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@ -170,7 +170,7 @@ int main(int argc, char ** argv) {
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const float * embd = llama_get_embeddings(ctx_cts);
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int n = 768*261;
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int n = 1282*261;
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LOG("result:\n");
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for (int i = 0; i < 10; ++i) {
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@ -9631,8 +9631,11 @@ static bool llm_load_tensors(
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}
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// output
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model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0);
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model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
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model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0);
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model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {768}, 0);
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model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
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model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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@ -17419,17 +17422,23 @@ struct llm_build_context {
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cur = inpL;
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cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
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cur = llm_build_norm(ctx0, cur, hparams,
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model.output_norm,
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model.output_norm_b,
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LLM_NORM, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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cb(cur, "result_output_no_bias", -1);
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cur = ggml_add(ctx0, cur, model.output_b);
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cb(cur, "result_output", -1);
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printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);
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//cur = llm_build_norm(ctx0, cur, hparams,
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// model.output_norm, NULL,
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// LLM_NORM_RMS, cb, -1);
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//cb(cur, "result_norm", -1);
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//// lm_head
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//cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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//cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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@ -18588,7 +18597,7 @@ if (model.arch != LLM_ARCH_OUTETTS_VOC) {
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GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
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// TODO: TEMPORARY [OUTETTS]
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//ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
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ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*768*sizeof(float));
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ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*1282*sizeof(float));
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}
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} break;
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case LLAMA_POOLING_TYPE_MEAN:
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