Commit Graph

84 Commits (d3c08376affe5343d4ffd344023186f5c6877637)

Author SHA1 Message Date
Maksim Levental eecbf0bab6
Eager mode description in the README and small example and ResNet18 example. (#707) 2022-03-28 23:54:06 -05:00
Sean Silva 52c330cca2 Fix some more uses of "e2e" that I missed in the last commit. 2022-03-28 19:09:56 +00:00
Sean Silva e59a91620a Tidy up README and examples
- update diagram to use the name "Eager Mode" instead of
  `torch.dispatch`, which wasn't a very accurate name
- rename `resnet_inference.ipynb` to
  `torchscript_resnet_inference.ipynb` - this is in preparation to LTC
  and Eager Mode versions
- remove mention of TorchFX - turns out that all TorchFX modules are
  actually scriptable modules, so there is literally "zero code" vs
  using the TorchScript path
- remove LazyTensorCore example, and instead point at the current
  in-development `torch_mlir_ltc_backend` branch.

Note: there were actually some pretty useful utilities built out in the
examples directory, but they now live inside the Eager Mode
`python/torch_mlir/eager_mode/ir_building.py` (and need to be rolled
into a proper home with the upcoming rewrite of our top-level
`torch_mlir.compile` API).
2022-03-28 10:05:58 -07:00
Ahmed S. Taei 8383497704
[NFC] Rename external -> externals (#699) 2022-03-26 09:12:27 -07:00
Prashant Kumar 126dac3ded Cmake build commands fix.
The external projects torch-mlir and torch-mlir-dialects should be
placed inside double quotes.
2022-02-16 20:46:53 +05:30
Yi Zhang 869daf3c22 Add TMTensor dialect to torch-mlir
This is intended to explore support for non-structured ops that can't
be modeled by Linalg dialect. `tm_tensor.scan` and `tm_tensor.scatter`
are added as the first such ops. The dialect should aim to be
upstreamed in the future.
2022-02-15 16:45:38 -05:00
Sean Silva 76728491cb
Polish readme. 2022-01-19 13:19:46 -08:00
Sean Silva a75ae82530
README: Move Project Communication section higher
People might want to lurk on discord, and not even check out the code yet.
2021-11-08 11:18:00 -08:00
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
Boian Petkantchin 393bd211c7 In readme build instructions force CMake to use Python virtual env
By default CMake is quite happy to use any Python that it finds
to fit the version requirements.
2021-10-14 10:27:56 -07:00
Sean Silva 00e94c70ef Remove packaging info for now.
Need to do manylinux build for it to work across many machines.
2021-10-07 01:38:42 +00:00
Sean Silva de8ea59d52 Fix feedback from Anush. 2021-10-07 01:28:13 +00:00
Sean Silva a157d6c946 Add instructions for installing from packages. 2021-10-07 01:20:49 +00:00
Phoenix Meadowlark c3e0a1e1dc Add shell markdown formatting to README.md 2021-10-04 11:59:25 -07:00
Ramiro Leal-Cavazos 3aef3a9e30 Remove duplicate example + fix README typo 2021-09-30 13:23:25 -07:00
Sean Silva 05a01b10b3
Add example deps for torchscript_resnet18_e2e.py 2021-09-30 12:48:35 -07:00
powderluv 649d6e4f28 Update README.md 2021-09-29 10:14:20 -07:00
Sean Silva 4a64fa28e7
Add MLP1Layer IR sample. 2021-09-29 09:20:04 -07:00
powderluv 36ac87dd83
Update Readme with examples (#336) 2021-09-28 21:43:38 -07:00
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 2b99c8b990
Wordsmith readme 2021-09-28 13:53:33 -07:00
powderluv b55baf508a
Updates to Readme.md (#334) 2021-09-28 13:50:25 -07:00
Sean Silva de1a8c92f7 First pass cleaning the README 2021-09-27 17:20:50 -07:00
Anush Elangovan 06559efe94 Update diagram and Readme.md 2021-09-23 14:34:02 -07:00
powderluv 81a9cf7800 Update README.md 2021-09-23 11:50:02 -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 496051163f Rename npcomp-run-mlir to refback-run
This better represents its limited scope. This was causing confusion --
people were feeding it higher level ops that require frontend lowering.
2021-08-03 18:24:24 -07:00
Sean Silva 719f0cd709 Minor wordsmithing to README 2021-08-03 14:59:58 -07:00
Sean Silva a153cf4ef2 Refresh Dockerfile and instructions.
Related to https://github.com/llvm/mlir-npcomp/issues/266
2021-08-03 14:54:39 -07:00
Sean Silva 453e29ea05 Add E2E support for tests with heavy dependencies (heavydep tests).
The tests use the same (pure-Python) test framework as the
normal torchscript_e2e_test.sh, but the tests are added in
`build_tools/torchscript_e2e_heavydep_tests` instead of
`frontends/pytorch/e2e_testing/torchscript`. Any needed dependencies can
easily be configured in generate_serialized_tests.sh.

We add an initial machine translation model with a complex set of
dependencies to seed the curriculum there. I verified that this model
gets to the point of MLIR import (it fails there with a segfault due to
not being able to import the "Any" type).

This required moving a few files from the `torch_mlir` Python module
into multiple modules to isolate the code that depends on our C++
extensions (which now live in `torch_mlir` and
`torch_mlir_torchscript_e2e_test_configs`) from the pure Python code
(which now lives in `torch_mlir_torchscript`). This is an entirely
mechanical change, and lots of imports needed to be updated.

The dependency graph is:
```
       torch_mlir_torchscript_e2e_test_configs
                  /              |
                 /               |
                /                |
               V                 V
torch_mlir_torchscript       torch_mlir
```

The `torch_mlir_torchscript_e2e_test_configs` are then dependency-injected
into the `torch_mlir_torchscript` modules to successfully assemble a
working test harness (the code was already structured this way, but this
new file organization allows the isolation from C++ code to actually
happen).  This isolation is critical to allowing the serialized programs
to be transported across PyTorch versions and for the test harness to be
used seamlessly to generate the heavydep tests.

Also:
- Extend `_Tracer` class to support nested property (submodule) accesses.

Recommended review order:
- "user-level" docs in README.md
- code in `build_tools/torchscript_e2e_heavydep_tests`.
- changes in `torch_mlir_torchscript/e2e_test/framework.py`
- misc mechanical changes.
2021-08-03 14:09:56 -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
Yi Zhang 6fbf94f0b2 Update readme and scripts for setting the new PYTHONPATH
Add scripts for generating .env and update instructions in README.
2021-07-28 15:06:40 -04:00
Stella Laurenzo 2dbab50444
Rework the python build to a static assembly of MLIR+NPCOMP (#251)
* Adapt to python build system updates.

* Bump llvm to 310c9496d80961188e8d8f8ad306cdf44bd7541f (includes python build updates)
* Adds refback C-API.
* Re-layers all python builds.
* Rework CI.
2021-07-27 16:10:10 -07:00
Sean Silva c289d83407 Fix README.md
Use the new `tools/torchscript_e2e_test.sh`.

Also, fix a few whitespace/comment issues.
2021-07-02 10:57:21 -07:00
Sean Silva d5108b9dc1 Add IREE support in TorchScript e2e tests.
- 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).
2021-06-30 16:19:25 -07:00
Sean Silva 390d39a96c
README: mention installing nightly pytorch. 2021-04-27 11:37:09 -07:00
Sean Silva 3f9760dc33
Add communication channels to README 2021-04-02 13:00:47 -07:00
stephenneuendorffer f96c05abd4
Update README.md
Fix more links
2021-01-26 10:02:25 -08:00
stephenneuendorffer 142de3bab3
Update README.md
Links were broken
2021-01-26 09:52:46 -08:00
Stella Laurenzo f6d7ee06ef Make torch_mlir compatible with binary PyTorch installations.
* This has been anticipated for a long time in that it is quite hard to keep C++ binary compatibility across a system landscape as diverse as PyTorch, LLVM, and this project. This is why we based the PyTorch extension on the MLIR and NPCOMP C APIs only: that is the only sane linkage story for the entire matrix.
* Removes the few LLVM'isms in torch_mlir that had snuck in, using either STL or PyTorch support utilities. The new rule here is that LLVM C++ includes are forbidden at this level and (as stated in the design), torch_mlir should use the PyTorch runtime and support libraries (not introduce an incidental C++ dependency on LLVM).
* Also deletes mnist-playground as it was proving impossible to keep the grid of PyTorch vs system ABI divisions functioning. I am open to a less drastic course here (optional/disabled by default?)
* This gets us pretty close to just using PyTorch's extension builder API, which will be nice for distribution (i.e. it integrates well with the PyTorch ecosystem for deployment). I ended up just simplifying the in-tree CMake support for now.
* Fixes #138
2020-12-14 09:51:00 -08:00
Sean Silva 45ca371129 cmake_configure.sh: Add mlir native modules to PYTHONPATH
Also, update README.md to use the canonical .env file written by
`cmake_configure.sh`.
2020-11-20 17:29:57 -08:00
Stella Laurenzo 6850295ec5 Teach cmake how to find the installed PyTorch.
* In most situations, this eliminates the need to explicitly set a path to the Torch cmake files.
* Also upgrades to new Python3 find package. (should eliminate 2.x mismatches)
* Since PyTorch is located by asking Python where it is, this eliminates a lot of causes of mismatch. (one source of truth)
2020-11-13 17:19:25 -08:00
Stella Laurenzo 59b7c559f4 Tweak build flags for efficiency and document building without a container.
* Enables -gsplit-dwarf for both LLVM and NPCOMP, reducing the occurrence of the ~GB scale binaries.
* CMake shared linking seems incompatible with this, so shared objects are still "too big" but there are few of them.
* Reduces disk thrash on clean/install of everything.
2020-11-03 13:46:46 -08:00
Stella Laurenzo 9e52f6235b More progress on PyTorch acap device capture.
* Now gets far enough to capture batch_norm.
* Has some issues still with in-place ops.
* Can materialize constants.
* Includes an upgrade to PyTorch nightly, which has important bug fixes for fallback and boxed kernel dispatch.
* Fixes #78, #79, #80.
* Will do more testing in a follow-up once further bugs are fixed that facilitate getting at the other features.
2020-10-15 21:43:21 -07:00
Sean Silva d6b05c507a Fix up the docker script / instructions after attempting to use it. 2020-10-09 10:27:06 -07:00
Stella Laurenzo 51d51241b4 Add scripts/documentation for VSCode setup with a docker dev image.
* Forks a subset of my shell functions into docker_shell_funcs.sh, specifically needed to create docker images that run as yourself.
* Extends the readme with the three command bootstrap to get a dev container running.
* Step by step instructions for configuring VSCode for Intellisense in either npcomp or LLVM.
* Changes LLVM config options to enable tests. This setup is now suitable for upstream changes as well without rebuilding.
2020-10-07 21:27:20 -07:00
Stella Laurenzo 47c3a9f461 Add docker image/instructions for building against pytorch 1.6. 2020-09-24 17:40:25 -07:00
Stella Laurenzo 361abebb51 Update README to reference published docker tag. 2020-09-16 23:12:05 -07:00
Stella Laurenzo 678989a321
Update docker, instructions and some fixes for the pytorch 1.3 build. (#45)
* Includes pybind11 directly (for some reason using the pytorch helper header for this depends on a source file not in the image).
* Installs nnpack into the image.
* Installs new-clang and LLD and configures environment to use it (otherwise, link time is terrible).
* Fixes a gcc compile error (in the off chance you build with default gcc compiler).
* Tests are failing based on some dialect registration stuff that must not have been factored correctly. Will followup with a fix.
2020-09-16 21:57:46 -07:00