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import dataclasses
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import torch
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from torch import Tensor
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import torch.nn as nn
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from torch.nn import functional as F
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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, bias, device):
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super().__init__()
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self.weight = compress(weight.data.to(device), default_compression_config)
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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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return F.linear(input, weight, self.bias)
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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 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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