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