fixes to accomendate mcore changes

nemo/collections/nlp/models/language_modeling/megatron/falcon/falcon_decoder_layer.py

@@ -107,6 +107,7 @@ def forward(
         context_mask=None,
         rotary_pos_emb=None,
         inference_params=None,
+        packed_seq_params=None,
     ):
         # hidden_states: [s, b, h]
 
@@ -161,60 +162,3 @@ def forward(
         output = make_viewless_tensor(inp=hidden_states, requires_grad=hidden_states.requires_grad, keep_graph=True)
 
         return output, context
-
-    def sharded_state_dict(self, prefix=''):
-
-        state_dict = self.state_dict(keep_vars=True)
-
-        tensor_parallel_layers_axis_map = {
-            'self_attention.linear_qkv.weight': 0,
-            'self_attention.linear_qkv.bias': 0,
-            'self_attention.linear_proj.weight': 1,
-            'mlp.linear_fc1.weight': 0,
-            'mlp.linear_fc1.bias': 0,
-            'mlp.linear_fc2.weight': 1,
-        }
-
-        offset = self._get_layer_offset()
-        num_layers = self.config.num_layers
-
-        sharded_state_dict = {}
-
-        for layer_name in state_dict.keys():
-            tensor = state_dict[layer_name]
-            global_layer_offset = self.layer_number - 1  # self.layer_number starts at 1
-            layer_key = f'{prefix}{global_layer_offset - offset}.{layer_name}'  # module list index in TransformerBlock
-            sharded_offsets = [(0, global_layer_offset, num_layers)]  # PP sharding
-
-            if layer_name in tensor_parallel_layers_axis_map:
-                tp_axis = tensor_parallel_layers_axis_map[layer_name]
-                # TP sharding
-                sharded_offsets.append(
-                    [
-                        tp_axis + 1,  # +1 for PP dimension
-                        parallel_state.get_tensor_model_parallel_rank(),
-                        parallel_state.get_tensor_model_parallel_world_size(),
-                    ]
-                )
-                replica_id = parallel_state.get_data_parallel_rank()
-            else:
-                replica_id = (
-                    parallel_state.get_data_parallel_rank() * parallel_state.get_data_parallel_world_size()
-                    + parallel_state.get_tensor_model_parallel_rank()
-                )
-
-            if layer_name.endswith('._extra_state'):
-                sharded_state_dict[layer_key] = ShardedObject(
-                    f'{prefix}{layer_name}', tensor, (num_layers,), (global_layer_offset,), replica_id,
-                )
-
-            else:
-                sharded_state_dict[layer_key] = ShardedTensor.from_rank_offsets(
-                    f'{prefix}{layer_name}',
-                    tensor,
-                    *sharded_offsets,
-                    replica_id=replica_id,
-                    prepend_axis_num=1,  # for PP sharding
-                )
-
-        return sharded_state_dict