def test_step_with_greedy_embedding_helper():
batch_size = 5
vocabulary_size = 7
cell_depth = vocabulary_size # cell's logits must match vocabulary size
input_depth = 10
start_tokens = np.random.randint(0, vocabulary_size, size=batch_size)
end_token = 1
embeddings = np.random.randn(vocabulary_size,
input_depth).astype(np.float32)
embeddings_t = tf.constant(embeddings)
cell = tf.keras.layers.LSTMCell(vocabulary_size)
sampler = sampler_py.GreedyEmbeddingSampler()
initial_state = cell.get_initial_state(batch_size=batch_size,
dtype=tf.float32)
my_decoder = basic_decoder.BasicDecoder(cell=cell, sampler=sampler)
(first_finished, first_inputs, first_state) = my_decoder.initialize(
embeddings_t,
start_tokens=start_tokens,
end_token=end_token,
initial_state=initial_state,
)
output_size = my_decoder.output_size
output_dtype = my_decoder.output_dtype
assert (basic_decoder.BasicDecoderOutput(cell_depth, tf.TensorShape(
[])) == output_size)
assert basic_decoder.BasicDecoderOutput(tf.float32,
tf.int32) == output_dtype
(
step_outputs,
step_state,
step_next_inputs,
step_finished,
) = my_decoder.step(tf.constant(0), first_inputs, first_state)
assert len(first_state) == 2
assert len(step_state) == 2
assert isinstance(step_outputs, basic_decoder.BasicDecoderOutput)
assert (batch_size, cell_depth) == step_outputs[0].shape
assert (batch_size, ) == step_outputs[1].shape
assert (batch_size, cell_depth) == first_state[0].shape
assert (batch_size, cell_depth) == first_state[1].shape
assert (batch_size, cell_depth) == step_state[0].shape
assert (batch_size, cell_depth) == step_state[1].shape
expected_sample_ids = np.argmax(step_outputs.rnn_output, -1)
expected_step_finished = expected_sample_ids == end_token
expected_step_next_inputs = embeddings[expected_sample_ids]
np.testing.assert_equal(
np.asanyarray([False, False, False, False, False]),
first_finished,
)
np.testing.assert_equal(expected_step_finished, step_finished)
assert output_dtype.sample_id == step_outputs.sample_id.dtype
np.testing.assert_equal(expected_sample_ids, step_outputs.sample_id)
np.testing.assert_equal(expected_step_next_inputs, step_next_inputs)
def test_dynamic_decode_tflite_conversion():
if test_utils.is_gpu_available():
pytest.skip("cpu-only test")
units = 10
vocab_size = 20
cell = tf.keras.layers.LSTMCell(units)
sampler = sampler_py.GreedyEmbeddingSampler()
embeddings = tf.random.uniform([vocab_size, units])
my_decoder = basic_decoder.BasicDecoder(cell=cell, sampler=sampler)
@tf.function
def _decode(start_tokens, end_token):
batch_size = tf.size(start_tokens)
initial_state = cell.get_initial_state(batch_size=batch_size, dtype=tf.float32)
return decoder.dynamic_decode(
my_decoder,
maximum_iterations=5,
enable_tflite_convertible=True,
decoder_init_input=embeddings,
decoder_init_kwargs=dict(
initial_state=initial_state,
start_tokens=start_tokens,
end_token=end_token,
),
)
concrete_function = _decode.get_concrete_function(
tf.TensorSpec([1], dtype=tf.int32), tf.TensorSpec([], dtype=tf.int32)
)
if tf.__version__[:3] >= "2.7":
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_function], _decode
)
else:
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_function])
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS,
]
_ = converter.convert()
with pytest.raises(tf.errors.InvalidArgumentError, match="batch size"):
# Batch size > 1 should throw an error.
_decode.get_concrete_function(
tf.TensorSpec([2], dtype=tf.int32), tf.TensorSpec([], dtype=tf.int32)
)
def testStepWithGreedyEmbeddingHelper(self):
batch_size = 5
vocabulary_size = 7
cell_depth = vocabulary_size # cell's logits must match vocabulary size
input_depth = 10
start_tokens = np.random.randint(0, vocabulary_size, size=batch_size)
end_token = 1
with self.cached_session(use_gpu=True):
embeddings = np.random.randn(vocabulary_size, input_depth).astype(
np.float32
)
embeddings_t = tf.constant(embeddings)
cell = tf.keras.layers.LSTMCell(vocabulary_size)
sampler = sampler_py.GreedyEmbeddingSampler()
initial_state = cell.get_initial_state(
batch_size=batch_size, dtype=tf.float32
)
my_decoder = basic_decoder.BasicDecoder(cell=cell, sampler=sampler)
(first_finished, first_inputs, first_state) = my_decoder.initialize(
embeddings_t,
start_tokens=start_tokens,
end_token=end_token,
initial_state=initial_state,
)
output_size = my_decoder.output_size
output_dtype = my_decoder.output_dtype
self.assertEqual(
basic_decoder.BasicDecoderOutput(cell_depth, tf.TensorShape([])),
output_size,
)
self.assertEqual(
basic_decoder.BasicDecoderOutput(tf.float32, tf.int32), output_dtype
)
(
step_outputs,
step_state,
step_next_inputs,
step_finished,
) = my_decoder.step(tf.constant(0), first_inputs, first_state)
batch_size_t = my_decoder.batch_size
self.assertLen(first_state, 2)
self.assertLen(step_state, 2)
self.assertTrue(isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual((batch_size, cell_depth), step_outputs[0].get_shape())
self.assertEqual((batch_size,), step_outputs[1].get_shape())
self.assertEqual((batch_size, cell_depth), first_state[0].get_shape())
self.assertEqual((batch_size, cell_depth), first_state[1].get_shape())
self.assertEqual((batch_size, cell_depth), step_state[0].get_shape())
self.assertEqual((batch_size, cell_depth), step_state[1].get_shape())
self.evaluate(tf.compat.v1.global_variables_initializer())
eval_result = self.evaluate(
{
"batch_size": batch_size_t,
"first_finished": first_finished,
"first_inputs": first_inputs,
"first_state": first_state,
"step_outputs": step_outputs,
"step_state": step_state,
"step_next_inputs": step_next_inputs,
"step_finished": step_finished,
}
)
expected_sample_ids = np.argmax(eval_result["step_outputs"].rnn_output, -1)
expected_step_finished = expected_sample_ids == end_token
expected_step_next_inputs = embeddings[expected_sample_ids]
self.assertAllEqual(
[False, False, False, False, False], eval_result["first_finished"]
)
self.assertAllEqual(expected_step_finished, eval_result["step_finished"])
self.assertEqual(
output_dtype.sample_id, eval_result["step_outputs"].sample_id.dtype
)
self.assertAllEqual(
expected_sample_ids, eval_result["step_outputs"].sample_id
)
self.assertAllEqual(
expected_step_next_inputs, eval_result["step_next_inputs"]
)
