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# Copyright (C) 2020-2021 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:
# Utilities used in vela unit tests
import numpy as np
from ethosu.vela import architecture_features
from ethosu.vela.data_type import DataType
from ethosu.vela.nn_graph import Subgraph
from ethosu.vela.operation import Op
from ethosu.vela.operation import Operation
from ethosu.vela.tensor import create_const_tensor
from ethosu.vela.tensor import QuantizationParameters
from ethosu.vela.tensor import Tensor
def create_arch():
return architecture_features.create_default_arch(architecture_features.Accelerator.Ethos_U55_128)
def default_quant_params():
qp = QuantizationParameters()
qp.scale_f32 = np.float32(1)
qp.zero_point = 0
return qp
def create_elemwise_op(
op_type,
name,
ifm_shape,
ifm2_shape,
ofm_shape,
datatype=DataType.uint8,
ifm_quant=default_quant_params(),
ifm2_quant=default_quant_params(),
ofm_quant=default_quant_params(),
):
# Creates elementwise operation with constant IFM/IFM2
if datatype.size_in_bytes() == 1:
np_type = np.uint8
elif datatype.size_in_bytes() == 2:
np_type = np.int16
else:
np_type = np.int32
op = Operation(op_type, name)
op.add_input_tensor(
create_const_tensor(name + "_ifm", ifm_shape, datatype, np.zeros(ifm_shape), np_type, quantization=ifm_quant)
)
if ifm2_shape is not None:
op.add_input_tensor(
create_const_tensor(
name + "_ifm2", ifm2_shape, datatype, np.zeros(ifm2_shape), np_type, quantization=ifm2_quant
)
)
ofm = Tensor(ofm_shape, datatype, name + "_ofm")
ofm.quantization = ofm_quant
op.set_output_tensor(ofm)
op.set_ifm_ofm_shapes()
return op
def create_op_with_quant_tensors(
op_type, ifm_shape, ofm_shape, weights_shape=None, bias_shape=None, datatype=DataType.uint8, set_ifm_ofm_shapes=True
):
ifm = Tensor(ifm_shape, datatype, "in")
ifm.quantization = default_quant_params()
ofm = Tensor(ofm_shape, datatype, "out")
ofm.quantization = default_quant_params()
op = Operation(op_type, "op")
op.add_input_tensor(ifm)
op.set_output_tensor(ofm)
# Optional weight tensor
if weights_shape is not None:
if datatype.size_in_bytes() == 1:
np_type = np.uint8
elif datatype.size_in_bytes() == 2:
np_type = np.int16
else:
np_type = np.int32
qp = default_quant_params()
if op.type is not Op.FullyConnected:
qp.zero_point = np.zeros(weights_shape)
weights = create_const_tensor(
"weights", weights_shape, datatype, np.zeros(weights_shape), np_type, quantization=qp
)
op.add_input_tensor(weights)
# Optional bias tensor
if bias_shape is not None:
qp = default_quant_params()
if op.type is not Op.FullyConnected:
qp.zero_point = np.zeros(bias_shape)
bias = create_const_tensor("bias", bias_shape, DataType.int32, np.zeros(bias_shape), np.int32, quantization=qp)
op.add_input_tensor(bias)
if set_ifm_ofm_shapes:
op.set_ifm_ofm_shapes()
return op
def create_op(op_type, inputs, output, attrs=None, set_ifm_ofm_shapes=True):
op = Operation(op_type, output.name + "_op")
for input in inputs:
op.add_input_tensor(input)
op.set_output_tensor(output)
if attrs is not None:
op.attrs = attrs
if set_ifm_ofm_shapes:
op.set_ifm_ofm_shapes()
return op
def create_subgraph(op_list):
# Creates subgraph using the given list of operations
sg = Subgraph()
all_inputs = set(tens for op in op_list for tens in op.inputs)
# Reversing, so that the resulting subgraph has same order as op_list
for op in op_list[::-1]:
for tens in op.outputs:
if tens not in all_inputs and tens not in sg.output_tensors:
sg.output_tensors.append(tens)
return sg
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