234 lines
10 KiB
C++
234 lines
10 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
==============================================================================*/
|
|
|
|
// Enable the use of M_* math constants.
|
|
// NOTE: this must be first in the file to ensure that if cmath is transitively
|
|
// included by any other header it has the define set on first processing.
|
|
// https://docs.microsoft.com/en-us/cpp/c-runtime-library/math-constants
|
|
#define _USE_MATH_DEFINES
|
|
#include <cmath>
|
|
#include <numeric>
|
|
|
|
#include "mlir-hlo/Dialect/mhlo/IR/chlo_ops.h"
|
|
#include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
|
|
#include "mlir-hlo/Dialect/mhlo/transforms/map_chlo_to_hlo_op.h"
|
|
#include "mlir-hlo/Dialect/mhlo/transforms/rewriters.h"
|
|
#include "mlir-hlo/utils/broadcast_utils.h"
|
|
#include "mlir/Dialect/SCF/SCF.h"
|
|
#include "mlir/Dialect/Shape/IR/Shape.h"
|
|
#include "mlir/Dialect/StandardOps/IR/Ops.h"
|
|
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
|
#include "mlir/IR/Attributes.h"
|
|
#include "mlir/IR/BuiltinTypes.h"
|
|
#include "mlir/IR/MLIRContext.h"
|
|
#include "mlir/IR/OperationSupport.h"
|
|
#include "mlir/IR/PatternMatch.h"
|
|
#include "mlir/Transforms/DialectConversion.h"
|
|
|
|
namespace mlir {
|
|
namespace chlo {
|
|
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<tensor::CastOp>(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
|
|
// to the corresponding mhlo non-broadcasting op.
|
|
template <typename ChloOpTy, typename HloOpTy, typename Adaptor>
|
|
struct ConvertTrivialNonBroadcastBinaryOp
|
|
: public OpConversionPattern<ChloOpTy> {
|
|
using OpConversionPattern<ChloOpTy>::OpConversionPattern;
|
|
LogicalResult matchAndRewrite(
|
|
ChloOpTy op, ArrayRef<Value> operands,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
// Only rewrite for statically determinable non-broadcasting cases.
|
|
typename ChloOpTy::Adaptor transformed(operands);
|
|
auto lhs_type =
|
|
transformed.lhs().getType().template dyn_cast<RankedTensorType>();
|
|
auto rhs_type =
|
|
transformed.rhs().getType().template dyn_cast<RankedTensorType>();
|
|
if (!lhs_type || !rhs_type) return failure();
|
|
|
|
// Requires rank broadcast.
|
|
if (lhs_type.getRank() != rhs_type.getRank()) return failure();
|
|
// Any dynamic dimension may require broadcasting and requires more
|
|
// analysis.
|
|
if (!lhs_type.hasStaticShape() || !rhs_type.hasStaticShape())
|
|
return failure();
|
|
|
|
for (auto extents : llvm::zip(lhs_type.getShape(), rhs_type.getShape())) {
|
|
auto lhs_extent = std::get<0>(extents);
|
|
auto rhs_extent = std::get<1>(extents);
|
|
if (lhs_extent != rhs_extent) {
|
|
return failure();
|
|
}
|
|
}
|
|
|
|
rewriter.replaceOp(
|
|
op, {Adaptor::CreateOp(op, op.getResult().getType(), operands[0],
|
|
operands[1], rewriter)});
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// Converts a binary op with ranked broadcasting operands to explicitly
|
|
// broadcast and invoke the corresponding mhlo non-broadcasting op.
|
|
// Note that dynamic broadcasting supported by this pattern is only valid for
|
|
// "numpy" broadcasting semantics as defined here:
|
|
// https://docs.scipy.org/doc/numpy/reference/ufuncs.html
|
|
// Specifically, this includes the following cases:
|
|
// - Same rank broadcast (operands have the same static rank).
|
|
// - Different-rank broadcast, either without a broadcast_dims attribte or
|
|
// with the broadcast_dims attribute set to map to a prefix padding.
|
|
// - Legal combinations of degenerate (1-dim) implicit broadcasting.
|
|
// The restriction on broadcast_dims derives from the definition of the
|
|
// `shape.broadcast` op, which only supports prefix-padding.
|
|
template <typename ChloOpTy, typename HloOpTy, typename Adaptor>
|
|
struct ConvertRankedDynamicBroadcastBinaryOp
|
|
: public OpConversionPattern<ChloOpTy> {
|
|
using OpConversionPattern<ChloOpTy>::OpConversionPattern;
|
|
LogicalResult matchAndRewrite(
|
|
ChloOpTy op, ArrayRef<Value> operands,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
// Only support ranked operands.
|
|
typename ChloOpTy::Adaptor transformed(operands);
|
|
Value lhs = transformed.lhs();
|
|
Value rhs = transformed.rhs();
|
|
auto lhs_type = lhs.getType().dyn_cast<RankedTensorType>();
|
|
auto rhs_type = rhs.getType().dyn_cast<RankedTensorType>();
|
|
auto result_type =
|
|
op.getResult().getType().template dyn_cast<RankedTensorType>();
|
|
if (!lhs_type || !rhs_type || !result_type) return failure();
|
|
|
|
// Check for "numpy"-style rank broadcast.
|
|
auto broadcast_dimensions = op.broadcast_dimensions();
|
|
if (broadcast_dimensions &&
|
|
!hlo::IsLegalNumpyRankedBroadcast(lhs, rhs, *broadcast_dimensions)) {
|
|
// Note: It is unclear whether the general specification of explicit
|
|
// broadcast_dimensions on binary ops is a feature we want to carry
|
|
// forward. While it can technically be implemented for ranked-dynamic,
|
|
// it is incompatible with unranked inputs. If this warning is emitted
|
|
// in real programs, it is an indication that the feature should be
|
|
// implemented versus just falling back on the more standard definition
|
|
// of numpy-like prefix-padding.
|
|
op.emitWarning() << "unsupported non prefix-padded dynamic rank "
|
|
<< "broadcast_dimensions = " << *broadcast_dimensions;
|
|
return failure();
|
|
}
|
|
|
|
// Compute result shape.
|
|
auto loc = op.getLoc();
|
|
|
|
// Insert a constraint on the shapes being broadcastable and insert all
|
|
// future code into an assuming block reliant on the constraint.
|
|
Value lhs_shape = rewriter.create<shape::ShapeOfOp>(loc, lhs);
|
|
Value rhs_shape = rewriter.create<shape::ShapeOfOp>(loc, rhs);
|
|
auto broadcastable_cstr =
|
|
rewriter.create<shape::CstrBroadcastableOp>(loc, lhs_shape, rhs_shape);
|
|
auto assuming_op = rewriter.create<shape::AssumingOp>(
|
|
loc, ArrayRef<Type>{result_type}, broadcastable_cstr.result());
|
|
|
|
OpBuilder::InsertionGuard guard(rewriter);
|
|
rewriter.createBlock(&assuming_op.doRegion());
|
|
|
|
int64_t result_rank = std::max(lhs_type.getRank(), rhs_type.getRank());
|
|
Value result_extents =
|
|
hlo::ComputeBinaryElementwiseBroadcastingResultExtents(
|
|
loc, lhs, rhs, rewriter, /*unsafe_as_extent_tensor=*/true);
|
|
|
|
// Note that we unconditionally emit DynamicBroadcastInDim ops and let
|
|
// downstream canonicalizations fold them away if possible. This is
|
|
// because, in the dynamic case, there are many corner cases regarding
|
|
// when it is safe to omit, and some of them require analysis to prove
|
|
// properly.
|
|
auto lhs_broadcast_dimensions = llvm::to_vector<4>(
|
|
llvm::seq<int64_t>(result_rank - lhs_type.getRank(), result_rank));
|
|
Value broadcasted_lhs = rewriter.create<mhlo::DynamicBroadcastInDimOp>(
|
|
loc,
|
|
RankedTensorType::get(result_type.getShape(),
|
|
lhs_type.getElementType()),
|
|
lhs, result_extents,
|
|
rewriter.getI64TensorAttr(lhs_broadcast_dimensions));
|
|
auto rhs_broadcast_dimensions = llvm::to_vector<4>(
|
|
llvm::seq<int64_t>(result_rank - rhs_type.getRank(), result_rank));
|
|
Value broadcasted_rhs = rewriter.create<mhlo::DynamicBroadcastInDimOp>(
|
|
loc,
|
|
RankedTensorType::get(result_type.getShape(),
|
|
rhs_type.getElementType()),
|
|
rhs, result_extents,
|
|
rewriter.getI64TensorAttr(rhs_broadcast_dimensions));
|
|
|
|
// And generate the final non-broadcasted binary op.
|
|
Value final_result = Adaptor::CreateOp(op, result_type, broadcasted_lhs,
|
|
broadcasted_rhs, rewriter);
|
|
rewriter.create<shape::AssumingYieldOp>(loc, final_result);
|
|
rewriter.replaceOp(op, {assuming_op.getResult(0)});
|
|
return success();
|
|
}
|
|
};
|
|
|
|
#include "generated_chlo_legalize_to_hlo.inc"
|
|
} // namespace
|
|
|
|
void PopulateLegalizeChloToHloPatterns(MLIRContext *context,
|
|
OwningRewritePatternList *patterns) {
|
|
populateWithGenerated(context, *patterns);
|
|
|
|
// Instantiate conversion templates for conforming binary elementwise ops
|
|
// that do not have different dtypes between operands and results and do
|
|
// not have special attributes that need to be preserved.
|
|
PopulateForBroadcastingBinaryOp<ConvertTrivialNonBroadcastBinaryOp>(
|
|
context, patterns, 10);
|
|
PopulateForBroadcastingBinaryOp<ConvertRankedDynamicBroadcastBinaryOp>(
|
|
context, patterns, 5);
|
|
|
|
// Other patterns.
|
|
patterns->insert<ConvertConstantLikeOp>(context);
|
|
}
|
|
|
|
} // namespace chlo
|
|
} // namespace mlir
|