def test_greedy(self, padded_decode):
greedy_obj = sampling_module.SamplingModule(
length_normalization_fn=None,
dtype=tf.float32,
symbols_to_logits_fn=self._get_test_symbols_to_logits_fn(),
vocab_size=3,
max_decode_length=4,
eos_id=10,
padded_decode=padded_decode)
ids, _ = greedy_obj.generate(initial_ids=tf.constant([9, 1]),
initial_cache=self.cache)
self.assertAllEqual([[9, 1, 2, 2, 2], [1, 1, 1, 2, 2]], ids)
def test_topk(self, padded_decode):
top_k_obj = sampling_module.SamplingModule(
length_normalization_fn=length_norm,
dtype=tf.float32,
symbols_to_logits_fn=self._get_test_symbols_to_logits_fn(),
vocab_size=3,
max_decode_length=4,
eos_id=10,
sample_temperature=tf.constant(0.1),
top_k=tf.constant(3),
padded_decode=padded_decode)
ids, _ = top_k_obj.generate(initial_ids=tf.constant([9, 1]),
initial_cache=self.cache)
self.assertAllEqual([2, 5], ids.shape)
def test_topp(self, padded_decode):
top_p_obj = sampling_module.SamplingModule(
length_normalization_fn=length_norm,
dtype=tf.float32,
symbols_to_logits_fn=self._get_test_symbols_to_logits_fn(),
vocab_size=3,
max_decode_length=4,
eos_id=10,
sample_temperature=tf.constant(1.0),
top_p=tf.constant(0.9),
padded_decode=padded_decode,
enable_greedy=False)
tf.random.set_seed(1)
ids, _ = top_p_obj.generate(initial_ids=tf.constant([9, 1]),
initial_cache=self.cache)
top_p_expected = tf.constant([[9, 1, 0, 2, 2], [1, 0, 1, 2, 0]])
self.assertAllEqual(top_p_expected, ids)
def test_sampling_equivalent_greedy(self, padded_decode):
# Ensure that p=0.0 with no sample temperature is same as greedy.
top_p_obj = sampling_module.SamplingModule(
length_normalization_fn=length_norm,
dtype=tf.float32,
symbols_to_logits_fn=self._get_test_symbols_to_logits_fn(),
vocab_size=3,
max_decode_length=4,
eos_id=10,
sample_temperature=0.0,
top_p=tf.constant(0.0),
padded_decode=padded_decode,
enable_greedy=False)
ids, _ = top_p_obj.generate(initial_ids=tf.constant([9, 1]),
initial_cache=self.cache)
self.assertAllEqual(greedy_expected, ids)
# Ensure that k=1 with no sample temperature is same as greedy.
top_k_obj = sampling_module.SamplingModule(
length_normalization_fn=length_norm,
dtype=tf.float32,
symbols_to_logits_fn=self._get_test_symbols_to_logits_fn(),
vocab_size=3,
max_decode_length=4,
eos_id=10,
sample_temperature=0.0,
top_k=tf.constant(1),
padded_decode=padded_decode,
enable_greedy=False)
ids, _ = top_k_obj.generate(initial_ids=tf.constant([9, 1]),
initial_cache=self.cache)
# Ensure that low sample temperature results in Sharp Distribution (greedy).
low_temperature_obj = sampling_module.SamplingModule(
length_normalization_fn=length_norm,
dtype=tf.float32,
symbols_to_logits_fn=self._get_test_symbols_to_logits_fn(),
vocab_size=3,
max_decode_length=4,
eos_id=10,
sample_temperature=0.0001,
padded_decode=padded_decode)
ids, _ = low_temperature_obj.generate(initial_ids=tf.constant([9, 1]),
initial_cache=self.cache)
self.assertAllEqual(greedy_expected, ids)
# Ensure that high sample temperature results in Flat Distribution (random).
high_temperature_obj = sampling_module.SamplingModule(
length_normalization_fn=length_norm,
dtype=tf.float32,
symbols_to_logits_fn=self._get_test_symbols_to_logits_fn(),
vocab_size=3,
max_decode_length=4,
eos_id=10,
sample_temperature=10.0,
padded_decode=padded_decode,
enable_greedy=False)
tf.random.set_seed(1)
ids, _ = high_temperature_obj.generate(initial_ids=tf.constant([9, 1]),
initial_cache=self.cache)
expected = tf.constant([[9, 0, 0, 2, 2], [1, 0, 0, 0, 0]])
self.assertAllEqual(expected, ids)
