Piecewise CUDA Graph Support & Torch Compile Backend (#10062)

Signed-off-by: Oasis-Git <ayw.sirius19@gmail.com>

python/sglang/srt/distributed/device_communicators/custom_all_reduce.py

@@ -185,7 +185,7 @@ class CustomAllreduce:
             # is enough for 131072 such tuples. The largest model I've seen only
             # needs less than 10000 of registered tuples.
             self.rank_data = torch.empty(
-                8 * 1024 * 1024, dtype=torch.uint8, device=self.device
+                max_size, dtype=torch.uint8, device=self.device
             )
             self._ptr = ops.init_custom_ar(
                 self.meta_ptrs, self.rank_data, rank, self.full_nvlink
@@ -202,7 +202,7 @@ class CustomAllreduce:
             )
             handles, offsets = self._gather_ipc_meta(shard_data)
             self.rank_data = torch.empty(
-                8 * 1024 * 1024, dtype=torch.uint8, device=self.device
+                max_size, dtype=torch.uint8, device=self.device
             )
             self._ptr = ops.init_custom_ar(
                 self.meta, self.rank_data, handles, offsets, rank, self.full_nvlink

python/sglang/srt/distributed/parallel_state.py

@@ -239,6 +239,7 @@ class GroupCoordinator:
         use_npu_communicator: bool,
         use_message_queue_broadcaster: bool = False,
         group_name: Optional[str] = None,
+        torch_compile: Optional[bool] = None,
     ):
         # Set group info
         group_name = group_name or "anonymous"
@@ -326,10 +327,18 @@ class GroupCoordinator:
         self.qr_comm: Optional[QuickAllReduce] = None
         if use_custom_allreduce and self.world_size > 1:
             # Initialize a custom fast all-reduce implementation.
+            if torch_compile is not None and torch_compile:
+                # For piecewise CUDA graph, the requirement for custom allreduce is larger to
+                # avoid illegal cuda memory access.
+                ca_max_size = 256 * 1024 * 1024
+            else:
+                ca_max_size = 8 * 1024 * 1024
             try:
+                # print(f"ca_max_size: {ca_max_size}")
                 self.ca_comm = CustomAllreduce(
                     group=self.cpu_group,
                     device=self.device,
+                    max_size=ca_max_size,
                 )
             except Exception as e:
                 logger.warning(
@@ -1260,6 +1269,7 @@ def init_model_parallel_group(
     group_name: Optional[str] = None,
     use_mscclpp_allreduce: Optional[bool] = None,
     use_symm_mem_allreduce: Optional[bool] = None,
+    torch_compile: Optional[bool] = None,
 ) -> GroupCoordinator:
     if use_custom_allreduce is None:
         use_custom_allreduce = _ENABLE_CUSTOM_ALL_REDUCE
@@ -1280,6 +1290,7 @@ def init_model_parallel_group(
         use_npu_communicator=True,
         use_message_queue_broadcaster=use_message_queue_broadcaster,
         group_name=group_name,
+        torch_compile=torch_compile,
     )
 
 
@@ -1439,6 +1450,7 @@ def initialize_model_parallel(
     pipeline_model_parallel_size: int = 1,
     backend: Optional[str] = None,
     duplicate_tp_group: bool = False,
+    torch_compile: Optional[bool] = None,
 ) -> None:
     """
     Initialize model parallel groups.
@@ -1494,6 +1506,7 @@ def initialize_model_parallel(
             "SGLANG_USE_MESSAGE_QUEUE_BROADCASTER", "true"
         ),
         group_name="tp",
+        torch_compile=torch_compile,
     )
 
     if duplicate_tp_group:
@@ -1509,6 +1522,7 @@ def initialize_model_parallel(
                 "SGLANG_USE_MESSAGE_QUEUE_BROADCASTER", "true"
             ),
             group_name="pdmux_prefill_tp",
+            torch_compile=torch_compile,
         )
         _TP.pynccl_comm.disabled = False
         _PDMUX_PREFILL_TP_GROUP.pynccl_comm.disabled = False
@@ -1518,7 +1532,6 @@ def initialize_model_parallel(
 
     global _MOE_EP
     assert _MOE_EP is None, "expert model parallel group is already initialized"
-
     if moe_ep_size == tensor_model_parallel_size:
         _MOE_EP = _TP
     else:
@@ -1539,7 +1552,6 @@ def initialize_model_parallel(
 
     global _MOE_TP
     assert _MOE_TP is None, "expert model parallel group is already initialized"
-
     if moe_tp_size == tensor_model_parallel_size:
         _MOE_TP = _TP
     else:

python/sglang/srt/layers/layernorm.py

@@ -43,11 +43,16 @@ _is_cpu = is_cpu()
 _is_xpu = is_xpu()
 
 if _is_cuda:
-    if _is_flashinfer_available:
-        from flashinfer.norm import fused_add_rmsnorm
-    else:
-        from sgl_kernel import fused_add_rmsnorm
-    from sgl_kernel import gemma_fused_add_rmsnorm, gemma_rmsnorm, rmsnorm
+    # if _is_flashinfer_available:
+    #     from flashinfer.norm import fused_add_rmsnorm
+    # else:
+    from sgl_kernel import (
+        fused_add_rmsnorm,
+        gemma_fused_add_rmsnorm,
+        gemma_rmsnorm,
+        rmsnorm,
+    )
+
 
 if _use_aiter:
     from aiter import rmsnorm2d_fwd as rms_norm

python/sglang/srt/layers/radix_attention.py

@@ -17,12 +17,18 @@ from __future__ import annotations
 from enum import Enum
 from typing import TYPE_CHECKING, Optional
 
+import torch
 from torch import nn
 
 if TYPE_CHECKING:
     from sglang.srt.layers.quantization.base_config import QuantizationConfig
     from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 
+from sglang.srt.model_executor.compilation.piecewise_context_manager import (
+    get_forward_context,
+)
+from sglang.srt.utils import direct_register_custom_op
+
 
 class AttentionType(Enum):
     """
@@ -105,12 +111,58 @@ class RadixAttention(nn.Module):
             else:
                 k = k.view(-1, self.tp_k_head_num, self.v_head_dim)
 
-        return forward_batch.attn_backend.forward(
-            q,
-            k,
-            v,
-            self,
-            forward_batch,
-            save_kv_cache,
-            **kwargs,
-        )
+        if forward_batch.forward_mode.is_extend() and get_forward_context() is not None:
+            output = torch.zeros_like(q)
+            torch.ops.sglang.unified_attention_with_output(
+                q, k, v, output, save_kv_cache, self.layer_id
+            )
+            return output
+        else:
+            return forward_batch.attn_backend.forward(
+                q,
+                k,
+                v,
+                self,
+                forward_batch,
+                save_kv_cache,
+                **kwargs,
+            )
+
+
+def unified_attention_with_output(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    output: torch.Tensor,
+    save_kv_cache: bool,
+    layer_id: int,
+) -> None:
+    context = get_forward_context()
+    forward_batch = context.forward_batch
+    attention_layers = context.attention_layers
+    attention_layer = attention_layers[layer_id]
+    ret = forward_batch.attn_backend.forward(
+        query, key, value, attention_layer, forward_batch, save_kv_cache
+    )
+    assert output.shape == ret.shape
+    output.copy_(ret)
+    return
+
+
+def unified_attention_with_output_fake(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    output: torch.Tensor,
+    save_kv_cache: bool,
+    layer_id: int,
+) -> None:
+    return
+
+
+direct_register_custom_op(
+    op_name="unified_attention_with_output",
+    op_func=unified_attention_with_output,
+    mutates_args=["output"],
+    fake_impl=unified_attention_with_output_fake,
+)

python/sglang/srt/model_executor/compilation/backend.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/backend.py
+
+
+import ast
+import dataclasses
+import logging
+import os
+import pprint
+import time
+from collections.abc import Sequence
+from contextlib import contextmanager
+from typing import Any, Callable, Optional
+
+import torch
+import torch.fx as fx
+from torch._dispatch.python import enable_python_dispatcher
+
+from sglang.srt.model_executor.compilation.compilation_config import CompilationConfig
+from sglang.srt.model_executor.compilation.compilation_counter import (
+    compilation_counter,
+)
+from sglang.srt.model_executor.compilation.compiler_interface import InductorAdaptor
+from sglang.srt.model_executor.compilation.cuda_piecewise_backend import (
+    CUDAPiecewiseBackend,
+)
+from sglang.srt.model_executor.compilation.pass_manager import PostGradPassManager
+
+logger = logging.getLogger(__name__)
+
+
+def make_compiler():
+    return InductorAdaptor()
+
+
+class CompilerManager:
+    def __init__(
+        self,
+    ):
+        self.cache = dict()
+        self.is_cache_updated = False
+        self.compiler = make_compiler()
+
+    def compute_hash(self):
+        return self.compiler.compute_hash()
+
+    def initialize_cache(
+        self, cache_dir: str, disable_cache: bool = False, prefix: str = ""
+    ):
+        self.disable_cache = disable_cache
+        self.cache_dir = cache_dir
+        self.cache_file_path = os.path.join(cache_dir, "sglang_compile_cache.py")
+
+        if not disable_cache and os.path.exists(self.cache_file_path):
+            with open(self.cache_file_path) as f:
+                self.cache = ast.literal_eval(f.read())
+
+        self.compiler.initialize_cache(
+            cache_dir=cache_dir, disable_cache=disable_cache, prefix=prefix
+        )
+
+    def save_to_file(self):
+        if self.disable_cache or not self.is_cache_updated:
+            return
+        printer = pprint.PrettyPrinter(indent=4)
+        data = printer.pformat(self.cache)
+        with open(self.cache_file_path, "w") as f:
+            f.write(data)
+
+    def load(
+        self,
+        graph: fx.GraphModule,
+        example_inputs: list[Any],
+        graph_index: int,
+        runtime_shape: Optional[int] = None,
+    ) -> Optional[Callable]:
+        handle = self.cache[(runtime_shape, graph_index, self.compiler.name)]
+        compiled_graph = self.compiler.load(
+            handle, graph, example_inputs, graph_index, runtime_shape
+        )
+        if runtime_shape is None:
+            logger.debug(
+                "Directly load the %s-th graph for dynamic shape from %s via "
+                "handle %s",
+                graph_index,
+                self.compiler.name,
+                handle,
+            )
+        else:
+            logger.debug(
+                "Directly load the %s-th graph for shape %s from %s via " "handle %s",
+                graph_index,
+                str(runtime_shape),
+                self.compiler.name,
+                handle,
+            )
+        return compiled_graph
+
+    def compile(
+        self,
+        graph: fx.GraphModule,
+        example_inputs,
+        inductor_config: dict[str, Any],
+        graph_index: int = 0,
+        num_graphs: int = 1,
+        runtime_shape: Optional[int] = None,
+    ) -> Any:
+        if graph_index == 0:
+            # before compiling the first graph, record the start time
+            global compilation_start_time
+            compilation_start_time = time.time()
+
+        compilation_counter.num_backend_compilations += 1
+
+        compiled_graph = None
+
+        # TODO(Yuwei): support cache loading
+
+        # no compiler cached the graph, or the cache is disabled,
+        # we need to compile it
+        if isinstance(self.compiler, InductorAdaptor):
+            maybe_key = None
+        else:
+            maybe_key = f"artifact_shape_{runtime_shape}_subgraph_{graph_index}"
+        compiled_graph, handle = self.compiler.compile(
+            graph, example_inputs, inductor_config, runtime_shape, maybe_key
+        )
+
+        assert compiled_graph is not None, "Failed to compile the graph"
+
+        # store the artifact in the cache
+        if handle is not None:
+            self.cache[(runtime_shape, graph_index, self.compiler.name)] = handle
+            compilation_counter.num_cache_entries_updated += 1
+            self.is_cache_updated = True
+            if graph_index == 0:
+                # adds some info logging for the first graph
+                if runtime_shape is None:
+                    logger.info("Cache the graph for dynamic shape for later use")
+                else:
+                    logger.info(
+                        "Cache the graph of shape %s for later use", str(runtime_shape)
+                    )
+            if runtime_shape is None:
+                logger.debug(
+                    "Store the %s-th graph for dynamic shape from %s via " "handle %s",
+                    graph_index,
+                    self.compiler.name,
+                    handle,
+                )
+            else:
+                logger.debug(
+                    "Store the %s-th graph for shape %s from %s via handle %s",
+                    graph_index,
+                    str(runtime_shape),
+                    self.compiler.name,
+                    handle,
+                )
+
+        # after compiling the last graph, record the end time
+        if graph_index == num_graphs - 1:
+            now = time.time()
+            elapsed = now - compilation_start_time
+            if runtime_shape is None:
+                logger.info("Compiling a graph for dynamic shape takes %.2f s", elapsed)
+            else:
+                logger.info(
+                    "Compiling a graph for shape %s takes %.2f s",
+                    runtime_shape,
+                    elapsed,
+                )
+
+        return compiled_graph
+
+
+@dataclasses.dataclass
+class SplitItem:
+    submod_name: str
+    graph_id: int
+    is_splitting_graph: bool
+    graph: fx.GraphModule
+
+
+def split_graph(
+    graph: fx.GraphModule, ops: list[str]
+) -> tuple[fx.GraphModule, list[SplitItem]]:
+    # split graph by ops
+    subgraph_id = 0
+    node_to_subgraph_id = {}
+    split_op_graphs = []
+    for node in graph.graph.nodes:
+        if node.op in ("output", "placeholder"):
+            continue
+        if node.op == "call_function" and str(node.target) in ops:
+            subgraph_id += 1
+            node_to_subgraph_id[node] = subgraph_id
+            split_op_graphs.append(subgraph_id)
+            subgraph_id += 1
+        else:
+            node_to_subgraph_id[node] = subgraph_id
+
+    # `keep_original_order` is important!
+    # otherwise pytorch might reorder the nodes and
+    # the semantics of the graph will change when we
+    # have mutations in the graph
+    split_gm = torch.fx.passes.split_module.split_module(
+        graph, None, lambda node: node_to_subgraph_id[node], keep_original_order=True
+    )
+
+    outputs = []
+
+    names = [name for (name, module) in split_gm.named_modules()]
+
+    for name in names:
+        if "." in name or name == "":
+            # recursive child module or the root module
+            continue
+
+        module = getattr(split_gm, name)
+
+        graph_id = int(name.replace("submod_", ""))
+        outputs.append(SplitItem(name, graph_id, (graph_id in split_op_graphs), module))
+
+    # sort by intetger graph_id, rather than string name
+    outputs.sort(key=lambda x: x.graph_id)
+
+    return split_gm, outputs
+
+
+# we share the global graph pool among all the backends
+global_graph_pool = None
+
+compilation_start_time = 0.0
+
+
+class PiecewiseCompileInterpreter(torch.fx.Interpreter):
+    def __init__(
+        self,
+        module: torch.fx.GraphModule,
+        compile_submod_names: list[str],
+        inductor_config: dict[str, Any],
+        graph_pool,
+        compile_config: CompilationConfig,
+        sglang_backend: "SGLangBackend",
+    ):
+        super().__init__(module)
+        from torch._guards import detect_fake_mode
+
+        self.fake_mode = detect_fake_mode()
+        self.compile_submod_names = compile_submod_names
+        self.graph_pool = graph_pool
+        self.sglang_backend = sglang_backend
+        # When True, it annoyingly dumps the torch.fx.Graph on errors.
+        self.extra_traceback = False
+        self.inductor_config = inductor_config
+        self.compile_config = compile_config
+
+    def run(self, *args):
+        fake_args = [
+            self.fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t
+            for t in args
+        ]
+        with self.fake_mode, enable_python_dispatcher():
+            return super().run(*fake_args)
+
+    def call_module(
+        self,
+        target: torch.fx.node.Target,
+        args: tuple[torch.fx.node.Argument, ...],
+        kwargs: dict[str, Any],
+    ) -> Any:
+        assert isinstance(target, str)
+        output = super().call_module(target, args, kwargs)
+
+        if target in self.compile_submod_names:
+            index = self.compile_submod_names.index(target)
+            submod = self.fetch_attr(target)
+            sym_shape_indices = [
+                i for i, x in enumerate(args) if isinstance(x, torch.SymInt)
+            ]
+            global compilation_start_time
+            compiled_graph_for_dynamic_shape = (
+                self.sglang_backend.compiler_manager.compile(
+                    submod,
+                    args,
+                    self.inductor_config,
+                    graph_index=index,
+                    num_graphs=len(self.compile_submod_names),
+                    runtime_shape=None,
+                )
+            )
+
+            self.module.__dict__[target] = CUDAPiecewiseBackend(
+                submod,
+                self.compile_config,
+                self.inductor_config,
+                self.graph_pool,
+                index,
+                len(self.compile_submod_names),
+                sym_shape_indices,
+                compiled_graph_for_dynamic_shape,
+                self.sglang_backend,
+            )
+
+            compilation_counter.num_piecewise_capturable_graphs_seen += 1
+
+        return output
+
+
+model_tag: str = "backbone"
+
+
+@contextmanager
+def set_model_tag(tag: str):
+    """Context manager to set the model tag."""
+    global model_tag
+    assert (
+        tag != model_tag
+    ), f"Model tag {tag} is the same as the current tag {model_tag}."
+    old_tag = model_tag
+    model_tag = tag
+    try:
+        yield
+    finally:
+        model_tag = old_tag
+
+
+class SGLangBackend:
+
+    graph_pool: Any
+    _called: bool = False
+    # the graph we compiled
+    graph: fx.GraphModule
+    # the stiching graph module for all the piecewise graphs
+    split_gm: fx.GraphModule
+    piecewise_graphs: list[SplitItem]
+    returned_callable: Callable
+    # Inductor passes to run on the graph pre-defunctionalization
+    post_grad_passes: Sequence[Callable]
+    sym_tensor_indices: list[int]
+    input_buffers: list[torch.Tensor]
+    compiler_manager: CompilerManager
+
+    def __init__(
+        self,
+        config: CompilationConfig,
+        graph_pool: Any,
+    ):
+        assert graph_pool is not None
+        self.graph_pool = graph_pool
+
+        self.post_grad_pass_manager = PostGradPassManager()
+        self.sym_tensor_indices = []
+        self.input_buffers = []
+
+        self.compiler_manager = CompilerManager()
+        self.inductor_config = {
+            "enable_auto_functionalized_v2": False,
+        }
+        self.compile_config = config
+
+    def configure_post_pass(self):
+        self.post_grad_pass_manager.configure()
+        self.inductor_config["post_grad_custom_post_pass"] = self.post_grad_pass_manager
+
+    def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable:
+        base_cache_dir = os.path.expanduser(
+            os.getenv("SGLANG_CACHE_DIR", "~/.cache/sglang/")
+        )
+
+        cache_hash = self.compiler_manager.compute_hash()
+        cache_dir = os.path.join(
+            base_cache_dir,
+            "torch_compile_cache",
+            cache_hash,
+        )
+
+        os.makedirs(cache_dir, exist_ok=True)
+        rank = 0
+        dp_rank = 0
+        local_cache_dir = os.path.join(cache_dir, f"rank_{rank}_{dp_rank}", model_tag)
+        os.makedirs(local_cache_dir, exist_ok=True)
+        self.compiler_manager.initialize_cache(
+            local_cache_dir, disable_cache=False, prefix=""
+        )
+        compilation_counter.num_graphs_seen += 1
+
+        assert not self._called, "SGLangBackend can only be called once"
+
+        self.graph = graph
+        self.configure_post_pass()
+
+        self.split_gm, self.piecewise_graphs = split_graph(
+            graph, ["sglang.unified_attention_with_output"]
+        )
+
+        from torch._dynamo.utils import lazy_format_graph_code
+
+        # depyf will hook lazy_format_graph_code and dump the graph
+        # for debugging, no need to print the graph here
+        lazy_format_graph_code("before split", self.graph)
+        lazy_format_graph_code("after split", self.split_gm)
+
+        compilation_counter.num_piecewise_graphs_seen += len(self.piecewise_graphs)
+
+        submod_names_to_compile = [
+            item.submod_name
+            for item in self.piecewise_graphs
+            if not item.is_splitting_graph
+        ]
+
+        PiecewiseCompileInterpreter(
+            self.split_gm,
+            submod_names_to_compile,
+            self.inductor_config,
+            self.graph_pool,
+            self.compile_config,
+            self,
+        ).run(*example_inputs)
+
+        graph_path = os.path.join(local_cache_dir, "computation_graph.py")
+        if not os.path.exists(graph_path):
+            # code adapted from https://github.com/thuml/depyf/blob/dab831108a752d1facc00acdd6d4243891845c37/depyf/explain/patched_lazy_format_graph_code.py#L30 # noqa
+            # use `print_readable` because it can include submodules
+            src = (
+                "from __future__ import annotations\nimport torch\n"
+                + self.split_gm.print_readable(print_output=False)
+            )
+            src = src.replace("<lambda>", "GraphModule")
+            with open(graph_path, "w") as f:
+                f.write(src)
+
+            logger.debug("Computation graph saved to %s", graph_path)
+
+        self._called = True
+        return self.split_gm

python/sglang/srt/model_executor/compilation/compilation_config.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/compilation_config.py
+
+from typing import List
+
+
+# TODO(Yuwei): support better compile config support
+class CompilationConfig:
+    def __init__(self, capture_sizes: List[int]):
+        self.traced_files = set()
+        self.capture_sizes = capture_sizes
+
+    def add_traced_file(self, file_path: str):
+        self.traced_files.add(file_path)
+
+    def get_traced_files(self):
+        return self.traced_files
+
+    def get_capture_sizes(self):
+        return self.capture_sizes

python/sglang/srt/model_executor/compilation/compilation_counter.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/compilation_counter.py
+
+import copy
+import dataclasses
+from contextlib import contextmanager
+
+
+@dataclasses.dataclass
+class CompilationCounter:
+    num_models_seen: int = 0
+    num_graphs_seen: int = 0
+    # including the splitting ops
+    num_piecewise_graphs_seen: int = 0
+    # not including the splitting ops
+    num_piecewise_capturable_graphs_seen: int = 0
+    num_backend_compilations: int = 0
+    # Number of gpu_model_runner attempts to trigger CUDAGraphs capture
+    num_gpu_runner_capture_triggers: int = 0
+    # Number of CUDAGraphs captured
+    num_cudagraph_captured: int = 0
+    # InductorAdapter.compile calls
+    num_inductor_compiles: int = 0
+    # EagerAdapter.compile calls
+    num_eager_compiles: int = 0
+    # The number of time vLLM's compiler cache entry was updated
+    num_cache_entries_updated: int = 0
+    # The number of standalone_compile compiled artifacts saved
+    num_compiled_artifacts_saved: int = 0
+    # Number of times a model was loaded with CompilationLevel.DYNAMO_AS_IS
+    dynamo_as_is_count: int = 0
+
+    def clone(self) -> "CompilationCounter":
+        return copy.deepcopy(self)
+
+    @contextmanager
+    def expect(self, **kwargs):
+        old = self.clone()
+        yield
+        for k, v in kwargs.items():
+            assert getattr(self, k) - getattr(old, k) == v, (
+                f"{k} not as expected, before it is {getattr(old, k)}"
+                f", after it is {getattr(self, k)}, "
+                f"expected diff is {v}"
+            )
+
+
+compilation_counter = CompilationCounter()

python/sglang/srt/model_executor/compilation/compile.py

+import contextvars
+import inspect
+import logging
+import os
+import sys
+import types
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import Any, Callable, Optional, Union
+
+import torch
+
+from sglang.srt.model_executor.compilation.compilation_config import CompilationConfig
+
+logger = logging.getLogger(__name__)
+
+_COMPILE_ENABLED = contextvars.ContextVar("_COMPILE_ENABLED", default=False)
+
+
+@contextmanager
+def set_compiled(enabled: bool = True):
+    token = _COMPILE_ENABLED.set(enabled)
+    try:
+        yield
+    finally:
+        _COMPILE_ENABLED.reset(token)
+
+
+@dataclass
+class IntermediateTensors:
+    """For all pipeline stages except the last, we need to return the hidden
+    states and residuals to be sent to the next stage. This data structure
+    contains the hidden states and residuals for a request.
+
+    Each stage also needs to handle its own finished_sending and
+    finished_recving in case of kv transfer.
+    """
+
+    tensors: dict[str, torch.Tensor]
+    # [req_ids]
+    finished_sending: Optional[set[str]] = None
+    finished_recving: Optional[set[str]] = None
+
+    def __init__(self, tensors):
+        # manually define this function, so that
+        # Dynamo knows `IntermediateTensors()` comes from this file.
+        # Otherwise, dataclass will generate this function by evaluating
+        # a string, and we will lose the information about the source file.
+        self.tensors = tensors
+
+    def __getitem__(self, key: Union[str, slice]):
+        if isinstance(key, str):
+            return self.tensors[key]
+        elif isinstance(key, slice):
+            return self.__class__({k: v[key] for k, v in self.tensors.items()})
+
+    def __setitem__(self, key: str, value: torch.Tensor):
+        self.tensors[key] = value
+
+    def items(self):
+        return self.tensors.items()
+
+    def __len__(self):
+        return len(self.tensors)
+
+    def __eq__(self, other: object):
+        return isinstance(other, self.__class__) and self
+
+    def __repr__(self) -> str:
+        return f"IntermediateTensors(tensors={self.tensors})"
+
+
+def _normalize_dims(dims, ndim: int):
+    dims = [dims] if isinstance(dims, int) else list(dims)
+    return [d if d >= 0 else ndim + d for d in dims]
+
+
+class _MaybeIntermediateTensors:
+    """Duck-typed check to support your IntermediateTensors without importing."""
+
+    def __init__(self, obj):
+        self.is_intermediate = hasattr(obj, "tensors") and isinstance(
+            getattr(obj, "tensors"), dict
+        )
+        self.obj = obj
+
+
+def _mark_dynamic_on_value(val, dims):
+    if isinstance(val, torch.Tensor):
+        torch._dynamo.mark_dynamic(val, _normalize_dims(dims, val.ndim))
+    else:
+        mit = _MaybeIntermediateTensors(val)
+        if mit.is_intermediate:
+            for t in mit.obj.tensors.values():
+                torch._dynamo.mark_dynamic(t, _normalize_dims(dims, t.ndim))
+        # else: ignore (None or non-tensor)
+
+
+def _infer_dynamic_arg_dims_from_annotations(forward_fn):
+    sig = inspect.signature(forward_fn)
+    dyn = {}
+    for name, p in sig.parameters.items():
+        ann = p.annotation
+        # Accept torch.Tensor / Optional[torch.Tensor] / your IntermediateTensors types by name
+        if (
+            ann is torch.Tensor
+            or getattr(getattr(ann, "__args__", [None])[0], "__name__", "") == "Tensor"
+        ):
+            dyn[name] = 0
+        elif getattr(ann, "__name__", "") in ("IntermediateTensors",) or any(
+            getattr(a, "__name__", "") == "IntermediateTensors"
+            for a in getattr(ann, "__args__", [])
+        ):
+            dyn[name] = 0
+    if not dyn:
+        raise ValueError("No dynamic dims inferred; pass dynamic_arg_dims explicitly.")
+    return dyn
+
+
+def install_torch_compiled(
+    module: torch.nn.Module,
+    *,
+    dynamic_arg_dims: dict[str, Union[int, list[int]]] | None = None,
+    backend_factory: Optional[Callable[[torch.fx.GraphModule, list], Callable]] = None,
+    compile_config: CompilationConfig = None,
+    fullgraph: bool = True,
+    graph_pool: Any = None,
+):
+    unbound_fwd = module.__class__.forward
+    if not callable(unbound_fwd):
+        raise TypeError("module.__class__.forward must be callable")
+    original_code = unbound_fwd.__code__
+
+    dyn_map = dynamic_arg_dims or _infer_dynamic_arg_dims_from_annotations(unbound_fwd)
+
+    if backend_factory is None:
+        from sglang.srt.model_executor.compilation.backend import SGLangBackend
+
+        backend_factory = lambda gm, ex: SGLangBackend(compile_config, graph_pool)(
+            gm, ex
+        )
+
+    compiled_codes: list[type(original_code)] = []
+    state = {"compiled": False, "compiled_callable": None}
+
+    def bytecode_hook(old_code, new_code):
+        if old_code is not original_code:
+            return
+        frame = sys._getframe()
+        while frame and frame.f_back:
+            frame = frame.f_back
+            if (
+                frame.f_code.co_name == "_compile"
+                and os.path.basename(frame.f_code.co_filename) == "convert_frame.py"
+            ):
+                break
+        try:
+            dynamo_frame = frame.f_locals["frame"]
+        except Exception:
+            return
+        if dynamo_frame.f_code is not old_code:
+            return
+        if dynamo_frame.f_locals.get("self") is not module:
+            return
+        compiled_codes.append(new_code)
+
+    torch._dynamo.convert_frame.register_bytecode_hook(bytecode_hook)
+
+    def _ensure_compiled(self, *args, **kwargs):
+        """Compile on first use (with flag ON)."""
+        if state["compiled"]:
+            return
+        # Mark dynamic dims only when we are about to compile
+        sig = inspect.signature(unbound_fwd)
+        ba = sig.bind(self, *args, **kwargs)
+        ba.apply_defaults()
+        for name, dims in (dyn_map or {}).items():
+            if name in ba.arguments:
+                val = ba.arguments[name]
+                if val is not None:
+                    _mark_dynamic_on_value(val, dims)
+
+        # Avoid cross-instance cache reuse
+        torch._dynamo.eval_frame.remove_from_cache(unbound_fwd.__code__)
+
+        bound = types.MethodType(unbound_fwd, self)
+        compiled_callable = torch.compile(
+            bound, fullgraph=fullgraph, backend=backend_factory
+        )
+
+        # Trigger Dynamo so bytecode hook can capture
+        compiled_callable(*args, **kwargs)
+
+        state["compiled"] = True
+        state["compiled_callable"] = compiled_callable
+
+    def trampoline(self, *args, **kwargs):
+        use_compiled = _COMPILE_ENABLED.get()
+        if use_compiled:
+            if not state["compiled"]:
+                _ensure_compiled(self, *args, **kwargs)
+
+            compiled_callable = state["compiled_callable"]
+            return compiled_callable(*args, **kwargs)
+        else:
+            # Explicitly run the original uncompiled forward
+            return unbound_fwd(self, *args, **kwargs)
+
+    module.forward = types.MethodType(trampoline, module)
+    return module

python/sglang/srt/model_executor/compilation/compiler_interface.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/compiler_interface.py
+
+import contextlib
+import copy
+import hashlib
+import os
+from contextlib import ExitStack
+from typing import Any, Callable, Optional
+from unittest.mock import patch
+
+import torch
+import torch._inductor.compile_fx
+import torch.fx as fx
+
+from sglang.srt.model_executor.compilation.compilation_counter import (
+    compilation_counter,
+)
+from sglang.srt.model_executor.compilation.inductor_pass import pass_context
+
+
+class CompilerInterface:
+    """
+    The interface for a compiler that can be used by vLLM.
+    """
+
+    # The name of the compiler, e.g. inductor.
+    # This is a class-level attribute.
+    name: str
+
+    def initialize_cache(
+        self, cache_dir: str, disable_cache: bool = False, prefix: str = ""
+    ):
+        """
+        when the vLLM process uses `cache_dir` as the cache directory,
+        the compiler should initialize itself with the cache directory,
+        e.g. by re-directing its own cache directory to a sub-directory.
+
+        prefix can be used in combination with cache_dir to figure out the base
+        cache directory, e.g. there're multiple parts of model being compiled,
+        but we want to share the same cache directory for all of them.
+
+        e.g.
+        cache_dir = "/path/to/dir/backbone", prefix = "backbone"
+        cache_dir = "/path/to/dir/eagle_head", prefix = "eagle_head"
+        """
+        pass
+
+    def compute_hash(self) -> str:
+        """
+        Gather all the relevant information from the vLLM config,
+        to compute a hash so that we can cache the compiled model.
+
+        See [`VllmConfig.compute_hash`][vllm.config.VllmConfig.compute_hash]
+        to check what information
+        is already considered by default. This function should only
+        consider the information that is specific to the compiler.
+        """
+        return ""
+
+    def compile(
+        self,
+        graph: fx.GraphModule,
+        example_inputs: list[Any],
+        compiler_config: dict[str, Any],
+        runtime_shape: Optional[int] = None,
+        key: Optional[str] = None,
+    ) -> tuple[Optional[Callable], Optional[Any]]:
+        """
+        Compile the graph with the given example inputs and compiler config,
+        with a runtime shape. If the `runtime_shape` is None, it means
+        the `example_inputs` have a dynamic shape. Otherwise, the
+        `runtime_shape` specifies the shape of the inputs. Right now we only
+        support one variable shape for all inputs, which is the batchsize
+        (number of tokens) during inference.
+
+        Dynamo will make sure `graph(*example_inputs)` is valid.
+
+        The function should return a compiled callable function, as well as
+        a handle that can be used to directly load the compiled function.
+
+        The handle should be a plain Python object, preferably a string or a
+        file path for readability.
+
+        If the compiler doesn't support caching, it should return None for the
+        handle. If the compiler fails to compile the graph, it should return
+        None for the compiled function as well.
+
+        `key` is required for StandaloneInductorAdapter, it specifies where to
+        save the compiled artifact. The compiled artifact gets saved to
+        `cache_dir/key`.
+        """
+        return None, None
+
+    def load(
+        self,
+        handle: Any,
+        graph: fx.GraphModule,
+        example_inputs: list[Any],
+        graph_index: int,
+        runtime_shape: Optional[int] = None,
+    ) -> Callable:
+        """
+        Load the compiled function from the handle.
+        Raises an error if the handle is invalid.
+
+        The handle is the second return value of the `compile` function.
+        """
+        raise NotImplementedError("caching is not supported")
+
+
+def get_inductor_factors() -> list[Any]:
+    factors: list[Any] = []
+    # summarize system state
+    from torch._inductor.codecache import CacheBase
+
+    system_factors = CacheBase.get_system()
+    factors.append(system_factors)
+
+    # summarize pytorch state
+    from torch._inductor.codecache import torch_key
+
+    torch_factors = torch_key()
+    factors.append(torch_factors)
+    return factors
+
+
+class AlwaysHitShapeEnv:
+    """
+    Why do we need this class:
+
+    For normal `torch.compile` usage, every compilation will have
+    one Dynamo bytecode compilation and one Inductor compilation.
+    The Inductor compilation happens under the context of the
+    Dynamo bytecode compilation, and that context is used to
+    determine the dynamic shape information, etc.
+
+    For our use case, we only run Dynamo bytecode compilation once,
+    and run Inductor compilation multiple times with different shapes
+    plus a general shape. The compilation for specific shapes happens
+    outside of the context of the Dynamo bytecode compilation. At that
+    time, we don't have shape environment to provide to Inductor, and
+    it will fail the Inductor code cache lookup.
+
+    By providing a dummy shape environment that always hits, we can
+    make the Inductor code cache lookup always hit, and we can
+    compile the graph for different shapes as needed.
+
+    The following dummy methods are obtained by trial-and-error
+    until it works.
+    """
+
+    def __init__(self) -> None:
+        self.guards: list[Any] = []
+
+    def evaluate_guards_expression(self, *args, **kwargs):
+        return True
+
+    def get_pruned_guards(self, *args, **kwargs):
+        return []
+
+    def produce_guards_expression(self, *args, **kwargs):
+        return ""
+
+
+class InductorAdaptor(CompilerInterface):
+    """
+    The adaptor for the Inductor compiler, version 2.5, 2.6, 2.7.
+    """
+
+    name = "inductor"
+
+    def compute_hash(self) -> str:
+        factors = get_inductor_factors()
+        hash_str = hashlib.md5(
+            str(factors).encode(), usedforsecurity=False
+        ).hexdigest()[:10]
+        return hash_str
+
+    def initialize_cache(
+        self, cache_dir: str, disable_cache: bool = False, prefix: str = ""
+    ):
+        self.cache_dir = cache_dir
+        self.prefix = prefix
+        self.base_cache_dir = cache_dir[: -len(prefix)] if prefix else cache_dir
+        if disable_cache:
+            return
+        # redirect the cache directory to a sub-directory
+        # set flags so that Inductor and Triton store their cache
+        # in the cache_dir, then users only need to copy the cache_dir
+        # to another machine to reuse the cache.
+        inductor_cache = os.path.join(self.base_cache_dir, "inductor_cache")
+        os.makedirs(inductor_cache, exist_ok=True)
+        os.environ["TORCHINDUCTOR_CACHE_DIR"] = inductor_cache
+        triton_cache = os.path.join(self.base_cache_dir, "triton_cache")
+        os.makedirs(triton_cache, exist_ok=True)
+        os.environ["TRITON_CACHE_DIR"] = triton_cache
+
+    def compile(
+        self,
+        graph: fx.GraphModule,
+        example_inputs: list[Any],
+        compiler_config: dict[str, Any],
+        runtime_shape: Optional[int] = None,
+        key: Optional[str] = None,
+    ) -> tuple[Optional[Callable], Optional[Any]]:
+        compilation_counter.num_inductor_compiles += 1
+        from torch._inductor.compile_fx import compile_fx
+
+        current_config = {}
+        if compiler_config is not None:
+            current_config.update(compiler_config)
+
+        # disable remote cache
+        current_config["fx_graph_cache"] = True
+        current_config["fx_graph_remote_cache"] = False
+
+        set_inductor_config(current_config, runtime_shape)
+
+        # inductor can inplace modify the graph, so we need to copy it
+        # see https://github.com/pytorch/pytorch/issues/138980
+        graph = copy.deepcopy(graph)
+
+        # it's the first time we compile this graph
+        # the assumption is that we don't have nested Inductor compilation.
+        # compiled_fx_graph_hash will only be called once, and we can hook
+        # it to get the hash of the compiled graph directly.
+
+        hash_str, file_path = None, None
+        from torch._inductor.codecache import FxGraphCache, compiled_fx_graph_hash
+
+        if torch.__version__.startswith("2.5"):
+            original_load = FxGraphCache.load
+            original_load_name = "torch._inductor.codecache.FxGraphCache.load"
+
+            def hijack_load(*args, **kwargs):
+                inductor_compiled_graph = original_load(*args, **kwargs)
+                nonlocal file_path
+                compiled_fn = inductor_compiled_graph.current_callable
+                file_path = compiled_fn.__code__.co_filename  # noqa
+                if not file_path.startswith(self.base_cache_dir):
+                    # hooked in the align_inputs_from_check_idxs function
+                    # in torch/_inductor/utils.py
+                    for cell in compiled_fn.__closure__:
+                        if not callable(cell.cell_contents):
+                            continue
+                        if cell.cell_contents.__code__.co_filename.startswith(
+                            self.base_cache_dir
+                        ):
+                            # this is the real file path compiled from Inductor
+                            file_path = cell.cell_contents.__code__.co_filename
+                            break
+                return inductor_compiled_graph
+
+            hijacked_compile_fx_inner = (
+                torch._inductor.compile_fx.compile_fx_inner
+            )  # noqa
+        elif torch.__version__ >= "2.6":
+            # function renamed in 2.6
+            original_load_name = None
+
+            def hijacked_compile_fx_inner(*args, **kwargs):
+                output = torch._inductor.compile_fx.compile_fx_inner(*args, **kwargs)
+                nonlocal hash_str
+                inductor_compiled_graph = output
+                if inductor_compiled_graph is not None:
+                    nonlocal file_path
+                    compiled_fn = inductor_compiled_graph.current_callable
+                    file_path = compiled_fn.__code__.co_filename  # noqa
+                    if not file_path.startswith(self.base_cache_dir):
+                        # hooked in the align_inputs_from_check_idxs function
+                        # in torch/_inductor/utils.py
+                        for cell in compiled_fn.__closure__:
+                            if not callable(cell.cell_contents):
+                                continue
+                            code = cell.cell_contents.__code__
+                            if code.co_filename.startswith(self.base_cache_dir):
+                                # this is the real file path
+                                # compiled from Inductor
+                                file_path = code.co_filename
+                                break
+                    hash_str = inductor_compiled_graph._fx_graph_cache_key
+                return output
+
+        def hijack_compiled_fx_graph_hash(*args, **kwargs):
+            out = compiled_fx_graph_hash(*args, **kwargs)
+            nonlocal hash_str
+            hash_str = out[0]
+            return out
+
+        def _check_can_cache(*args, **kwargs):
+            # no error means it can be cached.
+            # Inductor refuses to cache the graph outside of Dynamo
+            # tracing context, and also disables caching for graphs
+            # with high-order ops.
+            # For vLLM, in either case, we want to cache the graph.
+            # see https://github.com/pytorch/pytorch/blob/9f5ebf3fc609105a74eab4ccc24932d6353ff566/torch/_inductor/codecache.py#L1221 # noqa
+            return
+
+        def _get_shape_env() -> AlwaysHitShapeEnv:
+            return AlwaysHitShapeEnv()
+
+        with ExitStack() as stack:
+            # hijack to get the compiled graph itself
+            if original_load_name is not None:
+                stack.enter_context(patch(original_load_name, hijack_load))
+
+            # for hijacking the hash of the compiled graph
+            stack.enter_context(
+                patch(
+                    "torch._inductor.codecache.compiled_fx_graph_hash",
+                    hijack_compiled_fx_graph_hash,
+                )
+            )
+
+            # for providing a dummy shape environment
+            stack.enter_context(
+                patch(
+                    "torch._inductor.codecache.FxGraphCache._get_shape_env",
+                    _get_shape_env,
+                )
+            )
+
+            from torch._functorch._aot_autograd.autograd_cache import AOTAutogradCache
+
+            # torch 2.8+ on main uses _get_shape_env in AOTAutogradCache
+            if hasattr(AOTAutogradCache, "_get_shape_env"):
+                stack.enter_context(
+                    patch(
+                        "torch._functorch._aot_autograd.autograd_cache.AOTAutogradCache._get_shape_env",
+                        _get_shape_env,
+                    )
+                )
+
+            # for forcing the graph to be cached
+            stack.enter_context(
+                patch(
+                    "torch._inductor.codecache.FxGraphCache._check_can_cache",
+                    _check_can_cache,
+                )
+            )
+
+            # Dynamo metrics context, see method for more details.
+            stack.enter_context(self.metrics_context())
+
+            # Disable remote caching. When these are on, on remote cache-hit,
+            # the monkey-patched functions never actually get called.
+            # vLLM today assumes and requires the monkey-patched functions to
+            # get hit.
+            # TODO(zou3519): we're going to replace this all with
+            # standalone_compile sometime.
+
+            stack.enter_context(
+                torch._inductor.config.patch(fx_graph_remote_cache=False)
+            )
+            # InductorAdaptor (unfortunately) requires AOTAutogradCache
+            # to be turned off to run. It will fail to acquire the hash_str
+            # and error if not.
+            # StandaloneInductorAdaptor (PyTorch 2.8+) fixes this problem.
+            stack.enter_context(
+                torch._functorch.config.patch(enable_autograd_cache=False)
+            )
+            stack.enter_context(
+                torch._functorch.config.patch(enable_remote_autograd_cache=False)
+            )
+
+            with pass_context(runtime_shape):
+                compiled_graph = compile_fx(
+                    graph,
+                    example_inputs,
+                    inner_compile=hijacked_compile_fx_inner,
+                    config_patches=current_config,
+                )
+        return compiled_graph, (hash_str, file_path)
+
+    def load(
+        self,
+        handle: Any,
+        graph: fx.GraphModule,
+        example_inputs: list[Any],
+        graph_index: int,
+        runtime_shape: Optional[int] = None,
+    ) -> Callable:
+        assert isinstance(handle, tuple)
+        assert isinstance(handle[0], str)
+        assert isinstance(handle[1], str)
+        hash_str = handle[0]
+
+        from torch._functorch._aot_autograd.autograd_cache import AOTAutogradCache
+        from torch._inductor.codecache import FxGraphCache
+
+        with ExitStack() as exit_stack:
+            exit_stack.enter_context(
+                patch(
+                    "torch._inductor.codecache.FxGraphCache._get_shape_env",
+                    lambda *args, **kwargs: AlwaysHitShapeEnv(),
+                )
+            )
+            # torch 2.8+ on main uses _get_shape_env in AOTAutogradCache
+            if hasattr(AOTAutogradCache, "_get_shape_env"):
+                exit_stack.enter_context(
+                    patch(
+                        "torch._functorch._aot_autograd.autograd_cache.AOTAutogradCache._get_shape_env",
+                        lambda *args, **kwargs: AlwaysHitShapeEnv(),
+                    )
+                )
+
+            # Dynamo metrics context, see method for more details.
+            exit_stack.enter_context(self.metrics_context())
+
+            if torch.__version__.startswith("2.5"):
+                inductor_compiled_graph = FxGraphCache._lookup_graph(
+                    hash_str, example_inputs, True, False
+                )
+                assert inductor_compiled_graph is not None, (
+                    "Inductor cache lookup failed. Please remove"
+                    f"the cache directory and try again."  # noqa
+                )
+            elif torch.__version__ >= "2.6":
+                from torch._inductor.output_code import CompiledFxGraphConstantsWithGm
+
+                constants = CompiledFxGraphConstantsWithGm(graph)
+                inductor_compiled_graph, _ = FxGraphCache._lookup_graph(
+                    hash_str, example_inputs, True, None, constants
+                )
+                assert inductor_compiled_graph is not None, (
+                    "Inductor cache lookup failed. Please remove"
+                    f"the cache directory and try again."  # noqa
+                )
+
+        # Inductor calling convention (function signature):
+        # f(list) -> tuple
+        # Dynamo calling convention (function signature):
+        # f(*args) -> Any
+
+        # need to know if the graph returns a tuple
+        from torch._inductor.compile_fx import graph_returns_tuple
+
+        returns_tuple = graph_returns_tuple(graph)
+
+        # this is the callable we return to Dynamo to run
+        def compiled_graph(*args):
+            # convert args to list
+            list_args = list(args)
+            graph_output = inductor_compiled_graph(list_args)
+            # unpack the tuple if needed
+            if returns_tuple:
+                return graph_output
+            else:
+                return graph_output[0]
+
+        return compiled_graph
+
+    def metrics_context(self) -> contextlib.AbstractContextManager:
+        """
+        This method returns the Dynamo metrics context (if it exists,
+        otherwise a null context). It is used by various compile components.
+        Present in torch>=2.6, it's used inside FxGraphCache in
+        torch==2.6 (but not after). It might also be used in various other
+        torch.compile internal functions.
+
+        Because it is re-entrant, we always set it (even if entering via Dynamo
+        and the context was already entered). We might want to revisit if it
+        should be set at a different level of compilation.
+
+        This is likely a bug in PyTorch: public APIs should not rely on
+        manually setting up internal contexts. But we also rely on non-public
+        APIs which might not provide these guarantees.
+        """
+        import torch._dynamo.utils
+
+        return torch._dynamo.utils.get_metrics_context()
+
+
+def set_inductor_config(config, runtime_shape):
+    if isinstance(runtime_shape, int):
+        # for a specific batchsize, tuning triton kernel parameters
+        # can be beneficial
+        config["max_autotune"] = True
+        config["coordinate_descent_tuning"] = True

python/sglang/srt/model_executor/compilation/cuda_piecewise_backend.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/cuda_piecewise_backend.py
+
+import dataclasses
+import logging
+from contextlib import ExitStack
+from typing import Any, Callable, Optional, Union
+from unittest.mock import patch
+
+import torch
+import torch.fx as fx
+
+import sglang.srt.model_executor.compilation.weak_ref_tensor_jit
+from sglang.srt.model_executor.compilation.compilation_config import CompilationConfig
+from sglang.srt.model_executor.compilation.compilation_counter import (
+    compilation_counter,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def weak_ref_tensor(tensor: Any) -> Any:
+    """
+    Create a weak reference to a tensor.
+    The new tensor will share the same data as the original tensor,
+    but will not keep the original tensor alive.
+    """
+    if isinstance(tensor, torch.Tensor):
+        # TODO(yuwei): introduce weak_ref_tensor from sgl_kernel
+        return torch.ops.jit_weak_ref_tensor.weak_ref_tensor(tensor)
+    return tensor
+
+
+def weak_ref_tensors(
+    tensors: Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor]]
+) -> Union[torch.Tensor, list[Any], tuple[Any], Any]:
+    """
+    Convenience function to create weak references to tensors,
+    for single tensor, list of tensors or tuple of tensors.
+    """
+    if isinstance(tensors, torch.Tensor):
+        return weak_ref_tensor(tensors)
+    if isinstance(tensors, list):
+        return [weak_ref_tensor(t) for t in tensors]
+    if isinstance(tensors, tuple):
+        return tuple(weak_ref_tensor(t) for t in tensors)
+    raise ValueError("Invalid type for tensors")
+
+
+@dataclasses.dataclass
+class ConcreteSizeEntry:
+    runtime_shape: int
+    need_to_compile: bool  # the size is in compile_sizes
+    use_cudagraph: bool  # the size is in cudagraph_capture_sizes
+
+    compiled: bool = False
+    runnable: Callable = None  # type: ignore
+    num_finished_warmup: int = 0
+    cudagraph: Optional[torch.cuda.CUDAGraph] = None
+    output: Optional[Any] = None
+
+    # for cudagraph debugging, track the input addresses
+    # during capture, and check if they are the same during replay
+    input_addresses: Optional[list[int]] = None
+
+
+class CUDAPiecewiseBackend:
+
+    def __init__(
+        self,
+        graph: fx.GraphModule,
+        compile_config: CompilationConfig,
+        inductor_config: dict[str, Any],
+        graph_pool: Any,
+        piecewise_compile_index: int,
+        total_piecewise_compiles: int,
+        sym_shape_indices: list[int],
+        compiled_graph_for_general_shape: Callable,
+        sglang_backend,
+    ):
+        """
+        The backend for piecewise compilation.
+        It mainly handles the compilation and cudagraph capturing.
+
+        We will compile `self.graph` once for the general shape,
+        and then compile for different shapes specified in
+        `compilation_config.compile_sizes`.
+
+        Independently, we will capture cudagraph for different shapes.
+
+        If a shape needs both compilation and cudagraph, we will
+        compile it first, and then capture cudagraph.
+        """
+        self.graph = graph
+        self.inductor_config = inductor_config
+        self.graph_pool = graph_pool
+        self.piecewise_compile_index = piecewise_compile_index
+        self.total_piecewise_compiles = total_piecewise_compiles
+        self.sglang_backend = sglang_backend
+
+        self.is_first_graph = piecewise_compile_index == 0
+        self.is_last_graph = piecewise_compile_index == total_piecewise_compiles - 1
+
+        self.compile_sizes: set[int] = set([])
+        self.compile_config = compile_config
+        self.cudagraph_capture_sizes: set[int] = set(compile_config.get_capture_sizes())
+
+        self.first_run_finished = False
+
+        self.compiled_graph_for_general_shape = compiled_graph_for_general_shape  # noqa
+
+        self.sym_shape_indices = sym_shape_indices
+
+        self.is_debugging_mode = True
+
+        # the entries for different shapes that we need to either
+        # compile or capture cudagraph
+        self.concrete_size_entries: dict[int, ConcreteSizeEntry] = {}
+
+        # to_be_compiled_sizes tracks the remaining sizes to compile,
+        # and updates during the compilation process, so we need to copy it
+        self.to_be_compiled_sizes: set[int] = self.compile_sizes.copy()
+        for shape in self.compile_sizes.union(self.cudagraph_capture_sizes):
+            self.concrete_size_entries[shape] = ConcreteSizeEntry(
+                runtime_shape=shape,
+                need_to_compile=shape in self.compile_sizes,
+                use_cudagraph=shape in self.cudagraph_capture_sizes,
+            )
+
+    def check_for_ending_compilation(self):
+        if self.is_last_graph and not self.to_be_compiled_sizes:
+            # no specific sizes to compile
+            # save the hash of the inductor graph for the next run
+            self.sglang_backend.compiler_manager.save_to_file()
+
+    def __call__(self, *args) -> Any:
+        if not self.first_run_finished:
+            self.first_run_finished = True
+            self.check_for_ending_compilation()
+            return self.compiled_graph_for_general_shape(*args)
+        runtime_shape = args[self.sym_shape_indices[0]]
+        if runtime_shape not in self.concrete_size_entries:
+            # we don't need to do anything for this shape
+            return self.compiled_graph_for_general_shape(*args)
+
+        entry = self.concrete_size_entries[runtime_shape]
+
+        if entry.runnable is None:
+            entry.runnable = self.compiled_graph_for_general_shape
+
+        if entry.need_to_compile and not entry.compiled:
+            entry.compiled = True
+            self.to_be_compiled_sizes.remove(runtime_shape)
+            # args are real arguments
+            entry.runnable = self.sglang_backend.compiler_manager.compile(
+                self.graph,
+                args,
+                self.inductor_config,
+                graph_index=self.piecewise_compile_index,
+                num_graphs=self.total_piecewise_compiles,
+                runtime_shape=runtime_shape,
+            )
+
+            # finished compilations for all required shapes
+            if self.is_last_graph and not self.to_be_compiled_sizes:
+                self.check_for_ending_compilation()
+
+        # Skip CUDA graphs if this entry doesn't use them OR
+        # if we're supposed to skip them globally
+        # skip_cuda_graphs = get_forward_context().skip_cuda_graphs
+        # if not entry.use_cudagraph or skip_cuda_graphs:
+        #     return entry.runnable(*args)
+
+        if entry.cudagraph is None:
+            if entry.num_finished_warmup < 1:  # noqa
+                entry.num_finished_warmup += 1
+                return entry.runnable(*args)
+
+            input_addresses = [
+                x.data_ptr() for x in args if isinstance(x, torch.Tensor)
+            ]
+            entry.input_addresses = input_addresses
+            cudagraph = torch.cuda.CUDAGraph()
+
+            with ExitStack() as stack:
+                if not self.is_first_graph:
+                    # during every model forward, we will capture
+                    # many pieces of cudagraphs (roughly one per layer).
+                    # running gc again and again across layers will
+                    # make the cudagraph capture very slow.
+                    # therefore, we only run gc for the first graph,
+                    # and disable gc for the rest of the graphs.
+                    stack.enter_context(patch("gc.collect", lambda: None))
+                    stack.enter_context(patch("torch.cuda.empty_cache", lambda: None))
+
+                # mind-exploding: carefully manage the reference and memory.
+                with torch.cuda.graph(cudagraph, pool=self.graph_pool):
+                    # `output` is managed by pytorch's cudagraph pool
+                    output = entry.runnable(*args)
+                    if self.is_last_graph:
+                        # by converting it to weak ref,
+                        # the original `output` will immediately be released
+                        # to save memory. It is only safe to do this for
+                        # the last graph, because the output of the last graph
+                        # will not be used by any other cuda graph.
+                        output = weak_ref_tensors(output)
+
+            # here we always use weak ref for the output
+            # to save memory
+            entry.output = weak_ref_tensors(output)
+            entry.cudagraph = cudagraph
+
+            compilation_counter.num_cudagraph_captured += 1
+
+            # important: we need to return the output, rather than
+            # the weak ref of the output, so that pytorch can correctly
+            # manage the memory during cuda graph capture
+            return output
+
+        if self.is_debugging_mode:
+            # check if the input addresses are the same
+            new_input_addresses = [
+                x.data_ptr() for x in args if isinstance(x, torch.Tensor)
+            ]
+            assert new_input_addresses == entry.input_addresses, (
+                "Input addresses for cudagraphs are different during replay."
+                f" Expected {entry.input_addresses}, got {new_input_addresses}"
+            )
+
+        entry.cudagraph.replay()
+        return entry.output

python/sglang/srt/model_executor/compilation/fix_functionalization.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/fix_functionalization.py
+
+import logging
+import operator
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch._higher_order_ops.auto_functionalize import auto_functionalized
+
+from sglang.srt.model_executor.compilation.fx_utils import is_func
+from sglang.srt.model_executor.compilation.inductor_pass import SGLangInductorPass
+
+logger = logging.getLogger(__name__)
+
+
+class FixFunctionalizationPass(SGLangInductorPass):
+    """
+    This pass defunctionalizes certain nodes to avoid redundant tensor copies.
+    After this pass, DCE (dead-code elimination) should never be run,
+    as de-functionalized nodes may appear as dead code.
+
+    To add new nodes to defunctionalize, add to the if-elif chain in __call__.
+    """
+
+    def __call__(self, graph: torch.fx.Graph):
+        self.begin()
+        self.dump_graph(graph, "before_fix_functionalization")
+
+        self.nodes_to_remove: list[torch.fx.Node] = []
+        count = 0
+        for node in graph.nodes:
+            if not is_func(node, auto_functionalized):
+                continue  # Avoid deep if-elif nesting
+            count += 1
+
+        self.dump_graph(graph, "before_fix_functionalization_cleanup")
+
+        # Remove the nodes all at once
+        count_removed = len(self.nodes_to_remove)
+        for node in self.nodes_to_remove:
+            graph.erase_node(node)
+
+        logger.debug(
+            "De-functionalized %s nodes, removed %s nodes", count, count_removed
+        )
+        self.dump_graph(graph, "after_fix_functionalization")
+        self.end_and_log()
+
+    def _remove(self, node_or_nodes: Union[torch.fx.Node, Iterable[torch.fx.Node]]):
+        """
+        Stage a node (or nodes) for removal at the end of the pass.
+        """
+        if isinstance(node_or_nodes, torch.fx.Node):
+            self.nodes_to_remove.append(node_or_nodes)
+        else:
+            self.nodes_to_remove.extend(node_or_nodes)
+
+    def defunctionalize(
+        self,
+        graph: torch.fx.Graph,
+        node: torch.fx.Node,
+        mutated_args: dict[int, Union[torch.fx.Node, str]],
+        args: Optional[tuple[Union[torch.fx.Node, str], ...]] = None,
+    ):
+        """
+        De-functionalize a node by replacing it with a call to the original.
+        It also replaces the getitem users with the mutated arguments.
+        See replace_users_with_mutated_args and insert_defunctionalized.
+        """
+        self.replace_users_with_mutated_args(node, mutated_args)
+        self.insert_defunctionalized(graph, node, args=args)
+        self._remove(node)
+
+    def replace_users_with_mutated_args(
+        self, node: torch.fx.Node, mutated_args: dict[int, Union[torch.fx.Node, str]]
+    ):
+        """
+        Replace all getitem users of the auto-functionalized node with the
+        mutated arguments.
+        :param node: The auto-functionalized node
+        :param mutated_args: The mutated arguments, indexed by getitem index.
+        If the value of an arg is a string, `node.kwargs[arg]` is used.
+        """
+        for idx, user in self.getitem_users(node).items():
+            arg = mutated_args[idx]
+            arg = node.kwargs[arg] if isinstance(arg, str) else arg
+            user.replace_all_uses_with(arg)
+            self._remove(user)
+
+    def getitem_users(self, node: torch.fx.Node) -> dict[int, torch.fx.Node]:
+        """
+        Returns the operator.getitem users of the auto-functionalized node,
+        indexed by the index they are getting.
+        """
+        users = {}
+        for user in node.users:
+            if is_func(user, operator.getitem):
+                idx = user.args[1]
+                users[idx] = user
+        return users
+
+    def insert_defunctionalized(
+        self,
+        graph: torch.fx.Graph,
+        node: torch.fx.Node,
+        args: Optional[tuple[Union[torch.fx.Node, str], ...]] = None,
+    ):
+        """
+        Insert a new defunctionalized node into the graph before node.
+        If one of the kwargs is 'out', provide args directly,
+        as node.kwargs cannot be used.
+        See https://github.com/pytorch/pytorch/blob/a00faf440888ffb724bad413f329a49e2b6388e7/torch/_inductor/lowering.py#L351
+
+        :param graph: Graph to insert the defunctionalized node into
+        :param node: The auto-functionalized node to defunctionalize
+        :param args: If we cannot use kwargs, specify args directly.
+        If an arg is a string, `node.kwargs[arg]` is used.
+        """  # noqa: E501
+        assert is_func(
+            node, auto_functionalized
+        ), f"node must be auto-functionalized, is {node} instead"
+
+        # Create a new call to the original function
+        with graph.inserting_before(node):
+            function = node.args[0]
+            if args is None:
+                graph.call_function(function, kwargs=node.kwargs)
+            else:
+                # Args passed as strings refer to items in node.kwargs
+                args = tuple(
+                    node.kwargs[arg] if isinstance(arg, str) else arg for arg in args
+                )
+                graph.call_function(function, args=args)

python/sglang/srt/model_executor/compilation/fx_utils.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/fx_utils.py
+
+import operator
+from collections.abc import Iterable, Iterator
+from typing import Optional
+
+from torch import fx
+from torch._higher_order_ops.auto_functionalize import auto_functionalized
+from torch._ops import OpOverload
+
+
+def is_func(node: fx.Node, target) -> bool:
+    return node.op == "call_function" and node.target == target
+
+
+def is_auto_func(node: fx.Node, op: OpOverload) -> bool:
+    return is_func(node, auto_functionalized) and node.args[0] == op
+
+
+# Returns the first specified node with the given op (if it exists)
+def find_specified_fn_maybe(
+    nodes: Iterable[fx.Node], op: OpOverload
+) -> Optional[fx.Node]:
+    for node in nodes:
+        if node.target == op:
+            return node
+    return None
+
+
+# Returns the first specified node with the given op
+def find_specified_fn(nodes: Iterable[fx.Node], op: OpOverload) -> fx.Node:
+    node = find_specified_fn_maybe(nodes, op)
+    assert node is not None, f"Could not find {op} in nodes {nodes}"
+    return node
+
+
+# Returns the first auto_functionalized node with the given op (if it exists)
+def find_auto_fn_maybe(nodes: Iterable[fx.Node], op: OpOverload) -> Optional[fx.Node]:
+    for node in nodes:
+        if is_func(node, auto_functionalized) and node.args[0] == op:  # noqa
+            return node
+    return None
+
+
+# Returns the first auto_functionalized node with the given op
+def find_auto_fn(nodes: Iterable[fx.Node], op: OpOverload) -> fx.Node:
+    node = find_auto_fn_maybe(nodes, op)
+    assert node is not None, f"Could not find {op} in nodes {nodes}"
+    return node
+
+
+# Returns the getitem node that extracts the idx-th element from node
+# (if it exists)
+def find_getitem_maybe(node: fx.Node, idx: int) -> Optional[fx.Node]:
+    for user in node.users:
+        if is_func(user, operator.getitem) and user.args[1] == idx:
+            return user
+    return None
+
+
+# Returns the getitem node that extracts the idx-th element from node
+def find_getitem(node: fx.Node, idx: int) -> fx.Node:
+    ret = find_getitem_maybe(node, idx)
+    assert ret is not None, f"Could not find getitem {idx} in node {node}"
+    return ret
+
+
+# An auto-functionalization-aware utility for finding nodes with a specific op
+def find_op_nodes(op: OpOverload, graph: fx.Graph) -> Iterator[fx.Node]:
+    if not op._schema.is_mutable:
+        yield from graph.find_nodes(op="call_function", target=op)
+
+    for n in graph.find_nodes(op="call_function", target=auto_functionalized):
+        if n.args[0] == op:
+            yield n
+
+
+# Asserts that the node only has one user and returns it
+# Even if a node has only 1 user, it might share storage with another node,
+# which might need to be taken into account.
+def get_only_user(node: fx.Node) -> fx.Node:
+    assert len(node.users) == 1
+    return next(iter(node.users))

python/sglang/srt/model_executor/compilation/inductor_pass.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/inductor_pass.py
+
+import hashlib
+import inspect
+import json
+import logging
+import time
+import types
+from contextlib import contextmanager
+from typing import Any, Callable, Optional, Union
+
+import torch
+from torch import fx
+from torch._dynamo.utils import lazy_format_graph_code
+from torch._inductor.custom_graph_pass import CustomGraphPass
+
+logger = logging.getLogger(__name__)
+
+_pass_context = None
+
+
+class PassContext:
+
+    def __init__(self, runtime_shape: Optional[int]):
+        self.runtime_shape = runtime_shape
+
+
+def get_pass_context() -> PassContext:
+    """Get the current pass context."""
+    assert _pass_context is not None
+    return _pass_context
+
+
+@contextmanager
+def pass_context(runtime_shape: Optional[int]):
+    """A context manager that stores the current pass context,
+    usually it is a list of sizes to specialize.
+    """
+    global _pass_context
+    prev_context = _pass_context
+    _pass_context = PassContext(runtime_shape)
+    try:
+        yield
+    finally:
+        _pass_context = prev_context
+
+
+class InductorPass(CustomGraphPass):
+    """
+    A custom graph pass that uses a hash of its source as the UUID.
+    This is defined as a convenience and should work in most cases.
+    """
+
+    def uuid(self) -> Any:
+        """
+        Provide a unique identifier for the pass, used in Inductor code cache.
+        This should depend on the pass implementation, so that changes to the
+        pass result in recompilation.
+        By default, the object source is hashed.
+        """
+        return InductorPass.hash_source(self)
+
+    @staticmethod
+    def hash_source(*srcs: Union[str, Any]):
+        """
+        Utility method to hash the sources of functions or objects.
+        :param srcs: strings or objects to add to the hash.
+        Objects and functions have their source inspected.
+        :return:
+        """
+        hasher = hashlib.sha256()
+        for src in srcs:
+            if isinstance(src, str):
+                src_str = src
+            elif isinstance(src, types.FunctionType):
+                src_str = inspect.getsource(src)
+            else:
+                src_str = inspect.getsource(src.__class__)
+            hasher.update(src_str.encode("utf-8"))
+        return hasher.hexdigest()
+
+    @staticmethod
+    def hash_dict(dict_: dict[Any, Any]):
+        """
+        Utility method to hash a dictionary, can alternatively be used for uuid.
+        :return: A sha256 hash of the json rep of the dictionary.
+        """
+        encoded = json.dumps(dict_, sort_keys=True).encode("utf-8")
+        return hashlib.sha256(encoded).hexdigest()
+
+    def is_applicable_for_shape(self, shape: Optional[int]):
+        return True
+
+
+class CallableInductorPass(InductorPass):
+    """
+    This class is a wrapper for a callable that automatically provides an
+    implementation of the UUID.
+    """
+
+    def __init__(
+        self, callable: Callable[[fx.Graph], None], uuid: Optional[Any] = None
+    ):
+        self.callable = callable
+        self._uuid = self.hash_source(callable) if uuid is None else uuid
+
+    def __call__(self, graph: torch.fx.Graph):
+        self.callable(graph)
+
+    def uuid(self) -> Any:
+        return self._uuid
+
+
+class SGLangInductorPass(InductorPass):
+
+    def __init__(
+        self,
+    ):
+        self.pass_name = self.__class__.__name__
+
+    def dump_graph(self, graph: torch.fx.Graph, stage: str):
+        lazy_format_graph_code(stage, graph.owning_module)
+
+    def begin(self):
+        self._start_time = time.perf_counter_ns()
+
+    def end_and_log(self):
+        self._end_time = time.perf_counter_ns()
+        duration_ms = float(self._end_time - self._start_time) / 1.0e6
+        logger.debug("%s completed in %.1f ms", self.pass_name, duration_ms)
+
+
+class PrinterInductorPass(SGLangInductorPass):
+
+    def __init__(self, name: str):
+        super().__init__()
+        self.name = name
+
+    def __call__(self, graph: torch.fx.Graph):
+        self.dump_graph(graph, self.name)

python/sglang/srt/model_executor/compilation/pass_manager.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/pass_manager.py
+
+import logging
+
+from torch import fx as fx
+
+from sglang.srt.model_executor.compilation.fix_functionalization import (
+    FixFunctionalizationPass,
+)
+from sglang.srt.model_executor.compilation.inductor_pass import (
+    CustomGraphPass,
+    InductorPass,
+    SGLangInductorPass,
+    get_pass_context,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class PostGradPassManager(CustomGraphPass):
+    """
+    The pass manager for post-grad passes.
+    It handles configuration, adding custom passes, and running passes.
+    It supports uuid for the Inductor code cache. That includes torch<2.6
+    support using pickling (in .inductor_pass.CustomGraphPass).
+
+    The order of the post-grad post-passes is:
+    1. passes (constructor parameter)
+    2. default passes (NoopEliminationPass, FusionPass)
+    3. config["post_grad_custom_post_pass"] (if it exists)
+    4. fix_functionalization
+    This way, all passes operate on a functionalized graph.
+    """
+
+    def __init__(self):
+        self.passes: list[SGLangInductorPass] = []
+
+    def __call__(self, graph: fx.Graph):
+        shape = get_pass_context().runtime_shape
+        for pass_ in self.passes:
+            if pass_.is_applicable_for_shape(shape):
+                pass_(graph)
+
+        # always run fix_functionalization last
+        self.fix_functionalization(graph)
+
+    def configure(
+        self,
+    ):
+        self.pass_config = dict()
+        self.fix_functionalization = FixFunctionalizationPass()
+
+    def add(self, pass_: InductorPass):
+        assert isinstance(pass_, InductorPass)
+        self.passes.append(pass_)
+
+    def uuid(self):
+        """
+        The PostGradPassManager is set as a custom pass in the Inductor and
+        affects compilation caching. Its uuid depends on the UUIDs of all
+        dependent passes and the pass config. See InductorPass for more info.
+        """
+        pass_manager_uuid = "fshdakhsa"
+        state = {"pass_config": pass_manager_uuid, "passes": []}
+        for pass_ in self.passes:
+            state["passes"].append(pass_.uuid())
+        state["passes"].append(self.fix_functionalization.uuid())
+        return InductorPass.hash_dict(state)

python/sglang/srt/model_executor/compilation/piecewise_context_manager.py

+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import Any, List, Optional
+
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+
+
+@dataclass
+class ForwardContext:
+    def __init__(self):
+        self.forward_batch = None
+        self.attention_layer = None
+
+    def set_forward_batch(self, forward_batch: ForwardBatch):
+        self.forward_batch = forward_batch
+
+    def set_attention_layers(self, layers: List[Any]):
+        self.attention_layers = layers
+
+
+_forward_context: Optional[ForwardContext] = None
+
+
+def get_forward_context() -> Optional[ForwardContext]:
+    if _forward_context is None:
+        return None
+    return _forward_context
+
+
+@contextmanager
+def set_forward_context(forward_batch: ForwardBatch, attention_layers: List[Any]):
+    global _forward_context
+    prev_forward_context = _forward_context
+    _forward_context = ForwardContext()
+    _forward_context.set_forward_batch(forward_batch)
+    _forward_context.set_attention_layers(attention_layers)
+    try:
+        yield
+    finally:
+        _forward_context = prev_forward_context

python/sglang/srt/model_executor/compilation/weak_ref_tensor.cpp

+// Adapted from: https://github.com/vllm-project/vllm/blob/main/csrc/ops.h
+
+#include <torch/extension.h>
+#include <vector>
+
+static at::Tensor weak_ref_tensor(at::Tensor &tensor) {
+  TORCH_CHECK(tensor.is_cuda(), "weak_ref_tensor expects a CUDA tensor");
+
+  void *data_ptr = tensor.data_ptr();
+  std::vector<int64_t> sizes = tensor.sizes().vec();
+  std::vector<int64_t> strides = tensor.strides().vec();
+
+  auto options = tensor.options();
+
+  auto new_tensor = torch::from_blob(data_ptr, sizes, strides, options);
+
+  return new_tensor;
+}
+
+TORCH_LIBRARY(jit_weak_ref_tensor, ops) {
+  ops.def("weak_ref_tensor(Tensor input) -> Tensor");
+}
+
+TORCH_LIBRARY_IMPL(jit_weak_ref_tensor, CUDA, ops) {
+  ops.impl("weak_ref_tensor", weak_ref_tensor);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {}

python/sglang/srt/model_executor/compilation/weak_ref_tensor_jit.py

+import os
+
+import torch
+from torch.utils.cpp_extension import load
+
+_abs_path = os.path.dirname(os.path.abspath(__file__))
+
+load(
+    name="weak_ref_tensor_ext",
+    sources=[f"{_abs_path}/weak_ref_tensor.cpp"],
+    extra_cflags=["-O3"],
+)
+
+x = torch.arange(12, device="cuda").reshape(3, 4)
+y = torch.ops.jit_weak_ref_tensor.weak_ref_tensor(x)
+print("alias:", x.data_ptr() == y.data_ptr())

python/sglang/srt/model_executor/model_runner.py

@@ -108,8 +108,15 @@ from sglang.srt.mem_cache.memory_pool import (
 )
 from sglang.srt.model_executor.cpu_graph_runner import CPUGraphRunner
 from sglang.srt.model_executor.cuda_graph_runner import CudaGraphRunner
-from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_executor.forward_batch_info import (
+    ForwardBatch,
+    ForwardMode,
+    PPProxyTensors,
+)
 from sglang.srt.model_executor.npu_graph_runner import NPUGraphRunner
+from sglang.srt.model_executor.piecewise_cuda_graph_runner import (
+    PiecewiseCudaGraphRunner,
+)
 from sglang.srt.model_loader import get_model
 from sglang.srt.model_loader.loader import DefaultModelLoader, get_model_loader
 from sglang.srt.model_loader.remote_instance_weight_loader_utils import (
@@ -307,6 +314,26 @@ class ModelRunner:
         self._model_update_group = {}
         self._weights_send_group = {}
 
+        if (
+            self.server_args.enable_piecewise_cuda_graph
+            and self.can_run_piecewise_cuda_graph()
+        ):
+            self.attention_layers = []
+            for layer in self.model.model.layers:
+                if hasattr(layer, "self_attn") and hasattr(layer.self_attn, "attn"):
+                    self.attention_layers.append(layer.self_attn.attn)
+            if len(self.attention_layers) < self.model_config.num_hidden_layers:
+                # TODO(yuwei): support Non-Standard GQA
+                log_info_on_rank0(
+                    logger,
+                    "Disable piecewise CUDA graph because some layers do not apply Standard GQA",
+                )
+                self.piecewise_cuda_graph_runner = None
+            else:
+                self.piecewise_cuda_graph_runner = PiecewiseCudaGraphRunner(self)
+        else:
+            self.piecewise_cuda_graph_runner = None
+
     def initialize(self, min_per_gpu_memory: float):
         server_args = self.server_args
 
@@ -692,6 +719,7 @@ class ModelRunner:
                 pipeline_model_parallel_size=self.pp_size,
                 expert_model_parallel_size=self.moe_ep_size,
                 duplicate_tp_group=self.server_args.enable_pdmux,
+                torch_compile=self.server_args.enable_piecewise_cuda_graph,
             )
             initialize_dp_attention(
                 server_args=self.server_args,
@@ -1411,6 +1439,27 @@ class ModelRunner:
                 f"Use Sliding window memory pool. full_layer_tokens={self.full_max_total_num_tokens}, swa_layer_tokens={self.swa_max_total_num_tokens}"
             )
 
+    def can_run_piecewise_cuda_graph(self):
+        if self.server_args.disable_cuda_graph:
+            log_info_on_rank0(
+                logger, "Disable piecewise CUDA graph because disable_cuda_graph is set"
+            )
+            return False
+        if self.server_args.enable_torch_compile:
+            log_info_on_rank0(
+                logger,
+                "Disable piecewise CUDA graph because piecewise_cuda_graph has conflict with torch compile",
+            )
+            return False
+        if self.pp_size > 1:
+            # TODO(yuwei): support PP
+            log_info_on_rank0(
+                logger,
+                "Disable piecewise CUDA graph because piecewise_cuda_graph does not support PP",
+            )
+            return False
+        return True
+
     def init_memory_pool(
         self,
         total_gpu_memory: int,
@@ -1932,6 +1981,11 @@ class ModelRunner:
             kwargs["input_embeds"] = forward_batch.input_embeds.bfloat16()
         if not self.is_generation:
             kwargs["get_embedding"] = True
+
+        if self.piecewise_cuda_graph_runner is not None:
+            if self.piecewise_cuda_graph_runner.can_run(forward_batch):
+                return self.piecewise_cuda_graph_runner.replay(forward_batch, **kwargs)
+
         return self.model.forward(
             forward_batch.input_ids,
             forward_batch.positions,

python/sglang/srt/server_args.py

@@ -417,7 +417,10 @@ class ServerArgs:
     enable_single_batch_overlap: bool = False
     tbo_token_distribution_threshold: float = 0.48
     enable_torch_compile: bool = False
+    enable_piecewise_cuda_graph: bool = False
     torch_compile_max_bs: int = 32
+    piecewise_cuda_graph_max_tokens: int = 4096
+    piecewise_cuda_graph_tokens: Optional[List[int]] = None
     torchao_config: str = ""
     enable_nan_detection: bool = False
     enable_p2p_check: bool = False
@@ -675,6 +678,11 @@ class ServerArgs:
         else:
             self.cuda_graph_max_bs = max(self.cuda_graph_bs)
 
+        if self.piecewise_cuda_graph_tokens is None:
+            self.piecewise_cuda_graph_tokens = (
+                self._generate_piecewise_cuda_graph_tokens()
+            )
+
         if self.mem_fraction_static is None:
             # Constant meta data (e.g., from attention backend)
             reserved_mem = 512
@@ -753,6 +761,25 @@ class ServerArgs:
 
         return capture_bs
 
+    def _generate_piecewise_cuda_graph_tokens(self):
+        """
+        Generate the list of batch sizes for piecewise CUDA graph capture
+        based on piecewise_cuda_graph_max_tokens.
+        """
+        capture_sizes = (
+            list(range(4, 33, 4))
+            + list(range(48, 257, 16))
+            + list(range(288, 513, 32))
+            + list(range(640, 4096 + 1, 128))
+            + list(range(4352, self.piecewise_cuda_graph_max_tokens + 1, 256))
+        )
+
+        capture_sizes = [
+            s for s in capture_sizes if s <= self.piecewise_cuda_graph_max_tokens
+        ]
+
+        return capture_sizes
+
     def _handle_hpu_backends(self):
         if self.device == "hpu":
             self.attention_backend = "torch_native"
@@ -2649,12 +2676,29 @@ class ServerArgs:
             action="store_true",
             help="Optimize the model with torch.compile. Experimental feature.",
         )
+        parser.add_argument(
+            "--enable-piecewise-cuda-graph",
+            action="store_true",
+            help="Optimize the model with piecewise cuda graph for extend/prefill only. Experimental feature.",
+        )
+        parser.add_argument(
+            "--piecewise-cuda-graph-tokens",
+            type=json_list_type,
+            default=ServerArgs.piecewise_cuda_graph_tokens,
+            help="Set the list of tokens when using piecewise cuda graph.",
+        )
         parser.add_argument(
             "--torch-compile-max-bs",
             type=int,
             default=ServerArgs.torch_compile_max_bs,
             help="Set the maximum batch size when using torch compile.",
         )
+        parser.add_argument(
+            "--piecewise-cuda-graph-max-tokens",
+            type=int,
+            default=ServerArgs.piecewise_cuda_graph_max_tokens,
+            help="Set the maximum tokens when using piecewise cuda graph.",
+        )
         parser.add_argument(
             "--torchao-config",
             type=str,