Also contains the following changes:
- Remove derefineOp canonicalizer because it's not safe.
- Support for optional tensor and list tensors in reduceOpVariant. This
only works for some special detected and easy to handle cases. For list,
it covers the case list is got from a `ListConstruct`. For optional, it
covers the case optional is constructed from a `DerefineOp`.
- Remove the `inferReturnTypes` for `FromBuiltinTensorOp` because it's
not safe to deduce types from the input. For example, a built-in tensor
of i8 could be converted to si8 or ui8. It's better to let the user
specify the return type explicitly.
A few remain in examples/docs that will be naturally be updated in due
time.
This regresses the list support and the general direction of more widely
supported control flow, lists/dicts/globals that we were going for with
the TorchScript path. The idea is that we are deferring that work to
make torch-mlir a very clean standalone thing. We will reboot it,
probably using some of the tools of iree_pydm to make it simpler, and in
a more natural place (such as an iree-torch repo that depends on IREE and
torch-mlir to build a working PyTorch frontend solution for IREE -- it
was really weird that npcomp depended on IREE).
`tools/torchscript_e2e_test.sh` is all green.
This needs a few passes I put into torch-mlir/lib/RefBackend (not to be
confused with `npcomp/lib/RefBackend`, which will soon be deleted).
For the sake of review, since this brings together a lot of things, I
split this into its own commit. I temporarily commented out some "list"
stuff that we are going to remove as part of the torch-mlir refocus.
Implements a python package for taking a `torch.fx.GraphModule`
and turning it into MLIR in the `torch` dialect that can then
be further compiled by `npcomp`. This is a WIP, so the coverage
of PyTorch operations is very small.
Fixes https://github.com/llvm/mlir-npcomp/issues/311
The key change is that TorchPlugin is folded into
`torch_mlir.dialects.torch.importer.jit_ir` (it imports the PyTorch
JIT's IR, so that's a good, scoped name for it).
The CMake option `-DTORCH_MLIR_ENABLE_JIT_IR_IMPORTER=OFF` disables it,
which allows building without a PyTorch native dependency.
After this change, there are now just two subdirectories in the
`python_packages` directory in our combined build:
- `npcomp_core` with all the npcomp stuff
- `torch_mlir` with all the `torch-mlir` stuff.
The combined `torch_mlir` build will be packaged for use by `pip`.
There isn't anything super useful for wider use in `npcomp_core` so for
now we aren't going to package that one.
Our new dependency management solution relies:
- on the C++ side with the public iree-dialects project, which we
include and are using as representative of some missing upstream
ops (so we treat them "as if" they were upstream, with the hope of
upstreaming them after some codevelopment has happened)
- on the Python side, with simple PYTHONPATH manipulation or installed
Python packages. No CMake stuff required.
It just contained the e2e testing framework. We now fold it into the
main project to reduce complexity.
- `frontends/pytorch/python/` -> `python/torch_support`
- `frontends/pytorch/e2e_testing -> e2e_testing`
- `frontends/pytorch/examples -> examples`
- `frontends/pytorch/test` -> `python/test`
- `torch_mlir_torchscript` python module -> `npcomp_torchscript`
- `torch_mlir_torchscript_e2e_test_configs` python module ->
`npcomp_torchscript_e2e_test_configs`
This also changes the license of a handful of files from the
"pytorch-style" license to the regular LLVM/npcomp license. The only
people who committed to those files were myself and Yi.
This moves the bulk of the Python code (including the Torch interop)
from `frontends/pytorch` into `torch-mlir/TorchPlugin`. This also
required reconciling a bunch of other Python-related stuff, like the
`torch` dialects.
As I did this, it was simpler to just remove all the old numpy/basicpy
stuff because we were going to delete it anyway and it was faster than
debugging an intermediate state that would only last O(days) anyway.
torch-mlir has two top-level python packages (built into the
`python_packages` directory):
- `torch_mlir_dialects`: `torch` dialect Python bindings (does not
depend on PyTorch). This also involves building the aggregate CAPI for
`torch-mlir`.
- `torch_mlir`: bindings to the part of the code that links against
PyTorch (or C++ code that transitively does).
Additionally, there remain two more Python packages in npcomp (but
outside `torch-mlir`):
- `npcomp_torch`: Contains the e2e test framework and testing configs
that plug into RefBackend and IREE.
- `npcomp_core`: Contains the low-level interfaces to RefBackend and
IREE that `npcomp_torch` uses, along with its own
`MLIR_PYTHON_PACKAGE_PREFIX=npcomp.` aggregation of the core MLIR
python bindings. (all other functionality has been stripped out)
After all the basicpy/numpy deletions, the `npcomp` C++ code is now very
tiny. It basically just contains RefBackend and the `TorchConversion`
dialect/passes (e.g. `TorchToLinalg.cpp`).
Correspondingly, there are now 4 main testing targets paralleling the
Python layering (which is reflective of the deeper underlying dependency
structure)
- `check-torch-mlir`: checks the `torch-mlir` pure MLIR C++ code.
- `check-torch-mlir-plugin`: checks the code in `TorchPlugin` (e.g.
TorchScript import)
- `check-frontends-pytorch`: Checks the little code we have in
`frontends/pytorch` -- mainly things related to the e2e framework
itself.
- `check-npcomp`: Checks the pure MLIR C++ code inside npcomp.
There is a target `check-npcomp-all` that runs all of them.
The `torch-mlir/build_standalone.sh` script does a standalone build of
`torch-mlir`.
The e2e tests (`tools/torchscript_e2e_test.sh`) are working too.
The update_torch_ods script now lives in
`torch-mlir/build_tools/update_torch_ods.sh` and expects a standalone
build.
This change also required a fix upstream related to cross-shlib Python
dependencies, so we also update llvm-project to
8dca953dd39c0cd8c80decbeb38753f58a4de580 to get
https://reviews.llvm.org/D109776 (no other fixes were needed for the
integrate, thankfully).
This completes most of the large source code changes. Next will be
bringing the CI/packaging/examples back to life.
This creates the `external/torch-mlir` directory as an
LLVM_EXTERNAL_PROJECTS-compatible project (analogous to
`iree-dialects`) and completes movement/rename of all pure MLIR C/C++
compiler code into there. The next step will be to move all the Python
code / code that links/includes PyTorch C++ code (which currently lives
in `frontends/pytorch`) into a subdirectory here.
I call this "earthmoving" because it is mostly mechanical changes and
renames. As a quick summary (we can change this down the road easily)
- C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch`
- CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet`
- preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_`
- CMake `NPCOMPFoo -> TorchMLIRFoo`
The goal of this is to create a standalone project creating a center of
mass for entry into the MLIR ecosystem from PyTorch, suitable in scope
for eventual inclusion/ownership in PyTorch. The idea is that
`external/torch-mlir` will some day be pulled out into its own
repository, and then npcomp will simply pull it in as a submodule.
Layering-wise, what lives in `torch-mlir` lowers code from PyTorch
(currently TorchScript, but TorchFX or pytorch/xla-style tracing are
possible extensions) down to what we have been calling the "Torch
backend contract" which is cleaned up IR (inlining, simplifcation,
conversion to value tensors, ...) entirely in the `torch` dialect. This
is the branching off point for further lowering, of which npcomp takes
one opinion (outside `torch-mlir` of course!), namely the
`TorchConversion` dialect/transforms which lower to IR suitable for IREE
and other linalg-on-tensors based lower-level compilers.
Summary of changes:
- move `{include,lib,test}/Dialect/Torch` into `torch-mlir`
- move relevant parts of CAPI into `torch-mlir`.
- leave a few things related to the `torch-mlir` Python build commented
out, which should be resolved in a subsequent change.
This plumbs through a vertical slice of support for lists.
The main chunk of new code here is AnnotateABIPass which captures the
program signature at the Torch backend contract layer, right before we
start `TorchConversion`. The `TorchConversion` lowering process is lossy
w.r.t. types, so it's necessary to do this for all targets in general.
Like using `!iree.list` directly, we use IREE's ABI annotation
representation for this, although there is nothing very IREE-specific
about it (see
https://github.com/google/iree/blob/main/docs/developers/design_docs/function_abi.md)
We change `ListLiteralModule_basic` to use `!torch.int` because IREE
doesn't support f64 yet (and we don't yet have a way for users to say
that they want `!torch.float` to lower as f32).
Recommended review order:
- AnnotateABIPass and tests
- Arg marshaling in npcomp_backend.py and `iree.py`
- Updates to `list_programs.py` / `xfail_sets.py`
- Moving DeleteDeadIREEListsPass to Backend/Common, so that backends
that don't support lists can use it. RefBackend uses that pass, for
example.
This contains the following changes:
- Fix optional knowledge propagation. The initial knowledge should
always be NotNone for the operations we implemented.
- Add Folder for `prim.dtype`
We were not filling the `outs` with the neutral element of the
reduction, which resulted in reading uninitialized values (we were
getting lucky that sometimes the uninitialized buffers were all zero's).
Also,
- Slight tweak to error messages in the e2e framework.
Yi Zhang
Sean Silva
Anush Elangovan
powderluv
Ramiro Leal-Cavazos
George Petterson
Stella Laurenzo