yao_yf
4 years ago
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commit
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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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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 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_reshape_unexpand():
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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.reshape = P.Reshape()
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self.mul = P.Mul().shard(((1, 8), (1, 1, 8)))
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self.mul_weight = Parameter(Tensor(np.ones([96, 128]), dtype=ms.float32), name="weight")
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def construct(self, x):
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weight = self.reshape(self.mul_weight, (1, 128, 96))
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out = self.mul(x, weight)
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return out
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size = 8
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context.set_auto_parallel_context(device_num=size, global_rank=0)
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x = Tensor(np.ones([128, 96]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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_executor.compile(net, x)
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def test_reshape_unexpand_1():
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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.reshape = P.Reshape()
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self.mul = P.Mul().shard(((1, 8), (1, 1, 8)))
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self.mul_weight = Parameter(Tensor(np.ones([96, 128]), dtype=ms.float32), name="weight")
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def construct(self, x):
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weight = self.reshape(self.mul_weight, (1, 128, 96))
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out = self.mul(x, weight)
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return out
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size = 8
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context.set_auto_parallel_context(device_num=size, global_rank=0)
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x = Tensor(np.ones([128, 96]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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_executor.compile(net, x)
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def test_reshape_unexpand_2():
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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.reshape = P.Reshape()
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self.mul = P.Mul().shard(((1, 4, 2), (4, 2)))
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self.mul_weight = Parameter(Tensor(np.ones([128, 96]), dtype=ms.float32), name="weight")
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def construct(self, data):
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x = self.reshape(self.mul_weight, (1, 128, 96))
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out = self.mul(x, self.mul_weight)
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return out
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size = 8
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context.set_auto_parallel_context(device_num=size, global_rank=0)
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x = Tensor(np.ones([128, 96]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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_executor.compile(net, x)
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def test_reshape_unexpand_3():
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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.reshape = P.Reshape()
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self.relu1 = P.ReLU().shard(((4, 1),))
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self.relu2 = P.ReLU().shard(((1, 4),))
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def construct(self, data):
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x = self.relu1(data)
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x = self.reshape(x, (3, 4))
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x = self.relu2(x)
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return x
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size = 4
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context.set_auto_parallel_context(device_num=size, global_rank=0)
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x = Tensor(np.ones([4, 3]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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_executor.compile(net, x)
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def test_reshape_unexpand_4():
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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.reshape = P.Reshape()
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self.relu1 = P.ReLU().shard(((4, 1),))
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self.relu2 = P.ReLU().shard(((1, 2, 2),))
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def construct(self, data):
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x = self.relu1(data)
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x = self.reshape(x, (3, 2, 2))
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x = self.relu2(x)
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return x
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size = 4
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context.set_auto_parallel_context(device_num=size, global_rank=0)
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x = Tensor(np.ones([4, 3]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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_executor.compile(net, x)
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def test_reshape_unexpand_5():
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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.reshape = P.Reshape()
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self.relu1 = P.ReLU().shard(((2, 2, 1),))
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self.relu2 = P.ReLU().shard(((1, 4),))
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def construct(self, data):
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x = self.relu1(data)
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x = self.reshape(x, (3, 4))
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x = self.relu2(x)
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return x
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size = 4
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context.set_auto_parallel_context(device_num=size, global_rank=0)
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x = Tensor(np.ones([2, 2, 3]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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_executor.compile(net, x)
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def test_reshape_unexpand_6():
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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.reshape = P.Reshape()
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self.relu1 = P.ReLU().shard(((2, 1),))
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self.relu2 = P.ReLU().shard(((1, 1, 4),))
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def construct(self, data):
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x = self.relu1(data)
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x = self.reshape(x, (1, 3, 4))
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x = self.relu2(x)
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return x
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size = 4
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context.set_auto_parallel_context(device_num=size, global_rank=0)
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x = Tensor(np.ones([4, 3]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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_executor.compile(net, x)
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