[doc] FP8 training and communication document (#6050)

* Add FP8 training and communication document

* add fp8 docstring for plugins

* fix typo

* fix typo

colossalai/booster/plugin/gemini_plugin.py

@@ -323,7 +323,9 @@ class GeminiPlugin(DPPluginBase):
  enable_jit_fused (bool, optional): Whether to switch on JIT in Shardformer. Default to False.
  enable_sequence_parallelism (bool): Whether to turn on sequence parallelism in Shardformer. Defaults to False.
  enable_sequence_overlap (bool): Whether to turn on sequence overlap in Shardformer. Defaults to False.
+ use_fp8 (bool, optional): Whether to enable fp8 mixed precision training. Defaults to False.
  verbose (bool, optional): verbose mode. Debug info including chunk search result will be printed. Defaults to False.
+ fp8_communication (bool, optional): Whether to enable fp8 communication. Defaults to False.
  """
 
  def __init__(

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -981,7 +981,8 @@ class HybridParallelPlugin(PipelinePluginBase):
  gradient_checkpoint_config (GradientCheckpointConfig, optional): Configuration for gradient checkpointing. Defaults to None.
  enable_metadata_cache (bool, optional): Whether to enable metadata cache for pipeline parallelism. Defaults to True.
  make_vocab_size_divisible_by (int, optional): it's used when padding the vocabulary size, to make it choose an faster kenel. Default to 64.
- fp8_communication (bool, optional): Whether to enable fp8 communication in model parallelism
+ fp8_communication (bool, optional): Whether to enable fp8 communication. Defaults to False.
+ use_fp8 (bool, optional): Whether to enable fp8 mixed precision training. Defaults to False.
  overlap_p2p (bool, optional): Whether to overlap the p2p communication in pipeline parallelism
  inner_ring_size (int, optional): The inner ring size of 2D Ring Attention when sp mode is "ring_attn".
  It's advisable to not tune this (especially in single-node settings) and let it be heuristically set based on topology by default.

colossalai/booster/plugin/low_level_zero_plugin.py

@@ -327,6 +327,8 @@ class LowLevelZeroPlugin(DPPluginBase):
  overlap_communication (bool, optional): whether to overlap communication and computation. Defaults to True.
  cpu_offload (bool, optional): whether to offload grad, master weight and optimizer state to cpu. Defaults to False.
  verbose (bool, optional): verbose mode. Debug info including grad overflow will be printed. Defaults to False.
+ use_fp8 (bool, optional): Whether to enable fp8 mixed precision training. Defaults to False.
+ fp8_communication (bool, optional): Whether to enable fp8 communication. Defaults to False.
  """
 
  def __init__(

colossalai/booster/plugin/moe_hybrid_parallel_plugin.py

@@ -170,7 +170,9 @@ class MoeHybridParallelPlugin(HybridParallelPlugin):
  gradient_checkpoint_config (GradientCheckpointConfig, optional): Configuration for gradient checkpointing. Defaults to None.
  enable_metadata_cache (bool, optional): Whether to enable metadata cache for pipeline parallelism. Defaults to True.
  make_vocab_size_divisible_by (int, optional): it's used when padding the vocabulary size, to make it choose an faster kenel. Default to 64.
- overlap_p2p (bool, optional): Whether to overlap the p2p communication in pipeline parallelism
+ overlap_p2p (bool, optional): Whether to overlap the p2p communication in pipeline parallelism.
+ use_fp8 (bool, optional): Whether to enable fp8 mixed precision training. Defaults to False.
+ fp8_communication (bool, optional): Whether to enable fp8 communication. Defaults to False.
  """
 
  def __init__(

colossalai/booster/plugin/torch_ddp_plugin.py

@@ -169,6 +169,7 @@ class TorchDDPPlugin(DPPluginBase):
  check_reduction (bool, optional): Whether to check reduction. Defaults to False.
  gradient_as_bucket_view (bool, optional): Whether to use gradient as bucket view. Defaults to False.
  static_graph (bool, optional): Whether to use static graph. Defaults to False.
+ fp8_communication (bool, optional): Whether to enable fp8 communication. Defaults to False.
  """
 
  def __init__(

docs/source/en/features/mixed_precision_training_with_booster.md

@@ -9,6 +9,7 @@ Author: [Mingyan Jiang](https://github.com/jiangmingyan)
 **Related Paper**
 
 - [Accelerating Scientific Computations with Mixed Precision Algorithms](https://arxiv.org/abs/0808.2794)
+- [FP8 Formats for Deep Learning](https://arxiv.org/pdf/2209.05433)
 
 ## Introduction
 
@@ -60,7 +61,11 @@ However, there are other operations, like reductions, which require the dynamic
 
 ## AMP in Colossal-AI
 
-We supported three AMP training methods and allowed the user to train with AMP with no code. If you want to train with amp, just assign `mixed_precision` with `fp16` when you instantiate the `Booster`. Next we will support `bf16`, `fp8`.
+We supported three AMP training methods and allowed the user to train with AMP with no code. If you want to train with amp, just assign `mixed_precision` with `fp16` when you instantiate the `Booster`. Next we will support `bf16`.
+
+Currently we only support `fp8` mixed precision training for the `Linear` layer. Please specify the `use_fp8` parameter when create the plugin object.
+
+To reduce the communication volume inter nodes in low-bandwidth scenarios, we support FP8 communication compression. Please specify the `fp8_communication` parameter when create the plugin object.
 
 ### Start with Booster
 
@@ -74,7 +79,6 @@ instantiate `Booster` with `mixed_precision="fp16"`, then you can train with tor
  'fp16': torch amp
  'fp16_apex': apex amp,
  'bf16': bf16,
- 'fp8': fp8,
  'fp16_naive': naive amp
 """
 from colossalai import Booster
@@ -128,6 +132,10 @@ The output model is converted to AMP model of smaller memory consumption.
 If your input model is already too large to fit in a GPU, please instantiate your model weights in `dtype=torch.float16`.
 Otherwise, try smaller models or checkout more parallelization training techniques!
 
+### FP8 Communication
+
+In low-bandwidth scenarios, to reduce the communication load multiple nodes, we support FP8 communication compression, which can be enabled by using `fp8_communication=True` when you when create the plugin object (such as `GeminiPlugin`). The all-to-all, all-gather and P2P operations inter nodes will use FP8 format for data transmission. Currently the FP8 communication of reduction operators such as all-reduce and reduce-scatter is currently not supported due to lack of support of the NCCL library.
+
 ## Hands-on Practice
 
 Now we will introduce the use of AMP with Colossal-AI. In this practice, we will use Torch AMP as an example.

docs/source/zh-Hans/features/mixed_precision_training_with_booster.md

@@ -9,6 +9,7 @@
 **相关论文**
 
 - [Accelerating Scientific Computations with Mixed Precision Algorithms](https://arxiv.org/abs/0808.2794)
+- [FP8 Formats for Deep Learning](https://arxiv.org/pdf/2209.05433)
 
 ## 引言
 
@@ -56,9 +57,13 @@ AMP 代表自动混合精度训练。
 
 ## Colossal-AI 中的 AMP
 
-我们支持三种 AMP 训练方法，并允许用户在没有改变代码的情况下使用 AMP 进行训练。booster 支持 amp 特性注入，如果您要使用混合精度训练，则在创建 booster 实例时指定`mixed_precision`参数;后续将会拓展`bf16`,`pf8`的混合精度训练.
+我们支持三种 AMP 训练方法，并允许用户在没有改变代码的情况下使用 AMP 进行训练。booster 支持 amp 特性注入，如果您要使用混合精度训练，则在创建 booster 实例时指定`mixed_precision`参数; 后续将会拓展`bf16`.
 
-#### booster 启动方式
+我们目前只支持`Linear`层的`fp8`混合精度训练，如果您需要使用，请在创建 plugin实例时指定`use_fp8`参数。
+
+为了减少低带宽场景下多机之间的通讯负载，我们还支持了FP8通讯。如果您需要使用，请在创建 plugin实例时指定`fp8_communication`参数。
+
+### booster 启动方式
 
 您可以在创建 booster 实例时，指定`mixed_precision="fp16"`即使用 torch amp。
 
@@ -70,7 +75,6 @@ AMP 代表自动混合精度训练。
  'fp16': torch amp
  'fp16_apex': apex amp,
  'bf16': bf16,
- 'fp8': fp8,
  'fp16_naive': naive amp
 """
 from colossalai import Booster
@@ -118,6 +122,10 @@ booster = Booster(mixed_precision=mixed_precision,...)
 
 当使用`colossalai.booster`时, 首先需要实例化一个模型、一个优化器和一个标准。将输出模型转换为内存消耗较小的 AMP 模型。如果您的输入模型已经太大，无法放置在 GPU 中，请使用`dtype=torch.float16`实例化你的模型。或者请尝试更小的模型，或尝试更多的并行化训练技术！
 
+### FP8通讯
+
+在低带宽场景下，为了减少多机间的通讯负载，我们支持使用FP8的形式对通讯进行压缩，可以在初始化plugin实例（如`GeminiPlugin`）时使用fp8_communication=True来启用。此时多机之间all-to-all, all-gather以及P2P操作将使用FP8的格式进行数据传输。受限于NCCL库的支持，目前不支持缩减(Reduction)算子如Allreduce, ReduceScatter的FP8通讯。
+
 ## 实例
 
 下面我们将展现如何在 Colossal-AI 使用 AMP。在该例程中，我们使用 Torch AMP.