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
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>
Strict symbolic shapes allow us to assume numpy-style dynamic broadcasts
never occur. This allows us to strengthen the folder for broadcasts to
cases where the rank is the same and all shapes match (including dynamic
sentinel values).
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.
Set PyTorch and TorchVision version to nightly release 2023-09-28.
aten.baddbmm changes done because upstream PyTorch has now added
support for fp16 gemm on CPU.
Refer: 9399e0b1ff
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.
The LTC backend has drifted from being able to pass tests on the stable
PyTorch version, so pinning to nightly on ARM.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds to the lowering of `aten.view` handling for the
following cases:
- `(..., a.size(i))` -> `(..., a.size(i), 1, ..., 1)`
- `(..., a.size(i), 1, ..., 1)` -> `(..., a.size(i))`
Fixes: https://github.com/llvm/torch-mlir/issues/2448
Stella Laurenzo
Zhekun(Josh) Zhang
Daniel Garvey
Yuanqiang Liu
saienduri
xiaolou86
JianzheXiao
Ze Zhang
Sarthak Gupta
Vivek Khandelwal
Sambhav Jain
Chi_Liu
Ramiro Leal-Cavazos
Quinn Dawkins
Jae Hoon (Antonio) Kim