# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Communication APIs.
"""
from mindspore.common import Tensor
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...communication.management import get_rank, get_group_size, GlobalComm, _get_group
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, PrimitiveWithCheck, Primitive, prim_attr_register
from ...common.api import context
[文档]class ReduceOp:
"""
Operation options for reducing tensors. This is an enumerated type, not an operator.
Mainly used in data parallel mode.
The main calling methods are as follows:
- SUM: ReduceOp.SUM.
- MAX: ReduceOp.MAX.
- MIN: ReduceOp.MIN.
- PROD: ReduceOp.PROD.
There are four kinds of operation options, "SUM", "MAX", "MIN", and "PROD".
- SUM: Take the sum.
- MAX: Take the maximum.
- MIN: Take the minimum.
- PROD: Take the product.
Note:
For more, refer to example. This needs to run in an environment with multiple graphics cards.
The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.ops.html#communication-operator>`_.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> from mindspore.communication import init
>>> from mindspore import Tensor, ops
>>> from mindspore.ops import ReduceOp
>>> import mindspore.nn as nn
>>>
>>> init()
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.allreduce_sum = ops.AllReduce(ReduceOp.SUM)
...
... def construct(self, x):
... return self.allreduce_sum(x)
...
>>> input_ = Tensor(np.ones([2, 8]).astype(np.float32))
>>> net = Net()
>>> output = net(input_)
>>> print(output)
[[2. 2. 2. 2. 2. 2. 2. 2.]
[2. 2. 2. 2. 2. 2. 2. 2.]]
"""
SUM = "sum"
MAX = "max"
MIN = "min"
PROD = "prod"
target_dtypes = (mstype.int8, mstype.int32, mstype.float16, mstype.float32)
def check_hcom_group_valid(group, prim_name=None):
"""Check if hcom group is valid."""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if context.get_context("mode") == context.PYNATIVE_MODE and \
context.get_context("device_target") == "Ascend" and \
group != GlobalComm.WORLD_COMM_GROUP:
raise RuntimeError(f"{msg_prefix} 'group' only support 'hccl_world_group' in pynative mode, but got "
f"'group': {group}.")
[文档]class AllReduce(PrimitiveWithInfer):
"""
Reduces the tensor data across all devices in such a way that all devices will get the same final result.
Note:
The operation of AllReduce does not support "prod" currently.
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.ops.html#communication-operator>`_.
Args:
op (str): Specifies an operation used for element-wise reductions,
like sum, max, and min. Default: ReduceOp.SUM.
group (str): The communication group to work on. Default: "GlobalComm.WORLD_COMM_GROUP".
Inputs:
- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
Outputs:
Tensor, has the same shape of the input, i.e., :math:`(x_1, x_2, ..., x_R)`.
The contents depend on the specified operation.
Raises:
TypeError: If any of `op` and `group` is not a str,
or fusion is not an integer, or the input's dtype is bool.
ValueError: If the `op` is "prod".
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> # This example should be run with two devices. Refer to the tutorial > Distributed Training on mindspore.cn
>>> import numpy as np
>>> from mindspore.communication import init
>>> from mindspore import Tensor
>>> from mindspore.ops import ReduceOp
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>>
>>> init()
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.allreduce_sum = ops.AllReduce(ReduceOp.SUM)
...
... def construct(self, x):
... return self.allreduce_sum(x)
...
>>> input_ = Tensor(np.ones([2, 8]).astype(np.float32))
>>> net = Net()
>>> output = net(input_)
>>> print(output)
[[2. 2. 2. 2. 2. 2. 2. 2.]
[2. 2. 2. 2. 2. 2. 2. 2.]]
"""
@prim_attr_register
def __init__(self, op=ReduceOp.SUM, group=GlobalComm.WORLD_COMM_GROUP):
"""Initialize AllReduce."""
if not isinstance(op, type(ReduceOp.SUM)):
raise TypeError(f"For '{self.name}', the 'op' should be str, but got {type(op).__name__}.")
if not isinstance(_get_group(group), str):
raise TypeError(f"For '{self.name}', the 'group' should be str, "
f"but got {type(_get_group(group)).__name__}.")
check_hcom_group_valid(group, prim_name=self.name)
self.op = op
self.add_prim_attr('group', _get_group(group))
self.add_prim_attr('fusion', 0)
self.add_prim_attr('index', 0)
self.add_prim_attr('no_eliminate', True)
def infer_shape(self, x_shape):
return x_shape
def infer_dtype(self, x_dtype):
validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
return x_dtype
[文档]class AllGather(PrimitiveWithInfer):
"""
Gathers tensors from the specified communication group.
Note:
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.ops.html#communication-operator>`_.
Args:
group (str): The communication group to work on. Default: "GlobalComm.WORLD_COMM_GROUP".
Inputs:
- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
Outputs:
Tensor. If the number of devices in the group is N,
then the shape of output is :math:`(N, x_1, x_2, ..., x_R)`.
Raises:
TypeError: If `group` is not a str.
ValueError: If the local rank id of the calling process in the group
is larger than the group's rank size.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> # This example should be run with two devices. Refer to the tutorial > Distributed Training on mindspore.cn
>>> import numpy as np
>>> import mindspore.ops as ops
>>> import mindspore.nn as nn
>>> from mindspore.communication import init
>>> from mindspore import Tensor, context
>>>
>>> context.set_context(mode=context.GRAPH_MODE)
>>> init()
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.allgather = ops.AllGather()
...
... def construct(self, x):
... return self.allgather(x)
...
>>> input_x = Tensor(np.ones([2, 8]).astype(np.float32))
>>> net = Net()
>>> output = net(input_x)
>>> print(output)
[[1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1.]]
"""
@prim_attr_register
def __init__(self, group=GlobalComm.WORLD_COMM_GROUP):
"""Initialize AllGather."""
validator.check_value_type('group', _get_group(group), (str,), self.name)
self.rank = get_rank(_get_group(group))
self.rank_size = get_group_size(_get_group(group))
validator.check('rank', self.rank, 'rank_size', self.rank_size, Rel.LT, self.name)
self.add_prim_attr('rank_size', self.rank_size)
self.add_prim_attr('group', _get_group(group))
self.add_prim_attr('fusion', 0)
self.add_prim_attr('mean_flag', False)
self.add_prim_attr('no_eliminate', True)
def infer_shape(self, x_shape):
validator.check_positive_int(len(x_shape), "x shape", self.name)
if x_shape[0] > 0:
x_shape[0] = x_shape[0] * self.rank_size
return x_shape
def infer_dtype(self, x_dtype):
validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
return x_dtype
def __call__(self, tensor):
raise NotImplementedError
class _MiniStepAllGather(PrimitiveWithInfer):
"""
Auto parallel virtual operator. Do nothing in forward, do reducescatter in backward in mini-step. It is only for
internal use of parallel modules and cannot be called by users.
Args:
group (str): The communication group to work on. Default: None.
grad_accumulation_step (int): The grad accumulation step. Default: None.
"""
@prim_attr_register
def __init__(self, group=GlobalComm.WORLD_COMM_GROUP, grad_accumulation_step=None, mean_flag=None):
"""Initialize _MiniStepAllGather."""
validator.check_value_type('group', _get_group(group), (str,), self.name)
self.rank = get_rank(_get_group(group))
self.rank_size = get_group_size(_get_group(group))
validator.check('rank', self.rank, 'rank_size', self.rank_size, Rel.LT, self.name)
self.add_prim_attr('rank_size', self.rank_size)
self.add_prim_attr('group', _get_group(group))
self.add_prim_attr('fusion', 1)
self.grad_accumulation_step = grad_accumulation_step
self.mean_flag = mean_flag
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape, z_shape):
validator.check_positive_int(len(x_shape), "x shape", self.name)
if x_shape[0] > 0:
x_shape[0] = x_shape[0] * self.rank_size
return x_shape
def infer_dtype(self, x_dtype, z_shape):
validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
return x_dtype
class _MicroStepAllGather(PrimitiveWithInfer):
"""
Auto parallel virtual operator. Do nothing in forward, do reducescatter in backward in mini-step. It is only for
internal use of parallel modules and cannot be called by users.
Args:
group (str): The communication group to work on. Default: None.
"""
@prim_attr_register
def __init__(self, group=GlobalComm.WORLD_COMM_GROUP, mean_flag=None):
validator.check_value_type('group', _get_group(group), (str,), self.name)
self.rank = get_rank(_get_group(group))
self.rank_size = get_group_size(_get_group(group))
validator.check('rank', self.rank, 'rank_size', self.rank_size, Rel.LT, self.name)
self.add_prim_attr('rank_size', self.rank_size)
self.add_prim_attr('group', _get_group(group))
self.add_prim_attr('fusion', 1)
self.add_prim_attr('do_mirror', False)
self.mean_flag = mean_flag
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape, z_shape):
validator.check_positive_int(len(x_shape), "x shape", self.name)
if x_shape[0] > 0:
x_shape[0] = x_shape[0] * self.rank_size
return x_shape
def infer_dtype(self, x_dtype, z_dtype):
validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
return x_dtype
class _HostAllGather(PrimitiveWithInfer):
"""
Gathers tensors from the specified communication group on host.
Note:
The tensors must have the same shape and format in all processes of the collection.
_HostAllGather is a host-side operator, it depends on OpenMPI and must use build option -M on
to enable it. Using mpirun command to run it:
mpirun -output-filename log -merge-stderr-to-stdout -np 3 python test_host_all_gather.py
Args:
group (Union[tuple[int],list[int]]): The rand_ids of communication group to work on. Default: None.
Raises:
TypeError: If group is not a list nor tuple, or elements of group are not int.
ValueError: If group is not set, or rank_id from group not in [0, 7].
Inputs:
- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
Outputs:
Tensor. If the number of devices in the group is N,
then the shape of output is :math:`(N, x_1, x_2, ..., x_R)`.
"""
@prim_attr_register
def __init__(self, group=None):
"""Initialize _HostAllGather."""
if group is None:
raise ValueError(f"For '{self.name}', the 'group' cannot be None, but got {group}.")
validator.check_value_type('group', group, (tuple, list), self.name)
validator.check_int(len(group), 2, Rel.GE, "group size", self.name)
for r in group:
validator.check_int_range(r, 0, 7, Rel.INC_BOTH, "rank_id", self.name)
validator.check_value_type("rank_id", r, (int,), self.name)
self.group_size = len(group)
self.add_prim_attr('group', group)
self.add_prim_attr('no_eliminate', True)
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape):
validator.check_positive_int(len(x_shape), "x shape", self.name)
if x_shape[0] > 0:
x_shape[0] = x_shape[0] * self.group_size
return x_shape
def infer_dtype(self, x_dtype):
validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
return x_dtype
def __call__(self, tensor):
raise NotImplementedError
[文档]class ReduceScatter(PrimitiveWithInfer):
"""
Reduces and scatters tensors from the specified communication group.
Note:
The back propagation of the op is not supported yet. Stay tuned for more.
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.ops.html#communication-operator>`_.
Args:
op (str): Specifies an operation used for element-wise reductions,
like SUM, MAX, AVG. Default: ReduceOp.SUM.
group (str): The communication group to work on. Default: "GlobalComm.WORLD_COMM_GROUP".
Raises:
TypeError: If any of operation and group is not a string.
ValueError: If the first dimension of the input cannot be divided by the rank size.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> # This example should be run with two devices. Refer to the tutorial > Distributed Training on mindspore.cn
>>> from mindspore import Tensor, context
>>> from mindspore.communication import init
>>> from mindspore.ops import ReduceOp
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>> import numpy as np
>>>
>>> context.set_context(mode=context.GRAPH_MODE)
>>> init()
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.reducescatter = ops.ReduceScatter(ReduceOp.SUM)
...
... def construct(self, x):
... return self.reducescatter(x)
...
>>> input_ = Tensor(np.ones([8, 8]).astype(np.float32))
>>> net = Net()
>>> output = net(input_)
>>> print(output)
[[2. 2. 2. 2. 2. 2. 2. 2.]
[2. 2. 2. 2. 2. 2. 2. 2.]
[2. 2. 2. 2. 2. 2. 2. 2.]
[2. 2. 2. 2. 2. 2. 2. 2.]]
"""
@prim_attr_register
def __init__(self, op=ReduceOp.SUM, group=GlobalComm.WORLD_COMM_GROUP):
"""Initialize ReduceScatter."""
validator.check_value_type('op', op, (type(ReduceOp.SUM),), self.name)
validator.check_value_type('group', _get_group(group), (str,), self.name)
self.op = op
self.rank_size = get_group_size(_get_group(group))
self.add_prim_attr('rank_size', self.rank_size)
self.add_prim_attr('group', _get_group(group))
self.add_prim_attr('fusion', 0)
self.add_prim_attr('no_eliminate', True)
def infer_shape(self, x_shape):
if self.rank_size == 0:
raise ValueError(f"For '{self.name}', the 'rank_size' cannot be zero, but got {self.rank_size}.")
if x_shape[0] % self.rank_size != 0:
raise ValueError(f"For '{self.name}', the first dimension of 'x_shape' should be divided by 'rank_size', "
f"but got 'x_shape[0]': {x_shape[0]}, 'rank_size': {self.rank_size}.")
x_shape[0] = int(x_shape[0] / self.rank_size)
return x_shape
def infer_dtype(self, x_dtype):
validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
return x_dtype
def __call__(self, tensor):
raise NotImplementedError
class _HostReduceScatter(PrimitiveWithInfer):
"""
Reduces and scatters tensors from the specified communication group on host.
Note:
The tensors must have the same shape and format in all processes of the collection.
_HostReduceScatter is a host-side operator, it depends on OpenMPI and must use build option
-M on to enable it. Using mpirun command to run it:
mpirun -output-filename log -merge-stderr-to-stdout -np 3 python test_host_reduce_scatter.py
Args:
op (str): Specifies an operation used for element-wise reductions,
like sum, max, avg. Default: ReduceOp.SUM.
group (Union[tuple[int],list[int]]): The rand_ids of communication group to work on. Default: None.
Raises:
TypeError: If op is not a string and group is not a list nor tuple,
or elements of group are not int.
ValueError: If the first dimension of input can not be divided by group size,
or group is not set, or rank_id not in [0, 7].
"""
@prim_attr_register
def __init__(self, op=ReduceOp.SUM, group=None):
"""Initialize _HostReduceScatter."""
if group is None:
raise ValueError(f"For '{self.name}', the 'group' cannot be None, but got {group}.")
validator.check_value_type('op', op, (type(ReduceOp.SUM),), self.name)
validator.check_value_type('group', group, (tuple, list), self.name)
validator.check_int(len(group), 2, Rel.GE, "group size", self.name)
for r in group:
validator.check_int_range(r, 0, 7, Rel.INC_BOTH, "rank_id", self.name)
validator.check_value_type("rank_id", r, (int,), self.name)
self.op = op
self.group_size = len(group)
self.add_prim_attr('group', group)
self.add_prim_attr('no_eliminate', True)
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape):
if x_shape[0] % self.group_size != 0:
raise ValueError(f"For '{self.name}', the first dimension of 'x_shape' should be divided by 'group_size', "
f"but got 'x_shape[0]': {x_shape[0]}, 'rank_size': {self.group_size}.")
x_shape[0] = int(x_shape[0] / self.group_size)
return x_shape
def infer_dtype(self, x_dtype):
validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
return x_dtype
def __call__(self, tensor):
raise NotImplementedError
[文档]class Broadcast(PrimitiveWithInfer):
"""
Broadcasts the tensor to the whole group.
Note:
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.ops.html#communication-operator>`_.
Args:
root_rank (int): Source rank. Required in all processes except the one
that is sending the data.
group (str): The communication group to work on. Default: "GlobalComm.WORLD_COMM_GROUP".
Inputs:
- **input_x** (tuple[Tensor]) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
Outputs:
Tensor, has the same shape of the input, i.e., :math:`(x_1, x_2, ..., x_R)`.
The contents depend on the data of the `root_rank` device.
Raises:
TypeError: If root_rank is not an integer or group is not a string.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> # This example should be run with multiple processes.
>>> # Please refer to the Programming Guide > Distributed Training -> Distributed Parallel Usage Example
>>> # on mindspore.cn and focus on the contents of these three parts: Configuring Distributed Environment
>>> # Variables, Calling the Collective Communication Library, Running The Script.
>>> from mindspore import Tensor
>>> from mindspore import context
>>> from mindspore.communication import init
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>> import numpy as np
>>>
>>> context.set_context(mode=context.GRAPH_MODE)
>>> init()
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.broadcast = ops.Broadcast(1)
...
... def construct(self, x):
... return self.broadcast((x,))
...
>>> input_x = Tensor(np.ones([2, 4]).astype(np.int32))
>>> net = Net()
>>> output = net(input_x)
>>> print(output)
(Tensor(shape[2,4], dtype=Int32, value=
[[1, 1, 1, 1],
[1, 1, 1, 1]]),)
"""
@prim_attr_register
def __init__(self, root_rank, group=GlobalComm.WORLD_COMM_GROUP):
"""Initialize Broadcast."""
validator.check_value_type('root_rank', root_rank, (int,), self.name)
validator.check_value_type('group', _get_group(group), (str,), self.name)
check_hcom_group_valid(group, prim_name=self.name)
self.add_prim_attr('group', _get_group(group))
self.add_prim_attr('no_eliminate', True)
def infer_shape(self, x_shape):
return x_shape
def infer_dtype(self, x_dtype):
if not isinstance(x_dtype, tuple):
raise TypeError(f"For '{self.name}', the 'input_x' should be a tuple, but got {type(x_dtype).__name__}!")
for _ele in x_dtype:
validator.check_tensor_dtype_valid('x', _ele, target_dtypes, self.name)
return x_dtype
class _AllSwap(PrimitiveWithCheck):
"""
_AllSwap is a collective operation.
_AllSwap sends data from the all processes to the all processes in the specified group. It has two phases:
- The scatter phase: On each process, the operand is split into the send size of blocks along the
0-th axis, and the blocks are scattered to all processes, e.g., the ith block is send to the ith process.
- The gather phase: Each process concatenates the received blocks along the 0-th axis.
Note:
The tensors must have the same format in all processes of the collection.
Args:
group (str): The communication group name.
Inputs:
tensor_in (tensor): A 2-D tensor. On each process, divide blocks into number of the send size.
send_size (tensor): A 1-D int64 tensor. The element is the send data size for each process.
recv_size (tensor): A 1-D int64 tensor. The element is the receive data size for each process.
Returns:
tensor_out (tensor): The result tensor.
Raises:
TypeError: If group is not a string.
"""
@prim_attr_register
def __init__(self, group=GlobalComm.WORLD_COMM_GROUP):
"""Initialize _AllSwap"""
validator.check_value_type('group', _get_group(group), (str,), self.name)
self.init_prim_io_names(inputs=['tensor_in', 'send_size', 'recv_size'], outputs=['tensor_out'])
self.add_prim_attr('group', _get_group(group))
self.add_prim_attr('no_eliminate', True)
self.add_prim_attr('order_enforce_skip', True)
def __check__(self, tensor_in, send_size, recv_size):
validator.check_subclass("tensor_in", tensor_in['dtype'], mstype.tensor, self.name)
validator.check_tensor_dtype_valid("send_size", send_size['dtype'], [mstype.int64],
self.name)
validator.check_tensor_dtype_valid("recv_size", recv_size['dtype'], [mstype.int64],
self.name)
validator.check_equal_int(len(tensor_in['shape']), 2, "tensor_in", self.name)
validator.check_equal_int(len(send_size['shape']), 1, "send_size", self.name)
validator.check_equal_int(len(recv_size['shape']), 1, "recv_size", self.name)
out_shape = [-1] + [tensor_in['shape'][1]]
out = {'shape': out_shape,
'dtype': tensor_in['dtype'],
'value': None}
return out
[文档]class NeighborExchange(Primitive):
"""
NeighborExchange is a collective operation.
NeighborExchange sends data from the local rank to ranks in the send_rank_ids,
as while receive data from recv_rank_ids.
Note:
The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.ops.html#communication-operator>`_.
This operator requires a full-mesh network topology, each device has the same vlan id, and the ip & mask are
in the same subnet, please check the `details \
<https://www.mindspore.cn/tutorials/experts/zh-CN/r1.7/parallel/communicate_ops.html#注意事项>`_.
Args:
send_rank_ids (list(int)): Ranks which the data is sent to.
recv_rank_ids (list(int)): Ranks which the data is received from.
recv_shapes (tuple(list(int))): Data shape which received from recv_rank_ids.
send_shapes (tuple(list(int))): Data shape which send to the send_rank_ids.
recv_type (type): Data type which received from recv_rank_ids
group (str): The communication group to work on. Default: "GlobalComm.WORLD_COMM_GROUP".
Supported Platforms:
``Ascend``
Example:
>>> # This example should be run with 2 devices. Refer to the tutorial > Distributed Training on mindspore.cn
>>> import os
>>> import mindspore as ms
>>> from mindspore import Tensor
>>> from mindspore import context
>>> from mindspore.communication import init
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>> import numpy as np
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.neighborexchange = ops.NeighborExchange(send_rank_ids=[1], recv_rank_ids=[1],
... recv_shapes=([2, 2],), send_shapes=([3, 3],),
... recv_type=ms.float32)
...
...
... def construct(self, x):
... out = self.neighborexchange((x,))
...
>>> context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
>>> init()
>>> net = Net()
>>> input_x = Tensor(np.ones([3, 3]), dtype = ms.float32)
>>> output = net(input_x)
>>> print(output)
[[2. 2.], [2. 2.]]
"""
@prim_attr_register
def __init__(self, send_rank_ids, recv_rank_ids, recv_shapes, send_shapes, recv_type,
group=GlobalComm.WORLD_COMM_GROUP):
self.init_prim_io_names(inputs=['x'], outputs=['output'])
self.send_rank_ids = send_rank_ids
self.recv_rank_ids = recv_rank_ids
self.recv_shapes = recv_shapes
self.send_shapes = send_shapes
self.recv_type = recv_type
self.add_prim_attr('no_eliminate', True)
def __call__(self, tensor):
raise NotImplementedError
[文档]class AlltoAll(PrimitiveWithInfer):
"""
AlltoAll is a collective operation.
AlltoAll sends data from the all processes to the all processes in the specified group. It has two phases:
- The scatter phase: On each process, the operand is split into split_count number of blocks along the
split_dimensions, and the blocks are scattered to all processes, e.g., the ith block is send to the ith process.
- The gather phase: Each process concatenates the received blocks along the concat_dimension.
Note:
The tensors must have the same shape and format in all processes of the collection. The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.ops.html#communication-operator>`_.
This operator requires a full-mesh network topology, each device has the same vlan id, and the ip & mask are
in the same subnet, please check the `details \
<https://www.mindspore.cn/tutorials/experts/zh-CN/r1.7/parallel/communicate_ops.html#注意事项>`_.
Args:
split_count (int): On each process, divide blocks into split_count number.
split_dim (int): On each process, split blocks along the split_dim.
concat_dim (int): On each process, gather the received blocks along the concat_dimension.
group (str): The communication group to work on. Default: "GlobalComm.WORLD_COMM_GROUP".
Raises:
TypeError: If group is not a string.
Supported Platforms:
``Ascend``
Example:
>>> # This example should be run with 8 devices. Refer to the tutorial > Distributed Training on mindspore.cn
>>> import os
>>> import mindspore as ms
>>> from mindspore import Tensor
>>> from mindspore import context
>>> from mindspore.communication import init
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>> import numpy as np
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.alltoall = ops.AlltoAll(split_count = 8, split_dim = -2, concat_dim = -1)
...
... def construct(self, x):
... out = self.alltoall(x)
... return out
...
>>> context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
>>> init()
>>> net = Net()
>>> rank_id = int(os.getenv("RANK_ID"))
>>> input_x = Tensor(np.ones([1, 1, 8, 1]) * rank_id, dtype = ms.float32)
>>> output = net(input_x)
>>> print(output)
[[[[0. 1. 2. 3. 4. 5. 6. 7.]]]]
"""
@prim_attr_register
def __init__(self, split_count, split_dim, concat_dim, group=GlobalComm.WORLD_COMM_GROUP):
"""Initialize AlltoAll"""
validator.check_value_type('group', _get_group(group), (str,), self.name)
validator.check_is_int(split_count, int)
validator.check_is_int(split_dim, int)
validator.check_is_int(concat_dim, int)
self.split_count = split_count
self.split_dim = split_dim
self.concat_dim = concat_dim
self.add_prim_attr('group', _get_group(group))
self.add_prim_attr('no_eliminate', True)
def infer_shape(self, x_shape):
rank_size = get_group_size(_get_group(self.group))
if self.split_count != rank_size:
raise ValueError(f"For '{self.name}', the 'split_count' must be equal to 'rank_size', "
f"but got 'split_count': {self.split_count}, 'rank_size': {rank_size}.")
if x_shape[self.split_dim] % self.split_count != 0:
raise ValueError(f"For '{self.name}', the 'split_count' must be divisible by 'rank_size', "
f"but got 'split_count' {self.split_count}, 'rank_size' {x_shape[self.split_dim]}.")
x_shape[self.concat_dim] = x_shape[self.concat_dim] * self.split_count
x_shape[self.split_dim] = int(x_shape[self.split_dim] / self.split_count)
return x_shape
def infer_dtype(self, x_dtype):
validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
return x_dtype
def __call__(self, tensor):
raise NotImplementedError
[文档]class NeighborExchangeV2(Primitive):
"""
NeighborExchangeV2 is a collective operation.
NeighborExchangeV2 sends data from the local rank to ranks in the send_rank_ids,
as while receive data from recv_rank_ids.
Note:
The user needs to preset
communication environment variables before running the following example, please check the details on the
official website of `MindSpore \
<https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.ops.html#communication-operator>`_.
This operator requires a full-mesh network topology, each device has the same vlan id, and the ip & mask are
in the same subnet, please check the `details \
<https://www.mindspore.cn/tutorials/experts/zh-CN/r1.7/parallel/communicate_ops.html#注意事项>`_.
Args:
send_rank_ids (list(int)): Ranks which the data is sent to. 8 rank_ids represents 8 directions, if one
direction is not send to , set it -1.
recv_rank_ids (list(int)): Ranks which the data is received from. 8 rank_ids represents 8 directions,
if one direction is not recv from , set it -1.
send_lens (list(int)): Data lens which send to the send_rank_ids, 4 numbers represent the lens of
[top, bottom, left, right].
recv_lens (list(int)): Data lens which received from recv_rank_ids, 4 numbers represent the lens of
[top, bottom, left, right].
data_format (str): Data format, only support NCHW now.
group (str): The communication group to work on. Default: "GlobalComm.WORLD_COMM_GROUP".
Supported Platforms:
``Ascend``
Example:
>>> # This example should be run with 2 devices. Refer to the tutorial > Distributed Training on mindspore.cn
>>> import os
>>> import mindspore as ms
>>> from mindspore import Tensor
>>> from mindspore import context
>>> from mindspore.communication import init
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>> import numpy as np
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.neighborexchangev2 = ops.NeighborExchangeV2(send_rank_ids=[-1, -1, -1, -1, 1, -1, -1, -1],
... send_lens=[0, 1, 0, 0],
... recv_rank_ids=[-1, -1, -1, -1, 1, -1, -1, -1],
... recv_lens=[0, 1, 0, 0],
... data_format="NCHW")
...
... def construct(self, x):
... out = self.neighborexchangev2(x)
... return out
...
>>> context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
>>> init()
>>> input_x = Tensor(np.ones([1, 1, 2, 2]), dtype = ms.float32)
>>> net = Net()
>>> output = net(input_x)
>>> print(output)
[[[[1. 1.], [1. 1.], [2. 2.]]]]
"""
@prim_attr_register
def __init__(self, send_rank_ids, send_lens, recv_rank_ids, recv_lens, data_format,
group=GlobalComm.WORLD_COMM_GROUP):
self.init_prim_io_names(inputs=['x'], outputs=['output'])
self.send_rank_ids = send_rank_ids
self.recv_rank_ids = recv_rank_ids
self.send_lens = send_lens
self.recv_lens = recv_lens
self.format = data_format
self.add_prim_attr('group', _get_group(group))
self.add_prim_attr('no_eliminate', True)
def __call__(self, tensor):
raise NotImplementedError
class _MirrorOperator(PrimitiveWithInfer):
"""
Auto parallel virtual operator. Do nothing in forward, do all reduce and mean in backward. It is only for
internal use of parallel modules and cannot be called by users.
Args:
group (str): The communication group to work on. Default: None.
dev_num (int): The device number of the group. Default: None.
mean_flag (bool): Whether use mean in backward. Default: None.
"""
@prim_attr_register
def __init__(self, group=None, dev_num=None, mean_flag=None):
"""Initialize _MirrorOperator."""
self.group = group
self.dev_num = dev_num
self.mean_flag = mean_flag
self.add_prim_attr("fusion", 1)
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape):
return x_shape
def infer_dtype(self, x_dtype):
return x_dtype
mirror = _MirrorOperator()
class _MirrorMiniStepOperator(PrimitiveWithInfer):
"""
Auto parallel virtual operator. Do nothing in forward, do all reduce and mean in backward. It is only for
internal use of parallel modules and cannot be called by users.
Args:
group (str): The communication group to work on. Default: None.
dev_num (int): The device number of the group. Default: None.
mean_flag (bool): Whether use mean in backward. Default: None.
grad_accumulation_step (int): The grad accumulation step. Default: None.
"""
@prim_attr_register
def __init__(self, group=None, dev_num=None, mean_flag=None, grad_accumulation_step=None):
"""Initialize _MirrorMiniStepOperator."""
self.group = group
self.dev_num = dev_num
self.mean_flag = mean_flag
self.grad_accumulation_step = grad_accumulation_step
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape, z_shape):
return x_shape
def infer_dtype(self, x_dtype, z_shape):
return x_dtype
mirror_mini_step = _MirrorMiniStepOperator()
class _VirtualDiv(PrimitiveWithInfer):
"""
Auto parallel virtual operator. Do nothing in forward, do Div in backward.
Args:
divisor: float32
"""
@prim_attr_register
def __init__(self, divisor=None):
"""Initialize _VirtualDiv."""
self.divisor = divisor
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape):
return x_shape
def infer_dtype(self, x_dtype):
return x_dtype
virtual_div = _VirtualDiv()
class _VirtualAdd(PrimitiveWithInfer):
"""Auto parallel virtual operator. Do nothing in forward, do Add in backward."""
@prim_attr_register
def __init__(self):
"""Initialize _VirtualAdd."""
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape, y_shape):
return x_shape
def infer_dtype(self, x_dtype, y_dtype):
return x_dtype
class _VirtualDataset(PrimitiveWithInfer):
"""
Auto parallel virtual dataset operator.
It would insert VirtualDataset operator in forward computation and be deleted before backward computation.
"""
@prim_attr_register
def __init__(self):
"""Initialize _VirtualDataset."""
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, *args):
return args
def infer_dtype(self, *args):
return args
virtual_dataset = _VirtualDataset()
class _VirtualAssignAdd(PrimitiveWithInfer):
"""
Auto parallel virtual operator. Do nothing in forward, do AssignAdd in backward. It is only for
internal use of parallel modules and cannot be called by users.
"""
@prim_attr_register
def __init__(self):
"""Initialize _VirtualAssignAdd."""
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape, y_shape):
return x_shape
def infer_dtype(self, x_dtype, y_dtype):
return x_dtype
virtual_assign_add = _VirtualAssignAdd()
class _VirtualAccuGrad(PrimitiveWithInfer):
"""
Auto parallel virtual operator. Do nothing in forward, return y in backward. It is only for
internal use of parallel modules and cannot be called by users.
"""
@prim_attr_register
def __init__(self):
"""Initialize _VirtualAccuGrad."""
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape, y_shape):
return x_shape
def infer_dtype(self, x_dtype, y_dtype):
return x_dtype
virtual_accu_grad = _VirtualAccuGrad()
class _MirrorMicroStepOperator(PrimitiveWithInfer):
"""
Auto parallel virtual operator. Do nothing in forward, do all reduce and mean in backward. It is only for
internal use of parallel modules and cannot be called by users.
Args:
group (str): The communication group to work on. Default: None.
dev_num (int): The device number of the group. Default: None.
mean_flag (bool): Whether use mean in backward. Default: None.
"""
@prim_attr_register
def __init__(self, group=None, dev_num=None, mean_flag=None):
"""Initialize _MirrorMicroStepOperator."""
self.group = group
self.dev_num = dev_num
self.mean_flag = mean_flag
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape, z_shape):
return x_shape
def infer_dtype(self, x_dtype, z_shape):
return x_dtype
class _VirtualOutput(PrimitiveWithInfer):
"""
Auto parallel virtual out operator.
It would insert VirtualOutput operator in forward computation and be deleted before backward computation.
"""
@prim_attr_register
def __init__(self):
"""Initialize _VirtualOutput."""
self.add_prim_attr('order_enforce_skip', True)
def infer_shape(self, x_shape):
return x_shape
def infer_dtype(self, x_dtype):
return x_dtype
class _GetTensorSlice(PrimitiveWithInfer):
"""
Gets tensor slice by device matrix and tensor map.
Args:
dev_mat (tuple): The device matrix of the slice tensor.
tensor_map (tuple): The tensor map of the slice tensor.
"""
@prim_attr_register
def __init__(self):
"""Initialize _GetTensorSlice."""
self.add_prim_attr('order_enforce_skip', True)
def infer_value(self, x, dev_mat, tensor_map):
from mindspore.parallel._tensor import _load_tensor
validator.check_value_type("dev_mat", dev_mat, [tuple], self.name)
validator.check_value_type("tensor_map", tensor_map, [tuple], self.name)
return Tensor(_load_tensor(x, dev_mat, tensor_map))