llvm
/
torch-mlir
mirror of https://github.com/llvm/torch-mlir
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
torch-mlir/lib/Conversion/TorchToLinalg/Random.cpp

210 lines
8.5 KiB
C++
Raw Blame History

//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//
#include "torch-mlir/Conversion/TorchToLinalg/TorchToLinalg.h"
#include "../PassDetail.h"
#include "PopulatePatterns.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/Matchers.h"
#include "torch-mlir/Conversion/TorchToLinalg/Utils.h"
#include "torch-mlir/Conversion/Utils/Utils.h"
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
namespace {
// TODO: Dropout should probably be handled in DecomposeComplexOps instead of
// here.
class ConvertAtenDropoutOp : public OpConversionPattern<AtenDropoutOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenDropoutOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
bool train;
if (!matchPattern(op.getTrain(), m_TorchConstantBool(&train)))
return rewriter.notifyMatchFailure(op,
"Expected train to be constant bool.");
if (train)
return failure();
auto resultType = getTypeConverter()
->convertType(op->getResult(0).getType())
.cast<RankedTensorType>();
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType,
adaptor.getInput());
return success();
}
};
} // namespace
static Value toLinearIndex(OpBuilder &b, Location loc,
ArrayRef<Value> indicesIntValues,
ArrayRef<Value> shapeIntValues) {
assert(indicesIntValues.size() == shapeIntValues.size() &&
"Expected `indices` and `shape` to have the same size");
Value result =
b.create<arith::ConstantOp>(loc, b.getZeroAttr(b.getI64Type()));
for (auto [index, stride] : llvm::zip(indicesIntValues, shapeIntValues)) {
assert(index.getType().isa<mlir::IntegerType>() &&
stride.getType().isa<mlir::IntegerType>() &&
"Input arrays to `toLinearIndex` must only contain values of type "
"`mlir::IntegerType`");
Value mul = b.create<arith::MulIOp>(loc, result, stride);
result = b.create<arith::AddIOp>(loc, mul, index);
}
return result;
}
// Squares64 Algorithm for generating 64-bit random numbers.
// See: https://arxiv.org/abs/2004.06278
static Value randomUniformUInt(OpBuilder &b, Location loc, Value ctr,
Value key) {
auto mul = [&](Value lhs, Value rhs) -> Value {
return b.create<arith::MulIOp>(loc, lhs, rhs);
};
auto add = [&](Value lhs, Value rhs) -> Value {
return b.create<arith::AddIOp>(loc, lhs, rhs);
};
Value cst32 = b.create<arith::ConstantOp>(loc, b.getI64IntegerAttr(32));
auto shiftRight32 = [&](Value val) -> Value {
return b.create<arith::ShRUIOp>(loc, val, cst32);
};
auto swapLoHi = [&](Value val) -> Value {
Value leftShift = b.create<arith::ShLIOp>(loc, val, cst32);
Value rightShift = shiftRight32(val);
return b.create<arith::OrIOp>(loc, leftShift, rightShift);
};
auto bitwiseXOr = [&](Value lhs, Value rhs) -> Value {
return b.create<arith::XOrIOp>(loc, lhs, rhs);
};
Value t, x, y, z;
x = mul(ctr, key);
y = x;
z = add(y, key);
x = add(mul(x, x), y);
x = swapLoHi(x);
x = add(mul(x, x), z);
x = swapLoHi(x);
x = add(mul(x, x), y);
x = swapLoHi(x);
t = x = add(mul(x, x), z);
x = swapLoHi(x);
return bitwiseXOr(t, shiftRight32(add(mul(x, x), y)));
}
namespace {
class ConvertAtenUniformOp : public OpConversionPattern<AtenUniformOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenUniformOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op.getLoc();
Value self = adaptor.getSelf();
Value from = adaptor.getFrom();
Value to = adaptor.getTo();
Value generator = adaptor.getGenerator();
RankedTensorType resultType = self.getType().cast<RankedTensorType>();
Type elemTy = resultType.getElementType();
Type f64Ty = rewriter.getF64Type();
if (!isa<mlir::FloatType>(elemTy))
return rewriter.notifyMatchFailure(op, "This op only support float type");
if (!generator.getType().isa<Torch::NoneType>())
return rewriter.notifyMatchFailure(
op, "The generator has to be None because only global default "
"generator is supported");
// Get key, min and max used by `linalg.generic` compute payload.
Value key = rewriter.create<TorchConversion::GetNextSeedOp>(loc);
Value min = convertScalarToDtype(rewriter, loc, from, f64Ty);
Value max = convertScalarToDtype(rewriter, loc, to, f64Ty);
// Construct the `linalg.generic` op.
auto resultRank = resultType.getRank();
SmallVector<AffineMap, 1> indexingMaps(
1, rewriter.getMultiDimIdentityMap(resultRank));
SmallVector<utils::IteratorType> iteratorTypes(
resultRank, utils::IteratorType::parallel);
SmallVector<Value> sizes = getTensorSizes(rewriter, loc, self);
SmallVector<Value> sizesIntValues =
castIndexVectorToInt64Vector(rewriter, loc, sizes);
Value initTensor =
rewriter.create<tensor::EmptyOp>(loc, getAsOpFoldResult(sizes), elemTy);
Value uniformRes =
rewriter
.create<linalg::GenericOp>(
loc, initTensor.getType(), /*inputs=*/ValueRange{},
/*outputs=*/initTensor, indexingMaps, iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
SmallVector<Value> indicesIntValues;
for (int i = 0; i < resultRank; i++) {
indicesIntValues.push_back(castIndexToInt64(
b, loc, b.create<linalg::IndexOp>(loc, i)));
}
Value linearIndex =
toLinearIndex(b, loc, indicesIntValues, sizesIntValues);
Value randomVal = randomUniformUInt(b, loc, linearIndex, key);
// scale = (max - min) * const(F64, 5.4210108E-20)
// which is derived from rand(min,max) =
// rand()/(RAND_MAX/(max-min)) where RAND_MAX = 2^64 - 1
Value epsilon = b.create<arith::ConstantOp>(
loc, b.getFloatAttr(min.getType(), 5.4210108E-20));
Value range = b.create<arith::SubFOp>(loc, max, min);
Value scale = b.create<arith::MulFOp>(loc, range, epsilon);
// res = cast(F64, tempN) * scale + min
Value updateFloat =
b.create<arith::UIToFPOp>(loc, f64Ty, randomVal);
Value updateScaled =
b.create<arith::MulFOp>(loc, updateFloat, scale);
Value res = b.create<arith::AddFOp>(loc, updateScaled, min);
Value truncRes = res;
if (elemTy.isa<Float16Type, Float32Type>())
truncRes = b.create<arith::TruncFOp>(loc, elemTy, res);
b.create<linalg::YieldOp>(loc, truncRes);
})
.getResult(0);
Type newResultType = getTypeConverter()->convertType(op.getType());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, uniformRes);
return success();
}
};
} // namespace
void mlir::torch::torch_to_linalg::populateRandomPatternsAndLegality(
TypeConverter &typeConverter, RewritePatternSet &patterns,
ConversionTarget &target) {
MLIRContext *context = patterns.getContext();
target.addIllegalOp<AtenDropoutOp>();
patterns.add<ConvertAtenDropoutOp>(typeConverter, context);
target.addIllegalOp<AtenUniformOp>();
patterns.add<ConvertAtenUniformOp>(typeConverter, context);
}
Reference in New Issue View Git Blame Copy Permalink