# Copyright (c) 2022 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.

import argparse

import os

import re

import numpy as np

import paddle

from paddle.inference import _get_phi_kernel_name

paddle.enable_static()

def parse_args():

parser = argparse.ArgumentParser()

parser.add_argument(

'--model_dir',

type=str,

default="",

help='Directory of the inference models that named with pdmodel.',

)

parser.add_argument(

'--op_list',

type=str,

default="",

help='List of ops like "conv2d;pool2d;relu".',

)

return parser.parse_args()

def get_model_ops(model_file, ops_set):

model_bytes = paddle.static.load_from_file(model_file)

pg = paddle.static.deserialize_program(model_bytes)

for i in range(0, pg.desc.num_blocks()):

block = pg.desc.block(i)

size = block.op_size()

for j in range(0, size):

ops_set.add(block.op(j).type())

def get_model_phi_kernels(ops_set):

phi_set = set()

phi_raw_list = [

"add",

"subtract",

"multiply",

"multiply_sr",

"divide",

"maximum",

"minimum",

"remainder",

"floor_divide",

"elementwise_pow",

]

phi_odd_dist = {"batch_norm": "batch_norm_infer"}

for op in ops_set:

print(op)

phi_kernel = _get_phi_kernel_name(op)

print(phi_kernel)

phi_set.add(phi_kernel)

if phi_kernel in phi_raw_list:

phi_set.add(phi_kernel + "_raw")

if phi_kernel in phi_odd_dist.keys():

phi_set.add(phi_odd_dist[phi_kernel])

return phi_set

if __name__ == '__main__':

args = parse_args()

ops_set = set()

if args.op_list != "":

op_list = args.op_list.split(";")

for op in op_list:

ops_set.add(op)

if args.model_dir != "":

for root, dirs, files in os.walk(args.model_dir, topdown=True):

for name in files:

if re.match(r'.*pdmodel', name):

get_model_ops(os.path.join(root, name), ops_set)

phi_set = get_model_phi_kernels(ops_set)

ops = ";".join(ops_set)

kernels = ";".join(phi_set)

print("op_list: ", ops)

print("kernel_list: ", kernels)

ops = np.array([ops])

kernels = np.array([kernels])

np.savetxt("op_list.txt", ops, fmt='%s')

np.savetxt("kernel_list.txt", kernels, fmt='%s')