forked from mindspore-Ecosystem/mindspore
210 lines
6.5 KiB
Python
210 lines
6.5 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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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, y):
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predict = self.network(x, y)
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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):
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return grad_all(self.network)(x, y)
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def compile_net(net, x, y):
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net.set_auto_parallel()
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_executor.compile(net, x, y)
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def test_prelu_single_success1():
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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.prelu = P.PReLU()
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def construct(self, x, y):
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out = self.prelu(x, y)
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return out
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context.reset_auto_parallel_context()
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net = GradWrap(NetWithLoss(Net()))
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x = Tensor(np.random.rand(1, 33, 4, 4), ms.float32)
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w = Tensor(np.random.rand(33), ms.float32)
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compile_net(net, x, w)
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def test_prelu_single_success2():
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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.prelu = P.PReLU()
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def construct(self, x, y):
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out = self.prelu(x, y)
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return out
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context.reset_auto_parallel_context()
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net = GradWrap(NetWithLoss(Net()))
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x = Tensor(np.random.rand(1, 33, 4, 4), ms.float32)
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w = Tensor([0.1], ms.float32)
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compile_net(net, x, w)
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def test_prelu_parallel_success1():
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class Net(nn.Cell):
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def __init__(self, strategy):
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super().__init__()
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self.prelu = P.PReLU().set_strategy(strategy)
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def construct(self, x, y):
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out = self.prelu(x, y)
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return out
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context.reset_auto_parallel_context()
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context.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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strategy = ((1, 1, 1, 1), (1,))
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x = Tensor(np.random.rand(4, 4, 32, 64), dtype=ms.float32)
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w = Tensor(np.random.rand(4), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net(strategy)))
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compile_net(net, x, w)
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def test_prelu_parallel_success2():
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class Net(nn.Cell):
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def __init__(self, strategy):
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super().__init__()
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self.prelu = P.PReLU().set_strategy(strategy)
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def construct(self, x, y):
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out = self.prelu(x, y)
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return out
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(device_num=64, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy = ((2, 1, 4, 8), (1,))
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x = Tensor(np.random.rand(4, 4, 32, 64), dtype=ms.float32)
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w = Tensor(np.random.rand(4), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net(strategy)))
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compile_net(net, x, w)
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def test_prelu_parallel_success3():
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class NetWithLoss3(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss3, 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, w):
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predict = self.network(x, y, w)
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return self.loss(predict)
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class GradWrap3(nn.Cell):
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def __init__(self, network):
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super(GradWrap3, self).__init__()
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self.network = network
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def construct(self, x, y, w):
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return grad_all(self.network)(x, y, w)
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().set_strategy(strategy1)
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self.prelu = P.PReLU().set_strategy(strategy2)
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def construct(self, x, y, w):
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out = self.matmul(x, y)
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out = self.prelu(out, w)
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return out
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(device_num=64, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 4), (4, 2))
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strategy2 = ((32, 1), (1,))
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x = Tensor(np.random.rand(128, 64), dtype=ms.float32)
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y = Tensor(np.random.rand(64, 16), dtype=ms.float32)
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w = Tensor(np.random.rand(16), dtype=ms.float32)
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net = GradWrap3(NetWithLoss3(Net(strategy1, strategy2)))
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net.set_auto_parallel()
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_executor.compile(net, x, y, w)
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def test_prelu_parallel_success4():
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class Net(nn.Cell):
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def __init__(self, strategy):
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super().__init__()
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self.prelu = P.PReLU().set_strategy(strategy)
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def construct(self, x, y):
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out = self.prelu(x, y)
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return out
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(device_num=64, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy = ((2, 4, 4, 2), (4,))
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x = Tensor(np.random.rand(4, 16, 32, 64), dtype=ms.float32)
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w = Tensor(np.random.rand(16), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net(strategy)))
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compile_net(net, x, w)
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def test_prelu_parallel_success5():
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class Net(nn.Cell):
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def __init__(self, strategy):
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super().__init__()
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self.prelu = P.PReLU().set_strategy(strategy)
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def construct(self, x, y):
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out = self.prelu(x, y)
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return out
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(device_num=64, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy = ((2, 4, 4, 2), (1,))
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x = Tensor(np.random.rand(4, 16, 32, 64), dtype=ms.float32)
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w = Tensor(np.random.rand(1), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net(strategy)))
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compile_net(net, x, w)
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