f96f222526
### Description
Based on the ORT spec for ConvTranspose:
```
output_shape can also be explicitly specified in which case pads values are auto generated using these equations:
total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]
If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).
```
However the CPU EP logic differs. Basically, unless SAME_UPPER is
specified, the default behavior (for VALID,NOTSET,SAME_LOWER) should be
SAME_LOWER.
I think this is the pragmatic fix, however it's perhaps still not
totally up to standard.
In the case tested, the operator is actually only valid if padding is
inserted. Perhaps it "should" throw some error then, if auto_pad is not
SAME_UPPER or SAME_LOWER, as the spec also mentions:
"VALID mean no padding." (For convtranspose-1 but this was removed in
convtranspose-11, making it less clear.)
"NOTSET, which means explicit padding is used" (should technically
require explicit padding then, and not generate it)
HOWEVER, changing it to throw errors could do more harm than good. For
now, probably just best to make it consistent.
### Motivation and Context
We noticed that there was a discrepancy in one of the DML tests between
CPU and DML.
auto_pad is not specified, and DML is doing SAME_LOWER behavior by
default, where CPU EP is doing SAME_UPPER behavior.
```json
{
"graph_name": "ConvTranspose output_shape with even strides odd kernel autopad NOTSET",
"op_type": "ConvTranspose",
"dilations": [1,1],
"group": 1,
"strides": [2,2],
"kernel_shape": [3,3],
"output_shape": [1,1,4,4],
"X": {"dims": [1,1,2,2], "function": "iota"},
"W": {"dims": [1,1,3,3], "value": [1,2,3,4,5,6,7,8,9]},
"B": [1],
"Y": {"dims": [1,1,4,4], "value": [1,5,6,7,5,17,15,19,11,25,16,19,17,40,25,28]},
"T": "float32"
}
```
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
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For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
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License
This project is licensed under the MIT License.