While working on https://github.com/openxla/iree/pull/14917, I noticed that it is somewhat hard to take a dependency on torch-mlir such that one only builds deps for the target(s) of interest (in this case Linalg). I noticed that some ifdef'ey optionality was added for stablehlo, but this was not mirrored for the others. Further, it does the switching very deep in the dependency graph vs having top-level directories and defines gating entire features. In addition, I noticed that a lot of things in the Linalg path were broken down to a fine level of detail but were not actually shared/shareable outside of that target. I opted to clump these together into TorchToLinalg. It is easy enough to "promote" them to common with this new organization if the need arises.
General approach:
* Isolate each conversion target in one of TorchToLinalg, TorchToStablehlo, TorchToTosa.
* Gate each by top-level CMake flags and defines.
* Common conversions go in a Common/ directory (currently Arith and SCF).
* Pull target specific conversions out of TorchConversion/Transforms and put in their top-level directory.
* General maintenance on the build graph and registration stuff that had bitrotted and was blocking progress.
The main functional change for people taking a source dep is that `#include "torch-mlir/Conversion/Passes.h"` no longer is a one stop shop: For optional conversions, you have to include the dedicated `Passes.h` of each and take a library dep. See `InitAll.cpp` which does it right (and *is* a one stop shop still).
* view_as_real test case, allow dtype in testutils.randn
* abstract python upstream func implemented
* fixed upstream dtype func, implemented view_as_real backend op
* formatted AtenViewAsRealOp, removed change in e2etest/framework
* removed test suit from reshape_like.py, because it's moved to basic.py
* implemented C-API wrapper for mlirComplexF128 type
* fixed torch.complex dtype width in MLIR and Torch MLIR, deleted float16 dtype dict
* Changed IR input of aten fft_fft unit test
* code refactored
* code refactored and fixed ci test
* refactored: removed white spaces, and rolled back to having both input/output affine expr
* refactored: deleted output affine expr to reduce redundancy
* xfail ltc backend
* removed ComplexImag and ComplexReal from torchdynamo xfail set
* copied and pasted from main branch as there's no change to be made in this file
* refactored abstract_interp_lib_gen.py
* refactored: torchtypes.td, formatted, removed commented out code
* Support brevitas custom op (#2320)
* f16 change for brevitas
* Adapt the change of brevitas quant custom op name
* Add unit tests
* Make brevitas conversions isolated
* Address the comments
---------
Co-authored-by: dan <danimal197@gmail.com>
* [TOSA] Fix conversion for depthwise convolutions
* Add e2e tests for depthwise and grouped convolutions
Co-authored-by: Lucas Camphausen <lucas.camphausen@iml.fraunhofer.de>
This commit updates the `llvm-project` and `mlir-hlo` submodules to
commits:
llvm-project: a3f2751f782f3cdc6ba4790488ec20163a40ac37
mlir-hlo: 97c7e4b4506c3a2441c923e592833f45da439009
Changes made:
- Rename `getSuccessorEntryOperands` with `getEntrySuccessorOperands`
and remove `operands` from
`getSuccessorRegions` (https://reviews.llvm.org/D157506)
- Make `TypeConverter` a `const` (https://reviews.llvm.org/D157601)
* [MLIR][TORCH] Fix aten.cumsum lowering for int32 input (#2351)
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
[Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (#2340)
[Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend.
update PyTorch version to 2.1.0.dev20230729 (#2354)
- torch version: 2.1.0.dev20230729
- torch commit hash: b638df0afb83572724032c824c64e481bb4499a0
- torchvision version: 0.16.0.dev20230729
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
update PyTorch version to 2.1.0.dev20230730 (#2356)
- torch version: 2.1.0.dev20230730
- torch commit hash: 0ff243ff350268cc98fe03fa6364375ee2824742
- torchvision version: 0.16.0.dev20230730
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
update PyTorch version to 2.1.0.dev20230731 (#2359)
- torch version: 2.1.0.dev20230731
- torch commit hash: 6298ac688f8caafe30d71ff2ea2e20fbb32065c7
- torchvision version: 0.16.0.dev20230731
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
LTC->MLIR Debug Info support (#1922)
* LTC->MLIR Debug Info support
* SW-95317 Propagate Lazy->Jit->MLIR scope name.
* Enhance location information based on op names
Currently, the location information attached to the ops just considers
the filename, line number and column number. Attaching operation name
would help identify the type of computation by just looking at the
profile of execution.
* Update locations logic; updated debug-info.py test
* Use {scope}/{op_name} format to track names by default
---------
Co-authored-by: Gleb Kazantaev <gleb.kazantaev@cerebras.net>
Co-authored-by: Mark Browning <mark@cerebras.net>
Co-authored-by: Vimal Patel <vimal@polymagelabs.com>
build: update llvm tag to 41895843
Summary of changes:
- Update tags
llvm: 41895843b5915bb78e9d02aa711fa10f7174db43
mhlo: 4726d31f7025da66de0dea709bd56c462edb83c2
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
update PyTorch version to 2.1.0.dev20230802 (#2366)
- torch version: 2.1.0.dev20230802
- torch commit hash: c89b16917755c2abbef7b6420e340baf9ae8089e
- torchvision version: 0.16.0.dev20230802
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
Change Python version from 3.10 to 3.11 in installation instructions (#2370)
Add CITATION file (#2371)
Add packaging as an install dependency (#2369)
Needed by `torch_mlir._version`. Resolves#2368.
[Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op (#2358)
* [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op
update PyTorch version to 2.1.0.dev20230803 (#2372)
- torch version: 2.1.0.dev20230803
- torch commit hash: f89c73be3a3e8274d025ac46a33a780853841c9e
- torchvision version: 0.16.0.dev20230803
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
Prevent failed stable CI job from cancelling nightly jobs (#2373)
The CI jobs that use stable PyTorch are currently not required to pass
in order for a patch to get merged in `main`. This commit makes sure
that if a CI job for stable PyTorch fails, it does not cancel the
other required jobs.
[Torch Dialect] emit aten.tile op and decompose it into aten.repeat (#2355)
update
update xfail sets
update xfail_sets
update
fix xfail_sets
update:
update
update:
update
parent 22e88d523b1970b2e904eb5421d49d987a3d255e
author jianzhe.xiao <jianzhe.xiao@bytedance.com> 1691114110 +0800
committer jianzhe.xiao <jianzhe.xiao@bytedance.com> 1691114119 +0800
[Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (#2340)
[Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend.
update PyTorch version to 2.1.0.dev20230729 (#2354)
- torch version: 2.1.0.dev20230729
- torch commit hash: b638df0afb83572724032c824c64e481bb4499a0
- torchvision version: 0.16.0.dev20230729
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
update PyTorch version to 2.1.0.dev20230730 (#2356)
- torch version: 2.1.0.dev20230730
- torch commit hash: 0ff243ff350268cc98fe03fa6364375ee2824742
- torchvision version: 0.16.0.dev20230730
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
update PyTorch version to 2.1.0.dev20230731 (#2359)
- torch version: 2.1.0.dev20230731
- torch commit hash: 6298ac688f8caafe30d71ff2ea2e20fbb32065c7
- torchvision version: 0.16.0.dev20230731
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
LTC->MLIR Debug Info support (#1922)
* LTC->MLIR Debug Info support
* SW-95317 Propagate Lazy->Jit->MLIR scope name.
* Enhance location information based on op names
Currently, the location information attached to the ops just considers
the filename, line number and column number. Attaching operation name
would help identify the type of computation by just looking at the
profile of execution.
* Update locations logic; updated debug-info.py test
* Use {scope}/{op_name} format to track names by default
---------
Co-authored-by: Gleb Kazantaev <gleb.kazantaev@cerebras.net>
Co-authored-by: Mark Browning <mark@cerebras.net>
Co-authored-by: Vimal Patel <vimal@polymagelabs.com>
build: update llvm tag to 41895843
Summary of changes:
- Update tags
llvm: 41895843b5915bb78e9d02aa711fa10f7174db43
mhlo: 4726d31f7025da66de0dea709bd56c462edb83c2
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
update PyTorch version to 2.1.0.dev20230802 (#2366)
- torch version: 2.1.0.dev20230802
- torch commit hash: c89b16917755c2abbef7b6420e340baf9ae8089e
- torchvision version: 0.16.0.dev20230802
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
Change Python version from 3.10 to 3.11 in installation instructions (#2370)
Add CITATION file (#2371)
Add packaging as an install dependency (#2369)
Needed by `torch_mlir._version`. Resolves#2368.
[Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op (#2358)
* [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op
update PyTorch version to 2.1.0.dev20230803 (#2372)
- torch version: 2.1.0.dev20230803
- torch commit hash: f89c73be3a3e8274d025ac46a33a780853841c9e
- torchvision version: 0.16.0.dev20230803
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
Prevent failed stable CI job from cancelling nightly jobs (#2373)
The CI jobs that use stable PyTorch are currently not required to pass
in order for a patch to get merged in `main`. This commit makes sure
that if a CI job for stable PyTorch fails, it does not cancel the
other required jobs.
[Torch Dialect] emit aten.tile op and decompose it into aten.repeat (#2355)
update
update xfail sets
update xfail_sets
update
fix xfail_sets
update:
update
update:
add support for adaptive_pool_id
update xfail sets
update xfail_sets
update
fix xfail_sets
update:
update:
* update
---------
Co-authored-by: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
It's actually fine to not check the rank of the indices, because the conversion anyways flattens the index tensor to be (1, numElements) before applying tosa::gather, and then anyways reshapes the output tensor to the output shape of the aten.embedding.
The implementation at this place was a remnent of the times the pipeline was
run only once.
Rely instead on the backend verification, after optimizations have had an
opportunity to resolve some uncertainties. (e.g. `!torch.optional`).
* RecomposeComplexOps: Remove dead slice op
* lib/Dialect/Torch/IR/TorchOps.cpp: Fold slice ops even when they are on non-value tensors
* lib/Conversion/TorchToTosa/TorchToTosa.cpp: Fix slice start/end out of range/none
* lib/Dialect/Torch/IR/TorchOps.cpp: AtenSliceTensorOp::fold: Fold slices that go from 0:int_max
* More tests for aten.split.Tensor
Stella Laurenzo
Jerin Philip
Jiawei Wu
Vivek Khandelwal
Yuanqiang Liu
Bruce Kim
Quinn Dawkins
Arham Khan
JianzheXiao
jinchen62
Simon Camphausen
Ramiro Leal-Cavazos
Matthias Gehre
Alexandre Rames
AyaanShah2204