Add ci (#2650)

* add ci ut and workflow

* Automatically cancel any previous CI runs for the ci.yml workflow, keeping only the latest one active

Author: XieYunshen

scripts/run_ci.sh

@@ -0,0 +1,37 @@
+#!/bin/bash
+DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+echo "$DIR"
+
+python -m pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
+python -m pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu126/
+python -m pip install -r requirements.txt
+python -m pip install jsonschema aistudio_sdk==0.2.6
+bash build.sh || exit 1
+
+failed_files=()
+run_path="$DIR/../test/ci_use"
+pushd "$run_path" || exit 1  # 目录不存在时退出
+
+for file in test_*; do
+    if [ -f "$file" ]; then
+        abs_path=$(realpath "$file")
+        echo "Running pytest on $abs_path"
+        if ! python -m pytest -sv "$abs_path"; then
+            echo "Test failed: $file"
+            failed_files+=("$file")
+        fi
+    fi
+done
+popd
+
+
+if [ ${#failed_files[@]} -gt 0 ]; then
+    echo "The following tests failed:"
+    for f in "${failed_files[@]}"; do
+        echo "$f"
+    done
+    exit 1
+else
+    echo "All tests passed!"
+    exit 0
+fi

test/ci_use/test_qwen2_offline.py

@@ -0,0 +1,167 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+import traceback
+from fastdeploy import LLM, SamplingParams
+import os
+import subprocess
+import signal
+
+FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8313))
+
+def format_chat_prompt(messages):
+    """
+    Format multi-turn conversation into prompt string, suitable for chat models.
+    Uses Qwen2 style with <|im_start|> / <|im_end|> tokens.
+    """
+    prompt = ""
+    for msg in messages:
+        role, content = msg["role"], msg["content"]
+        if role == "user":
+            prompt += "<|im_start|>user\n{content}<|im_end|>\n".format(content=content)
+        elif role == "assistant":
+            prompt += "<|im_start|>assistant\n{content}<|im_end|>\n".format(content=content)
+    prompt += "<|im_start|>assistant\n"
+    return prompt
+
+
+@pytest.fixture(scope="module")
+def model_path():
+    """
+    Get model path from environment variable MODEL_PATH,
+    default to "./Qwen2-7B-Instruct" if not set.
+    """
+    base_path = os.getenv("MODEL_PATH")
+    if base_path:
+        return os.path.join(base_path, "Qwen2-7B-Instruct")
+    else:
+        return "./Qwen2-7B-Instruct"
+
+@pytest.fixture(scope="module")
+def llm(model_path):
+    """
+    Fixture to initialize the LLM model with a given model path
+    """
+    try:
+        output = subprocess.check_output(f"lsof -i:{FD_ENGINE_QUEUE_PORT} -t", shell=True).decode().strip()
+        for pid in output.splitlines():
+            os.kill(int(pid), signal.SIGKILL)
+            print(f"Killed process on port {FD_ENGINE_QUEUE_PORT}, pid={pid}")
+    except subprocess.CalledProcessError:
+        pass
+
+    try:
+        llm = LLM(
+            model=model_path,
+            tensor_parallel_size=1,
+            engine_worker_queue_port=FD_ENGINE_QUEUE_PORT,
+            max_model_len=4096
+        )
+        print("Model loaded successfully from {}.".format(model_path))
+        yield llm
+    except Exception:
+        print("Failed to load model from {}.".format(model_path))
+        traceback.print_exc()
+        pytest.fail("Failed to initialize LLM model from {}".format(model_path))
+
+
+def test_generate_prompts(llm):
+    """
+    Test basic prompt generation
+    """
+    # Only one prompt enabled for testing currently
+    prompts = [
+        "请介绍一下中国的四大发明。",
+        # "太阳和地球之间的距离是多少？",
+        # "写一首关于春天的古风诗。",
+    ]
+
+    sampling_params = SamplingParams(
+        temperature=0.8,
+        top_p=0.95,
+    )
+
+    try:
+        outputs = llm.generate(prompts, sampling_params)
+
+        # Verify basic properties of the outputs
+        assert len(outputs) == len(prompts), "Number of outputs should match number of prompts"
+
+        for i, output in enumerate(outputs):
+            assert output.prompt == prompts[i], "Prompt mismatch for case {}".format(i + 1)
+            assert isinstance(output.outputs.text, str), "Output text should be string for case {}".format(i + 1)
+            assert len(output.outputs.text) > 0, "Generated text should not be empty for case {}".format(i + 1)
+            assert isinstance(output.finished, bool), "'finished' should be boolean for case {}".format(i + 1)
+            assert output.metrics.model_execute_time > 0, "Execution time should be positive for case {}".format(i + 1)
+
+            print("=== Prompt generation Case {} Passed ===".format(i + 1))
+
+    except Exception:
+        print("Failed during prompt generation.")
+        traceback.print_exc()
+        pytest.fail("Prompt generation test failed")
+
+
+def test_chat_completion(llm):
+    """
+    Test chat completion with multiple turns
+    """
+    chat_cases = [
+        [
+            {"role": "user", "content": "你好，请介绍一下你自己。"},
+        ],
+        [
+            {"role": "user", "content": "你知道地球到月球的距离是多少吗？"},
+            {"role": "assistant", "content": "大约是38万公里左右。"},
+            {"role": "user", "content": "那太阳到地球的距离是多少？"},
+        ],
+        [
+            {"role": "user", "content": "请给我起一个中文名。"},
+            {"role": "assistant", "content": "好的，你可以叫“星辰”。"},
+            {"role": "user", "content": "再起一个。"},
+            {"role": "assistant", "content": "那就叫”大海“吧。"},
+            {"role": "user", "content": "再来三个。"},
+        ],
+    ]
+
+    sampling_params = SamplingParams(
+        temperature=0.8,
+        top_p=0.95,
+    )
+
+    for i, case in enumerate(chat_cases):
+        prompt = format_chat_prompt(case)
+        try:
+            outputs = llm.generate(prompt, sampling_params)
+
+            # Verify chat completion properties
+            assert len(outputs) == 1, "Should return one output per prompt"
+            assert isinstance(outputs[0].outputs.text, str), "Output text should be string"
+            assert len(outputs[0].outputs.text) > 0, "Generated text should not be empty"
+            assert outputs[0].metrics.model_execute_time > 0, "Execution time should be positive"
+
+            print("=== Chat Case {} Passed ===".format(i + 1))
+
+        except Exception:
+            print("[ERROR] Chat Case {} failed.".format(i + 1))
+            traceback.print_exc()
+            pytest.fail("Chat case {} failed".format(i + 1))
+
+
+if __name__ == "__main__":
+    """
+    Main entry point for the test script.
+    """
+    pytest.main(["-sv", __file__])