mirror of https://github.com/vladmandic/automatic
64 lines
2.8 KiB
Python
64 lines
2.8 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, int_mm_func # noqa: TID252
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from ...packed_int import unpack_int_symetric # noqa: TID252
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from ...dequantizer import quantize_int8, dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
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from .forward import check_mats
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def quantize_int8_matmul_input(input: torch.FloatTensor, scale: torch.FloatTensor) -> Tuple[torch.CharTensor, torch.FloatTensor]:
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input = input.flatten(0,-2).to(dtype=scale.dtype)
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input, input_scale = quantize_int8(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 int8_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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svd_up: torch.FloatTensor,
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svd_down: torch.FloatTensor,
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quantized_weight_shape: torch.Size,
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weights_dtype: str,
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) -> torch.FloatTensor:
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if quantized_weight_shape is not None:
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weight = unpack_int_symetric(weight, quantized_weight_shape, weights_dtype, dtype=torch.int8)
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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 = 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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input, scale = quantize_int8_matmul_input(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(int_mm_func(input, weight), scale, bias, return_dtype, output_shape)
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else:
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return dequantize_symmetric(int_mm_func(input, weight), scale, return_dtype, output_shape)
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def quantized_linear_forward_int8_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, self.scale, self.zero_point, self.svd_up, self.svd_down, skip_quantized_matmul=True), self.bias)
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if self.sdnq_dequantizer.re_quantize_for_matmul:
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weight, scale = self.sdnq_dequantizer.re_quantize_matmul(self.weight, self.scale, self.zero_point, None, None)
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quantized_weight_shape = None
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else:
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weight = self.weight
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scale = self.scale
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quantized_weight_shape = getattr(self.sdnq_dequantizer, "quantized_weight_shape", None)
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return int8_matmul(input, weight, self.bias, scale, self.svd_up, self.svd_down, quantized_weight_shape, self.sdnq_dequantizer.weights_dtype)
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int8_matmul = compile_func(int8_matmul)
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