vbatts/llama.cpp
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fix up

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joshcarp 2024-04-30 00:10:58 -04:00
parent 9858fd1457
commit 5eea11e241
1 changed files with 7 additions and 7 deletions
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14
llama.cpp
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@ -6410,7 +6410,7 @@ static struct ggml_tensor * llm_build_ffn(
case LLM_FFN_SILU2: case LLM_FFN_SILU2:
{ {
struct ggml_tensor * one = ggml_view_2d(ctx, cur, cur->ne[0]/2, cur->ne[1], cur->nb[1], 0); struct ggml_tensor * one = ggml_view_2d(ctx, cur, cur->ne[0]/2, cur->ne[1], cur->nb[1], 0);
int offset = (cur->ne[0]/2) * (cur->ne[1]); int offset = sizeof(float) * (cur->ne[0]/2) * (cur->ne[1]);
struct ggml_tensor * two = ggml_view_2d(ctx, cur, cur->ne[0]/2, cur->ne[1], cur->nb[1], offset); struct ggml_tensor * two = ggml_view_2d(ctx, cur, cur->ne[0]/2, cur->ne[1], cur->nb[1], offset);
cur = ggml_mul(ctx, ggml_silu(ctx, one), two); cur = ggml_mul(ctx, ggml_silu(ctx, one), two);
cb(cur, "ffn_silu", il); cb(cur, "ffn_silu", il);
@ -10768,9 +10768,9 @@ struct llm_build_context {
// [model_dim][(n_head_k+n_head_v+n_head_q)*head_dim] // [model_dim][(n_head_k+n_head_v+n_head_q)*head_dim]
// In most other impls, this is [model_dim][3*above] // In most other impls, this is [model_dim][3*above]
// This matches up with the dimensions of the huggingface version // This matches up with the dimensions of the huggingface version
Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_tokens, num_query_heads[il], cur->nb[1], cur->nb[2], 0 * sizeof(float) * (n_embd_head))); Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_tokens, num_query_heads[il], cur->nb[1], cur->nb[2], 0));
Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head,n_tokens, n_head_k, cur->nb[1], cur->nb[2], 1 * sizeof(float) * (n_embd_head))); Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head,n_tokens, n_head_k, cur->nb[1], cur->nb[2], 1 * sizeof(float) * (n_embd_head)));
Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head,n_tokens, n_head_k, cur->nb[1], cur->nb[2], 1 * sizeof(float) * (n_embd_head + n_embd_head))); Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head,n_tokens, n_head_k, cur->nb[1], cur->nb[2], 2 * sizeof(float) * (n_embd_head)));
// Q/K Layernorm // Q/K Layernorm
Qcur = llm_build_norm(ctx0, Qcur, modified_hparams, Qcur = llm_build_norm(ctx0, Qcur, modified_hparams,
model.layers[il].attn_q_norm, model.layers[il].attn_q_norm,
@ -10842,15 +10842,15 @@ struct llm_build_context {
// 4 == num groups // 4 == num groups
int64_t nev[GGML_MAX_DIMS] = {2*Vcur->ne[0], Vcur->ne[1], Vcur->ne[2], Vcur->ne[3]}; int64_t nev[GGML_MAX_DIMS] = {2*Vcur->ne[0], Vcur->ne[1], Vcur->ne[2], Vcur->ne[3]};
struct ggml_tensor * Vcur2 = ggml_new_tensor(ctx0, Vcur->type, GGML_MAX_DIMS, nev); struct ggml_tensor * Vcur2 = ggml_new_tensor(ctx0, Vcur->type, GGML_MAX_DIMS, nev);
Vcur2->op = GGML_OP_REPEAT; // Vcur2->op = GGML_OP_REPEAT;
Vcur2->grad = ggml_dup_tensor(ctx0, Vcur); Vcur2->grad = ggml_dup_tensor(ctx0, Vcur);
Vcur2 = ggml_reshape_2d(ctx0, Vcur2, modified_hparams.n_embd_k_gqa(), n_tokens); Vcur2 = ggml_reshape_2d(ctx0, Vcur2, modified_hparams.n_embd_k_gqa(), n_tokens);
int64_t nek[GGML_MAX_DIMS] = {2*Kcur->ne[0], Kcur->ne[1], Kcur->ne[2], Kcur->ne[3]}; int64_t nek[GGML_MAX_DIMS] = {2*Kcur->ne[0], Kcur->ne[1], Kcur->ne[2], Kcur->ne[3]};
struct ggml_tensor * Kcur2 = ggml_new_tensor(ctx0, Kcur->type, GGML_MAX_DIMS, nek); struct ggml_tensor * Kcur2 = ggml_new_tensor(ctx0, Kcur->type, GGML_MAX_DIMS, nek);
Kcur2->op = GGML_OP_REPEAT; // Kcur2->op = GGML_OP_REPEAT;
Kcur2->grad = ggml_dup_tensor(ctx0, Vcur); Kcur2->grad = ggml_dup_tensor(ctx0, Kcur);
Kcur2 = ggml_reshape_2d(ctx0, Vcur2, modified_hparams.n_embd_k_gqa(), n_tokens); Kcur2 = ggml_reshape_2d(ctx0, Kcur2, modified_hparams.n_embd_k_gqa(), n_tokens);
cb(Kcur, "Kcur", il); cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, modified_hparams, kv_self, gf, cur = llm_build_kv(ctx0, model, modified_hparams, kv_self, gf,
model.layers[il].wo, NULL, model.layers[il].wo, NULL,