[PD] Update prefill.py (#7190)

python/sglang/srt/disaggregation/base/conn.py

+from __future__ import annotations
+
 from abc import ABC, abstractmethod
-from typing import Optional
+from typing import TYPE_CHECKING, List, Optional
 
 import numpy as np
 import numpy.typing as npt
 
-from sglang.srt.disaggregation.utils import DisaggregationMode
 from sglang.srt.server_args import ServerArgs
 
+if TYPE_CHECKING:
+    from sglang.srt.disaggregation.utils import DisaggregationMode
+
 
 class KVArgs:
     engine_rank: int
-    kv_data_ptrs: list[int]
-    kv_data_lens: list[int]
-    kv_item_lens: list[int]
-    aux_data_ptrs: list[int]
-    aux_data_lens: list[int]
-    aux_item_lens: list[int]
+    kv_data_ptrs: List[int]
+    kv_data_lens: List[int]
+    kv_item_lens: List[int]
+    aux_data_ptrs: List[int]
+    aux_data_lens: List[int]
+    aux_item_lens: List[int]
     ib_device: str
+    ib_traffic_class: str
     gpu_id: int
+    # for different tp
+    decode_tp_size: int
+    # for pp prefill
+    prefill_pp_size: int
 
 
 class KVPoll:
@@ -45,7 +54,12 @@ class BaseKVSender(ABC):
 
     @abstractmethod
     def __init__(
-        self, mgr: BaseKVManager, bootstrap_addr: str, bootstrap_room: int
+        self,
+        mgr: BaseKVManager,
+        bootstrap_addr: str,
+        bootstrap_room: int,
+        dest_tp_ranks: List[int],
+        pp_rank: int,
     ): ...
 
     @abstractmethod

python/sglang/srt/disaggregation/common/utils.py

+import threading
+from collections import deque
+from typing import List, Tuple
+
+import numpy as np
+import numpy.typing as npt
+
+
+class FastQueue:
+    def __init__(self):
+        self._buf = deque()
+        self._cond = threading.Condition()
+
+    def put(self, item):
+        with self._cond:
+            self._buf.append(item)
+            # wake up a thread of wait()
+            self._cond.notify()
+
+    def get(self):
+        with self._cond:
+            # if queue is empty  ,block until is notified()
+            while not self._buf:
+                self._cond.wait()
+            return self._buf.popleft()
+
+
+def group_concurrent_contiguous(
+    src_indices: npt.NDArray[np.int64], dst_indices: npt.NDArray[np.int64]
+) -> Tuple[List[npt.NDArray[np.int64]], List[npt.NDArray[np.int64]]]:
+    """Vectorised NumPy implementation."""
+    if src_indices.size == 0:
+        return [], []
+
+    brk = np.where((np.diff(src_indices) != 1) | (np.diff(dst_indices) != 1))[0] + 1
+    src_groups = np.split(src_indices, brk)
+    dst_groups = np.split(dst_indices, brk)
+
+    src_groups = [g.tolist() for g in src_groups]
+    dst_groups = [g.tolist() for g in dst_groups]
+
+    return src_groups, dst_groups

python/sglang/srt/disaggregation/decode.py

@@ -33,8 +33,8 @@ from torch.distributed import ProcessGroup
 
 from sglang.srt.disaggregation.base import BaseKVManager, BaseKVReceiver, KVArgs, KVPoll
 from sglang.srt.disaggregation.utils import (
+    FAKE_BOOTSTRAP_HOST,
     DisaggregationMode,
-    FakeBootstrapHost,
     KVClassType,
     MetadataBuffers,
     ReqToMetadataIdxAllocator,
@@ -207,7 +207,7 @@ class DecodePreallocQueue:
 
     def add(self, req: Req) -> None:
         """Add a request to the pending queue."""
-        if req.bootstrap_host == FakeBootstrapHost:
+        if req.bootstrap_host == FAKE_BOOTSTRAP_HOST:
             # Fake transfer for warmup reqs
             kv_receiver_class = get_kv_class(TransferBackend.FAKE, KVClassType.RECEIVER)
         else:

python/sglang/srt/disaggregation/fake/conn.py

@@ -17,7 +17,14 @@ logger = logging.getLogger(__name__)
 
 # For warmup reqs, we don't kv transfer, we use the fake sender and receiver
 class FakeKVSender(BaseKVSender):
-    def __init__(self, mgr: BaseKVManager, bootstrap_addr: str, bootstrap_room: int):
+    def __init__(
+        self,
+        mgr: BaseKVManager,
+        bootstrap_addr: str,
+        bootstrap_room: int,
+        dest_tp_ranks: List[int],
+        pp_rank: int,
+    ):
         self.has_sent = False
 
     def poll(self) -> KVPoll:

python/sglang/srt/disaggregation/mooncake/conn.py

@@ -28,12 +28,12 @@ from sglang.srt.disaggregation.base.conn import (
     KVArgs,
     KVPoll,
 )
-from sglang.srt.disaggregation.mooncake.transfer_engine import MooncakeTransferEngine
-from sglang.srt.disaggregation.utils import (
-    DisaggregationMode,
+from sglang.srt.disaggregation.common.utils import (
     FastQueue,
     group_concurrent_contiguous,
 )
+from sglang.srt.disaggregation.mooncake.transfer_engine import MooncakeTransferEngine
+from sglang.srt.disaggregation.utils import DisaggregationMode
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     get_free_port,
@@ -677,7 +677,12 @@ class MooncakeKVManager(BaseKVManager):
 class MooncakeKVSender(BaseKVSender):
 
     def __init__(
-        self, mgr: MooncakeKVManager, bootstrap_addr: str, bootstrap_room: int
+        self,
+        mgr: MooncakeKVManager,
+        bootstrap_addr: str,
+        bootstrap_room: int,
+        dest_tp_ranks: List[int],
+        pp_rank: int,
     ):
         self.kv_mgr = mgr
         self.bootstrap_room = bootstrap_room

python/sglang/srt/disaggregation/nixl/conn.py

@@ -24,10 +24,8 @@ from sglang.srt.disaggregation.common.conn import (
     CommonKVManager,
     CommonKVReceiver,
 )
-from sglang.srt.disaggregation.utils import (
-    DisaggregationMode,
-    group_concurrent_contiguous,
-)
+from sglang.srt.disaggregation.common.utils import group_concurrent_contiguous
+from sglang.srt.disaggregation.utils import DisaggregationMode
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import get_local_ip_by_remote
 
@@ -350,7 +348,14 @@ class NixlKVManager(CommonKVManager):
 
 class NixlKVSender(BaseKVSender):
 
-    def __init__(self, mgr: NixlKVManager, bootstrap_addr: str, bootstrap_room: int):
+    def __init__(
+        self,
+        mgr: NixlKVManager,
+        bootstrap_addr: str,
+        bootstrap_room: int,
+        dest_tp_ranks: List[int],
+        pp_rank: int,
+    ):
         self.kv_mgr = mgr
         self.bootstrap_room = bootstrap_room
         self.aux_index = None

python/sglang/srt/disaggregation/prefill.py

@@ -27,10 +27,10 @@ from typing import TYPE_CHECKING, List, Optional
 
 import torch
 
-from sglang.srt.disaggregation.base import BaseKVManager, KVArgs, KVPoll
+from sglang.srt.disaggregation.base import BaseKVManager, KVPoll
 from sglang.srt.disaggregation.utils import (
+    FAKE_BOOTSTRAP_HOST,
     DisaggregationMode,
-    FakeBootstrapHost,
     KVClassType,
     MetadataBuffers,
     ReqToMetadataIdxAllocator,
@@ -51,7 +51,6 @@ if TYPE_CHECKING:
     from sglang.srt.managers.scheduler import GenerationBatchResult, Scheduler
     from sglang.srt.mem_cache.memory_pool import KVCache
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -68,35 +67,45 @@ class PrefillBootstrapQueue:
         metadata_buffers: MetadataBuffers,
         tp_rank: int,
         tp_size: int,
+        gpu_id: int,
         bootstrap_port: int,
         gloo_group: ProcessGroup,
-        transfer_backend: TransferBackend,
+        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.transfer_backend = transfer_backend
-        self.scheduler = scheduler
-        self.kv_manager = self._init_kv_manager()
+        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.pp_rank = pp_rank
+        self.pp_size = pp_size
         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
+        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 = KVArgs()
+        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.decode_tp_size = self.decode_tp_size // self.decode_dp_size
+        kv_args.prefill_pp_size = self.pp_size
         kv_data_ptrs, kv_data_lens, kv_item_lens = (
             self.token_to_kv_pool.get_contiguous_buf_infos()
         )
@@ -115,12 +124,12 @@ class PrefillBootstrapQueue:
         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,
@@ -130,23 +139,39 @@ class PrefillBootstrapQueue:
         )
         return kv_manager
 
-    def add(self, req: Req) -> None:
-        if req.bootstrap_host == FakeBootstrapHost:
-            # Fake transfer for warmup reqs
+    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)
         self.queue.append(req)
 
-    def extend(self, reqs: List[Req]) -> None:
+    def extend(self, reqs: List[Req], num_kv_heads: int) -> None:
         for req in reqs:
-            self.add(req)
+            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)
+            self.scheduler.stream_output([req], req.return_logprob)
+            return True
+        return False
 
     def _process_req(self, req: Req) -> None:
         """
@@ -154,19 +179,40 @@ class PrefillBootstrapQueue:
         """
         req.sampling_params.max_new_tokens = 1
 
-    def pop_bootstrapped(self) -> List[Req]:
-        """pop the reqs which has finished bootstrapping"""
+    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:
-            return []
+            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:
@@ -181,9 +227,10 @@ class PrefillBootstrapQueue:
                 )
                 self.scheduler.stream_output([req], req.return_logprob)
                 indices_to_remove.add(i)
+                failed_reqs.append(req)
                 continue
 
-            # KV.WaitingForInput
+            # KV.WaitingForInput - init here
             num_kv_indices = len(req.origin_input_ids)
             if self.req_to_metadata_buffer_idx_allocator.available_size() == 0:
                 break
@@ -192,9 +239,9 @@ class PrefillBootstrapQueue:
                 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)
 
@@ -202,7 +249,10 @@ class PrefillBootstrapQueue:
             entry for i, entry in enumerate(self.queue) if i not in indices_to_remove
         ]
 
-        return bootstrapped_reqs
+        if return_failed_reqs is False:
+            return bootstrapped_reqs
+        else:
+            return bootstrapped_reqs, failed_reqs
 
 
 class SchedulerDisaggregationPrefillMixin:
@@ -211,7 +261,7 @@ class SchedulerDisaggregationPrefillMixin:
     """
 
     @torch.no_grad()
-    def event_loop_normal_disagg_prefill(self: Scheduler):
+    def event_loop_normal_disagg_prefill(self: Scheduler) -> None:
         """A normal scheduler loop for prefill worker in disaggregation mode."""
 
         while True:
@@ -229,7 +279,6 @@ class SchedulerDisaggregationPrefillMixin:
                 or self.server_args.enable_sp_layernorm
             ):
                 batch, _ = self.prepare_dp_attn_batch(batch)
-
             self.cur_batch = batch
 
             if batch:
@@ -250,7 +299,7 @@ class SchedulerDisaggregationPrefillMixin:
             self.running_batch.batch_is_full = False
 
     @torch.no_grad()
-    def event_loop_overlap_disagg_prefill(self: Scheduler):
+    def event_loop_overlap_disagg_prefill(self: Scheduler) -> None:
         self.result_queue = deque()
 
         while True:
@@ -268,9 +317,7 @@ class SchedulerDisaggregationPrefillMixin:
                 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))
@@ -287,6 +334,9 @@ class SchedulerDisaggregationPrefillMixin:
 
             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:
@@ -309,7 +359,7 @@ class SchedulerDisaggregationPrefillMixin:
         launch_done: Optional[threading.Event] = None,
     ) -> None:
         """
-        Transfer kv for prefill completed requests and add it into disagg_prefill_infight_queue
+        Transfer kv for prefill completed requests and add it into disagg_prefill_inflight_queue
         Adapted from process_batch_result_prefill
         """
         (
@@ -325,7 +375,7 @@ class SchedulerDisaggregationPrefillMixin:
         )
 
         logprob_pt = 0
-        # Transfer kv for prefill completed requests and add it into disagg_prefill_infight_queue
+        # Transfer kv for prefill completed requests and add it into disagg_prefill_inflight_queue
         if self.enable_overlap:
             # wait
             logits_output, next_token_ids, _ = self.tp_worker.resolve_last_batch_result(
@@ -397,11 +447,15 @@ class SchedulerDisaggregationPrefillMixin:
         # 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:
+    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.
         """
-        assert len(self.disagg_prefill_inflight_queue) > 0
+        if len(self.disagg_prefill_inflight_queue) == 0:
+            return []
 
         done_reqs = []
 
@@ -413,6 +467,14 @@ class SchedulerDisaggregationPrefillMixin:
         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
@@ -434,11 +496,8 @@ class SchedulerDisaggregationPrefillMixin:
                     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
-            )
+            else:
+                assert False, f"Unexpected polling state {poll=}"
 
         # Stream requests which have finished transfer
         self.stream_output(
@@ -446,9 +505,32 @@ class SchedulerDisaggregationPrefillMixin:
             any(req.return_logprob for req in done_reqs),
             None,
         )
+        for req in done_reqs:
+            req: Req
+            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:

python/sglang/srt/disaggregation/utils.py

@@ -14,15 +14,15 @@ import requests
 import torch
 import torch.distributed as dist
 
-from sglang.srt.utils import get_ip, get_local_ip_by_remote
+from sglang.srt.utils import get_ip
 
 if TYPE_CHECKING:
     from sglang.srt.managers.schedule_batch import Req
 
-FakeBootstrapHost = "2.2.2.2"
-
-# env var for testing failure, convert to float explicitly
-FAILURE_PROB = float(os.getenv("DISAGGREGATION_TEST_FAILURE_PROB", 0))
+#########################
+# Constants & Enums
+#########################
+FAKE_BOOTSTRAP_HOST = "2.2.2.2"
 
 
 class DisaggregationMode(Enum):
@@ -31,6 +31,14 @@ class DisaggregationMode(Enum):
     DECODE = "decode"
 
 
+#########################
+# Synchronization
+#########################
+
+# env var for testing failure, convert to float explicitly
+FAILURE_PROB = float(os.getenv("DISAGGREGATION_TEST_FAILURE_PROB", 0))
+
+
 def poll_and_all_reduce(pollers, gloo_group):
     # at a certain prob, the poll is failed to simulate failure
     if FAILURE_PROB > 0:
@@ -47,6 +55,11 @@ def poll_and_all_reduce(pollers, gloo_group):
     return tensor_to_reduce.tolist()
 
 
+#########################
+# Metadata Buffers
+#########################
+
+
 class ReqToMetadataIdxAllocator:
     """A memory pool that maps a request to its first output token location."""
 
@@ -70,6 +83,91 @@ class ReqToMetadataIdxAllocator:
         self.free_slots.append(free_index)
 
 
+class MetadataBuffers:
+    def __init__(self, size: int, max_top_logprobs_num: int = 128):
+        # TODO: abort top_logprobs_num > 128 in PD
+
+        # We transfer the metadata of first output token to decode
+        # The minimal size for RDMA is 64Bytes, so we pad it to > 64Bytes
+        self.output_ids = torch.zeros((size, 16), dtype=torch.int32, device="cpu")
+        self.output_token_logprobs_val = torch.zeros(
+            (size, 16), dtype=torch.float32, device="cpu"
+        )
+        self.output_token_logprobs_idx = torch.zeros(
+            (size, 16), dtype=torch.int32, device="cpu"
+        )
+        self.output_top_logprobs_val = torch.zeros(
+            (size, max_top_logprobs_num), dtype=torch.float32, device="cpu"
+        )
+        self.output_top_logprobs_idx = torch.zeros(
+            (size, max_top_logprobs_num), dtype=torch.int32, device="cpu"
+        )
+
+    def get_buf_infos(self):
+        ptrs = [
+            self.output_ids.data_ptr(),
+            self.output_token_logprobs_val.data_ptr(),
+            self.output_token_logprobs_idx.data_ptr(),
+            self.output_top_logprobs_val.data_ptr(),
+            self.output_top_logprobs_idx.data_ptr(),
+        ]
+        data_lens = [
+            self.output_ids.nbytes,
+            self.output_token_logprobs_val.nbytes,
+            self.output_token_logprobs_idx.nbytes,
+            self.output_top_logprobs_val.nbytes,
+            self.output_top_logprobs_idx.nbytes,
+        ]
+        item_lens = [
+            self.output_ids[0].nbytes,
+            self.output_token_logprobs_val[0].nbytes,
+            self.output_token_logprobs_idx[0].nbytes,
+            self.output_top_logprobs_val[0].nbytes,
+            self.output_top_logprobs_idx[0].nbytes,
+        ]
+        return ptrs, data_lens, item_lens
+
+    def get_buf(self, idx: int):
+        return (
+            self.output_ids[idx],
+            self.output_token_logprobs_val[idx],
+            self.output_token_logprobs_idx[idx],
+            self.output_top_logprobs_val[idx],
+            self.output_top_logprobs_idx[idx],
+        )
+
+    def set_buf(self, req: Req):
+
+        self.output_ids[req.metadata_buffer_index][0] = req.output_ids[0]
+        if req.return_logprob:
+            if req.output_token_logprobs_val:  # not none or empty list
+                self.output_token_logprobs_val[req.metadata_buffer_index][0] = (
+                    req.output_token_logprobs_val[0]
+                )
+            if req.output_token_logprobs_idx:  # not none or empty list
+                self.output_token_logprobs_idx[req.metadata_buffer_index][0] = (
+                    req.output_token_logprobs_idx[0]
+                )
+
+            if req.output_top_logprobs_val:  # not none or empty list
+                self.output_top_logprobs_val[req.metadata_buffer_index][
+                    : len(req.output_top_logprobs_val[0])
+                ] = torch.tensor(
+                    req.output_top_logprobs_val[0], dtype=torch.float32, device="cpu"
+                )
+            if req.output_top_logprobs_idx:  # not none or empty list
+                self.output_top_logprobs_idx[req.metadata_buffer_index][
+                    : len(req.output_top_logprobs_idx[0])
+                ] = torch.tensor(
+                    req.output_top_logprobs_idx[0], dtype=torch.int32, device="cpu"
+                )
+
+
+#########################
+# Transfer Backend
+#########################
+
+
 class TransferBackend(Enum):
     MOONCAKE = "mooncake"
     NIXL = "nixl"
@@ -77,6 +175,7 @@ class TransferBackend(Enum):
 
 
 class KVClassType(Enum):
+    KVARGS = "kvargs"
     MANAGER = "manager"
     SENDER = "sender"
     RECEIVER = "receiver"
@@ -87,6 +186,7 @@ def get_kv_class(transfer_backend: TransferBackend, class_type: KVClassType):
     from sglang.srt.disaggregation.fake import FakeKVReceiver, FakeKVSender
 
     if transfer_backend == TransferBackend.MOONCAKE:
+        from sglang.srt.disaggregation.base import KVArgs
         from sglang.srt.disaggregation.mooncake import (
             MooncakeKVBootstrapServer,
             MooncakeKVManager,
@@ -95,6 +195,7 @@ def get_kv_class(transfer_backend: TransferBackend, class_type: KVClassType):
         )
 
         class_mapping = {
+            KVClassType.KVARGS: KVArgs,
             KVClassType.MANAGER: MooncakeKVManager,
             KVClassType.SENDER: MooncakeKVSender,
             KVClassType.RECEIVER: (MooncakeKVReceiver),
@@ -102,6 +203,7 @@ def get_kv_class(transfer_backend: TransferBackend, class_type: KVClassType):
         }
         return class_mapping.get(class_type)
     if transfer_backend == TransferBackend.NIXL:
+        from sglang.srt.disaggregation.base import KVArgs
         from sglang.srt.disaggregation.nixl import (
             NixlKVBootstrapServer,
             NixlKVManager,
@@ -110,6 +212,7 @@ def get_kv_class(transfer_backend: TransferBackend, class_type: KVClassType):
         )
 
         class_mapping = {
+            KVClassType.KVARGS: KVArgs,
             KVClassType.MANAGER: NixlKVManager,
             KVClassType.SENDER: NixlKVSender,
             KVClassType.RECEIVER: (NixlKVReceiver),
@@ -117,9 +220,11 @@ def get_kv_class(transfer_backend: TransferBackend, class_type: KVClassType):
         }
         return class_mapping.get(class_type)
     if transfer_backend == TransferBackend.FAKE:
+        from sglang.srt.disaggregation.base import KVArgs
         from sglang.srt.disaggregation.fake import FakeKVReceiver, FakeKVSender
 
         class_mapping = {
+            KVClassType.KVARGS: KVArgs,
             KVClassType.SENDER: FakeKVSender,
             KVClassType.RECEIVER: (FakeKVReceiver),
         }
@@ -128,6 +233,11 @@ def get_kv_class(transfer_backend: TransferBackend, class_type: KVClassType):
     raise ValueError(f"Unsupported transfer backend: {transfer_backend}")
 
 
+#########################
+# KV Pages
+#########################
+
+
 def kv_to_page_indices(kv_indices: np.ndarray, page_size: int):
     # 1. The page is guaranteed to be full except the last page.
     # 2. page index = kv_index // page_size
@@ -143,6 +253,11 @@ def kv_to_page_num(num_kv_indices: int, page_size: int):
     return (num_kv_indices + page_size - 1) // page_size
 
 
+#########################
+# PDLB Registry
+#########################
+
+
 @dataclasses.dataclass
 class PDRegistryRequest:
     """A request to register a machine itself to the LB."""
@@ -181,6 +296,11 @@ def register_disaggregation_server(
         )
 
 
+#########################
+# Misc
+#########################
+
+
 def is_mla_backend(target_kv_pool) -> bool:
     from sglang.srt.mem_cache.memory_pool import MLATokenToKVPool
 
@@ -200,119 +320,3 @@ def prepare_abort(req: Req, error_message: str, status_code=None):
         req.input_top_logprobs_idx = []
         req.input_token_ids_logprobs_val = []
         req.input_token_ids_logprobs_idx = []
-
-
-class MetadataBuffers:
-    def __init__(self, size: int, max_top_logprobs_num: int = 128):
-        # TODO: abort top_logprobs_num > 128 in PD
-
-        # We transfer the metadata of first output token to decode
-        # The minimal size for RDMA is 64Bytes, so we pad it to > 64Bytes
-        self.output_ids = torch.zeros((size, 16), dtype=torch.int32, device="cpu")
-        self.output_token_logprobs_val = torch.zeros(
-            (size, 16), dtype=torch.float32, device="cpu"
-        )
-        self.output_token_logprobs_idx = torch.zeros(
-            (size, 16), dtype=torch.int32, device="cpu"
-        )
-        self.output_top_logprobs_val = torch.zeros(
-            (size, max_top_logprobs_num), dtype=torch.float32, device="cpu"
-        )
-        self.output_top_logprobs_idx = torch.zeros(
-            (size, max_top_logprobs_num), dtype=torch.int32, device="cpu"
-        )
-
-    def get_buf_infos(self):
-        ptrs = [
-            self.output_ids.data_ptr(),
-            self.output_token_logprobs_val.data_ptr(),
-            self.output_token_logprobs_idx.data_ptr(),
-            self.output_top_logprobs_val.data_ptr(),
-            self.output_top_logprobs_idx.data_ptr(),
-        ]
-        data_lens = [
-            self.output_ids.nbytes,
-            self.output_token_logprobs_val.nbytes,
-            self.output_token_logprobs_idx.nbytes,
-            self.output_top_logprobs_val.nbytes,
-            self.output_top_logprobs_idx.nbytes,
-        ]
-        item_lens = [
-            self.output_ids[0].nbytes,
-            self.output_token_logprobs_val[0].nbytes,
-            self.output_token_logprobs_idx[0].nbytes,
-            self.output_top_logprobs_val[0].nbytes,
-            self.output_top_logprobs_idx[0].nbytes,
-        ]
-        return ptrs, data_lens, item_lens
-
-    def get_buf(self, idx: int):
-        return (
-            self.output_ids[idx],
-            self.output_token_logprobs_val[idx],
-            self.output_token_logprobs_idx[idx],
-            self.output_top_logprobs_val[idx],
-            self.output_top_logprobs_idx[idx],
-        )
-
-    def set_buf(self, req: Req):
-
-        self.output_ids[req.metadata_buffer_index][0] = req.output_ids[0]
-        if req.return_logprob:
-            if req.output_token_logprobs_val:  # not none or empty list
-                self.output_token_logprobs_val[req.metadata_buffer_index][0] = (
-                    req.output_token_logprobs_val[0]
-                )
-            if req.output_token_logprobs_idx:  # not none or empty list
-                self.output_token_logprobs_idx[req.metadata_buffer_index][0] = (
-                    req.output_token_logprobs_idx[0]
-                )
-
-            if req.output_top_logprobs_val:  # not none or empty list
-                self.output_top_logprobs_val[req.metadata_buffer_index][
-                    : len(req.output_top_logprobs_val[0])
-                ] = torch.tensor(
-                    req.output_top_logprobs_val[0], dtype=torch.float32, device="cpu"
-                )
-            if req.output_top_logprobs_idx:  # not none or empty list
-                self.output_top_logprobs_idx[req.metadata_buffer_index][
-                    : len(req.output_top_logprobs_idx[0])
-                ] = torch.tensor(
-                    req.output_top_logprobs_idx[0], dtype=torch.int32, device="cpu"
-                )
-
-
-class FastQueue:
-    def __init__(self):
-        self._buf = deque()
-        self._cond = threading.Condition()
-
-    def put(self, item):
-        with self._cond:
-            self._buf.append(item)
-            # wake up a thread of wait()
-            self._cond.notify()
-
-    def get(self):
-        with self._cond:
-            # if queue is empty  ,block until is notified()
-            while not self._buf:
-                self._cond.wait()
-            return self._buf.popleft()
-
-
-def group_concurrent_contiguous(
-    src_indices: npt.NDArray[np.int64], dst_indices: npt.NDArray[np.int64]
-) -> Tuple[List[npt.NDArray[np.int64]], List[npt.NDArray[np.int64]]]:
-    """Vectorised NumPy implementation."""
-    if src_indices.size == 0:
-        return [], []
-
-    brk = np.where((np.diff(src_indices) != 1) | (np.diff(dst_indices) != 1))[0] + 1
-    src_groups = np.split(src_indices, brk)
-    dst_groups = np.split(dst_indices, brk)
-
-    src_groups = [g.tolist() for g in src_groups]
-    dst_groups = [g.tolist() for g in dst_groups]
-
-    return src_groups, dst_groups

python/sglang/srt/entrypoints/http_server.py

@@ -43,7 +43,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import ORJSONResponse, Response, StreamingResponse
 
 from sglang.srt.disaggregation.utils import (
-    FakeBootstrapHost,
+    FAKE_BOOTSTRAP_HOST,
     register_disaggregation_server,
 )
 from sglang.srt.entrypoints.engine import _launch_subprocesses
@@ -878,7 +878,7 @@ def _wait_and_warmup(
                     "max_new_tokens": 8,
                     "ignore_eos": True,
                 },
-                "bootstrap_host": [FakeBootstrapHost] * server_args.dp_size,
+                "bootstrap_host": [FAKE_BOOTSTRAP_HOST] * server_args.dp_size,
                 # This is a hack to ensure fake transfer is enabled during prefill warmup
                 # ensure each dp rank has a unique bootstrap_room during prefill warmup
                 "bootstrap_room": [

python/sglang/srt/managers/scheduler.py

@@ -619,7 +619,7 @@ class Scheduler(
             self.disaggregation_mode == DisaggregationMode.DECODE
         ):  # *2 for the headroom.
             buffer_size = (self.req_to_token_pool.size) * 2
-            req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
+            self.req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
                 buffer_size
             )
             self.disagg_metadata_buffers = MetadataBuffers(buffer_size)
@@ -627,7 +627,7 @@ class Scheduler(
             # The decode requests polling kv cache
             self.disagg_decode_transfer_queue = DecodeTransferQueue(
                 gloo_group=self.attn_tp_cpu_group,
-                req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator,
+                req_to_metadata_buffer_idx_allocator=self.req_to_metadata_buffer_idx_allocator,
                 metadata_buffers=self.disagg_metadata_buffers,
                 scheduler=self,
                 tree_cache=self.tree_cache,
@@ -642,7 +642,7 @@ class Scheduler(
                     if self.draft_worker is None
                     else self.draft_worker.model_runner.token_to_kv_pool
                 ),
-                req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator,
+                req_to_metadata_buffer_idx_allocator=self.req_to_metadata_buffer_idx_allocator,
                 metadata_buffers=self.disagg_metadata_buffers,
                 scheduler=self,
                 transfer_queue=self.disagg_decode_transfer_queue,
@@ -660,7 +660,7 @@ class Scheduler(
         elif self.disaggregation_mode == DisaggregationMode.PREFILL:
             # *2 for the headroom.
             buffer_size = self.max_running_requests * 2
-            req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
+            self.req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
                 buffer_size
             )
             self.disagg_metadata_buffers = MetadataBuffers(buffer_size)
@@ -672,14 +672,20 @@ class Scheduler(
                     if self.draft_worker is None
                     else self.draft_worker.model_runner.token_to_kv_pool
                 ),
-                req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator,
+                req_to_metadata_buffer_idx_allocator=self.req_to_metadata_buffer_idx_allocator,
                 metadata_buffers=self.disagg_metadata_buffers,
                 tp_rank=self.tp_rank,
                 tp_size=self.tp_size,
+                gpu_id=self.gpu_id,
                 bootstrap_port=self.server_args.disaggregation_bootstrap_port,
                 gloo_group=self.attn_tp_cpu_group,
-                transfer_backend=self.transfer_backend,
+                max_total_num_tokens=self.max_total_num_tokens,
+                decode_tp_size=self.server_args.disaggregation_decode_tp,
+                decode_dp_size=self.server_args.disaggregation_decode_dp,
                 scheduler=self,
+                pp_rank=self.pp_rank,
+                pp_size=self.pp_size,
+                transfer_backend=self.transfer_backend,
             )
             # The prefill requests that are in the middle of kv sending
             self.disagg_prefill_inflight_queue: List[Req] = []
@@ -1110,7 +1116,9 @@ class Scheduler(
     def _add_request_to_queue(self, req: Req):
         req.queue_time_start = time.perf_counter()
         if self.disaggregation_mode == DisaggregationMode.PREFILL:
-            self.disagg_prefill_bootstrap_queue.add(req)
+            self.disagg_prefill_bootstrap_queue.add(
+                req, self.model_config.num_key_value_heads
+            )
         elif self.disaggregation_mode == DisaggregationMode.DECODE:
             self.disagg_decode_prealloc_queue.add(req)
         else:
@@ -1118,7 +1126,9 @@ class Scheduler(
 
     def _extend_requests_to_queue(self, reqs: List[Req]):
         if self.disaggregation_mode == DisaggregationMode.PREFILL:
-            self.disagg_prefill_bootstrap_queue.extend(reqs)
+            self.disagg_prefill_bootstrap_queue.extend(
+                reqs, self.model_config.num_key_value_heads
+            )
         elif self.disaggregation_mode == DisaggregationMode.DECODE:
             # If this is a decode server, we put the request to the decode pending prealloc queue
             self.disagg_decode_prealloc_queue.extend(reqs)

python/sglang/srt/server_args.py

@@ -227,6 +227,9 @@ class ServerArgs:
     disaggregation_mode: str = "null"
     disaggregation_transfer_backend: str = "mooncake"
     disaggregation_bootstrap_port: int = 8998
+    disaggregation_decode_tp: Optional[int] = None
+    disaggregation_decode_dp: Optional[int] = None
+    disaggregation_prefill_pp: Optional[int] = 1
     disaggregation_ib_device: Optional[str] = None
     num_reserved_decode_tokens: int = 512  # used for decode kv cache offload in PD
     pdlb_url: Optional[str] = None
@@ -505,12 +508,27 @@ class ServerArgs:
             self.triton_attention_num_kv_splits = 16
 
         # PD disaggregation
-        if self.disaggregation_mode == "prefill":
-            self.disable_cuda_graph = True
-            logger.warning("Cuda graph is disabled for prefill server")
-        elif self.disaggregation_mode == "decode":
+        if self.disaggregation_mode == "decode":
+            assert (
+                self.disaggregation_decode_tp is None
+            ), "Cannot set --disaggregation-decode-tp for the decode engine."
+            assert (
+                self.disaggregation_decode_dp is None
+            ), "Cannot set --disaggregation-decode-dp for the decode engine."
+
             self.disable_radix_cache = True
             logger.warning("KV cache is forced as chunk cache for decode server")
+        elif self.disaggregation_mode == "prefill":
+            if self.disaggregation_decode_tp is None:
+                self.disaggregation_decode_tp = self.tp_size
+            if self.disaggregation_decode_dp is None:
+                self.disaggregation_decode_dp = self.dp_size
+
+            self.disaggregation_prefill_pp = self.pp_size
+            self.validate_disagg_tp_size(self.tp_size, self.disaggregation_decode_tp)
+
+            self.disable_cuda_graph = True
+            logger.warning("Cuda graph is disabled for prefill server")
 
         os.environ["SGLANG_ENABLE_TORCH_COMPILE"] = (
             "1" if self.enable_torch_compile else "0"
@@ -520,6 +538,14 @@ class ServerArgs:
             "1" if self.disable_outlines_disk_cache else "0"
         )
 
+    def validate_disagg_tp_size(self, prefill_tp: int, decode_tp: int):
+        larger_tp = max(decode_tp, prefill_tp)
+        smaller_tp = min(decode_tp, prefill_tp)
+        assert larger_tp % smaller_tp == 0, (
+            "Different tp size is supported only when one tp is multiple of the other. "
+            f"decode_tp={decode_tp}, prefill_tp={prefill_tp}"
+        )
+
     @staticmethod
     def add_cli_args(parser: argparse.ArgumentParser):
         # Model and port args
@@ -1512,6 +1538,24 @@ class ServerArgs:
             default=ServerArgs.disaggregation_bootstrap_port,
             help="Bootstrap server port on the prefill server. Default is 8998.",
         )
+        parser.add_argument(
+            "--disaggregation-decode-tp",
+            type=int,
+            default=ServerArgs.disaggregation_decode_tp,
+            help="Decode tp size. If not set, it matches the tp size of the current engine. This is only set on the prefill server.",
+        )
+        parser.add_argument(
+            "--disaggregation-decode-dp",
+            type=int,
+            default=ServerArgs.disaggregation_decode_dp,
+            help="Decode dp size. If not set, it matches the dp size of the current engine. This is only set on the prefill server.",
+        )
+        parser.add_argument(
+            "--disaggregation-prefill-pp",
+            type=int,
+            default=ServerArgs.disaggregation_prefill_pp,
+            help="Prefill pp size. If not set, it is default to 1. This is only set on the decode server.",
+        )
         parser.add_argument(
             "--disaggregation-ib-device",
             type=str,