That way, downstreams don't have to duplicate this list.
Also, remove "external config" feature, since it is subsumed by just
importing the test suite.
* shape: add shape transfer function for aten.neg
Prior to this patch, the list of shape transfer functions did not
include `aten.neg`, which resulted in errors like below.
```
error: unsupported by backend lowering: tensor with unknown rank or dtype
note: see current operation: %0 = "torch.aten.neg"(%arg0) :
(!torch.vtensor<[256,256],f32>) -> !torch.vtensor<*,f32>
note: this is likely due to a missing shape transfer function in shape_lib_gen.py
```
This patch fixes the problem by adding a shape transfer function to
reflect the point-wise nature of this operation.
* linalg: add translation of aten.neg operation
This patch adds a translation rule to lower `aten.neg` operations on
tensors to an `arith.negf` operation wrapped inside a `linalg.generic`
operation. This patch also adds a rudimentary test.
This commit adds lowering of `aten::max_pool2d_with_indices_backward` op.
This commit also fixes formatting issues in basic.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
NB: `shouldnt_normalize2` and `shouldnt_normalize3` currently XPASS i.e., args *will* successfully normalize despite being incorrect due to an [upstream bug](https://github.com/pytorch/pytorch/issues/75342).
This commit adds the following support to the op `nll_loss_backward`:
- `input` tensor can be rank-1
- `weight` parameter
- `reduction` parameter
- `target`, `grad_output`, `total_weight` can be rank-0
- Checks that input tensors are of the expected type
Follows up on #623 for out-of-tree builds of torch-mlir, which
added building `torch-mir-dialects` as a subdirectory.
Our goal is to support both in-tree and out-of-tree builds of
`torch-mlir` with minimum hassle, for instance by using the same
variable names in both setups.
Specific changes to `externals/llvm-external-projects/torch-mlir-dialects/CMakeLists.txt`:
- We use `MLIR_FOUND` to detect that it is being build as a subdirectory
and the llvm+mlir cmake infrastructure is already set up (via
find_package in the parent build) as opposed to an in-tree build.
- For in-tree, the setting of variables and loading of llvm+mlir cmake
infrastructure is now conditionally performed.
- For in-tree, the names of cmake variables being defined for are
adjusted to match those `llvm-project` makes available through
`find_package(MLIR REQUIRED CONFIG)`, under the assumption that those
are the more "standardized" names.
Co-authored-by: Clément Fournier <clement.fournier@amd.com>
Co-authored-by: Liam Fitzpatrick <liam.fitzpatrick@xilinx.com>
This commit adds support for multi-dimensional tensors as input to the
`_index_put_impl_` op. The support was to some degree already there,
since `ScatterOp` already supports multi-dimensional tensors. This
commit also adds a bit more error checking to `index_put` and
refactors the code for creating `ScatterOp`s to mimic the way one
would make a `Linalg::GenericOp`.
The issue was in the canonicalizer for torch.aten.ge.int -- in cases
where the operands were swapped, it would miscompile. This issue is
fixed and folding support generalized to `torch.aten.size.int < 0` as
well.
Fixes#716
This commit decomposes different variants of `aten.where.*` op into
`aten.where.Self` op. It covers `aten.where.Scalar`,
`aten.where.ScalarSelf` and `aten.where.ScalarOther` ops.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit decomposes `aten.new_empty` op into `aten.empty.memory_format` op.
This commit also made a dtype fix to the constant tensor allocation like ops.
Earlier the dtype for the result was inferred from the result type; now, it's
being evaluated as per the original definition of the op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
A recent PyTorch commit made ConstantPad2d call a helper function with a
`Union[int, float]` type annotated. This commit adds minimal support for
representing and dealing with that.
https://github.com/pytorch/pytorch/pull/73287
Changes:
- Adding support for `!torch.union<T1, T2, T3>`/`Torch::UnionType`,
along with the importer and CAPI code.
- Add support in isValidSubtype for union types.
- Adding a canonicalizer for `torch.derefine` to help simplify some code
that derefines to a UnionType (this also fixes#664).
There is still more work to do for really supporting UnionType well,
such as canonicalizing UnionType's so that they can be compared with
pointer equality.
As per the docs on:
https://github.com/eregon/publish-release
> Note that the release must *not be marked as prerelease* for this to work.
For some reason, we were marking the release as pre-release before and
this was working, but the docs here seem pretty clear, so I'm going to
try it.
I am investigating the breakage.
Also, fix "externals" rename in setup.py and some cases where we weren't
using `requirements.txt` consistently.
Also, fix a case where the packaging script would get confused due to
".." in the path name.
The reified code to compute the shape of torch.aten.constant_pad_nd
uses negative indices when setting list elements. This was not
converted to a positive offset in one place in SimplifyShapeCalculations
which prevented computation of the static shape.
The logic in the rewriting phase had a bug in case of a read-only op
coming before mutation ops. The logic would use the op itself as the
"latest literal", but that is not correct, because later on we replace
the op itself with the *final* "latest literal", assuming that all uses
of the op have been rewritten -- that was working in general, except for
any read-only ops at the beginning.
Big thanks to @ljfitz for the tiny reproducer!
Fixes#704
- update diagram to use the name "Eager Mode" instead of
`torch.dispatch`, which wasn't a very accurate name
- rename `resnet_inference.ipynb` to
`torchscript_resnet_inference.ipynb` - this is in preparation to LTC
and Eager Mode versions
- remove mention of TorchFX - turns out that all TorchFX modules are
actually scriptable modules, so there is literally "zero code" vs
using the TorchScript path
- remove LazyTensorCore example, and instead point at the current
in-development `torch_mlir_ltc_backend` branch.
Note: there were actually some pretty useful utilities built out in the
examples directory, but they now live inside the Eager Mode
`python/torch_mlir/eager_mode/ir_building.py` (and need to be rolled
into a proper home with the upcoming rewrite of our top-level
`torch_mlir.compile` API).
This commit adds support for the cases of view op where the rank and
the shapes of the input and result are equal.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
In order to make sure that the TorchToLinalg conversions leave the
graph in a valid state, the final result of the conversion has to be
casted to the result type of the op. This commit adds this cast to ops
that did not have it.
Effectively, this mode works by compiling op by op as the NN is eagerly executed by PyTorch. Entailed in that compilation is building a representation of the op that can be `torch.jit.script`ed, importing using `ModuleBuilder`, and then executing (e.g., with `RefBackendLinalgOnTensorsBackend`). This mode includes a fallback to conventional PyTorch if anything in the torch-mlir compilation process fails (e.g., unsupported op).
Currently, all e2e tests pass execpt for two that involve an upstream PyTorch bug (https://github.com/pytorch/pytorch/issues/74400).
High priority next steps:
1. A compile cache in order to speed up reruns of the same NN.
2. Integration with IREE (though not in this repo).
3. Integration with `torch.distributed`.
Clément Fournier
Sean Silva
Vivek Khandelwal
Ashay Rane
powderluv
Maksim Levental
gpetters94
Prashant Kumar
Ahmed S. Taei
Ramiro Leal-Cavazos
Clément Fournier
Anup Gangwar
Gaurav Shukla
Liam Fitzpatrick
Qiang Fu