modify grad accu and comm fusion api

mindspore/common/parameter.py

@@ -288,20 +288,20 @@ class Parameter(Tensor_):
 
     @property
     def comm_fusion(self):
-        """Get the fusion type for communication operators corresponding to this parameter."""
-        return self.param_info.comm_fusion
-
-    @comm_fusion.setter
-    def comm_fusion(self, comm_fusion_):
         """
+        Get and Set the fusion type (int) for communication operators corresponding to this parameter.
+
         In `AUTO_PARALLEL` and `SEMI_AUTO_PARALLEL` mode, some communication operators used for parameters or
-        gradients aggregation are inserted automatically.Set the fusion type for communication operators generated
+        gradients aggregation are inserted automatically. Set the fusion type for communication operators generated
-        for this parameter. Only `Ascend` and `Graph` mode is supported.
+        for this parameter. The value of fusion must be greater than or equal to 0. When the value of fusion is 0,
+        operators will not be fused together.
 
-        Args:
+        Only `Ascend` and `Graph` mode is supported.
-            comm_fusion_ (int): The value of fusion must be greater than or equal to 0.
-                                When the value of fusion is 0, operators will not be fused together.
         """
+        return self.param_info.comm_fusion
+
+    @comm_fusion.setter
+    def comm_fusion(self, comm_fusion_):
         if context.get_context("mode") == context.PYNATIVE_MODE and "auto_parallel" in _get_parallel_mode():
             raise RuntimeError("`comm_fusion` does not support PYNATIVE_MODE")
         Validator.check_non_negative_int(comm_fusion_)

mindspore/context.py

@@ -344,7 +344,7 @@ def _context():
 @args_type_check(device_num=int, global_rank=int, gradients_mean=bool, gradient_fp32_sync=bool, parallel_mode=str,
                  auto_parallel_search_mode=str, parameter_broadcast=bool, strategy_ckpt_load_file=str,
                  strategy_ckpt_save_file=str, full_batch=bool, enable_parallel_optimizer=bool,
-                 all_reduce_fusion_config=list, pipeline_stages=int)
+                 all_reduce_fusion_config=list, pipeline_stages=int, grad_accumulation_step=int)
 def set_auto_parallel_context(**kwargs):
     r"""
     Set auto parallel context, which is valid only for Ascend and GPU target.
@@ -371,6 +371,7 @@ def set_auto_parallel_context(**kwargs):
     all_reduce_fusion_config     strategy_ckpt_save_file
     enable_parallel_optimizer    full_batch
                \                 pipeline_stages
+               \                 grad_accumulation_step
     ===========================  ===========================
 
     Args:
@@ -420,6 +421,8 @@ def set_auto_parallel_context(**kwargs):
                         the devices are distributed alone the pipeline. The total devices will be divided into
                         'pipeline_stags' stages. This currently could only be used when
                         parallel mode semi_auto_parallel is enabled. Default: 1.
+        grad_accumulation_step (int): Set the accumulation steps of gradients in auto and semi auto parallel mode.
+                        This should be a positive int. Default: 1.
 
     Raises:
         ValueError: If input key is not attribute in auto parallel context.

mindspore/parallel/_auto_parallel_context.py

@@ -18,7 +18,7 @@ import mindspore.context as context
 from mindspore.parallel._dp_allreduce_fusion import _set_fusion_strategy_by_idx, _set_fusion_strategy_by_size
 from mindspore.parallel._ps_context import _is_role_pserver
 from mindspore._c_expression import AutoParallelContext
-from mindspore._checkparam import args_type_check
+from mindspore._checkparam import args_type_check, Validator
 
 _MAX_GROUP_NAME_LEN = 127
 _DEFAULT_HCCL_FUSION_GROUP_NAME = "hccl_world_groupsum1"
@@ -257,6 +257,7 @@ class _AutoParallelContext:
             grad_accumulation_step (int): The grad accumulation step.
         """
         self.check_context_handle()
+        Validator.check_positive_int(grad_accumulation_step)
         self._context_handle.set_grad_accumulation_step(grad_accumulation_step)
 
     def get_grad_accumulation_step(self):