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
diff --git a/ethosu/vela/tflite_model_semantic.py b/ethosu/vela/tflite_model_semantic.py
new file mode 100644
index 0000000..c8b373a
--- /dev/null
+++ b/ethosu/vela/tflite_model_semantic.py
@@ -0,0 +1,527 @@
+# Copyright (C) 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 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 get_slice_offsets
+from .operation import Op
+from .supported_operators_util import docstring_format_args
+from .supported_operators_util import list_formatter
+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))
+
+ 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)
+
+ # 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)
+ 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)
+
+ # 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)
+
+ # 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)
+
+ # 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)
+
+ # LeakyRelu specific checks:
+ self.specific_constraints[Op.LeakyRelu].append(TFLiteSemantic.constraint_alpha_valid)
+
+ # 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)
+
+ 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
+
+ for constraint in self.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 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.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 = True
+ extra = []
+ for tens in op.outputs:
+ if len(tens.shape) != 2:
+ valid = False
+ extra.append(f"Tensor '{tens.name}' is {len(tens.shape)}D")
+ return valid, ", ".join(extra)
+
+ @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 ofm_scale < np.finfo(np.float32).tiny:
+ return (
+ False,
+ f"The quantization scale of the output tensor is {ofm_scale}, "
+ + f"minimum supported is: {np.finfo(np.float32).tiny}",
+ )
+ if op.ifm is not None and op.ifm.is_quantized():
+ ifm_scale = op.ifm.quantization.scale_f32
+ if np.isinf(ifm_scale / ofm_scale):
+ return (
+ False,
+ f"IFM scale divided by OFM scale is infinite, ifm_scale={ifm_scale} ofm_scale={ofm_scale}",
+ )
+ return True, "Op's quantization is ok"
+
+ @staticmethod
+ def constraint_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_splitv_inferred(op):
+ "Only one size is allowed to be inferred"
+ sizes = op.inputs[1].values
+ valid = np.count_nonzero(sizes == -1) <= 1
+ return valid, f"Op has multiple inferred sizes (-1): {sizes}"
+
+ @staticmethod
+ def constraint_axis_exists(op):
+ "Axis attribute must exist"
+ axis = op.attrs.get("axis")
+ valid = axis is not None
+ return valid, f"Op has axis={axis}"
+
+ @staticmethod
+ def constraint_axis_valid(op):
+ "Axis attribute must be in the range [0, <ofm_dimensions>)"
+ dims = len(op.ofm.shape)
+ axis = op.attrs["axis"]
+ axis += dims if axis < 0 else 0
+ valid = 0 <= axis < dims
+ return valid, f"Op has ofm_dimensions={dims} and axis attribute is: {axis}"
+
+ @staticmethod
+ def constraint_matching_dimensionality(op):
+ "All Input dimensionalities must match OFM dimensionality"
+ valid = True
+ extra = []
+ ofm_dim = len(op.ofm.shape)
+ tensors = [tens for tens in op.inputs if tens]
+ for tens in tensors:
+ dim = len(tens.shape)
+ if dim != ofm_dim:
+ valid = False
+ extra.append(f"Tensor '{tens.name}' has dimension: {dim}")
+ extra = ", ".join(extra)
+ return valid, f"Op has ofm_dimension={ofm_dim} and the list of mismatching inputs are: {extra}"
+
+ @staticmethod
+ def constraint_valid_dimensions(op):
+ "All Input dimensions must match OFM dimension in all axes except the one defined by the axis attribute"
+ valid = True
+ extra = []
+ ofm_shape = op.ofm.shape
+ ofm_dim = len(ofm_shape)
+ axis = op.attrs["axis"]
+ axis += ofm_dim if axis < 0 else 0
+ tensors = [tens for tens in op.inputs if tens]
+ for tens in tensors:
+ if any(tens.shape[dim] != ofm_shape[dim] for dim in range(ofm_dim) if dim != axis):
+ valid = False
+ extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}")
+ extra = ", ".join(extra)
+ return valid, f"Op has axis={axis}, ofm_shape={ofm_shape} and the list of mismatching inputs are: {extra}"
+
+ @staticmethod
+ def constraint_stridedslice_input_count(op):
+ "Exactly 4 Input tensors are required"
+ inputs = len(op.inputs)
+ valid = inputs == 4
+ return valid, f"Op has {inputs} inputs"
+
+ @staticmethod
+ def constraint_pad_input_count(op):
+ "Number of input tensors must be exactly 2"
+ inputs = len(op.inputs)
+ valid = inputs == 2
+ return valid, f"Op has {inputs} inputs"
+
+ @staticmethod
+ def constraint_pad_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}"
+
+ @staticmethod
+ def constraint_slice_ranges(op):
+ "Slice 'end' values must be greater than 'begin' values"
+ ifm, begin, end, _ = op.inputs
+ # Calculate offset begin/end
+ offset_begin = get_slice_offsets(ifm.shape, begin, op.attrs["begin_mask"], is_begin=True)
+ offset_end = get_slice_offsets(ifm.shape, end, op.attrs["end_mask"], is_begin=False)
+ # Check "end - begin" doesn't result in any zero or negative elements
+ valid = all((e - b) > 0 for b, e in zip(offset_begin, offset_end))
+ return valid, f"Op has begin_values={begin.values} and end_values={end.values}"
+
+ @staticmethod
+ def constraint_matching_inputs_types(op):
+ "Both Input data types must match"
+ ifm_dtype = op.ifm.dtype
+ ifm2_dtype = op.ifm2.dtype
+ valid = ifm_dtype == ifm2_dtype
+ return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}"
+
+ @staticmethod
+ def constraint_matching_signed(op):
+ "For IFM that are signed, OFM must also be signed"
+ valid = True
+ ifm_dtype = op.ifm.dtype
+ ofm_dtype = op.ofm.dtype
+ if ifm_dtype.type & BaseType.Signed:
+ valid = bool(ofm_dtype.type & BaseType.Signed)
+ return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}"
+
+ @staticmethod
+ def constraint_unsigned_valid(op):
+ "For IFM that are unsigned, OFM must either be the same type or int32"
+ valid = True
+ ifm_dtype = op.ifm.dtype
+ ofm_dtype = op.ofm.dtype
+ if ifm_dtype.type & BaseType.Unsigned:
+ valid = (ifm_dtype == ofm_dtype) or (ofm_dtype == DataType.int32)
+ return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}"
+
+ @staticmethod
+ def constraint_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_alpha_valid(op):
+ "Alpha must not be negative"
+ alpha = op.attrs["alpha"]
+ valid = alpha >= 0
+ return valid, f"Op has alpha={alpha}"
+
+ @staticmethod
+ def constraint_keep_dim_ifm_ofm(op):
+ "The IFM and OFM must have the same number of dimensions if keep_num_dims is set to true"
+ valid = True
+ if op.attrs.get("keep_num_dims"):
+ valid = len(op.ifm.shape) == len(op.ofm.shape)
+ return valid, f"Op has ifm shape={op.ifm.shape} and ofm shape={op.ofm.shape}"
+
+ @staticmethod
+ def constraint_mean_input_dims(op):
+ "Input tensor must be at least 2D"
+ dims = len(op.inputs[0].shape)
+ return 2 <= dims <= 4, f"Input is {dims}D"
+
+ @staticmethod
+ def constraint_mean_axis(op):
+ "Axis indices must correspond to height and width axes"
+ dims = len(op.inputs[0].shape)
+ axis = int(op.inputs[1].values) if op.inputs[1].shape == [] else list(op.inputs[1].values)
+ if dims == 2 or dims == 3:
+ valid = axis in (0, 1, [0], [1], [0, 1], [1, 0])
+ elif dims == 4:
+ valid = axis in (1, 2, [1], [2], [1, 2], [2, 1])
+ return valid, f"Axis is {axis}"
+
+
+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