# 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. # ============================================================================ """Bert test.""" # pylint: disable=missing-docstring, arguments-differ, W0612 import os import mindspore.common.dtype as mstype import mindspore.context as context from mindspore import Tensor from mindspore.ops import operations as P from mindspore.nn.optim import AdamWeightDecay from mindspore.train.loss_scale_manager import DynamicLossScaleManager from mindspore.nn import learning_rate_schedule as lr_schedules from tests.models.official.nlp.bert.src import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell from ...dataset_mock import MindData from ...ops_common import nn, np, batch_tuple_tensor, build_construct_graph _current_dir = os.path.dirname(os.path.realpath(__file__)) + "/../python/test_data" context.set_context(mode=context.GRAPH_MODE) def get_dataset(batch_size=1): dataset_types = (np.int32, np.int32, np.int32, np.int32, np.int32, np.int32, np.int32) dataset_shapes = ((batch_size, 128), (batch_size, 128), (batch_size, 128), (batch_size, 1), \ (batch_size, 20), (batch_size, 20), (batch_size, 20)) dataset = MindData(size=2, batch_size=batch_size, np_types=dataset_types, output_shapes=dataset_shapes, input_indexs=(0, 1)) return dataset def load_test_data(batch_size=1): dataset = get_dataset(batch_size) ret = dataset.next() ret = batch_tuple_tensor(ret, batch_size) return ret def get_config(version='base'): """ get_config definition """ if version == 'base': return BertConfig( seq_length=128, vocab_size=21128, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, use_relative_positions=True, dtype=mstype.float32, compute_type=mstype.float32) if version == 'large': return BertConfig( seq_length=128, vocab_size=21128, hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, intermediate_size=4096, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, use_relative_positions=True, dtype=mstype.float32, compute_type=mstype.float32) return BertConfig() class BertLearningRate(lr_schedules.LearningRateSchedule): def __init__(self, decay_steps, warmup_steps=100, learning_rate=0.1, end_learning_rate=0.0001, power=1.0): super(BertLearningRate, self).__init__() self.warmup_lr = lr_schedules.WarmUpLR(learning_rate, warmup_steps) self.decay_lr = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power) self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32)) self.greater = P.Greater() self.one = Tensor(np.array([1.0]).astype(np.float32)) self.cast = P.Cast() def construct(self, global_step): is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32) warmup_lr = self.warmup_lr(global_step) decay_lr = self.decay_lr(global_step) lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr return lr def test_bert_train(): """ the main function """ class ModelBert(nn.Cell): """ ModelBert definition """ def __init__(self, network, optimizer=None): super(ModelBert, self).__init__() self.optimizer = optimizer self.train_network = BertTrainOneStepCell(network, self.optimizer) self.train_network.set_train() def construct(self, arg0, arg1, arg2, arg3, arg4, arg5, arg6): return self.train_network(arg0, arg1, arg2, arg3, arg4, arg5, arg6) version = os.getenv('VERSION', 'large') batch_size = int(os.getenv('BATCH_SIZE', '1')) inputs = load_test_data(batch_size) config = get_config(version=version) netwithloss = BertNetworkWithLoss(config, True) lr = BertLearningRate(10) optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr) net = ModelBert(netwithloss, optimizer=optimizer) net.set_train() build_construct_graph(net, *inputs, execute=False) def test_bert_withlossscale_train(): class ModelBert(nn.Cell): def __init__(self, network, optimizer=None): super(ModelBert, self).__init__() self.optimizer = optimizer self.train_network = BertTrainOneStepWithLossScaleCell(network, self.optimizer) self.train_network.set_train() def construct(self, arg0, arg1, arg2, arg3, arg4, arg5, arg6, arg7): return self.train_network(arg0, arg1, arg2, arg3, arg4, arg5, arg6, arg7) version = os.getenv('VERSION', 'base') batch_size = int(os.getenv('BATCH_SIZE', '1')) scaling_sens = Tensor(np.ones([1]).astype(np.float32)) inputs = load_test_data(batch_size) + (scaling_sens,) config = get_config(version=version) netwithloss = BertNetworkWithLoss(config, True) lr = BertLearningRate(10) optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr) net = ModelBert(netwithloss, optimizer=optimizer) net.set_train() build_construct_graph(net, *inputs, execute=True) def bert_withlossscale_manager_train(): class ModelBert(nn.Cell): def __init__(self, network, optimizer=None): super(ModelBert, self).__init__() self.optimizer = optimizer manager = DynamicLossScaleManager() update_cell = LossScaleUpdateCell(manager) self.train_network = BertTrainOneStepWithLossScaleCell(network, self.optimizer, scale_update_cell=update_cell) self.train_network.set_train() def construct(self, arg0, arg1, arg2, arg3, arg4, arg5, arg6): return self.train_network(arg0, arg1, arg2, arg3, arg4, arg5, arg6) version = os.getenv('VERSION', 'base') batch_size = int(os.getenv('BATCH_SIZE', '1')) inputs = load_test_data(batch_size) config = get_config(version=version) netwithloss = BertNetworkWithLoss(config, True) lr = BertLearningRate(10) optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr) net = ModelBert(netwithloss, optimizer=optimizer) net.set_train() build_construct_graph(net, *inputs, execute=True) def bert_withlossscale_manager_train_feed(): class ModelBert(nn.Cell): def __init__(self, network, optimizer=None): super(ModelBert, self).__init__() self.optimizer = optimizer manager = DynamicLossScaleManager() update_cell = LossScaleUpdateCell(manager) self.train_network = BertTrainOneStepWithLossScaleCell(network, self.optimizer, scale_update_cell=update_cell) self.train_network.set_train() def construct(self, arg0, arg1, arg2, arg3, arg4, arg5, arg6, arg7): return self.train_network(arg0, arg1, arg2, arg3, arg4, arg5, arg6, arg7) version = os.getenv('VERSION', 'base') batch_size = int(os.getenv('BATCH_SIZE', '1')) scaling_sens = Tensor(np.ones([1]).astype(np.float32)) inputs = load_test_data(batch_size) + (scaling_sens,) config = get_config(version=version) netwithloss = BertNetworkWithLoss(config, True) lr = BertLearningRate(10) optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr) net = ModelBert(netwithloss, optimizer=optimizer) net.set_train() build_construct_graph(net, *inputs, execute=True)