# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Test network turn on mix_precision.""" import os import re import pytest import numpy as np from mindspore.common import dtype from mindspore import nn from mindspore import ops from mindspore import amp from mindspore import Tensor from mindspore import context from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.train.model import Model from utils import FakeData from utils import allclose_nparray from utils import FakeDataInitMode from utils import find_newest_validateir_file from utils import clean_all_ir_files def read_validateir_file(path_folder): filename = find_newest_validateir_file(path_folder) with open(os.path.join(filename), 'r') as f: contend = f.read() clean_all_ir_files(path_folder) return contend class Net(nn.Cell): def __init__(self, in_c, out_c): super().__init__() self.relu = nn.ReLU() self.bn1 = nn.BatchNorm2d(num_features=in_c, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros', moving_var_init='ones') self.bn2 = nn.BatchNorm2d(num_features=out_c, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros', moving_var_init='ones') self.conv = nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=3, stride=1, has_bias=True, pad_mode='same', weight_init='ones', bias_init='ones') self.mean = ops.ReduceMean(keep_dims=False) def construct(self, x): x = self.relu(x) x = self.bn1(x) x = self.conv(x) x = self.bn2(x) x = self.relu(x) x = self.mean(x, (2, 3)) return x @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_sit_auto_mix_precision_train_o3(): input_data = np.random.randn(32, 3, 224, 224).astype(np.float64) label_data = np.random.randn(32, 10).astype(np.float32) # graph mode context.set_context(mode=context.GRAPH_MODE) net = Net(3, 10) opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009, weight_decay=0.001, loss_scale=0.0001) loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False) train_network = amp.build_train_network(net, opt, loss, level="O3", loss_scale_manager=FixedLossScaleManager(drop_overflow_update=False)) out = train_network(Tensor(input_data), Tensor(label_data)) # pynative mode context.set_context(mode=context.PYNATIVE_MODE) net_pynative = Net(3, 10) opt_pynative = nn.Momentum(params=net_pynative.trainable_params(), learning_rate=0.001, momentum=0.0009, weight_decay=0.001, loss_scale=0.0001) loss_pynative = nn.SoftmaxCrossEntropyWithLogits(sparse=False) train_network_pynative = amp.build_train_network(net_pynative, opt_pynative, loss_pynative, level="O3", loss_scale_manager=FixedLossScaleManager( drop_overflow_update=False)) out_pynative = train_network_pynative(Tensor(input_data), Tensor(label_data)) assert np.allclose(out.asnumpy(), out_pynative.asnumpy(), 0.001, 0.001) @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.env_onecard def test_sit_auto_mix_precision_model_o0(): input_data = np.random.randn(32, 3, 224, 224).astype(np.float32) dataset1 = FakeData(size=32, batch_size=32, image_size=(3, 224, 224), num_classes=10, fakedata_mode=FakeDataInitMode.OnesInit) dataset1.set_label_data_type(np.float16) # graph mode context.set_context(mode=context.GRAPH_MODE) context.set_context(save_graphs=True, save_graphs_path='./test_amp_o0') net = Net(3, 10) net.to_float(dtype.float16) opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009) loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False) model = Model(net, loss, opt, amp_level="O0") model.train(1, dataset1, dataset_sink_mode=False) contend = read_validateir_file('./test_amp_o0') castnum = re.findall("Cast", contend) assert len(castnum) == 5 model.predict(Tensor(input_data)) contend = read_validateir_file('./test_amp_o0') castnum = re.findall("Cast", contend) assert len(castnum) == 11 @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_sit_auto_mix_precision_model_o2(): input_data = np.random.randn(32, 3, 224, 224).astype(np.float32) dataset1 = FakeData(size=32, batch_size=32, image_size=(3, 224, 224), num_classes=10, fakedata_mode=FakeDataInitMode.OnesInit) dataset2 = FakeData(size=32, batch_size=32, image_size=(3, 224, 224), num_classes=10, fakedata_mode=FakeDataInitMode.OnesInit) # graph mode context.set_context(mode=context.GRAPH_MODE) context.set_context(save_graphs=True, save_graphs_path='./test_amp_o2') net = Net(3, 10) opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009) loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False) model = Model(net, loss, opt, amp_level="O2") model.train(1, dataset1, dataset_sink_mode=False) contend = read_validateir_file('./test_amp_o2') castnum = re.findall("Cast", contend) assert len(castnum) == 14 out_graph = model.predict(Tensor(input_data)) # pynative mode context.set_context(mode=context.PYNATIVE_MODE) net_pynative = Net(3, 10) opt_pynative = nn.Momentum(params=net_pynative.trainable_params(), learning_rate=0.001, momentum=0.0009) loss_pynative = nn.SoftmaxCrossEntropyWithLogits(sparse=False) model_pynative = Model(net_pynative, loss_pynative, opt_pynative, amp_level="O2") model_pynative.train(1, dataset2, dataset_sink_mode=False) out_pynative = model_pynative.predict(Tensor(input_data)) allclose_nparray(out_graph.asnumpy(), out_pynative.asnumpy(), 0.001, 0.001)