forked from mindspore-Ecosystem/mindspore
add a self attention test case
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# 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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import numpy as np
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import mindspore as ms
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from mindspore import context, Tensor, Parameter
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from mindspore.nn import Cell, Momentum
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.ops import operations as P
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from mindspore.train import Model
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from tests.dataset_mock import MindData
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class Dataset(MindData):
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def __init__(self, predict, label, length=3):
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super(Dataset, self).__init__(size=length)
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self.predict = predict
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self.label = label
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self.index = 0
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.predict, self.label
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def reset(self):
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self.index = 0
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class Net(Cell):
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def __init__(self, mul_weight, strategy1=None, strategy2=None):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.neg = P.Neg().shard(strategy2)
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self.mul_weight = Parameter(mul_weight, "w1")
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def construct(self, x):
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out = self.mul(x, self.mul_weight)
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out = self.neg(out)
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return out
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_x = Tensor(np.ones([32, 128]), dtype=ms.float32)
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_b = Tensor(np.ones([32]), dtype=ms.int32)
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_w1 = Tensor(np.ones([512, 128]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(save_graphs=True)
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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dataset = Dataset(_x, _b)
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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model = Model(net, loss, optimizer=opt, amp_level="O2")
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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context.reset_auto_parallel_context()
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def test_neg_data_parallel():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((16, 1), (16, 1))
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strategy2 = ((16, 1),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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def test_neg_model_parallel():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((1, 16), (1, 16))
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strategy2 = ((1, 16),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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def test_neg_hybrid_parallel():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((4, 4), (4, 4))
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strategy2 = ((4, 4),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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def test_neg_auto_parallel():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
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net = Net(_w1)
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compile_net(net)
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def test_neg_repeat_calc():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((4, 4), (4, 4))
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strategy2 = ((2, 2),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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def test_neg_repeat_calc2():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((4, 2), (4, 2))
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strategy2 = ((4, 4),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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@ -0,0 +1,149 @@
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# Copyright 2019 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.context import set_auto_parallel_context
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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.common.parameter import Parameter
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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 compile_net(net, x):
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net.set_auto_parallel()
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_executor.compile(net, x)
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3, strategy4, strategy5):
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super().__init__()
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self.query_w = Parameter(initializer(
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"normal", [8, 16], ms.float32), name='query')
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self.query = P.MatMul().shard(strategy1)
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self.key_w = Parameter(initializer(
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"normal", [8, 16], ms.float32), name='key')
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self.key = P.MatMul().shard(strategy2)
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self.value_w = Parameter(initializer(
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"normal", [8, 16], ms.float32), name='value')
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self.value = P.MatMul().shard(strategy3)
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self.score = P.MatMul().shard(strategy4)
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self.context = P.MatMul().shard(strategy5)
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self.transpose1 = P.Transpose()
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self.transpose2 = P.Transpose()
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self.relu = P.ReLU()
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def construct(self, x):
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q = self.query(x, self.query_w)
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k = self.key(x, self.key_w)
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v = self.value(x, self.value_w)
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k = self.transpose1(k, (1, 0))
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s = self.score(q, k)
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v = self.transpose2(v, (1, 0))
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c = self.context(v, s)
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out = self.relu(c)
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return out
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def test_self_attention_standalone():
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set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="stand_alone")
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net = GradWrap(NetWithLoss(
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Net(None, None, None, None, None)))
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x = Tensor(np.ones([32, 8]), dtype=ms.float32)
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compile_net(net, x)
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def test_self_attention_semi():
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set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((2, 2), (2, 2))
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strategy3 = ((2, 2), (2, 2))
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strategy4 = ((2, 4), (4, 1))
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strategy5 = ((2, 1), (1, 4))
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net = GradWrap(NetWithLoss(
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Net(strategy1, strategy2, strategy3, strategy4, strategy5)))
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x = Tensor(np.ones([32, 8]), dtype=ms.float32)
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compile_net(net, x)
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def test_self_attention_dp():
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set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((8, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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strategy3 = ((8, 1), (1, 1))
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strategy4 = ((8, 1), (1, 1))
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strategy5 = ((8, 1), (1, 1))
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net = GradWrap(NetWithLoss(
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Net(strategy1, strategy2, strategy3, strategy4, strategy5)))
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x = Tensor(np.ones([32, 8]), dtype=ms.float32)
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compile_net(net, x)
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def test_self_attention_auto():
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set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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net = GradWrap(NetWithLoss(
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Net(None, None, None, None, None)))
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x = Tensor(np.ones([32, 8]), dtype=ms.float32)
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compile_net(net, x)
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