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Merged
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Nov 14, 2025
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DeepSeek: enable option to merge Q and K tensors #941

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0576d42
Merge Q and K for DeepSeek
Nov 11, 2025
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Nov 11, 2025
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1 change: 1 addition & 0 deletions src/llama-arch.h
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Expand Up @@ -262,6 +262,7 @@ enum llm_tensor {
LLM_TENSOR_ATTN_Q_A,
LLM_TENSOR_ATTN_Q_B,
LLM_TENSOR_ATTN_KV_A_MQA,
LLM_TENSOR_ATTN_KQ_A_MQA,
LLM_TENSOR_ATTN_KV_B,
LLM_TENSOR_ATTN_K_B,
LLM_TENSOR_ATTN_V_B,
Expand Down
133 changes: 81 additions & 52 deletions src/llama-build-context.cpp
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Expand Up @@ -5927,6 +5927,7 @@ ggml_cgraph * llm_build_context::build_deepseek2() {
const uint32_t n_embd_head_qk_rope = hparams.n_rot;
const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
const uint32_t kv_lora_rank = hparams.n_lora_kv;
const uint32_t q_lora_rank = hparams.n_lora_q;

struct ggml_tensor * cur;
struct ggml_tensor * inpL;
Expand All @@ -5953,68 +5954,96 @@ ggml_cgraph * llm_build_context::build_deepseek2() {

// self_attention
{
struct ggml_tensor * q = NULL;
if (!is_lite) {
// {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
cb(q, "q", il);

q = llm_build_norm(ctx0, q, hparams, model.layers[il].attn_q_a_norm, NULL, LLM_NORM_RMS, cb, il);
cb(q, "q", il);

// {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
cb(q, "q", il);
} else {
q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(q, "q", il);
ggml_tensor * q = nullptr;
ggml_tensor * kv_rope_compressed = nullptr;
ggml_tensor * q_rope;
ggml_tensor * q_nope;
ggml_tensor * k_rope;
ggml_tensor * kv_compressed;
if (model.layers[il].wkq_a_mqa) {
auto mqa = ggml_mul_mat(ctx0, model.layers[il].wkq_a_mqa, cur);
cb(mqa, "mqa", il);
size_t qnb1;
if (!is_lite) {
q = ggml_view_2d(ctx0, mqa, q_lora_rank, n_tokens, mqa->nb[1], 0);
q = llm_build_norm(ctx0, q, hparams, model.layers[il].attn_q_a_norm, NULL, LLM_NORM_RMS, cb, il);
q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
qnb1 = q->nb[1];
cb(q, "q", il);
kv_rope_compressed = ggml_view_2d(ctx0, mqa, kv_lora_rank + n_embd_head_qk_rope, n_tokens, mqa->nb[1],
q_lora_rank*ggml_element_size(mqa));
} else {
q = ggml_view_2d(ctx0, mqa, n_embd_k_gqa, n_tokens, mqa->nb[1], 0);
kv_rope_compressed = ggml_view_2d(ctx0, mqa, kv_lora_rank + n_embd_head_qk_rope, n_tokens, mqa->nb[1],
n_embd_k_gqa*ggml_element_size(mqa));
qnb1 = mqa->nb[1];
}
q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
ggml_row_size(q->type, hparams.n_embd_head_k), qnb1, 0);
q_rope = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
ggml_row_size(q->type, hparams.n_embd_head_k), qnb1, ggml_row_size(q->type, n_embd_head_qk_nope));
k_rope = ggml_view_3d(ctx0, kv_rope_compressed, n_embd_head_qk_rope, 1, n_tokens,
mqa->nb[1], mqa->nb[1], ggml_row_size(kv_rope_compressed->type, kv_lora_rank));
kv_compressed = ggml_view_2d(ctx0, kv_rope_compressed, kv_lora_rank, n_tokens, mqa->nb[1], 0);
}
else {
if (!is_lite) {
q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
cb(q, "q", il);

// split into {n_head * n_embd_head_qk_nope, n_tokens}
struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
ggml_row_size(q->type, hparams.n_embd_head_k),
ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
0);
cb(q_nope, "q_nope", il);
kv_rope_compressed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
cb(kv_rope_compressed, "kv_rope_compressed", il);

// and {n_head * n_embd_head_qk_rope, n_tokens}
struct ggml_tensor * q_rope = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
ggml_row_size(q->type, hparams.n_embd_head_k),
ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
ggml_row_size(q->type, n_embd_head_qk_nope));
cb(q_rope, "q_rope", il);
ggml_build_forward_expand(gf, q);
ggml_build_forward_expand(gf, kv_rope_compressed);

q_rope = ggml_rope_ext(
ctx0, q_rope, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor_scaled, beta_fast, beta_slow
);
cb(q_rope, "q_rope", il);
q = llm_build_norm(ctx0, q, hparams, model.layers[il].attn_q_a_norm, NULL, LLM_NORM_RMS, cb, il);
cb(q, "q", il);

q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
cb(q, "q", il);
} else {
q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(q, "q", il);

// {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
struct ggml_tensor * kv_rope_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
cb(kv_rope_compresseed, "kv_rope_compresseed", il);
kv_rope_compressed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
cb(kv_rope_compressed, "kv_rope_compressed", il);

// and {n_embd_head_qk_rope, n_tokens}
struct ggml_tensor * k_rope = ggml_view_3d(ctx0, kv_rope_compresseed, n_embd_head_qk_rope, 1, n_tokens,
kv_rope_compresseed->nb[1],
kv_rope_compresseed->nb[1],
ggml_row_size(kv_rope_compresseed->type, kv_lora_rank));
ggml_build_forward_expand(gf, q);
ggml_build_forward_expand(gf, kv_rope_compressed);
}

q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
ggml_row_size(q->type, hparams.n_embd_head_k),
ggml_row_size(q->type, hparams.n_embd_head_k * n_head), 0);

q_rope = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
ggml_row_size(q->type, hparams.n_embd_head_k),
ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
ggml_row_size(q->type, n_embd_head_qk_nope));

k_rope = ggml_view_3d(ctx0, kv_rope_compressed, n_embd_head_qk_rope, 1, n_tokens,
kv_rope_compressed->nb[1],
kv_rope_compressed->nb[1],
ggml_row_size(kv_rope_compressed->type, kv_lora_rank));

kv_compressed = ggml_view_2d(ctx0, kv_rope_compressed, kv_lora_rank, n_tokens,
kv_rope_compressed->nb[1], 0);
}
cb(q_nope, "q_nope", il);
cb(q_rope, "q_rope", il);
cb(k_rope, "k_rope", il);
cb(kv_compressed, "kv_compressed", il);

// shared RoPE key
k_rope = ggml_rope_ext(
ctx0, k_rope, inp_pos, nullptr,
q_rope = ggml_rope_ext(ctx0, q_rope, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor_scaled, beta_fast, beta_slow
);
cb(k_rope, "k_rope", il);
ext_factor, attn_factor_scaled, beta_fast, beta_slow);
cb(q_rope, "q_rope", il);

// split into {kv_lora_rank, n_tokens}
struct ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_rope_compresseed, kv_lora_rank, n_tokens,
kv_rope_compresseed->nb[1],
0);
cb(kv_compressed, "kv_compressed", il);
k_rope = ggml_rope_ext(ctx0, k_rope, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor_scaled, beta_fast, beta_slow);
cb(k_rope, "k_rope", il);

kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams, model.layers[il].attn_kv_a_norm, NULL, LLM_NORM_RMS, cb, il);
cb(kv_compressed, "kv_compressed", il);
Expand Down
35 changes: 32 additions & 3 deletions src/llama-load-tensors.cpp
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Expand Up @@ -1645,14 +1645,43 @@ bool create_tensors_helper::create_deepseek2_tensors(const LLM_TN & tn) {

layer.attn_kv_a_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank});

bool merged = false;
if (ml.merge_qkv) {
auto q_name = is_lite ? tn(LLM_TENSOR_ATTN_Q, "weight", i) : tn(LLM_TENSOR_ATTN_Q_A, "weight", i);
auto k_name = tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i);
auto wq = ml.require_tensor_meta(q_name.c_str());
auto wk = ml.require_tensor_meta(k_name.c_str());
GGML_ASSERT(wq && wk);
if (wq->type == wk->type) {
GGML_ASSERT(wq->ne[0] == wk->ne[0]);
layer.wkq_a_mqa = ggml_new_tensor_2d(ctx_split, wq->type, wq->ne[0], wq->ne[1] + wk->ne[1]);
snprintf(layer.wkq_a_mqa->name, GGML_MAX_NAME, "blk.%d.attn_qk_a_mqa.weight", i);
if (is_lite) {
layer.wq = ml.create_tensor_as_view(ctx_split, layer.wkq_a_mqa, q_name.c_str(), { wq->ne[0], wq->ne[1] }, 0);
} else {
layer.wq_a = ml.create_tensor_as_view(ctx_split, layer.wkq_a_mqa, q_name.c_str(), { wq->ne[0], wq->ne[1] }, 0);
}
layer.wkv_a_mqa = ml.create_tensor_as_view(ctx_split, layer.wkq_a_mqa, k_name.c_str(), { wk->ne[0], wk->ne[1] }, wq->ne[1]*wq->nb[1]);
merged = true;
use_mmap_buffer = false;
printf("============== Merged %s (%ld x %ld) and %s (%ld x %ld)\n", q_name.c_str(),
wq->ne[0], wq->ne[1], k_name.c_str(), wk->ne[0], wk->ne[1]);
}
}

if (!is_lite) {
layer.wq_a = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank});
if (!merged) {
layer.wq_a = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank});
}
layer.wq_b = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k});
} else {
} else if (!merged) {
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
}

layer.wkv_a_mqa = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i),{n_embd, kv_lora_rank + (n_embd_head_qk_rope)});
if (!merged) {
layer.wkv_a_mqa = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i),{n_embd, kv_lora_rank + (n_embd_head_qk_rope)});
}

layer.wkv_b = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i),
{kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, llama_model_loader::TENSOR_NOT_REQUIRED);
if (!layer.wkv_b) {
Expand Down
1 change: 1 addition & 0 deletions src/llama-model.cpp
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Expand Up @@ -805,6 +805,7 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_ATTN_Q_A, "blk.%d.attn_q_a" },
{ LLM_TENSOR_ATTN_Q_B, "blk.%d.attn_q_b" },
{ LLM_TENSOR_ATTN_KV_A_MQA, "blk.%d.attn_kv_a_mqa" },
{ LLM_TENSOR_ATTN_KQ_A_MQA, "blk.%d.attn_kq_a_mqa" },
{ LLM_TENSOR_ATTN_KV_B, "blk.%d.attn_kv_b" },
{ LLM_TENSOR_ATTN_K_B, "blk.%d.attn_k_b" },
{ LLM_TENSOR_ATTN_V_B, "blk.%d.attn_v_b" },
Expand Down
1 change: 1 addition & 0 deletions src/llama-model.h
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Expand Up @@ -160,6 +160,7 @@ struct llama_layer {
struct ggml_tensor * wq_a = nullptr;
struct ggml_tensor * wq_b = nullptr;
struct ggml_tensor * wkv_a_mqa = nullptr;
struct ggml_tensor * wkq_a_mqa = nullptr;
struct ggml_tensor * wkv_b = nullptr;
struct ggml_tensor * wk_b = nullptr;
struct ggml_tensor * wv_b = nullptr;
Expand Down