mirror of https://github.com/vladmandic/automatic
37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
# pylint: disable=relative-beyond-top-level,redefined-builtin,protected-access
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from typing import Tuple
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import torch
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from ...common import use_torch_compile # noqa: TID252
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def quantize_fp8_matmul_input(input: torch.FloatTensor) -> Tuple[torch.Tensor, torch.FloatTensor]:
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input = input.flatten(0,-2).to(dtype=torch.float32)
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input_scale = torch.amax(input.abs(), dim=-1, keepdims=True).div_(448)
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input = torch.div(input, input_scale).clamp_(-448, 448).to(dtype=torch.float8_e4m3fn)
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return input, input_scale
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def fp8_matmul(
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input: torch.FloatTensor,
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weight: torch.Tensor,
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bias: torch.FloatTensor,
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scale: torch.FloatTensor,
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) -> torch.FloatTensor:
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return_dtype = input.dtype
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output_shape = (*input.shape[:-1], weight.shape[-1])
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input, input_scale = quantize_fp8_matmul_input(input)
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return torch._scaled_mm(input, weight, scale_a=input_scale, scale_b=scale, bias=bias, out_dtype=return_dtype).view(output_shape)
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def quantized_linear_forward_fp8_matmul(self, input: torch.FloatTensor) -> torch.FloatTensor:
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if torch.numel(input) / input.shape[-1] < 32:
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return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, skip_quantized_matmul=True), self.bias)
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return fp8_matmul(input, self.weight, self.bias, self.sdnq_dequantizer.scale)
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if use_torch_compile:
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fp8_matmul = torch.compile(fp8_matmul, fullgraph=True, dynamic=False)
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