Chunked prefill support (#797)

python/sglang/srt/managers/controller/schedule_heuristic.py

@@ -38,24 +38,24 @@ class ScheduleHeuristic:
         self.max_total_num_tokens = max_total_num_tokens
         self.tree_cache = tree_cache
 
-    def get_priority_queue(self, forward_queue):
+    def get_priority_queue(self, waiting_queue):
         if self.schedule_heuristic == "lpm":
             # longest prefix match
-            forward_queue.sort(key=lambda x: -len(x.prefix_indices))
-            return forward_queue
+            waiting_queue.sort(key=lambda x: -len(x.prefix_indices))
+            return waiting_queue
         elif self.schedule_heuristic == "fcfs":
             # first come first serve
-            return forward_queue
+            return waiting_queue
         elif self.schedule_heuristic == "lof":
             # longest output first
-            forward_queue.sort(key=lambda x: -x.sampling_params.max_new_tokens)
-            return forward_queue
+            waiting_queue.sort(key=lambda x: -x.sampling_params.max_new_tokens)
+            return waiting_queue
         elif self.schedule_heuristic == "random":
-            random.shuffle(forward_queue)
-            return forward_queue
+            random.shuffle(waiting_queue)
+            return waiting_queue
         elif self.schedule_heuristic == "dfs-weight":
             last_node_to_reqs = defaultdict(list)
-            for req in forward_queue:
+            for req in waiting_queue:
                 last_node_to_reqs[req.last_node].append(req)
 
             node_to_weight = defaultdict(int)
@@ -67,7 +67,7 @@ class ScheduleHeuristic:
             self.get_dfs_priority(
                 self.tree_cache.root_node, node_to_weight, last_node_to_reqs, q
             )
-            assert len(q) == len(forward_queue)
+            assert len(q) == len(waiting_queue)
             return q
         else:
             raise ValueError(f"Unknown schedule_heuristic: {self.schedule_heuristic}")

python/sglang/srt/managers/controller/tp_worker.py

@@ -77,6 +77,10 @@ class ModelTpServer:
         self.schedule_heuristic = server_args.schedule_heuristic
         self.disable_regex_jump_forward = server_args.disable_regex_jump_forward
 
+        # Chunked prefill
+        self.chunked_prefill_size = server_args.chunked_prefill_size
+        self.current_inflight_req = None
+
         # Init model and tokenizer
         self.model_config = ModelConfig(
             server_args.model_path,
@@ -157,7 +161,7 @@ class ModelTpServer:
         self.token_to_kv_pool = self.model_runner.token_to_kv_pool
 
         # Init running status
-        self.forward_queue: List[Req] = []
+        self.waiting_queue: List[Req] = []
         self.running_batch: Batch = None
         self.out_pyobjs = []
         self.decode_forward_ct = 0
@@ -220,6 +224,7 @@ class ModelTpServer:
             # Run a new prefill batch
             self.forward_prefill_batch(new_batch)
             self.cache_filled_batch(new_batch)
+            self.filter_out_inflight(new_batch)
 
             if not new_batch.is_empty():
                 if self.running_batch is None:
@@ -261,7 +266,7 @@ class ModelTpServer:
             f"#token: {num_used}, "
             f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
             f"gen throughput (token/s): {throughput:.2f}, "
-            f"#queue-req: {len(self.forward_queue)}"
+            f"#queue-req: {len(self.waiting_queue)}"
         )
 
     def check_memory(self):
@@ -328,9 +333,10 @@ class ModelTpServer:
             ),
             self.max_req_input_len - 1 - len(req.origin_input_ids),
         )
-        self.forward_queue.append(req)
+        self.waiting_queue.append(req)
 
     def get_new_prefill_batch(self) -> Optional[Batch]:
+        # TODO(lsyin): organize this function
         running_bs = (
             len(self.running_batch.reqs) if self.running_batch is not None else 0
         )
@@ -338,7 +344,7 @@ class ModelTpServer:
             return
 
         # Compute matched prefix length
-        for req in self.forward_queue:
+        for req in self.waiting_queue:
             req.input_ids = req.origin_input_ids + req.output_ids
             prefix_indices, last_node = self.tree_cache.match_prefix(req.input_ids)
             if req.return_logprob:
@@ -348,7 +354,7 @@ class ModelTpServer:
             req.last_node = last_node
 
         # Get priority queue
-        self.forward_queue = self.scheduler.get_priority_queue(self.forward_queue)
+        self.waiting_queue = self.scheduler.get_priority_queue(self.waiting_queue)
 
         # Add requests if there is available space
         can_run_list = []
@@ -367,7 +373,33 @@ class ModelTpServer:
                 ]
             )
 
-        for req in self.forward_queue:
+        # Handle the current inflight request
+        take_inflight = 0
+        if self.current_inflight_req:
+            take_inflight = 1
+            r = self.current_inflight_req
+            r.input_ids = r.origin_input_ids + r.output_ids
+            truncated = (
+                len(r.input_ids) - len(r.prefix_indices) > self.chunked_prefill_size
+            )
+            r.extend_input_len = min(
+                len(r.input_ids) - len(r.prefix_indices), self.chunked_prefill_size
+            )
+            r.input_ids = r.input_ids[: len(r.prefix_indices) + r.extend_input_len]
+            can_run_list.append(r)
+
+            if not truncated:
+                # Finish inflight
+                self.current_inflight_req = None
+                new_batch_total_tokens += (
+                    r.extend_input_len + r.sampling_params.max_new_tokens
+                )
+                new_batch_input_tokens += r.extend_input_len
+            else:
+                new_batch_total_tokens += r.extend_input_len
+                new_batch_input_tokens += r.extend_input_len
+
+        for req in self.waiting_queue:
             if req.return_logprob and req.normalized_prompt_logprob is None:
                 # Need at least two tokens to compute normalized logprob
                 if req.extend_input_len < 2:
@@ -409,11 +441,36 @@ class ModelTpServer:
                     break
                 else:
                     # Add this request to the running batch
-                    can_run_list.append(req)
-                    new_batch_total_tokens += (
-                        req.extend_input_len + req.sampling_params.max_new_tokens
-                    )
-                    new_batch_input_tokens += req.extend_input_len
+                    if (
+                        new_batch_input_tokens + req.extend_input_len
+                        <= self.chunked_prefill_size
+                        or (
+                            req.return_logprob and req.normalized_prompt_logprob is None
+                        )
+                    ):
+                        can_run_list.append(req)
+                        new_batch_total_tokens += (
+                            req.extend_input_len + req.sampling_params.max_new_tokens
+                        )
+                        new_batch_input_tokens += req.extend_input_len
+                    else:
+                        trunc_len = self.chunked_prefill_size - new_batch_input_tokens
+
+                        if trunc_len <= 0:
+                            # Undo locking
+                            delta = self.tree_cache.dec_lock_ref(req.last_node)
+                            available_size += delta
+                            break
+
+                        req.extend_input_len = trunc_len
+                        req.input_ids = req.input_ids[
+                            : len(req.prefix_indices) + req.extend_input_len
+                        ]
+                        can_run_list.append(req)
+                        self.current_inflight_req = req
+                        new_batch_input_tokens += req.extend_input_len
+                        new_batch_total_tokens += req.extend_input_len
+                        break
             else:
                 break
 
@@ -440,7 +497,7 @@ class ModelTpServer:
                 f"#cached-token: {hit_tokens}, "
                 f"cache hit rate: {100.0 * tree_cache_hit_rate:.2f}%, "
                 f"#running-req: {running_bs}, "
-                f"#queue-req: {len(self.forward_queue) - len(can_run_list)}"
+                f"#queue-req: {len(self.waiting_queue) - len(can_run_list) + take_inflight}"
             )
 
         # Return the new batch
@@ -450,7 +507,7 @@ class ModelTpServer:
             self.token_to_kv_pool,
             self.tree_cache,
         )
-        self.forward_queue = [x for x in self.forward_queue if x not in can_run_list]
+        self.waiting_queue = [x for x in self.waiting_queue if x not in can_run_list]
         return new_batch
 
     def forward_prefill_batch(self, batch: Batch):
@@ -482,9 +539,10 @@ class ModelTpServer:
         # Check finish conditions
         pt = 0
         for i, req in enumerate(batch.reqs):
-            req.completion_tokens_wo_jump_forward += 1
-            req.output_ids.append(next_token_ids[i])
-            req.check_finished()
+            if req is not self.current_inflight_req:
+                req.completion_tokens_wo_jump_forward += 1
+                req.output_ids.append(next_token_ids[i])
+                req.check_finished()
 
             if req.return_logprob:
                 self.add_logprob_return_values(i, req, pt, next_token_ids, output)
@@ -545,7 +603,7 @@ class ModelTpServer:
         req_pool_indices_cpu = batch.req_pool_indices.cpu().numpy()
         for i, req in enumerate(batch.reqs):
             new_prefix_indices, new_last_node = self.tree_cache.cache_req(
-                token_ids=tuple(req.origin_input_ids + req.output_ids)[:-1],
+                token_ids=tuple(req.input_ids),
                 last_uncached_pos=len(req.prefix_indices),
                 req_pool_idx=req_pool_indices_cpu[i],
                 del_in_memory_pool=False,
@@ -553,6 +611,10 @@ class ModelTpServer:
             )
             req.prefix_indices, req.last_node = new_prefix_indices, new_last_node
 
+            if req is self.current_inflight_req:
+                # inflight request would get a new req idx
+                self.req_to_token_pool.free(int(req_pool_indices_cpu[i]))
+
     def forward_decode_batch(self, batch: Batch):
         # Check if decode out of memory
         if not batch.check_decode_mem():
@@ -566,7 +628,7 @@ class ModelTpServer:
                 f"#retracted_reqs: {len(retracted_reqs)}, "
                 f"#new_token_ratio: {old_ratio:.4f} -> {self.new_token_ratio:.4f}"
             )
-            self.forward_queue.extend(retracted_reqs)
+            self.waiting_queue.extend(retracted_reqs)
         else:
             self.new_token_ratio = max(
                 self.new_token_ratio - self.new_token_ratio_decay,
@@ -576,7 +638,7 @@ class ModelTpServer:
         if not self.disable_regex_jump_forward:
             # Check for jump-forward
             jump_forward_reqs = batch.check_for_jump_forward(self.model_runner)
-            self.forward_queue.extend(jump_forward_reqs)
+            self.waiting_queue.extend(jump_forward_reqs)
             if batch.is_empty():
                 return
 
@@ -711,8 +773,18 @@ class ModelTpServer:
             else:
                 batch.reqs = []
 
+    def filter_out_inflight(self, batch: Batch):
+        # TODO(lsyin): reduce the overhead, make a special version for this
+        if self.current_inflight_req is None:
+            return
+
+        unfinished_indices = list(range(len(batch.reqs)))
+        unfinished_indices.remove(batch.reqs.index(self.current_inflight_req))
+
+        batch.filter_batch(unfinished_indices)
+
     def flush_cache(self):
-        if len(self.forward_queue) == 0 and (
+        if len(self.waiting_queue) == 0 and (
             self.running_batch is None or len(self.running_batch.reqs) == 0
         ):
             self.tree_cache.reset()
@@ -725,20 +797,20 @@ class ModelTpServer:
         else:
             warnings.warn(
                 f"Cache not flushed because there are pending requests. "
-                f"#queue-req: {len(self.forward_queue)}, "
+                f"#queue-req: {len(self.waiting_queue)}, "
                 f"#running-req: {0 if self.running_batch is None else len(self.running_batch.reqs)}"
             )
 
     def abort_request(self, recv_req):
         # Delete requests in the waiting queue
         to_del = None
-        for i, req in enumerate(self.forward_queue):
+        for i, req in enumerate(self.waiting_queue):
             if req.rid == recv_req.rid:
                 to_del = i
                 break
 
         if to_del is not None:
-            del self.forward_queue[to_del]
+            del self.waiting_queue[to_del]
 
         # Delete requests in the running batch
         if self.running_batch:

python/sglang/srt/memory_pool.py

@@ -45,7 +45,7 @@ class ReqToTokenPool:
 
         return select_index
 
-    def free(self, free_index: int):
+    def free(self, free_index):
         self.mem_state[free_index] = True
         if isinstance(free_index, (int,)):
             self.can_use_mem_size += 1

python/sglang/srt/server.py

@@ -175,6 +175,39 @@ def _set_torch_compile_config():
     torch._dynamo.config.accumulated_cache_size_limit = 256
 
 
+def set_envs_and_config(server_args: ServerArgs):
+    # Set global environments
+    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+    os.environ["NCCL_CUMEM_ENABLE"] = "0"
+    os.environ["NCCL_NVLS_ENABLE"] = "0"
+    os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+    # Set ulimit
+    set_ulimit()
+
+    # Enable show time cost for debugging
+    if server_args.show_time_cost:
+        enable_show_time_cost()
+
+    # Disable disk cache
+    if server_args.disable_disk_cache:
+        disable_cache()
+
+    # Fix triton bugs
+    if server_args.tp_size * server_args.dp_size > 1:
+        # FIXME: remove this after https://github.com/triton-lang/triton/pull/4295 is used as a dependency.
+        maybe_set_triton_cache_manager()
+
+    # Set torch compile config
+    if server_args.enable_torch_compile:
+        _set_torch_compile_config()
+
+    # Set global chat template
+    if server_args.chat_template:
+        # TODO: replace this with huggingface transformers template
+        load_chat_template_for_openai_api(server_args.chat_template)
+
+
 def launch_server(
     server_args: ServerArgs,
     model_overide_args: Optional[dict] = None,
@@ -190,16 +223,6 @@ def launch_server(
         format="%(message)s",
     )
 
-    # Set global environments
-    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-    os.environ["NCCL_CUMEM_ENABLE"] = "0"
-    os.environ["NCCL_NVLS_ENABLE"] = "0"
-    os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
-    set_ulimit()
-    if server_args.show_time_cost:
-        enable_show_time_cost()
-    if server_args.disable_disk_cache:
-        disable_cache()
     if not server_args.disable_flashinfer:
         assert_pkg_version(
             "flashinfer",
@@ -208,14 +231,8 @@ def launch_server(
             "reinstall the latest version by following the instructions "
             "at https://docs.flashinfer.ai/installation.html.",
         )
-    if server_args.tp_size * server_args.dp_size > 1:
-        # FIXME: remove this after https://github.com/triton-lang/triton/pull/4295 is used as a dependency.
-        maybe_set_triton_cache_manager()
-    if server_args.chat_template:
-        # TODO: replace this with huggingface transformers template
-        load_chat_template_for_openai_api(server_args.chat_template)
-    if server_args.enable_torch_compile:
-        _set_torch_compile_config()
+
+    set_envs_and_config(server_args)
 
     # Allocate ports
     server_args.port, server_args.additional_ports = allocate_init_ports(

python/sglang/srt/server_args.py

@@ -65,6 +65,9 @@ class ServerArgs:
     dp_size: int = 1
     load_balance_method: str = "round_robin"
 
+    # Chunked Prefill
+    chunked_prefill_size: Optional[int] = None
+
     # Optimization/debug options
     disable_flashinfer: bool = False
     disable_flashinfer_sampling: bool = False
@@ -83,6 +86,8 @@ class ServerArgs:
     node_rank: Optional[int] = None
 
     def __post_init__(self):
+        if self.chunked_prefill_size is None:
+            self.chunked_prefill_size = int(10**9)
         if self.tokenizer_path is None:
             self.tokenizer_path = self.model_path
         if self.mem_fraction_static is None:
@@ -223,7 +228,7 @@ class ServerArgs:
         parser.add_argument(
             "--max-num-reqs",
             type=int,
-            default=None,
+            default=ServerArgs.max_num_reqs,
             help="The maximum number of requests to serve in the memory pool. If the model have a large context length, you may need to decrease this value to avoid out-of-memory errors.",
         )
         parser.add_argument(
@@ -311,10 +316,18 @@ class ServerArgs:
             help="The nccl init address of multi-node server.",
         )
         parser.add_argument(
-            "--nnodes", type=int, default=1, help="The number of nodes."
+            "--nnodes", type=int, default=ServerArgs.nnodes, help="The number of nodes."
         )
         parser.add_argument("--node-rank", type=int, help="The node rank.")
 
+        # Chunked prefill
+        parser.add_argument(
+            "--chunked-prefill-size",
+            type=int,
+            default=ServerArgs.chunked_prefill_size,
+            help="The size of the chunked prefill.",
+        )
+
         # Optimization/debug options
         parser.add_argument(
             "--disable-flashinfer",
@@ -393,6 +406,10 @@ class ServerArgs:
             self.dp_size > 1 and self.node_rank is not None
         ), "multi-node data parallel is not supported"
 
+        assert not (
+            self.chunked_prefill_size is not None and self.disable_radix_cache
+        ), "chunked prefill is not supported with radix cache disabled currently"
+
 
 @dataclasses.dataclass
 class PortArgs: