Simplify the interface of tp_worker (#1718)

python/sglang/srt/managers/scheduler.py

@@ -91,6 +91,7 @@ class Scheduler:
         port_args: PortArgs,
         gpu_id: int,
         tp_rank: int,
+        dp_rank: Optional[int],
     ):
         # Parse args
         self.server_args = server_args
@@ -144,13 +145,24 @@ class Scheduler:
 
         # Launch a tensor parallel worker
         self.tp_worker = TpModelWorker(
+            server_args=server_args,
             gpu_id=gpu_id,
             tp_rank=tp_rank,
-            server_args=server_args,
+            dp_rank=dp_rank,
             nccl_port=port_args.nccl_port,
         )
-        self.tp_cpu_group = self.tp_worker.model_runner.tp_group.cpu_group
-        self.device = self.tp_worker.device
+
+        # Init states for overlap schedule
+        if self.server_args.enable_overlap_schedule:
+            self.forward_batch_generation = (
+                self.tp_worker.forward_batch_generation_non_blocking
+            )
+            self.resolve_next_token_ids = (
+                lambda bid, x: self.tp_worker.resolve_future_token_ids(bid)
+            )
+        else:
+            self.forward_batch_generation = self.tp_worker.forward_batch_generation
+            self.resolve_next_token_ids = lambda bid, x: x.tolist()
 
         # Get token and memory info from the model worker
         (
@@ -159,11 +171,11 @@ class Scheduler:
             self.max_running_requests,
             self.max_req_input_len,
             self.random_seed,
+            self.device,
         ) = self.tp_worker.get_token_and_memory_info()
+        self.tp_cpu_group = self.tp_worker.get_tp_cpu_group()
+        self.pad_input_ids_func = self.tp_worker.get_pad_input_ids_func()
         set_random_seed(self.random_seed)
-        self.pad_input_ids_func = getattr(
-            self.tp_worker.model_runner.model, "pad_input_ids", None
-        )
 
         # Print debug info
         logger.info(
@@ -173,9 +185,8 @@ class Scheduler:
             f"context_len={self.model_config.context_len}"
         )
 
-        # Init cache
-        self.req_to_token_pool = self.tp_worker.model_runner.req_to_token_pool
-        self.token_to_kv_pool = self.tp_worker.model_runner.token_to_kv_pool
+        # Init memory pool and cache
+        self.req_to_token_pool, self.token_to_kv_pool = self.tp_worker.get_memory_pool()
 
         if (
             server_args.chunked_prefill_size is not None
@@ -253,20 +264,6 @@ class Scheduler:
                 with_stack=True,
             )
 
-        # Init states for overlap schedule
-        if self.server_args.enable_overlap_schedule:
-            self.forward_batch_generation = (
-                self.tp_worker.forward_batch_generation_non_blocking
-            )
-            self.resolve_next_token_ids = (
-                lambda bid, x: self.tp_worker.resolve_future_token_ids(bid)
-            )
-            self.cache_finished_req = self.tree_cache.cache_finished_req
-        else:
-            self.forward_batch_generation = self.tp_worker.forward_batch_generation
-            self.resolve_next_token_ids = lambda bid, x: x.tolist()
-            self.cache_finished_req = self.tree_cache.cache_finished_req
-
     @torch.inference_mode()
     def event_loop_normal(self):
         self.last_batch = None
@@ -779,7 +776,7 @@ class Scheduler:
                     req.check_finished()
 
                     if req.finished():
-                        self.cache_finished_req(req)
+                        self.tree_cache.cache_finished_req(req)
                     elif not batch.decoding_reqs or req not in batch.decoding_reqs:
                         self.tree_cache.cache_unfinished_req(req)
 
@@ -808,7 +805,7 @@ class Scheduler:
                     req.check_finished()
 
                 if req.finished():
-                    self.cache_finished_req(req)
+                    self.tree_cache.cache_finished_req(req)
                 else:
                     self.tree_cache.cache_unfinished_req(req)
 
@@ -845,7 +842,7 @@ class Scheduler:
                 )
 
             if req.finished():
-                self.cache_finished_req(req)
+                self.tree_cache.cache_finished_req(req)
 
             if req.return_logprob:
                 req.output_token_logprobs.append(
@@ -1069,7 +1066,7 @@ class Scheduler:
             for req in self.running_batch.reqs:
                 if req.rid == recv_req.rid and not req.finished():
                     req.finished_reason = FINISH_ABORT()
-                    self.cache_finished_req(req)
+                    self.tree_cache.cache_finished_req(req)
                     break
 
     def update_weights(self, recv_req: UpdateWeightReqInput):
@@ -1112,7 +1109,7 @@ def run_scheduler_process(
     suppress_other_loggers()
 
     try:
-        scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank)
+        scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank, dp_rank)
         pipe_writer.send("ready")
         if server_args.enable_overlap_schedule:
             scheduler.event_loop_overlap()

python/sglang/srt/managers/tp_worker.py

@@ -20,6 +20,7 @@ import logging
 import threading
 import time
 from queue import Queue
+from typing import Optional
 
 import torch
 
@@ -40,9 +41,10 @@ class TpModelWorker:
 
     def __init__(
         self,
+        server_args: ServerArgs,
         gpu_id: int,
         tp_rank: int,
-        server_args: ServerArgs,
+        dp_rank: Optional[int],
         nccl_port: int,
     ):
         # Parse args
@@ -116,6 +118,19 @@ class TpModelWorker:
             self.max_running_requests,
             self.max_req_input_len,
             self.random_seed,
+            self.device,
+        )
+
+    def get_pad_input_ids_func(self):
+        return getattr(self.model_runner.model, "pad_input_ids", None)
+
+    def get_tp_cpu_group(self):
+        return self.model_runner.tp_group.cpu_group
+
+    def get_memory_pool(self):
+        return (
+            self.model_runner.req_to_token_pool,
+            self.model_runner.token_to_kv_pool,
         )
 
     def init_overlap_status(self):