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|
# 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_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 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_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)
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_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_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_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):
"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."
@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}"
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
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