ikawrakow/ik_llama.cpp
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Deepseek MLA Optimizations V2 (#195)

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* Avoid allocating MHA KV cache when MLA is turned on

* Added missing gguf-py file

* Added final optimizations

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>

* Make sure we do have wk_b and wv_b before enabling MLA

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
saood06
2025-02-09 01:36:54 -06:00
committed by GitHub
parent 3aaf602da5
commit d58dee869a
2 changed files with 53 additions and 21 deletions
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8
gguf-py/gguf/tensor_mapping.py
View File

@@ -446,6 +446,14 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.kv_b_proj", # deepseek2
),
MODEL_TENSOR.ATTN_K_B: (
"model.layers.{bid}.self_attn.k_b_proj", # deepseek2
),
MODEL_TENSOR.ATTN_V_B: (
"model.layers.{bid}.self_attn.v_b_proj", # deepseek2
),
MODEL_TENSOR.ATTN_Q_A_NORM: (
"model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2
),

66
src/llama.cpp
View File

@@ -3173,8 +3173,17 @@ static bool llama_kv_cache_init(
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
ggml_tensor * k;
ggml_tensor * v;
if (cparams.mla_attn && model.layers[i].wk_b && model.layers[i].wv_b) {
k = ggml_new_tensor_1d(ctx, type_k, 1);
v = ggml_new_tensor_1d(ctx, type_v, 1);
}
else {
k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
}
ggml_format_name(k, "cache_k_l%d", i);
ggml_format_name(v, "cache_v_l%d", i);
cache.k_l.push_back(k);
@@ -13368,6 +13377,10 @@ struct llm_build_context {
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
// whether to use n_tokens as the matrix dimension during multiplication or n_head
// n_tokens is higher during prompt processing, this allows to optimize for this case
bool pp_opt = n_tokens > n_head;
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
@@ -13496,43 +13509,54 @@ struct llm_build_context {
struct ggml_tensor * wk_b = ggml_view_3d(ctx0, model.layers[il].wk_b, n_embd_head_qk_nope, kv_lora_rank, n_head, ggml_row_size(model.layers[il].wk_b->type, n_embd_head_qk_nope), ggml_row_size(model.layers[il].wk_b->type, kv_lora_rank * n_embd_head_qk_nope), 0);
cb(wk_b, "wk_b", il);
struct ggml_tensor * q_nope_perm = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
cb(q_nope_perm, "q_nope_perm", il);
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
cb(q_nope, "q_nope_perm", il);
struct ggml_tensor * q_nope2 = ggml_mul_mat(ctx0, wk_b, q_nope_perm);
struct ggml_tensor * q_nope2 = ggml_mul_mat(ctx0, wk_b, q_nope);
cb(q_nope2, "q_nope2", il);
struct ggml_tensor * q_nope2_perm = ggml_permute(ctx0, q_nope2, 0, 2, 1, 3);
cb(q_nope2_perm, "q_nope2_perm", il);
struct ggml_tensor * kq_nope = ggml_mul_mat(ctx0, kv_cache, q_nope2_perm);
if (!pp_opt) {
q_nope2 = ggml_permute(ctx0, q_nope2, 0, 2, 1, 3);
cb(q_nope2, "q_nope2_perm", il);
}
struct ggml_tensor * kq_nope = ggml_mul_mat(ctx0, kv_cache, q_nope2);
cb(kq_nope, "kq_nope", il);
// Huh? This is not used anywhere
//struct ggml_tensor * q_pe_perm = ggml_permute(ctx0, q_pe, 0, 3, 2, 1);
//cb(q_pe_perm, "q_pe_perm", il);
if (!pp_opt) {
kq_nope = ggml_permute(ctx0, kq_nope, 0, 2, 1, 3);
cb(kq_nope, "kq_nope_perm", il);
}
if (pp_opt) {
q_pe = ggml_permute(ctx0, q_pe, 0, 2, 1, 3);
cb(q_pe, "q_pe_perm", il);
}
struct ggml_tensor * kq_pe = ggml_mul_mat(ctx0, kr_cache, q_pe);
cb(kq_pe, "kq_pe", il);
if (!pp_opt) {
kq_pe = ggml_permute(ctx0, kq_pe, 0, 2, 1, 3);
cb(kq_pe, "kq_pe_perm", il);
}
struct ggml_tensor * kq = ggml_add(ctx0, kq_nope, kq_pe);
cb(kq, "kq", il);
// We need this copy because soft_max expects a contiguous tensor
kq = ggml_cont(ctx0, ggml_permute(ctx0, kq, 0, 2, 1, 3));
cb(kq, "kq_perm", il);
kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
struct ggml_tensor * kq_perm = ggml_permute(ctx0, kq, 0, 2, 1, 3);
cb(kq_perm, "kq_soft_max_ext_perm", il);
if (!pp_opt) {
kq = ggml_permute(ctx0, kq, 0, 2, 1, 3);
cb(kq, "kq_soft_max_ext_perm", il);
}
struct ggml_tensor * kqv_compressed = ggml_mul_mat(ctx0, kv_cache_trans, kq_perm);
struct ggml_tensor * kqv_compressed = ggml_mul_mat(ctx0, kv_cache_trans, kq);
cb(kqv_compressed, "kqv_compressed", il);
kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 2, 1, 3);
cb(kqv_compressed, "kqv_compressed_perm", il);
if (!pp_opt) {
kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 2, 1, 3);
cb(kqv_compressed, "kqv_compressed_perm", il);
}
struct ggml_tensor * wv_b = ggml_view_3d(ctx0, model.layers[il].wv_b, kv_lora_rank, n_embd_head_v, n_head, ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank), ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank * n_embd_head_v), 0);
cb(wv_b, "wv_b", il);