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llvm-project/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp

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//===- TosaOps.cpp - MLIR Dialect for TOSA --------------------------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// \file
// This file implements the TOSA Specification:
// https://developer.mlplatform.org/w/tosa/
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/Dialect/Tosa/Utils/QuantUtils.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/Matchers.h"
#include "mlir/Transforms/FoldUtils.h"
#include "mlir/Transforms/InliningUtils.h"
#include "mlir/Transforms/RegionUtils.h"
using namespace mlir;
using namespace mlir::tosa;
#include "mlir/Dialect/Tosa/IR/TosaOpsDialect.cpp.inc"
//===----------------------------------------------------------------------===//
// Tosa dialect structs and interface includes.
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Tosa/IR/TosaInterfaces.cpp.inc"
#include "mlir/Dialect/Tosa/IR/TosaStructs.cpp.inc"
namespace {
//===----------------------------------------------------------------------===//
// Dialect Function Inliner Interface.
//===----------------------------------------------------------------------===//
struct TosaInlinerInterface : public DialectInlinerInterface {
using DialectInlinerInterface::DialectInlinerInterface;
//===--------------------------------------------------------------------===//
// Analysis Hooks.
//===--------------------------------------------------------------------===//
/// All operations can be inlined by default.
bool isLegalToInline(Operation *op, Region *region, bool wouldBeCloned,
BlockAndValueMapping &map) const final {
return true;
}
/// All regions with If and While parent operators can be inlined.
bool isLegalToInline(Region *dest, Region *src, bool wouldBeCloned,
BlockAndValueMapping &map) const final {
return (isa<tosa::IfOp>(dest->getParentOp()) ||
isa<tosa::WhileOp>(dest->getParentOp()));
}
};
} // end anonymous namespace
//===----------------------------------------------------------------------===//
// TOSA control flow support.
//===----------------------------------------------------------------------===//
/// Returns the while loop body.
Region &tosa::WhileOp::getLoopBody() { return body(); }
bool tosa::WhileOp::isDefinedOutsideOfLoop(Value value) {
return !body().isAncestor(value.getParentRegion());
}
LogicalResult WhileOp::moveOutOfLoop(ArrayRef<mlir::Operation *> ops) {
if (ops.empty())
return success();
Operation *tosaWhileOp = this->getOperation();
for (auto *op : ops)
op->moveBefore(tosaWhileOp);
return success();
}
//===----------------------------------------------------------------------===//
// Tosa dialect initialization.
//===----------------------------------------------------------------------===//
void TosaDialect::initialize() {
addOperations<
#define GET_OP_LIST
#include "mlir/Dialect/Tosa/IR/TosaOps.cpp.inc"
>();
addInterfaces<TosaInlinerInterface>();
}
Operation *TosaDialect::materializeConstant(OpBuilder &builder, Attribute value,
Type type, Location loc) {
// Tosa dialect constants only support ElementsAttr unlike standard dialect
// constant which supports all attributes.
if (value.isa<ElementsAttr>())
return builder.create<tosa::ConstOp>(loc, type, value.cast<ElementsAttr>());
return nullptr;
}
//===----------------------------------------------------------------------===//
// Operator Folders.
//===----------------------------------------------------------------------===//
OpFoldResult ConstOp::fold(ArrayRef<Attribute> operands) {
assert(operands.empty() && "constant has no operands");
return valueAttr();
}
//===----------------------------------------------------------------------===//
// TOSA Operator Verifiers.
//===----------------------------------------------------------------------===//
template <typename T>
static LogicalResult verifyConvOp(T op) {
// All TOSA conv ops have an input() and weight().
auto inputType = op.input().getType().template dyn_cast<RankedTensorType>();
auto weightType = op.weight().getType().template dyn_cast<RankedTensorType>();
// Must be ranked tensor types
if (!inputType || !weightType)
return failure();
auto inputEType = inputType.getElementType();
auto weightEType = weightType.getElementType();
bool inputIsQuant = !inputEType.template isa<FloatType>();
bool weightIsQuant = !weightEType.template isa<FloatType>();
// Either both must be quantized or both unquantized.
if (inputIsQuant != weightIsQuant)
return failure();
// Quantized type must have constructed the quantizationattr, and unquantized
// types should not have a quantizationattr.
if ((inputIsQuant && !op.quantization_info()) ||
(!inputIsQuant && op.quantization_info()))
return failure();
return success();
}
//===----------------------------------------------------------------------===//
// TOSA Operator Quantization Builders.
//===----------------------------------------------------------------------===//
/// This builder is called on all convolution operators except TransposeConv,
/// which has specialized output shape semantics. The builder also defines the
/// bitwidth of the output given the bit width of the input & weight content.
static void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
Type outputType, Value input, Value weight,
Value bias, ArrayAttr pad,
ArrayAttr stride, ArrayAttr dilation) {
result.addOperands({input, weight, bias});
result.addAttribute("pad", pad);
result.addAttribute("stride", stride);
result.addAttribute("dilation", dilation);
auto quantAttr = buildConvOpQuantizationAttr(builder, input, weight);
if (quantAttr) {
result.addAttribute("quantization_info", quantAttr);
result.addTypes(
buildConvOpResultTypeInfo(builder, outputType, input, weight));
} else {
result.addTypes(outputType);
}
}
/// Handles tosa.transpose_conv2d which has outpad and output shape attributes.
static void
buildTransConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
Type outputType, Value input, Value weight,
Value bias, ArrayAttr outpad, ArrayAttr stride,
ArrayAttr dilation, ArrayAttr outputShape) {
result.addOperands({input, weight, bias});
result.addAttribute("out_pad", outpad);
result.addAttribute("stride", stride);
result.addAttribute("dilation", dilation);
result.addAttribute("out_shape", outputShape);
auto quantAttr = ::buildConvOpQuantizationAttr(builder, input, weight);
if (quantAttr) {
result.addAttribute("quantization_info", quantAttr);
result.addTypes(
buildConvOpResultTypeInfo(builder, outputType, input, weight));
} else {
result.addTypes(outputType);
}
}
/// The tosa.fully_connected op has its own builder as it does not have
/// strides/dilation/padding.
static void buildFCOpWithQuantInfo(OpBuilder &builder, OperationState &result,
Type outputType, Value input, Value weight,
Value bias) {
result.addOperands({input, weight, bias});
auto quantAttr = ::buildConvOpQuantizationAttr(builder, input, weight);
if (quantAttr) {
result.addAttribute("quantization_info", quantAttr);
result.addTypes(
buildConvOpResultTypeInfo(builder, outputType, input, weight));
} else {
result.addTypes(outputType);
}
}
/// The tosa.matmul op is also intended to be generated where a fully_connected
/// op must be constructed where the weight is not a constant. In this case,
/// the fully_connected op must be expressed using matmul.
/// TODO: Add link to the leglization document explaining this.
static void buildMatMulOpWithQuantInfo(OpBuilder &builder,
OperationState &result, Type outputType,
Value a, Value b) {
result.addOperands({a, b});
auto quantAttr = ::buildMatMulOpQuantizationAttr(builder, a, b);
if (quantAttr) {
result.addAttribute("quantization_info", quantAttr);
auto inputType = a.getType().dyn_cast<RankedTensorType>();
assert(inputType && "Input must be a ranked tensor type!");
auto inputQType = inputType.getElementType()
.dyn_cast<mlir::quant::UniformQuantizedType>();
assert(inputQType && "Tensor must have quantized datatype!");
unsigned inputBits = inputQType.getStorageTypeIntegralWidth();
auto outputShapedType = outputType.dyn_cast<RankedTensorType>();
assert(outputShapedType && "Output must be a ranked tensor type");
auto outputShape = outputShapedType.getShape();
IntegerType accElementType;
if (inputBits == 16)
accElementType = builder.getIntegerType(48);
else
accElementType = builder.getI32Type();
auto accType = RankedTensorType::get(outputShape, accElementType);
result.addTypes(accType);
} else {
result.addTypes(outputType);
}
}
/// Both the tosa.avg_pool2d and unary ops use the same UnaruOpQuantizationAttr
/// but avg_pool operator has its own builder as it has additional parameters
/// not part of the unary ops.
static void buildAvgPool2dOpWithQuantInfo(OpBuilder &builder,
OperationState &result,
Type outputType, Value input,
ArrayAttr kernel, ArrayAttr stride,
ArrayAttr pad) {
result.addOperands(input);
result.addAttribute("kernel", kernel);
result.addAttribute("stride", stride);
result.addAttribute("pad", pad);
auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType);
if (quantAttr)
result.addAttribute("quantization_info", quantAttr);
result.types.push_back(outputType);
}
/// This builder is called on single-parameter unary operators that have scale
/// relationship between their input and output, expressed by the
/// UnaryOpQuantizationAttr.
static void buildUnaryOpWithQuantInfo(OpBuilder &builder,
OperationState &result, Type outputType,
Value input) {
result.addOperands(input);
auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType);
if (quantAttr)
result.addAttribute("quantization_info", quantAttr);
result.types.push_back(outputType);
}
/// This builder is called on TOSA pad operator that needs to create its own
/// OptionalAttr quantization_attr parameter to scale the padding values
/// correctly.
static void buildPadOpWithQuantInfo(OpBuilder &builder, OperationState &result,
Type outputType, Value input,
Value paddings) {
result.addOperands({input, paddings});
auto quantAttr = buildPadOpQuantizationAttr(builder, input);
if (quantAttr)
result.addAttribute("quantization_info", quantAttr);
result.types.push_back(outputType);
}
//===----------------------------------------------------------------------===//
// TOSA Operator Return Type Inference.
//===----------------------------------------------------------------------===//
static void getI64Values(ArrayAttr arrayAttr, SmallVector<int64_t> &values) {
for (auto it : arrayAttr) {
values.push_back(it.cast<IntegerAttr>().getValue().getSExtValue());
}
}
LogicalResult tosa::ArgMaxOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
ShapedType inputTy = operands[0].getType().cast<ShapedType>();
IntegerAttr axis = attributes.get("axis").cast<IntegerAttr>();
int32_t axisVal = axis.getValue().getSExtValue();
if (!inputTy.hasRank()) {
inferredReturnShapes.push_back(ShapedTypeComponents());
return success();
}
SmallVector<int64_t> outShape;
outShape.reserve(inputTy.getRank() - 1);
for (int i = 0, s = inputTy.getRank(); i < s; i++) {
if (i == axisVal)
continue;
outShape.push_back(inputTy.getDimSize(i));
}
inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
return success();
}
LogicalResult tosa::ConcatOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
// Infer all dimension sizes by reducing based on inputs.
int32_t axis =
attributes.get("axis").cast<IntegerAttr>().getValue().getSExtValue();
llvm::SmallVector<int64_t> outputShape;
bool hasRankedInput = false;
for (auto operand : operands) {
ShapedType operandTy = operand.getType().cast<ShapedType>();
if (!operandTy.hasRank())
continue;
// Copy the Operand's rank.
if (!hasRankedInput)
outputShape.resize(operandTy.getRank(), -1);
// Copy shapes until the dim is non-dynamic.
for (int i = 0, s = operandTy.getRank(); i < s; i++) {
if (i == axis || operandTy.isDynamicDim(i))
continue;
if (outputShape[i] == -1)
outputShape[i] = operandTy.getDimSize(i);
if (outputShape[i] != operandTy.getDimSize(i))
return failure();
}
hasRankedInput = true;
}
if (!hasRankedInput) {
inferredReturnShapes.push_back(ShapedTypeComponents());
return success();
}
// Determine the dimension size along the concatenation axis.
int concatDimSize = 0;
for (auto operand : operands) {
ShapedType operandTy = operand.getType().cast<ShapedType>();
// We need to know the length of the concatenation axis of all inputs to
// determine the dimension size of the output shape.
if (!operandTy.hasRank() || operandTy.isDynamicDim(axis)) {
concatDimSize = -1;
break;
}
concatDimSize += operandTy.getDimSize(axis);
}
outputShape[axis] = concatDimSize;
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
LogicalResult tosa::FullyConnectedOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
ShapedType inputTy = operands[0].getType().cast<ShapedType>();
ShapedType weightTy = operands[1].getType().cast<ShapedType>();
ShapedType biasTy = operands[2].getType().cast<ShapedType>();
// All shapes are dynamic.
SmallVector<int64_t> outShape;
outShape.resize(2, -1);
if (inputTy.hasRank()) {
outShape[0] = inputTy.getDimSize(0);
}
if (weightTy.hasRank()) {
outShape[1] = weightTy.getDimSize(0);
}
if (biasTy.hasRank()) {
outShape[1] = outShape[1] == -1 ? biasTy.getDimSize(0) : outShape[1];
}
inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
return success();
}
LogicalResult tosa::MatMulOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
ShapedType lhsTy = operands[0].getType().cast<ShapedType>();
ShapedType rhsTy = operands[1].getType().cast<ShapedType>();
// All shapes are dynamic.
SmallVector<int64_t> outShape;
outShape.resize(3, -1);
if (lhsTy.hasRank()) {
outShape[0] = lhsTy.getDimSize(0);
outShape[1] = lhsTy.getDimSize(1);
}
if (rhsTy.hasRank()) {
outShape[0] = outShape[0] == -1 ? rhsTy.getDimSize(0) : outShape[0];
outShape[2] = rhsTy.getDimSize(2);
}
inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
return success();
}
LogicalResult tosa::PadOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
ShapedType inputTy = operands[0].getType().cast<ShapedType>();
ShapedType paddingTy = operands[1].getType().cast<ShapedType>();
SmallVector<int64_t> outputShape;
// If both inputs have unknown shape, we cannot determine the shape of the
// output.
if (!inputTy.hasRank() && !paddingTy.hasRank()) {
inferredReturnShapes.push_back(ShapedTypeComponents());
return success();
}
// If the input rank is unknown we can info the output rank using the padding
// shape's first dim.
if (!inputTy.hasRank()) {
if (paddingTy.isDynamicDim(0)) {
inferredReturnShapes.push_back(ShapedTypeComponents());
return success();
}
outputShape.resize(paddingTy.getDimSize(0), -1);
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
DenseIntElementsAttr paddings;
// If the paddings value is not a constant, all dimensions must be dynamic.
if (!matchPattern(operands[1], m_Constant(&paddings))) {
outputShape.resize(inputTy.getRank(), -1);
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
SmallVector<int64_t> paddingValues;
for (auto val : paddings) {
paddingValues.push_back(val.getSExtValue());
}
outputShape.reserve(inputTy.getRank());
for (int i = 0, s = inputTy.getRank(); i < s; i++) {
if (inputTy.isDynamicDim(i)) {
outputShape.push_back(-1);
continue;
}
outputShape.push_back(inputTy.getDimSize(i) + paddingValues[i * 2] +
paddingValues[i * 2 + 1]);
}
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
LogicalResult tosa::SliceOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
auto sizes = attributes.get("size").cast<ArrayAttr>().getValue();
SmallVector<int64_t> outputShape;
outputShape.reserve(sizes.size());
for (auto val : sizes) {
outputShape.push_back(val.cast<IntegerAttr>().getValue().getSExtValue());
}
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
LogicalResult tosa::TableOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
ShapedType inputTy = operands[0].getType().cast<ShapedType>();
if (!inputTy.hasRank()) {
inferredReturnShapes.push_back(ShapedTypeComponents());
return success();
}
inferredReturnShapes.push_back(inputTy.getShape());
return success();
}
LogicalResult tosa::TileOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
auto multiples = attributes.get("multiples").cast<ArrayAttr>().getValue();
ShapedType inputTy = operands[0].getType().cast<ShapedType>();
SmallVector<int64_t> outputShape;
if (!inputTy.hasRank()) {
outputShape.resize(multiples.size(), -1);
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
// We need the multiple values to determine the output shape.
SmallVector<int64_t> multipleValues;
multipleValues.reserve(multiples.size());
for (auto val : multiples) {
multipleValues.push_back(val.cast<IntegerAttr>().getValue().getSExtValue());
}
// Any non dynamic dimension can be multiplied to a known size.
outputShape.reserve(multiples.size());
for (int i = 0, s = inputTy.getRank(); i < s; i++) {
int dim = inputTy.getDimSize(i);
if (dim != -1)
dim *= multipleValues[i];
outputShape.push_back(dim);
}
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
LogicalResult tosa::ReshapeOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
ShapedType type = operands.front().getType().cast<ShapedType>();
auto newShape = attributes.get("new_shape").cast<ArrayAttr>();
llvm::SmallVector<int64_t> newShapeValue;
getI64Values(newShape, newShapeValue);
// We cannot infer from the total number of elements so we must take the
// shape attribute as exact.
if (!type.hasRank() || !type.hasStaticShape()) {
inferredReturnShapes.push_back(ShapedTypeComponents(newShapeValue));
return success();
}
// Determine the number of elements covered by the slice of all static
// dimensions. This allows us to infer the length of the remaining dynamic
// dimension.
int64_t numElements = type.getNumElements();
int64_t staticMul = 1;
for (auto val : newShapeValue) {
if (val != -1) {
staticMul *= val;
}
}
// Determine the length of the dynamic dimension.
for (auto &val : newShapeValue) {
if (val == -1)
val = numElements / staticMul;
}
inferredReturnShapes.push_back(ShapedTypeComponents(newShapeValue));
return success();
}
LogicalResult tosa::TransposeOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
ShapedType inputTy = operands[0].getType().cast<ShapedType>();
ShapedType permsTy = operands[1].getType().cast<ShapedType>();
// If input rank and permutation length is unknown, the output rank is
// unknown.
if (!inputTy.hasRank() && (!permsTy.hasRank() || permsTy.isDynamicDim(0))) {
inferredReturnShapes.push_back(ShapedTypeComponents());
return success();
}
// Without the input dims we cannot determine the output dim sizes but we
// can determine the output rank.
SmallVector<int64_t> outputShape;
if (!inputTy.hasRank()) {
outputShape.resize(permsTy.getDimSize(0), -1);
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
// Rank-0 means no permutations matter.
if (inputTy.getRank() == 0) {
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
// Check whether the input dimensions are all the same.
bool allTheSame = true;
for (int i = 1, s = inputTy.getRank(); i < s; i++) {
if (inputTy.getDimSize(0) != inputTy.getDimSize(i)) {
allTheSame = false;
break;
}
}
// If all of the input dimensions are the same we don't care about the
// permutation.
if (allTheSame) {
outputShape.resize(inputTy.getRank(), inputTy.getDimSize(0));
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
DenseIntElementsAttr perms;
outputShape.resize(inputTy.getRank(), -1);
// If the permuations are a constant we can directly determine the output
// shape.
if (matchPattern(operands[1], m_Constant(&perms))) {
llvm::SmallVector<int64_t> permValues;
for (auto val : perms) {
permValues.push_back(val.getSExtValue());
}
outputShape.reserve(inputTy.getRank());
for (int i = 0, s = inputTy.getRank(); i < s; i++) {
outputShape[i] = inputTy.getDimSize(permValues[i]);
}
}
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
LogicalResult tosa::GatherOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
llvm::SmallVector<int64_t> outputShape;
outputShape.resize(3, -1);
if (auto ty = operands[0].getType().dyn_cast<RankedTensorType>()) {
outputShape[0] = ty.getDimSize(0);
outputShape[2] = ty.getDimSize(2);
}
if (auto ty = operands[1].getType().dyn_cast<RankedTensorType>()) {
if (outputShape[0] == -1)
outputShape[0] = ty.getDimSize(0);
if (outputShape[1] == -1)
outputShape[1] = ty.getDimSize(1);
}
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
LogicalResult tosa::ScatterOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
llvm::SmallVector<int64_t> outputShape;
outputShape.resize(3, -1);
if (auto ty = operands[0].getType().dyn_cast<RankedTensorType>()) {
outputShape[0] = ty.getDimSize(0);
outputShape[1] = ty.getDimSize(1);
outputShape[2] = ty.getDimSize(2);
}
if (auto ty = operands[1].getType().dyn_cast<RankedTensorType>()) {
if (outputShape[0] == -1)
outputShape[0] = ty.getDimSize(0);
}
if (auto ty = operands[2].getType().dyn_cast<RankedTensorType>()) {
if (outputShape[0] == -1)
outputShape[0] = ty.getDimSize(0);
if (outputShape[2] == -1)
outputShape[2] = ty.getDimSize(2);
}
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
static LogicalResult ReduceInferReturnTypes(
Value operand, IntegerAttr axis,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
auto operandTy = operand.getType().cast<ShapedType>();
if (!operandTy.hasRank()) {
inferredReturnShapes.push_back(ShapedTypeComponents());
return success();
}
int64_t axisVal = axis.getValue().getSExtValue();
SmallVector<int64_t> outputShape;
outputShape.reserve(operandTy.getRank());
for (auto dim : operandTy.getShape()) {
outputShape.push_back(dim);
}
outputShape[axisVal] = 1;
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
#define REDUCE_SHAPE_INFER(OP) \
LogicalResult OP::inferReturnTypeComponents( \
MLIRContext *context, ::llvm::Optional<Location> location, \
ValueRange operands, DictionaryAttr attributes, RegionRange regions, \
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) { \
return ReduceInferReturnTypes(operands[0], \
attributes.get("axis").cast<IntegerAttr>(), \
inferredReturnShapes); \
}
REDUCE_SHAPE_INFER(tosa::ReduceAllOp)
REDUCE_SHAPE_INFER(tosa::ReduceAnyOp)
REDUCE_SHAPE_INFER(tosa::ReduceMaxOp)
REDUCE_SHAPE_INFER(tosa::ReduceMinOp)
REDUCE_SHAPE_INFER(tosa::ReduceProdOp)
REDUCE_SHAPE_INFER(tosa::ReduceSumOp)
#undef REDUCE_SHAPE_INFER
static LogicalResult resolveBroadcastShape(ValueRange operands,
SmallVector<int64_t> &outShape) {
int64_t outRank = 0;
for (auto operand : operands) {
auto type = operand.getType().cast<ShapedType>();
if (!type.hasRank())
return failure();
outRank = std::max<int64_t>(outRank, type.getRank());
}
outShape.resize(outRank, 1);
for (auto operand : operands) {
auto type = operand.getType().cast<ShapedType>();
auto shape = type.getShape();
auto rankDiff = outShape.size() - shape.size();
for (size_t i = 0; i < shape.size(); i++) {
auto dim1 = outShape[i + rankDiff];
auto dim2 = shape[i];
auto resolvedDim = dim1;
if (dim1 == 1) {
resolvedDim = dim2;
} else if (dim2 == 1) {
resolvedDim = dim1;
} else if (dim1 != dim2) {
return failure();
}
outShape[i + rankDiff] = resolvedDim;
}
}
return success();
}
static LogicalResult NAryInferReturnTypes(
ValueRange operands,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
llvm::SmallVector<int64_t> outShape;
if (resolveBroadcastShape(operands, outShape).failed()) {
inferredReturnShapes.push_back(ShapedTypeComponents());
} else {
inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
}
return success();
}
#define NARY_SHAPE_INFER(OP) \
LogicalResult OP::inferReturnTypeComponents( \
MLIRContext *context, ::llvm::Optional<Location> location, \
ValueRange operands, DictionaryAttr attributes, RegionRange regions, \
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) { \
return NAryInferReturnTypes(operands, inferredReturnShapes); \
}
NARY_SHAPE_INFER(tosa::AbsOp)
NARY_SHAPE_INFER(tosa::AddOp)
NARY_SHAPE_INFER(tosa::ArithmeticRightShiftOp)
NARY_SHAPE_INFER(tosa::BitwiseAndOp)
NARY_SHAPE_INFER(tosa::BitwiseOrOp)
NARY_SHAPE_INFER(tosa::BitwiseXorOp)
NARY_SHAPE_INFER(tosa::BitwiseNotOp)
NARY_SHAPE_INFER(tosa::CeilOp)
NARY_SHAPE_INFER(tosa::ClampOp)
NARY_SHAPE_INFER(tosa::ClzOp)
NARY_SHAPE_INFER(tosa::DivOp)
NARY_SHAPE_INFER(tosa::EqualOp)
NARY_SHAPE_INFER(tosa::ExpOp)
NARY_SHAPE_INFER(tosa::FloorOp)
NARY_SHAPE_INFER(tosa::GreaterEqualOp)
NARY_SHAPE_INFER(tosa::GreaterOp)
NARY_SHAPE_INFER(tosa::LogOp)
NARY_SHAPE_INFER(tosa::LogicalAndOp)
NARY_SHAPE_INFER(tosa::LogicalLeftShiftOp)
NARY_SHAPE_INFER(tosa::LogicalNotOp)
NARY_SHAPE_INFER(tosa::LogicalOrOp)
NARY_SHAPE_INFER(tosa::LogicalRightShiftOp)
NARY_SHAPE_INFER(tosa::LogicalXorOp)
NARY_SHAPE_INFER(tosa::MaximumOp)
NARY_SHAPE_INFER(tosa::MinimumOp)
NARY_SHAPE_INFER(tosa::MulOp)
NARY_SHAPE_INFER(tosa::NegateOp)
NARY_SHAPE_INFER(tosa::PowOp)
NARY_SHAPE_INFER(tosa::ReciprocalOp)
NARY_SHAPE_INFER(tosa::ReluNOp)
NARY_SHAPE_INFER(tosa::ReverseOp)
NARY_SHAPE_INFER(tosa::RsqrtOp)
NARY_SHAPE_INFER(tosa::SelectOp)
NARY_SHAPE_INFER(tosa::SubOp)
NARY_SHAPE_INFER(tosa::TanhOp)
NARY_SHAPE_INFER(tosa::SigmoidOp)
#undef PRED_SHAPE_INFER
static LogicalResult poolingInferReturnTypes(
ValueRange operands, DictionaryAttr attributes,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
RankedTensorType inputTy = operands[0].getType().dyn_cast<RankedTensorType>();
llvm::SmallVector<int64_t> outputShape;
outputShape.resize(4, -1);
// We only know the rank if the input type is unranked.
if (!inputTy) {
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
// Batch and number of channels are identical for pooling layer.
outputShape[0] = inputTy.getDimSize(0);
outputShape[3] = inputTy.getDimSize(3);
int32_t height = inputTy.getDimSize(1);
int32_t width = inputTy.getDimSize(2);
llvm::SmallVector<int64_t> kernel;
llvm::SmallVector<int64_t> stride;
llvm::SmallVector<int64_t> pad;
getI64Values(attributes.get("kernel").cast<ArrayAttr>(), kernel);
getI64Values(attributes.get("stride").cast<ArrayAttr>(), stride);
getI64Values(attributes.get("pad").cast<ArrayAttr>(), pad);
if (height != -1) {
int32_t padded = height + pad[0] + pad[1] - kernel[0];
outputShape[1] = padded / stride[0] + 1;
}
if (width != -1) {
int32_t padded = width + pad[2] + pad[3] - kernel[1];
outputShape[2] = padded / stride[1] + 1;
}
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
LogicalResult AvgPool2dOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
return poolingInferReturnTypes(operands, attributes, inferredReturnShapes);
}
LogicalResult MaxPool2dOp::inferReturnTypeComponents(
MLIRContext *context, ::llvm::Optional<Location> location,
ValueRange operands, DictionaryAttr attributes, RegionRange regions,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
return poolingInferReturnTypes(operands, attributes, inferredReturnShapes);
}
//===----------------------------------------------------------------------===//
// TOSA Operator Definitions.
//===----------------------------------------------------------------------===//
#define GET_OP_CLASSES
#include "mlir/Dialect/Tosa/IR/TosaOps.cpp.inc"
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