[PluggableLayer][1/N] Define PluggableLayer (Fix ci) (#32744)

Signed-off-by: whx-sjtu <2952154980@qq.com>

benchmarks/kernels/benchmark_activation.py

@@ -7,7 +7,7 @@ import itertools
 import torch
 
 import vllm.model_executor.layers.activation  # noqa F401
-from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.custom_op import op_registry
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
@@ -33,14 +33,14 @@ def benchmark_activation(
     torch.set_default_device(device)
 
     if func_name == "gelu_and_mul":
-        layer = CustomOp.op_registry[func_name](approximate="none")
+        layer = op_registry[func_name](approximate="none")
     elif func_name == "gelu_and_mul_tanh":
-        layer = CustomOp.op_registry["gelu_and_mul"](approximate="tanh")
+        layer = op_registry["gelu_and_mul"](approximate="tanh")
     elif func_name == "fatrelu_and_mul":
         threshold = 0.5
-        layer = CustomOp.op_registry[func_name](threshold)
+        layer = op_registry[func_name](threshold)
     else:
-        layer = CustomOp.op_registry[func_name]()
+        layer = op_registry[func_name]()
 
     x = torch.randn(num_tokens, dim, dtype=dtype, device=device)
     compiled_layer = torch.compile(layer.forward_native)

docs/design/custom_op.md

@@ -8,15 +8,6 @@ This document will introduce how CustomOp works in vLLM and how to implement a n
 
 `CustomOp` manages two dictionaries of all custom ops (i.e., op classes, indexed by registered name) in its class, for vLLM and OOT plugins respectively.
 
-??? code
-
-    ```python
-    class CustomOp(nn.Module):
-
-        op_registry: dict[str, type["CustomOp"]] = {}
-        op_registry_oot: dict[str, type["CustomOp"]] = {}
-    ```
-
 We can use `@CustomOp.register("op_name")` to register an op class to the `CustomOp` system. After this, the `op_name` and its class will be added into the `op_registry` dictionary. In addition, We can also register an OOT op by `@CustomOp.register_oot("op_name")`. We will introduce this mechanism in detail later.
 
 When a `CustomOp` is called (i.e., call its `forward()` method), if it is enabled (i.e., with `--compilation_config.custom_ops '["+op_name"]'`), it will automatically dispatch the forward method to the appropriate backend according to `current_platform`. Otherwise (i.e., it is disabled), it will only call the `forward_native()` method to use PyTorch-native implementation of this forward method.

tests/kernels/utils.py

@@ -13,7 +13,7 @@ import torch
 from torch._prims_common import TensorLikeType
 
 from tests.kernels.quant_utils import native_w8a8_block_matmul
-from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.custom_op import op_registry
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
 from vllm.utils.torch_utils import make_tensor_with_pad
@@ -883,7 +883,7 @@ def torch_experts(
 
     f32 = torch.float32
 
-    act = CustomOp.op_registry[activation]
+    act = op_registry[activation]
 
     for i in range(num_experts):
         mask = topk_ids == i

tests/model_executor/test_enabled_custom_ops.py

@@ -11,7 +11,7 @@ from vllm.config import (
     get_cached_compilation_config,
     set_current_vllm_config,
 )
-from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.custom_op import CustomOp, op_registry
 from vllm.model_executor.layers.activation import (
     GeluAndMul,
     ReLUSquaredActivation,
@@ -98,17 +98,17 @@ def test_enabled_ops(
         ops_enabled = [bool(x) for x in ops_enabled]
 
         assert RMSNorm(1024).enabled() == ops_enabled[0]
-        assert CustomOp.op_registry["rms_norm"].enabled() == ops_enabled[0]
+        assert op_registry["rms_norm"].enabled() == ops_enabled[0]
 
         assert SiluAndMul().enabled() == ops_enabled[1]
-        assert CustomOp.op_registry["silu_and_mul"].enabled() == ops_enabled[1]
+        assert op_registry["silu_and_mul"].enabled() == ops_enabled[1]
 
         assert GeluAndMul().enabled() == ops_enabled[2]
-        assert CustomOp.op_registry["gelu_and_mul"].enabled() == ops_enabled[2]
+        assert op_registry["gelu_and_mul"].enabled() == ops_enabled[2]
 
         # If registered, subclasses should follow their own name
         assert Relu3().enabled() == ops_enabled[3]
-        assert CustomOp.op_registry["relu3"].enabled() == ops_enabled[3]
+        assert op_registry["relu3"].enabled() == ops_enabled[3]
 
         # Unregistered subclass
         class SiluAndMul2(SiluAndMul):

vllm/config/compilation.py

@@ -1033,13 +1033,13 @@ class CompilationConfig:
             # check if op name exists in model
             op_name = op[1:]
             if op_name not in all_ops_in_model:
-                from vllm.model_executor.custom_op import CustomOp
+                from vllm.model_executor.custom_op import op_registry
 
                 # Does op exist at all or is it just not present in this model?
                 # Note: Only imported op classes appear in the registry.
                 missing_str = (
                     "doesn't exist (or wasn't imported/registered)"
-                    if op_name not in CustomOp.op_registry
+                    if op_name not in op_registry
                     else "not present in model"
                 )
 

vllm/model_executor/custom_op.py

@@ -11,6 +11,86 @@ from vllm.platforms import current_platform
 logger = init_logger(__name__)
 
 
+# Dictionary of all custom ops (classes, indexed by registered name).
+# To check if an op with a name is enabled, call .enabled() on the class.
+# Examples:
+# - MyOp.enabled()
+# - op_registry["my_op"].enabled()
+op_registry: dict[str, type["CustomOp"] | type["PluggableLayer"]] = {}
+op_registry_oot: dict[str, type["CustomOp"] | type["PluggableLayer"]] = {}
+
+
+class PluggableLayer(nn.Module):
+    """
+    Base class for pluggable layers.
+
+    A PluggableLayer is a *module-composing* abstraction: it may instantiate other
+    ``torch.nn.Module`` objects as sub-layers, and its functionality depends on
+    these sub-layers following a generalized invocation sequence. Also, it is stateful
+    and may hold parameters or buffers.
+
+    Unlike :class:`CustomOp`, PluggableLayer does NOT provide per-platform
+    ``forward_*`` dispatch. Instead, it supports out-of-tree (OOT) replacement
+    of the entire layer class at instantiation time, allowing customized
+    initialization and submodule composition.
+    """
+
+    def __new__(cls, *args, **kwargs):
+        try:
+            layer_class_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"@PluggableLayer.register, or it's the PluggableLayer itself."
+            ) from None
+
+        if layer_class_name not in op_registry_oot:
+            layer_cls_to_instantiate = cls
+        else:
+            layer_cls_to_instantiate = op_registry_oot[layer_class_name]
+            logger.debug(
+                "Instantiating pluggable layer: %s using %s",
+                layer_class_name,
+                str(layer_cls_to_instantiate),
+            )
+        return super().__new__(layer_cls_to_instantiate)
+
+    # Decorator to register pluggable layers.
+    @classmethod
+    def register(cls, name: str):
+        def decorator(op_cls):
+            assert name not in op_registry, f"Duplicate op name: {name}"
+            op_cls.name = name
+            op_registry[name] = op_cls
+            return op_cls
+
+        return decorator
+
+    # Decorator to register out-of-tree(oot) pluggable layers.
+    # For OOT pluggable layers:
+    #   if in-tree layer class is registered with an oot_custom_layer,
+    #   the oot_custom_layer will be used instead.
+    @classmethod
+    def register_oot(cls, _decorated_layer_cls=None, name: str | None = None):
+        def decorator(layer_cls):
+            reg_name = name if name is not None else cls.__name__
+            assert reg_name not in op_registry_oot, f"Duplicate layer name: {reg_name}"
+            layer_cls.name = reg_name
+            op_registry_oot[reg_name] = layer_cls
+            return layer_cls
+
+        if _decorated_layer_cls is None:
+            # Called with parentheses: @PluggableLayer.register_oot()
+            # or @PluggableLayer.register_oot(name="...")
+            return decorator
+        elif isinstance(_decorated_layer_cls, type):  # Check if it's a class
+            # Called without parentheses: @PluggableLayer.register_oot
+            return decorator(_decorated_layer_cls)
+        else:
+            raise TypeError("Decorator can only be applied to classes.")
+
+
 class CustomOp(nn.Module):
     """
     Base class for custom ops.
@@ -27,10 +107,10 @@ class CustomOp(nn.Module):
                 f"@CustomOp.register, or it's the CustomOp base class itself."
             ) from None
 
-        if op_name not in cls.op_registry_oot:
+        if op_name not in op_registry_oot:
             op_cls_to_instantiate = cls
         else:
-            op_cls_to_instantiate = cls.op_registry_oot[op_name]
+            op_cls_to_instantiate = op_registry_oot[op_name]
             logger.debug(
                 "Instantiating custom op: %s using %s",
                 op_name,
@@ -150,21 +230,13 @@ class CustomOp(nn.Module):
 
         return not count_none > 0 or count_all > 0
 
-    # Dictionary of all custom ops (classes, indexed by registered name).
-    # To check if an op with a name is enabled, call .enabled() on the class.
-    # Examples:
-    # - 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
     def register(cls, name: str):
         def decorator(op_cls):
-            assert name not in cls.op_registry, f"Duplicate op name: {name}"
+            assert name not in op_registry, f"Duplicate op name: {name}"
             op_cls.name = name
-            cls.op_registry[name] = op_cls
+            op_registry[name] = op_cls
             return op_cls
 
         return decorator
@@ -182,9 +254,9 @@ class CustomOp(nn.Module):
     def register_oot(cls, _decorated_op_cls=None, name: str | None = 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}"
+            assert reg_name not in op_registry_oot, f"Duplicate op name: {reg_name}"
             op_cls.name = reg_name
-            cls.op_registry_oot[reg_name] = op_cls
+            op_registry_oot[reg_name] = op_cls
             return op_cls
 
         if _decorated_op_cls is None:

vllm/model_executor/layers/mla.py

@@ -6,7 +6,7 @@ import torch
 
 from vllm.attention.layer import MLAAttention
 from vllm.config import CacheConfig
-from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.custom_op import PluggableLayer
 from vllm.model_executor.layers.quantization import QuantizationConfig
 
 
@@ -30,13 +30,13 @@ class MLAModules:
 
 
 # --8<-- [start:multi_head_latent_attention]
-@CustomOp.register("multi_head_latent_attention")
-class MultiHeadLatentAttentionWrapper(CustomOp):
-    """MLA layer registered as CustomOp to allow OOT backends to add
+@PluggableLayer.register("multi_head_latent_attention")
+class MultiHeadLatentAttentionWrapper(PluggableLayer):
+    """Pluggable MLA layer which allows OOT backends to add
     custom implementations of the outer MLA layer (including rope & o_proj).
-    Note that currently MLA ignores the enable/disable mechanism of CustomOp
-    because there is only one in-tree implementation in forward_native.
-    TODO: implement this with a new PluggableLayer mechanism.
+    Note that currently oot platforms can still use CustomOp.register_oot to
+    replace MLA layer entirly, although we use PluggableLayer to register
+    this layer now.
 
     This class takes positions and hidden_states as input.
     The input tensors can either contain prefill tokens or decode tokens.
@@ -110,7 +110,7 @@ class MultiHeadLatentAttentionWrapper(CustomOp):
 
         self.prefix = prefix
 
-    def forward_native(
+    def forward(
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
@@ -174,6 +174,3 @@ class MultiHeadLatentAttentionWrapper(CustomOp):
         )
 
         return self.o_proj(attn_out)[0]
-
-    def forward_cuda(self, *args, **kwargs):
-        return self.forward_native(*args, **kwargs)