Commit Graph

439 Commits

Author SHA1 Message Date
A. Unique TensorFlower 8ff5f9d94a Integrate LLVM at llvm/llvm-project@186f2ac612
Updates LLVM usage to match
[186f2ac612ad](https://github.com/llvm/llvm-project/commit/186f2ac612ad)

PiperOrigin-RevId: 380647265
2021-06-21 13:08:34 -07:00
Benjamin Kramer 03d2cb606d [mhlo] Make sure reifyResultTypes returns a tensor of index
Dynamic broadcast/reshape/iota take i32/i64 shape inputs, but users of
reification expect index shapes. Insert an appropriate cast if necessary.

PiperOrigin-RevId: 380613128
2021-06-21 10:42:38 -07:00
A. Unique TensorFlower a6b8882739 Integrate LLVM at llvm/llvm-project@b650778dc4
Updates LLVM usage to match
[b650778dc4ac](https://github.com/llvm/llvm-project/commit/b650778dc4ac)

PiperOrigin-RevId: 380565709
2021-06-21 06:40:22 -07:00
Adrian Kuegel 4c282fb542 Avoid Broadcast op if all shapes are (known to) be equal.
The rank specialization case for shapes which are either of the same shape or a
scalar doesn't need to compute the final result shape.

PiperOrigin-RevId: 380129316
2021-06-18 00:22:35 -07:00
A. Unique TensorFlower d4a7901284 Integrate LLVM at llvm/llvm-project@366df11a35
Updates LLVM usage to match
[366df11a3539](https://github.com/llvm/llvm-project/commit/366df11a3539)

PiperOrigin-RevId: 380081103
2021-06-17 17:29:07 -07:00
Abhishek Varma da6593e960 PR #50073: [MLIR] Add GatherOp lowering from lmhlo to Affine.
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/50073

-- Lowering of `GatherOp` is added from lmhlo to Affine. The lowering
   has been added as a part of `lhlo-legalize-to-affine` pass.

Signed-off-by: Abhishek Varma <abhishek.varma@polymagelabs.com>
Copybara import of the project:

--
5b3dcd4ab31a69f305cd079b869ee35ba6dc8bf5 by Abhishek Varma <abhishek.varma@polymagelabs.com>:

[MLIR] Add GatherOp lowering from lmhlo to Affine.

-- Lowering of `GatherOp` is added from lmhlo to Affine. The lowering
   has been added as a part of `lhlo-legalize-to-affine` pass.

Signed-off-by: Abhishek Varma <abhishek.varma@polymagelabs.com>
PiperOrigin-RevId: 380052157
2021-06-17 14:55:49 -07:00
A. Unique TensorFlower 470ac45f45 [MLIR][HLO] Remove unused pass `TransformUnrankedHloPass`
The pass was replaced by the new generalized rank specialization and the two
passes `mhlo-rank-specialization-cluster` and `mhlo-rank-specialization-to-scf`.

PiperOrigin-RevId: 379935562
2021-06-17 05:20:49 -07:00
A. Unique TensorFlower 9f47ff607b [MLIR][KernelGen] Better rank specialization for clusters containing `mhlo.select`
Merge `mhlo.select` into rank specialization clusters. Infer shape equalities
correctly from `mhlo.select` (and also from `mhlo.clamp`). This allows to lower
the relu kernel completely flattened.

PiperOrigin-RevId: 379925793
2021-06-17 04:06:08 -07:00
Mehdi Amini 8c8e81cb69 Fix pass definition to inherit from the TableGen generated base class (NFC)
PiperOrigin-RevId: 379860210
2021-06-16 19:05:11 -07:00
Wenyi Zhao 88cc0c6c46 PR #50271: [MLIR][DISC] Bufferize GatherOp and DynamicGatherOp
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/50271

support hlo-to-lhlo conversion for GatherOp and DynamicGatherOp
Copybara import of the project:

--
117a1b1bcaac7ecc5224b02863eede5c1b9618fe by Wenyi Zhao <reyizero@gmail.com>:

[MLIR][DISC] Bufferize GatherOp and DynamicGatherOp

PiperOrigin-RevId: 379801972
2021-06-16 13:47:56 -07:00
Wenyi Zhao 34dc5f2a79 PR #50020: [MLIR][DISC] support fusion on buffer
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/50020

This pass implements the logic to group kLoop/kInput fusion patterns on
buffer level. The reason for this is that we can avoid a lot of
headaches to handle `shape-only` consumers specially (e.g. memref.dim,
shape.shapeOf) since shapes are already resolved in buffer world. It may
be better to move this pass to tensor level after more shape
inference/constraint infras are ready on mhlo level.
Copybara import of the project:

--
e31f8344b59aa9860097197585215ea1689b8ff4 by Wenyi Zhao <reyizero@gmail.com>:

[MLIR][DISC] support fusion on buffer

This pass implements the logic to group kLoop/kInput fusion patterns on
buffer level. The reason for this is that we can avoid a lot of
headaches to handle `shape-only` consumers specially (e.g. memref.dim,
shape.shapeOf) since shapes are already resolved in buffer world. It may
be better to move this pass to tensor level after more shape
inference/constraint infras are ready on mhlo level.

--
35f2eb2791241b0ab5db1ddcaf1b4006278ddccf by Wenyi Zhao <reyizero@gmail.com>:

fix

--
923c8d61f7fe00a2a0df22d5be396508f0667964 by Wenyi Zhao <reyizero@gmail.com>:

fix sanity check failure

PiperOrigin-RevId: 379743424
2021-06-16 09:51:29 -07:00
A. Unique TensorFlower 82696f8598 [MLIR][HLO] Annotate `mhlo.clamp` and `mhlo.select` as element-wise broadcasting
The operations allow for a limited form of broadcasting which allows some
operands to be scalars. As such they are neither strictly `Elementwise`, nor
`Broadcasting`. They do fulfill the requirements for `BroadcastingElementwise`
though.

PiperOrigin-RevId: 379719961
2021-06-16 07:59:26 -07:00
Hanhan Wang b44ab8ad49 Add support for lowering DataMovementOp ops to Linalg on unsigned types.
PiperOrigin-RevId: 379527360
2021-06-15 10:58:22 -07:00
Feiwen 3afbe312f8 PR #49919: [MLIR][DISC] pattern conversion from tf2mhlo: ConvertUnpackOpDynamic, ConvertSignOpDynamic, ConvertSigmoidGradOpDynamic
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/49919

We are porting our MLIR-based dynamic shape compiler to tf community (From OP def, Patttern, to Optimization pass, etc).
This is the 5th PR about tf2mhlo pattern conversion, which including ConvertUnpackOpDynamic, ConvertSignOpDynamic, ConvertSigmoidGradOpDynamic.
The rest pattern conversions we will add:
- ConvertSqueezeOpxxx
- ConvertStridedSliceOpxxx
- ConvertPrintOp
Copybara import of the project:

--
21b3c3eb05b12956bcdb8b98cc54d9371dbf034d by azazhu <azazhu@gmail.com>:

[MLIR][DISC] pattern conversion from tf2mhlo: ConvertUnpackOpDynamic, ConvertSignOpDynamic, ConvertSigmoidGradOpDynamic

--
634630a4e2e426357290650bd579b35efecab5b3 by azazhu <azazhu@gmail.com>:

[MLIR][DISC] refine ConvertUnpackOpDynamic, ConvertSignOpDynamic, ConvertSigmoidGradOpDynamic

--
39a2bedd6dafb369ae960c5197b7a352bfdfbc80 by azazhu <azazhu@gmail.com>:

add RealDynamicSliceOp's canonicalize and fix CI

--
a1c38dd0963d602ed4812da0d77a096a95920ddb by azazhu <azazhu@gmail.com>:

fix CI for ConvertUnpackOpDynamic

--
5a8b4eb389ed6dc554104356c37f2f1550802b8c by azazhu <azazhu@gmail.com>:

fix typo in ConvertSigmoidGradOpDynamic

PiperOrigin-RevId: 379521079
2021-06-15 10:33:32 -07:00
Chris Jones 5fbdac34a9 [XLA:GPU] Add AllReduce{Start,Done} to MLIR LHLO dialect.
PiperOrigin-RevId: 379455720
2021-06-15 03:55:19 -07:00
Geoffrey Martin-Noble f9f7a63870 Add missing dep on RAL pass generation
Without this I see errors about being unable to find the generated header in our project's build.

PiperOrigin-RevId: 379377718
2021-06-14 17:02:26 -07:00
Wenyi Zhao 7f94bd923b PR #50236: [MLIR][DISC] Bufferize TransposeOp and ConcatenateOp
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/50236

support hlo-to-lhlo conversion for TransposeOp and ConcatenateOp
Copybara import of the project:

--
62860e717f2a14fbd3ddfb634aa6ff132d245a72 by Wenyi Zhao <reyizero@gmail.com>:

[MLIR][DISC] Bufferize TransposeOp and ConcatenateOp

--
ce2ff57c1edee1172cd2f36346cc0b34ec1c7467 by Wenyi Zhao <reyizero@gmail.com>:

fix

PiperOrigin-RevId: 379330954
2021-06-14 12:37:45 -07:00
Wenyi Zhao 23ebbb28d1 PR #50191: [MLIR][DISC] Add RAL (Runtime abstraction layer) Dialect
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/50191

DISC is a e2e flow, including both compiler side and runtime side. For
runtime side, we have different targeting environments (e.g. tensorflow,
pytorch, or sometimes even a standalone binary). In order to simplify
the design of the compiler side, we design a Runtime Abstraction Layer
(RAL) to sperate the compiler side and runtime side. Thus the compiler
side only need to target RAL itself and it is the responsibility of RAL
to handle the differences between different targeting environments.

One of the most important functions of RAL is to manage stateful
resources. To this end, it provides a context object, and hides all
stateful operations behind this context, thus the compiler side itself
doesn't need to care about the resource initialization. For example, a
kernel must be loaded before it can be launched on GPU. However, the
loading operation should only be taken once during the whole lifetime of
the context in order to achieve the best performance. Based on the
initialization-free interfaces provided by RAL, compiler side can focus
on its core optimization logic and lets the RAL to manage the resource
status.

The context mentioned above is passed as a parameter to the entry
function and all RAL APIs should always use the context as their first
argument. This CR also provides a pass to help to ensure this property.
The pass rewrites the entry function to make sure their first argument
is the context. For entry function, the pass also rewrites its inputs
and outputs. To be concrete, all the original inputs and outputs of the
entry function are received from and sent to RAL through a sequence of
RAL API calls correspondingly. The motivation behind this is to hide the
implementation details of I/Os. This design may also potentially enable
partial execution of the compiled module when some of the inputs are
ready.
Copybara import of the project:

--
c4f20a89aed71181e75bcc5265723b88bde23240 by Wenyi Zhao <reyizero@gmail.com>:

[MLIR][DISC] Add RAL (Runtime abstraction layer) Dialect

DISC is a e2e flow, including both compiler side and runtime side. For
runtime side, we have different targeting environments (e.g. tensorflow,
pytorch, or sometimes even a standalone binary). In order to simplify
the design of the compiler side, we design a Runtime Abstraction Layer
(RAL) to sperate the compiler side and runtime side. Thus the compiler
side only need to target RAL itself and it is the responsibility of RAL
to handle the differences between different targeting environments.

One of the most important functions of RAL is to manage stateful
resources. To this end, it provides a context object, and hides all
stateful operations behind this context, thus the compiler side itself
doesn't need to care about the resource initialization. For example, a
kernel must be loaded before it can be launched on GPU. However, the
loading operation should only be taken once during the whole lifetime of
the context in order to achieve the best performance. Based on the
initialization-free interfaces provided by RAL, compiler side can focus
on its core optimization logic and lets the RAL to manage the resource
status.

The context mentioned above is passed as a parameter to the entry
function and all RAL APIs should always use the context as their first
argument. This CR also provides a pass to help to ensure this property.
The pass rewrites the entry function to make sure their first argument
is the context. For entry function, the pass also rewrites its inputs
and outputs. To be concrete, all the original inputs and outputs of the
entry function are received from and sent to RAL through a sequence of
RAL API calls correspondingly. The motivation behind this is to hide the
implementation details of I/Os. This design may also potentially enable
partial execution of the compiled module when some of the inputs are
ready.

--
1991d4f80ab6087943956e1c0fec4940a22ab08d by Wenyi Zhao <reyizero@gmail.com>:

fix

PiperOrigin-RevId: 379317586
2021-06-14 11:27:43 -07:00
Rahul Joshi a6011d0279 [HLO] Add AllReduceScatter to MHLO and LMHLO dialects.
PiperOrigin-RevId: 379296198
2021-06-14 09:37:07 -07:00
Wenyi Zhao 8388303fd2 PR #50211: [MLIR][DISC] Bufferize RealDynamicSliceOp and ReduceOp
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/50211

support hlo-to-lhlo conversion for RealDynamicSliceOp and ReduceOp
Copybara import of the project:

--
c417b336670a1fc256f7026dfe8080e46d13d79a by Wenyi Zhao <reyizero@gmail.com>:

[MLIR][DISC] Bufferize RealDynamicSliceOp and ReduceOp

PiperOrigin-RevId: 378972113
2021-06-11 16:33:15 -07:00
Jacques Pienaar 95ba03534f Allow variadic operands/result in MHLO while
This just adds support for it in the op, but keeps the production/uses as is (e.g., single tensor or tuple) matching what XLA export requires. In follow up here, would be to add pass for export to retuple and then the canonical form could be changed. Tuple'ing given control flow via regions & multi-result operations does not add representational power and all the get_tuple_element ops obscure the computation.

The old form allowed single tensor or tuple. The new variadic number of tensor or tuples as tuples may be nested, so the input could have (Tensor<..>, Tuple<Tensor<...>, Tuple<...>, ...>, Tensor<...>) and HLO_Tensor doesn't allow Tuples.

PiperOrigin-RevId: 378934388
2021-06-11 13:08:28 -07:00
A. Unique TensorFlower bd5752f0bf [MLIR][HLO] Find shape equivalences and use them for better rank specialization
Find shape equivalence classes among the operands and use them for better rank
specialization. If all operands are known to be of the same shape, we can
flatten them to rank one. If there are two shape equivalence classes, we can
generalize the scalar rank specialization cases.

PiperOrigin-RevId: 378844575
2021-06-11 04:00:26 -07:00
Wenyi Zhao 6660234d80 PR #50100: [MLIR][DISC] Bufferize DynamicIotaOp and DynamicPadOp
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/50100

support hlo-to-lhlo conversion for DynamicIotaOp and DynamicPadOp
Copybara import of the project:

--
c3aae94954e35d3f8ad265f619ef9765665a5115 by Wenyi Zhao <reyizero@gmail.com>:

[MLIR][DISC] Bufferize DynamicIotaOp and DynamicPadOp

--
adc6996d70b804d61310d56a33fac975d70c8636 by Wenyi Zhao <reyizero@gmail.com>:

minor

PiperOrigin-RevId: 378733284
2021-06-10 14:20:45 -07:00
A. Unique TensorFlower 14093b7906 [XLA:GPU] Add AllReduce{Start,Done} to MLIR LHLO dialect.
PiperOrigin-RevId: 378681070
2021-06-10 10:27:22 -07:00
Chris Jones 968226b9d7 [XLA:GPU] Add AllReduce{Start,Done} to MLIR LHLO dialect.
PiperOrigin-RevId: 378640706
2021-06-10 06:54:42 -07:00
Adrian Kuegel 6088eb697c Fix Cosh approximation for F16.
We should upcast F16 to F32 to prevent precision loss.
E.g. cosh(-9) would evaluate to 4042 previously instead of 4052.
This allows to enable the MLIR generated kernel for F16 type.
Also move template instantiation for Sinh to inside the #ifdef block.
This was missed in a previous commit.

PiperOrigin-RevId: 378635042
2021-06-10 06:16:44 -07:00
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
A. Unique TensorFlower b9e45007d5 [MLIR][HLO] Extend broadcast propagation pass to enable more fusion
Move element-wise operations into assuming regions. This enables fusion
opportunities within the region.

PiperOrigin-RevId: 378362725
2021-06-09 03:03:37 -07:00
A. Unique TensorFlower d828b457b3 Handle empty tensors in SimplifyConcatSlice.
If the result of the slice is an empty tensor, do nothing.
This fixes a crash: we can't create a `concat` with an
empty operand range.

PiperOrigin-RevId: 378354956
2021-06-09 02:15:47 -07:00
Adrian Kuegel 9a8c254526 Support complex types for Sinh.
Because mhlo::ConstantLike doesn't support complex types, we need to use
GetScalarOfType and broadcast it to the needed shape.
Disable the tf2xla fallback, now that MLIR fully supports Sinh.

PiperOrigin-RevId: 378123151
2021-06-08 04:23:19 -07:00
A. Unique TensorFlower c47869f931 [MLIR][HLO] Rename `move-up-dynamic-broadcasts-for-fusion` to `broadcast-propagation`
PiperOrigin-RevId: 378102608
2021-06-08 01:51:10 -07:00
Benjamin Kramer d1c60df2fe [MHLO:linalg] Be more aggressive about turning mhlo.const into std.constant
On tensors the only difference between these ops is that mhlo.const supports unsigned types.

PiperOrigin-RevId: 377970948
2021-06-07 11:58:23 -07:00
Hanhan Wang 25b93c8d66 Add support for lowering mhlo.iota/dynamic_iota to Linalg on unsigned types.
PiperOrigin-RevId: 377956338
2021-06-07 10:59:33 -07:00
Adrian Kuegel 5315997402 Fix Sinh approximation for F16.
We should upcast F16 to F32 to prevent precision loss.
E.g. sinh(-9) would evaluate to -4042 previously instead of -4052.
This allows to enable the MLIR generated kernel for F16 type.

PiperOrigin-RevId: 377901896
2021-06-07 06:38:42 -07:00
Tobias Gysi fc723380e6 Update lhlo to use the new structured op interface.
Replace deprecated methods in lhlo_fuse_linalg.cc. The new structured op interface has been introduced in https://reviews.llvm.org/D103394.

PiperOrigin-RevId: 377875452
2021-06-07 03:11:03 -07:00
Wenyi Zhao ade873a5e0 PR #49970: [MLIR][DISC] bufferize DynamicReshape and DynamicBroadcastInDim
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/49970

1, add hlo-to-lhlo support for DynamicReshape and DynamicBroadcastInDim

2, add a flag `convert-to-lmhlo-only` to seperate following two case:
   - hlo-to-lhlo only. Simply lowers all mhlo ops to their lmhlo
     counterparts, do not apply any optimization (e.g. elide any
     buffer copy). Buffer optimization is not easy in dynamic
     shape world especially when involving control flow, thus we
     leave this to another dedicated pass.

   - hlo-to-lhlo-or-memref-directly. Lowers some metadata-only mhlo
     ops (e.g. reshape) to memref dialect directly and Lowers others
     to their lmhlo counterparts.
Copybara import of the project:

--
562bd65a368f6194405c4ae6900e3b4388a5ec03 by Wenyi Zhao <reyizero@gmail.com>:

[MLIR][DISC] bufferize DynamicReshape and DynamicBroadcastInDim

1, add hlo-to-lhlo support for DynamicReshape and DynamicBroadcastInDim

2, add a flag `convert-to-lmhlo-only` to seperate following two case:
   - hlo-to-lhlo only. Simply lowers all mhlo ops to their lmhlo
     counterparts, do not apply any optimization (e.g. elide any
     buffer copy). Buffer optimization is not easy in dynamic
     shape world especially when involving control flow, thus we
     leave this to another dedicated pass.

   - hlo-to-lhlo-or-memref-directly. Lowers some metadata-only mhlo
     ops (e.g. reshape) to memref dialect directly and Lowers others
     to their lmhlo counterparts.

PiperOrigin-RevId: 377603395
2021-06-04 15:36:03 -07:00
A. Unique TensorFlower db05388a3c Integrate LLVM at llvm/llvm-project@da3ed58b97
Updates LLVM usage to match
[da3ed58b97c1](https://github.com/llvm/llvm-project/commit/da3ed58b97c1)

PiperOrigin-RevId: 377432380
2021-06-03 20:45:18 -07:00
A. Unique TensorFlower aba16adfa5 Add `mhlo.all_gather` op to MHLO dialect.
Adds import/export/verifier support as well.
Also makes `channel_handle` uniform across mhlo.all_reduce and mhlo.all-gather.

PiperOrigin-RevId: 377323468
2021-06-03 10:45:29 -07:00
A. Unique TensorFlower 4620410f18 Integrate LLVM at llvm/llvm-project@b25546a4b4
Updates LLVM usage to match
[b25546a4b406](https://github.com/llvm/llvm-project/commit/b25546a4b406)

PiperOrigin-RevId: 377077163
2021-06-02 09:32:59 -07:00
A. Unique TensorFlower 75a1c450ea [MLIR][KernelGen] Fix Windows build failure
Fix usage of default constructor. Instead, always use the parameterized
constructor and make the maximum supported rank explicit.

PiperOrigin-RevId: 377037155
2021-06-02 05:34:44 -07:00
wyzhao 968d4b8709 PR #49598: [MLIR][DISC] legalize tensor_load inserted during hlo-to-lhlo conversion
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/49598

This PR implements logic for lowering memref.tensor_load ops that are
inserted during `mhlo-legalize-to-lmhlo`
Copybara import of the project:

--
80eb377af4e02182e1aecc943a41ca5d7d1c2100 by Wenyi Zhao <reyizero@gmail.com>:

[MLIR][DISC] legalize tensor_load inserted during hlo-to-lhlo conversion

This PR implements logic for lowering memref.tensor_load ops that are
inserted during `mhlo-legalize-to-lmhlo`.

--
ac452fe3dcd591211cd5c59be9189fe2f7153b41 by Wenyi Zhao <reyizero@gmail.com>:

minor fix

--
6b36017f8632a06adbc3e05a62975fa641d0260f by Wenyi Zhao <reyizero@gmail.com>:

minor refine

--
846005cc76d0033112e47825c2e9a97790b6925f by Wenyi Zhao <reyizero@gmail.com>:

minor fix

--
f6a4becaa287d5ca323b2d152a4d0ae053730fd9 by Wenyi Zhao <reyizero@gmail.com>:

fix

--
5555749f60f7fce8f57962860ef65efccf0362ba by Wenyi Zhao <reyizero@gmail.com>:

fix

--
8873b9b6d9315c1199ca9f7c133ecf377ecd2fa6 by Wenyi Zhao <reyizero@gmail.com>:

fix

PiperOrigin-RevId: 376942547
2021-06-01 16:27:56 -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 c7c245eaf1 [MLIR][KernelGen] Add MLIR-generated Xlogy kernel
Add the first MLIR-generated kernel that relies on an in-TF lowering. Fusion for
this kernel relies on the generalized rank specialization for operation groups.

PiperOrigin-RevId: 376805435
2021-06-01 04:48:18 -07:00
A. Unique TensorFlower f16e5a3a67 [MLIR][HLO] Use canonicalization patterns in broadcast propagation pass
Replace local canonicalization patterns with those from upstream.

PiperOrigin-RevId: 376794178
2021-06-01 03:14:31 -07:00
A. Unique TensorFlower 31536431e0 [MLIR][HLO] Eliminate duplicate broadcastable constraints
PiperOrigin-RevId: 376718433
2021-05-31 13:50:23 -07:00
A. Unique TensorFlower 0f341012c6 [MLIR][HLO] Eliminate duplicate broadcastable constraints
PiperOrigin-RevId: 376715240
2021-05-31 13:08:02 -07:00
A. Unique TensorFlower 511a1db4f3 [MLIR][HLO] Use canonicalization patterns in broadcast propagation pass
Replace local canonicalization patterns with those from upstream.

PiperOrigin-RevId: 376708719
2021-05-31 12:01:26 -07:00
A. Unique TensorFlower 5f5db13715 [MLIR][HLO] Use canonicalization patterns in broadcast propagation pass
Replace local canonicalization patterns with those from upstream.

PiperOrigin-RevId: 376707588
2021-05-31 11:44:50 -07:00
A. Unique TensorFlower cc1b22e618 [HLO][Linalg] Support scalar broadcasts in point-wise converter
This is needed for operations that support this limited form of broadcasting,
namely `mhlo.select`.

PiperOrigin-RevId: 376655844
2021-05-31 03:50:23 -07:00