verif/generator/tosa_error_if.py

# Copyright (c) 2021-2024, ARM Limited.
# SPDX-License-Identifier: Apache-2.0
import logging
import math

import numpy as np
from generator.tosa_utils import MAX_RESIZE_DIMENSION
from generator.tosa_utils import product
from generator.tosa_utils import usableDTypes
from generator.tosa_utils import valueToName
from tosa.DType import DType
from tosa.Op import Op
from tosa.ResizeMode import ResizeMode

logging.basicConfig()
logger = logging.getLogger("tosa_verif_build_tests")


class ErrorIf(object):
    MaxDimExceeded = "MaxDimExceeded"
    ScaleSmallerEqualZero = "ScaleSmallerEqualZero"
    ScaleNLargerMax = "ScaleNLargerMax"
    ScaleDLargerMax = "ScaleDLargerMax"
    OffsetSmallerMin = "OffsetSmallerMin"
    OffsetLargerEqualMax = "OffsetLargerEqualMax"
    BorderSmallerMin = "BorderSmallerMin"
    BorderLargerEqualMax = "BorderLargerEqualMax"
    ResizeOutputShapeMismatch = "ResizeOutputShapeMismatch"
    ResizeOutputShapeNonInteger = "ResizeOutputShapeNonInteger"
    WrongInputType = "WrongInputType"
    WrongOutputType = "WrongOutputType"
    WrongInputList = "WrongInputList"
    WrongOutputList = "WrongOutputList"
    WrongRank = "WrongRank"
    BatchMismatch = "BatchMismatch"
    ChannelMismatch = "ChannelMismatch"
    RankMismatch = "RankMismatch"
    DimensionMismatch = "DimensionMismatch"
    InputZeroPointNotZero = "InputZeroPointNotZero"
    WeightZeroPointNotZero = "WeightZeroPointNotZero"
    OutputZeroPointNotZero = "OutputZeroPointNotZero"
    AxisSmallerZero = "AxisSmallerZero"
    AxisLargerRank = "AxisLargerRank"
    ArgmaxOutputShapeMismatch = "ArgmaxOutputShapeMismatch"
    ArgmaxOutputRankMismatch = "ArgmaxOutputRankMismatch"
    ShapeOfAxisNotOne = "ShapeOfAxisNotOne"
    KernelSmallerOne = "KernelSmallerOne"
    StrideSmallerOne = "StrideSmallerOne"
    DilationSmallerOne = "DilationSmallerOne"
    PadSmallerZero = "PadSmallerZero"
    PadLargerEqualKernel = "PadLargerEqualKernel"
    PadOutputShapeMismatch = "PadOutputShapeMismatch"
    PoolingOutputShapeMismatch = "PoolingOutputShapeMismatch"
    PoolingOutputShapeNonInteger = "PoolingOutputShapeNonInteger"
    ConvOutputShapeMismatch = "ConvOutputShapeMismatch"
    ConvOutputShapeNonInteger = "ConvOutputShapeNonInteger"
    ScaleNotTrue = "ScaleNotTrue"
    ScaleTrue = "ScaleTrue"
    TensorSizeInputOutputMismatch = "TensorSizeInputOutputMismatch"
    StartSmallerZero = "StartSmallerZero"
    SizeSmallerEqualZero = "SizeSmallerEqualZero"
    StartSizeOutsideBounds = "StartSizeOutsideBounds"
    SizeOutputShapeMismatch = "SizeOutputShapeMismatch"
    InputSizeStartLengthMismatch = "InputSizeStartLengthMismatch"
    IndexOutsideBounds = "IndexOutsideBounds"
    IndexUsedTwice = "IndexUsedTwice"
    MaxSmallerMin = "MaxSmallerMin"
    ConcatInputRankMismatch = "ConcatInputRankMismatch"
    ConcatInputDimMismatch = "ConcatInputDimMismatch"
    ConcatShapeSumMismatch = "ConcatShapeSumMismatch"
    CondIfInputListThenGraphMismatch = "CondIfInputListThenGraphMismatch"
    CondIfInputListElseGraphMismatch = "CondIfInputListElseGraphMismatch"
    CondIfOutputListThenGraphMismatch = "CondIfOutputListThenGraphMismatch"
    CondIfOutputListElseGraphMismatch = "CondIfOutputListElseGraphMismatch"
    InputListOutputListMismatch = "InputListOutputListMismatch"
    InputListCondGraphMismatch = "InputListCondGraphMismatch"
    InputListBodyGraphInputMismatch = "InputListBodyGraphInputMismatch"
    InputListBodyGraphOutputMismatch = "InputListBodyGraphOutputMismatch"
    CondGraphOutputNotMatchingBool = "CondGraphOutputNotMatchingBool"
    U16InputZeroPointNotValid = "U16InputZeroPointNotValid"
    U16OutputZeroPointNotValid = "U16OutputZeroPointNotValid"
    CondIfCondNotMatchingBool = "CondIfCondNotMatchingBool"
    CondIfCondShapeNotSizeOne = "CondIfCondShapeNotSizeOne"
    CondGraphOutputShapeNotSizeOne = "CondGraphOutputShapeNotSizeOne"
    KernelNotPowerOfTwo = "KernelNotPowerOfTwo"
    FFTInputShapeMismatch = "FFTInputShapeMismatch"
    FFTOutputShapeMismatch = "FFTOutputShapeMismatch"
    ReshapeOutputSizeMultiInference = "ReshapeOutputSizeMultiInference"
    ReshapeOutputSizeNonInteger = "ReshapeOutputSizeNonInteger"
    BroadcastShapesMismatch = "BroadcastShapesMismatch"
    WrongAccumulatorType = "WrongAccumulatorType"


class TosaErrorIfArgGen:
    @staticmethod
    def eiResizeErrorIf(
        testGen,
        error_name,
        mode,
        dtype,
        shapeList,
        outputDType,
        scale,
        offset,
        border,
    ):
        if error_name == ErrorIf.ScaleSmallerEqualZero:
            index = testGen.randInt(low=0, high=4)
            scale[index] = testGen.rng.choice([-2, -1, 0])
        elif error_name == ErrorIf.ScaleNLargerMax:
            index = testGen.rng.choice([0, 2])
            scale[index] = (1 << 11) + testGen.rng.choice([1, 2, 3])
        elif error_name == ErrorIf.ScaleDLargerMax:
            index = testGen.rng.choice([1, 3])
            scale[index] = 16 * scale[index - 1] + testGen.rng.choice([0, 1, 2])

        if error_name == ErrorIf.OffsetLargerEqualMax:
            index = testGen.rng.choice([0, 1])
            offset[index] = 16 * scale[index * 2] + testGen.rng.choice([0, 1, 2])
        elif error_name == ErrorIf.OffsetSmallerMin:
            index = testGen.rng.choice([0, 1])
            offset[index] = -scale[index * 2] - testGen.rng.choice([1, 2, 3])

        if error_name == ErrorIf.BorderLargerEqualMax:
            index = testGen.rng.choice([0, 1])
            border[index] = scale[index * 2] + testGen.rng.choice([0, 1, 2])
        elif error_name == ErrorIf.BorderSmallerMin:
            index = testGen.rng.choice([0, 1])
            border[index] = -16 * scale[index * 2] - testGen.rng.choice([1, 2, 3])

        if error_name == ErrorIf.WrongOutputType:
            if mode == ResizeMode.NEAREST and dtype == DType.INT8:
                incorrect_types = (
                    DType.INT4,
                    DType.INT16,
                    DType.INT32,
                    DType.INT48,
                    DType.FP32,
                    DType.FP16,
                )
            elif mode == ResizeMode.NEAREST and dtype == DType.INT16:
                incorrect_types = (
                    DType.INT4,
                    DType.INT8,
                    DType.INT32,
                    DType.INT48,
                    DType.FP32,
                    DType.FP16,
                )
            elif mode == ResizeMode.BILINEAR and dtype == DType.INT8:
                incorrect_types = (
                    DType.INT4,
                    DType.INT8,
                    DType.INT16,
                    DType.INT48,
                    DType.FP32,
                    DType.FP16,
                )
            elif mode == ResizeMode.BILINEAR and dtype == DType.INT16:
                incorrect_types = (
                    DType.INT4,
                    DType.INT8,
                    DType.INT16,
                    DType.INT32,
                    DType.FP32,
                    DType.FP16,
                )
            elif dtype == DType.FP16:
                incorrect_types = (
                    DType.INT4,
                    DType.INT8,
                    DType.INT16,
                    DType.INT32,
                    DType.INT48,
                    DType.FP32,
                )
            elif dtype == DType.BF16:
                incorrect_types = (
                    DType.INT4,
                    DType.INT8,
                    DType.INT16,
                    DType.INT32,
                    DType.INT48,
                    DType.FP32,
                )
            elif dtype == DType.FP32:
                incorrect_types = (
                    DType.INT4,
                    DType.INT8,
                    DType.INT16,
                    DType.INT32,
                    DType.INT48,
                    DType.FP16,
                )
            outputDType = testGen.rng.choice(a=incorrect_types)

        return scale, offset, border, outputDType

    @staticmethod
    def eiPoolingErrorIf(testGen, error_name, stride, pad, kernel):
        if (
            error_name == ErrorIf.StrideSmallerOne
            # padding must not exceed the kernel size
            and pad[0] < kernel[0]
            and pad[1] < kernel[0]
            and pad[2] < kernel[1]
            and pad[3] < kernel[1]
        ):
            wrongStride = (
                testGen.rng.choice([0, -1, -2, -3]),
                testGen.rng.choice([0, -1, -2, -3]),
            )
            return wrongStride, pad, kernel
        elif error_name == ErrorIf.PadSmallerZero:
            wrongPad = (
                testGen.rng.choice([-1, -2, -3]),
                testGen.rng.choice([-1, -2, -3]),
                testGen.rng.choice([-1, -2, -3]),
                testGen.rng.choice([-1, -2, -3]),
            )
            return stride, wrongPad, kernel
        elif error_name == ErrorIf.KernelSmallerOne:
            wrongKernel = (
                testGen.rng.choice([0, -1, -2, -3]),
                testGen.rng.choice([0, -1, -2, -3]),
            )
            return stride, pad, wrongKernel
        elif error_name == ErrorIf.PadLargerEqualKernel:
            wrongPad = (
                testGen.rng.choice([kernel[0], kernel[0] + 1, kernel[0] + 2]),
                testGen.rng.choice([kernel[0], kernel[0] + 1, kernel[0] + 2]),
                testGen.rng.choice([kernel[1], kernel[1] + 1, kernel[1] + 2]),
                testGen.rng.choice([kernel[1], kernel[1] + 1, kernel[1] + 2]),
            )
            return stride, wrongPad, kernel
        else:
            return None, None, None

    @staticmethod
    def eiRescaleWrongOutputType(input_dtype, output_dtype):
        if input_dtype == DType.INT8:
            if output_dtype not in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]:
                return True
        elif input_dtype == DType.INT16:
            if output_dtype not in [
                DType.UINT8,
                DType.INT8,
                DType.UINT16,
                DType.INT16,
                DType.INT32,
            ]:
                return True
        elif input_dtype == DType.INT32:
            if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]:
                return True
        elif input_dtype == DType.INT48:
            if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]:
                return True
        elif input_dtype == DType.UINT8:
            if output_dtype not in [DType.INT8, DType.INT16]:
                return True
        elif input_dtype == DType.UINT16:
            if output_dtype != DType.INT16:
                return True
        return False

    @staticmethod
    def eiInvalidateInputOutputList(testGen, error_name, input_list, output_list):
        # Mess up input/output tensors for ERROR_IF checks
        if error_name == "WrongInputList":
            add_input = testGen.rng.choice([True, False])
            if add_input:
                input_list.append("eiDummyInput")
            else:
                input_list = input_list[:-1]
        elif error_name == "WrongOutputList":
            add_output = testGen.rng.choice([True, False])
            if add_output:
                output_list.append("eiDummyOutput")
            else:
                output_list = []
        return input_list, output_list

    @staticmethod
    def eiRestrictDimensions(shape, max_dim=32, max_items=100000):
        """Restrict the dimensions and overall size of a shape to
        max_dim and max_items.
        """
        new_shape = [min(d, max_dim) for d in shape] if max(shape) > max_dim else shape
        while product(new_shape) > max_items:
            new_shape = [max(d - 1, 1) for d in new_shape]
        return new_shape

    def eiSliceErrorIf(testGen, error_name, input_shape, start, size):
        if error_name == ErrorIf.StartSmallerZero:
            newStart = []
            for i in range(len(input_shape)):
                newStart.append(testGen.rng.choice([-3, -2, -1]))
            return newStart, size
        elif error_name == ErrorIf.SizeSmallerEqualZero:
            newSize = []
            for i in range(len(input_shape)):
                newSize.append(testGen.rng.choice([-3, -2, -1, 0]))
            return start, newSize
        elif error_name == ErrorIf.StartSizeOutsideBounds:
            newStart, newSize = [], []
            for i in range(len(input_shape)):
                newStart.append(input_shape[i] - 1)
                newSize.append(testGen.rng.choice([2, 3, 4]))
            return newStart, newSize
        elif error_name == ErrorIf.InputSizeStartLengthMismatch:
            remove = testGen.rng.choice([True, False])

            # Get an empty tensor when diminishing dimension on 1-d tensor.
            if len(start) == 1 or len(size) == 1:
                remove = False

            if remove:
                newStart = start[1:]
                newSize = size[1:]
            else:
                newStart = start
                newStart.append(1)
                newSize = size
                newSize.append(1)
            return newStart, newSize
        else:
            return start, size

    @staticmethod
    def eiCastErrorIf(testGen, input_dtype):
        # if input_dtype in [DType.BOOL, DType.FP32]:
        #    outputDType = [DType.BOOL, DType.INT48, DType.FP32]
        if input_dtype in [DType.BOOL]:
            outputDType = [
                DType.BOOL,
                DType.INT48,
                DType.FP32,
                DType.FP16,
                DType.BF16,
                DType.FP8E4M3,
                DType.FP8E5M2,
            ]
        elif input_dtype in [DType.FP32]:
            outputDType = [DType.BOOL, DType.INT48, DType.FP32]
        elif input_dtype in [DType.FP16, DType.BF16]:
            outputDType = [DType.BOOL, DType.INT48]
        elif input_dtype in [DType.INT8, DType.INT16, DType.INT32]:
            outputDType = [DType.INT48]
        elif input_dtype in [DType.FP8E4M3, DType.FP8E5M2]:
            outputDType = [
                DType.BOOL,
                DType.INT8,
                DType.INT16,
                DType.INT32,
                DType.INT48,
            ]
        else:
            assert False, f"input_dtype ({input_dtype}) not supported"
        return outputDType


class TosaErrorValidator:
    @staticmethod
    def evValidateErrorIfs(serializer, validator_fcns, error_name, **kwargs):
        """Check ERROR_IF statements are caught and set the expected result.

        Args:
            serializer: the serializer to set the expected result in
            validator_fcns: a sequence of validator functions to verify the result
            error_name: the name of the ERROR_IF condition to check for
            kwargs: keyword arguments for the validator functions
        Returns:
            True if the result matches the expected result; otherwise False
        """
        if validator_fcns is None:
            # Nothing to do
            return True
        overall_result = True
        for val_fcn in validator_fcns:
            val_result = val_fcn(True, **kwargs)
            validator_name = val_result["error_name"]
            error_result = val_result["error_result"]
            error_reason = val_result["error_reason"]

            # expect an error IFF the error_name and validator_name match
            expected_result = error_result == (error_name == validator_name)
            overall_result &= expected_result

            if expected_result and error_result:
                serializer.setExpectedReturnCode(2, True, desc=error_reason)
            elif error_result:  # and not expected_result
                logger.error(
                    f"Unexpected ERROR_IF: Op: {valueToName(Op, kwargs['op']['op'])}"
                    f" Expected: {error_name}, Got: {validator_name}"
                )
            elif not expected_result:  # and not error_result
                logger.error(
                    f"Missed ERROR_IF: Op: {valueToName(Op, kwargs['op']['op'])}"
                    f" Expected: {error_name}"
                )

            if not expected_result:
                for k, v in sorted(kwargs.items()):
                    if k != "op":
                        if k.endswith("dtype"):
                            v = valueToName(DType, v)
                        logger.error(f"  {k} = {v}")

        return overall_result

    @staticmethod
    def evWrongInputType(check=False, **kwargs):
        error_result = False

        # Find the unsupported input data types
        op = kwargs["op"]
        input_dtypes = op["types"]
        allowed_input_dtypes = {
            t[0] if isinstance(t, list) else t for t in input_dtypes
        }
        wrong_input_dtypes = list(usableDTypes(excludes=allowed_input_dtypes))

        # Turn the wrong dtypes into required list of types
        if op["op"] in [
            Op.FULLY_CONNECTED,
            Op.CONV2D,
            Op.CONV3D,
            Op.DEPTHWISE_CONV2D,
            Op.TRANSPOSE_CONV2D,
        ]:
            wrong_input_dtypes = [[t, t, t] for t in wrong_input_dtypes]

        if op["op"] == Op.CLAMP:
            wrong_input_dtypes.remove(DType.INT48)

        if check:
            input_dtype = kwargs["input_dtype"]
            if input_dtype not in allowed_input_dtypes:
                error_result = True

        info_dict = {
            "error_name": ErrorIf.WrongInputType,
            "error_result": error_result,
            "error_reason": "Input data type not supported for this operator",
            "param_reqs": {"rank": None, "dtype": wrong_input_dtypes, "shape": None},
        }
        return info_dict

    @staticmethod
    def evWrongOutputType(check=False, **kwargs):
        error_result = False

        if check:
            input_dtype = kwargs["input_dtype"]
            output_dtype = kwargs["output_dtype"]
            op = kwargs["op"]

            if op["op"] == Op.RESIZE:
                mode = kwargs["mode"]
                if (
                    (
                        mode == ResizeMode.NEAREST
                        and input_dtype == DType.INT8
                        and output_dtype != DType.INT8
                    )
                    or (
                        mode == ResizeMode.NEAREST
                        and input_dtype == DType.INT16
                        and output_dtype != DType.INT16
                    )
                    or (
                        mode == ResizeMode.BILINEAR
                        and input_dtype == DType.INT8
                        and output_dtype != DType.INT32
                    )
                    or (
                        mode == ResizeMode.BILINEAR
                        and input_dtype == DType.INT16
                        and output_dtype != DType.INT48
                    )
                    or (input_dtype == DType.FP16 and output_dtype != DType.FP16)
                    or (input_dtype == DType.BF16 and output_dtype != DType.BF16)
                    or (input_dtype == DType.FP32 and output_dtype != DType.FP32)
                ):
                    error_result = True

            elif op["op"] == Op.RESCALE:
                error_result = TosaErrorIfArgGen.eiRescaleWrongOutputType(
                    input_dtype, output_dtype
                )

            elif op["op"] in [Op.FULLY_CONNECTED, Op.MATMUL]:
                if (
                    (input_dtype == DType.INT8 and output_dtype != DType.INT32)
                    or (input_dtype == DType.INT16 and output_dtype != DType.INT48)
                    or (
                        input_dtype == DType.FP16
                        and output_dtype not in (DType.FP16, DType.FP32)
                    )
                    or (input_dtype == DType.BF16 and output_dtype != DType.FP32)
                    or (input_dtype == DType.FP32 and output_dtype != DType.FP32)
                    or (input_dtype == DType.FP8E4M3 and output_dtype != DType.FP16)
                    or (input_dtype == DType.FP8E5M2 and output_dtype != DType.FP16)
                ):
                    error_result = True

            elif op["op"] == Op.ARGMAX:
                if (
                    input_dtype
                    in [
                        DType.INT8,
                        DType.INT16,
                        DType.FP16,
                        DType.BF16,
                        DType.FP32,
                        DType.FP8E4M3,
                        DType.FP8E5M2,
                    ]
                    and output_dtype != DType.INT32
                ):
                    error_result = True

            elif op["op"] == Op.MUL:
                if (
                    input_dtype not in (DType.FP16, DType.BF16, DType.FP32)
                    and output_dtype != DType.INT32
                ):
                    error_result = True
                elif input_dtype == DType.FP16 and output_dtype != DType.FP16:
                    error_result = True
                elif input_dtype == DType.BF16 and output_dtype != DType.BF16:
                    error_result = True
                elif input_dtype == DType.FP32 and output_dtype != DType.FP32:
                    error_result = True

            elif op["op"] == Op.TABLE:
                if input_dtype == DType.INT8 and output_dtype != DType.INT8:
                    error_result = True
                elif input_dtype == DType.INT16 and output_dtype != DType.INT32:
                    error_result = True

            elif op["op"] in [Op.EQUAL, Op.GREATER_EQUAL, Op.GREATER]:
                if output_dtype != DType.BOOL:
                    error_result = True

            elif op["op"] == Op.CAST:
                if (
                    (
                        input_dtype == DType.BOOL
                        and output_dtype not in [DType.INT8, DType.INT16, DType.INT32]
                    )
                    or (
                        input_dtype == DType.INT8
                        and output_dtype
                        not in [
                            DType.BOOL,
                            DType.INT16,
                            DType.INT32,
                            DType.FP32,
                            DType.FP16,
                            DType.BF16,
                        ]
                    )
                    or (
                        input_dtype == DType.INT16
                        and output_dtype
                        not in [
                            DType.BOOL,
                            DType.INT8,
                            DType.INT32,
                            DType.FP32,
                            DType.FP16,
                            DType.BF16,
                        ]
                    )
                    or (
                        input_dtype == DType.INT32
                        and output_dtype
                        not in [
                            DType.BOOL,
                            DType.INT8,
                            DType.INT16,
                            DType.FP32,
                            DType.FP16,
                            DType.BF16,
                        ]
                    )
                    or (
                        input_dtype == DType.FP16
                        and output_dtype
                        not in [
                            DType.INT8,
                            DType.INT16,
                            DType.INT32,
                            DType.FP32,
                            DType.FP8E4M3,
                            DType.FP8E5M2,
                        ]
                    )
                    or (
                        input_dtype == DType.BF16
                        and output_dtype
                        not in [
                            DType.INT8,
                            DType.INT16,
                            DType.INT32,
                            DType.FP32,
                            DType.FP8E4M3,
                            DType.FP8E5M2,
                        ]
                    )
                    or (
                        input_dtype == DType.FP32
                        and output_dtype
                        not in [
                            DType.INT8,
                            DType.INT16,
                            DType.INT32,
                            DType.FP16,
                            DType.BF16,
                            DType.FP8E4M3,
                            DType.FP8E5M2,
                        ]
                    )
                    or (
                        input_dtype in [DType.FP8E4M3, DType.FP8E5M2]
                        and output_dtype
                        not in [
                            DType.FP16,
                            DType.BF16,
                            DType.FP32,
                        ]
                    )
                ):
                    error_result = True

            elif op["op"] in [Op.FFT2D, Op.RFFT2D]:
                if not all([ty == input_dtype for ty in output_dtype]):
                    error_result = True

            elif op["op"] in {
                Op.CONV2D,
                Op.CONV3D,
                Op.DEPTHWISE_CONV2D,
                Op.TRANSPOSE_CONV2D,
            }:
                if (
                    input_dtype == DType.INT8
                    and output_dtype != DType.INT32
                    or input_dtype == DType.INT16
                    and output_dtype != DType.INT48
                    or input_dtype == DType.FP16
                    and output_dtype not in (DType.FP16, DType.FP32)
                    or input_dtype == DType.BF16
                    and output_dtype != DType.FP32
                    or input_dtype == DType.FP32
                    and output_dtype != DType.FP32
                    or input_dtype == DType.FP8E4M3
                    and output_dtype != DType.FP16
                    or input_dtype == DType.FP8E5M2
                    and output_dtype != DType.FP16
                ):
                    error_result = True
                # invalid input types are ignored, to avoid reporting multiple errors

            elif op["op"] in {Op.ADD_SHAPE, Op.SUB_SHAPE, Op.MUL_SHAPE, Op.DIV_SHAPE}:
                if output_dtype != DType.SHAPE:
                    error_result = True

            else:
                if output_dtype != input_dtype:
                    error_result = True

        info_dict = {
            "error_name": ErrorIf.WrongOutputType,
            "error_result": error_result,
            "error_reason": (
                "Output data type not supported for this configuration of operator"
            ),
            "param_reqs": {"rank": None, "dtype": None, "shape": None},
        }
        return info_dict

    @staticmethod
    def evWrongRank(check=False, **kwargs):
        all_ranks = (1, 2, 3, 4, 5)

        # Make a list of incorrect ranks
        assert "op" in kwargs
        op = kwargs["op"]
        rmin, rmax = op["rank"]
        rank_range = range(rmin, rmax + 1)
        incorrect_ranks = list(set(all_ranks) - set(rank_range))
        # Remove small incorrect ranks to avoid index errors
        incorrect_ranks = [rank for rank in incorrect_ranks if rank > rmin]
        # Set minimum incorrect rank to 3 to avoid index error
        if op["op"] in [Op.RESIZE]:
            incorrect_ranks = [3, 5]
        elif op["op"] in [Op.TRANSPOSE]:
            incorrect_ranks = [7, 8]
        elif op["op"] in [Op.CONV3D]:
            incorrect_ranks = [6, 7]

        error_name = ErrorIf.WrongRank
        param_reqs = {"rank": incorrect_ranks, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Rank not supported for this operator"

        if check:
            input_shape = kwargs["input_shape"]

            if (
                op["op"] in [Op.RESIZE, Op.AVG_POOL2D, Op.MAX_POOL2D]
                and len(input_shape) != 4
            ):
                error_result = True
            elif op["op"] == Op.FULLY_CONNECTED and len(input_shape) != 2:
                error_result = True
            elif op["op"] == Op.MATMUL and len(input_shape) != 3:
                error_result = True
            else:
                if len(input_shape) not in rank_range:
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evWrongInputList(check=False, **kwargs):
        error_name = ErrorIf.WrongInputList
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Op input list does not match expected input"

        if check:
            input_list = kwargs["input_list"]
            num_operands = kwargs["num_operands"]
            if len(input_list) != num_operands:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evWrongOutputList(check=False, **kwargs):
        error_name = ErrorIf.WrongOutputList
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Op output list does not match expected output"

        if check:
            op = kwargs["op"]
            output_list = kwargs["output_list"]
            expected_length = 1
            if op["op"] in [Op.FFT2D, Op.RFFT2D]:
                expected_length = 2

            if len(output_list) != expected_length:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evMaxDimExceeded(check=False, **kwargs):
        error_name = ErrorIf.MaxDimExceeded
        param_reqs = {
            "rank": [4, 4],
            "dtype": [DType.INT8],
            "shape": [[1, 16584, 5, 1], [1, 2, 16499, 4]],
        }
        error_result = False
        error_reason = f"At least one maximum dimension is greater than or equal to {MAX_RESIZE_DIMENSION}"

        if check:
            input_shape = kwargs["input_shape"]
            output_shape = kwargs["output_shape"]
            if (
                (input_shape[1] >= MAX_RESIZE_DIMENSION)
                or (input_shape[2] >= MAX_RESIZE_DIMENSION)
                or (output_shape[1] >= MAX_RESIZE_DIMENSION)
                or (output_shape[2] >= MAX_RESIZE_DIMENSION)
            ):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evBatchMismatch(check=False, **kwargs):
        error_name = ErrorIf.BatchMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input batch size not equal to output batch size"

        assert "op" in kwargs
        op = kwargs["op"]
        rmin, rmax = op["rank"]
        rank_range = range(rmin, rmax + 1)

        if check:
            input_shape = kwargs["input_shape"]

            for output in kwargs["result_tensors"]:
                output_shape = (
                    output.shape
                )  # Note batch is expected to be the first dim
                if (len(input_shape) in rank_range) and (
                    input_shape[0] != output_shape[0]
                ):
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evChannelMismatch(check=False, **kwargs):
        error_name = ErrorIf.ChannelMismatch
        param_reqs = {"rank": [4, 4], "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input channel size not equal to output channel size"

        assert "op" in kwargs
        op = kwargs["op"]
        rmin, rmax = op["rank"]
        rank_range = range(rmin, rmax + 1)

        if check:
            input_shape = kwargs["input_shape"]
            for output in kwargs["result_tensors"]:
                output_shape = output.shape  # Note this is just (N, OH, OW, C)
                if (len(input_shape) in rank_range) and (
                    input_shape[3] != output_shape[3]
                ):
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evScaleSmallerEqualZero(check=False, **kwargs):
        error_name = ErrorIf.ScaleSmallerEqualZero
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Scale value smaller than or equal zero"

        if check:
            scale = kwargs["scale"]

            if min(scale) <= 0:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evScaleNLargerMax(check=False, **kwargs):
        error_name = ErrorIf.ScaleNLargerMax
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Scale N value larger than maximum value"

        if check:
            scale = kwargs["scale"]

            if scale[0] > (1 << 11) or scale[2] > (1 << 11):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evScaleDLargerMax(check=False, **kwargs):
        error_name = ErrorIf.ScaleDLargerMax
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Scale D value larger than maximum value"

        if check:
            scale = kwargs["scale"]

            if (scale[0] > 0 and scale[1] >= (16 * scale[0])) or (
                scale[2] > 0 and scale[3] >= (16 * scale[2])
            ):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evOffsetSmallerMin(check=False, **kwargs):
        error_name = ErrorIf.OffsetSmallerMin
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Offset value smaller than minimum value"

        if check:
            scale = kwargs["scale"]
            offset = kwargs["offset"]

            if scale[0] > 0 and scale[0] <= (1 << 11) and (offset[0] < -scale[0]):
                error_result = True
            elif scale[2] > 0 and scale[2] <= (1 << 11) and (offset[1] < -scale[2]):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evOffsetLargerEqualMax(check=False, **kwargs):
        error_name = ErrorIf.OffsetLargerEqualMax
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Offset value larger than or equal to maximum value"

        if check:
            scale = kwargs["scale"]
            offset = kwargs["offset"]

            if scale[0] > 0 and scale[0] <= (1 << 11) and (offset[0] >= 16 * scale[0]):
                error_result = True
            elif (
                scale[2] > 0 and scale[2] <= (1 << 11) and (offset[1] >= 16 * scale[2])
            ):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evBorderSmallerMin(check=False, **kwargs):
        error_name = ErrorIf.BorderSmallerMin
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Border value smaller than minimum value"

        if check:
            scale = kwargs["scale"]
            border = kwargs["border"]

            if (
                scale[0] > 0
                and scale[0] <= (1 << 11)
                and (border[0] < (-16 * scale[0]))
            ):
                error_result = True
            elif (
                scale[2] > 0
                and scale[2] <= (1 << 11)
                and (border[1] < (-16 * scale[2]))
            ):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evBorderLargerEqualMax(check=False, **kwargs):
        error_name = ErrorIf.BorderLargerEqualMax
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Border value larger than or equal to maximum value"

        if check:
            scale = kwargs["scale"]
            border = kwargs["border"]

            if scale[0] > 0 and scale[0] <= (1 << 11) and (border[0] >= scale[0]):
                error_result = True
            elif scale[2] > 0 and scale[2] <= (1 << 11) and (border[1] >= scale[2]):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def checkResizeParams(scale, offset, border):
        return (
            min(scale) > 0
            and max(scale[0], scale[2]) <= (1 << 11)
            and scale[1] < 16 * scale[0]
            and scale[3] < 16 * scale[2]
            and offset[0] >= -scale[0]
            and offset[1] >= -scale[2]
            and offset[0] < 16 * scale[0]
            and offset[1] < 16 * scale[2]
            and border[0] >= -16 * scale[0]
            and border[1] >= -16 * scale[2]
            and border[0] < scale[0]
            and border[1] < scale[2]
        )

    @staticmethod
    def evResizeOutputShapeMismatch(check=False, **kwargs):
        error_name = ErrorIf.ResizeOutputShapeMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = (
            "Mismatch between output shape provided and expected output shape"
        )

        if check:
            input_shape = kwargs["input_shape"]
            output_shape = kwargs["output_shape"]
            scale = kwargs["scale"]
            offset = kwargs["offset"]
            border = kwargs["border"]

            # Ensure parameters are valid
            params_valid = TosaErrorValidator.checkResizeParams(scale, offset, border)

            if (
                params_valid
                and max(output_shape) < MAX_RESIZE_DIMENSION
                and max(input_shape) < MAX_RESIZE_DIMENSION
            ):
                output_y = (
                    (input_shape[1] - 1) * scale[0] - offset[0] + border[0]
                ) // scale[1] + 1
                output_x = (
                    (input_shape[2] - 1) * scale[2] - offset[1] + border[1]
                ) // scale[3] + 1

                if [output_y, output_x] != output_shape[1:-1]:
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evResizeOutputShapeNonInteger(check=False, **kwargs):
        error_name = ErrorIf.ResizeOutputShapeNonInteger
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Parameters do not yield exact integer output dimensions"

        if check:
            input_shape = kwargs["input_shape"]
            scale = kwargs["scale"]
            offset = kwargs["offset"]
            border = kwargs["border"]

            # Ensure parameters are valid
            params_valid = TosaErrorValidator.checkResizeParams(scale, offset, border)

            if params_valid:
                remainder_y = (
                    (input_shape[1] - 1) * scale[0] - offset[0] + border[0]
                ) % scale[1]
                remainder_x = (
                    (input_shape[2] - 1) * scale[2] - offset[1] + border[1]
                ) % scale[3]

                if max(remainder_y, remainder_x) > 0:
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evRankMismatch(check=False, **kwargs):
        error_name = ErrorIf.RankMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input Rank does not match output rank"

        if check:
            input1_shape = kwargs["input1"].shape
            input2_shape = (
                kwargs["input2"].shape if "input2" in kwargs else input1_shape
            )
            # In case of SELECT op
            input3_shape = (
                kwargs["input3"].shape if "input3" in kwargs else input2_shape
            )

            for output in kwargs["result_tensors"]:
                output_shape = output.shape
                if (
                    (len(input1_shape) != len(output_shape))
                    or (len(input2_shape) != len(output_shape))
                    or (len(input3_shape) != len(output_shape))
                ):
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evDimensionMismatch(check=False, **kwargs):
        error_name = ErrorIf.DimensionMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input Dimensions do not match output"

        if check:
            input1_shape = kwargs["input1"].shape
            input2_shape = kwargs["input2"].shape
            # In case of SELECT op
            input3_shape = (
                kwargs["input3"].shape if "input3" in kwargs else input2_shape
            )

            op = kwargs["op"]
            if op["op"] in (Op.ADD_SHAPE, Op.SUB_SHAPE, Op.MUL_SHAPE, Op.DIV_SHAPE):
                output_shape = kwargs["result_tensors"][0].shape
                if input1_shape != output_shape:
                    error_result = True

            elif len(input1_shape) == len(input2_shape) == len(input3_shape):
                calculated_shape = TosaErrorValidator.calculateBroadcastShape(
                    input3_shape,
                    TosaErrorValidator.calculateBroadcastShape(
                        input1_shape, input2_shape
                    ),
                )
                if calculated_shape is not None:
                    # Valid inputs - check for output mismatch
                    for output in kwargs["result_tensors"]:
                        output_shape = output.shape
                        if calculated_shape != output_shape:
                            error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def _getZeroPoint(qinfo, index):
        """Return zero point value from quantization info.

        Generally input_zp is index 0, output_zp is index 1
        """
        return qinfo[index]

    @staticmethod
    def evInputZeroPointNotZero(check=False, **kwargs):
        op = kwargs["op"]
        error_result = False

        # Quantizable types
        qTypes = (DType.INT8, DType.UINT8, DType.UINT16)

        # This does not apply to quantizable types
        inputDtypes = [
            dtype
            for dtype in op["types"]
            if (isinstance(dtype, list) and dtype[0] not in qTypes)
            or (not isinstance(dtype, list) and dtype not in qTypes)
        ]

        if check:
            input_dtype = kwargs["input_dtype"]
            input_zero_point = TosaErrorValidator._getZeroPoint(kwargs["qinfo"], 0)
            if op["op"] == Op.MATMUL:
                input2_zero_point = TosaErrorValidator._getZeroPoint(kwargs["qinfo"], 1)
                for dtype, zp in (
                    (kwargs["input_dtype"], input_zero_point),
                    (kwargs["input2_dtype"], input2_zero_point),
                ):
                    if dtype not in qTypes and zp != 0:
                        error_result = True
                        break
            else:
                error_result = input_dtype not in qTypes and input_zero_point != 0

        info_dict = {
            "error_name": ErrorIf.InputZeroPointNotZero,
            "error_result": error_result,
            "error_reason": "Input DType not INT8 and zero point not 0",
            "param_reqs": {"rank": None, "dtype": inputDtypes, "shape": None},
        }
        return info_dict

    @staticmethod
    def evWeightZeroPointNotZero(check=False, **kwargs):
        op = kwargs["op"]

        # exclude inputs with INT8 weights
        inputDtypes = [
            t for t in op["types"] if not isinstance(t, list) or t[1] != DType.INT8
        ]

        error_name = ErrorIf.WeightZeroPointNotZero
        param_reqs = {"rank": None, "dtype": inputDtypes, "shape": None}
        error_result = False
        error_reason = "Weight DType not INT8 and zero point not 0"

        if check:
            weight_dtype = kwargs["weight_dtype"]
            weight_zero_point = TosaErrorValidator._getZeroPoint(kwargs["qinfo"], 1)
            if weight_dtype != DType.INT8 and weight_zero_point != 0:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evOutputZeroPointNotZero(check=False, **kwargs):
        op = kwargs["op"]
        inputDtypes = [
            t for t in op["types"] if t not in [DType.INT8, DType.UINT8, DType.UINT16]
        ]

        error_name = ErrorIf.OutputZeroPointNotZero
        param_reqs = {"rank": None, "dtype": inputDtypes, "shape": None}
        error_result = False
        error_reason = "Output DType not INT8 and zero point not 0"

        if check:
            input_dtype = kwargs["input_dtype"]
            output_dtype = kwargs["output_dtype"]
            output_zero_point = TosaErrorValidator._getZeroPoint(kwargs["qinfo"], 1)
            if op["op"] == Op.AVG_POOL2D:
                if input_dtype != DType.INT8 and output_zero_point != 0:
                    error_result = True
            elif (
                output_dtype not in [DType.INT8, DType.UINT8, DType.UINT16]
                and output_zero_point != 0
            ):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evU16InputZeroPointNotValid(check=False, **kwargs):
        error_name = ErrorIf.U16InputZeroPointNotValid
        param_reqs = {"rank": None, "dtype": [DType.UINT16], "shape": None}
        error_result = False
        error_reason = "Input DType is UINT16 and zero point not 0 or 32678"

        if check:
            input_dtype = kwargs["input_dtype"]
            input_zero_point = TosaErrorValidator._getZeroPoint(kwargs["qinfo"], 0)
            error_result = input_dtype == DType.UINT16 and input_zero_point not in [
                0,
                32768,
            ]

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evU16OutputZeroPointNotValid(check=False, **kwargs):
        error_name = ErrorIf.U16OutputZeroPointNotValid
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Output DType is UINT16 and zero point not 0 or 32678"

        if check:
            output_dtype = kwargs["output_dtype"]
            output_zero_point = TosaErrorValidator._getZeroPoint(kwargs["qinfo"], 1)

            error_result = output_dtype == DType.UINT16 and output_zero_point not in [
                0,
                32768,
            ]

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evAxisSmallerZero(check=False, **kwargs):
        error_name = ErrorIf.AxisSmallerZero
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Axis smaller than zero"

        if check:
            axis = kwargs["axis"]
            if axis < 0:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evAxisLargerRank(check=False, **kwargs):
        error_name = ErrorIf.AxisLargerRank
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Axis larger than rank"

        if check:
            axis = kwargs["axis"]
            shape = kwargs["input_shape"]
            if axis > len(shape):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evShapeOfAxisNotOne(check=False, **kwargs):
        error_name = ErrorIf.ShapeOfAxisNotOne
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "shape[axis] is not equal to 1"

        if check:
            axis = kwargs["axis"]
            shape = kwargs["output_shape"]
            if (0 <= axis < len(shape)) and shape[axis] != 1:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evPadSmallerZero(check=False, **kwargs):
        error_name = ErrorIf.PadSmallerZero
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "At least one pad is smaller than zero"

        if check:
            op = kwargs["op"]
            pad = kwargs["pad"]
            if op["op"] == Op.PAD:
                for padding in pad:
                    if min(padding) < 0:
                        error_result = True
            else:
                if min(pad) < 0:
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evPadLargerEqualKernel(check=False, **kwargs):
        error_name = ErrorIf.PadLargerEqualKernel
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "At least one pad is larger than kernel dimension"

        if check:
            pad = kwargs["pad"]
            op = kwargs["op"]
            if op["op"] == Op.TRANSPOSE_CONV2D:
                # transpose_conv2d
                kernel = kwargs["weight_shape"][1:-1]
                if (
                    pad[0] <= -kernel[0]
                    or pad[1] <= -kernel[0]
                    or pad[2] <= -kernel[1]
                    or pad[3] <= -kernel[1]
                ):
                    error_result = True
            else:
                # pooling op
                kernel = kwargs["kernel"]
                if min(pad) > 0 and min(kernel) > 1:
                    if (
                        pad[0] >= kernel[0]
                        or pad[1] >= kernel[0]
                        or pad[2] >= kernel[1]
                        or pad[3] >= kernel[1]
                    ):
                        error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evPadOutputShapeMismatch(check=False, **kwargs):
        error_name = ErrorIf.PadOutputShapeMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Pad output shape mismatch for requested padding"

        if check:
            pad = kwargs["pad"]
            input_shape = kwargs["input_shape"]
            output_shape = kwargs["output_shape"]
            for dim, padding in enumerate(pad):
                expected_size = input_shape[dim] + padding[0] + padding[1]
                if expected_size != output_shape[dim]:
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def checkPoolingParams(kernel, stride, pad):
        return (
            min(kernel) >= 1
            and min(stride) >= 1
            and min(pad) >= 0
            and not (
                pad[0] >= kernel[0]
                or pad[1] >= kernel[0]
                or pad[2] >= kernel[1]
                or pad[3] >= kernel[1]
            )
        )

    @staticmethod
    def evPoolingOutputShapeMismatch(check=False, **kwargs):
        error_name = ErrorIf.PoolingOutputShapeMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = (
            "Mismatch between output shape provided and expected output shape"
        )

        if check:
            pad = kwargs["pad"]
            pad_top, pad_bottom, pad_left, pad_right = pad[0], pad[1], pad[2], pad[3]

            kernel = kwargs["kernel"]
            kernel_y, kernel_x = kernel[0], kernel[1]

            input_shape = kwargs["input_shape"]
            IH, IW = input_shape[1], input_shape[2]

            output_shape = kwargs["output_shape"]
            OH, OW = output_shape[1], output_shape[2]

            stride = kwargs["stride"]
            stride_y, stride_x = stride[0], stride[1]

            # calculate correct height, width dimensions
            if stride_x != 0 and stride_y != 0:
                y_correct = ((IH + pad_top + pad_bottom - kernel_y) // stride_y) + 1
                x_correct = ((IW + pad_left + pad_right - kernel_x) // stride_x) + 1

            # ensure parameters are valid
            params_valid = TosaErrorValidator.checkPoolingParams(kernel, stride, pad)

            if params_valid and (OH != y_correct or OW != x_correct):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evPoolingOutputShapeNonInteger(check=False, **kwargs):
        error_name = ErrorIf.PoolingOutputShapeNonInteger
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Parameters do not yield exact integer output dimensions"

        if check:
            pad = kwargs["pad"]
            pad_top, pad_bottom, pad_left, pad_right = pad[0], pad[1], pad[2], pad[3]

            kernel = kwargs["kernel"]
            kernel_y, kernel_x = kernel[0], kernel[1]

            input_shape = kwargs["input_shape"]
            IH, IW = input_shape[1], input_shape[2]

            stride = kwargs["stride"]
            stride_y, stride_x = stride[0], stride[1]

            # calculate remainder of height, width dimensions
            if stride_x != 0 and stride_y != 0:
                y_remainder = (IH + pad_top + pad_bottom - kernel_y) % stride_y
                x_remainder = (IW + pad_left + pad_right - kernel_x) % stride_x

            # ensure parameters are valid
            params_valid = TosaErrorValidator.checkPoolingParams(kernel, stride, pad)
            if params_valid and (y_remainder != 0 or x_remainder != 0):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def checkConvParams(op, weight_shape, stride, pad, dilation):
        if op == Op.TRANSPOSE_CONV2D:
            pad_ok = (
                pad[0] > -weight_shape[1]
                and pad[1] > -weight_shape[1]
                and pad[2] > -weight_shape[2]
                and pad[3] > -weight_shape[2]
            )
        else:
            pad_ok = min(pad) >= 0

        return (
            # Check kernel sizes
            min(weight_shape[1:-1]) >= 1
            and min(stride) >= 1
            and pad_ok
            and (dilation is None or min(dilation) >= 1)
        )

    @staticmethod
    def evConvOutputShapeMismatch(check=False, **kwargs):
        error_name = ErrorIf.ConvOutputShapeMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = (
            "Mismatch between output shape provided and expected output shape"
        )

        if check:
            op = kwargs["op"]
            pad = kwargs["pad"]
            weight_shape = kwargs["weight_shape"]
            input_shape = kwargs["input_shape"]
            output_shape = kwargs["output_shape"]
            dilation = kwargs["dilation"] if op["op"] != Op.TRANSPOSE_CONV2D else None
            stride = kwargs["stride"]

            kernel_offset = 0 if op["op"] == Op.DEPTHWISE_CONV2D else 1

            # calculate correct dimensions
            dims_correct = []
            if min(stride) > 0:
                for index in range(len(stride)):
                    pad_offset = index * 2
                    if op["op"] == Op.TRANSPOSE_CONV2D:
                        dims_correct.append(
                            (input_shape[index + 1] - 1) * stride[index]
                            + pad[pad_offset]
                            + pad[pad_offset + 1]
                            + weight_shape[index + kernel_offset]
                        )
                    else:
                        dims_correct.append(
                            (
                                input_shape[index + 1]
                                - 1
                                + pad[pad_offset]
                                + pad[pad_offset + 1]
                                - (weight_shape[index + kernel_offset] - 1)
                                * dilation[index]
                            )
                            // stride[index]
                            + 1
                        )

            # ensure parameters are valid
            params_valid = TosaErrorValidator.checkConvParams(
                op["op"], weight_shape, stride, pad, dilation
            )

            if params_valid and output_shape[1:-1] != dims_correct:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evConvOutputShapeNonInteger(check=False, **kwargs):
        error_name = ErrorIf.ConvOutputShapeNonInteger
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Parameters do not yield exact integer output dimensions"

        if check:
            op = kwargs["op"]
            pad = kwargs["pad"]
            weight_shape = kwargs["weight_shape"]
            input_shape = kwargs["input_shape"]
            dilation = kwargs["dilation"]
            stride = kwargs["stride"]

            kernel_offset = 0 if op["op"] == Op.DEPTHWISE_CONV2D else 1

            # calculate correct height, width dimensions
            remainders = []
            if min(stride) > 0:
                for index in range(len(stride)):
                    pad_offset = index * 2
                    remainders.append(
                        (
                            input_shape[index + 1]
                            - 1
                            + pad[pad_offset]
                            + pad[pad_offset + 1]
                            - (weight_shape[index + kernel_offset] - 1)
                            * dilation[index]
                        )
                        % stride[index]
                    )

            # ensure parameters are valid
            params_valid = TosaErrorValidator.checkConvParams(
                op["op"], weight_shape, stride, pad, dilation
            )
            if params_valid and max(remainders) > 0:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evArgmaxOutputShapeMismatch(check=False, **kwargs):
        error_name = ErrorIf.ArgmaxOutputShapeMismatch
        param_reqs = {"rank": [2, 4], "dtype": None, "shape": None}
        error_result = False
        error_reason = (
            "Mismatch between output shape provided and expected output shape"
        )

        if check:
            output_shape = kwargs["output_shape"]
            input_shape = kwargs["input_shape"]
            axis = kwargs["axis"]

            dimension_match = True
            axis_shift = 0

            # Check that rank is correct before trying to check dimensions
            if (len(input_shape) - 1) == len(output_shape):
                for i in range(len(input_shape)):
                    if i == axis:
                        axis_shift = 1
                        continue
                    if input_shape[i] != output_shape[i - axis_shift]:
                        dimension_match = False

                if not dimension_match:
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evArgmaxOutputRankMismatch(check=False, **kwargs):
        error_name = ErrorIf.ArgmaxOutputRankMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = (
            "Mismatch between output shape provided and expected output shape"
        )

        if check:
            output_shape = kwargs["output_shape"]
            input_shape = kwargs["input_shape"]
            axis = kwargs["axis"]
            valid_params = axis >= 0 and axis < len(input_shape)

            if valid_params and (len(input_shape) - 1) != len(output_shape):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evKernelSmallerOne(check=False, **kwargs):
        error_name = ErrorIf.KernelSmallerOne
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "At least one kernel dimension is smaller than zero"

        if check:
            kernel = kwargs["kernel"]
            if min(kernel) < 1:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evStrideSmallerOne(check=False, **kwargs):
        error_name = ErrorIf.StrideSmallerOne
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "At least one stride dimension is smaller than zero"

        if check:
            stride = kwargs["stride"]
            if min(stride) < 1:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evDilationSmallerOne(check=False, **kwargs):
        error_result = check and min(kwargs["dilation"]) < 1
        return {
            "error_name": ErrorIf.DilationSmallerOne,
            "error_reason": "At least one dilation is smaller than one",
            "param_reqs": {"rank": None, "dtype": None, "shape": None},
            "error_result": error_result,
        }

    @staticmethod
    def evScaleTrue(check=False, **kwargs):
        error_name = ErrorIf.ScaleTrue
        param_reqs = {"rank": None, "dtype": [DType.INT48], "shape": None}
        error_result = False
        error_reason = "Scale set to true but input type is INT48"

        if check:
            input_dtype = kwargs["input_dtype"]
            scale32 = kwargs["scale32"]
            if scale32 and input_dtype == DType.INT48:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evScaleNotTrue(check=False, **kwargs):
        error_name = ErrorIf.ScaleNotTrue
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Scale set to false but double round set to true"

        if check:
            scale32 = kwargs["scale32"]
            double_round = kwargs["double_round"]
            if not scale32 and double_round:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evTensorSizeInputOutputMismatch(check=False, **kwargs):
        error_name = ErrorIf.TensorSizeInputOutputMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input tensor size does not match output tensor size"
        op = kwargs["op"]

        if check:
            input_shape = kwargs["input_shape"]
            output_shape = kwargs["output_shape"]
            shape_inferencing = False
            if -1 in output_shape and op["op"] == Op.RESHAPE:
                shape_inferencing = True
            input_size = np.prod(input_shape)
            output_size = np.prod(output_shape)
            if input_size != output_size and not shape_inferencing:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evStartSmallerZero(check=False, **kwargs):
        error_name = ErrorIf.StartSmallerZero
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Starting point smaller than zero"

        if check:
            input_shape = kwargs["input_shape"]
            start = kwargs["start"]
            rank = len(input_shape)
            if len(start) == rank:
                for index in range(rank):
                    if start[index] < 0:
                        error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evSizeSmallerEqualZero(check=False, **kwargs):
        error_name = ErrorIf.SizeSmallerEqualZero
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Size smaller than or equal to zero"

        if check:
            input_shape = kwargs["input_shape"]
            size = kwargs["size"]
            rank = len(input_shape)
            if len(size) == rank:
                for index in range(rank):
                    if size[index] <= 0:
                        error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evStartSizeOutsideBounds(check=False, **kwargs):
        error_name = ErrorIf.StartSizeOutsideBounds
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "starting point plus size larger than input dimension"

        if check:
            input_shape = kwargs["input_shape"]
            start = kwargs["start"]
            size = kwargs["size"]
            rank = len(input_shape)
            if len(start) == rank and len(size) == rank:
                for index in range(rank):
                    if start[index] + size[index] > input_shape[index]:
                        error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evSizeOutputShapeMismatch(check=False, **kwargs):
        error_name = ErrorIf.SizeOutputShapeMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Size does not match output dimension"

        if check:
            input_shape = kwargs["input_shape"]
            output_shape = kwargs["output_shape"]
            size = kwargs["size"]

            if len(input_shape) == len(output_shape):
                rank = len(input_shape)
                if len(size) == rank:
                    for index in range(rank):
                        if size[index] != output_shape[index]:
                            error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evInputSizeStartLengthMismatch(check=False, **kwargs):
        error_name = ErrorIf.InputSizeStartLengthMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "rank of input not equal to length of start or size"

        if check:
            input_shape = kwargs["input_shape"]
            start = kwargs["start"]
            size = kwargs["size"]
            rank = len(input_shape)
            if rank != len(start) or rank != len(size):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evIndexOutsideBounds(check=False, **kwargs):
        error_name = ErrorIf.IndexOutsideBounds
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Index outside of allowed bounds"

        if check:
            input_shape = kwargs["input_shape"]
            perms = kwargs["perms"]
            rank = len(input_shape)

            for index in perms:
                if index < 0 or index > rank:
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evIndexUsedTwice(check=False, **kwargs):
        error_name = ErrorIf.IndexUsedTwice
        param_reqs = {"rank": [2, 4], "dtype": None, "shape": None}
        error_result = False
        error_reason = "Index used multiple times"

        if check:
            perms = kwargs["perms"]

            unique_indices = []
            for index in perms:
                if index in unique_indices:
                    error_result = True
                else:
                    unique_indices.append(index)

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evMaxSmallerMin(check=False, **kwargs):
        error_name = ErrorIf.MaxSmallerMin
        param_reqs = {"rank": [2, 4], "dtype": None, "shape": None}
        error_result = False
        error_reason = "Max value smaller than min value"

        if check:
            max_val = kwargs["max_val"]
            min_val = kwargs["min_val"]
            if max_val < min_val:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evConcatInputRankMismatch(check=False, **kwargs):
        error_name = ErrorIf.ConcatInputRankMismatch
        param_reqs = {"rank": [2, 4], "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input ranks are not identical"

        if check:
            inputs = kwargs["inputs"]
            input_shape = kwargs["input_shape"]
            for input in inputs:
                if len(input.shape) != len(input_shape):
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evConcatInputDimMismatch(check=False, **kwargs):
        error_name = ErrorIf.ConcatInputDimMismatch
        param_reqs = {"rank": [2, 4], "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input dimensions differ on too many axes"

        if check:
            inputs = kwargs["inputs"]
            input_shape = kwargs["input_shape"]
            axis = kwargs["axis"]

            # Ensure rank is valid before checking dims.
            valid_rank = True
            for input in inputs:
                if len(input.shape) != len(input_shape):
                    valid_rank = False

            if valid_rank:
                for input in inputs:
                    for i, dim in enumerate(input.shape):
                        if dim != input_shape[i] and axis != i:
                            error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evConcatShapeSumMismatch(check=False, **kwargs):
        error_name = ErrorIf.ConcatShapeSumMismatch
        param_reqs = {"rank": [2, 4], "dtype": None, "shape": None}
        error_result = False
        error_reason = "Sum of dimensions on axis not equal to output dimension"

        if check:
            inputs = kwargs["inputs"]
            input_shape = kwargs["input_shape"]
            output_shape = kwargs["output_shape"]
            axis = kwargs["axis"]

            # Ensure rank is valid before checking dims.
            valid_params = True
            for input in inputs:
                if len(input.shape) != len(input_shape):
                    valid_params = False
            if axis < 0 or axis > len(input_shape):
                valid_params = False

            if valid_params:
                axis_dim_sum = 0
                for input in inputs:
                    axis_dim_sum += input.shape[axis]

                if axis_dim_sum != output_shape[axis]:
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evInputListThenGraphMismatch(check=False, **kwargs):
        error_name = ErrorIf.CondIfInputListThenGraphMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input list shape does not match then-graph shape"

        if check:
            a = kwargs["a"]
            b = kwargs["b"]
            basicBlocks = kwargs["basicBlocks"]
            then_block = basicBlocks[1]
            then_inputs = then_block.inputs
            then_tens = then_block.tensors
            if (a.shape != then_tens[then_inputs[0]].shape) or (
                b.shape != then_tens[then_inputs[1]].shape
            ):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evInputListElseGraphMismatch(check=False, **kwargs):
        error_name = ErrorIf.CondIfInputListElseGraphMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input list shape does not match else-graph shape"

        if check:
            a = kwargs["a"]
            b = kwargs["b"]
            basicBlocks = kwargs["basicBlocks"]
            else_block = basicBlocks[2]
            else_inputs = else_block.inputs
            else_tens = else_block.tensors
            if (a.shape != else_tens[else_inputs[0]].shape) or (
                b.shape != else_tens[else_inputs[1]].shape
            ):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evOutputListThenGraphMismatch(check=False, **kwargs):
        error_name = ErrorIf.CondIfOutputListThenGraphMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Output list shape does not match then-graph shape"

        if check:
            basicBlocks = kwargs["basicBlocks"]
            cond_block = basicBlocks[0]
            cond_outputs = cond_block.outputs
            cond_tens = cond_block.tensors
            then_block = basicBlocks[1]
            then_outputs = then_block.outputs
            then_tens = then_block.tensors
            if then_tens[then_outputs[0]].shape != cond_tens[cond_outputs[0]].shape:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evOutputListElseGraphMismatch(check=False, **kwargs):
        error_name = ErrorIf.CondIfOutputListElseGraphMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Output list shape does not match else-graph shape"

        if check:
            basicBlocks = kwargs["basicBlocks"]
            cond_block = basicBlocks[0]
            cond_outputs = cond_block.outputs
            cond_tens = cond_block.tensors
            else_block = basicBlocks[2]
            else_outputs = else_block.outputs
            else_tens = else_block.tensors
            if else_tens[else_outputs[0]].shape != cond_tens[cond_outputs[0]].shape:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evCondIfCondNotMatchingBool(check=False, **kwargs):
        error_name = ErrorIf.CondIfCondNotMatchingBool
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Conditional tensor does not match bool type"

        if check:
            cond = kwargs["cond"]
            if cond.dtype != DType.BOOL:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evCondIfCondShapeNotSizeOne(check=False, **kwargs):
        error_name = ErrorIf.CondIfCondShapeNotSizeOne
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Conditional tensor is not equal to a size of one"

        if check:
            cond = kwargs["cond"]
            # Size of 1 is equivalent to rank 0
            if len(cond.shape) != 0:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evInputListOutputListMismatch(check=False, **kwargs):
        error_name = ErrorIf.InputListOutputListMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input list does not match output list"

        if check:
            basicBlocks = kwargs["basicBlocks"]
            while_block = basicBlocks[0]
            while_inputs = while_block.inputs
            while_outputs = while_block.outputs
            while_tens = while_block.tensors
            if while_tens[while_inputs[1]].shape != while_tens[while_outputs[0]].shape:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evInputListCondGraphMismatch(check=False, **kwargs):
        error_name = ErrorIf.InputListCondGraphMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input list does not match cond graph"

        if check:
            basicBlocks = kwargs["basicBlocks"]
            while_block = basicBlocks[0]
            while_inputs = while_block.inputs
            while_tens = while_block.tensors
            cond_block = basicBlocks[1]
            cond_inputs = cond_block.inputs
            cond_tens = cond_block.tensors
            if (
                while_tens[while_inputs[0]].shape != cond_tens[cond_inputs[0]].shape
            ) or (while_tens[while_inputs[1]].shape != cond_tens[cond_inputs[2]].shape):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evInputListBodyGraphInputMismatch(check=False, **kwargs):
        error_name = ErrorIf.InputListBodyGraphInputMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input list does not match body graph input"

        if check:
            basicBlocks = kwargs["basicBlocks"]
            while_block = basicBlocks[0]
            while_inputs = while_block.inputs
            while_tens = while_block.tensors
            body_block = basicBlocks[2]
            body_outputs = body_block.inputs
            body_tens = body_block.tensors
            if (
                while_tens[while_inputs[0]].shape != body_tens[body_outputs[0]].shape
            ) or (
                while_tens[while_inputs[1]].shape != body_tens[body_outputs[2]].shape
            ):
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evInputListBodyGraphOutputMismatch(check=False, **kwargs):
        error_name = ErrorIf.InputListBodyGraphOutputMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Input list does not match body graph output"

        if check:
            basicBlocks = kwargs["basicBlocks"]
            while_block = basicBlocks[0]
            while_inputs = while_block.inputs
            while_tens = while_block.tensors
            body_block = basicBlocks[2]
            body_outputs = body_block.outputs
            body_tens = body_block.tensors
            if (
                while_tens[while_inputs[0]].shape != body_tens[body_outputs[0]].shape
            ) or (
                while_tens[while_inputs[1]].shape != body_tens[body_outputs[2]].shape
            ):
                error_result = True
        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evCondGraphOutputNotMatchingBool(check=False, **kwargs):
        error_name = ErrorIf.CondGraphOutputNotMatchingBool
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Cond graph output is not a match list of booleans"

        if check:
            basicBlocks = kwargs["basicBlocks"]
            cond_block = basicBlocks[1]
            cond_outputs = cond_block.outputs
            cond_tens = cond_block.tensors
            if cond_tens[cond_outputs[0]].dtype != DType.BOOL:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evCondGraphOutputShapeNotSizeOne(check=False, **kwargs):
        error_name = ErrorIf.CondGraphOutputShapeNotSizeOne
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Cond graph output is not a shape of size one"

        if check:
            basicBlocks = kwargs["basicBlocks"]
            cond_block = basicBlocks[1]
            cond_outputs = cond_block.outputs
            cond_tens = cond_block.tensors
            # Size of 1 is equivalent to rank 0
            if len(cond_tens[cond_outputs[0]].shape) != 0:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evKernelNotPowerOfTwo(check=False, **kwargs):
        error_name = ErrorIf.KernelNotPowerOfTwo
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "kernel height and/or width not a power of two"

        def is_power_of_two(x):
            return math.log(x, 2).is_integer()

        if check:
            shape = kwargs["input_shape"]
            if len(shape) == 3:
                valid_kernel = is_power_of_two(shape[1]) and is_power_of_two(shape[2])
                error_result = not valid_kernel

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evFFTInputShapeMismatch(check=False, **kwargs):
        error_name = ErrorIf.FFTInputShapeMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Mismatch between real and imaginary input shapes"

        if check:
            input1 = kwargs["input1"]
            input2 = kwargs["input2"]

            if input1.shape != input2.shape:
                error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def evFFTOutputShapeMismatch(check=False, **kwargs):
        error_name = ErrorIf.FFTOutputShapeMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = (
            "Mismatch between provided and expected output kernel (H, W) shape"
        )

        if check:
            op = kwargs["op"]
            input_shape = kwargs["input_shape"]

            if len(input_shape) == 3:
                output_shapes = kwargs["output_shape"]

                # Ignoring batch size (N) from input shape
                expected_shape = input_shape[1:]
                if op["op"] == Op.RFFT2D:
                    expected_shape[1] = expected_shape[1] // 2 + 1

                # Ignoring batch size (N) from output shapes
                output_shape_0 = output_shapes[0][1:]
                output_shape_1 = output_shapes[1][1:]
                # Ensure sure the kernel sizes (H, W) of both outputs match the expected
                if output_shape_0 != output_shape_1 or output_shape_0 != expected_shape:
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    @staticmethod
    def calculateBroadcastShape(input_shape_a, input_shape_b):
        if input_shape_a is not None and input_shape_b is not None:
            calculated_shape = input_shape_a.copy()
            for idx in range(len(calculated_shape)):
                if calculated_shape[idx] == 1:
                    calculated_shape[idx] = input_shape_b[idx]
                elif (
                    input_shape_b[idx] != 1
                    and input_shape_b[idx] != calculated_shape[idx]
                ):
                    return None
            return calculated_shape
        else:
            return None

    @staticmethod
    def evBroadcastShapesMismatch(check=False, **kwargs):
        error_name = ErrorIf.BroadcastShapesMismatch
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "Broadcast shape calculating failed"

        if check:
            input_shape_a = kwargs["input1"].shape
            input_shape_b = kwargs["input2"].shape
            input_shape_c = (
                kwargs["input3"].shape if "input3" in kwargs else input_shape_b
            )

            if len(input_shape_a) == len(input_shape_b) == len(input_shape_c):
                calculated_shape = TosaErrorValidator.calculateBroadcastShape(
                    input_shape_c,
                    TosaErrorValidator.calculateBroadcastShape(
                        input_shape_a, input_shape_b
                    ),
                )
                error_result = calculated_shape is None

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict

    def evWrongAccumulatorType(check=False, **kwargs):
        error_name = ErrorIf.WrongAccumulatorType
        param_reqs = {"rank": None, "dtype": None, "shape": None}
        error_result = False
        error_reason = "An unsupported accumulator data type was requested"

        if check:
            op = kwargs["op"]
            input_dtype = kwargs["input_dtype"]
            accum_dtype = kwargs["accum_dtype"]
            if op["op"] == Op.AVG_POOL2D:
                if (
                    input_dtype
                    in (
                        DType.INT8,
                        DType.INT16,
                    )
                    and accum_dtype != DType.INT32
                ):
                    error_result = True
                elif (
                    input_dtype
                    in (
                        DType.FP32,
                        DType.BF16,
                    )
                    and accum_dtype != DType.FP32
                ):
                    error_result = True
                elif input_dtype == DType.FP16 and accum_dtype not in (
                    DType.FP16,
                    DType.FP32,
                ):
                    error_result = True
                elif (
                    input_dtype in (DType.FP8E4M3, DType.FP8E5M2)
                    and accum_dtype != DType.FP16
                ):
                    error_result = True

        info_dict = {
            "error_name": error_name,
            "error_result": error_result,
            "error_reason": error_reason,
            "param_reqs": param_reqs,
        }
        return info_dict


class TosaInvalidValidator:
    @staticmethod
    def ivWrongDataTypeOrModeResize(**kwargs):
        input_dtype = kwargs["input_dtype"]
        args_dict = kwargs["args"]
        mode = args_dict["mode"]
        output_dtype = args_dict["output_dtype"]

        if mode == ResizeMode.BILINEAR:
            # Invalid output data type / Invalid input datatype
            return (
                not (input_dtype == DType.INT8 and output_dtype == DType.INT32)
                and not (input_dtype == DType.INT16 and output_dtype == DType.INT48)
                and not (input_dtype == DType.FP16 and output_dtype == DType.FP16)
                and not (input_dtype == DType.BF16 and output_dtype == DType.BF16)
                and not (input_dtype == DType.FP32 and output_dtype == DType.FP32)
            )
        elif mode == ResizeMode.NEAREST:
            # Invalid output data type / Invalid input datatype
            return (input_dtype != output_dtype) or (
                input_dtype
                not in [DType.INT8, DType.INT16, DType.FP16, DType.BF16, DType.FP32]
            )
        else:
            # Invalid resize mode
            return True

    @staticmethod
    def ivHeightWidthInvalid(**kwargs):
        opName = kwargs["opName"]

        inputShapes = kwargs["shapeList"]
        input_shape = inputShapes[0]

        args = kwargs["args"]

        if isinstance(args, dict):
            args_dict = args
        else:
            # Create args_dict from list elements
            # TODO - Remove this once all NWHC operators agFunctions have been
            # converted to args_dict output

            # Skip accum_dtype arg (apart from MaxPool2D that doesn't have one)
            stride_idx, pad_idx = (1, 2) if opName != "max_pool2d" else (0, 1)
            args_dict = {"stride": args[stride_idx], "pad": args[pad_idx]}
            # Alias different info for each op
            args_dict["kernel"] = args[pad_idx + 1]
            args_dict["out_shape"] = args[pad_idx + 1]
            args_dict["dilation"] = args[pad_idx + 1]

        # Common info for all ops
        strides = args_dict["stride"]
        padding = args_dict["pad"]

        if opName.endswith("pool2d"):
            # avg_pool2d, max_pool2d
            kernel_shape = args_dict["kernel"]
            h = (
                input_shape[1] + padding[0] + padding[1] + strides[0] - kernel_shape[0]
            ) // strides[0]
            w = (
                input_shape[2] + padding[2] + padding[3] + strides[1] - kernel_shape[1]
            ) // strides[1]
            # return True if any dimension is < 1
            return h < 1 or w < 1

        if opName.startswith("transpose_conv2d"):
            # transpose_conv2d
            output_shape = args_dict["out_shape"]
            filter_shape = inputShapes[1]
            kernel_shape = filter_shape[1:-1]

            def get_out_size(in_size, stride, kernel_size, out_pad, in_pad):
                """Calculate the transpose_conv2d output size for a dimension."""
                return (in_size - 1) * stride + kernel_size + in_pad + out_pad

            h = get_out_size(
                input_shape[1],
                strides[0],
                kernel_shape[0],
                padding[0],
                padding[1],
            )
            w = get_out_size(
                input_shape[2],
                strides[1],
                kernel_shape[1],
                padding[2],
                padding[3],
            )
            if output_shape[1] == h and output_shape[2] == w:
                return False
            # output shape does not match the expected shape
            return True

        if "conv2d" in opName or "conv3d" in opName:
            # conv2d, conv3d, depthwise_conv2d
            dilations = args_dict["dilation"]
            filter_shape = inputShapes[1]
            kernel_shape = (
                filter_shape[0:2]
                if opName.startswith("depthwise_conv2d")
                else filter_shape[1:-1]
            )

            for i in range(len(kernel_shape)):
                pad_offset = i * 2
                dim = (
                    input_shape[i + 1]
                    - 1
                    + padding[pad_offset]
                    + padding[pad_offset + 1]
                    - (kernel_shape[i] - 1) * dilations[i]
                ) // strides[i] + 1
                # return True if any dimension is < 1
                if dim < 1:
                    return True
            return False

        assert False, f"Unrecognized Op: {opName}"

    @staticmethod
    def ivNonPositiveOutputShape(**kwargs):
        args = kwargs["args"]
        output_shape = args["out_shape"]
        if output_shape[1] <= 0 or output_shape[2] <= 0:
            # Negative output shape
            return True
        return False