forked from huawei/mindspore2022
421 lines
14 KiB
Python
421 lines
14 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""comm_ops"""
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from ..._checkparam import Validator as validator
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from ..._checkparam import Rel
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from ...communication.management import get_rank, get_group_size, GlobalComm, get_group
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from ...common import dtype as mstype
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from ..primitive import PrimitiveWithInfer, prim_attr_register
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class ReduceOp:
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"""
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Operation options for reduce tensors.
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There are four kinds of operation options, "SUM","MAX","MIN","PROD".
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- SUM: Take the sum.
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- MAX: Take the maximum.
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- MIN: Take the minimum.
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- PROD: Take the product.
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"""
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SUM = "sum"
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MAX = "max"
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MIN = "min"
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PROD = "prod"
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class AllReduce(PrimitiveWithInfer):
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"""
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Reduces the tensor data across all devices in such a way that all devices will get the same final result.
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Note:
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The operation of AllReduce does not support "prod" currently.
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Tensor must have same shape and format in all processes participating in the collective.
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Args:
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op (str): Specifies an operation used for element-wise reductions,
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like sum, max, min. Default: ReduceOp.SUM.
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group (str): The communication group to work on. Default: "hccl_world_group".
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Raises:
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TypeError: If any of op and group is not a string
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or fusion is not a integer or the input's dtype is bool.
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ValueError: If op is "prod"
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Inputs:
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- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
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Outputs:
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Tensor, has the same shape of the input, i.e., :math:`(x_1, x_2, ..., x_R)`.
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The contents depend on the specified operation.
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Examples:
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>>> from mindspore.communication import init
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>>> import mindspore.ops.operations as P
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>>> init('nccl')
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>>> class Net(nn.Cell):
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>>> def __init__(self):
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>>> super(Net, self).__init__()
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>>> self.allreduce_sum = P.AllReduce(ReduceOp.SUM, group="nccl_world_group")
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>>>
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>>> def construct(self, x):
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>>> return self.allreduce_sum(x)
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>>>
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>>> input_ = Tensor(np.ones([2, 8]).astype(np.float32))
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>>> net = Net()
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>>> output = net(input_)
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"""
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@prim_attr_register
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def __init__(self, op=ReduceOp.SUM, group=GlobalComm.WORLD_COMM_GROUP):
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if not isinstance(op, type(ReduceOp.SUM)):
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raise TypeError("The operation of AllReduce should be str.")
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if op == ReduceOp.PROD:
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raise RuntimeError("The operation of AllReduce 'prod' is not supported yet.")
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if not isinstance(get_group(group), str):
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raise TypeError("The group of AllReduce should be str.")
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self.op = op
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self.add_prim_attr('group', get_group(group))
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self.add_prim_attr('fusion', 0)
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def vm_impl(self, x):
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"""Implement by vm mode."""
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x = x.asnumpy()
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return Tensor(x)
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def infer_shape(self, x_shape):
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return x_shape
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def infer_dtype(self, x_dtype):
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if x_dtype.element_type() == mstype.bool_:
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raise TypeError("AllReduce does not support 'Bool' as the dtype of input!")
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return x_dtype
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class AllGather(PrimitiveWithInfer):
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"""
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Gathers tensors from the specified communication group.
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Note:
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Tensor must have the same shape and format in all processes participating in the collective.
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Args:
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group (str): The communication group to work on. Default: "hccl_world_group".
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Raises:
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TypeError: If group is not a string.
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ValueError: If the local rank id of the calling process in the group
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is larger than the group's rank size.
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Inputs:
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- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
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Outputs:
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Tensor. If the number of devices in the group is N,
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then the shape of output is :math:`(N, x_1, x_2, ..., x_R)`.
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Examples:
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>>> from mindspore.communication import init
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>>> import mindspore.ops.operations as P
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>>> init('nccl')
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>>> class Net(nn.Cell):
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>>> def __init__(self):
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>>> super(Net, self).__init__()
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>>> self.allgather = P.AllGather(group="nccl_world_group")
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>>>
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>>> def construct(self, x):
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>>> return self.allgather(x)
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>>>
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>>> input_ = Tensor(np.ones([2, 8]).astype(np.float32))
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>>> net = Net()
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>>> output = net(input_)
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"""
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@prim_attr_register
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def __init__(self, group=GlobalComm.WORLD_COMM_GROUP):
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validator.check_value_type('group', get_group(group), (str,), self.name)
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self.rank = get_rank(get_group(group))
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self.rank_size = get_group_size(get_group(group))
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validator.check('rank', self.rank, 'rank_size', self.rank_size, Rel.LT, self.name)
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self.add_prim_attr('rank_size', self.rank_size)
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self.add_prim_attr('group', get_group(group))
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def infer_shape(self, x_shape):
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x_shape[0] = x_shape[0] * self.rank_size
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return x_shape
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def infer_dtype(self, x_dtype):
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if x_dtype.element_type() == mstype.bool_:
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raise TypeError(f"{self.name} does not support 'Bool' as the dtype of input!")
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return x_dtype
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def __call__(self, tensor):
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raise NotImplementedError
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class ReduceScatter(PrimitiveWithInfer):
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"""
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Reduces and scatters tensors from the specified communication group.
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Note:
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The back propagation of the op is not surported yet. Stay tuned for more.
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Tensor must have the same shape and format in all processes participating in the collective.
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Args:
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op (str): Specifies an operation used for element-wise reductions,
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like sum, max, avg. Default: ReduceOp.SUM.
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group (str): The communication group to work on. Default: "hccl_world_group".
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Raises:
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TypeError: If any of op and group is not a string
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ValueError: If the first dimension of input can not be divided by rank size.
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Examples:
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>>> from mindspore.communication import init
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>>> import mindspore.ops.operations as P
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>>> init('nccl')
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>>> class Net(nn.Cell):
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>>> def __init__(self):
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>>> super(Net, self).__init__()
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>>> self.reducescatter = P.ReduceScatter(ReduceOp.SUM, group="nccl_world_group")
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>>>
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>>> def construct(self, x):
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>>> return self.reducescatter(x)
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>>>
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>>> input_ = Tensor(np.ones([2, 8]).astype(np.float32))
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>>> net = Net()
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>>> output = net(input_)
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"""
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@prim_attr_register
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def __init__(self, op=ReduceOp.SUM, group=GlobalComm.WORLD_COMM_GROUP):
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validator.check_value_type('op', op, (type(ReduceOp.SUM),), self.name)
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validator.check_value_type('group', get_group(group), (str,), self.name)
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self.op = op
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self.rank_size = get_group_size(get_group(group))
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self.add_prim_attr('rank_size', self.rank_size)
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self.add_prim_attr('group', get_group(group))
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def infer_shape(self, x_shape):
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if x_shape[0] % self.rank_size != 0:
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raise ValueError(f"For '{self.name}' the first dimension of x should be divided by rank_size.")
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x_shape[0] = int(x_shape[0]/self.rank_size)
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return x_shape
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def infer_dtype(self, x_dtype):
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if x_dtype.element_type() == mstype.bool_:
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raise TypeError(f"{self.name} does not support 'Bool' as the dtype of input!")
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return x_dtype
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def __call__(self, tensor):
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raise NotImplementedError
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class Broadcast(PrimitiveWithInfer):
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"""
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Broadcasts the tensor to the whole group.
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Note:
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Tensor must have the same shape and format in all processes participating in the collective.
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Args:
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root_rank (int): Source rank. Required in all processes except the one
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that is sending the data.
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group (str): The communication group to work on. Default: "hccl_world_group".
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Inputs:
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- **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
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Outputs:
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Tensor, has the same shape of the input, i.e., :math:`(x_1, x_2, ..., x_R)`.
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The contents depend on the data of the `root_rank` device.
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Raises:
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TypeError: If root_rank is not a integer or group is not a string.
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Examples:
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>>> from mindspore.communication import init
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>>> import mindspore.ops.operations as P
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>>> init('nccl')
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>>> class Net(nn.Cell):
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>>> def __init__(self):
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>>> super(Net, self).__init__()
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>>> self.broadcast = P.Broadcast(1)
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>>>
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>>> def construct(self, x):
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>>> return self.broadcast((x,))
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>>>
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>>> input_ = Tensor(np.ones([2, 8]).astype(np.float32))
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>>> net = Net()
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>>> output = net(input_)
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"""
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@prim_attr_register
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def __init__(self, root_rank, group=GlobalComm.WORLD_COMM_GROUP):
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validator.check_value_type('root_rank', root_rank, (int,), self.name)
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validator.check_value_type('group', get_group(group), (str,), self.name)
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self.add_prim_attr('group', get_group(group))
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def infer_shape(self, x_shape):
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return x_shape
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def infer_dtype(self, x_dtype):
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if not isinstance(x_dtype, tuple):
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raise TypeError(f"{self.name}'s input should be a tuple!")
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for _ele in x_dtype:
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if _ele.element_type() == mstype.bool_:
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raise TypeError(f"{self.name} does not support 'Bool' as the dtype of input!")
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return x_dtype
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class _AlltoAll(PrimitiveWithInfer):
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"""
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AlltoAll is a collective operation.
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AlltoAll sends data from the all processes to the all processes in the specified group. It has two phases:
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- The scatter phase: On each process, the operand is split into split_count number of blocks along the
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split_dimensions, and the blocks are scattered to all processes, e.g., the ith block is send to the ith process.
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- The gather phase: Each process concatenates the received blocks along the concat_dimension.
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Note:
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Tensor must have the same shape and format in all processes participating in the collective.
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Args:
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split_count (int): On each process, divide blocks into split_count number.
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split_dim (int): On each process, split blocks along the split_dim.
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concat_dim (int): On each process, gather the received blocks along the concat_dimension.
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group (str): The communication group to work on. Default: "hccl_world_group".
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Raises:
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TypeError: If group is not a string.
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"""
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@prim_attr_register
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def __init__(self, split_count, split_dim, concat_dim, group=GlobalComm.WORLD_COMM_GROUP):
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"""init AlltoAll"""
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validator.check_value_type('group', get_group(group), (str,), self.name)
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self.split_count = split_count
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self.split_dim = split_dim
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self.concat_dim = concat_dim
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self.add_prim_attr('group', get_group(group))
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def infer_shape(self, x_shape):
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x_shape[self.concat_dim] = x_shape[self.concat_dim] * self.split_count
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x_shape[self.split_dim] = int(x_shape[self.split_dim] / self.split_count)
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return x_shape
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def infer_dtype(self, x_dtype):
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if x_dtype.element_type() == mstype.bool_:
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raise TypeError(f"{self.name} does not support 'Bool' as the dtype of input!")
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return x_dtype
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def __call__(self, tensor):
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return
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class _MirrorOperator(PrimitiveWithInfer):
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"""
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Auto parallel virtual operator. Do nothing in forward, do all reduce and mean in backward. It is only for
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internal use of parallel modules and cannot be called by users.
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Args:
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group (str): The communication group to work on. Default: None.
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dev_num (int): The device number of the group. Default: None.
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mean_flag (bool): Whether use mean in backward. Default: None.
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"""
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@prim_attr_register
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def __init__(self, group=None, dev_num=None, mean_flag=None):
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self.group = group
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self.dev_num = dev_num
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self.mean_flag = mean_flag
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def infer_shape(self, x_shape):
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return x_shape
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def infer_dtype(self, x_dtype):
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return x_dtype
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mirror = _MirrorOperator()
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class _VirtualDiv(PrimitiveWithInfer):
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"""
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Auto parallel virtual operator. Do nothing in forward, do Div in backward.
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Args:
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divisor: float32
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"""
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@prim_attr_register
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def __init__(self, divisor=None):
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self.divisor = divisor
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def infer_shape(self, x_shape):
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return x_shape
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def infer_dtype(self, x_dtype):
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return x_dtype
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virtual_div = _VirtualDiv()
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class _VirtualDataset(PrimitiveWithInfer):
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"""
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Auto parallel virtual dataset operator.
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It would insert Broadcast operator in forward computation and be deleted before backward computation.
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"""
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@prim_attr_register
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def __init__(self):
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"""init"""
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def infer_shape(self, *args):
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if len(args) == 1:
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return args[0]
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return args
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def infer_dtype(self, *args):
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if len(args) == 1:
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return args[0]
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return args
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virtual_dataset = _VirtualDataset()
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class _GetTensorSlice(PrimitiveWithInfer):
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"""
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Gets tensor slice by device matrix and tensor map.
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Args:
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dev_mat (tuple): The device matrix of the slice tensor.
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tensor_map (tuple): The tensor map of the slice tensor.
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"""
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@prim_attr_register
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def __init__(self):
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"""init ChunkTensor"""
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def infer_value(self, x, dev_mat, tensor_map):
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from mindspore.parallel._tensor import _load_tensor
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validator.check_value_type("dev_mat", dev_mat, [tuple], self.name)
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validator.check_value_type("tensor_map", tensor_map, [tuple], self.name)
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return _load_tensor(x, dev_mat, tensor_map)
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