forked from huawei/mindspore2022
81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
# Copyright 2021 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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import numpy as np
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import mindspore as ms
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import mindspore.context as context
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from mindspore import Tensor, Parameter
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import mindspore.nn as nn
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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from mindspore.ops.operations._inner_ops import NeighborExchange
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class MatMulNet(nn.Cell):
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def __init__(self, weight1):
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super(MatMulNet, self).__init__()
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self.matmul = P.MatMul()
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self.mul = P.Mul()
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self.alltoallv = NeighborExchange(send_rank_ids=[0], recv_rank_ids=[1, 2], recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 32], [32, 16]), recv_type=ms.float32)
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self.weight1 = Parameter(weight1, "w1")
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def construct(self, x1, x2):
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out = self.matmul(x1, x2)
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out = self.mul(out, self.weight1)
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out = self.alltoallv((out, x1))
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return out[0]
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class MatMulNet2(nn.Cell):
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def __init__(self, weight1):
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super(MatMulNet2, self).__init__()
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self.matmul = P.MatMul()
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self.mul = P.Mul()
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self.alltoallv = NeighborExchange(send_rank_ids=[0], recv_rank_ids=[1, 2], recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 32],), recv_type=ms.float32)
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self.weight1 = Parameter(weight1, "w1")
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def construct(self, x1, x2):
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out = self.matmul(x1, x2)
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out = self.mul(out, self.weight1)
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out = self.alltoallv((out,))
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return out[0]
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_w1 = Tensor(np.ones([32, 32]), dtype=ms.float32)
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_x1 = Tensor(np.ones([32, 16]), dtype=ms.float32)
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_x2 = Tensor(np.ones([16, 32]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_train()
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_executor.compile(train_net, _x1, _x2)
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def test_NeighborExchange_two_inputs():
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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net = MatMulNet(_w1)
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compile_net(net)
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def test_NeighborExchange_single_input():
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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net = MatMulNet2(_w1)
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compile_net(net)
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