Uniform args parsing and vocab only mode for convert examples
This commit is contained in:
KerfuffleV2
parent
2ea133895c
commit
58fa4dc870
4 changed files with 205 additions and 184 deletions
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@ -33,11 +33,10 @@ def bytes_to_unicode():
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bs.append(b)
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cs.append(2**8+n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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return dict(zip(bs, (chr(n) for n in cs)))
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def count_model_parts(dir_model: str) -> int:
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def count_model_parts(dir_model: Path) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("pytorch_model-"):
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@ -8,6 +8,7 @@ import struct
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import json
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import numpy as np
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import torch
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import argparse
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from typing import Any, List
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from pathlib import Path
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@ -37,7 +38,7 @@ def bytes_to_unicode():
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return dict(zip(bs, (chr(n) for n in cs)))
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def count_model_parts(dir_model: str) -> int:
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def count_model_parts(dir_model: Path) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("pytorch_model-"):
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@ -48,17 +49,22 @@ def count_model_parts(dir_model: str) -> int:
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return num_parts
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if len(sys.argv) < 3:
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print(f"Usage: python {sys.argv[0]} dir-model ftype\n")
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print(" ftype == 0 -> float32")
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print(" ftype == 1 -> float16")
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file")
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parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
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parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
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parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
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parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
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return parser.parse_args()
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args = parse_args()
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dir_model = args.model
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ftype = args.ftype
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if not dir_model.is_dir():
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print(f'Error: {args.model} is not a directory', file = sys.stderr)
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sys.exit(1)
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# output in the same directory as the model
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dir_model = sys.argv[1]
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last_dir = os.path.basename(os.path.normpath(dir_model))
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# possible tensor data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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@ -66,19 +72,15 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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ftype = 1
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if len(sys.argv) > 2:
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ftype = int(sys.argv[2])
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if ftype < 0 or ftype > 1:
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print("Invalid ftype: " + str(ftype))
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if args.outfile is not None:
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fname_out = args.outfile
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else:
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# output in the same directory as the model by default
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fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
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sys.exit(1)
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print("gguf: loading model "+dir_model.name)
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fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
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print("gguf: loading model "+last_dir)
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with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
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with open(dir_model / "config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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if hparams["architectures"][0] != "GPTNeoXForCausalLM":
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@ -96,7 +98,7 @@ print("gguf: get model metadata")
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block_count = hparams["num_hidden_layers"]
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gguf_writer.add_name(last_dir)
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gguf_writer.add_name(dir_model.name)
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gguf_writer.add_context_length(hparams["max_position_embeddings"])
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gguf_writer.add_embedding_length(hparams["hidden_size"])
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gguf_writer.add_block_count(block_count)
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@ -112,45 +114,49 @@ print("gguf: get tokenizer metadata")
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tokens: List[bytearray] = []
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if Path(dir_model + "/tokenizer.json").is_file():
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# gpt2 tokenizer
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gguf_writer.add_tokenizer_model("gpt2")
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tokenizer_json_file = dir_model / 'tokenizer.json'
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if not tokenizer_json_file.is_file():
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print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
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sys.exit(1)
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with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
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tokenizer_json = json.load(f)
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# gpt2 tokenizer
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gguf_writer.add_tokenizer_model("gpt2")
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print("gguf: get gpt2 tokenizer vocab")
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with open(tokenizer_json_file, "r", encoding="utf-8") as f:
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tokenizer_json = json.load(f)
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vocab_size = len(tokenizer_json["model"]["vocab"])
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print("gguf: get gpt2 tokenizer vocab")
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# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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vocab_size = len(tokenizer_json["model"]["vocab"])
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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byte_encoder = bytes_to_unicode()
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byte_decoder = {v: k for k, v in byte_encoder.items()}
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# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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for i in range(vocab_size):
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if i in reverse_vocab:
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try:
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text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
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except KeyError:
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text = bytearray()
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for c in reverse_vocab[i]:
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if ord(c) < 256: # single byte character
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text.append(byte_decoder[ord(c)])
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else: # multibyte special token character
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text.extend(c.encode('utf-8'))
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else:
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print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
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pad_token = f"[PAD{i}]".encode("utf8")
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text = bytearray(pad_token)
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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byte_encoder = bytes_to_unicode()
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byte_decoder = {v: k for k, v in byte_encoder.items()}
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tokens.append(text)
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for i in range(vocab_size):
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if i in reverse_vocab:
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try:
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text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
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except KeyError:
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text = bytearray()
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for c in reverse_vocab[i]:
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if ord(c) < 256: # single byte character
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text.append(byte_decoder[ord(c)])
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else: # multibyte special token character
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text.extend(c.encode('utf-8'))
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else:
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print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
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pad_token = f"[PAD{i}]".encode("utf8")
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text = bytearray(pad_token)
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gguf_writer.add_token_list(tokens)
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tokens.append(text)
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special_vocab = gguf.SpecialVocab(Path(dir_model), load_merges = True)
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gguf_writer.add_token_list(tokens)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
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special_vocab.add_to_gguf(gguf_writer)
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# TENSORS
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@ -168,6 +174,8 @@ else:
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)
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for part_name in part_names:
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if args.vocab_only:
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break
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print("gguf: loading model part '" + part_name + "'")
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model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
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@ -216,10 +224,11 @@ print("gguf: write header")
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gguf_writer.write_header_to_file()
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print("gguf: write metadata")
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gguf_writer.write_kv_data_to_file()
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print("gguf: write tensors")
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gguf_writer.write_tensors_to_file()
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if not args.vocab_only:
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print("gguf: write tensors")
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gguf_writer.write_tensors_to_file()
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gguf_writer.close()
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print("gguf: model successfully exported to '" + fname_out + "'")
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print(f"gguf: model successfully exported to '{fname_out}'")
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print("")
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@ -10,6 +10,7 @@ import struct
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import json
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import numpy as np
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import torch
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import argparse
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from typing import Any, List, TypeAlias
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from pathlib import Path
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@ -20,7 +21,7 @@ from sentencepiece import SentencePieceProcessor
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NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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def count_model_parts(dir_model: str) -> int:
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def count_model_parts(dir_model: Path) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("consolidated."):
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@ -31,19 +32,22 @@ def count_model_parts(dir_model: str) -> int:
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return num_parts
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if len(sys.argv) < 3:
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print(f"Usage: python {sys.argv[0]} dir-model ftype\n")
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print(" ftype == 0 -> float32")
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print(" ftype == 1 -> float16")
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Convert a PyTorch 7B LLaMA model to a GGML compatible file")
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parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
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parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
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parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
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parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
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return parser.parse_args()
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args = parse_args()
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dir_model = args.model
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ftype = args.ftype
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if not dir_model.is_dir():
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print(f'Error: {args.model} is not a directory', file = sys.stderr)
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sys.exit(1)
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# output in the same directory as the model
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dir_model = sys.argv[1]
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last_dir = os.path.basename(os.path.normpath(dir_model))
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# possible tensor data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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@ -51,19 +55,15 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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ftype = 1
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if len(sys.argv) > 2:
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ftype = int(sys.argv[2])
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if ftype < 0 or ftype > 1:
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print("Invalid ftype: " + str(ftype))
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if args.outfile is not None:
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fname_out = args.outfile
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else:
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# output in the same directory as the model by default
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fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
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sys.exit(1)
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print("gguf: loading model "+dir_model.name)
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fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
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print("gguf: loading model "+last_dir)
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with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
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with open(dir_model / "config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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if hparams["architectures"][0] != "LlamaForCausalLM":
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@ -107,7 +107,7 @@ else:
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sys.exit()
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gguf_writer.add_name(last_dir)
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gguf_writer.add_name(dir_model.name)
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gguf_writer.add_source_hf_repo(hf_repo)
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gguf_writer.add_tensor_data_layout("Meta AI original pth")
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gguf_writer.add_context_length(ctx_length)
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@ -133,54 +133,59 @@ tokens: List[bytes] = []
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scores: List[float] = []
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toktypes: List[int] = []
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if Path(dir_model + "/tokenizer.model").is_file():
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# vocab type sentencepiece
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print("gguf: get sentencepiece tokenizer vocab and scores")
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tokenizer_model_file = dir_model / 'tokenizer.model'
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if not tokenizer_model_file.is_file():
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print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
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sys.exit(1)
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tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
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# vocab type sentencepiece
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print("gguf: get sentencepiece tokenizer vocab and scores")
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for i in range(tokenizer.vocab_size()):
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text: bytes
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score: float
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tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
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piece = tokenizer.id_to_piece(i)
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text = piece.encode("utf-8")
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score = tokenizer.get_score(i)
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for i in range(tokenizer.vocab_size()):
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text: bytes
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score: float
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toktype = 1 # defualt to normal token type
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if tokenizer.is_unknown(i):
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toktype = 2
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if tokenizer.is_control(i):
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toktype = 3
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piece = tokenizer.id_to_piece(i)
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text = piece.encode("utf-8")
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score = tokenizer.get_score(i)
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# toktype = 4 is user-defined = tokens from added_tokens.json
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toktype = 1 # defualt to normal token type
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if tokenizer.is_unknown(i):
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toktype = 2
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if tokenizer.is_control(i):
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toktype = 3
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if tokenizer.is_unused(i):
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toktype = 5
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if tokenizer.is_byte(i):
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toktype = 6
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# toktype = 4 is user-defined = tokens from added_tokens.json
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tokens.append(text)
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scores.append(score)
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toktypes.append(toktype)
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if tokenizer.is_unused(i):
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toktype = 5
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if tokenizer.is_byte(i):
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toktype = 6
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if Path(dir_model + "/added_tokens.json").is_file():
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with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
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addtokens_json = json.load(f)
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tokens.append(text)
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scores.append(score)
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toktypes.append(toktype)
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print("gguf: get added tokens")
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added_tokens_file = dir_model / 'added_tokens.json'
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if added_tokens_file.is_file():
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with open(added_tokens_file, "r", encoding="utf-8") as f:
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addtokens_json = json.load(f)
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for key in addtokens_json:
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tokens.append( key.encode("utf-8") )
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scores.append(-1000.0)
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toktypes.append(4) # user-defined token type
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print("gguf: get added tokens")
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gguf_writer.add_tokenizer_model("llama")
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_scores(scores)
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gguf_writer.add_token_types(toktypes)
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for key in addtokens_json:
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tokens.append( key.encode("utf-8") )
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scores.append(-1000.0)
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toktypes.append(4) # user-defined token type
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special_vocab = gguf.SpecialVocab(Path(dir_model))
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gguf_writer.add_tokenizer_model("llama")
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_scores(scores)
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gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(dir_model)
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special_vocab.add_to_gguf(gguf_writer)
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# TENSORS
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@ -193,6 +198,8 @@ print("gguf: get tensor metadata")
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part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts))
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for part_name in part_names:
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if args.vocab_only:
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break
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print("gguf: loading model part '" + part_name + "'")
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model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
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@ -241,11 +248,11 @@ print("gguf: write header")
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gguf_writer.write_header_to_file()
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print("gguf: write metadata")
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gguf_writer.write_kv_data_to_file()
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print("gguf: write tensors")
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gguf_writer.write_tensors_to_file()
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if not args.vocab_only:
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print("gguf: write tensors")
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gguf_writer.write_tensors_to_file()
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gguf_writer.close()
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print("gguf: model successfully exported to '" + fname_out + "'")
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print(f"gguf: model successfully exported to '{fname_out}'")
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print("")
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@ -8,6 +8,7 @@ import struct
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import json
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import numpy as np
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import torch
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import argparse
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from typing import Any, List, Optional, TypeAlias
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from pathlib import Path
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@ -43,40 +44,38 @@ def count_model_parts(dir_model: str) -> int:
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return num_parts
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||||
|
||||
if len(sys.argv) < 3:
|
||||
print(f"Usage: python {sys.argv[0]} dir-model ftype\n")
|
||||
print(" ftype == 0 -> float32")
|
||||
print(" ftype == 1 -> float16")
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description="Convert a HuggingFace 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()
|
||||
|
||||
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)
|
||||
|
||||
|
||||
# output in the same directory as the model
|
||||
dir_model = sys.argv[1]
|
||||
last_dir = os.path.basename(os.path.normpath(dir_model))
|
||||
|
||||
|
||||
# possible tensor data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
|
||||
|
||||
# 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))
|
||||
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'
|
||||
|
||||
sys.exit(1)
|
||||
print("gguf: loading model "+dir_model.name)
|
||||
|
||||
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
|
||||
|
||||
print("gguf: loading model "+last_dir)
|
||||
|
||||
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":
|
||||
|
|
@ -115,7 +114,7 @@ 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)
|
||||
|
|
@ -141,55 +140,60 @@ 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, scores and token types")
|
||||
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)
|
||||
|
||||
tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
|
||||
# vocab type sentencepiece
|
||||
print("gguf: get sentencepiece tokenizer vocab, scores and token types")
|
||||
|
||||
for i in range(tokenizer.vocab_size()):
|
||||
text: bytes
|
||||
score: float
|
||||
tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
|
||||
|
||||
piece = tokenizer.id_to_piece(i)
|
||||
text = piece.encode("utf-8")
|
||||
score = tokenizer.get_score(i)
|
||||
for i in range(tokenizer.vocab_size()):
|
||||
text: bytes
|
||||
score: float
|
||||
|
||||
toktype = 1 # defualt to normal token type
|
||||
if tokenizer.is_unknown(i):
|
||||
toktype = 2
|
||||
if tokenizer.is_control(i):
|
||||
toktype = 3
|
||||
piece = tokenizer.id_to_piece(i)
|
||||
text = piece.encode("utf-8")
|
||||
score = tokenizer.get_score(i)
|
||||
|
||||
# toktype = 4 is user-defined = tokens from added_tokens.json
|
||||
toktype = 1 # defualt to normal token type
|
||||
if tokenizer.is_unknown(i):
|
||||
toktype = 2
|
||||
if tokenizer.is_control(i):
|
||||
toktype = 3
|
||||
|
||||
if tokenizer.is_unused(i):
|
||||
toktype = 5
|
||||
if tokenizer.is_byte(i):
|
||||
toktype = 6
|
||||
# toktype = 4 is user-defined = tokens from added_tokens.json
|
||||
|
||||
tokens.append(text)
|
||||
scores.append(score)
|
||||
toktypes.append(toktype)
|
||||
if tokenizer.is_unused(i):
|
||||
toktype = 5
|
||||
if tokenizer.is_byte(i):
|
||||
toktype = 6
|
||||
|
||||
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)
|
||||
tokens.append(text)
|
||||
scores.append(score)
|
||||
toktypes.append(toktype)
|
||||
|
||||
print("gguf: get added tokens")
|
||||
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)
|
||||
|
||||
for key in addtokens_json:
|
||||
tokens.append( key.encode("utf-8") )
|
||||
scores.append(-1000.0)
|
||||
toktypes.append(4) # user-defined token type
|
||||
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)
|
||||
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(Path(dir_model))
|
||||
special_vocab = gguf.SpecialVocab(dir_model)
|
||||
special_vocab.add_to_gguf(gguf_writer)
|
||||
|
||||
# TENSORS
|
||||
|
|
@ -207,6 +211,8 @@ else:
|
|||
)
|
||||
|
||||
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")
|
||||
|
||||
|
|
@ -261,11 +267,11 @@ 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("")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue