Sync from v0.13

tests/entrypoints/pooling/embed/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
+import torch.nn.functional as F
+
+from vllm import LLM, PoolingParams
+from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.platforms import current_platform
+
+if current_platform.is_rocm():
+    pytest.skip(
+        "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
+    )
+
+MODEL_NAME = "intfloat/multilingual-e5-small"
+
+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,
+        seed=0,
+    )
+
+    yield weakref.proxy(llm)
+
+    del llm
+
+    cleanup_dist_env_and_memory()
+
+
+@pytest.mark.skip_global_cleanup
+def test_token_embed(llm: LLM):
+    outputs = llm.encode(prompts, pooling_task="token_embed", use_tqdm=False)
+    multi_vector = outputs[0].outputs.data
+    assert multi_vector.shape == (11, 384)
+
+
+def test_pooling_params(llm: LLM):
+    def get_outputs(normalize):
+        outputs = llm.embed(
+            prompts, pooling_params=PoolingParams(normalize=normalize), use_tqdm=False
+        )
+        return torch.tensor([x.outputs.embedding for x in outputs])
+
+    default = get_outputs(normalize=None)
+    w_normal = get_outputs(normalize=True)
+    wo_normal = get_outputs(normalize=False)
+
+    assert torch.allclose(default, w_normal, atol=1e-2), "Default should use normal."
+    assert not torch.allclose(w_normal, wo_normal, atol=1e-2), (
+        "wo_normal should not use normal."
+    )
+    assert torch.allclose(w_normal, F.normalize(wo_normal, p=2, dim=-1), atol=1e-2), (
+        "w_normal should be close to normal(wo_normal)."
+    )