add chunk offload optimizer

paddlenlp/trainer/training_args.py

@@ -614,6 +614,16 @@ class TrainingArguments:
             )
         },
     )
+    sharding_offload_opt_buffersize_GB: int = field(
+        default=-1,
+        metadata={
+            "help": (
+                "Set the size of the optimizer offload buffer when need_hack_offload_optimizer() is True. This option only takes effect when "
+                "use DygraphShardingOptimizerV2. The default value is -1, which means that all of the optimizer states will be offloaded. Only "
+                "works when export HACK_OFFLOAD_OPTIMIZER=1. "
+            )
+        },
+    )
 
     save_sharded_model: bool = field(
         default=False,
@@ -1531,6 +1541,11 @@ def is_context_parallel_supported():
                                 self.sharding_comm_buffer_size_MB
                             )
 
+                        if hasattr(strategy.hybrid_configs["sharding_configs"], "offload_opt_buffer_size"):
+                            strategy.hybrid_configs["sharding_configs"].offload_opt_buffer_size = int(
+                                self.sharding_offload_opt_buffersize_GB
+                            )
+
                         if "split_param" in sharding_parallel_config:
                             strategy.hybrid_configs["sharding_configs"].split_param = True
                             assert self.amp_master_grad, "Currently sharding stage1 v2 only support amp_master_grad"
@@ -1631,6 +1646,7 @@ def is_context_parallel_supported():
                 self.sharding_parallel_degree
                 * self.tensor_parallel_degree
                 * self.sep_parallel_degree
+                * self.context_parallel_degree
                 * self.pipeline_parallel_degree
             )
 

paddlenlp/trainer/utils/offload_optimizer.py

@@ -58,9 +58,11 @@ def new_opt_op(*args):
                     reload(arg)
 
             ret = origin_op(*args)
-
+            is_offload_opt = getattr(args[0], "is_offload_opt", False)
             for i, arg in enumerate(args):
-                if i >= 2 and isinstance(arg, paddle.Tensor):  # do not offload parameter and gradient
+                if (
+                    i >= 2 and isinstance(arg, paddle.Tensor) and is_offload_opt
+                ):  # do not offload parameter and gradient
                     offload(arg)
             return ret
 
@@ -74,7 +76,11 @@ def new_insert_sync(self, sync_var, *args, **kwargs):
         origin_place = sync_var.place
         reload(sync_var)
         ret = origin_insert_sync(self, sync_var, *args, **kwargs)
-        new_sync_var = to_device(sync_var, origin_place)
+        is_offload_opt = getattr(sync_var, "is_offload_opt", False)
+        if is_offload_opt:
+            new_sync_var = to_device(sync_var, origin_place)
+        else:
+            new_sync_var = sync_var
         assert new_sync_var is sync_var, "to_device must be inplace operation"
         return ret