prefill.py

"""
Life cycle of a request in the prefill server

1. Bootstrap Queue
    a. Initialize a sender for each request
    b. Use the queue to store requests whose bootstrap (handshake and preallocation) has not finished
    c. Poll senders to check bootstrap state
    d. Once bootstrap is complete, move request to Waiting Queue

2. Waiting Queue
    a. Use PrefillAdder to pop requests
    b. Run forward
    c. Add the request to Inflight Queue

3. Inflight Queue
    a. Poll (non-blocking) the sender of the request
    b. Once the transfer has finished, return the request
"""

from __future__ import annotations

import logging
import threading
from collections import deque
from http import HTTPStatus
from typing import TYPE_CHECKING, List, Optional

import torch

from sglang.srt.disaggregation.base import BaseKVManager, KVArgs, KVPoll
from sglang.srt.disaggregation.utils import (
    DisaggregationMode,
    FakeBootstrapHost,
    KVClassType,
    MetadataBuffers,
    ReqToMetadataIdxAllocator,
    TransferBackend,
    get_kv_class,
    is_mla_backend,
    kv_to_page_indices,
    kv_to_page_num,
    poll_and_all_reduce,
    prepare_abort,
)
from sglang.srt.managers.schedule_batch import FINISH_LENGTH, Req, ScheduleBatch
from sglang.srt.model_executor.forward_batch_info import ForwardMode

if TYPE_CHECKING:
    from torch.distributed import ProcessGroup

    from sglang.srt.managers.scheduler import GenerationBatchResult, Scheduler
    from sglang.srt.mem_cache.memory_pool import KVCache


logger = logging.getLogger(__name__)


class PrefillBootstrapQueue:
    """
    Store the requests in bootstrapping
    """

    def __init__(
        self,
        token_to_kv_pool: KVCache,
        draft_token_to_kv_pool: Optional[KVCache],
        req_to_metadata_buffer_idx_allocator: ReqToMetadataIdxAllocator,
        metadata_buffers: MetadataBuffers,
        tp_rank: int,
        tp_size: int,
        bootstrap_port: int,
        gloo_group: ProcessGroup,
        transfer_backend: TransferBackend,
        scheduler: Scheduler,
    ):
        self.token_to_kv_pool = token_to_kv_pool
        self.draft_token_to_kv_pool = draft_token_to_kv_pool

        self.is_mla_backend = is_mla_backend(token_to_kv_pool)

        self.metadata_buffers = metadata_buffers
        self.req_to_metadata_buffer_idx_allocator = req_to_metadata_buffer_idx_allocator
        self.tp_rank = tp_rank
        self.tp_size = tp_size
        self.transfer_backend = transfer_backend
        self.scheduler = scheduler
        self.kv_manager = self._init_kv_manager()
        self.queue: List[Req] = []
        self.gloo_group = gloo_group
        self.bootstrap_port = bootstrap_port

    def store_prefill_results(self, idx: int, token_id: int):
        assert token_id >= 0, f"token_id: {token_id} is negative"
        output_id_buffer = self.metadata_buffers[0]
        output_id_buffer[idx] = token_id

    def _init_kv_manager(self) -> BaseKVManager:
        kv_args = KVArgs()
        kv_args.engine_rank = self.tp_rank
        kv_data_ptrs, kv_data_lens, kv_item_lens = (
            self.token_to_kv_pool.get_contiguous_buf_infos()
        )

        if self.draft_token_to_kv_pool is not None:
            # We should also transfer draft model kv cache. The indices are
            # always shared with a target model.
            draft_kv_data_ptrs, draft_kv_data_lens, draft_kv_item_lens = (
                self.draft_token_to_kv_pool.get_contiguous_buf_infos()
            )
            kv_data_ptrs += draft_kv_data_ptrs
            kv_data_lens += draft_kv_data_lens
            kv_item_lens += draft_kv_item_lens

        kv_args.kv_data_ptrs = kv_data_ptrs
        kv_args.kv_data_lens = kv_data_lens
        kv_args.kv_item_lens = kv_item_lens

        # Define req -> input ids buffer
        kv_args.aux_data_ptrs, kv_args.aux_data_lens, kv_args.aux_item_lens = (
            self.metadata_buffers.get_buf_infos()
        )
        kv_args.ib_device = self.scheduler.server_args.disaggregation_ib_device
        kv_args.gpu_id = self.scheduler.gpu_id
        kv_manager_class = get_kv_class(self.transfer_backend, KVClassType.MANAGER)
        kv_manager = kv_manager_class(
            kv_args,
            DisaggregationMode.PREFILL,
            self.scheduler.server_args,
            self.is_mla_backend,
        )
        return kv_manager

    def add(self, req: Req) -> None:
        if req.bootstrap_host == FakeBootstrapHost:
            # Fake transfer for warmup reqs
            kv_sender_class = get_kv_class(TransferBackend.FAKE, KVClassType.SENDER)
        else:
            kv_sender_class = get_kv_class(self.transfer_backend, KVClassType.SENDER)
        req.disagg_kv_sender = kv_sender_class(
            mgr=self.kv_manager,
            bootstrap_addr=f"{req.bootstrap_host}:{self.bootstrap_port}",
            bootstrap_room=req.bootstrap_room,
        )
        self._process_req(req)
        self.queue.append(req)

    def extend(self, reqs: List[Req]) -> None:
        for req in reqs:
            self.add(req)

    def _process_req(self, req: Req) -> None:
        """
        Set max_new_tokens = 1, so PrefillAdder memory estimation is accurate
        """
        req.sampling_params.max_new_tokens = 1

    def pop_bootstrapped(self) -> List[Req]:
        """pop the reqs which has finished bootstrapping"""
        bootstrapped_reqs = []
        indices_to_remove = set()

        if len(self.queue) == 0:
            return []

        polls = poll_and_all_reduce(
            [req.disagg_kv_sender for req in self.queue], self.gloo_group
        )

        for i, (req, poll) in enumerate(zip(self.queue, polls)):
            if poll == KVPoll.Bootstrapping:
                continue
            elif poll == KVPoll.Failed:
                error_message = f"Prefill bootstrap failed for request rank={self.tp_rank} {req.rid=} {req.bootstrap_room=}"
                try:
                    req.disagg_kv_sender.failure_exception()
                except Exception as e:
                    error_message += f" with exception {e}"
                logger.error(error_message)
                prepare_abort(
                    req, error_message, status_code=HTTPStatus.INTERNAL_SERVER_ERROR
                )
                self.scheduler.stream_output([req], req.return_logprob)
                indices_to_remove.add(i)
                continue

            # KV.WaitingForInput
            num_kv_indices = len(req.origin_input_ids)
            if self.req_to_metadata_buffer_idx_allocator.available_size() == 0:
                break

            req.metadata_buffer_index = (
                self.req_to_metadata_buffer_idx_allocator.alloc()
            )
            assert req.metadata_buffer_index is not None
            num_pages = kv_to_page_num(num_kv_indices, self.token_to_kv_pool.page_size)
            req.disagg_kv_sender.init(num_pages, req.metadata_buffer_index)

            bootstrapped_reqs.append(req)
            indices_to_remove.add(i)

        self.queue = [
            entry for i, entry in enumerate(self.queue) if i not in indices_to_remove
        ]

        return bootstrapped_reqs


class SchedulerDisaggregationPrefillMixin:
    """
    Mixin for Scheduler to handle disaggregation prefill
    """

    @torch.no_grad()
    def event_loop_normal_disagg_prefill(self: Scheduler):
        """A normal scheduler loop for prefill worker in disaggregation mode."""

        while True:
            recv_reqs = self.recv_requests()
            self.process_input_requests(recv_reqs)
            self.waiting_queue.extend(
                self.disagg_prefill_bootstrap_queue.pop_bootstrapped()
            )
            self.process_prefill_chunk()
            batch = self.get_new_batch_prefill()

            # Handle DP attention
            if (
                self.server_args.enable_dp_attention
                or self.server_args.enable_sp_layernorm
            ):
                batch, _ = self.prepare_dp_attn_batch(batch)

            self.cur_batch = batch

            if batch:
                result = self.run_batch(batch)
                self.process_batch_result_disagg_prefill(batch, result)

            if len(self.disagg_prefill_inflight_queue) > 0:
                self.process_disagg_prefill_inflight_queue()

            if batch is None and len(self.disagg_prefill_inflight_queue) == 0:
                self.check_memory()
                self.new_token_ratio = self.init_new_token_ratio

            self.last_batch = batch
            # HACK (byronhsu): reset the batch_is_full flag because we never enter update_running_batch which resets it
            # Otherwise, it hangs under high concurrency
            self.running_batch.batch_is_full = False

    @torch.no_grad()
    def event_loop_overlap_disagg_prefill(self: Scheduler):
        self.result_queue = deque()

        while True:
            recv_reqs = self.recv_requests()
            self.process_input_requests(recv_reqs)
            self.waiting_queue.extend(
                self.disagg_prefill_bootstrap_queue.pop_bootstrapped()
            )
            self.process_prefill_chunk()
            batch = self.get_new_batch_prefill()

            # Handle DP attention
            if (
                self.server_args.enable_dp_attention
                or self.server_args.enable_sp_layernorm
            ):
                batch, _ = self.prepare_dp_attn_batch(batch)

            self.cur_batch = batch

            if batch:
                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()
                self.process_batch_result_disagg_prefill(tmp_batch, tmp_result)

            if len(self.disagg_prefill_inflight_queue) > 0:
                self.process_disagg_prefill_inflight_queue()

            if batch is None and len(self.disagg_prefill_inflight_queue) == 0:
                self.check_memory()
                self.new_token_ratio = self.init_new_token_ratio

            self.last_batch = batch
            # HACK (byronhsu): reset the batch_is_full flag because we never enter update_running_batch which resets it
            # Otherwise, it hangs under high concurrency
            self.running_batch.batch_is_full = False

    def process_batch_result_disagg_prefill(
        self: Scheduler,
        batch: ScheduleBatch,
        result: GenerationBatchResult,
        launch_done: Optional[threading.Event] = None,
    ) -> None:
        """
        Transfer kv for prefill completed requests and add it into disagg_prefill_infight_queue
        Adapted from process_batch_result_prefill
        """
        (
            logits_output,
            next_token_ids,
            extend_input_len_per_req,
            extend_logprob_start_len_per_req,
        ) = (
            result.logits_output,
            result.next_token_ids,
            result.extend_input_len_per_req,
            result.extend_logprob_start_len_per_req,
        )

        logprob_pt = 0
        # Transfer kv for prefill completed requests and add it into disagg_prefill_infight_queue
        if self.enable_overlap:
            # wait
            logits_output, next_token_ids, _ = self.tp_worker.resolve_last_batch_result(
                launch_done
            )
        else:
            next_token_ids = result.next_token_ids.tolist()
            if batch.return_logprob:
                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()
                    )
        for i, (req, next_token_id) in enumerate(
            zip(batch.reqs, next_token_ids, strict=True)
        ):
            req: Req
            if req.is_chunked <= 0:
                # There is no output_ids for prefill
                req.output_ids.append(next_token_id)
                self.tree_cache.cache_unfinished_req(req)  # update the tree and lock
                self.disagg_prefill_inflight_queue.append(req)
                if req.return_logprob:
                    assert extend_logprob_start_len_per_req is not None
                    assert extend_input_len_per_req is not None
                    extend_logprob_start_len = extend_logprob_start_len_per_req[i]
                    extend_input_len = extend_input_len_per_req[i]
                    num_input_logprobs = extend_input_len - extend_logprob_start_len
                    self.add_logprob_return_values(
                        i,
                        req,
                        logprob_pt,
                        next_token_ids,
                        num_input_logprobs,
                        logits_output,
                    )
                    logprob_pt += num_input_logprobs
                self.send_kv_chunk(req, last_chunk=True)

                if req.grammar is not None:
                    req.grammar.accept_token(next_token_id)
                    req.grammar.finished = req.finished()
            else:
                # being chunked reqs' prefill is not finished
                req.is_chunked -= 1

                if req.return_logprob:
                    extend_logprob_start_len = extend_logprob_start_len_per_req[i]
                    extend_input_len = extend_input_len_per_req[i]
                    if extend_logprob_start_len < extend_input_len:
                        # Update input logprobs.
                        num_input_logprobs = extend_input_len - extend_logprob_start_len
                        self.add_input_logprob_return_values(
                            i,
                            req,
                            logits_output,
                            logprob_pt,
                            num_input_logprobs,
                            last_prefill_chunk=False,
                        )
                        logprob_pt += num_input_logprobs

                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)

    def process_disagg_prefill_inflight_queue(self: Scheduler) -> None:
        """
        Poll the requests in the middle of transfer. If done, return the request.
        """
        assert len(self.disagg_prefill_inflight_queue) > 0

        done_reqs = []

        polls = poll_and_all_reduce(
            [req.disagg_kv_sender for req in self.disagg_prefill_inflight_queue],
            self.attn_tp_cpu_group,
        )

        undone_reqs: List[Req] = []
        # Check .poll() for the reqs in disagg_prefill_inflight_queue. If Success, respond to the client and remove it from the queue
        for req, poll in zip(self.disagg_prefill_inflight_queue, polls):
            if poll in [KVPoll.WaitingForInput, KVPoll.Transferring]:
                undone_reqs.append(req)
            elif poll == KVPoll.Success:  # transfer done
                self.tree_cache.cache_finished_req(req)  # unlock the tree
                req.finished_reason = FINISH_LENGTH(length=0)
                # FIXME: clean up req's data in transfer engine
                done_reqs.append(req)
            elif poll == KVPoll.Failed:
                error_message = f"Prefill transfer failed for request rank={self.tp_rank} {req.rid=} {req.bootstrap_room=}"
                try:
                    req.disagg_kv_sender.failure_exception()
                except Exception as e:
                    error_message += f" with exception {e}"
                logger.warning(error_message)
                self.tree_cache.cache_finished_req(req)  # unlock the tree
                prepare_abort(
                    req, error_message, status_code=HTTPStatus.INTERNAL_SERVER_ERROR
                )
                done_reqs.append(req)

        for req in done_reqs:
            self.disagg_prefill_bootstrap_queue.req_to_metadata_buffer_idx_allocator.free(
                req.metadata_buffer_index
            )

        # Stream requests which have finished transfer
        self.stream_output(
            done_reqs,
            any(req.return_logprob for req in done_reqs),
            None,
        )

        self.disagg_prefill_inflight_queue = undone_reqs

    def process_prefill_chunk(self: Scheduler) -> None:
        if self.last_batch and self.last_batch.forward_mode.is_extend():
            if self.chunked_req:
                # Move the chunked request out of the batch so that we can merge
                # only finished requests to running_batch.
                self.last_batch.filter_batch(chunked_req_to_exclude=self.chunked_req)
                self.tree_cache.cache_unfinished_req(self.chunked_req)
                if self.enable_overlap:
                    # Delay KV transfer to process_batch_result_disagg_prefill when overlap is enabled to ensure results are resolved
                    self.chunked_req.tmp_end_idx = min(
                        len(self.chunked_req.fill_ids),
                        len(self.chunked_req.origin_input_ids),
                    )
                else:
                    self.send_kv_chunk(self.chunked_req)
                # chunked request keeps its rid but will get a new req_pool_idx
                self.req_to_token_pool.free(self.chunked_req.req_pool_idx)
                self.running_batch.batch_is_full = False

    def send_kv_chunk(
        self: Scheduler,
        req: Req,
        last_chunk: bool = False,
        end_idx: Optional[int] = None,
    ) -> None:
        """
        Send a prefilled chunk to the decode server
        """
        page_size = self.token_to_kv_pool_allocator.page_size
        start_idx = req.start_send_idx
        end_idx = (
            end_idx
            if end_idx is not None
            else min(len(req.fill_ids), len(req.origin_input_ids))
        )

        if not last_chunk:
            # if not the last chunk and the last page is partial, delay the last partial page to the next send
            end_idx = end_idx - end_idx % page_size

        kv_indices = (
            self.req_to_token_pool.req_to_token[req.req_pool_idx, start_idx:end_idx]
            .cpu()
            .numpy()
        )
        req.start_send_idx = end_idx
        if last_chunk:
            self.disagg_metadata_buffers.set_buf(req)
        page_indices = kv_to_page_indices(kv_indices, page_size)
        if len(page_indices) == 0:
            logger.info(
                f"Skip sending kv chunk for request {req.rid=} {req.bootstrap_room=} because page_indices is empty"
            )
            return
        req.disagg_kv_sender.send(page_indices)