vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

cleaning up to remove spaces and satisfy failed checks

Browse source
This commit is contained in:
Aniket 2023-08-08 20:40:17 -04:00
parent 5520876c3c
commit d14c066f0c
1 changed files with 21 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

43
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -55,17 +55,17 @@ void malloc_weights(TransformerWeights* w, Config* p) {
// we calloc instead of malloc to keep valgrind happy // we calloc instead of malloc to keep valgrind happy
w->token_embedding_table = new float[p->vocab_size * p->dim](); 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); 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](); 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); 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](); 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); 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](); 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); 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](); 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); 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](); w->wv = new float[p->n_layers * p->dim * p->dim]();
@ -200,7 +200,7 @@ struct my_llama_model {
struct train_params { struct train_params {
const char * fn_vocab_model; const char * fn_vocab_model;
const char * fn_llama2c_model; const char * fn_llama2c_model;
const char * fn_llama2c_output_model; const char * fn_llama2c_output_model;
const char * fn_train_data; const char * fn_train_data;
const char * fn_checkpoint_in; const char * fn_checkpoint_in;
const char * fn_checkpoint_out; const char * fn_checkpoint_out;
@ -295,7 +295,6 @@ void init_model(struct my_llama_model * model) {
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.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.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); 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->tok_embeddings, "tok_embeddings.weight");
ggml_set_name(model->norm, "norm.weight"); ggml_set_name(model->norm, "norm.weight");
@ -506,7 +505,7 @@ void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * kar
case 2: case 2:
ct = 0; ct = 0;
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) { for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) { 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]); float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]);
*ptr = karpathy_weights[ct]; *ptr = karpathy_weights[ct];
ct++; ct++;
@ -517,14 +516,14 @@ void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * kar
ct = 0; ct = 0;
for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) { for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) {
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) { for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) { 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]); 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]; *ptr = karpathy_weights[ct];
ct++; ct++;
} }
} }
} }
break; break;
} }
} }
@ -559,8 +558,8 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
// float* -> struct ggml_tensor // float* -> struct ggml_tensor
stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table); 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->output, w->token_embedding_table);
stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight); stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
//print_row(model->norm, 0); //print_row(model->norm, 0);
// for rms-att-weight // for rms-att-weight
@ -568,7 +567,7 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
const auto & hparams = model->hparams; const auto & hparams = model->hparams;
//int n_ff = model->hparams.n_embd; //int n_ff = model->hparams.n_embd;
int n_ff = get_n_ff(&hparams); int n_ff = get_n_ff(&hparams);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
auto & layer = model->layers[i]; auto & layer = model->layers[i];
// 1d // 1d
@ -580,7 +579,7 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
stuff_karpathy_weights_into_gg(layer.wk , &w->wk[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.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.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.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.w2 , &w->w2[i*n_ff*row_length]);
stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]); stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]);
@ -589,7 +588,7 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
write_tensor(&file, model->tok_embeddings); write_tensor(&file, model->tok_embeddings);
write_tensor(&file, model->norm); write_tensor(&file, model->norm);
write_tensor(&file, model->output); // ? write_tensor(&file, model->output); // ?
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i]; auto & layer = model->layers[i];
write_tensor(&file, layer.attention_norm); write_tensor(&file, layer.attention_norm);
@ -660,8 +659,8 @@ void print_usage(int /*argc*/, char ** argv, const struct train_params * params)
fprintf(stderr, "options:\n"); fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n"); fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " --vocab-model FNAME model path from which to load vocab (default '%s')\n", params->fn_vocab_model); fprintf(stderr, " --vocab-model FNAME model path from which to load vocab (default '%s')\n", params->fn_vocab_model);
fprintf(stderr, " --llama2c-model FNAME model path from which to load Karpathy's llama2.c model\n"); fprintf(stderr, " --llama2c-model FNAME model path from which to load Karpathy's llama2.c model\n");
fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model); fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
fprintf(stderr, "\n"); fprintf(stderr, "\n");
} }
@ -688,13 +687,13 @@ bool params_parse(int argc, char ** argv, struct train_params * params) {
invalid_param = true; invalid_param = true;
break; break;
} }
params->fn_llama2c_model = argv[i]; params->fn_llama2c_model = argv[i];
} else if (arg == "--llama2c-output-model") { } else if (arg == "--llama2c-output-model") {
if (++i >= argc) { if (++i >= argc) {
invalid_param = true; invalid_param = true;
break; break;
} }
params->fn_llama2c_output_model = argv[i]; params->fn_llama2c_output_model = argv[i];
} else if (arg == "-h" || arg == "--help") { } else if (arg == "-h" || arg == "--help") {
print_usage(argc, argv, &default_params); print_usage(argc, argv, &default_params);
exit(0); exit(0);
@ -720,7 +719,7 @@ int main(int argc, char ** argv) {
} }
Config config; Config config;
TransformerWeights weights; TransformerWeights weights;
{ {
FILE *file = fopen(params.fn_llama2c_model, "rb"); 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; } if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; }
// read in the config header // read in the config header
@ -741,7 +740,7 @@ int main(int argc, char ** argv) {
{ {
std::vector<const char *> strings; std::vector<const char *> strings;
std::vector<float> scores; std::vector<float> scores;
int n_vocab = llama_n_vocab(lctx); int n_vocab = llama_n_vocab(lctx);
strings.resize(n_vocab, NULL); strings.resize(n_vocab, NULL);
scores.resize(n_vocab, 0); scores.resize(n_vocab, 0);
n_vocab = llama_get_vocab(lctx, strings.data(), scores.data(), n_vocab); n_vocab = llama_get_vocab(lctx, strings.data(), scores.data(), n_vocab);
@ -749,7 +748,7 @@ int main(int argc, char ** argv) {
vocab.id_to_token.resize(n_vocab); vocab.id_to_token.resize(n_vocab);
for (int i=0; i<n_vocab; ++i) { for (int i=0; i<n_vocab; ++i) {
std::string tok = std::string(strings[i]); std::string tok = std::string(strings[i]);
float score = scores[i]; float score = scores[i];
vocab.id_to_token[i].tok = tok; vocab.id_to_token[i].tok = tok;
vocab.id_to_token[i].score = score; vocab.id_to_token[i].score = score;
vocab.token_to_id.emplace(tok, i); vocab.token_to_id.emplace(tok, i);
@ -759,7 +758,7 @@ int main(int argc, char ** argv) {
model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx); model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
model.hparams.n_ctx = params.n_ctx; model.hparams.n_ctx = params.n_ctx;
model.hparams.n_embd = config.dim; //params.n_embd; model.hparams.n_embd = config.dim; //params.n_embd;
model.hparams.n_mult = 32;//params.n_mult; model.hparams.n_mult = 32;//params.n_mult;
model.hparams.n_head = config.n_heads; //params.n_head; model.hparams.n_head = config.n_heads; //params.n_head;
model.hparams.n_layer = config.n_layers; //params.n_layer; model.hparams.n_layer = config.n_layers; //params.n_layer;
model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head); model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
@ -781,4 +780,4 @@ int main(int argc, char ** argv) {
ggml_free(model.ctx); ggml_free(model.ctx);
free_weights(&weights); free_weights(&weights);
return 0; return 0;
} }