# SPDX-License-Identifier: Apache-2.0 import contextlib import hashlib import math import os import queue import random import struct import threading import time from collections import defaultdict, deque from collections.abc import Iterator from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from typing import TYPE_CHECKING, Any, List, Optional, OrderedDict, Tuple import msgspec import numpy as np import numpy.typing as npt import torch import torch_npu import zmq from mooncake.engine import TransferEngine # type: ignore from vllm import envs from vllm.config import VllmConfig from vllm.distributed.kv_transfer.kv_connector.v1.base import ( KVConnectorBase_V1, KVConnectorMetadata, KVConnectorRole) from vllm.distributed.parallel_state import (get_tensor_model_parallel_rank, get_tp_group) from vllm.utils import get_ip, logger, make_zmq_path, make_zmq_socket from vllm.v1.core.sched.output import SchedulerOutput from vllm.v1.request import RequestStatus import vllm_npu.envs as envs_ascend from vllm_npu.ascend_config import get_ascend_config, init_ascend_config from vllm_npu.distributed.mooncake.transfer_engine import get_global_te from vllm_npu.distributed.utils import get_transfer_timeout_value if TYPE_CHECKING: from vllm.attention.backends.abstract import AttentionMetadata from vllm.forward_context import ForwardContext from vllm.v1.core.kv_cache_manager import KVCacheBlocks from vllm.v1.request import Request GET_META_MSG = b"get_meta_msg" DONE_RECVING_MSG = b"done_recving_msg" class MooncakeAgentMetadata(msgspec.Struct, omit_defaults=True, dict=True): engine_id: str te_rpc_port: int kv_caches_base_addr: list[int] num_blocks: int @dataclass class ReqMeta: local_block_ids: list[int] remote_block_ids: list[int] remote_host: str remote_port: int remote_engine_id: str class KVCacheTaskTracker: def __init__(self): super().__init__() self.done_task_lock = threading.Lock() self.finished_requests: set[str] = set() # Only used in prefill node. Tracks requests whose kv blocks freeing is # intentionally delayed. Each entry is a tuple of (request_id, # timestamp). If a request remains in this queue for too long, it will # be force-freed. self.record_finished_requests: set[str] = set() self.delayed_free_requests: OrderedDict[str, float] = OrderedDict() def add_not_transfer_request(self, request_id: str): with self.done_task_lock: self.finished_requests.add(request_id) def update_done_task_count(self, request_id: str): with self.done_task_lock: self.finished_requests.add(request_id) if request_id in self.delayed_free_requests: self._remove_delayed_requests(request_id) else: self.record_finished_requests.add(request_id) def get_and_clear_finished_requests(self) -> set[str]: """ Get and clear the requests that have been completed. Returns: A set of request IDs that have been completed. """ with self.done_task_lock: finished_requests = self.finished_requests.copy() expired_requests = self._retrieve_expired_requests() finished_requests.update(expired_requests) self.finished_requests.clear() return finished_requests def add_delayed_request(self, request_id: str, delay_start_time: float): """Add a delayed free request.""" with self.done_task_lock: if request_id not in self.record_finished_requests: self.delayed_free_requests[request_id] = delay_start_time else: self.record_finished_requests.discard(request_id) def _retrieve_expired_requests(self): """Retrieve all expired delayed requests.""" expired_requests: set[str] = set() # Free delayed requests if they exceed the timeout current_time = time.time() while self.delayed_free_requests: request_id = next(iter(self.delayed_free_requests)) delay_start_time = self.delayed_free_requests[request_id] if (current_time - delay_start_time > envs.VLLM_NIXL_ABORT_REQUEST_TIMEOUT): self.delayed_free_requests.popitem(last=False) expired_requests.add(request_id) logger.info("Force freed request: %s", request_id) else: break return expired_requests def _remove_delayed_requests(self, request_id: str): """Remove all delayed free requests matching the given request_id.""" self.delayed_free_requests.pop(request_id) class KVCacheSendingThread(threading.Thread): def __init__(self, tp_rank: int, decode_tp_size: int, local_engine_id: str, side_channel_host: str, side_channel_port: int, metadata: MooncakeAgentMetadata, ready_event: threading.Event, kv_caches: dict[str, Any]): super().__init__(daemon=True, name="KVCacheSendingThread") self.tp_rank = tp_rank self.decode_tp_size = decode_tp_size self.local_engine_id = local_engine_id self.side_channel_host = side_channel_host self.side_channel_port = side_channel_port self.metadata = metadata self.ready_event = ready_event self.kv_caches = kv_caches self.task_tracker = KVCacheTaskTracker() def get_and_clear_finished_requests(self) -> set[str]: """ Get and clear the requests that have been completed. Returns: A set of request IDs that have been completed. """ return self.task_tracker.get_and_clear_finished_requests() def add_not_transfer_request(self, request_id: str): self.task_tracker.add_not_transfer_request(request_id) def add_delayed_request(self, request_id: str, delay_start_time: float): return self.task_tracker.add_delayed_request(request_id, delay_start_time) def run(self): """Run the thread to handle KV cache transfer requests.""" encoder = msgspec.msgpack.Encoder() encoded_data = encoder.encode(self.metadata) size_in_bytes = len(encoded_data) logger.debug("Size of encoded MooncakeAgentMetadata: %s bytes", str(size_in_bytes)) # Listen for new requests for metadata. # NOTE(rob): we need each rank to have a unique port. This hack to keeps # us moving. We will switch when moving to etcd or where we have a # single ZMQ socket in the scheduler. handshake_port = self.side_channel_port + self.tp_rank path = make_zmq_path("tcp", self.side_channel_host, handshake_port) logger.info("Starting listening on path: %s", path) with zmq_ctx(zmq.ROUTER, path) as sock: # type: ignore self.ready_event.set() decoder = msgspec.msgpack.Decoder(type=tuple) while True: try: frames = sock.recv_multipart() if len(frames) < 2: logger.error("Invalid message format: %s", frames) continue identity = frames[0] payload = [f for f in frames[1:] if f != b""] if len(payload) != 1: logger.error("Invalid message format: %s", frames) continue msg = decoder.decode(payload[0]) if msg[0] == GET_META_MSG: sock.send_multipart((identity, b"", encoded_data)) elif msg[0] == DONE_RECVING_MSG: logger.debug("Got DONE_RECVING_MSG for request %s", msg[1]) request_id = msg[1] self.task_tracker.update_done_task_count(request_id) # Acknowledge the request completion. while True: try: # Send ACK to the sender. sock.send_multipart( (identity, b"", b"ACK"), flags=zmq.NOBLOCK) # type: ignore break except zmq.Again: # type: ignore # If the socket is not ready, retry sending. logger.debug( "Socket not ready, retrying to send ACK for " "request %s", msg[1]) time.sleep(0.01) else: logger.error( "Connection listener got unexpected message %s", msg) except Exception as e: logger.error("Connection listener got exception %s: %s", type(e), e) class KVCacheRecvingThread(threading.Thread): def __init__(self, tp_rank: int, tp_size: int, engine: TransferEngine, local_engine_id: str, local_handshake_port: int, local_kv_caches_base_addr: list[int], block_len: list[int], ready_event: threading.Event, vllm_config: VllmConfig, kv_caches: dict[str, Any]): super().__init__(daemon=True, name="KVCacheRecvingThread") self.tp_rank = tp_rank self.tp_size = tp_size self.local_engine_id = local_engine_id self.local_handshake_port = local_handshake_port self.engine = engine self.ready_event = ready_event self.kv_caches_base_addr: dict[str, dict[int, list[int]]] = \ defaultdict(dict) self.kv_caches_base_addr[local_engine_id][local_handshake_port] = \ local_kv_caches_base_addr self.remote_te_port: dict[str, dict[int, int]] = \ defaultdict(dict) self.block_len = block_len # TODO(jianzs): find a better way to detect MLA. self.use_mla = len(block_len) == 2 self.use_sparse = len(block_len) == 3 self.request_queue: queue.Queue[Any] = queue.Queue() self.executor = ThreadPoolExecutor(max_workers=32) self.task_tracker = KVCacheTaskTracker() self.encoder = msgspec.msgpack.Encoder() self.decoder = msgspec.msgpack.Decoder(MooncakeAgentMetadata) self.remote_sockets_lock = threading.Lock() self.remote_sockets: dict[ # type: ignore str, deque[zmq.Socket]] = defaultdict( # type: ignore deque) self.remote_poller = zmq.Poller() # type: ignore self.timeout = 1.0 # seconds self.vllm_config = vllm_config self.model_config = self.vllm_config.model_config self.num_key_value_heads = self.model_config.hf_config.num_key_value_heads self.kv_caches = kv_caches def add_request(self, request_id: str, local_block_ids: list[int], remote_block_ids: list[int], remote_engine_id: str, remote_host: str, remote_handshake_port: int, offset: int, num_need_pulls: int): """Add a new request to the queue for processing.""" logger.debug(f"Adding request {request_id} to the queue.") self.request_queue.put({ "request_id": request_id, "local_block_ids": local_block_ids, "remote_block_ids": remote_block_ids, "remote_engine_id": remote_engine_id, "remote_host": remote_host, "remote_handshake_port": remote_handshake_port, "offset": offset, "num_need_pulls": num_need_pulls }) def get_and_clear_finished_requests(self) -> set[str]: """ Get and clear the requests that have been completed. Returns: A set of request IDs that have been completed. """ return self.task_tracker.get_and_clear_finished_requests() def run(self): """Run the thread to handle KV cache transfer requests.""" self.ready_event.set() while True: try: request_data = self.request_queue.get() if request_data is None: logger.warning("Received a None request!") self.request_queue.task_done() continue self._handle_request(request_data) except Exception as e: logger.error(f"Error in KVCacheTransferThread: {e}") def _handle_request(self, req_meta: dict[str, Any]): request_id = req_meta["request_id"] remote_host = req_meta["remote_host"] remote_handshake_port = req_meta["remote_handshake_port"] offset = req_meta["offset"] num_need_pulls = req_meta["num_need_pulls"] try: logger.debug( f"Starting to transfer KV cache for request {request_id}.") self._transfer_kv_cache(req_meta) logger.debug( f"Finished transferring KV cache for request {request_id}.") except Exception as e: logger.error("Failed to transfer KV cache for request " f"{request_id}: {e}") finally: # Always send the done signal to the remote host to ensure proper # resource cleanup. Failing to do so may cause a memory leak on the # remote host. self._send_done_recv_signal(request_id, remote_host, remote_handshake_port) if offset == num_need_pulls - 1: self.task_tracker.update_done_task_count(request_id) self.request_queue.task_done() def _transfer_kv_cache(self, req_meta: dict[str, Any]): """Handle a KV cache transfer request.""" request_id = req_meta["request_id"] remote_block_ids = req_meta["remote_block_ids"] local_block_ids = req_meta["local_block_ids"] remote_engine_id = req_meta["remote_engine_id"] remote_host = req_meta["remote_host"] remote_handshake_port = req_meta["remote_handshake_port"] offset = req_meta["offset"] self.num_need_pulls = req_meta["num_need_pulls"] # Full prefix cache hit: do not need to read remote blocks, just notify # P worker that we have the blocks we need. num_local_blocks = len(local_block_ids) if num_local_blocks == 0: return num_remote_blocks = len(remote_block_ids) assert num_local_blocks <= num_remote_blocks if num_local_blocks < num_remote_blocks: remote_block_ids = remote_block_ids[-num_local_blocks:] # Check if we have the remote metadata cached. if remote_engine_id not in self.kv_caches_base_addr or \ remote_handshake_port not in self.kv_caches_base_addr[remote_engine_id]: self._get_remote_metadata(remote_host, remote_handshake_port) if self.num_need_pulls == 1: grouped_remote_block_ids, grouped_local_block_ids = \ group_concurrent_contiguous(remote_block_ids, local_block_ids) else: remote_block_ids = list(map(lambda x: [x], remote_block_ids)) local_block_ids = list(map(lambda x: [x], local_block_ids)) grouped_remote_block_ids, grouped_local_block_ids = remote_block_ids, local_block_ids num_transfer_groups = len(grouped_remote_block_ids) remote_kv_caches_base_addrs = \ self.kv_caches_base_addr[remote_engine_id][remote_handshake_port] local_kv_caches_base_addrs = \ self.kv_caches_base_addr[self.local_engine_id][self.local_handshake_port] remote_transfer_port = self.remote_te_port[remote_engine_id][ remote_handshake_port] num_blocks = len(local_block_ids) session_id = f"{remote_host}:{remote_transfer_port}" req_start_time = time.perf_counter() src_list, dst_list, length_list = [], [], [] for k, (src_layer_base_addr, dst_layer_base_addr) in enumerate( zip(local_kv_caches_base_addrs, remote_kv_caches_base_addrs)): if self.use_mla: block_len = (self.block_len[k % 2]) elif self.use_sparse: block_len = (self.block_len[k % 3]) else: block_len = (self.block_len[0]) inner_block_len = block_len // self.num_need_pulls for remote_block_id, local_block_id in zip( grouped_remote_block_ids, grouped_local_block_ids): src = src_layer_base_addr + local_block_id[ 0] * block_len + offset * inner_block_len dst = dst_layer_base_addr + remote_block_id[0] * inner_block_len length = inner_block_len * len(local_block_id) src_list.append(src) dst_list.append(dst) length_list.append(length) ret = self.engine.batch_transfer_sync_read(session_id, src_list, dst_list, length_list) if ret < 0: logger.error("Mooncake transfer failed for request %s", req_meta["request_id"]) raise RuntimeError(f"Mooncake transfer failed, ret: {ret}") req_end_time = time.perf_counter() req_transfer_elapsed = (req_end_time - req_start_time) * 1000 logger.info( "KV cache transfer for request %s took %.2f ms (%d groups," " %d blocks). local_ip %s local_device_id %s remote_session_id %s", request_id, req_transfer_elapsed, num_transfer_groups, num_blocks, get_ip(), self.tp_rank, session_id) if self.num_need_pulls > 1 and offset == self.num_need_pulls - 1: self._cat_kv_cache(grouped_local_block_ids) def _cat_kv_cache(self, block_ids: list[list[int]]): # Get necessary parameters k_cache = list(self.kv_caches.values())[0][0] kv_shape = k_cache.shape dtype = k_cache.dtype device = k_cache.device head_dim = self.model_config.hf_config.head_dim block_size = self.vllm_config.cache_config.block_size num_kv_head = max( self.model_config.hf_config.num_key_value_heads // self.tp_size, 1) flat_block_ids = [item for sublist in block_ids for item in sublist] block_ids_tensor = torch.tensor(flat_block_ids, dtype=torch.int32) num_blocks = len(flat_block_ids) block_len = num_blocks * block_size # Create device tensors for copy operations block_table = block_ids_tensor.view(1, -1).to(device=device) block_len_tensor = torch.tensor([block_len], dtype=torch.int32).to(device=device) seq_start_tensor = torch.tensor([0], dtype=torch.int32).to(device=device) # Initialize buffers k_buffer = torch.empty(block_len, num_kv_head, head_dim, dtype=dtype, device=device) v_buffer = torch.empty(block_len, num_kv_head, head_dim, dtype=dtype, device=device) # Create slot mapping for reshape operations block_offsets = torch.arange(0, block_size, dtype=torch.int32) slot_mapping = (block_offsets.reshape( (1, block_size)) + block_ids_tensor.reshape( (num_blocks, 1)) * block_size) slot_mapping = slot_mapping.flatten().to(device=device) # Process each layer in the KV cache for _, (k_cache_layer, v_cache_layer) in self.kv_caches.items(): if len( k_cache_layer.shape ) == 3: # kv shape in torchair model is [num_block, block_size, num_kv_head*head_dim] k_cache_layer = k_cache_layer.view(kv_shape[0], kv_shape[1], num_kv_head, head_dim) v_cache_layer = v_cache_layer.view(kv_shape[0], kv_shape[1], num_kv_head, head_dim) # Load cache data into buffers torch_npu.atb.npu_paged_cache_load( k_cache_layer, v_cache_layer, block_table, block_len_tensor, seq_starts=seq_start_tensor, key=k_buffer, value=v_buffer, ) # Transpose KV cache k_buffer = self._transpose_kv_cache_between_head( k_buffer, num_blocks, block_size, block_len, num_kv_head) v_buffer = self._transpose_kv_cache_between_head( v_buffer, num_blocks, block_size, block_len, num_kv_head) # Reshape and cache the processed buffers torch_npu._npu_reshape_and_cache( key=k_buffer, value=v_buffer, key_cache=k_cache_layer, value_cache=v_cache_layer, slot_indices=slot_mapping, ) # Clean up buffers del k_buffer, v_buffer def _transpose_kv_cache_between_head(self, buffer: torch.Tensor, num_blocks: int, block_size: int, block_len: int, num_kv_head: int) -> torch.Tensor: buffer = buffer.view(num_blocks, self.num_need_pulls, block_size, -1) buffer.transpose_(1, 2) return buffer.contiguous().view(block_len, num_kv_head, -1) def _get_remote_metadata(self, remote_host: str, remote_handshake_port: int) -> None: """Get the metadata from the remote host.""" sock: Optional[zmq.Socket] = None # type: ignore try: sock = self._get_remote_socket(remote_host, remote_handshake_port) ensure_zmq_send(sock, self.encoder.encode((GET_META_MSG, ""))) metadata_bytes = ensure_zmq_recv(sock, self.remote_poller) agent_meta = self.decoder.decode(metadata_bytes) engine_id = agent_meta.engine_id assert engine_id != self.local_engine_id, ( f"Conflict engine id {engine_id} with local engine id " f"{self.local_engine_id}.") self.kv_caches_base_addr[engine_id][remote_handshake_port] = \ agent_meta.kv_caches_base_addr self.remote_te_port[engine_id][remote_handshake_port] = \ agent_meta.te_rpc_port finally: if sock is not None: self._return_remote_socket(sock, remote_host, remote_handshake_port) logger.debug("Returned socket to pool for %s:%d", remote_host, remote_handshake_port) def _send_done_recv_signal(self, request_id: str, remote_host: str, remote_handshake_port: int): logger.debug("Sending done recving signal for request %s to %s:%d", request_id, remote_host, remote_handshake_port) sock: Optional[zmq.Socket] = None # type: ignore try: sock = self._get_remote_socket(remote_host, remote_handshake_port) data_bytes = self.encoder.encode((DONE_RECVING_MSG, request_id)) ensure_zmq_send(sock, data_bytes) resp = ensure_zmq_recv(sock, self.remote_poller, timeout=self.timeout) logger.debug( f"Received response for request {request_id}: {resp.decode('utf-8')}" ) if resp != b"ACK": logger.error("Failed to receive ACK for request %s from %s:%d", request_id, remote_host, remote_handshake_port) raise RuntimeError( f"Failed to receive ACK, resp: {resp.decode('utf-8')}") finally: if sock is not None: self._return_remote_socket(sock, remote_host, remote_handshake_port) logger.debug("Returned socket to pool for %s:%d", remote_host, remote_handshake_port) def _get_remote_socket( self, remote_host: str, remote_handshake_port: int) -> zmq.Socket: # type: ignore """Get a socket to the remote host.""" remote_path = make_zmq_path("tcp", remote_host, remote_handshake_port) with self.remote_sockets_lock: if self.remote_sockets[remote_path]: return self.remote_sockets[remote_path].popleft() ctx = zmq.Context() # type: ignore sock = make_zmq_socket( ctx=ctx, path=remote_path, socket_type=zmq.REQ, # type: ignore bind=False) sock.setsockopt( zmq.SNDTIMEO, # type: ignore int(self.timeout * 1000)) self.remote_poller.register(sock, zmq.POLLIN) # type: ignore return sock def _return_remote_socket( self, sock: zmq.Socket, # type: ignore remote_host: str, remote_handshake_port: int) -> None: """Return the remote socket to the pool.""" remote_path = make_zmq_path("tcp", remote_host, remote_handshake_port) with self.remote_sockets_lock: self.remote_sockets[remote_path].append(sock) class MooncakeConnectorMetadata(KVConnectorMetadata): def __init__(self): self.requests: dict[str, ReqMeta] = {} self.requests_to_send: dict[str, float] = {} def add_new_req( self, request_id: str, local_block_ids: list[int], kv_transfer_params: dict[str, Any], ): self.requests[request_id] = ReqMeta( local_block_ids=local_block_ids, remote_block_ids=kv_transfer_params["remote_block_ids"], remote_engine_id=kv_transfer_params["remote_engine_id"], remote_host=kv_transfer_params["remote_host"], remote_port=kv_transfer_params["remote_port"], ) class MooncakeConnector(KVConnectorBase_V1): def __init__(self, vllm_config: VllmConfig, role: KVConnectorRole): assert vllm_config.kv_transfer_config is not None self.engine_id = vllm_config.kv_transfer_config.engine_id if role == KVConnectorRole.SCHEDULER: self.connector_scheduler: Optional[MooncakeConnectorScheduler] = \ MooncakeConnectorScheduler(vllm_config, str(self.engine_id)) self.connector_worker: Optional[MooncakeConnectorWorker] = None elif role == KVConnectorRole.WORKER: self.connector_scheduler = None self.connector_worker = MooncakeConnectorWorker( vllm_config, str(self.engine_id)) ############################################################ # Scheduler Side Methods ############################################################ def get_num_new_matched_tokens( self, request: "Request", num_computed_tokens: int) -> tuple[int, bool]: assert self.connector_scheduler is not None return self.connector_scheduler.get_num_new_matched_tokens( request, num_computed_tokens) def update_state_after_alloc(self, request: "Request", blocks: "KVCacheBlocks", num_external_tokens: int): assert self.connector_scheduler is not None return self.connector_scheduler.update_state_after_alloc( request, blocks, num_external_tokens) def build_connector_meta( self, scheduler_output: SchedulerOutput, ) -> KVConnectorMetadata: assert self.connector_scheduler is not None return self.connector_scheduler.build_connector_meta(scheduler_output) def request_finished( self, request: "Request", block_ids: list[int], ) -> tuple[bool, Optional[dict[str, Any]]]: assert self.connector_scheduler is not None return self.connector_scheduler.request_finished(request, block_ids) ############################################################ # Worker Side Methods ############################################################ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]): assert self.connector_worker is not None self.connector_worker.register_kv_caches(kv_caches) def get_finished(self, finished_req_ids: set[str]) -> tuple[set[str], set[str]]: """Get the finished recving and sending requests.""" assert self.connector_worker is not None return self.connector_worker.get_finished() def start_load_kv(self, forward_context: "ForwardContext", **kwargs) -> None: assert self.connector_worker is not None assert isinstance(self._connector_metadata, MooncakeConnectorMetadata) self.connector_worker.start_load_kv(self._connector_metadata) def wait_for_layer_load(self, layer_name: str) -> None: """MooncakeConnector does not do layerwise saving.""" pass def save_kv_layer(self, layer_name: str, kv_layer: torch.Tensor, attn_metadata: "AttentionMetadata", **kwargs) -> None: """MooncakeConnector does not save explicitly.""" pass def wait_for_save(self): """MooncakeConnector does not save explicitly.""" pass class MooncakeConnectorScheduler: """Implementation of Scheduler side methods""" def __init__(self, vllm_config: VllmConfig, engine_id: str): self.vllm_config = vllm_config init_ascend_config(vllm_config) self.ascend_config = get_ascend_config() self.block_size = vllm_config.cache_config.block_size self.engine_id = engine_id self.local_ip = get_ip() logger.info("Initializing Mooncake Scheduler %s", engine_id) self.side_channel_host = get_ip() self.max_device_id = vllm_config.parallel_config.tensor_parallel_size * \ vllm_config.parallel_config.data_parallel_size # Handshake base port self.side_channel_port = ( vllm_config.kv_transfer_config.kv_port + vllm_config.parallel_config.data_parallel_rank * vllm_config.parallel_config.tensor_parallel_size) # Requests that need to start recv. # New requests are added by update_state_after_alloc in # the scheduler. Used to make metadata passed to Worker. self._reqs_need_recv: dict[str, tuple[Request, list[int]]] = {} self._reqs_need_send: dict[str, float] = {} def get_num_new_matched_tokens( self, request: "Request", num_computed_tokens: int) -> tuple[int, bool]: """ For remote prefill, pull all prompt blocks from remote asynchronously relative to engine execution. Args: request (Request): the request object. num_computed_tokens (int): the number of locally computed tokens for this request Returns: * the number of tokens that can be loaded from the external KV cache beyond what is already computed. * true if the external KV cache tokens will be loaded asynchronously (between scheduler steps). """ params = request.kv_transfer_params logger.debug( "MooncakeConnector get_num_new_matched_tokens: " "num_computed_tokens=%s, kv_transfer_params=%s", num_computed_tokens, params) if params is not None and params.get("do_remote_prefill"): # Remote prefill: get all prompt blocks from remote. assert num_computed_tokens % self.block_size == 0 # Note: We use the full token count as transmit data here. count = max(len(request.prompt_token_ids) - num_computed_tokens, 0) return count, count > 0 # No remote prefill for this request. return 0, False def update_state_after_alloc(self, request: "Request", blocks: "KVCacheBlocks", num_external_tokens: int): params = request.kv_transfer_params logger.debug( "MooncakeConnector update_state_after_alloc: " "num_external_tokens=%s, kv_transfer_params=%s", num_external_tokens, params) if params is not None and params.get("do_remote_prefill"): if params.get("remote_block_ids"): if all(p in params for p in ("remote_engine_id", "remote_host", "remote_port")): local_block_ids = (blocks.get_unhashed_block_ids() if num_external_tokens > 0 else []) # Get unhashed blocks to pull from remote. self._reqs_need_recv[request.request_id] = ( request, local_block_ids) else: logger.warning( "Got invalid KVTransferParams: %s. This " "request will not utilize KVTransfer", params) else: assert num_external_tokens == 0 # Only trigger 1 KV transfer per request. params["do_remote_prefill"] = False def build_connector_meta( self, scheduler_output: SchedulerOutput, ) -> KVConnectorMetadata: meta = MooncakeConnectorMetadata() # Loop through scheduled reqs and convert to ReqMeta. for req_id, (req, block_ids) in self._reqs_need_recv.items(): assert req.kv_transfer_params is not None # For the case where there are no remote blocks to pull # (block_ids is empty), we don't need to schedule # an async read on the worker side. meta.add_new_req( request_id=req_id, local_block_ids=block_ids, kv_transfer_params=req.kv_transfer_params, ) # Clear the list once workers start the transfers self._reqs_need_recv.clear() meta.requests_to_send = self._reqs_need_send self._reqs_need_send = {} return meta def request_finished( self, request: "Request", block_ids: list[int], ) -> tuple[bool, Optional[dict[str, Any]]]: """ Once a request is finished, determine whether request blocks should be freed now or will be sent asynchronously and freed later. """ params = request.kv_transfer_params logger.debug( "MooncakeConnector request_finished, request_status=%s, " "kv_transfer_params=%s", request.status, params) if (params is None or not params.get("do_remote_decode") or request.status != RequestStatus.FINISHED_LENGTH_CAPPED): return False, None computed_block_ids = block_ids delay_free_blocks = len(computed_block_ids) > 0 if delay_free_blocks: logger.info("Delaying free of %d blocks for request %s", len(computed_block_ids), request.request_id) self._reqs_need_send[request.request_id] = time.time() return delay_free_blocks, dict( do_remote_prefill=True, do_remote_decode=False, remote_block_ids=computed_block_ids, remote_engine_id=self.engine_id, remote_host=self.side_channel_host, remote_port=self.side_channel_port, last_token_id=request.output_token_ids[-1], ) class MooncakeConnectorWorker: """Implementation of Worker side methods""" def __init__(self, vllm_config: VllmConfig, engine_id: str): self._get_prefill_decode_size(vllm_config) os.environ["ASCEND_TRANSFER_TIMEOUT"] = str( get_transfer_timeout_value()) if self._prefill_tp_size < self._decode_tp_size: raise ValueError( f"prefill_tp_size: {self._prefill_tp_size} must be greater than" f" or equal to the decode_tp_size: {self._decode_tp_size}") # Metadata. self.vllm_config = vllm_config self.ascend_config = get_ascend_config() self.engine_id = engine_id self.tp_rank = get_tensor_model_parallel_rank() self.tp_size = vllm_config.parallel_config.tensor_parallel_size self.tp_group = get_tp_group() self.dp_rank = vllm_config.parallel_config.data_parallel_rank self.dp_size = vllm_config.parallel_config.data_parallel_size_local self.kv_caches: dict[str, torch.Tensor] = {} self.side_channel_host = get_ip() self.max_device_id = self.tp_size * self.dp_size self.kv_role = vllm_config.kv_transfer_config.kv_role self.num_key_value_heads = self.vllm_config.model_config.hf_config.num_key_value_heads # Handshake base port self.side_channel_port = ( vllm_config.kv_transfer_config.kv_port + vllm_config.parallel_config.data_parallel_rank * vllm_config.parallel_config.tensor_parallel_size) self.handshake_port = self.side_channel_port + self.tp_rank self.sockets: dict = {} # get tp device id # TODO(kw): https://github.com/vllm-project/vllm-ascend/pull/940 # introducing some changes device_ids_str = envs_ascend.PHYSICAL_DEVICES if device_ids_str is None: device_ids = list( range(self.dp_rank * self.tp_size, (self.dp_rank + 1) * self.tp_size)) else: device_ids = list(map(int, device_ids_str.split(','))) start_index = self.dp_rank * self.tp_size end_index = start_index + self.tp_size if len(device_ids) < end_index: raise ValueError( f"Not enough physical devices available for DP rank {self.dp_rank}. " f"Expected at least {end_index} devices, but found {len(device_ids)} " "in PHYSICAL_DEVICES.") device_ids = device_ids[start_index:end_index] assert len(device_ids) > self.tp_rank # type: ignore self.device_id = device_ids[self.tp_rank] # type: ignore if vllm_config.kv_transfer_config.get_from_extra_config( 'use_ascend_direct', True): hostname = self.side_channel_host else: hostname = f"{self.side_channel_host}:0:npu_{self.device_id}" logger.info("Initializing Mooncake work %s", engine_id) self.engine = get_global_te(hostname, device_name=None) self.te_rpc_port = self.engine.get_rpc_port() # Background thread for sending or receiving KV caches. self.kv_send_thread: Optional[KVCacheSendingThread] = None self.kv_recv_thread: Optional[KVCacheRecvingThread] = None # kv_transfer variables self.vllm_config = vllm_config self.block_size = vllm_config.cache_config.block_size if self.vllm_config.model_config.is_deepseek_mla: self.num_need_pulls = 1 else: num_d_block_heads = max(1, self.num_key_value_heads // self.tp_size) num_p_block_heads = max( 1, self.num_key_value_heads // self._prefill_tp_size) self.num_need_pulls = num_d_block_heads // num_p_block_heads def _get_prefill_decode_size(self, vllm_config: VllmConfig): # get prefill tp and dp size from extra config prefill_parallel_config: dict[ str, Any] = vllm_config.kv_transfer_config.get_from_extra_config( "prefill", {}) assert "tp_size" in prefill_parallel_config.keys() self._prefill_tp_size = prefill_parallel_config["tp_size"] assert "dp_size" in prefill_parallel_config.keys() self._prefill_dp_size = prefill_parallel_config["dp_size"] # get decode tp and dp size from extra config decode_parallel_config: dict[ str, Any] = vllm_config.kv_transfer_config.get_from_extra_config( "decode", {}) assert "tp_size" in decode_parallel_config.keys() self._decode_tp_size = decode_parallel_config["tp_size"] assert "dp_size" in decode_parallel_config.keys() self._decode_dp_size = decode_parallel_config["dp_size"] def _initialize( self, hostname: str, device_name: Optional[str], ) -> None: """Initialize the mooncake instance.""" device_name = device_name if device_name is not None else "" ret_value = self.engine.initialize(hostname, "P2PHANDSHAKE", "ascend", device_name) if ret_value != 0: raise RuntimeError( f"Mooncake initialization failed with ret_value: {ret_value}") def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]): """Register the KV Cache data.""" _, first_kv_cache_tuple = next(iter(kv_caches.items())) first_kv_cache = first_kv_cache_tuple[0] # TODO(tms): Find a more robust way to detect and handle MLA self.use_mla = first_kv_cache_tuple[0].size( -1) != first_kv_cache_tuple[1].size(-1) and len( first_kv_cache_tuple) == 2 self.use_sparse = len(first_kv_cache_tuple) == 3 if self.use_mla: # MLA case.[num_block, block_size, 1, hidden_dim] self.num_blocks = first_kv_cache.shape[0] block_rank = 3 # [block_size, latent_dim] block_shape_norm = first_kv_cache_tuple[0].shape[-block_rank:] block_shape_pe = first_kv_cache_tuple[1].shape[-block_rank:] self.block_len = [ first_kv_cache[0].element_size() * math.prod(block_shape_norm), first_kv_cache[1].element_size() * math.prod(block_shape_pe) ] logger.info( "num_blocks: %s, block_shape_norm: %s, block_shape_pe: %s", self.num_blocks, block_shape_norm, block_shape_pe) elif self.use_sparse: self.num_blocks = first_kv_cache.shape[0] block_rank = 3 # [block_size, latent_dim] block_shape_norm = first_kv_cache_tuple[0].shape[-block_rank:] block_shape_pe = first_kv_cache_tuple[1].shape[-block_rank:] block_shape_k = first_kv_cache_tuple[2].shape[-block_rank:] self.block_len = [ first_kv_cache[0].element_size() * math.prod(block_shape_norm), first_kv_cache[1].element_size() * math.prod(block_shape_pe), first_kv_cache[2].element_size() * math.prod(block_shape_k) ] logger.info( "num_blocks: %s, block_shape_norm: %s, block_shape_pe: %s, block_shape_k: %s", self.num_blocks, block_shape_norm, block_shape_pe, block_shape_k) else: # eager:[num_block, block_size, num_head, hidden_dim] # torchair:[num_block, block_size, num_head*hidden_dim] self.num_blocks = first_kv_cache.shape[0] kv_elem_size = first_kv_cache.element_size() block_rank = len( first_kv_cache.shape ) - 1 # [block_size, kv_heads, head_dim] or [block_size, kv_heads*head_dim] block_shape = first_kv_cache.shape[-block_rank:] self.block_len = [kv_elem_size * math.prod(block_shape)] logger.info("num_blocks: %s, block_shape: %s", self.num_blocks, block_shape) logger.info( "Registering KV_Caches. use_mla: %s, use_sparse: %s, shape %s", self.use_mla, self.use_sparse, first_kv_cache.shape) self.kv_caches = kv_caches kv_caches_base_addr = [] for cache_or_caches in kv_caches.values(): # Normalize to always be a list of caches if self.use_mla: for i, cache in enumerate(cache_or_caches, 0): base_addr = cache.data_ptr() region_len = self.num_blocks * self.block_len[i % 2] kv_caches_base_addr.append(base_addr) self._register(base_addr, region_len) elif self.use_sparse: for i, cache in enumerate(cache_or_caches, 0): base_addr = cache.data_ptr() region_len = self.num_blocks * self.block_len[i % 3] kv_caches_base_addr.append(base_addr) self._register(base_addr, region_len) else: cache_list = [ cache_or_caches ] if self.use_mla or self.use_sparse else cache_or_caches for cache in cache_list: base_addr = cache.data_ptr() region_len = self.num_blocks * self.block_len[0] kv_caches_base_addr.append(base_addr) self._register(base_addr, region_len) # After KV Caches registered, start the sending or receiving thread. metadata = MooncakeAgentMetadata( engine_id=self.engine_id, te_rpc_port=self.te_rpc_port, kv_caches_base_addr=kv_caches_base_addr, num_blocks=self.num_blocks, ) ready_event = threading.Event() if self.kv_role == 'kv_producer': self.kv_send_thread = KVCacheSendingThread( self.tp_rank, self._decode_tp_size, self.engine_id, self.side_channel_host, self.side_channel_port, metadata, ready_event, self.kv_caches) self.kv_send_thread.start() else: self.kv_recv_thread = KVCacheRecvingThread( self.tp_rank, self.tp_size, self.engine, self.engine_id, self.handshake_port, kv_caches_base_addr, self.block_len, ready_event, self.vllm_config, self.kv_caches) self.kv_recv_thread.start() ready_event.wait() def _register(self, ptr, length): logger.debug( "Registering KV cache: ptr=0x%x, length=%d, num_blocks=%d, " "block_lens=%s", ptr, length, self.num_blocks, self.block_len) ret_value = self.engine.register_memory(ptr, length) if ret_value != 0: raise RuntimeError("Mooncake memory registration failed.") def get_finished(self) -> tuple[set[str], set[str]]: done_sending = ( self.kv_send_thread. get_and_clear_finished_requests( # type: ignore[union-attr] ) if self.kv_role == 'kv_producer' else set()) done_recving = ( self.kv_recv_thread. get_and_clear_finished_requests( # type: ignore[union-attr] ) if self.kv_role == 'kv_consumer' else set()) if self.tp_rank == 0: logger.debug( "Number of completed KV cache send requests: %d, receive " "requests: %d", len(done_sending), len(done_recving)) return done_sending, done_recving def start_load_kv(self, metadata: MooncakeConnectorMetadata): """Start loading KV blocks from remote engine.""" for req_id, meta in metadata.requests.items(): logger.debug( "start_load_kv for request %s from remote engine %s. " "Num local_block_ids: %s. Num remote_block_ids: %s. ", req_id, meta.remote_engine_id, len(meta.local_block_ids), len(meta.remote_block_ids)) choosen_rank_list = self._get_remote_tp_rank(req_id) remote_handshake_port_list = [ x + meta.remote_port for x in choosen_rank_list ] for i in range(self.num_need_pulls): assert self.kv_recv_thread is not None self.kv_recv_thread.add_request( request_id=req_id, local_block_ids=meta.local_block_ids, remote_block_ids=meta.remote_block_ids, remote_engine_id=meta.remote_engine_id, remote_host=meta.remote_host, remote_handshake_port=remote_handshake_port_list[i], offset=i, num_need_pulls=self.num_need_pulls) if self.kv_send_thread is not None: for req_id, delay_start_time in metadata.requests_to_send.items(): if self.tp_rank in self._prefill_get_remote_tp_rank(req_id): self.kv_send_thread.add_delayed_request( req_id, delay_start_time) else: self.kv_send_thread.add_not_transfer_request(req_id) def _prefill_get_remote_tp_rank(self, req_id: str) -> List[int]: return sum(self._get_remote_tp_ranks_for_req(req_id), []) def _get_remote_tp_rank(self, req_id: str) -> List[int]: return self._get_remote_tp_ranks_for_req(req_id)[self.tp_rank] def _get_remote_tp_ranks_for_req(self, req_id: str) -> List[List[int]]: if self._prefill_tp_size == self._decode_tp_size: result = list(map(lambda x: [x], range(self._prefill_tp_size))) return result seed = string_to_int64_hash(req_id) rand = random.Random(seed) sampled_nums = [] ori_data = np.arange(self._prefill_tp_size) # random split prefill tp list if self._prefill_tp_size > self.num_key_value_heads or self.vllm_config.model_config.is_deepseek_mla or self.use_sparse: # use deepseek mla, num_key_value_heads == 128, but consider as 1 if self.vllm_config.model_config.is_deepseek_mla or self.use_sparse: num_kv_head = 1 else: num_kv_head = self.num_key_value_heads num_groups = len(ori_data) // num_kv_head ori_data = ori_data.reshape(-1, num_groups) rand_group_index = rand.sample(range(num_groups), \ max(self._decode_tp_size // num_kv_head, 1)) # random choose a group choosen_group = ori_data[:, [rand_group_index]] flattened = choosen_group.reshape(-1).tolist() sampled_nums = [ flattened[i:i + self.num_need_pulls] for i in range(0, len(flattened), self.num_need_pulls) ] # non-random split else: group_size = self._prefill_tp_size // self._decode_tp_size for i in range(self._decode_tp_size): ori_data_slice = ori_data[i * group_size:(i + 1) * group_size] sampled_nums.append(ori_data_slice.tolist()) return sampled_nums @contextlib.contextmanager def zmq_ctx(socket_type: Any, addr: str) -> Iterator[zmq.Socket]: # type: ignore """Context manager for a ZMQ socket""" if socket_type not in (zmq.ROUTER, zmq.REQ, zmq.DEALER): # type: ignore raise ValueError(f"Unexpected socket type: {socket_type}") ctx: Optional[zmq.Context] = None # type: ignore try: ctx = zmq.Context() # type: ignore yield make_zmq_socket(ctx=ctx, path=addr, socket_type=socket_type, bind=socket_type == zmq.ROUTER) # type: ignore finally: if ctx is not None: ctx.destroy(linger=0) def group_concurrent_contiguous( src: List[int], dst: List[int] ) -> Tuple[List[npt.NDArray[np.int64]], List[npt.NDArray[np.int64]]]: """Vectorised NumPy implementation.""" src_indices: npt.NDArray[np.int64] = np.array(src, dtype=np.int64) dst_indices: npt.NDArray[np.int64] = np.array(dst, dtype=np.int64) 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 def string_to_int64_hash(input_str): """ Hash the string using SHA-256 and convert it into an int64 integer. """ hashed_bytes = hashlib.sha256(input_str.encode("utf-8")).digest() trunked_bytes = hashed_bytes[:8] uint64_value = struct.unpack(" 0: logger.warning( f"Send failed: {e}, retrying... ({retries_left} " "attempts left)") time.sleep(0.1) else: logger.error(f"Send failed after all retries: {e}") raise RuntimeError(f"Failed to send data after {max_retries} " f"retries: {e}") def ensure_zmq_recv( socket: zmq.Socket, # type: ignore poller: zmq.Poller, # type: ignore timeout: float = 1.0, max_retries: int = 3) -> bytes: retries_left = max_retries while True: try: if dict(poller.poll(int(timeout * 1000))): # milliseconds data = socket.recv() return data else: raise zmq.ZMQError("Receive timeout") # type: ignore except zmq.ZMQError as e: # type: ignore retries_left -= 1 if retries_left > 0: logger.warning(f"Receive failed: {e}, retrying... " f"({retries_left} attempts left)") time.sleep(0.1) else: logger.error(f"Receive failed after all retries: {e}") raise RuntimeError( f"Failed to receive data after {max_retries} " f"retries: {e}")