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# SPDX-FileCopyrightText: Copyright 2020-2021, 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:
# 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 Graph
from ethosu.vela.nn_graph import PassPlacement
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
op = Operation(op_type, name)
op.add_input_tensor(
create_const_tensor(name + "_ifm", ifm_shape, datatype, np.zeros(ifm_shape), 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), 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:
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), 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), 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:
if input: # Add regular tensor input
op.add_input_tensor(input)
else: # Add optional (None) inputs for operators with sparse input positioning
op.inputs.append(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_lstm_op(batches, times, features, outputs, datatype):
input_shape = [batches, times, features]
output_shape = [batches, times, outputs]
weight_shape = [features, outputs]
state_shape = [batches, outputs]
bias_shape = [outputs]
ifm = Tensor(input_shape, datatype, "in")
ifm.quantization = default_quant_params()
ofm = Tensor(output_shape, datatype, "out")
ofm.quantization = default_quant_params()
bias_dtype = DataType.int64 if datatype == DataType.int16 else DataType.int32
bias = create_const_tensor("bias", bias_shape, bias_dtype, [0] * outputs)
weight_q = default_quant_params()
weight = create_const_tensor("weight", weight_shape, DataType.int8, np.ones(weight_shape), quantization=weight_q)
output_state = Tensor(state_shape, datatype, "output_state")
output_state.quantization = default_quant_params()
output_state.is_variable = True
cell_state = Tensor(state_shape, DataType.int16, "cell_state")
cell_state.quantization = default_quant_params()
cell_state.is_variable = True
intermediate = Tensor([], DataType.float32, "intermediate")
hidden_scale_intermediate = Tensor([], datatype, "effective_hidden_scale_intermediate")
hidden_scale_intermediate.quantization = default_quant_params()
peephole = None
projection = None
normalisation = None
inputs = [
ifm,
weight,
weight,
weight,
weight,
weight,
weight,
weight,
weight,
peephole,
peephole,
peephole,
bias,
bias,
bias,
bias,
projection,
projection,
output_state,
cell_state,
normalisation,
normalisation,
normalisation,
normalisation,
]
op = create_op(Op.UnidirectionalSequenceLstm, inputs, ofm)
op.intermediates = [intermediate, intermediate, intermediate, intermediate, hidden_scale_intermediate]
return op
def create_subgraph(op_list):
# Creates subgraph using the given list of operations
sg = Subgraph()
sg.placement = PassPlacement.Npu
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
def create_graph(op_list):
# Creates subgraph using the given list of operations
nng = Graph()
sg = create_subgraph(op_list)
nng.subgraphs.append(sg)
return nng
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