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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Executes XLNet benchmarks and accuracy tests."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import time
# pylint: disable=g-bad-import-order
from absl import flags
from absl.testing import flagsaver
import tensorflow as tf
# pylint: enable=g-bad-import-order
from official.benchmark import bert_benchmark_utils as benchmark_utils
from official.benchmark import owner_utils
from official.nlp.xlnet import run_classifier
from official.nlp.xlnet import run_squad
from official.benchmark import benchmark_wrappers
# pylint: disable=line-too-long
PRETRAINED_CHECKPOINT_PATH = 'gs://cloud-tpu-checkpoints/xlnet/large/xlnet_model-1'
CLASSIFIER_TRAIN_DATA_PATH = 'gs://tf-perfzero-data/xlnet/imdb/spiece.model.len-512.train.tf_record'
CLASSIFIER_EVAL_DATA_PATH = 'gs://tf-perfzero-data/xlnet/imdb/spiece.model.len-512.dev.eval.tf_record'
SQUAD_DATA_PATH = 'gs://tf-perfzero-data/xlnet/squadv2_cased/'
# pylint: enable=line-too-long
FLAGS = flags.FLAGS
class XLNetBenchmarkBase(benchmark_utils.BertBenchmarkBase):
"""Base class to hold methods common to test classes in the module."""
def __init__(self, output_dir=None, tpu=None):
super(XLNetBenchmarkBase, self).__init__(output_dir=output_dir, tpu=tpu)
self.num_epochs = None
self.num_steps_per_epoch = None
@flagsaver.flagsaver
def _run_xlnet_classifier(self):
"""Starts XLNet classification task."""
run_classifier.main(unused_argv=None)
@flagsaver.flagsaver
def _run_xlnet_squad(self):
"""Starts XLNet classification task."""
run_squad.main(unused_argv=None)
class XLNetClassifyAccuracy(XLNetBenchmarkBase):
"""Short accuracy test for XLNet classifier model.
Tests XLNet classification task model accuracy. The naming
convention of below test cases follow
`benchmark_(number of gpus)_gpu_(dataset type)` format.
"""
def __init__(self, output_dir=None, tpu=None, **kwargs):
self.train_data_path = CLASSIFIER_TRAIN_DATA_PATH
self.eval_data_path = CLASSIFIER_EVAL_DATA_PATH
self.pretrained_checkpoint_path = PRETRAINED_CHECKPOINT_PATH
super(XLNetClassifyAccuracy, self).__init__(output_dir=output_dir, tpu=tpu)
@benchmark_wrappers.enable_runtime_flags
def _run_and_report_benchmark(self,
training_summary_path,
min_accuracy=0.95,
max_accuracy=0.97):
"""Starts XLNet accuracy benchmark test."""
start_time_sec = time.time()
self._run_xlnet_classifier()
wall_time_sec = time.time() - start_time_sec
with tf.io.gfile.GFile(training_summary_path, 'rb') as reader:
summary = json.loads(reader.read().decode('utf-8'))
super(XLNetClassifyAccuracy, self)._report_benchmark(
stats=summary,
wall_time_sec=wall_time_sec,
min_accuracy=min_accuracy,
max_accuracy=max_accuracy)
def _setup(self):
super(XLNetClassifyAccuracy, self)._setup()
FLAGS.test_data_size = 25024
FLAGS.train_batch_size = 16
FLAGS.seq_len = 512
FLAGS.mem_len = 0
FLAGS.n_layer = 24
FLAGS.d_model = 1024
FLAGS.d_embed = 1024
FLAGS.n_head = 16
FLAGS.d_head = 64
FLAGS.d_inner = 4096
FLAGS.untie_r = True
FLAGS.n_class = 2
FLAGS.ff_activation = 'gelu'
FLAGS.strategy_type = 'mirror'
FLAGS.learning_rate = 2e-5
FLAGS.train_steps = 4000
FLAGS.warmup_steps = 500
FLAGS.iterations = 200
FLAGS.bi_data = False
FLAGS.init_checkpoint = self.pretrained_checkpoint_path
FLAGS.train_tfrecord_path = self.train_data_path
FLAGS.test_tfrecord_path = self.eval_data_path
@owner_utils.Owner('tf-model-garden')
def benchmark_8_gpu_imdb(self):
"""Run XLNet model accuracy test with 8 GPUs."""
self._setup()
FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_imdb')
# Sets timer_callback to None as we do not use it now.
self.timer_callback = None
summary_path = os.path.join(FLAGS.model_dir,
'summaries/training_summary.txt')
self._run_and_report_benchmark(summary_path)
@owner_utils.Owner('tf-model-garden')
def benchmark_2x2_tpu_imdb(self):
"""Run XLNet model accuracy test on 2x2 tpu."""
self._setup()
FLAGS.strategy_type = 'tpu'
FLAGS.model_dir = self._get_model_dir('benchmark_2x2_tpu_imdb')
# Sets timer_callback to None as we do not use it now.
self.timer_callback = None
summary_path = os.path.join(FLAGS.model_dir,
'summaries/training_summary.txt')
self._run_and_report_benchmark(summary_path)
class XLNetSquadAccuracy(XLNetBenchmarkBase):
"""Short accuracy test for XLNet squad model.
Tests XLNet squad task model accuracy. The naming
convention of below test cases follow
`benchmark_(number of gpus)_gpu_(dataset type)` format.
"""
def __init__(self, output_dir=None, tpu=None, **kwargs):
self.train_data_path = SQUAD_DATA_PATH
self.predict_file = os.path.join(SQUAD_DATA_PATH, "dev-v2.0.json")
self.test_data_path = os.path.join(SQUAD_DATA_PATH, "12048.eval.tf_record")
self.spiece_model_file = os.path.join(SQUAD_DATA_PATH, "spiece.cased.model")
self.pretrained_checkpoint_path = PRETRAINED_CHECKPOINT_PATH
super(XLNetSquadAccuracy, self).__init__(output_dir=output_dir, tpu=tpu)
@benchmark_wrappers.enable_runtime_flags
def _run_and_report_benchmark(self,
training_summary_path,
min_accuracy=87.0,
max_accuracy=89.0):
"""Starts XLNet accuracy benchmark test."""
start_time_sec = time.time()
self._run_xlnet_squad()
wall_time_sec = time.time() - start_time_sec
with tf.io.gfile.GFile(training_summary_path, 'rb') as reader:
summary = json.loads(reader.read().decode('utf-8'))
super(XLNetSquadAccuracy, self)._report_benchmark(
stats=summary,
wall_time_sec=wall_time_sec,
min_accuracy=min_accuracy,
max_accuracy=max_accuracy)
def _setup(self):
super(XLNetSquadAccuracy, self)._setup()
FLAGS.train_batch_size = 16
FLAGS.seq_len = 512
FLAGS.mem_len = 0
FLAGS.n_layer = 24
FLAGS.d_model = 1024
FLAGS.d_embed = 1024
FLAGS.n_head = 16
FLAGS.d_head = 64
FLAGS.d_inner = 4096
FLAGS.untie_r = True
FLAGS.ff_activation = 'gelu'
FLAGS.strategy_type = 'mirror'
FLAGS.learning_rate = 3e-5
FLAGS.train_steps = 8000
FLAGS.warmup_steps = 1000
FLAGS.iterations = 1000
FLAGS.bi_data = False
FLAGS.init_checkpoint = self.pretrained_checkpoint_path
FLAGS.train_tfrecord_path = self.train_data_path
FLAGS.test_tfrecord_path = self.test_data_path
FLAGS.spiece_model_file = self.spiece_model_file
FLAGS.predict_file = self.predict_file
FLAGS.adam_epsilon = 1e-6
FLAGS.lr_layer_decay_rate = 0.75
@owner_utils.Owner('tf-model-garden')
def benchmark_8_gpu_squadv2(self):
"""Run XLNet model squad v2 accuracy test with 8 GPUs."""
self._setup()
FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_squadv2')
FLAGS.predict_dir = FLAGS.model_dir
# Sets timer_callback to None as we do not use it now.
self.timer_callback = None
summary_path = os.path.join(FLAGS.model_dir,
'summaries/training_summary.txt')
self._run_and_report_benchmark(summary_path)
@owner_utils.Owner('tf-model-garden')
def benchmark_2x2_tpu_squadv2(self):
"""Run XLNet model squad v2 accuracy test on 2x2 tpu."""
self._setup()
FLAGS.strategy_type = 'tpu'
FLAGS.model_dir = self._get_model_dir('benchmark_2x2_tpu_squadv2')
FLAGS.predict_dir = FLAGS.model_dir
# Sets timer_callback to None as we do not use it now.
self.timer_callback = None
summary_path = os.path.join(FLAGS.model_dir,
'summaries/training_summary.txt')
self._run_and_report_benchmark(summary_path)
if __name__ == '__main__':
tf.test.main()
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