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!
This commit is contained in:
Kerfuffle
GitHub
parent
ad9ddcff6e
commit
dc07dc492e
10 changed files with 728 additions and 748 deletions
|
|
@ -8,8 +8,9 @@ import struct
|
|||
import json
|
||||
import numpy as np
|
||||
import torch
|
||||
import argparse
|
||||
|
||||
from typing import Any, List, Optional
|
||||
from typing import Any, List, Optional, TypeAlias
|
||||
from pathlib import Path
|
||||
from sentencepiece import SentencePieceProcessor
|
||||
|
||||
|
|
@ -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)
|
||||
|
|
@ -141,110 +140,61 @@ 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)
|
||||
|
||||
|
||||
print("gguf: get special token ids")
|
||||
|
||||
if Path(dir_model + "/tokenizer.json").is_file():
|
||||
# Look for special tokens in tokenizer.json if it exists
|
||||
|
||||
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
|
||||
tokenizer = json.load(f)
|
||||
|
||||
if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
|
||||
|
||||
with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
|
||||
tokenizer_config = json.load(f)
|
||||
|
||||
if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None:
|
||||
for key in tokenizer["added_tokens"]:
|
||||
if key["content"] == tokenizer_config["bos_token"]["content"]:
|
||||
gguf_writer.add_bos_token_id(key["id"])
|
||||
|
||||
if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None:
|
||||
for key in tokenizer["added_tokens"]:
|
||||
if key["content"] == tokenizer_config["eos_token"]["content"]:
|
||||
gguf_writer.add_eos_token_id(key["id"])
|
||||
|
||||
if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None:
|
||||
for key in tokenizer["added_tokens"]:
|
||||
if key["content"] == tokenizer_config["unk_token"]["content"]:
|
||||
gguf_writer.add_unk_token_id(key["id"])
|
||||
|
||||
if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None:
|
||||
for key in tokenizer["added_tokens"]:
|
||||
if key["content"] == tokenizer_config["sep_token"]["content"]:
|
||||
gguf_writer.add_sep_token_id(key["id"])
|
||||
|
||||
if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None:
|
||||
for key in tokenizer["added_tokens"]:
|
||||
if key["content"] == tokenizer_config["pad_token"]["content"]:
|
||||
gguf_writer.add_pad_token_id(key["id"])
|
||||
else:
|
||||
# If no tokenizer.json: Look for special tokens in config.json
|
||||
|
||||
if "bos_token_id" in hparams and hparams["bos_token_id"] != None:
|
||||
gguf_writer.add_bos_token_id(hparams["bos_token_id"])
|
||||
|
||||
if "eos_token_id" in hparams and hparams["eos_token_id"] != None:
|
||||
gguf_writer.add_eos_token_id(hparams["eos_token_id"])
|
||||
|
||||
if "unk_token_id" in hparams and hparams["unk_token_id"] != None:
|
||||
gguf_writer.add_unk_token_id(hparams["unk_token_id"])
|
||||
|
||||
if "sep_token_id" in hparams and hparams["sep_token_id"] != None:
|
||||
gguf_writer.add_sep_token_id(hparams["sep_token_id"])
|
||||
|
||||
if "pad_token_id" in hparams and hparams["pad_token_id"] != None:
|
||||
gguf_writer.add_pad_token_id(hparams["pad_token_id"])
|
||||
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
|
||||
|
||||
|
|
@ -254,13 +204,15 @@ tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
|
|||
print("gguf: get tensor metadata")
|
||||
|
||||
if num_parts == 0:
|
||||
part_names = ("pytorch_model.bin",)
|
||||
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(f"{dir_model}/{part_name}", map_location="cpu")
|
||||
|
||||
|
|
@ -286,11 +238,8 @@ for part_name in part_names:
|
|||
data = reverse_hf_permute(data, head_count, head_count_kv)
|
||||
|
||||
# map tensor names
|
||||
if name.endswith(".weight") and name[:-7] in tensor_map:
|
||||
name = tensor_map[name[:-7]] + ".weight"
|
||||
elif name.endswith(".bias") and name[:-5] in tensor_map:
|
||||
name = tensor_map[name[:-5]] + ".bias"
|
||||
else:
|
||||
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print("Can not map tensor '" + name + "'")
|
||||
sys.exit()
|
||||
|
||||
|
|
@ -309,20 +258,20 @@ for part_name in part_names:
|
|||
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
|
||||
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
|
||||
|
||||
gguf_writer.add_tensor(name, data)
|
||||
gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
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