Commit Graph

19 Commits

Author SHA1 Message Date
A. Unique TensorFlower 9f67417b41 [MLIR][HLO] Avoid duplicate cluster operands when merging
When merging rank specialization clusters, avoid duplicating operands. A fewer
number of operands usually allows better rank specialization.

PiperOrigin-RevId: 378445946
2021-06-09 10:54:55 -07:00
A. Unique TensorFlower b580722041 [MLIR][KernelGen] Merge rank specialization clusters
Merge adjacent rank specialization clusters. Combine their operands, bodies, and
results.

PiperOrigin-RevId: 378433222
2021-06-09 10:07:47 -07:00
Adrian Kuegel b6d8160611 Add Broadcasting and BroadcastingElementwise traits to ConstantLikeOp.
This allows to include such ops in rank specialization clusters.

PiperOrigin-RevId: 378380915
2021-06-09 05:09:26 -07:00
A. Unique TensorFlower 557e56362e [MLIR][KernelGen] Simplify rank specialization tests with smaller target rank
For the tests rank specialize only up to rank 3. The remaining cases for higher
ranks are analogous.

PiperOrigin-RevId: 377024370
2021-06-02 03:48:07 -07:00
A. Unique TensorFlower d1828625ab [MLIR][KernelGen] Make maximum supported rank in rank specialization configurable
The maximum supported target rank of 5 is sufficient for all operations but
`select`. Make the maximum target rank configurable in the rank specialization.
This reduces the number of generated kernels for operations that don't require
it.

PiperOrigin-RevId: 376822496
2021-06-01 06:54:31 -07:00
A. Unique TensorFlower 4ebcebf31c [MLIR][HLO] Exploit scalar properties in rank specialization lowering
Take advantage of the fact that scalars are already ranked and that they are
neutral elements to broadcasting. Do not reshape scalars, do not consider them
for broadcasting, and materialize ranked operations on scalars accordingly.

PiperOrigin-RevId: 375968371
2021-05-26 09:59:13 -07:00
A. Unique TensorFlower cb46298a07 [MLIR][HLO] Support all smaller ranks in rank specialization cases
Rank specialization cases can be applied to all argument tensors of smaller
ranks than the expected maximum rank. This is crucial if all operands are
effectively scalars and the maximum reduced rank is 0.

PiperOrigin-RevId: 375712020
2021-05-25 08:38:53 -07:00
A. Unique TensorFlower 97e6103933 [MLIR][HLO] Reshape to scalars in rank specialization
Scalars were incorrectly casted to scalar tensors when they have to be reshaped.

PiperOrigin-RevId: 375049088
2021-05-21 03:12:16 -07:00
A. Unique TensorFlower 3daf65578a [MLIR][HLO] Add scalar cases for binary rank specialization
For rank specialization clusters that have only two operands, we can materialize
two extra cases in which either of them is a scalar. This avoids redundant index
computations in these cases.

PiperOrigin-RevId: 375037390
2021-05-21 01:35:44 -07:00
A. Unique TensorFlower c62fd89663 [MLIR][HLO] Add equal shapes case to rank specialization
Also restructure lowering implementation to facilitate the addition or removal
of special cases.

PiperOrigin-RevId: 374626365
2021-05-19 05:38:42 -07:00
A. Unique TensorFlower 6af3d2df91 [MLIR][HLO] Add rank specialization with multiple non-scalar operands
Add lowering pattern for rank specialization clusters with more than one
non-scalar operand. The lowering resembles that of the `TransformUnrankedHlo`
pass and switches cases for maximal ranks from 1 through 8.

PiperOrigin-RevId: 374377002
2021-05-18 03:02:45 -07:00
A. Unique TensorFlower 474e419729 [MLIR][HLO] Generalize rank specialization with single operand
The pattern can be generalized to also rank specialize operations with a single
non-scalar operand. Also extract helper functions that can be reused in
following specializations.

PiperOrigin-RevId: 374198381
2021-05-17 08:12:55 -07:00
A. Unique TensorFlower c514c73390 [MLIR][HLO] Extend rank specialization clustering pass
Also cluster operations that operate on same shape operands. These implicitly
satisfy the broadcasting semantics requirement. Also, add test cases for some
cases that appear in the current MLIR-generated kernels.

PiperOrigin-RevId: 374191950
2021-05-17 07:31:36 -07:00
A. Unique TensorFlower ccd70d5717 [MLIR][HLO] Add `rank-specialization-to-scf` pass
Currently the lowering is only implemented for the unary case. The n-ary case
will follow.

PiperOrigin-RevId: 374162772
2021-05-17 03:56:23 -07:00
A. Unique TensorFlower 76341f3720 [MLIR][HLO] Add mixed test for `rank-specialization-cluster` pass
PiperOrigin-RevId: 373762814
2021-05-14 04:40:40 -07:00
A. Unique TensorFlower 420c42a0a1 [MLIR][HLO] Support CHLO unary operations in rank specialization clustering
PiperOrigin-RevId: 373397321
2021-05-12 10:20:43 -07:00
A. Unique TensorFlower 596918a6f1 [MLIR][HLO] Allow rank specialization clustering with `chlo.broadcast_select` op
PiperOrigin-RevId: 373379990
2021-05-12 08:56:49 -07:00
A. Unique TensorFlower 875803e5e1 [MLIR][HLO] Add more tests for `rank-specialization-cluster` pass
PiperOrigin-RevId: 373343750
2021-05-12 04:46:30 -07:00
A. Unique TensorFlower 313d24bc8f [MLIR][HLO] Add `rank-specialization-cluster` pass
Add a pass to cluster unranked C/HLO operations in one
`chlo.rank_specialization_cluster` op. The C/HLO operations are moved to the
body of the operation. Later passes can use this to rank-specialize all these
operations together.

PiperOrigin-RevId: 373336725
2021-05-12 03:46:01 -07:00