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# 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 TosaSupportedOperators class which is a collection of all supported operators and parameter checks.
from collections import defaultdict
from .data_type import DataType
from .operation import Op
from .supported_operators_util import docstring_format_args
from .supported_operators_util import list_formatter
from .tosa_mapping import optype_to_tosa_op_type
class TosaSupportedOperators:
# TODO currently sparsely populated
# Categorised lists of supported operators
convolution_ops = set((Op.Conv2DBias,))
convolution_like_ops = convolution_ops
mac_main_ops = convolution_like_ops
type_conversion_ops = set((Op.Rescale,))
relu_ops = set((Op.Clamp, Op.ReluN,))
activation_ops = relu_ops
npu_post_ops = activation_ops
supported_operators = mac_main_ops | type_conversion_ops | npu_post_ops
# Supported data types
# TODO will differ compared to TensorFlow Lite, currently set to the same
supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32))
def __init__(self):
# Setup the generic constraints. Note: the order matters
self.generic_constraints = []
self.generic_constraints.append(TosaSupportedOperators.constraint_tens_dtype)
# Setup specific constraints. Note: the order matters
self.specific_constraints = defaultdict(list)
def is_operator_supported(self, op):
ext_type = optype_to_tosa_op_type(op.type)
if op.type not in TosaSupportedOperators.supported_operators:
if op.type not in (Op.Placeholder, Op.SubgraphInput, Op.Const):
print(f"Info: {ext_type} '{op.name}' is not a NPU op")
return False
for constraint in self.generic_constraints + self.specific_constraints[op.type]:
valid, extra = constraint(op)
if not valid:
print(f"Warning: {ext_type} '{op.name}' is not supported on the NPU")
print(f" - {constraint.__doc__}")
if extra:
print(f" {extra}")
return False
return True
# TODO this function is the same for TensorFlow Lite, but input might differ
@classmethod
@docstring_format_args([list_formatter(supported_op_dtypes)])
def constraint_tens_dtype(cls, op):
"Tensors must be of type: {}"
valid = True
extra = []
tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
if not tensors:
tensors = [tens for tens in op.inputs if tens]
for tens in tensors:
if tens.dtype not in cls.supported_op_dtypes:
valid = False
extra.append(f"Tensor '{tens.name}' has data type: {tens.dtype}")
return valid, ", ".join(extra)
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