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
This is consistent with the design of LMHLO FusionOp, and it simplifies the
usage. Before the change, those redundant operands ended up unused as all sub-regions can already capture needed buffers.
PiperOrigin-RevId: 362381155
- Extract verification of source target pairs attached to collective permute into a common
helper function and use that to verify both MHLO and LMHLO variants.
- Change MlirGpuTestBase::ParseMlirModule to allow returning back a failure, and use
that to update the mlir_gpu_compile_test to check the new behavior.
PiperOrigin-RevId: 362156962
For now, the pass only reifies the required shape computations. Moving
broadcasts will follow to allow for fusion across them.
PiperOrigin-RevId: 362033715
Previously this would be too strict and fail if dynamic and static dims were
compared. Dynamic/unknown are treated as "maybe equal" to a static value without further info, so at this layer don't flag as invalid unless truly are.
PiperOrigin-RevId: 360189086
This op is useful for rank specialization of broadcasts. Kernel Generator
needs to generate one kernel for each rank, so if we can minimize the rank
of the broadcast shape, we can support more cases with the same number of
special-cased kernels.
PiperOrigin-RevId: 360137827
Adrian Kuegel
wyzhao
Feiwen
Rahul Joshi
A. Unique TensorFlower
Stella Laurenzo
Jacques Pienaar
Itai Zukerman
dfki-jugr
Benjamin Kramer
Hanhan Wang
Geoffrey Martin-Noble
Tim Shen