[Feature] Batch-Invariant Support for FA2 and LoRA (#30018)

Signed-off-by: quanliu <18646313696@163.com>
Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>

tests/v1/determinism/test_batch_invariance.py

@@ -10,6 +10,7 @@ from utils import (
     BACKENDS,
     _extract_step_logprobs,
     _random_prompt,
+    is_device_capability_below_90,
     resolve_model_name,
     skip_unsupported,
 )
@@ -17,6 +18,8 @@ from utils import (
 import vllm.model_executor.layers.batch_invariant as batch_invariant
 from vllm import LLM, SamplingParams
 
+IS_DEVICE_CAPABILITY_BELOW_90 = is_device_capability_below_90()
+
 
 @skip_unsupported
 @pytest.mark.timeout(1000)
@@ -190,6 +193,7 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
         max_model_len=8192,
         dtype="bfloat16",  # not everything is supported
         gpu_memory_utilization=0.9,
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
     )
 
     # Use more realistic prompts for better token generation
@@ -393,6 +397,8 @@ def test_simple_generation(backend, monkeypatch: pytest.MonkeyPatch):
         gpu_memory_utilization=0.9,
         max_model_len=2048,
         dtype="bfloat16",
+        enable_prefix_caching=False,
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
     )
 
     prompt = "the capital of france is"
@@ -459,6 +465,7 @@ def test_logprobs_without_batch_invariance_should_fail(
         max_num_seqs=32,
         max_model_len=8192,
         dtype="bfloat16",
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
     )
 
     # build ragged prompts to change shapes significantly across BS=1 vs BS=N
@@ -682,6 +689,7 @@ def test_decode_logprobs_match_prefill_logprobs(
         max_num_seqs=32,
         max_model_len=8192,
         dtype="bfloat16",
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
     )
 
     # Use a few test prompts
@@ -925,6 +933,8 @@ def LLM_with_max_seqs(
         max_model_len=max_model_len,
         dtype="bfloat16",
         tensor_parallel_size=int(os.getenv("VLLM_TP_SIZE", "1")),
+        enable_prefix_caching=False,
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
         # Enable for MOE models
         # enable_expert_parallel=True,
     )

tests/v1/determinism/utils.py

@@ -11,8 +11,10 @@ from vllm.platforms import current_platform
 from vllm.utils.flashinfer import has_flashinfer
 
 skip_unsupported = pytest.mark.skipif(
-    not (current_platform.is_cuda() and current_platform.has_device_capability(90)),
-    reason="Requires CUDA and >= Hopper (SM90)",
+    not (current_platform.is_cuda() and current_platform.has_device_capability(80)),
+    # Supports testing on Ampere and Ada Lovelace devices.
+    # Note: For devices with SM < 90, batch invariance does not support CUDA Graphs.
+    reason="Requires CUDA and >= Ampere (SM80)",
 )
 
 BACKENDS: list[str] = [
@@ -97,3 +99,7 @@ def _extract_step_logprobs(request_output):
             return t, inner.token_ids
 
     return None, None
+
+
+def is_device_capability_below_90() -> bool:
+    return not current_platform.has_device_capability(90)

vllm/model_executor/layers/batch_invariant.py

@@ -935,7 +935,11 @@ def enable_batch_invariant_mode():
 
     # Batch invariant matmuls are no longer needed after cublas overrides
     if not is_torch_equal_or_newer("2.10.0.dev"):
-        if current_platform.is_device_capability(100):
+        if (
+            current_platform.is_device_capability(100)
+            or current_platform.is_device_capability(80)
+            or current_platform.is_device_capability(89)
+        ):
             # For PyTorch 2.9, B200 uses GEMV for bs=1
             # Requires https://github.com/pytorch/pytorch/pull/166735
             _batch_invariant_LIB.impl("aten::mm", mm_batch_invariant, "CUDA")