[Bug] Fix batch invariant test `has` to `is` (#27032)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

tests/v1/generation/test_batch_invariance.py

@@ -10,6 +10,11 @@ import torch
 from vllm import LLM, SamplingParams
 from vllm.platforms import current_platform
 
+hopper_only = pytest.mark.skipif(
+    not (current_platform.is_cuda() and current_platform.is_device_capability(90)),
+    reason="Requires CUDA and Hopper (SM90)",
+)
+
 
 @pytest.fixture(autouse=True)
 def enable_batch_invariant_mode():
@@ -66,10 +71,7 @@ def _random_prompt(min_words: int = 1024, max_words: int = 1024 * 2) -> str:
     return base_prompt
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
+@hopper_only
 @pytest.mark.timeout(1000)
 def test_v1_generation_is_deterministic_across_batch_sizes_with_needle():
     """
@@ -214,14 +216,7 @@ def _extract_step_logprobs(request_output):
     return None, None
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
-@pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="Requires CUDA to match production inference path.",
-)
+@hopper_only
 @pytest.mark.parametrize("backend", ["FLASH_ATTN", "FLASHINFER"])
 @pytest.mark.forked
 def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(backend):
@@ -436,10 +431,7 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(backend):
         pytest.fail(msg)
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
+@hopper_only
 def test_simple_generation():
     """
     Simple test that runs the model with a basic prompt and prints the output.
@@ -485,14 +477,7 @@ def test_simple_generation():
             llm.shutdown()
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
-@pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="Requires CUDA to match production inference path.",
-)
+@hopper_only
 @pytest.mark.parametrize("backend", ["FLASH_ATTN", "FLASHINFER"])
 @pytest.mark.forked
 def test_logprobs_WITHOUT_batch_invariance_should_FAIL(backend):
@@ -707,14 +692,7 @@ def test_logprobs_WITHOUT_batch_invariance_should_FAIL(backend):
             os.environ["VLLM_KERNEL_OVERRIDE_BATCH_INVARIANT"] = old_value
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
-@pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="Requires CUDA to match production inference path.",
-)
+@hopper_only
 @pytest.mark.parametrize("backend", ["FLASH_ATTN"])
 @pytest.mark.forked
 def test_decode_logprobs_match_prefill_logprobs(backend):

tests/v1/generation/test_rms_norm_batch_invariant.py

@@ -14,14 +14,13 @@ from vllm.model_executor.layers.batch_invariant import rms_norm as triton_rms_no
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.platforms import current_platform
 
-
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason="Requires CUDA for RMS norm kernels"
+hopper_only = pytest.mark.skipif(
+    not (current_platform.is_cuda() and current_platform.is_device_capability(90)),
+    reason="Requires CUDA and Hopper (SM90)",
 )
+
+
+@hopper_only
 @pytest.mark.parametrize("batch_size", [1, 4, 16, 64])
 @pytest.mark.parametrize("hidden_size", [512, 2048, 4096, 8192])
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@@ -70,13 +69,7 @@ def test_rms_norm_batch_invariant_vs_standard(
     )
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason="Requires CUDA for RMS norm kernels"
-)
+@hopper_only
 @pytest.mark.parametrize("batch_size", [1, 16, 128])
 @pytest.mark.parametrize("seq_len", [1, 32, 512])
 @pytest.mark.parametrize("hidden_size", [2048, 4096])
@@ -118,13 +111,7 @@ def test_rms_norm_3d_input(batch_size: int, seq_len: int, hidden_size: int):
     )
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason="Requires CUDA for RMS norm kernels"
-)
+@hopper_only
 def test_rms_norm_numerical_stability():
     """
     Test RMS norm numerical stability with extreme values.
@@ -184,13 +171,7 @@ def test_rms_norm_numerical_stability():
         )
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason="Requires CUDA for RMS norm kernels"
-)
+@hopper_only
 def test_rms_norm_formula():
     """
     Test that RMS norm follows the correct mathematical formula.
@@ -223,13 +204,7 @@ def test_rms_norm_formula():
     )
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason="Requires CUDA for RMS norm kernels"
-)
+@hopper_only
 @pytest.mark.parametrize("hidden_size", [128, 1024, 4096, 16384])
 def test_rms_norm_different_hidden_sizes(hidden_size: int):
     """
@@ -267,13 +242,7 @@ def test_rms_norm_different_hidden_sizes(hidden_size: int):
     )
 
 
-@pytest.mark.skipif(
-    not current_platform.has_device_capability(90),
-    reason="Batch invariance tests only supported on Hopper (SM90)",
-)
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason="Requires CUDA for RMS norm kernels"
-)
+@hopper_only
 def test_rms_norm_determinism():
     """
     Test that batch-invariant RMS norm produces deterministic results.