44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
from dataclasses import dataclass
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import torch
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from EETQ import quant_weights, w8_a16_gemm
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from text_generation_server.utils.weights import UnquantizedWeight
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@dataclass
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class EETQWeight(UnquantizedWeight):
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weight: torch.Tensor
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def get_linear(self, bias: torch.Tensor):
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try:
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from text_generation_server.layers.eetq import EETQLinear
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return EETQLinear(self.weight, bias)
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except ImportError:
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raise ImportError(
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"Please install EETQ from https://github.com/NetEase-FuXi/EETQ"
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)
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class EETQLinear(torch.nn.Module):
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def __init__(
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self,
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weight,
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bias,
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) -> None:
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super().__init__()
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device = weight.device
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if weight.dtype != torch.float16:
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weight = weight.to(dtype=torch.float16)
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weight = torch.t(weight).contiguous().cpu()
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weight, scale = quant_weights(weight, torch.int8, False)
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self.weight = weight.cuda(device)
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self.scale = scale.cuda(device)
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self.bias = bias.cuda(device) if bias is not None else None
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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output = w8_a16_gemm(input, self.weight, self.scale)
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output = output + self.bias if self.bias is not None else output
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return output
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