chore: import upstream snapshot with attribution

tests/kernels/quantization/test_w4a16_kernel_selection.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for W4A16 kernel selection logic (ROCm).
+
+Run `pytest tests/kernels/quantization/test_w4a16_kernel_selection.py`.
+"""
+
+import pytest
+import torch
+
+from vllm.model_executor.kernels.linear import (
+    MPLinearLayerConfig,
+    choose_mp_linear_kernel,
+)
+from vllm.platforms import current_platform
+from vllm.scalar_type import scalar_types
+
+
+@pytest.mark.skipif(not current_platform.is_rocm(), reason="ROCm only")
+def test_choose_mp_linear_kernel_picks_triton_w4a16_for_uint4b8():
+    # int4 weights, 16-bit activations (CT W4A16 typical config).
+    K, N = 1024, 256
+    config = MPLinearLayerConfig(
+        full_weight_shape=(K, N),
+        partition_weight_shape=(K, N),
+        weight_type=scalar_types.uint4b8,  # symmetric int4 (bias=8)
+        act_type=torch.float16,
+        group_size=128,
+        zero_points=False,
+        has_g_idx=False,
+    )
+
+    kernel_type = choose_mp_linear_kernel(config)
+    assert kernel_type.__name__ == "TritonW4A16LinearKernel"
+
+
+@pytest.mark.skipif(not current_platform.is_rocm(), reason="ROCm only")
+def test_choose_mp_linear_kernel_picks_triton_w4a16_for_uint4_asymmetric():
+    # Asymmetric int4 weights should also be supported (explicit zero points).
+    K, N = 512, 512
+    config = MPLinearLayerConfig(
+        full_weight_shape=(K, N),
+        partition_weight_shape=(K, N),
+        weight_type=scalar_types.uint4,  # asymmetric int4 (explicit zeros)
+        act_type=torch.bfloat16,
+        group_size=64,
+        zero_points=True,
+        has_g_idx=False,
+    )
+
+    kernel_type = choose_mp_linear_kernel(config)
+    assert kernel_type.__name__ == "TritonW4A16LinearKernel"