support multi-step draft-model with cudagraph

Author: freeliuzc

fastdeploy/spec_decode/mtp.py

@@ -636,7 +636,7 @@ def insert_prefill_inputs(self, req_dicts: List[Request], num_running_requests:
         self.model_inputs["not_need_stop"][0] = True
         self.model_inputs["seq_lens_this_time"] = self.seq_lens_this_time_buffer
 
-    def _initialize_forward_meta(self, step_use_cudagraph: bool = False):
+    def _initialize_forward_meta(self, step_use_cudagraph: bool = False, is_dummy_run: bool = False, substep: int = 0):
         """
         Initialize forward meta and attention meta data
         """
@@ -672,7 +672,12 @@ def _initialize_forward_meta(self, step_use_cudagraph: bool = False):
         for attn_backend in self.attn_backends:
             attn_backend.init_attention_metadata(self.forward_meta)
 
-        self.forward_meta.step_use_cudagraph = step_use_cudagraph and self.draft_model_use_cudagraph
+        # Notes(liuzichang):
+        # 1. CUDA Graph capture sizes must be recorded in descending order (large → small).
+        # 2. In multi-step execution, only the first step should be captured.
+        self.forward_meta.step_use_cudagraph = (
+            step_use_cudagraph and self.draft_model_use_cudagraph and not (substep > 0 and is_dummy_run)
+        )
 
     def exist_prefill(self):
         """
@@ -827,7 +832,9 @@ def _propose(self, step_use_cudagraph: bool = False):
                 self.model_inputs["output_padding_offset"].copy_(output_padding_offset, False)
 
                 # Initialize forward meta data
-                self._initialize_forward_meta(step_use_cudagraph=step_use_cudagraph)
+                self._initialize_forward_meta(
+                    step_use_cudagraph=step_use_cudagraph, is_dummy_run=is_dummy_run, substep=substep
+                )
                 self.forward_meta.batch_id_per_token.copy_(batch_id_per_token, False)
 
                 # Padding inputs for cuda graph
@@ -852,9 +859,10 @@ def _propose(self, step_use_cudagraph: bool = False):
                     top_p_normalized_logprobs=self.model_inputs["top_p_normalized_logprobs"],
                     share_inputs=self.model_inputs,
                 )
-
+                # Note(liuzichang):
+                # paddle.clone would raise error 700 in cudaGraph mode
                 if self.num_model_steps > 1:
-                    self.last_seq_lens_this_time = paddle.clone(self.model_inputs["seq_lens_this_time"])
+                    self.last_seq_lens_this_time.copy_(self.model_inputs["seq_lens_this_time"], False)
 
                 model_output = self.model(
                     ids_remove_padding=self.model_inputs["ids_remove_padding"],

fastdeploy/worker/gpu_model_runner.py

@@ -1950,51 +1950,12 @@ def capture_model(self) -> None:
                         ),
                         batch_size=int(capture_size / (self.speculative_config.num_speculative_tokens + 1)),
                         in_capturing=True,
-                        expected_decode_len=self.speculative_config.num_speculative_tokens,
+                        expected_decode_len=self.speculative_config.num_speculative_tokens * 2 + 1,
                         accept_all_drafts=True,
                     )
                     logger.info(
-                        f"Warm up the Target model with the num_tokens:{capture_size}, expected_decode_len:{self.speculative_config.num_speculative_tokens}"
+                        f"Warm up the model with the num_tokens:{capture_size}, expected_decode_len:{self.speculative_config.num_speculative_tokens}"
                     )
-                if self.graph_opt_config.draft_model_use_cudagraph:
-                    # Capture Draft Model without bsz 1
-                    # NOTE(liujundong): expected_decode_len = 1, will affect mtp capture in cudagraph
-                    for batch_size in sorted(capture_sizes, reverse=True):
-                        if batch_size == 1:
-                            logger.info("Skip token_num = 1, when capture Draft model for mtp")
-                        else:
-                            assert batch_size % 2 == 0
-                            self._dummy_run(
-                                num_tokens=(
-                                    self.scheduler_config.max_num_seqs
-                                    if self.scheduler_config.splitwise_role == "decode"
-                                    else self.scheduler_config.max_num_batched_tokens
-                                ),
-                                batch_size=int(batch_size / 2),
-                                in_capturing=True,
-                                expected_decode_len=3,
-                                accept_all_drafts=True,
-                            )
-                            logger.info(
-                                f"Warm up the Draft model with the num_tokens:{batch_size}, expected_decode_len:{3}"
-                            )
-                    # Capture Draft Model with bsz 1
-                    if 1 in capture_sizes:
-                        self._dummy_run(
-                            num_tokens=(
-                                self.scheduler_config.max_num_seqs
-                                if self.scheduler_config.splitwise_role == "decode"
-                                else self.scheduler_config.max_num_batched_tokens
-                            ),
-                            batch_size=int(1),
-                            in_capturing=True,
-                            expected_decode_len=3,
-                            accept_all_drafts=False,
-                            reject_all_drafts=True,
-                        )
-                        logger.info(
-                            f"Warm up the Draft model with the num_tokens:{batch_size}, expected_decode_len:{3}"
-                        )
             else:
                 for batch_size in sorted(capture_sizes, reverse=True):
                     self._dummy_run(