| 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)
|
|
|
|
|
| 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
|
|
|
