This mostly copy-pastes the reduce minimum implementation to reduce max
to improve test coverage. We also improve the aten lowering for min/max
dim for unsigned types.
The addition of an e2e test is actually provided in the Shark-Testsuite.
This adds 2 test cases for the gridsampler e2e test.
Also as intended there were some items found which needed correction, so
the Gridsampler op has also a change.
Current implementation depends on using `aten.view` which has issues
inferring tensor collapse/expand operations during the lowering to
`linalg`. Using flatten and unsqueeze better infers what the later
reshape behavior.
Add e2d support for `aten.linalg_norm` by decompose it to
`aten.linalg_vector_norm`.
Lowering to `aten.linalg_matrix_norm` is still unsupported.
To Test:
`python -m e2e_testing.main -v`
---------
Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
Existing lowering via aten.view does not work as well for dynamic shapes
as the lowering to tensor.expand must re-infer dynamic shape matching.
Better to directly lower.
A bunch of small fixes are interlinked and trigger crashes if not
addressed as a group. This includes:
- aten view when expand from a rank-0 tensor
- slice folder with negative indices
- `aten._shape_as_tensor` folder on a rank-0 tensor
- `aten.cat` of a tensor with a length-0 tensor
The corrective transpose at the end is computed incorrectly. Is it
actually computin the inverse transpose. Inverting the permutations
fixes the issue.
Torch lowering only supported the most recent version. Refactored the
lowering so more easily handle default values and optional operands /
attributes.
Added Support for float dtype in in torch.arange in TOSA Dialect
This resolves the following issue :-
https://github.com/llvm/torch-mlir/issues/2762
The following test cases are passing after this change
1. ArangeDtypeIntModule_basic
2. ArangeFloatModule_basic
3. ArangeNegativeStartFloatModule_basic
4. ArangeStartFloatModule_basic
5. ArangeStartNegativeStepFloatModule_basic
6. ArangeStartOutDtypeModule_basic
7. ArangeStartStepFloatModule_basic
---------
Co-authored-by: James Newling <james.newling@gmail.com>
We collapsed and broadcasted scatter indices to a single element
version. We should instead upport `tm_tensor.scatter`s support for
multiple indices and the implicitly broadcasted behavior. This avoids
the serialization and materializing a needlessly large indices tensor.
There is no reason to treat `ConstantOfShape` as a specialized import
any as there exists a onnx-to-torch equivalent. Dropping the import
coding and adding support for resource conversion substantially
increases test coverage for dynamically shaped tests.
Strided slicing can occur with a negative stride. In these cases we need
to bound end differently. This included removing a function that was
generating bad limits.
Simple folder for limited size aten tensor operations. This is primarily
useful for shape computation folding as they unfortunately can use
`aten` operators. Add, sub, mul are common examples of these folders.
Onnx slice lowering used arange needlessly instead of directly
constructing the constant dimension values. This makes lowerings to
linalg struggle as multiple folders are required to get what is a
constant index value.
Even though the reference compiler is not about performance, inlining
the generated sparse helper methods has a rather big positive impact on
performance, leaving a much better first impression. Therefore, we added
this inlining pass (which leaves all other PyTorch modules unaffected,
since they tend to be one big main() method to start with).
testing:
$./tools/e2e_test.sh --config linalg
Summary:
Passed: 1164
Expectedly Failed: 8
$ python -m e2e_testing.main --config=torchdynamo
Summary:
Passed: 976
Expectedly Failed: 162
We can route the torch tests via `onnx` using the `torch.onnx.export`
tooling. We can then reimport, lower to torch, and compile to linalg to
validate the onnx path is working correctly.
The current implementation exposes some failures in the `onnx` path so
we cannot enable the onnx test suite yet due to segmentation faults.
This commit adds the OnnxToTorch lowering for cosh, acosh, asin, asinh,
and atanh op.
This commit also adds the TorchToLinalg lowering for acosh, asin, asinh,
and atanh op.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
Some operations include a backend matcher for specialized operations. We
map these back to generics so they appropriately match to the high
performance versions. This is done for the attention operation.
This PR introduces a sparse_jit wrapper that can run simple models with
sparse tensor inputs end-to-end. The implementation shows all required
components on modifying sparse tensor types with a 1:N relation on the
call sites. Two tests shows that the JIT runs end-to-end while computing
the correct results.
More details to follow (generalizing to COO and different ranks, as well
as support for *output* sparse tensors), but the general concepts are
all here now.
**_Update: Thanks to Rob, bump to proper LLVM/MLIR hash is done!_**
_**NOTE that all parameter passing changes are nicely done "downstream"
in MLIR, so very little changes are required in torch-mlir code
proper**_
---------
Co-authored-by: Franz Haniel <77495327+frafranz@users.noreply.github.com>
Co-authored-by: Franz Haniel <franz.haniel@amd.com>
The lowering decomposes AtenTraceOp into an AtenDiagonalOp followed by
AtenSumOp.
The progress is tracked in
https://github.com/nod-ai/SHARK-Turbine/issues/333.
---------
Co-authored-by: Franz Haniel <franz.haniel@amd.com>
There is no lowering support for math::AbsIOp, so if the operand is an
integer type, it will fail to lower to math::AbsFOp since the op operand
#0 must be floating-point-like.
This adds a few passes that will ensure linalg with sparse tensors are
properly lowered to loops and can run using the ExecutionEngine for
testing (a few details on parameter passing from PyTorch still TBD)
Test results:
$ ./tools/e2e_test.sh --config linalg
Summary:
Passed: 1144
Expectedly Failed: 8
$ python -m e2e_testing.main --config=torchdynamo -v
Summary:
Passed: 960
Expectedly Failed: 163
Filed issue:
https://github.com/pytorch/pytorch/issues/119407
Folds aten::index_select ops under the following conditions:
1. If the input and output are the same shape, the indexing operation is
a NOP, so just return the input.
2. If the input has shape <1x1x...xNx...x1> (all 1's except for one
dim), and the output shape is <1x1x...x1> (all 1's), then there is a
single index, so extract the single element value and return a tensor
with that value.
---------
Co-authored-by: Dave Liddell <dliddell@xilinx.com>
Lowering of torch.aten.all.dim to linalg.
Per PyTorch documentation:
> This function matches the behaviour of NumPy in returning output of
dtype bool for all supported dtypes except uint8. For uint8 the dtype of
output is uint8 itself.
Since there is no support for ui8 in torch-mlir currently
(https://github.com/llvm/torch-mlir/pull/1384#issuecomment-1260011334)
implementation returns failure for that case.
Link to related RFC:
https://discourse.llvm.org/t/rfc-rename-torch-mlir-compile-apis-and-introduce-fx-based-analogs/76646
This commit updates the documentation, tests, CMake files, and API for
the proposed changes in the RFC. There is a new torch_mlir/fx.py for
user level APIs related to importing modules and a corresponding test
for this path can be found at test/python/fx_importer/basic_test.py.
---------
Co-authored-by: MaheshRavishankar <mravisha@amd.com>
If a tensor is initialized by a list with a single constant integer,
this folder turns it into a torch.vtensor.literal
---------
Co-authored-by: Dave Liddell <dliddell@xilinx.com>
Rob Suderman
Andreas Falkenberg
Ze Zhang
Chi_Liu
mmakevic
Abhishek-TyRnT
Vivek Khandelwal
ptrifunovic98
Aart Bik
aldesilv
Yuanqiang Liu
Franz Haniel
Avinash Sharma
Dave Liddell
saienduri
Xida Ren (Cedar)