381 lines
13 KiB
Python
381 lines
13 KiB
Python
import abc
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import logging
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import threading
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from enum import IntEnum
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from functools import wraps
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import psutil
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import torch
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from sglang.srt.mem_cache.memory_pool import KVCache, MHATokenToKVPool, MLATokenToKVPool
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from sglang.srt.utils import debug_timing
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logger = logging.getLogger(__name__)
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class MemoryStateInt(IntEnum):
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IDLE = 0
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RESERVED = 1
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PROTECTED = 2
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SYNCED = 3
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BACKUP = 4
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def synchronized(debug_only=False):
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def _decorator(func):
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@wraps(func)
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def wrapper(self, *args, **kwargs):
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if (not debug_only) or self.debug:
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return func(self, *args, **kwargs)
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with self.lock:
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return func(self, *args, **kwargs)
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else:
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return True
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return wrapper
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return _decorator
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class HostKVCache(abc.ABC):
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def __init__(
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self,
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device_pool: KVCache,
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host_to_device_ratio: float,
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host_size: int,
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pin_memory: bool,
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device: str,
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page_size: int,
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):
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self.device_pool = device_pool
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self.dtype = device_pool.store_dtype
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self.pin_memory = pin_memory
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self.device = device
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self.page_size = page_size
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self.size_per_token = self.get_size_per_token()
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if host_size > 0:
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self.size = int(host_size * 1e9 // self.size_per_token)
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else:
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self.size = int(device_pool.size * host_to_device_ratio)
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# Align the host memory pool size to the page size
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self.size = self.size - (self.size % self.page_size)
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self.start_layer = device_pool.start_layer
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self.end_layer = device_pool.end_layer
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assert (
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self.size > device_pool.size
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), "The host memory should be larger than the device memory with the current protocol"
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# Verify there is enough available host memory.
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host_mem = psutil.virtual_memory()
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requested_bytes = self.size * self.size_per_token
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# preserve at least 10GB for other usage
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ten_gb = 10 * (1024**3)
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if requested_bytes > host_mem.available - ten_gb:
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raise ValueError(
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f"Not enough host memory available. Requesting "
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f"{requested_bytes / 1e9:.2f} GB but only have "
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f"{host_mem.available / 1e9:.2f} GB free. Please reduce the "
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f"size of the hierarchical cache."
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)
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else:
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logger.info(
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f"Allocating {requested_bytes / 1e9:.2f} GB host memory for hierarchical KV cache."
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)
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self.kv_buffer = self.init_kv_buffer()
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# A lock for synchronized operations on memory allocation and state transitions.
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self.lock = threading.RLock()
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self.debug = logger.isEnabledFor(logging.DEBUG)
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self.clear()
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@abc.abstractmethod
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def get_size_per_token(self):
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raise NotImplementedError()
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@abc.abstractmethod
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def init_kv_buffer(self):
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raise NotImplementedError()
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@abc.abstractmethod
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def transfer(self, indices, flat_data):
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raise NotImplementedError()
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@abc.abstractmethod
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def get_flat_data(self, indices):
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raise NotImplementedError()
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@abc.abstractmethod
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def get_flat_data_by_layer(self, indices, layer_id):
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raise NotImplementedError()
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@abc.abstractmethod
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def assign_flat_data(self, indices, flat_data):
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raise NotImplementedError()
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@synchronized()
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def clear(self):
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# Initialize memory states and tracking structures.
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self.mem_state = torch.zeros(
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(self.size,), dtype=torch.uint8, device=self.device
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)
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self.free_slots = torch.arange(self.size, dtype=torch.int64)
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def available_size(self):
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return len(self.free_slots)
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@synchronized()
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def alloc(self, need_size: int) -> torch.Tensor:
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if need_size > self.available_size():
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return None
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select_index = self.free_slots[:need_size]
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self.free_slots = self.free_slots[need_size:]
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if self.debug:
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self.mem_state[select_index] = MemoryStateInt.RESERVED
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return select_index
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@synchronized()
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def free(self, indices: torch.Tensor) -> int:
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self.free_slots = torch.cat([self.free_slots, indices])
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if self.debug:
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self.mem_state[indices] = MemoryStateInt.IDLE
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return len(indices)
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@synchronized(debug_only=True)
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def get_state(self, indices: torch.Tensor) -> MemoryStateInt:
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assert len(indices) > 0, "The indices should not be empty"
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states = self.mem_state[indices]
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assert (
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states == states[0]
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).all(), "The memory slots should have the same state {}".format(states)
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return MemoryStateInt(states[0].item())
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@synchronized(debug_only=True)
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def is_reserved(self, indices: torch.Tensor) -> bool:
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return self.get_state(indices) == MemoryStateInt.RESERVED
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@synchronized(debug_only=True)
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def is_protected(self, indices: torch.Tensor) -> bool:
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return self.get_state(indices) == MemoryStateInt.PROTECTED
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@synchronized(debug_only=True)
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def is_synced(self, indices: torch.Tensor) -> bool:
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return self.get_state(indices) == MemoryStateInt.SYNCED
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@synchronized(debug_only=True)
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def is_backup(self, indices: torch.Tensor) -> bool:
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return self.get_state(indices) == MemoryStateInt.BACKUP
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@synchronized(debug_only=True)
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def update_backup(self, indices: torch.Tensor):
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if not self.is_synced(indices):
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raise ValueError(
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f"The host memory slots should be in SYNCED state before turning into BACKUP. "
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f"Current state: {self.get_state(indices)}"
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)
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self.mem_state[indices] = MemoryStateInt.BACKUP
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@synchronized(debug_only=True)
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def update_synced(self, indices: torch.Tensor):
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self.mem_state[indices] = MemoryStateInt.SYNCED
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@synchronized(debug_only=True)
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def protect_write(self, indices: torch.Tensor):
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if not self.is_reserved(indices):
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raise ValueError(
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f"The host memory slots should be RESERVED before write operations. "
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f"Current state: {self.get_state(indices)}"
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)
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self.mem_state[indices] = MemoryStateInt.PROTECTED
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@synchronized(debug_only=True)
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def protect_load(self, indices: torch.Tensor):
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if not self.is_backup(indices):
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raise ValueError(
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f"The host memory slots should be in BACKUP state before load operations. "
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f"Current state: {self.get_state(indices)}"
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)
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self.mem_state[indices] = MemoryStateInt.PROTECTED
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@synchronized(debug_only=True)
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def complete_io(self, indices: torch.Tensor):
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if not self.is_protected(indices):
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raise ValueError(
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f"The host memory slots should be PROTECTED during I/O operations. "
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f"Current state: {self.get_state(indices)}"
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)
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self.mem_state[indices] = MemoryStateInt.SYNCED
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class MHATokenToKVPoolHost(HostKVCache):
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device_pool: MHATokenToKVPool
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def __init__(
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self,
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device_pool: MHATokenToKVPool,
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host_to_device_ratio: float,
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host_size: int,
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page_size: int,
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pin_memory: bool = True,
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device: str = "cpu",
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):
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super().__init__(
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device_pool, host_to_device_ratio, host_size, pin_memory, device, page_size
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)
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def get_size_per_token(self):
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self.head_num = self.device_pool.head_num
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self.head_dim = self.device_pool.head_dim
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self.layer_num = self.device_pool.layer_num
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return self.head_dim * self.head_num * self.layer_num * self.dtype.itemsize * 2
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def init_kv_buffer(self):
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return torch.empty(
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(2, self.layer_num, self.size, self.head_num, self.head_dim),
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dtype=self.dtype,
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device=self.device,
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pin_memory=self.pin_memory,
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)
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@debug_timing
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def transfer(self, indices, flat_data):
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# backup prepared data from device to host
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self.kv_buffer[:, :, indices] = flat_data.to(
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device=self.device, non_blocking=False
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)
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def get_flat_data(self, indices):
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return self.kv_buffer[:, :, indices]
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def get_flat_data_by_layer(self, indices, layer_id):
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return self.kv_buffer[:, layer_id - self.start_layer, indices]
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def assign_flat_data(self, indices, flat_data):
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self.kv_buffer[:, :, indices] = flat_data
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def write_page_all_layers(self, host_indices, device_indices, device_pool):
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device_indices_cpu = device_indices[:: self.page_size].cpu()
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for i in range(len(device_indices_cpu)):
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h_index = host_indices[i * self.page_size]
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d_index = device_indices_cpu[i]
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for j in range(self.layer_num):
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self.kv_buffer[0, j, h_index : h_index + self.page_size].copy_(
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device_pool.k_buffer[j][d_index : d_index + self.page_size],
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non_blocking=True,
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)
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self.kv_buffer[1, j, h_index : h_index + self.page_size].copy_(
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device_pool.v_buffer[j][d_index : d_index + self.page_size],
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non_blocking=True,
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)
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def load_page_per_layer(self, host_indices, device_indices, device_pool, layer_id):
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device_indices_cpu = device_indices[:: self.page_size].cpu()
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for i in range(len(device_indices_cpu)):
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h_index = host_indices[i * self.page_size]
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d_index = device_indices_cpu[i]
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device_pool.k_buffer[layer_id - self.start_layer][
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d_index : d_index + self.page_size
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].copy_(
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self.kv_buffer[
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0, layer_id - self.start_layer, h_index : h_index + self.page_size
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],
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non_blocking=True,
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)
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device_pool.v_buffer[layer_id - self.start_layer][
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d_index : d_index + self.page_size
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].copy_(
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self.kv_buffer[
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1, layer_id - self.start_layer, h_index : h_index + self.page_size
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],
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non_blocking=True,
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)
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class MLATokenToKVPoolHost(HostKVCache):
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device_pool: MLATokenToKVPool
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def __init__(
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self,
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device_pool: MLATokenToKVPool,
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host_to_device_ratio: float,
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host_size: int,
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page_size: int,
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pin_memory: bool = True,
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device: str = "cpu",
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):
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super().__init__(
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device_pool, host_to_device_ratio, host_size, pin_memory, device, page_size
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)
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def get_size_per_token(self):
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self.kv_lora_rank = self.device_pool.kv_lora_rank
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self.qk_rope_head_dim = self.device_pool.qk_rope_head_dim
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self.layer_num = self.device_pool.layer_num
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return (
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(self.kv_lora_rank + self.qk_rope_head_dim)
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* 1
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* self.dtype.itemsize
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* self.layer_num
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)
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def init_kv_buffer(self):
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return torch.empty(
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(
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self.layer_num,
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self.size,
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1,
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self.kv_lora_rank + self.qk_rope_head_dim,
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),
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dtype=self.dtype,
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device=self.device,
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pin_memory=self.pin_memory,
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)
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@debug_timing
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def transfer(self, indices, flat_data):
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# backup prepared data from device to host
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self.kv_buffer[:, indices] = flat_data.to(
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device=self.device, non_blocking=False
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)
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def get_flat_data(self, indices):
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return self.kv_buffer[:, indices]
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def get_flat_data_by_layer(self, indices, layer_id):
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return self.kv_buffer[layer_id - self.start_layer, indices]
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def assign_flat_data(self, indices, flat_data):
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self.kv_buffer[:, indices] = flat_data
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def write_page_all_layers(self, host_indices, device_indices, device_pool):
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device_indices_cpu = device_indices[:: self.page_size].cpu()
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for i in range(len(device_indices_cpu)):
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h_index = host_indices[i * self.page_size]
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d_index = device_indices_cpu[i]
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for j in range(self.layer_num):
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self.kv_buffer[j, h_index : h_index + self.page_size].copy_(
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device_pool.kv_buffer[j][d_index : d_index + self.page_size],
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non_blocking=True,
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)
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def load_page_per_layer(self, host_indices, device_indices, device_pool, layer_id):
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device_indices_cpu = device_indices[:: self.page_size].cpu()
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for i in range(len(device_indices_cpu)):
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h_index = host_indices[i * self.page_size]
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d_index = device_indices_cpu[i]
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device_pool.kv_buffer[layer_id - self.start_layer][
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d_index : d_index + self.page_size
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].copy_(
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self.kv_buffer[
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layer_id - self.start_layer, h_index : h_index + self.page_size
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],
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non_blocking=True,
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)
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