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llama: update to commit 2016f07b (#10352)

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This commit is contained in:
Jeffrey Morgan
2025-04-25 09:26:02 +09:00
committed by GitHub
parent 11dde41824
commit e9e5f61c45
46 changed files with 1967 additions and 1753 deletions
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29
llama/llama.cpp/src/llama-graph.cpp vendored
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@@ -1194,6 +1194,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
ggml_tensor * v,
ggml_tensor * kq_b,
ggml_tensor * kq_mask,
ggml_tensor * v_mla,
bool v_trans,
float kq_scale) const {
//const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
@@ -1205,8 +1206,6 @@ ggml_tensor * llm_graph_context::build_attn_mha(
//const auto & n_embd_head_k = hparams.n_embd_head_k;
//const auto & n_embd_head_v = hparams.n_embd_head_v;
const auto n_embd_head_v = v_trans ? v->ne[1] : v->ne[0];
const auto n_tokens = q->ne[1];
const auto n_head = q->ne[2];
const auto n_kv = k->ne[1];
@@ -1235,7 +1234,12 @@ ggml_tensor * llm_graph_context::build_attn_mha(
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
cur = ggml_reshape_2d(ctx0, cur, n_embd_head_v*n_head, n_tokens);
if (v_mla) {
cur = ggml_reshape_4d(ctx0, cur, v_mla->ne[0], 1, n_head, n_tokens);
cur = ggml_mul_mat(ctx0, v_mla, cur);
}
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens);
} else {
ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
@@ -1273,9 +1277,14 @@ ggml_tensor * llm_graph_context::build_attn_mha(
ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
// for MLA with the absorption optimization, we need to "decompress" from MQA back to MHA
if (v_mla) {
kqv = ggml_mul_mat(ctx0, v_mla, kqv);
}
cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_head_v*n_head, n_tokens);
cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens);
if (!cparams.offload_kqv) {
// all nodes between the KV store and the attention output are run on the CPU
@@ -1310,6 +1319,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k_cur,
ggml_tensor * v_cur,
ggml_tensor * kq_b,
ggml_tensor * v_mla,
float kq_scale,
int il) const {
GGML_UNUSED(n_tokens);
@@ -1331,7 +1341,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3);
//cb(k, "v", il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, false, kq_scale);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, false, kq_scale);
cb(cur, "kqv_out", il);
@@ -1385,6 +1395,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k_cur,
ggml_tensor * v_cur,
ggml_tensor * kq_b,
ggml_tensor * v_mla,
float kq_scale,
int il) const {
// these nodes are added to the graph together so that they are not reordered
@@ -1470,7 +1481,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_element_size(kv_self->v_l[il])*n_ctx*n_embd_head_v,
0);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_trans, kq_scale);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, v_trans, kq_scale);
cb(cur, "kqv_out", il);
if (wo) {
@@ -1529,6 +1540,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k_cur,
ggml_tensor * v_cur,
ggml_tensor * kq_b,
ggml_tensor * v_mla,
float kq_scale,
int il) const {
// these nodes are added to the graph together so that they are not reordered
@@ -1548,7 +1560,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3);
//cb(k, "v", il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, false, kq_scale);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, false, kq_scale);
cb(cur, "kqv_out", il);
@@ -1717,4 +1729,3 @@ void llm_graph_context::build_pooling(
ggml_build_forward_expand(gf, cur);
}