CUDA graph executable dedup via cudaGraphExecUpdate (#29625)

Co-authored-by: cctry <cctry@fb.com>

python/sglang/srt/environ.py

@@ -740,8 +740,10 @@ class Envs:
     # Unified Radix Tree
     SGLANG_ENABLE_UNIFIED_RADIX_TREE = EnvBool(False)
 
-    # Breakable CUDA Graph
+    # CUDA Graph
     SGLANG_USE_BREAKABLE_CUDA_GRAPH = EnvBool(False)
+    # Guards CUDA graph executable dedup via cudaGraphExecUpdate.
+    SGLANG_ENABLE_CUDA_GRAPH_DEDUP = EnvBool(False)
 
     # Release & Resume Memory
     SGLANG_MEMORY_SAVER_CUDA_GRAPH = EnvBool(False)

python/sglang/srt/model_executor/runner_backend/breakable_cuda_graph_backend.py

@@ -30,6 +30,9 @@ from sglang.srt.model_executor.forward_batch_info import PPProxyTensors
 from sglang.srt.model_executor.runner_backend.base_cuda_graph_backend import (
     BaseCudaGraphBackend,
 )
+from sglang.srt.model_executor.runner_backend.cuda_graph_dedup_mixin import (
+    DedupedCudaGraphMixin,
+)
 from sglang.srt.model_executor.runner_backend_utils.breakable_cuda_graph import (
     BreakableCUDAGraph,
     BreakableCUDAGraphCapture,
@@ -50,7 +53,7 @@ if TYPE_CHECKING:
     from sglang.srt.model_executor.runner.shape_key import ShapeKey
 
 
-class BreakableCudaGraphBackend(BaseCudaGraphBackend):
+class BreakableCudaGraphBackend(DedupedCudaGraphMixin, BaseCudaGraphBackend):
     """Segmented capture: graphs break at attention / mamba boundaries;
     attention metadata is recomputed at replay outside captured segments.
     """
@@ -62,6 +65,7 @@ class BreakableCudaGraphBackend(BaseCudaGraphBackend):
         enable_memory_saver: bool = False,
         debug_eager: bool = False,
     ) -> None:
+        self._model_runner = cuda_graph_runner.model_runner
         self._graphs: Dict[Any, BreakableCUDAGraph] = {}
         self._outputs: Dict[Any, Any] = {}
         self._pool = None
@@ -89,11 +93,15 @@ class BreakableCudaGraphBackend(BaseCudaGraphBackend):
         set_graph_pool_id(self._pool)
         self._capture_stream = stream
         self._shared_output_buffer = None
+        self.begin_cuda_graph_capture()
         try:
             with self.replay_session():
                 yield
         finally:
-            self._capture_stream = None
+            try:
+                self.end_cuda_graph_capture()
+            finally:
+                self._capture_stream = None
 
     def capture_one(
         self,
@@ -109,7 +117,7 @@ class BreakableCudaGraphBackend(BaseCudaGraphBackend):
             if post_warmup_hook is not None:
                 post_warmup_hook()
 
-        graph = BreakableCUDAGraph()
+        graph = BreakableCUDAGraph(self.deduped_cuda_graph)
         captured_fn = (
             eager_on_graph(True)(forward_fn) if self._debug_eager else forward_fn
         )
@@ -205,6 +213,7 @@ class BreakableCudaGraphBackend(BaseCudaGraphBackend):
         return self._outputs[shape_key]
 
     def cleanup(self) -> None:
+        self.close()
         self._graphs.clear()
         self._outputs.clear()
         self._pool = None

python/sglang/srt/model_executor/runner_backend/cuda_graph_dedup_mixin.py

@@ -0,0 +1,375 @@
+"""Shared CUDA graph executable-dedup plumbing for CUDA graph backends."""
+
+from __future__ import annotations
+
+import heapq
+import logging
+from dataclasses import dataclass, field
+
+import torch
+
+try:
+    from cuda.bindings import driver as cuda_drv
+    from cuda.bindings import runtime as cuda_rt
+except ImportError:
+    cuda_drv = None
+    cuda_rt = None
+
+from sglang.srt.environ import envs
+from sglang.srt.model_executor.runner_backend_utils.breakable_cuda_graph.cuda_utils import (
+    checkCudaErrors,
+)
+from sglang.srt.utils import get_bool_env_var
+
+logger = logging.getLogger(__name__)
+
+
+def dedup_update(graph_exec: int, raw_graph: int) -> tuple[bool, str]:
+    assert cuda_rt is not None
+    err, info = cuda_rt.cudaGraphExecUpdate(graph_exec, raw_graph)
+    if info is None:
+        return False, f"err={int(err)}"
+    result = info.result
+    ok = (
+        err == cuda_rt.cudaError_t.cudaSuccess
+        and result == cuda_rt.cudaGraphExecUpdateResult.cudaGraphExecUpdateSuccess
+    )
+    return ok, "" if ok else f"err={int(err)} result={result}"
+
+
+def maybe_cuda_result(result):
+    return None if int(result[0]) != 0 else checkCudaErrors(result)
+
+
+def kernel_name(params) -> str:
+    assert cuda_drv is not None
+    for handle, getter in (
+        (getattr(params, "kern", None), cuda_drv.cuKernelGetName),
+        (getattr(params, "func", None), cuda_drv.cuFuncGetName),
+    ):
+        if handle is None or int(handle) == 0:
+            continue
+        name = maybe_cuda_result(getter(handle))
+        if name is not None:
+            return name.decode("utf-8", "replace")
+    return f"func:{int(getattr(params, 'func', 0))}"
+
+
+def kernel_attrs(node) -> tuple[tuple[str, object], ...]:
+    assert cuda_drv is not None
+    attrs = []
+    for name, attr_name, get_value in (
+        (
+            "cooperative",
+            "CU_LAUNCH_ATTRIBUTE_COOPERATIVE",
+            lambda v: int(v.cooperative),
+        ),
+        (
+            "clusterDim",
+            "CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION",
+            lambda v: (
+                int(v.clusterDim.x),
+                int(v.clusterDim.y),
+                int(v.clusterDim.z),
+            ),
+        ),
+        (
+            "clusterSchedulingPolicyPreference",
+            "CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE",
+            lambda v: int(v.clusterSchedulingPolicyPreference),
+        ),
+        (
+            "preferredClusterDim",
+            "CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION",
+            lambda v: (
+                int(v.preferredClusterDim.x),
+                int(v.preferredClusterDim.y),
+                int(v.preferredClusterDim.z),
+            ),
+        ),
+        (
+            "sharedMemCarveout",
+            "CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT",
+            lambda v: int(v.sharedMemCarveout),
+        ),
+    ):
+        attr = getattr(cuda_drv.CUkernelNodeAttrID, attr_name, None)
+        if attr is None:
+            continue
+        value = maybe_cuda_result(cuda_drv.cuGraphKernelNodeGetAttribute(node, attr))
+        if value is not None:
+            attrs.append((name, get_value(value)))
+    return tuple(attrs)
+
+
+def kernel_node_payload(node):
+    assert cuda_drv is not None
+    params = checkCudaErrors(cuda_drv.cuGraphKernelNodeGetParams(node))
+    return (
+        kernel_name(params),
+        (int(params.gridDimX), int(params.gridDimY), int(params.gridDimZ)),
+        (int(params.blockDimX), int(params.blockDimY), int(params.blockDimZ)),
+        int(params.sharedMemBytes),
+        kernel_attrs(node),
+    )
+
+
+def graph_node_payload(node):
+    assert cuda_drv is not None
+    node_type = checkCudaErrors(cuda_drv.cuGraphNodeGetType(node))
+    match node_type:
+        case cuda_drv.CUgraphNodeType.CU_GRAPH_NODE_TYPE_KERNEL:
+            payload = kernel_node_payload(node)
+        case cuda_drv.CUgraphNodeType.CU_GRAPH_NODE_TYPE_MEMCPY:
+            params = checkCudaErrors(cuda_drv.cuGraphMemcpyNodeGetParams(node))
+            payload = (int(params.srcMemoryType), int(params.dstMemoryType))
+        case cuda_drv.CUgraphNodeType.CU_GRAPH_NODE_TYPE_MEMSET:
+            params = checkCudaErrors(cuda_drv.cuGraphMemsetNodeGetParams(node))
+            payload = (int(params.elementSize),)
+        case cuda_drv.CUgraphNodeType.CU_GRAPH_NODE_TYPE_GRAPH:
+            child_graph = checkCudaErrors(cuda_drv.cuGraphChildGraphNodeGetGraph(node))
+            payload = graph_signature(child_graph)
+        case cuda_drv.CUgraphNodeType.CU_GRAPH_NODE_TYPE_EMPTY:
+            payload = ()
+        case _:
+            payload = ()
+    return (node_type.name, payload)
+
+
+def graph_signature(raw_graph: int):
+    assert cuda_drv is not None
+    _, num_nodes = checkCudaErrors(cuda_drv.cuGraphGetNodes(raw_graph, 0))
+    nodes, _ = checkCudaErrors(cuda_drv.cuGraphGetNodes(raw_graph, num_nodes))
+    node_indices = {int(node): i for i, node in enumerate(nodes)}
+
+    _, _, _, num_edges = checkCudaErrors(cuda_drv.cuGraphGetEdges(raw_graph, 0))
+    from_nodes, to_nodes, _, _ = checkCudaErrors(
+        cuda_drv.cuGraphGetEdges(raw_graph, num_edges)
+    )
+    edges = [
+        (node_indices[int(src)], node_indices[int(dst)])
+        for src, dst in zip(from_nodes, to_nodes)
+    ]
+
+    children = [[] for _ in nodes]
+    indegree = [0] * len(nodes)
+    for src, dst in edges:
+        children[src].append(dst)
+        indegree[dst] += 1
+
+    ready = [i for i, degree in enumerate(indegree) if degree == 0]
+    heapq.heapify(ready)
+    order = []
+    while ready:
+        node_idx = heapq.heappop(ready)
+        order.append(node_idx)
+        for child_idx in sorted(children[node_idx]):
+            indegree[child_idx] -= 1
+            if indegree[child_idx] == 0:
+                heapq.heappush(ready, child_idx)
+    assert len(order) == len(nodes), "CUDA graph contains a dependency cycle"
+
+    topo_indices = {node_idx: i for i, node_idx in enumerate(order)}
+    topo_edges = tuple(
+        sorted((topo_indices[src], topo_indices[dst]) for src, dst in edges)
+    )
+    return (
+        tuple(graph_node_payload(nodes[node_idx]) for node_idx in order),
+        topo_edges,
+    )
+
+
+@dataclass(slots=True)
+class GraphExecGroup:
+    graph_exec: int
+    current_raw_graph: int
+    compat_exec: int | None
+    graphs: list[DedupedCudaGraph] = field(default_factory=list)
+
+
+@dataclass(eq=False, slots=True)
+class DedupedCudaGraph:
+    raw_graph: int
+    original_graph: object | None
+    registry: DedupedCudaGraphRegistry
+    group: GraphExecGroup | None = None
+
+    def replay(self, stream: int | None = None) -> None:
+        if stream is None:
+            stream = torch.cuda.current_stream().cuda_stream
+        self.registry.replay(self, stream)
+
+
+class DedupedCudaGraphRegistry:
+    def __init__(self):
+        self.groups: dict[tuple, GraphExecGroup] = {}
+        self.sealed = False
+
+    def instantiate(self, raw_graph: int) -> int:
+        assert cuda_rt is not None
+        graph_exec = checkCudaErrors(
+            cuda_rt.cudaGraphInstantiateWithFlags(raw_graph, 0)
+        )
+        return graph_exec
+
+    def destroy_exec(self, graph_exec: int) -> None:
+        assert cuda_rt is not None
+        checkCudaErrors(cuda_rt.cudaGraphExecDestroy(graph_exec))
+
+    def register(self, captured_graph) -> DedupedCudaGraph:
+        assert not self.sealed
+        raw_graph = captured_graph.raw_cuda_graph()
+        signature = graph_signature(raw_graph)
+        graph = DedupedCudaGraph(raw_graph, captured_graph, self)
+
+        group = self.groups.get(signature)
+        if group is not None:
+            assert group.compat_exec is not None
+            ok, detail = dedup_update(group.compat_exec, graph.raw_graph)
+            assert ok, f"CUDA graph dedup register update failed ({detail})"
+            graph.group = group
+            group.graphs.append(graph)
+            return graph
+
+        group = GraphExecGroup(
+            graph_exec=self.instantiate(graph.raw_graph),
+            current_raw_graph=graph.raw_graph,
+            compat_exec=self.instantiate(graph.raw_graph),
+            graphs=[graph],
+        )
+        graph.group = group
+        self.groups[signature] = group
+        return graph
+
+    def seal(self) -> None:
+        if self.sealed:
+            return
+        self.sealed = True
+        for group in self.groups.values():
+            if group.compat_exec is not None:
+                self.destroy_exec(group.compat_exec)
+                group.compat_exec = None
+
+    def stats(self) -> tuple[int, int]:
+        return sum(len(group.graphs) for group in self.groups.values()), len(
+            self.groups
+        )
+
+    def replay(self, graph: DedupedCudaGraph, stream: int) -> None:
+        assert cuda_rt is not None
+        group = graph.group
+        assert (
+            group is not None
+        ), "captured CUDA graph does not belong to this dedup state"
+
+        raw_graph = graph.raw_graph
+        graph_exec = group.graph_exec
+        if group.current_raw_graph != raw_graph:
+            ok, detail = dedup_update(graph_exec, raw_graph)
+            assert ok, (
+                "CUDA graph dedup replay update failed "
+                f"({detail}); captured graph is not compatible with its dedup group"
+            )
+            group.current_raw_graph = raw_graph
+
+        checkCudaErrors(cuda_rt.cudaGraphLaunch(graph_exec, stream))
+
+    def close(self) -> None:
+        self.sealed = True
+
+        for group in self.groups.values():
+            if group.compat_exec is not None:
+                self.destroy_exec(group.compat_exec)
+                group.compat_exec = None
+            self.destroy_exec(group.graph_exec)
+            for graph in group.graphs:
+                if graph.original_graph is not None:
+                    graph.original_graph.reset()
+                graph.original_graph = None
+                graph.group = None
+            group.graphs.clear()
+
+        self.groups.clear()
+
+
+class DedupedCudaGraphMixin:
+    deduped_cuda_graph: DedupedCudaGraphRegistry | None = None
+
+    def _dedup_registries(self) -> list[DedupedCudaGraphRegistry]:
+        registries = getattr(self, "_deduped_cuda_graph_registries", None)
+        if registries is None:
+            registries = []
+            self._deduped_cuda_graph_registries = registries
+        return registries
+
+    def _memory_saver_cuda_graph_enabled(self) -> bool:
+        adapter = getattr(self, "_memory_saver_adapter", None)
+        if adapter is not None and getattr(adapter, "enabled", False):
+            return True
+
+        model_runner = getattr(self, "model_runner", None)
+        if model_runner is None:
+            model_runner = getattr(self, "_model_runner", None)
+        server_args = getattr(model_runner, "server_args", None)
+        return bool(
+            server_args is not None
+            and getattr(server_args, "enable_memory_saver", False)
+            and get_bool_env_var("SGLANG_MEMORY_SAVER_CUDA_GRAPH")
+        )
+
+    def build_deduped_cuda_graph(self):
+        if not envs.SGLANG_ENABLE_CUDA_GRAPH_DEDUP.get():
+            return None
+        if cuda_drv is None or cuda_rt is None:
+            return None
+        try:
+            graph = torch.cuda.CUDAGraph(keep_graph=True)
+            if not hasattr(graph, "raw_cuda_graph"):
+                return None
+            return DedupedCudaGraphRegistry()
+        except TypeError:
+            return None
+        except Exception as e:
+            logger.warning(
+                "[CudaGraph][dedup] %s init failed (%s); using plain executables.",
+                type(self).__name__,
+                e,
+            )
+            return None
+
+    def begin_cuda_graph_capture(self) -> None:
+        if self.deduped_cuda_graph is not None:
+            self.end_cuda_graph_capture()
+
+        if self._memory_saver_cuda_graph_enabled():
+            self.deduped_cuda_graph = None
+            return
+
+        self.deduped_cuda_graph = self.build_deduped_cuda_graph()
+        if self.deduped_cuda_graph is not None:
+            self._dedup_registries().append(self.deduped_cuda_graph)
+
+    def end_cuda_graph_capture(self) -> None:
+        dedup = self.deduped_cuda_graph
+        self.deduped_cuda_graph = None
+        if dedup is not None:
+            captured, execs = dedup.stats()
+            dedup.seal()
+            logger.info("captured %d CUDA graphs, deduped to %d execs", captured, execs)
+
+    def close(self) -> None:
+        registries = self._dedup_registries()
+        seen: set[int] = set()
+        for registry in [self.deduped_cuda_graph, *registries]:
+            if registry is None or id(registry) in seen:
+                continue
+            seen.add(id(registry))
+            registry.close()
+        registries.clear()
+        self.deduped_cuda_graph = None
+
+    def __del__(self):
+        try:
+            self.close()
+        except Exception:
+            pass

python/sglang/srt/model_executor/runner_backend_utils/breakable_cuda_graph/breakable_cuda_graph.py

@@ -247,9 +247,10 @@ class BreakableCUDAGraph:
     """Container holding one torch.cuda.CUDAGraph per segment plus an
     eager break function between consecutive segments."""
 
-    def __init__(self) -> None:
-        self._segments: list[torch.cuda.CUDAGraph] = []
+    def __init__(self, deduped_cuda_graph=None) -> None:
+        self._segments: list[Any] = []
         self._break_fns: list[Callable[[], Any]] = []
+        self._deduped_cuda_graph = deduped_cuda_graph
 
     def replay(self) -> None:
         stream = torch.cuda.current_stream()
@@ -262,6 +263,16 @@ class BreakableCUDAGraph:
         finally:
             _current_stream_var.reset(token)
 
+    def _append_segment(
+        self, graph: torch.cuda.CUDAGraph, needs_instantiate: bool
+    ) -> None:
+        if self._deduped_cuda_graph is not None:
+            self._segments.append(self._deduped_cuda_graph.register(graph))
+            return
+        if needs_instantiate:
+            graph.instantiate()
+        self._segments.append(graph)
+
 
 class BreakableCUDAGraphCapture:
     """Context manager that captures the enclosed code as one or more
@@ -292,6 +303,8 @@ class BreakableCUDAGraphCapture:
         self._capture_token = None
         self._stream_token = None
         self._forked_token = None
+        self._current_graph: torch.cuda.CUDAGraph | None = None
+        self._current_graph_needs_instantiate = False
 
     def __enter__(self):
         _install_wait_stream_hook()
@@ -320,11 +333,21 @@ class BreakableCUDAGraphCapture:
         return False
 
     def _begin_new_segment(self) -> None:
-        graph = torch.cuda.CUDAGraph()
+        # keep_graph retains the raw graph for dedup; skip it on the plain path.
+        if self.cuda_graph._deduped_cuda_graph is not None:
+            try:
+                graph = torch.cuda.CUDAGraph(keep_graph=True)
+                self._current_graph_needs_instantiate = True
+            except TypeError:
+                graph = torch.cuda.CUDAGraph()
+                self._current_graph_needs_instantiate = False
+        else:
+            graph = torch.cuda.CUDAGraph()
+            self._current_graph_needs_instantiate = False
         graph.capture_begin(
             pool=self._pool, capture_error_mode=self._capture_error_mode
         )
-        self.cuda_graph._segments.append(graph)
+        self._current_graph = graph
 
     def _end_current_segment(self) -> None:
         # Auto-join any side streams forked during this segment but not joined.
@@ -336,7 +359,12 @@ class BreakableCUDAGraphCapture:
                 if _is_stream_capturing(side):
                     _original_wait_stream(main_stream, side)
             forked.clear()
-        self.cuda_graph._segments[-1].capture_end()
+        graph = self._current_graph
+        assert graph is not None
+        graph.capture_end()
+        self.cuda_graph._append_segment(graph, self._current_graph_needs_instantiate)
+        self._current_graph = None
+        self._current_graph_needs_instantiate = False
 
 
 @eager_on_graph(True)