BART metrics.json and validation checkpoint metrics seem to disagree
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 3.1.0
- Platform: Linux 4.14
- Python version:3.7.9
- PyTorch version (GPU?):1.6.0 (True)
- Using GPU in script?: True
- Using distributed or parallel set-up in script?: True

### Who can help
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 albert, bert, GPT2, XLM: @LysandreJik 
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 Translation: @sshleifer
 Summarization: @sshleifer
 TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 Text Generation: @TevenLeScao
 blenderbot: @mariamabarham
 Bart: @sshleifer
 Marian: @sshleifer
 T5: @patrickvonplaten
 Longformer/Reformer: @patrickvonplaten
 TransfoXL/XLNet: @TevenLeScao 
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 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
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 documentation: @sgugger
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 examples/seq2seq: @sshleifer 
## Information

Model I am using (BART):

The problem arises when using:
* [*] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [*] my own task or dataset: (give details below)
I am using a small subset of https://github.com/pubmedqa/pubmedqa to generate questions from the given passage.

## To reproduce

Steps to reproduce the behavior:

1. Do distributed training
2. Run the finetune script with appropriate arguments.

The saved checkpoint says:
val_avg_rouge2=13.0975-step_count=4.ckpt

However the metrics.json file says that val_avg_rouge2=6.46015 and there are better step_counts in comparison.

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## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
The best metric in metrics.json should be chosen for saving checkpoints.


I can replicate in pl 0.8.5 and pl 0.9.0, great catch.

Smaller command to replicate:

```
export MAX_LEN=128
export m=export m=sshleifer/student_marian_en_ro_6_3
python finetune.py \
  --learning_rate=3e-4 \
  --do_train \
  --do_predict \
  --fp16 \
  --val_check_interval 0.25 \
  --data_dir $ENRO_DIR \
  --max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
  --freeze_encoder --freeze_embeds \
  --train_batch_size=64 --eval_batch_size=64 \
  --tokenizer_name $m --model_name_or_path $m \
  --warmup_steps 500 --sortish_sampler --logger_name wandb \
  --fp16_opt_level=O1 --task translation --num_sanity_val_steps=0 \
  --model_name_or_path $m --gpus 8 --num_train_epochs=1 \
  --data_dir wmt_en_ro --output_dir dmar_pl_only_v2 --save_top_k=10
```

You will only call have 4-5 entries in metrics, but 10 checkpoints.


<|||||>Every single rank is saving checkpoints.<|||||>@sshleifer Wow, so is that like a race condition where the last RANK "wins"? What about the weights of model? Would they be same across all the ranks, or would they be a problem as well?<|||||>
Posted here https://github.com/PyTorchLightning/pytorch-lightning/issues/3597

Weights will be the same across all ranks. You just have suboptimal checkpoint saving logic. You can kind of workaround by passing --save_top_k=5 and then manually picking which one you like by looking at metrics.json.<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
