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148 lines
5.1 KiB
148 lines
5.1 KiB
# 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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import mindspore.context as context
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from mindspore import Tensor, Parameter
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import mindspore.nn as nn
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, mul_weight, axis=0, out_nums=1, strategy1=None, strategy2=None, strategy3=None):
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super(Net, self).__init__()
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self.split = P.Split(axis, out_nums).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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self.matmul = P.MatMul(transpose_b=True).shard(strategy2)
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self.matmul2 = P.MatMul().shard(strategy3)
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self.weight = Parameter(mul_weight, "w1")
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def construct(self, x):
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out = self.mul(x, self.weight)
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out1, out2, out3 = self.split(out)
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out = self.matmul(out1, out2)
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out = self.matmul2(out, out3)
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return out
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class Net1(nn.Cell):
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def __init__(self, mul_weight, axis=0, out_nums=1, strategy1=None, strategy2=None):
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super(Net1, self).__init__()
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self.split = P.Split(axis, out_nums).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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self.weight = Parameter(mul_weight, "w1")
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def construct(self, x):
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out1, out2 = self.split(self.weight)
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out = self.mul(x, out1)
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out = self.mul(out, out2)
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return out
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class Net2(nn.Cell):
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def __init__(self, mul_weight, axis=0, out_nums=1, strategy1=None, strategy2=None):
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super(Net2, self).__init__()
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self.split = P.Split(axis, out_nums).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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self.weight = Parameter(mul_weight, "w1")
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def construct(self, x):
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out = self.mul(x, self.weight)
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out1, _ = self.split(out)
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return out1
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_w = Tensor(np.ones([48, 64]), dtype=ms.float32)
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_x = Tensor(np.ones([48, 64]), dtype=ms.float32)
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_w1 = Tensor(np.ones([96, 64, 32]), dtype=ms.float32)
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_x1 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
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_w2 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(mode=context.GRAPH_MODE, 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)
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context.reset_auto_parallel_context()
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def compile_net1(net):
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context.set_context(mode=context.GRAPH_MODE, 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, _x1)
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context.reset_auto_parallel_context()
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def test_split_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),)
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net1(_w1, 0, 2, strategy1, strategy2)
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compile_net1(net)
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def test_split_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),)
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net1(_w1, 0, 2, strategy1, strategy2)
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compile_net1(net)
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def test_split_tensor():
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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, 8),)
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strategy2 = ((1, 8), (1, 8))
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strategy3 = ((1, 1), (1, 8))
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net = Net(_w, 0, 3, strategy1, strategy2, strategy3)
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compile_net(net)
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def test_split_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 = ((1, 4, 2),)
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net2(_w2, 0, 2, strategy1, strategy2)
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compile_net1(net)
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def test_split_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 = ((1, 2, 2),)
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net2(_w2, 0, 2, strategy1, strategy2)
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compile_net1(net)
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def test_split_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 = None
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net2(_w2, 0, 2, strategy1, strategy2)
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compile_net1(net)
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def test_split_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, 0, 2)
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compile_net1(net)
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