diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
new file mode 100644
index 0000000..e3492cd
--- /dev/null
+++ b/verif/generator/tosa_arg_gen.py
@@ -0,0 +1,1809 @@
+# Copyright (c) 2021-2022, ARM Limited.
+# SPDX-License-Identifier: Apache-2.0
+import itertools
+import math
+
+import numpy as np
+import serializer.tosa_serializer as ts
+from generator.tosa_error_if import ErrorIf
+from generator.tosa_error_if import TosaErrorIfArgGen
+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 getQinfo(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):
+        qinfo = ts.TosaSerializerQuantInfo()
+        if error_name == ErrorIf.InputZeroPointNotZero:
+            qinfo.UnaryQuantInfo(
+                TosaQuantGen.getQinfo(testGen, dtype, error_name),
+                TosaQuantGen.getQinfo(testGen, dtype),
+            )
+        elif error_name == ErrorIf.OutputZeroPointNotZero:
+            qinfo.UnaryQuantInfo(
+                TosaQuantGen.getQinfo(testGen, dtype),
+                TosaQuantGen.getQinfo(testGen, dtype, error_name),
+            )
+        else:
+            qinfo.UnaryQuantInfo(
+                TosaQuantGen.getQinfo(testGen, dtype),
+                TosaQuantGen.getQinfo(testGen, dtype),
+            )
+        return qinfo
+
+    @staticmethod
+    def qgConv(testGen, op, dtype_or_dtypeList, error_name=None):
+        qinfo = ts.TosaSerializerQuantInfo()
+        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:
+            input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0], error_name)
+            weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1])
+        elif error_name == ErrorIf.WeightZeroPointNotZero:
+            input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0])
+            weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1], error_name)
+        else:
+            input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0])
+            weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1])
+
+        qinfo.ConvQuantInfo(input_zp, weights_zp)
+        return qinfo
+
+    @staticmethod
+    def qgMatmul(testGen, op, dtype, error_name=None):
+        qinfo = ts.TosaSerializerQuantInfo()
+        if error_name == ErrorIf.InputZeroPointNotZero:
+            qinfo.MatMulQuantInfo(
+                TosaQuantGen.getQinfo(testGen, dtype, error_name),
+                TosaQuantGen.getQinfo(testGen, dtype, error_name),
+            )
+        else:
+            qinfo.MatMulQuantInfo(
+                TosaQuantGen.getQinfo(testGen, dtype),
+                TosaQuantGen.getQinfo(testGen, dtype),
+            )
+        return qinfo
+
+    @staticmethod
+    def qgPad(testGen, op, dtype, error_name=None):
+        qinfo = ts.TosaSerializerQuantInfo()
+        if error_name == ErrorIf.InputZeroPointNotZero:
+            qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype, error_name))
+        else:
+            qinfo.PadQuantInfo(TosaQuantGen.getQinfo(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, qinfo, 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, qinfo, error_name=None):
+        if dtypeList[0] != DType.FLOAT 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 negatable ranges
+            if dtypeList[0] == DType.INT8:
+                # Must be within int8, adjustable by input_zp and then negatable
+                # and be within int8
+                # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp
+                max_val = min(127, 127 + qinfo.ints[0][1])
+                min_val = max(-127, -127 + qinfo.ints[0][1])
+            elif dtypeList[0] == DType.INT16:
+                max_val = 32767
+                min_val = -max_val
+            else:
+                assert (
+                    dtypeList[0] == DType.INT32
+                ), "Op.NEGATE found with unsupported input type"
+                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, qinfo, error_name
+            )
+
+    @staticmethod
+    def tvgAddSub(testGen, op, dtypeList, shapeList, testArgs, qinfo, 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, qinfo, error_name
+            )
+
+    @staticmethod
+    def tvgCondIfWhileLoop(
+        testGen, op, dtypeList, shapeList, testArgs, qinfo, 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, qinfo, error_name
+            )
+
+    @staticmethod
+    def tvgArithmeticRightShift(
+        testGen, op, dtypeList, shapeList, testArgs, qinfo, 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, qinfo, error_name=None):
+        # Set datatype of condition tensor to boolean
+        dtypeList[0] = DType.BOOL
+
+        return TosaTensorValuesGen.tvgDefault(
+            testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name
+        )
+
+    @staticmethod
+    def tvgIntDiv(testGen, op, dtypeList, shapeList, testArgs, qinfo, 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, qinfo, error_name
+            )
+
+    @staticmethod
+    def tvgMul(testGen, op, dtypeList, shapeList, testArgs, qinfo, 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, qinfo, error_name
+            )
+
+    @staticmethod
+    def tvgConcat(testGen, op, dtypeList, shapeList, testArgs, qinfo, 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, qinfo, 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, qinfo, 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, qinfo, error_name
+            )
+
+    @staticmethod
+    def tvgReduceSum(
+        testGen, op, dtypeList, shapeList, testArgs, qinfo, 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, qinfo, 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:
+            s_vals = [x for x in range(1, 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 100
+        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]))
+                    ):
+                        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] * 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:
+            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 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] * 2))}
+
+        if not error_name:
+            # add some oversize argument values
+            if max(ifm_shape) < 64:
+                bigPadding = 9
+                paddings.update(
+                    {x for x in itertools.product(*([[0, bigPadding]] * 2))}
+                )
+            bigStride = 8
+            strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))})
+            bigDilation = 7
+            dilations.update({x for x in itertools.product(*([[1, bigDilation]] * 2))})
+
+        # There are too many parameter combinations, so generate them sparsely,
+        # very sparse for negative tests
+        sparsity_factor = 2 if error_name else 100
+        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:
+                        # Determine the output shape
+                        oh = (
+                            ifm_shape[1]
+                            - filter_shape[1]
+                            - (filter_shape[1] - 1) * (d[0] - 1)
+                            + 2 * p[0]
+                        ) // s[0] + 1
+                        ow = (
+                            ifm_shape[2]
+                            - filter_shape[2]
+                            - (filter_shape[2] - 1) * (d[1] - 1)
+                            + 2 * p[1]
+                        ) // s[1] + 1
+                        os = [ifm_shape[0], oh, ow, filter_shape[0]]
+                        arg_list.append(
+                            (
+                                "st{}_pad{}_dilat{}_os{}".format(
+                                    "".join([str(x) for x in s]),
+                                    "".join([str(x) for x in p]),
+                                    "".join([str(x) for x in d]),
+                                    "x".join([str(x) for x in os]),
+                                ),
+                                [s, p, d, 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))}
+        s_vals = [x for x in range(1, 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(*([[1, bigStride]] * 2))})
+            bigKernel = 6
+            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]
+                    ):
+                        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 dtype in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]:
+            if (
+                dtype in [DType.UINT8, DType.INT8]
+                and error_name == ErrorIf.OutputZeroPointNotZero
+            ):
+                continue
+            if (
+                inDtype == DType.UINT8
+                and dtype != DType.INT8
+                and error_name != ErrorIf.WrongOutputType
+            ):
+                # The only output dtype for UINT8 is INT8, skip all other combinations
+                continue
+            if (
+                inDtype != DType.INT8
+                and dtype == DType.UINT8
+                and error_name != ErrorIf.WrongOutputType
+            ):
+                # The only input dtype for UINT8 is INT8, skip all other combinations
+                continue
+            if (
+                error_name == ErrorIf.WrongOutputType
+                and not TosaErrorIfArgGen.eiRescaleWrongOutputType(inDtype, dtype)
+            ):
+                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[dtype],
+                                    int(scale32),
+                                    int(double_round),
+                                    int(per_channel),
+                                ),
+                                [dtype, 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)
+
+                # Toss in a -1 sometimes
+                minusOne = testGen.randInt(0, newRank * 4)
+                if minusOne < newRank:
+                    newShape[minusOne] = -1
+
+                # 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]
+        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
+
+            for outputDType in outputDTypeList:
+                for perm in range(testGen.args.num_rand_permutations):
+                    # Randomly generate legal output dimensions and shift
+                    # and then compute the stride and offset based on them
+                    # A output_dim of 1 will cause offset to exceed allowed range
+                    # so minimum value 2 produced below
+                    output_dims = [testGen.randInt(1) + 1, testGen.randInt(1) + 1]
+                    while (float(ifm_shape[1]) / float(output_dims[0])) >= 16:
+                        output_dims[0] += 1
+                    while (float(ifm_shape[2]) / float(output_dims[1])) >= 16:
+                        output_dims[1] += 1
+
+                    in_center_h = (ifm_shape[1] - 1) / 2.0
+                    in_center_w = (ifm_shape[2] - 1) / 2.0
+                    out_center_h = (output_dims[0] - 1) / 2.0
+                    out_center_w = (output_dims[1] - 1) / 2.0
+
+                    fp_stride_y = float(ifm_shape[1]) / float(output_dims[0])
+                    fp_stride_x = float(ifm_shape[2]) / float(output_dims[1])
+                    fp_offset_y = in_center_h - fp_stride_y * out_center_h
+                    fp_offset_x = in_center_w - fp_stride_x * out_center_w
+
+                    if outputDType == DType.FLOAT:
+                        float_op = True
+                        arg_str = (
+                            "mode{}_shift{}_odim{}x{}_out{}"
+                            "_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}"
+                        )
+                        shift = 0
+                        stride = [0, 0]
+                        offset = [0, 0]
+                        stride_fp = [fp_stride_y, fp_stride_x]
+                        offset_fp = [fp_offset_y, fp_offset_x]
+
+                    else:
+                        float_op = False
+                        arg_str = "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}"
+                        shift = testGen.randInt(1, 12)
+                        # Now search for a shift value (1 to 11) that will produce
+                        # a valid and predictable resize operation
+                        count = 0
+                        while count < 12:
+                            unit = float(1 << shift)
+                            stride_y = int(round(fp_stride_y * unit))
+                            stride_x = int(round(fp_stride_x * unit))
+                            offset_y = int(round(fp_offset_y * unit))
+                            offset_x = int(round(fp_offset_x * unit))
+
+                            if (
+                                stride_y <= 0
+                                or stride_x <= 0
+                                or stride_y >= (16 << shift)
+                                or stride_x >= (16 << shift)
+                                or offset_y >= (16 << shift)
+                                or offset_x >= (16 << shift)
+                                or offset_y <= (-16 << shift)
+                                or offset_x <= (-16 << shift)
+                            ):
+                                # Change the shift value and check again
+                                count += 1
+                                shift = (shift % 11) + 1
+                                continue
+
+                            def RESIZE_REQUIRE_CALC(
+                                length_in, length_out, stride, offset, shift
+                            ):
+                                # Perform the pseudo loop to look for out of bounds
+                                for pos in range(0, length_out):
+                                    a = pos * stride + offset
+                                    ia = a >> shift
+                                    ia0 = max(ia, 0)
+                                    ia1 = min(ia + 1, length_in - 1)
+                                    if ia0 > ia1:
+                                        # Found a problem value
+                                        break
+                                return ia0, ia1
+
+                            iy0, iy1 = RESIZE_REQUIRE_CALC(
+                                ifm_shape[1], output_dims[0], stride_y, offset_y, shift
+                            )
+                            ix0, ix1 = RESIZE_REQUIRE_CALC(
+                                ifm_shape[2], output_dims[1], stride_x, offset_x, shift
+                            )
+                            if ix0 > ix1 or iy0 > iy1:
+                                # Change the shift value and check again
+                                count += 1
+                                shift = (shift % 11) + 1
+                                continue
+                            break
+
+                        if count >= 12:
+                            # Couldn't find a good set of values for this test, skip it
+                            continue
+
+                        stride = [stride_y, stride_x]
+                        offset = [offset_y, offset_x]
+
+                        stride_fp = [0.0, 0.0]
+                        offset_fp = [0.0, 0.0]
+
+                    # Common for all data types
+                    if error_name is not None:
+                        (
+                            shift,
+                            stride,
+                            stride_fp,
+                            offset,
+                            offset_fp,
+                            outputDTypeNew,
+                        ) = TosaErrorIfArgGen.eiResizeErrorIf(
+                            testGen,
+                            error_name,
+                            mode,
+                            dtype,
+                            shapeList,
+                            outputDType,
+                            shift,
+                            stride,
+                            stride_fp,
+                            offset,
+                            offset_fp,
+                        )
+                    else:
+                        outputDTypeNew = outputDType
+
+                    arg_list.append(
+                        (
+                            arg_str.format(
+                                "N" if mode == ResizeMode.NEAREST else "B",
+                                shift,
+                                output_dims[0],
+                                output_dims[1],
+                                testGen.typeStr(outputDTypeNew),
+                                stride_fp[0] if float_op else stride[0],
+                                stride_fp[1] if float_op else stride[1],
+                                offset_fp[0] if float_op else offset[0],
+                                offset_fp[1] if float_op else offset[1],
+                            ),
+                            [
+                                mode,
+                                stride,
+                                offset,
+                                shift,
+                                stride_fp,
+                                offset_fp,
+                                output_dims,
+                                dtype,
+                                outputDTypeNew,
+                            ],
+                        )
+                    )
+
+        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