mirror of https://github.com/vladmandic/automatic
53 lines
2.4 KiB
Python
53 lines
2.4 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 compile_func # noqa: TID252
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from ...dequantizer import quantize_fp8, dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
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from .forward import check_mats
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def quantize_fp8_matmul_input_tensorwise(input: torch.FloatTensor, scale: torch.FloatTensor) -> Tuple[torch.Tensor, torch.FloatTensor]:
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input = input.flatten(0,-2).to(dtype=scale.dtype)
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input, input_scale = quantize_fp8(input, dim=-1)
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scale = torch.mul(input_scale, scale)
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if scale.dtype == torch.float16: # fp16 will overflow
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scale = scale.to(dtype=torch.float32)
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return input, scale
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def fp8_matmul_tensorwise(
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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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svd_up: torch.FloatTensor,
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svd_down: 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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if svd_up is not None:
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input.flatten(0,-2)
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if bias is not None:
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bias = torch.addmm(bias.to(dtype=svd_down.dtype), torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
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else:
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bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
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dummy_input_scale = torch.ones(1, device=input.device, dtype=torch.float32)
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input, scale = quantize_fp8_matmul_input_tensorwise(input, scale)
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input, weight = check_mats(input, weight)
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if bias is not None:
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return dequantize_symmetric_with_bias(torch._scaled_mm(input, weight, scale_a=dummy_input_scale, scale_b=dummy_input_scale, bias=None, out_dtype=scale.dtype), scale, bias, return_dtype, output_shape)
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else:
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return dequantize_symmetric(torch._scaled_mm(input, weight, scale_a=dummy_input_scale, scale_b=dummy_input_scale, bias=None, out_dtype=scale.dtype), scale, return_dtype, output_shape)
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def quantized_linear_forward_fp8_matmul_tensorwise(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, self.scale, self.zero_point, self.svd_up, self.svd_down, skip_quantized_matmul=True), self.bias)
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return fp8_matmul_tensorwise(input, self.weight, self.bias, self.scale, self.svd_up, self.svd_down)
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fp8_matmul_tensorwise = compile_func(fp8_matmul_tensorwise)
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