Add stable ci (#3460)

* add stable ci

* fix

* update

* fix

* rename tests dir;fix stable ci bug

* add timeout limit

* update

tests/layers/test_sampler.py

@@ -0,0 +1,79 @@
+"""
+# 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 paddle
+
+from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
+from fastdeploy.model_executor.layers.sample.sampler import Sampler
+
+
+def _create_fake_logits(batch_size: int, vocab_size: int) -> paddle.Tensor:
+    fake_logits = paddle.full(shape=[batch_size, vocab_size], fill_value=1e-2, dtype="float32")
+    return fake_logits
+
+
+def _create_penalty_tensor(batch_size: int, penalty_value: float) -> paddle.Tensor:
+    return paddle.full(shape=[batch_size, 1], fill_value=penalty_value, dtype="float32")
+
+
+def _create_tokens_tensor(
+    batch_size: int,
+    max_seq_len: int,
+) -> paddle.Tensor:
+    pre_token_ids = paddle.full(shape=[batch_size, max_seq_len], fill_value=-1, dtype="int64")
+    return pre_token_ids
+
+
+def _create_default_sampling_metadata(
+    batch_size: int,
+    min_seq_len: int,
+    max_seq_len: int,
+) -> SamplingMetadata:
+
+    fake_sampling_metadata = SamplingMetadata(
+        temperature=paddle.full(shape=[batch_size, 1], fill_value=0.9, dtype="float32"),
+        top_p=paddle.full(shape=[batch_size, 1], fill_value=0.7, dtype="float32"),
+        prompt_ids=paddle.full(shape=[batch_size, max_seq_len], fill_value=0, dtype="int64"),
+        prompt_lens=paddle.full(shape=[batch_size, 1], fill_value=5, dtype="int64"),
+        step_idx=paddle.full(shape=[batch_size, 1], fill_value=0, dtype="int64"),
+        pre_token_ids=_create_tokens_tensor(batch_size, max_seq_len),
+        frequency_penalties=_create_penalty_tensor(batch_size, 0.0),
+        presence_penalties=_create_penalty_tensor(batch_size, 0.0),
+        repetition_penalties=_create_penalty_tensor(batch_size, 1.0),
+        min_dec_lens=paddle.full(shape=[batch_size, 1], fill_value=min_seq_len, dtype="int64"),
+        bad_words_token_ids=paddle.full(shape=[batch_size], fill_value=-1, dtype="int64"),
+        eos_token_ids=paddle.full(shape=[batch_size], fill_value=-2, dtype="int64"),
+        min_p=paddle.randn([batch_size]),
+        seed=paddle.to_tensor([[2025]]),
+    )
+    return fake_sampling_metadata
+
+
+def test_sampler():
+    batch_size = 32
+    vocab_size = 1024
+    min_seq_len = 1
+    max_seq_len = 1024
+
+    sampler = Sampler()
+    logits = _create_fake_logits(batch_size, vocab_size)
+    sampling_metadata = _create_default_sampling_metadata(batch_size, min_seq_len, max_seq_len)
+    next_tokens = sampler(logits, sampling_metadata)
+    print(next_tokens)
+
+
+if __name__ == "__main__":
+    test_sampler()