This is the start of a push to getting ResNet running.
This involves throwing in the towel on an O0 pipelinie for now. See note
in the code. We keep an options struct with `optimize` flag, but it
default to true for now.
These tests pass on the reference backend.
- Add aten.linear op + shape xfer function + ATen->Linalg lowering.
- Note: this needs to be more automated, and needs to cover more cases.
- Current not implemented caveats:
- size-1 broadcasting for bias vector (either static-size-1 or ? case)
- higher-rank aten.linear ops (not produced by torch.nn.Linear though)
- type promotion (still don't even know the exact rules here)
- Add folder for torch.derefine op. Now the inliner can clean it up as
it inlines. (call boundaries are a main place we need to insert
torch.derefine) This is brittle -- the other important case is control
flow which will need to be handled via an extension to
RefineTypes.cpp (as will more robust call handling). River has an
in-flight patch to update it to the new dataflow framework so I didn't
want to do anything intrusive here.
- Also adjust torch.derefine syntax to use the keyword `to` instead of
`->`, as most type-only, cast-like ops do.
- Move frontend lowering pipelines to c++ (this helps with reproducing
failures in npcomp-opt)
- Add debugging printouts when compilation fails on RefBackendTestConfig
The experience now when a test fails during MLIR lowering is now like this:
```
NPCOMP TorchScript Object Graph IR -> NPCOMP Backend IR lowering failed with the following diagnostics:
failed to legalize operation 'torch.global_slot'
Module does not conform to npcomp's backend contract. See dialect conversion legality information above.
Error can be reproduced with:
$ npcomp-opt -torchscript-to-npcomp-backend-pipeline /tmp/ResNet18Module.mlir
```
And when TorchScript->MLIR import fails it looks like this:
```
PyTorch TorchScript module -> NPCOMP Object Graph IR import failed with the following diagnostics:
unhandled prim operation: %18 : int = prim::min(%17) # /usr/local/google/home/silvasean/.local/lib/python3.9/site-packages/torch/nn/functional.py:4532:4
```
Also,
- Add `--filter=<regex>` to e2e test harness to filter tests.
- Add a few prim ops that were needed to import ResNet18
- Fix torch.prim.Loop.condition assemblyFormat (it previously would not
round-trip in the case of no loop-carried variables)
The E2E tests can be run with
```
npcpy frontends/pytorch/e2e_testing/torchscript/main.py
```
This commit adds a couple items supporting that end, including new sugar
for annotations (no more raw use of ClassAnnotator!).
Recommended review order:
1. `frontends/pytorch/e2e_testing/torchscript/main.py` for
the harness + `basic.py` in that directory for examples of tests.
2. Annotation sugar in `frontends/pytorch/python/torch_mlir/torchscript/annotations.py`
and unittest in `frontends/pytorch/test/ivalue_import/annotations/sugar.py`
3. Global test registry / sugar in
`frontends/pytorch/python/torch_mlir/torchscript/e2e_test/registry.py`
4. `frontends/pytorch/python/torch_mlir/torchscript/e2e_test/framework.py`
for the meat of the testing framework (start at `run_tests`), and
looking at the backend configs in
`frontends/pytorch/python/torch_mlir/torchscript/e2e_test/configs`
for examples of backends. This is likely the bulk of review time.
5. Unit tests of the framework logic in `frontends/pytorch/test/torchscript_e2e_test`
There's TODO's scattered throughout, but this seems functional enough to
start pulling stuff into and kicking the tires. A few missing pieces:
1. Marking test expected pass/fail per backend.
2. Figuring out how best to fit this into dev workflows.
3. IREE TestConfig.
Also, forgive this Python newbie... Any advice on Python code structure
/ library design would be much appreciated.
Sean Silva