- 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)
Currently the docs are split into two places, the `docs/` directory
and the Github Wiki of Torch-MLIR. This commit moves the wiki docs to
`docs/` to consolidate everything into one place. This has the added
benefit that users will get all the documentation when they clone the
repository.
Note: there are 4 files in the wiki, but only one is truly needed
- Torch-ops-E2E-implementation.md: only file needed
- Coding-Style.md: the contents of this file are already in
Torch-ops-E2E-implementation.md
- Weekly-LLVM-Update.md: this is outdated. We no longer have a weekly
schedule for llvm updates
- Home.md: Contains links to talks and resources that are already
present in the documentation in `docs/` or in
Torch-ops-E2E-implementation.md
Co-authored-by: Yi Zhang <cathyzhyi@google.com>
Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
Co-authored-by: Sean Silva <silvasean@google.com>
Co-authored-by: Daniel Ellis <1346302+dellis23@users.noreply.github.com>
Co-authored-by: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
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.
NonValueSemantic Ops like Add_, div_, etc. expect result DType to be the
same as the first input. However, current implementation would result in
wrong result type for case like:
```python
a = torch.randn(3, 3).half() # float16
b = torch.randn(3, 3) # float32
a += b # i.e. torch.ops.aten.add_(a, b)
```
torch expects `a` to be float16, but dtype refinement would infer
float32 type, since it's replaced by `aten.add`.
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.
The last llvm bump in https://github.com/llvm/torch-mlir/pull/2511
pointed to
b44b3494f6,
however the bazel build upstream was not clean at this point:
```
ERROR: /root/.cache/bazel/_bazel_root/b89349c08f7224396763d14fe35cba11/external/llvm-project/mlir/BUILD.bazel:5837:18: TdGenerate
external/llvm-project/mlir/include/mlir/Dialect/LLVMIR/NVVMOpsInterface.h.inc failed: (Exit 1): mlir-tblgen failed: error executing command ...
external/llvm-project/mlir/include/mlir/Dialect/LLVMIR/NVVMOps.td:20:9: error: Could not find include file 'mlir/Dialect/LLVMIR/BasicPtxBuilderInterface.td'
include "mlir/Dialect/LLVMIR/BasicPtxBuilderInterface.td"
^
external/llvm-project/mlir/include/mlir/Dialect/LLVMIR/NVVMOps.td:20:9: error: Unexpected token at top level
include "mlir/Dialect/LLVMIR/BasicPtxBuilderInterface.td"
^
```
The bazel fixes followed in a subsequent commit at
28b27c1b10.
This PR bumps LLVM by a few more commits (to include the bazel fixes)
which helps restore Torch-MLIR's bazel build back to 🟢 .
GHA workflow to test bazel build:
https://github.com/sjain-stanford/torch-mlir/actions/runs/6555101471/job/17803082508
James Newling
Stella Laurenzo
Shehroze Khan
lzw
Yuanqiang Liu
Ramiro Leal-Cavazos
saienduri
JianzheXiao
Jiawei Wu
Sambhav Jain
Zhekun(Josh) Zhang
Daniel Garvey
xiaolou86
Ze Zhang
Sarthak Gupta
Vivek Khandelwal