PaddlePaddle
diff --git a/‎python/paddle/distributed/auto_parallel/api.py
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+187-110Lines changed: 187 additions & 110 deletions b/‎python/paddle/distributed/auto_parallel/api.py
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+187-110Lines changed: 187 additions & 110 deletions
@@ -369,58 +369,97 @@ def forward(
         local_tensor_list,
         local_mesh_list,
         local_placements,
-        idx,
-        global_dims,
         mesh,
         placements,
+        global_dims,
+        idx=None,
     ):
-        local_tensor = local_tensor_list[idx]
-        if local_tensor.is_dist():
-            local_mesh = local_tensor.process_mesh
-            local_val = local_tensor._local_value()
-        else:
-            local_val = local_tensor
-            local_mesh = None
-
-        ctx.global_mesh = copy.deepcopy(mesh)
-        ctx.placements = copy.deepcopy(placements)
-        ctx.local_dims = local_tensor.shape
-        ctx.local_mesh_list = copy.deepcopy(local_mesh_list)
-        ctx.local_placements = copy.deepcopy(local_placements)
+        # NOTE: _local_value/Paddle.Tensor is only supported in dynamic mode
+        if paddle.in_dynamic_mode():
+            local_tensor = local_tensor_list[idx]
+            if local_tensor.is_dist():
+                local_mesh = local_tensor.process_mesh
+                local_val = local_tensor._local_value()
+            else:
+                local_val = local_tensor
+                local_mesh = None
+
+            ctx.save_for_backward(
+                copy.deepcopy(mesh),  #  global_mesh
+                local_tensor.shape,  #  local_dims
+                copy.deepcopy(local_mesh_list),  #  local_mesh_list
+                copy.deepcopy(local_placements),  #  local_placements
+            )
 
-        place = paddle.framework._current_expected_place()
-        place = paddle.framework._get_paddle_place(place)
+            place = paddle.framework._current_expected_place()
+            place = paddle.framework._get_paddle_place(place)
 
-        global_tensor = paddle.Tensor(
-            local_val,
-            dims=global_dims,
-            process_mesh=mesh,
-            placements=placements,
-            place=place,
-        )
-        global_tensor.stop_gradient = local_tensor.stop_gradient
-        return global_tensor
+            global_tensor = paddle.Tensor(
+                local_val,
+                dims=global_dims,
+                process_mesh=mesh,
+                placements=placements,
+                place=place,
+            )
+            global_tensor.stop_gradient = local_tensor.stop_gradient
+            return global_tensor
+        else:
+            ctx.save_for_backward(
+                copy.deepcopy(mesh),  #  global_mesh
+                copy.deepcopy(placements),  #  global_placements
+                copy.deepcopy(local_mesh_list),  #  local_mesh_list
+                copy.deepcopy(local_placements),  #  local_placements
+            )
+            dist_tensor = paddle._C_ops.moe_global_mesh_tensor(
+                local_tensor_list,
+                local_mesh_list,
+                local_placements,
+                mesh,
+                placements,
+                global_dims,
+            )
+            dist_tensor.stop_gradient = local_tensor_list[0].stop_gradient
+            dist_tensor.persistable = local_tensor_list[0].persistable
+            return dist_tensor
 
     @staticmethod
     def backward(ctx, grad_tensor):
-        if ctx.local_mesh_list is None:
-            return grad_tensor._local_value()
-        else:
-            place = paddle.framework._current_expected_place()
-            place = paddle.framework._get_paddle_place(place)
-            out = []
-            for i, local_mesh in enumerate(ctx.local_mesh_list):
-                out.append(
-                    paddle.Tensor(
-                        grad_tensor._local_value(),
-                        dims=ctx.local_dims,
-                        process_mesh=local_mesh,
-                        placements=ctx.local_placements,
-                        place=place,
+        if paddle.in_dynamic_mode():
+            global_mesh, local_dims, local_mesh_list, local_placements = (
+                ctx.saved_tensor()
+            )
+            if local_mesh_list is None:
+                return grad_tensor._local_value()
+            else:
+                place = paddle.framework._current_expected_place()
+                place = paddle.framework._get_paddle_place(place)
+                out = []
+                for i, local_mesh in enumerate(local_mesh_list):
+                    out.append(
+                        paddle.Tensor(
+                            grad_tensor._local_value(),
+                            dims=local_dims,
+                            process_mesh=local_mesh,
+                            placements=local_placements,
+                            place=place,
+                        )
                     )
-                )
-                out[-1].get_tensor()._unsafe_set_skip_check_mesh(True)
-            return out
+                    out[-1].get_tensor()._unsafe_set_skip_check_mesh(True)
+                return out
+        else:
+            (
+                global_mesh,
+                global_placements,
+                local_mesh_list,
+                local_placements,
+            ) = ctx.saved_tensor()
+            return paddle._C_ops.moe_sub_mesh_tensors(
+                grad_tensor,
+                local_mesh_list,
+                local_placements,
+                global_mesh,
+                global_placements,
+            )
 
 
 def _get_sub_meshes_and_local_placements(
@@ -469,6 +508,7 @@ def moe_global_mesh_tensor(
     local_tensor = local_tensor_list[local_tensor_idx]
 
     if paddle.in_dynamic_mode():
+        # NOTE: _local_value and Paddle.Tensor() is only supported in dynamic mode
         if local_coord[0].size == 0:
             local_tensor_shape = _cal_local_shape(
                 local_tensor_list[0].shape, local_mesh_list[0], local_placements
@@ -498,27 +538,25 @@ def moe_global_mesh_tensor(
             resharded_local_tensor_list,
             local_mesh_list,
             local_placements,
-            local_tensor_idx,
-            global_dims,
             mesh,
             placements,
+            global_dims,
+            local_tensor_idx,
         )
     elif paddle.framework.in_pir_mode():
         global_dims = _cal_global_shape(
             local_tensor._local_shape, mesh, placements
         )
-        dist_tensor = paddle._C_ops.moe_global_mesh_tensor(
+        return paddle.jit.dy2static.py_layer.StaticPyLayer(
+            _moe_global_mesh_tensor
+        ).apply(
             local_tensor_list,
             local_mesh_list,
             local_placements,
             mesh,
             placements,
             global_dims,
         )
-        dist_tensor.stop_gradient = local_tensor_list[0].stop_gradient
-        dist_tensor.persistable = local_tensor_list[0].persistable
-
-        return dist_tensor
     else:
         raise NotImplementedError(
             "dtensor_from_local_list() are only supported in dynamic and pir mode."
@@ -536,75 +574,114 @@ def forward(
         global_mesh=None,
         global_placements=None,
     ):
-        ctx.local_mesh_list = copy.deepcopy(local_mesh_list)
-        ctx.local_placements = local_placements
-        ctx.local_mesh_dim = local_mesh_dim
-        ctx.global_mesh = copy.deepcopy(global_mesh)
-        ctx.global_placements = global_placements
-        ctx.global_shape = dist_tensor.shape
-
-        if global_mesh is None and global_placements is None:
-            return dist_tensor._local_value()
-        else:
-            if global_mesh is None or global_placements is None:
-                raise ValueError(
-                    "the args global_mesh and global_placements should be set together"
-                )
-            ori_mesh = dist_tensor.process_mesh
-            if global_mesh != dist_tensor.process_mesh:
-                raise ValueError(
-                    "the global_mesh should be the same as dist_tensor's process_mesh."
-                )
-            assert check_placements_equal(
-                global_placements, dist_tensor.placements
-            ), f"the global_placements ({global_placements}) is not equal to dist_tensor's placements ({dist_tensor.placements})."
-            local_shape = _cal_local_shape(
-                dist_tensor.shape, global_mesh, global_placements
-            )
-            for idx, placement in enumerate(local_placements):
-                if placement.is_shard():
-                    shard_dim = placement.get_dim()
-                    local_dim_size = local_shape[shard_dim]
-                    local_shape[shard_dim] = (
-                        local_dim_size * local_mesh_list[0].shape[idx]
+        ctx.save_for_backward(
+            copy.deepcopy(local_mesh_list),  # local_mesh_list,
+            local_placements,  # local_placements,
+            local_mesh_dim,  # local_mesh_dim,
+            copy.deepcopy(global_mesh),  # global_mesh,
+            global_placements,  # global_placements,
+            dist_tensor.shape,  # global_shape,
+        )
+        if paddle.in_dynamic_mode():
+            if global_mesh is None and global_placements is None:
+                return dist_tensor._local_value()
+            else:
+                if global_mesh is None or global_placements is None:
+                    raise ValueError(
+                        "the args global_mesh and global_placements should be set together"
                     )
-
-            place = paddle.framework._current_expected_place()
-            place = paddle.framework._get_paddle_place(place)
-            local_tensor_list = []
-            for i, local_mesh in enumerate(local_mesh_list):
-                local_tensor = paddle.Tensor(
-                    dist_tensor._local_value(),
-                    dims=local_shape,
-                    process_mesh=local_mesh,
-                    placements=local_placements,
-                    place=place,
+                ori_mesh = dist_tensor.process_mesh
+                if global_mesh != dist_tensor.process_mesh:
+                    raise ValueError(
+                        "the global_mesh should be the same as dist_tensor's process_mesh."
+                    )
+                assert check_placements_equal(
+                    global_placements, dist_tensor.placements
+                ), f"the global_placements ({global_placements}) is not equal to dist_tensor's placements ({dist_tensor.placements})."
+                local_shape = _cal_local_shape(
+                    dist_tensor.shape, global_mesh, global_placements
                 )
-                local_tensor.get_tensor()._unsafe_set_skip_check_mesh(True)
+                for idx, placement in enumerate(local_placements):
+                    if placement.is_shard():
+                        shard_dim = placement.get_dim()
+                        local_dim_size = local_shape[shard_dim]
+                        local_shape[shard_dim] = (
+                            local_dim_size * local_mesh_list[0].shape[idx]
+                        )
+
+                place = paddle.framework._current_expected_place()
+                place = paddle.framework._get_paddle_place(place)
+                local_tensor_list = []
+                for i, local_mesh in enumerate(local_mesh_list):
+                    local_tensor = paddle.Tensor(
+                        dist_tensor._local_value(),
+                        dims=local_shape,
+                        process_mesh=local_mesh,
+                        placements=local_placements,
+                        place=place,
+                    )
+                    local_tensor.get_tensor()._unsafe_set_skip_check_mesh(True)
+                    local_tensor.stop_gradient = dist_tensor.stop_gradient
+                    local_tensor_list.append(local_tensor)
+                return local_tensor_list
+        elif paddle.framework.in_pir_mode():
+            local_tensors = paddle._C_ops.moe_sub_mesh_tensors(
+                dist_tensor,
+                local_mesh_list,
+                local_placements,
+                global_mesh,
+                global_placements,
+            )
+            for local_tensor in local_tensors:
                 local_tensor.stop_gradient = dist_tensor.stop_gradient
-                local_tensor_list.append(local_tensor)
-            return local_tensor_list
+                local_tensor.persistable = dist_tensor.persistable
+            return local_tensors
 
     @staticmethod
     def backward(ctx, *grad_tensor):
+        (
+            local_mesh_list,
+            local_placements,
+            local_mesh_dim,
+            global_mesh,
+            global_placements,
+            global_shape,
+        ) = ctx.saved_tensor()
         place = paddle.framework._current_expected_place()
         place = paddle.framework._get_paddle_place(place)
-        mesh = ctx.global_mesh
+        mesh = global_mesh
         process_ids = np.array(mesh.process_ids).reshape(mesh.shape)
         local_coord = np.where(process_ids == dist.get_rank())
         if local_coord[0].size == 0:
             local_tensor_idx = 0
         else:
-            local_tensor_idx = local_coord[ctx.local_mesh_dim][0]
+            local_tensor_idx = local_coord[local_mesh_dim][0]
         local_grad = grad_tensor[local_tensor_idx]
-        global_tensor = paddle.Tensor(
-            local_grad._local_value(),
-            dims=ctx.global_shape,
-            process_mesh=mesh,
-            placements=ctx.global_placements,
-            place=place,
-        )
-        return global_tensor
+
+        if paddle.in_dynamic_mode():
+            place = paddle.framework._current_expected_place()
+            place = paddle.framework._get_paddle_place(place)
+            global_tensor = paddle.Tensor(
+                local_grad._local_value(),
+                dims=global_shape,
+                process_mesh=mesh,
+                placements=global_placements,
+                place=place,
+            )
+            return global_tensor
+        elif paddle.framework.in_pir_mode():
+            global_dims = _cal_global_shape(
+                local_grad._local_shape, mesh, global_placements
+            )
+
+            return paddle._C_ops.moe_global_mesh_tensor(
+                grad_tensor,
+                local_mesh_list,
+                local_placements,
+                global_mesh,
+                global_placements,
+                global_dims,
+            )
 
 
 def moe_sub_mesh_tensors(
@@ -627,17 +704,17 @@ def moe_sub_mesh_tensors(
             global_placements,
         )
     elif paddle.framework.in_pir_mode():
-        local_tensors = paddle._C_ops.moe_sub_mesh_tensors(
+
+        return paddle.jit.dy2static.py_layer.StaticPyLayer(
+            _moe_sub_mesh_tensors
+        ).apply(
             dist_tensor,
             local_mesh_list,
             local_placements,
+            local_mesh_dim,
             global_mesh,
             global_placements,
         )
-        for local_tensor in local_tensors:
-            local_tensor.stop_gradient = dist_tensor.stop_gradient
-            local_tensor.persistable = dist_tensor.persistable
-        return local_tensors
     else:
         raise NotImplementedError(
             "moe_sub_mesh_tensors is only supported in dynamic mode."