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112 lines
4.1 KiB
112 lines
4.1 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.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore import context
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from mindspore.common import dtype as mstype
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from mindspore.nn.optim import Momentum
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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from mindspore.train import Model
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from ....dataset_mock import MindData
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context.set_context(mode=context.GRAPH_MODE)
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class MindDataSet(MindData):
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def __init__(self, dataset_types, dataset_shapes):
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super(MindDataSet, self).__init__(size=2, batch_size=32,
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np_types=dataset_types,
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output_shapes=dataset_shapes,
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input_indexs=(0, 1))
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def __next__(self):
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if self._size < self._iter_num:
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raise StopIteration
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self._iter_num += 1
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next_ = []
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for shape, type_ in zip(self._output_shapes, self._np_types):
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next_.append(Tensor(np.ones(shape).astype(type_)))
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return tuple(next_)
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class Net(nn.Cell):
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def __init__(self, in_features, out_features):
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super(Net, self).__init__()
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self.weight = Parameter(Tensor(np.ones([out_features, in_features]).astype(np.float32)), name="weight")
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self.bias = Parameter(Tensor(np.ones([out_features]).astype(np.float32)), name="bias")
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self.matmul = P.MatMul()
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self.add = P.TensorAdd()
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def construct(self, input_):
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output = self.add(self.matmul(input_, self.weight), self.bias)
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return output
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class NetFP16(nn.Cell):
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def __init__(self, in_features, out_features):
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super(NetFP16, self).__init__()
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self.weight = Parameter(Tensor(np.ones([out_features, in_features]).astype(np.float32)), name="weight")
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self.bias = Parameter(Tensor(np.ones([out_features]).astype(np.float32)), name="bias")
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self.matmul = P.MatMul()
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self.add = P.TensorAdd()
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self.cast = P.Cast()
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def construct(self, input_):
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output = self.cast(
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self.add(self.matmul(self.cast(input_, mstype.float16), self.cast(self.weight, mstype.float16)),
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self.cast(self.bias, mstype.float16)), mstype.float32)
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return output
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def get_axis(x):
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shape_op = P.Shape()
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shape = shape_op(x)
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length = F.tuple_len(shape)
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perm = F.make_range(0, length)
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return perm
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class MSELoss(nn.Cell):
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def __init__(self):
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super(MSELoss, self).__init__()
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self.reduce_sum = P.ReduceSum()
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self.square = P.Square()
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self.reduce_mean = P.ReduceMean()
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def construct(self, data, label):
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diff = data - label
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return self.reduce_mean(self.square(diff), get_axis(diff))
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def test_auto_parallel_flag():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=1)
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dataset_types = (np.float32, np.float32)
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dataset_shapes = ((16, 16), (16, 16))
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dataset = MindDataSet(dataset_types, dataset_shapes)
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net = NetFP16(16, 16)
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net.set_train()
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scale_manager = FixedLossScaleManager()
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loss = MSELoss()
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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model = Model(net, loss_fn=loss, optimizer=optimizer, metrics=None, loss_scale_manager=scale_manager)
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model.train(2, dataset)
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assert model._train_network.get_flags()["auto_parallel"]
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context.reset_auto_parallel_context()
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