Source code for torch.nn.intrinsic.quantized.modules.linear_relu

import torch
import torch.nn.quantized as nnq
import torch.nn.intrinsic as nni

[docs]class LinearReLU(nnq.Linear): r""" A LinearReLU module fused from Linear and ReLU modules We adopt the same interface as :class:`torch.nn.quantized.Linear`. Attributes: Same as torch.nn.quantized.Linear Examples:: >>> m = nn.intrinsic.LinearReLU(20, 30) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 30]) """ _FLOAT_MODULE = nni.LinearReLU def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8): super().__init__(in_features, out_features, bias, dtype) def forward(self, x: torch.Tensor) -> torch.Tensor: return torch.ops.quantized.linear_relu( x, self._packed_params._packed_params, self.scale, self.zero_point) def _get_name(self): return 'QuantizedLinearReLU' @classmethod def from_float(cls, mod): return super(LinearReLU, cls).from_float(mod) @classmethod def from_reference(cls, ref_linear_relu, output_scale, output_zero_point): return super().from_reference(ref_linear_relu[0], output_scale, output_zero_point)


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