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[CI] add test generation demo (#3270)

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* Create test_generation.py

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* Update test_generation.py

* Update test_generation.py

* Update test_generation.py

* Update test_generation.py

* Update test_generation.py

* Update test_generation.py

* Update test_generation.py

* Update test_generation.py

* Update setup.py

* Delete test/plugins/test_model_runner_register.py

---------

Co-authored-by: YUNSHEN XIE <1084314248@qq.com>
This commit is contained in:
ltd0924
2025-08-19 17:12:40 +08:00
committed by GitHub
parent fef447e350
commit d587fb257f
3 changed files with 124 additions and 36 deletions
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124
test/entrypoints/test_generation.py Normal file
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@@ -0,0 +1,124 @@
"""
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
import os
import unittest
import weakref
from fastdeploy.engine.request import RequestOutput
from fastdeploy.engine.sampling_params import SamplingParams
from fastdeploy.entrypoints.llm import LLM
MODEL_NAME = os.getenv("MODEL_PATH") + "/ERNIE-4.5-0.3B-Paddle"
class TestGeneration(unittest.TestCase):
"""Test case for generation functionality"""
TOKEN_IDS = [
[0],
[0, 1],
[0, 1, 3],
[0, 2, 4, 6],
]
PROMPTS = [
"Hello, my name is",
"The capital of China is",
"The future of AI is",
"人工智能是",
]
@classmethod
def setUpClass(cls):
try:
llm = LLM(
model=MODEL_NAME,
max_num_batched_tokens=4096,
tensor_parallel_size=1,
engine_worker_queue_port=int(os.getenv("FD_ENGINE_QUEUE_PORT")),
)
cls.llm = weakref.proxy(llm)
except Exception as e:
print(f"Setting up LLM failed: {e}")
raise unittest.SkipTest(f"LLM initialization failed: {e}")
@classmethod
def tearDownClass(cls):
"""Clean up after all tests have run"""
if hasattr(cls, "llm"):
del cls.llm
def assert_outputs_equal(self, o1: list[RequestOutput], o2: list[RequestOutput]):
self.assertEqual([o.outputs for o in o1], [o.outputs for o in o2])
def test_consistency_single_prompt_tokens(self):
"""Test consistency between different prompt input formats"""
sampling_params = SamplingParams(temperature=1.0, top_p=0.0)
for prompt_token_ids in self.TOKEN_IDS:
with self.subTest(prompt_token_ids=prompt_token_ids):
output1 = self.llm.generate(prompts=prompt_token_ids, sampling_params=sampling_params)
output2 = self.llm.generate(
{"prompt": "", "prompt_token_ids": prompt_token_ids}, sampling_params=sampling_params
)
self.assert_outputs_equal(output1, output2)
def test_api_consistency_multi_prompt_tokens(self):
"""Test consistency with multiple prompt tokens"""
sampling_params = SamplingParams(
temperature=1.0,
top_p=0.0,
)
output1 = self.llm.generate(prompts=self.TOKEN_IDS, sampling_params=sampling_params)
output2 = self.llm.generate(
[{"prompt": "", "prompt_token_ids": p} for p in self.TOKEN_IDS],
sampling_params=sampling_params,
)
self.assert_outputs_equal(output1, output2)
def test_multiple_sampling_params(self):
"""Test multiple sampling parameters combinations"""
sampling_params = [
SamplingParams(temperature=0.01, top_p=0.95),
SamplingParams(temperature=0.3, top_p=0.95),
SamplingParams(temperature=0.7, top_p=0.95),
SamplingParams(temperature=0.99, top_p=0.95),
]
# Multiple SamplingParams should be matched with each prompt
outputs = self.llm.generate(prompts=self.PROMPTS, sampling_params=sampling_params)
self.assertEqual(len(self.PROMPTS), len(outputs))
# Exception raised if size mismatch
with self.assertRaises(ValueError):
self.llm.generate(prompts=self.PROMPTS, sampling_params=sampling_params[:3])
# Single SamplingParams should be applied to every prompt
single_sampling_params = SamplingParams(temperature=0.3, top_p=0.95)
outputs = self.llm.generate(prompts=self.PROMPTS, sampling_params=single_sampling_params)
self.assertEqual(len(self.PROMPTS), len(outputs))
# sampling_params is None, default params should be applied
outputs = self.llm.generate(prompts=self.PROMPTS, sampling_params=None)
self.assertEqual(len(self.PROMPTS), len(outputs))
if __name__ == "__main__":
unittest.main()

1
test/plugins/setup.py
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@@ -22,6 +22,5 @@ setup(
"fastdeploy.model_register_plugins": [
"fd_add_dummy_model = fd_add_dummy_model:register",
],
"fastdeploy.model_runner_plugins": ["fd_add_dummy_model_runner = fd_add_dummy_model_runner:get_runner"],
},
)

35
test/plugins/test_model_runner_register.py
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@@ -1,35 +0,0 @@
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from fastdeploy.plugins import load_model_runner_plugins
class TestModelRunnerRegistryPlugins(unittest.TestCase):
def test_model_runner_callable(self):
runner_class = load_model_runner_plugins()
device_id = 1
# create runner
runner = runner_class(device_id)
# test func
res = runner.get_rank()
self.assertEqual(res, device_id)
if __name__ == "__main__":
unittest.main()