[xdoctest] reformat example code with google style in 192-197 (#55926)

* Update input.py

* Update input.py

* Update gather.py

* Update broadcast.py

* Update batch_isend_irecv.py

* Update all_to_all.py

* Update all_reduce.py

* Update all_gather.py

* rollback

* Apply suggestions from code review

* Apply suggestions from code review

* Apply suggestions from code review

python/paddle/distributed/communication/all_gather.py

@@ -63,7 +63,7 @@ def all_gather(tensor_list, tensor, group=None, sync_op=True):
             ...     data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
             >>> dist.all_gather(tensor_list, data)
             >>> print(tensor_list)
-            [[[4, 5, 6], [4, 5, 6]], [[1, 2, 3], [1, 2, 3]]] (2 GPUs)
+            >>> # [[[4, 5, 6], [4, 5, 6]], [[1, 2, 3], [1, 2, 3]]] (2 GPUs)
     """
     return stream.all_gather(tensor_list, tensor, group, sync_op)
 
@@ -99,7 +99,7 @@ def all_gather_object(object_list, obj, group=None):
             ...     obj = {"bar": [4, 5, 6]}
             >>> dist.all_gather_object(object_list, obj)
             >>> print(object_list)
-            [{'foo': [1, 2, 3]}, {'bar': [4, 5, 6]}] (2 GPUs)
+            >>> # [{'foo': [1, 2, 3]}, {'bar': [4, 5, 6]}] (2 GPUs)
     """
     assert (
         framework.in_dynamic_mode()

python/paddle/distributed/communication/all_reduce.py

@@ -42,18 +42,18 @@ def all_reduce(tensor, op=ReduceOp.SUM, group=None, sync_op=True):
     Examples:
         .. code-block:: python
 
-            # required: distributed
-            import paddle
-            import paddle.distributed as dist
-
-            dist.init_parallel_env()
-            if dist.get_rank() == 0:
-                data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
-            else:
-                data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
-            dist.all_reduce(data)
-            print(data)
-            # [[5, 7, 9], [5, 7, 9]] (2 GPUs)
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle
+            >>> import paddle.distributed as dist
+
+            >>> dist.init_parallel_env()
+            >>> if dist.get_rank() == 0:
+            ...     data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
+            >>> else:
+            ...     data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
+            >>> dist.all_reduce(data)
+            >>> print(data)
+            >>> # [[5, 7, 9], [5, 7, 9]] (2 GPUs)
     """
     return stream.all_reduce(
         tensor, op=op, group=group, sync_op=sync_op, use_calc_stream=False

python/paddle/distributed/communication/all_to_all.py

@@ -40,22 +40,22 @@ def alltoall(in_tensor_list, out_tensor_list, group=None, sync_op=True):
     Examples:
         .. code-block:: python
 
-            # required: distributed
-            import paddle
-            import paddle.distributed as dist
-
-            dist.init_parallel_env()
-            out_tensor_list = []
-            if dist.get_rank() == 0:
-                data1 = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
-                data2 = paddle.to_tensor([[7, 8, 9], [10, 11, 12]])
-            else:
-                data1 = paddle.to_tensor([[13, 14, 15], [16, 17, 18]])
-                data2 = paddle.to_tensor([[19, 20, 21], [22, 23, 24]])
-            dist.alltoall([data1, data2], out_tensor_list)
-            print(out_tensor_list)
-            # [[[1, 2, 3], [4, 5, 6]], [[13, 14, 15], [16, 17, 18]]] (2 GPUs, out for rank 0)
-            # [[[7, 8, 9], [10, 11, 12]], [[19, 20, 21], [22, 23, 24]]] (2 GPUs, out for rank 1)
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle
+            >>> import paddle.distributed as dist
+
+            >>> dist.init_parallel_env()
+            >>> out_tensor_list = []
+            >>> if dist.get_rank() == 0:
+            ...     data1 = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
+            ...     data2 = paddle.to_tensor([[7, 8, 9], [10, 11, 12]])
+            >>> else:
+            ...     data1 = paddle.to_tensor([[13, 14, 15], [16, 17, 18]])
+            ...     data2 = paddle.to_tensor([[19, 20, 21], [22, 23, 24]])
+            >>> dist.alltoall([data1, data2], out_tensor_list)
+            >>> print(out_tensor_list)
+            >>> # [[[1, 2, 3], [4, 5, 6]], [[13, 14, 15], [16, 17, 18]]] (2 GPUs, out for rank 0)
+            >>> # [[[7, 8, 9], [10, 11, 12]], [[19, 20, 21], [22, 23, 24]]] (2 GPUs, out for rank 1)
     """
     return stream.alltoall(
         out_tensor_list, in_tensor_list, group, sync_op, False
@@ -92,46 +92,46 @@ def alltoall_single(
     Examples:
         .. code-block:: python
 
-            # required: distributed
-            import paddle
-            import paddle.distributed as dist
-
-            dist.init_parallel_env()
-            rank = dist.get_rank()
-            size = dist.get_world_size()
-
-            # case 1 (2 GPUs)
-            data = paddle.arange(2, dtype='int64') + rank * 2
-            # data for rank 0: [0, 1]
-            # data for rank 1: [2, 3]
-            output = paddle.empty([2], dtype='int64')
-            dist.alltoall_single(data, output)
-            print(output)
-            # output for rank 0: [0, 2]
-            # output for rank 1: [1, 3]
-
-            # case 2 (2 GPUs)
-            in_split_sizes = [i + 1 for i in range(size)]
-            # in_split_sizes for rank 0: [1, 2]
-            # in_split_sizes for rank 1: [1, 2]
-            out_split_sizes = [rank + 1 for i in range(size)]
-            # out_split_sizes for rank 0: [1, 1]
-            # out_split_sizes for rank 1: [2, 2]
-            data = paddle.ones([sum(in_split_sizes), size], dtype='float32') * rank
-            # data for rank 0: [[0., 0.], [0., 0.], [0., 0.]]
-            # data for rank 1: [[1., 1.], [1., 1.], [1., 1.]]
-            output = paddle.empty([(rank + 1) * size, size], dtype='float32')
-            group = dist.new_group([0, 1])
-            task = dist.alltoall_single(data,
-                                        output,
-                                        in_split_sizes,
-                                        out_split_sizes,
-                                        sync_op=False,
-                                        group=group)
-            task.wait()
-            print(output)
-            # output for rank 0: [[0., 0.], [1., 1.]]
-            # output for rank 1: [[0., 0.], [0., 0.], [1., 1.], [1., 1.]]
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle
+            >>> import paddle.distributed as dist
+
+            >>> dist.init_parallel_env()
+            >>> rank = dist.get_rank()
+            >>> size = dist.get_world_size()
+
+            >>> # case 1 (2 GPUs)
+            >>> data = paddle.arange(2, dtype='int64') + rank * 2
+            >>> # data for rank 0: [0, 1]
+            >>> # data for rank 1: [2, 3]
+            >>> output = paddle.empty([2], dtype='int64')
+            >>> dist.alltoall_single(data, output)
+            >>> print(output)
+            >>> # output for rank 0: [0, 2]
+            >>> # output for rank 1: [1, 3]
+
+            >>> # case 2 (2 GPUs)
+            >>> in_split_sizes = [i + 1 for i in range(size)]
+            >>> # in_split_sizes for rank 0: [1, 2]
+            >>> # in_split_sizes for rank 1: [1, 2]
+            >>> out_split_sizes = [rank + 1 for i in range(size)]
+            >>> # out_split_sizes for rank 0: [1, 1]
+            >>> # out_split_sizes for rank 1: [2, 2]
+            >>> data = paddle.ones([sum(in_split_sizes), size], dtype='float32') * rank
+            >>> # data for rank 0: [[0., 0.], [0., 0.], [0., 0.]]
+            >>> # data for rank 1: [[1., 1.], [1., 1.], [1., 1.]]
+            >>> output = paddle.empty([(rank + 1) * size, size], dtype='float32')
+            >>> group = dist.new_group([0, 1])
+            >>> task = dist.alltoall_single(data,
+            ...                             output,
+            ...                             in_split_sizes,
+            ...                             out_split_sizes,
+            ...                             sync_op=False,
+            ...                             group=group)
+            >>> task.wait()
+            >>> print(output)
+            >>> # output for rank 0: [[0., 0.], [1., 1.]]
+            >>> # output for rank 1: [[0., 0.], [0., 0.], [1., 1.], [1., 1.]]
 
     """
     return stream.alltoall_single(

python/paddle/distributed/communication/batch_isend_irecv.py

@@ -41,23 +41,23 @@ class P2POp:
     Examples:
         .. code-block:: python
 
-            # required: distributed
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
 
-            import paddle
-            import paddle.distributed as dist
+            >>> import paddle
+            >>> import paddle.distributed as dist
 
-            dist.init_parallel_env()
-            rank = dist.get_rank()
-            world_size = dist.get_world_size()
+            >>> dist.init_parallel_env()
+            >>> rank = dist.get_rank()
+            >>> world_size = dist.get_world_size()
 
-            send_t = paddle.arange(2) + rank
-            # paddle.tensor([0, 1])  # Rank-0
-            # paddle.tensor([1, 2])  # Rank-1
+            >>> send_t = paddle.arange(2) + rank
+            >>> # paddle.tensor([0, 1])  # Rank-0
+            >>> # paddle.tensor([1, 2])  # Rank-1
 
-            recv_t = paddle.empty(shape=[2], dtype=send_t.dtype)
+            >>> recv_t = paddle.empty(shape=[2], dtype=send_t.dtype)
 
-            send_op = dist.P2POp(dist.isend, send_t, (rank + 1) % world_size)
-            recv_op = dist.P2POp(dist.irecv, recv_t, (rank - 1 + world_size) % world_size)
+            >>> send_op = dist.P2POp(dist.isend, send_t, (rank + 1) % world_size)
+            >>> recv_op = dist.P2POp(dist.irecv, recv_t, (rank - 1 + world_size) % world_size)
 
     """
 
@@ -127,32 +127,32 @@ def batch_isend_irecv(p2p_op_list):
     Examples:
         .. code-block:: python
 
-            # required: distributed
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
 
-            import paddle
-            import paddle.distributed as dist
+            >>> import paddle
+            >>> import paddle.distributed as dist
 
-            dist.init_parallel_env()
-            rank = dist.get_rank()
-            world_size = dist.get_world_size()
+            >>> dist.init_parallel_env()
+            >>> rank = dist.get_rank()
+            >>> world_size = dist.get_world_size()
 
-            send_t = paddle.arange(2) + rank
-            # paddle.tensor([0, 1])  # Rank-0
-            # paddle.tensor([1, 2])  # Rank-1
+            >>> send_t = paddle.arange(2) + rank
+            >>> # paddle.tensor([0, 1])  # Rank-0
+            >>> # paddle.tensor([1, 2])  # Rank-1
 
-            recv_t = paddle.empty(shape=[2], dtype=send_t.dtype)
+            >>> recv_t = paddle.empty(shape=[2], dtype=send_t.dtype)
 
-            send_op = dist.P2POp(dist.isend, send_t, (rank + 1) % world_size)
-            recv_op = dist.P2POp(dist.irecv, recv_t, (rank - 1 + world_size) % world_size)
+            >>> send_op = dist.P2POp(dist.isend, send_t, (rank + 1) % world_size)
+            >>> recv_op = dist.P2POp(dist.irecv, recv_t, (rank - 1 + world_size) % world_size)
 
-            tasks = dist.batch_isend_irecv([send_op, recv_op])
+            >>> tasks = dist.batch_isend_irecv([send_op, recv_op])
 
-            for task in tasks:
-                task.wait()
+            >>> for task in tasks:
+            ...     task.wait()
 
-            print(recv_t)
-            # paddle.tensor([1, 2])     # Rank-0
-            # paddle.tensor([0, 1])     # Rank-1
+            >>> print(recv_t)
+            >>> # paddle.tensor([1, 2])     # Rank-0
+            >>> # paddle.tensor([0, 1])     # Rank-1
     """
     _check_p2p_op_list(p2p_op_list)
     group = p2p_op_list[0].group

python/paddle/distributed/communication/broadcast.py

@@ -48,18 +48,18 @@ def broadcast(tensor, src, group=None, sync_op=True):
     Examples:
         .. code-block:: python
 
-            # required: distributed
-            import paddle
-            import paddle.distributed as dist
-
-            dist.init_parallel_env()
-            if dist.get_rank() == 0:
-                data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
-            else:
-                data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
-            dist.broadcast(data, src=1)
-            print(data)
-            # [[1, 2, 3], [1, 2, 3]] (2 GPUs)
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle
+            >>> import paddle.distributed as dist
+
+            >>> dist.init_parallel_env()
+            >>> if dist.get_rank() == 0:
+            ...     data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
+            >>> else:
+            ...     data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
+            >>> dist.broadcast(data, src=1)
+            >>> print(data)
+            >>> # [[1, 2, 3], [1, 2, 3]] (2 GPUs)
     """
     return stream.broadcast(
         tensor,
@@ -89,17 +89,17 @@ def broadcast_object_list(object_list, src, group=None):
     Examples:
         .. code-block:: python
 
-            # required: distributed
-            import paddle.distributed as dist
-
-            dist.init_parallel_env()
-            if dist.get_rank() == 0:
-                object_list = [{"foo": [1, 2, 3]}]
-            else:
-                object_list = [{"bar": [4, 5, 6]}]
-            dist.broadcast_object_list(object_list, src=1)
-            print(object_list)
-            # [{"bar": [4, 5, 6]}] (2 GPUs)
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle.distributed as dist
+
+            >>> dist.init_parallel_env()
+            >>> if dist.get_rank() == 0:
+            ...     object_list = [{"foo": [1, 2, 3]}]
+            >>> else:
+            ...     object_list = [{"bar": [4, 5, 6]}]
+            >>> dist.broadcast_object_list(object_list, src=1)
+            >>> print(object_list)
+            >>> # [{"bar": [4, 5, 6]}] (2 GPUs)
     """
     assert (
         framework.in_dynamic_mode()

python/paddle/distributed/communication/gather.py

@@ -38,21 +38,21 @@ def gather(tensor, gather_list=None, dst=0, group=None, sync_op=True):
     Examples:
         .. code-block:: python
 
-            # required: distributed
-            import paddle
-            import paddle.distributed as dist
-
-            dist.init_parallel_env()
-            gather_list = []
-            if dist.get_rank() == 0:
-                data = paddle.to_tensor([1, 2, 3])
-                dist.gather(data, gather_list, dst=0)
-            else:
-                data = paddle.to_tensor([4, 5, 6])
-                dist.gather(data1, gather_list, dst=0)
-            print(gather_list)
-            # [[1, 2, 3], [4, 5, 6]] (2 GPUs, out for rank 0)
-            # [] (2 GPUs, out for rank 1)
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle
+            >>> import paddle.distributed as dist
+
+            >>> dist.init_parallel_env()
+            >>> gather_list = []
+            >>> if dist.get_rank() == 0:
+            ...     data = paddle.to_tensor([1, 2, 3])
+            ...     dist.gather(data, gather_list, dst=0)
+            >>> else:
+            ...     data = paddle.to_tensor([4, 5, 6])
+            ...     dist.gather(data1, gather_list, dst=0)
+            >>> print(gather_list)
+            >>> # [[1, 2, 3], [4, 5, 6]] (2 GPUs, out for rank 0)
+            >>> # [] (2 GPUs, out for rank 1)
     """
     assert (
         framework.in_dynamic_mode()