refactor(runner): split BaseRunner (shared) from BaseCudaGraphRunner (#28385)

python/sglang/srt/model_executor/runner/__init__.py

@@ -6,8 +6,11 @@ delegates capture/replay mechanics to a pluggable
 BaseCudaGraphBackend chosen via cuda_graph_config.
 
 Public API:
-  - BaseCudaGraphRunner — abstract base; shared init + bucket
+  - BaseRunner — minimal abstract base shared by the cuda-graph runners
-    padding + capture-loop scaffolding.
+    and the eager runner (shared __init__ + abstract
+    can_run_graph/load_batch/execute).
+  - BaseCudaGraphRunner — abstract cuda-graph base; bucket padding +
+    capture-loop scaffolding on top of BaseRunner.
   - DecodeCudaGraphRunner — concrete decode-phase runner.
   - PrefillCudaGraphRunner — concrete prefill-phase runner.
   - Buffer dataclasses, capture-mode flags, the global memory pool,
@@ -22,6 +25,7 @@ from sglang.srt.model_executor.runner.base_cuda_graph_runner import (  # noqa: F
     freeze_gc,
     get_batch_sizes_to_capture,
 )
+from sglang.srt.model_executor.runner.base_runner import BaseRunner  # noqa: F401
 from sglang.srt.model_executor.runner.decode_cuda_graph_runner import (
     DecodeCudaGraphRunner,
 )

python/sglang/srt/model_executor/runner/base_cuda_graph_runner.py

@@ -18,18 +18,15 @@ from __future__ import annotations
 import bisect
 import gc
 import logging
-from abc import ABC, abstractmethod
+from abc import abstractmethod
 from contextlib import contextmanager
 from typing import TYPE_CHECKING, Any, List, Sequence, Tuple
 
-import torch
+from sglang.srt.model_executor.runner.base_runner import BaseRunner
-
-from sglang.srt.batch_overlap.two_batch_overlap import TboCudaGraphRunnerPlugin
 from sglang.srt.runtime_context import get_parallel
 from sglang.srt.utils import require_gathered_buffer
 
 if TYPE_CHECKING:
-    from sglang.srt.model_executor.forward_batch_info import ForwardBatch
     from sglang.srt.model_executor.input_buffers import ForwardInputBuffers
     from sglang.srt.model_executor.model_runner import ModelRunner
     from sglang.srt.model_executor.runner_backend.base_cuda_graph_backend import (
@@ -103,7 +100,7 @@ def get_batch_sizes_to_capture(
     return capture_bs, compile_bs
 
 
-class BaseCudaGraphRunner(ABC):
+class BaseCudaGraphRunner(BaseRunner):
     """Abstract base for phase-specific cuda-graph runners.
 
     A subclass (DecodeCudaGraphRunner / PrefillCudaGraphRunner) owns one
@@ -112,19 +109,18 @@ class BaseCudaGraphRunner(ABC):
     selection, static buffer population, attention metadata init,
     replay dispatch, and output slicing.
 
-    Methods:
+    Adds the capture/shape machinery on top of BaseRunner:
-      - can_run_graph(forward_batch) — should forward_batch go through cuda
-        graph replay (vs eager fallback)?
       - capture_prepare(size, ...) — build the dummy ForwardBatch and
         per-shape local state needed by capture_one_shape.
       - capture() — one-time setup; iterates over shapes and calls
         capture_one_shape for each.
       - capture_one_shape(size, ...) — drive one model forward at this
         shape into the backend's captured artifact.
-      - load_batch(forward_batch, ...) — pad to the nearest captured
+      - _pad_to_bucket(...) — round a raw shape up to the nearest captured
-        bucket, populate static input buffers, init attention metadata.
+        bucket.
-      - execute(forward_batch, ...) — dispatch one batch through cuda
+
-        graph replay.
+    Inherits from BaseRunner: __init__ and the abstract
+    can_run_graph / load_batch / execute.
 
     Notes:
       - buffers and backend are populated by the subclass before
@@ -135,22 +131,11 @@ class BaseCudaGraphRunner(ABC):
     buffers: ForwardInputBuffers
     backend: BaseCudaGraphBackend
 
-    def __init__(self, model_runner: ModelRunner) -> None:
-        self.model_runner = model_runner
-        self.device = model_runner.device
-        self.device_module = torch.get_device_module(self.device)
-        self.tp_size = model_runner.server_args.tp_size
-        self.dp_size = model_runner.server_args.dp_size
-        self.pp_size = model_runner.server_args.pp_size
-        self.attn_tp_size = get_parallel().attn_tp_size
-        self.attn_tp_rank = get_parallel().attn_tp_rank
-        self.tbo_plugin = TboCudaGraphRunnerPlugin()
-
     @staticmethod
     def _pad_to_bucket(raw_size: int, buckets: Sequence[int]) -> int:
         """Return the smallest buckets[i] >= raw_size.
 
-        Caller's can_run must reject raw_size > max(buckets) before
+        Caller's can_run_graph must reject raw_size > max(buckets) before
         reaching load_batch; this assertion makes the contract
         explicit (bisect_left returns len(buckets) when the value
         exceeds all buckets, which would otherwise IndexError below
@@ -158,14 +143,11 @@ class BaseCudaGraphRunner(ABC):
         """
         assert raw_size <= buckets[-1], (
             f"size {raw_size} exceeds max captured bucket {buckets[-1]}; "
-            f"can_run should have rejected this batch"
+            f"can_run_graph should have rejected this batch"
         )
         index = bisect.bisect_left(buckets, raw_size)
         return buckets[index]
 
-    @abstractmethod
-    def can_run_graph(self, forward_batch: ForwardBatch) -> bool: ...
-
     @abstractmethod
     def capture_prepare(self, size: int, *args, **kwargs) -> Any: ...
 
@@ -174,17 +156,3 @@ class BaseCudaGraphRunner(ABC):
 
     @abstractmethod
     def capture_one_shape(self, size: int, *args, **kwargs) -> Any: ...
-
-    @abstractmethod
-    def load_batch(
-        self,
-        forward_batch: ForwardBatch,
-        **kwargs,
-    ) -> Any: ...
-
-    @abstractmethod
-    def execute(
-        self,
-        forward_batch: ForwardBatch,
-        **kwargs,
-    ) -> Any: ...

python/sglang/srt/model_executor/runner/base_runner.py

@@ -0,0 +1,61 @@
+# Copyright 2023-2026 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Base class shared by EagerRunner and BaseCudaGraphRunner."""
+
+from __future__ import annotations
+
+import logging
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any
+
+import torch
+
+from sglang.srt.batch_overlap.two_batch_overlap import TboCudaGraphRunnerPlugin
+from sglang.srt.runtime_context import get_parallel
+
+if TYPE_CHECKING:
+    from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+    from sglang.srt.model_executor.model_runner import ModelRunner
+
+logger = logging.getLogger(__name__)
+
+
+class BaseRunner(ABC):
+    def __init__(self, model_runner: ModelRunner) -> None:
+        self.model_runner = model_runner
+        self.device = model_runner.device
+        self.device_module = torch.get_device_module(self.device)
+        self.tp_size = model_runner.server_args.tp_size
+        self.dp_size = model_runner.server_args.dp_size
+        self.pp_size = model_runner.server_args.pp_size
+        self.attn_tp_size = get_parallel().attn_tp_size
+        self.attn_tp_rank = get_parallel().attn_tp_rank
+        self.tbo_plugin = TboCudaGraphRunnerPlugin()
+
+    @abstractmethod
+    def can_run_graph(self, forward_batch: ForwardBatch) -> bool: ...
+
+    @abstractmethod
+    def load_batch(
+        self,
+        forward_batch: ForwardBatch,
+        **kwargs,
+    ) -> Any: ...
+
+    @abstractmethod
+    def execute(
+        self,
+        forward_batch: ForwardBatch,
+        **kwargs,
+    ) -> Any: ...