2023-03-07 09:49:01 +00:00
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<!-- doc-test-command: torchrun --standalone --nproc_per_node=1 nvme_offload.py -->
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2023-02-09 06:21:38 +00:00
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# NVMe offload
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作者: Hongxin Liu
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**前置教程:**
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- [基于Chunk内存管理的零冗余优化器 (ZeRO)](../features/zero_with_chunk.md)
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2023-03-04 09:32:22 +00:00
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**相关论文**
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- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840)
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- [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857)
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2023-02-09 06:21:38 +00:00
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## 引言
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如果模型具有`N`个参数,在使用 Adam 时,优化器状态具有`8N`个参数。对于十亿规模的模型,优化器状态至少需要 32 GB 内存。 GPU显存限制了我们可以训练的模型规模,这称为GPU显存墙。如果我们将优化器状态 offload 到磁盘,我们可以突破 GPU 内存墙。
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我们实现了一个用户友好且高效的异步 Tensor I/O 库:[TensorNVMe](https://github.com/hpcaitech/TensorNVMe)。有了这个库,我们可以简单地实现 NVMe offload。
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> 该库与各种磁盘(HDD、SATA SSD 和 NVMe SSD)兼容。由于 HDD 或 SATA SSD 的 I/O 带宽较低,建议仅在 NVMe 磁盘上使用此库。
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在优化参数时,我们可以将优化过程分为三个阶段:读取、计算和 offload。我们以流水线的方式执行优化过程,这可以重叠计算和 I/O。
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<figure style={{textAlign: "center"}}>
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<img src="https://s2.loli.net/2022/08/16/CvRnowrsNyB4hza.jpg"/>
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<figcaption>优化过程</figcaption>
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</figure>
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## 使用
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首先,请确保您安装了 [TensorNVMe](https://github.com/hpcaitech/TensorNVMe):
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```shell
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pip install packaging
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pip install tensornvme
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```
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我们为 Adam ([CPUAdam](https://colossalai.readthedocs.io/en/latest/colossalai/colossalai.nn.optimizer.cpu_adam.html) 和 [HybridAdam](https://colossalai.readthedocs.io/en/latest/colossalai/colossalai.nn.optimizer.hybrid_adam.html)) 实现了优化器状态的 NVMe offload。
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2023-03-07 09:49:01 +00:00
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<!--- doc-test-ignore-start -->
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2023-02-09 06:21:38 +00:00
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```python
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from colossalai.nn.optimizer import CPUAdam, HybridAdam
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optimizer = HybridAdam(model.parameters(), lr=1e-3, nvme_offload_fraction=1.0, nvme_offload_dir='./')
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```
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2023-03-07 09:49:01 +00:00
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<!--- doc-test-ignore-end -->
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2023-02-09 06:21:38 +00:00
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`nvme_offload_fraction` 是要 offload 到 NVMe 的优化器状态的比例。 `nvme_offload_dir` 是保存 NVMe offload 文件的目录。如果 `nvme_offload_dir` 为 `None`,将使用随机临时目录。
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它与 ColossalAI 中的所有并行方法兼容。
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2023-03-07 09:49:01 +00:00
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> ⚠ 它只会卸载在 CPU 上的优化器状态。这意味着它只会影响 CPU 训练或者使用卸载的 Zero/Gemini。
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## Exampls
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Let's start from two simple examples -- training GPT with different methods. These examples relies on `transformers`.
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首先让我们从两个简单的例子开始 -- 用不同的方法训练 GPT。这些例子依赖`transformers`。
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我们首先应该安装依赖:
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```shell
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pip install psutil transformers
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```
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首先,我们导入必要的包和模块:
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```python
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import os
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import time
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from typing import Dict, Optional
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import psutil
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import torch
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import torch.nn as nn
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from transformers.models.gpt2.configuration_gpt2 import GPT2Config
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from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
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import colossalai
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper
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from colossalai.utils.model.colo_init_context import ColoInitContext
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```
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然后我们定义一个损失函数:
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```python
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class GPTLMLoss(nn.Module):
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def __init__(self):
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super().__init__()
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self.loss_fn = nn.CrossEntropyLoss()
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def forward(self, logits, labels):
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)),
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shift_labels.view(-1))
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```
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我们定义一些工具函数,用来生成随机数据、计算模型参数量和获取当前进程内存占用:
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```python
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def get_data(batch_size: int, seq_len: int,
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vocab_size: int, device: Optional[str] = None) -> Dict[str, torch.Tensor]:
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device = torch.cuda.current_device() if device is None else device
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input_ids = torch.randint(vocab_size, (batch_size, seq_len),
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device=device)
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attn_mask = torch.ones_like(input_ids)
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return dict(input_ids=input_ids, attention_mask=attn_mask)
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def get_model_numel(model: nn.Module) -> int:
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return sum(p.numel() for p in model.parameters())
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def get_mem_usage() -> int:
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proc = psutil.Process(os.getpid())
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return proc.memory_info().rss
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```
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我们首先尝试在 CPU 上训练 GPT 模型:
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```python
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def train_cpu(nvme_offload_fraction: float = 0.0):
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config = GPT2Config()
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model = GPT2LMHeadModel(config)
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criterion = GPTLMLoss()
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optimizer = HybridAdam(model.parameters(), nvme_offload_fraction=nvme_offload_fraction)
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print(f'Model numel: {get_model_numel(model) / 1024**3:.3f} B')
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start = time.time()
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for step in range(3):
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data = get_data(4, 128, config.vocab_size, device='cpu')
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outputs = model(**data)
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loss = criterion(outputs.logits, data['input_ids'])
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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print(f'[{step}] loss: {loss.item():.3f}')
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print(f'Time: {time.time() - start:.3f} s')
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print(f'Mem usage: {get_mem_usage() / 1024**2:.3f} MB')
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```
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不使用 NVME 卸载:
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```python
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train_cpu(0.0)
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```
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我们可能得到如下输出:
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```
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Model numel: 0.116 B
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[0] loss: 10.953
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[1] loss: 10.974
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[2] loss: 10.965
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Time: 7.739 s
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Mem usage: 5966.445 MB
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```
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然后使用(全量) NVME 卸载:
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```python
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train_cpu(1.0)
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```
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我们可能得到:
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```
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Model numel: 0.116 B
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[0] loss: 10.951
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[1] loss: 10.994
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[2] loss: 10.984
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Time: 8.527 s
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Mem usage: 4968.016 MB
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```
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对于有1.16亿参数的 GPT2-S 来说,它的优化器状态大约需要占用 0.928 GB 内存。NVME 卸载节省了大约 998 MB 内存,符合我们的预期。
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然后我们可以用 Gemini 来训练 GPT 模型。放置策略应该设置为`"auto"`、 `"cpu"` 或 `"const"`。
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```python
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def train_gemini_cpu(nvme_offload_fraction: float = 0.0):
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colossalai.launch_from_torch({})
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config = GPT2Config()
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with ColoInitContext(device=torch.cuda.current_device()):
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model = GPT2LMHeadModel(config)
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criterion = GPTLMLoss()
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optimizer = HybridAdam(model.parameters(), nvme_offload_fraction=nvme_offload_fraction)
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print(f'Model numel: {get_model_numel(model) / 1024**3:.3f} B')
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gemini_config = dict(strict_ddp_mode=True, device=torch.cuda.current_device(),
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placement_policy='cpu', pin_memory=True, hidden_dim=config.n_embd)
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model = zero_model_wrapper(model, zero_stage=3, gemini_config=gemini_config)
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optimizer = zero_optim_wrapper(model, optimizer, initial_scale=2**5)
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start = time.time()
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for step in range(3):
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data = get_data(4, 128, config.vocab_size)
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outputs = model(**data)
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loss = criterion(outputs.logits, data['input_ids'])
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optimizer.backward(loss)
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optimizer.step()
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optimizer.zero_grad()
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print(f'[{step}] loss: {loss.item():.3f}')
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print(f'Time: {time.time() - start:.3f} s')
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print(f'Mem usage: {get_mem_usage() / 1024**2:.3f} MB')
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```
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不使用 NVME 卸载:
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```python
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train_gemini_cpu(0.0)
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```
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我们可能得到:
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```
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Model numel: 0.116 B
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searching chunk configuration is completed in 0.27 s.
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used number: 118.68 MB, wasted number: 0.75 MB
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total wasted percentage is 0.63%
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[0] loss: 10.953
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[1] loss: 10.938
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[2] loss: 10.969
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Time: 2.997 s
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Mem usage: 5592.227 MB
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```
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然后使用(全量) NVME 卸载:
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```python
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train_gemini_cpu(1.0)
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```
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我们可能得到:
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```
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Model numel: 0.116 B
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searching chunk configuration is completed in 0.27 s.
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used number: 118.68 MB, wasted number: 0.75 MB
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total wasted percentage is 0.63%
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[0] loss: 10.953
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[1] loss: 10.938
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[2] loss: 10.969
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Time: 3.691 s
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Mem usage: 5298.344 MB
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```
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NVME 卸载节省了大约 294 MB 内存。注意使用 Gemini 的 `pin_memory` 功能可以加速训练,但是会增加内存占用。所以这个结果也是符合我们预期的。如果我们关闭 `pin_memory`,我们仍然可以观察到大约 900 MB 的内存占用下降。
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## API 参考
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{{ autodoc:colossalai.nn.optimizer.HybridAdam }}
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{{ autodoc:colossalai.nn.optimizer.CPUAdam }}
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