[s2s] add support for overriding config params (#6149)

2020-07-29 22:09:46 -07:00
parent 54f9fbeff8
commit 3212b8850d
4 changed files with 106 additions and 17 deletions
--- a/examples/seq2seq/README.md
+++ b/examples/seq2seq/README.md
@@ -3,7 +3,7 @@
 This directory contains examples for finetuning and evaluating transformers on summarization and translation tasks.
 Summarization support is more mature than translation support.
 Please tag @sshleifer with any issues/unexpected behaviors, or send a PR!
-For `bertabs` instructions, see [`bertabs/README.md`](bertabs/README.md). 
+For `bertabs` instructions, see [`bertabs/README.md`](bertabs/README.md).


 ### Data
@@ -35,23 +35,23 @@ export ENRO_DIR=${PWD}/wmt_en_ro
 this should make a directory called `wmt_en_ro/` with files like `test.source`.
 ```

-If you are using your own data, it must be formatted as one directory with 6 files: train.source, train.target, val.source, val.target, test.source, test.target.  
+If you are using your own data, it must be formatted as one directory with 6 files: train.source, train.target, val.source, val.target, test.source, test.target.
 The `.source` files are the input, the `.target` files are the desired output.

- 
+
 ### Tips and Tricks

 General Tips:
- since you need to run from `examples/seq2seq`, and likely need to modify code, the easiest workflow is fork transformers, clone your fork, and run `pip install -e .` before you get started.   
+- since you need to run from `examples/seq2seq`, and likely need to modify code, the easiest workflow is fork transformers, clone your fork, and run `pip install -e .` before you get started.
 - try `--freeze_encoder` or `--freeze_embeds` for faster training/larger batch size.  (3hr per epoch with bs=8, see the "xsum_shared_task" command below)
 - `fp16_opt_level=O1` (the default works best).
 - In addition to the pytorch-lightning .ckpt checkpoint, a transformers checkpoint will be saved.
 Load it with `BartForConditionalGeneration.from_pretrained(f'{output_dir}/best_tfmr)`.
 - At the moment, `--do_predict` does not work in a multi-gpu setting. You need to use `evaluate_checkpoint` or the `run_eval.py` code.
- This warning can be safely ignored: 
+- This warning can be safely ignored:
    > "Some weights of BartForConditionalGeneration were not initialized from the model checkpoint at facebook/bart-large-xsum and are newly initialized: ['final_logits_bias']"
 - Both finetuning and eval are 30% faster with `--fp16`. For that you need to [install apex](https://github.com/NVIDIA/apex#quick-start).
- Read scripts before you run them! 
+- Read scripts before you run them!

 Summarization Tips:
 - (summ) 1 epoch at batch size 1 for bart-large takes 24 hours and requires 13GB GPU RAM with fp16 on an NVIDIA-V100.
@@ -60,12 +60,25 @@ Summarization Tips:
 - `--max_target_length=60 --val_max_target_length=60 --test_max_target_length=100 ` is a reasonable setting for XSUM.
 - `wandb` can be used by specifying `--logger_name wandb`. It is useful for reproducibility. Specify the environment variable `WANDB_PROJECT='hf_xsum'` to do the XSUM shared task.
 - If you are finetuning on your own dataset, start from `distilbart-cnn-12-6` if you want long summaries and `distilbart-xsum-12-6` if you want short summaries.
-(It rarely makes sense to start from `bart-large` unless you are a researching finetuning methods). 
+(It rarely makes sense to start from `bart-large` unless you are a researching finetuning methods).

 **Update 2018-07-18**
 Datasets: Seq2SeqDataset will be used for all models besides MBart, for which MBartDataset will be used.**
 A new dataset is needed to support multilingual tasks.

+### Finetuning Training Params
+
+To override the pretrained model's training params, you can pass them to `./finetune.sh`:
+
+```bash
+./finetune.sh \
+    [...]
+    --encoder_layerdrop 0.1 \
+    --decoder_layerdrop 0.1 \
+    --dropout 0.1 \
+    --attention_dropout 0.1 \
+```
+
 ### Summarization Finetuning
 Run/modify `finetune.sh`

@@ -90,7 +103,7 @@ Best performing command:
 ```bash
 # optionally
 export ENRO_DIR='wmt_en_ro' # Download instructions above
-# export WANDB_PROJECT="MT" # optional 
+# export WANDB_PROJECT="MT" # optional
 export MAX_LEN=200
 export BS=4
 export GAS=8 # gradient accumulation steps
@@ -109,8 +122,8 @@ export BS=4
 export GAS=1  # gradient accumulation steps
 ./train_mbart_cc25_enro.sh --output_dir enro_finetune_baseline --gpus 8 --logger_name wandb
 ```
-### Finetuning Outputs 
-As you train, `output_dir` will be filled with files, that look kind of like this (comments are mine). 
+### Finetuning Outputs
+As you train, `output_dir` will be filled with files, that look kind of like this (comments are mine).
 Some of them are metrics, some of them are checkpoints, some of them are metadata. Here is a quick tour:

 ```bash
@@ -128,8 +141,8 @@ output_dir
 ├── student  # this is a huggingface checkpoint generated by SummarizationDistiller. It is the student before it gets finetuned.
 │   ├── config.json
 │   └── pytorch_model.bin
-├── test_generations.txt 
-# ^^ are the summaries or translations produced by your best checkpoint on the test data. Populated when training is done 
+├── test_generations.txt
+# ^^ are the summaries or translations produced by your best checkpoint on the test data. Populated when training is done
 ├── test_results.txt  # a convenience file with the test set metrics. This data is also in metrics.json['test']
 ├── hparams.pkl  # the command line args passed after some light preprocessing. Should be saved fairly quickly.
 ```
@@ -191,7 +204,7 @@ python run_eval.py sshleifer/distilbart-cnn-12-6 $DATA_DIR/val.source dbart_val_
 ![DBART](https://huggingface.co/front/thumbnails/distilbart_large.png)

 For the CNN/DailyMail dataset, (relatively longer, more extractive summaries), we found a simple technique that works:
-you just copy alternating layers from `bart-large-cnn` and finetune more on the same data. 
+you just copy alternating layers from `bart-large-cnn` and finetune more on the same data.

 For the XSUM dataset, that didn’t work as well so we used that same initialization strategy followed by a combination of Distillbert’s ce_loss and the hidden states MSE loss used in the tinybert paper.

@@ -207,7 +220,7 @@ They are initialized by copying layers from the associated `bart-large-{cnn|xsum
 The command that produced `sshleifer/distilbart-cnn-12-6` is
 ```bash
 ./train_distilbart_cnn.sh
-```  
+```
 runtime: 6H on NVIDIA RTX 24GB GPU

 *Note*: You can get the same simple distillation logic by using `./run_distiller.sh --no_teacher` followed by identical arguments as the ones in `train_distilbart_cnn.sh`.
@@ -223,15 +236,15 @@ This is how `sshleifer/distilbart-xsum*` checkpoints were produced.
 The command that produced `sshleifer/distilbart-xsum-12-6` is:

 ```bash
-./train_distilbart_xsum.sh  
+./train_distilbart_xsum.sh
 ```

-runtime: 13H on V-100 16GB GPU. 
+runtime: 13H on V-100 16GB GPU.

 ### Contributing
 - follow the standard contributing guidelines and code of conduct.
 - add tests to `test_seq2seq_examples.py`
 - To run only the seq2seq tests, you must be in the root of the repository and run:
 ```bash
-pytest examples/seq2seq/  
+pytest examples/seq2seq/
 ```
--- a/examples/seq2seq/finetune.sh
+++ b/examples/seq2seq/finetune.sh
@@ -10,4 +10,8 @@ python finetune.py \
    --do_predict \
    --n_val 1000 \
    --val_check_interval 0.1 \
+    --encoder_layerdrop 0.1 \
+    --decoder_layerdrop 0.1 \
+    --dropout 0.1 \
+    --attention_dropout 0.1 \
    $@
--- a/examples/seq2seq/test_seq2seq_examples.py
+++ b/examples/seq2seq/test_seq2seq_examples.py
@@ -277,6 +277,55 @@ def test_finetune(model):
        assert bart.decoder.embed_tokens == bart.shared


+def test_finetune_extra_model_args():
+    args_d: dict = CHEAP_ARGS.copy()
+
+    task = "summarization"
+    tmp_dir = make_test_data_dir()
+
+    args_d.update(
+        data_dir=tmp_dir,
+        tokenizer_name=None,
+        train_batch_size=2,
+        eval_batch_size=2,
+        do_predict=False,
+        task=task,
+        src_lang="en_XX",
+        tgt_lang="ro_RO",
+        freeze_encoder=True,
+        freeze_embeds=True,
+    )
+
+    # test models whose config includes the extra_model_args
+    model = BART_TINY
+    output_dir = tempfile.mkdtemp(prefix="output_1_")
+    args_d1 = args_d.copy()
+    args_d1.update(
+        model_name_or_path=model, output_dir=output_dir,
+    )
+    extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
+    for p in extra_model_params:
+        args_d1[p] = 0.5
+    args = argparse.Namespace(**args_d1)
+    model = main(args)
+    for p in extra_model_params:
+        assert getattr(model.config, p) == 0.5, f"failed to override the model config for param {p}"
+
+    # test models whose config doesn't include the extra_model_args
+    model = T5_TINY
+    output_dir = tempfile.mkdtemp(prefix="output_2_")
+    args_d2 = args_d.copy()
+    args_d2.update(
+        model_name_or_path=model, output_dir=output_dir,
+    )
+    unsupported_param = "encoder_layerdrop"
+    args_d2[unsupported_param] = 0.5
+    args = argparse.Namespace(**args_d2)
+    with pytest.raises(Exception) as excinfo:
+        model = main(args)
+    assert str(excinfo.value) == f"model config doesn't have a `{unsupported_param}` attribute"
+
+
 def test_pack_dataset():
    tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")