a1f142b7ad
There is a lot hack code for v0.11.0, which makes the code hard to
upgrade to newer vLLM version. Since v0.11.0 will release soon. Let's
drop v0.11.0 support first. Then we'll upgrade to v0.11.2 soon.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
1155 lines
50 KiB
Python
1155 lines
50 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import contextlib
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import copy
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import hashlib
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import math
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import os
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import queue
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import struct
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import threading
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import time
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from collections import defaultdict, deque
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from collections.abc import Iterator
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from concurrent.futures import ThreadPoolExecutor
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, Callable, List, Optional, Tuple
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import httpx
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import msgspec
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import numpy as np
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import numpy.typing as npt
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import torch
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import torch_npu
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import zmq
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from mooncake.engine import TransferEngine # type: ignore
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from vllm.config import VllmConfig
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from vllm.distributed.kv_transfer.kv_connector.v1.base import (
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KVConnectorBase_V1, KVConnectorMetadata, KVConnectorRole)
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from vllm.distributed.parallel_state import (get_tensor_model_parallel_rank,
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get_tp_group, get_world_group)
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from vllm.utils import logger
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from vllm.utils.network_utils import get_ip, make_zmq_path, make_zmq_socket
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from vllm.v1.core.sched.output import SchedulerOutput
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.distributed.utils import (align_memory,
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get_transfer_timeout_value,
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kv_alltoall_and_rearrange)
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if TYPE_CHECKING:
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from vllm.attention.backends.abstract import AttentionMetadata
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from vllm.forward_context import ForwardContext
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from vllm.v1.core.kv_cache_manager import KVCacheBlocks
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from vllm.v1.request import Request
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GET_META_MSG = b"get_meta_msg"
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DONE_SENDING_MSG = b"done_sending_msg"
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class MooncakeAgentMetadata(msgspec.Struct, omit_defaults=True, dict=True):
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te_rpc_port: int
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kv_caches_base_addr: list[int]
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@dataclass
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class ReqMeta:
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local_block_ids: list[int]
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token_ids: list[int]
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# Not None if layer-wise is disabled
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remote_block_ids: list[int]
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remote_engine_id: Optional[str]
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remote_host: Optional[str]
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remote_port: Optional[int]
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remote_te_rpc_port: Optional[int]
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remote_kv_caches_base_addr: Optional[list[int]]
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metaserver: Optional[str]
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class KVCacheSendingLayerThread(threading.Thread):
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def __init__(self,
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engine: TransferEngine,
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total_layers: int,
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ready_event: threading.Event,
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tp_rank: int,
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pd_head_ratio: int,
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num_head_replica: int,
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kv_cache_base_addr: list[int],
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use_mla: bool,
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block_len: list[int],
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first_kv_cache: torch.Tensor,
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callback_func: Callable[..., None] = lambda x: None):
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super().__init__(daemon=True, name="KVCacheSendingLayerThread")
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self.engine = engine
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self.tp_rank = tp_rank
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self.pd_head_ratio = pd_head_ratio
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self.num_head_replica = num_head_replica
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self.kv_caches_base_addr = kv_cache_base_addr
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self.total_layers = total_layers
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self.use_mla = use_mla
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self.block_len = block_len
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self.model_stream = torch_npu.npu.current_stream()
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self.current_layer = -1
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if self.pd_head_ratio > 1:
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# regesit kv buffer for tp inequal
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alignment = 2 * 1024 * 1024
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self.k_buffer = torch.zeros(first_kv_cache.numel() + alignment,
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dtype=first_kv_cache.dtype,
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device=first_kv_cache.device)
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self.k_buffer = align_memory(
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self.k_buffer, alignment)[:first_kv_cache.numel()].view(
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-1, first_kv_cache.shape[-1])
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self.v_buffer = torch.zeros(first_kv_cache.numel() + alignment,
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dtype=first_kv_cache.dtype,
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device=first_kv_cache.device)
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self.v_buffer = align_memory(
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self.v_buffer, alignment)[:first_kv_cache.numel()].view(
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-1, first_kv_cache.shape[-1])
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for tensor in (self.k_buffer, self.v_buffer):
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assert tensor.data_ptr(
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) % alignment == 0, "The address of the registered kv cache should be aligned to 2M"
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ret_value = self.engine.register_memory(
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tensor.data_ptr(), tensor.numel())
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logger.info(
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f"Register memory for prefill when pd head ratio > 1 {tensor.data_ptr()} {tensor.numel()} {ret_value=}"
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)
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if ret_value != 0:
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raise RuntimeError("Mooncake memory registration failed. ")
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self.send_queue = queue.Queue[Tuple[str, ReqMeta, int, torch.Tensor,
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torch.Tensor]]()
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self.ready_event = ready_event
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self.callback_func = callback_func
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def run(self):
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local_rank = get_world_group().local_rank
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device = torch.device(f"npu:{local_rank}")
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torch.npu.set_device(device)
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self.ready_event.set()
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while True:
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req_id, req_meta, layer_index, key, value = self.send_queue.get()
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self._handle_request(req_id, req_meta, layer_index, key, value)
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def _handle_request(self, req_id, req_meta, layer_index, key, value):
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try:
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logger.debug(
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f"Starting to transfer KV cache for request {req_id} {req_meta.remote_te_rpc_port=}."
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)
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self._transfer_kv_cache(req_id, req_meta, layer_index, key, value)
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logger.debug(
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f"Finished transferring KV cache for request {req_id} {req_meta.remote_te_rpc_port=}."
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)
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except Exception as e:
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logger.error("Failed to transfer KV cache for request "
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f"{req_id}: {e}")
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def _transfer_kv_cache(self, req_id, req_meta, layer_index, key, value):
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# send kv layer to remote
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if len(req_meta.local_block_ids) == 0:
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return
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# not need to send kv cache
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if self.tp_rank % self.num_head_replica != 0:
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return
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remote_host = req_meta.remote_host
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remote_block_ids = req_meta.remote_block_ids
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remote_te_port = req_meta.remote_te_rpc_port
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remote_kv_base_addrs = req_meta.remote_kv_caches_base_addr
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local_kv_base_addr = self.kv_caches_base_addr
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local_block_ids = req_meta.local_block_ids
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if self.pd_head_ratio == 1:
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layer_local_kv_base_addr = [
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local_kv_base_addr[i]
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for i in [2 * layer_index, 2 * layer_index + 1]
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]
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layer_remote_kv_base_addr = [
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remote_kv_base_addrs[i]
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for i in [2 * layer_index, 2 * layer_index + 1]
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]
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grouped_remote_block_ids, grouped_local_block_ids = \
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group_concurrent_contiguous(remote_block_ids, local_block_ids)
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session_id = f"{remote_host}:{remote_te_port}"
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src_list, dst_list, length_list = [], [], []
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for k, (src_layer_base_addr, dst_layer_base_addr) in enumerate(
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zip(layer_local_kv_base_addr, layer_remote_kv_base_addr)):
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block_len = self.block_len[
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k % 2] if self.use_mla else self.block_len[0]
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for group_remote_block_id, group_local_block_id in zip(
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grouped_remote_block_ids, grouped_local_block_ids):
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src = src_layer_base_addr + group_local_block_id[
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0] * block_len
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dst = dst_layer_base_addr + group_remote_block_id[
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0] * block_len
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length = len(group_local_block_id) * block_len
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src_list.append(src)
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dst_list.append(dst)
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length_list.append(length)
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if self.current_layer != layer_index:
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self.current_layer = layer_index
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self.model_stream.synchronize()
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ret = self.engine.batch_transfer_sync_write(
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session_id, src_list, dst_list, length_list)
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if ret < 0:
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logger.error("Mooncake transfer failed for request %s", req_id)
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raise RuntimeError(f"Mooncake transfer failed, ret: {ret}")
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else:
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key = key.view(-1, key.shape[-1])
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value = value.view(-1, key.shape[-1])
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self.k_buffer[:key.shape[0]].copy_(key) # [:4, 128] ->
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self.v_buffer[:value.shape[0]].copy_(value)
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layer_local_kv_base_addr = [
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self.k_buffer.data_ptr(),
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self.v_buffer.data_ptr()
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]
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layer_remote_kv_base_addr = [
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remote_kv_base_addrs[i]
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for i in [2 * layer_index, 2 * layer_index + 1]
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]
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grouped_remote_block_ids, _ = group_concurrent_contiguous(
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remote_block_ids)
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session_id = f"{remote_host}:{remote_te_port}"
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src_list, dst_list, length_list = [], [], []
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for k, (src_layer_base_addr, dst_layer_base_addr) in enumerate(
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zip(layer_local_kv_base_addr, layer_remote_kv_base_addr)):
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src_layer_addr = src_layer_base_addr
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for group_remote_block_id in grouped_remote_block_ids:
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block_len = self.block_len[0]
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remote_block_len = self.block_len[0] * self.pd_head_ratio
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src_list.append(src_layer_addr)
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if src_layer_addr + len(
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group_remote_block_id
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) * block_len > src_layer_base_addr + key.numel(
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) * key.element_size():
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length = src_layer_base_addr + key.numel(
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) * key.element_size() - src_layer_addr
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else:
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length = len(group_remote_block_id) * block_len
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length_list.append(length)
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dst_list.append(dst_layer_base_addr +
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group_remote_block_id[0] *
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remote_block_len + length *
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((self.tp_rank // self.num_head_replica) %
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self.pd_head_ratio))
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src_layer_addr += length
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self.model_stream.synchronize()
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ret = self.engine.batch_transfer_sync_write(
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session_id, src_list, dst_list, length_list)
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if ret < 0:
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logger.error("Mooncake transfer failed for request %s", req_id)
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raise RuntimeError(f"Mooncake transfer failed, ret: {ret}")
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if layer_index == (self.total_layers - 1):
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self.callback_func(req_id, req_meta)
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class KVCacheRecvingLayerThread(threading.Thread):
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def __init__(self, tp_rank: int, side_channel_port: int, tp_size: int,
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pd_head_ratio: int, local_engine_id: str,
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metadata: MooncakeAgentMetadata,
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ready_event: threading.Event):
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super().__init__(daemon=True, name="KVCacheRecvingLayerThread")
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self.tp_rank = tp_rank
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self.tp_size = tp_size
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self.pd_head_ratio = pd_head_ratio
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self.local_engine_id = local_engine_id
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self.side_channel_host = get_ip()
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self.side_channel_port = side_channel_port
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self.lock = threading.Lock()
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self.done_requests = set[str]()
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self.task_tracker = dict[str, int]()
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self.ready_event = ready_event
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self.metadata = metadata
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def get_and_clear_finished_requests(self) -> set[str]:
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"""
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Get and clear the requests that have been completed.
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Returns:
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A set of request IDs that have been completed.
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"""
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with self.lock:
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finished_requests = self.done_requests
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self.done_requests = set()
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return finished_requests
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def update_task(self, req_id):
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with self.lock:
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self.task_tracker[req_id] += 1
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if self.task_tracker[req_id] == self.pd_head_ratio:
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self.task_tracker.pop(req_id)
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self.done_requests.add(req_id)
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def run(self):
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"""Run the thread to handle KV cache transfer requests."""
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handshake_port = self.side_channel_port + self.tp_rank
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path = make_zmq_path("tcp", self.side_channel_host, handshake_port)
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logger.info("Starting listening on path: %s", path)
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encoder = msgspec.msgpack.Encoder()
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encoded_data = encoder.encode(self.metadata)
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with zmq_ctx(zmq.ROUTER, path) as sock: # type: ignore
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self.ready_event.set()
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decoder = msgspec.msgpack.Decoder(type=tuple)
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while True:
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try:
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frames = sock.recv_multipart()
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if len(frames) < 2:
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logger.error("Invalid message format: %s", frames)
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continue
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identity = frames[0]
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payload = [f for f in frames[1:] if f != b""]
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if len(payload) != 1:
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logger.error("Invalid message format: %s", frames)
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continue
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msg = decoder.decode(payload[0])
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if msg[0] == GET_META_MSG:
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logger.info("Got GET META INFO for request %s", msg[0])
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sock.send_multipart((identity, b"", encoded_data))
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elif msg[0] == DONE_SENDING_MSG:
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logger.debug("Got DONE_RECVING_MSG for request %s",
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msg[1])
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request_id = msg[1]
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self.update_task(request_id)
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sock.send_multipart((identity, b"", b"ACK"))
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else:
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logger.error(
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"Connection listener got unexpected message %s",
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msg)
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except Exception as e:
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logger.error("Failed to decode message: %s", e)
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class MooncakeLayerwiseConnectorMetadata(KVConnectorMetadata):
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def __init__(self):
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self.requests: dict[str, ReqMeta] = {}
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def add_new_req(self,
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request_id: str,
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local_block_ids: list[int],
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kv_transfer_params: dict[str, Any],
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token_ids: Optional[list[int]] = None):
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self.requests[request_id] = ReqMeta(
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token_ids=token_ids or [],
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local_block_ids=local_block_ids,
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remote_block_ids=kv_transfer_params.get("remote_block_ids", []),
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remote_engine_id=kv_transfer_params.get("remote_engine_id", None),
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remote_host=kv_transfer_params.get("remote_host", None),
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remote_port=kv_transfer_params.get("remote_port", None),
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remote_te_rpc_port=kv_transfer_params.get("remote_te_rpc_port",
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None),
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remote_kv_caches_base_addr=kv_transfer_params.get(
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"remote_kv_caches_base_addr", None),
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metaserver=kv_transfer_params.get("metaserver", None),
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)
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class MooncakeLayerwiseConnector(KVConnectorBase_V1):
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def __init__(self, vllm_config: VllmConfig, role: KVConnectorRole):
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assert vllm_config.kv_transfer_config is not None
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self.engine_id = vllm_config.kv_transfer_config.engine_id
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self._connector_metadata = MooncakeLayerwiseConnectorMetadata()
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if role == KVConnectorRole.SCHEDULER:
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self.connector_scheduler: Optional[MooncakeLayerwiseConnectorScheduler] = \
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MooncakeLayerwiseConnectorScheduler(vllm_config, str(self.engine_id))
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self.connector_worker: Optional[
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MooncakeLayerwiseConnectorWorker] = None
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elif role == KVConnectorRole.WORKER:
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self.connector_scheduler = None
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self.connector_worker = MooncakeLayerwiseConnectorWorker(
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vllm_config, str(self.engine_id))
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############################################################
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# Scheduler Side Methods
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############################################################
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def get_num_new_matched_tokens(
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self, request: "Request",
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num_computed_tokens: int) -> tuple[int, bool]:
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assert self.connector_scheduler is not None
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return self.connector_scheduler.get_num_new_matched_tokens(
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request, num_computed_tokens)
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def update_state_after_alloc(self, request: "Request",
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blocks: "KVCacheBlocks",
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num_external_tokens: int):
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assert self.connector_scheduler is not None
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return self.connector_scheduler.update_state_after_alloc(
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request, blocks, num_external_tokens)
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def build_connector_meta(
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self,
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scheduler_output: SchedulerOutput,
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) -> KVConnectorMetadata:
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assert self.connector_scheduler is not None
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return self.connector_scheduler.build_connector_meta(scheduler_output)
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def request_finished(
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self,
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request: "Request",
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block_ids: list[int],
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) -> tuple[bool, Optional[dict[str, Any]]]:
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assert self.connector_scheduler is not None
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return self.connector_scheduler.request_finished(request, block_ids)
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############################################################
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# Worker Side Methods
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############################################################
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def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
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assert self.connector_worker is not None
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self.connector_worker.register_kv_caches(kv_caches)
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def get_finished(self,
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finished_req_ids: set[str]) -> tuple[set[str], set[str]]:
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"""Get the finished recving and sending requests."""
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assert self.connector_worker is not None
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return self.connector_worker.get_finished()
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def start_load_kv(self, forward_context: "ForwardContext",
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**kwargs) -> None:
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assert self.connector_worker is not None
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assert isinstance(self._connector_metadata,
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MooncakeLayerwiseConnectorMetadata)
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self.connector_worker.start_load_kv(self._connector_metadata)
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def wait_for_layer_load(self, layer_name: str) -> None:
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"""MooncakeLayerwiseConnector does not do layerwise saving."""
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assert self.connector_worker is not None
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assert isinstance(self._connector_metadata,
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MooncakeLayerwiseConnectorMetadata)
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self.connector_worker.wait_for_layer_load(layer_name)
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def save_kv_layer(self, layer_name: str, kv_layer: torch.Tensor,
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attn_metadata: "AttentionMetadata", **kwargs) -> None:
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"""MooncakeLayerwiseConnector does not save explicitly."""
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assert self.connector_worker is not None
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assert isinstance(self._connector_metadata,
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MooncakeLayerwiseConnectorMetadata)
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self.connector_worker.save_kv_layer(layer_name, kv_layer,
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attn_metadata,
|
|
self._connector_metadata)
|
|
|
|
def wait_for_save(self):
|
|
"""MooncakeLayerwiseConnector does not save explicitly."""
|
|
pass
|
|
|
|
|
|
class MooncakeLayerwiseConnectorScheduler:
|
|
"""Implementation of Scheduler side methods"""
|
|
|
|
def __init__(self, vllm_config: VllmConfig, engine_id: str):
|
|
self.vllm_config = vllm_config
|
|
self.block_size = vllm_config.cache_config.block_size
|
|
self.engine_id = engine_id
|
|
logger.info("Initializing Mooncake Scheduler %s", engine_id)
|
|
|
|
self.side_channel_host = get_ip()
|
|
self.max_device_id = vllm_config.parallel_config.tensor_parallel_size * \
|
|
vllm_config.parallel_config.data_parallel_size
|
|
|
|
# Handshake base port
|
|
self.side_channel_port = (
|
|
vllm_config.kv_transfer_config.kv_port +
|
|
vllm_config.parallel_config.data_parallel_rank *
|
|
vllm_config.parallel_config.tensor_parallel_size)
|
|
|
|
# Requests that need to start recv.
|
|
# New requests are added by update_state_after_alloc in
|
|
# the scheduler. Used to make metadata passed to Worker.
|
|
self._reqs_need_recv: dict[str, tuple[Request, list[int],
|
|
list[int]]] = {}
|
|
self._reqs_need_send_layerwise: dict[str, tuple[
|
|
int, list[int],
|
|
Request]] = {} # req_id, (len(prompt), local_block_ids, request)
|
|
|
|
def get_num_new_matched_tokens(
|
|
self, request: "Request",
|
|
num_computed_tokens: int) -> tuple[int, bool]:
|
|
"""
|
|
For remote prefill, pull all prompt blocks from remote
|
|
asynchronously relative to engine execution.
|
|
|
|
Args:
|
|
request (Request): the request object.
|
|
num_computed_tokens (int): the number of locally
|
|
computed tokens for this request
|
|
Returns:
|
|
* the number of tokens that can be loaded from the
|
|
external KV cache beyond what is already computed.
|
|
* true if the external KV cache tokens will be loaded
|
|
asynchronously (between scheduler steps).
|
|
"""
|
|
|
|
params = request.kv_transfer_params
|
|
logger.debug(
|
|
"MooncakeLayerwiseConnector get_num_new_matched_tokens: "
|
|
"num_computed_tokens=%s, kv_transfer_params=%s",
|
|
num_computed_tokens, params)
|
|
|
|
if params is not None and params.get("do_remote_prefill"):
|
|
# Remote prefill: get all prompt blocks from remote.
|
|
assert num_computed_tokens % self.block_size == 0
|
|
# Note: We use the full token count as transmit data here.
|
|
count = max(len(request.prompt_token_ids) - num_computed_tokens, 0)
|
|
return count, count > 0
|
|
|
|
# No remote prefill for this request.
|
|
return 0, False
|
|
|
|
def update_state_after_alloc(self, request: "Request",
|
|
blocks: "KVCacheBlocks",
|
|
num_external_tokens: int):
|
|
|
|
params = request.kv_transfer_params
|
|
logger.debug(
|
|
"MooncakeLayerwiseConnector update_state_after_alloc: "
|
|
"num_external_tokens=%s, kv_transfer_params=%s",
|
|
num_external_tokens, params)
|
|
|
|
if params is not None and params.get("do_remote_prefill"):
|
|
local_block_ids = (blocks.get_unhashed_block_ids()
|
|
if num_external_tokens > 0 else [])
|
|
# Get unhashed blocks to pull from remote.
|
|
self._reqs_need_recv[request.request_id] = (
|
|
request,
|
|
[], #request._all_token_ids,
|
|
local_block_ids)
|
|
|
|
params["do_remote_prefill"] = False
|
|
|
|
# Layerwise prefiller add request need send
|
|
if params is not None and params.get("do_remote_decode"):
|
|
local_block_ids = (blocks.get_block_ids()[0])
|
|
self._reqs_need_send_layerwise[request.request_id] = (len(
|
|
request.all_token_ids), local_block_ids, request)
|
|
|
|
def build_connector_meta(
|
|
self,
|
|
scheduler_output: SchedulerOutput,
|
|
) -> KVConnectorMetadata:
|
|
meta = MooncakeLayerwiseConnectorMetadata()
|
|
|
|
# Loop through scheduled reqs and convert to ReqMeta.
|
|
for req_id, (req, token_ids,
|
|
block_ids) in self._reqs_need_recv.items():
|
|
assert req.kv_transfer_params is not None
|
|
# For the case where there are no remote blocks to pull
|
|
# (block_ids is empty), we don't need to schedule
|
|
# an async read on the worker side.
|
|
meta.add_new_req(request_id=req_id,
|
|
local_block_ids=block_ids,
|
|
kv_transfer_params=req.kv_transfer_params,
|
|
token_ids=token_ids)
|
|
|
|
# Clear the list once workers start the transfers
|
|
self._reqs_need_recv.clear()
|
|
|
|
cached_reqs = scheduler_output.scheduled_cached_reqs
|
|
new_reqs = scheduler_output.scheduled_new_reqs
|
|
for req_id, new_blocks in zip(cached_reqs.req_ids,
|
|
cached_reqs.new_block_ids):
|
|
if req_id in self._reqs_need_send_layerwise and new_blocks is not None:
|
|
total_tokens, block_ids, req = self._reqs_need_send_layerwise[
|
|
req_id]
|
|
block_ids.extend(new_blocks[0])
|
|
|
|
computed_tokens = dict(
|
|
list(zip(cached_reqs.req_ids, cached_reqs.num_computed_tokens)) +
|
|
[(x.req_id, x.num_computed_tokens) for x in new_reqs])
|
|
for req_id, scheduled_tokens in scheduler_output.num_scheduled_tokens.items(
|
|
):
|
|
if req_id in self._reqs_need_send_layerwise:
|
|
total_tokens, block_ids, req = self._reqs_need_send_layerwise[
|
|
req_id]
|
|
current_tokens = computed_tokens.get(req_id,
|
|
0) + scheduled_tokens
|
|
if current_tokens == total_tokens:
|
|
meta.add_new_req(request_id=req_id,
|
|
local_block_ids=block_ids,
|
|
kv_transfer_params=req.kv_transfer_params,
|
|
token_ids=[])
|
|
self._reqs_need_send_layerwise.pop(req_id)
|
|
return meta
|
|
|
|
def request_finished(
|
|
self,
|
|
request: "Request",
|
|
block_ids: list[int],
|
|
) -> tuple[bool, Optional[dict[str, Any]]]:
|
|
"""
|
|
Once a request is finished, determine whether request blocks
|
|
should be freed now or will be sent asynchronously and freed later.
|
|
"""
|
|
# layer_wise push, not need delay_free_blocks
|
|
return False, None
|
|
|
|
|
|
class MooncakeLayerwiseConnectorWorker:
|
|
"""Implementation of Worker side methods"""
|
|
|
|
def __init__(self, vllm_config: VllmConfig, engine_id: str):
|
|
self._get_prefill_decode_size(vllm_config)
|
|
os.environ["ASCEND_TRANSFER_TIMEOUT"] = str(
|
|
get_transfer_timeout_value())
|
|
if self._prefill_tp_size < self._decode_tp_size:
|
|
raise ValueError(
|
|
f"prefill_tp_size: {self._prefill_tp_size} must be greater than"
|
|
f" or equal to the decode_tp_size: {self._decode_tp_size}")
|
|
|
|
if TransferEngine is None:
|
|
raise RuntimeError("mooncake is not available")
|
|
logger.info("Initializing Mooncake work %s", engine_id)
|
|
self.engine = TransferEngine()
|
|
|
|
# Metadata.
|
|
self.vllm_config = vllm_config
|
|
self.local_engine_id: str = " "
|
|
self.engine_id = engine_id
|
|
self.tp_rank = get_tensor_model_parallel_rank()
|
|
self.tp_size = vllm_config.parallel_config.tensor_parallel_size
|
|
self.tp_group = get_tp_group()
|
|
self.dp_rank = vllm_config.parallel_config.data_parallel_rank
|
|
self.dp_size = vllm_config.parallel_config.data_parallel_size_local
|
|
self.kv_caches: dict[str, torch.Tensor] = {}
|
|
self.side_channel_host = get_ip()
|
|
self.max_device_id = self.tp_size * self.dp_size
|
|
self.total_layers = vllm_config.model_config.get_num_layers(
|
|
vllm_config.parallel_config)
|
|
|
|
self.executor = ThreadPoolExecutor(32)
|
|
self.metaserver_client = httpx.Client(
|
|
limits=httpx.Limits(max_connections=100000),
|
|
timeout=None) if self.tp_rank == 0 else None
|
|
|
|
# Handshake base port
|
|
self.side_channel_port = (
|
|
vllm_config.kv_transfer_config.kv_port +
|
|
vllm_config.parallel_config.data_parallel_rank *
|
|
vllm_config.parallel_config.tensor_parallel_size)
|
|
self.handshake_port = self.side_channel_port + self.tp_rank
|
|
self.sockets: dict = {}
|
|
|
|
# get tp device id
|
|
# TODO(kw): https://github.com/vllm-project/vllm-ascend/pull/940
|
|
# introducing some changes
|
|
device_ids_str = envs_ascend.PHYSICAL_DEVICES
|
|
if device_ids_str is None:
|
|
device_ids = list(
|
|
range(self.dp_rank * self.tp_size,
|
|
(self.dp_rank + 1) * self.tp_size))
|
|
else:
|
|
device_ids = list(map(int, device_ids_str.split(',')))
|
|
start_index = self.dp_rank * self.tp_size
|
|
end_index = start_index + self.tp_size
|
|
if len(device_ids) < end_index:
|
|
raise ValueError(
|
|
f"Not enough physical devices available for DP rank {self.dp_rank}. "
|
|
f"Expected at least {end_index} devices, but found {len(device_ids)} "
|
|
"in PHYSICAL_DEVICES.")
|
|
device_ids = device_ids[start_index:end_index]
|
|
assert len(device_ids) > self.tp_rank # type: ignore
|
|
self.device_id = device_ids[self.tp_rank] # type: ignore
|
|
|
|
if vllm_config.kv_transfer_config.get_from_extra_config(
|
|
'use_ascend_direct', True):
|
|
hostname = self.side_channel_host
|
|
else:
|
|
hostname = f"{self.side_channel_host}:0:npu_{self.device_id}"
|
|
self._initialize(hostname=hostname, device_name=None)
|
|
self.te_rpc_port = self.engine.get_rpc_port()
|
|
|
|
# Background thread for sending or receiving KV caches.
|
|
self.kv_recv_layer_thread: Optional[KVCacheRecvingLayerThread] = None
|
|
self.kv_send_layer_thread: Optional[KVCacheSendingLayerThread] = None
|
|
|
|
self.vllm_config = vllm_config
|
|
self.block_size = vllm_config.cache_config.block_size
|
|
self.kv_caches_base_addr: list[int] = []
|
|
|
|
self.pd_tp_ratio = get_ascend_config().pd_tp_ratio
|
|
self.pd_head_ratio = get_ascend_config().pd_head_ratio
|
|
self.num_head_replica = get_ascend_config().num_head_replica
|
|
|
|
self.first_kv_cache = None
|
|
self.remote_poller = zmq.Poller() # type: ignore
|
|
self.decoder = msgspec.msgpack.Decoder(MooncakeAgentMetadata)
|
|
self.encoder = msgspec.msgpack.Encoder()
|
|
|
|
self.remote_kv_caches_base_addr: dict[str, dict[int, list[int]]] = \
|
|
defaultdict(dict)
|
|
self.remote_te_port: dict[str, dict[int, int]] = \
|
|
defaultdict(dict)
|
|
self.remote_sockets_lock = threading.Lock()
|
|
self.remote_sockets: dict[ # type: ignore
|
|
str, deque[zmq.Socket]] = defaultdict( # type: ignore
|
|
deque)
|
|
self.remote_poller = zmq.Poller() # type: ignore
|
|
self.timeout = 1.0 # seconds
|
|
|
|
def _get_prefill_decode_size(self, vllm_config: VllmConfig):
|
|
# get prefill tp and dp size from extra config
|
|
prefill_parallel_config: dict[
|
|
str, Any] = vllm_config.kv_transfer_config.get_from_extra_config(
|
|
"prefill", {})
|
|
|
|
assert "tp_size" in prefill_parallel_config.keys()
|
|
self._prefill_tp_size = prefill_parallel_config["tp_size"]
|
|
|
|
assert "dp_size" in prefill_parallel_config.keys()
|
|
self._prefill_dp_size = prefill_parallel_config["dp_size"]
|
|
|
|
# get decode tp and dp size from extra config
|
|
decode_parallel_config: dict[
|
|
str, Any] = vllm_config.kv_transfer_config.get_from_extra_config(
|
|
"decode", {})
|
|
assert "tp_size" in decode_parallel_config.keys()
|
|
self._decode_tp_size = decode_parallel_config["tp_size"]
|
|
assert "dp_size" in decode_parallel_config.keys()
|
|
self._decode_dp_size = decode_parallel_config["dp_size"]
|
|
|
|
def _initialize(
|
|
self,
|
|
hostname: str,
|
|
device_name: Optional[str],
|
|
) -> None:
|
|
"""Initialize the mooncake instance."""
|
|
device_name = device_name if device_name is not None else ""
|
|
ret_value = self.engine.initialize(hostname, "P2PHANDSHAKE", "ascend",
|
|
device_name)
|
|
if ret_value != 0:
|
|
raise RuntimeError(
|
|
f"Mooncake initialization failed with ret_value: {ret_value}")
|
|
|
|
def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
|
|
"""Register the KV Cache data."""
|
|
|
|
_, first_kv_cache_tuple = next(iter(kv_caches.items()))
|
|
first_kv_cache = first_kv_cache_tuple[0]
|
|
self.first_kv_cache = first_kv_cache
|
|
|
|
# TODO(tms): Find a more robust way to detect and handle MLA
|
|
self.use_mla = first_kv_cache_tuple[0].size(
|
|
-1) != first_kv_cache_tuple[1].size(-1)
|
|
if self.use_mla:
|
|
# MLA case.[num_block, block_size, 1, hidden_dim]
|
|
self.num_blocks = first_kv_cache.shape[0]
|
|
block_rank = 3 # [block_size, latent_dim]
|
|
block_shape_norm = first_kv_cache_tuple[0].shape[-block_rank:]
|
|
block_shape_pe = first_kv_cache_tuple[1].shape[-block_rank:]
|
|
self.block_len = [
|
|
first_kv_cache[0].element_size() * math.prod(block_shape_norm),
|
|
first_kv_cache[1].element_size() * math.prod(block_shape_pe)
|
|
]
|
|
logger.info(
|
|
"num_blocks: %s, block_shape_norm: %s, block_shape_pe: %s",
|
|
self.num_blocks, block_shape_norm, block_shape_pe)
|
|
else:
|
|
# [num_block, block_size, num_head, hidden_dim]
|
|
self.num_blocks = first_kv_cache.shape[0]
|
|
kv_elem_size = first_kv_cache.element_size()
|
|
block_rank = 3 # [block_size, kv_heads, head_dim]
|
|
block_shape = first_kv_cache.shape[-block_rank:]
|
|
self.block_len = [kv_elem_size * math.prod(block_shape)]
|
|
logger.info("num_blocks: %s, block_shape: %s", self.num_blocks,
|
|
block_shape)
|
|
|
|
logger.info("Registering KV_Caches. use_mla: %s, shape %s",
|
|
self.use_mla, first_kv_cache.shape)
|
|
|
|
self.kv_caches = kv_caches
|
|
kv_caches_base_addr = []
|
|
for cache_or_caches in kv_caches.values():
|
|
# Normalize to always be a list of caches
|
|
if self.use_mla:
|
|
for i, cache in enumerate(cache_or_caches, 0):
|
|
base_addr = cache.data_ptr()
|
|
region_len = self.num_blocks * self.block_len[i % 2]
|
|
kv_caches_base_addr.append(base_addr)
|
|
self._register(base_addr, region_len)
|
|
else:
|
|
cache_list = [cache_or_caches
|
|
] if self.use_mla else cache_or_caches
|
|
for cache in cache_list:
|
|
base_addr = cache.data_ptr()
|
|
region_len = self.num_blocks * self.block_len[0]
|
|
kv_caches_base_addr.append(base_addr)
|
|
self._register(base_addr, region_len)
|
|
self.kv_caches_base_addr = kv_caches_base_addr
|
|
|
|
# After KV Caches registered, start the sending or receiving thread.
|
|
metadata = MooncakeAgentMetadata(
|
|
te_rpc_port=self.te_rpc_port,
|
|
kv_caches_base_addr=self.kv_caches_base_addr,
|
|
)
|
|
if self.vllm_config.kv_transfer_config.is_kv_producer:
|
|
ready_event = threading.Event()
|
|
self.kv_send_layer_thread = KVCacheSendingLayerThread(
|
|
engine=self.engine,
|
|
total_layers=self.total_layers,
|
|
ready_event=ready_event,
|
|
tp_rank=self.tp_rank,
|
|
pd_head_ratio=self.pd_head_ratio,
|
|
num_head_replica=self.num_head_replica,
|
|
kv_cache_base_addr=self.kv_caches_base_addr,
|
|
use_mla=self.use_mla,
|
|
block_len=self.block_len,
|
|
first_kv_cache=first_kv_cache,
|
|
callback_func=self.send_done_send_signal)
|
|
self.kv_send_layer_thread.start()
|
|
ready_event.wait()
|
|
|
|
if self.vllm_config.kv_transfer_config.is_kv_consumer:
|
|
ready_event = threading.Event()
|
|
self.kv_recv_layer_thread = KVCacheRecvingLayerThread(
|
|
self.tp_rank, self.side_channel_port, self.tp_size,
|
|
self.pd_head_ratio, self.engine_id, metadata, ready_event)
|
|
self.kv_recv_layer_thread.start()
|
|
ready_event.wait()
|
|
|
|
def _register(self, ptr, length):
|
|
logger.info(
|
|
"Registering KV cache: ptr=0x%x, length=%d, num_blocks=%d, "
|
|
"block_lens=%s", ptr, length, self.num_blocks, self.block_len)
|
|
ret_value = self.engine.register_memory(ptr, length)
|
|
if ret_value != 0:
|
|
raise RuntimeError("Mooncake memory registration failed.")
|
|
|
|
def _access_metaserver(self, url, message):
|
|
success = False
|
|
retry = 0
|
|
while retry < 3 and success is False:
|
|
retry += 1
|
|
try:
|
|
self.metaserver_client.post(url, json=message)
|
|
success = True
|
|
except Exception as e:
|
|
logger.error(
|
|
f"Failed to connect to metaserver: {url}, retry {retry} time."
|
|
)
|
|
if retry == 3:
|
|
raise e
|
|
|
|
def get_finished(self) -> tuple[set[str], set[str]]:
|
|
done_recving = (
|
|
self.kv_recv_layer_thread.
|
|
get_and_clear_finished_requests( # type: ignore[union-attr]
|
|
) if self.vllm_config.kv_transfer_config.is_kv_consumer else set())
|
|
if len(done_recving) > 0:
|
|
logger.info(
|
|
"Number of completed KV cache recv requests: %d, receive "
|
|
"requests: %d", 0, len(done_recving))
|
|
return set(), done_recving
|
|
|
|
def start_load_kv(self, metadata: MooncakeLayerwiseConnectorMetadata):
|
|
"""Start loading KV blocks from remote engine."""
|
|
self.current_layer = 0
|
|
if self.vllm_config.kv_transfer_config.is_kv_consumer:
|
|
for req_id, meta in metadata.requests.items():
|
|
if self.tp_rank % self.tp_size == 0:
|
|
logger.info(
|
|
f"Send request: {req_id} to proxy metaserver: {meta.metaserver}"
|
|
)
|
|
# All parameters here should appear in the returned dict of
|
|
# request_finished in the scheduler side except "request_id".
|
|
kv_transfer_params = dict(
|
|
token_ids=meta.token_ids,
|
|
request_id=req_id,
|
|
do_remote_prefill=False,
|
|
do_remote_decode=True,
|
|
remote_block_ids=meta.local_block_ids,
|
|
remote_engine_id=self.engine_id,
|
|
remote_host=self.side_channel_host,
|
|
remote_port=self.side_channel_port,
|
|
)
|
|
future = self.executor.submit(
|
|
self._access_metaserver,
|
|
url=meta.metaserver,
|
|
message=kv_transfer_params,
|
|
)
|
|
|
|
def handle_exception(future):
|
|
if future.exception():
|
|
logger.error(
|
|
f"Access metaserver fail: {future.exception()}"
|
|
)
|
|
|
|
future.add_done_callback(handle_exception)
|
|
assert self.kv_recv_layer_thread is not None
|
|
with self.kv_recv_layer_thread.lock:
|
|
self.kv_recv_layer_thread.task_tracker[req_id] = 0
|
|
|
|
def save_kv_layer(self, layer_name: str, kv_layer: Tuple[torch.Tensor,
|
|
torch.Tensor],
|
|
attn_metadata: "AttentionMetadata",
|
|
connector_metadata: MooncakeLayerwiseConnectorMetadata,
|
|
**kwargs) -> None:
|
|
"""MooncakeLayerwiseConnector does not save explicitly."""
|
|
if self.vllm_config.kv_transfer_config.is_kv_producer and connector_metadata.requests.keys(
|
|
):
|
|
# enable decode prefix cache
|
|
for request in connector_metadata.requests.values():
|
|
assert len(request.local_block_ids) >= len(
|
|
request.remote_block_ids
|
|
), "When prefix cache enabled, remote KVCacheBlocks num should not larger than local KVCacheBlocks num."
|
|
request.local_block_ids = request.local_block_ids[
|
|
-len(request.remote_block_ids):]
|
|
if self.pd_head_ratio != 1:
|
|
|
|
def sort_kv_cache(input_kv: list[list[int]]):
|
|
return torch.cat([
|
|
torch.chunk(tensor, self.pd_head_ratio, dim=0)[x]
|
|
for x in range(self.pd_head_ratio)
|
|
for tensor in input_kv
|
|
])
|
|
|
|
total_block_ids = [
|
|
request.local_block_ids
|
|
for request in connector_metadata.requests.values()
|
|
]
|
|
keys = [
|
|
kv_layer[0][block_ids].reshape(
|
|
-1, *kv_layer[0].shape[2:]).clone()
|
|
for block_ids in total_block_ids
|
|
]
|
|
values = [
|
|
kv_layer[1][block_ids].reshape(
|
|
-1, *kv_layer[1].shape[2:]).clone()
|
|
for block_ids in total_block_ids
|
|
]
|
|
key_block_size = keys[0].size(0) // len(total_block_ids[0])
|
|
value_block_size = values[0].size(0) // len(total_block_ids[0])
|
|
keys = sort_kv_cache(keys) # [req1_key, req2_key]
|
|
values = sort_kv_cache(values)
|
|
(keys,
|
|
values) = kv_alltoall_and_rearrange(self.pd_head_ratio, keys,
|
|
values)
|
|
key_start_id = 0
|
|
value_start_id = 0
|
|
else:
|
|
key = None
|
|
value = None
|
|
for req_id, req_meta in connector_metadata.requests.items():
|
|
logger.debug(
|
|
f"Add request {req_id} to kv send layer thread. {req_meta=}"
|
|
)
|
|
if self.pd_head_ratio != 1:
|
|
key_block_num = len(
|
|
req_meta.local_block_ids) * key_block_size
|
|
value_block_num = len(
|
|
req_meta.local_block_ids) * value_block_size
|
|
key = keys[key_start_id:key_start_id + key_block_num]
|
|
value = values[value_start_id:value_start_id +
|
|
value_block_num]
|
|
key_start_id += key_block_num
|
|
value_start_id += value_block_num
|
|
req_meta_update = self.update_decoder_info(req_id, req_meta)
|
|
assert self.kv_send_layer_thread is not None
|
|
self.kv_send_layer_thread.send_queue.put(
|
|
(req_id, req_meta_update, self.current_layer, key, value))
|
|
self.current_layer += 1
|
|
|
|
def _get_remote_socket(
|
|
self, remote_host: str,
|
|
remote_handshake_port: int) -> zmq.Socket: # type: ignore
|
|
"""Get a socket to the remote host."""
|
|
remote_path = make_zmq_path("tcp", remote_host, remote_handshake_port)
|
|
with self.remote_sockets_lock:
|
|
if self.remote_sockets[remote_path]:
|
|
return self.remote_sockets[remote_path].popleft()
|
|
|
|
ctx = zmq.Context() # type: ignore
|
|
sock = make_zmq_socket(
|
|
ctx=ctx,
|
|
path=remote_path,
|
|
socket_type=zmq.REQ, # type: ignore
|
|
bind=False)
|
|
sock.setsockopt(
|
|
zmq.SNDTIMEO, # type: ignore
|
|
int(self.timeout * 1000))
|
|
self.remote_poller.register(sock, zmq.POLLIN) # type: ignore
|
|
return sock
|
|
|
|
def update_decoder_info(self, req_id, req_meta):
|
|
req_meta_update = copy.deepcopy(req_meta)
|
|
if self.pd_tp_ratio > 1:
|
|
req_meta_update.remote_port = req_meta_update.remote_port + self.tp_rank // self.pd_tp_ratio
|
|
else:
|
|
req_meta_update.remote_port = req_meta_update.remote_port + self.tp_rank
|
|
if req_meta_update.remote_engine_id not in self.remote_kv_caches_base_addr or \
|
|
req_meta_update.remote_port not in self.remote_kv_caches_base_addr[req_meta_update.remote_engine_id]:
|
|
try:
|
|
encoded_data = self.encoder.encode((GET_META_MSG, req_id))
|
|
sock = self._get_remote_socket(req_meta_update.remote_host,
|
|
req_meta_update.remote_port)
|
|
ensure_zmq_send(sock, encoded_data)
|
|
metadata_bytes = ensure_zmq_recv(sock, self.remote_poller)
|
|
agent_meta = self.decoder.decode(metadata_bytes)
|
|
except Exception as e:
|
|
logger.error(
|
|
f"Query to port and kv base addr for request {req_id} from {req_meta_update.remote_host}:{req_meta_update.remote_port} fail with error: {e}"
|
|
)
|
|
assert req_meta_update.remote_engine_id != self.engine_id, (
|
|
f"Conflict engine id {req_meta_update.remote_engine_id} with local engine id "
|
|
f"{self.local_engine_id}.")
|
|
self.remote_kv_caches_base_addr[req_meta_update.remote_engine_id][
|
|
req_meta_update.remote_port] = agent_meta.kv_caches_base_addr
|
|
self.remote_te_port[req_meta_update.remote_engine_id][
|
|
req_meta_update.remote_port] = agent_meta.te_rpc_port
|
|
logger.info(
|
|
f"Query to port and kv base addr for request {req_id} from {req_meta_update.remote_host}:{req_meta_update.remote_port} success {agent_meta.kv_caches_base_addr=} {agent_meta.te_rpc_port=}"
|
|
)
|
|
req_meta_update.remote_te_rpc_port = self.remote_te_port[
|
|
req_meta_update.remote_engine_id][req_meta_update.remote_port]
|
|
req_meta_update.remote_kv_caches_base_addr = self.remote_kv_caches_base_addr[
|
|
req_meta_update.remote_engine_id][req_meta_update.remote_port]
|
|
return req_meta_update
|
|
|
|
def send_done_send_signal(self, req_id, req_meta):
|
|
logger.info("Sending done sending signal for request %s to %s:%d",
|
|
req_id, req_meta.remote_host, req_meta.remote_port)
|
|
try:
|
|
path = make_zmq_path("tcp", req_meta.remote_host,
|
|
req_meta.remote_port)
|
|
msg_encoder = msgspec.msgpack.Encoder()
|
|
encoded_data = msg_encoder.encode((DONE_SENDING_MSG, req_id))
|
|
with zmq_ctx(zmq.REQ, path) as sock: # type: ignore
|
|
ensure_zmq_send(sock, encoded_data)
|
|
ack = sock.recv()
|
|
if ack != b"ACK":
|
|
raise ValueError(f"Unexpected ACK response: {ack}")
|
|
except Exception as e:
|
|
logger.error(
|
|
f"Sending done sending signal for request {req_id} to {req_meta.remote_host}:{req_meta.remote_port} fail with error: {e}"
|
|
)
|
|
|
|
def wait_for_layer_load(self, layer_name: str) -> None:
|
|
pass
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def zmq_ctx(socket_type: Any,
|
|
addr: str) -> Iterator[zmq.Socket]: # type: ignore
|
|
"""Context manager for a ZMQ socket"""
|
|
|
|
if socket_type not in (zmq.ROUTER, zmq.REQ, zmq.DEALER): # type: ignore
|
|
raise ValueError(f"Unexpected socket type: {socket_type}")
|
|
|
|
ctx: Optional[zmq.Context] = None # type: ignore
|
|
try:
|
|
ctx = zmq.Context() # type: ignore
|
|
yield make_zmq_socket(ctx=ctx,
|
|
path=addr,
|
|
socket_type=socket_type,
|
|
bind=socket_type == zmq.ROUTER) # type: ignore
|
|
finally:
|
|
if ctx is not None:
|
|
ctx.destroy(linger=0)
|
|
|
|
|
|
def group_concurrent_contiguous(
|
|
src: List[int],
|
|
dst: List[int] = []
|
|
) -> Tuple[List[npt.NDArray[np.int64]], List[npt.NDArray[np.int64]]]:
|
|
"""Vectorised NumPy implementation."""
|
|
if not dst:
|
|
src_only_indices: npt.NDArray[np.int64] = np.array(src, dtype=np.int64)
|
|
|
|
if src_only_indices.size == 0:
|
|
return [], []
|
|
|
|
brk = np.where((np.diff(src_only_indices) != 1))[0] + 1
|
|
src_groups = np.split(src_only_indices, brk)
|
|
src_groups = [g.tolist() for g in src_groups]
|
|
|
|
return src_groups, []
|
|
|
|
else:
|
|
src_indices: npt.NDArray[np.int64] = np.array(src, dtype=np.int64)
|
|
dst_indices: npt.NDArray[np.int64] = np.array(dst, dtype=np.int64)
|
|
|
|
if src_indices.size == 0:
|
|
return [], []
|
|
|
|
brk = np.where((np.diff(src_indices) != 1)
|
|
| (np.diff(dst_indices) != 1))[0] + 1
|
|
src_groups = np.split(src_indices, brk)
|
|
dst_groups = np.split(dst_indices, brk)
|
|
|
|
src_groups = [g.tolist() for g in src_groups]
|
|
dst_groups = [g.tolist() for g in dst_groups]
|
|
|
|
return src_groups, dst_groups
|
|
|
|
|
|
def string_to_int64_hash(input_str):
|
|
"""
|
|
Hash the string using SHA-256 and convert it into an int64 integer.
|
|
"""
|
|
hashed_bytes = hashlib.sha256(input_str.encode("utf-8")).digest()
|
|
trunked_bytes = hashed_bytes[:8]
|
|
uint64_value = struct.unpack("<Q", trunked_bytes)[0]
|
|
return uint64_value
|
|
|
|
|
|
def ensure_zmq_send(
|
|
socket: zmq.Socket, # type: ignore
|
|
data: bytes,
|
|
max_retries: int = 3):
|
|
retries_left = max_retries
|
|
while True:
|
|
try:
|
|
socket.send(data)
|
|
return
|
|
except zmq.ZMQError as e: # type: ignore
|
|
retries_left -= 1
|
|
if retries_left > 0:
|
|
logger.warning(
|
|
f"Send failed: {e}, retrying... ({retries_left} "
|
|
"attempts left)")
|
|
time.sleep(0.1)
|
|
else:
|
|
logger.error(f"Send failed after all retries: {e}")
|
|
raise RuntimeError(f"Failed to send data after {max_retries} "
|
|
f"retries: {e}")
|
|
|
|
|
|
def ensure_zmq_recv(
|
|
socket: zmq.Socket, # type: ignore
|
|
poller: zmq.Poller, # type: ignore
|
|
timeout: float = 1.0,
|
|
max_retries: int = 3) -> bytes:
|
|
retries_left = max_retries
|
|
while True:
|
|
try:
|
|
if dict(poller.poll(int(timeout * 1000))): # milliseconds
|
|
data = socket.recv()
|
|
return data
|
|
else:
|
|
raise zmq.ZMQError("Receive timeout") # type: ignore
|
|
except zmq.ZMQError as e: # type: ignore
|
|
retries_left -= 1
|
|
if retries_left > 0:
|
|
logger.warning(f"Receive failed: {e}, retrying... "
|
|
f"({retries_left} attempts left)")
|
|
time.sleep(0.1)
|
|
else:
|
|
logger.error(f"Receive failed after all retries: {e}")
|
|
raise RuntimeError(
|
|
f"Failed to receive data after {max_retries} "
|
|
f"retries: {e}")
|