- Use a common base class to for AllReduce, AllGather, and AllToAll in the ODS spec.
- Add basic verification for replica groups attribute.
PiperOrigin-RevId: 354969654
Shape inference in case of ops with complex element types need to use the element type of complex as the result element type and not the full operand type.
Before:
"mhlo.abs"(%arg0) : (tensor<4xcomplex<f32>>) -> tensor<4xtensor<4xcomplex<f32>>>
After:
"mhlo.abs"(%arg0) : (tensor<4xcomplex<f32>>) -> tensor<4xf32>
PiperOrigin-RevId: 348123967
Casting negative s32 number to u64 directly will have leading 1s in the representation which is not what we want to get a single u64 out of two s32 seeds. Fixed this by first getting unsigned number of the same bit-width.
PiperOrigin-RevId: 345902167
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
Casting negative s32 number to u64 directly will have leading 1s in the representation which is not what we want to get a single u64 out of two s32 seeds. Fixed this by first getting unsigned number of the same bit-width.
PiperOrigin-RevId: 345618958
Casting negative s32 number to u64 directly will have leading 1s in the representation which is not what we want to get a single u64 out of two s32 seeds. Fixed this by first getting unsigned number of the same bit-width.
PiperOrigin-RevId: 345605910
- Split out enum definitions in hlo dialect into a separate .td file (similar to structs)
and generate enum decl/defs for these enums.
- Also split out the LHLO GPU enums into a separate .td file and generate enum
decl/defs for these enums as well.
- Remove unused dialect from ConvolutionAttributes and generate lhlo_gpu enums.
- Add appropriate namespace for all the enums.
PiperOrigin-RevId: 345277240
Casting negative s32 number to u64 directly will have leading 1s in the representation which is not what we want to get a single u64 out of two s32 seeds. Fixed this by first getting unsigned number of the same bit-width.
PiperOrigin-RevId: 345239817
Casting negative s32 number to u64 directly will have leading 1s in the representation which is not what we want to get a single u64 out of two s32 seeds. Fixed this by first getting unsigned number of the same bit-width.
PiperOrigin-RevId: 345227848
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
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
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
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
This https://reviews.llvm.org/D89254 diff introduced implicit matching between same name arguments. Modify usages accordingly.
PiperOrigin-RevId: 338090110
XLA HLO concat does not accept scalars, so fail verification if this occurs. Avoids segfault when accessing an empty output shape.
PiperOrigin-RevId: 337618167
- 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
MHLO concatenate should support dynamic inputs. Its possible that the output
shape can be inferred from a dimension in one input that is not dynamic in
another.
PiperOrigin-RevId: 331054181
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/42509
Add folder for mhlo GetDimensionSizeOp (`mhlo.get_dimension_size`).
`get_dimension_size` folds to a constant when the corresponding tensor
dimension size is statically known / constant.
Copybara import of the project:
--
5994915525ec2e932125aa1f133ce2260ba100af by Uday Bondhugula <uday@polymagelabs.com>:
[MLIR] Add folder for mhlo get_dimension_size
Add folder for mhlo GetDimensionSizeOp. get_dimension_size folds to a
constant when the corresponding tensor dimension size is statically
known / constant.
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/42509 from polymage-labs:get_dimension_size_fold 5994915525ec2e932125aa1f133ce2260ba100af
PiperOrigin-RevId: 328222517
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/42508
An lmhlo.constant op on an memref that is locally allocated and with
no users other than dealloc's can be deleted. Add a canonicalization
pattern for this.
Copybara import of the project:
--
8758c409a15f567e7cb8e1077faa020f5705c85a by Uday Bondhugula <uday@polymagelabs.com>:
[MLIR] Erase dead lmhlo.constant ops
An lmhlo.constant op on an memref that is locally allocated and with
no other users (other than dealloc's) can be deleted. Add a
canonicalization patter for this.
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/42508 from polymage-labs:lhlo_constant_erase 8758c409a15f567e7cb8e1077faa020f5705c85a
PiperOrigin-RevId: 328042416
This allows specifying a constant whose shape is only known when operand shape is. Also use it to update tf.Acos legalization.
PiperOrigin-RevId: 325860604
HLO requires that the element types match for all start index parameters. Right now we don't catch this invalid case until export, so adding a check in the verifier so that we catch this sooner.
This also requires a small tweak to the TF InplaceUpdate op lowering.
PiperOrigin-RevId: 325463796
For input and output dimensions which must match, we shouldn't fail in the case where one dim is dynamic and the other is static. This is insufficient information to conclude a dimension mismatch.
PiperOrigin-RevId: 325344738
Create safe or unsafe variants of `shape.broadcast` depending on the context.
The representation by means of an extent tensor is only legal if the operands
are known to be broadcastable. Currently, there is no use in a safe context in
the codebase but it will be used for shape inference eventually.
PiperOrigin-RevId: 325228073
Create safe or unsafe variants of `shape.broadcast` depending on the context.
The representation by means of an extent tensor is only legal if the operands
are known to be broadcastable. Currently, there is no use in a safe context in
the codebase but it will be used for shape inference eventually.
PiperOrigin-RevId: 325079842
Create safe or unsafe variants of `shape.broadcast` depending on the context.
The representation by means of an extent tensor is only legal if the operands
are known to be broadcastable. Currently, there is no use in a safe context in
the codebase but it will be used for shape inference eventually.
PiperOrigin-RevId: 325056915
Shuffle files around, use TableGen to register passes, and introduce
a `mlir-hlo-opt.cpp` file to hold the main entry point of the -opt tool
and stop relying on static registration for dialect/passes.
PiperOrigin-RevId: 323674455
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/40745
Fold broadcast_in_dim op if the operand is the result of a tensor splat.
Copybara import of the project:
--
26c9f631448b8d6ffd20ece39ea8d4132b5550c7 by Uday Bondhugula <uday@polymagelabs.com>:
[MLIR] Add constant folder for xla_hlo.broadcast_in_dim op
Fold broadcast_in_dim op if the operand is the result of a tensor
splat.
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/40745 from polymage-labs:broadcast_in_dim_fold 26c9f631448b8d6ffd20ece39ea8d4132b5550c7
PiperOrigin-RevId: 320365164
Following on the plan of isolating the compiler/mlir/hlo directory.
Another xla_lhlo dialect will be created under compiler/mlir/xla/ later.
PiperOrigin-RevId: 320210326
There is no reason to have a multidimensional iota for codegen.
This should be canonicalized to a single dimensional iota followed
by a broadcast. Changing iota to on a single dimension and a broadcast
substantially simplifies implementing iota operations.
PiperOrigin-RevId: 320095470
We're preparing to restructure the MLIR HLO ecosystem with 5 dialects:
- chlo: client dialect with explicit broadcast and multiple composite operations
- mhlo: hlo with dynamic shape, decouple from XLA for evolution purpose
- lmhlo: same as above, but after buffer assignment.
- xla_hlo: mapping 1:1 to the XLA HloInstruction class.
- xla_lhlo: same as above, but after buffer assignment.
The first three dialects are intended to live in the new tensorflow/compiler/mlir/hlo
path, the latter two will be created in tensorflow/compiler/mlir/xla.
This patch only moves the directory, will followup with other transformations and tests.
The structure of the new directory follows: https://llvm.discourse.group/t/rfc-canonical-file-paths-to-dialects/621 as we intend to make it a standalone buildable component (see also https://github.com/google/mlir-npcomp as another example).
PiperOrigin-RevId: 319273229
Stephan Herhut
Adrian Kuegel
Rahul Joshi
A. Unique TensorFlower
Jacques Pienaar
Tres Popp
Alexander Belyaev
Smit Hinsu
River Riddle
Phoenix Meadowlark
Dmitry Volodin
Richard Uhler
Thomas Joerg
Roman Dzhabarov
Benjamin Kramer
Robert Suderman
Hanhan Wang
Mehdi Amini
Uday Bondhugula
Andy Ly
Lucy Fox
Uday Bondhugula