def test_step(cell_type,
context_gating,
num_embed=2,
encoder_num_hidden=5,
decoder_num_hidden=5):
vocab_size, batch_size, source_seq_len = 10, 10, 7,
# (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)
config_coverage = sockeye.coverage.CoverageConfig(
type="tanh", num_hidden=2, layer_normalization=False)
config_attention = sockeye.rnn_attention.AttentionConfig(
type="coverage",
num_hidden=2,
input_previous_word=False,
source_num_hidden=decoder_num_hidden,
query_num_hidden=decoder_num_hidden,
layer_normalization=False,
config_coverage=config_coverage)
attention = sockeye.rnn_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)
config_rnn = sockeye.rnn.RNNConfig(cell_type=cell_type,
num_hidden=decoder_num_hidden,
num_layers=1,
dropout_inputs=0.,
dropout_states=0.,
residual=False,
forget_bias=0.)
config_decoder = sockeye.decoder.RecurrentDecoderConfig(
max_seq_len_source=source_seq_len,
rnn_config=config_rnn,
attention_config=config_attention,
context_gating=context_gating)
decoder = sockeye.decoder.RecurrentDecoder(config=config_decoder)
if cell_type == C.GRU_TYPE:
layer_states = [
gaussian_vector(shape=states_shape, return_symbol=True)
for _ in range(config_rnn.num_layers)
]
elif cell_type == C.LSTM_TYPE:
layer_states = [
gaussian_vector(shape=states_shape, return_symbol=True)
for _ in range(config_rnn.num_layers * 2)
]
else:
raise ValueError
state, attention_state = decoder._step(
word_vec_prev=word_vec_prev,
state=sockeye.decoder.RecurrentDecoderState(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, config_coverage.num_hidden)
def test_step(cell_type, context_gating,
num_embed=2,
encoder_num_hidden=5,
decoder_num_hidden=5):
vocab_size, batch_size, source_seq_len = 10, 10, 7,
# (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)
config_coverage = sockeye.coverage.CoverageConfig(type="tanh",
num_hidden=2,
layer_normalization=False)
config_attention = sockeye.rnn_attention.AttentionConfig(type="coverage",
num_hidden=2,
input_previous_word=False,
source_num_hidden=decoder_num_hidden,
query_num_hidden=decoder_num_hidden,
layer_normalization=False,
config_coverage=config_coverage)
attention = sockeye.rnn_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)
config_rnn = sockeye.rnn.RNNConfig(cell_type=cell_type,
num_hidden=decoder_num_hidden,
num_layers=1,
dropout_inputs=0.,
dropout_states=0.,
residual=False,
forget_bias=0.)
config_decoder = sockeye.decoder.RecurrentDecoderConfig(max_seq_len_source=source_seq_len,
rnn_config=config_rnn,
attention_config=config_attention,
context_gating=context_gating)
decoder = sockeye.decoder.RecurrentDecoder(config=config_decoder)
if cell_type == C.GRU_TYPE:
layer_states = [gaussian_vector(shape=states_shape, return_symbol=True) for _ in range(config_rnn.num_layers)]
elif cell_type == C.LSTM_TYPE:
layer_states = [gaussian_vector(shape=states_shape, return_symbol=True) for _ in range(config_rnn.num_layers*2)]
else:
raise ValueError
state, attention_state = decoder._step(word_vec_prev=word_vec_prev,
state=sockeye.decoder.RecurrentDecoderState(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, config_coverage.num_hidden)
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)
