Make assertions only if actually chunking forward #13598
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Asking @patrickvonplaten for review as it's on reformer |
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@LysandreJik This appears when tracing sentence-transformers/all-MiniLM-L12-v2, which doesn't seem to be a reformer 🤔 |
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Hey @joshdevins, I'm happy with the PR! Can you rebase to current master - it should fix the failing tests I think. Also could you maybe replace the |
This moves the assertion on checking input dimensions into a block that will only be called if the function is actually going to do chunking forward. This is often not the case at inference time and PyTorch tracing a model with this assertion in it leads to a tracing warning. TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! input_tensor.shape[chunk_dim] == tensor_shape for input_tensor in input_tensors
stas00
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to stas00/transformers
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Oct 12, 2021
This moves the assertion on checking input dimensions into a block that will only be called if the function is actually going to do chunking forward. This is often not the case at inference time and PyTorch tracing a model with this assertion in it leads to a tracing warning. TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! input_tensor.shape[chunk_dim] == tensor_shape for input_tensor in input_tensors
Albertobegue
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to Albertobegue/transformers
that referenced
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Jan 13, 2022
This moves the assertion on checking input dimensions into a block that will only be called if the function is actually going to do chunking forward. This is often not the case at inference time and PyTorch tracing a model with this assertion in it leads to a tracing warning. TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! input_tensor.shape[chunk_dim] == tensor_shape for input_tensor in input_tensors
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This moves the assertion on checking input dimensions into a block that will only be called if the function is actually going to do chunking forward. This is often not the case at inference time and PyTorch tracing a model with this assertion in it leads to a tracing warning.