Commit Graph

170 Commits (127c7d8e27ede211af77392b5277ba06fb515fb7)

Author SHA1 Message Date
Prashant Kumar 127c7d8e27 Add lowering of `torch.log` op
The lowering of `torch.log` op has been added.

Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
2021-11-02 21:18:00 +05:30
George Petterson 6dde5b347e Add rsub 2021-11-02 09:56:48 -04:00
Prashant Kumar 53b4275ef5 Add lowering of `aten.Int.Tensor` op.
The lowering of `aten.Int.Tensor` op has been added.
The changes has been made as a part of `convert-torch-to-linalg` pass.

Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
2021-11-01 21:58:08 +05:30
Sean Silva c46d48f9f5 Make error reporting a bit better.
- Split out TOSA in the CI.
- Add summary of unexpected test outcomes. This works better when there
  are many XFAIL'ing tests, as it only prints out the error_str on
  FAIL, not on XFAIL. Example here:
  https://gist.github.com/silvasean/c7886ec7b3d35c21563cb09f7c3407da
2021-10-28 13:20:16 -07:00
Sean Silva b02b65cf6e Fix for upstream Torch change.
After https://github.com/pytorch/pytorch/pull/65967 the `graph()` method
is only available on `torch::jit::GraphFunction` now.

Fixes https://github.com/llvm/torch-mlir/issues/388
2021-10-28 11:12:05 -07:00
Prateek Gupta c33a2ca952 [TORCH][MLIR] Add E2E support for aten.permute.
This commit adds lowering of aten.permute to linalg.generic operation.

Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
2021-10-28 10:25:26 -04:00
stephenneuendorffer 614b889dc6
Enable python extensions when building out of tree (#363) 2021-10-27 17:04:12 -07:00
Sean Silva 30df2ec71b Add min/max/clamp support.
Part of #380

Also
- BoolType is not considered as Scalar
- e2e framework fixes for nan handling
- `tu.rand(..., low=, high=)` support
- delete unused variable (fix warning)
- Add IouOfModule from #380 to e2e test suite (this is a common
  calculation in vision models)

 Your branch is ahead of 'origin/main' by 1 commit.
2021-10-27 13:29:21 -07:00
Prashant Kumar 5009cbf55c Add lowering of aten.matmul op.
Lowering of `aten.matmul` op is added from torch to linalg dialect.
The different cases correspond to
https://pytorch.org/docs/stable/generated/torch.matmul.html.
TODO: Broadcasting in case of batch-matmul is yet to be taken care of.

Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
2021-10-26 12:45:09 -04:00
Boian Petkantchin e276dbbaa6
Add aten::gelu lowering (#374)
* Print more exception info on error during test execution

* Fix formatting

* Add aten::gelu lowering

Co-authored-by: Boian Petkantchin <boian@nod-labs.com>
2021-10-25 16:16:01 -07:00
Sean Silva a6943ef90c Rename `tosa-to-linalg-on-tensors` to `tosa-to-linalg`
The pass name changed upstream.
2021-10-25 20:43:54 +00:00
Stella Laurenzo a23d77100b Set some wheel building optimization options.
* Also adds a requirements.txt and updates docs to reference it versus stringy pip install.
* Adds doc with instructions on creating a wheel.

Fixes #370
2021-10-25 18:30:53 +00:00
Stella Laurenzo fe69bb339c
Bump llvm-project to 3d92722f74993969243d1400bc3257ca3d03902f. (#369)
* Picks up Python configure changes (was pinned to a bad intermediate commit).
* Uses the new mlir_configure_python_dev_packages() to ensure CMake python is found consistently.
* Fixes the JIT importer to build as a MODULE vs SHARED (needed for linking to Python as a module, per config changes).
* Adds some notes to the README to help folks build a smaller set focused just on this project.
2021-10-21 21:09:00 -07:00
Yi Zhang abfaf8c577 Add aten.ne.bool to make CI pass 2021-10-21 14:45:41 -04:00
George Petterson 8853dfbc74 Add broadcast 2021-10-19 13:33:31 -04:00
Yi Zhang a459e09ab7 E2e support for aten.softmax.int and aten.embedding
- Added a DecomposeComplexOps pass to decompose complex torchOps.
- Refactored `visitAtenArgmaxOp` and `visitAtenAnyDimOp` to
`visitReductionAlongDimIntOp`.
- Moved some helper functions into
torch-mlir/Dialect/Torch/Utils/Utils.h to be shared by multiple files.
- Added support for f64 tensor as argument and return types.
2021-10-18 17:57:45 -04:00
dan 7750d2173a add argmax lowering
Add argmax lowering from torch to linalg
2021-10-13 14:31:16 -04:00
Sean Silva 19e9fc4ef1 Bring some more order to the e2e error reporting situation.
- Move `run_pipeline_with_repro_report` to a more common place, and use it
  consistently
- Attach a `torch.debug_module_name` to the enclosing `builtin.module`
  op to allow for self-contained error reporting (not needing to pass
  the names around.
- Remove redundant error reporting in linalg_on_tensors_backend.py and
  tosa_backend.py (their respective backend abstract base classes now
  take care of the error reports themselves)
- Save off original value of sys.stderr, rather than always resetting to
  `sys.__stderr__`. This is just more hygienic, and allows nesting if
  desired.
2021-10-08 13:00:12 -07:00
Sean Silva 0c5c84d63d Add a basic TOSA E2E backend.
We lower through linalg-on-tensors and use RefBackend to run it.
This adds enough support for a "tanh" op. Adding more ops should be
fairly mechanical now that things are wired up. Run with:
```
./tools/torchscript_e2e_test.sh -c tosa
```

The backend structure is very similar to linalg-on-tensors based E2E
backends and is a nice parallel (see `tosa_backend.py`). Actually, this
forced a nice refactoring to the layering here. We removed
`torchscript-module-to-linalg-on-tensors-backend-pipeline` and instead
require separately running
```
torchscript-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline
```
This highlights the step that lowers to the "torch backend contract"
of cleaned up `torch` dialect ops is a critical step in the lowering.
Going forward, that is the key load-bearing contract of the torch-mlir
project, not the linalg-on-tensors backend contract.

Recommended review order:
- `TorchToTosa.cpp` / `TorchToTosa/basic.mlir`
- `python/torch_mlir_e2e_test/torchscript/configs/tosa_backend.py` and
  the new `utils.py` file there.
- `python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py` and
  `abc.py` in that directory for the TOSA backend e2e interface.
- other misc mechanical changes
2021-10-08 09:59:45 -07:00
dan 2e1498ad11 add i64 support to refbackend 2021-10-05 15:12:44 -04:00
Yi Zhang 98ba255288 E2e support for layernorm. 2021-10-04 14:15:13 -04:00
Sean Silva f0ed9e2d8d Fix update_torch_ods.sh 2021-10-01 17:47:25 +00:00
Sean Silva 5b6902e31c Dual license the torch-mlir project.
This commit (with approval from all contributors) dual licenses
the torch-mlir project under both the standard LLVM license and the
standard PyTorch license. This will facilitate moving code between
torch-mlir and the two upstream projects.

The standard file comment is now:

```
// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
```

See `LICENSE` in the project root for the terms of both licenses.
2021-10-01 10:46:08 -07:00
Sean Silva 5917f1dc47 Remove last mentions of IREE. 2021-10-01 17:28:07 +00:00
Yi Zhang 89225b0cd8 Add BertSequenceClassification model to e2e
Use torch tracing to get the module because the original model is not
TorchScriptable out of box.
2021-09-30 13:30:29 -04:00
Ramiro Leal-Cavazos b59f2cb673
Implement the lazytensor package (#331)
Implement the `lazytensor` python package for converting
lazy computations captured by the Lazy Tensor Core into MLIR.
This PR also fixes a few things with `torchfx` and its example
2021-09-28 17:25:06 -07:00
Sean Silva 4fad753073 Move external/torch-mlir to the root of the repo. 2021-09-27 17:11:08 -07:00
Sean Silva d8f603a4e5 Remove old stuff in prep for move-to-root. 2021-09-27 17:11:08 -07:00
Sean Silva 404bd74ddf Port the bulk of the remaining code to torch-mlir
This leaves no real code outside torch-mlir.

This also renames the "npcomp backend contract" to "linalg on tensors
backend contract" as the name of the abstraction layer that RefBackend
(IREE too) accepts.
2021-09-27 12:48:33 -07:00
Yi Zhang cd7053dfde Add runtime check 2021-09-24 12:01:36 -04:00
Yi Zhang c9cc4cb2e9 Add i64 tensor argument support and bring back GatherModule_basic 2021-09-24 12:01:36 -04:00
Sean Silva 01c6c54dd8 Fix dependency. 2021-09-23 21:39:31 -07:00
Sean Silva 2213584c4f VerifyBackendContract -> VerifyLinalgOnTensorsBackendContract
This moves it into TorchConversion since it is only needed there.

This removes the Backend/ directory.
2021-09-23 21:39:31 -07:00
Sean Silva 1a0b953ea7 Eliminate almost all mentions of IREE.
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).
2021-09-22 16:06:38 -07:00
Sean Silva 8779d920b2 Remove "refjit" terminology.
We now use RefBackend/refbackend consistently.
2021-09-22 15:41:23 -07:00
Sean Silva a25163fbfa Remove old RefBackend
It is superceded by the new one.
2021-09-22 15:33:28 -07:00
Sean Silva f9c48d0b89 Bring up new RefBackend.
`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.
2021-09-22 14:20:22 -07:00
Sean Silva 6d8e7f1bb1 Implement Python relayout from #311
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.
2021-09-21 09:29:40 -07:00
Sean Silva 5f3b1ce0b8 Fold torch_mlir_dialects python package into `torch_mlir`.
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.
2021-09-17 09:27:49 -07:00
Sean Silva 0eb767ea45 Remove frontends/pytorch directory.
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.
2021-09-17 09:27:49 -07:00
Sean Silva d94d6800fa Bring CI back to life.
This brings back `check-npcomp-all` and the refbackend e2e tests
coverage.
2021-09-16 12:07:32 -07:00
Sean Silva b6be96d722 [torch-mlir earthmoving (2/N)] Python code movement.
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.
2021-09-15 13:40:30 -07:00
Sean Silva 28a7738189 [torch-mlir earthmoving (1/N)] C/C++ code movement.
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.
2021-09-10 21:44:37 -07:00
Sean Silva a7252f9a06 Add basic support for lists.
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.
2021-09-09 20:48:55 -07:00
dan d7320f3bda fixed some python imports
Change required to enable
./tools/torchscript_e2e_test.sh --config=iree
2021-08-27 14:58:45 -04:00
Stella Laurenzo 4148f88576 Merge npcomp and mlir python namespaces.
* Now the parts of the MLIR API are directly exported under the npcomp module (i.e. `npcomp.ir`, etc).
* Has required fixes for https://reviews.llvm.org/D108489
* Deletes npcomp.tracing vs fixing it because it was a very early experiment that will not be carried forward.
* This makes the npcomp python distribution completely standalone and separate from an mlir installation.
* Makes most of npcomp itself relocatable for future use as a library.
* Most things are a namespace package now. In the future we can s/torch_mlir/npcomp.frontends.torch/ and have it layer properly.
2021-08-22 21:00:42 -07:00
Sean Silva 902c2e579b Add resnet inference jupyter notebook.
This takes the example from torchscript_resnet18_e2e.py and puts it into
a slightly cleaned up notebook form.

It's still a little rough around the edges. Areas for improvement:
- Installation / setup.
- API usability.

Also,
- Add `npcomp-backend-to-iree-frontend-pipeline` since we will be adding
  more stuff there.
- Slight cleanups.
2021-08-09 14:34:43 -07:00
Sean Silva f168cacd6d Remove TCF and TCP.
These were legacy concepts that are now superceded by direct Torch to
linalg-on-tensors lowering. These were based on some very early thinking
related to the layering of frontends vs codegen, which is now obsolete
because:
- We expected a lot more centralization at the frontend (TCF) level. It
  turns out that frontend needs really vary a lot, and there is no grand
  unifying TCF dialect plausible. The additional layer isn't worth it.
- Linalg-on-tensors obsoletes the primary need for TCP. There are still
  a few things not representable with linalg-on-tensors, but the support
  is growing and the whole "not included in linalg-on-tensors" direction
  needs to be rethought. Our TCP dialect didn't cover any of the
  actually important things in this space (such as sort, FFT, top-k,
  etc.).

See historical [slides](https://drive.google.com/file/d/1iljcpTQ5NPaMfGpoPDFml1XkYxjK_6A4/view) / [recording](https://drive.google.com/file/d/1jSPa8TwPKUt0WuLquGc8OgSUVYJHMvWZ/view)
for more details on the origin story here.

Their presence was confusing users too
[bug](https://github.com/llvm/mlir-npcomp/issues/248).

Also,
- Trim down npcomp-run-mlir testing. It was testing TCF to TCP
  lowering for the most part. The essential stuff is retained and
  rephrased with linalg-on-tensors. (we should probably rename it
  "refback-run" or something, as it is just a way to invoke RefBackend)
- test/Python/Backend/RefJIT/simple_invoke_numpy.py is XFAIL'ed. Our
  "anti-framework" direction seems to be the likely future path.
2021-08-02 12:08:39 -07:00
Stella Laurenzo 445472c51e Build packages for npcomp-torch.
* Adds a minimal setup.py for frontends/pytorch
* Makes npcomp-core export its headers and libraries
* Adds a script to build packages.
* Adds CI step to package and smoke test.
* Will need some more tweaks and coordination prior to deploying (version locking etc).
2021-07-29 19:58:59 -07:00
Stella Laurenzo cd44a35177
Bump llvm-project to 5b2e7f50a6798fd9b9c79d9d62fdebcd9e78525b. (#260) 2021-07-29 12:26:54 -07:00