Comments (2)
Is 1.13.1 the pytorch version being used? If so please test with torch>2.0. It would also be helpful to upload the resulting onnx model.
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It would also help to create the issue in pytorch repo. I guess it could be an issue in pytorch-to-onnx exporter, or in onnxruntime implementation, or a potential mismatch in the ONNX op spec.
But it is a bit weird, in that it uses only a linear layer and Relu ... both of which must be very well tested by now in all 3 components.
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Related Issues (20)
- Performance Discrepancy Between PyTorch and ONNX Model Conversion: Unexpected Increase in Latency HOT 1
- [Feature request] Implement the reference runtime with the array api
- Inference session with 'optimized_model_filepath' expands Gelu operator into atomic operators
- [Performance] The unpack_int4 function has a low efficiency. HOT 5
- Shape inference segfaults when adding tensors of shapes `(0,)` and `(1,)` HOT 1
- Build Error: Protobuf pinned version missing `<cstdint>` definition on its headers
- Refactor the subbyte module
- No Adapter From Version $14 for Mul HOT 3
- Deprecation / Update Policy for onnx dependencies? HOT 2
- How to parse a function with variadics? HOT 5
- Split onnx model in architecture and weights HOT 2
- a) Feature Request: Function sample_dirichlet, b) State of probabilistic model support? HOT 3
- Using onnx shape inference some operator doesn't support shape inference HOT 1
- Convert a model with custom pytorch CUDA kernel HOT 1
- Want to substitute Expand operator with some other operator in ONNX due to compatibility issues with hardware HOT 1
- Squeeze-11's output shape is mistakenly inferred when input has dynamic axes and squeezing axes is not specified
- NMS Operator Output Different From Torchvision Implementation HOT 1
- n-bit data type HOT 1
- Model split failure using onnx.utils.extract_model HOT 3
- Change the example in the documentation of Transpose HOT 1
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