wehub-resource-sync
176 lines
6.6 KiB
Python
176 lines
6.6 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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RUN PaddleNLP CI Case
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"""
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import os
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import re
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import subprocess
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import sys
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def get_mode_info(case_path):
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"""
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Return: model_info{path,exec_file_list}
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Examples:
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pegasus = {
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"path": "applications/text_summarization/pegasus/"
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"deploy_path": "deploy/paddle_inference/"
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"prepare": "run_prepare.py"
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"train_exec_file": "train.py"
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"eval_exec_file": None
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"predict_exec_file": predict.py
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“export_exec_file”: export_model.py
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"infer_exec_file": inference_pegasus.py
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}
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"""
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model_info = {
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"path": case_path,
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"deploy_path": None,
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"prepare_exec_file": None,
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"train_exec_file": [],
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"eval_exec_file": None,
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"predict_exec_file": None,
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"export_exec_file": None,
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"infer_exec_file": None,
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}
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for root, dirs, files in os.walk(case_path):
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infer_deploy_path = case_path + "/deploy/paddle_inference"
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python_deploy_path = case_path + "/deploy/python"
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if files and root == case_path:
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for file in files:
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# TODO .sh file incompatible windows
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if file.split(".")[-1] == "py":
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if re.compile("prepare.py").findall(file):
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model_info["prepare_exec_file"] = file
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elif re.compile("train.py").findall(file):
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model_info["train_exec_file"].append(file)
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elif re.compile("finetune").findall(file):
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model_info["train_exec_file"].append(file)
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elif re.compile("eval.py").findall(file):
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model_info["eval_exec_file"] = file
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elif re.compile("predict.py").findall(file):
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model_info["predict_exec_file"] = file
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elif re.compile("export_model.py").findall(file):
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model_info["export_exec_file"] = file
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elif re.compile("run_").findall(file):
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model_info["train_exec_file"].append(file)
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else:
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continue
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elif files and root == infer_deploy_path:
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for file in files:
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if file.split(".")[-1] == "py":
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model_info["deploy_path"] = "deploy/paddle_inference"
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model_info["infer_exec_file"] = file
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elif files and root == python_deploy_path:
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for file in files:
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if file.split(".")[-1] == "py":
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model_info["deploy_path"] = "deploy/python"
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model_info["infer_exec_file"] = file
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print("model_info", model_info)
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return model_info
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def save_log(exit_code, output, case_name, file_name):
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"""
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save model log
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"""
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root_path = "/workspace/PaddleNLP"
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# root_path = '/ssd1/paddlenlp/zhangjunjun/PaddleNLP'
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if exit_code == 0:
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log_file = root_path + "/model_logs/" + os.path.join(case_name + "_" + file_name + "_SUCCESS.log")
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print("{} SUCCESS".format(file_name))
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with open(log_file, "a") as flog:
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flog.write("%s" % (output))
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else:
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log_file = root_path + "/model_logs/" + os.path.join(case_name + "_" + file_name + "_FAIL.log")
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print("{} FAIL".format(file_name))
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with open(log_file, "a") as flog:
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flog.write("%s" % (output))
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def run_normal_case(case_path):
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"""
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Run new normal case
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params:
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case_path: model path based PaddleNLP from git diff
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"""
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case_name = case_path.split("/")[-1]
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model_info = get_mode_info(case_path)
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deploy_path = model_info["deploy_path"]
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prepare_exec_file = model_info["prepare_exec_file"]
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eval_exec_file = model_info["eval_exec_file"]
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predict_exec_file = model_info["predict_exec_file"]
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export_exec_file = model_info["export_exec_file"]
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infer_exec_file = model_info["infer_exec_file"]
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os.chdir(case_path)
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if prepare_exec_file:
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prepare_output = subprocess.getstatusoutput("python %s " % (prepare_exec_file))
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save_log(prepare_output[0], prepare_output[1], case_name, prepare_exec_file.split(".")[0])
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if model_info["train_exec_file"]:
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for train_file in model_info["train_exec_file"]:
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train_output = subprocess.getstatusoutput(
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"python -m paddle.distributed.launch %s --device gpu --max_steps 2 \
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--save_steps 2 --output_dir ./output/"
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% (train_file)
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)
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save_log(train_output[0], train_output[1], case_name, train_file.split(".")[0])
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else:
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print("Train Skipped")
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if eval_exec_file:
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eval_output = subprocess.getstatusoutput("python %s --init_checkpoint_dir ./output/" % (eval_exec_file))
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save_log(eval_output[0], eval_output[1], case_name, eval_exec_file.split(".")[0])
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else:
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print("Evaluation Skipped")
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if predict_exec_file:
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predict_output = subprocess.getstatusoutput("python %s --init_checkpoint_dir ./output/" % (predict_exec_file))
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save_log(predict_output[0], predict_output[1], case_name, predict_exec_file.split(".")[0])
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else:
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print("Predict Skipped")
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if export_exec_file:
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export_output = subprocess.getstatusoutput(
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"python %s --export_output_dir ./inference_model/" % (export_exec_file)
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)
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save_log(export_output[0], export_output[1], case_name, export_exec_file.split(".")[0])
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else:
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print("Export model Skipped")
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if infer_exec_file:
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infer_output = subprocess.getstatusoutput(
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"cd %s && python %s --inference_model_dir ../../inference_model/" % (deploy_path, infer_exec_file)
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)
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save_log(infer_output[0], infer_output[1], case_name, infer_exec_file.split(".")[0])
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else:
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print("python inference Skipped")
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if __name__ == "__main__":
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# path ="applications/text_summarization/pegasus"
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path = sys.argv[1]
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if os.path.isdir(path):
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run_normal_case(path)
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else:
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print("not model file path, skepped ")
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