Iluvatar-mrv100 SDK 4.3.0

vllm/distributed/kv_transfer/kv_pipe/base.py

@@ -0,0 +1,66 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+This file defines an interface `KVPipeBase`
+that provides an abstraction for sending and receiving tensors, or None, via
+distributed communications.
+
+All classes instantiated from this interface are assumed to be a FIFO pipe.
+
+If your distributed communication platform already supports key-value lookup,
+you can bypass this interface and directly start from `kv_lookup_buffer`.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+import torch
+
+
+class KVPipeBase(ABC):
+    """
+    This class provides an interface for sending and receiving tensors, or
+    None, by distributed communications.
+    """
+
+    @abstractmethod
+    def send_tensor(self, tensor: Optional[torch.Tensor]) -> None:
+        """Send a tensor, or None, via the pipe.
+        
+        Need to support sending None -- important for error handling.
+        
+        TODO: add a `key` argument so that we can use traditional 
+        key-value database as the distributed communication mechanism behind 
+        the pipe.
+
+        Args:
+            tensor (Optional[torch.Tensor]): The tensor to be sent. Can be None.
+
+        Raises:
+            NotImplementedError: This method must be implemented in subclasses.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def recv_tensor(self) -> Optional[torch.Tensor]:
+        """Receive a tensor (can be None) from the pipeline.
+
+        Returns:
+            Optional[torch.Tensor]: The tensor received from the pipeline. Can 
+                                    be None.
+
+        Raises:
+            NotImplementedError: This method must be implemented in subclasses.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def close(self) -> None:
+        """Close the pipeline and release resources.
+
+        This method is responsible for closing the communication pipeline 
+        and releasing any resources associated with it.
+
+        Raises:
+            NotImplementedError: This method must be implemented in subclasses.
+        """
+        raise NotImplementedError

vllm/distributed/kv_transfer/kv_pipe/flagcx_p2p_nccl_pipe.py

@@ -0,0 +1,458 @@
+# Mainly adopted from https://github.com/vllm-project/vllm/blob/1ad957950ffc1552af5abda78c03d88ddb67945b/vllm/distributed/kv_transfer/kv_pipe/p2p_nccl_pipe.py.
+# Below is the original copyright:
+# SPDX-License-Identifier: Apache-2.0
+import os
+import logging
+import threading
+import time
+import typing
+from collections import deque
+from typing import Any, Deque, Dict, List, Optional
+
+import msgpack
+import torch
+import zmq
+import ctypes
+import sys
+sys.path.append(os.getenv('FLAGCX_PATH'))
+from plugin.interservice.flagcx_wrapper import (
+    FLAGCXLibrary,
+    buffer_type,
+    cudaStream_t,
+    flagcxComm_t,
+    flagcxDataTypeEnum,
+)
+from vllm.config import KVTransferConfig
+from vllm.utils import current_stream, get_ip
+
+logger = logging.getLogger(__name__)
+
+
+class P2pNcclPipe:
+
+    def __init__(self,
+                 local_rank: int,
+                 config: KVTransferConfig,
+                 hostname: str = "",
+                 port_offset: int = 0,
+                 library_path: Optional[str] = None) -> None:
+        self.config = config
+        self.rank = port_offset
+        self.local_rank = local_rank
+        self.device = torch.device(f"cuda:{self.local_rank}")
+        flagcx_path = os.getenv('FLAGCX_PATH')
+        library_path=os.path.join(flagcx_path, "build/lib/libflagcx.so")
+        self.flagcx = FLAGCXLibrary(library_path)
+
+        if not hostname:
+            hostname = get_ip()
+        port = self.config.kv_port + port_offset
+        if port == 0:
+            raise ValueError("Port cannot be 0")
+        self._hostname = hostname
+        self._port = port
+
+        # Each card corresponds to a ZMQ address.
+        self.zmq_address = f"{self._hostname}:{self._port}"
+
+        # The `http_port` must be consistent with the port of OpenAI.
+        self.http_address = (
+            f"{self._hostname}:"
+            f"{self.config.kv_connector_extra_config['http_port']}")
+
+        # If `proxy_ip` or `proxy_port` is `""`,
+        # then the ping thread will not be enabled.
+        proxy_ip = self.config.get_from_extra_config("proxy_ip", "")
+        proxy_port = self.config.get_from_extra_config("proxy_port", "")
+        if proxy_ip == "" or proxy_port == "":
+            self.proxy_address = ""
+        else:
+            self.proxy_address = proxy_ip + ":" + proxy_port
+
+        self.context = zmq.Context()
+        self.router_socket = self.context.socket(zmq.ROUTER)
+        self.router_socket.bind(f"tcp://{self.zmq_address}")
+
+        self.poller = zmq.Poller()
+        self.poller.register(self.router_socket, zmq.POLLIN)
+
+        self.send_store_cv = threading.Condition()
+        self.send_queue_cv = threading.Condition()
+        self.recv_store_cv = threading.Condition()
+        self.comm_cv = threading.Condition()
+
+        # The sending type includes tree mutually exclusive options:
+        # PUT, GET, PUT_ASYNC.
+        self.send_type = self.config.get_from_extra_config("send_type", "PUT")
+        if self.send_type == "GET":
+            self.send_store: Dict[str,
+                                  torch.Tensor] = {}  # tensor_id: torch.Tensor
+        else:
+            # PUT or PUT_ASYNC
+            self.send_queue: Deque[
+                List[Any]] = deque()  # tensor_id: torch.Tensor
+            if self.send_type == "PUT_ASYNC":
+                self._send_thread = threading.Thread(target=self._send_async,
+                                                     daemon=True)
+                self._send_thread.start()
+
+        self.recv_store: Dict[str,
+                              torch.Tensor] = {}  # tensor_id: torch.Tensor
+        self.socks: Dict[str, Any] = {}  # remote_address: client socket
+        self.comms: Dict[str, Any] = {}  # remote_address: (ncclComm_t, rank)
+
+        self.buffer_size = 0
+        self.buffer_size_threshold = self.config.kv_buffer_size
+
+        self._listener_thread = threading.Thread(
+            target=self._listen_for_requests, daemon=True)
+        self._listener_thread.start()
+
+        self._ping_thread = None
+        if port_offset == 0 and self.proxy_address != "":
+            self._ping_thread = threading.Thread(target=self._ping,
+                                                 daemon=True)
+            self._ping_thread.start()
+
+    def _create_connect(self, remote_address: typing.Optional[str] = None):
+        assert remote_address is not None
+        if remote_address not in self.socks:
+            sock = self.context.socket(zmq.DEALER)
+            sock.setsockopt_string(zmq.IDENTITY, self.zmq_address)
+            sock.connect(f"tcp://{remote_address}")
+            self.socks[remote_address] = sock
+            if remote_address in self.comms:
+                logger.info("👋comm exists, remote_address:%s, comms:%s",
+                            remote_address, self.comms)
+                return sock, self.comms[remote_address]
+
+            unique_id = self.flagcx.flagcxGetUniqueId().contents
+            data = {"cmd": "NEW", "unique_id": bytes(unique_id.internal)}
+            sock.send(msgpack.dumps(data))
+
+            with torch.cuda.device(self.device):
+                rank = 0
+                comm = self.flagcx.flagcxCommInitRank(
+                    2, ctypes.byref(unique_id), rank)
+                self.comms[remote_address] = (comm, rank)
+                logger.info("🤝ncclCommInitRank Success, %s👉%s, MyRank: %s",
+                            self.zmq_address, remote_address, rank)
+
+        return self.socks[remote_address], self.comms[remote_address]
+
+    def send_tensor(
+        self,
+        tensor_id: str,
+        tensor: torch.Tensor,
+        remote_address: typing.Optional[str] = None,
+    ) -> bool:
+        if remote_address is None:
+            with self.recv_store_cv:
+                self.recv_store[tensor_id] = tensor
+                self.recv_store_cv.notify()
+            return True
+        else:
+            if self.send_type == "PUT":
+                return self._send_sync(tensor_id, tensor, remote_address)
+            elif self.send_type == "PUT_ASYNC":
+                with self.send_queue_cv:
+                    self.send_queue.append([tensor_id, remote_address, tensor])
+                    self.send_queue_cv.notify()
+            else:  # GET
+                with self.send_store_cv:
+                    tensor_size = tensor.element_size() * tensor.numel()
+                    while (self.buffer_size + tensor_size
+                           > self.buffer_size_threshold):
+                        oldest_tenser_id = next(iter(self.send_store))
+                        oldest_tenser = self.send_store.pop(oldest_tenser_id)
+                        oldest_tenser_size = oldest_tenser.element_size(
+                        ) * oldest_tenser.numel()
+                        self.buffer_size -= oldest_tenser_size
+                        logger.info(
+                            "⛔[GET]Send to %s, tensor_id:%s, tensor_size:%d,"
+                            " buffer_size:%d, oldest_tenser_size:%d, rank:%d",
+                            remote_address, tensor_id, tensor_size,
+                            self.buffer_size, oldest_tenser_size, self.rank)
+
+                    self.send_store[tensor_id] = tensor
+                    self.buffer_size += tensor_size
+                    logger.info(
+                        "🔵[GET]Send to %s, tensor_id:%s, tensor_size:%d, "
+                        "shape:%s, rank:%d, buffer_size:%d(%.2f%%)",
+                        remote_address, tensor_id, tensor_size, tensor.shape,
+                        self.rank, self.buffer_size,
+                        self.buffer_size / self.buffer_size_threshold * 100)
+
+        return True
+
+    def recv_tensor(
+        self,
+        tensor_id: str,
+        remote_address: typing.Optional[str] = None,
+    ) -> torch.Tensor:
+        if self.send_type == "PUT" or self.send_type == "PUT_ASYNC":
+            start_time = time.time()
+            with self.recv_store_cv:
+                while tensor_id not in self.recv_store:
+                    self.recv_store_cv.wait()
+                tensor = self.recv_store[tensor_id]
+                self.recv_store[tensor_id] = None
+                while len(self.recv_store) > 10000:
+                    self.recv_store.pop(next(iter(self.recv_store)))
+
+            duration = time.time() - start_time
+            if tensor is not None:
+                self.buffer_size -= (tensor.element_size() * tensor.numel())
+                logger.info(
+                    "🔵[PUT]Recv From %s, tensor_id:%s, shape:%s, "
+                    "duration:%.3fms, size:%.3fGB, rank:%d", remote_address,
+                    tensor_id, tensor.shape, duration * 1000,
+                    tensor.element_size() * tensor.numel() / 1024**3,
+                    self.rank)
+            else:
+                logger.warning(
+                    "🔴[PUT]Recv From %s, tensor_id:%s, duration:%.3fms, "
+                    "rank:%d", remote_address, tensor_id, duration * 1000,
+                    self.rank)
+            return tensor
+
+        # GET
+        if remote_address is None:
+            return None
+
+        if remote_address not in self.socks:
+            self._create_connect(remote_address)
+
+        sock = self.socks[remote_address]
+        comm, rank = self.comms[remote_address]
+
+        data = {"cmd": "GET", "tensor_id": tensor_id}
+        sock.send(msgpack.dumps(data))
+
+        message = sock.recv()
+        data = msgpack.loads(message)
+        if data["ret"] != 0:
+            logger.warning("🔴[GET]Recv From %s, tensor_id: %s, ret: %d",
+                           remote_address, tensor_id, data["ret"])
+            return None
+
+        tensor = torch.empty(data["shape"],
+                             dtype=getattr(torch, data["dtype"]),
+                             device=self.device)
+
+        start_time = time.time()
+        self._recv(comm, tensor, rank ^ 1)
+        duration = time.time() - start_time
+        logger.info(
+            "🔵[GET]Recv From %s, tensor_id:%s, shape:%s, duration:%.3fms, "
+            "size:%.3fGB, rank:%d", remote_address, tensor_id, tensor.shape,
+            duration * 1000,
+            tensor.element_size() * tensor.numel() / 1024**3, self.rank)
+
+        return tensor
+
+    def _listen_for_requests(self):
+        while True:
+            socks = dict(self.poller.poll())
+            if self.router_socket in socks:
+                remote_address, message = self.router_socket.recv_multipart()
+                data = msgpack.loads(message)
+                logger.debug("Received message from %s, data:%s",
+                             remote_address.decode(), data)
+                if data["cmd"] == "NEW":
+                    unique_id = self.flagcx.unique_id_from_bytes(
+                        bytes(data["unique_id"]))
+                    with torch.cuda.device(self.device):
+                        rank = 1
+                        # comm: ncclComm_t = self.nccl.ncclCommInitRank(
+                        #     2, unique_id, rank)
+                        comm = self.flagcx.flagcxCommInitRank(
+                            2, ctypes.byref(unique_id), rank)
+                        self.comms[remote_address.decode()] = (comm, rank)
+                        logger.info(
+                            "🤝ncclCommInitRank Success, %s👈%s, MyRank:%s",
+                            self.zmq_address, remote_address.decode(), rank)
+                elif data["cmd"] == "PUT":
+                    tensor_id = data["tensor_id"]
+                    try:
+                        tensor = torch.empty(data["shape"],
+                                             dtype=getattr(
+                                                 torch, data["dtype"]),
+                                             device=self.device)
+
+                        tensor_size = tensor.element_size() * tensor.numel()
+                        if (self.buffer_size + tensor_size
+                                > self.buffer_size_threshold):
+                            self.router_socket.send_multipart(
+                                [remote_address, b"2"])
+                            logger.warning(
+                                "🔴[PUT]Recv Tensor, Out Of Threshold, "
+                                "%s👈%s, data:%s", self.zmq_address,
+                                remote_address.decode(), data)
+                            tensor = None
+                        else:
+                            self.buffer_size += tensor_size
+                            self.router_socket.send_multipart(
+                                [remote_address, b"0"])
+                            comm, rank = self.comms[remote_address.decode()]
+                            self._recv(comm, tensor, rank ^ 1)
+                            logger.info(
+                                "🔵[PUT]Recv Tensor, %s👈%s, MyRank:%s, "
+                                "data:%s, shape:%s", self.zmq_address,
+                                remote_address.decode(), rank, data,
+                                tensor.shape)
+
+                    except torch.cuda.OutOfMemoryError:
+                        self.router_socket.send_multipart(
+                            [remote_address, b"1"])
+                        tensor = None
+                        logger.warning(
+                            "🔴[PUT]Recv Tensor, Out Of Memory, %s👈%s, "
+                            "data:%s", self.zmq_address,
+                            remote_address.decode(), data)
+
+                    with self.recv_store_cv:
+                        self.recv_store[tensor_id] = tensor
+                        self.recv_store_cv.notify()
+
+                elif data["cmd"] == "GET":
+                    tensor_id = data["tensor_id"]
+                    with self.send_store_cv:
+                        tensor = self.send_store.pop(tensor_id, None)
+                        if tensor is not None:
+                            data = {
+                                "ret": 0,
+                                "shape": tensor.shape,
+                                "dtype":
+                                str(tensor.dtype).replace("torch.", "")
+                            }
+                            # LRU
+                            self.send_store[tensor_id] = tensor
+                        else:
+                            data = {"ret": 1}
+
+                    self.router_socket.send_multipart(
+                        [remote_address, msgpack.dumps(data)])
+
+                    if data["ret"] == 0:
+                        self._send(comm, tensor.to(self.device), rank ^ 1)
+
+                    logger.info(
+                        "🔵[GET]Send Tensor, %s👉%s, "
+                        "MyRank:%s, data:%s", self.zmq_address,
+                        remote_address.decode(), rank, data)
+                else:
+                    logger.warning(
+                        "🚧Unexpected, Received message from %s, data:%s",
+                        remote_address, data)
+
+    # Asynchronous sending may cause conflicts between P2P NCCL and
+    # NCCL used in TP/PP, which can lead to deadlock issues.
+    def _send_async(self):
+        while True:
+            with self.send_queue_cv:
+                while not self.send_queue:
+                    self.send_queue_cv.wait()
+                tensor_id, remote_address, tensor = self.send_queue.popleft()
+                if not self.send_queue:
+                    self.send_queue_cv.notify()
+            self._send_sync(tensor_id, tensor, remote_address)
+
+    def wait_for_sent(self):
+        if self.send_type == "PUT_ASYNC":
+            start_time = time.time()
+            with self.send_queue_cv:
+                while self.send_queue:
+                    self.send_queue_cv.wait()
+            duration = time.time() - start_time
+            logger.info(
+                "🚧[PUT_ASYNC]It took %.3fms to wait for the send_queue"
+                " to be empty, rank:%d", duration * 1000, self.rank)
+
+    def _send_sync(
+        self,
+        tensor_id: str,
+        tensor: torch.Tensor,
+        remote_address: typing.Optional[str] = None,
+    ) -> bool:
+        if remote_address is None:
+            return False
+        if remote_address not in self.socks:
+            self._create_connect(remote_address)
+
+        sock = self.socks[remote_address]
+        comm, rank = self.comms[remote_address]
+        data = {
+            "cmd": "PUT",
+            "tensor_id": tensor_id,
+            "shape": tensor.shape,
+            "dtype": str(tensor.dtype).replace("torch.", "")
+        }
+        sock.send(msgpack.dumps(data))
+
+        response = sock.recv()
+        if response != b"0":
+            # with self.send_queue_cv:
+            #     self.send_queue.append([tensor_id, remote_address, tensor])
+            #     self.send_queue_cv.notify()
+            logger.warning(
+                "🔴Send Tensor, Peer Out Of Memory/Threshold, %s 👉 %s, "
+                "MyRank:%s, data:%s, tensor:%s, size:%fGB, response:%s",
+                self.zmq_address, remote_address, rank, data, tensor.shape,
+                tensor.element_size() * tensor.numel() / 1024**3,
+                response.decode())
+            return False
+
+        self._send(comm, tensor.to(self.device), rank ^ 1)
+        logger.info("🔵Send Tensor, %s👉%s, MyRank:%s, data:%s, tensor:%s",
+                    self.zmq_address, remote_address, rank, data, tensor.shape)
+        return True
+
+    def _ping(self):
+        sock = self.context.socket(zmq.DEALER)
+        sock.setsockopt_string(zmq.IDENTITY, self.zmq_address)
+        logger.debug("ping start, zmq_address:%s", self.zmq_address)
+        sock.connect(f"tcp://{self.proxy_address}")
+        data = {
+            "type": "P" if self.config.is_kv_producer else "D",
+            "http_address": self.http_address,
+            "zmq_address": self.zmq_address
+        }
+        while True:
+            sock.send(msgpack.dumps(data))
+            time.sleep(3)
+
+    def _send(self, comm, tensor: torch.Tensor, dst: int, stream=None):
+        assert tensor.device == self.device, (
+            f"this nccl communicator is created to work on {self.device}, "
+            f"but the input tensor is on {tensor.device}")
+        if stream is None:
+            stream = current_stream()
+
+        with self.comm_cv:
+            flagcx_stream = self.flagcx.adaptor_stream_copy(stream)
+            self.flagcx.flagcxSend(buffer_type(tensor.data_ptr()), tensor.numel(),
+                            flagcxDataTypeEnum.from_torch(tensor.dtype), dst,
+                            comm, flagcx_stream)
+            self.flagcx.adaptor_stream_free(flagcx_stream)
+
+    def _recv(self, comm, tensor: torch.Tensor, src: int, stream=None):
+        assert tensor.device == self.device, (
+            f"this nccl communicator is created to work on {self.device}, "
+            f"but the input tensor is on {tensor.device}")
+        if stream is None:
+            stream = current_stream()
+
+        with self.comm_cv:
+            flagcx_stream = self.flagcx.adaptor_stream_copy(stream)
+            self.flagcx.flagcxRecv(buffer_type(tensor.data_ptr()), tensor.numel(),
+                            flagcxDataTypeEnum.from_torch(tensor.dtype), src,
+                            comm, flagcx_stream)
+            self.flagcx.adaptor_stream_free(flagcx_stream)
+
+    def close(self) -> None:
+        self._listener_thread.join()
+        if self.send_type == "PUT_ASYNC":
+            self._send_thread.join()
+        if self._ping_thread is not None:
+            self._ping_thread.join()

vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py

@@ -0,0 +1,274 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import os
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import zmq
+from safetensors.torch import load as safetensors_load
+from safetensors.torch import save as safetensors_save
+
+from vllm.config import KVTransferConfig
+from vllm.distributed.kv_transfer.kv_pipe.base import KVPipeBase
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+NONE_INT = -150886311
+
+
+@dataclass
+class MooncakeTransferEngineConfig:
+    prefill_url: str
+    decode_url: str
+    metadata_backend: Union[str, None]
+    metadata_server: str
+    protocol: str
+    device_name: str
+
+    @staticmethod
+    def from_file(file_path: str) -> 'MooncakeTransferEngineConfig':
+        """Load the config from a JSON file."""
+        with open(file_path) as fin:
+            config = json.load(fin)
+        return MooncakeTransferEngineConfig(
+            prefill_url=config.get("prefill_url"),
+            decode_url=config.get("decode_url"),
+            metadata_backend=config.get("metadata_backend", None),
+            metadata_server=config.get("metadata_server"),
+            protocol=config.get("protocol", "tcp"),
+            device_name=config.get("device_name", ""),
+        )
+
+    @staticmethod
+    def load_from_env() -> 'MooncakeTransferEngineConfig':
+        """Load config from a file specified in the environment variable."""
+        config_file_path = os.getenv('MOONCAKE_CONFIG_PATH')
+        if config_file_path is None:
+            raise ValueError(
+                "The environment variable 'MOONCAKE_CONFIG_PATH' is not set.")
+        return MooncakeTransferEngineConfig.from_file(config_file_path)
+
+
+class MooncakeTransferEngine:
+    """Handles the transfer of data using mooncake_vllm_adaptor and ZeroMQ."""
+
+    def __init__(self, kv_rank: int, local_rank: int):
+        try:
+            import mooncake_vllm_adaptor as mva
+        except ImportError as e:
+            raise ImportError(
+                "Please install mooncake by following the instructions at "
+                "https://github.com/kvcache-ai/Mooncake/blob/main/doc/en/build.md "  # noqa: E501
+                "to run vLLM with MooncakeConnector.") from e
+
+        self.engine = mva.mooncake_vllm_adaptor()
+        self.local_rank = local_rank
+
+        try:
+            self.config = MooncakeTransferEngineConfig.load_from_env()
+            logger.info("Mooncake Configuration loaded successfully.")
+        except ValueError as e:
+            logger.error(e)
+            raise
+        except Exception as exc:
+            logger.error(
+                "An error occurred while loading the configuration: %s", exc)
+            raise
+        prefill_host, base_prefill_port = self.config.prefill_url.split(':')
+        decode_host, base_decode_port = self.config.decode_url.split(':')
+
+        # Avoid ports conflict when running prefill and decode on the same node
+        if prefill_host == decode_host and \
+                base_prefill_port == base_decode_port:
+            base_decode_port = str(int(base_decode_port) + 100)
+
+        prefill_port = int(base_prefill_port) + self.local_rank
+        decode_port = int(base_decode_port) + self.local_rank
+        self.prefill_url = ':'.join([prefill_host, str(prefill_port)])
+        self.decode_url = ':'.join([decode_host, str(decode_port)])
+
+        self.initialize(self.prefill_url if kv_rank == 0 else self.decode_url,
+                        self.config.metadata_server, self.config.protocol,
+                        self.config.device_name, self.config.metadata_backend)
+
+        self.remote_url = (self.decode_url
+                           if kv_rank == 0 else self.prefill_url)
+
+        # Initialize ZeroMQ context and sockets
+        self.context = zmq.Context()  # type: ignore[attr-defined]
+        self.sender_socket = self.context.socket(zmq.constants.PUSH)
+        self.receiver_socket = self.context.socket(zmq.constants.PULL)
+        self.sender_ack = self.context.socket(zmq.constants.PULL)
+        self.receiver_ack = self.context.socket(zmq.constants.PUSH)
+
+        self.buffer_cleaner = ThreadPoolExecutor(max_workers=1)
+        self._setup_metadata_sockets(kv_rank, prefill_host, base_prefill_port,
+                                     decode_host, base_decode_port)
+
+    def _setup_metadata_sockets(self, kv_rank: int, p_host: str, p_port: str,
+                                d_host: str, d_port: str) -> None:
+        """Set up ZeroMQ sockets for sending and receiving data."""
+        # Offsets < 8 are left for initialization in case tp and pp are enabled
+        p_rank_offset = int(p_port) + 8 + self.local_rank * 2
+        d_rank_offset = int(d_port) + 8 + self.local_rank * 2
+        if kv_rank == 0:
+            self.sender_socket.bind(f"tcp://*:{p_rank_offset + 1}")
+            self.receiver_socket.connect(f"tcp://{d_host}:{d_rank_offset + 1}")
+            self.sender_ack.connect(f"tcp://{d_host}:{d_rank_offset + 2}")
+            self.receiver_ack.bind(f"tcp://*:{p_rank_offset + 2}")
+        else:
+            self.receiver_socket.connect(f"tcp://{p_host}:{p_rank_offset + 1}")
+            self.sender_socket.bind(f"tcp://*:{d_rank_offset + 1}")
+            self.receiver_ack.bind(f"tcp://*:{d_rank_offset + 2}")
+            self.sender_ack.connect(f"tcp://{p_host}:{p_rank_offset + 2}")
+
+    def initialize(self, local_hostname: str, metadata_server: str,
+                   protocol: str, device_name: str,
+                   metadata_backend: Union[str, None]) -> None:
+        """Initialize the mooncake instance."""
+        if metadata_backend is None:
+            self.engine.initialize(local_hostname, metadata_server, protocol,
+                                   device_name)
+        else:
+            supported_backend = ["etcd", "redis"]
+            metadata_backend = metadata_backend.lower()
+            if metadata_backend not in supported_backend:
+                raise ValueError(
+                    "Mooncake Configuration error. `metadata_backend`"
+                    f" should be one of {supported_backend}.")
+
+            self.engine.initializeExt(local_hostname, metadata_server,
+                                      protocol, device_name, metadata_backend)
+
+    def allocate_managed_buffer(self, length: int) -> int:
+        """Allocate a managed buffer of the specified length."""
+        ret = self.engine.allocateManagedBuffer(length)
+        if ret <= 0:
+            logger.error("Allocation Return Error")
+            raise Exception("Allocation Return Error")
+        return ret
+
+    def free_managed_buffer(self, buffer: int, length: int) -> int:
+        """Free a previously allocated managed buffer."""
+        return self.engine.freeManagedBuffer(buffer, length)
+
+    def transfer_sync(self, buffer: int, peer_buffer_address: int,
+                      length: int) -> int:
+        """Synchronously transfer data to the specified address."""
+        ret = self.engine.transferSync(self.remote_url, buffer,
+                                       peer_buffer_address, length)
+        if ret < 0:
+            logger.error("Transfer Return Error")
+            raise Exception("Transfer Return Error")
+        return ret
+
+    def write_bytes_to_buffer(self, buffer: int, user_data: bytes,
+                              length: int) -> int:
+        """Write bytes to the allocated buffer."""
+        return self.engine.writeBytesToBuffer(buffer, user_data, length)
+
+    def read_bytes_from_buffer(self, buffer: int, length: int) -> bytes:
+        """Read bytes from the allocated buffer."""
+        return self.engine.readBytesFromBuffer(buffer, length)
+
+    def wait_for_ack(self, src_ptr: int, length: int) -> None:
+        """Asynchronously wait for ACK from the receiver."""
+        ack = self.sender_ack.recv_pyobj()
+        if ack != b'ACK':
+            logger.error("Failed to receive ACK from the receiver")
+
+        self.free_managed_buffer(src_ptr, length)
+
+    def send_bytes(self, user_data: bytes) -> None:
+        """Send bytes to the remote process."""
+        length = len(user_data)
+        src_ptr = self.allocate_managed_buffer(length)
+        self.write_bytes_to_buffer(src_ptr, user_data, length)
+        self.sender_socket.send_pyobj((src_ptr, length))
+        self.buffer_cleaner.submit(self.wait_for_ack, src_ptr, length)
+
+    def recv_bytes(self) -> bytes:
+        """Receive bytes from the remote process."""
+        src_ptr, length = self.receiver_socket.recv_pyobj()
+        dst_ptr = self.allocate_managed_buffer(length)
+        self.transfer_sync(dst_ptr, src_ptr, length)
+        ret = self.read_bytes_from_buffer(dst_ptr, length)
+
+        # Buffer cleanup
+        self.receiver_ack.send_pyobj(b'ACK')
+        self.free_managed_buffer(dst_ptr, length)
+
+        return ret
+
+
+class MooncakePipe(KVPipeBase):
+    """MooncakeTransferEngine based Pipe implementation."""
+
+    def __init__(self,
+                 local_rank: int,
+                 config: KVTransferConfig,
+                 device: Optional[str] = None):
+        """Initialize the mooncake pipe and set related parameters."""
+        self.config = config
+        self.local_rank = local_rank
+        self.kv_rank = self.config.kv_rank
+        if device is None:
+            self.device = self._select_device(self.config.kv_buffer_device)
+        else:
+            self.device = self._select_device(device)
+
+        self.transfer_engine = MooncakeTransferEngine(self.kv_rank,
+                                                      self.local_rank)
+        self.transport_thread: Optional[ThreadPoolExecutor] = None
+        self.none_tensor = torch.tensor([NONE_INT], device=self.device)
+
+    def _select_device(self, device: str) -> torch.device:
+        """Select available device (CUDA or CPU)."""
+        logger.info("Selecting device: %s", device)
+        if device == "cuda":
+            return torch.device(f"cuda:{self.local_rank}")
+        else:
+            return torch.device("cpu")
+
+    def tensor_hash(self, tensor: torch.Tensor) -> int:
+        """Calculate the hash value of the tensor."""
+        return hash(tensor.data_ptr())
+
+    def _send_impl(self, tensor: torch.Tensor) -> None:
+        """Implement the tensor sending logic using safetensors."""
+        self.transfer_engine.send_bytes(safetensors_save({"tensor": tensor}))
+
+    def _recv_impl(self) -> torch.Tensor:
+        """Implement the tensor receiving logic using safetensors."""
+        data = self.transfer_engine.recv_bytes()
+        return safetensors_load(data)["tensor"].to(self.device)
+
+    def send_tensor(self, tensor: Optional[torch.Tensor]) -> None:
+        """Send tensor to the target process."""
+        if self.transport_thread is None:
+            self.transport_thread = ThreadPoolExecutor(max_workers=1)
+        tensor = tensor if tensor is not None else self.none_tensor
+        assert (len(tensor.shape) > 0)
+        self.transport_thread.submit(self._send_impl, tensor)
+
+    def recv_tensor(self) -> Optional[torch.Tensor]:
+        """Receive tensor from other processes."""
+        if self.transport_thread is None:
+            self.transport_thread = ThreadPoolExecutor(max_workers=1)
+        tensor = self.transport_thread.submit(self._recv_impl).result()
+        if tensor.numel() == 1 and tensor.item() == NONE_INT:
+            return None
+        else:
+            return tensor
+
+    def close(self) -> None:
+        """Cleanup logic when closing the pipe."""
+        self.transfer_engine.sender_socket.close()
+        self.transfer_engine.receiver_socket.close()
+        self.transfer_engine.sender_ack.close()
+        self.transfer_engine.receiver_ack.close()
+        self.transfer_engine.context.term()  # Terminate the ZMQ context
+        logger.info("Closed the transfer engine and cleaned up resources.")

vllm/distributed/kv_transfer/kv_pipe/p2p_nccl_pipe.py

@@ -0,0 +1,445 @@
+# Copied adopted from https://github.com/vllm-project/vllm/blob/1ad957950ffc1552af5abda78c03d88ddb67945b/vllm/distributed/kv_transfer/kv_pipe/p2p_nccl_pipe.py.
+# Below is the original copyright:
+# SPDX-License-Identifier: Apache-2.0
+import logging
+import threading
+import time
+import typing
+from collections import deque
+from typing import Any, Deque, Dict, List, Optional
+
+import msgpack
+import torch
+import zmq
+
+from vllm.config import KVTransferConfig
+from vllm.distributed.device_communicators.pynccl_wrapper import (
+    NCCLLibrary, buffer_type, cudaStream_t, ncclComm_t, ncclDataTypeEnum)
+from vllm.utils import current_stream, get_ip
+
+logger = logging.getLogger(__name__)
+
+
+class P2pNcclPipe:
+
+    def __init__(self,
+                 local_rank: int,
+                 config: KVTransferConfig,
+                 hostname: str = "",
+                 port_offset: int = 0,
+                 library_path: Optional[str] = None) -> None:
+        self.config = config
+        self.rank = port_offset
+        self.local_rank = local_rank
+        self.device = torch.device(f"cuda:{self.local_rank}")
+        self.nccl = NCCLLibrary(library_path)
+
+        if not hostname:
+            hostname = get_ip()
+        port = self.config.kv_port + port_offset
+        if port == 0:
+            raise ValueError("Port cannot be 0")
+        self._hostname = hostname
+        self._port = port
+
+        # Each card corresponds to a ZMQ address.
+        self.zmq_address = f"{self._hostname}:{self._port}"
+
+        # The `http_port` must be consistent with the port of OpenAI.
+        self.http_address = (
+            f"{self._hostname}:"
+            f"{self.config.kv_connector_extra_config['http_port']}")
+
+        # If `proxy_ip` or `proxy_port` is `""`,
+        # then the ping thread will not be enabled.
+        proxy_ip = self.config.get_from_extra_config("proxy_ip", "")
+        proxy_port = self.config.get_from_extra_config("proxy_port", "")
+        if proxy_ip == "" or proxy_port == "":
+            self.proxy_address = ""
+        else:
+            self.proxy_address = proxy_ip + ":" + proxy_port
+
+        self.context = zmq.Context()
+        self.router_socket = self.context.socket(zmq.ROUTER)
+        self.router_socket.bind(f"tcp://{self.zmq_address}")
+
+        self.poller = zmq.Poller()
+        self.poller.register(self.router_socket, zmq.POLLIN)
+
+        self.send_store_cv = threading.Condition()
+        self.send_queue_cv = threading.Condition()
+        self.recv_store_cv = threading.Condition()
+
+        self.send_stream = torch.cuda.Stream()
+        self.recv_stream = torch.cuda.Stream()
+
+        # The sending type includes tree mutually exclusive options:
+        # PUT, GET, PUT_ASYNC.
+        self.send_type = self.config.get_from_extra_config("send_type", "PUT")
+        if self.send_type == "GET":
+            self.send_store: Dict[str,
+                                  torch.Tensor] = {}  # tensor_id: torch.Tensor
+        else:
+            # PUT or PUT_ASYNC
+            self.send_queue: Deque[
+                List[Any]] = deque()  # tensor_id: torch.Tensor
+            if self.send_type == "PUT_ASYNC":
+                self._send_thread = threading.Thread(target=self._send_async,
+                                                     daemon=True)
+                self._send_thread.start()
+
+        self.recv_store: Dict[str,
+                              torch.Tensor] = {}  # tensor_id: torch.Tensor
+        self.socks: Dict[str, Any] = {}  # remote_address: client socket
+        self.comms: Dict[str, Any] = {}  # remote_address: (ncclComm_t, rank)
+
+        self.buffer_size = 0
+        self.buffer_size_threshold = self.config.kv_buffer_size
+
+        self._listener_thread = threading.Thread(
+            target=self._listen_for_requests, daemon=True)
+        self._listener_thread.start()
+
+        self._ping_thread = None
+        if port_offset == 0 and self.proxy_address != "":
+            self._ping_thread = threading.Thread(target=self._ping,
+                                                 daemon=True)
+            self._ping_thread.start()
+
+    def _create_connect(self, remote_address: typing.Optional[str] = None):
+        assert remote_address is not None
+        if remote_address not in self.socks:
+            sock = self.context.socket(zmq.DEALER)
+            sock.setsockopt_string(zmq.IDENTITY, self.zmq_address)
+            sock.connect(f"tcp://{remote_address}")
+            self.socks[remote_address] = sock
+            if remote_address in self.comms:
+                logger.info("👋comm exists, remote_address:%s, comms:%s",
+                            remote_address, self.comms)
+                return sock, self.comms[remote_address]
+
+            unique_id = self.nccl.ncclGetUniqueId()
+            data = {"cmd": "NEW", "unique_id": bytes(unique_id.internal)}
+            sock.send(msgpack.dumps(data))
+
+            with torch.cuda.device(self.device):
+                rank = 0
+                comm: ncclComm_t = self.nccl.ncclCommInitRank(
+                    2, unique_id, rank)
+                self.comms[remote_address] = (comm, rank)
+                logger.info("🤝ncclCommInitRank Success, %s👉%s, MyRank: %s",
+                            self.zmq_address, remote_address, rank)
+
+        return self.socks[remote_address], self.comms[remote_address]
+
+    def send_tensor(
+        self,
+        tensor_id: str,
+        tensor: torch.Tensor,
+        remote_address: typing.Optional[str] = None,
+    ) -> bool:
+        if remote_address is None:
+            with self.recv_store_cv:
+                self.recv_store[tensor_id] = tensor
+                self.recv_store_cv.notify()
+            return True
+        else:
+            if self.send_type == "PUT":
+                return self._send_sync(tensor_id, tensor, remote_address)
+            elif self.send_type == "PUT_ASYNC":
+                with self.send_queue_cv:
+                    self.send_queue.append([tensor_id, remote_address, tensor])
+                    self.send_queue_cv.notify()
+            else:  # GET
+                with self.send_store_cv:
+                    tensor_size = tensor.element_size() * tensor.numel()
+                    while (self.buffer_size + tensor_size
+                           > self.buffer_size_threshold):
+                        oldest_tenser_id = next(iter(self.send_store))
+                        oldest_tenser = self.send_store.pop(oldest_tenser_id)
+                        oldest_tenser_size = oldest_tenser.element_size(
+                        ) * oldest_tenser.numel()
+                        self.buffer_size -= oldest_tenser_size
+                        logger.info(
+                            "⛔[GET]Send to %s, tensor_id:%s, tensor_size:%d,"
+                            " buffer_size:%d, oldest_tenser_size:%d, rank:%d",
+                            remote_address, tensor_id, tensor_size,
+                            self.buffer_size, oldest_tenser_size, self.rank)
+
+                    self.send_store[tensor_id] = tensor
+                    self.buffer_size += tensor_size
+                    logger.info(
+                        "🔵[GET]Send to %s, tensor_id:%s, tensor_size:%d, "
+                        "shape:%s, rank:%d, buffer_size:%d(%.2f%%)",
+                        remote_address, tensor_id, tensor_size, tensor.shape,
+                        self.rank, self.buffer_size,
+                        self.buffer_size / self.buffer_size_threshold * 100)
+
+        return True
+
+    def recv_tensor(
+        self,
+        tensor_id: str,
+        remote_address: typing.Optional[str] = None,
+    ) -> torch.Tensor:
+        if self.send_type == "PUT" or self.send_type == "PUT_ASYNC":
+            start_time = time.time()
+            with self.recv_store_cv:
+                while tensor_id not in self.recv_store:
+                    self.recv_store_cv.wait()
+                tensor = self.recv_store[tensor_id]
+                self.recv_store[tensor_id] = None
+                while len(self.recv_store) > 10000:
+                    self.recv_store.pop(next(iter(self.recv_store)))
+
+            duration = time.time() - start_time
+            if tensor is not None:
+                self.buffer_size -= (tensor.element_size() * tensor.numel())
+                logger.info(
+                    "🔵[PUT]Recv From %s, tensor_id:%s, shape:%s, "
+                    "duration:%.3fms, size:%.3fGB, rank:%d", remote_address,
+                    tensor_id, tensor.shape, duration * 1000,
+                    tensor.element_size() * tensor.numel() / 1024**3,
+                    self.rank)
+            else:
+                logger.warning(
+                    "🔴[PUT]Recv From %s, tensor_id:%s, duration:%.3fms, "
+                    "rank:%d", remote_address, tensor_id, duration * 1000,
+                    self.rank)
+            return tensor
+
+        # GET
+        if remote_address is None:
+            return None
+
+        if remote_address not in self.socks:
+            self._create_connect(remote_address)
+
+        sock = self.socks[remote_address]
+        comm, rank = self.comms[remote_address]
+
+        data = {"cmd": "GET", "tensor_id": tensor_id}
+        sock.send(msgpack.dumps(data))
+
+        message = sock.recv()
+        data = msgpack.loads(message)
+        if data["ret"] != 0:
+            logger.warning("🔴[GET]Recv From %s, tensor_id: %s, ret: %d",
+                           remote_address, tensor_id, data["ret"])
+            return None
+
+        tensor = torch.empty(data["shape"],
+                             dtype=getattr(torch, data["dtype"]),
+                             device=self.device)
+
+        start_time = time.time()
+        self._recv(comm, tensor, rank ^ 1, self.recv_stream)
+        duration = time.time() - start_time
+        logger.info(
+            "🔵[GET]Recv From %s, tensor_id:%s, shape:%s, duration:%.3fms, "
+            "size:%.3fGB, rank:%d", remote_address, tensor_id, tensor.shape,
+            duration * 1000,
+            tensor.element_size() * tensor.numel() / 1024**3, self.rank)
+
+        return tensor
+
+    def _listen_for_requests(self):
+        while True:
+            socks = dict(self.poller.poll())
+            if self.router_socket in socks:
+                remote_address, message = self.router_socket.recv_multipart()
+                data = msgpack.loads(message)
+                logger.debug("Received message from %s, data:%s",
+                             remote_address.decode(), data)
+                if data["cmd"] == "NEW":
+                    unique_id = self.nccl.unique_id_from_bytes(
+                        bytes(data["unique_id"]))
+                    with torch.cuda.device(self.device):
+                        rank = 1
+                        comm: ncclComm_t = self.nccl.ncclCommInitRank(
+                            2, unique_id, rank)
+                        self.comms[remote_address.decode()] = (comm, rank)
+                        logger.info(
+                            "🤝ncclCommInitRank Success, %s👈%s, MyRank:%s",
+                            self.zmq_address, remote_address.decode(), rank)
+                elif data["cmd"] == "PUT":
+                    tensor_id = data["tensor_id"]
+                    try:
+                        tensor = torch.empty(data["shape"],
+                                             dtype=getattr(
+                                                 torch, data["dtype"]),
+                                             device=self.device)
+
+                        tensor_size = tensor.element_size() * tensor.numel()
+                        if (self.buffer_size + tensor_size
+                                > self.buffer_size_threshold):
+                            self.router_socket.send_multipart(
+                                [remote_address, b"2"])
+                            logger.warning(
+                                "🔴[PUT]Recv Tensor, Out Of Threshold, "
+                                "%s👈%s, data:%s", self.zmq_address,
+                                remote_address.decode(), data)
+                            tensor = None
+                        else:
+                            self.buffer_size += tensor_size
+                            self.router_socket.send_multipart(
+                                [remote_address, b"0"])
+                            comm, rank = self.comms[remote_address.decode()]
+                            self._recv(comm, tensor, rank ^ 1,
+                                       self.recv_stream)
+                            logger.info(
+                                "🔵[PUT]Recv Tensor, %s👈%s, MyRank:%s, "
+                                "data:%s, shape:%s", self.zmq_address,
+                                remote_address.decode(), rank, data,
+                                tensor.shape)
+
+                    except torch.cuda.OutOfMemoryError:
+                        self.router_socket.send_multipart(
+                            [remote_address, b"1"])
+                        tensor = None
+                        logger.warning(
+                            "🔴[PUT]Recv Tensor, Out Of Memory, %s👈%s, "
+                            "data:%s", self.zmq_address,
+                            remote_address.decode(), data)
+
+                    with self.recv_store_cv:
+                        self.recv_store[tensor_id] = tensor
+                        self.recv_store_cv.notify()
+
+                elif data["cmd"] == "GET":
+                    tensor_id = data["tensor_id"]
+                    with self.send_store_cv:
+                        tensor = self.send_store.pop(tensor_id, None)
+                        if tensor is not None:
+                            data = {
+                                "ret": 0,
+                                "shape": tensor.shape,
+                                "dtype":
+                                str(tensor.dtype).replace("torch.", "")
+                            }
+                            # LRU
+                            self.send_store[tensor_id] = tensor
+                        else:
+                            data = {"ret": 1}
+
+                    self.router_socket.send_multipart(
+                        [remote_address, msgpack.dumps(data)])
+
+                    if data["ret"] == 0:
+                        comm, rank = self.comms[remote_address.decode()]
+                        self._send(comm, tensor.to(self.device), rank ^ 1,
+                                   self.send_stream)
+
+                    logger.info(
+                        "🔵[GET]Send Tensor, %s👉%s, "
+                        "MyRank:%s, data:%s", self.zmq_address,
+                        remote_address.decode(), rank, data)
+                else:
+                    logger.warning(
+                        "🚧Unexpected, Received message from %s, data:%s",
+                        remote_address, data)
+
+    def _send_async(self):
+        while True:
+            with self.send_queue_cv:
+                while not self.send_queue:
+                    self.send_queue_cv.wait()
+                tensor_id, remote_address, tensor = self.send_queue.popleft()
+                if not self.send_queue:
+                    self.send_queue_cv.notify()
+            self._send_sync(tensor_id, tensor, remote_address)
+
+    def wait_for_sent(self):
+        if self.send_type == "PUT_ASYNC":
+            start_time = time.time()
+            with self.send_queue_cv:
+                while self.send_queue:
+                    self.send_queue_cv.wait()
+            duration = time.time() - start_time
+            logger.info(
+                "🚧[PUT_ASYNC]It took %.3fms to wait for the send_queue"
+                " to be empty, rank:%d", duration * 1000, self.rank)
+
+    def _send_sync(
+        self,
+        tensor_id: str,
+        tensor: torch.Tensor,
+        remote_address: typing.Optional[str] = None,
+    ) -> bool:
+        if remote_address is None:
+            return False
+        if remote_address not in self.socks:
+            self._create_connect(remote_address)
+
+        sock = self.socks[remote_address]
+        comm, rank = self.comms[remote_address]
+        data = {
+            "cmd": "PUT",
+            "tensor_id": tensor_id,
+            "shape": tensor.shape,
+            "dtype": str(tensor.dtype).replace("torch.", "")
+        }
+        sock.send(msgpack.dumps(data))
+
+        response = sock.recv()
+        if response != b"0":
+            # with self.send_queue_cv:
+            #     self.send_queue.append([tensor_id, remote_address, tensor])
+            #     self.send_queue_cv.notify()
+            logger.warning(
+                "🔴Send Tensor, Peer Out Of Memory/Threshold, %s 👉 %s, "
+                "MyRank:%s, data:%s, tensor:%s, size:%fGB, response:%s",
+                self.zmq_address, remote_address, rank, data, tensor.shape,
+                tensor.element_size() * tensor.numel() / 1024**3,
+                response.decode())
+            return False
+
+        self._send(comm, tensor.to(self.device), rank ^ 1, self.send_stream)
+        logger.info("🔵Send Tensor, %s👉%s, MyRank:%s, data:%s, tensor:%s",
+                    self.zmq_address, remote_address, rank, data, tensor.shape)
+        return True
+
+    def _ping(self):
+        sock = self.context.socket(zmq.DEALER)
+        sock.setsockopt_string(zmq.IDENTITY, self.zmq_address)
+        logger.debug("ping start, zmq_address:%s", self.zmq_address)
+        sock.connect(f"tcp://{self.proxy_address}")
+        data = {
+            "type": "P" if self.config.is_kv_producer else "D",
+            "http_address": self.http_address,
+            "zmq_address": self.zmq_address
+        }
+        while True:
+            sock.send(msgpack.dumps(data))
+            time.sleep(3)
+
+    def _send(self, comm, tensor: torch.Tensor, dst: int, stream=None):
+        assert tensor.device == self.device, (
+            f"this nccl communicator is created to work on {self.device}, "
+            f"but the input tensor is on {tensor.device}")
+        if stream is None:
+            stream = current_stream()
+
+        with torch.cuda.stream(stream):
+            self.nccl.ncclSend(buffer_type(tensor.data_ptr()), tensor.numel(),
+                               ncclDataTypeEnum.from_torch(tensor.dtype), dst,
+                               comm, cudaStream_t(stream.cuda_stream))
+
+    def _recv(self, comm, tensor: torch.Tensor, src: int, stream=None):
+        assert tensor.device == self.device, (
+            f"this nccl communicator is created to work on {self.device}, "
+            f"but the input tensor is on {tensor.device}")
+        if stream is None:
+            stream = current_stream()
+
+        with torch.cuda.stream(stream):
+            self.nccl.ncclRecv(buffer_type(tensor.data_ptr()), tensor.numel(),
+                               ncclDataTypeEnum.from_torch(tensor.dtype), src,
+                               comm, cudaStream_t(stream.cuda_stream))
+
+    def close(self) -> None:
+        self._listener_thread.join()
+        if self.send_type == "PUT_ASYNC":
+            self._send_thread.join()
+        if self._ping_thread is not None:
+            self._ping_thread.join()

vllm/distributed/kv_transfer/kv_pipe/pynccl_pipe.py

@@ -0,0 +1,279 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+    This module implements a PyNccl pipe for sending and receiving
+    Optional[torch.Tensor] between distributed ranks with advanced
+    communication features.
+
+    Key Features:
+    - Supports sending and receiving tensors with metadata
+    - Handles both CUDA and CPU device communications
+    - Implements a non-blocking tensor transfer mechanism
+    - Manages buffer size and provides backpressure control
+    - Supports distributed process groups with configurable parameters
+"""
+
+import threading
+import time
+from concurrent.futures import ThreadPoolExecutor
+from typing import Callable, Dict, Optional, Tuple
+
+import torch
+
+from vllm.config import KVTransferConfig
+from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
+from vllm.distributed.kv_transfer.kv_pipe.base import KVPipeBase
+from vllm.distributed.utils import StatelessProcessGroup
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class BrokenPipeException(Exception):
+
+    def __init__(self, message):
+        self.message = message
+        super().__init__(self.message)
+
+
+Metadata = Dict[str, Optional[torch.Tensor]]
+
+
+class PyNcclPipe(KVPipeBase):
+
+    METADATA_LENGTH = 16
+    MAX_TENSOR_DIMENSIONS = 14
+    METADATA_DTYPE = torch.int64
+
+    def __init__(self,
+                 local_rank: int,
+                 config: KVTransferConfig,
+                 device: Optional[str] = None,
+                 port_offset: int = 0):
+        self.config = config
+        self.local_rank = local_rank
+        self.kv_rank = self.config.kv_rank
+        self.kv_parallel_size = self.config.kv_parallel_size
+        if device is None:
+            self.device = self._select_device(self.config.kv_buffer_device)
+        else:
+            self.device = self._select_device(device)
+
+        # build distributed connection and send/recv implementation
+        store_timeout = self.config.get_from_extra_config("store_timeout", 300)
+        self.group = StatelessProcessGroup.create(
+            host=self.config.kv_ip,
+            port=self.config.kv_port + port_offset,
+            rank=self.kv_rank,
+            world_size=self.kv_parallel_size,
+            store_timeout=store_timeout,
+        )
+        # add a barrier to make sure the connection is initiated properly
+        self.group.barrier()
+        impl = self._get_device_send_recv_impl(self.group)
+        self.device_send_func, self.device_recv_func = impl
+        # set target rank
+        self.target_rank_for_send = (self.kv_rank + 1) % self.kv_parallel_size
+        self.target_rank_for_recv = (self.kv_rank - 1) % self.kv_parallel_size
+
+        # transportation-related variables
+        self.transport_thread: Optional[ThreadPoolExecutor] = None
+        self.buffer_size = 0
+        self.buffer_size_lock = threading.Lock()
+        self.buffer_size_thresh = self.config.kv_buffer_size
+
+    def _get_device_send_recv_impl(
+        self, group: StatelessProcessGroup
+    ) -> Tuple[Callable[[torch.Tensor, int], None], Callable[
+        [torch.Tensor, int], None]]:
+
+        send: Callable[[torch.Tensor, int], None]
+        recv: Callable[[torch.Tensor, int], None]
+        if self.device.type == "cuda":
+            # use PyNCCL for send / recv
+            comm = PyNcclCommunicator(group, device=self.local_rank)
+            comm.disabled = False
+            send, recv = comm.send, comm.recv  # type: ignore
+        else:
+            # This send / recv implementation here is NOT intended to transfer
+            # KV caches (and should NOT be repurposed to transfer KV caches).
+            # Currently it is only used to transmit control-plane messages
+            # for PyNcclBuffer.
+            send = group.send_obj
+
+            def my_recv(x, src):
+                x[...] = group.recv_obj(src)
+
+            recv = my_recv
+
+        return send, recv
+
+    def _select_device(self, device: str):
+        logger.info("Selecting device: %s", device)
+        if device == "cuda":
+            return torch.device(f"cuda:{self.local_rank}")
+        else:
+            return torch.device("cpu")
+
+    def _make_metadata(self, tensor: Optional[torch.Tensor]) -> Metadata:
+        """
+        Create the metadata as a dictionary based on the input tensor.
+
+        Parameters:
+            - tensor: The input tensor or None if no tensor is provided.
+
+        Returns:
+            - metadata: A dictionary with the following keys:
+                - "dtype": The data type of the tensor or None.
+                - "shape": The shape of the tensor or None.
+        """
+        if tensor is None:
+            return {"dtype": None, "shape": None}
+        else:
+            return {"dtype": tensor.dtype, "shape": tensor.shape}
+
+    def _prepare_recv_buffer(self, metadata: Metadata) -> torch.Tensor:
+        """
+        Create a buffer to receive the tensor based on the provided metadata.
+
+        Parameters:
+            - metadata: A dictionary with keys "dtype" and "shape", describing
+              the tensor's data type and shape.
+
+        Returns:
+            - buffer: A tensor of the specified type and shape, allocated on
+              self.device.
+        """
+        return torch.empty(metadata["shape"],
+                           dtype=metadata["dtype"],
+                           device=self.device)
+
+    def _send_metadata(self, metadata: Metadata):
+        """
+        Send the metadata dictionary to the target rank.
+
+        Parameters:
+            - metadata: A dictionary with keys "dtype" and "shape".
+        """
+        self.group.send_obj(metadata, self.target_rank_for_send)
+
+    def _recv_metadata(self) -> Metadata:
+        """
+        Receive the metadata dictionary from the target rank.
+
+        Returns:
+            - metadata: A dictionary with keys "dtype" and "shape" describing
+              the tensor.
+        """
+        return self.group.recv_obj(self.target_rank_for_recv)
+
+    def _send_impl(self, tensor: Optional[torch.Tensor]) -> None:
+        """
+        The actual implementation of sending the tensor and its metadata to the
+        target rank.
+
+        Parameters:
+            - tensor: The input tensor to be sent, or None if no tensor is
+              being sent.
+        """
+        metadata = self._make_metadata(tensor)
+        self._send_metadata(metadata)
+        if tensor is not None:
+            self.device_send_func(tensor.to(self.device),
+                                  self.target_rank_for_send)
+
+    def _recv_impl(self) -> Optional[torch.Tensor]:
+        """
+        The actual implementation of receiving a tensor and its metadata from
+        the target rank.
+
+        Returns:
+            - buffer: The received tensor, or None if no tensor is received.
+        """
+        metadata = self._recv_metadata()
+        if metadata["dtype"] is None:
+            return None
+        buffer = self._prepare_recv_buffer(metadata)
+        self.device_recv_func(buffer, self.target_rank_for_recv)
+
+        return buffer
+
+    def send_tensor_wrapper(self, tensor: Optional[torch.Tensor],
+                            tensor_size: int) -> None:
+        """
+        Wrapper for _send_impl to handle exceptions and update buffer size.
+        """
+        try:
+            self._send_impl(tensor)
+
+            with self.buffer_size_lock:
+                self.buffer_size -= tensor_size
+        except Exception as e:
+            logger.error("[rank%d]: Exception when trying to send %s, msg: %s",
+                         torch.distributed.get_rank(), str(tensor), str(e))
+            import traceback
+            traceback.print_exc()
+
+    def block_if_full(self):
+        """
+        Block the current thread if the buffer size is larger than the
+        threshold.
+        """
+        while self.buffer_size > self.buffer_size_thresh:
+            logger.debug("KV cache transfer pipe is full. Waiting...")
+            time.sleep(0.05)
+
+    def send_tensor(self, tensor: Optional[torch.Tensor]) -> None:
+        """
+        Sends a tensor and its metadata to the destination rank in a
+        non-blocking way.
+
+        Parameters:
+            - tensor: The tensor to send, or None if no tensor is being sent.
+        """
+        if self.transport_thread is None:
+            self.transport_thread = ThreadPoolExecutor(max_workers=1)
+
+        if tensor is not None:
+            tensor_size = tensor.element_size() * tensor.numel()
+        else:
+            tensor_size = 0
+
+        self.block_if_full()
+
+        with self.buffer_size_lock:
+            self.buffer_size += tensor_size
+
+        self.transport_thread.submit(self.send_tensor_wrapper, tensor,
+                                     tensor_size)
+
+    def recv_tensor(self) -> Optional[torch.Tensor]:
+        """
+        Receives a tensor and its metadata from the source rank. Blocking call.
+
+        Returns:
+            - tensor: The received tensor, or None if no tensor is received.
+        """
+        if self.transport_thread is None:
+            self.transport_thread = ThreadPoolExecutor(max_workers=1)
+
+        future = self.transport_thread.submit(self._recv_impl)
+
+        try:
+            tensor = future.result()
+        except Exception as e:
+            logger.error("Encountering exception in KV receiving thread")
+            logger.error("%s", e)
+            logger.error("My device: %s", self.device)
+            import traceback
+            traceback.print_exc()
+            raise e
+
+        return tensor
+
+    def close(self):
+        """
+        Close the pipe and release associated resources.
+        """
+        if hasattr(self,
+                   "transport_thread") and self.transport_thread is not None:
+            self.transport_thread.shutdown()