[CI] Split pooling from entrypoints Test (#24632)

Signed-off-by: wang.yuqi <noooop@126.com>

tests/entrypoints/pooling/llm/test_encode.py

@@ -0,0 +1,79 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import weakref
+
+import pytest
+
+from vllm import LLM, PoolingParams
+from vllm.distributed import cleanup_dist_env_and_memory
+
+MODEL_NAME = "intfloat/multilingual-e5-small"
+
+PROMPTS = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+TOKEN_IDS = [
+    # Using ID={0, 1, 2, 3} results in NaN values,
+    # so we add this offset of 1000
+    [1000],
+    [1000, 1001],
+    [1000, 1002, 1001],
+    [1000, 1003, 1001, 1002],
+]
+
+
+@pytest.fixture(scope="module")
+def llm():
+    # pytest caches the fixture so we use weakref.proxy to
+    # enable garbage collection
+    llm = LLM(model=MODEL_NAME,
+              max_num_batched_tokens=32768,
+              tensor_parallel_size=1,
+              gpu_memory_utilization=0.75,
+              enforce_eager=True,
+              seed=0)
+
+    yield weakref.proxy(llm)
+
+    del llm
+
+    cleanup_dist_env_and_memory()
+
+
+@pytest.mark.skip_global_cleanup
+def test_multiple_pooling_params(llm: LLM):
+    pooling_params = [
+        PoolingParams(),
+        PoolingParams(),
+        PoolingParams(),
+        PoolingParams(),
+    ]
+
+    # Multiple PoolingParams should be matched with each prompt
+    outputs = llm.encode(PROMPTS, pooling_params=pooling_params)
+    assert len(PROMPTS) == len(outputs)
+
+    # Exception raised, if the size of params does not match the size of prompts
+    with pytest.raises(ValueError):
+        outputs = llm.encode(PROMPTS, pooling_params=pooling_params[:3])
+
+    # Single PoolingParams should be applied to every prompt
+    single_pooling_params = PoolingParams()
+    outputs = llm.encode(PROMPTS, pooling_params=single_pooling_params)
+    assert len(PROMPTS) == len(outputs)
+
+    # pooling_params is None, default params should be applied
+    outputs = llm.encode(PROMPTS, pooling_params=None)
+    assert len(PROMPTS) == len(outputs)
+
+
+@pytest.mark.skip_global_cleanup
+def test_right_side_truncation(llm: LLM):
+    # Embeddings models should truncate the end of the prompt
+    tokenizer = llm.get_tokenizer()
+    assert tokenizer.truncation_side == "right"