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.
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 fixes the `ConstantPad2dStaticModule` test case by adding
the lowering of `aten.pad` operation. Previously the test case
mapped to `aten.constant_pad_nd` operation.
The `aten.pad` now decomposes into `aten.constant_pad_nd` operation.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
This patch updates the `torch_mlir::convertTensorToMlirElementsAttr()`
method to enable the creation of tensors whose base type is BFloat16.
This patch also adds a test to validate the IR generation, and it
updates the test for importing tensors of various types.
1. This commit adds lowering of "while-like" prim loop to scf.while
operation.
2. Adds lowering of "for-like" prim loops to scf.for operation.
Signed-Off-By: Prateek Gupta <prateek@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>
I wasn't able to find exactly what frontend situation created it, but
`torch.jit.trace` will sometimes create functions where the
`jit::Block`'s param node has refined tensor types. So we need to adjust
the function's formal param types to those refined types.
The updated LLVM code includes a patch to create bfloat16 array
attributes, thus enabling a different patch to torch-mlir to flesh out
support for the bfloat16 type.
Prior to this patch, the result type for several tensor operations could
only be float32, float64, or null. This patch adds bf16 to the list of
allowed result types.
* shape: add shape transfer function for aten.neg
Prior to this patch, the list of shape transfer functions did not
include `aten.neg`, which resulted in errors like below.
```
error: unsupported by backend lowering: tensor with unknown rank or dtype
note: see current operation: %0 = "torch.aten.neg"(%arg0) :
(!torch.vtensor<[256,256],f32>) -> !torch.vtensor<*,f32>
note: this is likely due to a missing shape transfer function in shape_lib_gen.py
```
This patch fixes the problem by adding a shape transfer function to
reflect the point-wise nature of this operation.
* linalg: add translation of aten.neg operation
This patch adds a translation rule to lower `aten.neg` operations on
tensors to an `arith.negf` operation wrapped inside a `linalg.generic`
operation. This patch also adds a rudimentary test.
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
This commit decomposes different variants of `aten.where.*` op into
`aten.where.Self` op. It covers `aten.where.Scalar`,
`aten.where.ScalarSelf` and `aten.where.ScalarOther` ops.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit decomposes `aten.new_empty` op into `aten.empty.memory_format` op.
This commit also made a dtype fix to the constant tensor allocation like ops.
Earlier the dtype for the result was inferred from the result type; now, it's
being evaluated as per the original definition of the op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
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.
The reified code to compute the shape of torch.aten.constant_pad_nd
uses negative indices when setting list elements. This was not
converted to a positive offset in one place in SimplifyShapeCalculations
which prevented computation of the static shape.
The logic in the rewriting phase had a bug in case of a read-only op
coming before mutation ops. The logic would use the op itself as the
"latest literal", but that is not correct, because later on we replace
the op itself with the *final* "latest literal", assuming that all uses
of the op have been rewritten -- that was working in general, except for
any read-only ops at the beginning.
Big thanks to @ljfitz for the tiny reproducer!
Fixes#704
- This commit adds decomposition of `aten.dropout` op. It also covers the
training mode of the same op.
- It also adds lowering of `aten.sub.float` op.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit adds the op `ValsemVariantAtenCopyOp` that represents
`AtenCopy_Op` without the underscore. This is needed to make sure
that the `ReduceOpVariants` pass turns the in-place op into an op
that takes value tensors as inputs, otherwise the
`MaximizeValueSemantics` pass will not be able to add value
semantics correctly.
This commit also adds the lowering of `ValsemVariantAtenCopyOp`.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds support for type refinement when
`torch.tensor_static_info_cast`s are involved, even when there are
users of the casted tensor that don't allow type refinements.
Originally the canonicalization pattern for
`torch.tensor_static_info_cast` would check if all the users of the
casted tensor allowed type refinements before making any changes. This
means that if at least one of the users did not allow type
refinements, the pattern would fail. This becomes an issue when doing
shape calculations because the calculations need the shape information
of each input tensor to be available before the calculation can be
simplified.
Vivek Khandelwal
Henry Tu
Sean Silva
Ashay Rane
Ramiro Leal-Cavazos
Yi Zhang
Prateek Gupta
Gaurav Shukla
Liam Fitzpatrick
Maksim Levental
Anup Gangwar
Vigilans