Remove sampling info events and overlap thread file (#11300)

python/sglang/srt/disaggregation/decode.py

@@ -783,16 +783,6 @@ class SchedulerDisaggregationDecodeMixin:
                         self.prepare_mlp_sync_batch(batch)
                     result = self.run_batch(batch)
                     self.result_queue.append((batch.copy(), result))
-
-                    if (self.last_batch is None) or (not self.last_batch_in_queue):
-                        # Create a dummy first batch to start the pipeline for overlap schedule.
-                        # It is now used for triggering the sampling_info_done event.
-                        tmp_batch = ScheduleBatch(
-                            reqs=None,
-                            forward_mode=ForwardMode.DUMMY_FIRST,
-                            next_batch_sampling_info=self.tp_worker.cur_sampling_info,
-                        )
-                        self.set_next_batch_sampling_info_done(tmp_batch)
                     last_batch_in_queue = True
 
             elif prepare_mlp_sync_flag:
@@ -806,9 +796,6 @@ class SchedulerDisaggregationDecodeMixin:
             # Process the results of the previous batch but skip if the last batch is extend
             if self.last_batch and self.last_batch_in_queue:
                 tmp_batch, tmp_result = self.result_queue.popleft()
-                tmp_batch.next_batch_sampling_info = (
-                    self.tp_worker.cur_sampling_info if batch else None
-                )
                 self.process_batch_result(tmp_batch, tmp_result)
 
             queue_size = (

python/sglang/srt/disaggregation/prefill.py

@@ -338,21 +338,8 @@ class SchedulerDisaggregationPrefillMixin:
                 result = self.run_batch(batch)
                 self.result_queue.append((batch.copy(), result))
 
-                if self.last_batch is None:
-                    # Create a dummy first batch to start the pipeline for overlap schedule.
-                    # It is now used for triggering the sampling_info_done event.
-                    tmp_batch = ScheduleBatch(
-                        reqs=None,
-                        forward_mode=ForwardMode.DUMMY_FIRST,
-                        next_batch_sampling_info=self.tp_worker.cur_sampling_info,
-                    )
-                    self.set_next_batch_sampling_info_done(tmp_batch)
-
             if self.last_batch:
                 tmp_batch, tmp_result = self.result_queue.popleft()
-                tmp_batch.next_batch_sampling_info = (
-                    self.tp_worker.cur_sampling_info if batch else None
-                )
                 self.process_batch_result_disagg_prefill(tmp_batch, tmp_result)
 
             if len(self.disagg_prefill_inflight_queue) > 0:
@@ -491,8 +478,6 @@ class SchedulerDisaggregationPrefillMixin:
                 if self.enable_overlap:
                     self.send_kv_chunk(req, last_chunk=False, end_idx=req.tmp_end_idx)
 
-        # We need to remove the sync in the following function for overlap schedule.
-        self.set_next_batch_sampling_info_done(batch)
         self.maybe_send_health_check_signal()
 
     def process_disagg_prefill_inflight_queue(

python/sglang/srt/managers/schedule_batch.py

@@ -891,7 +891,6 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
 
     # Sampling info
     sampling_info: SamplingBatchInfo = None
-    next_batch_sampling_info: SamplingBatchInfo = None
 
     # Batched arguments to model runner
     input_ids: torch.Tensor = None  # shape: [b], int64

python/sglang/srt/managers/scheduler.py

@@ -1012,22 +1012,9 @@ class Scheduler(
                 result = self.run_batch(batch)
                 self.result_queue.append((batch.copy(), result))
 
-                if self.last_batch is None:
-                    # Create a dummy first batch to start the pipeline for overlap schedule.
-                    # It is now used for triggering the sampling_info_done event.
-                    tmp_batch = ScheduleBatch(
-                        reqs=None,
-                        forward_mode=ForwardMode.DUMMY_FIRST,
-                        next_batch_sampling_info=self.tp_worker.cur_sampling_info,
-                    )
-                    self.process_batch_result(tmp_batch, None)
-
             if self.last_batch:
                 # Process the results of the last batch
                 tmp_batch, tmp_result = self.result_queue.popleft()
-                tmp_batch.next_batch_sampling_info = (
-                    self.tp_worker.cur_sampling_info if batch else None
-                )
                 self.process_batch_result(tmp_batch, tmp_result)
             elif batch is None:
                 # When the server is idle, do self-check and re-init some states
@@ -2100,7 +2087,7 @@ class Scheduler(
                 self.record_batch_in_overlap(model_worker_batch)
 
                 # Sampling info will be modified during forward
-                model_worker_batch.sampling_info = self.tp_worker.cur_sampling_info = (
+                model_worker_batch.sampling_info = (
                     model_worker_batch.sampling_info.copy_for_forward()
                 )
 
@@ -2219,9 +2206,6 @@ class Scheduler(
             if self.enable_overlap:
                 if result.copy_done is not None:
                     result.copy_done.synchronize()
-                self.set_next_batch_sampling_info_done(batch)
-        elif batch.forward_mode.is_dummy_first():
-            self.set_next_batch_sampling_info_done(batch)
 
         self.maybe_send_health_check_signal()
 
@@ -2431,13 +2415,6 @@ class Scheduler(
             self._add_request_to_queue(req)
         self.grammar_queue = self.grammar_queue[num_ready_reqs:]
 
-    def set_next_batch_sampling_info_done(self, batch: ScheduleBatch):
-        if batch.next_batch_sampling_info:
-            if batch.next_batch_sampling_info.grammars is not None:
-                batch.next_batch_sampling_info.update_regex_vocab_mask()
-                self.default_stream.synchronize()
-            batch.next_batch_sampling_info.sampling_info_done.set()
-
     def watchdog_thread(self):
         """A watch dog thread that will try to kill the server itself if one forward batch takes too long."""
         self.watchdog_last_forward_ct = 0

python/sglang/srt/managers/scheduler_output_processor_mixin.py

@@ -173,8 +173,6 @@ class SchedulerOutputProcessorMixin:
                                 )
                             logprob_pt += num_input_logprobs
 
-            self.set_next_batch_sampling_info_done(batch)
-
         else:  # embedding or reward model
             embeddings = result.embeddings.tolist()
 
@@ -295,7 +293,6 @@ class SchedulerOutputProcessorMixin:
                     self.abort_request(AbortReq(rid=req.rid))
                 req.grammar.finished = req.finished()
 
-        self.set_next_batch_sampling_info_done(batch)
         self.stream_output(batch.reqs, batch.return_logprob)
         self.token_to_kv_pool_allocator.free_group_end()
 

python/sglang/srt/managers/tp_worker_overlap_thread.py

-# Copyright 2023-2024 SGLang Team
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""A tensor parallel worker."""
-from __future__ import annotations
-
-import dataclasses
-import logging
-import signal
-import threading
-from queue import Queue
-from typing import TYPE_CHECKING, List, Optional, Tuple
-
-import psutil
-import torch
-
-from sglang.srt.managers.io_struct import (
-    DestroyWeightsUpdateGroupReqInput,
-    GetWeightsByNameReqInput,
-    InitWeightsSendGroupForRemoteInstanceReqInput,
-    InitWeightsUpdateGroupReqInput,
-    LoadLoRAAdapterReqInput,
-    SendWeightsToRemoteInstanceReqInput,
-    UnloadLoRAAdapterReqInput,
-    UpdateWeightFromDiskReqInput,
-    UpdateWeightsFromDistributedReqInput,
-    UpdateWeightsFromTensorReqInput,
-)
-from sglang.srt.managers.overlap_utils import FutureMap
-from sglang.srt.managers.schedule_batch import ModelWorkerBatch
-from sglang.srt.managers.tp_worker import TpModelWorker
-from sglang.srt.model_executor.forward_batch_info import ForwardBatchOutput
-from sglang.srt.server_args import ServerArgs
-from sglang.srt.utils import DynamicGradMode
-from sglang.utils import get_exception_traceback
-
-if TYPE_CHECKING:
-    from sglang.srt.managers.cache_controller import LayerDoneCounter
-
-logger = logging.getLogger(__name__)
-
-
-class TpModelWorkerClient:
-    """A tensor parallel model worker."""
-
-    def __init__(
-        self,
-        server_args: ServerArgs,
-        gpu_id: int,
-        tp_rank: int,
-        moe_ep_rank: int,
-        pp_rank: int,
-        dp_rank: Optional[int],
-        nccl_port: int,
-    ):
-        # Load the model
-        self.worker = TpModelWorker(
-            server_args, gpu_id, tp_rank, moe_ep_rank, pp_rank, dp_rank, nccl_port
-        )
-        self.max_running_requests = self.worker.max_running_requests
-        self.device = self.worker.device
-        self.gpu_id = gpu_id
-
-        # Init future mappings
-        self.future_map = FutureMap(self.max_running_requests, self.device)
-
-        # Launch threads
-        self.input_queue = Queue[Tuple[ModelWorkerBatch, int, torch.Event]]()
-        self.output_queue = Queue()
-        self.forward_stream = torch.get_device_module(self.device).Stream()
-        self.forward_thread = threading.Thread(
-            target=self.forward_thread_func,
-        )
-        self.forward_thread.start()
-        self.parent_process = psutil.Process().parent()
-        self.scheduler_stream = torch.get_device_module(self.device).current_stream()
-        if self.device == "cpu":
-            self.scheduler_stream.synchronize = lambda: None  # No-op for CPU
-
-        self.hicache_layer_transfer_counter = None
-
-    def register_hicache_layer_transfer_counter(self, counter: LayerDoneCounter):
-        self.hicache_layer_transfer_counter = counter
-
-    def get_worker_info(self):
-        return self.worker.get_worker_info()
-
-    def get_tokens_per_layer_info(self):
-        return self.worker.get_tokens_per_layer_info()
-
-    @property
-    def sliding_window_size(self) -> Optional[int]:
-        return self.worker.sliding_window_size
-
-    @property
-    def is_hybrid(self) -> bool:
-        return self.worker.is_hybrid
-
-    def get_pad_input_ids_func(self):
-        return self.worker.get_pad_input_ids_func()
-
-    def get_tp_group(self):
-        return self.worker.get_tp_group()
-
-    def get_attention_tp_group(self):
-        return self.worker.get_attention_tp_group()
-
-    def get_attention_tp_cpu_group(self):
-        return self.worker.get_attention_tp_cpu_group()
-
-    def get_memory_pool(self):
-        return (
-            self.worker.model_runner.req_to_token_pool,
-            self.worker.model_runner.token_to_kv_pool_allocator,
-        )
-
-    def get_kv_cache(self):
-        return self.worker.model_runner.token_to_kv_pool
-
-    def forward_thread_func(self):
-        try:
-            with torch.get_device_module(self.device).stream(self.forward_stream):
-                self.forward_thread_func_()
-        except Exception:
-            traceback = get_exception_traceback()
-            logger.error(f"TpModelWorkerClient hit an exception: {traceback}")
-            self.parent_process.send_signal(signal.SIGQUIT)
-
-    @DynamicGradMode()
-    def forward_thread_func_(self):
-        batch_pt = 0
-        batch_lists: List = [None] * 2
-
-        while True:
-            model_worker_batch, future_map_ct, sync_event = self.input_queue.get()
-            if not model_worker_batch:
-                break
-
-            sync_event.wait()
-
-            # Keep a reference of model_worker_batch by storing it into a list.
-            # Otherwise, the tensor members of model_worker_batch will be released
-            # by pytorch and cause CUDA illegal memory access errors.
-            batch_lists[batch_pt % 2] = model_worker_batch
-            batch_pt += 1
-
-            # Create event
-            copy_done = torch.get_device_module(self.device).Event()
-
-            # Resolve future tokens in the input
-            self.future_map.resolve_future(model_worker_batch)
-
-            # Run forward
-            forward_batch_output = self.worker.forward_batch_generation(
-                model_worker_batch,
-                model_worker_batch.launch_done,
-            )
-
-            logits_output, next_token_ids, can_run_cuda_graph = (
-                forward_batch_output.logits_output,
-                forward_batch_output.next_token_ids,
-                forward_batch_output.can_run_cuda_graph,
-            )
-
-            # Update the future token ids map
-            bs = len(model_worker_batch.seq_lens)
-            if model_worker_batch.is_prefill_only:
-                # For prefill-only requests, create dummy token IDs on CPU
-                next_token_ids = torch.zeros(bs, dtype=torch.long)
-
-            # store the future indices into future map
-            self.future_map.store_to_map(future_map_ct, bs, next_token_ids)
-
-            # Copy results to the CPU
-            if model_worker_batch.return_logprob:
-                if logits_output.next_token_logprobs is not None:
-                    logits_output.next_token_logprobs = (
-                        logits_output.next_token_logprobs.to("cpu", non_blocking=True)
-                    )
-                if logits_output.input_token_logprobs is not None:
-                    logits_output.input_token_logprobs = (
-                        logits_output.input_token_logprobs.to("cpu", non_blocking=True)
-                    )
-            if logits_output.hidden_states is not None:
-                logits_output.hidden_states = logits_output.hidden_states.to(
-                    "cpu", non_blocking=True
-                )
-            # Only copy to CPU if not already on CPU
-            if next_token_ids.device.type != "cpu":
-                next_token_ids = next_token_ids.to("cpu", non_blocking=True)
-            copy_done.record()
-
-            self.output_queue.put(
-                (copy_done, logits_output, next_token_ids, can_run_cuda_graph)
-            )
-
-    def resolve_last_batch_result(self, launch_done: Optional[threading.Event] = None):
-        """
-        This function is called to resolve the last batch result and
-        wait for the current batch to be launched. Used in overlap mode.
-        """
-        copy_done, logits_output, next_token_ids, can_run_cuda_graph = (
-            self.output_queue.get()
-        )
-
-        if launch_done is not None:
-            launch_done.wait()
-        copy_done.synchronize()
-
-        if logits_output.next_token_logprobs is not None:
-            logits_output.next_token_logprobs = (
-                logits_output.next_token_logprobs.tolist()
-            )
-        if logits_output.input_token_logprobs is not None:
-            logits_output.input_token_logprobs = tuple(
-                logits_output.input_token_logprobs.tolist()
-            )
-        next_token_ids = next_token_ids.tolist()
-        return logits_output, next_token_ids, can_run_cuda_graph
-
-    def forward_batch_generation(
-        self, model_worker_batch: ModelWorkerBatch
-    ) -> ForwardBatchOutput:
-        # Create a new copy of sampling_info because it will be updated in-place by the scheduler for the next batch.
-        model_worker_batch.sampling_info = self.cur_sampling_info = (
-            model_worker_batch.sampling_info.copy_for_forward()
-        )
-
-        # A cuda stream sync here to avoid the cuda illegal memory access error.
-        sync_event = torch.get_device_module(self.device).Event()
-        sync_event.record(self.scheduler_stream)
-
-        # Push a new batch to the queue
-        bs = len(model_worker_batch.seq_lens)
-        cur_future_map_ct = self.future_map.update_ct(bs)
-        self.input_queue.put((model_worker_batch, cur_future_map_ct, sync_event))
-
-        # get this forward batch's future token ids
-        future_next_token_ids = self.future_map.update_next_future(
-            cur_future_map_ct, bs
-        )
-        return ForwardBatchOutput(
-            next_token_ids=future_next_token_ids,
-            can_run_cuda_graph=False,
-        )
-
-    def update_weights_from_disk(self, recv_req: UpdateWeightFromDiskReqInput):
-        success, message = self.worker.update_weights_from_disk(recv_req)
-        return success, message
-
-    def init_weights_update_group(self, recv_req: InitWeightsUpdateGroupReqInput):
-        success, message = self.worker.init_weights_update_group(recv_req)
-        return success, message
-
-    def destroy_weights_update_group(self, recv_req: DestroyWeightsUpdateGroupReqInput):
-        success, message = self.worker.destroy_weights_update_group(recv_req)
-        return success, message
-
-    def init_weights_send_group_for_remote_instance(
-        self, recv_req: InitWeightsSendGroupForRemoteInstanceReqInput
-    ):
-        success, message = self.worker.init_weights_send_group_for_remote_instance(
-            recv_req
-        )
-        return success, message
-
-    def send_weights_to_remote_instance(
-        self, recv_req: SendWeightsToRemoteInstanceReqInput
-    ):
-        success, message = self.worker.send_weights_to_remote_instance(recv_req)
-        return success, message
-
-    def update_weights_from_distributed(
-        self, recv_req: UpdateWeightsFromDistributedReqInput
-    ):
-        success, message = self.worker.update_weights_from_distributed(recv_req)
-        return success, message
-
-    def update_weights_from_tensor(self, recv_req: UpdateWeightsFromTensorReqInput):
-        success, message = self.worker.update_weights_from_tensor(recv_req)
-        return success, message
-
-    def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput):
-        return self.worker.get_weights_by_name(recv_req)
-
-    def load_lora_adapter(self, recv_req: LoadLoRAAdapterReqInput):
-        return self.worker.load_lora_adapter(recv_req)
-
-    def unload_lora_adapter(self, recv_req: UnloadLoRAAdapterReqInput):
-        return self.worker.unload_lora_adapter(recv_req)
-
-    def can_run_lora_batch(self, lora_ids: list[str]) -> bool:
-        return self.worker.can_run_lora_batch(lora_ids)
-
-    def __delete__(self):
-        self.input_queue.put((None, None))
-        self.copy_queue.put((None, None, None))

python/sglang/srt/model_executor/forward_batch_info.py

@@ -75,10 +75,6 @@ class ForwardMode(IntEnum):
     # Used in speculative decoding: extend a batch in the draft model.
     DRAFT_EXTEND = auto()
 
-    # A dummy first batch to start the pipeline for overlap scheduler.
-    # It is now used for triggering the sampling_info_done event for the first prefill batch.
-    DUMMY_FIRST = auto()
-
     # Split Prefill for PD multiplexing
     SPLIT_PREFILL = auto()
 
@@ -128,9 +124,6 @@ class ForwardMode(IntEnum):
     def is_cpu_graph(self):
         return self == ForwardMode.DECODE
 
-    def is_dummy_first(self):
-        return self == ForwardMode.DUMMY_FIRST
-
     def is_split_prefill(self):
         return self == ForwardMode.SPLIT_PREFILL
 

python/sglang/srt/model_executor/model_runner.py

@@ -2057,15 +2057,11 @@ class ModelRunner:
     def _preprocess_logits(
         self, logits_output: LogitsProcessorOutput, sampling_info: SamplingBatchInfo
     ):
-        # Apply logit bias
-        if sampling_info.sampling_info_done:
-            # Overlap mode: the function update_regex_vocab_mask was executed
-            # in process_batch_result of the last batch.
-            if sampling_info.grammars:
-                sampling_info.sampling_info_done.wait()
-        else:
-            # Normal mode: Put CPU-heavy tasks here. They will be overlapped with the forward pass.
-            sampling_info.update_regex_vocab_mask()
+        # NOTE: In overlap mode, the function update_regex_vocab_mask (in sample)
+        #       was executed after we processed last batch's results.
+
+        # Calculate logits bias and apply it to next_token_logits.
+        sampling_info.update_regex_vocab_mask()
         sampling_info.apply_logits_bias(logits_output.next_token_logits)
 
     def sample(

python/sglang/srt/sampling/sampling_batch_info.py

@@ -44,12 +44,9 @@ class SamplingBatchInfo:
     vocab_mask: Optional[torch.Tensor] = None
     apply_mask_func: Optional[Callable[[torch.Tensor, torch.Tensor], None]] = None
 
-    # An event used for overlap schedule
-    sampling_info_done: Optional[threading.Event] = None
-
     # Penalizer
     penalizer_orchestrator: Optional[penaltylib.BatchedPenalizerOrchestrator] = None
-    linear_penalty: torch.Tensor = None
+    acc_linear_penalties: torch.Tensor = None  # Used in the overlap mode
 
     # Whether any request has custom logit processor
     has_custom_logit_processor: bool = False
@@ -217,19 +214,19 @@ class SamplingBatchInfo:
 
     def update_penalties(self):
         if self.penalizer_orchestrator.is_required:
-            self.linear_penalty = torch.zeros(
+            self.acc_linear_penalties = torch.zeros(
                 (len(self.temperatures), self.vocab_size),
                 dtype=torch.float32,
                 device=self.temperatures.device,
             )
-            self.penalizer_orchestrator.apply(self.linear_penalty)
+            self.penalizer_orchestrator.apply(self.acc_linear_penalties)
         else:
-            self.linear_penalty = None
+            self.acc_linear_penalties = None
 
     def apply_logits_bias(self, logits: torch.Tensor):
-        if self.linear_penalty is not None:
+        if self.acc_linear_penalties is not None:
             # Used in the overlap mode
-            logits.add_(self.linear_penalty)
+            logits.add_(self.acc_linear_penalties)
 
         if self.penalizer_orchestrator and self.penalizer_orchestrator.is_required:
             # Used in the non-overlap mode
@@ -373,11 +370,7 @@ class SamplingBatchInfo:
     def copy_for_forward(self):
         # Accumulate the penalty into a pre-allocated buffer to get rid of the dependency of `penalizer_orchestrator` later
         self.update_penalties()
-        return dataclasses.replace(
-            self,
-            sampling_info_done=threading.Event(),
-            penalizer_orchestrator=None,
-        )
+        return dataclasses.replace(self, penalizer_orchestrator=None)
 
 
 def merge_bias_tensor(