torch.nn.qat
This module implements versions of the key nn modules Conv2d() and Linear() which run in FP32 but with rounding applied to simulate the effect of INT8 quantization.
Conv2d
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class torch.nn.qat.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', qconfig=None)
[source] -
A Conv2d module attached with FakeQuantize modules for weight, used for quantization aware training.
We adopt the same interface as
torch.nn.Conv2d
, please see https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d for documentation.Similar to
torch.nn.Conv2d
, with FakeQuantize modules initialized to default.- Variables
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~Conv2d.weight_fake_quant – fake quant module for weight
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classmethod from_float(mod)
[source] -
Create a qat module from a float module or qparams_dict
Args:
mod
a float module, either produced by torch.quantization utilities or directly from user
Linear
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class torch.nn.qat.Linear(in_features, out_features, bias=True, qconfig=None)
[source] -
A linear module attached with FakeQuantize modules for weight, used for quantization aware training.
We adopt the same interface as
torch.nn.Linear
, please see https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.Similar to
torch.nn.Linear
, with FakeQuantize modules initialized to default.- Variables
-
~Linear.weight – fake quant module for weight
-
classmethod from_float(mod)
[source] -
Create a qat module from a float module or qparams_dict
Args:
mod
a float module, either produced by torch.quantization utilities or directly from user
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.8.0/torch.nn.qat.html