srt/disaggregation/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 time
from collections import deque
from http import HTTPStatus
from typing import TYPE_CHECKING, List, Optional, Type

import torch

from sglang.srt.disaggregation.base import BaseKVManager, KVPoll
from sglang.srt.disaggregation.utils import (
    FAKE_BOOTSTRAP_HOST,
    DisaggregationMode,
    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,
    RequestStage,
    ScheduleBatch,
)
from sglang.srt.mem_cache.memory_pool import (
    HybridLinearKVPool,
    NSATokenToKVPool,
    SWAKVPool,
)
from sglang.srt.utils import broadcast_pyobj, point_to_point_pyobj, require_mlp_sync

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,
        gpu_id: int,
        bootstrap_port: int,
        gloo_group: ProcessGroup,
        max_total_num_tokens: int,
        decode_tp_size: int,
        decode_dp_size: int,
        scheduler: Scheduler,
        pp_rank: int,
        pp_size: int,
        transfer_backend: TransferBackend,
    ):
        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.decode_tp_size = decode_tp_size
        self.decode_dp_size = decode_dp_size
        self.pp_rank = pp_rank
        self.pp_size = pp_size
        self.gpu_id = gpu_id
        self.bootstrap_port = bootstrap_port
        self.queue: List[Req] = []
        self.gloo_group = gloo_group
        self.max_total_num_tokens = max_total_num_tokens
        self.scheduler = scheduler
        self.transfer_backend = transfer_backend
        self.kv_manager = self._init_kv_manager()

    def _init_kv_manager(self) -> BaseKVManager:
        kv_args_class = get_kv_class(self.transfer_backend, KVClassType.KVARGS)
        kv_args = kv_args_class()
        kv_args.engine_rank = self.tp_rank
        kv_args.pp_rank = self.pp_rank
        kv_args.system_dp_rank = self.scheduler.dp_rank
        kv_args.decode_tp_size = self.decode_tp_size // self.decode_dp_size
        kv_args.prefill_pp_size = self.pp_size
        kv_args.prefill_start_layer = self.token_to_kv_pool.start_layer
        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
        if not self.is_mla_backend:
            kv_args.kv_head_num = self.token_to_kv_pool.head_num
        kv_args.page_size = self.token_to_kv_pool.page_size

        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

        if hasattr(self.token_to_kv_pool, "get_state_buf_infos"):
            state_data_ptrs, state_data_lens, state_item_lens = (
                self.token_to_kv_pool.get_state_buf_infos()
            )
            kv_args.state_data_ptrs = state_data_ptrs
            kv_args.state_data_lens = state_data_lens
            kv_args.state_item_lens = state_item_lens

            if isinstance(self.token_to_kv_pool, SWAKVPool):
                kv_args.state_type = "swa"
            elif isinstance(self.token_to_kv_pool, HybridLinearKVPool):
                kv_args.state_type = "mamba"
            elif isinstance(self.token_to_kv_pool, NSATokenToKVPool):
                kv_args.state_type = "nsa"
            else:
                kv_args.state_type = "none"
        else:
            kv_args.state_data_ptrs = []
            kv_args.state_data_lens = []
            kv_args.state_item_lens = []
            kv_args.state_type = "none"

        kv_manager_class: Type[BaseKVManager] = get_kv_class(
            self.transfer_backend, KVClassType.MANAGER
        )
        kv_manager: BaseKVManager = kv_manager_class(
            kv_args,
            DisaggregationMode.PREFILL,
            self.scheduler.server_args,
            self.is_mla_backend,
        )
        return kv_manager

    def add(self, req: Req, num_kv_heads: int) -> None:
        if self._check_if_req_exceed_kv_capacity(req):
            return

        if req.bootstrap_host == FAKE_BOOTSTRAP_HOST:
            kv_sender_class = get_kv_class(TransferBackend.FAKE, KVClassType.SENDER)
        else:
            kv_sender_class = get_kv_class(self.transfer_backend, KVClassType.SENDER)

        dest_tp_ranks = [self.tp_rank]

        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,
            dest_tp_ranks=dest_tp_ranks,
            pp_rank=self.pp_rank,
        )
        self._process_req(req)
        req.add_latency(RequestStage.PREFILL_PREPARE)
        self.queue.append(req)

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

    def _check_if_req_exceed_kv_capacity(self, req: Req) -> bool:
        if len(req.origin_input_ids) > self.max_total_num_tokens:
            message = f"Request {req.rid} exceeds the maximum number of tokens: {len(req.origin_input_ids)} > {self.max_total_num_tokens}"
            logger.error(message)
            prepare_abort(req, message, status_code=HTTPStatus.BAD_REQUEST)
            self.scheduler.stream_output([req], req.return_logprob)
            return True
        return False

    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,
        return_failed_reqs: bool = False,
        rids_to_check: Optional[List[str]] = None,
    ) -> List[Req]:
        """
        pop the reqs which has finished bootstrapping

        return_failed_reqs: For PP, on rank 0, also return the failed reqs to notify the next rank
        rids_to_check: For PP, on rank > 0, check the rids from the previous rank has consensus with the current rank.
        """

        bootstrapped_reqs = []
        failed_reqs = []
        indices_to_remove = set()

        if len(self.queue) == 0:
            if return_failed_reqs is False:
                return []
            else:
                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 rids_to_check is not None:
                # if req not in reqs_info_to_check, skip
                if req.rid not in rids_to_check:
                    continue
                # Either waiting for input or failed
                assert poll == KVPoll.WaitingForInput or poll == KVPoll.Failed

            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)
                failed_reqs.append(req)
                if self.scheduler.enable_metrics:
                    self.scheduler.metrics_collector.increment_bootstrap_failed_reqs()
                continue

            # KV.WaitingForInput - init here
            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)
            req.time_stats.wait_queue_entry_time = time.perf_counter()
            req.add_latency(RequestStage.PREFILL_BOOTSTRAP)

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

        if return_failed_reqs is False:
            return bootstrapped_reqs
        else:
            return bootstrapped_reqs, failed_reqs


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

    @torch.no_grad()
    def event_loop_normal_disagg_prefill(self: Scheduler) -> None:
        """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()

            if require_mlp_sync(self.server_args):
                batch = self.prepare_mlp_sync_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.self_check_during_idle()

            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) -> None:
        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()

            if require_mlp_sync(self.server_args):
                batch = self.prepare_mlp_sync_batch(batch)
            self.cur_batch = batch

            batch_result = None
            if batch:
                batch_result = self.run_batch(batch)
                self.result_queue.append((batch.copy(), batch_result))

            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()

            self.launch_batch_sample_if_needed(batch_result)

            if batch is None and len(self.disagg_prefill_inflight_queue) == 0:
                self.self_check_during_idle()

            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,
    ) -> None:
        """
        Transfer kv for prefill completed requests and add it into disagg_prefill_inflight_queue
        Adapted from process_batch_result_prefill
        """
        (
            logits_output,
            next_token_ids,
            extend_input_len_per_req,
            extend_logprob_start_len_per_req,
            copy_done,
        ) = (
            result.logits_output,
            result.next_token_ids,
            result.extend_input_len_per_req,
            result.extend_logprob_start_len_per_req,
            result.copy_done,
        )

        if copy_done is not None:
            copy_done.synchronize()

        logprob_pt = 0
        # Transfer kv for prefill completed requests and add it into disagg_prefill_inflight_queue
        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()
                )

        hidden_state_offset = 0
        for i, (req, next_token_id) in enumerate(
            zip(batch.reqs, next_token_ids, strict=True)
        ):
            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
                req.add_latency(RequestStage.PREFILL_FORWARD)
                self.disagg_prefill_inflight_queue.append(req)
                if self.spec_algorithm.is_eagle() and batch.spec_info is not None:
                    req.output_topk_p = batch.spec_info.topk_p[i]
                    req.output_topk_index = batch.spec_info.topk_index[i]
                    req.hidden_states_tensor = (
                        batch.spec_info.hidden_states[i].cpu().clone()
                    )
                else:
                    req.hidden_states_tensor = None
                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)
                req.time_stats.prefill_transfer_queue_entry_time = time.perf_counter()

                if req.grammar is not None:
                    # FIXME: this try-except block is for handling unexpected xgrammar issue.
                    try:
                        req.grammar.accept_token(next_token_id)
                    except ValueError as e:
                        # Grammar accept_token can raise ValueError if the token is not in the grammar.
                        # This can happen if the grammar is not set correctly or the token is invalid.
                        error_message = f"Grammar accept_token failed for req {req.rid} with token {next_token_id}: {e}"
                        self.tree_cache.cache_finished_req(req)
                        prepare_abort(
                            req,
                            error_message,
                            status_code=HTTPStatus.INTERNAL_SERVER_ERROR,
                        )
                    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)

        self.maybe_send_health_check_signal()

    def process_disagg_prefill_inflight_queue(
        self: Scheduler, rids_to_check: Optional[List[str]] = None
    ) -> List[Req]:
        """
        Poll the requests in the middle of transfer. If done, return the request.
        rids_to_check: For PP, on rank > 0, check the rids from the previous rank has consensus with the current rank.
        """
        if len(self.disagg_prefill_inflight_queue) == 0:
            return []

        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 rids_to_check is not None:
                if req.rid not in rids_to_check:
                    undone_reqs.append(req)
                    continue

                assert poll == KVPoll.Success or poll == KVPoll.Failed

            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
                if hasattr(req.disagg_kv_sender, "clear"):
                    req.disagg_kv_sender.clear()
                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)
                if self.enable_metrics:
                    self.metrics_collector.increment_transfer_failed_reqs()
            else:
                assert False, f"Unexpected polling state {poll=}"

        for req in done_reqs:
            req.time_stats.completion_time = time.perf_counter()

        # Stream requests which have finished transfer
        self.stream_output(
            done_reqs,
            any(req.return_logprob for req in done_reqs),
            None,
        )
        for req in done_reqs:
            req: Req
            req.add_latency(RequestStage.PREFILL_TRANSFER_KV_CACHE)
            self.req_to_metadata_buffer_idx_allocator.free(req.metadata_buffer_index)
            req.metadata_buffer_index = -1

        self.disagg_prefill_inflight_queue = undone_reqs

        return done_reqs

    def get_transferred_rids(self: Scheduler) -> List[str]:
        """
        Used by PP, get the transferred rids but **do not pop**
        """
        polls = poll_and_all_reduce(
            [req.disagg_kv_sender for req in self.disagg_prefill_inflight_queue],
            self.tp_worker.get_tp_group().cpu_group,
        )

        transferred_rids: List[str] = []

        for req, poll in zip(self.disagg_prefill_inflight_queue, polls):
            if poll == KVPoll.Success or poll == KVPoll.Failed:
                transferred_rids.append(req.rid)

        return transferred_rids

    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, chunked=True)
                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
        state_indices = None
        if last_chunk:
            self.disagg_metadata_buffers.set_buf(req)

            # Prepare extra pool indices for hybrid models
            if isinstance(
                self.token_to_kv_pool_allocator.get_kvcache(), HybridLinearKVPool
            ):
                # Mamba hybrid model: send single mamba state index
                state_indices = [
                    self.req_to_token_pool.req_index_to_mamba_index_mapping[
                        req.req_pool_idx
                    ]
                    .cpu()
                    .numpy()
                ]
            elif isinstance(self.token_to_kv_pool_allocator.get_kvcache(), SWAKVPool):
                # SWA hybrid model: send last window KV indices
                seq_len = len(req.fill_ids)
                window_size = self.sliding_window_size
                window_start = max(0, seq_len - window_size)
                window_start = (window_start // page_size) * page_size

                window_kv_indices_full = self.req_to_token_pool.req_to_token[
                    req.req_pool_idx, window_start:seq_len
                ]

                # Translate to SWA pool indices
                window_kv_indices_swa = (
                    self.token_to_kv_pool_allocator.translate_loc_from_full_to_swa(
                        window_kv_indices_full
                    )
                )
                state_indices = window_kv_indices_swa.cpu().numpy()
                state_indices = kv_to_page_indices(state_indices, page_size)
            elif isinstance(
                self.token_to_kv_pool_allocator.get_kvcache(), NSATokenToKVPool
            ):
                seq_len = len(req.fill_ids)
                kv_indices_full = self.req_to_token_pool.req_to_token[
                    req.req_pool_idx, :seq_len
                ]
                state_indices = kv_indices_full.cpu().numpy()
                state_indices = kv_to_page_indices(state_indices, page_size)

        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, state_indices)

    def send_pyobj_to_next_stage(self, data):
        if self.attn_tp_rank == 0:
            dp_offset = self.attn_dp_rank * self.attn_tp_size
            point_to_point_pyobj(
                data,
                self.pp_rank * self.tp_size + dp_offset,
                self.world_group.device_group,
                self.pp_rank * self.tp_size + dp_offset,
                ((self.pp_rank + 1) % self.pp_size) * self.tp_size + dp_offset,
            )

    def recv_pyobj_from_prev_stage(self):
        if self.attn_tp_rank == 0:
            dp_offset = self.attn_dp_rank * self.attn_tp_size
            data = point_to_point_pyobj(
                [],
                self.pp_rank * self.tp_size + dp_offset,
                self.world_group.device_group,
                ((self.pp_rank - 1) % self.pp_size) * self.tp_size + dp_offset,
                self.pp_rank * self.tp_size + dp_offset,
            )
        else:
            data = None

        if self.tp_size != 1:
            data = broadcast_pyobj(
                data, self.tp_group.rank, self.tp_cpu_group, src=self.tp_group.ranks[0]
            )
        return data