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
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
XLA HLO concat does not accept scalars, so fail verification if this occurs. Avoids segfault when accessing an empty output shape.
PiperOrigin-RevId: 337618167
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
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
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
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
Smit Hinsu
A. Unique TensorFlower
Adrian Kuegel
Tres Popp
Dmitry Volodin
Richard Uhler
Thomas Joerg
Jacques Pienaar
Benjamin Kramer
Robert Suderman
Hanhan Wang
Uday Bondhugula
Andy Ly
Lucy Fox
Mehdi Amini
Stephan Herhut
Uday Bondhugula