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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)
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