Reenable features for ROCm attention backends (#36185)

Signed-off-by: Rohan138 <rohanpotdar138@gmail.com>

vllm/v1/attention/backend.py

@@ -252,7 +252,7 @@ class AttentionBackend(ABC):
             else:
                 invalid_reasons.append("non-MLA not supported")
         if has_sink and not cls.supports_sink():
-            invalid_reasons.append("sink setting not supported")
+            invalid_reasons.append("attention sinks not supported")
         if use_sparse != cls.is_sparse():
             if use_sparse:
                 invalid_reasons.append("sparse not supported")

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

@@ -8,6 +8,7 @@ import torch
 
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig
+from vllm.config.cache import CacheDType
 from vllm.model_executor.layers.attention.mla_attention import (
     MLACommonBackend,
     MLACommonDecodeMetadata,
@@ -21,6 +22,15 @@ from vllm.v1.kv_cache_interface import AttentionSpec
 
 
 class AiterMLABackend(MLACommonBackend):
+    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
+    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
+        "auto",
+        "bfloat16",
+        "fp8",
+        "fp8_e4m3",
+        "fp8_e5m2",
+    ]
+
     @staticmethod
     def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
         return [1]

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

@@ -9,6 +9,7 @@ import torch
 
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig
+from vllm.config.cache import CacheDType
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention.mla_attention import (
     get_mla_dims,
@@ -21,6 +22,7 @@ from vllm.v1.attention.backend import (
     AttentionMetadata,
     AttentionMetadataBuilder,
     CommonAttentionMetadata,
+    MultipleOf,
     SparseMLAAttentionImpl,
 )
 from vllm.v1.attention.backends.mla.flashmla_sparse import (
@@ -77,7 +79,15 @@ def fetch_id_to_ragged_triton(
 
 class ROCMAiterMLASparseBackend(AttentionBackend):
     accept_output_buffer: bool = True
-    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.bfloat16]
+    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
+    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
+        "auto",
+        "bfloat16",
+    ]
+
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [1]
 
     @staticmethod
     def get_name() -> str:
@@ -105,10 +115,6 @@ class ROCMAiterMLASparseBackend(AttentionBackend):
     ) -> tuple[int, ...]:
         return (num_blocks, block_size, head_size)
 
-    @classmethod
-    def get_supported_head_sizes(cls) -> list[int]:
-        return [576]
-
     @classmethod
     def is_mla(cls) -> bool:
         return True
@@ -117,11 +123,6 @@ class ROCMAiterMLASparseBackend(AttentionBackend):
     def is_sparse(cls) -> bool:
         return True
 
-    @classmethod
-    def supports_block_size(cls, block_size: int | None) -> bool:
-        # The only supported block_size is 1
-        return block_size is None or block_size == 1
-
 
 @dataclass
 class ROCMAiterMLASparseMetadata(AttentionMetadata):

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

@@ -45,11 +45,6 @@ class TritonMLABackend(MLACommonBackend):
     def supports_compute_capability(cls, capability: DeviceCapability) -> bool:
         return True
 
-    @classmethod
-    def supports_block_size(cls, block_size: int | None) -> bool:
-        # The only unsupported block_size is 1
-        return block_size is None or block_size != 1
-
 
 class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):
     can_return_lse_for_decode: bool = True

vllm/v1/attention/backends/rocm_aiter_fa.py

@@ -9,6 +9,7 @@ import torch
 
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig, get_layers_from_vllm_config
+from vllm.config.cache import CacheDType
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
 from vllm.platforms import current_platform
@@ -732,6 +733,13 @@ class AiterFlashAttentionMetadataBuilder(
 class AiterFlashAttentionBackend(AttentionBackend):
     accept_output_buffer: bool = True
     supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
+    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
+        "auto",
+        "bfloat16",
+        "fp8",
+        "fp8_e4m3",
+        "fp8_e5m2",
+    ]
 
     @staticmethod
     def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:

vllm/v1/attention/backends/rocm_aiter_unified_attn.py

@@ -11,7 +11,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
     kFp8StaticTensorSym,
 )
-from vllm.v1.attention.backend import AttentionLayer, AttentionType
+from vllm.v1.attention.backend import AttentionLayer, AttentionType, MultipleOf
 from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
 from vllm.v1.attention.backends.rocm_attn import (
     RocmAttentionBackend,
@@ -25,6 +25,22 @@ logger = init_logger(__name__)
 class RocmAiterUnifiedAttentionBackend(RocmAttentionBackend):
     accept_output_buffer: bool = True
 
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [MultipleOf(16)]
+
+    @classmethod
+    def supports_head_size(cls, head_size: int) -> bool:
+        return head_size >= 32
+
+    @classmethod
+    def supports_mm_prefix(cls) -> bool:
+        return True
+
+    @classmethod
+    def supports_sink(cls) -> bool:
+        return True
+
     forward_includes_kv_cache_update: bool = False
 
     @staticmethod

vllm/v1/attention/backends/rocm_attn.py

@@ -9,6 +9,7 @@ import torch
 
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig
+from vllm.config.cache import CacheDType
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
@@ -163,6 +164,13 @@ class RocmAttentionBackend(AttentionBackend):
         torch.bfloat16,
         torch.float32,
     ]
+    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
+        "auto",
+        "bfloat16",
+        "fp8",
+        "fp8_e4m3",
+        "fp8_e5m2",
+    ]
 
     @staticmethod
     def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
@@ -185,15 +193,12 @@ class RocmAttentionBackend(AttentionBackend):
         return [32, 64, 80, 96, 128, 160, 192, 224, 256]
 
     @classmethod
-    def validate_head_size(cls, head_size: int) -> None:
-        if not cls.supports_head_size(head_size):
-            attn_type = cls.__name__.removesuffix("Backend")
-            raise ValueError(
-                f"Head size {head_size} is not supported by {attn_type}. "
-                f"Supported head sizes are: {cls.get_supported_head_sizes()}. "
-                "Set --attention-backend=FLEX_ATTENTION to use "
-                "FlexAttention backend which supports all head sizes."
-            )
+    def supports_mm_prefix(cls) -> bool:
+        return True
+
+    @classmethod
+    def supports_sink(cls) -> bool:
+        return True
 
     forward_includes_kv_cache_update: bool = False
 
@@ -275,8 +280,6 @@ class RocmAttentionImpl(AttentionImpl):
 
         self.num_queries_per_kv = self.num_heads // self.num_kv_heads
 
-        RocmAttentionBackend.validate_head_size(head_size)
-
         self.fp8_dtype = current_platform.fp8_dtype()
 
         self.sinks = sinks