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

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Merged
simon-mo merged 26 commits into from
Jan 8, 2026
Merged

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

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d83f58a
fix warnings
LucasWilkinson Jan 5, 2026
e0b5d17
review comments
LucasWilkinson Jan 5, 2026
68ac7fa
remove comment
LucasWilkinson Jan 5, 2026
f5da41a
cleanup
LucasWilkinson Jan 5, 2026
3ad46a6
more test fixes
LucasWilkinson Jan 6, 2026
189024f
fix test
LucasWilkinson Jan 6, 2026
5c00f92
cleanup
LucasWilkinson Jan 6, 2026
ca05e30
test fixes
LucasWilkinson Jan 6, 2026
9708141
test fixes
LucasWilkinson Jan 6, 2026
d8df862
Update vllm/config/vllm.py
LucasWilkinson Jan 6, 2026
8c84ef5
Format error message for unset vLLM config
ProExpertProg Jan 6, 2026
317ebb9
test fixes
LucasWilkinson Jan 6, 2026
c75b087
fix more tests
LucasWilkinson Jan 7, 2026
593e4f8
more test fixes
LucasWilkinson Jan 7, 2026
aca7c35
fix more tests
LucasWilkinson Jan 7, 2026
174ad86
fix
LucasWilkinson Jan 7, 2026
d851ee1
fix
LucasWilkinson Jan 7, 2026
6f6503e
fix tests
LucasWilkinson Jan 7, 2026
d36204a
fixes
LucasWilkinson Jan 7, 2026
4fe4001
fixes
LucasWilkinson Jan 7, 2026
818dcbf
wip
LucasWilkinson Jan 7, 2026
a7cdeb8
fix tests
LucasWilkinson Jan 8, 2026
59b675c
fixes
LucasWilkinson Jan 8, 2026
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fix
LucasWilkinson Jan 8, 2026
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LucasWilkinson Jan 8, 2026
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53 changes: 29 additions & 24 deletions tests/compile/distributed/test_async_tp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
ModelConfig,
PassConfig,
VllmConfig,
set_current_vllm_config,
)
from vllm.distributed import (
tensor_model_parallel_all_gather,
Expand Down Expand Up @@ -340,38 +341,42 @@ def async_tp_pass_on_test_model(
)

async_tp_pass = AsyncTPPass(vllm_config)
backend = TestBackend(async_tp_pass)

assert (
async_tp_pass.compilation_config.splitting_ops
== vllm_config.compilation_config.splitting_ops
)
assert (
async_tp_pass.compilation_config.use_inductor_graph_partition
== vllm_config.compilation_config.use_inductor_graph_partition
)
# Set the global vllm_config for TestBackend which calls
# get_current_vllm_config()
with set_current_vllm_config(vllm_config):
backend = TestBackend(async_tp_pass)

model = test_model_cls(hidden_size, dtype) # Pass dtype to model constructor
assert (
async_tp_pass.compilation_config.splitting_ops
== vllm_config.compilation_config.splitting_ops
)
assert (
async_tp_pass.compilation_config.use_inductor_graph_partition
== vllm_config.compilation_config.use_inductor_graph_partition
)

hidden_states = torch.randn(
(batch_size * seq_len, hidden_size), dtype=dtype, requires_grad=False
)
model = test_model_cls(hidden_size, dtype) # Pass dtype to model constructor

hidden_states = torch.randn(
(batch_size * seq_len, hidden_size), dtype=dtype, requires_grad=False
)

if dynamic:
torch._dynamo.mark_dynamic(hidden_states, 0)
if dynamic:
torch._dynamo.mark_dynamic(hidden_states, 0)

compiled_model = torch.compile(model, backend=backend)
compiled_model(hidden_states)
compiled_model = torch.compile(model, backend=backend)
compiled_model(hidden_states)

assert async_tp_pass.matched_count == 1
assert async_tp_pass.matched_count == 1

# In pre-nodes, all gather or reduce scatter should exist,
# fused_matmul_reduce_scatter or fused_all_gather_matmul should not
backend.check_before_ops(model.ops_in_model_before(), fully_replaced=False)
# In pre-nodes, all gather or reduce scatter should exist,
# fused_matmul_reduce_scatter or fused_all_gather_matmul should not
backend.check_before_ops(model.ops_in_model_before(), fully_replaced=False)

# In post-nodes, fused_matmul_reduce_scatter or \
# fused_all_gather_matmul should exist
backend.check_after_ops(model.ops_in_model_after())
# In post-nodes, fused_matmul_reduce_scatter or \
# fused_all_gather_matmul should exist
backend.check_after_ops(model.ops_in_model_after())


@create_new_process_for_each_test()
Expand Down
2 changes: 1 addition & 1 deletion tests/compile/test_config.py
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Expand Up @@ -430,7 +430,7 @@ def test_cudagraph_sizes_post_init(
)


def test_cached_compilation_config():
def test_cached_compilation_config(default_vllm_config):
import torch
from torch._inductor.utils import run_and_get_code

Expand Down
11 changes: 11 additions & 0 deletions tests/conftest.py
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Expand Up @@ -189,6 +189,17 @@ def dist_init():
cleanup_dist_env_and_memory()


@pytest.fixture
def default_vllm_config():
"""Set a default VllmConfig for tests that directly test CustomOps or pathways
that use get_current_vllm_config() outside of a full engine context.
"""
from vllm.config import VllmConfig, set_current_vllm_config

with set_current_vllm_config(VllmConfig()):
yield


@pytest.fixture()
def should_do_global_cleanup_after_test(request) -> bool:
"""Allow subdirectories to skip global cleanup by overriding this fixture.
Expand Down
2 changes: 1 addition & 1 deletion tests/kernels/attention/test_flashinfer_trtllm_attention.py
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Expand Up @@ -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
Expand Down
4 changes: 3 additions & 1 deletion tests/kernels/attention/test_mha_attn.py
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Expand Up @@ -36,7 +36,7 @@ def clear_cache():


@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.
"""
Expand Down Expand Up @@ -116,6 +116,7 @@ def ref_attention(
@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,
Expand Down Expand Up @@ -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,
Expand Down
2 changes: 2 additions & 0 deletions tests/kernels/core/test_activation.py
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Expand Up @@ -45,6 +45,7 @@
@pytest.mark.parametrize("device", CUDA_DEVICES)
@torch.inference_mode()
def test_act_and_mul(
default_vllm_config,
activation: str,
num_tokens: int,
d: int,
Expand Down Expand Up @@ -122,6 +123,7 @@ def _get_rtol(output) -> float:
@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,
Expand Down
1 change: 1 addition & 0 deletions tests/kernels/core/test_fused_qk_norm_rope.py
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Expand Up @@ -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,
Expand Down
1 change: 1 addition & 0 deletions tests/kernels/core/test_fused_quant_layernorm.py
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Expand Up @@ -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,
Expand Down
1 change: 1 addition & 0 deletions tests/kernels/core/test_layernorm.py
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Expand Up @@ -26,6 +26,7 @@
@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,
Expand Down
2 changes: 2 additions & 0 deletions tests/kernels/core/test_mrope.py
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Expand Up @@ -90,6 +90,7 @@ class MRoPETestInfo(NamedTuple):
@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,
Expand Down Expand Up @@ -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,
Expand Down
3 changes: 2 additions & 1 deletion tests/kernels/core/test_pos_encoding.py
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Expand Up @@ -62,6 +62,7 @@ def _get_batch_tensor_shape(
@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,
Expand Down Expand Up @@ -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 = (
Expand Down
1 change: 1 addition & 0 deletions tests/kernels/core/test_rotary_embedding.py
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Expand Up @@ -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,
Expand Down
8 changes: 2 additions & 6 deletions tests/kernels/moe/test_cpu_fused_moe.py
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Expand Up @@ -6,7 +6,7 @@

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

Expand All @@ -24,11 +24,6 @@
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,
Expand Down Expand Up @@ -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,
Expand Down
7 changes: 6 additions & 1 deletion tests/kernels/moe/test_moe.py
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Expand Up @@ -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."""
Expand Down
13 changes: 10 additions & 3 deletions tests/kernels/quantization/test_fp8_quant_group.py
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Expand Up @@ -23,7 +23,12 @@
@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.

Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down
2 changes: 1 addition & 1 deletion tests/kernels/quantization/test_int8_kernel.py
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Expand Up @@ -102,7 +102,7 @@ def setup_cuda():
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
Expand Down
1 change: 1 addition & 0 deletions tests/kernels/quantization/test_silu_mul_nvfp4_quant.py
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Expand Up @@ -31,6 +31,7 @@
@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:
Expand Down
1 change: 1 addition & 0 deletions tests/kernels/test_fused_quant_activation.py
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Expand Up @@ -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,
Expand Down
4 changes: 2 additions & 2 deletions tests/lora/conftest.py
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Expand Up @@ -82,7 +82,7 @@ class DummyLoRAModel(nn.Sequential, SupportsLoRA):


@pytest.fixture
def dummy_model() -> nn.Module:
def dummy_model(default_vllm_config) -> nn.Module:
model = DummyLoRAModel(
OrderedDict(
[
Expand Down Expand Up @@ -115,7 +115,7 @@ def dummy_model() -> nn.Module:


@pytest.fixture
def dummy_model_gate_up() -> nn.Module:
def dummy_model_gate_up(default_vllm_config) -> nn.Module:
model = DummyLoRAModel(
OrderedDict(
[
Expand Down
13 changes: 8 additions & 5 deletions tests/lora/test_layers.py
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Expand Up @@ -252,7 +252,9 @@ def check_punica_wrapper(punica_wrapper) -> bool:
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("vocab_size", [512, 32000, 64000, 128000])
@pytest.mark.parametrize("stage", STAGES)
def test_embeddings(dist_init, num_loras, device, vocab_size, stage) -> None:
def test_embeddings(
default_vllm_config, dist_init, num_loras, device, vocab_size, stage
) -> None:
# For multi-GPU testing of Triton kernel, we must explicitly set the CUDA
# device, see: https://github.com/triton-lang/triton/issues/2925
# Same below.
Expand Down Expand Up @@ -353,7 +355,7 @@ def create_random_embedding_layer():
@pytest.mark.parametrize("vocab_size", [512, 32000, 64000, 256512])
@pytest.mark.parametrize("stage", STAGES)
def test_lm_head_logits_processor(
dist_init, num_loras, device, vocab_size, stage
default_vllm_config, dist_init, num_loras, device, vocab_size, stage
) -> None:
if current_platform.is_cuda_alike():
torch.cuda.set_device(device)
Expand Down Expand Up @@ -470,6 +472,7 @@ def _pretest():
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("stage", STAGES)
def test_linear_replicated(
default_vllm_config,
dist_init,
num_loras,
device,
Expand Down Expand Up @@ -580,7 +583,7 @@ def create_random_linear_replicated_layer():
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("stage", STAGES)
def test_linear_parallel(
dist_init, num_loras, orientation, fully_shard, device, stage
default_vllm_config, dist_init, num_loras, orientation, fully_shard, device, stage
) -> None:
if current_platform.is_cuda_alike():
torch.cuda.set_device(device)
Expand Down Expand Up @@ -705,7 +708,7 @@ def create_random_linear_parallel_layer():
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("stage", STAGES)
def test_column_parallel_packed(
dist_init, num_loras, repeats, fully_shard, device, stage
default_vllm_config, dist_init, num_loras, repeats, fully_shard, device, stage
) -> None:
if current_platform.is_cuda_alike():
torch.cuda.set_device(device)
Expand Down Expand Up @@ -851,7 +854,7 @@ class FakeConfig:
@pytest.mark.parametrize(
"seed", list(range(VOCAB_PARALLEL_EMBEDDING_TEST_NUM_RANDOM_SEEDS))
)
def test_vocab_parallel_embedding_indices(tp_size, seed):
def test_vocab_parallel_embedding_indices(tp_size, seed, default_vllm_config):
random.seed(seed)
vocab_size = random.randint(4000, 64000)
added_vocab_size = random.randint(0, 1024)
Expand Down
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