def test_coverage_attention(attention_coverage_type,
attention_coverage_num_hidden,
batch_size=3,
encoder_num_hidden=2,
decoder_num_hidden=2):
# source: (batch_size, seq_len, encoder_num_hidden)
source = mx.sym.Variable("source")
# source_length: (batch_size, )
source_length = mx.sym.Variable("source_length")
source_seq_len = 10
attention = sockeye.attention.get_attention(
input_previous_word=False,
attention_type="coverage",
attention_num_hidden=5,
rnn_num_hidden=0,
max_seq_len=source_seq_len,
attention_coverage_type=attention_coverage_type,
attention_coverage_num_hidden=attention_coverage_num_hidden)
attention_state = attention.get_initial_state(source_length,
source_seq_len)
attention_func = attention.on(source, source_length, source_seq_len)
attention_input = attention.make_input(0, mx.sym.Variable("word_vec_prev"),
mx.sym.Variable("decoder_state"))
attention_state = attention_func(attention_input, attention_state)
sym = mx.sym.Group([
attention_state.context, attention_state.probs,
attention_state.dynamic_source
])
source_shape = (batch_size, source_seq_len, encoder_num_hidden)
source_length_shape = (batch_size, )
decoder_state_shape = (batch_size, decoder_num_hidden)
executor = sym.simple_bind(ctx=mx.cpu(),
source=source_shape,
source_length=source_length_shape,
decoder_state=decoder_state_shape)
source_length_vector = integer_vector(shape=source_length_shape,
max_value=source_seq_len)
executor.arg_dict["source"][:] = gaussian_vector(shape=source_shape)
executor.arg_dict["source_length"][:] = source_length_vector
executor.arg_dict["decoder_state"][:] = gaussian_vector(
shape=decoder_state_shape)
exec_output = executor.forward()
context_result = exec_output[0].asnumpy()
attention_prob_result = exec_output[1].asnumpy()
dynamic_source_result = exec_output[2].asnumpy()
expected_probs = (1 / source_length_vector).reshape((batch_size, 1))
expected_dynamic_source = (1 / source_length_vector).reshape(
(batch_size, 1))
assert context_result.shape == (batch_size, encoder_num_hidden)
assert attention_prob_result.shape == (batch_size, source_seq_len)
assert dynamic_source_result.shape == (batch_size, source_seq_len,
attention_coverage_num_hidden)
assert (np.sum(np.isclose(attention_prob_result, expected_probs),
axis=1) == source_length_vector).all()
def test_attention(attention_type,
batch_size=1,
encoder_num_hidden=2,
decoder_num_hidden=2):
# source: (batch_size, seq_len, encoder_num_hidden)
source = mx.sym.Variable("source")
# source_length: (batch_size,)
source_length = mx.sym.Variable("source_length")
source_seq_len = 3
config_attention = sockeye.attention.AttentionConfig(
type=attention_type,
num_hidden=2,
input_previous_word=False,
rnn_num_hidden=2,
layer_normalization=False,
config_coverage=None)
attention = sockeye.attention.get_attention(config_attention,
max_seq_len=source_seq_len)
attention_state = attention.get_initial_state(source_length,
source_seq_len)
attention_func = attention.on(source, source_length, source_seq_len)
attention_input = attention.make_input(0, mx.sym.Variable("word_vec_prev"),
mx.sym.Variable("decoder_state"))
attention_state = attention_func(attention_input, attention_state)
sym = mx.sym.Group([attention_state.context, attention_state.probs])
executor = sym.simple_bind(ctx=mx.cpu(),
source=(batch_size, source_seq_len,
encoder_num_hidden),
source_length=(batch_size, ),
decoder_state=(batch_size, decoder_num_hidden))
# TODO: test for other inputs (that are not equal at each source position)
executor.arg_dict["source"][:] = np.asarray([[[1., 2.], [1., 2.], [3.,
4.]]])
executor.arg_dict["source_length"][:] = np.asarray([2.0])
executor.arg_dict["decoder_state"][:] = np.asarray([[5, 6]])
exec_output = executor.forward()
context_result = exec_output[0].asnumpy()
attention_prob_result = exec_output[1].asnumpy()
# expecting uniform attention_weights of 0.5: 0.5 * seq1 + 0.5 * seq2
assert np.isclose(context_result, np.asarray([[1., 2.]])).all()
# equal attention to first two and no attention to third
assert np.isclose(attention_prob_result, np.asarray([[0.5, 0.5,
0.]])).all()
def test_last_state_attention(batch_size=1, encoder_num_hidden=2):
"""
EncoderLastStateAttention is a bit different from other attention mechanisms as it doesn't take a query argument
and doesn't return a probability distribution over the inputs (aka alignment).
"""
# source: (batch_size, seq_len, encoder_num_hidden)
source = mx.sym.Variable("source")
# source_length: (batch_size,)
source_length = mx.sym.Variable("source_length")
source_seq_len = 3
config_attention = sockeye.attention.AttentionConfig(
type="fixed",
num_hidden=0,
input_previous_word=False,
rnn_num_hidden=0,
layer_normalization=False,
config_coverage=None)
attention = sockeye.attention.get_attention(config_attention,
max_seq_len=source_seq_len)
attention_state = attention.get_initial_state(source_length,
source_seq_len)
attention_func = attention.on(source, source_length, source_seq_len)
attention_input = attention.make_input(0, mx.sym.Variable("word_vec_prev"),
mx.sym.Variable("decoder_state"))
attention_state = attention_func(attention_input, attention_state)
sym = mx.sym.Group([attention_state.context, attention_state.probs])
executor = sym.simple_bind(ctx=mx.cpu(),
source=(batch_size, source_seq_len,
encoder_num_hidden),
source_length=(batch_size, ))
# TODO: test for other inputs (that are not equal at each source position)
executor.arg_dict["source"][:] = np.asarray([[[1., 2.], [1., 2.], [3.,
4.]]])
executor.arg_dict["source_length"][:] = np.asarray([2.0])
exec_output = executor.forward()
context_result = exec_output[0].asnumpy()
attention_prob_result = exec_output[1].asnumpy()
# expecting attention on last state based on source_length
assert np.isclose(context_result, np.asarray([[1., 2.]])).all()
assert np.isclose(attention_prob_result, np.asarray([[0., 1.0, 0.]])).all()
def test_step(cell_type,
context_gating,
num_embed=2,
encoder_num_hidden=5,
decoder_num_hidden=5):
attention_num_hidden, vocab_size, num_layers, \
batch_size, source_seq_len, coverage_num_hidden = 2, 10, 1, 10, 7, 2
# (batch_size, source_seq_len, encoder_num_hidden)
source = mx.sym.Variable("source")
source_shape = (batch_size, source_seq_len, encoder_num_hidden)
# (batch_size,)
source_length = mx.sym.Variable("source_length")
source_length_shape = (batch_size, )
# (batch_size, num_embed)
word_vec_prev = mx.sym.Variable("word_vec_prev")
word_vec_prev_shape = (batch_size, num_embed)
# (batch_size, decoder_num_hidden)
hidden_prev = mx.sym.Variable("hidden_prev")
hidden_prev_shape = (batch_size, decoder_num_hidden)
# List(mx.sym.Symbol(batch_size, decoder_num_hidden)
states_shape = (batch_size, decoder_num_hidden)
attention = sockeye.attention.get_attention(
input_previous_word=False,
attention_type="coverage",
attention_num_hidden=attention_num_hidden,
rnn_num_hidden=decoder_num_hidden,
max_seq_len=source_seq_len,
attention_coverage_type="tanh",
attention_coverage_num_hidden=coverage_num_hidden,
layer_normalization=False)
attention_state = attention.get_initial_state(source_length,
source_seq_len)
attention_func = attention.on(source, source_length, source_seq_len)
decoder = sockeye.decoder.get_decoder(num_embed=num_embed,
vocab_size=vocab_size,
num_layers=num_layers,
rnn_num_hidden=decoder_num_hidden,
attention=attention,
cell_type=cell_type,
residual=False,
forget_bias=0.,
dropout=0.,
weight_tying=False,
lexicon=None,
context_gating=context_gating)
if cell_type == C.GRU_TYPE:
layer_states = [
gaussian_vector(shape=states_shape, return_symbol=True)
for _ in range(num_layers)
]
elif cell_type == C.LSTM_TYPE:
layer_states = [
gaussian_vector(shape=states_shape, return_symbol=True)
for _ in range(num_layers * 2)
]
state, attention_state = decoder._step(word_vec_prev=word_vec_prev,
state=sockeye.decoder.DecoderState(
hidden_prev, layer_states),
attention_func=attention_func,
attention_state=attention_state)
sym = mx.sym.Group(
[state.hidden, attention_state.probs, attention_state.dynamic_source])
executor = sym.simple_bind(ctx=mx.cpu(),
source=source_shape,
source_length=source_length_shape,
word_vec_prev=word_vec_prev_shape,
hidden_prev=hidden_prev_shape)
executor.arg_dict["source"][:] = gaussian_vector(source_shape)
executor.arg_dict["source_length"][:] = integer_vector(
source_length_shape, source_seq_len)
executor.arg_dict["word_vec_prev"][:] = gaussian_vector(
word_vec_prev_shape)
executor.arg_dict["hidden_prev"][:] = gaussian_vector(hidden_prev_shape)
executor.arg_dict["states"] = layer_states
hidden_result, attention_probs_result, attention_dynamic_source_result = executor.forward(
)
assert hidden_result.shape == hidden_prev_shape
assert attention_probs_result.shape == (batch_size, source_seq_len)
assert attention_dynamic_source_result.shape == (batch_size,
source_seq_len,
coverage_num_hidden)