from collections import deque from typing import Deque, Dict, Iterable, List, Optional, Set, Tuple import torch import torch.distributed as dist from torch.distributed import ProcessGroup from colossalai.utils import get_current_device from .chunk import Chunk, ChunkFullError, TensorState class ChunkManager: """ A manager class to manipulate the tensors in chunks. Args: chunk_configuration (Dict[int, Dict]): the configuration dictionary of this chunk manager. init_device (torch.device): optional, the device on which the chunk is initialized. The default is None. """ def __init__(self, chunk_configuration, init_device: Optional[torch.device] = None) -> None: self.device = init_device or get_current_device() self.dp_degree_chunk_size_dict: Dict[int, int] = dict() self.kwargs_config = chunk_configuration for k, v in self.kwargs_config.items(): self.dp_degree_chunk_size_dict[k] = v.pop('chunk_size') v['init_device'] = self.device self.chunk_groups: Dict[str, Deque[Chunk]] = dict() self.tensor_chunk_map: Dict[torch.Tensor, Chunk] = dict() self.accessed_chunks: Set[Chunk] = set() self.accessed_mem: int = 0 self.total_mem: Dict[str, int] = {'cpu': 0, 'cuda': 0} def register_tensor(self, tensor: torch.Tensor, group_type: str, config_key: int, process_group: ProcessGroup, cpu_offload: bool = False, pin_memory: bool = False) -> None: """ Register a tensor to the chunk manager. Then, the tensor should be accessed by `get_chunks`. Args: tensor: the tensor appended to the chunk group_type: the data type of the group. config_key: the key of the group's name, the size of the dp world cpu_offload: if True, the chunk will be closed on CPU pin_memory: whether the chunk is pinned in the cpu memory """ assert tensor not in self.tensor_chunk_map assert isinstance(tensor, torch.Tensor), "Please feed Tensor to this ChunkManager" assert config_key in self.dp_degree_chunk_size_dict chunk_size = self.dp_degree_chunk_size_dict[config_key] chunk_kwargs = self.kwargs_config[config_key] group_name = "{}_{}".format(group_type, config_key) chunk_group = self.__get_chunk_group(group_name) try: # append the tensor to the last chunk chunk_group[-1].append_tensor(tensor) except (IndexError, ChunkFullError): # the except statement will be triggered when there is no chunk or # the last chunk in the chunk group is full # this will create a new chunk and allocate this chunk to its corresponding process if chunk_group: # the chunk group is not empty # close the last chunk self.__close_one_chunk(chunk_group[-1]) if tensor.numel() > chunk_size: chunk_size = tensor.numel() dp_size = dist.get_world_size(process_group) chunk_size = chunk_size + (-chunk_size % dp_size) chunk = Chunk( chunk_size=chunk_size, process_group=process_group, dtype=tensor.dtype, cpu_shard_init=cpu_offload, pin_memory=pin_memory, **chunk_kwargs, ) chunk_group.append(chunk) chunk.append_tensor(tensor) self.__add_memory_usage(chunk.memory_usage) self.tensor_chunk_map[tensor] = chunk_group[-1] def close_all_groups(self): """Close all the chunks of all groups. """ for group_name in self.chunk_groups: self.__close_one_chunk(self.chunk_groups[group_name][-1]) def access_chunk(self, chunk: Chunk) -> None: """Make the chunk can be used for calculation. """ if chunk in self.accessed_chunks: return self.__sub_memory_usage(chunk.memory_usage) if chunk.device_type == 'cpu': chunk.shard_move(get_current_device()) self.__add_accessed_chunk(chunk) self.__add_memory_usage(chunk.memory_usage) def release_chunk(self, chunk: Chunk) -> None: """Scatter the chunk in CUDA. """ if chunk not in self.accessed_chunks: return if chunk.can_release: self.__sub_memory_usage(chunk.memory_usage) self.__sub_accessed_chunk(chunk) self.__add_memory_usage(chunk.memory_usage) def move_chunk(self, chunk: Chunk, device: torch.device, force_copy: bool = False) -> None: """Move the shard of the chunk to the target device. """ if not chunk.can_move or chunk.device_type == device.type: return self.__sub_memory_usage(chunk.memory_usage) chunk.shard_move(device, force_copy) self.__add_memory_usage(chunk.memory_usage) def trans_tensor_state(self, tensor: torch.Tensor, state: TensorState) -> None: """Transit tensor state according to pre-defined state machine. """ chunk = self.tensor_chunk_map[tensor] chunk.tensor_trans_state(tensor, state) def reduce_chunk(self, chunk: Chunk) -> bool: """Reduce or all reduce the chunk. """ if not chunk.can_reduce: return False self.__sub_memory_usage(chunk.memory_usage) chunk.reduce() self.__sub_accessed_chunk(chunk) self.__add_memory_usage(chunk.memory_usage) return True def fake_release_chunk(self, chunk: Chunk) -> None: """Release gathered chunk in a fake mode. This function is used for keep-gathered chunk in the inference mode. """ assert chunk.keep_gathered assert chunk.tensor_state_cnter[TensorState.HOLD] == chunk.num_tensors self.__sub_accessed_chunk(chunk) def copy_tensor_to_chunk_slice(self, tensor: torch.Tensor, data: torch.Tensor) -> None: """ Copy data to the chunk. Args: tensor (torch.Tensor): the tensor used to retrieve meta information data (torch.Tensor): the tensor to be copied to the chunk """ chunk = self.tensor_chunk_map[tensor] chunk.copy_tensor_to_chunk_slice(tensor, data) def get_chunk(self, tensor: torch.Tensor) -> Chunk: """ Return the chunk owning the tensor. Args: tensor (torch.Tensor): a torch tensor object """ return self.tensor_chunk_map[tensor] def get_cuda_movable_chunks(self) -> List[Chunk]: """ Get all chunks that can be moved. """ chunk_list = [] for chunk in self.accessed_chunks: if chunk.can_release: chunk_list.append(chunk) chunk_list.sort(key=lambda x: x.count_id) return chunk_list def get_chunks(self, tensors: Iterable[torch.Tensor]) -> Tuple[Chunk, ...]: """ Get all chunks owning the input tensors. Args: tensors (Iterable[torch.Tensor]): the tensors used to look for chunks """ chunks = [] for tensor in tensors: chunk = self.get_chunk(tensor) if chunk not in chunks: chunks.append(chunk) return tuple(chunks) def add_extern_static_tensor(self, tensor: torch.Tensor) -> None: """Add extern static tensor to chunk manager. Those tensors won't be managed by chunk manager, but we want to monitor memory usage of them. They are "static", which means their shape, dtype, device never change. Thus, their memory usage never changes. Args: tensor (torch.Tensor): An extern static tensor. E.g. optimizer state. """ assert tensor not in self.tensor_chunk_map self.total_mem[tensor.device.type] += tensor.numel() * tensor.element_size() def __repr__(self) -> str: msg = [ 'Chunk Manager Information:\n', 'Total memory: ' + ', '.join([f'{k}={v}B' for k, v in self.total_mem.items()]) + '\n' ] for group_name, group in self.chunk_groups.items(): msg.append(f'Group {group_name}:\n') for i, chunk in enumerate(group): msg.append(f'[{i}] {chunk}\n') return ''.join(msg) def __get_chunk_group(self, group_name: str) -> Deque[Chunk]: """Register a chunk group. """ if group_name not in self.chunk_groups: self.chunk_groups[group_name] = deque() return self.chunk_groups[group_name] def __close_one_chunk(self, chunk: Chunk): self.__sub_memory_usage(chunk.memory_usage) chunk.close_chunk() self.__add_memory_usage(chunk.memory_usage) def __sub_memory_usage(self, usage: Dict[str, int]): for k, v in usage.items(): self.total_mem[k] -= v def __add_memory_usage(self, usage: Dict[str, int]): for k, v in usage.items(): self.total_mem[k] += v def __add_accessed_chunk(self, chunk: Chunk): chunk.access_chunk() self.accessed_chunks.add(chunk) self.accessed_mem += chunk.chunk_mem def __sub_accessed_chunk(self, chunk: Chunk): chunk.release_chunk() self.accessed_chunks.remove(chunk) self.accessed_mem -= chunk.chunk_mem