llama : add llama_vocab
, functions -> methods, naming (#11110)
* llama : functions -> methods (#11110) * llama : add struct llama_vocab to the API (#11156) ggml-ci * hparams : move vocab params to llama_vocab (#11159) ggml-ci * vocab : more pimpl (#11165) ggml-ci * vocab : minor tokenization optimizations (#11160) ggml-ci Co-authored-by: Diego Devesa <slarengh@gmail.com> * lora : update API names (#11167) ggml-ci * llama : update API names to use correct prefix (#11174) * llama : update API names to use correct prefix ggml-ci * cont ggml-ci * cont ggml-ci * minor [no ci] * vocab : llama_vocab_add_[be]os -> llama_vocab_get_add_[be]os (#11174) ggml-ci * vocab : llama_vocab_n_vocab -> llama_vocab_n_tokens (#11174) ggml-ci --------- Co-authored-by: Diego Devesa <slarengh@gmail.com>
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68 changed files with 5855 additions and 5400 deletions
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@ -235,7 +235,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t
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else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
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use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
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if (qs.model.type == MODEL_70B) {
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if (qs.model.type == LLM_TYPE_70B) {
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// In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
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// 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
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// nearly negligible increase in model size by quantizing this tensor with more bits:
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@ -525,18 +525,20 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
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auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
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kv_overrides = v->data();
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}
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llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides);
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ml.init_mappings(false); // no prefetching
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llama_model model;
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llm_load_arch (ml, model);
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llm_load_hparams(ml, model);
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llm_load_stats (ml, model);
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llama_model model(llama_model_default_params());
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model.load_arch (ml);
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model.load_hparams(ml);
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model.load_stats (ml);
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struct quantize_state_impl qs(model, params);
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if (params->only_copy) {
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ftype = model.ftype;
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ftype = ml.ftype;
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}
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const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
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if (params->imatrix) {
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