[Cherry-pick] add comm buffer size (#8963)

docs/trainer.md

@@ -506,6 +506,14 @@ Trainer 是一个简单，但功能完整的 Paddle 训练和评估模块，并
                         with 8 cards, then set sharding_degree=8, sharding will only communication inside machine.
                         default -1 means sharding parameters between all workers. (`int`, *optional*, defaults to `-1`)
 
+  --sharding_comm_buffer_size_MB
+                        设置sharding的通信中fuse梯度的大小。此选项只在sharding选项开启时候生效。
+                        默认值为-1，表示所有通信fuse的梯度大小按照默认配置，默认配置是256MB。
+                        (`int`, 可选, 默认为 `-1`)
+
+                        Set the size of the fuse gradient in sharding communication. This option only takes effect when the sharding option is turned on.The default value is -1, which means that the gradient size of all communication fuses follows the default configuration, which is 256MB.
+                        (`int`, optional, default `-1`)
+
   --tensor_parallel_degree
                         张量并行是Megatron论文针对Transformer结构的张量切分方法.
                         此方法将一层transformer的计算划分到了不同卡上.

paddlenlp/trainer/training_args.py

@@ -549,6 +549,17 @@ class TrainingArguments:
             )
         },
     )
+    sharding_comm_buffer_size_MB: int = field(
+        default=-1,
+        metadata={
+            "help": (
+                "Set the size of the fuse gradient in sharding communication. This option only takes effect when "
+                "the sharding option is turned on.The default value is -1, which means that the gradient size of "
+                "all communication fuses follows the default configuration, which is 256MB. "
+            )
+        },
+    )
+
     save_sharded_model: bool = field(
         default=False,
         metadata={
@@ -1293,6 +1304,11 @@ def is_segment_parallel_supported():
                             )
 
                     try:
+                        if self.sharding_comm_buffer_size_MB > 0:
+                            strategy.hybrid_configs["sharding_configs"].comm_buffer_size_MB = int(
+                                self.sharding_comm_buffer_size_MB
+                            )
+
                         if "split_param" in sharding_parallel_config:
                             strategy.hybrid_configs["sharding_configs"].split_param = True