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[Enhancement] Change the order of condition to make fx wok #2883

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Aug 3, 2023
Merged

[Enhancement] Change the order of condition to make fx wok #2883

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c82bd84
change the order of condition, so that torch.fx can trace these modules
youkaichao Aug 3, 2023
616fc72
guard the test of torch.fx on MaxPool2d, MaxPool3d under torch 1.10
youkaichao Aug 3, 2023
fc883ec
modify skip test conditions
youkaichao Aug 3, 2023
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14 changes: 7 additions & 7 deletions mmcv/cnn/bricks/wrappers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ def backward(ctx, grad: torch.Tensor) -> tuple:
class Conv2d(nn.Conv2d):

def forward(self, x: torch.Tensor) -> torch.Tensor:
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
if obsolete_torch_version(TORCH_VERSION, (1, 4)) and x.numel() == 0:
out_shape = [x.shape[0], self.out_channels]
for i, k, p, s, d in zip(x.shape[-2:], self.kernel_size,
self.padding, self.stride, self.dilation):
Expand All @@ -62,7 +62,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
class Conv3d(nn.Conv3d):

def forward(self, x: torch.Tensor) -> torch.Tensor:
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
if obsolete_torch_version(TORCH_VERSION, (1, 4)) and x.numel() == 0:
out_shape = [x.shape[0], self.out_channels]
for i, k, p, s, d in zip(x.shape[-3:], self.kernel_size,
self.padding, self.stride, self.dilation):
Expand All @@ -84,7 +84,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
class ConvTranspose2d(nn.ConvTranspose2d):

def forward(self, x: torch.Tensor) -> torch.Tensor:
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
if obsolete_torch_version(TORCH_VERSION, (1, 4)) and x.numel() == 0:
out_shape = [x.shape[0], self.out_channels]
for i, k, p, s, d, op in zip(x.shape[-2:], self.kernel_size,
self.padding, self.stride,
Expand All @@ -106,7 +106,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
class ConvTranspose3d(nn.ConvTranspose3d):

def forward(self, x: torch.Tensor) -> torch.Tensor:
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
if obsolete_torch_version(TORCH_VERSION, (1, 4)) and x.numel() == 0:
out_shape = [x.shape[0], self.out_channels]
for i, k, p, s, d, op in zip(x.shape[-3:], self.kernel_size,
self.padding, self.stride,
Expand All @@ -127,7 +127,7 @@ class MaxPool2d(nn.MaxPool2d):

def forward(self, x: torch.Tensor) -> torch.Tensor:
# PyTorch 1.9 does not support empty tensor inference yet
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
if obsolete_torch_version(TORCH_VERSION, (1, 9)) and x.numel() == 0:
out_shape = list(x.shape[:2])
for i, k, p, s, d in zip(x.shape[-2:], _pair(self.kernel_size),
_pair(self.padding), _pair(self.stride),
Expand All @@ -145,7 +145,7 @@ class MaxPool3d(nn.MaxPool3d):

def forward(self, x: torch.Tensor) -> torch.Tensor:
# PyTorch 1.9 does not support empty tensor inference yet
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
if obsolete_torch_version(TORCH_VERSION, (1, 9)) and x.numel() == 0:
out_shape = list(x.shape[:2])
for i, k, p, s, d in zip(x.shape[-3:], _triple(self.kernel_size),
_triple(self.padding),
Expand All @@ -164,7 +164,7 @@ class Linear(torch.nn.Linear):

def forward(self, x: torch.Tensor) -> torch.Tensor:
# empty tensor forward of Linear layer is supported in Pytorch 1.6
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 5)):
if obsolete_torch_version(TORCH_VERSION, (1, 5)) and x.numel() == 0:
out_shape = [x.shape[0], self.out_features]
empty = NewEmptyTensorOp.apply(x, out_shape)
if self.training:
Expand Down
20 changes: 20 additions & 0 deletions tests/test_cnn/test_wrappers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,8 @@
import pytest
import torch
import torch.nn as nn
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION

from mmcv.cnn.bricks import (Conv2d, Conv3d, ConvTranspose2d, ConvTranspose3d,
Linear, MaxPool2d, MaxPool3d)
Expand Down Expand Up @@ -374,3 +376,21 @@ def test_nn_op_forward_called():
wrapper = Linear(3, 3)
wrapper(x_normal)
nn_module_forward.assert_called_with(x_normal)


@pytest.mark.skipif(
digit_version(TORCH_VERSION) < digit_version('1.10'),
reason='MaxPool2d and MaxPool3d will fail fx for torch<=1.9')
def test_fx_compatibility():
from torch import fx

# ensure the fx trace can pass the network
for Net in (MaxPool2d, MaxPool3d):
net = Net(1)
gm_module = fx.symbolic_trace(net) # noqa: F841
for Net in (Linear, ):
net = Net(1, 1)
gm_module = fx.symbolic_trace(net) # noqa: F841
for Net in (Conv2d, ConvTranspose2d, Conv3d, ConvTranspose3d):
net = Net(1, 1, 1)
gm_module = fx.symbolic_trace(net) # noqa: F841
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