forked from apache/tvm
-
Notifications
You must be signed in to change notification settings - Fork 30
/
tophub.py
243 lines (198 loc) · 7.75 KB
/
tophub.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
234
235
236
237
238
239
240
241
242
243
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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
#
# http://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.
"""
TopHub: Tensor Operator Hub
To get the best performance, we typically need auto-tuning for the specific devices.
TVM releases pre-tuned parameters in TopHub for some common networks and hardware targets.
TVM will download these parameters for you when you call relay.build.
"""
# pylint: disable=invalid-name
import logging
import os
import sys
from .task import ApplyHistoryBest
from ..target import Target
from ..contrib.download import download
from .record import load_from_file
from .utils import EmptyContext
# environment variable to read TopHub location
AUTOTVM_TOPHUB_LOC_VAR = "TOPHUB_LOCATION"
# default location of TopHub
AUTOTVM_TOPHUB_DEFAULT_LOC = "https://raw.githubusercontent.com/tlc-pack/tophub/main/tophub"
# value of AUTOTVM_TOPHUB_LOC_VAR to specify to not read from TopHub
AUTOTVM_TOPHUB_NONE_LOC = "NONE"
# root path to store TopHub files
AUTOTVM_TOPHUB_ROOT_PATH = os.path.join(os.path.expanduser("~"), ".tvm", "tophub")
# the version of each package
PACKAGE_VERSION = {
"arm_cpu": "v0.07",
"llvm": "v0.04",
"cuda": "v0.09",
"rocm": "v0.05",
"opencl": "v0.04",
"mali": "v0.06",
"intel_graphics": "v0.02",
"vta": "v0.09",
"amd_apu": "v0.01",
}
logger = logging.getLogger("autotvm")
def _alias(name):
"""convert alias for some packages"""
table = {
"vtacpu": "vta",
"metal": "opencl",
"webgpu": "opencl",
"vulkan": "opencl",
"nvptx": "cuda",
"amd_apu": "amd_apu",
}
return table.get(name, name)
def _get_tophub_location():
location = os.getenv(AUTOTVM_TOPHUB_LOC_VAR, None)
return AUTOTVM_TOPHUB_DEFAULT_LOC if location is None else location
def context(target, extra_files=None):
"""Return the dispatch context with pre-tuned parameters.
This function will load the corresponding *.log files in AUTOTVM_TOPHUB_ROOT_PATH.
If cannot find them, it will download them from TopHub github repo.
Users can also add their own files in argument `extra_files`.
Parameters
----------
target: Target or List of Target
The compilation target
extra_files: list of str, optional
Extra log files to load
"""
tophub_location = _get_tophub_location()
if tophub_location == AUTOTVM_TOPHUB_NONE_LOC:
return EmptyContext()
best_context = ApplyHistoryBest([])
targets = target if isinstance(target, (list, tuple)) else [target]
for tgt in targets:
if isinstance(tgt, str):
tgt = Target(tgt)
possible_names = []
device = tgt.attrs.get("device", "")
if device != "":
possible_names.append(_alias(device))
possible_names.append(tgt.kind.name)
all_packages = list(PACKAGE_VERSION.keys())
for name in possible_names:
name = _alias(name)
if name in all_packages:
if not check_backend(tophub_location, name):
continue
filename = "%s_%s.log" % (name, PACKAGE_VERSION[name])
best_context.load(os.path.join(AUTOTVM_TOPHUB_ROOT_PATH, filename))
break # only load one file to avoid some fallback template mismatch problem
if extra_files:
for filename in extra_files:
best_context.load(filename)
return best_context
def check_backend(tophub_location, backend):
"""Check whether have pre-tuned parameters of the certain target.
If not, will download it.
Parameters
----------
backend: str
The name of backend.
Returns
----------
success: bool
Whether the check is successful.
"""
backend = _alias(backend)
assert backend in PACKAGE_VERSION, 'Cannot find backend "%s" in TopHub' % backend
version = PACKAGE_VERSION[backend]
package_name = "%s_%s.log" % (backend, version)
if os.path.isfile(os.path.join(AUTOTVM_TOPHUB_ROOT_PATH, package_name)):
return True
# pylint: disable=import-outside-toplevel
if sys.version_info >= (3,):
import urllib.request as urllib2
else:
import urllib2
try:
download_package(tophub_location, package_name)
return True
except urllib2.URLError as e:
logging.warning("Failed to download tophub package for %s: %s", backend, e)
return False
def download_package(tophub_location, package_name):
"""Download pre-tuned parameters of operators for a backend
Parameters
----------
tophub_location: str
The location to download TopHub parameters from
package_name: str
The name of package
"""
rootpath = AUTOTVM_TOPHUB_ROOT_PATH
if not os.path.isdir(rootpath):
# make directory
splits = os.path.split(rootpath)
for j in range(1, len(splits) + 1):
path = os.path.join(*splits[:j])
if not os.path.isdir(path):
os.mkdir(path)
download_url = "{0}/{1}".format(tophub_location, package_name)
logger.info("Download pre-tuned parameters package from %s", download_url)
download(download_url, os.path.join(rootpath, package_name), True, verbose=0)
# global cache for load_reference_log
REFERENCE_LOG_CACHE = {}
def load_reference_log(backend, model, workload_name):
"""Load reference log from TopHub to support fallback in template.
Template will use these reference logs to choose fallback config.
Parameters
----------
backend: str
The backend name
model: str
The name of the device model
workload_name: str
The name of the workload. (The first item in the workload tuple)
"""
backend = _alias(backend)
version = PACKAGE_VERSION[backend]
package_name = "%s_%s.log" % (backend, version)
filename = os.path.join(AUTOTVM_TOPHUB_ROOT_PATH, package_name)
global REFERENCE_LOG_CACHE
key = (backend, model, workload_name)
if key not in REFERENCE_LOG_CACHE:
tmp = []
# If TOPHUB_LOCATION is not AUTOTVM_TOPHUB_NONE_LOC,
# Download the config file from tophub if not exists.
if not os.path.exists(filename):
tophub_location = _get_tophub_location()
if tophub_location != AUTOTVM_TOPHUB_NONE_LOC:
download_package(tophub_location, package_name)
if os.path.isfile(filename): # in case download failed
find = False
inp = None
counts = {}
for inp, res in load_from_file(filename):
counts[inp.target.model] = counts.get(inp.target.model, 0) + 1
if model == inp.target.model:
find = True
break
# if device model is not find, use the device model with the most tuned workloads
if not find and counts:
model = max(counts.items(), key=lambda k: k[1])[0]
for inp, res in load_from_file(filename):
if model == inp.target.model and inp.task.workload[0] == workload_name:
tmp.append((inp, res))
REFERENCE_LOG_CACHE[key] = tmp
return REFERENCE_LOG_CACHE[key]