[examples] SummarizationModule improvements (#4951)

examples/lightning_base.py

@@ -2,6 +2,8 @@ import argparse
 import logging
 import os
 import random
+from pathlib import Path
+from typing import Any, Dict
 
 import numpy as np
 import pytorch_lightning as pl
@@ -13,10 +15,13 @@ from transformers import (
     AutoModel,
     AutoModelForPreTraining,
     AutoModelForQuestionAnswering,
+    AutoModelForSeq2SeqLM,
     AutoModelForSequenceClassification,
     AutoModelForTokenClassification,
     AutoModelWithLMHead,
     AutoTokenizer,
+    PretrainedConfig,
+    PreTrainedTokenizer,
     get_linear_schedule_with_warmup,
 )
 
@@ -31,6 +36,8 @@ MODEL_MODES = {
     "pretraining": AutoModelForPreTraining,
     "token-classification": AutoModelForTokenClassification,
     "language-modeling": AutoModelWithLMHead,
+    "summarization": AutoModelForSeq2SeqLM,
+    "translation": AutoModelForSeq2SeqLM,
 }
 
 
@@ -38,33 +45,59 @@ def set_seed(args: argparse.Namespace):
     random.seed(args.seed)
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    if args.n_gpu > 0:
+    if args.gpus > 0:
         torch.cuda.manual_seed_all(args.seed)
 
 
 class BaseTransformer(pl.LightningModule):
-    def __init__(self, hparams: argparse.Namespace, num_labels=None, mode="base", **config_kwargs):
+    def __init__(
+        self,
+        hparams: argparse.Namespace,
+        num_labels=None,
+        mode="base",
+        config=None,
+        tokenizer=None,
+        model=None,
+        **config_kwargs
+    ):
         "Initialize a model."
 
         super().__init__()
         self.hparams = hparams
+        self.step_count = 0
+        self.tfmr_ckpts = {}
+        self.output_dir = Path(self.hparams.output_dir)
         cache_dir = self.hparams.cache_dir if self.hparams.cache_dir else None
+        if config is None:
             self.config = AutoConfig.from_pretrained(
                 self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,
                 **({"num_labels": num_labels} if num_labels is not None else {}),
                 cache_dir=cache_dir,
                 **config_kwargs,
             )
+        else:
+            self.config: PretrainedConfig = config
+        if tokenizer is None:
             self.tokenizer = AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
                 cache_dir=cache_dir,
             )
-        self.model = MODEL_MODES[mode].from_pretrained(
+        else:
+            self.tokenizer: PreTrainedTokenizer = tokenizer
+        if model is None:
+            self.model_type = MODEL_MODES[mode]
+            self.model = self.model_type.from_pretrained(
                 self.hparams.model_name_or_path,
                 from_tf=bool(".ckpt" in self.hparams.model_name_or_path),
                 config=self.config,
                 cache_dir=cache_dir,
             )
+        else:
+            self.model_type = None
+            self.model = model
+
+    def load_hf_checkpoint(self, *args, **kwargs):
+        self.model = self.model_type.from_pretrained(*args, **kwargs)
 
     def is_logger(self):
         return self.trainer.proc_rank <= 0
@@ -138,6 +171,15 @@ class BaseTransformer(pl.LightningModule):
             ),
         )
 
+    @pl.utilities.rank_zero_only
+    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
+        save_path = self.output_dir.joinpath("best_tfmr")
+        save_path.mkdir(exist_ok=True)
+        self.model.config.save_step = self.step_count
+        self.model.save_pretrained(save_path)
+        self.tokenizer.save_pretrained(save_path)
+        self.tfmr_ckpts[self.step_count] = save_path
+
     @staticmethod
     def add_model_specific_args(parser, root_dir):
         parser.add_argument(
@@ -152,7 +194,7 @@ class BaseTransformer(pl.LightningModule):
         )
         parser.add_argument(
             "--tokenizer_name",
-            default="",
+            default=None,
             type=str,
             help="Pretrained tokenizer name or path if not the same as model_name",
         )
@@ -165,7 +207,7 @@ class BaseTransformer(pl.LightningModule):
         parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
         parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
         parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-        parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
+        parser.add_argument("--warmup_steps", default=500, type=int, help="Linear warmup over warmup_steps.")
         parser.add_argument(
             "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform."
         )
@@ -199,7 +241,8 @@ class LoggingCallback(pl.Callback):
                         writer.write("{} = {}\n".format(key, str(metrics[key])))
 
 
-def add_generic_args(parser, root_dir):
+def add_generic_args(parser, root_dir) -> None:
+    #  TODO(SS): allow all pl args? parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir",
         default=None,
@@ -221,8 +264,8 @@ def add_generic_args(parser, root_dir):
         help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
         "See details at https://nvidia.github.io/apex/amp.html",
     )
-
-    parser.add_argument("--n_gpu", type=int, default=1)
+    parser.add_argument("--fast_dev_run", action="store_true")
+    parser.add_argument("--gpus", type=int, default=1)
     parser.add_argument("--n_tpu_cores", type=int, default=0)
     parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
     parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
@@ -235,28 +278,32 @@ def add_generic_args(parser, root_dir):
     )
 
     parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
-
-
-def generic_train(model: BaseTransformer, args: argparse.Namespace):
+    parser.add_argument("--resume_from_checkpoint", type=str, default=None)
+    parser.add_argument("--val_check_interval", default=1.0, type=float)
+
+
+def generic_train(
+    model: BaseTransformer,
+    args: argparse.Namespace,
+    early_stopping_callback=False,
+    logger=True,  # can pass WandbLogger() here
+    extra_callbacks=[],
+    checkpoint_callback=None,
+    logging_callback=None,
+    **extra_train_kwargs
+):
     # init model
     set_seed(args)
-
-    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
-        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
-
+    odir = Path(model.hparams.output_dir)
+    odir.mkdir(exist_ok=True)
+    if checkpoint_callback is None:
         checkpoint_callback = pl.callbacks.ModelCheckpoint(
-        filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=5
+            filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=1
         )
+    if logging_callback is None:
+        logging_callback = LoggingCallback()
 
-    train_params = dict(
-        accumulate_grad_batches=args.gradient_accumulation_steps,
-        gpus=args.n_gpu,
-        max_epochs=args.num_train_epochs,
-        early_stop_callback=False,
-        gradient_clip_val=args.max_grad_norm,
-        checkpoint_callback=checkpoint_callback,
-        callbacks=[LoggingCallback()],
-    )
+    train_params = {}
 
     if args.fp16:
         train_params["use_amp"] = args.fp16
@@ -269,12 +316,27 @@ def generic_train(model: BaseTransformer, args: argparse.Namespace):
         train_params["num_tpu_cores"] = args.n_tpu_cores
         train_params["gpus"] = 0
 
-    if args.n_gpu > 1:
+    if args.gpus > 1:
         train_params["distributed_backend"] = "ddp"
 
-    trainer = pl.Trainer(**train_params)
+    trainer = pl.Trainer(
+        logger=logger,
+        accumulate_grad_batches=args.gradient_accumulation_steps,
+        gpus=args.gpus,
+        max_epochs=args.num_train_epochs,
+        early_stop_callback=early_stopping_callback,
+        gradient_clip_val=args.max_grad_norm,
+        checkpoint_callback=checkpoint_callback,
+        callbacks=[logging_callback] + extra_callbacks,
+        fast_dev_run=args.fast_dev_run,
+        val_check_interval=args.val_check_interval,
+        weights_summary=None,
+        resume_from_checkpoint=args.resume_from_checkpoint,
+        **train_params,
+    )
 
     if args.do_train:
         trainer.fit(model)
-
+    trainer.logger.log_hyperparams(args)
+    trainer.logger.save()
     return trainer

examples/requirements.txt

@@ -5,5 +5,6 @@ psutil
 sacrebleu
 rouge-score
 tensorflow_datasets
-pytorch-lightning==0.7.3  # April 10, 2020 release
+pytorch-lightning==0.7.6
 matplotlib
+git-python==1.0.3

examples/summarization/README.md

-### Get CNN Data
-To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
+### Data
 
+CNN/DailyMail data
 ```bash
+cd examples/summarization
 wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
 tar -xzvf cnn_dm.tgz
+export CNN_DIR=${PWD}/cnn_dm
 ```
 
 this should make a directory called cnn_dm/ with files like `test.source`.
 To use your own data, copy that files format. Each article to be summarized is on its own line.
 
+XSUM Data:
+```bash
+cd examples/summarization
+wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz
+tar -xzvf xsum.tar.gz
+export XSUM_DIR=${PWD}/xsum
+```
+
+
 ### Evaluation
 
 To create summaries for each article in dataset, run:
 ```bash
-python evaluate_cnn.py <path_to_test.source> test_generations.txt <model-name>  --score_path rouge_scores.txt
+python run_eval.py <path_to_test.source> test_generations.txt <model-name>  --score_path rouge_scores.txt
 ```
-The default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
+The default batch size, 4, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
+
 
 ### Training
-Run/modify `finetune_bart.sh` or `finetune_t5.sh`
+Run/modify `finetune.sh`
 
-### Stanford CoreNLP Setup
-```
-ptb_tokenize () {
-    cat $1 | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > $2
-}
-
-sudo apt install openjdk-8-jre-headless
-sudo apt-get install ant
-wget http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
-unzip stanford-corenlp-full-2018-10-05.zip
-cd stanford-corenlp-full-2018-10-05
-export CLASSPATH=stanford-corenlp-3.9.2.jar:stanford-corenlp-3.9.2-models.jar
+The following command should work on a 16GB GPU:
+```bash
+export me=`git config user.name`
+./finetune.sh \
+    --data_dir $XSUM_DIR \
+    --train_batch_size=1 \
+    --eval_batch_size=1 \
+    --output_dir="$me"_xsum_results \
+    --num_train_epochs 1
 ```
-Then run `ptb_tokenize` on `test.target` and your generated hypotheses.
-### Rouge Setup
-Install `files2rouge` following the instructions at [here](https://github.com/pltrdy/files2rouge).
-I also needed to run `sudo apt-get install libxml-parser-perl`
-
-```python
-from files2rouge import files2rouge
-from files2rouge import settings
-files2rouge.run(<path_to_tokenized_hypo>,
-                <path_to_tokenized_target>,
-               saveto='rouge_output.txt')
+
+Tips:
+- 1 epoch at batch size 1 for bart-large takes 24 hours, requires 13GB GPU RAM with fp16 on an NVIDIA-V100. 
+- try `bart-base`, `--freeze_encoder` or `--freeze_embeds` for faster training/larger batch size.  (3hr/epoch with bs=8, see below)
+- `fp16_opt_level=O1` (the default works best).
+- If you are finetuning on your own dataset, start from `bart-large-cnn` if you want long summaries and `bart-large-xsum` if you want short summaries.
+(It rarely makes sense to start from `bart-large` unless you are a researching finetuning methods).
+- 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.
+- If you want to run experiments on improving the summarization finetuning process, try the XSUM Shared Task (below). It's faster to train than CNNDM because the summaries are shorter.    
+
+### XSUM Shared Task
+Compare XSUM results with others by using `--logger wandb_shared`. This requires `wandb` registration.
+Here is an example command
+```bash
+export me=`git config user.name`
+./finetune.sh \
+    --data_dir $XSUM_DIR \
+    --output_dir "$me"_xsum_frozen_embs \
+    --logger wandb_shared \
+    --train_batch_size 16 --eval_batch_size 16 --freeze_embeds --freeze_encoder \
+    --num_train_epochs 6
 ```
+
+Results can be viewed [here](https://app.wandb.ai/sshleifer/hf_summarization/table?workspace=user-)

examples/summarization/callbacks.py

+import logging
+import os
+from pathlib import Path
+
+import numpy as np
+import pytorch_lightning as pl
+import torch
+from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.utilities import rank_zero_only
+
+
+def count_trainable_parameters(model):
+    model_parameters = filter(lambda p: p.requires_grad, model.parameters())
+    params = sum([np.prod(p.size()) for p in model_parameters])
+    return params
+
+
+logger = logging.getLogger(__name__)
+
+
+class Seq2SeqLoggingCallback(pl.Callback):
+    def _write_logs(
+        self, trainer: pl.Trainer, pl_module: pl.LightningModule, type_path: str, save_generations=True
+    ) -> None:
+        logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****")
+        if not pl_module.is_logger():
+            return
+        metrics = trainer.callback_metrics
+        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]})
+        # Log results
+        od = Path(pl_module.hparams.output_dir)
+        if type_path == "test":
+            results_file = od / "test_results.txt"
+            generations_file = od / "test_generations.txt"
+        else:
+            results_file = od / f"{type_path}_results_{trainer.global_step:05d}.txt"
+            generations_file = od / f"{type_path}_generations_{trainer.global_step:05d}.txt"
+
+        with open(results_file, "a+") as writer:
+            for key in sorted(metrics):
+                if key in ["log", "progress_bar", "preds"]:
+                    continue
+                val = metrics[key]
+                if isinstance(val, torch.Tensor):
+                    val = val.item()
+                msg = f"{key}: {val:.6f}\n"
+                writer.write(msg)
+
+        if not save_generations:
+            return
+
+        if "preds" in metrics:
+            content = "\n".join(metrics["preds"])
+            generations_file.open("w+").write(content)
+
+    @rank_zero_only
+    def on_train_start(self, trainer, pl_module):
+        try:
+            npars = pl_module.model.model.num_parameters()
+        except AttributeError:
+            npars = pl_module.model.num_parameters()
+
+        n_trainable_pars = count_trainable_parameters(pl_module)
+        # mp stands for million parameters
+        trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6})
+
+    @rank_zero_only
+    def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
+        return self._write_logs(trainer, pl_module, "val")
+
+    @rank_zero_only
+    def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
+        return self._write_logs(trainer, pl_module, "test")
+
+
+def get_rouge2_checkpoint_callback(output_dir):
+    """Saves the best model by validation ROUGE2 score."""
+    checkpoint_callback = ModelCheckpoint(
+        filepath=os.path.join(output_dir, "{val_avg_rouge2:.4f}-{step_count}"),
+        monitor="val_rouge",
+        mode="max",
+        save_top_k=1,
+        period=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
+    )
+    return checkpoint_callback

examples/summarization/distillation.py

+import argparse
+import gc
+import os
+from pathlib import Path
+from typing import List
+
+import pytorch_lightning as pl
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from lightning_base import generic_train
+from transformers import AdamW, BartConfig, BartForConditionalGeneration, T5Config, T5ForConditionalGeneration
+
+
+try:
+    from .finetune import SummarizationModule
+    from .initialization_utils import init_student, copy_layers
+    from .utils import (
+        use_task_specific_params,
+        SummarizationDataset,
+        pickle_load,
+        freeze_params,
+        assert_all_frozen,
+        any_requires_grad,
+    )
+    from .finetune import main as ft_main
+except ImportError:
+    from finetune import SummarizationModule
+    from finetune import main as ft_main
+    from initialization_utils import init_student, copy_layers
+    from utils import (
+        use_task_specific_params,
+        SummarizationDataset,
+        pickle_load,
+        freeze_params,
+        assert_all_frozen,
+        any_requires_grad,
+    )
+
+
+class SummarizationDistiller(SummarizationModule):
+    loss_names = ["loss", "ce_loss", "mlm_loss", "enc_mse_loss", "hid_loss_enc", "hid_loss_dec"]
+
+    def __init__(self, hparams):
+        assert Path(hparams.data_dir).exists()
+
+        d_layers_to_copy, student, student_cfg, teacher = self.pre_init(hparams)
+
+        super().__init__(hparams, model=student, config=student_cfg)
+        self.teacher = teacher
+        use_task_specific_params(self.teacher, "summarization")
+        freeze_params(self.teacher)
+        self.sanity_check_gradients()
+        self.ce_loss_fct = nn.KLDivLoss(reduction="batchmean")
+        self.temperature = 2.0
+        self.alpha_mlm = hparams.alpha_mlm
+        self.alpha_ce = hparams.alpha_ce
+        self.alpha_hid = hparams.alpha_hid
+        # self.alpha_cos = hparams.alpha_cos
+        self.alpha_encoder_loss = self.hparams.alpha_encoder_loss
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def sanity_check_gradients(self):
+        assert_all_frozen(self.teacher)
+        assert_all_frozen(self.model.model.decoder.embed_tokens)
+        assert_all_frozen(self.model.model.encoder.embed_tokens)
+        if self.different_encoder:
+            assert any_requires_grad(self.model.model.encoder)
+        else:
+            freeze_params(self.model.model.encoder)
+            del self.teacher.model.encoder
+
+    def pre_init(self, hparams):
+        # Dump empty student model at a path, then call from_pretrained on it
+        teacher = BartForConditionalGeneration.from_pretrained(hparams.teacher).eval()
+        student_updates = {
+            "decoder_layers": hparams.student_decoder_layers,
+            "encoder_layers": hparams.student_encoder_layers,
+        }
+        d_layers_to_copy = get_layers_to_copy(student_updates["decoder_layers"], teacher.config.decoder_layers)
+        e_layers_to_copy: List = get_layers_to_copy(student_updates["encoder_layers"], teacher.config.encoder_layers)
+        hparams.d_layer_to_copy = d_layers_to_copy
+        hparams.e_layer_to_copy = e_layers_to_copy
+        kw = teacher.config.to_diff_dict()
+        kw.update(student_updates)
+        # Copy weights
+        student_cfg = BartConfig(**kw)
+        student = BartForConditionalGeneration(student_cfg)
+        student, _ = init_student(student, teacher)
+        self.copy_to_student(d_layers_to_copy, e_layers_to_copy, hparams, student, teacher)
+        Path(hparams.output_dir).mkdir(exist_ok=True)
+        return d_layers_to_copy, student, student_cfg, teacher
+
+    def copy_to_student(self, d_layers_to_copy, e_layers_to_copy, hparams, student, teacher):
+        if teacher.config.model_type == "t5":
+            return self.copy_t5_to_student(d_layers_to_copy, e_layers_to_copy, hparams, student, teacher)
+        self.different_encoder: bool = hparams.student_encoder_layers != teacher.config.encoder_layers
+        self.different_decoder = hparams.student_decoder_layers != teacher.config.decoder_layers
+        if self.different_decoder:
+            copy_layers(teacher.model.decoder.layers, student.model.decoder.layers, d_layers_to_copy)
+        if self.different_encoder:
+            copy_layers(teacher.model.encoder.layers, student.model.encoder.layers, e_layers_to_copy)
+
+    def copy_t5_to_student(self, d_layers_to_copy, e_layers_to_copy, hparams, student, teacher):
+        self.different_encoder: bool = hparams.student_encoder_layers != teacher.config.num_layers
+        self.different_decoder = hparams.student_decoder_layers != teacher.config.num_layers
+        if self.different_decoder:
+            copy_layers(teacher.decoder.block, student.decoder.block, d_layers_to_copy)
+        if self.different_encoder:
+            copy_layers(teacher.encoder.block, student.encoder.block, e_layers_to_copy)
+
+    def get_dataset(self, type_path) -> SummarizationDataset:
+        n_obs = self.n_obs[type_path]
+        dataset = SummarizationDataset(self.tokenizer, type_path=type_path, n_obs=n_obs, **self.dataset_kwargs)
+        return dataset
+
+    def calc_mse_loss(self, teacher_outputs: torch.Tensor, student_outputs: torch.Tensor, mask) -> torch.FloatTensor:
+        if mask is not None:
+            # mask has False at padding_idx
+            sel_mask = mask[:, :, None].expand_as(student_outputs).bool()
+            s_logits_slct = torch.masked_select(student_outputs, sel_mask)
+            t_logits_slct = torch.masked_select(teacher_outputs, sel_mask)
+        else:
+            t_logits_slct = teacher_outputs
+            s_logits_slct = student_outputs
+        return F.mse_loss(s_logits_slct, t_logits_slct)
+
+    def calc_ce_loss(self, mask, s_logits, t_logits):
+        if mask is not None:
+            # mask has False at padding_idx
+            sel_mask = mask[:, :, None].expand_as(s_logits)
+            s_logits_slct = torch.masked_select(
+                s_logits, sel_mask
+            )  # (bs * seq_length * voc_size) modulo the 1s in mask
+            t_logits_slct = torch.masked_select(
+                t_logits, sel_mask
+            )  # (bs * seq_length * voc_size) modulo the 1s in mask
+        else:
+            t_logits_slct = t_logits
+            s_logits_slct = s_logits  # (bs * seq_length * voc_size) modulo the 1s in mask
+        s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1))  # (bs * seq_length, voc_size) modulo the 1s in mask
+        t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1))  # (bs * seq_length, voc_size) modulo the 1s in mask
+        assert t_logits_slct.size() == s_logits_slct.size()
+        loss_ce = (
+            self.ce_loss_fct(
+                F.log_softmax(s_logits_slct / self.temperature, dim=-1),
+                F.softmax(t_logits_slct / self.temperature, dim=-1),
+            )
+            * (self.temperature) ** 2
+        )
+        return loss_ce, s_logits_slct, t_logits_slct
+
+    def configure_optimizers(self):
+        "Prepare optimizer and schedule (linear warmup and decay)"
+
+        model = self.model
+        no_decay = ["bias", "LayerNorm.weight"]
+        optimizer_grouped_parameters = [
+            {
+                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
+                "weight_decay": self.hparams.weight_decay,
+            },
+            {
+                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
+                "weight_decay": 0.0,
+            },
+        ]
+        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
+        self.opt = optimizer
+        return [optimizer]
+
+    @staticmethod
+    def add_model_specific_args(parser, root_dir):
+        SummarizationModule.add_model_specific_args(parser, root_dir)
+        parser.add_argument("--teacher", default="facebook/bart-large-cnn", type=str)
+        parser.add_argument("--alpha_ce", default=0.8, type=float)
+        parser.add_argument("--alpha_mlm", default=0.2, type=float)
+        # parser.add_argument("--alpha_cos", default=0.0, type=float)
+        parser.add_argument("--alpha_encoder_loss", default=0.0, type=float)
+        parser.add_argument("--alpha_hid", default=0.0, type=float, required=False)
+        parser.add_argument(
+            "--student_decoder_layers", default=12, type=int, required=False,
+        )
+        parser.add_argument(
+            "--student_encoder_layers", default=12, type=int, required=False,
+        )
+        parser.add_argument(
+            "--no_teacher", action="store_true", default=False,
+        )
+        parser.add_argument(  # TODO: remove
+            "--enc_only", action="store_true", default=False,
+        )
+        return parser
+
+    def _step(self, batch):
+        # assert is_frozen(self.teacher)
+        pad_token_id = self.tokenizer.pad_token_id
+        input_ids, src_mask, y = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
+        decoder_input_ids = y[:, :-1].contiguous()
+        labels = y[:, 1:].clone()
+        labels[y[:, 1:] == pad_token_id] = -100
+        # noinspection PyCallingNonCallable
+        sloss, slogits, dec_hidden, enc_outputs, enc_hidden_state = self(
+            input_ids,
+            attention_mask=src_mask,
+            decoder_input_ids=decoder_input_ids,
+            labels=labels,
+            output_hidden_states=True,
+            output_attentions=False,
+        )
+
+        def zero_tensor():
+            return torch.tensor(0.0).type_as(sloss)
+
+        loss_encoder, hid_loss_enc, hid_loss_dec = zero_tensor(), zero_tensor(), zero_tensor()
+        if self.different_encoder:
+            with torch.no_grad():
+                teacher_enc_outputs, teacher_enc_hid, _ = self.teacher.model.encoder(
+                    input_ids, attention_mask=src_mask, output_hidden_states=True
+                )
+            if self.hparams.alpha_encoder_loss > 0:
+                loss_encoder = self.calc_mse_loss(enc_outputs, teacher_enc_outputs, src_mask)
+
+            hid_loss_enc = self.calc_hidden_loss(
+                src_mask, enc_hidden_state, teacher_enc_hid, self.hparams.e_layer_to_copy
+            )
+
+        teacher_enc_outputs = (enc_outputs,)
+        assert isinstance(teacher_enc_outputs, tuple), type(teacher_enc_outputs)
+
+        with torch.no_grad():
+            tloss, tlogits, tdec_hidden, _ = self.teacher(
+                input_ids,
+                attention_mask=src_mask,
+                encoder_outputs=teacher_enc_outputs,
+                decoder_input_ids=decoder_input_ids,
+                lm_labels=labels,
+                output_hidden_states=True,
+            )
+        dec_mask = decoder_input_ids.ne(pad_token_id)
+        loss_ce, s_logits_slct, t_logits_slct = self.calc_ce_loss(dec_mask, slogits, tlogits)
+        if self.alpha_hid > 0:
+            hid_loss_dec = self.calc_hidden_loss(dec_mask, dec_hidden, tdec_hidden, self.hparams.d_layer_to_copy)
+
+        blended_loss = (
+            self.alpha_ce * loss_ce
+            + self.alpha_mlm * sloss
+            + self.hparams.alpha_encoder_loss * loss_encoder
+            + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
+        )
+        return blended_loss, loss_ce, sloss, loss_encoder, hid_loss_enc, hid_loss_dec
+
+    def calc_hidden_loss(self, attention_mask, hidden_states, hidden_states_T, matches):
+        assert not isinstance(
+            hidden_states, torch.Tensor
+        ), f"expected list or tuple for hidden_states, got tensor of shape {hidden_states.shape}"
+        assert not isinstance(
+            hidden_states_T, torch.Tensor
+        ), f"expected list or tuple for hidden_states_T, got tensor of shape {hidden_states_T.shape}"
+        mask = attention_mask.to(hidden_states[0])
+        valid_count = mask.sum() * hidden_states[0].size(-1)
+        hidden_losses = [
+            (F.mse_loss(hidden_states[i], hidden_states_T[j], reduction="none") * mask.unsqueeze(-1)).sum()
+            / valid_count
+            for i, j in enumerate(matches)
+        ]
+        return sum(hidden_losses)
+
+
+class T5SummarizationDistiller(SummarizationDistiller):
+    def pre_init(self, hparams):
+        teacher = T5ForConditionalGeneration.from_pretrained(hparams.teacher)
+        n_layer = hparams.student_decoder_layers
+        assert n_layer == hparams.student_encoder_layers  # TODO(SS): relax this
+        d_layers_to_copy = get_layers_to_copy(n_layer, len(teacher.decoder.block))
+        e_layers_to_copy: List = get_layers_to_copy(n_layer, len(teacher.encoder.block))
+        student_updates = {"num_layers": n_layer}
+        hparams.d_layer_to_copy = d_layers_to_copy
+        hparams.e_layer_to_copy = e_layers_to_copy
+        kw = teacher.config.to_diff_dict()
+
+        kw.update(student_updates)
+        # Copy weights
+        student_cfg = T5Config(**kw)
+        student = T5ForConditionalGeneration(student_cfg)
+        student, _ = init_student(student, teacher)
+        self.copy_to_student(d_layers_to_copy, e_layers_to_copy, hparams, student, teacher)
+        Path(hparams.output_dir).mkdir(exist_ok=True)
+        task_specific_params = student.config.task_specific_params
+        if task_specific_params is not None:
+            student.config.update(task_specific_params.get("summarization", {}))
+        return d_layers_to_copy, student, student_cfg, teacher
+
+    def freeze_embeds(self):
+        freeze_params(self.model.shared)
+        for d in [self.model.encoder, self.model.decoder]:
+            freeze_params(d.embed_tokens)
+
+    def sanity_check_gradients(self):
+        """T5"""
+        assert_all_frozen(self.teacher)
+        assert_all_frozen(self.model.decoder.embed_tokens)
+        assert_all_frozen(self.model.encoder.embed_tokens)
+        if self.different_encoder:
+            assert any_requires_grad(self.model.encoder)
+        else:
+            freeze_params(self.model.encoder)
+            del self.teacher.model.encoder
+        if self.different_decoder:
+            assert any_requires_grad(self.model.decoder)
+        else:
+            freeze_params(self.model.decoder)  # TODO(SS): very suspicious
+
+    def _step(self, batch):
+        pad_token_id = self.tokenizer.pad_token_id
+        source_ids, source_mask, y = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
+        decoder_input_ids = y[:, :-1].contiguous()
+        labels = y[:, 1:].clone()
+        labels[y[:, 1:] == pad_token_id] = -100
+        # noinspection PyCallingNonCallable
+        dec_mask = decoder_input_ids.ne(pad_token_id)
+
+        sloss, slogits, dec_hidden, enc_outputs, enc_hidden_state = self(
+            source_ids,
+            attention_mask=source_mask,
+            decoder_input_ids=decoder_input_ids,
+            labels=labels,
+            output_hidden_states=True,
+            output_attentions=False,
+            use_cache=False,
+        )
+
+        def zero_tensor():
+            return torch.tensor(0.0).type_as(sloss)
+
+        loss_encoder, hid_loss_enc, hid_loss_dec = zero_tensor(), zero_tensor(), zero_tensor()
+        if self.different_encoder:
+            with torch.no_grad():
+                teacher_enc_outputs, teacher_enc_hid = self.teacher.encoder(
+                    source_ids, attention_mask=source_mask, output_hidden_states=True, use_cache=False,
+                )
+            if self.hparams.alpha_encoder_loss > 0:
+                loss_encoder = self.calc_mse_loss(enc_outputs, teacher_enc_outputs, source_mask)
+
+            hid_loss_enc = self.calc_hidden_loss(
+                source_mask, enc_hidden_state, teacher_enc_hid, self.hparams.e_layer_to_copy
+            )
+
+        teacher_enc_outputs = (enc_outputs,)
+        assert isinstance(teacher_enc_outputs, tuple), type(teacher_enc_outputs)
+
+        with torch.no_grad():
+            tloss, tlogits, tdec_hidden, _ = self.teacher(
+                source_ids,
+                attention_mask=source_mask,
+                encoder_outputs=teacher_enc_outputs,
+                decoder_input_ids=decoder_input_ids,
+                lm_labels=labels,
+                output_hidden_states=True,
+                use_cache=False,
+            )
+
+        loss_ce, s_logits_slct, t_logits_slct = self.calc_ce_loss(dec_mask, slogits, tlogits)
+        if self.alpha_hid > 0:
+            hid_loss_dec = self.calc_hidden_loss(dec_mask, dec_hidden, tdec_hidden, self.hparams.d_layer_to_copy)
+
+        blended_loss = (
+            self.alpha_ce * loss_ce
+            + self.alpha_mlm * sloss
+            + self.hparams.alpha_encoder_loss * loss_encoder
+            + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
+        )
+        return blended_loss, loss_ce, sloss, loss_encoder, hid_loss_enc, hid_loss_dec
+
+
+def create_module(args):
+    t5 = "t5" in args.model_name_or_path
+    if args.no_teacher:
+        assert not args.enc_only
+        module_cls = SummarizationModule
+    elif t5:
+        module_cls = T5SummarizationDistiller
+    elif args.enc_only:
+        raise ValueError("Deleted that")
+    else:
+        module_cls = SummarizationDistiller
+    args.setup_cls: str = module_cls.__name__
+    model = module_cls(args)
+    return model
+
+
+def evaluate_checkpoint(ckpt_path: Path, dest_dir=None):
+    exp_dir = ckpt_path.parent
+    if dest_dir is None:
+        dest_dir = exp_dir
+    clash = list(dest_dir.glob("test_generations*"))
+    if clash:
+        print(f"SKIPPING to avoid overwriting {clash}")
+    ckpt = torch.load(ckpt_path, map_location="cpu")
+    if "hparams" in ckpt:
+        args = argparse.Namespace(**ckpt["hparams"])
+    else:
+        args = argparse.Namespace(**pickle_load(exp_dir / "hparams.pkl"))
+    args.resume_from_checkpoint = str(ckpt_path)
+    args.do_train = False
+    args.output_dir = str(dest_dir)
+    args.n_gpu = 1
+    args.eval_batch_size = 16
+    Path(args.output_dir).mkdir(exist_ok=True)
+    model = create_module(args)
+    trainer: pl.Trainer = generic_train(model, args, early_stopping_callback=False)
+    trainer.test(model)
+
+
+def get_layers_to_copy(n_to_get, tot):
+    all_layers = list(range(tot))
+    if tot == 12:  # Alternating for special cases
+        layers_to_copy = {  # maps # layers in student -> which teacher layers to copy
+            6: [0, 2, 4, 7, 9, 11],
+            1: [11],
+            3: [0, 6, 11],
+            2: [0, 11],
+            4: [0, 4, 8, 11],
+            9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
+            12: all_layers,
+        }
+        return layers_to_copy[n_to_get]
+    else:
+        return all_layers[:n_to_get]
+
+
+def distill_main(args):
+    Path(args.output_dir).mkdir(exist_ok=True)
+    if len(os.listdir(args.output_dir)) > 3 and args.do_train:
+        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
+
+    model = create_module(args)
+    return ft_main(args, model=model)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser = SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
+    args = parser.parse_args()
+
+    distill_main(args)

examples/summarization/finetune.py

@@ -3,91 +3,169 @@ import glob
 import logging
 import os
 import time
+from pathlib import Path
+from typing import Dict, List, Tuple
 
+import numpy as np
+import pytorch_lightning as pl
 import torch
 from torch.utils.data import DataLoader
 
-from lightning_base import BaseTransformer, add_generic_args, generic_train, get_linear_schedule_with_warmup
+from lightning_base import BaseTransformer, add_generic_args, generic_train
+from transformers import get_linear_schedule_with_warmup
 
 
 try:
-    from .utils import SummarizationDataset
+    from .utils import (
+        use_task_specific_params,
+        SummarizationDataset,
+        lmap,
+        flatten_list,
+        pickle_save,
+        save_git_info,
+        freeze_params,
+        calculate_rouge,
+        get_git_info,
+        ROUGE_KEYS,
+    )
+    from .callbacks import Seq2SeqLoggingCallback, get_rouge2_checkpoint_callback
 except ImportError:
-    from utils import SummarizationDataset
-
+    from utils import (
+        use_task_specific_params,
+        SummarizationDataset,
+        lmap,
+        flatten_list,
+        pickle_save,
+        save_git_info,
+        freeze_params,
+        calculate_rouge,
+        get_git_info,
+        ROUGE_KEYS,
+    )
+    from callbacks import Seq2SeqLoggingCallback, get_rouge2_checkpoint_callback
 
 logger = logging.getLogger(__name__)
 
 
-class SummarizationTrainer(BaseTransformer):
+class SummarizationModule(BaseTransformer):
+    mode = "summarization"
+    loss_names = ["loss"]
 
-    mode = "language-modeling"
+    def __init__(self, hparams, **kwargs):
+        super().__init__(hparams, num_labels=None, mode=self.mode, **kwargs)
+        use_task_specific_params(self.model, "summarization")
+        save_git_info(self.hparams.output_dir)
+        self.metrics_save_path = Path(self.output_dir) / "metrics.pkl"
+        self.hparams_save_path = Path(self.output_dir) / "hparams.pkl"
+        self.step_count = 0
+        self.metrics = {"train": [], "val": [], "test": []}
 
-    def __init__(self, hparams):
-        super().__init__(hparams, num_labels=None, mode=self.mode)
         self.dataset_kwargs: dict = dict(
             data_dir=self.hparams.data_dir,
             max_source_length=self.hparams.max_source_length,
-            max_target_length=self.hparams.max_target_length,
+            prefix=self.model.config.prefix or "",
         )
-
-    def forward(self, input_ids, attention_mask=None, decoder_input_ids=None, lm_labels=None):
-        return self.model(
-            input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, lm_labels=lm_labels,
+        n_observations_per_split = {
+            "train": self.hparams.n_train,
+            "val": self.hparams.n_val,
+            "test": self.hparams.n_test,
+        }
+        self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
+
+        self.target_lens = {
+            "train": self.hparams.max_target_length,
+            "val": self.hparams.val_max_target_length,
+            "test": self.hparams.test_max_target_length,
+        }
+        assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}"
+        assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}"
+
+        if self.hparams.freeze_embeds:
+            self.freeze_embeds()
+        if self.hparams.freeze_encoder:
+            freeze_params(self.model.model.encoder)  # TODO: this will break for t5
+        self.hparams.git_sha = get_git_info()["repo_sha"]
+        self.num_workers = 4 if self.hparams.gpus <= 1 else None  # passing num_workers breaks lightning for multigpu
+
+    def freeze_embeds(self):
+        """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
+        if self.model.config.model_type == "bart":
+            freeze_params(self.model.model.shared)
+            for d in [self.model.model.encoder, self.model.model.decoder]:
+                freeze_params(d.embed_positions)
+                freeze_params(d.embed_tokens)
+        else:
+            freeze_params(self.model.shared)
+            for d in [self.model.encoder, self.model.decoder]:
+                freeze_params(d.embed_tokens)
+
+    def forward(self, input_ids, **kwargs):
+        return self.model(input_ids, **kwargs)
+
+    def ids_to_clean_text(self, generated_ids: List[int]):
+        gen_text = self.tokenizer.batch_decode(
+            generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
         )
+        return lmap(str.strip, gen_text)
 
-    def _step(self, batch):
+    def _step(self, batch: dict) -> Tuple:
         pad_token_id = self.tokenizer.pad_token_id
-        source_ids, source_mask, y = batch["source_ids"], batch["source_mask"], batch["target_ids"]
+        source_ids, source_mask, y = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
         y_ids = y[:, :-1].contiguous()
         lm_labels = y[:, 1:].clone()
         lm_labels[y[:, 1:] == pad_token_id] = -100
-        outputs = self(source_ids, attention_mask=source_mask, decoder_input_ids=y_ids, lm_labels=lm_labels,)
-
+        outputs = self(source_ids, attention_mask=source_mask, decoder_input_ids=y_ids, labels=lm_labels,)
         loss = outputs[0]
-
-        return loss
-
-    def training_step(self, batch, batch_idx):
-        loss = self._step(batch)
-
-        tensorboard_logs = {"train_loss": loss}
-        return {"loss": loss, "log": tensorboard_logs}
-
-    def validation_step(self, batch, batch_idx):
-        loss = self._step(batch)
-        return {"val_loss": loss}
-
-    def validation_end(self, outputs):
-        avg_loss = torch.stack([x["val_loss"] for x in outputs]).mean()
-        tensorboard_logs = {"val_loss": avg_loss}
-        return {"avg_val_loss": avg_loss, "log": tensorboard_logs}
-
-    def test_step(self, batch, batch_idx):
+        return (loss,)
+
+    def training_step(self, batch, batch_idx) -> Dict:
+        loss_tensors = self._step(batch)
+        logs = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
+        return {"loss": loss_tensors[0], "log": logs}
+
+    def validation_step(self, batch, batch_idx) -> Dict:
+        return self._generative_step(batch)
+
+    def validation_end(self, outputs, prefix="val") -> Dict:
+        self.step_count += 1
+        losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
+        loss = losses["loss"]
+        rouges = {k: np.array([x[k] for x in outputs]).mean() for k in ROUGE_KEYS + ["gen_time", "summ_len"]}
+        rouge_tensor: torch.FloatTensor = torch.tensor(rouges["rouge2"]).type_as(loss)
+        rouges.update({k: v.item() for k, v in losses.items()})
+        losses.update(rouges)
+        metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
+        metrics["step_count"] = self.step_count
+        self.save_metrics(metrics, prefix)  # writes to self.metrics_save_path
+        preds = flatten_list([x["preds"] for x in outputs])
+        return {"log": metrics, "preds": preds, f"{prefix}_loss": loss, f"{prefix}_rouge": rouge_tensor}
+
+    def save_metrics(self, metrics, prefix) -> None:
+        self.metrics[prefix].append(metrics)
+        pickle_save(self.metrics, self.metrics_save_path)
+
+    def _generative_step(self, batch):
         pad_token_id = self.tokenizer.pad_token_id
         source_ids, source_mask, y = SummarizationDataset.trim_seq2seq_batch(batch, pad_token_id)
-        # NOTE: the following kwargs get more speed and lower quality summaries than those in evaluate_cnn.py
-        generated_ids = self.model.generate(
-            input_ids=source_ids,
-            attention_mask=source_mask,
-            num_beams=1,
-            max_length=80,
-            repetition_penalty=2.5,
-            length_penalty=1.0,
-            early_stopping=True,
-            use_cache=True,
-        )
-        preds = [
-            self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-            for g in generated_ids
-        ]
-        target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]
-        loss = self._step(batch)
+        # TODO(SS): task specific params
+
+        t0 = time.time()
+        generated_ids = self.model.generate(input_ids=source_ids, attention_mask=source_mask, use_cache=True,)
+        gen_time = time.time() - t0
+        preds = self.ids_to_clean_text(generated_ids)
+        target = self.ids_to_clean_text(y)
+        loss_tensors = self._step(batch)
+        base_metrics = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
+        rouge: Dict = calculate_rouge(preds, target)
+        summ_len = np.mean(lmap(len, generated_ids))
+        base_metrics.update(gen_time=gen_time, summ_len=summ_len, preds=preds, target=target, **rouge)
+        return base_metrics
 
-        return {"val_loss": loss, "preds": preds, "target": target}
+    def test_step(self, batch, batch_idx):
+        return self._generative_step(batch)
 
     def test_end(self, outputs):
-        return self.validation_end(outputs)
+        return self.validation_end(outputs, prefix="test")
 
     def test_epoch_end(self, outputs):
         output_test_predictions_file = os.path.join(self.hparams.output_dir, "test_predictions.txt")
@@ -102,15 +180,43 @@ class SummarizationTrainer(BaseTransformer):
 
         return self.test_end(outputs)
 
+    def validation_epoch_end(self, outputs):
+        self.validation_end(outputs, "val")
+
+    def get_dataset(self, type_path) -> SummarizationDataset:
+        n_obs = self.n_obs[type_path]
+        max_target_length = self.target_lens[type_path]
+        dataset = SummarizationDataset(
+            self.tokenizer,
+            type_path=type_path,
+            n_obs=n_obs,
+            max_target_length=max_target_length,
+            **self.dataset_kwargs,
+        )
+        return dataset
+
     def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False) -> DataLoader:
-        dataset = SummarizationDataset(self.tokenizer, type_path=type_path, **self.dataset_kwargs)
-        dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=dataset.collate_fn, shuffle=shuffle)
+        dataset = self.get_dataset(type_path)
+        sampler = None
+        if self.hparams.sortish_sampler and type_path == "train":
+            assert self.hparams.gpus <= 1  # TODO: assert earlier
+            sampler = dataset.make_sortish_sampler(batch_size)
+            shuffle = False
+
+        dataloader = DataLoader(
+            dataset,
+            batch_size=batch_size,
+            collate_fn=dataset.collate_fn,
+            shuffle=shuffle,
+            num_workers=self.num_workers,
+            sampler=sampler,
+        )
         return dataloader
 
     def train_dataloader(self) -> DataLoader:
         dataloader = self.get_dataloader("train", batch_size=self.hparams.train_batch_size, shuffle=True)
         t_total = (
-            (len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.n_gpu)))
+            (len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.gpus)))
             // self.hparams.gradient_accumulation_steps
             * float(self.hparams.num_train_epochs)
         )
@@ -129,7 +235,7 @@ class SummarizationTrainer(BaseTransformer):
     @staticmethod
     def add_model_specific_args(parser, root_dir):
         BaseTransformer.add_model_specific_args(parser, root_dir)
-        # Add BART specific options
+        add_generic_args(parser, root_dir)
         parser.add_argument(
             "--max_source_length",
             default=1024,
@@ -144,41 +250,82 @@ class SummarizationTrainer(BaseTransformer):
             help="The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded.",
         )
-
+        parser.add_argument(
+            "--val_max_target_length",
+            default=142,  # these defaults are optimized for CNNDM. For xsum, see README.md.
+            type=int,
+            help="The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded.",
+        )
+        parser.add_argument(
+            "--test_max_target_length",
+            default=142,
+            type=int,
+            help="The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded.",
+        )
         parser.add_argument(
             "--data_dir",
-            default=None,
             type=str,
             required=True,
-            help="The input data dir. Should contain the dataset files for the CNN/DM summarization task.",
+            help="The input data dir. Should contain train.source, train.target, val.source, val.target, test.source, test.target",
         )
+        parser.add_argument("--freeze_encoder", action="store_true")
+        parser.add_argument("--freeze_embeds", action="store_true")
+        parser.add_argument("--sortish_sampler", action="store_true", default=False)
+        parser.add_argument("--logger", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
+        parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
+        parser.add_argument("--n_val", type=int, default=500, required=False, help="# examples. -1 means use all.")
+        parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
         return parser
 
 
-def main(args):
-
-    # If output_dir not provided, a folder will be generated in pwd
-    if not args.output_dir:
-        args.output_dir = os.path.join("./results", f"{args.task}_{time.strftime('%Y%m%d_%H%M%S')}",)
-        os.makedirs(args.output_dir)
-    model = SummarizationTrainer(args)
-    trainer = generic_train(model, args)
+def main(args, model=None) -> SummarizationModule:
+    Path(args.output_dir).mkdir(exist_ok=True)
+    if len(os.listdir(args.output_dir)) > 3 and args.do_train:
+        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
+    if model is None:
+        model: BaseTransformer = SummarizationModule(args)
+    if (
+        args.logger == "default"
+        or args.fast_dev_run
+        or str(args.output_dir).startswith("/tmp")
+        or str(args.output_dir).startswith("/var")
+    ):
+        logger = True  # don't pollute wandb logs unnecessarily
+    elif args.logger == "wandb":
+        from pytorch_lightning.loggers import WandbLogger
+
+        logger = WandbLogger(name=model.output_dir.name)
+    elif args.logger == "wandb_shared":
+        from pytorch_lightning.loggers import WandbLogger
+
+        # TODO: separate LB for CNN, we should use Path(args.data_dir).name to determine the correct LB.
+        logger = WandbLogger(name=model.output_dir.name, project="hf_summarization")
+    trainer: pl.Trainer = generic_train(
+        model,
+        args,
+        logging_callback=Seq2SeqLoggingCallback(),
+        checkpoint_callback=get_rouge2_checkpoint_callback(args.output_dir),
+        logger=logger,
+        # TODO: early stopping callback seems messed up
+    )
+    if not args.do_predict:
+        return model
 
-    # Optionally, predict on dev set and write to output_dir
-    if args.do_predict:
-        # See https://github.com/huggingface/transformers/issues/3159
-        # pl use this format to create a checkpoint:
-        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
-        # /pytorch_lightning/callbacks/model_checkpoint.py#L169
-        checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True)))
-        model = model.load_from_checkpoint(checkpoints[-1])
-        trainer.test(model)
+    model.hparams.test_checkpoint = ""
+    checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "*.ckpt"), recursive=True)))
+    if checkpoints:
+        model.hparams.test_checkpoint = checkpoints[-1]
+        trainer.resume_from_checkpoint = checkpoints[-1]
+    trainer.logger.log_hyperparams(model.hparams)
+    trainer.test(model)  # this breaks in DDP, known lightning issue. See evaluate_checkpoint to recover metrics.
+    return model
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    add_generic_args(parser, os.getcwd())
-    parser = SummarizationTrainer.add_model_specific_args(parser, os.getcwd())
+    parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
     args = parser.parse_args()
 
     main(args)

examples/summarization/finetune_bart.sh → examples/summarization/finetune.sh

-export OUTPUT_DIR_NAME=bart_sum
-export CURRENT_DIR=${PWD}
-export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+export OUTPUT_DIR=bart_cnn_finetune
 
 # Make output directory if it doesn't exist
 mkdir -p $OUTPUT_DIR
@@ -8,11 +6,18 @@ mkdir -p $OUTPUT_DIR
 # Add parent directory to python path to access lightning_base.py
 export PYTHONPATH="../":"${PYTHONPATH}"
 
+
+# --model_name_or_path=t5-base for t5
+
 python finetune.py \
---data_dir=./cnn-dailymail/cnn_dm \
---model_name_or_path=bart-large \
---learning_rate=3e-5 \
---train_batch_size=4 \
---eval_batch_size=4 \
---output_dir=$OUTPUT_DIR \
---do_train  $@
+    --model_name_or_path=facebook/bart-large \
+    --learning_rate=3e-5 \
+    --fp16 \
+    --gpus 1 \
+    --do_train \
+    --do_predict \
+    --n_val 1000 \
+    --val_check_interval 0.1 \
+    --sortish_sampler \
+    --max_target_length=56 \
+    $@

examples/summarization/initialization_utils.py

+from typing import List
+
+from torch import nn
+
+
+def init_student(student, teacher):
+    teacher_state_dict = teacher.state_dict()
+    info = student.load_state_dict(teacher_state_dict, strict=False)
+    assert info.missing_keys == [], info.missing_keys
+    return student, info
+
+
+def copy_decoder_layers(teacher, student, l2copy=[0, 2, 4, 7, 9, 11]):
+    copy_layers(teacher.model.decoder.layers, student.model.decoder.layers, l2copy)
+
+
+def copy_layers(teacher_layers: nn.ModuleList, student_layers: nn.ModuleList, layers_to_copy: List) -> None:
+    layers_to_copy = nn.ModuleList([l for i, l in enumerate(teacher_layers) if i in layers_to_copy])
+    assert len(student_layers) == len(layers_to_copy), f"{len(student_layers)} != {len(layers_to_copy)}"
+    student_layers.load_state_dict(layers_to_copy.state_dict())

examples/summarization/run_distiller.sh

+#CNN_DIR = /home/shleifer/transformers_fork/examples/summarization/bart/cnn_dm
+
+# Add parent directory to python path to access lightning_base.py
+export PYTHONPATH="../":"${PYTHONPATH}"
+
+python distillation.py \
+--learning_rate=3e-4 \
+--do_train \
+--do_predict \
+--fp16 \
+--val_check_interval 0.1 \
+$@

examples/summarization/evaluate_cnn.py → examples/summarization/run_eval.py

 import argparse
+import json
 from pathlib import Path
 
 import torch
-from rouge_score import rouge_scorer, scoring
 from tqdm import tqdm
 
-from transformers import AutoModelWithLMHead, AutoTokenizer
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 
+try:
+    from .finetune import calculate_rouge, use_task_specific_params
+except ImportError:
+    from finetune import calculate_rouge, use_task_specific_params
+
 DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
 
@@ -18,17 +23,17 @@ def chunks(lst, n):
 
 
 def generate_summaries(
-    examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE
-):
+    examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE, fp16=False,
+) -> None:
     fout = Path(out_file).open("w", encoding="utf-8")
-    model = AutoModelWithLMHead.from_pretrained(model_name).to(device)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
+    if fp16:
+        model = model.half()
 
     tokenizer = AutoTokenizer.from_pretrained(model_name)
 
     # update config with summarization specific params
-    task_specific_params = model.config.task_specific_params
-    if task_specific_params is not None:
-        model.config.update(task_specific_params.get("summarization", {}))
+    use_task_specific_params(model, "summarization")
 
     for batch in tqdm(list(chunks(examples, batch_size))):
         if "t5" in model_name:
@@ -44,56 +49,29 @@ def generate_summaries(
             fout.flush()
 
 
-def calculate_rouge(output_lns, reference_lns, score_path):
-    score_file = Path(score_path).open("w")
-    scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
-    aggregator = scoring.BootstrapAggregator()
-
-    for reference_ln, output_ln in zip(reference_lns, output_lns):
-        scores = scorer.score(reference_ln, output_ln)
-        aggregator.add_scores(scores)
-
-    result = aggregator.aggregate()
-    score_file.write(
-        "ROUGE_1: \n{} \n\n ROUGE_2: \n{} \n\n ROUGE_L: \n{} \n\n".format(
-            result["rouge1"], result["rouge2"], result["rougeL"]
-        )
-    )
-
-
 def run_generate():
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "input_path", type=str, help="like cnn_dm/test.source or cnn_dm/test_articles_input.txt",
-    )
-    parser.add_argument(
-        "output_path", type=str, help="where to save summaries",
-    )
-    parser.add_argument(
-        "model_name",
-        type=str,
-        default="facebook/bart-large-cnn",
-        help="like bart-large-cnn,'t5-small', 't5-base', 't5-large', 't5-3b', 't5-11b",
-    )
+    parser.add_argument("input_path", type=str, help="like cnn_dm/test.source")
+    parser.add_argument("output_path", type=str, help="where to save summaries")
+    parser.add_argument("model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
     parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test_reference_summaries.txt")
-    parser.add_argument(
-        "--score_path", type=str, required=False, help="where to save the rouge score",
-    )
-    parser.add_argument(
-        "--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.",
-    )
-    parser.add_argument(
-        "--bs", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
-    )
+    parser.add_argument("--score_path", type=str, required=False, help="where to save the rouge score in json format")
+    parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.")
+    parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
+    parser.add_argument("--fp16", action="store_true")
     args = parser.parse_args()
     examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
 
-    generate_summaries(examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device)
+    generate_summaries(
+        examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device, fp16=args.fp16
+    )
     if args.score_path is not None:
         output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
         reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
 
-        calculate_rouge(output_lns, reference_lns, args.score_path)
+        rouge: dict = calculate_rouge(output_lns, reference_lns)
+
+        json.dump(rouge, open("score_path", "w+"))
 
 
 if __name__ == "__main__":

examples/summarization/test_summarization_examples.py

@@ -7,28 +7,40 @@ import unittest
 from pathlib import Path
 from unittest.mock import patch
 
+import torch
 from torch.utils.data import DataLoader
 
 from transformers import BartTokenizer
 
-from .evaluate_cnn import run_generate
+from .distillation import distill_main, evaluate_checkpoint
 from .finetune import main
-from .utils import SummarizationDataset
+from .run_eval import generate_summaries, run_generate
+from .utils import SummarizationDataset, lmap, pickle_load
 
 
 logging.basicConfig(level=logging.DEBUG)
 
 logger = logging.getLogger()
-
-DEFAULT_ARGS = {
+FP16_EVER = False
+CHEAP_ARGS = {
+    "logger": "default",
+    "alpha_hid": 0,
+    "freeze_embeds": True,
+    "enc_only": False,
+    "tgt_suffix": "",
+    "resume_from_checkpoint": None,
+    "sortish_sampler": True,
+    "student_decoder_layers": 1,
+    "val_check_interval": 1.0,
     "output_dir": "",
     "fp16": False,
+    "no_teacher": False,
     "fp16_opt_level": "O1",
-    "n_gpu": 1,
+    "gpus": 1 if torch.cuda.is_available() else 0,
     "n_tpu_cores": 0,
     "max_grad_norm": 1.0,
     "do_train": True,
-    "do_predict": False,
+    "do_predict": True,
     "gradient_accumulation_steps": 1,
     "server_ip": "",
     "server_port": "",
@@ -36,7 +48,7 @@ DEFAULT_ARGS = {
     "model_type": "bart",
     "model_name_or_path": "sshleifer/bart-tiny-random",
     "config_name": "",
-    "tokenizer_name": "",
+    "tokenizer_name": "facebook/bart-large",
     "cache_dir": "",
     "do_lower_case": False,
     "learning_rate": 3e-05,
@@ -48,6 +60,17 @@ DEFAULT_ARGS = {
     "eval_batch_size": 2,
     "max_source_length": 12,
     "max_target_length": 12,
+    "val_max_target_length": 12,
+    "test_max_target_length": 12,
+    "fast_dev_run": False,
+    "no_cache": False,
+    "n_train": -1,
+    "n_val": -1,
+    "n_test": -1,
+    "student_encoder_layers": 1,
+    "alpha_loss_encoder": 0.0,
+    "freeze_encoder": False,
+    "auto_scale_batch_size": False,
 }
 
 
@@ -56,6 +79,9 @@ def _dump_articles(path: Path, articles: list):
         f.write("\n".join(articles))
 
 
+BDIR = Path("~/transformers_fork/examples/summarization/bart/").absolute()
+
+
 def make_test_data_dir():
     tmp_dir = Path(tempfile.gettempdir())
     articles = [" Sam ate lunch today", "Sams lunch ingredients"]
@@ -66,6 +92,169 @@ def make_test_data_dir():
     return tmp_dir
 
 
+@unittest.skip("These wont' pass until hidden_states kwarg is merged.")
+class TestSummarizationDistiller(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
+        return cls
+
+    @unittest.skipUnless(torch.cuda.device_count() > 1, "skipping multiGPU test")
+    def test_bdc_multigpu(self):
+        updates = dict(
+            student_encoder_layers=2,
+            student_decoder_layers=1,
+            no_teacher=True,
+            freeze_encoder=True,
+            gpus=2,
+            sortish_sampler=False,
+        )
+        self._bart_distiller_cli(updates)
+
+    @unittest.skipUnless(torch.cuda.is_available(), "skipping fp16 test")
+    def test_bdc_fp16(self):
+        updates = dict(
+            student_encoder_layers=2,
+            student_decoder_layers=1,
+            alpha_hid=3.0,
+            freeze_encoder=True,
+            gpus=1,
+            fp16=FP16_EVER,
+            fp16_opt_level="O1",
+        )
+        self._bart_distiller_cli(updates)
+
+    @unittest.skipUnless(torch.cuda.is_available(), "skipping fp16 test")
+    def test_bdc_t5_eval_fp16(self):
+        updates = dict(
+            fp16=FP16_EVER,
+            gpus=1,
+            model_type="t5",
+            model_name_or_path="patrickvonplaten/t5-tiny-random",
+            do_train=False,
+            do_predict=True,
+            tokenizer_name=None,
+            no_teacher=True,
+        )
+        self._bart_distiller_cli(updates, check_contents=False)
+
+    @unittest.skipUnless(torch.cuda.is_available(), "skipping fp16 test")
+    def test_bdc_t5_train_fp16(self):
+        updates = dict(
+            fp16=FP16_EVER,
+            gpus=1,
+            model_type="t5",
+            model_name_or_path="patrickvonplaten/t5-tiny-random",
+            do_train=True,
+            do_predict=True,
+            tokenizer_name="patrickvonplaten/t5-tiny-random",
+            no_teacher=True,
+        )
+        self._bart_distiller_cli(updates)
+
+    def test_bdc_no_teacher(self):
+        updates = dict(student_encoder_layers=2, student_decoder_layers=1, no_teacher=True,)
+        self._bart_distiller_cli(updates)
+
+    def test_bdc_yes_teacher(self):
+        updates = dict(student_encoder_layers=2, student_decoder_layers=1,)
+        self._bart_distiller_cli(updates)
+
+    def test_bdc_checkpointing(self):
+
+        updates = dict(
+            student_encoder_layers=2,
+            student_decoder_layers=1,
+            num_train_epochs=4,
+            val_check_interval=0.25,
+            alpha_hid=2.0,
+        )
+        model = self._bart_distiller_cli(updates, check_contents=False)
+
+        ckpts = list(Path(model.output_dir).glob("*.ckpt"))
+        self.assertEqual(1, len(ckpts))
+        transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
+        self.assertEqual(len(transformer_ckpts), len(ckpts))
+        new_transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
+        self.assertEqual(len(new_transformer_ckpts), 1)
+        examples = lmap(str.strip, model.hparams.data_dir.joinpath("test.source").open().readlines())
+        out_path = tempfile.mktemp()
+        generate_summaries(examples, out_path, new_transformer_ckpts[0].parent)
+        self.assertTrue(Path(out_path).exists())
+
+        evaluate_checkpoint(ckpts[0], dest_dir=Path(tempfile.mkdtemp()))
+
+    def test_bdc_t5(self):
+        updates = dict(
+            student_encoder_layers=1,
+            student_decoder_layers=1,
+            alpha_hid=2.0,
+            teacher="patrickvonplaten/t5-tiny-random",
+            model_type="t5",
+            model_name_or_path="patrickvonplaten/t5-tiny-random",
+            tokenizer_name="patrickvonplaten/t5-tiny-random",
+        )
+        self._bart_distiller_cli(updates)
+
+    def test_bdc_t5_eval(self):
+        updates = dict(
+            model_type="t5",
+            model_name_or_path="patrickvonplaten/t5-tiny-random",
+            do_train=False,
+            do_predict=True,
+            tokenizer_name="patrickvonplaten/t5-tiny-random",
+            no_teacher=True,
+        )
+        self._bart_distiller_cli(updates, check_contents=False)
+
+    def _bart_distiller_cli(self, updates, check_contents=True):
+        default_updates = dict(
+            model_type="bart",
+            train_batch_size=1,
+            eval_batch_size=2,
+            num_train_epochs=2,
+            alpha_mlm=0.2,
+            alpha_ce=0.8,
+            do_predict=True,
+            gpus=1 if torch.cuda.is_available() else 0,
+            model_name_or_path="sshleifer/tinier_bart",
+            teacher=CHEAP_ARGS["model_name_or_path"],
+            val_check_interval=0.5,
+            alpha_encoder_loss=0.4,
+        )
+        default_updates.update(updates)
+        args_d: dict = CHEAP_ARGS.copy()
+        tmp_dir = make_test_data_dir()
+        output_dir = tempfile.mkdtemp(prefix="output_")
+
+        args_d.update(data_dir=tmp_dir, output_dir=output_dir, **default_updates)
+        model = distill_main(argparse.Namespace(**args_d))
+        if not check_contents:
+            return model
+        contents = os.listdir(output_dir)
+        ckpt_name = "val_avg_rouge2=0.0000-step_count=2.ckpt"  # "val_avg_rouge2=0.0000-epoch=1.ckpt"  # "epoch=1-val_avg_rouge2=0.0000.ckpt"
+        contents = {os.path.basename(p) for p in contents}
+        self.assertIn(ckpt_name, contents)
+        self.assertIn("metrics.pkl", contents)
+        self.assertIn("test_generations.txt", contents)
+        self.assertIn("val_generations_1.txt", contents)
+        self.assertIn("val_1_results.txt", contents)
+        self.assertIn("test_results.txt", contents)
+        # self.assertEqual(len(contents), 15)
+
+        metrics = pickle_load(Path(output_dir) / "metrics.pkl")
+        import pandas as pd
+
+        val_df = pd.DataFrame(metrics["val"])
+        train_df = pd.DataFrame(metrics["train"])
+        test_df = pd.DataFrame(metrics["test"])
+        desired_n_evals = args_d["num_train_epochs"] * 2 + 1
+        self.assertEqual(val_df.shape[0], desired_n_evals)  #
+        self.assertEqual(test_df.shape[1], val_df.shape[1])
+        self.assertEqual(train_df.shape[0], 0)
+        return model
+
+
 class TestBartExamples(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
@@ -79,49 +268,31 @@ class TestBartExamples(unittest.TestCase):
         output_file_name = Path(tempfile.gettempdir()) / "utest_output_bart_sum.hypo"
         articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
         _dump_articles(tmp, articles)
-        testargs = ["evaluate_cnn.py", str(tmp), str(output_file_name), "sshleifer/bart-tiny-random"]
+        testargs = ["run_eval.py", str(tmp), str(output_file_name), "sshleifer/bart-tiny-random"]
         with patch.object(sys, "argv", testargs):
             run_generate()
             self.assertTrue(Path(output_file_name).exists())
             os.remove(Path(output_file_name))
 
-    def test_bart_run_sum_cli(self):
-        args_d: dict = DEFAULT_ARGS.copy()
-        tmp_dir = make_test_data_dir()
-        output_dir = tempfile.mkdtemp(prefix="output_")
-        args_d.update(
-            data_dir=tmp_dir, model_type="bart", train_batch_size=2, eval_batch_size=2, n_gpu=0, output_dir=output_dir,
-        )
-        main(argparse.Namespace(**args_d))
-        args_d.update({"do_train": False, "do_predict": True})
-
-        main(argparse.Namespace(**args_d))
-        contents = os.listdir(output_dir)
-        expected_contents = {
-            "checkpointepoch=0.ckpt",
-            "test_results.txt",
-        }
-        created_files = {os.path.basename(p) for p in contents}
-        self.assertSetEqual(expected_contents, created_files)
-
     def test_t5_run_sum_cli(self):
-        args_d: dict = DEFAULT_ARGS.copy()
+        args_d: dict = CHEAP_ARGS.copy()
+
         tmp_dir = make_test_data_dir()
         output_dir = tempfile.mkdtemp(prefix="output_")
         args_d.update(
             data_dir=tmp_dir,
             model_type="t5",
             model_name_or_path="patrickvonplaten/t5-tiny-random",
+            tokenizer_name=None,  # "patrickvonplaten/t5-tiny-random",
             train_batch_size=2,
             eval_batch_size=2,
-            n_gpu=0,
+            gpus=0,
             output_dir=output_dir,
             do_predict=True,
         )
-        main(argparse.Namespace(**args_d))
-
-        # args_d.update({"do_train": False, "do_predict": True})
-        # main(argparse.Namespace(**args_d))
+        assert "n_train" in args_d
+        args = argparse.Namespace(**args_d)
+        main(args)
 
     def test_bart_summarization_dataset(self):
         tmp_dir = Path(tempfile.gettempdir())
@@ -138,42 +309,16 @@ class TestBartExamples(unittest.TestCase):
         )
         dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
         for batch in dataloader:
-            self.assertEqual(batch["source_mask"].shape, batch["source_ids"].shape)
+            self.assertEqual(batch["attention_mask"].shape, batch["input_ids"].shape)
             # show that articles were trimmed.
-            self.assertEqual(batch["source_ids"].shape[1], max_len_source)
-            self.assertGreater(20, batch["source_ids"].shape[1])  # trimmed significantly
+            self.assertEqual(batch["input_ids"].shape[1], max_len_source)
+            self.assertGreater(20, batch["input_ids"].shape[1])  # trimmed significantly
 
             # show that targets were truncated
-            self.assertEqual(batch["target_ids"].shape[1], trunc_target)  # Truncated
+            self.assertEqual(batch["decoder_input_ids"].shape[1], trunc_target)  # Truncated
             self.assertGreater(max_len_target, trunc_target)  # Truncated
 
 
-class TestT5Examples(unittest.TestCase):
-    def test_t5_cli(self):
-        output_file_name = "output_t5_sum.txt"
-        score_file_name = "score_t5_sum.txt"
-        articles = ["New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
-        stream_handler = logging.StreamHandler(sys.stdout)
-        logger.addHandler(stream_handler)
-        tmp = Path(tempfile.gettempdir()) / "utest_generations_t5_sum.hypo"
-        with tmp.open("w", encoding="utf-8") as f:
-            f.write("\n".join(articles))
-
-        output_file_name = Path(tempfile.gettempdir()) / "utest_output_t5_sum.hypo"
-        score_file_name = Path(tempfile.gettempdir()) / "utest_score_t5_sum.hypo"
-
-        testargs = [
-            "evaluate_cnn.py",
-            str(tmp),
-            str(output_file_name),
-            "patrickvonplaten/t5-tiny-random",
-            "--reference_path",
-            str(tmp),
-            "--score_path",
-            str(score_file_name),
-        ]
-
-        with patch.object(sys, "argv", testargs):
-            run_generate()
-            self.assertTrue(Path(output_file_name).exists())
-            self.assertTrue(Path(score_file_name).exists())
+def list_to_text_file(lst, path):
+    dest = Path(path)
+    dest.open("w+").writelines(lst)

examples/summarization/utils.py

+import itertools
+import json
 import os
+import pickle
+from pathlib import Path
+from typing import Dict, Iterable, List
 
+import git
+import numpy as np
 import torch
-from torch.utils.data import Dataset
+from rouge_score import rouge_scorer, scoring
+from torch import nn
+from torch.utils.data import Dataset, Sampler
+from tqdm import tqdm
 
+from transformers import BartTokenizer
 
-def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return_tensors="pt"):
+
+def encode_file(
+    tokenizer,
+    data_path,
+    max_length,
+    pad_to_max_length=True,
+    return_tensors="pt",
+    overwrite_cache=False,
+    prefix="",
+    tok_name="",
+):
+    cache_path = Path(f"{data_path}_{tok_name}{max_length}.pt")
+    if not overwrite_cache and cache_path.exists():
+        try:
+            examples = torch.load(cache_path)
+            assert isinstance(examples, list)
+            return examples
+
+        except Exception:
+            print(f"failed to load from {cache_path}, retokenizing {data_path}")
+    data_path = Path(data_path)
+
+    lns = lmap(str.strip, data_path.open().readlines())
+    lns = [prefix + text for text in lns]
+    assert lns, f"found empty file at {data_path}"
     examples = []
-    with open(data_path, "r") as f:
-        for text in f.readlines():
+    for text in tqdm(lns, desc=f"Tokenizing {data_path.name}"):
         tokenized = tokenizer.batch_encode_plus(
-                [text], max_length=max_length, pad_to_max_length=pad_to_max_length, return_tensors=return_tensors,
+            [text],  # DONT ADD SPACES
+            max_length=max_length,
+            pad_to_max_length=pad_to_max_length,
+            add_prefix_space=True,
+            return_tensors=return_tensors,
         )
         examples.append(tokenized)
+    torch.save(lmap(dict, examples), cache_path.open("wb"))
     return examples
 
 
+def lmap(f, x):
+    return list(map(f, x))
+
+
+T5_PREFIX = "summarize: "  # HACK, fixme
+
+
 def trim_batch(
     input_ids, pad_token_id, attention_mask=None,
 ):
@@ -30,15 +76,38 @@ class SummarizationDataset(Dataset):
     def __init__(
         self,
         tokenizer,
-        data_dir="./cnn-dailymail/cnn_dm/",
+        data_dir,
         type_path="train",
         max_source_length=1024,
         max_target_length=56,
+        n_obs=None,
+        overwrite_cache=False,
+        prefix="",
     ):
         super().__init__()
-        self.tokenizer = tokenizer
+        tok_name = "T5" if not isinstance(tokenizer, BartTokenizer) else ""
+        self.source = encode_file(
+            tokenizer,
+            os.path.join(data_dir, type_path + ".source"),
+            max_source_length,
+            overwrite_cache=overwrite_cache,
+            prefix=prefix,
+            tok_name=tok_name,
+        )
+        if type_path == "train":
+            tgt_path = os.path.join(data_dir, type_path + ".target")
+        else:
+            tgt_path = os.path.join(data_dir, type_path + ".target")
+
+        self.target = encode_file(
+            tokenizer, tgt_path, max_target_length, overwrite_cache=overwrite_cache, tok_name=tok_name
+        )
         self.source = encode_file(tokenizer, os.path.join(data_dir, type_path + ".source"), max_source_length)
         self.target = encode_file(tokenizer, os.path.join(data_dir, type_path + ".target"), max_target_length)
+        if n_obs is not None:
+            self.source = self.source[:n_obs]
+            self.target = self.target[:n_obs]
+        self.pad_token_id = tokenizer.pad_token_id
 
     def __len__(self):
         return len(self.source)
@@ -47,19 +116,141 @@ class SummarizationDataset(Dataset):
         source_ids = self.source[index]["input_ids"].squeeze()
         target_ids = self.target[index]["input_ids"].squeeze()
         src_mask = self.source[index]["attention_mask"].squeeze()
-        return {"source_ids": source_ids, "source_mask": src_mask, "target_ids": target_ids}
+        return {"input_ids": source_ids, "attention_mask": src_mask, "decoder_input_ids": target_ids}
 
     @staticmethod
     def trim_seq2seq_batch(batch, pad_token_id):
-        y = trim_batch(batch["target_ids"], pad_token_id)
-        source_ids, source_mask = trim_batch(batch["source_ids"], pad_token_id, attention_mask=batch["source_mask"])
+        y = trim_batch(batch["decoder_input_ids"], pad_token_id)
+        source_ids, source_mask = trim_batch(batch["input_ids"], pad_token_id, attention_mask=batch["attention_mask"])
         return source_ids, source_mask, y
 
-    def collate_fn(self, batch):
-        input_ids = torch.stack([x["source_ids"] for x in batch])
-        masks = torch.stack([x["source_mask"] for x in batch])
-        target_ids = torch.stack([x["target_ids"] for x in batch])
-        pad_token_id = self.tokenizer.pad_token_id
+    def collate_fn(self, batch) -> dict:
+        input_ids = torch.stack([x["input_ids"] for x in batch])
+        masks = torch.stack([x["attention_mask"] for x in batch])
+        target_ids = torch.stack([x["decoder_input_ids"] for x in batch])
+        pad_token_id = self.pad_token_id
         y = trim_batch(target_ids, pad_token_id)
         source_ids, source_mask = trim_batch(input_ids, pad_token_id, attention_mask=masks)
-        return {"source_ids": source_ids, "source_mask": source_mask, "target_ids": y}
+        batch = {"input_ids": source_ids, "attention_mask": source_mask, "decoder_input_ids": y}
+        return batch
+
+    @property
+    def src_lens(self):  # Can delete?
+        return lmap(len, self.source)
+
+    @property
+    def tgt_lens(self):
+        return lmap(len, self.target)
+
+    def make_sortish_sampler(self, batch_size):
+        return SortishSampler(self.source, batch_size)
+
+
+class SortishSampler(Sampler):
+    "Go through the text data by order of src length with a bit of randomness. From fastai repo."
+
+    def __init__(self, data, batch_size):
+        self.data, self.bs = data, batch_size
+
+    def key(self, i):
+        return len(self.data[i])
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __iter__(self):
+        idxs = np.random.permutation(len(self.data))
+        sz = self.bs * 50
+        ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)]
+        sort_idx = np.concatenate([sorted(s, key=self.key, reverse=True) for s in ck_idx])
+        sz = self.bs
+        ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
+        max_ck = np.argmax([self.key(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
+        ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
+        sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int)
+        sort_idx = np.concatenate((ck_idx[0], sort_idx))
+        return iter(sort_idx)
+
+
+def use_task_specific_params(model, task):
+    # update config with summarization specific params
+    task_specific_params = model.config.task_specific_params
+    if task_specific_params is not None:
+        model.config.update(task_specific_params.get(task, {}))
+
+
+def pickle_load(path):
+    """pickle.load(path)"""
+    with open(path, "rb") as f:
+        return pickle.load(f)
+
+
+def pickle_save(obj, path):
+    """pickle.dump(obj, path)"""
+    with open(path, "wb") as f:
+        return pickle.dump(obj, f)
+
+
+def flatten_list(summary_ids: List[List]):
+    return [x for x in itertools.chain.from_iterable(summary_ids)]
+
+
+def save_git_info(folder_path: str):
+    """
+    Log commit info.
+    """
+    repo_infos = get_git_info()
+
+    with open(os.path.join(folder_path, "git_log.json"), "w") as f:
+        json.dump(repo_infos, f, indent=4)
+
+
+def get_git_info():
+    repo = git.Repo(search_parent_directories=True)
+    repo_infos = {
+        "repo_id": str(repo),
+        "repo_sha": str(repo.head.object.hexsha),
+        "repo_branch": str(repo.active_branch),
+    }
+    return repo_infos
+
+
+ROUGE_KEYS = ["rouge1", "rouge2", "rougeL"]
+
+
+def calculate_rouge(output_lns: List[str], reference_lns: List[str]) -> Dict:
+    scorer = rouge_scorer.RougeScorer(ROUGE_KEYS, use_stemmer=True)
+    aggregator = scoring.BootstrapAggregator()
+
+    for reference_ln, output_ln in zip(reference_lns, output_lns):
+        scores = scorer.score(reference_ln, output_ln)
+        aggregator.add_scores(scores)
+
+    result = aggregator.aggregate()
+    return {k: v.mid.fmeasure for k, v in result.items()}
+
+
+def freeze_params(model: nn.Module):
+    for par in model.parameters():
+        par.requires_grad = False
+
+
+def grad_status(model: nn.Module) -> Iterable:
+    return (par.requires_grad for par in model.parameters())
+
+
+def any_requires_grad(model: nn.Module) -> bool:
+    return any(grad_status(model))
+
+
+def assert_all_frozen(model):
+    model_grads: List[bool] = list(grad_status(model))
+    n_require_grad = sum(lmap(int, model_grads))
+    npars = len(model_grads)
+    assert not any(model_grads), f"{n_require_grad/npars:.1%} of {npars} weights require grad"
+
+
+def assert_not_all_frozen(model):
+    model_grads: List[bool] = list(grad_status(model))
+    npars = len(model_grads)
+    assert any(model_grads), f"none of {npars} weights require grad"

src/transformers/modeling_bart.py

@@ -59,7 +59,7 @@ BART_GENERATION_EXAMPLE = r"""
     Examples::
 
         from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
-        # see ``examples/summarization/bart/evaluate_cnn.py`` for a longer example
+        # see ``examples/summarization/bart/run_eval.py`` for a longer example
         model = BartForConditionalGeneration.from_pretrained('bart-large-cnn')
         tokenizer = BartTokenizer.from_pretrained('bart-large-cnn')
         ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."