The lowering of aten.fill.Scalar has been added.
The changes have been made as a part of -torch-convert-to-linalg pass.
Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
This commit fixes a type promotion bug when NumToTensor was given a
float as an argument. In particular, the rules for type promotion of a
scalar vary depending on if the scalar is part of a tensor op or
not. NumToTensor falls under the second category, but it was being
treated as part of the first category.
aten.log_softmax_back_data op lowering and required
tests has been added. Some NFC have also been added.
Signed-off-by: Prashant Kumar prashant@nod-labs.com
This commit adds lowering of `aten.mul.Scalar` and also adds
decomposition of `aten.addmm` to `aten.mul.Scalar`, `aten.add.Tensor`
and `aten.mm` ops.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
Now, aten::linear supports rank 3 inputs. This is a fix
for upcoming bert-inference task. The correct way should be
to support broadcasting in `aten.matmul` op and decompose
`aten.linear` into right ops.
This commit adds new operation `aten.gelu_backward` in the aten
dialect and adds lowering of this operation from aten to linalg.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
This change is to unblock the work of some backprop ops returning more
than one tensors. We will need to think of a more scalable approach
in the future if more flexible return types combinations are needed.
- Remove use of conversion construction macros
- Add mul and div op conversions
- Add corresponding tests
Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
This is to facilitate scalar type conversion in the TorchToLinalg. As
part of adding the helper, this PR also:
- Updated `AtenAddTensorOp`, `AtenSubTensorOp` to use the helpers to
support more type variants.
- Added e2e type promotion testing.
- Added i32 memref return/arg type to support e2e testing.
Support for returning elemental types. Previously, only
memref types as returning types was supported. All the hacky ways
to write tests which return elemental types should be taken care of.
Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
The lowering of `aten.Int.Tensor` op has been added.
The changes has been made as a part of `convert-torch-to-linalg` pass.
Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
- This commit adds lowering of `aten.View` to `linalg.TensorExpandShape`.
- This lowering will be successful only when one or more static
dimensions are expanded.
- It also fixes a typo in `ConvertAtenFlattenUsingIntsOp` conversion
pattern.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
Part of #380
Also
- BoolType is not considered as Scalar
- e2e framework fixes for nan handling
- `tu.rand(..., low=, high=)` support
- delete unused variable (fix warning)
- Add IouOfModule from #380 to e2e test suite (this is a common
calculation in vision models)
Your branch is ahead of 'origin/main' by 1 commit.
Lowering of `aten.matmul` op is added from torch to linalg dialect.
The different cases correspond to
https://pytorch.org/docs/stable/generated/torch.matmul.html.
TODO: Broadcasting in case of batch-matmul is yet to be taken care of.
Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
* Print more exception info on error during test execution
* Fix formatting
* Add aten::gelu lowering
Co-authored-by: Boian Petkantchin <boian@nod-labs.com>
Includes a fix to use `add_mlir_public_c_api_library` for Torch-MLIR's CAPI library, which is now required (note: upstream sample has it the right way).
Disabled a TOSA test per discussion: https://github.com/llvm/torch-mlir/issues/379
Summary:
This commit fixes an off-by-one error in how negative dimensiosn were
being handled in the lowering of transpose. This commit also adds
tests to transpose and unsqueeze to test negative dimensions.
- Added a DecomposeComplexOps pass to decompose complex torchOps.
- Refactored `visitAtenArgmaxOp` and `visitAtenAnyDimOp` to
`visitReductionAlongDimIntOp`.
- Moved some helper functions into
torch-mlir/Dialect/Torch/Utils/Utils.h to be shared by multiple files.
- Added support for f64 tensor as argument and return types.
We lower through linalg-on-tensors and use RefBackend to run it.
This adds enough support for a "tanh" op. Adding more ops should be
fairly mechanical now that things are wired up. Run with:
```
./tools/torchscript_e2e_test.sh -c tosa
```
The backend structure is very similar to linalg-on-tensors based E2E
backends and is a nice parallel (see `tosa_backend.py`). Actually, this
forced a nice refactoring to the layering here. We removed
`torchscript-module-to-linalg-on-tensors-backend-pipeline` and instead
require separately running
```
torchscript-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline
```
This highlights the step that lowers to the "torch backend contract"
of cleaned up `torch` dialect ops is a critical step in the lowering.
Going forward, that is the key load-bearing contract of the torch-mlir
project, not the linalg-on-tensors backend contract.
Recommended review order:
- `TorchToTosa.cpp` / `TorchToTosa/basic.mlir`
- `python/torch_mlir_e2e_test/torchscript/configs/tosa_backend.py` and
the new `utils.py` file there.
- `python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py` and
`abc.py` in that directory for the TOSA backend e2e interface.
- other misc mechanical changes
This is a simple way for externals to plug their backends into the test
suite. They just implement the `TestConfig` class for their backend and
write a small script that exposes it.
I have a pending PR for iree-samples that successfully integrates this.
ds1231h
Prashant Kumar
Daniel Garvey
dan
Liam Fitzpatrick
Prateek Gupta
nodlabs
Vivek Khandelwal
Ramiro Leal-Cavazos
Gaurav Shukla
Yi Zhang
Suraj Sudhir
Suraj Sudhir
George Petterson
Wang Kangyu
Sean Silva
Boian Petkantchin
Stella Laurenzo