# NVMe offload Author: Hongxin Liu **Prerequisite:** - [Zero Redundancy Optimizer with chunk-based memory management](../features/zero_with_chunk.md) **Related Paper** - [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840) - [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857) ## Introduction If a model has `N` parameters, when using Adam, it has `8N` optimizer states. For billion-scale models, optimizer states take at least 32 GB memory. GPU memory limits the model scale we can train, which is called GPU memory wall. If we offload optimizer states to the disk, we can break through GPU memory wall. We implement a user-friendly and efficient asynchronous Tensor I/O library: [TensorNVMe](https://github.com/hpcaitech/TensorNVMe). With this library, we can simply implement NVMe offload. > This library is compatible with all kinds of disk (HDD, SATA SSD, and NVMe SSD). As I/O bandwidth of HDD or SATA SSD is low, it's recommended to use this lib only on NVMe disk. When optimizing a parameter, we can divide the optimization process into three stages: read, compute and offload. We perform the optimization process in a pipelined fashion, which can overlap computation and I/O.
Optimization process
## Usage First, please make sure you installed [TensorNVMe](https://github.com/hpcaitech/TensorNVMe): ```shell pip install packaging pip install tensornvme ``` We implement NVMe offload of optimizer states for Adam ([CPUAdam](https://colossalai.readthedocs.io/en/latest/colossalai/colossalai.nn.optimizer.cpu_adam.html) and [HybridAdam](https://colossalai.readthedocs.io/en/latest/colossalai/colossalai.nn.optimizer.hybrid_adam.html)). ```python from colossalai.nn.optimizer import CPUAdam, HybridAdam optimizer = HybridAdam(model.parameters(), lr=1e-3, nvme_offload_fraction=1.0, nvme_offload_dir='./') ``` `nvme_offload_fraction` is the fraction of optimizer states to be offloaded to NVMe. `nvme_offload_dir` is the directory to save NVMe offload files. If `nvme_offload_dir` is `None`, a random temporary directory will be used. It's compatible with all parallel methods in ColossalAI. > ⚠ It only offloads optimizer states on CPU. This means it only affects CPU training or Zero/Gemini with offloading. ## Examples Let's start from two simple examples -- training GPT with different methods. These examples relies on `transformers`. We should install dependencies first: ```shell pip install psutil transformers ``` First, we import essential packages and modules: ```python import os import time from typing import Dict, Optional import psutil import torch import torch.nn as nn from transformers.models.gpt2.configuration_gpt2 import GPT2Config from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel import colossalai from colossalai.nn.optimizer import HybridAdam from colossalai.utils.model.colo_init_context import ColoInitContext from colossalai.booster import Booster from colossalai.booster.plugin import GeminiPlugin ``` Then we define a loss function: ```python class GPTLMLoss(nn.Module): def __init__(self): super().__init__() self.loss_fn = nn.CrossEntropyLoss() def forward(self, logits, labels): shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) ``` And we define some utility functions, which generates random data, computes the number of parameters of a model and get memory usage of current process: ```python def get_data(batch_size: int, seq_len: int, vocab_size: int, device: Optional[str] = None) -> Dict[str, torch.Tensor]: device = torch.cuda.current_device() if device is None else device input_ids = torch.randint(vocab_size, (batch_size, seq_len), device=device) attn_mask = torch.ones_like(input_ids) return dict(input_ids=input_ids, attention_mask=attn_mask) def get_model_numel(model: nn.Module) -> int: return sum(p.numel() for p in model.parameters()) def get_mem_usage() -> int: proc = psutil.Process(os.getpid()) return proc.memory_info().rss ``` We first try to train GPT model on CPU: ```python def train_cpu(nvme_offload_fraction: float = 0.0): config = GPT2Config() model = GPT2LMHeadModel(config) criterion = GPTLMLoss() optimizer = HybridAdam(model.parameters(), nvme_offload_fraction=nvme_offload_fraction) print(f'Model numel: {get_model_numel(model) / 1024**3:.3f} B') start = time.time() for step in range(3): data = get_data(4, 128, config.vocab_size, device='cpu') outputs = model(**data) loss = criterion(outputs.logits, data['input_ids']) loss.backward() optimizer.step() optimizer.zero_grad() print(f'[{step}] loss: {loss.item():.3f}') print(f'Time: {time.time() - start:.3f} s') print(f'Mem usage: {get_mem_usage() / 1024**2:.3f} MB') ``` Run without NVME offload: ```python train_cpu(0.0) ``` We may get below output: ``` Model numel: 0.116 B [0] loss: 10.953 [1] loss: 10.974 [2] loss: 10.965 Time: 7.739 s Mem usage: 5966.445 MB ``` And then run with (full) NVME offload: ```python train_cpu(1.0) ``` We may get: ``` Model numel: 0.116 B [0] loss: 10.951 [1] loss: 10.994 [2] loss: 10.984 Time: 8.527 s Mem usage: 4968.016 MB ``` For GPT2-S, which has 0.116 billion parameters, its optimizer states take about 0.928 GB memory. And NVME offload saves about 998 MB memory, which meets our expectations. Then we can train GPT model with Gemini. The placement policy of Gemini should be `"auto"`, `"cpu"` or `"const"`. ```python def train_gemini_cpu(nvme_offload_fraction: float = 0.0): colossalai.launch_from_torch({}) config = GPT2Config() with ColoInitContext(device=torch.cuda.current_device()): model = GPT2LMHeadModel(config) criterion = GPTLMLoss() optimizer = HybridAdam(model.parameters(), nvme_offload_fraction=nvme_offload_fraction) print(f'Model numel: {get_model_numel(model) / 1024**3:.3f} B') plugin = GeminiPlugin( strict_ddp_mode=True, device=torch.cuda.current_device(), placement_policy='cpu', pin_memory=True, hidden_dim=config.n_embd, initial_scale=2**5 ) booster = Booster(plugin) model, optimizer, criterion, _* = booster.boost(model, optimizer, criterion) start = time.time() for step in range(3): data = get_data(4, 128, config.vocab_size) outputs = model(**data) loss = criterion(outputs.logits, data['input_ids']) booster.backward(loss, optimizer) optimizer.step() optimizer.zero_grad() print(f'[{step}] loss: {loss.item():.3f}') print(f'Time: {time.time() - start:.3f} s') print(f'Mem usage: {get_mem_usage() / 1024**2:.3f} MB') ``` Run without NVME offload: ```python train_gemini_cpu(0.0) ``` We may get: ``` Model numel: 0.116 B searching chunk configuration is completed in 0.27 s. used number: 118.68 MB, wasted number: 0.75 MB total wasted percentage is 0.63% [0] loss: 10.953 [1] loss: 10.938 [2] loss: 10.969 Time: 2.997 s Mem usage: 5592.227 MB ``` And run with (full) NVME offload: ```python train_gemini_cpu(1.0) ``` We may get: ``` Model numel: 0.116 B searching chunk configuration is completed in 0.27 s. used number: 118.68 MB, wasted number: 0.75 MB total wasted percentage is 0.63% [0] loss: 10.953 [1] loss: 10.938 [2] loss: 10.969 Time: 3.691 s Mem usage: 5298.344 MB ``` NVME offload saves about 294 MB memory. Note that enabling `pin_memory` of Gemini can accelerate training but increase memory usage. So this result also meets our expectation. If we disable `pin_memory`, we can also observe a memory usage drop about 900 MB. ## API Reference {{ autodoc:colossalai.nn.optimizer.HybridAdam }} {{ autodoc:colossalai.nn.optimizer.CPUAdam }}