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torch-mlir/lib/Conversion/TorchToLinalg/TensorScalarInterop.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 "mlir/Dialect/Arith/IR/Arith.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;
namespace {
class ConvertAtenSizeIntOp : public OpConversionPattern<AtenSizeIntOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenSizeIntOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op->getLoc();
Value self = adaptor.getSelf();
Value dim = adaptor.getDim();
auto type = cast<RankedTensorType>(self.getType());
Value inputRank = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI64IntegerAttr(type.getRank()));
Value dimPositive = toPositiveDimDynamic(rewriter, loc, dim, inputRank);
if (!isAssumingStrictSymbolicShapes(rewriter)) {
assertIsValidDim(rewriter, loc, dimPositive, inputRank);
}
Value size = rewriter.create<tensor::DimOp>(
loc, adaptor.getSelf(), castIntToIndex(rewriter, loc, dimPositive));
rewriter.replaceOp(op, castIndexToInt64(rewriter, loc, size));
return success();
}
};
} // namespace
namespace {
class ConvertAtenNumelOp : public OpConversionPattern<AtenNumelOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenNumelOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op.getLoc();
Value tensorSize = getTensorSize(rewriter, loc, adaptor.getSelf());
rewriter.replaceOp(op, tensorSize);
return success();
}
};
} // namespace
namespace {
// Casts a tensor of exactly one element to an elemental type.
template <typename OpTy>
class ConvertAtenTensorToScalarLikeOp : public OpConversionPattern<OpTy> {
public:
using OpConversionPattern<OpTy>::OpConversionPattern;
LogicalResult
matchAndRewrite(OpTy op,
typename OpConversionPattern<OpTy>::OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op.getLoc();
Value input = adaptor.getA();
SmallVector<Value> inputSizes = getTensorSizes(rewriter, loc, input);
int64_t inputRank = inputSizes.size();
Type inputDtype = cast<BaseTensorType>(op.getA().getType()).getDtype();
// The `input` tensor must contain exactly one element, i.e., either the
// `input` is a zero rank tensor or all the dimensions of the `input` tensor
// are unit.
Value constantOne =
rewriter.create<arith::ConstantOp>(loc, rewriter.getI64IntegerAttr(1));
for (int64_t i = 0; i < inputRank; i++)
checkDimEqualHelper(rewriter, loc, inputSizes[i], constantOne);
// Extract the only element from the `input` tensor.
Value constantZero =
rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(0));
SmallVector<Value> indices(inputRank, constantZero);
Value result = rewriter.create<tensor::ExtractOp>(loc, input, indices);
Type resultType =
this->getTypeConverter()->convertType(op->getResult(0).getType());
rewriter.replaceOp(op, convertScalarToDtype(rewriter, loc, result,
resultType, inputDtype));
return success();
}
};
} // namespace
namespace {
class ConvertAtenScalarToTensorLike : public ConversionPattern {
public:
ConvertAtenScalarToTensorLike(TypeConverter &typeConverter,
MLIRContext *context)
: ConversionPattern(typeConverter, MatchAnyOpTypeTag(), /*benefit=*/1,
context) {}
LogicalResult
matchAndRewrite(Operation *op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const override {
if (!isa<AtenTensorIntOp, AtenTensorFloatOp>(op))
return rewriter.notifyMatchFailure(
op, "not a supported Scalar to Tensor like op");
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op->getLoc();
Value elemVal, dtype, device, requires_grad;
if (AtenTensorIntOp tensorIntOp = dyn_cast<AtenTensorIntOp>(op)) {
AtenTensorIntOp::Adaptor adaptor(operands);
elemVal = adaptor.getT();
dtype = tensorIntOp.getDtype();
device = tensorIntOp.getDevice();
requires_grad = tensorIntOp.getRequiresGrad();
}
if (AtenTensorFloatOp tensorFloatOp = dyn_cast<AtenTensorFloatOp>(op)) {
AtenTensorFloatOp::Adaptor adaptor(operands);
elemVal = adaptor.getT();
dtype = tensorFloatOp.getDtype();
device = tensorFloatOp.getDevice();
requires_grad = tensorFloatOp.getRequiresGrad();
}
// TODO: Dtype conversion.
if (!dtype.getType().isa<Torch::NoneType>())
return rewriter.notifyMatchFailure(op, "Unimplemented non-None dtype");
// TODO: Device information.
if (!device.getType().isa<Torch::NoneType>())
return rewriter.notifyMatchFailure(
op, "Unimplemented non-None device information");
RankedTensorType resultType = getTypeConverter()
->convertType(op->getResult(0).getType())
.cast<RankedTensorType>();
Type outElementType = resultType.getElementType();
Value elemValProm =
convertScalarToDtype(rewriter, loc, elemVal, outElementType);
Value zeroDTensor =
createInitTensor(rewriter, loc, {}, outElementType, elemValProm);
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, zeroDTensor);
return success();
}
};
} // namespace
namespace {
class ConvertPrimNumToTensorScalarOp
: public OpConversionPattern<PrimNumToTensorScalarOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(PrimNumToTensorScalarOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op.getLoc();
RankedTensorType resultType = getTypeConverter()
->convertType(op->getResult(0).getType())
.cast<RankedTensorType>();
Type outElementType = resultType.getElementType();
Value elemVal = adaptor.getA();
Value elemValProm =
convertScalarToDtype(rewriter, loc, elemVal, outElementType);
Value zeroDTensor =
createInitTensor(rewriter, loc, {}, outElementType, elemValProm);
rewriter.replaceOp(op, zeroDTensor);
return success();
}
};
} // namespace
namespace {
class ConvertAtenFullOp : public OpConversionPattern<AtenFullOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenFullOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op.getLoc();
SmallVector<Value> inShape;
if (!getListConstructElements(adaptor.getSize(), inShape)) {
return rewriter.notifyMatchFailure(
op, "unimplemented: the size list is not from list construct");
}
auto resultTy = cast<RankedTensorType>(
this->getTypeConverter()->convertType(op.getResult().getType()));
if (resultTy.getRank() != static_cast<int64_t>(inShape.size()))
return rewriter.notifyMatchFailure(
op, "rank of shape and result shape do not match");
SmallVector<OpFoldResult> filteredShape;
for (int i = 0, s = resultTy.getRank(); i < s; ++i) {
if (resultTy.isDynamicDim(i)) {
filteredShape.push_back(inShape[i]);
continue;
}
filteredShape.push_back(rewriter.getIndexAttr(resultTy.getDimSize(i)));
}
Value full = adaptor.getFillValue();
if (full.getType() != resultTy.getElementType()) {
if (isa<mlir::FloatType>(full.getType())) {
full = rewriter.create<arith::TruncFOp>(loc, resultTy.getElementType(),
full);
} else if (isa<mlir::IntegerType>(full.getType())) {
full = rewriter.create<arith::TruncIOp>(loc, resultTy.getElementType(),
full);
}
}
Value outTensor = rewriter.create<tensor::EmptyOp>(
loc, filteredShape, resultTy.getElementType());
rewriter.replaceOpWithNewOp<linalg::FillOp>(op, full, outTensor);
return success();
}
};
} // namespace
namespace {
// Converts a tensor with one element to a scalar value.
template <typename OpTy>
class ConvertAtenImplicitLikeOp : public OpConversionPattern<OpTy> {
public:
using OpConversionPattern<OpTy>::OpConversionPattern;
LogicalResult
matchAndRewrite(OpTy op,
typename OpConversionPattern<OpTy>::OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
rewriter.replaceOpWithNewOp<tensor::ExtractOp>(op, adaptor.getA());
return success();
}
};
} // namespace
void mlir::torch::torch_to_linalg::
populateTensorScalarInteropPatternsAndLegality(TypeConverter &typeConverter,
RewritePatternSet &patterns,
ConversionTarget &target) {
MLIRContext *context = patterns.getContext();
target.addIllegalOp<AtenSizeIntOp>();
patterns.add<ConvertAtenSizeIntOp>(typeConverter, context);
target.addIllegalOp<AtenNumelOp>();
patterns.add<ConvertAtenNumelOp>(typeConverter, context);
target.addIllegalOp<AtenIntTensorOp, AtenFloatTensorOp, AtenBoolTensorOp>();
patterns.add<ConvertAtenTensorToScalarLikeOp<AtenIntTensorOp>>(typeConverter,
context);
patterns.add<ConvertAtenTensorToScalarLikeOp<AtenFloatTensorOp>>(
typeConverter, context);
patterns.add<ConvertAtenTensorToScalarLikeOp<AtenBoolTensorOp>>(typeConverter,
context);
target.addIllegalOp<AtenTensorIntOp, AtenTensorFloatOp>();
patterns.add<ConvertAtenScalarToTensorLike>(typeConverter, context);
target.addIllegalOp<PrimNumToTensorScalarOp>();
patterns.add<ConvertPrimNumToTensorScalarOp>(typeConverter, context);
target.addIllegalOp<AtenFullOp>();
patterns.add<ConvertAtenFullOp>(typeConverter, context);
patterns.add<ConvertAtenImplicitLikeOp<AtenScalarImplicitOp>>(typeConverter,
context);
patterns.add<ConvertAtenImplicitLikeOp<AtenFloatImplicitOp>>(typeConverter,
context);
patterns.add<ConvertAtenImplicitLikeOp<AtenIntImplicitOp>>(typeConverter,
context);
target.addIllegalOp<AtenScalarImplicitOp, AtenFloatImplicitOp,
AtenIntImplicitOp>();
}
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