In IREE, we use indexed generic op to handle the initial value. However, we
lower it to a generic op that carries an init_tensor here, and leave the handle
of initialization problem to later passes.
PiperOrigin-RevId: 354294807
If mhlo.reshape is not purely collapsing some consecutive operand
dimensions into result dimensions, we will generate two linalg
reshape op for it: the first one collapses all operand dimensions
into one dimension, and the second one expands it to all result
dimensions. For this case, the number of collapsed/expanded dimensions
should be coming strictly from the operand/result. It is different
from the case where we can generate one linalg reshape. For that case,
the reassociation map should have rank equal to the largest among
operand/result shape.
PiperOrigin-RevId: 354293826
This is the right behavior for TF and JAX and matches what TF does on GPU. It
doesn't match TF on CPU, but that's really a TF bug.
PiperOrigin-RevId: 353657779
This is the right behavior for TF and JAX and matches what TF does on GPU. It
doesn't match TF on CPU, but that's really a TF bug.
PiperOrigin-RevId: 353628258
This is the right behavior for TF and JAX and matches what TF does on GPU. It
doesn't match TF on CPU, but that's really a TF bug.
PiperOrigin-RevId: 353624935
We prototyped the lowering from mhlo.dot to linalg.matmul in IREE. Since Linalg
now supports matmul in tensors world, we can move the lowering logic to tensors
world, and upstream to legalize_to_linalg.cc. The patch lowers the mhlo.dot to
the linalg.matmul/matvec/dot in tensors world.
PiperOrigin-RevId: 351184911
This updates the tests to no longer rely on tensor_store. Once all users of this behavior have adopted, the tensor_store support will be removed.
PiperOrigin-RevId: 348624899
For floating point operations, this uses std.pow.
For integer operations, this lowers to a loop.
This adds a dependency on scf.
PiperOrigin-RevId: 348537232
- MLIR MemRefs do not preserve layout information correctly when unit dimensions
are involved. Operations like convolution that use cuDNN however need the correct
layout to be preserved so that we do not end up creating an incompatible combination
of input/filter/output layout that is not supported by cuDNN.
- Add these layouts to convolution attributes in the form of I32ArrayAttr for representing
the layout in "minor_to_major" form similar to XLA.
PiperOrigin-RevId: 348034757
It didn't return 0 for 0.0 and -0.0.
Currently we emit -0.0 for -0.0 which is correct according to the HLO dialect.
For the TF_SignOp we should emit 0.0 in that case, we will leave that as a TODO.
Enable the tests which work now, and add another one for Int64.
Also improve the registration code, we should not register the Int32 kernel.
PiperOrigin-RevId: 347981124
It didn't return 0 for 0.0 and -0.0.
Currently we emit -0.0 for -0.0 which is correct according to the HLO dialect.
For the TF_SignOp we should emit 0.0 in that case, we will leave that as a TODO.
Enable the tests which work now, and add another one for Int64.
Also improve the registration code, we should not register the Int32 kernel.
PiperOrigin-RevId: 347602378
It didn't return 0 for 0.0 and -0.0.
Currently we emit -0.0 for -0.0 which is correct according to the HLO dialect.
For the TF_SignOp we should emit 0.0 in that case, we will leave that as a TODO.
Enable the tests which work now, and add another one for Int64.
Also improve the registration code, we should not register the Int32 kernel.
PiperOrigin-RevId: 347590340
- 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
Two tensors having the same SSA-value isn't sufficient for equality for floating types, as `NaN != NaN`. As written this causes `tf.IsNan` to [miscompile](https://github.com/google/iree/issues/4061).
PiperOrigin-RevId: 345730640
- Add a variant of the fused convolution that does not need a side input and side input scale.
- Rename the existing one to `ConvForwardFusedSideInputOp`.
- Update tests to exercise all variants of the convolution ops in the GPU dialect.
- Eliminate unused `LHLO_ExtentBuffer` and changed LHLO_Buffer to allow any integer element
type to match what XLA can generate sometimes for scratch buffers.
PiperOrigin-RevId: 345701569
- Map Custom call for GEMM in XLA HLO to Gemm/Gemm bias operations in LHLO GPU
dialect.
- Make 'algorithm' an optional attribute to better match with XLA HLO backend config.
- Replace 'alpha' with 'alpha_real' and 'alpha_complex' to support complex GEMM correctly.
- Generate GemmThunk off of LHLO GPU Gemm operations.
PiperOrigin-RevId: 345250840
Boolean element values should be fetched as an unsigned integer and not signed integer which would return -1 for true.
Added to a TODO to handle unsigned types correctly as well as we don't seem to be using unsigned types.
PiperOrigin-RevId: 343927564
- Restructured LHLO GPU Cholesky to better match XLA HLO by eliminating the
untyped buffer and changing is_upper attribute to is_lower.
- Change LhloDialectEmitter to emit LHLO GPU Cholesky operation.
PiperOrigin-RevId: 343873516
- Also fixed kernel_gen embed_memref_prints to mark the declarations inserted private.
- This is in prep for proposed MLIR change to disallow public declarations.
PiperOrigin-RevId: 342081252
- 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
This specific pattern can be replaced with the shape
passed to dynamic_reshape. This is implemented as a
canonicalization on mhlo.dynamic_reshape to fit in
the infrastructure of canonicalization.
PiperOrigin-RevId: 342009365
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
This is to match with HLO semantics and general dimension semantics in MLIR.
Also,
* Define minimal verifier for these ops.
* Add folder for SetDimensionSize op on static shaped dimension.
* Fix assumption of ranked shape in GetDimensionSize op.
PiperOrigin-RevId: 341150923
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/44405
Splitting #43857 by top-level directories.
Copybara import of the project:
--
fa5da7d5478649d11321dcac9f867b0a57e4798a by Dmitry Volodin <mr.molkree@gmail.com>:
fix typos in compiler dir
--
4d3c9f047f7ecb8ab299f1bf28a86fd39096eee7 by Dmitry Volodin <mr.molkree@gmail.com>:
fix one test as "atleast" in it comes from Bazel
--
9440ebaaa9fc4a735f7f72f0c8f0de4ec58afbd6 by Dmitry Volodin <mr.molkree@gmail.com>:
a bit more
PiperOrigin-RevId: 340819994
The slice indices must be rank-1 and have the same number of elements of the
rank of the operand. Give reasonable error messages for violations of these
requirements instead of a misleading error message that the types of the
indices don't all match.
PiperOrigin-RevId: 340660822
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
The lowering assumes that the 'gather' op attributes are identical in both MHLO and LMHLO. But that's not true; some time ago the MHLO version was changed to pack 4 of its attributes into a struct. By doing the same for the LMHLO version we both fix the lowering for this op and resolve a longstanding TODO.
PiperOrigin-RevId: 337943946
XLA HLO concat does not accept scalars, so fail verification if this occurs. Avoids segfault when accessing an empty output shape.
PiperOrigin-RevId: 337618167
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
- Introduce operations in a new lmhlo_gpu dialect that map to GPU library function calls
in the XLA:GPU backend.
- Add basic unit tests as well.
PiperOrigin-RevId: 337132166
Legalize `atan2` analogously to XLA. `atan2` is first reduced to `atan` on the
interval [-1, 1] and subsequently approximated. This CL also adds e2e tests for
trigonometric approximations.
PiperOrigin-RevId: 334794336
- And add conversion from MHLO CustomCall to LHLO CustomCall
- According to XLA documentation, the called function should not be side effecting,
so marking the argument MemRefs as MemRead.
PiperOrigin-RevId: 334737196
Hanhan Wang
Lei Zhang
A. Unique TensorFlower
Rahul Joshi
Benjamin Kramer
Alexander Belyaev
Stephan Herhut
Tres Popp
Smit Hinsu
Adrian Kuegel
Phoenix Meadowlark
Robert Suderman
Dmitry Volodin
Richard Uhler
Sean Silva
Jacques Pienaar
Tim Shen