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torch-mlir/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp

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//===------------------------------------------------------------*- C++ -*-===//
//
// This file is licensed 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/TorchOnnxToTorch/Patterns.h"
#include "torch-mlir/Conversion/TorchOnnxToTorch/Utils.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/SmallVector.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::onnx_c;
// Simple rewrites for the default domain.
// See: https://onnx.ai/onnx/operators/
// For operators that are effectively version invariant, we register with
// sinceVersion==1. We interpret this to include the following spec
// diffs that are irrelevant to this level of lowering:
// * Supported element types.
// * Limited broadcasting to full broadcasting support.
//
// There are a lot of spec revisions that basically generalized elementwise
// to be more normal and a direct translation vs a special case. This
// results in a lot of ONNX test cases that all reduce to the exact same
// thing here, so we simplify.
// utilities
namespace {
// In case the ReduceSum Op was not the first operation performed on the data,
// we provide the original operand through storeResult, which will be modified
// if the result will be passed onto another operation, and will be used for
// noop_with_empty_axes handling before that.
LogicalResult reducedSumImpl(OpBinder binder,
ConversionPatternRewriter &rewriter, Value data,
Torch::ValueTensorType resultType,
Value &storeResult, int64_t keepDims,
int64_t noop_with_empty_axes,
bool isIntermediateOp) {
SmallVector<Value> axesList;
Value axesVal;
if (!binder.tensorOperandAtIndex(axesVal, 1)) {
auto inputType = dyn_cast<Torch::ValueTensorType>(data.getType());
if (!inputType.hasSizes() || !resultType.hasSizes()) {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: expected input and result to have shapes");
}
if (inputType.areAllSizesKnown() && resultType.areAllSizesKnown()) {
SmallVector<int64_t> inputShape{inputType.getSizes()};
SmallVector<int64_t> resultShape{resultType.getSizes()};
// if the shapes are equal, none of the dims is reduced
if (llvm::equal(inputShape, resultShape)) {
// simply fill in the op and return
rewriter.replaceOp(binder.op, data);
return success();
}
if (areAllElementsDistinct(inputShape)) {
// The check for the input shape elements to be distinct is added
// for the cases like:
// Input: [3, 2, 2] -> Output: [3, 2]
// For the above case, from the input and output shape it can't be
// inferred whether the dim:1 is reduced or dim:2. To avoid these
// type of cases, the check has been placed.
SmallVector<int64_t> reduceDims;
unsigned resultShapeCounter = 0;
for (unsigned i = 0; i < inputShape.size(); i++) {
if (resultShapeCounter < resultShape.size() &&
inputShape[i] == resultShape[resultShapeCounter]) {
resultShapeCounter++;
} else {
reduceDims.push_back(i);
if (resultShapeCounter < resultShape.size() &&
resultShape[resultShapeCounter] == 1)
resultShapeCounter++;
}
}
for (auto i : reduceDims) {
axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(i)));
}
}
}
if (axesList.empty()) {
Torch::BaseTensorType axesType =
cast<Torch::BaseTensorType>(axesVal.getType());
auto axesTy = dyn_cast<Torch::ValueTensorType>(axesVal.getType());
auto axesShape = axesTy.getSizes();
if (axesShape.size() != 1 || axesShape[0] == Torch::kUnknownSize)
return failure();
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(0));
SmallVector<int64_t> selectSizes{1};
auto selType = rewriter.getType<Torch::ValueTensorType>(
selectSizes, axesType.getOptionalDtype());
int64_t numAxes = axesShape[0];
for (int64_t i = 0; i < numAxes; ++i) {
Value iv = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selType, axesVal, zero, iv);
Value dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
axesList.push_back(dim);
}
}
}
SmallVector<int64_t> axesInts;
if (!binder.s64IntegerArrayAttr(axesInts, "axes", {})) {
for (int64_t i = 0, s = axesInts.size(); i < s; ++i) {
Value iv = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(axesInts[i]));
axesList.push_back(iv);
}
}
// Do not include absolute value in the noop
if (axesList.empty() && noop_with_empty_axes) {
rewriter.replaceOp(binder.op, storeResult);
return success();
}
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
axesList);
Value keepDimBool =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
Value dType = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
// If we are using the ReducedSum as an intermediate op to be passed into
// another operation, we might not want to replace the Op. So we create a new
// Op and store the result in a variable.
if (!isIntermediateOp) {
rewriter.replaceOpWithNewOp<Torch::AtenSumDimIntListOp>(
binder.op, resultType, data, dimValueList, keepDimBool,
/*dtype=*/dType);
} else {
storeResult = rewriter.create<Torch::AtenSumDimIntListOp>(
binder.getLoc(), resultType, data, dimValueList, keepDimBool,
/*dtype=*/dType);
}
return success();
}
} // namespace
void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
OnnxCustomOpConversionPattern &patterns) {
patterns.onOp(
"QuantizeLinear", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
llvm::SmallVector<Value> operands;
if (binder.tensorOperands(operands, 3) ||
binder.tensorResultType(resultType))
return failure();
Value operand = operands[0];
Value scale = operands[1];
Value zeropoint = operands[2];
auto scaleTy = dyn_cast<Torch::ValueTensorType>(scale.getType());
if (!scaleTy || !scaleTy.hasSizes())
return rewriter.notifyMatchFailure(binder.op, "requires known rank");
if (!resultType.hasDtype())
return rewriter.notifyMatchFailure(binder.op,
"requires known result dtype");
if (scaleTy.getSizes().size() == 0) {
Type qTy = resultType.getDtype();
if (qTy.isUnsignedInteger(8)) {
qTy = rewriter.getType<Torch::QUInt8Type>();
} else if (qTy.isSignedInteger(8)) {
qTy = rewriter.getType<Torch::QInt8Type>();
} else if (qTy.isSignedInteger(32)) {
qTy = rewriter.getType<Torch::QInt32Type>();
} else {
return rewriter.notifyMatchFailure(binder.op,
"unsupported result dtype");
}
auto qTensorTy = rewriter.getType<Torch::ValueTensorType>(
resultType.getOptionalSizes(), qTy);
auto torchqTy = Torch::getScalarTypeForType(qTy);
Value tyConst = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
static_cast<int64_t>(torchqTy)));
scale = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(), scale);
zeropoint = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), zeropoint);
auto quantize = rewriter.create<Torch::AtenQuantizePerTensorOp>(
binder.getLoc(), qTensorTy, operand, scale, zeropoint, tyConst);
rewriter.replaceOpWithNewOp<Torch::AtenIntReprOp>(
binder.op, resultType, quantize);
return success();
}
return failure();
});
patterns.onOp(
"QLinearConv", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
llvm::SmallVector<Value> operands;
if ((binder.tensorOperands(operands, 8) &&
binder.tensorOperands(operands, 9)) ||
binder.tensorResultType(resultType))
return failure();
Value a = operands[0];
Value aScale = operands[1];
Value aZp = operands[2];
Value b = operands[3];
Value bScale = operands[4];
Value bZp = operands[5];
Value cScale = operands[6];
Value cZp = operands[7];
Value c = operands.size() == 9 ? operands[8] : nullptr;
auto check = [](Value v) {
auto vTy = cast<Torch::ValueTensorType>(v.getType());
return llvm::all_of(vTy.getSizes(), [](int64_t d) { return d == 1; });
};
if (!check(aScale) || !check(aZp) || !check(bScale) || !check(bZp) ||
!check(cScale) || !check(cScale))
return rewriter.notifyMatchFailure(
binder.op, "not supported for non per-tensor quantization");
auto extract = [&rewriter, &binder](Value v) {
auto vTy = cast<Torch::ValueTensorType>(v.getType());
Type extractTy = rewriter.getType<Torch::FloatType>();
if (isa<IntegerType>(vTy.getDtype()))
extractTy = rewriter.getType<Torch::IntType>();
return rewriter.create<Torch::AtenItemOp>(binder.getLoc(), extractTy,
v);
};
aZp = extract(aZp);
bZp = extract(bZp);
cZp = extract(cZp);
aScale = extract(aScale);
bScale = extract(bScale);
cScale = extract(cScale);
auto make = [&rewriter, &binder](Value v, Value scale,
Value zp) -> Value {
auto ty = cast<Torch::ValueTensorType>(v.getType());
auto newTy = getQTorchTypeFromTorchIntType(ty);
return rewriter.create<Torch::Aten_MakePerTensorQuantizedTensorOp>(
binder.getLoc(), newTy, v, scale, zp);
};
a = make(a, aScale, aZp);
b = make(b, bScale, bZp);
auto cTy = rewriter.getType<Torch::ValueTensorType>(
resultType.getOptionalSizes(),
rewriter.getIntegerType(32, /*issigned=*/true));
// TODO(suderman): insert convolution operator.
llvm::SmallVector<Value> newOperands = {a, b};
if (c)
newOperands.push_back(c);
cTy = rewriter.getType<Torch::ValueTensorType>(
resultType.getOptionalSizes(),
rewriter.getType<Torch::QInt32Type>());
llvm::SmallVector<NamedAttribute> newAttributes;
newAttributes.push_back(
rewriter.getNamedAttr("name", rewriter.getStringAttr("onnx.Conv")));
for (auto namedAttr : binder.op->getAttrDictionary()) {
if (namedAttr.getName().getValue().compare("name") == 0)
continue;
llvm::errs() << namedAttr.getName() << "\n";
newAttributes.push_back(namedAttr);
}
c = rewriter
.create<Torch::OperatorOp>(binder.getLoc(), cTy, newOperands,
newAttributes,
binder.op->getRegions().size())
.getResult(0);
Value outScale = rewriter.create<Torch::AtenMulFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(), aScale,
bScale);
Value outZp = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
c = rewriter.create<Torch::Aten_MakePerTensorQuantizedTensorOp>(
binder.getLoc(), cTy, c, outScale, outZp);
cTy = rewriter.getType<Torch::ValueTensorType>(
resultType.getOptionalSizes(), rewriter.getF32Type());
c = rewriter.create<Torch::AtenDequantizeSelfOp>(binder.getLoc(), cTy,
c);
cTy = dyn_cast<Torch::ValueTensorType>(
getQTorchTypeFromTorchIntType(resultType));
Value dtyVal = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(
rewriter.getIntegerType(64),
static_cast<int64_t>(
Torch::getScalarTypeForType(cTy.getDtype()))));
c = rewriter.create<Torch::AtenQuantizePerTensorOp>(
binder.getLoc(), cTy, c, cScale, cZp, dtyVal);
rewriter.replaceOpWithNewOp<Torch::AtenIntReprOp>(binder.op, resultType,
c);
return success();
});
patterns.onOp(
"QLinearMatMul", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
llvm::SmallVector<Value> operands;
if (binder.tensorOperands(operands, 8) ||
binder.tensorResultType(resultType))
return failure();
Value a = operands[0];
Value aScale = operands[1];
Value aZp = operands[2];
Value b = operands[3];
Value bScale = operands[4];
Value bZp = operands[5];
Value cScale = operands[6];
Value cZp = operands[7];
auto check = [](Value v) {
auto vTy = cast<Torch::ValueTensorType>(v.getType());
for (auto dim : vTy.getSizes())
if (dim != 1)
return false;
return true;
};
if (!check(aScale) || !check(aZp) || !check(bScale) || !check(bZp) ||
!check(cScale) || !check(cScale))
return rewriter.notifyMatchFailure(
binder.op, "not supported for non per-tensor quantization");
Value emptyList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
rewriter.getType<Torch::ListType>(
rewriter.getType<Torch::IntType>()),
ValueRange{});
auto extract = [&rewriter, &binder, &emptyList](Value v) {
auto vTy = cast<Torch::ValueTensorType>(v.getType());
if (!vTy.getSizes().empty()) {
vTy = rewriter.getType<Torch::ValueTensorType>(
ArrayRef<int64_t>({}), vTy.getOptionalDtype());
v = rewriter.create<Torch::AtenReshapeOp>(binder.getLoc(), vTy, v,
emptyList);
}
Type extractTy = rewriter.getType<Torch::FloatType>();
if (isa<IntegerType>(vTy.getDtype()))
extractTy = rewriter.getType<Torch::IntType>();
return rewriter.create<Torch::AtenItemOp>(binder.getLoc(), extractTy,
v);
};
aZp = extract(aZp);
bZp = extract(bZp);
cZp = extract(cZp);
aScale = extract(aScale);
bScale = extract(bScale);
cScale = extract(cScale);
auto make = [&rewriter, &binder](Value v, Value scale,
Value zp) -> Value {
auto ty = cast<Torch::ValueTensorType>(v.getType());
auto newTy = getQTorchTypeFromTorchIntType(ty);
return rewriter.create<Torch::Aten_MakePerTensorQuantizedTensorOp>(
binder.getLoc(), newTy, v, scale, zp);
};
a = make(a, aScale, aZp);
b = make(b, bScale, bZp);
auto cTy = rewriter.getType<Torch::ValueTensorType>(
resultType.getOptionalSizes(),
rewriter.getIntegerType(32, /*issigned=*/true));
Value c;
if (cTy.getSizes().size() == 2) {
c = rewriter.create<Torch::AtenMmOp>(binder.getLoc(), cTy, a, b);
} else {
c = rewriter.create<Torch::AtenBmmOp>(binder.getLoc(), cTy, a, b);
}
cTy = rewriter.getType<Torch::ValueTensorType>(
resultType.getOptionalSizes(),
rewriter.getType<Torch::QInt32Type>());
Value mmScale = rewriter.create<Torch::AtenMulFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(), aScale,
bScale);
Value mmZp = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
c = rewriter.create<Torch::Aten_MakePerTensorQuantizedTensorOp>(
binder.getLoc(), cTy, c, mmScale, mmZp);
cTy = rewriter.getType<Torch::ValueTensorType>(
resultType.getOptionalSizes(), rewriter.getF32Type());
c = rewriter.create<Torch::AtenDequantizeSelfOp>(binder.getLoc(), cTy,
c);
cTy = dyn_cast<Torch::ValueTensorType>(
getQTorchTypeFromTorchIntType(resultType));
Value dtyVal = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(
rewriter.getIntegerType(64),
static_cast<int64_t>(
Torch::getScalarTypeForType(cTy.getDtype()))));
c = rewriter.create<Torch::AtenQuantizePerTensorOp>(
binder.getLoc(), cTy, c, cScale, cZp, dtyVal);
rewriter.replaceOpWithNewOp<Torch::AtenIntReprOp>(binder.op, resultType,
c);
return success();
});
patterns.onOp("Reciprocal", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenReciprocalOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Relu", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value x;
if (binder.tensorOperand(x) || binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenReluOp>(binder.op, resultType,
x);
return success();
});
patterns.onOp("Round", 11,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenRoundOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Scatter", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
int64_t axis;
if (binder.s64IntegerAttr(axis, "axis", {}))
return rewriter.notifyMatchFailure(binder.op, "axis bind failure");
Torch::ValueTensorType resultTy;
Value data, indices, updates;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorOperandAtIndex(indices, 1) ||
binder.tensorOperandAtIndex(updates, 2) ||
binder.tensorResultType(resultTy))
return failure();
auto dataTy = cast<Torch::ValueTensorType>(data.getType()),
indicesTy = cast<Torch::ValueTensorType>(indices.getType()),
updatesTy = cast<Torch::ValueTensorType>(updates.getType());
int64_t dataRank = dataTy.getSizes().size(),
indicesRank = indicesTy.getSizes().size(),
updatesRank = updatesTy.getSizes().size();
if ((dataRank < 1) || (indicesRank < 1) || (updatesRank < 1) ||
(axis < -dataRank) || (axis >= dataRank))
return failure();
Value axisValue = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(axis));
rewriter.replaceOpWithNewOp<Torch::AtenScatterSrcOp>(
binder.op, resultTy, data, axisValue, indices, updates);
return success();
});
patterns.onOp(
"ScatterElements", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
SmallVector<Value> valList;
int64_t axis;
std::string reduction;
int64_t numOperands = binder.op->getNumOperands();
if (binder.tensorOperands(valList, numOperands) ||
binder.s64IntegerAttr(axis, "axis", 0) ||
binder.customOpNameStringAttr(reduction, "reduction", "none") ||
binder.tensorResultType(resultType))
return failure();
Value data = valList[0];
Value indices = valList[1];
Value updates = valList[2];
// ONNX allows negative axis.
if (axis < 0)
axis +=
cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
Value constAxis = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), axis));
if (reduction == "none") {
rewriter.replaceOpWithNewOp<Torch::AtenScatterSrcOp>(
binder.op, resultType, data, constAxis, indices, updates);
return success();
}
// TODO: Implement max and min cases
if (reduction == "mul") {
reduction = "multiply";
} else if (reduction == "max" || reduction == "min") {
return rewriter.notifyMatchFailure(
binder.op, "max/min reduction unsupported for scatter elements");
}
Value cstStrReduction =
rewriter.create<Torch::ConstantStrOp>(binder.getLoc(), reduction);
rewriter.replaceOpWithNewOp<Torch::AtenScatterReduceOp>(
binder.op, resultType, data, constAxis, indices, updates,
cstStrReduction);
return success();
});
patterns.onOp(
"SequenceConstruct", 11,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
SmallVector<Value> operands;
Torch::ListType resultType;
if (binder.tensorOperands(operands, binder.getNumOperands()) ||
binder.tensorListResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::PrimListConstructOp>(
binder.op, resultType, operands);
return success();
});
patterns.onOp(
"SequenceLength", 11,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
// onnx.SequenceLength takes a sequence(list) of tensors, and returns
// a zero rank tensor with the length.
Torch::ValueTensorType resultType;
Value x;
if (binder.tensorListOperand(x) || binder.tensorResultType(resultType))
return failure();
Value cstFalse =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
Value none = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value len = rewriter.create<Torch::AtenLenTOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), x);
// AtenLenTOp returns a torch.int, so we have to
// put that in a tensor.
rewriter.replaceOpWithNewOp<Torch::AtenTensorIntOp>(
binder.op, resultType, len, none, none, cstFalse);
return success();
});
patterns.onOp(
"Sigmoid", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value x;
if (binder.tensorOperand(x) || binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenSigmoidOp>(binder.op, resultType,
x);
return success();
});
patterns.onOp("Sin", 7,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenSinOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp("Tanh", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenTanhOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp("Sqrt", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenSqrtOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Sub", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value x;
Value y;
if (binder.tensorOperands(x, y) || binder.tensorResultType(resultType))
return failure();
Value const1 = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 1));
rewriter.replaceOpWithNewOp<Torch::AtenSubTensorOp>(
binder.op, resultType, x, y, const1);
return success();
});
patterns.onOp(
"Sum", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
if (binder.op->getNumOperands() == 1) {
Torch::ValueTensorType resultType;
Value x;
if (binder.tensorOperand(x) || binder.tensorResultType(resultType))
return failure();
rewriter.replaceOp(binder.op, x);
return success();
}
Torch::ValueTensorType resultType;
SmallVector<Value> valList;
int64_t numOperands = binder.op->getNumOperands();
if (binder.tensorOperands(valList, numOperands) ||
binder.tensorResultType(resultType))
return failure();
Value const1 = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 1));
// Short circuit to binary add
if (numOperands == 2) {
rewriter.replaceOpWithNewOp<Torch::AtenAddTensorOp>(
binder.op, resultType, valList[0], valList[1], const1);
return success();
}
// When binder.op->getNumOperands() > 2
Value curr = rewriter.create<Torch::AtenAddTensorOp>(
binder.getLoc(), resultType, valList[0], valList[1], const1);
for (int i = 2; i < numOperands; i++) {
if (i == numOperands - 1) {
curr = rewriter.create<Torch::AtenAddTensorOp>(
binder.getLoc(), resultType, curr, valList[i], const1);
} else {
SmallVector<int64_t> resultBroadcastShapeInt;
SmallVector<Value> resultBroadcastShapeValue;
Torch::computeBroadcastShape(rewriter, binder.getLoc(), curr,
valList[i], resultBroadcastShapeInt,
resultBroadcastShapeValue);
auto baseType = Torch::ValueTensorType::get(
binder.op->getContext(), resultBroadcastShapeInt,
resultType.getOptionalDtype());
curr = rewriter.create<Torch::AtenAddTensorOp>(
binder.getLoc(), baseType, curr, valList[i], const1);
}
}
rewriter.replaceOp(binder.op, curr);
return success();
});
patterns.onOp("Where", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
SmallVector<Value> valList;
int64_t numOperands = binder.op->getNumOperands();
if (binder.tensorOperands(valList, numOperands) ||
binder.tensorResultType(resultType))
return failure();
Value condition = valList[0];
Value x = valList[1];
Value y = valList[2];
rewriter.replaceOpWithNewOp<Torch::AtenWhereSelfOp>(
binder.op, resultType, condition, x, y);
return success();
});
patterns.onOp(
"Xor", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value x;
Value y;
if (binder.tensorOperands(x, y) || binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenLogicalXorOp>(binder.op,
resultType, x, y);
return success();
});
patterns.onOp(
"Squeeze", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
SmallVector<Value> inputOperands;
if (binder.tensorOperands(inputOperands, binder.op->getNumOperands()) ||
binder.tensorResultType(resultType))
return failure();
Value data = inputOperands[0];
auto inputType = dyn_cast<Torch::ValueTensorType>(data.getType());
if (!inputType.hasSizes() || !resultType.hasSizes())
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: expected input and result to have shapes");
int64_t inputRank = inputType.getSizes().size();
int64_t resultRank = resultType.getSizes().size();
int64_t rankDiff = inputRank - resultRank;
if (rankDiff == 0) {
// In this case, no dimension is squeezed. Hence just replace the op
// with input.
rewriter.replaceOp(binder.op, data);
return success();
}
if (inputOperands.size() == 1) {
// Case: `axes` value is not present which means squeeze all the
// dimensions with shape value 1.
rewriter.replaceOpWithNewOp<Torch::AtenSqueezeOp>(binder.op,
resultType, data);
return success();
}
SmallVector<Value> dimList;
if (inputType.areAllSizesKnown() && resultType.areAllSizesKnown()) {
// If the input shape and result shape is statically known then the
// list of dims to be squeezed can be derived from those shapes. As a
// result, we don't have to wait for the dim values to be known at
// runtime which is also expected by the downstream pipeline.
SmallVector<int64_t> inputShape(inputType.getSizes());
SmallVector<int64_t> resultShape(resultType.getSizes());
SmallVector<int64_t> squeezeDims;
unsigned resultShapeCounter = 0;
for (unsigned i = 0; i < inputRank; i++) {
if (resultShapeCounter < resultRank &&
inputShape[i] == resultShape[resultShapeCounter]) {
resultShapeCounter++;
} else {
squeezeDims.push_back(i);
}
}
for (auto i : squeezeDims) {
dimList.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(i)));
}
}
if (dimList.empty()) {
Value axes = inputOperands[1];
Torch::BaseTensorType axesType =
cast<Torch::BaseTensorType>(axes.getType());
SmallVector<int64_t> selectSizes{1};
Type selectResultType = axesType.getWithSizesAndDtype(
selectSizes, axesType.getOptionalDtype());
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
for (int i = 0; i < rankDiff; i++) {
// Go through the axes list and get each dim in the list
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selectResultType, axes, zero, selectIndex);
Value dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
dimList.push_back(dim);
}
}
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
rewriter.getType<Torch::ListType>(
rewriter.getType<Torch::IntType>()),
dimList);
rewriter.replaceOpWithNewOp<Torch::PrimsSqueezeOp>(
binder.op, resultType, data, dimValueList);
return success();
});
patterns.onOp(
"Unsqueeze", 13,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
// Unlike squeeze where we are able to lower to Torch::PrimsSqueezeOp,
// pytorch does not support torch.unsqueeze to insert multiple new dims.
// discussion can be found here:
// https://github.com/pytorch/pytorch/issues/9410
// So, for now, we unroll into multiple unsqueezes.
Location loc = binder.getLoc();
Torch::ValueTensorType resultType;
Value data, axes;
if (binder.tensorOperands(data, axes) ||
binder.tensorResultType(resultType))
return failure();
auto inputType = dyn_cast<Torch::ValueTensorType>(data.getType());
if (!inputType.hasSizes() || !resultType.hasSizes())
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: expected input and result to have shapes");
int64_t inputRank = inputType.getSizes().size();
int64_t resultRank = resultType.getSizes().size();
int64_t rankDiff = resultRank - inputRank;
if (rankDiff == 0) {
// In this case, no dimension is unsqueezed. Hence just replace the op
// with input.
rewriter.replaceOp(binder.op, data);
return success();
}
SmallVector<int64_t> unsqueezeDims;
SmallVector<int64_t> inputShape(inputType.getSizes());
if (inputType.areAllSizesKnown() && resultType.areAllSizesKnown()) {
// If the input shape and result shape is statically known then the
// list of dims to be squeezed can be derived from those shapes. As a
// result, we don't have to wait for the dim values to be known at
// runtime which is also expected by the downstream pipeline.
SmallVector<int64_t> resultShape(resultType.getSizes());
unsigned inputShapeCounter = 0;
for (unsigned i = 0; i < resultRank; i++) {
if (inputShapeCounter < inputRank &&
inputShape[inputShapeCounter] == resultShape[i]) {
inputShapeCounter++;
} else {
unsqueezeDims.push_back(i);
}
}
} else {
SmallVector<int64_t> unsqueezeDimsInts;
if (!matchPattern(axes, m_OnnxListOfConstantInts(unsqueezeDimsInts)))
return rewriter.notifyMatchFailure(
binder.op, "only support constant int axes values");
for (auto dim : unsqueezeDimsInts)
unsqueezeDims.push_back(dim < 0 ? dim + resultRank : dim);
// If we don't sort, unsqueezing first on 4 and then on 0 would fail
// for shape = {x,y,z}, and axes [4,0]
llvm::sort(unsqueezeDims.begin(), unsqueezeDims.end());
}
Value result = data;
SmallVector<int64_t> unsqueezeShape = inputShape;
for (auto dim : unsqueezeDims) {
unsqueezeShape.insert(unsqueezeShape.begin() + dim, 1);
Type unsqueezeType = resultType.getWithSizesAndDtype(
unsqueezeShape, resultType.getOptionalDtype());
Value cstDim = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(dim));
result = rewriter.create<Torch::AtenUnsqueezeOp>(loc, unsqueezeType,
result, cstDim);
}
rewriter.replaceOp(binder.op, result);
return success();
});
patterns.onOp(
"Softmax", 13, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value input;
int64_t axis;
if (binder.tensorOperand(input) ||
binder.s64IntegerAttr(axis, "axis", -1) ||
binder.tensorResultType(resultType))
return failure();
// ONNX allows negative axis.
if (axis < 0)
axis +=
cast<Torch::ValueTensorType>(input.getType()).getSizes().size();
Value constAxis = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), axis));
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
rewriter.replaceOpWithNewOp<Torch::AtenSoftmaxIntOp>(
binder.op, resultType, input, constAxis, /*dtype=*/noneVal);
return success();
});
patterns.onOp(
"Selu", 6, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
// y = gamma * (alpha * e^x - alpha) for x <= 0, y = gamma * x for x > 0
Torch::ValueTensorType resultType;
float alpha, gamma;
Value operand;
// Refer https://onnx.ai/onnx/operators/onnx__Selu.html for the default
// alpha and gamma values.
if (binder.tensorOperand(operand) ||
binder.f32FloatAttr(alpha, "alpha", 1.67326) ||
binder.f32FloatAttr(gamma, "gamma", 1.0507) ||
binder.tensorResultType(resultType))
return failure();
Value vAlpha = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), alpha));
Value vScale = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), gamma));
Value vInputScale = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), 1.0));
rewriter.replaceOpWithNewOp<Torch::AtenEluOp>(
binder.op, resultType, operand, vAlpha, vScale, vInputScale);
return success();
});
patterns.onOp("ReduceL1", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
int64_t keepDims, noop_with_empty_axes;
Value operand;
if (binder.tensorOperandAtIndex(operand, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes,
"noop_with_empty_axes", 0))
return failure();
Value data = rewriter.create<Torch::AtenAbsOp>(
binder.getLoc(), operand.getType(), operand);
return reducedSumImpl(binder, rewriter, data, resultType,
/*storeValue=*/operand, keepDims,
noop_with_empty_axes, false);
});
patterns.onOp(
"ReduceL2", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
int64_t keepDims, noop_with_empty_axes;
if (binder.tensorOperandAtIndex(operand, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
0))
return failure();
// A ReduceL2 op is equivalent to the following sequence of operations:
// Mul(x, x) -> ReduceSum -> CastF32 -> Sqrt -> CastLike(resultType)
Value squareOfOperand = rewriter.create<Torch::AtenMulTensorOp>(
binder.getLoc(), operand.getType(), operand, operand);
auto reducedSum =
reducedSumImpl(binder, rewriter, squareOfOperand, resultType,
operand, keepDims, noop_with_empty_axes, true);
if (failed(reducedSum))
return rewriter.notifyMatchFailure(
binder.op,
"Failed to perform sum operation on square of operand");
Value castDType = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(/*Float32Type*/ 6));
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value constFalse =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
// Perform an AtenToDtype op on the squared sum of the operand, stored
// now in operand itself.
auto size = dyn_cast<Torch::ValueTensorType>(operand.getType())
.getOptionalSizes();
auto f32ResultType = rewriter.getType<Torch::ValueTensorType>(
size, rewriter.getF32Type());
Value operandCast = rewriter.create<Torch::AtenToDtypeOp>(
binder.getLoc(), f32ResultType, operand, castDType,
/*non_blocking=*/constFalse, /*copy=*/constFalse,
/*memory_format=*/noneVal);
Value operandSqrt = rewriter.create<Torch::AtenSqrtOp>(
binder.getLoc(), f32ResultType, operandCast);
Value resultDtype = Torch::getDtypeIntValueForType(
rewriter, binder.getLoc(), resultType.getDtype());
rewriter.replaceOpWithNewOp<Torch::AtenToDtypeOp>(
binder.op, resultType, operandSqrt, resultDtype,
/*non_blocking=*/constFalse, /*copy=*/constFalse,
/*memory_format=*/noneVal);
return success();
});
patterns.onOp("ReduceLogSum", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value data;
int64_t keepDims, noop_with_empty_axes;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes,
"noop_with_empty_axes", 0))
return failure();
auto reducedSumBool =
reducedSumImpl(binder, rewriter, data, resultType,
/*storeValue=*/data, keepDims,
noop_with_empty_axes, true);
if (failed(reducedSumBool))
return rewriter.notifyMatchFailure(
binder.op,
"Failed to perform sum operation on square of operand");
rewriter.replaceOpWithNewOp<Torch::AtenLogOp>(
binder.op, resultType, data);
return success();
});
patterns.onOp(
"ReduceLogSumExp", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value data;
int64_t keepDims, noop_with_empty_axes;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
0))
return failure();
// out = Log(reducesum(exp(data)))
Value castDType = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(/*Float64Type*/ 7));
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value constFalse =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
auto size =
dyn_cast<Torch::ValueTensorType>(data.getType()).getOptionalSizes();
auto f64ResultType = rewriter.getType<Torch::ValueTensorType>(
size, rewriter.getF64Type());
Value dataCast = rewriter.create<Torch::AtenToDtypeOp>(
binder.getLoc(), f64ResultType, data, castDType,
/*non_blocking=*/constFalse, /*copy=*/constFalse,
/*memory_format=*/noneVal);
Value dataExp = rewriter.create<Torch::AtenExpOp>(
binder.getLoc(), f64ResultType, dataCast);
auto f64ReduceType = rewriter.getType<Torch::ValueTensorType>(
resultType.getOptionalSizes(), rewriter.getF64Type());
auto reducedSumBool = reducedSumImpl(
binder, rewriter, dataExp, f64ReduceType,
/*storeValue=*/data, keepDims, noop_with_empty_axes, true);
if (failed(reducedSumBool))
return rewriter.notifyMatchFailure(
binder.op,
"Failed to perform sum operation on square of operand");
Value finalResult = rewriter.create<Torch::AtenLogOp>(
binder.getLoc(), f64ReduceType, data);
Value resultDtype = Torch::getDtypeIntValueForType(
rewriter, binder.getLoc(), resultType.getDtype());
rewriter.replaceOpWithNewOp<Torch::AtenToDtypeOp>(
binder.op, resultType, finalResult, resultDtype,
/*non_blocking=*/constFalse, /*copy=*/constFalse,
/*memory_format=*/noneVal);
return success();
});
patterns.onOp("ReduceSum", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value data;
int64_t keepDims, noop_with_empty_axes;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes,
"noop_with_empty_axes", 0))
return failure();
return reducedSumImpl(binder, rewriter, data, resultType,
/*storeValue=*/data, keepDims,
noop_with_empty_axes, false);
});
patterns.onOp("ReduceSumSquare", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value data;
int64_t keepDims, noop_with_empty_axes;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes,
"noop_with_empty_axes", 0))
return failure();
Value dataSquare = rewriter.create<Torch::AtenMulTensorOp>(
binder.getLoc(), data.getType(), data, data);
return reducedSumImpl(binder, rewriter, dataSquare,
resultType,
/*storeValue=*/data, keepDims,
noop_with_empty_axes, false);
});
patterns.onOp(
"ReduceMean", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value data;
int64_t keepDims, noop_with_empty_axes;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
0))
return failure();
SmallVector<Value> axesList;
Value axesVal;
if (!binder.tensorOperandAtIndex(axesVal, 1)) {
auto inputType = dyn_cast<Torch::ValueTensorType>(data.getType());
if (!inputType.hasSizes() || !resultType.hasSizes()) {
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: expected input and result to have shapes");
}
// If the input shape and result shape is statically known then the
// list of dims to be squeezed can be derived from those shapes. As a
// result, we don't have to wait for the dim values to be known at
// runtime which is also expected by the downstream pipeline.
if (inputType.areAllSizesKnown() && resultType.areAllSizesKnown()) {
SmallVector<int64_t> inputShape{inputType.getSizes()};
SmallVector<int64_t> resultShape{resultType.getSizes()};
if (llvm::equal(inputShape, resultShape)) {
// Case: none of the dimension is reduced.
rewriter.replaceOp(binder.op, data);
return success();
}
if (areAllElementsDistinct(inputShape)) {
// The check for the input shape elements to be distinct is added
// for the cases like:
// Input: [3, 2, 2] -> Output: [3, 2]
// For the above case, from the input and output shape it can't be
// inferred whether the dim:1 is reduced or dim:2. To avoid these
// type of cases, the check has been placed.
SmallVector<int64_t> reduceDims;
unsigned resultShapeCounter = 0;
for (unsigned i = 0; i < inputShape.size(); i++) {
if (resultShapeCounter < resultShape.size() &&
inputShape[i] == resultShape[resultShapeCounter]) {
resultShapeCounter++;
} else {
reduceDims.push_back(i);
if (resultShapeCounter < resultShape.size() &&
resultShape[resultShapeCounter] == 1)
resultShapeCounter++;
}
}
for (auto i : reduceDims) {
axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(i)));
}
}
}
if (axesList.empty()) {
Torch::BaseTensorType axesType =
cast<Torch::BaseTensorType>(axesVal.getType());
auto axesTy = dyn_cast<Torch::ValueTensorType>(axesVal.getType());
auto axesShape = axesTy.getSizes();
if (axesShape.size() != 1 || axesShape[0] == Torch::kUnknownSize)
return failure();
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(0));
SmallVector<int64_t> selectSizes{1};
auto selType = rewriter.getType<Torch::ValueTensorType>(
selectSizes, axesType.getOptionalDtype());
int64_t numAxes = axesShape[0];
for (int64_t i = 0; i < numAxes; ++i) {
Value iv = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selType, axesVal, zero, iv);
Value dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
axesList.push_back(dim);
}
}
}
SmallVector<int64_t> axesInts;
if (!binder.s64IntegerArrayAttr(axesInts, "axes", {})) {
for (int64_t i = 0, s = axesInts.size(); i < s; ++i) {
Value iv = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(axesInts[i]));
axesList.push_back(iv);
}
}
// deal with case when axes is empty
if (axesList.empty() && noop_with_empty_axes) {
rewriter.replaceOp(binder.op, data);
return success();
}
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
axesList);
Value keepDimBool =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
rewriter.replaceOpWithNewOp<Torch::AtenMeanDimOp>(
binder.op, resultType, data, dimValueList, keepDimBool,
/*dtype=*/noneVal);
return success();
});
patterns.onOp(
"ReduceMax", 13,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
// AtenAmaxOp allows us to pass a list of dims
Torch::ValueTensorType resultType;
Value data;
Value axes;
int64_t keepDims;
int64_t noop_with_empty_axes;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
0))
return failure();
auto dataTy = cast<Torch::BaseTensorType>(data.getType());
Torch::IntType torchIntTy = rewriter.getType<Torch::IntType>();
// If any of the input dims are 0 we set to the upper limit:
if (llvm::any_of(dataTy.getSizes(), [](int64_t d) { return d == 0; }) &&
(llvm::any_of(dataTy.getSizes(),
[](int64_t d) { return d == Torch::kUnknownSize; }) ||
keepDims)) {
auto dty = dataTy.getDtype();
Value scalar;
if (FloatType fpTy = dyn_cast<FloatType>(dty)) {
auto inf = APFloat::getInf(fpTy.getFloatSemantics());
scalar = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(),
inf.convertToDouble()));
}
if (IntegerType intTy = dyn_cast<IntegerType>(dty)) {
auto mx =
intTy.isSigned()
? APInt::getSignedMaxValue(intTy.getIntOrFloatBitWidth())
: APInt::getMaxValue(intTy.getIntOrFloatBitWidth());
scalar = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), torchIntTy,
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
mx.getSExtValue()));
}
llvm::SmallVector<Value> fillDims;
for (int i = 0, s = resultType.getSizes().size(); i < s; ++i) {
auto staticDim = resultType.getSizes()[i];
if (staticDim != Torch::kUnknownSize) {
fillDims.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), torchIntTy,
rewriter.getI64IntegerAttr(staticDim)));
continue;
}
Value iv = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), torchIntTy, rewriter.getI64IntegerAttr(i));
fillDims.push_back(rewriter.create<Torch::AtenSizeIntOp>(
binder.getLoc(), torchIntTy, data, iv));
}
Value none = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value fillDimsList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(), Torch::ListType::get(torchIntTy), fillDims);
rewriter.replaceOpWithNewOp<Torch::AtenFullOp>(
binder.op, resultType, fillDimsList, scalar, none, none, none,
none);
return success();
}
// Previous version of the operation had the axes as an attribute:
SmallVector<Value> axesList;
llvm::SmallVector<int64_t> axesAttr;
if (!binder.s64IntegerArrayAttr(axesAttr, "axes", {})) {
for (int i = 0, s = axesAttr.size(); i < s; ++i) {
axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), torchIntTy,
rewriter.getI64IntegerAttr(axesAttr[i])));
}
}
// Extract the axes values from the axes operand:
if (!binder.tensorOperandAtIndex(axes, 1)) {
Torch::BaseTensorType axesType =
cast<Torch::BaseTensorType>(axes.getType());
SmallVector<int64_t> selectSizes{1};
Type selectResultType = axesType.getWithSizesAndDtype(
selectSizes, axesType.getOptionalDtype());
auto sizes = axesType.getSizes();
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
// Extract the value of each axes:
for (int i = 0; i < sizes[0]; i++) {
// Go through the axes list and get each dim in the list
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selectResultType, axes, zero, selectIndex);
Value dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
axesList.push_back(dim);
}
}
// Handle the noop case:
if (axesList.empty() && noop_with_empty_axes) {
rewriter.replaceOp(binder.op, data);
return success();
}
// Deal with case when no axes arg is passed but not a noop:
if (axesList.empty()) {
int64_t numDims = dyn_cast<Torch::ValueTensorType>(data.getType())
.getSizes()
.size();
for (int i = 0; i < numDims; i++) {
Value curr = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
axesList.push_back(curr);
}
}
// Handle negative axis:
Value rankVal = rewriter.create<Torch::AtenDimOp>(binder.getLoc(),
torchIntTy, data);
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(0));
for (Value &axes : axesList) {
Value isNegative =
rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), axes, zero);
isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
isNegative);
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
binder.getLoc(), isNegative, rankVal);
axes = rewriter.create<Torch::AtenAddIntOp>(binder.getLoc(), axes,
finalOffset);
}
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(), Torch::ListType::get(torchIntTy), axesList);
Value keepDimBool =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
rewriter.replaceOpWithNewOp<Torch::AtenAmaxOp>(
binder.op, resultType, data, dimValueList, keepDimBool);
return success();
});
patterns.onOp(
"ReduceMin", 13,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
// AtenAminOp allows us to pass a list of dims
Torch::ValueTensorType resultType;
Value data;
Value axes;
int64_t keepDims;
int64_t noop_with_empty_axes;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
0))
return failure();
auto dataTy = cast<Torch::BaseTensorType>(data.getType());
Torch::IntType torchIntTy = rewriter.getType<Torch::IntType>();
// If any of the input dims are 0 we set to the upper limit:
if (llvm::any_of(dataTy.getSizes(), [](int64_t d) { return d == 0; }) &&
(llvm::any_of(dataTy.getSizes(),
[](int64_t d) { return d == Torch::kUnknownSize; }) ||
keepDims)) {
auto dty = dataTy.getDtype();
Value scalar;
if (FloatType fpTy = dyn_cast<FloatType>(dty)) {
auto inf = APFloat::getInf(fpTy.getFloatSemantics());
scalar = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(),
inf.convertToDouble()));
}
if (IntegerType intTy = dyn_cast<IntegerType>(dty)) {
auto mx =
intTy.isSigned()
? APInt::getSignedMaxValue(intTy.getIntOrFloatBitWidth())
: APInt::getMaxValue(intTy.getIntOrFloatBitWidth());
scalar = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), torchIntTy,
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
mx.getSExtValue()));
}
llvm::SmallVector<Value> fillDims;
for (int i = 0, s = resultType.getSizes().size(); i < s; ++i) {
auto staticDim = resultType.getSizes()[i];
if (staticDim != Torch::kUnknownSize) {
fillDims.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), torchIntTy,
rewriter.getI64IntegerAttr(staticDim)));
continue;
}
Value iv = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), torchIntTy, rewriter.getI64IntegerAttr(i));
fillDims.push_back(rewriter.create<Torch::AtenSizeIntOp>(
binder.getLoc(), torchIntTy, data, iv));
}
Value none = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value fillDimsList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(), Torch::ListType::get(torchIntTy), fillDims);
rewriter.replaceOpWithNewOp<Torch::AtenFullOp>(
binder.op, resultType, fillDimsList, scalar, none, none, none,
none);
return success();
}
// Previous version of the operation had the axes as an attribute:
SmallVector<Value> axesList;
llvm::SmallVector<int64_t> axesAttr;
if (!binder.s64IntegerArrayAttr(axesAttr, "axes", {})) {
for (int i = 0, s = axesAttr.size(); i < s; ++i) {
axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), torchIntTy,
rewriter.getI64IntegerAttr(axesAttr[i])));
}
}
// Extract the axes values from the axes operand:
if (!binder.tensorOperandAtIndex(axes, 1)) {
Torch::BaseTensorType axesType =
cast<Torch::BaseTensorType>(axes.getType());
SmallVector<int64_t> selectSizes{1};
Type selectResultType = axesType.getWithSizesAndDtype(
selectSizes, axesType.getOptionalDtype());
auto sizes = axesType.getSizes();
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
// Extract the value of each axes:
for (int i = 0; i < sizes[0]; i++) {
// Go through the axes list and get each dim in the list
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selectResultType, axes, zero, selectIndex);
Value dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
axesList.push_back(dim);
}
}
// Handle the noop case:
if (axesList.empty() && noop_with_empty_axes) {
rewriter.replaceOp(binder.op, data);
return success();
}
// Deal with case when no axes arg is passed but not a noop:
if (axesList.empty()) {
int64_t numDims = dyn_cast<Torch::ValueTensorType>(data.getType())
.getSizes()
.size();
for (int i = 0; i < numDims; i++) {
Value curr = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
axesList.push_back(curr);
}
}
// Handle negative axis:
Value rankVal = rewriter.create<Torch::AtenDimOp>(binder.getLoc(),
torchIntTy, data);
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(0));
for (Value &axes : axesList) {
Value isNegative =
rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), axes, zero);
isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
isNegative);
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
binder.getLoc(), isNegative, rankVal);
axes = rewriter.create<Torch::AtenAddIntOp>(binder.getLoc(), axes,
finalOffset);
}
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(), Torch::ListType::get(torchIntTy), axesList);
Value keepDimBool =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
rewriter.replaceOpWithNewOp<Torch::AtenAminOp>(
binder.op, resultType, data, dimValueList, keepDimBool);
return success();
});
patterns.onOp("Shape", 9,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::Aten_ShapeAsTensorOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp("Sinh", 9,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenSinhOp>(
binder.op, resultType, operand);
return success();
});
// split with fixed-size parts
// Arguments:
// - input: the tensor to split
// Attributes:
// - axis: the axis along which to split the input
// - num_outputs: the number of outputs to produce
// Outputs:
// - outputs: the produced outputs. Variadic with num_outputs elements.
// Note: torch.aten gives a list of tensors, but ONNX gives a variadic list of
// tensors
// so we need to unpack the list
patterns.onOp(
"Split", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Value self;
int64_t axis;
int64_t numOutputs;
if (binder.tensorOperand(self))
return rewriter.notifyMatchFailure(
binder.op, "Not converting to AtenSplitTensorOp due to input "
"tensor mismatch");
if (binder.s64IntegerAttr(axis, "axis", 0))
return rewriter.notifyMatchFailure(binder.op,
"Failed to get axis attribute");
if (binder.s64IntegerAttr(numOutputs, "num_outputs", 2))
return rewriter.notifyMatchFailure(
binder.op, "Failed to get num_outputs attribute");
auto loc = binder.getLoc();
auto result0Ty =
cast<Torch::ValueTensorType>(binder.op->getResult(0).getType());
auto resultNTy = cast<Torch::ValueTensorType>(
binder.op->getResults().back().getType());
auto selfTy = cast<Torch::ValueTensorType>(self.getType());
int64_t dim = axis;
if (dim < 0)
dim += selfTy.getSizes().size();
Value dimValue = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(dim));
Value vNumOutputs = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getType<Torch::IntType>(),
rewriter.getI64IntegerAttr(numOutputs));
Value one = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(1));
Value zero = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(0));
Value vDimSize = rewriter.create<Torch::AtenSizeIntOp>(
loc, rewriter.getType<Torch::IntType>(), self, dimValue);
Value addNumOutputs =
rewriter.create<Torch::AtenAddIntOp>(loc, vDimSize, vNumOutputs);
Value subOne =
rewriter.create<Torch::AtenSubIntOp>(loc, addNumOutputs, one);
Value splitSize =
rewriter.create<Torch::AtenFloordivIntOp>(loc, subOne, vNumOutputs);
llvm::SmallVector<Value> outputs;
Value step = one;
Value start = zero;
for (int i = 0; i < numOutputs - 1; ++i) {
Value end =
rewriter.create<Torch::AtenAddIntOp>(loc, start, splitSize);
Value slice = rewriter.create<Torch::AtenSliceTensorOp>(
loc, result0Ty, self, dimValue, start, end, step);
start = end;
outputs.push_back(slice);
}
Value end = vDimSize;
Value lastSlice = rewriter.create<Torch::AtenSliceTensorOp>(
loc, resultNTy, self, dimValue, start, end, step);
outputs.push_back(lastSlice);
rewriter.replaceOp(binder.op, outputs);
return success();
});
// split with variable parts
// Arguments:
// - input: the tensor to split
// - split: the sizes of the splits to be produced
// Attributes:
// - axis: the axis along which to split the input
// - num_outputs: the number of outputs to produce
// Outputs:
// - outputs: the produced outputs. Variadic with num_outputs elements.
// Note: torch.aten gives a list of tensors, but ONNX gives a variadic list of
// tensors
// so we need to unpack the list
patterns.onOp(
"Split", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Value self;
Value split;
int64_t axis;
int64_t num_outputs;
if (binder.tensorOperandAtIndex(self, 0) ||
binder.tensorOperandAtIndex(split, 1))
return rewriter.notifyMatchFailure(
binder.op, "Not converting to AtenSplitWithSizesOp due to input "
"tensor mismatch");
if (binder.s64IntegerAttr(axis, "axis", 0))
return rewriter.notifyMatchFailure(binder.op,
"Failed to get axis attribute");
if (binder.s64IntegerAttr(num_outputs, "num_outputs", 0))
return rewriter.notifyMatchFailure(
binder.op, "Failed to get num_outputs attribute");
auto result0Ty =
cast<Torch::ValueTensorType>(binder.op->getResult(0).getType());
auto selfTy =
cast<Torch::ValueTensorType>(binder.op->getOperand(0).getType());
int64_t dim = axis;
if (dim < 0)
dim += selfTy.getSizes().size();
llvm::SmallVector<int64_t> intermediateShape(result0Ty.getSizes());
for (auto result : binder.op->getResultTypes()) {
int64_t d = cast<Torch::ValueTensorType>(result).getSizes()[dim];
intermediateShape[dim] = d == intermediateShape[dim] ? d : -1;
}
Torch::PrimTolistOp splitToList = rewriter.create<Torch::PrimTolistOp>(
binder.getLoc(),
Torch::ListType::get(rewriter.getType<Torch::IntType>()), split);
Value dimValue = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), dim));
// TODO: Attempting to use the shape expected by the ONNX mlir as ground
// truth. For now just use dynamic shapes.
auto resultOuterType =
Torch::ListType::get(rewriter.getType<Torch::ValueTensorType>(
/*std::optional<llvm::ArrayRef<int64_t>>=*/intermediateShape,
result0Ty.getOptionalDtype()));
Torch::AtenSplitWithSizesOp new_op =
rewriter.create<Torch::AtenSplitWithSizesOp>(
binder.getLoc(), resultOuterType, self,
splitToList.getResult(0), dimValue);
// the onnx op is variadic with multiple results, but AtenSplitWithSizes
// outputs a list so we need to unpack the list
rewriter.replaceOpWithNewOp<Torch::PrimListUnpackOp>(
binder.op, binder.op->getResults().getType(), new_op.getResult());
return success();
});
patterns.onOp("Tan", 7,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenTanOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Transpose", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
auto loc = binder.getLoc();
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
auto operandType = cast<Torch::ValueTensorType>(operand.getType());
TensorType tensorType = operandType.toBuiltinTensor();
if (!tensorType || !tensorType.hasRank())
return failure();
// Default permutation is to reverse orders:
int64_t rank = tensorType.getRank();
llvm::SmallVector<int64_t> reverse(rank);
for (int64_t i = 0; i < rank; ++i) {
reverse[i] = rank - i - 1;
}
llvm::SmallVector<int64_t> permutations;
if (failed(binder.s64IntegerArrayAttr(permutations, "perm", reverse)))
return rewriter.notifyMatchFailure(binder.op,
"Failed to obtain permutations");
if (static_cast<int64_t>(permutations.size()) != rank)
return rewriter.notifyMatchFailure(
binder.op, "Permutation length does not match operand rank");
llvm::SmallVector<int64_t> shape(tensorType.getShape());
llvm::SmallVector<int64_t> current(rank);
for (int64_t i = 0; i < rank; ++i) {
current[i] = i;
}
for (auto &dim : permutations)
dim = dim < 0 ? dim + rank : dim;
// We need to override to the destination if known:
if (resultType.hasSizes()) {
for (int i = 0; i < rank; ++i) {
shape[permutations[i]] = resultType.getSizes()[i];
}
}
// Convert dynamic shape dimension:
for (unsigned i = 0; i < shape.size(); i++) {
if (shape[i] == ShapedType::kDynamic)
shape[i] = Torch::kUnknownSize;
}
for (int64_t i = 0; i < rank; ++i) {
if (current[i] == permutations[i])
continue;
int64_t target = i + 1;
for (; target < rank; ++target) {
if (current[target] == permutations[i])
break;
}
std::swap(shape[i], shape[target]);
std::swap(current[i], current[target]);
Value dim0 = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value dim1 = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), target));
operand = rewriter.create<Torch::AtenTransposeIntOp>(
loc,
Torch::ValueTensorType::get(tensorType.getContext(), shape,
operandType.getOptionalDtype()),
operand, dim0, dim1);
}
rewriter.replaceOp(binder.op, operand);
return success();
});
patterns.onOp(
"Slice", 13, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultTorchType;
Value operand, starts, ends;
// Handle if axes are not provided
if (binder.tensorOperandAtIndex(operand, 0) ||
binder.tensorOperandAtIndex(starts, 1) ||
binder.tensorOperandAtIndex(ends, 2) ||
binder.tensorResultType(resultTorchType)) {
return failure();
}
auto context = rewriter.getContext();
auto operandTorchTy = cast<Torch::ValueTensorType>(operand.getType());
auto operandTy =
dyn_cast<RankedTensorType>(operandTorchTy.toBuiltinTensor());
if (!operandTy)
return rewriter.notifyMatchFailure(
binder.op,
"Expected tensor operator argument to be a ranked tensor type");
auto startsTorchTy = cast<Torch::ValueTensorType>(starts.getType());
auto startsTy =
dyn_cast<RankedTensorType>(startsTorchTy.toBuiltinTensor());
int startSize = startsTy.getDimSize(0);
auto endsTorchTy = cast<Torch::ValueTensorType>(ends.getType());
auto endsTy = dyn_cast<RankedTensorType>(endsTorchTy.toBuiltinTensor());
int endSize = endsTy.getDimSize(0);
auto resultTy =
dyn_cast<RankedTensorType>(resultTorchType.toBuiltinTensor());
if (!resultTy)
return rewriter.notifyMatchFailure(
binder.op, "Expected result type to be a ranked tensor type");
Location loc = binder.getLoc();
// Binding `axes` from its arguments or through a default value
Value axes;
if (binder.getNumOperands() >= 4) {
if (binder.tensorOperandAtIndex(axes, 3)) {
return failure();
}
}
// Binding `steps` from its arguments or through a default value
Value steps;
if (binder.getNumOperands() >= 5) {
if (binder.tensorOperandAtIndex(steps, 4)) {
return failure();
}
} else {
// The default `steps` value is a 1d tensor filled with ones with a
// size equal to the size of `starts` and `ends`.
Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
Value sizeStepInput = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), startSize));
Value sizeStepsInput = rewriter.create<Torch::PrimListConstructOp>(
loc,
Torch::ListType::get(
Torch::IntType::get(binder.op->getContext())),
sizeStepInput);
steps = rewriter.create<Torch::AtenOnesOp>(
loc, startsTorchTy, sizeStepsInput, none, none, none, none);
}
if (!(endsTy.getRank() == 1 && startsTy.getRank() == 1 &&
startSize == endSize))
return rewriter.notifyMatchFailure(
binder.op, "Expected the rank of starts and ends tensors to be 1 "
"and their dimensions to match");
if (axes) {
auto axesTorchTy = cast<Torch::ValueTensorType>(axes.getType());
auto axesTy =
dyn_cast<RankedTensorType>(axesTorchTy.toBuiltinTensor());
int64_t numAxes = axesTy.getDimSize(0);
if (!(axesTy && numAxes == endSize))
return rewriter.notifyMatchFailure(
binder.op, "Axes should be the same size of starts and ends");
}
auto stepsTy = dyn_cast<RankedTensorType>(
cast<Torch::ValueTensorType>(steps.getType()).toBuiltinTensor());
if (!(stepsTy && stepsTy.getDimSize(0) == endsTy.getDimSize(0)))
return rewriter.notifyMatchFailure(
binder.op, "Steps should be the same size of starts and ends");
Value zero = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
auto select = [&](Value v, Value k) -> Value {
auto ty = cast<Torch::ValueTensorType>(v.getType());
auto sel = rewriter.create<Torch::AtenIndexSelectOp>(
loc,
Torch::ValueTensorType::get(ty.getContext(), ArrayRef<int64_t>{1},
ty.getOptionalDtype()),
v, zero, k);
Value item = rewriter.create<Torch::AtenItemOp>(
loc, rewriter.getType<Torch::IntType>(), sel);
return item;
};
llvm::SmallVector<int64_t> intermediateShape(operandTy.getShape());
for (int i = 0, s = operandTy.getRank(); i < s; ++i) {
if (operandTy.getDimSize(i) != resultTy.getDimSize(i))
intermediateShape[i] = -1;
if (intermediateShape[i] == ShapedType::kDynamic)
intermediateShape[i] = Torch::kUnknownSize;
}
auto intermediateType = Torch::ValueTensorType::get(
context, intermediateShape, resultTorchType.getOptionalDtype());
for (int i = 0; i < endSize; ++i) {
Value k = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value kTensor = rewriter.create<Torch::PrimNumToTensorScalarOp>(
loc,
Torch::ValueTensorType::get(
context, ArrayRef<int64_t>{1},
rewriter.getIntegerType(64, /*signed*/ 1)),
k);
Value start = select(starts, kTensor);
Value end = select(ends, kTensor);
Value axis = axes ? select(axes, kTensor) : k;
Value step = select(steps, kTensor);
auto sliceType = intermediateType;
sliceType = i == (endSize - 1) ? resultTorchType : sliceType;
operand = rewriter.create<Torch::AtenSliceTensorOp>(
loc, sliceType, operand, axis, start, end, step);
}
rewriter.replaceOp(binder.op, operand);
return success();
});
patterns.onOp(
"Reshape", 5, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value data;
Value shape;
int64_t allowzero;
if (binder.tensorOperands(data, shape) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(allowzero, "allowzero", 0))
return failure();
// If the result shape is static then we can create a result shape list
// directly using the result shape values (integers).
if (resultType.hasSizes()) {
bool hasStaticShape = resultType.areAllSizesKnown();
ArrayRef<int64_t> resultShapeInt = resultType.getSizes();
if (hasStaticShape) {
SmallVector<Value> resultShape;
for (int64_t dim : resultShapeInt) {
resultShape.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(dim)));
}
Value resultShapeList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(
Torch::IntType::get(binder.op->getContext())),
resultShape);
rewriter.replaceOpWithNewOp<Torch::AtenReshapeOp>(
binder.op, resultType, data, resultShapeList);
return success();
}
}
Torch::BaseTensorType shapeType =
cast<Torch::BaseTensorType>(shape.getType());
SmallVector<Value> dimList;
SmallVector<int64_t> selectSizes;
selectSizes.push_back(1);
Type selectResultType = shapeType.getWithSizesAndDtype(
llvm::ArrayRef(selectSizes), shapeType.getOptionalDtype());
auto shapeSizes =
dyn_cast<Torch::ValueTensorType>(shape.getType()).getSizes();
auto dataSizes =
dyn_cast<Torch::ValueTensorType>(data.getType()).getSizes();
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
if (allowzero == 0) {
// convert shape (tensor) into torch int list while dealing with zero
// vals
for (int i = 0; i < shapeSizes[0]; i++) {
// Go through the shape list and get each dim in the list
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selectResultType, shape, zero, selectIndex);
Value dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
// deal with zero axis values: replace with original dim value in
// input
Value isZero =
rewriter.create<Torch::AtenEqIntOp>(binder.getLoc(), dim, zero);
isZero =
rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(), isZero);
int64_t dataRank = dataSizes.size();
if (i < dataRank) {
auto torchIntTy = rewriter.getType<Torch::IntType>();
auto int64Ty = rewriter.getIntegerType(64, true);
auto dimTy = rewriter.getType<Torch::ValueTensorType>(
ArrayRef<int64_t>(), int64Ty);
auto boolTy = rewriter.getType<Torch::ValueTensorType>(
ArrayRef<int64_t>(), rewriter.getI1Type());
Value iv = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(i));
Value inDim = rewriter.create<Torch::AtenSizeIntOp>(
binder.getLoc(), torchIntTy, data, iv);
isZero = rewriter.create<Torch::PrimNumToTensorScalarOp>(
binder.getLoc(), boolTy, isZero);
inDim = rewriter.create<Torch::PrimNumToTensorScalarOp>(
binder.getLoc(), dimTy, inDim);
dim = rewriter.create<Torch::PrimNumToTensorScalarOp>(
binder.getLoc(), dimTy, dim);
Value finalDim = rewriter.create<Torch::AtenWhereSelfOp>(
binder.getLoc(), dimTy, isZero, inDim, dim);
dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
finalDim);
}
dimList.push_back(dim);
}
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(
Torch::IntType::get(binder.op->getContext())),
dimList);
rewriter.replaceOpWithNewOp<Torch::AtenReshapeOp>(
binder.op, resultType, data, dimValueList);
return success();
}
// convert axes (tensor) into torch int list
for (int i = 0; i < shapeSizes[0]; i++) {
// Go through the axes list and get each dim in the list
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selectResultType, shape, zero, selectIndex);
Value dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
dimList.push_back(dim);
}
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
dimList);
rewriter.replaceOpWithNewOp<Torch::AtenReshapeOp>(binder.op, resultType,
data, dimValueList);
return success();
});
patterns.onOp(
"ReduceProd", 13,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
// ReduceProd allows us to pass a list of dims but AtenProdDimIn only
// allow one dim as input.
Torch::ValueTensorType resultType;
Value data;
Value axes;
int64_t keepDims;
int64_t noop_with_empty_axes;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
0))
return failure();
auto dataTy = cast<Torch::BaseTensorType>(data.getType());
Torch::IntType torchIntTy = rewriter.getType<Torch::IntType>();
if (!resultType.hasSizes() || !resultType.areAllSizesKnown() ||
!dataTy.areAllSizesKnown())
return rewriter.notifyMatchFailure(
binder.op,
"Expected the input and result type to have known sizes");
int64_t rank = dataTy.getSizes().size();
SmallVector<Value> axesList;
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(0));
// Previous version of the operation had the axes as an attribute:
llvm::SmallVector<int64_t> axesAttr;
if (!binder.s64IntegerArrayAttr(axesAttr, "axes", {})) {
for (int i = 0, s = axesAttr.size(); i < s; ++i) {
axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), torchIntTy,
rewriter.getI64IntegerAttr(axesAttr[i])));
}
}
// Handle cases that axes are explicitly specified.
// Extract the axes values from the axes operand.
// This really shouldn't happen but it helps pass weird tests.
// TODO: Derive the chosen axes from the data type and final result type
// instead of using the dynamic axes at operand[1].
if (!binder.tensorOperandAtIndex(axes, 1)) {
Torch::BaseTensorType axesType =
cast<Torch::BaseTensorType>(axes.getType());
auto sizes = axesType.getSizes();
for (int i = 0; i < sizes[0]; i++) {
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(),
axesType.getWithSizesAndDtype(llvm::SmallVector<int64_t>{1},
axesType.getOptionalDtype()),
axes, zero, selectIndex);
Value dim = rewriter.create<Torch::AtenItemOp>(binder.getLoc(),
torchIntTy, extract);
axesList.push_back(dim);
}
}
// Handle the noop case:
// When axes is empty and noop_with_empty_axes is set to true, input
// tensor will not be reduced, and the output tensor would be
// equivalent to input tensor.
if (axesList.empty() && noop_with_empty_axes) {
rewriter.replaceOp(binder.op, data);
return success();
}
// Handle case when no axes arg is passed but not a noop:
// Manually set positive axis to all dims.
if (axesList.empty()) {
for (int i = 0; i < rank; i++) {
Value dimValue = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(i));
axesList.push_back(dimValue);
}
}
// Handle negative axis:
Value rankVal = rewriter.create<Torch::AtenDimOp>(binder.getLoc(),
torchIntTy, data);
for (Value &axes : axesList) {
Value isNegative =
rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), axes, zero);
isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
isNegative);
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
binder.getLoc(), isNegative, rankVal);
axes = rewriter.create<Torch::AtenAddIntOp>(binder.getLoc(), axes,
finalOffset);
}
// Handle multiple axes case:
// ReduceProd on each dim, always set keepDimsBool == True to avoid
// segfault.
Value trueVal =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
SmallVector<int64_t> intermediateShape(rank, Torch::kUnknownSize);
Value dataReduceProd = data;
for (int i = 0, numAxes = axesList.size(); i < numAxes; i++) {
auto axis = axesList[i];
if (keepDims && i == numAxes - 1) {
dataReduceProd = rewriter.create<Torch::AtenProdDimIntOp>(
binder.getLoc(),
dataTy.getWithSizesAndDtype(resultType.getSizes(),
dataTy.getOptionalDtype()),
dataReduceProd, axis, trueVal, noneVal);
rewriter.replaceOp(binder.op, dataReduceProd);
return success();
}
Type resultTyReduceProd = dataTy.getWithSizesAndDtype(
ArrayRef(intermediateShape), dataTy.getOptionalDtype());
dataReduceProd = rewriter.create<Torch::AtenProdDimIntOp>(
binder.getLoc(), resultTyReduceProd, dataReduceProd, axis,
trueVal, noneVal);
}
// Derived the final shape of the tensor after prod loop of each axis.
SmallVector<int64_t> dataReduceProdSize;
auto dataSize = dataTy.getSizes();
auto resultTypeSizes = resultType.getSizes();
if (!keepDims) {
// Handle the keepDimsBool == False case:
// 2 point algorithm to derive the static shape after prod loop.
int j = 0;
for (int i = 0; i < rank; i++) {
if (resultTypeSizes.size() && dataSize[i] == resultTypeSizes[j]) {
dataReduceProdSize.push_back(resultTypeSizes[i]);
j++;
continue;
}
dataReduceProdSize.push_back(1);
}
}
// Handle the keepDimsBool == False case:
// Reshape the prod loop result to the final result shape.
SmallVector<Value> dataReduceProdShape;
for (auto dim : dataReduceProdSize)
dataReduceProdShape.push_back(rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(dim)));
Value dataReduceProdShapeList =
rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
rewriter.getType<Torch::ListType>(
rewriter.getType<Torch::IntType>()),
dataReduceProdShape);
rewriter.replaceOpWithNewOp<Torch::AtenReshapeOp>(
binder.op, resultType, dataReduceProd, dataReduceProdShapeList);
return success();
});
patterns.onOp(
"Range", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
// ONNX.Range(start, limit, delta) -- limit is exclusive
Torch::ValueTensorType resultType;
Value start, limit, delta;
auto loc = binder.getLoc();
Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
if (binder.tensorOperandAtIndex(start, 0) ||
binder.tensorOperandAtIndex(limit, 1) ||
binder.tensorOperandAtIndex(delta, 2) ||
binder.tensorResultType(resultType))
return failure();
// Convert a 0-dimensional/Scalar Tensor ([]) to Scalar Torch Numeric
// Value torch.tensor(1.1) equivalent in ONNX to 1.1 as an example
// type of start, limit, delta can be one of: double, float, int16,
// int32, int64 Assuming start, limit and delta to be same type (could
// they be different?)
Torch::BaseTensorType startTensorType =
cast<Torch::BaseTensorType>(start.getType());
bool isFloatDType = startTensorType.getDtype().isF64() ||
startTensorType.getDtype().isF32();
bool isIntDType = startTensorType.getDtype().isInteger(16) ||
startTensorType.getDtype().isInteger(32) ||
startTensorType.getDtype().isInteger(64);
if (!isFloatDType && !isIntDType) {
return rewriter.notifyMatchFailure(
binder.op, "Expected the start, limit, delta to be one of "
"double, float, int16, int32, int64");
}
Value scalarStart, scalarLimit, scalarDelta;
if (isFloatDType) {
scalarStart = getItemOp<Torch::FloatType>(binder, rewriter, start);
scalarLimit = getItemOp<Torch::FloatType>(binder, rewriter, limit);
scalarDelta = getItemOp<Torch::FloatType>(binder, rewriter, delta);
} else {
scalarStart = getItemOp<Torch::IntType>(binder, rewriter, start);
scalarLimit = getItemOp<Torch::IntType>(binder, rewriter, limit);
scalarDelta = getItemOp<Torch::IntType>(binder, rewriter, delta);
}
rewriter.replaceOpWithNewOp<Torch::AtenArangeStartStepOp>(
binder.op, resultType, scalarStart, scalarLimit, scalarDelta, none,
none, none, none);
return success();
});
patterns.onOp(
"Size", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
auto loc = binder.getLoc();
auto &op = binder.op;
auto operandTy = cast<Torch::BaseTensorType>(operand.getType());
if (!operandTy.hasSizes())
return rewriter.notifyMatchFailure(op, "input rank unknown");
llvm::SmallVector<Value> dims;
int64_t rank = operandTy.getSizes().size();
for (int i = 0; i < rank; ++i) {
auto iv = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(i));
Value dim = rewriter.create<Torch::AtenSizeIntOp>(
loc, rewriter.getType<Torch::IntType>(), operand, iv);
dims.push_back(dim);
}
Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(loc, false);
Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
if (dims.empty()) {
Value one = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(1));
rewriter.replaceOpWithNewOp<Torch::AtenTensorIntOp>(
op, resultType, one, none, none, cstFalse);
return success();
}
Value prod = dims[0];
for (int i = 1, s = dims.size(); i < s; ++i)
prod = rewriter.create<Torch::AtenMulIntOp>(loc, prod, dims[i]);
rewriter.replaceOpWithNewOp<Torch::AtenTensorIntOp>(
op, resultType, prod, none, none, cstFalse);
return success();
});
patterns.onOp(
"Tile", 6, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
Value repeatDims;
if (binder.tensorOperands(operand, repeatDims) ||
binder.tensorResultType(resultType))
return failure();
// convert repeatDims tensor to list of ints
auto repeatDimsSizes =
dyn_cast<Torch::ValueTensorType>(repeatDims.getType()).getSizes();
SmallVector<Value> dimList;
SmallVector<int64_t> selectSizes;
selectSizes.push_back(1);
Torch::BaseTensorType shapeType =
cast<Torch::BaseTensorType>(repeatDims.getType());
Type selectResultType = shapeType.getWithSizesAndDtype(
llvm::ArrayRef(selectSizes), shapeType.getOptionalDtype());
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
for (int i = 0; i < repeatDimsSizes[0]; i++) {
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selectResultType, repeatDims, zero, selectIndex);
Value dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
dimList.push_back(dim);
}
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
dimList);
rewriter.replaceOpWithNewOp<Torch::AtenTileOp>(binder.op, resultType,
operand, dimValueList);
return success();
});
patterns.onOp(
"TopK", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType Values_type, Indices_type;
Value input, kValue;
int64_t axis;
bool largest, sorted;
if (binder.tensorOperandAtIndex(input, 0) ||
binder.tensorOperandAtIndex(kValue, 1) ||
binder.s64IntegerAttr(axis, "axis", -1) ||
binder.s64BoolAttr(largest, "largest", true) ||
binder.s64BoolAttr(sorted, "sorted", true) ||
binder.tensorResultTypeAtIndex(Values_type, 0) ||
binder.tensorResultTypeAtIndex(Indices_type, 1))
return failure();
std::optional<unsigned> maybeRank = Torch::getTensorRank(input);
if (!maybeRank)
return rewriter.notifyMatchFailure(binder.op,
"Unimplemented: unranked tensor");
unsigned rank = *maybeRank;
axis = Torch::toPositiveDim(axis, rank);
Value cstAxis = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(axis));
Value cstLargest =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), largest);
Value cstSorted =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), sorted);
Value kValueInt = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), kValue);
rewriter.replaceOpWithNewOp<Torch::AtenTopkOp>(
binder.op, Values_type, Indices_type, input, kValueInt, cstAxis,
cstLargest, cstSorted);
return success();
});
patterns.onOp("Sign", 9,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenSignOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Softplus", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value input;
if (binder.tensorOperand(input) ||
binder.tensorResultType(resultType)) {
return failure();
}
// out = ln(exp(x) + 1)
Value exp = rewriter.create<Torch::AtenExpOp>(binder.getLoc(),
resultType, input);
rewriter.replaceOpWithNewOp<Torch::AtenLog1pOp>(binder.op, resultType,
exp);
return success();
});
patterns.onOp("Softsign", 22,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value input;
if (binder.tensorOperand(input) ||
binder.tensorResultType(resultType)) {
return failure();
}
Value absX = rewriter.create<Torch::AtenAbsOp>(
binder.getLoc(), resultType, input);
Value constOne = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1));
Value absXPlusOne = rewriter.create<Torch::AtenAddScalarOp>(
binder.getLoc(), resultType, absX, constOne, constOne);
rewriter.replaceOpWithNewOp<Torch::AtenDivTensorOp>(
binder.op, resultType, input, absXPlusOne);
return success();
});
patterns.onOp(
"Trilu", 14, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value input;
int64_t upper;
if (binder.tensorOperandAtIndex(input, 0) ||
binder.s64IntegerAttr(upper, "upper", 1) ||
binder.tensorResultType(resultType)) {
return failure();
}
Value diagonal;
if (binder.tensorOperandAtIndex(diagonal, 1)) {
diagonal = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(0));
} else {
diagonal = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), diagonal);
}
if (upper) {
rewriter.replaceOpWithNewOp<Torch::AtenTriuOp>(binder.op, resultType,
input, diagonal);
return success();
}
rewriter.replaceOpWithNewOp<Torch::AtenTrilOp>(binder.op, resultType,
input, diagonal);
return success();
});
patterns.onOp("ThresholdedRelu", 10,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value input;
float alpha;
if (binder.tensorOperand(input) ||
binder.f32FloatAttr(alpha, "alpha", 1.0)) {
return failure();
}
Value cstAlpha = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), alpha));
Value value = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), 0.0));
rewriter.replaceOpWithNewOp<Torch::AtenThresholdOp>(
binder.op, resultType, input, cstAlpha, value);
return success();
});
patterns.onOp(
"RandomNormal", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
SmallString<64> name("torch.onnx.seed");
auto seedAttr = binder.op->getAttr(name);
if (seedAttr)
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: support not present for seed attribute");
Torch::ValueTensorType resultType;
int64_t dtypeIntOnnx;
float mean, scale;
SmallVector<int64_t> shape;
if (binder.s64IntegerAttr(dtypeIntOnnx, "dtype", 1) ||
binder.f32FloatAttr(mean, "mean", 0.0) ||
binder.f32FloatAttr(scale, "scale", 1.0) ||
binder.s64IntegerArrayAttr(shape, "shape", {}) ||
binder.tensorResultType(resultType)) {
return failure();
}
std::optional<int64_t> dtypeIntTorch =
onnxDtypeIntToTorchDtypeInt(dtypeIntOnnx);
if (!dtypeIntTorch.has_value()) {
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented support for the given dtype conversion");
}
Value constDtype = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(dtypeIntTorch.value()));
Value shapeList = createConstantIntList(binder, rewriter, shape);
Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value self = rewriter.create<Torch::AtenEmptyMemoryFormatOp>(
binder.op->getLoc(), resultType, shapeList,
/*dtype=*/constDtype,
/*layout=*/cstNone,
/*device=*/cstNone, /*pinMemory=*/cstNone,
/*memoryFormat=*/cstNone);
Value cstMean = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), mean));
Value cstStd = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), scale));
rewriter.replaceOpWithNewOp<Torch::AtenNormalFunctionalOp>(
binder.op, resultType, self, cstMean, cstStd,
/*generator=*/cstNone);
return success();
});
patterns.onOp(
"RandomNormalLike", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
SmallString<64> name("torch.onnx.seed");
auto seedAttr = binder.op->getAttr(name);
if (seedAttr)
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: support not present for seed attribute");
Torch::ValueTensorType resultType;
int64_t dtypeIntOnnx;
float mean, scale;
SmallVector<int64_t> shape;
Value input;
if (binder.tensorOperand(input) ||
binder.s64IntegerAttr(dtypeIntOnnx, "dtype", 1) ||
binder.f32FloatAttr(mean, "mean", 0.0) ||
binder.f32FloatAttr(scale, "scale", 1.0) ||
binder.tensorResultType(resultType)) {
return failure();
}
std::optional<int64_t> dtypeIntTorch =
onnxDtypeIntToTorchDtypeInt(dtypeIntOnnx);
if (!dtypeIntTorch.has_value()) {
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented support for the given dtype conversion");
}
Value constDtype = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(dtypeIntTorch.value()));
Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value cstFalse =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
input = rewriter.create<Torch::AtenToDtypeOp>(
binder.op->getLoc(), resultType, input, constDtype,
/*non_blocking=*/cstFalse, /*copy=*/cstFalse,
/*memory_format=*/cstNone);
Value cstMean = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), mean));
Value cstStd = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), scale));
rewriter.replaceOpWithNewOp<Torch::AtenNormalFunctionalOp>(
binder.op, resultType, input, cstMean, cstStd,
/*generator=*/cstNone);
return success();
});
patterns.onOp(
"RandomUniform", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
SmallString<64> name("torch.onnx.seed");
auto seedAttr = binder.op->getAttr(name);
if (seedAttr)
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: support not present for seed attribute");
Torch::ValueTensorType resultType;
int64_t dtypeIntOnnx;
float high, low;
SmallVector<int64_t> shape;
if (binder.s64IntegerAttr(dtypeIntOnnx, "dtype", 1) ||
binder.f32FloatAttr(high, "high", 1.0) ||
binder.f32FloatAttr(low, "low", 0.0) ||
binder.s64IntegerArrayAttr(shape, "shape", {}) ||
binder.tensorResultType(resultType)) {
return failure();
}
std::optional<int64_t> dtypeIntTorch =
onnxDtypeIntToTorchDtypeInt(dtypeIntOnnx);
if (!dtypeIntTorch.has_value()) {
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented support for the given dtype conversion");
}
Value constDtype = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(dtypeIntTorch.value()));
Value shapeList = createConstantIntList(binder, rewriter, shape);
Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value self = rewriter.create<Torch::AtenEmptyMemoryFormatOp>(
binder.op->getLoc(), resultType, shapeList,
/*dtype=*/constDtype,
/*layout=*/cstNone,
/*device=*/cstNone, /*pinMemory=*/cstNone,
/*memoryFormat=*/cstNone);
Value cstHigh = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), high));
Value cstLow = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), low));
rewriter.replaceOpWithNewOp<Torch::AtenUniformOp>(
binder.op, resultType, self, cstLow, cstHigh,
/*generator=*/cstNone);
return success();
});
patterns.onOp(
"RandomUniformLike", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
SmallString<64> name("torch.onnx.seed");
auto seedAttr = binder.op->getAttr(name);
if (seedAttr)
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: support not present for seed attribute");
Torch::ValueTensorType resultType;
int64_t dtypeIntOnnx;
float high, low;
SmallVector<int64_t> shape;
Value input;
if (binder.tensorOperand(input) ||
binder.s64IntegerAttr(dtypeIntOnnx, "dtype", 1) ||
binder.f32FloatAttr(high, "high", 1.0) ||
binder.f32FloatAttr(low, "low", 0.0) ||
binder.tensorResultType(resultType)) {
return failure();
}
std::optional<int64_t> dtypeIntTorch =
onnxDtypeIntToTorchDtypeInt(dtypeIntOnnx);
if (!dtypeIntTorch.has_value()) {
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented support for the given dtype conversion");
}
Value constDtype = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(dtypeIntTorch.value()));
Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value cstFalse =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
input = rewriter.create<Torch::AtenToDtypeOp>(
binder.op->getLoc(), resultType, input, constDtype,
/*non_blocking=*/cstFalse, /*copy=*/cstFalse,
/*memory_format=*/cstNone);
Value cstHigh = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), high));
Value cstLow = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), low));
rewriter.replaceOpWithNewOp<Torch::AtenUniformOp>(
binder.op, resultType, input, cstLow, cstHigh,
/*generator=*/cstNone);
return success();
});
patterns.onOp(
"SoftmaxCrossEntropyLoss", 12,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
int64_t ignoreIndex;
std::string reduction;
SmallVector<int64_t> shape;
Value scores, labels, weight;
if (binder.tensorOperandAtIndex(scores, 0) ||
binder.tensorOperandAtIndex(labels, 1) ||
binder.s64IntegerAttr(ignoreIndex, "ignore_index ", -100) ||
binder.customOpNameStringAttr(reduction, "reduction", "mean") ||
binder.tensorResultTypeAtIndex(resultType, 0)) {
return failure();
}
if (binder.tensorOperandAtIndex(weight, 2))
weight = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value cstIgnoreIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(ignoreIndex));
int64_t reductionInt = reduction == "none" ? 0
: reduction == "mean" ? 1
: 2;
Value cstReductionInt = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(reductionInt));
// The default PyTorch value for label smoothing is "0.0".
// Refer:
// https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html
Value cstLabelSmoothing = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), 0.0));
Value loss = rewriter.create<Torch::AtenCrossEntropyLossOp>(
binder.getLoc(), resultType, scores, labels, weight,
cstReductionInt, cstIgnoreIndex, cstLabelSmoothing);
if (binder.op->getNumResults() == 1) {
rewriter.replaceOp(binder.op, loss);
return success();
}
Torch::ValueTensorType resultTypeLogProb;
if (binder.tensorResultTypeAtIndex(resultTypeLogProb, 1))
return failure();
Value dim = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1));
Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value logProb = rewriter.create<Torch::AtenLogSoftmaxIntOp>(
binder.getLoc(), resultTypeLogProb, scores, dim, /*dtype=*/cstNone);
rewriter.replaceOp(binder.op, {loss, logProb});
return success();
});
patterns.onOp(
"Resize", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
llvm::SmallVector<Value> operands;
std::string mode, nearest_mode, coordTfMode;
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
if (auto attr = binder.op->getAttr("torch.onnx.antialias")) {
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: support not present for antialias attribute");
}
if (auto attr = binder.op->getAttr("torch.onnx.axes")) {
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: support not present for axes attribute");
}
if (auto attr = binder.op->getAttr("torch.onnx.exclude_outside")) {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: support not present for "
"exclude_outside attribute");
}
if (auto attr = binder.op->getAttr("torch.onnx.extrapolation_value")) {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: support not present for "
"extrapolation_value attribute");
}
if (auto attr =
binder.op->getAttr("torch.onnx.keep_aspect_ratio_policy")) {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: support not present for "
"keep_aspect_ratio_policy attribute");
}
if (binder.tensorOperandsList(operands) ||
binder.tensorResultType(resultType) ||
binder.customOpNameStringAttr(mode, "mode", "nearest") ||
binder.customOpNameStringAttr(
coordTfMode, "coordinate_transformation_mode", "half_pixel") ||
binder.customOpNameStringAttr(nearest_mode, "nearest_mode", ""))
return failure();
if (coordTfMode == "tf_crop_and_resize")
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: coordinate transformation mode: "
"tf_crop_and_resize");
if (mode == "nearest" && nearest_mode != "floor") {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: support not present for nearest_mode "
"except floor");
}
unsigned rank = dyn_cast<Torch::ValueTensorType>(operands[0].getType())
.getSizes()
.size();
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
Value cstFalse =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
Value cstTrue =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
Value modeStrValue;
auto extract = [&rewriter, &binder](Value x, Value v) {
auto xTy = cast<Torch::ValueTensorType>(x.getType());
Type extractTy = rewriter.getType<Torch::FloatType>();
if (isa<IntegerType>(xTy.getDtype()))
extractTy = rewriter.getType<Torch::IntType>();
return rewriter.create<Torch::AtenItemOp>(binder.getLoc(), extractTy,
v);
};
auto getValueList = [&](Value operand) {
SmallVector<Value> itemList;
auto sizes =
dyn_cast<Torch::ValueTensorType>(operand.getType()).getSizes();
Torch::BaseTensorType operandType =
cast<Torch::BaseTensorType>(operand.getType());
SmallVector<int64_t> selectSizes;
selectSizes.push_back(1);
Type selectResultType = operandType.getWithSizesAndDtype(
llvm::ArrayRef(selectSizes), operandType.getOptionalDtype());
MLIRContext *context = binder.op->getContext();
for (int i = 2; i < sizes[0]; i++) {
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value ext = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selectResultType, operand, zero, selectIndex);
Value item = extract(operand, ext);
itemList.push_back(item);
}
auto xTy = cast<Torch::ValueTensorType>(operand.getType());
Value ValueList;
if (isa<IntegerType>(xTy.getDtype())) {
ValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(context)), itemList);
} else {
ValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::FloatType::get(context)), itemList);
}
return ValueList;
};
Value scalesValueList = noneVal;
Value sizesValueList = noneVal;
Value alignCorners =
coordTfMode == "align_corners" ? cstTrue : cstFalse;
if (mode == "cubic") {
return rewriter.notifyMatchFailure(binder.op,
"unimplemented: bicubic mode");
}
// supported modes:
// bilinear (half_pixel), bilinear with align_corners,
// bilinear_pytorch_half_pixel, bilinear_asymmetric nearest
// (asymmetric), nearest with align_corners, nearest_half_pixel,
// nearest_pytorch_half_pixel
if (mode == "linear") {
std::string modeStr;
switch (rank) {
case 3:
modeStr = "linear";
break;
case 4:
modeStr = "bilinear";
break;
case 5:
modeStr = "trilinear";
break;
default:
return failure();
}
// Confusingly enough, the default coordTfMode for pytorch bilinear
// mode is apparently half_pixel, NOT pytorch_half_pixel
if (coordTfMode != "half_pixel" && coordTfMode != "align_corners")
modeStr = (modeStr + "_") + coordTfMode;
modeStrValue =
rewriter.create<Torch::ConstantStrOp>(binder.getLoc(), modeStr);
}
if (mode == "nearest") {
std::string modeStr = "nearest";
// The default coordTfMode for pytorch with mode = nearest is
// apparently asymmetric
if (coordTfMode != "asymmetric" && coordTfMode != "align_corners")
modeStr = (modeStr + "_") + coordTfMode;
modeStrValue =
rewriter.create<Torch::ConstantStrOp>(binder.getLoc(), modeStr);
}
if (operands.size() < 4) {
Value scaleOperand = operands[2];
scalesValueList = getValueList(scaleOperand);
sizesValueList = noneVal;
} else {
Value sizeOperand = operands[3];
scalesValueList = noneVal;
sizesValueList = getValueList(sizeOperand);
}
if (isa<Torch::NoneType>(scalesValueList.getType()) &&
isa<Torch::NoneType>(sizesValueList.getType())) {
return rewriter.notifyMatchFailure(binder.op, "unknown scaling mode");
}
rewriter
.replaceOpWithNewOp<Torch::Aten__InterpolateSizeListScaleListOp>(
binder.op, resultType, operands[0], sizesValueList,
scalesValueList, modeStrValue,
/* AnyTorchOptionalBoolType:$align_corners */ alignCorners,
/* AnyTorchOptionalBoolType:$recompute_scale_factor */ noneVal,
/*Torch_BoolType:$antialias*/ cstFalse);
return success();
});
patterns.onOp(
"SpaceToDepth", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value input;
int64_t blockSize;
std::string mode;
if (binder.tensorOperand(input) ||
binder.s64IntegerAttr(blockSize, "blocksize") ||
binder.customOpNameStringAttr(mode, "mode", "DCR") ||
binder.tensorResultType(resultType))
return failure();
auto inputTy = dyn_cast<Torch::BaseTensorType>(input.getType());
if (!inputTy || !inputTy.hasSizes()) {
return rewriter.notifyMatchFailure(
binder.op, "Expected input type having sizes");
}
SmallVector<int64_t> inputSizes{inputTy.getSizes()};
if (inputSizes.size() != 4) {
return rewriter.notifyMatchFailure(binder.op,
"Expected input rank to be 4");
}
Value b = rewriter.create<Torch::AtenSizeIntOp>(
binder.getLoc(), input,
rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(0)));
Value c = rewriter.create<Torch::AtenSizeIntOp>(
binder.getLoc(), input,
rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1)));
Value h = rewriter.create<Torch::AtenSizeIntOp>(
binder.getLoc(), input,
rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(2)));
Value w = rewriter.create<Torch::AtenSizeIntOp>(
binder.getLoc(), input,
rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(3)));
Value cstBlockSize = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(blockSize));
Value cstBlockSizeSquare = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(blockSize * blockSize));
Value hDivBlockSize = rewriter.create<Torch::AtenDivIntOp>(
binder.getLoc(), h, cstBlockSize);
Value wDivBlockSize = rewriter.create<Torch::AtenDivIntOp>(
binder.getLoc(), w, cstBlockSize);
hDivBlockSize = rewriter.create<Torch::AtenIntFloatOp>(binder.getLoc(),
hDivBlockSize);
wDivBlockSize = rewriter.create<Torch::AtenIntFloatOp>(binder.getLoc(),
wDivBlockSize);
// The implementation is as follows:
// tmp = np.reshape(
// x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize]
// )
// tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
// y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w //
// blocksize])
Value reshapeSizesList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(input.getContext())),
llvm::SmallVector<Value>{b, c, hDivBlockSize, cstBlockSize,
wDivBlockSize, cstBlockSize});
int64_t hDivBlockSizeInt = inputSizes[2] == Torch::kUnknownSize
? Torch::kUnknownSize
: inputSizes[2] / blockSize;
int64_t wDivBlockSizeInt = inputSizes[3] == Torch::kUnknownSize
? Torch::kUnknownSize
: inputSizes[3] / blockSize;
SmallVector<int64_t, 6> reshapeSizesInt{inputSizes[0], inputSizes[1],
hDivBlockSizeInt, blockSize,
wDivBlockSizeInt, blockSize};
Value reshapedInput = rewriter.create<Torch::AtenReshapeOp>(
binder.getLoc(),
inputTy.getWithSizesAndDtype(reshapeSizesInt,
inputTy.getOptionalDtype()),
input, reshapeSizesList);
SmallVector<int64_t, 6> permuteDimsInt{0, 3, 5, 1, 2, 4};
Value permutedInput;
if (failed(createTorchPermuteOp(binder, rewriter, binder.getLoc(),
reshapedInput, permuteDimsInt,
permutedInput)))
return rewriter.notifyMatchFailure(
binder.op, "Failed to create Torch Permute op");
Value cMulBlockSizeSquare = rewriter.create<Torch::AtenMulIntOp>(
binder.getLoc(), c, cstBlockSizeSquare);
reshapeSizesList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(input.getContext())),
llvm::SmallVector<Value>{b, cMulBlockSizeSquare, hDivBlockSize,
wDivBlockSize});
rewriter.replaceOpWithNewOp<Torch::AtenReshapeOp>(
binder.op, resultType, permutedInput, reshapeSizesList);
return success();
});
patterns.onOp(
"Shrink", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Location loc = binder.getLoc();
Torch::ValueTensorType resultType;
Value input;
float bias, lambd;
if (binder.tensorOperand(input) ||
binder.f32FloatAttr(bias, "bias", 0.0) ||
binder.f32FloatAttr(lambd, "lambd", 0.5) ||
binder.tensorResultType(resultType)) {
return failure();
}
Torch::ValueTensorType inputType =
cast<Torch::ValueTensorType>(input.getType());
if (!isa<mlir::FloatType>(inputType.getDtype()))
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: non-floating point dtype");
// The formula of this operator is: If x < -lambd, y = x + bias; If x >
// lambd, y = x - bias; Otherwise, y = 0.
// The implementation is based on the following algorithm:
// Shrink <bias,lambd>(input) => (output)
// {
// Lambd = Constant <value_float: float = @lambd> ()
// LambdCast = CastLike (Lambd, input)
// Bias = Constant <value_float: float = @bias> ()
// BiasCast = CastLike (Bias, input)
// Zero = Constant <value: tensor = float {0}> ()
// ZeroCast = CastLike (Zero, input)
// NegLmbda = Neg (LambdCast)
// InputLessThanNegLambda = Less (input, NegLmbda)
// InputAddBias = Add (input, BiasCast)
// InputSubBias = Sub (input, BiasCast)
// LambdaLessThanInput = Less (LambdCast, input)
// InputSubBiasOrZero = Where (LambdaLessThanInput, InputSubBias,
// ZeroCast) output = Where (InputLessThanNegLambda, InputAddBias,
// InputSubBiasOrZero)
// }
Value constLambd = rewriter.create<Torch::ConstantFloatOp>(
loc, rewriter.getFloatAttr(rewriter.getF64Type(), lambd));
Value constBias = rewriter.create<Torch::ConstantFloatOp>(
loc, rewriter.getFloatAttr(rewriter.getF64Type(), bias));
Value constZero = rewriter.create<Torch::ConstantFloatOp>(
loc, rewriter.getFloatAttr(rewriter.getF64Type(), 0.0));
Value constOne = rewriter.create<Torch::ConstantFloatOp>(
loc, rewriter.getFloatAttr(rewriter.getF64Type(), 1.0));
Value constNegLambd = rewriter.create<Torch::ConstantFloatOp>(
loc, rewriter.getFloatAttr(rewriter.getF64Type(), -lambd));
Value inputLTNegLambd = rewriter.create<Torch::AtenLtScalarOp>(
loc, inputType, input, constNegLambd);
Value inputPlusBias = rewriter.create<Torch::AtenAddScalarOp>(
loc, inputType, input, constBias, /*alpha=*/constOne);
Value inputSubBias = rewriter.create<Torch::AtenSubScalarOp>(
loc, inputType, input, constBias, /*alpha=*/constOne);
Value inputGTLambd = rewriter.create<Torch::AtenGtScalarOp>(
loc, inputType, input, constLambd);
Value inputSubBiasOrZero =
rewriter.create<Torch::AtenWhereScalarOtherOp>(
loc, resultType, inputGTLambd, inputSubBias, constZero);
rewriter.replaceOpWithNewOp<Torch::AtenWhereSelfOp>(
binder.op, resultType, inputLTNegLambd, inputPlusBias,
inputSubBiasOrZero);
return success();
});
}
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