9e1a344155
This diff completes switching Tosa to DenseArrayAttr. Test plan: ninja check-mlir check-all Differential revision: https://reviews.llvm.org/D141111
1379 lines
51 KiB
C++
1379 lines
51 KiB
C++
//===- 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/Quant/QuantOps.h"
|
|
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
|
#include "mlir/Dialect/Tosa/Utils/QuantUtils.h"
|
|
#include "mlir/Dialect/Tosa/Utils/ShapeUtils.h"
|
|
#include "mlir/IR/BuiltinTypes.h"
|
|
#include "mlir/IR/DialectImplementation.h"
|
|
#include "mlir/IR/Matchers.h"
|
|
#include "mlir/IR/PatternMatch.h"
|
|
#include "mlir/IR/TypeUtilities.h"
|
|
#include "mlir/Transforms/InliningUtils.h"
|
|
#include "llvm/ADT/DenseMap.h"
|
|
#include "llvm/ADT/TypeSwitch.h"
|
|
|
|
using namespace mlir;
|
|
using namespace mlir::tosa;
|
|
|
|
#include "mlir/Dialect/Tosa/IR/TosaOpsDialect.cpp.inc"
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Tosa dialect interface includes.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "mlir/Dialect/Tosa/IR/TosaInterfaces.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()));
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TOSA control flow support.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Returns the while loop body.
|
|
Region &tosa::WhileOp::getLoopBody() { return getBody(); }
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Tosa dialect initialization.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void TosaDialect::initialize() {
|
|
addOperations<
|
|
#define GET_OP_LIST
|
|
#include "mlir/Dialect/Tosa/IR/TosaOps.cpp.inc"
|
|
>();
|
|
addAttributes<
|
|
#define GET_ATTRDEF_LIST
|
|
#include "mlir/Dialect/Tosa/IR/TosaAttributes.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;
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TOSA Operator Verifiers.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
template <typename T> static LogicalResult verifyConvOp(T op) {
|
|
// All TOSA conv ops have an input() and weight().
|
|
auto inputType =
|
|
op.getInput().getType().template dyn_cast<RankedTensorType>();
|
|
auto weightType =
|
|
op.getWeight().getType().template dyn_cast<RankedTensorType>();
|
|
|
|
// Must be ranked tensor types
|
|
if (!inputType) {
|
|
op.emitOpError("expect a ranked tensor for input, got ") << op.getInput();
|
|
return failure();
|
|
}
|
|
if (!weightType) {
|
|
op.emitOpError("expect a ranked tensor for weight, got ") << op.getWeight();
|
|
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) {
|
|
op.emitOpError(
|
|
"expect both input and weight to be float or not together, got ")
|
|
<< inputEType << " and " << weightEType;
|
|
return failure();
|
|
}
|
|
|
|
// Quantized type must have constructed the quantizationattr, and unquantized
|
|
// types should not have a quantizationattr.
|
|
if ((inputIsQuant && !op.getQuantizationInfo()) ||
|
|
(!inputIsQuant && op.getQuantizationInfo())) {
|
|
op.emitOpError("quantizationattr is required for quantized type, and not "
|
|
"allowed for float type");
|
|
return failure();
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::AvgPool2dOp::verify() {
|
|
auto inputETy = getInput().getType().cast<ShapedType>().getElementType();
|
|
auto resultETy = getType().cast<ShapedType>().getElementType();
|
|
|
|
if (auto quantType = inputETy.dyn_cast<mlir::quant::UniformQuantizedType>())
|
|
inputETy = quantType.getStorageType();
|
|
|
|
if (auto quantType = resultETy.dyn_cast<mlir::quant::UniformQuantizedType>())
|
|
resultETy = quantType.getStorageType();
|
|
|
|
if (inputETy.isF32() && resultETy.isF32())
|
|
return success();
|
|
if (inputETy.isInteger(8) && resultETy.isInteger(8))
|
|
return success();
|
|
if (inputETy.isInteger(16) && resultETy.isInteger(16))
|
|
return success();
|
|
|
|
return emitOpError("input/output element types are incompatible.");
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// 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, DenseI64ArrayAttr pad,
|
|
DenseI64ArrayAttr stride,
|
|
DenseI64ArrayAttr 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, DenseI64ArrayAttr outpad,
|
|
DenseI64ArrayAttr stride, DenseI64ArrayAttr outputShape) {
|
|
result.addOperands({input, weight, bias});
|
|
result.addAttribute("out_pad", outpad);
|
|
result.addAttribute("stride", stride);
|
|
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<ShapedType>();
|
|
assert(inputType && "Input must be a shaped 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<ShapedType>();
|
|
assert(outputShapedType && "Output must be a shaped type");
|
|
|
|
IntegerType accElementType;
|
|
if (inputBits == 16)
|
|
accElementType = builder.getIntegerType(48);
|
|
else
|
|
accElementType = builder.getI32Type();
|
|
auto accType = outputShapedType.clone(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,
|
|
DenseArrayAttr kernel, DenseArrayAttr stride, DenseArrayAttr 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. No pad_const is interpreted as zero-padding.
|
|
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);
|
|
}
|
|
|
|
/// This builder is called on TOSA pad operator when an explicit pad_const
|
|
/// value is passed in. It also optionally constructs quantization_attr.
|
|
static void buildExplicitValuePadOpWithQuantInfo(OpBuilder &builder,
|
|
OperationState &result,
|
|
Type outputType, Value input,
|
|
Value paddings,
|
|
Value padConst) {
|
|
result.addOperands({input, paddings, padConst});
|
|
auto quantAttr = buildPadOpQuantizationAttr(builder, input);
|
|
if (quantAttr)
|
|
result.addAttribute("quantization_info", quantAttr);
|
|
result.types.push_back(outputType);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TOSA Operator Return Type Inference.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult resolveBroadcastShape(const ValueShapeRange &operands,
|
|
SmallVector<int64_t> &outShape) {
|
|
int64_t outRank = 0;
|
|
for (int i = 0, e = operands.size(); i != e; ++i) {
|
|
auto shape = operands.getShape(i);
|
|
if (!shape.hasRank()) {
|
|
// TODO(jennik): Update function to have better case handling for invalid
|
|
// operands and for ranked tensors.
|
|
return failure();
|
|
}
|
|
outRank = std::max<int64_t>(outRank, shape.getRank());
|
|
}
|
|
|
|
outShape.resize(outRank, 1);
|
|
|
|
for (int i = 0, e = operands.size(); i != e; ++i) {
|
|
auto shape = operands.getShape(i);
|
|
auto rankDiff = outShape.size() - shape.getRank();
|
|
|
|
for (size_t i = 0, e = shape.getRank(); i < e; ++i) {
|
|
auto dim1 = outShape[i + rankDiff];
|
|
auto dim2 = shape.getDimSize(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();
|
|
}
|
|
|
|
LogicalResult tosa::ArgMaxOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
ShapeAdaptor inputShape = operands.getShape(0);
|
|
IntegerAttr axis = attributes.get("axis").cast<IntegerAttr>();
|
|
int32_t axisVal = axis.getValue().getSExtValue();
|
|
|
|
if (!inputShape.hasRank()) {
|
|
inferredReturnShapes.push_back(ShapedTypeComponents());
|
|
return success();
|
|
}
|
|
|
|
SmallVector<int64_t> outShape;
|
|
outShape.reserve(inputShape.getRank() - 1);
|
|
for (int i = 0, s = inputShape.getRank(); i < s; i++) {
|
|
if (i == axisVal)
|
|
continue;
|
|
outShape.push_back(inputShape.getDimSize(i));
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::ConcatOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange 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) {
|
|
ShapeAdaptor operandShape = operands.getShape(operand);
|
|
if (!operandShape.hasRank())
|
|
continue;
|
|
|
|
// Copy the Operand's rank.
|
|
if (!hasRankedInput)
|
|
outputShape.resize(operandShape.getRank(), ShapedType::kDynamic);
|
|
|
|
// Copy shapes until the dim is non-dynamic.
|
|
for (int i = 0, s = operandShape.getRank(); i < s; i++) {
|
|
if (i == axis || operandShape.isDynamicDim(i))
|
|
continue;
|
|
if (outputShape[i] == ShapedType::kDynamic)
|
|
outputShape[i] = operandShape.getDimSize(i);
|
|
if (outputShape[i] != operandShape.getDimSize(i))
|
|
return failure();
|
|
}
|
|
|
|
hasRankedInput = true;
|
|
}
|
|
|
|
if (!hasRankedInput) {
|
|
inferredReturnShapes.push_back(ShapedTypeComponents());
|
|
return success();
|
|
}
|
|
|
|
// Determine the dimension size along the concatenation axis.
|
|
int64_t concatDimSize = 0;
|
|
for (auto operand : operands) {
|
|
ShapeAdaptor operandShape = operands.getShape(operand);
|
|
|
|
// We need to know the length of the concatenation axis of all inputs to
|
|
// determine the dimension size of the output shape.
|
|
if (!operandShape.hasRank() || operandShape.isDynamicDim(axis)) {
|
|
concatDimSize = ShapedType::kDynamic;
|
|
break;
|
|
}
|
|
|
|
concatDimSize += operandShape.getDimSize(axis);
|
|
}
|
|
|
|
outputShape[axis] = concatDimSize;
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::EqualOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
llvm::SmallVector<int64_t> outShape;
|
|
if (resolveBroadcastShape(operands, outShape).failed()) {
|
|
inferredReturnShapes.push_back(ShapedTypeComponents());
|
|
return success();
|
|
}
|
|
|
|
inferredReturnShapes.push_back(
|
|
ShapedTypeComponents(outShape, IntegerType::get(context, /*width=*/1)));
|
|
return success();
|
|
}
|
|
|
|
bool tosa::EqualOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
|
|
if (l.size() != r.size() || l.size() != 1)
|
|
return false;
|
|
return succeeded(verifyCompatibleShape(l[0], r[0]));
|
|
}
|
|
|
|
LogicalResult tosa::FullyConnectedOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
ShapeAdaptor inputShape = operands.getShape(0);
|
|
ShapeAdaptor weightShape = operands.getShape(1);
|
|
ShapeAdaptor biasShape = operands.getShape(2);
|
|
|
|
// All shapes are dynamic.
|
|
SmallVector<int64_t> outShape;
|
|
outShape.resize(2, ShapedType::kDynamic);
|
|
|
|
if (inputShape.hasRank()) {
|
|
outShape[0] = inputShape.getDimSize(0);
|
|
}
|
|
|
|
if (weightShape.hasRank()) {
|
|
outShape[1] = weightShape.getDimSize(0);
|
|
}
|
|
|
|
if (biasShape.hasRank()) {
|
|
outShape[1] = outShape[1] == ShapedType::kDynamic ? biasShape.getDimSize(0)
|
|
: outShape[1];
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult FullyConnectedOp::verify() { return verifyConvOp(*this); }
|
|
|
|
LogicalResult tosa::MatMulOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
ShapeAdaptor lhsShape = operands.getShape(0);
|
|
ShapeAdaptor rhsShape = operands.getShape(1);
|
|
|
|
// All shapes are dynamic.
|
|
SmallVector<int64_t> outShape;
|
|
outShape.resize(3, ShapedType::kDynamic);
|
|
|
|
if (lhsShape.hasRank()) {
|
|
outShape[0] = lhsShape.getDimSize(0);
|
|
outShape[1] = lhsShape.getDimSize(1);
|
|
}
|
|
|
|
if (rhsShape.hasRank()) {
|
|
outShape[0] = outShape[0] == ShapedType::kDynamic ? rhsShape.getDimSize(0)
|
|
: outShape[0];
|
|
outShape[2] = rhsShape.getDimSize(2);
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::PadOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
ShapeAdaptor inputShape = operands.getShape(0);
|
|
ShapeAdaptor paddingShape = operands.getShape(1);
|
|
SmallVector<int64_t> outputShape;
|
|
|
|
// If both inputs have unknown shape, we cannot determine the shape of the
|
|
// output.
|
|
if (!inputShape.hasRank() && !paddingShape.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 (!inputShape.hasRank()) {
|
|
if (paddingShape.isDynamicDim(0)) {
|
|
inferredReturnShapes.push_back(ShapedTypeComponents());
|
|
return success();
|
|
}
|
|
|
|
outputShape.resize(paddingShape.getDimSize(0), ShapedType::kDynamic);
|
|
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(inputShape.getRank(), ShapedType::kDynamic);
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
SmallVector<int64_t> paddingValues;
|
|
for (auto val : paddings) {
|
|
paddingValues.push_back(val.getSExtValue());
|
|
}
|
|
|
|
outputShape.reserve(inputShape.getRank());
|
|
for (int i = 0, s = inputShape.getRank(); i < s; i++) {
|
|
if (inputShape.isDynamicDim(i)) {
|
|
outputShape.push_back(ShapedType::kDynamic);
|
|
continue;
|
|
}
|
|
|
|
outputShape.push_back(inputShape.getDimSize(i) + paddingValues[i * 2] +
|
|
paddingValues[i * 2 + 1]);
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
static SmallVector<int64_t> convertToMlirShape(ArrayRef<int64_t> shape) {
|
|
return to_vector(llvm::map_range(shape, [](int64_t dim) {
|
|
return dim == -1 ? ShapedType::kDynamic : dim;
|
|
}));
|
|
}
|
|
|
|
LogicalResult tosa::SliceOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(
|
|
convertToMlirShape(SliceOpAdaptor(operands, attributes).getSize())));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::TableOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
ShapeAdaptor inputShape = operands.getShape(0);
|
|
|
|
if (!inputShape.hasRank()) {
|
|
inferredReturnShapes.push_back(ShapedTypeComponents());
|
|
return success();
|
|
}
|
|
|
|
inferredReturnShapes.resize(1);
|
|
inputShape.getDims(inferredReturnShapes[0]);
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::TileOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
TileOpAdaptor adaptor(operands, attributes);
|
|
ArrayRef<int64_t> multiples = adaptor.getMultiples();
|
|
ShapeAdaptor inputShape = operands.getShape(0);
|
|
SmallVector<int64_t> outputShape;
|
|
if (!inputShape.hasRank()) {
|
|
outputShape.resize(multiples.size(), ShapedType::kDynamic);
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
// Any non dynamic dimension can be multiplied to a known size.
|
|
outputShape.reserve(multiples.size());
|
|
for (int i = 0, s = inputShape.getRank(); i < s; i++) {
|
|
int64_t dim = inputShape.getDimSize(i);
|
|
if (dim != ShapedType::kDynamic)
|
|
dim *= multiples[i];
|
|
outputShape.push_back(dim);
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::ReshapeOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
ReshapeOpAdaptor adaptor(operands, attributes);
|
|
ShapeAdaptor inputShape = operands.getShape(0);
|
|
llvm::SmallVector<int64_t> newShapeValue =
|
|
convertToMlirShape(adaptor.getNewShape());
|
|
|
|
// We cannot infer from the total number of elements so we must take the
|
|
// shape attribute as exact.
|
|
if (!inputShape.hasRank() || !inputShape.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 = inputShape.getNumElements();
|
|
int64_t staticMul = 1;
|
|
for (auto val : newShapeValue) {
|
|
if (!ShapedType::isDynamic(val)) {
|
|
staticMul *= val;
|
|
}
|
|
}
|
|
|
|
// Determine the length of the dynamic dimension.
|
|
for (auto &val : newShapeValue) {
|
|
if (ShapedType::isDynamic(val))
|
|
val = numElements / staticMul;
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(newShapeValue));
|
|
return success();
|
|
}
|
|
|
|
mlir::LogicalResult tosa::ReshapeOp::verify() {
|
|
ShapedType inputType = getInput1().getType().cast<ShapedType>();
|
|
ShapedType outputType = getType().cast<ShapedType>();
|
|
|
|
if (inputType.hasStaticShape() && outputType.hasStaticShape()) {
|
|
int64_t inputElementsNum = inputType.getNumElements();
|
|
int64_t outputElementsNum = outputType.getNumElements();
|
|
if (inputElementsNum != outputElementsNum) {
|
|
return emitOpError() << "Cannot reshape " << inputElementsNum
|
|
<< " elements into " << outputElementsNum;
|
|
}
|
|
}
|
|
return mlir::success();
|
|
}
|
|
|
|
LogicalResult tosa::TransposeOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
ShapeAdaptor inputShape = operands.getShape(0);
|
|
ShapeAdaptor permsShape = operands.getShape(1);
|
|
|
|
// If input rank and permutation length is unknown, the output rank is
|
|
// unknown.
|
|
if (!inputShape.hasRank() || !permsShape.hasRank() ||
|
|
permsShape.isDynamicDim(0)) {
|
|
inferredReturnShapes.push_back(ShapedTypeComponents());
|
|
return success();
|
|
}
|
|
|
|
// This would imply the number of permutations does not match the rank of the
|
|
// input which is illegal.
|
|
if (permsShape.getDimSize(0) != inputShape.getRank()) {
|
|
return failure();
|
|
}
|
|
|
|
// Without the input dims we cannot determine the output dim sizes but we
|
|
// can determine the output rank.
|
|
SmallVector<int64_t> outputShape;
|
|
if (!inputShape.hasRank()) {
|
|
outputShape.resize(permsShape.getDimSize(0), ShapedType::kDynamic);
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
// Rank-0 means no permutations matter.
|
|
if (inputShape.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 = inputShape.getRank(); i < s; i++) {
|
|
if (inputShape.getDimSize(0) != inputShape.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(inputShape.getRank(), inputShape.getDimSize(0));
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
outputShape.resize(inputShape.getRank(), ShapedType::kDynamic);
|
|
// If the permuations are a constant we can directly determine the output
|
|
// shape.
|
|
if (ShapeAdaptor permShape = operands.getValueAsShape(1)) {
|
|
outputShape.reserve(inputShape.getRank());
|
|
for (int i = 0, s = inputShape.getRank(); i < s; i++) {
|
|
outputShape[i] = inputShape.getDimSize(permShape.getDimSize(i));
|
|
}
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::GatherOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
llvm::SmallVector<int64_t> outputShape;
|
|
outputShape.resize(3, ShapedType::kDynamic);
|
|
|
|
ShapeAdaptor valuesShape = operands.getShape(0);
|
|
if (valuesShape.hasRank()) {
|
|
outputShape[0] = valuesShape.getDimSize(0);
|
|
outputShape[2] = valuesShape.getDimSize(2);
|
|
}
|
|
|
|
ShapeAdaptor indicesShape = operands.getShape(1);
|
|
if (indicesShape.hasRank()) {
|
|
if (outputShape[0] == ShapedType::kDynamic)
|
|
outputShape[0] = indicesShape.getDimSize(0);
|
|
if (outputShape[1] == ShapedType::kDynamic)
|
|
outputShape[1] = indicesShape.getDimSize(1);
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::ResizeOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
ResizeOpAdaptor adaptor(operands, attributes);
|
|
llvm::SmallVector<int64_t, 4> outputShape;
|
|
outputShape.resize(4, ShapedType::kDynamic);
|
|
|
|
ShapeAdaptor inputShape = operands.getShape(adaptor.getInput());
|
|
if (!inputShape.hasRank())
|
|
return failure();
|
|
|
|
outputShape[0] = inputShape.getDimSize(0);
|
|
outputShape[3] = inputShape.getDimSize(3);
|
|
int64_t inputHeight = inputShape.getDimSize(1);
|
|
int64_t inputWidth = inputShape.getDimSize(2);
|
|
|
|
if ((inputHeight == ShapedType::kDynamic) ||
|
|
(inputWidth == ShapedType::kDynamic))
|
|
return failure();
|
|
|
|
llvm::ArrayRef<int64_t> scaleInt = adaptor.getScale();
|
|
llvm::ArrayRef<int64_t> offsetInt = adaptor.getOffset();
|
|
llvm::ArrayRef<int64_t> borderInt = adaptor.getBorder();
|
|
|
|
// Compute the output shape based on attributes: scale, offset, and border.
|
|
outputShape[1] =
|
|
(((inputHeight - 1) * scaleInt[0] - offsetInt[0] + borderInt[0]) /
|
|
scaleInt[1]) +
|
|
1;
|
|
|
|
outputShape[2] =
|
|
(((inputWidth - 1) * scaleInt[2] - offsetInt[1] + borderInt[1]) /
|
|
scaleInt[3]) +
|
|
1;
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult tosa::ScatterOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
llvm::SmallVector<int64_t> outputShape;
|
|
outputShape.resize(3, ShapedType::kDynamic);
|
|
|
|
ShapeAdaptor valuesInShape = operands.getShape(0);
|
|
if (valuesInShape.hasRank()) {
|
|
outputShape[0] = valuesInShape.getDimSize(0);
|
|
outputShape[1] = valuesInShape.getDimSize(1);
|
|
outputShape[2] = valuesInShape.getDimSize(2);
|
|
}
|
|
|
|
ShapeAdaptor indicesShape = operands.getShape(1);
|
|
if (indicesShape.hasRank()) {
|
|
if (outputShape[0] == ShapedType::kDynamic)
|
|
outputShape[0] = indicesShape.getDimSize(0);
|
|
}
|
|
|
|
ShapeAdaptor inputShape = operands.getShape(2);
|
|
if (inputShape.hasRank()) {
|
|
if (outputShape[0] == ShapedType::kDynamic)
|
|
outputShape[0] = inputShape.getDimSize(0);
|
|
if (outputShape[2] == ShapedType::kDynamic)
|
|
outputShape[2] = inputShape.getDimSize(2);
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
static LogicalResult ReduceInferReturnTypes(
|
|
ShapeAdaptor operandShape, IntegerAttr axis,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
if (!operandShape.hasRank()) {
|
|
inferredReturnShapes.push_back(ShapedTypeComponents());
|
|
return success();
|
|
}
|
|
|
|
SmallVector<int64_t> outputShape;
|
|
operandShape.getDims(outputShape);
|
|
int64_t axisVal = axis.getValue().getSExtValue();
|
|
outputShape[axisVal] = 1;
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
#define REDUCE_SHAPE_INFER(OP) \
|
|
LogicalResult OP::inferReturnTypeComponents( \
|
|
MLIRContext *context, ::std::optional<Location> location, \
|
|
ValueShapeRange operands, DictionaryAttr attributes, \
|
|
RegionRange regions, \
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) { \
|
|
return ReduceInferReturnTypes(operands.getShape(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 NAryInferReturnTypes(
|
|
const ValueShapeRange &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, ::std::optional<Location> location, \
|
|
ValueShapeRange 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::CastOp)
|
|
NARY_SHAPE_INFER(tosa::CeilOp)
|
|
NARY_SHAPE_INFER(tosa::ClampOp)
|
|
NARY_SHAPE_INFER(tosa::ClzOp)
|
|
NARY_SHAPE_INFER(tosa::DivOp)
|
|
NARY_SHAPE_INFER(tosa::ExpOp)
|
|
NARY_SHAPE_INFER(tosa::FloorOp)
|
|
NARY_SHAPE_INFER(tosa::GreaterEqualOp)
|
|
NARY_SHAPE_INFER(tosa::GreaterOp)
|
|
NARY_SHAPE_INFER(tosa::IdentityOp)
|
|
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::RescaleOp)
|
|
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(
|
|
const ValueShapeRange &operands, DictionaryAttr attributes,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
ShapeAdaptor inputShape = operands.getShape(0);
|
|
llvm::SmallVector<int64_t> outputShape;
|
|
outputShape.resize(4, ShapedType::kDynamic);
|
|
|
|
// We only know the rank if the input type is unranked.
|
|
if (!inputShape) {
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
// Batch and number of channels are identical for pooling layer.
|
|
outputShape[0] = inputShape.getDimSize(0);
|
|
outputShape[3] = inputShape.getDimSize(3);
|
|
|
|
int64_t height = inputShape.getDimSize(1);
|
|
int64_t width = inputShape.getDimSize(2);
|
|
|
|
ArrayRef<int64_t> kernel = attributes.get("kernel").cast<DenseI64ArrayAttr>();
|
|
ArrayRef<int64_t> stride = attributes.get("stride").cast<DenseI64ArrayAttr>();
|
|
ArrayRef<int64_t> pad = attributes.get("pad").cast<DenseI64ArrayAttr>();
|
|
|
|
if (!ShapedType::isDynamic(height)) {
|
|
int64_t padded = height + pad[0] + pad[1] - kernel[0];
|
|
outputShape[1] = padded / stride[0] + 1;
|
|
}
|
|
|
|
if (!ShapedType::isDynamic(width)) {
|
|
int64_t padded = width + pad[2] + pad[3] - kernel[1];
|
|
outputShape[2] = padded / stride[1] + 1;
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult Conv2DOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
llvm::SmallVector<int64_t> outputShape(4, ShapedType::kDynamic);
|
|
Conv2DOp::Adaptor adaptor(operands.getValues(), attributes);
|
|
|
|
int64_t inputWidth = ShapedType::kDynamic;
|
|
int64_t inputHeight = ShapedType::kDynamic;
|
|
int64_t weightWidth = ShapedType::kDynamic;
|
|
int64_t weightHeight = ShapedType::kDynamic;
|
|
|
|
// Input shape describes input width/height and batch.
|
|
|
|
ShapeAdaptor inputShape = operands.getShape(adaptor.getInput());
|
|
if (inputShape.hasRank()) {
|
|
outputShape[0] = inputShape.getDimSize(0);
|
|
inputHeight = inputShape.getDimSize(1);
|
|
inputWidth = inputShape.getDimSize(2);
|
|
}
|
|
|
|
// Weight shapes describes the filter width/height and the output channels.
|
|
ShapeAdaptor weightShape = operands.getShape(adaptor.getWeight());
|
|
if (weightShape.hasRank()) {
|
|
outputShape[3] = weightShape.getDimSize(0);
|
|
weightHeight = weightShape.getDimSize(1);
|
|
weightWidth = weightShape.getDimSize(2);
|
|
}
|
|
|
|
// Bias shape can describe the output channels.
|
|
ShapeAdaptor biasShape = operands.getShape(adaptor.getBias());
|
|
if (biasShape.hasRank()) {
|
|
outputShape[3] = ShapedType::isDynamic(outputShape[3])
|
|
? biasShape.getDimSize(0)
|
|
: outputShape[3];
|
|
}
|
|
|
|
llvm::ArrayRef<int64_t> dilation = adaptor.getDilation();
|
|
llvm::ArrayRef<int64_t> stride = adaptor.getStride();
|
|
llvm::ArrayRef<int64_t> padding = adaptor.getPad();
|
|
|
|
if (!ShapedType::isDynamic(inputHeight) &&
|
|
!ShapedType::isDynamic(weightHeight)) {
|
|
int64_t inputSize = inputHeight + padding[0] + padding[1];
|
|
int64_t filterSize = (weightHeight - 1) * dilation[0] + 1;
|
|
int64_t unstridedResult = inputSize - filterSize + 1;
|
|
outputShape[1] = (unstridedResult - 1) / stride[0] + 1;
|
|
}
|
|
|
|
if (!ShapedType::isDynamic(inputWidth) &&
|
|
!ShapedType::isDynamic(weightWidth)) {
|
|
int64_t inputSize = inputWidth + padding[2] + padding[3];
|
|
int64_t filterSize = (weightWidth - 1) * dilation[1] + 1;
|
|
int64_t unstridedResult = inputSize - filterSize + 1;
|
|
outputShape[2] = (unstridedResult - 1) / stride[1] + 1;
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult Conv2DOp::verify() { return verifyConvOp(*this); }
|
|
|
|
LogicalResult Conv3DOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
llvm::SmallVector<int64_t> outputShape(5, ShapedType::kDynamic);
|
|
Conv3DOp::Adaptor adaptor(operands.getValues(), attributes);
|
|
|
|
int64_t inputWidth = ShapedType::kDynamic;
|
|
int64_t inputHeight = ShapedType::kDynamic;
|
|
int64_t inputDepth = ShapedType::kDynamic;
|
|
|
|
int64_t weightWidth = ShapedType::kDynamic;
|
|
int64_t weightHeight = ShapedType::kDynamic;
|
|
int64_t weightDepth = ShapedType::kDynamic;
|
|
|
|
// Input shape describes input width/height and batch.
|
|
ShapeAdaptor inputShape = operands.getShape(adaptor.getInput());
|
|
if (inputShape.hasRank()) {
|
|
outputShape[0] = inputShape.getDimSize(0);
|
|
inputDepth = inputShape.getDimSize(1);
|
|
inputHeight = inputShape.getDimSize(2);
|
|
inputWidth = inputShape.getDimSize(3);
|
|
}
|
|
|
|
// Weight shapes describes the filter width/height and the output channels.
|
|
ShapeAdaptor weightShape = operands.getShape(adaptor.getWeight());
|
|
if (weightShape.hasRank()) {
|
|
outputShape[4] = weightShape.getDimSize(0);
|
|
weightDepth = weightShape.getDimSize(1);
|
|
weightHeight = weightShape.getDimSize(2);
|
|
weightWidth = weightShape.getDimSize(3);
|
|
}
|
|
|
|
// Bias shape can describe the output channels.
|
|
ShapeAdaptor biasShape = operands.getShape(adaptor.getBias());
|
|
if (biasShape.hasRank() && ShapedType::isDynamic(outputShape[4])) {
|
|
outputShape[4] = biasShape.getDimSize(0);
|
|
}
|
|
|
|
llvm::ArrayRef<int64_t> dilation = adaptor.getDilation();
|
|
llvm::ArrayRef<int64_t> stride = adaptor.getStride();
|
|
llvm::ArrayRef<int64_t> pad = adaptor.getPad();
|
|
|
|
if (!ShapedType::isDynamic(inputDepth) &&
|
|
!ShapedType::isDynamic(weightDepth)) {
|
|
int32_t inputSize = inputDepth + pad[0] + pad[1];
|
|
int32_t filterSize = (weightDepth - 1) * dilation[0] + 1;
|
|
int32_t unstridedResult = inputSize - filterSize + 1;
|
|
outputShape[1] = (unstridedResult - 1) / stride[0] + 1;
|
|
}
|
|
|
|
if (!ShapedType::isDynamic(inputHeight) &&
|
|
!ShapedType::isDynamic(weightHeight)) {
|
|
int32_t inputSize = inputHeight + pad[2] + pad[3];
|
|
int32_t filterSize = (weightHeight - 1) * dilation[1] + 1;
|
|
int32_t unstridedResult = inputSize - filterSize + 1;
|
|
outputShape[2] = (unstridedResult - 1) / stride[1] + 1;
|
|
}
|
|
|
|
if (!ShapedType::isDynamic(inputWidth) &&
|
|
!ShapedType::isDynamic(weightWidth)) {
|
|
int32_t inputSize = inputWidth + pad[4] + pad[5];
|
|
int32_t filterSize = (weightWidth - 1) * dilation[2] + 1;
|
|
int32_t unstridedResult = inputSize - filterSize + 1;
|
|
outputShape[3] = (unstridedResult - 1) / stride[2] + 1;
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult Conv3DOp::verify() { return verifyConvOp(*this); }
|
|
|
|
LogicalResult AvgPool2dOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
return poolingInferReturnTypes(operands, attributes, inferredReturnShapes);
|
|
}
|
|
|
|
LogicalResult MaxPool2dOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
return poolingInferReturnTypes(operands, attributes, inferredReturnShapes);
|
|
}
|
|
|
|
LogicalResult DepthwiseConv2DOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
llvm::SmallVector<int64_t> outputShape(4, ShapedType::kDynamic);
|
|
DepthwiseConv2DOp::Adaptor adaptor(operands.getValues(), attributes);
|
|
|
|
int64_t inputWidth = ShapedType::kDynamic;
|
|
int64_t inputHeight = ShapedType::kDynamic;
|
|
int64_t inputChannels = ShapedType::kDynamic;
|
|
|
|
int64_t weightWidth = ShapedType::kDynamic;
|
|
int64_t weightHeight = ShapedType::kDynamic;
|
|
int64_t depthChannels = ShapedType::kDynamic;
|
|
|
|
// Input shape describes input width/height and batch.
|
|
ShapeAdaptor inputShape = operands.getShape(adaptor.getInput());
|
|
if (inputShape.hasRank()) {
|
|
outputShape[0] = inputShape.getDimSize(0);
|
|
inputHeight = inputShape.getDimSize(1);
|
|
inputWidth = inputShape.getDimSize(2);
|
|
inputChannels = inputShape.getDimSize(3);
|
|
}
|
|
|
|
// Weight shapes describes the filter width/height and the output channels.
|
|
ShapeAdaptor weightShape = operands.getShape(adaptor.getWeight());
|
|
if (weightShape.hasRank()) {
|
|
weightHeight = weightShape.getDimSize(0);
|
|
weightWidth = weightShape.getDimSize(1);
|
|
inputChannels = ShapedType::isDynamic(inputChannels)
|
|
? weightShape.getDimSize(2)
|
|
: inputChannels;
|
|
depthChannels = weightShape.getDimSize(3);
|
|
}
|
|
|
|
// If both inputChannels and depthChannels are available we can determine
|
|
// the output channels.
|
|
if (!ShapedType::isDynamic(inputChannels) &&
|
|
!ShapedType::isDynamic(depthChannels)) {
|
|
outputShape[3] = inputChannels * depthChannels;
|
|
}
|
|
|
|
// Bias shape can describe the output channels.
|
|
ShapeAdaptor biasShape = operands.getShape(adaptor.getBias());
|
|
if (biasShape.hasRank()) {
|
|
outputShape[3] = ShapedType::isDynamic(outputShape[3])
|
|
? biasShape.getDimSize(0)
|
|
: outputShape[3];
|
|
}
|
|
|
|
llvm::ArrayRef<int64_t> dilation = adaptor.getDilation();
|
|
llvm::ArrayRef<int64_t> padding = adaptor.getPad();
|
|
llvm::ArrayRef<int64_t> stride = adaptor.getStride();
|
|
|
|
if (!ShapedType::isDynamic(inputHeight) &&
|
|
!ShapedType::isDynamic(weightHeight)) {
|
|
int64_t inputSize = inputHeight + padding[0] + padding[1];
|
|
int64_t filterSize = (weightHeight - 1) * dilation[0] + 1;
|
|
int64_t unstridedResult = inputSize - filterSize + 1;
|
|
outputShape[1] = (unstridedResult - 1) / stride[0] + 1;
|
|
}
|
|
|
|
if (!ShapedType::isDynamic(inputWidth) &&
|
|
!ShapedType::isDynamic(weightWidth)) {
|
|
int64_t inputSize = inputWidth + padding[2] + padding[3];
|
|
int64_t filterSize = (weightWidth - 1) * dilation[1] + 1;
|
|
int64_t unstridedResult = inputSize - filterSize + 1;
|
|
outputShape[2] = (unstridedResult - 1) / stride[1] + 1;
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult DepthwiseConv2DOp::verify() { return verifyConvOp(*this); }
|
|
|
|
LogicalResult TransposeConv2DOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
TransposeConv2DOp::Adaptor adaptor(operands.getValues(), attributes);
|
|
// outputShape is mutable.
|
|
llvm::SmallVector<int64_t> outputShape =
|
|
convertToMlirShape(adaptor.getOutShape());
|
|
|
|
int64_t inputWidth = ShapedType::kDynamic;
|
|
int64_t inputHeight = ShapedType::kDynamic;
|
|
int64_t weightWidth = ShapedType::kDynamic;
|
|
int64_t weightHeight = ShapedType::kDynamic;
|
|
|
|
// Input shape describes input width/height and batch.
|
|
ShapeAdaptor inputShape = operands.getShape(adaptor.getInput());
|
|
if (inputShape.hasRank()) {
|
|
outputShape[0] = ShapedType::isDynamic(outputShape[0])
|
|
? inputShape.getDimSize(0)
|
|
: outputShape[0];
|
|
inputHeight = inputShape.getDimSize(1);
|
|
inputWidth = inputShape.getDimSize(2);
|
|
}
|
|
|
|
// Weight shapes describes the filter width/height and the output channels.
|
|
ShapeAdaptor weightShape = operands.getShape(adaptor.getFilter());
|
|
if (weightShape.hasRank()) {
|
|
outputShape[3] = ShapedType::isDynamic(outputShape[3])
|
|
? weightShape.getDimSize(0)
|
|
: outputShape[3];
|
|
weightHeight = weightShape.getDimSize(1);
|
|
weightWidth = weightShape.getDimSize(2);
|
|
}
|
|
|
|
// Bias shape can describe the output channels.
|
|
ShapeAdaptor biasShape = operands.getShape(adaptor.getInput());
|
|
if (biasShape.hasRank()) {
|
|
outputShape[3] = ShapedType::isDynamic(outputShape[3])
|
|
? biasShape.getDimSize(0)
|
|
: outputShape[3];
|
|
}
|
|
|
|
llvm::ArrayRef<int64_t> padding = adaptor.getOutPad();
|
|
llvm::ArrayRef<int64_t> stride = adaptor.getStride();
|
|
|
|
if (!ShapedType::isDynamic(inputHeight) &&
|
|
!ShapedType::isDynamic(weightHeight)) {
|
|
int64_t calculateSize =
|
|
(inputHeight - 1) * stride[0] + padding[0] + padding[1] + weightHeight;
|
|
outputShape[1] =
|
|
ShapedType::isDynamic(outputShape[1]) ? calculateSize : outputShape[1];
|
|
}
|
|
|
|
if (!ShapedType::isDynamic(inputWidth) &&
|
|
!ShapedType::isDynamic(weightWidth)) {
|
|
int64_t calculateSize =
|
|
(inputWidth - 1) * stride[1] + padding[2] + padding[3] + weightWidth;
|
|
outputShape[2] =
|
|
ShapedType::isDynamic(outputShape[2]) ? calculateSize : outputShape[2];
|
|
}
|
|
|
|
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
|
|
return success();
|
|
}
|
|
|
|
LogicalResult IfOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
llvm::SmallVector<tosa::YieldOp> yieldOps;
|
|
for (Region *region : regions) {
|
|
for (auto &block : *region)
|
|
if (auto returnOp = dyn_cast<tosa::YieldOp>(block.getTerminator()))
|
|
yieldOps.push_back(returnOp);
|
|
}
|
|
|
|
if (yieldOps.empty())
|
|
return failure();
|
|
|
|
// Get the initial type information for the yield op.
|
|
llvm::SmallVector<ValueKnowledge> resultKnowledge;
|
|
resultKnowledge.reserve(yieldOps.front().getNumOperands());
|
|
for (auto operand : yieldOps.front().getOperands()) {
|
|
resultKnowledge.push_back(
|
|
ValueKnowledge::getKnowledgeFromType(operand.getType()));
|
|
}
|
|
|
|
for (auto yieldOp : yieldOps) {
|
|
if (resultKnowledge.size() != yieldOp.getNumOperands())
|
|
return failure();
|
|
|
|
for (const auto &it : llvm::enumerate(yieldOp.getOperands())) {
|
|
int32_t index = it.index();
|
|
auto meet = ValueKnowledge::meet(
|
|
resultKnowledge[index],
|
|
ValueKnowledge::getKnowledgeFromType(it.value().getType()));
|
|
if (!meet)
|
|
continue;
|
|
resultKnowledge[index] = meet;
|
|
}
|
|
}
|
|
|
|
for (const ValueKnowledge &result : resultKnowledge) {
|
|
inferredReturnShapes.push_back(result.getShapedTypeComponents());
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
LogicalResult WhileOp::inferReturnTypeComponents(
|
|
MLIRContext *context, ::std::optional<Location> location,
|
|
ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
|
|
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
|
|
llvm::SmallVector<tosa::YieldOp> yieldOps;
|
|
for (auto &block : *regions[1])
|
|
if (auto returnOp = dyn_cast<tosa::YieldOp>(block.getTerminator()))
|
|
yieldOps.push_back(returnOp);
|
|
|
|
// TOSA's while must have a tosa.yield as its terminator. If not found this
|
|
// tosa.while is invalid.
|
|
if (yieldOps.empty())
|
|
return failure();
|
|
|
|
// Get the initial type information from the operand types.
|
|
llvm::SmallVector<ValueKnowledge> resultKnowledge;
|
|
resultKnowledge.reserve(yieldOps.front().getNumOperands());
|
|
for (auto operand : yieldOps.front().getOperands()) {
|
|
resultKnowledge.push_back(
|
|
ValueKnowledge::getKnowledgeFromType(operand.getType()));
|
|
}
|
|
|
|
for (auto yieldOp : yieldOps) {
|
|
if (resultKnowledge.size() != yieldOp.getNumOperands())
|
|
return failure();
|
|
|
|
for (const auto &it : llvm::enumerate(yieldOp.getOperands())) {
|
|
int32_t index = it.index();
|
|
if (auto meet = ValueKnowledge::meet(
|
|
resultKnowledge[index],
|
|
ValueKnowledge::getKnowledgeFromType(it.value().getType()))) {
|
|
resultKnowledge[index] = meet;
|
|
}
|
|
}
|
|
}
|
|
|
|
for (const ValueKnowledge &result : resultKnowledge) {
|
|
inferredReturnShapes.push_back(result.getShapedTypeComponents());
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TOSA Attribute Definitions.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#define GET_ATTRDEF_CLASSES
|
|
#include "mlir/Dialect/Tosa/IR/TosaAttributes.cpp.inc"
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TOSA Operator Definitions.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#define GET_OP_CLASSES
|
|
#include "mlir/Dialect/Tosa/IR/TosaOps.cpp.inc"
|