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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python3 | ||
| # HF falcon--> gguf conversion | ||
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| from __future__ import annotations | ||
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| import argparse | ||
| import json | ||
| import os | ||
| import struct | ||
| import sys | ||
| from pathlib import Path | ||
| from typing import Any | ||
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| import numpy as np | ||
| import torch | ||
| from transformers import AutoTokenizer # type: ignore[import] | ||
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| if 'NO_LOCAL_GGUF' not in os.environ: | ||
| sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) | ||
| import gguf | ||
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| def bytes_to_unicode(): | ||
| # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py | ||
| """ | ||
| Returns list of utf-8 byte and a corresponding list of unicode strings. | ||
| The reversible bpe codes work on unicode strings. | ||
| This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. | ||
| When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. | ||
| This is a significant percentage of your normal, say, 32K bpe vocab. | ||
| To avoid that, we want lookup tables between utf-8 bytes and unicode strings. | ||
| And avoids mapping to whitespace/control characters the bpe code barfs on. | ||
| """ | ||
| bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) | ||
| cs = bs[:] | ||
| n = 0 | ||
| for b in range(2**8): | ||
| if b not in bs: | ||
| bs.append(b) | ||
| cs.append(2**8+n) | ||
| n += 1 | ||
| return dict(zip(bs, (chr(n) for n in cs))) | ||
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| def count_model_parts(dir_model: Path) -> int: | ||
| num_parts = 0 | ||
| for filename in os.listdir(dir_model): | ||
| if filename.startswith("pytorch_model-"): | ||
| num_parts += 1 | ||
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| if num_parts > 0: | ||
| print("gguf: found " + str(num_parts) + " model parts") | ||
| return num_parts | ||
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| def parse_args() -> argparse.Namespace: | ||
| parser = argparse.ArgumentParser(description="Convert a Refact 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], default=1, nargs='?', | ||
| help="output format - use 0 for float32, 1 for float16", | ||
| ) | ||
| return parser.parse_args() | ||
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| args = parse_args() | ||
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| dir_model = args.model | ||
| ftype = args.ftype | ||
| if not dir_model.is_dir(): | ||
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| print(f'Error: {args.model} is not a directory', file = sys.stderr) | ||
| sys.exit(1) | ||
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| # possible tensor data types | ||
| # ftype == 0 -> float32 | ||
| # ftype == 1 -> float16 | ||
|
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| # map from ftype to string | ||
| ftype_str = ["f32", "f16"] | ||
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| 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' | ||
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| print("gguf: loading model "+dir_model.name) | ||
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| with open(dir_model / "config.json", "r", encoding="utf-8") as f: | ||
| hparams = json.load(f) | ||
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| if hparams["architectures"][0] != "GPTRefactForCausalLM": | ||
| print("Model architecture not supported: " + hparams["architectures"][0]) | ||
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| sys.exit(1) | ||
|
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| # get number of model parts | ||
| num_parts = count_model_parts(dir_model) | ||
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| ARCH=gguf.MODEL_ARCH.REFACT | ||
| gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) | ||
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| print("gguf: get model metadata") | ||
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| # Get refact feed forward dimension | ||
| hidden_dim = hparams["n_embd"] | ||
| inner_dim = 4 * hidden_dim | ||
| hidden_dim = int(2 * inner_dim / 3) | ||
| multiple_of = 256 | ||
| ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) | ||
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| block_count = hparams["n_layer"] | ||
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| gguf_writer.add_name("Refact") | ||
| # refact uses Alibi. So this is from config.json which might be used by training. | ||
| gguf_writer.add_context_length(hparams["n_positions"]) | ||
| gguf_writer.add_embedding_length(hparams["n_embd"]) | ||
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| gguf_writer.add_feed_forward_length(ff_dim) | ||
| gguf_writer.add_block_count(block_count) | ||
| gguf_writer.add_head_count(hparams["n_head"]) | ||
| gguf_writer.add_head_count_kv(1) | ||
| gguf_writer.add_layer_norm_rms_eps(hparams["layer_norm_epsilon"]) | ||
| gguf_writer.add_file_type(ftype) | ||
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| # TOKENIZATION | ||
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| print("gguf: get tokenizer metadata") | ||
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| tokens: list[bytearray] = [] | ||
| scores: list[float] = [] | ||
| toktypes: list[int] = [] | ||
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| 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) | ||
|
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| # gpt2 tokenizer | ||
| gguf_writer.add_tokenizer_model("gpt2") | ||
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| with open(tokenizer_json_file, "r", encoding="utf-8") as f: | ||
| tokenizer_json = json.load(f) | ||
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| print("gguf: get gpt2 tokenizer vocab") | ||
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| # The number of tokens in tokenizer.json can differ from the expected vocab size. | ||
| # This causes downstream issues with mismatched tensor sizes when running the inference | ||
| vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer_json["model"]["vocab"]) | ||
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| tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) | ||
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| 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()} | ||
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| for i in range(vocab_size): | ||
| if i in reverse_vocab: | ||
| text = reverse_vocab[i] | ||
| 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) | ||
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| tokens.append(text) | ||
| scores.append(0.0) # dymmy | ||
| toktypes.append(gguf.TokenType.NORMAL) # dummy | ||
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| gguf_writer.add_token_list(tokens) | ||
| gguf_writer.add_token_scores(scores) | ||
| gguf_writer.add_token_types(toktypes) | ||
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| special_vocab = gguf.SpecialVocab(dir_model, load_merges = True) | ||
| special_vocab.add_to_gguf(gguf_writer) | ||
|
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| # TENSORS | ||
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| tensor_map = gguf.get_tensor_name_map(ARCH,block_count) | ||
|
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| # params for qkv transform | ||
| n_head = hparams["n_head"] | ||
| n_head_kv = 1 | ||
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| head_dim = hparams["n_embd"] // n_head | ||
|
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| # tensor info | ||
| print("gguf: get tensor metadata") | ||
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| if num_parts == 0: | ||
| part_names = iter(("pytorch_model.bin",)) | ||
| else: | ||
| part_names = ( | ||
| f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) | ||
| ) | ||
| for part_name in part_names: | ||
| if args.vocab_only: | ||
| break | ||
| print("gguf: loading model part '" + part_name + "'") | ||
| model_part = torch.load(dir_model / part_name, map_location="cpu") | ||
|
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| for name in model_part.keys(): | ||
| data = model_part[name] | ||
|
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| old_dtype = data.dtype | ||
|
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| # convert any unsupported data types to float32 | ||
| if data.dtype != torch.float16 and data.dtype != torch.float32: | ||
| data = data.to(torch.float32) | ||
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| data = data.squeeze().numpy() | ||
|
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| # map tensor names | ||
| new_name = tensor_map.get_name(name, try_suffixes = (".weight", )) | ||
| if new_name is None: | ||
| print("Can not map tensor '" + name + "'") | ||
| sys.exit() | ||
|
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| n_dims = len(data.shape) | ||
| data_dtype = data.dtype | ||
|
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| # if f32 desired, convert any float16 to float32 | ||
| if ftype == 0 and data_dtype == np.float16: | ||
| data = data.astype(np.float32) | ||
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| # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 | ||
| if ftype == 1 and data_dtype == np.float16 and n_dims == 1: | ||
| data = data.astype(np.float32) | ||
|
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| # if f16 desired, convert any float32 2-dim weight tensors to float16 | ||
| if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: | ||
| data = data.astype(np.float16) | ||
|
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| print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) | ||
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| gguf_writer.add_tensor(new_name, data) | ||
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| print("gguf: write header") | ||
| gguf_writer.write_header_to_file() | ||
| print("gguf: write metadata") | ||
| gguf_writer.write_kv_data_to_file() | ||
| if not args.vocab_only: | ||
| print("gguf: write tensors") | ||
| gguf_writer.write_tensors_to_file() | ||
|
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| gguf_writer.close() | ||
|
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| print(f"gguf: model successfully exported to '{fname_out}'") | ||
| print("") |
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