mirror of https://github.com/vladmandic/automatic
49 lines
2.1 KiB
Python
49 lines
2.1 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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from ...packed_int import unpack_int_symetric # noqa: TID252
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from ...dequantizer import dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
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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_scale = torch.amax(input.abs(), dim=-1, keepdims=True).div_(127)
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input = torch.div(input, input_scale).round_().clamp_(-128, 127).to(dtype=torch.int8)
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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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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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input, scale = quantize_int8_matmul_input(input, scale)
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if bias is not None:
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return dequantize_symmetric_with_bias(torch._int_mm(input, weight), scale, bias, return_dtype, output_shape)
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else:
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return dequantize_symmetric(torch._int_mm(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, skip_quantized_matmul=True), self.bias)
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return int8_matmul(input, self.weight, self.bias, self.sdnq_dequantizer.scale, getattr(self.sdnq_dequantizer, "quantized_weight_shape", None), self.sdnq_dequantizer.weights_dtype)
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if use_torch_compile:
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int8_matmul = torch.compile(int8_matmul, fullgraph=True, dynamic=False)
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