File size: 8,470 Bytes
b144856 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 | from __future__ import annotations
import math
from typing import Optional
import torch
from torch import Tensor, nn
def _select_quant_dtype(bits: int) -> torch.dtype:
if bits <= 0:
raise ValueError("Quantization bits must be positive.")
if bits <= 8:
return torch.int8
if bits <= 16:
return torch.int16
raise ValueError("Quantization bits above 16 are not supported.")
class QuantizedLinear(nn.Module):
"""Weight-only linear layer with per-group scales."""
def __init__(
self,
in_features: int,
out_features: int,
*,
weight_bits: int = 4,
group_size: int = 128,
bias: bool = True,
) -> None:
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight_bits = weight_bits
self.group_size = group_size
self.qmin = -(2 ** (weight_bits - 1))
self.qmax = (2 ** (weight_bits - 1)) - 1
self.num_groups = math.ceil(in_features / group_size)
self.quant_dtype = _select_quant_dtype(weight_bits)
weight_shape = (out_features, in_features)
scale_shape = (out_features, self.num_groups)
self.register_buffer("weight", torch.zeros(weight_shape, dtype=self.quant_dtype))
self.register_buffer(
"weight_scales", torch.ones(scale_shape, dtype=torch.float32)
)
self.bias = nn.Parameter(torch.zeros(out_features)) if bias else None
self._weight_cache: Optional[Tensor] = None
def _invalidate_cache(self) -> None:
self._weight_cache = None
def refresh_weight_cache(self) -> None:
self._weight_cache = self._dequantize_weight()
def _dequantize_weight(self) -> Tensor:
group_tensors = []
for group_idx in range(self.num_groups):
start = group_idx * self.group_size
end = min((group_idx + 1) * self.group_size, self.in_features)
block = self.weight[:, start:end].float()
scale = self.weight_scales[:, group_idx].unsqueeze(1)
group_tensors.append(block * scale)
return torch.cat(group_tensors, dim=1)
def forward(self, input: Tensor) -> Tensor:
if self._weight_cache is None or self._weight_cache.device != input.device:
self.refresh_weight_cache()
self._weight_cache = self._weight_cache.to(input.device)
weight = self._weight_cache
if weight.dtype != input.dtype:
weight = weight.to(input.dtype)
bias = self.bias
if bias is not None and bias.device != input.device:
bias = bias.to(input.device)
if bias is not None and bias.dtype != input.dtype:
bias = bias.to(input.dtype)
return nn.functional.linear(input, weight, bias)
def load_quant_state(self, weight: Tensor, weight_scales: Tensor) -> None:
if weight.shape != self.weight.shape:
raise ValueError(
f"Quantized weight shape mismatch: expected {tuple(self.weight.shape)}, "
f"got {tuple(weight.shape)}"
)
if weight_scales.shape != self.weight_scales.shape:
raise ValueError(
f"Scale tensor shape mismatch: expected {tuple(self.weight_scales.shape)}, "
f"got {tuple(weight_scales.shape)}"
)
self.weight.copy_(weight.to(dtype=self.quant_dtype))
self.weight_scales.copy_(weight_scales.to(dtype=torch.float32))
self._invalidate_cache()
def extra_repr(self) -> str:
return (
f"in_features={self.in_features}, out_features={self.out_features}, "
f"group_size={self.group_size}, bits={self.weight_bits}, bias={self.bias is not None}"
)
class SmoothQuantLinear(nn.Module):
"""Linear layer with SmoothQuant W8A8 (or configurable) quantization."""
def __init__(
self,
in_features: int,
out_features: int,
*,
weight_bits: int = 8,
activation_bits: int = 8,
bias: bool = True,
) -> None:
super().__init__()
if weight_bits <= 0 or weight_bits > 16:
raise ValueError("Weight bits must be in range [1, 16].")
if activation_bits <= 0 or activation_bits > 16:
raise ValueError("Activation bits must be in range [1, 16].")
self.in_features = in_features
self.out_features = out_features
self.weight_bits = weight_bits
self.activation_bits = activation_bits
self.weight_qmin = -(2 ** (weight_bits - 1))
self.weight_qmax = (2 ** (weight_bits - 1)) - 1
self.activation_qmin = -(2 ** (activation_bits - 1))
self.activation_qmax = (2 ** (activation_bits - 1)) - 1
self.quant_dtype = _select_quant_dtype(weight_bits)
weight_shape = (out_features, in_features)
self.register_buffer("weight", torch.zeros(weight_shape, dtype=self.quant_dtype))
self.register_buffer(
"weight_scales", torch.ones(out_features, 1, dtype=torch.float32)
)
self.register_buffer(
"input_scale", torch.ones(in_features, dtype=torch.float32)
)
self.register_buffer(
"activation_scale", torch.ones(in_features, dtype=torch.float32)
)
self.bias = nn.Parameter(torch.zeros(out_features)) if bias else None
self._weight_cache: Optional[Tensor] = None
def _invalidate_cache(self) -> None:
self._weight_cache = None
def refresh_weight_cache(self) -> None:
weight = self.weight.float() * self.weight_scales
self._weight_cache = weight
def forward(self, input: Tensor) -> Tensor:
if self._weight_cache is None or self._weight_cache.device != input.device:
self.refresh_weight_cache()
self._weight_cache = self._weight_cache.to(input.device)
activation_scale = self.activation_scale.to(input.device)
input_scale = self.input_scale.to(input.device)
scaled_input = input * input_scale
quantized = torch.round(scaled_input / activation_scale).clamp(
self.activation_qmin, self.activation_qmax
)
dequant_input = quantized * activation_scale
weight = self._weight_cache
if weight.dtype != dequant_input.dtype:
weight = weight.to(dequant_input.dtype)
bias = self.bias
if bias is not None and bias.device != input.device:
bias = bias.to(input.device)
if bias is not None and bias.dtype != dequant_input.dtype:
bias = bias.to(dequant_input.dtype)
return nn.functional.linear(dequant_input, weight, bias)
def load_quant_state(
self,
weight: Tensor,
weight_scales: Tensor,
input_scale: Tensor,
activation_scale: Tensor,
) -> None:
if weight.shape != self.weight.shape:
raise ValueError(
f"Quantized weight shape mismatch: expected {tuple(self.weight.shape)}, "
f"got {tuple(weight.shape)}"
)
if weight_scales.shape != self.weight_scales.shape:
raise ValueError(
f"Weight scale shape mismatch: expected {tuple(self.weight_scales.shape)}, "
f"got {tuple(weight_scales.shape)}"
)
if input_scale.shape != self.input_scale.shape:
raise ValueError(
f"Input scale shape mismatch: expected {tuple(self.input_scale.shape)}, "
f"got {tuple(input_scale.shape)}"
)
if activation_scale.shape != self.activation_scale.shape:
raise ValueError(
f"Activation scale shape mismatch: expected {tuple(self.activation_scale.shape)}, "
f"got {tuple(activation_scale.shape)}"
)
self.weight.copy_(weight.to(dtype=self.quant_dtype))
self.weight_scales.copy_(weight_scales.to(dtype=torch.float32))
self.input_scale.copy_(input_scale.to(dtype=torch.float32))
self.activation_scale.copy_(activation_scale.to(dtype=torch.float32))
self._invalidate_cache()
def extra_repr(self) -> str:
return (
f"in_features={self.in_features}, out_features={self.out_features}, "
f"weight_bits={self.weight_bits}, activation_bits={self.activation_bits}, "
f"bias={self.bias is not None}"
)
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