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YuBaoku
c6f0c5c3a6
* [CI] Optimize test execution with single-GPU parallelism and log collection * remove export CUDA_VISIBLE_DEVICES * fix path error * fix log_* path and debug * [CI] Optimize test execution with single-GPU parallelism and log collection
1427 lines
47 KiB
Python
1427 lines
47 KiB
Python
# 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 os
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import re
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import shutil
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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 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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print("log dir clean ")
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if os.path.exists("log") and os.path.isdir("log"):
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shutil.rmtree("log")
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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, "ernie-4_5-21b-a3b-bf16-paddle")
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else:
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model_path = "./ernie-4_5-21b-a3b-bf16-paddle"
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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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"wint4",
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"--graph-optimization-config",
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'{"cudagraph_capture_sizes": [1], "use_cudagraph":true}',
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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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# 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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# 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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# OpenAI Client chat.completions Test
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# ==========================
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@pytest.fixture
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def openai_client():
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ip = "0.0.0.0"
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service_http_port = str(FD_API_PORT)
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client = openai.Client(
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base_url=f"http://{ip}:{service_http_port}/v1",
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api_key="EMPTY_API_KEY",
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)
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return client
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# Non-streaming test
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def test_non_streaming_chat(openai_client):
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"""
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Test non-streaming chat functionality with the local service
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"""
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response = openai_client.chat.completions.create(
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model="default",
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant."},
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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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stream=False,
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)
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assert hasattr(response, "choices")
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assert len(response.choices) > 0
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assert hasattr(response.choices[0], "message")
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assert hasattr(response.choices[0].message, "content")
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def test_non_streaming_chat_finish_reason(openai_client):
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"""
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Test non-streaming chat functionality with the local service
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"""
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response = openai_client.chat.completions.create(
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model="default",
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant."},
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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=5,
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stream=False,
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)
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assert hasattr(response, "choices")
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assert response.choices[0].finish_reason == "length"
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response = openai_client.chat.completions.create(
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model="default",
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant."},
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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_completion_tokens=5,
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stream=False,
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)
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assert hasattr(response, "choices")
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assert response.choices[0].finish_reason == "length"
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response = openai_client.chat.completions.create(
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model="default",
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant."},
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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=5,
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stream=False,
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n=2,
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)
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assert hasattr(response, "choices")
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for choice in response.choices:
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assert choice.finish_reason == "length"
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response = openai_client.chat.completions.create(
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model="default",
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant."},
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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_completion_tokens=5,
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stream=False,
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n=2,
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)
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assert hasattr(response, "choices")
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for choice in response.choices:
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assert choice.finish_reason == "length"
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# Streaming test
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def test_streaming_chat(openai_client, capsys):
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"""
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Test streaming chat functionality with the local service
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"""
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response = openai_client.chat.completions.create(
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model="default",
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant."},
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{"role": "user", "content": "List 3 countries and their capitals."},
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{
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"role": "assistant",
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"content": "China(Beijing), France(Paris), Australia(Canberra).",
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},
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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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stream=True,
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)
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output = []
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for chunk in response:
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if hasattr(chunk.choices[0], "delta") and hasattr(chunk.choices[0].delta, "content"):
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output.append(chunk.choices[0].delta.content)
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assert len(output) > 2
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# ==========================
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# OpenAI Client completions Test
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# ==========================
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def test_non_streaming(openai_client):
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"""
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Test non-streaming chat functionality with the local service
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"""
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response = openai_client.completions.create(
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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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stream=False,
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)
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# Assertions to check the response structure
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assert hasattr(response, "choices")
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assert len(response.choices) > 0
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def test_streaming(openai_client, capsys):
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"""
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Test streaming functionality with the local service
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"""
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response = openai_client.completions.create(
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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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stream=True,
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)
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# Collect streaming output
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output = []
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for chunk in response:
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output.append(chunk.choices[0].text)
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assert len(output) > 0
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# ==========================
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# OpenAI Client additional chat/completions test
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# ==========================
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def test_non_streaming_chat_with_n(openai_client):
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"""
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Test n param option in non-streaming chat functionality with the local service
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"""
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response = openai_client.chat.completions.create(
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model="default",
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messages=[],
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temperature=1,
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max_tokens=5,
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extra_body={"prompt_token_ids": [5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937]},
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stream=False,
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n=2,
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)
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assert hasattr(response, "choices")
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assert len(response.choices) == 2
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assert hasattr(response, "usage")
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assert hasattr(response.usage, "prompt_tokens")
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assert response.usage.prompt_tokens == 9
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def test_streaming_chat_with_n(openai_client):
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"""
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Test n param option in streaming chat functionality with the local service
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"""
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response = openai_client.chat.completions.create(
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model="default",
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messages=[],
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temperature=1,
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max_tokens=5,
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extra_body={"prompt_token_ids": [5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937]},
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stream=True,
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stream_options={"include_usage": True},
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n=2,
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)
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count: list = [0, 0]
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for chunk in response:
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assert hasattr(chunk, "choices")
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assert hasattr(chunk, "usage")
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if len(chunk.choices) > 0:
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assert chunk.usage is None
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if chunk.choices[0].index == 0:
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count[0] = 1
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elif chunk.choices[0].index == 1:
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count[1] = 1
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else:
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assert hasattr(chunk.usage, "prompt_tokens")
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assert chunk.usage.prompt_tokens == 9
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assert sum(count) == 2
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def test_completions_non_streaming_with_n(openai_client):
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"""
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Test n param option in non-streaming completions functionality with the local service
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"""
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response = openai_client.completions.create(
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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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stream=False,
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n=2,
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)
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assert hasattr(response, "choices")
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assert len(response.choices) == 2
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assert hasattr(response.choices[0], "text")
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assert isinstance(response.choices[0].text, str)
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assert hasattr(response.choices[1], "text")
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assert isinstance(response.choices[1].text, str)
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def test_completions_streaming_with_n(openai_client):
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"""
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Test n param option in streaming completions functionality with the local service
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"""
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response = openai_client.completions.create(
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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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stream=True,
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n=2,
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)
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output_chunks = []
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count: list = [0, 0]
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for chunk in response:
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if chunk.choices[0].index == 0:
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count[0] = 1
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elif chunk.choices[0].index == 1:
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count[1] = 1
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assert hasattr(chunk, "choices")
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assert len(chunk.choices) > 0
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assert hasattr(chunk.choices[0], "text")
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output_chunks.append(chunk.choices[0].text)
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assert len(output_chunks) > 0
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assert sum(count) == 2
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def test_non_streaming_with_stop_str(openai_client):
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"""
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Test non-streaming chat functionality with the local service
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"""
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response = openai_client.chat.completions.create(
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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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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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assert hasattr(response, "choices")
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assert len(response.choices) > 0
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assert response.choices[0].message.content.endswith("</s>")
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response = openai_client.chat.completions.create(
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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": False},
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stream=False,
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)
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# Assertions to check the response structure
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assert hasattr(response, "choices")
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assert len(response.choices) > 0
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assert not response.choices[0].message.content.endswith("</s>")
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response = openai_client.completions.create(
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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=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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response = openai_client.completions.create(
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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=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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assert response.choices[0].text.endswith("</s>")
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def test_streaming_with_stop_str(openai_client):
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"""
|
|
Test non-streaming chat functionality with the local service
|
|
"""
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"min_tokens": 1, "include_stop_str_in_output": True},
|
|
stream=True,
|
|
)
|
|
# Assertions to check the response structure
|
|
last_token = ""
|
|
for chunk in response:
|
|
last_token = chunk.choices[0].delta.content
|
|
if last_token:
|
|
assert last_token.endswith("</s>"), f"last_token did not end with '</s>': {last_token!r}"
|
|
else:
|
|
print("Warning: empty output received, skipping test_streaming_with_stop_str.")
|
|
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"include_stop_str_in_output": False},
|
|
stream=True,
|
|
)
|
|
# Assertions to check the response structure
|
|
last_token = ""
|
|
for chunk in response:
|
|
last_token = chunk.choices[0].delta.content
|
|
assert last_token != "</s>"
|
|
|
|
response_1 = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
max_tokens=10,
|
|
stream=True,
|
|
)
|
|
last_token = ""
|
|
for chunk in response_1:
|
|
last_token = chunk.choices[0].text
|
|
assert not last_token.endswith("</s>")
|
|
|
|
response_1 = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
max_tokens=10,
|
|
extra_body={"include_stop_str_in_output": True},
|
|
stream=True,
|
|
)
|
|
last_token = ""
|
|
for chunk in response_1:
|
|
last_token = chunk.choices[0].text
|
|
assert last_token.endswith("</s>")
|
|
|
|
|
|
def test_non_streaming_chat_with_return_token_ids(openai_client, capsys):
|
|
"""
|
|
Test return_token_ids option in non-streaming chat functionality with the local service
|
|
"""
|
|
# enable return_token_ids
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"return_token_ids": True},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response, "choices")
|
|
assert len(response.choices) > 0
|
|
assert hasattr(response.choices[0], "message")
|
|
assert hasattr(response.choices[0].message, "prompt_token_ids")
|
|
assert isinstance(response.choices[0].message.prompt_token_ids, list)
|
|
assert hasattr(response.choices[0].message, "completion_token_ids")
|
|
assert isinstance(response.choices[0].message.completion_token_ids, list)
|
|
|
|
# disable return_token_ids
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"return_token_ids": False},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response, "choices")
|
|
assert len(response.choices) > 0
|
|
assert hasattr(response.choices[0], "message")
|
|
assert hasattr(response.choices[0].message, "prompt_token_ids")
|
|
assert response.choices[0].message.prompt_token_ids is None
|
|
assert hasattr(response.choices[0].message, "completion_token_ids")
|
|
assert response.choices[0].message.completion_token_ids is None
|
|
|
|
|
|
def test_streaming_chat_with_return_token_ids(openai_client, capsys):
|
|
"""
|
|
Test return_token_ids option in streaming chat functionality with the local service
|
|
"""
|
|
# enable return_token_ids
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"return_token_ids": True},
|
|
stream=True,
|
|
)
|
|
is_first_chunk = True
|
|
for chunk in response:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "delta")
|
|
assert hasattr(chunk.choices[0].delta, "prompt_token_ids")
|
|
assert hasattr(chunk.choices[0].delta, "completion_token_ids")
|
|
if is_first_chunk:
|
|
is_first_chunk = False
|
|
assert isinstance(chunk.choices[0].delta.prompt_token_ids, list)
|
|
assert chunk.choices[0].delta.completion_token_ids is None
|
|
else:
|
|
assert chunk.choices[0].delta.prompt_token_ids is None
|
|
assert isinstance(chunk.choices[0].delta.completion_token_ids, list)
|
|
|
|
# disable return_token_ids
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"return_token_ids": False},
|
|
stream=True,
|
|
)
|
|
for chunk in response:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "delta")
|
|
assert hasattr(chunk.choices[0].delta, "prompt_token_ids")
|
|
assert chunk.choices[0].delta.prompt_token_ids is None
|
|
assert hasattr(chunk.choices[0].delta, "completion_token_ids")
|
|
assert chunk.choices[0].delta.completion_token_ids is None
|
|
|
|
|
|
def test_non_streaming_completion_with_return_token_ids(openai_client, capsys):
|
|
"""
|
|
Test return_token_ids option in non-streaming completion functionality with the local service
|
|
"""
|
|
# enable return_token_ids
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"return_token_ids": True},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response, "choices")
|
|
assert len(response.choices) > 0
|
|
assert hasattr(response.choices[0], "prompt_token_ids")
|
|
assert isinstance(response.choices[0].prompt_token_ids, list)
|
|
assert hasattr(response.choices[0], "completion_token_ids")
|
|
assert isinstance(response.choices[0].completion_token_ids, list)
|
|
|
|
# disable return_token_ids
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"return_token_ids": False},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response, "choices")
|
|
assert len(response.choices) > 0
|
|
assert hasattr(response.choices[0], "prompt_token_ids")
|
|
assert response.choices[0].prompt_token_ids is None
|
|
assert hasattr(response.choices[0], "completion_token_ids")
|
|
assert response.choices[0].completion_token_ids is None
|
|
|
|
|
|
def test_streaming_completion_with_return_token_ids(openai_client, capsys):
|
|
"""
|
|
Test return_token_ids option in streaming completion functionality with the local service
|
|
"""
|
|
# enable return_token_ids
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"return_token_ids": True},
|
|
stream=True,
|
|
)
|
|
is_first_chunk = True
|
|
for chunk in response:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "prompt_token_ids")
|
|
assert hasattr(chunk.choices[0], "completion_token_ids")
|
|
if is_first_chunk:
|
|
is_first_chunk = False
|
|
assert isinstance(chunk.choices[0].prompt_token_ids, list)
|
|
assert chunk.choices[0].completion_token_ids is None
|
|
else:
|
|
assert chunk.choices[0].prompt_token_ids is None
|
|
assert isinstance(chunk.choices[0].completion_token_ids, list)
|
|
|
|
# disable return_token_ids
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"return_token_ids": False},
|
|
stream=True,
|
|
)
|
|
for chunk in response:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "prompt_token_ids")
|
|
assert chunk.choices[0].prompt_token_ids is None
|
|
assert hasattr(chunk.choices[0], "completion_token_ids")
|
|
assert chunk.choices[0].completion_token_ids is None
|
|
|
|
|
|
def test_non_streaming_chat_with_prompt_token_ids(openai_client, capsys):
|
|
"""
|
|
Test prompt_token_ids option in non-streaming chat functionality with the local service
|
|
"""
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"prompt_token_ids": [5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937]},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response, "choices")
|
|
assert len(response.choices) > 0
|
|
assert hasattr(response, "usage")
|
|
assert hasattr(response.usage, "prompt_tokens")
|
|
assert response.usage.prompt_tokens == 9
|
|
|
|
|
|
def test_streaming_chat_with_prompt_token_ids(openai_client, capsys):
|
|
"""
|
|
Test prompt_token_ids option in streaming chat functionality with the local service
|
|
"""
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"prompt_token_ids": [5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937]},
|
|
stream=True,
|
|
stream_options={"include_usage": True},
|
|
)
|
|
for chunk in response:
|
|
assert hasattr(chunk, "choices")
|
|
assert hasattr(chunk, "usage")
|
|
if len(chunk.choices) > 0:
|
|
assert chunk.usage is None
|
|
else:
|
|
assert hasattr(chunk.usage, "prompt_tokens")
|
|
assert chunk.usage.prompt_tokens == 9
|
|
|
|
|
|
def test_non_streaming_completion_with_prompt_token_ids(openai_client, capsys):
|
|
"""
|
|
Test prompt_token_ids option in streaming completion functionality with the local service
|
|
"""
|
|
# Test case for passing a token id list in `prompt_token_ids`
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt="",
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"prompt_token_ids": [5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937]},
|
|
stream=False,
|
|
)
|
|
assert len(response.choices) == 1
|
|
assert response.usage.prompt_tokens == 9
|
|
|
|
# Test case for passing a batch of token id lists in `prompt_token_ids`
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt="",
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"prompt_token_ids": [[5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937], [1, 2, 3]]},
|
|
stream=False,
|
|
)
|
|
assert len(response.choices) == 2
|
|
assert response.usage.prompt_tokens == 9 + 3
|
|
|
|
# Test case for passing a token id list in `prompt`
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt=[5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
stream=False,
|
|
)
|
|
assert len(response.choices) == 1
|
|
assert response.usage.prompt_tokens == 9
|
|
|
|
# Test case for passing a batch of token id lists in `prompt`
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt=[[5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937], [1, 2, 3]],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
stream=False,
|
|
)
|
|
assert len(response.choices) == 2
|
|
assert response.usage.prompt_tokens == 9 + 3
|
|
|
|
|
|
def test_streaming_completion_with_prompt_token_ids(openai_client, capsys):
|
|
"""
|
|
Test prompt_token_ids option in non-streaming completion functionality with the local service
|
|
"""
|
|
# Test case for passing a token id list in `prompt_token_ids`
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt="",
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"prompt_token_ids": [5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937]},
|
|
stream=True,
|
|
stream_options={"include_usage": True},
|
|
)
|
|
sum_prompt_tokens = 0
|
|
for chunk in response:
|
|
if len(chunk.choices) > 0:
|
|
assert chunk.usage is None
|
|
else:
|
|
sum_prompt_tokens += chunk.usage.prompt_tokens
|
|
assert sum_prompt_tokens == 9
|
|
|
|
# Test case for passing a batch of token id lists in `prompt_token_ids`
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt="",
|
|
temperature=1,
|
|
max_tokens=5,
|
|
extra_body={"prompt_token_ids": [[5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937], [1, 2, 3]]},
|
|
stream=True,
|
|
stream_options={"include_usage": True},
|
|
)
|
|
sum_prompt_tokens = 0
|
|
for chunk in response:
|
|
if len(chunk.choices) > 0:
|
|
assert chunk.usage is None
|
|
else:
|
|
sum_prompt_tokens += chunk.usage.prompt_tokens
|
|
assert sum_prompt_tokens == 9 + 3
|
|
|
|
# Test case for passing a token id list in `prompt`
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt=[5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
stream=True,
|
|
stream_options={"include_usage": True},
|
|
)
|
|
sum_prompt_tokens = 0
|
|
for chunk in response:
|
|
if len(chunk.choices) > 0:
|
|
assert chunk.usage is None
|
|
else:
|
|
sum_prompt_tokens += chunk.usage.prompt_tokens
|
|
assert sum_prompt_tokens == 9
|
|
|
|
# Test case for passing a batch of token id lists in `prompt`
|
|
response = openai_client.completions.create(
|
|
model="default",
|
|
prompt=[[5209, 626, 274, 45954, 1071, 3265, 3934, 1869, 93937], [1, 2, 3]],
|
|
temperature=1,
|
|
max_tokens=5,
|
|
stream=True,
|
|
stream_options={"include_usage": True},
|
|
)
|
|
sum_prompt_tokens = 0
|
|
for chunk in response:
|
|
if len(chunk.choices) > 0:
|
|
assert chunk.usage is None
|
|
else:
|
|
sum_prompt_tokens += chunk.usage.prompt_tokens
|
|
assert sum_prompt_tokens == 9 + 3
|
|
|
|
|
|
def test_non_streaming_chat_completion_disable_chat_template(openai_client, capsys):
|
|
"""
|
|
Test disable_chat_template option in chat functionality with the local service.
|
|
"""
|
|
enabled_response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
max_tokens=10,
|
|
temperature=0.0,
|
|
top_p=0,
|
|
extra_body={"disable_chat_template": False},
|
|
stream=False,
|
|
)
|
|
assert hasattr(enabled_response, "choices")
|
|
assert len(enabled_response.choices) > 0
|
|
|
|
# from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
|
|
# tokenizer = Ernie4_5Tokenizer.from_pretrained("PaddlePaddle/ERNIE-4.5-0.3B-Paddle", trust_remote_code=True)
|
|
# prompt = tokenizer.apply_chat_template([{"role": "user", "content": "Hello, how are you?"}], tokenize=False)
|
|
prompt = "<|begin_of_sentence|>User: Hello, how are you?\nAssistant: "
|
|
disabled_response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": prompt}],
|
|
max_tokens=10,
|
|
temperature=0,
|
|
top_p=0,
|
|
extra_body={"disable_chat_template": True},
|
|
stream=False,
|
|
)
|
|
assert hasattr(disabled_response, "choices")
|
|
assert len(disabled_response.choices) > 0
|
|
assert enabled_response.choices[0].message.content == disabled_response.choices[0].message.content
|
|
|
|
|
|
def test_non_streaming_chat_with_min_tokens(openai_client, capsys):
|
|
"""
|
|
Test min_tokens option in non-streaming chat functionality with the local service
|
|
"""
|
|
min_tokens = 1000
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
max_tokens=1010,
|
|
extra_body={"min_tokens": min_tokens},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response, "usage")
|
|
assert hasattr(response.usage, "completion_tokens")
|
|
assert response.usage.completion_tokens >= min_tokens
|
|
|
|
|
|
def test_non_streaming_min_max_token_equals_one(openai_client, capsys):
|
|
"""
|
|
Test chat/completion when min_tokens equals max_tokens equals 1.
|
|
Verify it returns exactly one token.
|
|
"""
|
|
# Test non-streaming chat
|
|
response = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello"}],
|
|
max_tokens=1,
|
|
temperature=0.0,
|
|
stream=False,
|
|
)
|
|
assert hasattr(response, "choices")
|
|
assert len(response.choices) > 0
|
|
assert hasattr(response.choices[0], "message")
|
|
assert hasattr(response.choices[0].message, "content")
|
|
# Verify usage shows exactly 1 completion token
|
|
assert hasattr(response, "usage")
|
|
assert response.usage.completion_tokens == 1
|
|
|
|
|
|
def test_non_streaming_chat_with_bad_words(openai_client, capsys):
|
|
"""
|
|
Test bad_words option in non-streaming chat functionality with the local service
|
|
"""
|
|
base_path = os.getenv("MODEL_PATH")
|
|
if base_path:
|
|
model_path = os.path.join(base_path, "ernie-4_5-21b-a3b-bf16-paddle")
|
|
else:
|
|
model_path = "./ernie-4_5-21b-a3b-bf16-paddle"
|
|
response_0 = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
stream=False,
|
|
extra_body={"return_token_ids": True},
|
|
)
|
|
|
|
assert hasattr(response_0, "choices")
|
|
assert len(response_0.choices) > 0
|
|
assert hasattr(response_0.choices[0], "message")
|
|
assert hasattr(response_0.choices[0].message, "completion_token_ids")
|
|
assert isinstance(response_0.choices[0].message.completion_token_ids, list)
|
|
|
|
from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
|
|
|
|
tokenizer = Ernie4_5Tokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
output_tokens_0 = []
|
|
output_ids_0 = []
|
|
for ids in response_0.choices[0].message.completion_token_ids:
|
|
output_tokens_0.append(tokenizer.decode(ids))
|
|
output_ids_0.append(ids)
|
|
|
|
# add bad words
|
|
bad_tokens = output_tokens_0[6:10]
|
|
bad_token_ids = output_ids_0[6:10]
|
|
response_1 = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
extra_body={"bad_words": bad_tokens, "return_token_ids": True},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response_1, "choices")
|
|
assert len(response_1.choices) > 0
|
|
assert hasattr(response_1.choices[0], "message")
|
|
assert hasattr(response_1.choices[0].message, "completion_token_ids")
|
|
assert isinstance(response_1.choices[0].message.completion_token_ids, list)
|
|
|
|
response_2 = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
extra_body={"bad_words_token_ids": bad_token_ids, "return_token_ids": True},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response_2, "choices")
|
|
assert len(response_2.choices) > 0
|
|
assert hasattr(response_2.choices[0], "message")
|
|
assert hasattr(response_2.choices[0].message, "completion_token_ids")
|
|
assert isinstance(response_2.choices[0].message.completion_token_ids, list)
|
|
|
|
assert not any(ids in response_1.choices[0].message.completion_token_ids for ids in bad_token_ids)
|
|
assert not any(ids in response_2.choices[0].message.completion_token_ids for ids in bad_token_ids)
|
|
|
|
|
|
def test_streaming_chat_with_bad_words(openai_client, capsys):
|
|
"""
|
|
Test bad_words option in streaming chat functionality with the local service
|
|
"""
|
|
response_0 = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
stream=True,
|
|
extra_body={"return_token_ids": True},
|
|
)
|
|
output_tokens_0 = []
|
|
output_ids_0 = []
|
|
is_first_chunk = True
|
|
for chunk in response_0:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "delta")
|
|
assert hasattr(chunk.choices[0].delta, "content")
|
|
assert hasattr(chunk.choices[0].delta, "completion_token_ids")
|
|
if is_first_chunk:
|
|
is_first_chunk = False
|
|
else:
|
|
assert isinstance(chunk.choices[0].delta.completion_token_ids, list)
|
|
output_tokens_0.append(chunk.choices[0].delta.content)
|
|
output_ids_0.extend(chunk.choices[0].delta.completion_token_ids)
|
|
|
|
# add bad words
|
|
bad_tokens = output_tokens_0[6:10]
|
|
bad_token_ids = output_ids_0[6:10]
|
|
response_1 = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
extra_body={"bad_words": bad_tokens, "return_token_ids": True},
|
|
stream=True,
|
|
)
|
|
output_tokens_1 = []
|
|
output_ids_1 = []
|
|
is_first_chunk = True
|
|
for chunk in response_1:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "delta")
|
|
assert hasattr(chunk.choices[0].delta, "content")
|
|
assert hasattr(chunk.choices[0].delta, "completion_token_ids")
|
|
if is_first_chunk:
|
|
is_first_chunk = False
|
|
else:
|
|
assert isinstance(chunk.choices[0].delta.completion_token_ids, list)
|
|
output_tokens_1.append(chunk.choices[0].delta.content)
|
|
output_ids_1.extend(chunk.choices[0].delta.completion_token_ids)
|
|
|
|
response_2 = openai_client.chat.completions.create(
|
|
model="default",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
extra_body={"bad_words_token_ids": bad_token_ids, "return_token_ids": True},
|
|
stream=True,
|
|
)
|
|
output_tokens_2 = []
|
|
output_ids_2 = []
|
|
is_first_chunk = True
|
|
for chunk in response_2:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "delta")
|
|
assert hasattr(chunk.choices[0].delta, "content")
|
|
assert hasattr(chunk.choices[0].delta, "completion_token_ids")
|
|
if is_first_chunk:
|
|
is_first_chunk = False
|
|
else:
|
|
assert isinstance(chunk.choices[0].delta.completion_token_ids, list)
|
|
output_tokens_2.append(chunk.choices[0].delta.content)
|
|
output_ids_2.extend(chunk.choices[0].delta.completion_token_ids)
|
|
|
|
assert not any(ids in output_ids_1 for ids in bad_token_ids)
|
|
assert not any(ids in output_ids_2 for ids in bad_token_ids)
|
|
|
|
|
|
def test_non_streaming_completion_with_bad_words(openai_client, capsys):
|
|
"""
|
|
Test bad_words option in non-streaming completion functionality with the local service
|
|
"""
|
|
base_path = os.getenv("MODEL_PATH")
|
|
if base_path:
|
|
model_path = os.path.join(base_path, "ernie-4_5-21b-a3b-bf16-paddle")
|
|
else:
|
|
model_path = "./ernie-4_5-21b-a3b-bf16-paddle"
|
|
|
|
response_0 = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
stream=False,
|
|
extra_body={"return_token_ids": True},
|
|
)
|
|
assert hasattr(response_0, "choices")
|
|
assert len(response_0.choices) > 0
|
|
assert hasattr(response_0.choices[0], "completion_token_ids")
|
|
assert isinstance(response_0.choices[0].completion_token_ids, list)
|
|
|
|
from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
|
|
|
|
tokenizer = Ernie4_5Tokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
output_tokens_0 = []
|
|
output_ids_0 = []
|
|
for ids in response_0.choices[0].completion_token_ids:
|
|
output_tokens_0.append(tokenizer.decode(ids))
|
|
output_ids_0.append(ids)
|
|
|
|
# add bad words
|
|
bad_tokens = output_tokens_0[6:10]
|
|
bad_token_ids = output_ids_0[6:10]
|
|
response_1 = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
extra_body={"bad_words": bad_tokens, "return_token_ids": True},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response_1, "choices")
|
|
assert len(response_1.choices) > 0
|
|
assert hasattr(response_1.choices[0], "completion_token_ids")
|
|
assert isinstance(response_1.choices[0].completion_token_ids, list)
|
|
|
|
response_2 = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
extra_body={"bad_words_token_ids": bad_token_ids, "return_token_ids": True},
|
|
stream=False,
|
|
)
|
|
assert hasattr(response_2, "choices")
|
|
assert len(response_2.choices) > 0
|
|
assert hasattr(response_2.choices[0], "completion_token_ids")
|
|
assert isinstance(response_2.choices[0].completion_token_ids, list)
|
|
|
|
assert not any(ids in response_1.choices[0].completion_token_ids for ids in bad_token_ids)
|
|
assert not any(ids in response_2.choices[0].completion_token_ids for ids in bad_token_ids)
|
|
|
|
|
|
def test_streaming_completion_with_bad_words(openai_client, capsys):
|
|
"""
|
|
Test bad_words option in streaming completion functionality with the local service
|
|
"""
|
|
response_0 = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
stream=True,
|
|
extra_body={"return_token_ids": True},
|
|
)
|
|
output_tokens_0 = []
|
|
output_ids_0 = []
|
|
is_first_chunk = True
|
|
for chunk in response_0:
|
|
if is_first_chunk:
|
|
is_first_chunk = False
|
|
else:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "text")
|
|
assert hasattr(chunk.choices[0], "completion_token_ids")
|
|
output_tokens_0.append(chunk.choices[0].text)
|
|
output_ids_0.extend(chunk.choices[0].completion_token_ids)
|
|
|
|
# add bad words
|
|
bad_token_ids = output_ids_0[6:10]
|
|
bad_tokens = output_tokens_0[6:10]
|
|
response_1 = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
extra_body={"bad_words": bad_tokens, "return_token_ids": True},
|
|
stream=True,
|
|
)
|
|
output_tokens_1 = []
|
|
output_ids_1 = []
|
|
is_first_chunk = True
|
|
for chunk in response_1:
|
|
if is_first_chunk:
|
|
is_first_chunk = False
|
|
else:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "text")
|
|
assert hasattr(chunk.choices[0], "completion_token_ids")
|
|
output_tokens_1.append(chunk.choices[0].text)
|
|
output_ids_1.extend(chunk.choices[0].completion_token_ids)
|
|
# add bad words token ids
|
|
response_2 = openai_client.completions.create(
|
|
model="default",
|
|
prompt="Hello, how are you?",
|
|
temperature=1,
|
|
top_p=0.0,
|
|
max_tokens=20,
|
|
extra_body={"bad_words_token_ids": bad_token_ids, "return_token_ids": True},
|
|
stream=True,
|
|
)
|
|
output_tokens_2 = []
|
|
output_ids_2 = []
|
|
is_first_chunk = True
|
|
for chunk in response_2:
|
|
if is_first_chunk:
|
|
is_first_chunk = False
|
|
else:
|
|
assert hasattr(chunk, "choices")
|
|
assert len(chunk.choices) > 0
|
|
assert hasattr(chunk.choices[0], "text")
|
|
assert hasattr(chunk.choices[0], "completion_token_ids")
|
|
output_tokens_2.append(chunk.choices[0].text)
|
|
output_ids_2.extend(chunk.choices[0].completion_token_ids)
|
|
|
|
assert not any(ids in output_ids_1 for ids in bad_token_ids)
|
|
assert not any(ids in output_ids_2 for ids in bad_token_ids)
|
|
|
|
|
|
def test_streaming_chat_finish_reason(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=5,
|
|
stream=True,
|
|
)
|
|
|
|
for chunk in response:
|
|
last_token = chunk.choices[0].finish_reason
|
|
assert last_token == "length"
|
|
|
|
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_completion_tokens=5,
|
|
stream=True,
|
|
)
|
|
|
|
for chunk in response:
|
|
last_token = chunk.choices[0].finish_reason
|
|
assert last_token == "length"
|
|
|
|
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_completion_tokens=5,
|
|
stream=True,
|
|
n=2,
|
|
)
|
|
finish_reason_1 = ""
|
|
finish_reason_1 = ""
|
|
|
|
for chunk in response:
|
|
last_token = chunk.choices[0].finish_reason
|
|
if last_token:
|
|
if chunk.choices[0].index == 0:
|
|
finish_reason_1 = last_token
|
|
else:
|
|
finish_reason_2 = last_token
|
|
assert finish_reason_1 == "length"
|
|
assert finish_reason_2 == "length"
|
|
|
|
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=5,
|
|
stream=True,
|
|
n=2,
|
|
)
|
|
finish_reason_1 = ""
|
|
finish_reason_1 = ""
|
|
|
|
for chunk in response:
|
|
last_token = chunk.choices[0].finish_reason
|
|
if last_token:
|
|
if chunk.choices[0].index == 0:
|
|
finish_reason_1 = last_token
|
|
else:
|
|
finish_reason_2 = last_token
|
|
assert finish_reason_1 == "length"
|
|
assert finish_reason_2 == "length"
|
|
|
|
|
|
def test_profile_reset_block_num():
|
|
"""测试profile reset_block_num功能，与baseline diff不能超过5%"""
|
|
log_dir = os.getenv("FD_LOG_DIR", "log")
|
|
log_file = os.path.join(log_dir, "config.log")
|
|
baseline = 31446
|
|
|
|
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)
|
|
print(f"Reset total_block_num: {actual_value}. baseline: {baseline}")
|
|
|
|
assert actual_value >= lower_bound, (
|
|
f"Reset total_block_num {actual_value} is lower than 95% of baseline {baseline}. "
|
|
f"Minimum allowed value: {lower_bound:.1f}"
|
|
)
|