ethosu/vela/tflite_model_semantic.py

# SPDX-FileCopyrightText: Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
#
# 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 TFLiteSemantic class which is a collection of TensorFlow lite model semantic 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 Op
from .supported_operators_util import docstring_format_args
from .supported_operators_util import list_formatter
from .tensor import check_quantized_tens_scaling_equal
from .tensor import shape_num_elements
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 TFLiteSemantic:
    # Categorised lists of operators
    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
    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 | set((Op.SquaredDifference,))
    shapeless_input_ops = binary_elem_wise_main_ops | set(
        (Op.Split, Op.SplitV, Op.Mean, Op.ExpandDims, Op.Quantize, Op.ArgMax)
    )
    reshape_ops = set(
        (
            Op.Reshape,
            Op.QuantizedReshape,
            Op.Squeeze,
            Op.ExpandDims,
        )
    )

    def __init__(self):
        # Setup the generic constraints. Note: the order matters
        self.generic_constraints = []
        self.generic_constraints.append(TFLiteSemantic.constraint_attributes_specified)
        self.generic_constraints.append(TFLiteSemantic.constraint_tens_no_dynamic)
        self.generic_constraints.append(TFLiteSemantic.constraint_tens_defined_shape)
        self.generic_constraints.append(TFLiteSemantic.constraint_tens_output_scalar)
        self.generic_constraints.append(TFLiteSemantic.constraint_tens_input_scalar)
        self.generic_constraints.append(TFLiteSemantic.constraint_tens_shape_size)

        self.generic_constraints.append(TFLiteSemantic.constraint_tens_quant_none_check)
        self.generic_constraints.append(TFLiteSemantic.constraint_tens_quant_scale)
        self.generic_constraints.append(TFLiteSemantic.constraint_quant_scale_inf)
        self.generic_constraints.append(TFLiteSemantic.constraint_none_const_tensors)

        # Setup specific constraints. Note: the order matters
        self.specific_constraints = defaultdict(list)

        # Conv-like checks:
        for op_type in TFLiteSemantic.convolution_like_ops:
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_stride_type)
            if op_type in TFLiteSemantic.convolution_ops:
                # Only Conv has groups
                self.specific_constraints[op_type].append(TFLiteSemantic.constraint_conv_groups_ifm_depth)
                self.specific_constraints[op_type].append(TFLiteSemantic.constraint_conv_groups_num_filters)
            if op_type not in TFLiteSemantic.transpose_convolution_ops:
                # Transpose Conv does not contain dilation
                self.specific_constraints[op_type].append(TFLiteSemantic.constraint_dilation_type)

        # Pooling checks:
        for op_type in TFLiteSemantic.pooling_ops:
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_stride_type)
        # AVG pooling specific checks:
        for op_type in TFLiteSemantic.avg_pooling_ops:
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types)
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_filter_type)
        # MAX pooling specific checks:
        for op_type in TFLiteSemantic.max_pooling_ops:
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types)
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_filter_type)

        # Concat specific checks:
        for op_type in (Op.Concat, Op.ConcatTFLite):
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_axis_exists)
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_axis_valid)
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_dimensionality)
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_valid_dimensions)
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_valid_dimensions_axis)

        # Element-wise checks:
        for op_type in TFLiteSemantic.elem_wise_main_ops:
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_either_shapes)
        # Unary specific checks:
        for op_type in TFLiteSemantic.unary_elem_wise_main_ops:
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types)
        # Binary Min/Max specific checks:
        for op_type in TFLiteSemantic.binary_elem_wise_min_max_ops:
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types)
        # Binary Add/Mul/Sub specific checks:
        for op_type in TFLiteSemantic.binary_elem_wise_add_mul_sub:
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_inputs_types)
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_signed)
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_unsigned_valid)

        # Ops reshaping dimensions: Reshape, Squeeze and ExpandDims
        for op_type in TFLiteSemantic.reshape_ops:
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_quant)
            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_elements)

        # Softmax specific checks:
        self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_matching_shapes)
        self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_matching_in_out_types)
        self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_beta_value_range)

        # Split specific checks:
        self.specific_constraints[Op.Split].append(TFLiteSemantic.constraint_split_axis)
        self.specific_constraints[Op.Split].append(TFLiteSemantic.constraint_split_num_splits)

        # SplitV specific checks:
        self.specific_constraints[Op.SplitV].append(TFLiteSemantic.constraint_splitv_inferred)

        # StridedSlice specific checks:
        self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_stridedslice_input_count)
        self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_stridedslice_inputs_const)
        self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_ellipsis_mask)
        self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_axis_masks)
        self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_slice_ranges)

        # FullyConnected specific checks:
        self.specific_constraints[Op.FullyConnected].append(TFLiteSemantic.constraint_fc_output_2d)
        self.specific_constraints[Op.FullyConnected].append(TFLiteSemantic.constraint_keep_dim_ifm_ofm)

        # Pad specific checks:
        self.specific_constraints[Op.Pad].append(TFLiteSemantic.constraint_pad_input_count)
        self.specific_constraints[Op.Pad].append(TFLiteSemantic.constraint_pad_constant)
        self.specific_constraints[Op.Pad].append(TFLiteSemantic.constraint_pad_output_shape)

        # HardSwish specific checks:
        self.specific_constraints[Op.HardSwish].append(TFLiteSemantic.constraint_input_8bit)
        self.specific_constraints[Op.HardSwish].append(TFLiteSemantic.constraint_matching_in_out_types)

        # Mean specific checks:
        self.specific_constraints[Op.Mean].append(TFLiteSemantic.constraint_mean_input_dims)
        self.specific_constraints[Op.Mean].append(TFLiteSemantic.constraint_mean_axis)

        # ArgMax specific checks:
        self.specific_constraints[Op.ArgMax].append(TFLiteSemantic.constraint_input_8bit)
        self.specific_constraints[Op.ArgMax].append(TFLiteSemantic.constraint_argmax_output)

        # UnidirectionalSequenceLstm specific checks:
        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_input_signed)
        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_matching_in_out_types)
        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_lstm_dimensions)
        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_lstm_inputs)
        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_lstm_intermediates)
        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_lstm_variables)

        # Exp specific checks
        self.specific_constraints[Op.Exp].append(TFLiteSemantic.constraint_input_signed)

        # Transpose specific checks
        self.specific_constraints[Op.Transpose].append(TFLiteSemantic.constraint_transpose_permutation_size)
        self.specific_constraints[Op.Transpose].append(TFLiteSemantic.constraint_transpose_permutation_values)

    def is_operator_semantic_valid(self, op):
        ext_type = optype_to_builtintype(op.type)

        if op.type in (Op.Placeholder, Op.SubgraphInput, Op.Const):
            return True

        # Generic constraints list filtered out to exclude certain constraints depending on op.type
        filtered_generic_constraints = []

        for constraint in self.generic_constraints:
            # Check constraint not in dictionary otherwise return empty array
            if constraint not in self.get_generic_constraint_exclude_list().get(op.type, []):
                filtered_generic_constraints.append(constraint)

        for constraint in filtered_generic_constraints + self.specific_constraints[op.type]:
            valid, extra = constraint(op)
            if not valid:
                print(
                    f"Warning: Unsupported TensorFlow Lite semantics for {ext_type} '{op.name}'. Placing on CPU instead"
                )
                print(f" - {constraint.__doc__}")
                if extra:
                    print(f"   {extra}")
                return False

        return True

    @staticmethod
    def get_generic_constraint_exclude_list():

        # Not all generic constraints can be applied to each operator
        generic_constraints_exclude_list = {
            Op.Shape: [
                TFLiteSemantic.constraint_tens_quant_none_check,
            ],
            Op.Quantize: [
                TFLiteSemantic.constraint_tens_no_dynamic,
                TFLiteSemantic.constraint_tens_output_scalar,
            ],
            Op.ArgMax: [
                TFLiteSemantic.constraint_tens_quant_none_check,
            ],
            Op.Transpose: [
                TFLiteSemantic.constraint_tens_quant_none_check,
            ],
        }
        return generic_constraints_exclude_list

    @staticmethod
    def constraint_none_const_tensors(op):
        "Constant tensors should not have NoneType-values"
        valid = True
        extra = ""
        for tens in filter(None, op.inputs):
            if len(tens.ops) > 0 and tens.ops[0].type == Op.Const and tens.values is None:
                valid = False
                extra = str(tens.name)
        return valid, f"Unexpected None value for constant tensor: {extra}"

    @staticmethod
    def constraint_attributes_specified(op):
        "All required operator attributes must be specified"
        # operators that have been created internally (i.e. not created as part of reading an input network) may not
        # have the read error attribute
        attribute_read_error = op.attrs.get("attribute_read_error", [])
        valid = len(attribute_read_error) == 0
        extra = ", ".join(attribute_read_error)
        return valid, f"Op has missing attributes: {extra}"

    @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)

    @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
                and 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)

    @staticmethod
    def constraint_fc_output_2d(op):
        """The output tensor(s) must have 2D shape"""
        valid = op.ifm.get_shape_as_2d(op.weights.shape[-2]) is not None
        extra = f"Op has non-2D output tensor '{op.ofm.name}'" if not valid else ""

        return valid, extra

    @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)}"

    @staticmethod
    def constraint_conv_groups_ifm_depth(op):
        """IFM depth must be a whole multiple of the filter kernel depth"""
        ifm_depth = op.ifm.shape[-1]  # nhwc
        kernel_ic = op.weights.shape[-2]  # hwio
        num_conv_groups = ifm_depth // kernel_ic

        if ifm_depth % kernel_ic == 0:
            op.attrs["num_conv_groups"] = num_conv_groups
            valid = True
        else:
            valid = False

        return valid, f"IFM depth = {ifm_depth} and filter kernel depth = {kernel_ic}"

    @staticmethod
    def constraint_conv_groups_num_filters(op):
        """Number of filter kernels must be equally divisible by the number of convolution groups"""
        ifm_depth = op.ifm.shape[-1]  # nhwc
        kernel_ic = op.weights.shape[-2]  # hwio
        kernel_oc = op.weights.shape[-1]  # hwio
        num_conv_groups = ifm_depth // kernel_ic

        if kernel_oc % num_conv_groups == 0:
            valid = True
        else:
            valid = False

        return valid, f"Filter kernels = {kernel_oc} and convolution groups = {num_conv_groups}"

    @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)}"

    @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 np.any(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.any(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_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)}"

    @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_split_axis(op):
        "Axis value must be in the range [-RANK(IFM) to +RANK(IFM))"
        axis_tens = op.inputs[0]
        input_tens = op.inputs[1]
        dims = len(input_tens.shape)
        # handle axis being a scalar or 1-D array
        if axis_tens.values.ndim == 0:
            axis = int(axis_tens.values)
        else:
            axis = int(axis_tens.values[0])
        axis += dims if axis < 0 else 0
        valid = 0 <= axis < dims
        return valid, f"Op has ifm_dimensions={dims} and axis value is: {axis}"

    @staticmethod
    def constraint_split_num_splits(op):
        "Axis must be divisible by number of splits"
        num_splits = op.attrs.get("num_splits")
        axis_tens = op.inputs[0]
        input_tens = op.inputs[1]
        dims = len(input_tens.shape)
        # handle axis being a scalar or 1-D array
        if axis_tens.values.ndim == 0:
            axis = int(axis_tens.values)
        else:
            axis = int(axis_tens.values[0])
        axis += dims if axis < 0 else 0
        valid = input_tens.shape[axis] % num_splits == 0
        return valid, f"Op has ifm shape={input_tens.shape} axis={axis} num_splits={num_splits}"

    @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_valid_dimensions_axis(op):
        """The size of the OFM axis must match the sum of all IFM axis 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

        sum_ifm_axis = 0
        tensors = [tens for tens in op.inputs if tens]
        for tens in tensors:
            sum_ifm_axis += tens.shape[axis]
            extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}")

        valid = sum_ifm_axis == ofm_shape[axis]
        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_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_pad_output_shape(op):
        "Shape of output tensor must equal to size of input tensor plus padding"
        input_shape = op.inputs[0].shape
        expected_output_shape = op.outputs[0].shape
        pad_tensor = op.inputs[1].values
        actual_output_shape = input_shape + pad_tensor.T[0] + pad_tensor.T[1]
        valid = np.array_equal(actual_output_shape, expected_output_shape)
        return valid, f"Op has wrong output tensor shape: {expected_output_shape}, has shape: {actual_output_shape}"

    @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_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}"

    def _get_slice_offsets(input_shape, offset_tens, offset_mask, is_begin=True):
        # For strided slice operator: get start or end offsets
        # input_shape: List[int], offset_tens: Tensor, offset_mask: int, is_begin: bool = True
        offsets = len(input_shape) * [0] if is_begin else input_shape[:]
        for idx in range(len(input_shape)):
            # If the i:th bit in the mask is not set then the value in offset_tens[i] should be used, otherwise it
            # should be ignored
            if (offset_mask & (1 << idx)) == 0:
                offsets[idx] = offset_tens.values[idx]
                if offsets[idx] < 0:
                    # Convert negative indexing to positive ones
                    offsets[idx] += input_shape[idx]
        return offsets

    @staticmethod
    def constraint_slice_ranges(op):
        "Slice 'end' values must be greater than 'begin' values"
        ifm, begin, end, _ = op.inputs
        shrink_axis_mask = op.attrs["shrink_axis_mask"]
        # Calculate offset begin/end
        offset_begin = TFLiteSemantic._get_slice_offsets(ifm.shape, begin, op.attrs["begin_mask"], is_begin=True)
        offset_end = TFLiteSemantic._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 = True
        # if a shrink mask bit is set then the end position provided by the operation should be ignored, and instead a
        # new end position should be calculated so that calculations in the graph optimiser, such as (end - start),
        # result in the correct value. otherwise, we just need to check that the begin and end values are valid
        for i in range(len(ifm.shape)):
            if (shrink_axis_mask & (1 << i)) != 0:
                offset_end[i] = offset_begin[i] + 1
            else:
                if offset_end[i] <= offset_begin[i]:
                    valid = False

        op.attrs["offset_begin"] = offset_begin
        op.attrs["offset_end"] = 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_input_signed(op):
        "IFM must be int8 or int16"
        ifm_dtype = op.ifm.dtype
        valid = (ifm_dtype == DataType.int8) or (ifm_dtype == DataType.int16)
        return valid, f"Op has ifm_dtype={ifm_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_argmax_output(op):
        "OFM must be int32 or int64"
        ofm_dtype = op.ofm.dtype
        valid = ofm_dtype in (DataType.int32, DataType.int64)
        return valid, f"Op has ofm_dtype={ofm_dtype}"

    @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_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):
        """Requirements for axis parameter:
        When IFM tensor is 2D:
          - Reduction in both axes is supported.
        When IFM tensor is 3D or 4D:
          - Reduction in Batch axis is only supported if batch size is 1.
          - Reduction in both Height and Width axes is supported.
          - Reduction in Depth axis is supported if at least one of H,W,C are of size 1."""
        input_shape = op.inputs[0].shape
        dims = len(input_shape)
        if op.inputs[1].shape == []:
            axis = [int(op.inputs[1].values)]
        else:
            axis = list(op.inputs[1].values)
        valid = True

        for ax in axis:
            if ax < 0 or ax >= dims:
                return False, "Axis parameter is out of bounds. axis: {axis}, dims: {dims}. "

            # Batch is only supported if batch shape is 1
            if dims == 4 and ax == 0:
                if input_shape[0] != 1:
                    valid = False
                    break

            # Depth is supported if any of h,w,c == 1
            if dims == 3:
                if ax == 2 and not any([s == 1 for s in input_shape]):
                    valid = False
                    break

            # Depth is supported if any of h,w,c == 1
            if dims == 4:
                if ax == 3 and not any([s == 1 for s in input_shape[1:]]):
                    valid = False
                    break

        return valid, f"Shape is {input_shape}, Axis is {axis}."

    @staticmethod
    def constraint_matching_in_out_quant(op):
        "Input and output quantisation must match."
        if not check_quantized_tens_scaling_equal(op.ifm, op.ofm):
            return False, "IFM and OFM quantisation parameters are not equal."
        return True, "IFM and OFM quantisation parameters matches."

    @staticmethod
    def constraint_matching_in_out_elements(op):
        "Input and output number of elements must match."
        if shape_num_elements(op.ifm.shape) != shape_num_elements(op.ofm.shape):
            return False, f"IFM {op.ifm.shape} and OFM {op.ofm.shape} number of elements are not equal."
        return True, "IFM and OFM number of elements are equal."

    @staticmethod
    def constraint_lstm_dimensions(op):
        "IFM and OFM must have 3D shape"
        valid = len(op.ifm.shape) == len(op.ofm.shape) == 3
        return valid, f"Op has ifm shape {op.ifm.shape} and ofm shape {op.ofm.shape}"

    @staticmethod
    def constraint_lstm_inputs(op):
        "Must have 24 input tensors"
        n_inputs = len(op.inputs)
        return n_inputs == 24, f"Op has {n_inputs} inputs"

    @staticmethod
    def constraint_lstm_intermediates(op):
        "Must have 5 intermediate tensors"
        n_intermediates = len(op.intermediates)
        return n_intermediates == 5, f"Op has {n_intermediates} intermediates"

    @staticmethod
    def constraint_lstm_variables(op):
        "State tensors must be variable"
        valid = True
        extra = []
        for tens in op.inputs[18:20]:
            if not tens.is_variable:
                valid = False
                extra.append(tens.name)
        extra = ", ".join(extra)
        return valid, f"Op has non-variable state tensor(s): {extra}"

    @staticmethod
    def constraint_transpose_permutation_size(op):
        "Permutation array must be a 1D tensor with RANK(IFM) elements"
        dims = len(op.inputs[0].shape)
        perm = op.inputs[1]
        valid = len(perm.shape) == 1 and perm.shape[0] == dims
        return valid, f"Op has ifm_dimension={dims} and permutation shape {perm.shape}"

    @staticmethod
    def constraint_transpose_permutation_values(op):
        "Permutation array must have constant values in the range [0, RANK(IFM))"
        dims = len(op.inputs[0].shape)
        perm = op.inputs[1]
        valid = False
        if perm.values is not None:
            valid = not any([val < 0 or val >= dims for val in perm.values])
        return valid, f"Op has ifm_dimension={dims} and permutation values are: {perm.values}"


def tflite_semantic_checker(nng):
    semantic_checker = TFLiteSemantic()
    for sg in nng.subgraphs:
        for op in sg.get_all_ops():
            op.run_on_npu = semantic_checker.is_operator_semantic_valid(op)
    return nng