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llama : print number of tensors per type + print arch + style

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This commit is contained in:
Georgi Gerganov
2023-08-18 10:36:45 +03:00
parent b275de745d
commit aa3efe87c8
1 changed files with 40 additions and 32 deletions
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72
llama.cpp
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@@ -126,13 +126,6 @@ static void llama_log_callback_default(llama_log_level level, const char * text,
// helpers // helpers
// //
template<typename T>
static std::string to_string(const T & val) {
std::stringstream ss;
ss << val;
return ss.str();
}
static void zeros(std::ofstream & file, size_t n) { static void zeros(std::ofstream & file, size_t n) {
char zero = 0; char zero = 0;
for (size_t i = 0; i < n; ++i) { for (size_t i = 0; i < n; ++i) {
@@ -1070,10 +1063,14 @@ struct llama_model_loader {
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n", LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(file_version)); __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(file_version));
std::map<enum ggml_type, uint32_t> n_type;
for (int i = 0; i < n_tensors; i++) { for (int i = 0; i < n_tensors; i++) {
const char * name = gguf_get_tensor_name(ctx_gguf, i); const char * name = gguf_get_tensor_name(ctx_gguf, i);
struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, name); struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, name);
n_type[meta->type]++;
LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str()); LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str());
} }
@@ -1083,6 +1080,15 @@ struct llama_model_loader {
LLAMA_LOG_INFO("%s: - kv %3d: %42s %-8s\n", __func__, i, name, gguf_type_name(type)); LLAMA_LOG_INFO("%s: - kv %3d: %42s %-8s\n", __func__, i, name, gguf_type_name(type));
} }
// print type counts
for (auto & kv : n_type) {
if (kv.second == 0) {
continue;
}
LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
}
} }
if (!llama_mmap::SUPPORTED) { if (!llama_mmap::SUPPORTED) {
@@ -1337,6 +1343,7 @@ static void llama_model_load_internal(
auto & hparams = model.hparams; auto & hparams = model.hparams;
std::string general_name = "n/a"; std::string general_name = "n/a";
std::string general_arch = "n/a";
// read hparams // read hparams
{ {
@@ -1372,6 +1379,7 @@ static void llama_model_load_internal(
// get general kv // get general kv
GGUF_GET(general_name, gguf_get_val_str, GGUF_TYPE_STRING, false, "general.name"); GGUF_GET(general_name, gguf_get_val_str, GGUF_TYPE_STRING, false, "general.name");
GGUF_GET(general_arch, gguf_get_val_str, GGUF_TYPE_STRING, false, "general.architecture");
// special tokens // special tokens
GGUF_GET(vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, "tokenizer.ggml.bos_token_id"); GGUF_GET(vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, "tokenizer.ggml.bos_token_id");
@@ -1440,47 +1448,47 @@ static void llama_model_load_internal(
tok_score.tok = std::move(word); tok_score.tok = std::move(word);
tok_score.score = scores[i]; tok_score.score = scores[i];
if( tok_score.tok == "<0x0A>" ) { // determine the newline token: 0x0A == 10 == '\n'
if (tok_score.tok == "<0x0A>") {
vocab.linefeed_id = i; vocab.linefeed_id = i;
} }
} }
} }
{ {
// hparams // hparams
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml->file_version)); LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml->file_version));
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab); LLAMA_LOG_INFO("%s: arch = %s\n", __func__, general_arch.c_str());
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train); LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, hparams.n_ctx); LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd); LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, hparams.n_ctx);
LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head); LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv); LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head);
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer); LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv);
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa()); LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_rms_eps); LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa());
LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base); LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale); LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type)); LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
LLAMA_LOG_INFO("%s: model size = %.2f B\n", __func__, ml->n_elements*1e-9); LLAMA_LOG_INFO("%s: model size = %.2f B\n", __func__, ml->n_elements*1e-9);
// TODO: print number of tensors for each quantization
// general kv // general kv
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, general_name.c_str()); LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, general_name.c_str());
// special tokens // special tokens
if( vocab.special_bos_id != -1 ) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].tok.c_str() ); } if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].tok.c_str() ); }
if( vocab.special_eos_id != -1 ) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].tok.c_str() ); } if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].tok.c_str() ); }
if( vocab.special_unk_id != -1 ) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].tok.c_str() ); } if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].tok.c_str() ); }
if( vocab.special_sep_id != -1 ) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].tok.c_str() ); } if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].tok.c_str() ); }
if( vocab.special_pad_id != -1 ) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].tok.c_str() ); } if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].tok.c_str() ); }
if( vocab.linefeed_id != -1 ) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].tok.c_str() ); } if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].tok.c_str() ); }
} }
if (vocab_only) { if (vocab_only) {
LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
return; return;
} }