[custom_op][vllm-plugin] update custom_op class to use op_registry (#19164)

Signed-off-by: Chendi.Xue <chendi.xue@intel.com>

tests/plugins/vllm_add_dummy_platform/setup.py

@@ -10,5 +10,7 @@ setup(
     entry_points={
         'vllm.platform_plugins': [
             "dummy_platform_plugin = vllm_add_dummy_platform:dummy_platform_plugin"  # noqa
-        ]
+        ],
+        "vllm.general_plugins":
+        ["dummy_custom_ops = vllm_add_dummy_platform:register_ops"],
     })

tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/__init__.py

@@ -6,3 +6,7 @@ from typing import Optional
 
 def dummy_platform_plugin() -> Optional[str]:
     return "vllm_add_dummy_platform.dummy_platform.DummyPlatform"
+
+
+def register_ops():
+    import vllm_add_dummy_platform.dummy_custom_ops  # noqa

tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/dummy_attention_backend.py

@@ -1,10 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from vllm.attention.backends.flash_attn import FlashAttentionBackend
+from vllm.attention.backends.placeholder_attn import (
+    PlaceholderAttentionBackend)
 
 
-class DummyAttentionBackend(FlashAttentionBackend):
+class DummyAttentionBackend(PlaceholderAttentionBackend):
 
     @staticmethod
     def get_name() -> str:

tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/dummy_custom_ops.py

@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
+
+
+# Register CustomRotaryEmbedding to CustomOP.
+@RotaryEmbedding.register_oot
+class DummyRotaryEmbedding(RotaryEmbedding):
+    """Original rotary positional embedding."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.addition_config = True
+
+    def forward_oot(self, *args,
+                    **kwargs) -> tuple[torch.Tensor, torch.Tensor]:
+        return super().forward_oot(*args, **kwargs)

tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/dummy_platform.py

@@ -1,12 +1,29 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import TYPE_CHECKING
 
-from vllm.platforms.cuda import CudaPlatform
+from vllm.platforms.interface import Platform, PlatformEnum
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+else:
+    VllmConfig = None
+from vllm import envs
 
 
-class DummyPlatform(CudaPlatform):
+class DummyPlatform(Platform):
+    _enum = PlatformEnum.OOT
     device_name = "DummyDevice"
+    device_type: str = "privateuseone"
+    dispatch_key: str = "PrivateUse1"
+
+    @classmethod
+    def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
+        if envs.VLLM_USE_V1:
+            compilation_config = vllm_config.compilation_config
+            # Activate custom ops for v1.
+            compilation_config.custom_ops = ["all"]
 
     def get_attn_backend_cls(self, backend_name, head_size, dtype,
                              kv_cache_dtype, block_size, use_v1, use_mla):
-        return "vllm_add_dummy_platform.dummy_attention_backend.DummyAttentionBackend"  # noqa E501
+        return "vllm_add_dummy_platform.dummy_attention_backend.DummyAttentionBackend"  # noqa E501

tests/plugins_tests/test_platform_plugins.py

@@ -5,6 +5,7 @@ import pytest
 import torch
 
 from vllm.attention.selector import get_attn_backend
+from vllm.plugins import load_general_plugins
 from vllm.utils import STR_BACKEND_ENV_VAR, STR_INVALID_VAL
 
 
@@ -32,3 +33,16 @@ def test_oot_attention_backend(monkeypatch: pytest.MonkeyPatch):
         m.setenv(STR_BACKEND_ENV_VAR, STR_INVALID_VAL)
         backend = get_attn_backend(16, torch.float16, "auto", 16, False)
         assert backend.get_name() == "Dummy_Backend"
+
+
+def test_oot_custom_op(monkeypatch: pytest.MonkeyPatch):
+    # simulate workload by running an example
+    load_general_plugins()
+    from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
+    layer = RotaryEmbedding(16, 16, 16, 16, True, torch.float16)
+    assert layer.__class__.__name__ == "DummyRotaryEmbedding", (
+        f"Expected DummyRotaryEmbedding, got {layer.__class__.__name__}, "
+        "possibly because the custom op is not registered correctly.")
+    assert hasattr(layer, "addition_config"), (
+        "Expected DummyRotaryEmbedding to have an 'addition_config' attribute, "
+        "which is set by the custom op.")

vllm/model_executor/custom_op.py

@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from typing import Optional
+
 import torch.nn as nn
 
 from vllm.config import get_current_vllm_config
@@ -16,6 +18,24 @@ class CustomOp(nn.Module):
     Dispatches the forward method to the appropriate backend.
     """
 
+    def __new__(cls, *args, **kwargs):
+        try:
+            op_name = cls.__name__
+        except AttributeError:
+            raise TypeError(
+                f"Cannot instantiate '{cls.__name__}': its 'name' attribute "
+                f"was not set, possibly because it was not decorated with "
+                f"@CustomOp.register, or it's the CustomOp base class itself."
+            ) from None
+
+        if op_name not in cls.op_registry_oot:
+            op_cls_to_instantiate = cls
+        else:
+            op_cls_to_instantiate = cls.op_registry_oot[op_name]
+            logger.debug("Instantiating custom op: %s using %s", op_name,
+                         str(op_cls_to_instantiate))
+        return super().__new__(op_cls_to_instantiate)
+
     def __init__(self):
         super().__init__()
         self._forward_method = self.dispatch_forward()
@@ -138,6 +158,7 @@ class CustomOp(nn.Module):
     # - MyOp.enabled()
     # - op_registry["my_op"].enabled()
     op_registry: dict[str, type['CustomOp']] = {}
+    op_registry_oot: dict[str, type['CustomOp']] = {}
 
     # Decorator to register custom ops.
     @classmethod
@@ -150,3 +171,38 @@ class CustomOp(nn.Module):
             return op_cls
 
         return decorator
+
+    # Decorator to register out-of-tree(oot) custom ops.
+    # For OOT custom ops:
+    #   if in-tree layer class is registered with an oot_custom_op layer,
+    #   the oot_custom_op layer will be used instead.
+    # Example:
+    # - @UnquantizedFusedMoEMethod.register_oot
+    #   class HPUUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod)
+    # or
+    # - @CustomOP.register_oot(name="UnquantizedFusedMoEMethod")
+    @classmethod
+    def register_oot(cls, _decorated_op_cls=None, name: Optional[str] = None):
+
+        def decorator(op_cls):
+            reg_name = name if name is not None else cls.__name__
+            assert reg_name not in cls.op_registry_oot, \
+                f"Duplicate op name: {reg_name}"
+            op_cls.name = reg_name
+            cls.op_registry_oot[reg_name] = op_cls
+            return op_cls
+
+        if _decorated_op_cls is None:
+            # Called with parentheses: @CustomOP.register_oot()
+            # or @CustomOP.register_oot(name="...")
+            # So, _decorated_op_cls is None.
+            # We return the actual decorator function.
+            return decorator
+        elif isinstance(_decorated_op_cls, type):  # Check if it's a class
+            # Called without parentheses: @CustomOP.register_oot
+            # The first argument is the class itself.
+            # We call the 'decorator' function immediately with the class.
+            return decorator(_decorated_op_cls)
+        else:
+            # Handle other unexpected cases if necessary
+            raise TypeError("Decorator can only be applied to classes.")