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14980df2df
When the AWQ quantizer was used with a layer that uses a bias, the bias tensor was not correctly passed/used. Instead, the value `true`/`1.0` was added to the linear transformation. Correctly pass through the bias when it is not `None`. Fixes #2106.
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
# Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
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import math
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from typing import Optional
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import torch
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import torch.nn as nn
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import awq_inference_engine # with CUDA kernels
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# class ScaledActivation(nn.Module):
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# def __init__(self, module, scales):
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# super().__init__()
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# self.act = module
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# self.scales = nn.Parameter(scales.data)
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#
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# def forward(self, x):
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# return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
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class WQLinear(nn.Module):
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def __init__(
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self, w_bit, group_size, qweight, qzeros, scales, bias: Optional[torch.Tensor]
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):
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super().__init__()
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if w_bit not in [4]:
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raise NotImplementedError("Only 4-bit are supported for now.")
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self.in_features = qweight.shape[0]
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self.out_features = qweight.shape[1] * 32 // w_bit
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self.w_bit = w_bit
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self.group_size = group_size if group_size != -1 else self.in_features
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# quick sanity check (make sure aligment)
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assert self.in_features % self.group_size == 0
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assert self.out_features % (32 // self.w_bit) == 0
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self.qweight = qweight
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self.qzeros = qzeros
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self.scales = scales
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self.bias = bias
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@torch.no_grad()
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def forward(self, x):
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out_shape = x.shape[:-1] + (self.out_features,)
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out = awq_inference_engine.gemm_forward_cuda(
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x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, 8
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)
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out = out + self.bias if self.bias is not None else out
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return out.reshape(out_shape)
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