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examples : update llama2.c converter to read vocab and write models in GGUF format (#2751)

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* llama2.c: direct gguf output (WIP)

* Simplify vector building logic

* llama2.c gguf conversion: fix token types in converter

* llama2.c: support copying vocab from a llama gguf model file

* llama2.c: update default path for vocab model + readme

* llama2.c: use defines for gguf keys

* llama2.c: escape whitespaces w/ U+2581 in vocab converter the llama.cpp way

* llama2.c converter: cleanups + take n_ff from config
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Olivier Chafik 2023-08-27 15:13:31 +01:00 committed by GitHub
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8
examples/convert-llama2c-to-ggml/README.md
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@ -12,18 +12,14 @@ usage: ./convert-llama2c-to-ggml [options]
options: options:
-h, --help show this help message and exit -h, --help show this help message and exit
--copy-vocab-from-model FNAME model path from which to copy vocab (default 'tokenizer.bin') --copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default 'models/7B/ggml-model-f16.gguf')
--llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model
--llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin') --llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin')
``` ```
An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows: 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` `$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.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`
Now you can use the model with a command like: Now you can use the model with a command like:

340
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
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@ -10,9 +10,48 @@
#include <ctime> #include <ctime>
#include <random> #include <random>
#include <stdexcept> #include <stdexcept>
#include <sstream>
#include <algorithm> #include <algorithm>
#include <string> #include <string>
// GGUF keys & tensor names.
#define KV_GENERAL_ARCHITECTURE "general.architecture"
#define KV_GENERAL_NAME "general.name"
#define KV_TOKENIZER_MODEL "tokenizer.ggml.model"
#define KV_TOKENIZER_LIST "tokenizer.ggml.tokens"
#define KV_TOKENIZER_TOKEN_TYPE "tokenizer.ggml.token_type"
#define KV_TOKENIZER_SCORES "tokenizer.ggml.scores"
#define KV_TOKENIZER_BOS_ID "tokenizer.ggml.bos_token_id"
#define KV_TOKENIZER_EOS_ID "tokenizer.ggml.eos_token_id"
#define KV_TOKENIZER_UNK_ID "tokenizer.ggml.unknown_token_id"
#define KV_TOKENIZER_SEP_ID "tokenizer.ggml.seperator_token_id"
#define KV_TOKENIZER_PAD_ID "tokenizer.ggml.padding_token_id"
#define KV_TOKENIZER_HF_JSON "tokenizer.huggingface.json"
#define KV_CONTEXT_LENGTH "llama.context_length"
#define KV_EMBEDDING_LENGTH "llama.embedding_length"
#define KV_BLOCK_COUNT "llama.block_count"
#define KV_FEED_FORWARD_LENGTH "llama.feed_forward_length"
#define KV_ATTENTION_HEAD_COUNT "llama.attention.head_count"
#define KV_ATTENTION_HEAD_COUNT_KV "llama.attention.head_count_kv"
#define KV_ATTENTION_LAYERNORM_RMS_EPS "llama.attention.layer_norm_rms_epsilon"
#define KV_ROPE_DIMENSION_COUNT "llama.rope.dimension_count"
#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) #if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data #pragma warning(disable: 4244 4267) // possible loss of data
#endif #endif
@ -20,6 +59,11 @@
#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt' #define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
#define LLAMA_FILE_VERSION_GGJT_V3 3 #define LLAMA_FILE_VERSION_GGJT_V3 3
#define TOKENIZER_NAME "llama"
#define UNKNOWN_TOKEN_ID 0
#define BOS_TOKEN_ID 1
#define EOS_TOKEN_ID 2
//////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc. //////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.
typedef struct { typedef struct {
int dim; // transformer dimension int dim; // transformer dimension
@ -183,6 +227,7 @@ struct my_llama_hparams {
uint32_t n_vocab = 32000; uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input? uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096; uint32_t n_embd = 4096;
uint32_t n_ff = 11008;
uint32_t n_mult = 4; uint32_t n_mult = 4;
uint32_t n_head = 32; uint32_t n_head = 32;
uint32_t n_layer = 32; uint32_t n_layer = 32;
@ -214,6 +259,8 @@ struct my_llama_layer {
struct my_llama_model { struct my_llama_model {
struct ggml_context * ctx = NULL; struct ggml_context * ctx = NULL;
std::string name;
my_llama_hparams hparams; my_llama_hparams hparams;
struct ggml_tensor * tok_embeddings; struct ggml_tensor * tok_embeddings;
@ -276,18 +323,13 @@ struct train_params {
int mem_compute1_gb; int mem_compute1_gb;
}; };
uint32_t get_n_ff(const struct my_llama_hparams* hparams) {
const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
return n_ff;
}
void print_params(struct my_llama_hparams * params) { void print_params(struct my_llama_hparams * params) {
printf("%s: n_vocab: %d\n", __func__, params->n_vocab); printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
printf("%s: n_ctx: %d\n", __func__, params->n_ctx); printf("%s: n_ctx: %d\n", __func__, params->n_ctx);
printf("%s: n_embd: %d\n", __func__, params->n_embd); printf("%s: n_embd: %d\n", __func__, params->n_embd);
printf("%s: n_mult: %d\n", __func__, params->n_mult); printf("%s: n_mult: %d\n", __func__, params->n_mult);
printf("%s: n_head: %d\n", __func__, params->n_head); 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_layer: %d\n", __func__, params->n_layer);
printf("%s: n_rot: %d\n", __func__, params->n_rot); printf("%s: n_rot: %d\n", __func__, params->n_rot);
} }
@ -299,7 +341,7 @@ void init_model(struct my_llama_model * model) {
const uint32_t n_layer = hparams.n_layer; const uint32_t n_layer = hparams.n_layer;
const uint32_t n_vocab = hparams.n_vocab; 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; struct ggml_context * ctx = model->ctx;
model->train_its = 0; model->train_its = 0;
@ -481,21 +523,6 @@ struct llama_file {
return std::string(chars.data(), len); return std::string(chars.data(), len);
} }
void write_raw(const void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, size, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() { ~llama_file() {
if (fp) { if (fp) {
std::fclose(fp); std::fclose(fp);
@ -503,30 +530,6 @@ 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) { bool is_ggml_file(const char *filename) {
llama_file file(filename, "rb"); llama_file file(filename, "rb");
if (file.size < 4) { if (file.size < 4) {
@ -536,48 +539,96 @@ bool is_ggml_file(const char *filename) {
return magic == GGUF_MAGIC; return magic == GGUF_MAGIC;
} }
static std::string llama_escape_whitespaces(const std::string& text) {
std::ostringstream out;
for (char c : text) {
if (c == ' ') out << "\xe2\x96\x81";
else out << c;
}
return out.str();
}
void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) { void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
#pragma message("TODO: implement reading vocabulary using gguf") if (is_ggml_file(filename)) {
// // heuristic to infer whether vocab is from ggml or from llama2.c vocabulary struct ggml_context * ctx_data = NULL;
// if (is_ggml_file(filename)) {
// struct gguf_init_params params = {
// struct llama_context_params llama_params = llama_context_default_params(); /*.no_alloc = */ false,
// llama_params.vocab_only = true; /*.ctx = */ &ctx_data,
// };
// struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params);
// struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params); struct gguf_context * ctx = gguf_init_from_file(filename, params);
// GGML_ASSERT(ctx != NULL);
// const int n_vocab = llama_n_vocab(lctx);
// vocab->id_to_token.resize(n_vocab); const int model_idx = gguf_find_key(ctx, KV_TOKENIZER_MODEL);
// for (int i=0; i<n_vocab; ++i) { GGML_ASSERT(model_idx >= 0);
// vocab->id_to_token[i].text = llama_token_get_text(lctx, i); std::string tokenizer_name = gguf_get_val_str(ctx, model_idx);
// vocab->id_to_token[i].score = llama_token_get_score(lctx, i); GGML_ASSERT(tokenizer_name == TOKENIZER_NAME);
// vocab->id_to_token[i].type = llama_token_get_type(lctx, i);
// vocab->token_to_id.emplace(vocab->id_to_token[i].text, i); const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST);
// } GGML_ASSERT(token_idx >= 0);
// llama_free(lctx);
// llama_free_model(lmodel); const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES);
// } else GGML_ASSERT(score_idx >= 0);
{ // assume llama2.c vocabulary const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename);
const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE);
GGML_ASSERT(toktype_idx >= 0);
const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
vocab->id_to_token.resize(n_vocab);
for (uint32_t i = 0; i < n_vocab; i++) {
std::string word = gguf_get_arr_str(ctx, token_idx, i);
vocab->token_to_id[word] = i;
auto & token_data = vocab->id_to_token[i];
token_data.text = std::move(word);
token_data.score = scores[i];
token_data.type = (llama_token_type) toktypes[i];
}
ggml_free(ctx_data);
gguf_free(ctx);
} else {
// assume llama2.c vocabulary
printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename);
llama_file file(filename, "rb"); llama_file file(filename, "rb");
const int n_vocab = config->vocab_size; const int n_vocab = config->vocab_size;
/* uint32_t max_token_length = */ file.read_u32(); // unused /* uint32_t max_token_length = */ file.read_u32(); // unused
vocab->id_to_token.resize(n_vocab); vocab->id_to_token.resize(n_vocab);
for (int i=0; i<n_vocab; ++i) { for (llama_vocab::id id=0; id<n_vocab; ++id) {
float_t score = file.read_f32(); float_t score = file.read_f32();
uint32_t len = file.read_u32(); uint32_t len = file.read_u32();
std::string text = file.read_string(len); std::string text = file.read_string(len);
// Special-case handling of <0xXX> single byte tokens.
char byte_val; unsigned char byte_val;
if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) { llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
char cstr[2] = { byte_val, 0 }; if (id == UNKNOWN_TOKEN_ID) {
text = cstr; text = "<unk>";
type = LLAMA_TOKEN_TYPE_UNKNOWN;
} else if (id == BOS_TOKEN_ID) {
text = "<s>";
type = LLAMA_TOKEN_TYPE_CONTROL;
} else if (id == EOS_TOKEN_ID) {
text = "</s>";
type = LLAMA_TOKEN_TYPE_CONTROL;
} else if (text.empty()) {
type = LLAMA_TOKEN_TYPE_CONTROL;
} else if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) {
// Text of byte tokens is already in the expected format.
type = LLAMA_TOKEN_TYPE_BYTE;
} else {
type = LLAMA_TOKEN_TYPE_NORMAL;
} }
vocab->id_to_token[i].text = text; text = llama_escape_whitespaces(text);
vocab->id_to_token[i].score = score;
vocab->id_to_token[i].type = LLAMA_TOKEN_TYPE_UNDEFINED; vocab->id_to_token[id].text = text;
vocab->token_to_id.emplace(text, i); vocab->id_to_token[id].score = score;
vocab->id_to_token[id].type = type;
vocab->token_to_id.emplace(text, id);
} }
} }
} }
@ -619,33 +670,6 @@ 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) { 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. // stuff AK weights into GG weights one by one.
// w->token_embedding_table -> model->tok_embeddings // w->token_embedding_table -> model->tok_embeddings
// float* -> struct ggml_tensor // float* -> struct ggml_tensor
@ -658,8 +682,7 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
// for rms-att-weight // for rms-att-weight
int row_length = model->hparams.n_embd; int row_length = model->hparams.n_embd;
const auto & hparams = model->hparams; const auto & hparams = model->hparams;
//int n_ff = model->hparams.n_embd; int n_ff = model->hparams.n_ff;
int n_ff = get_n_ff(&hparams);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
auto & layer = model->layers[i]; auto & layer = model->layers[i];
@ -677,28 +700,91 @@ 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.w2 , &w->w2[i*n_ff*row_length]);
stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]); stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]);
} }
struct gguf_context * ctx = gguf_init_empty();
std::vector<const char*> tokens;
std::vector<float> scores;
std::vector<llama_token_type> token_types;
for (const llama_vocab::token_data & token_data : vocab->id_to_token) {
tokens.push_back(token_data.text.c_str());
scores.push_back(token_data.score);
token_types.push_back(token_data.type);
}
gguf_set_arr_str(ctx, KV_TOKENIZER_LIST, tokens.data(), tokens.size());
gguf_set_arr_data(ctx, KV_TOKENIZER_SCORES, GGUF_TYPE_FLOAT32, scores.data(), scores.size());
gguf_set_arr_data(ctx, KV_TOKENIZER_TOKEN_TYPE, GGUF_TYPE_INT32, token_types.data(), token_types.size());
gguf_set_val_str(ctx, KV_TOKENIZER_MODEL, TOKENIZER_NAME);
gguf_set_val_str(ctx, KV_GENERAL_ARCHITECTURE, "llama");
gguf_set_val_str(ctx, KV_GENERAL_NAME, "llama");
// special tokens
gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID);
gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID);
gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID);
gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1);
gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1);
gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx);
gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff);
gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
// n_head_kv is optional, default to n_head
// gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...);
gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer);
gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot);
gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
// write tensors // write tensors
write_tensor(&file, model->tok_embeddings); ggml_set_name(model->tok_embeddings, TN_TOKEN_EMBD);
write_tensor(&file, model->norm); gguf_add_tensor(ctx, model->tok_embeddings);
write_tensor(&file, model->output); // ?
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) { for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i]; auto & layer = model->layers[i];
write_tensor(&file, layer.attention_norm); ggml_format_name(layer.wq, TN_ATTN_Q, i);
write_tensor(&file, layer.wq); gguf_add_tensor(ctx, layer.wq);
write_tensor(&file, layer.wk);
write_tensor(&file, layer.wv); ggml_format_name(layer.wk, TN_ATTN_K, i);
write_tensor(&file, layer.wo); gguf_add_tensor(ctx, layer.wk);
write_tensor(&file, layer.ffn_norm);
write_tensor(&file, layer.w1); ggml_format_name(layer.wv, TN_ATTN_V, i);
write_tensor(&file, layer.w2); gguf_add_tensor(ctx, layer.wv);
write_tensor(&file, layer.w3);
ggml_format_name(layer.wo, TN_ATTN_OUTPUT, i);
gguf_add_tensor(ctx, layer.wo);
ggml_format_name(layer.attention_norm, TN_ATTN_NORM, i);
gguf_add_tensor(ctx, layer.attention_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);
ggml_format_name(layer.ffn_norm, TN_FFN_NORM, i);
gguf_add_tensor(ctx, layer.ffn_norm);
} }
gguf_write_to_file(ctx, filename, false);
gguf_free(ctx);
} }
struct train_params get_default_train_params() { struct train_params get_default_train_params() {
struct train_params params; struct train_params params;
params.fn_vocab_model = "tokenizer.bin"; params.fn_vocab_model = "models/7B/ggml-model-f16.gguf";
params.fn_llama2c_output_model = "ak_llama_model.bin"; params.fn_llama2c_output_model = "ak_llama_model.bin";
params.fn_train_data = "shakespeare.txt"; params.fn_train_data = "shakespeare.txt";
params.fn_checkpoint_in = "checkpoint.bin"; params.fn_checkpoint_in = "checkpoint.bin";
@ -751,7 +837,7 @@ void print_usage(int /*argc*/, char ** argv, const struct train_params * params)
fprintf(stderr, "\n"); fprintf(stderr, "\n");
fprintf(stderr, "options:\n"); fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n"); fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " --copy-vocab-from-model FNAME llama2.c vocabulary or ggmlv3 model path from which to copy vocab (default '%s')\n", params->fn_vocab_model); fprintf(stderr, " --copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default '%s')\n", params->fn_vocab_model);
fprintf(stderr, " --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\n"); fprintf(stderr, " --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\n");
fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model); fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
fprintf(stderr, "\n"); fprintf(stderr, "\n");
@ -812,6 +898,14 @@ bool params_parse(int argc, char ** argv, struct train_params * params) {
return true; return true;
} }
std::string basename(const std::string &path) {
size_t pos = path.find_last_of("/");
if (pos == std::string::npos) {
return path;
}
return path.substr(pos + 1);
}
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
struct train_params params = get_default_train_params(); struct train_params params = get_default_train_params();
if (!params_parse(argc, argv, &params)) { if (!params_parse(argc, argv, &params)) {
@ -840,6 +934,7 @@ int main(int argc, char ** argv) {
model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx); model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
model.hparams.n_ctx = params.n_ctx; model.hparams.n_ctx = params.n_ctx;
model.hparams.n_embd = config.dim; //params.n_embd; 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_mult = 32;//params.n_mult;
model.hparams.n_head = config.n_heads; //params.n_head; model.hparams.n_head = config.n_heads; //params.n_head;
model.hparams.n_layer = config.n_layers; //params.n_layer; model.hparams.n_layer = config.n_layers; //params.n_layer;
@ -853,6 +948,7 @@ int main(int argc, char ** argv) {
model.ctx = ggml_init(lcparams); model.ctx = ggml_init(lcparams);
init_model(&model); init_model(&model);
model.name = basename(params.fn_llama2c_model);
save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model); save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model); printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model);