Merge remote-tracking branch 'upstream/r1.20.0' into hybrid-conformer…

…-export

nemo/collections/asr/metrics/rnnt_wer.py

@@ -1224,7 +1224,7 @@ def validation_epoch_end(self, outputs):
     def __init__(
         self, decoding: RNNTDecoding, batch_dim_index=0, use_cer=False, log_prediction=True, dist_sync_on_step=False
     ):
-        super(RNNTWER, self).__init__(dist_sync_on_step=dist_sync_on_step, compute_on_step=False)
+        super(RNNTWER, self).__init__(dist_sync_on_step=dist_sync_on_step)
         self.decoding = decoding
         self.batch_dim_index = batch_dim_index
         self.use_cer = use_cer

nemo/collections/asr/metrics/rnnt_wer_bpe.py

@@ -359,7 +359,7 @@ def __init__(
         log_prediction: bool = True,
         dist_sync_on_step=False,
     ):
-        super(RNNTBPEWER, self).__init__(dist_sync_on_step=dist_sync_on_step, compute_on_step=False)
+        super(RNNTBPEWER, self).__init__(dist_sync_on_step=dist_sync_on_step)
         self.decoding = decoding
         self.batch_dim_index = batch_dim_index
         self.use_cer = use_cer

nemo/collections/asr/metrics/wer.py

@@ -1125,7 +1125,7 @@ def __init__(
         fold_consecutive=True,
         dist_sync_on_step=False,
     ):
-        super().__init__(dist_sync_on_step=dist_sync_on_step, compute_on_step=False)
+        super().__init__(dist_sync_on_step=dist_sync_on_step)
 
         self.decoding = decoding
         self.use_cer = use_cer

nemo/collections/asr/metrics/wer_bpe.py

@@ -247,7 +247,7 @@ def __init__(
         fold_consecutive=True,
         dist_sync_on_step=False,
     ):
-        super().__init__(dist_sync_on_step=dist_sync_on_step, compute_on_step=False)
+        super().__init__(dist_sync_on_step=dist_sync_on_step)
         self.decoding = decoding
         self.tokenizer = self.decoding.tokenizer
         self.blank_id = self.decoding.tokenizer.tokenizer.vocab_size

nemo/collections/common/metrics/global_average_loss_metric.py

@@ -28,9 +28,6 @@ class GlobalAverageLossMetric(Metric):
     See :doc:`PyTorch Lightning Metrics<pytorch-lightning:metrics>` for the metric usage instruction.
 
     Args:
-        compute_on_step:
-            The method :meth:`forward` only calls ``update()`` and returns ``None`` if this is set to ``False``.
-            default: ``True``
         dist_sync_on_step:
             Synchronize metric state across processes at each method :meth:`forward` call before returning the
             value at the step
@@ -44,10 +41,8 @@ class GlobalAverageLossMetric(Metric):
 
     full_state_update = True
 
-    def __init__(self, compute_on_step=True, dist_sync_on_step=False, process_group=None, take_avg_loss=True):
-        super().__init__(
-            compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group
-        )
+    def __init__(self, dist_sync_on_step=False, process_group=None, take_avg_loss=True):
+        super().__init__(dist_sync_on_step=dist_sync_on_step, process_group=process_group)
         self.add_state("loss_sum", torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx='sum')
         self.add_state("num_measurements", torch.tensor(0, dtype=torch.int64), dist_reduce_fx='sum')
         self.take_avg_loss = take_avg_loss

nemo/collections/common/metrics/perplexity.py

@@ -29,8 +29,6 @@ class Perplexity(Metric):
     See `PyTorch Lightning Metrics <https://pytorch-lightning.readthedocs.io/en/stable/ecosystem/metrics.html>`_ for the metric usage instructions.
 
     Args:
-        compute_on_step:
-            Forward only calls ``update()`` and returns ``None`` if this is set to ``False``. default: ``True``
         dist_sync_on_step:
             Synchronize metric state across processes at each ``forward()``
             before returning the value at the step.
@@ -44,10 +42,8 @@ class Perplexity(Metric):
 
     full_state_update = True
 
-    def __init__(self, compute_on_step=True, dist_sync_on_step=False, process_group=None, validate_args=True):
-        super().__init__(
-            compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group
-        )
+    def __init__(self, dist_sync_on_step=False, process_group=None, validate_args=True):
+        super().__init__(dist_sync_on_step=dist_sync_on_step, process_group=process_group)
         self.validate_args = validate_args
         self.add_state('perplexities_sum', torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx='sum')
         # Total number of distributions seen since last reset

nemo/collections/nlp/metrics/sequence_perplexity.py

@@ -31,8 +31,6 @@ class SequencePerplexity(Metric):
     See :doc:`PyTorch Lightning Metrics<pytorch-lightning:metrics>` for the metric usage instructions.
 
     Args:
-        compute_on_step:
-            Forward only calls ``update()`` and returns ``None`` if this is set to ``False``. default: ``True``
         dist_sync_on_step:
             Synchronize metric state across processes at each ``forward()`` before returning the value at the step.
         process_group:
@@ -43,12 +41,9 @@ class SequencePerplexity(Metric):
                 to perform the allgather.
     """
 
-    def __init__(self, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None):
+    def __init__(self, dist_sync_on_step=False, process_group=None, dist_sync_fn=None):
         super().__init__(
-            compute_on_step=compute_on_step,
-            dist_sync_on_step=dist_sync_on_step,
-            process_group=process_group,
-            dist_sync_fn=dist_sync_fn,
+            dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn,
         )
 
         # Total sum of exponentiated average negative log likelihoods

nemo/collections/nlp/models/language_modeling/bert_lm_model.py

@@ -116,7 +116,7 @@ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
         # create extra bias
 
         # setup to track metrics
-        self.validation_perplexity = Perplexity(compute_on_step=False)
+        self.validation_perplexity = Perplexity()
 
         self.setup_optimization(cfg.optim)
 

nemo/collections/nlp/models/text2sparql/text2sparql_model.py

@@ -100,7 +100,7 @@ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
                 decoder=cfg.language_model.pretrained_decoder_model_name,
             )
 
-        self.validation_perplexity = Perplexity(compute_on_step=False)
+        self.validation_perplexity = Perplexity()
 
         self.setup_optimization(cfg.optim)
 

nemo/core/optim/optimizers.py

@@ -51,7 +51,6 @@
     AVAILABLE_OPTIMIZERS['fused_adam'] = FusedAdam
 except ModuleNotFoundError:
     HAVE_APEX = False
-    logging.warning("Apex was not found. Using the lamb or fused_adam optimizer will error out.")
 
 HAVE_APEX_DISTRIBUTED_ADAM = False
 if HAVE_APEX:

tests/collections/common/pl_utils.py

@@ -90,7 +90,7 @@ def _class_test(
                 calculated across devices for each batch (and not just at the end)
     """
     # Instanciate lightning metric
-    metric = metric_class(compute_on_step=True, dist_sync_on_step=dist_sync_on_step, **metric_args)
+    metric = metric_class(dist_sync_on_step=dist_sync_on_step, **metric_args)
 
     # verify metrics work after being loaded from pickled state
     pickled_metric = pickle.dumps(metric)
@@ -303,7 +303,7 @@ def _perplexity_class_test(
                 calculated across devices for each batch (and not just at the end)
     """
     # Instanciate lightning metric
-    perplexity = Perplexity(compute_on_step=True, dist_sync_on_step=dist_sync_on_step, **metric_args)
+    perplexity = Perplexity(dist_sync_on_step=dist_sync_on_step, **metric_args)
     if (probs is None) == (logits is None):
         with pytest.raises(ValueError):
             perplexity(probs, logits)
@@ -464,9 +464,7 @@ def _loss_class_test(
                 calculated across devices for each batch (and not just at the end)
     """
     # Instantiate lightning metric
-    loss_metric = GlobalAverageLossMetric(
-        compute_on_step=True, dist_sync_on_step=dist_sync_on_step, take_avg_loss=take_avg_loss
-    )
+    loss_metric = GlobalAverageLossMetric(dist_sync_on_step=dist_sync_on_step, take_avg_loss=take_avg_loss)
 
     # verify loss works after being loaded from pickled state
     pickled_metric = pickle.dumps(loss_metric)