275eb404bf
### Description
This PR refactors the `CPU` kernel for the `CumSum` operator. The new
implementation strives to have as little indirection as possible.
### Motivation and Context
Currently the `CumSum` operator perform very poorly in the case of 1D
tensors(it was slower than a python loop). This is caused by the
extensive use of the `SliceIterator`-s.
Here is a relevant snippet:
```python
import time
import ndonnx as ndx
import onnxruntime as ort
import numpy as np
import onnx
def test_cumsum(sz):
a = ndx.array(shape=(sz,), dtype=ndx.int64)
b = ndx.cumsum(a)
model = ndx.build({'a': a}, {'b': b})
onnx.save(model, "model.onnx")
input = np.ones(sz, np.int64)
start = time.time()
result = ort.InferenceSession(model.SerializeToString()).run(None, {'a': input})
end = time.time()
return end - start
def test_cumsum_by_hand(sz):
input = np.ones(sz, np.int64)
start = time.time()
answer = [0]
for i in input:
answer.append(answer[-1] + i)
end = time.time()
return end - start
print(test_cumsum(int(1e7)))
print(test_cumsum_by_hand(int(1e7)))
```
Before
```console
0.9794480800628662
0.4518160820007324
```
After
```console
0.02483987808227539
0.5496008396148682
```
The `model.onnx`:
<img width="214" alt="image"
src="https://github.com/user-attachments/assets/a213d6ff-86c3-49b5-a493-ebfd97deaa41">
The flame graph:
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| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
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| ORT_icon_for_light_bg.png | ||
| packages.config | ||
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| README.md | ||
| requirements-dev.txt | ||
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| requirements-training.txt | ||
| requirements.txt | ||
| SECURITY.md | ||
| setup.py | ||
| ThirdPartyNotices.txt | ||
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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
-
General Information: onnxruntime.ai
-
Usage documentation and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
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.