Simplify batch update (#2154)

README.md

@@ -35,7 +35,7 @@ SGLang is a fast serving framework for large language models and vision language
 It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.
 The core features include:
 
-- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ).
+- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, overhead-free CPU scheduler, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (FP8/INT4/AWQ/GPTQ).
 - **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
 - **Extensive Model Support**: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte, mcdse) and reward models (Skywork), with easy extensibility for integrating new models.
 - **Active Community**: SGLang is open-source and backed by an active community with industry adoption.

docs/backend/backend.md

@@ -79,7 +79,6 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
 ```
 python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
 ```
-- To enable the experimental overlapped scheduler, add `--enable-overlap-schedule`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currently.
 - To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. This does not work for FP8 currently.
 - To enable torchao quantization, add `--torchao-config int4wo-128`. It supports various quantization strategies.
 - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.

docs/index.rst

@@ -5,7 +5,7 @@ SGLang is a fast serving framework for large language models and vision language
 It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.
 The core features include:
 
-- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ).
+- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, overhead-free CPU scheduler, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (FP8/INT4/AWQ/GPTQ).
 - **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
 - **Extensive Model Support**: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte) and reward models (Skywork), with easy extensibility for integrating new models.
 - **Active Community**: SGLang is open-source and backed by an active community with industry adoption.

docs/references/hyperparameter_tuning.md

@@ -31,7 +31,6 @@ If you see out of memory (OOM) errors, you can try to tune the following paramet
 - You can also try to decrease `--mem-fraction-static`, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding.
 
 ### Try Advanced Options
-- To enable the experimental overlapped scheduler, add `--enable-overlap-schedule`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currently.
 - To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. This does not work for FP8 currently.
 
 ### Tune `--schedule-policy`

python/sglang/srt/managers/schedule_batch.py

@@ -467,6 +467,7 @@ class ScheduleBatch:
     extend_lens: List[int] = None
     extend_num_tokens: int = None
     decoding_reqs: List[Req] = None
+    extend_logprob_start_lens: List[int] = None
 
     # For encoder-decoder
     encoder_cached: Optional[List[bool]] = None
@@ -722,7 +723,6 @@ class ScheduleBatch:
         self.merge_batch(running_batch)
         self.input_ids = input_ids
         self.out_cache_loc = out_cache_loc
-        self.extend_num_tokens += running_bs
 
         # NOTE: prefix_indices is what has been cached, but we don't cache each decode step
         self.prefix_lens.extend(
@@ -732,6 +732,8 @@ class ScheduleBatch:
             ]
         )
         self.extend_lens.extend([1] * running_bs)
+        self.extend_num_tokens += running_bs
+        # TODO (lianmin): Revisit this. It should be seq_len - 1
         self.extend_logprob_start_lens.extend([0] * running_bs)
 
     def check_decode_mem(self):

python/sglang/srt/managers/scheduler.py

@@ -13,7 +13,6 @@
 # ==============================================================================
 """A scheduler that manages a tensor parallel GPU worker."""
 
-import dataclasses
 import logging
 import os
 import threading
@@ -28,7 +27,7 @@ import torch
 import zmq
 
 from sglang.global_config import global_config
-from sglang.srt.configs.model_config import AttentionArch, ModelConfig
+from sglang.srt.configs.model_config import ModelConfig
 from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.managers.io_struct import (
@@ -302,6 +301,9 @@ class Scheduler:
         ) / global_config.default_new_token_ratio_decay_steps
         self.new_token_ratio = self.init_new_token_ratio
 
+        # Tells whether the current running batch is full so that we can skip
+        # the check of whether to prefill new requests.
+        # This is an optimization to reduce the overhead of the prefill check.
         self.batch_is_full = False
 
         # Init watchdog thread
@@ -721,40 +723,30 @@ class Scheduler:
 
     def get_next_batch_to_run(self):
         # Merge the prefill batch into the running batch
-        if (
-            self.last_batch
-            and not self.last_batch.forward_mode.is_decode()
-            and not self.last_batch.is_empty()
-        ):
+        if self.last_batch and self.last_batch.forward_mode.is_extend():
             if self.being_chunked_req:
+                # Move the chunked request out of the batch
                 self.last_batch.filter_batch(being_chunked_req=self.being_chunked_req)
                 self.tree_cache.cache_unfinished_req(self.being_chunked_req)
-                # Inflight request keeps its rid but will get a new req_pool_idx.
+                # Inflight request keeps its rid but will get a new req_pool_idx
                 self.req_to_token_pool.free(self.being_chunked_req.req_pool_idx)
                 self.batch_is_full = False
+
             if not self.last_batch.is_empty():
                 if self.running_batch is None:
                     self.running_batch = self.last_batch
                 else:
                     self.running_batch.merge_batch(self.last_batch)
 
-        # Prefill first
+        # Run prefill first if possible
         new_batch = self.get_new_batch_prefill()
         if new_batch is not None:
             return new_batch
 
-        # Check memory
-        if self.running_batch is None:
-            return
-
         # Run decode
-        before_bs = self.running_batch.batch_size()
-        self.update_running_batch()
-        if not self.running_batch:
-            self.batch_is_full = False
+        if self.running_batch is None:
             return None
-        if before_bs != self.running_batch.batch_size():
-            self.batch_is_full = False
+        self.running_batch = self.update_running_batch(self.running_batch)
         return self.running_batch
 
     def get_new_batch_prefill(self) -> Optional[ScheduleBatch]:
@@ -866,15 +858,16 @@ class Scheduler:
 
         return new_batch
 
-    def update_running_batch(self):
+    def update_running_batch(self, batch: ScheduleBatch) -> Optional[ScheduleBatch]:
         """Update the current running decoding batch."""
         global test_retract
-        batch = self.running_batch
+
+        initial_bs = batch.batch_size()
 
         batch.filter_batch()
         if batch.is_empty():
-            self.running_batch = None
-            return
+            self.batch_is_full = False
+            return None
 
         # Check if decode out of memory
         if not batch.check_decode_mem() or (test_retract and batch.batch_size() > 10):
@@ -900,11 +893,15 @@ class Scheduler:
             jump_forward_reqs = batch.check_for_jump_forward(self.pad_input_ids_func)
             self.waiting_queue.extend(jump_forward_reqs)
             if batch.is_empty():
-                self.running_batch = None
-                return
+                self.batch_is_full = False
+                return None
+
+        if batch.batch_size() < initial_bs:
+            self.batch_is_full = False
 
         # Update batch tensors
         batch.prepare_for_decode(self.enable_overlap)
+        return batch
 
     def run_batch(self, batch: ScheduleBatch):
         """Run a batch."""
@@ -979,8 +976,10 @@ class Scheduler:
                 if req.is_retracted:
                     continue
 
+                if self.is_mixed_chunk and self.enable_overlap and req.finished():
+                    raise ValueError("Unhandled error!")
+
                 if req.is_being_chunked <= 0:
-                    # Inflight reqs' prefill is not finished
                     req.completion_tokens_wo_jump_forward += 1
                     req.output_ids.append(next_token_id)
                     req.check_finished()
@@ -990,14 +989,16 @@ class Scheduler:
                     elif not batch.decoding_reqs or req not in batch.decoding_reqs:
                         self.tree_cache.cache_unfinished_req(req)
 
-                    if req.grammar is not None:
-                        req.grammar.accept_token(next_token_id)
-
                     if req.return_logprob:
+                        # TODO (lianmin): need to think the case w/ mixed chunked prefill
                         logprob_pt += self.add_logprob_return_values(
                             i, req, logprob_pt, next_token_ids, logits_output
                         )
+
+                    if req.grammar is not None:
+                        req.grammar.accept_token(next_token_id)
                 else:
+                    # Inflight reqs' prefill is not finished
                     req.is_being_chunked -= 1
 
             if batch.next_batch_sampling_info:
@@ -1015,18 +1016,18 @@ class Scheduler:
                     continue
 
                 req.embedding = embeddings[i]
-                if req.is_being_chunked > 0:
-                    req.is_being_chunked -= 1
-                else:
-                    # Inflight reqs' prefill is not finished
-                    # dummy output token for embedding models
+                if req.is_being_chunked <= 0:
+                    # Dummy output token for embedding models
                     req.output_ids.append(0)
                     req.check_finished()
 
-                if req.finished():
-                    self.tree_cache.cache_finished_req(req)
+                    if req.finished():
+                        self.tree_cache.cache_finished_req(req)
+                    else:
+                        self.tree_cache.cache_unfinished_req(req)
                 else:
-                    self.tree_cache.cache_unfinished_req(req)
+                    # Inflight reqs' prefill is not finished
+                    req.is_being_chunked -= 1
 
         self.stream_output(batch.reqs)
 
@@ -1061,9 +1062,6 @@ class Scheduler:
             req.output_ids.append(next_token_id)
             req.check_finished()
 
-            if req.grammar is not None:
-                req.grammar.accept_token(next_token_id)
-
             if req.finished():
                 self.tree_cache.cache_finished_req(req)
 
@@ -1074,6 +1072,9 @@ class Scheduler:
                 if req.top_logprobs_num > 0:
                     req.output_top_logprobs.append(logits_output.output_top_logprobs[i])
 
+            if req.grammar is not None:
+                req.grammar.accept_token(next_token_id)
+
         if batch.next_batch_sampling_info:
             batch.next_batch_sampling_info.update_regex_vocab_mask()
             torch.cuda.current_stream().synchronize()

python/sglang/test/few_shot_gsm8k.py

@@ -135,7 +135,7 @@ def run_eval(args):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--num-shots", type=int, default=5)
-    parser.add_argument("--data-path", type=str, default="test.jsonl")
+    parser.add_argument("--data-path", type=str)
     parser.add_argument("--num-questions", type=int, default=200)
     parser.add_argument("--max-new-tokens", type=int, default=512)
     parser.add_argument("--parallel", type=int, default=128)