Upgrade PyTorch Lightning to 1.0.2 (#7852)

Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

examples/seq2seq/callbacks.py

@@ -102,7 +102,6 @@ def get_checkpoint_callback(output_dir, metric, save_top_k=1, lower_is_better=Fa
         monitor=f"val_{metric}",
         mode="min" if "loss" in metric else "max",
         save_top_k=save_top_k,
-        period=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
     )
     return checkpoint_callback
 

examples/seq2seq/finetune.py

@@ -182,7 +182,6 @@ class SummarizationModule(BaseTransformer):
         return self._generative_step(batch)
 
     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"]
@@ -252,7 +251,7 @@ class SummarizationModule(BaseTransformer):
     def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False) -> DataLoader:
         dataset = self.get_dataset(type_path)
 
-        if self.hparams.sortish_sampler and type_path != "test":
+        if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
             sampler = dataset.make_sortish_sampler(batch_size, distributed=self.hparams.gpus > 1)
             return DataLoader(
                 dataset,
@@ -263,7 +262,7 @@ class SummarizationModule(BaseTransformer):
                 sampler=sampler,
             )
 
-        elif self.hparams.max_tokens_per_batch is not None and type_path != "test":
+        elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
             batch_sampler = dataset.make_dynamic_sampler(
                 self.hparams.max_tokens_per_batch, distributed=self.hparams.gpus > 1
             )

examples/seq2seq/test_bash_script.py

@@ -144,6 +144,7 @@ class TestAll(TestCasePlus):
                 f"--num_train_epochs={epochs}",
                 "--warmup_steps=10",
                 "--val_check_interval=1.0",
+                "--do_predict",
             ]
         )
         with patch.object(sys, "argv", testargs):
@@ -151,7 +152,6 @@ class TestAll(TestCasePlus):
             parser = pl.Trainer.add_argparse_args(parser)
             parser = BartSummarizationDistiller.add_model_specific_args(parser, os.getcwd())
             args = parser.parse_args()
-            args.do_predict = False
             # assert args.gpus == gpus THIS BREAKS for multigpu
 
             model = distill_main(args)

examples/seq2seq/test_seq2seq_examples_multi_gpu.py

@@ -176,7 +176,6 @@ class TestSummarizationDistillerMultiGPU(TestCasePlus):
         print(metrics)
         last_step_stats = metrics["val"][-1]
         self.assertGreaterEqual(last_step_stats["val_avg_gen_time"], 0.01)
-        self.assertGreaterEqual(1.0, last_step_stats["val_avg_gen_time"])
         self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"], float)
         self.assertEqual(len(metrics["test"]), 1)
         desired_n_evals = int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1)