Merge branch 'v0.15.1-dev-pd' into 'v0.15.1-dev'

Merge v0.15.1-dev-pd into v0.15.1-dev

See merge request dcutoolkit/deeplearing/vllm!506

examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py

@@ -4,35 +4,87 @@
 import os
 import socket
 import threading
-import time
 import uuid
-from typing import Any
 
 import aiohttp
 import msgpack
 import zmq
+from typing import Any
 from quart import Quart, make_response, request
+from dataclasses import dataclass, field
+from vllm.distributed.device_communicators.pynccl_wrapper import NCCLLibrary
+from collections import deque, defaultdict
+import logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+# @dataclass
+# class Request:
+#     request_id: str
+#     p_http_address: str = ""
+#     p_dp_rank: int = -1
+#     d_http_address: str = ""
+#     d_dp_rank: int = -1
+
+@dataclass
+class Instance:
+    ins_type: str = "P"
+    http_address: str = ""
+    zmq_address: str = ""
+    p_unique_id: bytes = b""
+    dp_size: int = 0
+    pp_size: int = 0
+    tp_size: int = 0
+    # [dp, pp, tp] : zmq_address
+    rank_table: dict[int, dict[int, dict[int, str]]] = field(
+        default_factory=lambda: defaultdict(lambda: defaultdict(dict))
+    )
+    # [dp, pp, tp] : global rank
+    comm_rank_table: dict[int, dict[int, dict[int, int]]] = field(
+        default_factory=lambda: defaultdict(lambda: defaultdict(dict))
+    )
 
-count = 0
-prefill_instances: dict[str, Any] = {}  # http_address: (zmq_address, stamp)
-decode_instances: dict[str, Any] = {}  # http_address: (zmq_address, stamp)
+    def count_rank_table_elements(self):
+        count = 0
+        for first_dict in self.rank_table.values():
+            for second_dict in first_dict.values():
+                count += len(second_dict)
+        return count
+
+    def is_ready(self):
+        world_size = self.dp_size * self.pp_size * self.tp_size
+        inited_rank = self.count_rank_table_elements()
+        all_ranks_ready = world_size and inited_rank == world_size
+        if self.ins_type == "P" :
+            logger.info(f"""[Router] P is_ready? : {self.http_address} world_size = {world_size} inited_rank = {inited_rank}""")
+            # return all_ranks_ready and self.p_unique_id != b""
+            return all_ranks_ready
+        else :
+            logger.info(f"""[Router] D is_ready? : {self.http_address} world_size = {world_size} inited_rank = {inited_rank}""")
+            return all_ranks_ready 
 
-prefill_cv = threading.Condition()
-decode_cv = threading.Condition()
+count = 0
+# prefill_instances: dict[str, str] = {}  # http_address: zmq_address
+# decode_instances: dict[str, str] = {}  # http_address: zmq_address
+prefill_instances: dict[str, Instance] = {} 
+decode_instances: dict[str, Instance] = {} 
 
-DEFAULT_PING_SECONDS = 5
+pending_prefill_ins: list[str] = []
+pending_decode_ins: list[str] = []
+ready_prefill_ins: list[str] = []
+ready_decode_ins: list[str] = []
 
+pd_pair : dict[str, bytes] = {}
+router_nccl = NCCLLibrary()
 
-def _remove_oldest_instances(instances: dict[str, Any]) -> None:
-    oldest_key = next(iter(instances), None)
-    while oldest_key is not None:
-        value = instances[oldest_key]
-        if value[1] > time.time():
-            break
-        print(f"🔴Remove [HTTP:{oldest_key}, ZMQ:{value[0]}, stamp:{value[1]}]")
-        instances.pop(oldest_key, None)
-        oldest_key = next(iter(instances), None)
+prefill_cv = threading.Condition()
+decode_cv = threading.Condition()
+instance_cv = threading.Condition()
 
+sock_cache : dict[str, Any] = {} 
 
 def _listen_for_register(poller, router_socket):
     while True:
@@ -42,38 +94,69 @@ def _listen_for_register(poller, router_socket):
             # data: {"type": "P", "http_address": "ip:port",
             #        "zmq_address": "ip:port"}
             data = msgpack.loads(message)
-            if data["type"] == "P":
             global prefill_instances
-                global prefill_cv
-                with prefill_cv:
-                    node = prefill_instances.get(data["http_address"], None)
-                    prefill_instances[data["http_address"]] = (
-                        data["zmq_address"],
-                        time.time() + DEFAULT_PING_SECONDS,
-                    )
-                    _remove_oldest_instances(prefill_instances)
-
-            elif data["type"] == "D":
+            global instance_cv
             global decode_instances
-                global decode_cv
-                with decode_cv:
-                    node = decode_instances.get(data["http_address"], None)
-                    decode_instances[data["http_address"]] = (
-                        data["zmq_address"],
-                        time.time() + DEFAULT_PING_SECONDS,
-                    )
-                    _remove_oldest_instances(decode_instances)
+            if data["type"] == "P":
+                 with instance_cv:
+                    if data["http_address"] not in prefill_instances:
+                        prefill_instances[data["http_address"]] = Instance(http_address=data["http_address"])
+                    p_instance = prefill_instances[data["http_address"]]
+                    p_instance.rank_table[int(data["dp_rank"])][int(data["pp_rank"])][int(data["tp_rank"])] = data["zmq_address"]
+                    if p_instance.is_ready():
+                        pending_prefill_ins.append(p_instance.http_address)
+                        logger.info(f"""[Router] pending_prefill_ins appended {p_instance.http_address} ZMQ:{p_instance.zmq_address}""")
+                        instance_cv.notify()
+                    logger.info(f"""[Router] add P rank [{data["dp_rank"]}, {data["pp_rank"]}, {data["tp_rank"]}] : {data["zmq_address"]}""")
+            elif data["type"] == "D":
+                with instance_cv:
+                    if data["http_address"] not in decode_instances:
+                        decode_instances[data["http_address"]] = Instance(ins_type="D", http_address=data["http_address"])
+                    d_instance = decode_instances[data["http_address"]]
+                    d_instance.rank_table[int(data["dp_rank"])][int(data["pp_rank"])][int(data["tp_rank"])] = data["zmq_address"]
+                    if d_instance.is_ready():
+                        pending_decode_ins.append(d_instance.http_address)
+                        logger.info(f"""[Router] pending_decode_ins appended {d_instance.http_address} ZMQ:{d_instance.zmq_address}""")
+                        instance_cv.notify()
+                    logger.info(f"""[Router] add D rank [{data["dp_rank"]}, {data["pp_rank"]}, {data["tp_rank"]}] : {data["zmq_address"]}""")
+            elif data["type"] == "P_init":
+                with instance_cv:
+                    if data["http_address"] not in prefill_instances:
+                        prefill_instances[data["http_address"]] = Instance(http_address=data["http_address"], dp_size=int(data["dp_size"]), pp_size=int(data["pp_size"]), tp_size=int(data["tp_size"]))
+                        prefill_instances[data["http_address"]].zmq_address = data["zmq_address"]
+                        continue
+                    p_instance = prefill_instances[data["http_address"]]
+                    p_instance.dp_size=int(data["dp_size"])
+                    p_instance.pp_size=int(data["pp_size"])
+                    p_instance.tp_size=int(data["tp_size"])
+                    p_instance.zmq_address=data["zmq_address"]
+                    if p_instance.is_ready():
+                        pending_prefill_ins.append(p_instance.http_address)
+                        logger.info(f"""[Router] pending_prefill_ins appended {p_instance.http_address} ZMQ:{p_instance.zmq_address}""")
+                        instance_cv.notify()
+            elif data["type"] == "D_init":
+                with instance_cv:
+                    if data["http_address"] not in decode_instances:
+                        decode_instances[data["http_address"]] = Instance(ins_type="D", http_address=data["http_address"], dp_size=int(data["dp_size"]), pp_size=int(data["pp_size"]), tp_size=int(data["tp_size"]))
+                        decode_instances[data["http_address"]].zmq_address = data["zmq_address"]
+                        continue
+                    d_instance = decode_instances[data["http_address"]]
+                    d_instance.dp_size=int(data["dp_size"])
+                    d_instance.pp_size=int(data["pp_size"])
+                    d_instance.tp_size=int(data["tp_size"])
+                    d_instance.zmq_address=data["zmq_address"]
+                    if d_instance.is_ready():
+                        pending_decode_ins.append(d_instance.http_address)
+                        logger.info(f"""[Router] pending_decode_ins appended {d_instance.http_address} ZMQ:{d_instance.zmq_address}""")
+                        instance_cv.notify()
             else:
                 print(
                     "Unexpected, Received message from %s, data: %s",
                     remote_address,
                     data,
                 )
-                return
-
-            if node is None:
-                print(f"🔵Add [HTTP:{data['http_address']}, ZMQ:{data['zmq_address']}]")
 
+zmq_context = None
 
 def start_service_discovery(hostname, port):
     if not hostname:
@@ -81,8 +164,11 @@ def start_service_discovery(hostname, port):
     if port == 0:
         raise ValueError("Port cannot be 0")
     
-    context = zmq.Context()
-    router_socket = context.socket(zmq.ROUTER)
+    # context = zmq.Context()
+    # router_socket = context.socket(zmq.ROUTER)
+    global zmq_context
+    zmq_context = zmq.Context()
+    router_socket = zmq_context.socket(zmq.ROUTER)
     router_socket.bind(f"tcp://{hostname}:{port}")
 
     poller = zmq.Poller()
@@ -120,8 +206,110 @@ async def forward_request(url, data, request_id):
                     yield content
 
 
+def unique_id_dispatch(prefill_instance : str,
+                       decode_instance : str) :
+    global zmq_context
+    global sock_cache
+    global router_nccl
+    global pd_pair
+
+    pd_pair_id = prefill_instance.zmq_address + "_" + decode_instance.zmq_address
+    
+    if pd_pair_id in pd_pair:
+        logger.info(f"""[Router] pd pair {pd_pair_id} already exist""")
+        return
+
+    logger.info(f"""[Router] initing pd pair {pd_pair_id}""")
+
+    unique_id = router_nccl.ncclGetUniqueId()
+    unique_id = bytes(unique_id.internal)
+    rank = 0
+    p_rank_num = prefill_instance.dp_size * prefill_instance.pp_size * prefill_instance.tp_size
+    d_rank_num = decode_instance.dp_size * decode_instance.pp_size * decode_instance.tp_size
+    world_size = p_rank_num + d_rank_num
+
+    for dp_rank in range(prefill_instance.dp_size):
+        for pp_rank in range(prefill_instance.pp_size):
+            for tp_rank in range(prefill_instance.tp_size): 
+                if prefill_instance.rank_table[dp_rank][pp_rank][tp_rank] not in sock_cache:
+                    sock = zmq_context.socket(zmq.DEALER)
+                    sock.setsockopt_string(zmq.IDENTITY, "router")
+                    sock.connect(f"tcp://{prefill_instance.rank_table[dp_rank][pp_rank][tp_rank]}")
+                    sock_cache[prefill_instance.rank_table[dp_rank][pp_rank][tp_rank]] = sock
+                data = {
+                    "cmd": "comm_init",
+                    "pd_pair_id": pd_pair_id,
+                    "unique_id" : unique_id,
+                    "world_size": world_size,
+                    "rank": rank
+                }
+                sock_cache[prefill_instance.rank_table[dp_rank][pp_rank][tp_rank]].send(msgpack.dumps(data))
+                prefill_instance.comm_rank_table[dp_rank][pp_rank][tp_rank] = rank
+                rank += 1
+                logger.info(f"""[Router] dispatch unique_id of pd pair {pd_pair_id} to [P] [{dp_rank}, {pp_rank}, {tp_rank}]""")
+    
+    for dp_rank in range(decode_instance.dp_size):
+        for pp_rank in range(decode_instance.pp_size):
+            for tp_rank in range(decode_instance.tp_size):
+                if decode_instance.rank_table[dp_rank][pp_rank][tp_rank] not in sock_cache:
+                    sock = zmq_context.socket(zmq.DEALER)
+                    sock.setsockopt_string(zmq.IDENTITY, "router")
+                    sock.connect(f"tcp://{decode_instance.rank_table[dp_rank][pp_rank][tp_rank]}")
+                    sock_cache[decode_instance.rank_table[dp_rank][pp_rank][tp_rank]] = sock
+                data = {
+                    "cmd": "comm_init",
+                    "pd_pair_id": pd_pair_id,
+                    "unique_id" : unique_id,
+                    "world_size": world_size,
+                    "rank": rank
+                }
+                sock_cache[decode_instance.rank_table[dp_rank][pp_rank][tp_rank]].send(msgpack.dumps(data))
+                decode_instance.comm_rank_table[dp_rank][pp_rank][tp_rank] = rank
+                rank += 1
+                logger.info(f"""[Router] dispatch unique_id of pd pair {pd_pair_id} to [D] [{dp_rank}, {pp_rank}, {tp_rank}]""")
+    
+    pd_pair[pd_pair_id] = unique_id
+
+
+def pd_pair_init():
+    global prefill_instances
+    global decode_instances
+    global pending_prefill_ins
+    global pending_decode_ins
+    global ready_prefill_ins
+    global ready_decode_ins
+    global instance_cv
+
+    while True:
+        with instance_cv:
+            while len(pending_prefill_ins) == 0 and len(pending_decode_ins) == 0:
+                logger.info(f"""[Router] pd_pair_init: waiting for instance_cv""")
+                instance_cv.wait()
+            logger.info(f"""[Router] pd_pair_init: instance_cv finished waiting""")
+            while pending_prefill_ins:
+                p_ins = pending_prefill_ins[0]
+                logger.info(f"""[Router] pd_pair_init: processing {p_ins} from pending_prefill_ins""")
+                for d_ins in ready_decode_ins:
+                    unique_id_dispatch(prefill_instances[p_ins], decode_instances[d_ins])
+                ready_prefill_ins.append(p_ins)
+                pending_prefill_ins.remove(p_ins)
+            while pending_decode_ins:
+                d_ins = pending_decode_ins[0]
+                logger.info(f"""[Router] pd_pair_init: processing {d_ins} from pending_decode_ins""")
+                for p_ins in ready_prefill_ins:
+                    unique_id_dispatch(prefill_instances[p_ins], decode_instances[d_ins])
+                ready_decode_ins.append(d_ins)
+                pending_decode_ins.remove(d_ins)
+
+def start_pd_pair_init():
+    _thread = threading.Thread(
+        target=pd_pair_init, daemon=True
+    )
+    _thread.start()
+    return _thread
+
+
 @app.route("/v1/completions", methods=["POST"])
-@app.route("/v1/chat/completions", methods=["POST"])
 async def handle_request():
     try:
         original_request_data = await request.get_json()
@@ -129,45 +317,42 @@ async def handle_request():
         prefill_request = original_request_data.copy()
         # change max_tokens = 1 to let it only do prefill
         prefill_request["max_tokens"] = 1
-        if "max_completion_tokens" in prefill_request:
-            prefill_request["max_completion_tokens"] = 1
 
         global count
         global prefill_instances
         global prefill_cv
         with prefill_cv:
             prefill_list = list(prefill_instances.items())
-            prefill_addr, prefill_zmq_addr = prefill_list[count % len(prefill_list)]
-            prefill_zmq_addr = prefill_zmq_addr[0]
+            prefill_addr, prefill_instance = prefill_list[count % len(prefill_list)]
 
         global decode_instances
         global decode_cv
         with decode_cv:
             decode_list = list(decode_instances.items())
-            decode_addr, decode_zmq_addr = decode_list[count % len(decode_list)]
-            decode_zmq_addr = decode_zmq_addr[0]
+            decode_addr, decode_instance = decode_list[count % len(decode_list)]
 
         print(
             f"handle_request count: {count}, [HTTP:{prefill_addr}, "
-            f"ZMQ:{prefill_zmq_addr}] 👉 [HTTP:{decode_addr}, "
-            f"ZMQ:{decode_zmq_addr}]"
+            f"ZMQ:{prefill_instance.zmq_address}] 👉 [HTTP:{decode_addr}, "
+            f"ZMQ:{decode_instance.zmq_address}]"
         )
+        
         count += 1
 
         request_id = (
-            f"___prefill_addr_{prefill_zmq_addr}___decode_addr_"
-            f"{decode_zmq_addr}_{random_uuid()}"
+            f"___prefill_addr_{prefill_instance.zmq_address}___decode_addr_"
+            f"{decode_instance.zmq_address}_{random_uuid()}"
         )
 
         # finish prefill
         async for _ in forward_request(
-            f"http://{prefill_addr}{request.path}", prefill_request, request_id
+            f"http://{prefill_addr}/v1/completions", prefill_request, request_id
         ):
             continue
 
         # return decode
         generator = forward_request(
-            f"http://{decode_addr}{request.path}", original_request_data, request_id
+            f"http://{decode_addr}/v1/completions", original_request_data, request_id
         )
         response = await make_response(generator)
         response.timeout = None
@@ -186,5 +371,7 @@ async def handle_request():
 
 if __name__ == "__main__":
     t = start_service_discovery("0.0.0.0", 30001)
+    t_1 = start_pd_pair_init()
     app.run(host="0.0.0.0", port=10001)
     t.join()
+    t_1.join()

vllm/distributed/kv_transfer/kv_connector/factory.py

@@ -155,6 +155,11 @@ KVConnectorFactory.register_connector(
     "P2pNcclConnector",
 )
 
+KVConnectorFactory.register_connector(
+    "DuSwiftConnector",
+    "vllm.distributed.kv_transfer.kv_connector.v1.du.du_swift_connector",
+    "DuSwiftConnector")
+
 KVConnectorFactory.register_connector(
     "LMCacheConnectorV1",
     "vllm.distributed.kv_transfer.kv_connector.v1.lmcache_connector",

vllm/distributed/kv_transfer/kv_connector/v1/du/tensor_memory_pool.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import atexit
+import ctypes
+import math
+from dataclasses import dataclass
+
+import torch
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+@dataclass
+class MemoryBlock:
+    size: int
+    addr: int
+
+
+"""A memory pool for managing pinned host memory allocations for tensors.
+
+This class implements a buddy allocation system to efficiently manage pinned
+host memory for tensor storage. It supports allocation, deallocation, and
+tensor storage/retrieval operations.
+
+Key Features:
+- Uses power-of-two block sizes for efficient buddy allocation
+- Supports splitting and merging of memory blocks
+- Provides methods to store CUDA tensors in pinned host memory
+- Allows loading tensors from pinned memory back to device
+- Automatically cleans up memory on destruction
+
+Attributes:
+    max_block_size (int): Maximum block size (rounded to nearest power of two)
+    min_block_size (int): Minimum block size (rounded to nearest power of two)
+    free_lists (dict): Dictionary of free memory blocks by size
+    allocated_blocks (dict): Dictionary of currently allocated blocks
+    base_tensor (torch.Tensor): Base pinned memory tensor
+    base_address (int): Base memory address of the pinned memory region
+
+Example:
+    >>> pool = TensorMemoryPool(max_block_size=1024*1024)
+    >>> tensor = torch.randn(100, device='cuda')
+    >>> addr = pool.store_tensor(tensor)
+    >>> loaded_tensor = pool.load_tensor(addr, tensor.dtype,
+    ...                                  tensor.shape, 'cuda')
+    >>> pool.free(addr)
+"""
+
+
+class TensorMemoryPool:
+    """Initializes the memory pool with given size constraints.
+
+    Args:
+        max_block_size (int): Maximum size of memory blocks to manage
+        min_block_size (int, optional): Minimum size of memory blocks
+            to manage. Defaults to 512.
+
+    Raises:
+        ValueError: If block sizes are invalid or max_block_size is less
+            than min_block_size
+    """
+
+    def __init__(self, max_block_size: int, min_block_size: int = 128):
+        if max_block_size <= 0 or min_block_size <= 0:
+            raise ValueError("Block sizes must be positive")
+        if max_block_size < min_block_size:
+            raise ValueError(
+                "Max block size must be greater than min block size")
+
+        self.max_block_size = self._round_to_power_of_two(max_block_size)
+        self.min_block_size = self._round_to_power_of_two(min_block_size)
+
+        self.free_lists: dict[int, dict[int, MemoryBlock]] = {}
+        self.allocated_blocks: dict[int, MemoryBlock] = {}
+
+        self._initialize_free_lists()
+        self._allocate_pinned_memory()
+
+        atexit.register(self.cleanup)
+
+    def _round_to_power_of_two(self, size: int) -> int:
+        return 1 << (size - 1).bit_length()
+
+    def _initialize_free_lists(self):
+        size = self.max_block_size
+        while size >= self.min_block_size:
+            self.free_lists[size] = {}
+            size //= 2
+
+    def _allocate_pinned_memory(self):
+        self.base_tensor = torch.empty(self.max_block_size // 4,
+                                       dtype=torch.float32,
+                                       pin_memory=True)
+        self.base_address = self.base_tensor.data_ptr()
+        initial_block = MemoryBlock(size=self.max_block_size,
+                                    addr=self.base_address)
+        self.free_lists[self.max_block_size][
+            initial_block.addr] = initial_block
+        logger.debug("TensorMemoryPool, base_address:", self.base_address,
+                     self.base_address % self.max_block_size)
+
+    def allocate(self, size: int) -> int:
+        """Allocates a memory block of at least the requested size.
+
+        Args:
+            size (int): Minimum size of memory to allocate
+
+        Returns:
+            int: Address of the allocated memory block
+
+        Raises:
+            ValueError: If size is invalid or insufficient memory is available
+        """
+        if size <= 0:
+            raise ValueError("Allocation size must be positive")
+
+        required_size = self._round_to_power_of_two(
+            max(size, self.min_block_size))
+        if required_size > self.max_block_size:
+            raise ValueError("Requested size exceeds maximum block size")
+
+        current_size = required_size
+        while current_size <= self.max_block_size:
+            if self.free_lists[current_size]:
+                _, block = self.free_lists[current_size].popitem()
+                self._split_block(block, required_size)
+                self.allocated_blocks[block.addr] = block
+                return block.addr
+            current_size *= 2
+
+        raise ValueError("Insufficient memory")
+
+    def _split_block(self, block: MemoryBlock, required_size: int):
+        while (block.size > required_size
+               and block.size // 2 >= self.min_block_size):
+            buddy_size = block.size // 2
+            buddy_addr = block.addr + buddy_size
+
+            buddy = MemoryBlock(size=buddy_size, addr=buddy_addr)
+            block.size = buddy_size
+
+            self.free_lists[buddy_size][buddy.addr] = buddy
+
+    def free(self, addr: int):
+        """Frees an allocated memory block.
+
+        Args:
+            addr (int): Address of the block to free
+
+        Raises:
+            ValueError: If address is invalid or not allocated
+        """
+        if addr not in self.allocated_blocks:
+            raise ValueError("Invalid address to free")
+
+        block = self.allocated_blocks.pop(addr)
+        self._merge_buddies(block)
+
+    def _merge_buddies(self, block: MemoryBlock):
+        MAX_MERGE_DEPTH = 30
+        depth = 0
+
+        while depth < MAX_MERGE_DEPTH:
+            buddy_offset = block.size if (block.addr - self.base_address) % (
+                2 * block.size) == 0 else -block.size
+            buddy_addr = block.addr + buddy_offset
+            buddy = self.free_lists[block.size].get(buddy_addr)
+            if buddy:
+                del self.free_lists[buddy.size][buddy.addr]
+                merged_addr = min(block.addr, buddy.addr)
+                merged_size = block.size * 2
+                block = MemoryBlock(size=merged_size, addr=merged_addr)
+                depth += 1
+            else:
+                break
+        self.free_lists[block.size][block.addr] = block
+
+    def store_tensor(self, tensor: torch.Tensor) -> int:
+        """Stores a CUDA tensor in pinned host memory.
+
+        Args:
+            tensor (torch.Tensor): CUDA tensor to store
+
+        Returns:
+            int: Address where the tensor is stored
+
+        Raises:
+            ValueError: If tensor is not on CUDA or allocation fails
+        """
+        if not tensor.is_cuda:
+            raise ValueError("Only CUDA tensors can be stored")
+
+        size = tensor.element_size() * tensor.numel()
+        addr = self.allocate(size)
+        block = self.allocated_blocks[addr]
+
+        if block.size < size:
+            self.free(addr)
+            raise ValueError(
+                f"Allocated block size {block.size} is smaller than "
+                f"required size {size}")
+
+        try:
+            buffer = (ctypes.c_byte * block.size).from_address(block.addr)
+            cpu_tensor = torch.frombuffer(buffer,
+                                          dtype=tensor.dtype,
+                                          count=tensor.numel()).reshape(
+                                              tensor.shape)
+        except ValueError as err:
+            self.free(addr)
+            raise ValueError(f"Failed to create tensor view: {err}") from err
+
+        cpu_tensor.copy_(tensor)
+
+        return addr
+
+    def load_tensor(self, addr: int, dtype: torch.dtype,
+                    shape: tuple[int, ...], device) -> torch.Tensor:
+        """Loads a tensor from pinned host memory to the specified device.
+
+        Args:
+            addr (int): Address where tensor is stored
+            dtype (torch.dtype): Data type of the tensor
+            shape (tuple[int, ...]): Shape of the tensor
+            device: Target device for the loaded tensor
+
+        Returns:
+            torch.Tensor: The loaded tensor on the specified device
+
+        Raises:
+            ValueError: If address is invalid or sizes don't match
+        """
+        if addr not in self.allocated_blocks:
+            raise ValueError("Invalid address to load")
+
+        block = self.allocated_blocks[addr]
+        num_elements = math.prod(shape)
+        dtype_size = torch.tensor([], dtype=dtype).element_size()
+        required_size = num_elements * dtype_size
+
+        if required_size > block.size:
+            raise ValueError("Requested tensor size exceeds block size")
+
+        buffer = (ctypes.c_byte * block.size).from_address(block.addr)
+        cpu_tensor = torch.frombuffer(buffer, dtype=dtype,
+                                      count=num_elements).reshape(shape)
+
+        cuda_tensor = torch.empty(shape, dtype=dtype, device=device)
+
+        cuda_tensor.copy_(cpu_tensor)
+
+        return cuda_tensor
+
+    def cleanup(self):
+        """Cleans up all memory resources and resets the pool state."""
+        self.free_lists.clear()
+        self.allocated_blocks.clear()
+        if hasattr(self, 'base_tensor'):
+            del self.base_tensor
+
+    def __del__(self):
+        self.cleanup()

vllm/envs.py

@@ -47,6 +47,7 @@ if TYPE_CHECKING:
     VLLM_TRACE_FUNCTION: int = 0
     VLLM_USE_FLASHINFER_SAMPLER: bool | None = None
     VLLM_PP_LAYER_PARTITION: str | None = None
+    VLLM_PP_LAYER_PARTITION_D: Optional[str] = None
     VLLM_CPU_KVCACHE_SPACE: int | None = 0
     VLLM_CPU_OMP_THREADS_BIND: str = ""
     VLLM_CPU_NUM_OF_RESERVED_CPU: int | None = None
@@ -181,6 +182,7 @@ if TYPE_CHECKING:
     VLLM_XGRAMMAR_CACHE_MB: int = 0
     VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
     VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
+    VLLM_DISABLE_REQUEST_ID_RANDOMIZATION: bool = False
     VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost"
     VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5600
     VLLM_MOONCAKE_BOOTSTRAP_PORT: int = 8998
@@ -282,6 +284,8 @@ if TYPE_CHECKING:
     VLLM_USE_LIGHTOP_MOE_ALIGN: bool = False
     VLLM_USE_MERGE_ATTN_STATES_OPT: bool = False
     USE_FUSED_RMS_QUANT: bool = False
+    VLLM_P2P_ASYNC: bool = False
+    VLLM_P2P_BUF_TOKENS: int = 30000
     USE_FUSED_SILU_MUL_QUANT: bool = False
     VLLM_USE_PD_SPLIT: bool = False
     VLLM_USE_PP_SYNC: bool = False
@@ -759,6 +763,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
     else None,
     # Pipeline stage partition strategy
     "VLLM_PP_LAYER_PARTITION": lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),
+    
+    # Pipeline stage partition strategy
+    "VLLM_PP_LAYER_PARTITION_D":
+    lambda: os.getenv("VLLM_PP_LAYER_PARTITION_D", None),
+
     # (CPU backend only) CPU key-value cache space.
     # default is None and will be set as 4 GB
     "VLLM_CPU_KVCACHE_SPACE": lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0"))
@@ -1350,6 +1359,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_ALLOW_INSECURE_SERIALIZATION": lambda: bool(
         int(os.getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0"))
     ),
+    # Temporary: skip adding random suffix to internal request IDs. May be
+    # needed for KV connectors that match request IDs across instances.
+    "VLLM_DISABLE_REQUEST_ID_RANDOMIZATION": lambda: bool(
+        int(os.getenv("VLLM_DISABLE_REQUEST_ID_RANDOMIZATION", "1"))
+    ),
     # IP address used for NIXL handshake between remote agents.
     "VLLM_NIXL_SIDE_CHANNEL_HOST": lambda: os.getenv(
         "VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"
@@ -1813,7 +1827,12 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # vllm will use rmsquant fused op
     "USE_FUSED_RMS_QUANT":
         lambda: bool(int(os.getenv("USE_FUSED_RMS_QUANT", "0"))),
-        
+    # vllm pd separation will be used async
+    "VLLM_P2P_ASYNC":
+    lambda: bool(int(os.getenv("VLLM_P2P_ASYNC", "0"))),
+    # pd separation p2p async buf tokens
+    "VLLM_P2P_BUF_TOKENS":
+    lambda: int(os.getenv("VLLM_P2P_BUF_TOKENS", "30000")),
     # vllm will use silu_mul_quant fused op
     "USE_FUSED_SILU_MUL_QUANT":
         lambda: (os.getenv("USE_FUSED_SILU_MUL_QUANT", "False").lower() in

vllm/v1/engine/input_processor.py

@@ -6,6 +6,7 @@ import time
 from collections.abc import Mapping
 from typing import Any, Literal, cast
 
+import vllm.envs as envs
 from vllm.config import VllmConfig
 from vllm.exceptions import VLLMValidationError
 from vllm.inputs import (
@@ -474,6 +475,13 @@ class InputProcessor:
                 " passed to vLLM; use the request_id field."
             )
         request.external_req_id = request.request_id
+        if envs.VLLM_DISABLE_REQUEST_ID_RANDOMIZATION:
+            logger.warning_once(
+                "VLLM_DISABLE_REQUEST_ID_RANDOMIZATION is set and will be "
+                "removed in a future release. Duplicate externally-provided "
+                "request IDs may cause failures and/or subtle correctness errors."
+            )
+        else:
             request.request_id = f"{request.external_req_id}-{random_uuid():.8}"
 
     def process_inputs(