updated with latest PL and Ray (#15653)

examples/research_projects/rag/callbacks_rag.py

@@ -38,7 +38,7 @@ def get_checkpoint_callback(output_dir, metric):
         monitor=f"val_{metric}",
         mode="max",
         save_top_k=3,
-        period=1,  # maybe save a checkpoint every time val is run, not just end of epoch.
+        every_n_epochs=1,  # maybe save a checkpoint every time val is run, not just end of epoch.
     )
     return checkpoint_callback
 

examples/research_projects/rag/finetune_rag.py

@@ -254,7 +254,7 @@ def pad(self) -> int:
     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)}
+        logs = {name: loss.detach() for name, loss in zip(self.loss_names, loss_tensors)}
         # tokens per batch
         tgt_pad_token_id = (
             self.tokenizer.generator.pad_token_id
@@ -517,7 +517,7 @@ def main(args=None, model=None) -> GenerativeQAModule:
             raise RuntimeError("Please install Ray to use the Ray " "distributed retriever.")
         # Connect to an existing Ray cluster.
         try:
-            ray.init(address=args.ray_address)
+            ray.init(address=args.ray_address, namespace="rag")
         except (ConnectionError, ValueError):
             logger.warning(
                 "Connection to Ray cluster failed. Make sure a Ray"

examples/research_projects/rag/lightning_base.py

@@ -266,6 +266,15 @@ def add_model_specific_args(parser, root_dir):
         parser.add_argument("--adafactor", action="store_true")
 
 
+class InitCallback(pl.Callback):
+    # This method is better that using a custom DDP plugging with the latest pytorch-lightning (@shamanez)
+    def on_sanity_check_start(self, trainer, pl_module):
+        if (
+            trainer.is_global_zero and trainer.global_rank == 0
+        ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
+            pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.
+
+
 class LoggingCallback(pl.Callback):
     def on_batch_end(self, trainer, pl_module):
         lr_scheduler = trainer.lr_schedulers[0]["scheduler"]
@@ -368,19 +377,21 @@ def generic_train(
     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fp16:
         train_params["precision"] = 16
-        train_params["amp_level"] = args.fp16_opt_level
+        # train_params["amp_level"] = args.fp16_opt_level
 
     if args.gpus > 1:
-        train_params["accelerator"] = "ddp"
+        train_params["accelerator"] = "auto"  # "ddp"
+        train_params["strategy"] = "ddp"
 
     train_params["accumulate_grad_batches"] = args.accumulate_grad_batches
     train_params["profiler"] = None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
+    train_params["devices"] = "auto"
 
     trainer = pl.Trainer.from_argparse_args(
         args,
         weights_summary=None,
-        callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback],
-        plugins=[custom_ddp_plugin],
+        callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()],
+        # plugins=[custom_ddp_plugin],
         logger=logger,
         **train_params,
     )

examples/research_projects/rag/requirements.txt

@@ -2,6 +2,7 @@ faiss-cpu >= 1.6.3
 datasets >= 1.0.1
 psutil >= 5.7.0
 torch >= 1.4.0
+ray >= 1.10.0
+pytorch-lightning >= 1.5.10
 transformers
-pytorch-lightning
-GitPython
+GitPython