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torch-mlir/lib/Dialect/Torch/Transforms/DecomposeComplexOps.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 "PassDetail.h"
#include "mlir/Transforms/DialectConversion.h"
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Transforms/Passes.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "llvm/ADT/StringExtras.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
// Helper funtion to get rank of `Base tensor type`.
// -1 is returned if the tensorRank can't be determined.
static int getTensorRank(Value tensor) {
int tensorRank = -1;
BaseTensorType tensorType = tensor.getType().cast<BaseTensorType>();
if (tensorType.hasSizes()) {
ArrayRef<int64_t> tensorShape = tensorType.getSizes();
tensorRank = tensorShape.size();
}
return tensorRank;
}
static Value createSumAlongDimension(PatternRewriter &rewriter, Location loc,
Operation *op, Value input, Value dim,
bool keepDim) {
BaseTensorType tensorType = input.getType().cast<BaseTensorType>();
Value dimList = rewriter.create<PrimListConstructOp>(
loc, Torch::ListType::get(dim.getType()), dim);
Value keepDimCst = rewriter.create<ConstantBoolOp>(loc, keepDim);
Value dtype = rewriter.create<ConstantNoneOp>(loc);
SmallVector<int64_t> sizes;
int64_t dimInt;
if (tensorType.hasSizes()) {
ArrayRef<int64_t> inputShape = tensorType.getSizes();
int64_t inputRank = inputShape.size();
if (matchPattern(dim, m_TorchConstantInt(&dimInt))) {
dimInt = toPositiveDim(dimInt, inputRank);
if (!isValidDim(dimInt, inputRank)) {
(void)rewriter.notifyMatchFailure(op, "dim is not a valid dim");
return nullptr;
}
sizes.append(inputShape.begin(), inputShape.end());
sizes[dimInt] = 1;
} else {
sizes.resize(inputRank, kUnknownSize);
}
}
Type resultType = tensorType.getWithSizesAndDtype(
sizes.size() == 0 ? Optional<ArrayRef<int64_t>>()
: llvm::makeArrayRef(sizes),
tensorType.getDtype());
Value sum = rewriter.create<AtenSumDimIntListOp>(loc, resultType, input,
dimList, keepDimCst, dtype);
return sum;
}
// Helper for creating `aten::sub_tensor_op`.
static Value createTensorSub(PatternRewriter &rewriter, Location loc,
Type tensorType, Value lhs, Value rhs) {
Value alpha =
rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1));
Value sub =
rewriter.create<AtenSubTensorOp>(loc, tensorType, lhs, rhs, alpha);
return sub;
}
// Share code between `softmax_backward` and `log_softmax_backward` ops.
// Returns x - y * sum(z, dim).
static Value createSoftmaxBackwardCommonKernel(PatternRewriter &rewriter,
Location loc, Operation *op,
Type tensorType, Value x,
Value y, Value z, Value dim) {
Value sum = createSumAlongDimension(rewriter, loc, op, z, dim, /*keepDim=*/true);
if (!sum)
return nullptr;
auto broadcastSizeType =
Torch::ListType::get(Torch::IntType::get(op->getContext()));
Value broadcastSize = rewriter.create<AtenSizeOp>(loc, broadcastSizeType, z);
Value sumBroadcast =
rewriter.create<AtenBroadcastToOp>(loc, tensorType, sum, broadcastSize);
Value temp =
rewriter.create<AtenMulTensorOp>(loc, tensorType, y, sumBroadcast);
Value sub = createTensorSub(rewriter, loc, tensorType, x, temp);
return sub;
}
namespace {
class DecomposeAtenSizeOp : public OpRewritePattern<AtenSizeOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(AtenSizeOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value self = op.self();
MLIRContext *context = op.getContext();
int64_t rank = getTensorRank(self);
if (rank < 0)
return rewriter.notifyMatchFailure(op, "Unimplemented: unranked tensor");
SmallVector<Value> sizes;
for (int i = 0; i < rank; i++) {
Value dim = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(i));
sizes.push_back(rewriter.create<AtenSizeIntOp>(loc, self, dim));
}
Value sizeList = rewriter.create<PrimListConstructOp>(
loc, Torch::ListType::get(Torch::IntType::get(context)), sizes);
rewriter.replaceOp(op, sizeList);
return success();
}
};
} // namespace
// Calculates the softmax function on the given `input` tensor. Softmax(x) =
// exp(x)/sum(exp(x)).
template <typename OpTy>
static Value getSoftmaxResult(OpTy op, Type resultType,
PatternRewriter &rewriter) {
Location loc = op.getLoc();
Value dim = op.dim();
Value self = op.self();
// exp(x)
Value exp = rewriter.create<AtenExpOp>(loc, resultType, self);
// sum(exp(x))
Value sum =
createSumAlongDimension(rewriter, loc, op, exp, dim, /*keepDim=*/true);
if (!sum)
return nullptr;
// exp(x) / sum(exp(x))
return rewriter.create<AtenDivTensorOp>(loc, resultType, exp, sum);
}
// Decompose softmax into: exp(x) / sum(exp(x))
namespace {
class DecomposeAtenSoftmaxIntOp : public OpRewritePattern<AtenSoftmaxIntOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(AtenSoftmaxIntOp op,
PatternRewriter &rewriter) const override {
Value self = op.self();
if (!op.dtype().getType().isa<Torch::NoneType>())
return rewriter.notifyMatchFailure(
op, "Unimplemented non-None dtype for softmax");
BaseTensorType tensorType = self.getType().cast<BaseTensorType>();
if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
return rewriter.notifyMatchFailure(op, "Only support floating type");
Value result = getSoftmaxResult(op, tensorType, rewriter);
if (!result)
return failure();
rewriter.replaceOpWithNewOp<TensorStaticInfoCastOp>(op, op.getType(),
result);
return success();
}
};
} // namespace
namespace {
class DecomposeAten_SoftmaxOp : public OpRewritePattern<Aten_SoftmaxOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(Aten_SoftmaxOp op,
PatternRewriter &rewriter) const override {
Value self = op.self();
BaseTensorType tensorType = self.getType().cast<BaseTensorType>();
if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
return rewriter.notifyMatchFailure(op, "Only support floating type");
bool halfToFloat;
if (!matchPattern(op.half_to_float(), m_TorchConstantBool(&halfToFloat)))
return rewriter.notifyMatchFailure(
op, "Expected a boolean value for half_to_float");
// Currently, setting `halfToFloat` is not supported as the E2E testing for
// the same is not present on CPU.
if (halfToFloat)
return rewriter.notifyMatchFailure(
op, "halfToFloat is currently not supported.");
Value result = getSoftmaxResult(op, tensorType, rewriter);
if (!result)
return op.emitError("failed to get softmax result");
rewriter.replaceOpWithNewOp<TensorStaticInfoCastOp>(op, op.getType(),
result);
return success();
}
};
} // namespace
// Aten_SoftmaxBackwardDataOp(gradOutput, output, dim) =>
// newGrad = gradOutput * output
// result = newGrad - output * sum(newGrad, dim))
//
// Refer to
// https://github.com/pytorch/pytorch/blob/15fecc4c830a3907fde4b44c9962dc4144da50a4/torch/csrc/jit/codegen/cuda/ops/normalization.cpp#L31
namespace {
class DecomposeAten_SoftmaxBackwardDataOp
: public OpRewritePattern<Aten_SoftmaxBackwardDataOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(Aten_SoftmaxBackwardDataOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value gradOutput = op.grad_output();
Value output = op.output();
Value dim = op.dim();
BaseTensorType tensorType = gradOutput.getType().cast<BaseTensorType>();
if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
return rewriter.notifyMatchFailure(op, "Only support floating type");
Value newGrad =
rewriter.create<AtenMulTensorOp>(loc, tensorType, gradOutput, output);
Value result = createSoftmaxBackwardCommonKernel(
rewriter, loc, op, tensorType, newGrad, output, newGrad, dim);
if (!result)
return rewriter.notifyMatchFailure(
op,
"nullptr returned by createSoftmaxBackwardCommonKernel function.");
rewriter.replaceOp(op, result);
return success();
}
};
} // namespace
// AtenTanhBackwardOp(gradOutput, output) =>
// result = gradOutput * (1 - output^2)
// To get away from broadcasts the above formula is expanded i.e.,
// result = gradOutput - (gradOutput * output^2)
namespace {
class DecomposeAtenTanhBackwardOp
: public OpRewritePattern<AtenTanhBackwardOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(AtenTanhBackwardOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value gradOutput = op.grad_output();
// `output` is the value flowing out from tanh. Hence, tanh(x) = output.
// Since, dTanh(x) = (1 - tanh(x)^2) hence, dOutput = (1 - output^2).
Value output = op.output();
BaseTensorType tensorType = gradOutput.getType().cast<BaseTensorType>();
if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
return rewriter.notifyMatchFailure(op, "Only support floating type");
Value tanhSquare =
rewriter.create<AtenMulTensorOp>(loc, tensorType, output, output);
Value gradMulTanhSquare = rewriter.create<AtenMulTensorOp>(
loc, tensorType, tanhSquare, gradOutput);
Value newGrad = createTensorSub(rewriter, loc, tensorType, gradOutput,
gradMulTanhSquare);
rewriter.replaceOp(op, newGrad);
return success();
}
};
} // namespace
// Aten_LogSoftmaxBackwardDataOp(gradOutput, output, dim) =>
// result = gradOutput - (exp(output) * sum(gradOutput, dim))
namespace {
class DecomposeAten_LogSoftmaxBackwardDataOp
: public OpRewritePattern<Aten_LogSoftmaxBackwardDataOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(Aten_LogSoftmaxBackwardDataOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value gradOutput = op.grad_output();
Value output = op.output();
Value dim = op.dim();
BaseTensorType tensorType = gradOutput.getType().cast<BaseTensorType>();
if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
return rewriter.notifyMatchFailure(op, "Only support floating type");
Value expOut = rewriter.create<AtenExpOp>(loc, tensorType, output);
Value result = createSoftmaxBackwardCommonKernel(
rewriter, loc, op, tensorType, gradOutput, expOut, gradOutput, dim);
if (!result)
return rewriter.notifyMatchFailure(
op,
"nullptr returned by createSoftmaxBackwardCommonKernel function.");
rewriter.replaceOp(op, result);
return success();
}
};
} // namespace
// Decompose aten.log_softmax op into: log(softmax(x))
namespace {
class DecomposeAtenLogSoftmaxIntOp
: public OpRewritePattern<AtenLogSoftmaxIntOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(AtenLogSoftmaxIntOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value self = op.self();
Value dim = op.dim();
if (!op.dtype().getType().isa<Torch::NoneType>())
return rewriter.notifyMatchFailure(
op, "Unimplemented non-None dtype for log_softmax");
BaseTensorType tensorType = self.getType().cast<BaseTensorType>();
if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
return rewriter.notifyMatchFailure(op, "Only support floating type");
// softmax(x, dim)
Value softmax = rewriter.create<AtenSoftmaxIntOp>(loc, tensorType, self,
dim, op.dtype());
rewriter.replaceOpWithNewOp<AtenLogOp>(op, op.getType(), softmax);
return success();
}
};
} // namespace
// Decompose torch.matmul into: torch.mm and torch.bmm according to ranks.
namespace {
class DecomposeAtenMatmulOp : public OpRewritePattern<AtenMatmulOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(AtenMatmulOp op,
PatternRewriter &rewriter) const override {
Value lhs = op.self();
Value rhs = op.other();
int lhsRank = getTensorRank(lhs);
int rhsRank = getTensorRank(rhs);
// If both lhs and rhs ranks are 2 then map it to `aten.mm` op.
if (lhsRank == 2 && rhsRank == 2)
rewriter.replaceOpWithNewOp<AtenMmOp>(op, op.getType(), lhs, rhs);
// If both lhs and rhs ranks are 3 then map it to `aten.bmm` op.
if (lhsRank == 3 && rhsRank == 3)
rewriter.replaceOpWithNewOp<AtenBmmOp>(op, op.getType(), lhs, rhs);
return success();
}
};
} // namespace
// Decompose torch.expand into torch.broadcast_to op.
namespace {
class DecomposeAtenExpandOp : public OpRewritePattern<AtenExpandOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(AtenExpandOp op,
PatternRewriter &rewriter) const override {
bool implicit = false;
if (!matchPattern(op.implicit(), m_TorchConstantBool(&implicit)) ||
implicit) {
return rewriter.notifyMatchFailure(
op, "unimplemented: requires implicit to be false");
}
rewriter.replaceOpWithNewOp<AtenBroadcastToOp>(op, op.getType(), op.self(),
op.size());
return success();
}
};
} // namespace
// Decompose torch.addmm into torch.mm and torch.add.Tensor op.
namespace {
class DecomposeAtenAddmmOp : public OpRewritePattern<AtenAddmmOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(AtenAddmmOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value input = op.self();
Value mat1 = op.mat1();
Value mat2 = op.mat2();
// The operands `mat1`, `mat2` to aten.addmm must be of rank 2.
if (getTensorRank(mat1) != 2 || getTensorRank(mat2) != 2) {
return rewriter.notifyMatchFailure(
op, "expected mat1, mat2 operands to aten.addmm to be rank 2");
}
// TODO: Handle integer type operands.
if (!input.getType()
.cast<ValueTensorType>()
.getDtype()
.isa<mlir::FloatType>()) {
return rewriter.notifyMatchFailure(
op, "unimplemented: non-floating point dtype");
}
// matrix multiplication: matmul = mat1 @ mat2
Value matmul = rewriter.create<AtenMmOp>(loc, op.getType(), mat1, mat2);
// scaledInput = self * beta
Value scaledInput = rewriter.create<AtenMulScalarOp>(loc, input.getType(),
input, op.beta());
// result = scaledInput + alpha * matmul
rewriter.replaceOpWithNewOp<AtenAddTensorOp>(op, op.getType(), scaledInput,
matmul, op.alpha());
return success();
}
};
} // namespace
// Decompose torch.mean into: sum(x)/div(numTensorElements).
namespace {
class DecomposeAtenMeanOp : public OpRewritePattern<AtenMeanOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(AtenMeanOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value input = op.self();
Value output = op.result();
BaseTensorType inputTensorType = input.getType().cast<BaseTensorType>();
BaseTensorType outputTensorType = output.getType().cast<BaseTensorType>();
Value sum = rewriter.create<AtenSumOp>(loc, outputTensorType, input, op.dtype());
Value numTensorElements = rewriter.create<AtenNumelOp>(loc, input);
rewriter.replaceOpWithNewOp<AtenDivScalarOp>(op, outputTensorType, sum,
numTensorElements);
return success();
}
};
} // namespace
namespace {
template<typename OpTy, typename T1T2Op>
class DecomposeAtenAddCLikeOp : public OpRewritePattern<OpTy> {
using OpRewritePattern<OpTy>::OpRewritePattern;
LogicalResult matchAndRewrite(OpTy op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value input = op.self();
Value tensor1 = op.tensor1();
Value tensor2 = op.tensor2();
Value value = op.value();
Value product = rewriter.create<T1T2Op>(loc, op.getType(), tensor1, tensor2);
rewriter.replaceOpWithNewOp<AtenAddTensorOp>(op, op.getType(), input, product,
value);
return success();
}
};
} // namespace
namespace {
class DecomposeComplexOpsPass
: public DecomposeComplexOpsBase<DecomposeComplexOpsPass> {
void runOnOperation() override {
MLIRContext *context = &getContext();
RewritePatternSet patterns(context);
ConversionTarget target(*context);
target.addLegalDialect<Torch::TorchDialect>();
patterns.add<DecomposeAtenSoftmaxIntOp>(context);
target.addIllegalOp<AtenSoftmaxIntOp>();
patterns.add<DecomposeAten_SoftmaxOp>(context);
target.addIllegalOp<Aten_SoftmaxOp>();
patterns.add<DecomposeAtenLogSoftmaxIntOp>(context);
target.addIllegalOp<AtenLogSoftmaxIntOp>();
patterns.add<DecomposeAtenExpandOp>(context);
target.addIllegalOp<AtenExpandOp>();
patterns.add<DecomposeAtenSizeOp>(context);
target.addIllegalOp<AtenSizeOp>();
patterns.add<DecomposeAten_SoftmaxBackwardDataOp>(context);
target.addIllegalOp<Aten_SoftmaxBackwardDataOp>();
patterns.add<DecomposeAtenTanhBackwardOp>(context);
target.addIllegalOp<AtenTanhBackwardOp>();
patterns.add<DecomposeAtenAddmmOp>(context);
target.addIllegalOp<AtenAddmmOp>();
patterns.add<DecomposeAtenMeanOp>(context);
target.addIllegalOp<AtenMeanOp>();
patterns.add<DecomposeAtenMatmulOp>(context);
patterns.add<DecomposeAten_LogSoftmaxBackwardDataOp>(context);
target.addIllegalOp<Aten_LogSoftmaxBackwardDataOp>();
target.addDynamicallyLegalOp<AtenMatmulOp>([](AtenMatmulOp op) {
int lhsRank = getTensorRank(op.self());
int rhsRank = getTensorRank(op.other());
// Make aten.matmul legal if the following condition is satisfied.
return (lhsRank != 2 || rhsRank != 2) && (lhsRank != 3 || rhsRank != 3);
});
patterns.add<DecomposeAtenAddCLikeOp<AtenAddCMulOp, AtenMulTensorOp>>(context);
target.addIllegalOp<AtenAddCMulOp>();
patterns.add<DecomposeAtenAddCLikeOp<AtenAddCDivOp, AtenDivTensorOp>>(context);
target.addIllegalOp<AtenAddCDivOp>();
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns)))) {
return signalPassFailure();
}
}
};
} // namespace
std::unique_ptr<OperationPass<FuncOp>>
mlir::torch::Torch::createDecomposeComplexOpsPass() {
return std::make_unique<DecomposeComplexOpsPass>();
}
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