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torch-mlir/lib/Conversion/TorchToLinalg/Pooling.cpp

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//===----------------------------------------------------------------------===//
//
// 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
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//
#include "torch-mlir/Conversion/TorchToLinalg/TorchToLinalg.h"
#include "../PassDetail.h"
#include "PopulatePatterns.h"
#include "Utils.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/Matchers.h"
#include "torch-mlir/Conversion/Utils/Utils.h"
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
// Checks the validity of pooling parameters and stores them in the respective
// vector.
template <typename OpTy>
static LogicalResult
checkAndGetPoolingParameters(OpTy op, ConversionPatternRewriter &rewriter,
SmallVectorImpl<int64_t> &kernelSizeInts,
SmallVectorImpl<int64_t> &strideInts,
SmallVectorImpl<int64_t> &paddingInts,
SmallVectorImpl<int64_t> &dilationInts) {
// Pattern match against the op's original operands, because otherwise we
// will get the lowered version of the operands which is harder to pattern
// match.
if (!matchPattern(op.kernel_size(), m_TorchConstantIntList(kernelSizeInts)))
return rewriter.notifyMatchFailure(op, "only support kernel size ints");
if (!matchPattern(op.stride(), m_TorchConstantIntList(strideInts)))
return rewriter.notifyMatchFailure(op, "only support constant int strides");
if (!matchPattern(op.padding(), m_TorchConstantIntList(paddingInts)))
return rewriter.notifyMatchFailure(op,
"only support constant int paddings");
if (!matchPattern(op.dilation(), m_TorchConstantIntList(dilationInts)))
return rewriter.notifyMatchFailure(op,
"only support constant int dilations");
bool ceilMode;
if (!matchPattern(op.ceil_mode(), m_TorchConstantBool(&ceilMode)))
return rewriter.notifyMatchFailure(op,
"only support constant bool ceil_mode");
// TODO: Add support for ceil_mode equal to `True`.
if (ceilMode)
return rewriter.notifyMatchFailure(op, "only ceil_mode false is supported");
return success();
}
// Computes maxpool2d for AtenMaxPool2dOp and AtenMaxPool2dWithIndicesOp.
static LogicalResult
computeMaxPool2d(Operation *op, ConversionPatternRewriter &rewriter, Value self,
SmallVectorImpl<int64_t> &kernelSizeInts,
SmallVectorImpl<int64_t> &strideInts,
SmallVectorImpl<int64_t> &paddingInts,
SmallVectorImpl<int64_t> &dilationInts, Value &result) {
Location loc = op->getLoc();
Type elementType = self.getType().cast<RankedTensorType>().getElementType();
if (!elementType.isa<mlir::FloatType>())
return op->emitError("unimplemented: non-floating point type");
SmallVector<int64_t, 4> paddingIncludingNC = {0, 0};
paddingIncludingNC.insert(paddingIncludingNC.end(), paddingInts.begin(),
paddingInts.end());
auto smallestFPValueAttr = rewriter.getFloatAttr(
elementType, APFloat::getLargest(
elementType.cast<mlir::FloatType>().getFloatSemantics(),
/*Negative=*/true));
Value smallestFPValue =
rewriter.create<arith::ConstantOp>(loc, smallestFPValueAttr);
Value paddedInput =
torch_to_linalg::getPaddedTensor(op, rewriter, self, paddingIncludingNC,
paddingIncludingNC, smallestFPValue);
Value N = getDimOp(rewriter, loc, self, 0);
Value C = getDimOp(rewriter, loc, self, 1);
Value H = getDimOp(rewriter, loc, self, 2);
Value W = getDimOp(rewriter, loc, self, 3);
SmallVector<Value> paddingIntValues =
getAsConstantIntValues(rewriter, loc, paddingInts);
SmallVector<Value> dilationIntValues =
getAsConstantIntValues(rewriter, loc, dilationInts);
SmallVector<Value> kernelSizeIntValues =
getAsConstantIntValues(rewriter, loc, kernelSizeInts);
SmallVector<Value> strideIntValues =
getAsConstantIntValues(rewriter, loc, strideInts);
Value hOut = torch_to_linalg::getOutputDimForConvOps(
rewriter, loc, H, paddingIntValues[0], dilationIntValues[0],
kernelSizeIntValues[0], strideIntValues[0]);
Value wOut = torch_to_linalg::getOutputDimForConvOps(
rewriter, loc, W, paddingIntValues[1], dilationIntValues[1],
kernelSizeIntValues[1], strideIntValues[1]);
// Create output tensor initialized with smallest floating point value.
Value outTensorInitialized =
createInitTensor(rewriter, loc, ValueRange{N, C, hOut, wOut}, elementType,
smallestFPValue);
auto stridesAttr = rewriter.getI64VectorAttr(strideInts);
auto dilationAttr = rewriter.getI64VectorAttr(dilationInts);
Value windowTensor = rewriter.create<linalg::InitTensorOp>(
loc, getAsConstantIndexValues(rewriter, loc, kernelSizeInts),
elementType);
result = rewriter
.create<linalg::PoolingNchwMaxOp>(
loc, outTensorInitialized.getType(),
ValueRange{paddedInput, windowTensor}, outTensorInitialized,
stridesAttr, dilationAttr)
.getResult(0);
return success();
}
namespace {
class ConvertAtenMaxPool2dOp : public OpConversionPattern<AtenMaxPool2dOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenMaxPool2dOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Value self = adaptor.self();
int64_t selfRank = self.getType().cast<RankedTensorType>().getRank();
// TODO: Add support for 3D inputs.
if (selfRank == 3)
return rewriter.notifyMatchFailure(
op, "unimplemented: only support 4D input");
Value maxPool2d;
SmallVector<int64_t, 2> kernelSizeInts, strideInts, paddingInts,
dilationInts;
if (failed(checkAndGetPoolingParameters<AtenMaxPool2dOp>(
op, rewriter, kernelSizeInts, strideInts, paddingInts,
dilationInts)))
return rewriter.notifyMatchFailure(op, "invalid pooling parameters");
if (failed(computeMaxPool2d(op, rewriter, self, kernelSizeInts, strideInts,
paddingInts, dilationInts, maxPool2d)))
return rewriter.notifyMatchFailure(op, "unable to compute maxpool2d");
Type newResultType = getTypeConverter()->convertType(op.getType());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, maxPool2d);
return success();
}
};
} // namespace
namespace {
// Returns the result of maxpool2d over the input tensor. And the corresponding
// indices of the input tensor for the values of the result tensor.
//
// The result of the maxpool2d operation is calculated using the helper function
// written above. For finding the indices, we follow the below method:
//
// Let's say the input tensor is a 4-d tensor. The maxpool2d and indices will
// also be a 4-d tensor. Then:
// for i in range(N):
// for j in range(C):
// for m in range(Hout):
// for n in range(Wout):
// for p in range(kH):
// for r in range(kW):
// indexH = m * stride[0] + p * dilation[0]
// indexW = n * stride[0] + r * dilation[0]
// if paddedInput[i, j, indexH, indexW] ==
// maxPool2d[i, j, m, n]:
// indices[i, j, m, n] = (indexH - padding[0]) * W +
// (indexW - padding[1])
//
class ConvertAtenMaxPool2dWithIndicesOp
: public OpConversionPattern<AtenMaxPool2dWithIndicesOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenMaxPool2dWithIndicesOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op->getLoc();
Value self = adaptor.self();
RankedTensorType selfType = self.getType().cast<RankedTensorType>();
Type elementType = selfType.getElementType();
RankedTensorType indicesRankedTensorType =
getTypeConverter()
->convertType(op->getResult(1).getType())
.cast<RankedTensorType>();
// TODO: Add support for 3D inputs.
if (selfType.getRank() == 3)
return rewriter.notifyMatchFailure(
op, "unimplemented: only support 4D input");
SmallVector<int64_t, 2> kernelSizeInts, strideInts, paddingInts,
dilationInts;
if (failed(checkAndGetPoolingParameters<AtenMaxPool2dWithIndicesOp>(
op, rewriter, kernelSizeInts, strideInts, paddingInts,
dilationInts)))
return rewriter.notifyMatchFailure(op, "invalid pooling parameters");
// Contains the result of maxpool2d operation over the input.
Value maxPool2d;
if (failed(computeMaxPool2d(op, rewriter, self, kernelSizeInts, strideInts,
paddingInts, dilationInts, maxPool2d)))
return rewriter.notifyMatchFailure(op, "unable to compute maxpool2d");
SmallVector<int64_t, 4> paddingIncludingNC = {0, 0};
paddingIncludingNC.insert(paddingIncludingNC.end(), paddingInts.begin(),
paddingInts.end());
auto smallestFPValueAttr = rewriter.getFloatAttr(
elementType,
APFloat::getLargest(
elementType.cast<mlir::FloatType>().getFloatSemantics(),
/*Negative=*/true));
Value smallestFPValue =
rewriter.create<arith::ConstantOp>(loc, smallestFPValueAttr);
Value paddedInput =
torch_to_linalg::getPaddedTensor(op, rewriter, self, paddingIncludingNC,
paddingIncludingNC, smallestFPValue);
SmallVector<Value> resultShape(getTensorSizes(rewriter, loc, maxPool2d));
Value cstMinusOne =
rewriter.create<arith::ConstantOp>(loc, rewriter.getI64IntegerAttr(-1));
Value indicesTensor =
createInitTensor(rewriter, loc, resultShape,
indicesRankedTensorType.getElementType(), cstMinusOne);
SmallVector<Value> kernelSize =
getAsConstantIndexValues(rewriter, loc, kernelSizeInts);
SmallVector<Value> padding =
getAsConstantIndexValues(rewriter, loc, paddingInts);
SmallVector<Value> dilation =
getAsConstantIndexValues(rewriter, loc, dilationInts);
SmallVector<Value> stride =
getAsConstantIndexValues(rewriter, loc, strideInts);
Value windowTensor = rewriter.create<linalg::InitTensorOp>(
loc, kernelSize, indicesRankedTensorType.getElementType());
SmallVector<AffineExpr> inputExprs, outputExprs, kernelExprs;
for (unsigned i = 0; i < 4; i++) {
inputExprs.push_back(rewriter.getAffineDimExpr(i));
outputExprs.push_back(rewriter.getAffineDimExpr(i));
}
kernelExprs.push_back(rewriter.getAffineDimExpr(4));
kernelExprs.push_back(rewriter.getAffineDimExpr(5));
// Here we have six dimensions, each corresponding to N, C, Hout, Wout, kH,
// and kW, respectively, as described in the algorithm above.
SmallVector<AffineMap> indexingMaps =
AffineMap::inferFromExprList({inputExprs, kernelExprs, outputExprs});
SmallVector<StringRef> iteratorTypes(4, getParallelIteratorTypeName());
iteratorTypes.push_back(getReductionIteratorTypeName());
iteratorTypes.push_back(getReductionIteratorTypeName());
// Input format is : [N, C, H, W]
Value inputShapeW = getDimOp(rewriter, loc, self, 3);
Value indicesResult =
rewriter
.create<linalg::GenericOp>(
loc, /*resultTensorTypes=*/indicesTensor.getType(),
/*inputs=*/ValueRange({maxPool2d, windowTensor}),
/*outputs=*/indicesTensor,
/*indexingMaps=*/indexingMaps,
/*iteratorTypes=*/iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value maxVal = args[0], res = args[2];
Value i = b.create<linalg::IndexOp>(loc, 0);
Value j = b.create<linalg::IndexOp>(loc, 1);
Value m = b.create<linalg::IndexOp>(loc, 2);
Value n = b.create<linalg::IndexOp>(loc, 3);
Value p = b.create<linalg::IndexOp>(loc, 4);
Value r = b.create<linalg::IndexOp>(loc, 5);
Value mTimesStride =
b.create<arith::MulIOp>(loc, m, stride[0]);
Value pTimesDilation =
b.create<arith::MulIOp>(loc, p, dilation[0]);
Value indexH = b.create<arith::AddIOp>(loc, mTimesStride,
pTimesDilation);
Value nTimesStride =
b.create<arith::MulIOp>(loc, n, stride[1]);
Value rTimesDilation =
b.create<arith::MulIOp>(loc, r, dilation[1]);
Value indexW = b.create<arith::AddIOp>(loc, nTimesStride,
rTimesDilation);
Value input = b.create<tensor::ExtractOp>(
loc, paddedInput, ValueRange{i, j, indexH, indexW});
Value pred = b.create<arith::CmpFOp>(
loc, arith::CmpFPredicate::OEQ, input, maxVal);
Value indexHMinusPadding =
b.create<arith::SubIOp>(loc, indexH, padding[0]);
Value indexWMinusPadding =
b.create<arith::SubIOp>(loc, indexW, padding[1]);
Value outIndex = b.create<arith::MulIOp>(
loc, indexHMinusPadding, inputShapeW);
outIndex = b.create<arith::AddIOp>(loc, outIndex,
indexWMinusPadding);
Value result = b.create<arith::SelectOp>(
loc, pred, castIndexToInt64(b, loc, outIndex), res);
Value predInvalidIndex = b.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, res, cstMinusOne);
Value out = b.create<arith::SelectOp>(loc, predInvalidIndex,
result, res);
b.create<linalg::YieldOp>(loc, out);
})
.getResult(0);
Type maxPool2dResultType =
getTypeConverter()->convertType(op->getResult(0).getType());
Type indicesResultType =
getTypeConverter()->convertType(op->getResult(1).getType());
Value outMaxpool2d =
rewriter.create<tensor::CastOp>(loc, maxPool2dResultType, maxPool2d);
Value outIndices =
rewriter.create<tensor::CastOp>(loc, indicesResultType, indicesResult);
rewriter.replaceOp(op, {outMaxpool2d, outIndices});
return success();
}
};
} // namespace
namespace {
class ConvertAtenAdaptiveAvgPool2dOp
: public OpConversionPattern<AtenAdaptiveAvgPool2dOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenAdaptiveAvgPool2dOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = op->getLoc();
MLIRContext *context = op->getContext();
Value input = adaptor.self(); /* in form of N*C*H*W */
RankedTensorType inputType = input.getType().cast<RankedTensorType>();
Type elementType = inputType.getElementType();
if (!elementType.isa<mlir::FloatType>())
return op.emitError("unimplemented: non-floating point type");
auto inputRank = inputType.getRank();
if (inputRank != 4)
return rewriter.notifyMatchFailure(op, "input should be rank 4");
SmallVector<int64_t, 2> expects{1, 1};
// Pattern match against the op's original operands, because otherwise we
// will get the lowered version of the operands which is harder to pattern
// match.
if (!isConstantIntListMatching(op.output_size(), expects))
return rewriter.notifyMatchFailure(
op, "only support output_size with H and W both equal to constant 1");
Value N = getDimOp(rewriter, loc, input, 0);
Value C = getDimOp(rewriter, loc, input, 1);
Value initTensor = rewriter.create<linalg::InitTensorOp>(
loc, ValueRange{N, C}, elementType);
Value c0 = rewriter.create<arith::ConstantOp>(
loc, FloatAttr::get(elementType, 0.0));
Value initTensor0 =
rewriter.create<linalg::FillOp>(loc, c0, initTensor).getResult(0);
SmallVector<AffineExpr, 2> ncExprs;
ncExprs.push_back(mlir::getAffineDimExpr(0, context));
ncExprs.push_back(mlir::getAffineDimExpr(1, context));
auto ncIndexingMap = AffineMap::get(
/*dimCount=*/4,
/*symbolCount=*/0, ncExprs, context);
SmallVector<AffineMap, 2> indexingMaps = {
rewriter.getMultiDimIdentityMap(4), // input
ncIndexingMap, // output
};
SmallVector<StringRef, 4> iteratorTypesSum{"parallel", "parallel",
"reduction", "reduction"};
Value sumPool2d = rewriter
.create<linalg::GenericOp>(
loc, initTensor0.getType(), input, initTensor0,
/*indexingMaps=*/indexingMaps,
/*iteratorTypes=*/iteratorTypesSum,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value input = args[0], sum = args[1];
Value result = rewriter.create<arith::AddFOp>(
loc, sum, input);
b.create<linalg::YieldOp>(loc, result);
})
.getResult(0);
// Calculate H*W so that avg can be got from sum / (H*W)
Value H = getDimOp(rewriter, loc, input, 2);
Value W = getDimOp(rewriter, loc, input, 3);
auto castIndexToInt = [&](Value v) {
return rewriter.create<arith::IndexCastOp>(
loc, IntegerType::get(context, 64), v);
};
Value HtimesW = rewriter.create<arith::MulIOp>(loc, castIndexToInt(H),
castIndexToInt(W));
Value HtimesWf =
rewriter.create<arith::SIToFPOp>(loc, elementType, HtimesW);
Value c1Index = rewriter.create<arith::ConstantIndexOp>(loc, /*value=*/1);
Value outputTensor = rewriter.create<linalg::InitTensorOp>(
loc, ValueRange{N, C, c1Index, c1Index}, elementType);
SmallVector<AffineMap, 2> indexingMapsAvg{
ncIndexingMap, rewriter.getMultiDimIdentityMap(4)};
SmallVector<StringRef, 4> iteratorTypesAvg(4, "parallel");
Value avgPool2d =
rewriter
.create<linalg::GenericOp>(
loc, outputTensor.getType(), sumPool2d, outputTensor,
/*indexingMaps=*/indexingMapsAvg,
/*iteratorTypes=*/iteratorTypesAvg,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value avg = b.create<arith::DivFOp>(loc, args[0], HtimesWf);
b.create<linalg::YieldOp>(loc, avg);
})
.getResult(0);
Type newResultType = getTypeConverter()->convertType(op.getType());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, avgPool2d);
return success();
}
};
} // namespace
void mlir::torch::torch_to_linalg::populatePoolingPatternsAndLegality(
TypeConverter &typeConverter, RewritePatternSet &patterns,
ConversionTarget &target) {
MLIRContext *context = patterns.getContext();
target.addIllegalOp<AtenMaxPool2dOp>();
patterns.add<ConvertAtenMaxPool2dOp>(typeConverter, context);
target.addIllegalOp<AtenMaxPool2dWithIndicesOp>();
patterns.add<ConvertAtenMaxPool2dWithIndicesOp>(typeConverter, context);
target.addIllegalOp<AtenAdaptiveAvgPool2dOp>();
patterns.add<ConvertAtenAdaptiveAvgPool2dOp>(typeConverter, context);
}
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