ONNX Runtime improvements #1306
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* customize the wasm paths * update implementation * allow using 'webgpu' in nodejs binding * update version of onnxruntime-node * Upgrade onnxruntime-web to same version as onnxruntime-node * Update list of supported devices --------- Co-authored-by: Joshua Lochner <[email protected]>
* customize the wasm paths * update implementation
sroussey
reviewed
Jun 18, 2025
* Bump jinja version to support new features * [version] Update to 3.6.3
…1370) * Add special tokens in text-generation pipeline if tokenizer requires * Fix logits processors tests * Update bundles.test.js * Update comment * Formatting
Actually fixes #1343
* Optimize tensor.slice() The performance of executing `tensor.slice()` is super poor, especially for the 'logits' tensor with large dimensions. ``` const logits = outputs.logits.slice(null, -1, null);` ``` This is because currently implementation of the `slice` method manually iterates through each element and calculate indices which is a big time consuming if the tensor shape is large. For cases like `slice(null, -1, null)`, where the slicing operation is contiguous along certain dimensions, which can be optimized by bulk copy by using `TypeArray.subarray()` and `TypeArray.set()`. * nit * Add a few more tensor slice unit tests --------- Co-authored-by: Joshua Lochner <[email protected]>
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Merging into v4 branch 🔥 |
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Add support for native webgpu EP