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
|
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
# This script opens an existing model in onnx format and attempts to
# move initializers from model.graph.initializer field to model.graph.sparse_initializer field
# and convert them into ONNX COO flat index format.
from __future__ import annotations
import argparse
import logging
import sys
import numpy as np
import onnx
from onnx import ModelProto, TensorProto, numpy_helper
logger = logging.getLogger(__name__)
real_types = {int(TensorProto.FLOAT), int(TensorProto.DOUBLE)}
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True, type=str, help="input model path")
parser.add_argument("--output", required=True, type=str, help="output model path")
parser.add_argument(
"--exclude", required=False, type=str, help="semicolon separated list of initializer names to exclude"
)
parser.add_argument("--tolerance", required=False, type=float, default=1e-6, help="FP absolute tolerance.")
parser.add_argument(
"--sparsity_threshold",
required=False,
type=float,
default=0.5,
help="convert to sparse initializers if sparsity is at least this much",
)
parser.add_argument("--verbose", required=False, action="store_true")
parser.set_defaults(verbose=False)
args = parser.parse_args()
return args
def setup_logging(verbose): # type: (bool) -> None
log_handler = logging.StreamHandler(sys.stdout)
if verbose:
log_handler.setFormatter(logging.Formatter("[%(filename)s:%(lineno)s - %(funcName)20s()] %(message)s"))
logging_level = logging.DEBUG
else:
log_handler.setFormatter(logging.Formatter("%(filename)20s: %(message)s"))
logging_level = logging.INFO
log_handler.setLevel(logging_level)
logger.addHandler(log_handler)
logger.setLevel(logging_level)
def convert_tensor_to_sparse(tensor, sparsity_threshold, tolerance): # type: (TensorProto, float, float) -> Tuple[SparseTensorProto, float]
"""returns a tuple of sparse_tensor and sparsity level"""
values = []
indices = []
nnz_count = 0
tensor_data = numpy_helper.to_array(tensor).flatten()
data_len = len(tensor_data)
if tensor_data.dtype in real_types:
for index in range(data_len):
el = tensor_data[index]
if abs(el) <= tolerance:
values.append(el)
indices.append(index)
nnz_count += 1
else:
for index in range(data_len):
el = tensor_data[index]
if el != 0:
values.append(el)
indices.append(index)
nnz_count += 1
sparsity = 1.0 - float(nnz_count) / data_len
ind_data_type = TensorProto.INT8
ind_dtype = np.int8
ind_len = len(indices)
max_indices_value = 0
if ind_len > 0:
max_indices_value = indices[-1]
if max_indices_value <= np.iinfo(np.int8).max:
ind_data_type = TensorProto.INT8
ind_dtype = np.int8
elif max_indices_value <= np.iinfo(np.int16).max:
ind_data_type = TensorProto.INT16
ind_dtype = np.int16
elif max_indices_value <= np.iinfo(np.int32).max:
ind_data_type = TensorProto.INT32
ind_dtype = np.int32
else:
ind_data_type = TensorProto.INT64
ind_dtype = np.int64
logger.debug(
f"initializer={tensor.name}, dtype={tensor_data.dtype}, \
data_len={data_len}, nnz={nnz_count}, sparsity={sparsity}, \
max_indices_value={max_indices_value}, sparse_indices_type={ind_dtype}"
)
if sparsity < sparsity_threshold:
return (object(), sparsity)
tensor_data_bytes = tensor_data.nbytes
# create np array and cast data to the appropriate type
np_values = np.array(values).astype(tensor_data.dtype)
# create np array and cast data to the inferred index type
np_indices = np.array(indices).astype(ind_dtype)
total_sparse_bytes = np_values.nbytes + np_indices.nbytes
logger.debug(
f"initializer={tensor.name}, initializer_bytes={tensor_data_bytes}, \
sparse_initializer_bytes={total_sparse_bytes}"
)
# This check is usually useful for sparsity_threshold=0.5 where much
# depends on the size of the indices entries and the size of the original tensor.
# Big dense tensors command larger indices data type and for large float32 tensors
# int32 indices are often selected, thus we really want to guard against loosing
# rather than winning.
if tensor_data_bytes <= total_sparse_bytes:
sparsity = 1.0 - float(tensor_data_bytes) / total_sparse_bytes
logger.debug(f"initializer={tensor.name}, adjusted_sparsity={sparsity}")
return (object(), sparsity)
values_tensor = onnx.helper.make_tensor(tensor.name, tensor.data_type, [len(values)], np_values.tobytes(), raw=True)
indicies_tensor = onnx.helper.make_tensor(
tensor.name + "_indicies", ind_data_type, [ind_len], np_indices.tobytes(), raw=True
)
sparse_tensor = onnx.helper.make_sparse_tensor(values_tensor, indicies_tensor, tensor.dims)
return (sparse_tensor, sparsity)
def convert_initializers(model, exclude_names, sparsity_threshold, tolerance): # type: (ModelProto, List[str], float, float) -> None
graph = model.graph
converted_sparse = []
remaining_initializers = []
for initializer in graph.initializer:
if initializer.name in exclude_names:
logger.info(f"initializer={initializer.name} was excluded")
continue
if initializer.data_type == TensorProto.BOOL:
logger.info(f"initializer={initializer.name} contains bool, not converted")
remaining_initializers.append(initializer)
continue
sparse_tensor, sparsity = convert_tensor_to_sparse(initializer, sparsity_threshold, tolerance)
if sparsity >= sparsity_threshold:
logger.info(f"initializer={initializer.name} converted. sparsity={sparsity}")
converted_sparse.append(sparse_tensor)
else:
remaining_initializers.append(initializer)
logger.info(f"initializer={initializer.name} is not converted. sparsity={sparsity}")
graph.sparse_initializer.extend(converted_sparse)
del graph.initializer[:]
graph.initializer.extend(remaining_initializers)
def main():
args = parse_arguments()
setup_logging(args.verbose)
exclude_names = set() if args.exclude is None else set(args.exclude.split(";"))
model = ModelProto()
with open(args.input, "rb") as input_file:
model.ParseFromString(input_file.read())
convert_initializers(model, exclude_names, args.sparsity_threshold, args.tolerance)
with open(args.output, "wb") as output_file:
s = model.SerializeToString()
output_file.write(s)
if __name__ == "__main__":
main()
|
