- torch.aten.flatten.using_ints to linalg lowering
- torch.aten.max_pool2d to linalg lowering
- Support torch.aten.conv2d for more flexible dilation and strides values
The tests use the same (pure-Python) test framework as the
normal torchscript_e2e_test.sh, but the tests are added in
`build_tools/torchscript_e2e_heavydep_tests` instead of
`frontends/pytorch/e2e_testing/torchscript`. Any needed dependencies can
easily be configured in generate_serialized_tests.sh.
We add an initial machine translation model with a complex set of
dependencies to seed the curriculum there. I verified that this model
gets to the point of MLIR import (it fails there with a segfault due to
not being able to import the "Any" type).
This required moving a few files from the `torch_mlir` Python module
into multiple modules to isolate the code that depends on our C++
extensions (which now live in `torch_mlir` and
`torch_mlir_torchscript_e2e_test_configs`) from the pure Python code
(which now lives in `torch_mlir_torchscript`). This is an entirely
mechanical change, and lots of imports needed to be updated.
The dependency graph is:
```
torch_mlir_torchscript_e2e_test_configs
/ |
/ |
/ |
V V
torch_mlir_torchscript torch_mlir
```
The `torch_mlir_torchscript_e2e_test_configs` are then dependency-injected
into the `torch_mlir_torchscript` modules to successfully assemble a
working test harness (the code was already structured this way, but this
new file organization allows the isolation from C++ code to actually
happen). This isolation is critical to allowing the serialized programs
to be transported across PyTorch versions and for the test harness to be
used seamlessly to generate the heavydep tests.
Also:
- Extend `_Tracer` class to support nested property (submodule) accesses.
Recommended review order:
- "user-level" docs in README.md
- code in `build_tools/torchscript_e2e_heavydep_tests`.
- changes in `torch_mlir_torchscript/e2e_test/framework.py`
- misc mechanical changes.
We plumb through e2e a fair number of interesting cases:
- unary, binary, ternary elementwise ops
- ops like `torch.aten.add.Tensor` that also take a scalar parameter
- static size-1 broadcasting
We allow the static size-1 broadcasting case, but emit a runtime error
in the case of dynamic size-1 broadcasting. This seems like a sweet spot
subset of things that can be lowered directly to linalg, while not being
overly constraining to users. This is consistent with what IREE is doing
for CHLO->Linalg lowering as well
([code](50bf7a87e4/iree/compiler/InputConversion/MHLO/BroadcastingToLinalgPatterns.cpp (L1))).
To test the static size-1 case, we added support for the
`torch.aten.unsqueeze` op and lowering for it through
`linalg.tensor_expand_shape`. This involved a generalization of
`MaximizeValueSemantics` able to handle it (the solution there also
works for `torch.aten.flatten.using_ints` which we need for ResNet
anyway)
Also, a few minor additional changes:
- Add `VerifyInvariantsBeforeBackendLowering` pass, which catches a
large class of errors before we get to backend lowering (now that we
are doing dialect conversion, the errors are way nicer if we just emit
them up front rather than in the guts of a random pattern).
- Minor change to RefBackend to allow `linalg.tensor_expand_shape`.
Recommended review order:
- e2e tests in elementwise.py
- `ConvertElementwiseOp` in TorchToLinalg.cpp + elementwise.mlir test
- `ConvertAtenUnsqueezeOp` in TorchToLinalg.cpp + unsqueeze.mlir test
- RefineTypes.cpp + tests
- MaximizeValueSemantics changes + test
- VerifyInvariantsBeforeBackendLowering pass + test
Yi Zhang
Sean Silva