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llama.cpp fork for Ternary Bonsai 2 27B: MTP speculation, suffix cache reuse (SCR), q8 KV, two-slot fairness, fused DeltaNet kernels
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at main
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void llama_model_kimi_linear::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl); ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl); ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv); ml.get_key(LLM_KV_KDA_HEAD_DIM, hparams.n_embd_head_kda);
// MLA qk_rope_head_dim (for reference) // qk_rope_head_dim = 64, qk_nope_head_dim = 128, qk_head_dim = 192
// Mark KDA layers as recurrent using n_head_kv pattern (like Jamba) // Set n_head_kv = 0 for KDA layers (recurrent), n_head_kv = n_head for MLA layers (attention) for (uint32_t i = 0; i < hparams.n_layer(); ++i) { hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0; // KDA layers are recurrent }
// MoE parameters - Kimi uses moe_intermediate_size = 1024 ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
switch (hparams.n_layer()) { case 27: type = LLM_TYPE_48B_A3B; break; // Kimi-Linear-48B-A3B default: type = LLM_TYPE_UNKNOWN; }}
void llama_model_kimi_linear::load_arch_tensors(llama_model_loader &) { LLAMA_LOAD_LOCALS;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
for (int i = 0; i < n_layer; ++i) { auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
// Check for KDA specific tensors to determine layer type or if it's a mixed model // Assuming KDA layer if KDA tensors are present
// KDA uses head_dim = 128 (from linear_attn_config.head_dim) const int64_t n_embd_head_k_kda = hparams.n_embd_head_kda; const int64_t n_embd_head_v_kda = hparams.n_embd_head_kda; const int64_t ssm_d_conv = hparams.ssm_d_conv;
if (hparams.is_recr(i)) { // Conv1d weights: try 4D first, then 3D (quantization may remove trailing 1) // 4D: [d_conv, 1, d_inner, 1], 3D: [d_conv, 1, d_inner] layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED); if (!layer.ssm_q_conv) { layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, 0); }
// KDA Layer - Conv1d weights may be 3D or 4D layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED); if (!layer.ssm_k_conv) { layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, 0); } layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head, 1}, TENSOR_NOT_REQUIRED); if (!layer.ssm_v_conv) { layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head}, 0); }
// q, k, v projections // Python: q_proj, k_proj, v_proj create_tensor_qkv(layer, i, n_embd, n_embd_head_k_kda * n_head, n_embd_head_k_kda * n_head, n_embd_head_v_kda * n_head, 0);
// KDA specific projections // f_a_proj, f_b_proj layer.ssm_f_a = create_tensor(tn(LLM_TENSOR_SSM_F_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0); // head_dim layer.ssm_f_b = create_tensor(tn(LLM_TENSOR_SSM_F_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0); // projection_size
// b_proj (beta mixing coefficient) layer.ssm_beta = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), {n_embd, n_head}, 0);
// A_log - Shape in GGUF: [1, num_heads, 1, 1] (4D) or [1, num_heads] (2D after quantization) Note: -exp(A_log) is applied in convert_hf_to_gguf.py layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head, 1, 1}, TENSOR_NOT_REQUIRED); if (!layer.ssm_a) { layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0); }
// dt_bias - shape [n_embd_head_k_kda * n_head] = [4096] layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_embd_head_k_kda * n_head}, 0);
// g_a_proj, g_b_proj (output gate) layer.ssm_g_a = create_tensor(tn(LLM_TENSOR_SSM_G_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0); layer.ssm_g_b = create_tensor(tn(LLM_TENSOR_SSM_G_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0);
// o_norm (reusing SSM_NORM) layer.ssm_o_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {n_embd_head_k_kda}, 0); // FusedRMSNormGated
// o_proj layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v_kda * n_head, n_embd}, 0);
} else { // MLA Layer - use MLA-specific head dimensions const int64_t q_lora_rank = hparams.n_lora_q; const int64_t kv_lora_rank = hparams.n_lora_kv; const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, TENSOR_NOT_REQUIRED); layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
if (layer.attn_q_a_norm) { layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0); } else { // Kimi MLA without Q compression: wq = [n_embd, n_head * n_embd_head_k_mla] layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0); }
// Kimi: qk_rope_head_dim = 64 (actual RoPE dimension for MLA) // Note: hparams.n_rot may be 72 (from conversion) but actual is 64 const int64_t qk_rope_head_dim = hparams.n_rot(); // From config: qk_rope_head_dim layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0); // Support Legacy GGUFs that don't split wkv_b (MLA KV cache disabled) layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL); if (!layer.wkv_b) { // MLA KV cache enabled layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_k_mla - qk_rope_head_dim, kv_lora_rank, n_head}, 0); layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0); } layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0); }
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
// MoE intermediate size (different from dense FFN) const int64_t n_ff_exp = hparams.n_ff_exp;
// Kimi uses n_layer_dense_lead to determine which layers use dense FFN vs MoE // first_k_dense_replace = 1 means layer 0 uses dense FFN, layers 1+ use MoE if (i < (int) hparams.n_layer_dense_lead) { // Dense FFN layer - use normal n_ff layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } else { // MoE layer - use n_ff_exp (1024) instead of n_ff (9216) layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
// Shared experts use moe_intermediate_size * num_shared_experts // Kimi: shared_expert_intermediate_size = 1024 * 1 = 1024 // Tensors are 2D: [n_embd, n_ff_shexp] or [n_ff_shexp, n_embd] const int64_t n_ff_shexp_actual = n_ff_exp * (hparams.n_expert_shared > 0 ? hparams.n_expert_shared : 1); layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED); layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp_actual, n_embd}, TENSOR_NOT_REQUIRED); layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); } }}
std::unique_ptr<llm_graph_context> llama_model_kimi_linear::build_arch_graph(const llm_graph_params & params) const { return std::make_unique<graph>(*this, params);}
// Causal Conv1d function for Q,K,V// When qkv is 0, it is Q, 1 is K, 2 is Vstatic 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, 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_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_3d(ctx0, conv_states_all, d_conv - 1, d_inner, n_seqs, (d_conv - 1) * ggml_element_size(conv_states_all), // nb1: contiguous within one channel's conv taps n_embd_r_total * ggml_element_size(conv_states_all), // nb2: stride between sequences (skip over K,V states) (kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all)))); // offset to first seq's Q/K/V state // 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); Xcur = ggml_silu(ctx0, Xcur);
return ggml_reshape_4d(ctx0, Xcur, head_dim, n_head, n_seq_tokens, n_seqs);}
llama_model_kimi_linear::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_build_delta_net_base(params), model(model) { ggml_tensor * cur; ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd); cb(inpL, "model.embed_tokens", -1);
// Note: Kimi MLA does NOT use RoPE (rotary_emb=None in vLLM) // So we don't need inp_pos
auto * inp_kv = !hparams.is_mla() ? build_inp_mem_hybrid() : nullptr; auto * inp_k = hparams.is_mla() ? build_inp_mem_hybrid_k() : nullptr; auto * inp_rs = hparams.is_mla() ? inp_k->get_recr() : inp_kv->get_recr(); auto * inp_attn_kv = !hparams.is_mla() ? inp_kv->get_attn() : nullptr; auto * inp_attn_k = hparams.is_mla() ? inp_k->get_attn() : nullptr;
// Output ids for selecting which tokens to output ggml_tensor * inp_out_ids = build_inp_out_ids();
// Kimi dimension constants const int64_t n_head = hparams.n_head(); const int64_t head_dim = hparams.n_embd_head_kda; const int64_t d_conv = hparams.ssm_d_conv; const int64_t d_inner = n_head * head_dim; // 32 * 128 = 4096 const int64_t n_seqs = ubatch.n_seqs; const int64_t n_seq_tokens = ubatch.n_seq_tokens;
// Verify batch consistency for recurrent layers GGML_ASSERT(n_seqs != 0); GGML_ASSERT(ubatch.equal_seqs()); GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
// MLA params const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); const int64_t kv_lora_rank = hparams.n_lora_kv; // qk_rope_head_dim = 64 (from Kimi config) which is hparams.n_rot // Confirmed from tensor shape: wkv_a_mqa [2304, 576] = [n_embd, kv_lora_rank + qk_rope_head_dim] const int64_t n_embd_head_qk_rope = hparams.n_rot(); // config.qk_rope_head_dim const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; // 192 - 64 = 128 // Attention scale for MLA const float kq_scale_mla = 1.0f / sqrtf((float)n_embd_head_k_mla);
for (int il = 0; il < n_layer; ++il) { const auto & layer = model.layers[il]; ggml_tensor * inpSA = inpL;
// Attention Norm cur = build_norm(inpL, layer.attn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "attn_norm", il);
ggml_build_forward_expand(gf, cur);
if (hparams.is_recr(il)) { // === KDA Layer (Kimi Delta Attention) with Recurrent State === // Reference: vLLM kda.py const auto * mctx_cur = inp_rs->mctx; const auto kv_head = mctx_cur->get_head();
// 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); ggml_tensor * conv_state_all = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs); ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv, 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, 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, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
// g1 = -exp(A_log) * softplus(f_b(f_a(x)) + dt_bias) ggml_tensor * f_a = ggml_mul_mat(ctx0, layer.ssm_f_a, cur); ggml_tensor * g1 = ggml_mul_mat(ctx0, layer.ssm_f_b, f_a); cb(g1, "g1 f_b(f_a(cur))", il); g1 = ggml_add(ctx0, g1, layer.ssm_dt_b); g1 = ggml_softplus(ctx0, g1); g1 = ggml_reshape_3d(ctx0, g1, head_dim, n_head, n_tokens);
// A_log shape is [1, n_head] or [1, n_head, 1, 1], need to broadcast to [head_dim, n_head, n_tokens]. No need to -exp(a_log) because it was done in convert_hf_to_gguf.py // Reshape to [1, n_head, 1] for broadcasting with g1 [head_dim, n_head, n_tokens] ggml_tensor * A = ggml_reshape_3d(ctx0, layer.ssm_a, 1, n_head, 1); g1 = ggml_mul(ctx0, g1, A); cb(g1, "kda_g1", il);
g1 = ggml_reshape_4d(ctx0, g1, head_dim, n_head, n_seq_tokens, n_seqs);
// Compute beta (mixing coefficient) ggml_tensor * beta = ggml_mul_mat(ctx0, layer.ssm_beta, cur); beta = ggml_reshape_4d(ctx0, beta, 1, n_head, n_seq_tokens, n_seqs); cb(beta, "kda_beta", il);
beta = ggml_sigmoid(ctx0, beta);
// Reshape for KDA recurrence // {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);
// Get SSM state and compute KDA recurrence using ggml_kda_scan ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); ggml_tensor * state = build_rs(inp_rs, ssm_states_all, hparams.n_embd_s(), n_seqs); state = ggml_reshape_4d(ctx0, state, head_dim, head_dim, n_head, n_seqs);
const float eps_norm = hparams.f_norm_rms_eps;
Qcur = ggml_l2_norm(ctx0, Qcur, eps_norm); Kcur = ggml_l2_norm(ctx0, Kcur, eps_norm);
// Choose between build_delta_net_chunking and build_delta_net_recurrent based on n_tokens auto attn_out = build_delta_net(Qcur, Kcur, Vcur, g1, beta, state, il);
ggml_tensor * output = ggml_cont(ctx0, attn_out.first); ggml_tensor * new_state = attn_out.second; cb(output, "attn_output", il); cb(new_state, "new_state", il);
// Update the recurrent states ggml_build_forward_expand(gf, ggml_cpy(ctx0, new_state, ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs, kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
// Output gating g2 = g_b(g_a(x)) ggml_tensor * cur_2d = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs); ggml_tensor * g_a = ggml_mul_mat(ctx0, layer.ssm_g_a, cur_2d); ggml_tensor * g2 = ggml_mul_mat(ctx0, layer.ssm_g_b, g_a); cb(g2, "g2 g_b(g_a(cur_2d))", il); g2 = ggml_reshape_3d(ctx0, g2, head_dim, n_head, n_seq_tokens * n_seqs);
// Apply o_norm with sigmoid gating // Note: Kimi model uses sigmoid gating, not SiLU (despite FusedRMSNormGated default being swish) // Formula: output = RMSNorm(x) * sigmoid(g) ggml_tensor * attn_out_final = ggml_reshape_3d(ctx0, output, head_dim, n_head, n_seq_tokens * n_seqs); ggml_tensor * normed = build_norm(attn_out_final, layer.ssm_o_norm, nullptr, LLM_NORM_RMS, il); cb(normed, "kda_normed", il); ggml_tensor * gate = ggml_sigmoid(ctx0, g2); ggml_tensor * gated = ggml_mul(ctx0, normed, gate);
// Output projection gated = ggml_cont_2d(ctx0, gated, d_inner, n_tokens); cur = ggml_mul_mat(ctx0, layer.wo, gated); cb(cur, "kda_out", il);
} else { // === MLA Layer (Multi-head Latent Attention) without KV Cache === // Reference: vLLM mla.py // Step 1: Q projection and reshape // vLLM Kimi: q = q_proj(hidden_states), then view as [n_tokens, n_head, qk_head_dim] // Note: Kimi MLA does NOT use RoPE (rotary_emb=None in vLLM) ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.wq, cur);
// Step 2: KV compression // kv_cmpr_pe = kv_a_proj_with_mqa(hidden_states) -> [kv_lora_rank + qk_rope_head_dim, n_tokens] ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
// Split: kv_cmpr = kv_lora[:kv_lora_rank], k_pe = kv_lora[kv_lora_rank:] ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens, ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0); ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens, ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), ggml_row_size(kv_cmpr_pe->type, kv_lora_rank)); // Note: Kimi MLA does NOT apply RoPE (rotary_emb=None in vLLM) // k_pe is used directly without RoPE // Normalize kv_c kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
if (layer.wk_b && layer.wv_b) { // MLA KV cache enabled // extract q_nope ggml_tensor * q_nope = ggml_view_3d(ctx0, Qcur, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla), ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, 0); cb(q_nope, "q_nope", il);
// and {n_embd_head_qk_rope, n_head, n_tokens} ggml_tensor * q_pe = ggml_view_3d( ctx0, Qcur, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla), ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, ggml_row_size(Qcur->type, n_embd_head_qk_nope)); cb(q_pe, "q_pe", il);
// {n_embd_head_qk_nope, n_tokens, n_head} q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); cb(q_nope, "q_nope_perm", il);
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head} ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope); cb(q_nope_absorbed, "q_nope_absorbed", il);
// {kv_lora_rank, n_head, n_tokens} q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3); cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens} // note: rope must go first for in-place context shifting in build_rope_shift() Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0); cb(Qcur, "Qcur", il);
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens); cb(kv_cmpr, "kv_cmpr_reshape", il);
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens} ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0); cb(Kcur, "Kcur", il);
// {kv_lora_rank, 1, n_tokens} ggml_tensor * Vcur = kv_cmpr; cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn_k, layer.wo, NULL, layer.wo_s, Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale_mla, il); cb(cur, "mla_out", il); } else { // MLA KV cache disabled. Fall back to MHA KV cache. Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k_mla, n_head, n_tokens); cb(Qcur, "mla_Q", il); // KV decompression: kv = kv_b_proj(kv_c_normed) ggml_tensor * kv = ggml_mul_mat(ctx0, layer.wkv_b, kv_cmpr); const int64_t kv_per_head = n_embd_head_qk_nope + n_embd_head_v_mla;
// Split kv into k_nope and v ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(kv->type, kv_per_head), ggml_row_size(kv->type, kv_per_head * n_head), 0); ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v_mla, n_head, n_tokens, ggml_row_size(kv->type, kv_per_head), ggml_row_size(kv->type, kv_per_head * n_head), ggml_row_size(kv->type, n_embd_head_qk_nope)); Vcur = ggml_cont(ctx0, Vcur); cb(Vcur, "mla_V", il);
// Concatenate k_nope + k_pe (broadcast k_pe to all heads) // K = [k_nope, k_pe] where k_nope is [qk_nope_head_dim, n_head, n_tokens] // and k_pe is [qk_rope_head_dim, 1, n_tokens] broadcast to all heads // Need to broadcast k_pe from [qk_rope, 1, n_tokens] to [qk_rope, n_head, n_tokens] ggml_tensor * k_pe_target = ggml_new_tensor_3d(ctx0, k_pe->type, n_embd_head_qk_rope, n_head, n_tokens); ggml_tensor * k_pe_repeated = ggml_repeat(ctx0, k_pe, k_pe_target); ggml_tensor * Kcur = ggml_concat(ctx0, k_pe_repeated, k_nope, 0); cb(Kcur, "mla_K", il);
// Direct softmax attention (with MHA KV cache) // Use build_attn with inp_attn for proper mask handling cur = build_attn(inp_attn_kv, layer.wo, NULL, layer.wo_s, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il); cb(cur, "mla_out", il); } }
// On last layer, select only the output tokens if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); }
// Residual ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cb(ffn_inp, "ffn_inp", il);
// FFN Norm cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "ffn_norm", il);
if ((uint32_t) il < hparams.n_layer_dense_lead) { // Dense FFN layer cur = build_ffn(cur, layer.ffn_up, NULL, NULL, layer.ffn_gate, NULL, NULL, layer.ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); } else { // MoE layer // Kimi uses moe_renormalize=True and routed_scaling_factor (stored as expert_weights_scale) = 2.446 ggml_tensor * moe_out = build_moe_ffn(cur, layer.ffn_gate_inp, layer.ffn_up_exps, layer.ffn_gate_exps, layer.ffn_down_exps, layer.ffn_exp_probs_b, hparams.n_expert, hparams.n_expert_used, LLM_FFN_SILU, true, hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il);
// Shared expert { ggml_tensor * ffn_shexp = build_ffn(cur, layer.ffn_up_shexp, NULL, NULL, layer.ffn_gate_shexp, NULL, NULL, layer.ffn_down_shexp, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp); cb(cur, "ffn_out", il); } } // Residual cur = ggml_add(ctx0, cur, ffn_inp);
cur = build_cvec(cur, il); cb(cur, "l_out", il);
// input for next layer inpL = cur; } cur = inpL;
// Final Norm cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1); res->t_embd = cur;
// Output cur = ggml_mul_mat(ctx0, model.output, cur); cb(cur, "result_output", -1); res->t_logits = cur;
ggml_build_forward_expand(gf, cur);}