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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):
super().__init__()
self.weight = compress(weight.data.to(device), default_compression_config)
self.bias = bias
def forward(self, input: Tensor) -> Tensor:
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:
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 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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