[BugFix] support real batch_size (#3109)

* support real bsz

* fix

* fix xpu_model_runner.py,gpu_model_runner.py,gcu_model_runner.py,iluvatar_model_runner.py

* add event_loop_ep

* fix

* Add comments

* fix

* support mtp real_batch_size

* fix

* self.tmp_seq_lens_this_time->self.seq_lens_this_time_buffer

* fix

* fix VL real_seq_lens_this_time

* fix

* fix mtp

* fix

* fix mtp

* fix xpu

* fix

Author: lizexu123

fastdeploy/spec_decode/mtp.py

@@ -107,7 +107,7 @@ def dummy_prefill_inputs(self, num_tokens: int, batch_size: int, expected_decode
             idx = i
             self.model_inputs["input_ids"][idx : idx + 1, :input_length] = np.array([5] * input_length)
             self.model_inputs["eos_token_id"][:] = np.array([2], dtype="int64").reshape(-1, 1)
-            self.model_inputs["seq_lens_this_time"][idx : idx + 1] = input_length
+            self.seq_lens_this_time_buffer[idx : idx + 1] = input_length
             self.model_inputs["seq_lens_encoder"][idx : idx + 1] = input_length
             self.model_inputs["seq_lens_decoder"][idx : idx + 1] = 0
             self.model_inputs["step_idx"][idx : idx + 1] = 0
@@ -118,6 +118,7 @@ def dummy_prefill_inputs(self, num_tokens: int, batch_size: int, expected_decode
             self.model_inputs["block_tables"][idx : idx + 1, :block_num] = np.arange(
                 idx * block_num, (idx + 1) * block_num, 1
             )
+        self.model_inputs["seq_lens_this_time"] = self.seq_lens_this_time_buffer
 
     def initialize_kv_cache(self):
         """
@@ -263,7 +264,8 @@ def _init_model_inputs(self):
         # Same shape/dytpe with base model
         self.model_inputs["block_tables"] = paddle.clone(self.main_model_inputs["block_tables"])
         self.model_inputs["input_ids"] = paddle.clone(self.main_model_inputs["input_ids"])
-        self.model_inputs["seq_lens_this_time"] = paddle.clone(self.main_model_inputs["seq_lens_this_time"])
+        self.seq_lens_this_time_buffer = paddle.clone(self.main_model_inputs["seq_lens_this_time"])
+
         self.model_inputs["seq_lens_encoder"] = paddle.clone(self.main_model_inputs["seq_lens_encoder"])
         self.model_inputs["seq_lens_decoder"] = paddle.clone(self.main_model_inputs["seq_lens_decoder"])
         self.model_inputs["step_idx"] = paddle.clone(self.main_model_inputs["step_idx"])
@@ -338,7 +340,7 @@ def _init_model_inputs(self):
                 self.main_model_inputs["seq_lens_this_time"], fill_value=-1, dtype="int32"
             )
 
-    def insert_prefill_inputs(self, req_dicts: List[Request]):
+    def insert_prefill_inputs(self, req_dicts: List[Request], num_running_requests: int):
         """
         Process inputs for prefill tasks and insert it to model_inputs buffer
         """
@@ -372,7 +374,7 @@ def insert_prefill_inputs(self, req_dicts: List[Request]):
 
                 self.model_inputs["seq_lens_encoder"][idx : idx + 1] = 0
                 self.model_inputs["seq_lens_decoder"][idx : idx + 1] = length
-                self.model_inputs["seq_lens_this_time"][idx : idx + 1] = prefill_token_num
+                self.seq_lens_this_time_buffer[idx : idx + 1] = prefill_token_num
 
                 self.model_inputs["stop_flags"][idx : idx + 1] = False
                 self.model_inputs["batch_drop"][idx : idx + 1] = False
@@ -397,10 +399,10 @@ def insert_prefill_inputs(self, req_dicts: List[Request]):
                 if self.cache_config.enable_chunked_prefill:
                     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
+                    self.seq_lens_this_time_buffer[idx : idx + 1] = token_chunk_size
                 else:
                     self.model_inputs["seq_lens_encoder"][idx : idx + 1] = length
-                    self.model_inputs["seq_lens_this_time"][idx : idx + 1] = length
+                    self.seq_lens_this_time_buffer[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
@@ -413,6 +415,7 @@ def insert_prefill_inputs(self, req_dicts: List[Request]):
                     request.get("block_tables"), dtype="int32"
                 )
         self.model_inputs["not_need_stop"][0] = True
+        self.model_inputs["seq_lens_this_time"] = self.seq_lens_this_time_buffer[:num_running_requests]
 
     def _initialize_forward_meta(self):
         """

fastdeploy/worker/gcu_model_runner.py

@@ -152,9 +152,11 @@ def _init_logits_processor(self, request):
             schemata_key,
         )
 
-    def insert_prefill_inputs(self, req_dicts: List[Request]):
+    def insert_prefill_inputs(self, req_dicts: List[Request], num_running_requests: int = None):
         """
         Process inputs for prefill tasks and insert it to share_inputs buffer
+        req_dict: A list of Request dict
+        num_running_requests: batch_size
         """
 
         if req_dicts[-1].disaggregate_info is not None and req_dicts[-1].disaggregate_info["role"] == "prefill":
@@ -193,7 +195,7 @@ def get_attr_from_request(request, attr, default_value=None):
                 self.share_inputs["prompt_ids"][idx : idx + 1, :length] = np.array(request.prompt_token_ids)
                 self.share_inputs["seq_lens_encoder"][idx : idx + 1] = 0
                 self.share_inputs["seq_lens_decoder"][idx : idx + 1] = length
-                self.share_inputs["seq_lens_this_time"][idx : idx + 1] = 1
+                self.seq_lens_this_time_buffer[idx : idx + 1] = 1
                 self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = 0
                 self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = length
                 self.share_inputs["prompt_lens"][idx : idx + 1] = length
@@ -205,7 +207,7 @@ def get_attr_from_request(request, attr, default_value=None):
                         request.draft_token_ids[0:num_prefill_send_token],
                         dtype="int64",
                     )
-                    self.share_inputs["seq_lens_this_time"][idx : idx + 1] = num_prefill_send_token
+                    self.seq_lens_this_time_buffer[idx : idx + 1] = num_prefill_send_token
             else:
                 self.share_inputs["pre_ids"][idx : idx + 1] = -1
                 self.share_inputs["step_idx"][idx : idx + 1] = 0
@@ -222,14 +224,14 @@ def get_attr_from_request(request, attr, default_value=None):
                     )
                     self.share_inputs["seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
                     self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
-                    self.share_inputs["seq_lens_this_time"][idx : idx + 1] = token_chunk_size
+                    self.seq_lens_this_time_buffer[idx : idx + 1] = token_chunk_size
                     self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = token_chunk_size
                     self.share_inputs["seq_lens_encoder"][idx : idx + 1] = token_chunk_size
                     self.share_inputs["prompt_lens"][idx : idx + 1] = token_chunk_size
                 else:
                     self.share_inputs["seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
                     self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
-                    self.share_inputs["seq_lens_this_time"][idx : idx + 1] = length
+                    self.seq_lens_this_time_buffer[idx : idx + 1] = length
                     self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = length
                     self.share_inputs["seq_lens_encoder"][idx : idx + 1] = length
                     self.share_inputs["prompt_lens"][idx : idx + 1] = length
@@ -295,6 +297,7 @@ def get_attr_from_request(request, attr, default_value=None):
 
         if self.speculative_method in ["mtp"]:
             self.proposer.insert_prefill_inputs(req_dicts)
+        self.share_inputs["seq_lens_this_time"] = self.seq_lens_this_time_buffer[:num_running_requests]
 
     def _dummy_prefill_inputs(self, num_tokens: int, batch_size: int, expected_decode_len: int):
         """Set dummy prefill inputs to share_inputs"""
@@ -313,7 +316,7 @@ def _dummy_prefill_inputs(self, num_tokens: int, batch_size: int, expected_decod
             self.share_inputs["input_ids"][idx : idx + 1, :input_length] = np.array([5] * input_length)
             self.share_inputs["prompt_ids"][idx : idx + 1, :input_length] = np.array([5] * input_length)
             self.share_inputs["eos_token_id"][:] = np.array([2], dtype="int64").reshape(-1, 1)
-            self.share_inputs["seq_lens_this_time"][idx : idx + 1] = input_length
+            self.seq_lens_this_time_buffer[idx : idx + 1] = input_length
             self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = input_length
             self.share_inputs["seq_lens_encoder"][idx : idx + 1] = input_length
             self.share_inputs["seq_lens_decoder"][idx : idx + 1] = 0
@@ -331,6 +334,7 @@ def _dummy_prefill_inputs(self, num_tokens: int, batch_size: int, expected_decod
             self.share_inputs["block_tables"][idx : idx + 1, :block_num] = np.arange(
                 idx * block_num, (idx + 1) * block_num, 1
             )
+        self.share_inputs["seq_lens_this_time"] = self.seq_lens_this_time_buffer
 
     def _init_share_inputs(self, max_num_seqs: int):
         """
@@ -381,7 +385,7 @@ def _init_share_inputs(self, max_num_seqs: int):
         self.share_inputs["max_length"] = paddle.full(
             [max_num_seqs, 1], self.model_config.max_model_len, dtype="int64"
         )
-        self.share_inputs["seq_lens_this_time"] = paddle.full(max_num_seqs, 0, dtype="int32")
+        self.seq_lens_this_time_buffer = paddle.full(max_num_seqs, 0, dtype="int32")
         self.share_inputs["seq_lens_encoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
         self.share_inputs["seq_lens_decoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
         self.share_inputs["step_seq_lens_encoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
@@ -923,13 +927,15 @@ def _get_skip_idx(self, model_forward_batch: Optional[List[Request]] = None):
     def execute_model(
         self,
         model_forward_batch: Optional[List[Request]] = None,
+        num_running_requests: int = None,
     ) -> Optional[ModelRunnerOutput]:
         """
         The Entrance of model execute.
         Args:
             model_forward_batch: 'Request' contains information related to prompt and is an abstract
             class at the server level, which is too granular for ModelRunner.
             We plan to replace it with 'ModelForwardBatch'.
+            num_running_requests: batch_size
             intermediate_tensors:
         """
         # If `not_need_stop`` is False, it means the current worker is in an idle state.
@@ -1055,6 +1061,9 @@ class at the server level, which is too granular for ModelRunner.
 
         self._update_chunked_prefill(model_forward_batch)
         self._add_cache(model_forward_batch)
+        self.seq_lens_this_time_buffer[:num_running_requests].copy_(
+            self.share_inputs["seq_lens_this_time"][:num_running_requests], False
+        )
         return None
 
     def _add_cache(self, model_forward_batch) -> None:

fastdeploy/worker/gcu_worker.py

@@ -105,17 +105,18 @@ def initialize_cache(self, num_gpu_blocks: int) -> None:
     def execute_model(
         self,
         model_forward_batch: Optional[List[Request]] = None,
+        num_running_requests: int = None,
     ) -> Optional[ModelRunnerOutput]:
         """ """
-        output = self.model_runner.execute_model(model_forward_batch)
+        output = self.model_runner.execute_model(model_forward_batch, num_running_requests)
         return output
 
-    def preprocess_new_task(self, req_dicts: List[Request]) -> None:
+    def preprocess_new_task(self, req_dicts: List[Request], num_running_requests: int) -> None:
         """Process new requests and then start the decode loop
         TODO(gongshaotian):The scheduler should schedule the handling of prefill,
         and workers and modelrunners should not perceive it.
         """
-        self.model_runner.insert_prefill_inputs(req_dicts=req_dicts)
+        self.model_runner.insert_prefill_inputs(req_dicts=req_dicts, num_running_requests=num_running_requests)
 
     def graph_optimize_and_warm_up_model(self) -> None:
         """