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-rw-r--r--examples/train-text-from-scratch/train-text-from-scratch.cpp138
1 files changed, 68 insertions, 70 deletions
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
index 54dc2bee..31d6620a 100644
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp
@@ -1,4 +1,5 @@
#include "ggml.h"
+#include "common.h"
#include "llama.h"
#include <unordered_map>
#include <vector>
@@ -16,7 +17,7 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
-static const float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS;
+static const float rms_norm_eps = 1e-5f;
struct random_normal_distribution {
std::mt19937 gen;
@@ -169,14 +170,16 @@ struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struc
struct llama_vocab {
using id = int32_t;
using token = std::string;
+ using ttype = llama_token_type;
- struct token_score {
- token tok;
+ struct token_data {
+ token text;
float score;
+ ttype type;
};
std::unordered_map<token, id> token_to_id;
- std::vector<token_score> id_to_token;
+ std::vector<token_data> id_to_token;
};
struct my_llama_hparams {
@@ -1961,7 +1964,7 @@ void print_matrix(struct ggml_tensor * probs) {
void print_token(struct llama_context * ctx, llama_token token) {
- printf("%s", llama_token_to_str(ctx, token));
+ printf("%s", llama_token_to_str(ctx, token).c_str());
}
void print_tokens(struct llama_context* ctx, struct ggml_tensor * tokens) {
@@ -1995,7 +1998,7 @@ void print_tokens_batch(struct llama_context* ctx, struct ggml_tensor * tokens)
}
}
-void get_example_targets(const int * train_samples, size_t n_train_samples, const llama_token * train_data, size_t n_train_data, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs) {
+void get_example_targets(struct llama_context * lctx, const int * train_samples, size_t n_train_samples, const llama_token * train_data, size_t n_train_data, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs) {
int n_tokens = tokens_input->ne[0];
int n_vocab = target_logits->ne[0];
@@ -2004,7 +2007,7 @@ void get_example_targets(const int * train_samples, size_t n_train_samples, cons
ggml_set_f32(target_logits, -1.0f/n_vocab);
ggml_set_f32(target_probs, 0.0f);
- ggml_set_i32_1d(tokens_input, 0, llama_token_bos());
+ ggml_set_i32_1d(tokens_input, 0, llama_token_bos(lctx));
for (int i=1; i<n_tokens+1; ++i) {
int token = clamp(train_data[sample+i-1], 0, n_vocab-1);
set_f32_2d(target_logits, token, i-1, +1.0f);
@@ -2015,7 +2018,7 @@ void get_example_targets(const int * train_samples, size_t n_train_samples, cons
}
}
-void get_example_targets_batch(struct llama_context * /*lctx*/, const int * train_samples, size_t n_train_samples, const llama_token * train_data, size_t n_train_data, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs) {
+void get_example_targets_batch(struct llama_context * lctx, const int * train_samples, size_t n_train_samples, const llama_token * train_data, size_t n_train_data, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs) {
GGML_ASSERT(tokens_input->n_dims == 2);
GGML_ASSERT(target_logits->n_dims == 3);
GGML_ASSERT(target_probs->n_dims == 3);
@@ -2035,7 +2038,7 @@ void get_example_targets_batch(struct llama_context * /*lctx*/, const int * trai
size_t sample = train_samples[(example_id*n_batch + k) % n_train_samples];
GGML_ASSERT(sample+n_tokens-1 < n_train_data);
- set_i32_2d(tokens_input, 0, k, llama_token_bos());
+ set_i32_2d(tokens_input, 0, k, llama_token_bos(lctx));
for (int i=1; i<n_tokens+1; ++i) {
int token = clamp(train_data[sample+i-1], 0, n_vocab-1);
// print_token(lctx, token);
@@ -2188,11 +2191,10 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto
f.read_raw(buf.data(), f.size);
buf[f.size] = '\0';
- out.resize(buf.size());
-
- int n_tokens = llama_tokenize(lctx, buf.data(), out.data(), buf.size(), false);
- if (n_tokens >= 0) {
- out.resize(n_tokens);
+ int n_tokens = llama_tokenize(lctx, buf.data(), out.data(), out.size(), false);
+ if (n_tokens < 0) {
+ out.resize(-n_tokens);
+ llama_tokenize(lctx, buf.data(), out.data(), out.size(), false);
}
bool verify = false;
@@ -2200,17 +2202,17 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto
const char * in = buf.data();
const char * end = buf.data() + buf.size();
for (int i = 0; i < (int) out.size(); ++i) {
- const char * s = llama_token_to_str(lctx, out[i]);
- int len = strlen(s);
+ std::string s = llama_token_to_str(lctx, out[i]);
+ int len = s.length();
if (in >= end) {
printf("%s: unexpected end of original text.\n", __func__);
break;
}
- const bool matches = (strncmp(in, s, len) == 0);
+ const bool matches = (strncmp(in, s.c_str(), len) == 0);
if (matches) {
in += len;
} else {
- printf("%s: mismatch: expected '%s', but got '%s'\n", __func__, std::string(in, len).c_str(), s);
+ printf("%s: mismatch: expected '%s', but got '%s'\n", __func__, std::string(in, len).c_str(), s.c_str());
}
}
}
@@ -2294,7 +2296,7 @@ llama_token sample(struct my_llama_sampler * sampler, float * logits, const llam
const auto params = sampler->params;
// Apply penalties
- const float nl_logit = logits[llama_token_nl()];
+ const float nl_logit = logits[llama_token_nl(ctx)];
const int n_last = std::min(std::min(n_last_tokens, params.repeat_last_n), sampler->n_ctx);
@@ -2313,7 +2315,7 @@ llama_token sample(struct my_llama_sampler * sampler, float * logits, const llam
params.alpha_presence);
if (!params.penalize_nl) {
- logits[llama_token_nl()] = nl_logit;
+ logits[llama_token_nl(ctx)] = nl_logit;
}
llama_token token = 0;
@@ -2612,42 +2614,45 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
return;
}
- // 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
- 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));
- }
- // 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);
- }
+#pragma message("TODO: implement file saving using gguf")
+ (void) vocab;
+ (void) model;
+// // 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
+// uint32_t n_vocab = model->hparams.n_vocab;
+// for (uint32_t i = 0; i < n_vocab; i++) {
+// const auto & token_data = vocab->id_to_token.at(i);
+// file.write_u32((uint32_t) token_data.tok.size());
+// file.write_raw(token_data.tok.data(), token_data.tok.size());
+// file.write_raw(&token_data.score, sizeof(token_data.score));
+// }
+// // 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);
+// }
}
float cosine_decay(const int decay_steps, const float alpha, int step) {
@@ -3052,20 +3057,13 @@ int main(int argc, char ** argv) {
struct llama_vocab vocab;
{
- 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));
+ const int 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);
+ vocab.id_to_token[i].text = llama_token_get_text(lctx, i);
+ vocab.id_to_token[i].score = llama_token_get_score(lctx, i);
+ vocab.id_to_token[i].type = llama_token_get_type(lctx, i);
+ vocab.token_to_id.emplace(vocab.id_to_token[i].text, i);
}
}
@@ -3178,7 +3176,7 @@ int main(int argc, char ** argv) {
std::vector<int> train_samples;
train_samples.push_back(0);
for (int i = 1; i < (int) train_tokens.size() - n_tokens; ++i) {
- if (!params.samples_start_after_nl || (train_tokens[i-1] == llama_token_nl())) {
+ if (!params.samples_start_after_nl || (train_tokens[i-1] == llama_token_nl(lctx))) {
train_samples.push_back(i);
}
}
@@ -3338,7 +3336,7 @@ int main(int argc, char ** argv) {
struct ggml_tensor * target_logits = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens);
struct ggml_tensor * target_probs = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens);
- get_example_targets(train_samples.data(), train_samples.size(), train_tokens.data(), train_tokens.size(), rand()%train_samples.size(), tokens_input, target_logits, target_probs);
+ get_example_targets(lctx, train_samples.data(), train_samples.size(), train_tokens.data(), train_tokens.size(), rand()%train_samples.size(), tokens_input, target_logits, target_probs);
for (int i=sample_ctx; i<n_tokens; ++i) {
ggml_set_i32_1d(tokens_input, i, n_vocab/2);
}
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-15 17:33:12 +0000