| 53 | std::vector<llama_token> tokens; |
| 54 | |
| 55 | output_data(llama_context * ctx, const llama_model * model, const common_params & params) { |
| 56 | const llama_vocab * vocab = llama_model_get_vocab(model); |
| 57 | const bool add_bos = llama_vocab_get_add_bos(vocab); |
| 58 | |
| 59 | tokens = common_tokenize(ctx, params.prompt, add_bos, params.parse_special); |
| 60 | prompt = params.prompt; |
| 61 | |
| 62 | if (params.embedding) { |
| 63 | const int n_embd = llama_model_n_embd_out(model); |
| 64 | const bool pooling = has_pooling(ctx); |
| 65 | const int n_embd_count = pooling ? 1 : tokens.size(); |
| 66 | const int n_floats = n_embd * n_embd_count; |
| 67 | |
| 68 | float * embd_raw = pooling ? llama_get_embeddings_seq(ctx, 0) : llama_get_embeddings(ctx); |
| 69 | if (embd_raw == nullptr) { |
| 70 | throw std::runtime_error("failed to get embeddings from the model"); |
| 71 | } |
| 72 | |
| 73 | LOG_DBG("pooling_enabled: %s\n", pooling ? "true" : "false"); |
| 74 | LOG_DBG("n_embd: %d\n", n_embd); |
| 75 | LOG_DBG("n_floats: %d\n", n_floats); |
| 76 | LOG_DBG("n_embd_count: %d\n", n_embd_count); |
| 77 | |
| 78 | data_ptr = embd_raw; |
| 79 | data_size = n_floats; |
| 80 | type_suffix = "-embeddings"; |
| 81 | |
| 82 | if (params.embd_normalize >= 0) { |
| 83 | embd_norm.resize(n_floats); |
| 84 | for (int i = 0; i < n_embd_count; i++) { |
| 85 | common_embd_normalize(embd_raw+i*n_embd, embd_norm.data()+i*n_embd, n_embd, params.embd_normalize); |
| 86 | } |
| 87 | data_ptr = embd_norm.data(); |
| 88 | } |
| 89 | } else { |
| 90 | const float * logits = llama_get_logits_ith(ctx, tokens.size() - 1); |
| 91 | const int n_logits = llama_vocab_n_tokens(vocab); |
| 92 | |
| 93 | data_ptr = const_cast<float*>(logits); |
| 94 | data_size = n_logits; |
| 95 | type_suffix = ""; |
| 96 | } |
| 97 | } |
| 98 | }; |
| 99 | |
| 100 | static void save_output_data(const output_data & output, const std::string & model_name, const std::string & output_dir) { |
nothing calls this directly
no test coverage detected