vbatts/llama.cpp
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ggml: add names to tensors (#1268)

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* ggml: add names to tensors

* minor improvements to dot file formatting
This commit is contained in:
slaren 2023-05-02 16:03:00 +02:00 • committed by GitHub
parent f4cef87edf
commit 2d099e5193
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3 changed files with 68 additions and 20 deletions
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24
llama.cpp
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@ -659,6 +659,7 @@ struct llama_model_loader {
LLAMA_ASSERT(lt.ne.size() == 1);
tensor = ggml_new_tensor_1d(ggml_ctx, lt.type, lt.ne.at(0));
}
ggml_set_name(tensor, lt.name.c_str());
LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor
lt.ggml_tensor = tensor;
num_ggml_tensors_created++;
@ -798,6 +799,8 @@ static bool kv_cache_init(
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
ggml_set_name(cache.k, "cache_k");
ggml_set_name(cache.v, "cache_v");
return true;
}
@ -1084,6 +1087,7 @@ static bool llama_eval_internal(
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_set_name(embd, "embd");
memcpy(embd->data, tokens, N*ggml_element_size(embd));
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
@ -1110,6 +1114,8 @@ static bool llama_eval_internal(
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
ggml_set_name(Qcur, "Qcur");
ggml_set_name(Kcur, "Kcur");
// store key and value to memory
{
@ -1130,6 +1136,7 @@ static bool llama_eval_internal(
ggml_permute(ctx0,
Qcur,
0, 2, 1, 3);
ggml_set_name(Q, "Q");
struct ggml_tensor * K =
ggml_permute(ctx0,
@ -1137,21 +1144,26 @@ static bool llama_eval_internal(
ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
n_embd/n_head, n_head, n_past + N),
0, 2, 1, 3);
ggml_set_name(K, "K");
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
ggml_set_name(KQ, "KQ");
// KQ_scaled = KQ / sqrt(n_embd/n_head)
struct ggml_tensor * KQ_scaled =
ggml_scale(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
struct ggml_tensor * KQ_scale = ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head));
ggml_set_name(KQ_scale, "1/sqrt(n_embd/n_head)");
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
ggml_set_name(KQ_scaled, "KQ_scaled");
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
ggml_set_name(KQ_masked, "KQ_masked");
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
ggml_set_name(KQ_soft_max, "KQ_soft_max");
// split cached V into n_head heads
struct ggml_tensor * V =
@ -1160,9 +1172,11 @@ static bool llama_eval_internal(
n_ctx*ggml_element_size(kv_self.v),
n_ctx*ggml_element_size(kv_self.v)*n_embd/n_head,
il*n_ctx*ggml_element_size(kv_self.v)*n_embd);
ggml_set_name(V, "V");
#if 1
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
ggml_set_name(KQV, "KQV");
#else
// make V contiguous in memory to speed up the matmul, however we waste time on the copy
// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
@ -1173,11 +1187,13 @@ static bool llama_eval_internal(
// KQV_merged = KQV.permute(0, 2, 1, 3)
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
ggml_set_name(KQV_merged, "KQV_merged");
// cur = KQV_merged.contiguous().view(n_embd, N)
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
ggml_set_name(cur, "KQV_merged_contiguous");
// projection (no bias)
cur = ggml_mul_mat(ctx0,