67d0d7ac0a
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure. Reviewed By: mehdi_amini, rriddle Differential Review: https://reviews.llvm.org/D132838
276 lines
10 KiB
C++
276 lines
10 KiB
C++
//===- TosaMakeBroadcastable.cpp ------------------------------------------===//
|
|
//
|
|
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
|
|
// See https://llvm.org/LICENSE.txt for license information.
|
|
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
//
|
|
// Insert reshape to binary op's input if needed to match rank
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "mlir/Dialect/Func/IR/FuncOps.h"
|
|
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
|
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
|
|
#include "mlir/Dialect/Tosa/Transforms/Passes.h"
|
|
#include "mlir/Dialect/Tosa/Utils/QuantUtils.h"
|
|
#include "mlir/Pass/Pass.h"
|
|
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
|
|
|
|
namespace mlir {
|
|
namespace tosa {
|
|
#define GEN_PASS_DEF_TOSAMAKEBROADCASTABLE
|
|
#include "mlir/Dialect/Tosa/Transforms/Passes.h.inc"
|
|
} // namespace tosa
|
|
} // namespace mlir
|
|
|
|
using namespace mlir;
|
|
using namespace mlir::tosa;
|
|
|
|
/// There are two potential ways implementing broadcast:
|
|
/// a. https://www.tensorflow.org/xla/broadcasting#formal_definition
|
|
/// b. https://numpy.org/doc/stable/user/basics.broadcasting.html
|
|
/// This pass implements b (numpy style) now.
|
|
|
|
/// In this pass, we insert RESHAPE operators to increase the rank of the
|
|
/// lower rank operand as a first step in the broadcasting process. The TOSA
|
|
/// operators that support broadcast require that the rank of the operands
|
|
/// are equal.
|
|
|
|
// Examples:
|
|
// If lower=[c], higher=[a, b, c], [c] reshaped into [1, 1, c].
|
|
// If lower=[b, c], higher=[a, b, c], [b, c] reshaped into [1, b, c].
|
|
// If lower=[a], higher=[a, a], [a] reshaped into [1, a].
|
|
// If lower=[a], target=[a, b, a], [a] reshaped into [1, 1, a].
|
|
// If lower=[], target=[a, b, c], [] reshaped into [1, 1, 1].
|
|
|
|
static LogicalResult
|
|
computeReshapeOutput(ArrayRef<int64_t> higherRankShape,
|
|
ArrayRef<int64_t> lowerRankShape,
|
|
SmallVectorImpl<int64_t> &reshapeOutputShape) {
|
|
// Initialize new shapes with [1] * higherRank.
|
|
int64_t higherRank = higherRankShape.size();
|
|
int64_t lowerRank = lowerRankShape.size();
|
|
|
|
reshapeOutputShape.assign(higherRank, 1);
|
|
|
|
int64_t higherRankDim;
|
|
int64_t lowerRankDim;
|
|
|
|
for (int64_t i = higherRank - 1, j = lowerRank - 1; i >= 0 && j >= 0;
|
|
i--, j--) {
|
|
higherRankDim = higherRankShape[i];
|
|
lowerRankDim = lowerRankShape[j];
|
|
|
|
if (lowerRankDim == 1 && higherRankDim > 1)
|
|
reshapeOutputShape[i] = 1;
|
|
else if ((lowerRankDim > 1 && higherRankDim == 1) ||
|
|
(lowerRankDim == higherRankDim))
|
|
reshapeOutputShape[i] = lowerRankDim;
|
|
else if (higherRankDim != lowerRankDim)
|
|
return failure();
|
|
}
|
|
return success();
|
|
}
|
|
|
|
/// Common code to create the reshape op where necessary to make the rank of the
|
|
/// operations equal. Returns the updated input1 and input2 for the original
|
|
/// input. The caller is expected to use these to rewrite the original operator
|
|
/// with the RESHAPE now in the graph.
|
|
static LogicalResult reshapeLowerToHigher(PatternRewriter &rewriter,
|
|
Location loc,
|
|
RankedTensorType outputType,
|
|
Value input1, Value input2,
|
|
Value &outInput1, Value &outInput2) {
|
|
auto input1Ty = input1.getType().dyn_cast<RankedTensorType>();
|
|
auto input2Ty = input2.getType().dyn_cast<RankedTensorType>();
|
|
|
|
if (!input1Ty || !input2Ty)
|
|
return failure();
|
|
|
|
int64_t input1Rank = input1Ty.getRank();
|
|
int64_t input2Rank = input2Ty.getRank();
|
|
|
|
Value higherTensorValue, lowerTensorValue;
|
|
// Cannot rewrite as its already correct.
|
|
if (input1Rank == input2Rank)
|
|
return failure();
|
|
|
|
if (input1Rank > input2Rank) {
|
|
higherTensorValue = input1;
|
|
lowerTensorValue = input2;
|
|
} else {
|
|
higherTensorValue = input2;
|
|
lowerTensorValue = input1;
|
|
}
|
|
|
|
ArrayRef<int64_t> higherRankShape =
|
|
higherTensorValue.getType().cast<RankedTensorType>().getShape();
|
|
(void)higherRankShape;
|
|
ArrayRef<int64_t> lowerRankShape =
|
|
lowerTensorValue.getType().cast<RankedTensorType>().getShape();
|
|
|
|
SmallVector<int64_t, 4> reshapeOutputShape;
|
|
|
|
if (computeReshapeOutput(higherRankShape, lowerRankShape, reshapeOutputShape)
|
|
.failed())
|
|
return failure();
|
|
|
|
auto reshapeInputType = lowerTensorValue.getType().cast<RankedTensorType>();
|
|
auto reshapeOutputType = RankedTensorType::get(
|
|
ArrayRef<int64_t>(reshapeOutputShape), reshapeInputType.getElementType());
|
|
|
|
// Verify the rank agrees with the output type if the output type is ranked.
|
|
if (outputType) {
|
|
if (outputType.getShape().size() != reshapeOutputShape.size() ||
|
|
outputType.getShape().size() != higherRankShape.size())
|
|
return failure();
|
|
}
|
|
|
|
auto reshapeLower = rewriter.create<tosa::ReshapeOp>(
|
|
loc, reshapeOutputType, lowerTensorValue,
|
|
rewriter.getI64ArrayAttr(reshapeOutputShape));
|
|
|
|
if (input1Rank > input2Rank) {
|
|
outInput1 = higherTensorValue;
|
|
outInput2 = reshapeLower.getResult();
|
|
} else {
|
|
outInput1 = reshapeLower.getResult();
|
|
outInput2 = higherTensorValue;
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
template <typename OpTy>
|
|
struct ConvertTosaOp : public OpRewritePattern<OpTy> {
|
|
using OpRewritePattern<OpTy>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(OpTy tosaBinaryOp,
|
|
PatternRewriter &rewriter) const override {
|
|
|
|
Value input1 = tosaBinaryOp.getInput1();
|
|
Value input2 = tosaBinaryOp.getInput2();
|
|
Value output = tosaBinaryOp.getResult();
|
|
|
|
auto outputType = output.getType().dyn_cast<RankedTensorType>();
|
|
if (!outputType)
|
|
return failure();
|
|
|
|
Value outInput1, outInput2;
|
|
if (reshapeLowerToHigher(rewriter, tosaBinaryOp.getLoc(), outputType,
|
|
input1, input2, outInput1, outInput2)
|
|
.failed())
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<OpTy>(tosaBinaryOp, outputType, outInput1,
|
|
outInput2);
|
|
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// The MulOp has an extra parameter 'shift' not present in other elementwise
|
|
// binary ops, that necessitates special handling of its builder.
|
|
template <>
|
|
struct ConvertTosaOp<tosa::MulOp> : public OpRewritePattern<tosa::MulOp> {
|
|
using OpRewritePattern<tosa::MulOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(tosa::MulOp tosaBinaryOp,
|
|
PatternRewriter &rewriter) const override {
|
|
|
|
Value input1 = tosaBinaryOp.getInput1();
|
|
Value input2 = tosaBinaryOp.getInput2();
|
|
int32_t shift = tosaBinaryOp.getShift();
|
|
Value output = tosaBinaryOp.getResult();
|
|
auto outputType = output.getType().dyn_cast<RankedTensorType>();
|
|
if (!outputType)
|
|
return failure();
|
|
|
|
Value outInput1, outInput2;
|
|
if (reshapeLowerToHigher(rewriter, tosaBinaryOp.getLoc(), outputType,
|
|
input1, input2, outInput1, outInput2)
|
|
.failed())
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<tosa::MulOp>(tosaBinaryOp, outputType,
|
|
outInput1, outInput2, shift);
|
|
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// The ArithmeticRightShiftOp has an extra parameter 'round' not present in
|
|
// other elementwise binary ops, that necessitates special handling of its
|
|
// builder.
|
|
template <>
|
|
struct ConvertTosaOp<tosa::ArithmeticRightShiftOp>
|
|
: public OpRewritePattern<tosa::ArithmeticRightShiftOp> {
|
|
using OpRewritePattern<tosa::ArithmeticRightShiftOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(tosa::ArithmeticRightShiftOp tosaBinaryOp,
|
|
PatternRewriter &rewriter) const override {
|
|
|
|
Value input1 = tosaBinaryOp.getInput1();
|
|
Value input2 = tosaBinaryOp.getInput2();
|
|
int32_t round = tosaBinaryOp.getRound();
|
|
Value output = tosaBinaryOp.getResult();
|
|
auto outputType = output.getType().dyn_cast<RankedTensorType>();
|
|
if (!outputType)
|
|
return failure();
|
|
|
|
Value outInput1, outInput2;
|
|
if (reshapeLowerToHigher(rewriter, tosaBinaryOp.getLoc(), outputType,
|
|
input1, input2, outInput1, outInput2)
|
|
.failed())
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<tosa::ArithmeticRightShiftOp>(
|
|
tosaBinaryOp, outputType, outInput1, outInput2, round);
|
|
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
namespace {
|
|
/// Pass that enables broadcast by making all input arrays have the same
|
|
/// number of dimensions. Insert RESHAPE operations to lower rank operand
|
|
struct TosaMakeBroadcastable
|
|
: public tosa::impl::TosaMakeBroadcastableBase<TosaMakeBroadcastable> {
|
|
public:
|
|
void runOnOperation() override {
|
|
auto func = getOperation();
|
|
RewritePatternSet patterns(func.getContext());
|
|
MLIRContext *ctx = func.getContext();
|
|
// Add the generated patterns to the list.
|
|
patterns.add<ConvertTosaOp<tosa::BitwiseAndOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::BitwiseOrOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::BitwiseXorOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::AddOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::SubOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::MulOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::DivOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::MaximumOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::MinimumOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::EqualOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::GreaterOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::GreaterEqualOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::LogicalLeftShiftOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::ArithmeticRightShiftOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::LogicalRightShiftOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::LogicalAndOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::LogicalOrOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::LogicalXorOp>>(ctx);
|
|
patterns.add<ConvertTosaOp<tosa::PowOp>>(ctx);
|
|
(void)applyPatternsAndFoldGreedily(func, std::move(patterns));
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
std::unique_ptr<Pass> mlir::tosa::createTosaMakeBroadcastablePass() {
|
|
return std::make_unique<TosaMakeBroadcastable>();
|
|
}
|