Commit Graph

243 Commits (fd236b2c89158fa8cf4598ab4ca77c82da681f14)

Author SHA1 Message Date
Gleb Kazantaev c8b867b876
Added support for aten::norm.ScalarOpt_dim (#1774)
* Added support for aten::norm.ScalarOpt_dim

* Disable NormalizeModule_basic for linalg
2023-01-10 13:08:25 -05:00
Jiahao Li 8dc5d985eb
Add e2e support for aten logical or/and/xor/not ops (#1761) 2023-01-03 18:11:25 -08:00
Srirammaswamy a88e3766e8
Add E2E support for LeakyRelu and LeakyReluBackward ops (#1733)
Co-authored-by: srirammaswamy <srirammaswamy@gmail.com>
2023-01-03 08:30:16 -08:00
Ashay Rane ac780529b4
Revert e2e support for aten logical or/and/xor/not ops (#1757)
This reverts commit eaab9be207, since it
is causing the post-merge CI tests to fail, causing subsequent PRs to be
blocked.  Specifically, the tests
`ElementwiseAtenLogicalAndOpPromoteBroadcastModule_basic` and
`ElementwiseAtenLogicalXorOpPromoteBroadcastModule_basic` fail because
the oracle does not match the computed result.  This patch reverts the
commit to make the post-merge builds green again.
2022-12-29 21:01:06 -06:00
Shivam Gupta 2f45959f0d
Prelu lowering to linalg (#1712)
Prelu lowering to linalg
2022-12-28 08:51:33 +05:30
Jiahao Li eaab9be207
Add e2e support for aten logical or/and/xor/not ops (#1752) 2022-12-26 10:23:38 +08:00
Jiahao Li 60a139271d
Add aten.std.correction op and its decomposition (#1731) 2022-12-21 21:02:40 -08:00
Chi_Liu b2cefc0b64
[TOSA] Add aten.masked_fill.Tensor/Scalar support (#1735) 2022-12-21 08:56:07 -08:00
Abhishek Varma 66d7a412cb [RefineTypes] Fix knowledge dtype for `aten.embedding` op
-- The dtype of the result of `aten.embedding` should match that of
   the `weight` operand's (operand[0]) instead of hardcoding to f32.
-- This commit aims to provide a fix for the same.

Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
2022-12-20 19:56:12 +05:30
Prashant Kumar 8ba77ae2a5 Yapf Format `refbacked.py`. 2022-12-15 21:19:52 +05:30
Prashant Kumar 564403e3a1 Add float16 support in the refbackend.
This will require https://reviews.llvm.org/D139121 patch to go through.
2022-12-15 21:19:52 +05:30
Vivek Khandelwal d4862ec611 [MLIR][TORCH] Add e2e support for aten.var_mean op
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-12-12 15:46:54 +05:30
Vivek Khandelwal f783e19dcb Revert "[MLIR][TORCH] Fix mean and mean.dim op for large-sized inputs"
This reverts commit 55c7e66aa7.
2022-12-09 19:30:46 +05:30
Sean Silva 7731211d02 Remove eager_mode
This was an experimental attempt at rolling out own op-by-op executor
with `__torch_dispatch__`, but it proved difficult to make it robust.
Op-by-op execution is very easy to implement robustly now with the
PyTorch 2.0 stack, so we don't need eager_mode.

Downstream users were using eager_mode to implement lockstep numerical
accuracy debuggers. We implemented the same functionality with
TorchDynamo in https://github.com/llvm/torch-mlir/pull/1681 so now there
is not much reason to continue maintaining it.
2022-12-09 03:50:00 -08:00
Sean Silva e8511840c3 [cleanup] Use a single function pipeline for TOSA->Linalg
This should run faster and is overall clearer.
2022-12-08 09:02:38 -08:00
Sean Silva 69171c246a [RefBackend] Add elementwise fusion and buffer deallocation
This gives some decent improvements to memory consumption and latency of
testing. I would have expected buffer-deallocation to actually make a
big difference to the final process RSS but it doesn't appear to. Also
running buffer-deallocation later in the pipeline results in
miscompiles. I didn't have the time or interest to dig in deeper, but
something is off.

(numbers below are taken from a single run, but I did do a few runs to make
sure that the variance wasn't that great)

- Linalg-on-Tensors shows memory consumption improvements and some slight speedups.
```
./tools/e2e_test.sh -s -v -c refbackend
fuse=0 dealloc=0
RSS: 3071.33 MB
real    3m58.204s
user    6m22.299s
sys     0m51.235s
fuse=1 dealloc=0
RSS: 2515.89 MB
real    3m34.797s
user    5m56.902s
sys     0m44.933s
fuse=1 dealloc=post-bufferize:
RSS: 2290.25 MB
real    3m42.242s
user    6m0.560s
sys     0m46.335s
```

- TOSA ResNet18 gets significantly faster and uses significantly less memory.
```
time ./tools/e2e_test.sh -s -v -c tosa -f ResNet18
fuse=0 dealloc=0
rss 1328.56 MB
real    0m50.303s
user    0m55.355s
sys     0m12.260s
fuse=1 dealloc=0
rss 859MB
real    0m30.454s
user    0m35.551s
sys     0m11.879s
fuse=1 dealloc=post-bufferize:
rss 851MB
real    0m30.313s
user    0m39.889s
sys     0m11.941s
```

Big thanks to Ramiro for the methodology here for measuring the RSS with
`psutil`:
https://gist.github.com/ramiro050/5b5c2501f7389c008d9029210772c3a8
2022-12-08 03:14:42 -08:00
Ramiro Leal-Cavazos dd35488da5
build: update llvm tag to 798fa4b4 (#1684)
- Support for non-prefixed accessors has been removed. See:
  https://reviews.llvm.org/D136727
- Rename `operands` to `methodOperands` in `prim.CallMethod` since the
  name `operands` overlaps with a builtin method name. See:
  https://reviews.llvm.org/D136727
- Add passes in refbackend to lower memref.subview. See:
  https://reviews.llvm.org/D136377
- Replace `CopyToValueTensorOps` first in `RewriteViewLikeSubgraph` in
  maximize-value-semantics.

  The current implementation of the `RewriteViewLikeSubgraph` pass in
  maximize-value-semantics creates temporarily invalid IR. In
  particular, given a forward slice starting from a
  `CopyToNonValueTensorOp` and ending in `CopyToValueTensorOp`s, the
  pass first replaces all uses of the `CopyToNonValueTensorOp` with
  its operand, which results in all the `CopyToValueTensorOp` users
  having their operand have type `!torch.vtensor`, which is invalid.

  The correct way to do things is to first replace all the
  `CopyToValueTensorOp`s with their operand, and then replace all uses
  of the `CopyToNonValueTensorOp` with its operand.

  This only started failing now because the generated accessor
  `getOperand` for the `CopyToValueTensorOp` now returns a
  `TypedValue<NonValueTensorType>`, which has an assert checking that
  the value returned is of the expected type.
2022-12-07 12:20:41 -08:00
Sean Silva b1f9e09f85 [torchdynamo] Add ResNet18 example with TorchDynamo
This is a minor variation on our other resnet18 examples swapping in
TorchDynamo.

We replicate the refbackend_torchdynamo_backend out of the e2e test
config to avoid making that appear like a public API.

Also, some minor cleanups to TorchDynamoTestConfig.
2022-12-07 09:25:27 -08:00
Sean Silva c956c39c86 [cleanup] Remove disabled e2e test
This test has been disabled a long time, and since RefBackend is so slow
we don't want to add this unnecessarily. I believe it is covered by
downstream testing such as the Shark Tank.
2022-12-07 06:36:48 -08:00
Vivek Khandelwal 3e4bb2bd8e [MLIR][TORCH] Add E2E support for randn and randn.generator op
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-12-06 22:41:24 +05:30
Sean Silva 485c18bb2f [torchdynamo] Add "lockstep" numerical accuracy debugger.
Thanks to TorchDynamo's great layering and design, this is only about
100 lines of code for a basic lockstep debugger.

This should allow us to deprecate eager_mode, since AFAIK the only
interesting use case that it was really supporting is for downstream users to
write lockstep debuggers.

NOTE: The exact reporting and interface here is subject to change. Please
try it out and provide feedback (or patches :) ).
- make_fx should not drop source locations: https://github.com/pytorch/pytorch/issues/90276
- Report tensors better (huge tensors should be summarized)
- Maybe don't abort, but just warn?
- Allow customizing atol/rtol.
- How best to print the failing node? And include surrounding graph
context?
2022-12-06 07:57:45 -08:00
Vivek Khandelwal f416953600 [MLIR][TORCH] Add TorchConversionToMLProgram and MLProgramBufferize pass
This commit changes the `InsertRngGlobalsPass` to `TorchConversionToMLProgram`
pass. This commit also adds the `MLProgramBufferize` pass for the
bufferization of ml_program dialect ops to run on refbackend.

Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-12-02 13:20:46 +05:30
Ramiro Leal-Cavazos b4b92c990e
Replace LCG algorithm with squares64 algorithm in AtenUniformOp (#1633)
This commit replaces the LCG algorithm that was being used by the
`TorchToLinalg` lowering of `AtenUniformOp` to generate random numbers
with the `squares64` algorithm, for the LCG algorithm was producing
tensors that were highly correlated with one another.

Squares64 algorithm: https://arxiv.org/abs/2004.06278

Closes https://github.com/llvm/torch-mlir/issues/1608
2022-12-01 08:30:10 -08:00
Ramiro Leal-Cavazos 0983a7f93a
Fix modulus calculation in LCG algorithm of refbackend (#1658)
The current implementation sets the `nextSeed` value to `temp & 127`,
which is wrong. The last step of the LCG algorithm for the multiplier
and increment chosen should be `temp % 2^{64} = temp & (1 <<
63)`. However, because we are dealing with i64 values, the modulus
operation happens automatically, so it is not needed.

See Donald Knuth's values for LCG here:
https://en.wikipedia.org/wiki/Linear_congruential_generator
2022-11-30 08:46:52 -08:00
Abhishek Varma c27c1791f1 [MLIR][TORCH] Add e2e support for `aten.amax` op
-- This commit adds e2e support for `atend.amax` op.

Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
2022-11-30 17:54:37 +05:30
Daniel Ellis e2de20575f
Automatically strip overloads for FX-based models. 2022-11-29 22:19:09 -05:00
Ramiro Leal-Cavazos a8cbfff95b
Reduce memory usage of e2e tests by reducing input sizes (#1653)
There are a few e2e tests that take several very large tensors as
input, which leads to the e2e test suite leaking too much
memory. Running things locally resulted in a total memory usage of
12.5 GB when running the suite sequentially on the refbackend.

Many of the tests that take large tensors don't actually need
such large tensors to pass, and some that take several large tensors
as input are just doing the same thing multiple times. This commit
reduces the size of some of the tensors and removes repetitive parts
of tests to reduce the memory usage to a total of 3 GB.
2022-11-29 10:03:36 -08:00
Sean Silva 5a488ff085 Remove deprecated np.bool
`np.bool is bool` and will never be returned as a dtype of an
`np.ndarray`, so we don't need to handle it here.

```
>>> a = np.ndarray([1], dtype=bool)
>>> a.dtype.type is np.bool_
True
```

More info here:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
2022-11-29 01:46:21 -08:00
Sean Silva 5a27f826b8 Fix multiprocessing for `--config=torchdynamo`
For reasons that I haven't yet fully tracked down, the TorchDynamo
TestConfig seems to result in tensors that cannot be pickled. They seem
to be holding some sort of weak handles to a `torch.fx.graph.Graph`.

Here is the object structure that leads to the unpickleable object:
```
(<function _rebuild_tensor_v2 at 0x7f56346d56c0>, <class 'torch.Tensor'>, ( 1.0...
{<object object at 0x7f557529e6b0>: <WeakKeyDictionary at 0x7f556a3efbb0>}
{'data': {<weakref at 0x7f5615372ed0; to 'PythonKeyTracer' at 0x7f556a3ee5c0>: _...
<class 'torch.fx.graph.Graph'>
<class 'torch._ops.OpOverloadPacket'>
TypeError("cannot pickle 'torch._C.FunctionSchema' object")
```

Upstream bug filed: https://github.com/pytorch/pytorch/issues/89626
2022-11-28 04:03:11 -08:00
Shivam Gupta 853fd5c965
Fix RuntimeError while running examples/eager_mode.py (#1647) 2022-11-25 10:21:56 -06:00
Vivek Khandelwal d9cbf01d1e Revert "build: update llvm tag to 147fe9de"
This reverts commit e45ad313d4.
2022-11-25 12:41:56 +05:30
Vivek Khandelwal 9cac480a18 Revert "[MLIR][TORCH] Fix indentation and spacing for E2E tests"
This reverts commit 3790a4270e.
2022-11-25 12:41:56 +05:30
Sean Silva 28957adaac [torchdynamo] Initial TorchDynamo support
This adds a basic e2e Config for TorchDynamo using
Linalg-on-Tensors/RefBackend.
But TorchDynamo is pretty orthogonal to
various other pieces, so it should compose nicely with variations like:
- Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO)
- PyTorch functionalization and decompositions
- Taking the example inputs and compiling with all dynamic or all static
  shapes without duplicating tests.

This adds it to the CI, but there are still a lot of XFAIL's.

This also adds a helper `from torch_mlir.dynamo import
make_simple_dynamo_backend` which simplifies some of the steps for
making a Torch-MLIR-based TorchDynamo backend. We include "simple" in
the name because we are going to be exploring various things next from
the long-term roadmap.

The next steps are:
- Burn down all the XFAIL's.
- Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md).
  - Add functionalization/decompositions into the TorchDynamo flow and
    remove reliance on the current Torch-MLIR "frontend".
  - Write a pure-Python direct FX->MLIR importer.
  - Hook up the new PyTorch symbolic shape stuff.
  - Explore PrimTorch decompositions for simplifying backends.
2022-11-24 04:10:25 -08:00
Vivek Khandelwal 3790a4270e [MLIR][TORCH] Fix indentation and spacing for E2E tests
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-24 12:44:43 +05:30
Vivek Khandelwal e45ad313d4 build: update llvm tag to 147fe9de
Summary of changes:
- Update call to `hasNoEffect` utility
- `KDynamicSize` value changed to
  `std::numeric_limits<int64_t>::min()` from `-1`
- Update tags
  llvm: 147fe9de29dc13c14835127b35280c4d95c8e8ba
  mhlo: 1944b5fa6062ec4c065d726c9c5d64f1487ee8c5

Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-24 12:44:43 +05:30
Vivek Khandelwal 68f568b704 [MLIR][TORCH] Add E2E support for prims.convert_element_type op
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-22 09:36:36 +05:30
Vivek Khandelwal 55c7e66aa7 [MLIR][TORCH] Fix mean and mean.dim op for large-sized inputs
This commit fixes the aten.mean and aten.mean.dim op decomposition
for supporting large-sized inputs.
This commit also fixes the formatting for the file stats.py

Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-22 08:38:51 +05:30
Sean Silva 22307a1427 Clean up some parts of the test suite
The purpose of the test suite is to accelerate the development of the
compiler. However, we had various tests there that were not expected to
work, had no in-progress work being tested by the test, and nobody was
actively working on them. Having such tests in our test suite just adds
clutter and slows down development on the compiler.
2022-11-21 06:14:31 -08:00
Vivek Khandelwal 25ab8fcc1f [MLIR][TORCH] Fix numel tests for Roll PyTorch action
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-20 19:19:42 +05:30
Vivek Khandelwal 4cbd3927d7 [MLIR][TORCH] Add aten.sort.int op
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-20 19:00:41 +05:30
Abhishek Varma 1d949f3ac2 [MLIR][TORCH] Fix aten.upsample_nearest2d op
-- aten.upsample_nearest2d.vec op is not present
   owing to https://github.com/pytorch/pytorch/pull/85638
-- So this commit adds a lowering on aten.upsample_nearest2d.

Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
2022-11-18 13:41:47 +05:30
Sean Silva 39de4d6265 [cleanup] Make diagnostics better
Also remove some unused imports.
2022-11-17 02:09:54 -08:00
Vivek Khandelwal 5f7177da35 [MLIR][TORCH] Add decomposition for aten.var_mean.correction op
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-17 13:00:09 +05:30
Vivek Khandelwal a1d3afdba9 [MLIR][TORCH] Add E2E support for aten.randint.low op
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-16 09:54:18 +05:30
George Petterson 92f385bd9f [MLIR][TORCH] Add E2E support aten.convolution_backward op
This commit adds the decomposition for the `aten.convolution_backward`
and `aten.convolution_backward_overrideable` op.
2022-11-15 07:38:26 +05:30
Vivek Khandelwal a558034c1a [MLIR][TORCH] Fix aten.upsample_nearest2d_backward op
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-12 00:05:36 +05:30
Daniel Ellis a7ac0def45
Move single-tensor-tuple-return test to mlir unit test.
Also, add multiple return test.
2022-11-10 09:23:53 -05:00
Xiafei Qiu 4f173c6e0f
update llvm tag to a2620e00. (#1567)
- also update MHLO to 57ba12a2(branch greencommit/2022-11-07-a2620e00)
- change -pass-pipeline format to make tests pass.
2022-11-10 18:39:28 +08:00
Vivek Khandelwal fedf8c0640 [MLIR][TORCH] Add E2E support for aten.upsample_nearest2d_backward.vec op
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-04 22:10:07 +05:30
Vivek Khandelwal c86177730d [MLIR][TORCH] Add E2E support for aten.fill.Tensor op
This commit adds the decomposition for `aten.fill.Tensor` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-10-30 18:40:47 +05:30