wehub-resource-sync
149 lines
5.2 KiB
Python
149 lines
5.2 KiB
Python
#!/usr/bin/env python
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# 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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from __future__ import annotations
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import copy
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import json
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import os
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import socket
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import subprocess
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import sys
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import time
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import unittest
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import pytest
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import requests
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from paddlenlp.transformers import LlamaTokenizer
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def is_port_in_use(port):
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s.settimeout(0.01)
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try:
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s.bind(("localhost", port))
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return False
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except socket.error:
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return True
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class UITest(unittest.TestCase):
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def setUp(self):
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# start web ui
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self.flask_port = self.available_free_port()
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self.port = self.available_free_port([self.flask_port])
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self.model_path = "__internal_testing__/micro-random-llama"
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command = (
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"cd ./llm && PYTHONPATH=../:$PYTHONPATH"
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+ " {python} predict/flask_server.py --model_name_or_path {model_path} "
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+ '--port {port} --flask_port {flask_port} --src_length 1024 --dtype "float16"'
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).format(flask_port=self.flask_port, port=self.port, model_path=self.model_path, python=sys.executable)
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current_env = copy.copy(os.environ.copy())
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current_env.pop("http_proxy", None)
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current_env.pop("https_proxy", None)
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os.environ["http_proxy"] = ""
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os.environ["https_proxy"] = ""
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self.ui_process = subprocess.Popen(command, shell=True, stdout=sys.stdout, stderr=sys.stderr, env=current_env)
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self.tokenizer = LlamaTokenizer.from_pretrained(self.model_path)
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return super().setUp()
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def tearDown(self):
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self.ui_process.terminate()
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def available_free_port(self, exclude=None):
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exclude = exclude or []
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for port in range(8000, 10000):
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if port in exclude:
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continue
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if is_port_in_use(port):
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continue
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return port
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raise ValueError("can not get available port in [8000, 8200]")
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def wait_until_server_is_ready(self):
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while True:
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if is_port_in_use(self.flask_port) and is_port_in_use(self.port):
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break
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print("waiting for server ...")
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time.sleep(1)
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def get_gradio_ui_result(self, *args, **kwargs):
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_, _, file = self.client.predict(*args, **kwargs)
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with open(file, "r", encoding="utf-8") as f:
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content = json.load(f)
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return content[-1]["utterance"]
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@pytest.mark.timeout(4 * 60)
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def test_argument(self):
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self.wait_until_server_is_ready()
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def get_response(data):
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res = requests.post(f"http://localhost:{self.flask_port}/v1/chat/completions", json=data, stream=True)
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result_ = ""
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for line in res.iter_lines():
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if not line:
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continue
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decoded_line = line.decode("utf-8").strip()
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# 如果返回行以 "data:" 开头，则去除该前缀
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if decoded_line.startswith("data:"):
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data_str = decoded_line[len("data:") :].strip()
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else:
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data_str = decoded_line
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if data_str == "[DONE]":
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break
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chunk = json.loads(data_str)
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# 根据 OpenAI 的流式返回，每个 chunk 在 choices[0]["delta"] 中包含回复增量
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delta = chunk["choices"][0]["delta"].get("content", "")
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result_ += delta
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return result_
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# 测试用例1：greedy search 模式（top_p 为1.0）
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data = {
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"messages": [{"role": "user", "content": "你好"}],
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"temperature": 1.0,
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"max_tokens": 20,
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"top_p": 1.0,
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"stream": True,
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}
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result_1 = get_response(data)
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# 测试用例2：采样模式（top_p 为 0.7）
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data = {
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"messages": [{"role": "user", "content": "你好"}],
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"temperature": 1.0,
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"max_tokens": 20,
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"top_p": 0.7,
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"stream": True,
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}
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result_2 = get_response(data)
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# 对生成文本的长度进行简单检测
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assert 10 <= len(self.tokenizer.tokenize(result_1)) <= 50
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assert 10 <= len(self.tokenizer.tokenize(result_2)) <= 50
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# 测试用例3：更长的 max_tokens 参数
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data = {
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"messages": [{"role": "user", "content": "你好"}],
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"temperature": 1.0,
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"max_tokens": 100,
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"top_p": 0.7,
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"stream": True,
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}
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result_3 = get_response(data)
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assert result_3 != result_2
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assert 70 <= len(self.tokenizer.tokenize(result_3)) <= 150
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