diff --git a/examples/tts/tts.cpp b/examples/tts/tts.cpp index d3fee7373..17dc0eff2 100644 --- a/examples/tts/tts.cpp +++ b/examples/tts/tts.cpp @@ -170,13 +170,19 @@ int main(int argc, char ** argv) { const float * embd = llama_get_embeddings(ctx_cts); + int n = 768*261; + LOG("result:\n"); for (int i = 0; i < 10; ++i) { LOG("%8.3f ", embd[i]); } LOG("\n"); + for (int i = n - 10; i < n; ++i) { + LOG("%8.3f ", embd[i]); + } + LOG("\n"); double sum = 0.0; - for (int i = 0; i < 261*512; ++i) { + for (int i = 0; i < n; ++i) { sum += embd[i]; } LOG("sum: %f\n", sum); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 51cc85662..4c0cff48d 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -3874,7 +3874,7 @@ struct ggml_tensor * ggml_im2col( int d1, bool is_2D, enum ggml_type dst_type) { - if(is_2D) { + if (is_2D) { GGML_ASSERT(a->ne[2] == b->ne[2]); } else { GGML_ASSERT(a->ne[1] == b->ne[1]); diff --git a/src/llama.cpp b/src/llama.cpp index 4fc676a1e..7c4ad5691 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -3054,8 +3054,8 @@ struct llama_model { struct ggml_tensor * cls_out = nullptr; struct ggml_tensor * cls_out_b = nullptr; - // quantizer - struct ggml_tensor * qntz_cbook_embd = nullptr; + struct ggml_tensor * conv_1d = nullptr; + struct ggml_tensor * conv_1d_b = nullptr; std::vector layers; @@ -5036,7 +5036,7 @@ struct llama_model_loader { void done_getting_tensors() const { if (n_created != n_tensors) { - // TODO: TEMPORARY DISABLED + // TODO: TEMPORARY DISABLED [OUTETTS] //throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created)); } } @@ -7356,6 +7356,7 @@ static const std::map llm_tensor_info_mapping = { {LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, // this tensor is loaded for T5, but never used {LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}}, + {LLM_TENSOR_CONV1D, {LLM_TENSOR_LAYER_INPUT, GGML_OP_IM2COL}}, }; // checks if the weight tensor can be used with the specified buffer type and device @@ -7460,6 +7461,12 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H); op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state); } break; + case GGML_OP_IM2COL: + { + int n_embd = hparams.n_embd; + ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1); + op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16); + } break; default: GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name); } @@ -9428,6 +9435,9 @@ static bool llm_load_tensors( { model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.conv_1d = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, n_embd, 768}, 0); + model.conv_1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"), {768}, 0); + // 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); @@ -9671,7 +9681,7 @@ static struct ggml_tensor * llm_build_inp_embd( inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens); } else { - lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); + lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); inpL = lctx.inp_embd; ggml_set_input(lctx.inp_embd); } @@ -17009,7 +17019,13 @@ struct llm_build_context { inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); - cur = inpL; + cur = ggml_cont(ctx0, ggml_transpose(ctx0, inpL)); + + printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]); + printf("conv1d: %d %d %d\n", model.conv_1d->ne[0], model.conv_1d->ne[1], model.conv_1d->ne[2]); + cur = ggml_conv_1d_ph(ctx0, model.conv_1d, cur, 1, 1); + cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.conv_1d_b, 1, model.conv_1d_b->ne[0])); + 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, @@ -17309,7 +17325,7 @@ static struct ggml_cgraph * llama_build_graph( // add on pooling layer if (lctx.cparams.embeddings) { - // TODO: TEMPORARY DISABLED + // TODO: TEMPORARY DISABLED [OUTETTS] //result = llm.append_pooling(result); } @@ -17798,7 +17814,13 @@ 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; + + // TODO: TEMPORARY !!! [OUTETTS] +#if 0 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 // TODO: also consider shrinking the buffer @@ -18075,7 +18097,7 @@ static int llama_decode_internal( struct ggml_tensor * res = nullptr; struct ggml_tensor * embd = nullptr; -// TODO: TEMPORARY DISABLED +// TODO: TEMPORARY DISABLED [OUTETTS] if (model.arch != LLM_ARCH_OUTETTS_VOC) { // the output is always the last tensor in the graph res = ggml_graph_node(gf, -1); @@ -18170,7 +18192,9 @@ if (model.arch != LLM_ARCH_OUTETTS_VOC) { if (n_outputs_new) { 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_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float)); + // 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)); } } break; case LLAMA_POOLING_TYPE_MEAN: