gguf : deduplicate (#2629)
* gguf : better type names * dedup : CPU + Metal is working * ggml : fix warnings about unused results * llama.cpp : fix line feed and compiler warning * llama : fix strncpy warning + note token_to_str does not write null * llama : restore the original load/save session implementation Will migrate this to GGUF in the future * convert-llama-h5-to-gguf.py : support alt ctx param name * ggml : assert when using ggml_mul with non-F32 src1 * examples : dedup simple --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
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21 changed files with 1630 additions and 7398 deletions
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@ -17,7 +17,7 @@
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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static const float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS;
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static const float rms_norm_eps = 1e-5f;
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struct random_normal_distribution {
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std::mt19937 gen;
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@ -2612,42 +2612,45 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
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return;
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}
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// write_magic
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file.write_u32(LLAMA_FILE_MAGIC); // magic
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file.write_u32(LLAMA_FILE_VERSION); // version
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// write_hparams
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file.write_u32(model->hparams.n_vocab);
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file.write_u32(model->hparams.n_embd);
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file.write_u32(model->hparams.n_mult);
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file.write_u32(model->hparams.n_head);
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file.write_u32(model->hparams.n_layer);
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file.write_u32(model->hparams.n_rot);
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file.write_u32(LLAMA_FTYPE_ALL_F32);
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// write_vocab
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uint32_t n_vocab = model->hparams.n_vocab;
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for (uint32_t i = 0; i < n_vocab; i++) {
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const auto & token_score = vocab->id_to_token.at(i);
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file.write_u32((uint32_t) token_score.tok.size());
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file.write_raw(token_score.tok.data(), token_score.tok.size());
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file.write_raw(&token_score.score, sizeof(token_score.score));
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}
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// write tensors
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write_tensor(&file, model->tok_embeddings);
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write_tensor(&file, model->norm);
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write_tensor(&file, model->output);
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for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
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auto & layer = model->layers[i];
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write_tensor(&file, layer.attention_norm);
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write_tensor(&file, layer.wq);
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write_tensor(&file, layer.wk);
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write_tensor(&file, layer.wv);
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write_tensor(&file, layer.wo);
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write_tensor(&file, layer.ffn_norm);
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write_tensor(&file, layer.w1);
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write_tensor(&file, layer.w2);
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write_tensor(&file, layer.w3);
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}
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#pragma message("TODO: implement file saving using gguf")
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(void) vocab;
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(void) model;
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// // write_magic
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// file.write_u32(LLAMA_FILE_MAGIC); // magic
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// file.write_u32(LLAMA_FILE_VERSION); // version
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// // write_hparams
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// file.write_u32(model->hparams.n_vocab);
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// file.write_u32(model->hparams.n_embd);
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// file.write_u32(model->hparams.n_mult);
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// file.write_u32(model->hparams.n_head);
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// file.write_u32(model->hparams.n_layer);
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// file.write_u32(model->hparams.n_rot);
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// file.write_u32(LLAMA_FTYPE_ALL_F32);
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// // write_vocab
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// uint32_t n_vocab = model->hparams.n_vocab;
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// for (uint32_t i = 0; i < n_vocab; i++) {
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// const auto & token_score = vocab->id_to_token.at(i);
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// file.write_u32((uint32_t) token_score.tok.size());
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// file.write_raw(token_score.tok.data(), token_score.tok.size());
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// file.write_raw(&token_score.score, sizeof(token_score.score));
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// }
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// // write tensors
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// write_tensor(&file, model->tok_embeddings);
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// write_tensor(&file, model->norm);
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// write_tensor(&file, model->output);
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// for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
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// auto & layer = model->layers[i];
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//
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// write_tensor(&file, layer.attention_norm);
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// write_tensor(&file, layer.wq);
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// write_tensor(&file, layer.wk);
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// write_tensor(&file, layer.wv);
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// write_tensor(&file, layer.wo);
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// write_tensor(&file, layer.ffn_norm);
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// write_tensor(&file, layer.w1);
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// write_tensor(&file, layer.w2);
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// write_tensor(&file, layer.w3);
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// }
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}
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float cosine_decay(const int decay_steps, const float alpha, int step) {
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