[SOT] Add sot warmup (NVIDIA GPU Only) (#2929)

* add sot warmup

* fix code style

* change batch_size list

* add param to config

* rm free_list settings && set sot_warmup_sizes

* finish debug with dynamic dims by type annotations

* add profile_run guard

* rm sth useless

fastdeploy/config.py

@@ -319,6 +319,8 @@ class GraphOptimizationConfig:
         - With dyncmic graph backend: ...
         - With static grpah backend: WIP
     """
+    sot_warmup_sizes: Optional[list[int]] = field(default_factory=list)
+    """  Number of warmup runs for SOT warmup. """
     use_cudagraph: bool = False
     """Sizes to capture cudagraph.
     - None (default): capture sizes are inferred from llm config.

fastdeploy/engine/config.py

@@ -429,6 +429,7 @@ class GraphOptimizationConfig:
         graph_opt_level: Optional[int] = 0,
         use_cudagraph: Optional[bool] = None,
         cudagraph_capture_sizes: Optional[List[int]] = None,
+        sot_warmup_sizes: Optional[List[int]] = None,
         **kwargs,
     ):
         """
@@ -444,6 +445,7 @@ class GraphOptimizationConfig:
         self.graph_opt_level = graph_opt_level
         self.use_cudagraph = use_cudagraph
         self.cudagraph_capture_sizes = cudagraph_capture_sizes
+        self.sot_warmup_sizes = [] if sot_warmup_sizes is None else sot_warmup_sizes
 
     def to_json_string(self):
         """

fastdeploy/model_executor/graph_optimization/graph_optimization_backend.py

@@ -31,26 +31,15 @@ from fastdeploy.model_executor.graph_optimization.cudagraph_piecewise_backend im
 from fastdeploy.model_executor.graph_optimization.dynamic_dims_marker import (
     resolve_dynamic_dims,
 )
+from fastdeploy.model_executor.graph_optimization.utils import in_profile_run_mode
+from fastdeploy.model_executor.graph_optimization.utils import (
+    in_sot_warmup_mode as in_warmup_mode,
+)
 
 P = ParamSpec("P")
 T = TypeVar("T")
 
 
-# TODO(SigureMo): Replace this fn with real implementation by DrRyanHuang
-def create_in_warmup_mode():
-    cnt = 0
-
-    def in_warmup_mode():
-        nonlocal cnt
-        cnt += 1
-        return cnt < 32
-
-    return in_warmup_mode
-
-
-in_warmup_mode = create_in_warmup_mode()
-
-
 def apply_to_static_optimization(fn: Callable[P, T], backend: ToStaticBackend) -> Callable[P, T]:
     forward_fn = fn
     forward_sig = inspect.signature(forward_fn)
@@ -99,6 +88,8 @@ def apply_to_static_optimization(fn: Callable[P, T], backend: ToStaticBackend) -
 
     @functools.wraps(forward_fn)
     def static_forward(self, *args, **kwargs):
+        if in_profile_run_mode():
+            return forward_fn(self, *args, **kwargs)
         nonlocal need_warmup
         is_warmup = in_warmup_mode() and need_warmup
         if is_warmup:

fastdeploy/model_executor/graph_optimization/utils.py

@@ -0,0 +1,40 @@
+"""
+# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+import contextlib
+
+
+def create_guard(default_value):
+    _state = default_value
+
+    @contextlib.contextmanager
+    def state_guard(current_state):
+        nonlocal _state
+        old_state = _state
+        _state = current_state
+        try:
+            yield
+        finally:
+            _state = old_state
+
+    def get_state():
+        return _state
+
+    return state_guard, get_state
+
+
+sot_warmup_guard, in_sot_warmup_mode = create_guard(False)
+profile_run_guard, in_profile_run_mode = create_guard(False)

fastdeploy/worker/gpu_model_runner.py

@@ -25,6 +25,10 @@ from paddleformers.utils.log import logger
 
 from fastdeploy.config import FDConfig
 from fastdeploy.engine.request import Request
+from fastdeploy.model_executor.graph_optimization.utils import (
+    profile_run_guard,
+    sot_warmup_guard,
+)
 from fastdeploy.model_executor.guided_decoding import get_guided_backend
 from fastdeploy.model_executor.guided_decoding.base_guided_decoding import (
     LogitsProcessorBase,
@@ -113,8 +117,10 @@ class GPUModelRunner(ModelRunnerBase):
         # self.kv_caches: list[paddle.Tensor] = []
 
         # Cuda Graph
+        self.graph_opt_level = self.graph_opt_config.graph_opt_level
         self.use_cudagraph = self.graph_opt_config.use_cudagraph
         self.cudagraph_capture_sizes = list(reversed(self.graph_opt_config.cudagraph_capture_sizes))
+        self.sot_warmup_sizes = self.graph_opt_config.sot_warmup_sizes
 
         # Initialize share inputs
         self._init_share_inputs(self.parallel_config.max_num_seqs)
@@ -367,9 +373,6 @@ class GPUModelRunner(ModelRunnerBase):
     def _dummy_prefill_inputs(self, num_tokens: int, batch_size: int, expected_decode_len: int):
         """Set dummy prefill inputs to share_inputs"""
         # NOTE(gongshaotian): The maximum decoding length is equal to the expected decoded tokens plus the eos token
-        if self.enable_mm:
-            self.share_inputs["free_list"] = paddle.to_tensor([], dtype="int32")
-            self.share_inputs["free_list_len"][0] = 0
         max_dec_len = expected_decode_len + 1
         full_length = min(
             num_tokens // batch_size,
@@ -1007,6 +1010,17 @@ class GPUModelRunner(ModelRunnerBase):
         time_after_capture = time.perf_counter()
         logger.info(f"Cuda Graph capturing took {time_after_capture - time_before_capture} seconds")
 
+    @sot_warmup_guard(True)
+    def sot_warmup(self) -> None:
+        start_time = time.perf_counter()
+        for batch_size in self.sot_warmup_sizes:
+            self._dummy_run(
+                num_tokens=self.parallel_config.max_num_batched_tokens,
+                batch_size=batch_size,
+            )
+            logger.info(f"SOT warmup the model with the batch size:{batch_size}")
+        logger.info(f"SOT warmup took {time.perf_counter() - start_time} seconds")
+
     def _get_skip_idx(self, model_forward_batch: Optional[List[Request]] = None):
         """
         Get the index of the request that needs to be skipped during execution.
@@ -1208,6 +1222,7 @@ class GPUModelRunner(ModelRunnerBase):
         else:
             raise ValueError(f"{type(self.model)} has no attribute 'empty_input_forward")
 
+    @profile_run_guard(True)
     def profile_run(self) -> None:
         """Execute a forward pass with dummy inputs to profile the memory usage of the model"""
 

fastdeploy/worker/gpu_worker.py

@@ -189,6 +189,8 @@ class GpuWorker(WorkerBase):
         """
         Perform the warm-up and the graph optimization
         """
+        if self.model_runner.graph_opt_level >= 1:
+            self.model_runner.sot_warmup()
         # Triger cuda grpah capture
         self.model_runner.capture_model()
 

fastdeploy/worker/worker_process.py

@@ -632,6 +632,7 @@ def initialize_fd_config(args, ranks: int = 1, local_rank: int = 0) -> FDConfig:
             use_cudagraph=args.graph_optimization_config["use_cudagraph"],
             graph_opt_level=args.graph_optimization_config["graph_opt_level"],
             cudagraph_capture_sizes=args.graph_optimization_config["cudagraph_capture_sizes"],
+            sot_warmup_sizes=args.graph_optimization_config["sot_warmup_sizes"],
         )
 
     # Note(tangbinhan): used for load_checkpoint