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### Description This PR removes an internal `ORT_ENFORCE` when binding `torch.tensor` inputs using IO binding for end-to-end scripts. ### Motivation and Context In merged exports of PyTorch models to ONNX, each past key and past value in the past KV cache has an input shape of `(batch_size, num_heads, past_sequence_length, head_size)`. In the first pass through the model to process the prompt, `past_sequence_length = 0`. Therefore, each of these inputs is of shape `(batch_size, num_heads, 0, head_size)`. In subsequent passes, `past_sequence_length > 0`. When binding a `torch.tensor` of shape `(batch_size, num_heads, 0, head_size)` with `io_binding.bind_input`, the tensor's `data_ptr()` must be passed. For a `torch.tensor` of this shape, its `data_ptr()` returns 0. Because it returns 0, the existing `ORT_ENFORCE` is therefore false and an error is raised. By removing the internal `ORT_ENFORCE`, no error is raised and the model runs successfully. LLaMA-2 Example: Input Name | Input Size | Device | Device ID | Torch Dtype | data_ptr() ------------- | ----------- | ------- | ----------- | ------------- | ----------- input_ids | torch.Size([1, 11]) | cuda | 7 | torch.int64 | 140639561842688 attention_mask | torch.Size([1, 11]) | cuda | 7 | torch.int64 | 140639561843200 position_ids | torch.Size([1, 11]) | cuda | 7 | torch.int64 | 140639561844224 past_key_values.0.key | torch.Size([1, 32, 0, 128]) | cuda | 7 | torch.float32 | 0 past_key_values.0.value | torch.Size([1, 32, 0, 128]) | cuda | 7 | torch.float32 | 0 ... | ... | ... | ... | ... | ... |
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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 & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
| Mac | ||
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| iOS | ||
| Web | ||
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Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
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Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
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Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.