vbatts/llama.cpp
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llama : modified tensor permutations to multiply larger matrices during inference

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Stanisław Szymczyk 2025-01-26 22:23:13 +01:00
parent 202f323e66
commit 93c5937249
1 changed files with 10 additions and 10 deletions
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src/llama.cpp
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@ -6529,13 +6529,13 @@ 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, 3, 1);
struct ggml_tensor * q_nope_perm = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
cb(q_nope_perm, "q_nope_perm", il);
struct ggml_tensor * q_nope2 = ggml_mul_mat(ctx0, wk_b, q_nope_perm);
cb(q_nope2, "q_nope2", il);
struct ggml_tensor * q_nope2_perm = ggml_permute(ctx0, q_nope2, 0, 1, 3, 2);
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);
@ -6547,34 +6547,34 @@ struct llm_build_context {
struct ggml_tensor * kr_cache_perm = ggml_permute(ctx0, kr_cache, 0, 2, 3, 1);
cb(kr_cache_perm, "kr_cache_perm", il);
struct ggml_tensor * kq_pe = ggml_mul_mat(ctx0, kr_cache, q_pe_perm);
struct ggml_tensor * kq_pe = ggml_mul_mat(ctx0, kr_cache, q_pe);
cb(kq_pe, "kq_pe", il);
struct ggml_tensor * kq = ggml_add(ctx0, kq_nope, kq_pe);
cb(kq, "kq", il);
kq = ggml_permute(ctx0, kq, 0, 3, 1, 2);
kq = ggml_cont(ctx0, ggml_permute(ctx0, kq, 0, 2, 1, 3));
cb(kq, "kq_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);
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, 3, 1);
struct ggml_tensor * kq_perm = ggml_permute(ctx0, kq, 0, 2, 1, 3);
cb(kq_perm, "kq_soft_max_ext_perm", il);
struct ggml_tensor * kqv_compressed = ggml_mul_mat(ctx0, kv_cache_trans, kq_perm);
cb(kqv_compressed, "kqv_compressed", il);
kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 1, 3, 2);
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);
struct ggml_tensor * kqv = ggml_mul_mat(ctx0, wv_b, kqv_compressed);
cb(kqv, "kqv", il);
kqv = ggml_permute(ctx0, kqv, 0, 3, 1, 2);
kqv = ggml_cont(ctx0, ggml_permute(ctx0, kqv, 0, 2, 1, 3));
cb(kqv, "kqv_perm", il);
cur = ggml_view_2d(ctx0, kqv, n_embd_head_v*n_head, n_tokens, ggml_row_size(kqv->type, n_embd_head_v*n_head), 0);