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torch-mlir/pytorch-requirements.txt

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build: Update Roll PyTorch version (#3548) This commit also updates the PyTorch and Torchvision nightly links since they are now moved to a different location. PyTorch Nightly: https://download.pytorch.org/whl/nightly/cpu/torch/ Torchvision Nightly: https://download.pytorch.org/whl/nightly/cpu/torchvision/ Disables dtype checks for some ops, tracked by https://github.com/llvm/torch-mlir/issues/3552 Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-07-20 00:08:57 +08:00
-f https://download.pytorch.org/whl/nightly/cpu/torch/
build: create RollPyTorch to update PyTorch version in Torch-MLIR (#1419) This patch fetches the most recent nightly (binary) build of PyTorch, before pinning it in pytorch-requirements.txt, which is referenced in the top-level requirements.txt file. This way, end users will continue to be able to run `pip -r requirements.txt` without worrying whether doing so will break their Torch-MLIR build. This patch also fetches the git commit hash that corresponds to the nightly release, and this hash is passed to the out-of-tree build so that it can build PyTorch from source. If we were to sort the torch versions as numbers (in the usual descending order), then 1.9 appears before 1.13. To fix this problem, we use the `--version-sort` flag (along with `--reverse` for specifying a descending order). We also filter out lines that don't contain version numbers by only considering lines that start with a digit. As a matter of slight clarity, this patch renames the variable `torch_from_src` to `torch_from_bin`, since that variable is initialized to `TM_USE_PYTORCH_BINARY`. Co-authored-by: powderluv <powderluv@users.noreply.github.com>
2022-09-29 04:38:30 +08:00
--pre
build: manually update PyTorch version and fix CI failure (#3830) This commit sets the PyTorch and TorchVision version to nightly release 2024-10-29. This commit also fixes the CI failure after this commit https://github.com/llvm/torch-mlir/commit/54d9e2401376e7eb2c6c219e3b3555f45f8b2635 got merged. The issue was that the CI checks in the PR were run before the previous roll pytorch update but the PR was actually merged after the roll pytorch update. Hence, the failure was not caught before merging the PR. While exporting the fx_graph through fx_importer for `rrelu` and `rrelu_with_noise` op for train mode, it decomposes the `aten.rrelu_with_noise` op based on the PyTorch decomposition which is the default behavior. However, the decomposition contains an input mutation specifically here https://github.com/pytorch/pytorch/blob/9bbe4a67ad137032add6a3b0b74bda66f5ef83d2/torch/_decomp/decompositions.py#L325, resulting in the runtime failure. This issue would probably be fixed by https://github.com/pytorch/pytorch/pull/138503. Until then, the failing tests are added to the xfail set. Also, after the roll pytorch update following tests started passing for fx_importer, and fx_importer_stablehlo config. - "ElementwiseRreluTrainModule_basic" - "ElementwiseRreluTrainStaticModule_basic" - "ElementwiseRreluWithNoiseTrainModule_basic" - "ElementwiseRreluWithNoiseTrainStaticModule_basic" This commit also updates the dtype check for the `aten.linear` op since the op now expects both the input tensors to have the same dtype. Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-10-30 21:26:01 +08:00
torch==2.6.0.dev20241029