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://www.mlplatform.org/tosa/tosa_spec.html
//
//===----------------------------------------------------------------------===//
 
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
#include "mlir/Dialect/Quant/IR/Quant.h"
#include "mlir/Dialect/Shard/Interfaces/ShardingInterface.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tosa/Utils/QuantUtils.h"
#include "mlir/Dialect/Tosa/Utils/ShapeUtils.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/Dialect/Utils/VerificationUtils.h"
#include "mlir/IR/BuiltinTypeInterfaces.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/Interfaces/InferTypeOpInterface.h"
#include "mlir/Transforms/InliningUtils.h"
#include "llvm/ADT/APFloat.h"
#include "llvm/ADT/SmallVectorExtras.h"
 
#include <numeric>
#include <type_traits>
 
using namespace mlir;
using namespace mlir::tosa;
 
#include "mlir/Dialect/Tosa/IR/TosaOpsDialect.cpp.inc"
#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
 
//===----------------------------------------------------------------------===//
// Tosa dialect interface includes.
//===----------------------------------------------------------------------===//
 
#include "mlir/Dialect/Tosa/IR/TosaDialectBytecode.cpp.inc"
#include "mlir/Dialect/Tosa/IR/TosaEnums.cpp.inc"
#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,
                       IRMapping &map) const final {
    return true;
  }
 
  /// All regions with If and While parent operators can be inlined.
  bool isLegalToInline(Region *dest, Region *src, bool wouldBeCloned,
                       IRMapping &map) const final {
    return (isa<tosa::IfOp>(dest->getParentOp()) ||
            isa<tosa::WhileOp>(dest->getParentOp()));
  }
};
 
/// This class implements the bytecode interface for the Tosa dialect.
struct TosaDialectBytecodeInterface : public BytecodeDialectInterface {
  TosaDialectBytecodeInterface(Dialect *dialect)
      : BytecodeDialectInterface(dialect) {}
 
  //===--------------------------------------------------------------------===//
  // Attributes
 
  Attribute readAttribute(DialectBytecodeReader &reader) const override {
    return ::readAttribute(getContext(), reader);
  }
 
  LogicalResult writeAttribute(Attribute attr,
                               DialectBytecodeWriter &writer) const override {
    return ::writeAttribute(attr, writer);
  }
 
  //===--------------------------------------------------------------------===//
  // Types
 
  Type readType(DialectBytecodeReader &reader) const override {
    return ::readType(getContext(), reader);
  }
 
  LogicalResult writeType(Type type,
                          DialectBytecodeWriter &writer) const override {
    return ::writeType(type, writer);
  }
 
  void writeVersion(DialectBytecodeWriter &writer) const final {
    // TODO: Populate.
  }
 
  std::unique_ptr<DialectVersion>
  readVersion(DialectBytecodeReader &reader) const final {
    // TODO: Populate
    reader.emitError("Dialect does not support versioning");
    return nullptr;
  }
 
  LogicalResult upgradeFromVersion(Operation *topLevelOp,
                                   const DialectVersion &version) const final {
    return success();
  }
};
} // namespace
 
//===----------------------------------------------------------------------===//
// TOSA control flow support.
//===----------------------------------------------------------------------===//
 
/// Returns the while loop body.
SmallVector<Region *> tosa::WhileOp::getLoopRegions() {
  return {&getBodyGraph()};
}
 
//===----------------------------------------------------------------------===//
// TOSA variable operator support.
//===----------------------------------------------------------------------===//
 
static SmallVector<int64_t> convertToMlirShape(ArrayRef<int64_t> shape) {
  return map_to_vector(shape, [](int64_t dim) {
    return dim == -1 ? ShapedType::kDynamic : dim;
  });
}
 
// returns type of variable op
RankedTensorType mlir::tosa::getVariableType(tosa::VariableOp variableOp) {
  Type elementType = variableOp.getType();
  DenseIntElementsAttr varShapeAttr = variableOp.getVarShape();
  auto shape = convertToMlirShape(to_vector(varShapeAttr.getValues<int64_t>()));
  return RankedTensorType::get(shape, elementType);
}
 
//===----------------------------------------------------------------------===//
// Tosa dialect initialization.
//===----------------------------------------------------------------------===//
 
void TosaDialect::initialize() {
  addTypes<
#define GET_TYPEDEF_LIST
#include "mlir/Dialect/Tosa/IR/TosaOpsTypesBase.cpp.inc"
      >();
  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<TosaDialectBytecodeInterface, TosaInlinerInterface>();
  declarePromisedInterfaces<
      shard::ShardingInterface, ClampOp, SigmoidOp, TanhOp, AddOp,
      ArithmeticRightShiftOp, BitwiseAndOp, BitwiseOrOp, BitwiseXorOp, IntDivOp,
      LogicalAndOp, LogicalLeftShiftOp, LogicalRightShiftOp, LogicalOrOp,
      LogicalXorOp, MaximumOp, MinimumOp, MulOp, PowOp, SubOp, AbsOp,
      BitwiseNotOp, CeilOp, ClzOp, ExpOp, FloorOp, LogOp, LogicalNotOp,
      NegateOp, ReciprocalOp, RsqrtOp, SelectOp, EqualOp, GreaterOp,
      GreaterEqualOp, MatMulOp>();
}
 
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 (llvm::isa<shapeType>(type) && llvm::isa<DenseIntElementsAttr>(value)) {
    return tosa::ConstShapeOp::create(builder, loc, type,
                                      llvm::cast<DenseIntElementsAttr>(value));
  }
  if (llvm::isa<ElementsAttr>(value))
    return tosa::ConstOp::create(builder, loc, type,
                                 llvm::cast<ElementsAttr>(value));
  return nullptr;
}
 
//===----------------------------------------------------------------------===//
// Parsers and printers
//===----------------------------------------------------------------------===//
 
namespace {
 
ParseResult getShapeAndElementType(OpAsmParser &parser, Type parsedType,
                                   DenseElementsAttr &varShapeAttr,
                                   TypeAttr &typeAttr) {
  if (auto shapedType = dyn_cast<ShapedType>(parsedType)) {
    if (!shapedType.hasRank())
      return parser.emitError(parser.getCurrentLocation())
             << "expected ranked type";
 
    auto elementType = shapedType.getElementType();
    typeAttr = TypeAttr::get(elementType);
    ArrayRef<int64_t> shape = shapedType.getShape();
    Builder builder(parser.getContext());
    varShapeAttr = builder.getIndexTensorAttr(convertFromMlirShape(shape));
    return success();
  }
  return parser.emitError(parser.getCurrentLocation())
         << "expected shaped type";
}
 
} // namespace
 
// parses the optional initial value or type for a tosa variable
//  with initial value:
//    tosa.variable @name = dense<0.0> : tensor<1x8xf32>
//
//  without initial value:
//    tosa.variable @name : tensor<1x8xf32>
ParseResult mlir::tosa::parseVariableOpTypeOrInitialValue(
    OpAsmParser &parser, DenseElementsAttr &varShapeAttr, TypeAttr &typeAttr,
    Attribute &initialValueAttr) {
  if (succeeded(parser.parseOptionalEqual())) {
    if (failed(parser.parseAttribute(initialValueAttr))) {
      return parser.emitError(parser.getCurrentLocation())
             << "expected attribute";
    }
    if (auto typedAttr = dyn_cast<TypedAttr>(initialValueAttr)) {
      return getShapeAndElementType(parser, typedAttr.getType(), varShapeAttr,
                                    typeAttr);
    }
    return parser.emitError(parser.getCurrentLocation())
           << "expected Typed attr";
  }
 
  initialValueAttr = nullptr;
  Type parsedType;
  if (failed(parser.parseColonType(parsedType))) {
    return parser.emitError(parser.getCurrentLocation())
           << "expected type after colon";
  }
  return getShapeAndElementType(parser, parsedType, varShapeAttr, typeAttr);
}
 
void mlir::tosa::printVariableOpTypeOrInitialValue(
    OpAsmPrinter &p, Operation *op, DenseElementsAttr varShapeAttr,
    TypeAttr typeAttr, Attribute initialValueAttr) {
  bool needsSpace = false;
  if (!dyn_cast_or_null<TypedAttr>(initialValueAttr)) {
    auto shape =
        convertToMlirShape(to_vector(varShapeAttr.getValues<int64_t>()));
    Type elementType = typeAttr.getValue();
    RankedTensorType tensorType =
        RankedTensorType::get(ArrayRef<int64_t>(shape), elementType);
    auto tensorTypeAttr = TypeAttr::get(tensorType);
    p << ": ";
    p.printAttribute(tensorTypeAttr);
    needsSpace = true; // subsequent attr value needs a space separator
  }
  if (initialValueAttr) {
    if (needsSpace)
      p << ' ';
    p << "= ";
    p.printAttribute(initialValueAttr);
  }
}
 
//===----------------------------------------------------------------------===//
// Tosa utilities.
//===----------------------------------------------------------------------===//
 
static std::optional<int64_t> idivCheck(const int64_t lhs, const int64_t rhs) {
  if (lhs % rhs != 0)
    return std::nullopt;
  return lhs / rhs;
}
 
Type mlir::tosa::getStorageElementTypeOrSelf(Type type) {
  auto srcType = getElementTypeOrSelf(type);
  if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(srcType))
    srcType = getStorageElementTypeFromQuantized(quantType);
  return srcType;
}
 
Type mlir::tosa::getStorageElementTypeOrSelf(Value value) {
  return getStorageElementTypeOrSelf(value.getType());
}
 
static LogicalResult verifyRescaleValueAndZpTypes(Operation *op, Value val,
                                                  Value valZp, StringRef name) {
  Type eType = getStorageElementTypeOrSelf(val.getType());
  Type eZpType = getStorageElementTypeOrSelf(valZp.getType());
 
  bool bothInts =
      mlir::isa<IntegerType>(eType) && mlir::isa<IntegerType>(eZpType);
  bool sameBitWidth =
      (eType.getIntOrFloatBitWidth() == eZpType.getIntOrFloatBitWidth());
 
  if (!bothInts || !sameBitWidth) {
    return op->emitOpError()
           << "expected " << name << " and " << name
           << "_zp to both be integer of the same bitwidth, but got " << eType
           << " vs. " << eZpType;
  }
  return success();
}
 
// Create a pad-const const tensor with value of `val` of required data-type
Value mlir::tosa::createPadConstTensor(OpBuilder &builder, Location loc,
                                       Value src, int32_t val) {
  const auto srcType = getElementTypeOrSelf(src);
  const auto srcElemType = getStorageElementTypeOrSelf(src);
  const auto padConstType = mlir::RankedTensorType::get({1}, srcType);
  const auto padConstEType = mlir::RankedTensorType::get({1}, srcElemType);
  const auto padConstAttr{
      llvm::isa<FloatType>(srcElemType)
          ? DenseElementsAttr::get(padConstEType,
                                   builder.getFloatAttr(srcElemType, val))
          : DenseElementsAttr::get(padConstEType,
                                   builder.getIntegerAttr(srcElemType, val))};
  return tosa::ConstOp::create(builder, loc, padConstType, padConstAttr);
}
 
unsigned mlir::tosa::getBitWidth(Type type) {
  if (auto blockScaledTy = dyn_cast<tosa::BlockScaledType>(type))
    return getBitWidth(blockScaledTy.getValueType());
  if (dyn_cast<tosa::mxint8Type>(type))
    return 8;
  return type.getIntOrFloatBitWidth();
}
 
// Update dim size if current dim is dynamic, otherwise raise an error if sizes
// do not match
LogicalResult tryUpdateDimOrFailure(Operation *op, int64_t &currDim,
                                    const int64_t newDim,
                                    const StringRef operandName,
                                    const StringRef dimName) {
  if (ShapedType::isDynamic(currDim)) {
    currDim = newDim;
    return success();
  } else if (ShapedType::isStatic(newDim) && currDim != newDim) {
    return op->emitOpError("expected ")
           << dimName << " of " << operandName << " to match size " << currDim
           << ", got " << newDim;
  }
  return success();
}
 
static void printShapeToDiagnostic(InFlightDiagnostic &diag,
                                   ArrayRef<int64_t> shape) {
  auto printDim = [&](int64_t dim) {
    if (ShapedType::isDynamic(dim))
      diag << "?";
    else
      diag << dim;
  };
 
  llvm::interleaveComma(shape, diag, printDim);
}
 
static LogicalResult
verifyOutputShapeCompatibleWithExpected(Operation *op, ShapedType outputType,
                                        ArrayRef<int64_t> expectedShape,
                                        StringRef outputName = "output") {
  assert(outputType.hasRank() && "expected output type to be ranked");
 
  if (succeeded(verifyCompatibleShape(outputType.getShape(), expectedShape)))
    return success();
 
  InFlightDiagnostic diag = op->emitOpError("expected ");
  diag << outputName << " shape ";
  printShapeToDiagnostic(diag, outputType.getShape());
  diag << " to be compatible with inferred shape ";
  printShapeToDiagnostic(diag, expectedShape);
  return diag;
}
 
LogicalResult verifyConvOutputSize(
    Operation *op, const int64_t inputSize, const int64_t kernelSize,
    const int64_t outputSize, const int64_t padBefore, const int64_t padAfter,
    const int64_t stride, const int64_t dilation, const llvm::StringRef dimName,
    const llvm::StringRef dimAxis, const llvm::StringRef padBeforeName,
    const llvm::StringRef padAfterName) {
  if (inputSize == ShapedType::kDynamic || kernelSize == ShapedType::kDynamic)
    return success();
 
  // ERROR_IF: O != idiv_check(I - 1 + pa + pb - (K - 1) * d, s) + 1
 
  const std::optional<int64_t> calculatedOutSizeMinusOne = idivCheck(
      inputSize - 1 + padBefore + padAfter - (kernelSize - 1) * dilation,
      stride);
  if (!calculatedOutSizeMinusOne.has_value())
    return op->emitOpError("expected input_")
           << dimName << " - 1 + pad_" << padBeforeName << " + pad_"
           << padAfterName << " - (kernel_" << dimName << " - 1) * dilation_"
           << dimAxis << " to be wholly divisible by stride_" << dimAxis
           << ", got (" << inputSize << " - 1 + " << padBefore << " + "
           << padAfter << " - (" << kernelSize << " - 1) * " << dilation
           << ") / " << stride;
 
  const int64_t calculatedOutSize = calculatedOutSizeMinusOne.value() + 1;
  if (outputSize != ShapedType::kDynamic && calculatedOutSize != outputSize)
    return op->emitOpError("calculated output ")
           << dimName << " did not match expected: "
           << "calculated=" << calculatedOutSize << ", expected=" << outputSize;
 
  return success();
}
 
//===----------------------------------------------------------------------===//
// mxint8Type DenseElementTypeInterface implementation.
//===----------------------------------------------------------------------===//
size_t mlir::tosa::mxint8Type::getDenseElementBitSize() const { return 8; }
 
Attribute
mlir::tosa::mxint8Type::convertToAttribute(ArrayRef<char> rawData) const {
  assert(rawData.size() == 1 && "expected 1 byte for tosa.mxint8 element");
  const auto intType = IntegerType::get(getContext(), 8);
  return intType.convertToAttribute(rawData);
}
 
LogicalResult mlir::tosa::mxint8Type::convertFromAttribute(
    Attribute attr, SmallVectorImpl<char> &result) const {
  const auto intAttr = dyn_cast<IntegerAttr>(attr);
  if (!intAttr)
    return failure();
  const Type attrType = intAttr.getType();
  if (!attrType.isSignlessInteger(8))
    return failure();
  return cast<IntegerType>(attrType).convertFromAttribute(attr, result);
}
 
//===----------------------------------------------------------------------===//
// TOSA block scaling utilities.
//===----------------------------------------------------------------------===//
 
LogicalResult mlir::tosa::verifyBlockScaledTensorType(
    mlir::Type type, llvm::function_ref<mlir::InFlightDiagnostic()> emitError,
    bool allowScaleValues) {
  const auto tensorType = llvm::cast<ShapedType>(type);
  const BlockScaledType elemType =
      llvm::dyn_cast<BlockScaledType>(tensorType.getElementType());
  if (!elemType)
    return success();
 
  if (!allowScaleValues && elemType.hasScaleValues()) {
    if (emitError)
      emitError()
          << "block scaled tensor type with scale values is not allowed";
    return failure();
  }
 
  if (!tensorType.hasRank())
    return success();
 
  if (tensorType.getRank() == 0) {
    if (emitError)
      emitError() << "block scaled tensor type must have rank greater than "
                     "zero";
    return failure();
  }
 
  const ArrayRef<int64_t> tensorShape = tensorType.getShape();
  const uint32_t blockSize =
      BlockShapeAttr::getBlockShapeValue(elemType.getBlockShape());
 
  if (allowScaleValues && elemType.hasScaleValues() &&
      tensorType.hasStaticShape()) {
    const size_t numBlocks = tensorType.getNumElements() / blockSize;
    if (elemType.getScaleValues().size() != numBlocks) {
      if (emitError)
        emitError() << "block scaled tensor type with scale values must have "
                       "scale values for each block, expected "
                    << numBlocks << ", got "
                    << elemType.getScaleValues().size();
      return failure();
    }
  }
 
  const int64_t blockedDimension = tensorShape.back();
  if (ShapedType::isDynamic(blockedDimension))
    return success();
 
  if (blockedDimension % blockSize != 0) {
    if (emitError)
      emitError() << "last dimension of block scaled tensor type ("
                  << blockedDimension << ") must be divisible by block size ("
                  << blockSize << ")";
 
    return failure();
  }
 
  return success();
}
 
std::string mlir::tosa::getTosaTensorTypeErrorMessage(mlir::Type type) {
  MLIRContext *ctx = type.getContext();
  std::string message;
  ScopedDiagnosticHandler handler(
      ctx, [&](Diagnostic &diag) { message = diag.str(); });
 
  if (failed(verifyBlockScaledTensorType(
          type, [ctx] { return emitError(UnknownLoc::get(ctx)); })) &&
      !message.empty()) {
    return ": " + message;
  }
 
  return "";
}
 
static ParseResult parseScaleValues(AsmParser &parser,
                                    SmallVector<Attribute> &scaleValues,
                                    Type scaleType) {
  const auto parseScaleValue = [&]() -> ParseResult {
    const SMLoc loc = parser.getCurrentLocation();
 
    double floatValue;
    if (parser.parseFloat(floatValue))
      return failure();
 
    if (floatValue < 0.0)
      return parser.emitError(loc, "scale value must be non-negative, got ")
             << floatValue;
 
    Type attrType = scaleType;
    if (succeeded(parser.parseOptionalColon()) && parser.parseType(attrType))
      return failure();
 
    if (attrType != scaleType)
      return parser.emitError(loc, "parsed attribute type ")
             << attrType << " does not match expected scale type " << scaleType;
 
    scaleValues.push_back(FloatAttr::get(attrType, floatValue));
    return success();
  };
 
  return parser.parseCommaSeparatedList(parseScaleValue);
}
 
static void printScaleValues(AsmPrinter &printer,
                             ArrayRef<Attribute> scaleValues, Type) {
  llvm::interleaveComma(scaleValues, printer, [&](Attribute scaleValue) {
    printer.printAttributeWithoutType(scaleValue);
  });
}
 
size_t mlir::tosa::BlockScaledType::getDenseElementBitSize() const {
  const Type valueType = getValueType();
  if (isa<tosa::mxint8Type>(valueType))
    return 8;
  return valueType.getIntOrFloatBitWidth();
}
 
Attribute
mlir::tosa::BlockScaledType::convertToAttribute(ArrayRef<char> rawData) const {
  // Block scaled values are stored as a single byte. This is because possible
  // value data types are either 8-bit or sub-byte. Sub-byte types are aligned
  // to 8-bits.
  assert(rawData.size() == 1 && "expected 1 byte for block_scaled element");
  const Type valueType = getValueType();
  if (const auto mxint8Value = dyn_cast<tosa::mxint8Type>(valueType))
    return mxint8Value.convertToAttribute(rawData);
  if (!isa<FloatType>(valueType))
    return {};
  return mlir::detail::convertFloatTypeToAttribute(valueType, rawData);
}
 
LogicalResult mlir::tosa::BlockScaledType::convertFromAttribute(
    Attribute attr, SmallVectorImpl<char> &result) const {
  const Type valueType = getValueType();
  if (const auto mxint8Value = dyn_cast<tosa::mxint8Type>(valueType))
    return mxint8Value.convertFromAttribute(attr, result);
 
  const auto floatAttr = dyn_cast<FloatAttr>(attr);
  if (!floatAttr || floatAttr.getType() != valueType)
    return failure();
  // const APFloat value = floatAttr.getValue();
  return mlir::detail::convertFloatTypeFromAttribute(valueType, floatAttr,
                                                     result);
}
 
//===----------------------------------------------------------------------===//
// TOSA Operator shape inference
//===----------------------------------------------------------------------===//
 
template <typename A, std::enable_if_t<std::is_same_v<A, ArgMaxOp::Adaptor> ||
                                           std::is_same_v<A, ArgMinOp::Adaptor>,
                                       int> = 0>
LogicalResult inferArgMaxMinReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location, A adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput().getType());
  IntegerAttr axis = adaptor.getProperties().axis;
  int32_t axisVal = axis.getValue().getSExtValue();
 
  if (!inputShape.hasRank()) {
    inferredReturnShapes.push_back(ShapedTypeComponents());
    return success();
  }
 
  const auto inputRank = inputShape.getRank();
  SmallVector<int64_t> outShape;
  outShape.reserve(inputRank - 1);
  for (int i = 0, s = inputRank; i < s; i++) {
    if (i == axisVal)
      continue;
    outShape.push_back(inputShape.getDimSize(i));
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
  return success();
}
 
//===----------------------------------------------------------------------===//
// TOSA Operator Verifiers.
//===----------------------------------------------------------------------===//
template <typename T>
LogicalResult argMaxMinVerify(T op) {
  const ShapedType resultType = llvm::cast<ShapedType>(op.getType());
 
  if (const auto resultETy = resultType.getElementType();
      !resultETy.isIntOrIndex())
    return op.emitOpError("result tensor is not of integer type");
 
  const auto inputType = llvm::cast<ShapedType>(op.getInput().getType());
  if (!inputType.hasRank())
    return success();
 
  // Ensure axis is within the tensor rank
  const int64_t axis = op.getAxisAttr().getInt();
  if (((axis < 0) || axis >= inputType.getRank()))
    return op.emitOpError("specified axis is outside the rank of the tensor");
 
  if (!resultType.hasRank())
    return success();
 
  const ArrayRef<int64_t> inputShape = inputType.getShape();
  const ArrayRef<int64_t> outputShape = resultType.getShape();
  llvm::SmallVector<int64_t> expectedOutputShape(inputShape);
  expectedOutputShape.erase(expectedOutputShape.begin() + axis);
  if (failed(verifyCompatibleShape(expectedOutputShape, outputShape)))
    return op.emitOpError("expected output shape '")
           << expectedOutputShape << "', got '" << outputShape << "'";
 
  return success();
}
 
template <typename T>
static LogicalResult verifyConvOp(T op) {
  const auto inputType = llvm::dyn_cast<TensorType>(op.getInput().getType());
  const auto weightType = llvm::dyn_cast<TensorType>(op.getWeight().getType());
 
  auto inputEType = inputType.getElementType();
  auto weightEType = weightType.getElementType();
  auto biasEType =
      llvm::cast<ShapedType>(op.getBias().getType()).getElementType();
  auto resultEType =
      llvm::cast<ShapedType>(op.getResult().getType()).getElementType();
  bool biasIsFloat = llvm::isa<FloatType>(biasEType);
  bool resultIsFloat = llvm::isa<FloatType>(resultEType);
 
  if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(inputEType))
    inputEType = getStorageElementTypeFromQuantized(quantType);
 
  if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(weightEType))
    weightEType = getStorageElementTypeFromQuantized(quantType);
 
  if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(biasEType))
    biasEType = getStorageElementTypeFromQuantized(quantType);
 
  if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(resultEType))
    resultEType = getStorageElementTypeFromQuantized(quantType);
 
  if (biasIsFloat && resultIsFloat && (biasEType != resultEType)) {
    // for now, only enforce bias element type == result element type for
    // float types.
    op.emitOpError(
        "expect both bias and result to have same element type, got ")
        << biasEType << " and " << resultEType;
    return failure();
  }
 
  const bool isInputBlockScaled = llvm::isa<BlockScaledType>(inputEType);
  const bool isWeightBlockScaled = llvm::isa<BlockScaledType>(weightEType);
  const bool isInputFloat = llvm::isa<FloatType>(inputEType);
  const bool isWeightFloat = llvm::isa<FloatType>(weightEType);
 
  const bool isInputBSorFloat = isInputBlockScaled || isInputFloat;
  const bool isWeightBSorFloat = isWeightBlockScaled || isWeightFloat;
 
  // Either both must be float or both non-float.
  if (isInputBSorFloat != isWeightBSorFloat) {
    op.emitOpError(
        "expect both input and weight to be float or not together, got ")
        << inputEType << " and " << weightEType;
    return failure();
  }
 
  auto inputZpEType = getStorageElementTypeOrSelf(op.getInputZp().getType());
  if (!isInputBlockScaled && inputEType != inputZpEType) {
    return op.emitOpError("expect both input and its zero point are the same "
                          "element type, got ")
           << inputEType << " and " << inputZpEType;
  }
  if (isInputBlockScaled && !llvm::isa<Float32Type>(inputZpEType)) {
    return op.emitOpError(
               "expect block scaled input to have fp32 zero point, got ")
           << inputEType << " and " << inputZpEType;
  }
 
  auto weightZpEType = getStorageElementTypeOrSelf(op.getWeightZp().getType());
  if (!isWeightBlockScaled && weightEType != weightZpEType) {
    return op.emitOpError("expect both weight and its zero point are the same "
                          "element type, got ")
           << weightEType << " and " << weightZpEType;
  }
  if (isWeightBlockScaled && !llvm::isa<Float32Type>(weightZpEType)) {
    return op.emitOpError(
               "expect block scaled weight to have fp32 zero point, got ")
           << weightEType << " and " << weightZpEType;
  }
 
  FailureOr<int64_t> maybeIZp = op.getInputZeroPoint();
  if (succeeded(maybeIZp) && op.verifyInputZeroPoint(*maybeIZp).failed())
    return failure();
 
  FailureOr<int64_t> maybeWZp = op.getWeightZeroPoint();
  if (succeeded(maybeWZp) && op.verifyWeightZeroPoint(*maybeWZp).failed())
    return failure();
 
  return success();
}
 
LogicalResult tosa::ConstOp::verify() {
  Operation &op = *getOperation();
  auto attrType = llvm::dyn_cast<TensorType>(getValuesAttr().getType());
  auto outputType = llvm::dyn_cast<TensorType>(getOutput().getType());
 
  if (!attrType || !outputType) {
    emitOpError("expected tensors for attr/result type");
    return failure();
  }
 
  const Type attrElemType = attrType.getElementType();
  const Type resultElemType = outputType.getElementType();
 
  if (auto result =
          llvm::dyn_cast<mlir::quant::QuantizedType>(resultElemType)) {
    if (getStorageElementTypeFromQuantized(result) == attrElemType)
      return success();
  }
 
  if (auto attrBlockScaledType =
          llvm::dyn_cast<mlir::tosa::BlockScaledType>(attrElemType)) {
    if (!attrBlockScaledType.hasScaleValues())
      return op.emitOpError(
          "attribute block scaled type must have scale values");
 
    const auto emitAttributeError = [&op]() {
      return op.emitOpError("attribute block scaled type is invalid: ");
    };
 
    if (failed(verifyBlockScaledTensorType(attrType, emitAttributeError, true)))
      return failure();
 
    const BlockScaledType resultBlockScaledType =
        llvm::dyn_cast<mlir::tosa::BlockScaledType>(resultElemType);
    if (!resultBlockScaledType)
      return op.emitOpError(
          "result type must be block scaled type if attribute is block "
          "scaled type");
 
    if (attrBlockScaledType.getValueType() !=
            resultBlockScaledType.getValueType() ||
        attrBlockScaledType.getScaleType() !=
            resultBlockScaledType.getScaleType() ||
        attrBlockScaledType.getBlockShape() !=
            resultBlockScaledType.getBlockShape())
      return op.emitOpError(
                 "expected block scaled element type to be compatible "
                 "between attr and result, got ")
             << attrBlockScaledType << " vs. " << resultBlockScaledType;
 
    return success();
  }
 
  if (attrElemType != resultElemType)
    return emitOpError("expected same attr/result element types");
 
  return success();
}
 
//===----------------------------------------------------------------------===//
// ERROR_IF functions.
// ERROR_IF is a predicate that must set an error if the condition holds.
//===----------------------------------------------------------------------===//
 
template <typename T>
static LogicalResult verifyConvOpErrorIf(T op) {
  llvm::ArrayRef<int64_t> padding = op.getPad();
  if (llvm::any_of(padding, [](int64_t p) { return p < 0; }))
    return op.emitOpError("expect all padding values to be >= 0, got ")
           << padding;
 
  llvm::ArrayRef<int64_t> strides = op.getStride();
  if (llvm::any_of(strides, [](int64_t s) { return s < 1; }))
    return op.emitOpError("expect all stride values to be >= 1, got ")
           << strides;
 
  llvm::ArrayRef<int64_t> dilations = op.getDilation();
  if (llvm::any_of(dilations, [](int64_t d) { return d < 1; }))
    return op.emitOpError("expect all dilation values to be >= 1, got ")
           << dilations;
 
  const RankedTensorType outputType =
      llvm::dyn_cast<RankedTensorType>(op.getOutput().getType());
  if (!outputType)
    // Skip following checks if output is not ranked
    return success();
 
  const RankedTensorType inputType =
      llvm::dyn_cast<RankedTensorType>(op.getInput().getType());
  const RankedTensorType weightType =
      llvm::dyn_cast<RankedTensorType>(op.getWeight().getType());
 
  if (inputType && weightType) {
    // input = [_,IH,IW,_], weight = [_,KH,KW,_], output = [_,OH,OW,_]
    if constexpr (std::is_same<T, tosa::Conv2DOp>::value) {
      if (failed(verifyConvOutputSize(
              op, inputType.getDimSize(1), weightType.getDimSize(1),
              outputType.getDimSize(1), padding[0], padding[1], strides[0],
              dilations[0], "height", "y", "top", "bottom")))
        return failure();
 
      if (failed(verifyConvOutputSize(
              op, inputType.getDimSize(2), weightType.getDimSize(2),
              outputType.getDimSize(2), padding[2], padding[3], strides[1],
              dilations[1], "width", "x", "left", "right")))
        return failure();
    }
 
    // input = [_,IH,IW,_], weight = [KH,KW,_,_], output = [_,OH,OW,_]
    if constexpr (std::is_same<T, tosa::DepthwiseConv2DOp>::value) {
      if (failed(verifyConvOutputSize(
              op, inputType.getDimSize(1), weightType.getDimSize(0),
              outputType.getDimSize(1), padding[0], padding[1], strides[0],
              dilations[0], "height", "y", "top", "bottom")))
        return failure();
 
      if (failed(verifyConvOutputSize(
              op, inputType.getDimSize(2), weightType.getDimSize(1),
              outputType.getDimSize(2), padding[2], padding[3], strides[1],
              dilations[1], "width", "x", "left", "right")))
        return failure();
    }
 
    // input = [_,ID,IH,IW,_], weight = [_,KD,KH,KW,_], output = [_,OD,OH,OW,_]
    if constexpr (std::is_same<T, tosa::Conv3DOp>::value) {
      if (failed(verifyConvOutputSize(
              op, inputType.getDimSize(1), weightType.getDimSize(1),
              outputType.getDimSize(1), padding[0], padding[1], strides[0],
              dilations[0], "depth", "d", "front", "back")))
        return failure();
 
      if (failed(verifyConvOutputSize(
              op, inputType.getDimSize(2), weightType.getDimSize(2),
              outputType.getDimSize(2), padding[2], padding[3], strides[1],
              dilations[1], "height", "y", "top", "bottom")))
        return failure();
 
      if (failed(verifyConvOutputSize(
              op, inputType.getDimSize(3), weightType.getDimSize(3),
              outputType.getDimSize(3), padding[4], padding[5], strides[2],
              dilations[2], "width", "x", "left", "right")))
        return failure();
    }
  }
 
  const RankedTensorType biasType =
      llvm::dyn_cast<RankedTensorType>(op.getBias().getType());
  if (!biasType)
    // Skip following checks if bias is not ranked
    return success();
 
  const int64_t biasChannels = biasType.getDimSize(0);
  const int64_t outputChannels =
      outputType.getDimSize(outputType.getRank() - 1);
  if (biasChannels == ShapedType::kDynamic ||
      outputChannels == ShapedType::kDynamic)
    // Skip following checks if biasChannels or outputChannels is dynamic dim
    return success();
 
  if (biasChannels != outputChannels && biasChannels != 1)
    return op.emitOpError(
               "bias channels expected to be equal to output channels (")
           << outputChannels << ") or 1, got " << biasChannels;
 
  return success();
}
 
// Verify whether same type and shape of the given two types.
static LogicalResult errorIfTypeOrShapeMismatch(Operation *op, Type type1,
                                                StringRef name1, Type type2,
                                                StringRef name2) {
  auto shapeType1 = dyn_cast<ShapedType>(type1);
  auto shapeType2 = dyn_cast<ShapedType>(type2);
  if (!shapeType1 || !shapeType2)
    return failure();
 
  auto elemType1 = shapeType1.getElementType();
  auto elemType2 = shapeType2.getElementType();
  if (elemType1 != elemType2)
    return op->emitOpError()
           << "require same element type for " << name1 << " (" << elemType1
           << ") and " << name2 << " (" << elemType2 << ")";
 
  if (failed(verifyCompatibleShape(type1, type2)))
    return op->emitOpError()
           << "require same shapes for " << name1 << " (" << type1 << ") and "
           << name2 << " (" << type2 << ")";
 
  return success();
}
 
// Verify whether same length, type, and shape of the given two tensor lists.
static LogicalResult errorIfTypeOrShapeMismatch(Operation *op, ValueRange list1,
                                                StringRef name1,
                                                ValueRange list2,
                                                StringRef name2) {
  if (list1.size() != list2.size())
    return op->emitOpError()
           << "require same number of values in " << name1 << " ("
           << list1.size() << ") and " << name2 << " (" << list2.size() << ")";
 
  for (auto [type1, type2] :
       llvm::zip_equal(list1.getTypes(), list2.getTypes())) {
    if (errorIfTypeOrShapeMismatch(op, type1, name1, type2, name2).failed())
      return failure();
  }
 
  return success();
}
 
static inline LogicalResult errorIfShapeNotSizeOne(Operation *op, Type type) {
  ShapeAdaptor shapeAdaptor(type);
  if (!shapeAdaptor.hasRank() || !shapeAdaptor.hasStaticShape())
    return success();
 
  return shapeAdaptor.getNumElements() == 1 ? success() : failure();
}
 
template <typename T>
static LogicalResult verifyVariableOpErrorIf(T op, Type type, StringRef name) {
  Operation *symTableOp =
      op->template getParentWithTrait<OpTrait::SymbolTable>();
  if (!symTableOp)
    // If the operation is not the scope of a symbol table, we cannot
    // verify it against it's declaration.
    return success();
 
  SymbolTable symTable(symTableOp);
  const auto varOp = symTable.lookup<tosa::VariableOp>(op.getName());
 
  // Verify prior declaration
  if (!varOp)
    return op->emitOpError("'")
           << op.getName() << "' has not been declared by 'tosa.variable'";
 
  // Verify type and shape
  auto variableType = getVariableType(varOp);
  if (errorIfTypeOrShapeMismatch(op, type, name, variableType,
                                 "the input tensor")
          .failed())
    return failure();
  return success();
}
 
// verify that inType and outType have same element types
static LogicalResult verifySameElementTypes(Operation *op, Type aType,
                                            Type bType,
                                            StringRef aName = "input",
                                            StringRef bName = "output") {
  auto aTType = llvm::dyn_cast<TensorType>(aType);
  auto bTType = llvm::dyn_cast<TensorType>(bType);
  if (!aTType) {
    op->emitOpError("expect shaped tensor for") << aName << ", got " << aType;
    return failure();
  }
  if (!bTType) {
    op->emitOpError("expect shaped tensor for") << bName << ", got" << bType;
    return failure();
  }
  auto aElementType = aTType.getElementType();
  auto bElementType = bTType.getElementType();
  auto aQuantType =
      llvm::dyn_cast<mlir::quant::UniformQuantizedType>(aElementType);
  auto bQuantType =
      llvm::dyn_cast<mlir::quant::UniformQuantizedType>(bElementType);
  if ((aElementType.isIntOrIndexOrFloat() || aQuantType) &&
      (bElementType.isIntOrIndexOrFloat() || bQuantType) &&
      aElementType != bElementType) {
    // only check if both element types are int/index/float/UniformQuantized
    // eg, not sure how to check quant::QuantizedType
    // this happens in test_conv2d_q_grouped_convolution in
    // tfl-to-tosa-pipeline.mlir
    op->emitOpError("expect ")
        << aName << " and " << bName << " to have same element type, got "
        << aElementType << " and " << bElementType;
    return failure();
  }
  return success();
}
 
LogicalResult tosa::ArgMaxOp::verify() { return argMaxMinVerify(*this); }
 
LogicalResult tosa::ArgMinOp::verify() { return argMaxMinVerify(*this); }
 
static LogicalResult verifyPoolingOpImpl(Operation *op,
                                         ArrayRef<int64_t> kernel,
                                         ArrayRef<int64_t> strides,
                                         ArrayRef<int64_t> padding, Value input,
                                         Value output) {
  if (failed(verifySameElementTypes(op, input.getType(), output.getType())))
    return failure();
 
  const bool hasKernel = kernel.size() > 0;
  const bool hasStrides = strides.size() > 0;
  const bool hasPad = padding.size() > 0;
 
  if (hasKernel && llvm::any_of(kernel, [](int64_t s) { return s < 1; }))
    return op->emitOpError("expect all kernel values to be >= 1, got ")
           << kernel;
 
  if (hasStrides && llvm::any_of(strides, [](int64_t s) { return s < 1; }))
    return op->emitOpError("expect all stride values to be >= 1, got ")
           << strides;
 
  if (hasPad && llvm::any_of(padding, [](int64_t p) { return p < 0; }))
    return op->emitOpError("expect all padding values to be >= 0, got ")
           << padding;
 
  if (hasKernel && hasPad) {
    // Padding must be less than kernel size to avoid a divide-by-zero
    const int64_t kernelX = kernel[1];
    const int64_t padLeft = padding[2];
    const int64_t padRight = padding[3];
    if (padRight >= kernelX || padLeft >= kernelX)
      return op->emitOpError("expected left/right padding to be less than the "
                             "width of the kernel, got pad_left=")
             << padLeft << ", pad_right=" << padRight
             << ", kernel_x=" << kernelX;
 
    const int64_t kernelY = kernel[0];
    const int64_t padTop = padding[0];
    const int64_t padBottom = padding[1];
    if (padTop >= kernelY || padBottom >= kernelY)
      return op->emitOpError("expected top/bottom padding to be less than the "
                             "height of the kernel, got pad_top=")
             << padTop << ", pad_bottom=" << padBottom
             << ", kernel_y=" << kernelY;
  }
 
  const auto inputType = llvm::dyn_cast<RankedTensorType>(input.getType());
  const auto outputType = llvm::dyn_cast<RankedTensorType>(output.getType());
  if (!inputType || !outputType)
    return success();
 
  if (hasKernel && hasStrides && hasPad) {
    const auto verifyOutputSize =
        [op](const int64_t inputSize, const int64_t outputSize,
             const int64_t kernelSize, const int64_t strideSize,
             const int64_t padBefore, const int64_t padAfter,
             const llvm::StringRef dimName, const llvm::StringRef dimAxis,
             const llvm::StringRef padBeforeName,
             const llvm::StringRef padAfterName) -> LogicalResult {
      if (ShapedType::isDynamic(inputSize))
        return success();
 
      const std::optional<int64_t> calculatedOutSizeMinusOne =
          idivCheck(inputSize + padBefore + padAfter - kernelSize, strideSize);
      if (!calculatedOutSizeMinusOne.has_value())
        return op->emitOpError("expected input_")
               << dimName << " + pad_" << padBeforeName << " + pad_"
               << padAfterName << " - kernel_" << dimAxis
               << " to be wholly divisible by stride_" << dimAxis << ", got ("
               << inputSize << " + " << padBefore << " + " << padAfter << " - "
               << kernelSize << ") / " << strideSize;
 
      const int64_t calculatedOutSize = calculatedOutSizeMinusOne.value() + 1;
      if (ShapedType::isStatic(outputSize) && calculatedOutSize != outputSize)
        return op->emitOpError("calculated output ")
               << dimName << " did not match expected: " << "calculated="
               << calculatedOutSize << ", expected=" << outputSize;
 
      return success();
    };
 
    if (failed(verifyOutputSize(inputType.getDimSize(1),
                                outputType.getDimSize(1), kernel[0], strides[0],
                                padding[0], padding[1], "height", "y", "top",
                                "bottom")))
      return failure();
 
    if (failed(verifyOutputSize(
            inputType.getDimSize(2), outputType.getDimSize(2), kernel[1],
            strides[1], padding[2], padding[3], "width", "x", "left", "right")))
      return failure();
  }
  return success();
}
 
template <typename T>
static LogicalResult verifyPoolingOp(T op) {
  return verifyPoolingOpImpl(op.getOperation(), op.getKernel(), op.getStride(),
                             op.getPad(), op.getInput(), op.getOutput());
}
 
template <typename T>
static LogicalResult verifyAvgPoolCommonTypeAndZpChecks(T op) {
  const Type inputETy = getStorageElementTypeOrSelf(op.getInput().getType());
  const Type resultETy = getStorageElementTypeOrSelf(op.getOutput().getType());
  const Type inputZpETy =
      getStorageElementTypeOrSelf(op.getInputZp().getType());
  const Type outputZpETy =
      getStorageElementTypeOrSelf(op.getOutputZp().getType());
 
  auto accType = op.getAccType();
  if (llvm::isa<IntegerType>(inputETy) && !accType.isInteger(32))
    return op.emitOpError("accumulator type for integer tensor is not i32");
 
  if (inputETy.isF16() && !(accType.isF16() || accType.isF32()))
    return op.emitOpError("accumulator type for f16 tensor is not f16/f32");
 
  if (inputETy.isBF16() && !accType.isF32())
    return op.emitOpError("accumulator type for bf16 tensor is not f32");
 
  if (inputETy.isF32() && !accType.isF32())
    return op.emitOpError("accumulator type for f32 tensor is not f32");
 
  if (inputETy != inputZpETy)
    return op.emitOpError("expect both input and its zero point are the same "
                          "element type, got ")
           << inputETy << " and " << inputZpETy;
 
  if (resultETy != outputZpETy)
    return op.emitOpError("expect both output and its zero point are the same "
                          "element type, got ")
           << resultETy << " and " << outputZpETy;
 
  FailureOr<int64_t> maybeIZp = op.getInputZeroPoint();
  if (succeeded(maybeIZp) && op.verifyInputZeroPoint(*maybeIZp).failed())
    return failure();
 
  FailureOr<int64_t> maybeOZp = op.getOutputZeroPoint();
  if (succeeded(maybeOZp) && op.verifyOutputZeroPoint(*maybeOZp).failed())
    return failure();
 
  return success();
}
 
namespace {
struct AdaptivePoolingConstShapeValues {
  llvm::SmallVector<int64_t> kernel;
  llvm::SmallVector<int64_t> stride;
  llvm::SmallVector<int64_t> pad;
};
} // namespace
 
template <typename T>
static constexpr bool IsSupportedAdaptivePoolConstShapeVerifyOp =
    std::is_same_v<T, tosa::AvgPool2dAdaptiveOp> ||
    std::is_same_v<T, tosa::MaxPool2dAdaptiveOp>;
 
template <typename T,
          typename std::enable_if<IsSupportedAdaptivePoolConstShapeVerifyOp<T>,
                                  int>::type = 0>
static void extractAdaptivePoolingConstShapeOperands(
    T op, AdaptivePoolingConstShapeValues &values) {
  tosa::getConstShapeValues(op.getKernel().getDefiningOp(), values.kernel);
  tosa::getConstShapeValues(op.getStride().getDefiningOp(), values.stride);
  tosa::getConstShapeValues(op.getPad().getDefiningOp(), values.pad);
}
 
LogicalResult tosa::AvgPool2dOp::verify() {
  if (failed(verifyPoolingOp(*this)))
    return failure();
  if (failed(verifyAvgPoolCommonTypeAndZpChecks(*this)))
    return failure();
  return success();
}
 
LogicalResult tosa::AvgPool2dAdaptiveOp::verify() {
  AdaptivePoolingConstShapeValues values;
  extractAdaptivePoolingConstShapeOperands(*this, values);
 
  // If pad/stride/kernel are not constant, this is okay, we just can't check
  // their values. extractAdaptivePoolingConstShapeOperands will return an empty
  // list for each non CTC input. verifyPoolingOpImpl will need to handle values
  // not being present, and return success if they cannot be checked.
 
  if (failed(verifyPoolingOpImpl(getOperation(), values.kernel, values.stride,
                                 values.pad, getInput(), getOutput())))
    return failure();
 
  if (failed(verifyAvgPoolCommonTypeAndZpChecks(*this)))
    return failure();
 
  return success();
}
 
LogicalResult tosa::ClampOp::verify() {
  mlir::Type inputETy =
      llvm::cast<ShapedType>(getInput().getType()).getElementType();
  if (auto quantType =
          llvm::dyn_cast<mlir::quant::UniformQuantizedType>(inputETy)) {
    inputETy = getStorageElementTypeFromQuantized(quantType);
  }
  mlir::Type outputETy =
      llvm::cast<ShapedType>(getOutput().getType()).getElementType();
  if (auto quantType =
          llvm::dyn_cast<mlir::quant::UniformQuantizedType>(outputETy)) {
    outputETy = getStorageElementTypeFromQuantized(quantType);
  }
  if (inputETy != outputETy)
    return emitOpError("input/output element types are incompatible.");
 
  auto maxValAttr = getMaxValAttr();
  auto minValAttr = getMinValAttr();
 
  unsigned dataTypeBitWidth = inputETy.getIntOrFloatBitWidth();
 
  if (inputETy.isInteger(dataTypeBitWidth)) {
    // if input datatype is integer, check that the min_val/max_val attributes
    // are integer attributes, and that their type is the same as the input's
    // datatype
    auto intMaxValAttr = mlir::dyn_cast<mlir::IntegerAttr>(maxValAttr);
    auto intMinValAttr = mlir::dyn_cast<mlir::IntegerAttr>(minValAttr);
    if (!intMaxValAttr || !intMinValAttr ||
        (intMaxValAttr.getType() != intMinValAttr.getType()) ||
        (intMaxValAttr.getType() != inputETy))
      return emitOpError("min/max attributes types are incompatible with "
                         "input/output element types.");
 
    const bool isUnsigned = inputETy.isUnsignedInteger();
    const bool isBoolean = inputETy.isInteger(1);
    const APInt minVal = intMinValAttr.getValue();
    const APInt maxVal = intMaxValAttr.getValue();
    if ((isUnsigned || isBoolean) ? maxVal.ult(minVal) : maxVal.slt(minVal))
      return emitOpError("expected min_val <= max_val, got min_val=")
             << minValAttr << ", max_val=" << maxValAttr;
  } else {
    // otherwise, input datatype is float, check that the min_val/max_val
    // attributes share the same type and that their type is the same as the
    // input's datatype
    auto floatMaxValAttr = mlir::dyn_cast<mlir::FloatAttr>(maxValAttr);
    auto floatMinValAttr = mlir::dyn_cast<mlir::FloatAttr>(minValAttr);
    if (!floatMaxValAttr || !floatMinValAttr ||
        (floatMaxValAttr.getType() != floatMinValAttr.getType()) ||
        (floatMaxValAttr.getType() != inputETy))
      return emitOpError("min/max attributes types are incompatible with "
                         "input/output element types.");
 
    const APFloat minVal = floatMinValAttr.getValue();
    const APFloat maxVal = floatMaxValAttr.getValue();
    if (minVal.isNaN() || maxVal.isNaN())
      return emitOpError("min/max attributes should not be 'NaN', got min_val=")
             << minValAttr << ", max_val=" << maxValAttr;
 
    if (maxVal < minVal)
      return emitOpError("expected min_val <= max_val, got min_val=")
             << minValAttr << ", max_val=" << maxValAttr;
  }
 
  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, DenseI64ArrayAttr pad,
                                     DenseI64ArrayAttr stride,
                                     DenseI64ArrayAttr dilation,
                                     TypeAttr accType) {
  auto zps = createZPsAsConst(builder, input, weight);
  result.addOperands({input, weight, bias, zps.first, zps.second});
  result.addAttribute("pad", pad);
  result.addAttribute("stride", stride);
  result.addAttribute("dilation", dilation);
  result.addAttribute("acc_type", accType);
  Type finalOutputType = outputType;
  auto quantAttr = buildConvOpQuantizationAttr(builder, input, weight);
  if (quantAttr) {
    finalOutputType =
        buildConvOpResultTypeInfo(builder, outputType, input, weight);
  }
  result.addTypes(finalOutputType);
}
 
/// 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, TypeAttr accType) {
  auto zps = createZPsAsConst(builder, input, weight);
  result.addOperands({input, weight, bias, zps.first, zps.second});
  result.addAttribute("out_pad", outpad);
  result.addAttribute("stride", stride);
  result.addAttribute("acc_type", accType);
  Type finalOutputType = outputType;
  auto quantAttr = buildConvOpQuantizationAttr(builder, input, weight);
  if (quantAttr) {
    finalOutputType =
        buildConvOpResultTypeInfo(builder, outputType, input, weight);
  }
  result.addTypes(finalOutputType);
}
 
static void buildMatMulLikeOpWithQuantInfo(OpBuilder &builder,
                                           OperationState &result,
                                           Type outputType, Value a, Value b) {
  const std::pair<Value, Value> zps = createZPsAsConst(builder, a, b);
  result.addOperands({a, b, zps.first, zps.second});
 
  Type finalOutputType{outputType};
  if (buildMatMulOpQuantizationAttr(builder, a, b)) {
    auto eType = getStorageElementTypeOrSelf(a.getType());
    auto inputBits = eType.getIntOrFloatBitWidth();
 
    auto outputShapedType = llvm::dyn_cast<ShapedType>(outputType);
    assert(outputShapedType && "Output must be a shaped type");
 
    IntegerType accElementType;
    if (inputBits == 16)
      accElementType = builder.getIntegerType(48);
    else
      accElementType = builder.getI32Type();
 
    finalOutputType = outputShapedType.clone(accElementType);
  }
  result.addTypes(finalOutputType);
}
 
static void buildMatMulOpWithQuantInfo(OpBuilder &builder,
                                       OperationState &result, Type outputType,
                                       Value a, Value b) {
  buildMatMulLikeOpWithQuantInfo(builder, result, outputType, a, b);
}
 
static void buildMatMulTOpWithQuantInfo(OpBuilder &builder,
                                        OperationState &result, Type outputType,
                                        Value a, Value b) {
  buildMatMulLikeOpWithQuantInfo(builder, result, outputType, a, b);
}
 
/// Both the tosa.avg_pool2d and unary ops use the same
/// UnaryOpQuantizationAttr 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, TypeAttr accType) {
  const Location loc{result.location};
  int64_t inputZp{0};
  int64_t outputZp{0};
 
  if (auto quantAttr =
          buildUnaryOpQuantizationAttr(builder, input, outputType)) {
    inputZp = quantAttr.getInputZp();
    outputZp = quantAttr.getOutputZp();
  }
  const std::optional<Value> inputZpOp =
      createZeroPointTensor(builder, loc, input.getType(), inputZp);
  if (!inputZpOp) {
    (void)emitError(
        loc,
        "Failed to create input zero point tensor for quantized AVG_POOL2D op");
  }
  const std::optional<Value> outputZpOp =
      createZeroPointTensor(builder, loc, outputType, outputZp);
  if (!outputZpOp) {
    (void)emitError(loc, "Failed to create output zero point tensor for "
                         "quantized AVG_POOL2D op");
  }
 
  if (inputZpOp && outputZpOp) {
    result.addOperands({input, inputZpOp.value(), outputZpOp.value()});
  } else {
    // failed to create one or more zero points above: just add input as
    // operands this will trigger error in building the op because of missing
    // zero points
    result.addOperands({input});
  }
  result.addAttribute("kernel", kernel);
  result.addAttribute("stride", stride);
  result.addAttribute("pad", pad);
  result.addAttribute("acc_type", accType);
  result.types.push_back(outputType);
}
 
/// This builder mirrors avg_pool2d quant-info handling and materializes
/// kernel/stride/pad as const_shape operands for avg_pool2d_adaptive.
static void buildAvgPool2dAdaptiveOpWithQuantInfo(
    OpBuilder &builder, OperationState &result, Type outputType, Value input,
    DenseI64ArrayAttr kernel, DenseI64ArrayAttr stride, DenseI64ArrayAttr pad,
    TypeAttr accType) {
  const Location loc{result.location};
  int64_t inputZp{0};
  int64_t outputZp{0};
 
  if (auto quantAttr =
          buildUnaryOpQuantizationAttr(builder, input, outputType)) {
    inputZp = quantAttr.getInputZp();
    outputZp = quantAttr.getOutputZp();
  }
  const std::optional<Value> inputZpOp =
      createZeroPointTensor(builder, loc, input.getType(), inputZp);
  if (!inputZpOp) {
    (void)emitError(loc,
                    "Failed to create input zero point tensor for quantized "
                    "AVG_POOL2D_ADAPTIVE op");
  }
  const std::optional<Value> outputZpOp =
      createZeroPointTensor(builder, loc, outputType, outputZp);
  if (!outputZpOp) {
    (void)emitError(loc, "Failed to create output zero point tensor for "
                         "quantized AVG_POOL2D_ADAPTIVE op");
  }
 
  if (inputZpOp && outputZpOp) {
    ImplicitLocOpBuilder b(loc, builder);
    Value kernelShape = getTosaConstShape(b, kernel.asArrayRef());
    Value strideShape = getTosaConstShape(b, stride.asArrayRef());
    Value padShape = getTosaConstShape(b, pad.asArrayRef());
    result.addOperands({input, inputZpOp.value(), outputZpOp.value(),
                        kernelShape, strideShape, padShape});
  } else {
    // Failed to create one or more zero points above: just add input as
    // operands. This will trigger error in building the op because of missing
    // operands.
    result.addOperands({input});
  }
  result.addAttribute("acc_type", accType);
  result.types.push_back(outputType);
}
 
/// This builder is called on single-parameter negate operator
/// to construct input and output zero points based on their
/// types.
static void buildNegateOpWithQuantInfo(OpBuilder &builder,
                                       OperationState &result, Type outputType,
                                       Value input) {
  const Location loc{result.location};
  int64_t input1Zp{0};
  int64_t outputZp{0};
  auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType);
  if (quantAttr) {
    input1Zp = quantAttr.getInputZp();
    outputZp = quantAttr.getOutputZp();
  }
  const std::optional<Value> input1ZpOp =
      createZeroPointTensor(builder, loc, input.getType(), input1Zp);
  if (!input1ZpOp) {
    (void)emitError(
        loc, "Failed to create input1 zero point for quantized NEGATE op");
  }
 
  const std::optional<Value> outputZpOp =
      createZeroPointTensor(builder, loc, input.getType(), outputZp);
  if (!outputZpOp) {
    (void)emitError(
        loc, "Failed to create output zero point for quantized NEGATE op");
  }
 
  if (input1ZpOp && outputZpOp) {
    result.addOperands({input, input1ZpOp.value(), outputZpOp.value()});
  } else {
    // failed to create one or more zero points above: just add input as
    // operands. This will trigger error in building the op because of
    // missing zero points
    result.addOperands({input});
  }
 
  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) {
  const Location loc{result.location};
  int32_t zp{0};
  const auto quantAttr = buildPadOpQuantizationAttr(builder, input);
  if (quantAttr) {
    zp = static_cast<int32_t>(quantAttr.getInputZp());
  }
  const auto padConstOp{createPadConstTensor(builder, loc, input, zp)};
  result.addOperands({input, paddings, padConstOp});
  result.types.push_back(outputType);
}
 
static void buildVariableOp(OpBuilder &builder, OperationState &result,
                            StringRef name, Type variableType,
                            Attribute initialValue) {
  const Location loc{result.location};
  auto nameAttr = builder.getStringAttr(name);
 
  auto shapedType = dyn_cast<ShapedType>(variableType);
  if (!shapedType) {
    (void)emitError(loc, "variable type must be a shaped type");
    return;
  }
  if (!shapedType.hasRank()) {
    (void)emitError(loc, "variable type must be a ranked type");
    return;
  }
 
  auto elementType = shapedType.getElementType();
  auto elementTypeAttr = TypeAttr::get(elementType);
  ArrayRef<int64_t> shape = shapedType.getShape();
  auto varShapeAttr = builder.getIndexTensorAttr(convertFromMlirShape(shape));
 
  result.addAttribute("sym_name", nameAttr);
  result.addAttribute("var_shape", varShapeAttr);
  result.addAttribute("type", elementTypeAttr);
  result.addAttribute("initial_value", initialValue);
}
 
//===----------------------------------------------------------------------===//
// TOSA Operator Return Type Inference.
//===----------------------------------------------------------------------===//
static FailureOr<int64_t> resolveBroadcastDim(const int64_t dim1,
                                              const int64_t dim2) {
  if (dim1 == 1)
    return dim2;
  if (dim2 == 1)
    return dim1;
 
  if (ShapedType::isStatic(dim1) && ShapedType::isStatic(dim2) && dim1 != dim2)
    return failure();
 
  // Prefer static dimension over dynamic
  return ShapedType::isDynamic(dim1) ? dim2 : dim1;
}
 
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);
 
      const FailureOr<int64_t> maybeResolvedDim =
          resolveBroadcastDim(dim1, dim2);
      if (failed(maybeResolvedDim))
        return failure();
      const int64_t resolvedDim = *maybeResolvedDim;
      outShape[i + rankDiff] = resolvedDim;
    }
  }
 
  return success();
}
 
LogicalResult tosa::ArgMaxOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    ArgMaxOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  return inferArgMaxMinReturnTypeComponents(context, location, adaptor,
                                            inferredReturnShapes);
}
 
LogicalResult tosa::ArgMinOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    ArgMinOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  return inferArgMaxMinReturnTypeComponents(context, location, adaptor,
                                            inferredReturnShapes);
}
 
LogicalResult tosa::RFFT2dOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    RFFT2dOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInputReal().getType());
 
  if (!inputShape.hasRank())
    return failure();
 
  llvm::SmallVector<int64_t> outputShape;
  outputShape.resize(3, ShapedType::kDynamic);
  outputShape[0] = inputShape.getDimSize(0);
  outputShape[1] = inputShape.getDimSize(1);
  int64_t inWidth = inputShape.getDimSize(2);
 
  // Note that we can support this calculation symbolically
  // in the future e.g. [x, y, z] -> [x, y, z / 2 + 1]
  if (inWidth != ShapedType::kDynamic)
    outputShape[2] = inWidth / 2 + 1;
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
 
  return success();
}
 
static LogicalResult verifyDimIsPowerOfTwo(Operation *op, const int64_t dimSize,
                                           const llvm::StringRef dimName) {
  const bool isPowerOfTwo = (dimSize & (dimSize - 1)) == 0 && dimSize > 0;
  if (!isPowerOfTwo)
    return op->emitOpError("expected ")
           << dimName << " to be a power of two, got " << dimSize;
 
  return success();
}
 
LogicalResult tosa::RFFT2dOp::verify() {
  const auto outputTypes = getResultTypes();
  if (failed(verifyCompatibleShapes(outputTypes)))
    return emitOpError("expected output shapes to match, got ") << outputTypes;
 
  const auto inputType =
      llvm::dyn_cast<RankedTensorType>(getInputReal().getType());
  if (!inputType)
    return success();
 
  const int64_t height = inputType.getDimSize(1);
  if (ShapedType::isStatic(height) &&
      failed(verifyDimIsPowerOfTwo(*this, height, "height")))
    return failure();
 
  const int64_t width = inputType.getDimSize(2);
  if (ShapedType::isStatic(width) &&
      failed(verifyDimIsPowerOfTwo(*this, width, "width")))
    return failure();
 
  const auto outputType = llvm::dyn_cast<RankedTensorType>(outputTypes[0]);
  if (!outputType)
    return success();
 
  // Batch and height input/output dimensions should match
  if (failed(verifyCompatibleShape(inputType.getShape().drop_back(),
                                   outputType.getShape().drop_back())))
    return emitOpError("expected batch and height dimensions of input/output "
                       "to match, got input=")
           << inputType << " output=" << outputType;
 
  // Output width dimension expected to be input_width / 2 + 1
  const int64_t outputWidth = outputType.getDimSize(2);
  if (ShapedType::isStatic(width) && ShapedType::isStatic(outputWidth) &&
      (outputWidth != (width / 2) + 1))
    return emitOpError(
               "expected output width to be equal to input_width / 2 + 1, got ")
           << outputWidth;
 
  return success();
}
 
LogicalResult tosa::FFT2dOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    FFT2dOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  inferredReturnShapes.push_back(
      ShapedTypeComponents(ShapeAdaptor(adaptor.getInputReal().getType())));
  inferredReturnShapes.push_back(
      ShapedTypeComponents(ShapeAdaptor(adaptor.getInputImag().getType())));
  return success();
}
 
LogicalResult tosa::FFT2dOp::verify() {
  const auto inputRealType =
      llvm::dyn_cast<RankedTensorType>(getInputReal().getType());
  const auto inputImagType =
      llvm::dyn_cast<RankedTensorType>(getInputImag().getType());
  if (!inputRealType || !inputImagType)
    return success();
 
  const auto trySelectStaticDim = [](const int64_t a, const int64_t b) {
    return ShapedType::isDynamic(a) ? a : b;
  };
 
  const int64_t height = trySelectStaticDim(inputRealType.getDimSize(1),
                                            inputImagType.getDimSize(1));
  if (ShapedType::isStatic(height) &&
      failed(verifyDimIsPowerOfTwo(*this, height, "height")))
    return failure();
 
  const int64_t width = trySelectStaticDim(inputRealType.getDimSize(2),
                                           inputImagType.getDimSize(2));
  if (ShapedType::isStatic(width) &&
      failed(verifyDimIsPowerOfTwo(*this, width, "width")))
    return failure();
 
  return success();
}
 
LogicalResult tosa::ConcatOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    ConcatOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  // Infer all dimension sizes by reducing based on inputs.
  const Properties &prop = adaptor.getProperties();
  int32_t axis = prop.axis.getValue().getSExtValue();
  llvm::SmallVector<int64_t> outputShape;
  bool hasRankedInput = false;
  for (auto operand : adaptor.getOperands()) {
    ShapeAdaptor operandShape(operand.getType());
    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 emitOptionalError(location,
                                 "Cannot concat tensors with different sizes"
                                 " on the non-axis dimension ",
                                 i);
    }
 
    hasRankedInput = true;
  }
 
  if (adaptor.getInput1().empty())
    return failure();
 
  Type inputType =
      llvm::cast<TensorType>(adaptor.getInput1().getType()[0]).getElementType();
  if (!hasRankedInput) {
    inferredReturnShapes.push_back(ShapedTypeComponents(inputType));
    return success();
  }
 
  // Determine the dimension size along the concatenation axis.
  int64_t concatDimSize = 0;
  for (auto operand : adaptor.getOperands()) {
    ShapeAdaptor operandShape(operand.getType());
 
    // 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, inputType));
  return success();
}
 
LogicalResult tosa::ConcatOp::verify() {
  // check that each input has same element type as output
  auto outType = getOutput().getType();
  const Operation::operand_range inputList = getInput1();
 
  // Check there is at least one input
  if (inputList.empty())
    return emitOpError("expect at least one input");
 
  if (!llvm::all_of(inputList, [&](auto input) {
        return succeeded(verifySameElementTypes(
            *this, /* inType = */ input.getType(), outType));
      })) {
    return failure();
  }
 
  const int32_t axis = getAxis();
  ShapeAdaptor firstRankedInputShape = nullptr;
  for (const auto &input : inputList) {
    const Type inputType = input.getType();
    ShapeAdaptor currShape(inputType);
    if (currShape.hasRank()) {
      firstRankedInputShape = currShape;
      // Check axis is in expected range
      if (axis < 0 || axis >= firstRankedInputShape.getRank())
        return emitOpError("expect axis to be within range 0 < axis < "
                           "rank(input1[firstRankedTensorIdx]), got ")
               << axis;
      break;
    }
  }
 
  const auto allOperandsHasRank = [](const Value input) {
    return ShapeAdaptor(input.getType()).hasRank();
  };
  if (llvm::all_of(inputList, allOperandsHasRank)) {
    const int64_t firstInputRank = firstRankedInputShape.getRank();
 
    for (const auto &[index, input] : llvm::enumerate(inputList.drop_front())) {
      const ShapeAdaptor inputShape(input.getType());
      const int64_t inputRank = inputShape.getRank();
      const size_t operandNum = index + 1;
 
      // Check that each operand has the same rank
      if (inputRank != firstInputRank)
        return emitOpError(
                   "expect all operands to have the same rank, but got ")
               << firstInputRank << " vs " << inputRank << " on operands 0 and "
               << operandNum;
 
      // Check non-axis dims match
      for (int i = 0; i < inputRank; i++) {
        const int64_t inputDim = inputShape.getDimSize(i);
        const int64_t firstInputDim = firstRankedInputShape.getDimSize(i);
        if (i == axis || firstRankedInputShape.isDynamicDim(i) ||
            inputShape.isDynamicDim(i))
          continue;
        if (inputDim != firstInputDim)
          return emitOpError("expect all operand shapes to have the same sizes "
                             "on non-axis dimensions, but got ")
                 << inputDim << " vs " << firstInputDim << " at index " << i
                 << " on operands 0 and " << operandNum;
      }
    }
 
    const ShapeAdaptor outputShape(outType);
    if (outputShape.hasRank() && outputShape.getRank() != firstInputRank)
      return emitOpError("expect output rank to match inputs rank, got ")
             << outputShape.getRank() << " vs " << firstInputRank;
 
    // ERROR_IF(axis_sum != shape[axis]);
    int64_t axisSum = 0;
    for (const auto &input : inputList) {
      const ShapeAdaptor inputShape(input.getType());
      if (inputShape.isDynamicDim(axis)) {
        // make axisSum negative to indicate invalid value
        axisSum = -1;
        break;
      }
      axisSum += inputShape.getDimSize(axis);
    }
 
    if (axisSum >= 0 && outputShape.hasRank() &&
        !outputShape.isDynamicDim(axis) &&
        axisSum != outputShape.getDimSize(axis))
      return emitOpError("requires sum of axis dimensions of input1 "
                         "equal to output axis dimension, got ")
             << axisSum << " and " << outputShape.getDimSize(axis);
  }
 
  return success();
}
 
LogicalResult tosa::EqualOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    ValueShapeRange operands, DictionaryAttr attributes, PropertyRef properties,
    RegionRange regions,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  auto elementType = IntegerType::get(context, /*width=*/1);
 
  llvm::SmallVector<int64_t> outShape;
  if (resolveBroadcastShape(operands, outShape).failed()) {
    inferredReturnShapes.push_back(ShapedTypeComponents(elementType));
    return success();
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outShape, elementType));
  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]));
}
 
// MATMUL batch shapes are right aligned and may have different ranks. Missing
// leading dimensions are treated as one, while an unranked input remains
// unknown because it may contain any number of batch dimensions.
static int64_t getMatMulBatchDim(const ShapeAdaptor &shape, int64_t outputRank,
                                 int64_t axis) {
  if (!shape.hasRank())
    return ShapedType::kDynamic;
  const int64_t inputAxis = axis - (outputRank - shape.getRank());
  return inputAxis < 0 ? 1 : shape.getDimSize(inputAxis);
}
 
static FailureOr<SmallVector<int64_t>>
resolveMatMulOutputShape(const ShapeAdaptor &aShape, const ShapeAdaptor &bShape,
                         int64_t outputRank, bool transposeB) {
  if (outputRank < 2 ||
      (aShape.hasRank() &&
       (aShape.getRank() < 2 || aShape.getRank() > outputRank)) ||
      (bShape.hasRank() &&
       (bShape.getRank() < 2 || bShape.getRank() > outputRank)))
    return failure();
 
  SmallVector<int64_t> outputShape(outputRank, ShapedType::kDynamic);
  for (int64_t axis = 0; axis < outputRank - 2; ++axis) {
    const int64_t aDim = getMatMulBatchDim(aShape, outputRank, axis);
    const int64_t bDim = getMatMulBatchDim(bShape, outputRank, axis);
    FailureOr<int64_t> resolvedDim = resolveBroadcastDim(aDim, bDim);
    if (failed(resolvedDim))
      return failure();
    outputShape[axis] = *resolvedDim;
  }
 
  if (aShape.hasRank())
    outputShape[outputRank - 2] = aShape.getDimSize(aShape.getRank() - 2);
  if (bShape.hasRank())
    outputShape[outputRank - 1] =
        bShape.getDimSize(bShape.getRank() - (transposeB ? 2 : 1));
  return outputShape;
}
 
static LogicalResult inferMatMulReturnTypeComponents(
    const ShapeAdaptor &aShape, const ShapeAdaptor &bShape, bool transposeB,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  if ((aShape.hasRank() && aShape.getRank() < 2) ||
      (bShape.hasRank() && bShape.getRank() < 2))
    return failure();
 
  // An unranked input can have an arbitrary batch prefix, so the output rank
  // cannot yet be inferred.
  if (!aShape.hasRank() || !bShape.hasRank()) {
    inferredReturnShapes.emplace_back();
    return success();
  }
 
  const int64_t aChannels = aShape.getDimSize(aShape.getRank() - 1);
  const int64_t bChannels =
      bShape.getDimSize(bShape.getRank() - (transposeB ? 1 : 2));
  if (ShapedType::isStatic(aChannels) && ShapedType::isStatic(bChannels) &&
      aChannels != bChannels)
    return failure();
 
  const int64_t outputRank = std::max(aShape.getRank(), bShape.getRank());
  FailureOr<SmallVector<int64_t>> outputShape =
      resolveMatMulOutputShape(aShape, bShape, outputRank, transposeB);
  if (failed(outputShape))
    return failure();
 
  inferredReturnShapes.emplace_back(*outputShape);
  return success();
}
 
LogicalResult tosa::MatMulOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    MatMulOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  return inferMatMulReturnTypeComponents(ShapeAdaptor(adaptor.getA().getType()),
                                         ShapeAdaptor(adaptor.getB().getType()),
                                         /*transposeB=*/false,
                                         inferredReturnShapes);
}
 
template <typename T>
static LogicalResult verifyMatMulQuantizedOperandsType(T op, Type aElementType,
                                                       Type bElementType) {
  const auto aQuantizedEType =
      llvm::dyn_cast<quant::UniformQuantizedType>(aElementType);
  const auto bQuantizedEType =
      llvm::dyn_cast<quant::UniformQuantizedType>(bElementType);
 
  if (aQuantizedEType || bQuantizedEType) {
    if (!aQuantizedEType || !bQuantizedEType) {
      return op.emitOpError("expect operands to be both quantized or both not "
                            "quantized, got ")
             << aElementType << " and " << bElementType;
    }
    // both a and b have quantized element types
    auto aQuantWidth = aQuantizedEType.getStorageTypeIntegralWidth();
    auto bQuantWidth = bQuantizedEType.getStorageTypeIntegralWidth();
    if (aQuantWidth != bQuantWidth) {
      return op.emitOpError("expect quantized operands to have same widths, "
                            "got ")
             << aQuantWidth << " and " << bQuantWidth;
    }
  }
 
  return success();
}
 
template <typename T>
static LogicalResult verifyMatMulZeroPointType(T op, Value input, Value zp,
                                               StringRef inputName,
                                               StringRef zpName) {
  const Type inputElementType = getElementTypeOrSelf(input.getType());
  const Type inputStorageElementType = getStorageElementTypeOrSelf(input);
  const Type zpElementType = getStorageElementTypeOrSelf(zp);
  Type expectedElementType = inputStorageElementType;
 
  if (isa<BlockScaledType>(inputElementType))
    expectedElementType = Float32Type::get(op.getContext());
 
  if (expectedElementType == zpElementType)
    return success();
 
  InFlightDiagnostic diag = op.emitOpError("expect input ");
  diag << inputName << " and " << zpName;
  if (isa<BlockScaledType>(inputElementType))
    diag << " have compatible element types, got " << inputElementType
         << " and " << zpElementType;
  else
    diag << " have the same element type, got " << inputStorageElementType
         << " and " << zpElementType;
  return diag;
}
 
static SmallVector<int64_t> getMatMulBatchShape(const ShapeAdaptor &shape) {
  SmallVector<int64_t> batchShape;
  if (!shape.hasRank() || shape.getRank() < 2)
    return batchShape;
  batchShape.reserve(shape.getRank() - 2);
  for (int64_t i = 0, e = shape.getRank() - 2; i < e; ++i)
    batchShape.push_back(shape.getDimSize(i));
  return batchShape;
}
 
template <typename T>
static LogicalResult verifyMatMulShapes(T op, bool transposeB) {
  const ShapeAdaptor aShape(op.getA().getType());
  const ShapeAdaptor bShape(op.getB().getType());
  const auto outputType = cast<ShapedType>(op.getResult().getType());
 
  int64_t channels = aShape.hasRank() ? aShape.getDimSize(aShape.getRank() - 1)
                                      : ShapedType::kDynamic;
  if (bShape.hasRank() &&
      failed(tryUpdateDimOrFailure(
          op, channels,
          bShape.getDimSize(bShape.getRank() - (transposeB ? 1 : 2)), "b",
          "channels")))
    return failure();
 
  const int64_t minimumOutputRank =
      std::max(aShape.hasRank() ? aShape.getRank() : 2,
               bShape.hasRank() ? bShape.getRank() : 2);
  if (outputType.hasRank() && outputType.getRank() < minimumOutputRank)
    return op.emitOpError("expected output rank of at least ")
           << minimumOutputRank << ", got " << outputType.getRank();
 
  const bool bothInputsRanked = aShape.hasRank() && bShape.hasRank();
  if (!bothInputsRanked && !outputType.hasRank())
    return success();
 
  // When an input is unranked, use the declared output rank to validate every
  // result dimension constrained by the ranked input.
  const int64_t expectedOutputRank =
      bothInputsRanked ? minimumOutputRank : outputType.getRank();
  FailureOr<SmallVector<int64_t>> expectedOutputShape =
      resolveMatMulOutputShape(aShape, bShape, expectedOutputRank, transposeB);
  if (failed(expectedOutputShape)) {
    InFlightDiagnostic diag = op.emitOpError(
        "expected batch dimensions of a and b to be broadcast compatible, "
        "got a=[");
    printShapeToDiagnostic(diag, getMatMulBatchShape(aShape));
    diag << "] and b=[";
    printShapeToDiagnostic(diag, getMatMulBatchShape(bShape));
    diag << "]";
    return diag;
  }
 
  if (outputType.hasRank())
    return verifyOutputShapeCompatibleWithExpected(
        op.getOperation(), outputType, *expectedOutputShape);
  return success();
}
 
LogicalResult MatMulOp::verify() {
  const Type aElementType = getElementTypeOrSelf(getA());
  const Type bElementType = getElementTypeOrSelf(getB());
 
  if (failed(
          verifyMatMulQuantizedOperandsType(*this, aElementType, bElementType)))
    return failure();
 
  if (failed(verifyMatMulZeroPointType(*this, getA(), getAZp(), "a", "a_zp")) ||
      failed(verifyMatMulZeroPointType(*this, getB(), getBZp(), "b", "b_zp")))
    return failure();
 
  FailureOr<int64_t> maybeAZp = getAZeroPoint();
  if (succeeded(maybeAZp) && verifyAZeroPoint(*maybeAZp).failed())
    return failure();
 
  FailureOr<int64_t> maybeBZp = getBZeroPoint();
  if (succeeded(maybeBZp) && verifyBZeroPoint(*maybeBZp).failed())
    return failure();
 
  return verifyMatMulShapes(*this, /*transposeB=*/false);
}
 
LogicalResult tosa::MatMulTOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    MatMulTOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  return inferMatMulReturnTypeComponents(ShapeAdaptor(adaptor.getA().getType()),
                                         ShapeAdaptor(adaptor.getB().getType()),
                                         /*transposeB=*/true,
                                         inferredReturnShapes);
}
 
LogicalResult MatMulTOp::verify() {
  const Type aElementType = getElementTypeOrSelf(getA());
  const Type bElementType = getElementTypeOrSelf(getB());
 
  if (failed(
          verifyMatMulQuantizedOperandsType(*this, aElementType, bElementType)))
    return failure();
 
  if (failed(verifyMatMulZeroPointType(*this, getA(), getAZp(), "a", "a_zp")) ||
      failed(verifyMatMulZeroPointType(*this, getB(), getBZp(), "b", "b_zp")))
    return failure();
 
  FailureOr<int64_t> maybeAZp = getAZeroPoint();
  if (succeeded(maybeAZp) && verifyAZeroPoint(*maybeAZp).failed())
    return failure();
 
  FailureOr<int64_t> maybeBZp = getBZeroPoint();
  if (succeeded(maybeBZp) && verifyBZeroPoint(*maybeBZp).failed())
    return failure();
 
  return verifyMatMulShapes(*this, /*transposeB=*/true);
}
 
LogicalResult tosa::MatmulTBlockScaledOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    MatmulTBlockScaledOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  SmallVector<int64_t, 3> outShape(3, ShapedType::kDynamic);
 
  const auto aDataShape = cast<ShapedType>(adaptor.getAData().getType());
  if (aDataShape.hasRank()) {
    outShape[0] = aDataShape.getDimSize(0);
    outShape[1] = aDataShape.getDimSize(1);
  }
 
  const auto aScaleShape = cast<ShapedType>(adaptor.getAScale().getType());
  if (aScaleShape.hasRank()) {
    outShape[0] = ShapedType::isDynamic(outShape[0]) ? aScaleShape.getDimSize(0)
                                                     : outShape[0];
    outShape[1] = ShapedType::isDynamic(outShape[1]) ? aScaleShape.getDimSize(1)
                                                     : outShape[1];
  }
 
  // If B batch size is 1, it is broadcast across A's batch size
  const auto bDataShape = cast<ShapedType>(adaptor.getBData().getType());
  if (bDataShape.hasRank()) {
    const int64_t bDataBatchSize = bDataShape.getDimSize(0);
    if (bDataBatchSize != 1)
      outShape[0] =
          ShapedType::isDynamic(outShape[0]) ? bDataBatchSize : outShape[0];
    outShape[2] = bDataShape.getDimSize(1);
  }
 
  const auto bScaleShape = cast<ShapedType>(adaptor.getBScale().getType());
  if (bScaleShape.hasRank()) {
    const int64_t bScaleBatchSize = bScaleShape.getDimSize(0);
    if (bScaleBatchSize != 1)
      outShape[0] =
          ShapedType::isDynamic(outShape[0]) ? bScaleBatchSize : outShape[0];
    outShape[2] = ShapedType::isDynamic(outShape[2]) ? bScaleShape.getDimSize(1)
                                                     : outShape[2];
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
  return success();
}
 
LogicalResult MatmulTBlockScaledOp::verify() {
  // Verify same input data types
  const Type aDataType = getAData().getType();
  const Type bDataType = getBData().getType();
  if (failed(verifySameElementTypes(*this, aDataType, bDataType, "A_data",
                                    "B_data")))
    return failure();
 
  // Verify input shape compatibility
  int64_t N = ShapedType::kDynamic;
  int64_t D = ShapedType::kDynamic;
  int64_t H = ShapedType::kDynamic;
  int64_t W = ShapedType::kDynamic;
  int64_t C = ShapedType::kDynamic;
  int64_t multiplesOfC = ShapedType::kDynamic;
 
  const ShapeAdaptor aDataShape = ShapeAdaptor(aDataType);
  if (aDataShape.hasRank()) {
    N = aDataShape.getDimSize(0);
    H = aDataShape.getDimSize(1);
    C = aDataShape.getDimSize(2);
  }
 
  const ShapeAdaptor aScaleShape = ShapeAdaptor(getAScale().getType());
  if (aScaleShape.hasRank()) {
    if (failed(tryUpdateDimOrFailure(*this, N, aScaleShape.getDimSize(0),
                                     "a_scale", "batch")) ||
        failed(tryUpdateDimOrFailure(*this, H, aScaleShape.getDimSize(1),
                                     "a_scale", "height")))
      return failure();
    multiplesOfC = aScaleShape.getDimSize(2);
  }
 
  const ShapeAdaptor bDataShape = ShapeAdaptor(bDataType);
  if (bDataShape.hasRank()) {
    if (failed(tryUpdateDimOrFailure(*this, D, bDataShape.getDimSize(0),
                                     "b_data", "batch")) ||
        failed(tryUpdateDimOrFailure(*this, C, bDataShape.getDimSize(2),
                                     "b_data", "channels")))
      return failure();
    W = bDataShape.getDimSize(1);
  }
 
  const ShapeAdaptor bScaleShape = ShapeAdaptor(getBScale().getType());
  if (bScaleShape.hasRank()) {
    if (failed(tryUpdateDimOrFailure(*this, D, bScaleShape.getDimSize(0),
                                     "b_scale", "batch")) ||
        failed(tryUpdateDimOrFailure(*this, W, bScaleShape.getDimSize(1),
                                     "b_scale", "width")) ||
        failed(tryUpdateDimOrFailure(*this, multiplesOfC,
                                     bScaleShape.getDimSize(2), "b_scale",
                                     "C/block_size")))
      return failure();
  }
 
  // Verify batch size is broadcast compatible
  if (ShapedType::isStatic(N) && ShapedType::isStatic(D) && N != D && D != 1)
    return emitOpError("expect B matrix batch size to be broadcast compatible "
                       "with A, got D=")
           << D << " vs N=" << N;
 
  // Verify C is a multiple of block size
  const uint32_t blockSize = BlockSizeAttr::getBlockSizeValue(getBlockSize());
  if (blockSize != BlockSizeAttr::getBlockSizeValue(BlockSize::BLOCK_SIZE_32))
    return emitOpError("expect block size to be 32, got ") << blockSize;
  if (ShapedType::isStatic(C) && C % blockSize != 0)
    return emitOpError("expect C to be a multiple of block size, got C=")
           << C << ", block_size=" << blockSize;
 
  // Verify multiplesOfC is C / block size
  if (ShapedType::isStatic(C) && ShapedType::isStatic(multiplesOfC) &&
      multiplesOfC != C / blockSize)
    return emitOpError(
               "expect scale operands dimension 2 to equal C/block_size (")
           << C << "/" << blockSize << ")" << ", got " << multiplesOfC;
 
  // Verify output shape
  N = ShapedType::isDynamic(N) ? D : N;
  const SmallVector<int64_t, 3> expectedOutputShape = {N, H, W};
  const auto outputType = cast<ShapedType>(getResult().getType());
  if (outputType.hasRank() &&
      failed(
          verifyCompatibleShape(outputType.getShape(), expectedOutputShape))) {
    InFlightDiagnostic opError = emitOpError("expected output shape ");
    printShapeToDiagnostic(opError, outputType.getShape());
    opError << " to be compatible with expected output shape ";
    printShapeToDiagnostic(opError, expectedOutputShape);
    return opError;
  }
 
  return success();
}
 
LogicalResult tosa::PadOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    PadOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput1().getType());
  auto paddingRank =
      cast<tosa::shapeType>(adaptor.getPadding().getType()).getRank();
  SmallVector<int64_t> outputShape;
 
  // If the input rank is unknown, we can infer the output rank using the
  // padding shape's rank divided by 2.
  if (!inputShape.hasRank()) {
    outputShape.resize(paddingRank / 2, ShapedType::kDynamic);
    inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
    return success();
  }
 
  SmallVector<int64_t> paddingValues;
  // If the paddings value is not a constant, all dimensions must be dynamic.
  if (!tosa::getConstShapeValues(adaptor.getPadding().getDefiningOp(),
                                 paddingValues)) {
    outputShape.resize(inputShape.getRank(), ShapedType::kDynamic);
    inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
    return success();
  }
 
  outputShape.reserve(inputShape.getRank());
  for (int i = 0, s = inputShape.getRank(); i < s; i++) {
    if (inputShape.isDynamicDim(i)) {
      outputShape.push_back(ShapedType::kDynamic);
      continue;
    }
    auto padFront = paddingValues[i * 2];
    auto padBack = paddingValues[i * 2 + 1];
    outputShape.push_back(inputShape.getDimSize(i) + padFront + padBack);
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  return success();
}
 
LogicalResult tosa::PadOp::verify() {
  if (verifySameElementTypes(*this, /* inType = */ getInput1().getType(),
                             /* outType = */ getOutput().getType())
          .failed()) {
    return failure();
  }
 
  if (auto padConst = getPadConst()) {
    if (verifySameElementTypes(*this, /* inType = */ padConst.getType(),
                               /* outType = */ getOutput().getType())
            .failed()) {
      return failure();
    }
  }
 
  RankedTensorType inputType =
      llvm::dyn_cast<RankedTensorType>(getInput1().getType());
  RankedTensorType outputType =
      llvm::dyn_cast<RankedTensorType>(getOutput().getType());
  if (!inputType || !outputType)
    return success();
 
  if (failed(verifyRanksMatch(getOperation(), inputType, outputType, "input",
                              "output")))
    return failure();
 
  auto inputRank = inputType.getRank();
  auto paddingRank = cast<tosa::shapeType>(getPadding().getType()).getRank();
  if (paddingRank != inputRank * 2)
    return emitOpError() << "padding tensor must have " << inputRank
                         << " * 2 = " << inputRank * 2 << " elements, but got "
                         << paddingRank;
 
  return success();
}
 
LogicalResult tosa::SliceOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    SliceOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
 
  Type inputType = getElementTypeOrSelf(adaptor.getInput1().getType());
  SmallVector<int64_t> start;
  SmallVector<int64_t> size;
 
  if (!tosa::getConstShapeValues(adaptor.getStart().getDefiningOp(), start) ||
      !tosa::getConstShapeValues(adaptor.getSize().getDefiningOp(), size)) {
    auto rank = cast<tosa::shapeType>(adaptor.getSize().getType()).getRank();
    SmallVector<int64_t> fallback(rank, ShapedType::kDynamic);
    inferredReturnShapes.push_back(ShapedTypeComponents(fallback, inputType));
    return success();
  }
 
  // if size[i] is -1, all remaining elements in dimension i are included
  // in the slice, similar to TF.
  ShapeAdaptor inputShape(adaptor.getInput1().getType());
  // initialize outputShape to all unknown
  SmallVector<int64_t> outputShape(size.size(), ShapedType::kDynamic);
  if (inputShape.hasRank()) {
    for (size_t i = 0; i < size.size(); i++) {
      if (size[i] != 0 && size[i] >= -1 && start[i] >= 0 &&
          (ShapedType::isDynamic(inputShape.getDimSize(i)) ||
           start[i] < inputShape.getDimSize(i))) {
        // size[i] is not 0 and not < -1, and start[i] is in valid range
        if (ShapedType::isDynamic(inputShape.getDimSize(i))) {
          // input shape has unknown dim[i] - only valid if size[i] > 0
          if (size[i] > 0) {
            outputShape[i] = size[i];
          }
        } else {
          // input shape has known dim[i]
          if (size[i] == -1) {
            outputShape[i] = inputShape.getDimSize(i) - start[i];
          } else if (start[i] + size[i] <= inputShape.getDimSize(i)) {
            // start[i] + size[i] is within bound of input shape's dim[i]
            outputShape[i] = size[i];
          }
        }
      }
    }
  } else {
    outputShape = convertToMlirShape(size);
  }
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  return success();
}
 
LogicalResult tosa::SliceOp::verify() {
  const Value input = getInput1();
  const Value output = getOutput();
  if (verifySameElementTypes(*this, /* inType = */ input.getType(),
                             /* outType = */ output.getType())
          .failed())
    return failure();
 
  const Value start = getStart();
  const Value size = getSize();
  const ShapeAdaptor inputShape(input.getType());
  const ShapeAdaptor outputShape(output.getType());
 
  if (inputShape.hasRank()) {
    const auto inputRank = inputShape.getRank();
    if (outputShape.hasRank() && inputRank != outputShape.getRank())
      return emitOpError(
                 "expect input1 and output to have the same ranks, got ")
             << inputRank << " and " << outputShape.getRank();
 
    const auto startShapeRank =
        llvm::cast<tosa::shapeType>(start.getType()).getRank();
    if (inputRank != startShapeRank)
      return emitOpError("length of start is not equal to rank of input shape");
 
    const auto sizeShapeRank =
        llvm::cast<tosa::shapeType>(size.getType()).getRank();
    if (inputRank != sizeShapeRank)
      return emitOpError("length of size is not equal to rank of input shape");
  }
 
  return success();
}
 
LogicalResult tosa::MulOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    ValueShapeRange operands, DictionaryAttr attributes, PropertyRef properties,
    RegionRange regions,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  // mul op's output shape only depend on input1 and input2, not on shift
  ValueShapeRange twoInputs = operands.drop_back();
  llvm::SmallVector<int64_t> outShape;
  if (resolveBroadcastShape(twoInputs, outShape).failed()) {
    inferredReturnShapes.push_back(ShapedTypeComponents());
  } else {
    inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
  }
  return success();
}
 
LogicalResult tosa::MulOp::verify() {
  const Value output = getOutput();
  auto resElemType = getElementTypeOrSelf(output);
 
  // Verify if the element type among operands and result match tosa
  // specification.
  if (auto resIntType = dyn_cast<IntegerType>(resElemType)) {
    IntegerType lhsIntType =
        dyn_cast<IntegerType>(getElementTypeOrSelf(getInput1()));
    IntegerType rhsIntType =
        dyn_cast<IntegerType>(getElementTypeOrSelf(getInput2()));
    if (!lhsIntType || !rhsIntType || lhsIntType != rhsIntType)
      return emitOpError("requires the same element type for all operands");
 
    // Though the spec requires the element type of result to be i32, a more
    // relaxed way is provided at dialect level for easier cooperating with
    // other dialects.
    if (lhsIntType.getWidth() > resIntType.getWidth())
      return emitOpError("invalid data type size for operands or result");
 
  } else {
    // For other supported type, the spec requires requires the same element
    // type for all operands (excludes `shift` operand) and results.
    for (int i = 0; i < 2; ++i) {
      if (getElementTypeOrSelf(getOperand(i)) != resElemType)
        return emitOpError(
            "requires the same element type for all operands and results");
    }
  }
 
  // Verify the op has same ranks for all main operands (excludes extra operands
  // such as shift of mul op, so this is the only difference with the built-in
  // `SameOperandsAndResultRank` trait) and results types, if known.
  TypeRange operandTypes = getOperandTypes();
  ShapedType aType = cast<ShapedType>(operandTypes[0]);
  ShapedType bType = cast<ShapedType>(operandTypes[1]);
 
  const bool aHasRank = aType.hasRank();
  const bool bHasRank = bType.hasRank();
 
  bool hasExpectedOutputShape = false;
  SmallVector<int64_t> expectedOutputShape;
 
  if (aHasRank && bHasRank) {
    const int64_t aRank = aType.getRank();
    const int64_t bRank = bType.getRank();
    if (aRank != bRank)
      return emitOpError("a and b operands don't have matching ranks, got ")
             << aRank << " and " << bRank;
 
    // check for broadcast compatible shapes
    if (!mlir::OpTrait::util::getBroadcastedShape(
            aType.getShape(), bType.getShape(), expectedOutputShape))
      return emitOpError("a and b operands don't have broadcast-compatible "
                         "shapes, got ")
             << aType << " and " << bType;
    hasExpectedOutputShape = true;
  }
 
  ShapedType resultType = cast<ShapedType>(output.getType());
  if (!resultType.hasRank())
    return success();
 
  const int64_t resultRank = resultType.getRank();
  if (aHasRank && resultRank != aType.getRank())
    return emitOpError("result type has different rank than a, got ")
           << resultRank << " vs " << aType.getRank();
  if (bHasRank && resultRank != bType.getRank())
    return emitOpError("result type has different rank than b, got ")
           << resultRank << " vs " << bType.getRank();
 
  if (hasExpectedOutputShape &&
      failed(verifyOutputShapeCompatibleWithExpected(getOperation(), resultType,
                                                     expectedOutputShape)))
    return failure();
 
  return success();
}
 
LogicalResult tosa::TableOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    TableOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput1().getType());
 
  if (!inputShape.hasRank()) {
    inferredReturnShapes.push_back(ShapedTypeComponents());
    return success();
  }
 
  inferredReturnShapes.resize(1);
  inputShape.getDims(inferredReturnShapes[0]);
  return success();
}
 
LogicalResult tosa::TableOp::verify() {
  const TensorType inputType = getInput1().getType();
  const TensorType outputType = getOutput().getType();
 
  if (!inputType.hasRank() || !outputType.hasRank())
    return success();
 
  if (failed(verifyRanksMatch(getOperation(), inputType, outputType, "input",
                              "result")))
    return failure();
 
  auto inputDims = inputType.getShape();
  auto outputDims = outputType.getShape();
  for (auto it : llvm::enumerate(llvm::zip(inputDims, outputDims))) {
    int64_t dim = it.index();
    auto [inputDim, outputDim] = it.value();
    if (ShapedType::isStatic(outputDim) && outputDim != inputDim) {
      return emitOpError() << "dim(result, " << dim << ") = " << outputDim
                           << " doesn't match dim(input, " << dim
                           << ") = " << inputDim;
    }
  }
  return success();
}
 
LogicalResult
tosa::TileOp::getConstantMultiples(SmallVector<int64_t> &multiples) {
  // Multiples must be constants.
  DenseIntElementsAttr multiplesAttr;
  if (!matchPattern(getMultiples(), m_Constant(&multiplesAttr)))
    return failure();
  multiples =
      llvm::map_to_vector(multiplesAttr.getValues<APInt>(),
                          [](const APInt &val) { return val.getSExtValue(); });
  return success();
}
 
LogicalResult tosa::TileOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    TileOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  Type inputType = getElementTypeOrSelf(adaptor.getInput1().getType());
  SmallVector<int64_t> multiples;
  if (!tosa::getConstShapeValues(adaptor.getMultiples().getDefiningOp(),
                                 multiples)) {
    auto rank =
        cast<tosa::shapeType>(adaptor.getMultiples().getType()).getRank();
    SmallVector<int64_t> fallback(rank, ShapedType::kDynamic);
    inferredReturnShapes.push_back(ShapedTypeComponents(fallback, inputType));
    return success();
  }
  multiples = convertToMlirShape(multiples);
 
  ShapeAdaptor inputShape(adaptor.getInput1().getType());
  SmallVector<int64_t> outputShape;
  if (!inputShape.hasRank()) {
    outputShape.resize(multiples.size(), ShapedType::kDynamic);
    inferredReturnShapes.push_back(
        ShapedTypeComponents(outputShape, inputType));
    return success();
  }
  if (static_cast<size_t>(inputShape.getRank()) != multiples.size())
    return failure();
 
  // 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++) {
    if (multiples[i] == ShapedType::kDynamic) {
      outputShape.push_back(ShapedType::kDynamic);
    } else {
      int64_t dim = inputShape.getDimSize(i);
      if (dim != ShapedType::kDynamic)
        dim *= multiples[i];
      outputShape.push_back(dim);
    }
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape, inputType));
  return success();
}
 
LogicalResult tosa::TileOp::verify() {
  if (verifySameElementTypes(*this, /* intype = */ getInput1().getType(),
                             /* outType = */ getOutput().getType())
          .failed()) {
    return failure();
  }
  ShapedType inputType = llvm::cast<ShapedType>(getInput1().getType());
  ShapedType outputType = llvm::cast<ShapedType>(getType());
 
  shapeType multiplesType =
      llvm::cast<tosa::shapeType>(getMultiples().getType());
 
  auto multiplesRank = multiplesType.getRank();
 
  if (inputType.hasRank()) {
    if (inputType.getRank() != multiplesRank)
      return emitOpError("expect 'multiples' to have rank ")
             << inputType.getRank() << " but got " << multiplesRank << ".";
    if (outputType.hasRank() &&
        failed(verifyRanksMatch(getOperation(), inputType, outputType, "input",
                                "output")))
      return failure();
  } else if (outputType.hasRank() && outputType.getRank() != multiplesRank)
    return emitOpError("expect 'multiples' array to have length ")
           << outputType.getRank() << " but got " << multiplesRank << ".";
 
  SmallVector<int64_t> multiples;
  if (getConstantMultiples(multiples).succeeded() &&
      llvm::any_of(multiples, [](int64_t v) { return v <= 0 && v != -1; }))
    return emitOpError(
        "expect element of 'multiples' to be positive integer or -1.");
 
  return success();
}
 
bool tosa::ReshapeOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  if (l.size() != r.size() || l.size() != 1)
    return false;
  return getElementTypeOrSelf(l[0]) == getElementTypeOrSelf(r[0]);
}
 
LogicalResult tosa::ReshapeOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    ReshapeOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput1().getType());
  Type inputType = getElementTypeOrSelf(adaptor.getInput1().getType());
  llvm::SmallVector<int64_t> newShapeValue;
  if (!tosa::getConstShapeValues(adaptor.getShape().getDefiningOp(),
                                 newShapeValue)) {
    auto rank = cast<tosa::shapeType>(adaptor.getShape().getType()).getRank();
    SmallVector<int64_t> fallback(rank, ShapedType::kDynamic);
    inferredReturnShapes.push_back(ShapedTypeComponents(fallback, inputType));
    return success();
  }
  newShapeValue = convertToMlirShape(newShapeValue);
 
  // 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, inputType));
    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::isStatic(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, inputType));
  return success();
}
 
llvm::LogicalResult tosa::ReshapeOp::verify() {
  if (verifySameElementTypes(*this, /* inType = */ getInput1().getType(),
                             /* outType = */ getOutput().getType())
          .failed()) {
    return failure();
  }
  TensorType inputType = getInput1().getType();
 
  SmallVector<int64_t> shapeValues;
  if (!tosa::getConstShapeValues(getShape().getDefiningOp(), shapeValues)) {
    // skip following checks if shape is not constant
    return mlir::success();
  }
 
  int missingDims = llvm::count(shapeValues, kInferableDimSize);
  if (missingDims > 1)
    return emitOpError() << "expected at most one target dimension to be "
                         << kInferableDimSize;
 
  const auto outputType = dyn_cast<RankedTensorType>(getType());
  if (!outputType)
    return success();
 
  if ((int64_t)shapeValues.size() != outputType.getRank())
    return emitOpError() << "new shape does not match result rank";
 
  for (auto [newShapeDim, outputShapeDim] :
       zip(shapeValues, outputType.getShape())) {
    if (newShapeDim != kInferableDimSize &&
        newShapeDim != ShapedType::kDynamic &&
        outputShapeDim != ShapedType::kDynamic && newShapeDim != outputShapeDim)
      return emitOpError() << "new shape is inconsistent with result shape";
 
    if (newShapeDim != ShapedType::kDynamic && newShapeDim < kInferableDimSize)
      return emitOpError() << "new shape has invalid tensor dimension size "
                           << newShapeDim;
  }
 
  if (inputType.hasStaticShape()) {
    int64_t inputElementsNum = inputType.getNumElements();
    if (outputType.hasStaticShape()) {
      int64_t outputElementsNum = outputType.getNumElements();
      if (inputElementsNum != outputElementsNum) {
        return emitOpError() << "cannot reshape " << inputElementsNum
                             << " elements into " << outputElementsNum;
      }
    }
 
    int64_t newShapeElementsNum =
        llvm::accumulate(shapeValues, int64_t(1), [](int64_t acc, int64_t dim) {
          return (dim > 0) ? acc * dim : acc;
        });
    bool isStaticNewShape =
        llvm::all_of(shapeValues, [](int64_t s) { return s > 0; });
    if ((isStaticNewShape && inputElementsNum != newShapeElementsNum) ||
        (!isStaticNewShape && newShapeElementsNum > inputElementsNum)) {
      return emitOpError() << "cannot reshape " << inputElementsNum
                           << " elements into " << newShapeElementsNum;
    }
  }
 
  return mlir::success();
}
 
bool tosa::ReshapeBlockScaledOp::isCompatibleReturnTypes(TypeRange l,
                                                         TypeRange r) {
  if (l.size() != r.size() || l.size() < 1 || l.size() > 2)
    return false;
  bool ok = (getElementTypeOrSelf(l[0]) == getElementTypeOrSelf(r[0]));
  if (l.size() == 2)
    ok = ok && (getElementTypeOrSelf(l[1]) == getElementTypeOrSelf(r[1]));
  return ok;
}
 
LogicalResult tosa::ReshapeBlockScaledOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    ReshapeBlockScaledOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
 
  const auto numInputs = adaptor.getInput().size();
  ShapeAdaptor inputShape(adaptor.getInput()[0].getType());
  Type inputType = getElementTypeOrSelf(adaptor.getInput()[0].getType());
  llvm::SmallVector<int64_t> newShapeValue;
  const auto newShape = adaptor.getNewValueShape();
  if (!tosa::getConstShapeValues(newShape.getDefiningOp(), newShapeValue)) {
    auto rank = cast<tosa::shapeType>(newShape.getType()).getRank();
    SmallVector<int64_t> fallback(rank, ShapedType::kDynamic);
    inferredReturnShapes.push_back(ShapedTypeComponents(fallback, inputType));
    if (numInputs == 2)
      inferredReturnShapes.push_back(ShapedTypeComponents(
          fallback, getElementTypeOrSelf(adaptor.getInput()[1].getType())));
    return success();
  }
 
  const uint32_t blockSize =
      BlockSizeAttr::getBlockSizeValue(adaptor.getBlockSize());
 
  llvm::SmallVector<int64_t> newScaleShapeValue;
  if (numInputs == 2) {
    newScaleShapeValue.assign(newShapeValue.begin(), newShapeValue.end());
    if (!newScaleShapeValue.empty() &&
        ShapedType::isStatic(newScaleShapeValue.back()))
      newScaleShapeValue.back() /= blockSize;
  }
 
  inferredReturnShapes.push_back(
      ShapedTypeComponents(newShapeValue, inputType));
  if (numInputs == 2) {
    // Fix up scale shape - with special case for last dimension
    for (size_t idx = 0; idx < newShapeValue.size(); idx++) {
      if (ShapedType::isDynamic(newScaleShapeValue[idx])) {
        newScaleShapeValue[idx] = newShapeValue[idx];
        if (idx + 1 == newShapeValue.size())
          newScaleShapeValue[idx] /= blockSize;
      }
    }
 
    inferredReturnShapes.push_back(ShapedTypeComponents(
        newScaleShapeValue,
        getElementTypeOrSelf(adaptor.getInput()[1].getType())));
  }
  return success();
}
 
llvm::LogicalResult tosa::ReshapeBlockScaledOp::verify() {
  const Operation::operand_range inputList = getInput();
  const Operation::result_range outputList = getResults();
 
  if (inputList.size() == 0)
    return emitOpError("requires at least one input");
 
  if (inputList.size() > 2)
    return emitOpError("requires at most two inputs");
 
  if (inputList.size() != outputList.size())
    return emitOpError("requires number of results to match inputs");
 
  if (verifySameElementTypes(*this, /* inType = */ inputList[0].getType(),
                             /* outType = */ outputList[0].getType())
          .failed()) {
    return failure();
  }
 
  if (inputList.size() == 2 &&
      cast<tosa::shapeType>(getNewValueShape().getType()).getRank() == 0)
    return emitOpError("requires new shape to have a rank greater than 0");
 
  const auto inputType = llvm::cast<ShapedType>(inputList[0].getType());
  if (!inputType.hasRank())
    return success();
  const uint32_t blockSize = BlockSizeAttr::getBlockSizeValue(getBlockSize());
 
  if (inputList.size() == 2) {
    if (blockSize != BlockSizeAttr::getBlockSizeValue(BlockSize::BLOCK_SIZE_32))
      return emitOpError("expect block size to be 32, got ") << blockSize;
    if (llvm::any_of(inputList, [](Value v) {
          const auto input = cast<ShapedType>(v.getType());
          return input.hasRank() && input.getRank() == 0;
        }))
      return emitOpError(
          "requires all input shapes have a rank greater than 0");
    if (llvm::any_of(outputList, [](Value v) {
          const auto output = cast<ShapedType>(v.getType());
          return output.hasRank() && output.getRank() == 0;
        }))
      return emitOpError(
          "requires all result shapes have a rank greater than 0");
 
    if (verifySameElementTypes(*this, /* inType = */ inputList[1].getType(),
                               /* outType = */ outputList[1].getType())
            .failed()) {
      return failure();
    }
 
    const auto inputScaleType = llvm::cast<ShapedType>(inputList[1].getType());
    if (inputScaleType.hasRank()) {
      if (inputType.getRank() != inputScaleType.getRank())
        return emitOpError("input shapes do not have same rank");
 
      // Check all but the last dimension that the input shape dimensions match
      for (auto dimIdx = 0; dimIdx < inputType.getRank() - 1; dimIdx++) {
        const int64_t inputValueDim = inputType.getDimSize(dimIdx);
        const int64_t inputScaleDim = inputScaleType.getShape()[dimIdx];
        if (ShapedType::isStatic(inputValueDim) &&
            ShapedType::isStatic(inputScaleDim) &&
            inputValueDim != inputScaleDim)
          return emitOpError("input shapes for data and scale do not match on "
                             "dimension ")
                 << dimIdx;
      }
 
      // Verify last dimension of input is a multiple of block size
      const int64_t lastValueDim =
          inputType.getDimSize(inputType.getRank() - 1);
      if (ShapedType::isStatic(lastValueDim)) {
        if (lastValueDim % blockSize != 0)
          return emitOpError("expect last dimension of input_data (")
                 << lastValueDim << ") to be divisible by block_size ("
                 << blockSize << ")";
 
        const int64_t lastScaleDim =
            inputScaleType.getDimSize(inputScaleType.getRank() - 1);
        // Verify last dimension of scale is lastValueDim / block size
        if (ShapedType::isStatic(lastScaleDim) &&
            lastScaleDim != lastValueDim / blockSize)
          return emitOpError("expect last dimension of scale_data (")
                 << lastScaleDim << ") to be " << lastValueDim << "/"
                 << blockSize;
      }
    }
  } else {
    if (blockSize != BlockSizeAttr::getBlockSizeValue(BlockSize::BLOCK_SIZE_1))
      return emitOpError("expect block size to be 1, got ") << blockSize;
  }
 
  // Get the new value shape dimension values.
  SmallVector<int64_t> shapeValues;
  if (!tosa::getConstShapeValues(getNewValueShape().getDefiningOp(),
                                 shapeValues)) {
    // skip following checks if shape is not constant
    return mlir::success();
  }
 
  if (inputList.size() == 2) {
    const int64_t lastShapeDim = shapeValues.back();
    if (ShapedType::isStatic(lastShapeDim) && lastShapeDim % blockSize != 0)
      return emitOpError("expect last dimension of new shape (")
             << lastShapeDim << ") to be divisible by block_size (" << blockSize
             << ")";
  }
 
  const auto outputType = llvm::cast<ShapedType>(outputList[0].getType());
  if (!outputType.hasRank())
    return success();
 
  if (static_cast<int64_t>(shapeValues.size()) != outputType.getRank())
    return emitOpError() << "result does not match new shape rank";
 
  for (auto [newShapeDim, outputShapeDim] :
       zip(shapeValues, outputType.getShape())) {
    if (ShapedType::isStatic(newShapeDim) &&
        ShapedType::isStatic(outputShapeDim) && newShapeDim != outputShapeDim)
      return emitOpError() << "result shape is inconsistent with new shape";
  }
 
  if (outputList.size() == 2) {
    // Set up scale shape from new shape given
    SmallVector<int64_t> scaleShapeValues(shapeValues.begin(),
                                          shapeValues.end());
    scaleShapeValues.back() /= blockSize;
 
    const auto outputScaleType =
        llvm::cast<ShapedType>(outputList[1].getType());
    if (outputScaleType.hasRank()) {
      if ((int64_t)scaleShapeValues.size() != outputScaleType.getRank())
        return emitOpError() << "result scale does not match new shape rank";
 
      for (auto [newScaleShapeDim, outputScaleShapeDim] :
           zip(scaleShapeValues, outputScaleType.getShape())) {
        if (ShapedType::isStatic(newScaleShapeDim) &&
            ShapedType::isStatic(outputScaleShapeDim) &&
            newScaleShapeDim != outputScaleShapeDim)
          return emitOpError()
                 << "result scale shape is inconsistent with new shape";
      }
    }
  }
 
  if (inputType.hasStaticShape()) {
    int64_t inputElementsNum = inputType.getNumElements();
    if (outputType.hasStaticShape()) {
      int64_t outputElementsNum = outputType.getNumElements();
      if (inputElementsNum != outputElementsNum) {
        return emitOpError() << "cannot reshape " << inputElementsNum
                             << " elements into " << outputElementsNum;
      }
    }
 
    int64_t newShapeElementsNum =
        llvm::accumulate(shapeValues, int64_t(1), [](int64_t acc, int64_t dim) {
          return (dim > 0) ? acc * dim : acc;
        });
    bool isStaticNewShape =
        llvm::all_of(shapeValues, [](int64_t s) { return s > 0; });
    if ((isStaticNewShape && inputElementsNum != newShapeElementsNum) ||
        (!isStaticNewShape && newShapeElementsNum > inputElementsNum)) {
      return emitOpError() << "cannot reshape " << inputElementsNum
                           << " elements into " << newShapeElementsNum;
    }
  }
 
  return mlir::success();
}
 
// return failure if val is not a constant
// set zp to -1 if val is non-zero float or val is not integer nor float
// otherwise set zp to val's constant value
static FailureOr<int64_t> getZeroPoint(Value val, bool signExtend) {
  ElementsAttr zpAttr;
  if (!matchPattern(val, m_Constant(&zpAttr))) {
    return failure();
  }
 
  Type zpElemType = zpAttr.getElementType();
 
  if (llvm::isa<FloatType>(zpElemType)) {
    if (zpAttr.getValues<APFloat>()[0].isZero()) {
      return 0;
    }
    // return non-zero value to trigger error check
    return -1;
  }
 
  if (llvm::isa<IntegerType>(zpElemType)) {
    if (signExtend)
      return zpAttr.getValues<APInt>()[0].getSExtValue();
    return zpAttr.getValues<APInt>()[0].getZExtValue();
  }
 
  // return non-zero value to trigger error check
  return -1;
}
 
template <typename T>
static LogicalResult verifyZeroPoint(T op, Value val, const int64_t &zp,
                                     const std::string &operand) {
  Type zpElemType = getElementTypeOrSelf(val);
 
  if (!zpElemType.isInteger(8) && zp != 0) {
    // convert operand to lower case for error message
    std::string lower = operand;
    llvm::transform(lower, lower.begin(), ::tolower);
    return op.emitOpError()
           << lower << " zero point must be zero for non-int8 integer types";
  }
 
  return success();
}
 
static LogicalResult verifyZeroPoint(tosa::RescaleOp op, Value zpVal,
                                     const int64_t &zp,
                                     const std::string &operand) {
  bool isInputZp = (operand == "Input");
 
  bool tensorUnsigned =
      isInputZp ? op.getInputUnsigned() : op.getOutputUnsigned();
  StringRef tensorName = isInputZp ? "input" : "output";
 
  Type zpElemType = getElementTypeOrSelf(zpVal);
 
  if (zp != 0) {
    if (!zpElemType.isInteger(8) &&
        !(zpElemType.isInteger(16) && tensorUnsigned)) {
      return op.emitOpError()
             << "expect " << tensorName << "_zp of 0, got " << zp;
    }
    if (zpElemType.isInteger(16) && tensorUnsigned && zp != 32768) {
      return op.emitOpError() << "expect " << tensorName
                              << "_zp of 0 or 32768 for unsigned int16 "
                              << tensorName << ", got " << zp;
    }
  }
 
  return success();
}
 
#define ZERO_POINT_HELPER(OP, OPERAND_NAME, SIGN_EXTEND)                       \
  FailureOr<int64_t> tosa::OP::get##OPERAND_NAME##ZeroPoint() {                \
    return getZeroPoint(get##OPERAND_NAME##Zp(), SIGN_EXTEND);                 \
  }                                                                            \
  LogicalResult tosa::OP::verify##OPERAND_NAME##ZeroPoint(int64_t zp) {        \
    return verifyZeroPoint(*this, get##OPERAND_NAME##Zp(), zp, #OPERAND_NAME); \
  }
 
ZERO_POINT_HELPER(Conv2DOp, Input, true)
ZERO_POINT_HELPER(Conv2DOp, Weight, true)
ZERO_POINT_HELPER(Conv3DOp, Input, true)
ZERO_POINT_HELPER(Conv3DOp, Weight, true)
ZERO_POINT_HELPER(DepthwiseConv2DOp, Input, true)
ZERO_POINT_HELPER(DepthwiseConv2DOp, Weight, true)
ZERO_POINT_HELPER(TransposeConv2DOp, Input, true)
ZERO_POINT_HELPER(TransposeConv2DOp, Weight, true)
ZERO_POINT_HELPER(AvgPool2dOp, Input, true)
ZERO_POINT_HELPER(AvgPool2dOp, Output, true)
ZERO_POINT_HELPER(AvgPool2dAdaptiveOp, Input, true)
ZERO_POINT_HELPER(AvgPool2dAdaptiveOp, Output, true)
ZERO_POINT_HELPER(MatMulOp, A, true)
ZERO_POINT_HELPER(MatMulOp, B, true)
ZERO_POINT_HELPER(MatMulTOp, A, true)
ZERO_POINT_HELPER(MatMulTOp, B, true)
ZERO_POINT_HELPER(NegateOp, Input1, true)
ZERO_POINT_HELPER(NegateOp, Output, true)
ZERO_POINT_HELPER(RescaleOp, Input, !getInputUnsigned())
ZERO_POINT_HELPER(RescaleOp, Output, !getOutputUnsigned())
#undef ZERO_POINT_HELPER
 
LogicalResult tosa::TransposeOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    TransposeOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput1().getType());
 
  // If input rank and permutation length is unknown, the output rank is
  // unknown.
  if (!inputShape.hasRank()) {
    inferredReturnShapes.push_back(ShapedTypeComponents());
    return success();
  }
 
  const auto inputRank = inputShape.getRank();
 
  // This would imply the number of permutations does not match the rank of
  // the input which is illegal.
  if (adaptor.getPerms().size() != static_cast<size_t>(inputRank)) {
    return failure();
  }
 
  SmallVector<int64_t> outputShape;
  // Rank-0 means no permutations matter.
  if (inputRank == 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 = inputRank; 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(inputRank, inputShape.getDimSize(0));
    inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
    return success();
  }
 
  outputShape.resize(inputRank, ShapedType::kDynamic);
 
  // Constant permutation values must be within the input rank.
  if (llvm::any_of(adaptor.getPerms(),
                   [inputRank](const auto i) { return i >= inputRank; }))
    return failure();
 
  outputShape.reserve(inputRank);
  for (int i = 0, s = inputRank; i < s; i++) {
    outputShape[i] = inputShape.getDimSize(adaptor.getPerms()[i]);
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  return success();
}
 
LogicalResult tosa::TransposeOp::verify() {
  if (verifySameElementTypes(*this, /* inType = */ getInput1().getType(),
                             /* outType = */ getOutput().getType())
          .failed()) {
    return failure();
  }
 
  const ShapeAdaptor inputShape(getInput1().getType());
  const ShapeAdaptor outputShape(getOutput().getType());
 
  const llvm::ArrayRef<int32_t> constantPerms = getPerms();
 
  if (inputShape.hasRank() &&
      constantPerms.size() != static_cast<size_t>(inputShape.getRank()))
    return emitOpError() << "expected perms attribute to have size "
                         << inputShape.getRank()
                         << " (input rank) but got size "
                         << constantPerms.size();
 
  if (inputShape.hasRank() && outputShape.hasRank() &&
      inputShape.getRank() != outputShape.getRank())
    return emitOpError()
           << "expected input tensor rank to equal result tensor rank";
 
  if (outputShape.hasRank() &&
      constantPerms.size() != static_cast<size_t>(outputShape.getRank()))
    return emitOpError() << "expected perms attribute to have size "
                         << outputShape.getRank()
                         << " (output rank) but got size "
                         << constantPerms.size();
 
  if (!llvm::all_of(constantPerms,
                    [&constantPerms](int32_t s) {
                      return s >= 0 &&
                             static_cast<size_t>(s) < constantPerms.size();
                    }) ||
      !isPermutationVector(llvm::map_to_vector(
          constantPerms, [](int32_t v) -> int64_t { return v; })))
    return emitOpError() << "expected valid permutation indices";
 
  if (isa<BlockScaledType>(getInput1().getType().getElementType()) &&
      constantPerms.back() != static_cast<int32_t>(constantPerms.size()) - 1) {
    return emitOpError() << "expected no-op permutation on innermost dimension "
                            "for block scaled input";
  }
 
  // ERROR_IF(tensor_size(shape1) != tensor_size(shape))
  if (inputShape.hasStaticShape() && outputShape.hasStaticShape() &&
      inputShape.getNumElements() != outputShape.getNumElements())
    return emitOpError() << "expected input1 and output to have same numbers "
                            "of elements, got "
                         << inputShape.getNumElements() << " and "
                         << outputShape.getNumElements();
 
  // Verify that the types of the input and output tensors are properly
  // permuted.
  if (inputShape.hasRank() && outputShape.hasRank()) {
    for (auto i = 0; i < outputShape.getRank(); i++) {
      if (inputShape.isDynamicDim(constantPerms[i]) ||
          outputShape.isDynamicDim(i))
        continue;
 
      if (inputShape.getDimSize(constantPerms[i]) != outputShape.getDimSize(i))
        return emitOpError()
               << "expected output tensor dim " << i << " to match "
               << "input dim " << constantPerms[i] << " with value of "
               << inputShape.getDimSize(constantPerms[i]);
    }
  }
 
  return success();
}
 
LogicalResult TransposeOp::reifyResultShapes(
    OpBuilder &builder, ReifiedRankedShapedTypeDims &reifiedReturnShapes) {
 
  const llvm::ArrayRef<int32_t> transposePerms = getPerms();
 
  Value input = getInput1();
  auto inputType = cast<TensorType>(input.getType());
 
  SmallVector<OpFoldResult> returnedDims(inputType.getRank());
  for (auto dim : transposePerms) {
    int32_t dimInInput = transposePerms[dim];
    if (inputType.isDynamicDim(dimInInput))
      returnedDims[dim] =
          tensor::DimOp::create(builder, getLoc(), input, dimInInput)
              .getResult();
    else
      returnedDims[dim] =
          builder.getIndexAttr(inputType.getDimSize(dimInInput));
  }
 
  reifiedReturnShapes.emplace_back(std::move(returnedDims));
  return success();
}
 
LogicalResult tosa::GatherOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    GatherOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  llvm::SmallVector<int64_t> outputShape;
  outputShape.resize(3, ShapedType::kDynamic);
 
  ShapeAdaptor valuesShape(adaptor.getValues().getType());
  if (valuesShape.hasRank()) {
    outputShape[0] = valuesShape.getDimSize(0);
    outputShape[2] = valuesShape.getDimSize(2);
  }
 
  ShapeAdaptor indicesShape(adaptor.getIndices().getType());
  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::RowGatherOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    RowGatherOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  llvm::SmallVector<int64_t> outputShape;
  outputShape.resize(3, ShapedType::kDynamic);
 
  const ShapeAdaptor valuesShape(adaptor.getValues().getType());
  if (valuesShape.hasRank()) {
    outputShape[0] = valuesShape.getDimSize(0);
    outputShape[2] = valuesShape.getDimSize(2);
  }
 
  const ShapeAdaptor indicesShape(adaptor.getIndices().getType());
  if (indicesShape.hasRank()) {
    if (outputShape[0] == ShapedType::kDynamic)
      outputShape[0] = indicesShape.getDimSize(0);
 
    const FailureOr<int32_t> maybeRowCount =
        getConstantScalarIntValue<int32_t>(adaptor.getRowCount());
    if (succeeded(maybeRowCount)) {
      const int64_t indicesW = indicesShape.getDimSize(1);
      if (ShapedType::isStatic(indicesW))
        outputShape[1] = indicesW * maybeRowCount.value();
    }
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  return success();
}
 
LogicalResult tosa::RowGatherBlockScaledOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    RowGatherBlockScaledOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  const auto values = adaptor.getValues();
  if (values.empty())
    return failure();
 
  SmallVector<int64_t> dataShape(3, ShapedType::kDynamic);
  const ShapeAdaptor valuesShape(values.front().getType());
  if (valuesShape.hasRank()) {
    dataShape[0] = valuesShape.getDimSize(0);
    dataShape[2] = valuesShape.getDimSize(2);
  }
 
  const ShapeAdaptor indicesShape(adaptor.getIndices().getType());
  if (indicesShape.hasRank()) {
    if (dataShape[0] == ShapedType::kDynamic)
      dataShape[0] = indicesShape.getDimSize(0);
 
    if (auto rowCount =
            getConstantScalarIntValue<int32_t>(adaptor.getRowCount());
        succeeded(rowCount) && rowCount.value() > 0) {
      const int64_t indicesW = indicesShape.getDimSize(1);
      if (ShapedType::isStatic(indicesW))
        dataShape[1] = indicesW * rowCount.value();
    }
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(dataShape));
  if (values.size() == 1)
    return success();
 
  SmallVector<int64_t> scaleShape = dataShape;
  const uint32_t blockSize =
      BlockSizeAttr::getBlockSizeValue(adaptor.getBlockSize());
  if (ShapedType::isStatic(dataShape[2]))
    scaleShape[2] = dataShape[2] / blockSize;
 
  inferredReturnShapes.push_back(ShapedTypeComponents(scaleShape));
  return success();
}
 
LogicalResult tosa::GatherOp::verify() {
  if (verifySameElementTypes(*this, /* inType = */ getValues().getType(),
                             /* outType = */ getOutput().getType())
          .failed()) {
    return failure();
  }
 
  const ShapeAdaptor valuesShape(getValues().getType());
  const ShapeAdaptor indicesShape(getIndices().getType());
  const ShapeAdaptor outputShape(getOutput().getType());
 
  int64_t n = ShapedType::kDynamic;
  int64_t w = ShapedType::kDynamic;
  int64_t c = ShapedType::kDynamic;
 
  if (valuesShape.hasRank()) {
    n = valuesShape.getDimSize(0);
    c = valuesShape.getDimSize(2);
  }
  if (indicesShape.hasRank()) {
    const int64_t indicesN = indicesShape.getDimSize(0);
    w = indicesShape.getDimSize(1);
    if (n == ShapedType::kDynamic)
      n = indicesN;
    else if (indicesN != ShapedType::kDynamic && n != indicesN)
      return emitOpError() << "requires indices dimension 0 to have size " << n
                           << ", got " << indicesN;
  }
  if (outputShape.hasRank()) {
    const int64_t outputN = outputShape.getDimSize(0);
    const int64_t outputW = outputShape.getDimSize(1);
    const int64_t outputC = outputShape.getDimSize(2);
    if (n != ShapedType::kDynamic && outputN != ShapedType::kDynamic &&
        n != outputN)
      return emitOpError() << "requires output dimension 0 to have size " << n
                           << ", got " << outputN;
 
    if (w != ShapedType::kDynamic && outputW != ShapedType::kDynamic &&
        w != outputW)
      return emitOpError() << "requires output dimension 1 to have size " << w
                           << ", got " << outputW;
    if (c != ShapedType::kDynamic && outputC != ShapedType::kDynamic &&
        c != outputC)
      return emitOpError() << "requires output dimension 2 to have size " << c
                           << ", got " << outputC;
  }
  return success();
}
 
LogicalResult tosa::RowGatherOp::verify() {
  if (failed(verifySameElementTypes(*this, /* inType = */ getValues().getType(),
                                    /* outType = */ getOutput().getType())))
    return failure();
 
  const FailureOr<int32_t> maybeRowCount =
      getConstantScalarIntValue<int32_t>(getRowCount());
  if (succeeded(maybeRowCount) && maybeRowCount.value() <= 0)
    return emitOpError() << "requires row_count to be > 0, got "
                         << maybeRowCount.value();
 
  int64_t n = ShapedType::kDynamic;
  int64_t c = ShapedType::kDynamic;
  int64_t w = ShapedType::kDynamic;
 
  const ShapeAdaptor valuesShape(getValues().getType());
  if (valuesShape.hasRank()) {
    n = valuesShape.getDimSize(0);
    c = valuesShape.getDimSize(2);
  }
 
  const ShapeAdaptor indicesShape(getIndices().getType());
  if (indicesShape.hasRank()) {
    if (failed(tryUpdateDimOrFailure(*this, n, indicesShape.getDimSize(0),
                                     "indices", "batch")))
      return failure();
    w = indicesShape.getDimSize(1);
  }
 
  const ShapeAdaptor outputShape(getOutput().getType());
  if (outputShape.hasRank()) {
    if (failed(tryUpdateDimOrFailure(*this, n, outputShape.getDimSize(0),
                                     "output", "batch")) ||
        failed(tryUpdateDimOrFailure(*this, c, outputShape.getDimSize(2),
                                     "output", "channels")))
      return failure();
 
    if (succeeded(maybeRowCount) && maybeRowCount.value() > 0 &&
        ShapedType::isStatic(w)) {
      const int64_t expectedOutputRows = w * maybeRowCount.value();
      if (ShapedType::isStatic(outputShape.getDimSize(1)) &&
          outputShape.getDimSize(1) != expectedOutputRows)
        return emitOpError()
               << "requires output dimension to be equal to "
                  "indices[1]*row_count ("
               << expectedOutputRows << "), got " << outputShape.getDimSize(1);
    }
  }
 
  return success();
}
 
LogicalResult tosa::RowGatherBlockScaledOp::verify() {
  const OperandRange values = getValues();
  const ResultRange output = getOutput();
  if (values.empty() || values.size() > 2)
    return emitOpError()
           << "expects values tensor list length to be 1 or 2, got "
           << values.size();
  if (output.size() != values.size())
    return emitOpError()
           << "expects output tensor list length to match values tensor list "
              "length, got "
           << output.size() << " results for " << values.size()
           << " input tensors";
 
  const uint32_t blockSize = BlockSizeAttr::getBlockSizeValue(getBlockSize());
  if (values.size() == 1 && blockSize != 1)
    return emitOpError()
           << "requires block_size to be BLOCK_SIZE_1 when values tensor list "
              "length is 1";
  if (values.size() == 2 && blockSize == 1)
    return emitOpError()
           << "requires block_size to not be BLOCK_SIZE_1 when values tensor "
              "list length is 2";
 
  if (failed(verifySameElementTypes(*this, values[0].getType(),
                                    output[0].getType(), "values[0]",
                                    "output[0]")))
    return failure();
  if (values.size() == 2 && failed(verifySameElementTypes(
                                *this, values[1].getType(), output[1].getType(),
                                "values[1]", "output[1]")))
    return failure();
 
  if (auto rowCount = getConstantScalarIntValue<int32_t>(getRowCount());
      succeeded(rowCount) && rowCount.value() <= 0)
    return emitOpError() << "requires row_count to be > 0, got "
                         << rowCount.value();
 
  int64_t n = ShapedType::kDynamic;
  int64_t k = ShapedType::kDynamic;
  int64_t c = ShapedType::kDynamic;
  int64_t w = ShapedType::kDynamic;
  int64_t multiplesOfC = ShapedType::kDynamic;
 
  const ShapeAdaptor valuesDataShape(values[0].getType());
  if (valuesDataShape.hasRank()) {
    n = valuesDataShape.getDimSize(0);
    k = valuesDataShape.getDimSize(1);
    c = valuesDataShape.getDimSize(2);
  }
 
  if (ShapedType::isStatic(c) && c % blockSize != 0)
    return emitOpError() << "expects channels of values[0] (" << c
                         << ") to be divisible by block_size (" << blockSize
                         << ")";
 
  const ShapeAdaptor indicesShape(getIndices().getType());
  if (indicesShape.hasRank()) {
    if (failed(tryUpdateDimOrFailure(*this, n, indicesShape.getDimSize(0),
                                     "indices", "batch")))
      return failure();
    w = indicesShape.getDimSize(1);
  }
 
  const ShapeAdaptor outputDataShape(output[0].getType());
  if (outputDataShape.hasRank()) {
    if (failed(tryUpdateDimOrFailure(*this, n, outputDataShape.getDimSize(0),
                                     "output[0]", "batch")) ||
        failed(tryUpdateDimOrFailure(*this, c, outputDataShape.getDimSize(2),
                                     "output[0]", "channels")))
      return failure();
 
    if (auto rowCount = getConstantScalarIntValue<int32_t>(getRowCount());
        succeeded(rowCount) && rowCount.value() > 0 &&
        ShapedType::isStatic(w)) {
      const int64_t expectedOutputRows = w * rowCount.value();
      if (ShapedType::isStatic(outputDataShape.getDimSize(1)) &&
          outputDataShape.getDimSize(1) != expectedOutputRows)
        return emitOpError() << "requires output[0] dimension 1 to have size "
                             << expectedOutputRows << ", got "
                             << outputDataShape.getDimSize(1);
    }
  }
 
  if (values.size() == 2) {
    const ShapeAdaptor valuesScaleShape(values[1].getType());
    if (valuesScaleShape.hasRank()) {
      if (failed(tryUpdateDimOrFailure(*this, n, valuesScaleShape.getDimSize(0),
                                       "values[1]", "batch")) ||
          failed(tryUpdateDimOrFailure(*this, k, valuesScaleShape.getDimSize(1),
                                       "values[1]", "rows")))
        return failure();
      multiplesOfC = valuesScaleShape.getDimSize(2);
    }
 
    const ShapeAdaptor outputScaleShape(output[1].getType());
    if (outputScaleShape.hasRank()) {
      if (failed(tryUpdateDimOrFailure(*this, n, outputScaleShape.getDimSize(0),
                                       "output[1]", "batch")))
        return failure();
 
      if (auto rowCount = getConstantScalarIntValue<int32_t>(getRowCount());
          succeeded(rowCount) && rowCount.value() > 0 &&
          ShapedType::isStatic(w)) {
        const int64_t expectedOutputRows = w * rowCount.value();
        if (ShapedType::isStatic(outputScaleShape.getDimSize(1)) &&
            outputScaleShape.getDimSize(1) != expectedOutputRows)
          return emitOpError() << "requires output[1] dimension 1 to have size "
                               << expectedOutputRows << ", got "
                               << outputScaleShape.getDimSize(1);
      }
 
      if (ShapedType::isDynamic(multiplesOfC))
        multiplesOfC = outputScaleShape.getDimSize(2);
      else if (ShapedType::isStatic(outputScaleShape.getDimSize(2)) &&
               multiplesOfC != outputScaleShape.getDimSize(2))
        return emitOpError()
               << "expected channels of output[1] to match size "
               << multiplesOfC << ", got " << outputScaleShape.getDimSize(2);
    }
 
    if (ShapedType::isStatic(c) && ShapedType::isStatic(multiplesOfC) &&
        multiplesOfC != c / blockSize)
      return emitOpError()
             << "expects channels of scale tensors to equal C/block_size (" << c
             << "/" << blockSize << "), got " << multiplesOfC;
  }
 
  return success();
}
 
LogicalResult tosa::ResizeOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    ResizeOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  llvm::SmallVector<int64_t, 4> outputShape;
  outputShape.resize(4, ShapedType::kDynamic);
 
  ShapeAdaptor inputShape(adaptor.getInput().getType());
  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();
 
  SmallVector<int64_t> scaleInt, offsetInt, borderInt;
  if (!tosa::getConstShapeValues(adaptor.getScale().getDefiningOp(),
                                 scaleInt) ||
      !tosa::getConstShapeValues(adaptor.getOffset().getDefiningOp(),
                                 offsetInt) ||
      !tosa::getConstShapeValues(adaptor.getBorder().getDefiningOp(),
                                 borderInt)) {
    return failure();
  }
 
  // Non-positive scale values are invalid, so the output shape cannot be
  // inferred here. They are reported by the ERROR_IF check in tosa-validate.
  if (llvm::any_of(scaleInt, [](int64_t s) { return s <= 0; }))
    return failure();
 
  // Compute the output shape based on attributes: scale, offset, and border.
  const int64_t outputHeight =
      (((inputHeight - 1) * scaleInt[0] - offsetInt[0] + borderInt[0]) /
       scaleInt[1]) +
      1;
 
  const int64_t outputWidth =
      (((inputWidth - 1) * scaleInt[2] - offsetInt[1] + borderInt[1]) /
       scaleInt[3]) +
      1;
 
  if (outputHeight < 0 || outputWidth < 0) {
    return emitOptionalError(
        location,
        "calculated output height and width must be non-negative, "
        "got height = ",
        outputHeight, ", width = ", outputWidth);
  }
 
  outputShape[1] = outputHeight;
  outputShape[2] = outputWidth;
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  return success();
}
 
LogicalResult tosa::ResizeOp::verify() {
  const Type inputElementType = getElementTypeOrSelf(getInput().getType());
 
  if (isa<BlockScaledType>(inputElementType) &&
      getMode() != ResizeMode::NEAREST_NEIGHBOR)
    return emitOpError("requires NEAREST_NEIGHBOR mode for block scaled input");
 
  return success();
}
 
LogicalResult tosa::ScatterOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    ScatterOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  llvm::SmallVector<int64_t> outputShape;
  outputShape.resize(3, ShapedType::kDynamic);
 
  ShapeAdaptor valuesInShape(adaptor.getValuesIn().getType());
  if (valuesInShape.hasRank()) {
    outputShape[0] = valuesInShape.getDimSize(0);
    outputShape[1] = valuesInShape.getDimSize(1);
    outputShape[2] = valuesInShape.getDimSize(2);
  }
 
  ShapeAdaptor indicesShape(adaptor.getIndices().getType());
  if (indicesShape.hasRank()) {
    if (outputShape[0] == ShapedType::kDynamic)
      outputShape[0] = indicesShape.getDimSize(0);
  }
 
  ShapeAdaptor inputShape(adaptor.getInput().getType());
  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();
}
 
LogicalResult tosa::ScatterOp::verify() {
  if (verifySameElementTypes(*this, /* inType = */ getValuesIn().getType(),
                             /* outType = */ getValuesOut().getType())
          .failed() ||
      verifySameElementTypes(*this, /* inType = */ getInput().getType(),
                             /* outType = */ getValuesOut().getType())
          .failed()) {
    return failure();
  }
 
  const ShapeAdaptor valuesInShape(getValuesIn().getType());
  const ShapeAdaptor indicesShape(getIndices().getType());
  const ShapeAdaptor inputShape(getInput().getType());
  const ShapeAdaptor outputShape(getValuesOut().getType());
 
  int64_t n = ShapedType::kDynamic;
  int64_t k = ShapedType::kDynamic;
  int64_t w = ShapedType::kDynamic;
  int64_t c = ShapedType::kDynamic;
  if (valuesInShape.hasRank()) {
    n = valuesInShape.getDimSize(0);
    k = valuesInShape.getDimSize(1);
    c = valuesInShape.getDimSize(2);
  }
  if (indicesShape.hasRank()) {
    const int64_t indicesN = indicesShape.getDimSize(0);
    w = indicesShape.getDimSize(1);
    if (n == ShapedType::kDynamic)
      n = indicesN;
    else if (indicesN != ShapedType::kDynamic && n != indicesN)
      return emitOpError() << "requires indices dimension 0 to have size " << n
                           << ", got " << indicesN;
  }
  if (inputShape.hasRank()) {
    const int64_t inputN = inputShape.getDimSize(0);
    const int64_t inputW = inputShape.getDimSize(1);
    const int64_t inputC = inputShape.getDimSize(2);
    if (n == ShapedType::kDynamic)
      n = inputN;
    else if (inputN != ShapedType::kDynamic && n != inputN)
      return emitOpError() << "requires input dimension 0 to have size " << n
                           << ", got " << inputN;
    if (w == ShapedType::kDynamic)
      w = inputW;
    else if (inputW != ShapedType::kDynamic && w != inputW)
      return emitOpError() << "requires input dimension 1 to have size " << w
                           << ", got " << inputW;
 
    if (c == ShapedType::kDynamic)
      c = inputC;
    else if (inputC != ShapedType::kDynamic && c != inputC)
      return emitOpError() << "requires input dimension 2 to have size " << c
                           << ", got " << inputC;
  }
  if (outputShape.hasRank()) {
    const int64_t outputN = outputShape.getDimSize(0);
    const int64_t outputK = outputShape.getDimSize(1);
    const int64_t outputC = outputShape.getDimSize(2);
    if (n != ShapedType::kDynamic && outputN != ShapedType::kDynamic &&
        n != outputN)
      return emitOpError() << "requires values_out dimension 0 to have size "
                           << n << ", got " << outputN;
    if (k == ShapedType::kDynamic)
      k = outputK;
    else if (outputK != ShapedType::kDynamic && k != outputK)
      return emitOpError() << "requires values_out dimension 1 to have size "
                           << k << ", got " << outputK;
    if (c != ShapedType::kDynamic && outputC != ShapedType::kDynamic &&
        c != outputC)
      return emitOpError() << "requires values_out dimension 2 to have size "
                           << c << ", got " << outputC;
  }
  if (k != ShapedType::kDynamic && w != ShapedType::kDynamic && !(k >= w))
    return emitOpError() << "requires dimensions K >= W, got K=" << k
                         << " and W=" << w;
 
  return success();
}
 
static LogicalResult ReduceInferReturnTypes(
    ShapeAdaptor operandShape, Type inputType, IntegerAttr axis,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  int64_t axisVal = axis.getValue().getSExtValue();
  if (!operandShape.hasRank() || operandShape.getRank() <= axisVal) {
    inferredReturnShapes.push_back(ShapedTypeComponents(inputType));
    return success();
  }
 
  SmallVector<int64_t> outputShape;
  operandShape.getDims(outputShape);
  outputShape[axisVal] = 1;
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape, inputType));
  return success();
}
 
#define COMPATIBLE_RETURN_TYPES(OP)                                            \
  bool OP::isCompatibleReturnTypes(TypeRange l, TypeRange r) {                 \
    if (l.size() != r.size() || l.size() != 1)                                 \
      return false;                                                            \
    if (getElementTypeOrSelf(l[0]) != getElementTypeOrSelf(r[0]))              \
      return false;                                                            \
    return succeeded(verifyCompatibleShape(l[0], r[0]));                       \
  }
 
#define REDUCE_SHAPE_INFER(OP)                                                 \
  LogicalResult OP::inferReturnTypeComponents(                                 \
      MLIRContext *context, ::std::optional<Location> location,                \
      OP::Adaptor adaptor,                                                     \
      SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {           \
    Type inputType =                                                           \
        llvm::cast<TensorType>(adaptor.getInput().getType()).getElementType(); \
    ShapeAdaptor inputShape(adaptor.getInput().getType());                     \
    const Properties &prop = adaptor.getProperties();                          \
    return ReduceInferReturnTypes(inputShape, inputType, prop.axis,            \
                                  inferredReturnShapes);                       \
  }                                                                            \
  COMPATIBLE_RETURN_TYPES(OP)
 
REDUCE_SHAPE_INFER(tosa::ReduceAllOp)
REDUCE_SHAPE_INFER(tosa::ReduceAnyOp)
REDUCE_SHAPE_INFER(tosa::ReduceMaxOp)
REDUCE_SHAPE_INFER(tosa::ReduceMinOp)
REDUCE_SHAPE_INFER(tosa::ReduceProductOp)
REDUCE_SHAPE_INFER(tosa::ReduceSumOp)
#undef REDUCE_SHAPE_INFER
COMPATIBLE_RETURN_TYPES(tosa::ConcatOp)
#undef COMPATIBLE_RETURN_TYPES
 
template <typename T>
static LogicalResult verifyReduceOp(T op) {
  // All TOSA reduce Ops have input, output and axis.
  TensorType inputType = op.getInput().getType();
  TensorType outputType = op.getOutput().getType();
  int32_t reduceAxis = op.getAxis();
 
  if (reduceAxis < 0) {
    op.emitOpError("reduce axis must not be negative");
    return failure();
  }
  if (inputType.hasRank()) {
    int64_t inputRank = inputType.getRank();
    // We allow for a special case where the input/output shape has rank 0 and
    // axis is also 0.
    if (reduceAxis >= inputRank && (reduceAxis != 0 || inputRank != 0)) {
      op.emitOpError("expect input tensor rank (")
          << inputRank << ") to be larger than reduce axis (" << reduceAxis
          << ")";
      return failure();
    }
  }
  if (outputType.hasRank()) {
    int64_t outputRank = outputType.getRank();
    if (inputType.hasRank() && outputRank != inputType.getRank()) {
      op.emitOpError(
          "expect output tensor rank to be equal to input tensor rank");
      return failure();
    }
    if (reduceAxis >= outputRank && (reduceAxis != 0 || outputRank != 0)) {
      op.emitOpError("expect output tensor rank (")
          << outputRank << ") to be larger than reduce axis (" << reduceAxis
          << ")";
      return failure();
    }
    // We can only verify the reduced dimension size to be 1 if this is not
    // the special case of output rank == 0.
    if (outputRank != 0) {
      auto outputShape = outputType.getShape();
      if (!outputType.isDynamicDim(reduceAxis) &&
          outputShape[reduceAxis] != 1) {
        op.emitOpError("expect reduced dimension size to be 1, got ")
            << outputShape[reduceAxis];
        return failure();
      }
    }
  }
  return success();
}
 
LogicalResult tosa::ReduceAllOp::verify() { return verifyReduceOp(*this); }
LogicalResult tosa::ReduceAnyOp::verify() { return verifyReduceOp(*this); }
LogicalResult tosa::ReduceMaxOp::verify() { return verifyReduceOp(*this); }
LogicalResult tosa::ReduceMinOp::verify() { return verifyReduceOp(*this); }
LogicalResult tosa::ReduceProductOp::verify() { return verifyReduceOp(*this); }
LogicalResult tosa::ReduceSumOp::verify() { return verifyReduceOp(*this); }
 
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,                     \
      PropertyRef properties, 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::CosOp)
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::IntDivOp)
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::PowOp)
NARY_SHAPE_INFER(tosa::ReciprocalOp)
NARY_SHAPE_INFER(tosa::ReverseOp)
NARY_SHAPE_INFER(tosa::RsqrtOp)
NARY_SHAPE_INFER(tosa::SinOp)
NARY_SHAPE_INFER(tosa::SelectOp)
NARY_SHAPE_INFER(tosa::SubOp)
NARY_SHAPE_INFER(tosa::TanhOp)
NARY_SHAPE_INFER(tosa::ErfOp)
NARY_SHAPE_INFER(tosa::SigmoidOp)
#undef PRED_SHAPE_INFER
 
LogicalResult tosa::NegateOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    NegateOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput1().getType());
  inferredReturnShapes.push_back(ShapedTypeComponents(inputShape));
  return success();
}
 
LogicalResult tosa::NegateOp::verify() {
  // Verify same element type
  const Type input1Type = getInput1().getType();
  const Type outputType = getOutput().getType();
  if (verifySameElementTypes(*this, input1Type, outputType).failed())
    return failure();
 
  // Verify same shape
  const SmallVector<Type, 2> types = {input1Type, outputType};
  if (failed(verifyCompatibleShapes(types)))
    return emitOpError() << "requires the same shape for input1 and output";
 
  const Type input1EType = getStorageElementTypeOrSelf(getInput1().getType());
  const Type input1ZpEType =
      getStorageElementTypeOrSelf(getInput1Zp().getType());
  if (input1EType != input1ZpEType) {
    return emitOpError("expect both input1 and its zero point are the same "
                       "element type, got ")
           << input1EType << " and " << input1ZpEType;
  }
  const Type outputEType = getStorageElementTypeOrSelf(getOutput().getType());
  const Type outputZpEType =
      getStorageElementTypeOrSelf(getOutputZp().getType());
  if (outputEType != outputZpEType) {
    return emitOpError("expect both output and its zero point are the same "
                       "element type, got ")
           << outputEType << " and " << outputZpEType;
  }
 
  FailureOr<int64_t> maybeIZp = getInput1ZeroPoint();
  if (succeeded(maybeIZp) && verifyInput1ZeroPoint(*maybeIZp).failed())
    return failure();
 
  FailureOr<int64_t> maybeOZp = getOutputZeroPoint();
  if (succeeded(maybeOZp) && verifyOutputZeroPoint(*maybeOZp).failed())
    return failure();
 
  return success();
}
 
static LogicalResult poolingInferReturnTypes(
    ShapeAdaptor inputShape, ArrayRef<int64_t> kernel, ArrayRef<int64_t> stride,
    ArrayRef<int64_t> pad,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  llvm::SmallVector<int64_t> outputShape;
  outputShape.resize(4, ShapedType::kDynamic);
 
  // We only know the rank if the input type is unranked.
  if (!inputShape.hasRank()) {
    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);
 
  if (ShapedType::isStatic(height)) {
    int64_t padded = height + pad[0] + pad[1] - kernel[0];
    outputShape[1] = padded / stride[0] + 1;
  }
 
  if (ShapedType::isStatic(width)) {
    int64_t padded = width + pad[2] + pad[3] - kernel[1];
    outputShape[2] = padded / stride[1] + 1;
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  return success();
}
 
template <typename AdaptorT>
class ConvInferShapeAdaptor;
 
class ConvInferShapeAdaptorBase {
protected:
  static void updateIfDynamic(int64_t &current, int64_t candidate) {
    if (ShapedType::isDynamic(current))
      current = candidate;
  }
};
 
template <>
class ConvInferShapeAdaptor<Conv2DOp::Adaptor>
    : public ConvInferShapeAdaptorBase {
public:
  explicit ConvInferShapeAdaptor(Conv2DOp::Adaptor adaptor)
      : adaptor(adaptor) {}
 
  void inferInputShape(SmallVectorImpl<int64_t> &outputShape,
                       SmallVectorImpl<int64_t> &inputSpatial) {
    const ShapeAdaptor inputShape(adaptor.getInput().getType());
    if (!inputShape.hasRank())
      return;
 
    const int64_t outputBatch = inputShape.getDimSize(0);
    const int64_t inputHeight = inputShape.getDimSize(1);
    const int64_t inputWidth = inputShape.getDimSize(2);
 
    outputShape[0] = outputBatch;
    inputSpatial[0] = inputHeight;
    inputSpatial[1] = inputWidth;
  }
 
  void inferWeightShape(SmallVectorImpl<int64_t> &outputShape,
                        SmallVectorImpl<int64_t> &weightSpatial) {
    const ShapeAdaptor weightShape(adaptor.getWeight().getType());
    if (!weightShape.hasRank())
      return;
 
    const int64_t outputChannels = weightShape.getDimSize(0);
    const int64_t kernelHeight = weightShape.getDimSize(1);
    const int64_t kernelWidth = weightShape.getDimSize(2);
 
    outputShape[3] = outputChannels;
    weightSpatial[0] = kernelHeight;
    weightSpatial[1] = kernelWidth;
  }
 
  int64_t getNumSpatialDims() const { return 2; }
  int64_t getOutputRank() const { return 4; }
 
  LogicalResult getSpatialParameters(SmallVector<int64_t> &padValues,
                                     SmallVector<int64_t> &strideValues,
                                     SmallVector<int64_t> &dilationValues) {
    padValues.assign(adaptor.getPad().begin(), adaptor.getPad().end());
    strideValues.assign(adaptor.getStride().begin(), adaptor.getStride().end());
    dilationValues.assign(adaptor.getDilation().begin(),
                          adaptor.getDilation().end());
    return success();
  }
 
private:
  Conv2DOp::Adaptor adaptor;
};
 
template <>
class ConvInferShapeAdaptor<Conv2DBlockScaledOp::Adaptor>
    : public ConvInferShapeAdaptorBase {
public:
  explicit ConvInferShapeAdaptor(Conv2DBlockScaledOp::Adaptor adaptor)
      : adaptor(adaptor) {}
 
  void inferInputShape(SmallVectorImpl<int64_t> &outputShape,
                       SmallVectorImpl<int64_t> &inputSpatial) {
    const ShapeAdaptor inputDataShape(adaptor.getInputData().getType());
    if (inputDataShape.hasRank()) {
      const int64_t outputBatch = inputDataShape.getDimSize(0);
      const int64_t inputHeight = inputDataShape.getDimSize(1);
      const int64_t inputWidth = inputDataShape.getDimSize(2);
 
      outputShape[0] = outputBatch;
      inputSpatial[0] = inputHeight;
      inputSpatial[1] = inputWidth;
    }
 
    const ShapeAdaptor inputScaleShape(adaptor.getInputScale().getType());
    if (!inputScaleShape.hasRank())
      return;
 
    const int64_t scaleBatch = inputScaleShape.getDimSize(0);
    const int64_t scaleHeight = inputScaleShape.getDimSize(1);
    const int64_t scaleWidth = inputScaleShape.getDimSize(2);
 
    updateIfDynamic(outputShape[0], scaleBatch);
    updateIfDynamic(inputSpatial[0], scaleHeight);
    updateIfDynamic(inputSpatial[1], scaleWidth);
  }
 
  void inferWeightShape(SmallVectorImpl<int64_t> &outputShape,
                        SmallVectorImpl<int64_t> &weightSpatial) {
    const ShapeAdaptor weightDataShape(adaptor.getWeightData().getType());
    if (weightDataShape.hasRank()) {
      const int64_t outputChannels = weightDataShape.getDimSize(0);
      const int64_t kernelHeight = weightDataShape.getDimSize(1);
      const int64_t kernelWidth = weightDataShape.getDimSize(2);
 
      outputShape[3] = outputChannels;
      weightSpatial[0] = kernelHeight;
      weightSpatial[1] = kernelWidth;
    }
 
    const ShapeAdaptor weightScaleShape(adaptor.getWeightScale().getType());
    if (!weightScaleShape.hasRank())
      return;
 
    const int64_t scaleOutputChannels = weightScaleShape.getDimSize(0);
    const int64_t scaleKernelHeight = weightScaleShape.getDimSize(1);
    const int64_t scaleKernelWidth = weightScaleShape.getDimSize(2);
 
    updateIfDynamic(outputShape[3], scaleOutputChannels);
    updateIfDynamic(weightSpatial[0], scaleKernelHeight);
    updateIfDynamic(weightSpatial[1], scaleKernelWidth);
  }
 
  int64_t getNumSpatialDims() const { return 2; }
  int64_t getOutputRank() const { return 4; }
 
  LogicalResult getSpatialParameters(SmallVector<int64_t> &padValues,
                                     SmallVector<int64_t> &strideValues,
                                     SmallVector<int64_t> &dilationValues) {
    if (!tosa::getConstShapeValues(adaptor.getPad().getDefiningOp(),
                                   padValues) ||
        !tosa::getConstShapeValues(adaptor.getStride().getDefiningOp(),
                                   strideValues) ||
        !tosa::getConstShapeValues(adaptor.getDilation().getDefiningOp(),
                                   dilationValues))
      return failure();
    return success();
  }
 
private:
  Conv2DBlockScaledOp::Adaptor adaptor;
};
 
template <>
class ConvInferShapeAdaptor<Conv3DOp::Adaptor>
    : public ConvInferShapeAdaptorBase {
public:
  explicit ConvInferShapeAdaptor(Conv3DOp::Adaptor adaptor)
      : adaptor(adaptor) {}
 
  void inferInputShape(SmallVectorImpl<int64_t> &outputShape,
                       SmallVectorImpl<int64_t> &inputSpatial) {
    const ShapeAdaptor inputShape(adaptor.getInput().getType());
    if (!inputShape.hasRank())
      return;
 
    const int64_t outputBatch = inputShape.getDimSize(0);
    const int64_t inputDepth = inputShape.getDimSize(1);
    const int64_t inputHeight = inputShape.getDimSize(2);
    const int64_t inputWidth = inputShape.getDimSize(3);
 
    outputShape[0] = outputBatch;
    inputSpatial[0] = inputDepth;
    inputSpatial[1] = inputHeight;
    inputSpatial[2] = inputWidth;
  }
 
  void inferWeightShape(SmallVectorImpl<int64_t> &outputShape,
                        SmallVectorImpl<int64_t> &weightSpatial) {
    const ShapeAdaptor weightShape(adaptor.getWeight().getType());
    if (!weightShape.hasRank())
      return;
 
    const int64_t outputChannels = weightShape.getDimSize(0);
    const int64_t kernelDepth = weightShape.getDimSize(1);
    const int64_t kernelHeight = weightShape.getDimSize(2);
    const int64_t kernelWidth = weightShape.getDimSize(3);
 
    outputShape[4] = outputChannels;
    weightSpatial[0] = kernelDepth;
    weightSpatial[1] = kernelHeight;
    weightSpatial[2] = kernelWidth;
  }
 
  int64_t getNumSpatialDims() const { return 3; }
  int64_t getOutputRank() const { return 5; }
 
  LogicalResult getSpatialParameters(SmallVector<int64_t> &padValues,
                                     SmallVector<int64_t> &strideValues,
                                     SmallVector<int64_t> &dilationValues) {
    padValues.assign(adaptor.getPad().begin(), adaptor.getPad().end());
    strideValues.assign(adaptor.getStride().begin(), adaptor.getStride().end());
    dilationValues.assign(adaptor.getDilation().begin(),
                          adaptor.getDilation().end());
    return success();
  }
 
private:
  Conv3DOp::Adaptor adaptor;
};
 
template <typename AdaptorT>
LogicalResult inferConvReturnTypeComponents(
    AdaptorT adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ConvInferShapeAdaptor<AdaptorT> convShapeAdaptor(adaptor);
  llvm::SmallVector<int64_t> outputShape(convShapeAdaptor.getOutputRank(),
                                         ShapedType::kDynamic);
  llvm::SmallVector<int64_t> inputSpatial(convShapeAdaptor.getNumSpatialDims(),
                                          ShapedType::kDynamic);
  llvm::SmallVector<int64_t> weightSpatial(convShapeAdaptor.getNumSpatialDims(),
                                           ShapedType::kDynamic);
 
  convShapeAdaptor.inferInputShape(outputShape, inputSpatial);
  convShapeAdaptor.inferWeightShape(outputShape, weightSpatial);
 
  const ShapeAdaptor biasShape = adaptor.getBias().getType();
  if (biasShape.hasRank()) {
    const int64_t biasSize = biasShape.getDimSize(0);
    if (biasSize != 1) {
      const size_t outputChannelDim = convShapeAdaptor.getOutputRank() - 1;
      outputShape[outputChannelDim] =
          ShapedType::isDynamic(outputShape[outputChannelDim])
              ? biasSize
              : outputShape[outputChannelDim];
    }
  }
 
  SmallVector<int64_t> padValues;
  SmallVector<int64_t> strideValues;
  SmallVector<int64_t> dilationValues;
  if (failed(convShapeAdaptor.getSpatialParameters(padValues, strideValues,
                                                   dilationValues))) {
    inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
    return success();
  }
 
  for (int64_t dim = 0; dim < convShapeAdaptor.getNumSpatialDims(); ++dim) {
    if (!ShapedType::isStatic(inputSpatial[dim]) ||
        !ShapedType::isStatic(weightSpatial[dim]))
      continue;
    const int64_t inputSize =
        inputSpatial[dim] + padValues[2 * dim] + padValues[2 * dim + 1];
    const int64_t filterSize =
        (weightSpatial[dim] - 1) * dilationValues[dim] + 1;
    const int64_t unstridedResult = inputSize - filterSize + 1;
    outputShape[dim + 1] = (unstridedResult - 1) / strideValues[dim] + 1;
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  return success();
}
 
LogicalResult Conv2DOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    Conv2DOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  return inferConvReturnTypeComponents(adaptor, inferredReturnShapes);
}
 
LogicalResult Conv2DOp::verify() {
  if (verifyConvOp(*this).failed() || verifyConvOpErrorIf(*this).failed())
    return failure();
  return success();
}
 
LogicalResult Conv2DBlockScaledOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    Conv2DBlockScaledOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  return inferConvReturnTypeComponents(adaptor, inferredReturnShapes);
}
 
LogicalResult Conv2DBlockScaledOp::verify() {
  if (failed(verifySameElementTypes(*this, getInputData().getType(),
                                    getWeightData().getType(), "input_data",
                                    "weight_data")) ||
      failed(verifySameElementTypes(*this, getInputScale().getType(),
                                    getWeightScale().getType(), "input_scale",
                                    "weight_scale")) ||
      failed(verifySameElementTypes(*this, getBias().getType(),
                                    getOutput().getType(), "bias", "output")))
    return failure();
 
  // Verify input shape compatibility
  int64_t N = ShapedType::kDynamic;
  int64_t IH = ShapedType::kDynamic;
  int64_t IW = ShapedType::kDynamic;
  int64_t IC = ShapedType::kDynamic;
  int64_t multiplesOfIC = ShapedType::kDynamic;
  int64_t OC = ShapedType::kDynamic;
  int64_t KH = ShapedType::kDynamic;
  int64_t KW = ShapedType::kDynamic;
 
  const ShapeAdaptor inputDataShape(getInputData().getType());
  if (inputDataShape.hasRank()) {
    N = inputDataShape.getDimSize(0);
    IH = inputDataShape.getDimSize(1);
    IW = inputDataShape.getDimSize(2);
    IC = inputDataShape.getDimSize(3);
  }
 
  const ShapeAdaptor inputScaleShape(getInputScale().getType());
  if (inputScaleShape.hasRank()) {
    if (failed(tryUpdateDimOrFailure(*this, N, inputScaleShape.getDimSize(0),
                                     "input_scale", "batch size")) ||
        failed(tryUpdateDimOrFailure(*this, IH, inputScaleShape.getDimSize(1),
                                     "input_scale", "input height")) ||
        failed(tryUpdateDimOrFailure(*this, IW, inputScaleShape.getDimSize(2),
                                     "input_scale", "input width")))
      return failure();
    multiplesOfIC = inputScaleShape.getDimSize(3);
  }
 
  const ShapeAdaptor weightDataShape(getWeightData().getType());
  if (weightDataShape.hasRank()) {
    OC = weightDataShape.getDimSize(0);
    KH = weightDataShape.getDimSize(1);
    KW = weightDataShape.getDimSize(2);
    if (failed(tryUpdateDimOrFailure(*this, IC, weightDataShape.getDimSize(3),
                                     "weight_data", "input channels")))
      return failure();
  }
 
  const ShapeAdaptor weightScaleShape(getWeightScale().getType());
  if (weightScaleShape.hasRank()) {
    if (failed(tryUpdateDimOrFailure(*this, OC, weightScaleShape.getDimSize(0),
                                     "weight_scale", "output channels")) ||
        failed(tryUpdateDimOrFailure(*this, KH, weightScaleShape.getDimSize(1),
                                     "weight_scale", "kernel height")) ||
        failed(tryUpdateDimOrFailure(*this, KW, weightScaleShape.getDimSize(2),
                                     "weight_scale", "kernel width")) ||
        failed(tryUpdateDimOrFailure(*this, multiplesOfIC,
                                     weightScaleShape.getDimSize(3),
                                     "weight_scale", "input channel blocks")))
      return failure();
  }
 
  const uint32_t blockSize = BlockSizeAttr::getBlockSizeValue(getBlockSize());
  if (blockSize != BlockSizeAttr::getBlockSizeValue(BlockSize::BLOCK_SIZE_32))
    return emitOpError("expect block size to be 32, got ") << blockSize;
  // Verify IC is a multiple of block size
  if (ShapedType::isStatic(IC) && IC % blockSize != 0)
    return emitOpError("expect IC to be a multiple of block size, got IC=")
           << IC << ", block_size=" << blockSize;
 
  // Verify multiplesOfIC is IC / block size
  if (ShapedType::isStatic(IC) && ShapedType::isStatic(multiplesOfIC) &&
      multiplesOfIC != IC / blockSize)
    return emitOpError(
               "expect scale operands dimension 2 to equal IC/block_size (")
           << IC << "/" << blockSize << ")"
           << ", got " << multiplesOfIC;
 
  // Verify pad/stride/dilation values
  SmallVector<int64_t> padValues;
  if (tosa::getConstShapeValues(getPad().getDefiningOp(), padValues)) {
    if (llvm::any_of(padValues, [](int64_t p) { return p < 0; }))
      return emitOpError("expect all padding values to be >= 0, got ")
             << padValues;
  }
 
  SmallVector<int64_t> strideValues;
  if (tosa::getConstShapeValues(getStride().getDefiningOp(), strideValues)) {
    if (llvm::any_of(strideValues, [](int64_t s) { return s < 1; }))
      return emitOpError("expect all stride values to be >= 1, got ")
             << strideValues;
  }
 
  SmallVector<int64_t> dilationValues;
  if (tosa::getConstShapeValues(getDilation().getDefiningOp(),
                                dilationValues)) {
    if (llvm::any_of(dilationValues, [](int64_t d) { return d < 1; }))
      return emitOpError("expect all dilation values to be >= 1, got ")
             << dilationValues;
  }
 
  // Verify output shape compatibility
  const ShapeAdaptor outputShape(getOutput().getType());
  if (!padValues.empty() && !strideValues.empty() && !dilationValues.empty() &&
      outputShape.hasRank()) {
    if (failed(verifyConvOutputSize(*this, IH, KH, outputShape.getDimSize(1),
                                    padValues[0], padValues[1], strideValues[0],
                                    dilationValues[0], "height", "y", "top",
                                    "bottom")) ||
        failed(verifyConvOutputSize(*this, IW, KW, outputShape.getDimSize(2),
                                    padValues[2], padValues[3], strideValues[1],
                                    dilationValues[1], "width", "x", "left",
                                    "right")))
      return failure();
  }
 
  // Verify bias
  const ShapeAdaptor biasShape(getBias().getType());
  if (biasShape.hasRank() && outputShape.hasRank()) {
    const int64_t biasChannels = biasShape.getDimSize(0);
    const int64_t outputChannels =
        outputShape.getDimSize(outputShape.getRank() - 1);
    if (biasChannels == ShapedType::kDynamic ||
        outputChannels == ShapedType::kDynamic)
      // Skip following checks if biasChannels or outputChannels is dynamic dim
      return success();
 
    if (biasChannels != outputChannels && biasChannels != 1)
      return emitOpError(
                 "bias channels expected to be equal to output channels (")
             << outputChannels << ") or 1, got " << biasChannels;
  }
 
  return success();
}
 
LogicalResult Conv3DOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    Conv3DOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  return inferConvReturnTypeComponents(adaptor, inferredReturnShapes);
}
 
LogicalResult Conv3DOp::verify() {
  if (verifyConvOp(*this).failed() || verifyConvOpErrorIf(*this).failed())
    return failure();
  return success();
}
 
LogicalResult AvgPool2dOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    AvgPool2dOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput().getType());
  const Properties &prop = adaptor.getProperties();
  return poolingInferReturnTypes(inputShape, prop.kernel, prop.stride, prop.pad,
                                 inferredReturnShapes);
}
 
LogicalResult AvgPool2dAdaptiveOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    AvgPool2dAdaptiveOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput().getType());
 
  llvm::SmallVector<int64_t> kernelValues;
  llvm::SmallVector<int64_t> strideValues;
  llvm::SmallVector<int64_t> padValues;
  if (tosa::getConstShapeValues(adaptor.getKernel().getDefiningOp(),
                                kernelValues) &&
      tosa::getConstShapeValues(adaptor.getStride().getDefiningOp(),
                                strideValues) &&
      tosa::getConstShapeValues(adaptor.getPad().getDefiningOp(), padValues)) {
    return poolingInferReturnTypes(inputShape, kernelValues, strideValues,
                                   padValues, inferredReturnShapes);
  }
 
  llvm::SmallVector<int64_t> outputShape(4, ShapedType::kDynamic);
  if (inputShape.hasRank()) {
    // Keep N & C as pooling only changes H & W.
    outputShape[0] = inputShape.getDimSize(0);
    outputShape[3] = inputShape.getDimSize(3);
  }
 
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  return success();
}
 
LogicalResult MaxPool2dOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    MaxPool2dOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput().getType());
  const Properties &prop = adaptor.getProperties();
  return poolingInferReturnTypes(inputShape, prop.kernel, prop.stride, prop.pad,
                                 inferredReturnShapes);
}
 
LogicalResult MaxPool2dAdaptiveOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    MaxPool2dAdaptiveOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput().getType());
 
  llvm::SmallVector<int64_t> kernelValues;
  llvm::SmallVector<int64_t> strideValues;
  llvm::SmallVector<int64_t> padValues;
  if (tosa::getConstShapeValues(adaptor.getKernel().getDefiningOp(),
                                kernelValues) &&
      tosa::getConstShapeValues(adaptor.getStride().getDefiningOp(),
                                strideValues) &&
      tosa::getConstShapeValues(adaptor.getPad().getDefiningOp(), padValues)) {
    return poolingInferReturnTypes(inputShape, kernelValues, strideValues,
                                   padValues, inferredReturnShapes);
  }
 
  llvm::SmallVector<int64_t> outputShape(4, ShapedType::kDynamic);
  if (inputShape.hasRank()) {
    outputShape[0] = inputShape.getDimSize(0);
    outputShape[3] = inputShape.getDimSize(3);
  }
  inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
  return success();
}
 
LogicalResult MaxPool2dOp::verify() {
  if (failed(verifySameElementTypes(*this, /* intype = */ getInput().getType(),
                                    /* outType = */ getOutput().getType())))
    return failure();
 
  if (failed(verifyPoolingOp(*this)))
    return failure();
 
  return success();
}
 
LogicalResult MaxPool2dAdaptiveOp::verify() {
  if (failed(verifySameElementTypes(*this, /* intype = */ getInput().getType(),
                                    /* outType = */ getOutput().getType())))
    return failure();
 
  AdaptivePoolingConstShapeValues values;
  extractAdaptivePoolingConstShapeOperands(*this, values);
 
  if (failed(verifyPoolingOpImpl(getOperation(), values.kernel, values.stride,
                                 values.pad, getInput(), getOutput())))
    return failure();
 
  return success();
}
 
LogicalResult DepthwiseConv2DOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    DepthwiseConv2DOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  llvm::SmallVector<int64_t> outputShape(4, ShapedType::kDynamic);
 
  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(adaptor.getInput().getType());
  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(adaptor.getWeight().getType());
  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::isStatic(inputChannels) &&
      ShapedType::isStatic(depthChannels)) {
    outputShape[3] = inputChannels * depthChannels;
  }
 
  // Bias shape can describe the output channels.
  ShapeAdaptor biasShape(adaptor.getBias().getType());
  if (biasShape.hasRank() && ShapedType::isDynamic(outputShape[3])) {
    int64_t bc = biasShape.getDimSize(0);
    if (bc != ShapedType::kDynamic && bc != 1)
      outputShape[3] = bc;
  }
 
  llvm::ArrayRef<int64_t> dilation = adaptor.getDilation();
  llvm::ArrayRef<int64_t> padding = adaptor.getPad();
  llvm::ArrayRef<int64_t> stride = adaptor.getStride();
 
  if (ShapedType::isStatic(inputHeight) && ShapedType::isStatic(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::isStatic(inputWidth) && ShapedType::isStatic(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() {
  if (verifyConvOp(*this).failed() || verifyConvOpErrorIf(*this).failed())
    return failure();
  return success();
}
 
LogicalResult TransposeConv2DOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    TransposeConv2DOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  llvm::SmallVector<int64_t> outputShape(4, ShapedType::kDynamic);
 
  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(adaptor.getInput().getType());
  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(adaptor.getWeight().getType());
  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(adaptor.getBias().getType());
  if (biasShape.hasRank() && ShapedType::isDynamic(outputShape[3])) {
    int64_t bc = biasShape.getDimSize(0);
    if (bc != ShapedType::kDynamic && bc != 1)
      outputShape[3] = bc;
  }
 
  llvm::ArrayRef<int64_t> padding = adaptor.getOutPad();
  llvm::ArrayRef<int64_t> stride = adaptor.getStride();
 
  if (ShapedType::isStatic(inputHeight) && ShapedType::isStatic(weightHeight)) {
    int64_t calculateSize =
        (inputHeight - 1) * stride[0] + padding[0] + padding[1] + weightHeight;
    outputShape[1] =
        ShapedType::isDynamic(outputShape[1]) ? calculateSize : outputShape[1];
  }
 
  if (ShapedType::isStatic(inputWidth) && ShapedType::isStatic(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 TransposeConv2DOp::verify() {
  if (verifyConvOp(*this).failed())
    return failure();
 
  const llvm::ArrayRef<int64_t> strides = getStride();
  const int64_t strideY = strides[0];
  const int64_t strideX = strides[1];
 
  if (strideY < 1 || strideX < 1)
    return emitOpError("expect all stride values to be >= 1, got [")
           << strides << "]";
 
  const auto checkPadAgainstKernelDim =
      [this](int64_t padValue, int64_t kernelDimSize, llvm::StringRef padName,
             llvm::StringRef kernelDimName) -> LogicalResult {
    if (padValue <= -kernelDimSize)
      return emitOpError("expected ")
             << padName << " > -" << kernelDimName << ", but got: " << padName
             << "=" << padValue << " and " << kernelDimName << "="
             << kernelDimSize;
    return success();
  };
 
  const llvm::ArrayRef<int64_t> padding = getOutPad();
  const int64_t outPadTop = padding[0];
  const int64_t outPadBottom = padding[1];
  const int64_t outPadLeft = padding[2];
  const int64_t outPadRight = padding[3];
 
  const auto weightType =
      llvm::dyn_cast<RankedTensorType>(getWeight().getType());
 
  if (weightType) {
    const int64_t kernelHeight = weightType.getDimSize(1);
    if (ShapedType::isStatic(kernelHeight)) {
      if (failed(checkPadAgainstKernelDim(outPadTop, kernelHeight,
                                          "out_pad_top", "KH")))
        return failure();
 
      if (failed(checkPadAgainstKernelDim(outPadBottom, kernelHeight,
                                          "out_pad_bottom", "KH")))
        return failure();
    }
 
    const int64_t kernelWidth = weightType.getDimSize(2);
    if (ShapedType::isStatic(kernelWidth)) {
      if (failed(checkPadAgainstKernelDim(outPadLeft, kernelWidth,
                                          "out_pad_left", "KW")))
        return failure();
 
      if (failed(checkPadAgainstKernelDim(outPadRight, kernelWidth,
                                          "out_pad_right", "KW")))
        return failure();
    }
  }
 
  // Rest of the checks depend on the output type being a RankedTensorType
  const auto outputType =
      llvm::dyn_cast<RankedTensorType>(getOutput().getType());
  if (!outputType)
    return success();
 
  const auto inputType = llvm::dyn_cast<RankedTensorType>(getInput().getType());
  if (inputType && weightType) {
    const int64_t inputHeight = inputType.getDimSize(1);
    const int64_t kernelHeight = weightType.getDimSize(1);
    const int64_t outputHeight = outputType.getDimSize(1);
 
    if (ShapedType::isStatic(inputHeight) &&
        ShapedType::isStatic(outputHeight)) {
      if (outputHeight !=
          (inputHeight - 1) * strideY + outPadTop + outPadBottom + kernelHeight)
        return emitOpError(
                   "dimension mismatch: expected OH == (IH - 1) * stride_y "
                   "+ out_pad_top + out_pad_bottom + KH, but got ")
               << outputHeight << " != (" << inputHeight << " - 1) * "
               << strideY << " + " << outPadTop << " + " << outPadBottom
               << " + " << kernelHeight;
    }
 
    const int64_t inputWidth = inputType.getDimSize(2);
    const int64_t kernelWidth = weightType.getDimSize(2);
    const int64_t outputWidth = outputType.getDimSize(2);
 
    if (ShapedType::isStatic(inputWidth) && ShapedType::isStatic(outputWidth)) {
      if (outputWidth !=
          (inputWidth - 1) * strideX + outPadLeft + outPadRight + kernelWidth)
        return emitOpError(
                   "dimension mismatch: expected OW == (IW - 1) * stride_x "
                   "+ out_pad_left + out_pad_right + KW, but got ")
               << outputWidth << " != (" << inputWidth << " - 1) * " << strideX
               << " + " << outPadLeft << " + " << outPadRight << " + "
               << kernelWidth;
    }
  }
 
  const auto biasType = llvm::dyn_cast<RankedTensorType>(getBias().getType());
 
  if (!biasType)
    return success();
 
  const int64_t biasChannels = biasType.getDimSize(0);
 
  // Skip further checks if bias is dynamic
  if (biasChannels == ShapedType::kDynamic)
    return success();
 
  const int64_t outputChannels = outputType.getDimSize(3);
  if (!ShapedType::isDynamic(outputChannels) &&
      biasChannels != outputChannels && biasChannels != 1)
    return emitOpError(
               "bias channels expected to be equal to output channels (")
           << outputChannels << ") or 1, got " << biasChannels;
 
  return success();
}
 
LogicalResult RescaleOp::verify() {
  const auto inputType = llvm::cast<ShapedType>(getInput().getType());
  auto inputElementType =
      getStorageElementTypeOrSelf(inputType.getElementType());
  if (!mlir::isa<IntegerType>(inputElementType)) {
    emitOpError("expect input to have integer element type, got ")
        << inputElementType;
    return failure();
  }
 
  const auto outputType = llvm::cast<ShapedType>(getOutput().getType());
  auto outputElementType =
      getStorageElementTypeOrSelf(outputType.getElementType());
  if (!mlir::isa<IntegerType>(outputElementType)) {
    emitOpError("expect output to have integer element type, got ")
        << outputElementType;
    return failure();
  }
 
  if (verifyRescaleValueAndZpTypes(*this, getInput(), getInputZp(), "input")
          .failed())
    return failure();
 
  if (verifyRescaleValueAndZpTypes(*this, getOutput(), getOutputZp(), "output")
          .failed())
    return failure();
 
  const auto multiplierType = llvm::cast<ShapedType>(getMultiplier().getType());
  // multiplier element type must be i32 for scale32 = true
  if (getScale32() && !multiplierType.getElementType().isInteger(32)) {
    emitOpError("expect i32 element type for multiplier for scale32=true, got ")
        << multiplierType.getElementType();
    return failure();
  }
 
  // multiplier element type must be i16 for scale32 = false
  if (!getScale32() && !multiplierType.getElementType().isInteger(16)) {
    emitOpError(
        "expect i16 element type for multiplier for scale32=false, got ")
        << multiplierType.getElementType();
    return failure();
  }
 
  if (!inputType.hasRank())
    return success();
 
  // multiplier/shift must have shape = {numChannels},
  // where numChannel is 1 if per_channel = false
  // otherwise numChannel is dimension in input shape's last axis
  int64_t numChannels = 1;
  if (getPerChannel()) {
    if (inputType.getRank() < 1) {
      emitOpError("requires input to be at least rank 1 when per_channel is "
                  "true, but got rank ")
          << inputType.getRank();
      return failure();
    }
    numChannels = inputType.getDimSize(inputType.getRank() - 1);
  }
 
  if (outputType.hasRank()) {
    if (failed(verifyOutputShapeCompatibleWithExpected(
            getOperation(), outputType, inputType.getShape())))
      return failure();
  }
 
  if (multiplierType.hasRank()) {
    ArrayRef<int64_t> multiplierShape = multiplierType.getShape();
    // multiplier input has rank 1 by dialect definition
    if (multiplierShape[0] != ShapedType::kDynamic &&
        multiplierShape[0] != numChannels) {
      emitOpError("expect shape of { ")
          << numChannels << " } for multiplier input, got { "
          << multiplierShape[0] << " }";
      return failure();
    }
  }
 
  const auto shiftType = llvm::cast<ShapedType>(getShift().getType());
  if (shiftType.hasRank()) {
    ArrayRef<int64_t> shiftShape = shiftType.getShape();
    // shift input has rank 1 by dialect definition
    if (shiftShape[0] != ShapedType::kDynamic && shiftShape[0] != numChannels) {
      emitOpError("expect shape of { ")
          << numChannels << " } for shift input, got { " << shiftShape[0]
          << " }";
      return failure();
    }
  }
 
  return success();
}
 
LogicalResult RescaleOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    RescaleOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  ShapeAdaptor inputShape(adaptor.getInput().getType());
  inferredReturnShapes.push_back(ShapedTypeComponents(inputShape));
  return success();
}
 
LogicalResult CastOp::verify() {
  const ShapedType inputType = llvm::cast<ShapedType>(getInput().getType());
  const ShapedType outputType = llvm::cast<ShapedType>(getType());
  const Type inputElementType = inputType.getElementType();
  const Type outputElementType = outputType.getElementType();
 
  const bool inputIsBlockScaled = llvm::isa<BlockScaledType>(inputElementType);
  const bool outputIsBlockScaled =
      llvm::isa<BlockScaledType>(outputElementType);
 
  const bool isUnsigned = this->getInputUnsigned();
  const Type inputDataType = getStorageElementTypeOrSelf(inputType);
 
  if (isUnsigned)
    if (!inputDataType.isInteger() || inputDataType.isInteger(1))
      return emitOpError()
             << "attribute input_unsigned requires integer type inputs. Got: "
             << inputDataType;
 
  if (!inputIsBlockScaled && !outputIsBlockScaled)
    return success();
 
  if (inputIsBlockScaled && outputIsBlockScaled)
    return emitOpError()
           << "requires exactly one of input or output to have block scaled "
              "element type";
 
  const Type scalarElementType =
      inputIsBlockScaled ? outputElementType : inputElementType;
  if (!llvm::isa<FloatType>(scalarElementType))
    return emitOpError()
           << "requires non-block-scaled element type to be floating-point "
              "when casting to or from block scaled element type, got "
           << scalarElementType;
 
  return success();
}
 
LogicalResult CastFromBlockScaledOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    CastFromBlockScaledOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  const ShapeAdaptor inputShape(adaptor.getInputData().getType());
  inferredReturnShapes.push_back(ShapedTypeComponents(inputShape));
  return success();
}
 
LogicalResult CastFromBlockScaledOp::verify() {
  const Type inputDataType = getInputData().getType();
  const Type outputDataType = getResult().getType();
  if (failed(verifyCompatibleShape(inputDataType, outputDataType)))
    return emitOpError() << "require compatible shapes for input_data ("
                         << inputDataType << ") and " << "output_data ("
                         << outputDataType << ")";
 
  const ShapeAdaptor inputDataShape = ShapeAdaptor(inputDataType);
 
  if (inputDataShape.hasRank()) {
    const unsigned int blockSize =
        BlockSizeAttr::getBlockSizeValue(getBlockSize());
    if (blockSize != BlockSizeAttr::getBlockSizeValue(BlockSize::BLOCK_SIZE_32))
      return emitOpError("expect block size to be 32, got ") << blockSize;
    const int64_t inputDataLastDim =
        inputDataShape.getDimSize(inputDataShape.getRank() - 1);
    if (inputDataLastDim % blockSize != 0)
      return emitOpError() << "expect last dimension of input_data ("
                           << inputDataLastDim
                           << ") to be divisible by block_size (" << blockSize
                           << ")";
 
    const Type inputScaleType = getInputScale().getType();
    const ShapeAdaptor inputScaleShape = ShapeAdaptor(inputScaleType);
 
    if (inputScaleShape.hasRank()) {
      SmallVector<int64_t> inputDataDims, inputScaleDims;
      inputDataShape.getDims(inputDataDims);
      inputScaleShape.getDims(inputScaleDims);
 
      if (inputDataDims.size() != inputScaleDims.size() ||
          failed(verifyCompatibleShape(
              ArrayRef<int64_t>(inputDataDims).drop_back(1),
              ArrayRef<int64_t>(inputScaleDims).drop_back(1))))
        return emitOpError()
               << "require compatible shapes for input_data (" << inputDataType
               << ") and " << "input_scale (" << inputScaleType
               << ") except for the last dimension";
 
      const SmallVector<int64_t, 2> dimsToCheck{inputDataLastDim / blockSize,
                                                inputScaleDims.back()};
      if (ShapedType::isStatic(inputDataLastDim) &&
          failed(verifyCompatibleDims(dimsToCheck)))
        return emitOpError()
               << "expect last dimension of input_scale ("
               << inputScaleDims.back()
               << ") to be equal to last dimension of input_data / block_size ("
               << inputDataDims.back() / blockSize << ")";
    }
  }
 
  return success();
}
 
LogicalResult CastToBlockScaledOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    CastToBlockScaledOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  const ShapeAdaptor inputShape(adaptor.getInputData().getType());
  inferredReturnShapes.push_back(ShapedTypeComponents(inputShape));
  if (!inputShape.hasRank())
    return success();
 
  // Calculate output_scale shape if ranked input provided
  SmallVector<int64_t> outputScaleShape;
  inputShape.getDims(outputScaleShape);
  const int64_t lastDimLoc = inputShape.getRank() - 1;
  const int64_t lastDimSize = inputShape.getDimSize(lastDimLoc);
  if (ShapedType::isStatic(lastDimSize)) {
    const unsigned int blockSize =
        BlockSizeAttr::getBlockSizeValue(adaptor.getBlockSize());
    outputScaleShape[lastDimLoc] = lastDimSize / blockSize;
  }
  inferredReturnShapes.push_back(ShapedTypeComponents(outputScaleShape));
  return success();
}
 
LogicalResult CastToBlockScaledOp::verify() {
  const Type inputDataType = getInputData().getType();
  const Type outputDataType = getResult(0).getType();
  if (failed(verifyCompatibleShape(inputDataType, outputDataType)))
    return emitOpError() << "require compatible shapes for input_data ("
                         << inputDataType << ") and " << "output_data ("
                         << outputDataType << ")";
 
  const unsigned int blockSize =
      BlockSizeAttr::getBlockSizeValue(getBlockSize());
  if (blockSize != BlockSizeAttr::getBlockSizeValue(BlockSize::BLOCK_SIZE_32))
    return emitOpError("expect block size to be 32, got ") << blockSize;
  const ShapeAdaptor inputDataShape = ShapeAdaptor(inputDataType);
  if (inputDataShape.hasRank()) {
    const int64_t inputDataLastDim =
        inputDataShape.getDimSize(inputDataShape.getRank() - 1);
    if (ShapedType::isStatic(inputDataLastDim) &&
        inputDataLastDim % blockSize != 0)
      return emitOpError() << "expect last dimension of input_data ("
                           << inputDataLastDim
                           << ") to be divisible by block_size (" << blockSize
                           << ")";
  }
 
  const ShapeAdaptor outputDataShape = ShapeAdaptor(outputDataType);
  const Type outputScaleType = getResult(1).getType();
  const ShapeAdaptor outputScaleShape = ShapeAdaptor(outputScaleType);
  if (outputDataShape.hasRank() && outputScaleShape.hasRank()) {
    SmallVector<int64_t> outputDataDims, outputScaleDims;
    outputDataShape.getDims(outputDataDims);
    outputScaleShape.getDims(outputScaleDims);
 
    if (outputDataDims.size() != outputScaleDims.size() ||
        failed(verifyCompatibleShape(
            ArrayRef<int64_t>(outputDataDims).drop_back(1),
            ArrayRef<int64_t>(outputScaleDims).drop_back(1))))
      return emitOpError() << "require compatible shapes for output_data ("
                           << outputDataType << ") and " << "output_scale ("
                           << outputScaleType
                           << ") except for the last dimension";
 
    const int64_t outputDataLastDim = outputDataDims.back();
    const SmallVector<int64_t, 2> dimsToCheck{outputDataLastDim / blockSize,
                                              outputScaleDims.back()};
    if (ShapedType::isStatic(outputDataLastDim) &&
        failed(verifyCompatibleDims(dimsToCheck)))
      return emitOpError()
             << "expect last dimension of output_scale ("
             << outputScaleDims.back()
             << ") to be equal to last dimension of output_data / block_size ("
             << outputDataDims.back() / blockSize << ")";
  }
 
  return success();
}
 
LogicalResult IfOp::inferReturnTypeComponents(
    MLIRContext *context, ::std::optional<Location> location,
    IfOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  llvm::SmallVector<tosa::YieldOp> yieldOps;
  for (Region *region : adaptor.getRegions()) {
    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,
    WhileOp::Adaptor adaptor,
    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
  llvm::SmallVector<tosa::YieldOp> yieldOps;
  for (auto &block : adaptor.getBodyGraph())
    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();
}
 
static void printInitializationList(OpAsmPrinter &parser,
                                    Block::BlockArgListType blocksArgs,
                                    ValueRange initializers,
                                    StringRef prefix = "") {
  assert(blocksArgs.size() == initializers.size() &&
         "expected same length of arguments and initializers");
  if (initializers.empty())
    return;
 
  parser << prefix << '(';
  llvm::interleaveComma(
      llvm::zip(blocksArgs, initializers), parser,
      [&](auto it) { parser << std::get<0>(it) << " = " << std::get<1>(it); });
  parser << ")";
}
 
// parse and print of IfOp refer to the implementation of SCF dialect.
ParseResult IfOp::parse(OpAsmParser &parser, OperationState &result) {
  // Create the regions for 'then'.
  result.regions.reserve(2);
  Region *thenRegion = result.addRegion();
  Region *elseRegion = result.addRegion();
 
  OpAsmParser::UnresolvedOperand cond;
 
  if (parser.parseOperand(cond))
    return failure();
 
  SmallVector<OpAsmParser::Argument, 4> regionArgs;
  SmallVector<OpAsmParser::UnresolvedOperand, 4> operands;
 
  // Parse the optional block arguments
  OptionalParseResult listResult =
      parser.parseOptionalAssignmentList(regionArgs, operands);
  if (listResult.has_value() && failed(listResult.value()))
    return failure();
 
  // Parse a colon.
  if (failed(parser.parseColon()))
    return parser.emitError(parser.getCurrentLocation(),
                            "expected type for condition operand");
 
  // Parse the type of the condition operand
  Type condType;
  if (failed(parser.parseType(condType)))
    return parser.emitError(parser.getCurrentLocation(),
                            "expected type for condition operand");
 
  // Resolve operand with provided type
  if (failed(parser.resolveOperand(cond, condType, result.operands)))
    return failure();
 
  // Parse optional block arg types
  if (listResult.has_value()) {
    FunctionType functionType;
 
    if (failed(parser.parseType(functionType)))
      return parser.emitError(parser.getCurrentLocation())
             << "expected list of types for block arguments "
             << "followed by arrow type and list of return types";
 
    result.addTypes(functionType.getResults());
 
    if (functionType.getNumInputs() != operands.size()) {
      return parser.emitError(parser.getCurrentLocation())
             << "expected as many input types as operands " << "(expected "
             << operands.size() << " got " << functionType.getNumInputs()
             << ")";
    }
 
    // Resolve input operands.
    if (failed(parser.resolveOperands(operands, functionType.getInputs(),
                                      parser.getCurrentLocation(),
                                      result.operands)))
      return failure();
  } else {
    // Parse optional results type list.
    if (parser.parseOptionalArrowTypeList(result.types))
      return failure();
  }
 
  // Parse the 'then' region.
  if (parser.parseRegion(*thenRegion, /*arguments=*/{}, /*argTypes=*/{}))
    return failure();
 
  // If we find an 'else' keyword then parse the 'else' region.
  if (!parser.parseOptionalKeyword("else")) {
    if (parser.parseRegion(*elseRegion, /*arguments=*/{}, /*argTypes=*/{}))
      return failure();
  }
 
  // Parse the optional attribute list.
  if (parser.parseOptionalAttrDict(result.attributes))
    return failure();
  return success();
}
 
void IfOp::print(OpAsmPrinter &p) {
  p << " " << getCondition();
 
  printInitializationList(p, getThenGraph().front().getArguments(),
                          getInputList(), " ");
  p << " : ";
  p << getCondition().getType();
 
  if (!getInputList().empty()) {
    p << " (";
    llvm::interleaveComma(getInputList().getTypes(), p);
    p << ")";
  }
  p.printArrowTypeList(getResultTypes());
  p << " ";
 
  p.printRegion(getThenGraph());
 
  // Print the 'else' regions if it exists and has a block.
  auto &elseRegion = getElseGraph();
  if (!elseRegion.empty()) {
    p << " else ";
    p.printRegion(elseRegion);
  }
 
  p.printOptionalAttrDict((*this)->getDiscardableAttrDictionary().getValue());
}
 
LogicalResult IfOp::verify() {
  if (errorIfTypeOrShapeMismatch(*this, getThenGraph().front().getArguments(),
                                 "'then_graph' arguments", getInputList(),
                                 "'input_list'")
          .failed())
    return failure();
 
  if (errorIfTypeOrShapeMismatch(*this, getElseGraph().front().getArguments(),
                                 "'else_graph' arguments", getInputList(),
                                 "'input_list'")
          .failed())
    return failure();
 
  // MLIR will verify the absence of the terminator for us if otherwise.
  if (getThenGraph().front().mightHaveTerminator()) {
    auto thenYield =
        dyn_cast<tosa::YieldOp>(getThenGraph().front().getTerminator());
    if (thenYield && errorIfTypeOrShapeMismatch(
                         *this, thenYield.getInputs(), "'then_graph' results",
                         getOutputList(), "'output_list'")
                         .failed())
      return failure();
  }
 
  // MLIR will verify the absence of the terminator for us if otherwise.
  if (getElseGraph().front().mightHaveTerminator()) {
    auto elseYield =
        dyn_cast<tosa::YieldOp>(getElseGraph().front().getTerminator());
    if (elseYield && errorIfTypeOrShapeMismatch(
                         *this, elseYield.getInputs(), "'else_graph' results",
                         getOutputList(), "'output_list'")
                         .failed())
      return failure();
  }
 
  auto condType = getCondition().getType();
  if (errorIfShapeNotSizeOne(*this, condType).failed())
    return emitOpError() << "'condition' must be a size 1 tensor, got "
                         << condType;
 
  return success();
}
 
LogicalResult WhileOp::verify() {
  if (errorIfTypeOrShapeMismatch(*this, getInputList(), "'input_list'",
                                 getOutputList(), "'output_list'")
          .failed())
    return failure();
 
  if (errorIfTypeOrShapeMismatch(*this, getCondGraph().front().getArguments(),
                                 "'cond_graph' arguments", getInputList(),
                                 "'input_list'")
          .failed())
    return failure();
 
  if (errorIfTypeOrShapeMismatch(*this, getBodyGraph().front().getArguments(),
                                 "'body_graph' arguments", getInputList(),
                                 "'input_list'")
          .failed())
    return failure();
 
  if (getBodyGraph().front().mightHaveTerminator()) {
    auto bodyYield =
        dyn_cast<tosa::YieldOp>(getBodyGraph().front().getTerminator());
    if (bodyYield && errorIfTypeOrShapeMismatch(*this, bodyYield.getInputs(),
                                                "'body_graph' results",
                                                getInputList(), "'input_list'")
                         .failed())
      return failure();
  }
 
  // Condition block output must be a single element tensor with a single bool
  // value.
  if (!getCondGraph().front().mightHaveTerminator())
    return success();
 
  auto condYield =
      dyn_cast<tosa::YieldOp>(getCondGraph().front().getTerminator());
  if (!condYield)
    return success();
 
  if (condYield.getInputs().size() != 1)
    return emitOpError() << "require 'cond_graph' only have one result";
 
  auto condOutType = condYield.getInputs()[0].getType();
  if (errorIfShapeNotSizeOne(*this, condOutType).failed())
    return emitOpError() << "'cond_graph' result must be a size 1 tensor, got "
                         << condOutType;
 
  if (!getElementTypeOrSelf(condOutType).isInteger(1))
    return emitOpError() << "'cond_graph' result must be a boolean tensor, got "
                         << condOutType;
 
  return success();
}
 
LogicalResult ReverseOp::verify() {
  TensorType inputType = getInput1().getType();
  int32_t reverseAxis = getAxis();
 
  if (reverseAxis < 0)
    return emitOpError("expected non-negative reverse axis");
  if (inputType.hasRank()) {
    int64_t inputRank = inputType.getRank();
    // We allow for a special case where the input/output shape has rank 0 and
    // axis is also 0.
    if (reverseAxis >= inputRank && (reverseAxis != 0 || inputRank != 0))
      return emitOpError("expect input tensor rank (")
             << inputRank << ") to be larger than reverse axis (" << reverseAxis
             << ")";
  }
 
  return success();
}
 
LogicalResult tosa::SelectOp::verify() {
  // verify input2 and input3 have same element type as output
  if (verifySameElementTypes(*this, /* inType = */ getOnTrue().getType(),
                             /* outType = */ getOutput().getType())
          .failed() ||
      verifySameElementTypes(*this, /* inType = */ getOnFalse().getType(),
                             /* outType = */ getOutput().getType())
          .failed()) {
    return failure();
  }
  // verify input1 has element type of bool
  auto predicateType = llvm::dyn_cast<ShapedType>(getPred().getType());
  if (!predicateType) {
    return emitOpError("expect shaped tensor for input1, got ")
           << getInput1().getType();
  }
  auto predicateElementType = predicateType.getElementType();
  if (!predicateElementType.isInteger(1)) {
    return emitOpError("expect element type of bool for input1, got ")
           << predicateElementType;
  }
 
  return success();
}
 
LogicalResult tosa::VariableReadOp::verify() {
  if (verifyVariableOpErrorIf(*this, getOutput1().getType(), "'output1'")
          .failed())
    return failure();
 
  return success();
}
 
LogicalResult tosa::VariableWriteOp::verify() {
  if (verifyVariableOpErrorIf(*this, getInput1().getType(), "'input1'")
          .failed())
    return failure();
 
  return success();
}
 
// parse and print of WhileOp refer to the implementation of SCF dialect.
ParseResult WhileOp::parse(OpAsmParser &parser, OperationState &result) {
  SmallVector<OpAsmParser::Argument, 4> regionArgs;
  SmallVector<OpAsmParser::UnresolvedOperand, 4> operands;
  Region *cond = result.addRegion();
  Region *body = result.addRegion();
 
  OptionalParseResult listResult =
      parser.parseOptionalAssignmentList(regionArgs, operands);
  if (listResult.has_value() && failed(listResult.value()))
    return failure();
 
  FunctionType functionType;
  SMLoc typeLoc = parser.getCurrentLocation();
  if (failed(parser.parseColonType(functionType)))
    return failure();
 
  result.addTypes(functionType.getResults());
 
  if (functionType.getNumInputs() != operands.size()) {
    return parser.emitError(typeLoc)
           << "expected as many input types as operands " << "(expected "
           << operands.size() << " got " << functionType.getNumInputs() << ")";
  }
 
  // Resolve input operands.
  if (failed(parser.resolveOperands(operands, functionType.getInputs(),
                                    parser.getCurrentLocation(),
                                    result.operands)))
    return failure();
 
  // Propagate the types into the region arguments.
  for (size_t i = 0, e = regionArgs.size(); i != e; ++i)
    regionArgs[i].type = functionType.getInput(i);
 
  return failure(parser.parseRegion(*cond, regionArgs) ||
                 parser.parseKeyword("do") || parser.parseRegion(*body) ||
                 parser.parseOptionalAttrDictWithKeyword(result.attributes));
}
 
void WhileOp::print(OpAsmPrinter &parser) {
  printInitializationList(parser, getCondGraph().front().getArguments(),
                          getInputList(), " ");
  parser << " : ";
  parser.printFunctionalType(getInputList().getTypes(),
                             getResults().getTypes());
  parser << ' ';
  parser.printRegion(getCondGraph(), /*printEntryBlockArgs=*/false);
  parser << " do ";
  parser.printRegion(getBodyGraph());
  parser.printOptionalAttrDictWithKeyword(
      (*this)->getDiscardableAttrDictionary().getValue());
}
 
// Create a rank-1 const tensor for zero point of the source tensor.
std::optional<Value> mlir::tosa::createZeroPointTensor(OpBuilder &builder,
                                                       Location loc,
                                                       Type srcElemType,
                                                       int64_t zp) {
  srcElemType = getStorageElementTypeOrSelf(srcElemType);
  auto zpType = mlir::RankedTensorType::get({1}, srcElemType);
  if (llvm::isa<FloatType>(srcElemType)) {
    auto zpAttr = DenseElementsAttr::get(
        zpType, builder.getFloatAttr(srcElemType, static_cast<double>(zp)));
    return tosa::ConstOp::create(builder, loc, zpType, zpAttr);
  }
  if (llvm::isa<IntegerType>(srcElemType)) {
    auto zpAttr =
        DenseElementsAttr::get(zpType, builder.getIntegerAttr(srcElemType, zp));
    return tosa::ConstOp::create(builder, loc, zpType, zpAttr);
  }
  llvm::errs() << "zero point is not allowed for unsupported data types\n";
  return std::nullopt;
}
 
//===----------------------------------------------------------------------===//
// TOSA Shape and Shape Operators Helper functions.
//===----------------------------------------------------------------------===//
 
bool mlir::tosa::isa_tosa_shape_type(mlir::Type t) {
  return mlir::isa<tosa::shapeType>(t);
}
 
LogicalResult
mlir::tosa::shapeType::verify(function_ref<InFlightDiagnostic()> emitError,
                              int rank) {
  if (rank < 0)
    return emitError() << "invalid rank (must be >= 0): " << rank;
  return success();
}
 
LogicalResult OpTrait::tosa::verifyTosaResolvableShapeOperands(Operation *op) {
  for (auto v : op->getOperands()) {
    if (mlir::isa<::mlir::tosa::shapeType>(v.getType())) {
      Operation *definingOp = v.getDefiningOp();
      if (!definingOp || !definingOp->hasTrait<TosaShapeOperator>()) {
        return op->emitOpError("shape operand is not compile time resolvable");
      }
    }
  }
  return success();
}
 
LogicalResult
OpTrait::tosa::verifyTosaShapeOperatorWithSameRanks(Operation *op) {
  if (failed(OpTrait::impl::verifyAtLeastNOperands(op, 1)))
    return failure();
 
  // delegate function that returns rank of shape type
  auto getRank = [](const Type type) {
    return mlir::cast<mlir::tosa::shapeType>(type).getRank();
  };
  auto operandTypes = op->getOperandTypes();
  auto resultTypes = op->getResultTypes();
 
  auto rank = getRank(*op->getOperandTypes().begin());
  for (auto type : operandTypes) {
    if (getRank(type) != rank) {
      return op->emitOpError("operands don't have matching ranks");
    }
  }
  for (auto type : resultTypes) {
    if (getRank(type) != rank) {
      return op->emitOpError("result shape has different rank than operands");
    }
  }
  return success();
}
 
//===----------------------------------------------------------------------===//
// TOSA Shape Operators verify functions.
//===----------------------------------------------------------------------===//
 
LogicalResult tosa::ConstShapeOp::verify() {
  // check one dimensional rank
  auto valuesRank = getValues().getType().getRank();
  if (valuesRank != 1)
    return emitOpError("expect elements in attribute values with rank 1");
  // check that number of elements in values attr equal to rank of result shape
  auto count = getValues().getNumElements();
  auto rank = (cast<tosa::shapeType>(getResult().getType())).getRank();
  if (count != rank && (count != 1 || rank != 0)) {
    return emitOpError("expect number of elements in attribute values (")
           << count << ") to be equal to the rank (" << rank
           << ") for the result shape type";
  }
  return success();
}
 
LogicalResult tosa::DimOp::verify() {
  const tosa::shapeType outShapeType =
      cast<tosa::shapeType>(getResult().getType());
  if (outShapeType.getRank() != 1)
    return emitOpError("expect output shape type to contain one element, got ")
           << outShapeType;
 
  const ShapeAdaptor inputType(getInput1().getType());
  if (inputType.hasRank()) {
    const int64_t inputRank = inputType.getRank();
    const int64_t axis = getAxisAttr().getInt();
    if (axis < 0 || axis >= inputRank)
      return emitOpError("expect axis to be in the range [0, ")
             << inputRank << "), got " << axis;
  }
  return success();
}
 
LogicalResult tosa::ConcatShapeOp::verify() {
  const tosa::shapeType outShapeType =
      cast<tosa::shapeType>(getResult().getType());
  const int64_t outputRank = outShapeType.getRank();
  const Operation::operand_range inputList = getInput();
 
  if (inputList.size() == 0)
    return emitOpError("requires at least one input shape");
 
  if (llvm::any_of(inputList, [](Value v) {
        return cast<tosa::shapeType>(v.getType()).getRank() == 0;
      }))
    return emitOpError("requires all inputs shapes have a rank greater than 0");
 
  const int64_t inputsRank =
      llvm::accumulate(inputList, 0, [](int64_t acc, const Value &input) {
        const tosa::shapeType inShapeType =
            cast<tosa::shapeType>(input.getType());
        return acc + inShapeType.getRank();
      });
  if (outputRank != inputsRank)
    return emitOpError("requires output shape rank to be equal to the sum of "
                       "the input shape ranks (")
           << inputsRank << "), got " << outputRank;
 
  return success();
}
 
LogicalResult tosa::SliceShapeOp::verify() {
  std::optional<int32_t> start;
  DenseIntElementsAttr startAttr;
  if (matchPattern(getStart(), m_Constant(&startAttr)))
    start = startAttr.getValues<int32_t>()[0];
  if (start && start.value() < 0)
    return emitOpError("expected non-negative start index, got ")
           << start.value();
 
  std::optional<int32_t> size;
  DenseIntElementsAttr sizeAttr;
  if (matchPattern(getSize(), m_Constant(&sizeAttr)))
    size = sizeAttr.getValues<int32_t>()[0];
  if (size && size.value() <= 0)
    return emitOpError("expected positive size, got ") << size.value();
 
  if (!size)
    return success();
 
  const tosa::shapeType outShapeType =
      cast<tosa::shapeType>(getResult().getType());
  const int64_t outputRank = outShapeType.getRank();
  if (outputRank != size)
    return emitOpError(
               "expected output type size to be equal to size attribute, got ")
           << outputRank << " vs " << size.value();
 
  if (!start)
    return success();
 
  const tosa::shapeType inShapeType =
      cast<tosa::shapeType>(getInput().getType());
  const int64_t inputRank = inShapeType.getRank();
  const int64_t sliceSize = start.value() + size.value();
  if (sliceSize > inputRank)
    return emitOpError("expected start + size to be less than or equal to "
                       "input shape rank (")
           << inputRank << "), got " << sliceSize;
 
  return success();
}
 
//===----------------------------------------------------------------------===//
// TOSA Attribute Definitions.
//===----------------------------------------------------------------------===//
 
#define GET_ATTRDEF_CLASSES
#include "mlir/Dialect/Tosa/IR/TosaAttributes.cpp.inc"
 
//===----------------------------------------------------------------------===//
// TOSA Type Definitions.
//===----------------------------------------------------------------------===//
#define GET_TYPEDEF_CLASSES
#include "mlir/Dialect/Tosa/IR/TosaOpsTypesBase.cpp.inc"
 
//===----------------------------------------------------------------------===//
// TOSA Operator Definitions.
//===----------------------------------------------------------------------===//
 
static ParseResult parseOptionalBoolClause(OpAsmParser &parser,
                                           StringRef keyword,
                                           BoolAttr &result) {
  if (failed(parser.parseOptionalKeyword(keyword)))
    return success();
  if (parser.parseLParen() || parser.parseAttribute(result) ||
      parser.parseRParen())
    return failure();
  return success();
}
 
static void printOptionalBoolClause(OpAsmPrinter &printer, StringRef keyword,
                                    BoolAttr attr) {
  if (!attr)
    return;
  printer << keyword << '(';
  printer.printAttribute(attr);
  printer << ')';
}
 
static ParseResult parseLocalBound(OpAsmParser &parser, BoolAttr &result) {
  return parseOptionalBoolClause(parser, "local_bound", result);
}
 
static void printLocalBound(OpAsmPrinter &printer, Operation *, BoolAttr attr) {
  printOptionalBoolClause(printer, "local_bound", attr);
}
 
static ParseResult parseInputUnsigned(OpAsmParser &parser, BoolAttr &result) {
  return parseOptionalBoolClause(parser, "input_unsigned", result);
}
 
static void printInputUnsigned(OpAsmPrinter &printer, Operation *,
                               BoolAttr attr) {
  printOptionalBoolClause(printer, "input_unsigned", attr);
}
 
#define GET_OP_CLASSES
#include "mlir/Dialect/Tosa/IR/TosaOps.cpp.inc"