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211 lines
8.5 KiB
Python
211 lines
8.5 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 os
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from unittest.mock import MagicMock, patch
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import numpy as np
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import pytest
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from fastdeploy.engine.sampling_params import SamplingParams
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from fastdeploy.entrypoints.llm import LLM
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from fastdeploy.worker.output import Logprob, LogprobsTensors
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class DummyModelConfig:
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def __init__(self, max_logprobs=10, ori_vocab_size=50, enable_logprob=True):
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self.max_logprobs = max_logprobs
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self.ori_vocab_size = ori_vocab_size
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self.enable_logprob = enable_logprob
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class DummyCacheConfig:
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def __init__(self, enable_prefix_caching=False):
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self.enable_prefix_caching = enable_prefix_caching
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class DummyLLMEngineConfig:
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def __init__(self, model_config=None, cache_config=None):
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self.model_config = model_config or DummyModelConfig()
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self.cache_config = cache_config or DummyCacheConfig()
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class DummyLLMEngine:
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def __init__(self, model_config=None, cache_config=None):
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self.cfg = DummyLLMEngineConfig(model_config, cache_config)
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self.data_processor = MagicMock()
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# Mock tokenizer with sp_model attribute
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self.data_processor.tokenizer = MagicMock()
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self.data_processor.tokenizer.sp_model = MagicMock()
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self.data_processor.tokenizer.sp_model.__len__ = MagicMock(return_value=100)
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self.data_processor.tokenizer.vocab = MagicMock()
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self.data_processor.tokenizer.vocab.__len__ = MagicMock(return_value=100)
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self.data_processor.process_logprob_response.side_effect = lambda ids, **kwargs: f"TOKEN_{ids[0]}"
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self.add_requests = MagicMock()
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@pytest.fixture
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def mock_llm():
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llm = LLM.__new__(LLM)
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llm.llm_engine = DummyLLMEngine()
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return llm
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@pytest.fixture
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def mock_llm_with_prefix_caching():
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llm = LLM.__new__(LLM)
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llm.llm_engine = DummyLLMEngine(cache_config=DummyCacheConfig(enable_prefix_caching=True))
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return llm
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def test_prompt_logprobs_not_supported_with_stream(mock_llm):
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# Set FD_USE_GET_SAVE_OUTPUT_V1=1 to enable prompt_logprobs support
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with patch.dict(os.environ, {"FD_USE_GET_SAVE_OUTPUT_V1": "1"}):
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sampling = SamplingParams(prompt_logprobs=5)
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with pytest.raises(ValueError, match="prompt_logprobs is not supported with streaming"):
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mock_llm._add_request(["hi"], sampling, stream=True)
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def test_prompt_logprobs_not_supported_with_prefix_caching(mock_llm_with_prefix_caching):
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# Set FD_USE_GET_SAVE_OUTPUT_V1=1 to enable prompt_logprobs support
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with patch.dict(os.environ, {"FD_USE_GET_SAVE_OUTPUT_V1": "1"}):
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sampling = SamplingParams(prompt_logprobs=5)
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with pytest.raises(ValueError, match="prompt_logprobs is not supported with prefix caching enabled"):
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mock_llm_with_prefix_caching._add_request(["hi"], sampling)
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def test_num_logprobs_exceeds_max(mock_llm):
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# Set FD_USE_GET_SAVE_OUTPUT_V1=1 to allow logprobs > 20
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with patch.dict(os.environ, {"FD_USE_GET_SAVE_OUTPUT_V1": "1"}):
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sampling = SamplingParams(logprobs=20)
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with pytest.raises(ValueError, match="Number of logprobs requested"):
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mock_llm._add_request(["hi"], sampling)
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def test_max_logprobs_exceeds_vocab_size(mock_llm):
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# Test case where max_logprobs > ori_vocab_size
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mock_llm.llm_engine.cfg.model_config.max_logprobs = 150 # > vocab size (100)
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with pytest.raises(ValueError, match="max_logprobs \\(150\\) exceeds vocabulary size \\(100\\)"):
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mock_llm._add_request(["hi"], SamplingParams())
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def test_max_logprobs_less_than_minus_one(mock_llm):
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# Test case where max_logprobs < -1
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mock_llm.llm_engine.cfg.model_config.max_logprobs = -2
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with pytest.raises(ValueError, match="max_logprobs \\(-2\\) can't be less than -1"):
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mock_llm._add_request(["hi"], SamplingParams())
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def test_logprobs_minus_one_uses_vocab_size(mock_llm):
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# Test that logprobs=-1 uses vocab size
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with patch.dict(os.environ, {"FD_USE_GET_SAVE_OUTPUT_V1": "1"}):
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sampling = SamplingParams(logprobs=-1)
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mock_llm.llm_engine.cfg.model_config.max_logprobs = -1 # Allow unlimited
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mock_llm._add_request(["hi"], sampling)
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mock_llm.llm_engine.add_requests.assert_called_once()
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def test_num_prompt_logprobs_exceeds_max(mock_llm):
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# Set FD_USE_GET_SAVE_OUTPUT_V1=1 to enable prompt_logprobs support
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with patch.dict(os.environ, {"FD_USE_GET_SAVE_OUTPUT_V1": "1"}):
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sampling = SamplingParams(prompt_logprobs=20)
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with pytest.raises(ValueError, match="Number of logprobs requested"):
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mock_llm._add_request(["hi"], sampling)
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def test_logprobs_equal_to_minus_one_uses_ori_vocab_size(mock_llm):
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# Set FD_USE_GET_SAVE_OUTPUT_V1=1 to allow logprobs=-1
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with patch.dict(os.environ, {"FD_USE_GET_SAVE_OUTPUT_V1": "1"}):
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sampling = SamplingParams(logprobs=-1)
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mock_llm.llm_engine.cfg.model_config.max_logprobs = -1
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mock_llm._add_request(["hi"], sampling)
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mock_llm.llm_engine.add_requests.assert_called_once()
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# Get the first argument (tasks) which should be a dict
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call_args = mock_llm.llm_engine.add_requests.call_args
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tasks = call_args[0][0] # First positional argument
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assert isinstance(tasks, dict)
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assert "prompt" in tasks
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assert "request_id" in tasks
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def test_prompt_logprobs_equal_to_minus_one(mock_llm):
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# Set FD_USE_GET_SAVE_OUTPUT_V1=1 to enable prompt_logprobs support and allow -1
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with patch.dict(os.environ, {"FD_USE_GET_SAVE_OUTPUT_V1": "1"}):
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sampling = SamplingParams(prompt_logprobs=-1)
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mock_llm.llm_engine.cfg.model_config.max_logprobs = -1
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mock_llm._add_request(["hi"], sampling)
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mock_llm.llm_engine.add_requests.assert_called_once()
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def test_dynamic_vocab_size_from_sp_model(mock_llm):
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# Test that ori_vocab_size is dynamically obtained from sp_model
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mock_llm.llm_engine.data_processor.tokenizer.sp_model.__len__.return_value = 200
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mock_llm.llm_engine.cfg.model_config.max_logprobs = -1
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with patch.dict(os.environ, {"FD_USE_GET_SAVE_OUTPUT_V1": "1"}):
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sampling = SamplingParams(logprobs=-1)
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mock_llm._add_request(["hi"], sampling)
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# Should use the dynamic vocab size (200)
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mock_llm.llm_engine.add_requests.assert_called_once()
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def test_dynamic_vocab_size_from_vocab_fallback(mock_llm):
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# Test fallback to vocab when sp_model is not available
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del mock_llm.llm_engine.data_processor.tokenizer.sp_model
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mock_llm.llm_engine.data_processor.tokenizer.vocab.__len__.return_value = 300
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mock_llm.llm_engine.cfg.model_config.max_logprobs = -1
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with patch.dict(os.environ, {"FD_USE_GET_SAVE_OUTPUT_V1": "1"}):
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sampling = SamplingParams(logprobs=-1)
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mock_llm._add_request(["hi"], sampling)
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# Should use the vocab size (300)
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mock_llm.llm_engine.add_requests.assert_called_once()
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def test_build_prompt_logprobs_basic(mock_llm):
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# 构造 2 个 token，每个 token 对应 3 个 logprob 值
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token_ids = np.array([[1, 2, 3], [4, 5, 6]])
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logprobs = np.array([[-0.1, -0.2, -0.3], [-0.4, -0.5, -0.6]])
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ranks = np.array([1, 2])
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tensors = LogprobsTensors(token_ids, logprobs, ranks)
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result = mock_llm._build_prompt_logprobs(tensors, num_prompt_logprobs=2)
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# 检查结果格式
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assert isinstance(result, list)
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assert len(result) == 3
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for pos_dict in result:
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if pos_dict is not None:
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assert isinstance(pos_dict, dict)
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for logprob_obj in pos_dict.values():
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assert isinstance(logprob_obj, Logprob)
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assert logprob_obj.decoded_token.startswith("TOKEN_")
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def test_build_prompt_logprobs_handles_minus_one(mock_llm):
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token_ids = np.array([[7, 8]])
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logprobs = np.array([[-0.9, -1.0]])
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ranks = np.array([1])
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tensors = LogprobsTensors(token_ids, logprobs, ranks)
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result = mock_llm._build_prompt_logprobs(tensors, num_prompt_logprobs=-1)
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assert isinstance(result, list)
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assert len(result) == 2
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pos_dict = result[1]
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assert 7 in pos_dict
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assert pos_dict[7].decoded_token == "TOKEN_7"
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