[Misc] Fix Current vLLM config is not set. warnings, assert to avoid issues in the future (#31747)

Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>
Signed-off-by: Lucas Wilkinson <LucasWilkinson@users.noreply.github.com>
Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>

tests/kernels/attention/test_flashinfer_trtllm_attention.py

@@ -458,7 +458,7 @@ def test_flashinfer_trtllm_prefill_with_baseline(
     )
 
 
-def test_trtllm_attention_rejects_num_kv_heads_1() -> None:
+def test_trtllm_attention_rejects_num_kv_heads_1(default_vllm_config) -> None:
     """Test that TRTLLM attention correctly rejects num_kv_heads=1.
 
     When num_kv_heads=1 (MQA), the KV cache strides become degenerate

tests/kernels/attention/test_mha_attn.py

@@ -36,7 +36,7 @@ if current_platform.is_rocm():
 
 
 @pytest.mark.parametrize("device", devices)
-def test_mha_attn_platform(device: str):
+def test_mha_attn_platform(default_vllm_config, device: str):
     """
     Test the attention selector between different platform and device.
     """
@@ -116,6 +116,7 @@ CUDA_DEVICES = ["cuda"]
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 def test_mha_attn_forward(
+    default_vllm_config,
     batch_size: int,
     seq_len: int,
     num_heads: int,
@@ -162,6 +163,7 @@ def test_mha_attn_forward(
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 def test_mha_attn_varlen_forward(
+    default_vllm_config,
     var_seq_len: list[int],
     num_heads: int,
     num_kv_heads: int,

tests/kernels/core/test_activation.py

@@ -45,6 +45,7 @@ CUDA_DEVICES = [f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 e
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
 def test_act_and_mul(
+    default_vllm_config,
     activation: str,
     num_tokens: int,
     d: int,
@@ -122,6 +123,7 @@ def test_act_and_mul(
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
 def test_activation(
+    default_vllm_config,
     activation: type[torch.nn.Module],
     num_tokens: int,
     d: int,

tests/kernels/core/test_fused_qk_norm_rope.py

@@ -57,6 +57,7 @@ def _apply_qk_norm_rope(
 @pytest.mark.parametrize("rotary_ratio", [1.0, 0.5, 0.25])
 @torch.inference_mode()
 def test_fused_qk_norm_rope_matches_reference(
+    default_vllm_config,
     device: str,
     dtype: torch.dtype,
     is_neox: bool,

tests/kernels/core/test_fused_quant_layernorm.py

@@ -147,6 +147,7 @@ def ops_impl(
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
 def test_rms_norm(
+    default_vllm_config,
     num_tokens: int,
     hidden_size: int,
     add_residual: bool,

tests/kernels/core/test_layernorm.py

@@ -26,6 +26,7 @@ CUDA_DEVICES = [f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 e
 @pytest.mark.parametrize("strided_input", [False, True])
 @torch.inference_mode()
 def test_rms_norm(
+    default_vllm_config,
     num_tokens: int,
     hidden_size: int,
     add_residual: bool,

tests/kernels/core/test_mrope.py

@@ -90,6 +90,7 @@ num_tokens_list = [11, 8192]
 @pytest.mark.parametrize("dtype", [torch.bfloat16])
 @pytest.mark.parametrize("num_tokens", num_tokens_list)
 def test_mrope(
+    default_vllm_config,
     model_name: str,
     model_info: MRoPETestInfo,
     tp_size: int,
@@ -159,6 +160,7 @@ def test_mrope(
 @pytest.mark.parametrize("dtype", [torch.bfloat16])
 @pytest.mark.parametrize("num_tokens", num_tokens_list)
 def test_mrope_torch_compile_tracing(
+    default_vllm_config,
     model_name: str,
     model_info: MRoPETestInfo,
     tp_size: int,

tests/kernels/core/test_pos_encoding.py

@@ -62,6 +62,7 @@ TENSORS_SHAPES_FN = [
 @pytest.mark.parametrize("use_key", USE_KEY)
 @torch.inference_mode()
 def test_rotary_embedding(
+    default_vllm_config,
     is_neox_style: bool,
     tensor_shape_fn: Callable[[int, int, int, int], tuple[int, ...]],
     batch_size: int,
@@ -123,7 +124,7 @@ def test_rotary_embedding(
 
 
 @torch.inference_mode()
-def test_rope_module_cache():
+def test_rope_module_cache(default_vllm_config):
     MAX_POSITIONS = [123, 1234]
     ROPE_THETAS = [10000, 1000000]
     ROPE_PARAMETERS = (

tests/kernels/core/test_rotary_embedding.py

@@ -36,6 +36,7 @@ def rotary_embedding_opcheck(
 @pytest.mark.parametrize("use_key", [True, False])
 @pytest.mark.parametrize("head_stride_is_contiguous", [True, False])
 def test_rotary_embedding_opcheck(
+    default_vllm_config,
     dist_init,
     device,
     max_position,

tests/kernels/moe/test_cpu_fused_moe.py

@@ -6,7 +6,7 @@ import torch
 
 from tests.kernels.allclose_default import get_default_atol, get_default_rtol
 from vllm._custom_ops import cpu_fused_moe, cpu_prepack_moe_weight
-from vllm.model_executor.layers.activation import SiluAndMul, SwigluOAIAndMul
+from vllm.model_executor.layers.fused_moe.cpu_fused_moe import _CPU_MOE_ACT
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import set_random_seed
 
@@ -24,11 +24,6 @@ USE_BIAS = [True, False]
 ISA = ["amx", "vec"] if torch._C._cpu._is_amx_tile_supported() else ["vec"]
 DTYPE = [torch.bfloat16]
 
-_CPU_MOE_ACT = {
-    "silu": SiluAndMul(),
-    "swigluoai": SwigluOAIAndMul(),
-}
-
 
 def ref_fused_moe(
     input: torch.Tensor,
@@ -106,6 +101,7 @@ def ref_fused_moe(
 @pytest.mark.parametrize("act", ACT)
 @pytest.mark.parametrize("isa", ISA)
 def test_cpu_fused_moe(
+    default_vllm_config,
     batch_size: int,
     expert_num: int,
     hidden_size: int,

tests/kernels/moe/test_moe.py

@@ -468,7 +468,12 @@ def test_fused_moe_wn16(
 )
 @torch.inference_mode()
 def test_mixtral_moe(
-    dist_init, dtype: torch.dtype, padding: bool, use_rocm_aiter: bool, monkeypatch
+    default_vllm_config,
+    dist_init,
+    dtype: torch.dtype,
+    padding: bool,
+    use_rocm_aiter: bool,
+    monkeypatch,
 ):
     """Make sure our Mixtral MoE implementation agrees with the one from
     huggingface."""

tests/kernels/quantization/test_fp8_quant_group.py

@@ -23,7 +23,12 @@ from vllm.utils.torch_utils import set_random_seed
 @pytest.mark.parametrize("use_ue8m0", [True, False])
 @torch.inference_mode()
 def test_quantfp8_group_functionality(
-    batch_size: int, hidden_dim: int, group_size: int, seed: int, use_ue8m0: bool
+    default_vllm_config,
+    batch_size: int,
+    hidden_dim: int,
+    group_size: int,
+    seed: int,
+    use_ue8m0: bool,
 ) -> None:
     """Test QuantFP8 group quantization with various configurations.
 
@@ -82,7 +87,9 @@ def test_quantfp8_group_functionality(
 @pytest.mark.parametrize("seed", [42])
 @pytest.mark.parametrize("use_ue8m0", [True, False])
 @torch.inference_mode()
-def test_quantfp8_group_multidimensional(seed: int, use_ue8m0: bool) -> None:
+def test_quantfp8_group_multidimensional(
+    default_vllm_config, seed: int, use_ue8m0: bool
+) -> None:
     set_random_seed(seed)
 
     group_size = 64
@@ -135,7 +142,7 @@ def test_quantfp8_group_multidimensional(seed: int, use_ue8m0: bool) -> None:
 
 @pytest.mark.parametrize("seed", [42])
 @torch.inference_mode()
-def test_quantfp8_group_edge_cases(seed: int) -> None:
+def test_quantfp8_group_edge_cases(default_vllm_config, seed: int) -> None:
     set_random_seed(seed)
 
     batch_size = 16

tests/kernels/quantization/test_int8_kernel.py

@@ -102,7 +102,7 @@ SEEDS = [0]
     itertools.product(M, N, K, E, TOP_KS, DTYPES, SEEDS),
 )
 @torch.inference_mode()
-def test_w8a8_fp8_fused_moe(M, N, K, E, topk, dtype, seed):
+def test_w8a8_fp8_fused_moe(default_vllm_config, M, N, K, E, topk, dtype, seed):
     torch.manual_seed(seed)
     # Initialize int8 quantization parameters
     factor_for_scale = 1e-2

tests/kernels/quantization/test_silu_mul_nvfp4_quant.py

@@ -31,6 +31,7 @@ BLOCK_SIZE = 16
 @pytest.mark.parametrize("shape", SHAPES)
 @torch.inference_mode()
 def test_silu_mul_nvfp4_quant(
+    default_vllm_config,
     dtype: torch.dtype,
     shape: tuple[int, int],
 ) -> None:

tests/kernels/test_fused_quant_activation.py

@@ -39,6 +39,7 @@ def ops_impl(x: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
 def test_silu_and_mul(
+    default_vllm_config,
     num_tokens: int,
     hidden_size: int,
     dtype: torch.dtype,