Rename BlockAndValueMapping to IRMapping
Moved PrimTupleConstructOp type validation to its own verifier as the
tablegen version does not work for a combination of variadic input and
non-variadic output.
One of the potential values for a `torch_upstream::ScalarType` is
`Undefined`. This means that conversion of a `ScalarType` to another
type is a computation that can fail. To enforce handling of the
failure case, this commit makes the two helper functions that convert
`ScalarType`s into other types return `failure()` when the
`ScalarType` is `Undefined`.
This reverts commit eaab9be207, since it
is causing the post-merge CI tests to fail, causing subsequent PRs to be
blocked. Specifically, the tests
`ElementwiseAtenLogicalAndOpPromoteBroadcastModule_basic` and
`ElementwiseAtenLogicalXorOpPromoteBroadcastModule_basic` fail because
the oracle does not match the computed result. This patch reverts the
commit to make the post-merge builds green again.
Summary of changes:
- LLVM now includes <optional> instead of "llvm/ADT/Optional.h" in most
(although not all) places
(https://reviews.llvm.org/rG541ef3d61e9341cd38420c0dbca9250c4d0ea04c).
This patch replaces the affected instances of `llvm::Optional` with
`std::optional`.
- In the usages of llvm::Optional that remain, llvm::Optional::value()
is deprecated, so this patch replaces them with a dereference.
In order to verify if a given IR satisfies the backend contract, the
verifier needs to know if decompositions took place, and if so, which
ops were decomposed and which were not.
This commit adds two arguments to `verifyBackendContractPass` to
specify if decompositions took place and which ops to consider backend
legal, similar to the arguments of `LowerToBackendContractPass`.
Summary of changes:
- Replace `llvm::None` with `std::nullopt`, since the former is deprecated
(https://reviews.llvm.org/D139763)
- Use setter for symbol visibility instead of passing string attribute when
creating FuncOp
* [custom op] Generalize shape library logic to work with dtypes
This commit generalizes the shape library logic, so that dtype rules
for ops can also be expressed using the same mechanism. In other
words, each op can now have a shape function and a dtype function
specified in Python that is imported during lowering to calculate the
shapes and dtypes throught a program. For more information about how
to specify a dtype function, see the updated
`docs/adding_a_shape_and_dtype_function.md`.
For those not familiar with how the shape library works, the file
`docs/calculations_lib.md` provides an overview.
Currently `getTensorRank` returns -1 if it was unable to get the rank
of the tensor. However, not every use in the codebase was checking the
return value, and in some cases, the return value was casted to
unsigned leading to some infinte loops when an unranked tensor reached
a decomposition.
This commit changes the return of `getTensorRank` to
`Optional<unsigned>` to make it clear to the user that the function
can fail.
This commit also changes a couple of for loops that iterate a vector
in reverse order that can potentially become infinite loops into
range-based for loops.
A circular dependency was introduced in e7edcc62fd.
Specifically, the `makeShapeLLVMCompatible` and `makeShapeTorchCompatible` utilities were being called from `lib/Dialect/Torch/IR/TorchTypes.cpp` and `lib/Dialect/Torch/IR/TorchOps.cpp` defined under the `:TorchMLIRTorchDialect` bazel target, leading it to take a dependency on `:TorchMLIRConversionUtils` which already depends on `:TorchMLIRTorchDialect`, hence creating a circular dependency.
This commit resolves the same by moving said utilities from `lib/Conversion/Utils/Utils.cpp` to `lib/Dialect/Torch/Utils/Utils.cpp`. Please LMK if there's a better way to fix this and I will update the code.
This commit also adds the required targets to support building the new conversions from Torch to ML Program dialect that was introduced in f416953600.
Bazel build GHA triggered manually to verify: https://github.com/sjain-stanford/torch-mlir/actions/runs/3645944517
- Support for non-prefixed accessors has been removed. See:
https://reviews.llvm.org/D136727
- Rename `operands` to `methodOperands` in `prim.CallMethod` since the
name `operands` overlaps with a builtin method name. See:
https://reviews.llvm.org/D136727
- Add passes in refbackend to lower memref.subview. See:
https://reviews.llvm.org/D136377
- Replace `CopyToValueTensorOps` first in `RewriteViewLikeSubgraph` in
maximize-value-semantics.
The current implementation of the `RewriteViewLikeSubgraph` pass in
maximize-value-semantics creates temporarily invalid IR. In
particular, given a forward slice starting from a
`CopyToNonValueTensorOp` and ending in `CopyToValueTensorOp`s, the
pass first replaces all uses of the `CopyToNonValueTensorOp` with
its operand, which results in all the `CopyToValueTensorOp` users
having their operand have type `!torch.vtensor`, which is invalid.
The correct way to do things is to first replace all the
`CopyToValueTensorOp`s with their operand, and then replace all uses
of the `CopyToNonValueTensorOp` with its operand.
This only started failing now because the generated accessor
`getOperand` for the `CopyToValueTensorOp` now returns a
`TypedValue<NonValueTensorType>`, which has an assert checking that
the value returned is of the expected type.
This commit changes the `InsertRngGlobalsPass` to `TorchConversionToMLProgram`
pass. This commit also adds the `MLProgramBufferize` pass for the
bufferization of ml_program dialect ops to run on refbackend.
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
Summary of changes:
- Change ShapedType::kDynamicSize -> ShapedType::kDynamic
- llvm::NoneType has been deprecated, change convertScalarToDtype to use llvm::None
Summary of changes:
- Replace call to `MemoryEffectOpInterface::hasNoEffect`
with `isMemoryEffectFree`.
- Make fix for the dynamic dims, since
`kDynamicSize` value changed to
`std::numeric_limits<int64_t>::min()` from `-1` in llvm
- `makeShapeLLVMCompatible` and `makeShapeTorchCompatible`
utilities convert shapes in order to remain consistent
with the Torch and MLIR semantics.
- Update tags
llvm: 147fe9de29dc13c14835127b35280c4d95c8e8ba
mhlo: 1944b5fa6062ec4c065d726c9c5d64f1487ee8c5
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
-- 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 renames the patterns used to match on lists of constant
values to `m_TorchListOfConstant{valueType}s`. This is needed to avoid
ambiguity for when `valueType` has `Optional` in it. In particular, it
makes it clear whether the values in the list are optional or the list
itself is optional.
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.
Gleb Kazantaev
Ramiro Leal-Cavazos
Eric Kunze
Vivek Khandelwal
Jiahao Li
Maksim Levental
Srirammaswamy
Ashay Rane
Shivam Gupta
Chi_Liu
Tanyo Kwok
ataheridezfouli-groq
Sean Silva
Ahmed S. Taei
Sambhav Jain
Abhishek Varma
George Petterson