[AutoParallel]Refine ShardOptimizer (#62933)

* fix

* fix

* fix

* fix

* refine

* fix

* fix

* fix

Author: zhangbo9674

paddle/fluid/pybind/tensor.cc

@@ -1077,7 +1077,12 @@ void BindTensor(pybind11::module &m) {  // NOLINT
            [](DistTensor &self, const DistTensor &src) {
              self.unsafe_set_dims(src.dims());
              self.unsafe_set_dist_attr(src.dist_attr());
-             self.unsafe_mutable_value()->ShareDataWith(src.value());
+             if (!IsCurRankInMesh(self.process_mesh()) &&
+                 !IsCurRankInMesh(src.dist_attr().process_mesh())) {
+               self.unsafe_mutable_value()->ShareDataNoCheckWith(src.value());
+             } else {
+               self.unsafe_mutable_value()->ShareDataWith(src.value());
+             }
              return self;
            })
       .def("_clear", &DistTensor::clear);

python/paddle/distributed/auto_parallel/api.py

@@ -657,7 +657,9 @@ def __init__(self, optimizer, shard_fn=None):
                 self._shard_fn._shard_parameter(param)
 
     def _set_and_check_sharding_prop_from_param(self):
-        if len(self._shard_fn._mesh._shape) == 1:
+        if (self._shard_fn._mesh is not None) and (
+            len(self._shard_fn._mesh._shape) == 1
+        ):
             self._sharding_degree = self._shard_fn._mesh.get_dim_size(0)
             self._sharding_mesh_axis = 0
         else:
@@ -684,16 +686,12 @@ def _set_and_check_sharding_prop_from_param(self):
                     assert isinstance(
                         placements[self._sharding_mesh_axis], dist.Replicate
                     ), "The placement on sharding_mesh_axis should be Replicate"
+
                     # check the sharding degree since it has already been set
-                    if any(
-                        isinstance(placement, dist.Shard)
-                        for placement in placements
-                    ):
-                        for idx, placement in enumerate(placements):
-                            if isinstance(placement, dist.Replicate):
-                                assert (
-                                    mesh.dim_size(idx) == self._sharding_degree
-                                ), "The sharding degree of all parameters must be equal currently."
+                    assert (
+                        mesh.dim_size(self._sharding_mesh_axis)
+                        == self._sharding_degree
+                    ), "The sharding degree of all parameters must be equal currently."
 
         assert (
             self._sharding_degree is not None
@@ -889,7 +887,7 @@ class ShardingStage1(_ShardingStageBase):
     A builtin shard_fn for shard_optimizer interface, users can pass it to shard_optimizer to implement sharding optimization with stage 1.
 
     Args:
-        mesh(paddle.distributed.ProcessMesh): The `ProcessMesh` object describes the Cartesian topology of the used processes.
+        mesh(None|paddle.distributed.ProcessMesh): If mesh is not None, the `ProcessMesh` object describes the Cartesian topology of the used processes for dense type parameters. Note: Currently, only one mesh configuration is supported for all dense parameters. If there is a need for multiple mesh configurations, please configure them yourself in the upper layer networking code.
 
     Examples:
         .. code-block:: python
@@ -922,7 +920,7 @@ class ShardingStage1(_ShardingStageBase):
             >>> # python -m paddle.distributed.launch --gpus=0,1 {test_case}.py
     """
 
-    def __init__(self, mesh):
+    def __init__(self, mesh=None):
         super().__init__(mesh)
 
     def __call__(self, key, param, accumulator):
@@ -950,7 +948,7 @@ class ShardingStage2(_ShardingStageBase):
     A builtin shard_fn for shard_optimizer interface, users can pass it to shard_optimizer to implement sharding optimization with stage 2.
 
     Args:
-        mesh(paddle.distributed.ProcessMesh): The `ProcessMesh` object describes the Cartesian topology of the used processes.
+        mesh(None|paddle.distributed.ProcessMesh): If mesh is not None, the `ProcessMesh` object describes the Cartesian topology of the used processes for dense type parameters. Note: Currently, only one mesh configuration is supported for all dense parameters. If there is a need for multiple mesh configurations, please configure them yourself in the upper layer networking code.
 
     Examples:
         .. code-block:: python
@@ -983,7 +981,7 @@ class ShardingStage2(_ShardingStageBase):
             >>> # python -m paddle.distributed.launch --gpus=0,1 {test_case}.py
     """
 
-    def __init__(self, mesh):
+    def __init__(self, mesh=None):
         super().__init__(mesh)
 
     def __call__(self, key, param, accumulator):
@@ -1022,21 +1020,21 @@ def _grad_hook(grad):
         return grad
 
     def _register_hook_for_param_grad(self, param):
-        if param.is_dense():
+        if param.is_dense() and self._mesh is not None:
             placements = []
             for _ in range(len(self._mesh.shape)):
                 placements.append(dist.Replicate())
             param._to_dist_(placements, self._mesh)
-
-        param.register_hook(ShardingStage2._grad_hook)
+        if param.is_dist():
+            param.register_hook(ShardingStage2._grad_hook)
 
 
 class ShardingStage3(_ShardingStageBase):
     """
     A builtin shard_fn for shard_optimizer interface, users can pass it to shard_optimizer to implement sharding optimization with stage 3.
 
     Args:
-        mesh(paddle.distributed.ProcessMesh): The `ProcessMesh` object describes the Cartesian topology of the used processes.
+        mesh(None|paddle.distributed.ProcessMesh): If mesh is not None, the `ProcessMesh` object describes the Cartesian topology of the used processes for dense type parameters. Note: Currently, only one mesh configuration is supported for all dense parameters. If there is a need for multiple mesh configurations, please configure them yourself in the upper layer networking code.
 
     Examples:
         .. code-block:: python
@@ -1069,30 +1067,33 @@ class ShardingStage3(_ShardingStageBase):
             >>> # python -m paddle.distributed.launch --gpus=0,1 {test_case}.py
     """
 
-    def __init__(self, mesh):
+    def __init__(self, mesh=None):
         super().__init__(mesh)
 
     def _shard_parameter(self, param):
-        if param.is_dense():
+        if param.is_dense() and self._mesh is not None:
             placements = []
             for _ in range(len(self._mesh.shape)):
                 placements.append(dist.Replicate())
             param._to_dist_(placements, self._mesh)
-
-        new_placements = get_placement_with_sharding(
-            param, self._sharding_mesh_axis
-        )
-        shard_param = dist.reshard(param, param.process_mesh, new_placements)
-        # change the holder of param to new shard_param
-        param.get_tensor()._share_data_with(shard_param.get_tensor())
+        if param.is_dist():
+            new_placements = get_placement_with_sharding(
+                param, self._sharding_mesh_axis
+            )
+            shard_param = dist.reshard(
+                param, param.process_mesh, new_placements
+            )
+            # change the holder of param to new shard_param
+            param.get_tensor()._share_data_with(shard_param.get_tensor())
 
     def _unshard_parameter(self, param):
-        new_placements = param.placements
-        if isinstance(new_placements[self._sharding_mesh_axis], dist.Shard):
-            new_placements[self._sharding_mesh_axis] = dist.Replicate()
+        if param.is_dist():
+            new_placements = param.placements
+            if isinstance(new_placements[self._sharding_mesh_axis], dist.Shard):
+                new_placements[self._sharding_mesh_axis] = dist.Replicate()
 
-        new_param = dist.reshard(param, param.process_mesh, new_placements)
-        param.get_tensor()._share_data_with(new_param.get_tensor())
+            new_param = dist.reshard(param, param.process_mesh, new_placements)
+            param.get_tensor()._share_data_with(new_param.get_tensor())
 
     def __call__(self, key, param, accumulator):
         if param.is_dist():