mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-04 09:32:00 +00:00
88b5769487
* 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>
832 lines
30 KiB
C++
832 lines
30 KiB
C++
#include "ggml.h"
|
|
#include "llama.h"
|
|
|
|
#include <unordered_map>
|
|
#include <vector>
|
|
#include <cassert>
|
|
#include <climits>
|
|
#include <cstring>
|
|
#include <cstdarg>
|
|
#include <ctime>
|
|
#include <random>
|
|
#include <stdexcept>
|
|
#include <algorithm>
|
|
#include <string>
|
|
|
|
#if defined(_MSC_VER)
|
|
#pragma warning(disable: 4244 4267) // possible loss of data
|
|
#endif
|
|
|
|
//////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.
|
|
typedef struct {
|
|
int dim; // transformer dimension
|
|
int hidden_dim; // for ffn layers
|
|
int n_layers; // number of layers
|
|
int n_heads; // number of query heads
|
|
int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
|
|
int vocab_size; // vocabulary size, usually 256 (byte-level)
|
|
int seq_len; // max sequence length
|
|
} Config;
|
|
|
|
typedef struct {
|
|
// token embedding table
|
|
float* token_embedding_table; // (vocab_size, dim)
|
|
// weights for rmsnorms
|
|
float* rms_att_weight; // (layer, dim) rmsnorm weights
|
|
float* rms_ffn_weight; // (layer, dim)
|
|
// weights for matmuls
|
|
float* wq; // (layer, dim, dim)
|
|
float* wk; // (layer, dim, dim)
|
|
float* wv; // (layer, dim, dim)
|
|
float* wo; // (layer, dim, dim)
|
|
// weights for ffn
|
|
float* w1; // (layer, hidden_dim, dim)
|
|
float* w2; // (layer, dim, hidden_dim)
|
|
float* w3; // (layer, hidden_dim, dim)
|
|
// final rmsnorm
|
|
float* rms_final_weight; // (dim,)
|
|
// freq_cis for RoPE relatively positional embeddings
|
|
// float* freq_cis_real; // (seq_len, dim/2)
|
|
// float* freq_cis_imag; // (seq_len, dim/2)
|
|
// (optional) classifier weights for the logits, on the last layer
|
|
//float* wcls;
|
|
} TransformerWeights;
|
|
|
|
void malloc_weights(TransformerWeights* w, Config* p) {
|
|
// we calloc instead of malloc to keep valgrind happy
|
|
w->token_embedding_table = new float[p->vocab_size * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
|
|
|
|
w->rms_att_weight = new float[p->n_layers * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
|
|
|
|
w->rms_ffn_weight = new float[p->n_layers * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
|
|
|
|
w->wq = new float[p->n_layers * p->dim * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
|
|
|
|
w->wk = new float[p->n_layers * p->dim * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
|
|
|
|
w->wv = new float[p->n_layers * p->dim * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
|
|
|
|
w->wo = new float[p->n_layers * p->dim * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
|
|
|
|
w->w1 = new float[p->n_layers * p->hidden_dim * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
|
|
|
|
w->w2 = new float[p->n_layers * p->hidden_dim * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);
|
|
|
|
w->w3 = new float[p->n_layers * p->hidden_dim * p->dim]();
|
|
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
|
|
|
|
w->rms_final_weight = new float[p->dim]();
|
|
printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
|
|
}
|
|
|
|
int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f) {
|
|
if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
|
|
if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
|
|
if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
|
|
if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
|
|
if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
|
|
if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
|
|
if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
|
|
if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
|
|
if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->hidden_dim * p->dim)) return 1;
|
|
if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
|
|
if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast<size_t>(p->dim)) return 1;
|
|
return 0;
|
|
}
|
|
|
|
void free_weights(TransformerWeights* w) {
|
|
delete w->token_embedding_table;
|
|
delete w->rms_att_weight;
|
|
delete w->rms_ffn_weight;
|
|
delete w->wq;
|
|
delete w->wk;
|
|
delete w->wv;
|
|
delete w->wo;
|
|
delete w->w1;
|
|
delete w->w2;
|
|
delete w->w3;
|
|
delete w->rms_final_weight;
|
|
}
|
|
|
|
void print_sample_weights(TransformerWeights *w){
|
|
printf("----- Quick print of first of the weight vales of all the variables\n");
|
|
printf("%f\n", w->token_embedding_table[0]);
|
|
printf("%f\n", w->rms_att_weight[0]);
|
|
printf("%f\n", w->rms_ffn_weight[0]);
|
|
|
|
printf("%f\n", w->wq[0]);
|
|
printf("%f\n", w->wk[0]);
|
|
printf("%f\n", w->wv[0]);
|
|
printf("%f\n", w->wo[0]);
|
|
printf("%f\n", w->w1[0]);
|
|
printf("%f\n", w->w2[0]);
|
|
printf("%f\n", w->w3[0]);
|
|
printf("%f\n", w->rms_att_weight[0]);
|
|
}
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
//////////////////////////////////////// ggml structs and functions required to load models, configs and save the model.
|
|
|
|
struct llama_vocab {
|
|
using id = int32_t;
|
|
using token = std::string;
|
|
|
|
struct token_score {
|
|
token tok;
|
|
float score;
|
|
};
|
|
|
|
std::unordered_map<token, id> token_to_id;
|
|
std::vector<token_score> id_to_token;
|
|
};
|
|
|
|
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_mult = 4;
|
|
uint32_t n_head = 32;
|
|
uint32_t n_layer = 32;
|
|
uint32_t n_rot = 64;
|
|
bool operator!=(const my_llama_hparams& other) const {
|
|
return memcmp(this, &other, sizeof(my_llama_hparams));
|
|
}
|
|
};
|
|
|
|
struct my_llama_layer {
|
|
// normalization
|
|
struct ggml_tensor * attention_norm;
|
|
|
|
// attention
|
|
struct ggml_tensor * wq;
|
|
struct ggml_tensor * wk;
|
|
struct ggml_tensor * wv;
|
|
struct ggml_tensor * wo;
|
|
|
|
// normalization
|
|
struct ggml_tensor * ffn_norm;
|
|
|
|
// ff
|
|
struct ggml_tensor * w1;
|
|
struct ggml_tensor * w2;
|
|
struct ggml_tensor * w3;
|
|
};
|
|
|
|
struct my_llama_model {
|
|
struct ggml_context * ctx = NULL;
|
|
|
|
my_llama_hparams hparams;
|
|
|
|
struct ggml_tensor * tok_embeddings;
|
|
|
|
struct ggml_tensor * norm;
|
|
struct ggml_tensor * output;
|
|
|
|
std::vector<my_llama_layer> layers;
|
|
|
|
uint32_t train_its = 0;
|
|
uint32_t train_samples = 0;
|
|
uint32_t train_tokens = 0;
|
|
};
|
|
|
|
struct train_params {
|
|
const char * fn_vocab_model;
|
|
const char * fn_llama2c_model;
|
|
const char * fn_llama2c_output_model;
|
|
const char * fn_train_data;
|
|
const char * fn_checkpoint_in;
|
|
const char * fn_checkpoint_out;
|
|
const char * fn_model_out;
|
|
|
|
uint32_t seed;
|
|
|
|
int n_ctx;
|
|
int n_embd;
|
|
int n_mult;
|
|
int n_head;
|
|
int n_layer;
|
|
int n_rotmax;
|
|
|
|
int n_threads;
|
|
int n_batch;
|
|
int n_examples;
|
|
int n_predict;
|
|
|
|
int print_info_interval;
|
|
int print_details_interval;
|
|
|
|
bool samples_start_after_nl;
|
|
bool use_adam;
|
|
bool use_flash;
|
|
bool use_scratch;
|
|
|
|
// only adam
|
|
int warmup;
|
|
int cos_decay_steps;
|
|
float cos_decay_restart;
|
|
float cos_decay_alpha;
|
|
|
|
int lbfgs_n_iter;
|
|
int adam_n_iter;
|
|
float adam_alpha;
|
|
float adam_decay;
|
|
|
|
int mem_model_gb;
|
|
int mem_compute_gb;
|
|
int mem_compute0_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) {
|
|
printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
|
|
printf("%s: n_ctx: %d\n", __func__, params->n_ctx);
|
|
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_layer: %d\n", __func__, params->n_layer);
|
|
printf("%s: n_rot: %d\n", __func__, params->n_rot);
|
|
}
|
|
|
|
void init_model(struct my_llama_model * model) {
|
|
const auto & hparams = model->hparams;
|
|
|
|
const uint32_t n_embd = hparams.n_embd;
|
|
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);
|
|
struct ggml_context * ctx = model->ctx;
|
|
|
|
model->train_its = 0;
|
|
model->train_samples = 0;
|
|
model->train_tokens = 0;
|
|
|
|
model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
|
|
printf("[%s:GG] Allocating [%d] x [%d] = [%d] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab);
|
|
|
|
model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
printf("[%s:GG] Allocating [%d] float space for model->norm\n",__func__,n_embd);
|
|
|
|
model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
|
|
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab);
|
|
|
|
// printing the per-layer allocations here so we dont print in the for loop.
|
|
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wq for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
|
|
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wk for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
|
|
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wv for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
|
|
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wo for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
|
|
|
|
printf("[%s:GG] Allocating [%d] float space for layer.ffn_norm for [%d] layers\n",__func__,n_embd, n_layer);
|
|
|
|
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w1 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
|
|
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w2 for [%d] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer);
|
|
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w3 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
|
|
|
|
ggml_set_name(model->tok_embeddings, "tok_embeddings.weight");
|
|
ggml_set_name(model->norm, "norm.weight");
|
|
ggml_set_name(model->output, "output.weight");
|
|
|
|
model->layers.resize(n_layer);
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
auto & layer = model->layers[i];
|
|
|
|
std::string layers_i = "layers." + std::to_string(i);
|
|
|
|
layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
|
|
layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
|
|
layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
|
|
layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
|
|
layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
|
|
|
|
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
|
|
layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
|
|
layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
|
|
layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
|
|
|
|
ggml_set_name(layer.attention_norm, (layers_i + ".attention_norm.weight").c_str());
|
|
|
|
ggml_set_name(layer.wq, (layers_i + ".attention.wq.weight").c_str());
|
|
ggml_set_name(layer.wk, (layers_i + ".attention.wk.weight").c_str());
|
|
ggml_set_name(layer.wv, (layers_i + ".attention.wv.weight").c_str());
|
|
ggml_set_name(layer.wo, (layers_i + ".attention.wo.weight").c_str());
|
|
|
|
ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str());
|
|
|
|
ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str());
|
|
ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
|
|
ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
|
|
}
|
|
}
|
|
|
|
float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
|
|
float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
|
|
return *ptr;
|
|
}
|
|
|
|
int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
|
|
int32_t * ptr = (int32_t *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
|
|
return *ptr;
|
|
}
|
|
|
|
void print_row(struct ggml_tensor * probs, int i) {
|
|
for (int k = 0; k < probs->ne[0]; ++k) {
|
|
float p = get_f32_2d(probs, k, i);
|
|
printf(" %f", p);
|
|
}
|
|
printf("\n");
|
|
}
|
|
|
|
void print_matrix(struct ggml_tensor * probs) {
|
|
assert(probs->n_dims == 2);
|
|
for (int i = 0; i < probs->ne[1]; ++i) {
|
|
for (int k = 0; k < probs->ne[0]; ++k) {
|
|
float p = get_f32_2d(probs, k, i);
|
|
printf(" %.2f", p);
|
|
}
|
|
printf("\n");
|
|
}
|
|
}
|
|
|
|
#ifdef __GNUC__
|
|
#ifdef __MINGW32__
|
|
__attribute__((format(gnu_printf, 1, 2)))
|
|
#else
|
|
__attribute__((format(printf, 1, 2)))
|
|
#endif
|
|
#endif
|
|
static std::string format(const char * fmt, ...) {
|
|
va_list ap, ap2;
|
|
va_start(ap, fmt);
|
|
va_copy(ap2, ap);
|
|
int size = vsnprintf(NULL, 0, fmt, ap);
|
|
GGML_ASSERT(size >= 0 && size < INT_MAX);
|
|
std::vector<char> buf(size + 1);
|
|
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
|
|
GGML_ASSERT(size2 == size);
|
|
va_end(ap2);
|
|
va_end(ap);
|
|
return std::string(buf.data(), size);
|
|
}
|
|
|
|
struct llama_file {
|
|
// use FILE * so we don't have to re-open the file to mmap
|
|
FILE * fp;
|
|
size_t size;
|
|
|
|
llama_file(const char * fname, const char * mode) {
|
|
fp = std::fopen(fname, mode);
|
|
if (fp == NULL) {
|
|
size = 0;
|
|
} else {
|
|
seek(0, SEEK_END);
|
|
size = tell();
|
|
seek(0, SEEK_SET);
|
|
}
|
|
}
|
|
|
|
size_t tell() const {
|
|
#ifdef _WIN32
|
|
__int64 ret = _ftelli64(fp);
|
|
#else
|
|
long ret = std::ftell(fp);
|
|
#endif
|
|
GGML_ASSERT(ret != -1); // this really shouldn't fail
|
|
return (size_t) ret;
|
|
}
|
|
|
|
void seek(size_t offset, int whence) {
|
|
#ifdef _WIN32
|
|
int ret = _fseeki64(fp, (__int64) offset, whence);
|
|
#else
|
|
int ret = std::fseek(fp, (long) offset, whence);
|
|
#endif
|
|
GGML_ASSERT(ret == 0); // same
|
|
}
|
|
|
|
void read_raw(void * ptr, size_t size) {
|
|
if (size == 0) {
|
|
return;
|
|
}
|
|
errno = 0;
|
|
std::size_t ret = std::fread(ptr, size, 1, fp);
|
|
if (ferror(fp)) {
|
|
throw std::runtime_error(format("read error: %s", strerror(errno)));
|
|
}
|
|
if (ret != 1) {
|
|
throw std::runtime_error(std::string("unexpectedly reached end of file"));
|
|
}
|
|
}
|
|
|
|
std::uint32_t read_u32() {
|
|
std::uint32_t ret;
|
|
read_raw(&ret, sizeof(ret));
|
|
return ret;
|
|
}
|
|
std::float_t read_f32() {
|
|
std::float_t ret;
|
|
read_raw(&ret, sizeof(ret));
|
|
return ret;
|
|
}
|
|
|
|
std::string read_string(std::uint32_t len) {
|
|
std::vector<char> chars(len);
|
|
read_raw(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() {
|
|
if (fp) {
|
|
std::fclose(fp);
|
|
}
|
|
}
|
|
};
|
|
|
|
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) {
|
|
return false;
|
|
}
|
|
uint32_t magic = file.read_u32();
|
|
return magic == GGUF_MAGIC;
|
|
}
|
|
|
|
void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
|
|
// 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);
|
|
|
|
std::vector<const char *> strings;
|
|
std::vector<float> scores;
|
|
int n_vocab = llama_n_vocab(lctx);
|
|
strings.resize(n_vocab, NULL);
|
|
scores.resize(n_vocab, 0);
|
|
n_vocab = llama_get_vocab(lctx, strings.data(), scores.data(), n_vocab);
|
|
GGML_ASSERT(n_vocab == llama_n_vocab(lctx));
|
|
vocab->id_to_token.resize(n_vocab);
|
|
for (int i=0; i<n_vocab; ++i) {
|
|
std::string tok = std::string(strings[i]);
|
|
float score = scores[i];
|
|
vocab->id_to_token[i].tok = tok;
|
|
vocab->id_to_token[i].score = score;
|
|
vocab->token_to_id.emplace(tok, 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");
|
|
uint32_t n_vocab = config->vocab_size;
|
|
/* uint32_t max_token_length = */ file.read_u32(); // unused
|
|
vocab->id_to_token.resize(n_vocab);
|
|
for (uint32_t i=0; i<n_vocab; ++i) {
|
|
float_t score = file.read_f32();
|
|
uint32_t len = file.read_u32();
|
|
std::string tok = file.read_string(len);
|
|
vocab->id_to_token[i].tok = tok;
|
|
vocab->id_to_token[i].score = score;
|
|
vocab->token_to_id.emplace(tok, i);
|
|
}
|
|
}
|
|
}
|
|
|
|
void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * karpathy_weights){
|
|
int ct;
|
|
switch (gg_weights->n_dims){
|
|
case 1:
|
|
ct = 0;
|
|
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){
|
|
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0]);
|
|
*ptr = karpathy_weights[ct];
|
|
ct++;
|
|
}
|
|
break;
|
|
case 2:
|
|
ct = 0;
|
|
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
|
|
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]);
|
|
*ptr = karpathy_weights[ct];
|
|
ct++;
|
|
}
|
|
}
|
|
break;
|
|
case 3:
|
|
ct = 0;
|
|
for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) {
|
|
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
|
|
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1] + i2*gg_weights->nb[2]);
|
|
*ptr = karpathy_weights[ct];
|
|
ct++;
|
|
}
|
|
}
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
|
|
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")
|
|
(void) vocab;
|
|
(void) model;
|
|
(void) w;
|
|
// // write_magic
|
|
// file.write_u32(LLAMA_FILE_MAGIC); // magic
|
|
// file.write_u32(LLAMA_FILE_VERSION); // 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_score = vocab->id_to_token.at(i);
|
|
// file.write_u32((uint32_t) token_score.tok.size());
|
|
// file.write_raw(token_score.tok.data(), token_score.tok.size());
|
|
// file.write_raw(&token_score.score, sizeof(token_score.score));
|
|
// }
|
|
//
|
|
// // stuff AK weights into GG weights one by one.
|
|
// // w->token_embedding_table -> model->tok_embeddings
|
|
// // float* -> struct ggml_tensor
|
|
// stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table);
|
|
// stuff_karpathy_weights_into_gg(model->output, w->token_embedding_table);
|
|
//
|
|
// stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
|
|
// //print_row(model->norm, 0);
|
|
//
|
|
// // for rms-att-weight
|
|
// int row_length = model->hparams.n_embd;
|
|
// const auto & hparams = model->hparams;
|
|
// //int n_ff = model->hparams.n_embd;
|
|
// int n_ff = get_n_ff(&hparams);
|
|
//
|
|
// for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
|
|
// auto & layer = model->layers[i];
|
|
// // 1d
|
|
// stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
|
|
// stuff_karpathy_weights_into_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]);
|
|
//
|
|
// // from 3d matrix layer x dim x dim to 2d matrix dim x dim
|
|
// stuff_karpathy_weights_into_gg(layer.wq , &w->wq[i*row_length*row_length]);
|
|
// stuff_karpathy_weights_into_gg(layer.wk , &w->wk[i*row_length*row_length]);
|
|
// stuff_karpathy_weights_into_gg(layer.wv , &w->wv[i*row_length*row_length]);
|
|
// stuff_karpathy_weights_into_gg(layer.wo , &w->wo[i*row_length*row_length]);
|
|
//
|
|
// stuff_karpathy_weights_into_gg(layer.w1 , &w->w1[i*row_length*n_ff]);
|
|
// 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]);
|
|
// }
|
|
// // write tensors
|
|
// write_tensor(&file, model->tok_embeddings);
|
|
// write_tensor(&file, model->norm);
|
|
// write_tensor(&file, 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);
|
|
// }
|
|
}
|
|
|
|
struct train_params get_default_train_params() {
|
|
struct train_params params;
|
|
params.fn_vocab_model = "models/ggml-vocab.bin";
|
|
params.fn_llama2c_output_model = "ak_llama_model.bin";
|
|
params.fn_train_data = "shakespeare.txt";
|
|
params.fn_checkpoint_in = "checkpoint.bin";
|
|
params.fn_checkpoint_out = "checkpoint.bin";
|
|
params.fn_model_out = "ggml-checkpoint-f32.bin";
|
|
|
|
params.seed = -1;
|
|
|
|
params.n_ctx = 128;
|
|
params.n_embd = 256;
|
|
params.n_mult = 256;
|
|
params.n_head = 8;
|
|
params.n_layer = 16;
|
|
params.n_rotmax = 64;
|
|
|
|
params.n_threads = 6;
|
|
params.n_batch = 8;
|
|
params.n_examples = 8;
|
|
params.n_predict = 1024;
|
|
|
|
params.print_info_interval = 1;
|
|
params.print_details_interval = 2;
|
|
|
|
params.samples_start_after_nl = false;
|
|
params.use_adam = true;
|
|
params.use_flash = true;
|
|
params.use_scratch = true;
|
|
|
|
// only adam
|
|
params.warmup = 100;
|
|
params.cos_decay_steps = 1000;
|
|
params.cos_decay_restart = 1.1f;
|
|
params.cos_decay_alpha = 0.0f;
|
|
|
|
params.lbfgs_n_iter = 16;
|
|
params.adam_n_iter = 16;
|
|
params.adam_alpha = 1e-3f;
|
|
params.adam_decay = 1e-3f;
|
|
|
|
params.mem_model_gb = 2;
|
|
params.mem_compute_gb = 24;
|
|
params.mem_compute0_gb = 8;
|
|
params.mem_compute1_gb = 2;
|
|
|
|
return params;
|
|
}
|
|
|
|
void print_usage(int /*argc*/, char ** argv, const struct train_params * params) {
|
|
fprintf(stderr, "usage: %s [options]\n", argv[0]);
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "options:\n");
|
|
fprintf(stderr, " -h, --help show this help message and exit\n");
|
|
fprintf(stderr, " --copy-vocab-from-model FNAME llama2.c vocabulary or ggml model path 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-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
|
|
fprintf(stderr, "\n");
|
|
}
|
|
|
|
bool params_parse(int argc, char ** argv, struct train_params * params) {
|
|
bool invalid_param = false;
|
|
bool reqd_param_found = false;
|
|
std::string arg;
|
|
struct train_params default_params = get_default_train_params();
|
|
const std::string arg_prefix = "--";
|
|
|
|
for (int i = 1; i < argc; i++) {
|
|
arg = argv[i];
|
|
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
|
|
std::replace(arg.begin(), arg.end(), '_', '-');
|
|
}
|
|
|
|
if (arg == "--copy-vocab-from-model") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params->fn_vocab_model = argv[i];
|
|
} else if (arg == "--llama2c-model") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
reqd_param_found = true;
|
|
params->fn_llama2c_model = argv[i];
|
|
} else if (arg == "--llama2c-output-model") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params->fn_llama2c_output_model = argv[i];
|
|
} else if (arg == "-h" || arg == "--help") {
|
|
print_usage(argc, argv, &default_params);
|
|
exit(0);
|
|
} else {
|
|
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
|
print_usage(argc, argv, &default_params);
|
|
exit(1);
|
|
}
|
|
}
|
|
if (invalid_param) {
|
|
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
|
|
print_usage(argc, argv, &default_params);
|
|
exit(1);
|
|
}
|
|
if (!reqd_param_found){
|
|
fprintf(stderr, "error: please specify a llama2.c .bin file to be converted with argument --llama2c-model\n");
|
|
print_usage(argc, argv, &default_params);
|
|
exit(1);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
struct train_params params = get_default_train_params();
|
|
if (!params_parse(argc, argv, ¶ms)) {
|
|
return 1;
|
|
}
|
|
Config config;
|
|
TransformerWeights weights;
|
|
{
|
|
FILE *file = fopen(params.fn_llama2c_model, "rb");
|
|
if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; }
|
|
// read in the config header
|
|
if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
|
|
// read in the Transformer weights
|
|
malloc_weights(&weights, &config);
|
|
if(checkpoint_init_weights(&weights, &config, file)) { return 1; }
|
|
fclose(file);
|
|
}
|
|
|
|
struct llama_vocab vocab;
|
|
load_vocab(params.fn_vocab_model, &config, &vocab);
|
|
|
|
struct my_llama_model model;
|
|
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_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;
|
|
model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
|
|
print_params(&model.hparams);
|
|
struct ggml_init_params lcparams;
|
|
lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);
|
|
lcparams.mem_buffer = NULL;
|
|
lcparams.no_alloc = false;
|
|
|
|
model.ctx = ggml_init(lcparams);
|
|
|
|
init_model(&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);
|
|
|
|
ggml_free(model.ctx);
|
|
free_weights(&weights);
|
|
return 0;
|
|
}
|