diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md index fd561fcbc..c86c86325 100644 --- a/examples/convert-llama2c-to-ggml/README.md +++ b/examples/convert-llama2c-to-ggml/README.md @@ -19,11 +19,7 @@ options: An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows: -`$ ./convert-llama2c-to-ggml --copy-vocab-from-model ../llama2.c/tokenizer.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.ggmlv3.bin` - -For now the generated model is in the legacy GGJTv3 format, so you need to convert it to gguf manually: - -`$ python ./convert-llama-ggmlv3-to-gguf.py --eps 1e-5 --input stories42M.ggmlv3.bin --output stories42M.gguf.bin` +`$ ./convert-llama2c-to-ggml --copy-vocab-from-model ../llama2.c/tokenizer.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin` Now you can use the model with a command like: diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index f8a58dc7a..f8f7961f4 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -13,6 +13,20 @@ #include #include +// tensor names +#define TN_TOKEN_EMBD "token_embd.weight" +#define TN_OUTPUT_NORM "output_norm.weight" +#define TN_OUTPUT "output.weight" +#define TN_ATTN_NORM "blk.%d.attn_norm.weight" +#define TN_ATTN_Q "blk.%d.attn_q.weight" +#define TN_ATTN_K "blk.%d.attn_k.weight" +#define TN_ATTN_V "blk.%d.attn_v.weight" +#define TN_ATTN_OUTPUT "blk.%d.attn_output.weight" +#define TN_FFN_NORM "blk.%d.ffn_norm.weight" +#define TN_FFN_GATE "blk.%d.ffn_gate.weight" +#define TN_FFN_DOWN "blk.%d.ffn_down.weight" +#define TN_FFN_UP "blk.%d.ffn_up.weight" + #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data #endif @@ -183,6 +197,7 @@ struct my_llama_hparams { uint32_t n_vocab = 32000; uint32_t n_ctx = 512; // this is provided as user input? uint32_t n_embd = 4096; + uint32_t n_ff = 11008; uint32_t n_mult = 4; uint32_t n_head = 32; uint32_t n_layer = 32; @@ -287,7 +302,7 @@ void print_params(struct my_llama_hparams * params) { printf("%s: n_embd: %d\n", __func__, params->n_embd); printf("%s: n_mult: %d\n", __func__, params->n_mult); printf("%s: n_head: %d\n", __func__, params->n_head); - printf("%s: n_ff: %d\n", __func__, get_n_ff(params)); + printf("%s: n_ff: %d\n", __func__, params->n_ff); printf("%s: n_layer: %d\n", __func__, params->n_layer); printf("%s: n_rot: %d\n", __func__, params->n_rot); } @@ -299,7 +314,7 @@ void init_model(struct my_llama_model * model) { const uint32_t n_layer = hparams.n_layer; const uint32_t n_vocab = hparams.n_vocab; - const uint32_t n_ff = get_n_ff(&hparams); + const uint32_t n_ff = hparams.n_ff; struct ggml_context * ctx = model->ctx; model->train_its = 0; @@ -503,30 +518,6 @@ struct llama_file { } }; -void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) { - if (tensor == NULL) { - file->write_u32(0); - file->write_u32(0); - file->write_u32(GGML_TYPE_F32); - file->seek((0-file->tell()) & 31, SEEK_CUR); - return; - } - const char * name = ggml_get_name(tensor); - uint32_t name_len = strlen(name); - uint32_t nd = tensor->n_dims; - uint32_t ne[4] = { (uint32_t)tensor->ne[0], - (uint32_t)tensor->ne[1], - (uint32_t)tensor->ne[2], - (uint32_t)tensor->ne[3] }; - file->write_u32(nd); - file->write_u32(name_len); - file->write_u32(tensor->type); - file->write_raw(ne, sizeof(ne[0]) * nd); - file->write_raw(name, name_len); - file->seek((0-file->tell()) & 31, SEEK_CUR); - file->write_raw(tensor->data, ggml_nbytes(tensor)); -} - bool is_ggml_file(const char *filename) { llama_file file(filename, "rb"); if (file.size < 4) { @@ -537,48 +528,26 @@ bool is_ggml_file(const char *filename) { } void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) { -#pragma message("TODO: implement reading vocabulary using gguf") -// // heuristic to infer whether vocab is from ggml or from llama2.c vocabulary -// if (is_ggml_file(filename)) { -// -// struct llama_context_params llama_params = llama_context_default_params(); -// llama_params.vocab_only = true; -// -// struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params); -// struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params); -// -// const int n_vocab = llama_n_vocab(lctx); -// vocab->id_to_token.resize(n_vocab); -// for (int i=0; iid_to_token[i].text = llama_token_get_text(lctx, i); -// vocab->id_to_token[i].score = llama_token_get_score(lctx, i); -// vocab->id_to_token[i].type = llama_token_get_type(lctx, i); -// vocab->token_to_id.emplace(vocab->id_to_token[i].text, i); -// } -// llama_free(lctx); -// llama_free_model(lmodel); -// } else - { // assume llama2.c vocabulary - printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename); - llama_file file(filename, "rb"); - const int n_vocab = config->vocab_size; - /* uint32_t max_token_length = */ file.read_u32(); // unused - vocab->id_to_token.resize(n_vocab); - for (int i=0; i single byte tokens. - char byte_val; - if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) { - char cstr[2] = { byte_val, 0 }; - text = cstr; - } - vocab->id_to_token[i].text = text; - vocab->id_to_token[i].score = score; - vocab->id_to_token[i].type = LLAMA_TOKEN_TYPE_UNDEFINED; - vocab->token_to_id.emplace(text, i); + // assume llama2.c vocabulary + printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename); + llama_file file(filename, "rb"); + const int n_vocab = config->vocab_size; + /* uint32_t max_token_length = */ file.read_u32(); // unused + vocab->id_to_token.resize(n_vocab); + for (int i=0; i single byte tokens. + char byte_val; + if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) { + char cstr[2] = { byte_val, 0 }; + text = cstr; } + vocab->id_to_token[i].text = text; + vocab->id_to_token[i].score = score; + vocab->id_to_token[i].type = LLAMA_TOKEN_TYPE_UNDEFINED; + vocab->token_to_id.emplace(text, i); } } @@ -619,33 +588,6 @@ void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * kar } void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) { - struct llama_file file(filename, "wb"); - if (file.fp == NULL) { - return; - } - -#pragma message("TODO: implement file saving using gguf") - // write_magic - file.write_u32(LLAMA_FILE_MAGIC_GGJT); // magic - file.write_u32(LLAMA_FILE_VERSION_GGJT_V3); // version - // write_hparams - file.write_u32(model->hparams.n_vocab); - file.write_u32(model->hparams.n_embd); - file.write_u32(model->hparams.n_mult); - file.write_u32(model->hparams.n_head); - file.write_u32(model->hparams.n_layer); - file.write_u32(model->hparams.n_rot); - file.write_u32(LLAMA_FTYPE_ALL_F32); - - // write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk. - uint32_t n_vocab = model->hparams.n_vocab; - for (uint32_t i = 0; i < n_vocab; i++) { - const auto & token_data = vocab->id_to_token.at(i); - file.write_u32((uint32_t) token_data.text.size()); - file.write_raw(token_data.text.data(), token_data.text.size()); - file.write_raw(&token_data.score, sizeof(token_data.score)); - } - // stuff AK weights into GG weights one by one. // w->token_embedding_table -> model->tok_embeddings // float* -> struct ggml_tensor @@ -677,23 +619,91 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod stuff_karpathy_weights_into_gg(layer.w2 , &w->w2[i*n_ff*row_length]); stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]); } + + struct gguf_context * ctx = gguf_init_empty(); + + std::vector tokens; + std::transform(vocab->id_to_token.begin(), vocab->id_to_token.end(), std::back_inserter(tokens), + [](const llama_vocab::token_data & token_data) { return token_data.text.c_str(); }); + gguf_set_arr_str(ctx, "tokenizer.ggml.tokens", tokens.data(), tokens.size()); + + std::vector scores; + std::transform(vocab->id_to_token.begin(), vocab->id_to_token.end(), std::back_inserter(scores), + [](const llama_vocab::token_data & token_data) { return token_data.score; }); + gguf_set_arr_data(ctx, "tokenizer.ggml.scores", GGUF_TYPE_FLOAT32, scores.data(), scores.size()); + + std::vector token_types; + for (size_t i = 0; i < vocab->id_to_token.size(); ++i) { + // TODO: Refine this. + token_types.push_back(LLAMA_TOKEN_TYPE_UNDEFINED); + } + gguf_set_arr_data(ctx, "tokenizer.ggml.token_type", GGUF_TYPE_INT32, token_types.data(), token_types.size()); + + gguf_set_val_str(ctx, "tokenizer.ggml.model", "llama"); + + gguf_set_val_str(ctx, "general.name", "llama2.c"); + gguf_set_val_str(ctx, "general.architecture", "llama"); + + // special tokens + // gguf_set_val_u32(ctx, "tokenizer.ggml.bos_token_id", ...); + // gguf_set_val_u32(ctx, "tokenizer.ggml.eos_token_id", ...); + // gguf_set_val_u32(ctx, "tokenizer.ggml.unknown_token_id", ...); + // gguf_set_val_u32(ctx, "tokenizer.ggml.separator_token_id", ...); + // gguf_set_val_u32(ctx, "tokenizer.ggml.padding_token_id", ...); + + gguf_set_val_u32(ctx, "llama.context_length", model->hparams.n_ctx); + gguf_set_val_u32(ctx, "llama.embedding_length", model->hparams.n_embd); + gguf_set_val_u32(ctx, "llama.feed_forward_length", model->hparams.n_ff); + gguf_set_val_u32(ctx, "llama.attention.head_count", model->hparams.n_head); + gguf_set_val_u32(ctx, "llama.block_count", model->hparams.n_layer); + gguf_set_val_u32(ctx, "llama.rope.dimension_count", model->hparams.n_rot); + gguf_set_val_f32(ctx, "llama.attention.layer_norm_rms_epsilon", 1e-5f); + // // n_head_kv is optional, default to n_head + // gguf_set_val_u32(ctx, "llama.attention.head_count_kv", hparams.n_head_kv); + // write tensors - write_tensor(&file, model->tok_embeddings); - write_tensor(&file, model->norm); - write_tensor(&file, model->output); // ? + ggml_set_name(model->tok_embeddings, TN_TOKEN_EMBD); + gguf_add_tensor(ctx, model->tok_embeddings); + + ggml_set_name(model->norm, TN_OUTPUT_NORM); + gguf_add_tensor(ctx, model->norm); + + ggml_set_name(model->output, TN_OUTPUT); + gguf_add_tensor(ctx, model->output); // ? + for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { auto & layer = model->layers[i]; - write_tensor(&file, layer.attention_norm); - write_tensor(&file, layer.wq); - write_tensor(&file, layer.wk); - write_tensor(&file, layer.wv); - write_tensor(&file, layer.wo); - write_tensor(&file, layer.ffn_norm); - write_tensor(&file, layer.w1); - write_tensor(&file, layer.w2); - write_tensor(&file, layer.w3); + ggml_format_name(layer.attention_norm, TN_ATTN_NORM, i); + gguf_add_tensor(ctx, layer.attention_norm); + + ggml_format_name(layer.wq, TN_ATTN_Q, i); + gguf_add_tensor(ctx, layer.wq); + + ggml_format_name(layer.wk, TN_ATTN_K, i); + gguf_add_tensor(ctx, layer.wk); + + ggml_format_name(layer.wv, TN_ATTN_V, i); + gguf_add_tensor(ctx, layer.wv); + + ggml_format_name(layer.wo, TN_ATTN_OUTPUT, i); + gguf_add_tensor(ctx, layer.wo); + + ggml_format_name(layer.ffn_norm, TN_FFN_NORM, i); + gguf_add_tensor(ctx, layer.ffn_norm); + + ggml_format_name(layer.w1, TN_FFN_GATE, i); + gguf_add_tensor(ctx, layer.w1); + + ggml_format_name(layer.w2, TN_FFN_DOWN, i); + gguf_add_tensor(ctx, layer.w2); + + ggml_format_name(layer.w3, TN_FFN_UP, i); + gguf_add_tensor(ctx, layer.w3); } + + gguf_write_to_file(ctx, filename, false); + gguf_free(ctx); } struct train_params get_default_train_params() { @@ -840,6 +850,7 @@ int main(int argc, char ** argv) { model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx); model.hparams.n_ctx = params.n_ctx; model.hparams.n_embd = config.dim; //params.n_embd; + model.hparams.n_ff = config.hidden_dim; model.hparams.n_mult = 32;//params.n_mult; model.hparams.n_head = config.n_heads; //params.n_head; model.hparams.n_layer = config.n_layers; //params.n_layer;