88 lines
3.0 KiB
Python
88 lines
3.0 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 _cell_graph_executor
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from mindspore.nn import Cell
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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, mul_weight, strategy1=None, strategy2=None):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.neg = P.Neg().shard(strategy2)
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self.mul_weight = Parameter(mul_weight, "w1")
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def construct(self, x, b):
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out = self.mul(x, self.mul_weight)
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out = self.neg(out)
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return out
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class EvalNet(Cell):
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def __init__(self, network, strategy2=None):
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super().__init__()
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self.network = network
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self.relu = P.ReLU().shard(strategy2)
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def construct(self, x, b):
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out = self.network(x, b)
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out1 = self.relu(out)
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return out, out1
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_x = Tensor(np.ones([64, 64]), dtype=ms.float32)
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_w1 = Tensor(np.ones([64, 64]), dtype=ms.float32)
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_b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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def compile_net(net, input_data, label, is_train=True):
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net.set_auto_parallel()
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net.set_train(mode=is_train)
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phase = "train" if is_train else "eval"
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_cell_graph_executor.compile(net, input_data, label, phase=phase, auto_parallel_mode=True)
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def test_train_and_eval():
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"""
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Feature: test train and eval in semi auto parallel.
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Description: train and eval net in auto parallel.
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16)
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strategy1 = ((4, 4), (4, 4))
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strategy2 = ((4, 4),)
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net = Net(_w1, strategy1, strategy2)
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eval_net = EvalNet(net, strategy2=strategy2)
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compile_net(net, _x, _b, is_train=True)
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compile_net(eval_net, _x, _b, is_train=False)
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context.reset_auto_parallel_context()
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def test_train_and_eval_auto():
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"""
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Feature: test train and eval in semi auto parallel.
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Description: train and eval net in auto parallel.
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16)
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strategy1 = ((4, 4), (4, 4))
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strategy2 = ((4, 4),)
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net = Net(_w1, strategy1, strategy2)
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eval_net = EvalNet(net, strategy2=strategy2)
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compile_net(net, _x, _b, is_train=True)
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compile_net(eval_net, _x, _b, is_train=False)
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context.reset_auto_parallel_context()
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