MLBEDSW-4853: Refactor supported operators

Refactor supported operators by breaking out model semantics
into its own class. Model semantics checked right after model
read.

Signed-off-by: Jonas Ohlsson <jonas.ohlsson@arm.com>
Change-Id: If442b189efcd91dda01af60b2b3adedfacdf2fad

1 files changed, 0 insertions, 1087 deletions

diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
deleted file mode 100644
index 663c78f8..00000000
--- a/ethosu/vela/supported_operators.py
+++ /dev/null
@@ -1,1087 +0,0 @@
-# Copyright (C) 2020-2021 Arm Limited or its affiliates. All rights reserved.
-#
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the License); you may
-# not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an AS IS BASIS, WITHOUT
-# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# Description:
-# The SupportedOperators class which is a collection of all supported operators and parameter checks.
-from collections import defaultdict
-
-import numpy as np
-
-from .data_type import BaseType
-from .data_type import DataType
-from .numeric_util import is_integer
-from .operation import get_slice_offsets
-from .operation import Op
-from .operation import Padding
-from .supported_operators_util import docstring_format_args
-from .supported_operators_util import list_formatter
-from .tensor import check_quantized_tens_scaling_equal
-from .tflite_mapping import BUILTIN_OPERATOR_UNKNOWN
-from .tflite_mapping import optype_to_builtintype
-
-
-def _optype_formatter(op_list):
-    # Convert internal op types to external names
-    output = map(optype_to_builtintype, op_list)
-    # Remove UNKNOWNs
-    output = (x for x in output if x is not BUILTIN_OPERATOR_UNKNOWN)
-    return list_formatter(output)
-
-
-class SupportedOperators:
-    # Categorised lists of supported operators
-    npu_pre_ops = set((Op.SplitSliceRead,))
-    convolution_ops = set((Op.Conv2DBias, Op.Conv2D, Op.QuantizedConv2D,))
-    depthwise_convolution_ops = set((Op.DepthwiseConv2DBias,))
-    transpose_convolution_ops = set((Op.Conv2DBackpropInput,))
-    convolution_like_ops = convolution_ops | depthwise_convolution_ops | transpose_convolution_ops
-    max_pooling_ops = Op.op_set(Op.is_maxpool_op)
-    avg_pooling_ops = Op.op_set(Op.is_avgpool_op)
-    pooling_ops = set((Op.ReduceSum,)) | max_pooling_ops | avg_pooling_ops
-    resizing_ops = set((Op.ResizeBilinear,))
-    fc_vector_products = set((Op.QuantizedMatMul, Op.MatMul, Op.FullyConnected,))
-    mac_main_ops = (
-        # RNN/LSTM/GRU
-        set((Op.BlockLSTM,))
-        # conv/depthwiseconv/transposeconv
-        | convolution_like_ops
-        # pooling
-        | pooling_ops
-        # resizing/upscaling
-        | resizing_ops
-        # FC layers
-        | fc_vector_products
-        # Mean (converts to depthwise conv)
-        | set((Op.Mean,))
-    )
-    unary_elem_wise_main_ops = Op.op_set(Op.is_unary_elementwise_op)
-    binary_elem_wise_min_max_ops = set((Op.Minimum, Op.Maximum,))
-    binary_elem_wise_shift_ops = set((Op.SHL, Op.SHR,))
-    binary_elem_wise_add_mul_sub = set((Op.Add, Op.Mul, Op.Sub,))
-    binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
-    elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops
-    pad_ops = set((Op.Pad,))
-    supported_int32_tensor_ops = (
-        set((Op.ReduceSum, Op.CLZ,)) | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
-    )
-
-    relu_ops = set((Op.Relu, Op.Relu6, Op.ReluN1To1, Op.Clip,))
-    activation_ops = relu_ops | set((Op.Tanh, Op.Sigmoid, Op.Softmax, Op.HardSwish))
-    npu_post_ops = (
-        # activation functions
-        activation_ops
-        # concatenation write direction
-        | set((Op.ConcatSliceWrite,))
-        # Quantization
-        | set((Op.Quantize,))
-    )
-    split_ops = set((Op.Split, Op.SplitV, Op.StridedSlice, Op.Slice, Op.UnpackReshaped, Op.Unpack,))
-    concat_ops = set((Op.Concat, Op.ConcatTFLite, Op.PackReshaped, Op.Pack,))
-    memory_only_ops = set((Op.Reshape, Op.QuantizedReshape,)) | concat_ops | split_ops
-    shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV, Op.Mean))
-    per_axis_quant_ops = convolution_like_ops  # per-axis/channel quantization only currently supported for conv ops
-    supported_fused_activations = relu_ops | set((Op.Tanh, Op.Sigmoid, Op.LUT,))
-    supported_operators = npu_pre_ops | mac_main_ops | elem_wise_main_ops | pad_ops | npu_post_ops | memory_only_ops
-    # Supported data types
-    supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32))
-    supported_faf_dtypes = set((DataType.uint8, DataType.int8, DataType.int16))
-    supported_bias_dtypes = set((DataType.int32, DataType.int64))
-    supported_pad_dtypes = set((DataType.int32, DataType.int64))
-    # Defined ranges for allowed values:
-    tens_dim_range = (1, 65535)
-    stride_range = (1, 3)
-    dilation_range = (1, 2)
-    dilated_height_range = (1, 64)
-    dilated_product_range = (1, 64 * 64)
-    weights_limit = 127 * 65536
-    filter_range = (1, 8)
-    filter_height_range = (1, 256)
-    filter_product_range = (1, 256 * 256)
-    mean_kernel_product = 64 * 64
-    mean_kernel_product_int8 = 16 * 16
-    mean_kernel_product_avgpool = 256 * 256
-    # Supported consumers
-    supported_pad_consumers = convolution_ops | depthwise_convolution_ops | pooling_ops
-
-    def __init__(self):
-        # Setup the generic constraints. Note: the order matters
-        self.generic_constraints = []
-        self.generic_constraints.append(SupportedOperators.constraint_tens_no_dynamic)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_defined_shape)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_output_scalar)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_input_scalar)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_shape_size)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_dtype)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_int32_ops)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_dimension)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_quant_none_check)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale)
-        self.generic_constraints.append(SupportedOperators.constraint_tens_quant_per_axis)
-        self.generic_constraints.append(SupportedOperators.constraint_faf)
-        self.generic_constraints.append(SupportedOperators.constraint_faf_type)
-        self.generic_constraints.append(SupportedOperators.constraint_quant_scale_inf)
-
-        # Setup specific constraints. Note: the order matters
-        self.specific_constraints = defaultdict(list)
-
-        # Conv-like checks:
-        for op_type in SupportedOperators.convolution_like_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_type)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_range)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_dilation_type)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_dilation_range)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_dilated_height_range)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_dilated_product_range)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_type)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_const)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_limit)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_type)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_40bit)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_batch_size)
-        # Depthwise Conv specific checks:
-        for op_type in SupportedOperators.depthwise_convolution_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_depth_multiplier)
-        # Transpose Conv specific checks:
-        for op_type in SupportedOperators.transpose_convolution_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_stride)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_same)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_valid)
-
-        # Pooling checks:
-        for op_type in SupportedOperators.pooling_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_batch_size)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_type)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_range)
-        # AVG pooling specific checks:
-        for op_type in SupportedOperators.avg_pooling_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_type)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_range)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_height_range_valid_pad)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_product_range_valid_pad)
-        # MAX pooling specific checks:
-        for op_type in SupportedOperators.max_pooling_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_type)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_height_range)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_product_range)
-
-        # Resizing specific checks:
-        for op_type in SupportedOperators.resizing_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_resize)
-
-        # Vector Product specific checks:
-        for op_type in SupportedOperators.fc_vector_products:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_type)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_const)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_type)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_40bit)
-
-        # Concat specific checks:
-        for op_type in (Op.Concat, Op.ConcatTFLite):
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_axis_exists)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_axis_valid)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_dimensionality)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_valid_dimensions)
-
-        # Element-wise checks:
-        for op_type in SupportedOperators.elem_wise_main_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_elemwise_batch_size)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_either_shapes)
-        # Unary specific checks:
-        for op_type in SupportedOperators.unary_elem_wise_main_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types)
-        # Binary Min/Max specific checks:
-        for op_type in SupportedOperators.binary_elem_wise_min_max_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_quantization_parameters)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_broadcast_shapes)
-        # Binary Add/Mul/Sub specific checks:
-        for op_type in SupportedOperators.binary_elem_wise_add_mul_sub:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_inputs_types)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_signed)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_unsigned_valid)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_broadcast_shapes)
-        # Binary Shift specific checks:
-        for op_type in SupportedOperators.binary_elem_wise_shift_ops:
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_inputs_int32)
-            self.specific_constraints[op_type].append(SupportedOperators.constraint_broadcast_shapes)
-
-        # SHL specific checks:
-        self.specific_constraints[Op.SHL].append(SupportedOperators.constraint_output_int32)
-
-        # CLZ specific checks:
-        self.specific_constraints[Op.CLZ].append(SupportedOperators.constraint_output_int32)
-
-        # Softmax specific checks:
-        self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_matching_shapes)
-        self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_matching_in_out_types)
-        self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_beta_value_range)
-
-        # SplitV specific checks:
-        self.specific_constraints[Op.SplitV].append(SupportedOperators.constraint_splitv_inferred)
-
-        # StridedSlice specific checks:
-        self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_input_count)
-        self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_inputs_const)
-        self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_stride_values)
-        self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_ellipsis_mask)
-        self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_axis_masks)
-        self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_slice_ranges)
-
-        # LeakyRelu specific checks:
-        self.specific_constraints[Op.LeakyRelu].append(SupportedOperators.constraint_alpha_valid)
-
-        # FullyConnected specific checks:
-        self.specific_constraints[Op.FullyConnected].append(SupportedOperators.constraint_fc_output_2d)
-        self.specific_constraints[Op.FullyConnected].append(SupportedOperators.constraint_keep_dim_ifm_ofm)
-
-        # Pad specific checks:
-        self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_input_count)
-        self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_shape)
-        self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_padding_dimensions)
-        self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_type)
-        self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_constant)
-
-        # HardSwish specific checks:
-        self.specific_constraints[Op.HardSwish].append(SupportedOperators.constraint_input_8bit)
-        self.specific_constraints[Op.HardSwish].append(SupportedOperators.constraint_matching_in_out_types)
-        # Mean specific checks:
-        self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_input_8bit)
-        self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_input_dims)
-        self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_axis)
-        self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_height_width_product_avgpool)
-        self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_height_width_product)
-        self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_height_width_product_int8)
-
-    def is_operator_supported(self, op):
-        ext_type = optype_to_builtintype(op.type)
-        if op.type not in SupportedOperators.supported_operators:
-            if op.type not in (Op.Placeholder, Op.SubgraphInput, Op.Const):
-                print(f"Info: {ext_type} '{op.name}' is a CPU only op")
-            return False
-
-        for constraint in self.generic_constraints + self.specific_constraints[op.type]:
-            valid, extra = constraint(op)
-            if not valid:
-                print(f"Warning: {ext_type} '{op.name}' is not supported on the NPU. Placing on CPU instead")
-                print(f" - {constraint.__doc__}")
-                if extra:
-                    print(f"   {extra}")
-                return False
-
-        return True
-
-    @staticmethod
-    def constraint_tens_no_dynamic(op):
-        "Input(s) and Output tensors must not be dynamic"
-        valid = True
-        extra = []
-        tensors = [tens for tens in op.inputs + op.outputs if tens]
-        for tens in tensors:
-            if (tens.shape == []) and (tens.values is None):
-                valid = False
-                extra.append(tens.name)
-        extra = ", ".join(extra)
-        return valid, f"Op has dynamic tensor(s): {extra}"
-
-    @staticmethod
-    def constraint_tens_defined_shape(op):
-        "Input(s) and Output tensors must have a defined shape"
-        valid = True
-        extra = []
-        tensors = [tens for tens in op.inputs + op.outputs if tens]
-        for tens in tensors:
-            if not tens.has_fully_defined_shape():
-                valid = False
-                extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}")
-        return valid, ", ".join(extra)
-
-    @staticmethod
-    def constraint_tens_output_scalar(op):
-        "Output tensors cannot be scalar"
-        ofm = op.ofm
-        valid = ofm.shape != []
-        return valid, f"Output Tensor '{ofm.name}' is scalar"
-
-    @classmethod
-    @docstring_format_args([_optype_formatter(shapeless_input_ops)])
-    def constraint_tens_input_scalar(cls, op):
-        "Scalar Input tensors are only valid for op type: {}"
-        valid = True
-        extra = []
-        tensors = [tens for tens in op.inputs if tens]
-        for tens in tensors:
-            if (tens.shape == []) and (op.type not in cls.shapeless_input_ops):
-                valid = False
-                extra.append(tens.name)
-        extra = ", ".join(extra)
-        return valid, f"Op has scalar input tensor(s): {extra}"
-
-    @staticmethod
-    def constraint_tens_shape_size(op):
-        "Input(s) and Output tensors must not be greater than 4D"
-        valid = True
-        extra = []
-        tensors = [tens for tens in op.inputs + op.outputs if tens]
-        for tens in tensors:
-            if len(tens.shape) > 4:
-                valid = False
-                extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}")
-        return valid, ", ".join(extra)
-
-    @classmethod
-    @docstring_format_args([list_formatter(supported_op_dtypes)])
-    def constraint_tens_dtype(cls, op):
-        "Tensors must be of type: {}"
-        valid = True
-        extra = []
-        tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
-        if not tensors:
-            tensors = [tens for tens in op.inputs if tens]
-        for tens in tensors:
-            if tens.dtype not in cls.supported_op_dtypes:
-                valid = False
-                extra.append(f"Tensor '{tens.name}' has data type: {tens.dtype}")
-        return valid, ", ".join(extra)
-
-    @classmethod
-    @docstring_format_args([_optype_formatter(supported_int32_tensor_ops)])
-    def constraint_tens_int32_ops(cls, op):
-        "Tensors which are int32 are only valid when op type is: {}"
-        valid = True
-        extra = []
-        tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
-        if not tensors:
-            tensors = [tens for tens in op.inputs if tens]
-        for tens in tensors:
-            if (tens.dtype == DataType.int32) and (op.type not in cls.supported_int32_tensor_ops):
-                valid = False
-                extra.append(tens.name)
-        extra = ", ".join(extra)
-        return valid, f"Op has int32 tensor(s): {extra}"
-
-    @classmethod
-    @docstring_format_args(tens_dim_range)
-    def constraint_tens_dimension(cls, op):
-        "Tensor dimensions must be in the range [{}, {}]"
-        tens_min, tens_max = cls.tens_dim_range
-        valid = True
-        extra = []
-        tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
-        if not tensors:
-            tensors = [tens for tens in op.inputs if tens]
-        for tens in tensors:
-            if not all(tens_min <= dim <= tens_max for dim in tens.shape):
-                valid = False
-                extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}")
-        return valid, ", ".join(extra)
-
-    @staticmethod
-    def constraint_tens_quant_none_check(op):
-        "Input(s), Output and Weight tensors must have quantization parameters"
-        valid = True
-        extra = []
-        tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
-        for tens in tensors:
-            if tens.quantization is None:
-                valid = False
-                extra.append(tens.name)
-        extra = ", ".join(extra)
-        return valid, f"Op has tensors with missing quantization parameters: {extra}"
-
-    @staticmethod
-    def constraint_tens_quant_scale(op):
-        "Input(s), Output and Weight tensors with quantization scales must be finite"
-        valid = True
-        extra = []
-        tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
-        for tens in tensors:
-            if (tens.quantization.scale_f32 is not None) and np.isinf(tens.quantization.scale_f32).any():
-                valid = False
-                extra.append(f"Tensor '{tens.name}' has quantization scale: {tens.quantization.scale_f32}")
-        return valid, ", ".join(extra)
-
-    @classmethod
-    @docstring_format_args([_optype_formatter(per_axis_quant_ops)])
-    def constraint_tens_quant_per_axis(cls, op):
-        "Per-axis quantization is only supported for the following op types: {}"
-        valid = True
-        extra = []
-        if op.type not in cls.per_axis_quant_ops:
-            tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
-            for tens in tensors:
-                if tens.quantization.is_per_axis():
-                    valid = False
-                    extra.append(tens.name)
-        return valid, "The following tensor(s) have per-axis quantization parameters: " + ", ".join(extra)
-
-    @staticmethod
-    def constraint_fc_output_2d(op):
-        "The output tensor(s) must have 2D shape"
-        valid = True
-        extra = []
-        for tens in op.outputs:
-            if len(tens.shape) != 2:
-                valid = False
-                extra.append(f"Tensor '{tens.name}' is {len(tens.shape)}D")
-        return valid, ", ".join(extra)
-
-    @classmethod
-    @docstring_format_args([_optype_formatter(supported_fused_activations)])
-    def constraint_faf(cls, op):
-        "The fused activation function (if present) must be one of type: {}"
-        if op.activation is None:
-            res = True, "Op has no fused activation function"
-        else:
-            faf = op.activation.op_type
-            valid = faf in cls.supported_fused_activations
-            res = valid, f"Op has its fused activation function as: {faf}"
-        return res
-
-    @classmethod
-    @docstring_format_args([list_formatter(supported_faf_dtypes)])
-    def constraint_faf_type(cls, op):
-        "If a fused activation function is present, the Output tensor must be one of type: {}"
-        if op.activation is None:
-            res = True, "Op has no fused activation function"
-        else:
-            valid = op.ofm.dtype in cls.supported_faf_dtypes
-            ext_type = optype_to_builtintype(op.activation.op_type)
-            res = valid, f"Op has fused activation function {ext_type}, and Output tensor data type: {op.ofm.dtype}"
-        return res
-
-    @staticmethod
-    def constraint_stride_type(op):
-        "Stride values for both width and height must be integer types"
-        w, h = op.get_kernel_stride()
-        valid = is_integer(w) and is_integer(h)
-        return valid, f"Op has stride WxH as: {repr(w)}x{repr(h)}"
-
-    @classmethod
-    @docstring_format_args(stride_range)
-    def constraint_stride_range(cls, op):
-        "Stride values for both width and height must be in the range [{}, {}]"
-        w, h = op.get_kernel_stride()
-        stride_min, stride_max = cls.stride_range
-        valid = (stride_min <= w <= stride_max) and (stride_min <= h <= stride_max)
-        return valid, f"Op has stride WxH as: {w}x{h}"
-
-    @staticmethod
-    def constraint_dilation_type(op):
-        "Dilation factor values for both width and height must be integer types"
-        w, h = op.get_kernel_dilation()
-        valid = is_integer(w) and is_integer(h)
-        return valid, f"Op has dilation factor WxH as: {repr(w)}x{repr(h)}"
-
-    @classmethod
-    @docstring_format_args(dilation_range)
-    def constraint_dilation_range(cls, op):
-        "Dilation factor values for both width and height must be in the range [{}, {}]"
-        w, h = op.get_kernel_dilation()
-        dilation_min, dilation_max = cls.dilation_range
-        valid = (dilation_min <= w <= dilation_max) and (dilation_min <= h <= dilation_max)
-        return valid, f"Op has dilation factor WxH as: {w}x{h}"
-
-    @classmethod
-    @docstring_format_args(dilated_height_range)
-    def constraint_dilated_height_range(cls, op):
-        "Dilated kernel height must be in the range [{}, {}]"
-        h = op.kernel.area_height()
-        dilated_height_min, dilated_height_max = cls.dilated_height_range
-        valid = dilated_height_min <= h <= dilated_height_max
-        return valid, f"Op has dilated kernel height as: {h}"
-
-    @classmethod
-    @docstring_format_args(dilated_product_range)
-    def constraint_dilated_product_range(cls, op):
-        "Product of dilated kernel width and height must be in the range [{}, {}]"
-        product = op.kernel.area_width() * op.kernel.area_height()
-        dilated_product_min, dilated_product_max = cls.dilated_product_range
-        valid = dilated_product_min <= product <= dilated_product_max
-        return valid, f"Op has product of dilated kernel width and height as: {product}"
-
-    @staticmethod
-    def constraint_weights_type(op):
-        "Weight tensor must be 8-bit"
-        weights = op.weights
-        valid = weights.element_size() == 1
-        return valid, f"Tensor '{weights.name}' is {int(weights.element_size() * 8)}-bit"
-
-    @staticmethod
-    def constraint_weights_const(op):
-        "Weight tensor must be constant"
-        weights = op.weights
-        valid = weights.values is not None
-        return valid, f"Tensor '{weights.name}' has non-constant values"
-
-    @classmethod
-    @docstring_format_args([weights_limit])
-    def constraint_weights_limit(cls, op):
-        "The sum of the weights cannot exceed {}"
-        weights = op.weights
-        values = weights.values.astype(np.int64) - weights.quantization.zero_point
-        limit = np.amax(np.sum(np.absolute(values), axis=(0, 1, 2)))
-        valid = limit <= cls.weights_limit
-        return valid, f"Tensor '{weights.name}' has the sum of weights: {limit}"
-
-    @classmethod
-    @docstring_format_args([list_formatter(supported_bias_dtypes)])
-    def constraint_bias_type(cls, op):
-        "Optional Bias tensor must be of type: {}"
-        bias = op.bias
-        if bias:
-            valid = bias.dtype in cls.supported_bias_dtypes
-            return valid, f"Tensor '{bias.name}' has data type: {bias.dtype}"
-        return True, "Op has no bias tensor"
-
-    @staticmethod
-    def constraint_bias_40bit(op):
-        "Optional Bias tensor values must fit within 40-bits"
-        bias = op.bias
-        if bias and bias.dtype == DataType.int64 and bias.values is not None:
-            valid = all(len(bin(quant_value)[2:]) <= 40 for quant_value in bias.values)
-            return valid, f"Tensor '{bias.name}' has values larger than 40-bits"
-        return True, "Op has no bias tensor, or it fits in 40-bit"
-
-    @staticmethod
-    def constraint_batch_size(op):
-        "IFM Tensor batch size must be 1"
-        ifm = op.ifm
-        valid = ifm.shape[0] == 1
-        return valid, f"Tensor '{ifm.name}' has batch size: {ifm.shape[0]}"
-
-    @staticmethod
-    def constraint_quant_scale_inf(op):
-        "Input and Output tensors must have quantization scales that fit within float32 precision"
-        if op.ofm is not None and op.ofm.is_quantized():
-            ofm_scale = op.ofm.quantization.scale_f32
-            if ofm_scale < np.finfo(np.float32).tiny:
-                return (
-                    False,
-                    f"The quantization scale of the output tensor is {ofm_scale}, "
-                    + f"minimum supported is: {np.finfo(np.float32).tiny}",
-                )
-            if op.ifm is not None and op.ifm.is_quantized():
-                ifm_scale = op.ifm.quantization.scale_f32
-                if np.isinf(ifm_scale / ofm_scale):
-                    return (
-                        False,
-                        f"IFM scale divided by OFM scale is infinite, ifm_scale={ifm_scale} ofm_scale={ofm_scale}",
-                    )
-        return True, "Op's quantization is ok"
-
-    @staticmethod
-    def constraint_depth_multiplier(op):
-        "For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier"
-        depth_multiplier = op.attrs.get("depth_multiplier", 1)
-        if depth_multiplier > 1:
-            ifm_channels = op.ifm.shape[3]
-            ofm_channels = op.ofm.shape[3]
-            valid = (ifm_channels == 1) and (ofm_channels == depth_multiplier)
-            extra = (
-                f"Op has ifm_channels={ifm_channels}, ofm_channels={ofm_channels}"
-                f" and depth_multiplier={depth_multiplier}"
-            )
-            return valid, extra
-        return True, "Op has depth_multiplier=1"
-
-    @staticmethod
-    def constraint_tconv_stride(op):
-        "Stride values for both width and height must be 2"
-        w = op.kernel.stride.x
-        h = op.kernel.stride.y
-        valid = (w == 2) and (h == 2)
-        return valid, f"Op has stride WxH as: {w}x{h}"
-
-    @staticmethod
-    def constraint_tconv_same(op):
-        "SAME padding: OFM dimensions must equal IFM dimensions multiplied by stride"
-        if op.attrs["padding"] == Padding.SAME:
-            w = op.kernel.stride.x
-            h = op.kernel.stride.y
-            ifm_shape = op.ifm.shape
-            ofm_shape = op.ofm.shape
-            valid = (ofm_shape[1] == (ifm_shape[1] * h)) and (ofm_shape[2] == (ifm_shape[2] * w))
-            return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and stride WxH as {w}x{h}"
-        return True, "Op has padding=VALID"
-
-    @staticmethod
-    def constraint_tconv_valid(op):
-        """VALID padding: OFM dimensions must equal IFM dimensions multiplied by stride,
-                  minus difference between kernel size and stride"""
-        if op.attrs["padding"] == Padding.VALID:
-            s_w = op.kernel.stride.x
-            s_h = op.kernel.stride.y
-            k_w = op.kernel.width
-            k_h = op.kernel.height
-            ifm_shape = op.ifm.shape
-            ofm_shape = op.ofm.shape
-            height_check = ofm_shape[1] == (ifm_shape[1] * s_h + max(k_h - s_h, 0))
-            width_check = ofm_shape[2] == (ifm_shape[2] * s_w + max(k_w - s_w, 0))
-            valid = height_check and width_check
-            extra = (
-                f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape},"
-                f" stride WxH as {s_w}x{s_h} and kernel WxH as {k_w}x{k_h}"
-            )
-            return valid, extra
-        return True, "Op has padding=SAME"
-
-    @staticmethod
-    def constraint_matching_in_out_types(op):
-        "IFM and OFM data types must match"
-        ifm_dtype = op.ifm.dtype
-        ofm_dtype = op.ofm.dtype
-        valid = ifm_dtype == ofm_dtype
-        return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}"
-
-    @staticmethod
-    def constraint_beta_value_range(op):
-        "Beta value needs to be positive"
-        beta = op.attrs.get("beta", 1.0)
-        valid = beta >= 0
-        return valid, f"Op has beta={beta}"
-
-    @staticmethod
-    def constraint_filter_type(op):
-        "Kernel filter values for both width and height must be integer types"
-        w = op.kernel.width
-        h = op.kernel.height
-        valid = is_integer(w) and is_integer(h)
-        return valid, f"Op has kernel filter WxH as: {repr(w)}x{repr(h)}"
-
-    @classmethod
-    @docstring_format_args(filter_range)
-    def constraint_filter_range(cls, op):
-        "Kernel filter values for both width and height must be in the range [{}, {}]"
-        if op.attrs["padding"] == Padding.SAME:
-            w = op.kernel.width
-            h = op.kernel.height
-            filter_min, filter_max = cls.filter_range
-            valid = (filter_min <= w <= filter_max) and (filter_min <= h <= filter_max)
-            return valid, f"Op has kernel filter WxH as: {w}x{h}"
-        return True, "Op has padding=VALID"
-
-    @classmethod
-    @docstring_format_args(filter_height_range)
-    def constraint_filter_height_range(cls, op):
-        "Kernel filter height must be in the range [{}, {}]"
-        h = op.kernel.height
-        filter_height_min, filter_height_max = cls.filter_height_range
-        valid = filter_height_min <= h <= filter_height_max
-        return valid, f"Op has kernel filter height as: {h}"
-
-    @classmethod
-    @docstring_format_args(filter_product_range)
-    def constraint_filter_product_range(cls, op):
-        "Product of kernel filter width and height must be in the range [{}, {}]"
-        product = op.kernel.elements_wh()
-        filter_product_min, filter_product_max = cls.filter_product_range
-        valid = filter_product_min <= product <= filter_product_max
-        return valid, f"Op has product of kernel filter width and height as: {product}"
-
-    @staticmethod
-    @docstring_format_args(filter_height_range)
-    def constraint_filter_height_range_valid_pad(op):
-        "VALID padding: Kernel filter height must be in the range [{}, {}]"
-        if op.attrs["padding"] == Padding.VALID:
-            return SupportedOperators.constraint_filter_height_range(op)
-        return True, "Op has padding=SAME"
-
-    @staticmethod
-    @docstring_format_args(filter_product_range)
-    def constraint_filter_product_range_valid_pad(op):
-        "VALID padding: Product of kernel filter width and height must be in the range [{}, {}]"
-        if op.attrs["padding"] == Padding.VALID:
-            return SupportedOperators.constraint_filter_product_range(op)
-        return True, "Op has padding=SAME"
-
-    @staticmethod
-    def constraint_resize(op):
-        """The width and height of the IFM and OFM must match one of the following criteria:
-        IFM W and H must both be 1
-        IFM must match OFM
-        OFM W and H must be 2x IFM -1, if align_corners is True
-        OFM W and H must be 2x IFM, if align_corners is False"""
-        # Easier to start with False condition as very few cases result in a supported resize
-        valid = False
-        ifm_shape = op.ifm.shape
-        ofm_shape = op.ofm.shape
-        align_corners = op.attrs.get("align_corners", False)
-        if len(ifm_shape) == 4:
-            # Valid if IFM W and H are both 1, or IFM and OFM shape are the same
-            if ((ifm_shape[1] == 1) and (ifm_shape[2] == 1)) or (ifm_shape == ofm_shape):
-                valid = True
-            else:
-                upscaled_shape = np.array(ifm_shape[1:3])
-                out_shape = np.array(ofm_shape[1:3])
-                while (upscaled_shape < out_shape).all():
-                    upscaled_shape *= 2
-                    if align_corners:
-                        upscaled_shape -= 1
-                    # Valid if OFM is 2x IFM (-1 for align corners)
-                    if np.array_equal(out_shape, upscaled_shape):
-                        valid = True
-                        break
-        return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and align_corners={align_corners}"
-
-    @staticmethod
-    def constraint_matching_shapes(op):
-        "IFM and OFM shapes must match"
-        ifm_shape = op.ifm.shape
-        ofm_shape = op.ofm.shape
-        valid = ifm_shape == ofm_shape
-        return valid, f"Op has ifm_shape={ifm_shape} and ofm_shape={ofm_shape}"
-
-    @staticmethod
-    def constraint_splitv_inferred(op):
-        "Only one size is allowed to be inferred"
-        sizes = op.inputs[1].values
-        valid = np.count_nonzero(sizes == -1) <= 1
-        return valid, f"Op has multiple inferred sizes (-1): {sizes}"
-
-    @staticmethod
-    def constraint_axis_exists(op):
-        "Axis attribute must exist"
-        axis = op.attrs.get("axis")
-        valid = axis is not None
-        return valid, f"Op has axis={axis}"
-
-    @staticmethod
-    def constraint_axis_valid(op):
-        "Axis attribute must be in the range [0, <ofm_dimensions>)"
-        dims = len(op.ofm.shape)
-        axis = op.attrs["axis"]
-        axis += dims if axis < 0 else 0
-        valid = 0 <= axis < dims
-        return valid, f"Op has ofm_dimensions={dims} and axis attribute is: {axis}"
-
-    @staticmethod
-    def constraint_matching_dimensionality(op):
-        "All Input dimensionalities must match OFM dimensionality"
-        valid = True
-        extra = []
-        ofm_dim = len(op.ofm.shape)
-        tensors = [tens for tens in op.inputs if tens]
-        for tens in tensors:
-            dim = len(tens.shape)
-            if dim != ofm_dim:
-                valid = False
-                extra.append(f"Tensor '{tens.name}' has dimension: {dim}")
-        extra = ", ".join(extra)
-        return valid, f"Op has ofm_dimension={ofm_dim} and the list of mismatching inputs are: {extra}"
-
-    @staticmethod
-    def constraint_valid_dimensions(op):
-        "All Input dimensions must match OFM dimension in all axes except the one defined by the axis attribute"
-        valid = True
-        extra = []
-        ofm_shape = op.ofm.shape
-        ofm_dim = len(ofm_shape)
-        axis = op.attrs["axis"]
-        axis += ofm_dim if axis < 0 else 0
-        tensors = [tens for tens in op.inputs if tens]
-        for tens in tensors:
-            if any(tens.shape[dim] != ofm_shape[dim] for dim in range(ofm_dim) if dim != axis):
-                valid = False
-                extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}")
-        extra = ", ".join(extra)
-        return valid, f"Op has axis={axis}, ofm_shape={ofm_shape} and the list of mismatching inputs are: {extra}"
-
-    @staticmethod
-    def constraint_stridedslice_input_count(op):
-        "Exactly 4 Input tensors are required"
-        inputs = len(op.inputs)
-        valid = inputs == 4
-        return valid, f"Op has {inputs} inputs"
-
-    @staticmethod
-    def constraint_pad_input_count(op):
-        "Number of input tensors must be exactly 2"
-        inputs = len(op.inputs)
-        valid = inputs == 2
-        return valid, f"Op has {inputs} inputs"
-
-    @staticmethod
-    def constraint_pad_shape(op):
-        "The padding tensor must have the shape [3,2] or [4,2]"
-        valid = op.inputs[1].shape in ([3, 2], [4, 2])
-        return valid, f"The pad tensor has the shape: {op.inputs[1].shape}"
-
-    @classmethod
-    @docstring_format_args([list_formatter(supported_pad_dtypes)])
-    def constraint_pad_type(cls, op):
-        "Pad tensor must be of type: {}"
-        pad_tensor = op.inputs[1]
-        valid = pad_tensor.dtype in cls.supported_pad_dtypes
-        return valid, f"Tensor '{pad_tensor.name}' has data type: {pad_tensor.dtype}"
-
-    @staticmethod
-    def constraint_padding_dimensions(op):
-        "The pad tensor can only pad width and height"
-        pad_tensor = op.inputs[1].values
-
-        valid = sum(pad_tensor[-1, :]) == 0
-        if valid and len(pad_tensor) > 3:
-            valid = sum(pad_tensor[0, :]) == 0
-        return valid, f"First dimension padding: {pad_tensor[0,:]}, last dimension padding: {pad_tensor[-1,:]}"
-
-    @staticmethod
-    def constraint_pad_constant(op):
-        "The padding tensor must be constant"
-        pad_tensor = op.inputs[1].values
-        valid = pad_tensor is not None
-        return valid, f"Op has non-constant padding tensor: {op.inputs[1].values}"
-
-    @staticmethod
-    def constraint_stridedslice_inputs_const(op):
-        "Begin, End and Stride Input tensors must be constant"
-        valid = True
-        extra = []
-        _, begin, end, strides = op.inputs
-        if begin.values is None:
-            valid = False
-            extra.append(f"Begin tensor '{begin.name}'")
-        if end.values is None:
-            valid = False
-            extra.append(f"End tensor '{end.name}'")
-        if strides.values is None:
-            valid = False
-            extra.append(f"Stride tensor '{strides.name}'")
-        extra = ", ".join(extra)
-        return valid, f"Op has non-constant tensors: {extra}"
-
-    @staticmethod
-    def constraint_stridedslice_stride_values(op):
-        "All Strides values must be 1"
-        strides = op.inputs[3]
-        valid = all(stride == 1 for stride in strides.values)
-        return valid, f"Op has strides values {strides.values}"
-
-    @staticmethod
-    def constraint_ellipsis_mask(op):
-        "ellipsis_mask must be 0"
-        ellipsis = op.attrs["ellipsis_mask"]
-        valid = ellipsis == 0
-        return valid, f"Op has ellipsis mask as: {ellipsis}"
-
-    @staticmethod
-    def constraint_axis_masks(op):
-        "new_axis_mask and shrink_axis_mask cannot both be set"
-        new_axis = op.attrs["new_axis_mask"]
-        shrink_axis = op.attrs["shrink_axis_mask"]
-        valid = (new_axis == 0) or (shrink_axis == 0)
-        return valid, f"Op has new_axis_mask={new_axis} and shrink_axis_mask={shrink_axis}"
-
-    @staticmethod
-    def constraint_slice_ranges(op):
-        "Slice 'end' values must be greater than 'begin' values"
-        ifm, begin, end, _ = op.inputs
-        # Calculate offset begin/end
-        offset_begin = get_slice_offsets(ifm.shape, begin, op.attrs["begin_mask"], is_begin=True)
-        offset_end = get_slice_offsets(ifm.shape, end, op.attrs["end_mask"], is_begin=False)
-        # Check "end - begin" doesn't result in any zero or negative elements
-        valid = all((e - b) > 0 for b, e in zip(offset_begin, offset_end))
-        return valid, f"Op has begin_values={begin.values} and end_values={end.values}"
-
-    @staticmethod
-    def constraint_matching_inputs_types(op):
-        "Both Input data types must match"
-        ifm_dtype = op.ifm.dtype
-        ifm2_dtype = op.ifm2.dtype
-        valid = ifm_dtype == ifm2_dtype
-        return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}"
-
-    @staticmethod
-    def constraint_matching_signed(op):
-        "For IFM that are signed, OFM must also be signed"
-        valid = True
-        ifm_dtype = op.ifm.dtype
-        ofm_dtype = op.ofm.dtype
-        if ifm_dtype.type & BaseType.Signed:
-            valid = bool(ofm_dtype.type & BaseType.Signed)
-        return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}"
-
-    @staticmethod
-    def constraint_unsigned_valid(op):
-        "For IFM that are unsigned, OFM must either be the same type or int32"
-        valid = True
-        ifm_dtype = op.ifm.dtype
-        ofm_dtype = op.ofm.dtype
-        if ifm_dtype.type & BaseType.Unsigned:
-            valid = (ifm_dtype == ofm_dtype) or (ofm_dtype == DataType.int32)
-        return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}"
-
-    @staticmethod
-    def constraint_inputs_int32(op):
-        "Both Input data types must be int32"
-        ifm_dtype = op.ifm.dtype
-        ifm2_dtype = op.ifm2.dtype
-        valid = (ifm_dtype == DataType.int32) and (ifm2_dtype == DataType.int32)
-        return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}"
-
-    @staticmethod
-    def constraint_output_int32(op):
-        "OFM must be int32"
-        ofm_dtype = op.ofm.dtype
-        valid = ofm_dtype == DataType.int32
-        return valid, f"Op has ofm_dtype={ofm_dtype}"
-
-    @staticmethod
-    def constraint_input_8bit(op):
-        "IFM must be int8 or uint8"
-        ifm_dtype = op.ifm.dtype
-        valid = (ifm_dtype == DataType.int8) or (ifm_dtype == DataType.uint8)
-        return valid, f"Op has ifm_dtype={ifm_dtype}"
-
-    @staticmethod
-    def constraint_matching_quantization_parameters(op):
-        "Both Input quantization parameters must match OFM quantization parameters"
-        valid = True
-        extra = []
-        if not check_quantized_tens_scaling_equal(op.ofm, op.ifm):
-            valid = False
-            extra.append(op.ifm.name)
-        if op.ifm2 is not None and not check_quantized_tens_scaling_equal(op.ofm, op.ifm2):
-            valid = False
-            extra.append(op.ifm2.name)
-        extra = ", ".join(extra)
-        return valid, f"Op has tensors with different quantization parameters to the OFM '{op.ofm.name}': {extra}"
-
-    @staticmethod
-    def constraint_elemwise_batch_size(op):
-        "Batch size must be 1 for Input tensors with more than 2 dimensions"
-        valid = True
-        extra = []
-        for tens in (op.ifm, op.ifm2):
-            # Unary ops have ifm2 as None
-            if tens is not None:
-                if (len(tens.shape) > 2) and (tens.shape[0] != 1):
-                    valid = False
-                    extra.append(tens.name)
-        extra = ", ".join(extra)
-        return valid, f"Op has invalid input tensors: {extra}"
-
-    @staticmethod
-    def constraint_matching_either_shapes(op):
-        "At least one Input's shape must match the OFM's shape"
-        ifm_shape = op.ifm.shape
-        ifm2_shape = op.ifm2.shape if op.ifm2 else None
-        ofm_shape = op.ofm.shape
-        valid = (ifm_shape == ofm_shape) or (ifm2_shape == ofm_shape)
-        return valid, f"Op has ifm_shape={ifm_shape}, ifm2_shape={ifm2_shape} and ofm_shape={ofm_shape}"
-
-    @staticmethod
-    def constraint_broadcast_shapes(op):
-        "Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2"
-        ifm_shape = op.ifm.shape
-        ifm2_shape = op.ifm2.shape if op.ifm2 else None
-        ofm_shape = op.ofm.shape
-        valid = True
-        if ifm_shape is not None and ifm2_shape is not None:
-            # align trailing dimensions
-            size = min(len(ifm_shape), len(ifm2_shape))
-            for i, i2, o in zip(ifm_shape[-size:], ifm2_shape[-size:], ofm_shape[-size:]):
-                mi = max(i, i2)
-                # Input dimensions should match or one should be of dimension 1
-                # Output dimension should match the largest input dimension, together
-                # with constraint_match_either_shapes ensures broadcast from only one input
-                if not (i == i2 or i == 1 or i2 == 1) or o != mi:
-                    valid = False
-                    break
-
-        return valid, f"Op has ifm_shape={ifm_shape} and ifm2_shape={ifm2_shape}"
-
-    @staticmethod
-    def constraint_alpha_valid(op):
-        "Alpha must not be negative"
-        alpha = op.attrs["alpha"]
-        valid = alpha >= 0
-        return valid, f"Op has alpha={alpha}"
-
-    @staticmethod
-    def constraint_keep_dim_ifm_ofm(op):
-        "The IFM and OFM must have the same number of dimensions if keep_num_dims is set to true"
-        valid = True
-        if op.attrs.get("keep_num_dims"):
-            valid = len(op.ifm.shape) == len(op.ofm.shape)
-        return valid, f"Op has ifm shape={op.ifm.shape} and ofm shape={op.ofm.shape}"
-
-    @staticmethod
-    def constraint_mean_input_dims(op):
-        "Input tensor must be at least 2D"
-        dims = len(op.inputs[0].shape)
-        return 2 <= dims <= 4, f"Input is {dims}D"
-
-    @staticmethod
-    def constraint_mean_axis(op):
-        "Axis indices must correspond to height and width axes"
-        dims = len(op.inputs[0].shape)
-        axis = int(op.inputs[1].values) if op.inputs[1].shape == [] else list(op.inputs[1].values)
-        if dims == 2 or dims == 3:
-            valid = axis in (0, 1, [0], [1], [0, 1], [1, 0])
-        elif dims == 4:
-            valid = axis in (1, 2, [1], [2], [1, 2], [2, 1])
-        return valid, f"Axis is {axis}"
-
-    @classmethod
-    @docstring_format_args([mean_kernel_product_avgpool])
-    def constraint_mean_height_width_product_avgpool(cls, op):
-        """Product of height and width can be at most {}"""
-        shape = op.inputs[0].shape
-        hi = 0 if len(shape) < 4 else 1
-        h, w = shape[hi : hi + 2]
-        max_prod = cls.mean_kernel_product_avgpool
-        return h * w <= max_prod, f"Product of height and width is {h * w}"
-
-    @classmethod
-    @docstring_format_args([mean_kernel_product])
-    def constraint_mean_height_width_product(cls, op):
-        """Product of height and width can be at most {} when IFM and OFM have different scale or zero point,
-        or keep_dims is True"""
-        ifmq, ofmq = op.ifm.quantization, op.ofm.quantization
-        keep_dims = op.attrs.get("keep_dims")
-        # doesn't apply, size is checked by constraint_mean_height_width_product_avgpool
-        if not keep_dims and ifmq.scale_f32 == ofmq.scale_f32 and ifmq.zero_point == ofmq.zero_point:
-            return True, ""
-        shape = op.inputs[0].shape
-        hi = 0 if len(shape) < 4 else 1
-        h, w = shape[hi : hi + 2]
-        max_prod = cls.mean_kernel_product
-        return h * w <= max_prod, f"Product of height and width is {h * w}"
-
-    @classmethod
-    @docstring_format_args([mean_kernel_product_int8])
-    def constraint_mean_height_width_product_int8(cls, op):
-        """Product of IFM height and width can be at most {} when the following are true:
-        IFM dimensions are 4,
-        Axis indices are 1 and 2,
-        keep_dims is set to True and
-        IFM datatype is int8"""
-        shape = op.ifm.shape
-        axis = int(op.inputs[1].values) if op.inputs[1].shape == [] else list(op.inputs[1].values)
-        # doesn't apply, size is checked by constraint_mean_height_width_product_avgpool
-        # and constraint_mean_height_width_product
-        if (
-            len(shape) != 4
-            or op.ifm.dtype != DataType.int8
-            or not op.attrs.get("keep_dims")
-            or axis not in ([1, 2], [2, 1])
-        ):
-            return True, ""
-        hi = 0 if len(shape) < 4 else 1
-        h, w = shape[hi : hi + 2]
-        max_prod = cls.mean_kernel_product_int8
-        return h * w <= max_prod, f"Product of height and width is {h * w}"