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Georgi Gerganov 2024-12-11 10:22:12 +02:00
parent b09557dac6
commit 0150e413b2
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2 changed files with 22 additions and 13 deletions
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2
examples/tts/tts.cpp
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@ -170,7 +170,7 @@ int main(int argc, char ** argv) {
const float * embd = llama_get_embeddings(ctx_cts);
int n = 768*261;
int n = 1282*261;
LOG("result:\n");
for (int i = 0; i < 10; ++i) {

33
src/llama.cpp
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@ -9631,8 +9631,11 @@ static bool llm_load_tensors(
}
// output
model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0);
model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0);
model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {768}, 0);
model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
} break;
default:
throw std::runtime_error("unknown architecture");
@ -17419,17 +17422,23 @@ struct llm_build_context {
cur = inpL;
cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output_no_bias", -1);
cur = ggml_add(ctx0, cur, model.output_b);
cb(cur, "result_output", -1);
printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);
//cur = llm_build_norm(ctx0, cur, hparams,
// model.output_norm, NULL,
// LLM_NORM_RMS, cb, -1);
//cb(cur, "result_norm", -1);
//// lm_head
//cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
//cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
@ -18588,7 +18597,7 @@ if (model.arch != LLM_ARCH_OUTETTS_VOC) {
GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
// TODO: TEMPORARY [OUTETTS]
//ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*768*sizeof(float));
ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*1282*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_MEAN: