convert : various script cleanups/fixes + merges and special token handling (#2842)
* convert: Fix permute calls and method/func definitions * Cleanups for gguf-py * Minor types cleanups. * Initial implementation of handling merges and special tokens * convert: Handle special tokens and merges in vocab only mode convert: Vocab only mode no longer requires loading model tensors * gguf: Refactor tensor name mapping * convert: Fix type hint for special_token_types in SpecialVocab * Use common special vocab handling in various conversion scripts * First pass at implementing suggested changes * Second pass * gguf: SpecialVocab: Fix issue with special token content not in a dict gguf: SpecialVocab: Allow skipping handling of merges * convert-falcon-hf-to-gguf: Support --vocab-only option, bail out if no tokenizer.json * convert-gptneox-hf-to-gguf and convert: Only handle merges for BPE tokenizer * gguf: SpecialVocab: Actually set load_merges in object * Uniform args parsing and vocab only mode for convert examples * convert.py: Set gpt2 as tokenizer model when using BPE * Squish last type warning in gguf.py - yay!
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10 changed files with 728 additions and 748 deletions
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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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@ -34,11 +35,10 @@ 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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@ -49,17 +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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@ -67,19 +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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@ -97,7 +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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@ -111,86 +112,52 @@ gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"])
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print("gguf: get tokenizer metadata")
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tokens: List[str] = []
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merges: List[str] = []
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tokens: List[bytearray] = []
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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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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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# gpt2 tokenizer
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gguf_writer.add_tokenizer_model("gpt2")
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print("gguf: get gpt2 tokenizer merges")
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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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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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merges = tokenizer_json["model"]["merges"]
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print("gguf: get gpt2 tokenizer vocab")
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gguf_writer.add_token_merges(merges)
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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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tokens.append(text)
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gguf_writer.add_token_list(tokens)
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if "added_tokens" in tokenizer_json and Path(dir_model + "/tokenizer_config.json").is_file():
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print("gguf: get special token ids")
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with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
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tokenizer_config = json.load(f)
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# find special token ids
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if "bos_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["bos_token"]:
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gguf_writer.add_bos_token_id(key["id"])
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if "eos_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["eos_token"]:
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gguf_writer.add_eos_token_id(key["id"])
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if "unk_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["unk_token"]:
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gguf_writer.add_unk_token_id(key["id"])
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if "sep_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["sep_token"]:
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gguf_writer.add_sep_token_id(key["id"])
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if "pad_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["pad_token"]:
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gguf_writer.add_pad_token_id(key["id"])
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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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@ -200,13 +167,15 @@ tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
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print("gguf: get tensor metadata")
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if num_parts == 0:
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part_names = ("pytorch_model.bin",)
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part_names = iter(("pytorch_model.bin",))
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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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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@ -226,11 +195,8 @@ for part_name in part_names:
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data = data.squeeze().numpy()
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# map tensor names
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if name.endswith(".weight") and name[:-7] in tensor_map:
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name = tensor_map[name[:-7]] + ".weight"
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elif name.endswith(".bias") and name[:-5] in tensor_map:
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name = tensor_map[name[:-5]] + ".bias"
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else:
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new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
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if new_name is None:
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print("Can not map tensor '" + name + "'")
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sys.exit()
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@ -249,19 +215,20 @@ for part_name in part_names:
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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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(name, data)
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gguf_writer.add_tensor(new_name, data)
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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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