Lumpiasty/llama.cpp
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convert : add --fuse-qkv flag to fuse Q/K/V into QKV during HF-to-GGUF conversion (#22780)

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PikaPikachu
2026-09-07 00:47:05 +08:00
committed by GitHub
parent 5fdfa62829
commit 465e49b9ce
20 changed files with 396 additions and 125 deletions
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src/models/deepseek2.cpp
+2 -11
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@@ -475,21 +475,12 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
const int ocr_rope_type = GGML_ROPE_TYPE_NEOX;
GGML_ASSERT(n_embed_head == n_embd_head_k && n_embed_head == n_embd_head_v);
ggml_tensor * Qcur = NULL;
ggml_tensor * Kcur = NULL;
ggml_tensor * Vcur = NULL;
Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embed_head, n_head, n_head, il);
cb(Qcur, "q", il);
cb(Kcur, "k", il);
cb(Vcur, "v", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embed_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embed_head, n_head, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embed_head, n_head, n_tokens);
GGML_ASSERT(fabs(freq_base - 10000.0) < 1e-4);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);
src/models/deepseek2ocr.cpp
+1 -3
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@@ -40,9 +40,7 @@ void llama_model_deepseek2ocr::load_arch_tensors(llama_model_loader &) {
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd}, 0);
create_tensor_qkv(layer, i, n_embd, n_embd, n_embd, n_embd, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
// norm
src/models/gemma3n.cpp
+8 -1
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@@ -176,7 +176,14 @@ llama_model_gemma3n::graph::graph(const llama_model & model, const llm_graph_par
hparams.f_attention_scale, il);
} else {
// reuse KV cache of earlier layers
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
ggml_tensor * Qcur;
if (model.layers[il].wqkv) {
ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
const int64_t q_dim = n_embd_head * n_head;
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, q_dim, n_tokens, qkv->nb[1], 0));
} else {
Qcur = build_lora_mm(model.layers[il].wq, cur);
}
cb(Qcur, "Qcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
src/models/gemma4.cpp
+31 -9
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@@ -75,9 +75,13 @@ void llama_model_gemma4::load_arch_tensors(llama_model_loader &) {
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
// note: use_alternative_attention (v_proj is optional, if it's not present, use k_proj)
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, kv_flags);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, TENSOR_NOT_REQUIRED);
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i),
{n_embd, n_embd_head * n_head + n_embd_k + n_embd_v}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
if (!layer.wqkv) {
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, kv_flags);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, TENSOR_NOT_REQUIRED);
}
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head}, 0);
@@ -202,9 +206,17 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
// Q projection (shared for both non-KV and KV layers)
// this is to mirror Gemma4Attention in pytorch code
ggml_tensor * qkv_fused = nullptr;
ggml_tensor * Qcur;
{
if (model.layers[il].wqkv) {
qkv_fused = build_lora_mm(model.layers[il].wqkv, cur, model.layers[il].wqkv_s);
cb(qkv_fused, "wqkv", il);
const int64_t q_dim = n_embd_head * n_head;
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv_fused, q_dim, n_tokens, qkv_fused->nb[1], 0));
} else {
Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s);
}
{
cb(Qcur, "Qcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
@@ -219,12 +231,22 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
// self-attention
if (hparams.has_kv(il)) {
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
ggml_tensor * Kcur;
ggml_tensor * Vcur;
if (qkv_fused) {
const int64_t q_dim = n_embd_head * n_head;
const int64_t k_dim = n_embd_head * n_head_kv;
const int64_t v_dim = n_embd_head * n_head_kv;
const size_t esize = ggml_element_size(qkv_fused);
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv_fused, k_dim, n_tokens, qkv_fused->nb[1], q_dim * esize));
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv_fused, v_dim, n_tokens, qkv_fused->nb[1], (q_dim + k_dim) * esize));
} else {
Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
Vcur = model.layers[il].wv
? build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s)
: Kcur; // if v_proj is not present, use Kcur as Vcur
}
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = model.layers[il].wv
? build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s)
: Kcur; // if v_proj is not present, use Kcur as Vcur
cb(Vcur, "Vcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
src/models/jais2.cpp
+1 -7
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@@ -29,15 +29,9 @@ void llama_model_jais2::load_arch_tensors(llama_model_loader &) {
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
// attention biases - all have shape n_embd (output dimension of projections)
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd}, 0);
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd}, 0);
layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
src/models/kimi-linear.cpp
+15 -4
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@@ -195,7 +195,7 @@ static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_t
// 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);
ggml_tensor * x_proj = proj_w ? ggml_mul_mat(ctx0, proj_w, x) : 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);
@@ -295,9 +295,20 @@ llama_model_kimi_linear::graph::graph(const llama_model & model, const llm_graph
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);
ggml_tensor * q_in = cur, * k_in = cur, * v_in = cur;
ggml_tensor * q_w = layer.wq, * k_w = layer.wk, * v_w = layer.wv;
if (layer.wqkv) {
ggml_tensor * qkv = ggml_mul_mat(ctx0, layer.wqkv, cur);
const int64_t d_inner = head_dim * n_head;
const size_t esize = ggml_element_size(qkv);
q_in = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, d_inner, n_tokens, qkv->nb[1], 0));
k_in = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, d_inner, n_tokens, qkv->nb[1], d_inner * esize));
v_in = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, d_inner, n_tokens, qkv->nb[1], 2 * d_inner * esize));
q_w = nullptr; k_w = nullptr; v_w = nullptr;
}
ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, q_in, q_w, 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, k_in, k_w, 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, v_in, v_w, 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);
src/models/llada.cpp
+1 -6
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@@ -36,12 +36,7 @@ void llama_model_llada::load_arch_tensors(llama_model_loader &) {
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
// Use separate Q, K, V projections without bias, matching LLaDALlamaBlock
layer.wq =
create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
// No bias for QKV projections as per config: include_bias=false, include_qkv_bias=false
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
layer.wo =
create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
src/models/minimax-m2.cpp
+5 -6
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@@ -71,14 +71,13 @@ llama_model_minimax_m2::graph::graph(const llama_model & model, const llm_graph_
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
src/models/olmo2.cpp
+5 -6
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@@ -93,14 +93,13 @@ llama_model_olmo2::graph<iswa>::graph(const llama_model & model, const llm_graph
// self_attention
{
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
src/models/olmoe.cpp
+5 -6
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@@ -79,14 +79,13 @@ llama_model_olmoe::graph::graph(const llama_model & model, const llm_graph_param
// self_attention
{
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
src/models/qwen35.cpp
+12 -10
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@@ -263,8 +263,14 @@ ggml_tensor * llama_model_qwen35::graph::build_layer_attn(
// Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
// Qwen3Next uses a single Q projection that outputs query + gate
ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); // [ (n_embd_head * 2) * n_head, n_tokens ]
auto [Qcur_full, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "Qcur_full", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
ggml_element_size(Qcur_full) * n_embd_head * 2,
@@ -275,12 +281,6 @@ ggml_tensor * llama_model_qwen35::graph::build_layer_attn(
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);
cb(Vcur, "Vcur", il);
// Apply K normalization
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
@@ -554,7 +554,11 @@ llama_model_qwen35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);
auto [Qcur_full, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "mtp_Qcur_full", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,
@@ -573,12 +577,10 @@ llama_model_qwen35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
cb(gate, "mtp_gate", il);
ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
cb(Kcur, "mtp_Kcur_normed", il);
ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
cb(Vcur, "mtp_Vcur", il);
src/models/qwen35moe.cpp
+12 -10
View File
@@ -287,8 +287,14 @@ ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn(
// Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
// Qwen3Next uses a single Q projection that outputs query + gate
ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); // [ (n_embd_head * 2) * n_head, n_tokens ]
auto [Qcur_full, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "Qcur_full", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
ggml_element_size(Qcur_full) * n_embd_head * 2,
@@ -299,12 +305,6 @@ ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn(
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);
cb(Vcur, "Vcur", il);
// Apply K normalization
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
@@ -618,7 +618,11 @@ llama_model_qwen35moe::graph_mtp::graph_mtp(const llama_model & model, const llm
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);
auto [Qcur_full, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "mtp_Qcur_full", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,
@@ -637,12 +641,10 @@ llama_model_qwen35moe::graph_mtp::graph_mtp(const llama_model & model, const llm
gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
cb(gate, "mtp_gate", il);
ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
cb(Kcur, "mtp_Kcur_normed", il);
ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
cb(Vcur, "mtp_Vcur", il);
src/models/qwen3next.cpp
+12 -10
View File
@@ -244,8 +244,14 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn(
// Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
// Qwen3Next uses a single Q projection that outputs query + gate
ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s);
auto [Qcur_full, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "Qcur_full", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur_full = ggml_reshape_4d(ctx0, Qcur_full, n_embd_head * 2, n_head, n_tokens, 1);
@@ -260,12 +266,6 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn(
Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], n_embd_head * ggml_element_size(Qcur_full));
cb(gate, "gate", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);
cb(Vcur, "Vcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
@@ -692,7 +692,11 @@ llama_model_qwen3next::graph_mtp::graph_mtp(const llama_model & model, const llm
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);
auto [Qcur_full, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "mtp_Qcur_full", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,
@@ -703,12 +707,10 @@ llama_model_qwen3next::graph_mtp::graph_mtp(const llama_model & model, const llm
Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "mtp_Qcur_normed", il);
ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
cb(Kcur, "mtp_Kcur_normed", il);
ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
src/models/step35.cpp
+10 -6
View File
@@ -216,9 +216,11 @@ llama_model_step35::graph::graph(const llama_model & model, const llm_graph_para
{
cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head_k, n_head_l,
n_embd_head_k, n_head_kv_l,
n_embd_head_v, n_head_kv_l,
il, false);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
@@ -425,9 +427,11 @@ llama_model_step35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * Qcur = build_lora_mm(layer.wq, cur, layer.wq_s);
ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head_k, n_head_l,
n_embd_head_k, n_head_kv_l,
n_embd_head_v, n_head_kv_l,
il, false);
cb(Qcur, "mtp_Qcur", il);
cb(Kcur, "mtp_Kcur", il);
cb(Vcur, "mtp_Vcur", il);