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torch-mlir/lib/Conversion/TorchToTosa/TorchToTosa.cpp

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//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//
#include "torch-mlir/Conversion/TorchToTosa/TorchToTosa.h"
#include "torch-mlir/Conversion/TorchToTosa/TosaLegalizeCommon.h"
#include "torch-mlir/Conversion/TorchToTosa/TosaLegalizeUtils.h"
#include "../PassDetail.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
#include "mlir/Dialect/Traits.h"
#include "mlir/IR/Matchers.h"
#include "mlir/Transforms/DialectConversion.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionDialect.h"
#include "torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
namespace {
// These legalizations are for unary ops with only for floating point datatypes.
// There is no supported quantized integer mode for these.
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenUnaryFPOnlyOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value self = adaptor.self();
auto selfTy = self.getType().cast<TensorType>();
if (!selfTy)
return op.emitError("Only Tensor types supported in TOSA");
if (selfTy.getElementType().isa<mlir::FloatType>()) {
rewriter.replaceOpWithNewOp<TosaOpT>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
self);
return success();
} else {
return op.emitError(
"Only floating-point datatype legalization supported");
}
}
};
// These unary op legalizations are identical for floating-point
// or quantized types
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenUnaryOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
rewriter.replaceOpWithNewOp<TosaOpT>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
adaptor.self());
return success();
}
};
// These binary op legalizations are identical for floating-point
// or quantized types
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenBinaryOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs = adaptor.self();
auto lhsTy = lhs.getType().cast<TensorType>();
Value rhs = adaptor.other();
auto rhsTy = rhs.getType().cast<TensorType>();
if (!lhsTy || !rhsTy)
return op.emitError("Only Tensor types supported in TOSA");
auto lhsElemTy = lhsTy.getElementType();
auto rhsElemTy = rhsTy.getElementType();
if (lhsElemTy != rhsElemTy)
return op.emitError("Add: input datatypes mismatched");
rewriter.replaceOpWithNewOp<TosaOpT>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
lhs, rhs);
return success();
}
};
template <typename T>
static bool isInValidRange(bool isFloat, const double &doubleValue, bool isInt,
const int64_t &intValue) {
if (isFloat) {
// Do a round-trip check here instead of numeric limits due to
// compiler warnings around double <-> int conversion.
return (doubleValue == static_cast<double>(static_cast<T>(doubleValue)));
} else {
assert(isInt);
return (intValue >= std::numeric_limits<T>::min()) &&
(intValue <= std::numeric_limits<T>::max());
}
return true;
}
// FIXME: This will eventually go into a Tosa*Utils file.
LogicalResult torchScalarToTosaTensor(ConversionPatternRewriter &rewriter,
Operation *op, Value torchScalarValue,
Value &tosaTensor, Type dtype,
llvm::ArrayRef<int64_t> dshape) {
// Retrieve a const float or int value but create the out Tensor with dtype.
double doubleValue;
auto isFloat =
matchPattern(torchScalarValue, m_TorchConstantFloat(&doubleValue));
int64_t intValue;
auto isInt = matchPattern(torchScalarValue, m_TorchConstantInt(&intValue));
if (!isFloat && !isInt)
return op->emitError("Unable to extract the scalar constant");
if (dtype.isa<mlir::FloatType>()) {
tosaTensor = tosa::getConstTensor<float>(
rewriter, op, (isFloat ? doubleValue : intValue), dshape)
.getValue();
} else if (auto intType = dtype.dyn_cast<mlir::IntegerType>()) {
auto w = intType.getWidth();
if (w != 32 && w != 64)
return op->emitError("Unsupported integer type") << intType;
if (w == 32) {
if (!isInValidRange<int32_t>(isFloat, doubleValue, isInt, intValue)) {
return op->emitError("Supplied value of scalar constant exceeds limits "
"of destination type");
}
int32_t d = isFloat ? static_cast<int32_t>(doubleValue)
: static_cast<int32_t>(intValue);
tosaTensor =
tosa::getConstTensor<int32_t>(rewriter, op, {d}, dshape).getValue();
} else if (w == 64) {
if (!isInValidRange<int64_t>(isFloat, doubleValue, isInt, intValue)) {
return op->emitError("Supplied value of scalar constant exceeds limits "
"of destination type");
}
int64_t d = (isFloat ? static_cast<int64_t>(doubleValue) : intValue);
tosaTensor =
tosa::getConstTensor<int64_t>(rewriter, op, {d}, dshape).getValue();
}
} else
return op->emitError("Usupported element type");
return success();
}
LogicalResult torchAlphaToTosaTensor(ConversionPatternRewriter &rewriter,
Operation *op, Value alphaScalar,
Value &alphaTensor, Type dtype,
bool checkForUnity) {
if (succeeded(torchScalarToTosaTensor(rewriter, op, alphaScalar, alphaTensor,
dtype, {})))
return success();
// `alpha` has not been specified.
int64_t alphaValue;
if (!matchPattern(alphaScalar, m_TorchConstantInt(&alphaValue)))
return op->emitError("Currently only scalar constants are supported for "
"alpha in TOSA operation");
// When no alpha has been specified, this must be 1.
if (checkForUnity && alphaValue != 1)
return op->emitError("Unsupported integer value for alpha");
alphaTensor =
mlir::tosa::getTosaConstTensorSingleF32(rewriter, op, alphaValue);
return success();
}
// These binary op legalizations are specific to add/sub which have an
// alpha multiplier.
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenAddSubOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs = adaptor.self();
auto lhsType = lhs.getType().dyn_cast<TensorType>();
Value rhs = adaptor.other();
auto rhsType = rhs.getType().dyn_cast<TensorType>();
if (!lhsType)
return op.emitError("Only Tensor types supported in TOSA");
if (auto lhsElemTy = lhsType.getElementType().dyn_cast<IntegerType>()) {
if (lhsElemTy.getWidth() > 32)
return op.emitError(
"Integers with widths greater than 32 are not supported");
}
auto outType = OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<TensorType>();
Type outElemTy = outType.getElementType();
if (!outElemTy.isIntOrFloat()) {
return op.emitError(
"Only floating-point or integer datatype legalization supported");
}
Value rhsAsTensor;
if (!rhsType) {
if (failed(torchScalarToTosaTensor(rewriter, op, op.other(), rhsAsTensor,
outElemTy, {})))
return op.emitError("Currently only scalar constants are supported for "
"conversion in TOSA operation");
}
auto rhsTensor = rhsType ? rhs : rhsAsTensor;
// Handle alpha.
Value alphaTensor;
if (failed(torchAlphaToTosaTensor(rewriter, op.getOperation(), op.alpha(),
alphaTensor, outElemTy,
/*checkForUnity=*/false))) {
return op.emitError("Currently only scalar constants are supported for "
"alpha in conversion to TOSA operation");
}
auto multTensor = rewriter.create<tosa::MulOp>(
op.getLoc(), rhsType ? rhsType : RankedTensorType::get({}, outElemTy),
rhsTensor, alphaTensor, /*shift=*/0);
if (outElemTy.isa<mlir::FloatType>()) {
if (lhsType.getElementType() != outElemTy)
lhs = rewriter.create<tosa::CastOp>(op.getLoc(), outType, lhs);
rewriter.replaceOpWithNewOp<TosaOpT>(op, outType, lhs, multTensor);
return success();
} else {
return op.emitError(
"Only floating-point datatype legalization supported");
}
}
}; // namespace
// Binary op legalizations for comparator ops.
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenCompareOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs = adaptor.self();
auto lhsTy = lhs.getType().dyn_cast<TensorType>();
Value rhs = adaptor.other();
auto rhsTy = rhs.getType().dyn_cast<TensorType>();
if (!lhsTy)
return op.emitError("Only Tensor types supported in TOSA");
auto lhsElemTy = lhsTy.getElementType();
if (!lhsElemTy.isIntOrFloat())
return op.emitError(
"Only floating-point or integer datatype legalization supported");
// For bitwise operators, only integer datatype legalization is supported
if (lhsElemTy.isa<mlir::FloatType>() &&
std::is_same<AtenOpT, AtenBitwiseAndTensorOp>()) {
return op.emitError("For bitwise operators, only integer datatype "
"legalization is supported");
}
Value rhsAsTensor;
if (!rhsTy) {
if (failed(torchScalarToTosaTensor(rewriter, op, op.other(), rhsAsTensor,
lhsElemTy, {})))
return op.emitError("Currently only scalar constants are supported for "
"conversion in TOSA operation");
}
auto rhsTensor = rhsTy ? rhs : rhsAsTensor;
// There is no Lesser operator in TOSA.
auto swapLhsRhs = (std::is_same<AtenOpT, AtenLtTensorOp>() ||
std::is_same<AtenOpT, AtenLtScalarOp>());
auto resultOp = rewriter.create<TosaOpT>(
op.getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
(swapLhsRhs ? rhsTensor : lhs), (swapLhsRhs ? lhs : rhsTensor));
// There is no NE operator in TOSA.
if (std::is_same<AtenOpT, AtenNeTensorOp>() ||
std::is_same<AtenOpT, AtenNeScalarOp>())
rewriter.replaceOpWithNewOp<tosa::LogicalNotOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
resultOp.getResult());
else
rewriter.replaceOp(op, resultOp.getResult());
return success();
}
};
// Binary op legalizations for Mul variants.
template <typename AtenOpT>
class ConvertAtenMulOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs = adaptor.self();
auto lhsType = lhs.getType().dyn_cast<TensorType>();
if (!lhsType)
return op.emitError("Only Tensor types supported in TOSA");
auto outType = OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<TensorType>();
Type outElemTy = outType.getElementType();
if (!outElemTy.isIntOrFloat())
return op.emitError(
"Only floating-point or integer datatype legalization supported");
Value rhsTensor;
if (std::is_same<AtenOpT, AtenSquareOp>()) {
rhsTensor = lhs;
} else {
Value rhsAsTensor;
Value rhs = adaptor.other();
auto rhsType = rhs.getType().dyn_cast<TensorType>();
if (!rhsType) {
if (failed(torchScalarToTosaTensor(rewriter, op, op.other(),
rhsAsTensor, outElemTy, {})))
return op.emitError(
"Currently only scalar constants are supported for "
"conversion in TOSA operation");
}
rhsTensor = rhsType ? rhs : rhsAsTensor;
}
if (outElemTy.isa<mlir::FloatType>() ||
outElemTy.isa<mlir::IntegerType>()) {
if (lhsType.getElementType() != outElemTy)
lhs = rewriter.create<tosa::CastOp>(op.getLoc(), outType, lhs);
rewriter.replaceOpWithNewOp<tosa::MulOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
lhs, rhsTensor,
/*shift=*/0);
return success();
} else {
// Quantized multiplication may need to rescale inputs.
return op.emitError("Only floating-point or integer datatype "
"legalization currently supported");
}
}
};
template <typename AtenOpT>
class ConvertAtenDivOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs = adaptor.self();
auto lhsTy = lhs.getType().dyn_cast<TensorType>();
Value rhs = adaptor.other();
auto rhsTy = rhs.getType().dyn_cast<TensorType>();
if (!lhsTy)
return op.emitError("Only Tensor types supported in TOSA");
auto lhsElemTy = lhsTy.getElementType();
if (!lhsElemTy.isIntOrFloat())
return op.emitError(
"Only floating-point or integer datatype legalization supported");
Value rhsAsTensor;
if (!rhsTy) {
if (failed(torchScalarToTosaTensor(rewriter, op, op.other(), rhsAsTensor,
lhsElemTy, {})))
return op.emitError("Currently only scalar constants are supported for "
"conversion in TOSA operation");
}
auto rhsTensor = rhsTy ? rhs : rhsAsTensor;
if (lhsElemTy.isa<mlir::FloatType>()) {
auto rcpOp = rewriter.create<tosa::ReciprocalOp>(
op->getLoc(), rhsTy ? rhsTy : RankedTensorType::get({}, lhsElemTy),
rhsTensor);
rewriter.replaceOpWithNewOp<tosa::MulOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
lhs, rcpOp.getResult(), /*shift=*/0);
} else {
rewriter.replaceOpWithNewOp<tosa::DivOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
lhs, rhsTensor);
}
return success();
}
};
// This defines a template to construct ops whose legalizations are
// specialized.
template <typename AtenOpT>
class ConvertAtenOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override;
};
template <>
LogicalResult ConvertAtenOp<AtenTanhOp>::matchAndRewrite(
AtenTanhOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
Value self = adaptor.self();
auto selfTy = self.getType().cast<TensorType>();
if (selfTy && selfTy.getElementType().isa<mlir::FloatType>()) {
rewriter.replaceOpWithNewOp<tosa::TanhOp>(
op, getTypeConverter()->convertType(op.getType()), self);
return success();
} else {
// Sigmoid legalization in TOSA for quantized element-type uses
// specialized tosa.table construct.
return op.emitError(
"Only floating-point datatype legalization currently supported");
}
}
template <>
LogicalResult ConvertAtenOp<AtenSigmoidOp>::matchAndRewrite(
AtenSigmoidOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
Value self = adaptor.self();
auto selfTy = self.getType().cast<TensorType>();
if (selfTy && selfTy.getElementType().isa<mlir::FloatType>()) {
rewriter.replaceOpWithNewOp<tosa::SigmoidOp>(
op, getTypeConverter()->convertType(op.getType()), self);
return success();
} else {
// Sigmoid legalization in TOSA for quantized element-type uses
// specialized tosa.table construct.
return op.emitError(
"Only floating-point datatype legalization currently supported");
}
}
template <>
LogicalResult ConvertAtenOp<AtenReluOp>::matchAndRewrite(
AtenReluOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
Value self = adaptor.self();
auto selfTy = self.getType().cast<TensorType>();
// Maps to tosa.clamp which has both int and fp limits.
int64_t clampMin = 0;
Value clampIn = self;
if (selfTy) {
// Rescale the clampIn for quantized types. TBD
if (!selfTy.getElementType().isa<mlir::FloatType>()) {
return op.emitError(
"Only floating-point datatype legalization currently supported");
}
rewriter.replaceOpWithNewOp<tosa::ClampOp>(
op, getTypeConverter()->convertType(op.getType()), clampIn,
rewriter.getI64IntegerAttr(clampMin),
rewriter.getI64IntegerAttr(std::numeric_limits<int32_t>::max()),
rewriter.getF32FloatAttr(0.0f),
rewriter.getF32FloatAttr(std::numeric_limits<float>::max()));
return success();
} else {
return op.emitError("Only Tensor types supported in TOSA");
}
}
using ReductionConvFunc = llvm::Optional<Value> (*)(PatternRewriter &,
Operation *,
RankedTensorType, Value,
ElementsAttr, bool);
// They all constitute a common form invoking the appropriate
// converion function in TosaLegalizeCommon.cpp
template <typename AtenOpT, ReductionConvFunc ConversionFuncT>
class ConvertAtenReductionOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
// Each variant must implement corresponding parameter parsing options
virtual LogicalResult readReduceDimsAndKeepDims(
AtenOpT op, OpAdaptor adaptor, ConversionPatternRewriter &rewriter,
ElementsAttr &reduceDimsAttr, bool &keepDims) const {
return rewriter.notifyMatchFailure(
op, "Unimplemented reduce_dims and keep_dims parsing function");
}
// Common rewriter for all reduction ops, calls the specific implementation of
// readReduceDimsAndKeepDims() needed for the op variant.
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value self = adaptor.self();
auto selfTy = self.getType().cast<TensorType>();
if (!selfTy)
return op.emitError("Only Tensor types supported in TOSA");
auto outputTy = OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>();
if (!outputTy)
return op.emitError(
"Only ranked tensor type outputs permitted for reduce_mean");
ElementsAttr reduceDimsAttr;
bool keepDims;
if (failed(readReduceDimsAndKeepDims(op, adaptor, rewriter, reduceDimsAttr,
keepDims)))
return failure();
llvm::Optional<Value> result =
ConversionFuncT(rewriter, op, outputTy, self, reduceDimsAttr, keepDims);
if (!result)
return failure();
// TBD - support dtype casting.
rewriter.replaceOp(op, {result.getValue()});
return success();
}
};
// This reduction op legalization template handles op variants that have
// explicit reduce_dims dimensions (provided as a list) and keep_dims
// parameters.
template <typename AtenOpT, ReductionConvFunc ConversionFuncT>
class ConvertAtenMultipleDimsReductionOp
: public ConvertAtenReductionOp<AtenOpT, ConversionFuncT> {
using ConvertAtenReductionOp<AtenOpT,
ConversionFuncT>::ConvertAtenReductionOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult readReduceDimsAndKeepDims(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
ElementsAttr &reduceDimsAttr,
bool &keepDims) const override {
SmallVector<int64_t, 4> reduceDims;
if (!matchPattern(op.dim(), m_TorchConstantIntList(reduceDims)))
return rewriter.notifyMatchFailure(op,
"non-const dim parameter unsupported");
int64_t N = reduceDims.size();
auto reduceDimsType = RankedTensorType::get({N}, rewriter.getI64Type());
reduceDimsAttr = DenseIntElementsAttr::get(reduceDimsType,
llvm::makeArrayRef(reduceDims));
keepDims = false;
if (!matchPattern(op.keepdim(), m_TorchConstantBool(&keepDims)))
return rewriter.notifyMatchFailure(
op, "non-const keepdim parameter unsupported");
return success();
}
};
// This reduction op legalization template handles op variants that reduce in
// only one explicit dim which is provided as a number (rather than a list), and
// a keep_dims parameter.
template <typename AtenOpT, ReductionConvFunc ConversionFuncT>
class ConvertAtenOneDimReductionOp
: public ConvertAtenReductionOp<AtenOpT, ConversionFuncT> {
using ConvertAtenReductionOp<AtenOpT,
ConversionFuncT>::ConvertAtenReductionOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult readReduceDimsAndKeepDims(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
ElementsAttr &reduceDimsAttr,
bool &keepDims) const override {
int64_t reduceDim;
if (!matchPattern(op.dim(), m_TorchConstantInt(&reduceDim)))
return rewriter.notifyMatchFailure(op,
"non-const dim parameter unsupported");
auto reduceDimsType = RankedTensorType::get({1}, rewriter.getI64Type());
reduceDimsAttr = DenseIntElementsAttr::get(reduceDimsType,
llvm::makeArrayRef({reduceDim}));
keepDims = false;
if (!matchPattern(op.keepdim(), m_TorchConstantBool(&keepDims)))
return rewriter.notifyMatchFailure(
op, "non-const keepdim parameter unsupported");
return success();
}
};
// This reduction op legalization template handles op variants that reduce all
// dims does not keep dims.
template <typename AtenOpT, ReductionConvFunc ConversionFuncT>
class ConvertAtenAllDimsReductionOp
: public ConvertAtenReductionOp<AtenOpT, ConversionFuncT> {
public:
using ConvertAtenReductionOp<AtenOpT,
ConversionFuncT>::ConvertAtenReductionOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult readReduceDimsAndKeepDims(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
ElementsAttr &reduceDimsAttr,
bool &keepDims) const override {
auto self = adaptor.self();
auto selfTy = self.getType().template cast<RankedTensorType>();
// Select all dims to reduce
SmallVector<int64_t, 4> reduceDims;
for (int64_t i = 0; i < selfTy.getRank(); i++)
reduceDims.push_back(i);
int64_t N = selfTy.getRank();
auto reduceDimsType = RankedTensorType::get({N}, rewriter.getI64Type());
reduceDimsAttr = DenseIntElementsAttr::get(reduceDimsType,
llvm::makeArrayRef(reduceDims));
keepDims = false;
return success();
}
};
template <>
LogicalResult ConvertAtenOp<AtenArgmaxOp>::matchAndRewrite(
AtenArgmaxOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
Value self = adaptor.self();
auto selfTy = self.getType().template cast<RankedTensorType>();
if (!selfTy)
return op.emitError("Only ranked tensor types supported in TOSA argmax");
int64_t reduceDim;
if (!matchPattern(op.dim(), m_TorchConstantInt(&reduceDim))) {
// NoneType indicates reduce on all dims
reduceDim = -1;
}
bool keepDim = false;
if (!matchPattern(op.keepdim(), m_TorchConstantBool(&keepDim)))
return rewriter.notifyMatchFailure(
op, "non-const keepdim parameter unsupported");
auto resultTy = getTypeConverter()
->convertType(op.getResult().getType())
.cast<RankedTensorType>();
auto outputETy = resultTy.getElementType();
// Create a single instance of tosa.argmax.
// Multiple dims require chained construct.
auto buildArgmax = [&](int64_t reduceDim, Value input) -> Value {
auto inputTy = input.getType().cast<RankedTensorType>();
auto inputShape = inputTy.getShape();
SmallVector<int64_t> outputShapeArr = {};
int32_t i = 0;
for (auto &dim : inputShape) {
if (i++ != reduceDim) {
outputShapeArr.push_back(dim);
} else {
if (keepDim)
outputShapeArr.push_back(1);
}
}
// Tosa argmax output is i32, while Torch backend mandates i64.
auto outputReduceTy = RankedTensorType::get(
ArrayRef<int64_t>(outputShapeArr), rewriter.getI32Type());
auto reduceDimAttr =
rewriter.getIntegerAttr(rewriter.getI64Type(), reduceDim);
return rewriter
.create<tosa::ArgMaxOp>(op->getLoc(),
getTypeConverter()->convertType(outputReduceTy),
input, reduceDimAttr)
.getResult();
};
// Convert the final index to i64 for backend finalization, However, i64
// is not a defined type for tosa.cast, so using arith.extsi instead.
auto castToInt64 = [&](Value result) -> LogicalResult {
auto resTy = result.getType().cast<ShapedType>();
if (!resTy)
return op.emitError("Argmax: Result is not a shaped type");
auto resShape = resTy.getShape();
auto outTy =
RankedTensorType::get(resShape, outputETy); // rewriter.getI64Type());
rewriter.replaceOpWithNewOp<arith::ExtSIOp>(
op, getTypeConverter()->convertType(outTy), result);
return success();
};
if (reduceDim == -1) { // reducing on all dims
Value input = self;
for (int dim = 0; dim < selfTy.getRank(); dim++) {
// progressively reduce each 0-th dim
input = buildArgmax(0, input);
}
return castToInt64(input);
} else {
return castToInt64(buildArgmax(reduceDim, self));
}
return success();
}
template <typename AtenOpT>
class ConvertAtenSqueezeOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
// Each variant must implement corresponding parameter parsing options
virtual LogicalResult
generateSqueezedShape(AtenOpT op, RankedTensorType selfTy,
ConversionPatternRewriter &rewriter,
SmallVector<int64_t> &squeezedShape) const {
return rewriter.notifyMatchFailure(
op, "Unimplemented dim/dim-list parsing function");
}
// Common rewriter for all squeeze ops, calls the specific implementation of
// generateSqueezedShape() needed for the op variant.
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value self = adaptor.self();
auto selfTy = self.getType().template cast<RankedTensorType>();
if (!selfTy)
return op.emitError("Only ranked tensor types supported in TOSA argmax");
SmallVector<int64_t> newOutputShape;
if (failed(generateSqueezedShape(op, selfTy, rewriter, newOutputShape)))
return op.emitError("Squeeze could not compute new shape");
auto resultTy = OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getResult().getType())
.template cast<RankedTensorType>();
auto resultElemTy = resultTy.getElementType();
auto newOutputTy = RankedTensorType::get(newOutputShape, resultElemTy);
auto reshapeOp = rewriter.create<tosa::ReshapeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
newOutputTy),
self, rewriter.getI64ArrayAttr(newOutputShape));
rewriter.replaceOpWithNewOp<tensor::CastOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
newOutputTy),
reshapeOp);
return success();
}
};
template <typename AtenOpT>
class ConvertAtenSqueezeOneDimOp : public ConvertAtenSqueezeOp<AtenOpT> {
using ConvertAtenSqueezeOp<AtenOpT>::ConvertAtenSqueezeOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
generateSqueezedShape(AtenOpT op, RankedTensorType selfTy,
ConversionPatternRewriter &rewriter,
SmallVector<int64_t> &squeezedShape) const override {
int64_t squeezeDim;
if (!matchPattern(op.dim(), m_TorchConstantInt(&squeezeDim)))
return rewriter.notifyMatchFailure(op,
"non-const dim parameter unsupported");
// Handle negative dim
if (squeezeDim < 0)
squeezeDim = squeezeDim + selfTy.getRank();
auto selfShape = selfTy.getShape();
// Only dims statically known to have size=1 are reduced.
// Dynamic dims are treated as unknowns and will not be squeezed
// even if dim parameter says it should be.
uint32_t dimNum = 0;
for (auto &dim : selfShape) {
if (dim != 1 || squeezeDim != dimNum)
squeezedShape.push_back(dim);
dimNum++;
}
return success();
}
};
template <typename AtenOpT>
class ConvertAtenSqueezeAllDimsOp : public ConvertAtenSqueezeOp<AtenOpT> {
using ConvertAtenSqueezeOp<AtenOpT>::ConvertAtenSqueezeOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
generateSqueezedShape(AtenOpT op, RankedTensorType selfTy,
ConversionPatternRewriter &rewriter,
SmallVector<int64_t> &squeezedShape) const override {
auto selfShape = selfTy.getShape();
// Dims that may dynamically resolve to 1 are not reduced here. Only
// compile-time resolvable dims are handled here.
for (auto &dim : selfShape) {
if (dim != 1)
squeezedShape.push_back(dim);
}
return success();
}
};
template <>
LogicalResult ConvertAtenOp<AtenPowTensorScalarOp>::matchAndRewrite(
AtenPowTensorScalarOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
Value self = adaptor.self();
auto selfTy = self.getType().template cast<RankedTensorType>();
if (!selfTy)
return op.emitError("Only ranked tensor types supported in TOSA Pow");
if (!selfTy.getElementType().isa<mlir::FloatType>())
return op.emitError("Only floating-point datatype legalization supported");
Value expTensor;
Value expScalar = op.exponent();
if (failed(torchScalarToTosaTensor(rewriter, op, expScalar, expTensor,
selfTy.getElementType(), {})))
return op.emitError("Currently only scalar constants are supported for "
"conversion in TOSA Pow operation");
rewriter.replaceOpWithNewOp<tosa::PowOp>(
op, getTypeConverter()->convertType(op.getType()), self, expTensor);
return success();
}
// Perform the basic n-dim matmul operation encompassing the handling of
// broadcasting and dynamic shape propagation.
// All PyTorch ops that leverage matrix multiplication will derive this and
// implement their specialized input processing (e.g transpose), and output
// processing, e.g. GEMM or fully connected bias handling.
template <typename AtenOpT>
class ConvertAtenMatmulBaseOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
// Each variant must implement corresponding parameter parsing options.
// Maintain separate input read functions for each variant because it is not
// necessarily true with all variants that the first two operands are the lhs
// and rhs.
virtual LogicalResult readMatMulInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
Value &lhs, Value &rhs) const {
return rewriter.notifyMatchFailure(
op,
"Unimplemented matrix multiplication variant input parsing function");
}
LogicalResult performMatmul(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter, Value &lhs,
Value &rhs, Value &output) const {
auto lhsTy = lhs.getType().cast<RankedTensorType>();
auto rhsTy = rhs.getType().cast<RankedTensorType>();
auto lhsRank = lhsTy.getRank();
auto rhsRank = rhsTy.getRank();
auto lhsShape = lhsTy.getShape();
auto rhsShape = rhsTy.getShape();
auto lhsElemTy = lhsTy.getElementType();
auto rhsElemTy = rhsTy.getElementType();
if (lhsElemTy != rhsElemTy)
return op.emitError("Matmul: input datatypes mismatched");
// Legalization constructs may offer input shapes but expect output shapes
// to be inferred, e.g.
// func @forward(%arg0: !torch.vtensor<[14,19],f32>,
// %arg1: !torch.vtensor<[19,28],f32>) ->
// !torch.vtensor<[?,?],f32>
// This is tricky with matmul, since TOSA matmul is on 3D inputs.
// This means the need to reshape potentially both inputs and outputs,
// and reshape to unknown shape is undefined.
auto maxInputRank = lhsRank > rhsRank ? lhsRank : rhsRank;
// If performing dot product on vectors, the RHS is synthetically transposed
if (maxInputRank == 1)
maxInputRank++;
// Obtaining the rank broadcasted shapes of tensors makes it easier to
// construct the input and output reshaping logic.
auto getRankBroadcastedShape = [&](Value tensor,
bool isRHS) -> SmallVector<int64_t> {
auto tensorTy = tensor.getType().cast<TensorType>();
auto tensorShape = tensorTy.getShape();
auto tensorRank = tensorTy.getRank();
SmallVector<int64_t> bcastedShape;
auto bcastDims = maxInputRank - tensorRank;
if (isRHS && (tensorRank == 1) && bcastDims) {
// RHS with rank1 is special. It be synthetically transposed to dim[:-2]
for (int32_t i = 0; i < bcastDims - 1; i++)
bcastedShape.push_back(1);
bcastedShape.push_back(tensorShape[0]);
bcastedShape.push_back(1);
} else {
if (bcastDims > 0) { // rank broadcast
for (uint32_t i = 0; i < bcastDims; i++)
bcastedShape.push_back(1);
}
for (auto &dim : tensorShape)
bcastedShape.push_back(dim);
}
return bcastedShape;
};
// Step: Rank broadcast the two inputs.
auto lhsBroadcastedShape = getRankBroadcastedShape(lhs, false);
auto lhsBroadcastedTy =
RankedTensorType::get(lhsBroadcastedShape, lhsElemTy);
auto rhsBroadcastedShape = getRankBroadcastedShape(rhs, true);
auto rhsBroadcastedTy =
RankedTensorType::get(rhsBroadcastedShape, rhsElemTy);
auto rankBroadcastedLhs =
lhsRank == maxInputRank
? lhs
: rewriter.create<tosa::ReshapeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
lhsBroadcastedTy),
lhs, rewriter.getI64ArrayAttr(lhsBroadcastedShape));
auto rankBroadcastedRhs =
rhsRank == maxInputRank
? rhs
: rewriter.create<tosa::ReshapeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
rhsBroadcastedTy),
rhs, rewriter.getI64ArrayAttr(rhsBroadcastedShape));
// TOSA matmul is performed on two 3D inputs and generates a 3D output.
// Lower ranked tensors are dim-1 reshaped up to 3D
auto reshapeUpTo3DTensor = [&](Value tensor) -> Value {
auto tensorTy = tensor.getType().cast<TensorType>();
auto rank = tensorTy.getRank();
assert(rank <= 3 && "reshapeUpTo3D tensor must receive rank <= 3");
if (rank == 3)
return tensor;
auto shape = tensorTy.getShape();
SmallVector<int64_t> newShape({1, 1, 1});
if (rank == 2) { // batchsize = 1
newShape[1] = shape[0];
newShape[2] = shape[1];
} else { // rank 1
newShape[2] = shape[0];
}
auto newType = RankedTensorType::get(newShape, tensorTy.getElementType());
return rewriter.create<tosa::ReshapeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
newType),
tensor, rewriter.getI64ArrayAttr(newShape));
};
// Where broadcasting is required in one or more batch dims, the following
// is done.
// Where all batch dims are involved in broadcasting:
// Given A: 3x1x5x6 and B: 1x4x6x7
// 1. Reshape A to 1x15x6 (squeeze all batchdims into dim1)
// 2. Transpose B to 6x1x4x7, Reshape to 1x6x28
// 3. tosa.Matmul 1x15x6 1x6x28 = 1x15x28
// 4. Reshape out to 3x5x4x7, Transpose to 3x4x5x7
// Where there are batch dimensions that are broadcast and not, the
// treatment is to have dim0 correspond to product of all non-broadcast
// dimsizes:
// Given A: 4x8x16x32 B: 8x32x17
// 1. Reshape A to 8x64x32 (squeeze all unbroadcasted dims into dim0,
// broadcasted dims into dim1)
// 2. No transpose or reshape of B as its batchdims are not broadcast to.
// 3. tosa.Matmul 8x64x32 8x32x17 = 8x64x17
// 4. Reshape to 8x4x16x17, Transpose to 4x8x16x17
// Check if we need to perform the broadcast on batch dim
// Not needed if max rank < 3, or if maxrank == 3 and dim[0] matches
auto needsBatchDimBroadcast = [&]() -> bool {
if (maxInputRank < 3) {
return false;
} else {
if (maxInputRank == 3 &&
lhsBroadcastedShape[0] == rhsBroadcastedShape[0]) {
return false;
}
return true;
}
};
auto performBatchDimBroadcast = needsBatchDimBroadcast();
// Inputs to the tosa.matmul
Value matmulLhs, matmulRhs;
using TensorShape_t = struct {
int64_t dim;
int64_t shape;
};
// Transpose needs to done if transposeDims are not non-monotonically
// increasing. E.g. [0, 1, 2, 3]: No transpose [1, 0, 2, 3]: Transpose dim0
// and dim1 The order need not be sequential, since one or more dims may
// have been removed due to broadcasting.
auto isTransposeRequired = [](SmallVector<int32_t> transposedDims) -> bool {
int32_t lastDim = -1;
for (auto &dim : transposedDims) {
if (lastDim > dim)
return true;
lastDim = dim;
}
return false;
};
SmallVector<TensorShape_t> commonElems, lhsSqueezedElems, rhsSqueezedElems;
if (!performBatchDimBroadcast) {
// Simple with no broadcasting artifacts. Just reshape up to 3D
matmulLhs = reshapeUpTo3DTensor(rankBroadcastedLhs);
matmulRhs = reshapeUpTo3DTensor(rankBroadcastedRhs);
} else {
// In this case, either or both input matrices involve broadcasting on
// their batch dimensions. For example:
// 4x5x6, 1x6x7 -> 4x5x7
// 4x1x5x6, 1x3x6x7 -> 4x3x5x7
// Though maxInputRank is necessarily >=3 here, individual matrices may be
// lower rank.
// E.g. 3x4x5x6, 6 -> 3x4x5
// These are the accumulated products of the shape of each dim:
// 1. common dimensions: upper dimensions (dims other than two rightmost)
// whose shapes are the same for both LHS and RHS.
// 2. LHS squeezed dimensions: all dimensions of LHS that involve
// broadcasting in either direction, plus the LHS[-2] shape
// 3. RHS squeezed dimensions: all dimensions of RHS that involve
// broadcasting in either direction, plus the RHS[-1] shape
int64_t commonValue = 1, lhsSqueezedValue = 1, rhsSqueezedValue = 1;
// For both LHS and RHS, the dimensions are separated into the common,
// squeezed and remaining dim. E.g. given
// LHS = 3x4x5x6
// RHS = 1x4x6x7
// common = {{dim=1, shape=4}}
// lhs squeezed = {{dim=0, shape=3},
// {dim=2, shape=5}}
// rhs squeezed = {{dim=0, shape=1},
// {dim=2, shape=7}}
// The matmul dim is LHS[-1] and RHS[-2], i.e. 6.
// Once this is obtained, LHS and RHS are expressed as:
// LHS = {common, lhs_squeezed, matmul_dim}
// RHS = {common, matmul_dim, rhs_squeezed}
// The matmul is then performed to obtain output:
// matmul_out = {common, lhs_squeezed, rhs_squeezed}
// Finally, we reshape to 'unsqueeze' the LHS and RHS parts and transpose
// them back to their correct positions.
SmallVector<int64_t> transposedLhsShape;
SmallVector<int32_t> transposedLhsDims;
// Step: generate the common dim/shape information
for (uint32_t dim = 0; dim < maxInputRank - 2; dim++) {
bool isDynamicDim = ShapedType::isDynamic(lhsBroadcastedShape[dim]);
if (isDynamicDim ||
lhsBroadcastedShape[dim] == rhsBroadcastedShape[dim]) {
commonValue *= lhsBroadcastedShape[dim];
commonElems.push_back({dim, lhsBroadcastedShape[dim]});
}
}
// Step: generate the LHS squeezed dim/shape information.
bool hasDynamicDims = false;
for (uint32_t dim = 0; dim < maxInputRank - 2; dim++) {
bool isDynamicDim = ShapedType::isDynamic(lhsBroadcastedShape[dim]);
hasDynamicDims |= isDynamicDim;
if (!isDynamicDim &&
lhsBroadcastedShape[dim] != rhsBroadcastedShape[dim]) {
lhsSqueezedValue *= lhsBroadcastedShape[dim];
lhsSqueezedElems.push_back({dim, lhsBroadcastedShape[dim]});
}
}
// including LHS[-2]
lhsSqueezedElems.push_back(
{maxInputRank - 2, lhsBroadcastedShape[maxInputRank - 2]});
lhsSqueezedValue *= lhsBroadcastedShape[maxInputRank - 2];
// Step: Create the tosa.transpose array. If this array has a
// non-monotonic series of dims, perform transpose.
// First the common_elems
for (uint32_t i = 0; i < commonElems.size(); i++) {
transposedLhsShape.push_back(commonElems[i].shape);
transposedLhsDims.push_back(commonElems[i].dim);
}
// then the lhs_squeezed elems
for (uint32_t i = 0; i < lhsSqueezedElems.size(); i++) {
transposedLhsShape.push_back(lhsSqueezedElems[i].shape);
transposedLhsDims.push_back(lhsSqueezedElems[i].dim);
}
// then the final dim
transposedLhsDims.push_back(maxInputRank - 1);
transposedLhsShape.push_back(lhsBroadcastedShape[maxInputRank - 1]);
bool lhsNeedsTranspose = isTransposeRequired(transposedLhsDims);
auto lhsReshapeInput = rankBroadcastedLhs;
if (lhsNeedsTranspose) {
auto transposedLhsType =
RankedTensorType::get(transposedLhsShape, rhsElemTy);
llvm::Optional<Value> transposedLhsDimsConst =
tosa::getConstTensor<int32_t>(
rewriter, op,
/*vec=*/transposedLhsDims,
/*shape=*/{static_cast<int32_t>(transposedLhsDims.size())});
lhsReshapeInput =
rewriter
.create<tosa::TransposeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(transposedLhsType),
rankBroadcastedLhs, transposedLhsDimsConst.getValue())
.getResult();
}
// LHS = {common, lhs_squeezed, matmul_dim}
SmallVector<int64_t> newLhsShape(
{1, 1, lhsBroadcastedShape[maxInputRank - 1]});
newLhsShape[0] = commonValue;
newLhsShape[1] =
hasDynamicDims ? ShapedType::kDynamicSize : lhsSqueezedValue;
auto newLhsType = RankedTensorType::get(newLhsShape, lhsElemTy);
matmulLhs = rewriter.create<tosa::ReshapeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
newLhsType),
lhsReshapeInput, rewriter.getI64ArrayAttr(newLhsShape));
SmallVector<int64_t> transposedRhsShape;
SmallVector<int32_t> transposedRhsDims;
// Step: Create the RHS transpose sequence
// RHS = {common, matmul_dim, rhs_squeezed}
// first the common_dims
for (uint32_t i = 0; i < commonElems.size(); i++) {
transposedRhsShape.push_back(commonElems[i].shape);
transposedRhsDims.push_back(commonElems[i].dim);
}
// The matmul_dim of RHS
transposedRhsDims.push_back(maxInputRank - 2);
transposedRhsShape.push_back(rhsBroadcastedShape[maxInputRank - 2]);
// finally all the rhs_squeeze dims
hasDynamicDims = false;
for (uint32_t dim = 0; dim < maxInputRank - 2; dim++) {
bool isDynamicDim = ShapedType::isDynamic(rhsBroadcastedShape[dim]);
hasDynamicDims |= isDynamicDim;
if (!isDynamicDim &&
rhsBroadcastedShape[dim] != lhsBroadcastedShape[dim]) {
rhsSqueezedElems.push_back({dim, rhsBroadcastedShape[dim]});
rhsSqueezedValue *= rhsBroadcastedShape[dim];
}
}
rhsSqueezedElems.push_back(
{maxInputRank - 1, rhsBroadcastedShape[maxInputRank - 1]});
rhsSqueezedValue *= rhsBroadcastedShape[maxInputRank - 1];
for (uint32_t i = 0; i < rhsSqueezedElems.size(); i++) {
transposedRhsShape.push_back(rhsSqueezedElems[i].shape);
transposedRhsDims.push_back(rhsSqueezedElems[i].dim);
}
auto transposedRhsType =
RankedTensorType::get(transposedRhsShape, rhsElemTy);
if (hasDynamicDims)
rhsSqueezedValue = ShapedType::kDynamicSize;
SmallVector<int64_t> newRhsShape({commonValue,
rhsBroadcastedShape[maxInputRank - 2],
rhsSqueezedValue});
auto newRhsType = RankedTensorType::get(newRhsShape, rhsElemTy);
bool rhsNeedsTranspose = isTransposeRequired(transposedRhsDims);
auto transposedRhsValue = rankBroadcastedRhs;
if (rhsNeedsTranspose) {
llvm::Optional<Value> transposedRhsDimsConst =
tosa::getConstTensor<int32_t>(
rewriter, op,
/*vec=*/transposedRhsDims,
/*shape=*/{static_cast<int32_t>(transposedRhsDims.size())});
transposedRhsValue =
rewriter
.create<tosa::TransposeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(transposedRhsType),
rankBroadcastedRhs, transposedRhsDimsConst.getValue())
.getResult();
}
// reshape
matmulRhs = rewriter.create<tosa::ReshapeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
newRhsType),
transposedRhsValue, rewriter.getI64ArrayAttr(newRhsShape));
}
auto matmulLhsShape =
matmulLhs.getType().template cast<RankedTensorType>().getShape();
auto matmulRhsShape =
matmulRhs.getType().template cast<RankedTensorType>().getShape();
// The reshape/transpose should ensure the tosa.matmul always has same
// batch size for either matrix. If if shapes are dynamic, they'll be
// appropriately handled.
assert(matmulLhsShape[0] == matmulRhsShape[0] &&
"tosa.matmul needs same batchsize on LHS and RHS");
SmallVector<int64_t> matmulOutputShape(
{matmulLhsShape[0], matmulLhsShape[1], matmulRhsShape[2]});
Type outputElemTy;
if (lhsElemTy.isa<mlir::FloatType>()) {
outputElemTy = lhsElemTy;
} else { // qint8 emits i32 matmul output
outputElemTy = rewriter.getIntegerType(32);
}
auto mmOutputTy = RankedTensorType::get(matmulOutputShape, outputElemTy);
auto mmOpResult =
rewriter
.create<tosa::MatMulOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
mmOutputTy),
matmulLhs, matmulRhs)
.getResult();
// Perform the reshape to output shape. This is always required unless both
// inputs are rank=3, in which case the tosa.matmul output itself is
// correctly shaped.
bool performOpReshape =
!(lhsRank == 3 && rhsRank == 3 && lhsShape[0] == rhsShape[0]);
if (performOpReshape) {
// Since the output shape may be unknown, we construct it
// independently and reshape. Otherwise reshape may be expressed for
// an unknown to-be-inferred output shape. The final tensor.cast
// reshapes the known shape to the desired output shape.
auto computeOpShape = [&](SmallVector<int64_t> &reshapedOpShape,
SmallVector<int32_t> &transposedOpDims,
SmallVector<int64_t> &transposedOpShapes) {
if (maxInputRank == 1)
return;
if (maxInputRank == 2) {
if (lhsRank == 2)
reshapedOpShape.push_back(lhsShape[0]);
if (rhsRank == 2)
reshapedOpShape.push_back(rhsShape[1]);
return;
}
// Step: Construct the output transpose/reshape information
// First the common_dims
for (uint32_t i = 0; i < commonElems.size(); i++) {
reshapedOpShape.push_back(commonElems[i].shape);
transposedOpDims.push_back(commonElems[i].dim);
}
// Then the LHS squeezed dims
for (uint32_t i = 0; i < lhsSqueezedElems.size() - 1; i++) {
// Only dims that don't broadcast - broadcasting ones come from the
// other input.
if (lhsSqueezedElems[i].shape != 1) {
reshapedOpShape.push_back(lhsSqueezedElems[i].shape);
transposedOpDims.push_back(lhsSqueezedElems[i].dim);
}
}
// The last squeezed dim is lhs[-2] which needs to be
// checked separately for broadcasting
if (lhsRank > 1) {
reshapedOpShape.push_back(lhsBroadcastedShape[maxInputRank - 2]);
transposedOpDims.push_back(maxInputRank - 2);
}
// then the RHS squeezed dims except rhs[-1] which is handled like
// lhs[-2]
for (uint32_t i = 0; i < rhsSqueezedElems.size() - 1; i++) {
if (rhsSqueezedElems[i].shape != 1) {
reshapedOpShape.push_back(rhsSqueezedElems[i].shape);
transposedOpDims.push_back(rhsSqueezedElems[i].dim);
}
}
// rhs[-1]
if (rhsRank > 1) {
reshapedOpShape.push_back(rhsBroadcastedShape[maxInputRank - 1]);
transposedOpDims.push_back(maxInputRank - 1);
}
// Final transposed output shape construction
for (uint32_t i = 0; i < maxInputRank - 2; i++) {
if (lhsBroadcastedTy.isDynamicDim(i)) {
transposedOpShapes.push_back(ShapedType::kDynamicSize);
} else {
if (lhsBroadcastedShape[i] == rhsBroadcastedShape[i]) {
transposedOpShapes.push_back(lhsBroadcastedShape[i]);
} else {
transposedOpShapes.push_back(lhsBroadcastedShape[i] == 1
? rhsBroadcastedShape[i]
: lhsBroadcastedShape[i]);
}
}
}
if (lhsRank > 1)
transposedOpShapes.push_back(lhsBroadcastedShape[maxInputRank - 2]);
if (rhsRank > 1)
transposedOpShapes.push_back(rhsBroadcastedShape[maxInputRank - 1]);
return;
};
SmallVector<int64_t> reshapedOpShape, transposedOpShape;
SmallVector<int32_t> transposedOpDims;
computeOpShape(reshapedOpShape, transposedOpDims, transposedOpShape);
bool opNeedsTranspose = isTransposeRequired(transposedOpDims);
// Perform reshape
auto reshapedOpType =
RankedTensorType::get(reshapedOpShape, outputElemTy);
auto reshapedOp = rewriter.create<tosa::ReshapeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
reshapedOpType),
mmOpResult, rewriter.getI64ArrayAttr(reshapedOpShape));
if (opNeedsTranspose) {
llvm::Optional<Value> transposedOpShapeConst =
tosa::getConstTensor<int32_t>(
rewriter, op,
/*vec=*/transposedOpDims,
/*shape=*/{static_cast<int32_t>(transposedOpDims.size())});
auto transposedOpType =
RankedTensorType::get(transposedOpShape, outputElemTy);
output =
rewriter
.create<tosa::TransposeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(transposedOpType),
reshapedOp.getResult(), transposedOpShapeConst.getValue())
.getResult();
} else {
output = reshapedOp.getResult();
}
} else {
output = mmOpResult;
}
return success();
}
// The default version just reads two inputs, computes output and returns it.
// Other versions may add a bias, apply GEMM-style alpha/beta scaling etc.
virtual LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs, rhs;
if (failed(readMatMulInputs(op, adaptor, rewriter, lhs, rhs)))
return op.emitError("Failed to read matmul inputs");
Value output;
if (failed(performMatmul(op, adaptor, rewriter, lhs, rhs, output)))
return op.emitError("Failed to perform matmul operation");
rewriter.replaceOpWithNewOp<tensor::CastOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>(),
output);
return success();
}
};
// Legalizes the torch.matmul op for general n-dim matmul.
template <typename AtenOpT>
class ConvertAtenMatMulOp : public ConvertAtenMatmulBaseOp<AtenOpT> {
public:
using ConvertAtenMatmulBaseOp<AtenOpT>::ConvertAtenMatmulBaseOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult readMatMulInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
Value &lhs, Value &rhs) const override {
lhs = adaptor.self();
auto lhsTy = lhs.getType().cast<RankedTensorType>();
rhs = adaptor.other();
auto rhsTy = rhs.getType().cast<RankedTensorType>();
if (!lhsTy || !rhsTy)
return op.emitError("Only ranked tensor types supported in TOSA matmul");
return success();
}
};
// Implements handling of aten.mm and aten.bmm ops.
template <typename AtenOpT>
class ConvertAtenMmOp : public ConvertAtenMatmulBaseOp<AtenOpT> {
public:
using ConvertAtenMatmulBaseOp<AtenOpT>::ConvertAtenMatmulBaseOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult readMatMulInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
Value &lhs, Value &rhs) const override {
lhs = adaptor.self();
auto lhsTy = lhs.getType().cast<RankedTensorType>();
rhs = adaptor.mat2();
auto rhsTy = rhs.getType().cast<RankedTensorType>();
if (!lhsTy || !rhsTy)
return op.emitError("Only ranked tensor types supported in TOSA matmul");
auto lhsRank = lhsTy.getRank();
auto rhsRank = rhsTy.getRank();
if (isa<AtenMmOp>(op)) {
// Mm takes two 2D tensors.
if (lhsRank != 2 || rhsRank != 2)
return op.emitError("aten.mm called but matrix rank != 2");
} else if (isa<AtenBmmOp>(op)) {
// Bmm takes two 3D tensors.
if (lhsRank != 3 || rhsRank != 3)
return op.emitError("aten.bmm called but matrix rank != 3");
}
return success();
}
};
// Implements handling of aten.linear op.
template <typename AtenOpT>
class ConvertAtenLinearOp : public ConvertAtenMatmulBaseOp<AtenOpT> {
public:
using ConvertAtenMatmulBaseOp<AtenOpT>::ConvertAtenMatmulBaseOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult readMatMulInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
Value &lhs, Value &rhs) const override {
lhs = adaptor.input();
auto lhsTy = lhs.getType().cast<RankedTensorType>();
rhs = adaptor.weight();
auto rhsTy = rhs.getType().cast<RankedTensorType>();
if (!lhsTy || !rhsTy)
return op.emitError("Only ranked tensor types supported in TOSA matmul");
auto lhsRank = lhsTy.getRank();
auto rhsRank = rhsTy.getRank();
if (lhsRank != 2 && lhsRank != 3)
return op.emitError("aten.Linear called but input rank not 2 or 3");
if (rhsRank != 2 && rhsRank != 3)
return op.emitError("aten.Linear called but weight rank not 2 or 3");
// Protection against crash due to unguarded code in TOSA->LinAlg.
if (!lhsTy.hasStaticShape() || !rhsTy.hasStaticShape())
return op.emitError("aten.Linear needs statically shaped input");
return success();
}
// Override the default rewriter to perform RHS transpose and bias addition as
// well.
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs, rhs;
if (failed(readMatMulInputs(op, adaptor, rewriter, lhs, rhs)))
return op.emitError("Failed to read matmul inputs");
// The aten.Linear op has a bias tensor that is added to the matmul output.
auto bias = adaptor.bias();
auto biasTy = bias.getType();
// TOSA does not mandate that elementwise op tensors need to be ranked.
if (!biasTy.template isa<Torch::NoneType>() &&
!biasTy.template isa<TensorType>())
return op.emitError("Only tensor types supported in GEMM to "
"TOSA for bias tensor");
// RHS must have its last two dims transposed prior to matrix
// multiplication.
auto rhsTy = rhs.getType().cast<RankedTensorType>();
auto rhsRank = rhsTy.getRank();
auto rhsShape = rhsTy.getShape();
auto rhsElemTy = rhsTy.getElementType();
// Create a non-const shape array to transpose dims.
SmallVector<int64_t> transposedRhsShape;
for (auto &shape : rhsShape)
transposedRhsShape.push_back(shape);
SmallVector<int32_t> transposedRhsDims;
for (int32_t i = 0; i < rhsRank; i++)
transposedRhsDims.push_back(i);
// Swap the last two dims.
std::swap(transposedRhsShape[rhsRank - 1], transposedRhsShape[rhsRank - 2]);
std::swap(transposedRhsDims[rhsRank - 1], transposedRhsDims[rhsRank - 2]);
llvm::Optional<Value> transposedRhsShapeConst =
tosa::getConstTensor<int32_t>(
rewriter, op,
/*vec=*/transposedRhsDims,
/*shape=*/{static_cast<int32_t>(transposedRhsDims.size())});
auto transposedRhsType =
RankedTensorType::get(transposedRhsShape, rhsElemTy);
rhs = rewriter.create<tosa::TransposeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
transposedRhsType),
rhs, transposedRhsShapeConst.getValue());
Value matmulOutput;
if (failed(
this->performMatmul(op, adaptor, rewriter, lhs, rhs, matmulOutput)))
return op.emitError("Failed to perform matmul operation");
Value matmulPlusBias = matmulOutput;
if (!biasTy.template isa<Torch::NoneType>()) {
// Bias addition broadcasts to the matmul output shape.
matmulPlusBias =
rewriter
.create<tosa::AddOp>(op->getLoc(), matmulOutput.getType(),
matmulOutput, bias)
.getResult();
}
rewriter.replaceOpWithNewOp<tensor::CastOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>(),
matmulPlusBias);
return success();
}
};
template <>
LogicalResult ConvertAtenOp<AtenRsubScalarOp>::matchAndRewrite(
AtenRsubScalarOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
auto self = adaptor.self();
auto otherScalar = op.other();
auto alphaScalar = op.alpha();
auto selfTy = self.getType().template cast<RankedTensorType>();
if (!selfTy)
return op.emitError("Only ranked tensor types supported in TOSA Rsub");
if (!selfTy.getElementType().isa<mlir::FloatType>())
return op.emitError("Only floating-point datatype legalization supported");
Value otherTensor, alphaTensor;
if (failed(torchScalarToTosaTensor(rewriter, op, otherScalar, otherTensor,
selfTy.getElementType(), {})))
return op.emitError("Currently only scalar constants are supported for "
"conversion in TOSA Rsub operation");
if (failed(torchAlphaToTosaTensor(rewriter, op.getOperation(), alphaScalar,
alphaTensor, selfTy.getElementType(),
/*checkForUnity=*/true)))
return failure();
auto multTensor = rewriter.create<tosa::MulOp>(
op->getLoc(), getTypeConverter()->convertType(op.getType()), self,
alphaTensor, /*shift=*/0);
rewriter.replaceOpWithNewOp<tosa::SubOp>(
op, getTypeConverter()->convertType(op.getType()), otherTensor,
multTensor);
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenConvolutionOp>::matchAndRewrite(
AtenConvolutionOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
auto input = adaptor.input();
auto weight = adaptor.weight();
auto inputTy = input.getType().template cast<RankedTensorType>();
auto weightTy = weight.getType().template cast<RankedTensorType>();
auto outputTy = getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>();
if (!inputTy || !weightTy || !outputTy)
return op.emitError(
"Input, weight and output to Convolution must be ranked tensors");
auto inputElemTy = inputTy.getElementType();
auto weightElemTy = weightTy.getElementType();
auto inputShape = inputTy.getShape();
auto weightShape = weightTy.getShape();
if (inputTy.getRank() != 4)
return op.emitError("Unimplemented: only 2D convolutions supported");
if (!weightTy.hasStaticShape())
return op.emitError("Unimplemented: TOSA only supports static weight");
// Bias is optional. TOSA mandates a zero tensor here, so construct one if
// required.
auto bias = adaptor.bias();
if (adaptor.bias().getType().template isa<Torch::NoneType>()) {
// TBD: This is only valid for quantized 8-bit. For 16-bit, the bias (and
// accumulator) are 48-bit and not 32-bit, and requires the use of APInt to
// define a 48-bit int.
if (inputElemTy.isa<quant::QuantizedType>()) {
SmallVector<int32_t> zeroVec(weightShape[0], 0);
bias = tosa::getConstTensor<int32_t>(
rewriter, op, zeroVec, {static_cast<int32_t>(weightShape[0])})
.getValue();
} else {
SmallVector<float> zeroVec(weightShape[0], 0);
bias = tosa::getConstTensor<float>(rewriter, op, zeroVec,
{static_cast<int32_t>(weightShape[0])})
.getValue();
}
} else {
if (!bias.getType().cast<RankedTensorType>())
return op.emitError("Bias provided but not a ranked tensor");
}
auto biasElemTy = inputElemTy.template isa<mlir::FloatType>()
? inputElemTy
: rewriter.getI32Type();
SmallVector<int64_t, 2> stride;
if (!matchPattern(adaptor.stride(), m_TorchConstantIntList(stride)))
return rewriter.notifyMatchFailure(op, "non-const stride list unsupported");
SmallVector<int64_t, 2> padding_2d;
if (!matchPattern(adaptor.padding(), m_TorchConstantIntList(padding_2d)))
return rewriter.notifyMatchFailure(op,
"non-const padding list unsupported");
// TOSA uses 4D padding {t, b, l, r} while Torch defines 2D padding {t, l}.
// The Torch OFM computation uses 2*pad in each spatial direction, implying
// the same t=b and l=r values for TOSA.
SmallVector<int64_t> padding(
{padding_2d[0], padding_2d[0], padding_2d[1], padding_2d[1]});
SmallVector<int64_t, 2> dilation;
if (!matchPattern(adaptor.dilation(), m_TorchConstantIntList(dilation)))
return rewriter.notifyMatchFailure(op,
"non-const dilation list unsupported");
// TOSA works in NHWC and takes OHWI weights. Perform the necessary transpose.
llvm::Optional<Value> nchwToNhwcTransposeConst =
tosa::getConstTensor<int32_t>(rewriter, op,
/*vec=*/{0, 2, 3, 1},
/*shape=*/{static_cast<int32_t>(4)});
SmallVector<int64_t> transposedInputShape(
{inputShape[0], inputShape[2], inputShape[3], inputShape[1]});
auto transposedInputType =
RankedTensorType::get(transposedInputShape, inputElemTy);
auto transposedInput =
rewriter
.create<tosa::TransposeOp>(
op->getLoc(),
getTypeConverter()->convertType(transposedInputType), input,
nchwToNhwcTransposeConst.getValue())
.getResult();
SmallVector<int64_t> transposedWeightShape(
{weightShape[0], weightShape[2], weightShape[3], weightShape[1]});
auto transposedWeightType =
RankedTensorType::get(transposedWeightShape, weightElemTy);
auto transposedWeight =
rewriter
.create<tosa::TransposeOp>(
op->getLoc(),
getTypeConverter()->convertType(transposedWeightType), weight,
nchwToNhwcTransposeConst.getValue())
.getResult();
int64_t outputHDim, outputWDim;
if (inputTy.hasStaticShape()) {
outputHDim = (transposedInputShape[1] + padding[0] + padding[1] -
dilation[0] * (transposedWeightShape[1] - 1) - 1) /
stride[0] +
1;
outputWDim = (transposedInputShape[2] + padding[2] + padding[3] -
dilation[1] * (transposedWeightShape[2] - 1) - 1) /
stride[1] +
1;
} else {
outputHDim = ShapedType::kDynamicSize;
outputWDim = ShapedType::kDynamicSize;
}
// Output shape is NHWC, to be transposed back to NCHW. Output elemTy for
// quantized input is i32, which gets rescaled down to quantized output range.
SmallVector<int64_t> outputShape = {transposedInputShape[0], outputHDim,
outputWDim, transposedWeightShape[0]};
auto convOpTy = RankedTensorType::get(outputShape, biasElemTy);
Value convOpResult =
rewriter
.create<tosa::Conv2DOp>(op->getLoc(),
getTypeConverter()->convertType(convOpTy),
transposedInput, transposedWeight, bias,
rewriter.getI64ArrayAttr(padding),
rewriter.getI64ArrayAttr(stride),
rewriter.getI64ArrayAttr(dilation))
.getResult();
llvm::Optional<Value> nhwcToNchwTransposeConst =
tosa::getConstTensor<int32_t>(rewriter, op,
/*vec=*/{0, 3, 1, 2},
/*shape=*/{static_cast<int32_t>(4)});
SmallVector<int64_t> transposedOutputShape(
{outputShape[0], outputShape[3], outputShape[1], outputShape[2]});
auto transposedOutputType =
RankedTensorType::get(transposedOutputShape, biasElemTy);
auto transposedOutput =
rewriter
.create<tosa::TransposeOp>(
op->getLoc(),
getTypeConverter()->convertType(transposedOutputType),
convOpResult, nhwcToNchwTransposeConst.getValue())
.getResult();
Value rescaledResult = transposedOutput;
if (inputElemTy.template isa<quant::QuantizedType>()) {
rescaledResult = tosa::buildRescaleOpConvOutput(
rewriter, op, transposedOutput, inputTy, weightTy, outputTy);
}
rewriter.replaceOpWithNewOp<tensor::CastOp>(
op, getTypeConverter()->convertType(op.getType()), rescaledResult);
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenReshapeOp>::matchAndRewrite(
AtenReshapeOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
auto self = adaptor.self();
auto selfTy = self.getType().template cast<RankedTensorType>();
if (!selfTy)
return op.emitError("Only ranked tensor types supported in TOSA Reshape");
// Check that at most one dimension is -1
SmallVector<int64_t> newShape;
if (!matchPattern(op.shape(), m_TorchConstantIntList(newShape)))
return op.emitError("Only constant shape supported in TOSA Reshape");
int auto_sz = 0;
for (auto s : newShape)
auto_sz += (s == -1 ? 1 : 0);
if (auto_sz > 1)
op.emitError("At most one dimension may be specified as -1 to "
"automatically calculate its size");
auto newType = RankedTensorType::get(newShape, selfTy.getElementType());
rewriter.replaceOpWithNewOp<tosa::ReshapeOp>(
op, getTypeConverter()->convertType(newType), self,
rewriter.getI64ArrayAttr(newShape));
return success();
}
Value computeBatchNorm(Operation *op, ConversionPatternRewriter &rewriter,
Type outType, Value input, Value variance, Value eps,
Value mean, Value weight, Value bias) {
// For PyTorch:
// scale = gamma = weight
// offset = beta = bias
// Lowering:
// fused batchnorm = (input-mean) * scale * rsqrt(var+epsilon)) + offset
//
// shape_0 = ones(input.rank)
// shape_0[input.rank-1] = input.shape[input.rank-1]
// shape_1 = ones(1)
//
// bmean = reshape(mean, shape_0)
// bscale = reshape(scale, shape_0)
// boffset= reshape(offset, shape_0)
// beps = reshape(epsilon, shape_1)
//
// op1 = sub(input, bmean)
// op2 = add(var, beps)
// op3 = rsqrt(op2)
// bvar = reshape(op3, shape_0)
// op4 = mul(op1, bvar)
// op5 = mul(op4, bscale)
// op6 = add(op5, boffset)
auto op1SubInputMean =
rewriter.create<tosa::SubOp>(op->getLoc(), outType, input, mean);
auto op2AddVarEpsilon = rewriter.create<tosa::AddOp>(
op->getLoc(), variance.getType(), variance, eps);
auto op3RsqrtOp2 = rewriter.create<tosa::RsqrtOp>(
op->getLoc(), variance.getType(), op2AddVarEpsilon.getResult());
auto op4MulOp1Op3 = rewriter.create<tosa::MulOp>(op->getLoc(), outType,
op1SubInputMean.getResult(),
op3RsqrtOp2.getResult(), 0);
auto op5MulOp4Scale = rewriter.create<tosa::MulOp>(
op->getLoc(), outType, op4MulOp1Op3.getResult(), weight, 0);
return rewriter
.create<tosa::AddOp>(op->getLoc(), outType, op5MulOp4Scale.getResult(),
bias)
.getResult();
}
// This lowering is based on the TensorFlow to TOSA lowering.
template <>
LogicalResult ConvertAtenOp<AtenBatchNormOp>::matchAndRewrite(
AtenBatchNormOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a ranked tensor output
if (!adaptor.input().getType().dyn_cast<RankedTensorType>())
return op.emitError("Only ranked tensor types are supported");
auto outType = getTypeConverter()->convertType(op.getType());
// Note: cudnn_enabled is not handled.
// FIXME: Handle training and momentum.
if (op.momentum().getType().isa<Torch::NoneType>())
op.emitError("Unsupported None for momentum");
auto meanType = adaptor.running_mean().getType().dyn_cast<TensorType>();
auto varianceType = adaptor.running_var().getType().dyn_cast<TensorType>();
if (!varianceType || !meanType)
return op.emitError("Only ranked tensor types are supported");
// Normalization ops perform elementwise ops of a single mean/stdev value
// against the feature map and because input is NCHW, the rank-1 value must be
// reshaped so it sits on the same dim as 'C'.
auto reshapeToNormInputDim = [&](Operation *op,
ConversionPatternRewriter &rewriter,
TypeConverter *converter, Type outType,
const Value toBcast, Value &result) {
RankedTensorType toBcastType =
toBcast.getType().dyn_cast<RankedTensorType>();
if (toBcastType.getRank() > 1)
op->emitError("Rank cannot be more than 1");
RankedTensorType outTensorType = outType.cast<RankedTensorType>();
SmallVector<int64_t> newShape = {toBcastType.getShape()[0]};
for (auto i = 2; i < outTensorType.getRank(); ++i)
newShape.push_back(1);
auto newType =
RankedTensorType::get(newShape, outTensorType.getElementType());
result = rewriter.create<tosa::ReshapeOp>(
op->getLoc(), newType, toBcast, rewriter.getI64ArrayAttr(newShape));
return success();
};
Value meanVal, varianceVal, weightVal, biasVal;
assert(meanType.getNumElements() != 0 && varianceType.getNumElements() != 0);
if (failed(reshapeToNormInputDim(op.getOperation(), rewriter,
getTypeConverter(), outType,
adaptor.running_mean(), meanVal)))
op.emitError("Failed to reshape running mean");
if (failed(reshapeToNormInputDim(op.getOperation(), rewriter,
getTypeConverter(), outType,
adaptor.running_var(), varianceVal)))
op.emitError("Failed to reshape running variance");
if (failed(reshapeToNormInputDim(op.getOperation(), rewriter,
getTypeConverter(), outType,
adaptor.weight(), weightVal)))
op.emitError("Failed to reshape weight");
if (failed(reshapeToNormInputDim(op.getOperation(), rewriter,
getTypeConverter(), outType, adaptor.bias(),
biasVal)))
op.emitError("Failed to reshape bias");
double eps;
if (!matchPattern(op.eps(), m_TorchConstantFloat(&eps)))
return op.emitError("eps must be a scalar constant");
auto epsilonConst =
mlir::tosa::getTosaConstTensorSingleF32(rewriter, op, eps);
auto batchNorm =
computeBatchNorm(op, rewriter, outType, adaptor.input(), varianceVal,
epsilonConst, meanVal, weightVal, biasVal);
rewriter.replaceOp(op, {batchNorm});
return success();
}
// This lowering is loosely based on Torch to LinAlg lowering.
template <>
LogicalResult ConvertAtenOp<AtenNativeLayerNormOp>::matchAndRewrite(
AtenNativeLayerNormOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// The key difference from BatchNorm is that a specified set of dims
// (normalized_shape) are chosen to compute the mean and variance from input.
// Where as in BatchNorm the mean and variance are operands. tosa::ReduceSumOp
// is used to sum up the these dims for mean and for variance. The results
// eventually being reshaped for broadcasting.
// Not a ranked tensor output
if (!adaptor.input().getType().dyn_cast<RankedTensorType>())
return op.emitError("Only ranked tensor types are supported");
auto inputType = adaptor.input().getType().cast<RankedTensorType>();
if (inputType.getRank() > 4)
return op.emitError("Only up to 4D tensors are supported");
auto outType = getTypeConverter()->convertType(op.getType(0));
// Note: cudnn_enabled is not handled.
// FIXME: Handle the None cases for the optional parameters.
if (adaptor.weight().getType().isa<Torch::NoneType>())
return op.emitError("Unsupported None for weight");
if (adaptor.bias().getType().isa<Torch::NoneType>())
return op.emitError("Unsupported None for bias");
auto weightType = adaptor.weight().getType().cast<RankedTensorType>();
auto biasType = adaptor.bias().getType().cast<RankedTensorType>();
int64_t inputRank = inputType.getRank();
Type elemTy = inputType.getElementType();
// Check if all the arguments meet the requirements.
SmallVector<int64_t> normalizedShapeSizesInt;
if (!matchPattern(op.normalized_shape(),
m_TorchConstantIntList(normalizedShapeSizesInt))) {
return rewriter.notifyMatchFailure(op, "Unimplemented normalized_shape not"
"constructed from ListConstruct");
}
int64_t normalizedShapeRank = normalizedShapeSizesInt.size();
if (weightType.getRank() != normalizedShapeRank ||
biasType.getRank() != normalizedShapeRank ||
inputRank < normalizedShapeRank || normalizedShapeRank < 1)
return rewriter.notifyMatchFailure(op, "Input or weight or bias shape or"
"normalized shape not compatible");
// Check all the dimensions match the normalized_shape, only static shapes as
// of now
int64_t meanAndVarShapeRank = inputRank - normalizedShapeSizesInt.size();
for (auto en : llvm::enumerate((normalizedShapeSizesInt))) {
int64_t index = en.index();
int64_t value = en.value();
if (inputType.getShape()[index + meanAndVarShapeRank] != value ||
weightType.getShape()[index] != value ||
biasType.getShape()[index] != value)
return op.emitError("mismatching contracting dimension");
}
// Helper for computing mean and variance.
auto computeSumAndReshape = [&](Value toReduce, RankedTensorType toReduceType,
Type outType, SmallVector<int64_t> outShape) {
Value sumDiv = toReduce;
SmallVector<int64_t> toReduceShape(toReduceType.getShape().begin(),
toReduceType.getShape().end());
while (static_cast<int64_t>(toReduceShape.size()) != meanAndVarShapeRank) {
toReduceShape.back() = 1;
sumDiv = rewriter.create<tosa::ReduceSumOp>(
op.getLoc(),
RankedTensorType::get(toReduceShape, inputType.getElementType()),
sumDiv, rewriter.getI64IntegerAttr(toReduceShape.size() - 1));
toReduceShape.pop_back();
}
return rewriter.create<tosa::ReshapeOp>(op.getLoc(), outType, sumDiv,
rewriter.getI64ArrayAttr(outShape));
};
// TOSA has integer Div so, compute reciprocal of element count to be used in
// mul.
int64_t elemCnt = 1;
for (auto i : normalizedShapeSizesInt)
elemCnt *= i;
auto elemCntConst =
tosa::getConstTensor<float>(rewriter, op.getOperation(),
{static_cast<float>(elemCnt)}, {1})
.getValue();
Value elemCntRcp = rewriter.create<tosa::ReciprocalOp>(
op.getLoc(), elemCntConst.getType(), elemCntConst);
// Broadcast type and shape for various intermediate values.
SmallVector<int64_t> bcastOutShape;
for (auto en : llvm::enumerate(inputType.getShape())) {
bcastOutShape.push_back(
static_cast<int64_t>(en.index()) >= meanAndVarShapeRank ? 1
: en.value());
}
auto bcastOutType = RankedTensorType::get(bcastOutShape, elemTy);
// Compute mean.
Value sum = computeSumAndReshape(adaptor.input(), inputType, bcastOutType,
bcastOutShape);
Value meanVal = rewriter.create<tosa::MulOp>(op.getLoc(), bcastOutType, sum,
elemCntRcp, /*shift=*/0);
// Compute variance.
Value squareSumSub = rewriter.create<tosa::SubOp>(op.getLoc(), inputType,
adaptor.input(), meanVal);
Value squareSum = rewriter.create<tosa::MulOp>(op.getLoc(), inputType,
squareSumSub, squareSumSub, 0);
Value squareSumReduced =
computeSumAndReshape(squareSum, inputType, bcastOutType, bcastOutShape);
Value varianceVal = rewriter.create<tosa::MulOp>(
op.getLoc(), bcastOutType, squareSumReduced, elemCntRcp, /*shift=*/0);
// Reshape weight and bias.
SmallVector<int64_t> weightAndBiasBcastShape;
for (auto en : llvm::enumerate(inputType.getShape())) {
weightAndBiasBcastShape.push_back(
static_cast<int64_t>(en.index()) < meanAndVarShapeRank ? 1
: en.value());
}
auto weightAndMeanBcastType =
RankedTensorType::get(weightAndBiasBcastShape, elemTy);
Value weightVal = rewriter.create<tosa::ReshapeOp>(
op.getLoc(), weightAndMeanBcastType, adaptor.weight(),
rewriter.getI64ArrayAttr(weightAndBiasBcastShape));
Value biasVal = rewriter.create<tosa::ReshapeOp>(
op.getLoc(), weightAndMeanBcastType, adaptor.bias(),
rewriter.getI64ArrayAttr(weightAndBiasBcastShape));
double eps;
if (!matchPattern(op.eps(), m_TorchConstantFloat(&eps)))
return op.emitError("eps must be a scalar constant");
auto epsilonConst =
mlir::tosa::getTosaConstTensorSingleF32(rewriter, op, eps);
// Compute layer norm.
auto layerNorm =
computeBatchNorm(op, rewriter, outType, adaptor.input(), varianceVal,
epsilonConst, meanVal, weightVal, biasVal);
rewriter.replaceOp(op, {layerNorm, meanVal, varianceVal});
return success();
}
// Torch constants are converted to tosa.const .
template <>
LogicalResult ConvertAtenOp<ValueTensorLiteralOp>::matchAndRewrite(
ValueTensorLiteralOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
auto outputTy = getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>();
rewriter.replaceOpWithNewOp<tosa::ConstOp>(op, outputTy, adaptor.value());
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenFlattenUsingIntsOp>::matchAndRewrite(
AtenFlattenUsingIntsOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a ranked tensor type
auto selfType = adaptor.self().getType().dyn_cast<RankedTensorType>();
if (!selfType || !selfType.hasStaticShape())
return op.emitError(
"Only ranked tensor types with static shapes are currently supported");
int64_t selfRank = selfType.getRank();
int64_t start_dim, end_dim;
if (!matchPattern(op.start_dim(), m_TorchConstantInt(&start_dim)))
return op.emitError("start_dim must be a Scalar constant");
start_dim = toPositiveDim(start_dim, selfRank);
if (!matchPattern(op.end_dim(), m_TorchConstantInt(&end_dim)))
return op.emitError("end_dim must be a Scalar constant");
end_dim = toPositiveDim(end_dim, selfRank);
if (selfRank > 0 && !isValidDim(start_dim, selfRank))
return op.emitError("start_dim is statically invalid");
if (selfRank > 0 && !isValidDim(end_dim, selfRank))
return op.emitError("end_dim is statically invalid");
if (end_dim < start_dim)
return op.emitError("end_dim must be larger than start_dim");
SmallVector<int64_t> newShape;
for (auto s : llvm::enumerate(selfType.getShape())) {
int64_t idx = s.index();
if (idx < start_dim || idx > end_dim) {
newShape.push_back(s.value());
} else {
if (idx == start_dim)
newShape.push_back(s.value());
else
newShape.back() *= s.value();
}
}
// Handle the Scalar case
if (newShape.size() == 0)
newShape.push_back(1);
auto newType = RankedTensorType::get(newShape, selfType.getElementType());
auto reshapeOp = rewriter.create<tosa::ReshapeOp>(
op.getLoc(), newType, adaptor.self(), rewriter.getI64ArrayAttr(newShape));
rewriter.replaceOpWithNewOp<tensor::CastOp>(
op, getTypeConverter()->convertType(op.getType()), reshapeOp);
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenPermuteOp>::matchAndRewrite(
AtenPermuteOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a ranked tensor type
auto selfType = adaptor.self().getType().dyn_cast<RankedTensorType>();
if (!selfType)
return op.emitError(
"Only ranked tensor types with static shapes are currently supported");
SmallVector<int64_t> dimListInt;
if (!matchPattern(adaptor.dims(), m_TorchConstantIntList(dimListInt)))
return rewriter.notifyMatchFailure(
op, "Only constant dimensions are currently supported");
int64_t selfRank = selfType.getRank();
for (auto &d : dimListInt) {
d = toPositiveDim(d, selfRank);
if (!isValidDim(d, selfRank))
return op.emitError("Not all dims are valid");
}
auto transposeDimsConst = mlir::tosa::getConstTensor<int64_t>(
rewriter, op.getOperation(), dimListInt, {selfRank});
rewriter.replaceOpWithNewOp<tosa::TransposeOp>(
op, getTypeConverter()->convertType(op.getType()), adaptor.self(),
transposeDimsConst.getValue());
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenLog2Op>::matchAndRewrite(
AtenLog2Op op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a tensor type.
auto selfType = adaptor.self().getType().dyn_cast<TensorType>();
if (!selfType)
return op.emitError("Only tensor types are currently supported");
// Constant value of ln2.
SmallVector<int64_t> ln2Shape(selfType.getRank(), 1);
auto ln2Op =
tosa::getConstTensor<float>(rewriter, op, {0.69314718056}, ln2Shape)
.getValue();
auto rcpOp =
rewriter.create<tosa::ReciprocalOp>(op.getLoc(), ln2Op.getType(), ln2Op);
auto outType = getTypeConverter()->convertType(op.getType());
auto logOp =
rewriter.create<tosa::LogOp>(op.getLoc(), outType, adaptor.self());
rewriter.replaceOpWithNewOp<tosa::MulOp>(op, outType, logOp, rcpOp,
/*shift=*/0);
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenThresholdOp>::matchAndRewrite(
AtenThresholdOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a tensor type.
auto selfType = adaptor.self().getType().dyn_cast<TensorType>();
if (!selfType)
return op.emitError("Only tensor types are currently supported");
auto selfElemTy = selfType.getElementType();
if (!selfElemTy.isIntOrFloat())
return op.emitError(
"Only floating-point or integer datatype legalization supported");
// Integer types with width > 32 are not supported
auto selfIntType = selfElemTy.dyn_cast<IntegerType>();
if (selfIntType && selfIntType.getWidth() > 32) {
return op.emitError(
"Integer types with width greater than 32 are not supported");
}
SmallVector<int64_t> constTypeShape(selfType.getRank(), 1);
Value threshold, value;
if (failed(torchScalarToTosaTensor(rewriter, op, op.threshold(), threshold,
selfElemTy, constTypeShape)))
return op.emitError("Only scalar constant is supported for threshold");
if (failed(torchScalarToTosaTensor(rewriter, op, op.value(), value,
selfElemTy, constTypeShape)))
return op.emitError("Only scalar constant is supported for value");
// Threshold only clamps the upper values. tosa::ClampOp has the same
// value for both threshold and clamped value so cannot be used.
auto outType = getTypeConverter()->convertType(op.getType());
auto cmpOp = rewriter.create<tosa::GreaterOp>(
op.getLoc(),
RankedTensorType::get(selfType.getShape(), rewriter.getIntegerType(1)),
adaptor.self(), threshold);
rewriter.replaceOpWithNewOp<tosa::SelectOp>(op, outType, cmpOp,
adaptor.self(), value);
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenUnsqueezeOp>::matchAndRewrite(
AtenUnsqueezeOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a tensor type.
auto selfType = adaptor.self().getType().dyn_cast<TensorType>();
if (!selfType) {
return op.emitError("Only tensor types are currently supported");
}
auto selfRank = selfType.getRank();
auto selfElemTy = selfType.getElementType();
if (!selfElemTy.isIntOrFloat()) {
return op.emitError(
"Only floating-point or integer datatype legalization supported");
}
int64_t dim;
if (!matchPattern(op.dim(), m_TorchConstantInt(&dim)))
return op->emitError("dim must be a Scalar constant");
dim = toPositiveDim(dim, selfRank);
if (!isValidDim(dim, selfRank))
return op.emitError("dim is statically invalid");
SmallVector<int64_t> outShape;
for (auto en : llvm::enumerate(selfType.getShape())) {
if (static_cast<int64_t>(en.index()) == dim)
outShape.push_back(1);
outShape.push_back(en.value());
}
rewriter.replaceOpWithNewOp<tosa::ReshapeOp>(
op, getTypeConverter()->convertType(op.getType()), adaptor.self(),
rewriter.getI64ArrayAttr(outShape));
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenContiguousOp>::matchAndRewrite(
AtenContiguousOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a tensor type.
auto selfType = adaptor.self().getType().dyn_cast<TensorType>();
if (!selfType)
return op.emitError("Only tensor types are currently supported");
// FIXME: memory_format is not handled.
rewriter.replaceOp(op, adaptor.self());
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenDropoutOp>::matchAndRewrite(
AtenDropoutOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a tensor type.
auto selfType = adaptor.input().getType().dyn_cast<TensorType>();
if (!selfType)
return op.emitError("Only tensor types are currently supported");
// FIXME: train and p are not handled.
bool train;
if (!matchPattern(op.train(), m_TorchConstantBool(&train)))
op.emitError("train must be a Scalar constant");
if (train)
op.emitError("train must be false");
rewriter.replaceOpWithNewOp<tosa::CastOp>(
op, getTypeConverter()->convertType(op.getType()), adaptor.input());
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenViewOp>::matchAndRewrite(
AtenViewOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a tensor type.
auto selfType = adaptor.self().getType().dyn_cast<TensorType>();
if (!selfType)
return op.emitError("Only tensor types are currently supported");
auto selfElemTy = selfType.getElementType();
if (!selfElemTy.isIntOrFloat()) {
return op.emitError(
"Only floating-point or integer datatype legalization supported");
}
SmallVector<int64_t> outShape;
if (!matchPattern(op.size(), m_TorchConstantIntList(outShape)))
return op.emitError("size must consist of Scalar constants");
rewriter.replaceOpWithNewOp<tosa::ReshapeOp>(
op, getTypeConverter()->convertType(op.getType()), adaptor.self(),
rewriter.getI64ArrayAttr(outShape));
return success();
}
static Value approximateErfOp(ConversionPatternRewriter &rewriter,
Operation *op, Value x) {
// Using:
// https://en.wikipedia.org/wiki/Error_function#Numerical_approximations with
// maximum error as 5 x 10^-4 where a1 = 0.278393, a2 = 0.230389, a3 =
// 0.000972, a4 = 0.078108.
//
// Erf = 1 - 1 / (1 + a1X + a2X + a3X + a4X)^4
auto outType = x.getType().cast<TensorType>();
auto loc = op->getLoc();
auto absX = rewriter.create<tosa::AbsOp>(loc, outType, x);
auto zero = tosa::getConstTensor<float>(rewriter, op, 0, {}).getValue();
auto one = tosa::getConstTensor<float>(rewriter, op, 1, {}).getValue();
auto a1 = tosa::getConstTensor<float>(rewriter, op, 0.278393, {}).getValue();
auto a1X = rewriter.create<tosa::MulOp>(loc, outType, a1, absX, /*shift=*/0);
auto sum = rewriter.create<tosa::AddOp>(loc, outType, a1X, one);
auto a2 = tosa::getConstTensor<float>(rewriter, op, 0.230389, {}).getValue();
auto x2 = rewriter.create<tosa::MulOp>(loc, outType, absX, absX, /*shift=*/0);
auto a2X = rewriter.create<tosa::MulOp>(loc, outType, a2, x2, /*shift=*/0);
sum = rewriter.create<tosa::AddOp>(loc, outType, sum, a2X);
auto a3 = tosa::getConstTensor<float>(rewriter, op, 0.000972, {}).getValue();
auto x3 = rewriter.create<tosa::MulOp>(loc, outType, x2, absX, /*shift=*/0);
auto a3X = rewriter.create<tosa::MulOp>(loc, outType, a3, x3, /*shift=*/0);
sum = rewriter.create<tosa::AddOp>(loc, outType, sum, a3X);
auto a4 = tosa::getConstTensor<float>(rewriter, op, 0.078108, {}).getValue();
auto x4 = rewriter.create<tosa::MulOp>(loc, outType, x3, absX, /*shift=*/0);
auto a4X = rewriter.create<tosa::MulOp>(loc, outType, a4, x4, /*shift=*/0);
sum = rewriter.create<tosa::AddOp>(loc, outType, sum, a4X);
auto rcprl = rewriter.create<tosa::ReciprocalOp>(loc, outType, sum);
auto rcprl2 =
rewriter.create<tosa::MulOp>(loc, outType, rcprl, rcprl, /*shift=*/0);
auto rcprl4 =
rewriter.create<tosa::MulOp>(loc, outType, rcprl2, rcprl2, /*shift=*/0);
auto erf = rewriter.create<tosa::SubOp>(loc, outType, one, rcprl4);
// Deal with negative x.
auto cond = rewriter.create<tosa::GreaterEqualOp>(
loc,
RankedTensorType::get(outType.getShape(), rewriter.getIntegerType(1)), x,
zero);
auto negateErf = rewriter.create<tosa::NegateOp>(loc, outType, erf);
return rewriter.create<tosa::SelectOp>(loc, outType, cond, erf, negateErf);
}
static Value buildUnitNormalCdf(ConversionPatternRewriter &rewriter,
Operation *op, Value x) {
auto zero = tosa::getConstTensor<float>(rewriter, op, 0, {}).getValue();
auto one = tosa::getConstTensor<float>(rewriter, op, 1, {}).getValue();
auto loc = op->getLoc();
// buildNormalCdf, mean = zero, sigma = one
auto outType = x.getType();
auto mean = zero;
Value xMinusMean = rewriter.create<tosa::SubOp>(loc, outType, x, mean);
// rsqrt of 2
Value rsqrt2 =
tosa::getConstTensor<float>(rewriter, op, 0.70710678, {}).getValue();
Value erfArg = rewriter.create<tosa::MulOp>(loc, outType, xMinusMean, rsqrt2,
/*shift=*/0);
Value erf = approximateErfOp(rewriter, op, erfArg);
Value erfPlus1 = rewriter.create<tosa::AddOp>(loc, outType, one, erf);
Value oneHalf = tosa::getConstTensor<float>(rewriter, op, 0.5, {}).getValue();
Value normalCdf = rewriter.create<tosa::MulOp>(loc, outType, oneHalf,
erfPlus1, /*shift=*/0);
return normalCdf;
}
// This lowering is based on Torch to LinAlg lowering.
template <>
LogicalResult ConvertAtenOp<AtenGeluOp>::matchAndRewrite(
AtenGeluOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a tensor type.
auto selfType = adaptor.self().getType().dyn_cast<TensorType>();
if (!selfType)
return op.emitError("Only tensor types are currently supported");
auto selfElemTy = selfType.getElementType();
if (!selfElemTy.isa<mlir::FloatType>()) {
return op.emitError("Only floating-point datatype legalization supported");
}
// TODO: Handle approximate.
std::string approximate;
if (!matchPattern(op.approximate(), m_TorchConstantStr(approximate)) ||
approximate != "none") {
return op.emitError("Unsupported value of approximate");
}
Value cdf = buildUnitNormalCdf(rewriter, op, adaptor.self());
rewriter.replaceOpWithNewOp<tosa::MulOp>(
op, getTypeConverter()->convertType(op.getType()), adaptor.self(), cdf,
/*shift=*/0);
return success();
}
// This lowering is based on Torch to LinAlg lowering.
template <>
LogicalResult ConvertAtenOp<AtenGeluBackwardOp>::matchAndRewrite(
AtenGeluBackwardOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
// Not a tensor type.
auto selfType = adaptor.self().getType().dyn_cast<TensorType>();
if (!selfType)
return op.emitError("Only tensor types are currently supported");
auto selfElemTy = selfType.getElementType();
if (!selfElemTy.isa<mlir::FloatType>()) {
return op.emitError("Only floating-point datatype legalization supported");
}
// TODO: Handle approximate.
std::string approximate;
if (!matchPattern(op.approximate(), m_TorchConstantStr(approximate)) ||
approximate != "none") {
return op.emitError("Unsupported value of approximate");
}
auto loc = op->getLoc();
const double cstAlpha0 = 1.12837916709551257390;
const double cstAlpha1 = 0.70710678118654752440;
const double oneHalf = 0.5;
const double kAlpha = cstAlpha0 * cstAlpha1;
Value kAlphaHalf =
tosa::getConstTensor<float>(rewriter, op, kAlpha * oneHalf, {})
.getValue();
Value negOneHalf =
tosa::getConstTensor<float>(rewriter, op, -0.5, {}).getValue();
Value inputSquared = rewriter.create<tosa::MulOp>(
loc, selfType, adaptor.self(), adaptor.self(), /*shift=*/0);
Value negHalfInputSquared = rewriter.create<tosa::MulOp>(
loc, selfType, inputSquared, negOneHalf, /*shift=*/0);
Value dinput =
rewriter.create<tosa::ExpOp>(loc, selfType, negHalfInputSquared);
Value cdf = buildUnitNormalCdf(rewriter, op, adaptor.self());
Value dinputInput = rewriter.create<tosa::MulOp>(loc, selfType, dinput,
adaptor.self(), /*shift=*/0);
Value dinputInputAlpha = rewriter.create<tosa::MulOp>(
loc, selfType, dinputInput, kAlphaHalf, /*shift=*/0);
Value cdfExt =
rewriter.create<tosa::AddOp>(loc, selfType, dinputInputAlpha, cdf);
rewriter.replaceOpWithNewOp<tosa::MulOp>(
op, getTypeConverter()->convertType(op.getType()), adaptor.grad_output(),
cdfExt,
/*shift=*/0);
return success();
}
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenPoolingBaseOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
// Different pooling variants need to process inputs differently, e.g.
// adaptive pooling generates the kernel size rather than receive it. This
// function also transposes inputs.
virtual LogicalResult processInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
Value &input, ArrayAttr &kernel,
ArrayAttr &stride, ArrayAttr &pad,
Type &outputTy) const {
return rewriter.notifyMatchFailure(
op, "Unimplemented pooling input parsing function");
}
static int64_t getOutputDim(int64_t inputDim, int64_t kernelDim,
int64_t stride, int64_t padBefore,
int64_t padAfter, int64_t dilation) {
if (inputDim == ShapedType::kDynamicSize) {
return ShapedType::kDynamicSize;
} else {
return (
(inputDim + padBefore + padAfter - dilation * (kernelDim - 1) - 1) /
stride +
1);
}
}
// Apply the transposeDims vector on input to generate a transposed form.
Value transposeTensor(AtenOpT op, ConversionPatternRewriter &rewriter,
Value input, ArrayRef<int32_t> transposeDims) const {
auto inputTy = input.getType().template cast<RankedTensorType>();
auto inputElemTy = inputTy.getElementType();
auto inputShape = inputTy.getShape();
auto inputRank = inputTy.getRank();
llvm::Optional<Value> transposeDimsConst = tosa::getConstTensor<int32_t>(
rewriter, op,
/*vec=*/transposeDims,
/*shape=*/{static_cast<int32_t>(inputRank)});
SmallVector<int64_t> transposedInputShape;
for (auto &dim : transposeDims)
transposedInputShape.push_back(inputShape[dim]);
auto transposedInputType =
RankedTensorType::get(transposedInputShape, inputElemTy);
return rewriter
.create<tosa::TransposeOp>(op->getLoc(), transposedInputType, input,
transposeDimsConst.getValue())
.getResult();
}
Value transposePoolingInputToHwc(AtenOpT op,
ConversionPatternRewriter &rewriter,
Value input) const {
auto inputRank =
input.getType().template cast<RankedTensorType>().getRank();
SmallVector<int32_t> nchwToNhwc4DTransposeDims({0, 2, 3, 1});
SmallVector<int32_t> chwToHwc3DTransposeDims({1, 2, 0});
return transposeTensor(op, rewriter, input,
inputRank == 3 ? chwToHwc3DTransposeDims
: nchwToNhwc4DTransposeDims);
}
Value transposePoolingOutputToChw(AtenOpT op,
ConversionPatternRewriter &rewriter,
Value input) const {
auto inputTy = input.getType().template cast<RankedTensorType>();
auto inputRank = inputTy.getRank();
SmallVector<int32_t> nhwcToNchw4DTransposeDims({0, 3, 1, 2});
SmallVector<int32_t> hwcToChw3DTransposeDims({2, 0, 1});
return transposeTensor(op, rewriter, input,
inputRank == 3 ? hwcToChw3DTransposeDims
: nhwcToNchw4DTransposeDims);
}
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value input;
ArrayAttr kernel, stride, pad;
Type outputTy;
// Attempts to read input and kernel parameters, or synthesize them in the
// case of adaptive pooling. Also performs input CHW->HWC transpose.
if (failed(processInputs(op, adaptor, rewriter, input, kernel, stride, pad,
outputTy)))
return op.emitError("Failed to process inputs for pooling");
auto pooledOutput =
rewriter
.create<TosaOpT>(op->getLoc(), outputTy, input, kernel, stride, pad)
.getResult();
auto transposedOutput =
ConvertAtenPoolingBaseOp<AtenOpT, TosaOpT>::transposePoolingOutputToChw(
op, rewriter, pooledOutput);
rewriter.replaceOpWithNewOp<tensor::CastOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
transposedOutput);
return success();
}
};
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenAdaptivePoolingOp
: public ConvertAtenPoolingBaseOp<AtenOpT, TosaOpT> {
public:
using ConvertAtenPoolingBaseOp<AtenOpT, TosaOpT>::ConvertAtenPoolingBaseOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult processInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter, Value &input,
ArrayAttr &kernel, ArrayAttr &stride,
ArrayAttr &pad, Type &outputTy) const override {
auto inputXchw = adaptor.self();
auto inputTy = inputXchw.getType().template cast<RankedTensorType>();
if (!inputTy)
return op.emitError("Adaptive avgpool requires ranked tensor input");
auto inputShape = inputTy.getShape();
auto inputRank = inputTy.getRank();
auto inputElemTy = inputTy.getElementType();
// Rank sanity check.
if (inputTy.getRank() != 4 && inputRank != 3)
return op.emitError("NCHW->NHWC transpose requires 3D or 4D tensor");
int64_t inputHDim = inputShape[inputRank - 2];
int64_t inputWDim = inputShape[inputRank - 1];
SmallVector<int64_t> outputSize;
if (!matchPattern(op.output_size(), m_TorchConstantIntList(outputSize)))
return rewriter.notifyMatchFailure(
op, "Non-const output_size for adaptive pooling unsupported.");
SmallVector<int64_t> kernelDims;
int64_t outputHDim, outputWDim;
if (outputSize.size() == 1) {
outputHDim = outputWDim = outputSize[0];
} else {
if (outputSize.size() != 2)
return op.emitError(
"Adaptive avgpool output_size not 1 or 2 elements.");
// Assumes 'None' (e.g. output_size=(None, 5) ) is expressed as <=0.
outputHDim =
(outputSize[0] <= 0) ? inputShape[inputRank - 2] : outputSize[0];
outputWDim =
(outputSize[1] <= 0) ? inputShape[inputRank - 1] : outputSize[1];
}
// In adaptive pooling,
// stride = inputDim // outputDim
// kernel = inputDim - (outputDim-1)* stride
// pad = 0, dilation = 1
int64_t strideH = inputShape[inputRank - 2] / outputHDim;
int64_t strideW = inputShape[inputRank - 1] / outputWDim;
kernelDims.push_back(inputHDim - (outputHDim - 1) * strideH);
kernelDims.push_back(inputWDim - (outputWDim - 1) * strideW);
SmallVector<int64_t> outputShape;
if (inputRank > 3)
outputShape.push_back(inputShape[0]);
outputShape.push_back(outputHDim);
outputShape.push_back(outputWDim);
outputShape.push_back(inputShape[inputRank - 3]);
// Transpose to xHWC
input =
ConvertAtenPoolingBaseOp<AtenOpT, TosaOpT>::transposePoolingInputToHwc(
op, rewriter, inputXchw);
kernel = rewriter.getI64ArrayAttr(kernelDims);
stride = rewriter.getI64ArrayAttr({strideH, strideW});
// Adaptive pooling does unit dilation and zero pad.
pad = rewriter.getI64ArrayAttr({0, 0, 0, 0});
outputTy = RankedTensorType::get(outputShape, inputElemTy);
return success();
}
};
template <typename AtenOpT, typename tosaOp>
static Type getOutputTypeForNonAdaptivePoolingOp(
RankedTensorType inputTy, SmallVectorImpl<int64_t> &kernelSize,
SmallVectorImpl<int64_t> &strideArray, SmallVectorImpl<int64_t> &padArray,
SmallVectorImpl<int64_t> &dilationArray) {
auto inputShape = inputTy.getShape();
auto inputRank = inputTy.getRank();
auto inputElemTy = inputTy.getElementType();
int64_t outputHDim = ConvertAtenPoolingBaseOp<AtenOpT, tosaOp>::getOutputDim(
inputShape[inputRank - 2], kernelSize[0], strideArray[0], padArray[0],
padArray[0], dilationArray[0]);
int64_t outputWDim = ConvertAtenPoolingBaseOp<AtenOpT, tosaOp>::getOutputDim(
inputShape[inputRank - 1], kernelSize[1], strideArray[1], padArray[1],
padArray[1], dilationArray[1]);
SmallVector<int64_t> outputShape;
if (inputRank > 3)
outputShape.push_back(inputShape[0]);
outputShape.push_back(outputHDim);
outputShape.push_back(outputWDim);
outputShape.push_back(inputShape[inputRank - 3]);
return RankedTensorType::get(outputShape, inputElemTy);
}
// Checks the validity of pooling parameters and stores them in the respective
// vector. Also, gets the output type for the pooling op.
template <typename AtenOpT, typename tosaOp>
static LogicalResult getOutputTypeAndPoolingParameters(
AtenOpT op, ConversionPatternRewriter &rewriter, Value inputXchw,
SmallVectorImpl<int64_t> &dilationArray, Type &outputTy, ArrayAttr &kernel,
ArrayAttr &stride, ArrayAttr &pad) {
RankedTensorType inputTy = inputXchw.getType().cast<RankedTensorType>();
if (!inputTy)
return op.emitError("Pooling op requires ranked tensor input");
auto inputRank = inputTy.getRank();
// Rank sanity check.
if (inputTy.getRank() != 4 && inputRank != 3)
return op.emitError("NCHW->NHWC transpose requires 3D or 4D tensor");
SmallVector<int64_t, 2> kernelSizeInts, strideInts, paddingInts;
if (!matchPattern(op.kernel_size(), m_TorchConstantIntList(kernelSizeInts)))
return rewriter.notifyMatchFailure(
op, "Non-const kernel_size for pooling op unsupported");
if (!matchPattern(op.stride(), m_TorchConstantIntList(strideInts)))
return rewriter.notifyMatchFailure(
op, "Non-const stride for pooling op unsupported");
if (!matchPattern(op.padding(), m_TorchConstantIntList(paddingInts)))
return rewriter.notifyMatchFailure(
op, "Non-const padding factor for pooling op unsupported");
kernel = rewriter.getI64ArrayAttr(kernelSizeInts);
stride = rewriter.getI64ArrayAttr(strideInts);
pad = rewriter.getI64ArrayAttr(
{paddingInts[0], paddingInts[0], paddingInts[1], paddingInts[1]});
// FIXME: add ceil_mode support.
bool ceilMode;
if (!matchPattern(op.ceil_mode(), m_TorchConstantBool(&ceilMode)))
return rewriter.notifyMatchFailure(
op, "only support constant bool ceil_mode for pooling op");
if (ceilMode)
return rewriter.notifyMatchFailure(
op, "only support ceil_mode equals to False for pooling op");
outputTy = getOutputTypeForNonAdaptivePoolingOp<AtenOpT, tosaOp>(
inputTy, kernelSizeInts, strideInts, paddingInts, dilationArray);
return success();
}
class ConvertAtenMaxPool2dOp
: public ConvertAtenPoolingBaseOp<AtenMaxPool2dOp, tosa::MaxPool2dOp> {
public:
using ConvertAtenPoolingBaseOp<AtenMaxPool2dOp,
tosa::MaxPool2dOp>::ConvertAtenPoolingBaseOp;
LogicalResult processInputs(AtenMaxPool2dOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter, Value &input,
ArrayAttr &kernel, ArrayAttr &stride,
ArrayAttr &pad, Type &outputTy) const override {
SmallVector<int64_t, 2> dilationArray;
if (!matchPattern(op.dilation(), m_TorchConstantIntList(dilationArray)))
return rewriter.notifyMatchFailure(
op, "Non-const dilation for pooling op unsupported.");
// TOSA pooling only supports unit dilation.
if (dilationArray[0] > 1 || dilationArray[1] > 1)
return op.emitError("Cannot process non-unit pooling dilation.");
if (failed(getOutputTypeAndPoolingParameters<AtenMaxPool2dOp,
tosa::MaxPool2dOp>(
op, rewriter, adaptor.self(), dilationArray, outputTy, kernel,
stride, pad)))
return rewriter.notifyMatchFailure(
op, "invalid pooling parameters or input type");
// Transpose to xHWC
input = ConvertAtenPoolingBaseOp<AtenMaxPool2dOp, tosa::MaxPool2dOp>::
transposePoolingInputToHwc(op, rewriter, adaptor.self());
return success();
}
};
class ConvertAtenAvgPool2dOp
: public ConvertAtenPoolingBaseOp<AtenAvgPool2dOp, tosa::AvgPool2dOp> {
public:
using ConvertAtenPoolingBaseOp<AtenAvgPool2dOp,
tosa::AvgPool2dOp>::ConvertAtenPoolingBaseOp;
LogicalResult processInputs(AtenAvgPool2dOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter, Value &input,
ArrayAttr &kernel, ArrayAttr &stride,
ArrayAttr &pad, Type &outputTy) const override {
SmallVector<int64_t, 2> dilationArray{1, 1};
if (failed(getOutputTypeAndPoolingParameters<AtenAvgPool2dOp,
tosa::AvgPool2dOp>(
op, rewriter, adaptor.self(), dilationArray, outputTy, kernel,
stride, pad)))
return rewriter.notifyMatchFailure(
op, "invalid pooling parameters or input type");
// Transpose to xHWC
input = ConvertAtenPoolingBaseOp<AtenAvgPool2dOp, tosa::AvgPool2dOp>::
transposePoolingInputToHwc(op, rewriter, adaptor.self());
return success();
}
};
// Ref: Error checking based on the Torch to LinAlg lowering
template <typename AtenOpT, int fillVal>
class ConvertAtenConstPatternOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto outType = OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template dyn_cast<TensorType>();
if (!outType)
return op.emitError("Only Tensor types supported in TOSA");
Type outElemTy = outType.getElementType();
if (!outElemTy.isIntOrFloat())
return op.emitError(
"Only floating-point or integer datatype legalization supported");
// FIXME: Handle layout, device and pin_memory. Assume dtype has been
// processed to set output type correctly?
if (!op.layout().getType().template isa<Torch::NoneType>())
return op.emitError("Only default layout is supported");
bool pinMemory;
if (!op.pin_memory().getType().template isa<Torch::NoneType>() &&
(!matchPattern(op.pin_memory(), m_TorchConstantBool(&pinMemory)) ||
pinMemory)) {
return op.emitError(
"Unsupported pin_memory, should be either None or false");
}
SmallVector<int64_t> shape;
if (!matchPattern(op.size(), m_TorchConstantIntList(shape))) {
return op.emitError("Shape must be a list of Scalar constants");
}
int64_t size = 1;
for (auto s : shape)
size *= s;
SmallVector<int32_t> values(size, fillVal);
auto constOp =
tosa::getConstTensor<int32_t>(rewriter, op, values, shape).getValue();
rewriter.replaceOpWithNewOp<tosa::CastOp>(op, outType, constOp);
return success();
}
};
template <typename AtenOpT>
class ConvertAtenFillScalarOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto outType = OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template dyn_cast<TensorType>();
if (!outType || !outType.hasStaticShape())
return op.emitError(
"Only Tensor types with static shapes are currently supported");
Type outElemTy = outType.getElementType();
if (!outElemTy.isIntOrFloat()) {
return op.emitError(
"Only floating-point or integer datatype legalization supported");
}
Value constOp;
if (failed(torchScalarToTosaTensor(rewriter, op, op.value(), constOp,
outElemTy, outType.getShape())))
return op.emitError("Supplied value must be a Scalar constant");
rewriter.replaceOpWithNewOp<tosa::CastOp>(op, outType, constOp);
return success();
}
};
} // namespace
// -----------------------------------------------------------------------------
// TorchToTosa Pass
// -----------------------------------------------------------------------------
namespace {
class ConvertTorchToTosa : public ConvertTorchToTosaBase<ConvertTorchToTosa> {
public:
void getDependentDialects(DialectRegistry &registry) const override {
registry.insert<tosa::TosaDialect>();
registry.insert<tensor::TensorDialect>();
registry.insert<arith::ArithmeticDialect>();
TorchConversion::getBackendTypeConversionDependentDialects(registry);
}
void runOnOperation() override {
MLIRContext *context = &getContext();
ConversionTarget target(*context);
target.addLegalDialect<tosa::TosaDialect, tensor::TensorDialect,
arith::ArithmeticDialect>();
TypeConverter typeConverter;
typeConverter.addConversion([](Type type) { return type; });
TorchConversion::setupBackendTypeConversion(target, typeConverter);
RewritePatternSet patterns(context);
#define INSERT_UNARY_FPONLY_PATTERN(AtenOp, TosaOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenUnaryFPOnlyOp<AtenOp, TosaOp>>(typeConverter, \
context);
INSERT_UNARY_FPONLY_PATTERN(AtenLogOp, tosa::LogOp)
INSERT_UNARY_FPONLY_PATTERN(AtenExpOp, tosa::ExpOp)
#undef INSERT_UNARY_FPONLY_PATTERN
#define INSERT_UNARY_PATTERN(AtenOp, TosaOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenUnaryOp<AtenOp, TosaOp>>(typeConverter, context);
INSERT_UNARY_PATTERN(AtenNegOp, tosa::NegateOp)
INSERT_UNARY_PATTERN(AtenFloorOp, tosa::FloorOp)
INSERT_UNARY_PATTERN(AtenRsqrtOp, tosa::RsqrtOp)
INSERT_UNARY_PATTERN(AtenBitwiseNotOp, tosa::BitwiseNotOp)
INSERT_UNARY_PATTERN(AtenCeilOp, tosa::CeilOp)
INSERT_UNARY_PATTERN(AtenReciprocalOp, tosa::ReciprocalOp)
#undef INSERT_UNARY_PATTERN
#define INSERT_BINARY_PATTERN(AtenOp, TosaOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenBinaryOp<AtenOp, TosaOp>>(typeConverter, context);
INSERT_BINARY_PATTERN(AtenMaximumOp, tosa::MaximumOp)
INSERT_BINARY_PATTERN(AtenMinimumOp, tosa::MinimumOp)
#undef INSERT_BINARY_PATTERN
#define INSERT_BINARY_ADDSUB_PATTERN(AtenOp, TosaOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenAddSubOp<AtenOp, TosaOp>>(typeConverter, context);
INSERT_BINARY_ADDSUB_PATTERN(AtenAddTensorOp, tosa::AddOp)
INSERT_BINARY_ADDSUB_PATTERN(AtenAddScalarOp, tosa::AddOp)
INSERT_BINARY_ADDSUB_PATTERN(AtenSubTensorOp, tosa::SubOp)
INSERT_BINARY_ADDSUB_PATTERN(AtenSubScalarOp, tosa::SubOp)
#undef INSERT_BINARY_ADDSUB_PATTERN
#define INSERT_BINARY_COMPARE_PATTERN(AtenOp, TosaOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenCompareOp<AtenOp, TosaOp>>(typeConverter, context);
INSERT_BINARY_COMPARE_PATTERN(AtenGtTensorOp, tosa::GreaterOp)
INSERT_BINARY_COMPARE_PATTERN(AtenGtScalarOp, tosa::GreaterOp)
INSERT_BINARY_COMPARE_PATTERN(AtenLtTensorOp, tosa::GreaterOp)
INSERT_BINARY_COMPARE_PATTERN(AtenLtScalarOp, tosa::GreaterOp)
INSERT_BINARY_COMPARE_PATTERN(AtenEqTensorOp, tosa::EqualOp)
INSERT_BINARY_COMPARE_PATTERN(AtenEqScalarOp, tosa::EqualOp)
INSERT_BINARY_COMPARE_PATTERN(AtenNeTensorOp, tosa::EqualOp)
INSERT_BINARY_COMPARE_PATTERN(AtenNeScalarOp, tosa::EqualOp)
INSERT_BINARY_COMPARE_PATTERN(AtenBitwiseAndTensorOp, tosa::BitwiseAndOp)
#undef INSERT_BINARY_COMPARE_PATTERN
#define INSERT_BINARY_MUL_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenMulOp<AtenOp>>(typeConverter, context);
INSERT_BINARY_MUL_PATTERN(AtenMulTensorOp);
INSERT_BINARY_MUL_PATTERN(AtenMulScalarOp);
#undef INSERT_BINARY_MUL_PATTERN
#define INSERT_BINARY_DIV_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenDivOp<AtenOp>>(typeConverter, context);
INSERT_BINARY_DIV_PATTERN(AtenDivTensorOp);
INSERT_BINARY_DIV_PATTERN(AtenDivScalarOp);
#undef INSERT_BINARY_DIV_PATTERN
#define INSERT_NDIMS_REDUCTION_OP_PATTERN(AtenOp, ConversionFunc) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenMultipleDimsReductionOp<AtenOp, ConversionFunc>>( \
typeConverter, context);
INSERT_NDIMS_REDUCTION_OP_PATTERN(AtenMeanDimOp,
mlir::tosa::convertReduceMeanOp)
INSERT_NDIMS_REDUCTION_OP_PATTERN(AtenSumDimIntListOp,
mlir::tosa::convertReduceSumOp)
#undef INSERT_NDIMS_REDUCTION_OP_PATTERN
#define INSERT_ONEDIM_REDUCTION_OP_PATTERN(AtenOp, ConversionFunc) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenOneDimReductionOp<AtenOp, ConversionFunc>>( \
typeConverter, context);
INSERT_ONEDIM_REDUCTION_OP_PATTERN(AtenAnyDimOp,
mlir::tosa::convertReduceAnyOp)
#undef INSERT_ONEDIM_REDUCTION_OP_PATTERN
#define INSERT_ALLDIMS_REDUCTION_OP_PATTERN(AtenOp, ConversionFunc) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenAllDimsReductionOp<AtenOp, ConversionFunc>>( \
typeConverter, context);
INSERT_ALLDIMS_REDUCTION_OP_PATTERN(AtenAllOp,
mlir::tosa::convertReduceAllOp)
INSERT_ALLDIMS_REDUCTION_OP_PATTERN(AtenAnyOp,
mlir::tosa::convertReduceAnyOp)
INSERT_ALLDIMS_REDUCTION_OP_PATTERN(AtenSumOp,
mlir::tosa::convertReduceSumOp)
#undef INSERT_ALLDIMS_REDUCTION_OP_PATTERN
#define INSERT_SQUEEZE_OP_PATTERN(AtenOp, TemplateForm) \
target.addIllegalOp<AtenOp>(); \
patterns.add<TemplateForm<AtenOp>>(typeConverter, context);
INSERT_SQUEEZE_OP_PATTERN(AtenSqueezeOp, ConvertAtenSqueezeAllDimsOp)
INSERT_SQUEEZE_OP_PATTERN(AtenSqueezeDimOp, ConvertAtenSqueezeOneDimOp)
#undef INSERT_SQUEEZE_OP_PATTERN
#define INSERT_MATMUL_ATENOP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenMatMulOp<AtenOp>>(typeConverter, context);
INSERT_MATMUL_ATENOP_PATTERN(AtenMatmulOp);
#undef INSERT_MATMUL_ATEMOP_PATTERN
#define INSERT_MM_ATENOP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenMmOp<AtenOp>>(typeConverter, context);
INSERT_MM_ATENOP_PATTERN(AtenMmOp);
INSERT_MM_ATENOP_PATTERN(AtenBmmOp);
#undef INSERT_MM_ATEMOP_PATTERN
#define INSERT_LINEAR_ATENOP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenLinearOp<AtenOp>>(typeConverter, context);
INSERT_LINEAR_ATENOP_PATTERN(AtenLinearOp);
#undef INSERT_LINEAR_ATEMOP_PATTERN
#define INSERT_ADAPTIVE_POOLING_ATENOP_PATTERN(AtenOp, TosaOpT) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenAdaptivePoolingOp<AtenOp, TosaOpT>>(typeConverter, \
context);
INSERT_ADAPTIVE_POOLING_ATENOP_PATTERN(AtenAdaptiveAvgPool2dOp,
tosa::AvgPool2dOp);
#undef INSERT_ADAPTIVE_POOLING_ATEMOP_PATTERN
target.addIllegalOp<AtenMaxPool2dOp>();
patterns.add<ConvertAtenMaxPool2dOp>(typeConverter, context);
target.addIllegalOp<AtenAvgPool2dOp>();
patterns.add<ConvertAtenAvgPool2dOp>(typeConverter, context);
#define INSERT_CONSTANT_FILL_PATTERN(AtenOp, fillVal) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenConstPatternOp<AtenOp, fillVal>>(typeConverter, \
context);
INSERT_CONSTANT_FILL_PATTERN(AtenOnesOp, 1);
INSERT_CONSTANT_FILL_PATTERN(AtenZerosOp, 0);
#undef INSERT_CONSTANT_FILL_PATTERN
#define INSERT_FILL_SCALAR_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenFillScalarOp<AtenOp>>(typeConverter, context);
INSERT_FILL_SCALAR_PATTERN(AtenFill_ScalarOp);
#undef INSERT_FILL_SCALAR_PATTERN
#define INSERT_ATENOP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenOp<AtenOp>>(typeConverter, context);
INSERT_ATENOP_PATTERN(AtenTanhOp);
INSERT_ATENOP_PATTERN(AtenSigmoidOp);
INSERT_ATENOP_PATTERN(AtenReluOp);
INSERT_ATENOP_PATTERN(AtenArgmaxOp);
INSERT_ATENOP_PATTERN(AtenPowTensorScalarOp);
INSERT_ATENOP_PATTERN(AtenRsubScalarOp);
INSERT_ATENOP_PATTERN(AtenConvolutionOp);
INSERT_ATENOP_PATTERN(ValueTensorLiteralOp);
INSERT_ATENOP_PATTERN(AtenReshapeOp);
INSERT_ATENOP_PATTERN(AtenBatchNormOp);
INSERT_ATENOP_PATTERN(AtenNativeLayerNormOp);
INSERT_ATENOP_PATTERN(AtenFlattenUsingIntsOp);
INSERT_ATENOP_PATTERN(AtenPermuteOp);
INSERT_ATENOP_PATTERN(AtenLog2Op);
INSERT_ATENOP_PATTERN(AtenThresholdOp);
INSERT_ATENOP_PATTERN(AtenUnsqueezeOp);
INSERT_ATENOP_PATTERN(AtenContiguousOp);
INSERT_ATENOP_PATTERN(AtenDropoutOp);
INSERT_ATENOP_PATTERN(AtenViewOp);
INSERT_ATENOP_PATTERN(AtenGeluOp);
INSERT_ATENOP_PATTERN(AtenGeluBackwardOp);
#undef INSERT_ATENOP_PATTERN
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns))))
return signalPassFailure();
}
};
} // namespace
std::unique_ptr<OperationPass<func::FuncOp>>
mlir::torch::createConvertTorchToTosaPass() {
return std::make_unique<ConvertTorchToTosa>();
}
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