Upstreaming aiter triton attention backend as a new backend (#28701)

Signed-off-by: Aleksandr Malyshev <maleksan@amd.com>
Co-authored-by: Aleksandr Malyshev <maleksan@amd.com>

vllm/attention/backends/registry.py

@@ -46,6 +46,9 @@ class AttentionBackendEnum(Enum, metaclass=_AttentionBackendEnumMeta):
     XFORMERS = "vllm.v1.attention.backends.xformers.XFormersAttentionBackend"
     ROCM_ATTN = "vllm.v1.attention.backends.rocm_attn.RocmAttentionBackend"
     ROCM_AITER_MLA = "vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend"
+    ROCM_AITER_TRITON_MLA = (
+        "vllm.v1.attention.backends.mla.aiter_triton_mla.AiterTritonMLABackend"
+    )
     ROCM_AITER_FA = (
         "vllm.v1.attention.backends.rocm_aiter_fa.AiterFlashAttentionBackend"
     )

vllm/platforms/rocm.py

@@ -234,7 +234,6 @@ class RocmPlatform(Platform):
                     if rocm_aiter_ops.is_mla_enabled() or block_size == 1
                     else AttentionBackendEnum.TRITON_MLA
                 )
-
             if selected_backend == AttentionBackendEnum.TRITON_MLA:
                 if block_size != 1:
                     logger.info_once("Using Triton MLA backend.")
@@ -246,6 +245,9 @@ class RocmPlatform(Platform):
             if selected_backend == AttentionBackendEnum.ROCM_AITER_MLA:
                 logger.info("Using AITER MLA backend.")
                 return AttentionBackendEnum.ROCM_AITER_MLA.get_path()
+            if selected_backend == AttentionBackendEnum.ROCM_AITER_TRITON_MLA:
+                logger.info("Using AITER TRITON MLA backend.")
+                return AttentionBackendEnum.ROCM_AITER_TRITON_MLA.get_path()
 
             raise ValueError(
                 f" The selected backend, {selected_backend.name},"

vllm/v1/attention/backends/mla/aiter_triton_mla.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from vllm.v1.attention.backends.mla.common import MLACommonBackend
+from vllm.v1.attention.backends.mla.rocm_aiter_mla import (
+    AiterMLAImpl,
+    AiterMLAMetadataBuilder,
+)
+
+
+class AiterTritonMLABackend(MLACommonBackend):
+    @staticmethod
+    def get_name() -> str:
+        return "AITER_TRITON_MLA"
+
+    @staticmethod
+    def get_impl_cls() -> type["AiterTritonMLAImpl"]:
+        return AiterTritonMLAImpl
+
+    @staticmethod
+    def get_builder_cls() -> type["AiterMLAMetadataBuilder"]:
+        return AiterMLAMetadataBuilder
+
+
+class AiterTritonMLAImpl(AiterMLAImpl):
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: int,
+        alibi_slopes: list[float] | None,
+        sliding_window: int | None,
+        kv_cache_dtype: str,
+        logits_soft_cap: float | None,
+        attn_type: str,
+        kv_sharing_target_layer_name: str | None,
+        # MLA Specific Arguments
+        **mla_args,
+    ) -> None:
+        super().__init__(
+            num_heads,
+            head_size,
+            scale,
+            num_kv_heads,
+            alibi_slopes,
+            sliding_window,
+            kv_cache_dtype,
+            logits_soft_cap,
+            attn_type,
+            kv_sharing_target_layer_name,
+            **mla_args,
+        )
+        from aiter.ops.triton.mha import flash_attn_varlen_func
+
+        self.flash_attn_varlen_func = flash_attn_varlen_func
+
+    def _flash_attn_varlen_diff_headdims(
+        self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
+    ):
+        result = self.flash_attn_varlen_func(
+            q,
+            k,
+            v,
+            softmax_scale=softmax_scale,
+            return_lse=return_softmax_lse,
+            **kwargs,
+        )
+        # Transpose the LSE if Triton MHA is used:
+        # (q.shape[0], num_q_heads) to (num_q_heads, q.shape[0])
+        if type(result) is tuple and return_softmax_lse:
+            output, lse = result
+            lse = lse.T.contiguous()
+            return (output, lse)
+        return result