chore: reenable py313 by zewenli98 · Pull Request #3455 · pytorch/TensorRT (original) (raw)
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Description
Reenabled Python 3.13 builds since TensorRT has 3.13 wheels available now for TensorRT 10.9.
Type of change
- New feature (non-breaking change which adds functionality)
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- My code follows the style guidelines of this project (You can use the linters)
- I have performed a self-review of my own code
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- I have made corresponding changes to the documentation
- I have added tests to verify my fix or my feature
- New and existing unit tests pass locally with my changes
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that may beclosed by this pull request
| - name: Install Rust |
|---|
| run: | |
| curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y |
| source $HOME/.cargo/env |
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I think you don't need to install rust. Instead, you should try to bump the version of transformers in tests/py/requirements.txt. transformers 4.40.2 depended on tokenizers 0.19.x, but tokenizers 0.19.x didn't have wheels for cp313. That's why pip downloaded the source archive of tokenizers and then compiled unsuccessfully.
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The updated transformers doesn't match the results of BERT, compared with old transformers and TRT, so we fixed the version here. Any idea?
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LGTM
| transformers==4.40.2 |
|---|
| nvidia-modelopt[deploy,hf,torch]~=0.17.0 |
| transformers==4.49.0 |
| nvidia-modelopt[deploy,hf,torch]~=0.17.0; python_version < "3.13" |
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modelopt doesn't support 3.13 yet ? So, quantization also won't work right in Python 3.13?
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right, I think we've already directly skip quantization tests like
| @unittest.skipIf( |
|---|
| not importlib.util.find_spec("modelopt"), |
| "ModelOpt is required to run this test", |
| ) |
| @pytest.mark.unit |
| def test_base_fp8(ir): |
| import modelopt.torch.quantization as mtq |
| from modelopt.torch.quantization.utils import export_torch_mode |
| class SimpleNetwork(torch.nn.Module): |
| def __init__(self): |
| super(SimpleNetwork, self).__init__() |
| self.linear1 = torch.nn.Linear(in_features=10, out_features=5) |
| self.linear2 = torch.nn.Linear(in_features=5, out_features=1) |
| def forward(self, x): |
| x = self.linear1(x) |
| x = torch.nn.ReLU()(x) |
| x = self.linear2(x) |
| return x |
Users may not be aware of it though
| @@ -62,6 +64,22 @@ def not_implemented(*args: List[Any], **kwargs: Dict[str, Any]) -> Any: |
|---|
| return wrapper |
| def needs_refit(f: Callable[..., Any]) -> Callable[..., Any]: |
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Maybe we can make this a bit more generic (like for any feature in the FeatureSet)
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Think it mostly looks good, I think we should think about making the requires decorator a bit more generic and unify needs_torch_tensorrt_runtime and needs_refit, maybe something like needs_feature. Maybe something for after 2.7
Think it mostly looks good, I think we should think about making the requires decorator a bit more generic and unify
needs_torch_tensorrt_runtimeandneeds_refit, maybe something likeneeds_feature. Maybe something for after 2.7
sure, will think about it for the next release.
zewenli98 added a commit that referenced this pull request