def test_predict_fp16(self):
if context.num_gpus() >= 2:
self.skipTest(
'No need to test 2+ GPUs without a distribution strategy.')
self._prepare_files_and_flags('--dtype=fp16')
t = transformer_main.TransformerTask(FLAGS)
t.predict()
def test_train_save_full_model(self):
if context.num_gpus() >= 2:
self.skipTest(
'No need to test 2+ GPUs without a distribution strategy.')
FLAGS.save_weights_only = False
t = transformer_main.TransformerTask(FLAGS)
t.train()
def test_eval(self):
if context.num_gpus() >= 2:
self.skipTest(
'No need to test 2+ GPUs without a distribution strategy.')
if 'test_xla' in sys.argv[0]:
self.skipTest('TODO(xla): Make this test faster under XLA.')
self._prepare_files_and_flags()
t = transformer_main.TransformerTask(FLAGS)
t.eval()
def test_train_2_gpu(self):
if context.num_gpus() < 2:
self.skipTest(
'{} GPUs are not available for this test. {} GPUs are available'
.format(2, context.num_gpus()))
FLAGS.distribution_strategy = 'mirrored'
FLAGS.num_gpus = 2
FLAGS.param_set = 'base'
t = transformer_main.TransformerTask(FLAGS)
t.train()
def test_train_static_batch(self):
if context.num_gpus() >= 2:
self.skipTest(
'No need to test 2+ GPUs without a distribution strategy.')
FLAGS.distribution_strategy = 'one_device'
if tf.test.is_built_with_cuda():
FLAGS.num_gpus = 1
else:
FLAGS.num_gpus = 0
FLAGS.static_batch = True
t = transformer_main.TransformerTask(FLAGS)
t.train()
def test_train_no_dist_strat(self):
if context.num_gpus() >= 2:
self.skipTest(
'No need to test 2+ GPUs without a distribution strategy.')
t = transformer_main.TransformerTask(FLAGS)
t.train()
def test_train_fp16(self):
FLAGS.distribution_strategy = 'one_device'
FLAGS.dtype = 'fp16'
t = transformer_main.TransformerTask(FLAGS)
t.train()
def test_train_1_gpu_with_dist_strat(self):
FLAGS.distribution_strategy = 'one_device'
t = transformer_main.TransformerTask(FLAGS)
t.train()