diff --git a/src/models/kimi-linear.cpp b/src/models/kimi-linear.cpp index 50cebb9631..25eccd2f7d 100644 --- a/src/models/kimi-linear.cpp +++ b/src/models/kimi-linear.cpp @@ -3,6 +3,67 @@ #define CHUNK_SIZE 64 +// Causal Conv1d function for Q,K,V +// When qkv is 0, it is Q, 1 is K, 2 is V +static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_tensor * conv_states_all, ggml_tensor * conv_state_all, int64_t qkv, ggml_tensor * x, ggml_tensor * proj_w, ggml_tensor * conv_w, ggml_tensor * conv_b, int64_t d_conv, int64_t head_dim, int64_t n_head, int64_t n_seq_tokens, int64_t n_seqs, int64_t n_tokens, int64_t kv_head) { + const int64_t d_inner = head_dim * n_head; + const int64_t conv_state_size = (d_conv - 1) * d_inner; + const int64_t n_embd_r_total = 3 * conv_state_size; // Q + K + V + + // conv_state_all is [n_embd_r_total, n_seqs], split into Q, K, V + // Each conv state is [(d_conv-1) * d_inner] per sequence, need to reshape to [d_conv-1, d_inner, n_seqs] + // Memory layout: for each seq, Q state is first conv_state_size elements, then K, then V + // conv_state_all has stride: nb[0] = element_size, nb[1] = n_embd_r_total * element_size + // View Q conv state: offset 0, size conv_state_size per seq + // conv_state_all is [n_embd_r_total, n_seqs] with memory layout: + // state[i + seq * n_embd_r_total] where i = conv_step + channel * (d_conv-1) + {0, conv_state_size, 2*conv_state_size} for Q/K/V + // We want [d_conv-1, d_inner, n_seqs] view: + // nb1 = (d_conv-1) * element_size (stride between channels) + // nb2 = n_embd_r_total * element_size (stride between seqs) + ggml_tensor * conv_state_x = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs, + (d_conv - 1) * ggml_element_size(conv_state_all), // nb1: stride between channels + n_embd_r_total * ggml_element_size(conv_state_all), // nb2: stride between seqs + qkv * conv_state_size * ggml_element_size(conv_state_all)); + +// Causal Conv1d function for Q,K,V +// When qkv is 0, it is Q, 1 is K, 2 is V + // Step 1: Q, K, V projections -> [d_inner, n_tokens] + ggml_tensor * x_proj = ggml_mul_mat(ctx0, proj_w, x); + + // Reshape input: {d_inner, n_tokens} -> {d_inner, n_seq_tokens, n_seqs} + ggml_tensor * x_3d = ggml_reshape_3d(ctx0, x_proj, d_inner, n_seq_tokens, n_seqs); + + // Concat Q conv state and current input: {d_conv-1 + n_seq_tokens, d_inner, n_seqs} + ggml_tensor * conv_x = ggml_cont(ctx0, ggml_concat(ctx0, conv_state_x, ggml_transpose(ctx0, x_3d), 0)); + + // Save last (d_conv-1) columns back to Q conv state + ggml_tensor * last_conv_x = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs, + conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]); + ggml_build_forward_expand(gf, + ggml_cpy(ctx0, last_conv_x, + ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs, + (kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all)))); + // Reshape conv weight: GGUF [d_conv, 1, d_inner, 1] -> ggml_ssm_conv expects [d_conv, d_inner] + // GGUF stores as [d_conv, 1, d_inner, 1] with memory layout w[conv_step + channel * d_conv] + // vLLM stores as [d_inner, d_conv] with memory layout w[channel * d_conv + conv_step] + // ggml_ssm_conv computes: c[conv_step + channel * d_conv] + // GGUF layout: [d_conv, 1, d_inner] or [d_conv, 1, d_inner, 1] -> reshape to [d_conv, d_inner] + // Reshape conv weight from [d_conv, 1, d_inner, 1] to [d_conv, d_inner] for ggml_ssm_conv + ggml_tensor * conv_weight = ggml_reshape_2d(ctx0, conv_w, d_conv, d_inner); + + // Apply conv1d + // ggml_ssm_conv output: {d_inner, n_seq_tokens, n_seqs} + ggml_tensor * Xcur = ggml_ssm_conv(ctx0, conv_x, conv_weight); + // Reshape to 2D for bias add: {d_inner, n_tokens} + Xcur = ggml_reshape_2d(ctx0, Xcur, d_inner, n_tokens); + if (conv_b) { + Xcur = ggml_add(ctx0, Xcur, conv_b); + } + Xcur = ggml_silu(ctx0, Xcur); + + return ggml_reshape_4d(ctx0, Xcur, head_dim, n_head, n_seq_tokens, n_seqs); +} + llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params), model(model) { ggml_tensor * cur; @@ -78,138 +139,10 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll // Get conv states from r_l tensor (Q, K, V each have separate state) ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); cb(conv_states_all, "conv_states_all", il); - const int64_t conv_state_size = (d_conv - 1) * d_inner; - const int64_t n_embd_r_total = 3 * conv_state_size; // Q + K + V ggml_tensor * conv_state_all = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs); - // conv_state_all is [n_embd_r_total, n_seqs], split into Q, K, V - // Each conv state is [(d_conv-1) * d_inner] per sequence, need to reshape to [d_conv-1, d_inner, n_seqs] - // Memory layout: for each seq, Q state is first conv_state_size elements, then K, then V - // conv_state_all has stride: nb[0] = element_size, nb[1] = n_embd_r_total * element_size - // View Q conv state: offset 0, size conv_state_size per seq - // conv_state_all is [n_embd_r_total, n_seqs] with memory layout: - // state[i + seq * n_embd_r_total] where i = conv_step + channel * (d_conv-1) + {0, conv_state_size, 2*conv_state_size} for Q/K/V - // We want [d_conv-1, d_inner, n_seqs] view: - // nb1 = (d_conv-1) * element_size (stride between channels) - // nb2 = n_embd_r_total * element_size (stride between seqs) - ggml_tensor * conv_state_q = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs, - (d_conv - 1) * ggml_element_size(conv_state_all), // nb1: stride between channels - n_embd_r_total * ggml_element_size(conv_state_all), // nb2: stride between seqs - 0); // offset for Q - ggml_tensor * conv_state_k = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs, - (d_conv - 1) * ggml_element_size(conv_state_all), - n_embd_r_total * ggml_element_size(conv_state_all), - conv_state_size * ggml_element_size(conv_state_all)); // offset for K - ggml_tensor * conv_state_v = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs, - (d_conv - 1) * ggml_element_size(conv_state_all), - n_embd_r_total * ggml_element_size(conv_state_all), - 2 * conv_state_size * ggml_element_size(conv_state_all)); // offset for V - - // Step 1: Q, K, V projections -> [d_inner, n_tokens] - ggml_tensor * q_proj = ggml_mul_mat(ctx0, layer.wq, cur); - ggml_tensor * k_proj = ggml_mul_mat(ctx0, layer.wk, cur); - ggml_tensor * v_proj = ggml_mul_mat(ctx0, layer.wv, cur); - cb(q_proj, "kda_q_proj", il); - cb(k_proj, "kda_k_proj", il); - cb(v_proj, "kda_v_proj", il); - - // Step 2: Causal Conv1d for Q - // Reshape input: {d_inner, n_tokens} -> {d_inner, n_seq_tokens, n_seqs} - ggml_tensor * q_3d = ggml_reshape_3d(ctx0, q_proj, d_inner, n_seq_tokens, n_seqs); - - // Concat Q conv state and current input: {d_conv-1 + n_seq_tokens, d_inner, n_seqs} - ggml_tensor * conv_q = ggml_concat(ctx0, conv_state_q, ggml_transpose(ctx0, q_3d), 0); - - // Save last (d_conv-1) columns back to Q conv state - ggml_tensor * last_conv_q = ggml_view_3d(ctx0, conv_q, d_conv - 1, d_inner, n_seqs, - conv_q->nb[1], conv_q->nb[2], n_seq_tokens * conv_q->nb[0]); - ggml_build_forward_expand(gf, - ggml_cpy(ctx0, last_conv_q, - ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs, - kv_head * n_embd_r_total * ggml_element_size(conv_states_all)))); - // Reshape conv weight: GGUF [d_conv, 1, d_inner, 1] -> ggml_ssm_conv expects [d_conv, d_inner] - // GGUF stores as [d_conv, 1, d_inner, 1] with memory layout w[conv_step + channel * d_conv] - // vLLM stores as [d_inner, d_conv] with memory layout w[channel * d_conv + conv_step] - // ggml_ssm_conv computes: c[conv_step + channel * d_conv] - // GGUF layout: [d_conv, 1, d_inner] or [d_conv, 1, d_inner, 1] -> reshape to [d_conv, d_inner] - ggml_tensor * conv_weight = nullptr; - if (layer.ssm_q_conv) { - // Reshape conv weight from [d_conv, 1, d_inner, 1] to [d_conv, d_inner] for ggml_ssm_conv - conv_weight = ggml_reshape_2d(ctx0, layer.ssm_q_conv, d_conv, d_inner); - } - - // Apply conv1d - ggml_tensor * Qcur; - if (conv_weight) { - // Make conv_q contiguous for ggml_ssm_conv - conv_q = ggml_cont(ctx0, conv_q); - - // ggml_ssm_conv output: {d_inner, n_seq_tokens, n_seqs} - Qcur = ggml_ssm_conv(ctx0, conv_q, conv_weight); - cb(Qcur, "Q conv1d", il); - // Reshape to 2D for bias add: {d_inner, n_tokens} - Qcur = ggml_reshape_2d(ctx0, Qcur, d_inner, n_tokens); - if (layer.ssm_q_conv_b) { - Qcur = ggml_add(ctx0, Qcur, layer.ssm_q_conv_b); - } - Qcur = ggml_silu(ctx0, Qcur); - cb(Qcur, "Q conv1d b", il); - } else { - GGML_ABORT("KDA layer missing Q conv weight"); - } - - // K conv1d (with separate K conv state) - ggml_tensor * Kcur; - if (layer.ssm_k_conv) { - ggml_tensor * k_3d = ggml_reshape_3d(ctx0, k_proj, d_inner, n_seq_tokens, n_seqs); - ggml_tensor * conv_k = ggml_concat(ctx0, conv_state_k, ggml_transpose(ctx0, k_3d), 0); - - // Save K conv state - ggml_tensor * last_conv_k = ggml_view_3d(ctx0, conv_k, d_conv - 1, d_inner, n_seqs, - conv_k->nb[1], conv_k->nb[2], n_seq_tokens * conv_k->nb[0]); - ggml_build_forward_expand(gf, - ggml_cpy(ctx0, last_conv_k, - ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs, - (kv_head * n_embd_r_total + conv_state_size) * ggml_element_size(conv_states_all)))); - - ggml_tensor * k_conv_weight = ggml_reshape_2d(ctx0, layer.ssm_k_conv, d_conv, d_inner); - Kcur = ggml_ssm_conv(ctx0, conv_k, k_conv_weight); - cb(Kcur, "K conv1d", il); - Kcur = ggml_reshape_2d(ctx0, Kcur, d_inner, n_tokens); - if (layer.ssm_k_conv_b) { - Kcur = ggml_add(ctx0, Kcur, layer.ssm_k_conv_b); - } - Kcur = ggml_silu(ctx0, Kcur); - cb(Kcur, "K conv1d b", il); - } else { - GGML_ABORT("KDA layer missing K conv weight"); - } - - // V conv1d (with separate V conv state) - ggml_tensor * Vcur; - if (layer.ssm_v_conv) { - ggml_tensor * v_3d = ggml_reshape_3d(ctx0, v_proj, d_inner, n_seq_tokens, n_seqs); - ggml_tensor * conv_v = ggml_concat(ctx0, conv_state_v, ggml_transpose(ctx0, v_3d), 0); - - // Save V conv state - ggml_tensor * last_conv_v = ggml_view_3d(ctx0, conv_v, d_conv - 1, d_inner, n_seqs, - conv_v->nb[1], conv_v->nb[2], n_seq_tokens * conv_v->nb[0]); - ggml_build_forward_expand(gf, - ggml_cpy(ctx0, last_conv_v, - ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs, - (kv_head * n_embd_r_total + 2 * conv_state_size) * ggml_element_size(conv_states_all)))); - - ggml_tensor * v_conv_weight = ggml_reshape_2d(ctx0, layer.ssm_v_conv, d_conv, d_inner); - Vcur = ggml_ssm_conv(ctx0, conv_v, v_conv_weight); - cb(Vcur, "V conv1d", il); - Vcur = ggml_reshape_2d(ctx0, Vcur, d_inner, n_tokens); - if (layer.ssm_v_conv_b) { - Vcur = ggml_add(ctx0, Vcur, layer.ssm_v_conv_b); - } - Vcur = ggml_silu(ctx0, Vcur); - cb(Vcur, "V conv1d b", il); - } else { - GGML_ABORT("KDA layer missing V conv weight"); - } + ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv, layer.ssm_q_conv_b, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head); + ggml_tensor * Kcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 1, cur, layer.wk, layer.ssm_k_conv, layer.ssm_k_conv_b, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head); + ggml_tensor * Vcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 2, cur, layer.wv, layer.ssm_v_conv, layer.ssm_v_conv_b, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head); // Step 3: Compute g1 (forget gate) // g1 = -exp(A_log) * softplus(f_b(f_a(x)) + dt_bias) @@ -237,13 +170,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll // {n_embd, n_tokens} -> {n_embd, n_seq_tokens, n_seqs} cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs); - Qcur = ggml_reshape_4d(ctx0, Qcur, head_dim, n_head, n_seq_tokens, n_seqs); - Kcur = ggml_reshape_4d(ctx0, Kcur, head_dim, n_head, n_seq_tokens, n_seqs); - Vcur = ggml_reshape_4d(ctx0, Vcur, head_dim, n_head, n_seq_tokens, n_seqs); g1 = ggml_reshape_4d(ctx0, g1, head_dim, n_head, n_seq_tokens, n_seqs); - cb(Qcur, "kda_Q", il); - cb(Kcur, "kda_K", il); - cb(Vcur, "kda_V", il); // Step 6: Get SSM state and compute KDA recurrence using ggml_kda_scan ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);