Upgrade examples to pl=0.8.1(#5146)

examples/lightning_base.py

@@ -8,6 +8,7 @@ from typing import Any, Dict
 import numpy as np
 import pytorch_lightning as pl
 import torch
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
 
 from transformers import (
     AdamW,
@@ -60,10 +61,9 @@ class BaseTransformer(pl.LightningModule):
         model=None,
         **config_kwargs
     ):
-        "Initialize a model."
-
+        """Initialize a model, tokenizer and config."""
         super().__init__()
-        self.hparams = hparams
+        self.hparams = hparams  # TODO: move to self.save_hyperparameters()
         self.step_count = 0
         self.tfmr_ckpts = {}
         self.output_dir = Path(self.hparams.output_dir)
@@ -84,8 +84,8 @@ class BaseTransformer(pl.LightningModule):
             )
         else:
             self.tokenizer: PreTrainedTokenizer = tokenizer
+        self.model_type = MODEL_MODES[mode]
         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),
@@ -93,18 +93,13 @@ class BaseTransformer(pl.LightningModule):
                 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
-
     def configure_optimizers(self):
         "Prepare optimizer and schedule (linear warmup and decay)"
-
         model = self.model
         no_decay = ["bias", "LayerNorm.weight"]
         optimizer_grouped_parameters = [
@@ -121,23 +116,10 @@ class BaseTransformer(pl.LightningModule):
         self.opt = optimizer
         return [optimizer]
 
-    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
-        if self.trainer.use_tpu:
-            xm.optimizer_step(optimizer)
-        else:
-            optimizer.step()
-        optimizer.zero_grad()
-        self.lr_scheduler.step()
-
-    def get_tqdm_dict(self):
-        avg_loss = getattr(self.trainer, "avg_loss", 0.0)
-        tqdm_dict = {"loss": "{:.3f}".format(avg_loss), "lr": self.lr_scheduler.get_last_lr()[-1]}
-        return tqdm_dict
-
     def test_step(self, batch, batch_nb):
         return self.validation_step(batch, batch_nb)
 
-    def test_end(self, outputs):
+    def test_epoch_end(self, outputs):
         return self.validation_end(outputs)
 
     def train_dataloader(self):
@@ -208,6 +190,7 @@ class BaseTransformer(pl.LightningModule):
         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=500, type=int, help="Linear warmup over warmup_steps.")
+        parser.add_argument("--num_workers", default=4, type=int, help="kwarg passed to DataLoader")
         parser.add_argument(
             "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform."
         )
@@ -217,28 +200,26 @@ class BaseTransformer(pl.LightningModule):
 
 
 class LoggingCallback(pl.Callback):
+    @rank_zero_only
     def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
-        logger.info("***** Validation results *****")
-        if pl_module.is_logger():
-            metrics = trainer.callback_metrics
-            # Log results
+        rank_zero_info("***** Validation results *****")
+        metrics = trainer.callback_metrics
+        # Log results
+        for key in sorted(metrics):
+            if key not in ["log", "progress_bar"]:
+                rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
+
+    @rank_zero_only
+    def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
+        logger.info("***** Test results *****")
+        metrics = trainer.callback_metrics
+        # Log and save results to file
+        output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
+        with open(output_test_results_file, "w") as writer:
             for key in sorted(metrics):
                 if key not in ["log", "progress_bar"]:
                     logger.info("{} = {}\n".format(key, str(metrics[key])))
-
-    def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
-        logger.info("***** Test results *****")
-
-        if pl_module.is_logger():
-            metrics = trainer.callback_metrics
-
-            # Log and save results to file
-            output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
-            with open(output_test_results_file, "w") as writer:
-                for key in sorted(metrics):
-                    if key not in ["log", "progress_bar"]:
-                        logger.info("{} = {}\n".format(key, str(metrics[key])))
-                        writer.write("{} = {}\n".format(key, str(metrics[key])))
+                    writer.write("{} = {}\n".format(key, str(metrics[key])))
 
 
 def add_generic_args(parser, root_dir) -> None:

examples/requirements.txt

@@ -5,7 +5,7 @@ psutil
 sacrebleu
 rouge-score
 tensorflow_datasets
-pytorch-lightning==0.7.6
+pytorch-lightning==0.8.1
 matplotlib
 git-python==1.0.3
 faiss

examples/summarization/callbacks.py

@@ -19,12 +19,11 @@ logger = logging.getLogger(__name__)
 
 
 class Seq2SeqLoggingCallback(pl.Callback):
+    @rank_zero_only
     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

examples/summarization/distillation.py

@@ -271,6 +271,7 @@ class SummarizationDistiller(SummarizationModule):
 
 class T5SummarizationDistiller(SummarizationDistiller):
     def pre_init(self, hparams):
+        raise NotImplementedError("T5 Distillation does not work yet")
         teacher = T5ForConditionalGeneration.from_pretrained(hparams.teacher)
         n_layer = hparams.student_decoder_layers
         assert n_layer == hparams.student_encoder_layers  # TODO(SS): relax this

examples/summarization/finetune.py

@@ -85,7 +85,7 @@ class SummarizationModule(BaseTransformer):
         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
+        self.num_workers = hparams.num_workers
 
     def freeze_embeds(self):
         """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
@@ -126,7 +126,7 @@ class SummarizationModule(BaseTransformer):
     def validation_step(self, batch, batch_idx) -> Dict:
         return self._generative_step(batch)
 
-    def validation_end(self, outputs, prefix="val") -> Dict:
+    def validation_epoch_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"]
@@ -144,14 +144,12 @@ class SummarizationModule(BaseTransformer):
         self.metrics[prefix].append(metrics)
         pickle_save(self.metrics, self.metrics_save_path)
 
-    def _generative_step(self, batch):
+    def _generative_step(self, batch: dict) -> dict:
         pad_token_id = self.tokenizer.pad_token_id
         source_ids, source_mask, y = SummarizationDataset.trim_seq2seq_batch(batch, pad_token_id)
-        # 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
+        gen_time = time.time() - t0 / source_ids.shape[0]
         preds = self.ids_to_clean_text(generated_ids)
         target = self.ids_to_clean_text(y)
         loss_tensors = self._step(batch)
@@ -164,24 +162,8 @@ class SummarizationModule(BaseTransformer):
     def test_step(self, batch, batch_idx):
         return self._generative_step(batch)
 
-    def test_end(self, 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")
-        output_test_targets_file = os.path.join(self.hparams.output_dir, "test_targets.txt")
-        # write predictions and targets for later rouge evaluation.
-        with open(output_test_predictions_file, "w+") as p_writer, open(output_test_targets_file, "w+") as t_writer:
-            for output_batch in outputs:
-                p_writer.writelines(s + "\n" for s in output_batch["preds"])
-                t_writer.writelines(s + "\n" for s in output_batch["target"])
-            p_writer.close()
-            t_writer.close()
-
-        return self.test_end(outputs)
-
-    def validation_epoch_end(self, outputs):
-        self.validation_end(outputs, "val")
+        return self.validation_epoch_end(outputs, prefix="test")
 
     def get_dataset(self, type_path) -> SummarizationDataset:
         n_obs = self.n_obs[type_path]
@@ -310,6 +292,7 @@ def main(args, model=None) -> SummarizationModule:
         logger=logger,
         # TODO: early stopping callback seems messed up
     )
+    pickle_save(model.hparams, model.output_dir / "hparams.pkl")
     if not args.do_predict:
         return model
 

examples/summarization/run_distiller.sh

@@ -7,6 +7,5 @@ python distillation.py \
 --learning_rate=3e-4 \
 --do_train \
 --do_predict \
---fp16 \
 --val_check_interval 0.1 \
 $@

examples/summarization/run_eval.py

@@ -26,6 +26,7 @@ def generate_summaries(
     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_name = str(model_name)
     model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
     if fp16:
         model = model.half()

examples/summarization/test_summarization_examples.py

@@ -24,6 +24,7 @@ logger = logging.getLogger()
 FP16_EVER = False
 CHEAP_ARGS = {
     "logger": "default",
+    "num_workers": 2,
     "alpha_hid": 0,
     "freeze_embeds": True,
     "enc_only": False,
@@ -79,7 +80,8 @@ def _dump_articles(path: Path, articles: list):
         f.write("\n".join(articles))
 
 
-BDIR = Path("~/transformers_fork/examples/summarization/bart/").absolute()
+MSG = "T5 is broken at the moment"
+T5_TINY = "patrickvonplaten/t5-tiny-random"
 
 
 def make_test_data_dir():
@@ -92,7 +94,6 @@ 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):
@@ -108,47 +109,22 @@ class TestSummarizationDistiller(unittest.TestCase):
             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",
+            fp16=FP16_EVER,
         )
         self._bart_distiller_cli(updates)
 
-    @unittest.skipUnless(torch.cuda.is_available(), "skipping fp16 test")
-    def test_bdc_t5_eval_fp16(self):
+    def test_bdc_t5_train(self):
         updates = dict(
             fp16=FP16_EVER,
-            gpus=1,
+            gpus=1 if torch.cuda.is_available() else 0,
             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",
+            model_name_or_path=T5_TINY,
             do_train=True,
             do_predict=True,
-            tokenizer_name="patrickvonplaten/t5-tiny-random",
+            tokenizer_name=T5_TINY,
             no_teacher=True,
+            alpha_hid=2.0,
         )
         self._bart_distiller_cli(updates)
 
@@ -161,7 +137,6 @@ class TestSummarizationDistiller(unittest.TestCase):
         self._bart_distiller_cli(updates)
 
     def test_bdc_checkpointing(self):
-
         updates = dict(
             student_encoder_layers=2,
             student_decoder_layers=1,
@@ -184,32 +159,8 @@ class TestSummarizationDistiller(unittest.TestCase):
 
         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,
@@ -237,21 +188,14 @@ class TestSummarizationDistiller(unittest.TestCase):
         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("val_generations_00001.txt", contents)
+        self.assertIn("val_results_00001.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)
+        desired_n_evals = int(args_d["num_train_epochs"] * (1 / args_d["val_check_interval"]) + 1)
+        self.assertEqual(len(metrics["val"]), desired_n_evals)
+        self.assertEqual(len(metrics["train"]), 0)  # doesn't get logged here
         return model
 
 
@@ -281,9 +225,8 @@ class TestBartExamples(unittest.TestCase):
         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",
+            model_name_or_path=T5_TINY,
+            tokenizer_name=None,  # T5_TINY,
             train_batch_size=2,
             eval_batch_size=2,
             gpus=0,

examples/summarization/utils.py

@@ -45,8 +45,10 @@ def encode_file(
             max_length=max_length,
             pad_to_max_length=pad_to_max_length,
             add_prefix_space=True,
+            truncation=True,
             return_tensors=return_tensors,
         )
+        assert tokenized.input_ids.shape[1] == max_length
         examples.append(tokenized)
     torch.save(lmap(dict, examples), cache_path.open("wb"))
     return examples

examples/text-classification/run_pl_glue.py

@@ -108,7 +108,7 @@ class GLUETransformer(BaseTransformer):
 
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
 
-    def _eval_end(self, outputs):
+    def _eval_end(self, outputs) -> tuple:
         val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean().detach().cpu().item()
         preds = np.concatenate([x["pred"] for x in outputs], axis=0)
 
@@ -132,20 +132,14 @@ class GLUETransformer(BaseTransformer):
         logs = ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
-    def test_epoch_end(self, outputs):
-        # updating to test_epoch_end instead of deprecated test_end
+    def test_epoch_end(self, outputs) -> dict:
         ret, predictions, targets = self._eval_end(outputs)
-
-        # Converting to the dic required by pl
-        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
-        # pytorch_lightning/trainer/logging.py#L139
         logs = ret["log"]
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
     @staticmethod
     def add_model_specific_args(parser, root_dir):
-        # Add NER specific options
         BaseTransformer.add_model_specific_args(parser, root_dir)
         parser.add_argument(
             "--max_seq_length",

src/transformers/tokenization_auto.py

@@ -205,7 +205,7 @@ class AutoTokenizer:
         if not isinstance(config, PretrainedConfig):
             config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
 
-        if "bert-base-japanese" in pretrained_model_name_or_path:
+        if "bert-base-japanese" in str(pretrained_model_name_or_path):
             return BertJapaneseTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
 
         use_fast = kwargs.pop("use_fast", False)