diff options
Diffstat (limited to 'convert-llama-7b-pth-to-gguf.py')
| -rwxr-xr-x | convert-llama-7b-pth-to-gguf.py | 200 |
1 files changed, 75 insertions, 125 deletions
diff --git a/convert-llama-7b-pth-to-gguf.py b/convert-llama-7b-pth-to-gguf.py index 2ab08238..6e973a11 100755 --- a/convert-llama-7b-pth-to-gguf.py +++ b/convert-llama-7b-pth-to-gguf.py @@ -10,8 +10,9 @@ import struct import json import numpy as np import torch +import argparse -from typing import Any, List +from typing import Any, List, TypeAlias from pathlib import Path from sentencepiece import SentencePieceProcessor @@ -20,7 +21,7 @@ from sentencepiece import SentencePieceProcessor NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' -def count_model_parts(dir_model: str) -> int: +def count_model_parts(dir_model: Path) -> int: num_parts = 0 for filename in os.listdir(dir_model): if filename.startswith("consolidated."): @@ -31,18 +32,21 @@ def count_model_parts(dir_model: str) -> int: return num_parts -if len(sys.argv) < 3: - print(f"Usage: python {sys.argv[0]} dir-model ftype\n") - print(" ftype == 0 -> float32") - print(" ftype == 1 -> float16") - - sys.exit(1) +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a PyTorch 7B LLaMA model to a GGML compatible file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") + parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) + return parser.parse_args() +args = parse_args() -# output in the same directory as the model -dir_model = sys.argv[1] -last_dir = os.path.basename(os.path.normpath(dir_model)) - +dir_model = args.model +ftype = args.ftype +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file = sys.stderr) + sys.exit(1) # possible tensor data types # ftype == 0 -> float32 @@ -51,19 +55,15 @@ last_dir = os.path.basename(os.path.normpath(dir_model)) # map from ftype to string ftype_str = ["f32", "f16"] -ftype = 1 -if len(sys.argv) > 2: - ftype = int(sys.argv[2]) - if ftype < 0 or ftype > 1: - print("Invalid ftype: " + str(ftype)) - - sys.exit(1) - -fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf" +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' -print("gguf: loading model "+last_dir) +print("gguf: loading model "+dir_model.name) -with open(dir_model + "/config.json", "r", encoding="utf-8") as f: +with open(dir_model / "config.json", "r", encoding="utf-8") as f: hparams = json.load(f) if hparams["architectures"][0] != "LlamaForCausalLM": @@ -107,7 +107,7 @@ else: sys.exit() -gguf_writer.add_name(last_dir) +gguf_writer.add_name(dir_model.name) gguf_writer.add_source_hf_repo(hf_repo) gguf_writer.add_tensor_data_layout("Meta AI original pth") gguf_writer.add_context_length(ctx_length) @@ -133,109 +133,60 @@ tokens: List[bytes] = [] scores: List[float] = [] toktypes: List[int] = [] -if Path(dir_model + "/tokenizer.model").is_file(): - # vocab type sentencepiece - print("gguf: get sentencepiece tokenizer vocab and scores") - - tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model") - - for i in range(tokenizer.vocab_size()): - text: bytes - score: float - - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) - - toktype = 1 # defualt to normal token type - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 - - # toktype = 4 is user-defined = tokens from added_tokens.json - - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - - if Path(dir_model + "/added_tokens.json").is_file(): - with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f: - addtokens_json = json.load(f) - - print("gguf: get added tokens") - - for key in addtokens_json: - tokens.append( key.encode("utf-8") ) - scores.append(-1000.0) - toktypes.append(4) # user-defined token type - - gguf_writer.add_tokenizer_model("llama") - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) - - -print("gguf: get special token ids") - -if Path(dir_model + "/tokenizer.json").is_file(): - # Look for special tokens in tokenizer.json if it exists - - with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f: - tokenizer = json.load(f) +tokenizer_model_file = dir_model / 'tokenizer.model' +if not tokenizer_model_file.is_file(): + print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) + sys.exit(1) - if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file(): +# vocab type sentencepiece +print("gguf: get sentencepiece tokenizer vocab and scores") - with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f: - tokenizer_config = json.load(f) +tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) - if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["bos_token"]["content"]: - gguf_writer.add_bos_token_id(key["id"]) +for i in range(tokenizer.vocab_size()): + text: bytes + score: float - if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["eos_token"]["content"]: - gguf_writer.add_eos_token_id(key["id"]) + piece = tokenizer.id_to_piece(i) + text = piece.encode("utf-8") + score = tokenizer.get_score(i) - if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["unk_token"]["content"]: - gguf_writer.add_unk_token_id(key["id"]) + toktype = 1 # defualt to normal token type + if tokenizer.is_unknown(i): + toktype = 2 + if tokenizer.is_control(i): + toktype = 3 - if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["sep_token"]["content"]: - gguf_writer.add_sep_token_id(key["id"]) + # toktype = 4 is user-defined = tokens from added_tokens.json - if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["pad_token"]["content"]: - gguf_writer.add_pad_token_id(key["id"]) -else: - # If no tokenizer.json: Look for special tokens in config.json + if tokenizer.is_unused(i): + toktype = 5 + if tokenizer.is_byte(i): + toktype = 6 - if "bos_token_id" in hparams and hparams["bos_token_id"] != None: - gguf_writer.add_bos_token_id(hparams["bos_token_id"]) + tokens.append(text) + scores.append(score) + toktypes.append(toktype) - if "eos_token_id" in hparams and hparams["eos_token_id"] != None: - gguf_writer.add_eos_token_id(hparams["eos_token_id"]) +added_tokens_file = dir_model / 'added_tokens.json' +if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + addtokens_json = json.load(f) - if "unk_token_id" in hparams and hparams["unk_token_id"] != None: - gguf_writer.add_unk_token_id(hparams["unk_token_id"]) + print("gguf: get added tokens") - if "sep_token_id" in hparams and hparams["sep_token_id"] != None: - gguf_writer.add_sep_token_id(hparams["sep_token_id"]) + for key in addtokens_json: + tokens.append( key.encode("utf-8") ) + scores.append(-1000.0) + toktypes.append(4) # user-defined token type - if "pad_token_id" in hparams and hparams["pad_token_id"] != None: - gguf_writer.add_pad_token_id(hparams["pad_token_id"]) +gguf_writer.add_tokenizer_model("llama") +gguf_writer.add_token_list(tokens) +gguf_writer.add_token_scores(scores) +gguf_writer.add_token_types(toktypes) +special_vocab = gguf.SpecialVocab(dir_model) +special_vocab.add_to_gguf(gguf_writer) # TENSORS @@ -247,6 +198,8 @@ print("gguf: get tensor metadata") part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts)) for part_name in part_names: + if args.vocab_only: + break print("gguf: loading model part '" + part_name + "'") model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") @@ -266,11 +219,8 @@ for part_name in part_names: data = data.squeeze().numpy() # map tensor names - if name.endswith(".weight") and name[:-7] in tensor_map: - name = tensor_map[name[:-7]] + ".weight" - elif name.endswith(".bias") and name[:-5] in tensor_map: - name = tensor_map[name[:-5]] + ".bias" - else: + new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + if new_name is None: print("Can not map tensor '" + name + "'") sys.exit() @@ -289,20 +239,20 @@ for part_name in part_names: if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - gguf_writer.add_tensor(name, data) + gguf_writer.add_tensor(new_name, data) print("gguf: write header") gguf_writer.write_header_to_file() print("gguf: write metadata") gguf_writer.write_kv_data_to_file() -print("gguf: write tensors") -gguf_writer.write_tensors_to_file() +if not args.vocab_only: + print("gguf: write tensors") + gguf_writer.write_tensors_to_file() gguf_writer.close() - -print("gguf: model successfully exported to '" + fname_out + "'") +print(f"gguf: model successfully exported to '{fname_out}'") print("") |