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add bert performance test case

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pull/1801/head
wanghua 5 years ago
parent 75f791d8e5
commit 3f536ea1b7
1 changed files with 104 additions and 10 deletions
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tests/st/networks/models/bert/bert_tdt_lossscale.py
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@ -16,6 +16,7 @@
"""train bert network without lossscale"""
import os
import time
import pytest
import numpy as np
@ -85,14 +86,23 @@ def get_config(version='base', batch_size=1):
return bert_config
def me_de_train_dataset():
def me_de_train_dataset(sink_mode=False):
"""test me de train dataset"""
# apply repeat operations
repeat_count = 1
batch_size = 16
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)
new_repeat_count = repeat_count
if sink_mode:
repeat_count = 30
sink_steps = 100
ori_dataaet_size = ds.get_dataset_size()
new_size = sink_steps * batch_size
ds.set_dataset_size(new_size)
new_repeat_count = int(repeat_count * ori_dataaet_size // ds.get_dataset_size())
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)
@ -100,10 +110,11 @@ def me_de_train_dataset():
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
logger.info("data size: {}".format(ds.get_dataset_size()))
logger.info("repeat_count: {}".format(ds.get_repeat_count()))
return ds, new_repeat_count
def weight_variable(shape):
@ -127,20 +138,34 @@ class ModelCallback(Callback):
self.lossscale_list.append(cb_params.net_outputs[2].asnumpy())
print("epoch: {}, outputs are: {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
class TimeMonitor(Callback):
"""Time Monitor."""
def __init__(self, data_size):
super(TimeMonitor, self).__init__()
self.data_size = data_size
self.epoch_mseconds_list = []
self.per_step_mseconds_list = []
def epoch_begin(self, run_context):
self.epoch_time = time.time()
def epoch_end(self, run_context):
epoch_mseconds = (time.time() - self.epoch_time) * 1000
self.epoch_mseconds_list.append(epoch_mseconds)
self.per_step_mseconds_list.append(epoch_mseconds / self.data_size)
@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"""
def test_bert_percision():
"""test bert percision"""
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
ds = me_de_train_dataset()
ds, new_repeat_count = me_de_train_dataset()
version = os.getenv('VERSION', 'large')
batch_size = int(os.getenv('BATCH_SIZE', '16'))
batch_size = 16
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size()*ds.get_repeat_count(),
optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size()*new_repeat_count,
start_learning_rate=5e-5, end_learning_rate=1e-9,
power=10.0, warmup_steps=0, weight_decay=0.01)
scale_window = 3
@ -169,10 +194,12 @@ def test_bert_tdt():
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)
model.train(new_repeat_count, ds, callbacks=callback, dataset_sink_mode=False)
# assertion occurs while the loss value, overflow state or loss_scale value is wrong
loss_value = np.array(callback.loss_list)
assert np.allclose(loss_value[0], 12.207198, 0, 0.000001)
expect_loss_value = [12.207198, 11.980881, 11.984844, 11.879381, 11.832978, 12.411333, 12.009284,
12.621277, 12.223178, 12.427385]
print("loss value: {}".format(loss_value))
@ -188,6 +215,73 @@ def test_bert_tdt():
print("loss scale: {}".format(loss_scale))
assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
def test_bert_performance():
"""test bert performance"""
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
ds, new_repeat_count = me_de_train_dataset(sink_mode=True)
version = os.getenv('VERSION', 'large')
batch_size = 16
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size()*new_repeat_count,
start_learning_rate=5e-5, end_learning_rate=1e-9,
power=10.0, warmup_steps=0, weight_decay=0.01)
scale_window = 3
scale_manager = DynamicLossScaleManager(2 ** 16, 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:
param.init_data()
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)
time_monitor_callback = TimeMonitor(ds.get_dataset_size())
model.train(new_repeat_count, ds, callbacks=[time_monitor_callback, callback],
dataset_sink_mode=True)
# assertion occurs while the loss value, overflow state or loss_scale value is wrong
loss_value = np.array(callback.loss_list)
expect_loss_value = [10.237753, 10.213153, 10.212972]
print("loss value: {}".format(loss_value))
assert np.allclose(loss_value, expect_loss_value, 0, 0.0005)
overflow = np.array(callback.overflow_list)
expect_overflow = [False, False, False]
print("overflow: {}".format(overflow))
assert (overflow == expect_overflow).all()
loss_scale = np.array(callback.lossscale_list)
expect_loss_scale = [16384.0, 16384.0, 16384.0]
print("loss scale: {}".format(loss_scale))
assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
epoch_mseconds = np.array(time_monitor_callback.epoch_mseconds_list)[2]
expect_epoch_mseconds = 1726
print("epoch mseconds: {}".format(epoch_mseconds))
assert epoch_mseconds <= expect_epoch_mseconds + 5
per_step_mseconds = np.array(time_monitor_callback.per_step_mseconds_list)[2]
expect_per_step_mseconds = 17
print("per step mseconds: {}".format(per_step_mseconds))
assert per_step_mseconds <= expect_per_step_mseconds + 1
if __name__ == '__main__':
test_bert_tdt()
test_bert_percision()
test_bert_performance()

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