Making large AI models cheaper, faster and more accessible
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from enum import EnumMeta
class GeminiMemoryManager(object):
def __init__(self, states_cls: EnumMeta):
super().__init__()
self.states_cls = states_cls
self._cnter = 0 # the counter of instances
self.total_mem = dict()
self.state_mem = dict()
self.state_mem['cpu'] = dict()
self.state_mem['cuda'] = dict()
self.reset()
@property
def total_number(self):
return self._cnter
def reset(self):
self._cnter = 0 # the counter of instances
self.total_mem['cpu'] = 0 # memory occupation of instances in cpu
self.total_mem['cuda'] = 0 # memory of occupation of instances in cuda
# memory conditions for all states
for state in self.states_cls:
self.state_mem['cpu'][state] = 0
self.state_mem['cuda'][state] = 0
def register_new_instance(self):
self._cnter += 1
def delete_instance(self):
self._cnter -= 1
def print_info(self):
print(f"Total number: {self.total_number}",
f"Total CPU memory occupation: {self.total_mem['cpu']}",
f"Total CUDA memory occupation: {self.total_mem['cuda']}\n",
sep='\n')
for state in self.states_cls:
print(f"{state}: CPU memory occupation: {self.state_mem['cpu'][state]}",
f"{state}: CUDA memory occupation: {self.state_mem['cuda'][state]}\n",
sep='\n')