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3a6058e445
* add stable ci * fix * update * fix * rename tests dir;fix stable ci bug * add timeout limit * update
125 lines
4.4 KiB
Python
125 lines
4.4 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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import unittest
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import weakref
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from fastdeploy.engine.request import RequestOutput
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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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MODEL_NAME = os.getenv("MODEL_PATH") + "/ERNIE-4.5-0.3B-Paddle"
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class TestGeneration(unittest.TestCase):
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"""Test case for generation functionality"""
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TOKEN_IDS = [
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[0],
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[0, 1],
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[0, 1, 3],
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[0, 2, 4, 6],
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]
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PROMPTS = [
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"Hello, my name is",
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"The capital of China is",
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"The future of AI is",
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"人工智能是",
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]
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@classmethod
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def setUpClass(cls):
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try:
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llm = LLM(
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model=MODEL_NAME,
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max_num_batched_tokens=4096,
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tensor_parallel_size=1,
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engine_worker_queue_port=int(os.getenv("FD_ENGINE_QUEUE_PORT")),
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)
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cls.llm = weakref.proxy(llm)
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except Exception as e:
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print(f"Setting up LLM failed: {e}")
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raise unittest.SkipTest(f"LLM initialization failed: {e}")
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@classmethod
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def tearDownClass(cls):
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"""Clean up after all tests have run"""
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if hasattr(cls, "llm"):
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del cls.llm
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def assert_outputs_equal(self, o1: list[RequestOutput], o2: list[RequestOutput]):
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self.assertEqual([o.outputs for o in o1], [o.outputs for o in o2])
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def test_consistency_single_prompt_tokens(self):
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"""Test consistency between different prompt input formats"""
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sampling_params = SamplingParams(temperature=1.0, top_p=0.0)
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for prompt_token_ids in self.TOKEN_IDS:
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with self.subTest(prompt_token_ids=prompt_token_ids):
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output1 = self.llm.generate(prompts=prompt_token_ids, sampling_params=sampling_params)
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output2 = self.llm.generate(
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{"prompt": "", "prompt_token_ids": prompt_token_ids}, sampling_params=sampling_params
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)
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self.assert_outputs_equal(output1, output2)
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def test_api_consistency_multi_prompt_tokens(self):
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"""Test consistency with multiple prompt tokens"""
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sampling_params = SamplingParams(
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temperature=1.0,
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top_p=0.0,
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)
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output1 = self.llm.generate(prompts=self.TOKEN_IDS, sampling_params=sampling_params)
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output2 = self.llm.generate(
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[{"prompt": "", "prompt_token_ids": p} for p in self.TOKEN_IDS],
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sampling_params=sampling_params,
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)
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self.assert_outputs_equal(output1, output2)
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def test_multiple_sampling_params(self):
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"""Test multiple sampling parameters combinations"""
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sampling_params = [
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SamplingParams(temperature=0.01, top_p=0.95),
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SamplingParams(temperature=0.3, top_p=0.95),
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SamplingParams(temperature=0.7, top_p=0.95),
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SamplingParams(temperature=0.99, top_p=0.95),
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]
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# Multiple SamplingParams should be matched with each prompt
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outputs = self.llm.generate(prompts=self.PROMPTS, sampling_params=sampling_params)
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self.assertEqual(len(self.PROMPTS), len(outputs))
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# Exception raised if size mismatch
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with self.assertRaises(ValueError):
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self.llm.generate(prompts=self.PROMPTS, sampling_params=sampling_params[:3])
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# Single SamplingParams should be applied to every prompt
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single_sampling_params = SamplingParams(temperature=0.3, top_p=0.95)
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outputs = self.llm.generate(prompts=self.PROMPTS, sampling_params=single_sampling_params)
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self.assertEqual(len(self.PROMPTS), len(outputs))
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# sampling_params is None, default params should be applied
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outputs = self.llm.generate(prompts=self.PROMPTS, sampling_params=None)
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self.assertEqual(len(self.PROMPTS), len(outputs))
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if __name__ == "__main__":
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unittest.main()
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