| | 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 |
| |
|
