pytorch autograd_error ai_generated true

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

ID: pytorch/inplace-operation-gradient

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85%Fix Rate
88%Confidence
3Evidence
2023-01-01First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
2 active — — —

Root Cause

An in-place operation (+=, relu_(inplace=True), etc.) modified a tensor needed for backward pass, breaking autograd.

generic

Workarounds

  1. 92% success Replace inplace operations with out-of-place equivalents 
    x = x + 1 instead of x += 1; F.relu(x) instead of F.relu(x, inplace=True)

    Sources: https://pytorch.org/docs/stable/

  2. 88% success Clone tensors before modification 
    y = x.clone(); y.modify_inplace()  # x's gradient graph is preserved
  3. 90% success Set inplace=False in ReLU/Dropout layers 
    nn.ReLU(inplace=False)  # default is False anyway

Dead Ends

Common approaches that don't work:

  1. Wrap the operation in torch.no_grad() 88% fail

    no_grad disables gradient tracking entirely; the model will not learn

  2. Use .data to bypass autograd 85% fail

    Modifying .data breaks the computation graph silently; gradients become incorrect without errors