From 7fdd16b432d247121fe5fe7b21f8805f85266c85 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 8 Oct 2025 10:57:29 +0300 Subject: [PATCH 001/144] server : improve context checkpoint logic (#16440) --- src/llama-memory-recurrent.cpp | 5 +- tools/server/server.cpp | 86 ++++++++++++++++++++-------------- 2 files changed, 56 insertions(+), 35 deletions(-) diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp index 9402d9cb8d..d67f5a5f47 100644 --- a/src/llama-memory-recurrent.cpp +++ b/src/llama-memory-recurrent.cpp @@ -861,9 +861,12 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: bool llama_memory_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); + if (cell_count == 0) { + return true; + } + llama_batch_allocr balloc(hparams.n_pos_per_embd()); llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1); diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 307653764c..5980d43b49 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -3676,6 +3676,20 @@ struct server_context { alora_disabled_id = enabled_loras[0]; } + bool do_checkpoint = params_base.n_ctx_checkpoints > 0; + + // make a checkpoint of the parts of the memory that cannot be rolled back. + // checkpoints are created only if: + // - the model uses SWA and we are not using `swa_full` + // - the model architecture is marked as recurrent or hybrid + // + // TODO: try to make this conditional on the context or the memory module, instead of the model type + do_checkpoint = do_checkpoint && ( + llama_model_is_recurrent(model) || + llama_model_is_hybrid(model) || + (llama_model_n_swa(model) > 0 && !params_base.swa_full) + ); + // add prompt tokens for processing in the current batch while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) { // get next token to process @@ -3700,6 +3714,11 @@ struct server_context { slot.n_prompt_tokens_processed++; slot.n_past++; + + // process the last few tokens of the prompt separately in order to allow for a checkpoint to be created. + if (do_checkpoint && slot.n_prompt_tokens - slot.n_past == 64) { + break; + } } // SLT_INF(slot, "new cache_tokens: %s\n", slot.cache_tokens.str().c_str()); @@ -3730,6 +3749,39 @@ struct server_context { slot.i_batch = batch.n_tokens - 1; SLT_INF(slot, "prompt done, n_past = %d, n_tokens = %d\n", slot.n_past, batch.n_tokens); + + const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id); + const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id); + + // no need for empty or small checkpoints + do_checkpoint = do_checkpoint && (pos_min >= 0 && pos_max >= 64); + + // no need to create checkpoints that are too close together + do_checkpoint = do_checkpoint && (slot.ctx_checkpoints.empty() || pos_max > slot.ctx_checkpoints.back().pos_max + 64); + + if (do_checkpoint) { + while (slot.ctx_checkpoints.size() >= (size_t) params_base.n_ctx_checkpoints) { + // make room for the new checkpoint, if needed + const auto & cur = slot.ctx_checkpoints.front(); + SLT_WRN(slot, "erasing old context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", + cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024); + + slot.ctx_checkpoints.erase(slot.ctx_checkpoints.begin()); + } + + const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); + + auto & cur = slot.ctx_checkpoints.emplace_back(ctx_checkpoint{ + /*.pos_min = */ pos_min, + /*.pos_max = */ pos_max, + /*.data = */ std::vector(checkpoint_size), + }); + + llama_state_seq_get_data_ext(ctx, cur.data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); + + SLT_WRN(slot, "saved context checkpoint %d of %d (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", + (int) slot.ctx_checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024); + } } } @@ -3853,40 +3905,6 @@ struct server_context { // prompt evaluated for next-token prediction slot.state = SLOT_STATE_GENERATING; - - // make a checkpoint of the parts of the memory that cannot be rolled back. - // checkpoints are created only if: - // - the model uses SWA and we are not using `swa_full` - // - the model architecture is marked as recurrent or hybrid - // - // TODO: try to make this conditional on the context or the memory module, instead of the model type - const bool do_checkpoint = - (llama_model_is_recurrent(model) || llama_model_is_hybrid(model)) || - (llama_model_n_swa(model) > 0 && !params_base.swa_full); - - if (do_checkpoint && params_base.n_ctx_checkpoints > 0) { - while (slot.ctx_checkpoints.size() >= (size_t) params_base.n_ctx_checkpoints) { - // make room for the new checkpoint, if needed - const auto & cur = slot.ctx_checkpoints.front(); - SLT_WRN(slot, "erasing old context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", - cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024); - - slot.ctx_checkpoints.erase(slot.ctx_checkpoints.begin()); - } - - const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); - - auto & cur = slot.ctx_checkpoints.emplace_back(ctx_checkpoint{ - /*.pos_min = */ llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id), - /*.pos_max = */ llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id), - /*.data = */ std::vector(checkpoint_size), - }); - - llama_state_seq_get_data_ext(ctx, cur.data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); - - SLT_WRN(slot, "saved context checkpoint %d of %d (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", - (int) slot.ctx_checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024); - } } else if (slot.state != SLOT_STATE_GENERATING) { continue; // continue loop of slots } From b2c08c9ec4cd59ed88c1ebdd94f109af5ccc978e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 8 Oct 2025 10:57:53 +0300 Subject: [PATCH 002/144] metal : mark FA blocks (#16372) * metal : better unroll in the FA kernels * metal : index FA blocks * tests : restore [no ci] * metal : prevent division by zero in FA kernels * metal : fix -INF detection logic --- ggml/src/ggml-metal/ggml-metal-device.cpp | 48 +++++- ggml/src/ggml-metal/ggml-metal-device.h | 6 + ggml/src/ggml-metal/ggml-metal-impl.h | 29 +++- ggml/src/ggml-metal/ggml-metal-ops.cpp | 113 +++++++++++-- ggml/src/ggml-metal/ggml-metal-ops.h | 1 + ggml/src/ggml-metal/ggml-metal.cpp | 1 + ggml/src/ggml-metal/ggml-metal.metal | 191 +++++++++++++++++----- 7 files changed, 324 insertions(+), 65 deletions(-) diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index 46cc513459..e23abdda97 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -959,7 +959,53 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_pad( //ggml_metal_cv_set_int32(cv, ns20, FC_FLASH_ATTN_EXT_PAD + 21); //ggml_metal_cv_set_int32(cv, nsg, FC_FLASH_ATTN_EXT_PAD + 22); //ggml_metal_cv_set_int32(cv, nwg, FC_FLASH_ATTN_EXT_PAD + 23); - ggml_metal_cv_set_int32(cv, ncpsg, FC_FLASH_ATTN_EXT_PAD + 24); + //ggml_metal_cv_set_int32(cv, nqptg, FC_FLASH_ATTN_EXT_PAD + 24); + ggml_metal_cv_set_int32(cv, ncpsg, FC_FLASH_ATTN_EXT_PAD + 25); + + res = ggml_metal_library_compile_pipeline(lib, base, name, cv); + + ggml_metal_cv_free(cv); + + return res; +} + +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_blk( + ggml_metal_library_t lib, + const struct ggml_tensor * op, + int32_t nqptg, + int32_t ncpsg) { + assert(op->op == GGML_OP_FLASH_ATTN_EXT); + GGML_UNUSED(op); + + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_%s", + "flash_attn_ext_blk"); + + snprintf(name, 256, "%s_nqptg=%d_ncpsg=%d", + base, + nqptg, + ncpsg); + + ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name); + if (res) { + return res; + } + + ggml_metal_cv_t cv = ggml_metal_cv_init(); + + //ggml_metal_cv_set_bool(cv, has_mask, FC_FLASH_ATTN_EXT_BLK + 0); + //ggml_metal_cv_set_bool(cv, has_sinks, FC_FLASH_ATTN_EXT_BLK + 1); + //ggml_metal_cv_set_bool(cv, has_bias, FC_FLASH_ATTN_EXT_BLK + 2); + //ggml_metal_cv_set_bool(cv, has_scap, FC_FLASH_ATTN_EXT_BLK + 3); + + //ggml_metal_cv_set_int32(cv, ns10, FC_FLASH_ATTN_EXT_BLK + 20); + //ggml_metal_cv_set_int32(cv, ns20, FC_FLASH_ATTN_EXT_BLK + 21); + //ggml_metal_cv_set_int32(cv, nsg, FC_FLASH_ATTN_EXT_BLK + 22); + //ggml_metal_cv_set_int32(cv, nwg, FC_FLASH_ATTN_EXT_BLK + 23); + ggml_metal_cv_set_int32(cv, nqptg, FC_FLASH_ATTN_EXT_BLK + 24); + ggml_metal_cv_set_int32(cv, ncpsg, FC_FLASH_ATTN_EXT_BLK + 25); res = ggml_metal_library_compile_pipeline(lib, base, name, cv); diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index ef04950738..1034e4bbf6 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -141,6 +141,12 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_pad( bool has_mask, int32_t ncpsg); +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_blk( + ggml_metal_library_t lib, + const struct ggml_tensor * op, + int32_t nqptg, + int32_t ncpsg); + ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext( ggml_metal_library_t lib, const struct ggml_tensor * op, diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index 1524b3ab51..c9dff87305 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -70,11 +70,19 @@ // function constants offsets #define FC_FLASH_ATTN_EXT_PAD 100 -#define FC_FLASH_ATTN_EXT 200 -#define FC_FLASH_ATTN_EXT_VEC 300 -#define FC_FLASH_ATTN_EXT_VEC_REDUCE 400 -#define FC_MUL_MV 500 -#define FC_MUL_MM 600 +#define FC_FLASH_ATTN_EXT_BLK 200 +#define FC_FLASH_ATTN_EXT 300 +#define FC_FLASH_ATTN_EXT_VEC 400 +#define FC_FLASH_ATTN_EXT_VEC_REDUCE 500 +#define FC_MUL_MV 600 +#define FC_MUL_MM 700 + +// op-specific constants +#define OP_FLASH_ATTN_EXT_NQPTG 8 +#define OP_FLASH_ATTN_EXT_NCPSG 64 + +#define OP_FLASH_ATTN_EXT_VEC_NQPTG 1 +#define OP_FLASH_ATTN_EXT_VEC_NCPSG 32 // kernel argument structs // @@ -263,6 +271,17 @@ typedef struct { uint64_t nb33; } ggml_metal_kargs_flash_attn_ext_pad; +typedef struct { + int32_t ne01; + int32_t ne30; + int32_t ne31; + int32_t ne32; + int32_t ne33; + uint64_t nb31; + uint64_t nb32; + uint64_t nb33; +} ggml_metal_kargs_flash_attn_ext_blk; + typedef struct { int32_t ne01; int32_t ne02; diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index 125cc64dc5..1137e21077 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -1918,19 +1918,19 @@ size_t ggml_metal_op_flash_attn_ext_extra_pad(const ggml_tensor * op) { const bool has_mask = op->src[3] != nullptr; if (ggml_metal_op_flash_attn_ext_use_vec(op)) { - const bool has_kvpad = ne11 % 32 != 0; + const bool has_kvpad = ne11 % OP_FLASH_ATTN_EXT_VEC_NCPSG != 0; if (has_kvpad) { - res += 32*( + res += OP_FLASH_ATTN_EXT_VEC_NCPSG*( nb11*ne12*ne13 + nb21*ne22*ne23 + (has_mask ? ggml_type_size(GGML_TYPE_F16)*ne31*ne32*ne33 : 0)); } } else { - const bool has_kvpad = ne11 % 64 != 0; + const bool has_kvpad = ne11 % OP_FLASH_ATTN_EXT_NCPSG != 0; if (has_kvpad) { - res += 64*( + res += OP_FLASH_ATTN_EXT_NCPSG*( nb11*ne12*ne13 + nb21*ne22*ne23 + (has_mask ? ggml_type_size(GGML_TYPE_F16)*ne31*ne32*ne33 : 0)); @@ -1940,6 +1940,44 @@ size_t ggml_metal_op_flash_attn_ext_extra_pad(const ggml_tensor * op) { return res; } +size_t ggml_metal_op_flash_attn_ext_extra_blk(const ggml_tensor * op) { + assert(op->op == GGML_OP_FLASH_ATTN_EXT); + + GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne); + //GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb); + //GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne); + //GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb); + //GGML_TENSOR_LOCALS( int32_t, ne2, op->src[2], ne); + //GGML_TENSOR_LOCALS(uint64_t, nb2, op->src[2], nb); + GGML_TENSOR_LOCALS( int32_t, ne3, op->src[3], ne); + GGML_TENSOR_LOCALS(uint64_t, nb3, op->src[3], nb); + + size_t res = 0; + + const bool has_mask = op->src[3] != nullptr; + + if (!has_mask) { + return res; + } + + const bool is_vec = ggml_metal_op_flash_attn_ext_use_vec(op); + + // this optimization is not useful for the vector kernels + if (is_vec) { + return res; + } + + const int nqptg = is_vec ? OP_FLASH_ATTN_EXT_VEC_NQPTG : OP_FLASH_ATTN_EXT_NQPTG; + const int ncpsg = is_vec ? OP_FLASH_ATTN_EXT_VEC_NCPSG : OP_FLASH_ATTN_EXT_NCPSG; + + const int64_t ne1 = (ne01 + nqptg - 1)/nqptg; + const int64_t ne0 = (ne30 + ncpsg - 1)/ncpsg; + + res += GGML_PAD(ggml_type_size(GGML_TYPE_I8)*ne0*ne1*ne32*ne33, 32); + + return res; +} + size_t ggml_metal_op_flash_attn_ext_extra_tmp(const ggml_tensor * op) { assert(op->op == GGML_OP_FLASH_ATTN_EXT); @@ -2034,18 +2072,23 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) { ggml_metal_buffer_id bid_pad = bid_dst; bid_pad.offs += ggml_nbytes(op); - ggml_metal_buffer_id bid_tmp = bid_pad; - bid_tmp.offs += ggml_metal_op_flash_attn_ext_extra_pad(op); + ggml_metal_buffer_id bid_blk = bid_pad; + bid_blk.offs += ggml_metal_op_flash_attn_ext_extra_pad(op); + + ggml_metal_buffer_id bid_tmp = bid_blk; + bid_tmp.offs += ggml_metal_op_flash_attn_ext_extra_blk(op); if (!ggml_metal_op_flash_attn_ext_use_vec(op)) { // half8x8 kernel - const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !! - const int64_t ncpsg = 64; // cache values per simdgroup !! sync with kernel template arguments !! + const int nqptg = OP_FLASH_ATTN_EXT_NQPTG; // queries per threadgroup + const int ncpsg = OP_FLASH_ATTN_EXT_NCPSG; // cache values per simdgroup GGML_ASSERT(nqptg <= 32); GGML_ASSERT(nqptg % 8 == 0); GGML_ASSERT(ncpsg % 32 == 0); + bool need_sync = false; + const bool has_kvpad = ne11 % ncpsg != 0; if (has_kvpad) { @@ -2083,11 +2126,46 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) { ggml_metal_encoder_dispatch_threadgroups(enc, ncpsg, std::max(ne12, ne32), std::max(ne13, ne33), 32, 1, 1); - ggml_metal_op_concurrency_reset(ctx); + need_sync = true; } else { assert(ggml_metal_op_flash_attn_ext_extra_pad(op) == 0); } + if (has_mask) { + assert(ggml_metal_op_flash_attn_ext_extra_blk(op) != 0); + + ggml_metal_kargs_flash_attn_ext_blk args0 = { + /*.ne01 =*/ ne01, + /*.ne30 =*/ ne30, + /*.ne31 =*/ ne31, + /*.ne32 =*/ ne32, + /*.ne33 =*/ ne33, + /*.nb31 =*/ nb31, + /*.nb32 =*/ nb32, + /*.nb33 =*/ nb33, + }; + + ggml_metal_pipeline_t pipeline0 = ggml_metal_library_get_pipeline_flash_attn_ext_blk(lib, op, nqptg, ncpsg); + + ggml_metal_encoder_set_pipeline(enc, pipeline0); + ggml_metal_encoder_set_bytes (enc, &args0, sizeof(args0), 0); + ggml_metal_encoder_set_buffer (enc, bid_src3, 1); + ggml_metal_encoder_set_buffer (enc, bid_blk, 2); + + const int32_t nblk1 = ((ne01 + nqptg - 1)/nqptg); + const int32_t nblk0 = ((ne30 + ncpsg - 1)/ncpsg); + + ggml_metal_encoder_dispatch_threadgroups(enc, nblk0, nblk1, ne32*ne33, 32, 1, 1); + + need_sync = true; + } else { + assert(ggml_metal_op_flash_attn_ext_extra_blk(op) == 0); + } + + if (need_sync) { + ggml_metal_op_concurrency_reset(ctx); + } + const int is_q = ggml_is_quantized(op->src[1]->type) ? 1 : 0; // 2*(2*ncpsg) @@ -2164,7 +2242,8 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) { ggml_metal_encoder_set_buffer (enc, bid_src3, 4); ggml_metal_encoder_set_buffer (enc, bid_src4, 5); ggml_metal_encoder_set_buffer (enc, bid_pad, 6); - ggml_metal_encoder_set_buffer (enc, bid_dst, 7); + ggml_metal_encoder_set_buffer (enc, bid_blk, 7); + ggml_metal_encoder_set_buffer (enc, bid_dst, 8); ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0); @@ -2172,14 +2251,16 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) { #undef FATTN_SMEM } else { // half4x4 kernel - const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !! - const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! - const int64_t nkpsg = 1*ncpsg; + const int nqptg = OP_FLASH_ATTN_EXT_VEC_NQPTG; // queries per threadgroup + const int ncpsg = OP_FLASH_ATTN_EXT_VEC_NCPSG; // cache values per simdgroup !! sync with kernel template arguments !! + const int nkpsg = 1*ncpsg; GGML_ASSERT(nqptg <= 32); GGML_ASSERT(nqptg % 1 == 0); GGML_ASSERT(ncpsg % 32 == 0); + bool need_sync = false; + const bool has_kvpad = ne11 % ncpsg != 0; if (has_kvpad) { @@ -2217,11 +2298,15 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) { ggml_metal_encoder_dispatch_threadgroups(enc, ncpsg, std::max(ne12, ne32), std::max(ne13, ne33), 32, 1, 1); - ggml_metal_op_concurrency_reset(ctx); + need_sync = true; } else { assert(ggml_metal_op_flash_attn_ext_extra_pad(op) == 0); } + if (need_sync) { + ggml_metal_op_concurrency_reset(ctx); + } + // ne00 + 2*ncpsg*(nsg) // for each query, we load it as f16 in shared memory (ne00) // and store the soft_max values and the mask diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h index 6a6d8a7977..d4cb944621 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.h +++ b/ggml/src/ggml-metal/ggml-metal-ops.h @@ -40,6 +40,7 @@ size_t ggml_metal_op_mul_mat_id_extra_ids(const struct ggml_tensor * op); bool ggml_metal_op_flash_attn_ext_use_vec(const struct ggml_tensor * op); size_t ggml_metal_op_flash_attn_ext_extra_pad(const struct ggml_tensor * op); +size_t ggml_metal_op_flash_attn_ext_extra_blk(const struct ggml_tensor * op); size_t ggml_metal_op_flash_attn_ext_extra_tmp(const struct ggml_tensor * op); int ggml_metal_op_concat (ggml_metal_op_t ctx, int idx); diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp index e53f37b29c..7afc881fa7 100644 --- a/ggml/src/ggml-metal/ggml-metal.cpp +++ b/ggml/src/ggml-metal/ggml-metal.cpp @@ -194,6 +194,7 @@ static size_t ggml_backend_metal_buffer_type_get_alloc_size(ggml_backend_buffer_ case GGML_OP_FLASH_ATTN_EXT: { res += ggml_metal_op_flash_attn_ext_extra_pad(tensor); + res += ggml_metal_op_flash_attn_ext_extra_blk(tensor); res += ggml_metal_op_flash_attn_ext_extra_tmp(tensor); } break; default: diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index c52c6b48ad..45d91def88 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -4351,7 +4351,7 @@ kernel void kernel_leaky_relu_f32_4( constant bool FC_flash_attn_ext_pad_has_mask [[function_constant(FC_FLASH_ATTN_EXT_PAD + 0)]]; -constant int32_t FC_flash_attn_ext_pad_ncpsg [[function_constant(FC_FLASH_ATTN_EXT_PAD + 24)]]; +constant int32_t FC_flash_attn_ext_pad_ncpsg [[function_constant(FC_FLASH_ATTN_EXT_PAD + 25)]]; // pad the last chunk of C elements of k and v into a an extra pad buffer kernel void kernel_flash_attn_ext_pad( @@ -4419,6 +4419,65 @@ kernel void kernel_flash_attn_ext_pad( } } +constant int32_t FC_flash_attn_ext_blk_nqptg [[function_constant(FC_FLASH_ATTN_EXT_BLK + 24)]]; +constant int32_t FC_flash_attn_ext_blk_ncpsg [[function_constant(FC_FLASH_ATTN_EXT_BLK + 25)]]; + +// scan the blocks of the mask that are not masked +// 0 - masked (i.e. full of -INF, skip) +// 1 - not masked (i.e. at least one element of the mask is not -INF) +kernel void kernel_flash_attn_ext_blk( + constant ggml_metal_kargs_flash_attn_ext_blk & args, + device const char * mask, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]]) { + // block size C x Q + const int32_t Q = FC_flash_attn_ext_blk_nqptg; + const int32_t C = FC_flash_attn_ext_blk_ncpsg; + + constexpr short NW = N_SIMDWIDTH; + + const int32_t i3 = tgpig[2]/args.ne32; + const int32_t i2 = tgpig[2]%args.ne32; + const int32_t i1 = tgpig[1]; + const int32_t i0 = tgpig[0]; + + char res = i0*C + C > args.ne30 ? 1 : 0; + + device const half * mask_src = (device const half *) (mask + (i1*Q)*args.nb31 + i2*args.nb32 + i3*args.nb33) + i0*C + tiisg; + + // fast route + if (res == 0) { + if (simd_max(*mask_src) > -MAXHALF/2) { + res = 1; + } + } + + // detailed check of the elements of the block + if ((C > NW || Q > 1) && res == 0) { + half m = -MAXHALF; + + FOR_UNROLL (short j = 0; j < Q; ++j) { + FOR_UNROLL (short ii = 0; ii < C/NW; ++ii) { + m = max(m, mask_src[ii*NW]); + } + + mask_src += args.nb31/2; + } + + if (simd_max(m) > -MAXHALF/2) { + res = 1; + } + } + + const int32_t nblk1 = ((args.ne01 + Q - 1)/Q); + const int32_t nblk0 = ((args.ne30 + C - 1)/C); + + if (tiisg == 0) { + dst[((i3*args.ne32 + i2)*nblk1 + i1)*nblk0 + i0] = res; + } +} + constant bool FC_flash_attn_ext_has_mask [[function_constant(FC_FLASH_ATTN_EXT + 0)]]; constant bool FC_flash_attn_ext_has_sinks [[function_constant(FC_FLASH_ATTN_EXT + 1)]]; constant bool FC_flash_attn_ext_has_bias [[function_constant(FC_FLASH_ATTN_EXT + 2)]]; @@ -4473,6 +4532,7 @@ void kernel_flash_attn_ext_impl( device const char * mask, device const char * sinks, device const char * pad, + device const char * blk, device char * dst, threadgroup half * shmem_f16, uint3 tgpig, @@ -4538,6 +4598,13 @@ void kernel_flash_attn_ext_impl( pm2[jj] = (device const half2 *) ((device const char *) mask + (iq1 + j)*args.nb31 + (iq2%args.ne32)*args.nb32 + (iq3%args.ne33)*args.nb33); } + { + const int32_t nblk1 = ((args.ne01 + Q - 1)/Q); + const int32_t nblk0 = ((args.ne11 + C - 1)/C); + + blk += (((iq3%args.ne33)*args.ne32 + (iq2%args.ne32))*nblk1 + iq1/Q)*nblk0; + } + { q += iq1*args.nb01 + iq2*args.nb02 + iq3*args.nb03; @@ -4597,11 +4664,14 @@ void kernel_flash_attn_ext_impl( // loop over the KV cache // each simdgroup handles blocks of Q rows and C columns - for (int ic0 = 0; ic0 < args.ne11; ic0 += C) { - int ic = ic0; + for (int ic0 = 0; ; ++ic0) { + int ic = ic0*C; + if (ic >= args.ne11) { + break; + } // the last partial chunk uses the pad buffer as source - if (FC_flash_attn_ext_has_kvpad && ic0 + C > args.ne11) { + if (FC_flash_attn_ext_has_kvpad && ic + C > args.ne11) { k = pad; v = k + args.nb11*C*args.ne_12_2*args.ne_12_3; mask = v + args.nb21*C*args.ne_12_2*args.ne_12_3; @@ -4640,6 +4710,14 @@ void kernel_flash_attn_ext_impl( // read the mask into shared mem if (FC_flash_attn_ext_has_mask) { + if (blk[ic0] == 0) { + FOR_UNROLL (short jj = 0; jj < NQ; ++jj) { + pm2[jj] += NW; + } + + continue; + } + FOR_UNROLL (short jj = 0; jj < NQ; ++jj) { const short j = jj*NSG + sgitg; @@ -4652,6 +4730,9 @@ void kernel_flash_attn_ext_impl( pm2[jj] += NW; } +#if 0 + // note: old -INF block optimization - obsoleted by pre-computing non-masked blocks + threadgroup_barrier(mem_flags::mem_threadgroup); // used to detect blocks full of -INF @@ -4670,6 +4751,7 @@ void kernel_flash_attn_ext_impl( continue; } +#endif } // Q*K^T @@ -4687,26 +4769,24 @@ void kernel_flash_attn_ext_impl( constexpr short NC = (C/8)/NSG; - // TODO: not good to unroll for large contexts - not sure why? + // note: do not unroll for large heads + #pragma unroll (DK <= 64 ? NC : 1) for (short cc = 0; cc < NC; ++cc) { qk8x8_t mqk = make_filled_simdgroup_matrix((qk_t) 0.0f); - if (DK8 % 16 != 0) { + if (DK % 16 != 0) { k8x8_t mk; q8x8_t mq; FOR_UNROLL (short i = 0; i < DK8; ++i) { simdgroup_barrier(mem_flags::mem_none); - simdgroup_load(mk, pk, NS10, 0, true); - simdgroup_load(mq, pq, DK); + simdgroup_load(mk, pk + 8*i, NS10, 0, true); + simdgroup_load(mq, pq + 8*i, DK); simdgroup_barrier(mem_flags::mem_none); simdgroup_multiply_accumulate(mqk, mq, mk, mqk); - - pk += 8; - pq += 8; } } else { k8x8_t mk[2]; @@ -4715,26 +4795,22 @@ void kernel_flash_attn_ext_impl( FOR_UNROLL (short i = 0; i < DK8/2; ++i) { simdgroup_barrier(mem_flags::mem_none); - simdgroup_load(mk[0], pk + 0*8, NS10, 0, true); - simdgroup_load(mk[1], pk + 1*8, NS10, 0, true); + simdgroup_load(mq[0], pq + 0*8 + 16*i, DK); + simdgroup_load(mq[1], pq + 1*8 + 16*i, DK); - simdgroup_load(mq[0], pq + 0*8, DK); - simdgroup_load(mq[1], pq + 1*8, DK); + simdgroup_load(mk[0], pk + 0*8 + 16*i, NS10, 0, true); + simdgroup_load(mk[1], pk + 1*8 + 16*i, NS10, 0, true); simdgroup_barrier(mem_flags::mem_none); simdgroup_multiply_accumulate(mqk, mq[0], mk[0], mqk); simdgroup_multiply_accumulate(mqk, mq[1], mk[1], mqk); - - pk += 16; - pq += 16; } } simdgroup_store(mqk, ps, SH, 0, false); - pk += 8*(NSG*NS10 - DK8); - pq += 8*(NSG*0 - DK8); + pk += 8*(NSG*NS10); ps += 8*(NSG); } } else { @@ -4868,27 +4944,50 @@ void kernel_flash_attn_ext_impl( } { - auto sst = ss; - device const v_t * pv = (device const v_t *) (v + ic*args.nb21); pv += 8*sgitg; - FOR_UNROLL (short cc = 0; cc < C/8; ++cc) { - s8x8_t vs; - simdgroup_load(vs, sst, SH, 0, false); + if (DV <= 64) { + FOR_UNROLL (short cc = 0; cc < C/8; ++cc) { + s8x8_t vs; + simdgroup_load(vs, ss + 8*cc, SH, 0, false); - FOR_UNROLL (short ii = 0; ii < NO; ++ii) { - v8x8_t mv; + FOR_UNROLL (short ii = 0; ii < NO/2; ++ii) { + v8x8_t mv[2]; - simdgroup_load(mv, pv, NS20, 0, false); - simdgroup_multiply_accumulate(lo[ii], vs, mv, lo[ii]); + simdgroup_load(mv[0], pv + 0*NSG + 16*ii*NSG, NS20, 0, false); + simdgroup_load(mv[1], pv + 8*NSG + 16*ii*NSG, NS20, 0, false); - pv += 8*NSG; + simdgroup_multiply_accumulate(lo[2*ii + 0], vs, mv[0], lo[2*ii + 0]); + simdgroup_multiply_accumulate(lo[2*ii + 1], vs, mv[1], lo[2*ii + 1]); + } + + pv += 8*NS20; } + } else { + FOR_UNROLL (short cc = 0; cc < (C/8)/2; ++cc) { + s8x8_t vs[2]; - pv += 8*(NS20 - NO*NSG); - sst += 8; + simdgroup_load(vs[0], ss + 16*cc + 0, SH, 0, false); + simdgroup_load(vs[1], ss + 16*cc + 8, SH, 0, false); + + FOR_UNROLL (short ii = 0; ii < NO/2; ++ii) { + v8x8_t mv[4]; + + simdgroup_load(mv[0], pv + 0*NSG + 16*ii*NSG + 0*8*NS20, NS20, 0, false); + simdgroup_load(mv[1], pv + 8*NSG + 16*ii*NSG + 0*8*NS20, NS20, 0, false); + simdgroup_load(mv[2], pv + 0*NSG + 16*ii*NSG + 1*8*NS20, NS20, 0, false); + simdgroup_load(mv[3], pv + 8*NSG + 16*ii*NSG + 1*8*NS20, NS20, 0, false); + + simdgroup_multiply_accumulate(lo[2*ii + 0], vs[0], mv[0], lo[2*ii + 0]); + simdgroup_multiply_accumulate(lo[2*ii + 1], vs[0], mv[1], lo[2*ii + 1]); + simdgroup_multiply_accumulate(lo[2*ii + 0], vs[1], mv[2], lo[2*ii + 0]); + simdgroup_multiply_accumulate(lo[2*ii + 1], vs[1], mv[3], lo[2*ii + 1]); + } + + pv += 2*8*NS20; + } } } @@ -5002,7 +5101,7 @@ void kernel_flash_attn_ext_impl( device float4 * dst4 = (device float4 *) dst + ((uint64_t)iq3*args.ne2*args.ne1 + iq2 + (uint64_t)(iq1 + j)*args.ne1)*DV4; - const float scale = 1.0f/S[jj]; + const float scale = S[jj] == 0.0 ? 0.0f : 1.0f/S[jj]; if (DV4 % NW == 0) { FOR_UNROLL (short ii = 0; ii < DV4/NW; ++ii) { @@ -5047,8 +5146,8 @@ template< void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &), short DK, // K head size short DV, // V head size - short Q = 8, // queries per threadgroup - short C = 64> // cache items per threadgroup + short Q = OP_FLASH_ATTN_EXT_NQPTG, // queries per threadgroup + short C = OP_FLASH_ATTN_EXT_NCPSG> // cache items per threadgroup kernel void kernel_flash_attn_ext( constant ggml_metal_kargs_flash_attn_ext & args, device const char * q, @@ -5057,13 +5156,14 @@ kernel void kernel_flash_attn_ext( device const char * mask, device const char * sinks, device const char * pad, + device const char * blk, device char * dst, threadgroup half * shmem_f16 [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], ushort tiisg[[thread_index_in_simdgroup]], ushort sgitg[[simdgroup_index_in_threadgroup]]) { #define FWD_TMPL q_t, q4_t, q8x8_t, k_t, k4x4_t, k8x8_t, v_t, v4x4_t, v8x8_t, qk_t, qk8x8_t, s_t, s2_t, s8x8_t, o_t, o4_t, o8x8_t, kd4x4_t, nl_k, deq_k, vd4x4_t, nl_v, deq_v, DK, DV, Q, C -#define FWD_ARGS args, q, k, v, mask, sinks, pad, dst, shmem_f16, tgpig, tiisg, sgitg +#define FWD_ARGS args, q, k, v, mask, sinks, pad, blk, dst, shmem_f16, tgpig, tiisg, sgitg switch (FC_flash_attn_ext_nsg) { // note: disabled cases to reduce library load time //case 1: kernel_flash_attn_ext_impl(FWD_ARGS); break; @@ -5210,9 +5310,9 @@ template< void (*deq_v_t4)(device const vd4_t *, short, thread v4_t &), short DK, // K head size short DV, // V head size - short NE = 4, // head elements per thread - short Q = 1, // queries per threadgroup - short C = 32, // cache items per threadgroup + short NE, // head elements per thread + short Q, // queries per threadgroup + short C, // cache items per threadgroup short NSG> // number of simd groups void kernel_flash_attn_ext_vec_impl( constant ggml_metal_kargs_flash_attn_ext_vec & args, @@ -5327,8 +5427,8 @@ void kernel_flash_attn_ext_vec_impl( // loop over the KV cache // each simdgroup handles blocks of Q rows and C columns - for (int ic0 = (int) iwg*C*NSG; ic0 < args.ne11; ic0 += (int) NWG*C*NSG) { - int ic = ic0 + C*sgitg; + for (int ic0 = iwg*NSG + sgitg; ; ic0 += NWG*NSG) { + int ic = ic0*C; if (ic >= args.ne11) { break; } @@ -5621,7 +5721,7 @@ void kernel_flash_attn_ext_vec_impl( device float4 * dst4 = (device float4 *) dst; device float * dst1 = (device float *) dst + nrows*DV*NWG; // the S and M are stored after the results - const float S = NWG == 1 ? 1.0f/ss[0] : 1.0f; + const float S = NWG == 1 ? (ss[0] == 0.0f ? 0.0f : 1.0f/ss[0]) : 1.0f; // interleave the workgroup data for (short i = tiisg; i < DV4; i += NW) { @@ -5659,8 +5759,8 @@ template< short DK, // K head size short DV, // V head size short NE = 4, // head elements per thread - short Q = 1, // queries per threadgroup - short C = 32> // cache items per threadgroup + short Q = OP_FLASH_ATTN_EXT_VEC_NQPTG, // queries per threadgroup + short C = OP_FLASH_ATTN_EXT_VEC_NCPSG> // cache items per threadgroup kernel void kernel_flash_attn_ext_vec( constant ggml_metal_kargs_flash_attn_ext_vec & args, device const char * q, @@ -5799,7 +5899,8 @@ kernel void kernel_flash_attn_ext_vec_reduce( const float m = simd_max(M); const float ms = exp(M - m); - S = 1.0f/simd_sum(S*ms); + S = simd_sum(S*ms); + S = S == 0.0f ? 0.0f : 1.0f/S; const short DV4 = DV/4; From d2ee056e1df0fdf646270fb5621e9f92084b59a7 Mon Sep 17 00:00:00 2001 From: issixx <46835150+issixx@users.noreply.github.com> Date: Wed, 8 Oct 2025 17:20:18 +0900 Subject: [PATCH 003/144] server : fix cancel pending task (#16467) Co-authored-by: DevAI --- tools/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 5980d43b49..de6e1a322b 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -1937,7 +1937,7 @@ private: void cleanup_pending_task(int id_target) { // no need lock because this is called exclusively by post() auto rm_func = [id_target](const server_task & task) { - return task.id_target == id_target; + return task.id == id_target; }; queue_tasks.erase( std::remove_if(queue_tasks.begin(), queue_tasks.end(), rm_func), From 9d0882840e6c3fb62965d03af0e22880ea90e012 Mon Sep 17 00:00:00 2001 From: ai-fonsi Date: Wed, 8 Oct 2025 20:21:46 +0200 Subject: [PATCH 004/144] Disable CUDA host buffers on integrated GPUs (#16308) --- ggml/src/ggml-cuda/ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 26e72bbc2b..fb691528b7 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -231,7 +231,7 @@ static ggml_cuda_device_info ggml_cuda_init() { info.default_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; - info.devices[id].integrated = prop.integrated; + info.devices[id].integrated = false; // Temporarily disabled due to issues with corrupted output (e.g. #15034) info.devices[id].nsm = prop.multiProcessorCount; info.devices[id].smpb = prop.sharedMemPerBlock; info.devices[id].warp_size = prop.warpSize; From 12bbc3fa50b6df03318a4451c9a2210200a0b28d Mon Sep 17 00:00:00 2001 From: Pascal Date: Wed, 8 Oct 2025 22:18:41 +0200 Subject: [PATCH 005/144] refactor: centralize CoT parsing in backend for streaming mode (#16394) * refactor: unify reasoning handling via backend reasoning_content, drop frontend tag parsing - Updated the chat message component to surface backend-supplied reasoning via message.thinking while showing the raw assistant content without inline tag scrubbing - Simplified chat streaming to append content chunks directly, stream reasoning into the message model, and persist any partial reasoning when generation stops - Refactored the chat service SSE handler to rely on server-provided reasoning_content, removing legacy parsing logic - Refreshed Storybook data and streaming flows to populate the thinking field explicitly for static and streaming assistant messages * refactor: implement streaming-aware universal reasoning parser Remove the streaming mode limitation from --reasoning-format by refactoring try_parse_reasoning() to handle incremental parsing of tags across all formats. - Rework try_parse_reasoning() to track whitespace, partial tags, and multiple reasoning segments, allowing proper separation of reasoning_content and content in streaming mode - Parse reasoning tags before tool call handling in content-only and Llama 3.x formats to ensure inline blocks are captured correctly - Change default reasoning_format from 'auto' to 'deepseek' for consistent behavior - Add 'deepseek-legacy' option to preserve old inline behavior when needed - Update CLI help and documentation to reflect streaming support - Add parser tests for inline ... segments The parser now continues processing content after closes instead of stopping, enabling proper message.reasoning_content and message.content separation in both streaming and non-streaming modes. Fixes the issue where streaming responses would dump everything (including post-thinking content) into reasoning_content while leaving content empty. * refactor: address review feedback from allozaur - Passed the assistant message content directly to ChatMessageAssistant to drop the redundant derived state in the chat message component - Simplified chat streaming updates by removing unused partial-thinking handling and persisting partial responses straight from currentResponse - Refreshed the ChatMessage stories to cover standard and reasoning scenarios without the old THINK-tag parsing examples Co-authored-by: Aleksander Grygier * refactor: restore forced reasoning prefix to pass test-chat ([chat] All tests passed) - store the exact sequence seen on input when 'thinking_forced_open' enforces a reasoning block - inject this prefix before the first accumulated segment in 'reasoning_content', then clear it to avoid duplication - repeat the capture on every new 'start_think' detection to properly handle partial/streaming flows * refactor: address review feedback from ngxson * debug: say goodbye to curl -N, hello one-click raw stream - adds a new checkbox in the WebUI to display raw LLM output without backend parsing or frontend Markdown rendering * Update tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte Co-authored-by: Aleksander Grygier * webui: add Storybook example for raw LLM output and scope reasoning format toggle per story - Added a Storybook example that showcases the chat message component in raw LLM output mode with the provided trace sample - Updated every ChatMessage story to toggle the disableReasoningFormat setting so the raw-output rendering remains scoped to its own example * npm run format * chat-parser: address review feedback from ngxson Co-authored-by: Xuan Son Nguyen --------- Co-authored-by: Aleksander Grygier Co-authored-by: Xuan Son Nguyen --- common/arg.cpp | 3 +- common/chat-parser.cpp | 138 +++++++++- common/chat.cpp | 3 + common/common.h | 2 +- tests/test-chat-parser.cpp | 28 +++ tools/server/README.md | 2 +- .../app/chat/ChatMessages/ChatMessage.svelte | 20 +- .../ChatMessages/ChatMessageAssistant.svelte | 23 +- .../ChatSettings/ChatSettingsDialog.svelte | 6 + .../src/lib/constants/settings-config.ts | 3 + tools/server/webui/src/lib/services/chat.ts | 83 +------ .../webui/src/lib/stores/chat.svelte.ts | 18 +- tools/server/webui/src/lib/utils/thinking.ts | 143 ----------- .../src/stories/ChatMessage.stories.svelte | 235 +++++------------- 14 files changed, 276 insertions(+), 431 deletions(-) delete mode 100644 tools/server/webui/src/lib/utils/thinking.ts diff --git a/common/arg.cpp b/common/arg.cpp index ecc296485c..4204f6c690 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -3432,7 +3432,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--reasoning-format"}, "FORMAT", "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n" "- none: leaves thoughts unparsed in `message.content`\n" - "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n" + "- deepseek: puts thoughts in `message.reasoning_content`\n" + "- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`\n" "(default: auto)", [](common_params & params, const std::string & value) { params.reasoning_format = common_reasoning_format_from_name(value); diff --git a/common/chat-parser.cpp b/common/chat-parser.cpp index b3362519a6..7365782e7d 100644 --- a/common/chat-parser.cpp +++ b/common/chat-parser.cpp @@ -3,9 +3,12 @@ #include "log.h" #include "regex-partial.h" +#include +#include #include #include #include +#include #include using json = nlohmann::ordered_json; @@ -166,6 +169,27 @@ void common_chat_msg_parser::consume_literal(const std::string & literal) { } bool common_chat_msg_parser::try_parse_reasoning(const std::string & start_think, const std::string & end_think) { + std::string pending_reasoning_prefix; + + if (syntax_.reasoning_format == COMMON_REASONING_FORMAT_NONE) { + return false; + } + + auto set_reasoning_prefix = [&](size_t prefix_pos) { + if (!syntax_.thinking_forced_open || syntax_.reasoning_in_content) { + return; + } + if (prefix_pos + start_think.size() > input_.size()) { + pending_reasoning_prefix.clear(); + return; + } + // Capture the exact literal that opened the reasoning section so we can + // surface it back to callers. This ensures formats that force the + // reasoning tag open (e.g. DeepSeek R1) retain their original prefix + // instead of dropping it during parsing. + pending_reasoning_prefix = input_.substr(prefix_pos, start_think.size()); + }; + auto handle_reasoning = [&](const std::string & reasoning, bool closed) { auto stripped_reasoning = string_strip(reasoning); if (stripped_reasoning.empty()) { @@ -178,28 +202,116 @@ bool common_chat_msg_parser::try_parse_reasoning(const std::string & start_think add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "" : end_think); } } else { + if (!pending_reasoning_prefix.empty()) { + add_reasoning_content(pending_reasoning_prefix); + pending_reasoning_prefix.clear(); + } add_reasoning_content(stripped_reasoning); } }; - if (syntax_.reasoning_format != COMMON_REASONING_FORMAT_NONE) { - if (syntax_.thinking_forced_open || try_consume_literal(start_think)) { - if (auto res = try_find_literal(end_think)) { - handle_reasoning(res->prelude, /* closed */ true); - consume_spaces(); - return true; - } - auto rest = consume_rest(); + + const size_t saved_pos = pos_; + const size_t saved_content_size = result_.content.size(); + const size_t saved_reasoning_size = result_.reasoning_content.size(); + + auto restore_state = [&]() { + move_to(saved_pos); + result_.content.resize(saved_content_size); + result_.reasoning_content.resize(saved_reasoning_size); + }; + + // Allow leading whitespace to be preserved as content when reasoning is present at the start + size_t cursor = pos_; + size_t whitespace_end = cursor; + while (whitespace_end < input_.size() && std::isspace(static_cast(input_[whitespace_end]))) { + ++whitespace_end; + } + + if (whitespace_end >= input_.size()) { + restore_state(); + if (syntax_.thinking_forced_open) { + auto rest = input_.substr(saved_pos); if (!rest.empty()) { handle_reasoning(rest, /* closed */ !is_partial()); } - // Allow unclosed thinking tags, for now (https://github.com/ggml-org/llama.cpp/issues/13812, https://github.com/ggml-org/llama.cpp/issues/13877) - // if (!syntax_.thinking_forced_open) { - // throw common_chat_msg_partial_exception(end_think); - // } + move_to(input_.size()); return true; } + return false; + } + + cursor = whitespace_end; + const size_t remaining = input_.size() - cursor; + const size_t start_prefix = std::min(start_think.size(), remaining); + const bool has_start_tag = input_.compare(cursor, start_prefix, start_think, 0, start_prefix) == 0; + + if (has_start_tag && start_prefix < start_think.size()) { + move_to(input_.size()); + return true; + } + + if (has_start_tag) { + if (whitespace_end > pos_) { + add_content(input_.substr(pos_, whitespace_end - pos_)); + } + set_reasoning_prefix(cursor); + cursor += start_think.size(); + } else if (syntax_.thinking_forced_open) { + cursor = whitespace_end; + } else { + restore_state(); + return false; + } + while (true) { + if (cursor >= input_.size()) { + move_to(input_.size()); + return true; + } + + size_t end_pos = input_.find(end_think, cursor); + if (end_pos == std::string::npos) { + std::string_view remaining_view(input_.data() + cursor, input_.size() - cursor); + size_t partial_off = string_find_partial_stop(remaining_view, end_think); + size_t reasoning_end = partial_off == std::string::npos ? input_.size() : cursor + partial_off; + if (reasoning_end > cursor) { + handle_reasoning(input_.substr(cursor, reasoning_end - cursor), /* closed */ partial_off == std::string::npos && !is_partial()); + } + move_to(input_.size()); + return true; + } + + if (end_pos > cursor) { + handle_reasoning(input_.substr(cursor, end_pos - cursor), /* closed */ true); + } else { + handle_reasoning("", /* closed */ true); + } + + cursor = end_pos + end_think.size(); + + while (cursor < input_.size() && std::isspace(static_cast(input_[cursor]))) { + ++cursor; + } + + const size_t next_remaining = input_.size() - cursor; + if (next_remaining == 0) { + move_to(cursor); + return true; + } + + const size_t next_prefix = std::min(start_think.size(), next_remaining); + if (input_.compare(cursor, next_prefix, start_think, 0, next_prefix) == 0) { + if (next_prefix < start_think.size()) { + move_to(input_.size()); + return true; + } + set_reasoning_prefix(cursor); + cursor += start_think.size(); + continue; + } + + move_to(cursor); + return true; } - return false; } std::string common_chat_msg_parser::consume_rest() { diff --git a/common/chat.cpp b/common/chat.cpp index afbb2a2bdd..8587140e1f 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -1408,6 +1408,8 @@ static common_chat_params common_chat_params_init_apertus(const common_chat_temp return data; } static void common_chat_parse_llama_3_1(common_chat_msg_parser & builder, bool with_builtin_tools = false) { + builder.try_parse_reasoning("", ""); + if (!builder.syntax().parse_tool_calls) { builder.add_content(builder.consume_rest()); return; @@ -2862,6 +2864,7 @@ common_chat_params common_chat_templates_apply( } static void common_chat_parse_content_only(common_chat_msg_parser & builder) { + builder.try_parse_reasoning("", ""); builder.add_content(builder.consume_rest()); } diff --git a/common/common.h b/common/common.h index 8a8ecd667f..0d3638c9c6 100644 --- a/common/common.h +++ b/common/common.h @@ -433,7 +433,7 @@ struct common_params { std::string chat_template = ""; // NOLINT bool use_jinja = false; // NOLINT bool enable_chat_template = true; - common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO; + common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; int reasoning_budget = -1; bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response diff --git a/tests/test-chat-parser.cpp b/tests/test-chat-parser.cpp index 547ebb4871..0b275befb8 100644 --- a/tests/test-chat-parser.cpp +++ b/tests/test-chat-parser.cpp @@ -106,6 +106,34 @@ static void test_reasoning() { assert_equals("Cogito", builder.result().content); assert_equals("Ergo sum", builder.consume_rest()); } + { + const std::string variant("content_only_inline_think"); + common_chat_syntax syntax = { + /* .format = */ COMMON_CHAT_FORMAT_CONTENT_ONLY, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, + /* .reasoning_in_content = */ false, + /* .thinking_forced_open = */ false, + /* .parse_tool_calls = */ false, + }; + const std::string input = "PenseBonjour"; + auto msg = common_chat_parse(input, false, syntax); + assert_equals(variant, std::string("Pense"), msg.reasoning_content); + assert_equals(variant, std::string("Bonjour"), msg.content); + } + { + const std::string variant("llama_3_inline_think"); + common_chat_syntax syntax = { + /* .format = */ COMMON_CHAT_FORMAT_LLAMA_3_X, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, + /* .reasoning_in_content = */ false, + /* .thinking_forced_open = */ false, + /* .parse_tool_calls = */ false, + }; + const std::string input = "PlanRéponse"; + auto msg = common_chat_parse(input, false, syntax); + assert_equals(variant, std::string("Plan"), msg.reasoning_content); + assert_equals(variant, std::string("Réponse"), msg.content); + } // Test DeepSeek V3.1 parsing - reasoning content followed by "" and then regular content { common_chat_syntax syntax = { diff --git a/tools/server/README.md b/tools/server/README.md index e23b122ab1..f5ab9236d5 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -190,7 +190,7 @@ The project is under active development, and we are [looking for feedback and co | `--no-slots` | disables slots monitoring endpoint
(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) | | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) | | `--jinja` | use jinja template for chat (default: disabled)
(env: LLAMA_ARG_JINJA) | -| `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)
(default: auto)
(env: LLAMA_ARG_THINK) | +| `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: deepseek)
(env: LLAMA_ARG_THINK) | | `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte index c923bf9e04..fed0cf7126 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte @@ -1,7 +1,6 @@ { + const { updateConfig } = await import('$lib/stores/settings.svelte'); + updateConfig('disableReasoningFormat', false); + }} /> { + const { updateConfig } = await import('$lib/stores/settings.svelte'); + updateConfig('disableReasoningFormat', false); + }} /> { + const { updateConfig } = await import('$lib/stores/settings.svelte'); + updateConfig('disableReasoningFormat', false); + }} +/> + + { + const { updateConfig } = await import('$lib/stores/settings.svelte'); + updateConfig('disableReasoningFormat', true); + }} +/> + + { + const { updateConfig } = await import('$lib/stores/settings.svelte'); + updateConfig('disableReasoningFormat', false); // Phase 1: Stream reasoning content in chunks let reasoningText = 'I need to think about this carefully. Let me break down the problem:\n\n1. The user is asking for help with something complex\n2. I should provide a thorough and helpful response\n3. I need to consider multiple approaches\n4. The best solution would be to explain step by step\n\nThis approach will ensure clarity and understanding.'; @@ -187,126 +192,16 @@ message: processingMessage }} play={async () => { + const { updateConfig } = await import('$lib/stores/settings.svelte'); + updateConfig('disableReasoningFormat', false); // Import the chat store to simulate loading state const { chatStore } = await import('$lib/stores/chat.svelte'); - + // Set loading state to true to trigger the processing UI chatStore.isLoading = true; - + // Simulate the processing state hook behavior // This will show the "Generating..." text and parameter details - await new Promise(resolve => setTimeout(resolve, 100)); + await new Promise((resolve) => setTimeout(resolve, 100)); }} /> - - - - - - { - // Phase 1: Stream reasoning content - const thinkingContent = - 'Let me work through this problem systematically:\n\n1. First, I need to understand what the user is asking\n2. Then I should consider different approaches\n3. I need to evaluate the pros and cons\n4. Finally, I should provide a clear recommendation\n\nThis step-by-step approach will ensure accuracy.'; - - let currentContent = '\n'; - streamingThinkMessage.content = currentContent; - - for (let i = 0; i < thinkingContent.length; i++) { - currentContent += thinkingContent[i]; - streamingThinkMessage.content = currentContent; - await new Promise((resolve) => setTimeout(resolve, 5)); - } - - // Close the thinking block - currentContent += '\n\n\n'; - streamingThinkMessage.content = currentContent; - await new Promise((resolve) => setTimeout(resolve, 200)); - - // Phase 2: Stream main response content - const responseContent = - "Based on my analysis above, here's the solution:\n\n**Key Points:**\n- The approach should be systematic\n- We need to consider all factors\n- Implementation should be step-by-step\n\nThis ensures the best possible outcome."; - - for (let i = 0; i < responseContent.length; i++) { - currentContent += responseContent[i]; - streamingThinkMessage.content = currentContent; - await new Promise((resolve) => setTimeout(resolve, 10)); - } - - streamingThinkMessage.timestamp = Date.now(); - }} -> -
- -
- - - { - // Phase 1: Stream [THINK] reasoning content - const thinkingContent = - 'Using the DeepSeek format now:\n\n- This demonstrates the [THINK] bracket format\n- Should parse identically to <think> tags\n- The UI should display this in the thinking section\n- Main content should be separate\n\nBoth formats provide the same functionality.'; - - let currentContent = '[THINK]\n'; - streamingBracketMessage.content = currentContent; - - for (let i = 0; i < thinkingContent.length; i++) { - currentContent += thinkingContent[i]; - streamingBracketMessage.content = currentContent; - await new Promise((resolve) => setTimeout(resolve, 5)); - } - - // Close the thinking block - currentContent += '\n[/THINK]\n\n'; - streamingBracketMessage.content = currentContent; - await new Promise((resolve) => setTimeout(resolve, 200)); - - // Phase 2: Stream main response content - const responseContent = - "Here's my response after using the [THINK] format:\n\n**Observations:**\n- Both <think> and [THINK] formats work seamlessly\n- The parsing logic handles both cases\n- UI display is consistent across formats\n\nThis demonstrates the enhanced thinking content support."; - - for (let i = 0; i < responseContent.length; i++) { - currentContent += responseContent[i]; - streamingBracketMessage.content = currentContent; - await new Promise((resolve) => setTimeout(resolve, 10)); - } - - streamingBracketMessage.timestamp = Date.now(); - }} -> -
- -
- From e08db4259521de493b7aeb49dadf29ebd1ee966a Mon Sep 17 00:00:00 2001 From: Saba Fallah <10401143+sfallah@users.noreply.github.com> Date: Thu, 9 Oct 2025 08:39:18 +0200 Subject: [PATCH 006/144] model: EmbeddingGemma Adding Support for SentenceTransformers Dense Modules (#16367) * model: EmbeddingGemma sentence-transformers dense linear projections support * model: add support for EmbeddingGemma SentenceTransformers dense linear projections Adding support for the Dense modules used in EmbeddingGemma models. EmbeddingGemma is a SentenceTransformers model with additional modules beyond the base Transformer backbone. See: https://developers.googleblog.com/en/gemma-explained-embeddinggemma-architecture-and-recipe/ * model: add support for EmbeddingGemma SentenceTransformers dense linear projections - converting model with dense-layers is optional - introduced dense config params * Update convert_hf_to_gguf.py Co-authored-by: Daniel Bevenius * fixed formatting issues * Update src/llama-graph.cpp Co-authored-by: Georgi Gerganov * - removed pooling_type_opt, always allow overriding pooling_type - asserts checking dense features dims * fix python lint * fix ubuntu gcc build warning * - fixed thread-safety test - moved asserts to load_hparams * - tidying up code - simplifying graph-context expecting both dense weights * minor : add TODO --------- Co-authored-by: Daniel Bevenius Co-authored-by: Georgi Gerganov --- convert_hf_to_gguf.py | 72 ++++++++++++++++++++++++++++++++-- gguf-py/gguf/constants.py | 8 ++++ gguf-py/gguf/gguf_writer.py | 4 ++ gguf-py/gguf/tensor_mapping.py | 7 +++- src/llama-arch.cpp | 9 +++++ src/llama-arch.h | 8 ++++ src/llama-context.cpp | 6 +++ src/llama-graph.cpp | 17 ++++++++ src/llama-graph.h | 8 ++++ src/llama-hparams.h | 6 +++ src/llama-model.cpp | 26 ++++++++++-- src/llama-model.h | 6 +++ 12 files changed, 170 insertions(+), 7 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index a59ebfc0da..43d345bcb4 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -93,13 +93,15 @@ class ModelBase: # Mistral format specifics is_mistral_format: bool = False disable_mistral_community_chat_template: bool = False + sentence_transformers_dense_modules: 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, small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None, - disable_mistral_community_chat_template: bool = False): + disable_mistral_community_chat_template: bool = False, + sentence_transformers_dense_modules: bool = False): if type(self) is ModelBase or \ type(self) is TextModel or \ type(self) is MmprojModel: @@ -114,6 +116,7 @@ class ModelBase: self.lazy = not eager or (remote_hf_model_id is not None) self.dry_run = dry_run self.remote_hf_model_id = remote_hf_model_id + self.sentence_transformers_dense_modules = sentence_transformers_dense_modules if remote_hf_model_id is not None: self.is_safetensors = True @@ -5269,6 +5272,53 @@ class Gemma3Model(TextModel): @ModelBase.register("Gemma3TextModel") class EmbeddingGemma(Gemma3Model): model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING + module_paths = [] + dense_features_dims = {} + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if self.sentence_transformers_dense_modules: + # read modules.json to determine if model has Dense layers + modules_file = self.dir_model / "modules.json" + if modules_file.is_file(): + with open(modules_file, encoding="utf-8") as modules_json_file: + mods = json.load(modules_json_file) + for mod in mods: + if mod["type"] == "sentence_transformers.models.Dense": + mod_path = mod["path"] + # check if model.safetensors file for Dense layer exists + model_tensors_file = self.dir_model / mod_path / "model.safetensors" + if model_tensors_file.is_file(): + self.module_paths.append(mod_path) + # read config.json of the Dense layer to get in/out features + mod_conf_file = self.dir_model / mod_path / "config.json" + if mod_conf_file.is_file(): + with open(mod_conf_file, encoding="utf-8") as mod_conf_json_file: + mod_conf = json.load(mod_conf_json_file) + # hparams dense_2_feat_out and dense_3_feat_in are required when loading model's dense weights + prefix = self._get_dense_prefix(mod_path) + if mod_conf["in_features"] is not None and mod_conf["out_features"] is not None: + self.dense_features_dims[prefix] = (mod_conf["in_features"], mod_conf["out_features"]) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + from safetensors.torch import load_file + module_paths = list(self.module_paths) + for i, module_path in enumerate(module_paths): + tensors_file = self.dir_model / module_path / "model.safetensors" + local_tensors = load_file(tensors_file) + tensor_name = self._get_dense_prefix(module_path) + for name, local_tensor in local_tensors.items(): + if not name.endswith(".weight"): + continue + orig_name = name.replace("linear", tensor_name) + name = self.map_tensor_name(orig_name) + yield name, local_tensor.clone() + + @staticmethod + def _get_dense_prefix(module_path) -> str: + """Get the tensor name prefix for the Dense layer from module path.""" + tensor_name = "dense_2" if module_path == "2_Dense" else "dense_3" + return tensor_name def set_gguf_parameters(self): super().set_gguf_parameters() @@ -5285,6 +5335,10 @@ class EmbeddingGemma(Gemma3Model): logger.info(f"Using original sliding_window from config: {orig_sliding_window} " f"instead of {self.hparams['sliding_window']}") self.gguf_writer.add_sliding_window(orig_sliding_window) + if self.sentence_transformers_dense_modules: + for dense, dims in self.dense_features_dims.items(): + logger.info(f"Setting dense layer {dense} in/out features to {dims}") + self.gguf_writer.add_dense_features_dims(dense, dims[0], dims[1]) self._try_set_pooling_type() @@ -9335,6 +9389,13 @@ def parse_args() -> argparse.Namespace: ) ) + parser.add_argument( + "--sentence-transformers-dense-modules", action="store_true", + help=("Whether to include sentence-transformers dense modules." + "It can be used for sentence-transformers models, like google/embeddinggemma-300m" + "Default these modules are not included.") + ) + args = parser.parse_args() if not args.print_supported_models and args.model is None: parser.error("the following arguments are required: model") @@ -9397,9 +9458,13 @@ def main() -> None: if args.remote: hf_repo_id = args.model from huggingface_hub import snapshot_download + allowed_patterns = ["LICENSE", "*.json", "*.md", "*.txt", "tokenizer.model"] + if args.sentence_transformers_dense_modules: + # include sentence-transformers dense modules safetensors files + allowed_patterns.append("*.safetensors") local_dir = snapshot_download( repo_id=hf_repo_id, - allow_patterns=["LICENSE", "*.json", "*.md", "*.txt", "tokenizer.model"]) + allow_patterns=allowed_patterns) dir_model = Path(local_dir) logger.info(f"Downloaded config and tokenizer to {local_dir}") else: @@ -9467,7 +9532,8 @@ def main() -> None: split_max_tensors=args.split_max_tensors, split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run, small_first_shard=args.no_tensor_first_split, - remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template + remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template, + sentence_transformers_dense_modules=args.sentence_transformers_dense_modules ) if args.vocab_only: diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 9c99b90faa..f5e5fba800 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -128,6 +128,8 @@ class Keys: ALTUP_ACTIVE_IDX = "{arch}.altup.active_idx" ALTUP_NUM_INPUTS = "{arch}.altup.num_inputs" EMBD_LENGTH_PER_LAYER_INP = "{arch}.embedding_length_per_layer_input" + DENSE_FEAT_IN_SIZE = "{arch}.{dense}_feat_in" + DENSE_FEAT_OUT_SIZE = "{arch}.{dense}_feat_out" class Attention: HEAD_COUNT = "{arch}.attention.head_count" @@ -433,6 +435,8 @@ class MODEL_TENSOR(IntEnum): TOKEN_TYPES = auto() POS_EMBD = auto() OUTPUT = auto() + DENSE_2_OUT = auto() # embeddinggemma 2_Dense + DENSE_3_OUT = auto() # embeddinggemma 3_Dense OUTPUT_NORM = auto() ROPE_FREQS = auto() ROPE_FACTORS_LONG = auto() @@ -777,6 +781,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.POS_EMBD: "position_embd", MODEL_TENSOR.OUTPUT_NORM: "output_norm", MODEL_TENSOR.OUTPUT: "output", + MODEL_TENSOR.DENSE_2_OUT: "dense_2", # embeddinggemma 2_Dense + MODEL_TENSOR.DENSE_3_OUT: "dense_3", # embeddinggemma 2_Dense MODEL_TENSOR.ROPE_FREQS: "rope_freqs", MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long", MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short", @@ -1759,6 +1765,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_ARCH.GEMMA_EMBEDDING: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.DENSE_2_OUT, + MODEL_TENSOR.DENSE_3_OUT, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_Q_NORM, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index dfe4bfd490..306679e218 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -730,6 +730,10 @@ class GGUFWriter: def add_sliding_window_pattern(self, value: Sequence[bool]) -> None: self.add_array(Keys.Attention.SLIDING_WINDOW_PATTERN.format(arch=self.arch), value) + def add_dense_features_dims(self, dense:str, in_f:int, out_f:int) -> None: + self.add_uint32(Keys.LLM.DENSE_FEAT_IN_SIZE.format(arch=self.arch, dense=dense), in_f) + self.add_uint32(Keys.LLM.DENSE_FEAT_OUT_SIZE.format(arch=self.arch, dense=dense), out_f) + def add_logit_scale(self, value: float) -> None: self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 3e9a2dd8f8..c05aa6cc48 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -76,7 +76,12 @@ class TensorNameMap: "lm_head", # llama4 "model.transformer.ff_out", # llada ), - + MODEL_TENSOR.DENSE_2_OUT: ( + "dense_2_out", # embeddinggemma + ), + MODEL_TENSOR.DENSE_3_OUT: ( + "dense_3_out", # embeddinggemma + ), # Output norm MODEL_TENSOR.OUTPUT_NORM: ( "gpt_neox.final_layer_norm", # gptneox diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 45f0d0e2cb..869e4dccf0 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -219,6 +219,11 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" }, { LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" }, + // sentence-transformers dense modules feature dims + { LLM_KV_DENSE_2_FEAT_IN, "%s.dense_2_feat_in" }, + { LLM_KV_DENSE_2_FEAT_OUT, "%s.dense_2_feat_out" }, + { LLM_KV_DENSE_3_FEAT_IN, "%s.dense_3_feat_in" }, + { LLM_KV_DENSE_3_FEAT_OUT, "%s.dense_3_feat_out" }, { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" }, { LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" }, @@ -1071,6 +1076,8 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_DENSE_2_OUT, "dense_2" }, + { LLM_TENSOR_DENSE_3_OUT, "dense_3" }, { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, @@ -2281,6 +2288,8 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_OUTPUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, {LLM_TENSOR_CLS, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, {LLM_TENSOR_CLS_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DENSE_2_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output + {LLM_TENSOR_DENSE_3_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output {LLM_TENSOR_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, {LLM_TENSOR_DEC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, {LLM_TENSOR_ENC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, diff --git a/src/llama-arch.h b/src/llama-arch.h index 507fe5f379..c3ae71655b 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -271,6 +271,12 @@ enum llm_kv { LLM_KV_TOKENIZER_PREFIX_ID, LLM_KV_TOKENIZER_SUFFIX_ID, LLM_KV_TOKENIZER_MIDDLE_ID, + + // sentence-transformers dense layers in and out features + LLM_KV_DENSE_2_FEAT_IN, + LLM_KV_DENSE_2_FEAT_OUT, + LLM_KV_DENSE_3_FEAT_IN, + LLM_KV_DENSE_3_FEAT_OUT, }; enum llm_tensor { @@ -278,6 +284,8 @@ enum llm_tensor { LLM_TENSOR_TOKEN_EMBD_NORM, LLM_TENSOR_TOKEN_TYPES, LLM_TENSOR_POS_EMBD, + LLM_TENSOR_DENSE_2_OUT, + LLM_TENSOR_DENSE_3_OUT, LLM_TENSOR_OUTPUT, LLM_TENSOR_OUTPUT_NORM, LLM_TENSOR_ROPE_FREQS, diff --git a/src/llama-context.cpp b/src/llama-context.cpp index d8a8b5e647..e7526e7d0a 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -2346,6 +2346,12 @@ llama_context * llama_init_from_model( return nullptr; } + if (params.pooling_type != model->hparams.pooling_type) { + //user-specified pooling-type is different from the model default + LLAMA_LOG_WARN("%s: model default pooling_type is [%d], but [%d] was specified\n", __func__, + model->hparams.pooling_type, params.pooling_type); + } + try { auto * ctx = new llama_context(*model, params); return ctx; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 90cd885a60..a24853c63a 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -1853,6 +1853,23 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const { return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp)); } +void llm_graph_context::build_dense_out( + ggml_tensor * dense_2, + ggml_tensor * dense_3) const { + if (!cparams.embeddings || dense_2 == nullptr || dense_3 == nullptr) { + return; + } + ggml_tensor * cur = res->t_embd_pooled != nullptr ? res->t_embd_pooled : res->t_embd; + GGML_ASSERT(cur != nullptr && "missing t_embd_pooled/t_embd"); + + cur = ggml_mul_mat(ctx0, dense_2, cur); + cur = ggml_mul_mat(ctx0, dense_3, cur); + cb(cur, "result_embd_pooled", -1); + res->t_embd_pooled = cur; + ggml_build_forward_expand(gf, cur); +} + + void llm_graph_context::build_pooling( ggml_tensor * cls, ggml_tensor * cls_b, diff --git a/src/llama-graph.h b/src/llama-graph.h index 34b984afeb..dc84b79428 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -814,6 +814,14 @@ struct llm_graph_context { ggml_tensor * cls_b, ggml_tensor * cls_out, ggml_tensor * cls_out_b) const; + + // + // dense (out) + // + + void build_dense_out( + ggml_tensor * dense_2, + ggml_tensor * dense_3) const; }; // TODO: better name diff --git a/src/llama-hparams.h b/src/llama-hparams.h index f29b23eeff..4e7f73ec23 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -169,6 +169,12 @@ struct llama_hparams { uint32_t laurel_rank = 64; uint32_t n_embd_altup = 256; + // needed for sentence-transformers dense layers + uint32_t dense_2_feat_in = 0; // in_features of the 2_Dense + uint32_t dense_2_feat_out = 0; // out_features of the 2_Dense + uint32_t dense_3_feat_in = 0; // in_features of the 3_Dense + uint32_t dense_3_feat_out = 0; // out_features of the 3_Dense + // xIELU std::array xielu_alpha_n; std::array xielu_alpha_p; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 03c2f49d78..a5fe5b749c 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1218,12 +1218,21 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.set_swa_pattern(6); hparams.causal_attn = false; // embeddings do not use causal attention - hparams.rope_freq_base_train_swa = 10000.0f; + hparams.rope_freq_base_train_swa = 10000.0f; hparams.rope_freq_scale_train_swa = 1.0f; - ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + + //applied only if model converted with --sentence-transformers-dense-modules + ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false); + ml.get_key(LLM_KV_DENSE_2_FEAT_OUT, hparams.dense_2_feat_out, false); + ml.get_key(LLM_KV_DENSE_3_FEAT_IN, hparams.dense_3_feat_in, false); + ml.get_key(LLM_KV_DENSE_3_FEAT_OUT, hparams.dense_3_feat_out, false); + + GGML_ASSERT((hparams.dense_2_feat_in == 0 || hparams.dense_2_feat_in == hparams.n_embd) && "dense_2_feat_in must be equal to n_embd"); + GGML_ASSERT((hparams.dense_3_feat_out == 0 || hparams.dense_3_feat_out == hparams.n_embd) && "dense_3_feat_out must be equal to n_embd"); switch (hparams.n_layer) { case 24: type = LLM_TYPE_0_3B; break; @@ -3686,6 +3695,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) { output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); } + // Dense linear weights + dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED); + dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED); + + for (int i = 0; i < n_layer; ++i) { auto & layer = layers[i]; @@ -19893,6 +19907,12 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { // add on pooling layer llm->build_pooling(cls, cls_b, cls_out, cls_out_b); + // if the gguf model was converted with --sentence-transformers-dense-modules + // there will be two additional dense projection layers + // dense linear projections are applied after pooling + // TODO: move reranking logic here and generalize + llm->build_dense_out(dense_2_out_layers, dense_3_out_layers); + return llm->res->get_gf(); } diff --git a/src/llama-model.h b/src/llama-model.h index 20b59d952b..7f48662f28 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -438,6 +438,12 @@ struct llama_model { std::vector layers; + //Dense linear projections for SentenceTransformers models like embeddinggemma + // For Sentence Transformers models structure see + // https://sbert.net/docs/sentence_transformer/usage/custom_models.html#structure-of-sentence-transformer-models + struct ggml_tensor * dense_2_out_layers = nullptr; + struct ggml_tensor * dense_3_out_layers = nullptr; + llama_model_params params; // gguf metadata From b2602137557b2b28a39e03612717d85ead9a6f5a Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Thu, 9 Oct 2025 15:25:11 +0800 Subject: [PATCH 007/144] [SYCL] refactor soft_max, add soft_max_back (#16472) * refactor to support soft_max_ext * fix error and support soft_max_back * rm unused functions * fix format issue --------- Co-authored-by: Zhang Jianyu --- ggml/src/ggml-sycl/common.hpp | 86 ++++- ggml/src/ggml-sycl/dpct/helper.hpp | 20 ++ ggml/src/ggml-sycl/ggml-sycl.cpp | 25 +- ggml/src/ggml-sycl/softmax.cpp | 491 +++++++++++++++++++---------- ggml/src/ggml-sycl/softmax.hpp | 4 + 5 files changed, 437 insertions(+), 189 deletions(-) diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index 4e7449d06e..d66d7ade90 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -197,6 +197,7 @@ struct sycl_device_info { int cc; // compute capability // int nsm; // number of streaming multiprocessors // size_t smpb; // max. shared memory per block + size_t smpbo; // max. shared memory per block (with opt-in) bool vmm; // virtual memory support size_t total_vram; //sycl_hw_info hw_info; \\ device id and aarch, currently not used @@ -416,13 +417,6 @@ static __dpct_inline__ float warp_reduce_sum(float x, const sycl::nd_item<3>& item_ct1) { #pragma unroll for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { - /* - DPCT1096:98: The right-most dimension of the work-group used in the SYCL - kernel that calls this function may be less than "32". The function - "dpct::permute_sub_group_by_xor" may return an unexpected result on the - CPU device. Modify the size of the work-group to ensure that the value - of the right-most dimension is a multiple of "32". - */ x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask); } return x; @@ -440,17 +434,67 @@ warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3>& item_ct1) { return a; } +template +static __dpct_inline__ int warp_reduce_sum(int x) { + return sycl::reduce_over_group( + sycl::ext::oneapi::this_work_item::get_sub_group(), x, sycl::plus<>()); +} + +template +static __dpct_inline__ float warp_reduce_sum(float x) { +#pragma unroll + for (int offset = width / 2; offset > 0; offset >>= 1) { + x += dpct::permute_sub_group_by_xor( + sycl::ext::oneapi::this_work_item::get_sub_group(), x, offset, width); + } + return x; +} + +template +static __dpct_inline__ sycl::float2 warp_reduce_sum(sycl::float2 a) { +#pragma unroll + for (int offset = width / 2; offset > 0; offset >>= 1) { + a.x() += dpct::permute_sub_group_by_xor( + sycl::ext::oneapi::this_work_item::get_sub_group(), a.x(), offset, + width); + a.y() += dpct::permute_sub_group_by_xor( + sycl::ext::oneapi::this_work_item::get_sub_group(), a.y(), offset, + width); + } + return a; +} + +template +static __dpct_inline__ sycl::half2 warp_reduce_sum(sycl::half2 a) { +#pragma unroll + for (int offset = width / 2; offset > 0; offset >>= 1) { + a = a + dpct::permute_sub_group_by_xor( + sycl::ext::oneapi::this_work_item::get_sub_group(), a, offset, + width); + } + return a; +} + +static constexpr int ggml_sycl_get_physical_warp_size() { + // todo: for old iGPU + dGPU case, need to be changed. + return WARP_SIZE; +} + +template +static __dpct_inline__ float warp_reduce_max(float x) { +#pragma unroll + for (int offset = width / 2; offset > 0; offset >>= 1) { + x = sycl::fmax(x, dpct::permute_sub_group_by_xor( + sycl::ext::oneapi::this_work_item::get_sub_group(), x, + offset, width)); + } + return x; +} + static __dpct_inline__ float warp_reduce_max(float x, const sycl::nd_item<3>& item_ct1) { #pragma unroll for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { - /* - DPCT1096:97: The right-most dimension of the work-group used in the SYCL - kernel that calls this function may be less than "32". The function - "dpct::permute_sub_group_by_xor" may return an unexpected result on the - CPU device. Modify the size of the work-group to ensure that the value - of the right-most dimension is a multiple of "32". - */ x = sycl::fmax(x, dpct::permute_sub_group_by_xor( item_ct1.get_sub_group(), x, mask)); } @@ -558,4 +602,18 @@ struct scope_op_debug_print { std::string_view func_suffix; }; +static __dpct_inline__ float get_alibi_slope(const float max_bias, + const uint32_t h, + const uint32_t n_head_log2, + const float m0, + const float m1) { + if (max_bias <= 0.0f) { + return 1.0f; + } + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + return dpct::pow(base, exph); +} + #endif // GGML_SYCL_COMMON_HPP diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index d538965b09..f93cfa701f 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -277,6 +277,26 @@ namespace dpct } // namespace detail + // COPY from DPCT head files + /// dim3 is used to store 3 component dimensions. + class dim3 { + public: + unsigned x, y, z; + + constexpr dim3(unsigned x = 1, unsigned y = 1, unsigned z = 1) + : x(x), y(y), z(z) {} + + dim3(const sycl::id<3> &r) : dim3(r[2], r[1], r[0]) {} + + operator sycl::range<3>() const { return sycl::range<3>(z, y, x); } + }; // namespace dim3 + + inline dim3 operator*(const dim3 &a, const dim3 &b) { + return dim3{a.x * b.x, a.y * b.y, a.z * b.z}; + } + // COPY from DPCT head files + + /// Pitched 2D/3D memory data. class pitched_data { diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 4ac919ea2d..e4cc3c8ed8 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -87,6 +87,7 @@ static ggml_sycl_device_info ggml_sycl_init() { 100 * prop.get_major_version() + 10 * prop.get_minor_version(); info.devices[i].opt_feature.reorder = device.ext_oneapi_architecture_is(syclex::arch_category::intel_gpu); info.max_work_group_sizes[i] = prop.get_max_work_group_size(); + info.devices[i].smpbo = prop.get_local_mem_size(); } for (int id = 0; id < info.device_count; ++id) { @@ -3741,6 +3742,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_SOFT_MAX: ggml_sycl_op_soft_max(ctx, dst); break; + case GGML_OP_SOFT_MAX_BACK: + ggml_sycl_op_soft_max_back(ctx, dst); + break; case GGML_OP_ROPE: ggml_sycl_rope(ctx, dst); break; @@ -3778,6 +3782,7 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg return true; } catch (sycl::exception & e) { std::cerr << e.what() << "Exception caught at file:" << __FILE__ << ", line:" << __LINE__ << std::endl; + std::cerr << "Error OP "<op)<< std::endl; std::exit(1); } @@ -4386,19 +4391,15 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g return true; case GGML_OP_CONT: return op->src[0]->type != GGML_TYPE_BF16; - case GGML_OP_SOFT_MAX: - // TODO: support batching - if (op->src[0]->ne[3] != 1) { - return false; - } - // TODO: support attention sinks [TAG_ATTN_SINKS] - if (op->src[2]) { - return false; - } - // TODO: support broadcast - // ref: https://github.com/ggml-org/llama.cpp/pull/14435 - return !op->src[1] || (op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1); case GGML_OP_DIAG_MASK_INF: + return true; + case GGML_OP_SOFT_MAX: + return true; + case GGML_OP_SOFT_MAX_BACK: { + float max_bias = 0.0f; + memcpy(&max_bias, (const float *) op->op_params + 1, sizeof(float)); + return max_bias == 0.0f; + } case GGML_OP_ROPE: case GGML_OP_IM2COL: return true; diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp index 52fcf4b3db..83b7c71b66 100644 --- a/ggml/src/ggml-sycl/softmax.cpp +++ b/ggml/src/ggml-sycl/softmax.cpp @@ -1,37 +1,94 @@ #include "softmax.hpp" +#include +#include +#include -template -static void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par, - const int nrows_y, const float scale, const float max_bias, const float m0, - const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) { - const int ncols = ncols_template == 0 ? ncols_par : ncols_template; - const int tid = item_ct1.get_local_id(2); - const int rowx = item_ct1.get_group(2); - const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension +template static __dpct_inline__ float t2f32(T val) { + return (float) val; +} - const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template; +template <> float __dpct_inline__ t2f32(sycl::half val) { + return sycl::vec(val) + .convert()[0]; +} - const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; - const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; +struct soft_max_params { + + int64_t nheads; + uint32_t n_head_log2; + int64_t ncols; + int64_t nrows_x; + int64_t nrows_y; + int64_t ne00; + int64_t ne01; + int64_t ne02; + int64_t ne03; + int64_t nb11; + int64_t nb12; + int64_t nb13; + + int64_t ne12; + int64_t ne13; + float scale; + float max_bias; + float m0; + float m1; +}; + +// When ncols_template == 0 the bounds for the loops in this function are not known and can't be unrolled. +// As we want to keep pragma unroll for all other cases we supress the clang transformation warning here. +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wpass-failed" +#endif // __clang__ +template +static void soft_max_f32(const float * x, + const T * mask, + const float * sinks, + float * dst, + const soft_max_params p, + uint8_t * dpct_local) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int ncols = ncols_template == 0 ? p.ncols : ncols_template; + const int block_size = block_size_template == 0 + ? item_ct1.get_local_range(2) + : block_size_template; const int nthreads = block_size; const int nwarps = nthreads / WARP_SIZE; size_t nreduce = nwarps / WARP_SIZE; - float slope = 1.0f; - // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = rowx/nrows_y; // head index + const int tid = item_ct1.get_local_id(2); - const float base = h < n_head_log2 ? m0 : m1; - const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + const int64_t i03 = item_ct1.get_group(0); + const int64_t i02 = item_ct1.get_group(1); + const int64_t i01 = item_ct1.get_group(2); - slope = sycl::pow(base, float(exp)); - } + //TODO: noncontigous inputs/outputs + const int rowx = item_ct1.get_group(2) + + item_ct1.get_group(1) * item_ct1.get_group_range(2) + + item_ct1.get_group(0) * item_ct1.get_group_range(2) * + item_ct1.get_group_range(1); - float *vals = vals_smem ? buf + sycl::max(nwarps, WARP_SIZE) : dst + rowx * ncols; - float max_val = -INFINITY; + const int64_t i11 = i01; + const int64_t i12 = i02 % p.ne12; + const int64_t i13 = i03 % p.ne13; + x += int64_t(rowx)*ncols; + mask += (i11*p.nb11 + i12*p.nb12 + i13*p.nb13) / sizeof(T) * (mask != nullptr); + dst += int64_t(rowx)*ncols; + + const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + + const float slope = get_alibi_slope(p.max_bias, i02, p.n_head_log2, p.m0, p.m1); + + float * buf_iw = (float *) dpct_local; + + // shared memory buffer to cache values between iterations: + float *vals = use_shared ? buf_iw + sycl::max(nwarps, WARP_SIZE) : dst; + float max_val = sinks ? sinks[i02] : -INFINITY; +#pragma unroll for (int col0 = 0; col0 < ncols; col0 += block_size) { const int col = col0 + tid; @@ -39,42 +96,35 @@ static void soft_max_f32(const float * x, const T * mask, float * dst, const int break; } - const int ix = rowx*ncols + col; - const int iy = rowy*ncols + col; - - const float val = x[ix]*scale + (mask ? slope*static_cast(mask[iy]) : 0.0f); + const float val = x[col]*p.scale + (mask ? slope*t2f32(mask[col]) : 0.0f); vals[col] = val; - max_val = sycl::max(max_val, val); + max_val = sycl::max(max_val, val); } - // find the max value in the block - max_val = warp_reduce_max(max_val, item_ct1); + max_val = warp_reduce_max(max_val); + if (block_size > WARP_SIZE) { if (warp_id == 0) { - buf[lane_id] = -INFINITY; - for (size_t i = 1; i < nreduce; i += 1) { - buf[lane_id + i * WARP_SIZE] = -INFINITY; - } + buf_iw[lane_id] = -INFINITY; } - item_ct1.barrier(sycl::access::fence_space::local_space); + item_ct1.barrier(); if (lane_id == 0) { - buf[warp_id] = max_val; + buf_iw[warp_id] = max_val; } - item_ct1.barrier(sycl::access::fence_space::local_space); - max_val = buf[lane_id]; - for (size_t i = 1; i < nreduce; i += 1) { - max_val = sycl::max(max_val, buf[lane_id + i * WARP_SIZE]); - } - max_val = warp_reduce_max(max_val, item_ct1); - } + item_ct1.barrier(); + + max_val = buf_iw[lane_id]; + max_val = warp_reduce_max(max_val); + } + float tmp = 0.0f; // partial sum - float tmp = 0.f; #pragma unroll for (int col0 = 0; col0 < ncols; col0 += block_size) { const int col = col0 + tid; - if (ncols_template == 0 && col >= ncols) { + + if (ncols_template == 0 && col >= ncols) { break; } @@ -82,32 +132,33 @@ static void soft_max_f32(const float * x, const T * mask, float * dst, const int tmp += val; vals[col] = val; } - // find the sum of exps in the block - tmp = warp_reduce_sum(tmp, item_ct1); + tmp = warp_reduce_sum(tmp); if (block_size > WARP_SIZE) { - item_ct1.barrier(sycl::access::fence_space::local_space); + item_ct1.barrier(); if (warp_id == 0) { - buf[lane_id] = 0.f; + buf_iw[lane_id] = 0.0f; for (size_t i = 1; i < nreduce; i += 1) { - buf[lane_id + i * WARP_SIZE] = 0.f; + buf_iw[lane_id + i * WARP_SIZE] = 0.f; } } - item_ct1.barrier(sycl::access::fence_space::local_space); + item_ct1.barrier(); if (lane_id == 0) { - buf[warp_id] = tmp; + buf_iw[warp_id] = tmp; } - item_ct1.barrier(sycl::access::fence_space::local_space); + item_ct1.barrier(); - tmp = buf[lane_id]; + tmp = buf_iw[lane_id]; for (size_t i = 1; i < nreduce; i += 1) { - tmp += buf[lane_id + i * WARP_SIZE]; + tmp += buf_iw[lane_id + i * WARP_SIZE]; } - tmp = warp_reduce_sum(tmp, item_ct1); + tmp = warp_reduce_sum(tmp); } - - const float inv_sum = 1.f / tmp; + if (sinks) { + tmp += sycl::native::exp(sinks[i02] - max_val); + } + const float inv_sum = 1.0f / tmp; #pragma unroll for (int col0 = 0; col0 < ncols; col0 += block_size) { @@ -117,145 +168,259 @@ static void soft_max_f32(const float * x, const T * mask, float * dst, const int return; } - const int idst = rowx*ncols + col; - dst[idst] = vals[col] * inv_sum; + dst[col] = vals[col] * inv_sum; + } +} +#ifdef __clang__ +#pragma clang diagnostic pop +#endif // __clang__ + +static void soft_max_back_f32(const float *grad, const float *dstf, float *dst, + const int ncols, const float scale) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int tid = item_ct1.get_local_id(2); + const int rowx = item_ct1.get_group(2); + + grad += int64_t(rowx)*ncols; + dstf += int64_t(rowx)*ncols; + dst += int64_t(rowx)*ncols; + + float dgf_dot = 0.0f; // dot product of dst from forward pass and gradients + + for (int col = tid; col < ncols; col += WARP_SIZE) { + dgf_dot += dstf[col]*grad[col]; + } + + dgf_dot = warp_reduce_sum(dgf_dot); + + for (int col = tid; col < ncols; col += WARP_SIZE) { + dst[col] = scale * (grad[col] - dgf_dot) * dstf[col]; } } -template -static void soft_max_f32_submitter(const float * x, const T * mask, float * dst, const int ncols_par, - const int nrows_y, const float scale, const float max_bias, const float m0, - const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims, - const size_t n_local_scratch, queue_ptr stream) { +template +static void launch_soft_max_kernels(const float * x, + const T * mask, + const float * sinks, + float * dst, + const soft_max_params & p, + dpct::queue_ptr stream, + dpct::dim3 block_dims, + dpct::dim3 block_nums, + size_t nbytes_shared) +{ + auto launch_kernel = [=](auto I) -> bool { + constexpr int ncols = decltype(I)::value; + constexpr int block = (ncols > 1024 ? 1024 : ncols); + if (p.ncols == ncols) { + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor dpct_local_acc_ct1( + sycl::range<1>(nbytes_shared), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size( + WARP_SIZE)]] { + soft_max_f32( + x, mask, sinks, dst, p, + dpct_local_acc_ct1 + .get_multi_ptr() + .get()); + GGML_UNUSED(item_ct1); + }); + }); + return true; + } + return false; + }; + + // unary fold over launch_kernel + if ((launch_kernel(std::integral_constant{}) || ...)) { + return; + } + stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor local_buf_acc(n_local_scratch, cgh); + sycl::local_accessor dpct_local_acc_ct1( + sycl::range<1>(nbytes_shared), cgh); cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - soft_max_f32(x, mask, dst, ncols_par, - nrows_y, scale, max_bias, m0, - m1, n_head_log2, item_ct1, - get_pointer(local_buf_acc)); - }); + [=](sycl::nd_item<3> item_ct1) + [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + soft_max_f32( + x, mask, sinks, dst, p, + dpct_local_acc_ct1 + .get_multi_ptr() + .get()); + GGML_UNUSED(item_ct1); + }); }); } -template -static void soft_max_f32_sycl(const float * x, const T * mask, - float * dst, const int ncols_x, const int nrows_x, - const int nrows_y, const float scale, const float max_bias, - queue_ptr stream, int device) { +template +static void soft_max_f32_sycl(const float *x, const T *mask, + const float *sinks, float *dst, + const soft_max_params ¶ms, + dpct::queue_ptr stream, int device) { int nth = WARP_SIZE; int max_block_size = ggml_sycl_info().max_work_group_sizes[device]; + const int64_t ncols_x = params.ncols; + while (nth < ncols_x && nth < max_block_size) nth *= 2; if (nth>max_block_size) nth = max_block_size; - const sycl::range<3> block_dims(1, 1, nth); - const sycl::range<3> block_nums(1, 1, nrows_x); - const size_t n_val_tmp = nth / WARP_SIZE; - const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + n_val_tmp); + const dpct::dim3 block_dims(nth, 1, 1); + const dpct::dim3 block_nums(params.ne01, params.ne02, params.ne03); + const size_t nbytes_shared = + (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE) * sizeof(float); - const uint32_t n_head_kv = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + const int id = get_current_device_id(); + const size_t smpbo = ggml_sycl_info().devices[id].smpbo; - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - const size_t local_mem_size = stream->get_device().get_info(); - if (n_local_scratch*sizeof(float) < local_mem_size) { - if (ncols_x > max_block_size) { - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - return; - } - switch (ncols_x) { - case 32: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 64: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 128: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 256: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 512: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 1024: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 2048: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 4096: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - default: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - } + if (nbytes_shared <= smpbo) { + launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>( + x, mask, sinks, dst, params, stream, block_dims, block_nums, + nbytes_shared); } else { - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, WARP_SIZE, stream); + const size_t nbytes_shared_low = WARP_SIZE * sizeof(float); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor dpct_local_acc_ct1( + sycl::range<1>(nbytes_shared_low), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + soft_max_f32( + x, mask, sinks, dst, params, + dpct_local_acc_ct1 + .get_multi_ptr() + .get()); + GGML_UNUSED(item_ct1); + }); + }); } } +static void soft_max_back_f32_sycl(const float * grad, + const float * dstf, + float * dst, + const int ncols, + const int nrows, + const float scale, + dpct::queue_ptr stream) { + const dpct::dim3 block_dims(WARP_SIZE, 1, 1); + const dpct::dim3 block_nums(nrows, 1, 1); + + stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + soft_max_back_f32(grad, dstf, dst, ncols, scale); + GGML_UNUSED(item_ct1); + }); +} + void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + const ggml_tensor * src2 = dst->src[2]; + + const float * src0_d = (const float *) src0->data; + const void * src1_d = src1 ? (const void *) src1->data : nullptr; + const void * src2_d = src2 ? (const void *) src2->data : nullptr; + float * dst_d = (float *) dst->data; + + dpct::queue_ptr stream = ctx.stream(); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - GGML_ASSERT(!dst->src[1] || dst->src[1]->type == GGML_TYPE_F16 || dst->src[1]->type == GGML_TYPE_F32); // src1 contains mask and it is optional + // src1 contains mask and it is optional + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); - const int64_t ne00 = dst->src[0]->ne[0]; - const int64_t nrows_x = ggml_nrows(dst->src[0]); - const int64_t nrows_y = dst->src[0]->ne[1]; + const int64_t nrows_x = ggml_nrows(src0); + const int64_t nrows_y = src0->ne[1]; - float scale = 1.0f; + const int64_t ne00 = src0->ne[0]; + + float scale = 1.0f; float max_bias = 0.0f; - memcpy(&scale, dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, dst->op_params + 1, sizeof(float)); + memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float)); - const float * src0_dd = static_cast(dst->src[0]->data); - float * dst_dd = static_cast(dst->data); + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); - ggml_sycl_set_device(ctx.device); - dpct::queue_ptr main_stream = ctx.stream(); + const int64_t nb11 = src1 ? src1->nb[1] : 1; + const int64_t nb12 = src1 ? src1->nb[2] : 1; + const int64_t nb13 = src1 ? src1->nb[3] : 1; - if (dst->src[1] && dst->src[1]->type == GGML_TYPE_F16) { - const sycl::half * src1_dd = static_cast(dst->src[1]->data); - soft_max_f32_sycl(src0_dd, src1_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, - main_stream, ctx.device); - } else if (dst->src[1] && dst->src[1]->type == GGML_TYPE_F32) { - const float * src1_dd = static_cast(dst->src[1]->data); - soft_max_f32_sycl(src0_dd, src1_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device); + const int64_t ne12 = src1 ? src1->ne[2] : 1; + const int64_t ne13 = src1 ? src1->ne[3] : 1; + + const uint32_t n_head = src0->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + + soft_max_params params = {}; + params.nheads = src0->ne[2]; + params.n_head_log2 = n_head_log2; + params.ncols = ne00; + params.nrows_x = nrows_x; + params.nrows_y = nrows_y; + params.ne00 = src0->ne[0]; + params.ne01 = src0->ne[1]; + params.ne02 = src0->ne[2]; + params.ne03 = src0->ne[3]; + params.nb11 = nb11; + params.nb12 = nb12; + params.nb13 = nb13; + params.ne12 = ne12; + params.ne13 = ne13; + params.scale = scale; + params.max_bias = max_bias; + params.m0 = m0; + params.m1 = m1; + + if (use_f16) { + soft_max_f32_sycl(src0_d, (const sycl::half *)src1_d, + (const float *)src2_d, dst_d, params, stream, + ctx.device); } else { - /* mask unavailable */ - soft_max_f32_sycl(src0_dd, nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device); + soft_max_f32_sycl(src0_d, (const float *)src1_d, (const float *)src2_d, + dst_d, params, stream, ctx.device); } } + +void ggml_sycl_op_soft_max_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); + const ggml_tensor * src0 = dst->src[0]; // grad + const ggml_tensor * src1 = dst->src[1]; // forward pass output + + const float * src0_d = (const float *) src0->data; + const float * src1_d = (const float *) src1->data; + float * dst_d = (float *) dst->data; + + dpct::queue_ptr stream = ctx.stream(); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int64_t ncols = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float)); + + GGML_ASSERT(max_bias == 0.0f); + + soft_max_back_f32_sycl(src0_d, src1_d, dst_d, ncols, nrows, scale, stream); +} diff --git a/ggml/src/ggml-sycl/softmax.hpp b/ggml/src/ggml-sycl/softmax.hpp index 2cf8582ec9..23f1e5a9d6 100644 --- a/ggml/src/ggml-sycl/softmax.hpp +++ b/ggml/src/ggml-sycl/softmax.hpp @@ -15,6 +15,10 @@ #include "common.hpp" +#define SYCL_SOFT_MAX_BLOCK_SIZE 1024 + void ggml_sycl_op_soft_max(ggml_backend_sycl_context &ctx, ggml_tensor *dst); +void ggml_sycl_op_soft_max_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + #endif // GGML_SYCL_SOFTMAX_HPP From d80d6d2400b8faa80654c723cb5bdf9fc8f4db06 Mon Sep 17 00:00:00 2001 From: Charles Xu Date: Thu, 9 Oct 2025 09:29:17 +0200 Subject: [PATCH 008/144] kleidiai: kernel interface refactoring (#16460) --- ggml/src/ggml-cpu/kleidiai/kernels.cpp | 305 ++++++++++++++++-------- ggml/src/ggml-cpu/kleidiai/kernels.h | 76 +++--- ggml/src/ggml-cpu/kleidiai/kleidiai.cpp | 124 ++++------ 3 files changed, 292 insertions(+), 213 deletions(-) diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.cpp b/ggml/src/ggml-cpu/kleidiai/kernels.cpp index 7ba659124c..3eaa5e3f41 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kernels.cpp @@ -29,6 +29,108 @@ #define NELEMS(x) sizeof(x) / sizeof(*x) +template +static inline size_t kernel_offs_fn3(size_t a, size_t b, size_t c) { + return Fn(a, b, c); +} + +template +static inline size_t kernel_offs_fn2(size_t a, size_t b, size_t) { + return Fn(a, b); +} + +template +static inline void kernel_run_fn11(size_t m, size_t n, size_t k, size_t bl, + const void* lhs, const void* rhs, void* dst, + size_t dst_stride_row, size_t dst_stride_col, + float clamp_min, float clamp_max) { + Fn(m, n, k, bl, lhs, rhs, static_cast(dst), dst_stride_row, dst_stride_col, clamp_min, clamp_max); +} + +template +static inline void kernel_run_fn10(size_t m, size_t n, size_t k, size_t /*bl*/, + const void* lhs, const void* rhs, void* dst, + size_t dst_stride_row, size_t dst_stride_col, + float clamp_min, float clamp_max) { + Fn(m, n, k, lhs, rhs, dst, dst_stride_row, dst_stride_col, clamp_min, clamp_max); +} + +template +static inline size_t lhs_ps_fn6(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr) { + return Fn(m, k, bl, mr, kr, sr); +} + +template +static inline size_t lhs_ps_fn5(size_t m, size_t k, size_t /*bl*/, size_t mr, size_t kr, size_t sr) { + return Fn(m, k, mr, kr, sr); +} + +template +static inline size_t lhs_offs_fn6(size_t m_idx, size_t k, size_t bl, size_t mr, size_t kr, size_t sr) { + return Fn(m_idx, k, bl, mr, kr, sr); +} + +template +static inline size_t lhs_offs_fn5(size_t m_idx, size_t k, size_t /*bl*/, size_t mr, size_t kr, size_t sr) { + return Fn(m_idx, k, mr, kr, sr); +} + +template +static inline void lhs_pack_float_fn10(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr, + size_t m_idx_start, const void* lhs, size_t lhs_stride, void* lhs_packed) { + Fn(m, k, bl, mr, kr, sr, m_idx_start, static_cast(lhs), lhs_stride, lhs_packed); +} + +template +static inline void lhs_pack_void_fn10(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr, + size_t m_idx_start, const void* lhs, size_t lhs_stride, void* lhs_packed) { + Fn(m, k, bl, mr, kr, sr, m_idx_start, lhs, lhs_stride, lhs_packed); +} + +template +static inline void lhs_pack_void_fn9(size_t m, size_t k, size_t /*bl*/, size_t mr, size_t kr, size_t sr, + size_t m_idx_start, const void* lhs, size_t lhs_stride, void* lhs_packed) { + Fn(m, k, mr, kr, sr, m_idx_start, lhs, lhs_stride, lhs_packed); +} + +template +static inline size_t rhs_ps_fn5(size_t n, size_t k, size_t nr, size_t kr, size_t bl) { + return Fn(n, k, nr, kr, bl); +} + +template +static inline size_t rhs_ps_fn2(size_t n, size_t k, size_t /*nr*/, size_t /*kr*/, size_t /*bl*/) { + return Fn(n, k); +} + +template +static inline size_t rhs_stride_fn4(size_t k, size_t nr, size_t kr, size_t bl) { + return Fn(k, nr, kr, bl); +} + +template +static inline size_t rhs_stride_fn1(size_t k, size_t /*nr*/, size_t /*kr*/, size_t /*bl*/) { + return Fn(k); +} + +template +static inline void rhs_pack_fn12(size_t num_groups, size_t n, size_t k, size_t nr, size_t kr, size_t sr, size_t bl, + size_t /*rhs_stride*/, const void* rhs, const void* bias, const void* /*scale*/, + void* rhs_packed, size_t extra_bytes, const void* params) { + Fn(num_groups, n, k, nr, kr, sr, bl, + static_cast(rhs), + static_cast(bias), + rhs_packed, extra_bytes, + static_cast(params)); +} + +template +static inline void rhs_pack_fn13(size_t num_groups, size_t n, size_t k, size_t nr, size_t kr, size_t sr, size_t /*bl*/, + size_t rhs_stride, const void* rhs, const void* bias, const void* scale, + void* rhs_packed, size_t extra_bytes, const void* params) { + Fn(num_groups, n, k, nr, kr, sr, rhs_stride, rhs, bias, scale, rhs_packed, extra_bytes, params); +} + static const size_t INT4_PER_BYTE = 2; static const size_t INT4_BITS = 4; static const int Q4_0_ZERO_POINT = 8; @@ -122,17 +224,18 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, + /* .gemm_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32_neon, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32_neon, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32_neon, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p_f32_neon, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* SME GEMV */ /* .kern_info = */ { @@ -142,23 +245,24 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemv_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32_neon, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32_neon, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32_neon, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p_f32_neon, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* .rhs_info = */ { - /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon, - /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon, - /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon, - /* .to_float = */ dequantize_row_qsi4c32ps1s0scalef16, + /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon, + /* .to_float = */ dequantize_row_qsi4c32ps1s0scalef16, + /* .packed_size_ex = */ &rhs_ps_fn5, + /* .packed_stride_ex = */ &rhs_stride_fn4, + /* .pack_func_ex = */ &rhs_pack_fn12, }, /* .required_cpu = */ CPU_FEATURE_SME, /* .lhs_type = */ GGML_TYPE_F32, @@ -174,17 +278,17 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, - /* .run_kernel = */ kai_run_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_lhs_offset_ex = */ &kernel_offs_fn2, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2, + /* .run_kernel_ex = */ &kernel_run_fn10, }, /* .gemm_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_pack_bf16p2vlx2_f32_sme, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_pack_bf16p2vlx2_f32_sme, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_pack_bf16p2vlx2_f32_sme, - /* .pack_func = */ kai_run_lhs_pack_bf16p2vlx2_f32_sme, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_void_fn9, }, /* SME GEMV */ /* .kern_info = */ { @@ -194,23 +298,24 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, - /* .run_kernel = */ kai_run_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa, + /* .get_lhs_offset_ex = */ nullptr, + /* .get_rhs_packed_offset_ex = */ nullptr, + /* .run_kernel_ex = */ nullptr, }, /* .gemv_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_pack_bf16p2vlx2_f32_sme, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_pack_bf16p2vlx2_f32_sme, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_pack_bf16p2vlx2_f32_sme, - /* .pack_func = */ kai_run_lhs_pack_bf16p2vlx2_f32_sme, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_void_fn9, }, /* .rhs_info = */ { - /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme, - /* .packed_stride = */ NULL, - /* .pack_func = */ kai_run_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme, - /* .to_float = */ NULL, + /* .packed_stride = */ nullptr, + /* .to_float = */ nullptr, + /* .packed_size_ex = */ &rhs_ps_fn2, + /* .packed_stride_ex = */ &rhs_stride_fn1, + /* .pack_func_ex = */ &rhs_pack_fn13, }, /* .required_cpu = */ CPU_FEATURE_SME, /* .lhs_type = */ GGML_TYPE_F32, @@ -229,17 +334,17 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemm_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* DOTPROD GEMV */ /* .kern_info = */ { @@ -249,23 +354,24 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemv_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* .rhs_info = */ { - /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .to_float = */ dequantize_row_qsi4c32pscalef16, + /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, + /* .to_float = */ dequantize_row_qsi4c32pscalef16, + /* .packed_size_ex = */ &rhs_ps_fn5, + /* .packed_stride_ex = */ &rhs_stride_fn4, + /* .pack_func_ex = */ &rhs_pack_fn12, }, /* .required_cpu = */ CPU_FEATURE_DOTPROD, /* .lhs_type = */ GGML_TYPE_F32, @@ -283,17 +389,17 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemm_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p4x8sb_f32_neon, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p4x8sb_f32_neon, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* i8mm GEMV */ /* .kern_info = */ { @@ -303,23 +409,24 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemv_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* .rhs_info = */ { - /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .to_float = */ dequantize_row_qsi4c32pscalef16, + /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, + /* .to_float = */ dequantize_row_qsi4c32pscalef16, + /* .packed_size_ex = */ &rhs_ps_fn5, + /* .packed_stride_ex = */ &rhs_stride_fn4, + /* .pack_func_ex = */ &rhs_pack_fn12, }, /* .required_cpu = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM, /* .lhs_type = */ GGML_TYPE_F32, @@ -338,17 +445,17 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemm_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p4x8sb_f32_neon, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p4x8sb_f32_neon, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* i8mm GEMV */ /* .kern_info = */ { @@ -358,23 +465,24 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemv_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* .rhs_info = */ { - /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .to_float = */ dequantize_row_qsi4c32pscalef16, + /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, + /* .to_float = */ dequantize_row_qsi4c32pscalef16, + /* .packed_size_ex = */ &rhs_ps_fn5, + /* .packed_stride_ex = */ &rhs_stride_fn4, + /* .pack_func_ex = */ &rhs_pack_fn12, }, /* .required_cpu = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM, /* .lhs_type = */ GGML_TYPE_F32, @@ -392,17 +500,17 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemm_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* DOTPROD GEMV */ /* .kern_info = */ { @@ -412,23 +520,24 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, - /* .get_lhs_offset = */ kai_get_lhs_packed_offset_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, - /* .get_rhs_packed_offset = */ kai_get_rhs_packed_offset_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, - /* .run_kernel = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemv_lhs_info = */ { /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32, - /* .get_packed_offset = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32, - /* .packed_size = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32, - /* .pack_func = */ kai_run_lhs_quant_pack_qsi8d32p_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_float_fn10, }, /* .rhs_info = */ { - /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .pack_func = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, - /* .to_float = */ dequantize_row_qsi4c32pscalef16, + /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0, + /* .to_float = */ dequantize_row_qsi4c32pscalef16, + /* .packed_size_ex = */ &rhs_ps_fn5, + /* .packed_stride_ex = */ &rhs_stride_fn4, + /* .pack_func_ex = */ &rhs_pack_fn12, }, /* .required_cpu = */ CPU_FEATURE_DOTPROD, /* .lhs_type = */ GGML_TYPE_F32, @@ -443,6 +552,7 @@ ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features, c ggml_kleidiai_kernels * kernel = nullptr; if (tensor->op == GGML_OP_MUL_MAT && tensor->src[0] != nullptr && tensor->src[1] != nullptr) { +#if defined(__ARM_FEATURE_SME) || defined(__ARM_FEATURE_DOTPROD) || defined(__ARM_FEATURE_MATMUL_INT8) for (size_t i = 0; i < NELEMS(gemm_gemv_kernels); ++i) { if ((cpu_features & gemm_gemv_kernels[i].required_cpu) == gemm_gemv_kernels[i].required_cpu && gemm_gemv_kernels[i].lhs_type == tensor->src[1]->type && @@ -452,6 +562,7 @@ ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features, c break; } } +#endif } return kernel; @@ -460,12 +571,14 @@ ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features, c ggml_kleidiai_kernels * ggml_kleidiai_select_kernels_q4_0(cpu_feature features) { ggml_kleidiai_kernels * kernels = nullptr; +#if defined(__ARM_FEATURE_SME) || defined(__ARM_FEATURE_DOTPROD) || defined(__ARM_FEATURE_MATMUL_INT8) for (size_t i = 0; i < NELEMS(gemm_gemv_kernels); ++i) { if ((features & gemm_gemv_kernels[i].required_cpu) == gemm_gemv_kernels[i].required_cpu) { kernels = &gemm_gemv_kernels[i]; break; } } +#endif return kernels; } diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.h b/ggml/src/ggml-cpu/kleidiai/kernels.h index 2ad6ad6fd0..a84795a6b2 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.h +++ b/ggml/src/ggml-cpu/kleidiai/kernels.h @@ -4,8 +4,6 @@ #pragma once -#include -#include #include "ggml.h" enum cpu_feature { @@ -15,6 +13,7 @@ enum cpu_feature { CPU_FEATURE_SVE = 4, CPU_FEATURE_SME = 8 }; + inline cpu_feature& operator|=(cpu_feature& lhs, cpu_feature rhs) { lhs = static_cast(lhs | rhs); return lhs; @@ -30,63 +29,52 @@ struct kernel_info { size_t (*get_nr)(void); size_t (*get_kr)(void); size_t (*get_sr)(void); - std::variant< - std::function, - std::function - > get_lhs_offset; - std::variant< - std::function, - std::function - > get_rhs_packed_offset; + size_t (*get_dst_offset)(size_t m_idx, size_t n_idx, size_t stride); size_t (*get_dst_size)(size_t m, size_t n); - std::variant< - std::function, - std::function - > run_kernel; + + size_t (*get_lhs_offset_ex)(size_t m_idx, size_t k, size_t bl); + + size_t (*get_rhs_packed_offset_ex)(size_t n_idx, size_t k, size_t bl); + + void (*run_kernel_ex)( + size_t m, size_t n, size_t k, size_t bl, + const void* lhs_packed, const void* rhs_packed, + void* dst, size_t dst_stride_row, size_t dst_stride_col, + float clamp_min, float clamp_max); }; struct lhs_packing_info { size_t (*get_offset)(size_t m_idx, size_t lhs_stride); - std::variant< - std::function, - std::function - > get_packed_offset; - std::variant< - std::function, - std::function - > packed_size; - std::variant< - std::function, - std::function - > pack_func; + + size_t (*get_packed_offset_ex)(size_t m_idx, size_t k, size_t bl, size_t mr, size_t kr, size_t sr); + + size_t (*packed_size_ex)(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr); + + void (*pack_func_ex)(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr, + size_t m_idx_start, const void * lhs, size_t lhs_stride, void * lhs_packed); }; struct rhs_packing_info { - std::variant< - std::function, - std::function - > packed_size; size_t (*packed_stride)(size_t k, size_t nr, size_t kr, size_t bl); - std::variant< - std::function, - std::function - > pack_func; - void (*to_float)(const void *packed_data, int32_t row_idx, int64_t nc, float *out, size_t nr_pack, size_t packed_row_stride, - size_t kr, size_t bl, size_t num_bytes_multiplier); + + void (*to_float)(const void *packed_data, int32_t row_idx, int64_t nc, float *out, + size_t nr_pack, size_t packed_row_stride, size_t kr, size_t bl, + size_t num_bytes_multiplier); + + size_t (*packed_size_ex)(size_t n, size_t k, size_t nr, size_t kr, size_t bl); + + size_t (*packed_stride_ex)(size_t k, size_t nr, size_t kr, size_t bl); + + void (*pack_func_ex)(size_t num_groups, size_t n, size_t k, size_t nr, size_t kr, size_t sr, size_t bl, + size_t rhs_stride, const void * rhs, const void * bias, const void * scale, void * rhs_packed, size_t extra_bytes, const void * params); }; struct ggml_kleidiai_kernels { - kernel_info gemm; + kernel_info gemm; lhs_packing_info gemm_lhs_info; - kernel_info gemv; + kernel_info gemv; lhs_packing_info gemv_lhs_info; rhs_packing_info rhs_info; diff --git a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp index 44691e5dfd..8b3df7d780 100644 --- a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp @@ -8,6 +8,7 @@ #include #include #include +#include #if defined(__linux__) #include #include @@ -87,40 +88,6 @@ static inline int64_t ggml_ne(const ggml_tensor * tensor, int dim) { return tensor->ne[dim]; } -template -constexpr bool variant_any_invocable_impl(std::index_sequence) { - using V = std::remove_reference_t; - return (std::is_invocable_r_v< - Ret, - std::variant_alternative_t, - Args...> || ...); -} - -template -constexpr bool variant_any_invocable_v = - variant_any_invocable_impl( - std::make_index_sequence< - std::variant_size_v>>{}); - -template -static inline Ret variant_call(Variant && var, Args&&... args) { - static_assert(variant_any_invocable_v, Ret, Args...>, - "No alternative in Variant is invocable with the provided arguments and return type."); - - return std::visit( - [&](auto && f) -> Ret { - using F = std::decay_t; - if constexpr (std::is_invocable_r_v) { - return std::invoke(std::forward(f), std::forward(args)...); - } else { - GGML_ABORT("Invalid function type in variant_call"); - GGML_UNREACHABLE(); - } - }, - std::forward(var) - ); -} - namespace ggml::cpu::kleidiai { static size_t round_down(size_t x, size_t y) { @@ -145,7 +112,9 @@ class tensor_traits : public ggml::cpu::tensor_traits { return false; } ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, op); - GGML_ASSERT(kernels); + if (!kernels) { + return false; + } bool is_gemv = op->src[1]->ne[1] == 1; kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm; lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info; @@ -159,16 +128,18 @@ class tensor_traits : public ggml::cpu::tensor_traits { size_t sr = kernel->get_sr(); if (kernels->rhs_type == GGML_TYPE_Q4_0) { - size = variant_call(lhs_info->packed_size, m, k, QK4_0, mr, kr, sr); + if (!lhs_info->packed_size_ex) return false; + size = lhs_info->packed_size_ex(m, k, QK4_0, mr, kr, sr); } else if (kernels->rhs_type == GGML_TYPE_F16) { + if (!lhs_info->packed_size_ex || !kernels->rhs_info.packed_size_ex) return false; const int64_t lhs_batch_size0 = op->src[1]->ne[2]; const int64_t rhs_batch_size0 = op->src[0]->ne[2]; const int64_t r = lhs_batch_size0 / rhs_batch_size0; - size = variant_call(lhs_info->packed_size, m * r, k, mr, kr, sr) + - variant_call(kernels->rhs_info.packed_size, n, k) + + size = lhs_info->packed_size_ex(m * r, k, 0, mr, kr, sr) + + kernels->rhs_info.packed_size_ex(n, k, kernel->get_nr(), kernel->get_kr(), 0) + k * n * sizeof(float) + n * sizeof(float); } else { - GGML_ASSERT(false); + return false; } return true; @@ -196,12 +167,18 @@ class tensor_traits : public ggml::cpu::tensor_traits { GGML_TENSOR_BINARY_OP_LOCALS ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst); - GGML_ASSERT(kernels); + if (!kernels) { + return false; + } const bool is_gemv = src1->ne[1] == 1; kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm; lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info; GGML_ASSERT(kernel); + if (!kernels->rhs_info.pack_func_ex || + !kernel->get_lhs_offset_ex || !kernel->get_rhs_packed_offset_ex || !kernel->run_kernel_ex) { + return false; + } const int nth = params->nth; const int ith = params->ith; @@ -228,10 +205,10 @@ class tensor_traits : public ggml::cpu::tensor_traits { const int64_t kr = (int64_t) kernel->get_kr(); const int64_t sr = (int64_t) kernel->get_sr(); - const size_t lhs_packed_size = variant_call(lhs_info->packed_size, (size_t)m, (size_t)k, (size_t)mr, (size_t)kr, (size_t)sr); - const size_t rhs_packed_size = variant_call(kernels->rhs_info.packed_size, (size_t)n, (size_t)k); - const size_t kxn_size = (size_t)k * (size_t)n * sizeof(float); - const size_t bias_size = (size_t)n * sizeof(float); + const size_t lhs_packed_size = lhs_info->packed_size_ex(m, k, 0, mr, kr, sr); + const size_t rhs_packed_size = kernels->rhs_info.packed_size_ex(n, k, nr, kr, 0); + const size_t kxn_size = k * n * sizeof(float); + const size_t bias_size = n * sizeof(float); const size_t wsize_required = lhs_packed_size + rhs_packed_size + kxn_size + bias_size; GGML_ASSERT(wsize_required <= params->wsize); @@ -259,10 +236,8 @@ class tensor_traits : public ggml::cpu::tensor_traits { const int64_t m_count = (ith == num_threads - 1) ? num_m_per_threadN_1 : num_m_per_thread0; // Base packed offset (aligned) and per-row stride in bytes - const size_t base_packed_off = variant_call( - lhs_info->get_packed_offset, (size_t)m_start, (size_t)k, (size_t)mr, (size_t)kr, (size_t)sr); - const size_t next_block_off = variant_call( - lhs_info->get_packed_offset, (size_t)(m_start + mr), (size_t)k, (size_t)mr, (size_t)kr, (size_t)sr); + const size_t base_packed_off = lhs_info->get_packed_offset_ex(m_start, k, 0, mr, kr, sr); + const size_t next_block_off = lhs_info->get_packed_offset_ex(m_start + mr, k, 0, mr, kr, sr); const size_t row_stride_bytes = (next_block_off - base_packed_off) / (size_t)mr; int64_t remaining = m_count; @@ -278,9 +253,7 @@ class tensor_traits : public ggml::cpu::tensor_traits { const size_t dst_off = base_packed_off + (size_t)(cur - m_start) * row_stride_bytes; void * dst_ptr = lhs_packed + dst_off; - variant_call(lhs_info->pack_func, - (size_t)take, (size_t)k, (size_t)mr, (size_t)kr, (size_t)sr, - /*m_idx_start*/ 0, src_ptr, lhs_stride, dst_ptr); + lhs_info->pack_func_ex(take, k, 0, mr, kr, sr, 0, src_ptr, lhs_stride, dst_ptr); cur += take; remaining -= take; @@ -296,10 +269,8 @@ class tensor_traits : public ggml::cpu::tensor_traits { reinterpret_cast(rhs_batch_base), rhs_stride); - variant_call(kernels->rhs_info.pack_func, - /*num_groups*/ 1, (size_t)n, (size_t)k, (size_t)nr, (size_t)kr, (size_t)sr, - /*rhs_stride (bytes)*/ (size_t)(n * sizeof(float)), - rhs_kxn, bias, nullptr, rhs_packed, /*extra_bytes*/ 0, /*params*/ nullptr); + kernels->rhs_info.pack_func_ex(1, n, k, nr, kr, sr, 0, n * sizeof(float), + rhs_kxn, bias, nullptr, rhs_packed, 0, nullptr); } ggml_barrier(params->threadpool); @@ -320,20 +291,15 @@ class tensor_traits : public ggml::cpu::tensor_traits { const int64_t n_to_process = (ith == num_threads_n - 1) ? num_n_per_threadN_1 : num_n_per_thread0; // LHS packed base at row 0 (consistent with packing above) - const size_t lhs_packed_offset0 = variant_call( - lhs_info->get_packed_offset, (size_t)0, (size_t)k, (size_t)mr, (size_t)kr, (size_t)sr); - const size_t rhs_packed_offset = variant_call(kernel->get_rhs_packed_offset, (size_t)n_start, (size_t)k); - const size_t dst_offset = kernel->get_dst_offset((size_t)0, (size_t)n_start, dst_stride); + const size_t lhs_packed_offset0 = lhs_info->get_packed_offset_ex(0, k, 0, mr, kr, sr); + const size_t rhs_packed_offset = kernel->get_rhs_packed_offset_ex(n_start, k, 0); + const size_t dst_offset = kernel->get_dst_offset((size_t)0, (size_t)n_start, dst_stride); const void * lhs_ptr = lhs_packed + lhs_packed_offset0; const void * rhs_ptr = rhs_packed + rhs_packed_offset; float * dst_ptr = reinterpret_cast(dst_batch_base + dst_offset); - variant_call(kernel->run_kernel, - (size_t)m, (size_t)n_to_process, (size_t)k, - lhs_ptr, rhs_ptr, - dst_ptr, dst_stride, sizeof(float), - -FLT_MAX, FLT_MAX); + kernel->run_kernel_ex(m, n_to_process, k, 0, lhs_ptr, rhs_ptr, dst_ptr, dst_stride, sizeof(float), -FLT_MAX, FLT_MAX); } } @@ -354,13 +320,19 @@ class tensor_traits : public ggml::cpu::tensor_traits { GGML_TENSOR_BINARY_OP_LOCALS ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst); - GGML_ASSERT(kernels); + if (!kernels) { + return false; + } bool is_gemv = src1->ne[1] == 1; kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm; lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info; GGML_ASSERT(kernel); + if (!lhs_info->get_packed_offset_ex || !lhs_info->pack_func_ex || + !kernel->get_rhs_packed_offset_ex || !kernel->run_kernel_ex || !kernel->get_dst_offset) { + return false; + } const int ith = params->ith; const int nth_raw = params->nth; @@ -402,25 +374,26 @@ class tensor_traits : public ggml::cpu::tensor_traits { // Transform LHS const size_t src_stride = src1->nb[1]; const float * src_ptr = reinterpret_cast(lhs + lhs_info->get_offset(m_start, dst->src[1]->nb[1])); - const size_t lhs_packed_offset = variant_call(lhs_info->get_packed_offset, m_start, k, QK4_0, mr, kr, sr); + const size_t lhs_packed_offset = lhs_info->get_packed_offset_ex(m_start, k, QK4_0, mr, kr, sr); void * lhs_packed_ptr = static_cast(lhs_packed + lhs_packed_offset); - variant_call(lhs_info->pack_func, m_to_process, k, QK4_0, mr, kr, sr, 0, src_ptr, src_stride, lhs_packed_ptr); + // Pack this thread's chunk with m_idx_start = 0 and per-thread output pointer + lhs_info->pack_func_ex(m_to_process, k, QK4_0, mr, kr, sr, 0, src_ptr, src_stride, lhs_packed_ptr); } ggml_barrier(params->threadpool); // Perform the operation const size_t dst_stride = dst->nb[1]; - const size_t lhs_packed_offset = variant_call(lhs_info->get_packed_offset, 0, k, QK4_0, mr, kr, sr); - const size_t rhs_packed_offset = variant_call(kernel->get_rhs_packed_offset, n_start, k, QK4_0); + const size_t lhs_packed_offset = lhs_info->get_packed_offset_ex(0, k, QK4_0, mr, kr, sr); + const size_t rhs_packed_offset = kernel->get_rhs_packed_offset_ex(n_start, k, QK4_0); const size_t dst_offset = kernel->get_dst_offset(0, n_start, dst_stride); const void * rhs_ptr = static_cast(rhs_packed + rhs_packed_offset); const void* lhs_ptr = (const void*)((const char *)lhs_packed + lhs_packed_offset); float *dst_ptr = reinterpret_cast(static_cast(dst->data) + dst_offset); if (n_to_process > 0) { - variant_call(kernel->run_kernel, m, n_to_process, k, QK4_0, lhs_ptr, rhs_ptr, dst_ptr, dst_stride, + kernel->run_kernel_ex(m, n_to_process, k, QK4_0, lhs_ptr, rhs_ptr, dst_ptr, dst_stride, sizeof(float), -FLT_MAX, FLT_MAX); } @@ -429,7 +402,9 @@ class tensor_traits : public ggml::cpu::tensor_traits { bool compute_forward_get_rows(struct ggml_compute_params * params, struct ggml_tensor * dst) { GGML_ASSERT(dst->src[0]->type == GGML_TYPE_Q4_0); - GGML_ASSERT(ctx.kernels); + if (!ctx.kernels) { + return false; + } const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; @@ -438,6 +413,9 @@ class tensor_traits : public ggml::cpu::tensor_traits { rhs_packing_info * rhs_info = &ctx.kernels->rhs_info; kernel_info * kernel = &ctx.kernels->gemm; + if (!rhs_info->to_float || !kernel->get_nr) { + return false; + } const int64_t nc = ne00; const int64_t nr = ggml_nelements(src1); @@ -480,7 +458,7 @@ public: struct kai_rhs_pack_qs4cxs1s0_param params; params.lhs_zero_point = 1; params.rhs_zero_point = 8; - variant_call(ctx.kernels->rhs_info.pack_func, 1, n, k, nr, kr, sr, QK4_0, (const uint8_t*)data, nullptr, tensor->data, 0, ¶ms); + ctx.kernels->rhs_info.pack_func_ex(1, n, k, nr, kr, sr, QK4_0, 0, (const uint8_t*)data, nullptr, nullptr, tensor->data, 0, ¶ms); return 0; GGML_UNUSED(data_size); @@ -548,7 +526,7 @@ static size_t ggml_backend_cpu_kleidiai_buffer_type_get_alloc_size(ggml_backend_ const size_t nr = ctx.kernels->gemm.get_nr(); const size_t kr = ctx.kernels->gemm.get_kr(); - return variant_call(ctx.kernels->rhs_info.packed_size, n, k, nr, kr, QK4_0); + return ctx.kernels->rhs_info.packed_size_ex(n, k, nr, kr, QK4_0); GGML_UNUSED(buft); } From aa4711d369b0d4adb6802b98ad6ea362f838710e Mon Sep 17 00:00:00 2001 From: Chenguang Li <757486878@qq.com> Date: Thu, 9 Oct 2025 15:50:25 +0800 Subject: [PATCH 009/144] CANN: Improve ACL graph matching (#16166) * CANN: improve ACL graph matching Record `ne` and `nb` information for src tensors and include them in the graph matching check. This enhances the robustness of ACL graph matching by preventing incorrect matches when src tensors share the same data address but differ in shape or stride. * CANN: add op_params match --- ggml/src/ggml-cann/common.h | 9 +++++- ggml/src/ggml-cann/ggml-cann.cpp | 48 ++++++++++++++++++++++++-------- 2 files changed, 45 insertions(+), 12 deletions(-) diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h index b707b84359..debbcadc1e 100755 --- a/ggml/src/ggml-cann/common.h +++ b/ggml/src/ggml-cann/common.h @@ -341,11 +341,18 @@ private: #ifdef USE_ACL_GRAPH struct ggml_graph_node_properties { + // dst tensor void * node_address; - ggml_op node_op; int64_t ne[GGML_MAX_DIMS]; size_t nb[GGML_MAX_DIMS]; + + // src tensor void * src_address[GGML_MAX_SRC]; + int64_t src_ne[GGML_MAX_SRC][GGML_MAX_DIMS]; + size_t src_nb[GGML_MAX_SRC][GGML_MAX_DIMS]; + + // op + ggml_op node_op; int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)]; }; diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index b51b554e75..ad1adba6b3 100755 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -2186,7 +2186,15 @@ static void add_lru_matched_graph_node_properties( std::copy_n(node->nb, GGML_MAX_DIMS, prop.nb); for (int src = 0; src < GGML_MAX_SRC; ++src) { - prop.src_address[src] = node->src[src] ? node->src[src]->data : nullptr; + if (node->src[src]) { + prop.src_address[src] = node->src[src]->data; + std::copy_n(node->src[src]->ne, GGML_MAX_DIMS, prop.src_ne[src]); + std::copy_n(node->src[src]->nb, GGML_MAX_DIMS, prop.src_nb[src]); + } else { + prop.src_address[src] = nullptr; + std::fill_n(prop.src_ne[src], GGML_MAX_DIMS, 0); + std::fill_n(prop.src_nb[src], GGML_MAX_DIMS, 0); + } } memcpy(prop.op_params, node->op_params, GGML_MAX_OP_PARAMS); @@ -2206,14 +2214,18 @@ static void add_lru_matched_graph_node_properties( * @param graph_node_properties The stored properties of a CANN graph node. * @return true if all fields match (excluding GGML_OP_VIEW); false otherwise. */ -static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) { +static bool ggml_graph_node_has_matching_properties( + ggml_tensor * node, + ggml_graph_node_properties * graph_node_properties) { if (node->data != graph_node_properties->node_address && - node->op != GGML_OP_VIEW) { + node->op != GGML_OP_VIEW) { return false; } + if (node->op != graph_node_properties->node_op) { return false; } + for (int i = 0; i < GGML_MAX_DIMS; i++) { if (node->ne[i] != graph_node_properties->ne[i]) { return false; @@ -2222,17 +2234,31 @@ static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_gra return false; } } + for (int i = 0; i < GGML_MAX_SRC; i++) { - if (node->src[i] && - node->src[i]->data != graph_node_properties->src_address[i] && - node->op != GGML_OP_VIEW - ) { - return false; + if (node->src[i]) { + if (node->src[i]->data != graph_node_properties->src_address[i] && + node->op != GGML_OP_VIEW) { + return false; + } + + for (int d = 0; d < GGML_MAX_DIMS; d++) { + if (node->src[i]->ne[d] != graph_node_properties->src_ne[i][d]) { + return false; + } + if (node->src[i]->nb[d] != graph_node_properties->src_nb[i][d]) { + return false; + } + } + } else { + if (graph_node_properties->src_address[i] != nullptr) { + return false; + } } } - if (node->op == GGML_OP_SCALE && - memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) { - return false; + + if (node->op == GGML_OP_SCALE || node->op == GGML_OP_UNARY || node->op == GGML_OP_GLU) { + return memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) == 0; } return true; } From 2c0d875ae6c6043fbbafd2831e160955e9ca6af1 Mon Sep 17 00:00:00 2001 From: sudhiarm Date: Thu, 9 Oct 2025 09:13:18 +0100 Subject: [PATCH 010/144] ci: add ARM64 Kleidiai build and test support (#16462) --- .github/workflows/build.yml | 26 ++++++++++++++++++++++++++ ci/run.sh | 31 +++++++++++++++++++++++++++++++ 2 files changed, 57 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ec2ab5a58d..8d6ba5f9f3 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1515,3 +1515,29 @@ jobs: run: | vulkaninfo --summary GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp + + ggml-ci-arm64-cpu-kleidiai: + runs-on: ubuntu-22.04-arm + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.16 + with: + key: ggml-ci-arm64-cpu-kleidiai + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install -y build-essential libcurl4-openssl-dev + + - name: Test + id: ggml-ci + run: | + GG_BUILD_KLEIDIAI=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt + diff --git a/ci/run.sh b/ci/run.sh index f925956a84..bf0d53f20a 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -22,6 +22,9 @@ # # with MUSA support # GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt # +# # with KLEIDIAI support +# GG_BUILD_KLEIDIAI=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +# if [ -z "$2" ]; then echo "usage: $0 " @@ -115,6 +118,34 @@ if [ ! -z ${GG_BUILD_NO_SVE} ]; then CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm" fi +if [ -n "${GG_BUILD_KLEIDIAI}" ]; then + echo ">>===== Enabling KleidiAI support" + + CANDIDATES=("armv9-a+dotprod+i8mm" "armv8.6-a+dotprod+i8mm" "armv8.2-a+dotprod") + CPU="" + + for cpu in "${CANDIDATES[@]}"; do + if echo 'int main(){}' | ${CXX:-c++} -march="$cpu" -x c++ - -c -o /dev/null >/dev/null 2>&1; then + CPU="$cpu" + break + fi + done + + if [ -z "$CPU" ]; then + echo "ERROR: None of the required ARM baselines (armv9/armv8.6/armv8.2 + dotprod) are supported by this compiler." + exit 1 + fi + + echo ">>===== Using ARM baseline: ${CPU}" + + CMAKE_EXTRA="${CMAKE_EXTRA:+$CMAKE_EXTRA } \ + -DGGML_NATIVE=OFF \ + -DGGML_CPU_KLEIDIAI=ON \ + -DGGML_CPU_AARCH64=ON \ + -DGGML_CPU_ARM_ARCH=${CPU} \ + -DBUILD_SHARED_LIBS=OFF" +fi + ## helpers # download a file if it does not exist or if it is outdated From 56b4795842d852152222ca7a2d4304008facf1b9 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 9 Oct 2025 14:35:22 +0200 Subject: [PATCH 011/144] model-conversion : add support for SentenceTransformers (#16387) * model-conversion : add support for SentenceTransformers This commit adds support for models that use SentenceTransformer layers. The motivation for this is that if converted model includes any of the numbered layers specified in the original models repository then these changes enable these models to be used and verified. Currently the model-conversion only support the base model output without any of the additional transformation layers. Usage: Convert the model that also includes the SentenceTransformer layers: ```console (venv) $ export EMBEDDING_MODEL_PATH="~/google/embeddinggemma-300M" (venv) make embedding-convert-model ``` Verify the produced embeddings from the converted model against the original model embeddings: ```console (venv) make embedding-verify-logits-st ``` The original model can be run using SentenceTransformer: ```console (venv) make embedding-run-original-model-st ``` Run the converted model using "SentenceTransformer" layers whic enables pooling and normalization: ```console (venv) make embedding-run-converted-model-st ``` * add model-conversion example requirements * add support for -st flag in embedding model conversion This commit add support for the -st flag in the embedding model conversion script. This will enable models to be converted using sentence transformers dense layers. --- examples/model-conversion/Makefile | 23 ++- examples/model-conversion/README.md | 24 +++ examples/model-conversion/logits.cpp | 78 ++++++--- examples/model-conversion/requirements.txt | 1 + .../scripts/embedding/convert-model.sh | 18 +- .../scripts/embedding/run-converted-model.sh | 11 +- .../scripts/embedding/run-original-model.py | 161 +++++++++++------- .../scripts/utils/semantic_check.py | 119 ++++++++----- requirements/requirements-all.txt | 2 + 9 files changed, 297 insertions(+), 140 deletions(-) diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile index f0867cfe46..25b0514b29 100644 --- a/examples/model-conversion/Makefile +++ b/examples/model-conversion/Makefile @@ -116,20 +116,39 @@ embedding-convert-model: METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \ ./scripts/embedding/convert-model.sh +embedding-convert-model-st: + $(call validate_embedding_model_path,embedding-convert-model-st) + @MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \ + METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \ + ./scripts/embedding/convert-model.sh -st + embedding-run-original-model: $(call validate_embedding_model_path,embedding-run-original-model) @EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \ + USE_SENTENCE_TRANSFORMERS="$(USE_SENTENCE_TRANSFORMERS)" \ ./scripts/embedding/run-original-model.py \ - $(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") + $(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") \ + $(if $(USE_SENTENCE_TRANSFORMERS),--use-sentence-transformers) + +embedding-run-original-model-st: USE_SENTENCE_TRANSFORMERS=1 +embedding-run-original-model-st: embedding-run-original-model embedding-run-converted-model: @./scripts/embedding/run-converted-model.sh $(CONVERTED_EMBEDDING_MODEL) \ - $(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") + $(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") \ + $(if $(USE_POOLING),--pooling) + +embedding-run-converted-model-st: USE_POOLING=1 +embedding-run-converted-model-st: embedding-run-converted-model embedding-verify-logits: embedding-run-original-model embedding-run-converted-model @./scripts/embedding/compare-embeddings-logits.sh \ $(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") +embedding-verify-logits-st: embedding-run-original-model-st embedding-run-converted-model-st + @./scripts/embedding/compare-embeddings-logits.sh \ + $(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") + embedding-inspect-original-model: $(call validate_embedding_model_path,embedding-inspect-original-model) @EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" ./scripts/utils/inspect-org-model.py -m ${EMBEDDING_MODEL_PATH} diff --git a/examples/model-conversion/README.md b/examples/model-conversion/README.md index e95e05cd37..05d95d588b 100644 --- a/examples/model-conversion/README.md +++ b/examples/model-conversion/README.md @@ -189,6 +189,23 @@ This command will save two files to the `data` directory, one is a binary file containing logits which will be used for comparison with the converted model, and the other is a text file which allows for manual visual inspection. +#### Using SentenceTransformer with numbered layers +For models that have numbered SentenceTransformer layers (01_Pooling, 02_Dense, +03_Dense, 04_Normalize), use the `-st` targets to apply all these layers: + +```console +# Run original model with SentenceTransformer (applies all numbered layers) +(venv) $ make embedding-run-original-model-st + +# Run converted model with pooling enabled +(venv) $ make embedding-run-converted-model-st +``` + +This will use the SentenceTransformer library to load and run the model, which +automatically applies all the numbered layers in the correct order. This is +particularly useful when comparing with models that should include these +additional transformation layers beyond just the base model output. + ### Model conversion After updates have been made to [gguf-py](../../gguf-py) to add support for the new model the model can be converted to GGUF format using the following command: @@ -208,6 +225,13 @@ was done manually in the previous steps) and compare the logits: (venv) $ make embedding-verify-logits ``` +For models with SentenceTransformer layers, use the `-st` verification target: +```console +(venv) $ make embedding-verify-logits-st +``` +This convenience target automatically runs both the original model with SentenceTransformer +and the converted model with pooling enabled, then compares the results. + ### llama-server verification To verify that the converted model works with llama-server, the following command can be used: diff --git a/examples/model-conversion/logits.cpp b/examples/model-conversion/logits.cpp index 6dc334189f..bbd095e603 100644 --- a/examples/model-conversion/logits.cpp +++ b/examples/model-conversion/logits.cpp @@ -1,4 +1,7 @@ #include "llama.h" +#include "common.h" + + #include #include #include @@ -8,7 +11,10 @@ static void print_usage(int, char ** argv) { printf("\nexample usage:\n"); - printf("\n %s -m model.gguf [-ngl n_gpu_layers] -embd-mode [prompt]\n", argv[0]); + printf("\n %s -m model.gguf [-ngl n_gpu_layers] -embd-mode [-pooling] [-embd-norm ] [prompt]\n", argv[0]); + printf("\n"); + printf(" -embd-norm: normalization type for pooled embeddings (default: 2)\n"); + printf(" -1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm\n"); printf("\n"); } @@ -17,6 +23,8 @@ int main(int argc, char ** argv) { std::string prompt = "Hello, my name is"; int ngl = 0; bool embedding_mode = false; + bool pooling_enabled = false; + int32_t embd_norm = 2; // (-1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm) { int i = 1; @@ -41,9 +49,13 @@ int main(int argc, char ** argv) { return 1; } } else if (strcmp(argv[i], "-embd-mode") == 0) { + embedding_mode = true; + } else if (strcmp(argv[i], "-pooling") == 0) { + pooling_enabled = true; + } else if (strcmp(argv[i], "-embd-norm") == 0) { if (i + 1 < argc) { try { - embedding_mode = true; + embd_norm = std::stoi(argv[++i]); } catch (...) { print_usage(argc, argv); return 1; @@ -112,7 +124,7 @@ int main(int argc, char ** argv) { ctx_params.no_perf = false; if (embedding_mode) { ctx_params.embeddings = true; - ctx_params.pooling_type = LLAMA_POOLING_TYPE_NONE; + ctx_params.pooling_type = pooling_enabled ? LLAMA_POOLING_TYPE_MEAN : LLAMA_POOLING_TYPE_NONE; ctx_params.n_ubatch = ctx_params.n_batch; } @@ -143,17 +155,27 @@ int main(int argc, char ** argv) { return 1; } - float * logits; - int n_logits; + float * data_ptr; + int data_size; const char * type; + std::vector embd_out; if (embedding_mode) { - logits = llama_get_embeddings(ctx); - n_logits = llama_model_n_embd(model) * batch.n_tokens; + const int n_embd = llama_model_n_embd(model); + const int n_embd_count = pooling_enabled ? 1 : batch.n_tokens; + const int n_embeddings = n_embd * n_embd_count; + float * embeddings; type = "-embeddings"; - const int n_embd = llama_model_n_embd(model); - const int n_embd_count = batch.n_tokens; + if (llama_pooling_type(ctx) != LLAMA_POOLING_TYPE_NONE) { + embeddings = llama_get_embeddings_seq(ctx, 0); + embd_out.resize(n_embeddings); + printf("Normalizing embeddings using norm: %d\n", embd_norm); + common_embd_normalize(embeddings, embd_out.data(), n_embeddings, embd_norm); + embeddings = embd_out.data(); + } else { + embeddings = llama_get_embeddings(ctx); + } printf("Embedding dimension: %d\n", n_embd); printf("\n"); @@ -164,7 +186,7 @@ int main(int argc, char ** argv) { // Print first 3 values for (int i = 0; i < 3 && i < n_embd; i++) { - printf("%9.6f ", logits[j * n_embd + i]); + printf("%9.6f ", embeddings[j * n_embd + i]); } printf(" ... "); @@ -172,7 +194,7 @@ int main(int argc, char ** argv) { // Print last 3 values for (int i = n_embd - 3; i < n_embd; i++) { if (i >= 0) { - printf("%9.6f ", logits[j * n_embd + i]); + printf("%9.6f ", embeddings[j * n_embd + i]); } } @@ -180,27 +202,33 @@ int main(int argc, char ** argv) { } printf("\n"); - printf("Embeddings size: %d\n", n_logits); + printf("Embeddings size: %d\n", n_embeddings); + + data_ptr = embeddings; + data_size = n_embeddings; } else { - logits = llama_get_logits_ith(ctx, batch.n_tokens - 1); - n_logits = llama_vocab_n_tokens(vocab); + float * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1); + const int n_logits = llama_vocab_n_tokens(vocab); type = ""; printf("Vocab size: %d\n", n_logits); + + data_ptr = logits; + data_size = n_logits; } std::filesystem::create_directory("data"); - // Save logits to binary file + // Save data to binary file char bin_filename[512]; snprintf(bin_filename, sizeof(bin_filename), "data/llamacpp-%s%s.bin", model_name, type); - printf("Saving logits to %s\n", bin_filename); + printf("Saving data to %s\n", bin_filename); FILE * f = fopen(bin_filename, "wb"); if (f == NULL) { fprintf(stderr, "%s: error: failed to open binary output file\n", __func__); return 1; } - fwrite(logits, sizeof(float), n_logits, f); + fwrite(data_ptr, sizeof(float), data_size, f); fclose(f); // Also save as text for debugging @@ -211,27 +239,27 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: error: failed to open text output file\n", __func__); return 1; } - for (int i = 0; i < n_logits; i++) { - fprintf(f, "%d: %.6f\n", i, logits[i]); + for (int i = 0; i < data_size; i++) { + fprintf(f, "%d: %.6f\n", i, data_ptr[i]); } fclose(f); if (!embedding_mode) { printf("First 10 logits: "); - for (int i = 0; i < 10 && i < n_logits; i++) { - printf("%.6f ", logits[i]); + for (int i = 0; i < 10 && i < data_size; i++) { + printf("%.6f ", data_ptr[i]); } printf("\n"); printf("Last 10 logits: "); - for (int i = n_logits - 10; i < n_logits; i++) { - if (i >= 0) printf("%.6f ", logits[i]); + for (int i = data_size - 10; i < data_size; i++) { + if (i >= 0) printf("%.6f ", data_ptr[i]); } printf("\n\n"); } - printf("Logits saved to %s\n", bin_filename); - printf("Logits saved to %s\n", txt_filename); + printf("Data saved to %s\n", bin_filename); + printf("Data saved to %s\n", txt_filename); llama_free(ctx); llama_model_free(model); diff --git a/examples/model-conversion/requirements.txt b/examples/model-conversion/requirements.txt index ac9f69e10b..229b2ec75b 100644 --- a/examples/model-conversion/requirements.txt +++ b/examples/model-conversion/requirements.txt @@ -4,3 +4,4 @@ torchvision transformers huggingface-hub accelerate +sentence-transformers diff --git a/examples/model-conversion/scripts/embedding/convert-model.sh b/examples/model-conversion/scripts/embedding/convert-model.sh index 0929e42413..9926350c07 100755 --- a/examples/model-conversion/scripts/embedding/convert-model.sh +++ b/examples/model-conversion/scripts/embedding/convert-model.sh @@ -2,6 +2,21 @@ set -e +# Parse command line arguments +SENTENCE_TRANSFORMERS="" +while [[ $# -gt 0 ]]; do + case $1 in + -st|--sentence-transformers) + SENTENCE_TRANSFORMERS="--sentence-transformers-dense-modules" + shift + ;; + *) + echo "Unknown option: $1" + exit 1 + ;; + esac +done + MODEL_NAME="${MODEL_NAME:-$(basename "$EMBEDDING_MODEL_PATH")}" OUTPUT_DIR="${OUTPUT_DIR:-../../models}" TYPE="${OUTTYPE:-f16}" @@ -15,7 +30,8 @@ echo "Converted model path:: ${CONVERTED_MODEL}" python ../../convert_hf_to_gguf.py --verbose \ ${EMBEDDING_MODEL_PATH} \ --outfile ${CONVERTED_MODEL} \ - --outtype ${TYPE} + --outtype ${TYPE} \ + ${SENTENCE_TRANSFORMERS} echo "" echo "The environment variable CONVERTED_EMBEDDING MODEL can be set to this path using:" diff --git a/examples/model-conversion/scripts/embedding/run-converted-model.sh b/examples/model-conversion/scripts/embedding/run-converted-model.sh index f3e2676632..0f490e6c3b 100755 --- a/examples/model-conversion/scripts/embedding/run-converted-model.sh +++ b/examples/model-conversion/scripts/embedding/run-converted-model.sh @@ -5,6 +5,7 @@ set -e # Parse command line arguments CONVERTED_MODEL="" PROMPTS_FILE="" +USE_POOLING="" while [[ $# -gt 0 ]]; do case $1 in @@ -12,6 +13,10 @@ while [[ $# -gt 0 ]]; do PROMPTS_FILE="$2" shift 2 ;; + --pooling) + USE_POOLING="1" + shift + ;; *) if [ -z "$CONVERTED_MODEL" ]; then CONVERTED_MODEL="$1" @@ -47,4 +52,8 @@ echo $CONVERTED_MODEL cmake --build ../../build --target llama-logits -j8 # TODO: update logits.cpp to accept a --file/-f option for the prompt -../../build/bin/llama-logits -m "$CONVERTED_MODEL" -embd-mode "$PROMPT" +if [ -n "$USE_POOLING" ]; then + ../../build/bin/llama-logits -m "$CONVERTED_MODEL" -embd-mode -pooling "$PROMPT" +else + ../../build/bin/llama-logits -m "$CONVERTED_MODEL" -embd-mode "$PROMPT" +fi diff --git a/examples/model-conversion/scripts/embedding/run-original-model.py b/examples/model-conversion/scripts/embedding/run-original-model.py index 4a3e162413..640e200a97 100755 --- a/examples/model-conversion/scripts/embedding/run-original-model.py +++ b/examples/model-conversion/scripts/embedding/run-original-model.py @@ -14,6 +14,8 @@ unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME') parser = argparse.ArgumentParser(description='Process model with specified path') parser.add_argument('--model-path', '-m', help='Path to the model') parser.add_argument('--prompts-file', '-p', help='Path to file containing prompts (one per line)') +parser.add_argument('--use-sentence-transformers', action='store_true', + help='Use SentenceTransformer to apply all numbered layers (01_Pooling, 02_Dense, 03_Dense, 04_Normalize)') args = parser.parse_args() def read_prompt_from_file(file_path): @@ -31,41 +33,52 @@ model_path = os.environ.get('EMBEDDING_MODEL_PATH', args.model_path) if model_path is None: parser.error("Model path must be specified either via --model-path argument or EMBEDDING_MODEL_PATH environment variable") -tokenizer = AutoTokenizer.from_pretrained(model_path) +# Determine if we should use SentenceTransformer +use_sentence_transformers = args.use_sentence_transformers or os.environ.get('USE_SENTENCE_TRANSFORMERS', '').lower() in ('1', 'true', 'yes') -config = AutoConfig.from_pretrained(model_path) - -# This can be used to override the sliding window size for manual testing. This -# can be useful to verify the sliding window attention mask in the original model -# and compare it with the converted .gguf model. -if hasattr(config, 'sliding_window'): - original_sliding_window = config.sliding_window - #original_sliding_window = 6 - print(f"Modified sliding window: {original_sliding_window} -> {config.sliding_window}") - -print(f"Using unreleased model: {unreleased_model_name}") -if unreleased_model_name: - model_name_lower = unreleased_model_name.lower() - unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}" - class_name = f"{unreleased_model_name}Model" - print(f"Importing unreleased model module: {unreleased_module_path}") - - try: - model_class = getattr(importlib.import_module(unreleased_module_path), class_name) - model = model_class.from_pretrained(model_path, config=config) - except (ImportError, AttributeError) as e: - print(f"Failed to import or load model: {e}") - exit(1) +if use_sentence_transformers: + from sentence_transformers import SentenceTransformer + print("Using SentenceTransformer to apply all numbered layers") + model = SentenceTransformer(model_path) + tokenizer = model.tokenizer + config = model[0].auto_model.config # type: ignore else: - model = AutoModel.from_pretrained(model_path, config=config) -print(f"Model class: {type(model)}") -print(f"Model file: {type(model).__module__}") + tokenizer = AutoTokenizer.from_pretrained(model_path) + + config = AutoConfig.from_pretrained(model_path) + + # This can be used to override the sliding window size for manual testing. This + # can be useful to verify the sliding window attention mask in the original model + # and compare it with the converted .gguf model. + if hasattr(config, 'sliding_window'): + original_sliding_window = config.sliding_window + #original_sliding_window = 6 + print(f"Modified sliding window: {original_sliding_window} -> {config.sliding_window}") + + print(f"Using unreleased model: {unreleased_model_name}") + if unreleased_model_name: + model_name_lower = unreleased_model_name.lower() + unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}" + class_name = f"{unreleased_model_name}Model" + print(f"Importing unreleased model module: {unreleased_module_path}") + + try: + model_class = getattr(importlib.import_module(unreleased_module_path), class_name) + model = model_class.from_pretrained(model_path, config=config) + except (ImportError, AttributeError) as e: + print(f"Failed to import or load model: {e}") + exit(1) + else: + model = AutoModel.from_pretrained(model_path, config=config) + print(f"Model class: {type(model)}") + print(f"Model file: {type(model).__module__}") # Verify the model is using the correct sliding window -if hasattr(model.config, 'sliding_window'): - print(f"Model's sliding_window: {model.config.sliding_window}") -else: - print("Model config does not have sliding_window attribute") +if not use_sentence_transformers: + if hasattr(model.config, 'sliding_window'): # type: ignore + print(f"Model's sliding_window: {model.config.sliding_window}") # type: ignore + else: + print("Model config does not have sliding_window attribute") model_name = os.path.basename(model_path) @@ -75,34 +88,56 @@ if args.prompts_file: else: texts = ["Hello world today"] -encoded = tokenizer( - texts, - padding=True, - truncation=True, - return_tensors="pt" -) - -tokens = encoded['input_ids'][0] -token_strings = tokenizer.convert_ids_to_tokens(tokens) -for i, (token_id, token_str) in enumerate(zip(tokens, token_strings)): - print(f"{token_id:6d} -> '{token_str}'") - with torch.no_grad(): - outputs = model(**encoded) - hidden_states = outputs.last_hidden_state # Shape: [batch_size, seq_len, hidden_size] + if use_sentence_transformers: + embeddings = model.encode(texts, convert_to_numpy=True) + all_embeddings = embeddings # Shape: [batch_size, hidden_size] - # Extract embeddings for each token (matching LLAMA_POOLING_TYPE_NONE behavior) - all_embeddings = hidden_states[0].cpu().numpy() # Shape: [seq_len, hidden_size] + encoded = tokenizer( + texts, + padding=True, + truncation=True, + return_tensors="pt" + ) + tokens = encoded['input_ids'][0] + token_strings = tokenizer.convert_ids_to_tokens(tokens) + for i, (token_id, token_str) in enumerate(zip(tokens, token_strings)): + print(f"{token_id:6d} -> '{token_str}'") - print(f"Hidden states shape: {hidden_states.shape}") - print(f"All embeddings shape: {all_embeddings.shape}") - print(f"Embedding dimension: {all_embeddings.shape[1]}") + print(f"Embeddings shape (after all SentenceTransformer layers): {all_embeddings.shape}") + print(f"Embedding dimension: {all_embeddings.shape[1] if len(all_embeddings.shape) > 1 else all_embeddings.shape[0]}") # type: ignore + else: + # Standard approach: use base model output only + encoded = tokenizer( + texts, + padding=True, + truncation=True, + return_tensors="pt" + ) - # Print embeddings exactly like embedding.cpp does for LLAMA_POOLING_TYPE_NONE - n_embd = all_embeddings.shape[1] - n_embd_count = all_embeddings.shape[0] + tokens = encoded['input_ids'][0] + token_strings = tokenizer.convert_ids_to_tokens(tokens) + for i, (token_id, token_str) in enumerate(zip(tokens, token_strings)): + print(f"{token_id:6d} -> '{token_str}'") - print() # Empty line to match C++ output + outputs = model(**encoded) + hidden_states = outputs.last_hidden_state # Shape: [batch_size, seq_len, hidden_size] + + all_embeddings = hidden_states[0].cpu().numpy() # Shape: [seq_len, hidden_size] + + print(f"Hidden states shape: {hidden_states.shape}") + print(f"All embeddings shape: {all_embeddings.shape}") + print(f"Embedding dimension: {all_embeddings.shape[1]}") + + if len(all_embeddings.shape) == 1: + n_embd = all_embeddings.shape[0] # type: ignore + n_embd_count = 1 + all_embeddings = all_embeddings.reshape(1, -1) + else: + n_embd = all_embeddings.shape[1] # type: ignore + n_embd_count = all_embeddings.shape[0] # type: ignore + + print() for j in range(n_embd_count): embedding = all_embeddings[j] @@ -120,29 +155,23 @@ with torch.no_grad(): print() # New line - print() # Final empty line to match C++ output + print() data_dir = Path("data") data_dir.mkdir(exist_ok=True) bin_filename = data_dir / f"pytorch-{model_name}-embeddings.bin" txt_filename = data_dir / f"pytorch-{model_name}-embeddings.txt" - # Save all embeddings flattened (matching what embedding.cpp would save if it did) flattened_embeddings = all_embeddings.flatten() flattened_embeddings.astype(np.float32).tofile(bin_filename) with open(txt_filename, "w") as f: - f.write(f"# Model class: {model_name}\n") - f.write(f"# Tokens: {token_strings}\n") - f.write(f"# Shape: {all_embeddings.shape}\n") - f.write(f"# n_embd_count: {n_embd_count}, n_embd: {n_embd}\n\n") - + idx = 0 for j in range(n_embd_count): - f.write(f"# Token {j} ({token_strings[j]}):\n") - for i, value in enumerate(all_embeddings[j]): - f.write(f"{j}_{i}: {value:.6f}\n") - f.write("\n") - print(f"Total values: {len(flattened_embeddings)} ({n_embd_count} tokens × {n_embd} dimensions)") + for value in all_embeddings[j]: + f.write(f"{idx}: {value:.6f}\n") + idx += 1 + print(f"Total values: {len(flattened_embeddings)} ({n_embd_count} embeddings × {n_embd} dimensions)") print("") print(f"Saved bin embeddings to: {bin_filename}") print(f"Saved txt embeddings to: {txt_filename}") diff --git a/examples/model-conversion/scripts/utils/semantic_check.py b/examples/model-conversion/scripts/utils/semantic_check.py index 7fd417bcea..2ac8b6b7b4 100644 --- a/examples/model-conversion/scripts/utils/semantic_check.py +++ b/examples/model-conversion/scripts/utils/semantic_check.py @@ -35,7 +35,11 @@ def cosine_similarity(a, b=None): def load_embeddings_from_file(filename, n_tokens, n_embd): embeddings = np.fromfile(filename, dtype=np.float32) - return embeddings.reshape(n_tokens, n_embd) + # Check if this is pooled (single embedding) or per-token embeddings + if len(embeddings) == n_embd: + return embeddings.reshape(1, n_embd) + else: + return embeddings.reshape(n_tokens, n_embd) def test_single_prompt_similarity(python_emb, cpp_emb, tokens, prompt): np.set_printoptions(suppress=True, precision=6) @@ -48,58 +52,83 @@ def test_single_prompt_similarity(python_emb, cpp_emb, tokens, prompt): print(f"Embeddings shape: Python {python_emb.shape}, llama.cpp {cpp_emb.shape}") n_tokens = len(tokens) + is_pooled = python_emb.shape[0] == 1 - # 1. Direct embedding comparison - print(f"\n1. Raw Embedding Magnitude Comparison:") - # Check if the distance of each token embedding from the origin and compare - # if the vectors are on the same "sphere". This does not tell us about - # direction (meaning of the token embedding), just magnitude. - for i in range(n_tokens): - py_mag = np.linalg.norm(python_emb[i]) # calculate standard euclidean norm for Python embeddings - cpp_mag = np.linalg.norm(cpp_emb[i]) # calculate standard euclidean norm for llama.cpp embeddings + if is_pooled: + print(f"\n[Pooled Embeddings Mode - comparing single sentence embeddings]") + + # 1. Direct embedding comparison for pooled embeddings + print(f"\n1. Raw Embedding Magnitude Comparison:") + py_mag = np.linalg.norm(python_emb[0]) + cpp_mag = np.linalg.norm(cpp_emb[0]) ratio = py_mag / cpp_mag if cpp_mag > 0 else float('inf') - print(f" Token {i} ({tokens[i]}): Python={py_mag:.3f}, llama.cpp={cpp_mag:.3f}, ratio={ratio:.3f}") + print(f" Pooled embedding: Python={py_mag:.3f}, llama.cpp={cpp_mag:.3f}, ratio={ratio:.3f}") - # 2. Cosine similarity between tokens within each model - # Here we check the direction of token embeddings to see if the have the - # same meaning (similarity). This is done by calculating cosine similarity - # of a pair of token embeddings within each model. - print(f"\n2. Within-Model Token Similarities:") - print(" Python model:") - for i in range(n_tokens): - for j in range(i+1, n_tokens): - sim = cosine_similarity([python_emb[i]], [python_emb[j]])[0][0] - print(f" {tokens[i]} ↔ {tokens[j]}: {sim:.4f}") + # 2. Cross-model similarity for pooled embeddings + print(f"\n2. Cross-Model Pooled Embedding Similarity:") + sim = cosine_similarity([python_emb[0]], [cpp_emb[0]])[0][0] + print(f" Cosine similarity: {sim:.6f}") - print(" llama.cpp model:") - for i in range(n_tokens): - for j in range(i+1, n_tokens): - sim = cosine_similarity([cpp_emb[i]], [cpp_emb[j]])[0][0] - print(f" {tokens[i]} ↔ {tokens[j]}: {sim:.4f}") + return { + 'cross_model_similarities': [sim], + 'similarity_matrix_diff': np.array([[0.0]]), + 'max_diff': 0.0, + 'mean_diff': 0.0, + 'rms_diff': 0.0 + } + else: + # Original per-token comparison logic + # 1. Direct embedding comparison + print(f"\n1. Raw Embedding Magnitude Comparison:") + # Check if the distance of each token embedding from the origin and compare + # if the vectors are on the same "sphere". This does not tell us about + # direction (meaning of the token embedding), just magnitude. + for i in range(n_tokens): + py_mag = np.linalg.norm(python_emb[i]) # calculate standard euclidean norm for Python embeddings + cpp_mag = np.linalg.norm(cpp_emb[i]) # calculate standard euclidean norm for llama.cpp embeddings + ratio = py_mag / cpp_mag if cpp_mag > 0 else float('inf') + print(f" Token {i} ({tokens[i]}): Python={py_mag:.3f}, llama.cpp={cpp_mag:.3f}, ratio={ratio:.3f}") - # 3. Cross-model similarity (same token position) - print(f"\n3. Cross-Model Same-Token Similarities:") - for i in range(n_tokens): - sim = cosine_similarity([python_emb[i]], [cpp_emb[i]])[0][0] - print(f" Token {i} ({tokens[i]}): {sim:.4f}") + # 2. Cosine similarity between tokens within each model + # Here we check the direction of token embeddings to see if the have the + # same meaning (similarity). This is done by calculating cosine similarity + # of a pair of token embeddings within each model. + print(f"\n2. Within-Model Token Similarities:") + print(" Python model:") + for i in range(n_tokens): + for j in range(i+1, n_tokens): + sim = cosine_similarity([python_emb[i]], [python_emb[j]])[0][0] + print(f" {tokens[i]} ↔ {tokens[j]}: {sim:.4f}") - # 4. Similarity matrix comparison - print(f"\n4. Similarity Matrix Differences:") - py_sim_matrix = cosine_similarity(python_emb) - cpp_sim_matrix = cosine_similarity(cpp_emb) - diff_matrix = np.abs(py_sim_matrix - cpp_sim_matrix) + print(" llama.cpp model:") + for i in range(n_tokens): + for j in range(i+1, n_tokens): + sim = cosine_similarity([cpp_emb[i]], [cpp_emb[j]])[0][0] + print(f" {tokens[i]} ↔ {tokens[j]}: {sim:.4f}") - print(f" Max difference: {np.max(diff_matrix):.4f}") - print(f" Mean difference: {np.mean(diff_matrix):.4f}") - print(f" RMS difference: {np.sqrt(np.mean(diff_matrix**2)):.4f}") + # 3. Cross-model similarity (same token position) + print(f"\n3. Cross-Model Same-Token Similarities:") + for i in range(n_tokens): + sim = cosine_similarity([python_emb[i]], [cpp_emb[i]])[0][0] + print(f" Token {i} ({tokens[i]}): {sim:.4f}") - return { - 'cross_model_similarities': [cosine_similarity([python_emb[i]], [cpp_emb[i]])[0][0] for i in range(n_tokens)], - 'similarity_matrix_diff': diff_matrix, - 'max_diff': np.max(diff_matrix), - 'mean_diff': np.mean(diff_matrix), - 'rms_diff': np.sqrt(np.mean(diff_matrix**2)) - } + # 4. Similarity matrix comparison + print(f"\n4. Similarity Matrix Differences:") + py_sim_matrix = cosine_similarity(python_emb) + cpp_sim_matrix = cosine_similarity(cpp_emb) + diff_matrix = np.abs(py_sim_matrix - cpp_sim_matrix) + + print(f" Max difference: {np.max(diff_matrix):.4f}") + print(f" Mean difference: {np.mean(diff_matrix):.4f}") + print(f" RMS difference: {np.sqrt(np.mean(diff_matrix**2)):.4f}") + + return { + 'cross_model_similarities': [cosine_similarity([python_emb[i]], [cpp_emb[i]])[0][0] for i in range(n_tokens)], + 'similarity_matrix_diff': diff_matrix, + 'max_diff': np.max(diff_matrix), + 'mean_diff': np.mean(diff_matrix), + 'rms_diff': np.sqrt(np.mean(diff_matrix**2)) + } def read_prompt_from_file(file_path): try: diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 56b6752ac0..6c6bea9490 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -14,3 +14,5 @@ -r ./requirements-tool_bench.txt -r ./requirements-gguf_editor_gui.txt + +-r 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a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageThinkingBlock.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageThinkingBlock.svelte index 76861a66c6..9245ad5153 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageThinkingBlock.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageThinkingBlock.svelte @@ -4,7 +4,6 @@ import * as Collapsible from '$lib/components/ui/collapsible/index.js'; import { buttonVariants } from '$lib/components/ui/button/index.js'; import { Card } from '$lib/components/ui/card'; - import { MarkdownContent } from '$lib/components/app'; import { config } from '$lib/stores/settings.svelte'; interface Props { @@ -59,7 +58,9 @@

- +
+ {reasoningContent ?? ''} +
From d00cbea63c671cd85a57adaa50abf60b3b87d86f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 9 Oct 2025 18:54:51 +0300 Subject: [PATCH 013/144] server : host-memory prompt caching (#16391) * minor : code style * server : fix prompt similarity calculation * server : initial host-memory prompt caching * cont * server : refactor * cont * cont : make the server task of the slot const * cont : minor [no ci] * server : cache prompts and checkpoints only for completion tasks * server : improve prompt caching logic * cont : fix check for number of cached prompts [no ci] * server : improve caching logic, add -cram CLI arg * server : print prompt mismatch info * cont : better naming [no ci] * server : improve prompt cache loading logic * server : add option to debug the slot contents (#16482) * server : add option to debug the slot contents * Update tools/server/server.cpp --------- Co-authored-by: Xuan-Son Nguyen * server : add option to disable prompt cache --------- Co-authored-by: Xuan-Son Nguyen --- common/arg.cpp | 8 + common/chat.h | 6 +- common/common.h | 5 +- src/llama-kv-cache.cpp | 7 +- tools/server/server.cpp | 1147 +++++++++++------ tools/server/tests/unit/test_basic.py | 3 +- .../server/tests/unit/test_chat_completion.py | 6 +- tools/server/tests/unit/test_completion.py | 4 +- tools/server/tests/unit/test_ctx_shift.py | 13 +- tools/server/utils.hpp | 85 +- 10 files changed, 813 insertions(+), 471 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 4204f6c690..d17645cf2f 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1935,6 +1935,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.n_ctx_checkpoints = value; } ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--cache-ram", "-cram"}, "N", + string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)\n" + "[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)", params.cache_ram_mib), + [](common_params & params, int value) { + params.cache_ram_mib = value; + } + ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"--kv-unified", "-kvu"}, string_format("use single unified KV buffer for the KV cache of all sequences (default: %s)\n" diff --git a/common/chat.h b/common/chat.h index a1afe574bd..f7b36ec711 100644 --- a/common/chat.h +++ b/common/chat.h @@ -33,8 +33,8 @@ struct common_chat_msg_content_part { struct common_chat_msg { std::string role; std::string content; - std::vector content_parts = {}; - std::vector tool_calls = {}; + std::vector content_parts; + std::vector tool_calls; std::string reasoning_content; std::string tool_name; std::string tool_call_id; @@ -44,7 +44,7 @@ struct common_chat_msg { bool empty() const { return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() && tool_name.empty() && tool_call_id.empty(); } - void ensure_tool_call_ids_set(std::vector & ids_cache, const std::function & gen_tool_call_id) { + void set_tool_call_ids(std::vector & ids_cache, const std::function & gen_tool_call_id) { for (auto i = 0u; i < tool_calls.size(); i++) { if (ids_cache.size() <= i) { auto id = tool_calls[i].id; diff --git a/common/common.h b/common/common.h index 0d3638c9c6..040a44ebd8 100644 --- a/common/common.h +++ b/common/common.h @@ -378,7 +378,7 @@ struct common_params { bool simple_io = false; // improves compatibility with subprocesses and limited consoles bool cont_batching = true; // insert new sequences for decoding on-the-fly bool no_perf = false; // disable performance metrics - bool ctx_shift = false; // context shift on infinite text generation + bool ctx_shift = false; // context shift on infinite text generation bool swa_full = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) bool kv_unified = false; // enable unified KV cache @@ -425,7 +425,8 @@ struct common_params { int32_t timeout_write = timeout_read; // http write timeout in seconds int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting - int32_t n_ctx_checkpoints = 3; // max number of context checkpoints per slot + int32_t n_ctx_checkpoints = 8; // max number of context checkpoints per slot + int32_t cache_ram_mib = 8192; // 0 = no limit, 1 = 1 MiB, etc. std::string hostname = "127.0.0.1"; std::string public_path = ""; // NOLINT diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index 816f2d5de5..736693e174 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -123,11 +123,8 @@ llama_kv_cache::llama_kv_cache( throw std::runtime_error("failed to create ggml context for kv cache"); } - ggml_tensor * k; - ggml_tensor * v; - - k = ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream); - v = ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream); + ggml_tensor * k = ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream); + ggml_tensor * v = ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream); ggml_format_name(k, "cache_k_l%d", il); ggml_format_name(v, "cache_v_l%d", il); diff --git a/tools/server/server.cpp b/tools/server/server.cpp index de6e1a322b..41ecb279fe 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -9,7 +9,6 @@ #include "sampling.h" #include "speculative.h" #include "mtmd.h" -#include "mtmd-helper.h" // mime type for sending response #define MIMETYPE_JSON "application/json; charset=utf-8" @@ -158,7 +157,6 @@ struct slot_params { if (only_metrics) { return json { - {"n_predict", n_predict}, // Server configured n_predict {"seed", sampling.seed}, {"temperature", sampling.temp}, {"dynatemp_range", sampling.dynatemp_range}, @@ -181,7 +179,8 @@ struct slot_params { {"mirostat", sampling.mirostat}, {"mirostat_tau", sampling.mirostat_tau}, {"mirostat_eta", sampling.mirostat_eta}, - {"max_tokens", n_predict}, // User configured n_predict + {"max_tokens", n_predict}, + {"n_predict", n_predict}, // TODO: deduplicate? {"n_keep", n_keep}, {"n_discard", n_discard}, {"ignore_eos", sampling.ignore_eos}, @@ -209,7 +208,6 @@ struct slot_params { } return json { - {"n_predict", n_predict}, // Server configured n_predict {"seed", sampling.seed}, {"temperature", sampling.temp}, {"dynatemp_range", sampling.dynatemp_range}, @@ -234,7 +232,8 @@ struct slot_params { {"mirostat_tau", sampling.mirostat_tau}, {"mirostat_eta", sampling.mirostat_eta}, {"stop", antiprompt}, - {"max_tokens", n_predict}, // User configured n_predict + {"max_tokens", n_predict}, + {"n_predict", n_predict}, // TODO: deduplicate? {"n_keep", n_keep}, {"n_discard", n_discard}, {"ignore_eos", sampling.ignore_eos}, @@ -265,15 +264,15 @@ struct server_task { int id = -1; // to be filled by server_queue int index = -1; // used when there are multiple prompts (batch request) - server_task_type type; - // used by SERVER_TASK_TYPE_CANCEL int id_target = -1; + int id_slot = -1; // used by SERVER_TASK_TYPE_INFERENCE slot_params params; - server_tokens prompt_tokens; - int id_selected_slot = -1; + server_tokens tokens; + + server_task_type type; // used by SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE struct slot_action { @@ -289,6 +288,8 @@ struct server_task { // used by SERVER_TASK_TYPE_SET_LORA std::vector set_lora; + server_task() = default; + server_task(server_task_type type) : type(type) {} static slot_params params_from_json_cmpl( @@ -305,6 +306,7 @@ struct server_task { defaults.sampling = params_base.sampling; defaults.speculative = params_base.speculative; defaults.n_keep = params_base.n_keep; + defaults.n_predict = params_base.n_predict; defaults.antiprompt = params_base.antiprompt; // enabling this will output extra debug information in the HTTP responses from the server @@ -323,32 +325,32 @@ struct server_task { params.n_discard = json_value(data, "n_discard", defaults.n_discard); //params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms); - params.response_fields = json_value(data, "response_fields", std::vector()); + params.response_fields = json_value(data, "response_fields", std::vector()); - 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); - params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp); - params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range); - params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent); - params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n); - params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat); - params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq); - params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present); - params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier); - params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base); - params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length); - params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n); - params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat); - params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau); - params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta); - params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); - params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); - params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); + 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); + params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp); + params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range); + params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent); + params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n); + params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat); + params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq); + params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present); + params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier); + params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base); + params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length); + params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n); + params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat); + params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau); + params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta); + params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); + params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); + params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); params.post_sampling_probs = json_value(data, "post_sampling_probs", defaults.post_sampling_probs); params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min); @@ -690,7 +692,7 @@ struct server_task_result { // using shared_ptr for polymorphism of server_task_result using server_task_result_ptr = std::unique_ptr; -inline std::string stop_type_to_str(stop_type type) { +static inline std::string stop_type_to_str(stop_type type) { switch (type) { case STOP_TYPE_EOS: return "eos"; case STOP_TYPE_WORD: return "word"; @@ -764,13 +766,6 @@ struct completion_token_output { } }; -struct ctx_checkpoint { - llama_pos pos_min; - llama_pos pos_max; - - std::vector data; -}; - struct server_task_result_cmpl_final : server_task_result { int index = 0; @@ -797,11 +792,12 @@ struct server_task_result_cmpl_final : server_task_result { slot_params generation_params; // OAI-compat fields - bool verbose = false; - oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; - std::string oaicompat_model; - std::string oaicompat_cmpl_id; - common_chat_msg oaicompat_msg; + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + common_chat_msg oaicompat_msg; + std::vector oaicompat_msg_diffs; virtual int get_index() override { @@ -1373,17 +1369,17 @@ struct server_task_result_slot_save_load : server_task_result { { "save_ms", t_ms } }}, }; - } else { - return json { - { "id_slot", id_slot }, - { "filename", filename }, - { "n_restored", n_tokens }, - { "n_read", n_bytes }, - { "timings", { - { "restore_ms", t_ms } - }}, - }; } + + return json { + { "id_slot", id_slot }, + { "filename", filename }, + { "n_restored", n_tokens }, + { "n_read", n_bytes }, + { "timings", { + { "restore_ms", t_ms } + }}, + }; } }; @@ -1404,15 +1400,218 @@ struct server_task_result_apply_lora : server_task_result { } }; +struct server_prompt_checkpoint { + llama_pos pos_min; + llama_pos pos_max; + + std::vector data; + + size_t size() const { + return data.size(); + } +}; + +struct server_prompt { + server_tokens tokens; + + std::vector data; + + std::list checkpoints; + + size_t size() const { + size_t res = data.size(); + + for (const auto & checkpoint : checkpoints) { + res += checkpoint.size(); + } + + return res; + } + + int n_tokens() const { + return tokens.size(); + } +}; + +struct server_prompt_cache { + server_prompt_cache(int32_t limit_size_mib, size_t limit_tokens) { + this->limit_size = 1024ull*1024ull*(limit_size_mib < 0 ? 0 : limit_size_mib); + this->limit_tokens = limit_tokens; + } + + std::list states; + + // in bytes, 0 = no limit + size_t limit_size = 0; + + // in tokens, 0 = no limit + size_t limit_tokens = 0; + + size_t size() const { + size_t res = 0; + + for (const auto & state : states) { + res += state.size(); + } + + return res; + } + + size_t n_tokens() const { + size_t res = 0; + + for (const auto & state : states) { + res += state.n_tokens(); + } + + return res; + } + + server_prompt * alloc(const server_prompt & prompt, size_t state_size) { + // first check if the current state is contained fully in the cache + for (auto it = states.begin(); it != states.end(); ++it) { + const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens); + + if (cur_lcp_len == (int) prompt.tokens.size()) { + SRV_WRN("%s", " - prompt is already in the cache, skipping\n"); + return nullptr; + } + } + + // next, remove any cached prompts that are fully contained in the current prompt + for (auto it = states.begin(); it != states.end();) { + const int len = it->tokens.get_common_prefix(prompt.tokens); + + if (len == (int) it->tokens.size()) { + SRV_WRN(" - removing obsolete cached prompt with length %d\n", len); + + it = states.erase(it); + } else { + ++it; + } + } + + std::vector state_data; + + // check if we can allocate enough memory for the new state + try { + state_data.resize(state_size); + } catch (const std::bad_alloc & e) { + SRV_ERR("failed to allocate memory for prompt cache state: %s\n", e.what()); + + limit_size = std::max(1, 0.4*size()); + + SRV_WRN(" - cache size limit reduced to %.3f MiB\n", limit_size / (1024.0 * 1024.0)); + + update(); + + return nullptr; + } + + // TODO: for some reason we can't copy server_tokens, so we have to do this workaround + auto & cur = states.emplace_back(); + cur = { + /*.tokens =*/ server_tokens(prompt.tokens.get_text_tokens(), false), + /*.data =*/ std::move(state_data), + /*.checkpoints =*/ prompt.checkpoints, + }; + + return &cur; + } + + bool load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot) { + const int lcp_best = prompt.tokens.get_common_prefix(tokens_new); + + float f_keep_best = float(lcp_best) / prompt.tokens.size(); + float sim_best = float(lcp_best) / tokens_new.size(); + + SRV_WRN(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best); + + auto it_best = states.end(); + + // find the most similar cached prompt, that would also preserve the most context + for (auto it = states.begin(); it != states.end(); ++it) { + const int lcp_cur = it->tokens.get_common_prefix(tokens_new); + + const float f_keep_cur = float(lcp_cur) / it->tokens.size(); + const float sim_cur = float(lcp_cur) / tokens_new.size(); + + // don't trash large prompts + if (f_keep_cur < 0.25f) { + continue; + } + + if (f_keep_best < f_keep_cur && sim_best < sim_cur) { + f_keep_best = f_keep_cur; + sim_best = sim_cur; + + it_best = it; + } + } + + if (it_best != states.end()) { + SRV_WRN(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best); + + const size_t size = it_best->data.size(); + const size_t n = llama_state_seq_set_data_ext(ctx, it_best->data.data(), size, id_slot, 0); + if (n != size) { + SRV_WRN("failed to restore state with size %zu\n", size); + + return false; + } + + it_best->data.clear(); + it_best->data.shrink_to_fit(); + + prompt = std::move(*it_best); + + states.erase(it_best); + } + + return true; + } + + void update() { + if (limit_size > 0) { + // always keep at least one state, regardless of the limits + while (states.size() > 1 && size() > limit_size) { + if (states.empty()) { + break; + } + + SRV_WRN(" - cache size limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0)); + + states.pop_front(); + } + } + + if (limit_tokens > 0) { + while (states.size() > 1 && n_tokens() > limit_tokens) { + if (states.empty()) { + break; + } + + SRV_WRN(" - cache token limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0)); + + states.pop_front(); + } + } + + SRV_WRN(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens)\n", + states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens); + + for (const auto & state : states) { + SRV_WRN(" - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n", (const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0)); + } + } +}; + struct server_slot { int id; - int id_task = -1; - - // only used for completion/embedding/infill/rerank - server_task_type task_type = SERVER_TASK_TYPE_COMPLETION; llama_batch batch_spec = {}; + // TODO: change to unique_ptrs for consistency: llama_context * ctx = nullptr; llama_context * ctx_dft = nullptr; @@ -1421,15 +1620,8 @@ struct server_slot { common_speculative * spec = nullptr; - std::vector lora; - int32_t alora_invocation_start = -1; - - // the index relative to completion multi-task request - size_t index = 0; - - struct slot_params params; - - slot_state state = SLOT_STATE_IDLE; + std::unique_ptr task; + std::unique_ptr task_prev; // used for debugging // used to determine the slot that has been used the longest int64_t t_last_used = -1; @@ -1437,38 +1629,66 @@ struct server_slot { // generation props int32_t n_ctx = 0; // context size per slot int32_t n_past = 0; + int32_t n_keep = 0; int32_t n_decoded = 0; int32_t n_remaining = -1; int32_t i_batch = -1; - int32_t n_predict = -1; // TODO: disambiguate from params.n_predict - // n_prompt_tokens may not be equal to prompt_tokens.size(), because prompt maybe truncated - int32_t n_prompt_tokens = 0; int32_t n_prompt_tokens_cache = 0; int32_t n_prompt_tokens_processed = 0; - // input prompt tokens - server_tokens prompt_tokens; + int32_t n_prompt_tokens() const { + return task->tokens.size(); + } size_t last_nl_pos = 0; std::string generated_text; llama_tokens generated_tokens; + common_chat_msg chat_msg; - server_tokens cache_tokens; - std::vector generated_token_probs; - std::vector ctx_checkpoints; - bool has_next_token = true; bool has_new_line = false; bool truncated = false; + stop_type stop; std::string stopping_word; + // state + slot_state state = SLOT_STATE_IDLE; + + server_prompt prompt; + + void prompt_save(server_prompt_cache & prompt_cache) const { + assert(prompt.data.size() == 0); + + const size_t cur_size = llama_state_seq_get_size_ext(ctx, id, 0); + + SRV_WRN(" - saving prompt with length %d, total state size = %.3f MiB\n", + (int) prompt.tokens.size(), cur_size / (1024.0 * 1024.0)); + + auto * cur = prompt_cache.alloc(prompt, cur_size); + if (cur == nullptr) { + return; + } + + llama_state_seq_get_data_ext(ctx, cur->data.data(), cur_size, id, 0); + } + + void prompt_load(server_prompt_cache & prompt_cache, const server_tokens & tokens) { + bool res = prompt_cache.load(prompt, tokens, ctx, id); + if (!res) { + SLT_WRN(*this, "%s", "failed to load prompt from cache\n"); + } + } + + std::vector lora; + int32_t alora_invocation_start = -1; + // sampling json json_schema; @@ -1480,7 +1700,7 @@ struct server_slot { std::vector generated_tool_call_ids; // stats - size_t n_sent_text = 0; // number of sent text character + size_t n_sent_text = 0; // number of sent text character int64_t t_start_process_prompt; int64_t t_start_generation; @@ -1497,19 +1717,17 @@ struct server_slot { void reset() { SLT_DBG(*this, "%s", "\n"); - n_prompt_tokens = 0; n_prompt_tokens_cache = 0; - last_nl_pos = 0; - generated_text = ""; - has_new_line = false; - truncated = false; - stop = STOP_TYPE_NONE; - stopping_word = ""; - n_past = 0; - n_sent_text = 0; - task_type = SERVER_TASK_TYPE_COMPLETION; - chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + last_nl_pos = 0; + generated_text = ""; + has_new_line = false; + truncated = false; + stop = STOP_TYPE_NONE; + stopping_word = ""; + n_past = 0; + n_sent_text = 0; + chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY; generated_tokens.clear(); generated_token_probs.clear(); @@ -1521,16 +1739,23 @@ struct server_slot { n_draft_total = 0; n_draft_accepted = 0; + task.reset(); + task_prev.reset(); + // clear alora start alora_invocation_start = -1; } bool need_embd() const { - return server_task_type_need_embd(task_type); + GGML_ASSERT(task); + + return server_task_type_need_embd(task->type); } bool need_logits() const { - return server_task_type_need_logits(task_type); + GGML_ASSERT(task); + + return server_task_type_need_logits(task->type); } // if the context does not have a memory module then all embeddings have to be computed within a single ubatch @@ -1542,18 +1767,22 @@ struct server_slot { } bool can_batch_with(server_slot & other_slot) const { - return task_type == other_slot.task_type && are_lora_equal(lora, other_slot.lora); + GGML_ASSERT(task); + + return task->type == other_slot.task->type && are_lora_equal(lora, other_slot.lora); } bool has_budget(const common_params & global_params) { - if (params.n_predict == -1 && global_params.n_predict == -1) { + GGML_ASSERT(task); + + if (task->params.n_predict == -1 && global_params.n_predict == -1) { return true; // limitless } n_remaining = -1; - if (params.n_predict != -1) { - n_remaining = params.n_predict - n_decoded; + if (task->params.n_predict != -1) { + n_remaining = task->params.n_predict - n_decoded; } else if (global_params.n_predict != -1) { n_remaining = global_params.n_predict - n_decoded; } @@ -1566,7 +1795,7 @@ struct server_slot { } bool can_speculate() const { - return ctx_dft && params.speculative.n_max > 0 && params.cache_prompt; + return ctx_dft; } void add_token(const completion_token_output & token) { @@ -1579,11 +1808,17 @@ struct server_slot { void release() { if (is_processing()) { + GGML_ASSERT(task); + SLT_INF(*this, "stop processing: n_past = %d, truncated = %d\n", n_past, truncated); t_last_used = ggml_time_us(); t_token_generation = (ggml_time_us() - t_start_generation) / 1e3; state = SLOT_STATE_IDLE; + + task_prev = std::move(task); + task.reset(); + callback_on_release(id); } } @@ -1592,19 +1827,19 @@ struct server_slot { result_timings timings; timings.cache_n = n_prompt_tokens_cache; - timings.prompt_n = n_prompt_tokens_processed; - timings.prompt_ms = t_prompt_processing; + timings.prompt_n = n_prompt_tokens_processed; + timings.prompt_ms = t_prompt_processing; timings.prompt_per_token_ms = t_prompt_processing / n_prompt_tokens_processed; - timings.prompt_per_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; + timings.prompt_per_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; - timings.predicted_n = n_decoded; - timings.predicted_ms = t_token_generation; + timings.predicted_n = n_decoded; + timings.predicted_ms = t_token_generation; timings.predicted_per_token_ms = t_token_generation / n_decoded; - timings.predicted_per_second = 1e3 / t_token_generation * n_decoded; + timings.predicted_per_second = 1e3 / t_token_generation * n_decoded; // Add speculative metrics if (n_draft_total > 0) { - timings.draft_n = n_draft_total; + timings.draft_n = n_draft_total; timings.draft_n_accepted = n_draft_accepted; } @@ -1612,14 +1847,16 @@ struct server_slot { } const common_chat_msg & update_chat_msg(std::vector & diffs) { + GGML_ASSERT(task); + auto previous_msg = chat_msg; SRV_DBG("Parsing chat message: %s\n", generated_text.c_str()); auto new_msg = common_chat_parse( generated_text, /* is_partial= */ stop != STOP_TYPE_EOS, - params.oaicompat_chat_syntax); + task->params.oaicompat_chat_syntax); if (!new_msg.empty()) { - new_msg.ensure_tool_call_ids_set(generated_tool_call_ids, gen_tool_call_id); + new_msg.set_tool_call_ids(generated_tool_call_ids, gen_tool_call_id); chat_msg = new_msg; diffs = common_chat_msg_diff::compute_diffs(previous_msg, new_msg.empty() ? previous_msg : new_msg); } @@ -1627,9 +1864,11 @@ struct server_slot { } size_t find_stopping_strings(const std::string & text, const size_t last_token_size, bool is_full_stop) { + GGML_ASSERT(task); + size_t stop_pos = std::string::npos; - for (const std::string & word : params.antiprompt) { + for (const std::string & word : task->params.antiprompt) { size_t pos; if (is_full_stop) { @@ -1682,43 +1921,36 @@ struct server_slot { } json to_json(bool only_metrics = false) const { - if (only_metrics) { - return json { - {"id", id}, - {"id_task", id_task}, - {"n_ctx", n_ctx}, - {"speculative", can_speculate()}, - {"is_processing", is_processing()}, - {"params", params.to_json(true)}, - {"next_token", - { - {"has_next_token", has_next_token}, - {"has_new_line", has_new_line}, - {"n_remain", n_remaining}, - {"n_decoded", n_decoded}, - } - }, - }; - } + json res; - return json { + res = { {"id", id}, - {"id_task", id_task}, {"n_ctx", n_ctx}, {"speculative", can_speculate()}, {"is_processing", is_processing()}, - {"params", params.to_json()}, - {"prompt", prompt_tokens.detokenize(ctx, true)}, - {"next_token", + }; + + const auto & ptask = task ? task : task_prev; + + if (ptask) { + res["id_task"] = ptask->id; + res["params"] = ptask->params.to_json(only_metrics); + res["next_token"] = { { {"has_next_token", has_next_token}, {"has_new_line", has_new_line}, {"n_remain", n_remaining}, {"n_decoded", n_decoded}, - {"stopping_word", stopping_word}, } - }, - }; + }; + + if (!only_metrics) { + res["prompt"] = ptask->tokens.detokenize(ctx, true); + res["generated"] = generated_text; + } + } + + return res; } }; @@ -2109,11 +2341,14 @@ struct server_context { // slots / clients std::vector slots; - json default_generation_settings_for_props; + + int slots_debug = 0; server_queue queue_tasks; server_response queue_results; + std::unique_ptr prompt_cache; + server_metrics metrics; // Necessary similarity of prompt for slot selection @@ -2268,9 +2503,8 @@ struct server_context { slot.id = i; slot.ctx = ctx; slot.n_ctx = n_ctx_slot; - slot.n_predict = params_base.n_predict; slot.mctx = mctx; - slot.cache_tokens.has_mtmd = mctx != nullptr; + slot.prompt.tokens.has_mtmd = mctx != nullptr; if (model_dft) { slot.batch_spec = llama_batch_init(params_base.speculative.n_max + 1, 0, 1); @@ -2286,16 +2520,13 @@ struct server_context { SRV_ERR("%s", "failed to create speculator\n"); return; } - for (auto &pair : params_base.speculative.replacements) { + for (auto & pair : params_base.speculative.replacements) { common_speculative_add_replacement_tgt_dft(slot.spec, pair.first.c_str(), pair.second.c_str()); } } SLT_INF(slot, "new slot n_ctx_slot = %d\n", slot.n_ctx); - slot.params.sampling = params_base.sampling; - slot.params.n_keep = params_base.n_keep; - slot.callback_on_release = [this](int) { queue_tasks.pop_deferred_task(); }; @@ -2305,7 +2536,14 @@ struct server_context { slots.push_back(std::move(slot)); } - default_generation_settings_for_props = slots[0].to_json(); + { + const char * LLAMA_SERVER_SLOTS_DEBUG = getenv("LLAMA_SERVER_SLOTS_DEBUG"); + slots_debug = LLAMA_SERVER_SLOTS_DEBUG ? atoi(LLAMA_SERVER_SLOTS_DEBUG) : 0; + + if (slots_debug) { + SRV_WRN("slots debug = %d\n", slots_debug); + } + } // the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used) @@ -2316,11 +2554,25 @@ struct server_context { metrics.init(); + if (params_base.cache_ram_mib != 0) { + if (params_base.cache_ram_mib < 0) { + SRV_WRN("prompt cache is enabled, size limit: %s\n", "no limit"); + } else { + SRV_WRN("prompt cache is enabled, size limit: %d MiB\n", params_base.cache_ram_mib); + } + SRV_WRN("%s", "use `--cache-ram 0` to disable the prompt cache\n"); + + prompt_cache = std::make_unique(params_base.cache_ram_mib, n_ctx); + } else { + SRV_WRN("%s", "prompt cache is disabled - use `--cache-ram N` to enable it\n"); + } + SRV_WRN("%s", "for more info see https://github.com/ggml-org/llama.cpp/pull/16391\n"); + // thinking is enabled if: // 1. It's not explicitly disabled (reasoning_budget == 0) // 2. The chat template supports it const bool enable_thinking = params_base.use_jinja && params_base.reasoning_budget != 0 && common_chat_templates_support_enable_thinking(chat_templates.get()); - SRV_INF("Enable thinking? %d\n", enable_thinking); + SRV_INF("thinking = %d\n", enable_thinking); oai_parser_opt = { /* use_jinja */ params_base.use_jinja, @@ -2347,10 +2599,11 @@ struct server_context { server_slot * get_available_slot(const server_task & task) { server_slot * ret = nullptr; + bool update_cache = false; + // find the slot that has at least n% prompt similarity if (ret == nullptr && slot_prompt_similarity != 0.0f) { - int lcs_len = 0; - float similarity = 0; + float sim_best = 0; for (server_slot & slot : slots) { // skip the slot if it is not available @@ -2358,27 +2611,34 @@ struct server_context { continue; } + const auto & tokens = slot.prompt.tokens; + // skip the slot if it does not contains cached tokens - if (slot.cache_tokens.empty()) { + if (tokens.empty()) { continue; } - // length of the Longest Common Subsequence between the current slot's prompt and the input prompt - int cur_lcs_len = slot.cache_tokens.get_common_prefix(task.prompt_tokens); - - // fraction of the common subsequence length compared to the current slot's prompt length - float cur_similarity = static_cast(cur_lcs_len) / static_cast(slot.cache_tokens.size()); + // fraction of the Longest Common Prefix length with respect to the input prompt length + const float sim_cur = float(tokens.get_common_prefix(task.tokens)) / task.tokens.size(); // select the current slot if the criteria match - if (cur_lcs_len > lcs_len && cur_similarity > slot_prompt_similarity) { - lcs_len = cur_lcs_len; - similarity = cur_similarity; + if (sim_cur > sim_best && sim_cur > slot_prompt_similarity) { + sim_best = sim_cur; + ret = &slot; } } if (ret != nullptr) { - SLT_INF(*ret, "selected slot by lcs similarity, lcs_len = %d, similarity = %.3f (> %.3f thold)\n", lcs_len, similarity, slot_prompt_similarity); + const float f_keep = (sim_best*task.tokens.size()) / ret->prompt.tokens.size(); + + SLT_INF(*ret, "selected slot by LCP similarity, sim_best = %.3f (> %.3f thold), f_keep = %.3f\n", + sim_best, slot_prompt_similarity, f_keep); + + // if we are about to lose a large portion of the existing context - save it in the prompt cache + if (f_keep < 0.5f) { + update_cache = true; + } } } @@ -2401,6 +2661,36 @@ struct server_context { if (ret != nullptr) { SLT_INF(*ret, "selected slot by LRU, t_last = %" PRId64 "\n", t_last); + + update_cache = true; + } + } + + if (ret) { + const auto & tokens = ret->prompt.tokens; + + update_cache = update_cache && prompt_cache; + + // cache prompts only for completion tasks + update_cache = update_cache && task.type == SERVER_TASK_TYPE_COMPLETION; + + // don't update the cache if the slot's context is empty + update_cache = update_cache && tokens.size() > 0; + + // TODO: mtmd does not support prompt cache + update_cache = update_cache && (ret->mctx == nullptr); + + if (update_cache) { + SRV_WRN("%s", "updating prompt cache\n"); + + const int64_t t_start = ggml_time_us(); + + ret->prompt_save(*prompt_cache); + ret->prompt_load(*prompt_cache, task.tokens); + + prompt_cache->update(); + + SRV_WRN("prompt cache update took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0); } } @@ -2409,27 +2699,21 @@ struct server_context { bool launch_slot_with_task(server_slot & slot, server_task && task) { slot.reset(); - slot.id_task = task.id; - slot.index = task.index; - slot.task_type = task.type; - slot.params = std::move(task.params); - slot.prompt_tokens = std::move(task.prompt_tokens); - if (!are_lora_equal(slot.params.lora, slot.lora)) { + if (!are_lora_equal(task.params.lora, slot.lora)) { // if lora has changed, check to see if the cache should be cleared - if (lora_should_clear_cache(slot.lora, slot.params.lora)) { - SLT_INF(slot, "clearing cache for lora change. %zu loras -> %zu loras\n", slot.lora.size(), slot.params.lora.size()); - slot.cache_tokens.clear(); + if (lora_should_clear_cache(slot.lora, task.params.lora)) { + SLT_INF(slot, "clearing cache for lora change. %zu loras -> %zu loras\n", slot.lora.size(), task.params.lora.size()); + slot.prompt.tokens.clear(); } else { - SLT_INF(slot, "keeping cache for alora. %zu target loras\n", slot.params.lora.size()); + SLT_INF(slot, "keeping cache for alora. %zu target loras\n", task.params.lora.size()); } - slot.lora = slot.params.lora; + slot.lora = task.params.lora; } // if using alora, make sure it's only a single one requested and active - size_t alora_invocation_start = slot.prompt_tokens.size(); + size_t alora_invocation_start = task.tokens.size(); if (lora_all_alora(slot.lora)) { - const auto & enabled_ids = lora_get_enabled_ids(slot.lora); // TODO: This will error out if a user requests two aloras, but only // provides the activation string for one. We could, instead search @@ -2448,10 +2732,10 @@ struct server_context { // scan backwards through the prompt tokens to find the last // occurrence of the invocation sequence int match_idx = static_cast(n_invocation_tokens) - 1; - for (int i = slot.prompt_tokens.size() - 1; i >= 0; --i) { + for (int i = task.tokens.size() - 1; i >= 0; --i) { // the token in this position matches the next token to find in // the invocation sequence - if (slot.prompt_tokens[i] == invocation_tokens[match_idx]) { + if (task.tokens[i] == invocation_tokens[match_idx]) { // if it's a full match, we've found the start if (match_idx == 0) { alora_invocation_start = i; @@ -2466,7 +2750,7 @@ struct server_context { } // if the activation string is not found, disable the alora - if (alora_invocation_start == slot.prompt_tokens.size()) { + if (alora_invocation_start == task.tokens.size()) { SLT_DBG(slot, "alora %zu requested, but not found. deactivating\n", enabled_ids[0]); slot.lora[enabled_ids[0]].scale = 0.0f; } else { @@ -2475,24 +2759,20 @@ struct server_context { } } - if (!slot.prompt_tokens.validate(ctx)) { + if (!task.tokens.validate(ctx)) { send_error(task, "Prompt contains invalid tokens", ERROR_TYPE_INVALID_REQUEST); return false; } + SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str()); - if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) { - // Might be better to reject the request with a 400 ? - SLT_WRN(slot, "n_predict = %d exceeds server configuration, setting to %d\n", slot.params.n_predict, slot.n_predict); - slot.params.n_predict = slot.n_predict; - } - + // initialize samplers { if (slot.smpl != nullptr) { common_sampler_free(slot.smpl); } - slot.smpl = common_sampler_init(model, slot.params.sampling); + slot.smpl = common_sampler_init(model, task.params.sampling); if (slot.smpl == nullptr) { // for now, the only error that may happen here is invalid grammar send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST); @@ -2500,12 +2780,15 @@ struct server_context { } } + // initialize draft batch if (slot.ctx_dft) { llama_batch_free(slot.batch_spec); - slot.batch_spec = llama_batch_init(slot.params.speculative.n_max + 1, 0, 1); + slot.batch_spec = llama_batch_init(task.params.speculative.n_max + 1, 0, 1); } + slot.task = std::make_unique(std::move(task)); + slot.state = SLOT_STATE_STARTED; SLT_INF(slot, "%s", "processing task\n"); @@ -2527,7 +2810,7 @@ struct server_context { slot.sampled = result.tok; slot.generated_text += token_str; - if (slot.params.return_tokens) { + if (slot.task->params.return_tokens) { slot.generated_tokens.push_back(result.tok); } slot.has_next_token = true; @@ -2564,7 +2847,7 @@ struct server_context { } slot.add_token(result); - if (slot.params.stream) { + if (slot.task->params.stream) { send_partial_response(slot, result, false); } } @@ -2586,12 +2869,12 @@ struct server_context { slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; - SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.params.n_predict); + SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.task->params.n_predict); } if (slot.has_new_line) { // require that each new line has a whitespace prefix (i.e. indentation) of at least slot.params.n_indent - if (slot.params.n_indent > 0) { + if (slot.task->params.n_indent > 0) { // check the current indentation // TODO: improve by not doing it more than once for each new line if (slot.last_nl_pos > 0) { @@ -2603,7 +2886,7 @@ struct server_context { pos++; } - if (pos < slot.generated_text.size() && n_indent < slot.params.n_indent) { + if (pos < slot.generated_text.size() && n_indent < slot.task->params.n_indent) { slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; @@ -2630,11 +2913,11 @@ struct server_context { slot.has_new_line = true; // if we have seen a new line, we stop after a certain time limit, but only upon another new line - if (slot.params.t_max_predict_ms > 0 && (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) { + if (slot.task->params.t_max_predict_ms > 0 && (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.task->params.t_max_predict_ms)) { slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; - SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms); + SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.task->params.t_max_predict_ms); } } @@ -2645,7 +2928,7 @@ struct server_context { slot.has_next_token = false; SLT_DBG(slot, "stopped due to running out of context capacity, n_past = %d, n_prompt_tokens = %d, n_decoded = %d, n_ctx = %d\n", - slot.n_decoded, slot.n_prompt_tokens, slot.n_past, slot.n_ctx); + slot.n_decoded, slot.n_prompt_tokens(), slot.n_past, slot.n_ctx); } if (llama_vocab_is_eog(vocab, result.tok)) { @@ -2657,7 +2940,7 @@ struct server_context { const auto n_ctx_train = llama_model_n_ctx_train(model); - if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) { + if (slot.task->params.n_predict < 1 && slot.n_prompt_tokens() + slot.n_decoded >= n_ctx_train) { slot.truncated = true; slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; // stop prediction @@ -2665,7 +2948,7 @@ struct server_context { SLT_WRN(slot, "n_predict (%d) is set for infinite generation. " "Limiting generated tokens to n_ctx_train (%d) to avoid EOS-less generation infinite loop\n", - slot.params.n_predict, n_ctx_train); + slot.task->params.n_predict, n_ctx_train); } SLT_DBG(slot, "n_decoded = %d, n_remaining = %d, next token: %5d '%s'\n", slot.n_decoded, slot.n_remaining, result.tok, token_str.c_str()); @@ -2674,7 +2957,7 @@ struct server_context { } void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) const { - size_t n_probs = slot.params.sampling.n_probs; + size_t n_probs = slot.task->params.sampling.n_probs; size_t n_vocab = llama_vocab_n_tokens(vocab); if (post_sampling) { @@ -2728,7 +3011,7 @@ struct server_context { } void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { - send_error(slot.id_task, error, type, slot.n_prompt_tokens, slot.n_ctx); + send_error(slot.task->id, error, type, slot.n_prompt_tokens(), slot.n_ctx); } void send_error(const int id_task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER, const int32_t n_prompt_tokens = 0, const int32_t n_ctx = 0) { @@ -2749,7 +3032,7 @@ struct server_context { } // if multimodal is enabled, send an error and return false - bool ensure_no_mtmd(const int id_task) { + bool check_no_mtmd(const int id_task) { if (mctx) { send_error(id_task, "This feature is not supported by multimodal", ERROR_TYPE_NOT_SUPPORTED); return false; @@ -2760,14 +3043,14 @@ struct server_context { void send_partial_response(server_slot & slot, const completion_token_output & tkn, bool is_progress) { auto res = std::make_unique(); - res->id = slot.id_task; - res->index = slot.index; + res->id = slot.task->id; + res->index = slot.task->index; if (is_progress) { res->is_progress = true; - res->progress.total = slot.n_prompt_tokens; + res->progress.total = slot.n_prompt_tokens(); res->progress.cache = slot.n_prompt_tokens_cache; - res->progress.processed = slot.cache_tokens.size(); + res->progress.processed = slot.prompt.tokens.size(); res->progress.time_ms = (ggml_time_us() - slot.t_start_process_prompt / 1000); } else { res->content = tkn.text_to_send; @@ -2777,21 +3060,21 @@ struct server_context { } res->n_decoded = slot.n_decoded; - res->n_prompt_tokens = slot.n_prompt_tokens; - res->post_sampling_probs = slot.params.post_sampling_probs; + res->n_prompt_tokens = slot.n_prompt_tokens(); + res->post_sampling_probs = slot.task->params.post_sampling_probs; - res->verbose = slot.params.verbose; - res->oaicompat = slot.params.oaicompat; - res->oaicompat_model = slot.params.oaicompat_model; - res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id; + res->verbose = slot.task->params.verbose; + res->oaicompat = slot.task->params.oaicompat; + res->oaicompat_model = slot.task->params.oaicompat_model; + res->oaicompat_cmpl_id = slot.task->params.oaicompat_cmpl_id; // populate res.probs_output - if (slot.params.sampling.n_probs > 0) { + if (slot.task->params.sampling.n_probs > 0) { res->prob_output = tkn; // copy the token probs } // populate timings if this is final response or timings_per_token is enabled - if (slot.stop != STOP_TYPE_NONE || slot.params.timings_per_token) { + if (slot.stop != STOP_TYPE_NONE || slot.task->params.timings_per_token) { res->timings = slot.get_timings(); } @@ -2800,36 +3083,37 @@ struct server_context { void send_final_response(server_slot & slot) { auto res = std::make_unique(); - res->id = slot.id_task; - res->id_slot = slot.id; - res->index = slot.index; + res->id = slot.task->id; + res->id_slot = slot.id; + + res->index = slot.task->index; res->content = slot.generated_text; res->tokens = std::move(slot.generated_tokens); res->timings = slot.get_timings(); - res->prompt = slot.prompt_tokens.detokenize(ctx, true); - res->response_fields = std::move(slot.params.response_fields); + res->prompt = slot.task->tokens.detokenize(ctx, true); + res->response_fields = std::move(slot.task->params.response_fields); res->truncated = slot.truncated; res->n_decoded = slot.n_decoded; - res->n_prompt_tokens = slot.n_prompt_tokens; + res->n_prompt_tokens = slot.n_prompt_tokens(); res->n_tokens_cached = slot.n_past; res->has_new_line = slot.has_new_line; res->stopping_word = slot.stopping_word; res->stop = slot.stop; - res->post_sampling_probs = slot.params.post_sampling_probs; + res->post_sampling_probs = slot.task->params.post_sampling_probs; - res->verbose = slot.params.verbose; - res->stream = slot.params.stream; - res->include_usage = slot.params.include_usage; - res->oaicompat = slot.params.oaicompat; - res->oaicompat_model = slot.params.oaicompat_model; - res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id; - res->oaicompat_msg = slot.update_chat_msg(res->oaicompat_msg_diffs); + res->verbose = slot.task->params.verbose; + res->stream = slot.task->params.stream; + res->include_usage = slot.task->params.include_usage; + res->oaicompat = slot.task->params.oaicompat; + res->oaicompat_model = slot.task->params.oaicompat_model; + res->oaicompat_cmpl_id = slot.task->params.oaicompat_cmpl_id; + res->oaicompat_msg = slot.update_chat_msg(res->oaicompat_msg_diffs); // populate res.probs_output - if (slot.params.sampling.n_probs > 0) { - if (!slot.params.stream && slot.stop == STOP_TYPE_WORD) { + if (slot.task->params.sampling.n_probs > 0) { + if (!slot.task->params.stream && slot.stop == STOP_TYPE_WORD) { const llama_tokens stop_word_toks = common_tokenize(ctx, slot.stopping_word, false); size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size()); @@ -2843,17 +3127,17 @@ struct server_context { } } - res->generation_params = slot.params; // copy the parameters + res->generation_params = slot.task->params; // copy the parameters queue_results.send(std::move(res)); } void send_embedding(const server_slot & slot, const llama_batch & batch) { auto res = std::make_unique(); - res->id = slot.id_task; - res->index = slot.index; - res->n_tokens = slot.n_prompt_tokens; - res->oaicompat = slot.params.oaicompat; + res->id = slot.task->id; + res->index = slot.task->index; + res->n_tokens = slot.n_prompt_tokens(); + res->oaicompat = slot.task->params.oaicompat; const int n_embd = llama_model_n_embd(model); @@ -2880,12 +3164,12 @@ struct server_context { // normalize only when there is pooling if (llama_pooling_type(slot.ctx) != LLAMA_POOLING_TYPE_NONE) { - common_embd_normalize(embd, embd_res.data(), n_embd, slot.params.embd_normalize); + common_embd_normalize(embd, embd_res.data(), n_embd, slot.task->params.embd_normalize); res->embedding.push_back(embd_res); break; - } else { - res->embedding.emplace_back(embd, embd + n_embd); } + + res->embedding.emplace_back(embd, embd + n_embd); } SLT_DBG(slot, "%s", "sending embeddings\n"); @@ -2895,9 +3179,9 @@ struct server_context { void send_rerank(const server_slot & slot, const llama_batch & batch) { auto res = std::make_unique(); - res->id = slot.id_task; - res->index = slot.index; - res->n_tokens = slot.n_prompt_tokens; + res->id = slot.task->id; + res->index = slot.task->index; + res->n_tokens = slot.n_prompt_tokens(); for (int i = 0; i < batch.n_tokens; ++i) { if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { @@ -3034,7 +3318,7 @@ struct server_context { case SERVER_TASK_TYPE_EMBEDDING: case SERVER_TASK_TYPE_RERANK: { - const int id_slot = task.id_selected_slot; + const int id_slot = task.id_slot; server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task); @@ -3061,7 +3345,7 @@ struct server_context { { // release slot linked with the task id for (auto & slot : slots) { - if (slot.id_task == task.id_target) { + if (slot.task && slot.task->id == task.id_target) { slot.release(); break; } @@ -3079,7 +3363,7 @@ struct server_context { int n_processing_slots = 0; for (server_slot & slot : slots) { - json slot_data = slot.to_json(true); + json slot_data = slot.to_json(slots_debug == 0); if (slot.is_processing()) { n_processing_slots++; @@ -3121,7 +3405,7 @@ struct server_context { } break; case SERVER_TASK_TYPE_SLOT_SAVE: { - if (!ensure_no_mtmd(task.id)) { + if (!check_no_mtmd(task.id)) { break; } @@ -3138,13 +3422,13 @@ struct server_context { break; } - const size_t token_count = slot->cache_tokens.size(); + const size_t token_count = slot->prompt.tokens.size(); const int64_t t_start = ggml_time_us(); std::string filename = task.slot_action.filename; std::string filepath = task.slot_action.filepath; - const llama_tokens & tokens = slot->cache_tokens.get_text_tokens(); + const llama_tokens & tokens = slot->prompt.tokens.get_text_tokens(); const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, tokens.data(), token_count); const int64_t t_end = ggml_time_us(); @@ -3162,7 +3446,7 @@ struct server_context { } break; case SERVER_TASK_TYPE_SLOT_RESTORE: { - if (!ensure_no_mtmd(task.id)) break; + if (!check_no_mtmd(task.id)) break; int id_slot = task.slot_action.slot_id; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { @@ -3186,13 +3470,13 @@ struct server_context { size_t token_count = 0; size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id, tokens.data(), tokens.size(), &token_count); if (nread == 0) { - slot->cache_tokens.clear(); // KV may already been invalidated? + slot->prompt.tokens.clear(); // KV may already been invalidated? send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST); break; } tokens.resize(token_count); - slot->cache_tokens.clear(); - slot->cache_tokens.insert(tokens); + slot->prompt.tokens.clear(); + slot->prompt.tokens.insert(tokens); const int64_t t_end = ggml_time_us(); const double t_restore_ms = (t_end - t_start) / 1000.0; @@ -3209,7 +3493,9 @@ struct server_context { } break; case SERVER_TASK_TYPE_SLOT_ERASE: { - if (!ensure_no_mtmd(task.id)) break; + if (!check_no_mtmd(task.id)) { + break; + } int id_slot = task.slot_action.slot_id; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { @@ -3224,9 +3510,9 @@ struct server_context { } // Erase token cache - const size_t n_erased = slot->cache_tokens.size(); + const size_t n_erased = slot->prompt.tokens.size(); llama_memory_seq_rm(llama_get_memory(ctx), slot->id, -1, -1); - slot->cache_tokens.clear(); + slot->prompt.tokens.clear(); auto res = std::make_unique(); res->id = task.id; @@ -3282,8 +3568,8 @@ struct server_context { if (!params_base.ctx_shift) { // this check is redundant (for good) // we should never get here, because generation should already stopped in process_token() - slot.release(); send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER); + slot.release(); continue; } @@ -3294,9 +3580,16 @@ struct server_context { } // Shift context - const int n_keep = slot.params.n_keep + add_bos_token; + int n_keep = slot.task->params.n_keep < 0 ? slot.n_prompt_tokens() : slot.task->params.n_keep; + + if (add_bos_token) { + n_keep += 1; + } + + n_keep = std::min(slot.n_ctx - 4, n_keep); + const int n_left = slot.n_past - n_keep; - const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2); + const int n_discard = slot.task->params.n_discard ? slot.task->params.n_discard : (n_left / 2); SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard); @@ -3305,14 +3598,14 @@ struct server_context { // add generated tokens to cache { - llama_tokens new_tokens = slot.cache_tokens.get_text_tokens(); // copy + llama_tokens new_tokens = slot.prompt.tokens.get_text_tokens(); // copy for (size_t i = n_keep + n_discard; i < new_tokens.size(); i++) { new_tokens[i - n_discard] = new_tokens[i]; } - new_tokens.resize(slot.cache_tokens.size() - n_discard); - slot.cache_tokens.clear(); - slot.cache_tokens.insert(new_tokens); + new_tokens.resize(slot.prompt.tokens.size() - n_discard); + slot.prompt.tokens.clear(); + slot.prompt.tokens.insert(new_tokens); } slot.n_past -= n_discard; @@ -3328,7 +3621,8 @@ struct server_context { server_slot * slot_batched = nullptr; auto accept_special_token = [&](server_slot & slot, llama_token token) { - return params_base.special || slot.params.sampling.preserved_tokens.find(token) != slot.params.sampling.preserved_tokens.end(); + return params_base.special || + slot.task->params.sampling.preserved_tokens.find(token) != slot.task->params.sampling.preserved_tokens.end(); }; // frist, add sampled tokens from any ongoing sequences @@ -3349,10 +3643,10 @@ struct server_context { common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true); slot.n_past += 1; - slot.cache_tokens.push_back(slot.sampled); + slot.prompt.tokens.push_back(slot.sampled); SLT_DBG(slot, "slot decode token, n_ctx = %d, n_past = %d, n_cache_tokens = %d, truncated = %d\n", - slot.n_ctx, slot.n_past, (int) slot.cache_tokens.size(), slot.truncated); + slot.n_ctx, slot.n_past, (int) slot.prompt.tokens.size(), slot.truncated); } // process in chunks of params.n_batch @@ -3375,7 +3669,7 @@ struct server_context { // this slot still has a prompt to be processed if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) { - auto & prompt_tokens = slot.prompt_tokens; + const auto & input_tokens = slot.task->tokens; // TODO: maybe move branch to outside of this loop in the future if (slot.state == SLOT_STATE_STARTED) { @@ -3383,104 +3677,64 @@ struct server_context { slot.t_start_generation = 0; slot.n_past = 0; - slot.n_prompt_tokens = prompt_tokens.size(); slot.state = SLOT_STATE_PROCESSING_PROMPT; - SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens); + SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", + slot.n_ctx, slot.task->params.n_keep, slot.n_prompt_tokens()); // print prompt tokens (for debugging) /*if (1) { // first 16 tokens (avoid flooding logs) - for (int i = 0; i < std::min(16, prompt_tokens.size()); i++) { - SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str()); + for (int i = 0; i < std::min(16, input_tokens.size()); i++) { + SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, input_tokens[i], common_token_to_piece(ctx, input_tokens[i]).c_str()); } } else { // all - for (int i = 0; i < (int) prompt_tokens.size(); i++) { - SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str()); + for (int i = 0; i < (int) input_tokens.size(); i++) { + SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, input_tokens[i], common_token_to_piece(ctx, input_tokens[i]).c_str()); } }*/ // empty prompt passed -> release the slot and send empty response - if (prompt_tokens.empty()) { + if (input_tokens.empty()) { SLT_WRN(slot, "%s", "empty prompt - releasing slot\n"); - slot.release(); slot.print_timings(); send_final_response(slot); + slot.release(); + continue; } // TODO: support memory-less logits computation if (slot.need_logits() && !llama_get_memory(ctx)) { - slot.release(); send_error(slot, "the current context does not logits computation. skipping", ERROR_TYPE_SERVER); + slot.release(); continue; } if (!slot.can_split()) { - if (slot.n_prompt_tokens > n_ubatch) { - slot.release(); + if (slot.n_prompt_tokens() > n_ubatch) { send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER); + slot.release(); continue; } - if (slot.n_prompt_tokens > slot.n_ctx) { - slot.release(); + if (slot.n_prompt_tokens() > slot.n_ctx) { send_error(slot, "input is larger than the max context size. skipping", ERROR_TYPE_EXCEED_CONTEXT_SIZE); + slot.release(); continue; } } else { - if (!params_base.ctx_shift) { - // if context shift is disabled, we make sure prompt size is smaller than KV size - // TODO: there should be a separate parameter that control prompt truncation - // context shift should be applied only during the generation phase - if (slot.n_prompt_tokens >= slot.n_ctx) { - slot.release(); - send_error(slot, "the request exceeds the available context size. try increasing the context size or enable context shift", ERROR_TYPE_EXCEED_CONTEXT_SIZE); - continue; - } - } - if (slot.params.n_keep < 0) { - slot.params.n_keep = slot.n_prompt_tokens; - } - slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - - // if input prompt is too big, truncate it - if (slot.n_prompt_tokens >= slot.n_ctx) { - if (mctx) { - // we should never reach this - GGML_ABORT("not supported by multimodal"); - } - const int n_left = slot.n_ctx - slot.params.n_keep; - - const int n_block_size = n_left / 2; - const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - - const llama_tokens & curr_tokens = slot.prompt_tokens.get_text_tokens(); - llama_tokens new_tokens( - curr_tokens.begin(), - curr_tokens.begin() + slot.params.n_keep); - - new_tokens.insert( - new_tokens.end(), - curr_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, - curr_tokens.end()); - - prompt_tokens.clear(); - prompt_tokens.insert(new_tokens); - - slot.truncated = true; - slot.n_prompt_tokens = prompt_tokens.size(); - - SLT_WRN(slot, "input truncated, n_ctx = %d, n_keep = %d, n_left = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, n_left, slot.n_prompt_tokens); - - GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); + if (slot.n_prompt_tokens() >= slot.n_ctx) { + send_error(slot, "the request exceeds the available context size. try increasing the context size or enable context shift", ERROR_TYPE_EXCEED_CONTEXT_SIZE); + slot.release(); + continue; } - if (slot.params.cache_prompt) { + if (slot.task->params.cache_prompt) { // reuse any previously computed tokens that are common with the new prompt - slot.n_past = slot.cache_tokens.get_common_prefix(prompt_tokens); + slot.n_past = slot.prompt.tokens.get_common_prefix(input_tokens); // if there is an alora invoked, don't cache after the invocation start if (slot.alora_invocation_start >= 0) { @@ -3500,13 +3754,13 @@ struct server_context { SLT_DBG(slot, "trying to reuse chunks with size > %d, slot.n_past = %d\n", params_base.n_cache_reuse, slot.n_past); - while (head_c < slot.cache_tokens.size() && - head_p < prompt_tokens.size()) { + while (head_c < slot.prompt.tokens.size() && + head_p < input_tokens.size()) { size_t n_match = 0; - while (head_c + n_match < slot.cache_tokens.size() && - head_p + n_match < prompt_tokens.size() && - slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) { + while (head_c + n_match < slot.prompt.tokens.size() && + head_p + n_match < input_tokens.size() && + slot.prompt.tokens[head_c + n_match] == input_tokens[head_p + n_match]) { n_match++; } @@ -3523,7 +3777,7 @@ struct server_context { llama_memory_seq_add(llama_get_memory(ctx), slot.id, head_c, head_c + n_match, kv_shift); for (size_t i = 0; i < n_match; i++) { - slot.cache_tokens.set_token(head_p + i, slot.cache_tokens[head_c + i]); + slot.prompt.tokens.set_token(head_p + i, slot.prompt.tokens[head_c + i]); slot.n_past++; } @@ -3547,41 +3801,83 @@ struct server_context { // the largest pos_min required for a checkpoint to be useful const auto pos_min_thold = std::max(0, slot.n_past - n_swa); - if (slot.n_past > 0 && slot.n_past < (int) slot.cache_tokens.size()) { + if (slot.n_past > 0 && slot.n_past < (int) slot.prompt.tokens.size()) { const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id); if (pos_min == -1) { - SLT_ERR(slot, "n_past = %d, cache_tokens.size() = %d, seq_id = %d, pos_min = %d\n", slot.n_past, (int) slot.cache_tokens.size(), slot.id, pos_min); + SLT_ERR(slot, "n_past = %d, cache_tokens.size() = %d, seq_id = %d, pos_min = %d\n", slot.n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min); GGML_ABORT("pos_min == -1, but n_past > 0 - should not happen: https://github.com/ggml-org/llama.cpp/pull/13833#discussion_r2116181237"); } + // when the prompt prefix does not match, print the tokens around the mismatch + // this is useful for debugging prompt caching + { + const int np0 = std::max(slot.n_past - 4, 0); + const int np1 = std::min(slot.n_past + 6, std::min(slot.prompt.tokens.size(), slot.task->tokens.size())); + + std::stringstream ss0; + std::stringstream ss1; + + std::stringstream st0; + std::stringstream st1; + + ss0 << "old: ... "; + ss1 << "new: ... "; + + for (int i = np0; i < np1; i++) { + if (i == slot.n_past) { + ss0 << " | "; + ss1 << " | "; + } + + { + const auto token = slot.prompt.tokens[i]; + const auto piece = common_token_to_piece(ctx, token); + ss0 << piece; + st0 << std::setw(8) << token; + } + + { + const auto token = slot.task->tokens[i]; + const auto piece = common_token_to_piece(ctx, token); + ss1 << piece; + st1 << std::setw(8) << token; + } + } + + SLT_WRN(slot, "%s\n", ss0.str().c_str()); + SLT_WRN(slot, "%s\n", ss1.str().c_str()); + + SLT_WRN(slot, "%s\n", st0.str().c_str()); + SLT_WRN(slot, "%s\n", st1.str().c_str()); + } + if (pos_min > pos_min_thold) { - SLT_WRN(slot, "n_past = %d, cache_tokens.size() = %d, seq_id = %d, pos_min = %d, n_swa = %d\n", slot.n_past, (int) slot.cache_tokens.size(), slot.id, pos_min, n_swa); + SLT_WRN(slot, "n_past = %d, cache_tokens.size() = %d, seq_id = %d, pos_min = %d, n_swa = %d\n", slot.n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min, n_swa); // search for a context checkpoint const auto it = std::find_if( - slot.ctx_checkpoints.rbegin(), - slot.ctx_checkpoints.rend(), + slot.prompt.checkpoints.rbegin(), + slot.prompt.checkpoints.rend(), [&](const auto & cur) { // guarantee that a checkpoint will result in at least one token being processed [TAG_PROMPT_LOGITS] return cur.pos_min < pos_min_thold; } ); - bool do_reset = it == slot.ctx_checkpoints.rend(); - //printf("[DEBUG] `do_reset` was set to `%s`\n", do_reset ? "true" : "false"); + bool do_reset = it == slot.prompt.checkpoints.rend(); if (!do_reset) { // restore the context checkpoint - const size_t ctx_checkpoint_size = it->data.size(); - const size_t n = llama_state_seq_set_data_ext(ctx, it->data.data(), ctx_checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); + const size_t checkpoint_size = it->data.size(); + const size_t n = llama_state_seq_set_data_ext(ctx, it->data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); - if (n != ctx_checkpoint_size) { - SLT_ERR(slot, "failed to restore context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, (float) ctx_checkpoint_size / 1024 / 1024); + if (n != checkpoint_size) { + SLT_ERR(slot, "failed to restore context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, (float) checkpoint_size / 1024 / 1024); do_reset = true; //printf("[DEBUG] `do_reset` was set to `true` after failing to restore a checkpoint"); } else { slot.n_past = std::min(slot.n_past, std::max(it->pos_min + 1, it->pos_max)); - SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, (float) ctx_checkpoint_size / 1024 / 1024); + SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, (float) checkpoint_size / 1024 / 1024); } } @@ -3595,19 +3891,21 @@ struct server_context { { // erase any checkpoints with pos_min > pos_min_thold - for (int i = (int) slot.ctx_checkpoints.size() - 1; i >= 0; i--) { - const auto & cur = slot.ctx_checkpoints[i]; + for (auto it = slot.prompt.checkpoints.begin(); it != slot.prompt.checkpoints.end();) { + const auto & cur = *it; if (cur.pos_min > pos_min_thold) { SLT_WRN(slot, "erased invalidated context checkpoint (pos_min = %d, pos_max = %d, n_swa = %d, size = %.3f MiB)\n", cur.pos_min, cur.pos_max, n_swa, (float) cur.data.size() / 1024 / 1024); - slot.ctx_checkpoints.erase(slot.ctx_checkpoints.begin() + i); + it = slot.prompt.checkpoints.erase(it); + } else { + ++it; } } } } // [TAG_PROMPT_LOGITS] - if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) { - SLT_WRN(slot, "need to evaluate at least 1 token for each active slot (n_past = %d, n_prompt_tokens = %d)\n", slot.n_past, slot.n_prompt_tokens); + if (slot.n_past == slot.n_prompt_tokens() && slot.n_past > 0) { + SLT_WRN(slot, "need to evaluate at least 1 token for each active slot (n_past = %d, n_prompt_tokens = %d)\n", slot.n_past, slot.n_prompt_tokens()); slot.n_past--; SLT_WRN(slot, "n_past was set to %d\n", slot.n_past); } @@ -3618,7 +3916,7 @@ struct server_context { if (!slot.can_split()) { // cannot fit the prompt in the current batch - will try next iter - if (batch.n_tokens + slot.n_prompt_tokens > n_batch) { + if (batch.n_tokens + slot.n_prompt_tokens() > n_batch) { continue; } } @@ -3636,28 +3934,28 @@ struct server_context { SLT_INF(slot, "n_past = %d, memory_seq_rm [%d, end)\n", slot.n_past, slot.n_past); // remove the non-common part from the cache - slot.cache_tokens.keep_first(slot.n_past); + slot.prompt.tokens.keep_first(slot.n_past); // check if we should process the image - if (slot.n_past < slot.n_prompt_tokens && slot.prompt_tokens[slot.n_past] == LLAMA_TOKEN_NULL) { + if (slot.n_past < slot.n_prompt_tokens() && input_tokens[slot.n_past] == LLAMA_TOKEN_NULL) { // process the image int32_t new_n_past; - int32_t res = slot.prompt_tokens.process_chunk(ctx, mctx, slot.n_past, slot.id, new_n_past); - int32_t n_pos = new_n_past - slot.n_past; - + int32_t res = input_tokens.process_chunk(ctx, mctx, slot.n_past, slot.id, new_n_past); if (res != 0) { SLT_ERR(slot, "failed to process image, res = %d\n", res); - slot.release(); send_error(slot, "failed to process image", ERROR_TYPE_SERVER); + slot.release(); continue; } // add the image chunk to cache { - const auto & chunk = slot.prompt_tokens.find_chunk(slot.n_past); - slot.cache_tokens.push_back(chunk.get()); // copy + const auto & chunk = input_tokens.find_chunk(slot.n_past); + slot.prompt.tokens.push_back(chunk.get()); // copy } + const int32_t n_pos = new_n_past - slot.n_past; + slot.n_past += n_pos; slot.n_prompt_tokens_processed += n_pos; } @@ -3678,6 +3976,9 @@ struct server_context { bool do_checkpoint = params_base.n_ctx_checkpoints > 0; + // make checkpoints only for completion tasks + do_checkpoint = do_checkpoint && slot.task->type == SERVER_TASK_TYPE_COMPLETION; + // make a checkpoint of the parts of the memory that cannot be rolled back. // checkpoints are created only if: // - the model uses SWA and we are not using `swa_full` @@ -3691,9 +3992,9 @@ struct server_context { ); // add prompt tokens for processing in the current batch - while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) { + while (slot.n_past < slot.n_prompt_tokens() && batch.n_tokens < n_batch) { // get next token to process - llama_token cur_tok = slot.prompt_tokens[slot.n_past]; + llama_token cur_tok = input_tokens[slot.n_past]; if (cur_tok == LLAMA_TOKEN_NULL) { break; // end of text chunk } @@ -3707,36 +4008,33 @@ struct server_context { } // embedding requires all tokens in the batch to be output - const bool need_embd = server_task_type_need_embd(slot.task_type); - - common_batch_add(batch, cur_tok, slot.n_past, { slot.id }, need_embd); - slot.cache_tokens.push_back(cur_tok); + common_batch_add(batch, cur_tok, slot.n_past, { slot.id }, slot.need_embd()); + slot.prompt.tokens.push_back(cur_tok); slot.n_prompt_tokens_processed++; slot.n_past++; // process the last few tokens of the prompt separately in order to allow for a checkpoint to be created. - if (do_checkpoint && slot.n_prompt_tokens - slot.n_past == 64) { + if (do_checkpoint && slot.n_prompt_tokens() - slot.n_past == 64) { break; } } // SLT_INF(slot, "new cache_tokens: %s\n", slot.cache_tokens.str().c_str()); - SLT_INF(slot, "prompt processing progress, n_past = %d, n_tokens = %d, progress = %f\n", slot.n_past, batch.n_tokens, (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens); + SLT_INF(slot, "prompt processing progress, n_past = %d, n_tokens = %d, progress = %f\n", slot.n_past, batch.n_tokens, (float) slot.n_past / slot.n_prompt_tokens()); // entire prompt has been processed - if (slot.n_past == slot.n_prompt_tokens) { + if (slot.n_past == slot.n_prompt_tokens()) { slot.state = SLOT_STATE_DONE_PROMPT; GGML_ASSERT(batch.n_tokens > 0); - GGML_ASSERT((size_t) slot.n_prompt_tokens == slot.prompt_tokens.size()); common_sampler_reset(slot.smpl); // Process all prompt tokens through sampler system - for (int i = 0; i < slot.n_prompt_tokens; ++i) { - llama_token id = slot.prompt_tokens[i]; + for (int i = 0; i < slot.n_prompt_tokens(); ++i) { + llama_token id = input_tokens[i]; if (id != LLAMA_TOKEN_NULL) { common_sampler_accept(slot.smpl, id, false); } @@ -3757,21 +4055,22 @@ struct server_context { do_checkpoint = do_checkpoint && (pos_min >= 0 && pos_max >= 64); // no need to create checkpoints that are too close together - do_checkpoint = do_checkpoint && (slot.ctx_checkpoints.empty() || pos_max > slot.ctx_checkpoints.back().pos_max + 64); + do_checkpoint = do_checkpoint && (slot.prompt.checkpoints.empty() || pos_max > slot.prompt.checkpoints.back().pos_max + 64); if (do_checkpoint) { - while (slot.ctx_checkpoints.size() >= (size_t) params_base.n_ctx_checkpoints) { + while (slot.prompt.checkpoints.size() >= (size_t) params_base.n_ctx_checkpoints) { // make room for the new checkpoint, if needed - const auto & cur = slot.ctx_checkpoints.front(); + const auto & cur = slot.prompt.checkpoints.front(); + SLT_WRN(slot, "erasing old context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024); - slot.ctx_checkpoints.erase(slot.ctx_checkpoints.begin()); + slot.prompt.checkpoints.erase(slot.prompt.checkpoints.begin()); } const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); - auto & cur = slot.ctx_checkpoints.emplace_back(ctx_checkpoint{ + auto & cur = slot.prompt.checkpoints.emplace_back(server_prompt_checkpoint{ /*.pos_min = */ pos_min, /*.pos_max = */ pos_max, /*.data = */ std::vector(checkpoint_size), @@ -3779,8 +4078,8 @@ struct server_context { llama_state_seq_get_data_ext(ctx, cur.data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); - SLT_WRN(slot, "saved context checkpoint %d of %d (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", - (int) slot.ctx_checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024); + SLT_WRN(slot, "created context checkpoint %d of %d (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", + (int) slot.prompt.checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024); } } } @@ -3854,8 +4153,8 @@ struct server_context { if (!err.empty()) { SRV_ERR("%s, i = %d, n_batch = %d, ret = %d\n", err.c_str(), i, n_batch, ret); for (auto & slot : slots) { - slot.release(); send_error(slot, err); + slot.release(); } break; } @@ -3878,7 +4177,7 @@ struct server_context { for (auto & slot : slots) { // optionally send prompt processing progress if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_DONE_PROMPT) { - if (slot.params.stream && slot.params.return_progress) { + if (slot.task->params.stream && slot.task->params.return_progress) { send_partial_response(slot, {}, true); } } @@ -3888,7 +4187,7 @@ struct server_context { } if (slot.state == SLOT_STATE_DONE_PROMPT) { - if (slot.task_type == SERVER_TASK_TYPE_EMBEDDING) { + if (slot.task->type == SERVER_TASK_TYPE_EMBEDDING) { // prompt evaluated for embedding send_embedding(slot, batch_view); slot.release(); @@ -3896,7 +4195,7 @@ struct server_context { continue; // continue loop of slots } - if (slot.task_type == SERVER_TASK_TYPE_RERANK) { + if (slot.task->type == SERVER_TASK_TYPE_RERANK) { send_rerank(slot, batch_view); slot.release(); slot.i_batch = -1; @@ -3934,16 +4233,17 @@ struct server_context { result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok)); result.prob = 1.0f; // TODO: set it here instead of doing inside populate_token_probs - if (slot.params.sampling.n_probs > 0) { - populate_token_probs(slot, result, slot.params.post_sampling_probs, params_base.special, tok_idx); + if (slot.task->params.sampling.n_probs > 0) { + populate_token_probs(slot, result, slot.task->params.post_sampling_probs, params_base.special, tok_idx); } if (!process_token(result, slot)) { // release slot because of stop condition - slot.release(); slot.print_timings(); send_final_response(slot); metrics.on_prediction(slot); + slot.release(); + continue; } } @@ -3964,7 +4264,7 @@ struct server_context { } // determine the max draft that fits the current slot state - int n_draft_max = slot.params.speculative.n_max; + int n_draft_max = slot.task->params.speculative.n_max; // note: n_past is not yet increased for the `id` token sampled above // also, need to leave space for 1 extra token to allow context shifts @@ -3976,8 +4276,8 @@ struct server_context { SLT_DBG(slot, "max possible draft: %d\n", n_draft_max); - if (n_draft_max < slot.params.speculative.n_min) { - SLT_DBG(slot, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, slot.params.speculative.n_min); + if (n_draft_max < slot.task->params.speculative.n_min) { + SLT_DBG(slot, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, slot.task->params.speculative.n_min); continue; } @@ -3985,16 +4285,16 @@ struct server_context { llama_token id = slot.sampled; struct common_speculative_params params_spec; - params_spec.n_draft = n_draft_max; - params_spec.n_reuse = llama_n_ctx(slot.ctx_dft) - slot.params.speculative.n_max; - params_spec.p_min = slot.params.speculative.p_min; + params_spec.n_draft = n_draft_max; + params_spec.n_reuse = llama_n_ctx(slot.ctx_dft) - slot.task->params.speculative.n_max; + params_spec.p_min = slot.task->params.speculative.p_min; - const llama_tokens & cached_text_tokens = slot.cache_tokens.get_text_tokens(); + const llama_tokens & cached_text_tokens = slot.prompt.tokens.get_text_tokens(); llama_tokens draft = common_speculative_gen_draft(slot.spec, params_spec, cached_text_tokens, id); // ignore small drafts - if (slot.params.speculative.n_min > (int) draft.size()) { - SLT_DBG(slot, "ignoring small draft: %d < %d\n", (int) draft.size(), slot.params.speculative.n_min); + if (slot.task->params.speculative.n_min > (int) draft.size()) { + SLT_DBG(slot, "ignoring small draft: %d < %d\n", (int) draft.size(), slot.task->params.speculative.n_min); continue; } @@ -4023,8 +4323,8 @@ struct server_context { // update how many tokens out of those tested were accepted slot.n_draft_accepted += ids.size() - 1; - slot.cache_tokens.push_back(id); - slot.cache_tokens.insert({ids.begin(), ids.end() - 1}); + slot.prompt.tokens.push_back(id); + slot.prompt.tokens.insert({ids.begin(), ids.end() - 1}); llama_memory_seq_rm(llama_get_memory(ctx), slot.id, slot.n_past, -1); @@ -4038,11 +4338,11 @@ struct server_context { // TODO: set result.probs if (!process_token(result, slot)) { - // release slot because of stop condition - slot.release(); slot.print_timings(); send_final_response(slot); metrics.on_prediction(slot); + slot.release(); + break; } } @@ -4310,18 +4610,18 @@ int main(int argc, char ** argv) { } // TODO: get rid of this dynamic_cast - auto res_metrics = dynamic_cast(result.get()); - GGML_ASSERT(res_metrics != nullptr); + auto res_task = dynamic_cast(result.get()); + GGML_ASSERT(res_task != nullptr); // optionally return "fail_on_no_slot" error if (req.has_param("fail_on_no_slot")) { - if (res_metrics->n_idle_slots == 0) { + if (res_task->n_idle_slots == 0) { res_error(res, format_error_response("no slot available", ERROR_TYPE_UNAVAILABLE)); return; } } - res_ok(res, res_metrics->slots_data); + res_ok(res, res_task->slots_data); }; const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) { @@ -4349,56 +4649,56 @@ int main(int argc, char ** argv) { } // TODO: get rid of this dynamic_cast - auto res_metrics = dynamic_cast(result.get()); - GGML_ASSERT(res_metrics != nullptr); + auto res_task = dynamic_cast(result.get()); + GGML_ASSERT(res_task != nullptr); // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names json all_metrics_def = json { {"counter", {{ {"name", "prompt_tokens_total"}, {"help", "Number of prompt tokens processed."}, - {"value", (uint64_t) res_metrics->n_prompt_tokens_processed_total} + {"value", (uint64_t) res_task->n_prompt_tokens_processed_total} }, { {"name", "prompt_seconds_total"}, {"help", "Prompt process time"}, - {"value", (uint64_t) res_metrics->t_prompt_processing_total / 1.e3} + {"value", (uint64_t) res_task->t_prompt_processing_total / 1.e3} }, { {"name", "tokens_predicted_total"}, {"help", "Number of generation tokens processed."}, - {"value", (uint64_t) res_metrics->n_tokens_predicted_total} + {"value", (uint64_t) res_task->n_tokens_predicted_total} }, { {"name", "tokens_predicted_seconds_total"}, {"help", "Predict process time"}, - {"value", (uint64_t) res_metrics->t_tokens_generation_total / 1.e3} + {"value", (uint64_t) res_task->t_tokens_generation_total / 1.e3} }, { {"name", "n_decode_total"}, {"help", "Total number of llama_decode() calls"}, - {"value", res_metrics->n_decode_total} + {"value", res_task->n_decode_total} }, { {"name", "n_past_max"}, {"help", "Largest observed n_past."}, - {"value", res_metrics->n_past_max} + {"value", res_task->n_past_max} }, { {"name", "n_busy_slots_per_decode"}, {"help", "Average number of busy slots per llama_decode() call"}, - {"value", (float) res_metrics->n_busy_slots_total / std::max((float) res_metrics->n_decode_total, 1.f)} + {"value", (float) res_task->n_busy_slots_total / std::max((float) res_task->n_decode_total, 1.f)} }}}, {"gauge", {{ {"name", "prompt_tokens_seconds"}, {"help", "Average prompt throughput in tokens/s."}, - {"value", res_metrics->n_prompt_tokens_processed ? 1.e3 / res_metrics->t_prompt_processing * res_metrics->n_prompt_tokens_processed : 0.} + {"value", res_task->n_prompt_tokens_processed ? 1.e3 / res_task->t_prompt_processing * res_task->n_prompt_tokens_processed : 0.} },{ {"name", "predicted_tokens_seconds"}, {"help", "Average generation throughput in tokens/s."}, - {"value", res_metrics->n_tokens_predicted ? 1.e3 / res_metrics->t_tokens_generation * res_metrics->n_tokens_predicted : 0.} + {"value", res_task->n_tokens_predicted ? 1.e3 / res_task->t_tokens_generation * res_task->n_tokens_predicted : 0.} },{ {"name", "requests_processing"}, {"help", "Number of requests processing."}, - {"value", (uint64_t) res_metrics->n_processing_slots} + {"value", (uint64_t) res_task->n_processing_slots} },{ {"name", "requests_deferred"}, {"help", "Number of requests deferred."}, - {"value", (uint64_t) res_metrics->n_tasks_deferred} + {"value", (uint64_t) res_task->n_tasks_deferred} }}} }; @@ -4419,7 +4719,7 @@ int main(int argc, char ** argv) { } } - res.set_header("Process-Start-Time-Unix", std::to_string(res_metrics->t_start)); + res.set_header("Process-Start-Time-Unix", std::to_string(res_task->t_start)); res.set_content(prometheus.str(), "text/plain; version=0.0.4"); res.status = 200; // HTTP OK @@ -4543,9 +4843,22 @@ int main(int argc, char ** argv) { }; const auto handle_props = [¶ms, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) { + json default_generation_settings_for_props; + + { + slot_params params; + + params.sampling = ctx_server.params_base.sampling; + + default_generation_settings_for_props = json { + {"params", params.to_json(true)}, + {"n_ctx", ctx_server.slots[0].n_ctx}, + }; + } + // this endpoint is publicly available, please only return what is safe to be exposed json data = { - { "default_generation_settings", ctx_server.default_generation_settings_for_props }, + { "default_generation_settings", default_generation_settings_for_props }, { "total_slots", ctx_server.params_base.n_parallel }, { "model_path", ctx_server.params_base.model.path }, { "modalities", json { @@ -4650,12 +4963,12 @@ int main(int argc, char ** argv) { task.id = ctx_server.queue_tasks.get_new_id(); task.index = i; - task.prompt_tokens = std::move(inputs[i]); - task.params = server_task::params_from_json_cmpl( + task.tokens = std::move(inputs[i]); + task.params = server_task::params_from_json_cmpl( ctx_server.ctx, ctx_server.params_base, data); - task.id_selected_slot = json_value(data, "id_slot", -1); + task.id_slot = json_value(data, "id_slot", -1); // OAI-compat task.params.oaicompat = oaicompat; @@ -5024,9 +5337,9 @@ int main(int argc, char ** argv) { for (size_t i = 0; i < tokenized_prompts.size(); i++) { server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING); - task.id = ctx_server.queue_tasks.get_new_id(); - task.index = i; - task.prompt_tokens = std::move(tokenized_prompts[i]); + task.id = ctx_server.queue_tasks.get_new_id(); + task.index = i; + task.tokens = std::move(tokenized_prompts[i]); // OAI-compat task.params.oaicompat = oaicompat; @@ -5122,10 +5435,10 @@ int main(int argc, char ** argv) { tasks.reserve(documents.size()); for (size_t i = 0; i < documents.size(); i++) { auto tmp = format_rerank(ctx_server.model, ctx_server.vocab, ctx_server.mctx, query, documents[i]); - server_task task = server_task(SERVER_TASK_TYPE_RERANK); - task.id = ctx_server.queue_tasks.get_new_id(); - task.index = i; - task.prompt_tokens = std::move(tmp); + server_task task = server_task(SERVER_TASK_TYPE_RERANK); + task.id = ctx_server.queue_tasks.get_new_id(); + task.index = i; + task.tokens = std::move(tmp); tasks.push_back(std::move(task)); } @@ -5383,7 +5696,7 @@ int main(int argc, char ** argv) { #endif LOG_INF("%s: server is listening on %s - starting the main loop\n", __func__, - is_sock ? string_format("unix://%s", params.hostname.c_str()).c_str() : + is_sock ? string_format("unix://%s", params.hostname.c_str()).c_str() : string_format("http://%s:%d", params.hostname.c_str(), params.port).c_str()); // this call blocks the main thread until queue_tasks.terminate() is called diff --git a/tools/server/tests/unit/test_basic.py b/tools/server/tests/unit/test_basic.py index 829af2ebe7..720b136b05 100644 --- a/tools/server/tests/unit/test_basic.py +++ b/tools/server/tests/unit/test_basic.py @@ -66,8 +66,7 @@ def test_server_slots(): assert len(res.body) == server.n_slots assert server.n_ctx is not None and server.n_slots is not None assert res.body[0]["n_ctx"] == server.n_ctx / server.n_slots - assert "params" in res.body[0] - assert res.body[0]["params"]["seed"] == server.seed + assert "params" not in res.body[0] def test_load_split_model(): diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py index 2979ed4bb7..6e5a3488e7 100644 --- a/tools/server/tests/unit/test_chat_completion.py +++ b/tools/server/tests/unit/test_chat_completion.py @@ -19,8 +19,8 @@ def create_server(): (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, None), (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, 'chatml'), (None, "Book", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"), - ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None), - ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None), + ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 128, "length", False, None), + ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 128, "length", True, None), (None, "Book", [{"type": "text", "text": "What is"}, {"type": "text", "text": "the best book"}], 8, "Whillicter", 79, 8, "length", False, None), (None, "Book", [{"type": "text", "text": "What is"}, {"type": "text", "text": "the best book"}], 8, "Whillicter", 79, 8, "length", True, None), ] @@ -54,7 +54,7 @@ def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_conte "system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason", [ ("Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"), - ("You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"), + ("You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 128, "length"), ] ) def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason): diff --git a/tools/server/tests/unit/test_completion.py b/tools/server/tests/unit/test_completion.py index 11483e679a..00ba78cf67 100644 --- a/tools/server/tests/unit/test_completion.py +++ b/tools/server/tests/unit/test_completion.py @@ -16,7 +16,7 @@ def create_server(): @pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated,return_tokens", [ ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False, False), - ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False, True), + ("Write a joke about AI from a very long prompt which will not be truncated", 64, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False, True), ]) def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool, return_tokens: bool): global server @@ -41,7 +41,7 @@ def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, @pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [ ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False), - ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False), + ("Write a joke about AI from a very long prompt which will not be truncated", 64, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False), ]) def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool): global server diff --git a/tools/server/tests/unit/test_ctx_shift.py b/tools/server/tests/unit/test_ctx_shift.py index 92e49f2bb0..4adbbde64f 100644 --- a/tools/server/tests/unit/test_ctx_shift.py +++ b/tools/server/tests/unit/test_ctx_shift.py @@ -4,6 +4,12 @@ from utils import * server = ServerPreset.tinyllama2() +SHORT_TEXT = """ +Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. +Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. +Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. +""".strip() + LONG_TEXT = """ Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. @@ -21,19 +27,18 @@ def create_server(): def test_ctx_shift_enabled(): - # the prompt is 301 tokens + # the prompt is 226 tokens # the slot context is 512/2 = 256 tokens - # the prompt is truncated to keep the last (301 - 256/2) = 173 tokens # 96 tokens are generated thanks to shifting the context when it gets full global server server.enable_ctx_shift = True server.start() res = server.make_request("POST", "/completion", data={ "n_predict": 96, - "prompt": LONG_TEXT, + "prompt": SHORT_TEXT, }) assert res.status_code == 200 - assert res.body["timings"]["prompt_n"] == 173 + assert res.body["timings"]["prompt_n"] == 226 assert res.body["timings"]["predicted_n"] == 96 assert res.body["truncated"] is True diff --git a/tools/server/utils.hpp b/tools/server/utils.hpp index 4ca1423aaf..f175115f4f 100644 --- a/tools/server/utils.hpp +++ b/tools/server/utils.hpp @@ -31,10 +31,10 @@ using json = nlohmann::ordered_json; -#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) +#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) +#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) +#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) +#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) #define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) #define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) @@ -1102,6 +1102,7 @@ public: ~server_tokens() = default; // Prevent copying + // TODO: server_tokens should be copyable - remove this: server_tokens(const server_tokens&) = delete; server_tokens& operator=(const server_tokens&) = delete; @@ -1119,7 +1120,7 @@ public: } } - server_tokens(llama_tokens & tokens, bool has_mtmd) : has_mtmd(has_mtmd), tokens(tokens) {} + server_tokens(const llama_tokens & tokens, bool has_mtmd) : has_mtmd(has_mtmd), tokens(tokens) {} // for debugging std::string str() const { @@ -1144,9 +1145,8 @@ public: auto it = map_pos_to_media.find(pos); if (it != map_pos_to_media.end()) { return it->second; - } else { - throw std::runtime_error("Chunk not found"); } + throw std::runtime_error("Chunk not found"); } void push_back(llama_token tok) { @@ -1170,7 +1170,7 @@ public: map_pos_to_media[start_pos] = std::move(new_chunk); } else if (type == MTMD_INPUT_CHUNK_TYPE_TEXT) { size_t n_tokens; - auto text_tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens); + const auto * text_tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens); for (size_t i = 0; i < n_tokens; ++i) { push_back(text_tokens[i]); } @@ -1190,7 +1190,7 @@ public: // We could also just check, but this will prevent silently dropping MTMD data. GGML_ASSERT(has_mtmd); for (auto it = tokens.map_pos_to_media.begin(); it != tokens.map_pos_to_media.end(); ) { - auto chunk = tokens.map_pos_to_media[it->first].get(); + auto * chunk = tokens.map_pos_to_media[it->first].get(); mtmd::input_chunk_ptr new_chunk(mtmd_input_chunk_copy(chunk)); map_pos_to_media[start_pos+it->first] = std::move(new_chunk); } @@ -1271,33 +1271,52 @@ public: } size_t get_common_prefix(const server_tokens & b) const { - size_t max_idx = std::min(tokens.size(), b.tokens.size()); - for (size_t i = 0; i < max_idx; ++i) { - auto & ai = tokens[i]; - auto & bi = b.tokens[i]; + const size_t max_idx = std::min(tokens.size(), b.tokens.size()); - if (ai == LLAMA_TOKEN_NULL && bi == LLAMA_TOKEN_NULL) { - GGML_ASSERT(has_mtmd); - const auto & a_chunk = find_chunk(i); - const auto & b_chunk = b.find_chunk(i); - GGML_ASSERT(a_chunk && b_chunk); - std::string ai_id = mtmd_input_chunk_get_id(a_chunk.get()); - std::string bi_id = mtmd_input_chunk_get_id(b_chunk.get()); - size_t a_pos = mtmd_input_chunk_get_n_pos(a_chunk.get()); - size_t b_pos = mtmd_input_chunk_get_n_pos(b_chunk.get()); - if (ai_id == bi_id && a_pos == b_pos) { - GGML_ASSERT(a_pos > 0 && "Invalid media chunk"); // should never happen - i += a_pos - 1; // will be +1 by the for loop + if (!has_mtmd) { + for (size_t i = 0; i < max_idx; ++i) { + if (tokens[i] == b.tokens[i]) { continue; - } else { - return i; } - } else if (ai == bi) { - continue; - } else { + return i; } + + return max_idx; } + + for (size_t i = 0; i < max_idx; ++i) { + const llama_token ai = tokens[i]; + const llama_token bi = b.tokens[i]; + + if (ai == LLAMA_TOKEN_NULL && bi == LLAMA_TOKEN_NULL) { + const auto & a_chunk = find_chunk(i); + const auto & b_chunk = b.find_chunk(i); + + GGML_ASSERT(a_chunk && b_chunk); + + const std::string id_ai = mtmd_input_chunk_get_id(a_chunk.get()); + const std::string id_bi = mtmd_input_chunk_get_id(b_chunk.get()); + + const size_t pos_a = mtmd_input_chunk_get_n_pos(a_chunk.get()); + const size_t pos_b = mtmd_input_chunk_get_n_pos(b_chunk.get()); + + if (id_ai == id_bi && pos_a == pos_b) { + GGML_ASSERT(pos_a > 0 && "Invalid media chunk"); // should never happen + i += pos_a - 1; // will be +1 by the for loop + continue; + } + + return i; + } + + if (ai == bi) { + continue; + } + + return i; + } + return max_idx; // all tokens are equal } @@ -1308,7 +1327,7 @@ public: const int32_t n_vocab = llama_vocab_n_tokens(vocab); for (size_t i = 0; i < tokens.size(); ++i) { - auto & t = tokens[i]; + const auto & t = tokens[i]; if (t == LLAMA_TOKEN_NULL) { try { const auto & chunk = find_chunk(i); @@ -1330,8 +1349,8 @@ public: mtmd_context * mctx, llama_pos n_past, int32_t seq_id, - llama_pos & n_pos_out) { - auto & chunk = find_chunk(n_past); + llama_pos & n_pos_out) const { + const auto & chunk = find_chunk(n_past); const char * name = mtmd_input_chunk_get_type(chunk.get()) == MTMD_INPUT_CHUNK_TYPE_IMAGE ? "image" : "audio"; SRV_INF("processing %s...\n", name); From 1deee0f8d494981c32597dca8b5f8696d399b0f2 Mon Sep 17 00:00:00 2001 From: duduta Date: Thu, 9 Oct 2025 22:11:15 +0300 Subject: [PATCH 014/144] cpu : optimize the ggml NORM operation (#15953) * ggml-cpu: optimize norm operation to use intrinsics or Accelerate rename function add endif macro comment Co-authored-by: Georgi Gerganov Co-authored-by: Aaron Teo * implement s390x SIMD suggested by @taronaeo * add TODO comment * tidy up spaces --------- Co-authored-by: Georgi Gerganov Co-authored-by: Aaron Teo --- ggml/src/ggml-cpu/ops.cpp | 24 ++++++-------- ggml/src/ggml-cpu/vec.cpp | 66 +++++++++++++++++++++++++++++++++++++++ ggml/src/ggml-cpu/vec.h | 1 + 3 files changed, 77 insertions(+), 14 deletions(-) diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 8e1a2de14f..1c43865ff6 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -3467,31 +3467,27 @@ static void ggml_compute_forward_norm_f32( GGML_ASSERT(eps >= 0.0f); - // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ith; i01 < ne01; i01 += nth) { const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)x[i00]; - } - + float sum = 0.0; + ggml_vec_sum_f32(ne00, &sum, x); float mean = sum/ne00; float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + float variance = 0; - ggml_float sum2 = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - float v = x[i00] - mean; - y[i00] = v; - sum2 += (ggml_float)(v*v); - } +#ifdef GGML_USE_ACCELERATE + mean = -mean; + vDSP_vsadd(x, 1, &mean, y, 1, ne00); + vDSP_measqv(y, 1, &variance, ne00); +#else + variance = ggml_vec_cvar_f32(ne00, y, x, mean); +#endif //GGML_USE_ACCELERATE - float variance = sum2/ne00; const float scale = 1.0f/sqrtf(variance + eps); - ggml_vec_scale_f32(ne00, y, scale); } } diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp index 437192d525..b8e37052d3 100644 --- a/ggml/src/ggml-cpu/vec.cpp +++ b/ggml/src/ggml-cpu/vec.cpp @@ -404,6 +404,72 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float * } } +ggml_float ggml_vec_cvar_f32(const int n, float * y, const float * x, const float mean) { + int i = 0; + ggml_float sum = 0; +// TODO: optimize to process the remaining elements in groups using the smaller vector sizes from AVX2 and SSE +// ref: https://github.com/ggml-org/llama.cpp/pull/15953#pullrequestreview-3310928344 +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + __m512 val = _mm512_sub_ps(_mm512_loadu_ps(x + i), + _mm512_set1_ps(mean)); + _mm512_storeu_ps(y + i, val); + sum += (ggml_float)_mm512_reduce_add_ps(_mm512_mul_ps(val, val)); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + __m256 val = _mm256_sub_ps(_mm256_loadu_ps(x + i), + _mm256_set1_ps(mean)); + _mm256_storeu_ps(y + i, val); + val = _mm256_mul_ps(val,val); + __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), + _mm256_castps256_ps128(val)); + val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); + val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); + sum += (ggml_float)_mm_cvtss_f32(val2); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + __m128 val = _mm_sub_ps(_mm_loadu_ps(x + i), + _mm_set1_ps(mean)); + _mm_storeu_ps(y + i, val); + val = _mm_mul_ps(val, val); +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) + val = _mm_add_ps(val, _mm_movehl_ps(val, val)); + val = _mm_add_ss(val, _mm_movehdup_ps(val)); +#else + __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); + val = _mm_add_ps(val, tmp); + tmp = _mm_movehl_ps(tmp, val); + val = _mm_add_ss(val, tmp); +#endif // __AVX__ || __AVX2__ || __AVX512F__ + sum += (ggml_float)_mm_cvtss_f32(val); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + float32x4_t val = vsubq_f32(vld1q_f32(x + i), + vdupq_n_f32(mean)); + vst1q_f32(y + i, val); + val = vmulq_f32(val, val); + sum += (ggml_float)vaddvq_f32(val); + } +#elif defined(__VXE__) || defined(__VXE2__) + for (; i + 3 < n; i += 4) { + float32x4_t val = vec_sub(vec_xl(0, x + i), vec_splats(mean)); + vec_xst(val, 0, y + i); + val = vec_mul(val, val); + sum += (ggml_float)vec_hsum_f32x4(val); + } +#endif + for (; i < n; ++i) { + float val = x[i] - mean; + val *= val; + sum += (ggml_float)val; + y[i] = val; + } + return sum/n; +} + ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { int i = 0; ggml_float sum = 0; diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h index f95ca94e54..2751359ce4 100644 --- a/ggml/src/ggml-cpu/vec.h +++ b/ggml/src/ggml-cpu/vec.h @@ -44,6 +44,7 @@ void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t * void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * GGML_RESTRICT x, size_t bx, ggml_fp16_t * GGML_RESTRICT y, size_t by, int nrc); void ggml_vec_silu_f32(const int n, float * y, const float * x); +ggml_float ggml_vec_cvar_f32(const int n, float * y, const float * x, const float mean); //it will also center y ( y = y - mean ) ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max); ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max); From 1faa13a1187051af66b0fd9f0d6effe4c77f0b3e Mon Sep 17 00:00:00 2001 From: Pascal Date: Thu, 9 Oct 2025 22:54:57 +0200 Subject: [PATCH 015/144] =?UTF-8?q?webui:=20updated=20the=20chat=20service?= =?UTF-8?q?=20to=20only=20include=20max=5Ftokens=20in=20the=20req=E2=80=A6?= =?UTF-8?q?=20(#16489)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * webui: updated the chat service to only include max_tokens in the request payload when the setting is explicitly provided, while still mapping explicit zero or null values to the infinite-token sentinel * chore: update webui build output --- tools/server/public/index.html.gz | Bin 847731 -> 847723 bytes tools/server/webui/src/lib/services/chat.ts | 7 ++++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 550df72e93c334ec409f53abeeee6ae77e46df38..5026edcebed37b7176eeaec592c60899b558a027 100644 GIT binary patch delta 778381 zcmV(hK={A&-ZSgoGq5^!f7lKV=Z(QuVoQAcFUqrgw1t}owyNd4@4xr|TiL>Zm=v=NwWse}(Aem{fCBXH@`|s=|BL@X`__0%?=qVq05?o+LO=I~mkQ5w4p8)v;l=E_ zz|YoMCovl(e*Gm~_H2t%s%KzB9QkHgH^)>U9ZldtDfX3~v77i+f9#X&M4w+?&N{yB zwAzPmduw|SUs?Ig$Wz{zj*7?a5pcfM9GN|c%ElC|WvglE-3IQ-=ou$H17_)NmjEB3 zjg~&9pS#Mb^+xk2QtO~x(6zp00a*_4p5DU|Z>xuW8z0sFmI5dGF99 zu;lY)S<*C;~T-+J^V&E3DIDn2H0pfz@16(AFzcK^cFWiskxhQxQ& ze0_zm)i$jCRVq!q@&4uI@9SH2_Q4Vo%}JkS?j~gZ-JV#Of0ywHHx6yHzIVII*ixpZ 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zpE?8!Rv!Ku$w+aQ%`O>5<(l*Nu9^A?q-FeOgV7YUdeYO4U3u0BZiU;2$Wnx8Xr4EM;+09RkWggr**>oO;-HUB9Ubbs^@GSA z@2yW{fA(6iX}TaniZ6ddJ?X%t7>3WJ^%^Ubf1vrDt!y~jyB7zn7~Ty!yCekjJRX4(X+@_ocCGm$elay%K(qV#&D7kwdj#oSu_fem^@4+gp71Q74bx) zxwyh&My411D+FUCJVMy0s5Bc^p%ie_%pjSlf0!tKp#cq70VvjLhhtsr*GR%b>`$9o ze@w#9*QLYx*0_l_PGcah{t%Y$U`~&9|#4`X?2p+2b z*&wKbRhG}H^C=X}qv3$`2$1La3o~C=+tqk8M#L~w0RZ60_c8K&yDLOo6=UgcEJpBm zfBNlJ9P;*Lbw01C>c(SNiF4W#bcS5WZ)$#&cpWiiwIk0pTzXz3DAn%Z%_NcB3?VyB z`s3wb$h$4FzUXHRNl_!n_ssB$Lsm0DPZ;03j)H;?uy%eH9Ww&z+{5*);T(0ypa$|m zkkBVdtC7@}Qjm}ic%u^1=Z&tx?AUNjfA39ONg*4dP{O-=N#yAEbn9M+03@HW_Gk}b zrMF%CC=>n)&2^!1(ja56F4HgS#y_cDML|aDDMt532RonHUSTU_a%)F#aJNUgf3PJ! zi+&I`3gZd&BJjr{m-gV%r1qdVq3SrDLsch^AV`ksCM0X`%-!J{+yxW;e}n5($DFGw zsKrL&yI>%W;#Y_`5>Bz&a|FT0lEEQ~z0B@w%VP4kxlZH$Rg)9tY7c&C=u6AX-><4y zm*>aRaWH$l?wwX<1?@oly+qi1dNSINlC@<2@GCe{Xi(<<|NZ0NrpIhlo7;pl7sVVDXW141`{+Kd q$fJwJWBhn@e=z!hKOWsbo(?V_|K`zsy7)Z)&Hn=`8C5 Date: Fri, 10 Oct 2025 13:45:46 +0530 Subject: [PATCH 016/144] cmake : Dont define XOPENSOURCE on AIX (#16481) --- ggml/src/CMakeLists.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index c8f3d85964..892c23318a 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -145,6 +145,9 @@ endif() # which was introduced in POSIX.1-2008, forcing us to go higher if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD") add_compile_definitions(_XOPEN_SOURCE=700) +elseif (CMAKE_SYSTEM_NAME MATCHES "AIX") + # Don't define _XOPEN_SOURCE. We need _ALL_SOURCE, which is the default, + # in order to define _SC_PHYS_PAGES. else() add_compile_definitions(_XOPEN_SOURCE=600) endif() From cdb6da468cc33323955a523738d2e1675aeb5e9a Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Fri, 10 Oct 2025 13:22:27 +0300 Subject: [PATCH 017/144] server : log requests to /v1/completions (#16495) --- tools/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 41ecb279fe..39c950c15d 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -4368,7 +4368,7 @@ struct server_context { static void log_server_request(const httplib::Request & req, const httplib::Response & res) { // skip GH copilot requests when using default port - if (req.path == "/v1/health" || req.path == "/v1/completions") { + if (req.path == "/v1/health") { return; } From 68ee98ae181a5c83a5cc6261daeee69a1f588c15 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Fri, 10 Oct 2025 17:11:07 +0300 Subject: [PATCH 018/144] server : return HTTP 400 if prompt exceeds context length (#16486) In streaming mode when prompt exceeds context length, the server returns HTTP 200 status code with a JSON error in the body. This is very confusing and inconsistent with all other inference engines which return HTTP 4xx error in this case. This patch fixes this problem and makes the server return HTTP 400 in such cases. --- tools/server/server.cpp | 12 ++++++++-- .../server/tests/unit/test_chat_completion.py | 22 +++++++++++++++++++ tools/server/tests/utils.py | 8 +++++++ 3 files changed, 40 insertions(+), 2 deletions(-) diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 39c950c15d..5293a98f03 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -3727,7 +3727,7 @@ struct server_context { } } else { if (slot.n_prompt_tokens() >= slot.n_ctx) { - send_error(slot, "the request exceeds the available context size. try increasing the context size or enable context shift", ERROR_TYPE_EXCEED_CONTEXT_SIZE); + send_error(slot, "the request exceeds the available context size, try increasing it", ERROR_TYPE_EXCEED_CONTEXT_SIZE); slot.release(); continue; } @@ -4955,9 +4955,17 @@ int main(int argc, char ** argv) { // Everything else, including multimodal completions. inputs = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true); } - + const size_t n_ctx_slot = ctx_server.n_ctx / ctx_server.params_base.n_parallel; tasks.reserve(inputs.size()); for (size_t i = 0; i < inputs.size(); i++) { + auto n_prompt_tokens = inputs[i].size(); + if (n_prompt_tokens >= n_ctx_slot) { + json error_data = format_error_response("the request exceeds the available context size, try increasing it", ERROR_TYPE_EXCEED_CONTEXT_SIZE); + error_data["n_prompt_tokens"] = n_prompt_tokens; + error_data["n_ctx"] = n_ctx_slot; + res_error(res, error_data); + return; + } server_task task = server_task(type); task.id = ctx_server.queue_tasks.get_new_id(); diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py index 6e5a3488e7..d56d3d5f17 100644 --- a/tools/server/tests/unit/test_chat_completion.py +++ b/tools/server/tests/unit/test_chat_completion.py @@ -408,6 +408,28 @@ def test_context_size_exceeded(): assert res.body["error"]["n_ctx"] == server.n_ctx // server.n_slots +def test_context_size_exceeded_stream(): + global server + server.start() + try: + for _ in server.make_stream_request("POST", "/chat/completions", data={ + "messages": [ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ] * 100, # make the prompt too long + "stream": True}): + pass + assert False, "Should have failed" + except ServerError as e: + assert e.code == 400 + assert "error" in e.body + assert e.body["error"]["type"] == "exceed_context_size_error" + assert e.body["error"]["n_prompt_tokens"] > 0 + assert server.n_ctx is not None + assert server.n_slots is not None + assert e.body["error"]["n_ctx"] == server.n_ctx // server.n_slots + + @pytest.mark.parametrize( "n_batch,batch_count,reuse_cache", [ diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py index abd6fff10d..4ba3d43c33 100644 --- a/tools/server/tests/utils.py +++ b/tools/server/tests/utils.py @@ -35,6 +35,12 @@ class ServerResponse: body: dict | Any +class ServerError(Exception): + def __init__(self, code, body): + self.code = code + self.body = body + + class ServerProcess: # default options debug: bool = False @@ -297,6 +303,8 @@ class ServerProcess: response = requests.post(url, headers=headers, json=data, stream=True) else: raise ValueError(f"Unimplemented method: {method}") + if response.status_code != 200: + raise ServerError(response.status_code, response.json()) for line_bytes in response.iter_lines(): line = line_bytes.decode("utf-8") if '[DONE]' in line: From 81086cd6a3ca1252f0dc0f938171648399179c53 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 10 Oct 2025 17:17:31 +0300 Subject: [PATCH 019/144] vocab : mark EOT token for Granite models (#16499) * vocab : mark EOT token for Granite models * sampling : fallback to EOS when EOT is not found --- src/llama-sampling.cpp | 5 +++++ src/llama-vocab.cpp | 1 + 2 files changed, 6 insertions(+) diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index 2186f827bf..55d2e355fd 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -2541,8 +2541,13 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_ if (n_non_eog == 0) { cur_p->size = 1; cur_p->data[0].id = ctx->vocab->token_eot(); + if (cur_p->data[0].id == LLAMA_TOKEN_NULL) { + cur_p->data[0].id = ctx->vocab->token_eos(); + } cur_p->data[0].logit = 1.0f; + GGML_ASSERT(cur_p->data[0].id != LLAMA_TOKEN_NULL); + return; } diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index f965752a84..7fffd17149 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -2171,6 +2171,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { || t.first == "<|end|>" || t.first == "" || t.first == "<|endoftext|>" + || t.first == "<|end_of_text|>" // granite || t.first == "" || t.first == "_" || t.first == "<｜end▁of▁sentence｜>" // DeepSeek From e60f01d941bc5b7fae62dd57fee4cec76ec0ea6e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 10 Oct 2025 22:15:05 +0300 Subject: [PATCH 020/144] server : fix division by zero when reporting stats (#16501) --- tools/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 5293a98f03..60326e8e50 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -4226,7 +4226,7 @@ struct server_context { metrics.on_prompt_eval(slot); } - slot.t_token_generation = (t_current - slot.t_start_generation) / 1e3; + slot.t_token_generation = std::max(1, t_current - slot.t_start_generation) / 1e3; completion_token_output result; result.tok = id; From 477a66b03501cf3bd067f8968b77ca4d053ff1bd Mon Sep 17 00:00:00 2001 From: amirai21 <89905406+amirai21@users.noreply.github.com> Date: Sat, 11 Oct 2025 11:33:41 +0300 Subject: [PATCH 021/144] convert : correctly handle LLaMA tokenizer for Jamba (#16470) * fix: convert_hf_to_gguf - change Jamba non-sentencepiece mode (tokenizer.json) vocab construction * fix: convert_hf_to_gguf - jamba non-sentencepiece tokenizer to use _set_vocab_llama_hf func * fix: convert_hf_to_gguf - removed get_vocab_base_pre from jamba --- convert_hf_to_gguf.py | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 43d345bcb4..8c5132193e 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -5966,20 +5966,12 @@ class Mamba2Model(TextModel): class JambaModel(TextModel): model_arch = gguf.MODEL_ARCH.JAMBA - def get_vocab_base_pre(self, tokenizer) -> str: - del tokenizer # unused - - return "gpt-2" - def set_vocab(self): if (self.dir_model / "tokenizer.model").is_file(): - # Using Jamba's tokenizer.json causes errors on model load - # (something about "byte not found in vocab"), - # but there's a working tokenizer.model self._set_vocab_sentencepiece() else: - # Some Jamba models only have a tokenizer.json, which works. - self._set_vocab_gpt2() + self._set_vocab_llama_hf() + self.gguf_writer.add_add_space_prefix(False) def set_gguf_parameters(self): d_model = self.find_hparam(["hidden_size", "mamba_d_model"]) From 97870e64975b26c5e06a3540a8dc0ff601351e86 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Sat, 11 Oct 2025 04:02:26 -0700 Subject: [PATCH 022/144] cuda : avoid initializing unused devices (#16510) --- ggml/src/ggml-cuda/ggml-cuda.cu | 1 - 1 file changed, 1 deletion(-) diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index fb691528b7..856e9de2e1 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -3867,7 +3867,6 @@ ggml_backend_reg_t ggml_backend_cuda_reg() { dev_ctx->device = i; dev_ctx->name = GGML_CUDA_NAME + std::to_string(i); - ggml_cuda_set_device(i); cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, i)); dev_ctx->description = prop.name; From 31d0ff1869aa2ea31f4e96d5877d0343e9a2171b Mon Sep 17 00:00:00 2001 From: Yann Follet <131855179+YannFollet@users.noreply.github.com> Date: Sat, 11 Oct 2025 21:39:04 +0800 Subject: [PATCH 023/144] server / ranking : add sorting and management of top_n (#16403) * server / ranking : add sorting and management of top_n * Make the retro compatible if no top_n will return all results here is a script to make some test ```script URL=${1:-http://127.0.0.1:8181} curl "$URL/v1/rerank" -H "Content-Type: application/json" \ -d '{ "model": "M", "query": "What is the recipe to make bread ?", "return_text" : true, "texts" : true, "top_n": 6, "documents": [ "voici la recette pour faire du pain, il faut de la farine de l eau et du levain et du sel", "it is a bear", "bread recipe : floor, water, yest, salt", "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.", "here is the ingedients to bake bread : 500g floor, 350g water, 120g fresh refresh yest, 15g salt", "recipe to make cookies : floor, eggs, water, chocolat", "here is the recipe to make bread : 500g floor, 350g water, 120g fresh refresh yest, 15g salt", "il fait tres beau aujourd hui", "je n ai pas faim, je ne veux pas manger", "je suis a paris" ] }' | jq ``` * use resize() instead for(...) * simplify top_n init since no need to return error result to test : ./tests.sh unit/test_rerank.py -v -x ==================================================== test session starts ===================================================== platform linux -- Python 3.12.3, pytest-8.3.5, pluggy-1.6.0 -- /home/yann/dev/yann/llama.cpp/tools/server/tests/test/bin/python3 cachedir: .pytest_cache rootdir: /home/yann/dev/yann/llama.cpp/tools/server/tests configfile: pytest.ini plugins: anyio-4.11.0 collected 8 items unit/test_rerank.py::test_rerank PASSED [ 12%] unit/test_rerank.py::test_rerank_tei_format PASSED [ 25%] unit/test_rerank.py::test_invalid_rerank_req[documents0] PASSED [ 37%] unit/test_rerank.py::test_invalid_rerank_req[None] PASSED [ 50%] unit/test_rerank.py::test_invalid_rerank_req[123] PASSED [ 62%] unit/test_rerank.py::test_invalid_rerank_req[documents3] PASSED [ 75%] unit/test_rerank.py::test_rerank_usage[Machine learning is-A machine-Learning is-19] PASSED [ 87%] unit/test_rerank.py::test_rerank_usage[Which city?-Machine learning is -Paris, capitale de la-26] PASSED [100%] ===================================================== 8 passed in 4.31s ====================================================== * add rerank top_n unit test here is the result : ./tests.sh unit/test_rerank.py -v -x =================================================================== test session starts =================================================================== platform linux -- Python 3.12.3, pytest-8.3.5, pluggy-1.6.0 -- /home/yann/dev/yann/llama.cpp/tools/server/tests/test/bin/python3 cachedir: .pytest_cache rootdir: /home/yann/dev/yann/llama.cpp/tools/server/tests configfile: pytest.ini plugins: anyio-4.11.0 collected 16 items unit/test_rerank.py::test_rerank PASSED [ 6%] unit/test_rerank.py::test_rerank_tei_format PASSED [ 12%] unit/test_rerank.py::test_invalid_rerank_req[documents0] PASSED [ 18%] unit/test_rerank.py::test_invalid_rerank_req[None] PASSED [ 25%] unit/test_rerank.py::test_invalid_rerank_req[123] PASSED [ 31%] unit/test_rerank.py::test_invalid_rerank_req[documents3] PASSED [ 37%] unit/test_rerank.py::test_rerank_usage[Machine learning is-A machine-Learning is-19] PASSED [ 43%] unit/test_rerank.py::test_rerank_usage[Which city?-Machine learning is -Paris, capitale de la-26] PASSED [ 50%] unit/test_rerank.py::test_rerank_top_n[None-4] PASSED [ 56%] unit/test_rerank.py::test_rerank_top_n[2-2] PASSED [ 62%] unit/test_rerank.py::test_rerank_top_n[4-4] PASSED [ 68%] unit/test_rerank.py::test_rerank_top_n[99-4] PASSED [ 75%] unit/test_rerank.py::test_rerank_tei_top_n[None-4] PASSED [ 81%] unit/test_rerank.py::test_rerank_tei_top_n[2-2] PASSED [ 87%] unit/test_rerank.py::test_rerank_tei_top_n[4-4] PASSED [ 93%] unit/test_rerank.py::test_rerank_tei_top_n[99-4] PASSED [100%] =================================================================== 16 passed in 8.84s =================================================================== * editor config check fix --- tools/server/server.cpp | 14 ++--- tools/server/tests/unit/test_rerank.py | 42 +++++++++++++++ tools/server/utils.hpp | 73 ++++++++++++-------------- 3 files changed, 81 insertions(+), 48 deletions(-) diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 60326e8e50..cf12805b49 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -5401,15 +5401,6 @@ int main(int argc, char ** argv) { const json body = json::parse(req.body); - // TODO: implement - //int top_n = 1; - //if (body.count("top_n") != 1) { - // top_n = body.at("top_n"); - //} else { - // res_error(res, format_error_response("\"top_n\" must be provided", ERROR_TYPE_INVALID_REQUEST)); - // return; - //} - // if true, use TEI API format, otherwise use Jina API format // Jina: https://jina.ai/reranker/ // TEI: https://huggingface.github.io/text-embeddings-inference/#/Text%20Embeddings%20Inference/rerank @@ -5434,6 +5425,8 @@ int main(int argc, char ** argv) { return; } + int top_n = json_value(body, "top_n", (int)documents.size()); + // create and queue the task json responses = json::array(); bool error = false; @@ -5474,7 +5467,8 @@ int main(int argc, char ** argv) { body, responses, is_tei_format, - documents); + documents, + top_n); res_ok(res, root); }; diff --git a/tools/server/tests/unit/test_rerank.py b/tools/server/tests/unit/test_rerank.py index 0b63c7821e..ded8267109 100644 --- a/tools/server/tests/unit/test_rerank.py +++ b/tools/server/tests/unit/test_rerank.py @@ -102,3 +102,45 @@ def test_rerank_usage(query, doc1, doc2, n_tokens): assert res.status_code == 200 assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] assert res.body['usage']['prompt_tokens'] == n_tokens + + +@pytest.mark.parametrize("top_n,expected_len", [ + (None, len(TEST_DOCUMENTS)), # no top_n parameter + (2, 2), + (4, 4), + (99, len(TEST_DOCUMENTS)), # higher than available docs +]) +def test_rerank_top_n(top_n, expected_len): + global server + server.start() + data = { + "query": "Machine learning is", + "documents": TEST_DOCUMENTS, + } + if top_n is not None: + data["top_n"] = top_n + + res = server.make_request("POST", "/rerank", data=data) + assert res.status_code == 200 + assert len(res.body["results"]) == expected_len + + +@pytest.mark.parametrize("top_n,expected_len", [ + (None, len(TEST_DOCUMENTS)), # no top_n parameter + (2, 2), + (4, 4), + (99, len(TEST_DOCUMENTS)), # higher than available docs +]) +def test_rerank_tei_top_n(top_n, expected_len): + global server + server.start() + data = { + "query": "Machine learning is", + "texts": TEST_DOCUMENTS, + } + if top_n is not None: + data["top_n"] = top_n + + res = server.make_request("POST", "/rerank", data=data) + assert res.status_code == 200 + assert len(res.body) == expected_len diff --git a/tools/server/utils.hpp b/tools/server/utils.hpp index f175115f4f..fd0bc8de53 100644 --- a/tools/server/utils.hpp +++ b/tools/server/utils.hpp @@ -849,47 +849,44 @@ static json format_response_rerank( const json & request, const json & ranks, bool is_tei_format, - std::vector & texts) { - json res; - if (is_tei_format) { - // TEI response format - res = json::array(); - bool return_text = json_value(request, "return_text", false); - for (const auto & rank : ranks) { - int index = json_value(rank, "index", 0); - json elem = json{ - {"index", index}, - {"score", json_value(rank, "score", 0.0)}, - }; - if (return_text) { - elem["text"] = std::move(texts[index]); - } - res.push_back(elem); - } - } else { - // Jina response format - json results = json::array(); - int32_t n_tokens = 0; - for (const auto & rank : ranks) { - results.push_back(json{ - {"index", json_value(rank, "index", 0)}, - {"relevance_score", json_value(rank, "score", 0.0)}, - }); - - n_tokens += json_value(rank, "tokens_evaluated", 0); - } - - res = json{ - {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", "list"}, - {"usage", json{ - {"prompt_tokens", n_tokens}, - {"total_tokens", n_tokens} - }}, - {"results", results} + std::vector & texts, + int top_n) { + int32_t n_tokens = 0; + bool return_text = is_tei_format && json_value(request, "return_text", false); + std::vector elements; // Temporary vector to hold unsorted elements + std::string score_label = is_tei_format ? "score" : "relevance_score"; + for (const auto & rank : ranks) { + int index = json_value(rank, "index", 0); + json elem = json{ + {"index", index}, + {score_label, json_value(rank, "score", 0.0)}, }; + n_tokens += json_value(rank, "tokens_evaluated", 0); + if (return_text) { + elem["text"] = std::move(texts[index]); + } + elements.push_back(elem); } + std::sort(elements.begin(), elements.end(), [score_label](const json& a, const json& b) { + return json_value(a, score_label, 0.0) > json_value(b, score_label, 0.0); + }); + + elements.resize(std::min(top_n, (int)elements.size())); + json results = elements; + + if (is_tei_format) return results; + + json res = json{ + {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", "list"}, + {"usage", json{ + {"prompt_tokens", n_tokens}, + {"total_tokens", n_tokens} + }}, + {"results", results} + }; + return res; } From 4a8fbe0a5eb75f339782ebcf29c17848122184d3 Mon Sep 17 00:00:00 2001 From: Pascal Date: Sat, 11 Oct 2025 15:50:49 +0200 Subject: [PATCH 024/144] feat: render user content as markdown option (#16358) * feat: render user content as markdown option - Add a persisted 'renderUserContentAsMarkdown' preference to the settings defaults and info metadata so the choice survives reloads like other options - Surface the new 'Render user content as Markdown' checkbox in the General section of the chat settings dialog, beneath the PDF toggle - Render user chat messages with 'MarkdownContent' when the new setting is enabled, matching assistant formatting while preserving the existing card styling otherwise - chore: update webui build output * chore: update webui build output --- tools/server/public/index.html.gz | Bin 847723 -> 847816 bytes .../chat/ChatMessages/ChatMessageUser.svelte | 19 ++++++++++++++---- .../ChatSettings/ChatSettingsDialog.svelte | 5 +++++ .../src/lib/constants/settings-config.ts | 2 ++ 4 files changed, 22 insertions(+), 4 deletions(-) diff --git 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zEGB=O>oo3PH91kP_TZO>zO=mj{i=F(d44<{2eZfP-f3l4&nFasuv~}Kz!Z+k#?)PQ4``?VJiI1Z+`Q?_`hF%nZp}!F?>H-{BpMY zKc5B{Y*Sm?gfADR9F%6!761F_K5odPi^XI7cyxa-`hY(k-9MfVE+7Bq(S3UOJpRr9 M1MlT&)gT!x06xYNcmMzZ diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte index 66369b2f1c..cc2631b830 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte @@ -2,8 +2,9 @@ import { Check, X } from '@lucide/svelte'; import { Card } from '$lib/components/ui/card'; import { Button } from '$lib/components/ui/button'; - import { ChatAttachmentsList } from '$lib/components/app'; + import { ChatAttachmentsList, MarkdownContent } from '$lib/components/app'; import { INPUT_CLASSES } from '$lib/constants/input-classes'; + import { config } from '$lib/stores/settings.svelte'; import ChatMessageActions from './ChatMessageActions.svelte'; interface Props { @@ -55,6 +56,7 @@ let isMultiline = $state(false); let messageElement: HTMLElement | undefined = $state(); + const currentConfig = config(); $effect(() => { if (!messageElement || !message.content.trim()) return; @@ -123,9 +125,18 @@ class="max-w-[80%] rounded-[1.125rem] bg-primary px-3.75 py-1.5 text-primary-foreground data-[multiline]:py-2.5" data-multiline={isMultiline ? '' : undefined} > - - {message.content} - + {#if currentConfig.renderUserContentAsMarkdown} +
    + +
    + {:else} + + {message.content} + + {/if} {/if} diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte index 7c25e59258..dc617afdcd 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte @@ -80,6 +80,11 @@ key: 'showModelInfo', label: 'Show model information', type: 'checkbox' + }, + { + key: 'renderUserContentAsMarkdown', + label: 'Render user content as Markdown', + type: 'checkbox' } ] }, diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts index 63e4364ae5..154ec888ce 100644 --- a/tools/server/webui/src/lib/constants/settings-config.ts +++ b/tools/server/webui/src/lib/constants/settings-config.ts @@ -12,6 +12,7 @@ export const SETTING_CONFIG_DEFAULT: Record = pasteLongTextToFileLen: 2500, pdfAsImage: false, showModelInfo: false, + renderUserContentAsMarkdown: false, // make sure these default values are in sync with `common.h` samplers: 'top_k;typ_p;top_p;min_p;temperature', temperature: 0.8, @@ -84,6 +85,7 @@ export const SETTING_CONFIG_INFO: Record = { 'Ask for confirmation before automatically changing conversation title when editing the first message.', pdfAsImage: 'Parse PDF as image instead of text (requires vision-capable model).', showModelInfo: 'Display the model name used to generate each message below the message content.', + renderUserContentAsMarkdown: 'Render user messages using markdown formatting in the chat.', pyInterpreterEnabled: 'Enable Python interpreter using Pyodide. Allows running Python code in markdown code blocks.' }; From a3cb04744fb5c591985f53b749fef5407d07a145 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 11 Oct 2025 16:54:10 +0300 Subject: [PATCH 025/144] metal : fix mul-mm condition + fix mul-mv permuted kernels (#16494) --- ggml/src/ggml-metal/ggml-metal-ops.cpp | 5 +- ggml/src/ggml-metal/ggml-metal.metal | 66 +++++++++++++++----------- src/llama-model.cpp | 8 ++-- 3 files changed, 44 insertions(+), 35 deletions(-) diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index 1137e21077..5f9370449b 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -1546,9 +1546,8 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) { !ggml_is_transposed(op->src[1]) && // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - props_dev->has_simdgroup_mm && ne00 >= 64 && - (ne11 > ne11_mm_min || (ggml_is_quantized(op->src[0]->type) && ne12 > 1))) { - //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + props_dev->has_simdgroup_mm && ne00 >= 64 && ne11 > ne11_mm_min) { + //GGML_LOG_INFO("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); // some Metal matrix data types require aligned pointers // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index 45d91def88..ddc285042d 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -7487,7 +7487,7 @@ kernel void kernel_mul_mv_iq1_m_f32( kernel_mul_mv_iq1_m_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } -template +template void kernel_mul_mv_iq4_nl_f32_impl( args_t args, device const char * src0, @@ -7500,13 +7500,12 @@ void kernel_mul_mv_iq4_nl_f32_impl( const short NSG = FC_mul_mv_nsg; threadgroup float * shmem_f32 = (threadgroup float *) shmem; - const int nb = args.ne00/QK4_NL; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; - const int first_row = (r0 * NSG + sgitg) * nr0; + const int first_row = (r0 * NSG + sgitg) * NR0; const uint i12 = im%args.ne12; const uint i13 = im/args.ne12; @@ -7517,6 +7516,9 @@ void kernel_mul_mv_iq4_nl_f32_impl( device const block_iq4_nl * x = (device const block_iq4_nl *) (src0 + offset0); device const float * y = (device const float *) (src1 + offset1); + const int nb = args.ne00/QK4_NL; + const int ns01 = args.nb01/args.nb00; + const short ix = tiisg/2; // 0...15 const short it = tiisg%2; // 0 or 1 @@ -7524,24 +7526,25 @@ void kernel_mul_mv_iq4_nl_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; - float sumf[nr0]={0.f}; + float sumf[NR0]={0.f}; - device const float * yb = y + ix * QK4_NL + it * 8; + device const float * yb = y + ix*QK4_NL + it*8; uint32_t aux32[2]; thread const uint8_t * q8 = (thread const uint8_t *)aux32; float4 qf1, qf2; - for (int ib = ix; ib < nb; ib += 16) { + // [TAG_MUL_MV_WEIRD] + for (int ib = ix; ib < nb && ib < ns01; ib += 16) { device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; - for (short row = 0; row < nr0; row++) { - device const block_iq4_nl & xb = x[row*nb + ib]; + for (short row = 0; row < NR0; row++) { + device const block_iq4_nl & xb = x[row*ns01 + ib]; device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it); float4 acc1 = {0.f}, acc2 = {0.f}; @@ -7572,7 +7575,7 @@ void kernel_mul_mv_iq4_nl_f32_impl( device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; - for (int row = 0; row < nr0 && first_row + row < args.ne0; ++row) { + for (int row = 0; row < NR0 && first_row + row < args.ne0; ++row) { float sum_all = simd_sum(sumf[row]); if (tiisg == 0) { dst_f32[first_row + row] = sum_all; @@ -7594,7 +7597,7 @@ kernel void kernel_mul_mv_iq4_nl_f32( kernel_mul_mv_iq4_nl_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } -template +template void kernel_mul_mv_iq4_xs_f32_impl( args_t args, device const char * src0, @@ -7607,12 +7610,11 @@ void kernel_mul_mv_iq4_xs_f32_impl( const short NSG = FC_mul_mv_nsg; threadgroup float * shmem_f32 = (threadgroup float *) shmem; - const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; - const int first_row = (r0 * NSG + sgitg) * nr0; + const int first_row = (r0 * NSG + sgitg) * NR0; const uint i12 = im%args.ne12; const uint i13 = im/args.ne12; @@ -7623,6 +7625,9 @@ void kernel_mul_mv_iq4_xs_f32_impl( device const block_iq4_xs * x = (device const block_iq4_xs *) (src0 + offset0); device const float * y = (device const float *) (src1 + offset1); + const int nb = args.ne00/QK_K; + const int ns01 = args.nb01/args.nb00; + const short ix = tiisg/16; // 0 or 1 const short it = tiisg%16; // 0...15 const short ib = it/2; @@ -7632,7 +7637,7 @@ void kernel_mul_mv_iq4_xs_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; - float sumf[nr0]={0.f}; + float sumf[NR0]={0.f}; device const float * yb = y + ix * QK_K + ib * 32 + il * 8; @@ -7641,15 +7646,16 @@ void kernel_mul_mv_iq4_xs_f32_impl( float4 qf1, qf2; - for (int ibl = ix; ibl < nb; ibl += 2) { + // [TAG_MUL_MV_WEIRD] + for (int ibl = ix; ibl < nb && ibl < ns01; ibl += 2) { device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; - for (short row = 0; row < nr0; ++row) { - device const block_iq4_xs & xb = x[row*nb + ibl]; + for (short row = 0; row < NR0; ++row) { + device const block_iq4_xs & xb = x[row*ns01 + ibl]; device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il); float4 acc1 = {0.f}, acc2 = {0.f}; @@ -7679,7 +7685,7 @@ void kernel_mul_mv_iq4_xs_f32_impl( device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; - for (int row = 0; row < nr0 && first_row + row < args.ne0; ++row) { + for (int row = 0; row < NR0 && first_row + row < args.ne0; ++row) { float sum_all = simd_sum(sumf[row]); if (tiisg == 0) { dst_f32[first_row + row] = sum_all; @@ -7701,7 +7707,7 @@ kernel void kernel_mul_mv_iq4_xs_f32( kernel_mul_mv_iq4_xs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } -template +template void kernel_mul_mv_mxfp4_f32_impl( args_t args, device const char * src0, @@ -7714,13 +7720,12 @@ void kernel_mul_mv_mxfp4_f32_impl( const short NSG = FC_mul_mv_nsg; threadgroup float * shmem_f32 = (threadgroup float *) shmem; - const int nb = args.ne00/QK_MXFP4; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; - const int first_row = (r0 * NSG + sgitg) * nr0; + const int first_row = (r0 * NSG + sgitg) * NR0; const uint i12 = im%args.ne12; const uint i13 = im/args.ne12; @@ -7731,6 +7736,9 @@ void kernel_mul_mv_mxfp4_f32_impl( device const block_mxfp4 * x = (device const block_mxfp4 *) (src0 + offset0); device const float * y = (device const float *) (src1 + offset1); + const int nb = args.ne00/QK_MXFP4; + const int ns01 = args.nb01/args.nb00; // this can be larger than nb for permuted src0 tensors + const short ix = tiisg/2; // 0...15 const short it = tiisg%2; // 0 or 1 @@ -7738,20 +7746,22 @@ void kernel_mul_mv_mxfp4_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; - float sumf[nr0]={0.f}; + float sumf[NR0]={0.f}; - device const float * yb = y + ix * QK_MXFP4 + it * 8; + device const float * yb = y + ix*QK_MXFP4 + it*8; + + // note: just the check `ib < nb` is enough, but adding the redundant `&& ib < ns01` check makes the kernel a bit faster + // no idea why that is - needs some deeper investigation [TAG_MUL_MV_WEIRD] + for (int ib = ix; ib < nb && ib < ns01; ib += 16) { + device const float4 * y4 = (device const float4 *) yb; - for (int ib = ix; ib < nb; ib += 16) { - device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; -#pragma unroll(nr0) - for (short row = 0; row < nr0; row++) { - device const block_mxfp4 & xb = x[row*nb + ib]; + FOR_UNROLL (short row = 0; row < NR0; row++) { + device const block_mxfp4 & xb = x[row*ns01 + ib]; device const uint8_t * q2 = (device const uint8_t *)(xb.qs + 8*it); float4 acc1 = yl[0]*float4(shmem_f32[q2[0] & 0x0F], shmem_f32[q2[1] & 0x0F], shmem_f32[q2[2] & 0x0F], shmem_f32[q2[3] & 0x0F]); @@ -7769,7 +7779,7 @@ void kernel_mul_mv_mxfp4_f32_impl( device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; - for (int row = 0; row < nr0 && first_row + row < args.ne0; ++row) { + for (int row = 0; row < NR0 && first_row + row < args.ne0; ++row) { float sum_all = simd_sum(sumf[row]); if (tiisg == 0) { dst_f32[first_row + row] = sum_all; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index a5fe5b749c..36d495d6cf 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -16313,10 +16313,10 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba { } ggml_tensor * build_layer_ffn( - ggml_tensor * cur, - ggml_tensor * inpSA, - const llama_model & model, - const int il) { + ggml_tensor * cur, + ggml_tensor * inpSA, + const llama_model & model, + const int il) { // For Granite architectures - scale residual if (hparams.f_residual_scale) { From 11f0af5504252e453d57406a935480c909e3ff37 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 11 Oct 2025 20:54:32 +0200 Subject: [PATCH 026/144] CUDA: faster tile FA, add oob checks, more HSs (#16492) --- ggml/src/ggml-cuda/CMakeLists.txt | 2 + ggml/src/ggml-cuda/common.cuh | 7 +- ggml/src/ggml-cuda/fattn-common.cuh | 9 +- ggml/src/ggml-cuda/fattn-tile.cu | 771 +---------- ggml/src/ggml-cuda/fattn-tile.cuh | 1213 +++++++++++++++++ ggml/src/ggml-cuda/fattn-wmma-f16.cuh | 2 + ggml/src/ggml-cuda/fattn.cu | 76 +- .../fattn-tile-instance-dkq112-dv112.cu | 5 + .../fattn-tile-instance-dkq128-dv128.cu | 5 + .../fattn-tile-instance-dkq256-dv256.cu | 5 + .../fattn-tile-instance-dkq40-dv40.cu | 5 + .../fattn-tile-instance-dkq576-dv512.cu | 5 + .../fattn-tile-instance-dkq64-dv64.cu | 5 + .../fattn-tile-instance-dkq80-dv80.cu | 5 + .../fattn-tile-instance-dkq96-dv96.cu | 5 + .../template-instances/generate_cu_files.py | 18 +- ggml/src/ggml-hip/CMakeLists.txt | 2 + ggml/src/ggml-musa/CMakeLists.txt | 2 + 18 files changed, 1358 insertions(+), 784 deletions(-) create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq112-dv112.cu create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq128-dv128.cu create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq256-dv256.cu create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq40-dv40.cu create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq576-dv512.cu create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq64-dv64.cu create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq80-dv80.cu create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq96-dv96.cu diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt index bdcefe7b7e..3024775135 100644 --- a/ggml/src/ggml-cuda/CMakeLists.txt +++ b/ggml/src/ggml-cuda/CMakeLists.txt @@ -44,6 +44,8 @@ if (CUDAToolkit_FOUND) list(APPEND GGML_HEADERS_CUDA "../../include/ggml-cuda.h") file(GLOB GGML_SOURCES_CUDA "*.cu") + file(GLOB SRCS "template-instances/fattn-tile*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) file(GLOB SRCS "template-instances/fattn-mma*.cu") list(APPEND GGML_SOURCES_CUDA ${SRCS}) file(GLOB SRCS "template-instances/mmq*.cu") diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index d51abbeafa..e0abde5427 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -245,7 +245,8 @@ static bool fp16_available(const int cc) { } static bool fast_fp16_available(const int cc) { - return (GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && cc != 610) || GGML_CUDA_CC_IS_AMD(cc); + return GGML_CUDA_CC_IS_AMD(cc) || + (GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && ggml_cuda_highest_compiled_arch(cc) != 610); } // To be used for feature selection of external libraries, e.g. cuBLAS. @@ -571,6 +572,10 @@ static __device__ __forceinline__ void ggml_cuda_mad(half2 & acc, const half2 v, } // Aligned memory transfers of 8/16 bytes can be faster than 2 transfers with 4 bytes, especially on AMD. +// Important: do not use this function if dst and src both point at registers. +// Due to the strict aliasing rule the compiler can do incorrect optimizations if src and dst have different types. +// The function is intended for copies between registers and SRAM/VRAM to make the compiler emit the right instructions. +// If dst and src point at different address spaces then they are guaranteed to not be aliased. template static __device__ __forceinline__ void ggml_cuda_memcpy_1(void * __restrict__ dst, const void * __restrict__ src) { if constexpr (alignment != 0) { diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 33d2f0f49e..bc0c2523cc 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -793,8 +793,6 @@ void launch_fattn( GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) && "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big"); - GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding."); - ggml_cuda_pool & pool = ctx.pool(); cudaStream_t main_stream = ctx.stream(); const int id = ggml_cuda_get_device(); @@ -878,7 +876,7 @@ void launch_fattn( // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped. // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or // multiple sequences of possibly different lengths. - if (mask && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) { + if (mask && K->ne[1] % FATTN_KQ_STRIDE == 0 && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) { const int s31 = mask->nb[1] / sizeof(half2); const int s33 = mask->nb[3] / sizeof(half2); @@ -916,8 +914,7 @@ void launch_fattn( dst_tmp_meta.alloc(blocks_num.x*ncols * (2*2 + DV) * sizeof(float)); } else { - GGML_ASSERT(K->ne[1] % KQ_row_granularity == 0); - const int ntiles_KQ = K->ne[1] / KQ_row_granularity; // Max. number of parallel blocks limited by tensor size. + const int ntiles_KQ = (K->ne[1] + KQ_row_granularity - 1) / KQ_row_granularity; // Max. number of parallel blocks limited by tensor size. // parallel_blocks must not be larger than what the tensor size allows: parallel_blocks = std::min(parallel_blocks, ntiles_KQ); @@ -946,7 +943,7 @@ void launch_fattn( blocks_num.x = ntiles_x; blocks_num.y = parallel_blocks; - blocks_num.z = Q->ne[2]*Q->ne[3]; + blocks_num.z = (Q->ne[2]/ncols2)*Q->ne[3]; if (parallel_blocks > 1) { dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); diff --git a/ggml/src/ggml-cuda/fattn-tile.cu b/ggml/src/ggml-cuda/fattn-tile.cu index 68de623d80..3a5806d909 100644 --- a/ggml/src/ggml-cuda/fattn-tile.cu +++ b/ggml/src/ggml-cuda/fattn-tile.cu @@ -1,756 +1,45 @@ #include "common.cuh" -#include "fattn-common.cuh" #include "fattn-tile.cuh" #include "fattn-wmma-f16.cuh" -// kq_stride == number of KQ rows to process per iteration -// kq_nbatch == number of K columns to load in parallel for KQ calculation - -static int fattn_tile_get_kq_stride_host(const int D, const int ncols, const int cc, const int warp_size) { - if (GGML_CUDA_CC_IS_AMD(cc)) { - if (GGML_CUDA_CC_IS_RDNA(cc)) { - switch (D) { - case 64: - return 128; - case 128: - case 256: - return ncols <= 16 ? 128 : 64; - default: - GGML_ABORT("fatal error"); - return -1; - } - } - switch (D) { - case 64: - return ncols == 32 ? 128 : 64; - case 128: - return ncols == 32 ? 64 : 32; - case 256: - return 32; - default: - GGML_ABORT("fatal error"); - return -1; - } - } - if (fast_fp16_available(cc)) { - switch (D) { - case 64: - case 128: - case 256: - return ncols <= 16 ? 128 : 64; - default: - GGML_ABORT("fatal error"); - return -1; - } - } - switch (D) { - case 64: - return ncols <= 16 ? 128 : 64; - case 128: - return ncols <= 16 ? 64 : 32; - case 256: - return 32; - default: - GGML_ABORT("fatal error"); - return -1; - } - GGML_UNUSED(warp_size); -} - -static constexpr __device__ int fattn_tile_get_kq_stride_device(int D, int ncols, int warp_size) { -#ifdef GGML_USE_HIP -#ifdef RDNA - switch (D) { - case 64: - return 128; - case 128: - case 256: - return ncols <= 16 ? 128 : 64; - default: - return -1; - } -#else - switch (D) { - case 64: - return ncols == 32 ? 128 : 64; - case 128: - return ncols == 32 ? 64 : 32; - case 256: - return 32; - default: - return -1; - } -#endif // RDNA -#else -#ifdef FAST_FP16_AVAILABLE - switch (D) { - case 64: - case 128: - case 256: - return ncols <= 16 ? 128 : 64; - default: - return -1; - } -#else - switch (D) { - case 64: - return ncols <= 16 ? 128 : 64; - case 128: - return ncols <= 16 ? 64 : 32; - case 256: - return 32; - default: - return -1; - } -#endif // FAST_FP16_AVAILABLE -#endif // GGML_USE_HIP - GGML_UNUSED_VARS(ncols, warp_size); -} - -static constexpr __device__ int fattn_tile_get_kq_nbatch_device(int D, int ncols, int warp_size) { -#ifdef GGML_USE_HIP - switch (D) { - case 64: - return 64; - case 128: - case 256: - return 128; - default: - return -1; - } -#else -#ifdef FAST_FP16_AVAILABLE - switch (D) { - case 64: - return 64; - case 128: - case 256: - return 128; - default: - return -1; - } -#else - switch (D) { - case 64: - return 64; - case 128: - return 128; - case 256: - return ncols <= 16 ? 128 : 64; - default: - return -1; - } -#endif // FAST_FP16_AVAILABLE -#endif // GGML_USE_HIP - GGML_UNUSED_VARS(ncols, warp_size); -} - -static int fattn_tile_get_nthreads_host(const int cc, const int ncols) { - return 256; - GGML_UNUSED_VARS(cc, ncols); -} - -static constexpr __device__ int fattn_tile_get_nthreads_device(int ncols) { - return 256; - GGML_UNUSED(ncols); -} - -static constexpr __device__ int fattn_tile_get_occupancy_device(int ncols) { -#ifdef RDNA - return 3; -#else - return ncols <= 16 ? 3 : 2; -#endif // RDNA - GGML_UNUSED(ncols); -} - -template // D == head size -__launch_bounds__(fattn_tile_get_nthreads_device(ncols), fattn_tile_get_occupancy_device(ncols)) -static __global__ void flash_attn_tile( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - const char * __restrict__ sinks, - const int * __restrict__ KV_max, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const float logit_softcap, - const int32_t ne00, const int32_t ne01, const int32_t ne02, const int32_t ne03, - const int32_t nb01, const int32_t nb02, const int32_t nb03, - const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13, - const int32_t nb11, const int32_t nb12, const int64_t nb13, - const int32_t nb21, const int32_t nb22, const int64_t nb23, - const int32_t ne31, const int32_t ne32, const int32_t ne33, - const int32_t nb31, const int32_t nb32, const int64_t nb33) { -#ifdef FLASH_ATTN_AVAILABLE - - // Skip unused kernel variants for faster compilation: -#ifdef GGML_USE_WMMA_FATTN - NO_DEVICE_CODE; - return; -#endif // GGML_USE_WMMA_FATTN - - if (use_logit_softcap && !(D == 128 || D == 256)) { - GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, - max_bias, m0, m1, n_head_log2, logit_softcap, - ne00, ne01, ne02, ne03, - nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb11, nb12, nb13, - nb21, nb22, nb23, - ne31, ne32, ne33, - nb31, nb32, nb33); - NO_DEVICE_CODE; - return; - } - - constexpr int warp_size = 32; - constexpr int nwarps = fattn_tile_get_nthreads_device(ncols) / warp_size; - constexpr int kq_stride = fattn_tile_get_kq_stride_device(D, ncols, warp_size); - static_assert(kq_stride % warp_size == 0, "kq_stride not divisable by warp_size."); - constexpr int kq_nbatch = fattn_tile_get_kq_nbatch_device(D, ncols, warp_size); - static_assert(kq_nbatch % (2*warp_size) == 0, "bad kq_nbatch"); - - // In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = blockIdx.x * ncols; // Index of the Q/QKV column to work on. - - const int sequence = blockIdx.z / ne02; - const int head = blockIdx.z - sequence*ne02; - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float * Q_f = (const float *) (Q + nb03* sequence + nb02* head + nb01*ic0); - const half2 * K_h2 = (const half2 *) (K + nb13* sequence + nb12*(head / gqa_ratio)); - const half2 * V_h2 = (const half2 *) (V + nb13* sequence + nb12*(head / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0); - const float * sinksf = (const float *) (sinks); - - const int stride_KV2 = nb11 / sizeof(half2); - - const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1); - - constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); - constexpr int cpy_ne = cpy_nb / 4; - - constexpr int cpw = ncols/nwarps; // cols per warp - - // softmax_iter_j == number of KQ columns for which to calculate softmax in parallel. - // KQ is originall 2D but uses a Z-shaped memory pattern for larger reads/writes. -#ifdef FAST_FP16_AVAILABLE - constexpr int softmax_iter_j = cpw < 2*cpy_ne ? cpw : 2*cpy_ne; - - __shared__ half KQ[ncols/softmax_iter_j][kq_stride][softmax_iter_j]; - __shared__ half2 Q_tmp[ncols][D/2]; - __shared__ half2 KV_tmp[kq_stride * (kq_nbatch/2 + cpy_ne)]; // Padded to avoid memory bank conflicts. - half2 VKQ[cpw][D/(2*warp_size)] = {{{0.0f, 0.0f}}}; -#else - constexpr int softmax_iter_j = cpw < 1*cpy_ne ? cpw : 1*cpy_ne; - - __shared__ float KQ[ncols/softmax_iter_j][kq_stride][softmax_iter_j]; - __shared__ float Q_tmp[ncols][D]; - __shared__ float KV_tmp[kq_stride * (kq_nbatch + cpy_ne)]; // Padded to avoid memory bank conflicts. - float2 VKQ[cpw][D/(2*warp_size)] = {{{0.0f, 0.0f}}}; -#endif // FAST_FP16_AVAILABLE - static_assert(cpw % softmax_iter_j == 0, "bad softmax_iter_j"); - - float KQ_max[cpw]; -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - KQ_max[j0/nwarps] = -FLT_MAX/2.0f; - } - float KQ_sum[cpw] = {0.0f}; - - // Load Q data, convert to FP16 if fast. -#pragma unroll - for (int j0 = 0; j0 < cpw; ++j0) { - const int j = j0 + threadIdx.y*cpw; - - constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size; - -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) { - float tmp_f[cpy_ne_D] = {0.0f}; - if (ic0 + j < ne01) { - ggml_cuda_memcpy_1(tmp_f, &Q_f[j*(nb01/sizeof(float)) + i0 + threadIdx.x*cpy_ne_D]); - } - -#pragma unroll - for (int i1 = 0; i1 < cpy_ne_D; ++i1) { - tmp_f[i1] *= scale; - } - -#ifdef FAST_FP16_AVAILABLE - half2 tmp_h2[cpy_ne_D/2]; -#pragma unroll - for (int i1 = 0; i1 < cpy_ne_D; i1 += 2) { - tmp_h2[i1/2] = make_half2(tmp_f[i1 + 0], tmp_f[i1 + 1]); - } - ggml_cuda_memcpy_1(&Q_tmp[j][i0/2 + threadIdx.x*(cpy_ne_D/2)], tmp_h2); -#else - ggml_cuda_memcpy_1 (&Q_tmp[j][i0 + threadIdx.x* cpy_ne_D], tmp_f); -#endif // FAST_FP16_AVAILABLE - } - } - - __syncthreads(); - - // Main loop over KV cache: - const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11; - for (int k_VKQ_0 = blockIdx.y*kq_stride; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*kq_stride) { - // Calculate KQ tile and keep track of new maximum KQ values: - - float KQ_max_new[cpw]; -#pragma unroll - for (int j = 0; j < cpw; ++j) { - KQ_max_new[j] = KQ_max[j]; - } - - float KQ_acc[kq_stride/warp_size][cpw] = {{0.0f}}; // Accumulators for KQ matrix multiplication. - - // KQ = K @ Q matrix multiplication: -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += kq_nbatch) { -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += nwarps) { - const int i_KQ = i_KQ_0 + threadIdx.y; - -#ifdef FAST_FP16_AVAILABLE - constexpr int cpy_ne_kqnb = cpy_ne < kq_nbatch/(2*warp_size) ? cpy_ne : kq_nbatch/(2*warp_size); -#pragma unroll - for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; k_KQ_1 += warp_size*cpy_ne_kqnb) { - ggml_cuda_memcpy_1( - &KV_tmp[i_KQ*(kq_nbatch/2 + cpy_ne) + k_KQ_1 + threadIdx.x*cpy_ne_kqnb], - &K_h2[int64_t(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + k_KQ_1 + threadIdx.x*cpy_ne_kqnb]); - } -#else - constexpr int cpy_ne_kqnb = cpy_ne < kq_nbatch/warp_size ? cpy_ne : kq_nbatch/warp_size; -#pragma unroll - for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch; k_KQ_1 += warp_size*cpy_ne_kqnb) { - half2 tmp_h2[cpy_ne_kqnb/2]; - ggml_cuda_memcpy_1( - tmp_h2, &K_h2[int64_t(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + k_KQ_1/2 + threadIdx.x*(cpy_ne_kqnb/2)]); - - float2 tmp_f2[cpy_ne_kqnb/2]; -#pragma unroll - for (int k_KQ_2 = 0; k_KQ_2 < cpy_ne_kqnb/2; ++k_KQ_2) { - tmp_f2[k_KQ_2] = __half22float2(tmp_h2[k_KQ_2]); - } - ggml_cuda_memcpy_1( - &KV_tmp[i_KQ*(kq_nbatch + cpy_ne) + k_KQ_1 + threadIdx.x*cpy_ne_kqnb], tmp_f2); - } -#endif // FAST_FP16_AVAILABLE - } - - __syncthreads(); - -#ifdef FAST_FP16_AVAILABLE -#pragma unroll - for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; k_KQ_1 += cpy_ne) { - half2 K_k[kq_stride/warp_size][cpy_ne]; - half2 Q_k[cpw][cpy_ne]; -#else -#pragma unroll - for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch; k_KQ_1 += cpy_ne) { - float K_k[kq_stride/warp_size][cpy_ne]; - float Q_k[cpw][cpy_ne]; -#endif // FAST_FP16_AVAILABLE - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) { - const int i_KQ = i_KQ_0 + threadIdx.x; - -#ifdef FAST_FP16_AVAILABLE - ggml_cuda_memcpy_1(&K_k[i_KQ_0/warp_size], &KV_tmp[i_KQ*(kq_nbatch/2 + cpy_ne) + k_KQ_1]); -#else - ggml_cuda_memcpy_1(&K_k[i_KQ_0/warp_size], &KV_tmp[i_KQ*(kq_nbatch + cpy_ne) + k_KQ_1]); -#endif // FAST_FP16_AVAILABLE - } -#pragma unroll - for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) { - const int j_KQ = j_KQ_0 + threadIdx.y*cpw; - -#ifdef FAST_FP16_AVAILABLE - ggml_cuda_memcpy_1(&Q_k[j_KQ_0], &Q_tmp[j_KQ][k_KQ_0/2 + k_KQ_1]); -#else - ggml_cuda_memcpy_1(&Q_k[j_KQ_0], &Q_tmp[j_KQ][k_KQ_0 + k_KQ_1]); -#endif // FAST_FP16_AVAILABLE - } - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) { -#pragma unroll - for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) { -#pragma unroll - for (int k = 0; k < cpy_ne; ++k) { - ggml_cuda_mad(KQ_acc[i_KQ_0/warp_size][j_KQ_0], K_k[i_KQ_0/warp_size][k], Q_k[j_KQ_0][k]); - } - } - } - } - - if (k_KQ_0 + kq_nbatch < D) { - __syncthreads(); // Sync not needed on last iteration. - } - } - - // Apply logit softcap, mask, update KQ_max: -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) { - const int i_KQ = i_KQ_0 + threadIdx.x; - -#pragma unroll - for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) { - const int j_KQ = j_KQ_0 + threadIdx.y*cpw; - - if (use_logit_softcap) { - KQ_acc[i_KQ_0/warp_size][j_KQ_0] = logit_softcap * tanhf(KQ_acc[i_KQ_0/warp_size][j_KQ_0]); - } - - KQ_acc[i_KQ_0/warp_size][j_KQ_0] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; - - KQ_max_new[j_KQ_0] = fmaxf(KQ_max_new[j_KQ_0], KQ_acc[i_KQ_0/warp_size][j_KQ_0]); - } - } - - __syncthreads(); - - // Calculate KQ softmax, write to shared KQ buffer, re-scale VKQ accumulators: -#pragma unroll - for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) { -#ifdef FAST_FP16_AVAILABLE - half tmp[kq_stride/warp_size][softmax_iter_j]; -#else - float tmp[kq_stride/warp_size][softmax_iter_j]; -#endif // FAST_FP16_AVAILABLE - -#pragma unroll - for (int j1 = 0; j1 < softmax_iter_j; ++j1) { - KQ_max_new[j0+j1] = warp_reduce_max(KQ_max_new[j0+j1]); - const float KQ_max_scale = expf(KQ_max[j0+j1] - KQ_max_new[j0+j1]); - KQ_max[j0+j1] = KQ_max_new[j0+j1]; - - float KQ_sum_add = 0.0f; -#pragma unroll - for (int i0 = 0; i0 < kq_stride; i0 += warp_size) { - const float val = expf(KQ_acc[i0/warp_size][j0+j1] - KQ_max[j0+j1]); - KQ_sum_add += val; - tmp[i0/warp_size][j1] = val; - } - KQ_sum[j0+j1] = KQ_sum[j0+j1]*KQ_max_scale + KQ_sum_add; - -#ifdef FAST_FP16_AVAILABLE - const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - VKQ[j0+j1][i0/warp_size] *= KQ_max_scale_h2; - } -#else -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - VKQ[j0+j1][i0/warp_size].x *= KQ_max_scale; - VKQ[j0+j1][i0/warp_size].y *= KQ_max_scale; - } -#endif // FAST_FP16_AVAILABLE - } - -#pragma unroll - for (int i0 = 0; i0 < kq_stride; i0 += warp_size) { - const int i = i0 + threadIdx.x; - - ggml_cuda_memcpy_1( - KQ[j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j)][i], tmp[i0/warp_size]); - } - } - - // VKQ = V @ KQ matrix multiplication: - constexpr int V_cols_per_iter = kq_stride*kq_nbatch / D; // Number of V columns that fit in SRAM for K. - static_assert(kq_stride % V_cols_per_iter == 0, "bad V_cols_per_iter"); -#pragma unroll - for (int k0 = 0; k0 < kq_stride; k0 += V_cols_per_iter) { -#pragma unroll - for (int k1 = 0; k1 < V_cols_per_iter; k1 += nwarps) { - const int k_tile = k1 + threadIdx.y; - -#ifdef FAST_FP16_AVAILABLE - constexpr int cpy_ne_D = cpy_ne < D/(2*warp_size) ? cpy_ne : D/(2*warp_size); -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) { - ggml_cuda_memcpy_1( - &KV_tmp[k_tile*(D/2) + i0 + threadIdx.x*cpy_ne_D], - &V_h2[int64_t(k_VKQ_0 + k0 + k_tile)*stride_KV2 + i0 + threadIdx.x*cpy_ne_D]); - } -#else - constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) { - half2 tmp_h2[cpy_ne_D/2]; - ggml_cuda_memcpy_1( - tmp_h2, &V_h2[int64_t(k_VKQ_0 + k0 + k_tile)*stride_KV2 + i0/2 + threadIdx.x*(cpy_ne_D/2)]); - - float2 tmp_f2[cpy_ne_D/2]; -#pragma unroll - for (int i1 = 0; i1 < cpy_ne_D/2; ++i1) { - tmp_f2[i1] = __half22float2(tmp_h2[i1]); - } - ggml_cuda_memcpy_1( - &KV_tmp[k_tile*D + i0 + threadIdx.x*cpy_ne_D], tmp_f2); - } -#endif // FAST_FP16_AVAILABLE - } - - __syncthreads(); - -#ifdef FAST_FP16_AVAILABLE -#pragma unroll - for (int k1 = 0; k1 < V_cols_per_iter; ++k1) { - half2 V_k[(D/2)/warp_size]; - half2 KQ_k[cpw]; - - constexpr int cpy_ne_D = cpy_ne/2 < (D/2)/warp_size ? cpy_ne/2 : (D/2)/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) { - ggml_cuda_memcpy_1(&V_k[i0/warp_size], &KV_tmp[k1*(D/2) + i0 + threadIdx.x*cpy_ne_D]); - } -#pragma unroll - for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) { - const int j = j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j); - - half tmp[softmax_iter_j]; - ggml_cuda_memcpy_1( - &tmp, KQ[j][k0 + k1]); -#pragma unroll - for (int j1 = 0; j1 < softmax_iter_j; ++j1) { - KQ_k[j0+j1] = __half2half2(tmp[j1]); - } - } - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { -#pragma unroll - for (int j0 = 0; j0 < cpw; ++j0) { - VKQ[j0][i0/warp_size] += V_k[i0/warp_size]*KQ_k[j0]; - } - } - } -#else -#pragma unroll - for (int k1 = 0; k1 < V_cols_per_iter; ++k1) { - float2 V_k[(D/2)/warp_size]; - float KQ_k[cpw]; - - constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) { - ggml_cuda_memcpy_1(&V_k[i0/(2*warp_size)], &KV_tmp[k1*D + i0 + threadIdx.x*cpy_ne_D]); - } -#pragma unroll - for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) { - const int j = j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j); - - ggml_cuda_memcpy_1( - &KQ_k[j0], KQ[j][k0 + k1]); - } - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { -#pragma unroll - for (int j0 = 0; j0 < cpw; ++j0) { - VKQ[j0][i0/warp_size].x += V_k[i0/warp_size].x*KQ_k[j0]; - VKQ[j0][i0/warp_size].y += V_k[i0/warp_size].y*KQ_k[j0]; - } - } - } -#endif // FAST_FP16_AVAILABLE - - __syncthreads(); - } - } - - - // Attention sink: adjust running max and sum once per head - if (sinksf && blockIdx.y == 0) { - const float sink = sinksf[head]; - -#pragma unroll - for (int j0 = 0; j0 < cpw; ++j0) { - float KQ_max_new_j = fmaxf(KQ_max[j0], sink); - KQ_max_new_j = warp_reduce_max(KQ_max_new_j); - - const float KQ_max_scale = expf(KQ_max[j0] - KQ_max_new_j); - KQ_max[j0] = KQ_max_new_j; - - const float val = expf(sink - KQ_max[j0]); - KQ_sum[j0] = KQ_sum[j0] * KQ_max_scale; - if (threadIdx.x == 0) { - KQ_sum[j0] += val; - } - -#ifdef FAST_FP16_AVAILABLE - const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - VKQ[j0][i0/warp_size] *= KQ_max_scale_h2; - } -#else -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - VKQ[j0][i0/warp_size].x *= KQ_max_scale; - VKQ[j0][i0/warp_size].y *= KQ_max_scale; - } -#endif // FAST_FP16_AVAILABLE - } - } - -#pragma unroll - for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) { - KQ_sum[j_VKQ_0] = warp_reduce_sum(KQ_sum[j_VKQ_0]); - } - if (gridDim.y == 1) { -#pragma unroll - for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) { -#ifdef FAST_FP16_AVAILABLE - const half2 KQ_sum_j_inv = make_half2(1.0f/KQ_sum[j_VKQ_0], 1.0f/KQ_sum[j_VKQ_0]); -#pragma unroll - for (int i = 0; i < (D/2)/warp_size; ++i) { - VKQ[j_VKQ_0][i] *= KQ_sum_j_inv; - } -#else - const float KQ_sum_j_inv = 1.0f/KQ_sum[j_VKQ_0]; -#pragma unroll - for (int i = 0; i < (D/2)/warp_size; ++i) { - VKQ[j_VKQ_0][i].x *= KQ_sum_j_inv; - VKQ[j_VKQ_0][i].y *= KQ_sum_j_inv; - } -#endif // FAST_FP16_AVAILABLE - } - } - - // Write back results: -#pragma unroll - for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) { - const int j_VKQ = j_VKQ_0 + threadIdx.y*cpw; - - if (ic0 + j_VKQ >= ne01) { - return; - } - - const int j_dst_unrolled = ((sequence*ne01 + ic0 + j_VKQ)*ne02 + head)*gridDim.y + blockIdx.y; - -#ifdef FAST_FP16_AVAILABLE - constexpr int cpy_ne_D = cpy_ne/2 < (D/2)/warp_size ? cpy_ne/2 : (D/2)/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) { - float2 tmp[cpy_ne_D]; -#pragma unroll - for (int i1 = 0; i1 < cpy_ne_D; ++i1) { - tmp[i1] = __half22float2(VKQ[j_VKQ_0][i0/warp_size + i1]); - } - ggml_cuda_memcpy_1(&dst[j_dst_unrolled*D + 2*i0 + threadIdx.x*(2*cpy_ne_D)], tmp); - } -#else - constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) { - ggml_cuda_memcpy_1( - &dst[j_dst_unrolled*D + i0 + threadIdx.x*cpy_ne_D], &VKQ[j_VKQ_0][i0/(2*warp_size)]); - } -#endif // FAST_FP16_AVAILABLE - - if (gridDim.y != 1 && threadIdx.x == 0) { - dst_meta[j_dst_unrolled] = make_float2(KQ_max[j_VKQ_0], KQ_sum[j_VKQ_0]); - } - } -#else - GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, - max_bias, m0, m1, n_head_log2, logit_softcap, - ne00, ne01, ne02, ne03, - nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb11, nb12, nb13, - nb21, nb22, nb23, - ne31, ne32, ne33, - nb31, nb32, nb33); - NO_DEVICE_CODE; -#endif // FLASH_ATTN_AVAILABLE -} - -template -static void launch_fattn_tile_switch_ncols(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - - const int id = ggml_cuda_get_device(); - const int cc = ggml_cuda_info().devices[id].cc; - const int warp_size = 32; - - constexpr size_t nbytes_shared = 0; - -#ifdef GGML_USE_HIP - if constexpr (D <= 128) { - if (Q->ne[1] > 32) { - constexpr int cols_per_block = 64; - const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size; - fattn_kernel_t fattn_kernel = flash_attn_tile; - const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size); - launch_fattn - (ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size); - return; - } - } -#endif // GGML_USE_HIP - - if (Q->ne[1] > 16) { - constexpr int cols_per_block = 32; - const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size; - fattn_kernel_t fattn_kernel = flash_attn_tile; - const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size); - launch_fattn - (ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size); - return; - } - - constexpr int cols_per_block = 16; - const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size; - fattn_kernel_t fattn_kernel = flash_attn_tile; - const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size); - launch_fattn - (ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size); -} - -template -static void launch_fattn_tile_switch_head_size(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - switch (Q->ne[0]) { +void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + switch (K->ne[0]) { + case 40: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case< 40, 40>(ctx, dst); + } break; case 64: { - launch_fattn_tile_switch_ncols< 64, use_logit_softcap>(ctx, dst); + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case< 64, 64>(ctx, dst); + } break; + case 80: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case< 80, 80>(ctx, dst); + } break; + case 96: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case< 96, 96>(ctx, dst); + } break; + case 112: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case<112, 112>(ctx, dst); } break; case 128: { - launch_fattn_tile_switch_ncols<128, use_logit_softcap>(ctx, dst); + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case<128, 128>(ctx, dst); } break; case 256: { - launch_fattn_tile_switch_ncols<256, use_logit_softcap>(ctx, dst); + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case<256, 256>(ctx, dst); + } break; + case 576: { + GGML_ASSERT(V->ne[0] == 512); + ggml_cuda_flash_attn_ext_tile_case<576, 512>(ctx, dst); } break; default: { GGML_ABORT("Unsupported head size"); } break; } } - -void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - - float logit_softcap; - memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); - - if (logit_softcap == 0.0f) { - constexpr bool use_logit_softcap = false; - launch_fattn_tile_switch_head_size(ctx, dst); - } else { - constexpr bool use_logit_softcap = true; - launch_fattn_tile_switch_head_size(ctx, dst); - } -} diff --git a/ggml/src/ggml-cuda/fattn-tile.cuh b/ggml/src/ggml-cuda/fattn-tile.cuh index 10dc22d1bf..2efc9cc880 100644 --- a/ggml/src/ggml-cuda/fattn-tile.cuh +++ b/ggml/src/ggml-cuda/fattn-tile.cuh @@ -1,3 +1,1216 @@ #include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-wmma-f16.cuh" + +// nbatch_fa == number of KQ rows to process per iteration +// nbatch_K == number of K columns to load in parallel for KQ calculation + +// TODO optimize kernel parameters for FP16 NVIDIA (P100) +// TODO optimize kernel parameters for head sizes 40, 80, 96, 112 + +// The ROCm compiler cannot handle templating in __launch_bounds__. +// As a workaround, define a macro to package the kernel parameters as uint32_t: +#define GGML_CUDA_FATTN_TILE_CONFIG_CASE(DKQ_, DV_, ncols_, nthreads, occupancy, nbatch_fa, nbatch_K) \ + if (DKQ == (DKQ_) && DV == (DV_) && ncols == (ncols_)) { \ + static_assert((nthreads) <= 512, "bad nthreads"); \ + static_assert((occupancy) <= 8, "bad occupancy"); \ + static_assert((nbatch_fa) <= 256, "bad nbatch_fa"); \ + static_assert((nbatch_K) <= 256, "bad nbatch_K"); \ + return ((nthreads) << 0) | ((occupancy) << 10) | ((nbatch_fa) << 14) | ((nbatch_K) << 23); \ + } \ + +static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nvidia_fp16(const int DKQ, const int DV, const int ncols) { + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 64, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 64, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 128, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 64, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 64, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 64, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 64, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 64, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 64, 48) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 64, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 64, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 64, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 64, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 64, 56) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 2, 64, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 8, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 2, 64, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 4, 128, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 8, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2, 64, 64) + + return 0; +} + +static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nvidia_fp32(const int DKQ, const int DV, const int ncols) { + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 128, 3, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 128, 3, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 32, 48) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 32, 56) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 2, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 3, 32, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 8, 128, 3, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 128, 3, 32, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 2, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 4, 128, 3, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 8, 256, 2, 32, 256) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2, 32, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2, 32, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2, 32, 64) + + return 0; +} + +static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_amd(const int DKQ, const int DV, const int ncols) { + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 64, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 64, 3, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 128, 2, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 256, 2, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 64, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 64, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 64, 256, 2, 32, 48) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 64, 256, 2, 32, 56) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 2, 256, 2, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 8, 256, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 64, 256, 2, 64, 32) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 2, 256, 2, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 4, 256, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 8, 256, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2, 32, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2, 32, 128) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 32, 512, 1, 128, 64) + + return 0; +} + +static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_amd_rdna(const int DKQ, const int DV, const int ncols) { + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 64, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 64, 8, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 64, 8, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 128, 5, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 128, 5, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 128, 4, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 64, 128, 5, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 64, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 64, 256, 2, 32, 48) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 64, 256, 2, 32, 56) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 2, 64, 8, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 8, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 8, 128, 8, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 3, 128, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 3, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 64, 256, 3, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 2, 64, 8, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 4, 128, 6, 32, 256) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 8, 128, 6, 32, 256) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 5, 32, 256) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 3, 64, 128) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 4, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 32, 256, 2, 128, 64) + + return 0; +} + +static __host__ uint32_t ggml_cuda_fattn_tile_get_config(const int DKQ, const int DV, const int ncols, const int cc) { + if (GGML_CUDA_CC_IS_AMD(cc)) { + if (GGML_CUDA_CC_IS_RDNA(cc)) { + return ggml_cuda_fattn_tile_get_config_amd_rdna(DKQ, DV, ncols); + } + return ggml_cuda_fattn_tile_get_config_amd(DKQ, DV, ncols); + } + if (fast_fp16_available(cc)) { + return ggml_cuda_fattn_tile_get_config_nvidia_fp16(DKQ, DV, ncols); + } + return ggml_cuda_fattn_tile_get_config_nvidia_fp32(DKQ, DV, ncols); +} + +static constexpr __device__ uint32_t ggml_cuda_fattn_tile_get_config(const int DKQ, const int DV, const int ncols) { +#ifdef GGML_USE_HIP +#ifdef RDNA + return ggml_cuda_fattn_tile_get_config_amd_rdna(DKQ, DV, ncols); +#else + return ggml_cuda_fattn_tile_get_config_amd(DKQ, DV, ncols); +#endif // RDNA +#else +#ifdef FAST_FP16_AVAILABLE + return ggml_cuda_fattn_tile_get_config_nvidia_fp16(DKQ, DV, ncols); +#else + return ggml_cuda_fattn_tile_get_config_nvidia_fp32(DKQ, DV, ncols); +#endif // FAST_FP16_AVAILABLE +#endif // GGML_USE_HIP +} + +static __host__ int ggml_cuda_fattn_tile_get_nthreads(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 0) & ((1 << 10) - 1); +} + +static constexpr __device__ int ggml_cuda_fattn_tile_get_nthreads(const int DKQ, const int DV, const int ncols) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols) >> 0) & ((1 << 10) - 1); +} + +static __host__ int ggml_cuda_fattn_tile_get_occupancy(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 10) & ((1 << 4) - 1); +} + +static constexpr __device__ int ggml_cuda_fattn_tile_get_occupancy(const int DKQ, const int DV, const int ncols) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols) >> 10) & ((1 << 4) - 1); +} + +static __host__ int ggml_cuda_fattn_tile_get_nbatch_fa(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 14) & ((1 << 9) - 1); +} + +static constexpr __device__ int ggml_cuda_fattn_tile_get_nbatch_fa(const int DKQ, const int DV, const int ncols) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols) >> 14) & ((1 << 9) - 1); +} + +static __host__ int ggml_cuda_fattn_tile_get_nbatch_K(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 23) & ((1 << 9) - 1); +} + +static constexpr __device__ int ggml_cuda_fattn_tile_get_nbatch_K(const int DKQ, const int DV, const int ncols) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols) >> 23) & ((1 << 9) - 1); +} + +// TODO: deduplicate with mma-f16 +template +static __device__ __forceinline__ void flash_attn_tile_load_tile( + const half2 * const __restrict__ KV, half2 * const __restrict__ tile_KV, const int stride_KV, const int i_sup) { + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + auto load = [&] __device__ (const int n) { + const int stride_j = warp_size >> n; + + if (stride_j == 0) { + return; + } + + const int j0_start = stride_j == warp_size ? 0 : ((J/2)/cpy_ne) - ((J/2)/cpy_ne) % (2*stride_j); + const int j0_stop = ((J/2)/cpy_ne) - ((J/2)/cpy_ne) % (1*stride_j); + const int stride_i = warp_size / stride_j; + + if (j0_start == j0_stop) { + return; + } + +#pragma unroll + for (int i0 = 0; i0 < I; i0 += nwarps*stride_i) { + const int i = i0 + threadIdx.y*stride_i + (stride_j == warp_size ? 0 : threadIdx.x / stride_j); + + if (i0 + nwarps*stride_i <= I || i < I) { +#pragma unroll + for (int j0 = j0_start; j0 < j0_stop; j0 += stride_j) { + const int j = j0*cpy_ne + (stride_j == warp_size ? threadIdx.x : threadIdx.x % stride_j)*cpy_ne; + + const half2 zero[cpy_ne] = {{0.0f, 0.0f}}; + ggml_cuda_memcpy_1( + tile_KV + i*(J/2 + J_padding) + j, + !oob_check || i < i_sup ? KV + i*stride_KV + j : zero); + } + } + } + }; + // 1: max 64*16=512 bytes, 512 half + // 2: max 32*16=512 bytes, 256 half + // 3: max 16*16=256 bytes, 128 half + // 4: max 8*16=128 bytes, 64 half + // 5: max 4*16= 64 bytes, 32 half + // 6: max 2*16= 32 bytes, 16 half + // 7: max 1*16= 16 bytes, 8 half + static_assert(J % 8 == 0, "bad J"); + static_assert((J/2) % cpy_ne == 0, "bad J"); + ggml_cuda_unroll<7>{}(load); +} + +template +static __device__ __forceinline__ void flash_attn_tile_load_tile( + const half2 * const __restrict__ KV, float * const __restrict__ tile_KV, const int stride_KV, const int i_sup) { + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + auto load = [&] __device__ (const int n) { + const int stride_j = warp_size >> n; + + if (stride_j == 0) { + return; + } + + const int j0_start = stride_j == warp_size ? 0 : (J/cpy_ne) - (J/cpy_ne) % (2*stride_j); + const int j0_stop = (J/cpy_ne) - (J/cpy_ne) % (1*stride_j); + const int stride_i = warp_size / stride_j; + + if (j0_start == j0_stop) { + return; + } + +#pragma unroll + for (int i0 = 0; i0 < I; i0 += nwarps*stride_i) { + const int i = i0 + threadIdx.y*stride_i + (stride_j == warp_size ? 0 : threadIdx.x / stride_j); + + if (i0 + nwarps*stride_i <= I || i < I) { +#pragma unroll + for (int j0 = j0_start; j0 < j0_stop; j0 += stride_j) { + const int j = j0*(cpy_ne/2) + (stride_j == warp_size ? threadIdx.x : threadIdx.x % stride_j)*(cpy_ne/2); + + const half2 zero[cpy_ne/2] = {{0.0f, 0.0f}}; + half2 tmp_h2[cpy_ne/2]; + ggml_cuda_memcpy_1( + tmp_h2, !oob_check || i < i_sup ? KV + i*stride_KV + j : zero); + + float2 tmp_f2[cpy_ne/2]; +#pragma unroll + for (int l = 0; l < cpy_ne/2; ++l) { + tmp_f2[l] = __half22float2(tmp_h2[l]); + } + ggml_cuda_memcpy_1(tile_KV + i*(J + J_padding) + 2*j, tmp_f2); + } + } + } + }; + // 1: max 32*16=512 bytes, 128 float + // 2: max 16*16=256 bytes, 64 float + // 3: max 8*16=128 bytes, 32 float + // 4: max 4*16= 64 bytes, 16 float + // 5: max 2*16= 32 bytes, 8 float + static_assert(J % 8 == 0, "bad J"); + static_assert(J % cpy_ne == 0, "bad J"); + ggml_cuda_unroll<5>{}(load); +} + +// Function that performs a single iteration in for the KQ matrix multiplication: +template +static __device__ __forceinline__ void flash_attn_tile_iter_KQ( + T_vec_dot * const Q_tmp, + const half2 * const __restrict__ K_h2, + T_vec_dot * const KV_tmp, + const int stride_K2, + const int k_VKQ_0, + const int k_VKQ_sup, + const int k_KQ_0, + float * KQ_acc) { + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int ncols = ncols1*ncols2; + constexpr int cpw = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp + constexpr int np = nwarps > ncols ? nwarps/ncols : 1; // number of parallel warps per Q column + + flash_attn_tile_load_tile + (K_h2 + int64_t(k_VKQ_0)*stride_K2 + k_KQ_0/2, KV_tmp, stride_K2, k_VKQ_sup); + __syncthreads(); + +#ifdef FAST_FP16_AVAILABLE + static_assert((nbatch_K/2) % cpy_ne == 0, "bad nbatch_K"); +#pragma unroll + for (int k_KQ_1 = 0; k_KQ_1 < nbatch_K/2; k_KQ_1 += cpy_ne) { + half2 K_k[nbatch_fa/(np*warp_size)][cpy_ne]; + half2 Q_k[cpw][cpy_ne]; +#else + static_assert(nbatch_K % cpy_ne == 0, "bad nbatch_K"); +#pragma unroll + for (int k_KQ_1 = 0; k_KQ_1 < nbatch_K; k_KQ_1 += cpy_ne) { + float K_k[nbatch_fa/(np*warp_size)][cpy_ne]; + float Q_k[cpw][cpy_ne]; +#endif // FAST_FP16_AVAILABLE + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) { + const int i_KQ = i_KQ_0 + (threadIdx.y % np)*warp_size + threadIdx.x; + +#ifdef FAST_FP16_AVAILABLE + ggml_cuda_memcpy_1(&K_k[i_KQ_0/(np*warp_size)], &KV_tmp[i_KQ*(nbatch_K/2 + cpy_ne) + k_KQ_1]); +#else + ggml_cuda_memcpy_1(&K_k[i_KQ_0/(np*warp_size)], &KV_tmp[i_KQ*(nbatch_K + cpy_ne) + k_KQ_1]); +#endif // FAST_FP16_AVAILABLE + } +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (threadIdx.y / np)*cpw; + +#ifdef FAST_FP16_AVAILABLE + ggml_cuda_memcpy_1(&Q_k[jc0], &Q_tmp[jc*(DKQ/2) + k_KQ_0/2 + k_KQ_1]); +#else + ggml_cuda_memcpy_1(&Q_k[jc0], &Q_tmp[jc* DKQ + k_KQ_0 + k_KQ_1]); +#endif // FAST_FP16_AVAILABLE + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) { +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { +#pragma unroll + for (int k = 0; k < cpy_ne; ++k) { + ggml_cuda_mad(KQ_acc[i_KQ_0/(np*warp_size)*cpw + jc0], K_k[i_KQ_0/(np*warp_size)][k], Q_k[jc0][k]); + } + } + } + } + + if (k_KQ_0 + nbatch_K < DKQ) { + __syncthreads(); // Sync not needed on last iteration. + } +} + +// Function that performs a single iteration of the main loop over up to nbatch_fa tokens. +template +static __device__ __forceinline__ void flash_attn_tile_iter( + T_vec_dot * const Q_tmp, + const half2 * const __restrict__ K_h2, + const half2 * const __restrict__ V_h2, + const half * const __restrict__ mask, + const float logit_softcap, + const float slope, + T_KQ * const KQ, + T_vec_dot * const KV_tmp, + const int stride_K2, + const int stride_V2, + const int stride_mask, + float * const KQ_max, + float * const KQ_sum, + T_acc * const VKQ, + const int k_VKQ_0, + const int k_VKQ_max) { + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int ncols = ncols1*ncols2; + constexpr int cpw = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp + constexpr int np = nwarps > ncols ? nwarps/ncols : 1; // number of parallel warps per Q column + + constexpr int DVp = (DV + 2*warp_size - 1) & ~(2*warp_size - 1); // DV padded to multiple of 2*warp_size. + + // KQ_cs == KQ chunk size, number of KQ values in j direction to store as one contiguous chunk in memory. + // KQ is originally 2D but uses a Z-shaped 3D memory pattern like KQ[ncols/KQ_cs][DVp][KQ_cs]. +#ifdef FAST_FP16_AVAILABLE + constexpr int KQ_cs = cpw < 2*cpy_ne ? cpw : 2*cpy_ne; +#else + constexpr int KQ_cs = cpw < 1*cpy_ne ? cpw : 1*cpy_ne; +#endif // FAST_FP16_AVAILABLE + static_assert(cpw % KQ_cs == 0, "bad KQ_cs"); + const int k_VKQ_sup = k_VKQ_max - k_VKQ_0; // k supremum, only smaller k values have valid KV data + + float KQ_max_new[cpw]; +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + KQ_max_new[jc0] = KQ_max[jc0]; + } + + float KQ_acc[nbatch_fa/(np*warp_size) * cpw] = {0.0f}; // Accumulators for KQ matrix multiplication. + + // KQ = K @ Q matrix multiplication: + constexpr int nbatch_K_last = DKQ % nbatch_K; +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < DKQ - nbatch_K_last; k_KQ_0 += nbatch_K) { + flash_attn_tile_iter_KQ( + Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, KQ_acc); + } + if (nbatch_K_last > 0) { + constexpr int k_KQ_0 = DKQ - nbatch_K_last; + flash_attn_tile_iter_KQ( + Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, KQ_acc); + } + + // Apply logit softcap + mask, update KQ_max: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int j = (jc0 + (threadIdx.y / np)*cpw)/ncols2; + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) { + const int i_KQ = i_KQ_0 + (threadIdx.y % np)*warp_size + threadIdx.x; + + if (use_logit_softcap) { + KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0] = logit_softcap * tanhf(KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0]); + } + + KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0] += (ncols2 > 1 || mask) && (!oob_check || i_KQ < k_VKQ_sup) ? + slope*__half2float(mask[j*stride_mask + k_VKQ_0 + i_KQ]) : 0.0f; + + KQ_max_new[jc0] = fmaxf(KQ_max_new[jc0], KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0]); + } + + KQ_max_new[jc0] = warp_reduce_max(KQ_max_new[jc0]); + } + + if constexpr (np == 1) { + __syncthreads(); + } else { + static_assert(cpw == 1, "bad cpw"); + __shared__ float KQ_max_new_shared[nwarps]; + if (threadIdx.x == 0) { + KQ_max_new_shared[threadIdx.y] = KQ_max_new[0]; + } + __syncthreads(); + KQ_max_new[0] = KQ_max_new_shared[(threadIdx.y & ~(np-1)) + threadIdx.x % np]; + KQ_max_new[0] = warp_reduce_max(KQ_max_new[0]); + } + + // Calculate KQ softmax, write to shared KQ buffer, re-scale VKQ accumulators: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; jc0 += KQ_cs) { +#ifdef FAST_FP16_AVAILABLE + half tmp[nbatch_fa/(np*warp_size)][KQ_cs]; +#else + float tmp[nbatch_fa/(np*warp_size)][KQ_cs]; +#endif // FAST_FP16_AVAILABLE + +#pragma unroll + for (int jc1 = 0; jc1 < KQ_cs; ++jc1) { + const int jc = jc0 + jc1; + + const float KQ_max_scale = expf(KQ_max[jc] - KQ_max_new[jc]); + KQ_max[jc] = KQ_max_new[jc]; + + float KQ_sum_add = 0.0f; +#pragma unroll + for (int i0 = 0; i0 < nbatch_fa; i0 += np*warp_size) { + const float val = expf(KQ_acc[(i0/(np*warp_size))*cpw + jc] - KQ_max[jc]); + if (!oob_check || i0 + (threadIdx.y % np)*warp_size + threadIdx.x < k_VKQ_sup) { + KQ_sum_add += val; + } + tmp[i0/(np*warp_size)][jc1] = val; + } + KQ_sum[jc] = KQ_sum[jc]*KQ_max_scale + KQ_sum_add; + +#ifdef FAST_FP16_AVAILABLE + const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size] *= KQ_max_scale_h2; + } +#else +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size].x *= KQ_max_scale; + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size].y *= KQ_max_scale; + } +#endif // FAST_FP16_AVAILABLE + } + +#pragma unroll + for (int i0 = 0; i0 < nbatch_fa; i0 += np*warp_size) { + const int i = i0 + (threadIdx.y % np)*warp_size + threadIdx.x; + + ggml_cuda_memcpy_1( + KQ + (jc0/KQ_cs + (threadIdx.y / np)*(cpw/KQ_cs))*(nbatch_fa*KQ_cs) + i*KQ_cs, + tmp[i0/(np*warp_size)]); + } + } + + // VKQ = V @ KQ matrix multiplication: + static_assert(DV <= DKQ, "bad DV"); + static_assert(DV % nbatch_K == 0 || (nbatch_K % 3 == 0 && DV % (nbatch_K*2/3) == 0), "bad nbatch_K"); + constexpr int nbatch_V = (DV % nbatch_K == 0 ? nbatch_K : nbatch_K*2/3) * nbatch_fa / DV; // Number of V columns that fit in SRAM for K. + static_assert(nbatch_fa % nbatch_V == 0, "bad nbatch_V"); + static_assert(nbatch_V % np == 0, "bad nbatch_V"); +#pragma unroll + for (int k0 = 0; k0 < nbatch_fa; k0 += nbatch_V) { + flash_attn_tile_load_tile + (V_h2 + int64_t(k_VKQ_0 + k0)*stride_V2, KV_tmp, stride_V2, k_VKQ_sup - k0); + __syncthreads(); + +#ifdef FAST_FP16_AVAILABLE +#pragma unroll + for (int k1 = 0; k1 < nbatch_V; k1 += np) { + half2 V_k[(DVp/2)/warp_size]; + half2 KQ_k[cpw]; + + constexpr int cpy_ne_D = cpy_ne/2 < (DVp/2)/warp_size ? cpy_ne/2 : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + ggml_cuda_memcpy_1(&V_k[i0/warp_size], &KV_tmp[(k1 + threadIdx.y % np)*(DV/2) + i0 + threadIdx.x*cpy_ne_D]); + } +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; jc_VKQ_0 += KQ_cs) { + const int jc_KQ = jc_VKQ_0/KQ_cs + (threadIdx.y / np)*(cpw/KQ_cs); + + half tmp[KQ_cs]; + ggml_cuda_memcpy_1( + &tmp, KQ + jc_KQ*(nbatch_fa*KQ_cs) + (k0 + k1 + threadIdx.y % np)*KQ_cs); +#pragma unroll + for (int jc_VKQ_1 = 0; jc_VKQ_1 < KQ_cs; ++jc_VKQ_1) { + KQ_k[jc_VKQ_0+jc_VKQ_1] = __half2half2(tmp[jc_VKQ_1]); + } + } + +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; ++jc_VKQ_0) { + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size] += V_k[i0/warp_size]*KQ_k[jc_VKQ_0]; + } + } + } +#else +#pragma unroll + for (int k1 = 0; k1 < nbatch_V; k1 += np) { + float2 V_k[(DVp/2)/warp_size]; + float KQ_k[cpw]; + + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + ggml_cuda_memcpy_1(&V_k[i0/(2*warp_size)], &KV_tmp[(k1 + threadIdx.y % np)*DV + i0 + threadIdx.x*cpy_ne_D]); + } +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; jc_VKQ_0 += KQ_cs) { + const int jc_KQ = jc_VKQ_0/KQ_cs + (threadIdx.y / np)*(cpw/KQ_cs); + + ggml_cuda_memcpy_1( + &KQ_k[jc_VKQ_0], KQ + jc_KQ*(nbatch_fa*KQ_cs) + (k0 + k1 + threadIdx.y % np)*KQ_cs); + } + +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; ++jc_VKQ_0) { + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size].x += V_k[i0/warp_size].x*KQ_k[jc_VKQ_0]; + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size].y += V_k[i0/warp_size].y*KQ_k[jc_VKQ_0]; + } + } + } +#endif // FAST_FP16_AVAILABLE + + __syncthreads(); + } +} + +template // D == head size +__launch_bounds__(ggml_cuda_fattn_tile_get_nthreads(DKQ, DV, ncols1*ncols2), ggml_cuda_fattn_tile_get_occupancy(DKQ, DV, ncols1*ncols2)) +static __global__ void flash_attn_tile( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + const char * __restrict__ sinks, + const int * __restrict__ KV_max, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const float logit_softcap, + const int32_t ne00, const int32_t ne01, const int32_t ne02, const int32_t ne03, + const int32_t nb01, const int32_t nb02, const int32_t nb03, + const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13, + const int32_t nb11, const int32_t nb12, const int64_t nb13, + const int32_t nb21, const int32_t nb22, const int64_t nb23, + const int32_t ne31, const int32_t ne32, const int32_t ne33, + const int32_t nb31, const int32_t nb32, const int64_t nb33) { +#ifdef FLASH_ATTN_AVAILABLE + + // Skip unused kernel variants for faster compilation: + + if ( +#ifdef GGML_USE_WMMA_FATTN + (ncols2 != 1 && DV != 40 && DV != 512) || +#endif // GGML_USE_WMMA_FATTN + (use_logit_softcap && !(DV == 128 || DV == 256)) + ) { + GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, + max_bias, m0, m1, n_head_log2, logit_softcap, + ne00, ne01, ne02, ne03, + nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb11, nb12, nb13, + nb21, nb22, nb23, + ne31, ne32, ne33, + nb31, nb32, nb33); + NO_DEVICE_CODE; + return; + } + + static_assert(ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols1*ncols2) != 0, "kernel config not defined"); + + constexpr int ncols = ncols1*ncols2; + constexpr int warp_size = 32; + constexpr int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, ncols1*ncols2) / warp_size; + constexpr int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, ncols1*ncols2); + constexpr int nbatch_K = ggml_cuda_fattn_tile_get_nbatch_K (DKQ, DV, ncols1*ncols2); + + // In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int col_Q_0 = blockIdx.x * ncols1; // Index of the first Q column for this CUDA block to work on. + + const int sequence = blockIdx.z / (ne02/ncols2); + const int head0 = blockIdx.z*ncols2 - sequence*ne02; // == blockIdx.z % (ne02/ncols2) + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float * Q_f = (const float *) (Q + nb03*sequence + nb02* head0 + nb01*col_Q_0); + const half2 * K_h2 = (const half2 *) (K + nb13*sequence + nb12*(head0 / gqa_ratio)); + const half2 * V_h2 = (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio)); // K and V have same shape + + const half * maskh = mask ? (const half *) (mask + nb33*(sequence % ne33) + nb31*col_Q_0) : nullptr; + + const int stride_K2 = nb11 / sizeof(half2); + const int stride_V2 = nb21 / sizeof(half2); + const int stride_mask = nb31 / sizeof(half); + + const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head0, n_head_log2, m0, m1) : 1.0f; + + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int cpw = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp. + constexpr int np = nwarps > ncols ? nwarps/ncols : 1; // Number of parallel warps per Q column. + static_assert(cpw == 1 || np == 1, "bad cpw / np"); + static_assert(nbatch_fa % (np*warp_size) == 0, "nbatch_fa % (np*warp_size) != 0"); + + constexpr int DKQp = (DKQ + 2*warp_size - 1) & ~(2*warp_size - 1); // DKQ padded to multiple of 2*warp_size. + constexpr int DVp = (DV + 2*warp_size - 1) & ~(2*warp_size - 1); // DV padded to multiple of 2*warp_size. + + // Q_tmp == SRAM buffer to hold Q data for the entire lifetime of the kernel. + // KV_tmp == SRAM buffer to hold fragments of K/V data while iterating over ne11. + // KV_tmp is padded to avoid memory conflicts for K (cpy_ne) and OOB accesses for V (DVp-DV). + // KQ == SRAM buffer to hold KQ fragments between KQ and VKQ matrix multiplications. + // VKQ == Accumulators in registers for the final VKQ result. +#ifdef FAST_FP16_AVAILABLE + __shared__ half2 Q_tmp[ncols * DKQ/2]; + __shared__ half2 KV_tmp[nbatch_fa * (nbatch_K/2 + cpy_ne) + DVp-DV]; + __shared__ half KQ[ncols * nbatch_fa]; + half2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}}; +#else + __shared__ float Q_tmp[ncols * DKQ]; + __shared__ float KV_tmp[nbatch_fa * (nbatch_K + cpy_ne) + DVp-DV]; + __shared__ float KQ[ncols * nbatch_fa]; + float2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}}; +#endif // FAST_FP16_AVAILABLE + + float KQ_max[cpw]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + KQ_max[j0/nwarps] = -FLT_MAX/2.0f; + } + float KQ_sum[cpw] = {0.0f}; + + // Load Q data, convert to FP16 if fast: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (threadIdx.y / np)*cpw; + + const int j = jc / ncols2; + const int c = jc % ncols2; + + constexpr int cpy_ne_D = cpy_ne < DKQp/warp_size ? cpy_ne : DKQp/warp_size; + +#pragma unroll + for (int i0 = 0; i0 < DKQp; i0 += np*warp_size*cpy_ne_D) { + if (i0 + np*warp_size*cpy_ne_D <= DKQ || i0 + (threadIdx.y % np)*(warp_size*cpy_ne_D) + threadIdx.x*cpy_ne_D < DKQ) { + float tmp_f[cpy_ne_D] = {0.0f}; + if (ncols1 == 1 || col_Q_0 + j < ne01) { + ggml_cuda_memcpy_1 + (tmp_f, &Q_f[c*(nb02/sizeof(float)) + j*(nb01/sizeof(float)) + + i0 + (threadIdx.y % np)*(warp_size*cpy_ne_D) + threadIdx.x*cpy_ne_D]); + } + +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + tmp_f[i1] *= scale; + } + +#ifdef FAST_FP16_AVAILABLE + half2 tmp_h2[cpy_ne_D/2]; +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; i1 += 2) { + tmp_h2[i1/2] = make_half2(tmp_f[i1 + 0], tmp_f[i1 + 1]); + } + ggml_cuda_memcpy_1( + &Q_tmp[jc*(DKQ/2) + i0/2 + (threadIdx.y % np)*(warp_size*cpy_ne_D/2) + threadIdx.x*(cpy_ne_D/2)], + tmp_h2); +#else + ggml_cuda_memcpy_1( + &Q_tmp[jc* DKQ + i0 + (threadIdx.y % np)*(warp_size*cpy_ne_D) + threadIdx.x* cpy_ne_D], + tmp_f); +#endif // FAST_FP16_AVAILABLE + } + } + } + + __syncthreads(); + + // Main loop over KV cache: + const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11; + if (ncols2 == 1) { + // Branch with out-of-bounds checks. + int k_VKQ_0 = blockIdx.y*nbatch_fa; + while (k_VKQ_0 < k_VKQ_max - nbatch_fa) { + constexpr bool oob_check = false; + flash_attn_tile_iter + (Q_tmp, K_h2, V_h2, maskh, logit_softcap, slope, KQ, KV_tmp, + stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max); + k_VKQ_0 += gridDim.y*nbatch_fa; + } + if (k_VKQ_0 < k_VKQ_max) { + constexpr bool oob_check = true; + flash_attn_tile_iter + (Q_tmp, K_h2, V_h2, maskh, logit_softcap, slope, KQ, KV_tmp, + stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max); + } + } else { + // Branch without out-of-bounds checks. + for (int k_VKQ_0 = blockIdx.y*nbatch_fa; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*nbatch_fa) { + constexpr bool oob_check = false; + flash_attn_tile_iter + (Q_tmp, K_h2, V_h2, maskh, logit_softcap, slope, KQ, KV_tmp, + stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max); + } + } + +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + KQ_sum[jc0] = warp_reduce_sum(KQ_sum[jc0]); + } + + if constexpr (np > 1) { + static_assert(cpw == 1, "bad cpw"); + static_assert(nbatch_fa*nbatch_K >= nwarps*DVp, "KV_tmp too small"); + +#ifdef FAST_FP16_AVAILABLE + half2 * VKQ_combine = (half2 *) KV_tmp; +#else + float * VKQ_combine = (float *) KV_tmp; +#endif // FAST_FP16_AVAILABLE + float * KQ_sum_combine = (float *) Q_tmp; + + if (threadIdx.y % np != 0) { +#ifdef FAST_FP16_AVAILABLE + constexpr int cpy_ne_D = cpy_ne < (DVp/2)/warp_size ? cpy_ne : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + ggml_cuda_memcpy_1(&VKQ_combine[threadIdx.y*(DVp/2) + i0 + threadIdx.x*cpy_ne_D], &VKQ[i0/warp_size]); + } +#else + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + ggml_cuda_memcpy_1( + &VKQ_combine[threadIdx.y*DVp + i0 + threadIdx.x*cpy_ne_D], ((const float *) VKQ) + i0/warp_size); + } +#endif // FAST_FP16_AVAILABLE + + if (threadIdx.x == 0) { + KQ_sum_combine[threadIdx.y] = KQ_sum[0]; + } + + return; + } + + __syncthreads(); + +#pragma unroll + for (int ip = 1; ip < np; ++ip) { +#ifdef FAST_FP16_AVAILABLE + constexpr int cpy_ne_D = cpy_ne < (DVp/2)/warp_size ? cpy_ne : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + half2 tmp[cpy_ne_D]; + ggml_cuda_memcpy_1(tmp, &VKQ_combine[(threadIdx.y + ip)*(DVp/2) + i0 + threadIdx.x*cpy_ne_D]); +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + VKQ[i0/warp_size + i1] += tmp[i1]; + } + } +#else + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + float tmp[cpy_ne_D]; + ggml_cuda_memcpy_1(tmp, &VKQ_combine[(threadIdx.y + ip)*DVp + i0 + threadIdx.x*cpy_ne_D]); +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + ((float *)VKQ)[i0/warp_size + i1] += tmp[i1]; + } + } +#endif // FAST_FP16_AVAILABLE + + KQ_sum[0] += KQ_sum_combine[threadIdx.y + ip]; + } + } + + // Attention sink: adjust KQ max and sum only for the first of all parallel blocks: + if (sinks && blockIdx.y == 0) { +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (threadIdx.y/np)*cpw; + const float sink = ((const float *) sinks)[head0 + jc % ncols2]; + + float KQ_max_new_j = fmaxf(KQ_max[jc0], sink); + const float KQ_max_scale = expf(KQ_max[jc0] - KQ_max_new_j); + KQ_max[jc0] = KQ_max_new_j; + + const float val = expf(sink - KQ_max[jc0]); + KQ_sum[jc0] = KQ_sum[jc0]*KQ_max_scale + val; + +#ifdef FAST_FP16_AVAILABLE + const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size] *= KQ_max_scale_h2; + } +#else +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size].x *= KQ_max_scale; + VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size].y *= KQ_max_scale; + } +#endif // FAST_FP16_AVAILABLE + } + } + + if (gridDim.y == 1) { +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { +#ifdef FAST_FP16_AVAILABLE + const half2 KQ_sum_jc_inv = make_half2(1.0f/KQ_sum[jc0], 1.0f/KQ_sum[jc0]); +#pragma unroll + for (int i = 0; i < (DVp/2)/warp_size; ++i) { + VKQ[jc0*((DVp/2)/warp_size) + i] *= KQ_sum_jc_inv; + } +#else + const float KQ_sum_jc_inv = 1.0f/KQ_sum[jc0]; +#pragma unroll + for (int i = 0; i < (DVp/2)/warp_size; ++i) { + VKQ[jc0*((DVp/2)/warp_size) + i].x *= KQ_sum_jc_inv; + VKQ[jc0*((DVp/2)/warp_size) + i].y *= KQ_sum_jc_inv; + } +#endif // FAST_FP16_AVAILABLE + } + } + + // Write back results: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (threadIdx.y/np)*cpw; + + const int j = jc / ncols2; + const int c = jc % ncols2; + + if (ncols1 > 1 && col_Q_0 + j >= ne01) { + return; + } + + const int j_dst_unrolled = ((sequence*ne01 + col_Q_0 + j)*ne02 + head0 + c)*gridDim.y + blockIdx.y; + +#ifdef FAST_FP16_AVAILABLE + constexpr int cpy_ne_D = cpy_ne/2 < (DVp/2)/warp_size ? cpy_ne/2 : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + float2 tmp[cpy_ne_D]; +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + tmp[i1] = __half22float2(VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size + i1]); + } + if (i0 + warp_size*cpy_ne_D <= DV/2 || i0 + threadIdx.x*cpy_ne_D < DV/2) { + ggml_cuda_memcpy_1(&dst[j_dst_unrolled*DV + 2*i0 + threadIdx.x*(2*cpy_ne_D)], tmp); + } + } +#else + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + if (i0 + warp_size*cpy_ne_D <= DV || i0 + threadIdx.x*cpy_ne_D < DV) { + ggml_cuda_memcpy_1( + &dst[j_dst_unrolled*DV + i0 + threadIdx.x*cpy_ne_D], + &VKQ[jc0*((DVp/2)/warp_size) + i0/(2*warp_size)]); + } + } +#endif // FAST_FP16_AVAILABLE + + if (gridDim.y != 1 && threadIdx.x == 0) { + dst_meta[j_dst_unrolled] = make_float2(KQ_max[jc0], KQ_sum[jc0]); + } + } +#else + GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, + max_bias, m0, m1, n_head_log2, logit_softcap, + ne00, ne01, ne02, ne03, + nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb11, nb12, nb13, + nb21, nb22, nb23, + ne31, ne32, ne33, + nb31, nb32, nb33); + NO_DEVICE_CODE; +#endif // FLASH_ATTN_AVAILABLE +} + +template +static void launch_fattn_tile_switch_ncols1(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + + const int id = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[id].cc; + const int warp_size = 32; + + constexpr size_t nbytes_shared = 0; + +#ifdef GGML_USE_HIP + if constexpr (DV <= 128) { + if (Q->ne[1] > 32/ncols2) { + constexpr int cols_per_block = 64; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + } +#endif // GGML_USE_HIP + +#ifndef GGML_USE_HIP + if constexpr (DV <= 256) +#endif // GGML_USE_HIP + { + if (Q->ne[1] > 16/ncols2) { + constexpr int cols_per_block = 32; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + } + + if (Q->ne[1] > 8/ncols2) { + constexpr int cols_per_block = 16; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + + if constexpr (ncols2 <= 8) { + if (Q->ne[1] > 4/ncols2) { + constexpr int cols_per_block = 8; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + } + + if constexpr (ncols2 <= 4) { + if (Q->ne[1] > 2/ncols2) { + constexpr int cols_per_block = 4; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + } + + if constexpr (ncols2 <= 2) { + constexpr int cols_per_block = 2; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + + GGML_ABORT("fatal error"); +} + +template +static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * mask = dst->src[3]; + + float max_bias = 0.0f; + memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float)); + + GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); + const int gqa_ratio = Q->ne[2] / K->ne[2]; + + const bool nvidia = GGML_CUDA_CC_IS_NVIDIA(ggml_cuda_info().devices[ggml_cuda_get_device()].cc); + const int gqa_limit = nvidia && gqa_ratio <= 4 ? 16 : INT_MAX; + const bool use_gqa_opt = mask && max_bias == 0.0f && Q->ne[1] <= gqa_limit && K->ne[1] % FATTN_KQ_STRIDE == 0; + + if constexpr (DV == 512) { + if (use_gqa_opt && gqa_ratio % 16 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + } + + if constexpr (DV <= 256) { + if (use_gqa_opt && gqa_ratio % 8 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + + if (use_gqa_opt && gqa_ratio % 4 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + + if (use_gqa_opt && gqa_ratio % 2 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + GGML_ABORT("fatal error"); +} + +template +void ggml_cuda_flash_attn_ext_tile_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + + float logit_softcap; + memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); + + if (logit_softcap == 0.0f) { + constexpr bool use_logit_softcap = false; + launch_fattn_tile_switch_ncols2(ctx, dst); + } else { + constexpr bool use_logit_softcap = true; + launch_fattn_tile_switch_ncols2(ctx, dst); + } +} void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + +#define DECL_FATTN_TILE_CASE(DKQ, DV) \ + template void ggml_cuda_flash_attn_ext_tile_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_TILE_CASE( 40, 40); +extern DECL_FATTN_TILE_CASE( 64, 64); +extern DECL_FATTN_TILE_CASE( 80, 80); +extern DECL_FATTN_TILE_CASE( 96, 96); +extern DECL_FATTN_TILE_CASE(112, 112); +extern DECL_FATTN_TILE_CASE(128, 128); +extern DECL_FATTN_TILE_CASE(256, 256); +extern DECL_FATTN_TILE_CASE(576, 512); diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh index 1848d08836..7235f1b77a 100644 --- a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh @@ -1,3 +1,5 @@ +#pragma once + #include "common.cuh" #if (!defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA) || defined(GGML_USE_MUSA) diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 0c8e7b3e41..fe970adaec 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -198,6 +198,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const return BEST_FATTN_KERNEL_NONE; #endif// FLASH_ATTN_AVAILABLE + const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; const ggml_tensor * K = dst->src[1]; const ggml_tensor * V = dst->src[2]; @@ -206,37 +207,32 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const const int gqa_ratio = Q->ne[2] / K->ne[2]; GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); + float max_bias = 0.0f; + memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float)); + + // The effective batch size for the kernel can be increased by gqa_ratio. + // The kernel versions without this optimization are also used for ALiBi, if there is no mask, or if the KV cache is not padded, + const bool gqa_opt_applies = gqa_ratio % 2 == 0 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0; + const int cc = ggml_cuda_info().devices[device].cc; - // TODO: temporary until support is extended - // https://github.com/ggml-org/llama.cpp/pull/16148#issuecomment-3343525206 - if (K->ne[1] % FATTN_KQ_STRIDE != 0) { - return BEST_FATTN_KERNEL_NONE; - } - switch (K->ne[0]) { + case 40: case 64: - case 128: - case 256: - if (V->ne[0] != K->ne[0]) { - return BEST_FATTN_KERNEL_NONE; - } - break; case 80: case 96: + case 128: case 112: + case 256: if (V->ne[0] != K->ne[0]) { return BEST_FATTN_KERNEL_NONE; } - if (!ggml_cuda_should_use_wmma_fattn(cc) && !turing_mma_available(cc)) { - return BEST_FATTN_KERNEL_NONE; - } break; case 576: if (V->ne[0] != 512) { return BEST_FATTN_KERNEL_NONE; } - if (!turing_mma_available(cc) || gqa_ratio % 16 != 0) { + if (!gqa_opt_applies || gqa_ratio % 16 != 0) { return BEST_FATTN_KERNEL_NONE; } break; @@ -270,47 +266,57 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const return BEST_FATTN_KERNEL_NONE; } - const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0; - - // If Turing tensor cores available, use them except for some cases with batch size 1: - if (turing_mma_available(cc)) { - best_fattn_kernel best = BEST_FATTN_KERNEL_MMA_F16; + // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes: + const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0; + // If Turing tensor cores available, use them: + if (turing_mma_available(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40) { if (can_use_vector_kernel) { if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16) { if (cc >= GGML_CUDA_CC_ADA_LOVELACE && Q->ne[1] == 1 && Q->ne[3] == 1 && !(gqa_ratio > 4 && K->ne[1] >= 8192)) { - best = BEST_FATTN_KERNEL_VEC; + return BEST_FATTN_KERNEL_VEC; } } else { if (cc >= GGML_CUDA_CC_ADA_LOVELACE) { if (Q->ne[1] <= 2) { - best = BEST_FATTN_KERNEL_VEC; + return BEST_FATTN_KERNEL_VEC; } } else { if (Q->ne[1] == 1) { - best = BEST_FATTN_KERNEL_VEC; + return BEST_FATTN_KERNEL_VEC; } } } - if ((gqa_ratio % 2 != 0 || !mask) && Q->ne[1] == 1) { - best = BEST_FATTN_KERNEL_VEC; // GQA-specific optimizations in the mma kernel do not apply. + if (!gqa_opt_applies && Q->ne[1] == 1) { + return BEST_FATTN_KERNEL_VEC; } } - return best; + return BEST_FATTN_KERNEL_MMA_F16; } - // Use kernels specialized for small batch sizes if possible: - if (Q->ne[1] <= 8 && can_use_vector_kernel) { - return BEST_FATTN_KERNEL_VEC; - } - - // For large batch sizes, use the WMMA kernel if possible: - if (ggml_cuda_should_use_wmma_fattn(cc)) { + // Use the WMMA kernel if possible: + if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 576) { + if (can_use_vector_kernel && Q->ne[1] <= 2) { + return BEST_FATTN_KERNEL_VEC; + } return BEST_FATTN_KERNEL_WMMA_F16; } - // If there is no suitable kernel for tensor cores or small batch sizes, use the generic kernel for large batch sizes: + // If there are no tensor cores available, use the generic tile kernel: + if (can_use_vector_kernel) { + if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16) { + if (Q->ne[1] == 1) { + if (!gqa_opt_applies) { + return BEST_FATTN_KERNEL_VEC; + } + } + } else { + if (Q->ne[1] <= 2) { + return BEST_FATTN_KERNEL_VEC; + } + } + } return BEST_FATTN_KERNEL_TILE; } diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq112-dv112.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq112-dv112.cu new file mode 100644 index 0000000000..a8b15ad72a --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq112-dv112.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(112, 112); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq128-dv128.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq128-dv128.cu new file mode 100644 index 0000000000..1da1810550 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq128-dv128.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(128, 128); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq256-dv256.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq256-dv256.cu new file mode 100644 index 0000000000..bc65c723ec --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq256-dv256.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(256, 256); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq40-dv40.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq40-dv40.cu new file mode 100644 index 0000000000..10b330fa6c --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq40-dv40.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(40, 40); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq576-dv512.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq576-dv512.cu new file mode 100644 index 0000000000..254b7d2e1d --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq576-dv512.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(576, 512); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq64-dv64.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq64-dv64.cu new file mode 100644 index 0000000000..5caffac046 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq64-dv64.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(64, 64); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq80-dv80.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq80-dv80.cu new file mode 100644 index 0000000000..90abb3b186 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq80-dv80.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(80, 80); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq96-dv96.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq96-dv96.cu new file mode 100644 index 0000000000..7292c0aab8 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq96-dv96.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(96, 96); diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py index d410080fab..81a986f38c 100755 --- a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py +++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py @@ -3,8 +3,17 @@ from glob import glob import os +HEAD_SIZES_KQ = [40, 64, 80, 96, 112, 128, 256, 576] + TYPES_KV = ["GGML_TYPE_F16", "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0"] +SOURCE_FATTN_TILE = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE({head_size_kq}, {head_size_v}); +""" + SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. #include "../fattn-vec.cuh" @@ -51,6 +60,11 @@ def get_short_name(long_quant_name): for filename in glob("*.cu"): os.remove(filename) +for head_size_kq in HEAD_SIZES_KQ: + head_size_v = head_size_kq if head_size_kq != 576 else 512 + with open(f"fattn-tile-instance-dkq{head_size_kq}-dv{head_size_v}.cu", "w") as f: + f.write(SOURCE_FATTN_TILE.format(head_size_kq=head_size_kq, head_size_v=head_size_v)) + for type_k in TYPES_KV: for type_v in TYPES_KV: with open(f"fattn-vec-instance-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f: @@ -64,7 +78,9 @@ for ncols in [8, 16, 32, 64]: with open(f"fattn-mma-f16-instance-ncols1_{ncols1}-ncols2_{ncols2}.cu", "w") as f: f.write(SOURCE_FATTN_MMA_START) - for head_size_kq in [64, 80, 96, 112, 128, 256, 576]: + for head_size_kq in HEAD_SIZES_KQ: + if head_size_kq == 40: + continue if head_size_kq != 576 and ncols2 == 16: continue if head_size_kq == 576 and ncols2 != 16: diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt index 0e2b1847e0..934aefdcb4 100644 --- a/ggml/src/ggml-hip/CMakeLists.txt +++ b/ggml/src/ggml-hip/CMakeLists.txt @@ -53,6 +53,8 @@ file(GLOB GGML_HEADERS_ROCM "../ggml-cuda/*.cuh") list(APPEND GGML_HEADERS_ROCM "../../include/ggml-cuda.h") file(GLOB GGML_SOURCES_ROCM "../ggml-cuda/*.cu") +file(GLOB SRCS "../ggml-cuda/template-instances/fattn-tile*.cu") +list(APPEND GGML_SOURCES_ROCM ${SRCS}) file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu") list(APPEND GGML_SOURCES_ROCM ${SRCS}) file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") diff --git a/ggml/src/ggml-musa/CMakeLists.txt b/ggml/src/ggml-musa/CMakeLists.txt index f8477a2ef3..d76cb51977 100644 --- a/ggml/src/ggml-musa/CMakeLists.txt +++ b/ggml/src/ggml-musa/CMakeLists.txt @@ -30,6 +30,8 @@ if (MUSAToolkit_FOUND) list(APPEND GGML_HEADERS_MUSA "../ggml-musa/mudnn.cuh") file(GLOB GGML_SOURCES_MUSA "../ggml-cuda/*.cu") + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-tile*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu") list(APPEND GGML_SOURCES_MUSA ${SRCS}) file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") From 20cc625edc2264aae2779e71bef1593e6a4e8c43 Mon Sep 17 00:00:00 2001 From: sirus20x6 Date: Sun, 12 Oct 2025 00:15:00 -0500 Subject: [PATCH 027/144] ggml: Correct SVE implementation in ggml_vec_dot_f16_unroll (#16518) The previous SVE implementation for `ggml_vec_dot_f16_unroll` contained a bug due to a copy-paste error. The wrong variable was used in an FMA instruction, leading to incorrect results. This commit corrects the variable usage and improves the clarity of the code by renaming variables to avoid confusion. Co-authored-by: Aaron --- ggml/src/ggml-cpu/vec.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h index 2751359ce4..d3834182a6 100644 --- a/ggml/src/ggml-cpu/vec.h +++ b/ggml/src/ggml-cpu/vec.h @@ -144,14 +144,14 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG for (int i = 0; i < np; i += ggml_f16_step) { ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0); // 8 elements - ax1 = GGML_F16x_VEC_LOAD(x[0] + i + 0*ggml_f16_epr, 0); // 8 elemnst + ax1 = GGML_F16x_VEC_LOAD(x[0] + i + 0*ggml_f16_epr, 0); // 8 elements sum_00 = GGML_F16x_VEC_FMA(sum_00, ax1, ay1); // sum_00 = sum_00+ax1*ay1 ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 0*ggml_f16_epr, 0); // 8 elements sum_10 = GGML_F16x_VEC_FMA(sum_10, ax1, ay1); ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1); // next 8 elements - ax2 = GGML_F16x_VEC_LOAD(x[0] + i + 1*ggml_f16_epr, 1); // next 8 ekements + ax2 = GGML_F16x_VEC_LOAD(x[0] + i + 1*ggml_f16_epr, 1); // next 8 elements sum_01 = GGML_F16x_VEC_FMA(sum_01, ax2, ay2); ax2 = GGML_F16x_VEC_LOAD(x[1] + i + 1*ggml_f16_epr, 1); sum_11 = GGML_F16x_VEC_FMA(sum_11, ax2, ay2); @@ -160,7 +160,7 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG ax3 = GGML_F16x_VEC_LOAD(x[0] + i + 2*ggml_f16_epr, 2); sum_02 = GGML_F16x_VEC_FMA(sum_02, ax3, ay3); - ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 2*ggml_f16_epr, 2); + ax3 = GGML_F16x_VEC_LOAD(x[1] + i + 2*ggml_f16_epr, 2); sum_12 = GGML_F16x_VEC_FMA(sum_12, ax3, ay3); ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3); From a2fba89a426ff8005d303c73f0436e7e67368b70 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Sun, 12 Oct 2025 07:19:06 +0200 Subject: [PATCH 028/144] hparams : add check for layer index in is_recurrent (#16511) * hparams : add check for layer index in is_recurrent This commit adds a check in the is_recurrent method to ensure that the provided layer index is within the valid range. The motivation for this change is to prevent potential out-of-bounds and also be consistent with other methods in the class that perform similar checks, like is_swa. --- src/llama-hparams.cpp | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index c04ac58f1a..db65d69eab 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -140,7 +140,11 @@ uint32_t llama_hparams::n_embd_s() const { } bool llama_hparams::is_recurrent(uint32_t il) const { - return recurrent_layer_arr[il]; + if (il < n_layer) { + return recurrent_layer_arr[il]; + } + + GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer); } uint32_t llama_hparams::n_pos_per_embd() const { From 41aac5c69b5fb281bc1f486afb053f78101bb39e Mon Sep 17 00:00:00 2001 From: sirus20x6 Date: Sun, 12 Oct 2025 00:25:37 -0500 Subject: [PATCH 029/144] ggml : Fix FP16 ELU positive branch (#16519) Co-authored-by: Aaron --- ggml/src/ggml-cpu/vec.h | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h index d3834182a6..65c7dfb6b9 100644 --- a/ggml/src/ggml-cpu/vec.h +++ b/ggml/src/ggml-cpu/vec.h @@ -820,7 +820,8 @@ inline static void ggml_vec_tanh_f16 (const int n, ggml_fp16_t * y, const ggml_f inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } inline static void ggml_vec_elu_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { for (int i = 0; i < n; ++i) { - y[i] = GGML_CPU_FP32_TO_FP16(expm1f(GGML_CPU_FP16_TO_FP32(x[i]))); + const float v = GGML_CPU_FP16_TO_FP32(x[i]); + y[i] = GGML_CPU_FP32_TO_FP16((v > 0.f) ? v : expm1f(v)); } } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } From 4b2dae383df708e2afc49c4859a81cd074f5ac10 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 12 Oct 2025 09:29:13 +0300 Subject: [PATCH 030/144] common : update presets (#16504) * presets : add --embd-gemma-default and remove old embedding presets * presets : add gpt-oss presets * presets : add vision presets * cont : remove reasoning overrides [no ci] * cont : fix batch size for embedding gemma [no ci] --- common/arg.cpp | 294 ++++++++++++++++++++++++++---------------------- common/common.h | 2 +- 2 files changed, 162 insertions(+), 134 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index d17645cf2f..c0b7180711 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -3358,7 +3358,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex add_opt(common_arg( {"--chat-template-kwargs"}, "STRING", string_format("sets additional params for the json template parser"), - [](common_params & params, const std::string & value) { + [](common_params & params, const std::string & value) { auto parsed = json::parse(value); for (const auto & item : parsed.items()) { params.default_template_kwargs[item.key()] = item.value().dump(); @@ -3570,21 +3570,23 @@ common_params_context common_params_parser_init(common_params & params, llama_ex common_log_set_file(common_log_main(), value.c_str()); } )); - add_opt(common_arg({ "--log-colors" }, "[on|off|auto]", - "Set colored logging ('on', 'off', or 'auto', default: 'auto')\n" - "'auto' enables colors when output is to a terminal", - [](common_params &, const std::string & value) { - if (is_truthy(value)) { - common_log_set_colors(common_log_main(), LOG_COLORS_ENABLED); - } else if (is_falsey(value)) { - common_log_set_colors(common_log_main(), LOG_COLORS_DISABLED); - } else if (is_autoy(value)) { - common_log_set_colors(common_log_main(), LOG_COLORS_AUTO); - } else { - throw std::invalid_argument( - string_format("error: unkown value for --log-colors: '%s'\n", value.c_str())); - } - }).set_env("LLAMA_LOG_COLORS")); + add_opt(common_arg( + {"--log-colors"}, "[on|off|auto]", + "Set colored logging ('on', 'off', or 'auto', default: 'auto')\n" + "'auto' enables colors when output is to a terminal", + [](common_params &, const std::string & value) { + if (is_truthy(value)) { + common_log_set_colors(common_log_main(), LOG_COLORS_ENABLED); + } else if (is_falsey(value)) { + common_log_set_colors(common_log_main(), LOG_COLORS_DISABLED); + } else if (is_autoy(value)) { + common_log_set_colors(common_log_main(), LOG_COLORS_AUTO); + } else { + throw std::invalid_argument( + string_format("error: unkown value for --log-colors: '%s'\n", value.c_str())); + } + } + ).set_env("LLAMA_LOG_COLORS")); add_opt(common_arg( {"-v", "--verbose", "--log-verbose"}, "Set verbosity level to infinity (i.e. log all messages, useful for debugging)", @@ -3850,7 +3852,87 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_TTS})); - // model-specific + add_opt(common_arg( + {"--diffusion-steps"}, "N", + string_format("number of diffusion steps (default: %d)", params.diffusion.steps), + [](common_params & params, int value) { params.diffusion.steps = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-visual"}, + string_format("enable visual diffusion mode (show progressive generation) (default: %s)", params.diffusion.visual_mode ? "true" : "false"), + [](common_params & params) { params.diffusion.visual_mode = true; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-eps"}, "F", + string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps), + [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-algorithm"}, "N", + string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)", params.diffusion.algorithm), + [](common_params & params, int value) { params.diffusion.algorithm = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-alg-temp"}, "F", + string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp), + [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-block-length"}, "N", + string_format("llada block length for generation (default: %d)", params.diffusion.block_length), + [](common_params & params, int value) { params.diffusion.block_length = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-cfg-scale"}, "F", + string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale), + [](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-add-gumbel-noise"}, "F", + string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"), + [](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "-lr", "--learning-rate" }, "ALPHA", + string_format("adamw or sgd optimizer alpha (default: %.2g); note: sgd alpha recommended ~10x (no momentum)", (double) params.lr.lr0), + [](common_params & params, const std::string & value) { params.lr.lr0 = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg({ "-lr-min", "--learning-rate-min" }, "ALPHA", + string_format("(if >0) final learning rate after decay (if -decay-epochs is set, default=%.2g)", + (double) params.lr.lr_min), + [](common_params & params, const std::string & value) { params.lr.lr_min = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-decay-epochs", "--learning-rate-decay-epochs"}, "ALPHA", + string_format("(if >0) decay learning rate to -lr-min after this many epochs (exponential decay, default=%.2g)", (double) params.lr.decay_epochs), + [](common_params & params, const std::string & value) { params.lr.decay_epochs = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-wd", "--weight-decay"}, "WD", + string_format("adamw or sgd optimizer weight decay (0 is off; recommend very small e.g. 1e-9) (default: %.2g).", (double) params.lr.wd), + [](common_params & params, const std::string & value) { params.lr.wd = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-val-split", "--val-split"}, "FRACTION", + string_format("fraction of data to use as validation set for training (default: %.2g).", (double) params.val_split), + [](common_params & params, const std::string & value) { params.val_split = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-epochs", "--epochs"}, "N", + string_format("optimizer max # of epochs (default: %d)", params.lr.epochs), + [](common_params & params, int epochs) { params.lr.epochs = epochs; } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-opt", "--optimizer"}, "sgd|adamw", "adamw or sgd", + [](common_params & params, const std::string & name) { + params.optimizer = common_opt_get_optimizer(name.c_str()); + if (params.optimizer == GGML_OPT_OPTIMIZER_TYPE_COUNT) { + throw std::invalid_argument("invalid --optimizer, valid options: adamw, sgd"); + } + } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + + // presets add_opt(common_arg( {"--tts-oute-default"}, string_format("use default OuteTTS models (note: can download weights from the internet)"), @@ -3863,39 +3945,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_TTS})); add_opt(common_arg( - {"--embd-bge-small-en-default"}, - string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"), + {"--embd-gemma-default"}, + string_format("use default EmbeddingGemma model (note: can download weights from the internet)"), [](common_params & params) { - params.model.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF"; - params.model.hf_file = "bge-small-en-v1.5-q8_0.gguf"; - params.embd_normalize = 2; - params.n_ctx = 512; - params.verbose_prompt = true; - params.embedding = true; - } - ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); - - add_opt(common_arg( - {"--embd-e5-small-en-default"}, - string_format("use default e5-small-v2 model (note: can download weights from the internet)"), - [](common_params & params) { - params.model.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF"; - params.model.hf_file = "e5-small-v2-q8_0.gguf"; - params.embd_normalize = 2; - params.n_ctx = 512; - params.verbose_prompt = true; - params.embedding = true; - } - ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); - - add_opt(common_arg( - {"--embd-gte-small-default"}, - string_format("use default gte-small model (note: can download weights from the internet)"), - [](common_params & params) { - params.model.hf_repo = "ggml-org/gte-small-Q8_0-GGUF"; - params.model.hf_file = "gte-small-q8_0.gguf"; - params.embd_normalize = 2; - params.n_ctx = 512; + params.model.hf_repo = "ggml-org/embeddinggemma-300M-qat-q4_0-GGUF"; + params.model.hf_file = "embeddinggemma-300M-qat-Q4_0.gguf"; + params.port = 8011; + params.n_ubatch = 2048; + params.n_batch = 2048; + params.n_parallel = 32; + params.n_ctx = 2048*params.n_parallel; params.verbose_prompt = true; params.embedding = true; } @@ -3990,96 +4049,65 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( - { "--diffusion-steps" }, "N", - string_format("number of diffusion steps (default: %d)", params.diffusion.steps), - [](common_params & params, int value) { params.diffusion.steps = value; } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-visual" }, - string_format("enable visual diffusion mode (show progressive generation) (default: %s)", - params.diffusion.visual_mode ? "true" : "false"), - [](common_params & params) { params.diffusion.visual_mode = true; } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + {"--gpt-oss-20b-default"}, + string_format("use gpt-oss-20b (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gpt-oss-20b-GGUF"; + params.model.hf_file = "gpt-oss-20b-mxfp4.gguf"; + params.port = 8013; + params.n_ubatch = 2048; + params.n_batch = 32768; + params.n_parallel = 2; + params.n_ctx = 131072*params.n_parallel; + params.sampling.temp = 1.0f; + params.sampling.top_p = 1.0f; + params.sampling.top_k = 0; + params.sampling.min_p = 0.01f; + params.use_jinja = true; + //params.default_template_kwargs["reasoning_effort"] = "\"high\""; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( - { "--diffusion-eps" }, "F", - string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps), - [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-algorithm" }, "N", - string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)", - params.diffusion.algorithm), - [](common_params & params, int value) { params.diffusion.algorithm = value; } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-alg-temp" }, "F", - string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp), - [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + {"--gpt-oss-120b-default"}, + string_format("use gpt-oss-120b (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gpt-oss-120b-GGUF"; + params.port = 8013; + params.n_ubatch = 2048; + params.n_batch = 32768; + params.n_parallel = 2; + params.n_ctx = 131072*params.n_parallel; + params.sampling.temp = 1.0f; + params.sampling.top_p = 1.0f; + params.sampling.top_k = 0; + params.sampling.min_p = 0.01f; + params.use_jinja = true; + //params.default_template_kwargs["reasoning_effort"] = "\"high\""; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( - { "--diffusion-block-length" }, "N", - string_format("llada block length for generation (default: %d)", params.diffusion.block_length), - [](common_params & params, int value) { params.diffusion.block_length = value; } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-cfg-scale" }, "F", - string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale), - [](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-add-gumbel-noise" }, "F", - string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"), - [](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + {"--vision-gemma-4b-default"}, + string_format("use Gemma 3 4B QAT (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gemma-3-4b-it-qat-GGUF"; + params.port = 8014; + params.n_ctx = 0; + params.use_jinja = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); - - add_opt( - common_arg({ "-lr", "--learning-rate" }, "ALPHA", - string_format( - "adamw or sgd optimizer alpha (default: %.2g); note: sgd alpha recommended ~10x (no momentum)", - (double) params.lr.lr0), - [](common_params & params, const std::string & value) { params.lr.lr0 = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt( - common_arg({ "-lr-min", "--learning-rate-min" }, "ALPHA", - string_format( - "(if >0) final learning rate after decay (if -decay-epochs is set, default=%.2g)", - (double) params.lr.lr_min), - [](common_params & params, const std::string & value) { params.lr.lr_min = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt( - common_arg({ "-decay-epochs", "--learning-rate-decay-epochs" }, "ALPHA", - string_format( - "(if >0) decay learning rate to -lr-min after this many epochs (exponential decay, default=%.2g)", - (double) params.lr.decay_epochs), - [](common_params & params, const std::string & value) { params.lr.decay_epochs = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); add_opt(common_arg( - { "-wd", "--weight-decay" }, "WD", - string_format( - "adamw or sgd optimizer weight decay (0 is off; recommend very small e.g. 1e-9) (default: %.2g).", - (double) params.lr.wd), - [](common_params & params, const std::string & value) { params.lr.wd = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt(common_arg({ "-val-split", "--val-split" }, "FRACTION", - string_format("fraction of data to use as validation set for training (default: %.2g).", - (double) params.val_split), - [](common_params & params, const std::string & value) { params.val_split = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt(common_arg({ "-epochs", "--epochs" }, "N", - string_format("optimizer max # of epochs (default: %d)", params.lr.epochs), - [](common_params & params, int epochs) { params.lr.epochs = epochs; }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt(common_arg({ "-opt", "--optimizer" }, "sgd|adamw", "adamw or sgd", - [](common_params & params, const std::string & name) { - params.optimizer = common_opt_get_optimizer(name.c_str()); - if (params.optimizer == GGML_OPT_OPTIMIZER_TYPE_COUNT) { - throw std::invalid_argument("invalid --optimizer, valid options: adamw, sgd"); - } - }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); + {"--vision-gemma-12b-default"}, + string_format("use Gemma 3 12B QAT (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gemma-3-12b-it-qat-GGUF"; + params.port = 8014; + params.n_ctx = 0; + params.use_jinja = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); return ctx_arg; } diff --git a/common/common.h b/common/common.h index 040a44ebd8..a8cb630ea5 100644 --- a/common/common.h +++ b/common/common.h @@ -426,7 +426,7 @@ struct common_params { int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting int32_t n_ctx_checkpoints = 8; // max number of context checkpoints per slot - int32_t cache_ram_mib = 8192; // 0 = no limit, 1 = 1 MiB, etc. + int32_t cache_ram_mib = 8192; // -1 = no limit, 0 - disable, 1 = 1 MiB, etc. std::string hostname = "127.0.0.1"; std::string public_path = ""; // NOLINT From 2c301e91abb92d03c1a682b4b540ba835562a74b Mon Sep 17 00:00:00 2001 From: Aldehir Rojas Date: Sun, 12 Oct 2025 08:18:47 -0500 Subject: [PATCH 031/144] common : handle unicode during partial json parsing (#16526) * common : handle unicode during partial json parsing * common : set missing `ensure_ascii = true` during json dump --- common/chat-parser.cpp | 4 +-- common/json-partial.cpp | 51 ++++++++++++++++++++++++++++++++ tests/test-chat-parser.cpp | 58 +++++++++++++++++++++++++++++++++++++ tests/test-json-partial.cpp | 52 ++++++++++++++++++++++++++++++++- 4 files changed, 162 insertions(+), 3 deletions(-) diff --git a/common/chat-parser.cpp b/common/chat-parser.cpp index 7365782e7d..ff83102788 100644 --- a/common/chat-parser.cpp +++ b/common/chat-parser.cpp @@ -432,7 +432,7 @@ std::optional common_chat_msg_parse if (is_arguments_path({})) { // Entire JSON is the arguments and was parsed fully. return consume_json_result { - partial->json.dump(), + partial->json.dump(/* indent */ -1, /* indent_char */ ' ', /* ensure_ascii */ true), /* .is_partial = */ false, }; } @@ -444,7 +444,7 @@ std::optional common_chat_msg_parse std::vector path; std::function remove_unsupported_healings_and_dump_args = [&](const json & j) -> json { if (is_arguments_path(path)) { - auto arguments = j.dump(); + auto arguments = j.dump(/* indent */ -1, /* indent_char */ ' ', /* ensure_ascii */ true); if (is_partial() && !partial->healing_marker.marker.empty()) { auto idx = arguments.find(partial->healing_marker.json_dump_marker); if (idx != std::string::npos) { diff --git a/common/json-partial.cpp b/common/json-partial.cpp index d9d9169989..919927dc32 100644 --- a/common/json-partial.cpp +++ b/common/json-partial.cpp @@ -5,6 +5,7 @@ #include #include +#include using json = nlohmann::ordered_json; @@ -168,6 +169,47 @@ bool common_json_parse( } } + // Matches a potentially partial unicode escape sequence, e.g. \u, \uX, \uXX, \uXXX, \uXXXX + static const std::regex partial_unicode_regex(R"(\\u(?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F])?)?)?)?$)"); + + auto is_high_surrogate = [&](const std::string & s) { + // Check if a partial of a high surrogate (U+D800-U+DBFF) + return s.length() >= 4 && + s[0] == '\\' && s[1] == 'u' && + std::tolower(s[2]) == 'd' && + (s[3] == '8' || s[3] == '9' || std::tolower(s[3]) == 'a' || std::tolower(s[3]) == 'b'); + }; + + // Initialize the unicode marker to a low surrogate to handle the edge case + // where a high surrogate (U+D800-U+DBFF) is immediately followed by a + // backslash (\) + std::string unicode_marker_padding = "udc00"; + std::smatch last_unicode_seq; + + if (std::regex_search(str, last_unicode_seq, partial_unicode_regex)) { + std::smatch second_last_seq; + std::string prelude = str.substr(0, last_unicode_seq.position()); + + // Pad the escape sequence with 0s until it forms a complete sequence of 6 characters + unicode_marker_padding = std::string(6 - last_unicode_seq.length(), '0'); + + if (is_high_surrogate(last_unicode_seq.str())) { + // If the sequence is a partial match for a high surrogate, add a low surrogate (U+DC00-U+UDFF) + unicode_marker_padding += "\\udc00"; + } else if (std::regex_search(prelude, second_last_seq, partial_unicode_regex)) { + if (is_high_surrogate(second_last_seq.str())) { + // If this follows a high surrogate, pad it to be a low surrogate + if (last_unicode_seq.length() == 2) { + unicode_marker_padding = "dc00"; + } else if (last_unicode_seq.length() == 3) { + unicode_marker_padding = "c00"; + } else { + // The original unicode_marker_padding is already padded with 0s + } + } + } + } + const auto & magic_seed = out.healing_marker.marker = healing_marker;//"$llama.cpp.json$"; if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY) { @@ -186,6 +228,9 @@ bool common_json_parse( } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) { // Was inside an object value string after an escape str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing; + } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) { + // Was inside an object value string after a partial unicode escape + str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing; } else { // find last : auto last_pos = str.find_last_of(':'); @@ -205,6 +250,9 @@ bool common_json_parse( } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) { // Was inside an array value string after an escape str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing; + } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) { + // Was inside an array value string after a partial unicode escape + str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing; } else if (!was_maybe_number() && can_parse(str + ", 1" + closing)) { // Had just finished a value str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\"" + closing; @@ -230,6 +278,9 @@ bool common_json_parse( } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\": 1" + closing)) { // Was inside an object key string after an escape str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\": 1" + closing; + } else if (can_parse(str + unicode_marker_padding + "\": 1" + closing)) { + // Was inside an object key string after a partial unicode escape + str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\": 1" + closing; } else { auto last_pos = str.find_last_of(':'); if (last_pos == std::string::npos) { diff --git a/tests/test-chat-parser.cpp b/tests/test-chat-parser.cpp index 0b275befb8..4766518fe6 100644 --- a/tests/test-chat-parser.cpp +++ b/tests/test-chat-parser.cpp @@ -524,6 +524,64 @@ static void test_json_with_dumped_args() { R"({"foo": "bar", "args": {"arg1": [)", R"({"foo":"bar","args":"{\"arg1\":["})" ); + + // Unicode tests + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u0)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u0"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u00)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u00"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u000)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u000"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u0000)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u0000"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud8)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud8"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud80)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud80"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\u)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\u"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\ud)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\ud"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\udc)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\udc"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\udc0)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\udc0"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\udc00)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\udc00"})" + ); } static void test_positions() { diff --git a/tests/test-json-partial.cpp b/tests/test-json-partial.cpp index bc136beceb..39da9276ef 100644 --- a/tests/test-json-partial.cpp +++ b/tests/test-json-partial.cpp @@ -58,7 +58,7 @@ static void test_json_healing() { for (const auto & input : inputs) { common_json out; assert_equals(true, common_json_parse(input, "$foo", out)); - assert_equals(expected, out.json.dump()); + assert_equals(expected, out.json.dump(/* indent */ -1, /* indent_char */ ' ', /* ensure_ascii */ true)); assert_equals(expected_marker, out.healing_marker.json_dump_marker); } }; @@ -228,6 +228,56 @@ static void test_json_healing() { R"({"key":"$foo"})", R"(:"$foo)" ); + // Test unicode escape sequences + test( + { + R"({"a":"\u)", + }, + R"({"a":"\u0000$foo"})", + R"(0000$foo)" + ); + test( + { + R"({"a":"\u00)", + }, + R"({"a":"\u0000$foo"})", + R"(00$foo)" + ); + test( + { + R"({"a":"\ud300)", + }, + R"({"a":"\ud300$foo"})", + R"($foo)" + ); + test( + { + R"({"a":"\ud800)", + }, + R"({"a":"\ud800\udc00$foo"})", + R"(\udc00$foo)" + ); + test( + { + R"({"a":"\ud800\)", + }, + R"({"a":"\ud800\udc00$foo"})", + R"(udc00$foo)" + ); + test( + { + R"({"a":"\ud800\u)", + }, + R"({"a":"\ud800\udc00$foo"})", + R"(dc00$foo)" + ); + test( + { + R"({"a":"\ud800\udc00)", + }, + R"({"a":"\ud800\udc00$foo"})", + R"($foo)" + ); } int main() { From 8415f61e23d04427cd0d912fbb9d33b85f849456 Mon Sep 17 00:00:00 2001 From: Mathieu Baudier Date: Sun, 12 Oct 2025 15:48:03 +0200 Subject: [PATCH 032/144] ci : add Vulkan on Ubuntu with default packages build (#16532) * ci: build Vulkan on Ubuntu with default packages * ci: disable tests in Vulkan build with default Ubuntu packages --- .github/workflows/build.yml | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 8d6ba5f9f3..fe86863893 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -387,6 +387,39 @@ jobs: cd build ctest -L main --verbose + ubuntu-24-cmake-vulkan-deb: + runs-on: ubuntu-24.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.16 + with: + key: ubuntu-24-cmake-vulkan-deb + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + sudo apt-get install -y glslc libvulkan-dev libcurl4-openssl-dev + + - name: Configure + id: cmake_configure + run: | + cmake -B build \ + -DCMAKE_BUILD_TYPE=RelWithDebInfo \ + -DGGML_BACKEND_DL=ON \ + -DGGML_CPU_ALL_VARIANTS=ON \ + -DGGML_VULKAN=ON + + - name: Build + id: cmake_build + run: | + cmake --build build -j $(nproc) + ubuntu-24-cmake-vulkan: runs-on: ubuntu-24.04 From c7be9febcbafa9af7d1b9443f86475c59c9c5f87 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Sun, 12 Oct 2025 21:53:35 +0800 Subject: [PATCH 033/144] [SYCL] fix UT fault cases: count-equal, argsort, pad OPs (#16521) * fix/refactor OP argsort, pad * fix count-equal op * update SYCL OP list * fix format issue --------- Co-authored-by: Zhang Jianyu --- docs/ops.md | 18 +- docs/ops/SYCL.csv | 16344 +++++++++++++++++++------- ggml/src/ggml-sycl/backend.hpp | 2 + ggml/src/ggml-sycl/binbcast.cpp | 9 - ggml/src/ggml-sycl/binbcast.hpp | 6 - ggml/src/ggml-sycl/common.hpp | 3 +- ggml/src/ggml-sycl/count-equal.cpp | 79 + ggml/src/ggml-sycl/count-equal.hpp | 9 + ggml/src/ggml-sycl/element_wise.cpp | 78 - ggml/src/ggml-sycl/element_wise.hpp | 2 - ggml/src/ggml-sycl/ggml-sycl.cpp | 103 +- ggml/src/ggml-sycl/pad.cpp | 97 + ggml/src/ggml-sycl/pad.hpp | 24 + 13 files changed, 12381 insertions(+), 4393 deletions(-) create mode 100644 ggml/src/ggml-sycl/count-equal.cpp create mode 100644 ggml/src/ggml-sycl/count-equal.hpp create mode 100644 ggml/src/ggml-sycl/pad.cpp create mode 100644 ggml/src/ggml-sycl/pad.hpp diff --git a/docs/ops.md b/docs/ops.md index 0047ef3fa5..bd26c0eb45 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -31,7 +31,7 @@ Legend: | CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | +| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | | CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | | CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | @@ -51,7 +51,7 @@ Legend: | GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | | GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | | GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | @@ -65,11 +65,11 @@ Legend: | MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | | NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | | OPT_STEP_SGD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | -| PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | | PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | @@ -92,9 +92,9 @@ Legend: | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | | SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | -| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | +| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | | SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | | SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ | ❌ | | SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | @@ -102,9 +102,11 @@ Legend: | STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | | SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | | SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | | SWIGLU_OAI | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ | | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| TOPK_MOE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| XIELU | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index 5d022ee91a..d7efa43cdf 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -29,6 +29,8 @@ "SYCL0","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","XIELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" +"SYCL0","XIELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" "SYCL0","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" @@ -59,6 +61,8 @@ "SYCL0","EXP","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" +"SYCL0","XIELU","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" +"SYCL0","XIELU","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" @@ -89,6 +93,8 @@ "SYCL0","EXP","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","XIELU","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" +"SYCL0","XIELU","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" "SYCL0","ABS","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","ABS","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","SGN","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" @@ -119,6 +125,8 @@ "SYCL0","EXP","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" +"SYCL0","XIELU","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" +"SYCL0","XIELU","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","SYCL" "SYCL0","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","SYCL" "SYCL0","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","SYCL" @@ -239,99 +247,117 @@ "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=1,m=8,r=2,b=1,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,b=1,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,b=1,v=1","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,b=7,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,b=7,v=1","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f16,n=256,m=5,r=4,b=1,v=0","support","1","yes","SYCL" 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+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL" "SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL" "SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL" "SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL" @@ -2455,6 +4548,264 @@ "SYCL0","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","0","no","SYCL" "SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","0","no","SYCL" "SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","0","no","SYCL" +"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL" 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+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","1","yes","SYCL" @@ -2573,9 +4924,10 @@ "SYCL0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL" "SYCL0","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL" -"SYCL0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","SYCL" -"SYCL0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","SYCL" +"SYCL0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","SYCL" +"SYCL0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","SYCL" +"SYCL0","ARGMAX","type=f32,ne=[32,513,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[100,10,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[1024,10,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[1024,12,1,1]","support","1","yes","SYCL" @@ -2693,6 +5045,15 @@ "SYCL0","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","SYCL" @@ -2835,6 +5196,8 @@ "SYCL0","CPY","type_src=f16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=f16,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=f16,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" @@ -2879,6 +5242,8 @@ "SYCL0","CPY","type_src=bf16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=bf16,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=bf16,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" @@ -2923,6 +5288,8 @@ "SYCL0","CPY","type_src=f32,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=f32,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=f32,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" @@ -2967,6 +5334,8 @@ "SYCL0","CPY","type_src=q5_1,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q8_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q8_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=q3_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" @@ -2999,6 +5368,10 @@ "SYCL0","CPY","type_src=f16,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f16,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=f32,type_dst=i32,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=f32,type_dst=i32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=i32,type_dst=f32,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=i32,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","SYCL" "SYCL0","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","SYCL" "SYCL0","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","SYCL" @@ -3061,6 +5434,10 @@ "SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" "SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" +"SYCL0","ADD","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","SUB","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","MUL","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","DIV","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" @@ -3165,6 +5542,10 @@ "SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" "SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" +"SYCL0","ADD","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","SUB","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","MUL","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","DIV","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" @@ -3217,6 +5598,10 @@ "SYCL0","SUB","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","DIV","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","ADD","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" +"SYCL0","MUL","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" +"SYCL0","SUB","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" +"SYCL0","DIV","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=2","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[16,5,4,3],nr=[1,2,1,1],nf=3","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=4","support","1","yes","SYCL" @@ -3224,44 +5609,69 @@ "SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=6","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=7","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[16,5,4,3],nr=[2,2,2,2],nf=8","support","1","yes","SYCL" +"SYCL0","ADD","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" "SYCL0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" -"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000","support","1","yes","SYCL" -"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000","support","1","yes","SYCL" +"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","SYCL" +"SYCL0","SCALE","type=f32,ne=[100,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SOFTCAP","type=f32,ne=[10,10,10,10],softcap=50.000000","support","1","yes","SYCL" "SYCL0","SILU_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","0","no","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000000","support","0","no","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","0","no","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000100","support","0","no","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.100000","support","0","no","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=1","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=1","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=1","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=1","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=1,multi_add=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=1,multi_add=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=1,multi_add=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=1,multi_add=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=0,multi_add=0","support","1","yes","SYCL" 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+"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" "SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","SYCL" "SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","0","no","SYCL" "SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","SYCL" @@ -6006,9 +9325,13 @@ "SYCL0","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[16,10,10,10],order=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[60,10,10,10],order=0","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[8,1,1,1],order=1","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[16,10,10,10],order=1","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[60,10,10,10],order=1","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[1024,1,1,1],order=1","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[16384,1,1,1],order=1","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=0","support","1","yes","SYCL" @@ -6019,2115 +9342,6638 @@ "SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=1","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=257","support","0","no","SYCL" "SYCL0","SUM","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" -"SYCL0","SUM_ROWS","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[10,5,4,3],permute=0,slice=0","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=1,slice=0","support","0","no","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=0,slice=1","support","0","no","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=1,slice=1","support","0","no","SYCL" "SYCL0","MEAN","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","SUM","type=f32,ne=[33,1,1,1]","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[33,1,1,1],permute=0,slice=0","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[33,1,1,1]","support","0","no","SYCL" +"SYCL0","SUM","type=f32,ne=[33,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[33,1024,1,1],permute=0,slice=0","support","1","yes","SYCL" +"SYCL0","SUM","type=f32,ne=[33,256,1,1]","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[33,256,1,1],permute=0,slice=0","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[33,256,1,1]","support","0","no","SYCL" +"SYCL0","MEAN","type=f32,ne=[32769,1,1,1]","support","0","no","SYCL" "SYCL0","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","SYCL" "SYCL0","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","SYCL" +"SYCL0","GROUP_NORM_MUL_ADD","type=f32,ne=[64,64,320,1],num_groups=4,eps=0.000010","support","1","yes","SYCL" +"SYCL0","GROUP_NORM_MUL_ADD","type=f32,ne=[9,9,1280,1],num_groups=4,eps=0.000010","support","1","yes","SYCL" "SYCL0","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","1","yes","SYCL" "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","SYCL" +"SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","0","no","SYCL" "SYCL0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","SYCL" "SYCL0","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","0","no","SYCL" "SYCL0","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" 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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","SYCL" "SYCL0","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","TOPK_MOE","ne=[8,22,1,1],n_expert_used=4,with_norm=0","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[32,22,1,1],n_expert_used=8,with_norm=0","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[128,1,1,1],n_expert_used=128,with_norm=0","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[8,22,1,1],n_expert_used=4,with_norm=1","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[32,22,1,1],n_expert_used=8,with_norm=1","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[128,1,1,1],n_expert_used=128,with_norm=1","support","1","yes","SYCL" diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp index 410a67b019..6ff3215d5a 100644 --- a/ggml/src/ggml-sycl/backend.hpp +++ b/ggml/src/ggml-sycl/backend.hpp @@ -18,6 +18,7 @@ #include "concat.hpp" #include "conv.hpp" #include "convert.hpp" +#include "count-equal.hpp" #include "cpy.hpp" #include "dequantize.hpp" #include "dmmv.hpp" @@ -28,6 +29,7 @@ #include "mmvq.hpp" #include "norm.hpp" #include "outprod.hpp" +#include "pad.hpp" #include "quantize.hpp" #include "quants.hpp" #include "rope.hpp" diff --git a/ggml/src/ggml-sycl/binbcast.cpp b/ggml/src/ggml-sycl/binbcast.cpp index e0a1de0f32..0a3883ae1e 100644 --- a/ggml/src/ggml-sycl/binbcast.cpp +++ b/ggml/src/ggml-sycl/binbcast.cpp @@ -303,10 +303,6 @@ inline void ggml_sycl_op_sub(ggml_backend_sycl_context & ctx, ggml_tensor *dst) ggml_sycl_op_bin_bcast>(ctx, dst->src[0], dst->src[1], dst); } -inline void ggml_sycl_op_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { - ggml_sycl_op_bin_bcast>(ctx, dst->src[0], dst->src[1], dst); -} - inline void ggml_sycl_op_mul(ggml_backend_sycl_context & ctx, ggml_tensor *dst) { ggml_sycl_op_bin_bcast>(ctx, dst->src[0], dst->src[1], dst); @@ -332,11 +328,6 @@ void ggml_sycl_sub(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_op_sub(ctx, dst); } -void ggml_sycl_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { - scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); - ggml_sycl_op_count_equal(ctx, dst); -} - void ggml_sycl_mul(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); ggml_sycl_op_mul(ctx, dst); diff --git a/ggml/src/ggml-sycl/binbcast.hpp b/ggml/src/ggml-sycl/binbcast.hpp index 34c4064f52..9cce0f053a 100644 --- a/ggml/src/ggml-sycl/binbcast.hpp +++ b/ggml/src/ggml-sycl/binbcast.hpp @@ -16,12 +16,6 @@ static __dpct_inline__ float op_sub(const float a, const float b) { return a - b; } -static __dpct_inline__ float op_count_equal(const float a, const float b) { - return (a == b) ? 1.0f : 0.0f; -} - -void ggml_sycl_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst); - static __dpct_inline__ float op_mul(const float a, const float b) { return a * b; } diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index d66d7ade90..338fa08cda 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -195,7 +195,8 @@ struct optimize_feature { struct sycl_device_info { int cc; // compute capability - // int nsm; // number of streaming multiprocessors + int nsm; // number of streaming multiprocessors (CUDA) maps to the maximum + // number of compute units on a SYCL device. // size_t smpb; // max. shared memory per block size_t smpbo; // max. shared memory per block (with opt-in) bool vmm; // virtual memory support diff --git a/ggml/src/ggml-sycl/count-equal.cpp b/ggml/src/ggml-sycl/count-equal.cpp new file mode 100644 index 0000000000..b0a8b4820d --- /dev/null +++ b/ggml/src/ggml-sycl/count-equal.cpp @@ -0,0 +1,79 @@ +#include "count-equal.hpp" + +#include + +template +static void count_equal(const T *__restrict__ x, const T *__restrict__ y, + int64_t *__restrict__ dst, const int64_t dk, + const int64_t k) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int64_t i0 = (int64_t)item_ct1.get_group(2) * dk; + const int64_t i1 = sycl::min(i0 + dk, k); + + int nequal = 0; + + for (int64_t i = i0 + item_ct1.get_local_id(2); i < i1; i += WARP_SIZE) { + const T xi = x[i]; + const T yi = y[i]; + nequal += xi == yi; + } + + nequal = warp_reduce_sum(nequal); + + if (item_ct1.get_local_id(2) != 0) { + return; + } + + dpct::atomic_fetch_add( + (int *)dst, nequal); +} + +void ggml_sycl_count_equal(ggml_backend_sycl_context &ctx, ggml_tensor *dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == src1->type); + GGML_ASSERT( dst->type == GGML_TYPE_I64); + + GGML_ASSERT(ggml_are_same_shape(src0, src1)); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(ggml_is_contiguous(dst)); + + int64_t * dst_d = (int64_t *) dst->data; + + dpct::queue_ptr stream = ctx.stream(); + const int id = get_current_device_id(); + const int nsm = ggml_sycl_info().devices[id].nsm; + + const int64_t ne = ggml_nelements(src0); + GGML_ASSERT(ne < (1 << 30) && "atomicAdd implementation only supports int"); + const int64_t dne = + GGML_PAD((ne + 4 * nsm - 1) / (4 * nsm), SYCL_COUNT_EQUAL_CHUNK_SIZE); + + SYCL_CHECK(CHECK_TRY_ERROR(stream->memset(dst_d, 0, ggml_nbytes(dst)))); + + const dpct::dim3 block_dims(WARP_SIZE, 1, 1); + const dpct::dim3 block_nums( + std::min((int64_t)4 * nsm, (ne + SYCL_COUNT_EQUAL_CHUNK_SIZE - 1) / + SYCL_COUNT_EQUAL_CHUNK_SIZE), + 1, 1); + + switch (src0->type) { + case GGML_TYPE_I32: { + const int *src0_d = (const int *)src0->data; + const int *src1_d = (const int *)src1->data; + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + count_equal(src0_d, src1_d, dst_d, dne, ne); + GGML_UNUSED(item_ct1); + }); + + } break; + default: + GGML_ASSERT(false); + break; + } +} diff --git a/ggml/src/ggml-sycl/count-equal.hpp b/ggml/src/ggml-sycl/count-equal.hpp new file mode 100644 index 0000000000..f7f4fcbd0b --- /dev/null +++ b/ggml/src/ggml-sycl/count-equal.hpp @@ -0,0 +1,9 @@ +#ifndef GGML_SYCL_COUNT_EQUAL_HPP +#define GGML_SYCL_COUNT_EQUAL_HPP +#include "common.hpp" + +#define SYCL_COUNT_EQUAL_CHUNK_SIZE 128 + +void ggml_sycl_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +#endif //GGML_SYCL_COUNT_EQUAL_HPP diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index c2da2fb48a..aeeb387595 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -328,26 +328,6 @@ static void upscale(const T *x, T *dst, const int nb00, const int nb01, dst[index] = *(const T *)((const char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); } -template -static void pad(const T *x, T *dst, const int ne0, const int ne00, const int ne01, const int ne02, - const sycl::nd_item<3> &item_ct1) { - int nidx = SYCL_LOCAL_ID_CALC(item_ct1, 2); - if (nidx >= ne0) { - return; - } - - // operation - int offset_dst = nidx + item_ct1.get_group(1) * ne0 + - item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); - if (nidx < ne00 && item_ct1.get_group(1) < (size_t) ne01 && item_ct1.get_group(0) < (size_t) ne02) { - int offset_src = nidx + item_ct1.get_group(1) * ne00 + - item_ct1.get_group(0) * ne00 * ne01; - dst[offset_dst] = x[offset_src]; - } else { - dst[offset_dst] = static_cast(0.0f); - } -} - template static void clamp(const T * x, T * dst, const float min, const float max, const int k, const sycl::nd_item<1> &item_ct1) { @@ -431,18 +411,6 @@ static void upscale_sycl(const T *x, T *dst, const int nb00, const int nb01, }); } -template -static void pad_sycl(const T *x, T *dst, const int ne00, - const int ne01, const int ne02, const int ne0, - const int ne1, const int ne2, queue_ptr stream) { - int num_blocks = ceil_div(ne0, SYCL_PAD_BLOCK_SIZE); - sycl::range<3> gridDim(ne2, ne1, num_blocks); - stream->parallel_for( - sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { pad(x, dst, ne0, ne00, ne01, ne02, item_ct1); }); -} - template static inline void dispatch_ggml_sycl_op_unary(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) { #if defined (GGML_SYCL_F16) @@ -596,40 +564,6 @@ static inline void dispatch_ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx } } -template -static inline void dispatch_ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) { -#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); - GGML_ASSERT(dst->src[0]->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors - 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); - kernel_invoker(data_pts.src, data_pts.dst, (int)dst->src[0]->ne[0], (int)dst->src[0]->ne[1], (int)dst->src[0]->ne[2], (int)dst->ne[0], - (int)dst->ne[1], (int)dst->ne[2], main_stream, std::forward(args)...); - break; - } -#endif - case GGML_TYPE_F32: - { - auto data_pts = cast_data(dst); - kernel_invoker(data_pts.src, data_pts.dst, (int)dst->src[0]->ne[0], (int)dst->src[0]->ne[1], (int)dst->src[0]->ne[2], (int)dst->ne[0], - (int)dst->ne[1], (int)dst->ne[2], main_stream, std::forward(args)...); - break; - } - default: - GGML_ABORT("GGML tensor type not supported!\n"); - } -} } // namespace ggml_sycl_detail @@ -919,14 +853,6 @@ static inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, ggml_te }); } -static inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { - ggml_sycl_detail::dispatch_ggml_sycl_op_pad(ctx, dst, - [](const auto* src, auto* dst_ptr, int ne00, int ne01, int ne02, int ne0, int ne1, int ne2, - queue_ptr stream) { - ggml_sycl_detail::pad_sycl(src, dst_ptr, ne00, ne01, ne02, ne0, ne1, ne2, stream); - }); -} - static inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { float min_val; float max_val; @@ -1119,10 +1045,6 @@ void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_op_upscale(ctx, dst); } -void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { - scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); - ggml_sycl_op_pad(ctx, dst); -} void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp index 50749e87d7..4347431728 100644 --- a/ggml/src/ggml-sycl/element_wise.hpp +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -67,8 +67,6 @@ void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst); -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); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index e4cc3c8ed8..45b8c216c9 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -85,9 +85,11 @@ static ggml_sycl_device_info ggml_sycl_init() { info.devices[i].cc = 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + info.devices[i].nsm = prop.get_max_compute_units(); info.devices[i].opt_feature.reorder = device.ext_oneapi_architecture_is(syclex::arch_category::intel_gpu); - info.max_work_group_sizes[i] = prop.get_max_work_group_size(); info.devices[i].smpbo = prop.get_local_mem_size(); + + info.max_work_group_sizes[i] = prop.get_max_work_group_size(); } for (int id = 0; id < info.device_count; ++id) { @@ -1512,60 +1514,70 @@ static inline void ggml_sycl_swap(T & a, T & b) { template __dpct_inline__ static void k_argsort_f32_i32(const float *x, int *dst, const int ncols, int ncols_pad, - const sycl::nd_item<3> &item_ct1, uint8_t *dpct_local) { + const int tasks_per_thread, const sycl::nd_item<3> &item_ct1, + uint8_t *dpct_local) { // bitonic sort - int col = item_ct1.get_local_id(2); + int col_index = item_ct1.get_local_id(2); int row = item_ct1.get_group(1); - if (col >= ncols_pad) { - return; + for (int i = 0; i < tasks_per_thread; i++) { + int col = col_index * tasks_per_thread + i; + if (col >= ncols_pad) { + return; + } } const float * x_row = x + row * ncols; auto dst_row = (int *)dpct_local; // initialize indices - dst_row[col] = col; + for (int i=0;i 0; j /= 2) { - int ixj = col ^ j; - if (ixj > col) { - if ((col & k) == 0) { - if (dst_row[col] >= ncols || - (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] > x_row[dst_row[ixj]] : - x_row[dst_row[col]] < x_row[dst_row[ixj]])) - ) { - ggml_sycl_swap(dst_row[col], dst_row[ixj]); - } - } else { - if (dst_row[ixj] >= ncols || - (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] < x_row[dst_row[ixj]] : - x_row[dst_row[col]] > x_row[dst_row[ixj]])) - ) { - ggml_sycl_swap(dst_row[col], dst_row[ixj]); + for (int i = 0; i < tasks_per_thread; i++) { + int col = col_index * tasks_per_thread + i; + int ixj = col ^ j; + if (ixj > col) { + if ((col & k) == 0) { + if (dst_row[col] >= ncols || + (dst_row[ixj] < ncols && + (order == GGML_SORT_ORDER_ASC + ? x_row[dst_row[col]] > x_row[dst_row[ixj]] + : x_row[dst_row[col]] < + x_row[dst_row[ixj]]))) { + ggml_sycl_swap(dst_row[col], dst_row[ixj]); + } + } else { + if (dst_row[ixj] >= ncols || + (dst_row[col] < ncols && + (order == GGML_SORT_ORDER_ASC + ? x_row[dst_row[col]] < x_row[dst_row[ixj]] + : x_row[dst_row[col]] > + x_row[dst_row[ixj]]))) { + ggml_sycl_swap(dst_row[col], dst_row[ixj]); + } } } + item_ct1.barrier(sycl::access::fence_space::local_space); } - /* - DPCT1118:1: SYCL group functions and algorithms must be encountered - in converged control flow. You may need to adjust the code. - */ - item_ct1.barrier(sycl::access::fence_space::local_space); } } // copy the result to dst without the padding - if (col < ncols) { - dst[row * ncols + col] = dst_row[col]; + for (int i = 0; i < tasks_per_thread; i++) { + int col = col_index * tasks_per_thread + i; + if (col < ncols) { + dst[row * ncols + col] = dst_row[col]; + } } } - static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past, const sycl::nd_item<3> &item_ct1) { const int col = item_ct1.get_local_range(1) * item_ct1.get_group(1) + @@ -1738,11 +1750,20 @@ static int next_power_of_2(int x) { static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, const int nrows, ggml_sort_order order, - queue_ptr stream) { + queue_ptr stream, int device) { // bitonic sort requires ncols to be power of 2 const int ncols_pad = next_power_of_2(ncols); - const sycl::range<3> block_dims(1, 1, ncols_pad); + int nth = 1; + int max_block_size = ggml_sycl_info().max_work_group_sizes[device]; + while (nth < ncols_pad && nth < max_block_size) + nth *= 2; + if (nth > max_block_size) + nth = max_block_size; + + const int tasks_per_thread = ncols_pad / nth; + + const sycl::range<3> block_dims(1, 1, nth); const sycl::range<3> block_nums(1, nrows, 1); const size_t shared_mem = ncols_pad * sizeof(int); @@ -1755,8 +1776,9 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_argsort_f32_i32( - x, dst, ncols, ncols_pad, item_ct1, - dpct_local_acc_ct1.get_multi_ptr() + x, dst, ncols, ncols_pad, tasks_per_thread, item_ct1, + dpct_local_acc_ct1 + .get_multi_ptr() .get()); }); }); @@ -1769,8 +1791,9 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_argsort_f32_i32( - x, dst, ncols, ncols_pad, item_ct1, - dpct_local_acc_ct1.get_multi_ptr() + x, dst, ncols, ncols_pad, tasks_per_thread, item_ct1, + dpct_local_acc_ct1 + .get_multi_ptr() .get()); }); }); @@ -2142,7 +2165,8 @@ inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, ggml_tensor * enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - argsort_f32_i32_sycl(src0_dd, (int *) dst_dd, ncols, nrows, order, main_stream); + argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order, + main_stream, ctx.device); } inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { @@ -4413,8 +4437,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_ACC: return true; case GGML_OP_PAD: - return (ggml_get_op_params_i32(op, 0) == 0) && (ggml_get_op_params_i32(op, 2) == 0) && - (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0); + return ggml_is_contiguous(op->src[0]); case GGML_OP_LEAKY_RELU: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_RWKV_WKV6: diff --git a/ggml/src/ggml-sycl/pad.cpp b/ggml/src/ggml-sycl/pad.cpp new file mode 100644 index 0000000000..413712c584 --- /dev/null +++ b/ggml/src/ggml-sycl/pad.cpp @@ -0,0 +1,97 @@ +// +// MIT license +// Copyright (C) 2025 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +//#include "common.hpp" +#include "pad.hpp" + +static void pad_f32(const float * src, float * dst, + const int lp0, const int rp0, const int lp1, const int rp1, + const int lp2, const int rp2, const int lp3, const int rp3, + const int ne0, const int ne1, const int ne2, const int ne3) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + int i0 = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + int i1 = item_ct1.get_group(1); + int i2 = item_ct1.get_group(0) % ne2; + int i3 = item_ct1.get_group(0) / ne2; + if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } + + // operation + const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; + if ((i0 >= lp0 && i0 < ne0 - rp0) && + (i1 >= lp1 && i1 < ne1 - rp1) && + (i2 >= lp2 && i2 < ne2 - rp2) && + (i3 >= lp3 && i3 < ne3 - rp3)) { + const int64_t i00 = i0 - lp0; + const int64_t i01 = i1 - lp1; + const int64_t i02 = i2 - lp2; + const int64_t i03 = i3 - lp3; + const int64_t ne02 = ne2 - lp2 - rp2; + const int64_t ne01 = ne1 - lp1 - rp1; + const int64_t ne00 = ne0 - lp0 - rp0; + + const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + + i02 * (ne00 * ne01) + i01 * ne00 + i00; + + dst[dst_idx] = src[src_idx]; + } else { + dst[dst_idx] = 0.0f; + } +} + +static void pad_f32_sycl(const float *src, float *dst, const int lp0, + const int rp0, const int lp1, const int rp1, + const int lp2, const int rp2, const int lp3, + const int rp3, const int ne0, const int ne1, + const int ne2, const int ne3, + dpct::queue_ptr stream) { + int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE; + dpct::dim3 gridDim(num_blocks, ne1, ne2 * ne3); + stream->parallel_for( + sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + pad_f32(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1, + ne2, ne3); + }); +} + +void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const float * src0_d = (const float *)src0->data; + float * dst_d = (float *)dst->data; + dpct::queue_ptr stream = ctx.stream(); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_contiguous(src0)); + + const int32_t lp0 = ((const int32_t*)(dst->op_params))[0]; + const int32_t rp0 = ((const int32_t*)(dst->op_params))[1]; + const int32_t lp1 = ((const int32_t*)(dst->op_params))[2]; + const int32_t rp1 = ((const int32_t*)(dst->op_params))[3]; + const int32_t lp2 = ((const int32_t*)(dst->op_params))[4]; + const int32_t rp2 = ((const int32_t*)(dst->op_params))[5]; + const int32_t lp3 = ((const int32_t*)(dst->op_params))[6]; + const int32_t rp3 = ((const int32_t*)(dst->op_params))[7]; + + pad_f32_sycl(src0_d, dst_d, + lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream); +} + +void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + ggml_sycl_op_pad(ctx, dst); +} diff --git a/ggml/src/ggml-sycl/pad.hpp b/ggml/src/ggml-sycl/pad.hpp new file mode 100644 index 0000000000..b099e9b73a --- /dev/null +++ b/ggml/src/ggml-sycl/pad.hpp @@ -0,0 +1,24 @@ +// +// MIT license +// Copyright (C) 2025 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_PAD_HPP +#define GGML_SYCL_PAD_HPP + +#include "common.hpp" + +#define SYCL_PAD_BLOCK_SIZE 256 + +void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +#endif // GGML_SYCL_PAD_HPP From 81d54bbfd599811b354c39f04550888168be7780 Mon Sep 17 00:00:00 2001 From: Pascal Date: Sun, 12 Oct 2025 18:06:41 +0200 Subject: [PATCH 034/144] webui: remove client-side context pre-check and rely on backend for limits (#16506) * fix: make SSE client robust to premature [DONE] in agentic proxy chains * webui: remove client-side context pre-check and rely on backend for limits Removed the client-side context window pre-check and now simply sends messages while keeping the dialog imports limited to core components, eliminating the maximum context alert path Simplified streaming and non-streaming chat error handling to surface a generic 'No response received from server' error whenever the backend returns no content Removed the obsolete maxContextError plumbing from the chat store so state management now focuses on the core message flow without special context-limit cases * webui: cosmetic rename of error messages * Update tools/server/webui/src/lib/stores/chat.svelte.ts Co-authored-by: Aleksander Grygier * Update tools/server/webui/src/lib/stores/chat.svelte.ts Co-authored-by: Aleksander Grygier * Update tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte Co-authored-by: Aleksander Grygier * Update tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte Co-authored-by: Aleksander Grygier * chore: update webui build output --------- Co-authored-by: Aleksander Grygier --- tools/server/public/index.html.gz | Bin 847816 -> 847621 bytes .../app/chat/ChatScreen/ChatScreen.svelte | 36 ++--- .../app/dialogs/ChatErrorDialog.svelte | 60 ++++++++ .../dialogs/MaximumContextAlertDialog.svelte | 66 --------- .../webui/src/lib/components/app/index.ts | 3 +- tools/server/webui/src/lib/services/chat.ts | 89 ++++------- .../server/webui/src/lib/services/context.ts | 102 ------------- tools/server/webui/src/lib/services/index.ts | 1 - .../webui/src/lib/stores/chat.svelte.ts | 140 +++++------------- .../webui/src/lib/stores/server.svelte.ts | 2 +- tools/server/webui/src/routes/+layout.svelte | 8 +- 11 files changed, 141 insertions(+), 366 deletions(-) create mode 100644 tools/server/webui/src/lib/components/app/dialogs/ChatErrorDialog.svelte delete mode 100644 tools/server/webui/src/lib/components/app/dialogs/MaximumContextAlertDialog.svelte delete mode 100644 tools/server/webui/src/lib/services/context.ts diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 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a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte @@ -7,6 +7,7 @@ ChatMessages, ChatProcessingInfo, EmptyFileAlertDialog, + ChatErrorDialog, ServerErrorSplash, ServerInfo, ServerLoadingSplash, @@ -22,10 +23,11 @@ activeMessages, activeConversation, deleteConversation, + dismissErrorDialog, + errorDialog, isLoading, sendMessage, - stopGeneration, - setMaxContextError + stopGeneration } from '$lib/stores/chat.svelte'; import { supportsVision, @@ -34,7 +36,6 @@ serverWarning, serverStore } from '$lib/stores/server.svelte'; - import { contextService } from '$lib/services'; import { parseFilesToMessageExtras } from '$lib/utils/convert-files-to-extra'; import { isFileTypeSupported } from '$lib/utils/file-type'; import { filterFilesByModalities } from '$lib/utils/modality-file-validation'; @@ -79,6 +80,7 @@ showCenteredEmpty && !activeConversation() && activeMessages().length === 0 && !isLoading() ); + let activeErrorDialog = $derived(errorDialog()); let isServerLoading = $derived(serverLoading()); async function handleDeleteConfirm() { @@ -105,6 +107,12 @@ } } + function handleErrorDialogOpenChange(open: boolean) { + if (!open) { + dismissErrorDialog(); + } + } + function handleDragOver(event: DragEvent) { event.preventDefault(); } @@ -183,21 +191,6 @@ const extras = result?.extras; - // Check context limit using real-time slots data - const contextCheck = await contextService.checkContextLimit(); - - if (contextCheck && contextCheck.wouldExceed) { - const errorMessage = contextService.getContextErrorMessage(contextCheck); - - setMaxContextError({ - message: errorMessage, - estimatedTokens: contextCheck.currentUsage, - maxContext: contextCheck.maxContext - }); - - return false; - } - // Enable autoscroll for user-initiated message sending userScrolledUp = false; autoScrollEnabled = true; @@ -461,6 +454,13 @@ }} /> + +

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