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import dataclasses |
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import gc |
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import glob |
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import os |
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from accelerate import init_empty_weights |
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from accelerate.utils import set_module_tensor_to_device |
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from huggingface_hub import snapshot_download |
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import torch |
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from torch import Tensor |
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from torch.nn import functional as F |
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import torch.nn as nn |
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from tqdm import tqdm |
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from transformers import ( |
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AutoConfig, |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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AutoModel, |
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AutoModelForSeq2SeqLM, |
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) |
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@dataclasses.dataclass |
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class CompressionConfig: |
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"""Group-wise quantization.""" |
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num_bits: int |
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group_size: int |
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group_dim: int |
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symmetric: bool |
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enabled: bool = True |
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default_compression_config = CompressionConfig( |
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num_bits=8, group_size=256, group_dim=1, symmetric=True, enabled=True |
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) |
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class CLinear(nn.Module): |
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"""Compressed Linear Layer.""" |
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def __init__(self, weight=None, bias=None, device=None): |
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super().__init__() |
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if weight is None: |
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self.weight = None |
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elif isinstance(weight, Tensor): |
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self.weight = compress(weight.data.to(device), default_compression_config) |
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else: |
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self.weight = weight |
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self.bias = bias |
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def forward(self, input: Tensor) -> Tensor: |
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weight = decompress(self.weight, default_compression_config) |
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if self.bias is None: |
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return F.linear(input.to(weight.dtype), weight) |
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return F.linear(input.to(weight.dtype), weight, self.bias.to(weight.dtype)) |
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def compress_module(module, target_device): |
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for attr_str in dir(module): |
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target_attr = getattr(module, attr_str) |
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if type(target_attr) == torch.nn.Linear: |
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setattr( |
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module, |
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attr_str, |
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CLinear(target_attr.weight, target_attr.bias, target_device), |
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) |
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for name, child in module.named_children(): |
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compress_module(child, target_device) |
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def get_compressed_list(module, prefix=""): |
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compressed_list = [] |
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for attr_str in dir(module): |
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target_attr = getattr(module, attr_str) |
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if type(target_attr) == torch.nn.Linear: |
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full_name = ( |
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f"{prefix}.{attr_str}.weight" if prefix else f"{attr_str}.weight" |
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) |
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compressed_list.append(full_name) |
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for name, child in module.named_children(): |
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child_prefix = f"{prefix}.{name}" if prefix else name |
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for each in get_compressed_list(child, child_prefix): |
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compressed_list.append(each) |
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return compressed_list |
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def apply_compressed_weight(module, compressed_state_dict, target_device, prefix=""): |
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for attr_str in dir(module): |
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target_attr = getattr(module, attr_str) |
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if type(target_attr) == torch.nn.Linear: |
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full_name = ( |
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f"{prefix}.{attr_str}.weight" if prefix else f"{attr_str}.weight" |
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) |
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setattr( |
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module, |
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attr_str, |
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CLinear( |
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compressed_state_dict[full_name], target_attr.bias, target_device |
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), |
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) |
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for name, child in module.named_children(): |
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child_prefix = f"{prefix}.{name}" if prefix else name |
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apply_compressed_weight( |
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child, compressed_state_dict, target_device, child_prefix |
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) |
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def load_compress_model(model_path, device, torch_dtype, use_fast, revision="main"): |
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try: |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_path, use_fast=use_fast, revision=revision, trust_remote_code=True |
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) |
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except TypeError: |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_path, use_fast=~use_fast, revision=revision, trust_remote_code=True |
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) |
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with init_empty_weights(): |
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config = AutoConfig.from_pretrained( |
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model_path, |
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low_cpu_mem_usage=True, |
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torch_dtype=torch_dtype, |
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trust_remote_code=True, |
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revision=revision, |
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) |
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try: |
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if "T5Config" in str(type(config)): |
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model = AutoModelForSeq2SeqLM.from_config( |
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config, trust_remote_code=True |
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) |
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else: |
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model = AutoModelForCausalLM.from_config(config, trust_remote_code=True) |
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except NameError: |
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model = AutoModel.from_config(config, trust_remote_code=True) |
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linear_weights = get_compressed_list(model) |
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if os.path.exists(model_path): |
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base_pattern = os.path.join(model_path, "pytorch_model*.bin") |
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else: |
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model_path_temp = os.path.join( |
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os.path.expanduser("~"), |
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".cache/huggingface/hub", |
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"models--" + model_path.replace("/", "--"), |
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"snapshots/", |
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) |
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downloaded = False |
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if os.path.exists(model_path_temp): |
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temp_last_dir = os.listdir(model_path_temp)[-1] |
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model_path_temp = os.path.join(model_path_temp, temp_last_dir) |
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base_pattern = os.path.join(model_path_temp, "pytorch_model*.bin") |
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files = glob.glob(base_pattern) |
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if len(files) > 0: |
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downloaded = True |
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if downloaded: |
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model_path = model_path_temp |
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else: |
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model_path = snapshot_download(model_path, revision=revision) |
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base_pattern = os.path.join(model_path, "pytorch_model*.bin") |
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files = glob.glob(base_pattern) |
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use_safetensors = False |
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if len(files) == 0: |
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base_pattern = os.path.join(model_path, "*.safetensors") |
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files = glob.glob(base_pattern) |
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use_safetensors = True |
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if len(files) == 0: |
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raise ValueError( |
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f"Cannot find any model weight files. " |
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f"Please check your (cached) weight path: {model_path}" |
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) |
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compressed_state_dict = {} |
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if use_safetensors: |
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from safetensors.torch import load_file |
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for filename in tqdm(files): |
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if use_safetensors: |
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tmp_state_dict = load_file(filename) |
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else: |
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tmp_state_dict = torch.load( |
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filename, map_location=lambda storage, loc: storage |
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) |
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for name in tmp_state_dict: |
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if name in linear_weights: |
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tensor = tmp_state_dict[name].to(device, dtype=torch_dtype) |
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compressed_state_dict[name] = compress( |
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tensor, default_compression_config |
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) |
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else: |
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compressed_state_dict[name] = tmp_state_dict[name].to( |
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device, dtype=torch_dtype |
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) |
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tmp_state_dict[name] = None |
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tensor = None |
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gc.collect() |
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torch.cuda.empty_cache() |
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if device == "xpu": |
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torch.xpu.empty_cache() |
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if device == "npu": |
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torch.npu.empty_cache() |
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for name in model.state_dict(): |
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if name not in linear_weights: |
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set_module_tensor_to_device( |
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model, name, device, value=compressed_state_dict[name] |
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) |
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apply_compressed_weight(model, compressed_state_dict, device) |
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if torch_dtype == torch.float16: |
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model.half() |
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model.to(device) |
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model.eval() |
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return model, tokenizer |
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def compress(tensor, config): |
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"""Simulate group-wise quantization.""" |
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if not config.enabled: |
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return tensor |
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group_size, num_bits, group_dim, symmetric = ( |
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config.group_size, |
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config.num_bits, |
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config.group_dim, |
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config.symmetric, |
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) |
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assert num_bits <= 8 |
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original_shape = tensor.shape |
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num_groups = (original_shape[group_dim] + group_size - 1) // group_size |
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new_shape = ( |
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original_shape[:group_dim] |
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+ (num_groups, group_size) |
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+ original_shape[group_dim + 1 :] |
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) |
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pad_len = (group_size - original_shape[group_dim] % group_size) % group_size |
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if pad_len != 0: |
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pad_shape = ( |
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original_shape[:group_dim] + (pad_len,) + original_shape[group_dim + 1 :] |
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) |
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tensor = torch.cat( |
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[tensor, torch.zeros(pad_shape, dtype=tensor.dtype, device=tensor.device)], |
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dim=group_dim, |
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) |
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data = tensor.view(new_shape) |
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if symmetric: |
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B = 2 ** (num_bits - 1) - 1 |
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scale = B / torch.max(data.abs(), dim=group_dim + 1, keepdim=True)[0] |
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data = data * scale |
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data = data.clamp_(-B, B).round_().to(torch.int8) |
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return data, scale, original_shape |
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else: |
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B = 2**num_bits - 1 |
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mn = torch.min(data, dim=group_dim + 1, keepdim=True)[0] |
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mx = torch.max(data, dim=group_dim + 1, keepdim=True)[0] |
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scale = B / (mx - mn) |
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data = data - mn |
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data.mul_(scale) |
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data = data.clamp_(0, B).round_().to(torch.uint8) |
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return data, mn, scale, original_shape |
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def decompress(packed_data, config): |
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"""Simulate group-wise dequantization.""" |
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if not config.enabled: |
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return packed_data |
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group_size, num_bits, group_dim, symmetric = ( |
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config.group_size, |
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config.num_bits, |
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config.group_dim, |
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config.symmetric, |
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) |
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if symmetric: |
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data, scale, original_shape = packed_data |
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data = data / scale |
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else: |
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data, mn, scale, original_shape = packed_data |
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data = data / scale |
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data.add_(mn) |
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pad_len = (group_size - original_shape[group_dim] % group_size) % group_size |
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if pad_len: |
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padded_original_shape = ( |
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original_shape[:group_dim] |
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+ (original_shape[group_dim] + pad_len,) |
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+ original_shape[group_dim + 1 :] |
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) |
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data = data.reshape(padded_original_shape) |
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indices = [slice(0, x) for x in original_shape] |
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return data[indices].contiguous() |
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else: |
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return data.view(original_shape) |
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