Fixing wrapper and moving it to base class (NVIDIA#8055)

* Fixing wrapper and moving it to base class

* Fixing wrapper and moving it to base class

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update megatron_bert_model.py

Signed-off-by: Shanmugam Ramasamy <111910568+shanmugamr1992@users.noreply.github.com>

* Dummy changes

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fixing bugs for bert

---------

Signed-off-by: Shanmugam Ramasamy <111910568+shanmugamr1992@users.noreply.github.com>
Co-authored-by: Shanmugam Ramasamy <shanmugamr@shanmugamr-mlt.client.nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

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

@@ -231,6 +231,41 @@ def setup_transformer_engine_tp_groups(self):
                     tp_group = parallel_state.get_tensor_model_parallel_group()
                     child.set_tensor_parallel_group(tp_group)
 
+    def _wrap_model_for_O2(self):
+        """ Wraps self.model in a float16 wrapper if the model is using megatron amp O2.
+            Args:
+                model: The model to wrap. Can be a list of modules or a single module.
+            Returns:
+                The wrapped model. Returns a list of wrapped modules or a single wrapped module.
+        """
+        is_mcore_model = self.__dict__.get('mcore_gpt', False) or self.__dict__.get('mcore_bert', False)
+
+        Float16Wrapper = MCoreFloat16Module if is_mcore_model else Float16Module
+
+        nemo_args = {'config': self.model_parallel_config, 'precision': self.cfg.precision}
+
+        if type(self).__name__ == 'MegatronGPTModel':
+            nemo_args['share_token_embeddings'] = self.cfg.get('share_embeddings_and_output_weights', True)
+
+        mcore_args = {
+            'config': self.transformer_config,
+        }
+
+        args = mcore_args if is_mcore_model else nemo_args
+
+        # Model wrapper to convert both model and inputs to half precision
+        if isinstance(self.model, list):
+            converted_model = []
+            for module in self.model:
+                args['module'] = module
+                converted_model.append(Float16Wrapper(**args))
+            self.model = converted_model
+        else:
+            args['module'] = self.model
+            self.model = Float16Wrapper(**args)
+
+        args.pop('module')
+
     def get_model_module_list(self):
         if isinstance(self.model, list):
             return [
@@ -826,6 +861,7 @@ def is_data_parallel_rank_zero(self):
 
     def _get_total_params_across_model_parallel_groups_gpt_bert(self, model):
         """Returns the total number of parameters across all model parallel groups."""
+        is_mcore_model = self.__dict__.get('mcore_gpt', False) or self.__dict__.get('mcore_bert', False)
         # log number of parameters
         if isinstance(model, list):
             num_parameters_on_device = sum(
@@ -838,7 +874,7 @@ def _get_total_params_across_model_parallel_groups_gpt_bert(self, model):
             ):
                 word_embeddings_weight = (
                     model[-1].module.shared_embedding_or_output_weight()
-                    if getattr(self, 'mcore_gpt', False)
+                    if is_mcore_model
                     else model[-1].word_embeddings_weight()
                 )
                 # substract the embedding weights on the last virtual stage
@@ -853,7 +889,7 @@ def _get_total_params_across_model_parallel_groups_gpt_bert(self, model):
             ):
                 word_embeddings_weight = (
                     model.module.shared_embedding_or_output_weight()
-                    if getattr(self, 'mcore_gpt', False)
+                    if is_mcore_model
                     else model.word_embeddings_weight()
                 )
                 # substract the embedding weights on the last stage

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

@@ -136,40 +136,6 @@ def __init__(self, cfg: DictConfig, trainer: Trainer):
             self._nsys_profile_start_step *= grad_accum_steps
             self._nsys_profile_end_step *= grad_accum_steps
 
-    def _wrap_model_for_O2(self):
-        """ Wraps self.model in a float16 wrapper if the model is using megatron amp O2.
-            Args:
-                model: The model to wrap. Can be a list of modules or a single module.
-            Returns:
-                The wrapped model. Returns a list of wrapped modules or a single wrapped module.
-        """
-        Float16Wrapper = MCoreFloat16Module if self.mcore_bert else Float16Module
-
-        nemo_args = {
-            'config': self.model_parallel_config,
-            'precision': self.cfg.precision,
-        }
-        mcore_args = {
-            'config': self.transformer_config,
-        }
-
-        args = mcore_args if self.mcore_bert else nemo_args
-
-        # Model wrapper to convert both model and inputs to half precision
-        if isinstance(self.model, list):
-            converted_model = []
-            for module in self.model:
-                if not self.mcore_bert:
-                    args['module'] = module
-                converted_model.append(Float16Wrapper(**args))
-            self.model = converted_model
-        else:
-            if not self.mcore_bert:
-                args['module'] = self.model
-            self.model = Float16Wrapper(**args)
-
-        args.pop('module')
-
     def model_provider_func(self, pre_process, post_process):
         cfg = self.cfg
         num_tokentypes = 2 if cfg.bert_binary_head else 0
@@ -990,7 +956,7 @@ def configure_optimizers(self):
                     if isinstance(module, (Float16Module, MCoreFloat16Module)):
                         module = module.module
                     stage_bucket = []
-                    layers = module.transformer.layers if self.mcore_bert else module.language_model.encoder.layers
+                    layers = module.encoder.layers if self.mcore_bert else module.language_model.encoder.layers
                     for layer in layers:
                         stage_bucket.extend(
                             p for p in layer.parameters() if not getattr(p, '_disable_overlap_grad_sync', False)
@@ -1002,7 +968,7 @@ def configure_optimizers(self):
                 for module in modules:
                     if isinstance(module, (Float16Module, MCoreFloat16Module)):
                         module = module.module
-                    layers = module.transformer.layers if self.mcore_bert else module.language_model.encoder.layers
+                    layers = module.encoder.layers if self.mcore_bert else module.language_model.encoder.layers
                     for layer in layers:
                         buckets.append(
                             [p for p in layer.parameters() if not getattr(p, '_disable_overlap_grad_sync', False)]

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

@@ -1554,36 +1554,3 @@ def build_transformer_config(self) -> TransformerConfig:
             setattr(transformer_config, key, value)
 
         return transformer_config
-
-    def _wrap_model_for_O2(self):
-        """ Wraps self.model in a float16 wrapper if the model is using megatron amp O2.
-            Args:
-                model: The model to wrap. Can be a list of modules or a single module.
-            Returns:
-                The wrapped model. Returns a list of wrapped modules or a single wrapped module.
-        """
-        Float16Wrapper = MCoreFloat16Module if self.mcore_gpt else Float16Module
-
-        nemo_args = {
-            'config': self.model_parallel_config,
-            'precision': self.cfg.precision,
-            'share_token_embeddings': self.cfg.get('share_embeddings_and_output_weights', True),
-        }
-        mcore_args = {
-            'config': self.transformer_config,
-        }
-
-        args = mcore_args if self.mcore_gpt else nemo_args
-
-        # Model wrapper to convert both model and inputs to half precision
-        if isinstance(self.model, list):
-            converted_model = []
-            for module in self.model:
-                args['module'] = module
-                converted_model.append(Float16Wrapper(**args))
-            self.model = converted_model
-        else:
-            args['module'] = self.model
-            self.model = Float16Wrapper(**args)
-
-        args.pop('module')