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[seq2seq] make it easier to run the scripts (#7274)

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This commit is contained in:
Stas Bekman
2020-09-24 12:23:48 -07:00
committed by GitHub
parent 8d3bb781ee
commit eadd870b2f
18 changed files with 50 additions and 31 deletions
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examples/seq2seq/README.md
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@@ -100,7 +100,7 @@ All finetuning bash scripts call finetune.py (or distillation.py) with reasonabl
To see all the possible command line options, run:
```bash
./finetune.sh --help # this calls python finetune.py --help
./finetune.py --help
```
### Finetuning Training Params
@@ -197,7 +197,7 @@ If 'translation' is in your task name, the computed metric will be BLEU. Otherwi
For t5, you need to specify --task translation_{src}_to_{tgt} as follows:
```bash
export DATA_DIR=wmt_en_ro
python run_eval.py t5-base \
./run_eval.py t5-base \
$DATA_DIR/val.source t5_val_generations.txt \
--reference_path $DATA_DIR/val.target \
--score_path enro_bleu.json \
@@ -211,7 +211,7 @@ python run_eval.py t5-base \
This command works for MBART, although the BLEU score is suspiciously low.
```bash
export DATA_DIR=wmt_en_ro
python run_eval.py facebook/mbart-large-en-ro $DATA_DIR/val.source mbart_val_generations.txt \
./run_eval.py facebook/mbart-large-en-ro $DATA_DIR/val.source mbart_val_generations.txt \
--reference_path $DATA_DIR/val.target \
--score_path enro_bleu.json \
--task translation \
@@ -224,7 +224,7 @@ python run_eval.py facebook/mbart-large-en-ro $DATA_DIR/val.source mbart_val_gen
Summarization (xsum will be very similar):
```bash
export DATA_DIR=cnn_dm
python run_eval.py sshleifer/distilbart-cnn-12-6 $DATA_DIR/val.source dbart_val_generations.txt \
./run_eval.py sshleifer/distilbart-cnn-12-6 $DATA_DIR/val.source dbart_val_generations.txt \
--reference_path $DATA_DIR/val.target \
--score_path cnn_rouge.json \
--task summarization \
@@ -238,7 +238,7 @@ python run_eval.py sshleifer/distilbart-cnn-12-6 $DATA_DIR/val.source dbart_val_
### Multi-GPU Evalulation
here is a command to run xsum evaluation on 8 GPUS. It is more than linearly faster than run_eval.py in some cases
because it uses SortishSampler to minimize padding. You can also use it on 1 GPU. `data_dir` must have
`{type_path}.source` and `{type_path}.target`. Run `python run_distributed_eval.py --help` for all clargs.
`{type_path}.source` and `{type_path}.target`. Run `./run_distributed_eval.py --help` for all clargs.
```bash
python -m torch.distributed.launch --nproc_per_node=8 run_distributed_eval.py \
@@ -371,11 +371,11 @@ This feature can only be used:
- with fairseq installed
- on 1 GPU
- without sortish sampler
- after calling `python save_len_file.py $tok $data_dir`
- after calling `./save_len_file.py $tok $data_dir`
For example,
```bash
python save_len_file.py Helsinki-NLP/opus-mt-en-ro wmt_en_ro
./save_len_file.py Helsinki-NLP/opus-mt-en-ro wmt_en_ro
./dynamic_bs_example.sh --max_tokens_per_batch=2000 --output_dir benchmark_dynamic_bs
```
splits `wmt_en_ro/train` into 11,197 uneven lengthed batches and can finish 1 epoch in 8 minutes on a v100.