This commit adds the OnnxToTorch support for ReduceSumSquare ops.
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Co-authored-by: Ubuntu <archana@archana-cpu.judsoscro3wupi0qm4bjlj5m3b.bx.internal.cloudapp.net>
While playing with TorchDynamo on ResNet18. I notice following issues:
- `prims.convert_element_type` can’t be canonicalized even if the input
and the output share the same type
- `aten.max_pool2d_with_indices` is always used instead of
`aten.max_pool2d`, even if the second returned output (indices) has no
user
This PR fixes above issues by adding a folder to the
PrimsConvertElementTypeOp and a canonicalizer to the
AtenMaxPool2dWithIndicesOp
Lit test:
`cmake --build build --target check-torch-mlir-all`
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Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
This is probably a decent PR for learning about blocks and regions.
If you're here to learn about that, consider also looking at
lib/Conversion/TorchToSCF/TorchToSCF.cpp
While this doesn't include an e2e test, it is tested downstream in
https://github.com/nod-ai/SHARK-TestSuite/blob/main/e2eshark/onnx/operators/If/model.py
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Co-authored-by: Xida Ren <xida.ren.dev@gmail.com>
I spent a little while debugging numerics issues with some tests similar
to the ones in quantized_models.py, only to find that pytorch's
quantized conv transpose is catastrophically inaccurate. I'll upstream
the issue and only leave the tests here which are of the form quantize
-> dequantize -> op.
For some sparse programs (and I am sure other not-seen corner cases for
dense), some passes were missing in the reference pipeline, eventually
resulting in e.g. a unresolved unrealized cast issue. This PR adds some
very obvious missing passes to avoid this situation.
Fixes https://github.com/llvm/torch-mlir/issues/3258
In addition disabling the LTC builds since they are already covered in
CI (build_posix.sh) and I am not aware of a consumer of this flow in the
binary releases of torch-mlir (the main dependency there is from
source).
This scenario was uncovered in a downstream test that failed with a
previous snapshot of torch-mlir. See
https://github.com/cruise-automation/mlir-tcp/actions/runs/8605480116/job/23581829102?pr=65.
```
File "/home/runner/.cache/bazel/_bazel_runner/ce288f117ee4ca92dc028a6a28476a3d/sandbox/processwrapper-sandbox/2380/execroot/mlir-tcp/bazel-out/k8-opt-exec-2B5CBBC6/bin/test/AotCompile/broadcast_unit_dim_to_dynamic_with_unchanged_dim_dynamic_torch_exporter.runfiles/pip_deps_torch_mlir/site-packages/torch_mlir/extras/fx_importer.py", line 969, in value_info_to_type
raise NotImplementedError(
NotImplementedError: Could not deduce type from value info: tensor_meta=None, val=s1, sparsity=None
```
It seems to have resolved on current HEAD. Adding this test to ensure
coverage in the future.
This is a large change because prior to this point, Python files in the
project were not consistently formatted. This reformats them all with
black defaults.
Based on experience with prior projects, if you have a dev/long-term
branch with Python patches, you can minimize merge conflicts prior to
rebasing to include this commit by running `black` on your modified
Python files, squashing, and then rebasing/merging.
This is part 1 of ~3, formatting all miscellaneous text files and CPP files matched by a first run of pre-commit. These tend to be low change-traffic and are likely not disruptive.
Subsequent patches will format Python files and remaining CPP files.
Users can run via `pre-commit run` or set up a hook as described in the
instructions: https://pre-commit.com/
The CI is set to only run pre-commit on files changed in the patch. We
will run with `--all-files` in a separate patch.
Gridsampler
In onnx the interpolation mode is called 'linear' whereas in pytorch it
is called 'bilinear'. This led to the problem that everything other than
'bilinear' was rejected. It needed to be changed to linear.
Vivek Khandelwal
Xida Ren (Cedar)
Yuanqiang Liu
Rob Suderman
Vinayak Dev
Archana Ramalingam
Ze Zhang
Prashant Kumar
zjgarvey
Aart Bik
Xinyu Yang
Sambhav Jain
jinchen
Jae Hoon (Antonio) Kim
penguin_wwy
Stella Laurenzo
Andreas Falkenberg