Spaces:
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on
Zero
Running
on
Zero
File size: 8,250 Bytes
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from abc import abstractmethod
from contextlib import contextmanager
from typing import Tuple
import torch
import torch.nn as nn
import numpy as np
class MemoryController:
"""
Base class for memory management during training.
"""
_last_input_size = None
_last_mem_ratio = []
@contextmanager
def record(self):
pass
def update_run_states(self, input_size=None, mem_ratio=None):
if self._last_input_size is None:
self._last_input_size = input_size
elif self._last_input_size!= input_size:
raise ValueError(f'Input size should not change for different ElasticModules.')
self._last_mem_ratio.append(mem_ratio)
@abstractmethod
def get_mem_ratio(self, input_size):
pass
@abstractmethod
def state_dict(self):
pass
@abstractmethod
def log(self):
pass
class LinearMemoryController(MemoryController):
"""
A simple controller for memory management during training.
The memory usage is modeled as a linear function of:
- the number of input parameters
- the ratio of memory the model use compared to the maximum usage (with no checkpointing)
memory_usage = k * input_size * mem_ratio + b
The controller keeps track of the memory usage and gives the
expected memory ratio to keep the memory usage under a target
"""
def __init__(
self,
buffer_size=1000,
update_every=500,
target_ratio=0.8,
available_memory=None,
max_mem_ratio_start=0.1,
params=None,
device=None
):
self.buffer_size = buffer_size
self.update_every = update_every
self.target_ratio = target_ratio
self.device = device or torch.cuda.current_device()
self.available_memory = available_memory or torch.cuda.get_device_properties(self.device).total_memory / 1024**3
self._memory = np.zeros(buffer_size, dtype=np.float32)
self._input_size = np.zeros(buffer_size, dtype=np.float32)
self._mem_ratio = np.zeros(buffer_size, dtype=np.float32)
self._buffer_ptr = 0
self._buffer_length = 0
self._params = tuple(params) if params is not None else (0.0, 0.0)
self._max_mem_ratio = max_mem_ratio_start
self.step = 0
def __repr__(self):
return f'LinearMemoryController(target_ratio={self.target_ratio}, available_memory={self.available_memory})'
def _add_sample(self, memory, input_size, mem_ratio):
self._memory[self._buffer_ptr] = memory
self._input_size[self._buffer_ptr] = input_size
self._mem_ratio[self._buffer_ptr] = mem_ratio
self._buffer_ptr = (self._buffer_ptr + 1) % self.buffer_size
self._buffer_length = min(self._buffer_length + 1, self.buffer_size)
@contextmanager
def record(self):
torch.cuda.reset_peak_memory_stats(self.device)
self._last_input_size = None
self._last_mem_ratio = []
yield
self._last_memory = torch.cuda.max_memory_allocated(self.device) / 1024**3
self._last_mem_ratio = sum(self._last_mem_ratio) / len(self._last_mem_ratio)
self._add_sample(self._last_memory, self._last_input_size, self._last_mem_ratio)
self.step += 1
if self.step % self.update_every == 0:
self._max_mem_ratio = min(1.0, self._max_mem_ratio + 0.1)
self._fit_params()
def _fit_params(self):
memory_usage = self._memory[:self._buffer_length]
input_size = self._input_size[:self._buffer_length]
mem_ratio = self._mem_ratio[:self._buffer_length]
x = input_size * mem_ratio
y = memory_usage
k, b = np.polyfit(x, y, 1)
self._params = (k, b)
# self._visualize()
def _visualize(self):
import matplotlib.pyplot as plt
memory_usage = self._memory[:self._buffer_length]
input_size = self._input_size[:self._buffer_length]
mem_ratio = self._mem_ratio[:self._buffer_length]
k, b = self._params
plt.scatter(input_size * mem_ratio, memory_usage, c=mem_ratio, cmap='viridis')
x = np.array([0.0, 20000.0])
plt.plot(x, k * x + b, c='r')
plt.savefig(f'linear_memory_controller_{self.step}.png')
plt.cla()
def get_mem_ratio(self, input_size):
k, b = self._params
if k == 0: return np.random.rand() * self._max_mem_ratio
pred = (self.available_memory * self.target_ratio - b) / (k * input_size)
return min(self._max_mem_ratio, max(0.0, pred))
def state_dict(self):
return {
'params': self._params,
}
def load_state_dict(self, state_dict):
self._params = tuple(state_dict['params'])
def log(self):
return {
'params/k': self._params[0],
'params/b': self._params[1],
'memory': self._last_memory,
'input_size': self._last_input_size,
'mem_ratio': self._last_mem_ratio,
}
class ElasticModule(nn.Module):
"""
Module for training with elastic memory management.
"""
def __init__(self):
super().__init__()
self._memory_controller: MemoryController = None
@abstractmethod
def _get_input_size(self, *args, **kwargs) -> int:
"""
Get the size of the input data.
Returns:
int: The size of the input data.
"""
pass
@abstractmethod
def _forward_with_mem_ratio(self, *args, mem_ratio=0.0, **kwargs) -> Tuple[float, Tuple]:
"""
Forward with a given memory ratio.
"""
pass
def register_memory_controller(self, memory_controller: MemoryController):
self._memory_controller = memory_controller
def forward(self, *args, **kwargs):
if self._memory_controller is None or not torch.is_grad_enabled() or not self.training:
_, ret = self._forward_with_mem_ratio(*args, **kwargs)
else:
input_size = self._get_input_size(*args, **kwargs)
mem_ratio = self._memory_controller.get_mem_ratio(input_size)
mem_ratio, ret = self._forward_with_mem_ratio(*args, mem_ratio=mem_ratio, **kwargs)
self._memory_controller.update_run_states(input_size, mem_ratio)
return ret
class ElasticModuleMixin:
"""
Mixin for training with elastic memory management.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._memory_controller: MemoryController = None
@abstractmethod
def _get_input_size(self, *args, **kwargs) -> int:
"""
Get the size of the input data.
Returns:
int: The size of the input data.
"""
pass
@abstractmethod
@contextmanager
def with_mem_ratio(self, mem_ratio=1.0) -> float:
"""
Context manager for training with a reduced memory ratio compared to the full memory usage.
Returns:
float: The exact memory ratio used during the forward pass.
"""
pass
def register_memory_controller(self, memory_controller: MemoryController):
self._memory_controller = memory_controller
def forward(self, *args, **kwargs):
if self._memory_controller is None or not torch.is_grad_enabled() or not self.training:
ret = super().forward(*args, **kwargs)
else:
input_size = self._get_input_size(*args, **kwargs)
mem_ratio = self._memory_controller.get_mem_ratio(input_size)
with self.with_mem_ratio(mem_ratio) as exact_mem_ratio:
ret = super().forward(*args, **kwargs)
self._memory_controller.update_run_states(input_size, exact_mem_ratio)
return ret
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