diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index 3a0552a67..a13905b77 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-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 @@ -47,17 +48,22 @@ 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 Falcon 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 @@ -65,25 +71,21 @@ 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] != "RWForCausalLM": print("Model architecture not supported: " + hparams["architectures"][0]) - sys.exit() + sys.exit(1) # get number of model parts num_parts = count_model_parts(dir_model) @@ -117,49 +119,53 @@ tokens: List[bytearray] = [] scores: List[float] = [] toktypes: List[int] = [] -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) - scores.append(0.0) # dymmy - toktypes.append(gguf.TokenType.NORMAL) # dummy +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) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) + tokens.append(text) + scores.append(0.0) # dymmy + toktypes.append(gguf.TokenType.NORMAL) # dummy -special_vocab = gguf.SpecialVocab(Path(dir_model)) +gguf_writer.add_token_list(tokens) +gguf_writer.add_token_scores(scores) +gguf_writer.add_token_types(toktypes) + +special_vocab = gguf.SpecialVocab(dir_model, load_merges = True) special_vocab.add_to_gguf(gguf_writer) # TENSORS @@ -183,8 +189,10 @@ 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") + model_part = torch.load(dir_model / part_name, map_location="cpu") for name in model_part.keys(): data = model_part[name] @@ -244,10 +252,11 @@ 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("")