forked from mindspore-Ecosystem/mindspore
247 lines
9.1 KiB
Python
247 lines
9.1 KiB
Python
# 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.ops import composite as C
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from mindspore.ops import operations as P
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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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, y, b):
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predict = self.network(x, y, b)
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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, y, b):
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return C.grad_all(self.network)(x, y, b)
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def compile_net(net, x, y, b):
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net.set_auto_parallel()
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_executor.compile(net, x, y, b)
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def test_matmul_tanh():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul2 = P.MatMul().set_strategy(strategy2)
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self.tanh = P.Tanh().set_strategy(strategy3)
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def construct(self, x, y, b):
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out = self.tanh(self.matmul1(x, y))
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out = self.matmul2(out, b)
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return out
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((1, 1), (1, 16))
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strategy3 = ((4, 4),)
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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context.set_auto_parallel_context(device_num=16, global_rank=0)
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_activation():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul2 = P.MatMul().set_strategy(strategy2)
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self.activation = P.ReLU().set_strategy(strategy3)
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def construct(self, x, y, b):
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out = self.activation(self.matmul1(x, y))
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out = self.matmul2(out, b)
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return out
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((1, 1), (1, 16))
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strategy3 = ((4, 4),)
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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context.set_auto_parallel_context(device_num=16, global_rank=0)
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_softmax():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul2 = P.MatMul().set_strategy(strategy2)
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self.softmax = P.Softmax().set_strategy(strategy3)
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def construct(self, x, y, b):
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out = self.softmax(self.matmul1(x, y))
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out = self.matmul2(out, b)
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return out
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((1, 1), (1, 16))
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strategy3 = ((16, 1),)
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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context.set_auto_parallel_context(device_num=16, global_rank=0)
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_logsoftmax():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul2 = P.MatMul().set_strategy(strategy2)
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self.logsoftmax = P.LogSoftmax().set_strategy(strategy3)
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def construct(self, x, y, b):
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out = self.logsoftmax(self.matmul1(x, y))
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out = self.matmul2(out, b)
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return out
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strategy1 = ((4, 2), (2, 2))
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strategy2 = ((2, 4), (4, 2))
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strategy3 = ((16, 1),)
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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context.set_auto_parallel_context(device_num=16, global_rank=0)
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_activations():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul2 = P.MatMul().set_strategy(strategy2)
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self.gelu = P.Gelu().set_strategy(strategy3)
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self.tanh = P.Tanh().set_strategy(strategy3)
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self.softmax = P.Softmax().set_strategy(strategy3)
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self.logsoftmax = P.LogSoftmax().set_strategy(strategy3)
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def construct(self, x, y, b):
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out = self.gelu(self.tanh(self.matmul1(x, y)))
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out = self.logsoftmax(self.softmax(self.matmul2(out, b)))
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return out
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strategy1 = ((1, 2), (2, 2))
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strategy2 = ((2, 2), (2, 1))
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strategy3 = ((4, 1),)
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_activations_repeated_calculation():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3, strategy4, strategy5, strategy6):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul2 = P.MatMul().set_strategy(strategy2)
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self.gelu = P.Gelu().set_strategy(strategy3)
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self.tanh = P.Tanh().set_strategy(strategy4)
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self.softmax = P.Softmax().set_strategy(strategy5)
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self.logsoftmax = P.LogSoftmax().set_strategy(strategy6)
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def construct(self, x, y, b):
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out = self.gelu(self.tanh(self.matmul1(x, y)))
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out = self.logsoftmax(self.softmax(self.matmul2(out, b)))
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return out
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strategy1 = ((2, 4), (4, 8))
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strategy2 = ((2, 2), (2, 1))
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strategy3 = ((2, 1),)
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strategy4 = ((2, 2),)
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strategy5 = ((4, 1),)
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strategy6 = ((8, 1),)
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3, strategy4, strategy5, strategy6)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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context.set_auto_parallel_context(device_num=64, global_rank=0)
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_activations_axis_tuple():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3, strategy4, strategy5, strategy6):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul2 = P.MatMul().set_strategy(strategy2)
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self.gelu = P.Gelu().set_strategy(strategy3)
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self.tanh = P.Tanh().set_strategy(strategy4)
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self.softmax = P.Softmax(axis=(0, 1)).set_strategy(strategy5)
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self.logsoftmax = P.LogSoftmax().set_strategy(strategy6)
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def construct(self, x, y, b):
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out = self.gelu(self.tanh(self.matmul1(x, y)))
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out = self.logsoftmax(self.softmax(self.matmul2(out, b)))
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return out
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strategy1 = ((2, 4), (4, 8))
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strategy2 = ((2, 2), (2, 1))
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strategy3 = ((2, 1),)
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strategy4 = ((2, 2),)
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strategy5 = ((1, 1),)
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strategy6 = ((8, 1),)
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3, strategy4, strategy5, strategy6)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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context.set_auto_parallel_context(device_num=64, global_rank=0)
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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