def testSNLIClassifierAndTrainer(self):
with tf.device(self._test_device):
vocab_size = 40
batch_size = 2
d_embed = 10
sequence_length = 15
d_out = 4
config = _test_spinn_config(d_embed, d_out)
# Create fake embedding matrix.
embed = tf.random_normal((vocab_size, d_embed))
model = spinn.SNLIClassifier(config, embed)
trainer = spinn.SNLIClassifierTrainer(model, config.lr)
(labels, prem, prem_trans, hypo,
hypo_trans) = _generate_synthetic_snli_data_batch(sequence_length,
batch_size,
vocab_size)
# Invoke model under non-training mode.
logits = model(prem, prem_trans, hypo, hypo_trans, training=False)
self.assertEqual(tf.float32, logits.dtype)
self.assertEqual((batch_size, d_out), logits.shape)
# Invoke model under training model.
logits = model(prem, prem_trans, hypo, hypo_trans, training=True)
self.assertEqual(tf.float32, logits.dtype)
self.assertEqual((batch_size, d_out), logits.shape)
# Calculate loss.
loss1 = trainer.loss(labels, logits)
self.assertEqual(tf.float32, loss1.dtype)
self.assertEqual((), loss1.shape)
loss2, logits = trainer.train_batch(
labels, prem, prem_trans, hypo, hypo_trans)
self.assertEqual(tf.float32, loss2.dtype)
self.assertEqual((), loss2.shape)
self.assertEqual(tf.float32, logits.dtype)
self.assertEqual((batch_size, d_out), logits.shape)
# Training on the batch should have led to a change in the loss value.
self.assertNotEqual(loss1.numpy(), loss2.numpy())
def benchmarkEagerSpinnSNLIClassifier(self):
test_device = "gpu:0" if tfe.num_gpus() else "cpu:0"
with tf.device(test_device):
burn_in_iterations = 2
benchmark_iterations = 10
vocab_size = 1000
batch_size = 128
sequence_length = 15
d_embed = 200
d_out = 4
embed = tf.random_normal((vocab_size, d_embed))
config = _test_spinn_config(d_embed, d_out)
model = spinn.SNLIClassifier(config, embed)
trainer = spinn.SNLIClassifierTrainer(model, config.lr)
(labels, prem, prem_trans, hypo,
hypo_trans) = _generate_synthetic_snli_data_batch(sequence_length,
batch_size,
vocab_size)
for _ in range(burn_in_iterations):
trainer.train_batch(labels, prem, prem_trans, hypo, hypo_trans)
gc.collect()
start_time = time.time()
for _ in xrange(benchmark_iterations):
trainer.train_batch(labels, prem, prem_trans, hypo, hypo_trans)
wall_time = time.time() - start_time
# Named "examples"_per_sec to conform with other benchmarks.
extras = {"examples_per_sec": benchmark_iterations / wall_time}
self.report_benchmark(
name="Eager_SPINN_SNLIClassifier_Benchmark",
iters=benchmark_iterations,
wall_time=wall_time,
extras=extras)
