Move PoolerConfig from config/__init__.py to config/pooler.py (#25181)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/models/language/pooling/test_pooler_config_init_behaviour.py

@@ -0,0 +1,123 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+import torch.nn.functional as F
+
+from tests.models.utils import softmax
+from vllm.config import PoolerConfig
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        "jason9693/Qwen2.5-1.5B-apeach",
+        "papluca/xlm-roberta-base-language-detection"
+    ],
+)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_classify_models_using_activation(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+
+    with vllm_runner(
+            model,
+            max_model_len=512,
+            dtype=dtype,
+            pooler_config=PoolerConfig(activation=False)) as vllm_model:
+        wo_activation_out = vllm_model.classify(example_prompts)
+
+    with vllm_runner(
+            model,
+            max_model_len=512,
+            dtype=dtype,
+            pooler_config=PoolerConfig(activation=True)) as vllm_model:
+        w_activation_out = vllm_model.classify(example_prompts)
+
+    for wo_activation, w_activation in zip(wo_activation_out,
+                                           w_activation_out):
+        wo_activation = torch.tensor(wo_activation)
+        w_activation = torch.tensor(w_activation)
+
+        assert not torch.allclose(wo_activation, w_activation,
+                                  atol=1e-2), "pooler_config is not working"
+        assert torch.allclose(softmax(wo_activation), w_activation,
+                              1e-3 if dtype == "float" else 1e-2)
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        "intfloat/multilingual-e5-small",
+    ],
+)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_embed_models_using_normalize(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+
+    with vllm_runner(
+            model,
+            max_model_len=512,
+            dtype=dtype,
+            pooler_config=PoolerConfig(normalize=False)) as vllm_model:
+        wo_normalize = torch.tensor(vllm_model.embed(example_prompts))
+
+    with vllm_runner(model,
+                     max_model_len=512,
+                     dtype=dtype,
+                     pooler_config=PoolerConfig(normalize=True)) as vllm_model:
+        w_normalize = torch.tensor(vllm_model.embed(example_prompts))
+
+    assert not torch.allclose(
+        wo_normalize, w_normalize,
+        atol=1e-2), "pooler_config normalize is not working"
+    assert torch.allclose(
+        F.normalize(wo_normalize, p=2, dim=-1), w_normalize,
+        atol=1e-2), "w_normal should be close to normal(wo_normal)."
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        "internlm/internlm2-1_8b-reward",
+    ],
+)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_reward_models_using_softmax(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+
+    with vllm_runner(model,
+                     max_model_len=1024,
+                     dtype=dtype,
+                     pooler_config=PoolerConfig(softmax=False)) as vllm_model:
+        wo_softmax = vllm_model.encode(example_prompts)
+
+    with vllm_runner(model,
+                     max_model_len=1024,
+                     dtype=dtype,
+                     pooler_config=PoolerConfig(softmax=True)) as vllm_model:
+        w_softmax = vllm_model.encode(example_prompts)
+
+    for wo, w in zip(wo_softmax, w_softmax):
+        wo = torch.tensor(wo)
+        w = torch.tensor(w)
+
+        assert not torch.allclose(
+            wo, w, atol=1e-2), "pooler_config softmax is not working"
+        assert torch.allclose(
+            softmax(wo), w,
+            atol=1e-2), "w_softmax should be close to softmax(wo_softmax)."