[Feature] Batch invariant: Enable TRITON_MLA without prefix-caching (#29125)

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

tests/v1/determinism/test_batch_invariance.py

@@ -185,7 +185,7 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
     llm = LLM(
         model=model_name,
         tensor_parallel_size=tp_size,
-        enable_prefix_caching=False,
+        # enable_prefix_caching=False,
         max_num_seqs=32,
         max_model_len=8192,
         dtype="bfloat16",  # not everything is supported
@@ -393,7 +393,6 @@ def test_simple_generation(backend, monkeypatch: pytest.MonkeyPatch):
         gpu_memory_utilization=0.9,
         max_model_len=2048,
         dtype="bfloat16",
-        enable_prefix_caching=False,
     )
 
     prompt = "the capital of france is"
@@ -457,7 +456,6 @@ def test_logprobs_without_batch_invariance_should_fail(
     llm = LLM(
         model=model_name,
         tensor_parallel_size=tp_size,
-        enable_prefix_caching=False,
         max_num_seqs=32,
         max_model_len=8192,
         dtype="bfloat16",
@@ -681,7 +679,6 @@ def test_decode_logprobs_match_prefill_logprobs(
     llm = LLM(
         model=model_name,
         tensor_parallel_size=tp_size,
-        enable_prefix_caching=False,
         max_num_seqs=32,
         max_model_len=8192,
         dtype="bfloat16",
@@ -928,7 +925,6 @@ 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,
         # Enable for MOE models
         # enable_expert_parallel=True,
     )

tests/v1/determinism/test_online_batch_invariance.py

@@ -153,7 +153,10 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
     }
 
     tp_size = os.getenv("VLLM_TP_SIZE", "1")
-    server_args: list[str] = []
+    server_args: list[str] = [
+        "--max-model-len=8192",
+        "--max-num-seqs=32",
+    ]
     if tp_size:
         server_args += ["-tp", tp_size]
 

tests/v1/determinism/utils.py

@@ -17,6 +17,7 @@ skip_unsupported = pytest.mark.skipif(
 
 BACKENDS: list[str] = [
     "FLASH_ATTN",
+    "TRITON_MLA",
 ]
 
 if has_flashinfer():

vllm/attention/layer.py

@@ -25,6 +25,7 @@ from vllm.config.vllm import VllmConfig
 from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.model_executor.layers.batch_invariant import vllm_is_batch_invariant
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     UnquantizedLinearMethod,
@@ -251,6 +252,24 @@ class Attention(nn.Module, AttentionLayerBase):
         else:
             self.attn_backend = attn_backend
 
+        # prefix caching + batch invariance is currently not supported for
+        # FLASHINFER and TRITON_MLA.
+        if (
+            cache_config is not None
+            and cache_config.enable_prefix_caching
+            and vllm_is_batch_invariant()
+            and (
+                self.attn_backend.get_name() == "FLASHINFER"
+                or self.attn_backend.get_name() == "TRITON_MLA"
+            )
+        ):
+            logger.warning_once(
+                "Disabling prefix caching for FLASHINFER/TRITON_MLA "
+                "with batch invariance, as it is not yet supported.",
+                scope="local",
+            )
+            cache_config.enable_prefix_caching = False
+
         impl_cls = self.attn_backend.get_impl_cls()
         self.impl = impl_cls(
             num_heads,
@@ -628,6 +647,23 @@ class MLAAttention(nn.Module, AttentionLayerBase):
             use_mla=True,
             use_sparse=use_sparse,
         )
+
+        if (
+            cache_config is not None
+            and cache_config.enable_prefix_caching
+            and vllm_is_batch_invariant()
+            and (
+                self.attn_backend.get_name() == "TRITON_MLA"
+                or self.attn_backend.get_name() == "FLASHINFER"
+            )
+        ):
+            logger.warning_once(
+                "Disabling prefix caching for TRITON_MLA / FLASHINFER "
+                "with batch invariance, as it is not yet supported.",
+                scope="local",
+            )
+            cache_config.enable_prefix_caching = False
+
         impl_cls = cast(type[MLAAttentionImpl], self.attn_backend.get_impl_cls())
         self.impl = impl_cls(
             num_heads=self.num_heads,

vllm/model_executor/layers/batch_invariant.py

@@ -1006,11 +1006,11 @@ def override_envs_for_invariance():
         "FLASH_ATTN",  # best supported backend
         "FLASHINFER",
         "FLASH_ATTN_MLA",
+        "TRITON_MLA",
         # Not yet supported MLA backends
         # "FLASHMLA",
         # "FLEX_ATTENTION", # IMA issue even if we disable batch invariance
         # "FLASHINFER_MLA", https://github.com/vllm-project/vllm/pull/28967
-        # "TRITON_MLA",
     ]
     if curr_attn_backend not in supported_backends:
         error = (