fix wrong tracing location of fetch data

dlio_benchmark/data_loader/native_dali_data_loader.py

@@ -60,7 +60,9 @@ def next(self):
             pipeline.reset()
         for step in range(num_samples // batch_size):
             try:
-                for batch in self._dataset:
+                # TODO: @hariharan-devarajan: change below line when we bump the dftracer version to 
+                #       `dlp.iter(self._dataset, name=self.next.__qualname__)`
+                for batch in dlp.iter(self._dataset):
                     logging.debug(f"{utcnow()} Creating {len(batch)} batches by {self._args.my_rank} rank ")
                     yield batch
             except StopIteration:

dlio_benchmark/data_loader/tf_data_loader.py

@@ -100,7 +100,9 @@ def read(self):
     @dlp.log
     def next(self):
         super().next()
-        for batch in self._dataset:
+        # TODO: @hariharan-devarajan: change below line when we bump the dftracer version to 
+        #       `dlp.iter(self._dataset, name=self.next.__qualname__)`
+        for batch in dlp.iter(self._dataset):
             yield batch
         self.epoch_number += 1
         dlp.update(epoch=self.epoch_number)

dlio_benchmark/data_loader/torch_data_loader.py

@@ -166,7 +166,9 @@ def next(self):
         total = self._args.training_steps if self.dataset_type is DatasetType.TRAIN else self._args.eval_steps
         logging.debug(f"{utcnow()} Rank {self._args.my_rank} should read {total} batches")
         step = 1
-        for batch in self._dataset:
+        # TODO: @hariharan-devarajan: change below line when we bump the dftracer version to 
+        #       `dlp.iter(self._dataset, name=self.next.__qualname__)`
+        for batch in dlp.iter(self._dataset):
             dlp.update(step = step)
             step += 1
             yield batch

dlio_benchmark/main.py

@@ -224,7 +224,7 @@ def _eval(self, epoch):
         total = math.floor(self.num_samples * self.num_files_eval / self.batch_size_eval / self.comm_size)
         loader = self.framework.get_loader(DatasetType.VALID)
         t0 = time()
-        for batch in dlp.iter(loader.next()):
+        for batch in loader.next():
             self.stats.eval_batch_loaded(epoch, step, t0)
             eval_time = 0.0
             if self.eval_time > 0:
@@ -256,7 +256,7 @@ def _train(self, epoch):
 
         loader = self.framework.get_loader(dataset_type=DatasetType.TRAIN)
         t0 = time()
-        for batch in dlp.iter(loader.next()):
+        for batch in loader.next():
             if overall_step > max_steps or ((self.total_training_steps > 0) and (overall_step > self.total_training_steps)):
                 if self.args.my_rank == 0:
                     logging.info(f"{utcnow()} Maximum number of steps reached")