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* add reasoning_tokens into usage info initial commit * add unit tests * modify unit test * modify and add unit tests * fix unit test * move steam usage to processor * modify processor * modify test_logprobs * modify test_logprobs.py * modify stream reasoning tokens accumulation * fix unit test
178 lines
7.0 KiB
Python
178 lines
7.0 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import unittest
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from typing import List
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from unittest.mock import Mock
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from fastdeploy.entrypoints.openai.serving_completion import (
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CompletionRequest,
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OpenAIServingCompletion,
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RequestOutput,
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)
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from fastdeploy.utils import get_host_ip
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class TestOpenAIServingCompletion(unittest.TestCase):
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def test_check_master_tp4_dp1(self):
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engine_client = Mock()
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engine_client.tensor_parallel_size = 4
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max_chips_per_node = 8
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if engine_client.tensor_parallel_size <= max_chips_per_node:
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engine_client.is_master = True
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else:
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engine_client.is_master = False
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serving_completion = OpenAIServingCompletion(engine_client, None, "pid", None, 360)
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self.assertTrue(serving_completion._check_master())
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def test_check_master_tp4_dp4(self):
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engine_client = Mock()
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engine_client.tensor_parallel_size = 4
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max_chips_per_node = 8
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if engine_client.tensor_parallel_size <= max_chips_per_node:
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engine_client.is_master = True
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else:
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engine_client.is_master = False
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serving_completion = OpenAIServingCompletion(engine_client, None, "pid", "0.0.0.0, {get_host_ip()}", 360)
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self.assertTrue(serving_completion._check_master())
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def test_check_master_tp16_dp1_slave(self):
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engine_client = Mock()
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engine_client.tensor_parallel_size = 16
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max_chips_per_node = 8
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if engine_client.tensor_parallel_size <= max_chips_per_node:
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engine_client.is_master = True
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else:
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engine_client.is_master = False
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serving_completion = OpenAIServingCompletion(engine_client, None, "pid", f"0.0.0.0, {get_host_ip()}", 360)
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self.assertFalse(serving_completion._check_master())
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def test_check_master_tp16_dp1_master(self):
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engine_client = Mock()
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engine_client.tensor_parallel_size = 16
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max_chips_per_node = 8
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if engine_client.tensor_parallel_size <= max_chips_per_node:
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engine_client.is_master = True
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else:
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engine_client.is_master = False
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serving_completion = OpenAIServingCompletion(engine_client, None, "pid", f"{get_host_ip()}, 0.0.0.0", 360)
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self.assertTrue(serving_completion._check_master())
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def test_calc_finish_reason_tool_calls(self):
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# 创建一个模拟的engine_client，并设置reasoning_parser为"ernie_x1"
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engine_client = Mock()
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engine_client.reasoning_parser = "ernie_x1"
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# 创建一个OpenAIServingCompletion实例
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serving_completion = OpenAIServingCompletion(engine_client, None, "pid", "ips", 360)
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# 创建一个模拟的output，并设置finish_reason为"tool_call"
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output = {"tool_call": "tool_call"}
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# 调用calc_finish_reason方法
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result = serving_completion.calc_finish_reason(None, 100, output, False)
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# 断言结果为"tool_calls"
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assert result == "tool_calls"
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def test_calc_finish_reason_stop(self):
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# 创建一个模拟的engine_client，并设置reasoning_parser为"ernie_x1"
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engine_client = Mock()
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engine_client.reasoning_parser = "ernie_x1"
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# 创建一个OpenAIServingCompletion实例
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serving_completion = OpenAIServingCompletion(engine_client, None, "pid", "ips", 360)
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# 创建一个模拟的output，并设置finish_reason为其他值
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output = {"finish_reason": "other_reason"}
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# 调用calc_finish_reason方法
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result = serving_completion.calc_finish_reason(None, 100, output, False)
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# 断言结果为"stop"
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assert result == "stop"
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def test_calc_finish_reason_length(self):
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# 创建一个模拟的engine_client
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engine_client = Mock()
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# 创建一个OpenAIServingCompletion实例
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serving_completion = OpenAIServingCompletion(engine_client, None, "pid", "ips", 360)
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# 创建一个模拟的output
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output = {}
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# 调用calc_finish_reason方法
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result = serving_completion.calc_finish_reason(100, 100, output, False)
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# 断言结果为"length"
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assert result == "length"
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def test_request_output_to_completion_response(self):
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engine_client = Mock()
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# 创建一个OpenAIServingCompletion实例
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openai_serving_completion = OpenAIServingCompletion(engine_client, None, "pid", "ips", 360)
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final_res_batch: List[RequestOutput] = [
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{
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"outputs": {
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"token_ids": [1, 2, 3],
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"text": " world!",
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"top_logprobs": {
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"a": 0.1,
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"b": 0.2,
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},
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"reasoning_token_num": 10,
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},
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"output_token_ids": 3,
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},
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{
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"outputs": {
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"token_ids": [4, 5, 6],
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"text": " world!",
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"top_logprobs": {
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"a": 0.3,
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"b": 0.4,
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},
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"reasoning_token_num": 20,
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},
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"output_token_ids": 3,
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},
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]
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request: CompletionRequest = Mock()
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request.prompt = "Hello, world!"
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request.echo = True
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request.n = 2
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request_id = "test_request_id"
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created_time = 1655136000
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model_name = "test_model"
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prompt_batched_token_ids = [[1, 2, 3], [4, 5, 6]]
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completion_batched_token_ids = [[7, 8, 9], [10, 11, 12]]
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completion_response = openai_serving_completion.request_output_to_completion_response(
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final_res_batch=final_res_batch,
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request=request,
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request_id=request_id,
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created_time=created_time,
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model_name=model_name,
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prompt_batched_token_ids=prompt_batched_token_ids,
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completion_batched_token_ids=completion_batched_token_ids,
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prompt_tokens_list=["1", "1"],
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)
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assert completion_response.id == request_id
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assert completion_response.created == created_time
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assert completion_response.model == model_name
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assert len(completion_response.choices) == 2
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# 验证 choices 的 text 属性
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assert completion_response.choices[0].text == "Hello, world! world!"
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assert completion_response.choices[1].text == "Hello, world! world!"
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assert completion_response.usage.completion_tokens_details.reasoning_tokens == 30
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if __name__ == "__main__":
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unittest.main()
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