microsoft / microsoft/onnxruntime
[Feature Request] Add torch.Tensor support for InferenceSession input_feed
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Description
Describe the feature request
Having ONNX Runtime using torch.Tensor (in addition to the current numpy) tensors is useful for the scenarios in which numpy does not support the data type used in the original torch model, such as torch.bfloat16.
Describe scenario use case
Today, we are forced to transform the onnx graph to convert bfloat16 into float16 due to umpy's lack of support for bfloat16
Supporting torch.Tensor direcly also makes ORT closer to PyTorch's original model, without numpy as a middle man
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no files or tests. Start by locating the input_feed handling and its existing numpy conversion path, then determine how torch.Tensor and bfloat16 should be supported without that conversion. Done means an input_feed can accept torch.Tensor values for the described data types, with coverage demonstrating the behavior.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100