changet vector train.py local_rank to rank, test=doc

paddlespeech/vector/exps/ecapa_tdnn/train.py

@@ -54,7 +54,7 @@ def main(args, config):
     # stage1: we must call the paddle.distributed.init_parallel_env() api at the begining
     paddle.distributed.init_parallel_env()
     nranks = paddle.distributed.get_world_size()
-    local_rank = paddle.distributed.get_rank()
+    rank = paddle.distributed.get_rank()
     # set the random seed, it is the necessary measures for multiprocess training
     seed_everything(config.seed)
 
@@ -112,10 +112,10 @@ def main(args, config):
             state_dict = paddle.load(
                 os.path.join(args.load_checkpoint, 'model.pdopt'))
             optimizer.set_state_dict(state_dict)
-            if local_rank == 0:
+            if rank == 0:
                 logger.info(f'Checkpoint loaded from {args.load_checkpoint}')
         except FileExistsError:
-            if local_rank == 0:
+            if rank == 0:
                 logger.info('Train from scratch.')
 
         try:
@@ -219,7 +219,7 @@ def main(args, config):
             timer.count()  # step plus one in timer
 
             # stage 9-10: print the log information only on 0-rank per log-freq batchs
-            if (batch_idx + 1) % config.log_interval == 0 and local_rank == 0:
+            if (batch_idx + 1) % config.log_interval == 0 and rank == 0:
                 lr = optimizer.get_lr()
                 avg_loss /= config.log_interval
                 avg_acc = num_corrects / num_samples
@@ -250,7 +250,7 @@ def main(args, config):
 
         # stage 9-11: save the model parameters only on 0-rank per save-freq batchs
         if epoch % config.save_interval == 0 and batch_idx + 1 == steps_per_epoch:
-            if local_rank != 0:
+            if rank != 0:
                 paddle.distributed.barrier(
                 )  # Wait for valid step in main process
                 continue  # Resume trainning on other process
@@ -317,7 +317,7 @@ def main(args, config):
                 paddle.distributed.barrier()  # Main process
 
     # stage 10: create the final trained model.pdparams with soft link
-    if local_rank == 0:
+    if rank == 0:
         final_model = os.path.join(args.checkpoint_dir, "model.pdparams")
         logger.info(f"we will create the final model: {final_model}")
         if os.path.islink(final_model):