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https://github.com/PaddlePaddle/FastDeploy.git
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94be5ebdd1
* Implement test for MTP accept ratio Add test for MTP accept ratio with assertions on results and metrics. * Update test_ernie_21b_mtp.py * Refactor test_mtp_accept_ratio for baseline comparison Refactor test_mtp_accept_ratio to compare results against baseline file and metrics. * Fix formatting issues in test_ernie_21b_mtp.py --------- Co-authored-by: YuBaoku <49938469+EmmonsCurse@users.noreply.github.com>
368 lines
13 KiB
Python
368 lines
13 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 json
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import os
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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 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,
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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()
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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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mtp_model_path = os.path.join(model_path, "mtp")
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speculative_config = {"method": "mtp", "num_speculative_tokens": 1, "model": mtp_model_path}
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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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"2",
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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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"--speculative-config",
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json.dumps(speculative_config),
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"--graph-optimization-config",
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'{"use_cudagraph":true, "use_unique_memory_pool":true, "draft_model_use_cudagraph":true}',
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]
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# Start subprocess in new process group
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# 清除log目录
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if os.path.exists("log"):
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shutil.rmtree("log")
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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"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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clean()
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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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clean()
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print(f"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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def send_request(url, payload, timeout=60):
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"""
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发送请求到指定的URL，并返回响应结果。
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"""
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headers = {
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"Content-Type": "application/json",
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}
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try:
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res = requests.post(url, headers=headers, json=payload, timeout=timeout)
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print("🟢 接收响应中...\n")
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return res
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except requests.exceptions.Timeout:
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print(f"❌ 请求超时（超过 {timeout} 秒）")
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return None
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except requests.exceptions.RequestException as e:
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print(f"❌ 请求失败：{e}")
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return None
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def get_stream_chunks(response):
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"""解析流式返回，生成chunk List[dict]"""
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chunks = []
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if response.status_code == 200:
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for line in response.iter_lines(decode_unicode=True):
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if line:
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if line.startswith("data: "):
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line = line[len("data: ") :]
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if line.strip() == "[DONE]":
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break
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try:
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chunk = json.loads(line)
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chunks.append(chunk)
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except Exception as e:
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print(f"解析失败: {e}, 行内容: {line}")
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else:
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print(f"请求失败，状态码: {response.status_code}")
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print("返回内容：", response.text)
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return chunks
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def test_chat_usage_stream(api_url):
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"""测试流式chat usage"""
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payload = {
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"model": "default",
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"temperature": 0,
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"top_p": 0,
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"seed": 33,
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "牛顿的三大运动定律是什么？"},
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],
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"max_tokens": 50,
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"stream": True,
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"stream_options": {"include_usage": True, "continuous_usage_stats": True},
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"metadata": {"min_tokens": 10},
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}
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response = send_request(url=api_url, payload=payload)
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chunks = get_stream_chunks(response)
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result = "".join([x["choices"][0]["delta"]["content"] for x in chunks[:-1]])
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print("Prefill Response:", result)
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assert result != "", "结果为空"
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usage = chunks[-1]["usage"]
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total_tokens = usage["completion_tokens"] + usage["prompt_tokens"]
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assert payload["max_tokens"] >= usage["completion_tokens"], "completion_tokens大于max_tokens"
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assert payload["metadata"]["min_tokens"] <= usage["completion_tokens"], "completion_tokens小于min_tokens"
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assert usage["total_tokens"] == total_tokens, "total_tokens不等于prompt_tokens + completion_tokens"
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def test_chat_usage_non_stream(api_url):
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"""测试非流式chat usage"""
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payload = {
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"model": "default",
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"temperature": 0,
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"top_p": 0,
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"seed": 33,
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "牛顿的三大运动定律是什么？"},
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],
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"max_tokens": 50,
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"stream": False,
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"metadata": {"min_tokens": 10},
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}
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response = send_request(url=api_url, payload=payload).json()
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usage = response["usage"]
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result = response["choices"][0]["message"]["content"]
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assert result != "", "结果为空"
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total_tokens = usage["completion_tokens"] + usage["prompt_tokens"]
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assert payload["max_tokens"] >= usage["completion_tokens"], "completion_tokens大于max_tokens"
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assert payload["metadata"]["min_tokens"] <= usage["completion_tokens"], "completion_tokens小于min_tokens"
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assert usage["total_tokens"] == total_tokens, "total_tokens不等于prompt_tokens + completion_tokens"
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def test_non_chat_usage_stream(api_url):
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"""测试流式非chat usage"""
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payload = {
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"model": "default",
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"temperature": 0,
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"top_p": 0,
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"seed": 33,
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"prompt": "牛顿的三大运动定律是什么？",
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"max_tokens": 50,
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"stream": True,
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"stream_options": {"include_usage": True, "continuous_usage_stats": True},
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"metadata": {"min_tokens": 10},
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}
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api_url = api_url.replace("chat/completions", "completions")
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response = send_request(url=api_url, payload=payload)
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chunks = get_stream_chunks(response)
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result = "".join([x["choices"][0]["text"] for x in chunks[:-1]])
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# print("Prefill Response:", result)
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assert result != "", "结果为空"
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usage = chunks[-1]["usage"]
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total_tokens = usage["completion_tokens"] + usage["prompt_tokens"]
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assert payload["max_tokens"] >= usage["completion_tokens"], "completion_tokens大于max_tokens"
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assert payload["metadata"]["min_tokens"] <= usage["completion_tokens"], "completion_tokens小于min_tokens"
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assert usage["total_tokens"] == total_tokens, "total_tokens不等于prompt_tokens + completion_tokens"
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def test_non_chat_usage_non_stream(api_url):
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"""测试非流式非chat usage"""
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payload = {
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"model": "default",
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"temperature": 0,
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"top_p": 0,
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"seed": 33,
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"prompt": "牛顿的三大运动定律是什么？",
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"max_tokens": 50,
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"stream": False,
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"metadata": {"min_tokens": 10},
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}
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api_url = api_url.replace("chat/completions", "completions")
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response = send_request(url=api_url, payload=payload).json()
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usage = response["usage"]
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result = response["choices"][0]["text"]
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# print("Prefill Response:", result)
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assert result != "", "结果为空"
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total_tokens = usage["completion_tokens"] + usage["prompt_tokens"]
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assert payload["max_tokens"] >= usage["completion_tokens"], "completion_tokens大于max_tokens"
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assert payload["metadata"]["min_tokens"] <= usage["completion_tokens"], "completion_tokens小于min_tokens"
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assert usage["total_tokens"] == total_tokens, "total_tokens不等于prompt_tokens + completion_tokens"
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def test_mtp_accept_ratio(api_url):
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"""测试mtp接受率"""
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payload = {
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"model": "default",
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"messages": [
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{
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"role": "user",
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"content": "国外项目风险管理研究起步较早，理论体系成熟。早期研究集中于保险与金融领域，后逐步扩展至工程项目、"
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"公共管理等多领域。在理论层面，COSO《企业风险管理——整合框架》和ISO31000标准为风险管理提供了系统性"
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"指导，强调风险识别、评估、应对与监控的全流程管理。风险识别方法包括故障树分析、事件树分析等；风险评估"
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"则广泛应用VaR模型、蒙特卡洛模拟等量化工具。应对策略涵盖规避、转移、减轻和接受等，并衍生出风险共享、"
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"升级等复杂策略。此外，组织文化、管理层支持等因素对风险管理有效性影响显著。近年来，随着科技发展，"
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"人工智能、大数据等技术被引入风险管理，推动其向智能化、自动化方向发展。请介绍一下国外关于项目风险管理"
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"的文献研究综述，300字以内",
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},
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],
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"stream": True,
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"stream_options": {"include_usage": True, "continuous_usage_stats": True},
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"temperature": 0,
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"seed": 23,
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"top_p": 0,
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}
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print("fastdeploy answer is :")
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try:
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# TODO: 第一次和第二次存在diff，后面正常，暂时多请求一次
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response = send_request(url=api_url, payload=payload)
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chunks = get_stream_chunks(response)
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response = send_request(url=api_url, payload=payload)
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chunks = get_stream_chunks(response)
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for idx, chunk in enumerate(chunks):
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print(f"\nchunk[{idx}]:\n{json.dumps(chunk, ensure_ascii=False)}")
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result = "".join([x["choices"][0]["delta"]["content"] for x in chunks[:-1]])
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speculate_metrics = chunks[-2]["choices"][0]["speculate_metrics"]
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except Exception as e:
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print(f"解析失败: {e}")
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print("\nresult:\n", result)
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base_path = os.getenv("MODEL_PATH")
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baseline_path = os.path.join(base_path, "21b_mtp_accept_ratio_baseline.txt")
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with open(baseline_path, "r", encoding="utf-8") as f:
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baseline = f.read()
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baseline_ratio = {
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"accepted_tokens": 131,
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"rejected_tokens": 23,
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"accept_ratio": 0.4122137404580153,
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"average_accept_length": 1.7012987012987013,
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"accept_ratio_per_head": [0.7012987012987013],
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}
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response = send_request(url=api_url, payload=payload)
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chunks = get_stream_chunks(response)
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result_2 = "".join([x["choices"][0]["delta"]["content"] for x in chunks[:-1]])
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speculate_metrics_2 = chunks[-2]["choices"][0]["speculate_metrics"]
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print("chunks:", chunks[-2])
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print("baseline", speculate_metrics)
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print("speculate_metrics_2", speculate_metrics_2)
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assert result_2 == baseline, f"与baseline存在diff，result_2: {result}\n baseline: {baseline}"
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assert speculate_metrics_2 == baseline_ratio, (
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f"speculate_metrics存在diff，" f"speculate_metrics_2: {speculate_metrics_2}\n " f"baseline: {baseline_ratio}"
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)
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assert speculate_metrics_2["accept_ratio"] > 0, "accept_ratio异常"
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prompt_tokens = chunks[-1]["usage"]["prompt_tokens"]
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cached_tokens = chunks[-1]["usage"]["prompt_tokens_details"]["cached_tokens"]
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assert cached_tokens == prompt_tokens // 64 * 64, "cached_tokens数量有问题"
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