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fix n_embd + remove llama.cpp hacks

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Georgi Gerganov 2024-12-11 10:59:08 +02:00
parent dcf2230afb
commit 86d0ad5ef4
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3 changed files with 13 additions and 31 deletions
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2
examples/tts/convert_pt_to_hf.py
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@ -152,7 +152,7 @@ config = {
"architectures": [ "architectures": [
"OuteTTSVocoder" "OuteTTSVocoder"
], ],
"hidden_size": 512, "hidden_size": 1282,
"vocab_size": 4096, "vocab_size": 4096,
"n_head": 1, "n_head": 1,
"layer_norm_epsilon": 1e-6, "layer_norm_epsilon": 1e-6,

3
examples/tts/tts.cpp
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@ -168,9 +168,10 @@ int main(int argc, char ** argv) {
LOG_INF("%s: time for prompt: %.3f ms\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f); LOG_INF("%s: time for prompt: %.3f ms\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f);
const int n_embd = llama_n_embd(model_cts);
const float * embd = llama_get_embeddings(ctx_cts); const float * embd = llama_get_embeddings(ctx_cts);
int n = 1282*261; int n = n_embd*261;
LOG("result:\n"); LOG("result:\n");
for (int i = 0; i < 10; ++i) { for (int i = 0; i < 10; ++i) {

39
src/llama.cpp
View File

@ -9539,12 +9539,12 @@ static bool llm_load_tensors(
} break; } break;
case LLM_ARCH_OUTETTS_VOC: case LLM_ARCH_OUTETTS_VOC:
{ {
model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {512, n_vocab}, 0);
model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {768}, 0); model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {768}, 0);
model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {768}, 0); model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {768}, 0);
model.conv_1d = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, n_embd, 768}, 0); model.conv_1d = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, 512, 768}, 0);
model.conv_1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"), {768}, 0); model.conv_1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"), {768}, 0);
model.posnet_0_norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", 0), {768}, 0); model.posnet_0_norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", 0), {768}, 0);
@ -9636,8 +9636,8 @@ static bool llm_load_tensors(
model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0); 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_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {768}, 0);
model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, 0); model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, n_embd}, 0);
model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {1282}, 0); model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {n_embd}, 0);
model.hann_window = create_tensor(tn(LLM_TENSOR_HANN_WINDOW, "weight"), {1280}, 0); model.hann_window = create_tensor(tn(LLM_TENSOR_HANN_WINDOW, "weight"), {1280}, 0);
} break; } break;
@ -17432,14 +17432,12 @@ struct llm_build_context {
model.output_norm, model.output_norm,
model.output_norm_b, model.output_norm_b,
LLM_NORM, cb, -1); LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
// lm_head // lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); 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); cur = ggml_add(ctx0, cur, model.output_b);
cb(cur, "result_output", -1); cb(cur, "result_embd", -1);
printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]); printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);
@ -17732,8 +17730,7 @@ static struct ggml_cgraph * llama_build_graph(
// add on pooling layer // add on pooling layer
if (lctx.cparams.embeddings) { if (lctx.cparams.embeddings) {
// TODO: TEMPORARY DISABLED [OUTETTS] result = llm.append_pooling(result);
//result = llm.append_pooling(result);
} }
llm.free(); llm.free();
@ -18221,13 +18218,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
} }
const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output.get()) : 0; const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output.get()) : 0;
// TODO: TEMPORARY !!! [OUTETTS]
#if 0
const size_t new_size = (logits_size + embd_size) * sizeof(float); const size_t new_size = (logits_size + embd_size) * sizeof(float);
#else
const size_t new_size = 1024*1024*32;
#endif
// alloc only when more than the current capacity is required // alloc only when more than the current capacity is required
// TODO: also consider shrinking the buffer // TODO: also consider shrinking the buffer
@ -18501,14 +18492,9 @@ static int llama_decode_internal(
ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false); ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false);
struct ggml_tensor * res = nullptr;
struct ggml_tensor * embd = nullptr;
// TODO: TEMPORARY DISABLED [OUTETTS]
if (model.arch != LLM_ARCH_OUTETTS_VOC) {
// the output is always the last tensor in the graph // the output is always the last tensor in the graph
res = ggml_graph_node(gf, -1); struct ggml_tensor * res = ggml_graph_node(gf, -1);
embd = ggml_graph_node(gf, -2); struct ggml_tensor * embd = ggml_graph_node(gf, -2);
if (lctx.n_outputs == 0) { if (lctx.n_outputs == 0) {
// no output // no output
@ -18528,10 +18514,7 @@ if (model.arch != LLM_ARCH_OUTETTS_VOC) {
embd = nullptr; // do not extract embeddings when not needed embd = nullptr; // do not extract embeddings when not needed
GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor"); GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
} }
} else {
res = nullptr;
embd = ggml_graph_node(gf, -1);
}
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
ggml_backend_sched_alloc_graph(lctx.sched.get(), gf); ggml_backend_sched_alloc_graph(lctx.sched.get(), gf);
@ -18599,9 +18582,7 @@ if (model.arch != LLM_ARCH_OUTETTS_VOC) {
if (n_outputs_new) { if (n_outputs_new) {
GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs); GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size); 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_outputs_new*n_embd*sizeof(float));
ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*1282*sizeof(float));
} }
} break; } break;
case LLAMA_POOLING_TYPE_MEAN: case LLAMA_POOLING_TYPE_MEAN: