!474 [MS][bert][example]Add ST test script of bert model

Merge pull request !474 from wsc/lossscale_script
This commit is contained in:
mindspore-ci-bot 2020-04-27 09:16:16 +08:00 committed by Gitee
commit d7242002aa
2 changed files with 199 additions and 1 deletions

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@ -445,5 +445,5 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
succ = False succ = False
else: else:
succ = self.optimizer(grads) succ = self.optimizer(grads)
ret = (loss, cond) ret = (loss, cond, scaling_sens)
return F.depend(ret, succ) return F.depend(ret, succ)

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@ -0,0 +1,198 @@
# 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.
# ============================================================================
"""train bert network without lossscale"""
import os
import pytest
import numpy as np
from numpy import allclose
import mindspore.common.dtype as mstype
import mindspore.dataset.engine.datasets as de
import mindspore.dataset.transforms.c_transforms as C
from mindspore import context
from mindspore.common.tensor import Tensor
from mindspore.train.model import Model
from mindspore.train.callback import Callback, LossMonitor
from mindspore.train.loss_scale_manager import DynamicLossScaleManager
from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
from mindspore.nn.optim import Momentum
from mindspore import log as logger
_current_dir = os.path.dirname(os.path.realpath(__file__))
DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json"
def get_config(version='base', batch_size=1):
"""get config"""
if version == 'base':
bert_config = BertConfig(
batch_size=batch_size,
seq_length=128,
vocab_size=21136,
hidden_size=768,
num_hidden_layers=2,
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,
input_mask_from_dataset=True,
token_type_ids_from_dataset=True,
dtype=mstype.float32,
compute_type=mstype.float32)
elif version == 'large':
bert_config = BertConfig(
batch_size=batch_size,
seq_length=128,
vocab_size=21136,
hidden_size=1024,
num_hidden_layers=2,
num_attention_heads=16,
intermediate_size=4096,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
use_relative_positions=True,
input_mask_from_dataset=True,
token_type_ids_from_dataset=True,
dtype=mstype.float32,
compute_type=mstype.float16)
elif version == 'large_mixed':
bert_config = BertConfig(
batch_size=batch_size,
seq_length=128,
vocab_size=21136,
hidden_size=1024,
num_hidden_layers=24,
num_attention_heads=16,
intermediate_size=4096,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
use_relative_positions=True,
input_mask_from_dataset=True,
token_type_ids_from_dataset=True,
dtype=mstype.float32,
compute_type=mstype.float32)
else:
bert_config = BertConfig(batch_size=batch_size)
return bert_config
def me_de_train_dataset():
"""test me de train dataset"""
# apply repeat operations
repeat_count = 1
ds = de.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
"next_sentence_labels", "masked_lm_positions",
"masked_lm_ids", "masked_lm_weights"], shuffle=False)
type_cast_op = C.TypeCast(mstype.int32)
ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
ds = ds.map(input_columns="input_mask", operations=type_cast_op)
ds = ds.map(input_columns="input_ids", operations=type_cast_op)
# apply batch operations
batch_size = int(os.getenv('BATCH_SIZE', '16'))
ds = ds.batch(batch_size, drop_remainder=True)
ds = ds.repeat(repeat_count)
return ds
def weight_variable(shape):
"""weight variable"""
np.random.seed(1)
ones = np.random.uniform(-0.1, 0.1, size=shape).astype(np.float32)
return Tensor(ones)
class ModelCallback(Callback):
def __init__(self):
super(ModelCallback, self).__init__()
self.loss_list = []
self.overflow_list = []
self.lossscale_list = []
def step_end(self, run_context):
cb_params = run_context.original_args()
self.loss_list.append(cb_params.net_outputs[0])
self.overflow_list.append(cb_params.net_outputs[1])
self.lossscale_list.append(cb_params.net_outputs[2])
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_bert_tdt():
"""test bert tdt"""
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
context.set_context(enable_task_sink=True)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
ds = me_de_train_dataset()
version = os.getenv('VERSION', 'large')
batch_size = int(os.getenv('BATCH_SIZE', '16'))
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Momentum(netwithloss.trainable_params(), learning_rate=2e-5, momentum=0.9)
scale_window = 3
scale_manager = DynamicLossScaleManager(2**32, 2, scale_window)
netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer, scale_update_cell=scale_manager.get_update_cell())
netwithgrads.set_train(True)
model = Model(netwithgrads)
callback = ModelCallback()
params = netwithloss.trainable_params()
for param in params:
value = param.default_input
name = param.name
if isinstance(value, Tensor):
if name.split('.')[-1] in ['weight']:
if name.split('.')[-3] in ['cls2']:
logger.info("***************** BERT param name is 1 {}".format(name))
param.default_input = weight_variable(value.asnumpy().shape)
else:
logger.info("***************** BERT param name is 2 {}".format(name))
tempshape = value.asnumpy().shape
shape = (tempshape[1], tempshape[0])
weight_value = weight_variable(shape).asnumpy()
param.default_input = Tensor(np.transpose(weight_value, [1, 0]))
else:
logger.info("***************** BERT param name is 3 {}".format(name))
param.default_input = weight_variable(value.asnumpy().shape)
model.train(ds.get_repeat_count(), ds, callbacks=callback, dataset_sink_mode=False)
# assertion occurs while the loss_scale value is wrong
count = 0
for i in range(len(callback.overflow_list)):
if callback.overflow_list[i] == Tensor(True, mstype.bool_) and i > 0:
count = 0
assert callback.lossscale_list[i] == callback.lossscale_list[i - 1] * Tensor(0.5, mstype.float32)
if callback.overflow_list[i] == Tensor(False, mstype.bool_):
count = count + 1
if count == scale_window:
count = 0
assert callback.lossscale_list[i] == callback.lossscale_list[i - 1] * Tensor(2.0, mstype.float32)
if __name__ == '__main__':
test_bert_tdt()