* add support for mhlo
* Add Test for torch.ne
* fix torch.ne shape/add static test case
* add support for static torch.ne
---------
Co-authored-by: root <root@n31-177-039.byted.org>
The `copy_` op being replaced by `RecomposeSliceCopy_` operates on a
subset of the tensor being mutated, while the `index_put` op being
used to replace the `copy_` op operates on the entire tensor being
mutated. This means that the result type of the `index_put` should be
the type of the input to `index_put` and we need to make sure that
`copy_` does not have users before replacing to avoid type conflicts.
This commit also fixes the result type used for the
`AtenArangeStartStepOp`, and an off-by-1 error when creating the
indices vector.
Lastly, this commit also clamps the `end` value from the slice to the
size of the dimension.
When `use_tracing=True` is used to import a model into Torch-MLIR,
several casts get inserted in the IR to bridge the untyped inputs and
outputs with the typed body of the computation. These casts create
extra aliases of tensors that cause the current analysis in
`maximize-value-semantics` to fail.
In particular, the `maximize-value-semantics` analysis assumes that the
only valid alias right after an overwrite is the overwritten
alias. So, if there is a use of a casted version of the overwritten
alias after the overwrite, the analysis fails.
This commit improves the analysis by identifying all cast-like aliases
of the overwritten alias and allowing such aliases to be used after an
overwrite.
Because this issue only arises when using tracing, it cannot be
currently tested e2e, so only lit test is added.
This commit adds dtype functions for all the torch ops that did not
previously have one and removes the pass `RefineTypes`, since the
abstract interpretation library now takes care of all the dtype
propagation.
All dtype functions added are tested except for
- `aten.embedding`
- `aten._embedding_bag`
- `aten.embedding_bag`
These functions need a change to the testing framework to allow
specifying the actual data inside the tensor used for testing. I will
fix this in a follow up patch.
Co-authored-by: Jiahao Li <liplus17@163.com>
The current decomposition for `aten.randn.generator` does not specify
the `dtype` argument of the empty tensors created to store the random
values. This leads to invalid IR when the output type of the `randn`
op is not the default PyTorch dtype.
-- In Python we have the concept of negative dimension indexing.
-- We would want to normalize such dimensions to be +ve and within the
expected range instead.
-- This commit takes care of a few remaining set of Ops and their
lowerings by applying `toPositiveDim` and `isValidDim` to the
extracted integer `dim` value.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
-- This commit adds e2e support for atend.sort op.
-- 1. Adds aten.sort op in torch dialect.
-- 2. Adds tm_tensor.sort op in TMTensor dialect.
-- 3. Adds lowering of aten.sort -> tm_tensor.sort.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
`TorchToTMTensor` depends on `TorchMLIRTorchUtils` for
`mlir::torch::torch_upstream::get_reduction_enum`.
`TorchMLIRTorchConversionPasses` depends on multiple libs for both tblgen'd
headers and definitions. Test with `ninja TorchMLIRTorchConversionPasses` from
a clean build.
-- This commit adds e2e support for aten.randint by decomposing it into
an aten.randint.low by setting low=0.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
This commits adds the support for cases for index_put_op:
1.) where index is a 2-d tensor.
2.) where indices is a list of tensors and none, with exactly
2 non none tensors along the consecutive dimensions.
This commit also adds a utility to compute the broadcast shape
given the two input tensors.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit also adds the support for non-unit output padding in the
case of transposed convolution.
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
The ops `aten.convolution_overrideable` and
`aten.convolution_backward_overrideable` are currently not e2e tested
in Torch-MLIR. Moreover, there is no way to add e2e tests for them
because the ops cannot be called using the CPU backend (this also
prevents adding tested dtype functions for these ops). Since these two
ops are not expected to ever appear in PyTorch traces obtained through
standard means (https://github.com/pytorch/pytorch/issues/97481),
Torch-MLIR should not have to worry about them.
Yuanqiang Liu
Matthias Gehre
JianzheXiao
Vivek Khandelwal
Ramiro Leal-Cavazos
Zhekun Zhang
Gaurav Shukla
gpetters94
Prashant Kumar
Sean Silva
Ze Zhang
Eric Kunze
Roll PyTorch Action
Abhishek Varma
Alexandre Rames
Chi_Liu
Alexandre Rames