Add stable ci (#3460)

* add stable ci

* fix

* update

* fix

* rename tests dir;fix stable ci bug

* add timeout limit

* update

tests/entrypoints/test_generation.py

@@ -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()