def _build_model(
self,
init_params,
):
model = seq2seq_util.ModelHelper(init_params=init_params, )
self.encoder_inputs = model.net.AddExternalInput('encoder_inputs')
self.encoder_lengths = model.net.AddExternalInput('encoder_lengths')
self.decoder_inputs = model.net.AddExternalInput('decoder_inputs')
self.decoder_lengths = model.net.AddExternalInput('decoder_lengths')
self.targets = model.net.AddExternalInput('targets')
self.target_weights = model.net.AddExternalInput('target_weights')
optimizer_params = self.model_params['optimizer_params']
attention_type = self.model_params['attention']
assert attention_type in ['none', 'regular']
self.learning_rate = model.AddParam(
name='learning_rate',
init_value=float(optimizer_params['learning_rate']),
trainable=False,
)
self.global_step = model.AddParam(
name='global_step',
init_value=0,
trainable=False,
)
self.start_time = model.AddParam(
name='start_time',
init_value=time.time(),
trainable=False,
)
assert self.num_gpus < 2
assert len(self.encoder_params['encoder_layer_configs']) == 1
assert len(self.model_params['decoder_layer_configs']) == 1
encoder_num_units = (
self.encoder_params['encoder_layer_configs'][0]['num_units'])
decoder_num_units = (
self.model_params['decoder_layer_configs'][0]['num_units'])
(
encoder_outputs,
final_encoder_hidden_state,
final_encoder_cell_state,
) = self._embedding_encoder(
model=model,
encoder_type=self.encoder_type,
encoder_params=self.encoder_params,
inputs=self.encoder_inputs,
input_lengths=self.encoder_lengths,
vocab_size=self.source_vocab_size,
embedding_size=self.model_params['encoder_embedding_size'],
use_attention=(attention_type != 'none'),
)
# For bidirectional RNN, the num of units doubles after encodeing
if (self.encoder_type == 'rnn'
and self.encoder_params['use_bidirectional_encoder']):
encoder_num_units *= 2
if attention_type == 'none':
decoder_initial_hidden_state = model.FC(
final_encoder_hidden_state,
'decoder_initial_hidden_state',
encoder_num_units,
decoder_num_units,
axis=2,
)
decoder_initial_cell_state = model.FC(
final_encoder_cell_state,
'decoder_initial_cell_state',
encoder_num_units,
decoder_num_units,
axis=2,
)
else:
decoder_initial_hidden_state = model.param_init_net.ConstantFill(
[],
'decoder_initial_hidden_state',
shape=[decoder_num_units],
value=0.0,
)
decoder_initial_cell_state = model.param_init_net.ConstantFill(
[],
'decoder_initial_cell_state',
shape=[decoder_num_units],
value=0.0,
)
initial_attention_weighted_encoder_context = (
model.param_init_net.ConstantFill(
[],
'initial_attention_weighted_encoder_context',
shape=[encoder_num_units],
value=0.0,
))
sqrt3 = math.sqrt(3)
decoder_embeddings = model.AddParam(
name='decoder_embeddings',
init=('UniformFill',
dict(
shape=[
self.target_vocab_size,
self.model_params['decoder_embedding_size'],
],
min=-sqrt3,
max=sqrt3,
)),
)
embedded_decoder_inputs = model.net.Gather(
[decoder_embeddings, self.decoder_inputs],
['embedded_decoder_inputs'],
)
# seq_len x batch_size x decoder_embedding_size
with core.NameScope('', reset=True):
if attention_type == 'none':
decoder_outputs, _, _, _ = recurrent.LSTM(
model=model,
input_blob=embedded_decoder_inputs,
seq_lengths=self.decoder_lengths,
initial_states=(
decoder_initial_hidden_state,
decoder_initial_cell_state,
),
dim_in=self.model_params['decoder_embedding_size'],
dim_out=decoder_num_units,
scope='decoder',
outputs_with_grads=[0],
)
decoder_output_size = decoder_num_units
else:
(decoder_outputs, _, _, _, attention_weighted_encoder_contexts,
_) = recurrent.LSTMWithAttention(
model=model,
decoder_inputs=embedded_decoder_inputs,
decoder_input_lengths=self.decoder_lengths,
initial_decoder_hidden_state=decoder_initial_hidden_state,
initial_decoder_cell_state=decoder_initial_cell_state,
initial_attention_weighted_encoder_context=(
initial_attention_weighted_encoder_context),
encoder_output_dim=encoder_num_units,
encoder_outputs=encoder_outputs,
decoder_input_dim=self.
model_params['decoder_embedding_size'],
decoder_state_dim=decoder_num_units,
scope='decoder',
outputs_with_grads=[0, 4],
)
decoder_outputs, _ = model.net.Concat(
[decoder_outputs, attention_weighted_encoder_contexts],
[
'states_and_context_combination',
'_states_and_context_combination_concat_dims',
],
axis=2,
)
decoder_output_size = decoder_num_units + encoder_num_units
# we do softmax over the whole sequence
# (max_length in the batch * batch_size) x decoder embedding size
# -1 because we don't know max_length yet
decoder_outputs_flattened, _ = model.net.Reshape(
[decoder_outputs],
[
'decoder_outputs_flattened',
'decoder_outputs_and_contexts_combination_old_shape',
],
shape=[-1, decoder_output_size],
)
output_logits = self.output_projection(
model=model,
decoder_outputs=decoder_outputs_flattened,
decoder_output_size=decoder_output_size,
target_vocab_size=self.target_vocab_size,
decoder_softmax_size=self.model_params['decoder_softmax_size'],
)
targets, _ = model.net.Reshape(
[self.targets],
['targets', 'targets_old_shape'],
shape=[-1],
)
target_weights, _ = model.net.Reshape(
[self.target_weights],
['target_weights', 'target_weights_old_shape'],
shape=[-1],
)
output_probs, loss_per_word = model.net.SoftmaxWithLoss(
[output_logits, targets, target_weights],
['OutputProbs', 'loss_per_word'],
)
num_words = model.net.ReduceFrontSum(
target_weights,
'num_words',
)
self.total_loss_scalar = model.net.Mul(
[loss_per_word, num_words],
'total_loss_scalar',
)
self.forward_net = model.net.Clone(name=model.net.Name() +
'_forward_only', )
# print loss only in the forward net which evaluates loss after every
# epoch
self.forward_net.Print([self.total_loss_scalar], [])
# Note: average over batch.
# It is tricky because of two problems:
# 1. ReduceFrontSum from 1-D tensor returns 0-D tensor
# 2. If you want to multiply 0-D by 1-D tensor
# (by scalar batch_size_inverse_tensor),
# you need to use broadcasting. But gradient propogation
# is broken for op with broadcasting.
# total_loss_scalar, _ = model.net.Reshape(
# [total_loss_scalar],
# [total_loss_scalar, 'total_loss_scalar_old_shape'],
# shape=[1],
# )
batch_size_inverse_tensor = (model.param_init_net.ConstantFill(
[],
'batch_size_tensor',
shape=[],
value=1.0 / self.batch_size,
))
total_loss_scalar_average = model.net.Mul(
[self.total_loss_scalar, batch_size_inverse_tensor],
['total_loss_scalar_average'],
)
model.AddGradientOperators([
total_loss_scalar_average,
])
ONE = model.param_init_net.ConstantFill(
[],
'ONE',
shape=[1],
value=1.0,
)
logger.info('All trainable variables: ')
for param in model.params:
param_grad = model.param_to_grad[param]
if param in model.param_to_grad:
if isinstance(param_grad, core.GradientSlice):
param_grad_values = param_grad.values
param_grad_values = model.net.Clip(
[param_grad_values],
[param_grad_values],
min=0.0,
max=float(self.model_params['max_grad_value']),
)
model.net.ScatterWeightedSum(
[
param,
ONE,
param_grad.indices,
param_grad_values,
model.net.Negative(
[self.learning_rate],
'negative_learning_rate',
),
],
param,
)
else:
param_grad = model.net.Clip(
[param_grad],
[param_grad],
min=0.0,
max=float(self.model_params['max_grad_value']),
)
model.net.WeightedSum(
[
param,
ONE,
param_grad,
model.net.Negative(
[self.learning_rate],
'negative_learning_rate',
),
],
param,
)
self.model = model
def test_lstm_with_recurrent_attention(
self,
encoder_output_length,
encoder_output_dim,
decoder_input_length,
decoder_state_dim,
batch_size,
gc,
dc,
):
with core.DeviceScope(gc):
model = CNNModelHelper(name="external")
(
encoder_outputs,
decoder_inputs,
decoder_input_lengths,
initial_decoder_hidden_state,
initial_decoder_cell_state,
initial_attention_weighted_encoder_context,
) = model.net.AddExternalInputs(
"encoder_outputs",
"decoder_inputs",
"decoder_input_lengths",
"initial_decoder_hidden_state",
"initial_decoder_cell_state",
"initial_attention_weighted_encoder_context",
)
recurrent.LSTMWithAttention(
model=model,
decoder_inputs=decoder_inputs,
decoder_input_lengths=decoder_input_lengths,
initial_decoder_hidden_state=initial_decoder_hidden_state,
initial_decoder_cell_state=initial_decoder_cell_state,
initial_attention_weighted_encoder_context=(
initial_attention_weighted_encoder_context),
encoder_output_dim=encoder_output_dim,
encoder_outputs=encoder_outputs,
decoder_input_dim=decoder_state_dim,
decoder_state_dim=decoder_state_dim,
scope='external/LSTMWithAttention',
attention_type=AttentionType.Recurrent)
op = model.net._net.op[-1]
workspace.RunNetOnce(model.param_init_net)
# This is original decoder_inputs after linear layer
decoder_input_blob = op.input[0]
workspace.FeedBlob(
decoder_input_blob,
np.random.randn(
decoder_input_length,
batch_size,
decoder_state_dim * 4,
).astype(np.float32))
workspace.FeedBlob(
"external/LSTMWithAttention/encoder_outputs_transposed",
np.random.randn(
batch_size,
encoder_output_dim,
encoder_output_length,
).astype(np.float32),
)
workspace.FeedBlob(
"external/LSTMWithAttention/weighted_encoder_outputs",
np.random.randn(
encoder_output_length,
batch_size,
encoder_output_dim,
).astype(np.float32),
)
workspace.FeedBlob(
decoder_input_lengths,
np.random.randint(0, decoder_input_length + 1,
size=(batch_size, )).astype(np.int32))
workspace.FeedBlob(
initial_decoder_hidden_state,
np.random.randn(1, batch_size,
decoder_state_dim).astype(np.float32))
workspace.FeedBlob(
initial_decoder_cell_state,
np.random.randn(1, batch_size,
decoder_state_dim).astype(np.float32))
workspace.FeedBlob(
initial_attention_weighted_encoder_context,
np.random.randn(1, batch_size,
encoder_output_dim).astype(np.float32))
inputs = [workspace.FetchBlob(name) for name in op.input]
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=inputs,
reference=lstm_with_recurrent_attention_reference,
grad_reference=None,
output_to_grad=None,
outputs_to_check=range(6),
)
gradients_to_check = [
index for (index, input_name) in enumerate(op.input)
if input_name != "decoder_input_lengths"
]
for param in gradients_to_check:
self.assertGradientChecks(
device_option=gc,
op=op,
inputs=inputs,
outputs_to_check=param,
outputs_with_grads=[0, 4],
threshold=0.01,
stepsize=0.001,
)
def model_build_fun(self, model, forward_only=False, loss_scale=None):
encoder_inputs = model.net.AddExternalInput(
workspace.GetNameScope() + 'encoder_inputs', )
encoder_lengths = model.net.AddExternalInput(
workspace.GetNameScope() + 'encoder_lengths', )
decoder_inputs = model.net.AddExternalInput(
workspace.GetNameScope() + 'decoder_inputs', )
decoder_lengths = model.net.AddExternalInput(
workspace.GetNameScope() + 'decoder_lengths', )
targets = model.net.AddExternalInput(
workspace.GetNameScope() + 'targets', )
target_weights = model.net.AddExternalInput(
workspace.GetNameScope() + 'target_weights', )
attention_type = self.model_params['attention']
assert attention_type in ['none', 'regular']
(
encoder_outputs,
weighted_encoder_outputs,
final_encoder_hidden_state,
final_encoder_cell_state,
encoder_output_dim,
) = self._build_embedding_encoder(
model=model,
inputs=encoder_inputs,
input_lengths=encoder_lengths,
vocab_size=self.source_vocab_size,
embeddings=self.encoder_embeddings,
embedding_size=self.model_params['encoder_embedding_size'],
use_attention=(attention_type != 'none'),
num_gpus=self.num_gpus,
forward_only=forward_only,
)
assert len(self.model_params['decoder_layer_configs']) == 1
decoder_num_units = (
self.model_params['decoder_layer_configs'][0]['num_units'])
if attention_type == 'none':
decoder_initial_hidden_state = model.FC(
final_encoder_hidden_state,
'decoder_initial_hidden_state',
encoder_output_dim,
decoder_num_units,
axis=2,
)
decoder_initial_cell_state = model.FC(
final_encoder_cell_state,
'decoder_initial_cell_state',
encoder_output_dim,
decoder_num_units,
axis=2,
)
else:
decoder_initial_hidden_state = model.param_init_net.ConstantFill(
[],
'decoder_initial_hidden_state',
shape=[decoder_num_units],
value=0.0,
)
decoder_initial_cell_state = model.param_init_net.ConstantFill(
[],
'decoder_initial_cell_state',
shape=[decoder_num_units],
value=0.0,
)
initial_attention_weighted_encoder_context = (
model.param_init_net.ConstantFill(
[],
'initial_attention_weighted_encoder_context',
shape=[encoder_output_dim],
value=0.0,
))
if self.num_gpus == 0:
embedded_decoder_inputs = model.net.Gather(
[self.decoder_embeddings, decoder_inputs],
['embedded_decoder_inputs'],
)
else:
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
embedded_decoder_inputs_cpu = model.net.Gather(
[self.decoder_embeddings, decoder_inputs],
['embedded_decoder_inputs_cpu'],
)
embedded_decoder_inputs = model.CopyCPUToGPU(
embedded_decoder_inputs_cpu,
'embedded_decoder_inputs',
)
# seq_len x batch_size x decoder_embedding_size
if attention_type == 'none':
decoder_outputs, _, _, _ = recurrent.LSTM(
model=model,
input_blob=embedded_decoder_inputs,
seq_lengths=decoder_lengths,
initial_states=(
decoder_initial_hidden_state,
decoder_initial_cell_state,
),
dim_in=self.model_params['decoder_embedding_size'],
dim_out=decoder_num_units,
scope='decoder',
outputs_with_grads=[0],
)
decoder_output_size = decoder_num_units
else:
(decoder_outputs, _, _, _, attention_weighted_encoder_contexts,
_) = recurrent.LSTMWithAttention(
model=model,
decoder_inputs=embedded_decoder_inputs,
decoder_input_lengths=decoder_lengths,
initial_decoder_hidden_state=decoder_initial_hidden_state,
initial_decoder_cell_state=decoder_initial_cell_state,
initial_attention_weighted_encoder_context=(
initial_attention_weighted_encoder_context),
encoder_output_dim=encoder_output_dim,
encoder_outputs=encoder_outputs,
decoder_input_dim=self.model_params['decoder_embedding_size'],
decoder_state_dim=decoder_num_units,
scope='decoder',
outputs_with_grads=[0, 4],
)
decoder_outputs, _ = model.net.Concat(
[decoder_outputs, attention_weighted_encoder_contexts],
[
'states_and_context_combination',
'_states_and_context_combination_concat_dims',
],
axis=2,
)
decoder_output_size = decoder_num_units + encoder_output_dim
# we do softmax over the whole sequence
# (max_length in the batch * batch_size) x decoder embedding size
# -1 because we don't know max_length yet
decoder_outputs_flattened, _ = model.net.Reshape(
[decoder_outputs],
[
'decoder_outputs_flattened',
'decoder_outputs_and_contexts_combination_old_shape',
],
shape=[-1, decoder_output_size],
)
output_logits = self.output_projection(
model=model,
decoder_outputs=decoder_outputs_flattened,
decoder_output_size=decoder_output_size,
target_vocab_size=self.target_vocab_size,
decoder_softmax_size=self.model_params['decoder_softmax_size'],
)
targets, _ = model.net.Reshape(
[targets],
['targets', 'targets_old_shape'],
shape=[-1],
)
target_weights, _ = model.net.Reshape(
[target_weights],
['target_weights', 'target_weights_old_shape'],
shape=[-1],
)
output_probs = model.net.Softmax(
[output_logits],
['output_probs'],
engine=('CUDNN' if self.num_gpus > 0 else None),
)
label_cross_entropy = model.net.LabelCrossEntropy(
[output_probs, targets],
['label_cross_entropy'],
)
weighted_label_cross_entropy = model.net.Mul(
[label_cross_entropy, target_weights],
'weighted_label_cross_entropy',
)
total_loss_scalar = model.net.SumElements(
[weighted_label_cross_entropy],
'total_loss_scalar',
)
total_loss_scalar_weighted = model.net.Scale(
[total_loss_scalar],
'total_loss_scalar_weighted',
scale=1.0 / self.batch_size,
)
return [total_loss_scalar_weighted]
