chore: import upstream snapshot with attribution

tests/model_executor/test_gemma_hidden_act.py

@@ -0,0 +1,60 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.activation import (
+    GeluAndMul,
+    SiluAndMul,
+    get_act_and_mul_fn,
+    get_act_fn,
+)
+from vllm.model_executor.models.gemma3 import Gemma3MLP
+from vllm.model_executor.models.gemma4 import Gemma4MLP
+
+
+@pytest.mark.parametrize(
+    ("activation_name", "expected_type"),
+    [
+        ("gelu_pytorch_tanh", GeluAndMul),
+        ("silu", SiluAndMul),
+        ("swish", SiluAndMul),
+    ],
+)
+def test_get_act_and_mul_fn_supports_gemma_hidden_act_aliases(
+    activation_name: str,
+    expected_type: type[torch.nn.Module],
+    default_vllm_config,
+) -> None:
+    assert isinstance(get_act_and_mul_fn(activation_name), expected_type)
+
+
+def test_get_act_fn_supports_swish_alias() -> None:
+    assert isinstance(get_act_fn("swish"), torch.nn.SiLU)
+
+
+@pytest.mark.parametrize("mlp_cls", [Gemma3MLP, Gemma4MLP])
+@pytest.mark.parametrize(
+    ("activation_name", "expected_type"),
+    [
+        ("gelu_pytorch_tanh", GeluAndMul),
+        ("silu", SiluAndMul),
+        ("swish", SiluAndMul),
+    ],
+)
+def test_gemma_mlp_supports_hidden_act_variants(
+    mlp_cls: type[torch.nn.Module],
+    activation_name: str,
+    expected_type: type[torch.nn.Module],
+    default_vllm_config,
+    dist_init,
+) -> None:
+    mlp = mlp_cls(
+        hidden_size=16,
+        intermediate_size=32,
+        hidden_activation=activation_name,
+    )
+
+    assert isinstance(mlp.act_fn, expected_type)
+    assert mlp(torch.randn(3, 16)).shape == (3, 16)