verif/generator/tosa_arg_gen.py

# Copyright (c) 2021-2022, ARM Limited.
# SPDX-License-Identifier: Apache-2.0
import itertools
import math

import numpy as np
from generator.tosa_error_if import ErrorIf
from generator.tosa_error_if import TosaErrorIfArgGen
from generator.tosa_utils import MAX_RESIZE_DIMENSION
from serializer.tosa_serializer import DTypeNames
from tosa.DType import DType
from tosa.Op import Op
from tosa.ResizeMode import ResizeMode

# DTypeNames, DType, Op and ResizeMode are convenience variables to the
# flatc-generated types that should be enums, but aren't


class TosaQuantGen:
    """QuantizedInfo random generator helper functions.

    Specify with 'qgen': in the operator defintion.
    """

    def __init__(self):
        pass

    @staticmethod
    def getZeroPoint(testGen, dtype, error_name=None):

        if dtype == DType.INT8:
            return testGen.randInt(-128, 128)
        elif dtype == DType.UINT8:
            return testGen.randInt(0, 256)
        elif error_name in [
            ErrorIf.InputZeroPointNotZero,
            ErrorIf.WeightZeroPointNotZero,
            ErrorIf.OutputZeroPointNotZero,
        ]:
            zero_point = testGen.randInt(-128, 128)
            if zero_point == 0:
                zero_point = 1
            return zero_point
        return 0

    @staticmethod
    def qgUnary(testGen, op, dtype, error_name=None):
        if error_name == ErrorIf.InputZeroPointNotZero:
            qinfo = [
                TosaQuantGen.getZeroPoint(testGen, dtype, error_name),
                TosaQuantGen.getZeroPoint(testGen, dtype),
            ]
        elif error_name == ErrorIf.OutputZeroPointNotZero:
            qinfo = [
                TosaQuantGen.getZeroPoint(testGen, dtype),
                TosaQuantGen.getZeroPoint(testGen, dtype, error_name),
            ]
        else:
            qinfo = [
                TosaQuantGen.getZeroPoint(testGen, dtype),
                TosaQuantGen.getZeroPoint(testGen, dtype),
            ]
        return qinfo

    @staticmethod
    def qgConv(testGen, op, dtype_or_dtypeList, error_name=None):
        if isinstance(dtype_or_dtypeList, list):
            # a list of [input, weights, accumulator] dtypes
            dtypeList = dtype_or_dtypeList
        else:
            # an int, [input, weights, accumulator] dtypes are the same
            dtypeList = [dtype_or_dtypeList] * 3

        if error_name == ErrorIf.InputZeroPointNotZero:
            qinfo = [
                TosaQuantGen.getZeroPoint(testGen, dtypeList[0], error_name),
                TosaQuantGen.getZeroPoint(testGen, dtypeList[1]),
            ]
        elif error_name == ErrorIf.WeightZeroPointNotZero:
            qinfo = [
                TosaQuantGen.getZeroPoint(testGen, dtypeList[0]),
                TosaQuantGen.getZeroPoint(testGen, dtypeList[1], error_name),
            ]
        else:
            qinfo = [
                TosaQuantGen.getZeroPoint(testGen, dtypeList[0]),
                TosaQuantGen.getZeroPoint(testGen, dtypeList[1]),
            ]
        return qinfo

    @staticmethod
    def qgMatmul(testGen, op, dtype, error_name=None):
        if error_name == ErrorIf.InputZeroPointNotZero:
            qinfo = [
                TosaQuantGen.getZeroPoint(testGen, dtype, error_name),
                TosaQuantGen.getZeroPoint(testGen, dtype, error_name),
            ]
        else:
            qinfo = [
                TosaQuantGen.getZeroPoint(testGen, dtype),
                TosaQuantGen.getZeroPoint(testGen, dtype),
            ]
        return qinfo

    @staticmethod
    def computeMultiplierAndShift(scaleFp, scale32):
        # Derived from computeMultiplierAndShiftTosaScale32
        # Provide a floating-point scaling factor and the scale32 parameter
        # to compute the multiplier and shift

        if scale32:
            scaleBits = 31
        else:
            scaleBits = 15

        m, shift = math.frexp(scaleFp)

        if scaleFp < 0.0:
            m = -m

        multiplier = round(m * (1 << scaleBits))
        assert multiplier <= (1 << scaleBits)

        if multiplier == (1 << scaleBits):
            multiplier = multiplier // 2
            shift = shift + 1

        shift = (-shift) + scaleBits
        # print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(
        #   scaleFp, scaleBits, m, multiplier, shift))

        # Adjust multiplier such that shift is in allowed value range.
        if shift == 0:
            multiplier = multiplier // 4
            shift = shift + 2
        elif shift == 1:
            multiplier = multiplier // 2
            shift = shift + 1
        elif shift == 63:
            multiplier = multiplier * 2
            shift = shift - 1

        assert multiplier <= (1 << scaleBits)
        assert shift >= 2 and shift <= 62

        return multiplier, shift


class TosaTensorGen:
    """Tensor generators create a shape list for the placeholder and const tensor
    data operands for the operator.

    The actual random data is generated separately for each test.
    """

    def __init__(self):
        pass

    @staticmethod
    def tgBasic(testGen, opName, rank, error_name=None):
        pl, const = opName["operands"]
        shape = testGen.makeShape(rank)

        # Constrict the overall size of the shape when creating ERROR_IF tests
        if error_name:
            shape = TosaErrorIfArgGen.eiRestrictDimensions(shape)

        shape_list = []
        for i in range(pl + const):
            shape_list.append(shape.copy())

            if error_name == ErrorIf.RankMismatch:
                if rank == 1 and i != 1:
                    shape = testGen.makeShape(rank + testGen.rng.choice([1, 2, 3]))
                elif i != 1:
                    shape = testGen.makeShape(rank + testGen.rng.choice([-1, 1]))

        return shape_list

    @staticmethod
    def tgNHWC(testGen, opName, rank, error_name=None):
        pl, const = opName["operands"]

        if error_name != ErrorIf.WrongRank:
            assert rank == 4

        shape = testGen.makeShape(rank)

        # Constrict the batch size?
        if testGen.args.max_batch_size:
            shape[0] = (shape[0] % testGen.args.max_batch_size) + 1

        # Constrict the overall size of the shape when creating ERROR_IF tests
        if error_name and error_name != ErrorIf.MaxDimExceeded:
            shape = TosaErrorIfArgGen.eiRestrictDimensions(shape)

        shape_list = []
        for i in range(pl + const):
            shape_list.append(shape.copy())

        return shape_list

    @staticmethod
    def tgScatter(testGen, opName, rank, error_name=None):
        pl, const = opName["operands"]

        assert pl == 2
        assert const == 0
        if error_name != ErrorIf.WrongRank:
            assert rank == 3

        values_in_shape = testGen.makeShape(rank)

        # ignore max batch size if target shape is set
        if testGen.args.max_batch_size and not testGen.args.target_shapes:
            values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1

        W = testGen.randInt(
            testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1]
        )
        # Constrict W if one dimension is too large to keep tensor size reasonable
        if max(values_in_shape) > 5000:
            W = testGen.randInt(0, 16)

        input_shape = [values_in_shape[0], W, values_in_shape[2]]

        shape_list = []
        shape_list.append(values_in_shape.copy())
        shape_list.append(input_shape.copy())

        return shape_list

    @staticmethod
    def tgBroadcastFuzz(testGen, op, rank, error_name=None):
        shape = testGen.makeShape(rank)

        pl, const = op["operands"]

        shape_list = []

        # Choose one of the inputs to broadcast
        # Note: Simplifies OutputShaper code if we don't change first shape for errors
        bcast_idx = testGen.randInt(0 if error_name is None else 1, pl + const)
        for i in range(pl + const):
            shape_bcast = shape.copy()

            # If the chosen input, pick a random index to broadcast
            if i == bcast_idx:
                fuzz_idx = testGen.randInt(0, rank)
                if error_name == ErrorIf.DimensionMismatch:
                    shape_bcast[fuzz_idx] += 1
                elif error_name == ErrorIf.RankMismatch:
                    # Add one rank to the shape (or more for rank of 1)
                    extra_ranks = testGen.rng.choice([1, 2, 3]) if rank == 1 else 1
                    shape_bcast = np.concatenate(
                        (shape_bcast, testGen.makeShape(extra_ranks))
                    )
                    if rank != 1:
                        # Either keep the extra rank, or remove it
                        new_len = testGen.rng.choice([-2, len(shape_bcast)])
                        shape_bcast = shape_bcast[:new_len]
                else:
                    shape_bcast[fuzz_idx] = 1

            shape_list.append(shape_bcast)

        return shape_list

    @staticmethod
    def tgConv2D(testGen, op, rank, error_name=None):
        pl, const = op["operands"]

        if error_name != ErrorIf.WrongRank:
            assert rank == 4

        # IFM dimensions are NHWC
        ifm_shape = testGen.makeShape(rank)

        # Constrict the batch size?
        if testGen.args.max_batch_size:
            ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1

        # Constrict the overall size of the shape when creating ERROR_IF tests
        if error_name:
            ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(
                ifm_shape, max_dim=24, max_items=10000
            )

        # Get the filter height/width from the operator parameters
        filter_hw = op["filter"]

        # Generate a random OFM depth
        ofm_depth = testGen.makeShape(1)[0]

        # The filter dimensions are OHWI
        filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]])

        # The bias is OC
        bias_shape = np.asarray([ofm_depth])

        return [ifm_shape, filter_shape, bias_shape]

    @staticmethod
    def tgConv3D(testGen, op, rank, error_name=None):
        pl, const = op["operands"]

        if error_name != ErrorIf.WrongRank:
            assert rank == 5

        # IFM dimensions are NDHWC
        ifm_shape = testGen.makeShape(rank)

        # Constrict the batch size?
        if testGen.args.max_batch_size:
            ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1

        # Constrict the overall size of the shape when creating ERROR_IF tests
        if error_name:
            ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(
                ifm_shape, max_dim=24, max_items=10000
            )

        # Get the filter depth/height/width from the operator parameters
        filter_dhw = op["filter"]

        # Generate a random OFM channel
        ofm_channel = testGen.makeShape(1)[0]

        # The filter dimensions are ODHWI
        filter_shape = np.asarray(
            [ofm_channel, filter_dhw[0], filter_dhw[1], filter_dhw[2], ifm_shape[4]]
        )

        # The bias is OC
        bias_shape = np.asarray([ofm_channel])

        return [ifm_shape, filter_shape, bias_shape]

    @staticmethod
    def tgTransposeConv2D(testGen, op, rank, error_name=None):
        pl, const = op["operands"]

        if error_name != ErrorIf.WrongRank:
            assert rank == 4

        # IFM dimensions are NHWC
        ifm_shape = testGen.makeShape(rank)

        # Constrict the batch size?
        if testGen.args.max_batch_size:
            ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1

        # Constrict the overall size of the shape when creating ERROR_IF tests
        if error_name:
            ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(
                ifm_shape, max_dim=24, max_items=10000
            )

        # Get the filter height/width from the operator parameters
        filter_hw = op["filter"]

        # Generate a random OFM depth
        ofm_depth = testGen.makeShape(1)[0]

        # The filter dimensions are OHWI
        filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]])

        # The bias is OC
        bias_shape = np.asarray([ofm_depth])

        return [ifm_shape, filter_shape, bias_shape]

    @staticmethod
    def tgDepthwiseConv2D(testGen, op, rank, error_name=None):
        pl, const = op["operands"]

        if error_name != ErrorIf.WrongRank:
            assert rank == 4
        assert pl == 1 and const == 2

        # IFM dimensions are NHWC
        ifm_shape = testGen.makeShape(rank)

        # Constrict the batch size?
        if testGen.args.max_batch_size:
            ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1

        # Constrict the overall size of the shape when creating ERROR_IF tests
        if error_name:
            ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(
                ifm_shape, max_dim=24, max_items=10000
            )

        # Get the filter height/width from the operator parameters
        # Filter is KH, HW, C, M
        filter_hw = op["filter"]

        # Generate a random OFM depth, but don't let it get too big because
        # the output depth is M * C
        filter_m = (
            testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4)
        ) + 1

        # The filter dimensions are HWCM
        filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m])

        # The bias is M * C
        bias_shape = np.asarray([ifm_shape[3] * filter_m])

        return [ifm_shape, filter_shape, bias_shape]

    @staticmethod
    def tgFullyConnected(testGen, op, rank, error_name=None):
        pl, const = op["operands"]

        if error_name != ErrorIf.WrongRank:
            assert rank == 2

        input_shape = testGen.makeShape(rank)

        # Constrict the overall size of the shape when creating ERROR_IF tests
        if error_name:
            input_shape = TosaErrorIfArgGen.eiRestrictDimensions(input_shape)

        filter_oc = testGen.rng.integers(
            low=testGen.args.tensor_shape_range[0],
            high=testGen.args.tensor_shape_range[1],
            size=1,
        )[0]
        filter_shape = np.asarray([filter_oc, input_shape[1]])

        bias_shape = np.asarray([filter_oc])

        return [input_shape, filter_shape, bias_shape]

    @staticmethod
    def tgMatmul(testGen, op, rank, error_name=None):
        pl, const = op["operands"]

        if error_name != ErrorIf.WrongRank:
            assert rank == 3
        assert pl == 2 and const == 0

        a_shape = testGen.makeShape(rank)

        # Constrict the overall size of the shape when creating ERROR_IF tests
        if error_name:
            a_shape = TosaErrorIfArgGen.eiRestrictDimensions(a_shape)

        # Get a random number for b_oc even if target shape is defined
        b_oc = np.int32(
            testGen.rng.integers(
                low=testGen.args.tensor_shape_range[0],
                high=testGen.args.tensor_shape_range[1],
                size=1,
            )
        )[0]
        # If N or H is large let b_oc be 1 to reduce output tensor size
        if max(a_shape) > 1000:
            b_oc = 1

        b_shape = np.asarray([a_shape[0], a_shape[2], b_oc])
        return [a_shape, b_shape]

    @staticmethod
    def tgConcat(testGen, opName, rank, error_name=None):
        pl, const = opName["operands"]
        shape = testGen.makeShape(rank)

        # Create extra tensors to concat.
        # Take into account value of pl when getting maximum number of concats
        num_tensors = testGen.randInt(0, 4)
        shape_list = []
        for i in range(pl + const + num_tensors):
            if error_name == ErrorIf.ConcatInputRankMismatch and i != 0:
                remove = testGen.rng.choice([True, False])
                wrongShape = shape.copy()

                if remove and len(shape) > 1:
                    wrongShape = wrongShape[1:]
                else:
                    wrongShape = list(wrongShape)
                    wrongShape.append(testGen.rng.integers(1, 10))

                shape_list.append(wrongShape)
            else:
                shape_list.append(shape.copy())

        return shape_list

    @staticmethod
    def tgConcatConstInput(testGen, shapeList, axis, error_name=None):
        if error_name in [
            ErrorIf.AxisSmallerZero,
            ErrorIf.AxisLargerRank,
            ErrorIf.ConcatInputRankMismatch,
        ]:
            return shapeList

        # Split concat shape along axis to allow for multiple const inputs
        # without making too many large tensors
        if len(shapeList) == 2 or shapeList[0][axis] < len(shapeList):
            # If axis can't be split we still need to invalidate other dimensions
            if error_name == ErrorIf.ConcatInputDimMismatch:
                for shape in shapeList[1:]:
                    # Negative test shapeLists are created individually for each test,
                    # so no need to copy the shape before altering it.
                    shape[(axis + 1) % len(shape)] += testGen.rng.integers(5, 10)
            return shapeList

        # Create copy of shape we are going to split (so we don't alter shapeList)
        shape = shapeList[0].copy()
        # Add original shape as first input
        new_shapeList = [shape.copy()]
        length_on_axis = shape[axis]
        remaining_length = length_on_axis
        for i in range(len(shapeList) - 2):
            # Calculate split on axis and remaining value
            split_shape_val = int(shape[axis] / 2)
            remaining_length = remaining_length - split_shape_val

            # Append new shape, and set remaining shape
            shape[axis] = split_shape_val
            new_shapeList.append(shape.copy())

            # invalidate dimensions
            if error_name == ErrorIf.ConcatInputDimMismatch:
                shape[(axis + 1) % len(shape)] += testGen.rng.integers(5, 10)
            else:
                shape[axis] = remaining_length

            if i == len(shapeList) - 3:
                new_shapeList.append(shape.copy())

        return new_shapeList


class TosaTensorValuesGen:
    """Tensor Value generators create the random data for each test."""

    def __init__(self):
        pass

    @staticmethod
    def tvgDefault(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        pCount, cCount = op["operands"]

        tens = []
        tens.extend(
            testGen.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount])
        )
        tens.extend(testGen.buildConstTensors(shapeList[pCount:], dtypeList[pCount:]))

        return tens

    @staticmethod
    def tvgNegate(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        if dtypeList[0] == DType.INT32 and error_name is None:
            pCount, cCount = op["operands"]
            assert (
                pCount == 1 and cCount == 0
            ), "Op.NEGATE must have 1 placeholders, 0 consts"
            # Must create tensors with values within accumulator (int32) negatable
            # range
            max_val = (1 << 31) - 1
            min_val = -max_val
            arr = np.int32(
                testGen.rng.integers(low=min_val, high=(max_val + 1), size=shapeList[0])
            )
            placeholders = []
            placeholders.append(
                testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], arr)
            )
            return placeholders
        else:
            return TosaTensorValuesGen.tvgDefault(
                testGen, op, dtypeList, shapeList, testArgs, error_name
            )

    @staticmethod
    def tvgAddSub(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        if dtypeList[0] == DType.INT32 and error_name is None:
            # Make sure the operation does not cause value saturation - where
            # the number wraps due to limited number of bits to store the answer
            pCount, cCount = op["operands"]
            assert (
                pCount == 2 and cCount == 0
            ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts"
            placeholders = []
            add = op["op"] == Op.ADD
            a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
            b_arr = testGen.getRandTensor(shapeList[1], dtypeList[1])
            if add:
                res_arr = np.add(a_arr, b_arr, dtype=np.int64)
            else:
                res_arr = np.subtract(a_arr, b_arr, dtype=np.int64)

            # Work out the saturation limits
            max_i32 = (1 << 31) - 1
            min_i32 = -(1 << 31)
            max_arr = np.full(shapeList[1], max_i32)
            min_arr = np.full(shapeList[1], min_i32)

            # Find how much values exceed the maximum/minimums
            sat_max_arr = np.maximum(res_arr - max_arr, 0)
            sat_min_arr = np.minimum(res_arr - min_arr, 0)

            if not add:
                # Swap saturation values and negate values as we need to perform opposite operations
                sat_max_arr, sat_min_arr = -sat_min_arr, -sat_max_arr

            # Create new array of unsaturated values by clipping values as needed
            b_unsat_arr = b_arr
            if (sat_max_arr != 0).any():
                # Clip values that cause saturation
                b_unsat_arr = np.subtract(b_unsat_arr, sat_max_arr, dtype=np.int32)
                # Reduce axes in unsaturated tensor to match original tensor
                for axis, dim in enumerate(b_arr.shape):
                    if dim != b_unsat_arr.shape[axis]:
                        assert (
                            dim == 1
                        ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable"
                        b_unsat_arr = np.amin(b_unsat_arr, axis=axis, keepdims=True)

            if (sat_min_arr != 0).any():
                # Clip values that cause saturation
                b_unsat_arr = np.subtract(b_unsat_arr, sat_min_arr, dtype=np.int32)
                # Reduce axes in unsaturated tensor to match original tensor
                for axis, dim in enumerate(b_arr.shape):
                    if dim != b_unsat_arr.shape[axis]:
                        assert (
                            dim == 1
                        ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable"
                        b_unsat_arr = np.amax(b_unsat_arr, axis=axis, keepdims=True)

            placeholders.append(
                testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr)
            )
            placeholders.append(
                testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr)
            )

            return placeholders
        else:
            return TosaTensorValuesGen.tvgDefault(
                testGen, op, dtypeList, shapeList, testArgs, error_name
            )

    @staticmethod
    def tvgCondIfWhileLoop(
        testGen, op, dtypeList, shapeList, testArgs, error_name=None
    ):
        if dtypeList[0] in (
            DType.INT32,
            DType.INT16,
            DType.INT8,
        ):
            # Limit input tensors with cond_if_binary or while_loop to stop
            # saturation of add/sub ops with int32 and keep all logical shift
            # values between 0 to 31 for int16 or int8
            pCount, cCount = op["operands"]
            pRemain = pCount
            placeholders = []
            for idx, shape in enumerate(shapeList[:]):
                if dtypeList[0] == DType.INT32:
                    arr = testGen.getRandTensor(shapeList[idx], DType.INT16)
                else:
                    arr = np.int32(
                        testGen.rng.integers(low=0, high=32, size=shapeList[idx])
                    )
                if pRemain > 0:
                    placeholders.append(
                        testGen.ser.addPlaceholder(shape, dtypeList[idx], arr)
                    )
                    pRemain -= 1
                else:
                    placeholders.append(
                        testGen.ser.addConst(shape, dtypeList[idx], arr)
                    )

            return placeholders
        else:
            return TosaTensorValuesGen.tvgDefault(
                testGen, op, dtypeList, shapeList, testArgs, error_name
            )

    @staticmethod
    def tvgArithmeticRightShift(
        testGen, op, dtypeList, shapeList, testArgs, error_name=None
    ):
        pCount, cCount = op["operands"]
        # Force value of operand[1] to be within [0, num_bits]
        assert (
            pCount == 2 and cCount == 0
        ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts"

        placeholders = []
        for idx, shape in enumerate(shapeList[:]):
            if idx == 1:
                if dtypeList[idx] == DType.INT8:
                    arr = np.int32(testGen.rng.integers(low=0, high=8, size=shape))
                elif dtypeList[idx] == DType.INT16:
                    arr = np.int32(testGen.rng.integers(low=0, high=16, size=shape))
                elif dtypeList[idx] == DType.INT32:
                    arr = np.int32(testGen.rng.integers(low=0, high=32, size=shape))
                elif error_name == ErrorIf.WrongInputType:
                    arr = np.int32(testGen.rng.integers(low=0, high=8, size=shape))
                else:
                    raise Exception("OpArithmeticRightShift: invalid input dtype")
            else:
                arr = testGen.getRandTensor(shape, dtypeList[idx])
            placeholders.append(testGen.ser.addPlaceholder(shape, dtypeList[idx], arr))

        return placeholders

    @staticmethod
    def tvgSelect(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        # Set datatype of condition tensor to boolean
        dtypeList[0] = DType.BOOL

        return TosaTensorValuesGen.tvgDefault(
            testGen, op, dtypeList, shapeList, testArgs, error_name
        )

    @staticmethod
    def tvgIntDiv(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        if error_name is None:
            pCount, cCount = op["operands"]
            assert (
                pCount == 2 and cCount == 0
            ), "Op.INTDIV must have 2 placeholders, 0 consts"

            placeholders = []

            # Two invalid cases for Op.INTDIV:
            # 1. divisor == 0
            # 2. dividend == -(1<<31) and divisor == -1
            while True:
                dividend_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
                divisor_arr = testGen.getRandTensor(shapeList[1], dtypeList[1])

                if (divisor_arr == 0).any():
                    continue

                if (dividend_arr == -(2**31)).any() and (divisor_arr == -1).any():
                    continue

                break

            placeholders.append(
                testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr)
            )
            placeholders.append(
                testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr)
            )

            return placeholders
        else:
            return TosaTensorValuesGen.tvgDefault(
                testGen, op, dtypeList, shapeList, testArgs, error_name
            )

    @staticmethod
    def tvgMul(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        if error_name is None:
            pCount, cCount = op["operands"]
            assert (
                pCount == 2 and cCount == 0
            ), "Op.MUL must have 2 placeholders, 0 consts"

            tens = []
            if dtypeList[0] == DType.FLOAT:
                tens.extend(testGen.buildPlaceholderTensors(shapeList[:], dtypeList[:]))
            else:
                placeholders = []

                # Make sure multiply result in int32 range
                shift = testArgs[0]
                if dtypeList[0] == DType.INT8:
                    num_bits = 8
                elif dtypeList[0] == DType.INT16:
                    num_bits = 16
                elif dtypeList[0] == DType.INT32:
                    num_bits = 32
                elif error_name == ErrorIf.WrongInputType:
                    num_bits = 8
                else:
                    raise Exception("OpMul: invalid input dtype")

                for idx, shape in enumerate(shapeList[:]):
                    low = -(2 ** (num_bits - 1))
                    high = (2 ** (num_bits - 1)) - 1

                    a_arr = np.int32(
                        testGen.rng.integers(low=low, high=high, size=shapeList[0])
                    )
                    b_arr = np.int32(
                        testGen.rng.integers(low=low, high=high, size=shapeList[1])
                    )

                i = 0
                while True:

                    a_arr_64 = a_arr.astype(np.int64)
                    b_arr_64 = b_arr.astype(np.int64)

                    if shift > 0:
                        rounding = 1 << (shift - 1)
                        result_arr = ((a_arr_64 * b_arr_64) + rounding) >> shift
                    else:
                        result_arr = a_arr_64 * b_arr_64

                    if (result_arr > -(2**31)).all() and (
                        result_arr <= ((2**31) - 1)
                    ).all():
                        break

                    i = i + 1
                    a_arr = a_arr // 2
                    b_arr = b_arr // 2

                placeholders.append(
                    testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr)
                )
                placeholders.append(
                    testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr)
                )

                tens.extend(placeholders)

            return tens
        else:
            return TosaTensorValuesGen.tvgDefault(
                testGen, op, dtypeList, shapeList, testArgs, error_name
            )

    @staticmethod
    def tvgConcat(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        count = len(shapeList) - testGen.args.num_const_inputs_concat
        if count < 1:
            count = 1
        if testGen.args.num_const_inputs_concat == 0:
            count = len(shapeList)

        # Ensure axis is an int
        testArgs[0] = int(testArgs[0])

        shapeList = TosaTensorGen.tgConcatConstInput(
            testGen, shapeList, testArgs[0], error_name
        )

        tens = []
        tens.extend(
            testGen.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count])
        )
        tens.extend(testGen.buildConstTensors(shapeList[count:], dtypeList[count:]))

        return tens

    @staticmethod
    def tvgLogicalShift(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        pCount, cCount = op["operands"]
        assert (
            pCount == 2 and cCount == 0
        ), "Op.LOGICAL_LEFT_SHIFT or Op.LOGICAL_RIGHT_SHIFT must have 2 placeholders, 0 consts"
        values_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
        shift_arr = np.int32(testGen.rng.integers(low=0, high=32, size=shapeList[1]))
        placeholders = []
        placeholders.append(
            testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr)
        )
        placeholders.append(
            testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], shift_arr)
        )

        return placeholders

    @staticmethod
    def tvgEqual(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        if error_name is None:
            pCount, cCount = op["operands"]
            assert (
                pCount == 2 and cCount == 0
            ), "Op.EQUAL must have 2 placeholders, 0 consts"
            a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
            b_arr = testGen.getRandTensor(shapeList[1], dtypeList[1])
            # Using random numbers means that it will be very unlikely that
            # there are any matching (equal) values, therefore force that
            # there are twice the number of matching values as the tensor rank
            for num in range(0, len(shapeList[0]) * 2):
                a_index = []
                b_index = []
                # Choose an index in each axis for the whole shape
                for axis in range(0, len(shapeList[0])):
                    # Index can be up to the largest dimension in both shapes
                    index = np.int32(
                        testGen.rng.integers(
                            0, max(shapeList[0][axis], shapeList[1][axis])
                        )
                    )
                    # Reduce the index down to a shape's dim for broadcasting
                    a_index.append(min(shapeList[0][axis] - 1, index))
                    b_index.append(min(shapeList[1][axis] - 1, index))

                a_arr[tuple(a_index)] = b_arr[tuple(b_index)]

            placeholders = []
            placeholders.append(
                testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr)
            )
            placeholders.append(
                testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr)
            )
            return placeholders
        else:
            return TosaTensorValuesGen.tvgDefault(
                testGen, op, dtypeList, shapeList, testArgs, error_name
            )

    @staticmethod
    def tvgReduceSum(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
        if dtypeList[0] == DType.INT32:
            pCount, cCount = op["operands"]
            assert (
                pCount == 1 and cCount == 0
            ), "Op.REDUCE_SUM must have 1 placeholders, 0 consts"
            # Limit values so that the sum cannot exceed the range of an int32 during
            # summation of any axis
            range_val = int((1 << 31) / max(shapeList[0]))
            values_arr = np.int32(
                testGen.rng.integers(low=-range_val, high=range_val, size=shapeList[0])
            )
            placeholders = []
            placeholders.append(
                testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr)
            )
            return placeholders
        else:
            return TosaTensorValuesGen.tvgDefault(
                testGen, op, dtypeList, shapeList, testArgs, error_name
            )


class TosaArgGen:
    """Argument generators create exhaustive or random lists of attributes for
    operators that take attributes or other parameters.

    The return value is a list of (descriptive_name, [arglist]) tuples where
    the descriptive_name is appended to the test name and the arglist is expanded
    as arguments to the operator build function.
    """

    def __init__(self):
        pass

    @staticmethod
    def agNone(testGen, opName, shapeList, dtype, error_name=None):
        """A trivial argument generator for operators that don't take any
        non-tensor arguments"""
        return [("", [])]

    @staticmethod
    def agAxis(testGen, opName, shapeList, dtype, error_name=None):
        """Build the axis argument for operators that take a single axis"""
        axes = []
        shape = shapeList[0]

        if error_name == ErrorIf.AxisSmallerZero:
            small_axis = testGen.rng.integers(-5, 0)
            axes.append(("axis{}".format(small_axis), [small_axis]))
        elif error_name == ErrorIf.AxisLargerRank:
            large_axis = testGen.rng.integers(len(shape) + 1, len(shape) + 10)
            axes.append(("axis{}".format(large_axis), [large_axis]))
        else:
            for a in range(0, len(shape)):
                axes.append(("axis{}".format(a), [a]))

        return axes

    @staticmethod
    def agConv(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        ifm_shape = shapeList[0]
        filter_shape = shapeList[1]
        # determine the kernel shape from operator name (e.g. "conv2d_3x3" => [3,3])
        k = [int(x) for x in opName.split("_")[-1].split("x")]

        # Check the rank
        rank = 5 if opName.startswith("conv3d") else 4
        if error_name != ErrorIf.WrongRank:
            assert len(ifm_shape) == rank
            assert len(filter_shape) == rank

        # kernel rank omits batch and channels
        k_rank = rank - 2
        assert len(k) == k_rank

        # Generate comprehensive argument lists
        # - except for named errors, which use specific invalid value(s)
        if error_name == ErrorIf.PadSmallerZero:
            p_vals = [testGen.rng.choice(range(-5, 0))]
        else:
            p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)]
        paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))}
        if error_name == ErrorIf.StrideSmallerOne:
            # Can't use stride=0, as it is used to derive output shape, as a divisor
            s_vals = [testGen.rng.choice(range(-5, 0))]
        else:
            # Stride must be greater than 1 to force non-integer error
            startStride = 1 if error_name != ErrorIf.PoolingOutputShapeNonInteger else 2
            s_vals = [x for x in range(startStride, testGen.args.max_conv_stride + 1)]
        strides = {x for x in itertools.product(*([s_vals] * k_rank))}
        if error_name == ErrorIf.DilationSmallerOne:
            d_vals = [testGen.rng.choice(range(-5, 1))]
        else:
            d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)]
        dilations = {x for x in itertools.product(*([d_vals] * k_rank))}

        if not error_name and testGen.args.oversize:
            # add some oversize argument values
            if max(ifm_shape) < 64:
                bigPadding = 9
                paddings.update(
                    {x for x in itertools.product(*([[0, bigPadding]] * (k_rank * 2)))}
                )
            bigStride = 8
            strides.update({x for x in itertools.product(*([[1, bigStride]] * k_rank))})
            bigDilation = 7
            dilations.update(
                {x for x in itertools.product(*([[1, bigDilation]] * k_rank))}
            )

        # There are too many parameter combinations, so generate them sparsely,
        # very sparse for negative tests
        sparsity_factor = 2 if error_name else 120
        sparsity = len(paddings) * len(strides) * len(dilations) // sparsity_factor + 1
        # If there are only a small number of tests, just select them all
        if sparsity < 13:
            sparsity = 1
        # To get a variety of parameter combinations sparsity should not be a
        # multiple of 2, 3 or 5
        while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0:
            sparsity += 1

        n = 0
        for s in sorted(list(strides)):
            for p in sorted(list(paddings)):
                for d in sorted(list(dilations)):
                    if (
                        n % sparsity == 0
                        # padding must not exceed the kernel size ?
                        # and p[0] < k[0] and p[1] < k[0]
                        # and p[2] < k[1] and p[3] < k[1]
                        # and (k_rank < 3 or (p[4] < k[2] and p[5] < k[2]))
                        # the padded shape must exceed the kernel size
                        and (ifm_shape[1] + p[0] + p[1]) > k[0]
                        and (ifm_shape[2] + p[2] + p[3]) > k[1]
                        and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > k[2]))
                        # the padded shape must exceed the dilation
                        and (ifm_shape[1] + p[0] + p[1]) > d[0]
                        and (ifm_shape[2] + p[2] + p[3]) > d[1]
                        and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > d[2]))
                    ):
                        remainders = []
                        for index in range(k_rank):
                            pad_offset = index * 2
                            remainders.append(
                                (
                                    ifm_shape[index + 1]
                                    - 1
                                    + p[pad_offset]
                                    + p[pad_offset + 1]
                                    - (k[index] - 1) * d[index]
                                )
                                % s[index]
                            )
                        if (
                            # the parameters must produce integer exact output
                            error_name != ErrorIf.ConvOutputShapeNonInteger
                            and max(remainders) == 0
                        ) or (
                            error_name == ErrorIf.ConvOutputShapeNonInteger
                            and max(remainders) > 0
                        ):
                            arg_list.append(
                                (
                                    "st{}_pad{}_dilat{}".format(
                                        "".join([str(x) for x in s]),
                                        "".join([str(x) for x in p]),
                                        "".join([str(x) for x in d]),
                                    ),
                                    [s, p, d],
                                )
                            )
                    n += 1

        return arg_list

    @staticmethod
    def agTransposeConv2D(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        ifm_shape = shapeList[0]
        filter_shape = shapeList[1]

        # Must be rank 4
        if error_name != ErrorIf.WrongRank:
            assert len(ifm_shape) == 4
            assert len(filter_shape) == 4

        # Generate comprehensive argument lists
        # - except for named errors, which use specific invalid value(s)
        if error_name == ErrorIf.PadSmallerZero:
            p_vals = [testGen.rng.choice(range(-5, 0))]
        else:
            p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)]
        paddings = {x for x in itertools.product(*([p_vals] * 4))}
        if error_name == ErrorIf.StrideSmallerOne:
            # Can't use stride=0, as it is used to derive output shape, as a divisor
            s_vals = [testGen.rng.choice(range(-5, 0))]
        else:
            s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)]
        strides = {x for x in itertools.product(*([s_vals] * 2))}

        if not error_name and testGen.args.oversize:
            # add some oversize argument values
            if max(ifm_shape) < 64:
                bigPadding = 9
                paddings.update(
                    {x for x in itertools.product(*([[0, bigPadding]] * 4))}
                )
            bigStride = 8
            strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))})

        # There are too many parameter combinations, so generate them sparsely,
        # very sparse for negative tests
        sparsity_factor = 2 if error_name else 10
        sparsity = len(paddings) * len(strides) // sparsity_factor + 1
        # If there are only a small number of tests, just select them all
        if sparsity < 13:
            sparsity = 1
        # To get a variety of parameter combinations sparsity should not be a
        # multiple of 2, 3 or 5
        while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0:
            sparsity += 1

        n = 0
        for s in sorted(list(strides)):
            for p in sorted(list(paddings)):
                if n % sparsity == 0:
                    # Determine the output shape
                    oh = (ifm_shape[1] - 1) * s[0] - p[0] - p[1] + filter_shape[1]
                    ow = (ifm_shape[2] - 1) * s[1] - p[2] - p[3] + filter_shape[2]
                    os = [ifm_shape[0], oh, ow, filter_shape[0]]
                    arg_list.append(
                        (
                            "st{}_pad{}_os{}".format(
                                "".join([str(x) for x in s]),
                                "".join([str(x) for x in p]),
                                "x".join([str(x) for x in os]),
                            ),
                            [s, p, os],
                        )
                    )
                n += 1

        return arg_list

    @staticmethod
    def agPad(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []
        rank = len(shapeList[0])

        # Exhaustively test combinations of padding on each side of each dimension
        # - the range of padding values is defined by pad_min and pad_max
        # - for padding >9, the name format needs to be more distinctive
        pad_min, pad_max = 0, 1
        pad_values = [x for x in range(pad_min, pad_max + 1)]
        if error_name == ErrorIf.PadSmallerZero:
            pad_values = [x for x in range(-2, 0)]
        axis_pad_values = [x for x in itertools.product(pad_values, pad_values)]
        shape_pad_values = itertools.product(*([axis_pad_values] * rank))

        if dtype in [DType.BOOL, DType.INT8, DType.INT16, DType.INT32]:
            pad_const_int = testGen.getRandNumberDType(dtype)
            pad_const_fp = 0
        elif dtype == DType.FLOAT:
            pad_const_int = 0
            pad_const_fp = testGen.getRandNumberDType(dtype)
        else:
            return []

        for paddings in shape_pad_values:
            name = "pad"
            for r in range(rank):
                before, after = paddings[r]
                name = f"{name}{before}{after}"
            arg_list.append((name, [np.array(paddings), pad_const_int, pad_const_fp]))

        return arg_list

    @staticmethod
    def agPooling(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        shape = shapeList[0]
        if error_name != ErrorIf.WrongRank:
            assert len(shape) == 4

        # Generate comprehensive argument lists
        p_vals = [x for x in range(0, testGen.args.max_pooling_padding + 1)]
        paddings = {x for x in itertools.product(*([p_vals] * 4))}
        # Stride must be greater than 1 to force non-integer error
        startStride = 1 if error_name != ErrorIf.PoolingOutputShapeNonInteger else 2
        s_vals = [x for x in range(startStride, testGen.args.max_pooling_stride + 1)]
        strides = {x for x in itertools.product(*([s_vals] * 2))}
        k_vals = [x for x in range(2, testGen.args.max_pooling_kernel + 1)]
        kernels = {x for x in itertools.product(*([k_vals] * 2))}

        if testGen.args.oversize:
            # add some oversize argument values
            bigStride = 7
            strides.update(
                {x for x in itertools.product(*([[startStride, bigStride]] * 2))}
            )
            bigKernel = 9
            kernels.update({x for x in itertools.product(*([[2, bigKernel]] * 2))})
            if max(shape) < 64:
                # padding must be less than the kernel size
                bigPadding = bigKernel - 1
                paddings.update(
                    {x for x in itertools.product(*([[0, bigPadding]] * 4))}
                )

        # There are too many parameter combinations, so generate them sparsely,
        # very sparse for negative tests
        sparsity_factor = 2 if error_name else 500
        sparsity = len(paddings) * len(strides) * len(kernels) // sparsity_factor + 1

        n = 0
        for s in sorted(list(strides)):
            for p in sorted(list(paddings)):
                for k in sorted(list(kernels)):
                    if error_name in [
                        ErrorIf.StrideSmallerOne,
                        ErrorIf.KernelSmallerOne,
                        ErrorIf.PadSmallerZero,
                        ErrorIf.PadLargerEqualKernel,
                    ]:
                        sNew, pNew, kNew = TosaErrorIfArgGen.eiPoolingErrorIf(
                            testGen, error_name, s, p, k
                        )
                        if None not in [sNew, pNew, kNew] and n % sparsity == 0:
                            arg_list.append(
                                (
                                    "st{}_kern{}_pad{}".format(
                                        "".join([str(x) for x in sNew]),
                                        "".join([str(x) for x in kNew]),
                                        "".join([str(x) for x in pNew]),
                                    ),
                                    [sNew, pNew, kNew],
                                )
                            )
                    elif (
                        n % sparsity == 0
                        # padding must not exceed the kernel size
                        and p[0] < k[0]
                        and p[1] < k[0]
                        and p[2] < k[1]
                        and p[3] < k[1]
                        # the padded shape must exceed the kernel size
                        and (shape[1] + p[0] + p[1]) > k[0]
                        and (shape[2] + p[2] + p[3]) > k[1]
                    ):
                        remainder_h = (shape[1] + p[0] + p[1] - k[0]) % s[0]
                        remainder_w = (shape[2] + p[2] + p[3] - k[1]) % s[1]
                        if (
                            # the parameters must produce integer exact output
                            error_name != ErrorIf.PoolingOutputShapeNonInteger
                            and remainder_h == 0
                            and remainder_w == 0
                        ) or (
                            error_name == ErrorIf.PoolingOutputShapeNonInteger
                            and (remainder_h != 0 or remainder_w != 0)
                        ):
                            arg_list.append(
                                (
                                    "st{}_kern{}_pad{}".format(
                                        "".join([str(x) for x in s]),
                                        "".join([str(x) for x in k]),
                                        "".join([str(x) for x in p]),
                                    ),
                                    [s, p, k],
                                )
                            )
                    n += 1

        return arg_list

    @staticmethod
    def agCast(testGen, opName, shapeList, inDtype, error_name=None):
        arg_list = []

        # Enumerate the output types here
        if error_name == ErrorIf.WrongOutputType:
            dtypeList = TosaErrorIfArgGen.eiCastErrorIf(testGen, inDtype)
        elif inDtype == DType.INT8:
            dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT]
        elif inDtype == DType.INT16:
            dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT]
        elif inDtype == DType.INT32:
            dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT]
        elif inDtype == DType.BOOL:
            dtypeList = [DType.INT8, DType.INT16, DType.INT32]
        elif inDtype == DType.FLOAT:
            dtypeList = [DType.INT8, DType.INT16, DType.INT32]
        elif error_name == ErrorIf.WrongInputType:
            # Pick some potentially correct output type for incorrect input type
            dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT]
        else:
            raise Exception("Unexpected input dtype: {}".format(inDtype))

        for dtype in dtypeList:
            arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype]))

        return arg_list

    @staticmethod
    def agRescale(testGen, opName, shapeList, inDtype, error_name=None):
        arg_list = []

        # Enumerate the output types here
        for outDtype in [
            DType.UINT8,
            DType.INT8,
            DType.INT16,
            DType.INT32,
            DType.UINT16,
        ]:
            if (
                outDtype in [DType.UINT8, DType.INT8, DType.UINT16]
                and error_name == ErrorIf.OutputZeroPointNotZero
            ):
                continue
            if (
                outDtype != DType.UINT16
                and error_name == ErrorIf.U16OutputZeroPointNotValid
            ) or (
                inDtype != DType.UINT16
                and error_name == ErrorIf.U16InputZeroPointNotValid
            ):
                # ErrorIfs only valid with UINT16
                continue
            if (
                inDtype == DType.UINT8
                and outDtype not in [DType.INT8, DType.INT16]
                and error_name != ErrorIf.WrongOutputType
            ):
                # The only output dtypes for UINT8 are INT8/INT16, skip all others
                continue
            if (
                inDtype not in [DType.INT8, DType.INT16]
                and outDtype == DType.UINT8
                and error_name != ErrorIf.WrongOutputType
            ):
                # The only input dtypes for UINT8 are INT8/INT16, skip all others
                continue
            if (
                inDtype == DType.UINT16
                and outDtype != DType.INT16
                and error_name != ErrorIf.WrongOutputType
            ):
                # The only output dtype for UINT16 is INT16, skip all others
                continue
            if (
                inDtype != DType.INT16
                and outDtype == DType.UINT16
                and error_name != ErrorIf.WrongOutputType
            ):
                # The only input dtype for UINT16 is INT16, skip all others
                continue
            if (
                error_name == ErrorIf.WrongOutputType
                and not TosaErrorIfArgGen.eiRescaleWrongOutputType(inDtype, outDtype)
            ):
                continue

            for scale32 in [False, True]:
                if error_name == ErrorIf.ScaleTrue and not scale32:
                    continue
                elif error_name == ErrorIf.ScaleNotTrue and scale32:
                    continue
                for double_round in [False, True]:
                    if error_name == ErrorIf.ScaleNotTrue and not double_round:
                        continue
                    for per_channel in [False, True]:

                        if (
                            inDtype == DType.INT48
                            and scale32
                            and error_name != ErrorIf.ScaleTrue
                        ):
                            # Illegal condition.  Must be scale32=False
                            continue
                        if (
                            double_round
                            and not scale32
                            and error_name != ErrorIf.ScaleNotTrue
                        ):
                            # Illegal condition.  ERROR_IF(!scale32 && double_round)
                            continue

                        arg_list.append(
                            (
                                "out{}_sc{}_dr{}_pc{}".format(
                                    DTypeNames[outDtype],
                                    int(scale32),
                                    int(double_round),
                                    int(per_channel),
                                ),
                                [outDtype, scale32, double_round, per_channel],
                            )
                        )

        return arg_list

    @staticmethod
    def agMul(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        if dtype is DType.INT32:
            for p in range(testGen.args.num_rand_permutations):

                shift = testGen.randInt(0, 32)

                arg_list.append(("perm{}_shift{}".format(p, shift), [shift]))
        else:
            arg_list.append(("perm0_shift0", [0]))

        return arg_list

    @staticmethod
    def agArithmeticRightShift(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        arg_list.append(("roundTrue", [True]))
        arg_list.append(("roundFalse", [False]))

        return arg_list

    # Helper function for reshape.  Gets some factors of a larger number.
    @staticmethod
    def getFactors(val, start=1):
        factors = []

        for i in range(start, int(np.sqrt(val)) + 1):
            if (val % i) == 0:
                factors.append(i)

        return factors

    @staticmethod
    def agReshape(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        origShape = shapeList[0]

        totalElements = 1
        for s in origShape:
            totalElements *= s

        # This code is NOT fast.  Fortunately, the numbers are fairly small.
        factors = TosaArgGen.getFactors(totalElements)

        for p in range(testGen.args.num_rand_permutations):
            newRank = testGen.randInt(1, 7)
            if len(factors) < newRank:
                continue

            found = True
            # escape_counter breaks while loop if it continues on for too long
            escape_counter = 0
            while found:
                newShape = []
                # Generate newShape ensuring it isn't a duplicate
                remainingElements = totalElements
                shuffledFactors = testGen.rng.permutation(factors)
                for i in range(1, newRank):
                    # pick rank-1 factors
                    newShape.append(shuffledFactors[0])
                    remainingElements = remainingElements // shuffledFactors[0]
                    shuffledFactors = testGen.rng.permutation(
                        TosaArgGen.getFactors(remainingElements)
                    )
                newShape.append(remainingElements)

                # Check for duplicates
                found = False
                for name, other_shape in arg_list:
                    if other_shape[0] == newShape:
                        found = True
                        break

                escape_counter += 1
                if escape_counter >= 100:
                    break

                if not found:
                    arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape]))

        return arg_list

    @staticmethod
    def agTranspose(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        ifm_shape = shapeList[0]

        if error_name == ErrorIf.IndexOutsideBounds:
            incorrect_large_index = range(len(ifm_shape) + 1, 2 * len(ifm_shape) + 1)
            incorrect_small_index = range(-len(ifm_shape), 0)
            permutations = [p for p in itertools.permutations(incorrect_large_index)]
            permutations.extend(
                [p for p in itertools.permutations(incorrect_small_index)]
            )
        elif error_name == ErrorIf.IndexUsedTwice:
            # Create list with a duplicated index
            perm_range = list(range(len(ifm_shape)))
            index_choice = testGen.rng.choice(range(len(perm_range)))
            perm_range[(index_choice + 1) % len(perm_range)] = perm_range[index_choice]
            permutations = [p for p in itertools.permutations(perm_range)]

        else:
            # Get all permutations
            permutations = [p for p in itertools.permutations(range(len(ifm_shape)))]

        # Limit to possible permutations from shape dimension or argument setting
        limit = min(len(permutations), testGen.args.num_rand_permutations)

        # Get random permutation generator that uses all permutations
        random_permutations = testGen.rng.permutation(permutations)

        # Create list of required amount of permutations
        arg_list = [
            ("perm{}".format(p), [random_permutations[p].tolist()])
            for p in range(limit)
        ]
        return arg_list

    @staticmethod
    def agSlice(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        ifm_shape = shapeList[0]
        rank = len(ifm_shape)

        for p in range(testGen.args.num_rand_permutations):
            start = []
            size = []

            valid = True

            for i in range(rank):
                if ifm_shape[i] > 1:
                    start.append(testGen.randInt(0, ifm_shape[i]))
                    size.append(testGen.randInt(0, ifm_shape[i] - start[i]))

                    # Invalid slice size?
                    if size[i] == 0:
                        valid = False
                else:
                    start.append(0)
                    size.append(1)

            if valid:
                # If ERROR_IF test required then incorrect start, size will be returned
                start, size = TosaErrorIfArgGen.eiSliceErrorIf(
                    testGen, error_name, ifm_shape, start, size
                )
                arg_list.append(("perm{}".format(p), [start, size]))
        return arg_list

    @staticmethod
    def agTile(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        ifm_shape = shapeList[0]
        rank = len(ifm_shape)

        for p in range(testGen.args.num_rand_permutations):

            # Pick a few random, but small multiple values
            # because otherwise this has a tendency to generate
            # enormous tensors
            multiples = []
            for i in range(rank):
                if ifm_shape[i] > 1000:
                    # Multiple of 1 if ifm_shape dimension is large to reduce
                    # tensor size
                    multiples.append(1)
                elif max(ifm_shape) > 1000:
                    multiples.append(2)
                else:
                    multiples.append(testGen.randInt(1, 4))
            arg_list.append(("perm{}".format(p), [multiples]))

        return arg_list

    @staticmethod
    def agResize(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []
        ifm_shape = shapeList[0]

        def get_aspect_ratio_resize_params():
            common_aspect_ratios = ((3, 2), (16, 9), (4, 3))
            aspect_ratio = testGen.rng.choice(common_aspect_ratios)
            invert = testGen.rng.choice((False, True))
            letterbox = testGen.rng.choice((False, True))

            scale_y_n = aspect_ratio[0] if invert else aspect_ratio[1]
            scale_x_n = aspect_ratio[1] if invert else aspect_ratio[0]
            scale_y_d = scale_x_d = 1
            offset_x = offset_y = 0

            if letterbox:
                max_border = scale_y_n
                border_y = testGen.randInt(low=0, high=max_border)
                border_x = 0
            else:
                # Pillarboxing
                border_y = 0
                max_border = scale_x_n
                border_x = testGen.randInt(low=0, high=max_border)

            scale = (scale_y_n, scale_y_d, scale_x_n, scale_x_d)
            offset = (offset_y, offset_x)
            border = (border_y, border_x)

            return scale, offset, border

        def get_upscale_downscale_params():
            valid_params = False
            while not valid_params:
                upscale = testGen.rng.choice((False, True))

                # True if sampling begins from (0,0). Otherwise (-0.5,-0.5)
                origin_sampling = testGen.rng.choice((False, True))

                if upscale:
                    shift = testGen.randInt(low=1, high=4)
                    scale_x_d = scale_y_d = 1
                    scale_x_n = scale_y_n = (
                        1 << shift if origin_sampling else 2 << shift
                    )
                    border_x = border_y = 0 if origin_sampling else (1 << shift) - 1
                    offset_x = offset_y = 0 if origin_sampling else -(1 << shift) + 1
                else:
                    scale_x_n = 1
                    scale_y_n = 1

                    # Return list of valid scale_*_d values (max value 4) given input dim shape
                    def get_valid_denom(ifm_dim):
                        return [x for x in range(1, 5) if ifm_dim % x == 1]

                    # Generate list of valid downscale values and choose one randomly
                    valid_scale_y_ds = get_valid_denom(ifm_shape[1])
                    valid_scale_x_ds = get_valid_denom(ifm_shape[2])

                    if not valid_scale_y_ds and not valid_scale_x_ds:
                        # Bad parameters, skip
                        continue

                    if not valid_scale_y_ds:
                        scale_y_d = 1
                    else:
                        scale_y_d = testGen.rng.choice(valid_scale_y_ds)

                    if not valid_scale_x_ds:
                        scale_x_d = 1
                    else:
                        scale_x_d = testGen.rng.choice(valid_scale_x_ds)

                    border_x = border_y = 0
                    offset_y = testGen.randInt(0, 16 * scale_y_n)
                    offset_x = testGen.randInt(0, 16 * scale_x_n)
                valid_params = True

            scale = (scale_y_n, scale_y_d, scale_x_n, scale_x_d)
            offset = (offset_y, offset_x)
            border = (border_y, border_x)
            return scale, offset, border

        def get_rand_params():
            # Scale
            scale_y_n = testGen.randInt(low=1, high=(1 << 11))
            scale_x_n = testGen.randInt(low=1, high=(1 << 11))

            scale_y_d = testGen.randInt(low=1, high=(16 * scale_y_n))
            scale_x_d = testGen.randInt(low=1, high=(16 * scale_x_n))

            # Offsets and border within the scale
            offset_y = testGen.randInt(low=-scale_y_n, high=(16 * scale_y_n))
            offset_x = testGen.randInt(low=-scale_x_n, high=(16 * scale_x_n))
            border_y = testGen.randInt(low=(-16 * scale_y_n), high=scale_y_n)
            border_x = testGen.randInt(low=(-16 * scale_x_n), high=scale_x_n)

            scale = (scale_y_n, scale_y_d, scale_x_n, scale_x_d)
            offset = (offset_y, offset_x)
            border = (border_y, border_x)
            return scale, offset, border

        for mode in [ResizeMode.NEAREST, ResizeMode.BILINEAR]:
            # Exclude illegal {mode, type} configurations.  Pick legal output types
            if mode == ResizeMode.NEAREST and dtype == DType.INT8:
                outputDTypeList = [DType.INT8]
            elif mode == ResizeMode.NEAREST and dtype == DType.INT16:
                outputDTypeList = [DType.INT16]
            elif mode == ResizeMode.BILINEAR and dtype == DType.INT8:
                outputDTypeList = [DType.INT32]
            elif mode == ResizeMode.BILINEAR and dtype == DType.INT16:
                outputDTypeList = [DType.INT48]
            elif dtype == DType.FLOAT:
                outputDTypeList = [DType.FLOAT]
            elif error_name == ErrorIf.WrongInputType:
                # If an incorrect input type is used then we set a 'correct'
                # output type to avoid other errors
                outputDTypeList = [DType.INT8, DType.INT16, DType.INT32]
            else:
                continue

            arg_str = "mode{}_out{}_sc{}x{}x{}x{}_off{}x{}_bor{}x{}"

            for outputDType in outputDTypeList:
                perm = 0
                while perm < testGen.args.num_rand_permutations:
                    # Random choice of type of params we are testing
                    _rnd_param_fn = testGen.rng.choice(
                        (
                            get_rand_params,
                            get_upscale_downscale_params,
                            get_aspect_ratio_resize_params,
                        )
                    )
                    scale, offset, border = _rnd_param_fn()

                    # Expand params for bounds-checking
                    (scale_y_n, scale_y_d, scale_x_n, scale_x_d) = scale
                    (offset_y, offset_x) = offset
                    (border_y, border_x) = border

                    # Make sure output dimensions OH and OW are integers
                    partial_output_y = (
                        (ifm_shape[1] - 1) * scale_y_n - offset_y + border_y
                    )
                    partial_output_x = (
                        (ifm_shape[2] - 1) * scale_x_n - offset_x + border_x
                    )
                    if error_name == ErrorIf.ResizeOutputShapeNonInteger:
                        if (
                            partial_output_y % scale_y_d == 0
                            and partial_output_x % scale_x_d == 0
                        ):
                            # Skip this test as it doesn't produce NonInteger output
                            perm += 1
                            continue
                    else:
                        while partial_output_y % scale_y_d != 0:
                            scale_y_d -= 1
                        while partial_output_x % scale_x_d != 0:
                            scale_x_d -= 1

                    output_y = partial_output_y // scale_y_d + 1
                    output_x = partial_output_x // scale_x_d + 1

                    if (
                        output_y >= testGen.args.max_resize_output_dim
                        or output_x >= testGen.args.max_resize_output_dim
                    ) and error_name is None:
                        # Skip positive test if output dim will be too high
                        # Avoid high test latency and OOM issues
                        perm += 1
                        continue

                    if (
                        output_y <= 0
                        or output_y >= MAX_RESIZE_DIMENSION
                        or output_x <= 0
                        or output_x >= MAX_RESIZE_DIMENSION
                    ):
                        # Output dimensions out of scope
                        if error_name is not None and perm > 0:
                            # As long as we have one ERROR_IF test, don't worry
                            # about creating all the other permutations
                            perm += 1
                        continue

                    if error_name == ErrorIf.ResizeOutputShapeMismatch and (
                        (
                            output_y + scale_y_d >= MAX_RESIZE_DIMENSION
                            and output_y - scale_y_d < 1
                        )
                        or (
                            output_x + scale_x_d >= MAX_RESIZE_DIMENSION
                            and output_x - scale_x_d < 1
                        )
                    ):
                        # Can't create a negative test with these params as it
                        # will create invalid output size
                        if perm > 0:
                            perm += 1
                        continue

                    scale = [scale_y_n, scale_y_d, scale_x_n, scale_x_d]
                    offset = [offset_y, offset_x]
                    border = [border_y, border_x]

                    # Common for all data types
                    if error_name is not None:
                        (
                            scale,
                            offset,
                            border,
                            outputDTypeNew,
                        ) = TosaErrorIfArgGen.eiResizeErrorIf(
                            testGen,
                            error_name,
                            mode,
                            dtype,
                            shapeList,
                            outputDType,
                            scale,
                            offset,
                            border,
                        )
                    else:
                        outputDTypeNew = outputDType

                    arg_to_append = (
                        arg_str.format(
                            "N" if mode == ResizeMode.NEAREST else "B",
                            testGen.typeStr(outputDTypeNew),
                            scale[0],
                            scale[1],
                            scale[2],
                            scale[3],
                            offset[0],
                            offset[1],
                            border[0],
                            border[1],
                        ),
                        [
                            mode,
                            scale,
                            offset,
                            border,
                            dtype,
                            outputDTypeNew,
                        ],
                    )
                    if arg_to_append in arg_list:
                        # Skip already generated test params
                        continue

                    # Valid permutation
                    perm += 1
                    arg_list.append(arg_to_append)
        return arg_list

    @staticmethod
    def agTable(testGen, opName, shapeList, dtype, error_name=None):
        arg_list = []

        if dtype == DType.INT8:
            table = np.int32(
                testGen.rng.integers(low=-128, high=128, size=[256])
            ).tolist()
        else:  # INT16
            table = np.int32(
                testGen.rng.integers(low=-32768, high=32768, size=[513])
            ).tolist()

        arg_list.append(
            (
                "",
                [table],
            )
        )
        return arg_list

    def agCondIf(testGen, opName, shapeList, dtype, error_name=None):
        # CondIf generates the condition values here.
        # Convert to tensors in the build function, along with the
        # then and else blocks
        arg_list = []

        for c in [False, True]:
            arg_list.append(("cond{}".format(int(c)), [c]))

        return arg_list

    def agWhileLoop(testGen, opName, shapeList, dtype, error_name=None):
        # While loop: 0 iterations, 1, more than 1
        arg_list = []

        for iter in [0, 1, 4]:
            arg_list.append(("iter{}".format(iter), [iter]))

        return arg_list