Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23003
torch.quantization.fuse_module and torch.nn._intrinsic convRelu and LinearRelu
Fusion function to combine specific modules: (conv,bn) and (conv,bn,relu).
In all cases, replace modules in place. The first module is replaced with the _intrinsic fused module and the remaining modules are replaced by nn.Identity.
Support both training and eval. For training, the modules are "fused" with a sequential container. This is to allow for further module swaps for quantization aware training.
Also add: torch.nn._intrinsic for convRelu and LinearRelu.
TODO: Add tests for _intrinsic modules.
Conv BN fusion code is based on DsKhudia's implementation
Differential Revision: D16199720
fbshipit-source-id: 95fb9ffe72b361d280313b2ec57de2acd4f9dda2
Summary:
Add support for quantization aware training in eager mode
Modifications to Post training flow:
## Prepare
* Fusion: e.g. (Conv, Bn) → ConvBn (float)
* Swapping: To insert fake_quant to weight, we need to swap the float modules that has weight with different qat modules, e.g. Conv → torch.nn.qat.Conv , ConvBn → torch.nn._intrinsic.qat.ConvBn
```
* previously we were thinking about modify the weight in forward_pre hook and change it back in forward_hook:
* def forward_pre_hook(self, input):
self.float_weight = self.weight
self.weight = self.fake_quantize(self.float_weight)
def forward_hook(self, input):
self.weight = self.float_weight
```
* Assignments to self.weight are needed because we can’t change forward function and in forward function they are using self.weight.
* But we will need to keep two copies of weight in this case, so it’s probably better to just swap the module
* So we want to just swap Conv to torch.nn.qat.Conv and Linear to torch.nn.qat.Linear
* qat modules will have fake_quant for output and weights inserted in forward function
## Convert
* flow should be identical to ptq, but the swapping dictionary is slightly different since modules are changed in prepare step.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23082
ghstack-source-id: 86824650
Differential Revision: D16379374
fbshipit-source-id: 7d16d1acd87025065a24942ff92abf18e9fc8070
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22732
Add support for quantization aware training in eager mode
Modifications to Post training flow:
## Prepare
* Fusion: e.g. (Conv, Bn) → ConvBn (float)
* Swapping: To insert fake_quant to weight, we need to swap the float modules that has weight with different qat modules, e.g. Conv → torch.nn.qat.Conv , ConvBn → torch.nn._intrinsic.qat.ConvBn
```
* previously we were thinking about modify the weight in forward_pre hook and change it back in forward_hook:
* def forward_pre_hook(self, input):
self.float_weight = self.weight
self.weight = self.fake_quantize(self.float_weight)
def forward_hook(self, input):
self.weight = self.float_weight
```
* Assignments to self.weight are needed because we can’t change forward function and in forward function they are using self.weight.
* But we will need to keep two copies of weight in this case, so it’s probably better to just swap the module
* So we want to just swap Conv to torch.nn.qat.Conv and Linear to torch.nn.qat.Linear
* qat modules will have fake_quant for output and weights inserted in forward function
## Convert
* flow should be identical to ptq, but the swapping dictionary is slightly different since modules are changed in prepare step.
Reviewed By: zafartahirov
Differential Revision: D16199356
fbshipit-source-id: 62aeaf47c12c62a87d9cac208f25f7592e245d6c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22706
Moved the models used for quantization test from the test_quantization.py file to common_quantization.py
Reviewed By: jerryzh168
Differential Revision: D16189865
fbshipit-source-id: 409b43454b6b3fe278ac16b1affb9085d6ed6835
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22694
Move quantization and quantized utility functions for testing to common_quantized.py and common_quantization.py. Addditionally, add a quantized test case base class which contains common methods for checking the results of quantization on modules. As a consequence of the move, fixed the import at the top of test_quantized.py, and test_quantization to use the new utility
Reviewed By: jerryzh168
Differential Revision: D16172012
fbshipit-source-id: 329166af5555fc829f26bf1383d682c25c01a7d9
