- Add support for "expected failures" in test reporting. The new error
reports look like
[this](https://gist.github.com/silvasean/6ffd95e1d55302b699673da201da210d).
- We will now be able to put these tests into CI, since the harness
understand which tests are expected to pass and fail.
- Refactor RefBackendTestConfig to NpcompBackendTestConfig which
supports both RefBackend and IREE.
- Add instructions for installing IREE dependencies (both from packages
and for local builds of IREE)
- Add `tools/torchscript_e2e_test.sh` for invoking the e2e test
harness (this makes invoking a bit easier, as it doesn't rely on a
loose Python invocation).
- 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)
As described in the code comment:
```
When we import TorchScript IR, we import their entire "compilation unit",
which can contain numerous functions unrelated to the current program,
which breaks torch-globalization-pipeline; for example, there can be
random functions referencing types that haven't been imported
as part of the root `torch.nn.Module` we imported. Those will
be unreferenced private functions which symbol-dce will clean up nicely.
```
This situation is really easy to hit in jupyter notebooks, where the
same cell is evaluated multiple times. That results in the same
class name (at the Python level, e.g. class `Foo` in the top-level
main module). Internally to PyTorch, it handles this situation by
mangling in a unique number to the names of ClassType's and such. When
we import the new ClassType's, we see not just the new
torch::jit::Function's in the CompilationUnit, but, also all the old
ones, which reference ClassType's that are not reachable from the
`torch.nn.Module` that we imported.
Note: there is no way to avoid importing the whole CompilationUnit
(including these old remnants) without doing a fairly complicated call
graph reachability analysis of which functions are reachable from the
methods of the ClassType's we imported. It turns out that once we are
inside MLIR, we model visibility correctly so that `symbol-dce`
"Just Works" for this use case. That is to say, this is not a quick
hack, but rather seems like a totally palatable long-term solution.
This pass verifies that a given module satisfies the contract that we
have for backends. This is phrased as an "allowlist", because we want to
keep this interface tight. Also, this gives much better diagnostics than
a backend randomly crashing or failing to compile would (though they
could still be improved).
This was especially painful because if we had
`tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend
would convert it to a memref type and trip the "verify type invariants"
assertion which gives no location or anything and crashed the process,
which was very unpleasant.
We implement this with the dialect conversion framework, which works
reasonably well and was quick to put together and familiar, but is still
very "op oriented". We probably want to make this hand-rolled
eventually, especially the error reporting (the most useful kind of
error for a dialect conversion user is not necessarily the best for this
use case). Also, in production, these error will go to users, and need
to be surfaced carefully such as "the compiler needs a type annotation
on this function parameter" which in general requires some special
analysis, wordsmithing, and overall awareness of the e2e use case (such
as how much we can lean into certain source locations) to provide a
meaningful user-level diagnostic.
Also, add `inline` to the current frontend lowering pass pipeline to
allow slightly more complicated programs that otherwise would fail on
shape inference.
This is our first op with error semantics, and stresses the system.
There are a few design notes of special interest:
- RefineTypes.cpp's note about shape inference in the presence of code
that dynamically produces and error, and it is provable statically.
- ATenToLinalg.cpp's notes about future automation of the ATen->linalg
path.
- The notes in Passes.td about using low-tech `std.assert` ops instead
of `shape.assuming`.
Note: Doesn't work on IREE yet due to the `std.assert` op (needs to be
lowered to `vm.fail` on the IREE side).
Recommended review order:
- Changes in frontends/pytorch/examples/
- Changes in python/npcomp/compiler/pytorch/backend/
- Boilerplate for the `npcomp-iree-backend-lower-linkage` pass.
This change separates out a
`npcomp.compiler.pytorch.backend.frontend_lowering` module that does the
common lowering for all backends. The individual compiler backends
`npcomp.compiler.pytorch.backend.{refjit,iree}` now accept a loosely
defined "TCP + scalar code" IR mix that will be formalized in the
future as the interface to codegen backends.
This also required adding a small pass
`npcomp-iree-backend-lower-linkage` which adds `iree.module.export` onto
functions, and layering that into the frontend flow. The pass doesn't
require a C++-level dependency on IREE, which is nice for now. TBD how
we are going to handle lists (we hope we can get away with sneakerneting
some td files and relying on loose IR compatibility).
Running through IREE requires the ability to import `iree.compiler` and
`iree.runtime`, which can be obtained as follows:
```
python3 -m pip install iree-compiler-snapshot iree-runtime-snapshot -f https://github.com/google/iree/releases/tag/snapshot-20210406.200
PYTHONPATH="${PYTHONPATH}:${MY_IREE_BUILD}/bindings/python/"
```
This patch makes it painfully clear that we don't have any e2e testing
harness to really plug into, and also don't have a usable Python API to
our compiler stack (something usable in a jupyter notebook).
That will be addressed in subsequent commits. We've been flying by the
seat of our pants with this `examples` directory that isn't subject to
any kind of testing or real usability concerns.
This revamps the TORCH_TO_TCF_PASSES to reflect the new layering that we
are doing in the compiler. See comments there for the layering.
Also adds `frontends/pytorch/examples/torchscript_tanh_e2e.py` as an
"example". E2E testing story TBD (want to get IREE working first).
This pass incorporates torch.type_bound info and also removes NoneType
returns (eventually it will rewrite tuple types too, but can't yet
because !basicpy.TupleType doesn't track element types).
Recommend looking at adjust-calling-conventions.mlir first to see what
it is doing, and holding your nose for the implementation of the pass.
I decided to implement this with the conversion framework, because it
gives us *some* goodies for type conversion -- mainly avoiding large
amounts of tricky RAUW dances. Unfortunately, the conversion framework
isn't a perfect fit for a couple reasons:
- the incorporation of torch.type_bound is a context-sensitive rewrite
(requires looking at the arg attr, not just the type).
- NoneType conversion is 1->0, which requires some special handling
- (not implemented yet) 1->N tuple type conversions require special
handling.
It's a little bit scary, but on balance doing it the other way would
have its own downsides.
We already had the `promoteTrailingOutTensor` flag, but weren't using
it. A inplaceVariantKernelName flag needed to be added.
This change is a little dissatisfying, as the conversions done by the
RecognizeKernelsPass are currently non-orthogonal. In particular,
`kDropResultAndAliasArg0` probably won't work as intended if mixed with
these (we probably need to promote kDropResultAndAliasArg0 to not be an
arg-level thing anyway, as we have done with promoteTrailingOutTensor).
This involved adding a new op `numpy.overwrite_array`.
```
numpy.overwrite_array %arg2 overwrites %arg0 : tensor<2x3xf32>, !numpy.ndarray<[2,3]:f32>
```
This models the destructive update behavior. Note that in the above op,
we cannot simply RAUW %arg0 with a suitably conveted %arg2 (for example,
%arg0 might have uses that are not dominated by %arg2, or might have an
alias relation with some other array in the program). In general, we
need a pass analogous to "SSA-formation" which knows how to see through
these to uncover an underlying tensor program.
Also, add tanh_out_e2e.py/div_inplace_e2e.py and fix some bitrot in
refjit.py which is my running example I'm trying to get working.
* A bit gross because I took the chance to upgrade all of the backend bits to the new MLIR Python bindings and we still co-mingle the old and new for now.
* Since the Python created PassManagers are configured for explicit nesting, I had to upgrade some of the pass pipelines to be explicit.
* The demo in mul_maximum_e2e.py now compiles, runs through PyTorch and through the JIT, prints and asserts the same results.
* I am not claiming that this is the prettiest API in this patch: consider that this is just directly using low-level APIs and there should be an intervening high level API.
Sean Silva
Phoenix Meadowlark
Stella Laurenzo