llama
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1#include "ggml.h"
2#include "llama.h"
3#include "common.h"
4#include "log.h"
5
6#include <unordered_map>
7#include <vector>
8#include <cassert>
9#include <climits>
10#include <cstring>
11#include <cstdarg>
12#include <cinttypes>
13#include <ctime>
14#include <random>
15#include <stdexcept>
16#include <sstream>
17#include <algorithm>
18#include <string>
19
20// GGUF keys & tensor names.
21
22#define KV_GENERAL_ARCHITECTURE "general.architecture"
23#define KV_GENERAL_NAME "general.name"
24
25#define KV_TOKENIZER_MODEL "tokenizer.ggml.model"
26#define KV_TOKENIZER_LIST "tokenizer.ggml.tokens"
27#define KV_TOKENIZER_TOKEN_TYPE "tokenizer.ggml.token_type"
28#define KV_TOKENIZER_SCORES "tokenizer.ggml.scores"
29#define KV_TOKENIZER_BOS_ID "tokenizer.ggml.bos_token_id"
30#define KV_TOKENIZER_EOS_ID "tokenizer.ggml.eos_token_id"
31#define KV_TOKENIZER_UNK_ID "tokenizer.ggml.unknown_token_id"
32#define KV_TOKENIZER_SEP_ID "tokenizer.ggml.seperator_token_id"
33#define KV_TOKENIZER_PAD_ID "tokenizer.ggml.padding_token_id"
34#define KV_TOKENIZER_HF_JSON "tokenizer.huggingface.json"
35
36#define KV_CONTEXT_LENGTH "llama.context_length"
37#define KV_EMBEDDING_LENGTH "llama.embedding_length"
38#define KV_BLOCK_COUNT "llama.block_count"
39#define KV_FEED_FORWARD_LENGTH "llama.feed_forward_length"
40#define KV_ATTENTION_HEAD_COUNT "llama.attention.head_count"
41#define KV_ATTENTION_HEAD_COUNT_KV "llama.attention.head_count_kv"
42#define KV_ATTENTION_LAYERNORM_RMS_EPS "llama.attention.layer_norm_rms_epsilon"
43#define KV_ROPE_DIMENSION_COUNT "llama.rope.dimension_count"
44
45#define TN_TOKEN_EMBD "token_embd.weight"
46#define TN_OUTPUT_NORM "output_norm.weight"
47#define TN_OUTPUT "output.weight"
48#define TN_ATTN_NORM "blk.%d.attn_norm.weight"
49#define TN_ATTN_Q "blk.%d.attn_q.weight"
50#define TN_ATTN_K "blk.%d.attn_k.weight"
51#define TN_ATTN_V "blk.%d.attn_v.weight"
52#define TN_ATTN_OUTPUT "blk.%d.attn_output.weight"
53#define TN_FFN_NORM "blk.%d.ffn_norm.weight"
54#define TN_FFN_GATE "blk.%d.ffn_gate.weight"
55#define TN_FFN_DOWN "blk.%d.ffn_down.weight"
56#define TN_FFN_UP "blk.%d.ffn_up.weight"
57
58#if defined(_MSC_VER)
59#pragma warning(disable: 4244 4267) // possible loss of data
60#endif
61
62#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
63#define LLAMA_FILE_VERSION_GGJT_V3 3
64
65#define TOKENIZER_NAME "llama"
66#define UNKNOWN_TOKEN_ID 0
67#define BOS_TOKEN_ID 1
68#define EOS_TOKEN_ID 2
69
70//////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.
71typedef struct {
72int dim; // transformer dimension
73int hidden_dim; // for ffn layers
74int n_layers; // number of layers
75int n_heads; // number of query heads
76int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
77int vocab_size; // vocabulary size, usually 256 (byte-level)
78int seq_len; // max sequence length
79} Config;
80
81struct TransformerWeights {
82// token embedding table
83std::vector<float> token_embedding_table; // (vocab_size, dim)
84// weights for rmsnorms
85std::vector<float> rms_att_weight; // (layer, dim) rmsnorm weights
86std::vector<float> rms_ffn_weight; // (layer, dim)
87// weights for matmuls
88std::vector<float> wq; // (layer, dim, dim)
89std::vector<float> wk; // (layer, dim, dim)
90std::vector<float> wv; // (layer, dim, dim)
91std::vector<float> wo; // (layer, dim, dim)
92// weights for ffn
93std::vector<float> w1; // (layer, hidden_dim, dim)
94std::vector<float> w2; // (layer, dim, hidden_dim)
95std::vector<float> w3; // (layer, hidden_dim, dim)
96// final rmsnorm
97std::vector<float> rms_final_weight; // (dim,)
98// freq_cis for RoPE relatively positional embeddings
99// std::vector<float> freq_cis_real; // (seq_len, dim/2)
100// std::vector<float> freq_cis_imag; // (seq_len, dim/2)
101// (optional) classifier weights for the logits, on the last layer
102std::vector<float> wcls;
103};
104
105static void alloc_weights(TransformerWeights * w, const Config * p, bool shared_weights) {
106const int n_multiqueries = p->n_kv_heads <= 0 || p->n_kv_heads >= p->n_heads ? 1 : p->n_heads / p->n_kv_heads;
107try {
108w->token_embedding_table.resize(p->vocab_size * p->dim);
109LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
110
111w->rms_att_weight.resize(p->n_layers * p->dim);
112LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
113
114w->rms_ffn_weight.resize(p->n_layers * p->dim);
115LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
116
117w->wq.resize(p->n_layers * p->dim * p->dim);
118LOG_INF("%s: 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);
119
120w->wk.resize(p->n_layers * p->dim * p->dim / n_multiqueries);
121LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries);
122
123w->wv.resize(p->n_layers * p->dim * p->dim / n_multiqueries);
124LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries);
125
126w->wo.resize(p->n_layers * p->dim * p->dim);
127LOG_INF("%s: 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);
128
129w->w1.resize(p->n_layers * p->hidden_dim * p->dim);
130LOG_INF("%s: 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);
131
132w->w2.resize(p->n_layers * p->hidden_dim * p->dim);
133LOG_INF("%s: 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);
134
135w->w3.resize(p->n_layers * p->hidden_dim * p->dim);
136LOG_INF("%s: 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);
137
138w->rms_final_weight.resize(p->dim);
139LOG_INF("%s: Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
140
141if (shared_weights) {
142w->wcls = {};
143} else {
144w->wcls.resize(p->vocab_size * p->dim);
145LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
146}
147}
148catch (std::length_error &) {
149die("Invalid configuration. Failed to allocate memory for weights");
150}
151}
152
153static int checkpoint_init_weights(TransformerWeights * w, const Config * p, FILE * f, bool shared_weights) {
154if (fread(w->token_embedding_table.data(), sizeof(float), w->token_embedding_table.size(), f) != w->token_embedding_table.size()) return 1;
155if (fread(w->rms_att_weight.data(), sizeof(float), w->rms_att_weight.size(), f) != w->rms_att_weight.size()) return 1;
156if (fread(w->wq.data(), sizeof(float), w->wq.size(), f) != w->wq.size()) return 1;
157if (fread(w->wk.data(), sizeof(float), w->wk.size(), f) != w->wk.size()) return 1;
158if (fread(w->wv.data(), sizeof(float), w->wv.size(), f) != w->wv.size()) return 1;
159if (fread(w->wo.data(), sizeof(float), w->wo.size(), f) != w->wo.size()) return 1;
160if (fread(w->rms_ffn_weight.data(), sizeof(float), w->rms_ffn_weight.size(), f) != w->rms_ffn_weight.size()) return 1;
161if (fread(w->w1.data(), sizeof(float), w->w1.size(), f) != w->w1.size()) return 1;
162if (fread(w->w2.data(), sizeof(float), w->w2.size(), f) != w->w2.size()) return 1;
163if (fread(w->w3.data(), sizeof(float), w->w3.size(), f) != w->w3.size()) return 1;
164if (fread(w->rms_final_weight.data(), sizeof(float), w->rms_final_weight.size(), f) != w->rms_final_weight.size()) return 1;
165
166// Skip freq_cis_real & freq_cis_imag
167int head_size = p->dim / p->n_heads;
168fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR);
169
170if (!shared_weights && fread(w->wcls.data(), sizeof(float), w->wcls.size(), f) != w->wcls.size()) return 1;
171
172// Check we didn't forget to read anything
173auto curr = ftell(f);
174fseek(f, 0, SEEK_END);
175auto end = ftell(f);
176if (curr != end) {
177LOG_ERR("%s: Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\n", __func__, curr, end);
178return 1;
179}
180
181return 0;
182}
183
184static void print_sample_weights(TransformerWeights *w){
185LOG_INF("----- Quick print of first of the weight vales of all the variables\n");
186LOG_INF("%f\n", w->token_embedding_table[0]);
187LOG_INF("%f\n", w->rms_att_weight[0]);
188LOG_INF("%f\n", w->rms_ffn_weight[0]);
189
190LOG_INF("%f\n", w->wq[0]);
191LOG_INF("%f\n", w->wk[0]);
192LOG_INF("%f\n", w->wv[0]);
193LOG_INF("%f\n", w->wo[0]);
194LOG_INF("%f\n", w->w1[0]);
195LOG_INF("%f\n", w->w2[0]);
196LOG_INF("%f\n", w->w3[0]);
197LOG_INF("%f\n", w->rms_att_weight[0]);
198if (!w->wcls.empty()) LOG_INF("%f\n", w->wcls[0]);
199}
200////////////////////////////////////////////////////////////////////////////////////////////////////////////
201
202//////////////////////////////////////// ggml structs and functions required to load models, configs and save the model.
203
204struct llama_vocab {
205using id = int32_t;
206using token = std::string;
207using ttype = llama_token_type;
208
209struct token_data {
210token text;
211float score;
212ttype type;
213};
214
215std::unordered_map<token, id> token_to_id;
216std::vector<token_data> id_to_token;
217};
218
219struct my_llama_hparams {
220uint32_t n_vocab = 32000;
221uint32_t n_ctx = 512; // this is provided as user input?
222uint32_t n_embd = 4096;
223uint32_t n_ff = 11008;
224uint32_t n_mult = 4;
225uint32_t n_head = 32;
226uint32_t n_head_kv = 32;
227uint32_t n_layer = 32;
228uint32_t n_rot = 64;
229
230bool operator!=(const my_llama_hparams& other) const {
231return memcmp(this, &other, sizeof(my_llama_hparams));
232}
233};
234
235struct my_llama_layer {
236// normalization
237struct ggml_tensor * attention_norm;
238
239// attention
240struct ggml_tensor * wq;
241struct ggml_tensor * wk;
242struct ggml_tensor * wv;
243struct ggml_tensor * wo;
244
245// normalization
246struct ggml_tensor * ffn_norm;
247
248// ff
249struct ggml_tensor * w1;
250struct ggml_tensor * w2;
251struct ggml_tensor * w3;
252};
253
254struct my_llama_model {
255struct ggml_context * ctx = NULL;
256
257std::string name;
258
259my_llama_hparams hparams;
260
261struct ggml_tensor * tok_embeddings;
262
263struct ggml_tensor * norm;
264struct ggml_tensor * output;
265
266std::vector<my_llama_layer> layers;
267
268uint32_t train_its = 0;
269uint32_t train_samples = 0;
270uint32_t train_tokens = 0;
271};
272
273struct train_params {
274const char * fn_vocab_model;
275const char * fn_llama2c_model;
276const char * fn_llama2c_output_model;
277const char * fn_train_data;
278const char * fn_checkpoint_in;
279const char * fn_checkpoint_out;
280const char * fn_model_out;
281
282uint32_t seed;
283
284int n_ctx;
285int n_embd;
286int n_mult;
287int n_head;
288int n_layer;
289int n_rotmax;
290
291int n_threads;
292int n_batch;
293int n_examples;
294int n_predict;
295
296int print_info_interval;
297int print_details_interval;
298
299bool samples_start_after_nl;
300bool use_adam;
301bool use_flash;
302bool use_scratch;
303
304// only adam
305int warmup;
306int cos_decay_steps;
307float cos_decay_restart;
308float cos_decay_alpha;
309
310int lbfgs_n_iter;
311int adam_n_iter;
312float adam_alpha;
313float adam_decay;
314
315int mem_model_gb;
316int mem_compute_gb;
317int mem_compute0_gb;
318int mem_compute1_gb;
319};
320
321static void print_params(struct my_llama_hparams * params) {
322LOG_INF("%s: n_vocab: %u\n", __func__, params->n_vocab);
323LOG_INF("%s: n_ctx: %u\n", __func__, params->n_ctx);
324LOG_INF("%s: n_embd: %u\n", __func__, params->n_embd);
325LOG_INF("%s: n_mult: %u\n", __func__, params->n_mult);
326LOG_INF("%s: n_head: %u\n", __func__, params->n_head);
327LOG_INF("%s: n_head_kv: %u\n", __func__, params->n_head_kv);
328LOG_INF("%s: n_ff: %u\n", __func__, params->n_ff);
329LOG_INF("%s: n_layer: %u\n", __func__, params->n_layer);
330LOG_INF("%s: n_rot: %u\n", __func__, params->n_rot);
331}
332
333static void print_tensor_info(const struct ggml_context * ctx) {
334for (auto t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
335LOG_INF("%s: Allocating ", __func__);
336int64_t total = 1;
337int i = 0;
338for (; i < ggml_n_dims(t); ++i) {
339if (i > 0) LOG("x ");
340LOG("[%" PRId64 "] ", t->ne[i]);
341total *= t->ne[i];
342}
343if (i > 1) LOG("= [%" PRId64 "] ", total);
344LOG("float space for %s\n", ggml_get_name(t));
345}
346}
347
348static void init_model(struct my_llama_model * model) {
349const auto & hparams = model->hparams;
350
351const uint32_t n_embd = hparams.n_embd;
352const uint32_t n_layer = hparams.n_layer;
353const uint32_t n_vocab = hparams.n_vocab;
354
355const uint32_t n_multiqueries = hparams.n_head_kv <= 0 || hparams.n_head_kv >= hparams.n_head ? 1 : hparams.n_head / hparams.n_head_kv;
356
357const uint32_t n_ff = hparams.n_ff;
358struct ggml_context * ctx = model->ctx;
359
360model->train_its = 0;
361model->train_samples = 0;
362model->train_tokens = 0;
363
364model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
365model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
366model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
367
368ggml_set_name(model->tok_embeddings, "tok_embeddings.weight");
369ggml_set_name(model->norm, "norm.weight");
370ggml_set_name(model->output, "output.weight");
371
372model->layers.resize(n_layer);
373for (uint32_t i = 0; i < n_layer; ++i) {
374auto & layer = model->layers[i];
375
376std::string layers_i = "layers." + std::to_string(i);
377
378layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
379
380layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
381layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries);
382layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries);
383layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
384
385layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
386
387layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
388layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
389layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
390
391ggml_set_name(layer.attention_norm, (layers_i + ".attention_norm.weight").c_str());
392
393ggml_set_name(layer.wq, (layers_i + ".attention.wq.weight").c_str());
394ggml_set_name(layer.wk, (layers_i + ".attention.wk.weight").c_str());
395ggml_set_name(layer.wv, (layers_i + ".attention.wv.weight").c_str());
396ggml_set_name(layer.wo, (layers_i + ".attention.wo.weight").c_str());
397
398ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str());
399
400ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str());
401ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
402ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
403}
404
405print_tensor_info(ctx);
406}
407
408static float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
409float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
410return *ptr;
411}
412
413static int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
414int32_t * ptr = (int32_t *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
415return *ptr;
416}
417
418static void print_row(struct ggml_tensor * probs, int i) {
419for (int k = 0; k < probs->ne[0]; ++k) {
420float p = get_f32_2d(probs, k, i);
421LOG(" %f", p);
422}
423LOG("\n");
424}
425
426static void print_matrix(struct ggml_tensor * probs) {
427assert(ggml_is_matrix(probs));
428for (int i = 0; i < probs->ne[1]; ++i) {
429for (int k = 0; k < probs->ne[0]; ++k) {
430float p = get_f32_2d(probs, k, i);
431LOG(" %.2f", p);
432}
433LOG("\n");
434}
435}
436
437struct llama_file {
438// use FILE * so we don't have to re-open the file to mmap
439FILE * fp;
440size_t size;
441
442llama_file(const char * fname, const char * mode) {
443fp = std::fopen(fname, mode);
444if (fp == NULL) {
445size = 0;
446} else {
447seek(0, SEEK_END);
448size = tell();
449seek(0, SEEK_SET);
450}
451}
452
453size_t tell() const {
454#ifdef _WIN32
455__int64 ret = _ftelli64(fp);
456#else
457long ret = std::ftell(fp);
458#endif
459GGML_ASSERT(ret != -1); // this really shouldn't fail
460return (size_t) ret;
461}
462
463void seek(size_t offset, int whence) {
464#ifdef _WIN32
465int ret = _fseeki64(fp, (__int64) offset, whence);
466#else
467int ret = std::fseek(fp, (long) offset, whence);
468#endif
469GGML_ASSERT(ret == 0); // same
470}
471
472void read_raw(void * ptr, size_t size) {
473if (size == 0) {
474return;
475}
476errno = 0;
477std::size_t ret = std::fread(ptr, size, 1, fp);
478if (ferror(fp)) {
479die_fmt("fread failed: %s", strerror(errno));
480}
481if (ret != 1) {
482die("unexpectedly reached end of file");
483}
484}
485
486std::uint32_t read_u32() {
487std::uint32_t ret;
488read_raw(&ret, sizeof(ret));
489return ret;
490}
491std::float_t read_f32() {
492std::float_t ret;
493read_raw(&ret, sizeof(ret));
494return ret;
495}
496
497std::string read_string(std::uint32_t len) {
498std::vector<char> chars(len);
499read_raw(chars.data(), len);
500return std::string(chars.data(), len);
501}
502
503~llama_file() {
504if (fp) {
505std::fclose(fp);
506}
507}
508};
509
510static bool is_ggml_file(const char * filename) {
511llama_file file(filename, "rb");
512if (file.size < 4) {
513return false;
514}
515std::string magic = file.read_string(4);
516return magic == GGUF_MAGIC;
517}
518
519static std::string llama_escape_whitespaces(const std::string & text) {
520std::ostringstream out;
521for (char c : text) {
522if (c == ' ') out << "\xe2\x96\x81";
523else out << c;
524}
525return out.str();
526}
527
528static void load_vocab(const char * filename, const Config * config, struct llama_vocab * vocab) {
529if (is_ggml_file(filename)) {
530LOG_INF("%s: Loading vocabulary from gguf file %s\n", __func__, filename);
531struct ggml_context * ctx_data = NULL;
532
533struct gguf_init_params params = {
534/*.no_alloc = */ false,
535/*.ctx = */ &ctx_data,
536};
537
538struct gguf_context * ctx = gguf_init_from_file(filename, params);
539GGML_ASSERT(ctx != NULL);
540
541const int model_idx = gguf_find_key(ctx, KV_TOKENIZER_MODEL);
542GGML_ASSERT(model_idx >= 0);
543std::string tokenizer_name = gguf_get_val_str(ctx, model_idx);
544GGML_ASSERT(tokenizer_name == TOKENIZER_NAME);
545
546const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST);
547GGML_ASSERT(token_idx >= 0);
548
549const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES);
550GGML_ASSERT(score_idx >= 0);
551const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
552
553const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE);
554GGML_ASSERT(toktype_idx >= 0);
555const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
556
557const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
558if (n_vocab != static_cast<uint32_t>(config->vocab_size)) {
559die_fmt("vocab size mismatch: (gguf) %u != (llama2c) %d", n_vocab, config->vocab_size);
560}
561
562vocab->id_to_token.resize(n_vocab);
563
564for (uint32_t i = 0; i < n_vocab; i++) {
565std::string word = gguf_get_arr_str(ctx, token_idx, i);
566
567vocab->token_to_id[word] = i;
568
569auto & token_data = vocab->id_to_token[i];
570token_data.text = std::move(word);
571token_data.score = scores[i];
572token_data.type = (llama_token_type) toktypes[i];
573}
574ggml_free(ctx_data);
575gguf_free(ctx);
576} else {
577// assume llama2.c vocabulary
578LOG_INF("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename);
579llama_file file(filename, "rb");
580if (!file.fp) {
581die_fmt("%s: %s", strerror(errno), filename);
582}
583const int n_vocab = config->vocab_size;
584/* uint32_t max_token_length = */ file.read_u32(); // unused
585vocab->id_to_token.resize(n_vocab);
586for (llama_vocab::id id=0; id<n_vocab; ++id) {
587float_t score = file.read_f32();
588uint32_t len = file.read_u32();
589std::string text = file.read_string(len);
590
591unsigned char byte_val;
592llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
593if (id == UNKNOWN_TOKEN_ID) {
594text = "<unk>";
595type = LLAMA_TOKEN_TYPE_UNKNOWN;
596} else if (id == BOS_TOKEN_ID) {
597text = "<s>";
598type = LLAMA_TOKEN_TYPE_CONTROL;
599} else if (id == EOS_TOKEN_ID) {
600text = "</s>";
601type = LLAMA_TOKEN_TYPE_CONTROL;
602} else if (text.empty()) {
603type = LLAMA_TOKEN_TYPE_CONTROL;
604} else if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) {
605// Text of byte tokens is already in the expected format.
606type = LLAMA_TOKEN_TYPE_BYTE;
607} else {
608type = LLAMA_TOKEN_TYPE_NORMAL;
609}
610text = llama_escape_whitespaces(text);
611
612vocab->id_to_token[id].text = text;
613vocab->id_to_token[id].score = score;
614vocab->id_to_token[id].type = type;
615vocab->token_to_id.emplace(text, id);
616}
617}
618}
619
620static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) {
621int size = 1;
622for (int dim = 0; dim < ggml_n_dims(gg_weights); ++dim) {
623size *= gg_weights->ne[dim];
624}
625for (int ct = 0; ct < size; ++ct) {
626int64_t i0 = 0; int64_t i1 = 0;
627int64_t i2 = 0; int64_t i3 = 0;
628ggml_unravel_index(gg_weights, ct, &i0, &i1, &i2, &i3);
629ggml_set_f32_nd(gg_weights, i0, i1, i2, i3, karpathy_weights[ct]);
630}
631}
632
633static void save_as_llama_model(
634struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename
635) {
636// convert AK weights into GG weights one by one.
637// w->token_embedding_table -> model->tok_embeddings
638// float* -> struct ggml_tensor
639convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table.data());
640convert_weights_ak_to_gg(model->output, !w->wcls.empty() ? w->wcls.data() : w->token_embedding_table.data());
641
642convert_weights_ak_to_gg(model->norm, w->rms_final_weight.data());
643//print_row(model->norm, 0);
644
645// for rms-att-weight
646int row_length = model->hparams.n_embd;
647int n_ff = model->hparams.n_ff;
648
649const uint32_t n_multiqueries = model->hparams.n_head_kv <= 0 || model->hparams.n_head_kv >= model->hparams.n_head ? 1 : model->hparams.n_head / model->hparams.n_head_kv;
650
651for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
652auto & layer = model->layers[i];
653// 1d
654convert_weights_ak_to_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
655convert_weights_ak_to_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]);
656
657// from 3d matrix layer x dim x dim to 2d matrix dim x dim
658convert_weights_ak_to_gg(layer.wq , &w->wq[i*row_length*row_length]);
659convert_weights_ak_to_gg(layer.wo , &w->wo[i*row_length*row_length]);
660// from 3d matrix layer x dim x dim to 2d matrix dim x dim / n_multiqueries
661convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length/n_multiqueries]);
662convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length/n_multiqueries]);
663
664convert_weights_ak_to_gg(layer.w1 , &w->w1[i*row_length*n_ff]);
665convert_weights_ak_to_gg(layer.w2 , &w->w2[i*n_ff*row_length]);
666convert_weights_ak_to_gg(layer.w3 , &w->w3[i*row_length*n_ff]);
667}
668
669struct gguf_context * ctx = gguf_init_empty();
670
671std::vector<const char*> tokens;
672std::vector<float> scores;
673std::vector<llama_token_type> token_types;
674for (const llama_vocab::token_data & token_data : vocab->id_to_token) {
675tokens.push_back(token_data.text.c_str());
676scores.push_back(token_data.score);
677token_types.push_back(token_data.type);
678}
679gguf_set_arr_str(ctx, KV_TOKENIZER_LIST, tokens.data(), tokens.size());
680gguf_set_arr_data(ctx, KV_TOKENIZER_SCORES, GGUF_TYPE_FLOAT32, scores.data(), scores.size());
681gguf_set_arr_data(ctx, KV_TOKENIZER_TOKEN_TYPE, GGUF_TYPE_INT32, token_types.data(), token_types.size());
682
683gguf_set_val_str(ctx, KV_TOKENIZER_MODEL, TOKENIZER_NAME);
684
685gguf_set_val_str(ctx, KV_GENERAL_ARCHITECTURE, "llama");
686gguf_set_val_str(ctx, KV_GENERAL_NAME, "llama");
687
688// special tokens
689gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID);
690gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID);
691gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID);
692gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1);
693gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1);
694
695gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx);
696gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
697gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff);
698gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
699gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
700gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, model->hparams.n_head_kv);
701gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer);
702gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot);
703gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
704
705// write tensors
706ggml_set_name(model->tok_embeddings, TN_TOKEN_EMBD);
707gguf_add_tensor(ctx, model->tok_embeddings);
708
709ggml_set_name(model->norm, TN_OUTPUT_NORM);
710gguf_add_tensor(ctx, model->norm);
711
712ggml_set_name(model->output, TN_OUTPUT);
713gguf_add_tensor(ctx, model->output);
714
715for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
716auto & layer = model->layers[i];
717
718ggml_format_name(layer.wq, TN_ATTN_Q, i);
719gguf_add_tensor(ctx, layer.wq);
720
721ggml_format_name(layer.wk, TN_ATTN_K, i);
722gguf_add_tensor(ctx, layer.wk);
723
724ggml_format_name(layer.wv, TN_ATTN_V, i);
725gguf_add_tensor(ctx, layer.wv);
726
727ggml_format_name(layer.wo, TN_ATTN_OUTPUT, i);
728gguf_add_tensor(ctx, layer.wo);
729
730ggml_format_name(layer.attention_norm, TN_ATTN_NORM, i);
731gguf_add_tensor(ctx, layer.attention_norm);
732
733ggml_format_name(layer.w1, TN_FFN_GATE, i);
734gguf_add_tensor(ctx, layer.w1);
735
736ggml_format_name(layer.w2, TN_FFN_DOWN, i);
737gguf_add_tensor(ctx, layer.w2);
738
739ggml_format_name(layer.w3, TN_FFN_UP, i);
740gguf_add_tensor(ctx, layer.w3);
741
742ggml_format_name(layer.ffn_norm, TN_FFN_NORM, i);
743gguf_add_tensor(ctx, layer.ffn_norm);
744}
745
746gguf_write_to_file(ctx, filename, false);
747gguf_free(ctx);
748}
749
750static struct train_params get_default_train_params() {
751struct train_params params;
752params.fn_vocab_model = "models/7B/ggml-model-f16.gguf";
753params.fn_llama2c_output_model = "ak_llama_model.bin";
754params.fn_train_data = "shakespeare.txt";
755params.fn_checkpoint_in = "checkpoint.bin";
756params.fn_checkpoint_out = "checkpoint.bin";
757params.fn_model_out = "ggml-checkpoint-f32.bin";
758
759params.seed = -1;
760
761params.n_ctx = 128;
762params.n_embd = 256;
763params.n_mult = 256;
764params.n_head = 8;
765params.n_layer = 16;
766params.n_rotmax = 64;
767
768params.n_threads = 6;
769params.n_batch = 8;
770params.n_examples = 8;
771params.n_predict = 1024;
772
773params.print_info_interval = 1;
774params.print_details_interval = 2;
775
776params.samples_start_after_nl = false;
777params.use_adam = true;
778params.use_flash = false;
779params.use_scratch = true;
780
781// only adam
782params.warmup = 100;
783params.cos_decay_steps = 1000;
784params.cos_decay_restart = 1.1f;
785params.cos_decay_alpha = 0.0f;
786
787params.lbfgs_n_iter = 16;
788params.adam_n_iter = 16;
789params.adam_alpha = 1e-3f;
790params.adam_decay = 1e-3f;
791
792params.mem_model_gb = 2;
793params.mem_compute_gb = 24;
794params.mem_compute0_gb = 8;
795params.mem_compute1_gb = 2;
796
797return params;
798}
799
800static void print_usage(int /*argc*/, char ** argv, const struct train_params * params) {
801fprintf(stderr, "usage: %s [options]\n", argv[0]);
802fprintf(stderr, "\n");
803fprintf(stderr, "options:\n");
804fprintf(stderr, " -h, --help show this help message and exit\n");
805fprintf(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);
806fprintf(stderr, " --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\n");
807fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
808fprintf(stderr, "\n");
809}
810
811static bool params_parse(int argc, char ** argv, struct train_params * params) {
812bool invalid_param = false;
813bool reqd_param_found = false;
814std::string arg;
815struct train_params default_params = get_default_train_params();
816const std::string arg_prefix = "--";
817
818for (int i = 1; i < argc; i++) {
819arg = argv[i];
820if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
821std::replace(arg.begin(), arg.end(), '_', '-');
822}
823
824if (arg == "--copy-vocab-from-model") {
825if (++i >= argc) {
826invalid_param = true;
827break;
828}
829params->fn_vocab_model = argv[i];
830} else if (arg == "--llama2c-model") {
831if (++i >= argc) {
832invalid_param = true;
833break;
834}
835reqd_param_found = true;
836params->fn_llama2c_model = argv[i];
837} else if (arg == "--llama2c-output-model") {
838if (++i >= argc) {
839invalid_param = true;
840break;
841}
842params->fn_llama2c_output_model = argv[i];
843} else if (arg == "-h" || arg == "--help") {
844print_usage(argc, argv, &default_params);
845exit(0);
846} else {
847fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
848print_usage(argc, argv, &default_params);
849exit(1);
850}
851}
852if (invalid_param) {
853fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
854print_usage(argc, argv, &default_params);
855exit(1);
856}
857if (!reqd_param_found){
858fprintf(stderr, "error: please specify a llama2.c .bin file to be converted with argument --llama2c-model\n");
859print_usage(argc, argv, &default_params);
860exit(1);
861}
862
863return true;
864}
865
866static std::string basename(const std::string &path) {
867size_t pos = path.find_last_of("/\\");
868if (pos == std::string::npos) {
869return path;
870}
871return path.substr(pos + 1);
872}
873
874int main(int argc, char ** argv) {
875gpt_init();
876
877struct train_params params = get_default_train_params();
878if (!params_parse(argc, argv, ¶ms)) {
879return 1;
880}
881
882Config config;
883TransformerWeights weights = {};
884{
885LOG_INF("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model);
886FILE * file = fopen(params.fn_llama2c_model, "rb");
887if (!file) {
888LOG_ERR("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model);
889return 1;
890}
891// read in the config header
892if (fread(&config, sizeof(Config), 1, file) != 1) {
893LOG_ERR("%s: Unable to read llama2c config from %s!\n",__func__,params.fn_llama2c_model);
894return 1;
895}
896auto shared_weights = config.vocab_size > 0;
897config.vocab_size = abs(config.vocab_size);
898
899// read in the Transformer weights
900alloc_weights(&weights, &config, shared_weights);
901if (checkpoint_init_weights(&weights, &config, file, shared_weights)) {
902LOG_ERR("%s: Unable to initialize transformer weights from %s!",__func__,params.fn_llama2c_model);
903return 1;
904}
905fclose(file);
906}
907
908struct llama_vocab vocab;
909load_vocab(params.fn_vocab_model, &config, &vocab);
910
911struct my_llama_model model;
912model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
913model.hparams.n_ctx = params.n_ctx;
914model.hparams.n_embd = config.dim; //params.n_embd;
915model.hparams.n_ff = config.hidden_dim;
916model.hparams.n_mult = 32;//params.n_mult;
917model.hparams.n_head = config.n_heads; //params.n_head;
918model.hparams.n_head_kv = config.n_kv_heads;
919model.hparams.n_layer = config.n_layers; //params.n_layer;
920model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
921
922print_params(&model.hparams);
923
924struct ggml_init_params lcparams;
925lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);
926lcparams.mem_buffer = NULL;
927lcparams.no_alloc = false;
928
929model.ctx = ggml_init(lcparams);
930
931init_model(&model);
932model.name = basename(params.fn_llama2c_model);
933save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
934
935LOG_INF("%s: Saving llama.c model file %s in ggml format at %s\n", __func__, params.fn_llama2c_model, params.fn_llama2c_output_model);
936
937ggml_free(model.ctx);
938return 0;
939}
940