File size: 10,416 Bytes
6dc0c9c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 |
import dataclasses
import gc
import glob
import os
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
from huggingface_hub import snapshot_download
import torch
from torch import Tensor
from torch.nn import functional as F
import torch.nn as nn
from tqdm import tqdm
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
AutoModel,
AutoModelForSeq2SeqLM,
)
@dataclasses.dataclass
class CompressionConfig:
"""Group-wise quantization."""
num_bits: int
group_size: int
group_dim: int
symmetric: bool
enabled: bool = True
default_compression_config = CompressionConfig(
num_bits=8, group_size=256, group_dim=1, symmetric=True, enabled=True
)
class CLinear(nn.Module):
"""Compressed Linear Layer."""
def __init__(self, weight=None, bias=None, device=None):
super().__init__()
if weight is None:
self.weight = None
elif isinstance(weight, Tensor):
self.weight = compress(weight.data.to(device), default_compression_config)
else:
self.weight = weight
self.bias = bias
def forward(self, input: Tensor) -> Tensor:
weight = decompress(self.weight, default_compression_config)
if self.bias is None:
return F.linear(input.to(weight.dtype), weight)
return F.linear(input.to(weight.dtype), weight, self.bias.to(weight.dtype))
def compress_module(module, target_device):
for attr_str in dir(module):
target_attr = getattr(module, attr_str)
if type(target_attr) == torch.nn.Linear:
setattr(
module,
attr_str,
CLinear(target_attr.weight, target_attr.bias, target_device),
)
for name, child in module.named_children():
compress_module(child, target_device)
def get_compressed_list(module, prefix=""):
compressed_list = []
for attr_str in dir(module):
target_attr = getattr(module, attr_str)
if type(target_attr) == torch.nn.Linear:
full_name = (
f"{prefix}.{attr_str}.weight" if prefix else f"{attr_str}.weight"
)
compressed_list.append(full_name)
for name, child in module.named_children():
child_prefix = f"{prefix}.{name}" if prefix else name
for each in get_compressed_list(child, child_prefix):
compressed_list.append(each)
return compressed_list
def apply_compressed_weight(module, compressed_state_dict, target_device, prefix=""):
for attr_str in dir(module):
target_attr = getattr(module, attr_str)
if type(target_attr) == torch.nn.Linear:
full_name = (
f"{prefix}.{attr_str}.weight" if prefix else f"{attr_str}.weight"
)
setattr(
module,
attr_str,
CLinear(
compressed_state_dict[full_name], target_attr.bias, target_device
),
)
for name, child in module.named_children():
child_prefix = f"{prefix}.{name}" if prefix else name
apply_compressed_weight(
child, compressed_state_dict, target_device, child_prefix
)
def load_compress_model(model_path, device, torch_dtype, use_fast, revision="main"):
# partially load model
# `use_fast=True`` is not supported for some models.
try:
tokenizer = AutoTokenizer.from_pretrained(
model_path, use_fast=use_fast, revision=revision, trust_remote_code=True
)
except TypeError:
tokenizer = AutoTokenizer.from_pretrained(
model_path, use_fast=~use_fast, revision=revision, trust_remote_code=True
)
with init_empty_weights():
# `trust_remote_code` should be set as `True` for both AutoConfig and AutoModel
config = AutoConfig.from_pretrained(
model_path,
low_cpu_mem_usage=True,
torch_dtype=torch_dtype,
trust_remote_code=True,
revision=revision,
)
# some models are loaded by AutoModel but not AutoModelForCausalLM,
# such as chatglm, chatglm2
try:
# google/flan-* models are based on an AutoModelForSeq2SeqLM.
if "T5Config" in str(type(config)):
model = AutoModelForSeq2SeqLM.from_config(
config, trust_remote_code=True
)
else:
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
except NameError:
model = AutoModel.from_config(config, trust_remote_code=True)
linear_weights = get_compressed_list(model)
if os.path.exists(model_path):
# `model_path` is a local folder
base_pattern = os.path.join(model_path, "pytorch_model*.bin")
else:
# `model_path` is a cached Hugging Face repo
# We don't necessarily need to download the model' repo again if there is a cache.
# So check the default huggingface cache first.
model_path_temp = os.path.join(
os.path.expanduser("~"),
".cache/huggingface/hub",
"models--" + model_path.replace("/", "--"),
"snapshots/",
)
downloaded = False
if os.path.exists(model_path_temp):
temp_last_dir = os.listdir(model_path_temp)[-1]
model_path_temp = os.path.join(model_path_temp, temp_last_dir)
base_pattern = os.path.join(model_path_temp, "pytorch_model*.bin")
files = glob.glob(base_pattern)
if len(files) > 0:
downloaded = True
if downloaded:
model_path = model_path_temp
else:
model_path = snapshot_download(model_path, revision=revision)
base_pattern = os.path.join(model_path, "pytorch_model*.bin")
files = glob.glob(base_pattern)
use_safetensors = False
if len(files) == 0:
base_pattern = os.path.join(model_path, "*.safetensors")
files = glob.glob(base_pattern)
use_safetensors = True
if len(files) == 0:
raise ValueError(
f"Cannot find any model weight files. "
f"Please check your (cached) weight path: {model_path}"
)
compressed_state_dict = {}
if use_safetensors:
from safetensors.torch import load_file
for filename in tqdm(files):
if use_safetensors:
tmp_state_dict = load_file(filename)
else:
tmp_state_dict = torch.load(
filename, map_location=lambda storage, loc: storage
)
for name in tmp_state_dict:
if name in linear_weights:
tensor = tmp_state_dict[name].to(device, dtype=torch_dtype)
compressed_state_dict[name] = compress(
tensor, default_compression_config
)
else:
compressed_state_dict[name] = tmp_state_dict[name].to(
device, dtype=torch_dtype
)
tmp_state_dict[name] = None
tensor = None
gc.collect()
torch.cuda.empty_cache()
if device == "xpu":
torch.xpu.empty_cache()
if device == "npu":
torch.npu.empty_cache()
for name in model.state_dict():
if name not in linear_weights:
set_module_tensor_to_device(
model, name, device, value=compressed_state_dict[name]
)
apply_compressed_weight(model, compressed_state_dict, device)
if torch_dtype == torch.float16:
model.half()
model.to(device)
model.eval()
return model, tokenizer
def compress(tensor, config):
"""Simulate group-wise quantization."""
if not config.enabled:
return tensor
group_size, num_bits, group_dim, symmetric = (
config.group_size,
config.num_bits,
config.group_dim,
config.symmetric,
)
assert num_bits <= 8
original_shape = tensor.shape
num_groups = (original_shape[group_dim] + group_size - 1) // group_size
new_shape = (
original_shape[:group_dim]
+ (num_groups, group_size)
+ original_shape[group_dim + 1 :]
)
# Pad
pad_len = (group_size - original_shape[group_dim] % group_size) % group_size
if pad_len != 0:
pad_shape = (
original_shape[:group_dim] + (pad_len,) + original_shape[group_dim + 1 :]
)
tensor = torch.cat(
[tensor, torch.zeros(pad_shape, dtype=tensor.dtype, device=tensor.device)],
dim=group_dim,
)
data = tensor.view(new_shape)
# Quantize
if symmetric:
B = 2 ** (num_bits - 1) - 1
scale = B / torch.max(data.abs(), dim=group_dim + 1, keepdim=True)[0]
data = data * scale
data = data.clamp_(-B, B).round_().to(torch.int8)
return data, scale, original_shape
else:
B = 2**num_bits - 1
mn = torch.min(data, dim=group_dim + 1, keepdim=True)[0]
mx = torch.max(data, dim=group_dim + 1, keepdim=True)[0]
scale = B / (mx - mn)
data = data - mn
data.mul_(scale)
data = data.clamp_(0, B).round_().to(torch.uint8)
return data, mn, scale, original_shape
def decompress(packed_data, config):
"""Simulate group-wise dequantization."""
if not config.enabled:
return packed_data
group_size, num_bits, group_dim, symmetric = (
config.group_size,
config.num_bits,
config.group_dim,
config.symmetric,
)
# Dequantize
if symmetric:
data, scale, original_shape = packed_data
data = data / scale
else:
data, mn, scale, original_shape = packed_data
data = data / scale
data.add_(mn)
# Unpad
pad_len = (group_size - original_shape[group_dim] % group_size) % group_size
if pad_len:
padded_original_shape = (
original_shape[:group_dim]
+ (original_shape[group_dim] + pad_len,)
+ original_shape[group_dim + 1 :]
)
data = data.reshape(padded_original_shape)
indices = [slice(0, x) for x in original_shape]
return data[indices].contiguous()
else:
return data.view(original_shape)
|