mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
[CI] Fix unit_test error of unstable execution (#5660)
* [CI] Fix unit_test error of unstable execution
This commit is contained in:
YuBaoku
@@ -242,7 +242,7 @@ def test_non_streaming_chat(openai_client):
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{"role": "user", "content": "List 3 countries and their capitals."},
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],
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temperature=1,
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max_tokens=1024,
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max_tokens=64,
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stream=False,
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)
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@@ -269,7 +269,7 @@ def test_streaming_chat(openai_client, capsys):
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{"role": "user", "content": "OK, tell more."},
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],
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temperature=1,
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max_tokens=1024,
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max_tokens=64,
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stream=True,
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)
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@@ -293,7 +293,7 @@ def test_non_streaming(openai_client):
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model="default",
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prompt="Hello, how are you?",
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temperature=1,
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max_tokens=1024,
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max_tokens=64,
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stream=False,
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)
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@@ -310,7 +310,7 @@ def test_streaming(openai_client, capsys):
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model="default",
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prompt="Hello, how are you?",
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temperature=1,
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max_tokens=1024,
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max_tokens=64,
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stream=True,
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)
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@@ -334,8 +334,9 @@ def test_non_streaming_with_stop_str(openai_client):
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model="default",
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messages=[{"role": "user", "content": "Hello, how are you?"}],
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temperature=1,
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max_tokens=5,
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extra_body={"include_stop_str_in_output": True},
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top_p=0.0,
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max_tokens=10,
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extra_body={"min_tokens": 5, "include_stop_str_in_output": True},
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stream=False,
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)
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# Assertions to check the response structure
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@@ -360,7 +361,7 @@ def test_non_streaming_with_stop_str(openai_client):
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model="default",
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prompt="Hello, how are you?",
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temperature=1,
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max_tokens=1024,
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max_tokens=10,
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stream=False,
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)
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assert not response.choices[0].text.endswith("</s>")
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@@ -369,7 +370,7 @@ def test_non_streaming_with_stop_str(openai_client):
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model="default",
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prompt="Hello, how are you?",
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temperature=1,
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max_tokens=1024,
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max_tokens=10,
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extra_body={"include_stop_str_in_output": True},
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stream=False,
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)
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@@ -1436,6 +1437,7 @@ def test_structured_outputs_grammar(openai_client):
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grammar_param = {
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"temperature": 1,
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"top_p": 0.0,
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"max_tokens": 1024,
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"messages": [
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{
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@@ -776,6 +776,7 @@ def test_structured_outputs_choice(openai_client):
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"""
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choice_param = {
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"temperature": 1,
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"top_p": 0.0,
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"max_tokens": 1024,
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"messages": [{"role": "user", "content": "What is the landmark building in Shenzhen?"}],
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"extra_body": {
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@@ -855,6 +856,7 @@ def test_structured_outputs_grammar(openai_client):
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grammar_param = {
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"temperature": 1,
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"top_p": 0.0,
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"max_tokens": 1024,
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"messages": [
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{
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@@ -1,647 +0,0 @@
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# Copyright (c) 2025 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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import concurrent.futures
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import json
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import os
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import re
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import signal
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import subprocess
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import sys
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import time
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import openai
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import pytest
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import requests
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from jsonschema import validate
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tests_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
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sys.path.insert(0, tests_dir)
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from e2e.utils.serving_utils import (
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FD_API_PORT,
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FD_CACHE_QUEUE_PORT,
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FD_ENGINE_QUEUE_PORT,
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FD_METRICS_PORT,
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clean_ports,
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is_port_open,
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)
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@pytest.fixture(scope="session", autouse=True)
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def setup_and_run_server():
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"""
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Pytest fixture that runs once per test session:
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- Cleans ports before tests
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- Starts the API server as a subprocess
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- Waits for server port to open (up to 30 seconds)
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- Tears down server after all tests finish
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"""
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print("Pre-test port cleanup...")
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clean_ports()
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base_path = os.getenv("MODEL_PATH")
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if base_path:
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model_path = os.path.join(base_path, "Qwen2-7B-Instruct")
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else:
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model_path = "./Qwen2-7B-Instruct"
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log_path = "server.log"
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cmd = [
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sys.executable,
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"-m",
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"fastdeploy.entrypoints.openai.api_server",
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"--model",
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model_path,
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"--port",
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str(FD_API_PORT),
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"--tensor-parallel-size",
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"1",
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"--engine-worker-queue-port",
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str(FD_ENGINE_QUEUE_PORT),
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"--metrics-port",
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str(FD_METRICS_PORT),
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"--cache-queue-port",
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str(FD_CACHE_QUEUE_PORT),
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"--max-model-len",
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"32768",
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"--max-num-seqs",
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"128",
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"--quantization",
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"wint8",
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]
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# Start subprocess in new process group
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with open(log_path, "w") as logfile:
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process = subprocess.Popen(
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cmd,
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stdout=logfile,
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stderr=subprocess.STDOUT,
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start_new_session=True, # Enables killing full group via os.killpg
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)
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# Wait up to 300 seconds for API server to be ready
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for _ in range(300):
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if is_port_open("127.0.0.1", FD_API_PORT):
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print(f"API server is up on port {FD_API_PORT}")
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break
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time.sleep(1)
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else:
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print("[TIMEOUT] API server failed to start in 5 minutes. Cleaning up...")
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try:
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os.killpg(process.pid, signal.SIGTERM)
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except Exception as e:
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print(f"Failed to kill process group: {e}")
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raise RuntimeError(f"API server did not start on port {FD_API_PORT}")
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yield # Run tests
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print("\n===== Post-test server cleanup... =====")
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try:
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os.killpg(process.pid, signal.SIGTERM)
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print(f"API server (pid={process.pid}) terminated")
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except Exception as e:
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print(f"Failed to terminate API server: {e}")
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@pytest.fixture(scope="session")
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def api_url(request):
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"""
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Returns the API endpoint URL for chat completions.
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"""
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return f"http://0.0.0.0:{FD_API_PORT}/v1/chat/completions"
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@pytest.fixture(scope="session")
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def metrics_url(request):
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"""
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Returns the metrics endpoint URL.
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"""
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return f"http://0.0.0.0:{FD_METRICS_PORT}/metrics"
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@pytest.fixture
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def headers():
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"""
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Returns common HTTP request headers.
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"""
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return {"Content-Type": "application/json"}
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@pytest.fixture
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def consistent_payload():
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"""
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Returns a fixed payload for consistency testing,
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including a fixed random seed and temperature.
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"""
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return {
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"messages": [{"role": "user", "content": "用一句话介绍 PaddlePaddle"}],
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"temperature": 0.9,
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"top_p": 0, # fix top_p to reduce randomness
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"seed": 13, # fixed random seed
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}
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# ==========================
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# JSON Schema for validating chat API responses
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# ==========================
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chat_response_schema = {
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"type": "object",
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"properties": {
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"id": {"type": "string"},
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"object": {"type": "string"},
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"created": {"type": "number"},
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"model": {"type": "string"},
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"choices": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"message": {
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"type": "object",
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"properties": {
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"role": {"type": "string"},
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"content": {"type": "string"},
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},
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"required": ["role", "content"],
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},
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"index": {"type": "number"},
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"finish_reason": {"type": "string"},
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},
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"required": ["message", "index", "finish_reason"],
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},
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},
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},
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"required": ["id", "object", "created", "model", "choices"],
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}
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# ==========================
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# Helper function to calculate difference rate between two texts
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# ==========================
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def calculate_diff_rate(text1, text2):
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"""
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Calculate the difference rate between two strings
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based on the normalized Levenshtein edit distance.
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Returns a float in [0,1], where 0 means identical.
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"""
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if text1 == text2:
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return 0.0
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len1, len2 = len(text1), len(text2)
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dp = [[0] * (len2 + 1) for _ in range(len1 + 1)]
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for i in range(len1 + 1):
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for j in range(len2 + 1):
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if i == 0 or j == 0:
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dp[i][j] = i + j
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elif text1[i - 1] == text2[j - 1]:
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dp[i][j] = dp[i - 1][j - 1]
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else:
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dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1])
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edit_distance = dp[len1][len2]
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max_len = max(len1, len2)
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return edit_distance / max_len if max_len > 0 else 0.0
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# ==========================
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# Valid prompt test cases for parameterized testing
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# ==========================
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valid_prompts = [
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[{"role": "user", "content": "你好"}],
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[{"role": "user", "content": "用一句话介绍 FastDeploy"}],
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]
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@pytest.mark.parametrize("messages", valid_prompts)
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def test_valid_chat(messages, api_url, headers):
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"""
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Test valid chat requests.
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"""
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resp = requests.post(api_url, headers=headers, json={"messages": messages})
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assert resp.status_code == 200
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validate(instance=resp.json(), schema=chat_response_schema)
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# ==========================
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# Consistency test for repeated runs with fixed payload
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# ==========================
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def test_consistency_between_runs(api_url, headers, consistent_payload):
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"""
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Test that two runs with the same fixed input produce similar outputs.
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"""
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# First request
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resp1 = requests.post(api_url, headers=headers, json=consistent_payload)
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assert resp1.status_code == 200
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result1 = resp1.json()
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content1 = result1["choices"][0]["message"]["content"]
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# Second request
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resp2 = requests.post(api_url, headers=headers, json=consistent_payload)
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assert resp2.status_code == 200
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result2 = resp2.json()
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content2 = result2["choices"][0]["message"]["content"]
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# Calculate difference rate
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diff_rate = calculate_diff_rate(content1, content2)
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# Verify that the difference rate is below the threshold
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assert diff_rate < 0.05, f"Output difference too large ({diff_rate:.4%})"
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# ==========================
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# Invalid prompt tests
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# ==========================
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invalid_prompts = [
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[], # Empty array
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[{}], # Empty object
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[{"role": "user"}], # Missing content
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[{"content": "hello"}], # Missing role
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]
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@pytest.mark.parametrize("messages", invalid_prompts)
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def test_invalid_chat(messages, api_url, headers):
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"""
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Test invalid chat inputs
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"""
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resp = requests.post(api_url, headers=headers, json={"messages": messages})
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assert resp.status_code >= 400, "Invalid request should return an error status code"
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# ==========================
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# Test for input exceeding context length
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# ==========================
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def test_exceed_context_length(api_url, headers):
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"""
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Test case for inputs that exceed the model's maximum context length.
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"""
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# Construct an overly long message
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long_content = "你好，" * 20000
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messages = [{"role": "user", "content": long_content}]
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resp = requests.post(api_url, headers=headers, json={"messages": messages})
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# Check if the response indicates a token limit error or server error (500)
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try:
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response_json = resp.json()
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except Exception:
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response_json = {}
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# Check status code and response content
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assert (
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resp.status_code != 200 or "token" in json.dumps(response_json).lower()
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), f"Expected token limit error or similar, but got a normal response: {response_json}"
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# ==========================
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# Multi-turn Conversation Test
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# ==========================
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def test_multi_turn_conversation(api_url, headers):
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"""
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Test whether multi-turn conversation context is effective.
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"""
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messages = [
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{"role": "user", "content": "你是谁？"},
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{"role": "assistant", "content": "我是AI助手"},
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{"role": "user", "content": "你能做什么？"},
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]
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resp = requests.post(api_url, headers=headers, json={"messages": messages})
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assert resp.status_code == 200
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validate(instance=resp.json(), schema=chat_response_schema)
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# ==========================
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# Concurrent Performance Test
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# ==========================
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def test_concurrent_perf(api_url, headers):
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"""
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Send concurrent requests to test stability and response time.
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"""
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prompts = [{"role": "user", "content": "Introduce FastDeploy."}]
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def send_request():
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"""
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Send a single request
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"""
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resp = requests.post(api_url, headers=headers, json={"messages": prompts})
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assert resp.status_code == 200
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return resp.elapsed.total_seconds()
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with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
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futures = [executor.submit(send_request) for _ in range(8)]
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durations = [f.result() for f in futures]
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print("\nResponse time for each request:", durations)
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# ==========================
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# Metrics Endpoint Test
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# ==========================
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|
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|
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def test_metrics_endpoint(metrics_url):
|
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"""
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Test the metrics monitoring endpoint.
|
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"""
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resp = requests.get(metrics_url, timeout=5)
|
||||
|
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assert resp.status_code == 200, f"Unexpected status code: {resp.status_code}"
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assert "text/plain" in resp.headers["Content-Type"], "Content-Type is not text/plain"
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# Parse Prometheus metrics data
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metrics_data = resp.text
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lines = metrics_data.split("\n")
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metric_lines = [line for line in lines if not line.startswith("#") and line.strip() != ""]
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# 断言 具体值
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num_requests_running_found = False
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num_requests_waiting_found = False
|
||||
time_to_first_token_seconds_sum_found = False
|
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time_per_output_token_seconds_sum_found = False
|
||||
e2e_request_latency_seconds_sum_found = False
|
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request_inference_time_seconds_sum_found = False
|
||||
request_queue_time_seconds_sum_found = False
|
||||
request_prefill_time_seconds_sum_found = False
|
||||
request_decode_time_seconds_sum_found = False
|
||||
prompt_tokens_total_found = False
|
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generation_tokens_total_found = False
|
||||
request_prompt_tokens_sum_found = False
|
||||
request_generation_tokens_sum_found = False
|
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gpu_cache_usage_perc_found = False
|
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request_params_max_tokens_sum_found = False
|
||||
request_success_total_found = False
|
||||
cache_config_info_found = False
|
||||
available_batch_size_found = False
|
||||
hit_req_rate_found = False
|
||||
hit_token_rate_found = False
|
||||
cpu_hit_token_rate_found = False
|
||||
gpu_hit_token_rate_found = False
|
||||
|
||||
for line in metric_lines:
|
||||
if line.startswith("fastdeploy:num_requests_running"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "num_requests_running 值错误"
|
||||
num_requests_running_found = True
|
||||
elif line.startswith("fastdeploy:num_requests_waiting"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
num_requests_waiting_found = True
|
||||
assert float(value) >= 0, "num_requests_waiting 值错误"
|
||||
elif line.startswith("fastdeploy:time_to_first_token_seconds_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "time_to_first_token_seconds_sum 值错误"
|
||||
time_to_first_token_seconds_sum_found = True
|
||||
elif line.startswith("fastdeploy:time_per_output_token_seconds_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "time_per_output_token_seconds_sum 值错误"
|
||||
time_per_output_token_seconds_sum_found = True
|
||||
elif line.startswith("fastdeploy:e2e_request_latency_seconds_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "e2e_request_latency_seconds_sum_found 值错误"
|
||||
e2e_request_latency_seconds_sum_found = True
|
||||
elif line.startswith("fastdeploy:request_inference_time_seconds_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "request_inference_time_seconds_sum 值错误"
|
||||
request_inference_time_seconds_sum_found = True
|
||||
elif line.startswith("fastdeploy:request_queue_time_seconds_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "request_queue_time_seconds_sum 值错误"
|
||||
request_queue_time_seconds_sum_found = True
|
||||
elif line.startswith("fastdeploy:request_prefill_time_seconds_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "request_prefill_time_seconds_sum 值错误"
|
||||
request_prefill_time_seconds_sum_found = True
|
||||
elif line.startswith("fastdeploy:request_decode_time_seconds_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "request_decode_time_seconds_sum 值错误"
|
||||
request_decode_time_seconds_sum_found = True
|
||||
elif line.startswith("fastdeploy:prompt_tokens_total"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "prompt_tokens_total 值错误"
|
||||
prompt_tokens_total_found = True
|
||||
elif line.startswith("fastdeploy:generation_tokens_total"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "generation_tokens_total 值错误"
|
||||
generation_tokens_total_found = True
|
||||
elif line.startswith("fastdeploy:request_prompt_tokens_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "request_prompt_tokens_sum 值错误"
|
||||
request_prompt_tokens_sum_found = True
|
||||
elif line.startswith("fastdeploy:request_generation_tokens_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "request_generation_tokens_sum 值错误"
|
||||
request_generation_tokens_sum_found = True
|
||||
elif line.startswith("fastdeploy:gpu_cache_usage_perc"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "gpu_cache_usage_perc 值错误"
|
||||
gpu_cache_usage_perc_found = True
|
||||
elif line.startswith("fastdeploy:request_params_max_tokens_sum"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "request_params_max_tokens_sum 值错误"
|
||||
request_params_max_tokens_sum_found = True
|
||||
elif line.startswith("fastdeploy:request_success_total"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "request_success_total 值错误"
|
||||
request_success_total_found = True
|
||||
elif line.startswith("fastdeploy:cache_config_info"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "cache_config_info 值错误"
|
||||
cache_config_info_found = True
|
||||
elif line.startswith("fastdeploy:available_batch_size"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "available_batch_size 值错误"
|
||||
available_batch_size_found = True
|
||||
elif line.startswith("fastdeploy:hit_req_rate"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "hit_req_rate 值错误"
|
||||
hit_req_rate_found = True
|
||||
elif line.startswith("fastdeploy:hit_token_rate"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "hit_token_rate 值错误"
|
||||
hit_token_rate_found = True
|
||||
elif line.startswith("fastdeploy:cpu_hit_token_rate"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "cpu_hit_token_rate 值错误"
|
||||
cpu_hit_token_rate_found = True
|
||||
elif line.startswith("fastdeploy:gpu_hit_token_rate"):
|
||||
_, value = line.rsplit(" ", 1)
|
||||
assert float(value) >= 0, "gpu_hit_token_rate 值错误"
|
||||
gpu_hit_token_rate_found = True
|
||||
assert num_requests_running_found, "缺少 fastdeploy:num_requests_running 指标"
|
||||
assert num_requests_waiting_found, "缺少 fastdeploy:num_requests_waiting 指标"
|
||||
assert time_to_first_token_seconds_sum_found, "缺少 fastdeploy:time_to_first_token_seconds_sum 指标"
|
||||
assert time_per_output_token_seconds_sum_found, "缺少 fastdeploy:time_per_output_token_seconds_sum 指标"
|
||||
assert e2e_request_latency_seconds_sum_found, "缺少 fastdeploy:e2e_request_latency_seconds_sum_found 指标"
|
||||
assert request_inference_time_seconds_sum_found, "缺少 fastdeploy:request_inference_time_seconds_sum 指标"
|
||||
assert request_queue_time_seconds_sum_found, "缺少 fastdeploy:request_queue_time_seconds_sum 指标"
|
||||
assert request_prefill_time_seconds_sum_found, "缺少 fastdeploy:request_prefill_time_seconds_sum 指标"
|
||||
assert request_decode_time_seconds_sum_found, "缺少 fastdeploy:request_decode_time_seconds_sum 指标"
|
||||
assert prompt_tokens_total_found, "缺少 fastdeploy:prompt_tokens_total 指标"
|
||||
assert generation_tokens_total_found, "缺少 fastdeploy:generation_tokens_total 指标"
|
||||
assert request_prompt_tokens_sum_found, "缺少 fastdeploy:request_prompt_tokens_sum 指标"
|
||||
assert request_generation_tokens_sum_found, "缺少 fastdeploy:request_generation_tokens_sum 指标"
|
||||
assert gpu_cache_usage_perc_found, "缺少 fastdeploy:gpu_cache_usage_perc 指标"
|
||||
assert request_params_max_tokens_sum_found, "缺少 fastdeploy:request_params_max_tokens_sum 指标"
|
||||
assert request_success_total_found, "缺少 fastdeploy:request_success_total 指标"
|
||||
assert cache_config_info_found, "缺少 fastdeploy:cache_config_info 指标"
|
||||
assert available_batch_size_found, "缺少 fastdeploy:available_batch_size 指标"
|
||||
assert hit_req_rate_found, "缺少 fastdeploy:hit_req_rate 指标"
|
||||
assert hit_token_rate_found, "缺少 fastdeploy:hit_token_rate 指标"
|
||||
assert cpu_hit_token_rate_found, "缺少 fastdeploy:hit_token_rate 指标"
|
||||
assert gpu_hit_token_rate_found, "缺少 fastdeploy:gpu_hit_token_rate 指标"
|
||||
|
||||
|
||||
# ==========================
|
||||
# OpenAI Client chat.completions Test
|
||||
# ==========================
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def openai_client():
|
||||
ip = "0.0.0.0"
|
||||
service_http_port = str(FD_API_PORT)
|
||||
client = openai.Client(
|
||||
base_url=f"http://{ip}:{service_http_port}/v1",
|
||||
api_key="EMPTY_API_KEY",
|
||||
)
|
||||
return client
|
||||
|
||||
|
||||
# Non-streaming test
|
||||
def test_non_streaming_chat(openai_client):
|
||||
"""Test non-streaming chat functionality with the local service"""
|
||||
response = openai_client.chat.completions.create(
|
||||
model="default",
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful AI assistant."},
|
||||
{"role": "user", "content": "List 3 countries and their capitals."},
|
||||
],
|
||||
temperature=1,
|
||||
max_tokens=1024,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
assert hasattr(response, "choices")
|
||||
assert len(response.choices) > 0
|
||||
assert hasattr(response.choices[0], "message")
|
||||
assert hasattr(response.choices[0].message, "content")
|
||||
|
||||
|
||||
# Streaming test
|
||||
def test_streaming_chat(openai_client, capsys):
|
||||
"""Test streaming chat functionality with the local service"""
|
||||
response = openai_client.chat.completions.create(
|
||||
model="default",
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful AI assistant."},
|
||||
{"role": "user", "content": "List 3 countries and their capitals."},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "China(Beijing), France(Paris), Australia(Canberra).",
|
||||
},
|
||||
{"role": "user", "content": "OK, tell more."},
|
||||
],
|
||||
temperature=1,
|
||||
max_tokens=1024,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
output = []
|
||||
for chunk in response:
|
||||
if hasattr(chunk.choices[0], "delta") and hasattr(chunk.choices[0].delta, "content"):
|
||||
output.append(chunk.choices[0].delta.content)
|
||||
assert len(output) > 2
|
||||
|
||||
|
||||
# ==========================
|
||||
# OpenAI Client completions Test
|
||||
# ==========================
|
||||
|
||||
|
||||
def test_non_streaming(openai_client):
|
||||
"""Test non-streaming chat functionality with the local service"""
|
||||
response = openai_client.completions.create(
|
||||
model="default",
|
||||
prompt="Hello, how are you?",
|
||||
temperature=1,
|
||||
max_tokens=1024,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# Assertions to check the response structure
|
||||
assert hasattr(response, "choices")
|
||||
assert len(response.choices) > 0
|
||||
|
||||
|
||||
def test_streaming(openai_client, capsys):
|
||||
"""Test streaming functionality with the local service"""
|
||||
response = openai_client.completions.create(
|
||||
model="default",
|
||||
prompt="Hello, how are you?",
|
||||
temperature=1,
|
||||
max_tokens=1024,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
# Collect streaming output
|
||||
output = []
|
||||
for chunk in response:
|
||||
output.append(chunk.choices[0].text)
|
||||
assert len(output) > 0
|
||||
|
||||
|
||||
def test_profile_reset_block_num():
|
||||
"""测试profile reset_block_num功能，与baseline diff不能超过5%"""
|
||||
log_file = "./log/config.log"
|
||||
baseline = 32562
|
||||
|
||||
if not os.path.exists(log_file):
|
||||
pytest.fail(f"Log file not found: {log_file}")
|
||||
|
||||
with open(log_file, "r") as f:
|
||||
log_lines = f.readlines()
|
||||
|
||||
target_line = None
|
||||
for line in log_lines:
|
||||
if "Reset block num" in line:
|
||||
target_line = line.strip()
|
||||
break
|
||||
|
||||
if target_line is None:
|
||||
pytest.fail("日志中没有Reset block num信息")
|
||||
|
||||
match = re.search(r"total_block_num:(\d+)", target_line)
|
||||
if not match:
|
||||
pytest.fail(f"Failed to extract total_block_num from line: {target_line}")
|
||||
|
||||
try:
|
||||
actual_value = int(match.group(1))
|
||||
except ValueError:
|
||||
pytest.fail(f"Invalid number format: {match.group(1)}")
|
||||
|
||||
lower_bound = baseline * (1 - 0.05)
|
||||
upper_bound = baseline * (1 + 0.05)
|
||||
print(f"Reset total_block_num: {actual_value}. baseline: {baseline}")
|
||||
|
||||
assert lower_bound <= actual_value <= upper_bound, (
|
||||
f"Reset total_block_num {actual_value} 与 baseline {baseline} diff需要在5%以内"
|
||||
f"Allowed range: [{lower_bound:.1f}, {upper_bound:.1f}]"
|
||||
)
|
||||
@@ -481,7 +481,6 @@ def test_completions_streaming_with_n(openai_client):
|
||||
assert sum(count) == 2
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Temporarily skip this case due to unstable execution")
|
||||
def test_non_streaming_with_stop_str(openai_client):
|
||||
"""
|
||||
Test non-streaming chat functionality with the local service
|
||||
@@ -490,8 +489,9 @@ def test_non_streaming_with_stop_str(openai_client):
|
||||
model="default",
|
||||
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
||||
temperature=1,
|
||||
max_tokens=5,
|
||||
extra_body={"include_stop_str_in_output": True},
|
||||
top_p=0.0,
|
||||
max_tokens=10,
|
||||
extra_body={"min_tokens": 5, "include_stop_str_in_output": True},
|
||||
stream=False,
|
||||
)
|
||||
# Assertions to check the response structure
|
||||
@@ -516,7 +516,7 @@ def test_non_streaming_with_stop_str(openai_client):
|
||||
model="default",
|
||||
prompt="Hello, how are you?",
|
||||
temperature=1,
|
||||
max_tokens=1024,
|
||||
max_tokens=10,
|
||||
stream=False,
|
||||
)
|
||||
assert not response.choices[0].text.endswith("</s>")
|
||||
@@ -525,7 +525,7 @@ def test_non_streaming_with_stop_str(openai_client):
|
||||
model="default",
|
||||
prompt="Hello, how are you?",
|
||||
temperature=1,
|
||||
max_tokens=1024,
|
||||
max_tokens=10,
|
||||
extra_body={"include_stop_str_in_output": True},
|
||||
stream=False,
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user