ColossalAI/colossalai/zero/sharded_param/sharded_param.py

108 lines
3.7 KiB
Python

import torch
from colossalai.zero.sharded_param import ShardedTensor
from typing import Optional, Tuple
from colossalai.zero.sharded_param.tensor_utils import colo_tensor_mem_usage
from .tensorful_state import StatefulTensor, TensorState
from typing import List
EMPTY_TENSOR_DICT = {}
def get_empty_tensor(device: torch.device, dtype: torch.dtype):
key = (device, dtype)
if key not in EMPTY_TENSOR_DICT:
EMPTY_TENSOR_DICT[key] = torch.empty(0, dtype=dtype, device=device)
return EMPTY_TENSOR_DICT[key]
class ShardedParamV2(object):
def __init__(self, param: torch.nn.Parameter, set_data_none: bool = False) -> None:
self._sharded_data_tensor: ShardedTensor = ShardedTensor(param.data)
self.saved_grad: StatefulTensor = StatefulTensor(None, TensorState.FREE)
# This attribute must be initialized in ShardedModel
self.offload_grad: bool = False
# make sure the shared param is the only owner of payload
# The param.data maybe used to init the other part of the model.
# For example: File "resnet.py", line 190, in __init__
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
# So we can not empty the .data at this time
self.param = param
if set_data_none:
self.set_data_none()
def get_payload_tensors(self) -> List[StatefulTensor]:
"""returns stateful tensors kept by this class.
"""
return [self._sharded_data_tensor]
def set_data_none(self):
self.param.data = get_empty_tensor(self.sharded_data_tensor.device, self.sharded_data_tensor.dtype)
def set_grad_none(self):
self.saved_grad.set_null()
@property
def sharded_data_tensor(self):
return self._sharded_data_tensor
@property
def data_payload(self):
return self.sharded_data_tensor.payload
@property
def grad_payload(self):
assert not self.saved_grad.is_null()
return self.saved_grad.payload
@property
def param_is_sharded(self):
return self.sharded_data_tensor.is_sharded
def reset_data_payload(self, tensor: torch.Tensor):
assert type(tensor) is torch.Tensor
assert tensor.requires_grad is False
self.sharded_data_tensor.reset_payload(tensor)
def reset_grad_payload(self, tensor: torch.Tensor):
assert type(tensor) is torch.Tensor
assert tensor.requires_grad is False
self.saved_grad.reset_payload(tensor)
def get_memory_usage(self) -> Tuple[int, int]:
"""
get the memory usage of the param, including data and grad
Returns:
Tuple[int, int]: cuda mem usage in Byte, cpu memory usage in Byte
"""
cuda_mem_use, cpu_mem_use = 0, 0
def _update_mem_use(t: Optional[torch.Tensor]):
if t is None:
return
assert isinstance(t, torch.Tensor)
nonlocal cuda_mem_use
nonlocal cpu_mem_use
t_cuda, t_cpu = colo_tensor_mem_usage(t)
cuda_mem_use += t_cuda
cpu_mem_use += t_cpu
address_set = set()
_update_mem_use(self.data_payload)
address_set.add(self.data_payload.data_ptr())
if not self.saved_grad.is_null() and self.saved_grad.data_ptr() not in address_set:
_update_mem_use(self.grad_payload)
address_set.add(self.saved_grad.data_ptr())
if self.param.data is not None and self.param.data.data_ptr() not in address_set:
_update_mem_use(self.param.data)
address_set.add(self.param.data.data_ptr())
if self.param.grad is not None and self.param.grad.data_ptr() not in address_set:
_update_mem_use(self.param.grad)
return cuda_mem_use, cpu_mem_use