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

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

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Update input.py
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Merge branch 'PaddlePaddle:develop' into xdoc2
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52 changes: 26 additions & 26 deletions python/paddle/distributed/communication/all_gather.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,19 +51,19 @@ def all_gather(tensor_list, tensor, group=None, sync_op=True):
Examples:
.. code-block:: python

# required: distributed
import paddle
import paddle.distributed as dist

dist.init_parallel_env()
tensor_list = []
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_gather(tensor_list, data)
print(tensor_list)
# [[[4, 5, 6], [4, 5, 6]], [[1, 2, 3], [1, 2, 3]]] (2 GPUs)
>>> # doctest: +REQUIRES(env: DISTRIBUTED)
>>> import paddle
>>> import paddle.distributed as dist

>>> dist.init_parallel_env()
>>> tensor_list = []
>>> 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_gather(tensor_list, data)
>>> print(tensor_list)
[[[4, 5, 6], [4, 5, 6]], [[1, 2, 3], [1, 2, 3]]] (2 GPUs)
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恢复成注释形式吧

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"""
return stream.all_gather(tensor_list, tensor, group, sync_op)

Expand All @@ -87,19 +87,19 @@ def all_gather_object(object_list, obj, group=None):
Examples:
.. code-block:: python

# required: distributed
import paddle
import paddle.distributed as dist

dist.init_parallel_env()
object_list = []
if dist.get_rank() == 0:
obj = {"foo": [1, 2, 3]}
else:
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)
>>> # doctest: +REQUIRES(env: DISTRIBUTED)
>>> import paddle
>>> import paddle.distributed as dist

>>> dist.init_parallel_env()
>>> object_list = []
>>> if dist.get_rank() == 0:
... obj = {"foo": [1, 2, 3]}
>>> else:
... 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)
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"""
assert (
framework.in_dynamic_mode()
Expand Down
24 changes: 12 additions & 12 deletions python/paddle/distributed/communication/all_reduce.py
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Expand Up @@ -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)
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"""
return stream.all_reduce(
tensor, op=op, group=group, sync_op=sync_op, use_calc_stream=False
Expand Down
112 changes: 56 additions & 56 deletions python/paddle/distributed/communication/all_to_all.py
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Expand Up @@ -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)
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"""
return stream.alltoall(
out_tensor_list, in_tensor_list, group, sync_op, False
Expand Down Expand Up @@ -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.]]
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"""
return stream.alltoall_single(
Expand Down
60 changes: 30 additions & 30 deletions python/paddle/distributed/communication/batch_isend_irecv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)

"""

Expand Down Expand Up @@ -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
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"""
_check_p2p_op_list(p2p_op_list)
group = p2p_op_list[0].group
Expand Down
46 changes: 23 additions & 23 deletions python/paddle/distributed/communication/broadcast.py
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Expand Up @@ -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)
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"""
return stream.broadcast(
tensor,
Expand Down Expand Up @@ -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)
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"""
assert (
framework.in_dynamic_mode()
Expand Down
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