forked from mindspore-Ecosystem/mindspore
157 lines
5.5 KiB
Python
157 lines
5.5 KiB
Python
# 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.common.api import _executor
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from mindspore.nn import Cell, TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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class Net(Cell):
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def __init__(self, weight, weight2, strategy1=None, strategy2=None, is_parameter=True):
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super().__init__()
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self.concat = P.Concat(axis=0).set_strategy(strategy1)
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if is_parameter:
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self.weight = Parameter(weight, "w1")
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else:
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self.weight = weight
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self.mul = P.Mul().set_strategy(strategy2)
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self.weight2 = Parameter(weight2, "w2")
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def construct(self, x, b):
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out = self.concat((self.weight, self.weight2))
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out = self.mul(x, out)
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return out
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class Net2(Cell):
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def __init__(self, weight, strategy1=None, strategy2=None, axis=0):
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super().__init__()
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self.mul = P.Mul().set_strategy(strategy1)
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self.concat = P.Concat(axis=axis).set_strategy(strategy2)
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self.weight = Parameter(weight, "w")
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def construct(self, x, b):
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out = self.mul(x, b)
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out = self.concat((out, self.weight))
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return out
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class Net3(Cell):
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def __init__(self, weight, weight2, weight3, strategy1=None, strategy2=None, is_parameter=True):
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super().__init__()
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self.concat = P.Concat(axis=0).set_strategy(strategy1)
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if is_parameter:
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self.weight = Parameter(weight, "w1")
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else:
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self.weight = weight
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self.mul = P.Mul().set_strategy(strategy2)
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self.weight2 = Parameter(weight2, "w2")
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self.weight3 = Parameter(weight3, "w3")
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def construct(self, x, b):
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out = self.concat((self.weight, self.weight2, self.weight3))
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out = self.mul(x, out)
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return out
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_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
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_w1 = Tensor(np.ones([96, 64, 32]), dtype=ms.float32)
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_w2 = Tensor(np.ones([32, 64, 32]), dtype=ms.float32)
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_w3 = Tensor(np.ones([128, 16, 32]), dtype=ms.float32)
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_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
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w1 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
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w2 = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
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w3 = Tensor(np.ones([64, 64, 32]), 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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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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_executor.compile(train_net, _x, _b)
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context.reset_auto_parallel_context()
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def test_concat_parameter():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 4, 2), (1, 4, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
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compile_net(net)
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def test_concat_parameter_no_full_split():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 2, 2), (1, 2, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
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compile_net(net)
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def test_concat_tensor_and_parameter():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 2, 2), (1, 2, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net(_w1, _w2, strategy1, strategy2, is_parameter=False)
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compile_net(net)
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def test_concat_output():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net2(_w1, strategy1, strategy2)
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compile_net(net)
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def test_concat_output_no_full_split():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((1, 2, 2), (1, 2, 2))
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net = Net2(_w1, strategy1, strategy2)
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compile_net(net)
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def test_concat_no_strategy():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = None
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net = Net2(_w3, strategy1, strategy2, axis=1)
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compile_net(net)
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def test_concat_auto_parallel():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net2(_w2)
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compile_net(net)
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def test_concat_auto_parallel2():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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strategy1 = None
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strategy2 = None
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net = Net2(_w3, strategy1, strategy2, axis=1)
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compile_net(net)
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def test_concat_auto_parallel_3_tensor():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net3(w1, w2, w3)
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compile_net(net)
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