- Add this attribute to match the corresponding XLA HLO attribute on convolution
operations.
- A true value indicates a reversal of the corresponding kernel spatial dimension.
- Since XLA builder does not support this attribute, use a custom HLO converted to map
from mlir::mhlo::ConvOp to XLA.
PiperOrigin-RevId: 346891737
Use constant to generate the correct assertion message. This avoids
confusion when lowering the max rank specialization for debugging.
PiperOrigin-RevId: 344769021
M_PI and other math constants (used in chlo_legalize_hlo_patterns.td)
are not part of the C++ standard and must be enabled on MSVC
(similar to _GNU_SOURCE adding glibc symbols to posix headers).
PiperOrigin-RevId: 342432987
- Extend MHLO CustomCall to have multiple tensors as results.
- Extend LHLO CustomCall to have multiple memrefs for output operands.
- Fix HLO->LHLO and XLA HLO->LHLO mapping for CustomCall to setup the
operand_segment_sizes attribute correctly.
PiperOrigin-RevId: 342067762
- Extract code to create result memref's into a ConvertResults function.
- Also fix a bug when using reifyReturnTypes: use correct index for result_shape instead
of always using the first element.
PiperOrigin-RevId: 341852227
The conversion had a bug in computation of strides and sizes args for std.memref_reinterpret_cast. The previous version also relied on linalg::ReshapeOp to do broadcasting when the rank of the output was higher than the rank of the input. Now the broadcasting is entirely done via descriptor modification and linalg::ReshapeOp was replaced with CopyOp.
PiperOrigin-RevId: 341379871
Lowerings that depended on operations between real and complex types may
not infer the correct intermediate type. Removing these operations as
they are not technically legally generated operations. Updated tests
to validate this.
PiperOrigin-RevId: 341128903
Previously this started at rank 2 after checking for scalars and equal shapes. This resulted in cases such as <1xf32> + <2xf32> being treated as impossible.
PiperOrigin-RevId: 341043965
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/44499
The file `sink_constants_to_control_flow.cc` includes the header
`PassDetail.h`, which itself includes `mhlo_passes.h.inc`. The latter is
not guaranteed to be already generated since there was no dependency set
to MLIRMhloPassIncGen.
Copybara import of the project:
--
0ff51ccc88c1ba049eb2e9555afb54079bea39c9 by Marius Brehler <marius.brehler@iml.fraunhofer.de>:
Add missing dep on MLIRMhloPassIncGen target
The file `sink_constants_to_control_flow.cc` includes the header
`PassDetail.h`, which itself includes `mhlo_passes.h.inc`. The latter is
not guaranteed to be already generated since there was no dependency set
to MLIRMhloPassIncGen.
PiperOrigin-RevId: 340485068
Additionally:
- Forward listeners through new if/else op builders.
This corrects an error that led to incomplete legalization of broadcasted op
lowering.
- Use OpConversionPattern to ensure up to date operand values are used.
PiperOrigin-RevId: 339838833
Doesn't support tensors right now, as it's somewhat hairy to support both at
the same time. Since we use a generic lowering the result is messy
and needs a mem2reg pass to eliminate extra load/store/allocas.
PiperOrigin-RevId: 339562971
If unspecified, `compare_type` is FLOAT for float element types, SIGNED for signed element types and UNSIGNED for unsigned element types. compare_type can be TOTALORDER for float element types.
- Added import and export support the attribute.
- Restricted legalization from HLO to TF to the default compare types.
- Updated existing usage of the CompareOp
PiperOrigin-RevId: 339099219
As described in mlir/Transforms/Bufferize.h, patterns that don't need the special methods on a BufferizeTypeConverter should use a regular OpConversionPattern.
PiperOrigin-RevId: 338424819
The fusion heuristic identifies the root of a fusion by checking whether an
output of a linalg operation is a function result. It did not consider outputs
flowing through aliasing operations (like casts).
PiperOrigin-RevId: 337479910
Rahul Joshi
Christian Sigg
Alexander Belyaev
Stephan Herhut
Benjamin Kramer
Tres Popp
River Riddle
A. Unique TensorFlower
Adrian Kuegel
Lucy Fox
Ben Vanik
Smit Hinsu
Robert Suderman
Marius Brehler
Sean Silva
Nicolas Vasilache
Thomas Joerg
Hanhan Wang