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, 674 insertions, 0 deletions

diff --git a/ethosu/vela/tflite_supported_operators.py b/ethosu/vela/tflite_supported_operators.py
new file mode 100644
index 00000000..cb3d5048
--- /dev/null
+++ b/ethosu/vela/tflite_supported_operators.py
@@ -0,0 +1,674 @@
+# 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 TFLiteSupportedOperators class which is a collection of all TFLite supported operators and parameter checks.
+from collections import defaultdict
+
+import numpy as np
+
+from .data_type import DataType
+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 TFLiteSupportedOperators:
+    # 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
+    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
+
+    def __init__(self):
+        # Setup the generic constraints. Note: the order matters
+        self.generic_constraints = []
+        self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_dtype)
+        self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_int32_ops)
+        self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_dimension)
+        self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_quant_per_axis)
+        self.generic_constraints.append(TFLiteSupportedOperators.constraint_faf)
+        self.generic_constraints.append(TFLiteSupportedOperators.constraint_faf_type)
+
+        # Setup specific constraints. Note: the order matters
+        self.specific_constraints = defaultdict(list)
+
+        # Conv-like checks:
+        for op_type in TFLiteSupportedOperators.convolution_like_ops:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_stride_range)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_dilation_range)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_dilated_height_range)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_dilated_product_range)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_type)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_const)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_limit)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_type)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_40bit)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_batch_size)
+        # Depthwise Conv specific checks:
+        for op_type in TFLiteSupportedOperators.depthwise_convolution_ops:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_depth_multiplier)
+        # Transpose Conv specific checks:
+        for op_type in TFLiteSupportedOperators.transpose_convolution_ops:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_tconv_stride)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_tconv_same)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_tconv_valid)
+
+        # Pooling checks:
+        for op_type in TFLiteSupportedOperators.pooling_ops:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_batch_size)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_stride_range)
+        # AVG pooling specific checks:
+        for op_type in TFLiteSupportedOperators.avg_pooling_ops:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_range)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_height_range_valid_pad)
+            self.specific_constraints[op_type].append(
+                TFLiteSupportedOperators.constraint_filter_product_range_valid_pad
+            )
+        # MAX pooling specific checks:
+        for op_type in TFLiteSupportedOperators.max_pooling_ops:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_height_range)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_product_range)
+
+        # Resizing specific checks:
+        for op_type in TFLiteSupportedOperators.resizing_ops:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_resize)
+
+        # Vector Product specific checks:
+        for op_type in TFLiteSupportedOperators.fc_vector_products:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_type)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_const)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_type)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_40bit)
+
+        # Element-wise checks:
+        for op_type in TFLiteSupportedOperators.elem_wise_main_ops:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_elemwise_batch_size)
+        # Binary Min/Max specific checks:
+        for op_type in TFLiteSupportedOperators.binary_elem_wise_min_max_ops:
+            self.specific_constraints[op_type].append(
+                TFLiteSupportedOperators.constraint_matching_quantization_parameters
+            )
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_broadcast_shapes)
+        # Binary Add/Mul/Sub specific checks:
+        for op_type in TFLiteSupportedOperators.binary_elem_wise_add_mul_sub:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_broadcast_shapes)
+        # Binary Shift specific checks:
+        for op_type in TFLiteSupportedOperators.binary_elem_wise_shift_ops:
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_inputs_int32)
+            self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_broadcast_shapes)
+
+        # SHL specific checks:
+        self.specific_constraints[Op.SHL].append(TFLiteSupportedOperators.constraint_output_int32)
+
+        # CLZ specific checks:
+        self.specific_constraints[Op.CLZ].append(TFLiteSupportedOperators.constraint_output_int32)
+
+        # StridedSlice specific checks:
+        self.specific_constraints[Op.StridedSlice].append(
+            TFLiteSupportedOperators.constraint_stridedslice_stride_values
+        )
+
+        # Pad specific checks:
+        self.specific_constraints[Op.Pad].append(TFLiteSupportedOperators.constraint_pad_shape)
+        self.specific_constraints[Op.Pad].append(TFLiteSupportedOperators.constraint_padding_dimensions)
+        self.specific_constraints[Op.Pad].append(TFLiteSupportedOperators.constraint_pad_type)
+
+        # Mean specific checks:
+        self.specific_constraints[Op.Mean].append(TFLiteSupportedOperators.constraint_mean_height_width_product_avgpool)
+        self.specific_constraints[Op.Mean].append(TFLiteSupportedOperators.constraint_mean_height_width_product)
+        self.specific_constraints[Op.Mean].append(TFLiteSupportedOperators.constraint_mean_height_width_product_int8)
+
+    def is_operator_supported(self, op):
+        ext_type = optype_to_builtintype(op.type)
+        if op.type not in TFLiteSupportedOperators.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
+
+    @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)
+
+    @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)
+
+    @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
+
+    @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}"
+
+    @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_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"
+
+    @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 TFLiteSupportedOperators.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 TFLiteSupportedOperators.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_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_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_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_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_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}"
+
+    @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}"