polish readme

examples/language/gpt/README.md

@@ -39,9 +39,15 @@ If you want to test ZeRO1 and ZeRO2 in Colossal-AI, you need to ensure Colossal-
 For simplicity, the input data is randonly generated here.
 
 ## Training
-We provide two solutions. One utilizes the hybrid parallel strategies of Gemini, DDP/ZeRO, and Tensor Parallelism.
-The other one uses Pipeline Parallelism Only.
-In the future, we are going merge them together and they can be used orthogonally to each other.
+We provide two stable solutions.
+One utilizes the Gemini to implement hybrid parallel strategies of Gemini, DDP/ZeRO, and Tensor Parallelism for a huggingface GPT model.
+The other one use [Titans](https://github.com/hpcaitech/Titans), a distributed executed model zoo maintained by ColossalAI,to implement the hybrid parallel strategies of TP + ZeRO + PP.
+
+We recommend using Gemini to qucikly run your model in a distributed manner.
+It doesn't require significant changes to the model structures, therefore you can apply it on a new model easily.
+And use Titans as an advanced weapon to pursue a more extreme performance.
+Titans has included the some typical models, such as Vit and GPT.
+However, it requires some efforts to start if facing a new model structure.
 
 ### GeminiDPP/ZeRO + Tensor Parallelism
 ```bash
@@ -56,6 +62,11 @@ The `train_gpt_demo.py` provides three distributed plans, you can choose the pla
 - Pytorch DDP
 - Pytorch ZeRO
 
+### Titans (Tensor Parallelism) + ZeRO + Pipeline Parallelism
+
+Titans provides a customized GPT model, which uses distributed operators as building blocks.
+In [./titans/README.md], we provide a hybrid parallelism of ZeRO, TP and PP.
+You can switch parallel strategies using a config file.
 
 ## Performance
 

examples/language/gpt/titans/README.md

@@ -5,7 +5,7 @@
 You can download the preprocessed sample dataset for this demo via our [Google Drive sharing link](https://drive.google.com/file/d/1QKI6k-e2gJ7XgS8yIpgPPiMmwiBP_BPE/view?usp=sharing).
 
 
-You can also avoid dataset preparation by using `--use_dummy_data` during running.
+You can also avoid dataset preparation by using `--use_dummy_dataset` during running.
 
 ## Run this Demo
 
@@ -13,15 +13,15 @@ Use the following commands to install prerequisites.
 
 ```bash
 # assuming using cuda 11.3
-conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
-pip install colossalai==0.1.9+torch1.11cu11.3 -f https://release.colossalai.org
+pip install -r requirements.txt
 ```
 
 Use the following commands to execute training.
 
 ```Bash
 #!/usr/bin/env sh
-export DATA=/path/to/small-gpt-dataset.json'
+# if you want to use real dataset, then remove --use_dummy_dataset
+# export DATA=/path/to/small-gpt-dataset.json'
 
 # run on a single node
 colossalai run --nproc_per_node=<num_gpus> train_gpt.py --config configs/<config_file> --from_torch
@@ -34,14 +34,14 @@ colossalai run --nproc_per_node=<num_gpus> \
    train_gpt.py \
    --config configs/<config_file> \
    --from_torch \
-   --use_dummy_data
+   --use_dummy_dataset
 
 # run on multiple nodes with slurm
 srun python \
    train_gpt.py \
    --config configs/<config_file> \
    --host <master_node> \
-   --use_dummy_data
+   --use_dummy_dataset
 
 ```