92233062c1
This PR implements python enum bindings for *all* the enums - this includes `I*Attrs` (including positional/bit) and `Dialect/EnumAttr`.
There are a few parts to this:
1. CMake: a small addition to `declare_mlir_dialect_python_bindings` and `declare_mlir_dialect_extension_python_bindings` to generate the enum, a boolean arg `GEN_ENUM_BINDINGS` to make it opt-in (even though it works for basically all of the dialects), and an optional `GEN_ENUM_BINDINGS_TD_FILE` for handling corner cases.
2. EnumPythonBindingGen.cpp: there are two weedy aspects here that took investigation:
1. If an enum attribute is not a `Dialect/EnumAttr` then the `EnumAttrInfo` record is canonical, as far as both the cases of the enum **and the `AttrDefName`**. On the otherhand, if an enum is a `Dialect/EnumAttr` then the `EnumAttr` record has the correct `AttrDefName` ("load bearing", i.e., populates `ods.ir.AttributeBuilder('<NAME>')`) but its `enum` field contains the cases, which is an instance of `EnumAttrInfo`. The solution is to generate an one enum class for both `Dialect/EnumAttr` and "independent" `EnumAttrInfo` but to make that class interopable with two builder registrations that both do the right thing (see next sub-bullet).
2. Because we don't have a good connection to cpp `EnumAttr`, i.e., only the `enum class` getters are exposed (like `DimensionAttr::get(Dimension value)`), we have to resort to parsing e.g., `Attribute.parse(f'#gpu<dim {x}>')`. This means that the set of supported `assemblyFormat`s (for the enum) is fixed at compile of MLIR (currently 2, the only 2 I saw). There might be some things that could be done here but they would require quite a bit more C API work to support generically (e.g., casting ints to enum cases and binding all the getters or going generically through the `symbolize*` methods, like `symbolizeDimension(uint32_t)` or `symbolizeDimension(StringRef)`).
A few small changes:
1. In addition, since this patch registers default builders for attributes where people might've had their own builders already written, I added a `replace` param to `AttributeBuilder.insert` (`False` by default).
2. `makePythonEnumCaseName` can't handle all the different ways in which people write their enum cases, e.g., `llvm.CConv.Intel_OCL_BI`, which gets turned into `INTEL_O_C_L_B_I` (because `llvm::convertToSnakeFromCamelCase` doesn't look for runs of caps). So I dropped it. On the otherhand regularization does need to done because some enums have `None` as a case (and others might have other python keywords).
3. I turned on `llvm` dialect generation here in order to test `nvvm.WGMMAScaleIn`, which is an enum with [[ d7e26b5620/mlir/include/mlir/IR/EnumAttr.td (L22-L25) | no explicit discriminator ]] for the `neg` case.
Note, dialects that didn't get a `GEN_ENUM_BINDINGS` don't have any enums to generate.
Let me know if I should add more tests (the three trivial ones I added exercise both the supported `assemblyFormat`s and `replace=True`).
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D157934
154 lines
6.5 KiB
Python
154 lines
6.5 KiB
Python
# RUN: %PYTHON %s | FileCheck %s
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from mlir.ir import *
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from mlir.dialects import transform
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from mlir.dialects.transform import vector
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def run_apply_patterns(f):
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with Context(), Location.unknown():
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module = Module.create()
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with InsertionPoint(module.body):
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sequence = transform.SequenceOp(
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transform.FailurePropagationMode.Propagate,
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[],
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transform.AnyOpType.get(),
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)
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with InsertionPoint(sequence.body):
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apply = transform.ApplyPatternsOp(sequence.bodyTarget)
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with InsertionPoint(apply.patterns):
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f()
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transform.YieldOp()
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print("\nTEST:", f.__name__)
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print(module)
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return f
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@run_apply_patterns
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def non_configurable_patterns():
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# CHECK-LABEL: TEST: non_configurable_patterns
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# CHECK: apply_patterns
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# CHECK: transform.apply_patterns.vector.cast_away_vector_leading_one_dim
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vector.ApplyCastAwayVectorLeadingOneDimPatternsOp()
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# CHECK: transform.apply_patterns.vector.rank_reducing_subview_patterns
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vector.ApplyRankReducingSubviewPatternsOp()
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# CHECK: transform.apply_patterns.vector.transfer_permutation_patterns
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vector.ApplyTransferPermutationPatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_broadcast
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vector.ApplyLowerBroadcastPatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_masks
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vector.ApplyLowerMasksPatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_masked_transfers
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vector.ApplyLowerMaskedTransfersPatternsOp()
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# CHECK: transform.apply_patterns.vector.materialize_masks
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vector.ApplyMaterializeMasksPatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_outerproduct
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vector.ApplyLowerOuterProductPatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_gather
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vector.ApplyLowerGatherPatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_scan
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vector.ApplyLowerScanPatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_shape_cast
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vector.ApplyLowerShapeCastPatternsOp()
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@run_apply_patterns
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def configurable_patterns():
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# CHECK-LABEL: TEST: configurable_patterns
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# CHECK: apply_patterns
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# CHECK: transform.apply_patterns.vector.lower_transfer
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# CHECK-SAME: max_transfer_rank = 4
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vector.ApplyLowerTransferPatternsOp(max_transfer_rank=4)
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# CHECK: transform.apply_patterns.vector.transfer_to_scf
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# CHECK-SAME: max_transfer_rank = 3
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# CHECK-SAME: full_unroll = true
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vector.ApplyTransferToScfPatternsOp(max_transfer_rank=3, full_unroll=True)
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@run_apply_patterns
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def enum_configurable_patterns():
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# CHECK: transform.apply_patterns.vector.lower_contraction
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vector.ApplyLowerContractionPatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_contraction
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# CHECK-SAME: lowering_strategy = matmulintrinsics
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vector.ApplyLowerContractionPatternsOp(
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lowering_strategy=vector.VectorContractLowering.Matmul
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)
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# CHECK: transform.apply_patterns.vector.lower_contraction
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# CHECK-SAME: lowering_strategy = parallelarith
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vector.ApplyLowerContractionPatternsOp(
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lowering_strategy=vector.VectorContractLowering.ParallelArith
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)
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# CHECK: transform.apply_patterns.vector.lower_multi_reduction
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vector.ApplyLowerMultiReductionPatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_multi_reduction
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# This is the default mode, not printed.
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vector.ApplyLowerMultiReductionPatternsOp(
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lowering_strategy=vector.VectorMultiReductionLowering.InnerParallel
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)
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# CHECK: transform.apply_patterns.vector.lower_multi_reduction
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# CHECK-SAME: lowering_strategy = innerreduction
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vector.ApplyLowerMultiReductionPatternsOp(
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lowering_strategy=vector.VectorMultiReductionLowering.InnerReduction
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)
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# CHECK: transform.apply_patterns.vector.lower_transpose
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# CHECK-SAME: lowering_strategy = eltwise
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# CHECK-SAME: avx2_lowering_strategy = false
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vector.ApplyLowerTransposePatternsOp()
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# CHECK: transform.apply_patterns.vector.lower_transpose
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# CHECK-SAME: lowering_strategy = eltwise
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# CHECK-SAME: avx2_lowering_strategy = false
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vector.ApplyLowerTransposePatternsOp(
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lowering_strategy=vector.VectorTransposeLowering.EltWise
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)
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# CHECK: transform.apply_patterns.vector.lower_transpose
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# CHECK-SAME: lowering_strategy = flat_transpose
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# CHECK-SAME: avx2_lowering_strategy = false
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vector.ApplyLowerTransposePatternsOp(
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lowering_strategy=vector.VectorTransposeLowering.Flat
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)
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# CHECK: transform.apply_patterns.vector.lower_transpose
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# CHECK-SAME: lowering_strategy = shuffle_1d
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# CHECK-SAME: avx2_lowering_strategy = false
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vector.ApplyLowerTransposePatternsOp(
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lowering_strategy=vector.VectorTransposeLowering.Shuffle1D
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)
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# CHECK: transform.apply_patterns.vector.lower_transpose
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# CHECK-SAME: lowering_strategy = shuffle_16x16
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# CHECK-SAME: avx2_lowering_strategy = false
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vector.ApplyLowerTransposePatternsOp(
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lowering_strategy=vector.VectorTransposeLowering.Shuffle16x16
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)
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# CHECK: transform.apply_patterns.vector.lower_transpose
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# CHECK-SAME: lowering_strategy = flat_transpose
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# CHECK-SAME: avx2_lowering_strategy = true
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vector.ApplyLowerTransposePatternsOp(
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lowering_strategy=vector.VectorTransposeLowering.Flat,
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avx2_lowering_strategy=True,
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)
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# CHECK: transform.apply_patterns.vector.split_transfer_full_partial
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vector.ApplySplitTransferFullPartialPatternsOp()
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# CHECK: transform.apply_patterns.vector.split_transfer_full_partial
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# CHECK-SAME: split_transfer_strategy = none
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vector.ApplySplitTransferFullPartialPatternsOp(
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split_transfer_strategy=vector.VectorTransferSplit.None_
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)
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# CHECK: transform.apply_patterns.vector.split_transfer_full_partial
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# CHECK-SAME: split_transfer_strategy = "vector-transfer"
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vector.ApplySplitTransferFullPartialPatternsOp(
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split_transfer_strategy=vector.VectorTransferSplit.VectorTransfer
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)
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# CHECK: transform.apply_patterns.vector.split_transfer_full_partial
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# This is the default mode, not printed.
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vector.ApplySplitTransferFullPartialPatternsOp(
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split_transfer_strategy=vector.VectorTransferSplit.LinalgCopy
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)
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# CHECK: transform.apply_patterns.vector.split_transfer_full_partial
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# CHECK-SAME: split_transfer_strategy = "force-in-bounds"
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vector.ApplySplitTransferFullPartialPatternsOp(
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split_transfer_strategy=vector.VectorTransferSplit.ForceInBounds
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)
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