| | 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
|
| |
|
