Sync from v0.13

tests/entrypoints/pooling/reward/test_offline.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import weakref
+
+import pytest
+import torch
+
+from tests.models.utils import softmax
+from vllm import LLM, PoolingParams
+from vllm.distributed import cleanup_dist_env_and_memory
+
+MODEL_NAME = "internlm/internlm2-1_8b-reward"
+
+prompts = ["The chef prepared a delicious meal."]
+
+
+@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,
+        trust_remote_code=True,
+        seed=0,
+    )
+
+    yield weakref.proxy(llm)
+
+    del llm
+
+    cleanup_dist_env_and_memory()
+
+
+@pytest.mark.skip_global_cleanup
+def test_config(llm: LLM):
+    vllm_config = llm.llm_engine.vllm_config
+    assert vllm_config.cache_config.enable_prefix_caching
+    assert vllm_config.scheduler_config.enable_chunked_prefill
+
+
+def test_pooling_params(llm: LLM):
+    def get_outputs(use_activation):
+        outputs = llm.reward(
+            prompts,
+            pooling_params=PoolingParams(use_activation=use_activation),
+            use_tqdm=False,
+        )
+        return torch.cat([x.outputs.data for x in outputs])
+
+    default = get_outputs(use_activation=None)
+    w_activation = get_outputs(use_activation=True)
+    wo_activation = get_outputs(use_activation=False)
+
+    assert torch.allclose(default, w_activation, atol=1e-2), (
+        "Default should use activation."
+    )
+    assert not torch.allclose(w_activation, wo_activation, atol=1e-2), (
+        "wo_activation should not use activation."
+    )
+    assert torch.allclose(softmax(wo_activation), w_activation, atol=1e-2), (
+        "w_activation should be close to activation(wo_activation)."
+    )