mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
qwes5s5
213 lines
8.3 KiB
Python
213 lines
8.3 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 time
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import unittest
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from unittest.mock import MagicMock, patch
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import numpy as np
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from fastdeploy.engine.request import CompletionOutput, RequestMetrics, RequestOutput
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from fastdeploy.output.token_processor import TokenProcessor
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from fastdeploy.worker.output import LogprobsLists
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class TestTokenProcessorLogprobs(unittest.TestCase):
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def setUp(self):
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self.cfg = MagicMock()
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self.cfg.model_config.enable_logprob = True
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self.cfg.speculative_config.method = None
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self.cfg.parallel_config.local_data_parallel_id = 0
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self.cached_generated_tokens = MagicMock()
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self.engine_worker_queue = MagicMock()
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self.split_connector = MagicMock()
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self.processor = TokenProcessor(
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self.cfg, self.cached_generated_tokens, self.engine_worker_queue, self.split_connector
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)
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# Mock resource manager
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self.processor.resource_manager = MagicMock()
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self.processor.resource_manager.stop_flags = [False]
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# Create a proper task mock with time attributes
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self.task_mock = MagicMock()
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self.task_mock.request_id = "test_request"
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self.task_mock.pooling_params = None
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self.task_mock.messages = None
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self.task_mock.disaggregate_info = None
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self.task_mock.eos_token_ids = [2]
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self.task_mock.ic_req_data = {}
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self.task_mock.prompt_token_ids_len = 0
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now = time.time()
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self.task_mock.metrics = RequestMetrics(
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arrival_time=now,
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preprocess_start_time=now - 0.2,
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preprocess_end_time=now - 0.1,
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scheduler_recv_req_time=now + 0.1,
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inference_start_time=now + 0.2,
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)
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self.processor.resource_manager.tasks_list = [self.task_mock]
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# Mock logger
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self.processor.llm_logger = MagicMock()
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# Mock metrics to avoid prometheus dependency issues
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self.processor.main_process_metrics = MagicMock()
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self.processor._recycle_resources = MagicMock()
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# Mock the _process_per_token method to avoid prometheus issues
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self.processor._process_per_token = MagicMock()
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self.processor._process_per_token.return_value = RequestOutput(
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request_id="test_request",
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outputs=CompletionOutput(
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index=0,
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send_idx=0,
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token_ids=[],
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draft_token_ids=[],
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),
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finished=False,
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metrics=MagicMock(),
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)
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def test_process_logprobs_success(self):
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"""Test successful logprobs parsing"""
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stream_data = MagicMock()
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logprobs = MagicMock()
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logprobs.tolists.return_value = LogprobsLists(
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logprobs=[[0.5]], logprob_token_ids=[[1]], sampled_token_ranks=[0]
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)
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stream_data.logprobs = logprobs
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stream_data.tokens = np.array([1])
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stream_data.batch_id = 0
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result = self.processor._process_batch_output_use_zmq([stream_data])
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self.assertEqual(len(result), 1)
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self.processor.llm_logger.warning.assert_not_called()
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def test_process_logprobs_failure(self):
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"""Test failed logprobs parsing"""
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stream_data = MagicMock()
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stream_data.logprobs = MagicMock()
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stream_data.logprobs.tolists.side_effect = Exception("Test error")
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stream_data.tokens = np.array([1])
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stream_data.batch_id = 0
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with patch.object(self.processor.llm_logger, "warning"):
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result = self.processor._process_batch_output_use_zmq([stream_data])
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self.assertEqual(len(result), 1)
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self.assertIsNone(result[0].outputs.logprob)
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def test_process_prompt_logprobs_success(self):
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"""Test successful prompt_logprobs parsing"""
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stream_data = MagicMock()
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stream_data.logprobs = None
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stream_data.prompt_logprobs = np.array([0.1, 0.2])
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stream_data.tokens = np.array([1])
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stream_data.batch_id = 0
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result = self.processor._process_batch_output_use_zmq([stream_data])
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self.assertEqual(len(result), 1)
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self.processor.llm_logger.warning.assert_not_called()
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def test_process_prompt_logprobs_failure(self):
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"""Test failed prompt_logprobs parsing"""
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stream_data = MagicMock()
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stream_data.logprobs = None
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stream_data.prompt_logprobs = MagicMock()
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stream_data.prompt_logprobs.tolist.side_effect = AttributeError("'NoneType' object has no attribute 'tolist'")
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stream_data.tokens = np.array([1])
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stream_data.batch_id = 0
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with patch.object(self.processor.llm_logger, "warning"):
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result = self.processor._process_batch_output_use_zmq([stream_data])
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self.assertEqual(len(result), 1)
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self.assertIsNone(getattr(result[0], "prompt_logprobs", None))
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def test_process_batch_with_stop_flag(self):
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"""Test processing when stop flag is True"""
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self.processor.resource_manager.stop_flags = [True]
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stream_data = MagicMock()
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stream_data.batch_id = 0
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result = self.processor._process_batch_output_use_zmq([stream_data])
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self.assertEqual(len(result), 0)
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def test_process_batch_output_use_zmq_aborted_task_negative_token(self):
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"""Test aborted task receiving negative token triggers recycling logic"""
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# Set up task as aborted
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task_id = "test_aborted_request"
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self.task_mock.request_id = task_id
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self.processor.resource_manager.to_be_aborted_req_id_set = {task_id}
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self.processor.resource_manager.recycle_abort_task = MagicMock(
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side_effect=lambda rid: self.processor.resource_manager.to_be_aborted_req_id_set.discard(rid)
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)
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# Create stream data with negative token (PREEMPTED_TOKEN_ID = -9)
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stream_data = MagicMock()
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stream_data.tokens = np.array([1, 2, -9]) # Last token is PREEMPTED_TOKEN_ID
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stream_data.batch_id = 0
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# Mock _recycle_resources to track if it's called
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self.processor._recycle_resources = MagicMock()
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# Mock the llm_logger module and envs.ENABLE_V1_KVCACHE_SCHEDULER
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with (
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patch("fastdeploy.output.token_processor.llm_logger") as mock_logger,
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patch("fastdeploy.output.token_processor.envs.ENABLE_V1_KVCACHE_SCHEDULER", 1),
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):
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# Call the method
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result = self.processor._process_batch_output_use_zmq([stream_data])
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# Verify the recycling logic was triggered
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mock_logger.info.assert_any_call(f"start to recycle abort request_id {task_id}")
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self.processor.resource_manager.recycle_abort_task.assert_called_once_with(task_id)
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self.assertNotIn(task_id, self.processor.resource_manager.to_be_aborted_req_id_set)
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self.assertEqual(len(result), 0) # Aborted task is skipped (continue)
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def test_process_batch_output_use_zmq_non_aborted_task_negative_token(self):
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"""Test non-aborted task receiving negative token does not trigger recycling"""
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# Set up task as not aborted
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task_id = "test_normal_request"
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self.task_mock.request_id = task_id
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self.processor.resource_manager.abort_req_ids_set = set() # Empty set
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# Create stream data with negative token
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stream_data = MagicMock()
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stream_data.tokens = np.array([1, 2, -1]) # Last token is negative
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stream_data.batch_id = 0
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# Mock _recycle_resources to track if it's called
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self.processor._recycle_resources = MagicMock()
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# Call the method
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self.processor._process_batch_output_use_zmq([stream_data])
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# Verify recycling logic was NOT triggered
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self.processor._recycle_resources.assert_not_called()
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self.processor.llm_logger.info.assert_not_called()
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if __name__ == "__main__":
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unittest.main()
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