[research_projects] deal with security alerts (#15594)

* [research_projects] deal with security alerts

* add a note of the original PL ver and warning

examples/research_projects/pplm/README.md

@@ -10,6 +10,9 @@ Blog link: https://eng.uber.com/pplm
 
 Please check out the repo under uber-research for more information: https://github.com/uber-research/PPLM
 
+# Note
+
+⚠️ This project should be run with pytorch-lightning==1.0.4 which has a potential security vulnerability
 
 ## Setup
 
@@ -51,4 +54,3 @@ python run_pplm.py -D sentiment --class_label 2 --cond_text "My dog died" --leng
 1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model.
 
 2. Use `--class_label 3` for negative, and `--class_label 2` for positive
-

examples/research_projects/pplm/requirements.txt

@@ -5,7 +5,7 @@ psutil
 sacrebleu
 rouge-score
 tensorflow_datasets
-pytorch-lightning==1.0.4
+pytorch-lightning
 matplotlib
 git-python==1.0.3
 faiss-cpu

examples/research_projects/rag-end2end-retriever/README.md

@@ -15,6 +15,9 @@ This code can be modified to experiment with other research on retrival augmente
 
 To start training, use the bash script (finetune_rag_ray_end2end.sh) in this folder. This script also includes descriptions on each command-line argument used.
 
+# Note
+
+⚠️ This project should be run with pytorch-lightning==1.3.1 which has a potential security vulnerability
 
 # Testing
 

examples/research_projects/rag-end2end-retriever/requirements.txt

@@ -2,6 +2,6 @@ faiss-cpu >= 1.7.0
 datasets >= 1.6.2
 psutil >= 5.7.0
 torch >= 1.4.0
-pytorch-lightning ==  1.3.1
+pytorch-lightning
 nvidia-ml-py3 == 7.352.0
 ray >=  1.3.0

examples/research_projects/rag/README.md

@@ -11,6 +11,10 @@ Such contextualized inputs are passed to the generator.
 
 Read more about RAG  at https://arxiv.org/abs/2005.11401.
 
+# Note
+
+⚠️ This project should be run with pytorch-lightning==1.3.1 which has a potential security vulnerability
+
 # Finetuning
 
 Our finetuning logic is based on scripts from [`examples/seq2seq`](https://github.com/huggingface/transformers/tree/master/examples/seq2seq). We accept training data in the same format as specified there - we expect a directory consisting of 6 text files:

examples/research_projects/rag/requirements.txt

@@ -3,5 +3,5 @@ datasets >= 1.0.1
 psutil >= 5.7.0
 torch >= 1.4.0
 transformers
-pytorch-lightning==1.3.1
+pytorch-lightning
 GitPython

examples/research_projects/seq2seq-distillation/README.md

@@ -13,6 +13,10 @@ Author: Sam Shleifer (https://github.com/sshleifer)
 - `FSMTForConditionalGeneration`
 - `T5ForConditionalGeneration`
 
+# Note
+
+⚠️ This project should be run with pytorch-lightning==1.0.4 which has a potential security vulnerability
+
 ## Datasets
 
 #### XSUM

examples/research_projects/seq2seq-distillation/requirements.txt

@@ -4,7 +4,7 @@ psutil
 sacrebleu
 rouge-score
 tensorflow_datasets
-pytorch-lightning==1.0.4
+pytorch-lightning
 matplotlib
 git-python==1.0.3
 faiss-cpu