forked from huawei/mindspore2022
397 lines
13 KiB
Python
397 lines
13 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 _cell_graph_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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class NetWithLossTwoInput(nn.Cell):
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def __init__(self, network):
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super(NetWithLossTwoInput, 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 NetWithReduceLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithReduceLoss, self).__init__()
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self.mean = P.ReduceMean(keep_dims=False)
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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.mean(predict, ())
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class GradWrapTwoInput(nn.Cell):
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def __init__(self, network):
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super(GradWrapTwoInput, 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_graph(net, parallel_mode, device_num, x):
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context.set_auto_parallel_context(device_num=device_num, global_rank=0, parallel_mode=parallel_mode)
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net.set_auto_parallel()
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net.set_train()
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_cell_graph_executor.compile(net, x)
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def compile_graph_two_input(net, parallel_mode, device_num, x, y):
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context.set_auto_parallel_context(device_num=device_num, global_rank=0, parallel_mode=parallel_mode)
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net.set_auto_parallel()
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net.set_train()
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_cell_graph_executor.compile(net, x, y)
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def test_reshape_matmul():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: reshape - matmul net in auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul()
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self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
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def construct(self, x):
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out = self.reshape(x, (64, 28))
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out = self.matmul(out, self.matmul_weight)
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return out
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size = 8
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x = Tensor(np.ones([8 * size, 28, 1, 1]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, "auto_parallel", size, x)
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def test_reshape_reshape():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: reshape - reshape net in auto parallel.
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Expectation: compile done without error.
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"""
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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.relu = P.ReLU()
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def construct(self, x):
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x = self.relu(x)
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out = self.reshape(x, (64, 28))
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out = self.reshape(out, (64, 28, 1))
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return out
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size = 8
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x = Tensor(np.ones([8 * size, 28, 1, 1]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, "auto_parallel", size, x)
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def test_reshape_auto_1():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: relu - reshape - matmul net in auto parallel.
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Expectation: compile done without error.
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"""
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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.relu = P.ReLU()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul()
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self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
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def construct(self, x):
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out = self.relu(x)
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out = self.reshape(out, (64, 28))
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out = self.matmul(out, self.matmul_weight)
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return out
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size = 8
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x = Tensor(np.ones([8 * size, 28, 1, 1]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, "auto_parallel", size, x)
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def test_reshape_auto_2():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: reshape - matmul -reshape net in auto parallel.
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Expectation: compile done without error.
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"""
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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.relu = P.ReLU()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul()
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self.add_weight = Parameter(Tensor(np.ones([128, 32]), dtype=ms.float32), name="weight1")
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self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
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def construct(self, x):
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out = self.relu(x)
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out = self.reshape(out, (64, 28))
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out = self.matmul(out, self.matmul_weight)
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out = self.reshape(out, (128, 32))
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out = out + self.add_weight
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return out
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size = 8
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x = Tensor(np.ones([8 * size, 28, 1, 1]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, "auto_parallel", size, x)
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def test_reshape_auto_3():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: reshape as last node net in auto parallel.
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Expectation: compile done without error.
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"""
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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.relu = P.ReLU()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul()
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self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
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def construct(self, x):
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out = self.relu(x)
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out = self.matmul(out, self.matmul_weight)
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out = self.reshape(out, (8, 8, 8, 8))
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return out
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size = 8
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x = Tensor(np.ones([8 * size, 28]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, "auto_parallel", size, x)
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def test_reshape_auto_4():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: reshape - reshape net in auto parallel.
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Expectation: compile done without error.
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"""
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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.relu = P.ReLU()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul()
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self.matmul_weight = Parameter(Tensor(np.ones([28 * 64]), dtype=ms.float32), name="weight")
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def construct(self, x):
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out = self.relu(x)
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out = self.reshape(out, (64, 28))
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w = self.reshape(self.matmul_weight, (28, 64))
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out = self.matmul(out, w)
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return out
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size = 8
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x = Tensor(np.ones([8 * size, 28, 1, 1]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, "auto_parallel", size, x)
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def test_reshape_auto_5():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: modify wide&deep small net in auto parallel.
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Expectation: compile done without error.
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"""
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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.relu = P.ReLU()
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self.mul = P.Mul()
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self.reshape = P.Reshape()
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self.reduce_sum = P.ReduceSum()
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self.wide_w = Parameter(Tensor(np.ones([4, 1024 * 8, 64]), dtype=ms.float32), name="weight")
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def construct(self, x, y):
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mask = self.reshape(y, (4, 1024 * 8, 1))
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w_id = self.relu(x)
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wx = self.mul(w_id, mask)
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wide_out = self.reshape(self.reduce_sum(wx, 1), (-1, 1))
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deep_id = x + self.wide_w
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vx = self.mul(deep_id, mask)
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deep_in = self.reshape(vx, (-1, 1024 * 8 * 64))
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out = wide_out + deep_in
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return out
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size = 8
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context.set_auto_parallel_context(dataset_strategy="full_batch")
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x = Tensor(np.ones([4, 1024 * size, 1]), dtype=ms.float32)
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y = Tensor(np.ones([4, 1024 * size,]), dtype=ms.float32)
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net = GradWrapTwoInput(NetWithLossTwoInput(Net()))
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compile_graph_two_input(net, "auto_parallel", size, x, y)
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def test_reshape_auto_6():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: modify wide&deep small net in auto parallel.
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Expectation: compile done without error.
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"""
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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.relu = P.ReLU()
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self.mul = P.Mul()
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self.reshape = P.Reshape()
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self.reduce_mean = P.ReduceMean()
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self.wide_w = Parameter(Tensor(np.ones([4, 1024, 1]), dtype=ms.float32), name="weight")
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def construct(self, x, y):
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out1 = x + self.wide_w
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w = self.reshape(self.wide_w, (4, 1024))
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out1 = self.reduce_mean(out1, 1)
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out1 = out1 - w
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out2 = self.mul(y, w)
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out = out1 + out2
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return out
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size = 8
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context.set_auto_parallel_context(dataset_strategy="full_batch")
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x = Tensor(np.ones([4, 1024, 1]), dtype=ms.float32)
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y = Tensor(np.ones([4, 1024,]), dtype=ms.float32)
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net = GradWrapTwoInput(NetWithLossTwoInput(Net()))
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compile_graph_two_input(net, "auto_parallel", size, x, y)
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def test_reshape_auto_7():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: reshape weight net in semi auto parallel.
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Expectation: compile done without error.
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"""
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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, 2, 4), (2, 4)))
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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, x):
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weight = self.reshape(self.mul_weight, (1, 128, 96))
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out = self.mul(weight, self.mul_weight)
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return out
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size = 8
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x = Tensor(np.ones([128, 28]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, "semi_auto_parallel", size, x)
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def test_reshape_depend_reshape():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: reshape - depend -reshape net in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.reshape1 = P.Reshape()
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self.reshape2 = P.Reshape()
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self.relu = P.ReLU()
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self.depend = P.Depend()
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self.mul = P.Mul().shard(((2, 4), (2, 4)))
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self.mul_weight = Parameter(Tensor(np.ones([128, 96]), dtype=ms.float32), name="weight")
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self.add = P.Add().shard(((4, 2), (4, 2)))
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def construct(self, x, y):
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out1 = self.mul(x, self.mul_weight)
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y = self.relu(y)
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out2 = self.reshape1(y, (96, 32, 4))
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out3 = self.depend(out2, out1)
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out3 = self.reshape2(out3, (128, 96))
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out = out1 + out3
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return out
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size = 8
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x = Tensor(np.ones([128, 96]), dtype=ms.float32)
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y = Tensor(np.ones([256, 48]), dtype=ms.float32)
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net = GradWrapTwoInput(NetWithReduceLoss(Net()))
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compile_graph_two_input(net, "semi_auto_parallel", size, x, y)
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net_auto = GradWrapTwoInput(NetWithReduceLoss(Net()))
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compile_graph_two_input(net_auto, "auto_parallel", size, x, y)
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def test_appeq_reshape():
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"""
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Feature: distribute operator reshape in auto parallel.
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Description: app_eq - reshape - cast - relu net in semi auto parallel / auto parallel.
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Expectation: compile done without error.
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"""
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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.app_eq = P.ApproximateEqual(2.)
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self.reshape = P.Reshape()
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self.cast = P.Cast()
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self.relu = P.ReLU().shard(((1, 8),))
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def construct(self, x, y):
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out1 = self.app_eq(x, y)
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out2 = self.reshape(out1, (64, 192))
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out3 = self.cast(out2, ms.int32)
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out = self.relu(out3)
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return out
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size = 8
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x = Tensor(np.ones([128, 96]), dtype=ms.float32)
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y = Tensor(np.ones([128, 96]), dtype=ms.float32)
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net = GradWrapTwoInput(NetWithReduceLoss(Net()))
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compile_graph_two_input(net, "semi_auto_parallel", size, x, y)
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net_auto = GradWrapTwoInput(NetWithReduceLoss(Net()))
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context.set_auto_parallel_context(search_mode="recursive_programming")
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compile_graph_two_input(net_auto, "auto_parallel", size, x, y)
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