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prune_for_jetson.py
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prune_for_jetson.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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
#
# 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.
"""
This script simply removes all grad ops and kernels. You should use this script
when cmake ON_INFER=ON, which can greatly reduce the volume of the prediction library.
"""
import os
import re
import glob
def find_type_files(cur_dir, file_type, file_list=[]):
next_level_dirs = os.listdir(cur_dir)
for next_level_name in next_level_dirs:
next_level_dir = os.path.join(cur_dir, next_level_name)
if os.path.isfile(next_level_dir):
if os.path.splitext(next_level_dir)[1] == file_type:
file_list.append(next_level_dir)
elif os.path.isdir(next_level_dir):
find_type_files(next_level_dir, file_type, file_list)
return file_list
def find_kernel(content, pattern):
res = re.findall(pattern, content, flags=re.DOTALL)
ret = []
for p in res:
left, right = 0, 0
for c in p:
if c == '{':
left += 1
elif c == '}':
right += 1
if left == right:
ret.append(p)
return ret, len(ret)
def prune_phi_kernels():
tool_dir = os.path.dirname(os.path.abspath(__file__))
all_op = glob.glob(os.path.join(tool_dir, '../paddle/phi/kernels/**/*.cc'),
recursive=True)
all_op += glob.glob(os.path.join(tool_dir, '../paddle/phi/kernels/**/*.cu'),
recursive=True)
register_op_count = 0
for op_file in all_op:
need_continue = False
file_blacklist = [
"kernels/empty_kernel.cc", "/cast_kernel.c", "/batch_norm_kernel.c"
]
for bname in file_blacklist:
if op_file.find(bname) >= 0:
need_continue = True
break
if need_continue:
print("continue:", op_file)
continue
op_name = os.path.split(op_file)[1]
all_matches = []
with open(op_file, 'r', encoding='utf-8') as f:
content = ''.join(f.readlines())
op_pattern = 'PD_REGISTER_KERNEL\(.*?\).*?\{.*?\}'
op, op_count = find_kernel(content, op_pattern)
register_op_count += op_count
all_matches.extend(op)
for p in all_matches:
content = content.replace(p, '')
with open(op_file, 'w', encoding='utf-8') as f:
f.write(u'{}'.format(content))
print('We erase all grad op and kernel for Paddle-Inference lib.')
print('%50s%10s' % ('type', 'count'))
print('%50s%10s' % ('REGISTER_OPERATOR', register_op_count))
return True
def apply_patches():
work_path = os.path.dirname(os.path.abspath(__file__)) + "/../"
ret = os.system(
"cd %s && rm -f paddle/fluid/inference/api/tensorrt_predictor.* "
" && rm -f paddle/fluid/inference/api/paddle_tensorrt_predictor.h "
" && git apply tools/infer_prune_patches/*.patch && cd -" % work_path)
return ret == 0
def append_fluid_kernels():
op_white_list = ["load", "load_combine"]
#1. add to makefile
file_name = os.path.dirname(os.path.abspath(__file__)) \
+ "/../paddle/fluid/inference/tensorrt/CMakeLists.txt"
append_str = "\nfile(APPEND ${pybind_file} \"USE_NO_KERNEL_OP__(tensorrt_engine);\\n\")\n"
for op in op_white_list:
append_str = append_str + "file(APPEND ${pybind_file} \"USE_OP__(%s);\\n\")\n" % op
with open(file_name, 'r', encoding='utf-8') as f:
content = ''.join(f.readlines())
location_str = "nv_library(\n tensorrt_op_teller\n SRCS op_teller.cc\n DEPS framework_proto device_context)"
new_content = content.replace(location_str, location_str + append_str)
if new_content == content:
print("ERROR: can not find \"%s\" in file \"%s\"" %
(location_str, file_name))
return False
with open(file_name, 'w', encoding='utf-8') as f:
f.write(u'{}'.format(new_content))
#2. add op and kernel register
op_white_list.append("tensorrt_engine")
tool_dir = os.path.dirname(os.path.abspath(__file__))
all_op = glob.glob(os.path.join(tool_dir,
'../paddle/fluid/operators/**/*.cc'),
recursive=True)
all_op += glob.glob(os.path.join(tool_dir,
'../paddle/fluid/operators/**/*.cu'),
recursive=True)
for op_file in all_op:
with open(op_file, 'r', encoding='utf-8') as f:
content = ''.join(f.readlines())
for op in op_white_list:
patterns = {
"REGISTER_OPERATOR": "REGISTER_OPERATOR\(\s*%s\s*," % op,
"REGISTER_OP_CPU_KERNEL":
"REGISTER_OP_CPU_KERNEL\(\s*%s\s*," % op,
"REGISTER_OP_CUDA_KERNEL":
"REGISTER_OP_CUDA_KERNEL\(\s*%s\s*," % op
}
for k, p in patterns.items():
matches = re.findall(p, content, flags=re.DOTALL)
if len(matches) > 0:
content = content.replace(matches[0],
matches[0].replace(k, k + "__"))
with open(op_file, 'w', encoding='utf-8') as f:
f.write(u'{}'.format(content))
return True
if __name__ == '__main__':
print("================ step 1: apply patches =======================")
assert (apply_patches())
print("==============================================================\n")
print("================ step 2: append fluid op/kernels==============")
assert (append_fluid_kernels())
print("==============================================================\n")
print("================ step 3:prune phi kernels ====================")
assert (prune_phi_kernels())
print("==============================================================\n")