* Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO,
* run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
- Update MHLO commit to build with LLVM commit hash 00d648bd
- Update TorchToMhlo code to work with Stablehlo
- Re-enabled two failing TOSA tests, thus resolving Github Issue #1231
We use it for more than TorchScript testing now. This is a purely
mechanical change to adjust some file paths to remove "torchscript".
The most perceptible change here is that now e2e tests are run with
```
./tools/e2e_test.sh
instead of:
./tools/torchscript_e2e_test.sh
```
Change logic so that we never run the multiprocessing codepath with only
1 worker. That configuration was causing all subsequent tests to
spuriously fail if one test failed with a crash (this was easy to see
after sorting the tests). That configuration was the one used by the CI.
Also, sort tests to make output nicer.
Also, make verbose mode more verbose so that it is easy to see in `-s`
mode which test is crashing.
PyTorch recently added support for `dim=None` in the `torch.var`
(5ca9b2b6fa)
and `torch.std`op (eb0e30e0bc).
This commit adds the corresponding support in torch-mlir.
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
* Replace CHECK_EQ with TORCH_CHECK_EQ
* Check value of TORCH_MLIR_USE_INSTALLED_PYTORCH during LTC build
* Update LTC XFAIL with NewZerosModule ops
* Explicitly blacklist _like ops
* Automatically blacklist new_/_like ops
* Prune away unused Python dependencies from LTC
* Add flag to disable LTC
* Autogen dummy _REFERENCE_LAZY_BACKEND library when LTC is disabled
* Implement compute_shape_var
* Removed Var tests from XFAIL Set
* XFAIL tests using _local_scalar_dense or index.Tensor
* Add StdDim tests to XFAIL set
* Autogen aten::cat
- Pruned number of xfailed e2e LTC tests from 305 to 134
- Reviewed every failure to ensure the error genuinely warrants an xfail
- Fixed bug where non-tensor outputs of LTC computation had `.to('cpu')` called, which caused a failure and inflated the xfail count
- Fixed bug with `HBC_basic` test where a constant tensor was created in its constructor without being declared as a buffer, which prevented the device from being updated when the parent `torch.nn.Module` got moved to the `lazy` device
- Note that this test is still xfail'd due to some unsupported ops. Left a comment about some potential issues that may arise if it gets reenabled in the future
- Updated autogen `GeneratedTorchOps.td` to reflect the latest set of supported ops
- Renamed `aten.zero.functionalization` to `aten.zero` to reflect upstream PyTorch changes
* Added e2e LTC Torch MLIR tests
* Fix seed for reproducability
* Check if computation is None before getting debug string
* Updated unit tests, and added numeric tests
* Print name of the model layer that fails numeric validation
* Run LTC e2e test with CI/CD
* Set seed in main function, instead of beginning of execution
* Add comment to specify number of digits of precision
* Fixed typo
* Remove tests for LTC example models
* Added LTC option to torchscript e2e
* Implement compile and run for LTC e2e test
* xfail all tests that use ops that aren't currently supported
- Supports cases where the view op expands and collapses dims
simulataneously. This does not handle the case where it is neither
expanding nor collapsing (e.g. [2, 3] -> [3, 2])
- Additionally fixes a previous bug with adding 1-sized dims on both
sides of a tensor with aten.view
The biggest change here is to upgrade RefineTypes to the new sparse
dataflow framework.
Smaller changes:
- minor changes to type parsing
- suppress warnings in e2e tests
The original conversion pattern for `AtenBatchNormOp` required that
the input rank be greater than 2; however, the only
expectation in the conversion pattern and in Pytorch is that the input
rank is greater than 1, since the second dimension of the input must
match the size of the `weight`, `bias`, `runningMean`, and
`runningVar` inputs. This commit fixes the `inputRank` check.
A previous fix to the handling of size-1 dims in
`aten.view` (https://github.com/llvm/torch-mlir/pull/962) resulted in
the wrong grouping of dimensions when size-1 dims where between two
dims of size greater than 1. This commit fixes that.
This commit lowers `aten.matmul` to `linalg.BatchMatmul` under the
following conditions:
1. The result of matrix multiplication must have batch dimensions,
i.e., rank greater than 2.
2. The resultant matrix must have at most 1 dynamic batch dimension.
It also handles broadcasting of batch dimensions when batch dimensions
of the matrices are broadcastable.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
Vivek Khandelwal
武家伟
gpetters94
Quinn Dawkins
JakopinA
Ashay Rane
Sean Silva
Jae Hoon (Antonio) Kim
Henry Tu
Tanyo Kwok
Vidush Singhal
powderluv
Quinn Dawkins
Ramiro Leal-Cavazos
Suraj Sudhir
Gaurav Shukla