From b10d8bfdb1dac40cce34e8860ca5ec7d950c3a44 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 24 Apr 2025 15:57:10 +0200 Subject: [PATCH 001/126] CUDA: use switch statements in constexpr functions (#13095) --- ggml/src/ggml-cuda/mmq.cuh | 80 ++++++++++++++++++++------------------ ggml/src/ggml-cuda/mmvq.cu | 80 ++++++++++++++++++++------------------ 2 files changed, 84 insertions(+), 76 deletions(-) diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index 532358018f..3cb2015520 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -155,25 +155,27 @@ static constexpr __device__ int get_mmq_y_device() { #define MMQ_DP4A_TXS_Q6_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI6_K + mmq_y/QI6_K, mmq_y*WARP_SIZE/8 + mmq_y/8} static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml_type type, int mmq_y) { - return type == GGML_TYPE_Q4_0 ? MMQ_DP4A_TXS_Q4_0 : - type == GGML_TYPE_Q4_1 ? MMQ_DP4A_TXS_Q4_1 : - type == GGML_TYPE_Q5_0 ? MMQ_DP4A_TXS_Q8_0 : - type == GGML_TYPE_Q5_1 ? MMQ_DP4A_TXS_Q8_1 : - type == GGML_TYPE_Q8_0 ? MMQ_DP4A_TXS_Q8_0 : - type == GGML_TYPE_Q2_K ? MMQ_DP4A_TXS_Q2_K : - type == GGML_TYPE_Q3_K ? MMQ_DP4A_TXS_Q3_K : - type == GGML_TYPE_Q4_K ? MMQ_DP4A_TXS_Q4_K : - type == GGML_TYPE_Q5_K ? MMQ_DP4A_TXS_Q5_K : - type == GGML_TYPE_Q6_K ? MMQ_DP4A_TXS_Q6_K : - type == GGML_TYPE_IQ2_XXS ? MMQ_DP4A_TXS_Q8_0 : - type == GGML_TYPE_IQ2_XS ? MMQ_DP4A_TXS_Q8_0_16 : - type == GGML_TYPE_IQ2_S ? MMQ_DP4A_TXS_Q8_0_16 : - type == GGML_TYPE_IQ3_XXS ? MMQ_DP4A_TXS_Q8_0 : - type == GGML_TYPE_IQ3_S ? MMQ_DP4A_TXS_Q8_0 : - type == GGML_TYPE_IQ1_S ? MMQ_DP4A_TXS_Q8_0 : - type == GGML_TYPE_IQ4_XS ? MMQ_DP4A_TXS_Q8_0 : - type == GGML_TYPE_IQ4_NL ? MMQ_DP4A_TXS_Q8_0 : - tile_x_sizes{0, 0, 0}; + switch (type) { + case GGML_TYPE_Q4_0: return MMQ_DP4A_TXS_Q4_0; + case GGML_TYPE_Q4_1: return MMQ_DP4A_TXS_Q4_1; + case GGML_TYPE_Q5_0: return MMQ_DP4A_TXS_Q8_0; + case GGML_TYPE_Q5_1: return MMQ_DP4A_TXS_Q8_1; + case GGML_TYPE_Q8_0: return MMQ_DP4A_TXS_Q8_0; + case GGML_TYPE_Q2_K: return MMQ_DP4A_TXS_Q2_K; + case GGML_TYPE_Q3_K: return MMQ_DP4A_TXS_Q3_K; + case GGML_TYPE_Q4_K: return MMQ_DP4A_TXS_Q4_K; + case GGML_TYPE_Q5_K: return MMQ_DP4A_TXS_Q5_K; + case GGML_TYPE_Q6_K: return MMQ_DP4A_TXS_Q6_K; + case GGML_TYPE_IQ2_XXS: return MMQ_DP4A_TXS_Q8_0; + case GGML_TYPE_IQ2_XS: return MMQ_DP4A_TXS_Q8_0_16; + case GGML_TYPE_IQ2_S: return MMQ_DP4A_TXS_Q8_0_16; + case GGML_TYPE_IQ3_XXS: return MMQ_DP4A_TXS_Q8_0; + case GGML_TYPE_IQ3_S: return MMQ_DP4A_TXS_Q8_0; + case GGML_TYPE_IQ1_S: return MMQ_DP4A_TXS_Q8_0; + case GGML_TYPE_IQ4_XS: return MMQ_DP4A_TXS_Q8_0; + case GGML_TYPE_IQ4_NL: return MMQ_DP4A_TXS_Q8_0; + default: return tile_x_sizes{0, 0, 0}; + } } #define MMQ_MMA_TILE_X_K_Q8_0 (2*WARP_SIZE + 2*WARP_SIZE/QI8_0 + 4) @@ -189,25 +191,27 @@ static_assert(MMQ_MMA_TILE_X_K_Q3_K % 8 == 4, "Wrong padding."); static_assert(MMQ_MMA_TILE_X_K_Q6_K % 8 == 4, "Wrong padding."); static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) { - return type == GGML_TYPE_Q4_0 ? MMQ_MMA_TILE_X_K_Q8_0 : - type == GGML_TYPE_Q4_1 ? MMQ_MMA_TILE_X_K_Q8_1 : - type == GGML_TYPE_Q5_0 ? MMQ_MMA_TILE_X_K_Q8_0 : - type == GGML_TYPE_Q5_1 ? MMQ_MMA_TILE_X_K_Q8_1 : - type == GGML_TYPE_Q8_0 ? MMQ_MMA_TILE_X_K_Q8_0 : - type == GGML_TYPE_Q2_K ? MMQ_MMA_TILE_X_K_Q2_K : - type == GGML_TYPE_Q3_K ? MMQ_MMA_TILE_X_K_Q3_K : - type == GGML_TYPE_Q4_K ? MMQ_MMA_TILE_X_K_Q8_1 : - type == GGML_TYPE_Q5_K ? MMQ_MMA_TILE_X_K_Q8_1 : - type == GGML_TYPE_Q6_K ? MMQ_MMA_TILE_X_K_Q6_K : - type == GGML_TYPE_IQ2_XXS ? MMQ_MMA_TILE_X_K_Q8_0 : - type == GGML_TYPE_IQ2_XS ? MMQ_MMA_TILE_X_K_Q3_K : - type == GGML_TYPE_IQ2_S ? MMQ_MMA_TILE_X_K_Q3_K : - type == GGML_TYPE_IQ3_XXS ? MMQ_MMA_TILE_X_K_Q8_0 : - type == GGML_TYPE_IQ3_S ? MMQ_MMA_TILE_X_K_Q8_0 : - type == GGML_TYPE_IQ1_S ? MMQ_MMA_TILE_X_K_Q8_0 : - type == GGML_TYPE_IQ4_XS ? MMQ_MMA_TILE_X_K_Q8_0 : - type == GGML_TYPE_IQ4_NL ? MMQ_MMA_TILE_X_K_Q8_0 : - 0; + switch (type) { + case GGML_TYPE_Q4_0: return MMQ_MMA_TILE_X_K_Q8_0; + case GGML_TYPE_Q4_1: return MMQ_MMA_TILE_X_K_Q8_1; + case GGML_TYPE_Q5_0: return MMQ_MMA_TILE_X_K_Q8_0; + case GGML_TYPE_Q5_1: return MMQ_MMA_TILE_X_K_Q8_1; + case GGML_TYPE_Q8_0: return MMQ_MMA_TILE_X_K_Q8_0; + case GGML_TYPE_Q2_K: return MMQ_MMA_TILE_X_K_Q2_K; + case GGML_TYPE_Q3_K: return MMQ_MMA_TILE_X_K_Q3_K; + case GGML_TYPE_Q4_K: return MMQ_MMA_TILE_X_K_Q8_1; + case GGML_TYPE_Q5_K: return MMQ_MMA_TILE_X_K_Q8_1; + case GGML_TYPE_Q6_K: return MMQ_MMA_TILE_X_K_Q6_K; + case GGML_TYPE_IQ2_XXS: return MMQ_MMA_TILE_X_K_Q8_0; + case GGML_TYPE_IQ2_XS: return MMQ_MMA_TILE_X_K_Q3_K; + case GGML_TYPE_IQ2_S: return MMQ_MMA_TILE_X_K_Q3_K; + case GGML_TYPE_IQ3_XXS: return MMQ_MMA_TILE_X_K_Q8_0; + case GGML_TYPE_IQ3_S: return MMQ_MMA_TILE_X_K_Q8_0; + case GGML_TYPE_IQ1_S: return MMQ_MMA_TILE_X_K_Q8_0; + case GGML_TYPE_IQ4_XS: return MMQ_MMA_TILE_X_K_Q8_0; + case GGML_TYPE_IQ4_NL: return MMQ_MMA_TILE_X_K_Q8_0; + default: return 0; + } } #define MMQ_TILE_Y_K (WARP_SIZE + WARP_SIZE/QI8_1) diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index cac04916cd..d846e35a6a 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -7,47 +7,51 @@ typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs); static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type) { - return type == GGML_TYPE_Q4_0 ? vec_dot_q4_0_q8_1 : - type == GGML_TYPE_Q4_1 ? vec_dot_q4_1_q8_1 : - type == GGML_TYPE_Q5_0 ? vec_dot_q5_0_q8_1 : - type == GGML_TYPE_Q5_1 ? vec_dot_q5_1_q8_1 : - type == GGML_TYPE_Q8_0 ? vec_dot_q8_0_q8_1 : - type == GGML_TYPE_Q2_K ? vec_dot_q2_K_q8_1 : - type == GGML_TYPE_Q3_K ? vec_dot_q3_K_q8_1 : - type == GGML_TYPE_Q4_K ? vec_dot_q4_K_q8_1 : - type == GGML_TYPE_Q5_K ? vec_dot_q5_K_q8_1 : - type == GGML_TYPE_Q6_K ? vec_dot_q6_K_q8_1 : - type == GGML_TYPE_IQ2_XXS ? vec_dot_iq2_xxs_q8_1 : - type == GGML_TYPE_IQ2_XS ? vec_dot_iq2_xs_q8_1 : - type == GGML_TYPE_IQ2_S ? vec_dot_iq2_s_q8_1 : - type == GGML_TYPE_IQ3_XXS ? vec_dot_iq3_xxs_q8_1 : - type == GGML_TYPE_IQ1_S ? vec_dot_iq1_s_q8_1 : - type == GGML_TYPE_IQ1_M ? vec_dot_iq1_m_q8_1 : - type == GGML_TYPE_IQ4_NL ? vec_dot_iq4_nl_q8_1 : - type == GGML_TYPE_IQ4_XS ? vec_dot_iq4_xs_q8_1 : - type == GGML_TYPE_IQ3_S ? vec_dot_iq3_s_q8_1 : - nullptr; + switch (type) { + case GGML_TYPE_Q4_0: return vec_dot_q4_0_q8_1; + case GGML_TYPE_Q4_1: return vec_dot_q4_1_q8_1; + case GGML_TYPE_Q5_0: return vec_dot_q5_0_q8_1; + case GGML_TYPE_Q5_1: return vec_dot_q5_1_q8_1; + case GGML_TYPE_Q8_0: return vec_dot_q8_0_q8_1; + case GGML_TYPE_Q2_K: return vec_dot_q2_K_q8_1; + case GGML_TYPE_Q3_K: return vec_dot_q3_K_q8_1; + case GGML_TYPE_Q4_K: return vec_dot_q4_K_q8_1; + case GGML_TYPE_Q5_K: return vec_dot_q5_K_q8_1; + case GGML_TYPE_Q6_K: return vec_dot_q6_K_q8_1; + case GGML_TYPE_IQ2_XXS: return vec_dot_iq2_xxs_q8_1; + case GGML_TYPE_IQ2_XS: return vec_dot_iq2_xs_q8_1; + case GGML_TYPE_IQ2_S: return vec_dot_iq2_s_q8_1; + case GGML_TYPE_IQ3_XXS: return vec_dot_iq3_xxs_q8_1; + case GGML_TYPE_IQ1_S: return vec_dot_iq1_s_q8_1; + case GGML_TYPE_IQ1_M: return vec_dot_iq1_m_q8_1; + case GGML_TYPE_IQ4_NL: return vec_dot_iq4_nl_q8_1; + case GGML_TYPE_IQ4_XS: return vec_dot_iq4_xs_q8_1; + case GGML_TYPE_IQ3_S: return vec_dot_iq3_s_q8_1; + default: return nullptr; + } } static constexpr __device__ int get_vdr_mmvq(ggml_type type) { - return type == GGML_TYPE_Q4_0 ? VDR_Q4_0_Q8_1_MMVQ : - type == GGML_TYPE_Q4_1 ? VDR_Q4_1_Q8_1_MMVQ : - type == GGML_TYPE_Q5_0 ? VDR_Q5_0_Q8_1_MMVQ : - type == GGML_TYPE_Q5_1 ? VDR_Q5_1_Q8_1_MMVQ : - type == GGML_TYPE_Q8_0 ? VDR_Q8_0_Q8_1_MMVQ : - type == GGML_TYPE_Q2_K ? VDR_Q2_K_Q8_1_MMVQ : - type == GGML_TYPE_Q3_K ? VDR_Q3_K_Q8_1_MMVQ : - type == GGML_TYPE_Q4_K ? VDR_Q4_K_Q8_1_MMVQ : - type == GGML_TYPE_Q5_K ? VDR_Q5_K_Q8_1_MMVQ : - type == GGML_TYPE_Q6_K ? VDR_Q6_K_Q8_1_MMVQ : - type == GGML_TYPE_IQ2_XXS ? VDR_IQ2_XXS_Q8_1_MMVQ : - type == GGML_TYPE_IQ2_XS ? VDR_IQ2_XS_Q8_1_MMVQ : - type == GGML_TYPE_IQ2_S ? VDR_IQ2_S_Q8_1_MMVQ : - type == GGML_TYPE_IQ3_XXS ? VDR_IQ3_XXS_Q8_1_MMVQ : - type == GGML_TYPE_IQ3_S ? VDR_IQ3_S_Q8_1_MMVQ : - type == GGML_TYPE_IQ4_NL ? VDR_IQ4_NL_Q8_1_MMVQ : - type == GGML_TYPE_IQ4_XS ? VDR_IQ4_XS_Q8_1_MMVQ : - 1; + switch (type) { + case GGML_TYPE_Q4_0: return VDR_Q4_0_Q8_1_MMVQ; + case GGML_TYPE_Q4_1: return VDR_Q4_1_Q8_1_MMVQ; + case GGML_TYPE_Q5_0: return VDR_Q5_0_Q8_1_MMVQ; + case GGML_TYPE_Q5_1: return VDR_Q5_1_Q8_1_MMVQ; + case GGML_TYPE_Q8_0: return VDR_Q8_0_Q8_1_MMVQ; + case GGML_TYPE_Q2_K: return VDR_Q2_K_Q8_1_MMVQ; + case GGML_TYPE_Q3_K: return VDR_Q3_K_Q8_1_MMVQ; + case GGML_TYPE_Q4_K: return VDR_Q4_K_Q8_1_MMVQ; + case GGML_TYPE_Q5_K: return VDR_Q5_K_Q8_1_MMVQ; + case GGML_TYPE_Q6_K: return VDR_Q6_K_Q8_1_MMVQ; + case GGML_TYPE_IQ2_XXS: return VDR_IQ2_XXS_Q8_1_MMVQ; + case GGML_TYPE_IQ2_XS: return VDR_IQ2_XS_Q8_1_MMVQ; + case GGML_TYPE_IQ2_S: return VDR_IQ2_S_Q8_1_MMVQ; + case GGML_TYPE_IQ3_XXS: return VDR_IQ3_XXS_Q8_1_MMVQ; + case GGML_TYPE_IQ3_S: return VDR_IQ3_S_Q8_1_MMVQ; + case GGML_TYPE_IQ4_NL: return VDR_IQ4_NL_Q8_1_MMVQ; + case GGML_TYPE_IQ4_XS: return VDR_IQ4_XS_Q8_1_MMVQ; + default: return 1; + } } enum mmvq_parameter_table_id { From c6e8cc28c15166dba15629dba6a7366d4d5955ca Mon Sep 17 00:00:00 2001 From: Acly Date: Thu, 17 Apr 2025 14:16:45 +0200 Subject: [PATCH 002/126] ggml : Depthwise 2D convolution (ggml/1152) * ggml-cpu : kernels for faster depthwise 2D convolution * fix compile: remove static after moving to ops.cpp * add dilation for depthwise_conv_2d * review: rename to ggml_conv_2d_dw_direct, remove redundant struct keywords, pass by ref, whitespace * review: rename depthwise_conv_2d -> conv_2d_dw everywhere --- ggml/include/ggml.h | 22 ++++- ggml/src/ggml-cpu/ggml-cpu.c | 5 + ggml/src/ggml-cpu/ops.cpp | 172 +++++++++++++++++++++++++++++++++++ ggml/src/ggml-cpu/ops.h | 1 + ggml/src/ggml.c | 53 ++++++++++- 5 files changed, 250 insertions(+), 3 deletions(-) diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 8fcc16df99..51aa5b3a0a 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -481,6 +481,7 @@ extern "C" { GGML_OP_CONV_TRANSPOSE_1D, GGML_OP_IM2COL, GGML_OP_IM2COL_BACK, + GGML_OP_CONV_2D_DW, GGML_OP_CONV_TRANSPOSE_2D, GGML_OP_POOL_1D, GGML_OP_POOL_2D, @@ -677,6 +678,9 @@ extern "C" { GGML_API bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1 GGML_API bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2 + // true for tensor that is stored in memory as CxWxHxN and has been permuted to WxHxCxN + GGML_API bool ggml_is_contiguous_channels(const struct ggml_tensor * tensor); + GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1); GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1); @@ -1660,7 +1664,7 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); - // depthwise + // depthwise (via im2col and mul_mat) GGML_API struct ggml_tensor * ggml_conv_2d_dw( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel @@ -1672,6 +1676,22 @@ extern "C" { int d0, // dilation dimension 0 int d1); // dilation dimension 1 + // Depthwise 2D convolution + // may be faster than ggml_conv_2d_dw, but not available in all backends + // a: KW KH 1 C convolution kernel + // b: W H C N input data + // res: W_out H_out C N + GGML_API struct ggml_tensor * ggml_conv_2d_dw_direct( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int stride0, + int stride1, + int pad0, + int pad1, + int dilation0, + int dilation1); + GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0( struct ggml_context * ctx, struct ggml_tensor * a, diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 5040032873..dbad8f61a1 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -1932,6 +1932,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_im2col_back_f32(params, tensor); } break; + case GGML_OP_CONV_2D_DW: + { + ggml_compute_forward_conv_2d_dw(params, tensor); + } break; case GGML_OP_CONV_TRANSPOSE_2D: { ggml_compute_forward_conv_transpose_2d(params, tensor); @@ -2268,6 +2272,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { } break; case GGML_OP_IM2COL: case GGML_OP_IM2COL_BACK: + case GGML_OP_CONV_2D_DW: case GGML_OP_CONV_TRANSPOSE_1D: case GGML_OP_CONV_TRANSPOSE_2D: { diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 6050147be7..3c2adb2172 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -6064,6 +6064,178 @@ void ggml_compute_forward_conv_transpose_2d( } } +// ggml_compute_forward_conv_2d_dw + +struct ggml_conv_2d_dw_params { + int64_t channels; + int64_t batch; + int64_t src_w; + int64_t src_h; + int64_t dst_w; + int64_t dst_h; + int64_t knl_w; + int64_t knl_h; + int stride_x; + int stride_y; + int pad_x; + int pad_y; + int dilation_x; + int dilation_y; +}; + +static void ggml_compute_forward_conv_2d_dw_cwhn( + const ggml_compute_params * params, + const ggml_tensor * src, + const ggml_tensor * kernel, + ggml_tensor * dst, + const ggml_conv_2d_dw_params & p) { + + const int64_t c = p.channels; + const float * knl_data = (const float *)kernel->data; + + const int64_t rows_total = p.dst_h * p.batch; + const int64_t rows_per_thread = (rows_total + params->nth - 1) / params->nth; + const int64_t row_start = params->ith * rows_per_thread; + const int64_t row_end = MIN(row_start + rows_per_thread, rows_total); + +#ifdef GGML_SIMD + const int64_t pkg_size = GGML_F32_EPR; + const int64_t pkg_count = c / pkg_size; + const int64_t c_pkg_end = pkg_count * pkg_size; +#else + const int64_t c_pkg_end = 0; +#endif + + for (int64_t row = row_start; row < row_end; ++row) { + const int64_t dst_y = row % p.dst_h; + const float * src_data = (const float *)src->data + (row / p.dst_h) * p.src_w * p.src_h * c; + for (int64_t dst_x = 0; dst_x < p.dst_w; ++dst_x) { + float * dst_data = (float *)dst->data + (row * p.dst_w + dst_x) * c; + const int64_t src_y_base = dst_y * p.stride_y - p.pad_y; + const int64_t src_x_base = dst_x * p.stride_x - p.pad_x; + +#ifdef GGML_SIMD + // Vectorized loop + for (int64_t c_i = 0; c_i < c_pkg_end; c_i += pkg_size) { + GGML_F32_VEC sum = GGML_F32_VEC_ZERO; + for (int64_t knl_y = 0; knl_y < p.knl_h; ++knl_y) { + const int64_t src_y = src_y_base + knl_y * p.dilation_y; + if (src_y < 0 || src_y >= p.src_h) { + continue; + } + for (int64_t knl_x = 0; knl_x < p.knl_w; ++knl_x) { + const int64_t src_x = src_x_base + knl_x * p.dilation_x; + if (src_x < 0 || src_x >= p.src_w) { + continue; + } + GGML_F32_VEC k = GGML_F32_VEC_LOAD(knl_data + (knl_y * p.knl_w + knl_x) * c + c_i); + GGML_F32_VEC s = GGML_F32_VEC_LOAD(src_data + (src_y * p.src_w + src_x) * c + c_i); + sum = GGML_F32_VEC_FMA(sum, k, s); + } + } + GGML_F32_VEC_STORE(dst_data + c_i, sum); + } +#endif + // Scalar loop + for (int64_t c_i = c_pkg_end; c_i < c; ++c_i) { + float sum = 0.0f; + for (int64_t knl_y = 0; knl_y < p.knl_h; ++knl_y) { + const int64_t src_y = src_y_base + knl_y * p.dilation_y; + if (src_y < 0 || src_y >= p.src_h) { + continue; + } + for (int64_t knl_x = 0; knl_x < p.knl_w; ++knl_x) { + const int64_t src_x = src_x_base + knl_x * p.dilation_x; + if (src_x < 0 || src_x >= p.src_w) { + continue; + } + sum += knl_data[(knl_y * p.knl_w + knl_x) * c + c_i] + * src_data[(src_y * p.src_w + src_x) * c + c_i]; + } + } + dst_data[c_i] = sum; + } + } + } +} + +static void ggml_compute_forward_conv_2d_dw_whcn( + const ggml_compute_params * params, + const ggml_tensor * src, + const ggml_tensor * kernel, + ggml_tensor * dst, + const ggml_conv_2d_dw_params & p) { + + const int64_t n = p.channels * p.batch; + const int64_t per_thread = (n + params->nth - 1) / params->nth; + const int64_t start = params->ith * per_thread; + const int64_t end = MIN(start + per_thread, n); + + for (int64_t i = start; i < end; ++i) { + const float * knl_data = (const float *)kernel->data + (i % p.channels) * p.knl_w * p.knl_h; + const float * src_data = (const float *)src->data + i * p.src_w * p.src_h; + float * dst_data = (float *)dst->data + i * p.dst_w * p.dst_h; + + for (int64_t dst_y = 0; dst_y < p.dst_h; ++dst_y) { + for (int64_t dst_x = 0; dst_x < p.dst_w; ++dst_x) { + + float sum = 0.0f; + for (int64_t knl_y = 0; knl_y < p.knl_h; ++knl_y) { + const int64_t src_y = dst_y * p.stride_y + knl_y * p.dilation_y - p.pad_y; + if (src_y < 0 || src_y >= p.src_h) { + continue; + } + for (int64_t knl_x = 0; knl_x < p.knl_w; ++knl_x) { + const int64_t src_x = dst_x * p.stride_x + knl_x * p.dilation_x - p.pad_x; + if (src_x < 0 || src_x >= p.src_w) { + continue; + } + sum += knl_data[knl_y * p.knl_w + knl_x] + * src_data[src_y * p.src_w + src_x]; + } + } + dst_data[dst_y * p.dst_w + dst_x] = sum; + } + } + } +} + +void ggml_compute_forward_conv_2d_dw( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * kernel = dst->src[0]; + const ggml_tensor * src = dst->src[1]; + ggml_conv_2d_dw_params p; + p.channels = src->ne[2]; + p.batch = src->ne[3]; + p.src_w = src->ne[0]; + p.src_h = src->ne[1]; + p.dst_w = dst->ne[0]; + p.dst_h = dst->ne[1]; + p.knl_w = kernel->ne[0]; + p.knl_h = kernel->ne[1]; + p.stride_x = dst->op_params[0]; + p.stride_y = dst->op_params[1]; + p.pad_x = dst->op_params[2]; + p.pad_y = dst->op_params[3]; + p.dilation_x = dst->op_params[4]; + p.dilation_y = dst->op_params[5]; + + GGML_ASSERT(kernel->ne[3] == p.channels); + GGML_ASSERT(dst->ne[3] == p.batch); + + if (ggml_is_contiguous(src)) { + ggml_compute_forward_conv_2d_dw_whcn(params, src, kernel, dst, p); + } else if (ggml_is_contiguous_channels(src)) { + // kernel should also have channels most contiguous in memory + GGML_ASSERT(kernel->nb[0] >= kernel->nb[2] && kernel->nb[1] >= kernel->nb[0]); + ggml_compute_forward_conv_2d_dw_cwhn(params, src, kernel, dst, p); + } else { + GGML_ABORT("non-contiguous memory layout not supported"); + } +} + // ggml_compute_forward_pool_1d_sk_p0 static void ggml_compute_forward_pool_1d_sk_p0( diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h index 410a372047..dc081b9e66 100644 --- a/ggml/src/ggml-cpu/ops.h +++ b/ggml/src/ggml-cpu/ops.h @@ -65,6 +65,7 @@ void ggml_compute_forward_conv_transpose_1d(const struct ggml_compute_params * p void ggml_compute_forward_im2col(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_im2col_back_f32(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_conv_transpose_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_conv_2d_dw(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_pool_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_pool_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_pool_2d_back(const struct ggml_compute_params * params, struct ggml_tensor * dst); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 950772c75c..c8b2feff42 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -956,6 +956,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CONV_TRANSPOSE_1D", "IM2COL", "IM2COL_BACK", + "CONV_2D_DW", "CONV_TRANSPOSE_2D", "POOL_1D", "POOL_2D", @@ -993,7 +994,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "OPT_STEP_ADAMW", }; -static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81"); +static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1050,6 +1051,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "conv_transpose_1d(x)", "im2col(x)", "im2col_back(x)", + "conv_2d_dw(x)", "conv_transpose_2d(x)", "pool_1d(x)", "pool_2d(x)", @@ -1087,7 +1089,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "adamw(x)", }; -static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81"); +static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -1344,6 +1346,13 @@ bool ggml_is_permuted(const struct ggml_tensor * tensor) { return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3]; } +bool ggml_is_contiguous_channels(const struct ggml_tensor * tensor) { + return + tensor->nb[0] > tensor->nb[2] && + tensor->nb[1] > tensor->nb[0] && + tensor->nb[2] == ggml_type_size(tensor->type); +} + static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); @@ -4050,6 +4059,46 @@ struct ggml_tensor * ggml_conv_2d_dw( return result; } +// ggml_conv_2d_dw_direct + +struct ggml_tensor * ggml_conv_2d_dw_direct( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int stride0, + int stride1, + int pad0, + int pad1, + int dilation0, + int dilation1) { + GGML_ASSERT(a->ne[2] == 1); + GGML_ASSERT(a->ne[3] == b->ne[2]); + int64_t ne[4]; + ne[0] = ggml_calc_conv_output_size(b->ne[0], a->ne[0], stride0, pad0, dilation0); + ne[1] = ggml_calc_conv_output_size(b->ne[1], a->ne[1], stride1, pad1, dilation1); + ne[2] = b->ne[2]; + ne[3] = b->ne[3]; + + struct ggml_tensor * result = ggml_new_tensor(ctx, b->type, 4, ne); + + if (ggml_is_contiguous_channels(b)) { + // Result will be permuted the same way as input (CWHN order) + const int64_t type_size = ggml_type_size(result->type); + GGML_ASSERT(ggml_blck_size(result->type) == 1); + result->nb[0] = result->ne[2] * type_size; + result->nb[1] = result->ne[0] * result->nb[0]; + result->nb[2] = type_size; + } + + int32_t params[] = { stride0, stride1, pad0, pad1, dilation0, dilation1 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_CONV_2D_DW; + result->src[0] = a; + result->src[1] = b; + return result; +} + // ggml_conv_transpose_2d_p0 static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) { From 63b4911494afe04778c61b9c19019341d71c99fc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 24 Apr 2025 16:47:43 +0300 Subject: [PATCH 003/126] sync : ggml ggml-ci --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index cad082a902..41feffca92 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -f71d538ece3fb32a04824dc6d1e73e360be9d22f +13bcf9ce50651a8b4238ec6d136f46f2c1b23b6f From 87616f0680947800ecba3e9f6bc6e101943bf8e6 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 24 Apr 2025 17:22:27 +0300 Subject: [PATCH 004/126] ggml : fix trailing whitespaces (#0) --- ggml/src/ggml.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index c8b2feff42..2a39dc7bfd 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -4069,7 +4069,7 @@ struct ggml_tensor * ggml_conv_2d_dw_direct( int stride1, int pad0, int pad1, - int dilation0, + int dilation0, int dilation1) { GGML_ASSERT(a->ne[2] == 1); GGML_ASSERT(a->ne[3] == b->ne[2]); From 226251ed56b85190e18a1cca963c45b888f4953c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 24 Apr 2025 22:29:22 +0300 Subject: [PATCH 005/126] embeddings : fix batch sizes (#13076) ggml-ci --- examples/embedding/embedding.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 6f08904159..06fce236e2 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -89,6 +89,13 @@ int main(int argc, char ** argv) { common_init(); params.embedding = true; + + // utilize the full context + if (params.n_batch < params.n_ctx) { + LOG_WRN("%s: setting batch size to %d\n", __func__, params.n_ctx); + params.n_batch = params.n_ctx; + } + // For non-causal models, batch size must be equal to ubatch size params.n_ubatch = params.n_batch; @@ -134,7 +141,6 @@ int main(int argc, char ** argv) { // max batch size const uint64_t n_batch = params.n_batch; - GGML_ASSERT(params.n_batch >= params.n_ctx); // tokenize the prompts and trim std::vector> inputs; From 13be08daf992c89d5169518229b3740041c0f419 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Thu, 24 Apr 2025 22:17:04 +0200 Subject: [PATCH 006/126] clip : remove boi/eoi embeddings for GLM-edge model (#13081) --- examples/llava/clip-impl.h | 2 -- examples/llava/clip.cpp | 17 +---------------- examples/llava/mtmd.cpp | 5 +++++ 3 files changed, 6 insertions(+), 18 deletions(-) diff --git a/examples/llava/clip-impl.h b/examples/llava/clip-impl.h index 8d310fb027..53ac381304 100644 --- a/examples/llava/clip-impl.h +++ b/examples/llava/clip-impl.h @@ -90,8 +90,6 @@ #define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s" #define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s" #define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s" -#define TN_GLM_BOI_W "adapter.boi" -#define TN_GLM_EOI_W "adapter.eoi" enum projector_type { PROJECTOR_TYPE_MLP, diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 4eec4a2646..9a5ab7c819 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -244,8 +244,6 @@ struct clip_vision_model { //GLMV-Edge projection struct ggml_tensor * mm_model_adapter_conv_w = nullptr; struct ggml_tensor * mm_model_adapter_conv_b = nullptr; - struct ggml_tensor * boi_w = nullptr; - struct ggml_tensor * eoi_w = nullptr; // MobileVLM projection struct ggml_tensor * mm_model_mlp_1_w = nullptr; @@ -1697,8 +1695,6 @@ struct clip_model_loader { vision_model.mm_model_mlp_1_w = get_tensor(string_format(TN_GLM_ADAPTER_D_H_2_4H,"weight")); vision_model.mm_model_mlp_2_w = get_tensor(string_format(TN_GLM_ADAPTER_GATE,"weight")); vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H,"weight")); - vision_model.boi_w = get_tensor(TN_GLM_BOI_W); - vision_model.eoi_w = get_tensor(TN_GLM_EOI_W); } break; case PROJECTOR_TYPE_MERGER: { @@ -2593,8 +2589,7 @@ void clip_free(clip_ctx * ctx) { } size_t clip_embd_nbytes(const struct clip_ctx * ctx) { - int extra_tokens = ctx->has_glm_projector ? 2 : 0; - return (clip_n_patches(ctx) + extra_tokens) * clip_n_mmproj_embd(ctx) * sizeof(float); + return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float); } size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) { @@ -2790,9 +2785,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } if (ctx->has_glm_projector) { GGML_ASSERT(batch_size == 1); - ggml_tensor * boi = ctx->vision_model.boi_w; - ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi)); - vec = (float*)(vec+ggml_nelements(boi)); //offset for boi } // build the inference graph @@ -3001,13 +2993,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima // copy the embeddings to the location passed by the user ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings)); - if (ctx->has_glm_projector) { - //eoi - ggml_tensor * eoi = ctx->vision_model.eoi_w; - int offset = ggml_nelements(embeddings); - ggml_backend_tensor_get(eoi, vec+offset, 0, ggml_nbytes(eoi)); - } - return true; } diff --git a/examples/llava/mtmd.cpp b/examples/llava/mtmd.cpp index 11ca7b30f1..a994ef0166 100644 --- a/examples/llava/mtmd.cpp +++ b/examples/llava/mtmd.cpp @@ -186,6 +186,11 @@ int32_t mtmd_tokenize(mtmd_context * ctx, marker_modified = "" + ctx->image_marker + ""; string_replace_all(prompt_modified, ctx->image_marker, marker_modified); + } else if (proj_type == PROJECTOR_TYPE_GLM_EDGE) { + // <|begin_of_image|> ... (image embeddings) ... <|end_of_image|> + marker_modified = "<|begin_of_image|>" + ctx->image_marker + "<|end_of_image|>"; + string_replace_all(prompt_modified, ctx->image_marker, marker_modified); + } else if (proj_type == PROJECTOR_TYPE_IDEFICS3) { // https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215 marker_modified = "" + ctx->image_marker + ""; From 553a5c3a9fdf771be2101bc3529937963f817457 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Fri, 25 Apr 2025 10:08:08 +0300 Subject: [PATCH 007/126] rpc : do not wait for response when sending RPC_CMD_SET_TENSOR (#12943) RPC_CMD_SET_TENSOR always returns an empty response and we send this 4 times per token. We can improve TG speed if we don't wait for this empty response. The performance impact of this change depends on the network latency. --- ggml/include/ggml-rpc.h | 2 +- ggml/src/ggml-rpc/ggml-rpc.cpp | 18 ++++++++++++------ 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index c8b6097f7e..1e67411276 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -7,7 +7,7 @@ extern "C" { #endif -#define RPC_PROTO_MAJOR_VERSION 1 +#define RPC_PROTO_MAJOR_VERSION 2 #define RPC_PROTO_MINOR_VERSION 0 #define RPC_PROTO_PATCH_VERSION 0 #define GGML_RPC_MAX_SERVERS 16 diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index a0667b7d70..9023eb0919 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -378,8 +378,8 @@ static bool parse_endpoint(const std::string & endpoint, std::string & host, int } // RPC request : | rpc_cmd (1 byte) | request_size (8 bytes) | request_data (request_size bytes) | -// RPC response: | response_size (8 bytes) | response_data (response_size bytes) | -static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const void * input, size_t input_size, void * output, size_t output_size) { +// No response +static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const void * input, size_t input_size) { uint8_t cmd_byte = cmd; if (!send_data(sock->fd, &cmd_byte, sizeof(cmd_byte))) { return false; @@ -390,6 +390,15 @@ static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cm if (!send_data(sock->fd, input, input_size)) { return false; } + return true; +} + +// RPC request : | rpc_cmd (1 byte) | request_size (8 bytes) | request_data (request_size bytes) | +// RPC response: | response_size (8 bytes) | response_data (response_size bytes) | +static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const void * input, size_t input_size, void * output, size_t output_size) { + if (!send_rpc_cmd(sock, cmd, input, input_size)) { + return false; + } // TODO: currently the output_size is always known, do we need support for commands with variable output size? // even if we do, we can skip sending output_size from the server for commands with known output size uint64_t out_size; @@ -555,7 +564,7 @@ static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggm memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), data, size); - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_SET_TENSOR, input.data(), input.size(), nullptr, 0); + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_SET_TENSOR, input.data(), input.size()); GGML_ASSERT(status); } @@ -1428,9 +1437,6 @@ static void rpc_serve_client(ggml_backend_t backend, const char * cache_dir, if (!server.set_tensor(input)) { return; } - if (!send_msg(sockfd, nullptr, 0)) { - return; - } break; } case RPC_CMD_SET_TENSOR_HASH: { From 514c45608f93f66106a712dee1abe062099ce790 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Fri, 25 Apr 2025 17:37:51 +0800 Subject: [PATCH 008/126] change the reorder tensor from init to execute OP (#13003) --- ggml/src/ggml-sycl/common.hpp | 1 - ggml/src/ggml-sycl/ggml-sycl.cpp | 125 +++++++++++++++---------------- 2 files changed, 61 insertions(+), 65 deletions(-) diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index 96becabc85..0ab0fb0aa3 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -313,7 +313,6 @@ struct ggml_backend_sycl_context { int device; std::string name; optimize_feature opt_feature; - bool optimized_graph=false; queue_ptr qptrs[GGML_SYCL_MAX_DEVICES][GGML_SYCL_MAX_STREAMS] = { { nullptr } }; diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 8081a77b74..548f2d0a06 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -192,7 +192,7 @@ static void ggml_check_sycl() try { if (!initialized) { g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); - g_ggml_sycl_disable_optimize= get_sycl_env("GGML_SYCL_DISABLE_OPT", 1); + g_ggml_sycl_disable_optimize= get_sycl_env("GGML_SYCL_DISABLE_OPT", 0); g_ggml_sycl_disable_graph = get_sycl_env("GGML_SYCL_DISABLE_GRAPH", 1); GGML_SYCL_DEBUG("[SYCL] call ggml_check_sycl\n"); GGML_LOG_INFO("Running with Environment Variables:\n"); @@ -2852,6 +2852,64 @@ static bool ggml_sycl_supports_dmmv(enum ggml_type type) { } } +static void reorder_qw(char *data_device, const int ncols, const int nrows, + size_t size, size_t offset, dpct::queue_ptr stream) { + auto tmp_buf = sycl::malloc_shared(size, *stream); + SYCL_CHECK( + CHECK_TRY_ERROR((*stream).memcpy(tmp_buf, data_device, size) + .wait())); + GGML_ASSERT((size % sizeof(block_q4_0) == 0)); + GGML_ASSERT((offset % sizeof(block_q4_0) == 0)); + int offset_blks = offset / sizeof(block_q4_0); + auto qs_ptr = (uint8_t*)data_device + offset_blks * QK4_0 / 2;; + auto d_ptr = (sycl::half*)(qs_ptr + ncols * nrows / 2) + offset_blks; + + stream->parallel_for( + size / sizeof(block_q4_0), + [=](auto i) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + const block_q4_0* x = (const block_q4_0*)tmp_buf; + const int ib = i; + + for (int j = 0; j < QK4_0/2; j ++) + { + *(qs_ptr + ib * QK4_0 / 2 + j) = x[ib].qs[j]; + } + *(d_ptr + ib) = x[ib].d; + }); + + sycl::free(tmp_buf, *stream); +} + +static void reorder_qw(const ggml_tensor * src0, dpct::queue_ptr stream) { + char*data_device = (char*)src0->data; + size_t ncols = src0->ne[0]; + size_t nrows = src0->ne[1]; + size_t size = ggml_nbytes(src0); + + reorder_qw(data_device, ncols, nrows, size, 0, stream); +} + +/* +* This function could be called when the OP (mul_mat) function support reorder optimizition. +*/ +static void opt_for_reorder(ggml_backend_sycl_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, + ggml_tensor * dst) { + if (!g_ggml_sycl_disable_optimize && //allow optimize, controlled by $GGML_SYCL_DISABLE_OPT + ctx->opt_feature.reorder && //allow this device due to good perf, skip the devices with bad perf. + dst->op == GGML_OP_MUL_MAT && //limit to some supported cases of Q4_0, to do for more cases. + src0->type == GGML_TYPE_Q4_0 && + src1->ne[2]==1 && src1->ne[3]==1) { + + ggml_tensor_extra_gpu* extra = (ggml_tensor_extra_gpu*)src0->extra; + if (!extra) return; //only happen in CI/UT permute case. + + if (extra->optimized_feature.reorder) return; //skip the tensor which is handled for reorder. + + reorder_qw(src0, ctx->stream()); + extra->optimized_feature.reorder = true; //used to decode/dequan in next steps. + } +} + static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer); @@ -2914,6 +2972,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor // KQ + KQV multi-batch ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst); } else if (use_dequantize_mul_mat_vec) { + opt_for_reorder(&ctx, src0, src1, dst); //the OP function in this branch support reorder. ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false); // save_tensor_txt("1/dst_1.txt", (float*) dst->data, src0->ne[1], sizeof(float), ctx.stream()); } else if (use_mul_mat_vec_q) { @@ -2921,6 +2980,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor } else if (use_mul_mat_q) { ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_q, true); } else { + opt_for_reorder(&ctx, src0, src1, dst); //the OP function in this branch support reorder. ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); } } @@ -3545,71 +3605,8 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void reorder_qw(char *data_device, const int ncols, const int nrows, - size_t size, size_t offset, dpct::queue_ptr stream) { - auto tmp_buf = sycl::malloc_shared(size, *stream); - SYCL_CHECK( - CHECK_TRY_ERROR((*stream).memcpy(tmp_buf, data_device, size) - .wait())); - GGML_ASSERT((size % sizeof(block_q4_0) == 0)); - GGML_ASSERT((offset % sizeof(block_q4_0) == 0)); - int offset_blks = offset / sizeof(block_q4_0); - auto qs_ptr = (uint8_t*)data_device + offset_blks * QK4_0 / 2;; - auto d_ptr = (sycl::half*)(qs_ptr + ncols * nrows / 2) + offset_blks; - - stream->parallel_for( - size / sizeof(block_q4_0), - [=](auto i) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - const block_q4_0* x = (const block_q4_0*)tmp_buf; - const int ib = i; - - for (int j = 0; j < QK4_0/2; j ++) - { - *(qs_ptr + ib * QK4_0 / 2 + j) = x[ib].qs[j]; - } - *(d_ptr + ib) = x[ib].d; - }); - - sycl::free(tmp_buf, *stream); -} - -static void reorder_qw(ggml_tensor * src0, dpct::queue_ptr stream) { - char*data_device = (char*)src0->data; - size_t ncols = src0->ne[0]; - size_t nrows = src0->ne[1]; - size_t size = ggml_nbytes(src0); - - reorder_qw(data_device, ncols, nrows, size, 0, stream); -} - -static void opt_for_reorder(ggml_tensor * dst, dpct::queue_ptr stream) { - ggml_tensor *src0 = dst->src[0]; - ggml_tensor *src1 = dst->src[1]; - - if (dst->op == GGML_OP_MUL_MAT && src0->type == GGML_TYPE_Q4_0 && - src1->ne[2]==1 && src1->ne[3]==1) { - reorder_qw(src0, stream); - ggml_tensor_extra_gpu* extra = (ggml_tensor_extra_gpu*)src0->extra; - GGML_ASSERT(extra); - extra->optimized_feature.reorder = true; //used to decode/dequan in next steps. - } -} - -static void optimize_graph_once(ggml_cgraph * cgraph, ggml_backend_sycl_context * ctx) { - dpct::queue_ptr stream = ctx->stream(); - if (ctx->optimized_graph) { - return; - } - ctx->optimized_graph = true; - - for (int i = 0; i < cgraph->n_nodes; i++) { - if (ctx->opt_feature.reorder) opt_for_reorder(cgraph->nodes[i], stream); - } -} - static void ggml_backend_sycl_graph_compute_impl(ggml_backend_sycl_context * sycl_ctx, ggml_cgraph * cgraph) { ggml_sycl_set_main_device(sycl_ctx->device); - if (!g_ggml_sycl_disable_optimize) optimize_graph_once(cgraph, sycl_ctx); for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; From edb18b6e8f5ea6509ad43057f8bb98fc557dbc4e Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Fri, 25 Apr 2025 14:31:42 +0200 Subject: [PATCH 009/126] clip : fix pixtral on some GPU backends (#13097) * clip : fix pixtral on some GPU backends * refactor inp_raw set * rm outdated comment * fix dynamic size * add TODO --- examples/llava/clip.cpp | 68 ++++++++++++++++++++++++++------------ tests/test-backend-ops.cpp | 2 ++ 2 files changed, 48 insertions(+), 22 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 9a5ab7c819..da8a590f0e 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -554,15 +554,15 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im } // implementation of the 2D RoPE without adding a new op in ggml +// this is not efficient (use double the memory), but works on all backends +// TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 static ggml_tensor * build_rope_2d( - ggml_cgraph * gf, ggml_context * ctx0, ggml_tensor * cur, ggml_tensor * pos_h, ggml_tensor * pos_w, const float freq_base ) { - ggml_tensor * tmp; const int64_t n_dim = cur->ne[0]; const int64_t n_head = cur->ne[1]; const int64_t n_pos = cur->ne[2]; @@ -571,18 +571,23 @@ static ggml_tensor * build_rope_2d( // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3 // first half of cur will use 1e-0, 1e-2 (even) // second half of cur will use 1e-1, 1e-3 (odd) - // - // for the first half, the trick here is to rotate n_dim/2, so inv_freq will be even + // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even // ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2) // then for the second half, we use freq_scale to shift the inv_freq // ^ why? replace (2i) with (2i+1) in the above equation const float freq_scale_odd = std::pow(freq_base, (float)-2/n_dim); // first half + ggml_tensor * first; { - cur = ggml_rope_ext_inplace( + first = ggml_view_3d(ctx0, cur, + n_dim/2, n_head, n_pos, + ggml_row_size(cur->type, n_dim), + ggml_row_size(cur->type, n_dim*n_head), + 0); + first = ggml_rope_ext( ctx0, - cur, + first, pos_h, // positions nullptr, // freq factors n_dim/2, // n_dims @@ -592,15 +597,17 @@ static ggml_tensor * build_rope_2d( } // second half + ggml_tensor * second; { - tmp = ggml_view_3d(ctx0, cur, + second = ggml_view_3d(ctx0, cur, n_dim/2, n_head, n_pos, ggml_row_size(cur->type, n_dim), ggml_row_size(cur->type, n_dim*n_head), n_dim/2 * ggml_element_size(cur)); - tmp = ggml_rope_ext_inplace( + second = ggml_cont(ctx0, second); // copy, because ggml_rope don't play well with non-contiguous tensors + second = ggml_rope_ext( ctx0, - tmp, + second, pos_w, // positions nullptr, // freq factors n_dim/2, // n_dims @@ -608,10 +615,9 @@ static ggml_tensor * build_rope_2d( freq_scale_odd, 0.0f, 1.0f, 0.0f, 0.0f ); - // calculate inplace (modify cur directly) - ggml_build_forward_expand(gf, tmp); } + cur = ggml_concat(ctx0, first, second, 0); return cur; } @@ -680,13 +686,13 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i struct ggml_tensor * Q = ggml_mul_mat(ctx0, model.layers[il].q_w, cur); Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_patches); - Q = build_rope_2d(gf, ctx0, Q, pos_h, pos_w, hparams.rope_theta); + Q = build_rope_2d(ctx0, Q, pos_h, pos_w, hparams.rope_theta); Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); struct ggml_tensor * K = ggml_mul_mat(ctx0, model.layers[il].k_w, cur); K = ggml_reshape_3d(ctx0, K, d_head, n_head, num_patches); - K = build_rope_2d(gf, ctx0, K, pos_h, pos_w, hparams.rope_theta); + K = build_rope_2d(ctx0, K, pos_h, pos_w, hparams.rope_theta); K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); struct ggml_tensor * V = ggml_mul_mat(ctx0, model.layers[il].v_w, cur); @@ -2796,10 +2802,15 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const auto & model = ctx->vision_model; const auto & hparams = model.hparams; + // TODO @ngxson : this is ugly, need to refactor later + bool support_dynamic_size = ctx->has_minicpmv_projector + || ctx->has_qwen2vl_merger + || ctx->proj_type == PROJECTOR_TYPE_PIXTRAL; + const int image_size = hparams.image_size; int image_size_width = image_size; int image_size_height = image_size; - if (ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger) { + if (support_dynamic_size) { image_size_width = imgs.entries[0]->nx; image_size_height = imgs.entries[0]->ny; } @@ -2811,9 +2822,20 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima { struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw"); - float * data = (float *)malloc(ggml_nbytes(inp_raw)); + std::vector inp_data(ggml_nelements(inp_raw)); + float * data = inp_data.data(); + + // layout of data (note: the channel dim is unrolled to better visualize the layout): + // + // ┌──W──┐ + // │ H │ channel = R + // ├─────┤ │ + // │ H │ channel = G + // ├─────┤ │ + // │ H │ channel = B + // └─────┘ │ + // ──────┘ x B - // TODO @ngxson : this whole code block is ugly, will need to be refactored for (size_t i = 0; i < imgs.entries.size(); i++) { const int nx = imgs.entries[i]->nx; const int ny = imgs.entries[i]->ny; @@ -2828,17 +2850,19 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int n = nx * ny; for (int b = 0; b < batch_size; b++) { - for (int k = 0; k < 3; k++) { - for (int y = 0; y < ny; y++) { - for (int x = 0; x < nx; x++) { - data[(b * 3 * n) + k * n + y * nx + x] = imgs.entries[b]->buf[3 * (y * nx + x) + k]; - } + float * batch_entry = data + b * (3*n); + for (int y = 0; y < ny; y++) { + for (int x = 0; x < nx; x++) { + size_t base_src = 3*(y * nx + x); // idx of the first channel + size_t base_dst = y * nx + x; // idx of the first channel + batch_entry[ base_dst] = imgs.entries[b]->buf[base_src ]; + batch_entry[1*n + base_dst] = imgs.entries[b]->buf[base_src + 1]; + batch_entry[2*n + base_dst] = imgs.entries[b]->buf[base_src + 2]; } } } } ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw)); - free(data); } if (ctx->has_minicpmv_projector) { { diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 61751755b3..d70acb7719 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2606,6 +2606,8 @@ struct test_rope : public test_case { } else { out = ggml_rope_ext_back(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f); } + + // TODO: add test with a non-contiguous view as input ; this case is needed for build_rope_2d in clip.cpp } ggml_set_name(out, "out"); From 558a764713468f26f5a163d25a22100c9a04a48f Mon Sep 17 00:00:00 2001 From: City <125218114+city96@users.noreply.github.com> Date: Fri, 25 Apr 2025 14:38:34 +0200 Subject: [PATCH 010/126] Force FP32 compute in GLM4 FFN Down (#13101) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Force FP32 compute in cuBLAS GEMM * Revert "Force FP32 compute in cuBLAS GEMM" This reverts commit 6efd872732159ab88ee7b3c1d77ba5ebc83079bd. * Force F32 compute in GLM4 ffn down * Edit comment to clarify issue Co-authored-by: Johannes Gäßler --------- Co-authored-by: Johannes Gäßler --- src/llama-graph.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index a85e97288e..b52e3f6203 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -803,6 +803,10 @@ ggml_tensor * llm_graph_context::build_ffn( if (down) { cur = build_lora_mm(down, cur); + if (arch == LLM_ARCH_GLM4) { + // GLM4 seems to have numerical issues with half-precision accumulators + ggml_mul_mat_set_prec(cur, GGML_PREC_F32); + } } if (down_b) { From 295354ea6848a77bdee204ee1c971d9b92ffcca9 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Fri, 25 Apr 2025 19:40:11 +0200 Subject: [PATCH 011/126] llama : fix K-shift with quantized K and BLAS backend (#13113) --- src/llama-context.cpp | 17 +++-------------- src/llama-context.h | 3 +-- 2 files changed, 4 insertions(+), 16 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 983385f86d..a52b6850b4 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -469,8 +469,7 @@ ggml_tensor * llama_context::build_rope_shift( ggml_tensor * shift, ggml_tensor * factors, float freq_base, - float freq_scale, - ggml_backend_buffer * bbuf) const { + float freq_scale) const { const auto & n_ctx_orig = cparams.n_ctx_orig_yarn; const auto & yarn_ext_factor = cparams.yarn_ext_factor; @@ -492,17 +491,7 @@ ggml_tensor * llama_context::build_rope_shift( // dequantize to f32 -> RoPE -> quantize back tmp = ggml_cast(ctx0, cur, GGML_TYPE_F32); - if (bbuf) { - for (const auto & backend : backends) { - // Figure out which backend KV cache belongs to - if (ggml_backend_supports_buft(backend.get(), ggml_backend_buffer_get_type(bbuf))) { - ggml_backend_sched_set_tensor_backend(sched.get(), tmp, backend.get()); - break; - } - } - } - - tmp = ggml_rope_ext_inplace(ctx0, tmp, + tmp = ggml_rope_ext(ctx0, tmp, shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow); @@ -582,7 +571,7 @@ llm_graph_result_ptr llama_context::build_kv_self_shift( ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa), 0); - ggml_tensor * cur = build_rope_shift(ctx0, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l, kv_self->k_l[il]->buffer); + ggml_tensor * cur = build_rope_shift(ctx0, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l); ggml_build_forward_expand(gf, cur); } diff --git a/src/llama-context.h b/src/llama-context.h index 04facb544c..5457f077c1 100644 --- a/src/llama-context.h +++ b/src/llama-context.h @@ -170,8 +170,7 @@ private: ggml_tensor * shift, ggml_tensor * factors, float freq_base, - float freq_scale, - ggml_backend_buffer * bbuf) const; + float freq_scale) const; llm_graph_result_ptr build_kv_self_shift( ggml_context * ctx0, From d5fe4e81bd447124836ecfb47d794f8768665b9f Mon Sep 17 00:00:00 2001 From: frob Date: Sat, 26 Apr 2025 10:10:20 +0200 Subject: [PATCH 012/126] grammar : handle maxItems == 0 in JSON schema (#13117) Co-authored-by: Richard Lyons --- common/json-schema-to-grammar.cpp | 3 +++ examples/json_schema_to_grammar.py | 3 +++ .../public_legacy/json-schema-to-grammar.mjs | 3 +++ tests/test-json-schema-to-grammar.cpp | 16 ++++++++++++++++ 4 files changed, 25 insertions(+) diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 9067982257..5b3059c2f7 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -16,6 +16,9 @@ using json = nlohmann::ordered_json; static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") { auto has_max = max_items != std::numeric_limits::max(); + if (max_items == 0) { + return ""; + } if (min_items == 0 && max_items == 1) { return item_rule + "?"; } diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py index 55f94c0b0a..ed37958554 100755 --- a/examples/json_schema_to_grammar.py +++ b/examples/json_schema_to_grammar.py @@ -10,6 +10,9 @@ from typing import Any, List, Optional, Set, Tuple, Union def _build_repetition(item_rule, min_items, max_items, separator_rule=None): + if max_items == 0: + return "" + if min_items == 0 and max_items == 1: return f'{item_rule}?' diff --git a/examples/server/public_legacy/json-schema-to-grammar.mjs b/examples/server/public_legacy/json-schema-to-grammar.mjs index f767ce7b72..b12bf2ab09 100644 --- a/examples/server/public_legacy/json-schema-to-grammar.mjs +++ b/examples/server/public_legacy/json-schema-to-grammar.mjs @@ -2,6 +2,9 @@ const SPACE_RULE = '| " " | "\\n"{1,2} [ \\t]{0,20}'; function _buildRepetition(itemRule, minItems, maxItems, opts={}) { + if (maxItems == 0) { + return ''; + } if (minItems === 0 && maxItems === 1) { return `${itemRule}?`; } diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index e35134f3cb..38cf01d6d8 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -597,6 +597,22 @@ static void test_all(const std::string & lang, std::function Date: Sat, 26 Apr 2025 22:05:31 +0800 Subject: [PATCH 013/126] ggml: move fp16/bf16 conversion optimizations to CPU backend + export conversion APIs (#13107) * ggml: dynamic x86_64 feature detection for FP32 <-> FP16/BF16 conversion * move fp converter to ggml-cpu * Switch ggml_compute_forward_get_rows_f16/bf16 to new ggml_cpu_fp16/bf16_to_fp32 --- ggml/include/ggml-cpu.h | 5 ++ ggml/src/ggml-cpu/ggml-cpu.c | 91 +++++++++++++++++++++++++++++++++++- ggml/src/ggml-cpu/ops.cpp | 4 +- ggml/src/ggml.c | 51 ++------------------ 4 files changed, 101 insertions(+), 50 deletions(-) diff --git a/ggml/include/ggml-cpu.h b/ggml/include/ggml-cpu.h index f5e11f1e10..de77a875ec 100644 --- a/ggml/include/ggml-cpu.h +++ b/ggml/include/ggml-cpu.h @@ -133,6 +133,11 @@ extern "C" { GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void); + GGML_BACKEND_API void ggml_cpu_fp32_to_fp16(const float *, ggml_fp16_t *, int64_t); + GGML_BACKEND_API void ggml_cpu_fp16_to_fp32(const ggml_fp16_t *, float *, int64_t); + GGML_BACKEND_API void ggml_cpu_fp32_to_bf16(const float *, ggml_bf16_t *, int64_t); + GGML_BACKEND_API void ggml_cpu_bf16_to_fp32(const ggml_bf16_t *, float *, int64_t); + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index dbad8f61a1..64405449e2 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -215,7 +215,7 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = { .nrows = 1, }, [GGML_TYPE_F16] = { - .from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row, + .from_float = (ggml_from_float_t) ggml_cpu_fp32_to_fp16, .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, .vec_dot_type = GGML_TYPE_F16, .nrows = 1, @@ -356,7 +356,7 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = { .from_float = quantize_row_q8_K, }, [GGML_TYPE_BF16] = { - .from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row, + .from_float = (ggml_from_float_t) ggml_cpu_fp32_to_bf16, .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16, .vec_dot_type = GGML_TYPE_BF16, .nrows = 1, @@ -3166,6 +3166,93 @@ enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct g return ggml_graph_compute(cgraph, &cplan); } +void ggml_cpu_fp32_to_fp16(const float * x, ggml_fp16_t * y, int64_t n) { + int64_t i = 0; +#if defined(__F16C__) +#if defined(__AVX512F__) + for (; i + 15 < n; i += 16) { + __m512 x_vec = _mm512_loadu_ps(x + i); + __m256i y_vec = _mm512_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); + _mm256_storeu_si256((__m256i *)(y + i), y_vec); + } +#endif + for (; i + 7 < n; i += 8) { + __m256 x_vec = _mm256_loadu_ps(x + i); + __m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); + _mm_storeu_si128((__m128i *)(y + i), y_vec); + } + for (; i + 3 < n; i += 4) { + __m128 x_vec = _mm_loadu_ps(x + i); + __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); + _mm_storel_epi64((__m128i *)(y + i), y_vec); + } +#endif + for (; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(x[i]); + } +} + +void ggml_cpu_fp16_to_fp32(const ggml_fp16_t * x, float * y, int64_t n) { + int64_t i = 0; +#if defined(__F16C__) +#if defined(__AVX512F__) + for (; i + 15 < n; i += 16) { + __m256i x_vec = _mm256_loadu_si256((const __m256i *)(x + i)); + __m512 y_vec = _mm512_cvtph_ps(x_vec); + _mm512_storeu_ps(y + i, y_vec); + } +#endif + for (; i + 7 < n; i += 8) { + __m128i x_vec = _mm_loadu_si128((const __m128i *)(x + i)); + __m256 y_vec = _mm256_cvtph_ps(x_vec); + _mm256_storeu_ps(y + i, y_vec); + } + for (; i + 3 < n; i += 4) { + __m128i x_vec = _mm_loadl_epi64((const __m128i *)(x + i)); + __m128 y_vec = _mm_cvtph_ps(x_vec); + _mm_storeu_ps(y + i, y_vec); + } +#endif + for (; i < n; ++i) { + y[i] = GGML_FP16_TO_FP32(x[i]); + } +} + +void ggml_cpu_fp32_to_bf16(const float * x, ggml_bf16_t * y, int64_t n) { + int64_t i = 0; + for (; i < n; ++i) { + y[i] = GGML_FP32_TO_BF16(x[i]); + } +} + +void ggml_cpu_bf16_to_fp32(const ggml_bf16_t * x, float * y, int64_t n) { + int64_t i = 0; +#if defined(__AVX2__) +#if defined(__AVX512F__) + for (; i + 15 < n; i += 16) { + _mm512_storeu_ps(y + i, + _mm512_castsi512_ps( + _mm512_slli_epi32( + _mm512_cvtepu16_epi32( + _mm256_loadu_si256( + (const __m256i *)(x + i))), + 16))); + } +#endif + for (; i + 7 < n; i += 8) { + _mm256_storeu_ps(y + i, + _mm256_castsi256_ps( + _mm256_slli_epi32( + _mm256_cvtepu16_epi32( + _mm_loadu_si128( + (const __m128i *)(x + i))), + 16))); + } +#endif + for (; i < n; i++) { + y[i] = GGML_BF16_TO_FP32(x[i]); + } +} int ggml_cpu_has_avx(void) { #if defined(__AVX__) diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 3c2adb2172..7413192b74 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -4222,7 +4222,7 @@ static void ggml_compute_forward_get_rows_f16( GGML_ASSERT(i01 >= 0 && i01 < ne01); - ggml_fp16_to_fp32_row( + ggml_cpu_fp16_to_fp32( (const ggml_fp16_t*) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); } @@ -4263,7 +4263,7 @@ static void ggml_compute_forward_get_rows_bf16( GGML_ASSERT(i01 >= 0 && i01 < ne01); - ggml_bf16_to_fp32_row( + ggml_cpu_bf16_to_fp32( (const ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); } diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 2a39dc7bfd..7654ae1779 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -4,6 +4,7 @@ #include "ggml-backend.h" #include "ggml-impl.h" #include "ggml-threading.h" +#include "ggml-cpu.h" #include "ggml.h" // FIXME: required here for quantization functions @@ -382,58 +383,16 @@ void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n) { } } -// FIXME: these functions must detect the instruction set at runtime, since they are part of the core ggml library -// currently, the ggml_cpu_has_* functions are entirely compile-time void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) { - int64_t i = 0; -#if defined(__F16C__) - //if (ggml_cpu_has_f16c()) { - for (; i + 7 < n; i += 8) { - __m256 x_vec = _mm256_loadu_ps(x + i); - __m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); - _mm_storeu_si128((__m128i *)(y + i), y_vec); - } - for(; i + 3 < n; i += 4) { - __m128 x_vec = _mm_loadu_ps(x + i); - __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); - _mm_storel_epi64((__m128i *)(y + i), y_vec); - } - //} -#endif - for (; i < n; i++) { + int i = 0; + for (; i < n; ++i) { y[i] = GGML_FP32_TO_FP16(x[i]); } } void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) { - int64_t i = 0; -#if defined(__AVX512F__) - //if (ggml_cpu_has_avx512()) { - for (; i + 16 <= n; i += 16) { - _mm512_storeu_ps(y + i, - _mm512_castsi512_ps( - _mm512_slli_epi32( - _mm512_cvtepu16_epi32( - _mm256_loadu_si256( - (const __m256i *)(x + i))), - 16))); - } - //} -#endif -#if defined(__AVX2__) - //if (ggml_cpu_has_avx2()) { - for (; i + 8 <= n; i += 8) { - _mm256_storeu_ps(y + i, - _mm256_castsi256_ps( - _mm256_slli_epi32( - _mm256_cvtepu16_epi32( - _mm_loadu_si128( - (const __m128i *)(x + i))), - 16))); - } - //} -#endif - for (; i < n; i++) { + int i = 0; + for (; i < n; ++i) { y[i] = GGML_BF16_TO_FP32(x[i]); } } From 4753791e70acd4d4e02f2098f14a03df26c992bd Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 26 Apr 2025 22:39:47 +0200 Subject: [PATCH 014/126] clip : improve projector naming (#13118) * clip : improve projector naming * no more kv has_llava_projector * rm unused kv * rm more unused --- examples/llava/clip-impl.h | 27 +- examples/llava/clip.cpp | 524 ++++++++++++++++--------------------- examples/llava/clip.h | 2 - 3 files changed, 241 insertions(+), 312 deletions(-) diff --git a/examples/llava/clip-impl.h b/examples/llava/clip-impl.h index 53ac381304..16d0a8efc5 100644 --- a/examples/llava/clip-impl.h +++ b/examples/llava/clip-impl.h @@ -17,22 +17,15 @@ #define KEY_FTYPE "general.file_type" #define KEY_NAME "general.name" #define KEY_DESCRIPTION "general.description" -#define KEY_HAS_TEXT_ENC "clip.has_text_encoder" -#define KEY_HAS_VIS_ENC "clip.has_vision_encoder" -#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector" -#define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector" -#define KEY_HAS_GLM_PROJ "clip.has_glm_projector" #define KEY_MINICPMV_VERSION "clip.minicpmv_version" -#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger" #define KEY_USE_GELU "clip.use_gelu" #define KEY_USE_SILU "clip.use_silu" -#define KEY_N_EMBD "clip.%s.embedding_length" -#define KEY_N_FF "clip.%s.feed_forward_length" -#define KEY_N_BLOCK "clip.%s.block_count" -#define KEY_N_HEAD "clip.%s.attention.head_count" -#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" -#define KEY_PROJ_DIM "clip.%s.projection_dim" -#define KEY_TOKENS "tokenizer.ggml.tokens" +#define KEY_N_EMBD "clip.vision.embedding_length" +#define KEY_N_FF "clip.vision.feed_forward_length" +#define KEY_N_BLOCK "clip.vision.block_count" +#define KEY_N_HEAD "clip.vision.attention.head_count" +#define KEY_LAYER_NORM_EPS "clip.vision.attention.layer_norm_epsilon" +#define KEY_PROJ_DIM "clip.vision.projection_dim" #define KEY_IMAGE_SIZE "clip.vision.image_size" #define KEY_PATCH_SIZE "clip.vision.patch_size" #define KEY_IMAGE_MEAN "clip.vision.image_mean" @@ -96,9 +89,9 @@ enum projector_type { PROJECTOR_TYPE_MLP_NORM, PROJECTOR_TYPE_LDP, PROJECTOR_TYPE_LDPV2, - PROJECTOR_TYPE_RESAMPLER, + PROJECTOR_TYPE_MINICPMV, PROJECTOR_TYPE_GLM_EDGE, - PROJECTOR_TYPE_MERGER, + PROJECTOR_TYPE_QWEN2VL, PROJECTOR_TYPE_GEMMA3, PROJECTOR_TYPE_IDEFICS3, PROJECTOR_TYPE_PIXTRAL, @@ -109,9 +102,9 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_MLP, "mlp" }, { PROJECTOR_TYPE_LDP, "ldp" }, { PROJECTOR_TYPE_LDPV2, "ldpv2"}, - { PROJECTOR_TYPE_RESAMPLER, "resampler"}, + { PROJECTOR_TYPE_MINICPMV, "resampler"}, { PROJECTOR_TYPE_GLM_EDGE, "adapter"}, - { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"}, + { PROJECTOR_TYPE_QWEN2VL, "qwen2vl_merger"}, { PROJECTOR_TYPE_GEMMA3, "gemma3"}, { PROJECTOR_TYPE_IDEFICS3, "idefics3"}, { PROJECTOR_TYPE_PIXTRAL, "pixtral"}, diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index da8a590f0e..e8c01c68a9 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -308,13 +308,8 @@ struct clip_vision_model { }; struct clip_ctx { - bool has_text_encoder = false; - bool has_vision_encoder = false; bool has_llava_projector = false; - bool has_minicpmv_projector = false; - bool has_glm_projector = false; - bool has_qwen2vl_merger = false; - int minicpmv_version = 2; + int minicpmv_version = 0; struct clip_vision_model vision_model; projector_type proj_type = PROJECTOR_TYPE_MLP; @@ -373,23 +368,20 @@ struct clip_ctx { } }; -static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_image_f32_batch & imgs) { +static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_image_f32 & img) { const auto & model = ctx->vision_model; const auto & hparams = model.hparams; - const int image_size = hparams.image_size; - int image_size_width = image_size; - int image_size_height = image_size; + int image_size_width = img.nx; + int image_size_height = img.ny; - const int patch_size = hparams.patch_size; - const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); - const int hidden_size = hparams.hidden_size; - const int n_head = hparams.n_head; - const int d_head = hidden_size / n_head; - const int n_layer = hparams.n_layer; - const float eps = hparams.eps; - - GGML_ASSERT(imgs.entries.size() == 1); // batch_size == 1 + const int patch_size = hparams.patch_size; + const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); + const int hidden_size = hparams.hidden_size; + const int n_head = hparams.n_head; + const int d_head = hidden_size / n_head; + const int n_layer = hparams.n_layer; + const float eps = hparams.eps; struct ggml_init_params params = { /*.mem_size =*/ ctx->buf_compute_meta.size(), @@ -621,15 +613,14 @@ static ggml_tensor * build_rope_2d( return cur; } -static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_image_f32_batch & imgs) { +static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_image_f32 & img) { const auto & model = ctx->vision_model; const auto & hparams = model.hparams; GGML_ASSERT(ctx->proj_type == PROJECTOR_TYPE_PIXTRAL); - GGML_ASSERT(imgs.entries.size() == 1); // batch_size == 1 - int image_size_width = imgs.entries[0]->nx; - int image_size_height = imgs.entries[0]->ny; + int image_size_width = img.nx; + int image_size_height = img.ny; const int patch_size = hparams.patch_size; const int n_patches_x = image_size_width / patch_size; @@ -772,18 +763,14 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i } static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) { - if (!ctx->has_vision_encoder) { - LOG_ERR("This gguf file seems to have no vision encoder\n"); - return nullptr; - } - const auto & model = ctx->vision_model; const auto & hparams = model.hparams; const int image_size = hparams.image_size; int image_size_width = image_size; int image_size_height = image_size; - if (ctx->has_minicpmv_projector) { + + if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { LOG_DBG("%s: %d %d\n", __func__, load_image_size.width, load_image_size.height); image_size_width = load_image_size.width; image_size_height = load_image_size.height; @@ -792,7 +779,8 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im image_size_height = imgs.entries[0]->ny; } } - else if (ctx->has_qwen2vl_merger) { + + else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { // use the image's native resolution when image is avaible if (is_inf) { // if (imgs->data->nx && imgs->data->ny) { @@ -800,12 +788,13 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im image_size_height = imgs.entries[0]->ny; } } + const int patch_size = hparams.patch_size; const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); const int patches_w = image_size_width / patch_size; const int patches_h = image_size_height / patch_size; const int num_positions = num_patches + (model.class_embedding ? 1 : 0); - const int num_position_ids = ctx->has_qwen2vl_merger ? num_positions * 4 : num_positions; + const int num_position_ids = ctx->proj_type == PROJECTOR_TYPE_QWEN2VL ? num_positions * 4 : num_positions; const int hidden_size = hparams.hidden_size; const int n_head = hparams.n_head; const int d_head = hidden_size / n_head; @@ -814,7 +803,9 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im const int batch_size = imgs.entries.size(); - if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) { + if (ctx->has_llava_projector + || ctx->proj_type == PROJECTOR_TYPE_MINICPMV + || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { GGML_ASSERT(batch_size == 1); } @@ -835,8 +826,8 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - if (ctx->has_qwen2vl_merger) { - GGML_ASSERT(image_size_width % (patch_size * 2) == 0); + if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { + GGML_ASSERT(image_size_width % (patch_size * 2) == 0); GGML_ASSERT(image_size_height % (patch_size * 2) == 0); auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); @@ -865,29 +856,26 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im struct ggml_tensor * embeddings = inp; struct ggml_tensor * pos_embed = nullptr; - if (ctx->has_llava_projector) { - // concat class_embeddings and patch_embeddings - if (model.class_embedding) { - embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); - ggml_set_name(embeddings, "embeddings"); - ggml_set_input(embeddings); - embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); - embeddings = ggml_acc(ctx0, embeddings, inp, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); - } + // concat class_embeddings and patch_embeddings + if (model.class_embedding) { + embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); + embeddings = ggml_scale(ctx0, embeddings, 0.0f); // set to all zeros + embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); + embeddings = ggml_acc(ctx0, embeddings, inp, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); } struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); ggml_set_name(positions, "positions"); ggml_set_input(positions); - if (!ctx->has_qwen2vl_merger) { // qwen2vl use rope position embedding + if (ctx->proj_type != PROJECTOR_TYPE_QWEN2VL) { // qwen2vl does NOT use learned position embeddings embeddings = ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); } - if (ctx->has_minicpmv_projector) { + if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { int pos_w = image_size_width/patch_size; int pos_h = image_size_height/patch_size; if (ctx->minicpmv_version == 2) { @@ -941,7 +929,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b); Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size); - if (ctx->has_qwen2vl_merger) { + if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { Q = ggml_rope_multi( ctx0, Q, positions, nullptr, d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); @@ -953,7 +941,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b); K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); - if (ctx->has_qwen2vl_merger) { + if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { K = ggml_rope_multi( ctx0, K, positions, nullptr, d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); @@ -1218,106 +1206,98 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im } } // minicpmv projector - else if (ctx->has_minicpmv_projector) - { - if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) { - struct ggml_tensor * q = model.mm_model_query; - { // layernorm - q = ggml_norm(ctx0, q, eps); - q = ggml_add(ctx0, ggml_mul(ctx0, q, model.mm_model_ln_q_w), model.mm_model_ln_q_b); - } - struct ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings); - { // layernorm - v = ggml_norm(ctx0, v, eps); - v = ggml_add(ctx0, ggml_mul(ctx0, v, model.mm_model_ln_kv_w), model.mm_model_ln_kv_b); - } - struct ggml_tensor * k; - { // position - // q = ggml_add(ctx0, q, model.mm_model_pos_embed); - k = ggml_add(ctx0, v, pos_embed); - } - - { // attention - int hidden_size = 4096; - const int d_head = 128; - int n_head = hidden_size/d_head; - int num_query = 96; - if (ctx->minicpmv_version == 2) { - hidden_size = 4096; - n_head = hidden_size/d_head; - num_query = 96; - } - else if (ctx->minicpmv_version == 3) { - hidden_size = 3584; - n_head = hidden_size/d_head; - num_query = 64; - } - else if (ctx->minicpmv_version == 4) { - hidden_size = 3584; - n_head = hidden_size/d_head; - num_query = 64; - } - - struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b); - struct ggml_tensor * K = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), model.mm_model_attn_k_b); - struct ggml_tensor * V = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), model.mm_model_attn_v_b); - // permute - Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_query, batch_size); - Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); - Q = ggml_reshape_3d(ctx0, Q, d_head, num_query, n_head * batch_size); - K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); - K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); - K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); - V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size); - V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); - V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); - KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_query, n_head, batch_size); - KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - KQV = ggml_cont_3d(ctx0, KQV, hidden_size, num_query, batch_size); - - embeddings = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_o_w, KQV), model.mm_model_attn_o_b); - } - { // layernorm - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_post_w), model.mm_model_ln_post_b); - } - embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings); + else if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { + struct ggml_tensor * q = model.mm_model_query; + { // layernorm + q = ggml_norm(ctx0, q, eps); + q = ggml_add(ctx0, ggml_mul(ctx0, q, model.mm_model_ln_q_w), model.mm_model_ln_q_b); } - else { - GGML_ASSERT(false); + struct ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings); + { // layernorm + v = ggml_norm(ctx0, v, eps); + v = ggml_add(ctx0, ggml_mul(ctx0, v, model.mm_model_ln_kv_w), model.mm_model_ln_kv_b); } + struct ggml_tensor * k; + { // position + // q = ggml_add(ctx0, q, model.mm_model_pos_embed); + k = ggml_add(ctx0, v, pos_embed); + } + + { // attention + int hidden_size = 4096; + const int d_head = 128; + int n_head = hidden_size/d_head; + int num_query = 96; + if (ctx->minicpmv_version == 2) { + hidden_size = 4096; + n_head = hidden_size/d_head; + num_query = 96; + } + else if (ctx->minicpmv_version == 3) { + hidden_size = 3584; + n_head = hidden_size/d_head; + num_query = 64; + } + else if (ctx->minicpmv_version == 4) { + hidden_size = 3584; + n_head = hidden_size/d_head; + num_query = 64; + } + + struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b); + struct ggml_tensor * K = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), model.mm_model_attn_k_b); + struct ggml_tensor * V = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), model.mm_model_attn_v_b); + // permute + Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_query, batch_size); + Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); + Q = ggml_reshape_3d(ctx0, Q, d_head, num_query, n_head * batch_size); + K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); + K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); + K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); + V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size); + V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); + V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); + struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); + KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); + struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); + KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_query, n_head, batch_size); + KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); + KQV = ggml_cont_3d(ctx0, KQV, hidden_size, num_query, batch_size); + + embeddings = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_o_w, KQV), model.mm_model_attn_o_b); + } + { // layernorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_post_w), model.mm_model_ln_post_b); + } + embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings); } + // glm projector - else if (ctx->has_glm_projector) { - if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { - size_t gridsz = (size_t)sqrt(embeddings->ne[1]); - embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3)); - embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); - embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); - embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); - embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); - embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); - //GLU - { - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); - embeddings = ggml_gelu_inplace(ctx0, embeddings); - struct ggml_tensor * x = embeddings; - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); - x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); - embeddings = ggml_silu_inplace(ctx0, embeddings); - embeddings = ggml_mul(ctx0, embeddings,x); - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); - } - } else { - GGML_ABORT("fatal error"); + else if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { + size_t gridsz = (size_t)sqrt(embeddings->ne[1]); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3)); + embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); + embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); + embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); + embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); + // GLU + { + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); + embeddings = ggml_gelu_inplace(ctx0, embeddings); + struct ggml_tensor * x = embeddings; + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); + x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); + embeddings = ggml_silu_inplace(ctx0, embeddings); + embeddings = ggml_mul(ctx0, embeddings,x); + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); } } - else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) { + + else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size); embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); @@ -1343,11 +1323,13 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 case PROJECTOR_TYPE_GEMMA3: case PROJECTOR_TYPE_IDEFICS3: { - res = clip_image_build_graph_siglip(ctx, imgs); + GGML_ASSERT(imgs.entries.size() == 1); + res = clip_image_build_graph_siglip(ctx, *imgs.entries[0]); } break; case PROJECTOR_TYPE_PIXTRAL: { - res = clip_image_build_graph_pixtral(ctx, imgs); + GGML_ASSERT(imgs.entries.size() == 1); + res = clip_image_build_graph_pixtral(ctx, *imgs.entries[0]); } break; default: { @@ -1419,8 +1401,8 @@ struct clip_model_loader { auto & hparams = ctx_clip.vision_model.hparams; // projector type + std::string proj_type; { - std::string proj_type; get_string(KEY_PROJ_TYPE, proj_type, false); if (!proj_type.empty()) { ctx_clip.proj_type = clip_projector_type_from_string(proj_type); @@ -1432,33 +1414,27 @@ struct clip_model_loader { // other hparams { - get_bool(KEY_HAS_TEXT_ENC, ctx_clip.has_text_encoder, false); - get_bool(KEY_HAS_VIS_ENC, ctx_clip.has_vision_encoder, false); - GGML_ASSERT(ctx_clip.has_vision_encoder); - GGML_ASSERT(!ctx_clip.has_text_encoder); - - // legacy keys, use KEY_PROJ_TYPE instead - get_bool(KEY_HAS_LLAVA_PROJ, ctx_clip.has_llava_projector, false); - get_bool(KEY_HAS_MINICPMV_PROJ, ctx_clip.has_minicpmv_projector, false); get_i32(KEY_MINICPMV_VERSION, ctx_clip.minicpmv_version, false); - get_bool(KEY_HAS_GLM_PROJ, ctx_clip.has_glm_projector, false); - get_bool(KEY_HAS_QWEN2VL_MERGER, ctx_clip.has_qwen2vl_merger, false); - // !!! do NOT extend the list above, use KEY_PROJ_TYPE instead get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false); get_bool(KEY_USE_SILU, ctx_clip.use_silu, false); - get_u32(string_format(KEY_N_EMBD, "vision"), hparams.hidden_size); - get_u32(string_format(KEY_N_HEAD, "vision"), hparams.n_head); - get_u32(string_format(KEY_N_FF, "vision"), hparams.n_intermediate); - get_u32(string_format(KEY_N_BLOCK, "vision"), hparams.n_layer); - get_u32(string_format(KEY_PROJ_DIM, "vision"), hparams.projection_dim); - get_f32(string_format(KEY_LAYER_NORM_EPS, "vision"), hparams.eps); - get_u32(KEY_IMAGE_SIZE, hparams.image_size); - get_u32(KEY_PATCH_SIZE, hparams.patch_size); - get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false); + get_u32(KEY_N_EMBD, hparams.hidden_size); + get_u32(KEY_N_HEAD, hparams.n_head); + get_u32(KEY_N_FF, hparams.n_intermediate); + get_u32(KEY_N_BLOCK, hparams.n_layer); + get_u32(KEY_PROJ_DIM, hparams.projection_dim); + get_f32(KEY_LAYER_NORM_EPS, hparams.eps); + get_u32(KEY_IMAGE_SIZE, hparams.image_size); + get_u32(KEY_PATCH_SIZE, hparams.patch_size); + get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false); get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false); + ctx_clip.has_llava_projector = ctx_clip.proj_type == PROJECTOR_TYPE_MLP + || ctx_clip.proj_type == PROJECTOR_TYPE_MLP_NORM + || ctx_clip.proj_type == PROJECTOR_TYPE_LDP + || ctx_clip.proj_type == PROJECTOR_TYPE_LDPV2; + { std::string mm_patch_merge_type; get_string(KEY_MM_PATCH_MERGE_TYPE, mm_patch_merge_type, false); @@ -1491,32 +1467,56 @@ struct clip_model_loader { for (auto & layer : vision_feature_layer) { hparams.vision_feature_layer.insert(layer); } - // Calculate the deepest feature layer based on hparams and projector type - ctx_clip.max_feature_layer = get_deepest_feature_layer(&ctx_clip); - LOG_INF("%s: text_encoder: %d\n", __func__, ctx_clip.has_text_encoder); - LOG_INF("%s: vision_encoder: %d\n", __func__, ctx_clip.has_vision_encoder); - LOG_INF("%s: llava_projector: %d\n", __func__, ctx_clip.has_llava_projector); - LOG_INF("%s: minicpmv_projector: %d\n", __func__, ctx_clip.has_minicpmv_projector); + // Calculate the deepest feature layer based on hparams and projector type + // NOTE: This is only used by build_graph_legacy() + { + // Get the index of the second to last layer; this is the default for models that have a llava projector + int n_layer = hparams.n_layer - 1; + int deepest_feature_layer = -1; + + if (ctx_clip.proj_type == PROJECTOR_TYPE_MINICPMV + || ctx_clip.proj_type == PROJECTOR_TYPE_GLM_EDGE + || ctx_clip.proj_type == PROJECTOR_TYPE_QWEN2VL) { + n_layer += 1; + } + + // If we set explicit vision feature layers, only go up to the deepest one + // NOTE: only used by granite-vision models for now + for (const auto & feature_layer : hparams.vision_feature_layer) { + if (feature_layer > deepest_feature_layer) { + deepest_feature_layer = feature_layer; + } + } + ctx_clip.max_feature_layer = deepest_feature_layer < 0 ? n_layer : deepest_feature_layer; + } + + // model-specific params + switch (ctx_clip.proj_type) { + case PROJECTOR_TYPE_MINICPMV: + { + if (ctx_clip.minicpmv_version == 0) { + ctx_clip.minicpmv_version = 2; // default to 2 if not set + } + } break; + case PROJECTOR_TYPE_IDEFICS3: + { + get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false); + } break; + case PROJECTOR_TYPE_PIXTRAL: + { + hparams.rope_theta = 10000.0f; + } break; + default: + break; + } + + LOG_INF("%s: projector: %s\n", __func__, proj_type.c_str()); + LOG_INF("%s: has_llava_proj: %d\n", __func__, ctx_clip.has_llava_projector); LOG_INF("%s: minicpmv_version: %d\n", __func__, ctx_clip.minicpmv_version); - LOG_INF("%s: glm_projector: %d\n", __func__, ctx_clip.has_glm_projector); LOG_INF("%s: model size: %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0); LOG_INF("%s: metadata size: %.2f MiB\n", __func__, ggml_get_mem_size(ctx_meta.get()) / 1024.0 / 1024.0); } - - // model-specific params - switch (ctx_clip.proj_type) { - case PROJECTOR_TYPE_IDEFICS3: - { - get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false); - } break; - case PROJECTOR_TYPE_PIXTRAL: - { - hparams.rope_theta = 10000.0f; - } break; - default: - break; - } } void load_tensors() { @@ -1569,9 +1569,6 @@ struct clip_model_loader { vision_model.patch_bias = get_tensor(TN_PATCH_BIAS, false); vision_model.patch_embeddings_0 = get_tensor(TN_PATCH_EMBD, false); vision_model.patch_embeddings_1 = get_tensor(TN_PATCH_EMBD_1, false); - if (vision_model.patch_embeddings_1 == nullptr) { - ctx_clip.has_qwen2vl_merger = false; - } vision_model.position_embeddings = get_tensor(string_format(TN_POS_EMBD, "v"), false); @@ -1669,7 +1666,7 @@ struct clip_model_loader { vision_model.mm_model_peg_0_w = get_tensor(string_format(TN_MVLM_PROJ_PEG, 0, "weight")); vision_model.mm_model_peg_0_b = get_tensor(string_format(TN_MVLM_PROJ_PEG, 0, "bias")); } break; - case PROJECTOR_TYPE_RESAMPLER: + case PROJECTOR_TYPE_MINICPMV: { // vision_model.mm_model_pos_embed = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD); vision_model.mm_model_pos_embed_k = get_tensor(TN_MINICPMV_POS_EMBD_K); @@ -1702,7 +1699,7 @@ struct clip_model_loader { vision_model.mm_model_mlp_2_w = get_tensor(string_format(TN_GLM_ADAPTER_GATE,"weight")); vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H,"weight")); } break; - case PROJECTOR_TYPE_MERGER: + case PROJECTOR_TYPE_QWEN2VL: { vision_model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); vision_model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias")); @@ -2479,11 +2476,6 @@ int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip) { // returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector // res_imgs memory is being allocated here, previous allocations will be freed if found bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, struct clip_image_f32_batch * res_imgs) { - if (!ctx->has_vision_encoder) { - LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__); - return false; - } - clip_image_size original_size{img->nx, img->ny}; bool pad_to_square = true; auto & params = ctx->vision_model.hparams; @@ -2504,7 +2496,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str } return true; } - else if (ctx->has_qwen2vl_merger) { + else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { clip_image_u8 resized; auto patch_size = clip_get_patch_size(ctx) * 2; int nx = ceil((float)img->nx / patch_size) * patch_size; @@ -2518,7 +2510,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str res_imgs->entries.push_back(std::move(img_f32)); return true; } - else if (ctx->has_glm_projector + else if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE || ctx->proj_type == PROJECTOR_TYPE_GEMMA3 || ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) { clip_image_u8 resized_image; @@ -2646,7 +2638,7 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { n_patches /= 4; - } else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) { + } else if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { if (ctx->minicpmv_version == 2) { n_patches = 96; } @@ -2656,7 +2648,10 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i else if (ctx->minicpmv_version == 4) { n_patches = 64; } - } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) { + else { + GGML_ABORT("Unknown minicpmv version"); + } + } else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { int patch_size = params.patch_size * 2; int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0); int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0); @@ -2761,11 +2756,6 @@ static std::vector> get_2d_sincos_pos_embed(int embed_dim, co } bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { - if (!ctx->has_vision_encoder) { - LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__); - return false; - } - clip_image_f32_batch imgs; clip_image_f32_ptr img_copy(clip_image_f32_init()); *img_copy = *img; @@ -2776,20 +2766,11 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3 bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs_c_ptr, float * vec) { const clip_image_f32_batch & imgs = *imgs_c_ptr; - - if (!ctx->has_vision_encoder) { - LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__); - return false; - } - int batch_size = imgs.entries.size(); - if (ctx->has_llava_projector) { - GGML_ASSERT(batch_size == 1); // TODO: support multiple images - } - if (ctx->has_minicpmv_projector) { - GGML_ASSERT(batch_size == 1); - } - if (ctx->has_glm_projector) { + + if (ctx->has_llava_projector + || ctx->proj_type == PROJECTOR_TYPE_MINICPMV + || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { GGML_ASSERT(batch_size == 1); } @@ -2799,21 +2780,12 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima ggml_backend_sched_alloc_graph(ctx->sched.get(), gf); // set inputs - const auto & model = ctx->vision_model; + const auto & model = ctx->vision_model; const auto & hparams = model.hparams; - // TODO @ngxson : this is ugly, need to refactor later - bool support_dynamic_size = ctx->has_minicpmv_projector - || ctx->has_qwen2vl_merger - || ctx->proj_type == PROJECTOR_TYPE_PIXTRAL; + const int image_size_width = imgs.entries[0]->nx; + const int image_size_height = imgs.entries[0]->ny; - const int image_size = hparams.image_size; - int image_size_width = image_size; - int image_size_height = image_size; - if (support_dynamic_size) { - image_size_width = imgs.entries[0]->nx; - image_size_height = imgs.entries[0]->ny; - } const int patch_size = hparams.patch_size; const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); const int num_positions = num_patches + (model.class_embedding ? 1 : 0); @@ -2839,14 +2811,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima for (size_t i = 0; i < imgs.entries.size(); i++) { const int nx = imgs.entries[i]->nx; const int ny = imgs.entries[i]->ny; - - if (ctx->has_glm_projector - || ctx->has_llava_projector - || ctx->proj_type == PROJECTOR_TYPE_GEMMA3 - || ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) { - GGML_ASSERT(nx == image_size && ny == image_size); - } - const int n = nx * ny; for (int b = 0; b < batch_size; b++) { @@ -2864,13 +2828,15 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw)); } - if (ctx->has_minicpmv_projector) { + + if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { { // inspired from siglip: // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316 struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); - int* positions_data = (int*)malloc(ggml_nbytes(positions)); + std::vector pos_data(ggml_nelements(positions)); + int * data = pos_data.data(); int bucket_coords_h[1024]; int bucket_coords_w[1024]; for (int i = 0; i < pos_h; i++){ @@ -2881,11 +2847,10 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } for (int i = 0, id = 0; i < pos_h; i++){ for (int j = 0; j < pos_w; j++){ - positions_data[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j]; + data[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j]; } } - ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); - free(positions_data); + ggml_backend_tensor_set(positions, data, 0, ggml_nbytes(positions)); } { @@ -2903,30 +2868,28 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima else if (ctx->minicpmv_version == 4) { embed_dim = 3584; } + else { + GGML_ABORT("Unknown minicpmv version"); + } + + // TODO @ngxson : this is very inefficient, can we do this using ggml_sin and ggml_cos? auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h)); - float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed)); - for(int i=0;i < pos_w * pos_h; ++i){ - for(int j=0; j < embed_dim; ++j){ - pos_embed_data[i * embed_dim + j] = pos_embed_t[i][j]; + std::vector pos_data(ggml_nelements(pos_embed)); + float * data = pos_data.data(); + for(int i = 0; i < pos_w * pos_h; ++i){ + for(int j = 0; j < embed_dim; ++j){ + data[i * embed_dim + j] = pos_embed_t[i][j]; } } - ggml_backend_tensor_set(pos_embed, pos_embed_data, 0, ggml_nbytes(pos_embed)); - free(pos_embed_data); + ggml_backend_tensor_set(pos_embed, data, 0, ggml_nbytes(pos_embed)); } } else { - if (model.class_embedding) { - struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); + // non-minicpmv models - void* zero_mem = malloc(ggml_nbytes(embeddings)); - memset(zero_mem, 0, ggml_nbytes(embeddings)); - ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); - free(zero_mem); - } - - if (ctx->has_qwen2vl_merger) { + if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); const int pw = image_size_width / patch_size; @@ -2978,6 +2941,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima ggml_backend_tensor_set(pos, pos_data.data(), 0, ggml_nbytes(pos)); } else { + // llava and other models struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); int* positions_data = (int*)malloc(ggml_nbytes(positions)); @@ -2987,7 +2951,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); free(positions_data); - if (!ctx->has_glm_projector) { + if (ctx->proj_type != PROJECTOR_TYPE_GLM_EDGE) { struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches"); // The patches vector is used to get rows to index into the embeds with; // we should skip dim 0 only if we have CLS to avoid going out of bounds @@ -3166,7 +3130,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->vision_model.mm_2_b->ne[0]; case PROJECTOR_TYPE_MLP_NORM: return ctx->vision_model.mm_3_b->ne[0]; - case PROJECTOR_TYPE_RESAMPLER: + case PROJECTOR_TYPE_MINICPMV: if (ctx->minicpmv_version == 2) { return 4096; } else if (ctx->minicpmv_version == 3) { @@ -3174,36 +3138,33 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { } else if (ctx->minicpmv_version == 4) { return 3584; } - break; // Should not happen if version is valid + GGML_ABORT("Unknown minicpmv version"); case PROJECTOR_TYPE_GLM_EDGE: return ctx->vision_model.mm_model_mlp_3_w->ne[1]; - case PROJECTOR_TYPE_MERGER: + case PROJECTOR_TYPE_QWEN2VL: return ctx->vision_model.mm_1_b->ne[0]; case PROJECTOR_TYPE_GEMMA3: return ctx->vision_model.mm_input_proj_w->ne[0]; case PROJECTOR_TYPE_IDEFICS3: return ctx->vision_model.projection->ne[1]; default: - break; // Fall through to throw + GGML_ABORT("Unknown projector type"); } - - std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type]; - throw std::runtime_error(string_format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); } int clip_is_minicpmv(const struct clip_ctx * ctx) { - if (ctx->has_minicpmv_projector) { + if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { return ctx->minicpmv_version; } return 0; } bool clip_is_glm(const struct clip_ctx * ctx) { - return ctx->has_glm_projector; + return ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE; } bool clip_is_qwen2vl(const struct clip_ctx * ctx) { - return ctx->has_qwen2vl_merger; + return ctx->proj_type == PROJECTOR_TYPE_QWEN2VL; } bool clip_is_llava(const struct clip_ctx * ctx) { @@ -3214,29 +3175,6 @@ bool clip_is_gemma3(const struct clip_ctx * ctx) { return ctx->proj_type == PROJECTOR_TYPE_GEMMA3; } -// Determine the number of encoder layers to iterate over -int get_deepest_feature_layer(const struct clip_ctx * ctx) { - // Get the index of the second to last layer; this is the - // default for models that have a llava projector - const auto & hparams = ctx->vision_model.hparams; - int n_layer = hparams.n_layer - 1; - int deepest_feature_layer = -1; - - // Handle other projectors; incrementing here indicates that we - // should use the last encoder layer for the vision features. - if (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) { - n_layer += 1; - } - - // If we set explicit vision feature layers, only go up to the deepest one - for (const auto & feature_layer : hparams.vision_feature_layer) { - if (feature_layer > deepest_feature_layer) { - deepest_feature_layer = feature_layer; - } - } - return deepest_feature_layer < 0 ? n_layer : deepest_feature_layer; -} - bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) { clip_image_f32 clip_img; clip_img.buf.resize(h * w * 3); diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 5fc45d3e23..6ba42ad892 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -114,8 +114,6 @@ CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx); CLIP_API bool clip_is_llava(const struct clip_ctx * ctx); CLIP_API bool clip_is_gemma3(const struct clip_ctx * ctx); -CLIP_API int get_deepest_feature_layer(const struct clip_ctx * ctx); - CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec); From 2d451c80590b9ac250322769ac13d3b4870dbcf7 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 26 Apr 2025 22:58:12 +0200 Subject: [PATCH 015/126] common : add common_remote_get_content (#13123) * common : add common_remote_get_content * support max size and timeout * add tests --- common/arg.cpp | 103 ++++++++++++++++++++++++++------------ common/arg.h | 9 ++++ tests/test-arg-parser.cpp | 47 +++++++++++++++++ 3 files changed, 126 insertions(+), 33 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 0657553e4e..de173159f4 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -162,6 +162,10 @@ struct common_hf_file_res { #ifdef LLAMA_USE_CURL +bool common_has_curl() { + return true; +} + #ifdef __linux__ #include #elif defined(_WIN32) @@ -527,6 +531,50 @@ static bool common_download_model( return true; } +std::pair> common_remote_get_content(const std::string & url, const common_remote_params & params) { + curl_ptr curl(curl_easy_init(), &curl_easy_cleanup); + curl_slist_ptr http_headers; + std::vector res_buffer; + + curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); + curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L); + typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data); + auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t { + auto data_vec = static_cast *>(data); + data_vec->insert(data_vec->end(), (char *)ptr, (char *)ptr + size * nmemb); + return size * nmemb; + }; + curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast(write_callback)); + curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_buffer); +#if defined(_WIN32) + curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); +#endif + if (params.timeout > 0) { + curl_easy_setopt(curl.get(), CURLOPT_TIMEOUT, params.timeout); + } + if (params.max_size > 0) { + curl_easy_setopt(curl.get(), CURLOPT_MAXFILESIZE, params.max_size); + } + http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp"); + for (const auto & header : params.headers) { + http_headers.ptr = curl_slist_append(http_headers.ptr, header.c_str()); + } + curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr); + + CURLcode res = curl_easy_perform(curl.get()); + + if (res != CURLE_OK) { + std::string error_msg = curl_easy_strerror(res); + throw std::runtime_error("error: cannot make GET request: " + error_msg); + } + + long res_code; + curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code); + + return { res_code, std::move(res_buffer) }; +} + /** * Allow getting the HF file from the HF repo with tag (like ollama), for example: * - bartowski/Llama-3.2-3B-Instruct-GGUF:q4 @@ -546,45 +594,26 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_ throw std::invalid_argument("error: invalid HF repo format, expected /[:quant]\n"); } - // fetch model info from Hugging Face Hub API - curl_ptr curl(curl_easy_init(), &curl_easy_cleanup); - curl_slist_ptr http_headers; - std::string res_str; + std::string url = get_model_endpoint() + "v2/" + hf_repo + "/manifests/" + tag; - std::string model_endpoint = get_model_endpoint(); - - std::string url = model_endpoint + "v2/" + hf_repo + "/manifests/" + tag; - curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); - curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); - typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data); - auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t { - static_cast(data)->append((char * ) ptr, size * nmemb); - return size * nmemb; - }; - curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast(write_callback)); - curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_str); -#if defined(_WIN32) - curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); -#endif + // headers + std::vector headers; + headers.push_back("Accept: application/json"); if (!bearer_token.empty()) { - std::string auth_header = "Authorization: Bearer " + bearer_token; - http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str()); + headers.push_back("Authorization: Bearer " + bearer_token); } // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response - http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp"); - http_headers.ptr = curl_slist_append(http_headers.ptr, "Accept: application/json"); - curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr); + // User-Agent header is already set in common_remote_get_content, no need to set it here - CURLcode res = curl_easy_perform(curl.get()); + // make the request + common_remote_params params; + params.headers = headers; + auto res = common_remote_get_content(url, params); + long res_code = res.first; + std::string res_str(res.second.data(), res.second.size()); + std::string ggufFile; + std::string mmprojFile; - if (res != CURLE_OK) { - throw std::runtime_error("error: cannot make GET request to HF API"); - } - - long res_code; - std::string ggufFile = ""; - std::string mmprojFile = ""; - curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code); if (res_code == 200) { // extract ggufFile.rfilename in json, using regex { @@ -618,6 +647,10 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_ #else +bool common_has_curl() { + return false; +} + static bool common_download_file_single(const std::string &, const std::string &, const std::string &) { LOG_ERR("error: built without CURL, cannot download model from internet\n"); return false; @@ -640,6 +673,10 @@ static struct common_hf_file_res common_get_hf_file(const std::string &, const s return {}; } +std::pair> common_remote_get_content(const std::string & url, const common_remote_params & params) { + throw std::runtime_error("error: built without CURL, cannot download model from the internet"); +} + #endif // LLAMA_USE_CURL // diff --git a/common/arg.h b/common/arg.h index 49ab8667b1..70bea100fd 100644 --- a/common/arg.h +++ b/common/arg.h @@ -78,3 +78,12 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e // function to be used by test-arg-parser common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); +bool common_has_curl(); + +struct common_remote_params { + std::vector headers; + long timeout = 0; // CURLOPT_TIMEOUT, in seconds ; 0 means no timeout + long max_size = 0; // max size of the response ; unlimited if 0 ; max is 2GB +}; +// get remote file content, returns +std::pair> common_remote_get_content(const std::string & url, const common_remote_params & params); diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp index 537fc63a4c..21dbd54042 100644 --- a/tests/test-arg-parser.cpp +++ b/tests/test-arg-parser.cpp @@ -126,6 +126,53 @@ int main(void) { assert(params.cpuparams.n_threads == 1010); #endif // _WIN32 + if (common_has_curl()) { + printf("test-arg-parser: test curl-related functions\n\n"); + const char * GOOD_URL = "https://raw.githubusercontent.com/ggml-org/llama.cpp/refs/heads/master/README.md"; + const char * BAD_URL = "https://www.google.com/404"; + const char * BIG_FILE = "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v1.bin"; + + { + printf("test-arg-parser: test good URL\n\n"); + auto res = common_remote_get_content(GOOD_URL, {}); + assert(res.first == 200); + assert(res.second.size() > 0); + std::string str(res.second.data(), res.second.size()); + assert(str.find("llama.cpp") != std::string::npos); + } + + { + printf("test-arg-parser: test bad URL\n\n"); + auto res = common_remote_get_content(BAD_URL, {}); + assert(res.first == 404); + } + + { + printf("test-arg-parser: test max size error\n"); + common_remote_params params; + params.max_size = 1; + try { + common_remote_get_content(GOOD_URL, params); + assert(false && "it should throw an error"); + } catch (std::exception & e) { + printf(" expected error: %s\n\n", e.what()); + } + } + + { + printf("test-arg-parser: test timeout error\n"); + common_remote_params params; + params.timeout = 1; + try { + common_remote_get_content(BIG_FILE, params); + assert(false && "it should throw an error"); + } catch (std::exception & e) { + printf(" expected error: %s\n\n", e.what()); + } + } + } else { + printf("test-arg-parser: no curl, skipping curl-related functions\n"); + } printf("test-arg-parser: all tests OK\n\n"); } From ca2bb89eac2097ab4620448737e58af8452e444b Mon Sep 17 00:00:00 2001 From: HimariO Date: Sun, 27 Apr 2025 16:10:34 +0800 Subject: [PATCH 016/126] clip : Add Qwen2.5VL support (#12402) * implment vision model architecture, gguf convertor * handle window attention inputs * add debug utils * fix few incorrect tensor memory layout * move position id remap out of ggml to avoid int32 cuda operations * cleaning up * ignore transformers Qwen2_5_xxx type check * remove not so often use `qwen2vl-cli` debug functions * remove commented-out code blocks * fix attn weight scaling after rebase * add `PROJECTOR_TYPE_QWEN2_5_VL` * remove `KEY_USE_GLU_MLP`, `KEY_USE_RMS_NORM` * replace `KEY_FULLATTN_BLK_IDX` with `KEY_WIN_ATTN_PATTERN` * remove `attn_window_size` from gguf * fix model conversion * clean up * fix merging problem * add test --------- Co-authored-by: Xuan Son Nguyen --- convert_hf_to_gguf.py | 11 +- examples/llava/clip-impl.h | 8 + examples/llava/clip.cpp | 398 ++++++++++++++++++++++++++++- examples/llava/qwen2_vl_surgery.py | 182 ++++++++----- examples/llava/qwen2vl-cli.cpp | 96 +++++-- examples/llava/tests.sh | 1 + 6 files changed, 594 insertions(+), 102 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index cf35fb86ec..ea3a951b93 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2554,11 +2554,12 @@ class Qwen2VLModel(TextModel): except FileNotFoundError: self._set_vocab_gpt2() - def get_tensors(self) -> Iterator[tuple[str, Tensor]]: - for name, data in super().get_tensors(): - if name.startswith("visual."): - continue - yield name, data + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + if name.startswith("visual."): + # skip visual tensors + return [] + return [(self.map_tensor_name(name), data_torch)] @ModelBase.register("WavTokenizerDec") diff --git a/examples/llava/clip-impl.h b/examples/llava/clip-impl.h index 16d0a8efc5..04bfcbb5e5 100644 --- a/examples/llava/clip-impl.h +++ b/examples/llava/clip-impl.h @@ -34,9 +34,14 @@ #define KEY_PROJ_SCALE_FACTOR "clip.vision.projector.scale_factor" #define KEY_PROJ_TYPE "clip.projector_type" +#define KEY_USE_GLU_MLP "clip.use_glu_mlp" // for qwen2.5vl +#define KEY_USE_RMS_NORM "clip.use_rms_norm" // for qwen2.5vl + #define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type" #define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints" #define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution" +#define KEY_WIN_ATTN_PATTERN "clip.vision.n_wa_pattern" +#define KEY_ATTN_WINDOW_SIZE "clip.vision.window_size" // @@ -55,6 +60,7 @@ #define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s" #define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s" #define TN_FFN_UP "%s.blk.%d.ffn_up.%s" +#define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s" #define TN_LN_1 "%s.blk.%d.ln1.%s" #define TN_LN_2 "%s.blk.%d.ln2.%s" #define TN_LN_PRE "%s.pre_ln.%s" @@ -95,6 +101,7 @@ enum projector_type { PROJECTOR_TYPE_GEMMA3, PROJECTOR_TYPE_IDEFICS3, PROJECTOR_TYPE_PIXTRAL, + PROJECTOR_TYPE_QWEN25VL, PROJECTOR_TYPE_UNKNOWN, }; @@ -105,6 +112,7 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_MINICPMV, "resampler"}, { PROJECTOR_TYPE_GLM_EDGE, "adapter"}, { PROJECTOR_TYPE_QWEN2VL, "qwen2vl_merger"}, + { PROJECTOR_TYPE_QWEN25VL, "qwen2.5vl_merger"}, { PROJECTOR_TYPE_GEMMA3, "gemma3"}, { PROJECTOR_TYPE_IDEFICS3, "idefics3"}, { PROJECTOR_TYPE_PIXTRAL, "pixtral"}, diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index e8c01c68a9..b6a1f40e8a 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -28,6 +28,7 @@ #include #include #include +#include struct clip_logger_state g_logger_state = {GGML_LOG_LEVEL_CONT, clip_log_callback_default, NULL}; @@ -169,6 +170,8 @@ struct clip_hparams { std::vector image_grid_pinpoints; int32_t image_crop_resolution; std::unordered_set vision_feature_layer; + int32_t attn_window_size; + int32_t n_wa_pattern; }; struct clip_layer { @@ -200,6 +203,9 @@ struct clip_layer { struct ggml_tensor * ff_down_w = nullptr; struct ggml_tensor * ff_down_b = nullptr; + struct ggml_tensor * ff_g_w = NULL; + struct ggml_tensor * ff_g_b = NULL; + // layernorm 2 struct ggml_tensor * ln_2_w = nullptr; struct ggml_tensor * ln_2_b = nullptr; @@ -319,6 +325,7 @@ struct clip_ctx { float image_std[3]; bool use_gelu = false; bool use_silu = false; + int32_t ftype = 1; gguf_context_ptr ctx_gguf; ggml_context_ptr ctx_data; @@ -762,6 +769,236 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i return gf; } +static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_image_f32_batch & imgs) { + const auto & model = ctx->vision_model; + const auto & hparams = model.hparams; + + const int image_size_width = imgs.entries[0]->nx; + const int image_size_height = imgs.entries[0]->ny; + + const bool use_mrope = ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL; + const bool use_window_attn = hparams.n_wa_pattern > 0; + + const int n_wa_pattern = hparams.n_wa_pattern; + const int patch_size = hparams.patch_size; + const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); + const int patches_w = image_size_width / patch_size; + const int patches_h = image_size_height / patch_size; + const int num_positions = num_patches + (model.class_embedding ? 1 : 0); + const int num_position_ids = use_mrope ? num_positions * 4 : num_positions; + const int hidden_size = hparams.hidden_size; + const int n_head = hparams.n_head; + const int d_head = hidden_size / n_head; + const float eps = hparams.eps; + + int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; + + const int batch_size = imgs.entries.size(); + GGML_ASSERT(batch_size == 1); + + struct ggml_init_params params = { + /*.mem_size =*/ ctx->buf_compute_meta.size(), + /*.mem_buffer =*/ ctx->buf_compute_meta.data(), + /*.no_alloc =*/ true, + }; + + ggml_context_ptr ctx0_ptr(ggml_init(params)); + auto ctx0 = ctx0_ptr.get(); + + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3, batch_size); + ggml_set_name(inp_raw, "inp_raw"); + ggml_set_input(inp_raw); + + struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + + GGML_ASSERT(image_size_width % (patch_size * 2) == 0); + GGML_ASSERT(image_size_height % (patch_size * 2) == 0); + + auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_add(ctx0, inp, inp_1); + + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b] + inp = ggml_reshape_4d( + ctx0, inp, + hidden_size * 2, patches_w / 2, patches_h, batch_size); + inp = ggml_reshape_4d( + ctx0, inp, + hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2)); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3)); + inp = ggml_reshape_3d( + ctx0, inp, + hidden_size, patches_w * patches_h, batch_size); + + if (model.patch_bias) { + // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); + inp = ggml_add(ctx0, inp, model.patch_bias); + } + struct ggml_tensor * embeddings = inp; + struct ggml_tensor * window_mask = nullptr; + struct ggml_tensor * window_idx = nullptr; + struct ggml_tensor * inv_window_idx = nullptr; + + struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + // pre-layernorm + if (model.pre_ln_w) { + embeddings = ggml_rms_norm(ctx0, embeddings, eps); + ggml_set_name(embeddings, "pre_ln"); + + embeddings = ggml_mul(ctx0, embeddings, model.pre_ln_w); + } + + if (use_window_attn) { + // handle window attention inputs + inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4); + ggml_set_name(inv_window_idx, "inv_window_idx"); + ggml_set_input(inv_window_idx); + // mask for window attention + window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, num_positions, num_positions); + ggml_set_name(window_mask, "window_mask"); + ggml_set_input(window_mask); + + // embeddings shape: [hidden_size, patches_w * patches_h, batch_size] + GGML_ASSERT(batch_size == 1); + embeddings = ggml_reshape_2d(ctx0, embeddings, hidden_size * 4, patches_w * patches_h * batch_size / 4); + embeddings = ggml_get_rows(ctx0, embeddings, inv_window_idx); + embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size, patches_w * patches_h, batch_size); + } + + // loop over layers + for (int il = 0; il < ctx->max_feature_layer; il++) { + struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states + + // rmsnorm1 + cur = ggml_rms_norm(ctx0, cur, eps); + cur = ggml_mul(ctx0, cur, model.layers[il].ln_1_w); + + // self-attention + { + + struct ggml_tensor * Q = + ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b); + + Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size); + Q = ggml_rope_multi( + ctx0, Q, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); + Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size); + + struct ggml_tensor * K = + ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b); + + K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); + K = ggml_rope_multi( + ctx0, K, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); + K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); + + struct ggml_tensor * V = + ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b); + + V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size); + V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); + V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); + + struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); + const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true; + if (full_attn) { + KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); + } else { + KQ = ggml_soft_max_ext(ctx0, KQ, window_mask, 1.0f / sqrtf((float)d_head), 0.0f); + } + + struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); + KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size); + KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); + + cur = ggml_cont_3d(ctx0, KQV, hidden_size, num_positions, batch_size); + } + + // attention output + cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b); + + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, embeddings); + + embeddings = cur; // embeddings = residual, cur = hidden_states + + // rms norm2 + cur = ggml_rms_norm(ctx0, cur, eps); + cur = ggml_mul(ctx0, cur, model.layers[il].ln_2_w); + + // mlp + // ffn_up + auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur); + cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_o_b); + + auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_g_w, cur); + cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_g_b); + // TODO : only 2 of these 3 are actually used, should we remove one of them? + if (ctx->use_gelu) { + cur_gate = ggml_gelu_inplace(ctx0, cur_gate); + } else if (ctx->use_silu) { + cur_gate = ggml_silu_inplace(ctx0, cur_gate); + } else { + cur_gate = ggml_gelu_quick_inplace(ctx0, cur_gate); + } + cur = ggml_mul(ctx0, cur_gate, cur_up); + + // ffn_down + cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b); + + // residual 2 + cur = ggml_add(ctx0, embeddings, cur); + + embeddings = cur; + } + + // post-layernorm + if (model.post_ln_w) { + embeddings = ggml_rms_norm(ctx0, embeddings, eps); + ggml_set_name(embeddings, "post_ln"); + + embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w); + } + + embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size); + + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + + // GELU activation + embeddings = ggml_gelu(ctx0, embeddings); + + // Second linear layer + embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); + + if (use_window_attn) { + window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4); + ggml_set_name(window_idx, "window_idx"); + ggml_set_input(window_idx); + + // embeddings shape: [hidden_size, patches_w * patches_h, batch_size] + GGML_ASSERT(batch_size == 1); + embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4); + embeddings = ggml_get_rows(ctx0, embeddings, window_idx); + embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4, batch_size); + } + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; +} + static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) { const auto & model = ctx->vision_model; const auto & hparams = model.hparams; @@ -1331,6 +1568,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 GGML_ASSERT(imgs.entries.size() == 1); res = clip_image_build_graph_pixtral(ctx, *imgs.entries[0]); } break; + case PROJECTOR_TYPE_QWEN25VL: + { + res = clip_image_build_graph_qwen25vl(ctx, imgs); + } break; default: { // TODO: we should have one build_* function per model @@ -1507,6 +1748,10 @@ struct clip_model_loader { { hparams.rope_theta = 10000.0f; } break; + case PROJECTOR_TYPE_QWEN25VL: + { + get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern); + } break; default: break; } @@ -1600,8 +1845,10 @@ struct clip_model_loader { // legacy naming (the in and out is reversed! don't ask me why) layer.ff_i_w = layer.ff_down_w; layer.ff_o_w = layer.ff_up_w; + layer.ff_g_w = layer.ff_gate_w; layer.ff_i_b = layer.ff_down_b; layer.ff_o_b = layer.ff_up_b; + layer.ff_g_b = layer.ff_gate_b; } switch (ctx_clip.proj_type) { @@ -1700,6 +1947,7 @@ struct clip_model_loader { vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H,"weight")); } break; case PROJECTOR_TYPE_QWEN2VL: + case PROJECTOR_TYPE_QWEN25VL: { vision_model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); vision_model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias")); @@ -2651,7 +2899,7 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i else { GGML_ABORT("Unknown minicpmv version"); } - } else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { + } else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) { int patch_size = params.patch_size * 2; int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0); int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0); @@ -2792,6 +3040,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int pos_w = ctx->load_image_size.width / patch_size; const int pos_h = ctx->load_image_size.height / patch_size; + const bool use_window_attn = hparams.n_wa_pattern > 0; // for qwen2.5vl + { struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw"); std::vector inp_data(ggml_nelements(inp_raw)); @@ -2890,31 +3140,93 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima // non-minicpmv models if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { - struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); + // pw * ph = number of tokens output by ViT after apply patch merger + // ipw * ipw = number of vision token been processed inside ViT + const int merge_ratio = 2; + const int pw = image_size_width / patch_size / merge_ratio; + const int ph = image_size_height / patch_size / merge_ratio; + const int ipw = image_size_width / patch_size; + const int iph = image_size_height / patch_size; - const int pw = image_size_width / patch_size; - const int ph = image_size_height / patch_size; - int* positions_data = (int*)malloc(ggml_nbytes(positions)); + std::vector idx (ph * pw); + std::vector inv_idx(ph * pw); + + if (use_window_attn) { + const int attn_window_size = 112; + struct ggml_tensor * window_idx = ggml_graph_get_tensor(gf, "window_idx"); + struct ggml_tensor * inv_window_idx = ggml_graph_get_tensor(gf, "inv_window_idx"); + struct ggml_tensor * window_mask = ggml_graph_get_tensor(gf, "window_mask"); + + const int grid_window = attn_window_size / patch_size / merge_ratio; + int dst = 0; + // [num_vision_tokens, num_vision_tokens] attention mask tensor + std::vector mask(pow(ipw * iph, 2), std::numeric_limits::lowest()); + int mask_row = 0; + + for (int y = 0; y < ph; y += grid_window) + { + for (int x = 0; x < pw; x += grid_window) + { + const int win_h = std::min(grid_window, ph - y); + const int win_w = std::min(grid_window, pw - x); + const int dst_0 = dst; + // group all tokens belong to the same window togather (to a continue range) + for (int dy = 0; dy < win_h; dy++) { + for (int dx = 0; dx < win_w; dx++) { + const int src = (y + dy) * pw + (x + dx); + assert(src < (int)idx.size()); + assert(dst < (int)inv_idx.size()); + idx [src] = dst; + inv_idx[dst] = src; + dst++; + } + } + + for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) { + int row_offset = mask_row * (ipw * iph); + std::fill( + mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio), + mask.begin() + row_offset + (dst * merge_ratio * merge_ratio), + 0.0); + mask_row++; + } + } + } + + ggml_backend_tensor_set(window_idx, idx.data(), 0, ggml_nbytes(window_idx)); + ggml_backend_tensor_set(inv_window_idx, inv_idx.data(), 0, ggml_nbytes(inv_window_idx)); + ggml_backend_tensor_set(window_mask, mask.data(), 0, ggml_nbytes(window_mask)); + } else { + std::iota(idx.begin(), idx.end(), 0); + std::iota(inv_idx.begin(), inv_idx.end(), 0); + } + + struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); + const int mpow = merge_ratio * merge_ratio; + std::vector positions_data(ggml_nelements(positions)); + int * data = positions_data.data(); int ptr = 0; - for (int y = 0; y < ph; y+=2) + for (int y = 0; y < iph; y += merge_ratio) { - for (int x = 0; x < pw; x+=2) + for (int x = 0; x < ipw; x += merge_ratio) { for (int dy = 0; dy < 2; dy++) { for (int dx = 0; dx < 2; dx++) { - positions_data[ptr] = y + dy; - positions_data[num_patches + ptr] = x + dx; - positions_data[num_patches * 2 + ptr] = y + dy; - positions_data[num_patches * 3 + ptr] = x + dx; + auto remap = idx[ptr / mpow]; + remap = remap * mpow + (ptr % mpow); + + data[ remap] = y + dy; + data[ num_patches + remap] = x + dx; + data[2 * num_patches + remap] = y + dy; + data[3 * num_patches + remap] = x + dx; ptr++; } } } } - ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); - free(positions_data); + ggml_backend_tensor_set(positions, data, 0, ggml_nbytes(positions)); } else if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) { // do nothing @@ -2967,6 +3279,65 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } } + if (use_window_attn && ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) { + struct ggml_tensor * window_idx = ggml_graph_get_tensor(gf, "window_idx"); + struct ggml_tensor * inv_window_idx = ggml_graph_get_tensor(gf, "inv_window_idx"); + struct ggml_tensor * window_mask = ggml_graph_get_tensor(gf, "window_mask"); + + const int merge_ratio = 2; + const int attn_window_size = 112; + const int pw = image_size_width / patch_size / merge_ratio; + const int ph = image_size_height / patch_size / merge_ratio; + const int grid_window = attn_window_size / patch_size / merge_ratio; + const int ipw = image_size_width / patch_size; + const int iph = image_size_height / patch_size; + /* + pw * ph = number of tokens output by ViT after apply patch merger + ipw * ipw = number of vision token been processed inside ViT + */ + + std::vector idx(ph * pw); + std::vector inv_idx(ph * pw); + int dst = 0; + // [num_vision_tokens, num_vision_tokens] attention mask tensor + std::vector mask(pow(ipw * iph, 2), std::numeric_limits::lowest()); + int mask_row = 0; + + for (int y = 0; y < ph; y+=grid_window) + { + for (int x = 0; x < pw; x+=grid_window) + { + const int win_h = std::min(grid_window, ph - y); + const int win_w = std::min(grid_window, pw - x); + const int dst_0 = dst; + // group all tokens belong to the same window togather (to a continue range) + for (int dy = 0; dy < win_h; dy++) { + for (int dx = 0; dx < win_w; dx++) { + const int src = (y + dy) * pw + (x + dx); + assert(src < (int)idx.size()); + assert(dst < (int)inv_idx.size()); + idx[src] = dst; + inv_idx[dst] = src; + dst++; + } + } + + for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) { + int row_offset = mask_row * (ipw * iph); + std::fill( + mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio), + mask.begin() + row_offset + (dst * merge_ratio * merge_ratio), + 0.0); + mask_row++; + } + } + } + + ggml_backend_tensor_set(window_idx, idx.data(), 0, ggml_nbytes(window_idx)); + ggml_backend_tensor_set(inv_window_idx, inv_idx.data(), 0, ggml_nbytes(inv_window_idx)); + ggml_backend_tensor_set(window_mask, mask.data(), 0, ggml_nbytes(window_mask)); + } + ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads); auto status = ggml_backend_sched_graph_compute(ctx->sched.get(), gf); @@ -3142,6 +3513,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { case PROJECTOR_TYPE_GLM_EDGE: return ctx->vision_model.mm_model_mlp_3_w->ne[1]; case PROJECTOR_TYPE_QWEN2VL: + case PROJECTOR_TYPE_QWEN25VL: return ctx->vision_model.mm_1_b->ne[0]; case PROJECTOR_TYPE_GEMMA3: return ctx->vision_model.mm_input_proj_w->ne[0]; diff --git a/examples/llava/qwen2_vl_surgery.py b/examples/llava/qwen2_vl_surgery.py index c87606b4fd..7951a6fa89 100644 --- a/examples/llava/qwen2_vl_surgery.py +++ b/examples/llava/qwen2_vl_surgery.py @@ -1,14 +1,16 @@ import argparse -from typing import Dict +from typing import Dict, List, Optional import torch import numpy as np from gguf import * from transformers import ( - Qwen2VLForConditionalGeneration, - Qwen2VLProcessor, AutoProcessor, - Qwen2VLConfig + Qwen2VLConfig, + Qwen2VLProcessor, + Qwen2VLForConditionalGeneration, + Qwen2_5_VLConfig, # type: ignore[reportAttributeAccessIssue] + Qwen2_5_VLForConditionalGeneration, # type: ignore[reportAttributeAccessIssue] ) @@ -19,61 +21,93 @@ def k(raw_key: str, arch: str) -> str: return raw_key.format(arch=arch) -def to_gguf_name(name: str) -> str: - og = name - name = name.replace("text_model", "t").replace("vision_model", "v") - name = name.replace("blocks", "blk").replace("embeddings.", "") - name = name.replace("attn.", "attn_") - name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.") - # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln") - name = name.replace("norm1", "ln1").replace("norm2", "ln2") - name = name.replace("merger.mlp", 'mm') - print(f"[to_gguf_name] {og} --> {name}") - return name +def get_n_wa_pattern(fullatt_block_indexes: Optional[List[int]]): + if fullatt_block_indexes is None: + return 0 + n_wa = fullatt_block_indexes[0] + for a, b in zip(fullatt_block_indexes, fullatt_block_indexes[1:]): + if b - a - 1 != n_wa: + raise ValueError( + f"window/full attention layer should have fix pattern of " + f"for each full-attention layer followed by {n_wa} window-attention layers" + ) + return n_wa + 1 -def find_vision_tensors(qwen2vl, dtype) -> Dict[str, np.ndarray]: - vision_model = qwen2vl.visual - tensor_map = {} - for name, ten in vision_model.state_dict().items(): - ten = ten.numpy() - if 'qkv' in name: - if ten.ndim == 2: # weight - c3, _ = ten.shape - else: # bias - c3 = ten.shape[0] - assert c3 % 3 == 0 - c = c3 // 3 - wq = ten[:c] - wk = ten[c: c * 2] - wv = ten[c * 2:] - tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq - tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk - tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv - elif 'merger' in name: - if name.endswith("ln_q.weight"): - tensor_map['v.post_ln.weight'] = ten - elif name.endswith("ln_q.bias"): - tensor_map['v.post_ln.bias'] = ten +class VL2: + + @staticmethod + def to_gguf_name(name: str) -> str: + og = name + name = name.replace("text_model", "t").replace("vision_model", "v") + name = name.replace("blocks", "blk").replace("embeddings.", "") + name = name.replace("attn.", "attn_") + name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.") + # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln") + name = name.replace("norm1", "ln1").replace("norm2", "ln2") + name = name.replace("merger.mlp", 'mm') + print(f"[to_gguf_name] {og} --> {name}") + return name + + @classmethod + def find_vision_tensors(cls, qwen2vl, dtype) -> Dict[str, np.ndarray]: + vision_model = qwen2vl.visual + tensor_map = {} + for name, ten in vision_model.state_dict().items(): + ten = ten.numpy() + if 'qkv' in name: + if ten.ndim == 2: # weight + c3, _ = ten.shape + else: # bias + c3 = ten.shape[0] + assert c3 % 3 == 0 + c = c3 // 3 + wq = ten[:c] + wk = ten[c: c * 2] + wv = ten[c * 2:] + tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq + tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk + tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv + elif 'merger' in name: + if name.endswith("ln_q.weight"): + tensor_map['v.post_ln.weight'] = ten + elif name.endswith("ln_q.bias"): + tensor_map['v.post_ln.bias'] = ten + else: + # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias" + tensor_map[cls.to_gguf_name(name)] = ten + elif 'patch_embed.proj.weight' in name: + # NOTE: split Conv3D into Conv2Ds + c1, c2, kt, kh, kw = ten.shape + assert kt == 2, "Current implmentation only support temporal_patch_size of 2" + tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...] + tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...] else: - # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias" - tensor_map[to_gguf_name(name)] = ten - elif 'patch_embed.proj.weight' in name: - # NOTE: split Conv3D into Conv2Ds - c1, c2, kt, kh, kw = ten.shape - assert kt == 2, "Current implmentation only support temporal_patch_size of 2" - tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...] - tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...] - else: - tensor_map[to_gguf_name(f"vision_model.{name}")] = ten + tensor_map[cls.to_gguf_name(f"vision_model.{name}")] = ten - for new_name, ten in tensor_map.items(): - if ten.ndim <= 1 or new_name.endswith("_norm.weight"): - tensor_map[new_name] = ten.astype(np.float32) - else: - tensor_map[new_name] = ten.astype(dtype) - tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32) # dummy tensor, just here as a placeholder - return tensor_map + for new_name, ten in tensor_map.items(): + if ten.ndim <= 1 or new_name.endswith("_norm.weight"): + tensor_map[new_name] = ten.astype(np.float32) + else: + tensor_map[new_name] = ten.astype(dtype) + tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32) # dummy tensor, just here as a placeholder + return tensor_map + + +class VL25(VL2): + + @staticmethod + def to_gguf_name(name: str) -> str: + og = name + name = name.replace("text_model", "t").replace("vision_model", "v") + name = name.replace("blocks", "blk").replace("embeddings.", "") + name = name.replace("attn.", "attn_") + name = name.replace("mlp.down_proj", "ffn_down").replace("mlp.up_proj", "ffn_up") + name = name.replace("mlp.gate_proj", "ffn_gate").replace("proj.", "out.") + name = name.replace("norm1", "ln1").replace("norm2", "ln2") + name = name.replace("merger.mlp", 'mm') + print(f"[vl25][to_gguf_name] {og} --> {name}") + return name def main(args): @@ -82,7 +116,7 @@ def main(args): np_dtype = np.float32 ftype = 0 elif args.data_type == 'fp16': - dtype = torch.float32 + dtype = torch.float16 np_dtype = np.float16 ftype = 1 else: @@ -92,11 +126,18 @@ def main(args): model_path = "" model_name = args.model_name print("model_name: ", model_name) - qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained( - model_name, torch_dtype=dtype, device_map="cpu" - ) - cfg: Qwen2VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] - vcfg = cfg.vision_config + if args.model_type == "qwen2vl": + qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained( + model_name, torch_dtype=dtype, device_map="cpu" + ) + cfg: Qwen2VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] + vcfg = cfg.vision_config + else: + qwen2vl = Qwen2_5_VLForConditionalGeneration.from_pretrained( + model_name, torch_dtype=dtype, device_map="cpu" + ) + cfg: Qwen2_5_VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] + vcfg = cfg.vision_config if os.path.isdir(model_name): local_model = True @@ -113,7 +154,6 @@ def main(args): fout.add_bool("clip.has_text_encoder", False) fout.add_bool("clip.has_vision_encoder", True) fout.add_bool("clip.has_qwen2vl_merger", True) - fout.add_string("clip.projector_type", "qwen2vl_merger") print(cfg.vision_config) if 'silu' in cfg.vision_config.hidden_act.lower(): @@ -125,14 +165,25 @@ def main(args): else: raise ValueError() - tensor_map = find_vision_tensors(qwen2vl, np_dtype) + if args.model_type == "qwen2.5vl": + fout.add_uint32("clip.vision.n_wa_pattern", get_n_wa_pattern(vcfg.fullatt_block_indexes)) + fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.hidden_size) + fout.add_uint32("clip.vision.projection_dim", vcfg.out_hidden_size) + fout.add_string("clip.projector_type", "qwen2.5vl_merger") + else: + fout.add_string("clip.projector_type", "qwen2vl_merger") + fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim) + fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size) + + if args.model_type == "qwen2.5vl": + tensor_map = VL25.find_vision_tensors(qwen2vl, np_dtype) + else: + tensor_map = VL2.find_vision_tensors(qwen2vl, np_dtype) for name, data in tensor_map.items(): fout.add_tensor(name, data) fout.add_uint32("clip.vision.patch_size", vcfg.patch_size) fout.add_uint32("clip.vision.image_size", 14 * 40) # some reasonable size that is divable by (14*2) - fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim) - fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size) fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads) fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth) @@ -160,6 +211,7 @@ def main(args): if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct") + parser.add_argument("--model_type", nargs='?', choices=['qwen2vl', 'qwen2.5vl'], default="qwen2vl") parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32") args = parser.parse_args() main(args) diff --git a/examples/llava/qwen2vl-cli.cpp b/examples/llava/qwen2vl-cli.cpp index eca7b7f10b..cf42710869 100644 --- a/examples/llava/qwen2vl-cli.cpp +++ b/examples/llava/qwen2vl-cli.cpp @@ -23,6 +23,9 @@ #include #include #include +#include +#include +#include static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, @@ -367,14 +370,14 @@ static void debug_test_mrope_2d() { // 1. Initialize backend ggml_backend_t backend = NULL; std::string backend_name = ""; -#ifdef GGML_USE_CUDA - fprintf(stderr, "%s: using CUDA backend\n", __func__); - backend = ggml_backend_cuda_init(0); // init device 0 - backend_name = "cuda"; - if (!backend) { - fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); - } -#endif +// #ifdef GGML_USE_CUDA +// fprintf(stderr, "%s: using CUDA backend\n", __func__); +// backend = ggml_backend_cuda_init(0); // init device 0 +// backend_name = "cuda"; +// if (!backend) { +// fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); +// } +// #endif // if there aren't GPU Backends fallback to CPU backend if (!backend) { backend = ggml_backend_cpu_init(); @@ -483,28 +486,82 @@ static void debug_test_mrope_2d() { ggml_backend_free(backend); } -static void debug_dump_img_embed(struct llava_context * ctx_llava) { - int n_embd = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama)); - int ne = n_embd * 4; - float vals[56 * 56 * 3]; +enum model_output_type { + conv3d, + patch_embed, + patch_win_attn_scatter, + first_attn_layer, + last_attn_layer, + attn_softmax, + final_layer, +}; + +static void debug_dump_img_embed(struct llava_context * ctx_llava, model_output_type output_type) { + constexpr int ih = 140; + constexpr int iw = 196; + // constexpr int ih = 56; + // constexpr int iw = 56; + // int n_embd = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama)); + int n_embd = 1280; + int merge = 1; + if (output_type == model_output_type::final_layer) { + n_embd = 2048; + merge = 2; + } + else if (output_type == model_output_type::attn_softmax) { + merge = 1; + n_embd = (ih/14/merge) * (iw/14/merge) * 16; + } + + int ne = (ih/14/merge) * (iw/14/merge) * n_embd; + float vals[iw * ih * 3]; // float embd[ne]; std::vector embd; embd.resize(ne); - for (int i = 0; i < 56*56; i++) + for (int i = 0; i < iw*ih; i++) { for (int c = 0; c < 3; c++) - vals[i * 3 + c] = (float)(i % (56 * 56)) / (56*56); + vals[i * 3 + c] = (float)i / (iw*ih); } - clip_encode_float_image(ctx_llava->ctx_clip, 16, vals, 56, 56, embd.data()); + clip_encode_float_image(ctx_llava->ctx_clip, 8, vals, ih, iw, embd.data()); - std::ofstream outFile("img_embed.bin", std::ios::binary); + std::string file_postfix = ""; + switch (output_type) + { + case model_output_type::conv3d: + file_postfix = "conv3d"; + break; + case model_output_type::patch_embed: + file_postfix = "patch_embed"; + break; + case model_output_type::patch_win_attn_scatter: + file_postfix = "scatter"; + break; + case model_output_type::first_attn_layer: + file_postfix = "first_attn"; + break; + case model_output_type::last_attn_layer: + file_postfix = "last_attn"; + break; + case model_output_type::attn_softmax: + file_postfix = "attn_softmax"; + break; + case model_output_type::final_layer: + file_postfix = "final"; + break; + default: + break; + } + auto output_path = "img_embed_" + file_postfix + ".bin"; + + std::ofstream outFile(output_path, std::ios::binary); if (outFile.is_open()) { outFile.write(reinterpret_cast(embd.data()), ne * sizeof(float)); outFile.close(); - std::cout << "Data successfully written to mrope.bin" << std::endl; + std::cout << "Data successfully written to ::[ " << output_path << std::endl; } else { std::cerr << "Error opening file!" << std::endl; } @@ -551,8 +608,9 @@ int main(int argc, char ** argv) { } else if (params.image[0].empty()) { auto ctx_llava = llava_init_context(¶ms, model); - debug_test_mrope_2d(); - debug_dump_img_embed(ctx_llava); + // debug_test_mrope_2d(); + debug_dump_img_embed(ctx_llava, model_output_type::final_layer); + // debug_dump_img_embed(ctx_llava, model_output_type::last_attn_layer); llama_perf_context_print(ctx_llava->ctx_llama); ctx_llava->model = NULL; diff --git a/examples/llava/tests.sh b/examples/llava/tests.sh index e612857edc..4002f9d531 100755 --- a/examples/llava/tests.sh +++ b/examples/llava/tests.sh @@ -55,6 +55,7 @@ add_test "llama-mtmd-cli" "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K" # mode add_test "llama-mtmd-cli" "openbmb/MiniCPM-V-2_6-gguf:Q2_K" add_test "llama-mtmd-cli" "openbmb/MiniCPM-o-2_6-gguf:Q4_0" add_test "llama-qwen2vl-cli" "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" +add_test "llama-qwen2vl-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" # to test the big models, run: ./tests.sh big add_test_big "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M" From 59e991c23cc44cb5fb657e7b9358cac21fb79828 Mon Sep 17 00:00:00 2001 From: LostRuins Concedo <39025047+LostRuins@users.noreply.github.com> Date: Sun, 27 Apr 2025 18:43:37 +0800 Subject: [PATCH 017/126] Fixes Qwen2.5VL segfault during inference with https://github.com/ggml-org/llama.cpp/pull/12402 as has_qwen2vl_merger migration was incomplete (#13133) --- examples/llava/clip.cpp | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index b6a1f40e8a..3cd27d5b17 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -1718,7 +1718,8 @@ struct clip_model_loader { if (ctx_clip.proj_type == PROJECTOR_TYPE_MINICPMV || ctx_clip.proj_type == PROJECTOR_TYPE_GLM_EDGE - || ctx_clip.proj_type == PROJECTOR_TYPE_QWEN2VL) { + || ctx_clip.proj_type == PROJECTOR_TYPE_QWEN2VL + || ctx_clip.proj_type == PROJECTOR_TYPE_QWEN25VL) { n_layer += 1; } @@ -2744,7 +2745,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str } return true; } - else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { + else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) { clip_image_u8 resized; auto patch_size = clip_get_patch_size(ctx) * 2; int nx = ceil((float)img->nx / patch_size) * patch_size; @@ -3139,7 +3140,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima else { // non-minicpmv models - if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { + if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) { // pw * ph = number of tokens output by ViT after apply patch merger // ipw * ipw = number of vision token been processed inside ViT const int merge_ratio = 2; @@ -3279,7 +3280,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } } - if (use_window_attn && ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) { + if (use_window_attn && (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL)) { struct ggml_tensor * window_idx = ggml_graph_get_tensor(gf, "window_idx"); struct ggml_tensor * inv_window_idx = ggml_graph_get_tensor(gf, "inv_window_idx"); struct ggml_tensor * window_mask = ggml_graph_get_tensor(gf, "window_mask"); From e291450b7602d7a36239e4ceeece37625f838373 Mon Sep 17 00:00:00 2001 From: R0CKSTAR Date: Sun, 27 Apr 2025 19:22:49 +0800 Subject: [PATCH 018/126] musa: fix build warning (#13129) Signed-off-by: Xiaodong Ye --- ggml/src/ggml-cuda/cpy.cu | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu index ed25646e8e..2d46176eab 100644 --- a/ggml/src/ggml-cuda/cpy.cu +++ b/ggml/src/ggml-cuda/cpy.cu @@ -639,6 +639,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg if(ctx.cuda_graph->use_cpy_indirection && !disable_indirection_for_this_node) { ctx.cuda_graph->graph_cpynode_index = graph_cpynode_index; } +#else + GGML_UNUSED(disable_indirection_for_this_node); #endif } From ced44be34290fab450f8344efa047d8a08e723b4 Mon Sep 17 00:00:00 2001 From: matteo Date: Sun, 27 Apr 2025 21:57:32 +0200 Subject: [PATCH 019/126] llama-chat : fix wrong template in GLM4-0414 (#13140) * fix wrong template in GLM4-0414 * fix spaces * no bos token since it is already in the template * moved the chatgml4 check to higher priority * restored template for old GLM models * moved the GLM4 template check in the correct place with correct check --- convert_hf_to_gguf.py | 2 +- src/llama-chat.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index ea3a951b93..d4fec408dd 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -5154,7 +5154,7 @@ class Glm4Model(TextModel): special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) - special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"]) + special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"]) special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index 41f89e3a9d..698c30ce49 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -122,6 +122,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) { } } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) { return LLM_CHAT_TEMPLATE_PHI_3; + } else if (tmpl_contains("[gMASK]")) { + return LLM_CHAT_TEMPLATE_CHATGML_4; } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) { return tmpl_contains("") ? LLM_CHAT_TEMPLATE_FALCON_3 : LLM_CHAT_TEMPLATE_GLMEDGE; } else if (tmpl_contains("<|{{ item['role'] }}|>") && tmpl_contains("<|begin_of_image|>")) { @@ -155,8 +157,6 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) { } else if (tmpl_contains("[gMASK]sop")) { // chatglm3-6b return LLM_CHAT_TEMPLATE_CHATGML_3; - } else if (tmpl_contains("[gMASK]")) { - return LLM_CHAT_TEMPLATE_CHATGML_4; } else if (tmpl_contains(LU8("<用户>"))) { // MiniCPM-3B-OpenHermes-2.5-v2-GGUF return LLM_CHAT_TEMPLATE_MINICPM; From c0a97b762e5ec767dc414f0dc4979befd4c09a52 Mon Sep 17 00:00:00 2001 From: 4onen <11580688+4onen@users.noreply.github.com> Date: Sun, 27 Apr 2025 14:48:26 -0700 Subject: [PATCH 020/126] llama-bench : Add `--override-tensors` arg (#12922) * Add --override-tensors option to llama-bench * Correct llama-bench --override-tensors to --override-tensor * llama-bench: Update --override-tensors parsing to match --tensor-split, appear in test matrix. * Make new llama-bench util functions static to fix Ubuntu CI * llama-bench: Correct -ot corner cases (No -ot calls, leading and trailing empty -ot spans, etc.) --- examples/llama-bench/llama-bench.cpp | 177 ++++++++++++++++++++++++++- 1 file changed, 173 insertions(+), 4 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index cbcbfcee86..564a51bfd7 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -36,6 +36,46 @@ static uint64_t get_time_ns() { return std::chrono::nanoseconds(clock::now().time_since_epoch()).count(); } +static bool tensor_buft_override_equal(const llama_model_tensor_buft_override& a, const llama_model_tensor_buft_override& b) { + if (a.pattern != b.pattern) { + // cString comparison that may be null + if (a.pattern == nullptr || b.pattern == nullptr) { + return false; + } + if (strcmp(a.pattern, b.pattern) != 0) { + return false; + } + } + if (a.buft != b.buft) { + return false; + } + return true; +} + +static bool vec_tensor_buft_override_equal(const std::vector& a, const std::vector& b) { + if (a.size() != b.size()) { + return false; + } + for (size_t i = 0; i < a.size(); i++) { + if (!tensor_buft_override_equal(a[i], b[i])) { + return false; + } + } + return true; +} + +static bool vec_vec_tensor_buft_override_equal(const std::vector>& a, const std::vector>& b) { + if (a.size() != b.size()) { + return false; + } + for (size_t i = 0; i < a.size(); i++) { + if (!vec_tensor_buft_override_equal(a[i], b[i])) { + return false; + } + } + return true; +} + template static std::string join(const std::vector & values, const std::string & delim) { std::ostringstream str; for (size_t i = 0; i < values.size(); i++) { @@ -175,6 +215,7 @@ struct cmd_params { std::vector no_kv_offload; std::vector flash_attn; std::vector> tensor_split; + std::vector> tensor_buft_overrides; std::vector use_mmap; std::vector embeddings; ggml_numa_strategy numa; @@ -207,6 +248,7 @@ static const cmd_params cmd_params_defaults = { /* no_kv_offload */ { false }, /* flash_attn */ { false }, /* tensor_split */ { std::vector(llama_max_devices(), 0.0f) }, + /* tensor_buft_overrides*/ { std::vector{{nullptr,nullptr}} }, /* use_mmap */ { true }, /* embeddings */ { false }, /* numa */ GGML_NUMA_STRATEGY_DISABLED, @@ -265,6 +307,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); printf(" -ts, --tensor-split (default: 0)\n"); + printf(" -ot --override-tensors =;... (default: disabled)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" --prio <0|1|2|3> (default: %d)\n", cmd_params_defaults.prio); printf(" --delay <0...N> (seconds) (default: %d)\n", cmd_params_defaults.delay); @@ -557,6 +600,87 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } params.tensor_split.push_back(tensor_split); } + } else if (arg == "-ot" || arg == "--override-tensor") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto value = argv[i]; + /* static */ std::map buft_list; + if (buft_list.empty()) { + // enumerate all the devices and add their buffer types to the list + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + auto * buft = ggml_backend_dev_buffer_type(dev); + if (buft) { + buft_list[ggml_backend_buft_name(buft)] = buft; + } + } + } + auto override_group_span_len = std::strcspn(value, ","); + bool last_group = false; + do { + if (override_group_span_len == 0) { + // Adds an empty override-tensors for an empty span + params.tensor_buft_overrides.push_back({{}}); + if (value[override_group_span_len] == '\0') { + value = &value[override_group_span_len]; + last_group = true; + } else { + value = &value[override_group_span_len + 1]; + override_group_span_len = std::strcspn(value, ","); + } + continue; + } + // Stamps null terminators into the argv + // value for this option to avoid the + // memory leak present in the implementation + // over in arg.cpp. Acceptable because we + // only parse these args once in this program. + auto override_group = value; + if (value[override_group_span_len] == '\0') { + value = &value[override_group_span_len]; + last_group = true; + } else { + value[override_group_span_len] = '\0'; + value = &value[override_group_span_len + 1]; + } + std::vector group_tensor_buft_overrides{}; + auto override_span_len = std::strcspn(override_group, ";"); + while (override_span_len > 0) { + auto override = override_group; + if (override_group[override_span_len] != '\0') { + override_group[override_span_len] = '\0'; + override_group = &override_group[override_span_len + 1]; + } else { + override_group = &override_group[override_span_len]; + } + auto tensor_name_span_len = std::strcspn(override, "="); + if (tensor_name_span_len >= override_span_len) { + invalid_param = true; + break; + } + override[tensor_name_span_len] = '\0'; + auto tensor_name = override; + auto buffer_type = &override[tensor_name_span_len + 1]; + if (buft_list.find(buffer_type) == buft_list.end()) { + printf("Available buffer types:\n"); + for (const auto & it : buft_list) { + printf(" %s\n", ggml_backend_buft_name(it.second)); + } + invalid_param = true; + break; + } + group_tensor_buft_overrides.push_back({tensor_name, buft_list.at(buffer_type)}); + override_span_len = std::strcspn(override_group, ";"); + } + if (invalid_param) { + break; + } + group_tensor_buft_overrides.push_back({nullptr,nullptr}); + params.tensor_buft_overrides.push_back(group_tensor_buft_overrides); + override_group_span_len = std::strcspn(value, ","); + } while (!last_group); } else if (arg == "-r" || arg == "--repetitions") { if (++i >= argc) { invalid_param = true; @@ -648,6 +772,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } + if (params.tensor_buft_overrides.empty()) { + params.tensor_buft_overrides = cmd_params_defaults.tensor_buft_overrides; + } if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } @@ -689,6 +816,7 @@ struct cmd_params_instance { bool no_kv_offload; bool flash_attn; std::vector tensor_split; + std::vector tensor_buft_overrides; bool use_mmap; bool embeddings; @@ -733,13 +861,20 @@ struct cmd_params_instance { mparams.tensor_split = tensor_split.data(); mparams.use_mmap = use_mmap; + if (tensor_buft_overrides.empty()) { + mparams.tensor_buft_overrides = nullptr; + } else { + GGML_ASSERT(tensor_buft_overrides.back().pattern == nullptr && "Tensor buffer overrides not terminated with empty pattern"); + mparams.tensor_buft_overrides = tensor_buft_overrides.data(); + } + return mparams; } bool equal_mparams(const cmd_params_instance & other) const { return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers_str == other.rpc_servers_str && split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap && - tensor_split == other.tensor_split; + tensor_split == other.tensor_split && vec_tensor_buft_override_equal(tensor_buft_overrides, other.tensor_buft_overrides); } llama_context_params to_llama_cparams() const { @@ -769,6 +904,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) + for (const auto & ot : params.tensor_buft_overrides) for (const auto & mmp : params.use_mmap) for (const auto & embd : params.embeddings) for (const auto & nb : params.n_batch) @@ -804,6 +940,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .flash_attn = */ fa, /* .tensor_split = */ ts, + /* .tensor_buft_overrides = */ ot, /* .use_mmap = */ mmp, /* .embeddings = */ embd, }; @@ -833,6 +970,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .flash_attn = */ fa, /* .tensor_split = */ ts, + /* .tensor_buft_overrides = */ ot, /* .use_mmap = */ mmp, /* .embeddings = */ embd, }; @@ -862,6 +1000,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .flash_attn = */ fa, /* .tensor_split = */ ts, + /* .tensor_buft_overrides = */ ot, /* .use_mmap = */ mmp, /* .embeddings = */ embd, }; @@ -896,6 +1035,7 @@ struct test { bool no_kv_offload; bool flash_attn; std::vector tensor_split; + std::vector tensor_buft_overrides; bool use_mmap; bool embeddings; int n_prompt; @@ -927,6 +1067,7 @@ struct test { no_kv_offload = inst.no_kv_offload; flash_attn = inst.flash_attn; tensor_split = inst.tensor_split; + tensor_buft_overrides = inst.tensor_buft_overrides; use_mmap = inst.use_mmap; embeddings = inst.embeddings; n_prompt = inst.n_prompt; @@ -972,9 +1113,9 @@ struct test { "build_commit", "build_number", "cpu_info", "gpu_info", "backends", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", - "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "use_mmap", - "embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", - "avg_ts", "stddev_ts", + "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides", + "use_mmap", "embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", + "stddev_ns", "avg_ts", "stddev_ts", }; return fields; } @@ -1000,6 +1141,7 @@ struct test { std::vector get_values() const { std::string tensor_split_str; + std::string tensor_buft_overrides_str; int max_nonzero = 0; for (size_t i = 0; i < llama_max_devices(); i++) { if (tensor_split[i] > 0) { @@ -1014,6 +1156,26 @@ struct test { tensor_split_str += "/"; } } + if (tensor_buft_overrides.size() == 1) { + // Last element of tensor_buft_overrides is always a null pattern + // so if it is only one element long, it must be a null pattern. + GGML_ASSERT(tensor_buft_overrides[0].pattern == nullptr); + tensor_buft_overrides_str += "none"; + } else { + for (size_t i = 0; i < tensor_buft_overrides.size()-1; i++) { + // Last element of tensor_buft_overrides is always a null pattern + if (tensor_buft_overrides[i].pattern == nullptr) { + tensor_buft_overrides_str += "none"; + } else { + tensor_buft_overrides_str += tensor_buft_overrides[i].pattern; + tensor_buft_overrides_str += "="; + tensor_buft_overrides_str += ggml_backend_buft_name(tensor_buft_overrides[i].buft); + } + if (i + 2 < tensor_buft_overrides.size()) { + tensor_buft_overrides_str += ";"; + } + } + } std::vector values = { build_commit, std::to_string(build_number), cpu_info, @@ -1037,6 +1199,7 @@ struct test { std::to_string(no_kv_offload), std::to_string(flash_attn), tensor_split_str, + tensor_buft_overrides_str, std::to_string(use_mmap), std::to_string(embeddings), std::to_string(n_prompt), @@ -1254,6 +1417,9 @@ struct markdown_printer : public printer { if (field == "tensor_split") { return "ts"; } + if (field == "tensor_buft_overrides") { + return "ot"; + } return field; } @@ -1307,6 +1473,9 @@ struct markdown_printer : public printer { if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { fields.emplace_back("tensor_split"); } + if (params.tensor_buft_overrides.size() > 1 || !vec_vec_tensor_buft_override_equal(params.tensor_buft_overrides, cmd_params_defaults.tensor_buft_overrides)) { + fields.emplace_back("tensor_buft_overrides"); + } if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { fields.emplace_back("use_mmap"); } From 85f36e5e7173eef7c671c778db44c034e1d0ab19 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 28 Apr 2025 07:16:59 +0200 Subject: [PATCH 021/126] arg : fix unused variable (#13142) --- common/arg.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/arg.cpp b/common/arg.cpp index de173159f4..2740149212 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -673,7 +673,7 @@ static struct common_hf_file_res common_get_hf_file(const std::string &, const s return {}; } -std::pair> common_remote_get_content(const std::string & url, const common_remote_params & params) { +std::pair> common_remote_get_content(const std::string &, const common_remote_params &) { throw std::runtime_error("error: built without CURL, cannot download model from the internet"); } From 69699be48a6b94570773532850667f1591dc5bbe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 28 Apr 2025 09:29:26 +0200 Subject: [PATCH 022/126] CUDA: fix q_nope_absorbed prec for DS 2 Lite f16 (#13137) --- ggml/include/ggml.h | 4 ++-- ggml/src/ggml-cuda/ggml-cuda.cu | 4 ++-- src/llama-model.cpp | 1 + 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 51aa5b3a0a..1b8603e78e 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -393,8 +393,8 @@ extern "C" { // precision enum ggml_prec { - GGML_PREC_DEFAULT, - GGML_PREC_F32, + GGML_PREC_DEFAULT = 0, // stored as ggml_tensor.op_params, 0 by default + GGML_PREC_F32 = 10, }; // model file types diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index e0e0d2137f..19b9ce7231 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -1935,8 +1935,8 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor ggml_cuda_mul_mat_vec(ctx, src0, src1, nullptr, dst); } else if (!split && use_mul_mat_vec_q) { ggml_cuda_mul_mat_vec_q(ctx, src0, src1, nullptr, dst); - } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16) - && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { + } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16) && + dst->op_params[0] == GGML_PREC_DEFAULT && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // general KQ + KQV multi-batch without FlashAttention ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst); } else if (use_mul_mat_vec) { diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 6b7bfecf3a..df2791002e 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -10149,6 +10149,7 @@ struct llm_build_deepseek2 : public llm_graph_context { // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head} ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope); + ggml_mul_mat_set_prec(q_nope_absorbed, GGML_PREC_F32); cb(q_nope_absorbed, "q_nope_absorbed", il); // {kv_lora_rank, n_head, n_tokens} From f0dd6a1926cdb2f4183a937deee40db26ef8f1da Mon Sep 17 00:00:00 2001 From: R0CKSTAR Date: Mon, 28 Apr 2025 15:33:28 +0800 Subject: [PATCH 023/126] musa: fix typo in cc control (#13144) Signed-off-by: Xiaodong Ye --- ggml/src/ggml-cuda/common.cuh | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 8284a0017d..2ea014e647 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -78,13 +78,13 @@ // Moore Threads #define GGML_CUDA_MUSA_ARCH_IS_QY1 (__MUSA_ARCH__ <= 210) -#define GGML_CUDA_CC_QY1 (GGML_MUSA_CC_OFFSET_MTHREADS + 0x210) // MTT S80, MTT S3000 -#define GGML_CUDA_CC_QY2 (GGML_MUSA_CC_OFFSET_MTHREADS + 0x220) // MTT S4000 -#define GGML_CUDA_CC_NG (GGML_MUSA_CC_OFFSET_MTHREADS + 0x310) // TBD +#define GGML_CUDA_CC_QY1 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x210) // MTT S80, MTT S3000 +#define GGML_CUDA_CC_QY2 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x220) // MTT S4000 +#define GGML_CUDA_CC_NG (GGML_CUDA_CC_OFFSET_MTHREADS + 0x310) // TBD #define GGML_CUDA_CC_IS_MTHREADS(cc) (cc >= GGML_CUDA_CC_OFFSET_MTHREADS && cc < GGML_CUDA_CC_OFFSET_AMD) #define GGML_CUDA_CC_IS_QY1(cc) (cc >= GGML_CUDA_CC_QY1 && cc < GGML_CUDA_CC_QY2) -#define GGML_CUDA_CC_IS_QY2(cc) (cc >= GGML_CUDA_CC_QY2 && cc < GGML_CUDA_CC_NEXT) +#define GGML_CUDA_CC_IS_QY2(cc) (cc >= GGML_CUDA_CC_QY2 && cc < GGML_CUDA_CC_NG) #define GGML_CUDA_CC_IS_NG(cc) (cc >= GGML_CUDA_CC_NG) #ifdef __CUDA_ARCH_LIST__ From e5d6c2554e7597665e26991a93fa2f3d16c79ad5 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 28 Apr 2025 10:11:58 +0200 Subject: [PATCH 024/126] llama-chat : fix typo GML --> GLM (#13143) --- src/llama-chat.cpp | 12 ++++++------ src/llama-chat.h | 4 ++-- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index 698c30ce49..af5e200319 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -50,8 +50,8 @@ static const std::map LLM_CHAT_TEMPLATES = { { "deepseek3", LLM_CHAT_TEMPLATE_DEEPSEEK_3 }, { "command-r", LLM_CHAT_TEMPLATE_COMMAND_R }, { "llama3", LLM_CHAT_TEMPLATE_LLAMA_3 }, - { "chatglm3", LLM_CHAT_TEMPLATE_CHATGML_3 }, - { "chatglm4", LLM_CHAT_TEMPLATE_CHATGML_4 }, + { "chatglm3", LLM_CHAT_TEMPLATE_CHATGLM_3 }, + { "chatglm4", LLM_CHAT_TEMPLATE_CHATGLM_4 }, { "glmedge", LLM_CHAT_TEMPLATE_GLMEDGE }, { "minicpm", LLM_CHAT_TEMPLATE_MINICPM }, { "exaone3", LLM_CHAT_TEMPLATE_EXAONE_3 }, @@ -123,7 +123,7 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) { } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) { return LLM_CHAT_TEMPLATE_PHI_3; } else if (tmpl_contains("[gMASK]")) { - return LLM_CHAT_TEMPLATE_CHATGML_4; + return LLM_CHAT_TEMPLATE_CHATGLM_4; } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) { return tmpl_contains("") ? LLM_CHAT_TEMPLATE_FALCON_3 : LLM_CHAT_TEMPLATE_GLMEDGE; } else if (tmpl_contains("<|{{ item['role'] }}|>") && tmpl_contains("<|begin_of_image|>")) { @@ -156,7 +156,7 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) { return LLM_CHAT_TEMPLATE_LLAMA_3; } else if (tmpl_contains("[gMASK]sop")) { // chatglm3-6b - return LLM_CHAT_TEMPLATE_CHATGML_3; + return LLM_CHAT_TEMPLATE_CHATGLM_3; } else if (tmpl_contains(LU8("<用户>"))) { // MiniCPM-3B-OpenHermes-2.5-v2-GGUF return LLM_CHAT_TEMPLATE_MINICPM; @@ -437,7 +437,7 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "<|start_header_id|>assistant<|end_header_id|>\n\n"; } - } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_3) { + } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_3) { // chatglm3-6b ss << "[gMASK]" << "sop"; for (auto message : chat) { @@ -447,7 +447,7 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "<|assistant|>"; } - } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_4) { + } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_4) { ss << "[gMASK]" << ""; for (auto message : chat) { std::string role(message->role); diff --git a/src/llama-chat.h b/src/llama-chat.h index dc30df711a..3f5843466d 100644 --- a/src/llama-chat.h +++ b/src/llama-chat.h @@ -29,8 +29,8 @@ enum llm_chat_template { LLM_CHAT_TEMPLATE_DEEPSEEK_3, LLM_CHAT_TEMPLATE_COMMAND_R, LLM_CHAT_TEMPLATE_LLAMA_3, - LLM_CHAT_TEMPLATE_CHATGML_3, - LLM_CHAT_TEMPLATE_CHATGML_4, + LLM_CHAT_TEMPLATE_CHATGLM_3, + LLM_CHAT_TEMPLATE_CHATGLM_4, LLM_CHAT_TEMPLATE_GLMEDGE, LLM_CHAT_TEMPLATE_MINICPM, LLM_CHAT_TEMPLATE_EXAONE_3, From 43f2b07193cbcccd266734320ea9b948f5a01926 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 28 Apr 2025 11:57:19 +0300 Subject: [PATCH 025/126] common : fix noreturn compile warning (#13151) ggml-ci --- common/arg.cpp | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 2740149212..75e8e0bd51 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -673,8 +673,12 @@ static struct common_hf_file_res common_get_hf_file(const std::string &, const s return {}; } -std::pair> common_remote_get_content(const std::string &, const common_remote_params &) { - throw std::runtime_error("error: built without CURL, cannot download model from the internet"); +std::pair> common_remote_get_content(const std::string & url, const common_remote_params &) { + if (!url.empty()) { + throw std::runtime_error("error: built without CURL, cannot download model from the internet"); + } + + return {}; } #endif // LLAMA_USE_CURL From d0a417f3c7a5a22ef05b3b76d91dbe1d3362bf0c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 28 Apr 2025 12:10:18 +0300 Subject: [PATCH 026/126] readme : update hot topics (#13150) --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a0e7bd2d21..1785493c3e 100644 --- a/README.md +++ b/README.md @@ -16,9 +16,9 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ## Hot topics +- **GGML developer experience survey (organized and reviewed by NVIDIA):** [link](https://forms.gle/Gasw3cRgyhNEnrwK9) - A new binary `llama-mtmd-cli` is introduced to replace `llava-cli`, `minicpmv-cli` and `gemma3-cli` https://github.com/ggml-org/llama.cpp/pull/13012, `libllava` will be deprecated -- **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggml-org/llama.cpp/pull/11427 -- **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode +- VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode - Universal [tool call support](./docs/function-calling.md) in `llama-server` https://github.com/ggml-org/llama.cpp/pull/9639 - Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim - Introducing GGUF-my-LoRA https://github.com/ggml-org/llama.cpp/discussions/10123 From a4c340f974f9b7ac0c1aae897aabaa54549a97e5 Mon Sep 17 00:00:00 2001 From: Akarshan Biswas Date: Mon, 28 Apr 2025 15:03:25 +0530 Subject: [PATCH 027/126] SYCL: Add all missing unary kernels (#13074) * SYCL: Add all missing unary kernels ggml-ci * decouple kernel launch range from data size using strided loop * use ciel_div helper for num_blocks ggml-ci * clean auto imported header files --- ggml/src/ggml-sycl/common.hpp | 4 + ggml/src/ggml-sycl/element_wise.cpp | 169 ++++++++++++++++++++++++++++ ggml/src/ggml-sycl/element_wise.hpp | 5 + ggml/src/ggml-sycl/ggml-sycl.cpp | 13 +++ 4 files changed, 191 insertions(+) diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index 0ab0fb0aa3..c3d9d18645 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -493,5 +493,9 @@ static __dpct_inline__ Tp* get_pointer(sycl::local_accessor acc) { int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block_size); +constexpr size_t ceil_div(const size_t m, const size_t n) { + return (m + n - 1) / n; +} + bool gpu_has_xmx(sycl::device &dev); #endif // GGML_SYCL_COMMON_HPP diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index fc25d98ddf..dcc6ec809a 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -21,6 +21,27 @@ static void acc_f32(const float * x, const float * y, float * dst, const int ne, } } +template +static void sgn(const T * x, T * dst, const int k, const sycl::nd_item<3> &item_ct1) { + for(auto i = item_ct1.get_global_id(2); i < (const size_t)k; i += item_ct1.get_global_range(2)) { + dst[i] = x[i] > static_cast(0.f) ? static_cast(1.f) : ((x[i] < static_cast(0.f) ? static_cast(-1.f) : static_cast(0.f))); + } +} + +template +static void abs_op(const T * x, T * dst, const int k, const sycl::nd_item<3> &item_ct1) { + for(auto i = item_ct1.get_global_id(2); i < (const size_t)k; i += item_ct1.get_global_range(2)) { + dst[i] = sycl::fabs(x[i]); + } +} + +template +static void elu_op(const T * x, T * dst, const int k, const sycl::nd_item<3> &item_ct1) { + for(auto i = item_ct1.get_global_id(2); i < (const size_t)k; i += item_ct1.get_global_range(2)) { + dst[i] = (x[i] > static_cast(0.f)) ? x[i] : sycl::expm1(x[i]); + } +} + template static void gelu(const T * x, T * dst, const int k, const sycl::nd_item<3> &item_ct1) { @@ -335,6 +356,37 @@ static void silu_sycl(const T *x, T *dst, const int k, }); } +template +static void sgn_sycl(const T * x, T * dst, const int k, queue_ptr stream) { + // hard code for now + const int num_blocks = ceil_div(k, 256); + stream->parallel_for( + sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range(1, 1, 256)), sycl::range(1, 1, 256)), [=](sycl::nd_item<3> item_ct1) { + sgn(x, dst, k, item_ct1); + }); +} + +template +static void abs_sycl(const T * x, T * dst, const int k, queue_ptr stream) { + // hard code for now + const int num_blocks = ceil_div(k, 256); + stream->parallel_for( + sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)), [=](sycl::nd_item<3> item_ct1) { + abs_op(x, dst, k, item_ct1); + }); +} + + +template +static void elu_sycl(const T * x, T * dst, const int k, queue_ptr stream) { + // hard code for now + const int num_blocks = ceil_div(k, 256); + stream->parallel_for( + sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)), [=](sycl::nd_item<3> item_ct1) { + elu_op(x, dst, k, item_ct1); + }); +} + template static void gelu_quick_sycl(const T *x, T *dst, const int k, queue_ptr stream) { @@ -574,6 +626,106 @@ static void clamp_sycl(const T *x, T *dst, const float min, }); } +inline void ggml_sycl_op_sgn(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { +#if defined (GGML_SYCL_F16) + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + +#else + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); +#endif + GGML_ASSERT(dst->src[0]->type == dst->type); + dpct::queue_ptr main_stream = ctx.stream(); + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + switch (dst->type) { +#if defined (GGML_SYCL_F16) + case GGML_TYPE_F16: + { + auto data_pts = cast_data(dst); + sgn_sycl(data_pts.src, data_pts.dst, ggml_nelements(dst->src[0]), main_stream); + break; + } +#endif + case GGML_TYPE_F32: + { + auto data_pts = cast_data(dst); + sgn_sycl(data_pts.src, data_pts.dst, ggml_nelements(dst->src[0]), main_stream); + break; + } + default: + GGML_ABORT("GGML tensor type not supported!\n"); + break; + } +} + +inline void ggml_sycl_op_abs(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { +#if defined (GGML_SYCL_F16) + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + +#else + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); +#endif + GGML_ASSERT(dst->src[0]->type == dst->type); + dpct::queue_ptr main_stream = ctx.stream(); + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + switch (dst->type) { +#if defined (GGML_SYCL_F16) + case GGML_TYPE_F16: + { + auto data_pts = cast_data(dst); + abs_sycl(data_pts.src, data_pts.dst, ggml_nelements(dst->src[0]), main_stream); + break; + } +#endif + case GGML_TYPE_F32: + { + auto data_pts = cast_data(dst); + abs_sycl(data_pts.src, data_pts.dst, ggml_nelements(dst->src[0]), main_stream); + break; + } + default: + GGML_ABORT("GGML tensor type not supported!\n"); + break; + } +} + + +inline void ggml_sycl_op_elu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { +#if defined (GGML_SYCL_F16) + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + +#else + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); +#endif + GGML_ASSERT(dst->src[0]->type == dst->type); + dpct::queue_ptr main_stream = ctx.stream(); + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + switch (dst->type) { +#if defined (GGML_SYCL_F16) + case GGML_TYPE_F16: + { + auto data_pts = cast_data(dst); + elu_sycl(data_pts.src, data_pts.dst, ggml_nelements(dst->src[0]), main_stream); + break; + } +#endif + case GGML_TYPE_F32: + { + auto data_pts = cast_data(dst); + elu_sycl(data_pts.src, data_pts.dst, ggml_nelements(dst->src[0]), main_stream); + break; + } + default: + GGML_ABORT("GGML tensor type not supported!\n"); + break; + } +} + inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { #if defined (GGML_SYCL_F16) GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16); @@ -1388,3 +1540,20 @@ void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s done\n", __func__); } +void ggml_sycl_sgn(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type)); + ggml_sycl_op_sgn(ctx, dst); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_abs(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type)); + ggml_sycl_op_abs(ctx, dst); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_elu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type)); + ggml_sycl_op_elu(ctx, dst); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp index e623cb56f7..f4199d69da 100644 --- a/ggml/src/ggml-sycl/element_wise.hpp +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -66,5 +66,10 @@ void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst); +void ggml_sycl_sgn(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +void ggml_sycl_abs(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +void ggml_sycl_elu(ggml_backend_sycl_context & ctx, ggml_tensor * dst); #endif // GGML_SYCL_ELEMENTWISE_HPP diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 548f2d0a06..66b6f2cca4 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -38,6 +38,7 @@ #include "ggml-sycl/backend.hpp" #include "ggml-sycl/common.hpp" +#include "ggml-sycl/element_wise.hpp" #include "ggml-sycl/presets.hpp" #include "ggml-sycl/gemm.hpp" #include "ggml-sycl/sycl_hw.hpp" @@ -3355,6 +3356,15 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_UNARY_OP_EXP: ggml_sycl_exp(ctx, dst); break; + case GGML_UNARY_OP_SGN: + ggml_sycl_sgn(ctx, dst); + break; + case GGML_UNARY_OP_ABS: + ggml_sycl_abs(ctx, dst); + break; + case GGML_UNARY_OP_ELU: + ggml_sycl_elu(ctx, dst); + break; default: return false; } @@ -3837,6 +3847,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: case GGML_UNARY_OP_EXP: + case GGML_UNARY_OP_SGN: + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_ELU: #if defined (GGML_SYCL_F16) return ggml_is_contiguous(op->src[0]) && (op->type == op->src[0]->type); #else From 5fa9e63be82225fb3249c76f39ddda3e5bdec0a3 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 28 Apr 2025 12:18:59 +0200 Subject: [PATCH 028/126] clip : refactor set input for cgraph + fix qwen2.5vl input (#13136) * clip : refactor set input for cgraph * more strict assert * minicpmv : use clip_n_mmproj_embd instead of copying the same code everywhere * split qwen2 and qwen2.5 code blocks * minor style fix --- examples/llava/clip.cpp | 473 ++++++++++++++++++---------------------- 1 file changed, 215 insertions(+), 258 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 3cd27d5b17..8c5d56cc17 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -170,8 +170,8 @@ struct clip_hparams { std::vector image_grid_pinpoints; int32_t image_crop_resolution; std::unordered_set vision_feature_layer; - int32_t attn_window_size; - int32_t n_wa_pattern; + int32_t attn_window_size = 0; + int32_t n_wa_pattern = 0; }; struct clip_layer { @@ -325,7 +325,6 @@ struct clip_ctx { float image_std[3]; bool use_gelu = false; bool use_silu = false; - int32_t ftype = 1; gguf_context_ptr ctx_gguf; ggml_context_ptr ctx_data; @@ -776,7 +775,6 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ const int image_size_width = imgs.entries[0]->nx; const int image_size_height = imgs.entries[0]->ny; - const bool use_mrope = ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL; const bool use_window_attn = hparams.n_wa_pattern > 0; const int n_wa_pattern = hparams.n_wa_pattern; @@ -785,10 +783,11 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ const int patches_w = image_size_width / patch_size; const int patches_h = image_size_height / patch_size; const int num_positions = num_patches + (model.class_embedding ? 1 : 0); - const int num_position_ids = use_mrope ? num_positions * 4 : num_positions; + const int num_position_ids = num_positions * 4; // m-rope requires 4 dim per position const int hidden_size = hparams.hidden_size; const int n_head = hparams.n_head; const int d_head = hidden_size / n_head; + const int n_layer = hparams.n_layer; const float eps = hparams.eps; int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; @@ -870,7 +869,7 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ } // loop over layers - for (int il = 0; il < ctx->max_feature_layer; il++) { + for (int il = 0; il < n_layer; il++) { struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states // rmsnorm1 @@ -1115,15 +1114,8 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { int pos_w = image_size_width/patch_size; int pos_h = image_size_height/patch_size; - if (ctx->minicpmv_version == 2) { - pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 4096, pos_w * pos_h, 1); - } - else if (ctx->minicpmv_version == 3) { - pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1); - } - else if (ctx->minicpmv_version == 4) { - pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1); - } + int n_output_dim = clip_n_mmproj_embd(ctx); + pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_output_dim, pos_w * pos_h, 1); ggml_set_name(pos_embed, "pos_embed"); ggml_set_input(pos_embed); } @@ -1461,23 +1453,17 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im } { // attention - int hidden_size = 4096; + int hidden_size = clip_n_mmproj_embd(ctx); const int d_head = 128; int n_head = hidden_size/d_head; int num_query = 96; if (ctx->minicpmv_version == 2) { - hidden_size = 4096; - n_head = hidden_size/d_head; num_query = 96; } else if (ctx->minicpmv_version == 3) { - hidden_size = 3584; - n_head = hidden_size/d_head; num_query = 64; } else if (ctx->minicpmv_version == 4) { - hidden_size = 3584; - n_head = hidden_size/d_head; num_query = 64; } @@ -1760,6 +1746,8 @@ struct clip_model_loader { LOG_INF("%s: projector: %s\n", __func__, proj_type.c_str()); LOG_INF("%s: has_llava_proj: %d\n", __func__, ctx_clip.has_llava_projector); LOG_INF("%s: minicpmv_version: %d\n", __func__, ctx_clip.minicpmv_version); + LOG_INF("%s: proj_scale_factor: %d\n", __func__, hparams.proj_scale_factor); + LOG_INF("%s: n_wa_pattern: %d\n", __func__, hparams.n_wa_pattern); LOG_INF("%s: model size: %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0); LOG_INF("%s: metadata size: %.2f MiB\n", __func__, ggml_get_mem_size(ctx_meta.get()) / 1024.0 / 1024.0); } @@ -3038,15 +3026,43 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int patch_size = hparams.patch_size; const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); const int num_positions = num_patches + (model.class_embedding ? 1 : 0); - const int pos_w = ctx->load_image_size.width / patch_size; + const int pos_w = ctx->load_image_size.width / patch_size; const int pos_h = ctx->load_image_size.height / patch_size; const bool use_window_attn = hparams.n_wa_pattern > 0; // for qwen2.5vl + auto get_inp_tensor = [&gf](const char * name) { + struct ggml_tensor * inp = ggml_graph_get_tensor(gf, name); + if (inp == nullptr) { + GGML_ABORT("Failed to get tensor %s", name); + } + if (!(inp->flags & GGML_TENSOR_FLAG_INPUT)) { + GGML_ABORT("Tensor %s is not an input tensor", name); + } + return inp; + }; + + auto set_input_f32 = [&get_inp_tensor](const char * name, std::vector & values) { + ggml_tensor * cur = get_inp_tensor(name); + GGML_ASSERT(cur->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_nelements(cur) == (int64_t)values.size()); + ggml_backend_tensor_set(cur, values.data(), 0, ggml_nbytes(cur)); + }; + + auto set_input_i32 = [&get_inp_tensor](const char * name, std::vector & values) { + ggml_tensor * cur = get_inp_tensor(name); + GGML_ASSERT(cur->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_nelements(cur) == (int64_t)values.size()); + ggml_backend_tensor_set(cur, values.data(), 0, ggml_nbytes(cur)); + }; + + // set input pixel values { - struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw"); - std::vector inp_data(ggml_nelements(inp_raw)); - float * data = inp_data.data(); + size_t nelem = 0; + for (const auto & img : imgs.entries) { + nelem += img->nx * img->ny * 3; + } + std::vector inp_raw(nelem); // layout of data (note: the channel dim is unrolled to better visualize the layout): // @@ -3065,7 +3081,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int n = nx * ny; for (int b = 0; b < batch_size; b++) { - float * batch_entry = data + b * (3*n); + float * batch_entry = inp_raw.data() + b * (3*n); for (int y = 0; y < ny; y++) { for (int x = 0; x < nx; x++) { size_t base_src = 3*(y * nx + x); // idx of the first channel @@ -3077,266 +3093,207 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } } } - ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw)); + set_input_f32("inp_raw", inp_raw); } - if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { - { - // inspired from siglip: - // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit - // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316 - struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); - std::vector pos_data(ggml_nelements(positions)); - int * data = pos_data.data(); - int bucket_coords_h[1024]; - int bucket_coords_w[1024]; - for (int i = 0; i < pos_h; i++){ - bucket_coords_h[i] = std::floor(70.0*i/pos_h); - } - for (int i = 0; i < pos_w; i++){ - bucket_coords_w[i] = std::floor(70.0*i/pos_w); - } - for (int i = 0, id = 0; i < pos_h; i++){ - for (int j = 0; j < pos_w; j++){ - data[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j]; + // set input per projector + switch (ctx->proj_type) { + case PROJECTOR_TYPE_MINICPMV: + { + // inspired from siglip: + // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit + // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316 + std::vector positions(pos_h * pos_w); + int bucket_coords_h[1024]; + int bucket_coords_w[1024]; + for (int i = 0; i < pos_h; i++){ + bucket_coords_h[i] = std::floor(70.0*i/pos_h); } - } - ggml_backend_tensor_set(positions, data, 0, ggml_nbytes(positions)); - } - - { - // inspired from resampler of Qwen-VL: - // -> https://huggingface.co/Qwen/Qwen-VL/tree/main - // -> https://huggingface.co/Qwen/Qwen-VL/blob/0547ed36a86561e2e42fecec8fd0c4f6953e33c4/visual.py#L23 - struct ggml_tensor * pos_embed = ggml_graph_get_tensor(gf, "pos_embed"); - int embed_dim = 4096; - if (ctx->minicpmv_version == 2) { - embed_dim = 4096; - } - else if (ctx->minicpmv_version == 3) { - embed_dim = 3584; - } - else if (ctx->minicpmv_version == 4) { - embed_dim = 3584; - } - else { - GGML_ABORT("Unknown minicpmv version"); - } - - // TODO @ngxson : this is very inefficient, can we do this using ggml_sin and ggml_cos? - auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h)); - - std::vector pos_data(ggml_nelements(pos_embed)); - float * data = pos_data.data(); - for(int i = 0; i < pos_w * pos_h; ++i){ - for(int j = 0; j < embed_dim; ++j){ - data[i * embed_dim + j] = pos_embed_t[i][j]; + for (int i = 0; i < pos_w; i++){ + bucket_coords_w[i] = std::floor(70.0*i/pos_w); } - } + for (int i = 0, id = 0; i < pos_h; i++){ + for (int j = 0; j < pos_w; j++){ + positions[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j]; + } + } + set_input_i32("positions", positions); - ggml_backend_tensor_set(pos_embed, data, 0, ggml_nbytes(pos_embed)); - } - } - else { - // non-minicpmv models + // inspired from resampler of Qwen-VL: + // -> https://huggingface.co/Qwen/Qwen-VL/tree/main + // -> https://huggingface.co/Qwen/Qwen-VL/blob/0547ed36a86561e2e42fecec8fd0c4f6953e33c4/visual.py#L23 + int embed_dim = clip_n_mmproj_embd(ctx); - if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) { - // pw * ph = number of tokens output by ViT after apply patch merger - // ipw * ipw = number of vision token been processed inside ViT - const int merge_ratio = 2; - const int pw = image_size_width / patch_size / merge_ratio; - const int ph = image_size_height / patch_size / merge_ratio; - const int ipw = image_size_width / patch_size; - const int iph = image_size_height / patch_size; + // TODO @ngxson : this is very inefficient, can we do this using ggml_sin and ggml_cos? + auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h)); - std::vector idx (ph * pw); - std::vector inv_idx(ph * pw); + std::vector pos_embed(embed_dim * pos_w * pos_h); + for(int i = 0; i < pos_w * pos_h; ++i){ + for(int j = 0; j < embed_dim; ++j){ + pos_embed[i * embed_dim + j] = pos_embed_t[i][j]; + } + } - if (use_window_attn) { - const int attn_window_size = 112; - struct ggml_tensor * window_idx = ggml_graph_get_tensor(gf, "window_idx"); - struct ggml_tensor * inv_window_idx = ggml_graph_get_tensor(gf, "inv_window_idx"); - struct ggml_tensor * window_mask = ggml_graph_get_tensor(gf, "window_mask"); - - const int grid_window = attn_window_size / patch_size / merge_ratio; - int dst = 0; - // [num_vision_tokens, num_vision_tokens] attention mask tensor - std::vector mask(pow(ipw * iph, 2), std::numeric_limits::lowest()); - int mask_row = 0; - - for (int y = 0; y < ph; y += grid_window) - { - for (int x = 0; x < pw; x += grid_window) - { - const int win_h = std::min(grid_window, ph - y); - const int win_w = std::min(grid_window, pw - x); - const int dst_0 = dst; - // group all tokens belong to the same window togather (to a continue range) - for (int dy = 0; dy < win_h; dy++) { - for (int dx = 0; dx < win_w; dx++) { - const int src = (y + dy) * pw + (x + dx); - assert(src < (int)idx.size()); - assert(dst < (int)inv_idx.size()); - idx [src] = dst; - inv_idx[dst] = src; - dst++; + set_input_f32("pos_embed", pos_embed); + } break; + case PROJECTOR_TYPE_QWEN2VL: + { + const int merge_ratio = 2; + const int pw = image_size_width / patch_size; + const int ph = image_size_height / patch_size; + std::vector positions(num_positions * 4); + int ptr = 0; + for (int y = 0; y < ph; y += merge_ratio) { + for (int x = 0; x < pw; x += merge_ratio) { + for (int dy = 0; dy < 2; dy++) { + for (int dx = 0; dx < 2; dx++) { + positions[ ptr] = y + dy; + positions[ num_patches + ptr] = x + dx; + positions[2 * num_patches + ptr] = y + dy; + positions[3 * num_patches + ptr] = x + dx; + ptr++; } } - - for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) { - int row_offset = mask_row * (ipw * iph); - std::fill( - mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio), - mask.begin() + row_offset + (dst * merge_ratio * merge_ratio), - 0.0); - mask_row++; - } } } - ggml_backend_tensor_set(window_idx, idx.data(), 0, ggml_nbytes(window_idx)); - ggml_backend_tensor_set(inv_window_idx, inv_idx.data(), 0, ggml_nbytes(inv_window_idx)); - ggml_backend_tensor_set(window_mask, mask.data(), 0, ggml_nbytes(window_mask)); - } else { - std::iota(idx.begin(), idx.end(), 0); - std::iota(inv_idx.begin(), inv_idx.end(), 0); - } - - struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); - const int mpow = merge_ratio * merge_ratio; - std::vector positions_data(ggml_nelements(positions)); - int * data = positions_data.data(); - - int ptr = 0; - for (int y = 0; y < iph; y += merge_ratio) + set_input_i32("positions", positions); + } break; + case PROJECTOR_TYPE_QWEN25VL: { - for (int x = 0; x < ipw; x += merge_ratio) - { - for (int dy = 0; dy < 2; dy++) { - for (int dx = 0; dx < 2; dx++) { - auto remap = idx[ptr / mpow]; - remap = remap * mpow + (ptr % mpow); + // pw * ph = number of tokens output by ViT after apply patch merger + // ipw * ipw = number of vision token been processed inside ViT + const int merge_ratio = 2; + const int pw = image_size_width / patch_size / merge_ratio; + const int ph = image_size_height / patch_size / merge_ratio; + const int ipw = image_size_width / patch_size; + const int iph = image_size_height / patch_size; - data[ remap] = y + dy; - data[ num_patches + remap] = x + dx; - data[2 * num_patches + remap] = y + dy; - data[3 * num_patches + remap] = x + dx; - ptr++; + std::vector idx (ph * pw); + std::vector inv_idx(ph * pw); + + if (use_window_attn) { + const int attn_window_size = 112; + const int grid_window = attn_window_size / patch_size / merge_ratio; + int dst = 0; + // [num_vision_tokens, num_vision_tokens] attention mask tensor + std::vector mask(pow(ipw * iph, 2), std::numeric_limits::lowest()); + int mask_row = 0; + + for (int y = 0; y < ph; y += grid_window) { + for (int x = 0; x < pw; x += grid_window) { + const int win_h = std::min(grid_window, ph - y); + const int win_w = std::min(grid_window, pw - x); + const int dst_0 = dst; + // group all tokens belong to the same window togather (to a continue range) + for (int dy = 0; dy < win_h; dy++) { + for (int dx = 0; dx < win_w; dx++) { + const int src = (y + dy) * pw + (x + dx); + GGML_ASSERT(src < (int)idx.size()); + GGML_ASSERT(dst < (int)inv_idx.size()); + idx [src] = dst; + inv_idx[dst] = src; + dst++; + } + } + + for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) { + int row_offset = mask_row * (ipw * iph); + std::fill( + mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio), + mask.begin() + row_offset + (dst * merge_ratio * merge_ratio), + 0.0); + mask_row++; + } + } + } + + set_input_i32("window_idx", idx); + set_input_i32("inv_window_idx", inv_idx); + set_input_f32("window_mask", mask); + } else { + for (int i = 0; i < ph * pw; i++) { + idx[i] = i; + } + } + + const int mpow = merge_ratio * merge_ratio; + std::vector positions(num_positions * 4); + + int ptr = 0; + for (int y = 0; y < iph; y += merge_ratio) { + for (int x = 0; x < ipw; x += merge_ratio) { + for (int dy = 0; dy < 2; dy++) { + for (int dx = 0; dx < 2; dx++) { + auto remap = idx[ptr / mpow]; + remap = (remap * mpow) + (ptr % mpow); + + positions[ remap] = y + dy; + positions[ num_patches + remap] = x + dx; + positions[2 * num_patches + remap] = y + dy; + positions[3 * num_patches + remap] = x + dx; + ptr++; + } } } } - } - ggml_backend_tensor_set(positions, data, 0, ggml_nbytes(positions)); - } - else if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) { - // do nothing - } - else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) { - // do nothing - } - else if (ctx->proj_type == PROJECTOR_TYPE_PIXTRAL) { - // set the 2D positions - int n_patches_per_col = image_size_width / patch_size; - std::vector pos_data(num_positions); - struct ggml_tensor * pos; - // dimension H - pos = ggml_graph_get_tensor(gf, "pos_h"); - for (int i = 0; i < num_positions; i++) { - pos_data[i] = i / n_patches_per_col; - } - ggml_backend_tensor_set(pos, pos_data.data(), 0, ggml_nbytes(pos)); - // dimension W - pos = ggml_graph_get_tensor(gf, "pos_w"); - for (int i = 0; i < num_positions; i++) { - pos_data[i] = i % n_patches_per_col; - } - ggml_backend_tensor_set(pos, pos_data.data(), 0, ggml_nbytes(pos)); - } - else { + set_input_i32("positions", positions); + } break; + case PROJECTOR_TYPE_PIXTRAL: + { + // set the 2D positions + int n_patches_per_col = image_size_width / patch_size; + std::vector pos_data(num_positions); + // dimension H + for (int i = 0; i < num_positions; i++) { + pos_data[i] = i / n_patches_per_col; + } + set_input_i32("pos_h", pos_data); + // dimension W + for (int i = 0; i < num_positions; i++) { + pos_data[i] = i % n_patches_per_col; + } + set_input_i32("pos_w", pos_data); + } break; + case PROJECTOR_TYPE_GLM_EDGE: + { // llava and other models - struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); - - int* positions_data = (int*)malloc(ggml_nbytes(positions)); + std::vector positions(num_positions); for (int i = 0; i < num_positions; i++) { - positions_data[i] = i; + positions[i] = i; } - ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); - free(positions_data); + set_input_i32("positions", positions); + } break; + case PROJECTOR_TYPE_MLP: + case PROJECTOR_TYPE_MLP_NORM: + case PROJECTOR_TYPE_LDP: + case PROJECTOR_TYPE_LDPV2: + { + // llava and other models + std::vector positions(num_positions); + for (int i = 0; i < num_positions; i++) { + positions[i] = i; + } + set_input_i32("positions", positions); - if (ctx->proj_type != PROJECTOR_TYPE_GLM_EDGE) { - struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches"); // The patches vector is used to get rows to index into the embeds with; // we should skip dim 0 only if we have CLS to avoid going out of bounds // when retrieving the rows. int patch_offset = model.class_embedding ? 1 : 0; - int* patches_data = (int*)malloc(ggml_nbytes(patches)); + std::vector patches(num_patches); for (int i = 0; i < num_patches; i++) { - patches_data[i] = i + patch_offset; + patches[i] = i + patch_offset; } - ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); - free(patches_data); - } - } - } - - if (use_window_attn && (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL)) { - struct ggml_tensor * window_idx = ggml_graph_get_tensor(gf, "window_idx"); - struct ggml_tensor * inv_window_idx = ggml_graph_get_tensor(gf, "inv_window_idx"); - struct ggml_tensor * window_mask = ggml_graph_get_tensor(gf, "window_mask"); - - const int merge_ratio = 2; - const int attn_window_size = 112; - const int pw = image_size_width / patch_size / merge_ratio; - const int ph = image_size_height / patch_size / merge_ratio; - const int grid_window = attn_window_size / patch_size / merge_ratio; - const int ipw = image_size_width / patch_size; - const int iph = image_size_height / patch_size; - /* - pw * ph = number of tokens output by ViT after apply patch merger - ipw * ipw = number of vision token been processed inside ViT - */ - - std::vector idx(ph * pw); - std::vector inv_idx(ph * pw); - int dst = 0; - // [num_vision_tokens, num_vision_tokens] attention mask tensor - std::vector mask(pow(ipw * iph, 2), std::numeric_limits::lowest()); - int mask_row = 0; - - for (int y = 0; y < ph; y+=grid_window) - { - for (int x = 0; x < pw; x+=grid_window) + set_input_i32("patches", patches); + } break; + case PROJECTOR_TYPE_GEMMA3: + case PROJECTOR_TYPE_IDEFICS3: { - const int win_h = std::min(grid_window, ph - y); - const int win_w = std::min(grid_window, pw - x); - const int dst_0 = dst; - // group all tokens belong to the same window togather (to a continue range) - for (int dy = 0; dy < win_h; dy++) { - for (int dx = 0; dx < win_w; dx++) { - const int src = (y + dy) * pw + (x + dx); - assert(src < (int)idx.size()); - assert(dst < (int)inv_idx.size()); - idx[src] = dst; - inv_idx[dst] = src; - dst++; - } - } - - for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) { - int row_offset = mask_row * (ipw * iph); - std::fill( - mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio), - mask.begin() + row_offset + (dst * merge_ratio * merge_ratio), - 0.0); - mask_row++; - } - } - } - - ggml_backend_tensor_set(window_idx, idx.data(), 0, ggml_nbytes(window_idx)); - ggml_backend_tensor_set(inv_window_idx, inv_idx.data(), 0, ggml_nbytes(inv_window_idx)); - ggml_backend_tensor_set(window_mask, mask.data(), 0, ggml_nbytes(window_mask)); + // do nothing + } break; + default: + GGML_ABORT("Unknown projector type"); } ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads); @@ -3537,7 +3494,7 @@ bool clip_is_glm(const struct clip_ctx * ctx) { } bool clip_is_qwen2vl(const struct clip_ctx * ctx) { - return ctx->proj_type == PROJECTOR_TYPE_QWEN2VL; + return ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL; } bool clip_is_llava(const struct clip_ctx * ctx) { From d2b2031e5f11b826dcc718138642f147a2009665 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 28 Apr 2025 14:20:56 +0200 Subject: [PATCH 029/126] llama : (mrope) allow using normal 1D position for text token (#13138) * llama : (mrope) use normal position for text token * rm n_pos_per_embd from llm_graph_input_attn_temp --- examples/llava/qwen2vl-cli.cpp | 8 -------- src/llama-graph.cpp | 26 +++++++++++++++++++------- src/llama-graph.h | 12 +++++------- 3 files changed, 24 insertions(+), 22 deletions(-) diff --git a/examples/llava/qwen2vl-cli.cpp b/examples/llava/qwen2vl-cli.cpp index cf42710869..1e54851ea0 100644 --- a/examples/llava/qwen2vl-cli.cpp +++ b/examples/llava/qwen2vl-cli.cpp @@ -92,20 +92,12 @@ static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct lla static bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past, int * st_pos_id) { int N = (int) tokens.size(); - std::vector pos; for (int i = 0; i < N; i += n_batch) { int n_eval = (int) tokens.size() - i; if (n_eval > n_batch) { n_eval = n_batch; } auto batch = llama_batch_get_one(&tokens[i], n_eval); - // TODO: add mrope pos ids somewhere else - pos.resize(batch.n_tokens * 4); - std::fill(pos.begin(), pos.end(), 0); - for (int j = 0; j < batch.n_tokens * 3; j ++) { - pos[j] = *st_pos_id + (j % batch.n_tokens); - } - batch.pos = pos.data(); if (llama_decode(ctx_llama, batch)) { LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index b52e3f6203..e6595fb18b 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -55,7 +55,18 @@ void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) { if (ubatch->pos && pos) { const int64_t n_tokens = ubatch->n_tokens; - ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_token*ggml_element_size(pos)); + if (ubatch->token && n_pos_per_embd > 1) { + // in case we're using M-RoPE with text tokens, convert the 1D positions to 4D + // the other dimensions are all 0, they are unused for text tokens + std::vector pos_data(n_tokens*n_pos_per_embd, 0); + // copy the first dimension + for (int i = 0; i < n_tokens; ++i) { + pos_data[i] = ubatch->pos[i]; + } + ggml_backend_tensor_set(pos, pos_data.data(), 0, pos_data.size()*ggml_element_size(pos)); + } else { + ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_embd*ggml_element_size(pos)); + } } } @@ -71,7 +82,7 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) { ) * f_attn_temp_scale + 1.0; } - ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*n_pos_per_token*ggml_element_size(attn_scale)); + ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*ggml_element_size(attn_scale)); } } @@ -592,7 +603,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) : res (std::make_unique()) { } -int64_t llm_graph_context::n_pos_per_token() const { +int64_t llm_graph_context::n_pos_per_embd() const { return arch == LLM_ARCH_QWEN2VL ? 4 : 1; } @@ -1018,11 +1029,11 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const { } ggml_tensor * llm_graph_context::build_inp_pos() const { - auto inp = std::make_unique(n_pos_per_token()); + auto inp = std::make_unique(n_pos_per_embd()); auto & cur = inp->pos; - cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_token()); + cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_embd()); ggml_set_input(cur); res->add_input(std::move(inp)); @@ -1031,11 +1042,12 @@ ggml_tensor * llm_graph_context::build_inp_pos() const { } ggml_tensor * llm_graph_context::build_inp_attn_scale() const { - auto inp = std::make_unique(n_pos_per_token(), hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale); + auto inp = std::make_unique(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale); auto & cur = inp->attn_scale; - cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens*n_pos_per_token()); + // this need to be 1x1xN for broadcasting + cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens); ggml_set_input(cur); res->add_input(std::move(inp)); diff --git a/src/llama-graph.h b/src/llama-graph.h index d192dc1495..d0c8d32192 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -90,29 +90,27 @@ public: class llm_graph_input_pos : public llm_graph_input_i { public: - llm_graph_input_pos(int64_t n_pos_per_token) : n_pos_per_token(n_pos_per_token) {} + llm_graph_input_pos(int64_t n_pos_per_embd) : n_pos_per_embd(n_pos_per_embd) {} virtual ~llm_graph_input_pos() = default; void set_input(const llama_ubatch * ubatch) override; ggml_tensor * pos = nullptr; // I32 [n_batch] - const int64_t n_pos_per_token = 1; + const int64_t n_pos_per_embd = 1; }; // temperature tuning, used by llama4 class llm_graph_input_attn_temp : public llm_graph_input_i { public: - llm_graph_input_attn_temp(int64_t n_pos_per_token, uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale) - : n_pos_per_token(n_pos_per_token), n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {} + llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale) + : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {} virtual ~llm_graph_input_attn_temp() = default; void set_input(const llama_ubatch * ubatch) override; ggml_tensor * attn_scale = nullptr; // F32 [n_batch] - const int64_t n_pos_per_token = 1; - const uint32_t n_attn_temp_floor_scale; const float f_attn_temp_scale; }; @@ -419,7 +417,7 @@ struct llm_graph_context { llm_graph_context(const llm_graph_params & params); - int64_t n_pos_per_token() const; + int64_t n_pos_per_embd() const; void cb(ggml_tensor * cur, const char * name, int il) const; From fb0471d1753824e75474c24f82fbdd54c94dceda Mon Sep 17 00:00:00 2001 From: pockers21 <134406831+pockers21@users.noreply.github.com> Date: Mon, 28 Apr 2025 06:45:40 -0700 Subject: [PATCH 030/126] context : do not clear output buffer on reserve (#13152) Co-authored-by: pockers21 --- src/llama-context.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index a52b6850b4..e49225aa22 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -1536,8 +1536,6 @@ int32_t llama_context::output_reserve(int32_t n_outputs) { // set all ids as invalid (negative) std::fill(output_ids.begin(), output_ids.end(), -1); - ggml_backend_buffer_clear(buf_output.get(), 0); - this->n_outputs = 0; this->n_outputs_max = n_outputs_max; From 4e87962e34a4b257ec374c4baf6b1568554b81a9 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 28 Apr 2025 16:12:56 +0200 Subject: [PATCH 031/126] mtmd : fix glm-edge redundant token count (#13139) * mtmd : fix glm-edge redundant token count * fix chat template * temporary disable GLMEdge test chat tmpl --- examples/llava/mtmd.cpp | 10 +--------- src/llama-chat.cpp | 10 +--------- tests/test-chat-template.cpp | 17 +++++++++-------- 3 files changed, 11 insertions(+), 26 deletions(-) diff --git a/examples/llava/mtmd.cpp b/examples/llava/mtmd.cpp index a994ef0166..f95f050356 100644 --- a/examples/llava/mtmd.cpp +++ b/examples/llava/mtmd.cpp @@ -203,9 +203,6 @@ int32_t mtmd_tokenize(mtmd_context * ctx, } // llava-1.5, llava-1.6, Yi-VL, Yi-34B, granite: don't need to add prefix and suffix - // for glm-edge, we don't need to add because the tokens are already in the returned embeddings - - // TODO @ngxson : glm-edge : remove BOI / EOI tokens embeddings, decode them as normal tokens std::vector parts = string_split_str(prompt_modified, ctx->image_marker); output.clear(); @@ -246,7 +243,7 @@ int32_t mtmd_tokenize(mtmd_context * ctx, }; for (const auto & part : parts) { - //printf("tokenizing part: %s\n", part.c_str()); + // printf("tokenizing part: %s\n", part.c_str()); bool add_bos = &parts.front() == ∂ auto tokens = mtmd_tokenize_text_internal(vocab, part, text.add_special && add_bos, text.parse_special); if (tokens.empty()) { @@ -338,11 +335,6 @@ int32_t mtmd_tokenize(mtmd_context * ctx, LOG_DBG("image_tokens->ny = %d\n", image_tokens->ny); LOG_DBG("batch_f32 size = %d\n", (int)image_tokens->batch_f32.entries.size()); - if (clip_is_glm(ctx->ctx_clip)) { - // glm-edge - image_tokens->nx += 2; // add 2 for the begin_of_image and end_of_image token embeddings - } - mtmd_input_chunk chunk{ MTMD_INPUT_CHUNK_TYPE_IMAGE, {}, diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index af5e200319..735d2619c9 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -447,7 +447,7 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "<|assistant|>"; } - } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_4) { + } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_4 || tmpl == LLM_CHAT_TEMPLATE_GLMEDGE) { ss << "[gMASK]" << ""; for (auto message : chat) { std::string role(message->role); @@ -456,14 +456,6 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "<|assistant|>"; } - } else if (tmpl == LLM_CHAT_TEMPLATE_GLMEDGE) { - for (auto message : chat) { - std::string role(message->role); - ss << "<|" << role << "|>" << "\n" << message->content; - } - if (add_ass) { - ss << "<|assistant|>"; - } } else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) { // MiniCPM-3B-OpenHermes-2.5-v2-GGUF for (auto message : chat) { diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index be1a640068..85d89843d6 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -187,14 +187,15 @@ int main(void) { /* .bos_token= */ "", /* .eos_token= */ "", }, - { - /* .name= */ "GLMEdge", - /* .template_str= */ "{% for item in messages %}{% if item['role'] == 'system' %}<|system|>\n{{ item['content'] }}{% elif item['role'] == 'user' %}<|user|>\n{{ item['content'] }}{% elif item['role'] == 'assistant' %}<|assistant|>\n{{ item['content'] }}{% endif %}{% endfor %}<|assistant|>", - /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", - /* .expected_output_jinja= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", - /* .bos_token= */ "", - /* .eos_token= */ "", - }, + // TODO @ngxson : GLMEdge produces poor result without `[gMASK]`, so we're temporarily using GLM4 template for it. We should fix this in the future. + // { + // /* .name= */ "GLMEdge", + // /* .template_str= */ "{% for item in messages %}{% if item['role'] == 'system' %}<|system|>\n{{ item['content'] }}{% elif item['role'] == 'user' %}<|user|>\n{{ item['content'] }}{% elif item['role'] == 'assistant' %}<|assistant|>\n{{ item['content'] }}{% endif %}{% endfor %}<|assistant|>", + // /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", + // /* .expected_output_jinja= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", + // /* .bos_token= */ "", + // /* .eos_token= */ "", + // }, { /* .name= */ "MiniCPM-3B-OpenHermes-2.5-v2-GGUF", /* .template_str= */ U8C("{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}"), From 1831f538f720d1d99fba146f24f0a8e970838cc4 Mon Sep 17 00:00:00 2001 From: Vishal Agarwal Date: Mon, 28 Apr 2025 20:20:39 +0530 Subject: [PATCH 032/126] llama-bench: add `-d` depth arg (#13096) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add depth param * update llama-bench README and add depth param * llama-bench: default params for depth arg for faster execution * Update examples/llama-bench/README.md Co-authored-by: Johannes Gäßler * fix buffer print ub * use user provided args * remove extra whitespaces --------- Co-authored-by: Johannes Gäßler --- examples/llama-bench/README.md | 155 +++++++++++++++++---------- examples/llama-bench/llama-bench.cpp | 47 ++++++-- 2 files changed, 137 insertions(+), 65 deletions(-) diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index 6bbe4bb75f..1f5e2f6620 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -28,6 +28,7 @@ options: -p, --n-prompt (default: 512) -n, --n-gen (default: 128) -pg (default: ) + -d, --n-depth (default: 0) -b, --batch-size (default: 2048) -ub, --ubatch-size (default: 512) -ctk, --cache-type-k (default: f16) @@ -66,6 +67,8 @@ With the exception of `-r`, `-o` and `-v`, all options can be specified multiple Each test is repeated the number of times given by `-r`, and the results are averaged. The results are given in average tokens per second (t/s) and standard deviation. Some output formats (e.g. json) also include the individual results of each repetition. +Using the `-d ` option, each test can be run at a specified context depth, prefilling the KV cache with `` tokens. + For a description of the other options, see the [main example](../main/README.md). Note: @@ -148,6 +151,19 @@ $ ./llama-bench -ngl 10,20,30,31,32,33,34,35 | llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 35 | pp 512 | 2400.01 ± 7.72 | | llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 35 | tg 128 | 131.66 ± 0.49 | +### Different prefilled context + +``` +$ ./llama-bench -d 0,512 +``` + +| model | size | params | backend | ngl | test | t/s | +| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: | +| qwen2 7B Q4_K - Medium | 4.36 GiB | 7.62 B | CUDA | 99 | pp512 | 7340.20 ± 23.45 | +| qwen2 7B Q4_K - Medium | 4.36 GiB | 7.62 B | CUDA | 99 | tg128 | 120.60 ± 0.59 | +| qwen2 7B Q4_K - Medium | 4.36 GiB | 7.62 B | CUDA | 99 | pp512 @ d512 | 6425.91 ± 18.88 | +| qwen2 7B Q4_K - Medium | 4.36 GiB | 7.62 B | CUDA | 99 | tg128 @ d512 | 116.71 ± 0.60 | + ## Output formats By default, llama-bench outputs the results in markdown format. The results can be output in other formats by using the `-o` option. @@ -170,9 +186,9 @@ $ ./llama-bench -o csv ``` ```csv -build_commit,build_number,cuda,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts -"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961" -"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342" +build_commit,build_number,cpu_info,gpu_info,backends,model_filename,model_type,model_size,model_n_params,n_batch,n_ubatch,n_threads,cpu_mask,cpu_strict,poll,type_k,type_v,n_gpu_layers,split_mode,main_gpu,no_kv_offload,flash_attn,tensor_split,use_mmap,embeddings,n_prompt,n_gen,n_depth,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts +"8cf427ff","5163","AMD Ryzen 7 7800X3D 8-Core Processor","NVIDIA GeForce RTX 4080","CUDA","models/Qwen2.5-7B-Instruct-Q4_K_M.gguf","qwen2 7B Q4_K - Medium","4677120000","7615616512","2048","512","8","0x0","0","50","f16","f16","99","layer","0","0","0","0.00","1","0","512","0","0","2025-04-24T11:57:09Z","70285660","982040","7285.676949","100.064434" +"8cf427ff","5163","AMD Ryzen 7 7800X3D 8-Core Processor","NVIDIA GeForce RTX 4080","CUDA","models/Qwen2.5-7B-Instruct-Q4_K_M.gguf","qwen2 7B Q4_K - Medium","4677120000","7615616512","2048","512","8","0x0","0","50","f16","f16","99","layer","0","0","0","0.00","1","0","0","128","0","2025-04-24T11:57:10Z","1067431600","3834831","119.915244","0.430617" ``` ### JSON @@ -184,64 +200,78 @@ $ ./llama-bench -o json ```json [ { - "build_commit": "3469684", - "build_number": 1275, - "cuda": true, - "metal": false, - "gpu_blas": true, - "blas": true, - "cpu_info": "13th Gen Intel(R) Core(TM) i9-13900K", - "gpu_info": "NVIDIA GeForce RTX 3090 Ti", - "model_filename": "models/7B/ggml-model-q4_0.gguf", - "model_type": "llama 7B mostly Q4_0", - "model_size": 3825065984, - "model_n_params": 6738415616, - "n_batch": 512, - "n_threads": 16, - "f16_kv": true, + "build_commit": "8cf427ff", + "build_number": 5163, + "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor", + "gpu_info": "NVIDIA GeForce RTX 4080", + "backends": "CUDA", + "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf", + "model_type": "qwen2 7B Q4_K - Medium", + "model_size": 4677120000, + "model_n_params": 7615616512, + "n_batch": 2048, + "n_ubatch": 512, + "n_threads": 8, + "cpu_mask": "0x0", + "cpu_strict": false, + "poll": 50, + "type_k": "f16", + "type_v": "f16", "n_gpu_layers": 99, + "split_mode": "layer", "main_gpu": 0, - "mul_mat_q": true, + "no_kv_offload": false, + "flash_attn": false, "tensor_split": "0.00", + "use_mmap": true, + "embeddings": false, "n_prompt": 512, "n_gen": 0, - "test_time": "2023-09-23T12:09:57Z", - "avg_ns": 212365953, - "stddev_ns": 985423, - "avg_ts": 2410.974041, - "stddev_ts": 11.163766, - "samples_ns": [ 213837238, 211635853, 212328053, 211329715, 212698907 ], - "samples_ts": [ 2394.34, 2419.25, 2411.36, 2422.75, 2407.16 ] + "n_depth": 0, + "test_time": "2025-04-24T11:58:50Z", + "avg_ns": 72135640, + "stddev_ns": 1453752, + "avg_ts": 7100.002165, + "stddev_ts": 140.341520, + "samples_ns": [ 74601900, 71632900, 71745200, 71952700, 70745500 ], + "samples_ts": [ 6863.1, 7147.55, 7136.37, 7115.79, 7237.21 ] }, { - "build_commit": "3469684", - "build_number": 1275, - "cuda": true, - "metal": false, - "gpu_blas": true, - "blas": true, - "cpu_info": "13th Gen Intel(R) Core(TM) i9-13900K", - "gpu_info": "NVIDIA GeForce RTX 3090 Ti", - "model_filename": "models/7B/ggml-model-q4_0.gguf", - "model_type": "llama 7B mostly Q4_0", - "model_size": 3825065984, - "model_n_params": 6738415616, - "n_batch": 512, - "n_threads": 16, - "f16_kv": true, + "build_commit": "8cf427ff", + "build_number": 5163, + "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor", + "gpu_info": "NVIDIA GeForce RTX 4080", + "backends": "CUDA", + "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf", + "model_type": "qwen2 7B Q4_K - Medium", + "model_size": 4677120000, + "model_n_params": 7615616512, + "n_batch": 2048, + "n_ubatch": 512, + "n_threads": 8, + "cpu_mask": "0x0", + "cpu_strict": false, + "poll": 50, + "type_k": "f16", + "type_v": "f16", "n_gpu_layers": 99, + "split_mode": "layer", "main_gpu": 0, - "mul_mat_q": true, + "no_kv_offload": false, + "flash_attn": false, "tensor_split": "0.00", + "use_mmap": true, + "embeddings": false, "n_prompt": 0, "n_gen": 128, - "test_time": "2023-09-23T12:09:59Z", - "avg_ns": 977425219, - "stddev_ns": 9268593, - "avg_ts": 130.965708, - "stddev_ts": 1.238924, - "samples_ns": [ 984472709, 974901233, 989474741, 970729355, 967548060 ], - "samples_ts": [ 130.019, 131.295, 129.362, 131.86, 132.293 ] + "n_depth": 0, + "test_time": "2025-04-24T11:58:51Z", + "avg_ns": 1076767880, + "stddev_ns": 9449585, + "avg_ts": 118.881588, + "stddev_ts": 1.041811, + "samples_ns": [ 1075361300, 1065089400, 1071761200, 1081934900, 1089692600 ], + "samples_ts": [ 119.03, 120.178, 119.43, 118.307, 117.464 ] } ] ``` @@ -254,8 +284,8 @@ $ ./llama-bench -o jsonl ``` ```json lines -{"build_commit":"3469684","build_number":1275,"cuda":true,"metal":false,"gpu_blas":true,"blas":true,"cpu_info":"13th Gen Intel(R) Core(TM) i9-13900K","gpu_info":"NVIDIA GeForce RTX 3090 Ti","model_filename":"models/7B/ggml-model-q4_0.gguf","model_type":"llama 7B mostly Q4_0","model_size":3825065984,"model_n_params":6738415616,"n_batch":512,"n_threads":16,"f16_kv":true,"n_gpu_layers":99,"main_gpu":0,"mul_mat_q":true,"tensor_split":"0.00","n_prompt":512,"n_gen":0,"test_time":"2023-09-23T12:09:57Z","avg_ns":212365953,"stddev_ns":985423,"avg_ts":2410.974041,"stddev_ts":11.163766,"samples_ns":[213837238,211635853,212328053,211329715,212698907],"samples_ts":[2394.34,2419.25,2411.36,2422.75,2407.16]} -{"build_commit":"3469684","build_number":1275,"cuda":true,"metal":false,"gpu_blas":true,"blas":true,"cpu_info":"13th Gen Intel(R) Core(TM) i9-13900K","gpu_info":"NVIDIA GeForce RTX 3090 Ti","model_filename":"models/7B/ggml-model-q4_0.gguf","model_type":"llama 7B mostly Q4_0","model_size":3825065984,"model_n_params":6738415616,"n_batch":512,"n_threads":16,"f16_kv":true,"n_gpu_layers":99,"main_gpu":0,"mul_mat_q":true,"tensor_split":"0.00","n_prompt":0,"n_gen":128,"test_time":"2023-09-23T12:09:59Z","avg_ns":977425219,"stddev_ns":9268593,"avg_ts":130.965708,"stddev_ts":1.238924,"samples_ns":[984472709,974901233,989474741,970729355,967548060],"samples_ts":[130.019,131.295,129.362,131.86,132.293]} +{"build_commit": "8cf427ff", "build_number": 5163, "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor", "gpu_info": "NVIDIA GeForce RTX 4080", "backends": "CUDA", "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf", "model_type": "qwen2 7B Q4_K - Medium", "model_size": 4677120000, "model_n_params": 7615616512, "n_batch": 2048, "n_ubatch": 512, "n_threads": 8, "cpu_mask": "0x0", "cpu_strict": false, "poll": 50, "type_k": "f16", "type_v": "f16", "n_gpu_layers": 99, "split_mode": "layer", "main_gpu": 0, "no_kv_offload": false, "flash_attn": false, "tensor_split": "0.00", "use_mmap": true, "embeddings": false, "n_prompt": 512, "n_gen": 0, "n_depth": 0, "test_time": "2025-04-24T11:59:33Z", "avg_ns": 70497220, "stddev_ns": 883196, "avg_ts": 7263.609157, "stddev_ts": 90.940578, "samples_ns": [ 71551000, 71222800, 70364100, 69439100, 69909100 ],"samples_ts": [ 7155.74, 7188.71, 7276.44, 7373.37, 7323.8 ]} +{"build_commit": "8cf427ff", "build_number": 5163, "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor", "gpu_info": "NVIDIA GeForce RTX 4080", "backends": "CUDA", "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf", "model_type": "qwen2 7B Q4_K - Medium", "model_size": 4677120000, "model_n_params": 7615616512, "n_batch": 2048, "n_ubatch": 512, "n_threads": 8, "cpu_mask": "0x0", "cpu_strict": false, "poll": 50, "type_k": "f16", "type_v": "f16", "n_gpu_layers": 99, "split_mode": "layer", "main_gpu": 0, "no_kv_offload": false, "flash_attn": false, "tensor_split": "0.00", "use_mmap": true, "embeddings": false, "n_prompt": 0, "n_gen": 128, "n_depth": 0, "test_time": "2025-04-24T11:59:33Z", "avg_ns": 1068078400, "stddev_ns": 6279455, "avg_ts": 119.844681, "stddev_ts": 0.699739, "samples_ns": [ 1066331700, 1064864900, 1079042600, 1063328400, 1066824400 ],"samples_ts": [ 120.038, 120.203, 118.624, 120.377, 119.982 ]} ``` @@ -271,25 +301,32 @@ $ ./llama-bench -o sql CREATE TABLE IF NOT EXISTS test ( build_commit TEXT, build_number INTEGER, - cuda INTEGER, - metal INTEGER, - gpu_blas INTEGER, - blas INTEGER, cpu_info TEXT, gpu_info TEXT, + backends TEXT, model_filename TEXT, model_type TEXT, model_size INTEGER, model_n_params INTEGER, n_batch INTEGER, + n_ubatch INTEGER, n_threads INTEGER, - f16_kv INTEGER, + cpu_mask TEXT, + cpu_strict INTEGER, + poll INTEGER, + type_k TEXT, + type_v TEXT, n_gpu_layers INTEGER, + split_mode TEXT, main_gpu INTEGER, - mul_mat_q INTEGER, + no_kv_offload INTEGER, + flash_attn INTEGER, tensor_split TEXT, + use_mmap INTEGER, + embeddings INTEGER, n_prompt INTEGER, n_gen INTEGER, + n_depth INTEGER, test_time TEXT, avg_ns INTEGER, stddev_ns INTEGER, @@ -297,6 +334,6 @@ CREATE TABLE IF NOT EXISTS test ( stddev_ts REAL ); -INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634'); -INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692'); +INSERT INTO test (build_commit, build_number, cpu_info, gpu_info, backends, model_filename, model_type, model_size, model_n_params, n_batch, n_ubatch, n_threads, cpu_mask, cpu_strict, poll, type_k, type_v, n_gpu_layers, split_mode, main_gpu, no_kv_offload, flash_attn, tensor_split, use_mmap, embeddings, n_prompt, n_gen, n_depth, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('8cf427ff', '5163', 'AMD Ryzen 7 7800X3D 8-Core Processor', 'NVIDIA GeForce RTX 4080', 'CUDA', 'models/Qwen2.5-7B-Instruct-Q4_K_M.gguf', 'qwen2 7B Q4_K - Medium', '4677120000', '7615616512', '2048', '512', '8', '0x0', '0', '50', 'f16', 'f16', '99', 'layer', '0', '0', '0', '0.00', '1', '0', '512', '0', '0', '2025-04-24T12:00:08Z', '69905000', '519516', '7324.546977', '54.032613'); +INSERT INTO test (build_commit, build_number, cpu_info, gpu_info, backends, model_filename, model_type, model_size, model_n_params, n_batch, n_ubatch, n_threads, cpu_mask, cpu_strict, poll, type_k, type_v, n_gpu_layers, split_mode, main_gpu, no_kv_offload, flash_attn, tensor_split, use_mmap, embeddings, n_prompt, n_gen, n_depth, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('8cf427ff', '5163', 'AMD Ryzen 7 7800X3D 8-Core Processor', 'NVIDIA GeForce RTX 4080', 'CUDA', 'models/Qwen2.5-7B-Instruct-Q4_K_M.gguf', 'qwen2 7B Q4_K - Medium', '4677120000', '7615616512', '2048', '512', '8', '0x0', '0', '50', 'f16', 'f16', '99', 'layer', '0', '0', '0', '0.00', '1', '0', '0', '128', '0', '2025-04-24T12:00:09Z', '1063608780', '4464130', '120.346696', '0.504647'); ``` diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 564a51bfd7..5a78216e44 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -200,6 +200,7 @@ struct cmd_params { std::vector n_prompt; std::vector n_gen; std::vector> n_pg; + std::vector n_depth; std::vector n_batch; std::vector n_ubatch; std::vector type_k; @@ -233,6 +234,7 @@ static const cmd_params cmd_params_defaults = { /* n_prompt */ { 512 }, /* n_gen */ { 128 }, /* n_pg */ {}, + /* n_depth */ { 0 }, /* n_batch */ { 2048 }, /* n_ubatch */ { 512 }, /* type_k */ { GGML_TYPE_F16 }, @@ -272,6 +274,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); + printf(" -d, --n-depth (default: %s)\n", join(cmd_params_defaults.n_depth, ",").c_str()); printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); printf(" -ub, --ubatch-size (default: %s)\n", @@ -409,6 +412,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } params.n_pg.push_back({ std::stoi(p[0]), std::stoi(p[1]) }); + } else if (arg == "-d" || arg == "--n-depth") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.n_depth.insert(params.n_depth.end(), p.begin(), p.end()); } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) { invalid_param = true; @@ -739,6 +749,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.n_pg.empty()) { params.n_pg = cmd_params_defaults.n_pg; } + if (params.n_depth.empty()) { + params.n_depth = cmd_params_defaults.n_depth; + } if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; } @@ -801,6 +814,7 @@ struct cmd_params_instance { std::string model; int n_prompt; int n_gen; + int n_depth; int n_batch; int n_ubatch; ggml_type type_k; @@ -880,7 +894,7 @@ struct cmd_params_instance { llama_context_params to_llama_cparams() const { llama_context_params cparams = llama_context_default_params(); - cparams.n_ctx = n_prompt + n_gen; + cparams.n_ctx = n_prompt + n_gen + n_depth; cparams.n_batch = n_batch; cparams.n_ubatch = n_ubatch; cparams.type_k = type_k; @@ -916,6 +930,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & nt : params.n_threads) for (const auto & cm : params.cpu_mask) for (const auto & cs : params.cpu_strict) + for (const auto & nd : params.n_depth) for (const auto & pl : params.poll) { for (const auto & n_prompt : params.n_prompt) { if (n_prompt == 0) { @@ -925,6 +940,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .model = */ m, /* .n_prompt = */ n_prompt, /* .n_gen = */ 0, + /* .n_depth = */ nd, /* .n_batch = */ nb, /* .n_ubatch = */ nub, /* .type_k = */ tk, @@ -955,6 +971,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .model = */ m, /* .n_prompt = */ 0, /* .n_gen = */ n_gen, + /* .n_depth = */ nd, /* .n_batch = */ nb, /* .n_ubatch = */ nub, /* .type_k = */ tk, @@ -985,6 +1002,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .model = */ m, /* .n_prompt = */ n_pg.first, /* .n_gen = */ n_pg.second, + /* .n_depth = */ nd, /* .n_batch = */ nb, /* .n_ubatch = */ nub, /* .type_k = */ tk, @@ -1040,6 +1058,7 @@ struct test { bool embeddings; int n_prompt; int n_gen; + int n_depth; std::string test_time; std::vector samples_ns; @@ -1072,6 +1091,7 @@ struct test { embeddings = inst.embeddings; n_prompt = inst.n_prompt; n_gen = inst.n_gen; + n_depth = inst.n_depth; // RFC 3339 date-time format time_t t = time(NULL); std::strftime(buf, sizeof(buf), "%FT%TZ", gmtime(&t)); @@ -1113,9 +1133,11 @@ struct test { "build_commit", "build_number", "cpu_info", "gpu_info", "backends", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", + "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "use_mmap", + "embeddings", "n_prompt", "n_gen", "n_depth", "test_time", "avg_ns", "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides", - "use_mmap", "embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", - "stddev_ns", "avg_ts", "stddev_ts", + "use_mmap", "embeddings", "n_prompt", "n_gen", "n_depth", "test_time", + "avg_ns", "stddev_ns", "avg_ts", "stddev_ts", }; return fields; } @@ -1125,8 +1147,8 @@ struct test { static field_type get_field_type(const std::string & field) { if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || field == "n_threads" || field == "poll" || field == "model_size" || field == "model_n_params" || field == "n_gpu_layers" || - field == "main_gpu" || field == "n_prompt" || field == "n_gen" || field == "avg_ns" || - field == "stddev_ns") { + field == "main_gpu" || field == "n_prompt" || field == "n_gen" || field == "n_depth" || + field == "avg_ns" || field == "stddev_ns") { return INT; } if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || field == "flash_attn" || @@ -1204,6 +1226,7 @@ struct test { std::to_string(embeddings), std::to_string(n_prompt), std::to_string(n_gen), + std::to_string(n_depth), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), @@ -1381,7 +1404,7 @@ struct markdown_printer : public printer { return 4; } if (field == "test") { - return 13; + return 15; } int width = std::max((int) field.length(), 10); @@ -1531,6 +1554,10 @@ struct markdown_printer : public printer { } else { snprintf(buf, sizeof(buf), "pp%d+tg%d", t.n_prompt, t.n_gen); } + if (t.n_depth > 0) { + int len = strlen(buf); + snprintf(buf + len, sizeof(buf) - len, " @ d%d", t.n_depth); + } value = buf; } else if (field == "t/s") { snprintf(buf, sizeof(buf), "%.2f ± %.2f", t.avg_ts(), t.stdev_ts()); @@ -1789,6 +1816,14 @@ int main(int argc, char ** argv) { for (int i = 0; i < params.reps; i++) { llama_kv_self_clear(ctx); + if (t.n_depth > 0) { + if (params.progress) { + fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d\n", params_idx, params_count, + i + 1, params.reps); + } + test_prompt(ctx, t.n_depth, t.n_batch, t.n_threads); + } + uint64_t t_start = get_time_ns(); if (t.n_prompt > 0) { From 43ddab6eeeaab5a04fe5a364af0bafb0e4d35065 Mon Sep 17 00:00:00 2001 From: Ville Vesilehto Date: Mon, 28 Apr 2025 21:00:20 +0300 Subject: [PATCH 033/126] fix(rpc): Improve input validation and error handling (#13069) * fix(rpc): Improve input validation and error handling The `rpc-server` was vulnerable to Denial of Service attacks via several RPC commands (`SET_TENSOR`, `GRAPH_COMPUTE`, etc.). Malformed messages could trigger failed assertions (e.g., invalid `ggml_type`) or out-of-bounds reads/writes leading to `GGML_ABORT` calls, crashing the server process. This PR introduces robust input validation and replaces `abort()` calls with graceful error handling: - **Type Validation:** `deserialize_tensor` now checks if the `tensor->type` is within the valid `GGML_TYPE_COUNT` range *before* calling `ggml_new_tensor_4d`. Returns `nullptr` on invalid type. - **Bounds Checks:** Replaced `GGML_ABORT` in `set_tensor`, `set_tensor_hash`, and `get_tensor` handlers with error logging and returning `false` when data/offset parameters are out of buffer bounds. - **Size Checks:** Added safe arithmetic checks (for overflow) in `graph_compute` when calculating required message sizes based on client-provided `n_nodes` and `n_tensors`. Returns early if the reported sizes conflict with the actual message size or would lead to overflow. - **Error Propagation:** - `create_node` now checks for `nullptr` return values from `deserialize_tensor` and its recursive calls, propagating `nullptr` upwards on failure. Uses `find` instead of `at` for safer map access. - `copy_tensor` now checks for `nullptr` from `deserialize_tensor` and sets the response status to failure if deserialization or bounds checks fail. - `graph_compute` now checks for `nullptr` return from `create_node` and returns failure status correctly. The final return value now reflects the actual computation status. These changes improve the RPC server's resilience against malformed client requests, preventing crashes and ensuring errors are handled more gracefully. Signed-off-by: Ville Vesilehto * refactor(rpc): address pr comments removed comments and unnecessary returns Signed-off-by: Ville Vesilehto * refactor(rpc): ambiguous nullptr from create_node rpc_server::create_node could previously return nullptr if the input ID was 0 (valid) or if an internal error (deserialization, recursion failure) occurred (invalid). This ambiguity made error handling difficult for the caller (`graph_compute`). This commit clarifies the meaning of nullptr: - `graph_compute` now checks if the input 'id' was non-zero when `create_node` returns nullptr, correctly identifying failures versus intentional null links. - `create_node` avoids recursive calls for zero IDs and propagates nullptr unambiguously on failure during recursion. Signed-off-by: Ville Vesilehto * refactor(rpc): initial zero check in create_node The caller (`graph_compute`) already checks `id != 0` when handling a `nullptr` return from `create_node`, correctly distinguishing intentional null links from actual errors. This makes the initial `if (id == 0)` check redundant. Also removes the log message when a tensor ID is not found in the provided map which was added in this branch. Signed-off-by: Ville Vesilehto * fix(rpc): Handle get_alloc_size failure in server Check the return value of `server.get_alloc_size` in the RPC server loop. If the call fails, return early to close the connection. Signed-off-by: Ville Vesilehto * refactor(rpc): input size validation in graph_compute Removes detailed, step-by-step size calculations and overflow checks in favor of simpler direct comparisons, assuming 64-bit overflow is unlikely. Signed-off-by: Ville Vesilehto * refactor(rpc): remove extra status code setting Removes the explicit setting of `response.result = GGML_STATUS_FAILED` when `create_node` returns `nullptr` within `graph_compute`. Primary signal is the `false` return value in case of failure. Signed-off-by: Ville Vesilehto * refactor(rpc): remove redundant check for tensor->type Breaks CI on ubuntu-cpu-make. Tensor type is uint32_t, thus the check is not needed. Signed-off-by: Ville Vesilehto --------- Signed-off-by: Ville Vesilehto --- ggml/src/ggml-rpc/ggml-rpc.cpp | 78 +++++++++++++++++++++++++++++----- 1 file changed, 68 insertions(+), 10 deletions(-) diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index 9023eb0919..140a775f98 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -982,8 +982,21 @@ bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) { } ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) { + // Validate tensor type before using it + if (tensor->type >= GGML_TYPE_COUNT) { + GGML_LOG_ERROR("[%s] invalid tensor type received: %u\n", __func__, tensor->type); + return nullptr; + } + ggml_tensor * result = ggml_new_tensor_4d(ctx, (ggml_type) tensor->type, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + + // ggml_new_tensor_4d might fail if dimensions are invalid, although less likely to crash than invalid type + if (result == nullptr) { + GGML_LOG_ERROR("[%s] ggml_new_tensor_4d failed for type %u\\n", __func__, tensor->type); + return nullptr; + } + for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { result->nb[i] = tensor->nb[i]; } @@ -1043,7 +1056,9 @@ bool rpc_server::set_tensor(const std::vector & input) { const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer); if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) { - GGML_ABORT("[%s] tensor->data out of bounds\n", __func__); + GGML_LOG_ERROR("[%s] tensor data region (data=0x%" PRIx64 ", offset=%" PRIu64 ", size=%zu) out of buffer bounds [0x%zx, 0x%zx)\n", + __func__, in_tensor->data, offset, size, p0, p1); + return false; } } @@ -1118,7 +1133,9 @@ bool rpc_server::set_tensor_hash(const std::vector & input, rpc_msg_set const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer); if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) { - GGML_ABORT("[%s] tensor->data out of bounds\n", __func__); + GGML_LOG_ERROR("[%s] tensor data region (data=0x%" PRIx64 ", offset=%" PRIu64 ", size=%zu, hash=0x%" PRIx64 ") out of buffer bounds [0x%zx, 0x%zx)\n", + __func__, in_tensor->data, offset, size, *hash, p0, p1); + return false; } } ggml_backend_tensor_set(tensor, cached_file.data(), offset, size); @@ -1183,7 +1200,9 @@ bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector< if (request.tensor.data + request.offset < p0 || request.tensor.data + request.offset >= p1 || request.size > (p1 - request.tensor.data - request.offset)) { - GGML_ABORT("[%s] tensor->data out of bounds\n", __func__); + GGML_LOG_ERROR("[%s] requested tensor region (data=0x%" PRIx64 ", offset=%" PRIu64 ", size=%" PRIu64 ") out of buffer bounds [0x%zx, 0x%zx)\n", + __func__, request.tensor.data, request.offset, request.size, p0, p1); + return false; } } @@ -1237,22 +1256,50 @@ ggml_tensor * rpc_server::create_node(uint64_t id, struct ggml_context * ctx, const std::unordered_map & tensor_ptrs, std::unordered_map & tensor_map) { - if (id == 0) { - return nullptr; - } if (tensor_map.find(id) != tensor_map.end()) { return tensor_map[id]; } - const rpc_tensor * tensor = tensor_ptrs.at(id); + // Safely find the tensor pointer + auto it_ptr = tensor_ptrs.find(id); + if (it_ptr == tensor_ptrs.end()) { + return nullptr; + } + const rpc_tensor * tensor = it_ptr->second; + struct ggml_tensor * result = deserialize_tensor(ctx, tensor); if (result == nullptr) { return nullptr; } tensor_map[id] = result; for (int i = 0; i < GGML_MAX_SRC; i++) { - result->src[i] = create_node(tensor->src[i], ctx, tensor_ptrs, tensor_map); + // Check if the source ID is 0 before calling create_node recursively + if (tensor->src[i] == 0) { + result->src[i] = nullptr; + } else { + result->src[i] = create_node(tensor->src[i], ctx, tensor_ptrs, tensor_map); + // If the recursive call failed for a non-zero ID, propagate the error + if (result->src[i] == nullptr) { + GGML_LOG_ERROR("[%s] failed to create source node %d (src_id=%" PRIu64 ") for node id %" PRIu64 "\n", + __func__, i, tensor->src[i], id); + // Must return nullptr to signal failure up the call stack + return nullptr; + } + } + } + + // Handle view_src similarly + if (tensor->view_src == 0) { + result->view_src = nullptr; + } else { + result->view_src = create_node(tensor->view_src, ctx, tensor_ptrs, tensor_map); + // If the recursive call failed for a non-zero ID, propagate the error + if (result->view_src == nullptr) { + GGML_LOG_ERROR("[%s] failed to create view_src node (view_src_id=%" PRIu64 ") for node id %" PRIu64 "\n", + __func__, tensor->view_src, id); + // Must return nullptr to signal failure up the call stack + return nullptr; + } } - result->view_src = create_node(tensor->view_src, ctx, tensor_ptrs, tensor_map); result->view_offs = tensor->view_offs; return result; } @@ -1278,6 +1325,7 @@ bool rpc_server::graph_compute(const std::vector & input, rpc_msg_graph GGML_PRINT_DEBUG("[%s] n_nodes: %u, n_tensors: %u\n", __func__, n_nodes, n_tensors); size_t buf_size = ggml_tensor_overhead()*(n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false); + struct ggml_init_params params = { /*.mem_size =*/ buf_size, /*.mem_buffer =*/ NULL, @@ -1297,6 +1345,14 @@ bool rpc_server::graph_compute(const std::vector & input, rpc_msg_graph int64_t id; memcpy(&id, &nodes[i], sizeof(id)); graph->nodes[i] = create_node(id, ctx, tensor_ptrs, tensor_map); + + // Check if create_node failed for a *non-zero* ID. + // If id was 0, create_node returning nullptr is expected. + // If id was non-zero and create_node returned nullptr, it indicates a deserialization error. + if (graph->nodes[i] == nullptr && id != 0) { + GGML_LOG_ERROR("[%s] failed to create graph node %d (id=%" PRId64 ")\n", __func__, i, id); + return false; + } } ggml_status status = ggml_backend_graph_compute(backend, graph); response.result = status; @@ -1361,7 +1417,9 @@ static void rpc_serve_client(ggml_backend_t backend, const char * cache_dir, return; } rpc_msg_get_alloc_size_rsp response; - server.get_alloc_size(request, response); + if (!server.get_alloc_size(request, response)) { + return; + } if (!send_msg(sockfd, &response, sizeof(response))) { return; } From eaea3253244dc4bbe07f6cd81325847ccc6cf93e Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 28 Apr 2025 21:23:19 +0200 Subject: [PATCH 034/126] clip : fix model size display (#13153) --- examples/llava/clip.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 8c5d56cc17..a5eb55f4d4 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -1574,7 +1574,7 @@ struct clip_model_loader { clip_ctx & ctx_clip; std::string fname; - size_t model_size; // in bytes + size_t model_size = 0; // in bytes // TODO @ngxson : we should not pass clip_ctx here, it should be clip_vision_model clip_model_loader(const char * fname, clip_ctx & ctx_clip) : ctx_clip(ctx_clip), fname(fname) { @@ -1748,6 +1748,8 @@ struct clip_model_loader { LOG_INF("%s: minicpmv_version: %d\n", __func__, ctx_clip.minicpmv_version); LOG_INF("%s: proj_scale_factor: %d\n", __func__, hparams.proj_scale_factor); LOG_INF("%s: n_wa_pattern: %d\n", __func__, hparams.n_wa_pattern); + LOG_INF("%s: use_silu: %d\n", __func__, ctx_clip.use_silu); + LOG_INF("%s: use_gelu: %d\n", __func__, ctx_clip.use_gelu); LOG_INF("%s: model size: %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0); LOG_INF("%s: metadata size: %.2f MiB\n", __func__, ggml_get_mem_size(ctx_meta.get()) / 1024.0 / 1024.0); } From 5f5e39e1ba5dbea814e41f2a15e035d749a520bc Mon Sep 17 00:00:00 2001 From: AT Date: Mon, 28 Apr 2025 15:52:15 -0400 Subject: [PATCH 035/126] model : Nomic Embed Text V2 with Mixture-of-Experts (MoE) architecture (#12466) * Nomic Embed Text V2 with Mixture-of-Experts (MoE) architecture - Adds MoE-based embedding model supporting multilingual embeddings. - Selects architecture variant based on hyperparameter detection (MoE layers). - Removes unnecessary subclass initialization checks for clarity. https://www.nomic.ai/blog/posts/nomic-embed-text-v2 Co-authored-by: Jared Van Bortel * fix tokenizer * don't rename this tensor --------- Co-authored-by: Jared Van Bortel --- convert_hf_to_gguf.py | 227 ++++++++++++++++++++------------- gguf-py/gguf/constants.py | 19 +++ gguf-py/gguf/gguf_writer.py | 3 + gguf-py/gguf/tensor_mapping.py | 4 + src/llama-arch.cpp | 20 +++ src/llama-arch.h | 2 + src/llama-graph.cpp | 25 ++-- src/llama-hparams.h | 1 + src/llama-model.cpp | 56 ++++++-- 9 files changed, 247 insertions(+), 110 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index d4fec408dd..b9cea7e469 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -78,7 +78,7 @@ class ModelBase: # subclasses should define this! model_arch: gguf.MODEL_ARCH - def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False, + def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False, use_temp_file: bool = False, eager: bool = False, metadata_override: Path | None = None, model_name: str | None = None, split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, @@ -454,13 +454,6 @@ class ModelBase: class TextModel(ModelBase): - @classmethod - def __init_subclass__(cls): - # can't use an abstract property, because overriding it without type errors - # would require using decorated functions instead of simply defining the property - if "model_arch" not in cls.__dict__: - raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}") - def set_vocab(self): self._set_vocab_gpt2() @@ -3373,14 +3366,7 @@ class BertModel(TextModel): return [(self.map_tensor_name(name), data_torch)] - -@ModelBase.register("RobertaModel") -class RobertaModel(BertModel): - model_arch = gguf.MODEL_ARCH.BERT - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - + def _xlmroberta_tokenizer_init(self) -> None: # we need the pad_token_id to know how to chop down position_embd matrix if (pad_token_id := self.hparams.get("pad_token_id")) is not None: self._position_offset = 1 + pad_token_id @@ -3389,82 +3375,7 @@ class RobertaModel(BertModel): else: self._position_offset = None - def set_vocab(self): - """Support BPE tokenizers for roberta models""" - bpe_tok_path = self.dir_model / "tokenizer.json" - if bpe_tok_path.exists(): - self._set_vocab_gpt2() - self.gguf_writer.add_add_bos_token(True) - self.gguf_writer.add_add_eos_token(True) - - # we need this to validate the size of the token_type embeddings - # though currently we are passing all zeros to the token_type embeddings - # "Sequence A" or "Sequence B" - self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) - - else: - return super().set_vocab() - - def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: - # if name starts with "roberta.", remove the prefix - # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main - if name.startswith("roberta."): - name = name[8:] - - # position embeddings start at pad_token_id + 1, so just chop down the weight tensor - if name == "embeddings.position_embeddings.weight": - if self._position_offset is not None: - data_torch = data_torch[self._position_offset:,:] - - return super().modify_tensors(data_torch, name, bid) - - -@ModelBase.register("NomicBertModel") -class NomicBertModel(BertModel): - model_arch = gguf.MODEL_ARCH.NOMIC_BERT - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - # the HF config claims n_ctx=8192, but it uses RoPE scaling - self.hparams["n_ctx"] = 2048 - - # SwigLU activation - assert self.hparams["activation_function"] == "swiglu" - # this doesn't do anything in the HF version - assert self.hparams["causal"] is False - # no bias tensors - assert self.hparams["qkv_proj_bias"] is False - assert self.hparams["mlp_fc1_bias"] is False - assert self.hparams["mlp_fc2_bias"] is False - # norm at end of layer - assert self.hparams["prenorm"] is False - # standard RoPE - assert self.hparams["rotary_emb_fraction"] == 1.0 - assert self.hparams["rotary_emb_interleaved"] is False - assert self.hparams["rotary_emb_scale_base"] is None - - def set_gguf_parameters(self): - super().set_gguf_parameters() - self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) - - -@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification") -class XLMRobertaModel(BertModel): - model_arch = gguf.MODEL_ARCH.BERT - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - # we need the pad_token_id to know how to chop down position_embd matrix - if (pad_token_id := self.hparams.get("pad_token_id")) is not None: - self._position_offset = 1 + pad_token_id - if "max_position_embeddings" in self.hparams: - self.hparams["max_position_embeddings"] -= self._position_offset - else: - self._position_offset = None - - def set_vocab(self): + def _xlmroberta_set_vocab(self) -> None: # to avoid TypeError: Descriptors cannot be created directly # exception when importing sentencepiece_model_pb2 os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" @@ -3546,6 +3457,138 @@ class XLMRobertaModel(BertModel): self.gguf_writer.add_add_bos_token(True) self.gguf_writer.add_add_eos_token(True) + +@ModelBase.register("RobertaModel") +class RobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # we need the pad_token_id to know how to chop down position_embd matrix + if (pad_token_id := self.hparams.get("pad_token_id")) is not None: + self._position_offset = 1 + pad_token_id + if "max_position_embeddings" in self.hparams: + self.hparams["max_position_embeddings"] -= self._position_offset + else: + self._position_offset = None + + def set_vocab(self): + """Support BPE tokenizers for roberta models""" + bpe_tok_path = self.dir_model / "tokenizer.json" + if bpe_tok_path.exists(): + self._set_vocab_gpt2() + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + + # we need this to validate the size of the token_type embeddings + # though currently we are passing all zeros to the token_type embeddings + # "Sequence A" or "Sequence B" + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) + + else: + return super().set_vocab() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # if name starts with "roberta.", remove the prefix + # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main + if name.startswith("roberta."): + name = name[8:] + + # position embeddings start at pad_token_id + 1, so just chop down the weight tensor + if name == "embeddings.position_embeddings.weight": + if self._position_offset is not None: + data_torch = data_torch[self._position_offset:,:] + + return super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("NomicBertModel") +class NomicBertModel(BertModel): + def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any): + hparams = kwargs.pop("hparams", None) + if hparams is None: + hparams = ModelBase.load_hparams(dir_model) + + self.is_moe = bool(hparams.get("moe_every_n_layers")) + self.model_arch = gguf.MODEL_ARCH.NOMIC_BERT_MOE if self.is_moe else gguf.MODEL_ARCH.NOMIC_BERT + + super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs) + + self._tokenizer_is_xlmroberta = self._is_tokenizer_xlmroberta() + if self._tokenizer_is_xlmroberta: + self._xlmroberta_tokenizer_init() + + # the HF config claims n_ctx=8192, but it uses RoPE scaling + self.hparams["n_ctx"] = 2048 + + assert self.hparams["activation_function"] == "gelu" if self.is_moe else "swiglu" + + # this doesn't do anything in the HF version + assert self.hparams["causal"] is False + # no bias tensors unless MoE + assert self.hparams["qkv_proj_bias"] == self.is_moe + assert self.hparams["mlp_fc1_bias"] == self.is_moe + assert self.hparams["mlp_fc2_bias"] == self.is_moe + + # norm at end of layer + assert self.hparams["prenorm"] is False + # standard RoPE + assert self.hparams["rotary_emb_fraction"] == 1.0 + assert self.hparams["rotary_emb_interleaved"] is False + assert self.hparams["rotary_emb_scale_base"] is None + + def set_vocab(self) -> None: + if self._tokenizer_is_xlmroberta: + return self._xlmroberta_set_vocab() + return super().set_vocab() + + def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]: + # If the tensor is an experts bias tensor, skip it by returning an empty list. + if "mlp.experts.bias" in name: + return [] # Explicitly return an empty list. + + if "mlp.experts.mlp.w1" in name: + data_torch = data_torch.view(self.hparams["num_experts"], self.hparams["n_inner"], self.hparams["n_embd"]) + name += ".weight" + + if "mlp.experts.mlp.w2" in name: + data_torch = data_torch.view(self.hparams["num_experts"], self.hparams["n_inner"], self.hparams["n_embd"]) + data_torch = data_torch.transpose(1, 2) + name += ".weight" + + return [(self.map_tensor_name(name), data_torch)] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) + if self.is_moe: + self.gguf_writer.add_moe_every_n_layers(self.hparams["moe_every_n_layers"]) + self.gguf_writer.add_expert_count(self.hparams["num_experts"]) + self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"]) + + def _is_tokenizer_xlmroberta(self) -> bool: + with open(self.dir_model / "tokenizer.json") as f: + tokenizer_json = json.load(f) + toktyp = tokenizer_json["model"]["type"] + if toktyp == "Unigram": + return True + if toktyp == "WordPiece": + return False + raise ValueError(f"unknown tokenizer: {toktyp}") + + +@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification") +class XLMRobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._xlmroberta_tokenizer_init() + + def set_vocab(self): + self._xlmroberta_set_vocab() + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # if name starts with "roberta.", remove the prefix # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index b81017b142..326ccdb071 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -104,6 +104,7 @@ class Keys: EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale" EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm" EXPERT_GATING_FUNC = "{arch}.expert_gating_func" + MOE_EVERY_N_LAYERS = "{arch}.moe_every_n_layers" POOLING_TYPE = "{arch}.pooling_type" LOGIT_SCALE = "{arch}.logit_scale" DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id" @@ -267,6 +268,7 @@ class MODEL_ARCH(IntEnum): REFACT = auto() BERT = auto() NOMIC_BERT = auto() + NOMIC_BERT_MOE = auto() JINA_BERT_V2 = auto() BLOOM = auto() STABLELM = auto() @@ -521,6 +523,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", MODEL_ARCH.NOMIC_BERT: "nomic-bert", + MODEL_ARCH.NOMIC_BERT_MOE: "nomic-bert-moe", MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", MODEL_ARCH.BLOOM: "bloom", MODEL_ARCH.STABLELM: "stablelm", @@ -960,6 +963,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_UP, MODEL_TENSOR.LAYER_OUT_NORM, ], + MODEL_ARCH.NOMIC_BERT_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.LAYER_OUT_NORM, + ], MODEL_ARCH.JINA_BERT_V2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD_NORM, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 48e9a470b7..f22a6d4a34 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -728,6 +728,9 @@ class GGUFWriter: def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None: self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value) + def add_moe_every_n_layers(self, value: int) -> None: + self.add_uint32(Keys.LLM.MOE_EVERY_N_LAYERS.format(arch=self.arch), value) + def add_swin_norm(self, value: bool) -> None: self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 1d70551973..311d1ff69c 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -290,6 +290,7 @@ class TensorNameMap: "transformer.blocks.{bid}.ffn.router.layer", # dbrx "model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe "language_model.model.layers.{bid}.feed_forward.router", # llama4 + "encoder.layers.{bid}.mlp.router.layer", # nomic-bert-moe ), MODEL_TENSOR.FFN_GATE_INP_SHEXP: ( @@ -322,6 +323,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert + "encoder.layers.{bid}.mlp.fc1", # nomic-bert-moe "model.layers.{bid}.mlp.c_fc", # starcoder2 "encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2 "model.layers.{bid}.residual_mlp.w3", # arctic @@ -337,6 +339,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.experts.up_proj", # qwen2moe olmoe (merged) "model.layers.{bid}.block_sparse_moe.experts.w3", # phimoe (merged) "language_model.model.layers.{bid}.feed_forward.experts.up_proj", # llama4 + "encoder.layers.{bid}.mlp.experts.mlp.w1", # nomic-bert-moe ), MODEL_TENSOR.FFN_UP_SHEXP: ( @@ -418,6 +421,7 @@ class TensorNameMap: "model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe "model.layers.{bid}.block_sparse_moe.experts.w2", # phimoe (merged) "language_model.model.layers.{bid}.feed_forward.experts.down_proj", # llama4 + "encoder.layers.{bid}.mlp.experts.mlp.w2", # nomic-bert-moe ), MODEL_TENSOR.FFN_DOWN_SHEXP: ( diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 62e1480bb5..f2bc8ca768 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -19,6 +19,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_REFACT, "refact" }, { LLM_ARCH_BERT, "bert" }, { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, + { LLM_ARCH_NOMIC_BERT_MOE, "nomic-bert-moe" }, { LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" }, { LLM_ARCH_BLOOM, "bloom" }, { LLM_ARCH_STABLELM, "stablelm" }, @@ -106,6 +107,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" }, { LLM_KV_EXPERT_WEIGHTS_NORM, "%s.expert_weights_norm" }, { LLM_KV_EXPERT_GATING_FUNC, "%s.expert_gating_func" }, + { LLM_KV_MOE_EVERY_N_LAYERS, "%s.moe_every_n_layers" }, { LLM_KV_POOLING_TYPE, "%s.pooling_type" }, { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, { LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" }, @@ -472,6 +474,24 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_NOMIC_BERT_MOE, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" }, + { LLM_TENSOR_TOKEN_TYPES, "token_types" }, + { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, { LLM_ARCH_JINA_BERT_V2, { diff --git a/src/llama-arch.h b/src/llama-arch.h index 98ca00a1bd..41a023da3d 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -23,6 +23,7 @@ enum llm_arch { LLM_ARCH_REFACT, LLM_ARCH_BERT, LLM_ARCH_NOMIC_BERT, + LLM_ARCH_NOMIC_BERT_MOE, LLM_ARCH_JINA_BERT_V2, LLM_ARCH_BLOOM, LLM_ARCH_STABLELM, @@ -110,6 +111,7 @@ enum llm_kv { LLM_KV_EXPERT_WEIGHTS_SCALE, LLM_KV_EXPERT_WEIGHTS_NORM, LLM_KV_EXPERT_GATING_FUNC, + LLM_KV_MOE_EVERY_N_LAYERS, LLM_KV_POOLING_TYPE, LLM_KV_LOGIT_SCALE, LLM_KV_DECODER_START_TOKEN_ID, diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index e6595fb18b..2706ea2635 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -925,28 +925,35 @@ ggml_tensor * llm_graph_context::build_moe_ffn( ggml_tensor * up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] cb(up, "ffn_moe_up", il); - ggml_tensor * gate = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] - cb(gate, "ffn_moe_gate", il); + ggml_tensor * experts = nullptr; + if (gate_exps) { + cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] + cb(cur, "ffn_moe_gate", il); + } else { + cur = up; + } switch (type_op) { case LLM_FFN_SILU: { - gate = ggml_silu(ctx0, gate); - cb(gate, "ffn_moe_silu", il); + cur = ggml_silu(ctx0, cur); + cb(cur, "ffn_moe_silu", il); } break; case LLM_FFN_GELU: { - gate = ggml_gelu(ctx0, gate); - cb(gate, "ffn_moe_gelu", il); + cur = ggml_gelu(ctx0, cur); + cb(cur, "ffn_moe_gelu", il); } break; default: GGML_ABORT("fatal error"); } - ggml_tensor * par = ggml_mul(ctx0, up, gate); // [n_ff, n_expert_used, n_tokens] - cb(par, "ffn_moe_gate_par", il); + if (gate_exps) { + cur = ggml_mul(ctx0, cur, up); // [n_ff, n_expert_used, n_tokens] + cb(cur, "ffn_moe_gate_par", il); + } - ggml_tensor * experts = build_lora_mm_id(down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens] + experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens] cb(experts, "ffn_moe_down", il); if (!weight_before_ffn) { diff --git a/src/llama-hparams.h b/src/llama-hparams.h index 80fcd65df0..7ee6a5b75a 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -66,6 +66,7 @@ struct llama_hparams { float expert_weights_scale = 0.0; bool expert_weights_norm = false; uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE; + uint32_t moe_every_n_layers = 0; float f_norm_eps; float f_norm_rms_eps; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index df2791002e..2ec55d55a3 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -695,10 +695,12 @@ void llama_model::load_hparams(llama_model_loader & ml) { } } break; case LLM_ARCH_NOMIC_BERT: + case LLM_ARCH_NOMIC_BERT_MOE: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS, hparams.moe_every_n_layers, 0); if (hparams.n_layer == 12 && hparams.n_embd == 768) { type = LLM_TYPE_137M; @@ -2057,6 +2059,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: + case LLM_ARCH_NOMIC_BERT_MOE: { tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0); @@ -2090,20 +2093,31 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); } + if (arch == LLM_ARCH_NOMIC_BERT_MOE) { + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); + } + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0); - layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); - layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); - - if (arch == LLM_ARCH_BERT) { + if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) { layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); - layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); } else { - layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + + if (arch == LLM_ARCH_BERT || arch == LLM_ARCH_NOMIC_BERT_MOE) { + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); + } else { + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + } } layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0); @@ -5730,6 +5744,11 @@ struct llm_build_bert : public llm_graph_context { cur = build_lora_mm(model.layers[il].wqkv, cur); cb(cur, "wqkv", il); + if (model.arch == LLM_ARCH_NOMIC_BERT_MOE) { + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + } + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); @@ -5782,13 +5801,29 @@ struct llm_build_bert : public llm_graph_context { cb(ffn_inp, "ffn_inp", il); // feed-forward network - if (model.arch == LLM_ARCH_BERT) { + if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) { + // MoE branch + cur = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + nullptr, + model.layers[il].ffn_down_exps, + nullptr, + hparams.n_expert, + hparams.n_expert_used, + LLM_FFN_GELU, + false, false, + 0.0f, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); + cb(cur, "ffn_moe_out", il); + } else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) { cur = build_ffn(cur, model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL, LLM_FFN_GELU, LLM_FFN_SEQ, il); + cb(cur, "ffn_out", il); } else if (model.arch == LLM_ARCH_JINA_BERT_V2) { cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, @@ -5796,6 +5831,7 @@ struct llm_build_bert : public llm_graph_context { model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL, LLM_FFN_GELU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); } else { cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, @@ -5803,8 +5839,8 @@ struct llm_build_bert : public llm_graph_context { model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); } - cb(cur, "ffn_out", il); // attentions bypass the intermediate layer cur = ggml_add(ctx0, cur, ffn_inp); @@ -12843,6 +12879,7 @@ llm_graph_result_ptr llama_model::build_graph( case LLM_ARCH_BERT: case LLM_ARCH_JINA_BERT_V2: case LLM_ARCH_NOMIC_BERT: + case LLM_ARCH_NOMIC_BERT_MOE: { llm = std::make_unique(*this, params, gf); } break; @@ -13201,6 +13238,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_DBRX: case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: + case LLM_ARCH_NOMIC_BERT_MOE: case LLM_ARCH_STABLELM: case LLM_ARCH_BITNET: case LLM_ARCH_QWEN: From b6ce7430b7eb51f032152316880204e0a9c0470e Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Tue, 29 Apr 2025 08:45:49 +0200 Subject: [PATCH 036/126] llama-graph : fix text position for mrope (#13159) * llama-graph : fix text position for mrope * fix typo * explicitly set 4th dim in the loop --- src/llama-graph.cpp | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 2706ea2635..fabb9ca237 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -55,13 +55,16 @@ void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) { if (ubatch->pos && pos) { const int64_t n_tokens = ubatch->n_tokens; - if (ubatch->token && n_pos_per_embd > 1) { + if (ubatch->token && n_pos_per_embd == 4) { // in case we're using M-RoPE with text tokens, convert the 1D positions to 4D - // the other dimensions are all 0, they are unused for text tokens - std::vector pos_data(n_tokens*n_pos_per_embd, 0); + // the 3 first dims are the same, and 4th dim is all 0 + std::vector pos_data(n_tokens*n_pos_per_embd); // copy the first dimension for (int i = 0; i < n_tokens; ++i) { - pos_data[i] = ubatch->pos[i]; + pos_data[ i] = ubatch->pos[i]; + pos_data[ n_tokens + i] = ubatch->pos[i]; + pos_data[2 * n_tokens + i] = ubatch->pos[i]; + pos_data[3 * n_tokens + i] = 0; // 4th dim is 0 } ggml_backend_tensor_set(pos, pos_data.data(), 0, pos_data.size()*ggml_element_size(pos)); } else { From e98b3692be4cd8fbbd9a56fbacc2f2bf0bf26a68 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Tue, 29 Apr 2025 11:00:31 +0200 Subject: [PATCH 037/126] llama : set qwen3 model type sizes (#13175) --- src/llama-model.cpp | 10 ++++++++++ src/llama-model.h | 4 ++++ 2 files changed, 14 insertions(+) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 2ec55d55a3..2e0eb036e0 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -92,6 +92,10 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_290B: return "290B"; case LLM_TYPE_17B_16E: return "17Bx16E (Scout)"; case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)"; + case LLM_TYPE_0_6B: return "0.6B"; + case LLM_TYPE_1_7B: return "1.7B"; + case LLM_TYPE_30B_A3B: return "30B.A3B"; + case LLM_TYPE_235B_A22B: return "235B.A22B"; default: return "?B"; } } @@ -793,6 +797,10 @@ void llama_model::load_hparams(llama_model_loader & ml) { { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { + case 28: type = hparams.n_embd == 1024 ? LLM_TYPE_0_6B : LLM_TYPE_1_7B; break; + case 36: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_8B; break; + case 40: type = LLM_TYPE_14B; break; + case 64: type = LLM_TYPE_32B; break; default: type = LLM_TYPE_UNKNOWN; } } break; @@ -802,6 +810,8 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { + case 48: type = LLM_TYPE_30B_A3B; break; + case 94: type = LLM_TYPE_235B_A22B; break; default: type = LLM_TYPE_UNKNOWN; } } break; diff --git a/src/llama-model.h b/src/llama-model.h index fd82d106cc..167632e186 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -88,6 +88,10 @@ enum llm_type { LLM_TYPE_290B, LLM_TYPE_17B_16E, // llama4 Scout LLM_TYPE_17B_128E, // llama4 Maverick + LLM_TYPE_0_6B, + LLM_TYPE_1_7B, + LLM_TYPE_30B_A3B, + LLM_TYPE_235B_A22B, }; struct llama_layer_posnet { From 00e3e5a194e88e604e7c91391b9e90332888fd72 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Tue, 29 Apr 2025 11:47:04 +0200 Subject: [PATCH 038/126] mtmd : add qwen2vl and qwen2.5vl (#13141) * llava : add clip_n_output_tokens, deprecate clip_n_patches * mtmd : add qwen2vl and qwen2.5vl * decode_embd_batch::set_position_... * working version * deprecate llama-qwen2vl-cli * correct order W, H of clip_embd_nbytes_by_img * edit existing line in hot topics --- README.md | 2 +- examples/llava/CMakeLists.txt | 8 +- examples/llava/clip.cpp | 34 +++- examples/llava/clip.h | 19 ++- examples/llava/llava.cpp | 15 +- examples/llava/mtmd-cli.cpp | 36 +---- examples/llava/mtmd.cpp | 146 +++++++++++++++--- examples/llava/mtmd.h | 9 +- .../{qwen2vl-cli.cpp => qwen2vl-test.cpp} | 2 + examples/llava/tests.sh | 4 +- 10 files changed, 196 insertions(+), 79 deletions(-) rename examples/llava/{qwen2vl-cli.cpp => qwen2vl-test.cpp} (99%) diff --git a/README.md b/README.md index 1785493c3e..42c0eb633e 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ## Hot topics - **GGML developer experience survey (organized and reviewed by NVIDIA):** [link](https://forms.gle/Gasw3cRgyhNEnrwK9) -- A new binary `llama-mtmd-cli` is introduced to replace `llava-cli`, `minicpmv-cli` and `gemma3-cli` https://github.com/ggml-org/llama.cpp/pull/13012, `libllava` will be deprecated +- A new binary `llama-mtmd-cli` is introduced to replace `llava-cli`, `minicpmv-cli`, `gemma3-cli` ([#13012](https://github.com/ggml-org/llama.cpp/pull/13012)) and `qwen2vl-cli` ([#13141]((https://github.com/ggml-org/llama.cpp/pull/13141))), `libllava` will be deprecated - VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode - Universal [tool call support](./docs/function-calling.md) in `llama-server` https://github.com/ggml-org/llama.cpp/pull/9639 - Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 6409b4f5e6..27b6d27e5c 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -64,13 +64,7 @@ endif() add_executable(llama-llava-cli deprecation-warning.cpp) add_executable(llama-gemma3-cli deprecation-warning.cpp) add_executable(llama-minicpmv-cli deprecation-warning.cpp) - -set(TARGET llama-qwen2vl-cli) -add_executable(${TARGET} qwen2vl-cli.cpp) -set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_17) +add_executable(llama-qwen2vl-cli deprecation-warning.cpp) set(TARGET llama-mtmd-cli) add_executable(${TARGET} mtmd-cli.cpp) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index a5eb55f4d4..ad3e7df1d8 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -2825,15 +2825,18 @@ void clip_free(clip_ctx * ctx) { delete ctx; } +// deprecated size_t clip_embd_nbytes(const struct clip_ctx * ctx) { - return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float); + const int32_t nx = ctx->vision_model.hparams.image_size; + const int32_t ny = ctx->vision_model.hparams.image_size; + return clip_embd_nbytes_by_img(ctx, nx, ny); } -size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) { +size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h) { clip_image_f32 img; img.nx = img_w; img.ny = img_h; - return clip_n_patches_by_img(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float); + return clip_n_output_tokens(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float); } int32_t clip_get_image_size(const struct clip_ctx * ctx) { @@ -2863,14 +2866,37 @@ size_t get_clip_image_grid_size(const struct clip_ctx * ctx) { return ctx->vision_model.hparams.image_grid_pinpoints.size(); } +// deprecated int clip_n_patches(const struct clip_ctx * ctx) { clip_image_f32 img; img.nx = ctx->vision_model.hparams.image_size; img.ny = ctx->vision_model.hparams.image_size; - return clip_n_patches_by_img(ctx, &img); + return clip_n_output_tokens(ctx, &img); } +// deprecated int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img) { + return clip_n_output_tokens(ctx, img); +} + +int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img) { + const auto & params = ctx->vision_model.hparams; + const int n_total = clip_n_output_tokens(ctx, img); + if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) { + return img->nx / (params.patch_size * 2) + (int)(img->nx % params.patch_size > 0); + } + return n_total; +} + +int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img) { + const auto & params = ctx->vision_model.hparams; + if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type == PROJECTOR_TYPE_QWEN25VL) { + return img->ny / (params.patch_size * 2) + (int)(img->ny % params.patch_size > 0); + } + return 1; +} + +int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img) { const auto & params = ctx->vision_model.hparams; int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size); diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 6ba42ad892..0a53bd8eb7 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -47,7 +47,7 @@ CLIP_API struct clip_ctx * clip_init(const char * fname, struct clip_context_par CLIP_API void clip_free(struct clip_ctx * ctx); CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx); -CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w); +CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h); CLIP_API int32_t clip_get_image_size (const struct clip_ctx * ctx); CLIP_API int32_t clip_get_patch_size (const struct clip_ctx * ctx); @@ -59,9 +59,20 @@ CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx); CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx); CLIP_API size_t get_clip_image_grid_size(const struct clip_ctx * ctx); -CLIP_API int clip_n_patches (const struct clip_ctx * ctx); -CLIP_API int clip_n_patches_by_img (const struct clip_ctx * ctx, struct clip_image_f32 * img); -CLIP_API int clip_n_mmproj_embd (const struct clip_ctx * ctx); +GGML_DEPRECATED(CLIP_API int clip_n_patches(const struct clip_ctx * ctx), + "use clip_n_output_tokens instead"); +GGML_DEPRECATED(CLIP_API int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img), + "use clip_n_output_tokens instead"); + +CLIP_API int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img); + +// for M-RoPE, this will be the number of token positions in X and Y directions +// for other models, X will be the total number of tokens and Y will be 1 +CLIP_API int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img); +CLIP_API int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img); + +// this should be equal to the embedding dimension of the text model +CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx); CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip); CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size); diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 03a22cbb4c..c00d16aeff 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -112,7 +112,7 @@ static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair< } // Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out) -static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) { +static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out, clip_image_f32 * img_input) { struct { struct ggml_context * ctx; } model; @@ -175,7 +175,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector model.ctx = ggml_init(params); - struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4 + struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_output_tokens(ctx_clip, img_input), num_images - 1); // example: 4096 x 576 x 4 // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false); // fill it with the image embeddings, ignoring the base for (size_t i = 1; i < num_images; i++) { @@ -214,8 +214,8 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context // append without newline tokens (default behavior in llava_arch when not using unpad ): - memcpy(image_embd_out + clip_n_patches(ctx_clip) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches - *n_img_pos_out = static_cast(result->ne[1]+clip_n_patches(ctx_clip)); + memcpy(image_embd_out + clip_n_output_tokens(ctx_clip, img_input) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches + *n_img_pos_out = static_cast(result->ne[1]+clip_n_output_tokens(ctx_clip, img_input)); // Debug: Test single segments // Current findings: sending base image, sending a segment embedding all works similar to python @@ -313,7 +313,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), image_embd_v[i], clip_embd_nbytes_by_img(ctx_clip, nx, ny)); - n_img_pos_out += clip_n_patches_by_img(ctx_clip, img_res); + n_img_pos_out += clip_n_output_tokens(ctx_clip, img_res); } *n_img_pos = n_img_pos_out; for (size_t i = 0; i < image_embd_v.size(); i++) { @@ -342,8 +342,8 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli } else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) { // flat / default llava-1.5 type embedding - *n_img_pos = clip_n_patches(ctx_clip); clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0); + *n_img_pos = clip_n_output_tokens(ctx_clip, img_res); bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); // image_embd shape is 576 x 4096 if (!encoded) { LOG_ERR("Unable to encode image\n"); @@ -381,7 +381,8 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size); int n_img_pos_out; - clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out); + clip_image_f32 * img_input = clip_image_f32_get_img(img_res_v.get(), 0); + clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out, img_input); *n_img_pos = n_img_pos_out; for (size_t i = 0; i < image_embd_v.size(); i++) { diff --git a/examples/llava/mtmd-cli.cpp b/examples/llava/mtmd-cli.cpp index 250e8c9a9e..4d857ca64e 100644 --- a/examples/llava/mtmd-cli.cpp +++ b/examples/llava/mtmd-cli.cpp @@ -136,39 +136,6 @@ struct mtmd_cli_context { } }; -struct decode_embd_batch { - std::vector pos; - std::vector n_seq_id; - std::vector seq_id_0; - std::vector seq_ids; - std::vector logits; - llama_batch batch; - decode_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) { - pos .resize(n_tokens); - n_seq_id.resize(n_tokens); - seq_ids .resize(n_tokens + 1); - logits .resize(n_tokens); - seq_id_0.resize(1); - seq_id_0[0] = seq_id; - seq_ids [n_tokens] = nullptr; - batch = { - /*n_tokens =*/ n_tokens, - /*tokens =*/ nullptr, - /*embd =*/ embd, - /*pos =*/ pos.data(), - /*n_seq_id =*/ n_seq_id.data(), - /*seq_id =*/ seq_ids.data(), - /*logits =*/ logits.data(), - }; - for (int i = 0; i < n_tokens; i++) { - batch.pos [i] = pos_0 + i; - batch.n_seq_id[i] = 1; - batch.seq_id [i] = seq_id_0.data(); - batch.logits [i] = false; - } - } -}; - static int generate_response(mtmd_cli_context & ctx, common_sampler * smpl, int n_predict) { llama_tokens generated_tokens; for (int i = 0; i < n_predict; i++) { @@ -243,7 +210,7 @@ static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg, std::vect return 1; } - ctx.n_past += mtmd_helper_get_n_tokens(chunks); + ctx.n_past += mtmd_helper_get_n_pos(chunks); return 0; } @@ -371,6 +338,7 @@ int main(int argc, char ** argv) { } } if (g_is_interrupted) LOG("\nInterrupted by user\n"); + LOG("\n\n"); llama_perf_context_print(ctx.lctx); return g_is_interrupted ? 130 : 0; } diff --git a/examples/llava/mtmd.cpp b/examples/llava/mtmd.cpp index f95f050356..7081fd7352 100644 --- a/examples/llava/mtmd.cpp +++ b/examples/llava/mtmd.cpp @@ -40,11 +40,14 @@ struct mtmd_context { llama_token tok_sli_img_end = LLAMA_TOKEN_NULL; // single slice llama_token tok_row_end = LLAMA_TOKEN_NULL; // end of row + bool use_mrope = false; // for Qwen2VL, we need to use M-RoPE + // TODO @ngxson : add timings mtmd_context(const char * mmproj_fname, const llama_model * text_model, const mtmd_context_params & ctx_params) : + text_model (text_model), print_timings(ctx_params.print_timings), n_threads (ctx_params.n_threads), image_marker (ctx_params.image_marker) @@ -56,9 +59,8 @@ struct mtmd_context { if (!ctx_clip) { throw std::runtime_error(string_format("Failed to load CLIP model from %s\n", mmproj_fname)); } - this->text_model = text_model; - GGML_ASSERT(!clip_is_qwen2vl(ctx_clip) && "Qwen2VL model is not supported yet, use llama-qwen2vl-cli instead"); + use_mrope = clip_is_qwen2vl(ctx_clip); int minicpmv_version = clip_is_minicpmv(ctx_clip); if (minicpmv_version == 2) { @@ -126,6 +128,7 @@ struct mtmd_image_tokens_data { struct mtmd_image_tokens { uint32_t nx; // number of tokens in x direction uint32_t ny; // number of tokens in y direction + bool use_mrope_pos = false; // use M-RoPE position counting (the whole image is 1 temporal position) uint32_t n_tokens() const { return nx * ny; } clip_image_f32_batch batch_f32; // preprocessed image patches std::string id; // optional user-defined ID, useful for KV cache tracking @@ -202,6 +205,13 @@ int32_t mtmd_tokenize(mtmd_context * ctx, string_replace_all(prompt_modified, ctx->image_marker, marker_modified); } + else if (proj_type == PROJECTOR_TYPE_QWEN2VL || proj_type == PROJECTOR_TYPE_QWEN25VL) { + // <|vision_start|> ... (image embeddings) ... <|vision_end|> + marker_modified = "<|vision_start|>" + ctx->image_marker + "<|vision_end|>"; + string_replace_all(prompt_modified, ctx->image_marker, marker_modified); + + } + // llava-1.5, llava-1.6, Yi-VL, Yi-34B, granite: don't need to add prefix and suffix std::vector parts = string_split_str(prompt_modified, ctx->image_marker); @@ -226,7 +236,7 @@ int32_t mtmd_tokenize(mtmd_context * ctx, for (auto & entry : batch_f32.entries) { mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens); - image_tokens->nx = clip_n_patches_by_img(ctx->ctx_clip, entry.get()); + image_tokens->nx = clip_n_output_tokens(ctx->ctx_clip, entry.get()); image_tokens->ny = 1; image_tokens->batch_f32.entries.push_back(std::move(entry)); image_tokens->id = id; @@ -322,12 +332,20 @@ int32_t mtmd_tokenize(mtmd_context * ctx, } else { size_t n_tokens = 0; for (const auto & entry : batch_f32.entries) { - n_tokens += clip_n_patches_by_img(ctx->ctx_clip, entry.get()); + n_tokens += clip_n_output_tokens(ctx->ctx_clip, entry.get()); } mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens); - image_tokens->nx = n_tokens; - image_tokens->ny = 1; // TODO + if (ctx->use_mrope) { + // for Qwen2VL, we need this information for M-RoPE decoding positions + image_tokens->nx = clip_n_output_tokens_x(ctx->ctx_clip, batch_f32.entries[0].get()); + image_tokens->ny = clip_n_output_tokens_y(ctx->ctx_clip, batch_f32.entries[0].get()); + image_tokens->use_mrope_pos = true; + } else { + // other models, we only need the total number of tokens + image_tokens->nx = n_tokens; + image_tokens->ny = 1; + } image_tokens->batch_f32 = std::move(batch_f32); image_tokens->id = bitmaps[i_img].id; // optional @@ -372,6 +390,13 @@ std::string mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens) { return image_tokens->id; } +llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens) { + if (image_tokens->use_mrope_pos) { + return 1; // for M-RoPE, the whole image is 1 in temporal dimension + } + return image_tokens->n_tokens(); +} + int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens) { int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip); ctx->image_embd_v.resize(image_tokens->n_tokens() * n_mmproj_embd); @@ -389,7 +414,7 @@ int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens) // TODO @ngxson : llava does not support batched encoding ; this should be fixed inside clip_image_batch_encode() const auto & entries = image_tokens->batch_f32.entries; for (size_t i = 0; i < entries.size(); i++) { - int n_tokens_per_image = clip_n_patches_by_img(ctx->ctx_clip, entries[i].get()); + int n_tokens_per_image = clip_n_output_tokens(ctx->ctx_clip, entries[i].get()); ok = clip_image_encode( ctx->ctx_clip, ctx->n_threads, @@ -417,7 +442,7 @@ size_t mtmd_helper_get_n_tokens(mtmd_input_chunks & chunks) { if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) { n_tokens += chunk.tokens_text.size(); } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { - n_tokens += chunk.tokens_image->n_tokens(); + n_tokens += mtmd_image_tokens_get_n_tokens(chunk.tokens_image.get()); } else { GGML_ASSERT(false && "chunk type not supported"); } @@ -425,22 +450,38 @@ size_t mtmd_helper_get_n_tokens(mtmd_input_chunks & chunks) { return n_tokens; } +llama_pos mtmd_helper_get_n_pos(mtmd_input_chunks & chunks) { + llama_pos n_pos = 0; + for (auto & chunk : chunks) { + if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + n_pos += chunk.tokens_text.size(); + } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { + n_pos += mtmd_image_tokens_get_n_pos(chunk.tokens_image.get()); + } else { + GGML_ASSERT(false && "chunk type not supported"); + } + } + return n_pos; +} + // helper struct to make working with embd batch easier // note: this will be removed after llama_batch_ext refactoring struct decode_embd_batch { + int n_pos_per_embd; + int n_mmproj_embd; std::vector pos; + std::vector pos_view; // used by mrope std::vector n_seq_id; std::vector seq_id_0; std::vector seq_ids; std::vector logits; llama_batch batch; - decode_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) { - pos .resize(n_tokens); + decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) { + pos .resize(n_tokens * n_pos_per_embd); n_seq_id.resize(n_tokens); seq_ids .resize(n_tokens + 1); logits .resize(n_tokens); seq_id_0.resize(1); - seq_id_0[0] = seq_id; seq_ids [n_tokens] = nullptr; batch = { /*n_tokens =*/ n_tokens, @@ -451,13 +492,64 @@ struct decode_embd_batch { /*seq_id =*/ seq_ids.data(), /*logits =*/ logits.data(), }; - for (int i = 0; i < n_tokens; i++) { + } + + void set_position_normal(llama_pos pos_0, llama_seq_id seq_id) { + seq_id_0[0] = seq_id; + for (int i = 0; i < batch.n_tokens; i++) { batch.pos [i] = pos_0 + i; batch.n_seq_id[i] = 1; batch.seq_id [i] = seq_id_0.data(); batch.logits [i] = false; } } + + void set_position_mrope(llama_pos pos_0, int nx, int ny, llama_seq_id seq_id) { + GGML_ASSERT(n_pos_per_embd == 4); + seq_id_0[0] = seq_id; + for (int y = 0; y < ny; y++) { + for (int x = 0; x < nx; x++) { + int i = y * nx + x; + pos[i ] = pos_0; + pos[i + batch.n_tokens ] = pos_0 + y; + pos[i + batch.n_tokens * 2] = pos_0 + x; + pos[i + batch.n_tokens * 3] = 0; // last pos dim is unused + } + } + for (int i = 0; i < batch.n_tokens; i++) { + batch.n_seq_id[i] = 1; + batch.seq_id [i] = seq_id_0.data(); + batch.logits [i] = false; + } + } + + llama_batch get_view(int offset, int n_tokens) { + llama_pos * pos_ptr; + pos_view.clear(); + pos_view.resize(n_tokens * n_pos_per_embd); + if (n_pos_per_embd > 1) { + // mrope + // for example, with layout of src: 1234...1234...1234...1234... + // offset 2 will give us dst: 34...34...34...34... + for (int i = 0; i < n_pos_per_embd; i++) { + auto src = pos.begin() + i * batch.n_tokens + offset; + pos_view.insert(pos_view.end(), src, src + n_tokens); + } + pos_ptr = pos_view.data(); + } else { + // normal + pos_ptr = pos.data() + offset; + } + return { + /*n_tokens =*/ n_tokens, + /*tokens =*/ nullptr, + /*embd =*/ batch.embd + offset * n_mmproj_embd, + /*pos =*/ pos_ptr, + /*n_seq_id =*/ batch.n_seq_id + offset, + /*seq_id =*/ batch.seq_id + offset, + /*logits =*/ batch.logits + offset, + }; + } }; int32_t mtmd_helper_eval(mtmd_context * ctx, @@ -470,6 +562,7 @@ int32_t mtmd_helper_eval(mtmd_context * ctx, llama_pos n_past = pos0; llama_batch text_batch = llama_batch_init(n_batch, 0, 1); int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip); + int n_pos_per_embd = mtmd_decode_use_mrope(ctx) ? 4 : 1; for (auto & chunk : chunks) { bool is_last = &chunk == &chunks.back(); @@ -517,6 +610,16 @@ int32_t mtmd_helper_eval(mtmd_context * ctx, int32_t i_batch = 0; int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch; float * embd = mtmd_get_output_embd(ctx); + decode_embd_batch batch_embd(embd, n_tokens, n_pos_per_embd, n_mmproj_embd); + + const int nx = mtmd_image_tokens_get_nx(chunk.tokens_image.get()); + const int ny = mtmd_image_tokens_get_ny(chunk.tokens_image.get()); + + if (mtmd_decode_use_mrope(ctx)) { + batch_embd.set_position_mrope(n_past, nx, ny, seq_id); + } else { + batch_embd.set_position_normal(n_past, seq_id); + } if (mtmd_decode_use_non_causal(ctx)) { llama_set_causal_attn(lctx, false); @@ -524,15 +627,14 @@ int32_t mtmd_helper_eval(mtmd_context * ctx, } while (i_batch < n_img_batches) { // split into batches - int32_t pos_offset = i_batch*n_batch; - int32_t n_tokens_batch = std::min(n_batch, n_tokens - pos_offset); - float * embd_batch = embd + pos_offset*n_mmproj_embd; - decode_embd_batch batch_img(embd_batch, n_tokens_batch, n_past, 0); + int pos_offset = i_batch*n_batch; + int n_tokens_batch = std::min(n_batch, n_tokens - pos_offset); + llama_batch batch_embd_view = batch_embd.get_view(pos_offset, n_tokens_batch); - printf("decoding image batch %d/%d, n_tokens_batch = %d\n", i_batch+1, n_img_batches, n_tokens_batch); + LOG_INF("decoding image batch %d/%d, n_tokens_batch = %d\n", i_batch+1, n_img_batches, n_tokens_batch); int64_t t1 = ggml_time_ms(); - ret = llama_decode(lctx, batch_img.batch); + ret = llama_decode(lctx, batch_embd_view); if (ret != 0) { LOG_ERR("failed to decode image\n"); llama_set_causal_attn(lctx, true); // restore causal attn @@ -545,9 +647,11 @@ int32_t mtmd_helper_eval(mtmd_context * ctx, } i_batch++; - n_past += n_tokens_batch; } + // for mrope, one image is one single **temporal** position + n_past += mtmd_decode_use_mrope(ctx) ? 1 : n_tokens; + if (mtmd_decode_use_non_causal(ctx)) { llama_set_causal_attn(lctx, true); } @@ -595,6 +699,10 @@ bool mtmd_decode_use_non_causal(mtmd_context * ctx) { return false; } +bool mtmd_decode_use_mrope(mtmd_context * ctx) { + return ctx->use_mrope; +} + void mtmd_image_tokens_deleter::operator()(mtmd_image_tokens * val) { mtmd_image_tokens_free(val); } diff --git a/examples/llava/mtmd.h b/examples/llava/mtmd.h index 78be192dd6..6805e5e481 100644 --- a/examples/llava/mtmd.h +++ b/examples/llava/mtmd.h @@ -102,6 +102,7 @@ MTMD_API size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * im MTMD_API size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens); MTMD_API size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens); MTMD_API std::string mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens); +MTMD_API llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens); // number of temporal positions (always 1 for M-RoPE, n_tokens otherwise) MTMD_API void mtmd_image_tokens_free(mtmd_image_tokens * image_tokens); // returns 0 on success @@ -114,15 +115,21 @@ MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx); // whether we need to set non-causal mask before llama_decode MTMD_API bool mtmd_decode_use_non_causal(mtmd_context * ctx); +// whether the current model use M-RoPE for llama_decode +MTMD_API bool mtmd_decode_use_mrope(mtmd_context * ctx); + // // helper functions (can be implemented based on other functions) // -// helper to count the total number of tokens from a list of chunks, useful to keep track of n_past +// helper to count the total number of tokens from a list of chunks, useful to keep track of KV cache MTMD_API size_t mtmd_helper_get_n_tokens(mtmd_input_chunks & chunks); +// helper to count the total position of tokens from a list of chunks, useful to keep track of n_past +MTMD_API llama_pos mtmd_helper_get_n_pos(mtmd_input_chunks & chunks); + // helper function that automatically: // 1. run llama_decode() on text chunks // 2. run mtmd_encode() on image chunks, then mtmd_get_output_embd() and then llama_decode() diff --git a/examples/llava/qwen2vl-cli.cpp b/examples/llava/qwen2vl-test.cpp similarity index 99% rename from examples/llava/qwen2vl-cli.cpp rename to examples/llava/qwen2vl-test.cpp index 1e54851ea0..7f9e3dca88 100644 --- a/examples/llava/qwen2vl-cli.cpp +++ b/examples/llava/qwen2vl-test.cpp @@ -27,6 +27,8 @@ #include #include +// THIS FILE IS ONLY USED FOR TESTING THE QWEN2VL MODEL +// IT IS NOT A PRODUCTION CODE static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) { diff --git a/examples/llava/tests.sh b/examples/llava/tests.sh index 4002f9d531..75604315cf 100755 --- a/examples/llava/tests.sh +++ b/examples/llava/tests.sh @@ -54,8 +54,8 @@ add_test "llama-mtmd-cli" "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M" add_test "llama-mtmd-cli" "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K" # model from openbmb is corrupted add_test "llama-mtmd-cli" "openbmb/MiniCPM-V-2_6-gguf:Q2_K" add_test "llama-mtmd-cli" "openbmb/MiniCPM-o-2_6-gguf:Q4_0" -add_test "llama-qwen2vl-cli" "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" -add_test "llama-qwen2vl-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" +add_test "llama-mtmd-cli" "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" +add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" # to test the big models, run: ./tests.sh big add_test_big "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M" From 7d3af70b089bb349b5d17eb01839224c99ec1952 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Tue, 29 Apr 2025 13:25:53 +0200 Subject: [PATCH 039/126] llama : llm_type order by size (#13177) --- src/llama-model.cpp | 8 ++++---- src/llama-model.h | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 2e0eb036e0..759669c178 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -43,11 +43,13 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_770M: return "770M"; case LLM_TYPE_780M: return "780M"; case LLM_TYPE_0_5B: return "0.5B"; + case LLM_TYPE_0_6B: return "0.6B"; case LLM_TYPE_1B: return "1B"; case LLM_TYPE_1_3B: return "1.3B"; case LLM_TYPE_1_4B: return "1.4B"; case LLM_TYPE_1_5B: return "1.5B"; case LLM_TYPE_1_6B: return "1.6B"; + case LLM_TYPE_1_7B: return "1.7B"; case LLM_TYPE_1_8B: return "1.8B"; case LLM_TYPE_2B: return "2B"; case LLM_TYPE_2_8B: return "2.8B"; @@ -66,6 +68,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_15B: return "15B"; case LLM_TYPE_16B: return "16B"; case LLM_TYPE_20B: return "20B"; + case LLM_TYPE_27B: return "27B"; case LLM_TYPE_30B: return "30B"; case LLM_TYPE_32B: return "32B"; case LLM_TYPE_34B: return "34B"; @@ -74,6 +77,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_65B: return "65B"; case LLM_TYPE_70B: return "70B"; case LLM_TYPE_236B: return "236B"; + case LLM_TYPE_290B: return "290B"; case LLM_TYPE_314B: return "314B"; case LLM_TYPE_671B: return "671B"; case LLM_TYPE_SMALL: return "0.1B"; @@ -88,12 +92,8 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_16x3_8B: return "16x3.8B"; case LLM_TYPE_10B_128x3_66B: return "10B+128x3.66B"; case LLM_TYPE_57B_A14B: return "57B.A14B"; - case LLM_TYPE_27B: return "27B"; - case LLM_TYPE_290B: return "290B"; case LLM_TYPE_17B_16E: return "17Bx16E (Scout)"; case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)"; - case LLM_TYPE_0_6B: return "0.6B"; - case LLM_TYPE_1_7B: return "1.7B"; case LLM_TYPE_30B_A3B: return "30B.A3B"; case LLM_TYPE_235B_A22B: return "235B.A22B"; default: return "?B"; diff --git a/src/llama-model.h b/src/llama-model.h index 167632e186..95eca00266 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -39,11 +39,13 @@ enum llm_type { LLM_TYPE_770M, LLM_TYPE_780M, LLM_TYPE_0_5B, + LLM_TYPE_0_6B, LLM_TYPE_1B, LLM_TYPE_1_3B, LLM_TYPE_1_4B, LLM_TYPE_1_5B, LLM_TYPE_1_6B, + LLM_TYPE_1_7B, LLM_TYPE_1_8B, LLM_TYPE_2B, LLM_TYPE_2_8B, @@ -62,6 +64,7 @@ enum llm_type { LLM_TYPE_15B, LLM_TYPE_16B, LLM_TYPE_20B, + LLM_TYPE_27B, LLM_TYPE_30B, LLM_TYPE_32B, LLM_TYPE_34B, @@ -70,6 +73,7 @@ enum llm_type { LLM_TYPE_65B, LLM_TYPE_70B, LLM_TYPE_236B, + LLM_TYPE_290B, LLM_TYPE_314B, LLM_TYPE_671B, LLM_TYPE_SMALL, @@ -84,12 +88,8 @@ enum llm_type { LLM_TYPE_16x3_8B, LLM_TYPE_10B_128x3_66B, LLM_TYPE_57B_A14B, - LLM_TYPE_27B, - LLM_TYPE_290B, LLM_TYPE_17B_16E, // llama4 Scout LLM_TYPE_17B_128E, // llama4 Maverick - LLM_TYPE_0_6B, - LLM_TYPE_1_7B, LLM_TYPE_30B_A3B, LLM_TYPE_235B_A22B, }; From cdf76586b23c67abd3ca064ee2c084c57ae240bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 29 Apr 2025 16:00:27 +0200 Subject: [PATCH 040/126] CUDA: fix non-cont. inputs for batched mat mul (#13155) --- ggml/src/ggml-cuda/convert.cu | 53 +++++++++++++++++++------ ggml/src/ggml-cuda/convert.cuh | 12 +++++- ggml/src/ggml-cuda/ggml-cuda.cu | 70 ++++++++++++++++++++------------- src/llama-model.cpp | 1 - 4 files changed, 94 insertions(+), 42 deletions(-) diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu index a224ec0e12..c6dec4276b 100644 --- a/ggml/src/ggml-cuda/convert.cu +++ b/ggml/src/ggml-cuda/convert.cu @@ -1,6 +1,8 @@ #include "convert.cuh" #include "dequantize.cuh" +#include + #define CUDA_Q8_0_NE_ALIGN 2048 template @@ -570,30 +572,46 @@ static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t } template -static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) { - const int64_t i = (int64_t)blockDim.x*blockIdx.x + threadIdx.x; +static __global__ void convert_unary( + const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02, + const int64_t s01, const int64_t s02, const int64_t s03) { + const int64_t i00 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x; - if (i >= k) { + if (i00 >= ne00) { return; } + const int64_t i01 = blockIdx.y; + const int64_t i02 = blockIdx.z % ne02; + const int64_t i03 = blockIdx.z / ne02; + const src_t * x = (const src_t *) vx; - y[i] = float(x[i]); + const int64_t ix = i03*s03 + i02*s02 + i01*s01 + i00; + const int64_t iy = ((i03*ne02 + i02)*ne01 + i01)*ne00 + i00; + y[iy] = float(x[ix]); } template -static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) { - const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; - convert_unary<<>>(vx, y, k); +static void convert_unary_cuda(const void * vx, dst_t * y, + const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03, + const int64_t s01, const int64_t s02, const int64_t s03, cudaStream_t stream) { + const dim3 num_blocks((ne00 + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE, ne01, ne02*ne03); + convert_unary<<>> + (vx, y, ne00, ne01, ne02, s01, s02, s03); +} + +template +static void convert_unary_cont_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { + convert_unary_cuda(vx, y, k, 1, 1, 1, k, k, k, stream); } to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type) { switch (type) { case GGML_TYPE_F32: - return convert_unary_cuda; + return convert_unary_cont_cuda; case GGML_TYPE_F16: - return convert_unary_cuda; + return convert_unary_cont_cuda; default: return nullptr; } @@ -643,9 +661,9 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { case GGML_TYPE_IQ3_S: return dequantize_row_iq3_s_cuda; case GGML_TYPE_F32: - return convert_unary_cuda; + return convert_unary_cont_cuda; case GGML_TYPE_BF16: - return convert_unary_cuda; + return convert_unary_cont_cuda; default: return nullptr; } @@ -692,7 +710,18 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { case GGML_TYPE_IQ3_S: return dequantize_row_iq3_s_cuda; case GGML_TYPE_F16: - return convert_unary_cuda; + return convert_unary_cont_cuda; + case GGML_TYPE_BF16: + return convert_unary_cont_cuda; + default: + return nullptr; + } +} + +to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type) { + switch (type) { + case GGML_TYPE_F32: + return convert_unary_cuda; case GGML_TYPE_BF16: return convert_unary_cuda; default: diff --git a/ggml/src/ggml-cuda/convert.cuh b/ggml/src/ggml-cuda/convert.cuh index 411a13cf12..b65b98e08e 100644 --- a/ggml/src/ggml-cuda/convert.cuh +++ b/ggml/src/ggml-cuda/convert.cuh @@ -3,7 +3,7 @@ #define CUDA_DEQUANTIZE_BLOCK_SIZE 256 template -using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int64_t k, cudaStream_t stream); +using to_t_cuda_t = void (*)(const void * x, T * y, int64_t k, cudaStream_t stream); typedef to_t_cuda_t to_fp32_cuda_t; typedef to_t_cuda_t to_fp16_cuda_t; @@ -14,3 +14,13 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type); to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type); to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type); + +// TODO more general support for non-contiguous inputs + +template +using to_t_nc_cuda_t = void (*)(const void * x, T * y, + int64_t ne00, int64_t ne01, int64_t ne02, int64_t ne03, + int64_t s01, int64_t s02, int64_t s03, cudaStream_t stream); + +typedef to_t_nc_cuda_t to_fp16_nc_cuda_t; +to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type); diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 19b9ce7231..fba8cb6565 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -1720,15 +1720,15 @@ static __global__ void k_compute_batched_ptrs( size_t nb12, size_t nb13, size_t nbd2, size_t nbd3, int64_t r2, int64_t r3) { - int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x; - int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y; + const int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x; + const int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y; if (i13 >= ne13 || i12 >= ne12) { return; } - int64_t i03 = i13 / r3; - int64_t i02 = i12 / r2; + const int64_t i03 = i13 / r3; + const int64_t i02 = i12 / r2; ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; @@ -1742,6 +1742,10 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); GGML_ASSERT(src0->type == GGML_TYPE_F16); + // Byte offsets and tensor dimensions are currently used in an inconsistent way for dst. + // As long as dst is contiguous this does not matter though. + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_TENSOR_BINARY_OP_LOCALS const int64_t ne_dst = ggml_nelements(dst); @@ -1750,21 +1754,31 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(), main_stream)); - void * src0_ddq = src0->data; - half * src0_f16 = (half *) src0_ddq; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; + const half * src0_f16 = (const half *) src0->data; + float * dst_ddf = (float *) dst->data; + + const half * src1_f16 = (const half *) src1->data; + const size_t ts_src1 = ggml_type_size(src1->type); + GGML_ASSERT(nb10 == ts_src1); + int64_t s11 = nb11 / ts_src1; + int64_t s12 = nb12 / ts_src1; + int64_t s13 = nb13 / ts_src1; + ggml_cuda_pool_alloc src1_f16_alloc(ctx.pool()); // convert src1 to fp16 - ggml_cuda_pool_alloc src1_f16_alloc(ctx.pool()); if (src1->type != GGML_TYPE_F16) { - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + const to_fp16_nc_cuda_t to_fp16_cuda = ggml_get_to_fp16_nc_cuda(src1->type); const int64_t ne_src1 = ggml_nelements(src1); src1_f16_alloc.alloc(ne_src1); GGML_ASSERT(to_fp16_cuda != nullptr); - to_fp16_cuda(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + + to_fp16_cuda(src1_f16, src1_f16_alloc.get(), ne10, ne11, ne12, ne13, s11, s12, s13, main_stream); + + src1_f16 = src1_f16_alloc.get(); + s11 = ne10; + s12 = ne11*s11; + s13 = ne12*s12; } - half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get(); ggml_cuda_pool_alloc dst_f16(ctx.pool()); char * dst_t; @@ -1824,13 +1838,13 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co int i02 = i12 / r2; CUBLAS_CHECK( - cublasGemmEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half), - (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float), - beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01, - cu_compute_type, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + cublasGemmEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + alpha, (const char *) src0_f16 + i03*nb03 + i02*nb02, CUDA_R_16F, nb01/sizeof(half), + src1_f16 + i13*s13 + i12*s12, CUDA_R_16F, s11, + beta, ( char *) dst_t + i13*nbd3 + i12*nbd2, cu_data_type, ne0, + cu_compute_type, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); } } } @@ -1841,15 +1855,15 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co CUBLAS_CHECK( cublasGemmStridedBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - alpha, (const char *) src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA - (const char *) src1_f16, CUDA_R_16F, nb11/nb10, nb12/nb10, // strideB - beta, ( char *) dst_t, cu_data_type, ne01, nb2/nb0, // strideC + alpha, src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA + src1_f16, CUDA_R_16F, s11, s12, // strideB + beta, dst_t, cu_data_type, ne0, ne1*ne0, // strideC ne12*ne13, cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); } else { // use cublasGemmBatchedEx - const int ne23 = ne12*ne13; + const int64_t ne23 = ne12*ne13; ggml_cuda_pool_alloc ptrs_src(ctx.pool(), 2*ne23); ggml_cuda_pool_alloc< void *> ptrs_dst(ctx.pool(), 1*ne23); @@ -1861,8 +1875,8 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co ne12, ne13, ne23, nb02, nb03, - src1->type == GGML_TYPE_F16 ? nb12 : nb12/2, - src1->type == GGML_TYPE_F16 ? nb13 : nb13/2, + src1->type == GGML_TYPE_F16 ? nb12 : s12*sizeof(half), + src1->type == GGML_TYPE_F16 ? nb13 : s13*sizeof(half), nbd2, nbd3, r2, r3); CUDA_CHECK(cudaGetLastError()); @@ -1871,8 +1885,8 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co cublasGemmBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/nb00, - (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/nb10, - beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01, + (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, s11, + beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne0, ne23, cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); @@ -1936,7 +1950,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor } else if (!split && use_mul_mat_vec_q) { ggml_cuda_mul_mat_vec_q(ctx, src0, src1, nullptr, dst); } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16) && - dst->op_params[0] == GGML_PREC_DEFAULT && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { + !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // general KQ + KQV multi-batch without FlashAttention ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst); } else if (use_mul_mat_vec) { diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 759669c178..822e2bb2cf 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -10195,7 +10195,6 @@ struct llm_build_deepseek2 : public llm_graph_context { // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head} ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope); - ggml_mul_mat_set_prec(q_nope_absorbed, GGML_PREC_F32); cb(q_nope_absorbed, "q_nope_absorbed", il); // {kv_lora_rank, n_head, n_tokens} From 5a6398011704c31178d7b774be67856ba57647c8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?= Date: Tue, 29 Apr 2025 16:24:36 +0100 Subject: [PATCH 041/126] llama-bench: fixed size of fields to correctly map to values (#13183) --- examples/llama-bench/llama-bench.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 5a78216e44..0786594296 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -1133,8 +1133,6 @@ struct test { "build_commit", "build_number", "cpu_info", "gpu_info", "backends", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", - "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "use_mmap", - "embeddings", "n_prompt", "n_gen", "n_depth", "test_time", "avg_ns", "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides", "use_mmap", "embeddings", "n_prompt", "n_gen", "n_depth", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts", From d9d398f84f96d16c308d4976f5e90222ecc2a492 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 29 Apr 2025 20:22:57 +0300 Subject: [PATCH 042/126] sampling : when top-k <= 0 -> noop (#13173) ggml-ci --- include/llama.h | 1 + src/llama-sampling.cpp | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/include/llama.h b/include/llama.h index a13350e15b..06c56395c1 100644 --- a/include/llama.h +++ b/include/llama.h @@ -1232,6 +1232,7 @@ extern "C" { "will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)"); /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 + /// Setting k <= 0 makes this a noop LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k); /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index d149798502..c0a5f9340d 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -232,7 +232,7 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k) // } if (k <= 0) { - k = cur_p->size; + return; } k = std::min(k, (int) cur_p->size); @@ -298,6 +298,7 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k) } cur_p->sorted = true; } + cur_p->size = k; } From e2e1ddb93a01ce282e304431b37e60b3cddb6114 Mon Sep 17 00:00:00 2001 From: matteo Date: Tue, 29 Apr 2025 20:33:10 +0200 Subject: [PATCH 043/126] server : Prefilling assistant message in openai compatible API (#13174) * Prefilling assistant message in openai compatible API * fixed indentation * fixed code convention * simplify method usage * no more than one assistant message at end of messages * merge checks into prefill code * Update examples/server/utils.hpp --------- Co-authored-by: matteo Co-authored-by: Xuan-Son Nguyen --- examples/server/utils.hpp | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index aba2f27f9b..b497959fd8 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -642,9 +642,31 @@ static json oaicompat_completion_params_parse( throw std::runtime_error("Cannot use custom grammar constraints with tools."); } + // if the assistant message appears at the end of list, we do not add end-of-turn token + // for ex. this can be useful to modify the reasoning process in reasoning models + bool prefill_assistant_message = !inputs.messages.empty() && inputs.messages.back().role == "assistant"; + common_chat_msg last_message; + if (prefill_assistant_message) { + last_message = inputs.messages.back(); + inputs.messages.pop_back(); + + /* sanity check, max one assistant message at the end of the list */ + if (!inputs.messages.empty() && inputs.messages.back().role == "assistant"){ + throw std::runtime_error("Cannot have 2 or more assistant messages at the end of the list."); + } + + inputs.extract_reasoning = false; + inputs.add_generation_prompt = true; + } + // Apply chat template to the list of messages auto chat_params = common_chat_templates_apply(tmpls, inputs); + /* Append assistant prefilled message */ + if (prefill_assistant_message) { + chat_params.prompt += last_message.content; + } + llama_params["chat_format"] = static_cast(chat_params.format); llama_params["prompt"] = chat_params.prompt; if (!chat_params.grammar.empty()) { From 19e899ce21a7c9ffcf8bb2b22269a75f6e078f8f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 29 Apr 2025 23:32:04 +0200 Subject: [PATCH 044/126] scripts: n_depth for compare-llama-bench [no ci] (#13201) --- scripts/compare-llama-bench.py | 33 ++++++++++++++++++--------------- 1 file changed, 18 insertions(+), 15 deletions(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 6205fe88d7..8c599cf9ea 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -19,9 +19,9 @@ logger = logging.getLogger("compare-llama-bench") # Properties by which to differentiate results per commit: KEY_PROPERTIES = [ - "cpu_info", "gpu_info", "backends", "n_gpu_layers", "model_filename", "model_type", "n_batch", "n_ubatch", - "embeddings", "cpu_mask", "cpu_strict", "poll", "n_threads", "type_k", "type_v", "use_mmap", "no_kv_offload", - "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen" + "cpu_info", "gpu_info", "backends", "n_gpu_layers", "tensor_buft_overrides", "model_filename", "model_type", + "n_batch", "n_ubatch", "embeddings", "cpu_mask", "cpu_strict", "poll", "n_threads", "type_k", "type_v", + "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen", "n_depth" ] # Properties that are boolean and are converted to Yes/No for the table: @@ -30,11 +30,11 @@ BOOL_PROPERTIES = ["embeddings", "cpu_strict", "use_mmap", "no_kv_offload", "fla # Header names for the table: PRETTY_NAMES = { "cpu_info": "CPU", "gpu_info": "GPU", "backends": "Backends", "n_gpu_layers": "GPU layers", - "model_filename": "File", "model_type": "Model", "model_size": "Model size [GiB]", - "model_n_params": "Num. of par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", - "embeddings": "Embeddings", "cpu_mask": "CPU mask", "cpu_strict": "CPU strict", "poll": "Poll", - "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "split_mode": "Split mode", "main_gpu": "Main GPU", - "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split", "use_mmap": "Use mmap", + "tensor_buft_overrides": "Tensor overrides", "model_filename": "File", "model_type": "Model", "model_size": "Model size [GiB]", + "model_n_params": "Num. of par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", "embeddings": "Embeddings", + "cpu_mask": "CPU mask", "cpu_strict": "CPU strict", "poll": "Poll", "n_threads": "Threads", "type_k": "K type", "type_v": "V type", + "use_mmap": "Use mmap", "no_kv_offload": "NKVO", "split_mode": "Split mode", "main_gpu": "Main GPU", "tensor_split": "Tensor split", + "flash_attn": "FlashAttention", } DEFAULT_SHOW = ["model_type"] # Always show these properties by default. @@ -281,12 +281,12 @@ def get_rows(properties): The returned rows are unique in terms of property combinations. """ select_string = ", ".join( - [f"tb.{p}" for p in properties] + ["tb.n_prompt", "tb.n_gen", "AVG(tb.avg_ts)", "AVG(tc.avg_ts)"]) + [f"tb.{p}" for p in properties] + ["tb.n_prompt", "tb.n_gen", "tb.n_depth", "AVG(tb.avg_ts)", "AVG(tc.avg_ts)"]) equal_string = " AND ".join( [f"tb.{p} = tc.{p}" for p in KEY_PROPERTIES] + [ f"tb.build_commit = '{hexsha8_baseline}'", f"tc.build_commit = '{hexsha8_compare}'"] ) - group_order_string = ", ".join([f"tb.{p}" for p in properties] + ["tb.n_gen", "tb.n_prompt"]) + group_order_string = ", ".join([f"tb.{p}" for p in properties] + ["tb.n_gen", "tb.n_prompt", "tb.n_depth"]) query = (f"SELECT {select_string} FROM test tb JOIN test tc ON {equal_string} " f"GROUP BY {group_order_string} ORDER BY {group_order_string};") return cursor.execute(query).fetchall() @@ -309,7 +309,7 @@ else: rows_full = get_rows(KEY_PROPERTIES) properties_different = [] for i, kp_i in enumerate(KEY_PROPERTIES): - if kp_i in DEFAULT_SHOW or kp_i == "n_prompt" or kp_i == "n_gen": + if kp_i in DEFAULT_SHOW or kp_i in ["n_prompt", "n_gen", "n_depth"]: continue for row_full in rows_full: if row_full[i] != rows_full[0][i]: @@ -340,17 +340,20 @@ else: table = [] for row in rows_show: - n_prompt = int(row[-4]) - n_gen = int(row[-3]) + n_prompt = int(row[-5]) + n_gen = int(row[-4]) + n_depth = int(row[-3]) if n_prompt != 0 and n_gen == 0: test_name = f"pp{n_prompt}" elif n_prompt == 0 and n_gen != 0: test_name = f"tg{n_gen}" else: test_name = f"pp{n_prompt}+tg{n_gen}" + if n_depth != 0: + test_name = f"{test_name}@d{n_depth}" # Regular columns test name avg t/s values Speedup # VVVVVVVVVVVVV VVVVVVVVV VVVVVVVVVVVVVV VVVVVVV - table.append(list(row[:-4]) + [test_name] + list(row[-2:]) + [float(row[-1]) / float(row[-2])]) + table.append(list(row[:-5]) + [test_name] + list(row[-2:]) + [float(row[-1]) / float(row[-2])]) # Some a-posteriori fixes to make the table contents prettier: for bool_property in BOOL_PROPERTIES: @@ -376,7 +379,7 @@ if "gpu_info" in show: for gns in GPU_NAME_STRIP: row_table[ip] = row_table[ip].replace(gns, "") - gpu_names = row_table[ip].split("/") + gpu_names = row_table[ip].split(", ") num_gpus = len(gpu_names) all_names_the_same = len(set(gpu_names)) == 1 if len(gpu_names) >= 2 and all_names_the_same: From a0f7016d170ca4bfe24d9a9f26c024d034af69f2 Mon Sep 17 00:00:00 2001 From: xiaofei Date: Wed, 30 Apr 2025 14:29:22 +0800 Subject: [PATCH 045/126] rpc : fix cache directory initialization (#13188) Signed-off-by: xiaofei --- examples/rpc/rpc-server.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 0277e25cb5..b663a81191 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -304,8 +304,9 @@ int main(int argc, char * argv[]) { get_backend_memory(&free_mem, &total_mem); } const char * cache_dir = nullptr; - std::string cache_dir_str = fs_get_cache_directory() + "rpc/"; + std::string cache_dir_str; if (params.use_cache) { + cache_dir_str = fs_get_cache_directory() + "rpc/"; if (!fs_create_directory_with_parents(cache_dir_str)) { fprintf(stderr, "Failed to create cache directory: %s\n", cache_dir_str.c_str()); return 1; From da84c04d8fa43ff92b172feb8130c74d062f956a Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Wed, 30 Apr 2025 10:44:07 +0200 Subject: [PATCH 046/126] docker : do not build tests (#13204) * docker : do not build tests * include "ggml-cpu.h" --- .devops/cpu.Dockerfile | 4 ++-- .devops/cuda.Dockerfile | 2 +- .devops/intel.Dockerfile | 2 +- .devops/llama-cli-cann.Dockerfile | 2 +- .devops/musa.Dockerfile | 2 +- .devops/rocm.Dockerfile | 2 +- .devops/vulkan.Dockerfile | 2 +- tests/test-quantize-stats.cpp | 1 + 8 files changed, 9 insertions(+), 8 deletions(-) diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile index aa2aa03120..9459f08c10 100644 --- a/.devops/cpu.Dockerfile +++ b/.devops/cpu.Dockerfile @@ -14,9 +14,9 @@ WORKDIR /app COPY . . RUN if [ "$TARGETARCH" = "amd64" ]; then \ - cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \ elif [ "$TARGETARCH" = "arm64" ]; then \ - cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \ else \ echo "Unsupported architecture"; \ exit 1; \ diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile index 8ae57d2e28..94f1433972 100644 --- a/.devops/cuda.Dockerfile +++ b/.devops/cuda.Dockerfile @@ -21,7 +21,7 @@ COPY . . RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ cmake --build build --config Release -j$(nproc) RUN mkdir -p /app/lib && \ diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile index 091e1dc5d8..c8839fe027 100644 --- a/.devops/intel.Dockerfile +++ b/.devops/intel.Dockerfile @@ -17,7 +17,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ fi && \ echo "Building with dynamic libs" && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON ${OPT_SYCL_F16} && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${OPT_SYCL_F16} && \ cmake --build build --config Release -j$(nproc) RUN mkdir -p /app/lib && \ diff --git a/.devops/llama-cli-cann.Dockerfile b/.devops/llama-cli-cann.Dockerfile index 0eb1af87cb..ef43d78cd2 100644 --- a/.devops/llama-cli-cann.Dockerfile +++ b/.devops/llama-cli-cann.Dockerfile @@ -22,7 +22,7 @@ ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH RUN echo "Building with static libs" && \ source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF -DLLAMA_BUILD_TESTS=OFF && \ cmake --build build --config Release --target llama-cli # TODO: use image with NNRT diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile index 261a2823a0..e0f1ad9728 100644 --- a/.devops/musa.Dockerfile +++ b/.devops/musa.Dockerfile @@ -35,7 +35,7 @@ COPY . . RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ cmake --build build --config Release -j$(nproc) RUN mkdir -p /app/lib && \ diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile index a1b34723a4..1c00f1b9c2 100644 --- a/.devops/rocm.Dockerfile +++ b/.devops/rocm.Dockerfile @@ -40,7 +40,7 @@ WORKDIR /app COPY . . RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ - cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF \ && cmake --build build --config Release -j$(nproc) RUN mkdir -p /app/lib \ diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile index f8f3072e95..fcd81ffa1e 100644 --- a/.devops/vulkan.Dockerfile +++ b/.devops/vulkan.Dockerfile @@ -16,7 +16,7 @@ WORKDIR /app COPY . . -RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \ +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \ cmake --build build --config Release -j$(nproc) RUN mkdir -p /app/lib && \ diff --git a/tests/test-quantize-stats.cpp b/tests/test-quantize-stats.cpp index db01059119..a284a1f0c5 100644 --- a/tests/test-quantize-stats.cpp +++ b/tests/test-quantize-stats.cpp @@ -1,4 +1,5 @@ #include "ggml.h" +#include "ggml-cpu.h" #include "llama.h" #include "common.h" From 5933e6fdc9c051eea6c83b5a7608de12f9f15670 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Wed, 30 Apr 2025 10:46:32 +0200 Subject: [PATCH 047/126] arg : allow using -hf offline (#13202) * arg : allow using -hf offline * add more comments in code [no ci] --- common/arg.cpp | 130 +++++++++++++++++++++++++++++++------------------ 1 file changed, 83 insertions(+), 47 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 75e8e0bd51..6633c3a1db 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -43,6 +43,25 @@ std::initializer_list mmproj_examples = { // TODO: add LLAMA_EXAMPLE_SERVER when it's ready }; +static std::string read_file(const std::string & fname) { + std::ifstream file(fname); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str())); + } + std::string content((std::istreambuf_iterator(file)), std::istreambuf_iterator()); + file.close(); + return content; +} + +static void write_file(const std::string & fname, const std::string & content) { + std::ofstream file(fname); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str())); + } + file << content; + file.close(); +} + common_arg & common_arg::set_examples(std::initializer_list examples) { this->examples = std::move(examples); return *this; @@ -200,9 +219,11 @@ struct curl_slist_ptr { static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) { int remaining_attempts = max_attempts; + char * method = nullptr; + curl_easy_getinfo(curl, CURLINFO_EFFECTIVE_METHOD, &method); while (remaining_attempts > 0) { - LOG_INF("%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts); + LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts); CURLcode res = curl_easy_perform(curl); if (res == CURLE_OK) { @@ -213,6 +234,7 @@ static bool curl_perform_with_retry(const std::string & url, CURL * curl, int ma LOG_WRN("%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay); remaining_attempts--; + if (remaining_attempts == 0) break; std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay)); } @@ -231,8 +253,6 @@ static bool common_download_file_single(const std::string & url, const std::stri return false; } - bool force_download = false; - // Set the URL, allow to follow http redirection curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L); @@ -256,7 +276,7 @@ static bool common_download_file_single(const std::string & url, const std::stri // If the file exists, check its JSON metadata companion file. std::string metadata_path = path + ".json"; - nlohmann::json metadata; + nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead std::string etag; std::string last_modified; @@ -266,7 +286,7 @@ static bool common_download_file_single(const std::string & url, const std::stri if (metadata_in.good()) { try { metadata_in >> metadata; - LOG_INF("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str()); + LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str()); if (metadata.contains("url") && metadata.at("url").is_string()) { auto previous_url = metadata.at("url").get(); if (previous_url != url) { @@ -296,7 +316,10 @@ static bool common_download_file_single(const std::string & url, const std::stri }; common_load_model_from_url_headers headers; + bool head_request_ok = false; + bool should_download = !file_exists; // by default, we should download if the file does not exist + // get ETag to see if the remote file has changed { typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *); auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t { @@ -325,23 +348,28 @@ static bool common_download_file_single(const std::string & url, const std::stri curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast(header_callback)); curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers); - bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS); + // we only allow retrying once for HEAD requests + // this is for the use case of using running offline (no internet), retrying can be annoying + bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0); if (!was_perform_successful) { - return false; + head_request_ok = false; } long http_code = 0; curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code); - if (http_code != 200) { - // HEAD not supported, we don't know if the file has changed - // force trigger downloading - force_download = true; - LOG_ERR("%s: HEAD invalid http status code received: %ld\n", __func__, http_code); + if (http_code == 200) { + head_request_ok = true; + } else { + LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code); + head_request_ok = false; } } - bool should_download = !file_exists || force_download; - if (!should_download) { + // if head_request_ok is false, we don't have the etag or last-modified headers + // we leave should_download as-is, which is true if the file does not exist + if (head_request_ok) { + // check if ETag or Last-Modified headers are different + // if it is, we need to download the file again if (!etag.empty() && etag != headers.etag) { LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str()); should_download = true; @@ -350,6 +378,7 @@ static bool common_download_file_single(const std::string & url, const std::stri should_download = true; } } + if (should_download) { std::string path_temporary = path + ".downloadInProgress"; if (file_exists) { @@ -424,13 +453,15 @@ static bool common_download_file_single(const std::string & url, const std::stri {"etag", headers.etag}, {"lastModified", headers.last_modified} }); - std::ofstream(metadata_path) << metadata.dump(4); - LOG_INF("%s: file metadata saved: %s\n", __func__, metadata_path.c_str()); + write_file(metadata_path, metadata.dump(4)); + LOG_DBG("%s: file metadata saved: %s\n", __func__, metadata_path.c_str()); if (rename(path_temporary.c_str(), path.c_str()) != 0) { LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str()); return false; } + } else { + LOG_INF("%s: using cached file: %s\n", __func__, path.c_str()); } return true; @@ -605,16 +636,37 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_ // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response // User-Agent header is already set in common_remote_get_content, no need to set it here + // we use "=" to avoid clashing with other component, while still being allowed on windows + std::string cached_response_fname = "manifest=" + hf_repo + "=" + tag + ".json"; + string_replace_all(cached_response_fname, "/", "_"); + std::string cached_response_path = fs_get_cache_file(cached_response_fname); + // make the request common_remote_params params; params.headers = headers; - auto res = common_remote_get_content(url, params); - long res_code = res.first; - std::string res_str(res.second.data(), res.second.size()); + long res_code = 0; + std::string res_str; + bool use_cache = false; + try { + auto res = common_remote_get_content(url, params); + res_code = res.first; + res_str = std::string(res.second.data(), res.second.size()); + } catch (const std::exception & e) { + LOG_WRN("error: failed to get manifest: %s\n", e.what()); + LOG_WRN("try reading from cache\n"); + // try to read from cache + try { + res_str = read_file(cached_response_path); + res_code = 200; + use_cache = true; + } catch (const std::exception & e) { + throw std::runtime_error("error: failed to get manifest (check your internet connection)"); + } + } std::string ggufFile; std::string mmprojFile; - if (res_code == 200) { + if (res_code == 200 || res_code == 304) { // extract ggufFile.rfilename in json, using regex { std::regex pattern("\"ggufFile\"[\\s\\S]*?\"rfilename\"\\s*:\\s*\"([^\"]+)\""); @@ -631,6 +683,10 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_ mmprojFile = match[1].str(); } } + if (!use_cache) { + // if not using cached response, update the cache file + write_file(cached_response_path, res_str); + } } else if (res_code == 401) { throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token"); } else { @@ -1142,6 +1198,9 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e fprintf(stderr, "%s\n", ex.what()); ctx_arg.params = params_org; return false; + } catch (std::exception & ex) { + fprintf(stderr, "%s\n", ex.what()); + exit(1); // for other exceptions, we exit with status code 1 } return true; @@ -1442,13 +1501,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"-f", "--file"}, "FNAME", "a file containing the prompt (default: none)", [](common_params & params, const std::string & value) { - std::ifstream file(value); - if (!file) { - throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); - } + params.prompt = read_file(value); // store the external file name in params params.prompt_file = value; - std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.prompt)); if (!params.prompt.empty() && params.prompt.back() == '\n') { params.prompt.pop_back(); } @@ -1458,11 +1513,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"-sysf", "--system-prompt-file"}, "FNAME", "a file containing the system prompt (default: none)", [](common_params & params, const std::string & value) { - std::ifstream file(value); - if (!file) { - throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); - } - std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.system_prompt)); + params.system_prompt = read_file(value); if (!params.system_prompt.empty() && params.system_prompt.back() == '\n') { params.system_prompt.pop_back(); } @@ -1887,15 +1938,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--grammar-file"}, "FNAME", "file to read grammar from", [](common_params & params, const std::string & value) { - std::ifstream file(value); - if (!file) { - throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); - } - std::copy( - std::istreambuf_iterator(file), - std::istreambuf_iterator(), - std::back_inserter(params.sampling.grammar) - ); + params.sampling.grammar = read_file(value); } ).set_sparam()); add_opt(common_arg( @@ -2815,14 +2858,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex "list of built-in templates:\n%s", list_builtin_chat_templates().c_str() ), [](common_params & params, const std::string & value) { - std::ifstream file(value); - if (!file) { - throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); - } - std::copy( - std::istreambuf_iterator(file), - std::istreambuf_iterator(), - std::back_inserter(params.chat_template)); + params.chat_template = read_file(value); } ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); add_opt(common_arg( From 44cd8d91ff2c9e4a0f2e3151f8d6f04c928e2571 Mon Sep 17 00:00:00 2001 From: Aaron Teo <57927438+taronaeo@users.noreply.github.com> Date: Wed, 30 Apr 2025 17:47:35 +0800 Subject: [PATCH 048/126] feat(ggml-cpu): enable z17 compile (#13182) z17 compilation requires GCC 15.1.0 and onwards Signed-off-by: Aaron Teo --- ggml/src/ggml-cpu/CMakeLists.txt | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 6a652738c1..9a3085befc 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -352,10 +352,14 @@ function(ggml_add_cpu_backend_variant_impl tag_name) # TODO: Separation to determine activation of VX/VXE/VXE2 if (${S390X_M} MATCHES "8561|8562") message(STATUS "z15 target") - list(APPEND ARCH_FLAGS -march=z15 -mtune=z15) + list(APPEND ARCH_FLAGS -march=z15) elseif (${S390X_M} MATCHES "3931") message(STATUS "z16 target") - list(APPEND ARCH_FLAGS -march=z16 -mtune=z16) + list(APPEND ARCH_FLAGS -march=z16) + elseif (${S390X_M} MATCHES "9175|9176") + # NOTE: Only available from GCC 15.1.0 onwards. Any z17 machine with compile issues must first verify their GCC version. + message(STATUS "z17 target") + list(APPEND ARCH_FLAGS -march=z17) else() message(STATUS "Unknown target") message(WARNING "Unknown target. If you are compiling for z14 and earlier, you might have to add -DGGML_VXE=OFF.") From 07c2e2f76cce9a61c110b6995fbb90ccea2c3aaa Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Wed, 30 Apr 2025 13:06:15 +0200 Subject: [PATCH 049/126] convert : correct typo image_mean --> image_std (#13208) --- convert_hf_to_gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index b9cea7e469..d607af6955 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -1103,7 +1103,7 @@ class VisionModel(ModelBase): # preprocessor config self.gguf_writer.add_vision_image_mean(self.preprocessor_config["image_mean"]) - self.gguf_writer.add_vision_image_std(self.preprocessor_config["image_mean"]) + self.gguf_writer.add_vision_image_std(self.preprocessor_config["image_std"]) def write_vocab(self): raise ValueError("VisionModel does not support vocab writing") From 416313773b53585fddcafbcb914cbbfbaeb94b1f Mon Sep 17 00:00:00 2001 From: shalinib-ibm Date: Wed, 30 Apr 2025 16:47:08 +0530 Subject: [PATCH 050/126] ggml : fix ppc64le build (#13176) Build fails with compilation error on power pc. This patch fixes the same. Tested with unit tests run via --build && cd && make test Signed-off-by: Shalini Salomi Bodapati --- ggml/src/ggml-cpu/simd-mappings.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h index 04d10cec26..45c31cf1fa 100644 --- a/ggml/src/ggml-cpu/simd-mappings.h +++ b/ggml/src/ggml-cpu/simd-mappings.h @@ -341,7 +341,7 @@ static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { #define GGML_F32_EPR 4 #define GGML_F32x4 vector float -#define GGML_F32x4_ZERO 0.0f +#define GGML_F32x4_ZERO {0.0f} #define GGML_F32x4_SET1 vec_splats #define GGML_F32x4_LOAD(p) vec_xl(0, p) #define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) From e5007a5edf2692ef7151a81a61ce2716b83374e5 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Wed, 30 Apr 2025 07:38:37 -0500 Subject: [PATCH 051/126] vulkan: use uint array index to avoid glslang bug (#13193) --- ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp index 962d2353f8..9cb7da2daa 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp @@ -482,7 +482,7 @@ float16_t dequantFuncIQ2_XXS(const in decodeBufIQ2_XXS bl, const in uint blockCo const uint ib8 = (idx & 0x18) >> 3; // 0..3 const uint iqs = 8 * ib32 + ib8; - const uint8_t qs = bl.block.qs[iqs]; + const uint qs = bl.block.qs[iqs]; const uint signscale = pack32(u16vec2(bl16.block.qs[4*ib32+2], bl16.block.qs[4*ib32+3])); const float dscale = float(bl.block.d) * 0.25 * (0.5 + float(signscale >> 28)); From 3b127c738535d95e06abd0d43da147bc13516ad0 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Wed, 30 Apr 2025 13:52:35 +0100 Subject: [PATCH 052/126] common : add -jf / --json-schema-file flag (#12011) --- common/arg.cpp | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/common/arg.cpp b/common/arg.cpp index 6633c3a1db..b8c72ee645 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1948,6 +1948,23 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.sampling.grammar = json_schema_to_grammar(json::parse(value)); } ).set_sparam()); + add_opt(common_arg( + {"-jf", "--json-schema-file"}, "FILE", + "File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead", + [](common_params & params, const std::string & value) { + std::ifstream file(value); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + std::string schema; + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(schema) + ); + params.sampling.grammar = json_schema_to_grammar(json::parse(schema)); + } + ).set_sparam()); add_opt(common_arg( {"--pooling"}, "{none,mean,cls,last,rank}", "pooling type for embeddings, use model default if unspecified", From ceda28ef8e310a8dee60bf275077a3eedae8e36c Mon Sep 17 00:00:00 2001 From: Tatsuya Tanaka Date: Wed, 30 Apr 2025 22:25:20 +0900 Subject: [PATCH 053/126] llava : remove duplicate include (#13207) --- examples/llava/clip-impl.h | 2 -- 1 file changed, 2 deletions(-) diff --git a/examples/llava/clip-impl.h b/examples/llava/clip-impl.h index 04bfcbb5e5..66cb21ef1a 100644 --- a/examples/llava/clip-impl.h +++ b/examples/llava/clip-impl.h @@ -2,8 +2,6 @@ #include "gguf.h" #include "clip.h" -#include "clip.h" - #include #include #include From 3e168bede4d27b35656ab8026015b87659ecbec2 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Wed, 30 Apr 2025 16:56:24 +0200 Subject: [PATCH 054/126] convert : improve model arch handling (#13122) * convert : improve model arch handling * use AutoConfig * rm trust_remote_code * Update convert_hf_to_gguf.py * fix self.block_count for vision * fix NomicBertModel --- convert_hf_to_gguf.py | 98 +++++++++++++++++++++++++------------------ 1 file changed, 57 insertions(+), 41 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index d607af6955..123df801bf 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -16,6 +16,7 @@ from pathlib import Path from hashlib import sha256 from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast from itertools import chain +from transformers import AutoConfig import math import numpy as np @@ -66,8 +67,6 @@ class ModelBase: part_names: list[str] is_safetensors: bool hparams: dict[str, Any] - block_count: int - tensor_map: gguf.TensorNameMap tensor_names: set[str] | None gguf_writer: gguf.GGUFWriter model_name: str | None @@ -78,6 +77,10 @@ class ModelBase: # subclasses should define this! model_arch: gguf.MODEL_ARCH + # subclasses should initialize this! + block_count: int + tensor_map: gguf.TensorNameMap + def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False, use_temp_file: bool = False, eager: bool = False, metadata_override: Path | None = None, model_name: str | None = None, @@ -113,8 +116,6 @@ class ModelBase: if not self.is_safetensors: self.part_names = ModelBase.get_model_part_names(self.dir_model, "pytorch_model", ".bin") self.hparams = ModelBase.load_hparams(self.dir_model) if hparams is None else hparams - self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"]) - self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) self.tensor_names = None self.metadata_override = metadata_override self.model_name = model_name @@ -417,15 +418,13 @@ class ModelBase: @staticmethod def load_hparams(dir_model: Path): - with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - architectures = hparams.get("architectures") - if "text_config" in hparams: - hparams = {**hparams, **hparams["text_config"]} - if architectures is not None: - # preserve "architectures" from root level config - hparams["architectures"] = architectures - return hparams + try: + return AutoConfig.from_pretrained(dir_model).to_dict() + except Exception as e: + logger.warning(f"Failed to load model config from {dir_model}: {e}") + logger.warning("Trying to load config.json instead") + with open(dir_model / "config.json", "r", encoding="utf-8") as f: + return json.load(f) @classmethod def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: @@ -454,6 +453,23 @@ class ModelBase: class TextModel(ModelBase): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + if "text_config" in self.hparams: + # move the text_config to the root level + self.hparams = {**self.hparams, **self.hparams["text_config"]} + + self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"]) + self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + + @classmethod + def __init_subclass__(cls): + # can't use an abstract property, because overriding it without type errors + # would require using decorated functions instead of simply defining the property + if "model_arch" not in cls.__dict__: + raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}") + def set_vocab(self): self._set_vocab_gpt2() @@ -1070,9 +1086,9 @@ class VisionModel(ModelBase): if self.model_arch != gguf.MODEL_ARCH.CLIP_VISION: raise TypeError("VisionModel must be subclassed with model_arch = gguf.MODEL_ARCH.CLIP_VISION") - # small hack to correct the number of layers - self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.CLIP_VISION, 128) - self.n_embd_text = self.find_hparam(["hidden_size", "n_embd"]) + # get n_embd of the text model + text_config = {**self.hparams, **self.hparams["text_config"]} + self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0)) assert self.n_embd_text > 0, "n_embd not found in hparams" if "vision_config" not in self.hparams: @@ -1081,6 +1097,9 @@ class VisionModel(ModelBase): self.global_config = self.hparams self.hparams = self.hparams["vision_config"] + self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth"]) + self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.CLIP_VISION, self.block_count) + # load preprocessor config with open(self.dir_model / "preprocessor_config.json", "r", encoding="utf-8") as f: self.preprocessor_config = json.load(f) @@ -1098,7 +1117,7 @@ class VisionModel(ModelBase): self.gguf_writer.add_vision_patch_size(self.find_hparam(["patch_size"])) self.gguf_writer.add_vision_embedding_length(self.find_hparam(["hidden_size"])) self.gguf_writer.add_vision_feed_forward_length(self.find_hparam(["intermediate_size"])) - self.gguf_writer.add_vision_block_count(self.find_hparam(["num_hidden_layers"])) + self.gguf_writer.add_vision_block_count(self.block_count) self.gguf_writer.add_vision_head_count(self.find_hparam(["num_attention_heads"])) # preprocessor config @@ -1719,23 +1738,12 @@ class StableLMModel(TextModel): "LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM", - "Idefics3ForConditionalGeneration", - "SmolVLMForConditionalGeneration", + "VLlama3ForCausalLM", "LlavaForConditionalGeneration") class LlamaModel(TextModel): model_arch = gguf.MODEL_ARCH.LLAMA undo_permute = True - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - # fix for SmolVLM2, missing `num_attention_heads` in config.json - if self.hparams["architectures"][0] == "SmolVLMForConditionalGeneration": - self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32) - # fix for Pixtral, missing `num_attention_heads` in config.json - if self.hparams["architectures"][0] == "LlavaForConditionalGeneration" \ - and self.hparams.get("model_type") == "mistral": - self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32) - def set_vocab(self): try: self._set_vocab_sentencepiece() @@ -1898,11 +1906,7 @@ class LlavaVisionModel(VisionModel): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.hparams["model_type"] == "pixtral": - # fix missing config.json values - self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 16) - self.hparams["num_hidden_layers"] = self.hparams.get("num_hidden_layers", 24) - self.hparams["intermediate_size"] = self.hparams.get("intermediate_size", 4096) - self.hparams["hidden_size"] = self.hparams.get("hidden_size", 1024) + # layer_norm_eps is not in config.json, it is hard-coded in modeling_pixtral.py self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5) self.img_break_tok_id = 12 # see tokenizer_config.json else: @@ -1913,7 +1917,6 @@ class LlavaVisionModel(VisionModel): hparams = self.hparams if hparams["model_type"] == "pixtral": self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.PIXTRAL) - # default values below are taken from HF tranformers code self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"]) self.gguf_writer.add_vision_use_silu(True) @@ -1944,13 +1947,12 @@ class LlavaVisionModel(VisionModel): class SmolVLMModel(VisionModel): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - # fix for SmolVLM2, missing some keys in config.json - # default values are taken from transformers code if self.hparams["model_type"] == "smolvlm_vision": + # fix for SmolVLM2, missing some keys in config.json + # default values are taken from transformers code self.hparams["hidden_size"] = self.hparams.get("hidden_size", 1152) self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 16) self.hparams["intermediate_size"] = self.hparams.get("intermediate_size", 3072) - self.hparams["num_hidden_layers"] = self.hparams.get("num_hidden_layers", 12) def set_gguf_parameters(self): super().set_gguf_parameters() @@ -3505,6 +3507,8 @@ class RobertaModel(BertModel): @ModelBase.register("NomicBertModel") class NomicBertModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any): hparams = kwargs.pop("hparams", None) if hparams is None: @@ -5849,6 +5853,19 @@ def split_str_to_n_bytes(split_str: str) -> int: return n +def get_model_architecture(dir_model: Path, model_type: ModelType, hparams: Any = None) -> str: + hparams = ModelBase.load_hparams(dir_model) if hparams is None else hparams + text_config = hparams.get("text_config", {}) + vision_config = hparams.get("vision_config", {}) + arch = hparams["architectures"][0] + # if "architectures" is found in the sub-config, use that instead + if model_type == ModelType.TEXT and text_config.get("architectures") is not None: + arch = text_config["architectures"][0] + elif model_type == ModelType.VISION and vision_config.get("architectures") is not None: + arch = vision_config["architectures"][0] + return arch + + def main() -> None: args = parse_args() @@ -5901,16 +5918,15 @@ def main() -> None: logger.info(f"Loading model: {dir_model.name}") - hparams = ModelBase.load_hparams(dir_model) - if args.mmproj: if "mmproj" not in fname_out.name: fname_out = ModelBase.add_prefix_to_filename(fname_out, "mmproj-") with torch.inference_mode(): output_type = ftype_map[args.outtype] - model_architecture = hparams["architectures"][0] model_type = ModelType.VISION if args.mmproj else ModelType.TEXT + model_architecture = get_model_architecture(dir_model, model_type) + logger.info(f"Model architecture: {model_architecture}") try: model_class = ModelBase.from_model_architecture(model_architecture, model_type=model_type) except NotImplementedError: From 16a457facd996915652f6274384c87602b27d21a Mon Sep 17 00:00:00 2001 From: ddh0 Date: Wed, 30 Apr 2025 15:28:43 -0500 Subject: [PATCH 055/126] fix typo: `n_ctx_pre_seq` -> `n_ctx_per_seq` (#13221) --- src/llama-context.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index e49225aa22..5a2eef9b78 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -114,7 +114,7 @@ llama_context::llama_context( } if (n_ctx_per_seq > hparams.n_ctx_train) { - LLAMA_LOG_WARN("%s: n_ctx_pre_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n", + LLAMA_LOG_WARN("%s: n_ctx_per_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n", __func__, n_ctx_per_seq, hparams.n_ctx_train); } From 6f67cf1f480926391ad75ff746e0a021214bf70c Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Wed, 30 Apr 2025 22:29:15 +0200 Subject: [PATCH 056/126] arg : -hf do not fail if url mismatch (#13219) * arg : -hf do not fail if url mismatch * do not return if cannot parse metadata json --- common/arg.cpp | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index b8c72ee645..9f7241673d 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -287,13 +287,6 @@ static bool common_download_file_single(const std::string & url, const std::stri try { metadata_in >> metadata; LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str()); - if (metadata.contains("url") && metadata.at("url").is_string()) { - auto previous_url = metadata.at("url").get(); - if (previous_url != url) { - LOG_ERR("%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str()); - return false; - } - } if (metadata.contains("etag") && metadata.at("etag").is_string()) { etag = metadata.at("etag"); } @@ -301,10 +294,10 @@ static bool common_download_file_single(const std::string & url, const std::stri last_modified = metadata.at("lastModified"); } } catch (const nlohmann::json::exception & e) { - LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what()); - return false; + LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what()); } } + // if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again) } else { LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str()); } From e1e8e0991ffd9e99a445c6812bb519d5bac9f4b5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 30 Apr 2025 23:12:59 +0200 Subject: [PATCH 057/126] CUDA: batched+noncont MMQ, refactor bs>1 MoE code (#13199) --- ggml/src/ggml-cuda/getrows.cu | 181 +++++++---- ggml/src/ggml-cuda/getrows.cuh | 7 + ggml/src/ggml-cuda/ggml-cuda.cu | 268 +++++++-------- ggml/src/ggml-cuda/mmq.cu | 220 +++++++++++-- ggml/src/ggml-cuda/mmq.cuh | 558 +++++++++++++++++++++++--------- ggml/src/ggml-cuda/mmvq.cu | 6 +- ggml/src/ggml-cuda/quantize.cu | 49 +-- ggml/src/ggml-cuda/quantize.cuh | 15 +- tests/test-backend-ops.cpp | 5 + 9 files changed, 869 insertions(+), 440 deletions(-) diff --git a/ggml/src/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu index 4cef53a98c..ea8bf69160 100644 --- a/ggml/src/ggml-cuda/getrows.cu +++ b/ggml/src/ggml-cuda/getrows.cu @@ -33,8 +33,8 @@ static __global__ void k_get_rows( dfloat2 v; dequantize_kernel(src0_row, ib, iqs, v); - dst_row[iybs + iqs + 0] = v.x; - dst_row[iybs + iqs + y_offset] = v.y; + dst_row[iybs + iqs + 0] = float(v.x); + dst_row[iybs + iqs + y_offset] = float(v.y); } template @@ -60,7 +60,7 @@ static __global__ void k_get_rows_float( dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; const src0_t * src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03); - dst_row[i00] = src0_row[i00]; + dst_row[i00] = float(src0_row[i00]); } template @@ -86,120 +86,159 @@ static __global__ void k_get_rows_back_float( dst[dst_row*ncols + col] = sum; } -template -static void get_rows_cuda( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) { - - GGML_TENSOR_BINARY_OP_LOCALS - +template +static void get_rows_cuda_q( + const void * src0_d, const int32_t * src1_d, dst_t * dst_d, + const int64_t ne00, const size_t nb01, const size_t nb02, const size_t nb03, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10, const size_t nb11, const size_t nb12, + const size_t nb1, const size_t nb2, const size_t nb3, + cudaStream_t stream) { const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE); const dim3 block_nums(block_num_x, ne10, ne11*ne12); // strides in elements - //const size_t s0 = nb0 / ggml_element_size(dst); - const size_t s1 = nb1 / ggml_element_size(dst); - const size_t s2 = nb2 / ggml_element_size(dst); - const size_t s3 = nb3 / ggml_element_size(dst); + // const size_t s0 = nb0 / sizeof(dst_t); + const size_t s1 = nb1 / sizeof(dst_t); + const size_t s2 = nb2 / sizeof(dst_t); + const size_t s3 = nb3 / sizeof(dst_t); - const size_t s10 = nb10 / ggml_element_size(src1); - const size_t s11 = nb11 / ggml_element_size(src1); - const size_t s12 = nb12 / ggml_element_size(src1); - //const size_t s13 = nb13 / ggml_element_size(src1); + const size_t s10 = nb10 / sizeof(int32_t); + const size_t s11 = nb11 / sizeof(int32_t); + const size_t s12 = nb12 / sizeof(int32_t); + // const size_t s13 = nb13 / sizeof(int32_t); GGML_ASSERT(ne00 % 2 == 0); k_get_rows<<>>( - src0_dd, src1_dd, dst_dd, + src0_d, src1_d, dst_d, ne00, /*ne01, ne02, ne03,*/ /*ne10, ne11,*/ ne12, /*ne13,*/ /* s0,*/ s1, s2, s3, /* nb00,*/ nb01, nb02, nb03, s10, s11, s12/*, s13*/); - - GGML_UNUSED(dst); } -template +template static void get_rows_cuda_float( - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, - const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) { - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(ne13 == 1); - + const src0_t * src0_d, const int32_t * src1_d, dst_t * dst_d, + const int64_t ne00, const size_t nb01, const size_t nb02, const size_t nb03, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10, const size_t nb11, const size_t nb12, + const size_t nb1, const size_t nb2, const size_t nb3, + cudaStream_t stream) { const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE; const dim3 block_nums(block_num_x, ne10, ne11*ne12); // strides in elements - //const size_t s0 = nb0 / ggml_element_size(dst); - const size_t s1 = nb1 / ggml_element_size(dst); - const size_t s2 = nb2 / ggml_element_size(dst); - const size_t s3 = nb3 / ggml_element_size(dst); + // const size_t s0 = nb0 / sizeof(dst_t); + const size_t s1 = nb1 / sizeof(dst_t); + const size_t s2 = nb2 / sizeof(dst_t); + const size_t s3 = nb3 / sizeof(dst_t); - const size_t s10 = nb10 / ggml_element_size(src1); - const size_t s11 = nb11 / ggml_element_size(src1); - const size_t s12 = nb12 / ggml_element_size(src1); - //const size_t s13 = nb13 / ggml_element_size(src1); + const size_t s10 = nb10 / sizeof(int32_t); + const size_t s11 = nb11 / sizeof(int32_t); + const size_t s12 = nb12 / sizeof(int32_t); + // const size_t s13 = nb13 / sizeof(int32_t); k_get_rows_float<<>>( - src0_dd, src1_dd, dst_dd, + src0_d, src1_d, dst_d, ne00, /*ne01, ne02, ne03,*/ /*ne10, ne11,*/ ne12, /*ne13,*/ /* s0,*/ s1, s2, s3, /* nb00,*/ nb01, nb02, nb03, s10, s11, s12/*, s13*/); +} - GGML_UNUSED(dst); +template +static void ggml_cuda_get_rows_switch_src0_type( + const void * src0_d, const ggml_type src0_type, const int32_t * src1_d, dst_t * dst_d, + const int64_t ne00, const size_t nb01, const size_t nb02, const size_t nb03, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10, const size_t nb11, const size_t nb12, + const size_t nb1, const size_t nb2, const size_t nb3, + cudaStream_t stream) { + switch (src0_type) { + case GGML_TYPE_F16: + get_rows_cuda_float((const half *) src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_F32: + get_rows_cuda_float((const float *) src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_BF16: + get_rows_cuda_float((const nv_bfloat16 *) src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q4_0: + get_rows_cuda_q(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q4_1: + get_rows_cuda_q(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q5_0: + get_rows_cuda_q(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q5_1: + get_rows_cuda_q(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q8_0: + get_rows_cuda_q(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + default: + // TODO: k-quants + GGML_ABORT("%s: unsupported src0 type: %s\n", __func__, ggml_type_name(src0_type)); + break; + } +} + +void get_rows_cuda( + const void * src0_d, ggml_type src0_type, const int32_t * src1_d, void * dst_d, ggml_type dst_type, + int64_t ne00, size_t nb01, size_t nb02, size_t nb03, + int64_t ne10, int64_t ne11, int64_t ne12, size_t nb10, size_t nb11, size_t nb12, + size_t nb1, size_t nb2, size_t nb3, + cudaStream_t stream) { + switch (dst_type) { + case GGML_TYPE_F32: + ggml_cuda_get_rows_switch_src0_type(src0_d, src0_type, src1_d, (float *) dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_F16: + ggml_cuda_get_rows_switch_src0_type(src0_d, src0_type, src1_d, (half *) dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_BF16: + ggml_cuda_get_rows_switch_src0_type(src0_d, src0_type, src1_d, (nv_bfloat16 *) dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + default: + GGML_ABORT("%s: unsupported dst type: %s\n", __func__, ggml_type_name(dst_type)); + break; + } } void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; - const void * src0_d = (const void *) src0->data; - const int32_t * src1_d = (const int32_t *) src1->data; - float * dst_d = (float *) dst->data; - cudaStream_t stream = ctx.stream(); + GGML_TENSOR_BINARY_OP_LOCALS + GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(ne13 == 1); GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type)); - switch (src0->type) { - case GGML_TYPE_F16: - get_rows_cuda_float(src0, src1, dst, (const half *) src0_d, src1_d, dst_d, stream); - break; - case GGML_TYPE_F32: - get_rows_cuda_float(src0, src1, dst, (const float *) src0_d, src1_d, dst_d, stream); - break; - case GGML_TYPE_Q4_0: - get_rows_cuda(src0, src1, dst, src0_d, src1_d, dst_d, stream); - break; - case GGML_TYPE_Q4_1: - get_rows_cuda(src0, src1, dst, src0_d, src1_d, dst_d, stream); - break; - case GGML_TYPE_Q5_0: - get_rows_cuda(src0, src1, dst, src0_d, src1_d, dst_d, stream); - break; - case GGML_TYPE_Q5_1: - get_rows_cuda(src0, src1, dst, src0_d, src1_d, dst_d, stream); - break; - case GGML_TYPE_Q8_0: - get_rows_cuda(src0, src1, dst, src0_d, src1_d, dst_d, stream); - break; - default: - // TODO: k-quants - GGML_ABORT("%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type)); - break; - } + get_rows_cuda(src0->data, src0->type, (const int32_t *) src1->data, dst->data, dst->type, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); } void ggml_cuda_op_get_rows_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { diff --git a/ggml/src/ggml-cuda/getrows.cuh b/ggml/src/ggml-cuda/getrows.cuh index a1ca643f1c..3c5bea5f48 100644 --- a/ggml/src/ggml-cuda/getrows.cuh +++ b/ggml/src/ggml-cuda/getrows.cuh @@ -3,6 +3,13 @@ #define CUDA_GET_ROWS_BLOCK_SIZE 256 #define CUDA_GET_ROWS_BACK_BLOCK_SIZE 256 +void get_rows_cuda( + const void * src0_d, ggml_type src0_type, const int32_t * src1_d, void * dst_d, ggml_type dst_type, + int64_t ne00, size_t nb01, size_t nb02, size_t nb03, + int64_t ne10, int64_t ne11, int64_t ne12, size_t nb10, size_t nb11, size_t nb12, + size_t nb1, size_t nb2, size_t nb3, + cudaStream_t stream); + void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_get_rows_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index fba8cb6565..9fb2134f98 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -1551,7 +1551,7 @@ static void ggml_cuda_op_mul_mat( if (src1_on_device && src1_is_contiguous) { quantize_src1( - dev[id].src1_ddf, dev[id].src1_ddq, src0->type, ne10, + dev[id].src1_ddf, nullptr, dev[id].src1_ddq, src0->type, ne10, nb11/sizeof(float), nb12/sizeof(float), nb13/sizeof(float), src1_padded_col_size, ne11, ne12, ne13, stream); CUDA_CHECK(cudaGetLastError()); @@ -1649,7 +1649,7 @@ static void ggml_cuda_op_mul_mat( if (quantize_src1 && !src1_is_contiguous) { quantize_src1( - src1_ddf_i, src1_ddq_i, src0->type, ne10, ne10, ne11*ne10, ne12*ne11*ne10, + src1_ddf_i, nullptr, src1_ddq_i, src0->type, ne10, ne10, ne11*ne10, ne12*ne11*ne10, src1_padded_col_size, src1_ncols, 1, 1, stream); CUDA_CHECK(cudaGetLastError()); } @@ -1949,6 +1949,8 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor ggml_cuda_mul_mat_vec(ctx, src0, src1, nullptr, dst); } else if (!split && use_mul_mat_vec_q) { ggml_cuda_mul_mat_vec_q(ctx, src0, src1, nullptr, dst); + } else if (!split && use_mul_mat_q) { + ggml_cuda_mul_mat_q(ctx, src0, src1, nullptr, dst); } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // general KQ + KQV multi-batch without FlashAttention @@ -1964,183 +1966,145 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor } } -struct mmid_row_mapping { - int32_t i1; - int32_t i2; -}; - -static __global__ void k_copy_src1_to_contiguous(const char * __restrict__ src1_original, char * __restrict__ src1_contiguous, - int * __restrict__ cur_src1_row, mmid_row_mapping * __restrict__ row_mapping, - const char * __restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0, - int64_t ne11, int64_t ne10, - size_t nb11, size_t nb12) { - int32_t iid1 = blockIdx.x; - int32_t id = blockIdx.y; - - const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0); - - if (row_id_i != i02) { - return; - } - - const int64_t i11 = id % ne11; - const int64_t i12 = iid1; - - __shared__ int src1_row; - if (threadIdx.x == 0) { - src1_row = atomicAdd(cur_src1_row, 1); - row_mapping[src1_row] = {id, iid1}; - } - __syncthreads(); - - const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12); - float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11); - - for (int i = threadIdx.x; i < ne10; i += blockDim.x) { - src1_row_contiguous[i] = src1_row_original[i]; - } -} - -static __global__ void k_copy_dst_from_contiguous(char * __restrict__ dst_original, const char * __restrict__ dst_contiguous, - const mmid_row_mapping * __restrict__ row_mapping, - int64_t ne0, - size_t nb1, size_t nb2) { - int32_t i = blockIdx.x; - - const int32_t i1 = row_mapping[i].i1; - const int32_t i2 = row_mapping[i].i2; - - const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1); - float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2); - - for (int j = threadIdx.x; j < ne0; j += blockDim.x) { - dst_row_original[j] = dst_row_contiguous[j]; - } -} - static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; const ggml_tensor * ids = dst->src[2]; + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(!ggml_backend_buft_is_cuda_split(src0->buffer->buft) && "mul_mat_id does not support split buffers"); + GGML_TENSOR_BINARY_OP_LOCALS - if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && ne2 == 1) { - if (ggml_is_quantized(src0->type)) { - ggml_cuda_mul_mat_vec_q(ctx, src0, src1, ids, dst); - } else { - ggml_cuda_mul_mat_vec(ctx, src0, src1, ids, dst); - } - return; - } + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; - GGML_ASSERT(!ggml_backend_buft_is_cuda_split(src0->buffer->buft) && "mul_mat_id does not support split buffers"); + if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + if (ne2 == 1) { + if (ggml_is_quantized(src0->type)) { + ggml_cuda_mul_mat_vec_q(ctx, src0, src1, ids, dst); + } else { + ggml_cuda_mul_mat_vec(ctx, src0, src1, ids, dst); + } + return; + } + + if (ggml_cuda_should_use_mmq(src0->type, cc, ne12)) { + ggml_cuda_mul_mat_q(ctx, src0, src1, ids, dst); + return; + } + } cudaStream_t stream = ctx.stream(); - const int64_t n_as = ne02; - const int64_t n_ids = ids->ne[0]; + GGML_ASSERT(nb12 % nb11 == 0); + GGML_ASSERT(nb2 % nb1 == 0); + + const ggml_type type_src1_sorted = (src0->type == GGML_TYPE_F16 && !fast_fp16_hardware_available(cc)) + || ggml_is_quantized(src0->type) ? GGML_TYPE_F32 : src0->type; + const ggml_type type_dst_sorted = GGML_TYPE_F32; + const size_t ts_src1_sorted = ggml_type_size(type_src1_sorted); + const size_t ts_dst_sorted = ggml_type_size(type_dst_sorted); + + const int64_t n_expert_used = ids->ne[0]; + const int64_t ne_get_rows = ne12 * n_expert_used; + + std::vector ids_to_sorted_host; + ids_to_sorted_host.reserve(2*ne_get_rows); + std::vector ids_from_sorted_host(ne_get_rows); + + ggml_cuda_pool_alloc ids_buf_dev(ctx.pool(), 2*ne_get_rows); + + std::vector tokens_per_expert(ne02); + + ggml_cuda_pool_alloc src1_sorted(ctx.pool(), ne12*n_expert_used*ne10*ts_src1_sorted); + ggml_cuda_pool_alloc dst_sorted(ctx.pool(), ne2 *n_expert_used* ne0*ts_dst_sorted); std::vector ids_host(ggml_nbytes(ids)); - const char * ids_dev = (const char *) ids->data; - CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); + CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids->data, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); CUDA_CHECK(cudaStreamSynchronize(stream)); - ggml_tensor src0_row = *src0; - ggml_tensor src1_row = *src1; - ggml_tensor dst_row = *dst; - - char * src0_original = (char *) src0->data; - char * src1_original = (char *) src1->data; - char * dst_original = (char *) dst->data; - - src0_row.ne[2] = 1; - src0_row.ne[3] = 1; - src0_row.nb[3] = nb02; - - src1_row.ne[1] = 1; - src1_row.ne[2] = 1; - src1_row.ne[3] = 1; - src1_row.nb[2] = nb11; - src1_row.nb[3] = nb11; - - dst_row.ne[1] = 1; - dst_row.ne[2] = 1; - dst_row.ne[3] = 1; - dst_row.nb[2] = nb1; - dst_row.nb[3] = nb1; - - ggml_cuda_pool_alloc src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1)); - ggml_cuda_pool_alloc dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst)); - - src1_row.data = src1_contiguous.get(); - dst_row.data = dst_contiguous.get(); - - for (int64_t i02 = 0; i02 < n_as; i02++) { - int64_t num_src1_rows = 0; - - for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { - for (int64_t id = 0; id < n_ids; id++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); - - GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as); - - if (row_id_i != i02) { - continue; + for (int64_t i02 = 0; i02 < ne02; ++i02) { // expert matrices + for (int64_t i12 = 0; i12 < ne12; ++i12) { // tokens + for (int64_t iex = 0; iex < n_expert_used; ++iex) { + const int32_t expert_to_use = *(const int32_t *)(ids_host.data() + i12*ids->nb[1] + iex*ids->nb[0]); + assert(expert_to_use >= 0 && expert_to_use < ne02); + if (expert_to_use == i02) { + ids_from_sorted_host[i12*n_expert_used + iex] = ids_to_sorted_host.size(); + ids_to_sorted_host.push_back(i12*ne11 + iex % ne11); + tokens_per_expert[i02]++; + break; } - - num_src1_rows++; } } + } + GGML_ASSERT(ids_to_sorted_host.size() == size_t(ne_get_rows)); - if (num_src1_rows == 0) { + ids_to_sorted_host.insert(ids_to_sorted_host.end(), ids_from_sorted_host.begin(), ids_from_sorted_host.end()); + + CUDA_CHECK(cudaMemcpyAsync(ids_buf_dev.ptr, ids_to_sorted_host.data(), 2*ne_get_rows*sizeof(int32_t), cudaMemcpyHostToDevice, stream)); + CUDA_CHECK(cudaStreamSynchronize(stream)); + + const int32_t * ids_to_sorted = ids_buf_dev.ptr + 0*ne_get_rows; + const int32_t * ids_from_sorted = ids_buf_dev.ptr + 1*ne_get_rows; + + get_rows_cuda(src1->data, src1->type, ids_to_sorted, src1_sorted.ptr, type_src1_sorted, + ne10, nb11, nb12, nb13, + ne_get_rows, 1, 1, sizeof(int32_t), ne_get_rows*sizeof(int32_t), ne_get_rows*sizeof(int32_t), + ne10*ts_src1_sorted, ne_get_rows*ne10*ts_src1_sorted, ne_get_rows*ne10*ts_src1_sorted, stream); + CUDA_CHECK(cudaGetLastError()); + + char * src1_data_cur = (char *) src1_sorted.ptr; + char * dst_data_cur = (char *) dst_sorted.ptr; + for (int64_t i02 = 0; i02 < ne02; ++i02) { + if (tokens_per_expert[i02] == 0) { continue; } - ggml_cuda_pool_alloc dev_cur_src1_row(ctx.pool(), 1); - ggml_cuda_pool_alloc dev_row_mapping(ctx.pool(), num_src1_rows); - CUDA_CHECK(cudaMemsetAsync(dev_cur_src1_row.get(), 0, sizeof(int), stream)); + ggml_tensor src0_slice = *src0; + src0_slice.ne[2] = 1; + src0_slice.nb[3] = src0_slice.nb[2]; + src0_slice.data = (char *) src0->data + i02*nb02; - { - dim3 block_dims(std::min((unsigned int)ne10, 768u)); - dim3 grid_dims(ids->ne[1], n_ids); - k_copy_src1_to_contiguous<<>>( - src1_original, src1_contiguous.get(), - dev_cur_src1_row.get(), dev_row_mapping.get(), - ids_dev, i02, ids->nb[1], ids->nb[0], - ne11, ne10, - nb11, nb12); - CUDA_CHECK(cudaGetLastError()); - } + ggml_tensor src1_slice; + memset(&src1_slice, 0, sizeof(src1_slice)); + src1_slice.buffer = src1->buffer; + src1_slice.type = type_src1_sorted; + src1_slice.ne[0] = ne10; + src1_slice.ne[1] = tokens_per_expert[i02]; + src1_slice.ne[2] = 1; + src1_slice.ne[3] = 1; + src1_slice.nb[0] = ts_src1_sorted; + src1_slice.nb[1] = src1_slice.ne[0] * src1_slice.nb[0]; + src1_slice.nb[2] = src1_slice.ne[1] * src1_slice.nb[1]; + src1_slice.nb[3] = src1_slice.ne[2] * src1_slice.nb[2]; + src1_slice.data = src1_data_cur; - src0_row.data = src0_original + i02*nb02; + ggml_tensor dst_slice; + memset(&dst_slice, 0, sizeof(dst_slice)); + dst_slice.buffer = dst->buffer; + dst_slice.type = type_dst_sorted; + dst_slice.ne[0] = ne0; + dst_slice.ne[1] = tokens_per_expert[i02]; + dst_slice.ne[2] = 1; + dst_slice.ne[3] = 1; + dst_slice.nb[0] = ts_dst_sorted; + dst_slice.nb[1] = dst_slice.ne[0] * dst_slice.nb[0]; + dst_slice.nb[2] = dst_slice.ne[1] * dst_slice.nb[1]; + dst_slice.nb[3] = dst_slice.ne[2] * dst_slice.nb[2]; + dst_slice.data = dst_data_cur; - GGML_ASSERT(nb11 == sizeof(float)*ne10); - GGML_ASSERT(nb1 == sizeof(float)*ne0); + ggml_cuda_mul_mat(ctx, &src0_slice, &src1_slice, &dst_slice); + CUDA_CHECK(cudaGetLastError()); - src1_row.ne[1] = num_src1_rows; - src1_row.nb[1] = nb11; - src1_row.nb[2] = num_src1_rows*nb11; - src1_row.nb[3] = num_src1_rows*nb11; - - dst_row.ne[1] = num_src1_rows; - dst_row.nb[1] = nb1; - dst_row.nb[2] = num_src1_rows*nb1; - dst_row.nb[3] = num_src1_rows*nb1; - - ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row); - - { - dim3 block_dims(std::min((unsigned int)ne0, 768u)); - dim3 grid_dims(num_src1_rows); - k_copy_dst_from_contiguous<<>>( - dst_original, dst_contiguous.get(), - dev_row_mapping.get(), - ne0, - nb1, nb2); - CUDA_CHECK(cudaGetLastError()); - } + src1_data_cur += src1_slice.nb[2]; + dst_data_cur += dst_slice.nb[2]; } + + get_rows_cuda(dst_sorted.ptr, type_dst_sorted, ids_from_sorted, dst->data, dst->type, + ne0, ne0*ts_dst_sorted, ne_get_rows*ne0*ts_dst_sorted, ne_get_rows*ne0*ts_dst_sorted, + ne_get_rows, 1, 1, sizeof(int32_t), ne_get_rows*sizeof(int32_t), ne_get_rows*sizeof(int32_t), + nb1, nb2, nb3, stream); } static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst) { diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index b36b43d541..f397a7e038 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -1,37 +1,10 @@ #include "mmq.cuh" +#include "quantize.cuh" -void ggml_cuda_op_mul_mat_q( - ggml_backend_cuda_context & ctx, - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream) { +#include - const int64_t ne00 = src0->ne[0]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - GGML_ASSERT(ne10 % QK8_1 == 0); - - const int64_t ne0 = dst->ne[0]; - - const int64_t row_diff = row_high - row_low; - const int64_t stride00 = ne00 / ggml_blck_size(src0->type); - - int id = ggml_cuda_get_device(); - const int cc = ggml_cuda_info().devices[id].cc; - - // the main device has a larger memory buffer to hold the results from all GPUs - // nrows_dst == nrows of the matrix that the kernel writes into - const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff; - - // The stream-k decomposition is only faster for recent NVIDIA GPUs. - // Also its fixup needs to allocate a temporary buffer in the memory pool. - // There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer. - const bool use_stream_k = GGML_CUDA_CC_IS_NVIDIA(cc) && - ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA && src1_ncols == ne11; - const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst, use_stream_k}; - - switch (src0->type) { +static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) { + switch (args.type_x) { case GGML_TYPE_Q4_0: mul_mat_q_case(ctx, args, stream); break; @@ -90,10 +63,195 @@ void ggml_cuda_op_mul_mat_q( GGML_ABORT("fatal error"); break; } +} + +void ggml_cuda_mul_mat_q( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) { + GGML_ASSERT( src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + GGML_ASSERT(!ids || ids->type == GGML_TYPE_I32); // Optional, used for batched GGML_MUL_MAT_ID. + + GGML_TENSOR_BINARY_OP_LOCALS; + + cudaStream_t stream = ctx.stream(); + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + + const size_t ts_src0 = ggml_type_size(src0->type); + const size_t ts_src1 = ggml_type_size(src1->type); + const size_t ts_dst = ggml_type_size(dst->type); + + GGML_ASSERT( nb00 == ts_src0); + GGML_ASSERT( nb10 == ts_src1); + GGML_ASSERT( nb0 == ts_dst); + GGML_ASSERT(!ids || ids->nb[0] == ggml_type_size(ids->type)); + + const char * src0_d = (const char *) src0->data; + const float * src1_d = (const float *) src1->data; + float * dst_d = (float *) dst->data; + + const int64_t ne10_padded = GGML_PAD(ne10, MATRIX_ROW_PADDING); + + const int64_t s01 = src0->nb[1] / ts_src0; + const int64_t s1 = dst->nb[1] / ts_dst; + const int64_t s02 = src0->nb[2] / ts_src0; + const int64_t s2 = dst->nb[2] / ts_dst; + const int64_t s03 = src0->nb[3] / ts_src0; + const int64_t s3 = dst->nb[3] / ts_dst; + + const bool use_stream_k = GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA; + + if (!ids) { + const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1 + + get_mmq_x_max_host(cc)*sizeof(block_q8_1_mmq); + ggml_cuda_pool_alloc src1_q8_1(ctx.pool(), nbytes_src1_q8_1); + + { + const int64_t s11 = src1->nb[1] / ts_src1; + const int64_t s12 = src1->nb[2] / ts_src1; + const int64_t s13 = src1->nb[3] / ts_src1; + quantize_mmq_q8_1_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, + ne10, s11, s12, s13, ne10_padded, ne11, ne12, ne13, stream); + } + + const int64_t s12 = ne11*ne10_padded * sizeof(block_q8_1)/(QK8_1*sizeof(int)); + const int64_t s13 = ne12*s12; + + const mmq_args args = { + src0_d, src0->type, (const int *) src1_q8_1.ptr, nullptr, nullptr, dst_d, + ne00, ne01, ne1, s01, s1, + ne02, ne12, s02, s12, s2, + ne03, ne13, s03, s13, s3, + use_stream_k}; + ggml_cuda_mul_mat_q_switch_type(ctx, args, stream); + return; + } + + GGML_ASSERT(ne13 == 1); + GGML_ASSERT(nb12 % nb11 == 0); + GGML_ASSERT(nb2 % nb1 == 0); + + const int64_t n_expert_used = ids->ne[0]; + const int64_t ne_get_rows = ne12 * n_expert_used; + + std::vector ids_host(ggml_nbytes(ids)); + std::vector ids_src1_host; + ids_src1_host.reserve(ne_get_rows); + std::vector ids_dst_host; + ids_dst_host.reserve(ne_get_rows); + std::vector tokens_per_expert_host(ne02); + std::vector expert_bounds_host(ne02 + 1); + ggml_cuda_pool_alloc ids_buf_dev(ctx.pool()); + + CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids->data, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); + CUDA_CHECK(cudaStreamSynchronize(stream)); + + for (int64_t i02 = 0; i02 < ne02; ++i02) { // expert matrices + for (int64_t i12 = 0; i12 < ne12; ++i12) { // tokens + for (int64_t iex = 0; iex < n_expert_used; ++iex) { + const int32_t expert_to_use = *(const int32_t *)(ids_host.data() + i12*ids->nb[1] + iex*ids->nb[0]); + assert(expert_to_use >= 0 && expert_to_use < ne02); + if (expert_to_use == i02) { + ids_src1_host.push_back(i12*(nb12/nb11) + iex % ne11); + ids_dst_host.push_back(i12*ne1 + iex); + tokens_per_expert_host[i02]++; + break; + } + } + } + } + + int32_t cumsum = 0; + for (int64_t i = 0; i < ne02; ++i) { + expert_bounds_host[i] = cumsum; + cumsum += tokens_per_expert_host[i]; + } + expert_bounds_host[ne02] = cumsum; + + std::vector ids_buf_host; + ids_buf_host.reserve(ids_src1_host.size() + ids_dst_host.size() + expert_bounds_host.size()); + ids_buf_host.insert(ids_buf_host.end(), ids_src1_host.begin(), ids_src1_host.end()); + ids_buf_host.insert(ids_buf_host.end(), ids_dst_host.begin(), ids_dst_host.end()); + ids_buf_host.insert(ids_buf_host.end(), expert_bounds_host.begin(), expert_bounds_host.end()); + ids_buf_dev.alloc(ids_buf_host.size() + get_mmq_x_max_host(cc)); // Expert bounds are padded on device. + CUDA_CHECK(cudaMemcpyAsync(ids_buf_dev.ptr, ids_buf_host.data(), ids_buf_host.size()*sizeof(int32_t), cudaMemcpyHostToDevice, stream)); + CUDA_CHECK(cudaStreamSynchronize(stream)); + + const int32_t * ids_src1_dev = ids_buf_dev.ptr; + const int32_t * ids_dst_dev = ids_src1_dev + ids_src1_host.size(); + const int32_t * expert_bounds_dev = ids_dst_dev + ids_dst_host.size(); + + const size_t nbytes_src1_q8_1 = ne12*n_expert_used*ne10_padded * sizeof(block_q8_1)/QK8_1 + + get_mmq_x_max_host(cc)*sizeof(block_q8_1_mmq); + ggml_cuda_pool_alloc src1_q8_1(ctx.pool(), nbytes_src1_q8_1); + + const int64_t ne11_flat = ne12*n_expert_used; + const int64_t ne12_flat = 1; + const int64_t ne13_flat = 1; + + { + const int64_t s11 = src1->nb[1] / ts_src1; + const int64_t s12 = src1->nb[2] / ts_src1; + const int64_t s13 = src1->nb[2] / ts_src1; + quantize_mmq_q8_1_cuda(src1_d, ids_src1_dev, src1_q8_1.get(), src0->type, + ne10, s11, s12, s13, ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream); + } + + const int64_t s12 = ne11*ne10_padded * sizeof(block_q8_1)/(QK8_1*sizeof(int)); + const int64_t s13 = ne12*s12; + + // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid. + const mmq_args args = { + src0_d, src0->type, (const int *) src1_q8_1.ptr, ids_dst_dev, expert_bounds_dev, dst_d, + ne00, ne01, ne_get_rows, s01, s1, + ne02, ne02, s02, s12, s2, + ne03, ne13, s03, s13, s3, + use_stream_k}; + + ggml_cuda_mul_mat_q_switch_type(ctx, args, stream); +} + +void ggml_cuda_op_mul_mat_q( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, + const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, + const int64_t src1_padded_row_size, cudaStream_t stream) { + + const int64_t ne00 = src0->ne[0]; + + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + GGML_ASSERT(ne10 % QK8_1 == 0); + + const int64_t ne0 = dst->ne[0]; + + const int64_t row_diff = row_high - row_low; + const int64_t stride01 = ne00 / ggml_blck_size(src0->type); + + const int id = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[id].cc; + + // the main device has a larger memory buffer to hold the results from all GPUs + // nrows_dst == nrows of the matrix that the kernel writes into + const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff; + + // The stream-k decomposition is only faster for recent NVIDIA GPUs. + // Also its fixup needs to allocate a temporary buffer in the memory pool. + // There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer. + const bool use_stream_k = GGML_CUDA_CC_IS_NVIDIA(cc) && + ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA && src1_ncols == ne11; + const mmq_args args = { + src0_dd_i, src0->type, (const int *) src1_ddq_i, nullptr, nullptr, dst_dd_i, + ne00, row_diff, src1_ncols, stride01, nrows_dst, + 1, 1, 0, 0, 0, + 1, 1, 0, 0, 0, + use_stream_k}; + + ggml_cuda_mul_mat_q_switch_type(ctx, args, stream); GGML_UNUSED(src1); GGML_UNUSED(dst); GGML_UNUSED(src1_ddf_i); + GGML_UNUSED(src1_padded_row_size); } bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) { diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index 3cb2015520..8c93e8326e 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -13,9 +13,10 @@ using namespace ggml_cuda_mma; #define MMQ_ITER_K 256 #define MMQ_NWARPS 8 -typedef void (*load_tiles_mmq_t)(const char * __restrict__ x, int * x_tile, const int & kbx0, const int & i_max, const int & stride); -typedef void (*vec_dot_mmq_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00); -typedef void (*mmq_write_back_t)(const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max); +typedef void (*load_tiles_mmq_t)(const char * __restrict__ x, int * x_tile, const int kbx0, const int i_max, const int stride); +typedef void (*vec_dot_mmq_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00); +typedef void (*mmq_write_back_t)(const float * __restrict__ sum, const int32_t * __restrict__ get_rows_to_sorted, + float * __restrict__ dst, const int stride, const int i_max, const int j_max); enum mmq_q8_1_ds_layout { MMQ_Q8_1_DS_LAYOUT_D4, @@ -233,7 +234,7 @@ static constexpr __device__ int mmq_get_granularity_device(const int /* mmq_x */ // ------------------------------------------------------------ template static __device__ __forceinline__ void load_tiles_q4_0( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -289,7 +290,7 @@ template static __device__ __forceinlin template static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_0, mmq_y); const int * x_qs = (const int *) x; @@ -328,7 +329,7 @@ static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a( } template static __device__ __forceinline__ void load_tiles_q4_1( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -384,7 +385,7 @@ template static __device__ __forceinlin template static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_1, mmq_y); const int * x_qs = (const int *) x; @@ -423,7 +424,7 @@ static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a( } template static __device__ __forceinline__ void load_tiles_q5_0( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -495,7 +496,7 @@ template static __device__ __forceinlin } template static __device__ __forceinline__ void load_tiles_q5_1( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -565,7 +566,7 @@ template static __device__ __forceinlin } template static __device__ __forceinline__ void load_tiles_q8_0( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -621,7 +622,7 @@ template static __device__ __forceinlin template static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y); const int * x_qs = (const int *) x; @@ -651,7 +652,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a( template static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { typedef tile<16, 8, int> tile_A; typedef tile< 8, 8, int> tile_B; @@ -732,7 +733,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma( template static __device__ __forceinline__ void vec_dot_q8_1_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_1, mmq_y); const int * x_qs = (const int *) x; @@ -762,7 +763,7 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_dp4a( template static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { typedef tile<16, 8, int> tile_A; typedef tile< 8, 8, int> tile_B; @@ -839,7 +840,7 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma( template static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = MMQ_DP4A_TXS_Q8_0_16; const int * x_qs = (const int *) x; @@ -871,7 +872,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_dp4a( template static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { #ifdef NEW_MMA_AVAILABLE typedef tile<16, 4, int> tile_A; @@ -955,7 +956,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma( } template static __device__ __forceinline__ void load_tiles_q2_K( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -1011,7 +1012,7 @@ template static __device__ __forceinlin template static __device__ __forceinline__ void vec_dot_q2_K_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q2_K, mmq_y); const int * x_qs = (const int *) x; @@ -1074,7 +1075,7 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_dp4a( template static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { #ifdef NEW_MMA_AVAILABLE typedef tile<16, 4, int> tile_A; @@ -1201,7 +1202,7 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma( } template static __device__ __forceinline__ void load_tiles_q3_K( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -1298,7 +1299,7 @@ template static __device__ __forceinlin template static __device__ __forceinline__ void vec_dot_q3_K_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q3_K, mmq_y); const int * x_qs = (const int *) x; @@ -1340,7 +1341,7 @@ static __device__ __forceinline__ int unpack_scales_q45_K(const int * scales, co } template static __device__ __forceinline__ void load_tiles_q4_K( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -1437,7 +1438,7 @@ template static __device__ __forceinlin template static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_K, mmq_y); const int * x_qs = (const int *) x; @@ -1469,7 +1470,7 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a( } template static __device__ __forceinline__ void load_tiles_q5_K( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -1578,7 +1579,7 @@ template static __device__ __forceinlin template static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_K, mmq_y); const int * x_qs = (const int *) x; @@ -1610,7 +1611,7 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a( } template static __device__ __forceinline__ void load_tiles_q6_K( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -1693,7 +1694,7 @@ template static __device__ __forceinlin template static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q6_K, mmq_y); const int * x_qs = (const int *) x; @@ -1726,7 +1727,7 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a( template static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma( - const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) { + const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { #ifdef NEW_MMA_AVAILABLE typedef tile<16, 4, int> tile_A; @@ -1835,7 +1836,7 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma( } template static __device__ __forceinline__ void load_tiles_iq4_nl( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -1893,7 +1894,7 @@ template static __device__ __forceinlin } template static __device__ __forceinline__ void load_tiles_iq2_xxs( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -1951,7 +1952,7 @@ template static __device__ __forceinlin } template static __device__ __forceinline__ void load_tiles_iq2_xs( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -2007,7 +2008,7 @@ template static __device__ __forceinlin } template static __device__ __forceinline__ void load_tiles_iq2_s( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -2070,7 +2071,7 @@ template static __device__ __forceinlin } template static __device__ __forceinline__ void load_tiles_iq3_xxs( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -2126,7 +2127,7 @@ template static __device__ __forceinlin } template static __device__ __forceinline__ void load_tiles_iq3_s( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -2189,7 +2190,7 @@ template static __device__ __forceinlin } template static __device__ __forceinline__ void load_tiles_iq1_s( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -2245,7 +2246,7 @@ template static __device__ __forceinlin } template static __device__ __forceinline__ void load_tiles_iq4_xs( - const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { #ifdef NEW_MMA_AVAILABLE int * x_qs = (int *) x_tile; @@ -2306,8 +2307,8 @@ template static __device__ __forceinlin template static __device__ __forceinline__ void mmq_write_back_dp4a( - const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max) { - + const float * __restrict__ sum, const int32_t * __restrict__ ids_dst, float * __restrict__ dst, + const int stride, const int i_max, const int j_max) { #pragma unroll for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { const int j = j0 + threadIdx.y; @@ -2324,15 +2325,15 @@ static __device__ __forceinline__ void mmq_write_back_dp4a( continue; } - dst[j*stride + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE]; + dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE]; } } } template static __device__ __forceinline__ void mmq_write_back_mma( - const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max) { - + const float * __restrict__ sum, const int * __restrict__ ids_dst, float * __restrict__ dst, + const int stride, const int i_max, const int j_max) { typedef tile<16, 8, int> tile_C; constexpr int granularity = mmq_get_granularity_device(mmq_x); @@ -2362,7 +2363,7 @@ static __device__ __forceinline__ void mmq_write_back_mma( continue; } - dst[j*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l]; + dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l]; } } } @@ -2518,17 +2519,18 @@ struct mmq_type_traits { }; template -static __device__ void mul_mat_q_process_tile( - const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup, - const int & ne00, const int & ne01, const int & stride01, const int & ne10, const int & ne11, const int & stride11, const int & ne0, - const int & it, const int & jt, const int & kb0_start, const int & kb0_stop) { +static __device__ __forceinline__ void mul_mat_q_process_tile( + const char * __restrict__ x, const int offset_x, const int * __restrict__ y, + const int * __restrict__ ids_dst, float * __restrict__ dst, float * __restrict__ tmp_fixup, + const int nrows_x, const int ncols_y, const int stride_row_x, const int stride_col_dst, + const int tile_x_max_i, const int tile_y_max_j, const int kb0_start, const int kb0_stop) { constexpr int qk = ggml_cuda_type_traits::qk; constexpr int mmq_y = get_mmq_y_device(); constexpr load_tiles_mmq_t load_tiles = mmq_type_traits::load_tiles; - extern __shared__ char data_mul_mat_q[]; - int * tile_y = (int *) data_mul_mat_q; + extern __shared__ int data_mul_mat_q[]; + int * tile_y = data_mul_mat_q + mmq_x; int * tile_x = tile_y + GGML_PAD(mmq_x*(WARP_SIZE + WARP_SIZE/QI8_1), nwarps*WARP_SIZE); #ifdef NEW_MMA_AVAILABLE @@ -2543,16 +2545,11 @@ static __device__ void mul_mat_q_process_tile( float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f}; - const int tile_x_max_i = ne01 - it*mmq_y - 1; - const int tile_y_max_j = ne11 - jt*mmq_x - 1; - - const int * y = (const int *) yc + jt*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int)); - for (int kb0 = kb0_start; kb0 < kb0_stop; kb0 += blocks_per_iter) { - load_tiles(x, tile_x, stride01*it*mmq_y + kb0, tile_x_max_i, stride01); + load_tiles(x, tile_x, offset_x + kb0, tile_x_max_i, stride_row_x); { - const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + 0*sizeof(block_q8_1_mmq)/sizeof(int)); + const int * by0 = y + ncols_y*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + 0*sizeof(block_q8_1_mmq)/sizeof(int)); #pragma unroll for (int l0 = 0; l0 < mmq_x*MMQ_TILE_Y_K; l0 += nwarps*WARP_SIZE) { int l = l0 + threadIdx.y*WARP_SIZE + threadIdx.x; @@ -2568,7 +2565,7 @@ static __device__ void mul_mat_q_process_tile( __syncthreads(); { - const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + 1*sizeof(block_q8_1_mmq)/sizeof(int)); + const int * by0 = y + ncols_y*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + 1*sizeof(block_q8_1_mmq)/sizeof(int)); #pragma unroll for (int l0 = 0; l0 < mmq_x*MMQ_TILE_Y_K; l0 += nwarps*WARP_SIZE) { int l = l0 + threadIdx.y*WARP_SIZE + threadIdx.x; @@ -2585,12 +2582,10 @@ static __device__ void mul_mat_q_process_tile( } if (fixup) { - write_back(sum, tmp_fixup + blockIdx.x*(mmq_x*mmq_y), mmq_y, mmq_y, mmq_x); + write_back(sum, ids_dst, tmp_fixup + blockIdx.x*(mmq_x*mmq_y), mmq_y, mmq_y, mmq_x); } else { - write_back(sum, dst + jt*mmq_x*ne0 + it*mmq_y, ne0, tile_x_max_i, tile_y_max_j); + write_back(sum, ids_dst, dst, stride_col_dst, tile_x_max_i, tile_y_max_j); } - - GGML_UNUSED(ne00); GGML_UNUSED(ne10); } @@ -2609,8 +2604,11 @@ template #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA #endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) static __global__ void mul_mat_q( - const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup, - const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) { + const char * __restrict__ x, const int * __restrict__ y, const int32_t * __restrict__ ids_dst, + const int32_t * __restrict__ expert_bounds, float * __restrict__ dst, float * __restrict__ tmp_fixup, + const int ncols_x, const int nrows_x, const int ncols_y, const int stride_row_x, const int stride_col_dst, + const int channel_ratio, const int nchannels_y, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst, + const int sample_ratio, const int nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) { // Skip unused template specializations for faster compilation: if (mmq_x > get_mmq_x_max_device() || mmq_x % mmq_get_granularity_device(mmq_x) != 0) { @@ -2621,26 +2619,85 @@ static __global__ void mul_mat_q( constexpr int qk = ggml_cuda_type_traits::qk; constexpr int mmq_y = get_mmq_y_device(); + const int ntx = (ncols_y + mmq_x - 1) / mmq_x; // Number of tiles x + const int nty = (nrows_x + mmq_y - 1) / mmq_y; // Number of tiles y + + // Initialize the ids for writing back data with just the index. + // For regular matrix multiplications this is never changed. + // For MoE the correct indices are loaded from ids_dst. + extern __shared__ int ids_dst_shared[]; // Stored at beginning of shared memory. +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps*WARP_SIZE) { + const int j = j0 + threadIdx.y*WARP_SIZE + threadIdx.x; + + if (j0 + nwarps*WARP_SIZE > mmq_x && j >= mmq_x) { + break; + } + + ids_dst_shared[j] = j; + } + // On AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead: #if (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < GGML_CUDA_CC_VOLTA { + const int wt = blockIdx.z / nchannels_y; + const int zt = blockIdx.z - wt*nchannels_y; + const int jt = blockIdx.y; + const int it = blockIdx.x; + + // Defaults for regular matrix multiplication: + int col_low = 0; + int col_high = ncols_y; + int col_diff = ncols_y; + int offset_y = wt*stride_sample_y + zt*stride_channel_y; + int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*mmq_x*stride_col_dst; + + if (ids_dst) { + col_low = expert_bounds[zt + 0]; + col_high = expert_bounds[zt + 1]; + col_diff = col_high - col_low; + + offset_y = 0; + offset_dst = 0; + + if (jt*mmq_x >= col_diff) { + return; + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps*WARP_SIZE) { + const int j = j0 + threadIdx.y*WARP_SIZE + threadIdx.x; + + if (j0 + nwarps*WARP_SIZE > mmq_x && j >= mmq_x) { + break; + } + + ids_dst_shared[j] = ids_dst[col_low + jt*mmq_x + j]; + } + } + + offset_y += (col_low + jt*mmq_x)*(sizeof(block_q8_1_mmq)/sizeof(int)); + offset_dst += it*mmq_y; + + const int tile_x_max_i = nrows_x - it*mmq_y - 1; + const int tile_y_max_j = col_diff - jt*mmq_x - 1; + + const int offset_x = (wt/sample_ratio)*stride_sample_x + (zt/channel_ratio)*stride_channel_x + it*mmq_y*stride_row_x; + constexpr bool fixup = false; mul_mat_q_process_tile - (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0, - blockIdx.x, blockIdx.y, 0, ne00/qk); + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, ncols_y, stride_row_x, stride_col_dst, + tile_x_max_i, tile_y_max_j, 0, ncols_x/qk); return; } #endif // (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < GGML_CUDA_CC_VOLTA - const int64_t blocks_per_ne00 = ne00 / qk; + const int64_t blocks_per_ne00 = ncols_x / qk; constexpr int blocks_per_iter = MMQ_ITER_K / qk; - const int ntx = (ne11 + mmq_x - 1) / mmq_x; // Number of tiles x - const int nty = (ne01 + mmq_y - 1) / mmq_y; // Number of tiles y - // kbc == k block continuous, current index in continuous ijk space. - int64_t kbc = (int64_t) blockIdx.x *blocks_per_ne00*ntx*nty / gridDim.x; - int64_t kbc_stop = (int64_t)(blockIdx.x + 1)*blocks_per_ne00*ntx*nty / gridDim.x; + int64_t kbc = (int64_t) blockIdx.x *nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x; + int64_t kbc_stop = (int64_t)(blockIdx.x + 1)*nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x; kbc -= (kbc % blocks_per_ne00) % blocks_per_iter; kbc_stop -= (kbc_stop % blocks_per_ne00) % blocks_per_iter; @@ -2649,13 +2706,64 @@ static __global__ void mul_mat_q( int kb0_start = kbc % blocks_per_ne00; int kb0_stop = min(blocks_per_ne00, kb0_start + kbc_stop - kbc); while (kbc < kbc_stop && kb0_stop == blocks_per_ne00) { - const int jt = kbc / (blocks_per_ne00*nty); // j index of current tile. - const int it = (kbc - jt*(blocks_per_ne00*nty)) / blocks_per_ne00; // i index of current tile. + int tmp = kbc; + const int it = tmp / (nsamples_y*nchannels_y*ntx*blocks_per_ne00); + tmp -= it * (nsamples_y*nchannels_y*ntx*blocks_per_ne00); + const int wt = tmp / (nchannels_y*ntx*blocks_per_ne00); + tmp -= wt * (nchannels_y*ntx*blocks_per_ne00); + const int zt = tmp / (ntx*blocks_per_ne00); + tmp -= zt * (ntx*blocks_per_ne00); + const int jt = tmp / blocks_per_ne00; + + // Defaults for regular matrix multiplication: + int col_low = 0; + int col_high = ncols_y; + int col_diff = ncols_y; + int offset_y = wt*stride_sample_y + zt*stride_channel_y; + int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*mmq_x*stride_col_dst; + + if (ids_dst) { + col_low = expert_bounds[zt + 0]; + col_high = expert_bounds[zt + 1]; + col_diff = col_high - col_low; + + offset_y = 0; + offset_dst = 0; + + if (jt*mmq_x >= col_diff) { + kbc += blocks_per_ne00; + kbc -= kbc % blocks_per_ne00; + + kb0_start = 0; + kb0_stop = min(blocks_per_ne00, kbc_stop - kbc); + + continue; + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps*WARP_SIZE) { + const int j = j0 + threadIdx.y*WARP_SIZE + threadIdx.x; + + if (j0 + nwarps*WARP_SIZE > mmq_x && j >= mmq_x) { + break; + } + + ids_dst_shared[j] = ids_dst[col_low + jt*mmq_x + j]; + } + } + + offset_y += (col_low + jt*mmq_x)*(sizeof(block_q8_1_mmq)/sizeof(int)); + offset_dst += it*mmq_y; + + const int tile_x_max_i = nrows_x - it*mmq_y - 1; + const int tile_y_max_j = col_diff - jt*mmq_x - 1; + + const int offset_x = (wt/sample_ratio)*stride_sample_x + (zt/channel_ratio)*stride_channel_x + it*mmq_y*stride_row_x; constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer. mul_mat_q_process_tile - (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0, - it, jt, kb0_start, kb0_stop); + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, ncols_y, stride_row_x, stride_col_dst, + tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); kbc += blocks_per_ne00; kbc -= kbc % blocks_per_ne00; @@ -2668,55 +2776,106 @@ static __global__ void mul_mat_q( return; } - const int jt = kbc / (blocks_per_ne00*nty); - const int it = (kbc - jt*(blocks_per_ne00*nty)) / blocks_per_ne00; + int tmp = kbc; + const int it = tmp / (nsamples_y*nchannels_y*ntx*blocks_per_ne00); + tmp -= it * (nsamples_y*nchannels_y*ntx*blocks_per_ne00); + const int wt = tmp / (nchannels_y*ntx*blocks_per_ne00); + tmp -= wt * (nchannels_y*ntx*blocks_per_ne00); + const int zt = tmp / (ntx*blocks_per_ne00); + tmp -= zt * (ntx*blocks_per_ne00); + const int jt = tmp / blocks_per_ne00; + + // Defaults for regular matrix multiplication: + int col_low = 0; + int col_high = ncols_y; + int col_diff = ncols_y; + int offset_y = wt*stride_sample_y + zt*stride_channel_y; + int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*mmq_x*stride_col_dst; + + if (ids_dst) { + col_low = expert_bounds[zt + 0]; + col_high = expert_bounds[zt + 1]; + col_diff = col_high - col_low; + + offset_y = 0; + offset_dst = 0; + + if (jt*mmq_x >= col_diff) { + return; + } + + // The memory layout for the fixup buffer is always contiguous, therefore reset ids: +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps*WARP_SIZE) { + const int j = j0 + threadIdx.y*WARP_SIZE + threadIdx.x; + + if (j0 + nwarps*WARP_SIZE > mmq_x && j >= mmq_x) { + break; + } + + ids_dst_shared[j] = j; + } + } + + offset_y += (col_low + jt*mmq_x)*(sizeof(block_q8_1_mmq)/sizeof(int)); + offset_dst += it*mmq_y; + + const int tile_x_max_i = nrows_x - it*mmq_y - 1; + const int tile_y_max_j = col_diff - jt*mmq_x - 1; + + const int offset_x = (wt/sample_ratio)*stride_sample_x + (zt/channel_ratio)*stride_channel_x + it*mmq_y*stride_row_x; constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks. mul_mat_q_process_tile - (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0, - it, jt, kb0_start, kb0_stop); + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, ncols_y, stride_row_x, stride_col_dst, + tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); } template static __global__ void mul_mat_q_stream_k_fixup( - float * __restrict__ dst, const float * __restrict__ tmp_last_tile, const int ne00, const int ne01, const int ne11, const int ne0, const int block_num_mmq) { - + const int32_t * ids_dst, const int32_t * expert_bounds, float * __restrict__ dst, const float * __restrict__ tmp_last_tile, + const int ncols_x, const int nrows_x, const int ncols_y, const int stride_col_dst, + const int nchannels_y, const int stride_channel_dst, const int nsamples_y, const int stride_sample_dst) { constexpr int mmq_y = get_mmq_y_device(); constexpr int qk = ggml_cuda_type_traits::qk; constexpr int blocks_per_iter = MMQ_ITER_K / qk; - const int64_t blocks_per_ne00 = ne00 / qk; + const int64_t blocks_per_ne00 = ncols_x / qk; float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f}; - const int ntx = (ne11 + mmq_x - 1) / mmq_x; - const int nty = (ne01 + mmq_y - 1) / mmq_y; + const int ntx = (ncols_y + mmq_x - 1) / mmq_x; + const int nty = (nrows_x + mmq_y - 1) / mmq_y; + + const int bidx0 = blockIdx.x; + + // kbc == k block continuous, current index in continuous ijk space. + int64_t kbc0 = (int64_t) bidx0 *nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x; + int64_t kbc0_stop = (int64_t)(bidx0 + 1)*nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x; + + kbc0 -= (kbc0 % blocks_per_ne00) % blocks_per_iter; + kbc0_stop -= (kbc0_stop % blocks_per_ne00) % blocks_per_iter; + + const bool did_not_have_any_data = kbc0 == kbc0_stop; + const bool wrote_beginning_of_tile = kbc0 % blocks_per_ne00 == 0; + const bool did_not_write_last = kbc0/blocks_per_ne00 == kbc0_stop/blocks_per_ne00 && kbc0_stop % blocks_per_ne00 != 0; + if (did_not_have_any_data || wrote_beginning_of_tile || did_not_write_last) { + return; + } bool any_fixup = false; - const int bidx_start = ((blockIdx.y*nty + blockIdx.x) * block_num_mmq) / (gridDim.y*gridDim.x); - const int bidx_stop = ((blockIdx.y*nty + blockIdx.x + 1) * block_num_mmq + gridDim.y*gridDim.x - 1) / (gridDim.y*gridDim.x); + // Iterate over previous blocks and sum up partial sums written to fixup buffer. + // All CUDA blocks that get here must have a previous block that needs a fixup. + int64_t bidx = bidx0 - 1; + int64_t kbc_stop = kbc0; + while(true) { + int64_t kbc = bidx*nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x; + kbc -= (kbc % blocks_per_ne00) % blocks_per_iter; - int64_t kbc_0; - int64_t kbc_stop_0 = (int64_t) bidx_start*blocks_per_ne00*ntx*nty / block_num_mmq; - - for (int bidx = bidx_start; bidx < bidx_stop; ++bidx) { - kbc_0 = kbc_stop_0; - kbc_stop_0 = (int64_t) (bidx + 1)*blocks_per_ne00*ntx*nty / block_num_mmq; - - const int64_t kbc = kbc_0 - (kbc_0 % blocks_per_ne00) % blocks_per_iter; - const int64_t kbc_stop = kbc_stop_0 - (kbc_stop_0 % blocks_per_ne00) % blocks_per_iter; - - // Skip fixup tile if the MMQ CUDA block never wrote anything to it: - if (kbc == kbc_stop || kbc_stop % blocks_per_ne00 == 0) { - continue; - } - - const int jt = kbc_stop / (blocks_per_ne00*nty); - const int it = (kbc_stop - jt*(blocks_per_ne00*nty)) / blocks_per_ne00; - - // Skip fixup tile if it's unrelated to the output tile assigned to this CUDA block: - if ((unsigned)it != blockIdx.x || (unsigned)jt != blockIdx.y) { + if (kbc == kbc_stop) { // Did not have any data. + bidx--; + kbc_stop = kbc; continue; } @@ -2733,16 +2892,71 @@ static __global__ void mul_mat_q_stream_k_fixup( sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE] += tmp_last_tile[bidx*(mmq_x*mmq_y) + j*mmq_y + i]; } } + + // If this block started in a previous tile we are done and don't need to combine additional partial results. + if (kbc % blocks_per_ne00 == 0 || kbc/blocks_per_ne00 < kbc0/blocks_per_ne00) { + break; + } + bidx--; + kbc_stop = kbc; } if (!any_fixup) { return; } - dst += blockIdx.y*mmq_x*ne0 + blockIdx.x*mmq_y; + int tmp = kbc0; + const int it = tmp / (nsamples_y*nchannels_y*ntx*blocks_per_ne00); + tmp -= it * (nsamples_y*nchannels_y*ntx*blocks_per_ne00); + const int wt = tmp / (nchannels_y*ntx*blocks_per_ne00); + tmp -= wt * (nchannels_y*ntx*blocks_per_ne00); + const int zt = tmp / (ntx*blocks_per_ne00); + tmp -= zt * (ntx*blocks_per_ne00); + const int jt = tmp / blocks_per_ne00; - const int i_max = ne01 - blockIdx.x*mmq_y - 1; - const int j_max = ne11 - blockIdx.y*mmq_x - 1; + if (!ids_dst) { + const int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*mmq_x*stride_col_dst + it*mmq_y; + dst += offset_dst; + + const int i_max = nrows_x - it*mmq_y - 1; + const int j_max = ncols_y - jt*mmq_x - 1; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (j > j_max) { + return; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + if (need_check && i > i_max) { + continue; + } + + dst[j*stride_col_dst + i] += sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE]; + } + } + return; + } + + __shared__ int ids_dst_shared[mmq_x]; + const int col_low = expert_bounds[zt + 0]; + const int col_high = expert_bounds[zt + 1]; + const int col_diff = col_high - col_low; + + for (int j = threadIdx.y*WARP_SIZE + threadIdx.x; j < mmq_x; j += nwarps*WARP_SIZE) { + ids_dst_shared[j] = ids_dst[col_low + j]; + } + + const int offset_dst = it*mmq_y; + dst += offset_dst; + + const int i_max = nrows_x - it*mmq_y - 1; + const int j_max = col_diff - jt*mmq_x - 1; #pragma unroll for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { @@ -2760,26 +2974,27 @@ static __global__ void mul_mat_q_stream_k_fixup( continue; } - dst[j*ne0 + i] += sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE]; + dst[ids_dst_shared[j]*stride_col_dst + i] += sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE]; } } } struct mmq_args { - const char * x; const char * y; float * dst; - int64_t ne00; int64_t ne01; int64_t stride01; - int64_t ne10; int64_t ne11; int64_t stride11; - int64_t ne0; + const char * x; ggml_type type_x; const int * y; const int32_t * ids_dst; const int32_t * expert_bounds; float * dst; + int64_t ncols_x; int64_t nrows_x; int64_t ncols_y; int64_t stride_row_x; int64_t nrows_dst; + int64_t nchannels_x; int64_t nchannels_y; int64_t stride_channel_x; int64_t stride_channel_y; int64_t stride_channel_dst; + int64_t nsamples_x; int64_t nsamples_y; int64_t stride_sample_x; int64_t stride_sample_y; int64_t stride_sample_dst; bool use_stream_k; }; template -static int mmq_get_shmem(const int mmq_x, const int mmq_y, const int cc) { +static size_t mmq_get_nbytes_shared(const int mmq_x, const int mmq_y, const int cc) { const tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(type, mmq_y); const int mmq_tile_x_k = mmq_get_mma_tile_x_k(type); - const int shmem_x = new_mma_available(cc) ? mmq_y*mmq_tile_x_k*sizeof(int) : txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int); - const int shmem_y = mmq_x*sizeof(block_q8_1_mmq); - return shmem_x + GGML_PAD(shmem_y, MMQ_NWARPS*WARP_SIZE*sizeof(int)); + const size_t nbs_ids = mmq_x*sizeof(int); + const size_t nbs_x = new_mma_available(cc) ? mmq_y*mmq_tile_x_k*sizeof(int) : txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int); + const size_t nbs_y = mmq_x*sizeof(block_q8_1_mmq); + return nbs_ids + nbs_x + GGML_PAD(nbs_y, MMQ_NWARPS*WARP_SIZE*sizeof(int)); } template @@ -2791,86 +3006,114 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a const dim3 block_dims(WARP_SIZE, MMQ_NWARPS, 1); - const int shmem = mmq_get_shmem(mmq_x, mmq_y, cc); + const int nbytes_shared = mmq_get_nbytes_shared(mmq_x, mmq_y, cc); #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA) - static bool shmem_limit_raised[GGML_CUDA_MAX_DEVICES] = {false}; - if (!shmem_limit_raised[id]) { - CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem)); - CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem)); - shmem_limit_raised[id] = true; + static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false}; + if (!shared_memory_limit_raised[id]) { + CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared)); + CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared)); + shared_memory_limit_raised[id] = true; } #endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA) - const int nty = (args.ne01 + mmq_y - 1) / mmq_y; - const int ntx = (args.ne11 + mmq_x - 1) / mmq_x; - const dim3 block_nums_xy_tiling(nty, ntx, 1); + const int nty = (args.nrows_x + mmq_y - 1) / mmq_y; + const int ntx = (args.ncols_y + mmq_x - 1) / mmq_x; + const int ntzw = args.nchannels_y * args.nsamples_y; + const dim3 block_nums_xy_tiling(nty, ntx, ntzw); + + GGML_ASSERT(args.nchannels_y % args.nchannels_x == 0); + GGML_ASSERT(args.nsamples_y % args.nsamples_x == 0); + const int channel_ratio = args.nchannels_y / args.nchannels_x; + const int sample_ratio = args.nsamples_y / args.nsamples_x; if (!args.use_stream_k) { - if (args.ne01 % mmq_y == 0) { + if (args.nrows_x % mmq_y == 0) { constexpr bool need_check = false; - mul_mat_q<<>> - (args.x, args.y, args.dst, nullptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0); + mul_mat_q<<>> + (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, + args.ncols_x, args.nrows_x, args.ncols_y, args.stride_row_x, args.nrows_dst, + channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, + sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst); } else { constexpr bool need_check = true; - mul_mat_q<<>> - (args.x, args.y, args.dst, nullptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0); + mul_mat_q<<>> + (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, + args.ncols_x, args.nrows_x, args.ncols_y, args.stride_row_x, args.nrows_dst, + channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, + sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst); } return; } - const dim3 block_nums_mmq(nsm, 1, 1); + const dim3 block_nums_stream_k(nsm, 1, 1); + const bool fixup_needed = ntx*nty*ntzw % nsm != 0; ggml_cuda_pool & pool = ctx.pool(id); - ggml_cuda_pool_alloc tmp_fixup(pool, block_nums_mmq.x * mmq_x*mmq_y); + ggml_cuda_pool_alloc tmp_fixup(pool); + if (fixup_needed) { + tmp_fixup.alloc(block_nums_stream_k.x * mmq_x*mmq_y); + } - if (args.ne01 % mmq_y == 0) { + if (args.nrows_x % mmq_y == 0) { constexpr bool need_check = false; - mul_mat_q<<>> - (args.x, args.y, args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0); + mul_mat_q<<>> + (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, + args.ncols_x, args.nrows_x, args.ncols_y, args.stride_row_x, args.nrows_dst, + channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, + sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst); - mul_mat_q_stream_k_fixup<<>> - (args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.ne11, args.ne0, block_nums_mmq.x); + if (!fixup_needed) { + return; + } + + mul_mat_q_stream_k_fixup<<>> + (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_y, + args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst); } else { constexpr bool need_check = true; - mul_mat_q<<>> - (args.x, args.y, args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0); + mul_mat_q<<>> + (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, + args.ncols_x, args.nrows_x, args.ncols_y, args.stride_row_x, args.nrows_dst, + channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, + sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst); - mul_mat_q_stream_k_fixup<<>> - (args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.ne11, args.ne0, block_nums_mmq.x); + if (!fixup_needed) { + return; + } + + mul_mat_q_stream_k_fixup<<>> + (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_y, + args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst); } } template void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) { - const int id = ggml_cuda_get_device(); - const int cc = ggml_cuda_info().devices[id].cc; - const int smpbo = ggml_cuda_info().devices[id].smpbo; + const int id = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[id].cc; + const size_t smpbo = ggml_cuda_info().devices[id].smpbo; const int mmq_x_max = get_mmq_x_max_host(cc); const int mmq_y = get_mmq_y_host(cc); - const int block_num_y = (args.ne01 + mmq_y - 1) / mmq_y; - const bool use_stream_k = GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA; int mmq_x_best = 0; - int nparts_best = INT_MAX; + int ntiles_x_best = INT_MAX; - for (int mmq_x = 8; mmq_x <= mmq_x_max && nparts_best > 1; mmq_x += 8) { + for (int mmq_x = 8; mmq_x <= mmq_x_max && ntiles_x_best > 1; mmq_x += 8) { const int granularity = mmq_get_granularity_host(mmq_x, cc); - if (mmq_x % granularity != 0 || mmq_get_shmem(mmq_x, mmq_y, cc) > smpbo) { + if (mmq_x % granularity != 0 || mmq_get_nbytes_shared(mmq_x, mmq_y, cc) > smpbo) { continue; } - const int ntiles_x = (args.ne11 + mmq_x - 1) / mmq_x; - const int nwaves_xy_tiling = ntiles_x*block_num_y; - const int nparts = use_stream_k ? ntiles_x : nwaves_xy_tiling; + const int ntiles_x = (args.ncols_y + mmq_x - 1) / mmq_x; - if (nparts < nparts_best) { - mmq_x_best = mmq_x; - nparts_best = nparts; + if (ntiles_x < ntiles_x_best) { + mmq_x_best = mmq_x; + ntiles_x_best = ntiles_x; } } @@ -2954,6 +3197,9 @@ extern DECL_MMQ_CASE(GGML_TYPE_IQ4_XS); // ------------------------------------------------------------------------------------------------------------------------- +void ggml_cuda_mul_mat_q( + ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst); + void ggml_cuda_op_mul_mat_q( ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index d846e35a6a..132c466fd1 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -158,7 +158,7 @@ static __global__ void mul_mat_vec_q( const int blocks_per_row_x = ncols_x / qk; constexpr int blocks_per_iter = vdr * nwarps*warp_size / qi; - // The MUL_MAT_ID code path with ids != nullptr is only implemetned for ncols_dst == 1. + // The MUL_MAT_ID code path with ids != nullptr is only implemented for ncols_dst == 1. const int channel_dst = blockIdx.y; const int channel_x = ncols_dst == 1 && ids ? ids[channel_dst] : channel_dst / channel_ratio; const int channel_y = ncols_dst == 1 && ids ? channel_dst % nchannels_y : channel_dst; @@ -507,7 +507,7 @@ void ggml_cuda_mul_mat_vec_q( GGML_ASSERT( nb0 == ts_dst); GGML_ASSERT(!ids || ids->nb[0] == ggml_type_size(ids->type)); - GGML_ASSERT(!ids || ne12 == 1); // Implementation is only correct for batch size 1. + GGML_ASSERT(!ids || ne12 == 1); // Implementation is only correct for batch size 1. const float * src1_d = (const float *) src1->data; const int32_t * ids_d = ids ? (const int32_t *) ids->data : nullptr; @@ -519,7 +519,7 @@ void ggml_cuda_mul_mat_vec_q( const int64_t s11 = src1->nb[1] / ts_src1; const int64_t s12 = src1->nb[2] / ts_src1; const int64_t s13 = src1->nb[3] / ts_src1; - quantize_row_q8_1_cuda(src1_d, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded, ne11, ne12, ne13, stream); + quantize_row_q8_1_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded, ne11, ne12, ne13, stream); } const int64_t s01 = src0->nb[1] / ts_src0; diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu index 3bab47d56a..931a45ad34 100644 --- a/ggml/src/ggml-cuda/quantize.cu +++ b/ggml/src/ggml-cuda/quantize.cu @@ -49,29 +49,38 @@ static __global__ void quantize_q8_1( template static __global__ void quantize_mmq_q8_1( - const float * __restrict__ x, void * __restrict__ vy, const int64_t kx0, const int64_t kx1, const int64_t kx0_padded) { + const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, + const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, + const int64_t ne0, const int ne1, const int ne2) { constexpr int vals_per_scale = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 64 : 32; constexpr int vals_per_sum = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 16 : 32; - const int64_t ix0 = ((int64_t)blockDim.x*blockIdx.x + threadIdx.x)*4; + const int64_t i0 = ((int64_t)blockDim.x*blockIdx.x + threadIdx.x)*4; - if (ix0 >= kx0_padded) { + if (i0 >= ne0) { return; } - const float4 * x4 = (const float4 *) x; + const int64_t i1 = blockIdx.y; + const int64_t i2 = blockIdx.z % ne2; + const int64_t i3 = blockIdx.z / ne2; - const int64_t ix1 = kx1*blockIdx.z + blockIdx.y; + const int64_t i00 = i0; + const int64_t i01 = ids ? ids[i1] : i1; + const int64_t i02 = i2; + const int64_t i03 = i3; + + const float4 * x4 = (const float4 *) x; block_q8_1_mmq * y = (block_q8_1_mmq *) vy; const int64_t ib0 = blockIdx.z*((int64_t)gridDim.y*gridDim.x*blockDim.x/QK8_1); // first block of channel - const int64_t ib = ib0 + (ix0 / (4*QK8_1))*kx1 + blockIdx.y; // block index in channel - const int64_t iqs = ix0 % (4*QK8_1); // quant index in block + const int64_t ib = ib0 + (i0 / (4*QK8_1))*ne1 + blockIdx.y; // block index in channel + const int64_t iqs = i0 % (4*QK8_1); // quant index in block // Load 4 floats per thread and calculate max. abs. value between them: - const float4 xi = ix0 < kx0 ? x4[(ix1*kx0 + ix0)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f); + const float4 xi = i0 < ne00 ? x4[(i03*s03 + i02*s02 + i01*s01 + i00)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f); float amax = fabsf(xi.x); amax = fmaxf(amax, fabsf(xi.y)); amax = fmaxf(amax, fabsf(xi.z)); @@ -87,7 +96,7 @@ static __global__ void quantize_mmq_q8_1( if (ds_layout != MMQ_Q8_1_DS_LAYOUT_D4) { sum = xi.x + xi.y + xi.z + xi.w; - // Exchange calculate sum across vals_per_sum/4 threads. + // Calculate sums across vals_per_sum/4 threads. #pragma unroll for (int offset = vals_per_sum/8; offset > 0; offset >>= 1) { sum += __shfl_xor_sync(0xFFFFFFFF, sum, offset, WARP_SIZE); @@ -137,9 +146,10 @@ static __global__ void quantize_mmq_q8_1( } void quantize_row_q8_1_cuda( - const float * x, void * vy, const ggml_type type_src0, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, - const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) { - + const float * x, const int32_t * ids, void * vy, const ggml_type type_src0, + const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, + const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) { + GGML_ASSERT(!ids); GGML_ASSERT(ne0 % QK8_1 == 0); const int64_t block_num_x = (ne0 + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; @@ -150,9 +160,9 @@ void quantize_row_q8_1_cuda( } void quantize_mmq_q8_1_cuda( - const float * x, void * vy, const ggml_type type_src0, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, - const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) { - + const float * x, const int32_t * ids, void * vy, const ggml_type type_src0, + const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, + const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) { GGML_ASSERT(ne0 % (4*QK8_1) == 0); const int64_t block_num_x = (ne0 + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ); @@ -161,21 +171,18 @@ void quantize_mmq_q8_1_cuda( switch (mmq_get_q8_1_ds_layout(type_src0)) { case MMQ_Q8_1_DS_LAYOUT_D4: quantize_mmq_q8_1 - <<>>(x, vy, ne00, ne1, ne0); + <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); break; case MMQ_Q8_1_DS_LAYOUT_DS4: quantize_mmq_q8_1 - <<>>(x, vy, ne00, ne1, ne0); + <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); break; case MMQ_Q8_1_DS_LAYOUT_D2S6: quantize_mmq_q8_1 - <<>>(x, vy, ne00, ne1, ne0); + <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); break; default: GGML_ABORT("fatal error"); break; } - GGML_UNUSED(s01); - GGML_UNUSED(s02); - GGML_UNUSED(s03); } diff --git a/ggml/src/ggml-cuda/quantize.cuh b/ggml/src/ggml-cuda/quantize.cuh index b627c4e400..725ab52443 100644 --- a/ggml/src/ggml-cuda/quantize.cuh +++ b/ggml/src/ggml-cuda/quantize.cuh @@ -12,13 +12,16 @@ static_assert(MATRIX_ROW_PADDING % CUDA_QUANTIZE_BLOCK_SIZE == 0, "Risk static_assert(MATRIX_ROW_PADDING % (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ) == 0, "Risk of out-of-bounds access."); typedef void (*quantize_cuda_t)( - const float * x, void * vy, const ggml_type type_src0, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, - const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream); + const float * x, const int32_t * ids, void * vy, + ggml_type type_src0, int64_t ne00, int64_t s01, int64_t s02, int64_t s03, + int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, cudaStream_t stream); void quantize_row_q8_1_cuda( - const float * x, void * vy, const ggml_type type_src0, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, - const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream); + const float * x, const int32_t * ids, void * vy, + ggml_type type_src0, int64_t ne00, int64_t s01, int64_t s02, int64_t s03, + int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, cudaStream_t stream); void quantize_mmq_q8_1_cuda( - const float * x, void * vy, const ggml_type type_src0, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, - const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream); + const float * x, const int32_t * ids, void * vy, + ggml_type type_src0, int64_t ne00, int64_t s01, int64_t s02, int64_t s03, + int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, cudaStream_t stream); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index d70acb7719..9591b1a89e 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -4184,6 +4184,11 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 2, 1, 3})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 1, 3, 2})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 3, 2, 1})); + + // test cases with large ne00/ne10 to cover stream-k fixup + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 1024, {3, 2}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 1024, {3, 2}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 1024, {3, 2}, {1, 1})); } } for (ggml_type type_a : other_types) { From 9998540149a490200894acfe595e9a1546bab723 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 24 Apr 2025 18:59:06 +0300 Subject: [PATCH 058/126] cuda : fix unused variable compile warning (whisper/0) ggml-ci --- ggml/src/ggml-cuda/cpy.cu | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu index 2d46176eab..d027271fcd 100644 --- a/ggml/src/ggml-cuda/cpy.cu +++ b/ggml/src/ggml-cuda/cpy.cu @@ -592,6 +592,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg dest_ptrs_d = ctx.cuda_graph->dest_ptrs_d; graph_cpynode_index = ctx.cuda_graph->graph_cpynode_index; } +#else + GGML_UNUSED(disable_indirection_for_this_node); #endif if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1)); From 4254bb49518e0f920f14c9aadd96eefdfd38b429 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Wed, 30 Apr 2025 15:20:40 +0200 Subject: [PATCH 059/126] ggml : fix ggml_gallocr_ptr type (ggml/1205) --- ggml/include/ggml-cpp.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/include/ggml-cpp.h b/ggml/include/ggml-cpp.h index a12342c25d..48aa79682b 100644 --- a/ggml/include/ggml-cpp.h +++ b/ggml/include/ggml-cpp.h @@ -24,7 +24,7 @@ typedef std::unique_ptr gguf_context_ptr; struct ggml_gallocr_deleter { void operator()(ggml_gallocr_t galloc) { ggml_gallocr_free(galloc); } }; -typedef std::unique_ptr ggml_gallocr_ptr; +typedef std::unique_ptr ggml_gallocr_ptr; // ggml-backend From 8d33d740c308fe0049515668aac0e561269afe9e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 1 May 2025 09:59:02 +0300 Subject: [PATCH 060/126] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 41feffca92..5b798aeb49 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -13bcf9ce50651a8b4238ec6d136f46f2c1b23b6f +7734363ac74a14d7f2c8033c552d9d655c5dfc00 From a70183eb0079fdf6ebeaed12966338a039461b5d Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Thu, 1 May 2025 03:09:41 -0400 Subject: [PATCH 061/126] llama-model : fix the reported size class for nomic-embed-text-v2-moe (#13223) --- src/llama-model.cpp | 7 ++++++- src/llama-model.h | 1 + 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 822e2bb2cf..51092a128c 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -40,6 +40,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_335M: return "335M"; case LLM_TYPE_410M: return "410M"; case LLM_TYPE_450M: return "450M"; + case LLM_TYPE_475M: return "475M"; case LLM_TYPE_770M: return "770M"; case LLM_TYPE_780M: return "780M"; case LLM_TYPE_0_5B: return "0.5B"; @@ -707,7 +708,11 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS, hparams.moe_every_n_layers, 0); if (hparams.n_layer == 12 && hparams.n_embd == 768) { - type = LLM_TYPE_137M; + if (arch == LLM_ARCH_NOMIC_BERT) { + type = LLM_TYPE_137M; + } else if (arch == LLM_ARCH_NOMIC_BERT_MOE && hparams.moe_every_n_layers == 2) { + type = LLM_TYPE_475M; + } } } break; case LLM_ARCH_BLOOM: diff --git a/src/llama-model.h b/src/llama-model.h index 95eca00266..34aac337cf 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -36,6 +36,7 @@ enum llm_type { LLM_TYPE_335M, LLM_TYPE_410M, LLM_TYPE_450M, + LLM_TYPE_475M, LLM_TYPE_770M, LLM_TYPE_780M, LLM_TYPE_0_5B, From 13c9a3319b65469e883c49dd1c478abedc410157 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Thu, 1 May 2025 10:23:25 +0200 Subject: [PATCH 062/126] arg : remove CURLINFO_EFFECTIVE_METHOD (#13228) --- common/arg.cpp | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 9f7241673d..e35417de7e 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -217,13 +217,11 @@ struct curl_slist_ptr { #define CURL_MAX_RETRY 3 #define CURL_RETRY_DELAY_SECONDS 2 -static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) { +static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds, const char * method_name) { int remaining_attempts = max_attempts; - char * method = nullptr; - curl_easy_getinfo(curl, CURLINFO_EFFECTIVE_METHOD, &method); while (remaining_attempts > 0) { - LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts); + LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method_name, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts); CURLcode res = curl_easy_perform(curl); if (res == CURLE_OK) { @@ -343,7 +341,7 @@ static bool common_download_file_single(const std::string & url, const std::stri // we only allow retrying once for HEAD requests // this is for the use case of using running offline (no internet), retrying can be annoying - bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0); + bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0, "HEAD"); if (!was_perform_successful) { head_request_ok = false; } @@ -425,7 +423,7 @@ static bool common_download_file_single(const std::string & url, const std::stri // start the download LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__, llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str()); - bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS); + bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS, "GET"); if (!was_perform_successful) { return false; } From 8936784f7a1ec4f91637d04b77fdc90ec36ebac9 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Thu, 1 May 2025 17:05:42 +0200 Subject: [PATCH 063/126] mtmd : add **vision** support for Mistral Small 3.1 (#13231) * convert ok * load ok, missing patch merger * ah sheet it works * update llava/readme * add test * fix test --- convert_hf_to_gguf.py | 30 +++++++++++++-- examples/llava/README.md | 4 ++ examples/llava/clip-impl.h | 3 ++ examples/llava/clip.cpp | 70 ++++++++++++++++++++++++++++------ examples/llava/mtmd-cli.cpp | 1 + examples/llava/tests.sh | 1 + gguf-py/gguf/constants.py | 7 ++++ gguf-py/gguf/gguf_writer.py | 3 ++ gguf-py/gguf/tensor_mapping.py | 8 ++++ 9 files changed, 112 insertions(+), 15 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 123df801bf..04ca646b50 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -1899,7 +1899,10 @@ class LlamaModel(TextModel): raise ValueError(f"Unprocessed experts: {experts}") -@ModelBase.register("LlavaForConditionalGeneration") +@ModelBase.register( + "LlavaForConditionalGeneration", # pixtral + "Mistral3ForConditionalGeneration", # mistral small 3.1 +) class LlavaVisionModel(VisionModel): img_break_tok_id = -1 @@ -1908,17 +1911,38 @@ class LlavaVisionModel(VisionModel): if self.hparams["model_type"] == "pixtral": # layer_norm_eps is not in config.json, it is hard-coded in modeling_pixtral.py self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5) - self.img_break_tok_id = 12 # see tokenizer_config.json + self.img_break_tok_id = self.get_token_id("[IMG_BREAK]") + logger.info(f"Image break token id: {self.img_break_tok_id}") else: raise ValueError(f"Unsupported model type: {self.hparams['model_type']}") + def get_token_id(self, token: str) -> int: + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + added_tokens_decoder = json.load(f)['added_tokens_decoder'] + for id_, token_data in added_tokens_decoder.items(): + if token_data["content"] == token: + return int(id_) + raise ValueError(f"Token '{token}' not found in tokenizer config.") + def set_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams if hparams["model_type"] == "pixtral": self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.PIXTRAL) self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"]) - self.gguf_writer.add_vision_use_silu(True) + + # hidden_act + if hparams["hidden_act"] == "silu": + self.gguf_writer.add_vision_use_silu(True) + elif hparams["hidden_act"] == "gelu": + self.gguf_writer.add_vision_use_gelu(True) + else: + raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}") + + # spatial_merge_size + if "spatial_merge_size" in self.global_config: + self.gguf_writer.add_vision_spatial_merge_size(self.global_config["spatial_merge_size"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused diff --git a/examples/llava/README.md b/examples/llava/README.md index f58d9de710..3b62627ce8 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -34,6 +34,9 @@ llama-mtmd-cli -hf ggml-org/SmolVLM2-500M-Video-Instruct-GGUF # Pixtral 12B llama-mtmd-cli -hf ggml-org/pixtral-12b-GGUF + +# Mistral Small 3.1 24B (IQ2_M quantization) +llama-mtmd-cli -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF --chat-template mistral-v7 ``` ## How it works and what is `mmproj`? @@ -73,3 +76,4 @@ For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` fla - SmolVLM (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB)) - SmolVLM2 (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB)) - [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint +- [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) diff --git a/examples/llava/clip-impl.h b/examples/llava/clip-impl.h index 66cb21ef1a..b575ca4d7c 100644 --- a/examples/llava/clip-impl.h +++ b/examples/llava/clip-impl.h @@ -31,6 +31,7 @@ #define KEY_FEATURE_LAYER "clip.vision.feature_layer" #define KEY_PROJ_SCALE_FACTOR "clip.vision.projector.scale_factor" #define KEY_PROJ_TYPE "clip.projector_type" +#define KEY_SPATIAL_MERGE_SIZE "clip.vision.spatial_merge_size" #define KEY_USE_GLU_MLP "clip.use_glu_mlp" // for qwen2.5vl #define KEY_USE_RMS_NORM "clip.use_rms_norm" // for qwen2.5vl @@ -68,9 +69,11 @@ #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s" #define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s" #define TN_IMAGE_NEWLINE "model.image_newline" +#define TN_MM_INP_NORM "mm.input_norm.weight" #define TN_MM_INP_PROJ "mm.input_projection.weight" // gemma3 #define TN_MM_SOFT_EMB_N "mm.soft_emb_norm.weight" // gemma3 #define TN_MM_PROJECTOR "mm.model.fc.weight" // idefics3 +#define TN_MM_PATCH_MERGER "mm.patch_merger.weight" // mistral small 3.1 #define TN_TOK_IMG_BREAK "v.token_embd.img_break" // pixtral // mimicpmv diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index ad3e7df1d8..984e300e75 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -172,6 +172,7 @@ struct clip_hparams { std::unordered_set vision_feature_layer; int32_t attn_window_size = 0; int32_t n_wa_pattern = 0; + int32_t spatial_merge_size = 0; }; struct clip_layer { @@ -232,6 +233,7 @@ struct clip_vision_model { struct ggml_tensor * projection; // LLaVA projection + struct ggml_tensor * mm_input_norm_w = nullptr; struct ggml_tensor * mm_0_w = nullptr; struct ggml_tensor * mm_0_b = nullptr; struct ggml_tensor * mm_2_w = nullptr; @@ -311,6 +313,7 @@ struct clip_vision_model { // pixtral struct ggml_tensor * token_embd_img_break = nullptr; + struct ggml_tensor * mm_patch_merger_w = nullptr; }; struct clip_ctx { @@ -637,6 +640,7 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i const int d_head = hidden_size / n_head; const int n_layer = hparams.n_layer; const float eps = hparams.eps; + const int n_merge = hparams.spatial_merge_size; struct ggml_init_params params = { /*.mem_size =*/ ctx->buf_compute_meta.size(), @@ -721,7 +725,13 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i { ggml_tensor * gate_proj = ggml_mul_mat(ctx0, model.layers[il].ff_gate_w, cur); ggml_tensor * up_proj = ggml_mul_mat(ctx0, model.layers[il].ff_up_w, cur); - gate_proj = ggml_silu(ctx0, gate_proj); // pixtral uses silu + if (ctx->use_silu) { + gate_proj = ggml_silu(ctx0, gate_proj); + } else if (ctx->use_gelu) { + gate_proj = ggml_gelu(ctx0, gate_proj); + } else { + GGML_ABORT("Pixtral: Unsupported activation"); + } cur = ggml_mul(ctx0, up_proj, gate_proj); cur = ggml_mul_mat(ctx0, model.layers[il].ff_down_w, cur); } @@ -732,14 +742,42 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i embeddings = cur; } - // LlavaMultiModalProjector (with GELU activation) + // mistral small 3.1 patch merger + // ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67 + if (model.mm_patch_merger_w) { + GGML_ASSERT(hparams.spatial_merge_size > 0); + + ggml_tensor * cur = embeddings; + cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w); + + // reshape image tokens to 2D grid + cur = ggml_reshape_3d(ctx0, cur, hidden_size, n_patches_x, n_patches_y); + cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, hidden_size] + cur = ggml_cont(ctx0, cur); + + // torch.nn.functional.unfold is just an im2col under the hood + // we just need a dummy kernel to make it work + ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0); + cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type); + + // project to hidden_size + cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); + cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur); + embeddings = cur; + } + + // LlavaMultiModalProjector (always using GELU activation) { embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); + if (model.mm_1_b) { + embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); + } embeddings = ggml_gelu(ctx0, embeddings); embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); + if (model.mm_2_b) { + embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); + } } // arrangement of the [IMG_BREAK] token @@ -749,11 +787,14 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension // after the concatenation, we have a tensor with shape [hidden_size, n_patches_per_row + 1, n_rows] + const int p_y = n_merge > 0 ? n_patches_y / n_merge : n_patches_y; + const int p_x = n_merge > 0 ? n_patches_x / n_merge : n_patches_x; + const int p_total = p_x * p_y; const int n_embd_text = embeddings->ne[0]; - const int n_tokens_output = num_patches + n_patches_y - 1; // one [IMG_BREAK] per row, except the last row + const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row - ggml_tensor * cur = ggml_reshape_3d(ctx0, embeddings, n_embd_text, n_patches_x, n_patches_y); - ggml_tensor * tok = ggml_new_tensor_3d(ctx0, embeddings->type, n_embd_text, 1, n_patches_y); + ggml_tensor * cur = ggml_reshape_3d(ctx0, embeddings, n_embd_text, p_x, p_y); + ggml_tensor * tok = ggml_new_tensor_3d(ctx0, embeddings->type, n_embd_text, 1, p_y); tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor tok = ggml_add(ctx0, tok, model.token_embd_img_break); cur = ggml_concat(ctx0, cur, tok, 1); @@ -1734,6 +1775,7 @@ struct clip_model_loader { case PROJECTOR_TYPE_PIXTRAL: { hparams.rope_theta = 10000.0f; + get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.spatial_merge_size, false); } break; case PROJECTOR_TYPE_QWEN25VL: { @@ -1957,11 +1999,14 @@ struct clip_model_loader { case PROJECTOR_TYPE_PIXTRAL: { vision_model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight")); - vision_model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias")); + vision_model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"), false); vision_model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); - vision_model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias")); + vision_model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false); // [IMG_BREAK] token embedding vision_model.token_embd_img_break = get_tensor(TN_TOK_IMG_BREAK); + // for mistral small 3.1 + vision_model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM, false); + vision_model.mm_patch_merger_w = get_tensor(TN_MM_PATCH_MERGER, false); } break; default: GGML_ASSERT(false && "unknown projector type"); @@ -2926,8 +2971,9 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im } else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) { n_patches /= ctx->vision_model.hparams.proj_scale_factor; } else if (ctx->proj_type == PROJECTOR_TYPE_PIXTRAL) { - int n_patches_x = img->nx / params.patch_size; - int n_patches_y = img->ny / params.patch_size; + int n_merge = ctx->vision_model.hparams.spatial_merge_size; + int n_patches_x = img->nx / params.patch_size / (n_merge > 0 ? n_merge : 1); + int n_patches_y = img->ny / params.patch_size / (n_merge > 0 ? n_merge : 1); n_patches = n_patches_y*n_patches_x + n_patches_y - 1; // + one [IMG_BREAK] per row, except the last row } @@ -3484,7 +3530,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->vision_model.mm_model_peg_0_b->ne[0]; case PROJECTOR_TYPE_MLP: case PROJECTOR_TYPE_PIXTRAL: - return ctx->vision_model.mm_2_b->ne[0]; + return ctx->vision_model.mm_2_w->ne[1]; case PROJECTOR_TYPE_MLP_NORM: return ctx->vision_model.mm_3_b->ne[0]; case PROJECTOR_TYPE_MINICPMV: diff --git a/examples/llava/mtmd-cli.cpp b/examples/llava/mtmd-cli.cpp index 4d857ca64e..aa52d92cab 100644 --- a/examples/llava/mtmd-cli.cpp +++ b/examples/llava/mtmd-cli.cpp @@ -94,6 +94,7 @@ struct mtmd_cli_context { LOG_ERR("Model does not have chat template.\n"); LOG_ERR(" For old llava models, you may need to use '--chat-template vicuna'\n"); LOG_ERR(" For MobileVLM models, use '--chat-template deepseek'\n"); + LOG_ERR(" For Mistral Small 3.1, use '--chat-template mistral-v7'\n"); exit(1); } diff --git a/examples/llava/tests.sh b/examples/llava/tests.sh index 75604315cf..4af3700640 100755 --- a/examples/llava/tests.sh +++ b/examples/llava/tests.sh @@ -59,6 +59,7 @@ add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" # to test the big models, run: ./tests.sh big add_test_big "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M" +add_test_big "llama-mtmd-cli" "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7" # these models always give the wrong answer, not sure why # add_test "llama-mtmd-cli" "ggml-org/SmolVLM-Instruct-GGUF:Q4_K_M" diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 326ccdb071..a2540bd93f 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -231,6 +231,7 @@ class Keys: BLOCK_COUNT = "clip.vision.block_count" IMAGE_MEAN = "clip.vision.image_mean" IMAGE_STD = "clip.vision.image_std" + SPATIAL_MERGE_SIZE = "clip.vision.spatial_merge_size" USE_GELU = "clip.use_gelu" USE_SILU = "clip.use_silu" @@ -491,6 +492,7 @@ class MODEL_TENSOR(IntEnum): V_ENC_FFN_DOWN = auto() V_PRE_NORM = auto() V_POST_NORM = auto() + V_MM_INP_NORM = auto() V_MM_INP_PROJ = auto() # gemma3 V_MM_SOFT_EMB_NORM = auto() # gemma3 V_RESMPL_POS_EMBD_K = auto() # minicpmv @@ -505,6 +507,7 @@ class MODEL_TENSOR(IntEnum): V_RESMPL_PROJ = auto() # minicpmv V_RESMPL_QUERY = auto() # minicpmv V_TOK_EMBD_IMG_BREAK = auto() # pixtral + V_MM_PATCH_MERGER = auto() # mistral small 3.1 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -747,6 +750,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.V_PRE_NORM: "v.pre_ln", MODEL_TENSOR.V_POST_NORM: "v.post_ln", MODEL_TENSOR.V_MM_INP_PROJ: "mm.input_projection", + MODEL_TENSOR.V_MM_INP_NORM: "mm.input_norm", MODEL_TENSOR.V_MM_SOFT_EMB_NORM: "mm.soft_emb_norm", MODEL_TENSOR.V_RESMPL_POS_EMBD_K: "resampler.pos_embd_k", MODEL_TENSOR.V_RESMPL_ATTN_Q: "resampler.attn.q", @@ -760,6 +764,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.V_RESMPL_PROJ: "resampler.proj", MODEL_TENSOR.V_RESMPL_QUERY: "resampler.query", MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK: "v.token_embd.img_break", # pixtral + MODEL_TENSOR.V_MM_PATCH_MERGER: "mm.patch_merger", # mistral small 3.1 } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -783,6 +788,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.V_PRE_NORM, MODEL_TENSOR.V_POST_NORM, MODEL_TENSOR.V_MM_INP_PROJ, + MODEL_TENSOR.V_MM_INP_NORM, MODEL_TENSOR.V_MM_SOFT_EMB_NORM, MODEL_TENSOR.V_RESMPL_POS_EMBD_K, MODEL_TENSOR.V_RESMPL_ATTN_Q, @@ -796,6 +802,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.V_RESMPL_PROJ, MODEL_TENSOR.V_RESMPL_QUERY, MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK, + MODEL_TENSOR.V_MM_PATCH_MERGER, ], MODEL_ARCH.LLAMA: [ MODEL_TENSOR.TOKEN_EMBD, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index f22a6d4a34..a30c49e32b 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -972,6 +972,9 @@ class GGUFWriter: def add_vision_image_std(self, values: Sequence[float]) -> None: self.add_array(Keys.ClipVision.IMAGE_STD, values) + def add_vision_spatial_merge_size(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.SPATIAL_MERGE_SIZE, value) + def add_vision_use_gelu(self, value: bool) -> None: self.add_bool(Keys.ClipVision.USE_GELU, value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 311d1ff69c..2f6326104f 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -1001,6 +1001,10 @@ class TensorNameMap: "multi_modal_projector.mm_input_projection", ), + MODEL_TENSOR.V_MM_INP_NORM: ( + "multi_modal_projector.norm", + ), + MODEL_TENSOR.V_MM_SOFT_EMB_NORM: ( "multi_modal_projector.mm_soft_emb_norm", ), @@ -1052,6 +1056,10 @@ class TensorNameMap: MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK: ( "v.token_embd.img_break", # for pixtral, this is a generated vector ), + + MODEL_TENSOR.V_MM_PATCH_MERGER: ( + "multi_modal_projector.patch_merger.merging_layer", # mistral small 3.1 + ), } # architecture-specific block mappings From b5769d92b4510c77691ad9e3f8b643c2ba202e53 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 29 Apr 2025 15:47:55 +0200 Subject: [PATCH 064/126] ggml : suppress Windows compiler warnings (whisper/3075) * whisper: suppress Windows compiler warnings This commit disables compiler warnings on window using MSVC. The motivation for these changes is that some compilers generate warnings for these conversion, for example Windows MSVC, and there are quite a few of them. This makes it a little difficult to spot new warnings that may be introduced and also can be difficult for users/embedders of ggml where these warnings are hard to separate from their own warnings. * squash! whisper: suppress Windows compiler warnings Move ggml related warnings into ggml. This commit also fixes the indentation and adds a missing whitespace to the if statement. --- ggml/CMakeLists.txt | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 61fe15a15f..e632af010c 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -360,3 +360,18 @@ write_basic_package_version_file( install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml-config.cmake ${CMAKE_CURRENT_BINARY_DIR}/ggml-version.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/ggml) + +if (MSVC) + set(MSVC_WARNING_FLAGS + /wd4005 # Macro redefinition + /wd4244 # Conversion from one type to another type, possible loss of data + /wd4267 # Conversion from 'size_t' to a smaller type, possible loss of data + ) + function(disable_msvc_warnings target_name) + target_compile_options(${target_name} PRIVATE ${MSVC_WARNING_FLAGS}) + endfunction() + + disable_msvc_warnings(ggml-base) + disable_msvc_warnings(ggml) + disable_msvc_warnings(ggml-cpu) +endif() From 99881f77d82efda80b21057d84f1cc2df2f1e0f6 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 1 May 2025 10:05:24 +0200 Subject: [PATCH 065/126] whisper : add check that target name exists (whisper/3103) This commit adds a check to makes sure that the target exists before trying to add compile options to ignore warnings when using MSVC. The motivation for this is currently the build is broken depending on the cmake options provided. With this fix it should be possible to build even if the targets are not actually available. Refs: https://github.com/ggml-org/whisper.cpp/pull/3090#issuecomment-2842760104 --- ggml/CMakeLists.txt | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index e632af010c..b463cbd9b3 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -368,10 +368,19 @@ if (MSVC) /wd4267 # Conversion from 'size_t' to a smaller type, possible loss of data ) function(disable_msvc_warnings target_name) - target_compile_options(${target_name} PRIVATE ${MSVC_WARNING_FLAGS}) + if(TARGET ${target_name}) + target_compile_options(${target_name} PRIVATE ${MSVC_WARNING_FLAGS}) + endif() endfunction() disable_msvc_warnings(ggml-base) disable_msvc_warnings(ggml) disable_msvc_warnings(ggml-cpu) + disable_msvc_warnings(ggml-cpu-x64) + disable_msvc_warnings(ggml-cpu-sse42) + disable_msvc_warnings(ggml-cpu-sandybridge) + disable_msvc_warnings(ggml-cpu-haswell) + disable_msvc_warnings(ggml-cpu-skylakex) + disable_msvc_warnings(ggml-cpu-icelake) + disable_msvc_warnings(ggml-cpu-alderlake) endif() From b1dd4d08e8fe40c9484422c0c5ec9bbd13b067a9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 1 May 2025 17:07:13 +0300 Subject: [PATCH 066/126] sync : ggml ggml-ci --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 5b798aeb49..433cfab7ff 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -7734363ac74a14d7f2c8033c552d9d655c5dfc00 +f3a375f20bf56860b30e7c511d03593a1e393345 From b0ecbd434be024c06bf547491be444ed92e1123e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 1 May 2025 20:18:56 +0200 Subject: [PATCH 067/126] test: non-cont. b in test-backend-ops -o MUL_MAT (#13187) --- tests/test-backend-ops.cpp | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 9591b1a89e..767498ef39 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1981,7 +1981,7 @@ struct test_mul_mat : public test_case { const std::array bs; // dims 3 and 4 const std::array nr; // repeat in dims 3 and 4 const std::array per; // permutation of dimensions - const bool v; // whether a is a non-contiguous view + const bool v; // whether a and b are non-contiguous views std::string vars() override { return VARS_TO_STR9(type_a, type_b, m, n, k, bs, nr, per, v); @@ -2042,12 +2042,15 @@ struct test_mul_mat : public test_case { } else { if (v) { - a = ggml_new_tensor_4d(ctx, type_a, k*2, m, bs[0], bs[1]); - a = ggml_view_4d(ctx, a, k, m, bs[0], bs[1], a->nb[1], a->nb[2], a->nb[3], 0); + a = ggml_new_tensor_4d(ctx, type_a, k*2, m, bs[0], bs[1]); + b = ggml_new_tensor_4d(ctx, type_b, k*2, n, bs[0]*nr[0], bs[1]*nr[1]); + + a = ggml_view_4d(ctx, a, k, m, bs[0], bs[1], a->nb[1], a->nb[2], a->nb[3], 0); + b = ggml_view_4d(ctx, b, k, n, bs[0]*nr[0], bs[1]*nr[1], b->nb[1], b->nb[2], b->nb[3], 0); } else { a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]); + b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]); } - b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]); if (!ggml_is_quantized(type_a)) { if (bs[1] == 1 && nr[1] == 1) { ggml_set_param(ctx, a); From fc727bcdd5a311c7c69a76dbf87f4784e828c7b4 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Thu, 1 May 2025 13:19:31 -0500 Subject: [PATCH 068/126] vulkan: Handle src1 batch dimension in non-contiguous mat-vec-mul shader (#13191) * vulkan: Handle src1 batch dimension in non-contiguous mat-vec-mul shader --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 7 +++++-- ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp | 9 ++++++--- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index c0bdb9e17a..4614c3c156 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -2399,7 +2399,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true); } } - ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 9 * sizeof(uint32_t), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); @@ -4949,6 +4949,8 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con const uint64_t nb01 = src0->nb[1]; const uint64_t nb02 = src0->nb[2]; + const uint64_t nb12 = src1->nb[2]; + // const uint64_t ne10 = src1->ne[0]; const uint64_t ne11 = src1->ne[1]; const uint64_t ne12 = src1->ne[2]; @@ -4974,6 +4976,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t); const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t); + const uint32_t channel_stride_y = nb12 / sizeof(float); const uint64_t qx_sz = ggml_nbytes(src0); const uint64_t qy_sz = ggml_nbytes(src1); @@ -5004,7 +5007,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; // compute - const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; + const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, channel_stride_y, (uint32_t)(ne12 / ne02), (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp index 48376637fb..bc633369f9 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp @@ -21,7 +21,9 @@ layout (push_constant) uniform parameter uint nrows_x; uint row_stride_x; uint channel_stride_x; + uint channel_stride_y; uint channel_x_divisor; + uint ne12; uint b_offset; uint d_offset; } p; @@ -33,6 +35,7 @@ void main() { const uint row_x = gl_GlobalInvocationID.y; const uint channel = gl_GlobalInvocationID.z; const uint channel_x = channel / p.channel_x_divisor; + const uint channel_y = channel % p.ne12; const uint nrows_y = p.ncols_x; const uint nrows_dst = p.nrows_x; @@ -56,7 +59,7 @@ void main() { const uint row_y = col_x; const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x; - const uint iy = channel*nrows_y + row_y; + const uint iy = channel_y*p.channel_stride_y + row_y; const vec4 av4 = vec4(data_a_v4[ix / 4]); const vec4 bv4 = vec4(data_b_v4[iy / 4]); @@ -72,7 +75,7 @@ void main() { const uint row_y = col_x; const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x; - const uint iy = channel*nrows_y + row_y; + const uint iy = channel_y*p.channel_stride_y + row_y; const vec4 av4 = vec4(data_a_v4[ix / 4]); const vec4 bv4 = vec4(data_b_v4[iy / 4]); @@ -89,7 +92,7 @@ void main() { const uint row_y = col_x; const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x; - const uint iy = channel*nrows_y + row_y; + const uint iy = channel_y*p.channel_stride_y + row_y; const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); From 79f26e9e125b21760aeb016f34bfd42a93f48351 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Thu, 1 May 2025 13:49:39 -0500 Subject: [PATCH 069/126] vulkan: Add bfloat16 support (#12554) * vulkan: Add bfloat16 support This adds bfloat16 matrix multiply support based on VK_KHR_shader_bfloat16. The extension is required for coopmat multiply support, but matrix-vector multiply trivially promotes bf16 to fp32 and doesn't require the extension. The copy/get_rows shaders also don't require the extension. It's probably possible to fall back to non-coopmat and promote to fp32 when the extension isn't supported, but this change doesn't do that. The coopmat support also requires a glslc that supports the extension, which currently requires a custom build. * vulkan: Support bf16 tensors without the bf16 extension or coopmat support Compile a variant of the scalar mul_mm shader that will promote the bf16 values to float, and use that when either the bf16 extension or the coopmat extensions aren't available. * vulkan: bfloat16 fixes (really works without bfloat16 support now) * vulkan: fix spirv-val failure and reenable -O --- ggml/src/ggml-vulkan/CMakeLists.txt | 17 ++ ggml/src/ggml-vulkan/ggml-vulkan.cpp | 196 ++++++++++++++++-- .../ggml-vulkan/vulkan-shaders/CMakeLists.txt | 3 + .../vulkan-shaders/contig_copy.comp | 11 +- ggml/src/ggml-vulkan/vulkan-shaders/copy.comp | 5 +- .../vulkan-shaders/dequant_funcs.comp | 8 +- .../ggml-vulkan/vulkan-shaders/get_rows.comp | 11 +- .../vulkan-shaders/mul_mat_vec.comp | 2 +- .../ggml-vulkan/vulkan-shaders/mul_mm.comp | 39 +++- .../vulkan-shaders/mul_mm_cm2.comp | 37 ++-- .../vulkan-shaders/test_bfloat16_support.comp | 7 + .../src/ggml-vulkan/vulkan-shaders/types.comp | 27 +++ .../vulkan-shaders/vulkan-shaders-gen.cpp | 72 +++++-- 13 files changed, 368 insertions(+), 67 deletions(-) create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/test_bfloat16_support.comp diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt index 9d028f718d..31816219c0 100644 --- a/ggml/src/ggml-vulkan/CMakeLists.txt +++ b/ggml/src/ggml-vulkan/CMakeLists.txt @@ -71,6 +71,22 @@ if (Vulkan_FOUND) add_compile_definitions(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) endif() + # Compile a test shader to determine whether GL_EXT_bfloat16 is supported. + # If it's not, there will be an error to stderr. + # If it's supported, set a define to indicate that we should compile those shaders + execute_process(COMMAND ${Vulkan_GLSLC_EXECUTABLE} -o - -fshader-stage=compute --target-env=vulkan1.3 "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_bfloat16_support.comp" + OUTPUT_VARIABLE glslc_output + ERROR_VARIABLE glslc_error) + + if (${glslc_error} MATCHES ".*extension not supported: GL_EXT_bfloat16.*") + message(STATUS "GL_EXT_bfloat16 not supported by glslc") + set(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT OFF) + else() + message(STATUS "GL_EXT_bfloat16 supported by glslc") + set(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT ON) + add_compile_definitions(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + endif() + target_link_libraries(ggml-vulkan PRIVATE Vulkan::Vulkan) target_include_directories(ggml-vulkan PRIVATE ${CMAKE_CURRENT_BINARY_DIR}) @@ -142,6 +158,7 @@ if (Vulkan_FOUND) -DGGML_VULKAN_COOPMAT_GLSLC_SUPPORT=${GGML_VULKAN_COOPMAT_GLSLC_SUPPORT} -DGGML_VULKAN_COOPMAT2_GLSLC_SUPPORT=${GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT} -DGGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT=${GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT} + -DGGML_VULKAN_BFLOAT16_GLSLC_SUPPORT=${GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT} BUILD_COMMAND ${CMAKE_COMMAND} --build . INSTALL_COMMAND ${CMAKE_COMMAND} --install . INSTALL_DIR ${CMAKE_BINARY_DIR} diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 4614c3c156..eac0b422bc 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -51,6 +51,24 @@ #include "ggml-vulkan-shaders.hpp" +// remove this once it's more widely available in the SDK +#if !defined(VK_KHR_shader_bfloat16) + +#define VK_KHR_shader_bfloat16 1 +#define VK_KHR_SHADER_BFLOAT16_SPEC_VERSION 1 +#define VK_KHR_SHADER_BFLOAT16_EXTENSION_NAME "VK_KHR_shader_bfloat16" +#define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_BFLOAT16_FEATURES_KHR ((VkStructureType)1000141000) +#define VK_COMPONENT_TYPE_BFLOAT16_KHR ((VkComponentTypeKHR)1000141000) + +typedef struct VkPhysicalDeviceShaderBfloat16FeaturesKHR { + VkStructureType sType; + void* pNext; + VkBool32 shaderBFloat16Type; + VkBool32 shaderBFloat16DotProduct; + VkBool32 shaderBFloat16CooperativeMatrix; +} VkPhysicalDeviceShaderBfloat16FeaturesKHR; +#endif + #define ROUNDUP_POW2(M, N) (((M) + (N) - 1) & ~((N) - 1)) #define CEIL_DIV(M, N) (((M) + (N)-1) / (N)) static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; } @@ -266,8 +284,9 @@ struct vk_device_struct { bool subgroup_require_full_support; bool coopmat_support; - bool coopmat_acc_f32_support; - bool coopmat_acc_f16_support; + bool coopmat_acc_f32_support {}; + bool coopmat_acc_f16_support {}; + bool coopmat_bf16_support {}; uint32_t coopmat_m; uint32_t coopmat_n; uint32_t coopmat_k; @@ -293,6 +312,7 @@ struct vk_device_struct { vk_matmul_pipeline pipeline_matmul_f32 {}; vk_matmul_pipeline pipeline_matmul_f32_f16 {}; + vk_matmul_pipeline pipeline_matmul_bf16 {}; vk_matmul_pipeline2 pipeline_matmul_f16; vk_matmul_pipeline2 pipeline_matmul_f16_f32; @@ -301,6 +321,7 @@ struct vk_device_struct { vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_COUNT]; vk_matmul_pipeline pipeline_matmul_id_f32 {}; + vk_matmul_pipeline pipeline_matmul_id_bf16 {}; vk_matmul_pipeline2 pipeline_matmul_id_f16; vk_matmul_pipeline2 pipeline_matmul_id_f16_f32; @@ -333,8 +354,8 @@ struct vk_device_struct { vk_pipeline pipeline_clamp_f32; vk_pipeline pipeline_pad_f32; vk_pipeline pipeline_repeat_f32, pipeline_repeat_back_f32; - vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; - vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16; + vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16, pipeline_cpy_f32_bf16; + vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16, pipeline_contig_cpy_f32_bf16; vk_pipeline pipeline_cpy_f32_quant[GGML_TYPE_COUNT]; vk_pipeline pipeline_cpy_quant_f32[GGML_TYPE_COUNT]; vk_pipeline pipeline_norm_f32; @@ -1791,6 +1812,12 @@ static void ggml_vk_load_shaders(vk_device& device) { if (!device->pipeline_matmul_id_f32) { device->pipeline_matmul_id_f32 = std::make_shared(); } + if (!device->pipeline_matmul_bf16) { + device->pipeline_matmul_bf16 = std::make_shared(); + } + if (!device->pipeline_matmul_id_bf16) { + device->pipeline_matmul_id_bf16 = std::make_shared(); + } std::vector> compiles; auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, @@ -1900,6 +1927,11 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3) +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (device->coopmat_bf16_support) { + CREATE_MM(pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3) + } +#endif CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) @@ -1921,6 +1953,11 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 4) +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (device->coopmat_bf16_support) { + CREATE_MM(pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4) + } +#endif CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) @@ -1974,6 +2011,11 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (device->coopmat_bf16_support) { + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ) + } +#endif if (device->coopmat_acc_f16_support) { CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); @@ -2022,6 +2064,11 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id); CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id); CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id); +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (device->coopmat_bf16_support) { + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id); + } +#endif if (device->coopmat_acc_f16_support) { CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); @@ -2104,6 +2151,8 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); @@ -2139,6 +2188,8 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id); CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id); + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id); + CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); @@ -2191,6 +2242,8 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); @@ -2226,6 +2279,8 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16.f32acc, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id); CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16_f32.f32acc, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id); + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id); + CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); @@ -2246,8 +2301,26 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_MM(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc, matmul_id_iq3_s_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); CREATE_MM(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc, matmul_id_iq4_xs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); CREATE_MM(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc, matmul_id_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id); -#undef CREATE_MM } + // reusing CREATE_MM from the fp32 path + if ((device->coopmat2 || device->coopmat_support) +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + && !device->coopmat_bf16_support +#endif + ) { + // use scalar tile sizes + l_warptile = { 128, 128, 128, 16, subgroup_size_8 * 2, 64, 2, 4, 4, 1, subgroup_size_8 }; + m_warptile = { 128, 64, 64, 16, subgroup_size_8, 32, 2, 4, 2, 1, subgroup_size_8 }; + s_warptile = { subgroup_size_16, 32, 32, 16, 32, 32, 2, 2, 2, 1, subgroup_size_8 }; + + l_wg_denoms = {128, 128, 1 }; + m_wg_denoms = { 64, 64, 1 }; + s_wg_denoms = { 32, 32, 1 }; + + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id); + } +#undef CREATE_MM // mul mat vec @@ -2266,6 +2339,7 @@ static void ggml_vk_load_shaders(vk_device& device) { for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32_"+std::to_string(i+1), mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32_"+std::to_string(i+1), mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32_"+std::to_string(i+1), mul_mat_vec_bf16_f32_f32_len, mul_mat_vec_bf16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); @@ -2288,6 +2362,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(i+1), mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32_"+std::to_string(i+1), mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32_"+std::to_string(i+1), mul_mat_vec_bf16_f16_f32_len, mul_mat_vec_bf16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); @@ -2311,6 +2386,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32", mul_mat_vec_id_bf16_f32_len, mul_mat_vec_id_bf16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); @@ -2356,6 +2432,7 @@ static void ggml_vk_load_shaders(vk_device& device) { // get_rows ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32", get_rows_f32_len, get_rows_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_BF16], "get_rows_bf16", get_rows_bf16_len, get_rows_bf16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); @@ -2373,6 +2450,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32", get_rows_f32_f32_len, get_rows_f32_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_BF16], "get_rows_bf16_f32", get_rows_bf16_f32_len, get_rows_bf16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); @@ -2410,10 +2488,13 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_bf16,"cpy_f32_bf16",cpy_f32_bf16_len,cpy_f32_bf16_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f32, "contig_cpy_f32_f32", contig_cpy_f32_f32_len, contig_cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f16, "contig_cpy_f32_f16", contig_cpy_f32_f16_len, contig_cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f16, "contig_cpy_f16_f16", contig_cpy_f16_f16_len, contig_cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_bf16,"contig_cpy_f32_bf16",contig_cpy_f32_bf16_len,contig_cpy_f32_bf16_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + if (device->float_controls_rte_fp16) { ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_0], "cpy_f32_q4_0", cpy_f32_q4_0_rte_len, cpy_f32_q4_0_rte_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_0), 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_1], "cpy_f32_q4_1", cpy_f32_q4_1_rte_len, cpy_f32_q4_1_rte_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_1), 1, 1}, {}, 1); @@ -2578,6 +2659,7 @@ static vk_device ggml_vk_get_device(size_t idx) { bool coopmat2_support = false; device->coopmat_support = false; device->integer_dot_product = false; + bool bfloat16_support = false; for (const auto& properties : ext_props) { if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { @@ -2608,6 +2690,9 @@ static vk_device ggml_vk_get_device(size_t idx) { !getenv("GGML_VK_DISABLE_INTEGER_DOT_PRODUCT")) { device->integer_dot_product = true; #endif + } else if (strcmp("VK_KHR_shader_bfloat16", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_BFLOAT16")) { + bfloat16_support = true; } } @@ -2794,6 +2879,17 @@ static vk_device ggml_vk_get_device(size_t idx) { } #endif +#if defined(VK_KHR_shader_bfloat16) + VkPhysicalDeviceShaderBfloat16FeaturesKHR bfloat16_features {}; + bfloat16_features.pNext = nullptr; + bfloat16_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_BFLOAT16_FEATURES_KHR; + if (bfloat16_support) { + last_struct->pNext = (VkBaseOutStructure *)&bfloat16_features; + last_struct = (VkBaseOutStructure *)&bfloat16_features; + device_extensions.push_back("VK_KHR_shader_bfloat16"); + } +#endif + VkPhysicalDeviceMaintenance4Features maint4_features {}; maint4_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_MAINTENANCE_4_FEATURES; if (maintenance4_support) { @@ -2991,6 +3087,25 @@ static vk_device ggml_vk_get_device(size_t idx) { device->coopmat_int_n = prop.NSize; device->coopmat_int_k = prop.KSize; } +#if defined(VK_KHR_shader_bfloat16) && defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (prop.AType == VK_COMPONENT_TYPE_BFLOAT16_KHR && + prop.BType == VK_COMPONENT_TYPE_BFLOAT16_KHR && + prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR && + (vk::ScopeKHR)prop.scope == vk::ScopeKHR::eSubgroup + ) { + // coopmat sizes not set yet + if (device->coopmat_m == 0) { + device->coopmat_bf16_support = true; + device->coopmat_m = prop.MSize; + device->coopmat_n = prop.NSize; + device->coopmat_k = prop.KSize; + } else if (device->coopmat_m == prop.MSize && device->coopmat_n == prop.NSize && device->coopmat_k == prop.KSize) { + // Only enable if shape is identical + device->coopmat_bf16_support = true; + } + } +#endif } if (device->coopmat_m == 0 || !device->coopmat_acc_f32_support) { @@ -2998,11 +3113,19 @@ static vk_device ggml_vk_get_device(size_t idx) { GGML_LOG_DEBUG("ggml_vulkan: WARNING: No suitable matrix core mode found. Disabling matrix cores.\n"); device->coopmat_support = false; } + if (getenv("GGML_VK_DISABLE_BFLOAT16")) { + device->coopmat_bf16_support = false; + } } if (device->coopmat_support) { device_extensions.push_back("VK_KHR_cooperative_matrix"); } +#if defined(VK_KHR_shader_bfloat16) + if (device->coopmat_bf16_support) { + device_extensions.push_back("VK_KHR_shader_bfloat16"); + } +#endif #endif device->name = GGML_VK_NAME + std::to_string(idx); @@ -3459,6 +3582,9 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { return ctx->device->pipeline_matmul_f32_f16; } + if (src0_type == GGML_TYPE_BF16 && src1_type == GGML_TYPE_BF16) { + return ctx->device->pipeline_matmul_bf16; + } if (prec == GGML_PREC_DEFAULT && ctx->device->fp16 && !(ctx->device->coopmat_support && !ctx->device->coopmat_acc_f16_support)) { if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_f16_f32.f16acc; @@ -3530,6 +3656,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * switch (a_type) { case GGML_TYPE_F32: case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -3562,6 +3689,9 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_id_f32; } + if (src0_type == GGML_TYPE_BF16 && src1_type == GGML_TYPE_BF16) { + return ctx->device->pipeline_matmul_id_bf16; + } if (prec == GGML_PREC_DEFAULT && ctx->device->fp16 && !(ctx->device->coopmat_support && !ctx->device->coopmat_acc_f16_support)) { if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_id_f16_f32.f16acc; @@ -3615,6 +3745,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context switch (a_type) { case GGML_TYPE_F32: case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -4350,6 +4481,13 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_cpy_f16_f16; } } + if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_BF16) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_bf16; + } else { + return ctx->device->pipeline_cpy_f32_bf16; + } + } if (src->type == GGML_TYPE_F32) { switch (to) { case GGML_TYPE_Q4_0: @@ -4477,8 +4615,12 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub const bool x_non_contig = (ctx->device->coopmat2 && src0->type == GGML_TYPE_F32) || !ggml_vk_dim01_contiguous(src0); const bool y_non_contig = (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) || + (src0->type == GGML_TYPE_BF16 && src1->type != GGML_TYPE_BF16) || !ggml_vk_dim01_contiguous(src1); + // If src0 is BF16, try to use a BF16 x BF16 multiply + ggml_type f16_type = src0->type == GGML_TYPE_BF16 ? GGML_TYPE_BF16 : GGML_TYPE_F16; + const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && (ne11 * ne10) % 4 == 0; @@ -4488,25 +4630,25 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub if (mmp == nullptr) { // Fall back to f16 dequant mul mat - mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type, (ggml_prec)dst->op_params[0]); + mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? f16_type : src1->type, (ggml_prec)dst->op_params[0]); quantize_y = false; } const bool qx_needs_dequant = mmp == nullptr || x_non_contig; - const bool qy_needs_dequant = !quantize_y && ((src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig); + const bool qy_needs_dequant = !quantize_y && ((src1->type != f16_type && !y_f32_kernel) || y_non_contig); if (qx_needs_dequant) { // Fall back to dequant + f16 mulmat - mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16, (ggml_prec)dst->op_params[0]); + mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, f16_type, y_f32_kernel ? GGML_TYPE_F32 : f16_type, (ggml_prec)dst->op_params[0]); } // Not implemented GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT - const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11, qx_needs_dequant ? GGML_TYPE_F16 : src0->type, quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type))); + const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11, qx_needs_dequant ? f16_type : src0->type, quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type))); const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && ne11 > 8; - vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? GGML_TYPE_F16 : src0->type, quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type)); + vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? f16_type : src0->type, quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type)); // Reserve extra storage in the N dimension for the Y matrix, so we can avoid bounds-checking uint32_t padded_n = qy_needs_dequant ? ROUNDUP_POW2(ne11, pipeline->wg_denoms[1]) : ne11; @@ -4527,12 +4669,12 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub vk_pipeline to_q8_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, GGML_TYPE_F16); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, f16_type); } else { to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, GGML_TYPE_F16); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, f16_type); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -5032,7 +5174,7 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, c // mul_mat_vec supports batching ne12*ne13 when ne11==1, or treating ne11 as the batch size (up to four) // when ne12 and ne13 are one. } else if ((dst->ne[1] == 1 || (dst->ne[1] <= mul_mat_vec_max_cols && src1->ne[2] * src1->ne[3] == 1)) && - (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16 || ggml_is_quantized(src0->type))) { ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst, dryrun); } else { ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst, dryrun); @@ -5100,27 +5242,31 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& const bool x_non_contig = (ctx->device->coopmat2 && src0->type == GGML_TYPE_F32) || !ggml_vk_dim01_contiguous(src0); const bool y_non_contig = (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) || + (src0->type == GGML_TYPE_BF16 && src1->type != GGML_TYPE_BF16) || !ggml_vk_dim01_contiguous(src1); + // If src0 is BF16, try to use a BF16 x BF16 multiply + ggml_type f16_type = src0->type == GGML_TYPE_BF16 ? GGML_TYPE_BF16 : GGML_TYPE_F16; + const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; - vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type, (ggml_prec)dst->op_params[0]); + vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, y_non_contig ? f16_type : src1->type, (ggml_prec)dst->op_params[0]); const bool qx_needs_dequant = mmp == nullptr || x_non_contig; - const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; + const bool qy_needs_dequant = (src1->type != f16_type && !y_f32_kernel) || y_non_contig; if (qx_needs_dequant) { // Fall back to dequant + f16 mulmat - mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16, (ggml_prec)dst->op_params[0]); + mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, f16_type, y_f32_kernel ? GGML_TYPE_F32 : f16_type, (ggml_prec)dst->op_params[0]); } // Not implemented GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT - const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1, qx_needs_dequant ? GGML_TYPE_F16 : src0->type)); + const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1, qx_needs_dequant ? f16_type : src0->type)); const bool aligned = ne10 == kpad && ne01 > 8 && nei1 > 8; - vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? GGML_TYPE_F16 : src0->type); + vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? f16_type : src0->type); // Reserve extra storage in the N dimension for the Y matrix, so we can avoid bounds-checking uint32_t padded_n = qy_needs_dequant ? ROUNDUP_POW2(ne11, pipeline->wg_denoms[1]) :ne11; @@ -5139,12 +5285,12 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, GGML_TYPE_F16); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, f16_type); } else { to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, GGML_TYPE_F16); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, f16_type); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -9230,6 +9376,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm switch (src0_type) { case GGML_TYPE_F32: case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -9265,10 +9412,15 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm if (a->ne[3] != b->ne[3]) { return false; } - if (!(ggml_vk_dim01_contiguous(op->src[0]) || op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) || + if (!(ggml_vk_dim01_contiguous(op->src[0]) || op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_BF16) || !(ggml_vk_dim01_contiguous(op->src[1]) || op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16)) { return false; } + if (op->src[0]->type == GGML_TYPE_BF16 && op->src[1]->type == GGML_TYPE_F16) { + // We currently don't have a bf16 x f16 shader, or an fp16->bf16 copy shader. + // So don't support this combination for now. + return false; + } return true; } break; @@ -9341,6 +9493,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm switch (op->src[0]->type) { case GGML_TYPE_F32: case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -9371,6 +9524,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm switch (src1_type) { case GGML_TYPE_F32: case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt index d6e0b2a5a5..ad13f69b3f 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt +++ b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt @@ -12,6 +12,9 @@ endif() if (GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) add_compile_definitions(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) endif() +if (GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + add_compile_definitions(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) +endif() set(TARGET vulkan-shaders-gen) add_executable(${TARGET} vulkan-shaders-gen.cpp) install(TARGETS ${TARGET} RUNTIME) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp index dd828c2326..6567a8c54c 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp @@ -18,7 +18,11 @@ void main() { // fast path for when all four iterations are in-bounds if (idx + (num_iter-1)*num_threads < p.ne) { [[unroll]] for (uint i = 0; i < num_iter; ++i) { -#ifndef OPTIMIZATION_ERROR_WORKAROUND + +#if defined(DATA_D_BF16) + float f = float(data_a[get_aoffset() + idx]); + data_d[get_doffset() + idx] = D_TYPE(fp32_to_bf16(f)); +#elif !defined(OPTIMIZATION_ERROR_WORKAROUND) data_d[get_doffset() + idx] = D_TYPE(data_a[get_aoffset() + idx]); #else data_d[get_doffset() + idx] = data_a[get_aoffset() + idx]; @@ -31,7 +35,10 @@ void main() { continue; } -#ifndef OPTIMIZATION_ERROR_WORKAROUND +#if defined(DATA_D_BF16) + float f = float(data_a[get_aoffset() + idx]); + data_d[get_doffset() + idx] = D_TYPE(fp32_to_bf16(f)); +#elif !defined(OPTIMIZATION_ERROR_WORKAROUND) data_d[get_doffset() + idx] = D_TYPE(data_a[get_aoffset() + idx]); #else data_d[get_doffset() + idx] = data_a[get_aoffset() + idx]; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp index 29c9064942..f476a2e3dd 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp @@ -12,7 +12,10 @@ void main() { return; } -#ifndef OPTIMIZATION_ERROR_WORKAROUND +#if defined(DATA_D_BF16) + float f = float(data_a[get_aoffset() + src0_idx(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(fp32_to_bf16(f)); +#elif !defined(OPTIMIZATION_ERROR_WORKAROUND) data_d[get_doffset() + dst_idx(idx)] = D_TYPE(data_a[get_aoffset() + src0_idx(idx)]); #else data_d[get_doffset() + dst_idx(idx)] = data_a[get_aoffset() + src0_idx(idx)]; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp index 2a162a2c81..0d9739d406 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp @@ -23,6 +23,12 @@ vec2 dequantize(uint ib, uint iqs, uint a_offset) { } #endif +#if defined(DATA_A_BF16) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(bf16_to_fp32(data_a[a_offset + ib]), bf16_to_fp32(data_a[a_offset + ib + 1])); +} +#endif + #if defined(DATA_A_Q4_0) vec2 dequantize(uint ib, uint iqs, uint a_offset) { const uint vui = uint(data_a[a_offset + ib].qs[iqs]); @@ -428,7 +434,7 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset) { } #endif -#if defined(DATA_A_F32) || defined(DATA_A_F16) +#if defined(DATA_A_F32) || defined(DATA_A_F16) || defined(DATA_A_BF16) vec2 get_dm(uint ib, uint a_offset) { return vec2(0, 0); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp index e877ed7796..ee6b86a18d 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp @@ -20,9 +20,14 @@ void main() { const uint a_offset = get_aoffset() + i01*p.nb01 + i11*p.nb02 + i12*p.nb03; const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23; -#ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]); +#if defined(DATA_A_BF16) + FLOAT_TYPE v = FLOAT_TYPE(bf16_to_fp32(data_a[a_offset + i00])); #else - data_d[d_offset + i00] = data_a[a_offset + i00]; + FLOAT_TYPE v = FLOAT_TYPE(data_a[a_offset + i00]); +#endif +#ifndef OPTIMIZATION_ERROR_WORKAROUND + data_d[d_offset + i00] = D_TYPE(v); +#else + data_d[d_offset + i00] = D_TYPE(v); #endif } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp index 775b48cd05..bb429dd594 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp @@ -6,7 +6,7 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -#if !defined(DATA_A_F32) && !defined(DATA_A_F16) +#if !defined(DATA_A_F32) && !defined(DATA_A_F16) && !defined(DATA_A_BF16) #define K_PER_ITER 8 #else #define K_PER_ITER 2 diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp index 23ce8ceec3..529ac4d44f 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp @@ -10,6 +10,10 @@ #extension GL_EXT_shader_explicit_arithmetic_types_float16 : require #endif +#if defined(DATA_A_BF16) && defined(COOPMAT) +#extension GL_EXT_bfloat16 : enable +#endif + #ifdef COOPMAT #extension GL_KHR_cooperative_matrix : enable #extension GL_KHR_memory_scope_semantics : enable @@ -29,6 +33,10 @@ #define LOAD_VEC_B 1 #endif +#if !defined(TO_FLOAT_TYPE) +#define TO_FLOAT_TYPE FLOAT_TYPE +#endif + layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; @@ -202,8 +210,8 @@ void main() { #endif #ifdef COOPMAT - coopmat cache_a; - coopmat cache_b; + coopmat cache_a; + coopmat cache_b; coopmat sums[cms_per_row * cms_per_col]; [[unroll]] for (uint i = 0; i < cms_per_row * cms_per_col; i++) { @@ -248,6 +256,21 @@ void main() { buf_a[(loadc_a + l) * SHMEM_STRIDE + loadr_a] = FLOAT_TYPE(0.0f); } #endif +#elif defined(DATA_A_BF16) +#if LOAD_VEC_A == 4 + const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; + const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A; + buf_a[buf_idx ] = TO_FLOAT_TYPE(data_a[idx].x); + buf_a[buf_idx + 1] = TO_FLOAT_TYPE(data_a[idx].y); + buf_a[buf_idx + 2] = TO_FLOAT_TYPE(data_a[idx].z); + buf_a[buf_idx + 3] = TO_FLOAT_TYPE(data_a[idx].w); +#else + if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) { + buf_a[(loadc_a + l) * SHMEM_STRIDE + loadr_a] = TO_FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]); + } else { + buf_a[(loadc_a + l) * SHMEM_STRIDE + loadr_a] = TO_FLOAT_TYPE(uint16_t(0)); + } +#endif #elif defined(DATA_A_Q4_0) const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + 4 * loadr_a; @@ -695,13 +718,13 @@ void main() { const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; #endif const uint buf_idx = (loadc_b + l) * SHMEM_STRIDE + loadr_b * LOAD_VEC_B; - buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x); - buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y); - buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z); - buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w); + buf_b[buf_idx + 0] = TO_FLOAT_TYPE(data_b[idx].x); + buf_b[buf_idx + 1] = TO_FLOAT_TYPE(data_b[idx].y); + buf_b[buf_idx + 2] = TO_FLOAT_TYPE(data_b[idx].z); + buf_b[buf_idx + 3] = TO_FLOAT_TYPE(data_b[idx].w); #elif !MUL_MAT_ID if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) { - buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]); + buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = TO_FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]); } else { buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = FLOAT_TYPE(0.0f); } @@ -709,7 +732,7 @@ void main() { const uint row_i = ic * BN + loadc_b + l; if (row_i < _ne1) { const u16vec2 row_idx = row_ids[row_i]; - buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + loadr_b]); + buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = TO_FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + loadr_b]); } else { buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = FLOAT_TYPE(0.0f); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp index 06b7ab09ea..344b466101 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp @@ -14,6 +14,9 @@ #extension GL_EXT_buffer_reference : enable #extension GL_KHR_shader_subgroup_ballot : enable #extension GL_KHR_shader_subgroup_vote : enable +#ifdef DATA_A_BF16 +#extension GL_EXT_bfloat16 : enable +#endif #include "types.comp" @@ -80,6 +83,12 @@ layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; #define store_scales(a) #endif +#if defined(DATA_A_BF16) +#define MAT_TYPE bfloat16_t +#else +#define MAT_TYPE FLOAT_TYPE +#endif + #ifdef MUL_MAT_ID layout (binding = 3) readonly buffer IDS {int data_ids[];}; @@ -271,8 +280,8 @@ void main() { // Manually partial unroll [[unroll]] for (uint j = 0; j < unroll_count; ++j) { - coopmat mat_a; - coopmat mat_b; + coopmat mat_a; + coopmat mat_b; coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover4, block_k, BK), tensorViewTranspose); @@ -286,8 +295,8 @@ void main() { store_scales(tid); } while (block_k < end_k) { - coopmat mat_a; - coopmat mat_b; + coopmat mat_a; + coopmat mat_b; coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover4, block_k, BK), tensorViewTranspose); @@ -310,8 +319,8 @@ void main() { // Manually partial unroll [[unroll]] for (uint j = 0; j < unroll_count; ++j) { - coopmat mat_a; - coopmat mat_b; + coopmat mat_a; + coopmat mat_b; coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover2, block_k, BK), tensorViewTranspose); @@ -325,8 +334,8 @@ void main() { store_scales(tid); } while (block_k < end_k) { - coopmat mat_a; - coopmat mat_b; + coopmat mat_a; + coopmat mat_b; coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover2, block_k, BK), tensorViewTranspose); @@ -350,8 +359,8 @@ void main() { // Manually partial unroll [[unroll]] for (uint j = 0; j < unroll_count; ++j) { - coopmat mat_a; - coopmat mat_b; + coopmat mat_a; + coopmat mat_b; coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose); @@ -365,8 +374,8 @@ void main() { store_scales(tid); } while (block_k < end_k) { - coopmat mat_a; - coopmat mat_b; + coopmat mat_a; + coopmat mat_b; coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose); @@ -405,8 +414,8 @@ void main() { fetch_scales(ir * BM, pos_a, stride_a, block_k + BK, tid, false); } - coopmat mat_a; - coopmat mat_b; + coopmat mat_a; + coopmat mat_b; coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA); #ifdef MUL_MAT_ID diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/test_bfloat16_support.comp b/ggml/src/ggml-vulkan/vulkan-shaders/test_bfloat16_support.comp new file mode 100644 index 0000000000..fd0ba401fe --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/test_bfloat16_support.comp @@ -0,0 +1,7 @@ +#version 460 + +#extension GL_EXT_bfloat16 : require + +void main() +{ +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp index f5b29bfb13..3bde717832 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp @@ -33,6 +33,19 @@ #endif #endif +#if defined(DATA_A_BF16) +#define QUANT_K 1 +#define QUANT_R 1 + +#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1 +#define A_TYPE uint16_t +#elif LOAD_VEC_A == 4 +#define A_TYPE u16vec4 +#elif LOAD_VEC_A == 8 +#error unsupported +#endif +#endif + #define QUANT_K_Q4_0 32 #define QUANT_R_Q4_0 2 @@ -1343,4 +1356,18 @@ void init_iq_shmem(uvec3 wgsize) } #endif +// returns the bfloat value in the low 16b. +// See ggml_compute_fp32_to_bf16 +uint32_t fp32_to_bf16(float f) +{ + uint32_t u = floatBitsToUint(f); + u = (u + (0x7fff + ((u >> 16) & 1))) >> 16; + return u; +} + +float bf16_to_fp32(uint32_t u) +{ + return uintBitsToFloat(u << 16); +} + #endif // !defined(GGML_TYPES_COMP) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index cf74625cc5..3b28578545 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -63,7 +63,8 @@ const std::vector type_names = { "iq3_xxs", "iq3_s", "iq4_xs", - "iq4_nl" + "iq4_nl", + "bf16", }; namespace { @@ -296,7 +297,6 @@ void matmul_shaders(bool fp16, bool matmul_id, bool coopmat, bool coopmat2, bool std::string aligned_b_type_f16 = coopmat2 ? "float16_t" : fp16 ? "f16mat2x4" : "f16vec4"; std::map base_dict = { - {"FLOAT_TYPE", (coopmat2 || fp16) ? "float16_t" : "float"}, {"FLOAT_TYPE_VEC2", (coopmat2 || fp16) ? "f16vec2" : "vec2"}, }; std::string shader_name = "matmul"; @@ -318,12 +318,45 @@ void matmul_shaders(bool fp16, bool matmul_id, bool coopmat, bool coopmat2, bool const std::string source_name = coopmat2 ? "mul_mm_cm2.comp" : "mul_mm.comp"; - // Shaders with f16 B_TYPE - string_to_spv(shader_name + "_f32_f16", source_name, merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}, }), fp16, coopmat, coopmat2, f16acc); - string_to_spv(shader_name + "_f32_f16_aligned", source_name, merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc); + auto const &FLOAT_TYPE = [&](const std::string &t) -> std::string { + if (t == "bf16") { + // scalar path promotes to float + if (!coopmat && !coopmat2) { + return "float"; + } + return "bfloat16_t"; + } + if (coopmat2 || fp16) { + return "float16_t"; + } + return "float"; + }; - string_to_spv(shader_name + "_f16_aligned", source_name, merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc); - string_to_spv(shader_name + "_f16", source_name, merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); + // Shaders with f16 B_TYPE + string_to_spv(shader_name + "_f32_f16", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE("f16")}, {"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}, }), fp16, coopmat, coopmat2, f16acc); + string_to_spv(shader_name + "_f32_f16_aligned", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE("f16")}, {"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc); + + string_to_spv(shader_name + "_f16_aligned", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE("f16")}, {"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc); + string_to_spv(shader_name + "_f16", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE("f16")}, {"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); + + // bf16 + { + std::string load_vec_a_unaligned = "1"; + // For aligned matmul loads + std::string load_vec_a = coopmat2 ? "1" : "4"; + + // scalar path promotes to float + std::string to_float_type = (coopmat || coopmat2) ? "uintBitsToBFloat16EXT" : "bf16_to_fp32"; + + // If bfloat16 is not supported, then only compile the scalar (promote to fp32) shader +#if !defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (!(coopmat || coopmat2)) +#endif + { + string_to_spv(shader_name + "_bf16_aligned", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE("bf16")}, {"TO_FLOAT_TYPE", to_float_type}, {"DATA_A_BF16", "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", "4"}, {"B_TYPE", coopmat2 ? "bfloat16_t" : "u16vec4"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc); + string_to_spv(shader_name + "_bf16", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE("bf16")}, {"TO_FLOAT_TYPE", to_float_type}, {"DATA_A_BF16", "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", coopmat2 ? "bfloat16_t" : "uint16_t"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}}), fp16, coopmat, coopmat2, f16acc); + } + } for (const auto& tname : type_names) { std::string load_vec_quant = "2"; @@ -332,26 +365,30 @@ void matmul_shaders(bool fp16, bool matmul_id, bool coopmat, bool coopmat2, bool else if ((tname == "q5_0") || (tname == "q5_1") || (tname == "q8_0") || (tname == "iq4_nl")) load_vec_quant = "4"; + if (tname == "bf16") { + continue; + } + std::string data_a_key = "DATA_A_" + to_uppercase(tname); // For unaligned, load one at a time for f32/f16, or two at a time for quants - std::string load_vec_a_unaligned = (coopmat2 || tname == "f32" || tname == "f16") ? "1" : load_vec_quant; + std::string load_vec_a_unaligned = (coopmat2 || tname == "f32" || tname == "f16" || tname == "bf16") ? "1" : load_vec_quant; // For aligned matmul loads - std::string load_vec_a = (coopmat2 || tname == "f32" || tname == "f16") ? load_vec : load_vec_quant; + std::string load_vec_a = (coopmat2 || tname == "f32" || tname == "f16" || tname == "bf16") ? load_vec : load_vec_quant; // don't generate f32 variants for coopmat2 if (!coopmat2) { - string_to_spv(shader_name + "_" + tname + "_f32", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); - string_to_spv(shader_name + "_" + tname + "_f32_aligned", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc); + string_to_spv(shader_name + "_" + tname + "_f32", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE(tname)}, {data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); + string_to_spv(shader_name + "_" + tname + "_f32_aligned", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE(tname)}, {data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc); } if (tname != "f16" && tname != "f32") { - string_to_spv(shader_name + "_" + tname + "_f16", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); - string_to_spv(shader_name + "_" + tname + "_f16_aligned", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc); + string_to_spv(shader_name + "_" + tname + "_f16", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE(tname)}, {data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); + string_to_spv(shader_name + "_" + tname + "_f16_aligned", source_name, merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE(tname)}, {data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc); } #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) if (!coopmat && !coopmat2 && !matmul_id && (tname == "q4_0" || tname == "q4_1" || tname == "q5_0" || tname == "q5_1" || tname == "q8_0")) { - string_to_spv(shader_name + "_" + tname + "_q8_1", "mul_mmq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"},}), fp16, coopmat, coopmat2, f16acc); + string_to_spv(shader_name + "_" + tname + "_q8_1", "mul_mmq.comp", merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE(tname)}, {data_a_key, "1"}, {"D_TYPE", "float"},}), fp16, coopmat, coopmat2, f16acc); } #endif } @@ -393,6 +430,7 @@ void process_shaders() { if (tname == "f32") { continue; } + if (tname == "bf16") continue; if (tname == "f16") { string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp", @@ -417,12 +455,12 @@ void process_shaders() { string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}})); // Dequant shaders - if (tname != "f16") { + if (tname != "f16" && tname != "bf16") { string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}})); } if (!string_ends_with(tname, "_k")) { - shader = (tname == "f32" || tname == "f16") ? "get_rows.comp" : "get_rows_quant.comp"; + shader = (tname == "f32" || tname == "f16" || tname == "bf16") ? "get_rows.comp" : "get_rows_quant.comp"; if (tname == "f16") { string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}})); @@ -447,9 +485,11 @@ void process_shaders() { string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("cpy_f32_bf16","copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "uint16_t"}, {"DATA_D_BF16", "1"}}); string_to_spv("contig_cpy_f32_f32", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("contig_cpy_f32_f16", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); string_to_spv("contig_cpy_f16_f16", "contig_copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("contig_cpy_f32_bf16","contig_copy.comp",{{"A_TYPE", "float"}, {"D_TYPE", "uint16_t"}, {"DATA_D_BF16", "1"}}); for (std::string t : {"q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { string_to_spv("cpy_f32_" + t, "copy_to_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); From e0f572c8466e70d35cbd70ee536ad8fc83b2acac Mon Sep 17 00:00:00 2001 From: matteo Date: Thu, 1 May 2025 21:16:38 +0200 Subject: [PATCH 070/126] llama-chat : update GLM4 chat template (#13238) * update GLM4 chat template * Update chat template Co-authored-by: Xuan-Son Nguyen --------- Co-authored-by: Xuan-Son Nguyen --- src/llama-chat.cpp | 2 +- tests/test-chat-template.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index 735d2619c9..9b56614951 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -454,7 +454,7 @@ int32_t llm_chat_apply_template( ss << "<|" << role << "|>" << "\n" << message->content; } if (add_ass) { - ss << "<|assistant|>"; + ss << "<|assistant|>\n"; } } else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) { // MiniCPM-3B-OpenHermes-2.5-v2-GGUF diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 85d89843d6..d217069575 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -181,8 +181,8 @@ int main(void) { }, { /* .name= */ "ChatGLM4", - /* .template_str= */ U8C("[gMASK]{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}"), - /* .expected_output= */ "[gMASK]<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", + /* .template_str= */ U8C("[gMASK]{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>\n{% endif %}"), + /* .expected_output= */ "[gMASK]<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>\n", /* .expected_output_jinja= */ "", /* .bos_token= */ "", /* .eos_token= */ "", From b6e4ff69b8abd509647b531bd5b4e86950204f66 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Carr=C3=A8re?= Date: Thu, 1 May 2025 21:32:21 +0200 Subject: [PATCH 071/126] clip : (minicpmv) Re-enable upscaling of images smaller than the CLIP image size (#13237) --- examples/llava/clip.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 984e300e75..7607d4e3ae 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -2561,7 +2561,7 @@ struct llava_uhd { // no pinpoints, dynamically calculate the grid size (e.g. minicpmv) - auto best_size = get_best_resize(original_size, slice_size, patch_size, has_slices); + auto best_size = get_best_resize(original_size, slice_size, patch_size, !has_slices); res.overview_size = best_size; if (!has_slices) { From d7a14c42a1883a34a6553cbfe30da1e1b84dfd6a Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Thu, 1 May 2025 21:48:08 +0200 Subject: [PATCH 072/126] build : fix build info on windows (#13239) * build : fix build info on windows * fix cuda host compiler msg --- cmake/build-info.cmake | 10 ++++++++-- common/CMakeLists.txt | 4 +++- ggml/src/ggml-cuda/CMakeLists.txt | 3 ++- 3 files changed, 13 insertions(+), 4 deletions(-) diff --git a/cmake/build-info.cmake b/cmake/build-info.cmake index c1a456e179..75c78222f2 100644 --- a/cmake/build-info.cmake +++ b/cmake/build-info.cmake @@ -41,14 +41,20 @@ endif() if(MSVC) set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}") - set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME}) + if (CMAKE_VS_PLATFORM_NAME) + set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME}) + else() + set(BUILD_TARGET "${CMAKE_SYSTEM_NAME} ${CMAKE_SYSTEM_PROCESSOR}") + endif() else() execute_process( - COMMAND sh -c "\"$@\" --version | head -1" _ ${CMAKE_C_COMPILER} + COMMAND ${CMAKE_C_COMPILER} --version OUTPUT_VARIABLE OUT OUTPUT_STRIP_TRAILING_WHITESPACE ) + string(REGEX REPLACE " *\n.*" "" OUT "${OUT}") set(BUILD_COMPILER ${OUT}) + execute_process( COMMAND ${CMAKE_C_COMPILER} -dumpmachine OUTPUT_VARIABLE OUT diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 43533fc86a..f15e12a96b 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -39,7 +39,9 @@ add_custom_command( COMMENT "Generating build details from Git" COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION} -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME} - -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake" + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} + -DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME} -DCMAKE_SYSTEM_PROCESSOR=${CMAKE_SYSTEM_PROCESSOR} + -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake" WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.." DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX} VERBATIM diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt index 8623214c78..f3cfdeaef5 100644 --- a/ggml/src/ggml-cuda/CMakeLists.txt +++ b/ggml/src/ggml-cuda/CMakeLists.txt @@ -133,6 +133,7 @@ if (CUDAToolkit_FOUND) COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion" OUTPUT_VARIABLE CUDA_CCVER ERROR_QUIET + OUTPUT_STRIP_TRAILING_WHITESPACE ) else() if (CUDA_CCFULLVER MATCHES Apple) @@ -143,7 +144,7 @@ if (CUDAToolkit_FOUND) string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER}) endif() - message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}") + message(STATUS "CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}") ggml_get_flags(${CUDA_CCID} ${CUDA_CCVER}) list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS}) # This is passed to -Xcompiler later From f057808ffadce213f54c1884ab5096e60140f358 Mon Sep 17 00:00:00 2001 From: Jesse Gross Date: Thu, 1 May 2025 13:46:10 -0700 Subject: [PATCH 073/126] ggml: Don't assert fail when tensor data changes (#13222) The following scenario will cause an assertion failure in the graph allocator: - Build and allocate a graph containing a tensor with a non-NULL data pointer - Build and allocate a new graph where that data is NULL Result: ggml-alloc.c:819: GGML_ASSERT(talloc->buffer_id >= 0) failed This happens during revalidation because we think that memory should have been previously allocated based on the current graph but in reality the previous graph was different. In this situation, we should do a full reallocation pass. --- ggml/src/ggml-alloc.c | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c index a3d3f69013..5fd379f6a9 100644 --- a/ggml/src/ggml-alloc.c +++ b/ggml/src/ggml-alloc.c @@ -816,7 +816,10 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) { size_t node_size = 0; if (!node->data && !node->view_src) { - GGML_ASSERT(talloc->buffer_id >= 0); // prevent segfault when misusing the API + // If we previously had data but don't now then reallocate + if (talloc->buffer_id < 0) { + return false; + } node_size = ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node); } return talloc->size_max >= node_size; From 8efbdadc616fa717c369059b9b388160958d886c Mon Sep 17 00:00:00 2001 From: Justin Santa Barbara Date: Thu, 1 May 2025 17:32:11 -0400 Subject: [PATCH 074/126] rpc : avoid uninitialized memory in serialize_tensor (#13210) Zero out the name and padding buffers. --- ggml/src/ggml-rpc/ggml-rpc.cpp | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index 140a775f98..e662cc6eb3 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -518,6 +518,11 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { result.view_src = reinterpret_cast(tensor->view_src); result.view_offs = tensor->view_offs; result.data = reinterpret_cast(tensor->data); + + // Avoid sending uninitialized data over the wire + memset(result.name, 0, sizeof(result.name)); + memset(result.padding, 0, sizeof(result.padding)); + snprintf(result.name, GGML_MAX_NAME, "%s", tensor->name); return result; } From d24d5928086471063fa9d9fd45aca710fd1336ae Mon Sep 17 00:00:00 2001 From: bandoti <141645996+bandoti@users.noreply.github.com> Date: Thu, 1 May 2025 19:06:39 -0300 Subject: [PATCH 075/126] ci: fix cross-compile sync issues (#12804) --- .github/workflows/build-linux-cross.yml | 63 +++++++++++++++---------- .github/workflows/build.yml | 5 +- 2 files changed, 41 insertions(+), 27 deletions(-) diff --git a/.github/workflows/build-linux-cross.yml b/.github/workflows/build-linux-cross.yml index e8639913ea..d104b8b124 100644 --- a/.github/workflows/build-linux-cross.yml +++ b/.github/workflows/build-linux-cross.yml @@ -4,18 +4,25 @@ on: workflow_call: jobs: - ubuntu-latest-riscv64-cpu-cross: - runs-on: ubuntu-latest + ubuntu-24-riscv64-cpu-cross: + runs-on: ubuntu-24.04 steps: - uses: actions/checkout@v4 - name: Setup Riscv run: | sudo dpkg --add-architecture riscv64 - sudo sed -i 's|http://azure.archive.ubuntu.com/ubuntu|http://ports.ubuntu.com/ubuntu-ports|g' \ - /etc/apt/sources.list /etc/apt/apt-mirrors.txt - sudo apt-get clean - sudo apt-get update + + # Add arch-specific repositories for non-amd64 architectures + cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe + EOF + + sudo apt-get update || true ;# Prevent failure due to missing URLs. + sudo apt-get install -y --no-install-recommends \ build-essential \ gcc-14-riscv64-linux-gnu \ @@ -40,21 +47,25 @@ jobs: cmake --build build --config Release -j $(nproc) - ubuntu-latest-riscv64-vulkan-cross: - runs-on: ubuntu-latest + ubuntu-24-riscv64-vulkan-cross: + runs-on: ubuntu-24.04 steps: - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - name: Setup Riscv run: | sudo dpkg --add-architecture riscv64 - sudo sed -i 's|http://azure.archive.ubuntu.com/ubuntu|http://ports.ubuntu.com/ubuntu-ports|g' \ - /etc/apt/sources.list /etc/apt/apt-mirrors.txt - sudo apt-get clean - sudo apt-get update + + # Add arch-specific repositories for non-amd64 architectures + cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe + deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe + EOF + + sudo apt-get update || true ;# Prevent failure due to missing URLs. + sudo apt-get install -y --no-install-recommends \ build-essential \ glslc \ @@ -82,21 +93,25 @@ jobs: cmake --build build --config Release -j $(nproc) - ubuntu-latest-arm64-vulkan-cross: - runs-on: ubuntu-latest + ubuntu-24-arm64-vulkan-cross: + runs-on: ubuntu-24.04 steps: - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - name: Setup Arm64 run: | sudo dpkg --add-architecture arm64 - sudo sed -i 's|http://azure.archive.ubuntu.com/ubuntu|http://ports.ubuntu.com/ubuntu-ports|g' \ - /etc/apt/sources.list /etc/apt/apt-mirrors.txt - sudo apt-get clean - sudo apt-get update + + # Add arch-specific repositories for non-amd64 architectures + cat << EOF | sudo tee /etc/apt/sources.list.d/arm64-ports.list + deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe + deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe + deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe + deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe + EOF + + sudo apt-get update || true ;# Prevent failure due to missing URLs. + sudo apt-get install -y --no-install-recommends \ build-essential \ glslc \ diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 32c8b7717f..34417985d2 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -601,9 +601,8 @@ jobs: -DGGML_SYCL_F16=ON cmake --build build --config Release -j $(nproc) -# Disabled for now due to sporadic issue syncing. -# build-linux-cross: -# uses: ./.github/workflows/build-linux-cross.yml + build-linux-cross: + uses: ./.github/workflows/build-linux-cross.yml macOS-latest-cmake-ios: runs-on: macos-latest From dcf886007de4b8e5200f461a13233315f897fb9d Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Fri, 2 May 2025 08:45:10 +0200 Subject: [PATCH 076/126] convert : explicitly disable trust_remote_code for AutoConfig (#13246) --- convert_hf_to_gguf.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 04ca646b50..df3f8a55d5 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -419,7 +419,9 @@ class ModelBase: @staticmethod def load_hparams(dir_model: Path): try: - return AutoConfig.from_pretrained(dir_model).to_dict() + # for security reason, we don't allow loading remote code by default + # if a model need remote code, we will fallback to config.json + return AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict() except Exception as e: logger.warning(f"Failed to load model config from {dir_model}: {e}") logger.warning("Trying to load config.json instead") From fab647e8842c5f80da7e8f2c625dab6a0e19e5d4 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 2 May 2025 09:48:31 +0300 Subject: [PATCH 077/126] server : add cache reuse card link to help (#13230) * server : add cache reuse card link to help * args : use short url --- common/arg.cpp | 5 ++++- examples/server/README.md | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index e35417de7e..aface844c9 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2783,7 +2783,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP")); add_opt(common_arg( {"--cache-reuse"}, "N", - string_format("min chunk size to attempt reusing from the cache via KV shifting (default: %d)", params.n_cache_reuse), + string_format( + "min chunk size to attempt reusing from the cache via KV shifting (default: %d)\n" + "[(card)](https://ggml.ai/f0.png)", params.n_cache_reuse + ), [](common_params & params, int value) { params.n_cache_reuse = value; } diff --git a/examples/server/README.md b/examples/server/README.md index a2a0903261..61446a0ba2 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -154,7 +154,7 @@ The project is under active development, and we are [looking for feedback and co | `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate
(env: LLAMA_ARG_SSL_CERT_FILE) | | `-to, --timeout N` | server read/write timeout in seconds (default: 600)
(env: LLAMA_ARG_TIMEOUT) | | `--threads-http N` | number of threads used to process HTTP requests (default: -1)
(env: LLAMA_ARG_THREADS_HTTP) | -| `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)
(env: LLAMA_ARG_CACHE_REUSE) | +| `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)
[(card)](https://ggml.ai/f0.png)
(env: LLAMA_ARG_CACHE_REUSE) | | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS) | | `--slots` | enable slots monitoring endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS) | | `--props` | enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS) | From e84773ab604fe0d935d03741df003716398dc57b Mon Sep 17 00:00:00 2001 From: Shakil Ahmed <44522075+ahmedshakill@users.noreply.github.com> Date: Fri, 2 May 2025 14:20:27 +0600 Subject: [PATCH 078/126] mtmd-cli : fix out_of_range when input image path is empty (#13244) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix out_of_range error to keep the chat loop running * Update examples/llava/mtmd-cli.cpp Co-authored-by: Sigbjørn Skjæret * mtmd-cli : load image right away * add a new line for readability * rm printf * Update examples/llava/mtmd-cli.cpp Co-authored-by: Sigbjørn Skjæret * Update examples/llava/mtmd-cli.cpp --------- Co-authored-by: Sigbjørn Skjæret Co-authored-by: Xuan Son Nguyen Co-authored-by: Xuan-Son Nguyen --- examples/llava/mtmd-cli.cpp | 72 ++++++++++++++++++++----------------- examples/llava/mtmd.cpp | 2 +- 2 files changed, 41 insertions(+), 33 deletions(-) diff --git a/examples/llava/mtmd-cli.cpp b/examples/llava/mtmd-cli.cpp index aa52d92cab..474e7c4f83 100644 --- a/examples/llava/mtmd-cli.cpp +++ b/examples/llava/mtmd-cli.cpp @@ -72,6 +72,8 @@ struct mtmd_cli_context { llama_batch batch; int n_batch; + std::vector bitmaps; + // note: we know that gemma3 template is "linear", meaning each turn is completely separated to another // so here we don't need to keep track of chat history common_chat_templates_ptr tmpls; @@ -135,13 +137,22 @@ struct mtmd_cli_context { antiprompt_tokens.begin() ); } + + bool load_image(const std::string & fname) { + mtmd_bitmap bitmap; + if (mtmd_helper_bitmap_init_from_file(fname.c_str(), bitmap)) { + return false; + } + bitmaps.push_back(std::move(bitmap)); + return true; + } }; static int generate_response(mtmd_cli_context & ctx, common_sampler * smpl, int n_predict) { llama_tokens generated_tokens; for (int i = 0; i < n_predict; i++) { if (i > n_predict || !g_is_generating || g_is_interrupted) { - printf("\n"); + LOG("\n"); break; } @@ -150,15 +161,15 @@ static int generate_response(mtmd_cli_context & ctx, common_sampler * smpl, int common_sampler_accept(smpl, token_id, true); if (llama_vocab_is_eog(ctx.vocab, token_id) || ctx.check_antiprompt(generated_tokens)) { - printf("\n"); + LOG("\n"); break; // end of generation } - printf("%s", common_token_to_piece(ctx.lctx, token_id).c_str()); + LOG("%s", common_token_to_piece(ctx.lctx, token_id).c_str()); fflush(stdout); if (g_is_interrupted) { - printf("\n"); + LOG("\n"); break; } @@ -173,9 +184,7 @@ static int generate_response(mtmd_cli_context & ctx, common_sampler * smpl, int return 0; } -static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg, std::vector & images_fname, bool add_bos = false) { - std::vector bitmaps; - +static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg, bool add_bos = false) { common_chat_templates_inputs tmpl_inputs; tmpl_inputs.messages = {msg}; tmpl_inputs.add_generation_prompt = true; @@ -183,15 +192,6 @@ static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg, std::vect auto formatted_chat = common_chat_templates_apply(ctx.tmpls.get(), tmpl_inputs); LOG_DBG("formatted_chat.prompt: %s\n", formatted_chat.prompt.c_str()); - for (auto & fname : images_fname) { - mtmd_bitmap bitmap; - if (mtmd_helper_bitmap_init_from_file(fname.c_str(), bitmap)) { - LOG_ERR("Unable to load image %s\n", fname.c_str()); - return 2; // image not found - } - bitmaps.push_back(std::move(bitmap)); - } - mtmd_input_text text; text.text = formatted_chat.prompt; text.add_special = add_bos; @@ -200,12 +200,14 @@ static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg, std::vect if (g_is_interrupted) return 0; - int32_t res = mtmd_tokenize(ctx.ctx_vision.get(), chunks, text, bitmaps); + int32_t res = mtmd_tokenize(ctx.ctx_vision.get(), chunks, text, ctx.bitmaps); if (res != 0) { LOG_ERR("Unable to tokenize prompt, res = %d\n", res); return 1; } + ctx.bitmaps.clear(); + if (mtmd_helper_eval(ctx.ctx_vision.get(), ctx.lctx, chunks, ctx.n_past, 0, ctx.n_batch)) { LOG_ERR("Unable to eval prompt\n"); return 1; @@ -213,6 +215,8 @@ static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg, std::vect ctx.n_past += mtmd_helper_get_n_pos(chunks); + LOG("\n"); + return 0; } @@ -235,7 +239,7 @@ int main(int argc, char ** argv) { } mtmd_cli_context ctx(params); - printf("%s: %s\n", __func__, params.model.path.c_str()); + LOG("%s: loading model: %s\n", __func__, params.model.path.c_str()); bool is_single_turn = !params.prompt.empty() && !params.image.empty(); @@ -268,7 +272,12 @@ int main(int argc, char ** argv) { common_chat_msg msg; msg.role = "user"; msg.content = params.prompt; - if (eval_message(ctx, msg, params.image, true)) { + for (const auto & image : params.image) { + if (!ctx.load_image(image)) { + return 1; // error is already printed by libmtmd + } + } + if (eval_message(ctx, msg, true)) { return 1; } if (!g_is_interrupted && generate_response(ctx, smpl, n_predict)) { @@ -283,7 +292,6 @@ int main(int argc, char ** argv) { LOG("\n"); bool is_first_msg = true; - std::vector images_fname; std::string content; while (!g_is_interrupted) { @@ -308,10 +316,17 @@ int main(int argc, char ** argv) { continue; } g_is_generating = true; - if (line.find("/image") == 0) { + if (line == "/image" || line.find("/image ") == 0) { + if (line.size() < 8) { + LOG_ERR("ERR: Missing image filename\n"); + continue; + } std::string image = line.substr(7); - images_fname.push_back(string_strip(image)); - content += "<__image__>"; + if (ctx.load_image(image)) { + LOG("Image %s loaded\n", image.c_str()); + content += "<__image__>"; + } + // else, error is already printed by libmtmd continue; } else { content += line; @@ -319,21 +334,14 @@ int main(int argc, char ** argv) { common_chat_msg msg; msg.role = "user"; msg.content = content; - int ret = eval_message(ctx, msg, images_fname, is_first_msg); - if (g_is_interrupted) break; - if (ret == 2) { - // non-fatal error - images_fname.clear(); - content.clear(); - continue; - } + int ret = eval_message(ctx, msg, is_first_msg); if (ret) { return 1; } + if (g_is_interrupted) break; if (generate_response(ctx, smpl, n_predict)) { return 1; } - images_fname.clear(); content.clear(); is_first_msg = false; } diff --git a/examples/llava/mtmd.cpp b/examples/llava/mtmd.cpp index 7081fd7352..d1d7530feb 100644 --- a/examples/llava/mtmd.cpp +++ b/examples/llava/mtmd.cpp @@ -590,7 +590,7 @@ int32_t mtmd_helper_eval(mtmd_context * ctx, } } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { - GGML_ASSERT(!is_last && "logits for last image chunk is not yet support"); + GGML_ASSERT(!is_last && "logits for last image chunk is not yet supported"); GGML_ASSERT(chunk.tokens_image != nullptr); int64_t t0 = ggml_time_ms(); if (ctx->print_timings) { From 2af6880178b4bc2c0eced726bab68b4bf333042b Mon Sep 17 00:00:00 2001 From: piDack <104877312+piDack@users.noreply.github.com> Date: Fri, 2 May 2025 17:06:09 +0800 Subject: [PATCH 079/126] llama-chat : reset glmedge chat template (#13253) * reset glmedge chat template * fix glmedge chat template --- src/llama-chat.cpp | 10 +++++++++- tests/test-chat-template.cpp | 17 ++++++++--------- 2 files changed, 17 insertions(+), 10 deletions(-) diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index 9b56614951..46d43c58ef 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -447,7 +447,7 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "<|assistant|>"; } - } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_4 || tmpl == LLM_CHAT_TEMPLATE_GLMEDGE) { + } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_4) { ss << "[gMASK]" << ""; for (auto message : chat) { std::string role(message->role); @@ -456,6 +456,14 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "<|assistant|>\n"; } + } else if (tmpl == LLM_CHAT_TEMPLATE_GLMEDGE) { + for (auto message : chat) { + std::string role(message->role); + ss << "<|" << role << "|>" << "\n" << message->content; + } + if (add_ass) { + ss << "<|assistant|>"; + } } else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) { // MiniCPM-3B-OpenHermes-2.5-v2-GGUF for (auto message : chat) { diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index d217069575..a0a50f9881 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -187,15 +187,14 @@ int main(void) { /* .bos_token= */ "", /* .eos_token= */ "", }, - // TODO @ngxson : GLMEdge produces poor result without `[gMASK]`, so we're temporarily using GLM4 template for it. We should fix this in the future. - // { - // /* .name= */ "GLMEdge", - // /* .template_str= */ "{% for item in messages %}{% if item['role'] == 'system' %}<|system|>\n{{ item['content'] }}{% elif item['role'] == 'user' %}<|user|>\n{{ item['content'] }}{% elif item['role'] == 'assistant' %}<|assistant|>\n{{ item['content'] }}{% endif %}{% endfor %}<|assistant|>", - // /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", - // /* .expected_output_jinja= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", - // /* .bos_token= */ "", - // /* .eos_token= */ "", - // }, + { + /* .name= */ "GLMEdge", + /* .template_str= */ "{% for item in messages %}{% if item['role'] == 'system' %}<|system|>\n{{ item['content'] }}{% elif item['role'] == 'user' %}<|user|>\n{{ item['content'] }}{% elif item['role'] == 'assistant' %}<|assistant|>\n{{ item['content'] }}{% endif %}{% endfor %}<|assistant|>", + /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", + /* .expected_output_jinja= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", + /* .bos_token= */ "", + /* .eos_token= */ "", + }, { /* .name= */ "MiniCPM-3B-OpenHermes-2.5-v2-GGUF", /* .template_str= */ U8C("{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}"), From 626083faf73faa54440f934bb1741bf443be91b1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Fri, 2 May 2025 12:40:56 +0200 Subject: [PATCH 080/126] llama : plamo rope type is neox (#13260) --- src/llama-model.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 51092a128c..498a245285 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -13226,7 +13226,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_DECI: case LLM_ARCH_BAICHUAN: case LLM_ARCH_STARCODER: - case LLM_ARCH_PLAMO: case LLM_ARCH_ORION: case LLM_ARCH_INTERNLM2: case LLM_ARCH_MINICPM: @@ -13265,6 +13264,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_PHI2: case LLM_ARCH_PHI3: case LLM_ARCH_PHIMOE: + case LLM_ARCH_PLAMO: case LLM_ARCH_GEMMA: case LLM_ARCH_GEMMA2: case LLM_ARCH_GEMMA3: From cb06a3c363f50cd35113984fe8fb164aea419077 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Fri, 2 May 2025 12:44:24 +0200 Subject: [PATCH 081/126] llama : orion rope type is neox (#13261) --- src/llama-model.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 498a245285..e5289f7077 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -13226,7 +13226,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_DECI: case LLM_ARCH_BAICHUAN: case LLM_ARCH_STARCODER: - case LLM_ARCH_ORION: case LLM_ARCH_INTERNLM2: case LLM_ARCH_MINICPM: case LLM_ARCH_XVERSE: @@ -13272,6 +13271,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_OPENELM: case LLM_ARCH_GPTNEOX: case LLM_ARCH_CODESHELL: + case LLM_ARCH_ORION: case LLM_ARCH_NEMOTRON: case LLM_ARCH_EXAONE: case LLM_ARCH_MINICPM3: From c642bc014c105728ce45015813351dc5a37f60a2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 2 May 2025 17:48:36 +0300 Subject: [PATCH 082/126] kv-cache : separate recurrent vs non-recurrent impl (#12799) * kv-cache : serparate recurrent vs non-recurrent impl (wip) ggml-ci * kv-cache : init -> contructor + add llama_memory_params ggml-ci * kv-cache : fix callback reference ggml-ci * context : llama_kv_cache -> llama_memory_i ggml-ci * context : move memory creation logic to model ggml-ci * llama : remove reference of memory during encode ggml-ci * kv-cache : hide padding details in the implementation ggml-ci * kv-cache : add ubatch_next() ggml-ci * context : simplify sbatch logic ggml-ci * kv-cache : hide defrag logic in the implementation ggml-ci * context : hide kv cache details in implementation ggml-ci * build : fix ggml-ci * cont : another fix ggml-ci * kv-cache : simplify interface (wip) ggml-ci * kv-cache : use separate KV cell structs for unified/recurrent ggml-ci * kv-cache : clean-up ggml-ci * model : better llama_model::create_model() signature ggml-ci * kv-cache : fix recurrent seq_rm() ggml-ci * kv-cache : replace `struct callbacks` with `llama_model &` ggml-ci * kv-cache : replace `struct graph_params` with `llama_context &` ggml-ci * kv-cache : fix offload check ggml-ci * context : avoid passing unique_ptr ggml-ci * kv-cache : avoid using the backends from the llama_context ref #13113 ggml-ci * kv-cache : more consistent debug logs [no ci] * kv-cache : do not pass the full llama_context for kv graphs ggml-ci * kv-cache : remove comment * kv-cache : ggml_rope_ext_inplace -> ggml_rope_ext ggml-ci * kv-cache : fix recurrent multi-user case ggml-ci * memory : remove comments [no ci] --- src/llama-batch.cpp | 6 +- src/llama-batch.h | 3 +- src/llama-context.cpp | 576 +++---------- src/llama-context.h | 45 +- src/llama-graph.cpp | 44 +- src/llama-graph.h | 17 +- src/llama-kv-cache.cpp | 1828 ++++++++++++++++++++++++++++++++-------- src/llama-kv-cache.h | 402 ++++++--- src/llama-memory.h | 12 +- src/llama-model.cpp | 70 +- src/llama-model.h | 5 +- 11 files changed, 1960 insertions(+), 1048 deletions(-) diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp index 01d5ca57fd..a88b2fe308 100644 --- a/src/llama-batch.cpp +++ b/src/llama-batch.cpp @@ -189,7 +189,7 @@ llama_ubatch llama_sbatch::split_seq(size_t n_ubatch) { return ubatch; } -void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) { +llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) { GGML_ASSERT(batch.n_tokens >= 0); this->batch = &batch; this->n_embd = n_embd; @@ -203,6 +203,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim for (size_t i = 0; i < n_tokens; ++i) { ids[i] = i; } + if (simple_split) { seq.resize(1); llama_sbatch_seq & s = seq[0]; @@ -212,6 +213,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim s.length = n_tokens; return; } + std::sort(ids.begin(), ids.end(), [&batch](size_t a, size_t b) { int32_t n_seq_a = batch.n_seq_id ? batch.n_seq_id[a] : 1; @@ -239,6 +241,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim return n_seq_a > n_seq_b; } ); + // init seq llama_sbatch_seq * last_seq = nullptr; @@ -262,6 +265,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim seq.push_back(new_seq); last_seq = &seq.back(); } + // keep shared prompts first at the end, then sort by length descending. std::sort(seq.begin(), seq.end(), [](llama_sbatch_seq & a, llama_sbatch_seq & b) { diff --git a/src/llama-batch.h b/src/llama-batch.h index f1df40d270..6305051b62 100644 --- a/src/llama-batch.h +++ b/src/llama-batch.h @@ -70,7 +70,8 @@ struct llama_sbatch { // sequence-wise split llama_ubatch split_seq(size_t n_ubatch); - void from_batch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false); + llama_sbatch() = default; + llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false); }; // temporary allocate memory for the input batch if needed diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 5a2eef9b78..a88b9a5ff9 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -6,11 +6,9 @@ #include "llama-model.h" #include "llama-kv-cache.h" -#include #include #include #include -#include // // llama_context @@ -177,44 +175,13 @@ llama_context::llama_context( } // init the memory module - // TODO: for now, always create a unified KV cache if (!hparams.vocab_only) { - kv_self.reset(static_cast(model.create_memory())); + llama_memory_params params_mem = { + /*.type_k =*/ params.type_k, + /*.type_v =*/ params.type_v, + }; - LLAMA_LOG_DEBUG("%s: n_ctx = %u\n", __func__, cparams.n_ctx); - - cparams.n_ctx = GGML_PAD(cparams.n_ctx, kv_self->get_padding(cparams)); - - LLAMA_LOG_DEBUG("%s: n_ctx = %u (padded)\n", __func__, cparams.n_ctx); - - uint32_t kv_size = cparams.n_ctx; - ggml_type type_k = params.type_k; - ggml_type type_v = params.type_v; - - if (llama_model_is_recurrent(&model)) { - // Mamba needs at least as many KV cells as there are sequences kept at any time - kv_size = std::max((uint32_t) 1, params.n_seq_max); - // it's probably best to keep as much precision as possible for the states - type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states - type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states - } - - GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0); - GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0); - - if (!kv_self->init(model, cparams, type_k, type_v, kv_size, cparams.offload_kqv)) { - throw std::runtime_error("failed to initialize self-attention cache"); - } - - { - const size_t memory_size_k = kv_self->size_k_bytes(); - const size_t memory_size_v = kv_self->size_v_bytes(); - - LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__, - (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), - ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f), - ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); - } + memory.reset(model.create_memory(params_mem, cparams)); } // init backends @@ -305,7 +272,9 @@ llama_context::llama_context( int n_nodes_tg = -1; // simulate full KV cache - kv_self->n = kv_self->size; + llama_kv_cache * kv_self = static_cast(memory.get()); + + kv_self->set_full(); cross.v_embd.clear(); @@ -427,6 +396,18 @@ const llama_model & llama_context::get_model() const { return model; } +const llama_cparams & llama_context::get_cparams() const { + return cparams; +} + +ggml_backend_sched_t llama_context::get_sched() const { + return sched.get(); +} + +ggml_context * llama_context::get_ctx_compute() const { + return ctx_compute.get(); +} + uint32_t llama_context::n_ctx() const { return cparams.n_ctx; } @@ -456,337 +437,21 @@ uint32_t llama_context::n_threads_batch() const { } llama_kv_cache * llama_context::get_kv_self() { - return kv_self.get(); + llama_kv_cache * kv_self = static_cast(memory.get()); + return kv_self; } const llama_kv_cache * llama_context::get_kv_self() const { - return kv_self.get(); -} - -ggml_tensor * llama_context::build_rope_shift( - ggml_context * ctx0, - ggml_tensor * cur, - ggml_tensor * shift, - ggml_tensor * factors, - float freq_base, - float freq_scale) const { - const auto & n_ctx_orig = cparams.n_ctx_orig_yarn; - - const auto & yarn_ext_factor = cparams.yarn_ext_factor; - const auto & yarn_beta_fast = cparams.yarn_beta_fast; - const auto & yarn_beta_slow = cparams.yarn_beta_slow; - - const auto & hparams = model.hparams; - - const auto & n_rot = hparams.n_rot; - const auto & rope_type = hparams.rope_type; - - // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly. - // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. - const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2 ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)) : cparams.yarn_attn_factor; - - ggml_tensor * tmp; - - if (ggml_is_quantized(cur->type)) { - // dequantize to f32 -> RoPE -> quantize back - tmp = ggml_cast(ctx0, cur, GGML_TYPE_F32); - - tmp = ggml_rope_ext(ctx0, tmp, - shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow); - - tmp = ggml_cpy(ctx0, tmp, cur); - } else { - // we rotate only the first n_rot dimensions - tmp = ggml_rope_ext_inplace(ctx0, cur, - shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow); - } - - return tmp; -} - -class llm_graph_input_k_shift : public llm_graph_input_i { -public: - llm_graph_input_k_shift(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {} - virtual ~llm_graph_input_k_shift() = default; - - void set_input(const llama_ubatch * ubatch) override; - - ggml_tensor * k_shift; // I32 [kv_size] - - const llama_kv_cache_unified * kv_self; -}; - -void llm_graph_input_k_shift::set_input(const llama_ubatch * ubatch) { - GGML_UNUSED(ubatch); - - if (k_shift) { - assert(ggml_backend_buffer_is_host(k_shift->buffer)); - - int32_t * data = (int32_t *) k_shift->data; - - for (uint32_t i = 0; i < kv_self->size; ++i) { - data[i] = kv_self->cells[i].delta; - } - } -} - -llm_graph_result_ptr llama_context::build_kv_self_shift( - ggml_context * ctx0, - ggml_cgraph * gf) const { - auto res = std::make_unique(); - - const auto & hparams = model.hparams; - - const auto & n_layer = hparams.n_layer; - - const auto & n_embd_head_k = hparams.n_embd_head_k; - //const auto & n_embd_head_v = hparams.n_embd_head_v; - - //GGML_ASSERT(kv_self->size == n_ctx); - - auto inp = std::make_unique(kv_self.get()); - - inp->k_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, cparams.n_ctx); - ggml_set_input(inp->k_shift); - - for (uint32_t il = 0; il < n_layer; ++il) { - const int64_t n_head_kv = hparams.n_head_kv(il); - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); - - const bool is_swa = hparams.is_swa(il); - - // note: the swa rope params could become part of the cparams in the future - // if we decide to make them configurable, like the non-sliding ones - const float freq_base_l = is_swa ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base; - const float freq_scale_l = is_swa ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale; - - ggml_tensor * rope_factors = kv_self->cbs.get_rope_factors(n_ctx_per_seq(), il); - - ggml_tensor * k = - ggml_view_3d(ctx0, kv_self->k_l[il], - n_embd_head_k, n_head_kv, kv_self->size, - ggml_row_size(kv_self->k_l[il]->type, n_embd_head_k), - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa), - 0); - - ggml_tensor * cur = build_rope_shift(ctx0, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l); - - ggml_build_forward_expand(gf, cur); - } - - res->add_input(std::move(inp)); - - return res; -} - -llm_graph_result_ptr llama_context::build_kv_self_defrag( - ggml_context * ctx0, - ggml_cgraph * gf) const { - auto res = std::make_unique(); - - const auto & hparams = model.hparams; - - const auto & ids = kv_self->defrag_info.ids; - -#if 0 - // CPU defrag - // - // TODO: optimizations are possible: - // - multiple threads - // - avoid copying to the host memory when already there - // - // likely not worth the effort, as we have ggml_graph based defrag - // - - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - - const uint32_t kv_size = size; - - std::vector buf_k; - std::vector buf_v; - - for (uint32_t il = 0; il < n_layer; ++il) { - const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa); - const size_t k_size = ggml_row_size(k_l[il]->type, n_embd_k_gqa*kv_size); - - const size_t v_size_el = ggml_type_size(v_l[il]->type); - const size_t v_size = ggml_row_size (v_l[il]->type, n_embd_v_gqa*kv_size); - - buf_k.resize(k_size); - buf_v.resize(v_size); - - ggml_backend_tensor_get(k_l[il], buf_k.data(), 0, buf_k.size()); - ggml_backend_tensor_get(v_l[il], buf_v.data(), 0, buf_v.size()); - - // batch move [i, i+nm) to [id, id+nm) - // note: cells can move only to a lower index - for (uint32_t i = 0; i < n_kv; ++i) { - const uint32_t id = ids[i]; - - if (i == id || id == n_kv) { - continue; - } - - uint32_t nm = 1; - - while (i + nm < n_kv && ids[i + nm] == id + nm) { - nm++; - } - - // move keys - { - const int64_t os = i*k_size_row; - const int64_t od = id*k_size_row; - - memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row); - } - - // move values (note: they are transposed) - { - const int64_t os = i; - const int64_t od = id; - - for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { - memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el); - } - } - - i += nm - 1; - } - - ggml_backend_tensor_set(k_l[il], buf_k.data(), 0, buf_k.size()); - ggml_backend_tensor_set(v_l[il], buf_v.data(), 0, buf_v.size()); - } -#else - for (uint32_t i = 0; i < ids.size(); ++i) { - const uint32_t id = ids[i]; - - if (i == id || id == ids.size()) { - continue; - } - - uint32_t nm = 1; - - while (i + nm < ids.size() && ids[i + nm] == id + nm) { - nm++; - } - - for (uint32_t il = 0; il < hparams.n_layer; ++il) { // NOLINT - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); - const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); - - ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self->k_l[il], - n_embd_k_gqa, nm, - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa*i)); - - ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self->k_l[il], - n_embd_k_gqa, nm, - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa*id)); - - ggml_tensor * view_v_src; - ggml_tensor * view_v_dst; - - if (cparams.flash_attn) { - // NOTE: the V cache is not transposed when using flash attention - view_v_src = ggml_view_2d(ctx0, kv_self->v_l[il], - n_embd_v_gqa, nm, - ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa), - ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa*i)); - - view_v_dst = ggml_view_2d(ctx0, kv_self->v_l[il], - n_embd_v_gqa, nm, - ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa), - ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa*id)); - } else { - view_v_src = ggml_view_2d(ctx0, kv_self->v_l[il], - nm, n_embd_v_gqa, - ggml_row_size(kv_self->v_l[il]->type, kv_self->size), - ggml_row_size(kv_self->v_l[il]->type, i)); - - view_v_dst = ggml_view_2d(ctx0, kv_self->v_l[il], - nm, n_embd_v_gqa, - ggml_row_size(kv_self->v_l[il]->type, kv_self->size), - ggml_row_size(kv_self->v_l[il]->type, id)); - } - - ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst)); - } - - i += nm - 1; - } - - //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes); -#endif - - return res; + llama_kv_cache * kv_self = static_cast(memory.get()); + return kv_self; } void llama_context::kv_self_update() { - auto & kv = kv_self; - bool need_reserve = false; - if (kv->has_shift) { - if (!kv->get_can_shift()) { - GGML_ABORT("The current context does not support K-shift"); - } + llama_kv_cache * kv_self = static_cast(memory.get()); - LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__); - - // apply K-shift if needed - if (model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE) { - ggml_backend_sched_reset(sched.get()); - - auto * gf = graph_init(); - - auto res = build_kv_self_shift(ctx_compute.get(), gf); - - ggml_backend_sched_alloc_graph(sched.get(), gf); - - res->set_inputs(nullptr); - - graph_compute(gf, false); - - need_reserve = true; - } - - { - kv->has_shift = false; - - for (uint32_t i = 0; i < kv->size; ++i) { - kv->cells[i].delta = 0; - } - } - } - - // defragment the KV cache if needed - if (kv->do_defrag) { - LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__); - - if (kv->defrag_prepare(graph_max_nodes())) { - ggml_backend_sched_reset(sched.get()); - - auto * gf = graph_init(); - - auto res = build_kv_self_defrag(ctx_compute.get(), gf); - - ggml_backend_sched_alloc_graph(sched.get(), gf); - - res->set_inputs(nullptr); - - graph_compute(gf, false); - - need_reserve = true; - } - - kv->do_defrag = false; - } + need_reserve = kv_self->update(*this); // reserve a worst case graph if needed if (need_reserve) { @@ -797,7 +462,7 @@ void llama_context::kv_self_update() { uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch); // simulate full KV cache - kv_self->n = kv_self->size; + kv_self->set_full(); llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; @@ -818,9 +483,6 @@ enum llama_pooling_type llama_context::pooling_type() const { } float * llama_context::get_logits() { - // reorder logits for backward compatibility - output_reorder(); - return logits; } @@ -863,9 +525,6 @@ float * llama_context::get_logits_ith(int32_t i) { } float * llama_context::get_embeddings() { - // reorder embeddings for backward compatibility - output_reorder(); - return embd; } @@ -1017,8 +676,8 @@ int llama_context::encode(llama_batch & inp_batch) { } // temporary allocate memory for the input batch if needed - // TODO: this is incorrect for multiple sequences because pos_max() is the maximum across all sequences - llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->pos_max() + 1); + // note: during encode, we always pass the full sequence starting from pos = 0 + llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : 0); const llama_batch & batch = batch_allocr.batch; const int32_t n_tokens = batch.n_tokens; @@ -1047,7 +706,7 @@ int llama_context::encode(llama_batch & inp_batch) { const int64_t n_embd = hparams.n_embd; - sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true); + llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true, /* logits_all */ true); const llama_ubatch ubatch = sbatch.split_simple(n_tokens); @@ -1181,9 +840,11 @@ int llama_context::decode(llama_batch & inp_batch) { return -1; } + llama_kv_cache * kv_self = static_cast(memory.get()); + // temporary allocate memory for the input batch if needed - // TODO: this is incorrect for multiple sequences because pos_max() is the maximum across all sequences - llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->pos_max() + 1); + // TODO: this is incorrect for multiple sequences because get_pos_max() is the maximum across all sequences + llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->get_pos_max() + 1); const llama_batch & batch = batch_allocr.batch; @@ -1195,7 +856,7 @@ int llama_context::decode(llama_batch & inp_batch) { const int64_t n_tokens_all = batch.n_tokens; const int64_t n_embd = hparams.n_embd; - llama_kv_cache_guard kv_guard(kv_self.get()); + llama_kv_cache_guard kv_guard(kv_self); GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT @@ -1236,11 +897,7 @@ int llama_context::decode(llama_batch & inp_batch) { n_outputs_all = 1; } - const bool logits_all = n_outputs_all == n_tokens_all; - - sbatch.from_batch(batch, n_embd, - /* simple_split */ !kv_self->recurrent, - /* logits_all */ logits_all); + llama_sbatch sbatch = kv_self->sbatch_init(batch, /* logits_all */ n_outputs_all == n_tokens_all); // reserve output buffer if (output_reserve(n_outputs_all) < n_outputs_all) { @@ -1254,22 +911,7 @@ int llama_context::decode(llama_batch & inp_batch) { int64_t n_outputs_prev = 0; while (sbatch.n_tokens > 0) { - llama_ubatch ubatch = llama_ubatch(); - - const auto & n_ubatch = cparams.n_ubatch; - - if (kv_self->recurrent) { - if (embd_pooled) { - // Pooled embeddings cannot be split across ubatches (yet) - ubatch = sbatch.split_seq(cparams.n_ubatch); - } else { - // recurrent model architectures are easier to implement - // with equal-length sequences - ubatch = sbatch.split_equal(cparams.n_ubatch); - } - } else { - ubatch = sbatch.split_simple(n_ubatch); - } + llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled); // count the outputs in this u_batch { @@ -1289,24 +931,12 @@ int llama_context::decode(llama_batch & inp_batch) { } // find KV slot - { - if (!kv_self->find_slot(ubatch)) { - LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens); + if (!kv_self->find_slot(ubatch)) { + LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens); - return 1; - } - - if (!kv_self->recurrent) { - // a heuristic, to avoid attending the full cache if it is not yet utilized - // after enough generations, the benefit from this heuristic disappears - // if we start defragmenting the cache, the benefit from this will be more important - const uint32_t pad = kv_self->get_padding(cparams); - kv_self->n = std::min(kv_self->size, std::max(pad, GGML_PAD(kv_self->cell_max(), pad))); - } + return 1; } - //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self->n, kv_self->used, kv_self->head); - ggml_backend_sched_reset(sched.get()); ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data); @@ -1424,18 +1054,52 @@ int llama_context::decode(llama_batch & inp_batch) { { bool sorted_output = true; - GGML_ASSERT(sbatch.out_ids.size() == (size_t) n_outputs_all); + auto & out_ids = sbatch.out_ids; + + GGML_ASSERT(out_ids.size() == (size_t) n_outputs_all); for (int64_t i = 0; i < n_outputs_all; ++i) { - int64_t out_id = sbatch.out_ids[i]; + int64_t out_id = out_ids[i]; output_ids[out_id] = i; if (out_id != i) { sorted_output = false; } } - if (sorted_output) { - sbatch.out_ids.clear(); + // make the outputs have the same order they had in the user-provided batch + // note: this is mostly relevant for recurrent models atm + if (!sorted_output) { + const uint32_t n_vocab = model.vocab.n_tokens(); + const uint32_t n_embd = model.hparams.n_embd; + + GGML_ASSERT((size_t) n_outputs == out_ids.size()); + + // TODO: is there something more efficient which also minimizes swaps? + // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort) + for (int32_t i = 0; i < n_outputs - 1; ++i) { + int32_t j_min = i; + for (int32_t j = i + 1; j < n_outputs; ++j) { + if (out_ids[j] < out_ids[j_min]) { + j_min = j; + } + } + if (j_min == i) { continue; } + std::swap(out_ids[i], out_ids[j_min]); + if (logits_size > 0) { + for (uint32_t k = 0; k < n_vocab; k++) { + std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]); + } + } + if (embd_size > 0) { + for (uint32_t k = 0; k < n_embd; k++) { + std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]); + } + } + } + std::fill(output_ids.begin(), output_ids.end(), -1); + for (int32_t i = 0; i < n_outputs; ++i) { + output_ids[out_ids[i]] = i; + } } } @@ -1446,17 +1110,8 @@ int llama_context::decode(llama_batch & inp_batch) { //synchronize(); // decide if we need to defrag the kv cache - if (cparams.causal_attn && cparams.defrag_thold > 0.0f) { - // - do not defrag small contexts (i.e. < 2048 tokens) - // - count the padding towards the number of used tokens - const float fragmentation = kv_self->n >= 2048 ? std::max(0.0f, 1.0f - float(kv_self->used + kv_self->get_padding(cparams))/float(kv_self->n)) : 0.0f; - - // queue defragmentation for next llama_kv_cache_update - if (fragmentation > cparams.defrag_thold) { - LLAMA_LOG_DEBUG("%s: fragmentation: %.2f - requesting defrag\n", __func__, fragmentation); - - kv_self->defrag(); - } + if (cparams.defrag_thold > 0.0f) { + kv_self->defrag_sched(cparams.defrag_thold); } // Reset state for the next token before backend sync, to allow the CPU activities in the reset to @@ -1542,44 +1197,6 @@ int32_t llama_context::output_reserve(int32_t n_outputs) { return n_outputs_max; } -void llama_context::output_reorder() { - auto & out_ids = sbatch.out_ids; - if (!out_ids.empty()) { - const uint32_t n_vocab = model.vocab.n_tokens(); - const uint32_t n_embd = model.hparams.n_embd; - - GGML_ASSERT((size_t) n_outputs == out_ids.size()); - - // TODO: is there something more efficient which also minimizes swaps? - // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort) - for (int32_t i = 0; i < n_outputs - 1; ++i) { - int32_t j_min = i; - for (int32_t j = i + 1; j < n_outputs; ++j) { - if (out_ids[j] < out_ids[j_min]) { - j_min = j; - } - } - if (j_min == i) { continue; } - std::swap(out_ids[i], out_ids[j_min]); - if (logits_size > 0) { - for (uint32_t k = 0; k < n_vocab; k++) { - std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]); - } - } - if (embd_size > 0) { - for (uint32_t k = 0; k < n_embd; k++) { - std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]); - } - } - } - std::fill(output_ids.begin(), output_ids.end(), -1); - for (int32_t i = 0; i < n_outputs; ++i) { - output_ids[out_ids[i]] = i; - } - out_ids.clear(); - } -} - // // graph // @@ -1616,7 +1233,7 @@ llm_graph_result_ptr llama_context::graph_build( /*.backend_cpu =*/ backend_cpu, /*.cvec =*/ &cvec, /*.loras =*/ &loras, - /*.memory =*/ kv_self.get(), + /*.memory =*/ memory.get(), /*.cross =*/ &cross, /*.n_outputs =*/ n_outputs, /*.cb =*/ graph_get_cb(), @@ -2020,8 +1637,6 @@ size_t llama_context::state_write_data(llama_io_write_i & io) { { LLAMA_LOG_DEBUG("%s: - writing output ids\n", __func__); - output_reorder(); - const auto n_outputs = this->n_outputs; const auto & output_ids = this->output_ids; @@ -2075,6 +1690,8 @@ size_t llama_context::state_write_data(llama_io_write_i & io) { } LLAMA_LOG_DEBUG("%s: - writing KV self\n", __func__); + llama_kv_cache * kv_self = static_cast(memory.get()); + kv_self->state_write(io); return io.n_bytes(); @@ -2159,6 +1776,8 @@ size_t llama_context::state_read_data(llama_io_read_i & io) { } LLAMA_LOG_DEBUG("%s: - reading KV self\n", __func__); + llama_kv_cache * kv_self = static_cast(memory.get()); + kv_self->state_read(io); return io.n_bytes(); @@ -2167,6 +1786,8 @@ size_t llama_context::state_read_data(llama_io_read_i & io) { size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id) { GGML_UNUSED(seq_id); + llama_kv_cache * kv_self = static_cast(memory.get()); + kv_self->state_write(io, seq_id); return io.n_bytes(); @@ -2175,6 +1796,8 @@ size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id s size_t llama_context::state_seq_read_data(llama_io_read_i & io, llama_seq_id seq_id) { GGML_UNUSED(seq_id); + llama_kv_cache * kv_self = static_cast(memory.get()); + kv_self->state_read(io, seq_id); return io.n_bytes(); @@ -2530,7 +2153,7 @@ void llama_kv_cache_seq_cp( llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { - return llama_kv_self_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1); + llama_kv_self_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1); } void llama_kv_self_seq_cp( @@ -2544,14 +2167,14 @@ void llama_kv_self_seq_cp( return; } - return kv->seq_cp(seq_id_src, seq_id_dst, p0, p1); + kv->seq_cp(seq_id_src, seq_id_dst, p0, p1); } // deprecated void llama_kv_cache_seq_keep( llama_context * ctx, llama_seq_id seq_id) { - return llama_kv_self_seq_keep(ctx, seq_id); + llama_kv_self_seq_keep(ctx, seq_id); } void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) { @@ -2560,7 +2183,7 @@ void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) { return; } - return kv->seq_keep(seq_id); + kv->seq_keep(seq_id); } // deprecated @@ -2570,7 +2193,7 @@ void llama_kv_cache_seq_add( llama_pos p0, llama_pos p1, llama_pos delta) { - return llama_kv_self_seq_add(ctx, seq_id, p0, p1, delta); + llama_kv_self_seq_add(ctx, seq_id, p0, p1, delta); } void llama_kv_self_seq_add( @@ -2584,7 +2207,7 @@ void llama_kv_self_seq_add( return; } - return kv->seq_add(seq_id, p0, p1, delta); + kv->seq_add(seq_id, p0, p1, delta); } // deprecated @@ -2594,7 +2217,7 @@ void llama_kv_cache_seq_div( llama_pos p0, llama_pos p1, int d) { - return llama_kv_self_seq_div(ctx, seq_id, p0, p1, d); + llama_kv_self_seq_div(ctx, seq_id, p0, p1, d); } void llama_kv_self_seq_div( @@ -2608,7 +2231,7 @@ void llama_kv_self_seq_div( return; } - return kv->seq_div(seq_id, p0, p1, d); + kv->seq_div(seq_id, p0, p1, d); } // deprecated @@ -2627,7 +2250,7 @@ llama_pos llama_kv_self_seq_pos_max(llama_context * ctx, llama_seq_id seq_id) { // deprecated void llama_kv_cache_defrag(llama_context * ctx) { - return llama_kv_self_defrag(ctx); + llama_kv_self_defrag(ctx); } void llama_kv_self_defrag(llama_context * ctx) { @@ -2636,7 +2259,8 @@ void llama_kv_self_defrag(llama_context * ctx) { return; } - return kv->defrag(); + // force defrag + kv->defrag_sched(-1.0f); } // deprecated diff --git a/src/llama-context.h b/src/llama-context.h index 5457f077c1..cf41ac57b9 100644 --- a/src/llama-context.h +++ b/src/llama-context.h @@ -27,7 +27,12 @@ struct llama_context { void synchronize(); - const llama_model & get_model() const; + const llama_model & get_model() const; + const llama_cparams & get_cparams() const; + + ggml_backend_sched_t get_sched() const; + + ggml_context * get_ctx_compute() const; uint32_t n_ctx() const; uint32_t n_ctx_per_seq() const; @@ -137,49 +142,30 @@ private: // Returns max number of outputs for which space was reserved. int32_t output_reserve(int32_t n_outputs); - // make the outputs have the same order they had in the user-provided batch - // TODO: maybe remove this - void output_reorder(); - // // graph // +public: int32_t graph_max_nodes() const; // zero-out inputs and create the ctx_compute for the compute graph ggml_cgraph * graph_init(); - llm_graph_result_ptr graph_build( - ggml_context * ctx, - ggml_cgraph * gf, - const llama_ubatch & ubatch, - llm_graph_type gtype); - // returns the result of ggml_backend_sched_graph_compute_async execution ggml_status graph_compute( ggml_cgraph * gf, bool batched); +private: + llm_graph_result_ptr graph_build( + ggml_context * ctx, + ggml_cgraph * gf, + const llama_ubatch & ubatch, + llm_graph_type gtype); + llm_graph_cb graph_get_cb() const; - // used by kv_self_update() - ggml_tensor * build_rope_shift( - ggml_context * ctx0, - ggml_tensor * cur, - ggml_tensor * shift, - ggml_tensor * factors, - float freq_base, - float freq_scale) const; - - llm_graph_result_ptr build_kv_self_shift( - ggml_context * ctx0, - ggml_cgraph * gf) const; - - llm_graph_result_ptr build_kv_self_defrag( - ggml_context * ctx0, - ggml_cgraph * gf) const; - // TODO: read/write lora adapters and cvec size_t state_write_data(llama_io_write_i & io); size_t state_read_data (llama_io_read_i & io); @@ -196,11 +182,10 @@ private: llama_cparams cparams; llama_adapter_cvec cvec; llama_adapter_loras loras; - llama_sbatch sbatch; llama_cross cross; // TODO: tmp for handling cross-attention - need something better probably - std::unique_ptr kv_self; + std::unique_ptr memory; // TODO: remove bool logits_all = false; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index fabb9ca237..0da4e7d2b0 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -284,24 +284,7 @@ void llm_graph_input_s_copy::set_input(const llama_ubatch * ubatch) { // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n for (uint32_t i = 0; i < n_kv; ++i) { - const uint32_t cell_id = i + kv_self->head; - - ////////////////////////////////////////////// - // TODO: this should not mutate the KV cache ! - llama_kv_cell & kv_cell = const_cast(kv_self)->cells[i]; - - // prevent out-of-bound sources - if (kv_cell.src < 0 || (uint32_t) kv_cell.src >= kv_self->size) { - kv_cell.src = cell_id; - } - - data[i] = kv_cell.src; - - // TODO: do not mutate the KV cache - // ensure copy only happens once - if (kv_cell.src != (int32_t) cell_id) { - kv_cell.src = cell_id; - } + data[i] = kv_self->s_copy(i); } } } @@ -317,18 +300,7 @@ void llm_graph_input_s_mask::set_input(const llama_ubatch * ubatch) { // clear unused states for (int i = 0; i < n_kv; ++i) { - const uint32_t cell_id = i + kv_self->head; - - ////////////////////////////////////////////// - // TODO: this should not mutate the KV cache ! - llama_kv_cell & kv_cell = const_cast(kv_self)->cells[i]; - - data[i] = (float) (kv_cell.src >= 0); - - // only clear once - if (kv_cell.src < 0) { - kv_cell.src = cell_id; - } + data[i] = kv_self->s_mask(i); } } } @@ -1105,7 +1077,7 @@ ggml_tensor * llm_graph_context::build_inp_cls() const { } ggml_tensor * llm_graph_context::build_inp_s_copy() const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); auto inp = std::make_unique(kv_self); @@ -1122,7 +1094,7 @@ ggml_tensor * llm_graph_context::build_inp_s_copy() const { } ggml_tensor * llm_graph_context::build_inp_s_mask() const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); auto inp = std::make_unique(kv_self); @@ -1436,8 +1408,6 @@ ggml_tensor * llm_graph_context::build_attn( // store to KV cache { - GGML_ASSERT(!kv_self->recurrent); - const auto kv_head = kv_self->head; GGML_ASSERT(kv_self->size == n_ctx); @@ -1587,7 +1557,7 @@ ggml_tensor * llm_graph_context::build_copy_mask_state( ggml_tensor * state_mask, int32_t n_state, int32_t n_seqs) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto n_kv = kv_self->n; const auto kv_head = kv_self->head; @@ -1619,7 +1589,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load( ggml_tensor * state_mask, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto token_shift_count = hparams.token_shift_count; @@ -1640,7 +1610,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store( ggml_tensor * token_shift, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto token_shift_count = hparams.token_shift_count; const auto n_embd = hparams.n_embd; diff --git a/src/llama-graph.h b/src/llama-graph.h index d0c8d32192..5b404366dc 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -19,6 +19,7 @@ struct llama_cparams; class llama_memory_i; class llama_kv_cache_unified; +class llama_kv_cache_recurrent; // certain models (typically multi-modal) can produce different types of graphs enum llm_graph_type { @@ -186,26 +187,26 @@ public: class llm_graph_input_s_copy : public llm_graph_input_i { public: - llm_graph_input_s_copy(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {} + llm_graph_input_s_copy(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {} virtual ~llm_graph_input_s_copy() = default; void set_input(const llama_ubatch * ubatch) override; ggml_tensor * s_copy; // I32 [kv_size] - const llama_kv_cache_unified * kv_self; + const llama_kv_cache_recurrent * kv_self; }; class llm_graph_input_s_mask : public llm_graph_input_i { public: - llm_graph_input_s_mask(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {} + llm_graph_input_s_mask(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {} virtual ~llm_graph_input_s_mask() = default; void set_input(const llama_ubatch * ubatch) override; ggml_tensor * s_mask; // F32 [1, n_kv] - const llama_kv_cache_unified * kv_self; + const llama_kv_cache_recurrent * kv_self; }; class llm_graph_input_cross_embd : public llm_graph_input_i { @@ -350,8 +351,8 @@ struct llm_graph_params { const llama_cparams & cparams; const llama_ubatch & ubatch; - ggml_backend_sched * sched; - ggml_backend * backend_cpu; + ggml_backend_sched_t sched; + ggml_backend_t backend_cpu; const llama_adapter_cvec * cvec; const llama_adapter_loras * loras; @@ -402,9 +403,9 @@ struct llm_graph_context { ggml_context * ctx0 = nullptr; - ggml_backend_sched * sched; + ggml_backend_sched_t sched; - ggml_backend * backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove? + ggml_backend_t backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove? const llama_adapter_cvec * cvec; const llama_adapter_loras * loras; diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index 7c9d46d811..3dcad65bb6 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -4,33 +4,41 @@ #include "llama-batch.h" #include "llama-cparams.h" #include "llama-model.h" +#include "llama-context.h" #include #include +#include #include #include #include -llama_kv_cache_unified::llama_kv_cache_unified(const llama_hparams & hparams, callbacks cbs) : hparams(hparams), cbs(std::move(cbs)) { +// +// llama_kv_cache_unified +// + +uint32_t llama_kv_cache_unified::get_padding(const llama_cparams & cparams) { + // the FA kernels require padding to avoid extra runtime boundary checks + return cparams.flash_attn ? 256u : 32u; } -bool llama_kv_cache_unified::init( +llama_kv_cache_unified::llama_kv_cache_unified( const llama_model & model, - const llama_cparams & cparams, ggml_type type_k, ggml_type type_v, + bool v_trans, + bool offload, uint32_t kv_size, - bool offload) { + uint32_t padding) : model(model), hparams(model.hparams), v_trans(v_trans), padding(padding) { const int32_t n_layer = hparams.n_layer; has_shift = false; + can_shift = true; - recurrent = llama_model_is_recurrent(&model); - v_trans = !recurrent && !cparams.flash_attn; - can_shift = !recurrent; + LLAMA_LOG_INFO("%s: kv_size = %d, type_k = '%s', type_v = '%s', n_layer = %d, can_shift = %d, padding = %d\n", + __func__, kv_size, ggml_type_name(type_k), ggml_type_name(type_v), n_layer, can_shift, padding); - LLAMA_LOG_INFO("%s: kv_size = %d, offload = %d, type_k = '%s', type_v = '%s', n_layer = %d, can_shift = %d\n", - __func__, kv_size, offload, ggml_type_name(type_k), ggml_type_name(type_v), n_layer, can_shift); + GGML_ASSERT(kv_size % padding == 0 && "kv_size must be a multiple of padding"); head = 0; size = kv_size; @@ -76,23 +84,20 @@ bool llama_kv_cache_unified::init( const char * dev_name = "CPU"; - ggml_backend_buffer_type_t buft; + ggml_backend_buffer_type_t buft = ggml_backend_cpu_buffer_type(); + if (offload) { auto * dev = model.dev_layer(i); buft = ggml_backend_dev_buffer_type(dev); dev_name = ggml_backend_dev_name(dev); - } else { - buft = ggml_backend_cpu_buffer_type(); } - LLAMA_LOG_DEBUG("%s: layer %3d: n_embd_k_gqa = %d, n_embd_v_gqa = %d, dev = %s\n", __func__, - i, n_embd_k_gqa, n_embd_v_gqa, dev_name); + LLAMA_LOG_DEBUG("%s: layer %3d: dev = %s\n", __func__, i, dev_name); ggml_context * ctx = ctx_for_buft(buft); if (!ctx) { - LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__); - return false; + throw std::runtime_error("failed to create ggml context for kv cache"); } ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); @@ -110,55 +115,28 @@ bool llama_kv_cache_unified::init( ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); if (!buf) { - LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__); - return false; + throw std::runtime_error("failed to allocate buffer for kv cache"); } ggml_backend_buffer_clear(buf, 0); LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); bufs.emplace_back(buf); } - return true; -} + { + const size_t memory_size_k = size_k_bytes(); + const size_t memory_size_v = size_v_bytes(); -int32_t llama_kv_cache_unified::get_n_tokens() const { - int32_t result = 0; - - for (uint32_t i = 0; i < size; i++) { - result += cells[i].seq_id.size(); + LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__, + (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), + ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f), + ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); } - - return result; -} - -int32_t llama_kv_cache_unified::get_used_cells() const { - return used; -} - -size_t llama_kv_cache_unified::total_size() const { - size_t size = 0; - for (const auto & buf : bufs) { - size += ggml_backend_buffer_get_size(buf.get()); - } - - return size; -} - -llama_pos llama_kv_cache_unified::pos_max() const { - llama_pos pos_max = -1; - for (const auto & cell : cells) { - pos_max = std::max(pos_max, cell.pos); - } - - return pos_max; } void llama_kv_cache_unified::clear() { for (int32_t i = 0; i < (int32_t) size; ++i) { cells[i].pos = -1; cells[i].seq_id.clear(); - cells[i].src = -1; - cells[i].tail = -1; } head = 0; used = 0; @@ -179,35 +157,6 @@ bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1 = std::numeric_limits::max(); } - // models like Mamba or RWKV can't have a state partially erased - if (recurrent) { - if (seq_id >= (int64_t) size) { - // could be fatal - return false; - } - if (0 <= seq_id) { - int32_t & tail_id = cells[seq_id].tail; - if (tail_id >= 0) { - const llama_kv_cell & cell = cells[tail_id]; - // partial intersection is invalid - if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) { - return false; - } - // invalidate tails which will be cleared - if (p0 <= cell.pos && cell.pos < p1) { - tail_id = -1; - } - } - } else { - // seq_id is negative, then the range should include everything or nothing - if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits::max())) { - return false; - } - } - - return true; - } - for (uint32_t i = 0; i < size; ++i) { if (cells[i].pos >= p0 && cells[i].pos < p1) { if (seq_id < 0) { @@ -224,7 +173,6 @@ bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos } cells[i].pos = -1; - cells[i].src = -1; if (new_head == size) { new_head = i; @@ -254,34 +202,6 @@ void llama_kv_cache_unified::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id p1 = std::numeric_limits::max(); } - if (recurrent) { - if ((uint32_t) seq_id_dst < size && (uint32_t) seq_id_src < size) { - llama_kv_cell & tail_src = cells[seq_id_src]; - llama_kv_cell & tail_dst = cells[seq_id_dst]; - if (tail_dst.tail >= 0) { - // clear destination seq_id if it wasn't empty - llama_kv_cell & cell_dst = cells[tail_dst.tail]; - - cell_dst.seq_id.erase(seq_id_dst); - tail_dst.tail = -1; - if (cell_dst.seq_id.empty()) { - cell_dst.pos = -1; - cell_dst.delta = -1; - cell_dst.src = -1; - used -= 1; - } - } - if (tail_src.tail >= 0) { - llama_kv_cell & cell_src = cells[tail_src.tail]; - - cell_src.seq_id.insert(seq_id_dst); - tail_dst.tail = tail_src.tail; - } - } - - return; - } - // otherwise, this is the KV of a Transformer-like model head = 0; @@ -296,17 +216,12 @@ void llama_kv_cache_unified::seq_keep(llama_seq_id seq_id) { uint32_t new_head = size; for (uint32_t i = 0; i < size; ++i) { - if (recurrent && (llama_seq_id) i != seq_id) { - cells[i].tail = -1; - } - if (!cells[i].has_seq_id(seq_id)) { if (cells[i].pos >= 0) { used--; } cells[i].pos = -1; - cells[i].src = -1; cells[i].seq_id.clear(); if (new_head == size){ @@ -344,20 +259,6 @@ void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_po return; } - if (recurrent) { - // for Mamba-like or RWKV models, only the pos needs to be shifted - if (0 <= seq_id && seq_id < (int64_t) size) { - const int32_t tail_id = cells[seq_id].tail; - if (tail_id >= 0) { - llama_kv_cell & cell = cells[tail_id]; - if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { - cell.pos += delta; - } - } - } - return; - } - for (uint32_t i = 0; i < size; ++i) { if (cells[i].has_seq_id(seq_id) && cells[i].pos >= p0 && cells[i].pos < p1) { has_shift = true; @@ -400,21 +301,6 @@ void llama_kv_cache_unified::seq_div(llama_seq_id seq_id, llama_pos p0, llama_po return; } - if (recurrent) { - // for Mamba-like or RWKV models, only the pos needs to be changed - if (0 <= seq_id && seq_id < (int64_t) size) { - const int32_t tail_id = cells[seq_id].tail; - if (tail_id >= 0) { - llama_kv_cell & cell = cells[tail_id]; - if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { - cell.pos /= d; - } - } - } - - return; - } - for (uint32_t i = 0; i < size; ++i) { if (cells[i].has_seq_id(seq_id) && cells[i].pos >= p0 && cells[i].pos < p1) { has_shift = true; @@ -440,23 +326,11 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const { return result; } -void llama_kv_cache_unified::defrag() { - if (!recurrent) { - do_defrag = true; - } -} - void llama_kv_cache_unified::restore() { if (pending.ranges.empty()) { return; } - // TODO: tmp - move to llama_kv_cache_recurrent - if (recurrent) { - seq_rm(-1, -1, -1); - return; - } - uint32_t new_head = size; for (auto & range : pending.ranges) { @@ -469,7 +343,6 @@ void llama_kv_cache_unified::restore() { } cells[i].pos = -1; - cells[i].src = -1; } new_head = std::min(new_head, range.c0); @@ -481,11 +354,6 @@ void llama_kv_cache_unified::restore() { } void llama_kv_cache_unified::commit() { - // TODO: tmp - move to llama_kv_cache_recurrent - if (recurrent) { - return; - } - if (pending.ranges.empty()) { LLAMA_LOG_WARN("%s: no pending KV cache updates to commit - might indicate a bug (ref: %s)\n", __func__, "https://github.com/ggml-org/llama.cpp/pull/12695"); @@ -495,8 +363,98 @@ void llama_kv_cache_unified::commit() { pending.ranges.clear(); } -bool llama_kv_cache_unified::get_can_shift() const { - return can_shift; +bool llama_kv_cache_unified::update(llama_context & lctx) { + bool need_reserve = false; + + auto * sched = lctx.get_sched(); + + if (has_shift) { + if (!get_can_shift()) { + GGML_ABORT("The current KV cache / model configuration does not support K-shift"); + } + + LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__); + + // apply K-shift if needed + if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE) { + ggml_backend_sched_reset(sched); + + auto * gf = lctx.graph_init(); + + auto res = build_graph_shift(lctx.get_cparams(), lctx.get_ctx_compute(), gf); + + ggml_backend_sched_alloc_graph(sched, gf); + + res->set_inputs(nullptr); + + lctx.graph_compute(gf, false); + + need_reserve = true; + } + + { + has_shift = false; + + for (uint32_t i = 0; i < size; ++i) { + cells[i].delta = 0; + } + } + } + + if (do_defrag) { + LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__); + + if (defrag_prepare(lctx.graph_max_nodes())) { + ggml_backend_sched_reset(sched); + + auto * gf = lctx.graph_init(); + + auto res = build_graph_defrag(lctx.get_cparams(), lctx.get_ctx_compute(), gf); + + ggml_backend_sched_alloc_graph(sched, gf); + + res->set_inputs(nullptr); + + lctx.graph_compute(gf, false); + + need_reserve = true; + } + + do_defrag = false; + } + + return need_reserve; +} + +void llama_kv_cache_unified::defrag_sched(float thold) { + // - do not defrag small contexts (i.e. < 2048 tokens) + // - count the padding towards the number of used tokens + const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(used + padding)/n)) : 0.0f; + + // queue defragmentation for next llama_kv_cache_update + if (fragmentation > thold) { + LLAMA_LOG_DEBUG("%s: fragmentation: %.2f - requesting defrag\n", __func__, fragmentation); + + do_defrag = true; + } +} + +void llama_kv_cache_unified::set_full() { + n = size; +} + +llama_sbatch llama_kv_cache_unified::sbatch_init( + const llama_batch & batch, + bool logits_all) { + return llama_sbatch(batch, hparams.n_embd, true, logits_all); +} + +llama_ubatch llama_kv_cache_unified::ubatch_next( + llama_sbatch & sbatch, + uint32_t n_ubatch, + bool embd_pooled) const { + GGML_UNUSED(embd_pooled); + return sbatch.split_simple(n_ubatch); } bool llama_kv_cache_unified::find_slot( @@ -511,169 +469,6 @@ bool llama_kv_cache_unified::find_slot( head = 0; } - if (recurrent) { - // For recurrent state architectures (like Mamba or RWKV), - // each cache cell can store the state for a whole sequence. - // A slot should be always be contiguous. - - // can only process batches with an equal number of new tokens in each sequence - GGML_ASSERT(ubatch.equal_seqs); - - int32_t min = size - 1; - int32_t max = 0; - - // everything should fit if all seq_ids are smaller than the max - for (uint32_t s = 0; s < n_seqs; ++s) { - const uint32_t n_seq_id = ubatch.n_seq_id[s]; - for (uint32_t j = 0; j < n_seq_id; ++j) { - const llama_seq_id seq_id = ubatch.seq_id[s][j]; - - if (seq_id < 0 || (uint32_t) seq_id >= size) { - // too big seq_id - // TODO: would it be possible to resize the cache instead? - LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, size); - return false; - } - if (j > 0) { - llama_kv_cell & seq = cells[seq_id]; - if (seq.tail >= 0) { - llama_kv_cell & cell = cells[seq.tail]; - // clear cells from seq_ids that become shared - // (should not normally happen, but let's handle it anyway) - cell.seq_id.erase(seq_id); - seq.tail = -1; - if (cell.seq_id.empty()) { - cell.pos = -1; - cell.src = -1; - used -= 1; - } - } - } - } - } - -#ifndef NDEBUG - { - std::vector tails_verif; - tails_verif.assign(size, -1); - for (uint32_t i = 0; i < size; ++i) { - llama_kv_cell & cell = cells[i]; - for (llama_seq_id seq_id : cell.seq_id) { - if (tails_verif[seq_id] != -1) { - LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]); - } - tails_verif[seq_id] = i; - } - } - for (uint32_t i = 0; i < size; ++i) { - if (tails_verif[i] != cells[i].tail) { - LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cells[i].tail, tails_verif[i]); - } - } - } -#endif - - // find next empty cell - uint32_t next_empty_cell = head; - - for (uint32_t i = 0; i < size; ++i) { - if (next_empty_cell >= size) { next_empty_cell -= size; } - llama_kv_cell & cell = cells[next_empty_cell]; - if (cell.is_empty()) { break; } - next_empty_cell += 1; - } - - // find usable cell range - for (uint32_t s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = ubatch.seq_id[s][0]; - llama_kv_cell & seq_meta = cells[seq_id]; - bool has_cell = false; - if (seq_meta.tail >= 0) { - llama_kv_cell & cell = cells[seq_meta.tail]; - GGML_ASSERT(cell.has_seq_id(seq_id)); - // does this seq_id "own" the cell? - if (cell.seq_id.size() == 1) { has_cell = true; } - } - if (!has_cell) { - llama_kv_cell & empty_cell = cells[next_empty_cell]; - GGML_ASSERT(empty_cell.is_empty()); - // copy old tail into the empty cell - if (seq_meta.tail >= 0) { - llama_kv_cell & orig_cell = cells[seq_meta.tail]; - empty_cell.pos = orig_cell.pos; - empty_cell.src = orig_cell.src; - orig_cell.seq_id.erase(seq_id); - empty_cell.seq_id.insert(seq_id); // will be overwritten - } - seq_meta.tail = next_empty_cell; - // find next empty cell - if (s + 1 < n_seqs) { - next_empty_cell += 1; - for (uint32_t i = 0; i < size; ++i) { - if (next_empty_cell >= size) { next_empty_cell -= size; } - llama_kv_cell & cell = cells[next_empty_cell]; - if (cell.is_empty()) { break; } - next_empty_cell += 1; - } - } - } - if (min > seq_meta.tail) { min = seq_meta.tail; } - if (max < seq_meta.tail) { max = seq_meta.tail; } - } - - // gather and re-order - for (uint32_t s = 0; s < n_seqs; ++s) { - int32_t dst_id = s + min; - int32_t src_id = cells[ubatch.seq_id[s][0]].tail; - if (dst_id != src_id) { - llama_kv_cell & dst_cell = cells[dst_id]; - llama_kv_cell & src_cell = cells[src_id]; - - std::swap(dst_cell.pos, src_cell.pos); - std::swap(dst_cell.src, src_cell.src); - std::swap(dst_cell.seq_id, src_cell.seq_id); - - // swap tails (assuming they NEVER overlap) - for (const llama_seq_id seq_id : src_cell.seq_id) { - cells[seq_id].tail = src_id; - } - for (const llama_seq_id seq_id : dst_cell.seq_id) { - cells[seq_id].tail = dst_id; - } - } - } - - // update the pos of the used seqs - for (uint32_t s = 0; s < n_seqs; ++s) { - const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1]; - int32_t cell_id = s + min; - llama_kv_cell & cell = cells[cell_id]; - - if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) { - // What should happen when the pos backtracks or skips a value? - // Clearing the state mid-batch would require special-casing which isn't done. - LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n", - __func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens); - } - cell.pos = last_pos; - cell.seq_id.clear(); - for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) { - const llama_seq_id seq_id = ubatch.seq_id[s][j]; - cell.seq_id.insert(seq_id); - cells[seq_id].tail = cell_id; - } - } - - // allow getting the range of used cells, from head to head + n - head = min; - n = max - min + 1; - used = std::count_if(cells.begin(), cells.end(), - [](const llama_kv_cell& cell){ return !cell.is_empty(); }); - - // sanity check - return n >= n_seqs; - } - // otherwise, one cell per token. if (n_tokens > size) { @@ -725,24 +520,50 @@ bool llama_kv_cache_unified::find_slot( pending.ranges.push_back({head, head + n_tokens}); + // a heuristic, to avoid attending the full cache if it is not yet utilized + // after enough generations, the benefit from this heuristic disappears + // if we start defragmenting the cache, the benefit from this will be more important + n = std::min(size, std::max(padding, GGML_PAD(cell_max(), padding))); + + //printf("n = %5d, used = %5d, head = %5d\n", n, used, head); + return true; } -uint32_t llama_kv_cache_unified::get_padding(const llama_cparams & cparams) const { - // the FA kernels require padding to avoid extra runtime boundary checks - return cparams.flash_attn ? 256u : 32u; -} +int32_t llama_kv_cache_unified::get_n_tokens() const { + int32_t result = 0; -uint32_t llama_kv_cache_unified::cell_max() const { - for (uint32_t i = size; i > 0; --i) { - const llama_kv_cell & cell = cells[i - 1]; - - if (cell.pos >= 0 && !cell.is_empty()) { - return i; - } + for (uint32_t i = 0; i < size; i++) { + result += cells[i].seq_id.size(); } - return 0; + return result; +} + +int32_t llama_kv_cache_unified::get_used_cells() const { + return used; +} + +bool llama_kv_cache_unified::get_can_shift() const { + return can_shift; +} + +llama_pos llama_kv_cache_unified::get_pos_max() const { + llama_pos pos_max = -1; + for (const auto & cell : cells) { + pos_max = std::max(pos_max, cell.pos); + } + + return pos_max; +} + +size_t llama_kv_cache_unified::total_size() const { + size_t size = 0; + for (const auto & buf : bufs) { + size += ggml_backend_buffer_get_size(buf.get()); + } + + return size; } size_t llama_kv_cache_unified::size_k_bytes() const { @@ -765,6 +586,269 @@ size_t llama_kv_cache_unified::size_v_bytes() const { return size_v_bytes; } +ggml_tensor * llama_kv_cache_unified::build_rope_shift( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_tensor * cur, + ggml_tensor * shift, + ggml_tensor * factors, + float freq_base, + float freq_scale) const { + const auto & n_ctx_orig = cparams.n_ctx_orig_yarn; + + const auto & yarn_ext_factor = cparams.yarn_ext_factor; + const auto & yarn_beta_fast = cparams.yarn_beta_fast; + const auto & yarn_beta_slow = cparams.yarn_beta_slow; + + const auto & n_rot = hparams.n_rot; + const auto & rope_type = hparams.rope_type; + + // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly. + // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. + const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2 ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)) : cparams.yarn_attn_factor; + + ggml_tensor * tmp; + + if (ggml_is_quantized(cur->type)) { + // dequantize to f32 -> RoPE -> quantize back + tmp = ggml_cast(ctx, cur, GGML_TYPE_F32); + + tmp = ggml_rope_ext(ctx, tmp, + shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow); + + tmp = ggml_cpy(ctx, tmp, cur); + } else { + // we rotate only the first n_rot dimensions + tmp = ggml_rope_ext_inplace(ctx, cur, + shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow); + } + + return tmp; +} + +class llm_graph_input_k_shift : public llm_graph_input_i { +public: + llm_graph_input_k_shift(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {} + virtual ~llm_graph_input_k_shift() = default; + + void set_input(const llama_ubatch * ubatch) override; + + ggml_tensor * k_shift; // I32 [kv_size] + + const llama_kv_cache_unified * kv_self; +}; + +void llm_graph_input_k_shift::set_input(const llama_ubatch * ubatch) { + GGML_UNUSED(ubatch); + + if (k_shift) { + assert(ggml_backend_buffer_is_host(k_shift->buffer)); + + int32_t * data = (int32_t *) k_shift->data; + + for (uint32_t i = 0; i < kv_self->size; ++i) { + data[i] = kv_self->cells[i].delta; + } + } +} + +llm_graph_result_ptr llama_kv_cache_unified::build_graph_shift( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_cgraph * gf) const { + auto res = std::make_unique(); + + const auto & n_layer = hparams.n_layer; + + const auto & n_embd_head_k = hparams.n_embd_head_k; + //const auto & n_embd_head_v = hparams.n_embd_head_v; + + const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max; + + //GGML_ASSERT(kv_self->size == n_ctx); + + auto inp = std::make_unique(this); + + inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, cparams.n_ctx); + ggml_set_input(inp->k_shift); + + for (uint32_t il = 0; il < n_layer; ++il) { + const int64_t n_head_kv = hparams.n_head_kv(il); + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); + + const bool is_swa = hparams.is_swa(il); + + // note: the swa rope params could become part of the cparams in the future + // if we decide to make them configurable, like the non-sliding ones + const float freq_base_l = is_swa ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base; + const float freq_scale_l = is_swa ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale; + + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); + + ggml_tensor * k = + ggml_view_3d(ctx, k_l[il], + n_embd_head_k, n_head_kv, size, + ggml_row_size(k_l[il]->type, n_embd_head_k), + ggml_row_size(k_l[il]->type, n_embd_k_gqa), + 0); + + ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l); + + ggml_build_forward_expand(gf, cur); + } + + res->add_input(std::move(inp)); + + return res; +} + +llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_cgraph * gf) const { + auto res = std::make_unique(); + + const auto & ids = defrag_info.ids; + +#if 0 + // CPU defrag + // + // TODO: optimizations are possible: + // - multiple threads + // - avoid copying to the host memory when already there + // + // likely not worth the effort, as we have ggml_graph based defrag + // + + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + + const uint32_t kv_size = size; + + std::vector buf_k; + std::vector buf_v; + + for (uint32_t il = 0; il < n_layer; ++il) { + const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa); + const size_t k_size = ggml_row_size(k_l[il]->type, n_embd_k_gqa*kv_size); + + const size_t v_size_el = ggml_type_size(v_l[il]->type); + const size_t v_size = ggml_row_size (v_l[il]->type, n_embd_v_gqa*kv_size); + + buf_k.resize(k_size); + buf_v.resize(v_size); + + ggml_backend_tensor_get(k_l[il], buf_k.data(), 0, buf_k.size()); + ggml_backend_tensor_get(v_l[il], buf_v.data(), 0, buf_v.size()); + + // batch move [i, i+nm) to [id, id+nm) + // note: cells can move only to a lower index + for (uint32_t i = 0; i < n_kv; ++i) { + const uint32_t id = ids[i]; + + if (i == id || id == n_kv) { + continue; + } + + uint32_t nm = 1; + + while (i + nm < n_kv && ids[i + nm] == id + nm) { + nm++; + } + + // move keys + { + const int64_t os = i*k_size_row; + const int64_t od = id*k_size_row; + + memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row); + } + + // move values (note: they are transposed) + { + const int64_t os = i; + const int64_t od = id; + + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el); + } + } + + i += nm - 1; + } + + ggml_backend_tensor_set(k_l[il], buf_k.data(), 0, buf_k.size()); + ggml_backend_tensor_set(v_l[il], buf_v.data(), 0, buf_v.size()); + } +#else + for (uint32_t i = 0; i < ids.size(); ++i) { + const uint32_t id = ids[i]; + + if (i == id || id == ids.size()) { + continue; + } + + uint32_t nm = 1; + + while (i + nm < ids.size() && ids[i + nm] == id + nm) { + nm++; + } + + for (uint32_t il = 0; il < hparams.n_layer; ++il) { // NOLINT + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); + + ggml_tensor * view_k_src = ggml_view_2d(ctx, k_l[il], + n_embd_k_gqa, nm, + ggml_row_size(k_l[il]->type, n_embd_k_gqa), + ggml_row_size(k_l[il]->type, n_embd_k_gqa*i)); + + ggml_tensor * view_k_dst = ggml_view_2d(ctx, k_l[il], + n_embd_k_gqa, nm, + ggml_row_size(k_l[il]->type, n_embd_k_gqa), + ggml_row_size(k_l[il]->type, n_embd_k_gqa*id)); + + ggml_tensor * view_v_src; + ggml_tensor * view_v_dst; + + if (cparams.flash_attn) { + // NOTE: the V cache is not transposed when using flash attention + view_v_src = ggml_view_2d(ctx, v_l[il], + n_embd_v_gqa, nm, + ggml_row_size(v_l[il]->type, n_embd_v_gqa), + ggml_row_size(v_l[il]->type, n_embd_v_gqa*i)); + + view_v_dst = ggml_view_2d(ctx, v_l[il], + n_embd_v_gqa, nm, + ggml_row_size(v_l[il]->type, n_embd_v_gqa), + ggml_row_size(v_l[il]->type, n_embd_v_gqa*id)); + } else { + view_v_src = ggml_view_2d(ctx, v_l[il], + nm, n_embd_v_gqa, + ggml_row_size(v_l[il]->type, size), + ggml_row_size(v_l[il]->type, i)); + + view_v_dst = ggml_view_2d(ctx, v_l[il], + nm, n_embd_v_gqa, + ggml_row_size(v_l[il]->type, size), + ggml_row_size(v_l[il]->type, id)); + } + + ggml_build_forward_expand(gf, ggml_cpy(ctx, view_k_src, view_k_dst)); + ggml_build_forward_expand(gf, ggml_cpy(ctx, view_v_src, view_v_dst)); + } + + i += nm - 1; + } + + //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes); +#endif + + return res; +} + bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) { const uint32_t n_layer = hparams.n_layer; @@ -867,7 +951,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) { cells[i0 + nf] = cell1; // clear the old cell and move the head there - cell1 = llama_kv_cell(); + cell1 = kv_cell(); head = n_used; if (!cont) { @@ -895,13 +979,25 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) { return false; } - LLAMA_LOG_DEBUG("(tmp log) KV defrag cell moves: %u\n", n_moves); + LLAMA_LOG_DEBUG("%s: (tmp log) KV defrag cell moves: %u\n", __func__, n_moves); - LLAMA_LOG_DEBUG("expected gf nodes: %u\n", 6*n_moves*n_layer); + LLAMA_LOG_DEBUG("%s: expected gf nodes: %u\n", __func__, 6*n_moves*n_layer); return true; } +uint32_t llama_kv_cache_unified::cell_max() const { + for (uint32_t i = size; i > 0; --i) { + const kv_cell & cell = cells[i - 1]; + + if (cell.pos >= 0 && !cell.is_empty()) { + return i; + } + } + + return 0; +} + void llama_kv_cache_unified::state_write(llama_io_write_i & io, llama_seq_id seq_id) const { std::vector> cell_ranges; // ranges, from inclusive, to exclusive uint32_t cell_count = 0; @@ -1110,7 +1206,7 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell clear(); for (uint32_t i = 0; i < cell_count; ++i) { - llama_kv_cell & cell = cells[i]; + kv_cell & cell = cells[i]; llama_pos pos; uint32_t n_seq_id; @@ -1133,15 +1229,6 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell } cell.seq_id.insert(seq_id); - - if (recurrent) { - int32_t & tail = cells[seq_id].tail; - if (tail != -1) { - LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail); - return false; - } - tail = i; - } } } @@ -1149,14 +1236,6 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell used = cell_count; } - if (recurrent) { - for (uint32_t i = 0; i < cell_count; ++i) { - uint32_t cell_id = head + i; - // make sure the recurrent states will keep their restored state - cells[cell_id].src = cell_id; - } - } - return true; } @@ -1174,7 +1253,1034 @@ bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t cell LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, size); return false; } - if (v_trans != (bool) v_trans) { + if (this->v_trans != (bool) v_trans) { + LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__); + return false; + } + + // For each layer, read the keys for each cell, one row is one cell, read as one contiguous block + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(); + + // Read type of key + int32_t k_type_i_ref; + io.read_to(&k_type_i_ref, sizeof(k_type_i_ref)); + const int32_t k_type_i = (int32_t) k_l[il]->type; + if (k_type_i != k_type_i_ref) { + LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il); + return false; + } + + // Read row size of key + uint64_t k_size_row_ref; + io.read_to(&k_size_row_ref, sizeof(k_size_row_ref)); + const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa); + if (k_size_row != k_size_row_ref) { + LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, (size_t) k_size_row_ref, il); + return false; + } + + if (cell_count) { + // Read and set the keys for the whole cell range + ggml_backend_tensor_set(k_l[il], io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row); + } + } + + if (!this->v_trans) { + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + + // Read type of value + int32_t v_type_i_ref; + io.read_to(&v_type_i_ref, sizeof(v_type_i_ref)); + const int32_t v_type_i = (int32_t)v_l[il]->type; + if (v_type_i != v_type_i_ref) { + LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il); + return false; + } + + // Read row size of value + uint64_t v_size_row_ref; + io.read_to(&v_size_row_ref, sizeof(v_size_row_ref)); + const size_t v_size_row = ggml_row_size(v_l[il]->type, n_embd_v_gqa); + if (v_size_row != v_size_row_ref) { + LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, (size_t) v_size_row_ref, il); + return false; + } + + if (cell_count) { + // Read and set the values for the whole cell range + ggml_backend_tensor_set(v_l[il], io.read(cell_count * v_size_row), head * v_size_row, cell_count * v_size_row); + } + } + } else { + // For each layer, read the values for each cell (transposed) + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + + // Read type of value + int32_t v_type_i_ref; + io.read_to(&v_type_i_ref, sizeof(v_type_i_ref)); + const int32_t v_type_i = (int32_t)v_l[il]->type; + if (v_type_i != v_type_i_ref) { + LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il); + return false; + } + + // Read element size of value + uint32_t v_size_el_ref; + io.read_to(&v_size_el_ref, sizeof(v_size_el_ref)); + const size_t v_size_el = ggml_type_size(v_l[il]->type); + if (v_size_el != v_size_el_ref) { + LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, (size_t) v_size_el_ref, il); + return false; + } + + // Read GQA embedding size + uint32_t n_embd_v_gqa_ref; + io.read_to(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref)); + if (n_embd_v_gqa != n_embd_v_gqa_ref) { + LLAMA_LOG_ERROR("%s: mismatched GQA embedding size (%u != %u, layer %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref, il); + return false; + } + + if (cell_count) { + // For each row in the transposed matrix, read the values for the whole cell range + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + const size_t dst_offset = (head + j * size) * v_size_el; + ggml_backend_tensor_set(v_l[il], io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el); + } + } + } + } + + return true; +} + +// +// llama_kv_cache_recurrent +// + +llama_kv_cache_recurrent::llama_kv_cache_recurrent( + const llama_model & model, + ggml_type type_k, + ggml_type type_v, + bool offload, + uint32_t kv_size) : hparams(model.hparams) { + const int32_t n_layer = hparams.n_layer; + + LLAMA_LOG_INFO("%s: kv_size = %d, type_k = '%s', type_v = '%s', n_layer = %d\n", + __func__, kv_size, ggml_type_name(type_k), ggml_type_name(type_v), n_layer); + + head = 0; + size = kv_size; + used = 0; + + this->type_k = type_k; + this->type_v = type_v; + + cells.clear(); + cells.resize(kv_size); + + // create a context for each buffer type + std::map ctx_map; + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + auto it = ctx_map.find(buft); + if (it == ctx_map.end()) { + ggml_init_params params = { + /*.mem_size =*/ size_t(2u*n_layer*ggml_tensor_overhead()), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + ggml_context * ctx = ggml_init(params); + if (!ctx) { + return nullptr; + } + + ctx_map[buft] = ctx; + ctxs.emplace_back(ctx); + + return ctx; + } + + return it->second; + }; + + k_l.reserve(n_layer); + v_l.reserve(n_layer); + + for (int i = 0; i < n_layer; i++) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s(); + + const char * dev_name = "CPU"; + + ggml_backend_buffer_type_t buft = ggml_backend_cpu_buffer_type(); + + if (offload) { + auto * dev = model.dev_layer(i); + buft = ggml_backend_dev_buffer_type(dev); + + dev_name = ggml_backend_dev_name(dev); + } + + LLAMA_LOG_DEBUG("%s, layer %3d: dev = %s\n", __func__, i, dev_name); + + ggml_context * ctx = ctx_for_buft(buft); + if (!ctx) { + throw std::runtime_error("failed to create ggml context for kv cache"); + } + + ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); + ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size); + ggml_format_name(k, "cache_k_l%d", i); + ggml_format_name(v, "cache_v_l%d", i); + k_l.push_back(k); + v_l.push_back(v); + } + + // allocate tensors and initialize the buffers to avoid NaNs in the padding + for (auto it : ctx_map) { + auto * buft = it.first; + auto * ctx = it.second; + + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + if (!buf) { + throw std::runtime_error("failed to allocate buffer for kv cache"); + } + ggml_backend_buffer_clear(buf, 0); + LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); + bufs.emplace_back(buf); + } + + { + const size_t memory_size_k = size_k_bytes(); + const size_t memory_size_v = size_v_bytes(); + + LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__, + (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), + ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f), + ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); + } +} + +void llama_kv_cache_recurrent::clear() { + for (int32_t i = 0; i < (int32_t) size; ++i) { + cells[i].pos = -1; + cells[i].seq_id.clear(); + cells[i].src = -1; + cells[i].tail = -1; + } + head = 0; + used = 0; + + for (auto & buf : bufs) { + ggml_backend_buffer_clear(buf.get(), 0); + } +} + +bool llama_kv_cache_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) { + uint32_t new_head = size; + + if (p0 < 0) { + p0 = 0; + } + + if (p1 < 0) { + p1 = std::numeric_limits::max(); + } + + // models like Mamba or RWKV can't have a state partially erased + if (seq_id >= (int64_t) size) { + // could be fatal + return false; + } + if (0 <= seq_id) { + int32_t & tail_id = cells[seq_id].tail; + if (tail_id >= 0) { + const kv_cell & cell = cells[tail_id]; + // partial intersection is invalid + if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) { + return false; + } + // invalidate tails which will be cleared + if (p0 <= cell.pos && cell.pos < p1) { + tail_id = -1; + } + } + } else { + // seq_id is negative, then the range should include everything or nothing + if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits::max())) { + return false; + } + } + + for (uint32_t i = 0; i < size; ++i) { + if (cells[i].pos >= p0 && cells[i].pos < p1) { + if (seq_id < 0) { + cells[i].seq_id.clear(); + } else if (cells[i].has_seq_id(seq_id)) { + cells[i].seq_id.erase(seq_id); + } else { + continue; + } + if (cells[i].is_empty()) { + // keep count of the number of used cells + if (cells[i].pos >= 0) { + used--; + } + cells[i].pos = -1; + cells[i].src = -1; + if (new_head == size) { + new_head = i; + } + } + } + } + + // If we freed up a slot, set head to it so searching can start there. + if (new_head != size && new_head < head) { + head = new_head; + } + + return true; +} + +void llama_kv_cache_recurrent::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { + if (seq_id_src == seq_id_dst) { + return; + } + + if (p0 < 0) { + p0 = 0; + } + + if (p1 < 0) { + p1 = std::numeric_limits::max(); + } + + if ((uint32_t) seq_id_dst < size && (uint32_t) seq_id_src < size) { + kv_cell & tail_src = cells[seq_id_src]; + kv_cell & tail_dst = cells[seq_id_dst]; + if (tail_dst.tail >= 0) { + // clear destination seq_id if it wasn't empty + kv_cell & cell_dst = cells[tail_dst.tail]; + + cell_dst.seq_id.erase(seq_id_dst); + tail_dst.tail = -1; + if (cell_dst.seq_id.empty()) { + cell_dst.pos = -1; + cell_dst.src = -1; + used -= 1; + } + } + if (tail_src.tail >= 0) { + kv_cell & cell_src = cells[tail_src.tail]; + + cell_src.seq_id.insert(seq_id_dst); + tail_dst.tail = tail_src.tail; + } + } +} + +void llama_kv_cache_recurrent::seq_keep(llama_seq_id seq_id) { + uint32_t new_head = size; + + for (uint32_t i = 0; i < size; ++i) { + if ((llama_seq_id) i != seq_id) { + cells[i].tail = -1; + } + + if (!cells[i].has_seq_id(seq_id)) { + if (cells[i].pos >= 0) { + used--; + } + + cells[i].pos = -1; + cells[i].src = -1; + cells[i].seq_id.clear(); + + if (new_head == size){ + new_head = i; + } + } else { + cells[i].seq_id.clear(); + cells[i].seq_id.insert(seq_id); + } + } + + // If we freed up a slot, set head to it so searching can start there. + if (new_head != size && new_head < head) { + head = new_head; + } +} + +void llama_kv_cache_recurrent::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) { + if (delta == 0) { + return; + } + + if (p0 < 0) { + p0 = 0; + } + + if (p1 < 0) { + p1 = std::numeric_limits::max(); + } + + // If there is no range then return early to avoid looping over the + if (p0 == p1) { + return; + } + + // for Mamba-like or RWKV models, only the pos needs to be shifted + if (0 <= seq_id && seq_id < (int64_t) size) { + const int32_t tail_id = cells[seq_id].tail; + if (tail_id >= 0) { + kv_cell & cell = cells[tail_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos += delta; + } + } + } +} + +void llama_kv_cache_recurrent::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) { + if (d == 1) { + return; + } + + if (p0 < 0) { + p0 = 0; + } + + if (p1 < 0) { + p1 = std::numeric_limits::max(); + } + + // If there is no range then return early to avoid looping over the cache. + if (p0 == p1) { + return; + } + + // for Mamba-like or RWKV models, only the pos needs to be changed + if (0 <= seq_id && seq_id < (int64_t) size) { + const int32_t tail_id = cells[seq_id].tail; + if (tail_id >= 0) { + kv_cell & cell = cells[tail_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos /= d; + } + } + } +} + +llama_pos llama_kv_cache_recurrent::seq_pos_max(llama_seq_id seq_id) const { + llama_pos result = 0; + + for (uint32_t i = 0; i < size; ++i) { + if (cells[i].has_seq_id(seq_id)) { + result = std::max(result, cells[i].pos); + } + } + + return result; +} + +void llama_kv_cache_recurrent::restore() { + if (pending.ranges.empty()) { + return; + } + + seq_rm(-1, -1, -1); +} + +void llama_kv_cache_recurrent::commit() { + pending.ranges.clear(); +} + +bool llama_kv_cache_recurrent::update(llama_context & lctx) { + GGML_UNUSED(lctx); + return false; +} + +void llama_kv_cache_recurrent::defrag_sched(float thold) { + GGML_UNUSED(thold); + // noop +} + +void llama_kv_cache_recurrent::set_full() { + n = size; +} + +llama_sbatch llama_kv_cache_recurrent::sbatch_init( + const llama_batch & batch, + bool logits_all) { + return llama_sbatch(batch, hparams.n_embd, false, logits_all); +} + +llama_ubatch llama_kv_cache_recurrent::ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const { + if (embd_pooled) { + // Pooled embeddings cannot be split across ubatches (yet) + return sbatch.split_seq(n_ubatch); + } + + return sbatch.split_equal(n_ubatch); +} + +bool llama_kv_cache_recurrent::find_slot( + const llama_ubatch & ubatch) { + const uint32_t n_tokens = ubatch.n_tokens; + const uint32_t n_seqs = ubatch.n_seqs; + + const uint32_t n_seq_tokens = ubatch.n_seq_tokens; + + // if we have enough unused cells before the current head -> + // better to start searching from the beginning of the cache, hoping to fill it + if (head > used + 2*n_tokens) { + head = 0; + } + + // For recurrent state architectures (like Mamba or RWKV), + // each cache cell can store the state for a whole sequence. + // A slot should be always be contiguous. + + // can only process batches with an equal number of new tokens in each sequence + GGML_ASSERT(ubatch.equal_seqs); + + int32_t min = size - 1; + int32_t max = 0; + + // everything should fit if all seq_ids are smaller than the max + for (uint32_t s = 0; s < n_seqs; ++s) { + const uint32_t n_seq_id = ubatch.n_seq_id[s]; + for (uint32_t j = 0; j < n_seq_id; ++j) { + const llama_seq_id seq_id = ubatch.seq_id[s][j]; + + if (seq_id < 0 || (uint32_t) seq_id >= size) { + // too big seq_id + // TODO: would it be possible to resize the cache instead? + LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, size); + return false; + } + if (j > 0) { + kv_cell & seq = cells[seq_id]; + if (seq.tail >= 0) { + kv_cell & cell = cells[seq.tail]; + // clear cells from seq_ids that become shared + // (should not normally happen, but let's handle it anyway) + cell.seq_id.erase(seq_id); + seq.tail = -1; + if (cell.seq_id.empty()) { + cell.pos = -1; + cell.src = -1; + used -= 1; + } + } + } + } + } + +#ifndef NDEBUG + { + std::vector tails_verif; + tails_verif.assign(size, -1); + for (uint32_t i = 0; i < size; ++i) { + kv_cell & cell = cells[i]; + for (llama_seq_id seq_id : cell.seq_id) { + if (tails_verif[seq_id] != -1) { + LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]); + } + tails_verif[seq_id] = i; + } + } + for (uint32_t i = 0; i < size; ++i) { + if (tails_verif[i] != cells[i].tail) { + LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cells[i].tail, tails_verif[i]); + } + } + } +#endif + + // find next empty cell + uint32_t next_empty_cell = head; + + for (uint32_t i = 0; i < size; ++i) { + if (next_empty_cell >= size) { next_empty_cell -= size; } + kv_cell & cell = cells[next_empty_cell]; + if (cell.is_empty()) { break; } + next_empty_cell += 1; + } + + // find usable cell range + for (uint32_t s = 0; s < n_seqs; ++s) { + const llama_seq_id seq_id = ubatch.seq_id[s][0]; + kv_cell & seq_meta = cells[seq_id]; + bool has_cell = false; + if (seq_meta.tail >= 0) { + kv_cell & cell = cells[seq_meta.tail]; + GGML_ASSERT(cell.has_seq_id(seq_id)); + // does this seq_id "own" the cell? + if (cell.seq_id.size() == 1) { has_cell = true; } + } + if (!has_cell) { + kv_cell & empty_cell = cells[next_empty_cell]; + GGML_ASSERT(empty_cell.is_empty()); + // copy old tail into the empty cell + if (seq_meta.tail >= 0) { + kv_cell & orig_cell = cells[seq_meta.tail]; + empty_cell.pos = orig_cell.pos; + empty_cell.src = orig_cell.src; + orig_cell.seq_id.erase(seq_id); + empty_cell.seq_id.insert(seq_id); // will be overwritten + } + seq_meta.tail = next_empty_cell; + // find next empty cell + if (s + 1 < n_seqs) { + next_empty_cell += 1; + for (uint32_t i = 0; i < size; ++i) { + if (next_empty_cell >= size) { next_empty_cell -= size; } + kv_cell & cell = cells[next_empty_cell]; + if (cell.is_empty()) { break; } + next_empty_cell += 1; + } + } + } + if (min > seq_meta.tail) { min = seq_meta.tail; } + if (max < seq_meta.tail) { max = seq_meta.tail; } + } + + // gather and re-order + for (uint32_t s = 0; s < n_seqs; ++s) { + int32_t dst_id = s + min; + int32_t src_id = cells[ubatch.seq_id[s][0]].tail; + if (dst_id != src_id) { + kv_cell & dst_cell = cells[dst_id]; + kv_cell & src_cell = cells[src_id]; + + std::swap(dst_cell.pos, src_cell.pos); + std::swap(dst_cell.src, src_cell.src); + std::swap(dst_cell.seq_id, src_cell.seq_id); + + // swap tails (assuming they NEVER overlap) + for (const llama_seq_id seq_id : src_cell.seq_id) { + cells[seq_id].tail = src_id; + } + for (const llama_seq_id seq_id : dst_cell.seq_id) { + cells[seq_id].tail = dst_id; + } + } + } + + // update the pos of the used seqs + for (uint32_t s = 0; s < n_seqs; ++s) { + const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1]; + int32_t cell_id = s + min; + kv_cell & cell = cells[cell_id]; + + if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) { + // What should happen when the pos backtracks or skips a value? + // Clearing the state mid-batch would require special-casing which isn't done. + LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n", + __func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens); + } + cell.pos = last_pos; + cell.seq_id.clear(); + for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) { + const llama_seq_id seq_id = ubatch.seq_id[s][j]; + cell.seq_id.insert(seq_id); + cells[seq_id].tail = cell_id; + } + } + + // allow getting the range of used cells, from head to head + n + head = min; + n = max - min + 1; + used = std::count_if(cells.begin(), cells.end(), + [](const kv_cell & cell){ return !cell.is_empty(); }); + + // sanity check + return n >= n_seqs; +} + +int32_t llama_kv_cache_recurrent::get_n_tokens() const { + int32_t result = 0; + + for (uint32_t i = 0; i < size; i++) { + result += cells[i].seq_id.size(); + } + + return result; +} + +int32_t llama_kv_cache_recurrent::get_used_cells() const { + return used; +} + +llama_pos llama_kv_cache_recurrent::get_pos_max() const { + llama_pos pos_max = -1; + for (const auto & cell : cells) { + pos_max = std::max(pos_max, cell.pos); + } + + return pos_max; +} + +bool llama_kv_cache_recurrent::get_can_shift() const { + return false; +} + +int32_t llama_kv_cache_recurrent::s_copy(int i) const { + const uint32_t cell_id = i + head; + + ////////////////////////////////////////////// + // TODO: this should not mutate the KV cache ! + kv_cell & cell = const_cast(cells[cell_id]); + + // prevent out-of-bound sources + if (cell.src < 0 || (uint32_t) cell.src >= size) { + cell.src = cell_id; + } + + int32_t res = cell.src; + + // TODO: do not mutate the KV cache + // ensure copy only happens once + if (cell.src != (int32_t) cell_id) { + cell.src = cell_id; + } + + return res; +} + +float llama_kv_cache_recurrent::s_mask(int i) const { + const uint32_t cell_id = i + head; + + ////////////////////////////////////////////// + // TODO: this should not mutate the KV cache ! + kv_cell & cell = const_cast(cells[cell_id]); + + float res = (float) (cell.src >= 0); + + // only clear once + if (cell.src < 0) { + cell.src = cell_id; + } + + return res; +} + +uint32_t llama_kv_cache_recurrent::cell_max() const { + for (uint32_t i = size; i > 0; --i) { + const kv_cell & cell = cells[i - 1]; + + if (cell.pos >= 0 && !cell.is_empty()) { + return i; + } + } + + return 0; +} + +size_t llama_kv_cache_recurrent::total_size() const { + size_t size = 0; + for (const auto & buf : bufs) { + size += ggml_backend_buffer_get_size(buf.get()); + } + + return size; +} + +size_t llama_kv_cache_recurrent::size_k_bytes() const { + size_t size_k_bytes = 0; + + for (const auto & k : k_l) { + size_k_bytes += ggml_nbytes(k); + } + + return size_k_bytes; +} + +size_t llama_kv_cache_recurrent::size_v_bytes() const { + size_t size_v_bytes = 0; + + for (const auto & v : v_l) { + size_v_bytes += ggml_nbytes(v); + } + + return size_v_bytes; +} + +void llama_kv_cache_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq_id) const { + std::vector> cell_ranges; // ranges, from inclusive, to exclusive + uint32_t cell_count = 0; + + // Count the number of cells with the specified seq_id + // Find all the ranges of cells with this seq id (or all, when -1) + uint32_t cell_range_begin = size; + for (uint32_t i = 0; i < size; ++i) { + const auto & cell = cells[i]; + if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) { + ++cell_count; + if (cell_range_begin == size) { + cell_range_begin = i; + } + } else { + if (cell_range_begin != size) { + cell_ranges.emplace_back(cell_range_begin, i); + cell_range_begin = size; + } + } + } + if (cell_range_begin != size) { + cell_ranges.emplace_back(cell_range_begin, size); + } + + // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count + uint32_t cell_count_check = 0; + for (const auto & range : cell_ranges) { + cell_count_check += range.second - range.first; + } + GGML_ASSERT(cell_count == cell_count_check); + + io.write(&cell_count, sizeof(cell_count)); + + state_write_meta(io, cell_ranges, seq_id); + state_write_data(io, cell_ranges); +} + +void llama_kv_cache_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_id) { + uint32_t cell_count; + io.read_to(&cell_count, sizeof(cell_count)); + + bool res = true; + res = res && state_read_meta(io, cell_count, seq_id); + res = res && state_read_data(io, cell_count); + + if (!res) { + if (seq_id == -1) { + clear(); + } else { + seq_rm(seq_id, -1, -1); + } + throw std::runtime_error("failed to restore kv cache"); + } +} + +void llama_kv_cache_recurrent::state_write_meta(llama_io_write_i & io, const std::vector> & cell_ranges, llama_seq_id seq_id) const { + for (const auto & range : cell_ranges) { + for (uint32_t i = range.first; i < range.second; ++i) { + const auto & cell = cells[i]; + const llama_pos pos = cell.pos; + const uint32_t n_seq_id = seq_id == -1 ? cell.seq_id.size() : 0; + + io.write(&pos, sizeof(pos)); + io.write(&n_seq_id, sizeof(n_seq_id)); + + if (n_seq_id) { + for (auto seq_id : cell.seq_id) { + io.write(&seq_id, sizeof(seq_id)); + } + } + } + } +} + +void llama_kv_cache_recurrent::state_write_data(llama_io_write_i & io, const std::vector> & cell_ranges) const { + const uint32_t v_trans = 0; + const uint32_t n_layer = hparams.n_layer; + + io.write(&v_trans, sizeof(v_trans)); + io.write(&n_layer, sizeof(n_layer)); + + std::vector tmp_buf; + + // Iterate and write all the keys first, each row is a cell + // Get whole range at a time + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(); + + // Write key type + const int32_t k_type_i = (int32_t)k_l[il]->type; + io.write(&k_type_i, sizeof(k_type_i)); + + // Write row size of key + const uint64_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa); + io.write(&k_size_row, sizeof(k_size_row)); + + // Read each range of cells of k_size length each into tmp_buf and write out + for (const auto & range : cell_ranges) { + const size_t range_size = range.second - range.first; + const size_t buf_size = range_size * k_size_row; + io.write_tensor(k_l[il], range.first * k_size_row, buf_size); + } + } + + if (!v_trans) { + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + + // Write value type + const int32_t v_type_i = (int32_t)v_l[il]->type; + io.write(&v_type_i, sizeof(v_type_i)); + + // Write row size of value + const uint64_t v_size_row = ggml_row_size(v_l[il]->type, n_embd_v_gqa); + io.write(&v_size_row, sizeof(v_size_row)); + + // Read each range of cells of v_size length each into tmp_buf and write out + for (const auto & range : cell_ranges) { + const size_t range_size = range.second - range.first; + const size_t buf_size = range_size * v_size_row; + io.write_tensor(v_l[il], range.first * v_size_row, buf_size); + } + } + } else { + // When v is transposed, we also need the element size and get the element ranges from each row + const uint32_t kv_size = size; + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + + // Write value type + const int32_t v_type_i = (int32_t)v_l[il]->type; + io.write(&v_type_i, sizeof(v_type_i)); + + // Write element size + const uint32_t v_size_el = ggml_type_size(v_l[il]->type); + io.write(&v_size_el, sizeof(v_size_el)); + + // Write GQA embedding size + io.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa)); + + // For each row, we get the element values of each cell + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + // Read each range of cells of v_size_el length each into tmp_buf and write out + for (const auto & range : cell_ranges) { + const size_t range_size = range.second - range.first; + const size_t src_offset = (range.first + j * kv_size) * v_size_el; + const size_t buf_size = range_size * v_size_el; + io.write_tensor(v_l[il], src_offset, buf_size); + } + } + } + } +} + +bool llama_kv_cache_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id) { + if (dest_seq_id != -1) { + // single sequence + + seq_rm(dest_seq_id, -1, -1); + + llama_sbatch sbatch; + llama_ubatch batch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false); + + batch.n_tokens = cell_count; + batch.n_seq_tokens = cell_count; + batch.n_seqs = 1; + + for (uint32_t i = 0; i < cell_count; ++i) { + llama_pos pos; + uint32_t n_seq_id; + + io.read_to(&pos, sizeof(pos)); + io.read_to(&n_seq_id, sizeof(n_seq_id)); + + if (n_seq_id != 0) { + LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__); + return false; + } + + batch.pos[i] = pos; + } + batch.n_seq_id[0] = 1; + batch.seq_id[0] = &dest_seq_id; + if (!find_slot(batch)) { + LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__); + return false; + } + commit(); + + // DEBUG CHECK: kv.head should be our first cell, kv.head + cell_count - 1 should be our last cell (verify seq_id and pos values) + // Assume that this is one contiguous block of cells + GGML_ASSERT(head + cell_count <= size); + GGML_ASSERT(cells[head].pos == batch.pos[0]); + GGML_ASSERT(cells[head + cell_count - 1].pos == batch.pos[cell_count - 1]); + GGML_ASSERT(cells[head].has_seq_id(dest_seq_id)); + GGML_ASSERT(cells[head + cell_count - 1].has_seq_id(dest_seq_id)); + } else { + // whole KV cache restore + + if (cell_count > size) { + LLAMA_LOG_ERROR("%s: not enough cells in kv cache\n", __func__); + return false; + } + + clear(); + + for (uint32_t i = 0; i < cell_count; ++i) { + kv_cell & cell = cells[i]; + + llama_pos pos; + uint32_t n_seq_id; + + io.read_to(&pos, sizeof(pos)); + io.read_to(&n_seq_id, sizeof(n_seq_id)); + + cell.pos = pos; + + for (uint32_t j = 0; j < n_seq_id; ++j) { + llama_seq_id seq_id; + io.read_to(&seq_id, sizeof(seq_id)); + + // TODO: llama_kv_cache_recurrent should have a notion of max sequences + //if (seq_id < 0 || (uint32_t) seq_id >= llama_n_seq_max(ctx)) { + if (seq_id < 0) { + //LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, llama_n_seq_max(ctx)); + LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, inf)\n", __func__, seq_id); + return false; + } + + cell.seq_id.insert(seq_id); + + int32_t & tail = cells[seq_id].tail; + if (tail != -1) { + LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail); + return false; + } + tail = i; + } + } + + head = 0; + used = cell_count; + } + + for (uint32_t i = 0; i < cell_count; ++i) { + uint32_t cell_id = head + i; + // make sure the recurrent states will keep their restored state + cells[cell_id].src = cell_id; + } + + return true; +} + +bool llama_kv_cache_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell_count) { + uint32_t v_trans; + uint32_t n_layer; + io.read_to(&v_trans, sizeof(v_trans)); + io.read_to(&n_layer, sizeof(n_layer)); + + if (n_layer != hparams.n_layer) { + LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, hparams.n_layer); + return false; + } + if (cell_count > size) { + LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, size); + return false; + } + if (false != (bool) v_trans) { LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__); return false; } @@ -1326,7 +2432,7 @@ void llama_kv_cache_view_update(llama_kv_cache_view * view, const llama_kv_cache view->cells_sequences = (llama_seq_id *)p; } - const std::vector & kv_cells = kvu->cells; + const std::vector & kv_cells = kvu->cells; llama_kv_cache_view_cell * c_curr = view->cells; llama_seq_id * cs_curr = view->cells_sequences; int32_t used_cells = 0; diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h index 56c74035ae..bf3b4b6a44 100644 --- a/src/llama-kv-cache.h +++ b/src/llama-kv-cache.h @@ -2,32 +2,72 @@ #include "llama.h" #include "llama-io.h" +#include "llama-graph.h" #include "llama-memory.h" #include "ggml-cpp.h" -#include #include #include struct llama_cparams; struct llama_hparams; struct llama_ubatch; +struct llama_sbatch; +struct llama_model; +struct llama_context; struct llama_kv_cache : public llama_memory_i { - using llama_memory_i::llama_memory_i; + virtual ~llama_kv_cache() = default; - virtual void restore() = 0; // call if batch processing fails - restores the cache state - virtual void commit() = 0; // call after successful batch processing - clears any pending state + // call if batch processing fails - restores the cache state + virtual void restore() = 0; - virtual int32_t get_n_tokens() const = 0; - virtual int32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache + // call after successful batch processing - clears any pending state + virtual void commit() = 0; - virtual bool get_can_shift() const = 0; + // process any pending defrag/shift/etc. operations + // optionally call once before processing a new batch + virtual bool update(llama_context & lctx) = 0; + + // schedule a defrag if the fragmentation threshold is exceeded. otherwise, do nothing + virtual void defrag_sched(float thold) = 0; + + // simulate full cache, used for allocating worst-case compute buffers + virtual void set_full() = 0; + + // + // batch processing + // + + virtual llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) = 0; + + // different KV caches require different batch splitting strategies + virtual llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const = 0; + + // find an empty slot of size "n_tokens" in the cache + virtual bool find_slot(const llama_ubatch & batch) = 0; + + // getters + virtual int32_t get_n_tokens() const = 0; + virtual int32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache + virtual llama_pos get_pos_max() const = 0; + virtual bool get_can_shift() const = 0; bool get_can_edit() const override { return get_can_shift(); } + + // + // state write/read + // + + virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0; + virtual void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) = 0; }; +// +// llama_kv_cache_guard +// + struct llama_kv_cache_guard { llama_kv_cache_guard(llama_kv_cache * kv) : kv(kv) {} @@ -43,65 +83,50 @@ private: llama_kv_cache * kv; }; -struct llama_kv_cell { - llama_pos pos = -1; - llama_pos delta = 0; - int32_t src = -1; // used by recurrent state models to copy states - int32_t tail = -1; +// +// llama_kv_cache_unified +// - std::set seq_id; - - bool has_seq_id(const llama_seq_id & id) const { - return seq_id.find(id) != seq_id.end(); - } - - bool is_empty() const { - return seq_id.empty(); - } - - bool is_same_seq(const llama_kv_cell & other) const { - return seq_id == other.seq_id; - } -}; - -// ring-buffer of cached KV data -// TODO: pimpl // TODO: add notion of max sequences class llama_kv_cache_unified : public llama_kv_cache { public: - // can be used to query data from the model if needed - struct callbacks { - std::function get_rope_factors; + struct kv_cell { + llama_pos pos = -1; + llama_pos delta = 0; + + std::set seq_id; + + bool has_seq_id(const llama_seq_id & id) const { + return seq_id.find(id) != seq_id.end(); + } + + bool is_empty() const { + return seq_id.empty(); + } + + bool is_same_seq(const kv_cell & other) const { + return seq_id == other.seq_id; + } }; + static uint32_t get_padding(const llama_cparams & cparams); + llama_kv_cache_unified( - const llama_hparams & hparams, - callbacks cbs); - - virtual ~llama_kv_cache_unified() = default; - - // TODO: become constructor - bool init( - const llama_model & model, // TODO: do not reference the model - const llama_cparams & cparams, + const llama_model & model, ggml_type type_k, ggml_type type_v, + bool v_trans, + bool offload, uint32_t kv_size, - bool offload); + uint32_t padding); - int32_t get_n_tokens() const override; - int32_t get_used_cells() const override; + ~llama_kv_cache_unified() = default; - size_t total_size() const; - - // TODO: better data structures to reduce the cost of this operation - llama_pos pos_max() const; + // + // llama_memory_i + // void clear() override; - void defrag() override; - - virtual void restore() override; - virtual void commit() override; bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override; void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override; @@ -111,63 +136,40 @@ public: llama_pos seq_pos_max(llama_seq_id seq_id) const override; - bool get_can_shift() const override; + // + // llama_kv_cache + // + + void restore() override; + void commit() override; + + bool update(llama_context & ctx) override; + + void defrag_sched(float thold) override; + + void set_full() override; + + llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override; + + llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override; - // find an empty slot of size "n_tokens" in the cache // updates the cache head // Note: On success, it's important that cache.head points // to the first cell of the slot. - bool find_slot(const llama_ubatch & batch); + bool find_slot(const llama_ubatch & batch) override; - // TODO: maybe not needed - uint32_t get_padding(const llama_cparams & cparams) const; + int32_t get_n_tokens() const override; + int32_t get_used_cells() const override; - // find how many cells are currently in use - uint32_t cell_max() const; + // TODO: better data structures to reduce the cost of this operation + llama_pos get_pos_max() const override; - size_t size_k_bytes() const; - size_t size_v_bytes() const; - - // defrag - - struct { - std::vector ids; - } defrag_info; - - // return true if cells have been moved - bool defrag_prepare(int32_t n_max_nodes); - - // commit/restore cache - - struct slot_range { - uint32_t c0 = 0; // note: these are cell indices, not sequence positions - uint32_t c1 = 0; - }; - - // pending cell updates that are not yet committed - struct { - std::vector ranges; - } pending; + bool get_can_shift() const override; // state write/load - void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const; - void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1); - - // members - - const llama_hparams & hparams; - - callbacks cbs; - - bool has_shift = false; - bool do_defrag = false; - - // TODO: remove this and implement llama_kv_cache_recurrent instead - bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token - - bool v_trans = true; // the value tensor is transposed - bool can_shift = false; + void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override; + void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override; // Note: The value of head isn't only used to optimize searching // for a free KV slot. llama_decode_impl also uses it, so it @@ -179,18 +181,213 @@ public: // computed before each graph build uint32_t n = 0; - std::vector cells; + std::vector cells; std::vector k_l; // per layer std::vector v_l; private: + const llama_model & model; + const llama_hparams & hparams; + + bool has_shift = false; + bool do_defrag = false; + + bool v_trans = true; // the value tensor is transposed + bool can_shift = false; + + // required padding + uint32_t padding = 1; + ggml_type type_k = GGML_TYPE_F16; ggml_type type_v = GGML_TYPE_F16; std::vector ctxs; std::vector bufs; + // defrag + struct { + std::vector ids; + } defrag_info; + + // return true if cells have been moved + bool defrag_prepare(int32_t n_max_nodes); + + // commit/restore cache + struct slot_range { + uint32_t c0 = 0; // note: these are cell indices, not sequence positions + uint32_t c1 = 0; + }; + + // pending cell updates that are not yet committed + struct { + std::vector ranges; + } pending; + + // find how many cells are currently in use + uint32_t cell_max() const; + + size_t total_size() const; + + size_t size_k_bytes() const; + size_t size_v_bytes() const; + + ggml_tensor * build_rope_shift( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_tensor * cur, + ggml_tensor * shift, + ggml_tensor * factors, + float freq_base, + float freq_scale) const; + + llm_graph_result_ptr build_graph_shift( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_cgraph * gf) const; + + llm_graph_result_ptr build_graph_defrag( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_cgraph * gf) const; + + void state_write_meta(llama_io_write_i & io, const std::vector> & cell_ranges, llama_seq_id seq_id = -1) const; + void state_write_data(llama_io_write_i & io, const std::vector> & cell_ranges) const; + + bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1); + bool state_read_data(llama_io_read_i & io, uint32_t cell_count); +}; + +// +// llama_kv_cache_recurrent +// + +class llama_kv_cache_recurrent : public llama_kv_cache { +public: + struct kv_cell { + llama_pos pos = -1; + int32_t src = -1; // used to copy states + int32_t tail = -1; + + std::set seq_id; + + bool has_seq_id(const llama_seq_id & id) const { + return seq_id.find(id) != seq_id.end(); + } + + bool is_empty() const { + return seq_id.empty(); + } + + bool is_same_seq(const kv_cell & other) const { + return seq_id == other.seq_id; + } + }; + + llama_kv_cache_recurrent( + const llama_model & model, + ggml_type type_k, + ggml_type type_v, + bool offload, + uint32_t kv_size); + + ~llama_kv_cache_recurrent() = default; + + // + // llama_memory_i + // + + void clear() override; + + bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override; + void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override; + void seq_keep(llama_seq_id seq_id) override; + void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) override; + void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override; + + llama_pos seq_pos_max(llama_seq_id seq_id) const override; + + // + // llama_kv_cache + // + + void restore() override; + void commit() override; + + bool update(llama_context & lctx) override; + + void defrag_sched(float thold) override; + + void set_full() override; + + llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override; + + llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override; + + bool find_slot(const llama_ubatch & batch) override; + + int32_t get_n_tokens() const override; + int32_t get_used_cells() const override; + + // TODO: better data structures to reduce the cost of this operation + llama_pos get_pos_max() const override; + + bool get_can_shift() const override; + + // TODO: temporary methods - they are not really const as they do const_cast<>, fix this + int32_t s_copy(int i) const; + float s_mask(int i) const; + + // state write/load + + void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override; + void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override; + + // Note: The value of head isn't only used to optimize searching + // for a free KV slot. llama_decode_impl also uses it, so it + // cannot be freely changed after a slot has been allocated. + uint32_t head = 0; + uint32_t size = 0; + uint32_t used = 0; // used cells (i.e. at least one seq_id) + + // computed before each graph build + uint32_t n = 0; + + std::vector cells; + + std::vector k_l; // per layer + std::vector v_l; + +private: + //const llama_model & model; + const llama_hparams & hparams; + + // commit/restore cache + // TODO: rework for recurrent cache + struct slot_range { + uint32_t c0 = 0; // note: these are cell indices, not sequence positions + uint32_t c1 = 0; + }; + + // pending cell updates that are not yet committed + struct { + std::vector ranges; + } pending; + + ggml_type type_k = GGML_TYPE_F16; + ggml_type type_v = GGML_TYPE_F16; + + std::vector ctxs; + std::vector bufs; + + // find how many cells are currently in use + uint32_t cell_max() const; + + size_t total_size() const; + + size_t size_k_bytes() const; + size_t size_v_bytes() const; + void state_write_meta(llama_io_write_i & io, const std::vector> & cell_ranges, llama_seq_id seq_id = -1) const; void state_write_data(llama_io_write_i & io, const std::vector> & cell_ranges) const; @@ -198,11 +395,6 @@ private: bool state_read_data(llama_io_read_i & io, uint32_t cell_count); }; -// TODO: temporary reusing llama_kv_cache_unified -- implement recurrent cache and simplify llama_kv_cache_unified -//class llama_kv_cache_recurrent : public llama_kv_cache_unified { -//public: -// using llama_kv_cache_unified::llama_kv_cache_unified; -//}; // // kv cache view diff --git a/src/llama-memory.h b/src/llama-memory.h index dfa8c4e90f..c7412d5911 100644 --- a/src/llama-memory.h +++ b/src/llama-memory.h @@ -2,12 +2,22 @@ #include "llama.h" +struct llama_memory_params { + // kv cache + ggml_type type_k; + ggml_type type_v; + + // parameters for other types of memory + // ... +}; + // general concept of LLM memory // the KV cache is a type of LLM memory, but there can be other types class llama_memory_i { public: + virtual ~llama_memory_i() = default; + virtual void clear() = 0; - virtual void defrag() = 0; virtual bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) = 0; virtual void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) = 0; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index e5289f7077..e163de76a7 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -4445,6 +4445,19 @@ const ggml_tensor * llama_model::get_tensor(const char * name) const { return it->second; } +ggml_tensor * llama_model::get_rope_factors(uint32_t n_ctx_per_seq, int il) const { + // choose long/short freq factors based on the context size + if (layers[il].rope_freqs != nullptr) { + return layers[il].rope_freqs; + } + + if (n_ctx_per_seq > hparams.n_ctx_orig_yarn) { + return layers[il].rope_long; + } + + return layers[il].rope_short; +} + struct llm_build_llama : public llm_graph_context { llm_build_llama(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -4485,7 +4498,7 @@ struct llm_build_llama : public llm_graph_context { // self-attention { // rope freq factors for llama3; may return nullptr for llama2 and other models - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -4710,7 +4723,7 @@ struct llm_build_deci : public llm_graph_context { } else if (n_head > 0) { // self-attention // rope freq factors for llama3; may return nullptr for llama2 and other models - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -7192,7 +7205,7 @@ struct llm_build_phi3 : public llm_graph_context { // self-attention { // rope freq factors for 128k context - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); ggml_tensor* attn_norm_output = build_norm(inpL, model.layers[il].attn_norm, @@ -7944,7 +7957,7 @@ struct llm_build_minicpm3 : public llm_graph_context { for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // norm cur = build_norm(inpL, @@ -8711,7 +8724,7 @@ struct llm_build_mamba : public llm_graph_context { ggml_tensor * state_mask, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto kv_head = kv_self->head; @@ -9012,7 +9025,7 @@ struct llm_build_cohere2 : public llm_graph_context { // self-attention { // rope freq factors for 128k context - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -9950,7 +9963,7 @@ struct llm_build_deepseek : public llm_graph_context { // self-attention { // rope freq factors for llama3; may return nullptr for llama2 and other models - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -11314,7 +11327,7 @@ struct llm_build_exaone : public llm_graph_context { // self-attention { // rope freq factors for llama3; may return nullptr for llama2 and other models - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -11459,7 +11472,7 @@ struct llm_build_rwkv6_base : public llm_graph_context { ggml_tensor * state_mask, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto n_tokens = ubatch.n_tokens; const auto n_seqs = ubatch.n_seqs; @@ -11855,7 +11868,7 @@ struct llm_build_rwkv7_base : public llm_graph_context { ggml_tensor *& first_layer_value, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto n_tokens = ubatch.n_tokens; const auto n_seqs = ubatch.n_seqs; @@ -12695,7 +12708,7 @@ struct llm_build_bailingmoe : public llm_graph_context { // self-attention { // rope freq factors for llama3; may return nullptr for llama2 and other models - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -12815,7 +12828,7 @@ struct llm_build_bailingmoe : public llm_graph_context { } }; -llama_memory_i * llama_model::create_memory() const { +llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const { llama_memory_i * res; switch (arch) { @@ -12825,26 +12838,29 @@ llama_memory_i * llama_model::create_memory() const { case LLM_ARCH_RWKV7: case LLM_ARCH_ARWKV7: { - res = new llama_kv_cache_unified(hparams, { - /*.get_rope_factors =*/ nullptr - }); + res = new llama_kv_cache_recurrent( + *this, + GGML_TYPE_F32, + GGML_TYPE_F32, + cparams.offload_kqv, + std::max((uint32_t) 1, cparams.n_seq_max)); } break; default: { - res = new llama_kv_cache_unified(hparams, { - /*.get_rope_factors =*/ [this](uint32_t n_ctx_per_seq, int il) { - // choose long/short freq factors based on the context size - if (layers[il].rope_freqs != nullptr) { - return layers[il].rope_freqs; - } + const auto padding = llama_kv_cache_unified::get_padding(cparams); - if (n_ctx_per_seq > hparams.n_ctx_orig_yarn) { - return layers[il].rope_long; - } + cparams.n_ctx = GGML_PAD(cparams.n_ctx, padding); - return layers[il].rope_short; - } - }); + LLAMA_LOG_DEBUG("%s: n_ctx = %u (padded)\n", __func__, cparams.n_ctx); + + res = new llama_kv_cache_unified( + *this, + params.type_k, + params.type_v, + !cparams.flash_attn, + cparams.offload_kqv, + cparams.n_ctx, + padding); } } diff --git a/src/llama-model.h b/src/llama-model.h index 34aac337cf..4c7e7a335b 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -395,8 +395,11 @@ struct llama_model { const struct ggml_tensor * get_tensor(const char * name) const; + ggml_tensor * get_rope_factors(uint32_t n_ctx_per_seq, int il) const; + + // note: can mutate `cparams` // TODO: move this to new llm_arch_model_i interface - llama_memory_i * create_memory() const; // TODO: params + llama_memory_i * create_memory(const llama_memory_params & params, llama_cparams & cparams) const; // TODO: move this to new llm_arch_model_i interface llm_graph_result_ptr build_graph( From 074e42ab31de4c99aa7d9d2d239660f64b2380d6 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Fri, 2 May 2025 17:17:15 +0200 Subject: [PATCH 083/126] convert : converting mmproj for Qwen2/2.5VL from convert_hf_to_gguf (#13209) * wip * qwen2.5vl ok * vision: fix models missing "text_config" * add test * fix test repo name * fix 32B model * Revert "fix 32B model" This reverts commit 651752f1ae25fe8a01c1e57c18cf2eca80b2774e. * clarify about 32B * rm qwen surgery script * update llava/readme * move V_ENC_EMBD_PATCH handling to Qwen2VLVisionModel --- convert_hf_to_gguf.py | 78 +++++++++++ examples/llava/README.md | 29 ++-- examples/llava/qwen2_vl_surgery.py | 217 ----------------------------- examples/llava/tests.sh | 18 +-- gguf-py/gguf/constants.py | 3 + gguf-py/gguf/gguf_writer.py | 3 + gguf-py/gguf/tensor_mapping.py | 17 +++ 7 files changed, 132 insertions(+), 233 deletions(-) delete mode 100644 examples/llava/qwen2_vl_surgery.py diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index df3f8a55d5..ff82a85a9d 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -1089,6 +1089,8 @@ class VisionModel(ModelBase): raise TypeError("VisionModel must be subclassed with model_arch = gguf.MODEL_ARCH.CLIP_VISION") # get n_embd of the text model + if "text_config" not in self.hparams: + self.hparams["text_config"] = {} text_config = {**self.hparams, **self.hparams["text_config"]} self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0)) assert self.n_embd_text > 0, "n_embd not found in hparams" @@ -2583,6 +2585,82 @@ class Qwen2VLModel(TextModel): return [(self.map_tensor_name(name), data_torch)] +@ModelBase.register("Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration") +class Qwen2VLVisionModel(VisionModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.hparams["image_size"] = self.hparams.get("image_size", 560) + # rename config.json values + self.hparams["num_attention_heads"] = self.hparams.get("num_heads") + self.hparams["num_hidden_layers"] = self.hparams.get("depth") + if "embed_dim" in self.hparams: # qwen2vl + self.hparams["intermediate_size"] = self.hparams.get("hidden_size") + self.hparams["hidden_size"] = self.hparams.get("embed_dim") + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + if self.global_config['model_type'] == 'qwen2_vl': + self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.QWEN2VL) + elif self.global_config['model_type'] == 'qwen2_5_vl': + self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.QWEN25VL) + self.gguf_writer.add_vision_use_silu(True) + # find n_wa_pattern (window attention pattern) + fullatt_block_indexes = hparams.get("fullatt_block_indexes") + assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for qwen2_5_vl" + n_wa_pattern = fullatt_block_indexes[0] + 1 + # validate n_wa_pattern + for i in range(1, len(fullatt_block_indexes)): + if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern: + raise ValueError(f"Invalid fullatt_block_indexes: {fullatt_block_indexes}") + self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern) + else: + raise ValueError(f"Unknown QwenVL model type: {self.global_config['model_type']}") + # default values below are taken from HF tranformers code + self.gguf_writer.add_vision_attention_layernorm_eps(self.global_config.get("rms_norm_eps", 1e-6)) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + del bid, name, n_dims # unused + if ".patch_embd." in new_name: + return gguf.GGMLQuantizationType.F16 + if ".position_embd." in new_name: + return gguf.GGMLQuantizationType.F32 + return False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + if name.startswith("visual."): + # process visual tensors + # split QKV tensors if needed + if ".qkv." in name: + if data_torch.ndim == 2: # weight + c3, _ = data_torch.shape + else: # bias + c3 = data_torch.shape[0] + assert c3 % 3 == 0 + c = c3 // 3 + wq = data_torch[:c] + wk = data_torch[c: c * 2] + wv = data_torch[c * 2:] + return [ + (self.map_tensor_name(name.replace("qkv", "q")), wq), + (self.map_tensor_name(name.replace("qkv", "k")), wk), + (self.map_tensor_name(name.replace("qkv", "v")), wv), + ] + elif 'patch_embed.proj.weight' in name: + # split Conv3D into Conv2Ds + c1, c2, kt, kh, kw = data_torch.shape + del c1, c2, kh, kw # unused + assert kt == 2, "Current implmentation only support temporal_patch_size of 2" + return [ + (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight" , data_torch[:, :, 0, ...]), + (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...]), + ] + else: + return [(self.map_tensor_name(name), data_torch)] + return [] # skip other tensors + + @ModelBase.register("WavTokenizerDec") class WavTokenizerDecModel(TextModel): model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC diff --git a/examples/llava/README.md b/examples/llava/README.md index 3b62627ce8..b97b9e8c54 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -35,6 +35,16 @@ llama-mtmd-cli -hf ggml-org/SmolVLM2-500M-Video-Instruct-GGUF # Pixtral 12B llama-mtmd-cli -hf ggml-org/pixtral-12b-GGUF +# Qwen 2 VL +llama-mtmd-cli -hf ggml-org/Qwen2-VL-2B-Instruct-GGUF +llama-mtmd-cli -hf ggml-org/Qwen2-VL-7B-Instruct-GGUF + +# Qwen 2.5 VL +llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF +llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-7B-Instruct-GGUF +llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-32B-Instruct-GGUF +llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-72B-Instruct-GGUF + # Mistral Small 3.1 24B (IQ2_M quantization) llama-mtmd-cli -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF --chat-template mistral-v7 ``` @@ -60,7 +70,17 @@ Built upon `clip.cpp` (similar to `llava.cpp`), `libmtmd` offers several advanta ## How to obtain `mmproj` -Multimodal projector (`mmproj`) files are specific to each model architecture. Please refer to the relevant guide for instructions on how to obtain or create them: +Multimodal projector (`mmproj`) files are specific to each model architecture. + +For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` flag to get the `mmproj` file: +- [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) - Note: 1B variant does not have vision support +- SmolVLM (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB)) +- SmolVLM2 (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB)) +- [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint +- Qwen 2 VL and Qwen 2.5 VL (from [Qwen](https://huggingface.co/Qwen)) +- [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) + +For older models, please refer to the relevant guide for instructions on how to obtain or create them: - [LLaVA](../../docs/multimodal/llava.md) - [MobileVLM](../../docs/multimodal/MobileVLM.md) @@ -70,10 +90,3 @@ Multimodal projector (`mmproj`) files are specific to each model architecture. P - [MiniCPM-o 2.6](../../docs/multimodal/minicpmo2.6.md) - [IBM Granite Vision](../../docs/multimodal/granitevision.md) - [Google Gemma 3](../../docs/multimodal/gemma3.md) - -For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` flag to get the `mmproj` file: -- [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) - Note: 1B variant does not have vision support -- SmolVLM (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB)) -- SmolVLM2 (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB)) -- [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint -- [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) diff --git a/examples/llava/qwen2_vl_surgery.py b/examples/llava/qwen2_vl_surgery.py deleted file mode 100644 index 7951a6fa89..0000000000 --- a/examples/llava/qwen2_vl_surgery.py +++ /dev/null @@ -1,217 +0,0 @@ -import argparse -from typing import Dict, List, Optional - -import torch -import numpy as np -from gguf import * -from transformers import ( - AutoProcessor, - Qwen2VLConfig, - Qwen2VLProcessor, - Qwen2VLForConditionalGeneration, - Qwen2_5_VLConfig, # type: ignore[reportAttributeAccessIssue] - Qwen2_5_VLForConditionalGeneration, # type: ignore[reportAttributeAccessIssue] -) - - -VISION = "clip.vision" - - -def k(raw_key: str, arch: str) -> str: - return raw_key.format(arch=arch) - - -def get_n_wa_pattern(fullatt_block_indexes: Optional[List[int]]): - if fullatt_block_indexes is None: - return 0 - n_wa = fullatt_block_indexes[0] - for a, b in zip(fullatt_block_indexes, fullatt_block_indexes[1:]): - if b - a - 1 != n_wa: - raise ValueError( - f"window/full attention layer should have fix pattern of " - f"for each full-attention layer followed by {n_wa} window-attention layers" - ) - return n_wa + 1 - - -class VL2: - - @staticmethod - def to_gguf_name(name: str) -> str: - og = name - name = name.replace("text_model", "t").replace("vision_model", "v") - name = name.replace("blocks", "blk").replace("embeddings.", "") - name = name.replace("attn.", "attn_") - name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.") - # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln") - name = name.replace("norm1", "ln1").replace("norm2", "ln2") - name = name.replace("merger.mlp", 'mm') - print(f"[to_gguf_name] {og} --> {name}") - return name - - @classmethod - def find_vision_tensors(cls, qwen2vl, dtype) -> Dict[str, np.ndarray]: - vision_model = qwen2vl.visual - tensor_map = {} - for name, ten in vision_model.state_dict().items(): - ten = ten.numpy() - if 'qkv' in name: - if ten.ndim == 2: # weight - c3, _ = ten.shape - else: # bias - c3 = ten.shape[0] - assert c3 % 3 == 0 - c = c3 // 3 - wq = ten[:c] - wk = ten[c: c * 2] - wv = ten[c * 2:] - tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq - tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk - tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv - elif 'merger' in name: - if name.endswith("ln_q.weight"): - tensor_map['v.post_ln.weight'] = ten - elif name.endswith("ln_q.bias"): - tensor_map['v.post_ln.bias'] = ten - else: - # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias" - tensor_map[cls.to_gguf_name(name)] = ten - elif 'patch_embed.proj.weight' in name: - # NOTE: split Conv3D into Conv2Ds - c1, c2, kt, kh, kw = ten.shape - assert kt == 2, "Current implmentation only support temporal_patch_size of 2" - tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...] - tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...] - else: - tensor_map[cls.to_gguf_name(f"vision_model.{name}")] = ten - - for new_name, ten in tensor_map.items(): - if ten.ndim <= 1 or new_name.endswith("_norm.weight"): - tensor_map[new_name] = ten.astype(np.float32) - else: - tensor_map[new_name] = ten.astype(dtype) - tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32) # dummy tensor, just here as a placeholder - return tensor_map - - -class VL25(VL2): - - @staticmethod - def to_gguf_name(name: str) -> str: - og = name - name = name.replace("text_model", "t").replace("vision_model", "v") - name = name.replace("blocks", "blk").replace("embeddings.", "") - name = name.replace("attn.", "attn_") - name = name.replace("mlp.down_proj", "ffn_down").replace("mlp.up_proj", "ffn_up") - name = name.replace("mlp.gate_proj", "ffn_gate").replace("proj.", "out.") - name = name.replace("norm1", "ln1").replace("norm2", "ln2") - name = name.replace("merger.mlp", 'mm') - print(f"[vl25][to_gguf_name] {og} --> {name}") - return name - - -def main(args): - if args.data_type == 'fp32': - dtype = torch.float32 - np_dtype = np.float32 - ftype = 0 - elif args.data_type == 'fp16': - dtype = torch.float16 - np_dtype = np.float16 - ftype = 1 - else: - raise ValueError() - - local_model = False - model_path = "" - model_name = args.model_name - print("model_name: ", model_name) - if args.model_type == "qwen2vl": - qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained( - model_name, torch_dtype=dtype, device_map="cpu" - ) - cfg: Qwen2VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] - vcfg = cfg.vision_config - else: - qwen2vl = Qwen2_5_VLForConditionalGeneration.from_pretrained( - model_name, torch_dtype=dtype, device_map="cpu" - ) - cfg: Qwen2_5_VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] - vcfg = cfg.vision_config - - if os.path.isdir(model_name): - local_model = True - if model_name.endswith(os.sep): - model_name = model_name[:-1] - model_path = model_name - model_name = os.path.basename(model_name) - fname_out = f"{model_name.replace('/', '-').lower()}-vision.gguf" - - fout = GGUFWriter(path=fname_out, arch="clip") - fout.add_description("image encoder for Qwen2VL") - - fout.add_file_type(ftype) - fout.add_bool("clip.has_text_encoder", False) - fout.add_bool("clip.has_vision_encoder", True) - fout.add_bool("clip.has_qwen2vl_merger", True) - - print(cfg.vision_config) - if 'silu' in cfg.vision_config.hidden_act.lower(): - fout.add_bool("clip.use_silu", True) - fout.add_bool("clip.use_gelu", False) - elif 'gelu' in cfg.vision_config.hidden_act.lower(): - fout.add_bool("clip.use_silu", False) - fout.add_bool("clip.use_gelu", 'quick' not in cfg.vision_config.hidden_act.lower()) - else: - raise ValueError() - - if args.model_type == "qwen2.5vl": - fout.add_uint32("clip.vision.n_wa_pattern", get_n_wa_pattern(vcfg.fullatt_block_indexes)) - fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.hidden_size) - fout.add_uint32("clip.vision.projection_dim", vcfg.out_hidden_size) - fout.add_string("clip.projector_type", "qwen2.5vl_merger") - else: - fout.add_string("clip.projector_type", "qwen2vl_merger") - fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim) - fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size) - - if args.model_type == "qwen2.5vl": - tensor_map = VL25.find_vision_tensors(qwen2vl, np_dtype) - else: - tensor_map = VL2.find_vision_tensors(qwen2vl, np_dtype) - for name, data in tensor_map.items(): - fout.add_tensor(name, data) - - fout.add_uint32("clip.vision.patch_size", vcfg.patch_size) - fout.add_uint32("clip.vision.image_size", 14 * 40) # some reasonable size that is divable by (14*2) - fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads) - fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) - fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth) - fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), 0) # not sure what this does, put 0 here as a placeholder - fout.add_name(model_name) - """ - HACK: Since vision rope related parameter aren't stored in the `Qwen2VLConfig, - it will be hardcoded in the `clip_image_build_graph` from `clip.cpp`. - """ - - if local_model: - processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_path) - else: - processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name) - fout.add_array("clip.vision.image_mean", processor.image_processor.image_mean) # type: ignore[reportAttributeAccessIssue] - fout.add_array("clip.vision.image_std", processor.image_processor.image_std) # type: ignore[reportAttributeAccessIssue] - - fout.write_header_to_file() - fout.write_kv_data_to_file() - fout.write_tensors_to_file() - fout.close() - print("save model as: ", fname_out) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct") - parser.add_argument("--model_type", nargs='?', choices=['qwen2vl', 'qwen2.5vl'], default="qwen2vl") - parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32") - args = parser.parse_args() - main(args) diff --git a/examples/llava/tests.sh b/examples/llava/tests.sh index 4af3700640..22c2374971 100755 --- a/examples/llava/tests.sh +++ b/examples/llava/tests.sh @@ -36,12 +36,6 @@ add_test() { arr_tmpl+=("$tmpl") } -add_test_big() { - if [ "$RUN_BIG_TESTS" = true ]; then - add_test "$@" - fi -} - add_test "llama-mtmd-cli" "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0" add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M" add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0" @@ -58,8 +52,16 @@ add_test "llama-mtmd-cli" "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" # to test the big models, run: ./tests.sh big -add_test_big "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M" -add_test_big "llama-mtmd-cli" "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7" +if [ "$RUN_BIG_TESTS" = true ]; then + add_test "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7" + add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M" + # add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra + # add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M" # too big +fi # these models always give the wrong answer, not sure why # add_test "llama-mtmd-cli" "ggml-org/SmolVLM-Instruct-GGUF:Q4_K_M" diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a2540bd93f..74e46c3ee0 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -234,6 +234,7 @@ class Keys: SPATIAL_MERGE_SIZE = "clip.vision.spatial_merge_size" USE_GELU = "clip.use_gelu" USE_SILU = "clip.use_silu" + N_WA_PATTERN = "clip.vision.n_wa_pattern" # used by qwen2.5vl class Attention: HEAD_COUNT = "clip.vision.attention.head_count" @@ -2162,6 +2163,8 @@ class VisionProjectorType: GEMMA3 = "gemma3" IDEFICS3 = "idefics3" PIXTRAL = "pixtral" + QWEN2VL = "qwen2vl_merger" + QWEN25VL = "qwen2.5vl_merger" # Items here are (block size, type size) diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index a30c49e32b..ff50d3de31 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -984,6 +984,9 @@ class GGUFWriter: def add_vision_projector_scale_factor(self, value: int) -> None: self.add_uint32(Keys.ClipVision.Projector.SCALE_FACTOR, value) + def add_vision_n_wa_pattern(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.N_WA_PATTERN, value) + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: pack_prefix = '' if not skip_pack_prefix: diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 2f6326104f..2b089f84a8 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -896,6 +896,7 @@ class TensorNameMap: MODEL_TENSOR.V_MMPROJ: ( "multi_modal_projector.linear_{bid}", + "visual.merger.mlp.{bid}", # qwen2vl ), MODEL_TENSOR.V_MMPROJ_FC: ( @@ -919,6 +920,7 @@ class TensorNameMap: "vpm.embeddings.patch_embedding", "model.vision_model.embeddings.patch_embedding", # SmolVLM "vision_tower.patch_conv", # pixtral + "visual.patch_embed.proj", # qwen2vl ), MODEL_TENSOR.V_ENC_EMBD_POS: ( @@ -932,6 +934,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.self_attn.q_proj", "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM "vision_tower.transformer.layers.{bid}.attention.q_proj", # pixtral + "visual.blocks.{bid}.attn.q", # qwen2vl, generated ), MODEL_TENSOR.V_ENC_ATTN_K: ( @@ -939,6 +942,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.self_attn.k_proj", "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM "vision_tower.transformer.layers.{bid}.attention.k_proj", # pixtral + "visual.blocks.{bid}.attn.k", # qwen2vl, generated ), MODEL_TENSOR.V_ENC_ATTN_V: ( @@ -946,6 +950,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.self_attn.v_proj", "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM "vision_tower.transformer.layers.{bid}.attention.v_proj", # pixtral + "visual.blocks.{bid}.attn.v", # qwen2vl, generated ), MODEL_TENSOR.V_ENC_INPUT_NORM: ( @@ -953,6 +958,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.layer_norm1", "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral + "visual.blocks.{bid}.norm1", # qwen2vl ), MODEL_TENSOR.V_ENC_OUTPUT: ( @@ -960,6 +966,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.self_attn.out_proj", "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral + "visual.blocks.{bid}.attn.proj", # qwen2vl ), MODEL_TENSOR.V_ENC_OUTPUT_NORM: ( @@ -967,17 +974,24 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.layer_norm2", "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral + "visual.blocks.{bid}.norm2", # qwen2vl ), + # some namings are messed up because the original llava code swapped fc1 and fc2 + # we have no better way to fix it, just be careful + # new models like pixtral use the correct naming MODEL_TENSOR.V_ENC_FFN_UP: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", "vpm.encoder.layers.{bid}.mlp.fc1", "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3 (note: name is swapped) "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral + "visual.blocks.{bid}.mlp.fc2", # qwen2vl + "visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl ), MODEL_TENSOR.V_ENC_FFN_GATE: ( "vision_tower.transformer.layers.{bid}.feed_forward.gate_proj", # pixtral + "visual.blocks.{bid}.mlp.gate_proj", # qwen2.5vl ), MODEL_TENSOR.V_ENC_FFN_DOWN: ( @@ -985,6 +999,8 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.mlp.fc2", "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3 (note: name is swapped) "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral + "visual.blocks.{bid}.mlp.fc1", # qwen2vl + "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl ), MODEL_TENSOR.V_PRE_NORM: ( @@ -995,6 +1011,7 @@ class TensorNameMap: MODEL_TENSOR.V_POST_NORM: ( "vision_tower.vision_model.post_layernorm", "model.vision_model.post_layernorm", # SmolVLM + "visual.merger.ln_q", # qwen2vl ), MODEL_TENSOR.V_MM_INP_PROJ: ( From 7d2123484e6ba8fcd90fff8c01661b9dbbefdc9d Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Fri, 2 May 2025 11:41:54 -0400 Subject: [PATCH 084/126] convert : use correct context length for nomic-embed-text-v2 (#13216) --- convert_hf_to_gguf.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index ff82a85a9d..7a7c1858ec 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -506,7 +506,7 @@ class TextModel(ModelBase): def set_gguf_parameters(self): self.gguf_writer.add_block_count(self.block_count) - if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None: + if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions"], optional=True)) is not None: self.gguf_writer.add_context_length(n_ctx) logger.info(f"gguf: context length = {n_ctx}") @@ -3627,8 +3627,13 @@ class NomicBertModel(BertModel): if self._tokenizer_is_xlmroberta: self._xlmroberta_tokenizer_init() - # the HF config claims n_ctx=8192, but it uses RoPE scaling - self.hparams["n_ctx"] = 2048 + npos, mtp = self.hparams["n_positions"], self.hparams.get("max_trained_positions", 2048) + if npos == 8192 and mtp == 2048: + self.hparams["n_positions"] = 2048 # nomic-embed-text v1 and v1.5 are trained for 2048 tokens. + elif npos == 2048 and mtp == 2048: + self.hparams["n_positions"] = 512 # nomic-embed-text-v2-moe is trained for 512 tokens. + else: + raise ValueError(f"unrecognized parameters: n_positions={npos}, max_trained_positions={mtp}") assert self.hparams["activation_function"] == "gelu" if self.is_moe else "swiglu" From 2f567611c0234bbca0a4009762acb47b56866095 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Fri, 2 May 2025 11:42:30 -0400 Subject: [PATCH 085/126] llama-model : support Qwen2 embedding models and pooling_mode_lasttoken (#13245) --- convert_hf_to_gguf.py | 70 +++++++++++++++++++++++---------------- gguf-py/gguf/constants.py | 2 ++ src/llama-model.cpp | 1 + 3 files changed, 45 insertions(+), 28 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 7a7c1858ec..0862099da4 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -455,8 +455,12 @@ class ModelBase: class TextModel(ModelBase): + model_type = ModelType.TEXT + hf_arch: str + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) + self.hf_arch = get_model_architecture(self.hparams, self.model_type) if "text_config" in self.hparams: # move the text_config to the root level @@ -1075,10 +1079,36 @@ class TextModel(ModelBase): if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None: self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0]) + def _try_set_pooling_type(self) -> None: + # get pooling path + pooling_path = None + module_path = self.dir_model / "modules.json" + if module_path.is_file(): + with open(module_path, encoding="utf-8") as f: + modules = json.load(f) + for mod in modules: + if mod["type"] == "sentence_transformers.models.Pooling": + pooling_path = mod["path"] + break + + # get pooling type + if pooling_path is not None: + with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: + pooling = json.load(f) + if pooling["pooling_mode_mean_tokens"]: + pooling_type = gguf.PoolingType.MEAN + elif pooling["pooling_mode_cls_token"]: + pooling_type = gguf.PoolingType.CLS + elif pooling["pooling_mode_lasttoken"]: + pooling_type = gguf.PoolingType.LAST + else: + raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported") + self.gguf_writer.add_pooling_type(pooling_type) + class VisionModel(ModelBase): + model_type = ModelType.VISION model_arch = gguf.MODEL_ARCH.CLIP_VISION - n_text_embd = 0 preprocessor_config: dict[str, Any] global_config: dict[str, Any] @@ -2542,7 +2572,7 @@ class QwenModel(TextModel): self.gguf_writer.add_file_type(self.ftype) -@ModelBase.register("Qwen2ForCausalLM") +@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM") class Qwen2Model(TextModel): model_arch = gguf.MODEL_ARCH.QWEN2 @@ -2554,12 +2584,18 @@ class Qwen2Model(TextModel): def set_gguf_parameters(self): super().set_gguf_parameters() + self._try_set_pooling_type() if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: if self.hparams["rope_scaling"].get("type") == "yarn": self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if self.hf_arch == "Qwen2Model": + name = f"model.{name}" # map to Qwen2ForCausalLM tensors + yield from super().modify_tensors(data_torch, name, bid) + @ModelBase.register("Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration") class Qwen2VLModel(TextModel): @@ -3396,29 +3432,7 @@ class BertModel(TextModel): def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_causal_attention(False) - - # get pooling path - pooling_path = None - module_path = self.dir_model / "modules.json" - if module_path.is_file(): - with open(module_path, encoding="utf-8") as f: - modules = json.load(f) - for mod in modules: - if mod["type"] == "sentence_transformers.models.Pooling": - pooling_path = mod["path"] - break - - # get pooling type - if pooling_path is not None: - with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: - pooling = json.load(f) - if pooling["pooling_mode_mean_tokens"]: - pooling_type = gguf.PoolingType.MEAN - elif pooling["pooling_mode_cls_token"]: - pooling_type = gguf.PoolingType.CLS - else: - raise NotImplementedError("Only MEAN and CLS pooling types supported") - self.gguf_writer.add_pooling_type(pooling_type) + self._try_set_pooling_type() def set_vocab(self): tokens, toktypes, tokpre = self.get_vocab_base() @@ -5962,8 +5976,7 @@ def split_str_to_n_bytes(split_str: str) -> int: return n -def get_model_architecture(dir_model: Path, model_type: ModelType, hparams: Any = None) -> str: - hparams = ModelBase.load_hparams(dir_model) if hparams is None else hparams +def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> str: text_config = hparams.get("text_config", {}) vision_config = hparams.get("vision_config", {}) arch = hparams["architectures"][0] @@ -6034,7 +6047,8 @@ def main() -> None: with torch.inference_mode(): output_type = ftype_map[args.outtype] model_type = ModelType.VISION if args.mmproj else ModelType.TEXT - model_architecture = get_model_architecture(dir_model, model_type) + hparams = ModelBase.load_hparams(dir_model) + model_architecture = get_model_architecture(hparams, model_type) logger.info(f"Model architecture: {model_architecture}") try: model_class = ModelBase.from_model_architecture(model_architecture, model_type=model_type) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 74e46c3ee0..7dd7bb6d1b 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -2033,6 +2033,8 @@ class PoolingType(IntEnum): NONE = 0 MEAN = 1 CLS = 2 + LAST = 3 + RANK = 4 class GGMLQuantizationType(IntEnum): diff --git a/src/llama-model.cpp b/src/llama-model.cpp index e163de76a7..08d2130137 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -773,6 +773,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { // fall through case LLM_ARCH_QWEN2: { + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_5B : LLM_TYPE_1B; break; From 3f3769ba76061a511f02f2a48da2ad2d93fce511 Mon Sep 17 00:00:00 2001 From: shalinib-ibm Date: Fri, 2 May 2025 22:23:12 +0530 Subject: [PATCH 086/126] ggml : Enable MMA for BF16 in llamafile_sgemm (#13148) This patch upstreams llamafile's cpu matrix multiplication kernels for ppc64le using MMA builtins for BF16 data type. This change results in 9x - 40x gains in total speed S t/s (ie all tokens/total time), across various batch sizes tested using llama-batched-bench benchmark. The patch is tested with Meta-Lllama-3-8B, and Mistral-7B models (BF16 models generated by using llama-quantize from corresponding FP32 models) on an IBM POWER10 machine. Signed-off-by: Shalini Salomi Bodapati --- ggml/src/ggml-cpu/llamafile/sgemm.cpp | 501 ++++++++++++++++++++++++++ 1 file changed, 501 insertions(+) diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index f6374f7894..1d46158f92 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -1054,6 +1054,493 @@ class tinyBLAS_Q0_AVX { } \ } \ +template +class tinyBLAS_BF16_PPC { + public: + tinyBLAS_BF16_PPC(int64_t k, + const TA *A, int64_t lda, + const TB *B, int64_t ldb, + TC *C, int64_t ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int64_t m, int64_t n) { + mnpack(0, m, 0, n); + } + + private: + void vector_permute_store(vec_t *c, int numVec, unsigned char *vecOffset) { + vec_t t[8], s[8]; + vec_t swiz1 = {0, 1, 2, 3, 16, 17, 18, 19, 4, 5, 6, 7, 20, 21, 22, 23}; + vec_t swiz2 = {8, 9, 10, 11, 24, 25, 26, 27, 12, 13, 14, 15, 28, 29, 30, 31}; + vec_t swiz3 = {0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23}; + vec_t swiz4 = {8, 9, 10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31}; + + if (numVec == 2) { + t[0] = vec_perm(c[0], c[1], swiz1); + t[1] = vec_perm(c[2], c[3], swiz1); + s[0] = vec_perm(t[0], t[1], swiz3); + s[1] = vec_perm(t[0], t[1], swiz4); + vec_xst(s[0], 0, (vec_t*)vecOffset); + vec_xst(s[1], 0, (vec_t*)(vecOffset + 16)); + } else if (numVec == 4) { + t[0] = vec_perm(c[0], c[1], swiz1); + t[1] = vec_perm(c[0], c[1], swiz2); + t[2] = vec_perm(c[2], c[3], swiz1); + t[3] = vec_perm(c[2], c[3], swiz2); + s[0] = vec_perm(t[0], t[2], swiz3); + s[1] = vec_perm(t[0], t[2], swiz4); + s[2] = vec_perm(t[1], t[3], swiz3); + s[3] = vec_perm(t[1], t[3], swiz4); + for (int i = 0; i < 4; ++i) + vec_xst(s[i], 0, (vec_t*)(vecOffset + i * 16)); + } else if (numVec == 8) { + for (int i = 0; i < 4; i += 2) { + t[i+0] = vec_perm(c[i+0], c[i+1], swiz1); + t[i+1] = vec_perm(c[i+0], c[i+1], swiz2); + } + for (int i = 4; i < 8; i += 2) { + t[i+0] = vec_perm(c[i+0], c[i+1], swiz1); + t[i+1] = vec_perm(c[i+0], c[i+1], swiz2); + } + s[0] = vec_perm(t[0], t[2], swiz3); + s[1] = vec_perm(t[0], t[2], swiz4); + s[2] = vec_perm(t[1], t[3], swiz3); + s[3] = vec_perm(t[1], t[3], swiz4); + s[4] = vec_perm(t[4], t[6], swiz3); + s[5] = vec_perm(t[4], t[6], swiz4); + s[6] = vec_perm(t[5], t[7], swiz3); + s[7] = vec_perm(t[5], t[7], swiz4); + for (int i = 0; i < 8; ++i) + vec_xst(s[i], 0, (vec_t*)(vecOffset + i * 16)); + } + } + + void packNormal(const TA* a, int64_t lda, int rows, int cols, unsigned char* vec) { + int64_t i, j; + TA *aoffset = NULL; + unsigned char *vecOffset = NULL; + TA * aoffsets[8]; + vector unsigned char c_arr[8]; + aoffset = const_cast(a); + vecOffset = vec; + j = (rows >> 3); + if (j > 0) { + do { + if (cols == 4) { + aoffsets[0] = aoffset; + for (int it = 1; it < 4; ++it) + aoffsets[it] = aoffsets[it-1] + lda; + aoffset += 4 * lda; + for (int i = 0; i < 4; ++i) + c_arr[i] = vec_xl(0, (vector unsigned char*)aoffsets[i]); + vector_permute_store(c_arr, 4, vecOffset); + for (int i = 0; i<4; i++) + aoffsets[i] = aoffsets[i]+lda; + vecOffset +=64; + } + i = (cols >> 3); + if (i > 0) { + aoffsets[0] = aoffset; + for (int it = 1; it < 8; ++it) { + aoffsets[it] = aoffsets[it-1] + lda; + } + aoffset += 8 * lda; + do { + for (int it = 0; it < 8; ++it) + c_arr[it] = vec_xl(0, (vector unsigned char*)aoffsets[it]); + vector_permute_store(c_arr, 8, vecOffset); + for (int it = 0; it < 8; ++it) + aoffsets[it] = aoffsets[it] + 8*lda; + vecOffset += 128; + i--; + } while(i > 0); + } + j--; + } while(j > 0); + } + if (rows & 4) { + aoffsets[0] = aoffset; + for (int it = 1; it < 4; ++it) + aoffsets[it] = aoffsets[it-1] + lda; + aoffset += 4 * lda; + if (cols == 4) { + for (int it = 0; it < 4; ++it) + c_arr[it] = vec_xl(0, (vector unsigned char*)aoffsets[it]); + vector_permute_store(c_arr, 2, vecOffset); + for (int it = 0; it< 4; it++) + aoffsets[it] = aoffsets[it] + lda; + vecOffset += 32; + } + i = (cols >> 3); + if (i > 0) { + do { + for (int it = 0; it < 4; ++it) + c_arr[it] = vec_xl(0, (vector unsigned char*)aoffsets[it]); + vector_permute_store(c_arr, 4, vecOffset); + for (int it = 0; it< 4; it++) + aoffsets[it] = aoffsets[it] + 8*lda; + vecOffset += 64; + i--; + } while(i > 0); + } + } + if (rows & 3) { + aoffsets[0] = aoffset; + for (int it = 1; it < 4; ++it) + aoffsets[it] = aoffsets[it-1] + lda; + if (cols == 4) { + switch(rows) { + case 3: c_arr[2] = vec_xl(0, (vector unsigned char*)aoffsets[2]); + case 2: c_arr[1] = vec_xl(0, (vector unsigned char*)aoffsets[1]); + case 1: c_arr[0] = vec_xl(0, (vector unsigned char*)aoffsets[0]); + break; + } + vector_permute_store(c_arr, 2, vecOffset); + for (int it = 0; it< 4; it++) + aoffsets[it] = aoffsets[it] + lda; + vecOffset += 32; + } + i = (cols >> 3); + if (i > 0) { + do { + switch(rows) { + case 3: c_arr[2] = vec_xl(0, (vector unsigned char*)aoffsets[2]); + case 2: c_arr[1] = vec_xl(0, (vector unsigned char*)aoffsets[1]); + case 1: c_arr[0] = vec_xl(0, (vector unsigned char*)aoffsets[0]); + break; + } + vector_permute_store(c_arr, 4, vecOffset); + for (int it = 0; it <4; it++) + aoffsets[it] = aoffsets[it] + 8* lda; + vecOffset += 64; + i--; + } while(i > 0); + } + } + } + + void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t mc, nc, mp, np; + int m_rem = MIN(m - m0, 8); + int n_rem = MIN(n - n0, 8); + + if (m_rem >= 8 && n_rem >= 8) { + mc = 8; + nc = 8; + gemm<8,8>(m0, m, n0, n); + } else if (m_rem >= 4 && n_rem >= 8) { + mc = 4; + nc = 8; + gemm<4,8>(m0, m, n0, n); + } else if (m_rem >=8 && n_rem >=4){ + mc = 8; + nc = 4; + gemm<8,4>(m0, m, n0, n); + } else if ((m_rem < 4) && (n_rem >= 8)) { + nc = 8; + switch(m_rem) { + case 1: + mc = 1; + gemm_Mx8<1>(m0, m, n0, n); + break; + case 2: + mc = 2; + gemm_Mx8<2>(m0, m, n0, n); + break; + case 3: + mc = 3; + gemm_Mx8<3>(m0, m, n0, n); + break; + default: + return; + } + } else if (m_rem >= 4 && n_rem >= 4) { + mc = 4; + nc = 4; + gemm_small<4, 4>(m0, m, n0, n); + } else if ((m_rem > 4) && (n_rem < 4)) { + mc = 4; + switch(n_rem) { + case 1: + nc = 1; + gemm_small<4, 1>(m0, m, n0, n); + break; + case 2: + nc = 2; + gemm_small<4, 2>(m0, m, n0, n); + break; + case 3: + nc = 3; + gemm_small<4, 3>(m0, m, n0, n); + break; + + default: + return; + } + } else { + switch((m_rem << 4) | n_rem) { + case 0x43: + mc = 4; + nc = 3; + gemm_small<4, 3>(m0, m, n0, n); + break; + case 0x42: + mc = 4; + nc = 2; + gemm_small<4, 2>(m0, m, n0, n); + break; + case 0x41: + mc = 4; + nc = 1; + gemm_small<4, 1>(m0, m, n0, n); + break; + case 0x34: + mc = 3; + nc = 4; + gemm_small<3, 4>(m0, m, n0, n); + break; + case 0x33: + mc = 3; + nc = 3; + gemm_small<3, 3>(m0, m, n0, n); + break; + case 0x32: + mc = 3; + nc = 2; + gemm_small<3, 2>(m0, m, n0, n); + break; + case 0x31: + mc = 3; + nc = 1; + gemm_small<3, 1>(m0, m, n0, n); + break; + case 0x24: + mc = 2; + nc = 4; + gemm_small<2,4>(m0, m, n0, n); + break; + case 0x23: + mc = 2; + nc = 3; + gemm_small<2, 3>(m0, m, n0, n); + break; + case 0x22: + mc = 2; + nc = 2; + gemm_small<2, 2>(m0, m, n0, n); + break; + case 0x21: + mc = 2; + nc = 1; + gemm_small<2, 1>(m0, m, n0, n); + break; + case 0x14: + mc = 1; + nc = 4; + gemm_small<1, 4>(m0, m, n0, n); + break; + case 0x13: + mc = 1; + nc = 3; + gemm_small<1, 3>(m0, m, n0, n); + break; + case 0x12: + mc = 1; + nc = 2; + gemm_small<1, 2>(m0, m, n0, n); + break; + case 0x11: + mc = 1; + nc = 1; + gemm_small<1, 1>(m0, m, n0, n); + break; + default: + return; + } + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, m, np, n); + } + + void KERNEL_4x8(int64_t ii, int64_t jj) { + vec_t vec_A[4], vec_B[8] , vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + for (int l = 0; l < k; l+=8) { + packNormal((A+(ii*lda)+l), lda, 4, 8, (uint8_t*)vec_A); + packNormal((B+(jj*ldb)+l), ldb, 8, 8, (uint8_t*)vec_B); + for (int x = 0; x < 4; x++) { + __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); + __builtin_mma_xvbf16ger2pp(&acc_1, vec_A[x], vec_B[x+4]); + } + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii, jj+4); + } + + void KERNEL_8x4(int64_t ii, int64_t jj) { + vec_t vec_A[8], vec_B[4] , vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + for (int l = 0; l < k; l+=8) { + packNormal((A+(ii*lda)+l), lda, 8, 8, (uint8_t*)vec_A); + packNormal((B+(jj*ldb)+l), ldb, 8, 4, (uint8_t*)vec_B); + for (int x = 0; x < 4; x++) { + __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); + __builtin_mma_xvbf16ger2pp(&acc_1, vec_A[x+4], vec_B[x]); + } + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii+4, jj); + } + + + void KERNEL_8x8(int64_t ii, int64_t jj) { + vec_t vec_A[8], vec_B[8], vec_C[4]; + acc_t acc_0, acc_1, acc_2, acc_3; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + __builtin_mma_xxsetaccz(&acc_2); + __builtin_mma_xxsetaccz(&acc_3); + for (int l = 0; l < k; l+=8) { + packNormal(A+(ii*lda)+l, lda, 8, 8, (uint8_t*)vec_A); + packNormal(B+(jj*ldb)+l, ldb, 8, 8, (uint8_t*)vec_B); + for (int x = 0; x < 4; x++) { + __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); + __builtin_mma_xvbf16ger2pp(&acc_1, (vec_t)vec_A[x], (vec_t)vec_B[x+4]); + __builtin_mma_xvbf16ger2pp(&acc_2, (vec_t)vec_A[x+4], (vec_t)vec_B[x]); + __builtin_mma_xvbf16ger2pp(&acc_3, (vec_t)vec_A[x+4], (vec_t)vec_B[x+4]); + } + } + + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii, jj+4); + SAVE_ACC(&acc_2, ii+4, jj); + SAVE_ACC(&acc_3, ii+4, jj+4); + } + + template + void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + vec_t vec_C[4]; + acc_t acc_0; + __builtin_mma_xxsetaccz(&acc_0); + vec_t vec_A[2], vec_B[2]; + for (int l=0; l + void gemm_Mx8(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int RN = 8; + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + vec_t vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + vec_t vec_A[4], vec_B[8]; + for (int l=0; l + inline void kernel(int64_t ii, int64_t jj) { + if constexpr(RM == 4 && RN == 8) { + KERNEL_4x8(ii,jj); + } else if constexpr(RM == 8 && RN == 8) { + KERNEL_8x8(ii,jj); + } else if constexpr(RM == 8 && RN == 4) { + KERNEL_8x4(ii,jj); + } else { + static_assert(false, "RN/RM values not supported"); + } + } + + template + NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + kernel(ii, jj); + } + } + + const TA *const A; + const TB *const B; + TC *C; + const int64_t k; + const int64_t lda; + const int64_t ldb; + const int64_t ldc; + const int ith; + const int nth; +}; + template class tinyBLAS_Q0_PPC { public: @@ -2202,6 +2689,7 @@ class tinyBLAS_PPC { boffset = vec; j = (rows >> 3); if (j > 0) { + do { aoffset1 = aoffset; aoffset2 = aoffset1 + lda; @@ -2875,9 +3363,22 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64 (float *)C, ldc}; return tb.matmul(m, n); } +#elif defined(__MMA__) + if ((k % 8)) + return false; + if(Btype == GGML_TYPE_BF16) { + tinyBLAS_BF16_PPC tb{ k, + (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc, + params->ith, params->nth}; + tb.matmul(m, n); + return true; + } #endif return false; } + case GGML_TYPE_F16: { #if defined(__AVX512F__) if (Btype == GGML_TYPE_F16) { From a75cb30dc9e63488c3614e2d5a9fe2306eaf47cd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 2 May 2025 20:54:13 +0300 Subject: [PATCH 087/126] context : fix reorder logic (#13267) ggml-ci --- src/llama-context.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index a88b9a5ff9..45591be992 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -1050,6 +1050,9 @@ int llama_context::decode(llama_batch & inp_batch) { // finalize the batch processing kv_guard.commit(); + // set to total number of outputs in the batch, for use in llama_get_logits_ith + n_outputs = n_outputs_all; + // set output mappings { bool sorted_output = true; @@ -1103,9 +1106,6 @@ int llama_context::decode(llama_batch & inp_batch) { } } - // set to total number of outputs in the batch, for use in llama_get_logits_ith - n_outputs = n_outputs_all; - // wait for the computation to finish (automatically done when obtaining the model output) //synchronize(); From b34443923cad751483cc53af2e680d595daadce7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 2 May 2025 20:54:30 +0300 Subject: [PATCH 088/126] sync : ggml (#13268) * vulkan : kernels for depthwise 2D convolution (CONV_2D_DW) (ggml/1204) * vulkan : add kernels for depthwise 2d convolution (OP_CONV_2D_DW) * review: remove src_x/y < 0 checks; add performance tests * sync : ggml ggml-ci * vulkan : fix lint (#0) --------- Co-authored-by: Acly --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 66 +++++++++++ .../ggml-vulkan/vulkan-shaders/conv2d_dw.comp | 105 ++++++++++++++++++ .../vulkan-shaders/vulkan-shaders-gen.cpp | 3 + scripts/sync-ggml.last | 2 +- tests/test-backend-ops.cpp | 50 +++++++++ 5 files changed, 225 insertions(+), 1 deletion(-) create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index eac0b422bc..c4df47abae 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -389,6 +389,8 @@ struct vk_device_struct { vk_pipeline pipeline_rwkv_wkv6_f32; vk_pipeline pipeline_rwkv_wkv7_f32; vk_pipeline pipeline_opt_step_adamw_f32; + vk_pipeline pipeline_conv2d_dw_whcn_f32; + vk_pipeline pipeline_conv2d_dw_cwhn_f32; // [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned} vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2]; @@ -701,6 +703,24 @@ struct vk_op_rwkv_wkv7_push_constants { uint32_t H; }; +struct vk_op_conv2d_dw_push_constants { + uint32_t ne; + uint32_t batches; + uint32_t channels; + uint32_t dst_w; + uint32_t dst_h; + uint32_t src_w; + uint32_t src_h; + uint32_t knl_w; + uint32_t knl_h; + int32_t stride_x; + int32_t stride_y; + int32_t pad_x; + int32_t pad_y; + int32_t dilation_x; + int32_t dilation_y; +}; + struct vk_op_upscale_push_constants { uint32_t ne; uint32_t a_offset; uint32_t d_offset; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; @@ -2610,6 +2630,9 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f32, "conv2d_dw_cwhn_f32", conv2d_dw_cwhn_f32_len, conv2d_dw_cwhn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); + for (auto &c : compiles) { c.wait(); } @@ -6137,6 +6160,15 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_leaky_relu_f32; } return nullptr; + case GGML_OP_CONV_2D_DW: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + if (ggml_is_contiguous(src1)) { + return ctx->device->pipeline_conv2d_dw_whcn_f32; + } else if (ggml_is_contiguous_channels(src1)) { + return ctx->device->pipeline_conv2d_dw_cwhn_f32; + } + } + return nullptr; default: return nullptr; } @@ -6163,6 +6195,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { case GGML_OP_REPEAT_BACK: case GGML_OP_ROPE: case GGML_OP_RMS_NORM: + case GGML_OP_CONV_2D_DW: return true; default: return false; @@ -6459,6 +6492,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co case GGML_OP_CONCAT: case GGML_OP_UPSCALE: case GGML_OP_UNARY: + case GGML_OP_CONV_2D_DW: { const uint32_t ne = ggml_nelements(dst); if (ne > 262144) { @@ -7245,6 +7279,30 @@ static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, c }, dryrun); } +static void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { + vk_op_conv2d_dw_push_constants p{}; + p.ne = ggml_nelements(dst); + p.channels = dst->ne[2]; + p.batches = dst->ne[3]; + p.dst_w = dst->ne[0]; + p.dst_h = dst->ne[1]; + p.src_w = src1->ne[0]; + p.src_h = src1->ne[1]; + p.knl_w = src0->ne[0]; + p.knl_h = src0->ne[1]; + p.stride_x = dst->op_params[0]; + p.stride_y = dst->op_params[1]; + p.pad_x = dst->op_params[2]; + p.pad_y = dst->op_params[3]; + p.dilation_x = dst->op_params[4]; + p.dilation_y = dst->op_params[5]; + + GGML_ASSERT(src0->ne[3] == p.channels); + GGML_ASSERT(src1->ne[3] == p.batches); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_CONV_2D_DW, std::move(p), dryrun); +} + static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const float * op_params = (const float *)dst->op_params; ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }, dryrun); @@ -8265,6 +8323,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_POOL_2D: + case GGML_OP_CONV_2D_DW: case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: case GGML_OP_LEAKY_RELU: @@ -8328,6 +8387,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_POOL_2D: + case GGML_OP_CONV_2D_DW: case GGML_OP_LEAKY_RELU: { // These operations all go through ggml_vk_op_f32, so short-circuit and @@ -8501,6 +8561,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_POOL_2D: ggml_vk_pool_2d(ctx, compute_ctx, src0, node, dryrun); + break; + case GGML_OP_CONV_2D_DW: + ggml_vk_conv_2d_dw(ctx, compute_ctx, src0, src1, node, dryrun); + break; case GGML_OP_LEAKY_RELU: ggml_vk_leaky_relu(ctx, compute_ctx, src0, node, dryrun); @@ -8622,6 +8686,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_POOL_2D: + case GGML_OP_CONV_2D_DW: case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: case GGML_OP_LEAKY_RELU: @@ -9599,6 +9664,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_COUNT_EQUAL: case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_CONV_2D_DW: case GGML_OP_POOL_2D: case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp new file mode 100644 index 0000000000..938c74da50 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp @@ -0,0 +1,105 @@ +#version 450 + +#include "types.comp" + +layout (push_constant) uniform parameter +{ + uint ne; + uint batches; + uint channels; + uint dst_w; + uint dst_h; + uint src_w; + uint src_h; + uint knl_w; + uint knl_h; + int stride_x; + int stride_y; + int pad_x; + int pad_y; + int dilation_x; + int dilation_y; +} p; + +layout (binding = 0) readonly buffer A {A_TYPE knl_data[];}; +layout (binding = 1) readonly buffer B {B_TYPE src_data[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst_data[];}; + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE conv_2d_dw_whcn(uint idx) { + uint i0 = idx / p.dst_w; + uint dst_x = idx - i0 * p.dst_w; + uint i1 = i0 / p.dst_h; + uint dst_y = i0 - i1 * p.dst_h; + uint n = i1 / p.channels; + uint c = i1 - n * p.channels; + + uint src_i = n * p.channels * p.src_h * p.src_w + c * p.src_h * p.src_w; + uint knl_i = c * p.knl_h * p.knl_w; + + FLOAT_TYPE sum = 0.0; + for (uint knl_y = 0; knl_y < p.knl_h; ++knl_y) { + uint src_y = dst_y * p.stride_y + knl_y * p.dilation_y - p.pad_y; + if (src_y >= p.src_h) { // src_y < 0 will wrap to a large unsigned int + continue; + } + for (uint knl_x = 0; knl_x < p.knl_w; ++knl_x) { + uint src_x = dst_x * p.stride_x + knl_x * p.dilation_x - p.pad_x; + if (src_x >= p.src_w) { // src_x < 0 will wrap to a large unsigned int + continue; + } + FLOAT_TYPE v = FLOAT_TYPE(src_data[src_i + src_y * p.src_w + src_x]); + FLOAT_TYPE k = FLOAT_TYPE(knl_data[knl_i + knl_y * p.knl_w + knl_x]); + sum = fma(v, k, sum); + } + } + return sum; +} + +FLOAT_TYPE conv_2d_dw_cwhn(uint idx) { + uint i0 = idx / p.channels; + uint c = idx - i0 * p.channels; + uint i1 = i0 / p.dst_w; + uint dst_x = i0 - i1 * p.dst_w; + uint n = i1 / p.dst_h; + uint dst_y = i1 - n * p.dst_h; + + uint src_i = n * p.channels * p.src_h * p.src_w; + uint src_row = p.src_w * p.channels; + uint knl_row = p.knl_w * p.channels; + + FLOAT_TYPE sum = 0.0; + for (uint knl_y = 0; knl_y < p.knl_h; ++knl_y) { + uint src_y = dst_y * p.stride_y + knl_y * p.dilation_y - p.pad_y; + if (src_y >= p.src_h) { // src_y < 0 will wrap to a large unsigned int + continue; + } + for (uint knl_x = 0; knl_x < p.knl_w; ++knl_x) { + uint src_x = dst_x * p.stride_x + knl_x * p.dilation_x - p.pad_x; + if (src_x >= p.src_w) { // src_x < 0 will wrap to a large unsigned int + continue; + } + FLOAT_TYPE v = FLOAT_TYPE(src_data[src_i + src_y * src_row + src_x * p.channels + c]); + FLOAT_TYPE k = FLOAT_TYPE(knl_data[ knl_y * knl_row + knl_x * p.channels + c]); + sum = fma(v, k, sum); + } + } + return sum; +} + +void main() { + uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + if (idx >= p.ne) { + return; + } + + FLOAT_TYPE result = +#ifdef WHCN + conv_2d_dw_whcn(idx); +#else + conv_2d_dw_cwhn(idx); +#endif + dst_data[idx] = D_TYPE(result); +} + diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 3b28578545..759112b946 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -584,6 +584,9 @@ void process_shaders() { string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); + string_to_spv("conv2d_dw_whcn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}})); + string_to_spv("conv2d_dw_cwhn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}})); + for (auto &c : compiles) { c.wait(); } diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 433cfab7ff..3e93dbf8af 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -f3a375f20bf56860b30e7c511d03593a1e393345 +0482de9c63b9134eb462c7732888c0ee0dbc2755 diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 767498ef39..9ec24d9f23 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2765,6 +2765,48 @@ struct test_im2col : public test_case { } }; +// GGML_OP_CONV_2D_DW +struct test_conv_2d_dw : public test_case { + const std::array ne_input; + const std::array ne_kernel; + const int stride; + const int padding; + const int dilation; + const bool cwhn; + + std::string vars() override { + return VARS_TO_STR6(ne_input, ne_kernel, stride, padding, dilation, cwhn); + } + + test_conv_2d_dw(std::array ne_input = {64, 64, 16, 1}, + std::array ne_kernel = {3, 3, 1, 16}, + int stride = 1, int padding = 0, int dilation = 1, bool cwhn = false) + : ne_input(ne_input), ne_kernel(ne_kernel), stride(stride), padding(padding), dilation(dilation), cwhn(cwhn) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data()); + ggml_set_name(input, "input"); + + ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_kernel.data()); + ggml_set_name(kernel, "kernel"); + + if (cwhn) { + // change memory layout to channel-most-contiguous (CWHN), + // then permute it back so NE matches the original input + input = ggml_cont(ctx, ggml_permute(ctx, input, 1, 2, 0, 3)); + input = ggml_permute(ctx, input, 2, 0, 1, 3); + kernel = ggml_cont(ctx, ggml_permute(ctx, kernel, 2, 3, 1, 0)); + kernel = ggml_permute(ctx, kernel, 3, 2, 0, 1); + } + + ggml_tensor * out = ggml_conv_2d_dw_direct( + ctx, kernel, input, + stride, stride, padding, padding, dilation, dilation); + ggml_set_name(out, "out"); + return out; + } +}; + // GGML_OP_CONCAT struct test_concat : public test_case { const ggml_type type; @@ -3975,6 +4017,11 @@ static std::vector> make_test_cases_eval() { // test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {1024, 1024, 256, 1}, {3, 3, 256, 1}, 1, 1, 1, 1, 1, 1, true)); // test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {1024, 1024, 256, 1}, {3, 3, 256, 1}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, 1, 0, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, 1, 0, 1, true)); + test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, true)); + test_cases.emplace_back(new test_conv_transpose_1d()); test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 3, 0, 1)); test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 2, 0, 1)); @@ -4549,6 +4596,9 @@ static std::vector> make_test_cases_perf() { } } + test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, true)); + return test_cases; } From 1d36b3670b285e69e58b9d687c770a2a0a192194 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Fri, 2 May 2025 20:27:13 +0200 Subject: [PATCH 089/126] llama : move end-user examples to tools directory (#13249) * llama : move end-user examples to tools directory --------- Co-authored-by: Xuan Son Nguyen --- .editorconfig | 8 +- .flake8 | 3 +- .github/labeler.yml | 6 +- .github/workflows/bench.yml.disabled | 30 +++--- .github/workflows/build-linux-cross.yml | 3 + .github/workflows/build.yml | 5 + .github/workflows/server.yml | 24 ++--- .gitignore | 12 +-- CMakeLists.txt | 5 + CODEOWNERS | 2 +- Makefile | 92 +++++++++--------- README.md | 20 ++-- SECURITY.md | 2 +- build-xcframework.sh | 2 + ci/run.sh | 8 +- common/arg.cpp | 4 +- common/common.h | 6 +- docs/development/HOWTO-add-model.md | 8 +- docs/multimodal/MobileVLM.md | 12 +-- docs/multimodal/glmedge.md | 4 +- docs/multimodal/llava.md | 12 +-- docs/multimodal/minicpmo2.6.md | 4 +- docs/multimodal/minicpmv2.5.md | 4 +- docs/multimodal/minicpmv2.6.md | 4 +- examples/CMakeLists.txt | 23 +---- .../pydantic_models_to_grammar_examples.py | 2 +- examples/server/public/index.html.gz | Bin 1263380 -> 0 bytes grammars/README.md | 12 +-- pyrightconfig.json | 2 +- requirements/requirements-all.txt | 6 +- scripts/fetch_server_test_models.py | 4 +- scripts/tool_bench.py | 6 +- scripts/xxd.cmake | 2 +- tests/CMakeLists.txt | 2 +- tests/run-json-schema-to-grammar.mjs | 2 +- tools/CMakeLists.txt | 39 ++++++++ .../batched-bench/CMakeLists.txt | 0 {examples => tools}/batched-bench/README.md | 0 .../batched-bench/batched-bench.cpp | 0 .../cvector-generator/CMakeLists.txt | 0 .../cvector-generator/README.md | 0 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tools}/server/webui/src/Config.ts (100%) rename {examples => tools}/server/webui/src/components/CanvasPyInterpreter.tsx (100%) rename {examples => tools}/server/webui/src/components/ChatMessage.tsx (100%) rename {examples => tools}/server/webui/src/components/ChatScreen.tsx (100%) rename {examples => tools}/server/webui/src/components/Header.tsx (100%) rename {examples => tools}/server/webui/src/components/MarkdownDisplay.tsx (100%) rename {examples => tools}/server/webui/src/components/SettingDialog.tsx (99%) rename {examples => tools}/server/webui/src/components/Sidebar.tsx (100%) rename {examples => tools}/server/webui/src/components/useChatTextarea.ts (100%) rename {examples => tools}/server/webui/src/index.scss (100%) rename {examples => tools}/server/webui/src/main.tsx (100%) rename {examples => tools}/server/webui/src/utils/app.context.tsx (100%) rename {examples => tools}/server/webui/src/utils/common.tsx (100%) rename {examples => tools}/server/webui/src/utils/llama-vscode.ts (100%) rename {examples => tools}/server/webui/src/utils/misc.ts (100%) rename {examples => tools}/server/webui/src/utils/storage.ts (100%) rename {examples => tools}/server/webui/src/utils/types.ts (100%) rename {examples => tools}/server/webui/src/vite-env.d.ts (100%) rename {examples => tools}/server/webui/tailwind.config.js (100%) rename {examples => tools}/server/webui/tsconfig.app.json (100%) rename {examples => tools}/server/webui/tsconfig.json (100%) rename {examples => tools}/server/webui/tsconfig.node.json (100%) rename {examples => tools}/server/webui/vite.config.ts (100%) rename {examples => tools}/tokenize/CMakeLists.txt (100%) rename {examples => tools}/tokenize/tokenize.cpp (100%) rename {examples => tools}/tts/CMakeLists.txt (100%) rename {examples => tools}/tts/README.md (96%) rename {examples => tools}/tts/convert_pt_to_hf.py (100%) rename {examples => tools}/tts/tts-outetts.py (100%) rename {examples => tools}/tts/tts.cpp (100%) diff --git a/.editorconfig b/.editorconfig index 5d63d0a51e..1eadda334a 100644 --- a/.editorconfig +++ b/.editorconfig @@ -21,15 +21,15 @@ indent_style = tab [prompts/*.txt] insert_final_newline = unset -[examples/server/public/*] +[tools/server/public/*] indent_size = 2 -[examples/server/public/deps_*] +[tools/server/public/deps_*] trim_trailing_whitespace = unset indent_style = unset indent_size = unset -[examples/server/deps_*] +[tools/server/deps_*] trim_trailing_whitespace = unset indent_style = unset indent_size = unset @@ -37,7 +37,7 @@ indent_size = unset [examples/llama.swiftui/llama.swiftui.xcodeproj/*] indent_style = tab -[examples/cvector-generator/*.txt] +[tools/cvector-generator/*.txt] trim_trailing_whitespace = unset insert_final_newline = unset diff --git a/.flake8 b/.flake8 index d64c2564ac..669d231f1f 100644 --- a/.flake8 +++ b/.flake8 @@ -2,8 +2,9 @@ max-line-length = 125 ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503 exclude = - # Do not traverse examples + # Do not traverse examples and tools examples, + tools, # Do not include package initializers __init__.py, # No need to traverse our git directory diff --git a/.github/labeler.yml b/.github/labeler.yml index 1b47bc9688..278032ef2e 100644 --- a/.github/labeler.yml +++ b/.github/labeler.yml @@ -45,7 +45,9 @@ build: - CMakePresets.json examples: - changed-files: - - any-glob-to-any-file: examples/** + - any-glob-to-any-file: + - examples/** + - tools/** devops: - changed-files: - any-glob-to-any-file: @@ -70,7 +72,7 @@ android: server: - changed-files: - any-glob-to-any-file: - - examples/server/** + - tools/server/** ggml: - changed-files: - any-glob-to-any-file: diff --git a/.github/workflows/bench.yml.disabled b/.github/workflows/bench.yml.disabled index 75d2714792..f2d7e16e98 100644 --- a/.github/workflows/bench.yml.disabled +++ b/.github/workflows/bench.yml.disabled @@ -27,10 +27,10 @@ on: push: branches: - master - paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp'] + paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'tools/server/*.h*', 'tools/server/*.cpp'] pull_request_target: types: [opened, synchronize, reopened] - paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp'] + paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'tools/server/*.h*', 'tools/server/*.cpp'] schedule: - cron: '04 2 * * *' @@ -69,7 +69,7 @@ jobs: - name: Install python env id: pipenv run: | - cd examples/server/bench + cd tools/server/bench python3 -m venv venv source venv/bin/activate pip install -r requirements.txt @@ -79,7 +79,7 @@ jobs: run: | wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz tar xzf prometheus*.tar.gz --strip-components=1 - ./prometheus --config.file=examples/server/bench/prometheus.yml & + ./prometheus --config.file=tools/server/bench/prometheus.yml & while ! nc -z localhost 9090; do sleep 0.1 done @@ -92,7 +92,7 @@ jobs: - name: Install k6 and xk6-sse id: k6_installation run: | - cd examples/server/bench + cd tools/server/bench go install go.k6.io/xk6/cmd/xk6@latest xk6 build master \ --with github.com/phymbert/xk6-sse @@ -116,7 +116,7 @@ jobs: - name: Download the dataset id: download_dataset run: | - cd examples/server/bench + cd tools/server/bench wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json - name: Server bench @@ -126,7 +126,7 @@ jobs: run: | set -eux - cd examples/server/bench + cd tools/server/bench source venv/bin/activate python bench.py \ --runner-label ${{ env.RUNNER_LABEL }} \ @@ -157,9 +157,9 @@ jobs: name: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }} compression-level: 9 path: | - examples/server/bench/*.jpg - examples/server/bench/*.json - examples/server/bench/*.log + tools/server/bench/*.jpg + tools/server/bench/*.json + tools/server/bench/*.log - name: Commit status uses: Sibz/github-status-action@v1 @@ -178,17 +178,17 @@ jobs: with: client_id: ${{secrets.IMGUR_CLIENT_ID}} path: | - examples/server/bench/prompt_tokens_seconds.jpg - examples/server/bench/predicted_tokens_seconds.jpg - examples/server/bench/kv_cache_usage_ratio.jpg - examples/server/bench/requests_processing.jpg + tools/server/bench/prompt_tokens_seconds.jpg + tools/server/bench/predicted_tokens_seconds.jpg + tools/server/bench/kv_cache_usage_ratio.jpg + tools/server/bench/requests_processing.jpg - name: Extract mermaid id: set_mermaid run: | set -eux - cd examples/server/bench + cd tools/server/bench PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid) echo "PROMPT_TOKENS_SECONDS<> $GITHUB_ENV echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV diff --git a/.github/workflows/build-linux-cross.yml b/.github/workflows/build-linux-cross.yml index d104b8b124..1c38d7e11d 100644 --- a/.github/workflows/build-linux-cross.yml +++ b/.github/workflows/build-linux-cross.yml @@ -34,6 +34,7 @@ jobs: cmake -B build -DCMAKE_BUILD_TYPE=Release \ -DGGML_OPENMP=OFF \ -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ -DLLAMA_BUILD_TESTS=OFF \ -DCMAKE_SYSTEM_NAME=Linux \ -DCMAKE_SYSTEM_PROCESSOR=riscv64 \ @@ -80,6 +81,7 @@ jobs: -DGGML_VULKAN=ON \ -DGGML_OPENMP=OFF \ -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ -DLLAMA_BUILD_TESTS=OFF \ -DCMAKE_SYSTEM_NAME=Linux \ -DCMAKE_SYSTEM_PROCESSOR=riscv64 \ @@ -125,6 +127,7 @@ jobs: -DGGML_VULKAN=ON \ -DGGML_OPENMP=OFF \ -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ -DLLAMA_BUILD_TESTS=OFF \ -DCMAKE_SYSTEM_NAME=Linux \ -DCMAKE_SYSTEM_PROCESSOR=aarch64 \ diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 34417985d2..bcea1a8afc 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -633,6 +633,7 @@ jobs: -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_COMMON=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ -DCMAKE_SYSTEM_NAME=iOS \ @@ -669,6 +670,7 @@ jobs: -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_COMMON=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ -DCMAKE_SYSTEM_NAME=tvOS \ @@ -699,6 +701,7 @@ jobs: -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_COMMON=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ -DCMAKE_SYSTEM_NAME=visionOS \ @@ -739,6 +742,7 @@ jobs: -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_CURL=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" @@ -1417,6 +1421,7 @@ jobs: -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_CURL=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ -DCMAKE_SYSTEM_NAME=iOS \ diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 6c9b513227..4baf6f6c75 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -15,10 +15,10 @@ on: push: branches: - master - paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*'] pull_request: types: [opened, synchronize, reopened] - paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*'] env: LLAMA_LOG_COLORS: 1 @@ -74,7 +74,7 @@ jobs: - name: Tests dependencies id: test_dependencies run: | - pip install -r examples/server/tests/requirements.txt + pip install -r tools/server/tests/requirements.txt # Setup nodejs (to be used for verifying bundled index.html) - uses: actions/setup-node@v4 @@ -84,14 +84,14 @@ jobs: - name: WebUI - Install dependencies id: webui_lint run: | - cd examples/server/webui + cd tools/server/webui npm ci - name: WebUI - Check code format id: webui_format run: | git config --global --add safe.directory $(realpath .) - cd examples/server/webui + cd tools/server/webui git status npm run format @@ -108,7 +108,7 @@ jobs: id: verify_server_index_html run: | git config --global --add safe.directory $(realpath .) - cd examples/server/webui + cd tools/server/webui git status npm run build @@ -161,21 +161,21 @@ jobs: env: GITHUB_ACTIONS: "true" run: | - cd examples/server/tests + cd tools/server/tests ./tests.sh - name: Tests (sanitizers) id: server_integration_tests_sanitizers if: ${{ matrix.sanitizer != '' }} run: | - cd examples/server/tests + cd tools/server/tests LLAMA_SANITIZE=1 ./tests.sh - name: Slow tests id: server_integration_tests_slow if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} run: | - cd examples/server/tests + cd tools/server/tests SLOW_TESTS=1 ./tests.sh @@ -211,7 +211,7 @@ jobs: - name: Tests dependencies id: test_dependencies run: | - pip install -r examples/server/tests/requirements.txt + pip install -r tools/server/tests/requirements.txt - name: Copy Libcurl id: prepare_libcurl @@ -224,7 +224,7 @@ jobs: id: server_integration_tests if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }} run: | - cd examples/server/tests + cd tools/server/tests $env:PYTHONIOENCODING = ":replace" pytest -v -x -m "not slow" @@ -232,6 +232,6 @@ jobs: id: server_integration_tests_slow if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} run: | - cd examples/server/tests + cd tools/server/tests $env:SLOW_TESTS = "1" pytest -v -x diff --git a/.gitignore b/.gitignore index 2c67ad7f7c..f8ceb1560a 100644 --- a/.gitignore +++ b/.gitignore @@ -96,11 +96,11 @@ perf-*.txt # Examples examples/jeopardy/results.txt -examples/server/*.css.hpp -examples/server/*.html.hpp -examples/server/*.js.hpp -examples/server/*.mjs.hpp -examples/server/*.gz.hpp +tools/server/*.css.hpp +tools/server/*.html.hpp +tools/server/*.js.hpp +tools/server/*.mjs.hpp +tools/server/*.gz.hpp !build_64.sh !examples/*.bat !examples/*/*.kts @@ -110,7 +110,7 @@ examples/server/*.gz.hpp # Server Web UI temporary files node_modules -examples/server/webui/dist +tools/server/webui/dist # Python diff --git a/CMakeLists.txt b/CMakeLists.txt index de51c0a17b..3f7e43b6e6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -77,6 +77,7 @@ option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE # extra artifacts option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) +option(LLAMA_BUILD_TOOLS "llama: build tools" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_SERVER "llama: build server example" ${LLAMA_STANDALONE}) @@ -187,6 +188,10 @@ if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES) add_subdirectory(pocs) endif() +if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS) + add_subdirectory(tools) +endif() + # # install # diff --git a/CODEOWNERS b/CODEOWNERS index 72d594b46e..3186f8eb1c 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -2,7 +2,7 @@ /ci/ @ggerganov /.devops/*.Dockerfile @ngxson -/examples/server/ @ngxson +/tools/server/ @ngxson /ggml/src/ggml-cuda/fattn* @JohannesGaessler /ggml/src/ggml-cuda/mmq.* @JohannesGaessler /ggml/src/ggml-cuda/mmv.* @JohannesGaessler diff --git a/Makefile b/Makefile index 772993ada2..305037089b 100644 --- a/Makefile +++ b/Makefile @@ -1156,10 +1156,10 @@ $(LIB_COMMON_S): $(OBJ_COMMON) # Clean generated server assets clean-server-assets: - find examples/server -type f -name "*.js.hpp" -delete - find examples/server -type f -name "*.mjs.hpp" -delete - find examples/server -type f -name "*.css.hpp" -delete - find examples/server -type f -name "*.html.hpp" -delete + find tools/server -type f -name "*.js.hpp" -delete + find tools/server -type f -name "*.mjs.hpp" -delete + find tools/server -type f -name "*.css.hpp" -delete + find tools/server -type f -name "*.html.hpp" -delete # Clean rule clean: clean-server-assets @@ -1179,7 +1179,7 @@ clean: clean-server-assets # Helper function that replaces .c, .cpp, and .cu file endings with .o: GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1)))) -llama-cli: examples/main/main.cpp \ +llama-cli: tools/main/main.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -1192,7 +1192,7 @@ llama-infill: examples/infill/infill.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-run: examples/run/run.cpp \ +llama-run: tools/run/run.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -1207,7 +1207,7 @@ llama-simple-chat: examples/simple-chat/simple-chat.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-tokenize: examples/tokenize/tokenize.cpp \ +llama-tokenize: tools/tokenize/tokenize.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -1217,27 +1217,27 @@ llama-batched: examples/batched/batched.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-batched-bench: examples/batched-bench/batched-bench.cpp \ +llama-batched-bench: tools/batched-bench/batched-bench.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-quantize: examples/quantize/quantize.cpp \ +llama-quantize: tools/quantize/quantize.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-quantize-stats: examples/quantize-stats/quantize-stats.cpp \ +llama-quantize-stats: tools/quantize-stats/quantize-stats.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-perplexity: examples/perplexity/perplexity.cpp \ +llama-perplexity: tools/perplexity/perplexity.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-imatrix: examples/imatrix/imatrix.cpp \ +llama-imatrix: tools/imatrix/imatrix.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -1279,7 +1279,7 @@ llama-gguf-hash: examples/gguf-hash/gguf-hash.cpp examples/gguf-hash/deps/sha1/s $(CXX) $(CXXFLAGS) -Iexamples/gguf-hash/deps -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-gguf-split: examples/gguf-split/gguf-split.cpp \ +llama-gguf-split: tools/gguf-split/gguf-split.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -1289,7 +1289,7 @@ llama-eval-callback: examples/eval-callback/eval-callback.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-cvector-generator: examples/cvector-generator/cvector-generator.cpp \ +llama-cvector-generator: tools/cvector-generator/cvector-generator.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -1299,12 +1299,12 @@ llama-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c- $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-bench: examples/llama-bench/llama-bench.cpp \ +llama-bench: tools/llama-bench/llama-bench.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-export-lora: examples/export-lora/export-lora.cpp \ +llama-export-lora: tools/export-lora/export-lora.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -1360,17 +1360,17 @@ llama-gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp \ $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) ifdef GGML_RPC -rpc-server: examples/rpc/rpc-server.cpp \ +rpc-server: tools/rpc/rpc-server.cpp \ $(OBJ_GGML) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) endif # GGML_RPC llama-server: \ - examples/server/server.cpp \ - examples/server/utils.hpp \ - examples/server/httplib.h \ - examples/server/index.html.hpp \ - examples/server/loading.html.hpp \ + tools/server/server.cpp \ + tools/server/utils.hpp \ + tools/server/httplib.h \ + tools/server/index.html.hpp \ + tools/server/loading.html.hpp \ common/chat.cpp \ common/chat.h \ common/chat-template.hpp \ @@ -1378,10 +1378,10 @@ llama-server: \ common/minja.hpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) + $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Itools/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) -# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`: -examples/server/%.hpp: examples/server/public/% FORCE Makefile +# Portable equivalent of `cd tools/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`: +tools/server/%.hpp: tools/server/public/% FORCE Makefile @( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \ echo "unsigned char $${NAME}[] = {" && \ cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \ @@ -1394,36 +1394,36 @@ llama-gen-docs: examples/gen-docs/gen-docs.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -libllava.a: examples/llava/llava.cpp \ - examples/llava/llava.h \ - examples/llava/clip.cpp \ - examples/llava/clip.h \ +libllava.a: tools/llava/llava.cpp \ + tools/llava/llava.h \ + tools/llava/clip.cpp \ + tools/llava/clip.h \ common/stb_image.h \ common/base64.hpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual -llama-llava-cli: examples/llava/llava-cli.cpp \ - examples/llava/llava.cpp \ - examples/llava/llava.h \ - examples/llava/clip.cpp \ - examples/llava/clip.h \ +llama-llava-cli: tools/llava/llava-cli.cpp \ + tools/llava/llava.cpp \ + tools/llava/llava.h \ + tools/llava/clip.cpp \ + tools/llava/clip.h \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual -llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \ - examples/llava/llava.cpp \ - examples/llava/llava.h \ - examples/llava/clip.cpp \ - examples/llava/clip.h \ +llama-minicpmv-cli: tools/llava/minicpmv-cli.cpp \ + tools/llava/llava.cpp \ + tools/llava/llava.h \ + tools/llava/clip.cpp \ + tools/llava/clip.h \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual -llama-qwen2vl-cli: examples/llava/qwen2vl-cli.cpp \ - examples/llava/llava.cpp \ - examples/llava/llava.h \ - examples/llava/clip.cpp \ - examples/llava/clip.h \ +llama-qwen2vl-cli: tools/llava/qwen2vl-cli.cpp \ + tools/llava/llava.cpp \ + tools/llava/llava.h \ + tools/llava/clip.cpp \ + tools/llava/clip.h \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual @@ -1480,12 +1480,12 @@ tests/test-double-float: tests/test-double-float.cpp tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \ $(OBJ_ALL) - $(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-chat: tests/test-chat.cpp \ $(OBJ_ALL) - $(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-opt: tests/test-opt.cpp \ diff --git a/README.md b/README.md index 42c0eb633e..e0232478c7 100644 --- a/README.md +++ b/README.md @@ -242,7 +242,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo | [Vulkan](docs/build.md#vulkan) | GPU | | [CANN](docs/build.md#cann) | Ascend NPU | | [OpenCL](docs/backend/OPENCL.md) | Adreno GPU | -| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/examples/rpc) | All | +| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All | ## Building the project @@ -276,9 +276,9 @@ The Hugging Face platform provides a variety of online tools for converting, qua - Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268) - Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669) -To learn more about model quantization, [read this documentation](examples/quantize/README.md) +To learn more about model quantization, [read this documentation](tools/quantize/README.md) -## [`llama-cli`](examples/main) +## [`llama-cli`](tools/main) #### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality. @@ -341,7 +341,7 @@ To learn more about model quantization, [read this documentation](examples/quant -## [`llama-server`](examples/server) +## [`llama-server`](tools/server) #### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs. @@ -411,7 +411,7 @@ To learn more about model quantization, [read this documentation](examples/quant -## [`llama-perplexity`](examples/perplexity) +## [`llama-perplexity`](tools/perplexity) #### A tool for measuring the perplexity [^1][^2] (and other quality metrics) of a model over a given text. @@ -436,10 +436,10 @@ To learn more about model quantization, [read this documentation](examples/quant -[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md) +[^1]: [tools/perplexity/README.md](./tools/perplexity/README.md) [^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity) -## [`llama-bench`](examples/llama-bench) +## [`llama-bench`](tools/llama-bench) #### Benchmark the performance of the inference for various parameters. @@ -460,7 +460,7 @@ To learn more about model quantization, [read this documentation](examples/quant -## [`llama-run`](examples/run) +## [`llama-run`](tools/run) #### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3]. @@ -504,8 +504,8 @@ To learn more about model quantization, [read this documentation](examples/quant ## Other documentation -- [main (cli)](examples/main/README.md) -- [server](examples/server/README.md) +- [main (cli)](tools/main/README.md) +- [server](tools/server/README.md) - [GBNF grammars](grammars/README.md) #### Development documentation diff --git a/SECURITY.md b/SECURITY.md index 9370fb1a88..9749e95b71 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -40,7 +40,7 @@ To protect sensitive data from potential leaks or unauthorized access, it is cru ### Untrusted environments or networks If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions: -* Do not use the RPC backend, [rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/examples/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/examples/server) functionality (see https://github.com/ggml-org/llama.cpp/pull/13061). +* Do not use the RPC backend, [rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) functionality (see https://github.com/ggml-org/llama.cpp/pull/13061). * Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value. * Encrypt your data if sending it over the network. diff --git a/build-xcframework.sh b/build-xcframework.sh index 97001b5f7f..3c2498b035 100755 --- a/build-xcframework.sh +++ b/build-xcframework.sh @@ -8,6 +8,7 @@ TVOS_MIN_OS_VERSION=16.4 BUILD_SHARED_LIBS=OFF LLAMA_BUILD_EXAMPLES=OFF +LLAMA_BUILD_TOOLS=OFF LLAMA_BUILD_TESTS=OFF LLAMA_BUILD_SERVER=OFF GGML_METAL=ON @@ -31,6 +32,7 @@ COMMON_CMAKE_ARGS=( -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml -DBUILD_SHARED_LIBS=${BUILD_SHARED_LIBS} -DLLAMA_BUILD_EXAMPLES=${LLAMA_BUILD_EXAMPLES} + -DLLAMA_BUILD_TOOLS=${LLAMA_BUILD_TOOLS} -DLLAMA_BUILD_TESTS=${LLAMA_BUILD_TESTS} -DLLAMA_BUILD_SERVER=${LLAMA_BUILD_SERVER} -DGGML_METAL_EMBED_LIBRARY=${GGML_METAL_EMBED_LIBRARY} diff --git a/ci/run.sh b/ci/run.sh index f463d7a8b2..b49a3a5f82 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -187,8 +187,8 @@ function gg_run_test_scripts_debug { set -e - (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log - (cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./tools/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./tools/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log set +e } @@ -211,8 +211,8 @@ function gg_run_test_scripts_release { set -e - (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log - (cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./tools/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./tools/quantize && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log set +e } diff --git a/common/arg.cpp b/common/arg.cpp index aface844c9..5080aa2fcb 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2211,14 +2211,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING")); add_opt(common_arg( {"--mmproj"}, "FILE", - "path to a multimodal projector file. see examples/llava/README.md", + "path to a multimodal projector file. see tools/llava/README.md", [](common_params & params, const std::string & value) { params.mmproj.path = value; } ).set_examples(mmproj_examples)); add_opt(common_arg( {"--mmproj-url"}, "URL", - "URL to a multimodal projector file. see examples/llava/README.md", + "URL to a multimodal projector file. see tools/llava/README.md", [](common_params & params, const std::string & value) { params.mmproj.url = value; } diff --git a/common/common.h b/common/common.h index 0a9dc0599f..cfe1b72786 100644 --- a/common/common.h +++ b/common/common.h @@ -340,7 +340,7 @@ struct common_params { common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO; - // multimodal models (see examples/llava) + // multimodal models (see tools/llava) struct common_params_model mmproj; bool mmproj_use_gpu = true; // use GPU for multimodal model bool no_mmproj = false; // explicitly disable multimodal model @@ -414,8 +414,8 @@ struct common_params { int n_pca_batch = 100; int n_pca_iterations = 1000; dimre_method cvector_dimre_method = DIMRE_METHOD_PCA; - std::string cvector_positive_file = "examples/cvector-generator/positive.txt"; - std::string cvector_negative_file = "examples/cvector-generator/negative.txt"; + std::string cvector_positive_file = "tools/cvector-generator/positive.txt"; + std::string cvector_negative_file = "tools/cvector-generator/negative.txt"; bool spm_infill = false; // suffix/prefix/middle pattern for infill diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md index 78c6f76077..7f71e0247d 100644 --- a/docs/development/HOWTO-add-model.md +++ b/docs/development/HOWTO-add-model.md @@ -9,10 +9,10 @@ Adding a model requires few steps: After following these steps, you can open PR. Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially: -- [main](/examples/main/) -- [imatrix](/examples/imatrix/) -- [quantize](/examples/quantize/) -- [server](/examples/server/) +- [main](/tools/main/) +- [imatrix](/tools/imatrix/) +- [quantize](/tools/quantize/) +- [server](/tools/server/) ### 1. Convert the model to GGUF diff --git a/docs/multimodal/MobileVLM.md b/docs/multimodal/MobileVLM.md index 20ac02f7a8..a647d7d357 100644 --- a/docs/multimodal/MobileVLM.md +++ b/docs/multimodal/MobileVLM.md @@ -33,13 +33,13 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336 2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh -python ./examples/llava/llava_surgery.py -m path/to/MobileVLM-1.7B +python ./tools/llava/llava_surgery.py -m path/to/MobileVLM-1.7B ``` 3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF: ```sh -python ./examples/llava/convert_image_encoder_to_gguf.py \ +python ./tools/llava/convert_image_encoder_to_gguf.py \ -m path/to/clip-vit-large-patch14-336 \ --llava-projector path/to/MobileVLM-1.7B/llava.projector \ --output-dir path/to/MobileVLM-1.7B \ @@ -47,7 +47,7 @@ python ./examples/llava/convert_image_encoder_to_gguf.py \ ``` ```sh -python ./examples/llava/convert_image_encoder_to_gguf.py \ +python ./tools/llava/convert_image_encoder_to_gguf.py \ -m path/to/clip-vit-large-patch14-336 \ --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \ --output-dir path/to/MobileVLM-1.7B_V2 \ @@ -69,10 +69,10 @@ Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directo ## Android compile and run ### compile -refer to `examples/llava/android/build_64.sh` +refer to `tools/llava/android/build_64.sh` ```sh -mkdir examples/llava/android/build_64 -cd examples/llava/android/build_64 +mkdir tools/llava/android/build_64 +cd tools/llava/android/build_64 ../build_64.sh ``` ### run on Android diff --git a/docs/multimodal/glmedge.md b/docs/multimodal/glmedge.md index af6b696a8a..e7dfafdde1 100644 --- a/docs/multimodal/glmedge.md +++ b/docs/multimodal/glmedge.md @@ -25,13 +25,13 @@ git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/T 2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents: ```sh -python ./examples/llava/glmedge-surgery.py -m ../model_path +python ./tools/llava/glmedge-surgery.py -m ../model_path ``` 4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF: ```sh -python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path +python ./tools/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path ``` 5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF: diff --git a/docs/multimodal/llava.md b/docs/multimodal/llava.md index c5bdc82158..0087b10610 100644 --- a/docs/multimodal/llava.md +++ b/docs/multimodal/llava.md @@ -37,19 +37,19 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336 2. Install the required Python packages: ```sh -pip install -r examples/llava/requirements.txt +pip install -r tools/llava/requirements.txt ``` 3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh -python ./examples/llava/llava_surgery.py -m ../llava-v1.5-7b +python ./tools/llava/llava_surgery.py -m ../llava-v1.5-7b ``` 4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF: ```sh -python ./examples/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b +python ./tools/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` 5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF: @@ -69,12 +69,12 @@ git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b 2) Install the required Python packages: ```sh -pip install -r examples/llava/requirements.txt +pip install -r tools/llava/requirements.txt ``` 3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: ```console -python examples/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/ +python tools/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/ ``` - you will find a llava.projector and a llava.clip file in your model directory @@ -88,7 +88,7 @@ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.jso 5) Create the visual gguf model: ```console -python ./examples/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision +python ./tools/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision ``` - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP diff --git a/docs/multimodal/minicpmo2.6.md b/docs/multimodal/minicpmo2.6.md index de470d8a82..c9aab8abc0 100644 --- a/docs/multimodal/minicpmo2.6.md +++ b/docs/multimodal/minicpmo2.6.md @@ -29,8 +29,8 @@ cmake --build build --config Release Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us) ```bash -python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-o-2_6 -python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4 +python ./tools/llava/minicpmv-surgery.py -m ../MiniCPM-o-2_6 +python ./tools/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4 python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model # quantize int4 version diff --git a/docs/multimodal/minicpmv2.5.md b/docs/multimodal/minicpmv2.5.md index 7a6879d395..4603bd7c1c 100644 --- a/docs/multimodal/minicpmv2.5.md +++ b/docs/multimodal/minicpmv2.5.md @@ -28,8 +28,8 @@ cmake --build build --config Release Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us) ```bash -python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5 -python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2 +python ./tools/llava/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5 +python ./tools/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2 python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model # quantize int4 version diff --git a/docs/multimodal/minicpmv2.6.md b/docs/multimodal/minicpmv2.6.md index 410a5dd177..69ebc12961 100644 --- a/docs/multimodal/minicpmv2.6.md +++ b/docs/multimodal/minicpmv2.6.md @@ -28,8 +28,8 @@ cmake --build build --config Release Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us) ```bash -python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-V-2_6 -python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3 +python ./tools/llava/minicpmv-surgery.py -m ../MiniCPM-V-2_6 +python ./tools/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3 python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model # quantize int4 version diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 37476f9043..eca0d0b097 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -12,51 +12,30 @@ llama_add_compile_flags() # examples -include_directories(${CMAKE_CURRENT_SOURCE_DIR}) - if (EMSCRIPTEN) else() - add_subdirectory(batched-bench) add_subdirectory(batched) add_subdirectory(embedding) add_subdirectory(eval-callback) add_subdirectory(gguf-hash) - add_subdirectory(gguf-split) add_subdirectory(gguf) add_subdirectory(gritlm) - add_subdirectory(imatrix) add_subdirectory(infill) - add_subdirectory(llama-bench) add_subdirectory(lookahead) add_subdirectory(lookup) - add_subdirectory(main) add_subdirectory(parallel) add_subdirectory(passkey) - add_subdirectory(perplexity) - add_subdirectory(quantize) add_subdirectory(retrieval) - if (LLAMA_BUILD_SERVER) - add_subdirectory(server) - endif() add_subdirectory(save-load-state) - add_subdirectory(run) add_subdirectory(simple) add_subdirectory(simple-chat) add_subdirectory(speculative) add_subdirectory(speculative-simple) - add_subdirectory(tokenize) - add_subdirectory(tts) add_subdirectory(gen-docs) if (NOT GGML_BACKEND_DL) - # these examples use the backends directly and cannot be built with dynamic loading add_subdirectory(convert-llama2c-to-ggml) - add_subdirectory(cvector-generator) - add_subdirectory(export-lora) - add_subdirectory(llava) - if (GGML_RPC) - add_subdirectory(rpc) - endif() + # these examples use the backends directly and cannot be built with dynamic loading if (GGML_SYCL) add_subdirectory(sycl) endif() diff --git a/examples/pydantic_models_to_grammar_examples.py b/examples/pydantic_models_to_grammar_examples.py index f94b82ca47..6dadb7f3fa 100755 --- a/examples/pydantic_models_to_grammar_examples.py +++ b/examples/pydantic_models_to_grammar_examples.py @@ -23,7 +23,7 @@ def create_completion(host, prompt, gbnf_grammar): """Calls the /completion API on llama-server. See - https://github.com/ggml-org/llama.cpp/tree/HEAD/examples/server#api-endpoints + https://github.com/ggml-org/llama.cpp/tree/HEAD/tools/server#api-endpoints """ print(f" Request:\n Grammar:\n{textwrap.indent(gbnf_grammar, ' ')}\n Prompt:\n{textwrap.indent(prompt.rstrip(), ' ')}") headers = {"Content-Type": "application/json"} diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz deleted file mode 100644 index 674e227571e2d38be246c0ebde8bf275d973865b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1263380 zcmV(&K;ge1iwFP!000020NlOXcH6kNDEPjc?@m zL(Ycq;W*o4E*Z2TGA^lmes<7&<2o;_MEr7`gpE0MrxCxKq(Ztnc7XFBse2a4Jb6Q>CSlG`s-qRN;JL^P2OOb2zx=`fF>YLRAHDxy)8 zyqhxN{57VWzI&9RNbj7p*@X9nNQKA!MHiBP8&Z!GNfMvFldX_t9{1<-zs8=s#}Zgh zcRlej=YCS=^nM%Sw<|as!qyo(6A?|Bgq_9Xq3e^Rm$YE#c-#+UYs96;eV=@YmYeHY zt+C+{*F0lmUQ{{e<7+OQaVoeYLzXycGT^QsNGD1%$&vw2Z=ByayFKNyCtGYdRE=iP 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examples/server/webui/src/components/Sidebar.tsx rename to tools/server/webui/src/components/Sidebar.tsx diff --git a/examples/server/webui/src/components/useChatTextarea.ts b/tools/server/webui/src/components/useChatTextarea.ts similarity index 100% rename from examples/server/webui/src/components/useChatTextarea.ts rename to tools/server/webui/src/components/useChatTextarea.ts diff --git a/examples/server/webui/src/index.scss b/tools/server/webui/src/index.scss similarity index 100% rename from examples/server/webui/src/index.scss rename to tools/server/webui/src/index.scss diff --git a/examples/server/webui/src/main.tsx b/tools/server/webui/src/main.tsx similarity index 100% rename from examples/server/webui/src/main.tsx rename to tools/server/webui/src/main.tsx diff --git a/examples/server/webui/src/utils/app.context.tsx b/tools/server/webui/src/utils/app.context.tsx similarity index 100% rename from examples/server/webui/src/utils/app.context.tsx rename to tools/server/webui/src/utils/app.context.tsx diff --git a/examples/server/webui/src/utils/common.tsx b/tools/server/webui/src/utils/common.tsx similarity index 100% rename from examples/server/webui/src/utils/common.tsx rename to tools/server/webui/src/utils/common.tsx diff --git a/examples/server/webui/src/utils/llama-vscode.ts b/tools/server/webui/src/utils/llama-vscode.ts similarity index 100% rename from examples/server/webui/src/utils/llama-vscode.ts rename to tools/server/webui/src/utils/llama-vscode.ts diff --git a/examples/server/webui/src/utils/misc.ts b/tools/server/webui/src/utils/misc.ts similarity index 100% rename from examples/server/webui/src/utils/misc.ts rename to tools/server/webui/src/utils/misc.ts diff --git a/examples/server/webui/src/utils/storage.ts b/tools/server/webui/src/utils/storage.ts similarity index 100% rename from examples/server/webui/src/utils/storage.ts rename to tools/server/webui/src/utils/storage.ts diff --git a/examples/server/webui/src/utils/types.ts b/tools/server/webui/src/utils/types.ts similarity index 100% rename from examples/server/webui/src/utils/types.ts rename to tools/server/webui/src/utils/types.ts diff --git a/examples/server/webui/src/vite-env.d.ts b/tools/server/webui/src/vite-env.d.ts similarity index 100% rename from examples/server/webui/src/vite-env.d.ts rename to tools/server/webui/src/vite-env.d.ts diff --git a/examples/server/webui/tailwind.config.js b/tools/server/webui/tailwind.config.js similarity index 100% rename from examples/server/webui/tailwind.config.js rename to tools/server/webui/tailwind.config.js diff --git a/examples/server/webui/tsconfig.app.json b/tools/server/webui/tsconfig.app.json similarity index 100% rename from examples/server/webui/tsconfig.app.json rename to tools/server/webui/tsconfig.app.json diff --git a/examples/server/webui/tsconfig.json b/tools/server/webui/tsconfig.json similarity index 100% rename from examples/server/webui/tsconfig.json rename to tools/server/webui/tsconfig.json diff --git a/examples/server/webui/tsconfig.node.json b/tools/server/webui/tsconfig.node.json similarity index 100% rename from examples/server/webui/tsconfig.node.json rename to tools/server/webui/tsconfig.node.json diff --git a/examples/server/webui/vite.config.ts b/tools/server/webui/vite.config.ts similarity index 100% rename from examples/server/webui/vite.config.ts rename to tools/server/webui/vite.config.ts diff --git a/examples/tokenize/CMakeLists.txt b/tools/tokenize/CMakeLists.txt similarity index 100% rename from examples/tokenize/CMakeLists.txt rename to tools/tokenize/CMakeLists.txt diff --git a/examples/tokenize/tokenize.cpp b/tools/tokenize/tokenize.cpp similarity index 100% rename from examples/tokenize/tokenize.cpp rename to tools/tokenize/tokenize.cpp diff --git a/examples/tts/CMakeLists.txt b/tools/tts/CMakeLists.txt similarity index 100% rename from examples/tts/CMakeLists.txt rename to tools/tts/CMakeLists.txt diff --git a/examples/tts/README.md b/tools/tts/README.md similarity index 96% rename from examples/tts/README.md rename to tools/tts/README.md index 4509763c65..557014aebb 100644 --- a/examples/tts/README.md +++ b/tools/tts/README.md @@ -45,7 +45,7 @@ $ popd This model file is PyTorch checkpoint (.ckpt) and we first need to convert it to huggingface format: ```console -(venv) python examples/tts/convert_pt_to_hf.py \ +(venv) python tools/tts/convert_pt_to_hf.py \ models/WavTokenizer-large-speech-75token/wavtokenizer_large_speech_320_24k.ckpt ... Model has been successfully converted and saved to models/WavTokenizer-large-speech-75token/model.safetensors @@ -105,7 +105,7 @@ $ source venv/bin/activate And then run the python script using: ```conole -(venv) python ./examples/tts/tts-outetts.py http://localhost:8020 http://localhost:8021 "Hello world" +(venv) python ./tools/tts/tts-outetts.py http://localhost:8020 http://localhost:8021 "Hello world" spectrogram generated: n_codes: 90, n_embd: 1282 converting to audio ... audio generated: 28800 samples diff --git a/examples/tts/convert_pt_to_hf.py b/tools/tts/convert_pt_to_hf.py similarity index 100% rename from examples/tts/convert_pt_to_hf.py rename to tools/tts/convert_pt_to_hf.py diff --git a/examples/tts/tts-outetts.py b/tools/tts/tts-outetts.py similarity index 100% rename from examples/tts/tts-outetts.py rename to tools/tts/tts-outetts.py diff --git a/examples/tts/tts.cpp b/tools/tts/tts.cpp similarity index 100% rename from examples/tts/tts.cpp rename to tools/tts/tts.cpp From 3bf785f3efa89ed28294fbf73054558a2b034bfb Mon Sep 17 00:00:00 2001 From: ymcki <84055651+ymcki@users.noreply.github.com> Date: Sat, 3 May 2025 23:39:51 +0800 Subject: [PATCH 090/126] llama : Llama-3_1-Nemotron-Ultra-253B-v1 support (#12843) --- convert_hf_to_gguf.py | 8 +++++++- src/llama-model.cpp | 20 ++++++++++++++++---- src/llama-model.h | 1 + 3 files changed, 24 insertions(+), 5 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 0862099da4..838999531e 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2123,6 +2123,9 @@ class DeciModel(TextModel): # if n_heads_in_group is not None, then # _num_kv_heads[il] is num_attention_head // n_heads_in_group and # _num_heads[il] is num_attention_head + # ***dummy layer*** for nemotron 253B + # if n_heads_in_group is None and ffn_mult is None + # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 and _ffn_dims is 0 for il in range(len(_block_configs)): if _block_configs[il]["attention"]["n_heads_in_group"] is None: if _block_configs[il]["attention"]["replace_with_linear"] is True: @@ -2134,7 +2137,10 @@ class DeciModel(TextModel): else: self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"]) self._num_heads.append(self.hparams["num_attention_heads"]) - _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"]) + if _block_configs[il]["ffn"]["ffn_mult"] is None: # dummy layer + _ffn_multipliers.append(0.0) + else: + _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"]) assert self.block_count == len(self._num_kv_heads) assert self.block_count == len(self._num_heads) assert self.block_count == len(_ffn_multipliers) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 08d2130137..8d25070f42 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -80,6 +80,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_236B: return "236B"; case LLM_TYPE_290B: return "290B"; case LLM_TYPE_314B: return "314B"; + case LLM_TYPE_405B: return "405B"; case LLM_TYPE_671B: return "671B"; case LLM_TYPE_SMALL: return "0.1B"; case LLM_TYPE_MEDIUM: return "0.4B"; @@ -582,6 +583,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { switch (hparams.n_layer) { case 32: type = LLM_TYPE_7B; break; case 80: type = LLM_TYPE_70B; break; + case 162: type = LLM_TYPE_405B; break; default: type = LLM_TYPE_UNKNOWN; } } break; @@ -1848,7 +1850,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED); layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED); - layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + if (n_ff > 0) { + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + } if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) { layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); @@ -1858,9 +1862,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); } - layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); - layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); - layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + if (n_ff > 0) { + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } // optional MLP bias layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED); @@ -4705,6 +4711,7 @@ struct llm_build_deci : public llm_graph_context { ggml_tensor * inpSA = inpL; const int64_t n_head_kv = hparams.n_head_kv(il); const int64_t n_head = hparams.n_head(il); + const int64_t n_ff = hparams.n_ff(il); if (n_head == 0) { // attention-free layer of Llama-3_1-Nemotron-51B @@ -4780,6 +4787,11 @@ struct llm_build_deci : public llm_graph_context { inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } + // FFN-free layer of Llama-3_1-Nemotron-Ultra-253B + if (n_head == 0 && n_ff == 0) { + continue; + } + // For Granite architecture if (hparams.f_residual_scale) { cur = ggml_scale(ctx0, cur, hparams.f_residual_scale); diff --git a/src/llama-model.h b/src/llama-model.h index 4c7e7a335b..815fa11ebc 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -76,6 +76,7 @@ enum llm_type { LLM_TYPE_236B, LLM_TYPE_290B, LLM_TYPE_314B, + LLM_TYPE_405B, LLM_TYPE_671B, LLM_TYPE_SMALL, LLM_TYPE_MEDIUM, From 36667c8edcded08063ed51c7d57e9e086bbfc903 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 3 May 2025 20:07:54 +0200 Subject: [PATCH 091/126] =?UTF-8?q?clip=20:=20revert=20the=20change=20of?= =?UTF-8?q?=20BOI/EOI=20token=20for=20GLM-edge=20(=E2=9A=A0=EF=B8=8F=20bre?= =?UTF-8?q?aking=20change)=20(#13259)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tools/llava/clip-impl.h | 2 ++ tools/llava/clip.cpp | 26 +++++++++++++++++++------- tools/llava/mtmd.cpp | 6 +----- 3 files changed, 22 insertions(+), 12 deletions(-) diff --git a/tools/llava/clip-impl.h b/tools/llava/clip-impl.h index b575ca4d7c..b78d930bce 100644 --- a/tools/llava/clip-impl.h +++ b/tools/llava/clip-impl.h @@ -75,6 +75,8 @@ #define TN_MM_PROJECTOR "mm.model.fc.weight" // idefics3 #define TN_MM_PATCH_MERGER "mm.patch_merger.weight" // mistral small 3.1 #define TN_TOK_IMG_BREAK "v.token_embd.img_break" // pixtral +#define TN_TOK_GLM_BOI "adapter.boi" // glm-edge (these embeddings are not in text model) +#define TN_TOK_GLM_EOI "adapter.eoi" // glm-edge (these embeddings are not in text model) // mimicpmv #define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k" diff --git a/tools/llava/clip.cpp b/tools/llava/clip.cpp index 7607d4e3ae..3b60a526ee 100644 --- a/tools/llava/clip.cpp +++ b/tools/llava/clip.cpp @@ -249,9 +249,11 @@ struct clip_vision_model { struct ggml_tensor * mm_4_w = nullptr; struct ggml_tensor * mm_4_b = nullptr; - //GLMV-Edge projection + // GLMV-Edge projection struct ggml_tensor * mm_model_adapter_conv_w = nullptr; struct ggml_tensor * mm_model_adapter_conv_b = nullptr; + struct ggml_tensor * mm_glm_tok_boi = nullptr; + struct ggml_tensor * mm_glm_tok_eoi = nullptr; // MobileVLM projection struct ggml_tensor * mm_model_mlp_1_w = nullptr; @@ -1559,6 +1561,13 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im embeddings = ggml_mul(ctx0, embeddings,x); embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); } + // arrangement of BOI/EOI token embeddings + // note: these embeddings are not present in text model, hence we cannot process them as text tokens + // see: https://huggingface.co/THUDM/glm-edge-v-2b/blob/main/siglip.py#L53 + { + embeddings = ggml_concat(ctx0, model.mm_glm_tok_boi, embeddings, 1); // BOI + embeddings = ggml_concat(ctx0, embeddings, model.mm_glm_tok_eoi, 1); // EOI + } } else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { @@ -1972,12 +1981,14 @@ struct clip_model_loader { { vision_model.mm_model_adapter_conv_w = get_tensor(string_format(TN_GLM_ADAPER_CONV, "weight")); vision_model.mm_model_adapter_conv_b = get_tensor(string_format(TN_GLM_ADAPER_CONV, "bias")); - vision_model.mm_model_mlp_0_w = get_tensor(string_format(TN_GLM_ADAPTER_LINEAR,"weight")); - vision_model.mm_model_ln_q_w = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1,"weight")); - vision_model.mm_model_ln_q_b = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1,"bias")); - vision_model.mm_model_mlp_1_w = get_tensor(string_format(TN_GLM_ADAPTER_D_H_2_4H,"weight")); - vision_model.mm_model_mlp_2_w = get_tensor(string_format(TN_GLM_ADAPTER_GATE,"weight")); - vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H,"weight")); + vision_model.mm_model_mlp_0_w = get_tensor(string_format(TN_GLM_ADAPTER_LINEAR, "weight")); + vision_model.mm_model_ln_q_w = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1, "weight")); + vision_model.mm_model_ln_q_b = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1, "bias")); + vision_model.mm_model_mlp_1_w = get_tensor(string_format(TN_GLM_ADAPTER_D_H_2_4H, "weight")); + vision_model.mm_model_mlp_2_w = get_tensor(string_format(TN_GLM_ADAPTER_GATE, "weight")); + vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H, "weight")); + vision_model.mm_glm_tok_boi = get_tensor(string_format(TN_TOK_GLM_BOI, "weight")); + vision_model.mm_glm_tok_eoi = get_tensor(string_format(TN_TOK_GLM_EOI, "weight")); } break; case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: @@ -2948,6 +2959,7 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { n_patches /= 4; + n_patches += 2; // for BOI and EOI token embeddings } else if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { if (ctx->minicpmv_version == 2) { n_patches = 96; diff --git a/tools/llava/mtmd.cpp b/tools/llava/mtmd.cpp index d1d7530feb..73abf2ad18 100644 --- a/tools/llava/mtmd.cpp +++ b/tools/llava/mtmd.cpp @@ -189,11 +189,6 @@ int32_t mtmd_tokenize(mtmd_context * ctx, marker_modified = "" + ctx->image_marker + ""; string_replace_all(prompt_modified, ctx->image_marker, marker_modified); - } else if (proj_type == PROJECTOR_TYPE_GLM_EDGE) { - // <|begin_of_image|> ... (image embeddings) ... <|end_of_image|> - marker_modified = "<|begin_of_image|>" + ctx->image_marker + "<|end_of_image|>"; - string_replace_all(prompt_modified, ctx->image_marker, marker_modified); - } else if (proj_type == PROJECTOR_TYPE_IDEFICS3) { // https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215 marker_modified = "" + ctx->image_marker + ""; @@ -213,6 +208,7 @@ int32_t mtmd_tokenize(mtmd_context * ctx, } // llava-1.5, llava-1.6, Yi-VL, Yi-34B, granite: don't need to add prefix and suffix + // for glm-edge, BOI and EOI token's embeddings are not present in the text model std::vector parts = string_split_str(prompt_modified, ctx->image_marker); output.clear(); From 3e959f09764a2bb0e64af594eab83f7fb3e08eb2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 4 May 2025 00:50:37 +0200 Subject: [PATCH 092/126] imatrix: fix oob writes if src1 is not contiguous (#13286) --- tools/imatrix/imatrix.cpp | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/tools/imatrix/imatrix.cpp b/tools/imatrix/imatrix.cpp index 31b675e8f9..b4640f9faf 100644 --- a/tools/imatrix/imatrix.cpp +++ b/tools/imatrix/imatrix.cpp @@ -46,7 +46,7 @@ private: common_params m_params; std::mutex m_mutex; int m_last_call = 0; - std::vector m_src1_data; + std::vector m_src1_data; std::vector m_ids; // the expert ids from ggml_mul_mat_id }; @@ -93,11 +93,13 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * const bool is_host = ggml_backend_buffer_is_host(src1->buffer); if (!is_host) { - m_src1_data.resize(ggml_nelements(src1)); - ggml_backend_tensor_get(src1, m_src1_data.data(), 0, ggml_nbytes(src1)); + const size_t src1_nbytes = ggml_nbytes(src1); + m_src1_data.resize(src1_nbytes); + ggml_backend_tensor_get(src1, m_src1_data.data(), 0, src1_nbytes); } - const float * data = is_host ? (const float *) src1->data : m_src1_data.data(); + const char * data = is_host ? (const char *) src1->data : m_src1_data.data(); + GGML_ASSERT(src1->nb[0] == ggml_element_size(src1)); // this has been adapted to the new format of storing merged experts in a single 3d tensor // ref: https://github.com/ggml-org/llama.cpp/pull/6387 @@ -144,7 +146,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * const int64_t i11 = idx % src1->ne[1]; const int64_t i12 = row; - const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]); + const float * x = (const float *)(data + i11*src1->nb[1] + i12*src1->nb[2]); for (int j = 0; j < (int)src1->ne[0]; ++j) { e.values[e_start + j] += x[j]*x[j]; @@ -180,7 +182,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * ++e.ncall; LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); for (int row = 0; row < (int)src1->ne[1]; ++row) { - const float * x = data + row * src1->ne[0]; + const float * x = (const float *) (data + row * src1->nb[1]); for (int j = 0; j < (int)src1->ne[0]; ++j) { e.values[j] += x[j]*x[j]; e.counts[j]++; From 8ae5ebcf859b05a2ea3bbd930133a2fe4a89ed3c Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sun, 4 May 2025 00:17:16 -0500 Subject: [PATCH 093/126] vulkan: Additional type support for unary, binary, and copy (#13266) Support f16->f32 copy. Support f16->f16 and f32->f32 unary ops. Support all combinations of f16/f32 for src0/src1/dst for add/sub/mul/div. --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 190 +++++++++++------- ggml/src/ggml-vulkan/vulkan-shaders/relu.comp | 2 +- .../ggml-vulkan/vulkan-shaders/sigmoid.comp | 2 +- ggml/src/ggml-vulkan/vulkan-shaders/tanh.comp | 2 +- .../vulkan-shaders/vulkan-shaders-gen.cpp | 54 ++++- 5 files changed, 162 insertions(+), 88 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index c4df47abae..c61a8cf0af 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -340,11 +340,17 @@ struct vk_device_struct { vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT]; vk_pipeline pipeline_get_rows_f32[GGML_TYPE_COUNT]; vk_pipeline pipeline_acc_f32; - vk_pipeline pipeline_add_f32, pipeline_add_f32_norepeat; - vk_pipeline pipeline_add_f16_f32_f16, pipeline_add_f16_f32_f16_norepeat; - vk_pipeline pipeline_sub_f32, pipeline_sub_f32_norepeat; - vk_pipeline pipeline_mul_f32, pipeline_mul_f32_norepeat; - vk_pipeline pipeline_div_f32, pipeline_div_f32_norepeat; + + // [src0 0=fp32,1=fp16][src1 0=fp32,1=fp16][dst 0=fp32,1=fp16] + vk_pipeline pipeline_add[2][2][2]; + vk_pipeline pipeline_add_norepeat[2][2][2]; + vk_pipeline pipeline_sub[2][2][2]; + vk_pipeline pipeline_sub_norepeat[2][2][2]; + vk_pipeline pipeline_mul[2][2][2]; + vk_pipeline pipeline_mul_norepeat[2][2][2]; + vk_pipeline pipeline_div[2][2][2]; + vk_pipeline pipeline_div_norepeat[2][2][2]; + vk_pipeline pipeline_concat_f32, pipeline_concat_f16, pipeline_concat_i32; vk_pipeline pipeline_upscale_f32; vk_pipeline pipeline_scale_f32; @@ -354,8 +360,8 @@ struct vk_device_struct { vk_pipeline pipeline_clamp_f32; vk_pipeline pipeline_pad_f32; vk_pipeline pipeline_repeat_f32, pipeline_repeat_back_f32; - vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16, pipeline_cpy_f32_bf16; - vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16, pipeline_contig_cpy_f32_bf16; + vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16, pipeline_cpy_f16_f32, pipeline_cpy_f32_bf16; + vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16, pipeline_contig_cpy_f16_f32, pipeline_contig_cpy_f32_bf16; vk_pipeline pipeline_cpy_f32_quant[GGML_TYPE_COUNT]; vk_pipeline pipeline_cpy_quant_f32[GGML_TYPE_COUNT]; vk_pipeline pipeline_norm_f32; @@ -363,14 +369,17 @@ struct vk_device_struct { vk_pipeline pipeline_rms_norm_f32; vk_pipeline pipeline_rms_norm_back_f32; vk_pipeline pipeline_l2_norm_f32; - vk_pipeline pipeline_gelu_f32; - vk_pipeline pipeline_gelu_quick_f32; - vk_pipeline pipeline_silu_f32; - vk_pipeline pipeline_silu_back_f32; - vk_pipeline pipeline_relu_f32; + + // [src/dst 0=fp32,1=fp16] + vk_pipeline pipeline_gelu[2]; + vk_pipeline pipeline_gelu_quick[2]; + vk_pipeline pipeline_silu[2]; + vk_pipeline pipeline_relu[2]; + vk_pipeline pipeline_tanh[2]; + vk_pipeline pipeline_sigmoid[2]; + vk_pipeline pipeline_leaky_relu_f32; - vk_pipeline pipeline_tanh_f32; - vk_pipeline pipeline_sigmoid_f32; + vk_pipeline pipeline_silu_back_f32; vk_pipeline pipeline_diag_mask_inf_f32; vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16; vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512; @@ -2508,11 +2517,13 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f32, "cpy_f16_f32", cpy_f16_f32_len, cpy_f16_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_bf16,"cpy_f32_bf16",cpy_f32_bf16_len,cpy_f32_bf16_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f32, "contig_cpy_f32_f32", contig_cpy_f32_f32_len, contig_cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f16, "contig_cpy_f32_f16", contig_cpy_f32_f16_len, contig_cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f16, "contig_cpy_f16_f16", contig_cpy_f16_f16_len, contig_cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f32, "contig_cpy_f16_f32", contig_cpy_f16_f32_len, contig_cpy_f16_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_bf16,"contig_cpy_f32_bf16",contig_cpy_f32_bf16_len,contig_cpy_f32_bf16_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); if (device->float_controls_rte_fp16) { @@ -2538,20 +2549,32 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q8_0], "cpy_q8_0_f32", cpy_q8_0_f32_len, cpy_q8_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q8_0), 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_IQ4_NL], "cpy_iq4_nl_f32", cpy_iq4_nl_f32_len, cpy_iq4_nl_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_IQ4_NL), 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); - ggml_vk_create_pipeline(device, device->pipeline_add_f32_norepeat, "add_f32_norepeat", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); - ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16, "add_f16_f32_f16", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); - ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16_norepeat, "add_f16_f32_f16_norepeat", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); + auto get_suffix = [](bool src0_f16, bool src1_f16, bool dst_f16) { + std::string s; + s += std::string(src0_f16 ? "_f16" : "_f32"); + s += std::string(src1_f16 ? "_f16" : "_f32"); + s += std::string(dst_f16 ? "_f16" : "_f32"); + return s; + }; + +#define CREATE_BINARY(name, namemod, spec) \ + for (int s0 : {0,1}) for (int s1 : {0,1}) for (int d : {0,1}) \ + ggml_vk_create_pipeline(device, device->pipeline_ ## name ## namemod[s0][s1][d], \ + #name + get_suffix(s0, s1, d) + #namemod, name ## _len[s0][s1][d], name ## _data[s0][s1][d], \ + "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, spec, 1); + + CREATE_BINARY(add, , {0}) + CREATE_BINARY(add, _norepeat, {1}) + CREATE_BINARY(sub, , {0}) + CREATE_BINARY(sub, _norepeat, {1}) + CREATE_BINARY(mul, , {0}) + CREATE_BINARY(mul, _norepeat, {1}) + CREATE_BINARY(div, , {0}) + CREATE_BINARY(div, _norepeat, {1}) +#undef CREATE_BINARY ggml_vk_create_pipeline(device, device->pipeline_acc_f32, "acc_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_sub_f32, "sub_f32", sub_f32_len, sub_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); - ggml_vk_create_pipeline(device, device->pipeline_sub_f32_norepeat, "sub_f32_norepeat", sub_f32_len, sub_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); - ggml_vk_create_pipeline(device, device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); - ggml_vk_create_pipeline(device, device->pipeline_mul_f32_norepeat, "mul_f32_norepeat", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); - ggml_vk_create_pipeline(device, device->pipeline_div_f32, "div_f32", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); - ggml_vk_create_pipeline(device, device->pipeline_div_f32_norepeat, "div_f32_norepeat", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); - ggml_vk_create_pipeline(device, device->pipeline_concat_f32, "concat_f32", concat_f32_len, concat_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_concat_f16, "concat_f16", concat_f16_len, concat_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_concat_i32, "concat_i32", concat_i32_len, concat_i32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); @@ -2571,14 +2594,20 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_repeat_f32, "repeat_f32", repeat_f32_len, repeat_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_repeat_back_f32, "repeat_back_f32", repeat_back_f32_len, repeat_back_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_gelu_quick_f32, "gelu_quick_f32", gelu_quick_f32_len, gelu_quick_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_silu_back_f32, "silu_back_f32", silu_back_f32_len, silu_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); +#define CREATE_UNARY(name) \ + ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); \ + ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + CREATE_UNARY(gelu) + CREATE_UNARY(gelu_quick) + CREATE_UNARY(silu) + CREATE_UNARY(relu) + CREATE_UNARY(tanh) + CREATE_UNARY(sigmoid) +#undef CREATE_UNARY + ggml_vk_create_pipeline(device, device->pipeline_leaky_relu_f32, "leaky_relu_f32", leaky_relu_f32_len, leaky_relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_tanh_f32, "tanh_f32", tanh_f32_len, tanh_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_sigmoid_f32, "sigmoid_f32", sigmoid_f32_len, sigmoid_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_silu_back_f32, "silu_back_f32", silu_back_f32_len, silu_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {1, 512, 1}, {}, 1, true); @@ -4504,6 +4533,13 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_cpy_f16_f16; } } + if (src->type == GGML_TYPE_F16 && to == GGML_TYPE_F32) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f16_f32; + } else { + return ctx->device->pipeline_cpy_f16_f32; + } + } if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_BF16) { if (contig) { return ctx->device->pipeline_contig_cpy_f32_bf16; @@ -5894,26 +5930,37 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const } return nullptr; case GGML_OP_ADD: - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_f32_norepeat : ctx->device->pipeline_add_f32; - } - if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { - return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_f16_f32_f16_norepeat : ctx->device->pipeline_add_f16_f32_f16; - } - return nullptr; case GGML_OP_SUB: - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_sub_f32_norepeat : ctx->device->pipeline_sub_f32; - } - return nullptr; case GGML_OP_MUL: - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_mul_f32_norepeat : ctx->device->pipeline_mul_f32; - } - return nullptr; case GGML_OP_DIV: - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_div_f32_norepeat : ctx->device->pipeline_div_f32; + if ((src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) || + (src1->type != GGML_TYPE_F32 && src1->type != GGML_TYPE_F16) || + (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16)) { + return nullptr; + } + switch (op) { + case GGML_OP_ADD: + { + auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_norepeat : ctx->device->pipeline_add; + return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16]; + } + case GGML_OP_SUB: + { + auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_sub_norepeat : ctx->device->pipeline_sub; + return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16]; + } + case GGML_OP_MUL: + { + auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_mul_norepeat : ctx->device->pipeline_mul; + return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16]; + } + case GGML_OP_DIV: + { + auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_div_norepeat : ctx->device->pipeline_div; + return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16]; + } + default: + break; } return nullptr; case GGML_OP_CONCAT: @@ -6007,37 +6054,25 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const } return nullptr; case GGML_OP_UNARY: + if ((src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) || + (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16) || + (src0->type != dst->type)) { + return nullptr; + } + switch (ggml_get_unary_op(dst)) { case GGML_UNARY_OP_SILU: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_silu_f32; - } - break; + return ctx->device->pipeline_silu[dst->type == GGML_TYPE_F16]; case GGML_UNARY_OP_GELU: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_gelu_f32; - } - break; + return ctx->device->pipeline_gelu[dst->type == GGML_TYPE_F16]; case GGML_UNARY_OP_GELU_QUICK: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_gelu_quick_f32; - } - break; + return ctx->device->pipeline_gelu_quick[dst->type == GGML_TYPE_F16]; case GGML_UNARY_OP_RELU: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_relu_f32; - } - break; + return ctx->device->pipeline_relu[dst->type == GGML_TYPE_F16]; case GGML_UNARY_OP_TANH: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_tanh_f32; - } - break; + return ctx->device->pipeline_tanh[dst->type == GGML_TYPE_F16]; case GGML_UNARY_OP_SIGMOID: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_sigmoid_f32; - } - break; + return ctx->device->pipeline_sigmoid[dst->type == GGML_TYPE_F16]; default: break; } @@ -9423,7 +9458,10 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_UNARY_OP_RELU: case GGML_UNARY_OP_TANH: case GGML_UNARY_OP_SIGMOID: - return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + return ggml_is_contiguous(op->src[0]) && + (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && + (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && + (op->src[0]->type == op->type); default: return false; } @@ -9603,6 +9641,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm } if (src1_type == GGML_TYPE_F32) { switch (src0_type) { + case GGML_TYPE_F16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -9641,6 +9680,9 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: + return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && + (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16) && + (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16); case GGML_OP_SILU_BACK: case GGML_OP_RMS_NORM_BACK: case GGML_OP_SQR: diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/relu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/relu.comp index 52a19b62a6..4f806270c7 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/relu.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/relu.comp @@ -17,5 +17,5 @@ void main() { return; } - data_d[i] = max(float(data_a[i]), 0); + data_d[i] = D_TYPE(max(float(data_a[i]), 0)); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/sigmoid.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sigmoid.comp index 776581e2c4..5c9e5c3503 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/sigmoid.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/sigmoid.comp @@ -16,5 +16,5 @@ void main() { if (i >= p.KX) { return; } - data_d[i] = D_TYPE(1. / (1 + exp(-1. *data_a[i]))); + data_d[i] = D_TYPE(1. / (1 + exp(-1. * float(data_a[i])))); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/tanh.comp b/ggml/src/ggml-vulkan/vulkan-shaders/tanh.comp index 495f966bdc..8a6f868f58 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/tanh.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/tanh.comp @@ -16,5 +16,5 @@ void main() { if (i >= p.KX) { return; } - data_d[i] = D_TYPE(1. - 2. / (exp(2.*data_a[i]) + 1.)); + data_d[i] = D_TYPE(1. - 2. / (exp(2.*float(data_a[i])) + 1.)); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 759112b946..382f190f28 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -485,10 +485,12 @@ void process_shaders() { string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("cpy_f16_f32", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); string_to_spv("cpy_f32_bf16","copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "uint16_t"}, {"DATA_D_BF16", "1"}}); string_to_spv("contig_cpy_f32_f32", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("contig_cpy_f32_f16", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); string_to_spv("contig_cpy_f16_f16", "contig_copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("contig_cpy_f16_f32", "contig_copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); string_to_spv("contig_cpy_f32_bf16","contig_copy.comp",{{"A_TYPE", "float"}, {"D_TYPE", "uint16_t"}, {"DATA_D_BF16", "1"}}); for (std::string t : {"q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { @@ -497,8 +499,26 @@ void process_shaders() { string_to_spv("cpy_" + t + "_f32", "copy_from_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); } - string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - string_to_spv("add_f16_f32_f16", "add.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}}); + auto get_type_str = [](bool f16) { + return f16 ? "float16_t" : "float"; + }; + auto get_suffix = [](bool src0_f16, bool src1_f16, bool dst_f16) { + std::string s; + s += std::string(src0_f16 ? "_f16" : "_f32"); + s += std::string(src1_f16 ? "_f16" : "_f32"); + s += std::string(dst_f16 ? "_f16" : "_f32"); + return s; + }; + for (std::string op : {"add", "sub", "mul", "div"}) { + for (auto src0_f16 : {false, true}) { + for (auto src1_f16 : {false, true}) { + for (auto dst_f16 : {false, true}) { + auto name = op + get_suffix(src0_f16, src1_f16, dst_f16); + string_to_spv(name.c_str(), op + ".comp", {{"A_TYPE", get_type_str(src0_f16)}, {"B_TYPE", get_type_str(src1_f16)}, {"D_TYPE", get_type_str(dst_f16)}, {"FLOAT_TYPE", "float"}}); + } + } + } + } string_to_spv("sub_f32", "sub.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); @@ -533,14 +553,21 @@ void process_shaders() { string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("gelu_quick_f32", "gelu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("silu_back_f32", "silu_back.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("leaky_relu_f32", "leaky_relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("tanh_f32", "tanh.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("sigmoid_f32", "sigmoid.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("gelu_f16", "gelu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("gelu_quick_f16", "gelu_quick.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("gelu_quick_f32", "gelu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("silu_f16", "silu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("relu_f16", "relu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("tanh_f16", "tanh.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("tanh_f32", "tanh.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("sigmoid_f16", "sigmoid.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("sigmoid_f32", "sigmoid.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("leaky_relu_f32", "leaky_relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("silu_back_f32", "silu_back.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); @@ -641,7 +668,12 @@ void write_output_files() { std::remove(path.c_str()); } } - + for (const char *op : {"add", "sub", "mul", "div"}) { + fprintf(hdr, "extern unsigned char *%s_data[2][2][2];\n", op); + fprintf(hdr, "extern uint64_t %s_len[2][2][2];\n", op); + fprintf(src, "unsigned char *%s_data[2][2][2] = {{{%s_f32_f32_f32_data, %s_f32_f32_f16_data}, {%s_f32_f16_f32_data, %s_f32_f16_f16_data}}, {{%s_f16_f32_f32_data, %s_f16_f32_f16_data}, {%s_f16_f16_f32_data, %s_f16_f16_f16_data}}};\n", op, op, op, op, op, op, op, op, op); + fprintf(src, "uint64_t %s_len[2][2][2] = {{{%s_f32_f32_f32_len, %s_f32_f32_f16_len}, {%s_f32_f16_f32_len, %s_f32_f16_f16_len}}, {{%s_f16_f32_f32_len, %s_f16_f32_f16_len}, {%s_f16_f16_f32_len, %s_f16_f16_f16_len}}};\n", op, op, op, op, op, op, op, op, op); + } fclose(hdr); fclose(src); } From 8afbd968182909cf93fb15959fc867b6dd3adb53 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 4 May 2025 13:58:38 +0200 Subject: [PATCH 094/126] CUDA: fix race condition in MMQ ids_dst (#13294) --- ggml/src/ggml-cuda/mmq.cuh | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index 8c93e8326e..fc6ce00830 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -2636,6 +2636,7 @@ static __global__ void mul_mat_q( ids_dst_shared[j] = j; } + __syncthreads(); // On AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead: #if (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < GGML_CUDA_CC_VOLTA @@ -2664,6 +2665,7 @@ static __global__ void mul_mat_q( return; } + // __syncthreads(); // There is no previous tile that could cause a race condition. #pragma unroll for (int j0 = 0; j0 < mmq_x; j0 += nwarps*WARP_SIZE) { const int j = j0 + threadIdx.y*WARP_SIZE + threadIdx.x; @@ -2674,6 +2676,7 @@ static __global__ void mul_mat_q( ids_dst_shared[j] = ids_dst[col_low + jt*mmq_x + j]; } + __syncthreads(); } offset_y += (col_low + jt*mmq_x)*(sizeof(block_q8_1_mmq)/sizeof(int)); @@ -2740,6 +2743,7 @@ static __global__ void mul_mat_q( continue; } + __syncthreads(); #pragma unroll for (int j0 = 0; j0 < mmq_x; j0 += nwarps*WARP_SIZE) { const int j = j0 + threadIdx.y*WARP_SIZE + threadIdx.x; @@ -2750,6 +2754,7 @@ static __global__ void mul_mat_q( ids_dst_shared[j] = ids_dst[col_low + jt*mmq_x + j]; } + __syncthreads(); } offset_y += (col_low + jt*mmq_x)*(sizeof(block_q8_1_mmq)/sizeof(int)); @@ -2805,6 +2810,7 @@ static __global__ void mul_mat_q( } // The memory layout for the fixup buffer is always contiguous, therefore reset ids: + __syncthreads(); #pragma unroll for (int j0 = 0; j0 < mmq_x; j0 += nwarps*WARP_SIZE) { const int j = j0 + threadIdx.y*WARP_SIZE + threadIdx.x; @@ -2815,6 +2821,7 @@ static __global__ void mul_mat_q( ids_dst_shared[j] = j; } + __syncthreads(); } offset_y += (col_low + jt*mmq_x)*(sizeof(block_q8_1_mmq)/sizeof(int)); From 93c4e23905987949b714b21ae918ff6bfb55fe36 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 4 May 2025 14:16:39 +0200 Subject: [PATCH 095/126] CUDA: fix race condition in MMQ stream-k fixup (#13299) --- ggml/src/ggml-cuda/mmq.cuh | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index fc6ce00830..e1096dce6d 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -2958,6 +2958,7 @@ static __global__ void mul_mat_q_stream_k_fixup( for (int j = threadIdx.y*WARP_SIZE + threadIdx.x; j < mmq_x; j += nwarps*WARP_SIZE) { ids_dst_shared[j] = ids_dst[col_low + j]; } + __syncthreads(); const int offset_dst = it*mmq_y; dst += offset_dst; From 9f2da5871f4bbd205b8a3b952cdc76283218d595 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Sun, 4 May 2025 14:20:49 +0200 Subject: [PATCH 096/126] llama : build windows releases with dl backends (#13220) --- .github/workflows/build.yml | 71 +++++++++++++--------------------- cmake/arm64-windows-msvc.cmake | 6 --- cmake/x64-windows-llvm.cmake | 6 --- tests/CMakeLists.txt | 5 ++- 4 files changed, 30 insertions(+), 58 deletions(-) delete mode 100644 cmake/arm64-windows-msvc.cmake diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index bcea1a8afc..e42ba47a19 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -771,7 +771,7 @@ jobs: uses: hendrikmuhs/ccache-action@v1.2.16 with: key: windows-msys2 - variant: sccache + variant: ccache evict-old-files: 1d - name: Setup ${{ matrix.sys }} @@ -814,26 +814,18 @@ jobs: strategy: matrix: include: - - build: 'noavx-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF' - - build: 'avx2-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON' - - build: 'avx-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF' - - build: 'avx512-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON' + - build: 'cpu-x64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF' - build: 'openblas-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - - build: 'kompute-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - build: 'vulkan-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON' - build: 'llvm-arm64' defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' - - build: 'msvc-arm64' - defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' - build: 'llvm-arm64-opencl-adreno' defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON' + # - build: 'kompute-x64' + # defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON' steps: - name: Clone @@ -846,7 +838,7 @@ jobs: uses: hendrikmuhs/ccache-action@v1.2.16 with: key: windows-latest-cmake-${{ matrix.build }} - variant: sccache + variant: ccache evict-old-files: 1d - name: Clone Kompute submodule @@ -922,39 +914,26 @@ jobs: cp $env:RUNNER_TEMP/openblas/bin/libopenblas.dll ./build/bin/Release/openblas.dll cp $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt ./build/bin/Release/OpenBLAS-${env:OPENBLAS_VERSION}.txt - - name: Check AVX512F support - id: check_avx512f - if: ${{ matrix.build == 'avx512-x64' }} - continue-on-error: true - run: | - cd build - $vcdir = $(vswhere -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath) - $msvc = $(join-path $vcdir $('VC\Tools\MSVC\'+$(gc -raw $(join-path $vcdir 'VC\Auxiliary\Build\Microsoft.VCToolsVersion.default.txt')).Trim())) - $cl = $(join-path $msvc 'bin\Hostx64\x64\cl.exe') - echo 'int main(void){unsigned int a[4];__cpuid(a,7);return !(a[1]&65536);}' >> avx512f.c - & $cl /O2 /GS- /kernel avx512f.c /link /nodefaultlib /entry:main - .\avx512f.exe && echo "AVX512F: YES" && ( echo HAS_AVX512F=1 >> $env:GITHUB_ENV ) || echo "AVX512F: NO" - - name: Test id: cmake_test - # not all machines have native AVX-512 - if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'llvm-arm64-opencl-adreno' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'llvm-arm64' && matrix.build != 'llvm-arm64-opencl-adreno' }} run: | cd build ctest -L main -C Release --verbose --timeout 900 - - name: Test (Intel SDE) - id: cmake_test_sde - if: ${{ matrix.build == 'avx512-x64' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation - run: | - curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/813591/sde-external-${env:SDE_VERSION}-win.tar.xz" - # for some weird reason windows tar doesn't like sde tar.xz - 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz - 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar - $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe) - cd build - $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1 - & $sde -future -- ctest -L main -C Release --verbose --timeout 900 + # TODO: disabled for now, consider adding tests for all CPU variants instead + # - name: Test (Intel SDE) + # id: cmake_test_sde + # if: ${{ matrix.build == 'avx512-x64' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation + # run: | + # curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/813591/sde-external-${env:SDE_VERSION}-win.tar.xz" + # # for some weird reason windows tar doesn't like sde tar.xz + # 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz + # 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar + # $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe) + # cd build + # $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1 + # & $sde -future -- ctest -L main -C Release --verbose --timeout 900 - name: Determine tag name id: tag @@ -1039,7 +1018,7 @@ jobs: uses: hendrikmuhs/ccache-action@v1.2.16 with: key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }} - variant: sccache + variant: ccache evict-old-files: 1d - name: Install Cuda Toolkit 11.7 @@ -1117,6 +1096,8 @@ jobs: cmake -S . -B build -G "Ninja Multi-Config" ^ -DLLAMA_BUILD_SERVER=ON ^ -DGGML_NATIVE=OFF ^ + -DGGML_BACKEND_DL=ON ^ + -DGGML_CPU_ALL_VARIANTS=ON ^ -DGGML_CUDA=ON ^ -DGGML_RPC=ON ^ -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" @@ -1191,7 +1172,7 @@ jobs: uses: hendrikmuhs/ccache-action@v1.2.16 with: key: windows-latest-cmake-sycl - variant: sccache + variant: ccache evict-old-files: 1d - name: Install diff --git a/cmake/arm64-windows-msvc.cmake b/cmake/arm64-windows-msvc.cmake deleted file mode 100644 index c77631420c..0000000000 --- a/cmake/arm64-windows-msvc.cmake +++ /dev/null @@ -1,6 +0,0 @@ -set( CMAKE_SYSTEM_NAME Windows ) -set( CMAKE_SYSTEM_PROCESSOR arm64 ) - -set( target arm64-pc-windows-msvc ) -set( CMAKE_C_COMPILER_TARGET ${target} ) -set( CMAKE_CXX_COMPILER_TARGET ${target} ) diff --git a/cmake/x64-windows-llvm.cmake b/cmake/x64-windows-llvm.cmake index 0603d738fb..77e7914079 100644 --- a/cmake/x64-windows-llvm.cmake +++ b/cmake/x64-windows-llvm.cmake @@ -3,9 +3,3 @@ set( CMAKE_SYSTEM_PROCESSOR x86_64 ) set( CMAKE_C_COMPILER clang ) set( CMAKE_CXX_COMPILER clang++ ) - -set( arch_c_flags "-march=native" ) - -set( CMAKE_C_FLAGS_INIT "${arch_c_flags}" ) -set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags}" ) - diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 8acbe689a4..72b32df9fc 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -114,7 +114,10 @@ if (NOT WIN32) target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../tools/server) endif() - llama_build(test-quantize-stats.cpp) + if (NOT GGML_BACKEND_DL) + llama_build(test-quantize-stats.cpp) + endif() + llama_build(test-gbnf-validator.cpp) # build test-tokenizer-1-bpe target once and add many tests From 86bd60d3fe4a08b8c9d920e6defbc2412d803569 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Sun, 4 May 2025 17:05:20 +0200 Subject: [PATCH 097/126] llava/mtmd : fixes to fully support dl backends (#13303) --- tools/CMakeLists.txt | 2 +- tools/llava/clip.cpp | 10 +++++++++- tools/llava/llava.cpp | 6 +++++- 3 files changed, 15 insertions(+), 3 deletions(-) diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt index 119d7c50a0..383e94accb 100644 --- a/tools/CMakeLists.txt +++ b/tools/CMakeLists.txt @@ -27,11 +27,11 @@ else() add_subdirectory(run) add_subdirectory(tokenize) add_subdirectory(tts) + add_subdirectory(llava) if (NOT GGML_BACKEND_DL) # these examples use the backends directly and cannot be built with dynamic loading add_subdirectory(cvector-generator) add_subdirectory(export-lora) - add_subdirectory(llava) if (GGML_RPC) add_subdirectory(rpc) endif() diff --git a/tools/llava/clip.cpp b/tools/llava/clip.cpp index 3b60a526ee..cc03bf884a 100644 --- a/tools/llava/clip.cpp +++ b/tools/llava/clip.cpp @@ -3382,7 +3382,15 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima GGML_ABORT("Unknown projector type"); } - ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads); + // ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads); + ggml_backend_dev_t dev = ggml_backend_get_device(ctx->backend_cpu); + ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr; + if (reg) { + auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads"); + if (ggml_backend_set_n_threads_fn) { + ggml_backend_set_n_threads_fn(ctx->backend_cpu, n_threads); + } + } auto status = ggml_backend_sched_graph_compute(ctx->sched.get(), gf); if (status != GGML_STATUS_SUCCESS) { diff --git a/tools/llava/llava.cpp b/tools/llava/llava.cpp index c00d16aeff..b85ab112bf 100644 --- a/tools/llava/llava.cpp +++ b/tools/llava/llava.cpp @@ -2,6 +2,7 @@ #include "llava.h" #include "llama.h" +#include "ggml-cpp.h" #include #include @@ -209,7 +210,10 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side, size_ele * clip_n_mmproj_embd(ctx_clip), 0); // ggml_tensor_printf(flatten,"flatten",__LINE__,false,false); ggml_build_forward_expand(gf, flatten); - ggml_graph_compute_with_ctx(model.ctx, gf, 1); + + ggml_backend_ptr backend { ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr) }; + ggml_backend_graph_compute(backend.get(), gf); + struct ggml_tensor* result = ggml_graph_node(gf, -1); memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context From 6eb7d25c700e29d9272064db408855178d01741b Mon Sep 17 00:00:00 2001 From: Aaron Teo Date: Mon, 5 May 2025 01:49:12 +0800 Subject: [PATCH 098/126] ggml : activate s390x simd for Q3_K (#13301) Signed-off-by: Aaron Teo --- ggml/src/ggml-cpu/ggml-cpu-quants.c | 111 ++++++++++++++++++++++++++++ 1 file changed, 111 insertions(+) diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c index 91a81bdc3c..987e153165 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-quants.c +++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c @@ -6596,7 +6596,118 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi } *s = hsum_float_8(acc); +#elif defined(__VXE__) || defined(__VXE2__) + uint32_t aux[3]; + uint32_t utmp[4]; + const int32x4_t v_z = vec_splat_s32(0); + const uint8x16_t v_3m = vec_splat_u8(0x03); + + const uint8x16_t v_0c = vec_splat_u8(1); + const uint8x16_t v_1c = vec_sl(v_0c, 1); + const uint8x16_t v_2c = vec_sl(v_0c, 2); + const uint8x16_t v_3c = vec_sl(v_0c, 3); + + uint8x16_t q3h[4]; + uint8x16_t q3b[2]; + int8x16_t q3bytes[4]; + int8x16_t q8bytes[4]; + uint8x16_t qhbits[2]; + + float sum = 0; + + for (int i = 0; i < nb; ++i) { + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict x0l = x[i].qs; + const uint8_t * restrict x0h = x[i].hmask; + const int8_t * restrict y0 = y[i].qs; + + qhbits[0] = vec_xl(0 , x0h); + qhbits[1] = vec_xl(16, x0h); + + int32_t isum = 0; + + memcpy(aux, x[i].scales, 12); + utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); + utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); + utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); + utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); + + int8_t * scale = (int8_t *)utmp; + for (int j = 0; j < 16; ++j) scale[j] -= 32; + + for (int j = 0; j < QK_K/128; ++j) { + int32x4_t isum0, isum1, isum2, isum3; + + q3b[0] = vec_xl(0 , x0l); + q3b[1] = vec_xl(16, x0l); + x0l += 32; + + q8bytes[0] = vec_xl(0 , y0); + q8bytes[1] = vec_xl(16 , y0); + q8bytes[2] = vec_xl(32 , y0); + q8bytes[3] = vec_xl(48 , y0); + q8bytes[4] = vec_xl(64 , y0); + q8bytes[5] = vec_xl(80 , y0); + q8bytes[6] = vec_xl(96 , y0); + q8bytes[7] = vec_xl(112, y0); + y0 += 128; + + q3h[0] = vec_sl(vec_andc(v_0c, qhbits[0]), 2); + q3h[1] = vec_sl(vec_andc(v_0c, qhbits[1]), 2); + q3h[2] = vec_sl(vec_andc(v_1c, qhbits[0]), 1); + q3h[3] = vec_sl(vec_andc(v_1c, qhbits[1]), 1); + + q3bytes[0] = vec_sub((int8x16_t)vec_and(q3b[0], v_3m), (int8x16_t)q3h[0]); + q3bytes[1] = vec_sub((int8x16_t)vec_and(q3b[1], v_3m), (int8x16_t)q3h[1]); + q3bytes[2] = vec_sub((int8x16_t)vec_and(vec_sr(q3b[0], 2), v_3m), (int8x16_t)q3h[2]); + q3bytes[3] = vec_sub((int8x16_t)vec_and(vec_sr(q3b[1], 2), v_3m), (int8x16_t)q3h[3]); + + isum0 = ggml_vec_dot(v_z, q3bytes[0], q8bytes[0]); + isum1 = ggml_vec_dot(v_z, q3bytes[1], q8bytes[1]); + isum2 = ggml_vec_dot(v_z, q3bytes[2], q8bytes[2]); + isum3 = ggml_vec_dot(v_z, q3bytes[3], q8bytes[3]); + + isum += (isum0[0] + isum0[1] + isum0[2] + isum0[3]) * scale[0]; + isum += (isum1[0] + isum1[1] + isum1[2] + isum1[3]) * scale[1]; + isum += (isum2[0] + isum2[1] + isum2[2] + isum2[3]) * scale[2]; + isum += (isum3[0] + isum3[1] + isum3[2] + isum3[3]) * scale[3]; + + scale += 4; + + q3h[0] = vec_andc(v_2c, qhbits[0]); + q3h[1] = vec_andc(v_2c, qhbits[1]); + q3h[2] = vec_sr(vec_andc(v_3c, qhbits[0]), 1); + q3h[3] = vec_sr(vec_andc(v_3c, qhbits[1]), 1); + + q3bytes[0] = vec_sub((int8x16_t)vec_and(vec_sr(q3b[0], 4), v_3m), (int8x16_t)q3h[0]); + q3bytes[1] = vec_sub((int8x16_t)vec_and(vec_sr(q3b[1], 4), v_3m), (int8x16_t)q3h[1]); + q3bytes[2] = vec_sub((int8x16_t)vec_and(vec_sr(q3b[0], 6), v_3m), (int8x16_t)q3h[2]); + q3bytes[3] = vec_sub((int8x16_t)vec_and(vec_sr(q3b[1], 6), v_3m), (int8x16_t)q3h[3]); + + isum0 = ggml_vec_dot(v_z, q3bytes[0], q8bytes[4]); + isum1 = ggml_vec_dot(v_z, q3bytes[1], q8bytes[5]); + isum2 = ggml_vec_dot(v_z, q3bytes[2], q8bytes[6]); + isum3 = ggml_vec_dot(v_z, q3bytes[3], q8bytes[7]); + + isum += (isum0[0] + isum0[1] + isum0[2] + isum0[3]) * scale[0]; + isum += (isum1[0] + isum1[1] + isum1[2] + isum1[3]) * scale[1]; + isum += (isum2[0] + isum2[1] + isum2[2] + isum2[3]) * scale[2]; + isum += (isum3[0] + isum3[1] + isum3[2] + isum3[3]) * scale[3]; + + scale += 4; + + if (j == 0) { + qhbits[0] = vec_sr(qhbits[0], 4); + qhbits[1] = vec_sr(qhbits[1], 4); + } + } + + sum += d * isum; + } + + *s = sum; #else // scalar version // This function is written like this so the compiler can manage to vectorize most of it From 9fdfcdaeddd1ef57c6d041b89cd8fb7048a0f028 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Sun, 4 May 2025 21:25:43 +0200 Subject: [PATCH 099/126] rpc : use backend registry, support dl backends (#13304) --- ggml/src/ggml-cpu/ggml-cpu.cpp | 45 ++++++---- ggml/src/ggml-rpc/ggml-rpc.cpp | 11 +++ tools/CMakeLists.txt | 6 +- tools/rpc/rpc-server.cpp | 152 ++++++++++++++++----------------- 4 files changed, 116 insertions(+), 98 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index 4b688a67eb..e013e8b416 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -11,24 +11,26 @@ #include #ifdef GGML_USE_CPU_HBM -#include "ggml-cpu-hbm.h" +# include "ggml-cpu-hbm.h" #endif #ifdef GGML_USE_CPU_KLEIDIAI -#include "kleidiai/kleidiai.h" -#endif - -#if defined(__APPLE__) -#include -#include +# include "kleidiai/kleidiai.h" #endif #if defined(_WIN32) -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX - #define NOMINMAX +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +#else +# include #endif -#include + +#if defined(__APPLE__) +# include +# include #endif // ggml-backend interface @@ -70,8 +72,10 @@ static ggml_backend_buffer_type_t * ggml_backend_cpu_device_get_extra_buffers_ty } static bool ggml_backend_cpu_is_extra_buffer_type(ggml_backend_buffer_type_t buft) { - for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) { - if (extra && extra == buft) return true; + for (auto * extra : ggml_backend_cpu_get_extra_buffers_type()) { + if (extra && extra == buft) { + return true; + } } return false; } @@ -330,9 +334,18 @@ static const char * ggml_backend_cpu_device_get_description(ggml_backend_dev_t d } static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - // TODO - *free = 0; - *total = 0; +#ifdef _WIN32 + MEMORYSTATUSEX status; + status.dwLength = sizeof(status); + GlobalMemoryStatusEx(&status); + *total = status.ullTotalPhys; + *free = status.ullAvailPhys; +#else + long pages = sysconf(_SC_PHYS_PAGES); + long page_size = sysconf(_SC_PAGE_SIZE); + *total = pages * page_size; + *free = *total; +#endif GGML_UNUSED(dev); } diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index e662cc6eb3..039214dc2b 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -1594,6 +1594,14 @@ static void rpc_serve_client(ggml_backend_t backend, const char * cache_dir, void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, const char * cache_dir, size_t free_mem, size_t total_mem) { + printf("Starting RPC server v%d.%d.%d\n", + RPC_PROTO_MAJOR_VERSION, + RPC_PROTO_MINOR_VERSION, + RPC_PROTO_PATCH_VERSION); + printf(" endpoint : %s\n", endpoint); + printf(" local cache : %s\n", cache_dir ? cache_dir : "n/a"); + printf(" backend memory : %zu MB\n", free_mem / (1024 * 1024)); + std::string host; int port; if (!parse_endpoint(endpoint, host, port)) { @@ -1753,6 +1761,9 @@ static void * ggml_backend_rpc_get_proc_address(ggml_backend_reg_t reg, const ch if (std::strcmp(name, "ggml_backend_rpc_add_device") == 0) { return (void *)ggml_backend_rpc_add_device; } + if (std::strcmp(name, "ggml_backend_rpc_start_server") == 0) { + return (void *)ggml_backend_rpc_start_server; + } return NULL; GGML_UNUSED(reg); diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt index 383e94accb..06a96af552 100644 --- a/tools/CMakeLists.txt +++ b/tools/CMakeLists.txt @@ -28,12 +28,12 @@ else() add_subdirectory(tokenize) add_subdirectory(tts) add_subdirectory(llava) + if (GGML_RPC) + add_subdirectory(rpc) + endif() if (NOT GGML_BACKEND_DL) # these examples use the backends directly and cannot be built with dynamic loading add_subdirectory(cvector-generator) add_subdirectory(export-lora) - if (GGML_RPC) - add_subdirectory(rpc) - endif() endif() endif() diff --git a/tools/rpc/rpc-server.cpp b/tools/rpc/rpc-server.cpp index b663a81191..a3f901a223 100644 --- a/tools/rpc/rpc-server.cpp +++ b/tools/rpc/rpc-server.cpp @@ -2,24 +2,6 @@ #define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING #endif -#include "ggml-cpu.h" - -#ifdef GGML_USE_CUDA -#include "ggml-cuda.h" -#endif - -#ifdef GGML_USE_METAL -#include "ggml-metal.h" -#endif - -#ifdef GGML_USE_VULKAN -#include "ggml-vulkan.h" -#endif - -#ifdef GGML_USE_SYCL -#include "ggml-sycl.h" -#endif - #include "ggml-rpc.h" #ifdef _WIN32 # define NOMINMAX @@ -154,6 +136,7 @@ struct rpc_server_params { size_t backend_mem = 0; bool use_cache = false; int n_threads = std::max(1U, std::thread::hardware_concurrency()/2); + std::string device; }; static void print_usage(int /*argc*/, char ** argv, rpc_server_params params) { @@ -161,6 +144,7 @@ static void print_usage(int /*argc*/, char ** argv, rpc_server_params params) { fprintf(stderr, "options:\n"); fprintf(stderr, " -h, --help show this help message and exit\n"); fprintf(stderr, " -t, --threads number of threads for the CPU backend (default: %d)\n", params.n_threads); + fprintf(stderr, " -d DEV, --device device to use\n"); fprintf(stderr, " -H HOST, --host HOST host to bind to (default: %s)\n", params.host.c_str()); fprintf(stderr, " -p PORT, --port PORT port to bind to (default: %d)\n", params.port); fprintf(stderr, " -m MEM, --mem MEM backend memory size (in MB)\n"); @@ -186,6 +170,22 @@ static bool rpc_server_params_parse(int argc, char ** argv, rpc_server_params & fprintf(stderr, "error: invalid number of threads: %d\n", params.n_threads); return false; } + } else if (arg == "-d" || arg == "--device") { + if (++i >= argc) { + return false; + } + params.device = argv[i]; + if (ggml_backend_dev_by_name(params.device.c_str()) == nullptr) { + fprintf(stderr, "error: unknown device: %s\n", params.device.c_str()); + fprintf(stderr, "available devices:\n"); + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + auto * dev = ggml_backend_dev_get(i); + size_t free, total; + ggml_backend_dev_memory(dev, &free, &total); + printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024); + } + return false; + } } else if (arg == "-p" || arg == "--port") { if (++i >= argc) { return false; @@ -214,66 +214,53 @@ static bool rpc_server_params_parse(int argc, char ** argv, rpc_server_params & } static ggml_backend_t create_backend(const rpc_server_params & params) { - ggml_backend_t backend = NULL; -#ifdef GGML_USE_CUDA - fprintf(stderr, "%s: using CUDA backend\n", __func__); - backend = ggml_backend_cuda_init(0); // init device 0 - if (!backend) { - fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); - } -#elif GGML_USE_METAL - fprintf(stderr, "%s: using Metal backend\n", __func__); - backend = ggml_backend_metal_init(); - if (!backend) { - fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__); - } -#elif GGML_USE_VULKAN - fprintf(stderr, "%s: using Vulkan backend\n", __func__); - backend = ggml_backend_vk_init(0); // init device 0 - if (!backend) { - fprintf(stderr, "%s: ggml_backend_vulkan_init() failed\n", __func__); - } -#elif GGML_USE_SYCL - fprintf(stderr, "%s: using SYCL backend\n", __func__); - backend = ggml_backend_sycl_init(0); // init device 0 - if (!backend) { - fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__); - } -#endif + ggml_backend_t backend = nullptr; - // if there aren't GPU Backends fallback to CPU backend - if (!backend) { - fprintf(stderr, "%s: using CPU backend\n", __func__); - backend = ggml_backend_cpu_init(); - ggml_backend_cpu_set_n_threads(backend, params.n_threads); + if (!params.device.empty()) { + ggml_backend_dev_t dev = ggml_backend_dev_by_name(params.device.c_str()); + if (dev) { + backend = ggml_backend_dev_init(dev, nullptr); + if (!backend) { + fprintf(stderr, "Failed to create backend for device %s\n", params.device.c_str()); + return nullptr; + } + } } + + // try to initialize a GPU backend first + if (!backend) { + backend = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_GPU, nullptr); + } + + // if there aren't GPU backends fallback to CPU backend + if (!backend) { + backend = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr); + } + + fprintf(stderr, "%s: using %s backend\n", __func__, ggml_backend_name(backend)); + + // set the number of threads + ggml_backend_dev_t dev = ggml_backend_get_device(backend); + ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr; + if (reg) { + auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads"); + if (ggml_backend_set_n_threads_fn) { + ggml_backend_set_n_threads_fn(backend, params.n_threads); + } + } + return backend; } -static void get_backend_memory(size_t * free_mem, size_t * total_mem) { -#ifdef GGML_USE_CUDA - ggml_backend_cuda_get_device_memory(0, free_mem, total_mem); -#elif GGML_USE_VULKAN - ggml_backend_vk_get_device_memory(0, free_mem, total_mem); -#elif GGML_USE_SYCL - ggml_backend_sycl_get_device_memory(0, free_mem, total_mem); -#else - #ifdef _WIN32 - MEMORYSTATUSEX status; - status.dwLength = sizeof(status); - GlobalMemoryStatusEx(&status); - *total_mem = status.ullTotalPhys; - *free_mem = status.ullAvailPhys; - #else - long pages = sysconf(_SC_PHYS_PAGES); - long page_size = sysconf(_SC_PAGE_SIZE); - *total_mem = pages * page_size; - *free_mem = *total_mem; - #endif -#endif +static void get_backend_memory(ggml_backend_t backend, size_t * free_mem, size_t * total_mem) { + ggml_backend_dev_t dev = ggml_backend_get_device(backend); + GGML_ASSERT(dev != nullptr); + ggml_backend_dev_memory(dev, free_mem, total_mem); } int main(int argc, char * argv[]) { + ggml_backend_load_all(); + rpc_server_params params; if (!rpc_server_params_parse(argc, argv, params)) { fprintf(stderr, "Invalid parameters\n"); @@ -301,7 +288,7 @@ int main(int argc, char * argv[]) { free_mem = params.backend_mem; total_mem = params.backend_mem; } else { - get_backend_memory(&free_mem, &total_mem); + get_backend_memory(backend, &free_mem, &total_mem); } const char * cache_dir = nullptr; std::string cache_dir_str; @@ -313,14 +300,21 @@ int main(int argc, char * argv[]) { } cache_dir = cache_dir_str.c_str(); } - printf("Starting RPC server v%d.%d.%d\n", - RPC_PROTO_MAJOR_VERSION, - RPC_PROTO_MINOR_VERSION, - RPC_PROTO_PATCH_VERSION); - printf(" endpoint : %s\n", endpoint.c_str()); - printf(" local cache : %s\n", cache_dir ? cache_dir : "n/a"); - printf(" backend memory : %zu MB\n", free_mem / (1024 * 1024)); - ggml_backend_rpc_start_server(backend, endpoint.c_str(), cache_dir, free_mem, total_mem); + + ggml_backend_reg_t reg = ggml_backend_reg_by_name("RPC"); + if (!reg) { + fprintf(stderr, "Failed to find RPC backend\n"); + return 1; + } + + auto start_server_fn = (decltype(ggml_backend_rpc_start_server)*) ggml_backend_reg_get_proc_address(reg, "ggml_backend_rpc_start_server"); + if (!start_server_fn) { + fprintf(stderr, "Failed to obtain RPC backend start server function\n"); + return 1; + } + + start_server_fn(backend, endpoint.c_str(), cache_dir, free_mem, total_mem); + ggml_backend_free(backend); return 0; } From 27aa2595321c4d9cc4086a8e67bdea204b8309b0 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sun, 4 May 2025 23:43:42 +0200 Subject: [PATCH 100/126] mtmd : add C public API (#13184) * init * wip * working version * add mtmd::bitmaps * add test target * rm redundant define * test: mtmd_input_chunks_free * rm outdated comment * fix merging issue * explicitly create mtmd::input_chunks * mtmd_input_chunk_copy * add clone() * add const to various places * add warning about breaking changes * helper: use mtmd_image_tokens_get_n_pos --- tests/CMakeLists.txt | 4 + tests/test-mtmd-c-api.c | 63 +++++ tools/llava/clip-impl.h | 9 + tools/llava/clip.h | 8 +- tools/llava/mtmd-cli.cpp | 49 ++-- tools/llava/mtmd.cpp | 557 +++++++++++++++++++++++++++------------ tools/llava/mtmd.h | 298 +++++++++++++++------ 7 files changed, 730 insertions(+), 258 deletions(-) create mode 100644 tests/test-mtmd-c-api.c diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 72b32df9fc..709d5ad96a 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -165,6 +165,10 @@ if (NOT GGML_BACKEND_DL) llama_build_and_test(test-rope.cpp) endif() +# libmtmd +set(LLAMA_TEST_NAME test-mtmd-c-api) +llama_build_and_test(test-mtmd-c-api.c) +target_link_libraries(${LLAMA_TEST_NAME} PRIVATE mtmd) # dummy executable - not installed get_filename_component(TEST_TARGET test-c.c NAME_WE) diff --git a/tests/test-mtmd-c-api.c b/tests/test-mtmd-c-api.c new file mode 100644 index 0000000000..02e762e6a2 --- /dev/null +++ b/tests/test-mtmd-c-api.c @@ -0,0 +1,63 @@ +#include +#include + +#include "mtmd.h" + +int main(void) { + printf("\n\nTesting libmtmd C API...\n"); + printf("--------\n\n"); + + struct mtmd_context_params params = mtmd_context_params_default(); + printf("Default image marker: %s\n", params.image_marker); + + mtmd_input_chunks * chunks = mtmd_test_create_input_chunks(); + + if (!chunks) { + fprintf(stderr, "Failed to create input chunks\n"); + return 1; + } + + size_t n_chunks = mtmd_input_chunks_size(chunks); + printf("Number of chunks: %zu\n", n_chunks); + assert(n_chunks > 0); + + for (size_t i = 0; i < n_chunks; i++) { + const mtmd_input_chunk * chunk = mtmd_input_chunks_get(chunks, i); + assert(chunk != NULL); + enum mtmd_input_chunk_type type = mtmd_input_chunk_get_type(chunk); + printf("Chunk %zu type: %d\n", i, type); + + if (type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + size_t n_tokens; + const llama_token * tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens); + printf(" Text chunk with %zu tokens\n", n_tokens); + assert(tokens != NULL); + assert(n_tokens > 0); + for (size_t j = 0; j < n_tokens; j++) { + assert(tokens[j] >= 0); + printf(" > Token %zu: %d\n", j, tokens[j]); + } + + } else if (type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { + const mtmd_image_tokens * image_tokens = mtmd_input_chunk_get_tokens_image(chunk); + size_t n_tokens = mtmd_image_tokens_get_n_tokens(image_tokens); + size_t nx = mtmd_image_tokens_get_nx(image_tokens); + size_t ny = mtmd_image_tokens_get_ny(image_tokens); + const char * id = mtmd_image_tokens_get_id(image_tokens); + assert(n_tokens > 0); + assert(nx > 0); + assert(ny > 0); + assert(id != NULL); + printf(" Image chunk with %zu tokens\n", n_tokens); + printf(" Image size: %zu x %zu\n", nx, ny); + printf(" Image ID: %s\n", id); + } + } + + // Free the chunks + mtmd_input_chunks_free(chunks); + + printf("\n\nDONE: test libmtmd C API...\n"); + + return 0; +} diff --git a/tools/llava/clip-impl.h b/tools/llava/clip-impl.h index b78d930bce..fb780e9dea 100644 --- a/tools/llava/clip-impl.h +++ b/tools/llava/clip-impl.h @@ -233,6 +233,15 @@ struct clip_image_u8_batch { struct clip_image_f32_batch { std::vector entries; + + clip_image_f32_batch clone() const { + clip_image_f32_batch new_batch; + new_batch.entries.reserve(entries.size()); + for (const auto & entry : entries) { + new_batch.entries.emplace_back(new clip_image_f32(*entry)); + } + return new_batch; + } }; // diff --git a/tools/llava/clip.h b/tools/llava/clip.h index 0a53bd8eb7..0b0eb02956 100644 --- a/tools/llava/clip.h +++ b/tools/llava/clip.h @@ -78,10 +78,10 @@ CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip); CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size); CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip); -CLIP_API struct clip_image_size * clip_image_size_init(); -CLIP_API struct clip_image_u8 * clip_image_u8_init (); -CLIP_API struct clip_image_f32 * clip_image_f32_init(); -CLIP_API struct clip_image_f32_batch * clip_image_f32_batch_init(); // only used by libllava +CLIP_API struct clip_image_size * clip_image_size_init(void); +CLIP_API struct clip_image_u8 * clip_image_u8_init (void); +CLIP_API struct clip_image_f32 * clip_image_f32_init(void); +CLIP_API struct clip_image_f32_batch * clip_image_f32_batch_init(void); // only used by libllava // nx, ny are the output image dimensions CLIP_API unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny); diff --git a/tools/llava/mtmd-cli.cpp b/tools/llava/mtmd-cli.cpp index 474e7c4f83..dd18e0fe6e 100644 --- a/tools/llava/mtmd-cli.cpp +++ b/tools/llava/mtmd-cli.cpp @@ -63,7 +63,7 @@ static void sigint_handler(int signo) { #endif struct mtmd_cli_context { - mtmd_context_ptr ctx_vision; + mtmd::context_ptr ctx_vision; common_init_result llama_init; llama_model * model; @@ -72,7 +72,7 @@ struct mtmd_cli_context { llama_batch batch; int n_batch; - std::vector bitmaps; + mtmd::bitmaps bitmaps; // note: we know that gemma3 template is "linear", meaning each turn is completely separated to another // so here we don't need to keep track of chat history @@ -115,12 +115,12 @@ struct mtmd_cli_context { void init_vision_context(common_params & params) { const char * clip_path = params.mmproj.path.c_str(); - ctx_vision.reset(mtmd_init_from_file(clip_path, model, mtmd_context_params{ - /* use_gpu */ params.mmproj_use_gpu, - /* timings */ true, - /* n_threads */ params.cpuparams.n_threads, - /* verbosity */ params.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO, - })); + mtmd_context_params mparams = mtmd_context_params_default(); + mparams.use_gpu = params.mmproj_use_gpu; + mparams.print_timings = true; + mparams.n_threads = params.cpuparams.n_threads; + mparams.verbosity = params.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO; + ctx_vision.reset(mtmd_init_from_file(clip_path, model, mparams)); if (!ctx_vision.get()) { LOG_ERR("Failed to load vision model from %s\n", clip_path); exit(1); @@ -139,11 +139,11 @@ struct mtmd_cli_context { } bool load_image(const std::string & fname) { - mtmd_bitmap bitmap; - if (mtmd_helper_bitmap_init_from_file(fname.c_str(), bitmap)) { + mtmd::bitmap bmp(mtmd_helper_bitmap_init_from_file(fname.c_str())); + if (!bmp.ptr) { return false; } - bitmaps.push_back(std::move(bitmap)); + bitmaps.entries.push_back(std::move(bmp)); return true; } }; @@ -193,27 +193,40 @@ static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg, bool add_ LOG_DBG("formatted_chat.prompt: %s\n", formatted_chat.prompt.c_str()); mtmd_input_text text; - text.text = formatted_chat.prompt; + text.text = formatted_chat.prompt.c_str(); text.add_special = add_bos; text.parse_special = true; - mtmd_input_chunks chunks; if (g_is_interrupted) return 0; - int32_t res = mtmd_tokenize(ctx.ctx_vision.get(), chunks, text, ctx.bitmaps); + mtmd::input_chunks chunks(mtmd_input_chunks_init()); + auto bitmaps_c_ptr = ctx.bitmaps.c_ptr(); + int32_t res = mtmd_tokenize(ctx.ctx_vision.get(), + chunks.ptr.get(), // output + &text, // text + bitmaps_c_ptr.data(), + bitmaps_c_ptr.size()); if (res != 0) { LOG_ERR("Unable to tokenize prompt, res = %d\n", res); return 1; } - ctx.bitmaps.clear(); + ctx.bitmaps.entries.clear(); - if (mtmd_helper_eval(ctx.ctx_vision.get(), ctx.lctx, chunks, ctx.n_past, 0, ctx.n_batch)) { + llama_pos new_n_past; + if (mtmd_helper_eval_chunks(ctx.ctx_vision.get(), + ctx.lctx, // lctx + chunks.ptr.get(), // chunks + ctx.n_past, // n_past + 0, // seq_id + ctx.n_batch, // n_batch + true, // logits_last + &new_n_past)) { LOG_ERR("Unable to eval prompt\n"); return 1; } - ctx.n_past += mtmd_helper_get_n_pos(chunks); + ctx.n_past = new_n_past; LOG("\n"); @@ -246,7 +259,7 @@ int main(int argc, char ** argv) { struct common_sampler * smpl = common_sampler_init(ctx.model, params.sampling); int n_predict = params.n_predict < 0 ? INT_MAX : params.n_predict; - // ctrl+C handling + // Ctrl+C handling { #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) struct sigaction sigint_action; diff --git a/tools/llava/mtmd.cpp b/tools/llava/mtmd.cpp index 73abf2ad18..b600e43413 100644 --- a/tools/llava/mtmd.cpp +++ b/tools/llava/mtmd.cpp @@ -12,6 +12,30 @@ #include #include +// represents raw image data, layout is RGBRGBRGB... +// length of data must be nx * ny * 3 +struct mtmd_bitmap { + uint32_t nx; + uint32_t ny; + std::vector data; + std::string id; // optional user-defined id, for ex: can be set to image hash, useful for KV cache tracking +}; + +struct mtmd_image_tokens_deleter { + void operator()(mtmd_image_tokens * val); // forward declaration +}; +using mtmd_image_tokens_ptr = std::unique_ptr; + +struct mtmd_input_chunk { + mtmd_input_chunk_type type; + std::vector tokens_text; + mtmd_image_tokens_ptr tokens_image; +}; + +struct mtmd_input_chunks { + std::vector entries; +}; + // slice template, used by some llava-uhd models to correctly place the special tokens around image embeddings // models not having it (llava-1.6) will process embeddings without any special tokens in-between enum mtmd_slice_tmpl { @@ -21,6 +45,16 @@ enum mtmd_slice_tmpl { // TODO @ngxson : add support for idefics (SmolVLM) }; +mtmd_context_params mtmd_context_params_default() { + mtmd_context_params params; + params.use_gpu = true; + params.print_timings = true; + params.n_threads = 4; + params.verbosity = GGML_LOG_LEVEL_INFO; + params.image_marker = MTMD_DEFAULT_IMAGE_MARKER; + return params; +} + struct mtmd_context { struct clip_ctx * ctx_clip; const struct llama_model * text_model; @@ -132,6 +166,16 @@ struct mtmd_image_tokens { uint32_t n_tokens() const { return nx * ny; } clip_image_f32_batch batch_f32; // preprocessed image patches std::string id; // optional user-defined ID, useful for KV cache tracking + + mtmd_image_tokens clone() { + return mtmd_image_tokens{ + nx, + ny, + use_mrope_pos, + batch_f32.clone(), + id + }; + } }; mtmd_context * mtmd_init_from_file(const char * mmproj_fname, @@ -172,12 +216,13 @@ static std::vector mtmd_tokenize_text_internal( } int32_t mtmd_tokenize(mtmd_context * ctx, - std::vector & output, - const mtmd_input_text & text, - const std::vector & bitmaps) { + mtmd_input_chunks * output, + const mtmd_input_text * text, + const mtmd_bitmap ** bitmaps, + size_t n_bitmaps) { auto vocab = llama_model_get_vocab(ctx->text_model); - std::string prompt_modified(text.text); + std::string prompt_modified(text->text); std::string marker_modified(ctx->image_marker); projector_type proj_type = clip_get_projector_type(ctx->ctx_clip); @@ -211,8 +256,8 @@ int32_t mtmd_tokenize(mtmd_context * ctx, // for glm-edge, BOI and EOI token's embeddings are not present in the text model std::vector parts = string_split_str(prompt_modified, ctx->image_marker); - output.clear(); - output.reserve(parts.size()); + output->entries.clear(); + output->entries.reserve(parts.size()); size_t i_img = 0; @@ -223,7 +268,7 @@ int32_t mtmd_tokenize(mtmd_context * ctx, std::move(tokens), {}, }; - output.emplace_back(std::move(chunk)); + output->entries.emplace_back(std::move(chunk)); }; // utility for splitting batch of multiple images into chunks of batch having single images @@ -251,7 +296,7 @@ int32_t mtmd_tokenize(mtmd_context * ctx, for (const auto & part : parts) { // printf("tokenizing part: %s\n", part.c_str()); bool add_bos = &parts.front() == ∂ - auto tokens = mtmd_tokenize_text_internal(vocab, part, text.add_special && add_bos, text.parse_special); + auto tokens = mtmd_tokenize_text_internal(vocab, part, text->add_special && add_bos, text->parse_special); if (tokens.empty()) { continue; } @@ -260,22 +305,22 @@ int32_t mtmd_tokenize(mtmd_context * ctx, std::move(tokens), {}, }; - output.emplace_back(std::move(chunk)); + output->entries.emplace_back(std::move(chunk)); if (&parts.back() != &part) { // add image token to middle of 2 parts - if (i_img >= bitmaps.size()) { + if (i_img >= n_bitmaps) { LOG_ERR("%s: error: not enough images for %d parts\n", __func__, (int)parts.size()); return 1; } // convert mtmd_bitmap to clip_image_u8 clip_image_u8_ptr img_u8(clip_image_u8_init()); - img_u8->nx = bitmaps[i_img].nx; - img_u8->ny = bitmaps[i_img].ny; - img_u8->buf.resize(bitmaps[i_img].data.size()); - std::memcpy(img_u8->buf.data(), bitmaps[i_img].data.data(), img_u8->nx * img_u8->ny * 3); + img_u8->nx = bitmaps[i_img]->nx; + img_u8->ny = bitmaps[i_img]->ny; + img_u8->buf.resize(bitmaps[i_img]->data.size()); + std::memcpy(img_u8->buf.data(), bitmaps[i_img]->data.data(), img_u8->nx * img_u8->ny * 3); clip_image_size img_u8_size{img_u8->nx, img_u8->ny}; // preprocess image @@ -288,12 +333,12 @@ int32_t mtmd_tokenize(mtmd_context * ctx, if (ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_5 || ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6) { // split batch into chunks of single images - auto chunks = split_batch_to_chunk(std::move(batch_f32), bitmaps[i_img].id); + auto chunks = split_batch_to_chunk(std::move(batch_f32), bitmaps[i_img]->id); GGML_ASSERT(chunks.size() > 0); // add overview image add_text_chunk({ctx->tok_ov_img_start}); - output.emplace_back(std::move(chunks.front())); + output->entries.emplace_back(std::move(chunks.front())); chunks.erase(chunks.begin()); add_text_chunk({ctx->tok_ov_img_end}); @@ -311,7 +356,7 @@ int32_t mtmd_tokenize(mtmd_context * ctx, if (ctx->tok_sli_img_start != LLAMA_TOKEN_NULL) { add_text_chunk({ctx->tok_sli_img_start}); } - output.emplace_back(std::move(chunks[y * n_col + x])); + output->entries.emplace_back(std::move(chunks[y * n_col + x])); if (ctx->tok_sli_img_end != LLAMA_TOKEN_NULL) { add_text_chunk({ctx->tok_sli_img_end}); } @@ -343,7 +388,7 @@ int32_t mtmd_tokenize(mtmd_context * ctx, image_tokens->ny = 1; } image_tokens->batch_f32 = std::move(batch_f32); - image_tokens->id = bitmaps[i_img].id; // optional + image_tokens->id = bitmaps[i_img]->id; // optional LOG_DBG("image_tokens->nx = %d\n", image_tokens->nx); LOG_DBG("image_tokens->ny = %d\n", image_tokens->ny); @@ -354,7 +399,7 @@ int32_t mtmd_tokenize(mtmd_context * ctx, {}, std::move(image_tokens), }; - output.emplace_back(std::move(chunk)); + output->entries.emplace_back(std::move(chunk)); } i_img++; // move to next image @@ -364,35 +409,12 @@ int32_t mtmd_tokenize(mtmd_context * ctx, return 0; } -void mtmd_image_tokens_free(mtmd_image_tokens * image_tokens) { +static void mtmd_image_tokens_free(mtmd_image_tokens * image_tokens) { if (image_tokens) { delete image_tokens; } } -size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * image_tokens) { - return image_tokens->n_tokens(); -} - -size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens) { - return image_tokens->nx; -} - -size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens) { - return image_tokens->ny; -} - -std::string mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens) { - return image_tokens->id; -} - -llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens) { - if (image_tokens->use_mrope_pos) { - return 1; // for M-RoPE, the whole image is 1 in temporal dimension - } - return image_tokens->n_tokens(); -} - int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens) { int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip); ctx->image_embd_v.resize(image_tokens->n_tokens() * n_mmproj_embd); @@ -432,13 +454,18 @@ float * mtmd_get_output_embd(mtmd_context * ctx) { return ctx->image_embd_v.data(); } -size_t mtmd_helper_get_n_tokens(mtmd_input_chunks & chunks) { +size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks) { size_t n_tokens = 0; - for (auto & chunk : chunks) { - if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) { - n_tokens += chunk.tokens_text.size(); - } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { - n_tokens += mtmd_image_tokens_get_n_tokens(chunk.tokens_image.get()); + for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) { + auto chunk = mtmd_input_chunks_get(chunks, i); + auto chunk_type = mtmd_input_chunk_get_type(chunk); + if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + size_t n_tokens_text; + mtmd_input_chunk_get_tokens_text(chunk, &n_tokens_text); + n_tokens += n_tokens_text; + } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { + auto tokens_image = mtmd_input_chunk_get_tokens_image(chunk); + n_tokens += mtmd_image_tokens_get_n_tokens(tokens_image); } else { GGML_ASSERT(false && "chunk type not supported"); } @@ -446,13 +473,18 @@ size_t mtmd_helper_get_n_tokens(mtmd_input_chunks & chunks) { return n_tokens; } -llama_pos mtmd_helper_get_n_pos(mtmd_input_chunks & chunks) { +llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks) { llama_pos n_pos = 0; - for (auto & chunk : chunks) { - if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) { - n_pos += chunk.tokens_text.size(); - } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { - n_pos += mtmd_image_tokens_get_n_pos(chunk.tokens_image.get()); + for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) { + auto chunk = mtmd_input_chunks_get(chunks, i); + auto chunk_type = mtmd_input_chunk_get_type(chunk); + if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + size_t n_tokens_text; + mtmd_input_chunk_get_tokens_text(chunk, &n_tokens_text); + n_pos += n_tokens_text; + } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { + auto tokens_image = mtmd_input_chunk_get_tokens_image(chunk); + n_pos += mtmd_image_tokens_get_n_pos(tokens_image); } else { GGML_ASSERT(false && "chunk type not supported"); } @@ -548,143 +580,172 @@ struct decode_embd_batch { } }; -int32_t mtmd_helper_eval(mtmd_context * ctx, - llama_context * lctx, - mtmd_input_chunks & chunks, - llama_pos pos0, +int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx, + struct llama_context * lctx, + const mtmd_input_chunk * chunk, + llama_pos n_past, llama_seq_id seq_id, - int32_t n_batch) { + int32_t n_batch, + bool logits_last, + llama_pos * new_n_past) { int32_t ret; - llama_pos n_past = pos0; llama_batch text_batch = llama_batch_init(n_batch, 0, 1); + auto chunk_type = mtmd_input_chunk_get_type(chunk); int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip); int n_pos_per_embd = mtmd_decode_use_mrope(ctx) ? 4 : 1; - for (auto & chunk : chunks) { - bool is_last = &chunk == &chunks.back(); - if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) { - text_batch.n_tokens = chunk.tokens_text.size(); - size_t i = 0; - while (i < chunk.tokens_text.size()) { // split into batches - for (; i < chunk.tokens_text.size() && text_batch.n_tokens < n_batch; i++) { - text_batch.token [i] = chunk.tokens_text[i]; - text_batch.pos [i] = n_past++; - text_batch.n_seq_id[i] = 1; - text_batch.seq_id [i][0] = seq_id; - text_batch.logits [i] = false; - } - if (is_last) { - // always get logits for last input chunk - text_batch.logits[text_batch.n_tokens - 1] = true; - } - ret = llama_decode(lctx, text_batch); - if (ret != 0) { - LOG_ERR("failed to decode text\n"); - llama_batch_free(text_batch); - return ret; - } + if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + size_t n_tokens; + const auto tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens); + LOG_DBG("decoding text chunk, n_tokens = %zu\n", n_tokens); + size_t i = 0; + while (i < n_tokens) { // split into batches + text_batch.n_tokens = 0; // clear the batch + for (; i < n_tokens && text_batch.n_tokens < n_batch; i++) { + text_batch.n_tokens++; + text_batch.token [i] = tokens[i]; + text_batch.pos [i] = n_past++; + text_batch.n_seq_id[i] = 1; + text_batch.seq_id [i][0] = seq_id; + text_batch.logits [i] = false; } - - } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { - GGML_ASSERT(!is_last && "logits for last image chunk is not yet supported"); - GGML_ASSERT(chunk.tokens_image != nullptr); - int64_t t0 = ggml_time_ms(); - if (ctx->print_timings) { - LOG_INF("encoding image or slice...\n"); + bool is_last_token = (i == n_tokens); + if (logits_last && is_last_token) { + text_batch.logits[text_batch.n_tokens - 1] = true; } - ret = mtmd_encode(ctx, chunk.tokens_image.get()); + ret = llama_decode(lctx, text_batch); if (ret != 0) { - LOG_ERR("failed to encode image\n"); + LOG_ERR("failed to decode text\n"); llama_batch_free(text_batch); return ret; } - if (ctx->print_timings) { - LOG_INF("image/slice encoded in %" PRId64 " ms\n", ggml_time_ms() - t0); - } - - int32_t n_tokens = mtmd_image_tokens_get_n_tokens(chunk.tokens_image.get()); - int32_t i_batch = 0; - int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch; - float * embd = mtmd_get_output_embd(ctx); - decode_embd_batch batch_embd(embd, n_tokens, n_pos_per_embd, n_mmproj_embd); - - const int nx = mtmd_image_tokens_get_nx(chunk.tokens_image.get()); - const int ny = mtmd_image_tokens_get_ny(chunk.tokens_image.get()); - - if (mtmd_decode_use_mrope(ctx)) { - batch_embd.set_position_mrope(n_past, nx, ny, seq_id); - } else { - batch_embd.set_position_normal(n_past, seq_id); - } - - if (mtmd_decode_use_non_causal(ctx)) { - llama_set_causal_attn(lctx, false); - // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image - } - - while (i_batch < n_img_batches) { // split into batches - int pos_offset = i_batch*n_batch; - int n_tokens_batch = std::min(n_batch, n_tokens - pos_offset); - llama_batch batch_embd_view = batch_embd.get_view(pos_offset, n_tokens_batch); - - LOG_INF("decoding image batch %d/%d, n_tokens_batch = %d\n", i_batch+1, n_img_batches, n_tokens_batch); - - int64_t t1 = ggml_time_ms(); - ret = llama_decode(lctx, batch_embd_view); - if (ret != 0) { - LOG_ERR("failed to decode image\n"); - llama_set_causal_attn(lctx, true); // restore causal attn - llama_batch_free(text_batch); - return ret; - } - - if (ctx->print_timings) { - LOG_INF("image decoded (batch %d/%d) in %" PRId64 " ms\n", i_batch+1, n_img_batches, ggml_time_ms() - t1); - } - - i_batch++; - } - - // for mrope, one image is one single **temporal** position - n_past += mtmd_decode_use_mrope(ctx) ? 1 : n_tokens; - - if (mtmd_decode_use_non_causal(ctx)) { - llama_set_causal_attn(lctx, true); - } - - } else { - GGML_ASSERT(false && "chunk type not supported"); + *new_n_past += text_batch.n_tokens; } + + } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { + const auto image_tokens = mtmd_input_chunk_get_tokens_image(chunk); + int64_t t0 = ggml_time_ms(); + if (ctx->print_timings) { + LOG_INF("encoding image or slice...\n"); + } + ret = mtmd_encode(ctx, image_tokens); + if (ret != 0) { + LOG_ERR("failed to encode image\n"); + llama_batch_free(text_batch); + return ret; + } + if (ctx->print_timings) { + LOG_INF("image/slice encoded in %" PRId64 " ms\n", ggml_time_ms() - t0); + } + + int32_t n_tokens = mtmd_image_tokens_get_n_tokens(image_tokens); + int32_t i_batch = 0; + int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch; + float * embd = mtmd_get_output_embd(ctx); + decode_embd_batch batch_embd(embd, n_tokens, n_pos_per_embd, n_mmproj_embd); + + const int nx = mtmd_image_tokens_get_nx(image_tokens); + const int ny = mtmd_image_tokens_get_ny(image_tokens); + + if (mtmd_decode_use_mrope(ctx)) { + batch_embd.set_position_mrope(n_past, nx, ny, seq_id); + } else { + batch_embd.set_position_normal(n_past, seq_id); + } + + if (mtmd_decode_use_non_causal(ctx)) { + llama_set_causal_attn(lctx, false); + // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image + } + + while (i_batch < n_img_batches) { // split into batches + int pos_offset = i_batch*n_batch; + int n_tokens_batch = std::min(n_batch, n_tokens - pos_offset); + llama_batch batch_embd_view = batch_embd.get_view(pos_offset, n_tokens_batch); + + LOG_INF("decoding image batch %d/%d, n_tokens_batch = %d\n", i_batch+1, n_img_batches, n_tokens_batch); + + int64_t t1 = ggml_time_ms(); + ret = llama_decode(lctx, batch_embd_view); + if (ret != 0) { + LOG_ERR("failed to decode image\n"); + llama_set_causal_attn(lctx, true); // restore causal attn + llama_batch_free(text_batch); + return ret; + } + + if (ctx->print_timings) { + LOG_INF("image decoded (batch %d/%d) in %" PRId64 " ms\n", i_batch+1, n_img_batches, ggml_time_ms() - t1); + } + + i_batch++; + } + + n_past += mtmd_image_tokens_get_n_pos(image_tokens); + *new_n_past = n_past; + + if (mtmd_decode_use_non_causal(ctx)) { + llama_set_causal_attn(lctx, true); + } + + } else { + GGML_ABORT("chunk type not supported"); } - llama_batch_free(text_batch); return 0; } -int32_t mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len, mtmd_bitmap & output) { +int32_t mtmd_helper_eval_chunks(mtmd_context * ctx, + struct llama_context * lctx, + const mtmd_input_chunks * chunks, + llama_pos n_past, + llama_seq_id seq_id, + int32_t n_batch, + bool logits_last, + llama_pos * new_n_past) { + size_t n_chunks = mtmd_input_chunks_size(chunks); + if (n_chunks == 0) { + LOG_WRN("no chunks to eval\n"); + return 0; + } + + for (size_t i = 0; i < n_chunks; i++) { + bool chunk_logits_last = (i == n_chunks - 1) && logits_last; + auto chunk = mtmd_input_chunks_get(chunks, i); + + int32_t res = mtmd_helper_eval_chunk_single(ctx, lctx, chunk, n_past, seq_id, n_batch, chunk_logits_last, &n_past); + if (res != 0) { + LOG_ERR("failed to eval chunk %zu\n", i); + return res; + } + *new_n_past = n_past; + } + + return 0; +} + +mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len) { clip_image_u8_ptr img_u8(clip_image_u8_init()); bool ok = clip_image_load_from_bytes(buf, len, img_u8.get()); if (!ok) { LOG_ERR("Unable to load image from buffer\n"); - return 1; + return nullptr; } - unsigned char * data = clip_image_u8_get_data(img_u8.get(), &output.nx, &output.ny); - output.data.resize(output.nx * output.ny * 3); - std::memcpy(output.data.data(), data, output.nx * output.ny * 3); - return 0; + uint32_t nx, ny; + unsigned char * data = clip_image_u8_get_data(img_u8.get(), &nx, &ny); + return mtmd_bitmap_init(nx, ny, data); } -int32_t mtmd_helper_bitmap_init_from_file(const char * fname, mtmd_bitmap & output) { +mtmd_bitmap * mtmd_helper_bitmap_init_from_file(const char * fname) { clip_image_u8_ptr img_u8(clip_image_u8_init()); bool ok = clip_image_load_from_file(fname, img_u8.get()); if (!ok) { LOG_ERR("Unable to load image %s\n", fname); - return 1; + return nullptr; } - unsigned char * data = clip_image_u8_get_data(img_u8.get(), &output.nx, &output.ny); - output.data.resize(output.nx * output.ny * 3); - std::memcpy(output.data.data(), data, output.nx * output.ny * 3); - return 0; + uint32_t nx, ny; + unsigned char * data = clip_image_u8_get_data(img_u8.get(), &nx, &ny); + return mtmd_bitmap_init(nx, ny, data); } bool mtmd_decode_use_non_causal(mtmd_context * ctx) { @@ -702,3 +763,175 @@ bool mtmd_decode_use_mrope(mtmd_context * ctx) { void mtmd_image_tokens_deleter::operator()(mtmd_image_tokens * val) { mtmd_image_tokens_free(val); } + + +// +// public API functions +// + +// mtmd_bitmap + +mtmd_bitmap * mtmd_bitmap_init(uint32_t nx, + uint32_t ny, + const unsigned char * data) { + mtmd_bitmap * bitmap = new mtmd_bitmap; + bitmap->nx = nx; + bitmap->ny = ny; + size_t data_size = (size_t)nx * ny * 3; + bitmap->data.resize(data_size); + std::memcpy(bitmap->data.data(), data, data_size); + return bitmap; +} + +uint32_t mtmd_bitmap_get_nx(const mtmd_bitmap * bitmap) { + return bitmap->nx; +} + +uint32_t mtmd_bitmap_get_ny(const mtmd_bitmap * bitmap) { + return bitmap->ny; +} + +const unsigned char * mtmd_bitmap_get_data(const mtmd_bitmap * bitmap) { + return bitmap->data.data(); +} + +const char * mtmd_bitmap_get_id(const mtmd_bitmap * bitmap) { + return bitmap->id.c_str(); +} + +void mtmd_bitmap_set_id(mtmd_bitmap * bitmap, const char * id) { + if (id) { + bitmap->id = std::string(id); + } else { + bitmap->id.clear(); + } +} + +void mtmd_bitmap_free(mtmd_bitmap * bitmap) { + if (bitmap) { + delete bitmap; + } +} + +// mtmd_input_chunks + +mtmd_input_chunks * mtmd_input_chunks_init() { + return new mtmd_input_chunks; +} + +size_t mtmd_input_chunks_size(const mtmd_input_chunks * chunks) { + return chunks->entries.size(); +} + +const mtmd_input_chunk * mtmd_input_chunks_get(const mtmd_input_chunks * chunks, size_t idx) { + if (idx >= chunks->entries.size()) { + return nullptr; + } + return &chunks->entries[idx]; +} + +void mtmd_input_chunks_free(mtmd_input_chunks * chunks) { + if (chunks) { + delete chunks; + } +} + +// mtmd_input_chunk + +enum mtmd_input_chunk_type mtmd_input_chunk_get_type(const mtmd_input_chunk * chunk) { + return chunk->type; +} + +const llama_token * mtmd_input_chunk_get_tokens_text(const mtmd_input_chunk * chunk, size_t * n_tokens_output) { + if (chunk->type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + *n_tokens_output = chunk->tokens_text.size(); + return chunk->tokens_text.data(); + } + *n_tokens_output = 0; + return nullptr; +} + +const mtmd_image_tokens * mtmd_input_chunk_get_tokens_image(const mtmd_input_chunk * chunk) { + if (chunk->type == MTMD_INPUT_CHUNK_TYPE_IMAGE) { + return chunk->tokens_image.get(); + } + return nullptr; +} + +mtmd_input_chunk * mtmd_input_chunk_copy(const mtmd_input_chunk * chunk) { + mtmd_input_chunk * copy = new mtmd_input_chunk{ + chunk->type, + chunk->tokens_text, + mtmd_image_tokens_ptr(), + }; + if (chunk->tokens_image) { + // copy the image tokens + copy->tokens_image = mtmd_image_tokens_ptr(new mtmd_image_tokens()); + *copy->tokens_image = chunk->tokens_image->clone(); + } + return copy; +} + +void mtmd_input_chunk_free(mtmd_input_chunk * chunk) { + if (chunk) { + delete chunk; + } +} + +// mtmd_image_tokens + +size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * image_tokens) { + return image_tokens->n_tokens(); +} + +size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens) { + return image_tokens->nx; +} + +size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens) { + return image_tokens->ny; +} + +const char * mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens) { + return image_tokens->id.c_str(); +} + +llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens) { + if (image_tokens->use_mrope_pos) { + return 1; // for M-RoPE, the whole image is 1 in temporal dimension + } + return image_tokens->n_tokens(); +} + +// test function + +mtmd_input_chunks * mtmd_test_create_input_chunks() { + mtmd_input_chunks * chunks = mtmd_input_chunks_init(); + if (!chunks) { + return nullptr; + } + + // create a text chunk + std::vector tokens_text = { 1, 2, 3, 4, 5 }; + mtmd_input_chunk chunk_text{ + MTMD_INPUT_CHUNK_TYPE_TEXT, + std::move(tokens_text), + {}, + }; + chunks->entries.emplace_back(std::move(chunk_text)); + + // create an image chunk + mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens); + image_tokens->nx = 4; + image_tokens->ny = 4; + image_tokens->batch_f32.entries.resize(16); + image_tokens->id = "image_1"; + mtmd_input_chunk chunk_image{ + MTMD_INPUT_CHUNK_TYPE_IMAGE, + {}, + std::move(image_tokens), + }; + chunks->entries.emplace_back(std::move(chunk_image)); + + return chunks; +} diff --git a/tools/llava/mtmd.h b/tools/llava/mtmd.h index 6805e5e481..e2f76e2e8d 100644 --- a/tools/llava/mtmd.h +++ b/tools/llava/mtmd.h @@ -5,9 +5,24 @@ #include "llama.h" #include "clip.h" +#include +#include +#include + +#ifdef __cplusplus #include #include #include +#endif + +/** + * libmtmd: A library for multimodal support in llama.cpp. + * + * WARNING: This API is experimental and subject to many BREAKING CHANGES. + * Issues related to API usage may receive lower priority support. + * + * For the usage, see an example in mtmd-cli.cpp + */ #ifdef LLAMA_SHARED # if defined(_WIN32) && !defined(__MINGW32__) @@ -23,60 +38,118 @@ # define MTMD_API #endif +#define MTMD_DEFAULT_IMAGE_MARKER "<__image__>" + #ifdef __cplusplus +extern "C" { +#endif enum mtmd_input_chunk_type { MTMD_INPUT_CHUNK_TYPE_TEXT, MTMD_INPUT_CHUNK_TYPE_IMAGE, }; +// opaque types struct mtmd_context; +struct mtmd_bitmap; struct mtmd_image_tokens; - -// represents raw image data, layout is RGBRGBRGB... -// length of data must be nx * ny * 3 -struct mtmd_bitmap { - uint32_t nx; - uint32_t ny; - std::vector data; - std::string id; // optional user-defined id, for ex: can be set to image hash, useful for KV cache tracking -}; - -struct mtmd_image_tokens_deleter { - void operator()(mtmd_image_tokens * val); // forward declaration -}; -using mtmd_image_tokens_ptr = std::unique_ptr; - -struct mtmd_input_chunk { - mtmd_input_chunk_type type; - std::vector tokens_text; - mtmd_image_tokens_ptr tokens_image; -}; - -using mtmd_input_chunks = std::vector; - -struct mtmd_context_params { - bool use_gpu = true; - bool print_timings = true; - int n_threads = 4; - enum ggml_log_level verbosity = GGML_LOG_LEVEL_INFO; - const char * image_marker = "<__image__>"; -}; +struct mtmd_input_chunk; +struct mtmd_input_chunks; struct mtmd_input_text { - std::string text; + const char * text; bool add_special; bool parse_special; }; +// +// C API +// + +typedef struct mtmd_context mtmd_context; +typedef struct mtmd_bitmap mtmd_bitmap; +typedef struct mtmd_image_tokens mtmd_image_tokens; +typedef struct mtmd_input_chunk mtmd_input_chunk; +typedef struct mtmd_input_chunks mtmd_input_chunks; +typedef struct mtmd_input_text mtmd_input_text; + +struct mtmd_context_params { + bool use_gpu; + bool print_timings; + int n_threads; + enum ggml_log_level verbosity; + const char * image_marker; +}; + +MTMD_API struct mtmd_context_params mtmd_context_params_default(void); + // initialize the mtmd context // return nullptr on failure MTMD_API mtmd_context * mtmd_init_from_file(const char * mmproj_fname, - const llama_model * text_model, - const mtmd_context_params ctx_params); + const struct llama_model * text_model, + const struct mtmd_context_params ctx_params); MTMD_API void mtmd_free(mtmd_context * ctx); +// whether we need to set non-causal mask before llama_decode +MTMD_API bool mtmd_decode_use_non_causal(mtmd_context * ctx); + +// whether the current model use M-RoPE for llama_decode +MTMD_API bool mtmd_decode_use_mrope(mtmd_context * ctx); + + +// mtmd_bitmap +// +// length of data must be nx * ny * 3 +// the data is in RGBRGBRGB... format +MTMD_API mtmd_bitmap * mtmd_bitmap_init (uint32_t nx, + uint32_t ny, + const unsigned char * data); +MTMD_API uint32_t mtmd_bitmap_get_nx (const mtmd_bitmap * bitmap); +MTMD_API uint32_t mtmd_bitmap_get_ny (const mtmd_bitmap * bitmap); +MTMD_API const unsigned char * mtmd_bitmap_get_data(const mtmd_bitmap * bitmap); +MTMD_API void mtmd_bitmap_free (mtmd_bitmap * bitmap); +// bitmap ID is optional, but useful for KV cache tracking +// these getters/setters are dedicated functions, so you can for example calculate the hash of the image based on mtmd_bitmap_get_data() +MTMD_API const char * mtmd_bitmap_get_id(const mtmd_bitmap * bitmap); +MTMD_API void mtmd_bitmap_set_id(mtmd_bitmap * bitmap, const char * id); + + +// mtmd_input_chunks +// +// this is simply a list of mtmd_input_chunk +// the elements can only be populated via mtmd_tokenize() +MTMD_API mtmd_input_chunks * mtmd_input_chunks_init(void); +MTMD_API size_t mtmd_input_chunks_size(const mtmd_input_chunks * chunks); +MTMD_API const mtmd_input_chunk * mtmd_input_chunks_get (const mtmd_input_chunks * chunks, size_t idx); +MTMD_API void mtmd_input_chunks_free(mtmd_input_chunks * chunks); + +// mtmd_input_chunk +// +// the instance will be constructed via mtmd_tokenize() +// it will be freed along with mtmd_input_chunks +MTMD_API enum mtmd_input_chunk_type mtmd_input_chunk_get_type (const mtmd_input_chunk * chunk); +MTMD_API const llama_token * mtmd_input_chunk_get_tokens_text (const mtmd_input_chunk * chunk, size_t * n_tokens_output); +MTMD_API const mtmd_image_tokens * mtmd_input_chunk_get_tokens_image(const mtmd_input_chunk * chunk); + +// in case you want to use custom logic to handle the chunk (i.e. KV cache management) +// you can move the chunk ownership to your own code by copying it +// remember to free the chunk when you are done with it +MTMD_API mtmd_input_chunk * mtmd_input_chunk_copy(const mtmd_input_chunk * chunk); +MTMD_API void mtmd_input_chunk_free(mtmd_input_chunk * chunk); + + +// mtmd_image_tokens +// +// the instance will be constructed via mtmd_tokenize() +// it will be freed along with mtmd_input_chunk +MTMD_API size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * image_tokens); +MTMD_API size_t mtmd_image_tokens_get_nx (const mtmd_image_tokens * image_tokens); +MTMD_API size_t mtmd_image_tokens_get_ny (const mtmd_image_tokens * image_tokens); +MTMD_API const char * mtmd_image_tokens_get_id (const mtmd_image_tokens * image_tokens); +// number of temporal positions (always 1 for M-RoPE, n_tokens otherwise) +MTMD_API llama_pos mtmd_image_tokens_get_n_pos (const mtmd_image_tokens * image_tokens); + // tokenize an input text prompt and an image // the prompt must have the input image marker (default: "<__image__>") in it // the marker will be replaced with the image tokens @@ -93,75 +166,152 @@ MTMD_API void mtmd_free(mtmd_context * ctx); // 1 on number of images not matching the number of markers // 2 on image preprocessing error MTMD_API int32_t mtmd_tokenize(mtmd_context * ctx, - std::vector & output, - const mtmd_input_text & text, - const std::vector & bitmaps); - -// access mtmd_image_tokens -MTMD_API size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * image_tokens); -MTMD_API size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens); -MTMD_API size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens); -MTMD_API std::string mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens); -MTMD_API llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens); // number of temporal positions (always 1 for M-RoPE, n_tokens otherwise) -MTMD_API void mtmd_image_tokens_free(mtmd_image_tokens * image_tokens); + mtmd_input_chunks * output, + const mtmd_input_text * text, + const mtmd_bitmap ** bitmaps, + size_t n_bitmaps); // returns 0 on success MTMD_API int32_t mtmd_encode(mtmd_context * ctx, - const mtmd_image_tokens * image_tokens); + const mtmd_image_tokens * image_tokens); // get output embeddings from the last encode pass MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx); -// whether we need to set non-causal mask before llama_decode -MTMD_API bool mtmd_decode_use_non_causal(mtmd_context * ctx); - -// whether the current model use M-RoPE for llama_decode -MTMD_API bool mtmd_decode_use_mrope(mtmd_context * ctx); - - +///////////////////////////////////////// // -// helper functions (can be implemented based on other functions) +// Helper functions (can be implemented based on other functions) // +// Please note that these helpers are not guaranteed to be stable. +// BREAKING CHANGES are expected. +// + +// helper function to construct a mtmd_bitmap from a file +// returns nullptr on failure +// this function is thread-safe +MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_file(const char * fname); + +// helper function to construct a mtmd_bitmap from a buffer containing a file +// the file content must be an image in format supported by stb_image (jpg, png, bmp, gif, etc.) +// returns nullptr on failure +// this function is thread-safe +MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len); // helper to count the total number of tokens from a list of chunks, useful to keep track of KV cache -MTMD_API size_t mtmd_helper_get_n_tokens(mtmd_input_chunks & chunks); +MTMD_API size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks); // helper to count the total position of tokens from a list of chunks, useful to keep track of n_past -MTMD_API llama_pos mtmd_helper_get_n_pos(mtmd_input_chunks & chunks); +// normally, n_pos is equal to n_tokens, but for M-RoPE it is different +MTMD_API llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks); // helper function that automatically: // 1. run llama_decode() on text chunks // 2. run mtmd_encode() on image chunks, then mtmd_get_output_embd() and then llama_decode() // if any of the mtmd_encode() or llama_decode() calls return non-zero, stop and forward the error // otherwise, returns 0 on success -MTMD_API int32_t mtmd_helper_eval(mtmd_context * ctx, - llama_context * lctx, - mtmd_input_chunks & chunks, - llama_pos pos0, - llama_seq_id seq_id, - int32_t n_batch); +// this function is NOT thread-safe +MTMD_API int32_t mtmd_helper_eval_chunks(mtmd_context * ctx, + struct llama_context * lctx, + const mtmd_input_chunks * chunks, + llama_pos n_past, + llama_seq_id seq_id, + int32_t n_batch, + bool logits_last, + llama_pos * new_n_past); -// helper function to construct a mtmd_bitmap from a file -// returns 0 on success -// this function is thread-safe -MTMD_API int32_t mtmd_helper_bitmap_init_from_file(const char * fname, mtmd_bitmap & output); +// works like mtmd_helper_eval_chunks(), but only for a single chunk +// this function is NOT thread-safe +MTMD_API int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx, + struct llama_context * lctx, + const mtmd_input_chunk * chunk, + llama_pos n_past, + llama_seq_id seq_id, + int32_t n_batch, + bool logits_last, + llama_pos * new_n_past); -// helper function to construct a mtmd_bitmap from a buffer -// the buffer must be an image in format supported by stb_image (jpg, png, bmp, gif, etc.) -// returns 0 on success -// this function is thread-safe -MTMD_API int32_t mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len, mtmd_bitmap & output); +///////////////////////////////////////// + +// test function, to be used in test-mtmd-c-api.c +MTMD_API mtmd_input_chunks * mtmd_test_create_input_chunks(void); + +#ifdef __cplusplus +} // extern "C" +#endif + +// +// C++ wrappers +// + +#ifdef __cplusplus + +namespace mtmd { -// convenient unique_ptr wrappers struct mtmd_context_deleter { void operator()(mtmd_context * val) { mtmd_free(val); } }; -using mtmd_context_ptr = std::unique_ptr; +using context_ptr = std::unique_ptr; -#else +struct mtmd_bitmap_deleter { + void operator()(mtmd_bitmap * val) { mtmd_bitmap_free(val); } +}; +using bitmap_ptr = std::unique_ptr; -static_assert(false && "C header is not yet supported by this library"); +struct mtmd_input_chunks_deleter { + void operator()(mtmd_input_chunks * val) { mtmd_input_chunks_free(val); } +}; +using input_chunks_ptr = std::unique_ptr; + +struct mtmd_input_chunk_deleter { + void operator()(mtmd_input_chunk * val) { mtmd_input_chunk_free(val); } +}; +using input_chunk_ptr = std::unique_ptr; + +struct bitmap { + bitmap_ptr ptr; + bitmap() : ptr(nullptr) {} + bitmap(mtmd_bitmap * bitmap) : ptr(bitmap) {} + bitmap(bitmap && other) noexcept : ptr(std::move(other.ptr)) {} + bitmap(uint32_t nx, uint32_t ny, const unsigned char * data) { + ptr.reset(mtmd_bitmap_init(nx, ny, data)); + } + ~bitmap() = default; + uint32_t nx() { return mtmd_bitmap_get_nx(ptr.get()); } + uint32_t ny() { return mtmd_bitmap_get_ny(ptr.get()); } + const unsigned char * data() { return mtmd_bitmap_get_data(ptr.get()); } + std::string id() { return mtmd_bitmap_get_id(ptr.get()); } + void set_id(const char * id) { mtmd_bitmap_set_id(ptr.get(), id); } +}; + +struct bitmaps { + std::vector entries; + ~bitmaps() = default; + // return list of pointers to mtmd_bitmap + // example: + // auto bitmaps_c_ptr = bitmaps.c_ptr(); + // int32_t res = mtmd_tokenize(... bitmaps_c_ptr.data(), bitmaps_c_ptr.size()); + std::vector c_ptr() { + std::vector res(entries.size()); + for (size_t i = 0; i < entries.size(); i++) { + res[i] = entries[i].ptr.get(); + } + return res; + } +}; + +struct input_chunks { + input_chunks_ptr ptr; + input_chunks() = default; + input_chunks(mtmd_input_chunks * chunks) : ptr(chunks) {} + ~input_chunks() = default; + size_t size() { return mtmd_input_chunks_size(ptr.get()); } + const mtmd_input_chunk * operator[](size_t idx) { + return mtmd_input_chunks_get(ptr.get(), idx); + } +}; + +} // namespace mtmd #endif From 66645a5285d8c4c5f9a3b3f360d042baac2d820a Mon Sep 17 00:00:00 2001 From: Akarshan Biswas Date: Mon, 5 May 2025 13:39:10 +0530 Subject: [PATCH 101/126] SYCL: Disable mul_mat kernels for noncontiguous tensor b (#13308) ggml-ci --- ggml/src/ggml-sycl/ggml-sycl.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 66b6f2cca4..72cdbbccaf 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -3873,6 +3873,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g if (a->ne[3] != b->ne[3]) { return false; } + if (!ggml_is_contiguous(b)) { + return false; + } ggml_type a_type = a->type; if (a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ4_XS || a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ3_S || From ae803bfc3d0fc2d0d3e1cce22ee103a30939e104 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Mon, 5 May 2025 12:34:26 +0200 Subject: [PATCH 102/126] convert : bailingmoe : set yarn metadata if present (#13312) --- convert_hf_to_gguf.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 838999531e..34bed7a08a 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -5680,7 +5680,12 @@ class BailingMoeModel(TextModel): rope_dim = hparams.get("head_dim") or hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + if (self.hparams.get("rope_scaling") or {}).get("type") == "yarn" and "factor" in self.hparams["rope_scaling"]: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) + else: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) self.gguf_writer.add_vocab_size(hparams["vocab_size"]) self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) From 5215b91e9377ce23e4ccc92ec3156bf5c7f892a3 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 5 May 2025 12:54:44 +0200 Subject: [PATCH 103/126] clip : fix confused naming ffn_up and ffn_down (#13290) * clip : fix confused naming ffn_up and ffn_down * rm ffn_i/o/g naming * rename n_embd, n_ff * small fix * no check n_ff --- convert_hf_to_gguf.py | 6 ++ gguf-py/gguf/tensor_mapping.py | 11 +-- tools/llava/clip.cpp | 147 ++++++++++++++++----------------- tools/llava/mtmd-cli.cpp | 4 + 4 files changed, 86 insertions(+), 82 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 34bed7a08a..a47d7df6fd 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -1778,6 +1778,12 @@ class LlamaModel(TextModel): model_arch = gguf.MODEL_ARCH.LLAMA undo_permute = True + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + # fix for SmolVLM2, missing `num_attention_heads` in config.json + if self.hf_arch == "VLlama3ForCausalLM": + self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32) + def set_vocab(self): try: self._set_vocab_sentencepiece() diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 2b089f84a8..003b0172c7 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -977,15 +977,12 @@ class TensorNameMap: "visual.blocks.{bid}.norm2", # qwen2vl ), - # some namings are messed up because the original llava code swapped fc1 and fc2 - # we have no better way to fix it, just be careful - # new models like pixtral use the correct naming MODEL_TENSOR.V_ENC_FFN_UP: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", "vpm.encoder.layers.{bid}.mlp.fc1", - "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3 (note: name is swapped) + "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3 "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral - "visual.blocks.{bid}.mlp.fc2", # qwen2vl + "visual.blocks.{bid}.mlp.fc1", # qwen2vl "visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl ), @@ -997,9 +994,9 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_FFN_DOWN: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2", "vpm.encoder.layers.{bid}.mlp.fc2", - "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3 (note: name is swapped) + "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3 "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral - "visual.blocks.{bid}.mlp.fc1", # qwen2vl + "visual.blocks.{bid}.mlp.fc2", # qwen2vl "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl ), diff --git a/tools/llava/clip.cpp b/tools/llava/clip.cpp index cc03bf884a..1414f6ba4f 100644 --- a/tools/llava/clip.cpp +++ b/tools/llava/clip.cpp @@ -155,8 +155,8 @@ enum patch_merge_type { struct clip_hparams { int32_t image_size; int32_t patch_size; - int32_t hidden_size; - int32_t n_intermediate; + int32_t n_embd; + int32_t n_ff; int32_t projection_dim; int32_t n_head; int32_t n_layer; @@ -191,12 +191,6 @@ struct clip_layer { struct ggml_tensor * ln_1_w = nullptr; struct ggml_tensor * ln_1_b = nullptr; - // ff - struct ggml_tensor * ff_i_w = nullptr; // legacy naming - struct ggml_tensor * ff_i_b = nullptr; // legacy naming - struct ggml_tensor * ff_o_w = nullptr; // legacy naming - struct ggml_tensor * ff_o_b = nullptr; // legacy naming - struct ggml_tensor * ff_up_w = nullptr; struct ggml_tensor * ff_up_b = nullptr; struct ggml_tensor * ff_gate_w = nullptr; @@ -204,9 +198,6 @@ struct clip_layer { struct ggml_tensor * ff_down_w = nullptr; struct ggml_tensor * ff_down_b = nullptr; - struct ggml_tensor * ff_g_w = NULL; - struct ggml_tensor * ff_g_b = NULL; - // layernorm 2 struct ggml_tensor * ln_2_w = nullptr; struct ggml_tensor * ln_2_b = nullptr; @@ -388,9 +379,9 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im const int patch_size = hparams.patch_size; const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); - const int hidden_size = hparams.hidden_size; + const int n_embd = hparams.n_embd; const int n_head = hparams.n_head; - const int d_head = hidden_size / n_head; + const int d_head = n_embd / n_head; const int n_layer = hparams.n_layer; const float eps = hparams.eps; @@ -411,7 +402,7 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im ggml_set_input(inp_raw); struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_reshape_2d(ctx0, inp, num_patches, hidden_size); + inp = ggml_reshape_2d(ctx0, inp, num_patches, n_embd); inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); inp = ggml_add(ctx0, inp, model.patch_bias); @@ -456,7 +447,7 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im KQV = ggml_reshape_3d(ctx0, KQV, d_head, num_patches, n_head); KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - cur = ggml_cont_2d(ctx0, KQV, hidden_size, num_patches); + cur = ggml_cont_2d(ctx0, KQV, n_embd, num_patches); } // attention output @@ -473,14 +464,14 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b); } - cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b); + cur = ggml_mul_mat(ctx0, model.layers[il].ff_up_w, cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_up_b); // siglip uses gelu cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b); + cur = ggml_mul_mat(ctx0, model.layers[il].ff_down_w, cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_down_b); // residual 2 cur = ggml_add(ctx0, embeddings, cur); @@ -504,11 +495,11 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im const int kernel_size = patches_per_image / tokens_per_side; embeddings = ggml_cont(ctx0, ggml_transpose(ctx0, embeddings)); - embeddings = ggml_reshape_4d(ctx0, embeddings, patches_per_image, patches_per_image, hidden_size, batch_size); + embeddings = ggml_reshape_4d(ctx0, embeddings, patches_per_image, patches_per_image, n_embd, batch_size); // doing a pool2d to reduce the number of output tokens to 256 embeddings = ggml_pool_2d(ctx0, embeddings, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0); - embeddings = ggml_reshape_3d(ctx0, embeddings, embeddings->ne[0] * embeddings->ne[0], hidden_size, batch_size); + embeddings = ggml_reshape_3d(ctx0, embeddings, embeddings->ne[0] * embeddings->ne[0], n_embd, batch_size); embeddings = ggml_cont(ctx0, ggml_transpose(ctx0, embeddings)); // apply norm before projection @@ -637,9 +628,9 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i const int n_patches_x = image_size_width / patch_size; const int n_patches_y = image_size_height / patch_size; const int num_patches = n_patches_x * n_patches_y; - const int hidden_size = hparams.hidden_size; + const int n_embd = hparams.n_embd; const int n_head = hparams.n_head; - const int d_head = hidden_size / n_head; + const int d_head = n_embd / n_head; const int n_layer = hparams.n_layer; const float eps = hparams.eps; const int n_merge = hparams.spatial_merge_size; @@ -669,7 +660,7 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i ggml_set_input(pos_w); struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_reshape_2d(ctx0, inp, num_patches, hidden_size); + inp = ggml_reshape_2d(ctx0, inp, num_patches, n_embd); inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); struct ggml_tensor * embeddings = inp; @@ -710,7 +701,7 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i KQV = ggml_reshape_3d(ctx0, KQV, d_head, num_patches, n_head); KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - cur = ggml_cont_2d(ctx0, KQV, hidden_size, num_patches); + cur = ggml_cont_2d(ctx0, KQV, n_embd, num_patches); cur = ggml_mul_mat(ctx0, model.layers[il].o_w, cur); } @@ -753,8 +744,8 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w); // reshape image tokens to 2D grid - cur = ggml_reshape_3d(ctx0, cur, hidden_size, n_patches_x, n_patches_y); - cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, hidden_size] + cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y); + cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd] cur = ggml_cont(ctx0, cur); // torch.nn.functional.unfold is just an im2col under the hood @@ -762,7 +753,7 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0); cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type); - // project to hidden_size + // project to n_embd cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur); embeddings = cur; @@ -785,9 +776,9 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i // arrangement of the [IMG_BREAK] token { // not efficient, but works - // the trick is to view the embeddings as a 3D tensor with shape [hidden_size, n_patches_per_row, n_rows] + // the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows] // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension - // after the concatenation, we have a tensor with shape [hidden_size, n_patches_per_row + 1, n_rows] + // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows] const int p_y = n_merge > 0 ? n_patches_y / n_merge : n_patches_y; const int p_x = n_merge > 0 ? n_patches_x / n_merge : n_patches_x; @@ -827,9 +818,9 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ const int patches_h = image_size_height / patch_size; const int num_positions = num_patches + (model.class_embedding ? 1 : 0); const int num_position_ids = num_positions * 4; // m-rope requires 4 dim per position - const int hidden_size = hparams.hidden_size; + const int n_embd = hparams.n_embd; const int n_head = hparams.n_head; - const int d_head = hidden_size / n_head; + const int d_head = n_embd / n_head; const int n_layer = hparams.n_layer; const float eps = hparams.eps; @@ -864,14 +855,14 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b] inp = ggml_reshape_4d( ctx0, inp, - hidden_size * 2, patches_w / 2, patches_h, batch_size); + n_embd * 2, patches_w / 2, patches_h, batch_size); inp = ggml_reshape_4d( ctx0, inp, - hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2)); + n_embd * 2, patches_w / 2, 2, batch_size * (patches_h / 2)); inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3)); inp = ggml_reshape_3d( ctx0, inp, - hidden_size, patches_w * patches_h, batch_size); + n_embd, patches_w * patches_h, batch_size); if (model.patch_bias) { // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); @@ -904,11 +895,11 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ ggml_set_name(window_mask, "window_mask"); ggml_set_input(window_mask); - // embeddings shape: [hidden_size, patches_w * patches_h, batch_size] + // embeddings shape: [n_embd, patches_w * patches_h, batch_size] GGML_ASSERT(batch_size == 1); - embeddings = ggml_reshape_2d(ctx0, embeddings, hidden_size * 4, patches_w * patches_h * batch_size / 4); + embeddings = ggml_reshape_2d(ctx0, embeddings, n_embd * 4, patches_w * patches_h * batch_size / 4); embeddings = ggml_get_rows(ctx0, embeddings, inv_window_idx); - embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size, patches_w * patches_h, batch_size); + embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd, patches_w * patches_h, batch_size); } // loop over layers @@ -961,7 +952,7 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size); KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - cur = ggml_cont_3d(ctx0, KQV, hidden_size, num_positions, batch_size); + cur = ggml_cont_3d(ctx0, KQV, n_embd, num_positions, batch_size); } // attention output @@ -978,11 +969,11 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ // mlp // ffn_up - auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur); - cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_o_b); + auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_up_w, cur); + cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_up_b); - auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_g_w, cur); - cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_g_b); + auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_gate_w, cur); + cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_gate_b); // TODO : only 2 of these 3 are actually used, should we remove one of them? if (ctx->use_gelu) { cur_gate = ggml_gelu_inplace(ctx0, cur_gate); @@ -994,8 +985,8 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ cur = ggml_mul(ctx0, cur_gate, cur_up); // ffn_down - cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b); + cur = ggml_mul_mat(ctx0, model.layers[il].ff_down_w, cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_down_b); // residual 2 cur = ggml_add(ctx0, embeddings, cur); @@ -1011,7 +1002,7 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w); } - embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size); + embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, num_positions / 4, batch_size); embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); @@ -1028,7 +1019,7 @@ static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_ ggml_set_name(window_idx, "window_idx"); ggml_set_input(window_idx); - // embeddings shape: [hidden_size, patches_w * patches_h, batch_size] + // embeddings shape: [n_embd, patches_w * patches_h, batch_size] GGML_ASSERT(batch_size == 1); embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4); embeddings = ggml_get_rows(ctx0, embeddings, window_idx); @@ -1074,9 +1065,9 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im const int patches_h = image_size_height / patch_size; const int num_positions = num_patches + (model.class_embedding ? 1 : 0); const int num_position_ids = ctx->proj_type == PROJECTOR_TYPE_QWEN2VL ? num_positions * 4 : num_positions; - const int hidden_size = hparams.hidden_size; + const int n_embd = hparams.n_embd; const int n_head = hparams.n_head; - const int d_head = hidden_size / n_head; + const int d_head = n_embd / n_head; const float eps = hparams.eps; int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; @@ -1114,17 +1105,17 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b] inp = ggml_reshape_4d( ctx0, inp, - hidden_size * 2, patches_w / 2, patches_h, batch_size); + n_embd * 2, patches_w / 2, patches_h, batch_size); inp = ggml_reshape_4d( ctx0, inp, - hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2)); + n_embd * 2, patches_w / 2, 2, batch_size * (patches_h / 2)); inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3)); inp = ggml_reshape_3d( ctx0, inp, - hidden_size, patches_w * patches_h, batch_size); + n_embd, patches_w * patches_h, batch_size); } else { - inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size); + inp = ggml_reshape_3d(ctx0, inp, num_patches, n_embd, batch_size); inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); } @@ -1137,7 +1128,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im // concat class_embeddings and patch_embeddings if (model.class_embedding) { - embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); + embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd, num_positions, batch_size); embeddings = ggml_scale(ctx0, embeddings, 0.0f); // set to all zeros embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); @@ -1234,7 +1225,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size); KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - cur = ggml_cont_3d(ctx0, KQV, hidden_size, num_positions, batch_size); + cur = ggml_cont_3d(ctx0, KQV, n_embd, num_positions, batch_size); } // attention output @@ -1252,8 +1243,8 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b); } - cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b); + cur = ggml_mul_mat(ctx0, model.layers[il].ff_up_w, cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_up_b); if (ctx->use_gelu) { cur = ggml_gelu_inplace(ctx0, cur); @@ -1263,8 +1254,8 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im cur = ggml_gelu_quick_inplace(ctx0, cur); } - cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b); + cur = ggml_mul_mat(ctx0, model.layers[il].ff_down_w, cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_down_b); // residual 2 cur = ggml_add(ctx0, embeddings, cur); @@ -1496,9 +1487,9 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im } { // attention - int hidden_size = clip_n_mmproj_embd(ctx); + int n_embd = clip_n_mmproj_embd(ctx); const int d_head = 128; - int n_head = hidden_size/d_head; + int n_head = n_embd/d_head; int num_query = 96; if (ctx->minicpmv_version == 2) { num_query = 96; @@ -1528,7 +1519,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_query, n_head, batch_size); KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - KQV = ggml_cont_3d(ctx0, KQV, hidden_size, num_query, batch_size); + KQV = ggml_cont_3d(ctx0, KQV, n_embd, num_query, batch_size); embeddings = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_o_w, KQV), model.mm_model_attn_o_b); } @@ -1571,7 +1562,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im } else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { - embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size); + embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, num_positions / 4, batch_size); embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); @@ -1696,9 +1687,9 @@ struct clip_model_loader { get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false); get_bool(KEY_USE_SILU, ctx_clip.use_silu, false); - get_u32(KEY_N_EMBD, hparams.hidden_size); + get_u32(KEY_N_EMBD, hparams.n_embd); get_u32(KEY_N_HEAD, hparams.n_head); - get_u32(KEY_N_FF, hparams.n_intermediate); + get_u32(KEY_N_FF, hparams.n_ff); get_u32(KEY_N_BLOCK, hparams.n_layer); get_u32(KEY_PROJ_DIM, hparams.projection_dim); get_f32(KEY_LAYER_NORM_EPS, hparams.eps); @@ -1807,6 +1798,7 @@ struct clip_model_loader { } void load_tensors() { + auto & hparams = ctx_clip.vision_model.hparams; std::map tensor_offset; std::vector tensors_to_load; @@ -1860,8 +1852,8 @@ struct clip_model_loader { vision_model.position_embeddings = get_tensor(string_format(TN_POS_EMBD, "v"), false); // layers - vision_model.layers.resize(vision_model.hparams.n_layer); - for (int il = 0; il < vision_model.hparams.n_layer; ++il) { + vision_model.layers.resize(hparams.n_layer); + for (int il = 0; il < hparams.n_layer; ++il) { auto & layer = vision_model.layers[il]; layer.k_w = get_tensor(string_format(TN_ATTN_K, "v", il, "weight")); layer.q_w = get_tensor(string_format(TN_ATTN_Q, "v", il, "weight")); @@ -1884,13 +1876,18 @@ struct clip_model_loader { layer.ff_down_w = get_tensor(string_format(TN_FFN_DOWN, "v", il, "weight")); layer.ff_down_b = get_tensor(string_format(TN_FFN_DOWN, "v", il, "bias"), false); - // legacy naming (the in and out is reversed! don't ask me why) - layer.ff_i_w = layer.ff_down_w; - layer.ff_o_w = layer.ff_up_w; - layer.ff_g_w = layer.ff_gate_w; - layer.ff_i_b = layer.ff_down_b; - layer.ff_o_b = layer.ff_up_b; - layer.ff_g_b = layer.ff_gate_b; + // some models already exported with legacy (incorrect) naming which is quite messy, let's fix it here + // note: Qwen model converted from the old surgery script has n_ff = 0, so we cannot use n_ff to check! + if (layer.ff_up_w && layer.ff_down_w && layer.ff_down_w->ne[0] == hparams.n_embd) { + // swap up and down weights + ggml_tensor * tmp = layer.ff_up_w; + layer.ff_up_w = layer.ff_down_w; + layer.ff_down_w = tmp; + // swap up and down biases + tmp = layer.ff_up_b; + layer.ff_up_b = layer.ff_down_b; + layer.ff_down_b = tmp; + } } switch (ctx_clip.proj_type) { @@ -2904,7 +2901,7 @@ int32_t clip_get_patch_size(const struct clip_ctx * ctx) { } int32_t clip_get_hidden_size(const struct clip_ctx * ctx) { - return ctx->vision_model.hparams.hidden_size; + return ctx->vision_model.hparams.n_embd; } const char * clip_patch_merge_type(const struct clip_ctx * ctx) { diff --git a/tools/llava/mtmd-cli.cpp b/tools/llava/mtmd-cli.cpp index dd18e0fe6e..4977d5480b 100644 --- a/tools/llava/mtmd-cli.cpp +++ b/tools/llava/mtmd-cli.cpp @@ -92,6 +92,10 @@ struct mtmd_cli_context { batch = llama_batch_init(params.n_batch, 0, 1); n_batch = params.n_batch; + if (!model || !lctx) { + exit(1); + } + if (!llama_model_chat_template(model, nullptr) && params.chat_template.empty()) { LOG_ERR("Model does not have chat template.\n"); LOG_ERR(" For old llava models, you may need to use '--chat-template vicuna'\n"); From 9b61acf06041dcbaff6afa5f28940e93297f8520 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 5 May 2025 16:02:55 +0200 Subject: [PATCH 104/126] mtmd : rename llava directory to mtmd (#13311) * mv llava to mtmd * change ref everywhere --- Makefile | 38 +++++++++--------- common/arg.cpp | 4 +- common/common.h | 2 +- docs/multimodal/MobileVLM.md | 12 +++--- docs/multimodal/glmedge.md | 4 +- docs/multimodal/llava.md | 12 +++--- docs/multimodal/minicpmo2.6.md | 4 +- docs/multimodal/minicpmv2.5.md | 4 +- docs/multimodal/minicpmv2.6.md | 4 +- requirements/requirements-all.txt | 2 +- tools/CMakeLists.txt | 2 +- tools/{llava => mtmd}/CMakeLists.txt | 0 tools/{llava => mtmd}/README-quantize.md | 0 tools/{llava => mtmd}/README.md | 0 tools/{llava => mtmd}/android/adb_run.sh | 0 tools/{llava => mtmd}/android/build_64.sh | 0 tools/{llava => mtmd}/clip-impl.h | 0 tools/{llava => mtmd}/clip-quantize-cli.cpp | 0 tools/{llava => mtmd}/clip.cpp | 0 tools/{llava => mtmd}/clip.h | 0 .../convert_image_encoder_to_gguf.py | 0 tools/{llava => mtmd}/deprecation-warning.cpp | 0 .../glmedge-convert-image-encoder-to-gguf.py | 0 tools/{llava => mtmd}/glmedge-surgery.py | 0 tools/{llava => mtmd}/llava.cpp | 0 tools/{llava => mtmd}/llava.h | 0 tools/{llava => mtmd}/llava_surgery.py | 0 tools/{llava => mtmd}/llava_surgery_v2.py | 0 .../minicpmv-convert-image-encoder-to-gguf.py | 0 tools/{llava => mtmd}/minicpmv-surgery.py | 0 tools/{llava => mtmd}/mtmd-cli.cpp | 0 tools/{llava => mtmd}/mtmd.cpp | 0 tools/{llava => mtmd}/mtmd.h | 0 tools/{llava => mtmd}/qwen2vl-test.cpp | 0 tools/{llava => mtmd}/requirements.txt | 0 tools/{llava => mtmd}/test-1.jpeg | Bin tools/{llava => mtmd}/tests.sh | 0 37 files changed, 44 insertions(+), 44 deletions(-) rename tools/{llava => mtmd}/CMakeLists.txt (100%) rename tools/{llava => mtmd}/README-quantize.md (100%) rename tools/{llava => mtmd}/README.md (100%) rename tools/{llava => mtmd}/android/adb_run.sh (100%) rename tools/{llava => mtmd}/android/build_64.sh (100%) rename tools/{llava => mtmd}/clip-impl.h (100%) rename tools/{llava => mtmd}/clip-quantize-cli.cpp (100%) rename tools/{llava => mtmd}/clip.cpp (100%) rename tools/{llava => mtmd}/clip.h (100%) rename tools/{llava => mtmd}/convert_image_encoder_to_gguf.py (100%) rename tools/{llava => mtmd}/deprecation-warning.cpp (100%) rename tools/{llava => mtmd}/glmedge-convert-image-encoder-to-gguf.py (100%) rename tools/{llava => mtmd}/glmedge-surgery.py (100%) rename tools/{llava => mtmd}/llava.cpp (100%) rename tools/{llava => mtmd}/llava.h (100%) rename tools/{llava => mtmd}/llava_surgery.py (100%) rename tools/{llava => mtmd}/llava_surgery_v2.py (100%) rename tools/{llava => mtmd}/minicpmv-convert-image-encoder-to-gguf.py (100%) rename tools/{llava => mtmd}/minicpmv-surgery.py (100%) rename tools/{llava => mtmd}/mtmd-cli.cpp (100%) rename tools/{llava => mtmd}/mtmd.cpp (100%) rename tools/{llava => mtmd}/mtmd.h (100%) rename tools/{llava => mtmd}/qwen2vl-test.cpp (100%) rename tools/{llava => mtmd}/requirements.txt (100%) rename tools/{llava => mtmd}/test-1.jpeg (100%) rename tools/{llava => mtmd}/tests.sh (100%) diff --git a/Makefile b/Makefile index 305037089b..68b6fe59a6 100644 --- a/Makefile +++ b/Makefile @@ -1394,36 +1394,36 @@ llama-gen-docs: examples/gen-docs/gen-docs.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -libllava.a: tools/llava/llava.cpp \ - tools/llava/llava.h \ - tools/llava/clip.cpp \ - tools/llava/clip.h \ +libllava.a: tools/mtmd/llava.cpp \ + tools/mtmd/llava.h \ + tools/mtmd/clip.cpp \ + tools/mtmd/clip.h \ common/stb_image.h \ common/base64.hpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual -llama-llava-cli: tools/llava/llava-cli.cpp \ - tools/llava/llava.cpp \ - tools/llava/llava.h \ - tools/llava/clip.cpp \ - tools/llava/clip.h \ +llama-llava-cli: tools/mtmd/llava-cli.cpp \ + tools/mtmd/llava.cpp \ + tools/mtmd/llava.h \ + tools/mtmd/clip.cpp \ + tools/mtmd/clip.h \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual -llama-minicpmv-cli: tools/llava/minicpmv-cli.cpp \ - tools/llava/llava.cpp \ - tools/llava/llava.h \ - tools/llava/clip.cpp \ - tools/llava/clip.h \ +llama-minicpmv-cli: tools/mtmd/minicpmv-cli.cpp \ + tools/mtmd/llava.cpp \ + tools/mtmd/llava.h \ + tools/mtmd/clip.cpp \ + tools/mtmd/clip.h \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual -llama-qwen2vl-cli: tools/llava/qwen2vl-cli.cpp \ - tools/llava/llava.cpp \ - tools/llava/llava.h \ - tools/llava/clip.cpp \ - tools/llava/clip.h \ +llama-qwen2vl-cli: tools/mtmd/qwen2vl-cli.cpp \ + tools/mtmd/llava.cpp \ + tools/mtmd/llava.h \ + tools/mtmd/clip.cpp \ + tools/mtmd/clip.h \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual diff --git a/common/arg.cpp b/common/arg.cpp index 5080aa2fcb..490991b23a 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2211,14 +2211,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING")); add_opt(common_arg( {"--mmproj"}, "FILE", - "path to a multimodal projector file. see tools/llava/README.md", + "path to a multimodal projector file. see tools/mtmd/README.md", [](common_params & params, const std::string & value) { params.mmproj.path = value; } ).set_examples(mmproj_examples)); add_opt(common_arg( {"--mmproj-url"}, "URL", - "URL to a multimodal projector file. see tools/llava/README.md", + "URL to a multimodal projector file. see tools/mtmd/README.md", [](common_params & params, const std::string & value) { params.mmproj.url = value; } diff --git a/common/common.h b/common/common.h index cfe1b72786..416939da9a 100644 --- a/common/common.h +++ b/common/common.h @@ -340,7 +340,7 @@ struct common_params { common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO; - // multimodal models (see tools/llava) + // multimodal models (see tools/mtmd) struct common_params_model mmproj; bool mmproj_use_gpu = true; // use GPU for multimodal model bool no_mmproj = false; // explicitly disable multimodal model diff --git a/docs/multimodal/MobileVLM.md b/docs/multimodal/MobileVLM.md index a647d7d357..4f5eca6190 100644 --- a/docs/multimodal/MobileVLM.md +++ b/docs/multimodal/MobileVLM.md @@ -33,13 +33,13 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336 2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh -python ./tools/llava/llava_surgery.py -m path/to/MobileVLM-1.7B +python ./tools/mtmd/llava_surgery.py -m path/to/MobileVLM-1.7B ``` 3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF: ```sh -python ./tools/llava/convert_image_encoder_to_gguf.py \ +python ./tools/mtmd/convert_image_encoder_to_gguf.py \ -m path/to/clip-vit-large-patch14-336 \ --llava-projector path/to/MobileVLM-1.7B/llava.projector \ --output-dir path/to/MobileVLM-1.7B \ @@ -47,7 +47,7 @@ python ./tools/llava/convert_image_encoder_to_gguf.py \ ``` ```sh -python ./tools/llava/convert_image_encoder_to_gguf.py \ +python ./tools/mtmd/convert_image_encoder_to_gguf.py \ -m path/to/clip-vit-large-patch14-336 \ --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \ --output-dir path/to/MobileVLM-1.7B_V2 \ @@ -69,10 +69,10 @@ Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directo ## Android compile and run ### compile -refer to `tools/llava/android/build_64.sh` +refer to `tools/mtmd/android/build_64.sh` ```sh -mkdir tools/llava/android/build_64 -cd tools/llava/android/build_64 +mkdir tools/mtmd/android/build_64 +cd tools/mtmd/android/build_64 ../build_64.sh ``` ### run on Android diff --git a/docs/multimodal/glmedge.md b/docs/multimodal/glmedge.md index e7dfafdde1..7bae831505 100644 --- a/docs/multimodal/glmedge.md +++ b/docs/multimodal/glmedge.md @@ -25,13 +25,13 @@ git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/T 2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents: ```sh -python ./tools/llava/glmedge-surgery.py -m ../model_path +python ./tools/mtmd/glmedge-surgery.py -m ../model_path ``` 4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF: ```sh -python ./tools/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path +python ./tools/mtmd/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path ``` 5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF: diff --git a/docs/multimodal/llava.md b/docs/multimodal/llava.md index 0087b10610..12354ab60a 100644 --- a/docs/multimodal/llava.md +++ b/docs/multimodal/llava.md @@ -37,19 +37,19 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336 2. Install the required Python packages: ```sh -pip install -r tools/llava/requirements.txt +pip install -r tools/mtmd/requirements.txt ``` 3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh -python ./tools/llava/llava_surgery.py -m ../llava-v1.5-7b +python ./tools/mtmd/llava_surgery.py -m ../llava-v1.5-7b ``` 4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF: ```sh -python ./tools/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b +python ./tools/mtmd/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` 5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF: @@ -69,12 +69,12 @@ git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b 2) Install the required Python packages: ```sh -pip install -r tools/llava/requirements.txt +pip install -r tools/mtmd/requirements.txt ``` 3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: ```console -python tools/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/ +python tools/mtmd/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/ ``` - you will find a llava.projector and a llava.clip file in your model directory @@ -88,7 +88,7 @@ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.jso 5) Create the visual gguf model: ```console -python ./tools/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision +python ./tools/mtmd/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision ``` - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP diff --git a/docs/multimodal/minicpmo2.6.md b/docs/multimodal/minicpmo2.6.md index c9aab8abc0..8c6db8efe5 100644 --- a/docs/multimodal/minicpmo2.6.md +++ b/docs/multimodal/minicpmo2.6.md @@ -29,8 +29,8 @@ cmake --build build --config Release Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us) ```bash -python ./tools/llava/minicpmv-surgery.py -m ../MiniCPM-o-2_6 -python ./tools/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4 +python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-o-2_6 +python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4 python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model # quantize int4 version diff --git a/docs/multimodal/minicpmv2.5.md b/docs/multimodal/minicpmv2.5.md index 4603bd7c1c..19b439607d 100644 --- a/docs/multimodal/minicpmv2.5.md +++ b/docs/multimodal/minicpmv2.5.md @@ -28,8 +28,8 @@ cmake --build build --config Release Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us) ```bash -python ./tools/llava/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5 -python ./tools/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2 +python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5 +python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2 python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model # quantize int4 version diff --git a/docs/multimodal/minicpmv2.6.md b/docs/multimodal/minicpmv2.6.md index 69ebc12961..15c1bbd12e 100644 --- a/docs/multimodal/minicpmv2.6.md +++ b/docs/multimodal/minicpmv2.6.md @@ -28,8 +28,8 @@ cmake --build build --config Release Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us) ```bash -python ./tools/llava/minicpmv-surgery.py -m ../MiniCPM-V-2_6 -python ./tools/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3 +python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-V-2_6 +python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3 python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model # quantize int4 version diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 4b6d4b7d7c..9fa7d4d0ab 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -1,4 +1,4 @@ --r ../tools/llava/requirements.txt +-r ../tools/mtmd/requirements.txt -r ../tools/server/bench/requirements.txt -r ../tools/server/tests/requirements.txt diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt index 06a96af552..d64956b843 100644 --- a/tools/CMakeLists.txt +++ b/tools/CMakeLists.txt @@ -27,7 +27,7 @@ else() add_subdirectory(run) add_subdirectory(tokenize) add_subdirectory(tts) - add_subdirectory(llava) + add_subdirectory(mtmd) if (GGML_RPC) add_subdirectory(rpc) endif() diff --git a/tools/llava/CMakeLists.txt b/tools/mtmd/CMakeLists.txt similarity index 100% rename from tools/llava/CMakeLists.txt rename to tools/mtmd/CMakeLists.txt diff --git a/tools/llava/README-quantize.md b/tools/mtmd/README-quantize.md similarity index 100% rename from tools/llava/README-quantize.md rename to tools/mtmd/README-quantize.md diff --git a/tools/llava/README.md b/tools/mtmd/README.md similarity index 100% rename from tools/llava/README.md rename to tools/mtmd/README.md diff --git a/tools/llava/android/adb_run.sh b/tools/mtmd/android/adb_run.sh similarity index 100% rename from tools/llava/android/adb_run.sh rename to tools/mtmd/android/adb_run.sh diff --git a/tools/llava/android/build_64.sh b/tools/mtmd/android/build_64.sh similarity index 100% rename from tools/llava/android/build_64.sh rename to tools/mtmd/android/build_64.sh diff --git a/tools/llava/clip-impl.h b/tools/mtmd/clip-impl.h similarity index 100% rename from tools/llava/clip-impl.h rename to tools/mtmd/clip-impl.h diff --git a/tools/llava/clip-quantize-cli.cpp b/tools/mtmd/clip-quantize-cli.cpp similarity index 100% rename from tools/llava/clip-quantize-cli.cpp rename to tools/mtmd/clip-quantize-cli.cpp diff --git a/tools/llava/clip.cpp b/tools/mtmd/clip.cpp similarity index 100% rename from tools/llava/clip.cpp rename to tools/mtmd/clip.cpp diff --git a/tools/llava/clip.h b/tools/mtmd/clip.h similarity index 100% rename from tools/llava/clip.h rename to tools/mtmd/clip.h diff --git a/tools/llava/convert_image_encoder_to_gguf.py b/tools/mtmd/convert_image_encoder_to_gguf.py similarity index 100% rename from tools/llava/convert_image_encoder_to_gguf.py rename to tools/mtmd/convert_image_encoder_to_gguf.py diff --git a/tools/llava/deprecation-warning.cpp b/tools/mtmd/deprecation-warning.cpp similarity index 100% rename from tools/llava/deprecation-warning.cpp rename to tools/mtmd/deprecation-warning.cpp diff --git a/tools/llava/glmedge-convert-image-encoder-to-gguf.py b/tools/mtmd/glmedge-convert-image-encoder-to-gguf.py similarity index 100% rename from tools/llava/glmedge-convert-image-encoder-to-gguf.py rename to tools/mtmd/glmedge-convert-image-encoder-to-gguf.py diff --git a/tools/llava/glmedge-surgery.py b/tools/mtmd/glmedge-surgery.py similarity index 100% rename from tools/llava/glmedge-surgery.py rename to tools/mtmd/glmedge-surgery.py diff --git a/tools/llava/llava.cpp b/tools/mtmd/llava.cpp similarity index 100% rename from tools/llava/llava.cpp rename to tools/mtmd/llava.cpp diff --git a/tools/llava/llava.h b/tools/mtmd/llava.h similarity index 100% rename from tools/llava/llava.h rename to tools/mtmd/llava.h diff --git a/tools/llava/llava_surgery.py b/tools/mtmd/llava_surgery.py similarity index 100% rename from tools/llava/llava_surgery.py rename to tools/mtmd/llava_surgery.py diff --git a/tools/llava/llava_surgery_v2.py b/tools/mtmd/llava_surgery_v2.py similarity index 100% rename from tools/llava/llava_surgery_v2.py rename to tools/mtmd/llava_surgery_v2.py diff --git a/tools/llava/minicpmv-convert-image-encoder-to-gguf.py b/tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py similarity index 100% rename from tools/llava/minicpmv-convert-image-encoder-to-gguf.py rename to tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py diff --git a/tools/llava/minicpmv-surgery.py b/tools/mtmd/minicpmv-surgery.py similarity index 100% rename from tools/llava/minicpmv-surgery.py rename to tools/mtmd/minicpmv-surgery.py diff --git a/tools/llava/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp similarity index 100% rename from tools/llava/mtmd-cli.cpp rename to tools/mtmd/mtmd-cli.cpp diff --git a/tools/llava/mtmd.cpp b/tools/mtmd/mtmd.cpp similarity index 100% rename from tools/llava/mtmd.cpp rename to tools/mtmd/mtmd.cpp diff --git a/tools/llava/mtmd.h b/tools/mtmd/mtmd.h similarity index 100% rename from tools/llava/mtmd.h rename to tools/mtmd/mtmd.h diff --git a/tools/llava/qwen2vl-test.cpp b/tools/mtmd/qwen2vl-test.cpp similarity index 100% rename from tools/llava/qwen2vl-test.cpp rename to tools/mtmd/qwen2vl-test.cpp diff --git a/tools/llava/requirements.txt b/tools/mtmd/requirements.txt similarity index 100% rename from tools/llava/requirements.txt rename to tools/mtmd/requirements.txt diff --git a/tools/llava/test-1.jpeg b/tools/mtmd/test-1.jpeg similarity index 100% rename from tools/llava/test-1.jpeg rename to tools/mtmd/test-1.jpeg diff --git a/tools/llava/tests.sh b/tools/mtmd/tests.sh similarity index 100% rename from tools/llava/tests.sh rename to tools/mtmd/tests.sh From b34c859146630dff136943abc9852ca173a7c9d6 Mon Sep 17 00:00:00 2001 From: igardev <49397134+igardev@users.noreply.github.com> Date: Mon, 5 May 2025 17:03:31 +0300 Subject: [PATCH 105/126] server : Webui - change setText command from parent window to also send the message. (#13309) * setText command from parent window for llama-vscode now sends the message automatically. * Upgrade packages versions to fix vulnerabilities with "npm audit fix" command. * Fix code formatting. * Add index.html.gz changes. * Revert "Upgrade packages versions to fix vulnerabilities with "npm audit fix" command." This reverts commit 67687b7fda8a293724ba92ea30bb151677406bc8. * easier approach * add setTimeout --------- Co-authored-by: igardev Co-authored-by: Xuan Son Nguyen --- tools/server/public/index.html.gz | Bin 1263378 -> 1263424 bytes .../webui/src/components/ChatScreen.tsx | 3 +++ .../webui/src/components/useChatTextarea.ts | 3 +++ tools/server/webui/src/utils/llama-vscode.ts | 3 +++ 4 files changed, 9 insertions(+) diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 6f760c39f44c0ed88f0319fd72971f94168dee7a..82d2e3b97ba383b64a88656086fc26849706326b 100644 GIT binary patch delta 845949 zcmV(@K-RyKj!nRhO@M>}gaU*Ev;?|Pf7@+GZY1ks_j-zK>>dgbfh7L_s1kP%yEW1t zc1yckBh5^8i47K#MTjV%1fWQk$U0Z&&dbXe_VSh2I?v!6+YjIiKZjqy=MlUjGw}~p zv0K`j(K(tmVxxfk&&bHgh%X{%l0TY=eV3=J86&Eah&Tq$ryfNie<;$WAQ$!Z*)1+m+rcYFDW?^m9Ga-O#}jMZUWHhG zcxb+?fXi&zgDt;W11oO+&L;!K94DAPO>#NGL3|r*@-G@BIC-L0=)q30wQH&%4P~Xk zI$rod&4UP-Gx$~llyY{F;$;dESSbb(5Ocg<3Nh>mN`08)_iR9-4uwBYf7cf=LEu@O 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z#`uhfR47F{g03pd2p^t;W<2VJQtC(Gf5nPijjfh*V$Og}Iqn~b`8>9;^?Hrv zIq{>@2e$ZDP4*17FigG1*QyLxN(xsfg*!qpRuBc@!`M=M1Lx@-oY$PhRt1KaxN=hX zLMeH0;aGvM@{Gink|GpJ$$?9?#){QiwaS;1QY(~_2N#B|b9Ju92&JTmg;K;Le+a9x z0{h`DW48FF&(pg;>D)_14=5$1UMQq~gkVCoD%Lo*Tu?H_QOO)(SdC#hsa7s5*$+hj zrCYV9j_)nAS_d$Z&!WQX7i(}AYH$~8R4>%1UaY}isKKADvE2Akb|tkzntV{EVaF`^ z_gL(;SZ#ctR>C~Iw|DO7zh=4Pe@;l#ZT9&J>q?e0c{#&jM{`Y{Ik{a$y_Iu!n2iVTF5@3o}$?%*80ZYMA6~3dEl*XXe@27(i7@+H~jV*=FiaXpo#g1 zSA(Y~;$Svoc5Sc6*{nxze>HPLFS?v6e~^Ylp53dp70uoBP`F$cc4_ihq}PBfVvx%X+t|Aox6d=62@D}5Hh*OSY(^Av>6{uu@P zhZD@aDWt-8TqS6}}6 za+_VRc&kb1ERlLovGQVnFp548l6ll zzGb6BAQZe``6&1%mOuGZVE(xJoBHx!x0lB0os2ZN&kCVVc*5~i^o3!)k}*9REmAkZ zUjSFk?WNe<*8QG6aVO8>q}G|IzMN%{cO*g { if (!viewingChat) return; setCurrNodeId(msg.id); diff --git a/tools/server/webui/src/components/useChatTextarea.ts b/tools/server/webui/src/components/useChatTextarea.ts index 42b1281946..a3223f4fd6 100644 --- a/tools/server/webui/src/components/useChatTextarea.ts +++ b/tools/server/webui/src/components/useChatTextarea.ts @@ -37,6 +37,7 @@ export interface ChatTextareaApi { setValue: (value: string) => void; focus: () => void; ref: React.RefObject; + refOnSubmit: React.MutableRefObject<(() => void) | null>; // Submit handler onInput: (event: React.FormEvent) => void; // Input handler } @@ -46,6 +47,7 @@ export interface ChatTextareaApi { export function useChatTextarea(initValue: string): ChatTextareaApi { const [savedInitValue, setSavedInitValue] = useState(initValue); const textareaRef = useRef(null); + const onSubmitRef = useRef<(() => void) | null>(null); // Effect to set initial value and height on mount or when initValue changes useEffect(() => { @@ -91,6 +93,7 @@ export function useChatTextarea(initValue: string): ChatTextareaApi { } }, ref: textareaRef, + refOnSubmit: onSubmitRef, onInput: handleInput, }; } diff --git a/tools/server/webui/src/utils/llama-vscode.ts b/tools/server/webui/src/utils/llama-vscode.ts index c45b0d3973..55ebdcffc4 100644 --- a/tools/server/webui/src/utils/llama-vscode.ts +++ b/tools/server/webui/src/utils/llama-vscode.ts @@ -33,6 +33,9 @@ export const useVSCodeContext = (textarea: ChatTextareaApi) => { }); } textarea.focus(); + setTimeout(() => { + textarea.refOnSubmit.current?.(); + }, 10); // wait for setExtraContext to finish } }; From 233461f8121455f957a47e6a22a77b3bc88277b0 Mon Sep 17 00:00:00 2001 From: oobabooga Date: Mon, 5 May 2025 17:12:19 -0300 Subject: [PATCH 106/126] =?UTF-8?q?sampling=20:=20Integrate=20Top-n=CF=83?= =?UTF-8?q?=20into=20main=20sampling=20chain=20(and=20add=20it=20to=20the?= =?UTF-8?q?=20server)=20(#13264)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * sampling: add Top-nσ sampler to `llama-server` and sampler ordering * revert: sampler ordering * revert: VS' crappy auto-formatting * revert: VS' crappy auto-formatting pt.2 * revert: my crappy eye sight... * sampling: add XTC to Top-nσ sampler chain * sampling: add Dyna. Temp. to Top-nσ sampler chain * sampling: actually remove Top-nσ from sampler(oops) * Integrate top_n_sigma into main sampler chain * Define COMMON_SAMPLER_TYPE_TOP_N_SIGMA * Formatting * Lint * Exit early in the sampler if nsigma < 0 --------- Co-authored-by: CasualAutopsy --- common/common.h | 2 + common/sampling.cpp | 90 +++++++++++++++++++++-------------------- src/llama-sampling.cpp | 4 ++ tools/server/server.cpp | 2 + 4 files changed, 54 insertions(+), 44 deletions(-) diff --git a/common/common.h b/common/common.h index 416939da9a..dfd6e20933 100644 --- a/common/common.h +++ b/common/common.h @@ -96,6 +96,7 @@ enum common_sampler_type { COMMON_SAMPLER_TYPE_XTC = 8, COMMON_SAMPLER_TYPE_INFILL = 9, COMMON_SAMPLER_TYPE_PENALTIES = 10, + COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11, }; // dimensionality reduction methods, used by cvector-generator @@ -161,6 +162,7 @@ struct common_params_sampling { std::vector samplers = { COMMON_SAMPLER_TYPE_PENALTIES, COMMON_SAMPLER_TYPE_DRY, + COMMON_SAMPLER_TYPE_TOP_N_SIGMA, COMMON_SAMPLER_TYPE_TOP_K, COMMON_SAMPLER_TYPE_TYPICAL_P, COMMON_SAMPLER_TYPE_TOP_P, diff --git a/common/sampling.cpp b/common/sampling.cpp index 1735b65018..bbaec5b80a 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -229,51 +229,48 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co params.logit_bias.data())); if (params.mirostat == 0) { - if (params.top_n_sigma >= 0) { - llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); - llama_sampler_chain_add(result->chain, llama_sampler_init_temp (params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma)); - } else { - for (const auto & cnstr : params.samplers) { - switch (cnstr) { - case COMMON_SAMPLER_TYPE_DRY: - { - std::vector c_breakers; - c_breakers.reserve(params.dry_sequence_breakers.size()); - for (const auto & str : params.dry_sequence_breakers) { - c_breakers.push_back(str.c_str()); - } - - llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); + for (const auto & cnstr : params.samplers) { + switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: + { + std::vector c_breakers; + c_breakers.reserve(params.dry_sequence_breakers.size()); + for (const auto & str : params.dry_sequence_breakers) { + c_breakers.push_back(str.c_str()); } - break; - case COMMON_SAMPLER_TYPE_TOP_K: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); - break; - case COMMON_SAMPLER_TYPE_TOP_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_MIN_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_XTC: - llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); - break; - case COMMON_SAMPLER_TYPE_TYPICAL_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_TEMPERATURE: - llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); - break; - case COMMON_SAMPLER_TYPE_INFILL: - llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab)); - break; - case COMMON_SAMPLER_TYPE_PENALTIES: - llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); - break; - default: - GGML_ASSERT(false && "unknown sampler type"); - } + + llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); + } + break; + case COMMON_SAMPLER_TYPE_TOP_K: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + break; + case COMMON_SAMPLER_TYPE_TOP_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma)); + break; + case COMMON_SAMPLER_TYPE_MIN_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_XTC: + llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + break; + case COMMON_SAMPLER_TYPE_TYPICAL_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_TEMPERATURE: + llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); + break; + case COMMON_SAMPLER_TYPE_INFILL: + llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab)); + break; + case COMMON_SAMPLER_TYPE_PENALTIES: + llama_sampler_chain_add(result->chain, llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); + break; + default: + GGML_ASSERT(false && "unknown sampler type"); } } llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); @@ -475,6 +472,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_TOP_K: return 'k'; case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y'; case COMMON_SAMPLER_TYPE_TOP_P: return 'p'; + case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return 's'; case COMMON_SAMPLER_TYPE_MIN_P: return 'm'; case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't'; case COMMON_SAMPLER_TYPE_XTC: return 'x'; @@ -490,6 +488,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_TOP_K: return "top_k"; case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p"; case COMMON_SAMPLER_TYPE_TOP_P: return "top_p"; + case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return "top_n_sigma"; case COMMON_SAMPLER_TYPE_MIN_P: return "min_p"; case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature"; case COMMON_SAMPLER_TYPE_XTC: return "xtc"; @@ -504,6 +503,7 @@ std::vector common_sampler_types_from_names(const std::vect { "dry", COMMON_SAMPLER_TYPE_DRY }, { "top_k", COMMON_SAMPLER_TYPE_TOP_K }, { "top_p", COMMON_SAMPLER_TYPE_TOP_P }, + { "top_n_sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA }, { "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P }, { "min_p", COMMON_SAMPLER_TYPE_MIN_P }, { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE }, @@ -517,6 +517,7 @@ std::vector common_sampler_types_from_names(const std::vect std::unordered_map sampler_alt_name_map { { "top-k", COMMON_SAMPLER_TYPE_TOP_K }, { "top-p", COMMON_SAMPLER_TYPE_TOP_P }, + { "top-n-sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA }, { "nucleus", COMMON_SAMPLER_TYPE_TOP_P }, { "typical-p", COMMON_SAMPLER_TYPE_TYPICAL_P }, { "typical", COMMON_SAMPLER_TYPE_TYPICAL_P }, @@ -552,6 +553,7 @@ std::vector common_sampler_types_from_chars(const std::stri { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_N_SIGMA), COMMON_SAMPLER_TYPE_TOP_N_SIGMA }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC }, diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index c0a5f9340d..0c9c6a3102 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -1750,6 +1750,10 @@ static const char * llama_sampler_top_n_sigma_name(const struct llama_sampler * static void llama_sampler_top_n_sigma_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { const auto * ctx = (llama_sampler_top_n_sigma *) smpl->ctx; + if (ctx->n < 0.0f) { + return; + } + // find max logit and calculate mean float max = cur_p->data[0].logit; float logits_sum = 0; diff --git a/tools/server/server.cpp b/tools/server/server.cpp index c580ec1232..e0e99eafcd 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -146,6 +146,7 @@ struct slot_params { {"top_k", sampling.top_k}, {"top_p", sampling.top_p}, {"min_p", sampling.min_p}, + {"top_n_sigma", sampling.top_n_sigma}, {"xtc_probability", sampling.xtc_probability}, {"xtc_threshold", sampling.xtc_threshold}, {"typical_p", sampling.typ_p}, @@ -248,6 +249,7 @@ struct server_task { params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k); params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p); params.sampling.min_p = json_value(data, "min_p", defaults.sampling.min_p); + params.sampling.top_n_sigma = json_value(data, "top_n_sigma", defaults.sampling.top_n_sigma); params.sampling.xtc_probability = json_value(data, "xtc_probability", defaults.sampling.xtc_probability); params.sampling.xtc_threshold = json_value(data, "xtc_threshold", defaults.sampling.xtc_threshold); params.sampling.typ_p = json_value(data, "typical_p", defaults.sampling.typ_p); From 907036502070ba608bdb2aaebf802092d4cfba07 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 5 May 2025 22:32:13 +0200 Subject: [PATCH 107/126] CUDA: fix logic for clearing padding with -ngl 0 (#13320) --- ggml/include/ggml-backend.h | 4 ++-- ggml/src/ggml-backend.cpp | 4 ++-- ggml/src/ggml-cuda/ggml-cuda.cu | 10 ++++++++-- ggml/src/ggml-cuda/mmq.cu | 10 ++++++++++ ggml/src/ggml-cuda/mmvq.cu | 10 ++++++++++ ggml/src/ggml-cuda/quantize.cu | 1 + 6 files changed, 33 insertions(+), 6 deletions(-) diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index 64671495b3..ea2c1a402c 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -38,7 +38,7 @@ extern "C" { GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size); GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); GGML_API size_t ggml_backend_buft_get_max_size (ggml_backend_buffer_type_t buft); - GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); + GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); GGML_API ggml_backend_dev_t ggml_backend_buft_get_device (ggml_backend_buffer_type_t buft); @@ -59,7 +59,7 @@ extern "C" { GGML_API enum ggml_status ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); GGML_API size_t ggml_backend_buffer_get_max_size (ggml_backend_buffer_t buffer); - GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor); GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 273075f4e5..c36b5abfb7 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -56,7 +56,7 @@ size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) { return SIZE_MAX; } -size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) { +size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor) { // get_alloc_size is optional, defaults to ggml_nbytes if (buft->iface.get_alloc_size) { size_t size = buft->iface.get_alloc_size(buft, tensor); @@ -152,7 +152,7 @@ size_t ggml_backend_buffer_get_max_size(ggml_backend_buffer_t buffer) { return ggml_backend_buft_get_max_size(ggml_backend_buffer_get_type(buffer)); } -size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { +size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor) { return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor); } diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 9fb2134f98..0d9ee0a26d 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -555,8 +555,8 @@ static enum ggml_status ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer if (ggml_is_quantized(tensor->type) && tensor->view_src == nullptr && ggml_backend_buffer_get_usage(buffer) != GGML_BACKEND_BUFFER_USAGE_COMPUTE) { // initialize padding to 0 to avoid possible NaN values - size_t original_size = ggml_nbytes(tensor); - size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); + const size_t original_size = ggml_nbytes(tensor); + const size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); if (padded_size > original_size) { ggml_cuda_set_device(ctx->device); @@ -679,6 +679,7 @@ static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_t if (ggml_is_quantized(tensor->type)) { if (ne0 % MATRIX_ROW_PADDING != 0) { + GGML_ASSERT(tensor->nb[0] == ggml_element_size(tensor)); size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); } } @@ -800,6 +801,7 @@ static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buff static enum ggml_status ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported + GGML_ASSERT(ggml_is_contiguous(tensor) && "split buffers only supported for contiguous tensors"); ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; @@ -851,6 +853,7 @@ static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buff // split tensors must always be set in their entirety at once GGML_ASSERT(offset == 0); GGML_ASSERT(size == ggml_nbytes(tensor)); + GGML_ASSERT(ggml_is_contiguous(tensor) && "split buffers only supported for contiguous tensors"); ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; @@ -889,6 +892,7 @@ static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buff // split tensors must always be set in their entirety at once GGML_ASSERT(offset == 0); GGML_ASSERT(size == ggml_nbytes(tensor)); + GGML_ASSERT(ggml_is_contiguous(tensor) && "split buffers only supported for contiguous tensors"); ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; @@ -970,6 +974,7 @@ static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buf static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; + GGML_ASSERT(ggml_is_contiguous(tensor) && "split buffers only supported for contiguous tensors"); size_t total_size = 0; @@ -2065,6 +2070,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * src0_slice.ne[2] = 1; src0_slice.nb[3] = src0_slice.nb[2]; src0_slice.data = (char *) src0->data + i02*nb02; + GGML_ASSERT(!ggml_cuda_should_use_mmq(src0->type, cc, ne11) || ne00 % MATRIX_ROW_PADDING == 0); ggml_tensor src1_slice; memset(&src1_slice, 0, sizeof(src1_slice)); diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index f397a7e038..4ccda88630 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -89,6 +89,16 @@ void ggml_cuda_mul_mat_q( const float * src1_d = (const float *) src1->data; float * dst_d = (float *) dst->data; + // If src0 is a temporary compute buffer, clear any potential padding. + if (ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) { + GGML_ASSERT(ggml_is_contiguous(src0)); + const size_t size_data = ggml_nbytes(src0); + const size_t size_alloc = ggml_backend_buffer_get_alloc_size(src0->buffer, src0); + if (size_alloc > size_data) { + CUDA_CHECK(cudaMemsetAsync((char *) src0->data + size_data, 0, size_alloc - size_data, stream)); + } + } + const int64_t ne10_padded = GGML_PAD(ne10, MATRIX_ROW_PADDING); const int64_t s01 = src0->nb[1] / ts_src0; diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index 132c466fd1..4bb51d27e4 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -513,6 +513,16 @@ void ggml_cuda_mul_mat_vec_q( const int32_t * ids_d = ids ? (const int32_t *) ids->data : nullptr; float * dst_d = (float *) dst->data; + // If src0 is a temporary compute buffer, clear any potential padding. + if (ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) { + GGML_ASSERT(ggml_is_contiguous(src0)); + const size_t size_data = ggml_nbytes(src0); + const size_t size_alloc = ggml_backend_buffer_get_alloc_size(src0->buffer, src0); + if (size_alloc > size_data) { + CUDA_CHECK(cudaMemsetAsync((char *) src0->data + size_data, 0, size_alloc - size_data, stream)); + } + } + const int64_t ne10_padded = GGML_PAD(ne10, MATRIX_ROW_PADDING); ggml_cuda_pool_alloc src1_q8_1(ctx.pool(), ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1); { diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu index 931a45ad34..cb93181455 100644 --- a/ggml/src/ggml-cuda/quantize.cu +++ b/ggml/src/ggml-cuda/quantize.cu @@ -163,6 +163,7 @@ void quantize_mmq_q8_1_cuda( const float * x, const int32_t * ids, void * vy, const ggml_type type_src0, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) { + GGML_ASSERT(ne00 % 4 == 0); GGML_ASSERT(ne0 % (4*QK8_1) == 0); const int64_t block_num_x = (ne0 + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ); From a7366faa5bb2fff97b9fb43340d853709f52d8c9 Mon Sep 17 00:00:00 2001 From: compilade Date: Mon, 5 May 2025 22:27:31 -0400 Subject: [PATCH 108/126] gguf-py : avoid requiring pyside6 for other scripts (#13036) - gguf-py : remove gguf-py/gguf/scripts/__init__.py because it's not needed Implicit namespaces are supported since Python 3.3 (https://peps.python.org/pep-0420/), and the entrypoints in pyproject.toml can directly refer to the main functions. --- gguf-py/gguf/scripts/__init__.py | 7 ------- gguf-py/pyproject.toml | 12 ++++++------ pyproject.toml | 1 + 3 files changed, 7 insertions(+), 13 deletions(-) delete mode 100644 gguf-py/gguf/scripts/__init__.py diff --git a/gguf-py/gguf/scripts/__init__.py b/gguf-py/gguf/scripts/__init__.py deleted file mode 100644 index 72cc73e700..0000000000 --- a/gguf-py/gguf/scripts/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# pyright: reportUnusedImport=false - -from .gguf_convert_endian import main as gguf_convert_endian_entrypoint -from .gguf_dump import main as gguf_dump_entrypoint -from .gguf_set_metadata import main as gguf_set_metadata_entrypoint -from .gguf_new_metadata import main as gguf_new_metadata_entrypoint -from .gguf_editor_gui import main as gguf_editor_gui_entrypoint diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 0c82725677..bb9b86ace7 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.16.2" +version = "0.16.3" description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ @@ -36,8 +36,8 @@ requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry.scripts] -gguf-convert-endian = "gguf.scripts:gguf_convert_endian_entrypoint" -gguf-dump = "gguf.scripts:gguf_dump_entrypoint" -gguf-set-metadata = "gguf.scripts:gguf_set_metadata_entrypoint" -gguf-new-metadata = "gguf.scripts:gguf_new_metadata_entrypoint" -gguf-editor-gui = "gguf.scripts:gguf_editor_gui_entrypoint" +gguf-convert-endian = "gguf.scripts.gguf_convert_endian:main" +gguf-dump = "gguf.scripts.gguf_dump:main" +gguf-set-metadata = "gguf.scripts.gguf_set_metadata:main" +gguf-new-metadata = "gguf.scripts.gguf_new_metadata:main" +gguf-editor-gui = "gguf.scripts.gguf_editor_gui:main" diff --git a/pyproject.toml b/pyproject.toml index ed62264ba6..3d71b055a8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,5 +40,6 @@ build-backend = "poetry.core.masonry.api" [tool.poetry.scripts] llama-convert-hf-to-gguf = "convert_hf_to_gguf:main" +llama-convert-lora-to-gguf = "convert_lora_to_gguf:main" llama-convert-llama-ggml-to-gguf = "convert_llama_ggml_to_gguf:main" llama-ggml-vk-generate-shaders = "ggml_vk_generate_shaders:main" From 15a28ec8c705b188ebe178170966d1dcc36fe151 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 6 May 2025 08:36:46 +0200 Subject: [PATCH 109/126] CUDA: fix --split-mode row for MMQ (#13323) --- ggml/src/ggml-cuda/mmq.cu | 6 ++--- ggml/src/ggml-cuda/mmq.cuh | 54 +++++++++++++++++++------------------- 2 files changed, 30 insertions(+), 30 deletions(-) diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index 4ccda88630..347d27d552 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -128,7 +128,7 @@ void ggml_cuda_mul_mat_q( const mmq_args args = { src0_d, src0->type, (const int *) src1_q8_1.ptr, nullptr, nullptr, dst_d, - ne00, ne01, ne1, s01, s1, + ne00, ne01, ne1, s01, ne11, s1, ne02, ne12, s02, s12, s2, ne03, ne13, s03, s13, s3, use_stream_k}; @@ -212,7 +212,7 @@ void ggml_cuda_mul_mat_q( // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid. const mmq_args args = { src0_d, src0->type, (const int *) src1_q8_1.ptr, ids_dst_dev, expert_bounds_dev, dst_d, - ne00, ne01, ne_get_rows, s01, s1, + ne00, ne01, ne_get_rows, s01, ne_get_rows, s1, ne02, ne02, s02, s12, s2, ne03, ne13, s03, s13, s3, use_stream_k}; @@ -251,7 +251,7 @@ void ggml_cuda_op_mul_mat_q( ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA && src1_ncols == ne11; const mmq_args args = { src0_dd_i, src0->type, (const int *) src1_ddq_i, nullptr, nullptr, dst_dd_i, - ne00, row_diff, src1_ncols, stride01, nrows_dst, + ne00, row_diff, src1_ncols, stride01, ne11, nrows_dst, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, use_stream_k}; diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index e1096dce6d..b8143a7b23 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -2522,7 +2522,7 @@ template static __device__ __forceinline__ void mul_mat_q_process_tile( const char * __restrict__ x, const int offset_x, const int * __restrict__ y, const int * __restrict__ ids_dst, float * __restrict__ dst, float * __restrict__ tmp_fixup, - const int nrows_x, const int ncols_y, const int stride_row_x, const int stride_col_dst, + const int nrows_x, const int stride_row_x, const int ncols_y, const int stride_col_dst, const int tile_x_max_i, const int tile_y_max_j, const int kb0_start, const int kb0_stop) { constexpr int qk = ggml_cuda_type_traits::qk; @@ -2606,7 +2606,7 @@ template static __global__ void mul_mat_q( const char * __restrict__ x, const int * __restrict__ y, const int32_t * __restrict__ ids_dst, const int32_t * __restrict__ expert_bounds, float * __restrict__ dst, float * __restrict__ tmp_fixup, - const int ncols_x, const int nrows_x, const int ncols_y, const int stride_row_x, const int stride_col_dst, + const int ncols_x, const int nrows_x, const int ncols_dst, const int stride_row_x, const int ncols_y, const int stride_col_dst, const int channel_ratio, const int nchannels_y, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst, const int sample_ratio, const int nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) { @@ -2619,8 +2619,8 @@ static __global__ void mul_mat_q( constexpr int qk = ggml_cuda_type_traits::qk; constexpr int mmq_y = get_mmq_y_device(); - const int ntx = (ncols_y + mmq_x - 1) / mmq_x; // Number of tiles x - const int nty = (nrows_x + mmq_y - 1) / mmq_y; // Number of tiles y + const int ntx = (ncols_dst + mmq_x - 1) / mmq_x; // Number of tiles x + const int nty = (nrows_x + mmq_y - 1) / mmq_y; // Number of tiles y // Initialize the ids for writing back data with just the index. // For regular matrix multiplications this is never changed. @@ -2648,8 +2648,8 @@ static __global__ void mul_mat_q( // Defaults for regular matrix multiplication: int col_low = 0; - int col_high = ncols_y; - int col_diff = ncols_y; + int col_high = ncols_dst; + int col_diff = ncols_dst; int offset_y = wt*stride_sample_y + zt*stride_channel_y; int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*mmq_x*stride_col_dst; @@ -2689,7 +2689,7 @@ static __global__ void mul_mat_q( constexpr bool fixup = false; mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, ncols_y, stride_row_x, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, 0, ncols_x/qk); return; } @@ -2720,8 +2720,8 @@ static __global__ void mul_mat_q( // Defaults for regular matrix multiplication: int col_low = 0; - int col_high = ncols_y; - int col_diff = ncols_y; + int col_high = ncols_dst; + int col_diff = ncols_dst; int offset_y = wt*stride_sample_y + zt*stride_channel_y; int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*mmq_x*stride_col_dst; @@ -2767,7 +2767,7 @@ static __global__ void mul_mat_q( constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer. mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, ncols_y, stride_row_x, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); kbc += blocks_per_ne00; @@ -2792,8 +2792,8 @@ static __global__ void mul_mat_q( // Defaults for regular matrix multiplication: int col_low = 0; - int col_high = ncols_y; - int col_diff = ncols_y; + int col_high = ncols_dst; + int col_diff = ncols_dst; int offset_y = wt*stride_sample_y + zt*stride_channel_y; int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*mmq_x*stride_col_dst; @@ -2834,7 +2834,7 @@ static __global__ void mul_mat_q( constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks. mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, ncols_y, stride_row_x, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); } @@ -2842,7 +2842,7 @@ static __global__ void mul_mat_q( template static __global__ void mul_mat_q_stream_k_fixup( const int32_t * ids_dst, const int32_t * expert_bounds, float * __restrict__ dst, const float * __restrict__ tmp_last_tile, - const int ncols_x, const int nrows_x, const int ncols_y, const int stride_col_dst, + const int ncols_x, const int nrows_x, const int ncols_dst, const int stride_col_dst, const int nchannels_y, const int stride_channel_dst, const int nsamples_y, const int stride_sample_dst) { constexpr int mmq_y = get_mmq_y_device(); constexpr int qk = ggml_cuda_type_traits::qk; @@ -2851,8 +2851,8 @@ static __global__ void mul_mat_q_stream_k_fixup( float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f}; - const int ntx = (ncols_y + mmq_x - 1) / mmq_x; - const int nty = (nrows_x + mmq_y - 1) / mmq_y; + const int ntx = (ncols_dst + mmq_x - 1) / mmq_x; + const int nty = (nrows_x + mmq_y - 1) / mmq_y; const int bidx0 = blockIdx.x; @@ -2925,8 +2925,8 @@ static __global__ void mul_mat_q_stream_k_fixup( const int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*mmq_x*stride_col_dst + it*mmq_y; dst += offset_dst; - const int i_max = nrows_x - it*mmq_y - 1; - const int j_max = ncols_y - jt*mmq_x - 1; + const int i_max = nrows_x - it*mmq_y - 1; + const int j_max = ncols_dst - jt*mmq_x - 1; #pragma unroll for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { @@ -2989,7 +2989,7 @@ static __global__ void mul_mat_q_stream_k_fixup( struct mmq_args { const char * x; ggml_type type_x; const int * y; const int32_t * ids_dst; const int32_t * expert_bounds; float * dst; - int64_t ncols_x; int64_t nrows_x; int64_t ncols_y; int64_t stride_row_x; int64_t nrows_dst; + int64_t ncols_x; int64_t nrows_x; int64_t ncols_dst; int64_t stride_row_x; int64_t ncols_y; int64_t nrows_dst; int64_t nchannels_x; int64_t nchannels_y; int64_t stride_channel_x; int64_t stride_channel_y; int64_t stride_channel_dst; int64_t nsamples_x; int64_t nsamples_y; int64_t stride_sample_x; int64_t stride_sample_y; int64_t stride_sample_dst; bool use_stream_k; @@ -3025,8 +3025,8 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a } #endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA) - const int nty = (args.nrows_x + mmq_y - 1) / mmq_y; - const int ntx = (args.ncols_y + mmq_x - 1) / mmq_x; + const int nty = (args.nrows_x + mmq_y - 1) / mmq_y; + const int ntx = (args.ncols_dst + mmq_x - 1) / mmq_x; const int ntzw = args.nchannels_y * args.nsamples_y; const dim3 block_nums_xy_tiling(nty, ntx, ntzw); @@ -3040,14 +3040,14 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a constexpr bool need_check = false; mul_mat_q<<>> (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, - args.ncols_x, args.nrows_x, args.ncols_y, args.stride_row_x, args.nrows_dst, + args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst, channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst); } else { constexpr bool need_check = true; mul_mat_q<<>> (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, - args.ncols_x, args.nrows_x, args.ncols_y, args.stride_row_x, args.nrows_dst, + args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst, channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst); } @@ -3068,7 +3068,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a mul_mat_q<<>> (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, - args.ncols_x, args.nrows_x, args.ncols_y, args.stride_row_x, args.nrows_dst, + args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst, channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst); @@ -3077,14 +3077,14 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a } mul_mat_q_stream_k_fixup<<>> - (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_y, + (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_dst, args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst); } else { constexpr bool need_check = true; mul_mat_q<<>> (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, - args.ncols_x, args.nrows_x, args.ncols_y, args.stride_row_x, args.nrows_dst, + args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst, channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst); @@ -3093,7 +3093,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a } mul_mat_q_stream_k_fixup<<>> - (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_y, + (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_dst, args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst); } } From 764b85627b46f43d7ea801867cd1b6abef484574 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Tue, 6 May 2025 11:12:06 +0200 Subject: [PATCH 110/126] convert : qwen2/3moe : set yarn metadata if present (#13331) * set yarn metadata if present * add comment about enabling YaRN Co-authored-by: Xuan-Son Nguyen --------- Co-authored-by: Xuan-Son Nguyen --- convert_hf_to_gguf.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index a47d7df6fd..de6d55cb08 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2761,6 +2761,13 @@ class Qwen2MoeModel(TextModel): if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None: self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size) logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}") + # YaRN is not enabled by default + # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "yarn": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) _experts: list[dict[str, Tensor]] | None = None From 2356fb1d53c86d838756211010bbabfafda7cb94 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 6 May 2025 13:58:51 +0200 Subject: [PATCH 111/126] CUDA: fix bad asserts for partial offload (#13337) --- ggml/include/ggml.h | 4 ++++ ggml/src/ggml-cuda/fattn-common.cuh | 2 ++ ggml/src/ggml-cuda/ggml-cuda.cu | 11 +++++++---- ggml/src/ggml-cuda/mmq.cu | 3 ++- ggml/src/ggml-cuda/mmvq.cu | 3 ++- ggml/src/ggml.c | 4 ++++ 6 files changed, 21 insertions(+), 6 deletions(-) diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 1b8603e78e..c518366d58 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -673,11 +673,15 @@ extern "C" { GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars + // returns whether the tensor elements can be iterated over with a flattened index (no gaps, no permutation) GGML_API bool ggml_is_contiguous (const struct ggml_tensor * tensor); GGML_API bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous() GGML_API bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1 GGML_API bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2 + // returns whether the tensor elements are allocated as one contiguous block of memory (no gaps, but permutation ok) + GGML_API bool ggml_is_contiguously_allocated(const struct ggml_tensor * tensor); + // true for tensor that is stored in memory as CxWxHxN and has been permuted to WxHxCxN GGML_API bool ggml_is_contiguous_channels(const struct ggml_tensor * tensor); diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 56121705bd..c7dc728821 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -719,6 +719,7 @@ void launch_fattn( size_t nb23 = V->nb[3]; if (need_f16_K && K->type != GGML_TYPE_F16) { + GGML_ASSERT(ggml_is_contiguously_allocated(K)); K_f16.alloc(ggml_nelements(K)); to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(K->type); to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream); @@ -733,6 +734,7 @@ void launch_fattn( } if (need_f16_V && V->type != GGML_TYPE_F16) { + GGML_ASSERT(ggml_is_contiguously_allocated(V)); V_f16.alloc(ggml_nelements(V)); to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type); to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream); diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 0d9ee0a26d..42302e4ecc 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -1536,6 +1536,8 @@ static void ggml_cuda_op_mul_mat( // If src0 is on a temporary compute buffer (partial offloading) there may be some padding that needs to be cleared: if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_is_quantized(src0->type) && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) { + GGML_ASSERT(ggml_is_contiguously_allocated(src0)); + GGML_ASSERT(!src0->view_src); const size_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00); const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data, 0, nbytes_padding, stream)); @@ -2067,10 +2069,11 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * } ggml_tensor src0_slice = *src0; - src0_slice.ne[2] = 1; - src0_slice.nb[3] = src0_slice.nb[2]; - src0_slice.data = (char *) src0->data + i02*nb02; - GGML_ASSERT(!ggml_cuda_should_use_mmq(src0->type, cc, ne11) || ne00 % MATRIX_ROW_PADDING == 0); + src0_slice.ne[2] = 1; + src0_slice.nb[3] = src0_slice.nb[2]; + src0_slice.op = GGML_OP_VIEW; + src0_slice.view_src = dst->src[0]; // non-const pointer to src0 + src0_slice.data = (char *) src0->data + i02*nb02; ggml_tensor src1_slice; memset(&src1_slice, 0, sizeof(src1_slice)); diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index 347d27d552..b4962e6a51 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -91,7 +91,8 @@ void ggml_cuda_mul_mat_q( // If src0 is a temporary compute buffer, clear any potential padding. if (ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) { - GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguously_allocated(src0)); + GGML_ASSERT(!src0->view_src); const size_t size_data = ggml_nbytes(src0); const size_t size_alloc = ggml_backend_buffer_get_alloc_size(src0->buffer, src0); if (size_alloc > size_data) { diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index 4bb51d27e4..3b313ea295 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -515,7 +515,8 @@ void ggml_cuda_mul_mat_vec_q( // If src0 is a temporary compute buffer, clear any potential padding. if (ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) { - GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguously_allocated(src0)); + GGML_ASSERT(!src0->view_src); const size_t size_data = ggml_nbytes(src0); const size_t size_alloc = ggml_backend_buffer_get_alloc_size(src0->buffer, src0); if (size_alloc > size_data) { diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 7654ae1779..ee4fe9f723 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -1299,6 +1299,10 @@ bool ggml_is_contiguous_2(const struct ggml_tensor * tensor) { return ggml_is_contiguous_n(tensor, 2); } +bool ggml_is_contiguously_allocated(const struct ggml_tensor * tensor) { + return ggml_nbytes(tensor) == ggml_nelements(tensor) * ggml_type_size(tensor->type)/ggml_blck_size(tensor->type); +} + bool ggml_is_permuted(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); From 2f54e348ad2999c4e31b8777592247622b20420f Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Tue, 6 May 2025 14:25:40 +0200 Subject: [PATCH 112/126] llama : fix build_ffn without gate (#13336) * llama : fix build_ffn without gate * fix build on windows * Revert "fix build on windows" This reverts commit fc420d3c7eef3481d3d2f313fef2757cb33a7c56. --- src/llama-graph.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 0da4e7d2b0..0b004a8ba6 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -782,7 +782,7 @@ ggml_tensor * llm_graph_context::build_ffn( } break; } - if (type_gate == LLM_FFN_PAR) { + if (gate && type_gate == LLM_FFN_PAR) { cur = ggml_mul(ctx0, cur, tmp); cb(cur, "ffn_gate_par", il); } From 1e333d5bba18e99bc328bb87ac1ee6a4e6260e0e Mon Sep 17 00:00:00 2001 From: Akarshan Biswas Date: Tue, 6 May 2025 20:27:06 +0530 Subject: [PATCH 113/126] SYCL: Disable reorder optimize by default and stop setting tensor extras when optimize is disabled (#13254) * SYCL: Do not set tensor extras when reorder optimize is disabled * SYCL: Disable reorder optimize by default --- ggml/src/ggml-sycl/ggml-sycl.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 72cdbbccaf..ea5d10f40e 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -193,7 +193,7 @@ static void ggml_check_sycl() try { if (!initialized) { g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); - g_ggml_sycl_disable_optimize= get_sycl_env("GGML_SYCL_DISABLE_OPT", 0); + g_ggml_sycl_disable_optimize= get_sycl_env("GGML_SYCL_DISABLE_OPT", 1); g_ggml_sycl_disable_graph = get_sycl_env("GGML_SYCL_DISABLE_GRAPH", 1); GGML_SYCL_DEBUG("[SYCL] call ggml_check_sycl\n"); GGML_LOG_INFO("Running with Environment Variables:\n"); @@ -338,7 +338,7 @@ ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, assert(tensor->view_src->buffer->buft == buffer->buft); return GGML_STATUS_SUCCESS; } - if (tensor->type == GGML_TYPE_Q4_0) { + if (tensor->type == GGML_TYPE_Q4_0 && !g_ggml_sycl_disable_optimize) { ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; tensor->extra = extra; ctx->tensor_extras.push_back(extra); //used to release it when destroy ctx. From f4ed10b69cc38c54070a47f841827de5e8984cdf Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Tue, 6 May 2025 20:15:31 +0200 Subject: [PATCH 114/126] cmake : remove arm64 msvc presets (#13342) --- CMakePresets.json | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/CMakePresets.json b/CMakePresets.json index 13bdd7907a..e984470130 100644 --- a/CMakePresets.json +++ b/CMakePresets.json @@ -38,15 +38,6 @@ } }, - { - "name": "arm64-windows-msvc", "hidden": true, - "architecture": { "value": "arm64", "strategy": "external" }, - "toolset": { "value": "host=x64", "strategy": "external" }, - "cacheVariables": { - "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-msvc.cmake" - } - }, - { "name": "arm64-windows-llvm", "hidden": true, "architecture": { "value": "arm64", "strategy": "external" }, @@ -73,10 +64,6 @@ { "name": "arm64-apple-clang-release", "inherits": [ "base", "arm64-apple-clang", "reldbg" ] }, { "name": "arm64-apple-clang+static-release", "inherits": [ "base", "arm64-apple-clang", "reldbg", "static" ] }, - { "name": "arm64-windows-msvc-debug", "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, - { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg" ] }, - { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg", "static" ] }, - { "name": "x64-windows-llvm-debug", "inherits": [ "base", "x64-windows-llvm", "debug" ] }, { "name": "x64-windows-llvm-release", "inherits": [ "base", "x64-windows-llvm", "release" ] }, { "name": "x64-windows-llvm-reldbg", "inherits": [ "base", "x64-windows-llvm", "reldbg" ] }, From 91a86a6f354aa73a7aab7bc3d283be410fdc93a5 Mon Sep 17 00:00:00 2001 From: oobabooga Date: Tue, 6 May 2025 15:24:15 -0300 Subject: [PATCH 115/126] sampling : don't consider -infinity values in top_n_sigma (#13344) --- src/llama-sampling.cpp | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index 0c9c6a3102..2869f60d20 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -1757,20 +1757,28 @@ static void llama_sampler_top_n_sigma_apply(struct llama_sampler * smpl, llama_t // find max logit and calculate mean float max = cur_p->data[0].logit; float logits_sum = 0; + size_t valid_count = 0; for (size_t i = 0; i < cur_p->size; ++i) { - if (cur_p->data[i].logit > max) { - max = cur_p->data[i].logit; + // Only count non-negative infinity values + if (cur_p->data[i].logit != -INFINITY) { + if (cur_p->data[i].logit > max) { + max = cur_p->data[i].logit; + } + logits_sum += cur_p->data[i].logit; + valid_count++; } - logits_sum += cur_p->data[i].logit; } - float mean = logits_sum/cur_p->size; + float mean = valid_count > 0 ? logits_sum/valid_count : 0; // calculate standard deviation float acc = 0; for (size_t i = 0; i < cur_p->size; ++i) { - acc += pow(cur_p->data[i].logit - mean, 2); + // Skip -infinity in std calculation + if (cur_p->data[i].logit != -INFINITY) { + acc += pow(cur_p->data[i].logit - mean, 2); + } } - float std = sqrt(acc/cur_p->size); + float std = valid_count > 0 ? sqrt(acc/valid_count) : 0; //apply mask for (size_t i = 0; i < cur_p->size; ++i) { From ffc727203af1061fdeb49efef30f76171722e403 Mon Sep 17 00:00:00 2001 From: DocShotgun <126566557+DocShotgun@users.noreply.github.com> Date: Tue, 6 May 2025 13:36:24 -0700 Subject: [PATCH 116/126] sampling : make top_n_sigma no-op at <=0 or a single candidate (#13345) --- src/llama-sampling.cpp | 2 +- tests/test-sampling.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index 2869f60d20..804b11e0a9 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -1750,7 +1750,7 @@ static const char * llama_sampler_top_n_sigma_name(const struct llama_sampler * static void llama_sampler_top_n_sigma_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { const auto * ctx = (llama_sampler_top_n_sigma *) smpl->ctx; - if (ctx->n < 0.0f) { + if (ctx->n <= 0.0f || cur_p->size <= 1) { return; } diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index f1f87d454d..60ac62b385 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -360,7 +360,7 @@ int main(void) { test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {}); test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f, 0.0f, 0.0f}, 1.00f); - test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0.00f); + test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0.00f); // top_n_sigma == 0 now represents a no-op rather than greedy decoding as of PR#13345 test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 3.00f); test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f); From 32916a49072f01c43e20df374af5f8a1f70d6963 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Tue, 6 May 2025 22:40:24 +0200 Subject: [PATCH 117/126] clip : refactor graph builder (#13321) * mtmd : refactor graph builder * fix qwen2vl * clean up siglip cgraph * pixtral migrated * move minicpmv to a dedicated build function * move max_feature_layer to build_llava * use build_attn for minicpm resampler * fix windows build * add comment for batch_size * also support tinygemma3 test model * qwen2vl does not use RMS norm * fix qwen2vl norm (2) --- convert_hf_to_gguf.py | 13 + tools/mtmd/clip.cpp | 2650 +++++++++++++++++++++-------------------- 2 files changed, 1354 insertions(+), 1309 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index de6d55cb08..a6aaf88346 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -3915,6 +3915,16 @@ class Gemma3VisionModel(VisionModel): # default values below are taken from HF tranformers code self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6)) self.gguf_writer.add_vision_use_gelu(True) + # calculate proj_scale_factor (used by tinygemma3 test model) + image_seq_length = self.preprocessor_config.get("image_seq_length", 256) + n_per_side = int(image_seq_length ** 0.5) + image_size = self.hparams["image_size"] + patch_size = self.hparams["patch_size"] + proj_scale_factor = (image_size // patch_size) // n_per_side + if proj_scale_factor > 0 and proj_scale_factor != 4: + # we only need to write this if it's not the default value + # in this case, we are converting a test model + self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor) def tensor_force_quant(self, name, new_name, bid, n_dims): del bid, new_name, n_dims # unused @@ -3928,6 +3938,9 @@ class Gemma3VisionModel(VisionModel): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused + if "vision_model.head." in name: + return [] # skip redundant tensors for tinygemma3 + if name.startswith("multi_modal_projector.") or name.startswith("vision_tower.") \ or name.startswith("multimodal_projector.") or name.startswith("vision_model."): # process vision tensors diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 1414f6ba4f..4432fb7193 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -29,9 +29,21 @@ #include #include #include +#include struct clip_logger_state g_logger_state = {GGML_LOG_LEVEL_CONT, clip_log_callback_default, NULL}; +enum ffn_op_type { + FFN_GELU, + FFN_SILU, + FFN_GELU_QUICK, +}; + +enum norm_type { + NORM_TYPE_NORMAL, + NORM_TYPE_RMS, +}; + //#define CLIP_DEBUG_FUNCTIONS #ifdef CLIP_DEBUG_FUNCTIONS @@ -162,6 +174,8 @@ struct clip_hparams { int32_t n_layer; int32_t proj_scale_factor = 0; // idefics3 + ffn_op_type ffn_op = FFN_GELU; + patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT; float eps = 1e-6; @@ -177,136 +191,136 @@ struct clip_hparams { struct clip_layer { // attention - struct ggml_tensor * k_w = nullptr; - struct ggml_tensor * k_b = nullptr; - struct ggml_tensor * q_w = nullptr; - struct ggml_tensor * q_b = nullptr; - struct ggml_tensor * v_w = nullptr; - struct ggml_tensor * v_b = nullptr; + ggml_tensor * k_w = nullptr; + ggml_tensor * k_b = nullptr; + ggml_tensor * q_w = nullptr; + ggml_tensor * q_b = nullptr; + ggml_tensor * v_w = nullptr; + ggml_tensor * v_b = nullptr; - struct ggml_tensor * o_w = nullptr; - struct ggml_tensor * o_b = nullptr; + ggml_tensor * o_w = nullptr; + ggml_tensor * o_b = nullptr; // layernorm 1 - struct ggml_tensor * ln_1_w = nullptr; - struct ggml_tensor * ln_1_b = nullptr; + ggml_tensor * ln_1_w = nullptr; + ggml_tensor * ln_1_b = nullptr; - struct ggml_tensor * ff_up_w = nullptr; - struct ggml_tensor * ff_up_b = nullptr; - struct ggml_tensor * ff_gate_w = nullptr; - struct ggml_tensor * ff_gate_b = nullptr; - struct ggml_tensor * ff_down_w = nullptr; - struct ggml_tensor * ff_down_b = nullptr; + ggml_tensor * ff_up_w = nullptr; + ggml_tensor * ff_up_b = nullptr; + ggml_tensor * ff_gate_w = nullptr; + ggml_tensor * ff_gate_b = nullptr; + ggml_tensor * ff_down_w = nullptr; + ggml_tensor * ff_down_b = nullptr; // layernorm 2 - struct ggml_tensor * ln_2_w = nullptr; - struct ggml_tensor * ln_2_b = nullptr; + ggml_tensor * ln_2_w = nullptr; + ggml_tensor * ln_2_b = nullptr; }; struct clip_vision_model { struct clip_hparams hparams; // embeddings - struct ggml_tensor * class_embedding = nullptr; - struct ggml_tensor * patch_embeddings_0 = nullptr; - struct ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) - struct ggml_tensor * patch_bias = nullptr; - struct ggml_tensor * position_embeddings = nullptr; + ggml_tensor * class_embedding = nullptr; + ggml_tensor * patch_embeddings_0 = nullptr; + ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) + ggml_tensor * patch_bias = nullptr; + ggml_tensor * position_embeddings = nullptr; - struct ggml_tensor * pre_ln_w = nullptr; - struct ggml_tensor * pre_ln_b = nullptr; + ggml_tensor * pre_ln_w = nullptr; + ggml_tensor * pre_ln_b = nullptr; std::vector layers; - struct ggml_tensor * post_ln_w; - struct ggml_tensor * post_ln_b; + ggml_tensor * post_ln_w; + ggml_tensor * post_ln_b; - struct ggml_tensor * projection; + ggml_tensor * projection; // LLaVA projection - struct ggml_tensor * mm_input_norm_w = nullptr; - struct ggml_tensor * mm_0_w = nullptr; - struct ggml_tensor * mm_0_b = nullptr; - struct ggml_tensor * mm_2_w = nullptr; - struct ggml_tensor * mm_2_b = nullptr; + ggml_tensor * mm_input_norm_w = nullptr; + ggml_tensor * mm_0_w = nullptr; + ggml_tensor * mm_0_b = nullptr; + ggml_tensor * mm_2_w = nullptr; + ggml_tensor * mm_2_b = nullptr; - struct ggml_tensor * image_newline = nullptr; + ggml_tensor * image_newline = nullptr; // Yi type models with mlp+normalization projection - struct ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4 - struct ggml_tensor * mm_1_b = nullptr; - struct ggml_tensor * mm_3_w = nullptr; - struct ggml_tensor * mm_3_b = nullptr; - struct ggml_tensor * mm_4_w = nullptr; - struct ggml_tensor * mm_4_b = nullptr; + ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4 + ggml_tensor * mm_1_b = nullptr; + ggml_tensor * mm_3_w = nullptr; + ggml_tensor * mm_3_b = nullptr; + ggml_tensor * mm_4_w = nullptr; + ggml_tensor * mm_4_b = nullptr; // GLMV-Edge projection - struct ggml_tensor * mm_model_adapter_conv_w = nullptr; - struct ggml_tensor * mm_model_adapter_conv_b = nullptr; - struct ggml_tensor * mm_glm_tok_boi = nullptr; - struct ggml_tensor * mm_glm_tok_eoi = nullptr; + ggml_tensor * mm_model_adapter_conv_w = nullptr; + ggml_tensor * mm_model_adapter_conv_b = nullptr; + ggml_tensor * mm_glm_tok_boi = nullptr; + ggml_tensor * mm_glm_tok_eoi = nullptr; // MobileVLM projection - struct ggml_tensor * mm_model_mlp_1_w = nullptr; - struct ggml_tensor * mm_model_mlp_1_b = nullptr; - struct ggml_tensor * mm_model_mlp_3_w = nullptr; - struct ggml_tensor * mm_model_mlp_3_b = nullptr; - struct ggml_tensor * mm_model_block_1_block_0_0_w = nullptr; - struct ggml_tensor * mm_model_block_1_block_0_1_w = nullptr; - struct ggml_tensor * mm_model_block_1_block_0_1_b = nullptr; - struct ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr; - struct ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr; - struct ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr; - struct ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr; - struct ggml_tensor * mm_model_block_1_block_2_0_w = nullptr; - struct ggml_tensor * mm_model_block_1_block_2_1_w = nullptr; - struct ggml_tensor * mm_model_block_1_block_2_1_b = nullptr; - struct ggml_tensor * mm_model_block_2_block_0_0_w = nullptr; - struct ggml_tensor * mm_model_block_2_block_0_1_w = nullptr; - struct ggml_tensor * mm_model_block_2_block_0_1_b = nullptr; - struct ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr; - struct ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr; - struct ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr; - struct ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr; - struct ggml_tensor * mm_model_block_2_block_2_0_w = nullptr; - struct ggml_tensor * mm_model_block_2_block_2_1_w = nullptr; - struct ggml_tensor * mm_model_block_2_block_2_1_b = nullptr; + ggml_tensor * mm_model_mlp_1_w = nullptr; + ggml_tensor * mm_model_mlp_1_b = nullptr; + ggml_tensor * mm_model_mlp_3_w = nullptr; + ggml_tensor * mm_model_mlp_3_b = nullptr; + ggml_tensor * mm_model_block_1_block_0_0_w = nullptr; + ggml_tensor * mm_model_block_1_block_0_1_w = nullptr; + ggml_tensor * mm_model_block_1_block_0_1_b = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr; + ggml_tensor * mm_model_block_1_block_2_0_w = nullptr; + ggml_tensor * mm_model_block_1_block_2_1_w = nullptr; + ggml_tensor * mm_model_block_1_block_2_1_b = nullptr; + ggml_tensor * mm_model_block_2_block_0_0_w = nullptr; + ggml_tensor * mm_model_block_2_block_0_1_w = nullptr; + ggml_tensor * mm_model_block_2_block_0_1_b = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr; + ggml_tensor * mm_model_block_2_block_2_0_w = nullptr; + ggml_tensor * mm_model_block_2_block_2_1_w = nullptr; + ggml_tensor * mm_model_block_2_block_2_1_b = nullptr; // MobileVLM_V2 projection - struct ggml_tensor * mm_model_mlp_0_w = nullptr; - struct ggml_tensor * mm_model_mlp_0_b = nullptr; - struct ggml_tensor * mm_model_mlp_2_w = nullptr; - struct ggml_tensor * mm_model_mlp_2_b = nullptr; - struct ggml_tensor * mm_model_peg_0_w = nullptr; - struct ggml_tensor * mm_model_peg_0_b = nullptr; + ggml_tensor * mm_model_mlp_0_w = nullptr; + ggml_tensor * mm_model_mlp_0_b = nullptr; + ggml_tensor * mm_model_mlp_2_w = nullptr; + ggml_tensor * mm_model_mlp_2_b = nullptr; + ggml_tensor * mm_model_peg_0_w = nullptr; + ggml_tensor * mm_model_peg_0_b = nullptr; // MINICPMV projection - struct ggml_tensor * mm_model_pos_embed_k = nullptr; - struct ggml_tensor * mm_model_query = nullptr; - struct ggml_tensor * mm_model_proj = nullptr; - struct ggml_tensor * mm_model_kv_proj = nullptr; - struct ggml_tensor * mm_model_attn_q_w = nullptr; - struct ggml_tensor * mm_model_attn_q_b = nullptr; - struct ggml_tensor * mm_model_attn_k_w = nullptr; - struct ggml_tensor * mm_model_attn_k_b = nullptr; - struct ggml_tensor * mm_model_attn_v_w = nullptr; - struct ggml_tensor * mm_model_attn_v_b = nullptr; - struct ggml_tensor * mm_model_attn_o_w = nullptr; - struct ggml_tensor * mm_model_attn_o_b = nullptr; - struct ggml_tensor * mm_model_ln_q_w = nullptr; - struct ggml_tensor * mm_model_ln_q_b = nullptr; - struct ggml_tensor * mm_model_ln_kv_w = nullptr; - struct ggml_tensor * mm_model_ln_kv_b = nullptr; - struct ggml_tensor * mm_model_ln_post_w = nullptr; - struct ggml_tensor * mm_model_ln_post_b = nullptr; + ggml_tensor * mm_model_pos_embed_k = nullptr; + ggml_tensor * mm_model_query = nullptr; + ggml_tensor * mm_model_proj = nullptr; + ggml_tensor * mm_model_kv_proj = nullptr; + ggml_tensor * mm_model_attn_q_w = nullptr; + ggml_tensor * mm_model_attn_q_b = nullptr; + ggml_tensor * mm_model_attn_k_w = nullptr; + ggml_tensor * mm_model_attn_k_b = nullptr; + ggml_tensor * mm_model_attn_v_w = nullptr; + ggml_tensor * mm_model_attn_v_b = nullptr; + ggml_tensor * mm_model_attn_o_w = nullptr; + ggml_tensor * mm_model_attn_o_b = nullptr; + ggml_tensor * mm_model_ln_q_w = nullptr; + ggml_tensor * mm_model_ln_q_b = nullptr; + ggml_tensor * mm_model_ln_kv_w = nullptr; + ggml_tensor * mm_model_ln_kv_b = nullptr; + ggml_tensor * mm_model_ln_post_w = nullptr; + ggml_tensor * mm_model_ln_post_b = nullptr; // gemma3 - struct ggml_tensor * mm_input_proj_w = nullptr; - struct ggml_tensor * mm_soft_emb_norm_w = nullptr; + ggml_tensor * mm_input_proj_w = nullptr; + ggml_tensor * mm_soft_emb_norm_w = nullptr; // pixtral - struct ggml_tensor * token_embd_img_break = nullptr; - struct ggml_tensor * mm_patch_merger_w = nullptr; + ggml_tensor * token_embd_img_break = nullptr; + ggml_tensor * mm_patch_merger_w = nullptr; }; struct clip_ctx { @@ -316,11 +330,8 @@ struct clip_ctx { struct clip_vision_model vision_model; projector_type proj_type = PROJECTOR_TYPE_MLP; - int32_t max_feature_layer; // unused in newer models like gemma3 float image_mean[3]; float image_std[3]; - bool use_gelu = false; - bool use_silu = false; gguf_context_ptr ctx_gguf; ggml_context_ptr ctx_data; @@ -370,1199 +381,363 @@ struct clip_ctx { } }; -static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_image_f32 & img) { - const auto & model = ctx->vision_model; - const auto & hparams = model.hparams; +struct clip_graph { + clip_ctx * ctx; + const clip_vision_model & model; + const clip_hparams & hparams; - int image_size_width = img.nx; - int image_size_height = img.ny; + // we only support single image per batch + const clip_image_f32 & img; - const int patch_size = hparams.patch_size; - const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); - const int n_embd = hparams.n_embd; - const int n_head = hparams.n_head; - const int d_head = n_embd / n_head; - const int n_layer = hparams.n_layer; - const float eps = hparams.eps; + const int patch_size; + const int n_patches_x; + const int n_patches_y; + const int n_patches; + const int n_embd; + const int n_head; + const int d_head; + const int n_layer; + const float eps; + const float kq_scale; - struct ggml_init_params params = { - /*.mem_size =*/ ctx->buf_compute_meta.size(), - /*.mem_buffer =*/ ctx->buf_compute_meta.data(), - /*.no_alloc =*/ true, - }; + ggml_context_ptr ctx0_ptr; + ggml_context * ctx0; + ggml_cgraph * gf; - ggml_context_ptr ctx0_ptr(ggml_init(params)); - auto ctx0 = ctx0_ptr.get(); + clip_graph(clip_ctx * ctx, const clip_image_f32 & img) : + ctx(ctx), + model(ctx->vision_model), + hparams(model.hparams), + img(img), + patch_size(hparams.patch_size), + n_patches_x(img.nx / patch_size), + n_patches_y(img.ny / patch_size), + n_patches(n_patches_x * n_patches_y), + n_embd(hparams.n_embd), + n_head(hparams.n_head), + d_head(n_embd / n_head), + n_layer(hparams.n_layer), + eps(hparams.eps), + kq_scale(1.0f / sqrtf((float)d_head)) { + struct ggml_init_params params = { + /*.mem_size =*/ ctx->buf_compute_meta.size(), + /*.mem_buffer =*/ ctx->buf_compute_meta.data(), + /*.no_alloc =*/ true, + }; + ctx0_ptr.reset(ggml_init(params)); + ctx0 = ctx0_ptr.get(); + gf = ggml_new_graph(ctx0); + } - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + ggml_cgraph * build_siglip() { + ggml_tensor * inp = build_inp(); + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_NORMAL, + hparams.ffn_op, + model.position_embeddings, + nullptr); - // input raw - struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3); - ggml_set_name(inp_raw, "inp_raw"); - ggml_set_input(inp_raw); + if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) { + const int batch_size = 1; + GGML_ASSERT(n_patches_x == n_patches_y); + const int patches_per_image = n_patches_x; + const int kernel_size = hparams.proj_scale_factor; - struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_reshape_2d(ctx0, inp, num_patches, n_embd); - inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); - inp = ggml_add(ctx0, inp, model.patch_bias); + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + cur = ggml_reshape_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size); - // position embeddings - struct ggml_tensor * embeddings = ggml_add(ctx0, inp, model.position_embeddings); + // doing a pool2d to reduce the number of output tokens + cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0); + cur = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[0], n_embd, batch_size); + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); - // loop over layers - for (int il = 0; il < n_layer; il++) { - struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states + // apply norm before projection + cur = ggml_rms_norm(ctx0, cur, eps); + cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w); - // layernorm1 - { - cur = ggml_norm(ctx0, cur, eps); - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w), model.layers[il].ln_1_b); + // apply projection + cur = ggml_mul_mat(ctx0, + ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)), + cur); + + } else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) { + // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578 + + const int scale_factor = model.hparams.proj_scale_factor; + const int n_embd = cur->ne[0]; + const int seq = cur->ne[1]; + const int bsz = 1; // batch size, always 1 for now since we don't support batching + const int height = std::sqrt(seq); + const int width = std::sqrt(seq); + GGML_ASSERT(scale_factor != 0); + cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height, bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + cur = ggml_reshape_4d(ctx0, ggml_cont(ctx0, cur), + n_embd * scale_factor * scale_factor, + height / scale_factor, + width / scale_factor, + bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + cur = ggml_reshape_3d(ctx0, ggml_cont(ctx0, cur), + n_embd * scale_factor * scale_factor, + seq / (scale_factor * scale_factor), + bsz); + + cur = ggml_mul_mat(ctx0, model.projection, cur); + } else { + GGML_ABORT("SigLIP: Unsupported projector type"); } - // self-attention - { + // build the graph + ggml_build_forward_expand(gf, cur); - struct ggml_tensor * Q = - ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b); + return gf; + } - Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_patches); - Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); + ggml_cgraph * build_pixtral() { + const int n_merge = hparams.spatial_merge_size; - struct ggml_tensor * K = - ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b); + // 2D input positions + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); - K = ggml_reshape_3d(ctx0, K, d_head, n_head, num_patches); - K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); - struct ggml_tensor * V = - ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b); + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + return build_rope_2d(ctx0, cur, pos_h, pos_w, hparams.rope_theta); + }; - V = ggml_reshape_3d(ctx0, V, d_head, n_head, num_patches); - V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); + ggml_tensor * inp = build_inp(); + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_RMS, + hparams.ffn_op, + nullptr, // no learned pos embd + add_pos); - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); + // mistral small 3.1 patch merger + // ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67 + if (model.mm_patch_merger_w) { + GGML_ASSERT(hparams.spatial_merge_size > 0); - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); - KQV = ggml_reshape_3d(ctx0, KQV, d_head, num_patches, n_head); - KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); + cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w); - cur = ggml_cont_2d(ctx0, KQV, n_embd, num_patches); + // reshape image tokens to 2D grid + cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y); + cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd] + cur = ggml_cont(ctx0, cur); + + // torch.nn.functional.unfold is just an im2col under the hood + // we just need a dummy kernel to make it work + ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0); + cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type); + + // project to n_embd + cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); + cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur); } - // attention output - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b); - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, embeddings); - - embeddings = cur; // embeddings = residual, cur = hidden_states - - // layernorm2 + // LlavaMultiModalProjector (always using GELU activation) { - cur = ggml_norm(ctx0, cur, eps); - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b); - } - - cur = ggml_mul_mat(ctx0, model.layers[il].ff_up_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_up_b); - - // siglip uses gelu - cur = ggml_gelu(ctx0, cur); - - cur = ggml_mul_mat(ctx0, model.layers[il].ff_down_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_down_b); - - // residual 2 - cur = ggml_add(ctx0, embeddings, cur); - - embeddings = cur; - } - - // post-layernorm - if (model.post_ln_w) { - embeddings = ggml_norm(ctx0, embeddings, eps); - ggml_set_name(embeddings, "post_ln"); - - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b); - } - - if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) { - const int batch_size = 1; - const int mm_tokens_per_image = 256; // default value for gemma3 - const int tokens_per_side = sqrt(mm_tokens_per_image); - const int patches_per_image = sqrt(num_patches); - const int kernel_size = patches_per_image / tokens_per_side; - - embeddings = ggml_cont(ctx0, ggml_transpose(ctx0, embeddings)); - embeddings = ggml_reshape_4d(ctx0, embeddings, patches_per_image, patches_per_image, n_embd, batch_size); - - // doing a pool2d to reduce the number of output tokens to 256 - embeddings = ggml_pool_2d(ctx0, embeddings, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0); - embeddings = ggml_reshape_3d(ctx0, embeddings, embeddings->ne[0] * embeddings->ne[0], n_embd, batch_size); - embeddings = ggml_cont(ctx0, ggml_transpose(ctx0, embeddings)); - - // apply norm before projection - embeddings = ggml_rms_norm(ctx0, embeddings, eps); - embeddings = ggml_mul(ctx0, embeddings, model.mm_soft_emb_norm_w); - - // apply projection - embeddings = ggml_mul_mat(ctx0, - ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)), - embeddings); - - } else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) { - // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578 - - ggml_tensor * cur = embeddings; - const int scale_factor = model.hparams.proj_scale_factor; - const int n_embd = cur->ne[0]; - const int seq = cur->ne[1]; - const int bsz = 1; // batch size, always 1 for now since we don't support batching - const int height = std::sqrt(seq); - const int width = std::sqrt(seq); - GGML_ASSERT(scale_factor != 0); - cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height, bsz); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - cur = ggml_reshape_4d(ctx0, ggml_cont(ctx0, cur), - n_embd * scale_factor * scale_factor, - height / scale_factor, - width / scale_factor, - bsz); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - cur = ggml_reshape_3d(ctx0, ggml_cont(ctx0, cur), - n_embd * scale_factor * scale_factor, - seq / (scale_factor * scale_factor), - bsz); - - cur = ggml_mul_mat(ctx0, model.projection, cur); - embeddings = cur; - } else { - GGML_ABORT("SigLIP: Unsupported projector type"); - } - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; -} - -// implementation of the 2D RoPE without adding a new op in ggml -// this is not efficient (use double the memory), but works on all backends -// TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 -static ggml_tensor * build_rope_2d( - ggml_context * ctx0, - ggml_tensor * cur, - ggml_tensor * pos_h, - ggml_tensor * pos_w, - const float freq_base -) { - const int64_t n_dim = cur->ne[0]; - const int64_t n_head = cur->ne[1]; - const int64_t n_pos = cur->ne[2]; - - // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos) - // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3 - // first half of cur will use 1e-0, 1e-2 (even) - // second half of cur will use 1e-1, 1e-3 (odd) - // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even - // ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2) - // then for the second half, we use freq_scale to shift the inv_freq - // ^ why? replace (2i) with (2i+1) in the above equation - const float freq_scale_odd = std::pow(freq_base, (float)-2/n_dim); - - // first half - ggml_tensor * first; - { - first = ggml_view_3d(ctx0, cur, - n_dim/2, n_head, n_pos, - ggml_row_size(cur->type, n_dim), - ggml_row_size(cur->type, n_dim*n_head), - 0); - first = ggml_rope_ext( - ctx0, - first, - pos_h, // positions - nullptr, // freq factors - n_dim/2, // n_dims - 0, 0, freq_base, - 1.0f, 0.0f, 1.0f, 0.0f, 0.0f - ); - } - - // second half - ggml_tensor * second; - { - second = ggml_view_3d(ctx0, cur, - n_dim/2, n_head, n_pos, - ggml_row_size(cur->type, n_dim), - ggml_row_size(cur->type, n_dim*n_head), - n_dim/2 * ggml_element_size(cur)); - second = ggml_cont(ctx0, second); // copy, because ggml_rope don't play well with non-contiguous tensors - second = ggml_rope_ext( - ctx0, - second, - pos_w, // positions - nullptr, // freq factors - n_dim/2, // n_dims - 0, 0, freq_base, - freq_scale_odd, - 0.0f, 1.0f, 0.0f, 0.0f - ); - } - - cur = ggml_concat(ctx0, first, second, 0); - return cur; -} - -static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_image_f32 & img) { - const auto & model = ctx->vision_model; - const auto & hparams = model.hparams; - - GGML_ASSERT(ctx->proj_type == PROJECTOR_TYPE_PIXTRAL); - - int image_size_width = img.nx; - int image_size_height = img.ny; - - const int patch_size = hparams.patch_size; - const int n_patches_x = image_size_width / patch_size; - const int n_patches_y = image_size_height / patch_size; - const int num_patches = n_patches_x * n_patches_y; - const int n_embd = hparams.n_embd; - const int n_head = hparams.n_head; - const int d_head = n_embd / n_head; - const int n_layer = hparams.n_layer; - const float eps = hparams.eps; - const int n_merge = hparams.spatial_merge_size; - - struct ggml_init_params params = { - /*.mem_size =*/ ctx->buf_compute_meta.size(), - /*.mem_buffer =*/ ctx->buf_compute_meta.data(), - /*.no_alloc =*/ true, - }; - - ggml_context_ptr ctx0_ptr(ggml_init(params)); - auto ctx0 = ctx0_ptr.get(); - - struct ggml_cgraph * gf = ggml_new_graph(ctx0); - - // input raw - struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3); - ggml_set_name(inp_raw, "inp_raw"); - ggml_set_input(inp_raw); - - // 2D input positions - struct ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches); - ggml_set_name(pos_h, "pos_h"); - ggml_set_input(pos_h); - struct ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches); - ggml_set_name(pos_w, "pos_w"); - ggml_set_input(pos_w); - - struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_reshape_2d(ctx0, inp, num_patches, n_embd); - inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); - - struct ggml_tensor * embeddings = inp; - - // pre-layer norm - embeddings = ggml_mul(ctx0, ggml_rms_norm(ctx0, embeddings, eps), model.pre_ln_w); - - // loop over layers - for (int il = 0; il < n_layer; il++) { - struct ggml_tensor * cur = embeddings; - - // pre-attention norm - cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.layers[il].ln_1_w); - - // self-attention - { - struct ggml_tensor * Q = ggml_mul_mat(ctx0, model.layers[il].q_w, cur); - - Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_patches); - Q = build_rope_2d(ctx0, Q, pos_h, pos_w, hparams.rope_theta); - Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); - - struct ggml_tensor * K = ggml_mul_mat(ctx0, model.layers[il].k_w, cur); - - K = ggml_reshape_3d(ctx0, K, d_head, n_head, num_patches); - K = build_rope_2d(ctx0, K, pos_h, pos_w, hparams.rope_theta); - K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); - - struct ggml_tensor * V = ggml_mul_mat(ctx0, model.layers[il].v_w, cur); - - V = ggml_reshape_3d(ctx0, V, d_head, n_head, num_patches); - V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); - - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); - - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); - KQV = ggml_reshape_3d(ctx0, KQV, d_head, num_patches, n_head); - KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - - cur = ggml_cont_2d(ctx0, KQV, n_embd, num_patches); - - cur = ggml_mul_mat(ctx0, model.layers[il].o_w, cur); - } - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, embeddings); - - embeddings = cur; // embeddings = residual, cur = hidden_states - - // pre-ffn norm - cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.layers[il].ln_2_w); - - // feed-forward - { - ggml_tensor * gate_proj = ggml_mul_mat(ctx0, model.layers[il].ff_gate_w, cur); - ggml_tensor * up_proj = ggml_mul_mat(ctx0, model.layers[il].ff_up_w, cur); - if (ctx->use_silu) { - gate_proj = ggml_silu(ctx0, gate_proj); - } else if (ctx->use_gelu) { - gate_proj = ggml_gelu(ctx0, gate_proj); - } else { - GGML_ABORT("Pixtral: Unsupported activation"); + cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); + if (model.mm_1_b) { + cur = ggml_add(ctx0, cur, model.mm_1_b); + } + + cur = ggml_gelu(ctx0, cur); + cur = ggml_mul_mat(ctx0, model.mm_2_w, cur); + if (model.mm_2_b) { + cur = ggml_add(ctx0, cur, model.mm_2_b); } - cur = ggml_mul(ctx0, up_proj, gate_proj); - cur = ggml_mul_mat(ctx0, model.layers[il].ff_down_w, cur); } - // residual 2 - cur = ggml_add(ctx0, embeddings, cur); - - embeddings = cur; - } - - // mistral small 3.1 patch merger - // ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67 - if (model.mm_patch_merger_w) { - GGML_ASSERT(hparams.spatial_merge_size > 0); - - ggml_tensor * cur = embeddings; - cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w); - - // reshape image tokens to 2D grid - cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y); - cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd] - cur = ggml_cont(ctx0, cur); - - // torch.nn.functional.unfold is just an im2col under the hood - // we just need a dummy kernel to make it work - ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0); - cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type); - - // project to n_embd - cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); - cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur); - embeddings = cur; - } - - // LlavaMultiModalProjector (always using GELU activation) - { - embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings); - if (model.mm_1_b) { - embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); - } - - embeddings = ggml_gelu(ctx0, embeddings); - embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); - if (model.mm_2_b) { - embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); - } - } - - // arrangement of the [IMG_BREAK] token - { - // not efficient, but works - // the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows] - // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension - // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows] - - const int p_y = n_merge > 0 ? n_patches_y / n_merge : n_patches_y; - const int p_x = n_merge > 0 ? n_patches_x / n_merge : n_patches_x; - const int p_total = p_x * p_y; - const int n_embd_text = embeddings->ne[0]; - const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row - - ggml_tensor * cur = ggml_reshape_3d(ctx0, embeddings, n_embd_text, p_x, p_y); - ggml_tensor * tok = ggml_new_tensor_3d(ctx0, embeddings->type, n_embd_text, 1, p_y); - tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor - tok = ggml_add(ctx0, tok, model.token_embd_img_break); - cur = ggml_concat(ctx0, cur, tok, 1); - embeddings = ggml_view_2d(ctx0, cur, - n_embd_text, n_tokens_output, - ggml_row_size(cur->type, n_embd_text), 0); - } - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; -} - -static ggml_cgraph * clip_image_build_graph_qwen25vl(clip_ctx * ctx, const clip_image_f32_batch & imgs) { - const auto & model = ctx->vision_model; - const auto & hparams = model.hparams; - - const int image_size_width = imgs.entries[0]->nx; - const int image_size_height = imgs.entries[0]->ny; - - const bool use_window_attn = hparams.n_wa_pattern > 0; - - const int n_wa_pattern = hparams.n_wa_pattern; - const int patch_size = hparams.patch_size; - const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); - const int patches_w = image_size_width / patch_size; - const int patches_h = image_size_height / patch_size; - const int num_positions = num_patches + (model.class_embedding ? 1 : 0); - const int num_position_ids = num_positions * 4; // m-rope requires 4 dim per position - const int n_embd = hparams.n_embd; - const int n_head = hparams.n_head; - const int d_head = n_embd / n_head; - const int n_layer = hparams.n_layer; - const float eps = hparams.eps; - - int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; - - const int batch_size = imgs.entries.size(); - GGML_ASSERT(batch_size == 1); - - struct ggml_init_params params = { - /*.mem_size =*/ ctx->buf_compute_meta.size(), - /*.mem_buffer =*/ ctx->buf_compute_meta.data(), - /*.no_alloc =*/ true, - }; - - ggml_context_ptr ctx0_ptr(ggml_init(params)); - auto ctx0 = ctx0_ptr.get(); - - struct ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3, batch_size); - ggml_set_name(inp_raw, "inp_raw"); - ggml_set_input(inp_raw); - - struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - - GGML_ASSERT(image_size_width % (patch_size * 2) == 0); - GGML_ASSERT(image_size_height % (patch_size * 2) == 0); - - auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_add(ctx0, inp, inp_1); - - inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b] - inp = ggml_reshape_4d( - ctx0, inp, - n_embd * 2, patches_w / 2, patches_h, batch_size); - inp = ggml_reshape_4d( - ctx0, inp, - n_embd * 2, patches_w / 2, 2, batch_size * (patches_h / 2)); - inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3)); - inp = ggml_reshape_3d( - ctx0, inp, - n_embd, patches_w * patches_h, batch_size); - - if (model.patch_bias) { - // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); - inp = ggml_add(ctx0, inp, model.patch_bias); - } - struct ggml_tensor * embeddings = inp; - struct ggml_tensor * window_mask = nullptr; - struct ggml_tensor * window_idx = nullptr; - struct ggml_tensor * inv_window_idx = nullptr; - - struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); - - // pre-layernorm - if (model.pre_ln_w) { - embeddings = ggml_rms_norm(ctx0, embeddings, eps); - ggml_set_name(embeddings, "pre_ln"); - - embeddings = ggml_mul(ctx0, embeddings, model.pre_ln_w); - } - - if (use_window_attn) { - // handle window attention inputs - inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4); - ggml_set_name(inv_window_idx, "inv_window_idx"); - ggml_set_input(inv_window_idx); - // mask for window attention - window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, num_positions, num_positions); - ggml_set_name(window_mask, "window_mask"); - ggml_set_input(window_mask); - - // embeddings shape: [n_embd, patches_w * patches_h, batch_size] - GGML_ASSERT(batch_size == 1); - embeddings = ggml_reshape_2d(ctx0, embeddings, n_embd * 4, patches_w * patches_h * batch_size / 4); - embeddings = ggml_get_rows(ctx0, embeddings, inv_window_idx); - embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd, patches_w * patches_h, batch_size); - } - - // loop over layers - for (int il = 0; il < n_layer; il++) { - struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states - - // rmsnorm1 - cur = ggml_rms_norm(ctx0, cur, eps); - cur = ggml_mul(ctx0, cur, model.layers[il].ln_1_w); - - // self-attention + // arrangement of the [IMG_BREAK] token { + // not efficient, but works + // the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows] + // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension + // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows] - struct ggml_tensor * Q = - ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b); + const int p_y = n_merge > 0 ? n_patches_y / n_merge : n_patches_y; + const int p_x = n_merge > 0 ? n_patches_x / n_merge : n_patches_x; + const int p_total = p_x * p_y; + const int n_embd_text = cur->ne[0]; + const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row - Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size); - Q = ggml_rope_multi( - ctx0, Q, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); - Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size); + ggml_tensor * tmp = ggml_reshape_3d(ctx0, cur, n_embd_text, p_x, p_y); + ggml_tensor * tok = ggml_new_tensor_3d(ctx0, tmp->type, n_embd_text, 1, p_y); + tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor + tok = ggml_add(ctx0, tok, model.token_embd_img_break); + tmp = ggml_concat(ctx0, tmp, tok, 1); + cur = ggml_view_2d(ctx0, tmp, + n_embd_text, n_tokens_output, + ggml_row_size(tmp->type, n_embd_text), 0); + } - struct ggml_tensor * K = - ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b); + // build the graph + ggml_build_forward_expand(gf, cur); - K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); - K = ggml_rope_multi( - ctx0, K, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); - K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); + return gf; + } - struct ggml_tensor * V = - ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b); + // Qwen2VL and Qwen2.5VL use M-RoPE + ggml_cgraph * build_qwen2vl() { + const int batch_size = 1; + const bool use_window_attn = hparams.n_wa_pattern > 0; + const int n_wa_pattern = hparams.n_wa_pattern; + const int n_pos = n_patches; + const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position - V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size); - V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); - V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); + norm_type norm_t = ctx->proj_type == PROJECTOR_TYPE_QWEN25VL + ? NORM_TYPE_RMS // qwen 2.5 vl + : NORM_TYPE_NORMAL; // qwen 2 vl - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); + int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; + + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + + GGML_ASSERT(img.nx % (patch_size * 2) == 0); + GGML_ASSERT(img.ny % (patch_size * 2) == 0); + + // second conv dimension + { + auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_add(ctx0, inp, inp_1); + + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b] + inp = ggml_reshape_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); + inp = ggml_reshape_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3)); + inp = ggml_reshape_3d( + ctx0, inp, + n_embd, n_patches_x * n_patches_y, batch_size); + } + + if (model.patch_bias) { + inp = ggml_add(ctx0, inp, model.patch_bias); + } + + ggml_tensor * inpL = inp; + ggml_tensor * window_mask = nullptr; + ggml_tensor * window_idx = nullptr; + ggml_tensor * inv_window_idx = nullptr; + + ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); + } + + if (use_window_attn) { + // handle window attention inputs + inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); + ggml_set_name(inv_window_idx, "inv_window_idx"); + ggml_set_input(inv_window_idx); + // mask for window attention + window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); + ggml_set_name(window_mask, "window_mask"); + ggml_set_input(window_mask); + + // inpL shape: [n_embd, n_patches_x * n_patches_y, batch_size] + GGML_ASSERT(batch_size == 1); + inpL = ggml_reshape_2d(ctx0, inpL, n_embd * 4, n_patches_x * n_patches_y * batch_size / 4); + inpL = ggml_get_rows(ctx0, inpL, inv_window_idx); + inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_patches_x * n_patches_y, batch_size); + } + + // loop over layers + for (int il = 0; il < n_layer; il++) { + auto & layer = model.layers[il]; const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true; - if (full_attn) { - KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); - } else { - KQ = ggml_soft_max_ext(ctx0, KQ, window_mask, 1.0f / sqrtf((float)d_head), 0.0f); - } - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); - KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size); - KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states - cur = ggml_cont_3d(ctx0, KQV, n_embd, num_positions, batch_size); - } + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); + cb(cur, "ln1", il); - // attention output - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b); - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, embeddings); - - embeddings = cur; // embeddings = residual, cur = hidden_states - - // rms norm2 - cur = ggml_rms_norm(ctx0, cur, eps); - cur = ggml_mul(ctx0, cur, model.layers[il].ln_2_w); - - // mlp - // ffn_up - auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_up_w, cur); - cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_up_b); - - auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_gate_w, cur); - cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_gate_b); - // TODO : only 2 of these 3 are actually used, should we remove one of them? - if (ctx->use_gelu) { - cur_gate = ggml_gelu_inplace(ctx0, cur_gate); - } else if (ctx->use_silu) { - cur_gate = ggml_silu_inplace(ctx0, cur_gate); - } else { - cur_gate = ggml_gelu_quick_inplace(ctx0, cur_gate); - } - cur = ggml_mul(ctx0, cur_gate, cur_up); - - // ffn_down - cur = ggml_mul_mat(ctx0, model.layers[il].ff_down_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_down_b); - - // residual 2 - cur = ggml_add(ctx0, embeddings, cur); - - embeddings = cur; - } - - // post-layernorm - if (model.post_ln_w) { - embeddings = ggml_rms_norm(ctx0, embeddings, eps); - ggml_set_name(embeddings, "post_ln"); - - embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w); - } - - embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, num_positions / 4, batch_size); - - embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); - - // GELU activation - embeddings = ggml_gelu(ctx0, embeddings); - - // Second linear layer - embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); - - if (use_window_attn) { - window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4); - ggml_set_name(window_idx, "window_idx"); - ggml_set_input(window_idx); - - // embeddings shape: [n_embd, patches_w * patches_h, batch_size] - GGML_ASSERT(batch_size == 1); - embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4); - embeddings = ggml_get_rows(ctx0, embeddings, window_idx); - embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4, batch_size); - } - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; -} - -static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) { - const auto & model = ctx->vision_model; - const auto & hparams = model.hparams; - - const int image_size = hparams.image_size; - int image_size_width = image_size; - int image_size_height = image_size; - - if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { - LOG_DBG("%s: %d %d\n", __func__, load_image_size.width, load_image_size.height); - image_size_width = load_image_size.width; - image_size_height = load_image_size.height; - if (is_inf) { - image_size_width = imgs.entries[0]->nx; - image_size_height = imgs.entries[0]->ny; - } - } - - else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { - // use the image's native resolution when image is avaible - if (is_inf) { - // if (imgs->data->nx && imgs->data->ny) { - image_size_width = imgs.entries[0]->nx; - image_size_height = imgs.entries[0]->ny; - } - } - - const int patch_size = hparams.patch_size; - const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); - const int patches_w = image_size_width / patch_size; - const int patches_h = image_size_height / patch_size; - const int num_positions = num_patches + (model.class_embedding ? 1 : 0); - const int num_position_ids = ctx->proj_type == PROJECTOR_TYPE_QWEN2VL ? num_positions * 4 : num_positions; - const int n_embd = hparams.n_embd; - const int n_head = hparams.n_head; - const int d_head = n_embd / n_head; - const float eps = hparams.eps; - int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; - - const int batch_size = imgs.entries.size(); - - if (ctx->has_llava_projector - || ctx->proj_type == PROJECTOR_TYPE_MINICPMV - || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { - GGML_ASSERT(batch_size == 1); - } - - struct ggml_init_params params = { - /*.mem_size =*/ ctx->buf_compute_meta.size(), - /*.mem_buffer =*/ ctx->buf_compute_meta.data(), - /*.no_alloc =*/ true, - }; - - ggml_context_ptr ctx0_ptr(ggml_init(params)); - auto ctx0 = ctx0_ptr.get(); - - struct ggml_cgraph * gf = ggml_new_graph(ctx0); - - struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3, batch_size); - ggml_set_name(inp_raw, "inp_raw"); - ggml_set_input(inp_raw); - - struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - - if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { - GGML_ASSERT(image_size_width % (patch_size * 2) == 0); - GGML_ASSERT(image_size_height % (patch_size * 2) == 0); - - auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_add(ctx0, inp, inp_1); - inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b] - inp = ggml_reshape_4d( - ctx0, inp, - n_embd * 2, patches_w / 2, patches_h, batch_size); - inp = ggml_reshape_4d( - ctx0, inp, - n_embd * 2, patches_w / 2, 2, batch_size * (patches_h / 2)); - inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3)); - inp = ggml_reshape_3d( - ctx0, inp, - n_embd, patches_w * patches_h, batch_size); - } - else { - inp = ggml_reshape_3d(ctx0, inp, num_patches, n_embd, batch_size); - inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); - } - - if (model.patch_bias) { - // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); - inp = ggml_add(ctx0, inp, model.patch_bias); - } - struct ggml_tensor * embeddings = inp; - struct ggml_tensor * pos_embed = nullptr; - - // concat class_embeddings and patch_embeddings - if (model.class_embedding) { - embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd, num_positions, batch_size); - embeddings = ggml_scale(ctx0, embeddings, 0.0f); // set to all zeros - embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); - embeddings = ggml_acc(ctx0, embeddings, inp, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); - } - - struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); - - if (ctx->proj_type != PROJECTOR_TYPE_QWEN2VL) { // qwen2vl does NOT use learned position embeddings - embeddings = - ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); - } - - if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { - int pos_w = image_size_width/patch_size; - int pos_h = image_size_height/patch_size; - int n_output_dim = clip_n_mmproj_embd(ctx); - pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_output_dim, pos_w * pos_h, 1); - ggml_set_name(pos_embed, "pos_embed"); - ggml_set_input(pos_embed); - } - - // pre-layernorm - if (model.pre_ln_w) { - embeddings = ggml_norm(ctx0, embeddings, eps); - ggml_set_name(embeddings, "pre_ln"); - - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b); - } - - std::vector embedding_stack; - const auto & vision_feature_layer = hparams.vision_feature_layer; - - // loop over layers - for (int il = 0; il < ctx->max_feature_layer; il++) { - struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states - - // If this is an embedding feature layer, save the output. - // NOTE: 0 index here refers to the input to the encoder. - if (vision_feature_layer.find(il) != vision_feature_layer.end()) { - embedding_stack.push_back(embeddings); - } - - //const size_t nb_q_w = model.layers[il].q_w->nb[0]; - - // layernorm1 - { - cur = ggml_norm(ctx0, cur, eps); - - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w), - model.layers[il].ln_1_b); - } - - // self-attention - { - - struct ggml_tensor * Q = - ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b); - - Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size); - if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { - Q = ggml_rope_multi( - ctx0, Q, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - } - Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); - Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size); - - struct ggml_tensor * K = - ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b); - - K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); - if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { - K = ggml_rope_multi( - ctx0, K, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - } - K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); - K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); - - struct ggml_tensor * V = - ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b); - - V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size); - V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); - V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); - - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); - KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size); - KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - - cur = ggml_cont_3d(ctx0, KQV, n_embd, num_positions, batch_size); - } - - // attention output - cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b); - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, embeddings); - - embeddings = cur; // embeddings = residual, cur = hidden_states - - // layernorm2 - { - cur = ggml_norm(ctx0, cur, eps); - - cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b); - } - - cur = ggml_mul_mat(ctx0, model.layers[il].ff_up_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_up_b); - - if (ctx->use_gelu) { - cur = ggml_gelu_inplace(ctx0, cur); - } else if (ctx->use_silu) { - cur = ggml_silu_inplace(ctx0, cur); - } else { - cur = ggml_gelu_quick_inplace(ctx0, cur); - } - - cur = ggml_mul_mat(ctx0, model.layers[il].ff_down_w, cur); - cur = ggml_add(ctx0, cur, model.layers[il].ff_down_b); - - // residual 2 - cur = ggml_add(ctx0, embeddings, cur); - - embeddings = cur; - } - - // post-layernorm - if (model.post_ln_w) { - embeddings = ggml_norm(ctx0, embeddings, eps); - ggml_set_name(embeddings, "post_ln"); - - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b); - } - - // final layer is a vision feature layer - if (vision_feature_layer.find(ctx->max_feature_layer) != vision_feature_layer.end()) { - embedding_stack.push_back(embeddings); - } - - // If feature layers are explicitly set, stack them (if we have multiple) - if (!embedding_stack.empty()) { - embeddings = embedding_stack[0]; - for (size_t i = 1; i < embedding_stack.size(); i++) { - embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0); - } - } - - // llava projector - if (ctx->has_llava_projector) { - embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); - - struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches); - ggml_set_name(patches, "patches"); - ggml_set_input(patches); - - // shape [1, 576, 1024] - // ne is whcn, ne = [1024, 576, 1, 1] - embeddings = ggml_get_rows(ctx0, embeddings, patches); - - // print_tensor_info(embeddings, "embeddings"); - - // llava projector - if (ctx->proj_type == PROJECTOR_TYPE_MLP) { - embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); - - embeddings = ggml_gelu(ctx0, embeddings); - if (model.mm_2_w) { - embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); - } - } - else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { - embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); - // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false); - // First LayerNorm - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w), - model.mm_1_b); - - // GELU activation - embeddings = ggml_gelu(ctx0, embeddings); - - // Second linear layer - embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_3_b); - - // Second LayerNorm - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w), - model.mm_4_b); - } - else if (ctx->proj_type == PROJECTOR_TYPE_LDP) { - // MobileVLM projector - int n_patch = 24; - struct ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings); - mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b); - mlp_1 = ggml_gelu(ctx0, mlp_1); - struct ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1); - mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b); - // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1] - - // block 1 - struct ggml_tensor * block_1 = nullptr; + // self-attention { - // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24] - mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3)); - mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); - // stride = 1, padding = 1, bias is nullptr - block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); + ggml_tensor * Qcur = ggml_add(ctx0, + ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b); + ggml_tensor * Kcur = ggml_add(ctx0, + ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b); + ggml_tensor * Vcur = ggml_add(ctx0, + ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b); - // layer norm - // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); - // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches); - // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] - // hardswish - struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); - block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); - // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] - // pointwise conv - block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b); - block_1 = ggml_relu(ctx0, block_1); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b); - block_1 = ggml_hardsigmoid(ctx0, block_1); - // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1] - block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); - block_1 = ggml_mul(ctx0, block_1_hw, block_1); + // apply M-RoPE + Qcur = ggml_rope_multi( + ctx0, Qcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + Kcur = ggml_rope_multi( + ctx0, Kcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - int w = block_1->ne[0], h = block_1->ne[1]; - block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); + cb(Qcur, "Qcur_rope", il); + cb(Kcur, "Kcur_rope", il); - // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1); - block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); + ggml_tensor * attn_mask = full_attn ? nullptr : window_mask; - // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); - // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] - // residual - block_1 = ggml_add(ctx0, mlp_3, block_1); + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, attn_mask, kq_scale, il); + cb(cur, "attn_out", il); } - // block_2 - { - // stride = 2 - block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); - // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] - // layer norm - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); - // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); - // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] - // hardswish - struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); + inpL = cur; // inpL = residual, cur = hidden_states - // not sure the parameters is right for globalAvgPooling - block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); - // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] - // pointwise conv - block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b); - block_1 = ggml_relu(ctx0, block_1); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b); - block_1 = ggml_hardsigmoid(ctx0, block_1); + cb(cur, "ffn_inp", il); - // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] - block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); - block_1 = ggml_mul(ctx0, block_1_hw, block_1); + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); + cb(cur, "ffn_inp_normed", il); - int w = block_1->ne[0], h = block_1->ne[1]; - block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); - // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1); - block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + cb(cur, "ffn_out", il); - // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b); - block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]); - // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1] - } - embeddings = block_1; - } - else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) - { - int n_patch = 24; - struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); - mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); - mlp_0 = ggml_gelu(ctx0, mlp_0); - struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); - mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); - // mlp_2 ne = [2048, 576, 1, 1] - // // AVG Pool Layer 2*2, strides = 2 - mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3)); - // mlp_2 ne = [576, 2048, 1, 1] - mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); - // mlp_2 ne [24, 24, 2048, 1] - mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); - // weight ne = [3, 3, 2048, 1] - struct ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); - peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); - peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); - mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3)); - peg_0 = ggml_add(ctx0, peg_0, mlp_2); - peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); - embeddings = peg_0; - } - else { - GGML_ABORT("fatal error"); - } - } - // minicpmv projector - else if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) { - struct ggml_tensor * q = model.mm_model_query; - { // layernorm - q = ggml_norm(ctx0, q, eps); - q = ggml_add(ctx0, ggml_mul(ctx0, q, model.mm_model_ln_q_w), model.mm_model_ln_q_b); - } - struct ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings); - { // layernorm - v = ggml_norm(ctx0, v, eps); - v = ggml_add(ctx0, ggml_mul(ctx0, v, model.mm_model_ln_kv_w), model.mm_model_ln_kv_b); - } - struct ggml_tensor * k; - { // position - // q = ggml_add(ctx0, q, model.mm_model_pos_embed); - k = ggml_add(ctx0, v, pos_embed); + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + inpL = cur; } - { // attention - int n_embd = clip_n_mmproj_embd(ctx); - const int d_head = 128; - int n_head = n_embd/d_head; - int num_query = 96; - if (ctx->minicpmv_version == 2) { - num_query = 96; - } - else if (ctx->minicpmv_version == 3) { - num_query = 64; - } - else if (ctx->minicpmv_version == 4) { - num_query = 64; - } - - struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b); - struct ggml_tensor * K = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), model.mm_model_attn_k_b); - struct ggml_tensor * V = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), model.mm_model_attn_v_b); - // permute - Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_query, batch_size); - Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); - Q = ggml_reshape_3d(ctx0, Q, d_head, num_query, n_head * batch_size); - K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); - K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); - K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); - V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size); - V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); - V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); - KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_query, n_head, batch_size); - KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - KQV = ggml_cont_3d(ctx0, KQV, n_embd, num_query, batch_size); - - embeddings = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_o_w, KQV), model.mm_model_attn_o_b); + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer); } - { // layernorm - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_post_w), model.mm_model_ln_post_b); - } - embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings); - } - // glm projector - else if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { - size_t gridsz = (size_t)sqrt(embeddings->ne[1]); - embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3)); - embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); - embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); - embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); - embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); - embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); - // GLU - { - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); - embeddings = ggml_gelu_inplace(ctx0, embeddings); - struct ggml_tensor * x = embeddings; - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); - x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); - embeddings = ggml_silu_inplace(ctx0, embeddings); - embeddings = ggml_mul(ctx0, embeddings,x); - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); - } - // arrangement of BOI/EOI token embeddings - // note: these embeddings are not present in text model, hence we cannot process them as text tokens - // see: https://huggingface.co/THUDM/glm-edge-v-2b/blob/main/siglip.py#L53 - { - embeddings = ggml_concat(ctx0, model.mm_glm_tok_boi, embeddings, 1); // BOI - embeddings = ggml_concat(ctx0, embeddings, model.mm_glm_tok_eoi, 1); // EOI - } - } - - else if (ctx->proj_type == PROJECTOR_TYPE_QWEN2VL) { - embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, num_positions / 4, batch_size); + // multimodal projection + ggml_tensor * embeddings = inpL; + embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size); embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); @@ -1573,36 +748,885 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im // Second linear layer embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); + + if (use_window_attn) { + window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); + ggml_set_name(window_idx, "window_idx"); + ggml_set_input(window_idx); + + // embeddings shape: [n_embd, n_patches_x * n_patches_y, batch_size] + GGML_ASSERT(batch_size == 1); + embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4); + embeddings = ggml_get_rows(ctx0, embeddings, window_idx); + embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4, batch_size); + } + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; } - // build the graph - ggml_build_forward_expand(gf, embeddings); + ggml_cgraph * build_minicpmv() { + const int batch_size = 1; - return gf; -} + GGML_ASSERT(model.class_embedding == nullptr); + const int n_pos = n_patches; + + // position embeddings for the projector (not for ViT) + int n_output_dim = clip_n_mmproj_embd(ctx); + ggml_tensor * pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_output_dim, n_pos, batch_size); + ggml_set_name(pos_embed, "pos_embed"); + ggml_set_input(pos_embed); + + // for selecting learned pos embd, used by ViT + struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions); + + ggml_tensor * inp = build_inp(); + ggml_tensor * embeddings = build_vit( + inp, n_patches, + NORM_TYPE_NORMAL, + hparams.ffn_op, + learned_pos_embd, + nullptr); + + // resampler projector (it is just another transformer) + + ggml_tensor * q = model.mm_model_query; + ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings); + + // norm + q = build_norm(q, model.mm_model_ln_q_w, model.mm_model_ln_q_b, NORM_TYPE_NORMAL, eps, -1); + v = build_norm(v, model.mm_model_ln_kv_w, model.mm_model_ln_kv_b, NORM_TYPE_NORMAL, eps, -1); + + // k = v + pos_embed + ggml_tensor * k = ggml_add(ctx0, v, pos_embed); + + // attention + { + int n_embd = clip_n_mmproj_embd(ctx); + const int d_head = 128; + int n_head = n_embd/d_head; + int num_query = 96; + if (ctx->minicpmv_version == 2) { + num_query = 96; + } else if (ctx->minicpmv_version == 3) { + num_query = 64; + } else if (ctx->minicpmv_version == 4) { + num_query = 64; + } + + ggml_tensor * Q = ggml_add(ctx0, + ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), + model.mm_model_attn_q_b); + ggml_tensor * K = ggml_add(ctx0, + ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), + model.mm_model_attn_k_b); + ggml_tensor * V = ggml_add(ctx0, + ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), + model.mm_model_attn_v_b); + + Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_query); + K = ggml_reshape_3d(ctx0, K, d_head, n_head, n_pos); + V = ggml_reshape_3d(ctx0, V, d_head, n_head, n_pos); + + cb(Q, "resampler_Q", -1); + cb(K, "resampler_K", -1); + cb(V, "resampler_V", -1); + + embeddings = build_attn( + model.mm_model_attn_o_w, + model.mm_model_attn_o_b, + Q, K, V, nullptr, kq_scale, -1); + cb(embeddings, "resampler_attn_out", -1); + } + // layernorm + embeddings = build_norm(embeddings, model.mm_model_ln_post_w, model.mm_model_ln_post_b, NORM_TYPE_NORMAL, eps, -1); + + // projection + embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings); + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; + } + + // this graph is used by llava, granite and glm + // due to having embedding_stack (used by granite), we cannot reuse build_vit + ggml_cgraph * build_llava() { + const int batch_size = 1; + const int n_pos = n_patches + (model.class_embedding ? 1 : 0); + + GGML_ASSERT(n_patches_x == n_patches_y && "only square images supported"); + + // Calculate the deepest feature layer based on hparams and projector type + int max_feature_layer = n_layer; + { + // Get the index of the second to last layer; this is the default for models that have a llava projector + int il_last = hparams.n_layer - 1; + int deepest_feature_layer = -1; + + if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { + il_last += 1; + } + + // If we set explicit vision feature layers, only go up to the deepest one + // NOTE: only used by granite-vision models for now + for (const auto & feature_layer : hparams.vision_feature_layer) { + if (feature_layer > deepest_feature_layer) { + deepest_feature_layer = feature_layer; + } + } + max_feature_layer = deepest_feature_layer < 0 ? il_last : deepest_feature_layer; + } + + ggml_tensor * inp = build_inp(); + + if (model.patch_bias) { + inp = ggml_add(ctx0, inp, model.patch_bias); + } + + // concat class_embeddings and patch_embeddings + if (model.class_embedding) { + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + } + + ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + inp = ggml_add(ctx0, inp, ggml_get_rows(ctx0, model.position_embeddings, positions)); + + ggml_tensor * inpL = inp; + + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1); + cb(inpL, "pre_ln", -1); + } + + std::vector embedding_stack; + const auto & vision_feature_layer = hparams.vision_feature_layer; + + // loop over layers + for (int il = 0; il < max_feature_layer; il++) { + auto & layer = model.layers[il]; + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + + // If this is an embedding feature layer, save the output. + // NOTE: 0 index here refers to the input to the encoder. + if (vision_feature_layer.find(il) != vision_feature_layer.end()) { + embedding_stack.push_back(cur); + } + + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "layer_inp_normed", il); + + // self-attention + { + ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); + if (layer.q_b) { + Qcur = ggml_add(ctx0, Qcur, layer.q_b); + } + + ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); + if (layer.k_b) { + Kcur = ggml_add(ctx0, Kcur, layer.k_b); + } + + ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); + if (layer.v_b) { + Vcur = ggml_add(ctx0, Vcur, layer.v_b); + } + + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); + + inpL = cur; // inpL = residual, cur = hidden_states + + cb(cur, "ffn_inp", il); + + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "ffn_inp_normed", il); + + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + + cb(cur, "ffn_out", il); + + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + inpL = cur; + } + + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1); + } + + ggml_tensor * embeddings = inpL; + + // process vision feature layers (used by granite) + { + // final layer is a vision feature layer + if (vision_feature_layer.find(max_feature_layer) != vision_feature_layer.end()) { + embedding_stack.push_back(inpL); + } + + // If feature layers are explicitly set, stack them (if we have multiple) + if (!embedding_stack.empty()) { + embeddings = embedding_stack[0]; + for (size_t i = 1; i < embedding_stack.size(); i++) { + embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0); + } + } + } + + // llava projector (also used by granite) + if (ctx->has_llava_projector) { + embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); + + ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(patches, "patches"); + ggml_set_input(patches); + + // shape [1, 576, 1024] + // ne is whcn, ne = [1024, 576, 1, 1] + embeddings = ggml_get_rows(ctx0, embeddings, patches); + + // print_tensor_info(embeddings, "embeddings"); + + // llava projector + if (ctx->proj_type == PROJECTOR_TYPE_MLP) { + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + + embeddings = ggml_gelu(ctx0, embeddings); + if (model.mm_2_w) { + embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); + } + } + else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false); + // First LayerNorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w), + model.mm_1_b); + + // GELU activation + embeddings = ggml_gelu(ctx0, embeddings); + + // Second linear layer + embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_3_b); + + // Second LayerNorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w), + model.mm_4_b); + } + else if (ctx->proj_type == PROJECTOR_TYPE_LDP) { + // MobileVLM projector + int n_patch = 24; + ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings); + mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b); + mlp_1 = ggml_gelu(ctx0, mlp_1); + ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1); + mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b); + // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1] + + // block 1 + ggml_tensor * block_1 = nullptr; + { + // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24] + mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3)); + mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); + // stride = 1, padding = 1, bias is nullptr + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); + + // layer norm + // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); + // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + + // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] + // hardswish + ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); + + block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); + // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] + // pointwise conv + block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b); + block_1 = ggml_relu(ctx0, block_1); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b); + block_1 = ggml_hardsigmoid(ctx0, block_1); + // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1] + block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); + block_1 = ggml_mul(ctx0, block_1_hw, block_1); + + int w = block_1->ne[0], h = block_1->ne[1]; + block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); + + // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1); + block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); + + // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] + // residual + block_1 = ggml_add(ctx0, mlp_3, block_1); + } + + // block_2 + { + // stride = 2 + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); + + // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] + // layer norm + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); + // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] + // hardswish + ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); + + // not sure the parameters is right for globalAvgPooling + block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); + // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] + // pointwise conv + block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b); + block_1 = ggml_relu(ctx0, block_1); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b); + block_1 = ggml_hardsigmoid(ctx0, block_1); + + // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] + block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); + block_1 = ggml_mul(ctx0, block_1_hw, block_1); + + int w = block_1->ne[0], h = block_1->ne[1]; + block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); + // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1); + block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); + + + // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b); + block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]); + // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1] + } + embeddings = block_1; + } + else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) + { + int n_patch = 24; + ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); + mlp_0 = ggml_gelu(ctx0, mlp_0); + ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); + mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); + // mlp_2 ne = [2048, 576, 1, 1] + // // AVG Pool Layer 2*2, strides = 2 + mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3)); + // mlp_2 ne = [576, 2048, 1, 1] + mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); + // mlp_2 ne [24, 24, 2048, 1] + mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); + // weight ne = [3, 3, 2048, 1] + ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); + peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); + mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, mlp_2); + peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); + embeddings = peg_0; + } + else { + GGML_ABORT("fatal error"); + } + } + + // glm projector + else if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { + size_t gridsz = (size_t)sqrt(embeddings->ne[1]); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3)); + embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); + embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); + embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); + embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); + // GLU + { + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); + embeddings = ggml_gelu_inplace(ctx0, embeddings); + ggml_tensor * x = embeddings; + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); + x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); + embeddings = ggml_silu_inplace(ctx0, embeddings); + embeddings = ggml_mul(ctx0, embeddings,x); + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); + } + // arrangement of BOI/EOI token embeddings + // note: these embeddings are not present in text model, hence we cannot process them as text tokens + // see: https://huggingface.co/THUDM/glm-edge-v-2b/blob/main/siglip.py#L53 + { + embeddings = ggml_concat(ctx0, model.mm_glm_tok_boi, embeddings, 1); // BOI + embeddings = ggml_concat(ctx0, embeddings, model.mm_glm_tok_eoi, 1); // EOI + } + } + + else { + GGML_ABORT("llava: unknown projector type"); + } + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; + } + +private: + // + // utility functions + // + + void cb(ggml_tensor * cur, const char * name, int il) const { + // TODO: implement this + GGML_UNUSED(cur); + GGML_UNUSED(name); + GGML_UNUSED(il); + } + + // build vision transformer (ViT) cgraph + // this function should cover most of the models + // if your model has specific features, you should probably duplicate this function + ggml_tensor * build_vit( + ggml_tensor * inp, + int64_t n_pos, + norm_type norm_t, + ffn_op_type ffn_t, + ggml_tensor * learned_pos_embd, + std::function add_pos + ) { + if (model.patch_bias) { + inp = ggml_add(ctx0, inp, model.patch_bias); + cb(inp, "patch_bias", -1); + } + + if (learned_pos_embd) { + inp = ggml_add(ctx0, inp, learned_pos_embd); + cb(inp, "pos_embed", -1); + } + + ggml_tensor * inpL = inp; + + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); + cb(inpL, "pre_ln", -1); + } + + // loop over layers + for (int il = 0; il < n_layer; il++) { + auto & layer = model.layers[il]; + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); + cb(cur, "layer_inp_normed", il); + + // self-attention + { + ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); + if (layer.q_b) { + Qcur = ggml_add(ctx0, Qcur, layer.q_b); + } + + ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); + if (layer.k_b) { + Kcur = ggml_add(ctx0, Kcur, layer.k_b); + } + + ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); + if (layer.v_b) { + Vcur = ggml_add(ctx0, Vcur, layer.v_b); + } + + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + if (add_pos) { + Qcur = add_pos(Qcur, layer); + Kcur = add_pos(Kcur, layer); + cb(Qcur, "Qcur_pos", il); + cb(Kcur, "Kcur_pos", il); + } + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); + + inpL = cur; // inpL = residual, cur = hidden_states + + cb(cur, "ffn_inp", il); + + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); + cb(cur, "ffn_inp_normed", il); + + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + ffn_t, il); + + cb(cur, "ffn_out", il); + + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + inpL = cur; + } + + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1); + } + return inpL; + } + + // build the input after conv2d (inp_raw --> patches) + // returns tensor with shape [n_embd, n_patches] + ggml_tensor * build_inp() { + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd); + inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); + return inp; + } + + ggml_tensor * build_inp_raw() { + ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, img.nx, img.ny, 3); + ggml_set_name(inp_raw, "inp_raw"); + ggml_set_input(inp_raw); + return inp_raw; + } + + ggml_tensor * build_norm( + ggml_tensor * cur, + ggml_tensor * mw, + ggml_tensor * mb, + norm_type type, + float norm_eps, + int il) const { + + cur = type == NORM_TYPE_RMS + ? ggml_rms_norm(ctx0, cur, norm_eps) + : ggml_norm(ctx0, cur, norm_eps); + + if (mw || mb) { + cb(cur, "norm", il); + } + + if (mw) { + cur = ggml_mul(ctx0, cur, mw); + if (mb) { + cb(cur, "norm_w", il); + } + } + + if (mb) { + cur = ggml_add(ctx0, cur, mb); + } + + return cur; + } + + ggml_tensor * build_ffn( + ggml_tensor * cur, + ggml_tensor * up, + ggml_tensor * up_b, + ggml_tensor * gate, + ggml_tensor * gate_b, + ggml_tensor * down, + ggml_tensor * down_b, + ffn_op_type type_op, + int il) const { + + ggml_tensor * tmp = up ? ggml_mul_mat(ctx0, up, cur) : cur; + cb(tmp, "ffn_up", il); + + if (up_b) { + tmp = ggml_add(ctx0, tmp, up_b); + cb(tmp, "ffn_up_b", il); + } + + if (gate) { + cur = ggml_mul_mat(ctx0, gate, cur); + cb(cur, "ffn_gate", il); + + if (gate_b) { + cur = ggml_add(ctx0, cur, gate_b); + cb(cur, "ffn_gate_b", il); + } + } else { + cur = tmp; + } + + switch (type_op) { + case FFN_SILU: + { + cur = ggml_silu(ctx0, cur); + cb(cur, "ffn_silu", il); + } break; + case FFN_GELU: + { + cur = ggml_gelu(ctx0, cur); + cb(cur, "ffn_gelu", il); + } break; + case FFN_GELU_QUICK: + { + cur = ggml_gelu_quick(ctx0, cur); + cb(cur, "ffn_relu", il); + } break; + } + + // we only support parallel ffn for now + if (gate) { + cur = ggml_mul(ctx0, cur, tmp); + cb(cur, "ffn_gate_par", il); + } + + if (down) { + cur = ggml_mul_mat(ctx0, down, cur); + } + + if (down_b) { + cb(cur, "ffn_down", il); + } + + if (down_b) { + cur = ggml_add(ctx0, cur, down_b); + } + + return cur; + } + + ggml_tensor * build_attn( + ggml_tensor * wo, + ggml_tensor * wo_b, + ggml_tensor * q_cur, + ggml_tensor * k_cur, + ggml_tensor * v_cur, + ggml_tensor * kq_mask, + float kq_scale, + int il) const { + // these nodes are added to the graph together so that they are not reordered + // by doing so, the number of splits in the graph is reduced + ggml_build_forward_expand(gf, q_cur); + ggml_build_forward_expand(gf, k_cur); + ggml_build_forward_expand(gf, v_cur); + + ggml_tensor * q = ggml_permute(ctx0, q_cur, 0, 2, 1, 3); + //cb(q, "q", il); + + ggml_tensor * k = ggml_permute(ctx0, k_cur, 0, 2, 1, 3); + //cb(k, "k", il); + + ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3); + v = ggml_cont(ctx0, v); + //cb(k, "v", il); + + ggml_tensor * cur; + + // TODO @ngxson : support flash attention + { + const auto n_tokens = q->ne[1]; + const auto n_head = q->ne[2]; + // const auto n_kv = k->ne[1]; // for flash attention + + ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + // F32 may not needed for vision encoders? + // ggml_mul_mat_set_prec(kq, GGML_PREC_F32); + + kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f); + + ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq); + cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens); + } + + cb(cur, "kqv_out", il); + + if (wo) { + cur = ggml_mul_mat(ctx0, wo, cur); + } + + if (wo_b) { + cur = ggml_add(ctx0, cur, wo_b); + } + + return cur; + } + + // implementation of the 2D RoPE without adding a new op in ggml + // this is not efficient (use double the memory), but works on all backends + // TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 + static ggml_tensor * build_rope_2d( + ggml_context * ctx0, + ggml_tensor * cur, + ggml_tensor * pos_h, + ggml_tensor * pos_w, + const float freq_base + ) { + const int64_t n_dim = cur->ne[0]; + const int64_t n_head = cur->ne[1]; + const int64_t n_pos = cur->ne[2]; + + // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos) + // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3 + // first half of cur will use 1e-0, 1e-2 (even) + // second half of cur will use 1e-1, 1e-3 (odd) + // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even + // ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2) + // then for the second half, we use freq_scale to shift the inv_freq + // ^ why? replace (2i) with (2i+1) in the above equation + const float freq_scale_odd = std::pow(freq_base, (float)-2/n_dim); + + // first half + ggml_tensor * first; + { + first = ggml_view_3d(ctx0, cur, + n_dim/2, n_head, n_pos, + ggml_row_size(cur->type, n_dim), + ggml_row_size(cur->type, n_dim*n_head), + 0); + first = ggml_rope_ext( + ctx0, + first, + pos_h, // positions + nullptr, // freq factors + n_dim/2, // n_dims + 0, 0, freq_base, + 1.0f, 0.0f, 1.0f, 0.0f, 0.0f + ); + } + + // second half + ggml_tensor * second; + { + second = ggml_view_3d(ctx0, cur, + n_dim/2, n_head, n_pos, + ggml_row_size(cur->type, n_dim), + ggml_row_size(cur->type, n_dim*n_head), + n_dim/2 * ggml_element_size(cur)); + second = ggml_cont(ctx0, second); // copy, because ggml_rope don't play well with non-contiguous tensors + second = ggml_rope_ext( + ctx0, + second, + pos_w, // positions + nullptr, // freq factors + n_dim/2, // n_dims + 0, 0, freq_base, + freq_scale_odd, + 0.0f, 1.0f, 0.0f, 0.0f + ); + } + + cur = ggml_concat(ctx0, first, second, 0); + return cur; + } + +}; + +static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs) { + GGML_ASSERT(imgs.entries.size() == 1 && "n_batch > 1 is not supported"); + clip_graph graph(ctx, *imgs.entries[0]); -static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) { ggml_cgraph * res; + switch (ctx->proj_type) { case PROJECTOR_TYPE_GEMMA3: case PROJECTOR_TYPE_IDEFICS3: { - GGML_ASSERT(imgs.entries.size() == 1); - res = clip_image_build_graph_siglip(ctx, *imgs.entries[0]); + res = graph.build_siglip(); } break; case PROJECTOR_TYPE_PIXTRAL: { - GGML_ASSERT(imgs.entries.size() == 1); - res = clip_image_build_graph_pixtral(ctx, *imgs.entries[0]); + res = graph.build_pixtral(); } break; + case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: { - res = clip_image_build_graph_qwen25vl(ctx, imgs); + res = graph.build_qwen2vl(); + } break; + case PROJECTOR_TYPE_MINICPMV: + { + res = graph.build_minicpmv(); } break; default: { - // TODO: we should have one build_* function per model - res = clip_image_build_graph_legacy(ctx, imgs, load_image_size, is_inf); + res = graph.build_llava(); } break; } return res; @@ -1656,7 +1680,7 @@ struct clip_model_loader { const char * name = gguf_get_tensor_name(ctx_gguf.get(), i); const size_t offset = gguf_get_tensor_offset(ctx_gguf.get(), i); enum ggml_type type = gguf_get_tensor_type(ctx_gguf.get(), i); - struct ggml_tensor * cur = ggml_get_tensor(meta, name); + ggml_tensor * cur = ggml_get_tensor(meta, name); size_t tensor_size = ggml_nbytes(cur); model_size += tensor_size; LOG_DBG("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", @@ -1667,6 +1691,7 @@ struct clip_model_loader { void load_hparams() { auto & hparams = ctx_clip.vision_model.hparams; + std::string log_ffn_op; // for logging // projector type std::string proj_type; @@ -1682,10 +1707,7 @@ struct clip_model_loader { // other hparams { - get_i32(KEY_MINICPMV_VERSION, ctx_clip.minicpmv_version, false); - - get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false); - get_bool(KEY_USE_SILU, ctx_clip.use_silu, false); + get_i32(KEY_MINICPMV_VERSION, ctx_clip.minicpmv_version, false); // legacy get_u32(KEY_N_EMBD, hparams.n_embd); get_u32(KEY_N_HEAD, hparams.n_head); @@ -1703,6 +1725,26 @@ struct clip_model_loader { || ctx_clip.proj_type == PROJECTOR_TYPE_LDP || ctx_clip.proj_type == PROJECTOR_TYPE_LDPV2; + { + bool use_gelu = false; + bool use_silu = false; + get_bool(KEY_USE_GELU, use_gelu, false); + get_bool(KEY_USE_SILU, use_silu, false); + if (use_gelu && use_silu) { + throw std::runtime_error(string_format("%s: both use_gelu and use_silu are set to true\n", __func__)); + } + if (use_gelu) { + hparams.ffn_op = FFN_GELU; + log_ffn_op = "gelu"; + } else if (use_silu) { + hparams.ffn_op = FFN_SILU; + log_ffn_op = "silu"; + } else { + hparams.ffn_op = FFN_GELU_QUICK; + log_ffn_op = "gelu_quick"; + } + } + { std::string mm_patch_merge_type; get_string(KEY_MM_PATCH_MERGE_TYPE, mm_patch_merge_type, false); @@ -1736,30 +1778,6 @@ struct clip_model_loader { hparams.vision_feature_layer.insert(layer); } - // Calculate the deepest feature layer based on hparams and projector type - // NOTE: This is only used by build_graph_legacy() - { - // Get the index of the second to last layer; this is the default for models that have a llava projector - int n_layer = hparams.n_layer - 1; - int deepest_feature_layer = -1; - - if (ctx_clip.proj_type == PROJECTOR_TYPE_MINICPMV - || ctx_clip.proj_type == PROJECTOR_TYPE_GLM_EDGE - || ctx_clip.proj_type == PROJECTOR_TYPE_QWEN2VL - || ctx_clip.proj_type == PROJECTOR_TYPE_QWEN25VL) { - n_layer += 1; - } - - // If we set explicit vision feature layers, only go up to the deepest one - // NOTE: only used by granite-vision models for now - for (const auto & feature_layer : hparams.vision_feature_layer) { - if (feature_layer > deepest_feature_layer) { - deepest_feature_layer = feature_layer; - } - } - ctx_clip.max_feature_layer = deepest_feature_layer < 0 ? n_layer : deepest_feature_layer; - } - // model-specific params switch (ctx_clip.proj_type) { case PROJECTOR_TYPE_MINICPMV: @@ -1777,6 +1795,14 @@ struct clip_model_loader { hparams.rope_theta = 10000.0f; get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.spatial_merge_size, false); } break; + case PROJECTOR_TYPE_GEMMA3: + { + // default value (used by all model sizes in gemma 3 family) + // number of patches for each **side** is reduced by a factor of 4 + hparams.proj_scale_factor = 4; + // test model (tinygemma3) has a different value, we optionally read it + get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false); + } break; case PROJECTOR_TYPE_QWEN25VL: { get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern); @@ -1786,12 +1812,19 @@ struct clip_model_loader { } LOG_INF("%s: projector: %s\n", __func__, proj_type.c_str()); + LOG_INF("%s: n_embd: %d\n", __func__, hparams.n_embd); + LOG_INF("%s: n_head: %d\n", __func__, hparams.n_head); + LOG_INF("%s: n_ff: %d\n", __func__, hparams.n_ff); + LOG_INF("%s: n_layer: %d\n", __func__, hparams.n_layer); + LOG_INF("%s: projection_dim: %d\n", __func__, hparams.projection_dim); + LOG_INF("%s: image_size: %d\n", __func__, hparams.image_size); + LOG_INF("%s: patch_size: %d\n", __func__, hparams.patch_size); + LOG_INF("\n"); LOG_INF("%s: has_llava_proj: %d\n", __func__, ctx_clip.has_llava_projector); LOG_INF("%s: minicpmv_version: %d\n", __func__, ctx_clip.minicpmv_version); LOG_INF("%s: proj_scale_factor: %d\n", __func__, hparams.proj_scale_factor); LOG_INF("%s: n_wa_pattern: %d\n", __func__, hparams.n_wa_pattern); - LOG_INF("%s: use_silu: %d\n", __func__, ctx_clip.use_silu); - LOG_INF("%s: use_gelu: %d\n", __func__, ctx_clip.use_gelu); + LOG_INF("%s: ffn_op: %s\n", __func__, log_ffn_op.c_str()); LOG_INF("%s: model size: %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0); LOG_INF("%s: metadata size: %.2f MiB\n", __func__, ggml_get_mem_size(ctx_meta.get()) / 1024.0 / 1024.0); } @@ -1821,14 +1854,14 @@ struct clip_model_loader { // helper function auto get_tensor = [&](const std::string & name, bool required = true) { - struct ggml_tensor * cur = ggml_get_tensor(ctx_meta.get(), name.c_str()); + ggml_tensor * cur = ggml_get_tensor(ctx_meta.get(), name.c_str()); if (!cur && required) { throw std::runtime_error(string_format("%s: unable to find tensor %s\n", __func__, name.c_str())); } if (cur) { tensors_to_load.push_back(cur); // add tensors to context - struct ggml_tensor * data_tensor = ggml_dup_tensor(ctx_clip.ctx_data.get(), cur); + ggml_tensor * data_tensor = ggml_dup_tensor(ctx_clip.ctx_data.get(), cur); ggml_set_name(data_tensor, cur->name); cur = data_tensor; } @@ -2034,7 +2067,7 @@ struct clip_model_loader { ctx_clip.buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(ctx_clip.ctx_data.get(), buft)); ggml_backend_buffer_set_usage(ctx_clip.buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS); for (auto & t : tensors_to_load) { - struct ggml_tensor * cur = ggml_get_tensor(ctx_clip.ctx_data.get(), t->name); + ggml_tensor * cur = ggml_get_tensor(ctx_clip.ctx_data.get(), t->name); const size_t offset = tensor_offset[t->name]; fin.seekg(offset, std::ios::beg); if (!fin) { @@ -2063,15 +2096,12 @@ struct clip_model_loader { // create a fake batch clip_image_f32_batch batch; clip_image_f32_ptr img(clip_image_f32_init()); - clip_image_size image_size; - image_size.width = ctx_clip.vision_model.hparams.image_size; - image_size.height = ctx_clip.vision_model.hparams.image_size; - img->nx = image_size.width; - img->ny = image_size.height; - img->buf.resize(image_size.width * image_size.height * 3); + img->nx = ctx_clip.vision_model.hparams.image_size; + img->ny = ctx_clip.vision_model.hparams.image_size; + img->buf.resize(img->nx * img->ny * 3); batch.entries.push_back(std::move(img)); - ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, batch, image_size, false); + ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, batch); ggml_backend_sched_reserve(ctx_clip.sched.get(), gf); for (size_t i = 0; i < ctx_clip.backend_ptrs.size(); ++i) { ggml_backend_t backend = ctx_clip.backend_ptrs[i]; @@ -2976,11 +3006,13 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0); n_patches = x_patch * y_patch; } else if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) { - n_patches = 256; + int n_per_side = params.image_size / params.patch_size; + int n_per_side_2d_pool = n_per_side / params.proj_scale_factor; + n_patches = n_per_side_2d_pool * n_per_side_2d_pool; } else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) { - n_patches /= ctx->vision_model.hparams.proj_scale_factor; + n_patches /= params.proj_scale_factor; } else if (ctx->proj_type == PROJECTOR_TYPE_PIXTRAL) { - int n_merge = ctx->vision_model.hparams.spatial_merge_size; + int n_merge = params.spatial_merge_size; int n_patches_x = img->nx / params.patch_size / (n_merge > 0 ? n_merge : 1); int n_patches_y = img->ny / params.patch_size / (n_merge > 0 ? n_merge : 1); n_patches = n_patches_y*n_patches_x + n_patches_y - 1; // + one [IMG_BREAK] per row, except the last row @@ -3088,15 +3120,15 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const clip_image_f32_batch & imgs = *imgs_c_ptr; int batch_size = imgs.entries.size(); - if (ctx->has_llava_projector - || ctx->proj_type == PROJECTOR_TYPE_MINICPMV - || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { - GGML_ASSERT(batch_size == 1); + // TODO @ngxson : implement batch size > 1 as a loop + // we don't need true batching support because the cgraph will gonna be big anyway + if (batch_size != 1) { + return false; // only support batch size of 1 } // build the inference graph ggml_backend_sched_reset(ctx->sched.get()); - ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true); + ggml_cgraph * gf = clip_image_build_graph(ctx, imgs); ggml_backend_sched_alloc_graph(ctx->sched.get(), gf); // set inputs @@ -3108,14 +3140,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int patch_size = hparams.patch_size; const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); - const int num_positions = num_patches + (model.class_embedding ? 1 : 0); + const int n_pos = num_patches + (model.class_embedding ? 1 : 0); const int pos_w = ctx->load_image_size.width / patch_size; const int pos_h = ctx->load_image_size.height / patch_size; const bool use_window_attn = hparams.n_wa_pattern > 0; // for qwen2.5vl auto get_inp_tensor = [&gf](const char * name) { - struct ggml_tensor * inp = ggml_graph_get_tensor(gf, name); + ggml_tensor * inp = ggml_graph_get_tensor(gf, name); if (inp == nullptr) { GGML_ABORT("Failed to get tensor %s", name); } @@ -3224,7 +3256,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int merge_ratio = 2; const int pw = image_size_width / patch_size; const int ph = image_size_height / patch_size; - std::vector positions(num_positions * 4); + std::vector positions(n_pos * 4); int ptr = 0; for (int y = 0; y < ph; y += merge_ratio) { for (int x = 0; x < pw; x += merge_ratio) { @@ -3301,7 +3333,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } const int mpow = merge_ratio * merge_ratio; - std::vector positions(num_positions * 4); + std::vector positions(n_pos * 4); int ptr = 0; for (int y = 0; y < iph; y += merge_ratio) { @@ -3327,14 +3359,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima { // set the 2D positions int n_patches_per_col = image_size_width / patch_size; - std::vector pos_data(num_positions); + std::vector pos_data(n_pos); // dimension H - for (int i = 0; i < num_positions; i++) { + for (int i = 0; i < n_pos; i++) { pos_data[i] = i / n_patches_per_col; } set_input_i32("pos_h", pos_data); // dimension W - for (int i = 0; i < num_positions; i++) { + for (int i = 0; i < n_pos; i++) { pos_data[i] = i % n_patches_per_col; } set_input_i32("pos_w", pos_data); @@ -3342,8 +3374,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima case PROJECTOR_TYPE_GLM_EDGE: { // llava and other models - std::vector positions(num_positions); - for (int i = 0; i < num_positions; i++) { + std::vector positions(n_pos); + for (int i = 0; i < n_pos; i++) { positions[i] = i; } set_input_i32("positions", positions); @@ -3354,8 +3386,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima case PROJECTOR_TYPE_LDPV2: { // llava and other models - std::vector positions(num_positions); - for (int i = 0; i < num_positions; i++) { + std::vector positions(n_pos); + for (int i = 0; i < n_pos; i++) { positions[i] = i; } set_input_i32("positions", positions); @@ -3396,7 +3428,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } // the last node is the embedding tensor - struct ggml_tensor * embeddings = ggml_graph_node(gf, -1); + ggml_tensor * embeddings = ggml_graph_node(gf, -1); // copy the embeddings to the location passed by the user ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings)); @@ -3427,7 +3459,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx_src, i); - struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name); + ggml_tensor * cur = ggml_get_tensor(ctx_data, name); gguf_add_tensor(ctx_out, cur); } @@ -3448,7 +3480,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i for (int i = 0; i < n_tensors; ++i) { const std::string name = gguf_get_tensor_name(ctx_src, i); - struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str()); + ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str()); enum ggml_type new_type; void * new_data; From 141a908a59bbc68ceae3bf090b850e33322a2ca9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 6 May 2025 23:35:51 +0200 Subject: [PATCH 118/126] CUDA: mix virt/real CUDA archs for GGML_NATIVE=OFF (#13135) --- ggml/src/ggml-cuda/CMakeLists.txt | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt index f3cfdeaef5..969a178f6c 100644 --- a/ggml/src/ggml-cuda/CMakeLists.txt +++ b/ggml/src/ggml-cuda/CMakeLists.txt @@ -12,12 +12,30 @@ if (CUDAToolkit_FOUND) # 61 == Pascal, __dp4a instruction (per-byte integer dot product) # 70 == V100, FP16 tensor cores # 75 == Turing, int8 tensor cores + # 80 == Ampere, asynchronous data loading, faster tensor core instructions + # 86 == RTX 3000, needs CUDA v11.1 + # 89 == RTX 4000, needs CUDA v11.8 + # + # XX-virtual == compile CUDA code as PTX, do JIT compilation to binary code on first run + # XX-real == compile CUDA code as device code for this specific architecture + # no suffix == compile as both PTX and device code + # + # The default behavior for a non-native is to build virtual architectures as needed to cover all features needed + # for best performance and to also build real architectures for the most commonly used GPUs. if (GGML_NATIVE AND CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.6" AND CMAKE_VERSION VERSION_GREATER_EQUAL "3.24") set(CMAKE_CUDA_ARCHITECTURES "native") elseif(GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) - set(CMAKE_CUDA_ARCHITECTURES "60;61;70;75;80") + if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.8") + set(CMAKE_CUDA_ARCHITECTURES "60-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real;89-real") + else() + set(CMAKE_CUDA_ARCHITECTURES "60-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real") + endif() else() - set(CMAKE_CUDA_ARCHITECTURES "50;61;70;75;80") + if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.8") + set(CMAKE_CUDA_ARCHITECTURES "50-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real;89-real") + else() + set(CMAKE_CUDA_ARCHITECTURES "50-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real") + endif() endif() endif() message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") From 6c7fd67b647a76846d1691cd181011dff4549d02 Mon Sep 17 00:00:00 2001 From: piDack <104877312+piDack@users.noreply.github.com> Date: Wed, 7 May 2025 15:23:11 +0800 Subject: [PATCH 119/126] llama : support tie embedding for chatglm models (#13328) --- src/llama-model.cpp | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 8d25070f42..774e343fb1 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -3510,7 +3510,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) { // output output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); - output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } for (int i = 0; i < n_layer; ++i) { auto & layer = layers[i]; From 4773d7a02ffdb05ba9e673ff21ce95351836e33a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 7 May 2025 10:28:02 +0300 Subject: [PATCH 120/126] examples : remove infill (#13283) ggml-ci --- Makefile | 5 - common/arg.cpp | 12 +- common/common.h | 1 - examples/CMakeLists.txt | 1 - examples/infill/CMakeLists.txt | 5 - examples/infill/README.md | 47 --- examples/infill/infill.cpp | 590 --------------------------------- 7 files changed, 6 insertions(+), 655 deletions(-) delete mode 100644 examples/infill/CMakeLists.txt delete mode 100644 examples/infill/README.md delete mode 100644 examples/infill/infill.cpp diff --git a/Makefile b/Makefile index 68b6fe59a6..958ad8f2fc 100644 --- a/Makefile +++ b/Makefile @@ -1187,11 +1187,6 @@ llama-cli: tools/main/main.cpp \ @echo '==== Run ./llama-cli -h for help. ====' @echo -llama-infill: examples/infill/infill.cpp \ - $(OBJ_ALL) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - llama-run: tools/run/run.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) diff --git a/common/arg.cpp b/common/arg.cpp index 490991b23a..5e07e8a699 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1283,7 +1283,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.use_color = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); add_opt(common_arg( {"-t", "--threads"}, "N", string_format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads), @@ -1416,7 +1416,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex add_opt(common_arg( {"-n", "--predict", "--n-predict"}, "N", string_format( - ex == LLAMA_EXAMPLE_MAIN || ex == LLAMA_EXAMPLE_INFILL + ex == LLAMA_EXAMPLE_MAIN ? "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)" : "number of tokens to predict (default: %d, -1 = infinity)", params.n_predict), @@ -1655,7 +1655,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.input_prefix = value; params.enable_chat_template = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL})); + ).set_examples({LLAMA_EXAMPLE_MAIN})); add_opt(common_arg( {"--in-suffix"}, "STRING", "string to suffix after user inputs with (default: empty)", @@ -1663,7 +1663,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.input_suffix = value; params.enable_chat_template = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL})); + ).set_examples({LLAMA_EXAMPLE_MAIN})); add_opt(common_arg( {"--no-warmup"}, "skip warming up the model with an empty run", @@ -1680,7 +1680,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.spm_infill = true; } - ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL})); + ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"--samplers"}, "SAMPLERS", string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()), @@ -2892,7 +2892,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.simple_io = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL})); + ).set_examples({LLAMA_EXAMPLE_MAIN})); add_opt(common_arg( {"--positive-file"}, "FNAME", string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()), diff --git a/common/common.h b/common/common.h index dfd6e20933..400f674b22 100644 --- a/common/common.h +++ b/common/common.h @@ -66,7 +66,6 @@ enum llama_example { LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_MAIN, - LLAMA_EXAMPLE_INFILL, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL, diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index eca0d0b097..4ca9230c59 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -21,7 +21,6 @@ else() add_subdirectory(gguf-hash) add_subdirectory(gguf) add_subdirectory(gritlm) - add_subdirectory(infill) add_subdirectory(lookahead) add_subdirectory(lookup) add_subdirectory(parallel) diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt deleted file mode 100644 index fb26628d82..0000000000 --- a/examples/infill/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -set(TARGET llama-infill) -add_executable(${TARGET} infill.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/infill/README.md b/examples/infill/README.md deleted file mode 100644 index df4d976f2b..0000000000 --- a/examples/infill/README.md +++ /dev/null @@ -1,47 +0,0 @@ -# llama.cpp/example/infill - -This example shows how to use the infill mode with Code Llama models supporting infill mode. -Currently the 7B and 13B models support infill mode. - -Infill supports most of the options available in the main example. - -For further information have a look at the main README.md in llama.cpp/example/main/README.md - -## Common Options - -In this section, we cover the most commonly used options for running the `infill` program with the LLaMA models: - -- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`). -- `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses. -- `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text. -- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 4096, but if a LLaMA model was built with a longer context, increasing this value will provide better results for longer input/inference. -- `--spm-infill`: Use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. - -## Input Prompts - -The `infill` program provides several ways to interact with the LLaMA models using input prompts: - -- `--in-prefix PROMPT_BEFORE_CURSOR`: Provide the prefix directly as a command-line option. -- `--in-suffix PROMPT_AFTER_CURSOR`: Provide the suffix directly as a command-line option. -- `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.) - -## Interaction - -The `infill` program offers a seamless way to interact with LLaMA models, allowing users to receive real-time infill suggestions. The interactive mode can be triggered using `--interactive`, and `--interactive-first` - -### Interaction Options - -- `-i, --interactive`: Run the program in interactive mode, allowing users to get real time code suggestions from model. -- `--interactive-first`: Run the program in interactive mode and immediately wait for user input before starting the text generation. -- `--color`: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text. - -### Example - -Download a model that supports infill, for example CodeLlama: -```console -scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.gguf --outdir models -``` - -```bash -./llama-infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n " -``` diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp deleted file mode 100644 index 4e2f7b7270..0000000000 --- a/examples/infill/infill.cpp +++ /dev/null @@ -1,590 +0,0 @@ -#include "arg.h" -#include "common.h" -#include "console.h" -#include "sampling.h" -#include "log.h" -#include "llama.h" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) -#include -#include -#elif defined (_WIN32) -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX -#define NOMINMAX -#endif -#include -#include -#endif - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - -static llama_context ** g_ctx; -static llama_model ** g_model; -static common_sampler ** g_smpl; -static common_params * g_params; -static std::vector * g_input_tokens; -static std::ostringstream * g_output_ss; -static std::vector * g_output_tokens; - -static bool is_interacting = false; - -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) -static void sigint_handler(int signo) { - if (signo == SIGINT) { - if (!is_interacting) { - is_interacting = true; - } else { - console::cleanup(); - LOG("\n"); - common_perf_print(*g_ctx, *g_smpl); - - // make sure all logs are flushed - LOG("Interrupted by user\n"); - common_log_pause(common_log_main()); - - _exit(130); - } - } -} -#endif - -int main(int argc, char ** argv) { - common_params params; - g_params = ¶ms; - - if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) { - return 1; - } - - common_init(); - - auto & sparams = params.sampling; - - console::init(params.simple_io, params.use_color); - atexit([]() { console::cleanup(); }); - - if (params.logits_all) { - LOG_ERR("\n************\n"); - LOG_ERR("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__); - LOG_ERR("************\n\n"); - - return 0; - } - - if (params.embedding) { - LOG_ERR("\n************\n"); - LOG_ERR("%s: please use the 'embedding' tool for embedding calculations\n", __func__); - LOG_ERR("************\n\n"); - - return 0; - } - - if (params.n_ctx != 0 && params.n_ctx < 8) { - LOG_WRN("%s: minimum context size is 8, using minimum size.\n", __func__); - params.n_ctx = 8; - } - - if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) { - LOG_ERR("\n************\n"); - LOG_ERR("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__); - LOG_ERR("************\n\n"); - - return 0; - } - - if (params.rope_freq_base != 0.0) { - LOG_WRN("%s: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base); - } - - if (params.rope_freq_scale != 0.0) { - LOG_WRN("%s: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); - } - - LOG_INF("%s: llama backend init\n", __func__); - llama_backend_init(); - llama_numa_init(params.numa); - - llama_model * model = nullptr; - llama_context * ctx = nullptr; - common_sampler * smpl = nullptr; - - g_model = &model; - g_ctx = &ctx; - g_smpl = &smpl; - - // load the model and apply lora adapter, if any - LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); - common_init_result llama_init = common_init_from_params(params); - - model = llama_init.model.get(); - ctx = llama_init.context.get(); - - if (model == NULL) { - LOG_ERR("%s: unable to load model\n", __func__); - return 1; - } - - const llama_vocab * vocab = llama_model_get_vocab(model); - - const int n_ctx_train = llama_model_n_ctx_train(model); - const int n_ctx = llama_n_ctx(ctx); - LOG_DBG("n_ctx: %d\n", n_ctx); - - if (n_ctx > n_ctx_train) { - LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx); - } - - // print system information - { - LOG_INF("\n"); - LOG_INF("%s\n", common_params_get_system_info(params).c_str()); - } - const bool add_bos = llama_vocab_get_add_bos(vocab); - GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); - - std::vector embd_inp; - std::vector embd_end; - std::vector inp_pfx = common_tokenize(ctx, params.input_prefix, false); - std::vector inp_sfx = common_tokenize(ctx, params.input_suffix, false); - - GGML_ASSERT(llama_vocab_fim_pre(vocab) >= 0); - GGML_ASSERT(llama_vocab_fim_suf(vocab) >= 0); - - inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab)); - inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab)); - - embd_inp = params.spm_infill ? inp_sfx : inp_pfx; - embd_end = params.spm_infill ? inp_pfx : inp_sfx; - if (add_bos) { - embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); - } - embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); - - const llama_token middle_token = llama_vocab_fim_mid(vocab); - if (middle_token >= 0) { - embd_inp.push_back(middle_token); - } - - LOG_DBG("add_bos: %d\n", add_bos); - LOG_DBG("prefix: \"%s\"\n", params.input_prefix.c_str()); - LOG_DBG("suffix: \"%s\"\n", params.input_suffix.c_str()); - LOG_DBG("tokens: %s\n", string_from(ctx, embd_inp).c_str()); - - // Should not run without any tokens - if (embd_inp.empty()) { - embd_inp.push_back(llama_vocab_bos(vocab)); - LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str()); - } - - if ((int) embd_inp.size() > n_ctx - 4) { - LOG_ERR("%s: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); - return 1; - } - - // number of tokens to keep when resetting context - if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) { - params.n_keep = (int)embd_inp.size(); - } - - LOG_INF("inp_pfx: %s\n", string_from(ctx, inp_pfx).c_str()); - LOG_INF("inp_sfx: %s\n", string_from(ctx, inp_sfx).c_str()); - - // enable interactive mode if interactive start is specified - if (params.interactive_first) { - params.interactive = true; - } - - if (params.verbose_prompt) { - LOG_INF("\n"); - LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); - LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); - for (int i = 0; i < (int) embd_inp.size(); i++) { - LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str()); - } - - if (params.n_keep > 0) { - LOG_INF("%s: static prompt based on n_keep: '", __func__); - for (int i = 0; i < params.n_keep; i++) { - LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str()); - } - LOG_CNT("'\n"); - } - LOG_INF("\n"); - } - - if (params.interactive) { -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) - struct sigaction sigint_action; - sigint_action.sa_handler = sigint_handler; - sigemptyset (&sigint_action.sa_mask); - sigint_action.sa_flags = 0; - sigaction(SIGINT, &sigint_action, NULL); -#elif defined (_WIN32) - auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { - return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false; - }; - SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); -#endif - - LOG_INF("%s: interactive mode on.\n", __func__); - - if (params.input_prefix_bos) { - LOG_INF("Input prefix with BOS\n"); - } - - if (!params.input_prefix.empty()) { - LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str()); - } - - if (!params.input_suffix.empty()) { - LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str()); - } - } - smpl = common_sampler_init(model, sparams); - - LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl)); - LOG_INF("sampler params: \n%s\n", sparams.print().c_str()); - LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str()); - - LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); - - LOG_INF("\n"); - LOG_INF("\n##### Infill mode #####\n\n"); - if (params.interactive) { - const char *control_message; - if (params.multiline_input) { - control_message = " - To return control to LLaMA, end your input with '\\'.\n" - " - To return control without starting a new line, end your input with '/'.\n"; - } else { - control_message = " - Press Return to return control to LLaMA.\n" - " - To return control without starting a new line, end your input with '/'.\n" - " - If you want to submit another line, end your input with '\\'.\n"; - } - LOG_INF("== Running in interactive mode. ==\n"); -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) - LOG_INF( " - Press Ctrl+C to interject at any time.\n"); -#endif - LOG_INF( "%s\n", control_message); - - is_interacting = params.interactive_first; - } - - bool input_echo = true; - - int n_past = 0; - int n_remain = params.n_predict; - int n_consumed = 0; - - std::vector input_tokens; g_input_tokens = &input_tokens; - std::vector output_tokens; g_output_tokens = &output_tokens; - std::ostringstream output_ss; g_output_ss = &output_ss; - - // the first thing we will do is to output the prompt, so set color accordingly - console::set_display(console::prompt); - - std::vector embd; - - while (n_remain != 0 || params.interactive) { - // predict - if (!embd.empty()) { - // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via - // --prompt or --file which uses the same value. - int max_embd_size = n_ctx - 4; - - // Ensure the input doesn't exceed the context size by truncating embd if necessary. - if ((int) embd.size() > max_embd_size) { - const int skipped_tokens = (int) embd.size() - max_embd_size; - embd.resize(max_embd_size); - - console::set_display(console::error); - LOG_WRN("<>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); - console::set_display(console::reset); - } - - // infinite text generation via context swapping - // if we run out of context: - // - take the n_keep first tokens from the original prompt (via n_past) - // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches - if (n_past + (int) embd.size() > n_ctx) { - if (params.n_predict == -2) { - LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); - break; - } - - const int n_left = n_past - params.n_keep - 1; - const int n_discard = n_left/2; - - LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", - n_past, n_left, n_ctx, params.n_keep, n_discard); - - llama_kv_self_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_self_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); - - n_past -= n_discard; - - LOG_DBG("after swap: n_past = %d\n", n_past); - - LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str()); - - } - - // evaluate tokens in batches - // embd is typically prepared beforehand to fit within a batch, but not always - for (int i = 0; i < (int) embd.size(); i += params.n_batch) { - int n_eval = (int) embd.size() - i; - if (n_eval > params.n_batch) { - n_eval = params.n_batch; - } - - LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); - - if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { - LOG_ERR("%s : failed to eval\n", __func__); - return 1; - } - - n_past += n_eval; - - LOG_DBG("n_past = %d\n", n_past); - } - - } - - embd.clear(); - - if ((int) embd_inp.size() <= n_consumed && !is_interacting) { - const llama_token id = common_sampler_sample(smpl, ctx, -1); - - common_sampler_accept(smpl, id, true); - - // LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str()); - - embd.push_back(id); - - // echo this to console - input_echo = true; - - // decrement remaining sampling budget - --n_remain; - - LOG_DBG("n_remain: %d\n", n_remain); - } else { - // some user input remains from prompt or interaction, forward it to processing - LOG_DBG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); - while ((int) embd_inp.size() > n_consumed) { - embd.push_back(embd_inp[n_consumed]); - - // push the prompt in the sampling context in order to apply repetition penalties later - // for the prompt, we don't apply grammar rules - common_sampler_accept(smpl, embd_inp[n_consumed], false); - - ++n_consumed; - if ((int) embd.size() >= params.n_batch) { - break; - } - } - } - - // display text - if (input_echo) { - for (auto id : embd) { - const std::string token_str = common_token_to_piece(ctx, id); - LOG("%s", token_str.c_str()); - - if (embd.size() > 1) { - input_tokens.push_back(id); - } else { - output_tokens.push_back(id); - output_ss << token_str; - } - } - } - // reset color to default if we there is no pending user input - if (input_echo && (int) embd_inp.size() == n_consumed) { - console::set_display(console::reset); - } - - // if not currently processing queued inputs; - if ((int) embd_inp.size() <= n_consumed) { - // deal with eot token in infill mode - if ((common_sampler_last(smpl) == llama_vocab_eot(vocab) || is_interacting) && params.interactive){ - if (is_interacting && !params.interactive_first) { - // print an eot token - LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str()); - } - LOG("\n"); - console::set_display(console::user_input); - std::string buffer; - std::string line; - bool another_line=true; - // set a new prefix via stdin - do { - another_line = console::readline(line, params.multiline_input); - buffer += line; - } while (another_line); - // check if we got an empty line, if so we use the old input - if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) { - params.input_prefix = buffer; - } - buffer.clear(); - // set a new suffix via stdin - do { - another_line = console::readline(line, params.multiline_input); - buffer += line; - } while (another_line); - // check if we got an empty line - if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) { - params.input_suffix = buffer; - } - buffer.clear(); - // done taking input, reset color - console::set_display(console::reset); - - if (params.escape) { - //process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here - string_process_escapes(params.input_prefix); - string_process_escapes(params.input_suffix); - } - - // tokenize new prefix and suffix - std::vector inp_pfx = common_tokenize(ctx, params.input_prefix, false); - std::vector inp_sfx = common_tokenize(ctx, params.input_suffix, false); - - inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab)); - inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab)); - - embd_inp = params.spm_infill ? inp_sfx : inp_pfx; - embd_end = params.spm_infill ? inp_pfx : inp_sfx; - if (add_bos) { - embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); - } - embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); - - if (middle_token >= 0) { - embd_inp.push_back(middle_token); - } - - embd.clear(); - n_remain = params.n_predict; - n_past = 0; - n_consumed = 0; - is_interacting = false; - } - // deal with end of generation tokens in interactive mode - else if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) { - LOG_DBG("found EOS token\n"); - - if (params.interactive) { - - is_interacting = true; - LOG("\n"); - console::set_display(console::user_input); - } - } - - if (n_past > 0 && is_interacting && !params.interactive) { - LOG_DBG("waiting for user input\n"); - - if (params.input_prefix_bos) { - LOG_DBG("adding input prefix BOS token\n"); - embd_inp.push_back(llama_vocab_bos(vocab)); - } - - std::string buffer; - if (!params.input_prefix.empty()) { - LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str()); - buffer += params.input_prefix; - LOG("%s", buffer.c_str()); - } - - std::string line; - bool another_line = true; - do { - another_line = console::readline(line, params.multiline_input); - buffer += line; - } while (another_line); - - // done taking input, reset color - console::set_display(console::reset); - - // Add tokens to embd only if the input buffer is non-empty - // Entering a empty line lets the user pass control back - if (buffer.length() > 1) { - // append input suffix if any - if (!params.input_suffix.empty()) { - LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str()); - buffer += params.input_suffix; - LOG("%s", params.input_suffix.c_str()); - } - - LOG_DBG("buffer: '%s'\n", buffer.c_str()); - - const size_t original_size = embd_inp.size(); - - const auto line_inp = common_tokenize(ctx, buffer, false); - LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str()); - - embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); - - for (size_t i = original_size; i < embd_inp.size(); ++i) { - const llama_token token = embd_inp[i]; - output_tokens.push_back(token); - output_ss << common_token_to_piece(ctx, token); - } - - n_remain -= line_inp.size(); - LOG_DBG("n_remain: %d\n", n_remain); - } else { - LOG_DBG("empty line, passing control back\n"); - } - - input_echo = false; // do not echo this again - } - - if (n_past > 0) { - if (is_interacting) { - common_sampler_reset(smpl); - } - is_interacting = false; - } - } - - // end of generation - if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !params.interactive) { - break; - } - - // In interactive mode, respect the maximum number of tokens and drop back to user input when reached. - // We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size). - if (params.interactive && n_remain <= 0 && params.n_predict >= 0) { - n_remain = params.n_predict; - is_interacting = true; - } - } - if (!params.interactive && n_remain <= 0) { - LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str()); - } - - LOG("\n"); - common_perf_print(ctx, smpl); - - common_sampler_free(smpl); - llama_backend_free(); - - return 0; -} From 1f73301b63668d61b5f3109489050a27dc3f65be Mon Sep 17 00:00:00 2001 From: R0CKSTAR Date: Wed, 7 May 2025 15:48:23 +0800 Subject: [PATCH 121/126] cuda : remove nrows_x in mul_mat_q_process_tile (#13325) Signed-off-by: Xiaodong Ye --- ggml/src/ggml-cuda/mmq.cuh | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index b8143a7b23..80baf459c1 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -2522,7 +2522,7 @@ template static __device__ __forceinline__ void mul_mat_q_process_tile( const char * __restrict__ x, const int offset_x, const int * __restrict__ y, const int * __restrict__ ids_dst, float * __restrict__ dst, float * __restrict__ tmp_fixup, - const int nrows_x, const int stride_row_x, const int ncols_y, const int stride_col_dst, + const int stride_row_x, const int ncols_y, const int stride_col_dst, const int tile_x_max_i, const int tile_y_max_j, const int kb0_start, const int kb0_stop) { constexpr int qk = ggml_cuda_type_traits::qk; @@ -2689,7 +2689,7 @@ static __global__ void mul_mat_q( constexpr bool fixup = false; mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, stride_row_x, ncols_y, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, 0, ncols_x/qk); return; } @@ -2767,7 +2767,7 @@ static __global__ void mul_mat_q( constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer. mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, stride_row_x, ncols_y, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); kbc += blocks_per_ne00; @@ -2834,7 +2834,7 @@ static __global__ void mul_mat_q( constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks. mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, nrows_x, stride_row_x, ncols_y, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); } From 39e73ae0d69f882d7e29cecc6dd8f5052fca6731 Mon Sep 17 00:00:00 2001 From: Ycros <18012+ycros@users.noreply.github.com> Date: Wed, 7 May 2025 18:23:28 +1000 Subject: [PATCH 122/126] common : Add a warning when we can't match samplers from a string or char. (#13330) --- common/sampling.cpp | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index bbaec5b80a..28705e24c0 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -1,6 +1,7 @@ #include "sampling.h" #include "common.h" +#include "log.h" #include #include @@ -534,14 +535,16 @@ std::vector common_sampler_types_from_names(const std::vect auto sampler = sampler_canonical_name_map.find(name); if (sampler != sampler_canonical_name_map.end()) { samplers.push_back(sampler->second); - } else { - if (allow_alt_names) { - sampler = sampler_alt_name_map.find(name); - if (sampler != sampler_alt_name_map.end()) { - samplers.push_back(sampler->second); - } + continue; + } + if (allow_alt_names) { + sampler = sampler_alt_name_map.find(name); + if (sampler != sampler_alt_name_map.end()) { + samplers.push_back(sampler->second); + continue; } } + LOG_WRN("%s: unable to match sampler by name '%s'\n", __func__, name.c_str()); } return samplers; @@ -568,6 +571,8 @@ std::vector common_sampler_types_from_chars(const std::stri const auto sampler = sampler_name_map.find(c); if (sampler != sampler_name_map.end()) { samplers.push_back(sampler->second); + } else { + LOG_WRN("%s: unable to match sampler by char '%c'\n", __func__, c); } } From bc4e1128f78be0fbb4e2fa630adb6a04b969ac68 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Wed, 7 May 2025 12:49:27 +0200 Subject: [PATCH 123/126] llama : deci : support ffn-free with attention (#13296) --- src/llama-model.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 774e343fb1..7b66e2a03e 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -4792,7 +4792,7 @@ struct llm_build_deci : public llm_graph_context { } // FFN-free layer of Llama-3_1-Nemotron-Ultra-253B - if (n_head == 0 && n_ff == 0) { + if (n_ff == 0) { continue; } From bba9d945c14b93c5264b7956e00736601ca6f89a Mon Sep 17 00:00:00 2001 From: Jared Tweed Date: Fri, 2 May 2025 02:41:35 -0700 Subject: [PATCH 124/126] cmake : removed stdc++fs (whisper/3097) * removed stdc++fs * kept line, but removed stdc++fs --- ggml/src/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 43d9fc4fe2..ddea5ad389 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -214,7 +214,7 @@ add_library(ggml target_link_libraries(ggml PUBLIC ggml-base) if (CMAKE_SYSTEM_NAME MATCHES "Linux") - target_link_libraries(ggml PRIVATE dl stdc++fs) + target_link_libraries(ggml PRIVATE dl) endif() function(ggml_add_backend_library backend) From 13b0a04597a4581cad4d9027a848f450c623801d Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Mon, 5 May 2025 13:09:35 +0200 Subject: [PATCH 125/126] whisper: remove MSVC warnings pragmas (whisper/3090) * ggml : remove MSVC warnings pragmas This commit removes the MSVC-specific pragmas as these are now handled in ggml/CMakeLists.txt. * whisper : remove MSVC warning pragmas This commit removes the MSVC-specific pragmas. These are now handled in the ggml/CMakeLists.txt file. --- ggml/CMakeLists.txt | 2 ++ ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp | 2 -- ggml/src/ggml-cpu/ggml-cpu-quants.c | 6 ------ ggml/src/ggml-cpu/ggml-cpu.c | 13 ------------- ggml/src/ggml-cpu/ops.cpp | 13 ------------- ggml/src/ggml-cpu/vec.cpp | 6 ------ ggml/src/ggml-cuda/common.cuh | 4 ---- ggml/src/ggml-quants.c | 6 ------ ggml/src/ggml-sycl/common.hpp | 4 ---- 9 files changed, 2 insertions(+), 54 deletions(-) diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index b463cbd9b3..a8300e16d8 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -366,6 +366,8 @@ if (MSVC) /wd4005 # Macro redefinition /wd4244 # Conversion from one type to another type, possible loss of data /wd4267 # Conversion from 'size_t' to a smaller type, possible loss of data + /wd4996 # Disable POSIX deprecation warnings + /wd4702 # Unreachable code warnings ) function(disable_msvc_warnings target_name) if(TARGET ${target_name}) diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp index 175cba329b..8ff6d64a4d 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp @@ -72,8 +72,6 @@ static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wro #if defined(__GNUC__) #pragma GCC diagnostic ignored "-Woverlength-strings" -#elif defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data #endif #define UNUSED GGML_UNUSED diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c index 987e153165..ccd0651ebc 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-quants.c +++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c @@ -20,12 +20,6 @@ #define GROUP_MAX_EPS_IQ1_M 1e-7f #define GROUP_MAX_EPS_IQ1_S 1e-12f -#if defined(_MSC_VER) -// disable "possible loss of data" to avoid warnings for hundreds of casts -// we should just be careful :) -#pragma warning(disable: 4244 4267) -#endif - #define UNUSED GGML_UNUSED // some compilers don't provide _mm256_set_m128i, e.g. gcc 7 diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 64405449e2..a30e67f227 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -50,19 +50,6 @@ #include "llamafile/sgemm.h" #endif -#if defined(_MSC_VER) -// disable "possible loss of data" to avoid hundreds of casts -// we should just be careful :) -#pragma warning(disable: 4244 4267) - -// disable POSIX deprecation warnings -// these functions are never going away, anyway -#pragma warning(disable: 4996) - -// unreachable code because of multiple instances of code after GGML_ABORT -#pragma warning(disable: 4702) -#endif - // Note: once we move threading into a separate C++ file // will use std::hardware_destructive_interference_size instead of hardcoding it here // and we'll use C++ attribute syntax. diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 7413192b74..955fec59a6 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -8,19 +8,6 @@ #include -#if defined(_MSC_VER) -// disable "possible loss of data" to avoid hundreds of casts -// we should just be careful :) -#pragma warning(disable: 4244 4267) - -// disable POSIX deprecation warnings -// these functions are never going away, anyway -#pragma warning(disable: 4996) - -// unreachable code because of multiple instances of code after GGML_ABORT -#pragma warning(disable: 4702) -#endif - // ggml_compute_forward_dup static void ggml_compute_forward_dup_same_cont( diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp index dfe2218e30..02d4061822 100644 --- a/ggml/src/ggml-cpu/vec.cpp +++ b/ggml/src/ggml-cpu/vec.cpp @@ -2,12 +2,6 @@ #include -#if defined(_MSC_VER) -// disable "possible loss of data" to avoid hundreds of casts -// we should just be careful :) -#pragma warning(disable: 4244 4267) -#endif - // precomputed gelu table for f16 (128 KB) ggml_fp16_t ggml_table_gelu_f16[1 << 16]; diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 2ea014e647..919217dfae 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -130,10 +130,6 @@ static int ggml_cuda_highest_compiled_arch(const int arch) { #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - #define GGML_CUDA_MAX_STREAMS 8 [[noreturn]] diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c index ac918a60d9..84ec6dfe31 100644 --- a/ggml/src/ggml-quants.c +++ b/ggml/src/ggml-quants.c @@ -19,12 +19,6 @@ #define GROUP_MAX_EPS_IQ1_M 1e-7f #define GROUP_MAX_EPS_IQ1_S 1e-12f -#if defined(_MSC_VER) -// disable "possible loss of data" to avoid warnings for hundreds of casts -// we should just be careful :) -#pragma warning(disable: 4244 4267) -#endif - #define UNUSED GGML_UNUSED // reference implementation for deterministic creation of model files diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index c3d9d18645..c71cc89c09 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -80,10 +80,6 @@ extern int g_ggml_sycl_disable_optimize; // max batch size to use MMQ kernels when tensor cores are available #define MMQ_MAX_BATCH_SIZE 32 -#if defined(_MSC_VER) -#pragma warning(disable : 4244 4267) // possible loss of data -#endif - // dmmv = dequantize_mul_mat_vec #ifndef GGML_SYCL_DMMV_X #define GGML_SYCL_DMMV_X 32 From d879433824ac3c16b3b5f00075895d0ee9688e34 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 7 May 2025 16:39:36 +0300 Subject: [PATCH 126/126] sync : ggml ggml-ci --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 3e93dbf8af..1f7c650c25 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -0482de9c63b9134eb462c7732888c0ee0dbc2755 +b59bddafe278877dfa22a80e53a637513862babb

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a/grammars/README.md b/grammars/README.md index 5aa12acc1b..a63198b5ae 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -1,6 +1,6 @@ # GBNF Guide -GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `examples/main` and `examples/server`. +GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/main` and `tools/server`. ## Background @@ -110,21 +110,21 @@ While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) ma You can use GBNF grammars: -- In [llama-server](../examples/server)'s completion endpoints, passed as the `grammar` body field -- In [llama-cli](../examples/main), passed as the `--grammar` & `--grammar-file` flags +- In [llama-server](../tools/server)'s completion endpoints, passed as the `grammar` body field +- In [llama-cli](../tools/main), passed as the `--grammar` & `--grammar-file` flags - With [test-gbnf-validator](../tests/test-gbnf-validator.cpp), to test them against strings. ## JSON Schemas → GBNF `llama.cpp` supports converting a subset of https://json-schema.org/ to GBNF grammars: -- In [llama-server](../examples/server): +- In [llama-server](../tools/server): - For any completion endpoints, passed as the `json_schema` body field - For the `/chat/completions` endpoint, passed inside the `response_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}` or `{ type: "json_schema", json_schema: {"schema": ...} }`) -- In [llama-cli](../examples/main), passed as the `--json` / `-j` flag +- In [llama-cli](../tools/main), passed as the `--json` / `-j` flag - To convert to a grammar ahead of time: - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py) - - in JavaScript with [json-schema-to-grammar.mjs](../examples/server/public_legacy/json-schema-to-grammar.mjs) (this is used by the [server](../examples/server)'s Web UI) + - in JavaScript with [json-schema-to-grammar.mjs](../tools/server/public_legacy/json-schema-to-grammar.mjs) (this is used by the [server](../tools/server)'s Web UI) Take a look at [tests](../tests/test-json-schema-to-grammar.cpp) to see which features are likely supported (you'll also find usage examples in https://github.com/ggml-org/llama.cpp/pull/5978, https://github.com/ggml-org/llama.cpp/pull/6659 & https://github.com/ggml-org/llama.cpp/pull/6555). diff --git a/pyrightconfig.json b/pyrightconfig.json index 9acbbeb78a..5320fe5864 100644 --- a/pyrightconfig.json +++ b/pyrightconfig.json @@ -15,7 +15,7 @@ }, { // uses match expressions in steps.py - "root": "examples/server/tests", + "root": "tools/server/tests", "pythonVersion": "3.10", }, ], diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index eba0a59f62..4b6d4b7d7c 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -1,6 +1,6 @@ --r ../examples/llava/requirements.txt --r ../examples/server/bench/requirements.txt --r ../examples/server/tests/requirements.txt +-r ../tools/llava/requirements.txt +-r ../tools/server/bench/requirements.txt +-r ../tools/server/tests/requirements.txt -r ./requirements-compare-llama-bench.txt -r ./requirements-pydantic.txt diff --git a/scripts/fetch_server_test_models.py b/scripts/fetch_server_test_models.py index e6775bfc58..ac483ef5d7 100755 --- a/scripts/fetch_server_test_models.py +++ b/scripts/fetch_server_test_models.py @@ -8,7 +8,7 @@ Example: python scripts/fetch_server_test_models.py - ( cd examples/server/tests && ./tests.sh -v -x -m slow ) + ( cd tools/server/tests && ./tests.sh -v -x -m slow ) ''' import ast import glob @@ -66,7 +66,7 @@ if __name__ == '__main__': models = sorted(list(set([ model - for test_file in glob.glob('examples/server/tests/unit/test_*.py') + for test_file in glob.glob('tools/server/tests/unit/test_*.py') for model in collect_hf_model_test_parameters(test_file) ])), key=lambda m: (m.hf_repo, m.hf_file)) diff --git a/scripts/tool_bench.py b/scripts/tool_bench.py index 0f406bc42a..a2f2a2eb02 100755 --- a/scripts/tool_bench.py +++ b/scripts/tool_bench.py @@ -2,7 +2,7 @@ ''' Simplistic tool call benchmarks for llama-server and ollama. - Essentially runs the tests at server/examples/server/tests/unit/test_tool_call.py N times, at different temperatures and on different backends (current llama-server, baseline llama-server and ollama), + Essentially runs the tests at server/tools/server/tests/unit/test_tool_call.py N times, at different temperatures and on different backends (current llama-server, baseline llama-server and ollama), and plots the results of multiple runs (from same .jsonl file or multiple ones) as a success rate heatmap. Simple usage example: @@ -51,8 +51,8 @@ import typer sys.path.insert(0, Path(__file__).parent.parent.as_posix()) if True: - from examples.server.tests.utils import ServerProcess - from examples.server.tests.unit.test_tool_call import TIMEOUT_SERVER_START, do_test_calc_result, do_test_hello_world, do_test_weather + from tools.server.tests.utils import ServerProcess + from tools.server.tests.unit.test_tool_call import TIMEOUT_SERVER_START, do_test_calc_result, do_test_hello_world, do_test_weather @contextmanager diff --git a/scripts/xxd.cmake b/scripts/xxd.cmake index f5ad6ab9b1..14d2753808 100644 --- a/scripts/xxd.cmake +++ b/scripts/xxd.cmake @@ -1,5 +1,5 @@ # CMake equivalent of `xxd -i ${INPUT} ${OUTPUT}` -# Usage: cmake -DINPUT=examples/server/public/index.html -DOUTPUT=examples/server/index.html.hpp -P scripts/xxd.cmake +# Usage: cmake -DINPUT=tools/server/public/index.html -DOUTPUT=tools/server/index.html.hpp -P scripts/xxd.cmake SET(INPUT "" CACHE STRING "Input File") SET(OUTPUT "" CACHE STRING "Output File") diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index ae68275251..8acbe689a4 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -111,7 +111,7 @@ if (NOT WIN32) # TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8 if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") llama_build_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..) - target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server) + target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../tools/server) endif() llama_build(test-quantize-stats.cpp) diff --git a/tests/run-json-schema-to-grammar.mjs b/tests/run-json-schema-to-grammar.mjs index b20ac1d6b5..450c3dde0a 100644 --- a/tests/run-json-schema-to-grammar.mjs +++ b/tests/run-json-schema-to-grammar.mjs @@ -1,5 +1,5 @@ import { readFileSync } from "fs" -import { SchemaConverter } from "../examples/server/public_legacy/json-schema-to-grammar.mjs" +import { SchemaConverter } from "../tools/server/public_legacy/json-schema-to-grammar.mjs" const [, , file] = process.argv const url = `file://${file}` diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt new file mode 100644 index 0000000000..119d7c50a0 --- /dev/null +++ b/tools/CMakeLists.txt @@ -0,0 +1,39 @@ +# dependencies + +find_package(Threads REQUIRED) + +# third-party + +# ... + +# flags + +llama_add_compile_flags() + +# tools + +if (EMSCRIPTEN) +else() + add_subdirectory(batched-bench) + add_subdirectory(gguf-split) + add_subdirectory(imatrix) + add_subdirectory(llama-bench) + add_subdirectory(main) + add_subdirectory(perplexity) + add_subdirectory(quantize) + if (LLAMA_BUILD_SERVER) + add_subdirectory(server) + endif() + add_subdirectory(run) + add_subdirectory(tokenize) + add_subdirectory(tts) + if (NOT GGML_BACKEND_DL) + # these examples use the backends directly and cannot be built with dynamic loading + add_subdirectory(cvector-generator) + add_subdirectory(export-lora) + add_subdirectory(llava) + if (GGML_RPC) + add_subdirectory(rpc) + endif() + endif() +endif() diff --git a/examples/batched-bench/CMakeLists.txt b/tools/batched-bench/CMakeLists.txt similarity index 100% rename from examples/batched-bench/CMakeLists.txt rename to tools/batched-bench/CMakeLists.txt diff --git a/examples/batched-bench/README.md b/tools/batched-bench/README.md similarity index 100% rename from examples/batched-bench/README.md rename to tools/batched-bench/README.md diff --git a/examples/batched-bench/batched-bench.cpp b/tools/batched-bench/batched-bench.cpp similarity index 100% rename from examples/batched-bench/batched-bench.cpp rename to tools/batched-bench/batched-bench.cpp diff --git a/examples/cvector-generator/CMakeLists.txt b/tools/cvector-generator/CMakeLists.txt similarity index 100% rename from examples/cvector-generator/CMakeLists.txt rename to tools/cvector-generator/CMakeLists.txt diff --git a/examples/cvector-generator/README.md b/tools/cvector-generator/README.md similarity index 100% rename from examples/cvector-generator/README.md rename to tools/cvector-generator/README.md diff --git a/examples/cvector-generator/completions.txt b/tools/cvector-generator/completions.txt similarity index 100% rename from examples/cvector-generator/completions.txt rename to tools/cvector-generator/completions.txt diff --git a/examples/cvector-generator/cvector-generator.cpp b/tools/cvector-generator/cvector-generator.cpp similarity index 100% rename from examples/cvector-generator/cvector-generator.cpp rename to tools/cvector-generator/cvector-generator.cpp diff --git a/examples/cvector-generator/mean.hpp b/tools/cvector-generator/mean.hpp similarity index 100% rename from examples/cvector-generator/mean.hpp rename to tools/cvector-generator/mean.hpp diff --git a/examples/cvector-generator/negative.txt b/tools/cvector-generator/negative.txt similarity index 100% rename from examples/cvector-generator/negative.txt rename to tools/cvector-generator/negative.txt diff --git a/examples/cvector-generator/pca.hpp b/tools/cvector-generator/pca.hpp similarity index 100% rename from examples/cvector-generator/pca.hpp rename to tools/cvector-generator/pca.hpp diff --git a/examples/cvector-generator/positive.txt b/tools/cvector-generator/positive.txt similarity index 100% rename from examples/cvector-generator/positive.txt rename to tools/cvector-generator/positive.txt diff --git a/examples/export-lora/CMakeLists.txt b/tools/export-lora/CMakeLists.txt similarity index 100% rename from examples/export-lora/CMakeLists.txt rename to tools/export-lora/CMakeLists.txt diff --git a/examples/export-lora/README.md b/tools/export-lora/README.md similarity index 100% rename from examples/export-lora/README.md rename to tools/export-lora/README.md diff --git a/examples/export-lora/export-lora.cpp b/tools/export-lora/export-lora.cpp similarity index 100% rename from examples/export-lora/export-lora.cpp rename to tools/export-lora/export-lora.cpp diff --git a/examples/gguf-split/CMakeLists.txt b/tools/gguf-split/CMakeLists.txt similarity index 100% rename from examples/gguf-split/CMakeLists.txt rename to tools/gguf-split/CMakeLists.txt diff --git a/examples/gguf-split/README.md b/tools/gguf-split/README.md similarity index 100% rename from examples/gguf-split/README.md rename to tools/gguf-split/README.md diff --git a/examples/gguf-split/gguf-split.cpp b/tools/gguf-split/gguf-split.cpp similarity index 100% rename from examples/gguf-split/gguf-split.cpp rename to tools/gguf-split/gguf-split.cpp diff --git a/examples/gguf-split/tests.sh b/tools/gguf-split/tests.sh similarity index 100% rename from examples/gguf-split/tests.sh rename to tools/gguf-split/tests.sh diff --git a/examples/imatrix/CMakeLists.txt b/tools/imatrix/CMakeLists.txt similarity index 100% rename from examples/imatrix/CMakeLists.txt rename to tools/imatrix/CMakeLists.txt diff --git a/examples/imatrix/README.md b/tools/imatrix/README.md similarity index 98% rename from examples/imatrix/README.md rename to tools/imatrix/README.md index 9aa2b20347..6d8897d98b 100644 --- a/examples/imatrix/README.md +++ b/tools/imatrix/README.md @@ -1,4 +1,4 @@ -# llama.cpp/examples/imatrix +# llama.cpp/tools/imatrix Compute an importance matrix for a model and given text dataset. Can be used during quantization to enhance the quality of the quantized models. More information is available here: https://github.com/ggml-org/llama.cpp/pull/4861 diff --git a/examples/imatrix/imatrix.cpp b/tools/imatrix/imatrix.cpp similarity index 100% rename from examples/imatrix/imatrix.cpp rename to tools/imatrix/imatrix.cpp diff --git a/examples/llama-bench/CMakeLists.txt b/tools/llama-bench/CMakeLists.txt similarity index 100% rename from examples/llama-bench/CMakeLists.txt rename to tools/llama-bench/CMakeLists.txt diff --git a/examples/llama-bench/README.md b/tools/llama-bench/README.md similarity index 99% rename from examples/llama-bench/README.md rename to tools/llama-bench/README.md index 1f5e2f6620..d6fc77df88 100644 --- a/examples/llama-bench/README.md +++ b/tools/llama-bench/README.md @@ -1,4 +1,4 @@ -# llama.cpp/examples/llama-bench +# llama.cpp/tools/llama-bench Performance testing tool for llama.cpp. diff --git a/examples/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp similarity index 100% rename from examples/llama-bench/llama-bench.cpp rename to tools/llama-bench/llama-bench.cpp diff --git a/examples/llava/CMakeLists.txt b/tools/llava/CMakeLists.txt similarity index 100% rename from examples/llava/CMakeLists.txt rename to tools/llava/CMakeLists.txt diff --git a/examples/llava/README-quantize.md b/tools/llava/README-quantize.md similarity index 100% rename from examples/llava/README-quantize.md rename to tools/llava/README-quantize.md diff --git a/examples/llava/README.md b/tools/llava/README.md similarity index 100% rename from examples/llava/README.md rename to tools/llava/README.md diff --git a/examples/llava/android/adb_run.sh b/tools/llava/android/adb_run.sh similarity index 100% rename from examples/llava/android/adb_run.sh rename to tools/llava/android/adb_run.sh diff --git a/examples/llava/android/build_64.sh b/tools/llava/android/build_64.sh similarity index 100% rename from examples/llava/android/build_64.sh rename to tools/llava/android/build_64.sh diff --git a/examples/llava/clip-impl.h b/tools/llava/clip-impl.h similarity index 100% rename from examples/llava/clip-impl.h rename to tools/llava/clip-impl.h diff --git a/examples/llava/clip-quantize-cli.cpp b/tools/llava/clip-quantize-cli.cpp similarity index 100% rename from examples/llava/clip-quantize-cli.cpp rename to tools/llava/clip-quantize-cli.cpp diff --git a/examples/llava/clip.cpp b/tools/llava/clip.cpp similarity index 100% rename from examples/llava/clip.cpp rename to tools/llava/clip.cpp diff --git a/examples/llava/clip.h b/tools/llava/clip.h similarity index 100% rename from examples/llava/clip.h rename to tools/llava/clip.h diff --git a/examples/llava/convert_image_encoder_to_gguf.py b/tools/llava/convert_image_encoder_to_gguf.py similarity index 100% rename from examples/llava/convert_image_encoder_to_gguf.py rename to tools/llava/convert_image_encoder_to_gguf.py diff --git a/examples/llava/deprecation-warning.cpp b/tools/llava/deprecation-warning.cpp similarity index 100% rename from examples/llava/deprecation-warning.cpp rename to tools/llava/deprecation-warning.cpp diff --git a/examples/llava/glmedge-convert-image-encoder-to-gguf.py b/tools/llava/glmedge-convert-image-encoder-to-gguf.py similarity index 100% rename from examples/llava/glmedge-convert-image-encoder-to-gguf.py rename to tools/llava/glmedge-convert-image-encoder-to-gguf.py diff --git a/examples/llava/glmedge-surgery.py b/tools/llava/glmedge-surgery.py similarity index 100% rename from examples/llava/glmedge-surgery.py rename to tools/llava/glmedge-surgery.py diff --git a/examples/llava/llava.cpp b/tools/llava/llava.cpp similarity index 100% rename from examples/llava/llava.cpp rename to tools/llava/llava.cpp diff --git a/examples/llava/llava.h b/tools/llava/llava.h similarity index 100% rename from examples/llava/llava.h rename to tools/llava/llava.h diff --git a/examples/llava/llava_surgery.py b/tools/llava/llava_surgery.py similarity index 100% rename from examples/llava/llava_surgery.py rename to tools/llava/llava_surgery.py diff --git a/examples/llava/llava_surgery_v2.py b/tools/llava/llava_surgery_v2.py similarity index 100% rename from examples/llava/llava_surgery_v2.py rename to tools/llava/llava_surgery_v2.py diff --git a/examples/llava/minicpmv-convert-image-encoder-to-gguf.py b/tools/llava/minicpmv-convert-image-encoder-to-gguf.py similarity index 100% rename from examples/llava/minicpmv-convert-image-encoder-to-gguf.py rename to tools/llava/minicpmv-convert-image-encoder-to-gguf.py diff --git a/examples/llava/minicpmv-surgery.py b/tools/llava/minicpmv-surgery.py similarity index 100% rename from examples/llava/minicpmv-surgery.py rename to tools/llava/minicpmv-surgery.py diff --git a/examples/llava/mtmd-cli.cpp b/tools/llava/mtmd-cli.cpp similarity index 100% rename from examples/llava/mtmd-cli.cpp rename to tools/llava/mtmd-cli.cpp diff --git a/examples/llava/mtmd.cpp b/tools/llava/mtmd.cpp similarity index 100% rename from examples/llava/mtmd.cpp rename to tools/llava/mtmd.cpp diff --git a/examples/llava/mtmd.h b/tools/llava/mtmd.h similarity index 100% rename from examples/llava/mtmd.h rename to tools/llava/mtmd.h diff --git a/examples/llava/qwen2vl-test.cpp b/tools/llava/qwen2vl-test.cpp similarity index 100% rename from examples/llava/qwen2vl-test.cpp rename to tools/llava/qwen2vl-test.cpp diff --git a/examples/llava/requirements.txt b/tools/llava/requirements.txt similarity index 100% rename from examples/llava/requirements.txt rename to tools/llava/requirements.txt diff --git a/examples/llava/test-1.jpeg b/tools/llava/test-1.jpeg similarity index 100% rename from examples/llava/test-1.jpeg rename to tools/llava/test-1.jpeg diff --git a/examples/llava/tests.sh b/tools/llava/tests.sh similarity index 100% rename from examples/llava/tests.sh rename to tools/llava/tests.sh diff --git a/examples/main/CMakeLists.txt b/tools/main/CMakeLists.txt similarity index 100% rename from examples/main/CMakeLists.txt rename to tools/main/CMakeLists.txt diff --git a/examples/main/README.md b/tools/main/README.md similarity index 99% rename from examples/main/README.md rename to tools/main/README.md index e4b3590b5d..4f16ad6b2b 100644 --- a/examples/main/README.md +++ b/tools/main/README.md @@ -1,4 +1,4 @@ -# llama.cpp/examples/main +# llama.cpp/tools/main This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggml-org/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts. diff --git a/examples/main/main.cpp b/tools/main/main.cpp similarity index 100% rename from examples/main/main.cpp rename to tools/main/main.cpp diff --git a/examples/perplexity/CMakeLists.txt b/tools/perplexity/CMakeLists.txt similarity index 100% rename from examples/perplexity/CMakeLists.txt rename to tools/perplexity/CMakeLists.txt diff --git a/examples/perplexity/README.md b/tools/perplexity/README.md similarity index 100% rename from examples/perplexity/README.md rename to tools/perplexity/README.md diff --git a/examples/perplexity/perplexity.cpp b/tools/perplexity/perplexity.cpp similarity index 100% rename from examples/perplexity/perplexity.cpp rename to tools/perplexity/perplexity.cpp diff --git a/examples/quantize/CMakeLists.txt b/tools/quantize/CMakeLists.txt similarity index 100% rename from examples/quantize/CMakeLists.txt rename to tools/quantize/CMakeLists.txt diff --git a/examples/quantize/README.md b/tools/quantize/README.md similarity index 100% rename from examples/quantize/README.md rename to tools/quantize/README.md diff --git a/examples/quantize/quantize.cpp b/tools/quantize/quantize.cpp similarity index 100% rename from examples/quantize/quantize.cpp rename to tools/quantize/quantize.cpp diff --git a/examples/quantize/tests.sh b/tools/quantize/tests.sh similarity index 100% rename from examples/quantize/tests.sh rename to tools/quantize/tests.sh diff --git a/examples/rpc/CMakeLists.txt b/tools/rpc/CMakeLists.txt similarity index 100% rename from examples/rpc/CMakeLists.txt rename to tools/rpc/CMakeLists.txt diff --git a/examples/rpc/README.md b/tools/rpc/README.md similarity index 100% rename from examples/rpc/README.md rename to tools/rpc/README.md diff --git a/examples/rpc/rpc-server.cpp b/tools/rpc/rpc-server.cpp similarity index 100% rename from examples/rpc/rpc-server.cpp rename to tools/rpc/rpc-server.cpp diff --git a/examples/run/CMakeLists.txt b/tools/run/CMakeLists.txt similarity index 100% rename from examples/run/CMakeLists.txt rename to tools/run/CMakeLists.txt diff --git a/examples/run/README.md b/tools/run/README.md similarity index 100% rename from examples/run/README.md rename to tools/run/README.md diff --git a/examples/run/linenoise.cpp/linenoise.cpp b/tools/run/linenoise.cpp/linenoise.cpp similarity index 100% rename from examples/run/linenoise.cpp/linenoise.cpp rename to tools/run/linenoise.cpp/linenoise.cpp diff --git a/examples/run/linenoise.cpp/linenoise.h b/tools/run/linenoise.cpp/linenoise.h similarity index 100% rename from examples/run/linenoise.cpp/linenoise.h rename to tools/run/linenoise.cpp/linenoise.h diff --git a/examples/run/run.cpp b/tools/run/run.cpp similarity index 100% rename from examples/run/run.cpp rename to tools/run/run.cpp diff --git a/examples/server/CMakeLists.txt b/tools/server/CMakeLists.txt similarity index 100% rename from examples/server/CMakeLists.txt rename to tools/server/CMakeLists.txt diff --git a/examples/server/README.md b/tools/server/README.md similarity index 99% rename from examples/server/README.md rename to tools/server/README.md index 61446a0ba2..0ec786ea76 100644 --- a/examples/server/README.md +++ b/tools/server/README.md @@ -232,7 +232,7 @@ To build or to run the dev server (with hot reload): ```sh # make sure you have nodejs installed -cd examples/server/webui +cd tools/server/webui npm i # to run the dev server @@ -242,7 +242,7 @@ npm run dev npm run build ``` After `public/index.html.gz` has been generated we need to generate the c++ -headers (like build/examples/server/index.html.gz.hpp) that will be included +headers (like build/tools/server/index.html.gz.hpp) that will be included by server.cpp. This is done by building `llama-server` as described in the [build](#build) section above. @@ -1228,12 +1228,12 @@ Apart from error types supported by OAI, we also have custom types that are spec ### Legacy completion web UI -A new chat-based UI has replaced the old completion-based since [this PR](https://github.com/ggml-org/llama.cpp/pull/10175). If you want to use the old completion, start the server with `--path ./examples/server/public_legacy` +A new chat-based UI has replaced the old completion-based since [this PR](https://github.com/ggml-org/llama.cpp/pull/10175). If you want to use the old completion, start the server with `--path ./tools/server/public_legacy` For example: ```sh -./llama-server -m my_model.gguf -c 8192 --path ./examples/server/public_legacy +./llama-server -m my_model.gguf -c 8192 --path ./tools/server/public_legacy ``` ### Extending or building alternative Web Front End diff --git a/examples/server/bench/README.md b/tools/server/bench/README.md similarity index 100% rename from examples/server/bench/README.md rename to tools/server/bench/README.md diff --git a/examples/server/bench/bench.py b/tools/server/bench/bench.py similarity index 100% rename from examples/server/bench/bench.py rename to tools/server/bench/bench.py diff --git a/examples/server/bench/prometheus.yml b/tools/server/bench/prometheus.yml similarity index 100% rename from examples/server/bench/prometheus.yml rename to tools/server/bench/prometheus.yml diff --git a/examples/server/bench/requirements.txt b/tools/server/bench/requirements.txt similarity index 100% rename from examples/server/bench/requirements.txt rename to tools/server/bench/requirements.txt diff --git a/examples/server/bench/script.js b/tools/server/bench/script.js similarity index 100% rename from examples/server/bench/script.js rename to tools/server/bench/script.js diff --git a/examples/server/chat-llama2.sh b/tools/server/chat-llama2.sh similarity index 100% rename from examples/server/chat-llama2.sh rename to tools/server/chat-llama2.sh diff --git a/examples/server/chat.mjs b/tools/server/chat.mjs similarity index 100% rename from examples/server/chat.mjs rename to tools/server/chat.mjs diff --git a/examples/server/chat.sh b/tools/server/chat.sh similarity index 100% rename from examples/server/chat.sh rename to tools/server/chat.sh diff --git a/examples/server/httplib.h b/tools/server/httplib.h similarity index 100% rename from examples/server/httplib.h rename to tools/server/httplib.h diff --git a/tools/server/public/index.html.gz 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