[MTP] Support chunked_prefill in speculative decoding(MTP)

fastdeploy/spec_decode/base.py

@@ -61,3 +61,13 @@ def _run_impl(self, *args, **kwargs) -> Any:
         Implemention for different method
         """
         raise NotImplementedError
+
+    def is_chunk_prefill_enabled(self) -> bool:
+        """
+        Check whether chunk-based prefill is enabled.
+        Default is False.
+
+        Returns:
+            bool: True if chunk prefill is enabled; False otherwise.
+        """
+        return False

fastdeploy/spec_decode/mtp.py

@@ -405,17 +405,21 @@ def insert_prefill_inputs(self, req_dicts: List[Request]):
                                                                  1:length]
                 self.model_inputs["pre_ids"][idx:idx + 1] = -1
                 self.model_inputs["step_idx"][idx:idx + 1] = 0
-                # TODO(liuzichang) finish chunked_prefill
                 if self.parallel_config.enable_chunked_prefill:
-                    raise NotImplementedError(
-                        "MTP don't support chunked_prefill now")
+                    token_chunk_size = request.prefill_chunk_info[0]
+                    self.model_inputs["seq_lens_encoder"][idx:idx +
+                                                          1] = token_chunk_size
+                    self.model_inputs["seq_lens_this_time"][
+                        idx:idx + 1] = token_chunk_size
                 else:
                     self.model_inputs["seq_lens_encoder"][idx:idx + 1] = length
-                    self.model_inputs["seq_lens_decoder"][idx:idx + 1] = (
-                        request.get("seq_lens_decoder", 0))
                     self.model_inputs["seq_lens_this_time"][idx:idx +
                                                             1] = length
 
+                self.model_inputs["seq_lens_decoder"][idx:idx +
+                                                      1] = (request.get(
+                                                          "seq_lens_decoder",
+                                                          0))
                 self.model_inputs["stop_flags"][idx:idx + 1] = False
                 self.model_inputs["batch_drop"][idx:idx + 1] = False
 
@@ -578,7 +582,6 @@ def _propose(self, target_hidden_states):
                     self.model_inputs["output_padding_offset"],
                     self.parallel_config.max_model_len,
                 )
-                paddle.device.synchronize()
 
                 # 4. Compute logits, Sample
                 logits = self.model.compute_logits(hiddden_states)
@@ -595,6 +598,43 @@ def _propose(self, target_hidden_states):
 
                 self._post_process(sampled_token_ids)
 
+    def update_task_chunk_prefill(self, task):
+        """
+        Update single task's chunk_prefill info
+        """
+        idx = task.idx
+        start_idx = sum(task.prefill_chunk_info[:task.chunk_idx])
+
+        if task.chunk_idx == len(task.prefill_chunk_info):
+            self.model_inputs['seq_lens_encoder'][idx:idx + 1] = 0
+            self.model_inputs["step_idx"][idx:idx + 1] = 1
+            self.model_inputs["seq_lens_decoder"][idx:idx +
+                                                  1] = start_idx + task.get(
+                                                      "seq_lens_decoder", 0)
+        else:
+            token_chunk_size = task.prefill_chunk_info[task.chunk_idx]
+
+            if task.chunk_idx < len(task.prefill_chunk_info) - 1:
+                self.model_inputs['input_ids'][
+                    idx, :token_chunk_size] = np.array(
+                        task.prompt_token_ids[start_idx + 1:start_idx +
+                                              token_chunk_size + 1])
+            # Last prefill
+            else:
+                self.model_inputs['input_ids'][
+                    idx, :token_chunk_size - 1] = np.array(
+                        task.prompt_token_ids[start_idx + 1:start_idx +
+                                              token_chunk_size])
+
+            self.model_inputs["seq_lens_this_time"][idx:idx +
+                                                    1] = token_chunk_size
+            self.model_inputs['seq_lens_encoder'][idx:idx +
+                                                  1] = token_chunk_size
+            self.model_inputs["step_idx"][idx:idx + 1] = 0
+            self.model_inputs["seq_lens_decoder"][idx:idx +
+                                                  1] = start_idx + task.get(
+                                                      "seq_lens_decoder", 0)
+
     def _update_status(self):
         """
         Update main-model's forward info in next step.
@@ -624,6 +664,11 @@ def _update_status(self):
         )
 
     def _run_impl(self, full_hidden_states):
+        """"""
         target_hidden_states = self._prepare_inputs(full_hidden_states)
         self._propose(target_hidden_states=target_hidden_states)
         self._update_status()
+
+    def is_chunk_prefill_enabled(self):
+        """"""
+        return True

fastdeploy/worker/gpu_model_runner.py

@@ -898,6 +898,9 @@ def _update_chunked_prefill(self, tasks):
                 self.share_inputs["step_idx"][idx:idx + 1] = 0
                 self.share_inputs["seq_lens_decoder"][
                     idx:idx + 1] = start_idx + task.get("seq_lens_decoder", 0)
+            if self.speculative_decoding and self.proposer.is_chunk_prefill_enabled(
+            ):
+                self.proposer.update_task_chunk_prefill(task)
             task.chunk_idx += 1
 
     def _dummy_sampler_run(self) -> paddle.Tensor: