[V1] Enable Triton(ROCm) Attention backend for Nvidia GPUs

vllm/engine/arg_utils.py

@@ -1570,7 +1570,7 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
         # No FlashInfer or XFormers so far.
         V1_BACKENDS = [
             "FLASH_ATTN_VLLM_V1", "FLASH_ATTN", "PALLAS", "PALLAS_VLLM_V1",
-            "TRITON_MLA", "FLASHMLA"
+            "TRITON_ATTN_VLLM_V1", "TRITON_MLA", "FLASHMLA"
         ]
         if (envs.is_set("VLLM_ATTENTION_BACKEND")
                 and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS):

vllm/platforms/cuda.py

@@ -212,9 +212,14 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
                         return ("vllm.attention.backends."
                                 "flashmla.FlashMLABackend")
         if use_v1:
-            logger.info_once("Using Flash Attention backend on V1 engine.")
-            return ("vllm.v1.attention.backends.flash_attn."
-                    "FlashAttentionBackend")
+            if selected_backend == _Backend.TRITON_ATTN_VLLM_V1:
+                logger.info_once("Using Triton backend on V1 engine.")
+                return ("vllm.v1.attention.backends."
+                        "triton_attn.TritonAttentionBackend")
+            if cls.has_device_capability(80):
+                logger.info_once("Using Flash Attention backend on V1 engine.")
+                return ("vllm.v1.attention.backends."
+                        "flash_attn.FlashAttentionBackend")
         if selected_backend == _Backend.FLASHINFER:
             logger.info("Using FlashInfer backend.")
             return "vllm.attention.backends.flashinfer.FlashInferBackend"

vllm/platforms/interface.py

@@ -29,6 +29,7 @@ def in_wsl() -> bool:
 class _Backend(enum.Enum):
     FLASH_ATTN = enum.auto()
     FLASH_ATTN_VLLM_V1 = enum.auto()
+    TRITON_ATTN_VLLM_V1 = enum.auto()
     XFORMERS = enum.auto()
     ROCM_FLASH = enum.auto()
     TORCH_SDPA = enum.auto()

vllm/platforms/rocm.py

@@ -120,8 +120,9 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
         selected_backend = (_Backend.ROCM_FLASH if selected_backend
                             == _Backend.FLASH_ATTN else selected_backend)
         if envs.VLLM_USE_V1:
-            logger.info("Using ROCm Attention backend on V1 engine.")
-            return "vllm.v1.attention.backends.rocm_attn.ROCmAttentionBackend"
+            logger.info("Using Triton Attention backend on V1 engine.")
+            return ("vllm.v1.attention.backends."
+                    "triton_attn.TritonAttentionBackend")
         if selected_backend == _Backend.ROCM_FLASH:
             if not cls.has_device_capability(90):
                 # not Instinct series GPUs.

vllm/v1/attention/backends/rocm_attn.py → vllm/v1/attention/backends/triton_attn.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-"""Attention layer with PagedAttention on rocm"""
+"""Attention layer with PagedAttention and Triton prefix prefill."""
 from typing import Any, Optional
 
 import torch
@@ -16,7 +16,7 @@
 logger = init_logger(__name__)
 
 
-class ROCmAttentionBackend(AttentionBackend):
+class TritonAttentionBackend(AttentionBackend):
 
     accept_output_buffer: bool = True
 
@@ -26,11 +26,11 @@ def get_supported_head_sizes() -> list[int]:
 
     @staticmethod
     def get_name() -> str:
-        return "ROCM_ATTN_VLLM_V1"
+        return "TRITON_ATTN_VLLM_V1"
 
     @staticmethod
-    def get_impl_cls() -> type["ROCmAttentionImpl"]:
-        return ROCmAttentionImpl
+    def get_impl_cls() -> type["TritonAttentionImpl"]:
+        return TritonAttentionImpl
 
     @staticmethod
     def get_metadata_cls() -> type["AttentionMetadata"]:
@@ -56,7 +56,7 @@ def get_builder_cls() -> type["FlashAttentionMetadataBuilder"]:
         return FlashAttentionMetadataBuilder
 
 
-class ROCmAttentionImpl(AttentionImpl):
+class TritonAttentionImpl(AttentionImpl):
 
     def __init__(
         self,
@@ -73,7 +73,7 @@ def __init__(
     ) -> None:
         if blocksparse_params is not None:
             raise ValueError(
-                "ROCmAttention does not support block-sparse attention.")
+                "TritonAttention does not support block-sparse attention.")
         self.num_heads = num_heads
         self.head_size = head_size
         self.scale = float(scale)
@@ -90,17 +90,17 @@ def __init__(
         assert self.num_heads % self.num_kv_heads == 0
         self.num_queries_per_kv = self.num_heads // self.num_kv_heads
 
-        support_head_sizes = ROCmAttentionBackend.get_supported_head_sizes()
+        support_head_sizes = TritonAttentionBackend.get_supported_head_sizes()
         if head_size not in support_head_sizes:
             raise ValueError(
-                f"Head size {head_size} is not supported by ROCmAttention. "
+                f"Head size {head_size} is not supported by TritonAttention. "
                 f"Supported head sizes are: {support_head_sizes}.")
 
         if attn_type != AttentionType.DECODER:
             raise NotImplementedError("Encoder self-attention and "
                                       "encoder/decoder cross-attention "
                                       "are not implemented for "
-                                      "ROCmAttentionImpl")
+                                      "TritonAttentionImpl")
 
     def forward(
         self,