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119 lines
4.6 KiB
119 lines
4.6 KiB
# 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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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.common.api import _executor
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from mindspore.common.parameter import Parameter
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from mindspore.common.initializer import initializer
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from mindspore.nn import TrainOneStepCell, Momentum
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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grad_all = C.GradOperation(get_all=True)
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class NetWithLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x):
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predict = self.network(x)
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return self.loss(predict)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x):
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return grad_all(self.network)(x)
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def test_unique_column_split():
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.unique = P.Unique().shard(((1,),))
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self.relu = P.ReLU()
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self.mul = P.Mul()
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self.embedding_lookp = P.Gather().shard(((1, 8), (1,)))
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self.embedding_table = Parameter(initializer('normal', [2000, 128]),
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name='embedding_table')
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self.gatherv2 = P.Gather().shard(((1, 8), (1,)))
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self.reshape = P.Reshape()
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self.matmul = P.MatMul()
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self.mul_weight = Parameter(Tensor(np.full([32, 64, 1], 0.5, dtype=np.float32)), name="mul_weight")
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def construct(self, indices):
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indices_flatten = self.reshape(indices, (-1,))
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unique_id, unique_idx = self.unique(indices_flatten)
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unique_id_weight = self.embedding_lookp(self.embedding_table, unique_id, 0)
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weight_flatten = self.gatherv2(unique_id_weight, unique_idx, 0)
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weight = self.reshape(weight_flatten, (32, 64, 128))
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vx = self.mul(weight, self.mul_weight)
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return vx
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size = 8
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context.set_auto_parallel_context(device_num=size, global_rank=0, parallel_mode="auto_parallel")
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x = Tensor(np.ones([32, 64]), dtype=ms.int32)
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net = Net()
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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_auto_parallel()
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train_net.set_train()
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_executor.compile(train_net, x)
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def test_unique_row_split():
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.unique = P.Unique().shard(((1,),))
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self.relu = P.ReLU()
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self.mul = P.Mul()
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self.embedding_lookp = P.Gather().shard(((8, 1), (1,)))
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self.embedding_table = Parameter(initializer('normal', [2000, 128]),
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name='embedding_table')
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self.gatherv2 = P.Gather().shard(((1, 1), (1,)))
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self.reshape = P.Reshape()
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self.matmul = P.MatMul()
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self.mul_weight = Parameter(Tensor(np.full([32, 64, 1], 0.5, dtype=np.float32)), name="mul_weight")
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def construct(self, indices):
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indices_flatten = self.reshape(indices, (-1,))
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unique_id, unique_idx = self.unique(indices_flatten)
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unique_id_weight = self.embedding_lookp(self.embedding_table, unique_id, 0)
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weight_flatten = self.gatherv2(unique_id_weight, unique_idx, 0)
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weight = self.reshape(weight_flatten, (32, 64, 128))
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vx = self.mul(weight, self.mul_weight)
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return vx
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size = 8
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context.set_auto_parallel_context(device_num=size, global_rank=0, parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([32, 64]), dtype=ms.int32)
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net = Net()
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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_auto_parallel()
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train_net.set_train()
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_executor.compile(train_net, x)
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