forked from huawei/mindspore2022
805 lines
28 KiB
Python
805 lines
28 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 mindspore.common import Tensor
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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, PrimitiveWithCheck, prim_attr_register
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from ...common.api import context
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class ReduceOp:
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"""
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Operation options for reducing tensors.
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There are four kinds of operation options, "SUM", "MAX", "MIN", and "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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Supported Platforms:
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``Ascend`` ``GPU``
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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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target_dtypes = (mstype.int8, mstype.int32, mstype.float16, mstype.float32)
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def check_hcom_group_valid(group):
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"""Check if hcom group is valid."""
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if context.get_context("mode") == context.PYNATIVE_MODE and \
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context.get_context("device_target") == "Ascend" and \
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group != GlobalComm.WORLD_COMM_GROUP:
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raise RuntimeError("Only hccl_world_group is supported in Pynative mode, but got {}".format(group))
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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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The tensors must have the same shape and format in all processes of the collection.
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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, and min. Default: ReduceOp.SUM.
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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 specified operation.
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Raises:
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TypeError: If any of `op` and `group` is not a str,
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or fusion is not an integer, or the input's dtype is bool.
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ValueError: If the `op` is "prod".
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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> from mindspore.communication import init
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>>> from mindspore import Tensor
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>>> from mindspore.ops.operations.comm_ops import ReduceOp
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>>> import mindspore.nn as nn
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>>> import mindspore.ops.operations as ops
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>>>
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>>> init()
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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 = ops.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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>>> print(output)
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[[4. 5. 6. 0. 0. 0. 0. 0.]
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[0. 0. 0. 0. 0. 0. 0. 0.]]
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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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"""Initialize AllReduce."""
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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 not isinstance(_get_group(group), str):
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raise TypeError("The group of AllReduce should be str.")
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check_hcom_group_valid(group)
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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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self.add_prim_attr('index', 0)
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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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validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
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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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The tensors must have the same shape and format in all processes of the collection.
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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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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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Raises:
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TypeError: If `group` is not a str.
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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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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> # This example should be run with two devices. Refer to the tutorial > Distributed Training on mindspore.cn
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>>> import numpy as np
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>>> import mindspore.ops.operations as ops
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>>> import mindspore.nn as nn
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>>> from mindspore.communication import init
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>>> from mindspore import Tensor, context
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>>>
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>>> context.set_context(mode=context.GRAPH_MODE)
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>>> init()
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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 = ops.AllGather()
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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_x = Tensor(np.ones([2, 8]).astype(np.float32))
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>>> net = Net()
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>>> output = net(input_x)
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>>> print(output)
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[[1. 1. 1. 1. 1. 1. 1. 1.]
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[1. 1. 1. 1. 1. 1. 1. 1.]
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[1. 1. 1. 1. 1. 1. 1. 1.]
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[1. 1. 1. 1. 1. 1. 1. 1.]]
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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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"""Initialize AllGather."""
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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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self.add_prim_attr('fusion', 0)
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def infer_shape(self, x_shape):
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validator.check_positive_int(len(x_shape), "x shape", self.name)
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if x_shape[0] > 0:
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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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validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
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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 _MiniStepAllGather(PrimitiveWithInfer):
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"""
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Auto parallel virtual operator. Do nothing in forward, do reducescatter in backward in mini-step. 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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grad_accumulation_step (int): The grad accumulation step. Default: None.
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"""
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@prim_attr_register
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def __init__(self, group=GlobalComm.WORLD_COMM_GROUP, grad_accumulation_step=None, mean_flag=None):
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"""Initialize _MiniStepAllGather."""
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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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self.add_prim_attr('fusion', 1)
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self.grad_accumulation_step = grad_accumulation_step
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self.mean_flag = mean_flag
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def infer_shape(self, x_shape, z_shape):
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validator.check_positive_int(len(x_shape), "x shape", self.name)
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if x_shape[0] > 0:
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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, z_shape):
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validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
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return x_dtype
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class _HostAllGather(PrimitiveWithInfer):
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"""
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Gathers tensors from the specified communication group on host.
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Note:
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The tensors must have the same shape and format in all processes of the collection.
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_HostAllGather is a host-side operator, it depends on OpenMPI and must use build option -M on
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to enable it. Using mpirun command to run it:
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mpirun -output-filename log -merge-stderr-to-stdout -np 3 python test_host_all_gather.py
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Args:
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group (Union[tuple[int],list[int]]): The rand_ids of communication group to work on. Default: None.
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Raises:
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TypeError: If group is not a list nor tuple, or elements of group are not int.
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ValueError: If group is not set, or rank_id from group not in [0, 7].
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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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"""
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@prim_attr_register
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def __init__(self, group=None):
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"""Initialize _HostAllGather."""
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if group is None:
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raise ValueError(f"For '{self.name}' group must be set.")
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validator.check_value_type('group', group, (tuple, list), self.name)
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validator.check_int(len(group), 2, Rel.GE, "group size", self.name)
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for r in group:
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validator.check_int_range(r, 0, 7, Rel.INC_BOTH, "rank_id", self.name)
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validator.check_value_type("rank_id", r, (int,), self.name)
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self.group_size = len(group)
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self.add_prim_attr('group', group)
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def infer_shape(self, x_shape):
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validator.check_positive_int(len(x_shape), "x shape", self.name)
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if x_shape[0] > 0:
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x_shape[0] = x_shape[0] * self.group_size
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return x_shape
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def infer_dtype(self, x_dtype):
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validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
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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 supported yet. Stay tuned for more.
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The tensors must have the same shape and format in all processes of the collection.
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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 operation and group is not a string.
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ValueError: If the first dimension of the input cannot be divided by the rank size.
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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> # This example should be run with two devices. Refer to the tutorial > Distributed Training on mindspore.cn
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>>> from mindspore import Tensor, context
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>>> from mindspore.communication import init
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>>> from mindspore.ops.operations.comm_ops import ReduceOp
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>>> import mindspore.nn as nn
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>>> import mindspore.ops.operations as ops
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>>> import numpy as np
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>>>
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>>> context.set_context(mode=context.GRAPH_MODE)
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>>> init()
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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 = ops.ReduceScatter(ReduceOp.SUM)
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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([8, 8]).astype(np.float32))
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>>> net = Net()
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>>> output = net(input_)
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>>> print(output)
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[[2. 2. 2. 2. 2. 2. 2. 2.]
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[2. 2. 2. 2. 2. 2. 2. 2.]
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[2. 2. 2. 2. 2. 2. 2. 2.]
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[2. 2. 2. 2. 2. 2. 2. 2.]]
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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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"""Initialize ReduceScatter."""
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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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self.add_prim_attr('fusion', 0)
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def infer_shape(self, x_shape):
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if self.rank_size == 0:
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raise ValueError(f"For '{self.name}' rank_size can not be zero.")
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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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validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
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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 _HostReduceScatter(PrimitiveWithInfer):
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"""
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Reduces and scatters tensors from the specified communication group on host.
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Note:
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The tensors must have the same shape and format in all processes of the collection.
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_HostReduceScatter is a host-side operator, it depends on OpenMPI and must use build option
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-M on to enable it. Using mpirun command to run it:
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mpirun -output-filename log -merge-stderr-to-stdout -np 3 python test_host_reduce_scatter.py
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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 (Union[tuple[int],list[int]]): The rand_ids of communication group to work on. Default: None.
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Raises:
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TypeError: If op is not a string and group is not a list nor tuple,
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or elements of group are not int.
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ValueError: If the first dimension of input can not be divided by group size,
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or group is not set, or rank_id not in [0, 7].
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"""
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@prim_attr_register
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def __init__(self, op=ReduceOp.SUM, group=None):
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"""Initialize _HostReduceScatter."""
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if group is None:
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raise ValueError(f"For '{self.name}' group must be set.")
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validator.check_value_type('op', op, (type(ReduceOp.SUM),), self.name)
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validator.check_value_type('group', group, (tuple, list), self.name)
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validator.check_int(len(group), 2, Rel.GE, "group size", self.name)
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for r in group:
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validator.check_int_range(r, 0, 7, Rel.INC_BOTH, "rank_id", self.name)
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validator.check_value_type("rank_id", r, (int,), self.name)
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self.op = op
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self.group_size = len(group)
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self.add_prim_attr('group', group)
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def infer_shape(self, x_shape):
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if x_shape[0] % self.group_size != 0:
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raise ValueError(f"For '{self.name}' the first dimension of x should be divided by group_size.")
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x_shape[0] = int(x_shape[0] / self.group_size)
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return x_shape
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def infer_dtype(self, x_dtype):
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validator.check_tensor_dtype_valid('x', x_dtype, target_dtypes, self.name)
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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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The tensors must have the same shape and format in all processes of the collection.
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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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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> # This example should be run with multiple processes.
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>>> # Please refer to the tutorial > Distributed Training on mindspore.cn.
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>>> from mindspore import Tensor
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>>> from mindspore import context
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>>> from mindspore.communication import init
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>>> import mindspore.nn as nn
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>>> import mindspore.ops.operations as ops
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>>> import numpy as np
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>>>
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>>> context.set_context(mode=context.GRAPH_MODE)
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>>> init()
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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 = ops.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_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)
|
|
self.add_prim_attr('group', _get_group(group))
|
|
|
|
def infer_shape(self, x_shape):
|
|
return x_shape
|
|
|
|
def infer_dtype(self, x_dtype):
|
|
if not isinstance(x_dtype, tuple):
|
|
raise TypeError(f"{self.name}'s input should be a tuple!")
|
|
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))
|
|
|
|
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
|
|
|
|
|
|
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.
|
|
|
|
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: "hccl_world_group".
|
|
|
|
Raises:
|
|
TypeError: If group is not a string.
|
|
"""
|
|
|
|
@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)
|
|
self.split_count = split_count
|
|
self.split_dim = split_dim
|
|
self.concat_dim = concat_dim
|
|
self.add_prim_attr('group', _get_group(group))
|
|
|
|
def infer_shape(self, x_shape):
|
|
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):
|
|
return
|
|
|
|
|
|
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)
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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."""
|
|
|
|
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."""
|
|
|
|
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."""
|
|
|
|
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."""
|
|
|
|
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
|
|
|
|
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."""
|
|
|
|
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."""
|
|
|
|
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))
|