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[MLIR] Lower `chlo.constant_like` to MHLO 

Lower `chlo.constant_like` to a constant and, if needed, a broadcast.

PiperOrigin-RevId: 331964137
This commit is contained in:
A. Unique TensorFlower 2020-09-16 02:41:02 -07:00 committed by TensorFlow MLIR Team
parent da43c8596b
commit a6fdebdc6c
2 changed files with 64 additions and 0 deletions
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38
lib/Dialect/mhlo/transforms/chlo_legalize_to_hlo.cc
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@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
==============================================================================*/ ==============================================================================*/
#include <numeric>
#include "mlir-hlo/Dialect/mhlo/IR/chlo_ops.h" #include "mlir-hlo/Dialect/mhlo/IR/chlo_ops.h"
#include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h" #include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
#include "mlir-hlo/Dialect/mhlo/transforms/rewriters.h" #include "mlir-hlo/Dialect/mhlo/transforms/rewriters.h"
@ -31,6 +33,39 @@ namespace mlir {
namespace chlo { namespace chlo {
namespace { namespace {
struct ConvertConstantLikeOp : public OpConversionPattern<ConstantLikeOp> {
using OpConversionPattern<ConstantLikeOp>::OpConversionPattern;
LogicalResult matchAndRewrite(
ConstantLikeOp op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const override {
auto result_ty = op.getType().cast<ShapedType>();
// Unranked uses are not supported. Consider `transform-unranked-hlo`.
if (!result_ty.hasRank()) return failure();
// Lower to MHLO constant if statically shaped.
if (result_ty.hasStaticShape()) {
rewriter.replaceOpWithNewOp<mhlo::ConstOp>(
op, DenseElementsAttr::get(result_ty, op.value()));
return success();
}
// Lower to broadcasted constant.
ConstantLikeOp::Adaptor transformed(operands);
auto loc = op.getLoc();
Type extent_tensor_type = shape::getExtentTensorType(op.getContext());
Value constant = rewriter.create<mhlo::ConstOp>(loc, op.value());
Value uncasted_shape = rewriter.create<shape::ShapeOfOp>(
loc, extent_tensor_type, transformed.operand());
Type shape_ty =
RankedTensorType::get({result_ty.getRank()}, rewriter.getIndexType());
Value shape = rewriter.create<TensorCastOp>(loc, shape_ty, uncasted_shape);
rewriter.replaceOpWithNewOp<mhlo::DynamicBroadcastInDimOp>(
op, result_ty, constant, shape, rewriter.getI64TensorAttr({}));
return success();
}
};
// Converts binary ops that statically are determined to not broadcast directly // Converts binary ops that statically are determined to not broadcast directly
// to the corresponding mhlo non-broadcasting op. // to the corresponding mhlo non-broadcasting op.
template <typename ChloOpTy, typename HloOpTy, typename Adaptor> template <typename ChloOpTy, typename HloOpTy, typename Adaptor>
@ -505,6 +540,9 @@ void PopulateLegalizeChloToHloPatterns(MLIRContext *context,
context, patterns); context, patterns);
PopulateForBinaryOp<BroadcastCompareOp, mhlo::CompareOp, HloCompareAdaptor>( PopulateForBinaryOp<BroadcastCompareOp, mhlo::CompareOp, HloCompareAdaptor>(
context, patterns); context, patterns);
// Other patterns.
patterns->insert<ConvertConstantLikeOp>(context);
} }
} // namespace chlo } // namespace chlo

26
tests/chlo_legalize_to_mhlo.mlir Normal file
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@ -0,0 +1,26 @@
// RUN: mlir-hlo-opt --mhlo-test-chlo-legalize-to-hlo --split-input-file %s | FileCheck %s
// Lower statically shaped `constant_like` to constant.
// CHECK-LABEL: @constant_like_static_shape
func @constant_like_static_shape(%arg : tensor<1x2xi64>) -> tensor<1x2xf32> {
// CHECK: %[[RESULT:.*]] = mhlo.constant dense<3.200000e+00> : tensor<1x2xf32>
// CHECK: return %[[RESULT]]
%result = "chlo.constant_like"(%arg) { value = 3.2 : f32 }
: (tensor<1x2xi64>) -> tensor<1x2xf32>
return %result : tensor<1x2xf32>
}
// Lower dynamically shaped `constant_like` to broadcasted constant.
// CHECK-LABEL: constant_like_dynamic_shape
// CHECK-SAME: (%[[ARG:.*]]: tensor<?x?xi64>)
func @constant_like_dynamic_shape(%arg : tensor<?x?xi64>) -> tensor<?x?xf32> {
// CHECK: %[[CONSTANT:.*]] = mhlo.constant dense<3.200000e+00> : tensor<f32>
// CHECK: %[[UNCASTED_SHAPE:.*]] = shape.shape_of %[[ARG]] : tensor<?x?xi64> -> tensor<?xindex>
// CHECK: %[[SHAPE:.*]] = tensor_cast %[[UNCASTED_SHAPE]] : tensor<?xindex> to tensor<2xindex>
// CHECK: %[[BROADCASTED_CONSTANT:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[CONSTANT]], %[[SHAPE]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>, tensor<2xindex>) -> tensor<?x?xf32>
// CHECK: return %[[BROADCASTED_CONSTANT]] : tensor<?x?xf32>
%result = "chlo.constant_like"(%arg) { value = 3.2 : f32 }
: (tensor<?x?xi64>) -> tensor<?x?xf32>
return %result : tensor<?x?xf32>
}