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