Comments (3)
The default return value of grad_depends_{input,output}_data() was changed by:
MaxPoolingCudaCudnn should be kept until input/output data is used by backward calculation due to cudnn limitation.
Also, to prevent broken data that are used in backward by inplace, show error messages at graph construction:
Failed `input->allow_modify_data()`: Modifying data is prohibited by the parent function of the 0-th input data of 'ReLUCudaCudnn' (depth=3). (Parent is 'MaxPoolingCudaCudnn').
It is necessary to set False for all places where inplace=True when giving MaxPooling output to ReLU.
So, you can run it by changing following in mnist_lenet_prediction().
c1 = F.relu(F.max_pooling(c1, (2, 2)), inplace=False)
c2 = PF.convolution(c1, 16, (5, 5), name='conv2')
c2 = F.relu(F.max_pooling(c2, (2, 2)), inplace=False)
We will consider other ways such as ignoring inplace.
Sorry for inconvenience.
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Now we prepared PRs to ignore inplace option from ReLU, LeakyReLU, RandomErase, Add2, BcAdd2, Sub2, AddScalar, MulScalar.
Please be careful if you expect inplace behavior.
This change will be released by nnabla v1.21.0.
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v1.21.0 was released, so close this issue.
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