This commit adds verifiers to the ops `ToBuiltinTensorOp` and
`FromBuiltinTensorOp` that make sure that the input and output have
the same shape and data type.
This enables building Pytorch from source in the CI.
The build should mostly hit the ccache.
Release builds will follow once we have some runtime on the CI.
In the interest of merging upstream LLVM quickly, a previous patch
(7f08169) updated the torch-mlir build to register all dialects and
passes through Python bindings. This patch limits the dialects and
passes to only those that are used in torch-mlir.
Key to this change are the removal of
`MLIRPythonExtension.RegisterEverything` and the introduction of a new
Python module (`_mlir_libs/_site_initialize_0.py`), where we register
the dialects and passes used by torch-mlir.
This commit adds the decomposition for `aten.var.dim` op.
This commit also make changes in the decomposition for `aten.var` op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This patch adds a new pass `torch-verify-conversion-to-value-semantics`,
which looks for non-value semantics tensors to catch such tensors early
during compilation.
This pass requires `torch-refine-public-return` pass to ensure that
return operations are updated to use value tensors, followed by the
canonicalize pass to remove any dead ops that may use or produce
non-value 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.
Prior to this patch, the code in the `torch-simplify-shape-calculations`
pass iterated on the uses of an op's result while also modifying the
value. This caused the iterator to get invalidated, thus terminating
the loop early and producing incorrect IR. This patch makes use of
`llvm::make_early_inc_range()` to ensure that the iterator is not
invalidated while executing the loop body.
This commit does three things:
1. Reverts some of the shape lib changes merged in
https://github.com/llvm/torch-mlir/pull/844
2. Updates the signature of `aten.sum_dim_IntList` that was recently
updated in
23bdb570cf
3. Replaces `aten.zero.functional` with `aten.zero`, updated in 960758b0b7
`aten.select_scatter` op.
This commit adds:
1. Lowering of `aten.slice_scatter` op into `tensor.insert_slice`
op.
2. Decomposes the `aten.select_scatter` op into `aten.slice_scater`
op.
Signed-Off-By: Prateek Gupta <gprateek93@gmail.com>
The canonicalizer converts `torch.prim.dtype` ops into integer constants
for valid types, but the type may not be known until type refinement is
complete. However, type refinement cannot make progress until
`torch.prim.dtype` ops have been resolved to their corresponding integer
constants, thus creating a circular dependency.
This patch creates a tight coupling between type refinement and the
lowering of `torch.prim.dtype` ops by handling such ops as they are
encountered during type refinement. The unit test in this patch aims to
check whether the type refinement pass can now handle chains of
operations that alternate between type construction and type refinement.
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.
TorchScript nodes like `prim::Load` and `prim::Store` aren't supported
in torch-mlir because they can't be lowered to backends, but such nodes
can occur in the TorchScript IR.
This patch adds a rudimentary translation from such nodes to
corresponding ops in the Torch dialect. Since we expected such nodes to
go away during lowering because of the SymbolDCE pass, this patch does
not add code to lower these ops beyond the Torch dialect.
In the `pyhpc_turbulent_kinetic_energy` TorchBench benchmark, the shape
calculation occurs inside loops, but because `DropShapeCalculationsPass`
does not explicitly mark the Torch dialect as legal, the pass execution
fails.
This patch adds Torch to the list of legal dialects, and adds a test to
validate the translation.
Prior to this patch, the torch dialect included `AtenTriuOp` for
computing the upper triangular part of the input matrix, but there was
no code for lowering the op to the linalg dialect.
This patch adds code to generate a `linalg.generic` operation that
compares indices (computed using `linalg.index`) to choose between zero
or the original value (using `arith.select`). The lowering fails if the
number of dimensions are less than two. This patch also adds a few
end-to-end tests.
* [MLIR][TORCH] Add folder for torch_c.from_i64 & torch_c.to_i64
* add unit tests for each individual fold
* fix failure of NumelZeroRankModule & TestMultipleTensorAndPrimitiveTypesReturn
This commit decomposes `aten.baddbmm` op into `aten.bmm`,
`aten.mul.Scalar`, and `aten.add.Tensor` op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds the decomposition of `aten.adaptive_avg_pool2d` op into
`aten.avg_pool2d` op. The current decomposition only supports cases where
input size is equal to the output size.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
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
The op `aten.rand_like` was missing a shape function, unit tests, and
the `dtype` argument was being ignored in its decomposition. This
commit fixes all three things.
Fix the type promotion code for scalar only operation to return
TorchType which is the type tracked in ValueKnowledge.scalarType.
- Fix `getPromotedResultScalarType` to return Torch type.
- Add `getBuiltInTypeForTorchScalar` helper to convert scalar type
to builtin type before passing to the next level type promotion
helper `updateResultTypeState`.
- Add `setScalarType` helper to make setting ValueKnowledge.scalarType
easier.
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>
The main changes are:
- Added `ValueKnowledge.scalarType` to track scalar type information.
- Added `ValueKnowledge.kind` to indicate the value kind.
- Modified the meet and join helper functions. The ValueKnowledge has
slightly more complicated state now so the meet and join function need
to look at the `kind` field in addition to just the type field.
This also has a fix for the adjustment of types of TupleConstruct
inputs, which I found when using this new functionality on a model.
Some scenarios in tracing create situations where the output of
TupleConstruct has a more refined type than the inputs.
This introduces a helper `adjustStaticInformationForValues` which
subsumes the `derefineValues` helper and the tensor static information
adjustment we were doing.
武家伟
Kevin Kiningham
Tanyo Kwok
Ramiro Leal-Cavazos
powderluv
Ashay Rane
Ziheng Jiang
Vivek Khandelwal
Sean Silva
Prateek Gupta
Quinn Dawkins
erman-gurses
Maksim Levental
Henry Tu
Yi Zhang