[SOT] Extend SOT warmup support to new hardware (#3032)

* add new hardware

* add_sot_warmup4new_hardware

* fix conflict

* rm Optional

fastdeploy/worker/xpu_model_runner.py

@@ -26,6 +26,10 @@ from fastdeploy import envs
 from fastdeploy.config import FDConfig
 from fastdeploy.engine.request import Request, RequestType
 from fastdeploy.model_executor.forward_meta import ForwardMeta, XPUForwardMeta
+from fastdeploy.model_executor.graph_optimization.utils import (
+    profile_run_guard,
+    sot_warmup_guard,
+)
 from fastdeploy.model_executor.layers.attention import get_attention_backend
 from fastdeploy.model_executor.layers.attention.base_attention_backend import (
     AttentionBackend,
@@ -346,7 +350,9 @@ class XPUModelRunner(ModelRunnerBase):
         # self.kv_caches: list[paddle.Tensor] = []
 
         # Cuda Graph
+        self.graph_opt_level = self.graph_opt_config.graph_opt_level
         self.use_cudagraph = False
+        self.sot_warmup_sizes = self.graph_opt_config.sot_warmup_sizes
         self.input_ids = paddle.zeros(self.parallel_config.max_num_seqs, dtype="int32")
 
         # Initialize share inputs
@@ -764,6 +770,17 @@ class XPUModelRunner(ModelRunnerBase):
         logger.warn("XPU not support cuda graph currently")
         pass
 
+    @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 exist_prefill(self):
         """
         check whether prefill stage exist
@@ -901,6 +918,7 @@ class XPUModelRunner(ModelRunnerBase):
         self.num_gpu_blocks = self.parallel_config.total_block_num
         self.initialize_kv_cache()
 
+    @profile_run_guard(True)
     def profile_run(self) -> None:
         """Execute a forward pass with dummy inputs to profile the memory usage of the model."""