-- aten.upsample_nearest2d.vec op is not present
owing to https://github.com/pytorch/pytorch/pull/85638
-- So this commit adds a lowering on aten.upsample_nearest2d.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
This commit removes almost all of the valsem ops, since the value
semantics version of the ops now exist in PyTorch. The only op missing
is `aten.bernoulli_.float`. In addition, this commit also simplifies
the implementation of `aten.fill.Scalar` by moving it to the pattern
that converts elementwise ops.
-- This commit adds e2e support for `aten.Mish` op.
-- `aten.Mish` op is decomposed as following :-
Mish(x) = x * Tanh(Softplus(x))
Signed-off-by: Abhishek Varma <avarma094@gmail.com>
Signed-off-by: Abhishek Varma <avarma094@gmail.com>
This commit adds lowering of `aten.div.int` and `aten.bitwise_or.Tensor`
ops. Both these ops are required in order to support bloom_560m model.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
The auto-update of the PyTorch version broke the Torch-MLIR build
because it did not update the shape library. Going forward, we should
add the shape library update to the PyTorch version update action.
Strength the shape inference for aten.arange-like op by
1. registering aten.sub and aten.ceil.Scalar op and design folders for them.
2. register a new constant-like op: Torch::ConstantNumberOp and design canonicalizer for it.
As @oroppas identified, literal strings that are over 16,380 characters
cause the MSVC compiler to throw an error (C2026), eventually causing
the Windows build of Torch-MLIR to fail because the length of the
generated MLIR for the shape library crosses the allowed threshold.
This patch fixes the problem by making the Python script generate one
literal string per line to satisfy the MSVC compiler.
Thanks to @oroppas for the bulk of the effort required to resolve this!
* 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.
I recently fixed the handling of the `dim` argument in
`sum_mean_dim` (59fccab857). Therefore,
the checks that the `dim` input is `None` or `[]` are no longer needed.
Bumps the shape library:
- Updates the function signature for aten.arange.start_step
- upstream_shape_functions.mean_dim -> upstream_shape_functions.sum_mean_dim
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>
This commit fixes the shape calculation for:
1.) aten.mean.dim
2.) aten.var.dim
3.) aten.sum.dim_IntList op
Also, it fixes the lowering of `aten.mean.dim` and
`aten.sum.dim_IntList` for handling the cases of empty dim list.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com
- Includes a canonicalizer for `aten.add.t`needed for successfully lowering the shape function
- Only offers support for statically sized index tensors when there is more than one
- Dynamic shape support remains for single indexing tensors
Tanyo Kwok
Vivek Khandelwal
Abhishek Varma
George Petterson
Ramiro Leal-Cavazos
Prashant Kumar
Abhishek Varma
Gaurav Shukla
Daniel Ellis
Ashay Rane
武家伟
Sean Silva
Alex Tsao
Quinn Dawkins
Vidush Singhal
powderluv
Quinn Dawkins
Alec
PhaneeshB