Strict symbolic shapes allow us to assume numpy-style dynamic broadcasts
never occur. This allows us to strengthen the folder for broadcasts to
cases where the rank is the same and all shapes match (including dynamic
sentinel values).
* RecomposeComplexOps: Remove dead slice op
* lib/Dialect/Torch/IR/TorchOps.cpp: Fold slice ops even when they are on non-value tensors
* lib/Conversion/TorchToTosa/TorchToTosa.cpp: Fix slice start/end out of range/none
* lib/Dialect/Torch/IR/TorchOps.cpp: AtenSliceTensorOp::fold: Fold slices that go from 0:int_max
* More tests for aten.split.Tensor
Currently, the op `torch.tensor_static_info_cast` will not get
canonicalized away if the result type has any shape or dtype
information. This is because `isValidSubtype` only returns true when
the tensor types being compared are exactly the same or the supertype
has no shape and dtype information. Being unable to canonicalize away
the `torch.tensor_static_info_cast` gets in the way of further
optimizations, such as shape propagation.
This commit improves `isValidSubtype` by adding logic that compares
the shapes and dtypes of the two tensor types to determine of one type
is indeed a valid subtype of the other.
Fixes https://github.com/llvm/torch-mlir/issues/1926
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>
- 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
Prior to this patch, the canonicalizers for `AtenSizeOp` and
`AtenSizeIntOp` succeeded only if the tensor operand's type information
included the size of the requested dimension(s). We can extend the set
of optimizable cases by propagating types across operations whose result
type matches the input tensor type.
Specifically, this patch enables the canonicalizers for `AtenSizeOp` and
`AtenSizeIntOp` to see past `tensor_static_info_cast`,
`copy.to_vtensor`, and `copy.to_tensor` ops until it reaches the first
op whose result type contains size information for the requested
dimensions, with a maximum bound of 6 parent lookups to avoid indefinite
compilation times. All other encountered ops cause the canonicalizer to
give up.
A prior patch (63538de2) that added support for bfloat16 type did not
add the canonicalization pattern to fold `torch.prim.dtype` operations
on bfloat16 tensors into the integer constant 15. This patch fixes the
problem.
In addition to updating the llvm-project submodule, this patch also:
1. updates shape functions and tests so that `func` and `call`
operations refer to the `func` dialect
2. avoid duplicate registration of dialects
This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`,
`aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op.
This commit also fixes formatting for the file scalar.py and scalar_comparison.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit decomposes `aten.to.dtype_layout` op into `aten.to.dtype` op.
This commit also fixes the formatting for the file type_conversion.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds lowering of `aten.ceil.float` op.
This commit also fixes formatting for the file scalar.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
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
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.
Quinn Dawkins
JianzheXiao
Jiawei Wu
Alexandre Rames
Matthias Gehre
Jiawei Wu
Yuanqiang Liu
Zhekun Zhang
Vivek Khandelwal
Ramiro Leal-Cavazos
Ahmed S. Taei
Gaurav Shukla
gpetters94
Ashay Rane
Quinn Dawkins
erman-gurses
Maksim Levental
Sean Silva