| # 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 |
| 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_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 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) |
| |
| # 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_input_8bit) |
| 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) |
| |
| 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, |
| ], |
| } |
| 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_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_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) |
| axis = int(axis_tens.values) |
| 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) |
| axis = int(axis_tens.values) |
| 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_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_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_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): |
| "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}" |
| |
| @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." |
| |
| |
| 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 |