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65 lines
2.0 KiB
65 lines
2.0 KiB
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from torch import nn
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class QuantStub(nn.Module):
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r"""Quantize stub module, before calibration, this is same as an observer,
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it will be swapped as `nnq.Quantize` in `convert`.
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Args:
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qconfig: quantization configuration for the tensor,
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if qconfig is not provided, we will get qconfig from parent modules
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"""
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def __init__(self, qconfig=None):
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super().__init__()
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if qconfig:
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self.qconfig = qconfig
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def forward(self, x):
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return x
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class DeQuantStub(nn.Module):
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r"""Dequantize stub module, before calibration, this is same as identity,
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this will be swapped as `nnq.DeQuantize` in `convert`.
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Args:
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qconfig: quantization configuration for the tensor,
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if qconfig is not provided, we will get qconfig from parent modules
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"""
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def __init__(self, qconfig=None):
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super().__init__()
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if qconfig:
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self.qconfig = qconfig
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def forward(self, x):
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return x
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class QuantWrapper(nn.Module):
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r"""A wrapper class that wraps the input module, adds QuantStub and
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DeQuantStub and surround the call to module with call to quant and dequant
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modules.
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This is used by the `quantization` utility functions to add the quant and
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dequant modules, before `convert` function `QuantStub` will just be observer,
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it observes the input tensor, after `convert`, `QuantStub`
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will be swapped to `nnq.Quantize` which does actual quantization. Similarly
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for `DeQuantStub`.
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"""
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quant: QuantStub
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dequant: DeQuantStub
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module: nn.Module
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def __init__(self, module):
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super().__init__()
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qconfig = getattr(module, "qconfig", None)
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self.add_module('quant', QuantStub(qconfig))
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self.add_module('dequant', DeQuantStub(qconfig))
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self.add_module('module', module)
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self.train(module.training)
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def forward(self, X):
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X = self.quant(X)
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X = self.module(X)
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return self.dequant(X)
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