test_openai_server.py

import json
import time
import unittest

import openai

from sglang.srt.hf_transformers_utils import get_tokenizer
from sglang.srt.utils import kill_child_process
from sglang.test.test_utils import (
    DEFAULT_MODEL_NAME_FOR_TEST,
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
    DEFAULT_URL_FOR_TEST,
    popen_launch_server,
)


class TestOpenAIServer(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = DEFAULT_MODEL_NAME_FOR_TEST
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.api_key = "sk-123456"
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            api_key=cls.api_key,
        )
        cls.base_url += "/v1"
        cls.tokenizer = get_tokenizer(DEFAULT_MODEL_NAME_FOR_TEST)

    @classmethod
    def tearDownClass(cls):
        kill_child_process(cls.process.pid)

    def run_completion(
        self, echo, logprobs, use_list_input, parallel_sample_num, token_input
    ):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
        prompt = "The capital of France is"
        if token_input:
            prompt_input = self.tokenizer.encode(prompt)
            num_prompt_tokens = len(prompt_input)
        else:
            prompt_input = prompt
            num_prompt_tokens = len(self.tokenizer.encode(prompt))

        if use_list_input:
            prompt_arg = [prompt_input, prompt_input]
            num_choices = len(prompt_arg)
            num_prompt_tokens *= 2
        else:
            prompt_arg = prompt_input
            num_choices = 1

        response = client.completions.create(
            model=self.model,
            prompt=prompt_arg,
            temperature=0,
            max_tokens=32,
            echo=echo,
            logprobs=logprobs,
            n=parallel_sample_num,
        )

        assert len(response.choices) == num_choices * parallel_sample_num

        if echo:
            text = response.choices[0].text
            assert text.startswith(prompt)

        if logprobs:
            assert response.choices[0].logprobs
            assert isinstance(response.choices[0].logprobs.tokens[0], str)
            assert isinstance(response.choices[0].logprobs.top_logprobs[1], dict)
            ret_num_top_logprobs = len(response.choices[0].logprobs.top_logprobs[1])

            # FIXME: Sometimes, some top_logprobs are missing in the return value. The reason is that some output id maps to the same output token and duplicate in the map
            # assert ret_num_top_logprobs == logprobs, f"{ret_num_top_logprobs} vs {logprobs}"
            assert ret_num_top_logprobs > 0

            assert response.choices[0].logprobs.token_logprobs[0]

        assert response.id
        assert response.created
        assert (
            response.usage.prompt_tokens == num_prompt_tokens
        ), f"{response.usage.prompt_tokens} vs {num_prompt_tokens}"
        assert response.usage.completion_tokens > 0
        assert response.usage.total_tokens > 0

    def run_completion_stream(
        self, echo, logprobs, use_list_input, parallel_sample_num, token_input
    ):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
        prompt = "The capital of France is"
        if token_input:
            prompt_input = self.tokenizer.encode(prompt)
            num_prompt_tokens = len(prompt_input)
        else:
            prompt_input = prompt
            num_prompt_tokens = len(self.tokenizer.encode(prompt))

        if use_list_input:
            prompt_arg = [prompt_input, prompt_input]
            num_choices = len(prompt_arg)
            num_prompt_tokens *= 2
        else:
            prompt_arg = prompt_input
            num_choices = 1

        generator = client.completions.create(
            model=self.model,
            prompt=prompt_arg,
            temperature=0,
            max_tokens=32,
            echo=echo,
            logprobs=logprobs,
            stream=True,
            stream_options={"include_usage": True},
            n=parallel_sample_num,
        )

        is_firsts = {}
        for response in generator:
            usage = response.usage
            if usage is not None:
                assert usage.prompt_tokens > 0
                assert usage.completion_tokens > 0
                assert usage.total_tokens > 0
                continue

            index = response.choices[0].index
            is_first = is_firsts.get(index, True)

            if logprobs:
                assert response.choices[0].logprobs
                assert isinstance(response.choices[0].logprobs.tokens[0], str)
                if not (is_first and echo):
                    assert isinstance(
                        response.choices[0].logprobs.top_logprobs[0], dict
                    )
                    ret_num_top_logprobs = len(
                        response.choices[0].logprobs.top_logprobs[0]
                    )
                    # FIXME: Sometimes, some top_logprobs are missing in the return value. The reason is that some output id maps to the same output token and duplicate in the map
                    # assert ret_num_top_logprobs == logprobs, f"{ret_num_top_logprobs} vs {logprobs}"
                    assert ret_num_top_logprobs > 0

            if is_first:
                if echo:
                    assert response.choices[0].text.startswith(
                        prompt
                    ), f"{response.choices[0].text} and all args {echo} {logprobs} {token_input} {is_first}"
                is_firsts[index] = False
            assert response.id
            assert response.created

        for index in [i for i in range(parallel_sample_num * num_choices)]:
            assert not is_firsts.get(
                index, True
            ), f"index {index} is not found in the response"

    def run_chat_completion(self, logprobs, parallel_sample_num):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
        response = client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "You are a helpful AI assistant"},
                {
                    "role": "user",
                    "content": "What is the capital of France? Answer in a few words.",
                },
            ],
            temperature=0,
            logprobs=logprobs is not None and logprobs > 0,
            top_logprobs=logprobs,
            n=parallel_sample_num,
        )

        if logprobs:
            assert isinstance(
                response.choices[0].logprobs.content[0].top_logprobs[0].token, str
            )

            ret_num_top_logprobs = len(
                response.choices[0].logprobs.content[0].top_logprobs
            )
            assert (
                ret_num_top_logprobs == logprobs
            ), f"{ret_num_top_logprobs} vs {logprobs}"

        assert len(response.choices) == parallel_sample_num
        assert response.choices[0].message.role == "assistant"
        assert isinstance(response.choices[0].message.content, str)
        assert response.id
        assert response.created
        assert response.usage.prompt_tokens > 0
        assert response.usage.completion_tokens > 0
        assert response.usage.total_tokens > 0

    def run_chat_completion_stream(self, logprobs, parallel_sample_num=1):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
        generator = client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "You are a helpful AI assistant"},
                {"role": "user", "content": "What is the capital of France?"},
            ],
            temperature=0,
            logprobs=logprobs is not None and logprobs > 0,
            top_logprobs=logprobs,
            stream=True,
            stream_options={"include_usage": True},
            n=parallel_sample_num,
        )

        is_firsts = {}
        for response in generator:
            usage = response.usage
            if usage is not None:
                assert usage.prompt_tokens > 0
                assert usage.completion_tokens > 0
                assert usage.total_tokens > 0
                continue

            index = response.choices[0].index
            data = response.choices[0].delta

            if is_firsts.get(index, True):
                assert data.role == "assistant"
                is_firsts[index] = False
                continue

            if logprobs:
                assert response.choices[0].logprobs
                assert isinstance(
                    response.choices[0].logprobs.content[0].top_logprobs[0].token, str
                )
                assert isinstance(
                    response.choices[0].logprobs.content[0].top_logprobs, list
                )
                ret_num_top_logprobs = len(
                    response.choices[0].logprobs.content[0].top_logprobs
                )
                assert (
                    ret_num_top_logprobs == logprobs
                ), f"{ret_num_top_logprobs} vs {logprobs}"

            assert isinstance(data.content, str)
            assert response.id
            assert response.created

        for index in [i for i in range(parallel_sample_num)]:
            assert not is_firsts.get(
                index, True
            ), f"index {index} is not found in the response"

    def _create_batch(self, mode, client):
        if mode == "completion":
            input_file_path = "complete_input.jsonl"
            # write content to input file
            content = [
                {
                    "custom_id": "request-1",
                    "method": "POST",
                    "url": "/v1/completions",
                    "body": {
                        "model": "gpt-3.5-turbo-instruct",
                        "prompt": "List 3 names of famous soccer player: ",
                        "max_tokens": 20,
                    },
                },
                {
                    "custom_id": "request-2",
                    "method": "POST",
                    "url": "/v1/completions",
                    "body": {
                        "model": "gpt-3.5-turbo-instruct",
                        "prompt": "List 6 names of famous basketball player:  ",
                        "max_tokens": 40,
                    },
                },
                {
                    "custom_id": "request-3",
                    "method": "POST",
                    "url": "/v1/completions",
                    "body": {
                        "model": "gpt-3.5-turbo-instruct",
                        "prompt": "List 6 names of famous tenniss player:  ",
                        "max_tokens": 40,
                    },
                },
            ]

        else:
            input_file_path = "chat_input.jsonl"
            content = [
                {
                    "custom_id": "request-1",
                    "method": "POST",
                    "url": "/v1/chat/completions",
                    "body": {
                        "model": "gpt-3.5-turbo-0125",
                        "messages": [
                            {
                                "role": "system",
                                "content": "You are a helpful assistant.",
                            },
                            {
                                "role": "user",
                                "content": "Hello! List 3 NBA players and tell a story",
                            },
                        ],
                        "max_tokens": 30,
                    },
                },
                {
                    "custom_id": "request-2",
                    "method": "POST",
                    "url": "/v1/chat/completions",
                    "body": {
                        "model": "gpt-3.5-turbo-0125",
                        "messages": [
                            {"role": "system", "content": "You are an assistant. "},
                            {
                                "role": "user",
                                "content": "Hello! List three capital and tell a story",
                            },
                        ],
                        "max_tokens": 50,
                    },
                },
            ]

        with open(input_file_path, "w") as file:
            for line in content:
                file.write(json.dumps(line) + "\n")

        with open(input_file_path, "rb") as file:
            uploaded_file = client.files.create(file=file, purpose="batch")
        if mode == "completion":
            endpoint = "/v1/completions"
        elif mode == "chat":
            endpoint = "/v1/chat/completions"
        completion_window = "24h"
        batch_job = client.batches.create(
            input_file_id=uploaded_file.id,
            endpoint=endpoint,
            completion_window=completion_window,
        )

        return batch_job, content, uploaded_file

    def run_batch(self, mode):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
        batch_job, content, uploaded_file = self._create_batch(mode=mode, client=client)

        while batch_job.status not in ["completed", "failed", "cancelled"]:
            time.sleep(3)
            print(
                f"Batch job status: {batch_job.status}...trying again in 3 seconds..."
            )
            batch_job = client.batches.retrieve(batch_job.id)
        assert (
            batch_job.status == "completed"
        ), f"Batch job status is not completed: {batch_job.status}"
        assert batch_job.request_counts.completed == len(content)
        assert batch_job.request_counts.failed == 0
        assert batch_job.request_counts.total == len(content)

        result_file_id = batch_job.output_file_id
        file_response = client.files.content(result_file_id)
        result_content = file_response.read().decode("utf-8")  # Decode bytes to string
        results = [
            json.loads(line)
            for line in result_content.split("\n")
            if line.strip() != ""
        ]
        assert len(results) == len(content)
        for delete_fid in [uploaded_file.id, result_file_id]:
            del_pesponse = client.files.delete(delete_fid)
            assert del_pesponse.deleted

    def run_cancel_batch(self, mode):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
        batch_job, _, uploaded_file = self._create_batch(mode=mode, client=client)

        assert batch_job.status not in ["cancelling", "cancelled"]

        batch_job = client.batches.cancel(batch_id=batch_job.id)
        assert batch_job.status == "cancelling"

        while batch_job.status not in ["failed", "cancelled"]:
            batch_job = client.batches.retrieve(batch_job.id)
            print(
                f"Batch job status: {batch_job.status}...trying again in 3 seconds..."
            )
            time.sleep(3)

        assert batch_job.status == "cancelled"
        del_response = client.files.delete(uploaded_file.id)
        assert del_response.deleted

    def test_completion(self):
        for echo in [False, True]:
            for logprobs in [None, 5]:
                for use_list_input in [True, False]:
                    for parallel_sample_num in [1, 2]:
                        for token_input in [False, True]:
                            self.run_completion(
                                echo,
                                logprobs,
                                use_list_input,
                                parallel_sample_num,
                                token_input,
                            )

    def test_completion_stream(self):
        # parallel sampling adn list input are not supported in streaming mode
        for echo in [False, True]:
            for logprobs in [None, 5]:
                for use_list_input in [True, False]:
                    for parallel_sample_num in [1, 2]:
                        for token_input in [False, True]:
                            self.run_completion_stream(
                                echo,
                                logprobs,
                                use_list_input,
                                parallel_sample_num,
                                token_input,
                            )

    def test_chat_completion(self):
        for logprobs in [None, 5]:
            for parallel_sample_num in [1, 2]:
                self.run_chat_completion(logprobs, parallel_sample_num)

    def test_chat_completion_stream(self):
        for logprobs in [None, 5]:
            for parallel_sample_num in [1, 2]:
                self.run_chat_completion_stream(logprobs, parallel_sample_num)

    def test_batch(self):
        for mode in ["completion", "chat"]:
            self.run_batch(mode)

    def test_calcel_batch(self):
        for mode in ["completion", "chat"]:
            self.run_cancel_batch(mode)

    def test_regex(self):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)

        regex = (
            r"""\{\n"""
            + r"""   "name": "[\w]+",\n"""
            + r"""   "population": [\d]+\n"""
            + r"""\}"""
        )

        response = client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "You are a helpful AI assistant"},
                {"role": "user", "content": "Introduce the capital of France."},
            ],
            temperature=0,
            max_tokens=128,
            extra_body={"regex": regex},
        )
        text = response.choices[0].message.content

        try:
            js_obj = json.loads(text)
        except (TypeError, json.decoder.JSONDecodeError):
            print("JSONDecodeError", text)
            raise
        assert isinstance(js_obj["name"], str)
        assert isinstance(js_obj["population"], int)

    def test_penalty(self):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)

        response = client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "You are a helpful AI assistant"},
                {"role": "user", "content": "Introduce the capital of France."},
            ],
            temperature=0,
            max_tokens=32,
            frequency_penalty=1.0,
        )
        text = response.choices[0].message.content
        assert isinstance(text, str)


if __name__ == "__main__":
    unittest.main()