[Feature] Add colossalai strategy (#1299)

open-mmlab · Aug 18, 2023 · db32234 · db32234
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diff --git a/docs/en/api/optim.rst b/docs/en/api/optim.rst
@@ -25,7 +25,6 @@ Optimizer
     OptimWrapperDict
     DefaultOptimWrapperConstructor
     ZeroRedundancyOptimizer
-    DeepSpeedOptimWrapper
 
 .. autosummary::
    :toctree: generated

diff --git a/docs/en/api/strategy.rst b/docs/en/api/strategy.rst
@@ -16,3 +16,26 @@ mmengine._strategy
    DDPStrategy
    DeepSpeedStrategy
    FSDPStrategy
+   ColossalAIStrategy
+
+
+.. currentmodule:: mmengine._strategy.deepspeed
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   MMDeepSpeedEngineWrapper
+   DeepSpeedOptimWrapper
+
+
+.. currentmodule:: mmengine._strategy.colossalai
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   CollosalAIModelWrapper
+   ColossalAIOpitmWrapper
diff --git a/docs/en/common_usage/large_model_training.md b/docs/en/common_usage/large_model_training.md
@@ -25,7 +25,7 @@ pip install deepspeed
 After installing DeepSpeed, you need to configure the `strategy` and `optim_wrapper` parameters of FlexibleRunner as follows:
 
 - strategy: Set `type='DeepSpeedStrategy'` and configure other parameters. See [DeepSpeedStrategy](mmengine._strategy.DeepSpeedStrategy) for more details.
-- optim_wrapper: Set `type='DeepSpeedOptimWrapper'` and configure other parameters. See [DeepSpeedOptimWrapper](mmengine.optim.DeepSpeedOptimWrapper) for more details.
+- optim_wrapper: Set `type='DeepSpeedOptimWrapper'` and configure other parameters. See [DeepSpeedOptimWrapper](mmengine._strategy.deepspeed.DeepSpeedOptimWrapper) for more details.
 
 Here is an example configuration related to DeepSpeed:
 
@@ -182,3 +182,77 @@ torchrun --nproc-per-node 2 examples/distributed_training_with_flexible_runner.p
 ```
 
 </details>
+
+## ColossalAI
+
+[ColossalAI](https://colossalai.org/) is a comprehensive large-scale model training system that utilizes efficient parallelization techniques. Starting from MMEngine v0.8.5, it supports training models using optimization strategies from the ZeRO series in ColossalAI.
+
+Install ColossalAI with a version greater than v0.3.1. This version requirement is due to a [bug](https://github.com/hpcaitech/ColossalAI/issues/4393) in v0.3.1 that causes some program blocking, which has been fixed in later versions. If the highest available version of ColossalAI is still v0.3.1, it is recommended to install ColossalAI from the source code on the main branch.
+
+```{note}
+Note that if you encounter compilation errors like `nvcc fatal: Unsupported gpu architecture 'compute_90'` and your PyTorch version is higher than 2.0, you need to git clone the source code and follow the modifications in this [PR](https://github.com/hpcaitech/ColossalAI/pull/4357) before proceeding with the installation.
+```
+
+```bash
+pip install git+https://github.com/hpcaitech/ColossalAI
+```
+
+If the latest version of ColossalAI is higher than v0.3.1, you can directly install it using pip:
+
+```bash
+pip install colossalai
+```
+
+Once ColossalAI is installed, configure the `strategy` and `optim_wrapper` parameters for FlexibleRunner:
+
+- `strategy`: Specify `type='ColossalAIStrategy'` and configure the parameters. Detailed parameter descriptions can be found in [ColossalAIStrategy](mmengine._strategy.ColossalAI).
+- `optim_wrapper`: Default to no `type` parameter or specify `type=ColossalAIOpitmWrapper`. It is recommended to choose `HybridAdam` as the optimizer type. Other configurable types are listed in [ColossalAIOptimWrapper](mmengine._strategy.ColossalAIOptimWrapper).
+
+Here's the configuration related to ColossalAI:
+
+```python
+from mmengine.runner._flexible_runner import FlexibleRunner
+
+strategy = dict(type='ColossalAIStrategy')
+optim_wrapper = dict(optimizer=dict(type='HybridAdam', lr=1e-3))
+
+# Initialize FlexibleRunner
+runner = FlexibleRunner(
+    model=MMResNet50(),
+    work_dir='./work_dirs',
+    strategy=strategy,
+    train_dataloader=train_dataloader,
+    optim_wrapper=optim_wrapper,
+    param_scheduler=dict(type='LinearLR'),
+    train_cfg=dict(by_epoch=True, max_epochs=10, val_interval=1),
+    val_dataloader=val_dataloader,
+    val_cfg=dict(),
+    val_evaluator=dict(type=Accuracy))
+
+# Start training
+runner.train()
+```
+
+To initiate distributed training using two GPUs:
+
+```bash
+torchrun --nproc-per-node 2 examples/distributed_training_with_flexible_runner.py --use-colossalai
+```
+
+<details>
+<summary>Training Logs</summary>
+
+```
+08/18 11:56:34 - mmengine - INFO - Epoch(train)  [1][ 10/196]  lr: 3.3333e-04  eta: 0:10:31  time: 0.3238  data_time: 0.0344  memory: 597  loss: 3.8766
+08/18 11:56:35 - mmengine - INFO - Epoch(train)  [1][ 20/196]  lr: 3.3333e-04  eta: 0:06:56  time: 0.1057  data_time: 0.0338  memory: 597  loss: 2.3797
+08/18 11:56:36 - mmengine - INFO - Epoch(train)  [1][ 30/196]  lr: 3.3333e-04  eta: 0:05:45  time: 0.1068  data_time: 0.0342  memory: 597  loss: 2.3219
+08/18 11:56:37 - mmengine - INFO - Epoch(train)  [1][ 40/196]  lr: 3.3333e-04  eta: 0:05:08  time: 0.1059  data_time: 0.0337  memory: 597  loss: 2.2641
+08/18 11:56:38 - mmengine - INFO - Epoch(train)  [1][ 50/196]  lr: 3.3333e-04  eta: 0:04:45  time: 0.1062  data_time: 0.0338  memory: 597  loss: 2.2250
+08/18 11:56:40 - mmengine - INFO - Epoch(train)  [1][ 60/196]  lr: 3.3333e-04  eta: 0:04:31  time: 0.1097  data_time: 0.0339  memory: 597  loss: 2.1672
+08/18 11:56:41 - mmengine - INFO - Epoch(train)  [1][ 70/196]  lr: 3.3333e-04  eta: 0:04:21  time: 0.1096  data_time: 0.0340  memory: 597  loss: 2.1688
+08/18 11:56:42 - mmengine - INFO - Epoch(train)  [1][ 80/196]  lr: 3.3333e-04  eta: 0:04:13  time: 0.1098  data_time: 0.0338  memory: 597  loss: 2.1781
+08/18 11:56:43 - mmengine - INFO - Epoch(train)  [1][ 90/196]  lr: 3.3333e-04  eta: 0:04:06  time: 0.1097  data_time: 0.0338  memory: 597  loss: 2.0938
+08/18 11:56:44 - mmengine - INFO - Epoch(train)  [1][100/196]  lr: 3.3333e-04  eta: 0:04:01  time: 0.1097  data_time: 0.0339  memory: 597  loss: 2.1078
+08/18 11:56:45 - mmengine - INFO - Epoch(train)  [1][110/196]  lr: 3.3333e-04  eta: 0:04:01  time: 0.1395  data_time: 0.0340  memory: 597  loss: 2.0141
+08/18 11:56:46 - mmengine - INFO - Epoch(train)  [1][120/196]  lr: 3.3333
+```
diff --git a/docs/zh_cn/api/optim.rst b/docs/zh_cn/api/optim.rst
@@ -25,7 +25,6 @@ Optimizer
     OptimWrapperDict
     DefaultOptimWrapperConstructor
     ZeroRedundancyOptimizer
-    DeepSpeedOptimWrapper
 
 .. autosummary::
    :toctree: generated

diff --git a/docs/zh_cn/api/strategy.rst b/docs/zh_cn/api/strategy.rst
@@ -16,3 +16,26 @@ mmengine._strategy
    DDPStrategy
    DeepSpeedStrategy
    FSDPStrategy
+   ColossalAIStrategy
+
+
+.. currentmodule:: mmengine._strategy.deepspeed
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   MMDeepSpeedEngineWrapper
+   DeepSpeedOptimWrapper
+
+
+.. currentmodule:: mmengine._strategy.colossalai
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   CollosalAIModelWrapper
+   ColossalAIOpitmWrapper
diff --git a/docs/zh_cn/common_usage/large_model_training.md b/docs/zh_cn/common_usage/large_model_training.md
@@ -24,7 +24,7 @@ pip install deepspeed
 安装好 deepspeed 后，需配置 FlexibleRunner 的 strategy 和 optim_wrapper 参数：
 
 - strategy：指定 `type='DeepSpeedStrategy'` 并配置参数。参数的详细介绍可阅读 [DeepSpeedStrategy](mmengine._strategy.DeepSpeedStrategy)。
-- optim_wrapper：指定 `type='DeepSpeedOptimWrapper'` 并配置参数。参数的详细介绍可阅读 [DeepSpeedOptimWrapper](mmengine.optim.DeepSpeedOptimWrapper)。
+- optim_wrapper：指定 `type='DeepSpeedOptimWrapper'` 并配置参数。参数的详细介绍可阅读 [DeepSpeedOptimWrapper](mmengine._strategy.deepspeed.DeepSpeedOptimWrapper)。
 
 下面是 DeepSpeed 相关的配置：
 
@@ -181,3 +181,85 @@ torchrun --nproc-per-node 2 examples/distributed_training_with_flexible_runner.p
 ```
 
 </details>
+
+## ColossalAI
+
+[ColossalAI](https://colossalai.org/) 是一个具有高效并行化技术的综合大规模模型训练系统。MMEngine 自 v0.8.5 开始，支持使用 ColossalAI 中的 ZeRO 系列优化策略训练模型。
+
+安装版本大于 v0.3.1 的 ColossalAI。这个版本限制是由于 v0.3.1 存在一些程序阻塞的 [Bug](https://github.com/hpcaitech/ColossalAI/issues/4393)，而该 Bug 在之后的版本中已经修复。如果目前 ColossalAI 的最高版本仍为 v0.3.1，建议从源码安装主分支的 ColossalAI。
+
+```{note}
+需要注意的是，如果你的 PyTorch 版本高于 2.0，并遇到了 `nvcc fatal : Unsupported gpu architecture 'compute_90'` 类似的编译错误，则需要 git clone 源码，参考该 [PR](https://github.com/hpcaitech/ColossalAI/pull/4357) 进行修改源码，再进行安装
+```
+
+```bash
+pip install git+https://github.com/hpcaitech/ColossalAI
+```
+
+如果 ColossalAI 的最新版本大于 v0.3.1，可以直接使用 pip 安装：
+
+```bash
+pip install colossalai
+```
+
+安装好 ColossalAI 后，需配置 FlexibleRunner 的 strategy 和 optim_wrapper 参数：
+
+- strategy：指定 `type='ColossalAIStrategy'` 并配置参数。参数的详细介绍可阅读 [ColossalAIStrategy](mmengine._strategy.ColossalAI)。
+- optim_wrapper：缺省 `type` 参数，或指定 `type=ColossalAIOpitmWrapper`，优化器类型时建议选择 `HybridAdam`。其他可配置类型可阅读 [ColossalAIOptimWrapper](mmengine._strategy.ColossalAIOptimWrapper)。
+
+下面是 ColossalAI 相关的配置：
+
+```python
+from mmengine.runner._flexible_runner import FlexibleRunner
+
+strategy = dict(type='ColossalAIStrategy')
+optim_wrapper = dict(optimizer=dict(type='HybridAdam', lr=1e-3))
+
+# 初始化 FlexibleRunner
+runner = FlexibleRunner(
+    model=MMResNet50(),
+    work_dir='./work_dirs',
+    strategy=strategy,
+    train_dataloader=train_dataloader,
+    optim_wrapper=optim_wrapper,
+    param_scheduler=dict(type='LinearLR'),
+    train_cfg=dict(by_epoch=True, max_epochs=10, val_interval=1),
+    val_dataloader=val_dataloader,
+    val_cfg=dict(),
+    val_evaluator=dict(type=Accuracy))
+
+# 开始训练
+runner.train()
+```
+
+使用两张卡启动分布式训练：
+
+```bash
+torchrun --nproc-per-node 2 examples/distributed_training_with_flexible_runner.py --use-colossalai
+```
+
+<details>
+<summary>训练日志</summary>
+
+```
+08/18 11:56:34 - mmengine - INFO - Epoch(train)  [1][ 10/196]  lr: 3.3333e-04  eta: 0:10:31  time: 0.3238  data_time: 0.0344  memory: 597  loss: 3.8766
+08/18 11:56:35 - mmengine - INFO - Epoch(train)  [1][ 20/196]  lr: 3.3333e-04  eta: 0:06:56  time: 0.1057  data_time: 0.0338  memory: 597  loss: 2.3797
+08/18 11:56:36 - mmengine - INFO - Epoch(train)  [1][ 30/196]  lr: 3.3333e-04  eta: 0:05:45  time: 0.1068  data_time: 0.0342  memory: 597  loss: 2.3219
+08/18 11:56:37 - mmengine - INFO - Epoch(train)  [1][ 40/196]  lr: 3.3333e-04  eta: 0:05:08  time: 0.1059  data_time: 0.0337  memory: 597  loss: 2.2641
+08/18 11:56:38 - mmengine - INFO - Epoch(train)  [1][ 50/196]  lr: 3.3333e-04  eta: 0:04:45  time: 0.1062  data_time: 0.0338  memory: 597  loss: 2.2250
+08/18 11:56:40 - mmengine - INFO - Epoch(train)  [1][ 60/196]  lr: 3.3333e-04  eta: 0:04:31  time: 0.1097  data_time: 0.0339  memory: 597  loss: 2.1672
+08/18 11:56:41 - mmengine - INFO - Epoch(train)  [1][ 70/196]  lr: 3.3333e-04  eta: 0:04:21  time: 0.1096  data_time: 0.0340  memory: 597  loss: 2.1688
+08/18 11:56:42 - mmengine - INFO - Epoch(train)  [1][ 80/196]  lr: 3.3333e-04  eta: 0:04:13  time: 0.1098  data_time: 0.0338  memory: 597  loss: 2.1781
+08/18 11:56:43 - mmengine - INFO - Epoch(train)  [1][ 90/196]  lr: 3.3333e-04  eta: 0:04:06  time: 0.1097  data_time: 0.0338  memory: 597  loss: 2.0938
+08/18 11:56:44 - mmengine - INFO - Epoch(train)  [1][100/196]  lr: 3.3333e-04  eta: 0:04:01  time: 0.1097  data_time: 0.0339  memory: 597  loss: 2.1078
+08/18 11:56:45 - mmengine - INFO - Epoch(train)  [1][110/196]  lr: 3.3333e-04  eta: 0:04:01  time: 0.1395  data_time: 0.0340  memory: 597  loss: 2.0141
+08/18 11:56:46 - mmengine - INFO - Epoch(train)  [1][120/196]  lr: 3.3333e-04  eta: 0:03:56  time: 0.1090  data_time: 0.0338  memory: 597  loss: 2.0273
+08/18 11:56:48 - mmengine - INFO - Epoch(train)  [1][130/196]  lr: 3.3333e-04  eta: 0:03:52  time: 0.1096  data_time: 0.0339  memory: 597  loss: 2.0086
+08/18 11:56:49 - mmengine - INFO - Epoch(train)  [1][140/196]  lr: 3.3333e-04  eta: 0:03:49  time: 0.1096  data_time: 0.0339  memory: 597  loss: 1.9180
+08/18 11:56:50 - mmengine - INFO - Epoch(train)  [1][150/196]  lr: 3.3333e-04  eta: 0:03:46  time: 0.1092  data_time: 0.0339  memory: 597  loss: 1.9578
+08/18 11:56:51 - mmengine - INFO - Epoch(train)  [1][160/196]  lr: 3.3333e-04  eta: 0:03:43  time: 0.1097  data_time: 0.0339  memory: 597  loss: 1.9375
+08/18 11:56:52 - mmengine - INFO - Epoch(train)  [1][170/196]  lr: 3.3333e-04  eta: 0:03:40  time: 0.1092  data_time: 0.0339  memory: 597  loss: 1.9312
+08/18 11:56:53 - mmengine - INFO - Epoch(train)  [1][180/196]  lr: 3.3333e-04  eta: 0:03:37  time: 0.1070  data_time: 0.0339  memory: 597  loss: 1.9078
+```
+
+</details>
diff --git a/examples/distributed_training_with_flexible_runner.py b/examples/distributed_training_with_flexible_runner.py
@@ -1,6 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import argparse
 
+import torch
 import torch.nn.functional as F
 import torchvision
 import torchvision.transforms as transforms
@@ -44,6 +45,7 @@ def parse_args():
     parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
     parser.add_argument('--use-fsdp', action='store_true')
     parser.add_argument('--use-deepspeed', action='store_true')
+    parser.add_argument('--use-colossalai', action='store_true')
     args = parser.parse_args()
     return args
 
@@ -116,6 +118,25 @@ def main():
             model_wrapper=dict(auto_wrap_policy=size_based_auto_wrap_policy))
         optim_wrapper = dict(
             type='AmpOptimWrapper', optimizer=dict(type='AdamW', lr=1e-3))
+    elif args.use_colossalai:
+        from colossalai.tensor.op_wrapper import colo_op_impl
+
+        # ColossalAI overwrite some torch ops with their custom op to
+        # make it compatible with `ColoTensor`. However, a backward error
+        # is more likely to happen if there are inplace operation in the
+        # model.
+        # For example, layers like `conv` + `bn` + `relu` is OK when `relu` is
+        # inplace since PyTorch builtin ops `batch_norm` could handle it.
+        # However, if `relu` is an `inplaced` op while `batch_norm` is an
+        # custom op, an error will be raised since PyTorch thinks the custom op
+        # could not handle the backward graph modification caused by inplace
+        # op.
+        # In this example, the inplace op `add_` in resnet could raise an error
+        # since PyTorch consider the custom op before it could not handle the
+        # backward graph modification
+        colo_op_impl(torch.Tensor.add_)(torch.add)
+        strategy = dict(type='ColossalAIStrategy')
+        optim_wrapper = dict(optimizer=dict(type='HybridAdam', lr=1e-3))
     else:
         strategy = None
         optim_wrapper = dict(

diff --git a/mmengine/_strategy/__init__.py b/mmengine/_strategy/__init__.py
@@ -2,12 +2,14 @@
 from mmengine.utils import digit_version
 from mmengine.utils.dl_utils import TORCH_VERSION
 from .base import BaseStrategy
+from .colossalai import ColossalAIStrategy
 from .deepspeed import DeepSpeedStrategy
 from .distributed import DDPStrategy
 from .single_device import SingleDeviceStrategy
 
 __all__ = [
-    'BaseStrategy', 'DDPStrategy', 'SingleDeviceStrategy', 'DeepSpeedStrategy'
+    'BaseStrategy', 'DDPStrategy', 'SingleDeviceStrategy', 'DeepSpeedStrategy',
+    'ColossalAIStrategy'
 ]
 
 if digit_version(TORCH_VERSION) >= digit_version('2.0.0'):