1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
|
# 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
|