GeezerVison is showing github.com — llama_cpp_for_codeshell/convert-starcoder-hf-to-gguf.py at master · WisdomShell/llama_cpp_for_codeshell

Expand file tree

convert-starcoder-hf-to-gguf.py

Copy path

More file actions

Latest commit

History

executable file

202 lines (146 loc) · 6.15 KB

convert-starcoder-hf-to-gguf.py

Copy path

File metadata and controls

executable file

202 lines (146 loc) · 6.15 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

#!/usr/bin/env python3

# HF starcoder --> gguf conversion

from __future__ import annotations

import argparse

import json

import os

import struct

import sys

from pathlib import Path

from typing import Any

import numpy as np

import torch

from transformers import AutoTokenizer # type: ignore[import]

if 'NO_LOCAL_GGUF' not in os.environ:

sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))

import gguf

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

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 StarCoder 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, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], 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)

# 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)

if hparams["architectures"][0] != "GPTBigCodeForCausalLM":

print("Model architecture not supported: " + hparams["architectures"][0])

sys.exit(1)

# get number of model parts

num_parts = count_model_parts(dir_model)

ARCH=gguf.MODEL_ARCH.STARCODER

gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])

print("gguf: get model metadata")

block_count = hparams["n_layer"]

gguf_writer.add_name("StarCoder")

gguf_writer.add_context_length(hparams["n_positions"])

gguf_writer.add_embedding_length(hparams["n_embd"])

gguf_writer.add_feed_forward_length(4 * hparams["n_embd"])

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_eps(hparams["layer_norm_epsilon"])

gguf_writer.add_file_type(ftype)

# TOKENIZATION

print("gguf: get tokenizer metadata")

tokens: list[bytearray] = []

scores: list[float] = []

toktypes: list[int] = []

# gpt2 tokenizer

gguf_writer.add_tokenizer_model("gpt2")

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)

# 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.get("vocab_size", len(tokenizer.vocab))

assert max(tokenizer.vocab.values()) < vocab_size

reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}

for i in range(vocab_size):

tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")

scores.append(0.0) # dummy

toktypes.append(gguf.TokenType.NORMAL)

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

tensor_map = gguf.get_tensor_name_map(ARCH,block_count)

# params for qkv transform

n_head = hparams["n_head"]

n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1

head_dim = hparams["n_embd"] // n_head

# 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(dir_model / part_name, map_location="cpu")

for name in model_part.keys():

data = model_part[name]

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 + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))