[Feature] Enable TRITON_ATTN for Batch Invariance

docs/features/batch_invariance.md

@@ -108,6 +108,7 @@ Batch invariance has been tested and verified on the following models:
 - **Qwen3 (MoE)**: `Qwen/Qwen3-30B-A3B`, `Qwen/Qwen3-Next-80B-A3B-Instruct`
 - **Qwen2.5**: `Qwen/Qwen2.5-0.5B-Instruct`, `Qwen/Qwen2.5-1.5B-Instruct`, `Qwen/Qwen2.5-3B-Instruct`, `Qwen/Qwen2.5-7B-Instruct`, `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-32B-Instruct`
 - **Llama 3**: `meta-llama/Llama-3.1-8B-Instruct`, `meta-llama/Llama-3.2-1B-Instruct`
+- **GPT-OSS**: `openai/gpt-oss-20b`, `openai/gpt-oss-120b`
 
 Other models may also work, but these have been explicitly validated. If you encounter issues with a specific model, please report them on the [GitHub issue tracker](https://github.com/vllm-project/vllm/issues/new/choose).
 

tests/v1/determinism/utils.py

@@ -18,6 +18,7 @@
 
 BACKENDS: list[str] = [
     "FLASH_ATTN",
+    "TRITON_ATTN",
     "TRITON_MLA",
 ]
 

vllm/model_executor/layers/batch_invariant.py

@@ -1003,8 +1003,11 @@ def vllm_is_batch_invariant() -> bool:
 def override_envs_for_invariance(
     attention_backend: AttentionBackendEnum | None,
 ):
-    supported_backends = [
+    decode_invariant_backends = [
         AttentionBackendEnum.FLASH_ATTN,  # best supported backend
+        AttentionBackendEnum.TRITON_ATTN,
+    ]
+    supported_backends = decode_invariant_backends + [
         # FlashInfer temporarily disabled due to invariant CTA sizes.
         # See FlashInfer issue #2424
         # AttentionBackendEnum.FLASHINFER,
@@ -1025,9 +1028,9 @@ def override_envs_for_invariance(
             "one of the supported backends before enabling batch_invariant."
         )
         raise RuntimeError(error)
-    if attention_backend != supported_backends[0]:
+    if attention_backend not in decode_invariant_backends:
         warning = (
-            "You are using a decode-invariant form of batch invariance. "
+            "You are using a non-decode-invariant form of batch invariance. "
             "This will not be invariant between prefill and decode."
         )
         logger.warning_once(warning, scope="local")

vllm/v1/attention/ops/triton_unified_attention.py

@@ -10,10 +10,12 @@
 import torch
 
 from vllm.logger import init_logger
+from vllm.model_executor.layers.batch_invariant import vllm_is_batch_invariant
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 
 logger = init_logger(__name__)
+is_batch_invariant = vllm_is_batch_invariant()
 float8_info = torch.finfo(current_platform.fp8_dtype())
 
 
@@ -972,7 +974,8 @@ def unified_attention(
     # Launch the 2D kernel if
     # 1. No intermediate tiled softmax buffers for the 3D kernel have been allocated, or
     # 2. The batch includes at least one prefill request, or
-    # 3. The number of sequences exceeds the configured threshold
+    # 3. The number of sequences exceeds the configured threshold, or
+    # 4. Batch invariance is enabled
     if (
         seq_threshold_3D is None
         or num_par_softmax_segments is None
@@ -981,6 +984,7 @@ def unified_attention(
         or softmax_segm_expsum is None
         or max_seqlen_q > 1
         or num_seqs > seq_threshold_3D
+        or is_batch_invariant
     ):
         kernel_unified_attention_2d[
             (