Merge branch 'patch_3' into apply_plugin

vllm-project#11609

Author: wangxiyuan

tests/kernels/test_attention_selector.py

@@ -16,33 +16,36 @@
     "name", ["TORCH_SDPA", "ROCM_FLASH", "XFORMERS", "FLASHINFER", "OPENVINO"])
 @pytest.mark.parametrize("device", ["cpu", "openvino", "hip", "cuda"])
 def test_env(name: str, device: str, monkeypatch):
-    """Test that the attention selector can be set via environment variable.
-    Note that we do not test FlashAttn because it is the default backend.
-    """
+    """Test that the attention selector can be set via environment variable."""
 
     override_backend_env_variable(monkeypatch, name)
 
     if device == "cpu":
         with patch("vllm.attention.selector.current_platform", CpuPlatform()):
             backend = which_attn_to_use(16, torch.float16, torch.float16, 16,
                                         False)
-        assert backend.name == "TORCH_SDPA"
+        assert backend == "vllm.attention.backends.torch_sdpa.TorchSDPABackend"
     elif device == "hip":
         with patch("vllm.attention.selector.current_platform", RocmPlatform()):
             backend = which_attn_to_use(16, torch.float16, torch.float16, 16,
                                         False)
-        assert backend.name == "ROCM_FLASH"
+        assert backend == "vllm.attention.backends.rocm_flash_attn.ROCmFlashAttentionBackend"  # noqa: E501
     elif device == "openvino":
         with patch("vllm.attention.selector.current_platform",
                    OpenVinoPlatform()):
             backend = which_attn_to_use(16, torch.float16, torch.float16, 16,
                                         False)
-        assert backend.name == "OPENVINO"
+        assert backend == "vllm.attention.backends.openvino.OpenVINOAttentionBackend"  # noqa: E501
     else:
         with patch("vllm.attention.selector.current_platform", CudaPlatform()):
             backend = which_attn_to_use(16, torch.float16, torch.float16, 16,
                                         False)
-        assert backend.name == name
+        if name == "FLASHINFER":
+            assert backend == "vllm.attention.backends.flashinfer.FlashInferBackend"  # noqa: E501
+        if name == "XFORMERS":
+            assert backend == "vllm.attention.backends.xformers.XFormersBackend"
+        else:
+            assert backend == "vllm.attention.backends.flash_attn.FlashAttentionBackend"  # noqa: E501
 
 
 def test_flash_attn(monkeypatch):
@@ -55,32 +58,32 @@ def test_flash_attn(monkeypatch):
     # Unsupported CUDA arch
     with patch("torch.cuda.get_device_capability", return_value=(7, 5)):
         backend = which_attn_to_use(16, torch.float16, None, 16, False)
-        assert backend.name != STR_FLASH_ATTN_VAL
+        assert backend != "vllm.attention.backends.flash_attn.FlashAttentionBackend"  # noqa: E501
 
     # Unsupported data type
     backend = which_attn_to_use(16, torch.float8_e4m3fn, None, 16, False)
-    assert backend.name != STR_FLASH_ATTN_VAL
+    assert backend != "vllm.attention.backends.flash_attn.FlashAttentionBackend"
 
     # Unsupported kv cache data type
     backend = which_attn_to_use(16, torch.float16, "fp8", 16, False)
-    assert backend.name != STR_FLASH_ATTN_VAL
+    assert backend != "vllm.attention.backends.flash_attn.FlashAttentionBackend"
 
     # Unsupported block size
     backend = which_attn_to_use(16, torch.float16, None, 8, False)
-    assert backend.name != STR_FLASH_ATTN_VAL
+    assert backend != "vllm.attention.backends.flash_attn.FlashAttentionBackend"
 
     # flash-attn is not installed
     with patch.dict('sys.modules', {'vllm_flash_attn': None}):
         backend = which_attn_to_use(16, torch.float16, None, 16, False)
-        assert backend.name != STR_FLASH_ATTN_VAL
+        assert backend != "vllm.attention.backends.flash_attn.FlashAttentionBackend"  # noqa: E501
 
     # Unsupported head size
     backend = which_attn_to_use(17, torch.float16, None, 16, False)
-    assert backend.name != STR_FLASH_ATTN_VAL
+    assert backend != "vllm.attention.backends.flash_attn.FlashAttentionBackend"
 
     # Attention-free models should bypass env and use PlaceholderAttention
     backend = which_attn_to_use(16, torch.float16, torch.float16, 16, True)
-    assert backend.name != STR_FLASH_ATTN_VAL
+    assert backend != "vllm.attention.backends.flash_attn.FlashAttentionBackend"
 
 
 def test_invalid_env(monkeypatch):

vllm/attention/selector.py

@@ -9,7 +9,7 @@
 from vllm.attention.backends.abstract import AttentionBackend
 from vllm.logger import init_logger
 from vllm.platforms import _Backend, current_platform
-from vllm.utils import STR_BACKEND_ENV_VAR
+from vllm.utils import STR_BACKEND_ENV_VAR, resolve_obj_by_qualname
 
 logger = init_logger(__name__)
 
@@ -114,83 +114,32 @@ def _cached_get_attn_backend(
             BlocksparseFlashAttentionBackend)
         return BlocksparseFlashAttentionBackend
 
-    backend = which_attn_to_use(head_size, dtype, kv_cache_dtype, block_size,
-                                is_attention_free, use_v1)
-    if backend == _Backend.FLASH_ATTN:
-        logger.info("Using Flash Attention backend.")
-        from vllm.attention.backends.flash_attn import (  # noqa: F401
-            FlashAttentionBackend)
-        return FlashAttentionBackend
-    if backend == _Backend.FLASH_ATTN_VLLM_V1:
-        from vllm.v1.attention.backends.flash_attn import (  # noqa: F401
-            FlashAttentionBackend as FlashAttentionBackendV1)
-        return FlashAttentionBackendV1
-    if backend == _Backend.XFORMERS:
-        logger.info("Using XFormers backend.")
-        from vllm.attention.backends.xformers import (  # noqa: F401
-            XFormersBackend)
-        return XFormersBackend
-    elif backend == _Backend.ROCM_FLASH:
-        logger.info("Using ROCmFlashAttention backend.")
-        from vllm.attention.backends.rocm_flash_attn import (  # noqa: F401
-            ROCmFlashAttentionBackend)
-        return ROCmFlashAttentionBackend
-    elif backend == _Backend.TORCH_SDPA:
-        assert current_platform.is_cpu(), RuntimeError(
-            "Torch SDPA backend is only used for the CPU device.")
-        logger.info("Using Torch SDPA backend.")
-        from vllm.attention.backends.torch_sdpa import TorchSDPABackend
-        return TorchSDPABackend
-    elif backend == _Backend.OPENVINO:
-        logger.info("Using OpenVINO Attention backend.")
-        from vllm.attention.backends.openvino import OpenVINOAttentionBackend
-        return OpenVINOAttentionBackend
-    elif backend == _Backend.IPEX:
-        assert current_platform.is_xpu(), RuntimeError(
-            "IPEX attention backend is only used for the XPU device.")
-        logger.info("Using IPEX attention backend.")
-        from vllm.attention.backends.ipex_attn import IpexAttnBackend
-        return IpexAttnBackend
-    elif backend == _Backend.FLASHINFER:
-        logger.info("Using Flashinfer backend.")
-        from vllm.attention.backends.flashinfer import FlashInferBackend
-        return FlashInferBackend
-    elif backend == _Backend.HPU_ATTN:
-        logger.info("Using HPUAttention backend.")
-        from vllm.attention.backends.hpu_attn import HPUAttentionBackend
-        return HPUAttentionBackend
-    elif backend == _Backend.PALLAS:
-        logger.info("Using Pallas backend.")
-        from vllm.attention.backends.pallas import PallasAttentionBackend
-        return PallasAttentionBackend
-    elif backend == _Backend.NO_ATTENTION:
-        from vllm.attention.backends.placeholder_attn import (
-            PlaceholderAttentionBackend)
-        return PlaceholderAttentionBackend
-    else:
-        raise ValueError("Invalid attention backend.")
+    attention_cls = which_attn_to_use(head_size, dtype, kv_cache_dtype,
+                                      block_size, is_attention_free, use_v1)
+    assert attention_cls != "", (
+        f"Invalid attention backend for {current_platform.device_name}")
+
+    return resolve_obj_by_qualname(attention_cls)
 
 
 def which_attn_to_use(head_size: int,
                       dtype: torch.dtype,
                       kv_cache_dtype: Optional[str],
                       block_size: int,
                       is_attention_free: bool,
-                      use_v1: bool = False) -> _Backend:
+                      use_v1: bool = False) -> str:
     """Returns which flash attention backend to use."""
-    # Default case.
-    selected_backend = _Backend.FLASH_ATTN
-
     # If there are no attention layers (e.g. we are running Mamba),
     # use the placeholder NO_ATTENTION
     if is_attention_free:
-        return _Backend.NO_ATTENTION
+        return "vllm.attention.backends.placeholder_attn.PlaceholderAttentionBackend"  # noqa: E501
 
     # Check whether a particular choice of backend was
     # previously forced.
     #
     # THIS SELECTION OVERRIDES THE VLLM_ATTENTION_BACKEND
     # ENVIRONMENT VARIABLE.
+    selected_backend = None
     backend_by_global_setting: Optional[_Backend] = (
         get_global_forced_attn_backend())
     if backend_by_global_setting is not None:
@@ -201,64 +150,10 @@ def which_attn_to_use(head_size: int,
         if backend_by_env_var is not None:
             selected_backend = backend_name_to_enum(backend_by_env_var)
 
-    # get device-specific default attn_backend
-    default_backend = current_platform.get_default_attn_backend(
-        selected_backend)
-    if default_backend is not None:
-        return default_backend
-
-    if use_v1:
-        return _Backend.FLASH_ATTN_VLLM_V1
-
-    # FlashAttn in NVIDIA GPUs.
-    if selected_backend == _Backend.FLASH_ATTN:
-        if not current_platform.has_device_capability(80):
-            # Volta and Turing NVIDIA GPUs.
-            logger.info(
-                "Cannot use FlashAttention-2 backend for Volta and Turing "
-                "GPUs.")
-            selected_backend = _Backend.XFORMERS
-        elif dtype not in (torch.float16, torch.bfloat16):
-            logger.info(
-                "Cannot use FlashAttention-2 backend for dtype other than "
-                "torch.float16 or torch.bfloat16.")
-            selected_backend = _Backend.XFORMERS
-        elif kv_cache_dtype is not None and kv_cache_dtype.startswith("fp8"):
-            logger.info(
-                "Cannot use FlashAttention-2 backend for FP8 KV cache.")
-            logger.warning(
-                "Please use FlashInfer backend with FP8 KV Cache for "
-                "better performance by setting environment variable  "
-                "VLLM_ATTENTION_BACKEND=FLASHINFER")
-            selected_backend = _Backend.XFORMERS
-        elif block_size % 16 != 0:
-            logger.info(
-                "Cannot use FlashAttention-2 backend for block size not "
-                "divisible by 16.")
-            selected_backend = _Backend.XFORMERS
-
-    # FlashAttn is valid for the model, checking if the package is installed.
-    if selected_backend == _Backend.FLASH_ATTN:
-        try:
-            import vllm.vllm_flash_attn  # noqa: F401
-            from vllm.attention.backends.flash_attn import (  # noqa: F401
-                FlashAttentionBackend)
-
-            supported_sizes = FlashAttentionBackend.get_supported_head_sizes()
-            if head_size not in supported_sizes:
-                logger.info(
-                    "Cannot use FlashAttention-2 backend for head size %d.",
-                    head_size)
-                selected_backend = _Backend.XFORMERS
-        except ImportError:
-            logger.info(
-                "Cannot use FlashAttention-2 backend because the "
-                "vllm.vllm_flash_attn package is not found. "
-                "Make sure that vllm_flash_attn was built and installed "
-                "(on by default).")
-            selected_backend = _Backend.XFORMERS
-
-    return selected_backend
+    # get device-specific attn_backend
+    return current_platform.get_attn_backend_cls(selected_backend, head_size,
+                                                 dtype, kv_cache_dtype,
+                                                 block_size, use_v1)
 
 
 @contextmanager

vllm/platforms/cpu.py

@@ -28,10 +28,13 @@ def get_device_name(cls, device_id: int = 0) -> str:
         return "cpu"
 
     @classmethod
-    def get_default_attn_backend(cls, selected_backend: _Backend) -> _Backend:
+    def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
+                             dtype: torch.dtype, kv_cache_dtype: Optional[str],
+                             block_size: int, use_v1: bool) -> str:
         if selected_backend != _Backend.TORCH_SDPA:
             logger.info("Cannot use %s backend on CPU.", selected_backend)
-        return _Backend.TORCH_SDPA
+        logger.info("Using Torch SDPA backend.")
+        return "vllm.attention.backends.torch_sdpa.TorchSDPABackend"
 
     @classmethod
     def get_device_total_memory(cls, device_id: int = 0) -> int:

vllm/platforms/cuda.py

@@ -16,7 +16,7 @@
 import vllm.envs as envs
 from vllm.logger import init_logger
 
-from .interface import DeviceCapability, Platform, PlatformEnum
+from .interface import DeviceCapability, Platform, PlatformEnum, _Backend
 
 if TYPE_CHECKING:
     from vllm.config import VllmConfig
@@ -141,6 +141,82 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
         if cache_config and cache_config.block_size is None:
             cache_config.block_size = 16
 
+    @classmethod
+    def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
+                             kv_cache_dtype, block_size, use_v1) -> str:
+        if use_v1:
+            logger.info("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"
+        elif selected_backend == _Backend.XFORMERS:
+            logger.info("Using XFormers backend.")
+            return "vllm.attention.backends.xformers.XFormersBackend"
+        elif selected_backend == _Backend.FLASH_ATTN:
+            logger.info("Using FlashAttention backend.")
+            return "vllm.attention.backends.flash_attn.FlashAttentionBackend"
+        elif selected_backend:
+            raise ValueError(
+                f"Invalid attention backend for {cls.device_name}")
+
+        target_backend = _Backend.FLASH_ATTN
+        if not cls.has_device_capability(80):
+            # Volta and Turing NVIDIA GPUs.
+            logger.info(
+                "Cannot use FlashAttention-2 backend for Volta and Turing "
+                "GPUs.")
+            target_backend = _Backend.XFORMERS
+        elif dtype not in (torch.float16, torch.bfloat16):
+            logger.info(
+                "Cannot use FlashAttention-2 backend for dtype other than "
+                "torch.float16 or torch.bfloat16.")
+            target_backend = _Backend.XFORMERS
+        elif kv_cache_dtype is not None and \
+            kv_cache_dtype.startswith("fp8"):
+            logger.info(
+                "Cannot use FlashAttention-2 backend for FP8 KV cache.")
+            logger.warning(
+                "Please use FlashInfer backend with FP8 KV Cache for "
+                "better performance by setting environment variable  "
+                "VLLM_ATTENTION_BACKEND=FLASHINFER")
+            target_backend = _Backend.XFORMERS
+        elif block_size % 16 != 0:
+            logger.info(
+                "Cannot use FlashAttention-2 backend for block size not "
+                "divisible by 16.")
+            target_backend = _Backend.XFORMERS
+
+        # FlashAttn is valid for the model, checking if the package is
+        # installed.
+        if target_backend == _Backend.FLASH_ATTN:
+            try:
+                import vllm.vllm_flash_attn  # noqa: F401
+                from vllm.attention.backends.flash_attn import (  # noqa: F401
+                    FlashAttentionBackend)
+
+                supported_sizes = \
+                    FlashAttentionBackend.get_supported_head_sizes()
+                if head_size not in supported_sizes:
+                    logger.info(
+                        "Cannot use FlashAttention-2 backend for head size %d.",
+                        head_size)
+                    target_backend = _Backend.XFORMERS
+            except ImportError:
+                logger.info(
+                    "Cannot use FlashAttention-2 backend because the "
+                    "vllm.vllm_flash_attn package is not found. "
+                    "Make sure that vllm_flash_attn was built and installed "
+                    "(on by default).")
+                target_backend = _Backend.XFORMERS
+
+        if target_backend == _Backend.XFORMERS:
+            logger.info("Using XFormers backend.")
+            return "vllm.attention.backends.xformers.XFormersBackend"
+
+        logger.info("Using Flash Attention backend.")
+        return "vllm.attention.backends.flash_attn.FlashAttentionBackend"
+
 
 # NVML utils
 # Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`,

vllm/platforms/hpu.py

@@ -21,8 +21,11 @@ class HpuPlatform(Platform):
     dispatch_key: str = "HPU"
 
     @classmethod
-    def get_default_attn_backend(cls, selected_backend: _Backend) -> _Backend:
-        return _Backend.HPU_ATTN
+    def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
+                             dtype: torch.dtype, kv_cache_dtype: Optional[str],
+                             block_size: int, use_v1: bool) -> str:
+        logger.info("Using HPUAttention backend.")
+        return "vllm.attention.backends.hpu_attn.HPUAttentionBackend"
 
     @classmethod
     def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:

vllm/platforms/interface.py

@@ -115,9 +115,11 @@ def is_cuda_alike(self) -> bool:
         return self._enum in (PlatformEnum.CUDA, PlatformEnum.ROCM)
 
     @classmethod
-    def get_default_attn_backend(cls, selected_backend: _Backend):
-        """Get the default attention backend of a device."""
-        return None
+    def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
+                             dtype: torch.dtype, kv_cache_dtype: Optional[str],
+                             block_size: int, use_v1: bool) -> str:
+        """Get the attention backend class of a device."""
+        return ""
 
     @classmethod
     def get_device_capability(