diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index a13905b77..0fdea70e1 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -33,11 +33,10 @@ def bytes_to_unicode(): bs.append(b) cs.append(2**8+n) n += 1 - cs = [chr(n) for n in cs] - return dict(zip(bs, cs)) + return dict(zip(bs, (chr(n) for n in cs))) -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("pytorch_model-"): diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index 9c78fe14e..38e71e03b 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -8,6 +8,7 @@ import struct import json import numpy as np import torch +import argparse from typing import Any, List from pathlib import Path @@ -37,7 +38,7 @@ def bytes_to_unicode(): return dict(zip(bs, (chr(n) for n in cs))) -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("pytorch_model-"): @@ -48,17 +49,22 @@ 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") +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a GPT-NeoX 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 @@ -66,19 +72,15 @@ 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)) +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] != "GPTNeoXForCausalLM": @@ -96,7 +98,7 @@ print("gguf: get model metadata") block_count = hparams["num_hidden_layers"] -gguf_writer.add_name(last_dir) +gguf_writer.add_name(dir_model.name) gguf_writer.add_context_length(hparams["max_position_embeddings"]) gguf_writer.add_embedding_length(hparams["hidden_size"]) gguf_writer.add_block_count(block_count) @@ -112,45 +114,49 @@ print("gguf: get tokenizer metadata") tokens: List[bytearray] = [] -if Path(dir_model + "/tokenizer.json").is_file(): - # gpt2 tokenizer - gguf_writer.add_tokenizer_model("gpt2") +tokenizer_json_file = dir_model / 'tokenizer.json' +if not tokenizer_json_file.is_file(): + print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr) + sys.exit(1) - with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f: - tokenizer_json = json.load(f) +# gpt2 tokenizer +gguf_writer.add_tokenizer_model("gpt2") - print("gguf: get gpt2 tokenizer vocab") +with open(tokenizer_json_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) - vocab_size = len(tokenizer_json["model"]["vocab"]) +print("gguf: get gpt2 tokenizer vocab") - # ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py - tokenizer = AutoTokenizer.from_pretrained(dir_model) +vocab_size = len(tokenizer_json["model"]["vocab"]) - reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - byte_encoder = bytes_to_unicode() - byte_decoder = {v: k for k, v in byte_encoder.items()} +# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py +tokenizer = AutoTokenizer.from_pretrained(dir_model) - for i in range(vocab_size): - if i in reverse_vocab: - try: - text = bytearray([byte_decoder[c] for c in reverse_vocab[i]]) - except KeyError: - text = bytearray() - for c in reverse_vocab[i]: - if ord(c) < 256: # single byte character - text.append(byte_decoder[ord(c)]) - else: # multibyte special token character - text.extend(c.encode('utf-8')) - else: - print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.") - pad_token = f"[PAD{i}]".encode("utf8") - text = bytearray(pad_token) +reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} +byte_encoder = bytes_to_unicode() +byte_decoder = {v: k for k, v in byte_encoder.items()} - tokens.append(text) +for i in range(vocab_size): + if i in reverse_vocab: + try: + text = bytearray([byte_decoder[c] for c in reverse_vocab[i]]) + except KeyError: + text = bytearray() + for c in reverse_vocab[i]: + if ord(c) < 256: # single byte character + text.append(byte_decoder[ord(c)]) + else: # multibyte special token character + text.extend(c.encode('utf-8')) + else: + print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.") + pad_token = f"[PAD{i}]".encode("utf8") + text = bytearray(pad_token) - gguf_writer.add_token_list(tokens) + tokens.append(text) -special_vocab = gguf.SpecialVocab(Path(dir_model), load_merges = True) +gguf_writer.add_token_list(tokens) + +special_vocab = gguf.SpecialVocab(dir_model, load_merges = True) special_vocab.add_to_gguf(gguf_writer) # TENSORS @@ -168,6 +174,8 @@ 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") @@ -216,10 +224,11 @@ 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("") diff --git a/convert-llama-7b-pth-to-gguf.py b/convert-llama-7b-pth-to-gguf.py index 1400d770e..6e973a116 100755 --- a/convert-llama-7b-pth-to-gguf.py +++ b/convert-llama-7b-pth-to-gguf.py @@ -10,6 +10,7 @@ import struct import json import numpy as np import torch +import argparse from typing import Any, List, TypeAlias from pathlib import Path @@ -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."): @@ -31,19 +32,22 @@ 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") +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() + +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 @@ -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)) +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": @@ -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) @@ -133,54 +133,59 @@ 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_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 and scores") - 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") - gguf_writer.add_tokenizer_model("llama") - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) + for key in addtokens_json: + tokens.append( key.encode("utf-8") ) + scores.append(-1000.0) + toktypes.append(4) # user-defined token type -special_vocab = gguf.SpecialVocab(Path(dir_model)) +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 @@ -193,6 +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") @@ -241,11 +248,11 @@ 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("") diff --git a/convert-llama-hf-to-gguf.py b/convert-llama-hf-to-gguf.py index 472f70707..ab94b5eab 100755 --- a/convert-llama-hf-to-gguf.py +++ b/convert-llama-hf-to-gguf.py @@ -8,6 +8,7 @@ import struct import json import numpy as np import torch +import argparse from typing import Any, List, Optional, TypeAlias from pathlib import Path @@ -43,40 +44,38 @@ 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") +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("")