scheduler.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 scheduler that manages a tensor parallel GPU worker."""

import json
import logging
import os
import time
import warnings
from typing import List, Optional, Union

import torch
import zmq

from sglang.global_config import global_config
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.constrained.fsm_cache import FSMCache
from sglang.srt.constrained.jump_forward import JumpForwardCache
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 (
    AbortReq,
    BatchEmbeddingOut,
    BatchTokenIDOut,
    FlushCacheReq,
    ProfileReq,
    TokenizedEmbeddingReqInput,
    TokenizedGenerateReqInput,
    TokenizedRewardReqInput,
    UpdateWeightReqInput,
    UpdateWeightReqOutput,
)
from sglang.srt.managers.schedule_batch import (
    FINISH_ABORT,
    BaseFinishReason,
    ImageInputs,
    Req,
    ScheduleBatch,
)
from sglang.srt.managers.schedule_policy import (
    AddReqResult,
    PrefillAdder,
    SchedulePolicy,
)
from sglang.srt.managers.tp_worker import TpModelWorker
from sglang.srt.mem_cache.chunk_cache import ChunkCache
from sglang.srt.mem_cache.radix_cache import RadixCache
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import (
    broadcast_pyobj,
    configure_logger,
    is_generation_model,
    is_multimodal_model,
    kill_parent_process,
    pytorch_profile,
    set_random_seed,
    suppress_other_loggers,
)
from sglang.utils import get_exception_traceback

logger = logging.getLogger(__name__)

# Crash on warning if we are running CI tests
crash_on_warning = os.getenv("SGLANG_IS_IN_CI", "false") == "true"


class Scheduler:
    """A scheduler that manages a tensor parallel GPU worker."""

    def __init__(
        self,
        server_args: ServerArgs,
        port_args: PortArgs,
        gpu_id: int,
        tp_rank: int,
    ):
        # Parse args
        self.server_args = server_args
        self.tp_rank = tp_rank
        self.tp_size = server_args.tp_size
        self.schedule_policy = server_args.schedule_policy
        self.disable_regex_jump_forward = server_args.disable_regex_jump_forward
        self.lora_paths = server_args.lora_paths
        self.max_loras_per_batch = server_args.max_loras_per_batch

        # Init inter-process communication
        context = zmq.Context(2)

        if self.tp_rank == 0:
            self.recv_from_tokenizer = context.socket(zmq.PULL)
            self.recv_from_tokenizer.bind(f"ipc://{port_args.scheduler_input_ipc_name}")

            self.send_to_detokenizer = context.socket(zmq.PUSH)
            self.send_to_detokenizer.connect(f"ipc://{port_args.detokenizer_ipc_name}")
        else:
            self.recv_from_tokenizer = self.send_to_detokenizer = None

        # Init tokenizer
        self.model_config = ModelConfig(
            server_args.model_path,
            server_args.trust_remote_code,
            context_length=server_args.context_length,
            model_override_args=json.loads(server_args.json_model_override_args),
        )

        if server_args.skip_tokenizer_init:
            self.tokenizer = self.processor = None
        else:
            if is_multimodal_model(self.model_config.hf_config.architectures):
                self.processor = get_processor(
                    server_args.tokenizer_path,
                    tokenizer_mode=server_args.tokenizer_mode,
                    trust_remote_code=server_args.trust_remote_code,
                )
                self.tokenizer = self.processor.tokenizer
            else:
                self.tokenizer = get_tokenizer(
                    server_args.tokenizer_path,
                    tokenizer_mode=server_args.tokenizer_mode,
                    trust_remote_code=server_args.trust_remote_code,
                )
        self.is_generation = is_generation_model(
            self.model_config.hf_config.architectures, self.server_args.is_embedding
        )

        # Launch a tensor parallel worker
        self.tp_worker = TpModelWorker(
            gpu_id=gpu_id,
            tp_rank=tp_rank,
            server_args=server_args,
            nccl_port=port_args.nccl_port,
        )
        self.tp_cpu_group = self.tp_worker.model_runner.tp_group.cpu_group

        # Get token and memory info from the model worker
        (
            self.max_total_num_tokens,
            self.max_prefill_tokens,
            self.max_running_requests,
            self.max_req_input_len,
            self.random_seed,
        ) = self.tp_worker.get_token_and_memory_info()
        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(
            f"max_total_num_tokens={self.max_total_num_tokens}, "
            f"max_prefill_tokens={self.max_prefill_tokens}, "
            f"max_running_requests={self.max_running_requests}, "
            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

        if (
            server_args.chunked_prefill_size is not None
            and server_args.disable_radix_cache
        ):
            self.tree_cache = ChunkCache(
                req_to_token_pool=self.req_to_token_pool,
                token_to_kv_pool=self.token_to_kv_pool,
            )
        else:
            self.tree_cache = RadixCache(
                req_to_token_pool=self.req_to_token_pool,
                token_to_kv_pool=self.token_to_kv_pool,
                disable=server_args.disable_radix_cache,
            )
        self.tree_cache_metrics = {"total": 0, "hit": 0}
        self.policy = SchedulePolicy(self.schedule_policy, self.tree_cache)

        # Init running status
        self.waiting_queue: List[Req] = []
        self.running_batch: ScheduleBatch = None
        self.out_pyobjs = []
        self.decode_forward_ct = 0
        self.stream_interval = server_args.stream_interval
        self.num_generated_tokens = 0
        self.last_stats_tic = time.time()

        # Init chunked prefill
        self.chunked_prefill_size = server_args.chunked_prefill_size
        self.current_inflight_req = None
        self.is_mixed_chunk = (
            self.chunked_prefill_size is not None and server_args.enable_mixed_chunk
        )

        # Init the FSM cache for constrained generation
        if not server_args.skip_tokenizer_init:
            self.regex_fsm_cache = FSMCache(
                server_args.tokenizer_path,
                {
                    "tokenizer_mode": server_args.tokenizer_mode,
                    "trust_remote_code": server_args.trust_remote_code,
                },
                skip_tokenizer_init=server_args.skip_tokenizer_init,
                constrained_json_whitespace_pattern=server_args.constrained_json_whitespace_pattern,
            )
        self.jump_forward_cache = JumpForwardCache()

        # Init new token estimation
        assert (
            server_args.schedule_conservativeness >= 0
        ), "Invalid schedule_conservativeness"
        self.min_new_token_ratio = min(
            global_config.base_min_new_token_ratio
            * server_args.schedule_conservativeness,
            1.0,
        )
        self.new_token_ratio = self.min_new_token_ratio
        self.new_token_ratio_decay = global_config.new_token_ratio_decay
        self.batch_is_full = False

        if os.getenv("SGLANG_TORCH_PROFILER_DIR", "") == "":
            self.profiler = None
        else:
            self.torch_profiler_trace_dir = os.getenv("SGLANG_TORCH_PROFILER_DIR")
            logger.info(
                "Profiling enabled. Traces will be saved to: %s",
                self.torch_profiler_trace_dir,
            )
            self.profiler = torch.profiler.profile(
                activities=[
                    torch.profiler.ProfilerActivity.CPU,
                    torch.profiler.ProfilerActivity.CUDA,
                ],
                with_stack=True,
            )

    @torch.inference_mode()
    def event_loop(self):
        while True:
            recv_reqs = self.recv_requests()
            self.process_input_requests(recv_reqs)

            self.run_step()

            self.send_results()

    def recv_requests(self):
        if self.tp_rank == 0:
            recv_reqs = []

            while True:
                try:
                    recv_req = self.recv_from_tokenizer.recv_pyobj(zmq.NOBLOCK)
                except zmq.ZMQError:
                    break
                recv_reqs.append(recv_req)
        else:
            recv_reqs = None

        if self.tp_size != 1:
            recv_reqs = broadcast_pyobj(recv_reqs, self.tp_rank, self.tp_cpu_group)
        return recv_reqs

    def process_input_requests(self, recv_reqs: List):
        for recv_req in recv_reqs:
            if isinstance(recv_req, TokenizedGenerateReqInput):
                self.handle_generate_request(recv_req)
            elif isinstance(
                recv_req, (TokenizedEmbeddingReqInput, TokenizedRewardReqInput)
            ):
                self.handle_embedding_request(recv_req)
            elif isinstance(recv_req, FlushCacheReq):
                self.flush_cache()
            elif isinstance(recv_req, AbortReq):
                self.abort_request(recv_req)
            elif isinstance(recv_req, UpdateWeightReqInput):
                success, message = self.update_weights(recv_req)
                self.out_pyobjs.append(UpdateWeightReqOutput(success, message))
            elif isinstance(recv_req, ProfileReq):
                if recv_req == ProfileReq.START_PROFILE:
                    self.start_profile()
                else:
                    self.stop_profile()
            else:
                raise ValueError(f"Invalid request: {recv_req}")

    def handle_generate_request(
        self,
        recv_req: TokenizedGenerateReqInput,
    ):
        req = Req(
            recv_req.rid,
            recv_req.input_text,
            recv_req.input_ids,
            recv_req.sampling_params,
            lora_path=recv_req.lora_path,
        )
        req.tokenizer = self.tokenizer

        # Image inputs
        if recv_req.image_inputs is not None:
            req.image_inputs = ImageInputs.from_dict(
                recv_req.image_inputs, self.model_config.vocab_size
            )
            req.origin_input_ids = self.pad_input_ids_func(
                req.origin_input_ids_unpadded, req.image_inputs
            )

        req.return_logprob = recv_req.return_logprob
        req.top_logprobs_num = recv_req.top_logprobs_num
        req.stream = recv_req.stream
        req.logprob_start_len = recv_req.logprob_start_len

        if req.logprob_start_len == -1:
            # By default, only return the logprobs for output tokens
            req.logprob_start_len = len(recv_req.input_ids) - 1

        # Init regex FSM
        if (
            req.sampling_params.json_schema is not None
            or req.sampling_params.regex is not None
        ):
            if req.sampling_params.json_schema is not None:
                req.regex_fsm, computed_regex_string = self.regex_fsm_cache.query(
                    ("json", req.sampling_params.json_schema)
                )
            elif req.sampling_params.regex is not None:
                req.regex_fsm, computed_regex_string = self.regex_fsm_cache.query(
                    ("regex", req.sampling_params.regex)
                )
            if not self.disable_regex_jump_forward:
                req.jump_forward_map = self.jump_forward_cache.query(
                    computed_regex_string
                )

        # Truncate prompts that are too long
        if len(req.origin_input_ids) >= self.max_req_input_len:
            logger.warning(
                "Request length is longer than the KV cache pool size or "
                "the max context length. Truncated!!!"
            )
            req.origin_input_ids = req.origin_input_ids[: self.max_req_input_len]
        req.sampling_params.max_new_tokens = min(
            (
                req.sampling_params.max_new_tokens
                if req.sampling_params.max_new_tokens is not None
                else 1 << 30
            ),
            self.max_req_input_len - 1 - len(req.origin_input_ids),
        )

        self.waiting_queue.append(req)

    def handle_embedding_request(
        self,
        recv_req: Union[TokenizedEmbeddingReqInput, TokenizedRewardReqInput],
    ):
        req = Req(
            recv_req.rid,
            recv_req.input_text,
            recv_req.input_ids,
            recv_req.sampling_params,
        )
        req.tokenizer = self.tokenizer

        # Truncate prompts that are too long
        if len(req.origin_input_ids) >= self.max_req_input_len:
            logger.warning(
                "Request length is longer than the KV cache pool size or "
                "the max context length. Truncated!!!"
            )
            req.origin_input_ids = req.origin_input_ids[: self.max_req_input_len]

        self.waiting_queue.append(req)

    def send_results(self):
        if self.tp_rank == 0:
            for obj in self.out_pyobjs:
                self.send_to_detokenizer.send_pyobj(obj)
            self.out_pyobjs = []

    def print_decode_stats(self):
        num_used = self.max_total_num_tokens - (
            self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
        )
        throughput = self.num_generated_tokens / (time.time() - self.last_stats_tic)
        self.num_generated_tokens = 0
        self.last_stats_tic = time.time()
        logger.info(
            f"Decode batch. "
            f"#running-req: {len(self.running_batch.reqs)}, "
            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.waiting_queue)}"
        )

    def check_memory(self):
        available_size = (
            self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
        )
        if available_size != self.max_total_num_tokens:
            warnings.warn(
                "Warning: "
                f"available_size={available_size}, max_total_num_tokens={self.max_total_num_tokens}\n"
                "KV cache pool leak detected!"
            )
            exit(1) if crash_on_warning else None

        if len(self.req_to_token_pool.free_slots) != self.req_to_token_pool.size:
            warnings.warn(
                "Warning: "
                f"available req slots={len(self.req_to_token_pool.free_slots)}, "
                f"total slots={self.req_to_token_pool.size}\n"
                "Memory pool leak detected!"
            )
            exit(1) if crash_on_warning else None

    def run_step(self):
        new_batch = self.get_new_batch_prefill()
        if new_batch is not None:
            # Run a new prefill batch
            # replace run_batch with the uncommented line to use pytorch profiler
            # result = pytorch_profile(
            #     "profile_prefill_step", self.run_batch, new_batch, data_size=len(new_batch.reqs)
            # )
            result = self.run_batch(new_batch)
            self.process_batch_result(new_batch, result)
        else:
            if self.running_batch is not None:
                # Run a few decode batches continuously for reducing overhead
                for _ in range(global_config.num_continue_decode_steps):
                    batch = self.get_new_batch_decode()

                    if batch:
                        # replace run_batch with the uncommented line to use pytorch profiler
                        # result = pytorch_profile(
                        #     "profile_decode_step",
                        #     self.run_batch,
                        #     batch,
                        #     data_size=len(batch.reqs),
                        # )
                        result = self.run_batch(batch)
                        self.process_batch_result(batch, result)

                    if self.running_batch.is_empty():
                        self.running_batch = None

                    if self.running_batch is None:
                        break

                    if self.out_pyobjs and self.running_batch.has_stream:
                        break
            else:
                self.check_memory()
                self.new_token_ratio = global_config.init_new_token_ratio

    def get_new_batch_prefill(self) -> Optional[ScheduleBatch]:
        # Handle the cases where prefill is not allowed
        if (
            self.batch_is_full or len(self.waiting_queue) == 0
        ) and self.current_inflight_req is None:
            return None

        running_bs = (
            len(self.running_batch.reqs) if self.running_batch is not None else 0
        )
        if running_bs >= self.max_running_requests:
            self.batch_is_full = True
            return None

        # Get priority queue
        prefix_computed = self.policy.calc_priority(self.waiting_queue)

        # Prefill policy
        num_mixed_running = running_bs if self.is_mixed_chunk else 0
        adder = PrefillAdder(
            self.tree_cache,
            self.running_batch,
            self.new_token_ratio,
            self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size(),
            self.max_prefill_tokens,
            self.chunked_prefill_size,
            num_mixed_running,
        )

        has_inflight = self.current_inflight_req is not None
        if self.current_inflight_req is not None:
            self.current_inflight_req.init_next_round_input(
                None if prefix_computed else self.tree_cache
            )
            self.current_inflight_req = adder.add_inflight_req(
                self.current_inflight_req
            )

        if self.lora_paths is not None:
            lora_set = (
                set([req.lora_path for req in self.running_batch.reqs])
                if self.running_batch is not None
                else set([])
            )

        for req in self.waiting_queue:
            if (
                self.lora_paths is not None
                and len(
                    lora_set
                    | set([req.lora_path for req in adder.can_run_list])
                    | set([req.lora_path])
                )
                > self.max_loras_per_batch
            ):
                self.batch_is_full = True
                break

            if running_bs + len(adder.can_run_list) >= self.max_running_requests:
                self.batch_is_full = True
                break

            req.init_next_round_input(None if prefix_computed else self.tree_cache)
            res = adder.add_one_req(req)
            if res != AddReqResult.CONTINUE:
                if res == AddReqResult.NO_TOKEN:
                    self.batch_is_full = True
                break

        can_run_list = adder.can_run_list

        if adder.new_inflight_req is not None:
            assert self.current_inflight_req is None
            self.current_inflight_req = adder.new_inflight_req

        if len(can_run_list) == 0:
            return None

        self.waiting_queue = [x for x in self.waiting_queue if x not in can_run_list]

        # Print stats
        if self.tp_rank == 0:
            if isinstance(self.tree_cache, RadixCache):
                self.tree_cache_metrics["total"] += (
                    adder.log_input_tokens + adder.log_hit_tokens
                ) / 10**9
                self.tree_cache_metrics["hit"] += (adder.log_hit_tokens) / 10**9
                tree_cache_hit_rate = (
                    self.tree_cache_metrics["hit"] / self.tree_cache_metrics["total"]
                )
            else:
                tree_cache_hit_rate = 0.0

            num_used = self.max_total_num_tokens - (
                self.token_to_kv_pool.available_size()
                + self.tree_cache.evictable_size()
            )

            if num_mixed_running > 0:
                logger.info(
                    f"Prefill batch"
                    f"(mixed #running-req: {num_mixed_running}). "
                    f"#new-seq: {len(can_run_list)}, "
                    f"#new-token: {adder.log_input_tokens}, "
                    f"#cached-token: {adder.log_hit_tokens}, "
                    f"cache hit rate: {100.0 * tree_cache_hit_rate:.2f}%, "
                    f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
                    f"#queue-req: {len(self.waiting_queue) + has_inflight}"
                )
            else:
                logger.info(
                    f"Prefill batch. "
                    f"#new-seq: {len(can_run_list)}, "
                    f"#new-token: {adder.log_input_tokens}, "
                    f"#cached-token: {adder.log_hit_tokens}, "
                    f"cache hit rate: {100.0 * tree_cache_hit_rate:.2f}%, "
                    f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
                    f"#running-req: {running_bs}, "
                    f"#queue-req: {len(self.waiting_queue) + has_inflight}"
                )

        # Create a new batch
        new_batch = ScheduleBatch.init_new(
            can_run_list,
            self.req_to_token_pool,
            self.token_to_kv_pool,
            self.tree_cache,
        )
        new_batch.prepare_for_extend(self.model_config.vocab_size)

        # Mixed-style chunked prefill
        decoding_reqs = []
        if self.is_mixed_chunk and self.running_batch is not None:
            self.running_batch.prepare_for_decode()
            new_batch.mix_with_running(self.running_batch)
            decoding_reqs = self.running_batch.reqs
            self.running_batch = None
        new_batch.decoding_reqs = decoding_reqs

        return new_batch

    def get_new_batch_decode(self) -> Optional[ScheduleBatch]:
        batch = self.running_batch

        # Check if decode out of memory
        if not batch.check_decode_mem():
            old_ratio = self.new_token_ratio

            retracted_reqs, new_token_ratio = batch.retract_decode()
            self.new_token_ratio = new_token_ratio

            logger.info(
                "Decode out of memory happened. "
                f"#retracted_reqs: {len(retracted_reqs)}, "
                f"#new_token_ratio: {old_ratio:.4f} -> {self.new_token_ratio:.4f}"
            )
            self.waiting_queue.extend(retracted_reqs)
        else:
            self.new_token_ratio = max(
                self.new_token_ratio - self.new_token_ratio_decay,
                self.min_new_token_ratio,
            )

        # Check for jump-forward
        if not self.disable_regex_jump_forward:
            jump_forward_reqs = batch.check_for_jump_forward(self.pad_input_ids_func)
            self.waiting_queue.extend(jump_forward_reqs)
            if jump_forward_reqs:
                self.batch_is_full = False
            if batch.is_empty():
                return None

        # Update batch tensors
        batch.prepare_for_decode()
        return batch

    def run_batch(self, batch: ScheduleBatch):
        if self.is_generation:
            if batch.forward_mode.is_decode() or batch.extend_num_tokens != 0:
                model_worker_batch = batch.get_model_worker_batch()
                logits_output, next_token_ids = self.tp_worker.forward_batch_generation(
                    model_worker_batch
                )
            else:
                logits_output = None
                if self.tokenizer is not None:
                    next_token_ids = torch.full(
                        (batch.batch_size(),), self.tokenizer.eos_token_id
                    )
                else:
                    next_token_ids = torch.full((batch.batch_size(),), 0)
            return logits_output, next_token_ids
        else:  # embedding or reward model
            assert batch.extend_num_tokens != 0
            model_worker_batch = batch.get_model_worker_batch()
            embeddings = self.tp_worker.forward_batch_embedding(model_worker_batch)
            return embeddings

    def process_batch_result(self, batch: ScheduleBatch, result):
        if batch.forward_mode.is_decode():
            self.process_batch_result_decode(batch, result)
        else:
            self.process_batch_result_prefill(batch, result)

    def process_batch_result_prefill(self, batch: ScheduleBatch, result):
        if self.is_generation:
            logits_output, next_token_ids = result
            if batch.sampling_info.penalizer_orchestrator:
                batch.sampling_info.penalizer_orchestrator.cumulate_output_tokens(
                    next_token_ids
                )

            if logits_output:
                # Move logprobs to cpu
                if logits_output.next_token_logprobs is not None:
                    logits_output.next_token_logprobs = (
                        logits_output.next_token_logprobs[
                            torch.arange(
                                len(next_token_ids), device=next_token_ids.device
                            ),
                            next_token_ids,
                        ].tolist()
                    )
                    logits_output.input_token_logprobs = (
                        logits_output.input_token_logprobs.tolist()
                    )
                    logits_output.normalized_prompt_logprobs = (
                        logits_output.normalized_prompt_logprobs.tolist()
                    )

            next_token_ids = next_token_ids.tolist()

            # Check finish conditions
            logprob_pt = 0
            for i, req in enumerate(batch.reqs):
                if req is not self.current_inflight_req:
                    # Inflight reqs' prefill is not finished
                    req.completion_tokens_wo_jump_forward += 1
                    req.output_ids.append(next_token_ids[i])
                    req.check_finished()

                if req.regex_fsm is not None:
                    req.regex_fsm_state = req.regex_fsm.get_next_state(
                        req.regex_fsm_state, next_token_ids[i]
                    )

                if req.finished():
                    self.tree_cache.cache_finished_req(req)
                elif req not in batch.decoding_reqs:
                    # To reduce overhead, only cache prefill reqs
                    self.tree_cache.cache_unfinished_req(req)

                if req is self.current_inflight_req:
                    # Inflight request would get a new req idx
                    self.req_to_token_pool.free(req.req_pool_idx)

                if req.return_logprob:
                    logprob_pt += self.add_logprob_return_values(
                        i, req, logprob_pt, next_token_ids, logits_output
                    )
        else:  # embedding or reward model
            assert batch.extend_num_tokens != 0
            embeddings = result

            # Check finish conditions
            for i, req in enumerate(batch.reqs):
                req.embedding = embeddings[i]
                if req is not self.current_inflight_req:
                    # Inflight reqs' prefill is not finished
                    # dummy output token for embedding models
                    req.output_ids.append(0)
                    req.check_finished()

                if req.finished():
                    self.tree_cache.cache_finished_req(req)
                else:
                    self.tree_cache.cache_unfinished_req(req)

                if req is self.current_inflight_req:
                    # Inflight request would get a new req idx
                    self.req_to_token_pool.free(req.req_pool_idx)

        self.handle_finished_requests(batch)

        if not batch.is_empty():
            if self.running_batch is None:
                self.running_batch = batch
            else:
                self.running_batch.merge_batch(batch)

    def process_batch_result_decode(self, batch: ScheduleBatch, result):
        logits_output, next_token_ids = result
        if batch.sampling_info.penalizer_orchestrator:
            batch.sampling_info.penalizer_orchestrator.cumulate_output_tokens(
                next_token_ids
            )
        self.num_generated_tokens += len(batch.reqs)

        # Move logprobs to cpu
        if logits_output.next_token_logprobs is not None:
            next_token_logprobs = logits_output.next_token_logprobs[
                torch.arange(len(next_token_ids), device=next_token_ids.device),
                next_token_ids,
            ].tolist()

        next_token_ids = next_token_ids.tolist()

        # Check finish condition
        for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):
            req.completion_tokens_wo_jump_forward += 1
            req.output_ids.append(next_token_id)
            req.check_finished()

            if req.regex_fsm is not None:
                req.regex_fsm_state = req.regex_fsm.get_next_state(
                    req.regex_fsm_state, next_token_id
                )

            if req.finished():
                self.tree_cache.cache_finished_req(req)

            if req.return_logprob:
                req.output_token_logprobs.append(
                    (next_token_logprobs[i], next_token_id)
                )
                if req.top_logprobs_num > 0:
                    req.output_top_logprobs.append(logits_output.output_top_logprobs[i])

        self.handle_finished_requests(batch)

        self.decode_forward_ct = (self.decode_forward_ct + 1) % (1 << 30)
        if self.tp_rank == 0 and self.decode_forward_ct % 40 == 0:
            self.print_decode_stats()

    def add_logprob_return_values(
        self,
        i: int,
        req: Req,
        pt: int,
        next_token_ids: List[int],
        output: LogitsProcessorOutput,
    ):
        """Attach logprobs to the return values."""
        req.output_token_logprobs.append(
            (output.next_token_logprobs[i], next_token_ids[i])
        )

        # If logprob_start_len > 0, then first logprob_start_len prompt tokens will be ignored.
        num_input_logprobs = req.extend_input_len - req.extend_logprob_start_len

        if req.normalized_prompt_logprob is None:
            req.normalized_prompt_logprob = output.normalized_prompt_logprobs[i]

        if req.input_token_logprobs is None:
            input_token_logprobs = output.input_token_logprobs[
                pt : pt + num_input_logprobs - 1 - req.last_update_decode_tokens
            ]
            input_token_ids = req.fill_ids[
                len(req.fill_ids)
                - num_input_logprobs
                + 1 : len(req.fill_ids)
                - req.last_update_decode_tokens
            ]
            req.input_token_logprobs = list(zip(input_token_logprobs, input_token_ids))

            if (
                req.logprob_start_len == 0
            ):  # The first token does not have logprob, pad it.
                req.input_token_logprobs = [
                    (None, req.fill_ids[0])
                ] + req.input_token_logprobs

        if req.last_update_decode_tokens != 0:
            # Some decode tokens are re-computed in an extend batch
            req.output_token_logprobs.extend(
                list(
                    zip(
                        output.input_token_logprobs[
                            pt
                            + num_input_logprobs
                            - 1
                            - req.last_update_decode_tokens : pt
                            + num_input_logprobs
                            - 1
                        ],
                        req.fill_ids[
                            len(req.fill_ids)
                            - req.last_update_decode_tokens : len(req.fill_ids)
                        ],
                    )
                )
            )

        if req.top_logprobs_num > 0:
            if req.input_top_logprobs is None:
                req.input_top_logprobs = output.input_top_logprobs[i]
                if req.logprob_start_len == 0:
                    req.input_top_logprobs = [None] + req.input_top_logprobs

            if req.last_update_decode_tokens != 0:
                req.output_top_logprobs.extend(
                    output.input_top_logprobs[i][-req.last_update_decode_tokens :]
                )
            req.output_top_logprobs.append(output.output_top_logprobs[i])

        return num_input_logprobs

    def handle_finished_requests(self, batch: ScheduleBatch):
        output_rids = []
        output_meta_info = []
        output_finished_reason: List[BaseFinishReason] = []
        if self.is_generation:
            output_vids = []
            decoded_texts = []
            output_read_ids = []
            output_read_offsets = []
            output_skip_special_tokens = []
            output_spaces_between_special_tokens = []
        else:  # embedding or reward model
            output_embeddings = []
        unfinished_indices = []

        for i, req in enumerate(batch.reqs):
            if not req.finished() and req is not self.current_inflight_req:
                unfinished_indices.append(i)
            else:
                self.batch_is_full = False

            if req.finished() or (
                req.stream
                and (
                    self.decode_forward_ct % self.stream_interval == 0
                    or len(req.output_ids) == 1
                )
            ):
                output_rids.append(req.rid)
                output_finished_reason.append(req.finished_reason)
                if self.is_generation:
                    output_vids.append(req.vid)
                    decoded_texts.append(req.decoded_text)
                    read_ids, read_offset = req.init_incremental_detokenize()
                    output_read_ids.append(read_ids)
                    output_read_offsets.append(read_offset)
                    output_skip_special_tokens.append(
                        req.sampling_params.skip_special_tokens
                    )
                    output_spaces_between_special_tokens.append(
                        req.sampling_params.spaces_between_special_tokens
                    )

                    meta_info = {
                        "prompt_tokens": len(req.origin_input_ids),
                        "completion_tokens": len(req.output_ids),
                        "completion_tokens_wo_jump_forward": req.completion_tokens_wo_jump_forward,
                        "finish_reason": (
                            req.finished_reason.to_json()
                            if req.finished_reason is not None
                            else None
                        ),
                    }
                    if req.return_logprob:
                        (
                            meta_info["input_token_logprobs"],
                            meta_info["output_token_logprobs"],
                            meta_info["input_top_logprobs"],
                            meta_info["output_top_logprobs"],
                            meta_info["normalized_prompt_logprob"],
                        ) = (
                            req.input_token_logprobs,
                            req.output_token_logprobs,
                            req.input_top_logprobs,
                            req.output_top_logprobs,
                            req.normalized_prompt_logprob,
                        )
                    output_meta_info.append(meta_info)
                else:  # embedding or reward model
                    output_embeddings.append(req.embedding)
                    meta_info = {
                        "prompt_tokens": len(req.origin_input_ids),
                    }
                    output_meta_info.append(meta_info)

        # Send to detokenizer
        if output_rids:
            if self.is_generation:
                self.out_pyobjs.append(
                    BatchTokenIDOut(
                        output_rids,
                        output_vids,
                        decoded_texts,
                        output_read_ids,
                        output_read_offsets,
                        output_skip_special_tokens,
                        output_spaces_between_special_tokens,
                        output_meta_info,
                        output_finished_reason,
                    )
                )
            else:  # embedding or reward model
                self.out_pyobjs.append(
                    BatchEmbeddingOut(
                        output_rids,
                        output_embeddings,
                        output_meta_info,
                        output_finished_reason,
                    )
                )

        # Remove finished reqs: update batch tensors
        batch.filter_batch(unfinished_indices)

    def flush_cache(self):
        if len(self.waiting_queue) == 0 and (
            self.running_batch is None or len(self.running_batch.reqs) == 0
        ):
            self.tree_cache.reset()
            self.tree_cache_metrics = {"total": 0, "hit": 0}
            self.regex_fsm_cache.reset()
            self.req_to_token_pool.clear()
            self.token_to_kv_pool.clear()
            torch.cuda.empty_cache()
            logger.info("Cache flushed successfully!")
            if_success = True
        else:
            logging.warning(
                f"Cache not flushed because there are pending requests. "
                f"#queue-req: {len(self.waiting_queue)}, "
                f"#running-req: {0 if self.running_batch is None else len(self.running_batch.reqs)}"
            )
            if_success = False
        return if_success

    def abort_request(self, recv_req: AbortReq):
        # Delete requests in the waiting queue
        to_del = None
        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.waiting_queue[to_del]

        # Delete requests in the running batch
        if self.running_batch:
            for req in self.running_batch.reqs:
                if req.rid == recv_req.rid:
                    req.finished_reason = FINISH_ABORT()
                    break

    def update_weights(self, recv_req: UpdateWeightReqInput):
        success, message = self.tp_worker.update_weights(recv_req)
        if success:
            flash_cache_success = self.flush_cache()
            assert flash_cache_success, "Cache flush failed after updating weights"
        else:
            logger.error(message)
        return success, message

    def start_profile(self) -> None:
        if self.profiler is None:
            raise RuntimeError("Profiler is not enabled.")
        self.profiler.start()

    def stop_profile(self) -> None:
        if self.profiler is None:
            raise RuntimeError("Profiler is not enabled.")
        self.profiler.stop()
        self.profiler.export_chrome_trace(
            self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
        )
        logger.info("Profiler is done")


def run_scheduler_process(
    server_args: ServerArgs,
    port_args: PortArgs,
    gpu_id: int,
    tp_rank: int,
    dp_rank: Optional[int],
    pipe_writer,
):
    if dp_rank is None:
        configure_logger(server_args, prefix=f" TP{tp_rank}")
    else:
        configure_logger(server_args, prefix=f" DP{dp_rank} TP{tp_rank}")

    suppress_other_loggers()

    try:
        scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank)
        pipe_writer.send("ready")
        scheduler.event_loop()
    except Exception:
        msg = get_exception_traceback()
        logger.error(msg)
        kill_parent_process()