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.
- TensorFromElementsOp -> tensor::FromElementsOp
- `cmpi "eq", ...` -> `cmpi eq, ...`. Same for `cmpf`
- syntax change for private func ops
- some changes to the python bindings
* Most updates are mechanical except:
* python/npcomp/__init__.py and python/NpcompModule.cpp: New init/registration bits to replace some automatic things being done in the old bindings. Also an annoying linkage hack that I'll need to triage next.
* NpcompModule.cpp: New python helpers for custom types and other hard to reach items (for the new bindings).
* PybindUtils.h: Extended type casting so that the local extension can directly exchange Mlir* C types.
* python/npcomp/dialects/*: Build support and ODS bindings for local dialects.
* mlir_utils.py: Defines an ImportContext to replace the old/bad "Helper" class that tracked locations, and insertion points. This has a number of methods on it that would be good candidates to think about better ways to do them upstream.
* Also hoisted a few stand-alone samples to dedicated unit tests as they covered important things.
* More cleanup can be done, but keeping this patch as mechanical as possible to stay in NFC land (this is big enough).
* Going through TODOs on the PyTorch side, this is a big cause of them (not being able to have constants for signed/unsigned).
* Added complex while in here since we're at the phase where it is better to just have things complete than partially done.
* Organizes the BasicPyOps.td file by function.
* Renamed `to_boolean` -> `as_predicate_value` (trying to consistently use "predicate" to refer to i1/low-level types and Bool/Boolean to refer to Python bool types).
* Incorporates source fixes.
* Uses upstream pybind11 detection logic.
* Patches CI.
* This may break the CI, which will need to be fixed manually in a followup.
* IREE doesn't have proper install support, so there is some temporary hoaky hacking in our CMakeLists.txt to shuttle some symlinks around.
* Reworked the original numpy e2e with IREE test to pipe through iree-translate.
* Removed all of the C++-level dependencies.
* Will generalize and apply to the PyTorch backend in a followup.
* 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.
* Exposes the op registry via a get_registered_ops method.
* Moves the aten dialect generation scripts in prep for integrating them with this facility.
* Still need to add a systematic mechanism for discovering gradient ops.
* Work needed on the various _ suffixed inplace ops.
* Other randoms still not mapped.
* Outside of this commit, I do have enough commented/reworked to roughly build but that will take another handful of commits to get going.
* Add a new python script to auto-generate ATen op ODS definitions.
* There is still some work on some of the ops to annotate correct types.
* The ODS is not actually included into the dialect yet, but I'd like to commit it so that we can track changes.
* Will reconcile this with the ops produced by the existing script in a followup. Still need to do some more iteration to reach parity.
* This extracts metadata from python invocations (nearly) sufficient to generate ODS and a Torch IR translation table for most of the ops.
* It also has the side effect of creating a data structure with meaningfully runnable examples suitable for an automated regression test.
* There are some ops that are sufficiently complex/weird (like _convolution) that we'll just manually handle those.
* See example output: https://gist.github.com/stellaraccident/60a58457b15e9184e224fa98a2658769
This patch adds a pytorch interface to npcomp. This interface is modeled
after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar
to a gpu device or the xla backend). Usage is intended to be something like:
dev = torch_mlir.mlir_device()
t0 = torch.randn((4,4), device=dev)
t1 = torch.randn((4,4), device=dev)
t2 = t0 + t1
t2_mlir = torch_mlir.get_mlir( t2 )
t2_cpu = t2.to('cpu')
In this case t2_cpu would contain the result of the computation, and t2_mlir
contains the mlir description of the computation. Note that this also
properly returns backward paths synthesized by pytorch. There are several
parts of this:
1) A tensor type (implemented by tensor.* and tensor_impl.*)
2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*)
3) a temporary IR (implemented by ir.cpp)
There is also a reference lowering directly from the ATen dialect to C
function calls consisting of two parts:
1) The driver that uses the IR to generate MLIR, run Passes and compile the
result using mlir::ExecutionEngine (implemented by jit.cpp and
mlir_gen.cpp)
2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations
are implemented by callbacks into the torch C++ libraries.
Some aspects of this are known to be less than optimal, in particular:
1) There's some function definitions that don't live in the file corresponding
to their declaration.
2) More aspects of this (e.g. the IR) seem like they should be automatically
generated.
3) It's unclear to me how much of the 'IR' is actually necessary, or whether
MLIR could be created on the fly.
Note that this code is licensed in a way similar to pytorch, with the
intention that eventually (when npcomp reaches some maturity) it should be
pushed there. (see frontends/pytorch/LICENSE) The code is also structured
much closer to the pytorch coding style than the LLVM coding style.
* Enables e2e test.
* With what I've learned in upstream about test directory layout, I can consolidate most of the separate directories we have for these things. Will do that in a followup.
* Not pleased with the LLVM global initialization depends but serviceable for now.
* This starts to lay down the infra for reasoning about calls
* Adds the importer code to generate IR for function calls of compiler recognized static functions.
* Adds python bindings for invoking flow, HAL, and VM lowering pipelines.
* Adds pythong bindings for translating to VM module flatbuffer.
* Adds a new backend_test/iree directory and configure lit to find the IREE python rt bindings.
* Open code a simple_invoke.py that exercises the whole pipeline (need real APIs for a lot of this).
* Fails when invoking the function because I never implemented argument marshaling for scalars :(
* Plenty of stuff to do tomorrow.
* Conversions to std for numeric binary expressions, numeric to_boolean, and numeric comparisons.
* Added folders to constant ops to comply with requirements of the pass system.
* Extended the frontend with parameter/result annotation processing for primitives (can specify types for function arguments).
* Added (empty) directory/sources for IREEVM conversions. These are only enabled if IREE is enabled.
Sean Silva
Bairen Yi
Stella Laurenzo
Phoenix Meadowlark
stephenneuendorffer