add Bigbird ONNX config #16427
add Bigbird ONNX config #16427
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The documentation is not available anymore as the PR was closed or merged. |
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5040f17eba15504bad66b14a645bddd9b015ebb7 #15622 |
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Thank you for this very clean implementation @vumichien 🚀 ! I have a small question about the changes to the modeling file, but this looks good to me 😄
Could you please fix the merge conflicts with the serialization.mdx file and check that the slow tests pass by running:
RUN_SLOW=1 pytest tests/onnx/test_onnx_v2.py -k "bigbird"
| logits_mask = self.prepare_question_mask(question_lengths, seqlen) | ||
| if token_type_ids is None: | ||
| token_type_ids = (~logits_mask).long() | ||
| token_type_ids = torch.ones(logits_mask.size(), dtype=int) - logits_mask |
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Are these changes needed to enable inference with ONNX Runtime? Just trying to understand the need to change the modeling code
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@lewtun Yes, you are right. I have changed the type of logits_mask here from boolean to integer to enable inference with ONNX Runtime. The error looks like this if we use the original modeling code.
RuntimeError: 0INTERNAL ASSERT FAILED at "../torch/csrc/jit/ir/alias_analysis.cpp":611, please report a bug to PyTorch. We don't have an op for aten::fill_ but it isn't a special case. Argument types: Tensor, bool,
Candidates:
aten::fill_.Scalar(Tensor(a!) self, Scalar value) -> (Tensor(a!))
aten::fill_.Tensor(Tensor(a!) self, Tensor value) -> (Tensor(a!))
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Thanks for the clarification! This looks fine to me :)
… add-onnx-bigbird
| mask = torch.arange(0, maxlen).to(q_lengths.device) | ||
| mask.unsqueeze_(0) # -> (1, maxlen) | ||
| mask = mask < q_lengths | ||
| mask = torch.where(mask < q_lengths, 1, 0) |
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Also in here, I have to change the mask type
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@lewtun Thank you for reviewing my code. I have fixed the merge conflicts and all tests by running this |
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This all looks good to me @vumichien - thanks for iterating! Gently pinging @LysandreJik or @sgugger for final approval |
* add Bigbird ONNX config
5 participants
What does this PR do?
Add Bigbird OnnxConfig to make this model available for conversion.
Who can review?
@lewtun @LysandreJik