mirror of https://github.com/llvm/torch-mlir
2594 lines
114 KiB
C++
2594 lines
114 KiB
C++
//===------------------------------------------------------------*- 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
|
|
// Templatized function to get an item op of a type
|
|
namespace {
|
|
template <typename T>
|
|
Value getItemOp(OpBinder binder, ConversionPatternRewriter &rewriter,
|
|
Value &ofItem) {
|
|
return rewriter.create<Torch::AtenItemOp>(binder.getLoc(),
|
|
rewriter.getType<T>(), ofItem);
|
|
}
|
|
|
|
// 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(
|
|
"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(
|
|
"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();
|
|
|
|
Torch::ValueTensorType inputType =
|
|
operand.getType().cast<Torch::ValueTensorType>();
|
|
|
|
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 cstOne = rewriter.create<Torch::ConstantFloatOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
|
|
rewriter.getFloatAttr(rewriter.getF64Type(), 1.0));
|
|
|
|
Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
|
|
Value zeroTensor = rewriter.create<Torch::AtenZerosLikeOp>(
|
|
binder.getLoc(), resultType, operand, cstNone, cstNone, cstNone,
|
|
cstNone, cstNone);
|
|
Value exp = rewriter.create<Torch::AtenExpOp>(binder.getLoc(),
|
|
resultType, operand);
|
|
Value expMulAlpha = rewriter.create<Torch::AtenMulScalarOp>(
|
|
binder.getLoc(), resultType, exp, vAlpha);
|
|
Value expMulAlphaSubAlpha = rewriter.create<Torch::AtenSubScalarOp>(
|
|
binder.getLoc(), resultType, expMulAlpha, vAlpha, cstOne);
|
|
Value neg = rewriter.create<Torch::AtenMulScalarOp>(
|
|
binder.getLoc(), resultType, expMulAlphaSubAlpha, vScale);
|
|
Value pos = rewriter.create<Torch::AtenMulScalarOp>(
|
|
binder.getLoc(), resultType, operand, vScale);
|
|
Type compareType = inputType.getWithSizesAndDtype(
|
|
inputType.getOptionalSizes(), rewriter.getI1Type());
|
|
Value xLessThanZero = rewriter.create<Torch::AtenLtTensorOp>(
|
|
binder.getLoc(), compareType, operand, zeroTensor);
|
|
|
|
rewriter.replaceOpWithNewOp<Torch::AtenWhereSelfOp>(
|
|
binder.op, resultType, xLessThanZero, neg, pos);
|
|
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("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("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(
|
|
"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", 13,
|
|
[](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 = steps.getType()
|
|
.cast<Torch::ValueTensorType>()
|
|
.toBuiltinTensor()
|
|
.dyn_cast<RankedTensorType>();
|
|
|
|
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(
|
|
"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();
|
|
});
|
|
}
|