-
Notifications
You must be signed in to change notification settings - Fork 21.4k
/
check_backward_compatibility.py
233 lines (218 loc) · 10 KB
/
check_backward_compatibility.py
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
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
import argparse
import datetime
import re
import sys
from collections import defaultdict
import torch
from torch._C import parse_schema
# The date specifies how long the allowlist exclusion should apply to.
#
# - If we NEVER give BC guarantee for an operator, you can put the
# date arbitrarily far in the future.
# - Otherwise, pick a date that is far enough in the future that you
# believe you can land your diff before then.
#
# Allowlist entries can be removed after the date listed on them passes.
#
# Allowlist item format:
# [
# 0: function name regex
# 1: date until which the allowlist entry is valid
# 2: (optional) function argument regex
# ]
#
# NB: function name DOES NOT include overload name!
allow_list = [
("c10_experimental", datetime.date(2222, 1, 1)),
# Internal
("static", datetime.date(9999, 1, 1)),
# Internal, profiler-specific ops
("profiler::_call_end_callbacks_on_jit_fut*", datetime.date(9999, 1, 1)),
("profiler::_record_function_enter", datetime.date(9999, 1, 1)),
("tensorexpr::Group", datetime.date(2020, 9, 9)),
("aten::append*", datetime.date(2020, 4, 15)),
("aten::_min", datetime.date(2020, 9, 9)),
("aten::_max", datetime.date(2020, 9, 9)),
("aten::amax", datetime.date(2020, 10, 9)),
("aten::amin", datetime.date(2020, 10, 9)),
("aten::min_values", datetime.date(2020, 10, 9)),
("aten::max_values", datetime.date(2020, 10, 9)),
("aten::split_with_sizes", datetime.date(2020, 7, 29)),
("aten::eq", datetime.date(2020, 7, 30)),
("aten::log", datetime.date(2020, 7, 30)),
("aten::__and__", datetime.date(2020, 7, 30)),
("aten::__or__", datetime.date(2020, 7, 30)),
("aten::__xor__", datetime.date(2020, 7, 30)),
("aten::add", datetime.date(2020, 7, 30)),
("aten::__upsample_bilinear", datetime.date(2020, 7, 30)),
("aten::hash", datetime.date(2020, 7, 30)),
("aten::divmod", datetime.date(2020, 7, 30)),
("aten::sorted", datetime.date(2020, 8, 30)),
("aten::__contains__", datetime.date(2020, 7, 30)),
("aten::ne", datetime.date(2020, 7, 30)),
("aten::index", datetime.date(2020, 7, 30)),
("aten::isnan", datetime.date(2020, 7, 30)),
("aten::pow", datetime.date(2020, 7, 30)),
("aten::atan2", datetime.date(2020, 7, 30)),
("aten::copy_", datetime.date(2020, 7, 30)),
("aten::sort", datetime.date(2020, 7, 30)),
("aten::_convolution", datetime.date(2020, 10, 15)),
("aten::cudnn_convolution", datetime.date(2020, 10, 15)),
("aten::cudnn_convolution_transpose", datetime.date(2020, 10, 15)),
("aten::_convolution_double_backward", datetime.date(2020, 10, 15)),
("aten::cudnn_convolution_backward_input", datetime.date(2020, 10, 15)),
("aten::cudnn_convolution_backward", datetime.date(2020, 10, 15)),
("aten::cudnn_convolution_backward_weight", datetime.date(2020, 10, 15)),
("aten::cudnn_convolution_transpose_backward", datetime.date(2020, 10, 15)),
("aten::cudnn_convolution_transpose_backward_input", datetime.date(2020, 10, 15)),
("aten::cudnn_convolution_transpose_backward_weight", datetime.date(2020, 10, 15)),
("aten::_cudnn_init_dropout_state", datetime.date(2020, 7, 30)),
("aten::sparse_coo_tensor", datetime.date(2020, 7, 30)),
("aten::_sparse_coo_tensor_with_dims", datetime.date(2020, 7, 30)),
("aten::_sparse_coo_tensor_with_dims_and_tensors", datetime.date(2020, 7, 30)),
("aten::__lshift__", datetime.date(2020, 7, 30)),
("aten::__rshift__", datetime.date(2020, 7, 30)),
("aten::__round_to_zero_floordiv", datetime.date(2020, 7, 30)),
("aten::gcd", datetime.date(2020, 7, 30)),
("aten::unflatten", datetime.date(2020, 8, 14)),
("aten::linalg_outer", datetime.date(2020, 8, 30)),
# WARNING: overload name here doesn't do anything
("aten::linalg_outer.out", datetime.date(2020, 8, 30)),
("aten::linalg_norm", datetime.date(2020, 9, 30)),
("aten::linalg_norm.ord_str", datetime.date(2020, 9, 30)),
("aten::linalg_norm.out", datetime.date(2020, 9, 30)),
("aten::linalg_norm.ord_str_out", datetime.date(2020, 9, 30)),
("aten::_compute_linear_combination", datetime.date(2020, 9, 1)),
("aten::linspace", datetime.date(2020, 9, 30)),
("aten::linspace.out", datetime.date(2020, 9, 30)),
("aten::logspace", datetime.date(2020, 9, 30)),
("aten::logspace.out", datetime.date(2020, 9, 30)),
("__getstate__", datetime.date(2020, 9, 11), "Conv[23]dPackedParams"),
("_caffe2::LearningRate", datetime.date(2020, 10, 1)),
("aten::_var", datetime.date(2020, 10, 1)),
("aten::_std", datetime.date(2020, 10, 1)),
("aten::_foreach_add_", datetime.date(2020, 10, 1)),
("aten::stft", datetime.date(2020, 10, 1)),
("aten::istft", datetime.date(2020, 10, 1)),
("prim::MakeTestTensor", datetime.date(2020, 10, 1)),
("preprocess", datetime.date(2020, 10, 1)),
("compile", datetime.date(2020, 10, 1)),
("execute", datetime.date(2020, 10, 1)),
("aten::_addr", datetime.date(2020, 10, 31)),
("aten::_addr_", datetime.date(2020, 10, 31)),
("aten::_addr.out", datetime.date(2020, 10, 31)),
("aten::_foreach_add", datetime.date(2020, 10, 1)),
("aten::_foreach_sub_", datetime.date(2020, 10, 1)),
("aten::_foreach_div", datetime.date(2020, 10, 1)),
("aten::_foreach_sub", datetime.date(2020, 10, 1)),
("aten::_foreach_mul_scalar_list", datetime.date(2020, 11, 10)),
("aten::_foreach_mul_scalar_list_", datetime.date(2020, 11, 10)),
("aten::_amp_non_finite_check_and_unscale_", datetime.date(9999, 1, 1)),
("aten::choose_qparams_optimized", datetime.date(2020, 10, 5)),
("aten::smooth_l1_loss_backward", datetime.date(2020, 10, 15)),
("quantized::embedding_bag_byte", datetime.date(2020, 10, 15)),
("quantized::embedding_bag_4bit", datetime.date(2020, 10, 15)),
("quantized::embedding_byte", datetime.date(2020, 10, 15)),
("quantized::embedding_bag_byte_rowwise_offsets", datetime.date(2020, 10, 15)),
("quantized::embedding_bag_4bit_rowwise_offsets", datetime.date(2020, 10, 15)),
("aten::_foreach_sub_scalar_list", datetime.date(2020, 11, 10)),
("aten::_foreach_add_scalar_list_", datetime.date(2020, 11, 10)),
("aten::_foreach_add_scalar_list", datetime.date(2020, 11, 10)),
("aten::_foreach_sub_scalar_list_", datetime.date(2020, 11, 10)),
("aten::_foreach_div_scalar_list", datetime.date(2020, 11, 10)),
("aten::_foreach_div_scalar_list_", datetime.date(2020, 11, 10)),
("aten::_foreach_addcmul_", datetime.date(2020, 10, 15)),
("aten::_foreach_addcdiv_", datetime.date(2020, 10, 15)),
("aten::_foreach_addcdiv", datetime.date(2020, 10, 15)),
("aten::_foreach_addcmul", datetime.date(2020, 10, 15)),
("aten::conj", datetime.date(2020, 11, 10)),
]
def allow_listed(schema, allow_list):
for item in allow_list:
if item[1] < datetime.date.today():
continue
regexp = re.compile(item[0])
if regexp.search(schema.name):
if len(item) > 2:
# if arguments regex is present, use it
regexp_args = re.compile(item[2])
return bool(regexp_args.search(str(schema)))
return True
return False
# The nightly will fail to parse newly added syntax to schema declarations
# Add new schemas that will fail the nightly here
dont_parse_list = [
("_TorchScriptTesting.*", datetime.date(2099, 9, 17)),
("test_backend", datetime.date(2099, 9, 17)),
]
def dont_parse(schema_line):
for item in dont_parse_list:
if item[1] < datetime.date.today():
continue
regexp = re.compile(item[0])
if regexp.search(schema_line):
return True
return False
def check_bc(existing_schemas):
new_schemas = torch._C._jit_get_all_schemas()
new_schemas += torch._C._jit_get_custom_class_schemas()
new_schema_dict = defaultdict(list)
for s in new_schemas:
new_schema_dict[s.name].append(s)
is_bc = True
broken_ops = []
for existing_schema in existing_schemas:
if allow_listed(existing_schema, allow_list):
print("schema: ", str(existing_schema), " found on allowlist, skipping")
continue
print("processing existing schema: ", str(existing_schema))
matching_new_schemas = new_schema_dict.get(existing_schema.name, [])
found = False
for matching_new_schema in matching_new_schemas:
if matching_new_schema.is_backward_compatible_with(existing_schema):
found = True
break
if not found:
print(
"Can NOT find backward compatible schemas after changes "
"for schema {} from the following candidates:\n[\n{}\n]".format(
str(existing_schema),
"\n\t".join(str(s) for s in matching_new_schemas),
)
)
# TODO Print out more details about why candidates don't match.
broken_ops.append(str(existing_schema))
is_bc = False
if is_bc:
print("Found backward compatible schemas for all existing schemas")
else:
print(
"The PR is introducing backward incompatible changes to the "
"operator library. Please contact PyTorch team to confirm "
"whether this change is wanted or not. \n\nBroken ops: "
"[\n\t{}\n]".format("\n\t".join(broken_ops))
)
return is_bc
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Process some integers.")
parser.add_argument(
"--existing-schemas",
help="filename to load existing schemas",
type=str,
default="schemas.txt",
)
args = parser.parse_args()
existing_schema_dict = dict()
slist = []
with open(args.existing_schemas, "r") as f:
while True:
line = f.readline()
if not line:
break
if dont_parse(line.strip()):
print("Not parsing schema line: ", line.strip())
continue
s = parse_schema(line.strip())
slist.append(s)
if not check_bc(slist):
sys.exit(1)