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256 lines
9.4 KiB
256 lines
9.4 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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""" test_loss_scale """
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import context
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from mindspore import Tensor, Parameter
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from mindspore.nn.wrap.cell_wrapper import WithLossCell
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from mindspore.nn.wrap.loss_scale import TrainOneStepWithLossScaleCell
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from mindspore.ops import operations as P
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from mindspore.nn.optim import Momentum, RMSProp
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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from mindspore.train import Model
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from mindspore.nn.optim import Lamb
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from mindspore.train.loss_scale_manager import DynamicLossScaleManager
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def setup_module():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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class MindData:
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""" Stub for MindData """
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def __init__(self, size=None, batch_size=None, repeat_count=1,
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np_types=None, output_shapes=None, input_indexes=(), func_name=''):
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self._size = size
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self._batch_size = batch_size
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self._repeat_count = repeat_count
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self._np_types = np_types
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self._output_shapes = output_shapes
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self._input_indexes = input_indexes
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self._func_name = func_name
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self._iter_num = 0
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def get_dataset_size(self):
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return self._size
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def get_repeat_count(self):
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return self._repeat_count
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def get_batch_size(self):
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return self._batch_size
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def output_types(self):
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return self._np_types
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def output_shapes(self):
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return self._output_shapes
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def create_tuple_iterator(self, num_epochs=-1, do_copy=True):
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return self
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@property
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def input_indexes(self):
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return self._input_indexes
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@property
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def func_name(self):
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return self._func_name
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def send(self):
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pass
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def __len__(self):
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return self._size
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def __iter__(self):
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return self
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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_value = []
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for shape, typ in zip(self._output_shapes, self._np_types):
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next_value.append(Tensor(np.ndarray(shape, typ)))
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return tuple(next_value)
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def next(self):
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return self.__next__()
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def reset(self):
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self._iter_num = 0
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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_indexes=(0, 1), func_name='')
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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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res = []
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for shape, t in zip(self._output_shapes, self._np_types):
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res.append(Tensor(np.ones(shape).astype(t)))
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return tuple(res)
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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.Add()
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self.cast = P.Cast()
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def construct(self, x):
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output = self.cast(self.add(self.matmul(self.cast(x, mstype.float16),
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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.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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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_loss_scale_fp16_lr_overflow():
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inputs = Tensor(np.ones([16, 16]).astype(np.float32))
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label = Tensor(np.zeros([16, 16]).astype(np.float32))
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lr = Tensor(np.ones([1], np.float32) * 0.1)
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net = NetFP16(16, 16)
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net.set_train()
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loss = MSELoss()
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optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=0.9)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer,
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scale_sense=Tensor(np.full((1), np.finfo(np.float32).max),
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dtype=mstype.float32))
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output_1 = train_network(inputs, label)
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output_2 = train_network(inputs, label)
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assert output_1[0].asnumpy() == output_2[0].asnumpy()
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assert output_1[1].asnumpy() == output_2[1].asnumpy() == True
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_loss_scale_fp16_lr_overflow_set_sense_scale():
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inputs = Tensor(np.ones([16, 16]).astype(np.float32))
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label = Tensor(np.zeros([16, 16]).astype(np.float32))
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lr = Tensor(np.ones([1], np.float32) * 0.1)
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net = NetFP16(16, 16)
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net.set_train()
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loss = MSELoss()
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optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=0.9)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer,
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scale_sense=Tensor(np.full((1), np.finfo(np.float32).max),
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dtype=mstype.float32))
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output_1 = train_network(inputs, label)
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train_network.set_sense_scale(Tensor(np.full((1), np.finfo(np.float32).max), dtype=mstype.float32))
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output_2 = train_network(inputs, label)
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assert output_1[0].asnumpy() == output_2[0].asnumpy()
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assert output_1[1].asnumpy() == output_2[1].asnumpy() == True
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_loss_scale_fp16_model_train_overflow():
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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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loss = MSELoss()
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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scale_manager = DynamicLossScaleManager(init_loss_scale=16, scale_factor=2, scale_window=2)
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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, dataset_sink_mode=False)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_loss_scale_fp16_opt_rmsprop_overflow():
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inputs = Tensor(np.ones([16, 16]).astype(np.float32))
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label = Tensor(np.zeros([16, 16]).astype(np.float32))
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net = NetFP16(16, 16)
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net.set_train()
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loss = MSELoss()
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optimizer = RMSProp(net.trainable_params(), learning_rate=0.1)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer,
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scale_sense=Tensor(np.full(1, np.finfo(np.float32).max),
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dtype=mstype.float32))
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output_1 = train_network(inputs, label)
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output_2 = train_network(inputs, label)
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assert output_1[0].asnumpy() == output_2[0].asnumpy()
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assert output_1[1].asnumpy() == output_2[1].asnumpy() == True
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_loss_scale_fp16_overflow():
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inputs = Tensor(np.ones([16, 16]).astype(np.float32))
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label = Tensor(np.zeros([16, 16]).astype(np.float32))
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net = NetFP16(16, 16)
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net.set_train()
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loss = MSELoss()
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optimizer = Lamb(net.trainable_params(), learning_rate=0.01)
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net_with_loss = WithLossCell(net, loss)
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net_with_loss.set_grad()
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train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer,
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scale_sense=Tensor(np.full((1), np.finfo(np.float32).max),
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dtype=mstype.float32))
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output_1 = train_network(inputs, label)
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output_2 = train_network(inputs, label)
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assert output_1[0].asnumpy() == output_2[0].asnumpy()
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assert output_1[1].asnumpy() == output_2[1].asnumpy() == True
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