| import dataclasses
|
|
|
| import torch
|
| from torch import Tensor
|
| import torch.nn as nn
|
| from torch.nn import functional as F
|
|
|
|
|
| @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, bias, device):
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| super().__init__()
|
|
|
| self.weight = compress(weight.data.to(device), default_compression_config)
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| self.bias = bias
|
|
|
| def forward(self, input: Tensor) -> Tensor:
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| weight = decompress(self.weight, default_compression_config)
|
| return F.linear(input, weight, self.bias)
|
|
|
|
|
| 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:
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| setattr(
|
| module,
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| attr_str,
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| CLinear(target_attr.weight, target_attr.bias, target_device),
|
| )
|
| for name, child in module.named_children():
|
| compress_module(child, target_device)
|
|
|
|
|
| def compress(tensor, config):
|
| """Simulate group-wise quantization."""
|
| if not config.enabled:
|
| return tensor
|
|
|
| group_size, num_bits, group_dim, symmetric = (
|
| config.group_size,
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| config.num_bits,
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| 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_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)
|
|
|
|
|
| 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
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| 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,
|
| )
|
|
|
|
|
| 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)
|
|
|
|
|
| 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)
|
|
|
