Apply isort and black reformatting

Signed-off-by: michal2409 <michal2409@users.noreply.github.com>

nemo/collections/nlp/modules/common/megatron/adapters/mcore_mixins.py

@@ -142,11 +142,19 @@ def get_query_key_value_tensors(self, hidden_states, key_value_states=None):
         if SplitAlongDim is not None:
 
             # [sq, b, ng, (np/ng + 2) * hn] --> [sq, b, ng, np/ng * hn], [sq, b, ng, hn], [sq, b, ng, hn]
-            (query, key, value) = SplitAlongDim(mixed_qkv, 3, split_arg_list,)
+            (query, key, value) = SplitAlongDim(
+                mixed_qkv,
+                3,
+                split_arg_list,
+            )
         else:
 
             # [sq, b, ng, (np/ng + 2) * hn] --> [sq, b, ng, np/ng * hn], [sq, b, ng, hn], [sq, b, ng, hn]
-            (query, key, value) = torch.split(mixed_qkv, split_arg_list, dim=3,)
+            (query, key, value) = torch.split(
+                mixed_qkv,
+                split_arg_list,
+                dim=3,
+            )
 
         # [sq, b, ng, np/ng * hn] -> [sq, b, np, hn]
         query = query.reshape(query.size(0), query.size(1), -1, self.hidden_size_per_attention_head)
@@ -231,11 +239,21 @@ def forward(
 
         if self.checkpoint_core_attention:
             core_attn_out = self._checkpointed_attention_forward(
-                query, key, value, attention_mask, attn_mask_type=attn_mask_type, packed_seq_params=packed_seq_params,
+                query,
+                key,
+                value,
+                attention_mask,
+                attn_mask_type=attn_mask_type,
+                packed_seq_params=packed_seq_params,
             )
         else:
             core_attn_out = self.core_attention(
-                query, key, value, attention_mask, attn_mask_type=attn_mask_type, packed_seq_params=packed_seq_params,
+                query,
+                key,
+                value,
+                attention_mask,
+                attn_mask_type=attn_mask_type,
+                packed_seq_params=packed_seq_params,
             )
 
         if packed_seq_params is not None:
@@ -316,7 +334,9 @@ def forward(self, hidden_states):
                     intermediate_parallel = bias_gelu_impl(intermediate_parallel, bias_parallel)
             elif self.activation_func == F.silu and self.config.gated_linear_unit:
                 intermediate_parallel = bias_swiglu_impl(
-                    intermediate_parallel, bias_parallel, self.config.activation_func_fp8_input_store,
+                    intermediate_parallel,
+                    bias_parallel,
+                    self.config.activation_func_fp8_input_store,
                 )
 
             else:

nemo/collections/nlp/modules/common/megatron/adapters/parallel_adapters.py

@@ -277,7 +277,9 @@ def _get_init_fn(self, init_method: str):
             raise NotImplementedError("out_init_method should be zero, normal, kaiming or xavier")
         return init_fn
 
-    def adapter_unfreeze(self,):
+    def adapter_unfreeze(
+        self,
+    ):
         """
         Can be customized to allow for selective training of only some params in the PEFT.
         """
@@ -404,7 +406,7 @@ class LoraQAdapter(ParallelLinearAdapter):
 
 class LoraDenseAttentionAdapter(ParallelLinearAdapter):
     """
-    Lora Adapters are the same arch as regular adapters but with potentially different input and output feature sizes 
+    Lora Adapters are the same arch as regular adapters but with potentially different input and output feature sizes
     and they do not use an bottleneck activation function
     """
 
@@ -413,7 +415,7 @@ class LoraDenseAttentionAdapter(ParallelLinearAdapter):
 
 class LoraHto4HAdapter(ParallelLinearAdapter):
     """
-    Lora Adapters are the same arch as regular adapters but with potentially different input and output feature sizes 
+    Lora Adapters are the same arch as regular adapters but with potentially different input and output feature sizes
     and they do not use an bottleneck activation function
     """
 
@@ -422,7 +424,7 @@ class LoraHto4HAdapter(ParallelLinearAdapter):
 
 class Lora4HtoHAdapter(ParallelLinearAdapter):
     """
-    Lora Adapters are the same arch as regular adapters but with potentially different input and output feature sizes 
+    Lora Adapters are the same arch as regular adapters but with potentially different input and output feature sizes
     and they do not use an bottleneck activation function
     """
 
@@ -690,14 +692,20 @@ def set_inference_table(self, prompt_representation: torch.Tensor):
         self.is_inference_ready = True
         return True
 
-    def clear_inference_table(self,):
+    def clear_inference_table(
+        self,
+    ):
         self.inference_table.fill_(0.0)
         self.is_inference_ready = False
 
-    def get_inference_table(self,):
+    def get_inference_table(
+        self,
+    ):
         return self.inference_table.data
 
-    def inner_forward(self,):
+    def inner_forward(
+        self,
+    ):
         input_embeds = self.embedding(self.indices).unsqueeze(0)
         intermediate_parallel, bias_parallel = self.first(input_embeds)
         intermediate_parallel = fused_bias_gelu(intermediate_parallel, bias_parallel)