This commit adds the OnnxToTorch support for Atan, Bitshift, BitwiseAnd,
and BitwiseNot op.
This commit also adds the TorchToLinalg support for AtenBitwiseLeftShiftTensorOp.
Signed-Off By: vivekkhandelwal@nod-labs.com
Adds a pipeline to convert custom ops and metadata represented as
`torch.operator` custom ops to corresponding `torch` ops where possible.
This is part of a multi-part approach for building ONNX import in as a
regular feature of torch-mlir. It is focused on the conversions vs the
infra. We will end up maintaining a [pure-python
importer](https://github.com/nod-ai/SHARK-Turbine/blob/main/python/shark_turbine/importers/onnx_importer.py)
to go with this in torch-mlir, and we will also maintain test case
generation utilities derived from it.
I have left substantial documentation in the README of the conversion
directory, including the recommended approach that we will take to keep
building this out.
(note that this organizes the code to coincide with the refactoring in
#2442 versus the current flat arrangement)
Adds support for lowering to prims split_op.
Similar design to collapse op lowering in
https://github.com/llvm/torch-mlir/pull/2572, with some
small differences, because the split_dim op (in pytorch) is
view-changing whereas the collapse is not. The difference
means that
1) it must be registered in the function Torch::isViewLikeOp
2) it must be be added to the "expected fail" set for the torch dynamo backend.
This lifts the core of the jit_ir_importer and ltc out of the pt1
project, making them peers to it. As a side-effect of this layering, now
the "MLIR bits" (dialects, etc) are not commingled with the various
parts of the pt1 project, allowing pt1 and ltc to overlay cleanly onto a
more fundamental "just MLIR" Python core. Prior to this, the Python
namespace was polluted to the point that this could not happen.
That "just MLIR" Python core will be introduced in a followup, which
will create the space to upstream the FX and ONNX pure Python importers.
This primary non-NFC change to the API is:
* `torch_mlir.dialects.torch.importer.jit_ir` ->
`torch_mlir.jit_ir_importer`.
The rest is source code layering so that we can make the pt1 project
optional without losing the other features.
Progress on #2546.
- adds support for an optional verifier to the generated torch op
tablegen (GeneratedTorchOps.td)
- uses the above to add a verifier for the torch permute op.
Motivation: I hit an unclear error from linalg while developing a
decomposition pass for pixel_shuffle. The error would have been clearer
if the problem had been detected earlier in the invalid aten.permute op.
Testing: new tests added. To run added tests, from the base directory
run
```
./build/bin/llvm-lit test/Dialect/Torch/invalid.mlir
```
Steps taken:
1) add generator code to torch_ods_gen.py, run update_torch_ods.sh
2) add (custom) shape and type inference generator code to
abstract_interp_lib_gen.py, run update_abstract_interp_lib.sh
3) Implement lowering to tensor.collapse_dims. Requires the `start` and
`end` values to be constant, else lowering fails
4) Update xfail_sets.py (append to LTC_XFAIL_SET) after running
/tools/e2e_test.sh --filter Collapse --verbose -c XX for all support
backends (XX).
Motivation:
- Supporting the collapse operation will be useful for lowering of
pixel_shuffle (see Issue #2559)
For static tests (that is when the shape is know) for example:
```
@annotate_args([None, ([3, 18, 2, 2], torch.float32, True)])
```
The e2e passes. But only if the replacement op's return type is set as
undefined (optional shape and type must be explicitly made unset),
otherwise there's a error about the function return type.
For dynamic cases, for example if the above is replaced with
```
@annotate_args([None, ([-1, -1, -1, -1], torch.float32, True)])
```
There is a failure to lower to linalg from torch ("view op explicitly
labelled as illegal"). This seems to be because the support for lowering
from torch to linalg with dynamic shapes is limited.
This is a first step towards the structure we discussed here:
https://gist.github.com/stellaraccident/931b068aaf7fa56f34069426740ebf20
There are two primary goals:
1. Separate the core project (C++ dialects and conversions) from the
hard PyTorch dependencies. We move all such things into projects/pt1 as
a starting point since they are presently entangled with PT1-era APIs.
Additional work can be done to disentangle components from that
(specifically LTC is identified as likely ultimately living in a
`projects/ltc`).
2. Create space for native PyTorch2 Dynamo-based infra to be upstreamed
without needing to co-exist with the original TorchScript path.
Very little changes in this path with respect to build layering or
options. These can be updated in a followup without commingling
directory structure changes.
This also takes steps toward a couple of other layering enhancements:
* Removes the llvm-external-projects/torch-mlir-dialects sub-project,
collapsing it into the main tree.
* Audits and fixes up the core C++ build to account for issues found
while moving things. This is just an opportunistic pass through but
roughly ~halves the number of build actions for the project from the
high 4000's to the low 2000's.
It deviates from the discussed plan by having a `projects/` tree instead
of `compat/`. As I was thinking about it, this will better accommodate
the follow-on code movement.
Once things are roughly in place and the CI passing, followups will
focus on more in-situ fixes and cleanups.
Attempt to solve https://github.com/llvm/torch-mlir/issues/2490
Changes for Non Value Semantic Ops having the
`IsTrailingUnderscoreInplaceVariant` trait :
- AnyTorchTensorType -> Torch_NonValueTensorType
- AnyTorchOptionalTensorType -> AnyTorchOptionalNonValueTensorType
- AnyTorchListOfOptionalTensorType ->
AnyTorchListOfOptionalNonValueTensorType
- AnyTorchListOfTensorType -> AnyTorchListOfNonValueTensorType
Created three new tensor types for optional and list non value tensors.
Add aten.isclose op
Add its torch-to-tosa lowering
Update the TorchToTosa/basic.mlir tests
To test e2e tosa lowering:
`python -m e2e_testing.main -v -c=tosa`
---------
Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
Add aten.unflatten.int op
Add its torch-to-tosa lowering
Update the TorchToTosa/basic.mlir tests
To test e2e tosa lowering:
`python -m e2e_testing.main -v -c=tosa`
---------
Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
Add linspace/cumprod/roll ops to ODS and add shape inference functions
to make it work with LTC.
Also, add some tensor utils to LTC library for searching for non-detach
copy nodes.
When importing dynamic shaped programs from Dynamo, via torch.compile or
torch.export, we can assume that strict symbolic shape checks have been
done prior to generating torch IR. Among other shape checking, this
eliminates the case where an unknown dimension can be dynamically '1' in
a way that signals a broadcast.
Adds a `isAssumingStrictSymbolicShapes` utility which consults a
`torch.assume_strict_symbolic_shapes` attribute on an enclosing scope
and returns true if present.
In the linalg pipeline, many runtime checks are elided when this returns
true.
Making the same PR with #2457, as I accidentally thought the review was already made and merged it (reverted).
Add decompose empty_strided op.
Referring to #1776, this decomposition op only supports default stride values, because accessing the tensor or indexing over that, the indices are determined by the strides.
In MLIR, this is not implicitly supported but assumes that the strides are default while iterating over the tensor.
Corresponding commits:
* mlir-hlo: 16886a108eff5197f816ca0f1950cc5ff1b078d9
* stablehlo: 77a59815a82b34f7b08ed2d42a711d9920682d0e
* llvm-project: 4acc3ffbb0af5631bc7916aeff3570f448899647
* Adapt to ByteCodeOpInterface changes.
* Adapt to RegionBranchPoint changes: https://reviews.llvm.org/D159116
* Adapt inferReturnTypes to get the value from properties.
* Adapt invalid.mlir to properties syntax
* [TOSA] Align with custom assembly format change.
* [TOSA] handle change of axis to int32 type
* [TOSA] Restore improper convert to i32
Landing with Windows broken (it cannot be fixed because of the way the mlir-hlo dep is inserted). Will followup with an untangling.
---------
Co-authored-by: TatWai Chong <tatwai.chong@arm.com>
Co-authored-by: Eric Kunze <eric.kunze@arm.com>
* 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>
* LTC/TorchMLIR multi-output operations support
* Update torch-mlir jit lowering to support ops with dynamic number of outputs
* Added support for aten::split_copy, aten::split_with_sizes_copy
* Fix native function for aten::split; cleanup code
* Fix TorchMlirTensorList lowering
* Remove xfails
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>
* add support for mhlo
* Add Test for torch.ne
* fix torch.ne shape/add static test case
* add support for static torch.ne
---------
Co-authored-by: root <root@n31-177-039.byted.org>
Before inlining a global slot, the users of the global slot are
checked to see if they are `ReadOnly` or `MemoryEffectFree` to make
sure that the global slot is not being mutated. Because the op
`copy.to_vtensor` currently does not have the `ReadOnly` trait, if a
global slot is passed to `copy.to_vtensor`, the pass
`InlineGlobalSlots` will fail.
The op `copy.to_vtensor` is `ReadOnly`, since it does not modify the
contents of the input tensor; it simply makes a new copy. This commit
adds the trait as well as an e2e test that generates the case of a
global slot being passed to a `copy.to_vtensor`.
Lowering torch operations that allow different compatible data types
in its operands to tosa end up generating invalid tosa IR with mixed
data types. In tosa spec, certain operations (generally element-wise
operations) require all operands to have the same data type.
Add wrapper functions for those element-wise tosa ops to perform op
creation with type conversion if necessary.
This commit adds dtype functions for all the torch ops that did not
previously have one and removes the pass `RefineTypes`, since the
abstract interpretation library now takes care of all the dtype
propagation.
All dtype functions added are tested except for
- `aten.embedding`
- `aten._embedding_bag`
- `aten.embedding_bag`
These functions need a change to the testing framework to allow
specifying the actual data inside the tensor used for testing. I will
fix this in a follow up patch.
Co-authored-by: Jiahao Li <liplus17@163.com>
-- This commit adds e2e support for atend.sort op.
-- 1. Adds aten.sort op in torch dialect.
-- 2. Adds tm_tensor.sort op in TMTensor dialect.
-- 3. Adds lowering of aten.sort -> tm_tensor.sort.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
Vivek Khandelwal
Stella Laurenzo
James Newling
Zhekun(Josh) Zhang
Yuanqiang Liu
JianzheXiao
Jiawei Wu
Daniel Garvey
xiaolou86
Sarthak Gupta
Ze Zhang
Jae Hoon (Antonio) Kim
saienduri
Gleb Kazantaev
David Gens
Bruce Kim
Ramiro Leal-Cavazos
Arham Khan
Jerin Philip
Quinn Dawkins
jinchen62
Chi_Liu
Jiawei Wu
Abhishek Varma
Maksim Levental
Matthias Gehre
JianzheXiao
Gaurav Shukla
Prateek Gupta
TatWai Chong
gpetters94
Prashant Kumar
rahul shrivastava
Sean Silva
Abhishek Varma