llama.cpp/convert-baichuan-hf-to-gguf.py
2023-10-23 17:47:03 +02:00

317 lines
9.9 KiB
Python
Executable File

#!/usr/bin/env python3
# HF baichuan --> gguf conversion
from __future__ import annotations
import argparse
import json
import os
import struct
import sys
from pathlib import Path
from typing import TYPE_CHECKING, Any
import itertools
import numpy as np
import torch
from sentencepiece import SentencePieceProcessor # type: ignore[import]
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
import gguf
if TYPE_CHECKING:
from typing import TypeAlias
NDArray: TypeAlias = 'np.ndarray[Any, Any]'
# reverse HF permute back to original pth layout
def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
def reverse_hf_permute_part(weights: NDArray, n_part: int, n_head: int, n_head_kv: int| None = None) -> NDArray:
r = weights.shape[0] // 3
return (reverse_hf_permute(weights[r * n_part : r * n_part + r, ...], n_head, n_head_kv))
def reverse_hf_part(weights: NDArray, n_part: int) -> NDArray:
r = weights.shape[0] // 3
return weights[r * n_part : r * n_part + r, ...]
def count_model_parts(dir_model: str) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("pytorch_model-"):
num_parts += 1
if num_parts > 0:
print("gguf: found " + str(num_parts) + " model parts")
return num_parts
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], default=1, nargs='?',
help="output format - use 0 for float32, 1 for float16",
)
parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
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)
endianess = gguf.GGUFEndian.LITTLE
if args.bigendian:
endianess = gguf.GGUFEndian.BIG
endianess_str = "Big Endian" if args.bigendian else "Little Endian"
print(f"gguf: Conversion Endianess {endianess}")
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
# map from ftype to string
ftype_str = ["f32", "f16"]
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'
print("gguf: loading model "+dir_model.name)
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
print("hello print: ",hparams["architectures"][0])
if hparams["architectures"][0] != "BaichuanForCausalLM" and hparams["architectures"][0] != "BaiChuanForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit()
# get number of model parts
num_parts = count_model_parts(dir_model)
print(f"num_parts:{num_parts}\n")
ARCH=gguf.MODEL_ARCH.BAICHUAN
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
print("gguf: get model metadata")
block_count = hparams["num_hidden_layers"]
head_count = hparams["num_attention_heads"]
if "num_key_value_heads" in hparams:
head_count_kv = hparams["num_key_value_heads"]
else:
head_count_kv = head_count
if "_name_or_path" in hparams:
hf_repo = hparams["_name_or_path"]
else:
hf_repo = ""
if "max_sequence_length" in hparams:
ctx_length = hparams["max_sequence_length"]
elif "max_position_embeddings" in hparams:
ctx_length = hparams["max_position_embeddings"]
elif "model_max_length" in hparams:
ctx_length = hparams["model_max_length"]
else:
print("gguf: can not find ctx length parameter.")
sys.exit()
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)
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(head_count)
gguf_writer.add_head_count_kv(head_count_kv)
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
if "type" in hparams["rope_scaling"]:
if hparams["rope_scaling"]["type"] == "linear":
gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"])
# TOKENIZATION
print("gguf: get tokenizer metadata")
tokens: list[bytes] = []
scores: list[float] = []
toktypes: list[int] = []
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)
# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab, scores and token types")
tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
vocab_size = hparams.get('vocab_size')
if vocab_size is None:
vocab_size = tokenizer.vocab_size()
for i in range(vocab_size):
text: bytes
score: float
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
# toktype = 4 is user-defined = tokens from added_tokens.json
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
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)
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)
special_vocab = gguf.SpecialVocab(dir_model, n_vocab = len(tokens))
special_vocab.add_to_gguf(gguf_writer)
# TENSORS
tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
# tensor info
print("gguf: get tensor metadata")
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(f"{dir_model}/{part_name}", map_location="cpu")
tmp=model_part
for i in range(block_count):
if f"model.layers.{i}.self_attn.W_pack.weight" in model_part:
print(f"Unpacking and permuting layer {i}")
tmp[f"model.layers.{i}.self_attn.q_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],0,head_count,head_count)
tmp[f"model.layers.{i}.self_attn.k_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],1,head_count,head_count_kv)
tmp[f"model.layers.{i}.self_attn.v_proj.weight"]=reverse_hf_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],2)
del tmp[f"model.layers.{i}.self_attn.W_pack.weight"]
for name in model_part.keys():
data = model_part[name]
# we don't need these
if name.endswith(".rotary_emb.inv_freq"):
continue
old_dtype = data.dtype
# convert any unsupported data types to float32
if data.dtype != torch.float16 and data.dtype != torch.float32:
data = data.to(torch.float32)
data = data.squeeze().numpy()
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None:
print("Can not map tensor '" + name + "'")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# 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)
# 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)
print(name + " -> " + new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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()
if not args.vocab_only:
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print(f"gguf: model successfully exported to '{fname_out}'")
print("")