511 lines
23 KiB
C++
511 lines
23 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "mlir-hlo/Dialect/mhlo/IR/chlo_ops.h"
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#include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
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#include "mlir-hlo/Dialect/mhlo/transforms/map_chlo_to_hlo_op.h"
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#include "mlir-hlo/Dialect/mhlo/transforms/rewriters.h"
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#include "mlir/Dialect/SCF/SCF.h"
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#include "mlir/Dialect/Shape/IR/Shape.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/BuiltinOps.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/MLIRContext.h"
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#include "mlir/IR/Operation.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Transforms/DialectConversion.h"
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namespace mlir {
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namespace {
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// TODO(herhut): Generate these out of op definitions.
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#define MAP_XLA_OPERATION_CWISE_UNARY(fn, sep) \
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fn(AbsOp) sep fn(CeilOp) sep fn(ClzOp) sep fn(CosOp) sep fn(ExpOp) \
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sep fn(Expm1Op) sep fn(FloorOp) sep fn(ImagOp) sep fn(IsFiniteOp) \
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sep fn(LogOp) sep fn(Log1pOp) sep fn(LogisticOp) sep fn(NotOp) \
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sep fn(NegOp) sep fn(PopulationCountOp) sep fn(RealOp) \
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sep fn(RoundOp) sep fn(RsqrtOp) sep fn(SignOp) sep fn(SinOp) \
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sep fn(SqrtOp) sep fn(TanhOp)
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// TODO(herhut): Generate these out of op definitions.
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#define MAP_XLA_OPERATION_CWISE_BINARY(fn, sep) \
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fn(AddOp) sep fn(AndOp) sep fn(Atan2Op) sep fn(ComplexOp) sep fn(DivOp) \
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sep fn(MaxOp) sep fn(MinOp) sep fn(MulOp) sep fn(OrOp) sep fn(PowOp) \
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sep fn(RemOp) sep fn(ShiftLeftOp) sep fn(ShiftRightArithmeticOp) \
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sep fn(ShiftRightLogicalOp) sep fn(SubOp) sep fn(XorOp)
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// TODO(herhut): Generate these out of op definitions.
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#define MAP_CHLO_OPERATION_CWISE_UNARY(fn, sep) \
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fn(AcosOp) sep fn(AtanOp) sep fn(ConjOp) sep fn(ErfOp) sep fn(ErfcOp) \
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sep fn(SinhOp) sep fn(TanOp)
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template <typename OpTy>
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inline void AddLegalOpOnRankedTensor(ConversionTarget *target) {
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target->addDynamicallyLegalOp<OpTy>([](OpTy op) {
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return llvm::all_of(op.getOperation()->getOperandTypes(),
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[&](Type t) { return t.isa<RankedTensorType>(); });
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});
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}
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/// Element-wise operations on unranked tensors can be applied to the flattened
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/// tensor operands with the same effect. This pattern rewrites every such
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/// operation to
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/// (i) flatten the input tensor,
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/// (ii) apply the operation, and
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/// (iii) restore the original shape.
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template <typename OpTy>
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struct ElementwiseOpConversion : public OpRewritePattern<OpTy> {
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explicit ElementwiseOpConversion(MLIRContext *context)
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: OpRewritePattern<OpTy>(context) {}
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LogicalResult matchAndRewrite(OpTy op,
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PatternRewriter &rewriter) const override {
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// Don't apply conversion unless all operands are unranked.
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if (!llvm::all_of(op.getOperation()->getOperands(), [&](Value operand) {
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return operand.getType().isa<UnrankedTensorType>();
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})) {
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return failure();
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}
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// Get operands' shape.
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auto loc = op.getLoc();
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Type extentTensorTy = shape::getExtentTensorType(rewriter.getContext());
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SmallVector<Value, 3> operandShapes;
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for (Value operand : op.getOperation()->getOperands()) {
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Value shape =
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rewriter.create<shape::ShapeOfOp>(loc, extentTensorTy, operand);
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operandShapes.push_back(shape);
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}
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Value shape =
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operandShapes.size() == 1
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? operandShapes.front()
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: rewriter.create<shape::AnyOp>(loc, extentTensorTy, operandShapes);
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// Derive flat shape.
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Type indexTy = rewriter.getIndexType();
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Value numElements =
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rewriter.create<shape::NumElementsOp>(loc, indexTy, shape);
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Value flatShape = rewriter.create<TensorFromElementsOp>(loc, numElements);
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// Flatten operands.
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SmallVector<Value, 3> flatOperands;
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for (Value operand : op.getOperation()->getOperands()) {
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Type operandElementTy =
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operand.getType().template cast<ShapedType>().getElementType();
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Type flatTy =
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RankedTensorType::get({ShapedType::kDynamicSize}, operandElementTy);
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Value flat = rewriter.create<mhlo::DynamicReshapeOp>(loc, flatTy, operand,
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flatShape);
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flatOperands.push_back(flat);
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}
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// Apply operation to flattened operands.
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Type resultElementTy =
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op.getType().template cast<ShapedType>().getElementType();
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Type flatResultTy =
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RankedTensorType::get({ShapedType::kDynamicSize}, resultElementTy);
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Value flatResult =
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rewriter.create<OpTy>(loc, flatResultTy, flatOperands, op.getAttrs());
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// Restore original shape.
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rewriter.replaceOpWithNewOp<mhlo::DynamicReshapeOp>(op, op.getType(),
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flatResult, shape);
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return success();
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}
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};
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// Converts a broadcasting binary operation with a scalar operand and an
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// unranked operand to a ranked broadcasting operation by dynamically reshaping
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// the unranked operand to a 1D tensor. This will always be safe because
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// broadcasting from a scalar to another shape always works.
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template <typename ChloOpTy, typename HloOpTy, typename Adaptor>
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struct ConvertUnrankedScalarDynamicBroadcastBinaryOp
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: public OpConversionPattern<ChloOpTy> {
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using OpConversionPattern<ChloOpTy>::OpConversionPattern;
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LogicalResult matchAndRewrite(
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ChloOpTy op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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auto loc = op.getLoc();
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typename ChloOpTy::Adaptor transformed(operands);
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Value lhs = transformed.lhs();
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Value rhs = transformed.rhs();
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auto lhs_ranked_type = lhs.getType().dyn_cast<RankedTensorType>();
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auto lhs_unranked_type = lhs.getType().dyn_cast<UnrankedTensorType>();
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auto rhs_ranked_type = rhs.getType().dyn_cast<RankedTensorType>();
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auto rhs_unranked_type = rhs.getType().dyn_cast<UnrankedTensorType>();
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bool lhs_is_scalar = lhs_ranked_type &&
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lhs_ranked_type.getShape().empty() &&
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rhs_unranked_type;
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bool rhs_is_scalar = rhs_ranked_type &&
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rhs_ranked_type.getShape().empty() &&
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lhs_unranked_type;
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// Only support the case where exactly one operand is scalar and the other
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// is unranked. Other patterns in chlo-to-hlo legalization will create more
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// efficient lowerings for cases where both ranks are known or will handle
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// the more generic case of both inputs being unranked.
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if (!(lhs_is_scalar ^ rhs_is_scalar)) return failure();
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auto scalar_element_type = lhs_is_scalar ? lhs_ranked_type.getElementType()
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: rhs_ranked_type.getElementType();
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auto result_type = op.getResult().getType().template dyn_cast<TensorType>();
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auto result_element_type = result_type.getElementType();
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// Reshape the non-scalar value into a dynamically sized, rank-1 tensor
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Value shape =
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rewriter.create<shape::ShapeOfOp>(loc, lhs_is_scalar ? rhs : lhs);
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Value num_elements = rewriter.create<shape::NumElementsOp>(loc, shape);
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Value size_tensor =
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rewriter.create<TensorFromElementsOp>(loc, num_elements);
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Value reshaped = rewriter.create<mhlo::DynamicReshapeOp>(
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loc, RankedTensorType::get({-1}, scalar_element_type),
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lhs_is_scalar ? rhs : lhs, size_tensor);
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// Create a new ranked Chlo op that will be further lowered by other
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// patterns into Mhlo.
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SmallVector<Value, 2> new_operands{lhs_is_scalar ? lhs : reshaped,
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rhs_is_scalar ? rhs : reshaped};
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Value computed = rewriter.create<ChloOpTy>(
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loc, TypeRange{RankedTensorType::get({-1}, result_element_type)},
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new_operands, op.getAttrs());
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// Reshape the result back into an unranked tensor.
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rewriter.replaceOpWithNewOp<mhlo::DynamicReshapeOp>(op, result_type,
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computed, shape);
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return success();
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}
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};
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// Handles lowering of the following pattern to patterns that will be further
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// matched by other patterns until they result in LHLO:
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// %result = "chlo.op"(%lhs, %rhs) : (<*xTy>, <*xTy>) -> <*xTy>
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//
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// The sequence of specializations this handles is:
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// - Either operand being scalar
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// - Operands having equal shapes
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// - The resulting value being any of ranks [2,6]
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template <typename ChloOpTy, typename HloOpTy, typename Adaptor>
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struct ConvertUnrankedDynamicBroadcastBinaryOp
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: public OpConversionPattern<ChloOpTy> {
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using OpConversionPattern<ChloOpTy>::OpConversionPattern;
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LogicalResult matchAndRewrite(
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ChloOpTy op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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auto loc = op.getLoc();
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typename ChloOpTy::Adaptor transformed(operands);
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Value lhs = transformed.lhs();
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Value rhs = transformed.rhs();
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auto lhs_type = lhs.getType().dyn_cast<UnrankedTensorType>();
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auto rhs_type = rhs.getType().dyn_cast<UnrankedTensorType>();
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auto result_type = op.getResult().getType().template dyn_cast<TensorType>();
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// Only support unranked operands. If either operand is ranked, another
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// pattern will handle the lowering.
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if (!lhs_type || !rhs_type) return failure();
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// If lhs is scalar
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auto if_op = rewriter.create<scf::IfOp>(
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loc, result_type, IsScalarTensor(rewriter, op, lhs), true);
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OpBuilder if_lhs_scalar_builder =
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if_op.getThenBodyBuilder(rewriter.getListener());
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Value reshaped_lhs = if_lhs_scalar_builder.create<tensor::CastOp>(
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loc, RankedTensorType::get({}, lhs_type.getElementType()), lhs);
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Value if_lhs_scalar_result = if_lhs_scalar_builder.create<ChloOpTy>(
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loc, ArrayRef<Type>{result_type}, ArrayRef<Value>{reshaped_lhs, rhs},
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op.getAttrs());
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if_lhs_scalar_builder.create<scf::YieldOp>(loc, if_lhs_scalar_result);
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// If lhs is NOT scalar
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//
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// See if rhs is scalar
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OpBuilder else_lhs_scalar_builder =
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if_op.getElseBodyBuilder(rewriter.getListener());
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auto if_rhs_scalar_op = else_lhs_scalar_builder.create<scf::IfOp>(
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loc, result_type, IsScalarTensor(else_lhs_scalar_builder, op, rhs),
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true);
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else_lhs_scalar_builder.create<scf::YieldOp>(loc,
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if_rhs_scalar_op.getResult(0));
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OpBuilder if_rhs_scalar_builder =
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if_rhs_scalar_op.getThenBodyBuilder(rewriter.getListener());
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Value reshaped_rhs = if_rhs_scalar_builder.create<tensor::CastOp>(
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loc, RankedTensorType::get({}, lhs_type.getElementType()), rhs);
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Value if_rhs_scalar_result = if_rhs_scalar_builder.create<ChloOpTy>(
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loc, ArrayRef<Type>{result_type}, ArrayRef<Value>{lhs, reshaped_rhs},
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op.getAttrs());
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if_rhs_scalar_builder.create<scf::YieldOp>(loc, if_rhs_scalar_result);
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// If NEITHER shape is scalar
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//
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// See if shapes are equal.
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OpBuilder else_no_scalars_builder =
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if_rhs_scalar_op.getElseBodyBuilder(rewriter.getListener());
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Value shape_of_lhs =
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else_no_scalars_builder.create<shape::ShapeOfOp>(loc, lhs);
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Value shape_of_rhs =
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else_no_scalars_builder.create<shape::ShapeOfOp>(loc, rhs);
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Value equal_shapes = else_no_scalars_builder.create<shape::ShapeEqOp>(
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loc, shape_of_lhs, shape_of_rhs);
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auto if_eq_shapes_op = else_no_scalars_builder.create<scf::IfOp>(
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loc, result_type, equal_shapes, true);
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else_no_scalars_builder.create<scf::YieldOp>(loc,
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if_eq_shapes_op.getResult(0));
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OpBuilder if_eq_shapes_builder =
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if_eq_shapes_op.getThenBodyBuilder(rewriter.getListener());
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Value non_broadcast_op =
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Adaptor::CreateOp(op, result_type, lhs, rhs, if_eq_shapes_builder);
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if_eq_shapes_builder.create<scf::YieldOp>(loc, non_broadcast_op);
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// If shapes are not scalar, nor equal
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//
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// See if values are of a rank that we support.
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OpBuilder if_neq_shapes_builder =
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if_eq_shapes_op.getElseBodyBuilder(rewriter.getListener());
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if_neq_shapes_builder.create<scf::YieldOp>(
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loc, HandleBroadcastAndOp(if_neq_shapes_builder, op, lhs, rhs));
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rewriter.replaceOp(op, {if_op.getResult(0)});
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return success();
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}
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private:
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// Returns the dynamic result of checking the given value is a scalar tensor.
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Value IsScalarTensor(OpBuilder &rewriter, ChloOpTy op, Value tensor) const {
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auto loc = op.getLoc();
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Value shape_of_tensor = rewriter.create<shape::ShapeOfOp>(loc, tensor);
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Value rank_tensor = rewriter.create<shape::RankOp>(
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loc, rewriter.getIndexType(), shape_of_tensor);
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return rewriter.create<CmpIOp>(loc, rewriter.getI1Type(), CmpIPredicate::eq,
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rank_tensor,
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rewriter.create<ConstantIndexOp>(loc, 0));
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}
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Value GreaterRankIsN(OpBuilder &builder, Location loc, Value actual_rank,
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int targeted_rank) const {
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return builder.create<CmpIOp>(
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loc, CmpIPredicate::eq, actual_rank,
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builder.create<ConstantIndexOp>(loc, targeted_rank));
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}
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scf::IfOp createIfOpForRankSpecializedBroadcastAndOp(
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OpBuilder &builder, ChloOpTy op, Value actual_rank,
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int targeted_rank) const {
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// Create the if block to place the current specialized logic in.
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Value greater_rank_is_n =
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GreaterRankIsN(builder, op.getLoc(), actual_rank, targeted_rank);
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return builder.create<scf::IfOp>(op.getLoc(), op.getResult().getType(),
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greater_rank_is_n, true);
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}
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// Create the if statement and code for a broadcasting op with a result of a
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// given rank.
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void createRankSpecializedBroadcastAndOp(OpBuilder &if_builder, ChloOpTy op,
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Value lhs, Value rhs,
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int targeted_rank) const {
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auto loc = op.getLoc();
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// Handle shape broadcasting and inferrence.
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Value lhs_shape = if_builder.create<shape::ShapeOfOp>(loc, lhs);
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Value rhs_shape = if_builder.create<shape::ShapeOfOp>(loc, rhs);
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SmallVector<int64_t, 6> ranked_shape(targeted_rank, 1);
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auto unknown_rank_extent_tensor_type = RankedTensorType::get(
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{RankedTensorType::kDynamicSize}, if_builder.getIndexType());
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auto known_rank_extent_tensor_type =
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RankedTensorType::get({targeted_rank}, if_builder.getIndexType());
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auto reshaped_type = RankedTensorType::get(
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llvm::SmallVector<int64_t, 6>(targeted_rank,
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RankedTensorType::kDynamicSize),
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lhs.getType().template dyn_cast<TensorType>().getElementType());
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Value ranked_shape_val = if_builder.create<shape::ConstShapeOp>(
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loc, known_rank_extent_tensor_type,
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mlir::DenseIntElementsAttr::get(known_rank_extent_tensor_type,
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ranked_shape));
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Value extended_lhs = if_builder.create<shape::BroadcastOp>(
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loc, unknown_rank_extent_tensor_type, lhs_shape, ranked_shape_val,
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nullptr);
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Value extended_lhs_casted = if_builder.create<tensor::CastOp>(
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loc, known_rank_extent_tensor_type, extended_lhs);
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Value extended_rhs = if_builder.create<shape::BroadcastOp>(
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loc, unknown_rank_extent_tensor_type, rhs_shape, ranked_shape_val,
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nullptr);
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Value extended_rhs_casted = if_builder.create<tensor::CastOp>(
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loc, known_rank_extent_tensor_type, extended_rhs);
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// 1. Reshape operands to the given rank (with the same number of elements)
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// 2. Compute the ranked-broadcasted ChloOp (which will assert that the ops
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// can be broadcasted and do the actual broadcasting)
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// 3. Type erase the output back to unranked
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Value reshaped_lhs = if_builder.create<mhlo::DynamicReshapeOp>(
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loc, reshaped_type, lhs, extended_lhs_casted);
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Value reshaped_rhs = if_builder.create<mhlo::DynamicReshapeOp>(
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loc, reshaped_type, rhs, extended_rhs_casted);
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auto result_element_type = op.getResult()
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.getType()
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.template dyn_cast<TensorType>()
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.getElementType();
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auto result_type = RankedTensorType::get(
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llvm::SmallVector<int64_t, 6>(targeted_rank,
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RankedTensorType::kDynamicSize),
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result_element_type);
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Value result = if_builder.create<ChloOpTy>(
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loc, ArrayRef<Type>{result_type},
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ArrayRef<Value>{reshaped_lhs, reshaped_rhs}, op.getAttrs());
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Value reshaped_result = if_builder.create<tensor::CastOp>(
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loc, UnrankedTensorType::get(result_element_type), result);
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if_builder.create<scf::YieldOp>(loc, reshaped_result);
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}
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// Iterates over the desired ranks to be specialized and generates the code
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// snippet for each case.
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Value HandleBroadcastAndOp(OpBuilder &rewriter, ChloOpTy op, Value lhs,
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Value rhs) const {
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auto loc = op.getLoc();
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// Find the larger rank of the 2 operands.
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auto extent_tensor_type = RankedTensorType::get({ShapedType::kDynamicSize},
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rewriter.getIndexType());
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Value lhs_shape =
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rewriter.create<shape::ShapeOfOp>(loc, extent_tensor_type, lhs);
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Value rhs_shape =
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rewriter.create<shape::ShapeOfOp>(loc, extent_tensor_type, rhs);
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Value lhs_rank =
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rewriter.create<shape::RankOp>(loc, rewriter.getIndexType(), lhs_shape);
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Value rhs_rank =
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rewriter.create<shape::RankOp>(loc, rewriter.getIndexType(), rhs_shape);
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Value greater_rank_lhs =
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rewriter.create<CmpIOp>(loc, CmpIPredicate::sgt, lhs_rank, rhs_rank);
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Value greater_rank =
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rewriter.create<SelectOp>(loc, greater_rank_lhs, lhs_rank, rhs_rank);
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// Generate a list of nested if/else statements to handle rank
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// specializations from 1 to `kMaxRankSpecialization`.
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scf::IfOp if_op = createIfOpForRankSpecializedBroadcastAndOp(
|
|
rewriter, op, greater_rank, 1);
|
|
OpBuilder if_builder = if_op.getThenBodyBuilder(rewriter.getListener());
|
|
createRankSpecializedBroadcastAndOp(if_builder, op, lhs, rhs, 1);
|
|
|
|
// Put each subsequent rank specialization inside the else statement of the
|
|
// previous one.
|
|
OpBuilder else_builder = if_op.getElseBodyBuilder(rewriter.getListener());
|
|
constexpr int kMaxRankSpecialization = 6;
|
|
for (int i = 2; i < kMaxRankSpecialization; i++) {
|
|
auto inner_if = createIfOpForRankSpecializedBroadcastAndOp(
|
|
else_builder, op, greater_rank, i);
|
|
if_builder = inner_if.getThenBodyBuilder(rewriter.getListener());
|
|
createRankSpecializedBroadcastAndOp(if_builder, op, lhs, rhs, i);
|
|
else_builder.create<scf::YieldOp>(loc, inner_if.getResult(0));
|
|
else_builder = inner_if.getElseBodyBuilder(rewriter.getListener());
|
|
}
|
|
// Fire an assertion if none of the rank specializations applied (one of
|
|
// the ranks was greater than `kMaxRankSpecialization`).
|
|
else_builder.create<AssertOp>(
|
|
loc,
|
|
GreaterRankIsN(else_builder, op.getLoc(), greater_rank,
|
|
kMaxRankSpecialization),
|
|
"Input for dynamic binary op lowering was of a rank greater than " +
|
|
std::to_string(kMaxRankSpecialization));
|
|
// Add the rank 6 specialization to the innermost else block.
|
|
createRankSpecializedBroadcastAndOp(else_builder, op, lhs, rhs,
|
|
kMaxRankSpecialization);
|
|
|
|
// Return the result of the outermost if statement.
|
|
return if_op.getResult(0);
|
|
}
|
|
};
|
|
|
|
struct TransformUnrankedHloPass
|
|
: public PassWrapper<TransformUnrankedHloPass, FunctionPass> {
|
|
void getDependentDialects(DialectRegistry ®istry) const override {
|
|
registry.insert<shape::ShapeDialect, mhlo::MhloDialect>();
|
|
}
|
|
|
|
void runOnFunction() override {
|
|
// Setup conversion target.
|
|
MLIRContext &ctx = getContext();
|
|
ConversionTarget target(ctx);
|
|
target.addLegalDialect<mhlo::MhloDialect, StandardOpsDialect,
|
|
shape::ShapeDialect, scf::SCFDialect,
|
|
tensor::TensorDialect>();
|
|
target.addLegalOp<FuncOp>();
|
|
#define ADD_LEGAL_MHLO(op) AddLegalOpOnRankedTensor<mhlo::op>(&target)
|
|
#define ADD_LEGAL_CHLO(op) AddLegalOpOnRankedTensor<chlo::op>(&target)
|
|
MAP_XLA_OPERATION_CWISE_UNARY(ADD_LEGAL_MHLO, ;);
|
|
MAP_XLA_OPERATION_CWISE_BINARY(ADD_LEGAL_MHLO, ;);
|
|
MAP_CHLO_OPERATION_CWISE_UNARY(ADD_LEGAL_CHLO, ;);
|
|
#undef ADD_LEGAL_MHLO
|
|
#undef ADD_LEGAL_CHLO
|
|
AddLegalOpOnRankedTensor<mhlo::CompareOp>(&target);
|
|
AddLegalOpOnRankedTensor<mhlo::SelectOp>(&target);
|
|
target.addDynamicallyLegalDialect<chlo::HloClientDialect>(
|
|
[](Operation *op) {
|
|
return !llvm::any_of(op->getOperandTypes(), [](Type type) {
|
|
return type.isa<UnrankedTensorType>();
|
|
});
|
|
});
|
|
|
|
// Populate rewrite patterns.
|
|
OwningRewritePatternList patterns;
|
|
PopulateTransformUnrankedHloPatterns(&ctx, &patterns);
|
|
|
|
// Apply transformation.
|
|
if (failed(
|
|
applyPartialConversion(getFunction(), target, std::move(patterns))))
|
|
return signalPassFailure();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void PopulateTransformUnrankedHloPatterns(MLIRContext *context,
|
|
OwningRewritePatternList *patterns) {
|
|
#define MAP_UNARY(op) ElementwiseOpConversion<mhlo::op>
|
|
#define MAP_BINARY(op) ElementwiseOpConversion<mhlo::op>
|
|
#define MAP_CHLO_UNARY(op) ElementwiseOpConversion<chlo::op>
|
|
#define COMMA ,
|
|
// clang-format off
|
|
patterns->insert<
|
|
MAP_XLA_OPERATION_CWISE_UNARY(MAP_UNARY, COMMA),
|
|
MAP_XLA_OPERATION_CWISE_BINARY(MAP_BINARY, COMMA),
|
|
MAP_CHLO_OPERATION_CWISE_UNARY(MAP_CHLO_UNARY, COMMA),
|
|
ElementwiseOpConversion<mhlo::CompareOp>,
|
|
ElementwiseOpConversion<mhlo::SelectOp>>(context);
|
|
// clang-format on
|
|
#undef MAP_UNARY
|
|
#undef MAP_BINARY
|
|
#undef MAP_CHLO_UNARY
|
|
#undef COMMA
|
|
chlo::PopulateForBroadcastingBinaryOp<
|
|
ConvertUnrankedDynamicBroadcastBinaryOp>(context, patterns);
|
|
chlo::PopulateForBroadcastingBinaryOp<
|
|
ConvertUnrankedScalarDynamicBroadcastBinaryOp>(context, patterns);
|
|
}
|
|
|
|
std::unique_ptr<FunctionPass> createTransformUnrankedHloPass() {
|
|
return std::make_unique<TransformUnrankedHloPass>();
|
|
}
|
|
|
|
} // namespace mlir
|