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llama.cpp/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
Qin Yue Chen 8cf19d60dc gguf : support big endian platform (#3552) 
* check whether platform is 390x if yes->do not import immintrin.h

* support s390x big endian

* support --bigendian option for s390x
1. verified with baichuan7b-chat with float 16 on s390x
2. verified with baichuan7b-chat
3. verified with chinese-alpaca-2-13b-f16

* update format based on editor-config checker result

* Update convert-baichuan-hf-to-gguf.py

* 1. check in ggml.c if endianess is not match
2. update GGUF version
3. change get_pack_prefix to property
4. update information log

* always use "GGUF" as beginng of GGUF file

* Compare "GGUF" with file header char by char
1.  Set GGUF_MAGIC to "GGUF" string instead of int value
2. Compare "GGUF" char by char to ensure its byte order
3. Move bytes swap code from convert.py to gguf.py write_tensor_data

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-10-20 14:19:40 +03:00

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#include "ggml.h"
#include "llama.h"
#include "common.h"
#include <unordered_map>
#include <vector>
#include <cassert>
#include <climits>
#include <cstring>
#include <cstdarg>
#include <ctime>
#include <random>
#include <stdexcept>
#include <sstream>
#include <algorithm>
#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)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
#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.
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;
struct TransformerWeights {
// 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() {
delete[] token_embedding_table;
delete[] rms_att_weight;
delete[] rms_ffn_weight;
delete[] wq;
delete[] wk;
delete[] wv;
delete[] wo;
delete[] w1;
delete[] w2;
delete[] w3;
delete[] rms_final_weight;
delete[] wcls;
}
};
static void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) {
// 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);
if (shared_weights) {
w->wcls = NULL;
} else {
w->wcls = new float[p->vocab_size * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
}
}
static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) {
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;
// Skip freq_cis_real & freq_cis_imag
int head_size = p->dim / p->n_heads;
fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR);
if (!shared_weights && fread(w->wcls, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
// Check we didn't forget to read anything
auto curr = ftell(f);
fseek(f, 0, SEEK_END);
auto end = ftell(f);
if (curr != end) {
printf("Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\n", curr, end);
return 1;
}
return 0;
}
static 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]);
if (w->wcls) printf("%f\n", w->wcls[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;
using ttype = llama_token_type;
struct token_data {
token text;
float score;
ttype type;
};
std::unordered_map<token, id> token_to_id;
std::vector<token_data> 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_ff = 11008;
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;
std::string name;
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;
};
static 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__, params->n_ff);
printf("%s: n_layer: %d\n", __func__, params->n_layer);
printf("%s: n_rot: %d\n", __func__, params->n_rot);
}
static 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 = hparams.n_ff;
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());
}
}
static 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;
}
static 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;
}
static 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");
}
static 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)) {
die_fmt("fread failed: %s", strerror(errno));
}
if (ret != 1) {
die("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);
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
static bool is_ggml_file(const char * filename) {
llama_file file(filename, "rb");
if (file.size < 4) {
return false;
}
std::string magic = file.read_string(4);
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();
}
static void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
if (is_ggml_file(filename)) {
struct ggml_context * ctx_data = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ false,
/*.ctx = */ &ctx_data,
};
struct gguf_context * ctx = gguf_init_from_file(filename, params);
GGML_ASSERT(ctx != NULL);
const int model_idx = gguf_find_key(ctx, KV_TOKENIZER_MODEL);
GGML_ASSERT(model_idx >= 0);
std::string tokenizer_name = gguf_get_val_str(ctx, model_idx);
GGML_ASSERT(tokenizer_name == TOKENIZER_NAME);
const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST);
GGML_ASSERT(token_idx >= 0);
const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES);
GGML_ASSERT(score_idx >= 0);
const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
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");
if (!file.fp) {
die_fmt("%s: %s", strerror(errno), filename);
}
const int n_vocab = config->vocab_size;
/* uint32_t max_token_length = */ file.read_u32(); // unused
vocab->id_to_token.resize(n_vocab);
for (llama_vocab::id id=0; id<n_vocab; ++id) {
float_t score = file.read_f32();
uint32_t len = file.read_u32();
std::string text = file.read_string(len);
unsigned char byte_val;
llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
if (id == UNKNOWN_TOKEN_ID) {
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;
}
text = llama_escape_whitespaces(text);
vocab->id_to_token[id].text = text;
vocab->id_to_token[id].score = score;
vocab->id_to_token[id].type = type;
vocab->token_to_id.emplace(text, id);
}
}
}
static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const 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;
}
}
static void save_as_llama_model(
struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename
) {
// convert AK weights into GG weights one by one.
// w->token_embedding_table -> model->tok_embeddings
// float* -> struct ggml_tensor
convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table);
convert_weights_ak_to_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table);
convert_weights_ak_to_gg(model->norm, w->rms_final_weight);
//print_row(model->norm, 0);
// for rms-att-weight
int row_length = model->hparams.n_embd;
int n_ff = model->hparams.n_ff;
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
auto & layer = model->layers[i];
// 1d
convert_weights_ak_to_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
convert_weights_ak_to_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
convert_weights_ak_to_gg(layer.wq , &w->wq[i*row_length*row_length]);
convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length]);
convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length]);
convert_weights_ak_to_gg(layer.wo , &w->wo[i*row_length*row_length]);
convert_weights_ak_to_gg(layer.w1 , &w->w1[i*row_length*n_ff]);
convert_weights_ak_to_gg(layer.w2 , &w->w2[i*n_ff*row_length]);
convert_weights_ak_to_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
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];
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.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);
}
static struct train_params get_default_train_params() {
struct train_params params;
params.fn_vocab_model = "models/7B/ggml-model-f16.gguf";
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;
}
static 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 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-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
fprintf(stderr, "\n");
}
static 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;
}
static 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) {
struct train_params params = get_default_train_params();
if (!params_parse(argc, argv, &params)) {
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; }
auto shared_weights = config.vocab_size > 0;
config.vocab_size = abs(config.vocab_size);
// read in the Transformer weights
malloc_weights(&weights, &config, shared_weights);
if(checkpoint_init_weights(&weights, &config, file, shared_weights)) { 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_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;
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);
model.name = basename(params.fn_llama2c_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);
return 0;
}
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