Fix usage of default constructor. Instead, always use the parameterized
constructor and make the maximum supported rank explicit.
PiperOrigin-RevId: 377037155
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:
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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`.
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ac452fe3dcd591211cd5c59be9189fe2f7153b41 by Wenyi Zhao <reyizero@gmail.com>:
minor fix
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6b36017f8632a06adbc3e05a62975fa641d0260f by Wenyi Zhao <reyizero@gmail.com>:
minor refine
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846005cc76d0033112e47825c2e9a97790b6925f by Wenyi Zhao <reyizero@gmail.com>:
minor fix
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f6a4becaa287d5ca323b2d152a4d0ae053730fd9 by Wenyi Zhao <reyizero@gmail.com>:
fix
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5555749f60f7fce8f57962860ef65efccf0362ba by Wenyi Zhao <reyizero@gmail.com>:
fix
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8873b9b6d9315c1199ca9f7c133ecf377ecd2fa6 by Wenyi Zhao <reyizero@gmail.com>:
fix
PiperOrigin-RevId: 376942547
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
Replace the previously used `TransformUnrankedHloPass` which rank-specializes
only one operation at a time. The new generalized rank specialization clusters
compatible operations and rank-specializes them collectively.
PiperOrigin-RevId: 376127752
We can lower it to the DivOp in the complex dialect.
Also add tests to hlo-legalize-to-linalg.mlir for CompareOp lowering of complex
types. These were forgotten in a previous commit.
PiperOrigin-RevId: 375669125
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/49454
The new interface is more safe to be used during dialect conversion
(e.g. converting from tensor world to buffer world).
Copybara import of the project:
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a6968072d59bec3c3bbaef0121d297e807c37c91 by Wenyi Zhao <reyizero@gmail.com>:
[MLIR][DISC] Upgrade to use the new `reifyReturnTypeShapes` interface.
The new interface is more safe to be used during dialect conversion
(e.g. converting from tensor world to buffer world).
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55e7c6b7f2f99b99e226645a57e2433fae3e90ed by Wenyi Zhao <reyizero@gmail.com>:
minor fix
PiperOrigin-RevId: 375500273
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/49228
We are porting our MLIR-based dynamic shape compiler to tf community (From OP def, Patttern, to Optimization pass, etc).
This is the first PR, which including some dynamic shape OPs def in mhlo and lmhlo dialect.
For mhlo dialect, we add:
- HLO_RealDynamicSliceOp
- HLO_DynamicPadOp
- HLO_DynamicGatherOp
- HLO_DynamicConvOp
For lmhlo dialect, we add:
- LHLO_RealDynamicSliceOp
- LHLO_DynamicBroadcastInDimOp
- LHLO_DynamicGatherOp
- LHLO_DynamicPadOp
- LHLO_DynamicBitcastOp
- LHLO_DynamicConvOp
- LHLO_DynamicIotaOp
- LHLO_DynamicReshapeOp
- LHLO_DotGeneralOp
- LHLO_BitcastOp
Rest Ops to add:
* We will send a separate PR containing LHLO_DynamicWhileOp and LHLO_DynamicCaseOp for control flow.
* We will add a separate dedicated dialect like mhlo_ral, which including D2HOp/H2DOp/DebugPrintOp/TopKOp, etc.
Previous discussions：[RFC](https://groups.google.com/a/tensorflow.org/g/mlir/c/_X48poNcbDI/m/jCC8BWIICQAJ), [discussion_1](https://llvm.discourse.group/t/updates-on-mlir-based-dynamic-shape-compiler/2384), [Recording of meeting](https://drive.google.com/file/d/1_uEISlV5MUWdG9faKAdKlCWnPtGjRC-D/view?usp=sharing).
Copybara import of the project:
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e22d9e61106e00a1a1c6f368cc4a03e3bd1f414c by azazhu <azazhu@gmail.com>:
[DISC]fea: porting mhlo and lmhlo OPs
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9ec3e76290da07cbd53d7da5fa86ff67179441a1 by azazhu <azazhu@gmail.com>:
[DISC][MLIR] 1. add summary and description for dynamic OPs in mhlo and lmhlo; 2. rm InferOutputTypes; 3. add verify for RealDynamicSliceOp and DynamicPadOp
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0d68cd135555fd935991c12456b21329e628f23f by azazhu <azazhu@gmail.com>:
[DISC][MLIR] 1.remove D2H,H2D and DebugPrint Ops from mhlo/lmhlo dialect; 2. add type constraint to DynamicPadOp and RealDynamicSliceOp; 3.refine lmhlo type constraint; 4.rename RealDynamicSliceOp as name conflict.
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698762a77d60f6a844cb1ab3f32740d4ef3c5843 by azazhu <azazhu@gmail.com>:
[DISC][MLIR] 1. replace dyn_cast to cast 2. refine code
PiperOrigin-RevId: 375022260
* The former is typically invariant regardless of backend.
* The latter may need to be done differently depending on capabilities of the lowering target.
PiperOrigin-RevId: 374492924
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
Moved the corresponding `summary` and `description` fields into the subclasses.
Kept BASE_HLO_ConvOp for `hasWindowReversal()'.
PiperOrigin-RevId: 373173025
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/48667
Added RegionBranchOpInterfaces to lmhlo operations that use regions.
This is needed, since the bufferization features in MLIR have to reason about the control flow within these operations.
Copybara import of the project:
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572fd7d850a46630b812da84e9094280f89f259e by Julian Gross <julian.gross@dfki.de>:
Added RegionBranchOpInterfaces to lmhlo operations.
PiperOrigin-RevId: 372070825
This strips away the signedness with a type converter, using unrealized
conversion casts. The rest is mostly mechanically pushing the original op down
the pipeline so lowerings can see the original types.
Signed types stay signless for now. This can be changed in the HLO bridge later.
I did a pass over all ops and added unsigned lowerings where they were missing.
There may be more.
Currently the lowering will die at a later stage because it doesn't understand
the unrealized casts.
PiperOrigin-RevId: 371077494
Add a folder for maps whose body returns only one of the arguments. When this arises the fold replaces the map output with one of the operand tensors.
PiperOrigin-RevId: 369304322
- No need to walk the entire region, instead just iterate over the top level operations in
the region attached to the fusion op.
PiperOrigin-RevId: 366528833
We can use it also for ternary ops like Select if we change the signature so
that a ValueRange is passed in.
Also remove special casing for HloComplexAdaptor. It can be handled with the
generic adaptor as well.
PiperOrigin-RevId: 365777493
A. Unique TensorFlower
wyzhao
Mehdi Amini
Adrian Kuegel
Feiwen
Rahul Joshi
Stella Laurenzo
Jacques Pienaar
Itai Zukerman
dfki-jugr
Benjamin Kramer
Hanhan Wang
Geoffrey Martin-Noble