def __init__(self, num_mac_cells: int, hidden_dim: int):
self.cells = num_mac_cells
self.mac = Mac(hidden_dim)
self.hidden_dim = hidden_dim
self.acts = []
self.qenc = CudnnGru(hidden_dim // 2,
w_init=TruncatedNormal(stddev=0.05))
self.question_drop = DropoutLayer(0.92)
self.control_proj = FullyConnected(hidden_dim)
for _ in range(num_mac_cells):
self.acts.append(FullyConnected(hidden_dim))
class MacNetwork(Configurable):
""" Basic non-recurrent attention using the given SimilarityFunction """
def __init__(self, num_mac_cells: int, hidden_dim: int):
self.cells = num_mac_cells
self.mac = Mac(hidden_dim)
self.hidden_dim = hidden_dim
self.acts = []
self.qenc = CudnnGru(hidden_dim // 2,
w_init=TruncatedNormal(stddev=0.05))
self.question_drop = DropoutLayer(0.92)
self.control_proj = FullyConnected(hidden_dim)
for _ in range(num_mac_cells):
self.acts.append(FullyConnected(hidden_dim))
def apply(self,
is_train,
document,
questions,
document_mask=None,
question_mask=None):
# create question vec
# the cudnnGRU layer reverses the sequences and stuff for us so we just grab last hidden states.
question_hidden = self.qenc.apply(is_train, questions,
question_mask)[:, -1]
question_hidden = self.question_drop.apply(is_train, question_hidden)
# shared projection
question_vec = tf.tanh(
self.control_proj.apply(is_train, question_hidden))
# create initial memory and control states
init_control = question_hidden
init_memory = tf.get_variable(
'init_memory',
shape=(1, self.hidden_dim),
trainable=True,
)
init_memory = tf.tile(init_memory, [tf.shape(questions)[0], 1])
# going through the cells!
control, memory = init_control, init_memory
for i in range(self.cells):
# control projection stuff
position_cont = self.acts[i].apply(is_train, question_vec)
# call mac cell
with tf.variable_scope('macmsc', reuse=False if i == 0 else True):
next_control, next_mem, out = self.mac.apply(
is_train, document, questions, question_vec, control,
position_cont, memory, False if i == 0 else True,
document_mask, question_mask)
control, memory = next_control, next_mem
# no yes/no questions, so no need for outputting states.
return out
def main():
parser = argparse.ArgumentParser("Train our ELMo model on SQuAD")
parser.add_argument("loss_mode", choices=['default', 'confidence'])
parser.add_argument("output_dir")
parser.add_argument("--dim", type=int, default=90)
parser.add_argument("--l2", type=float, default=0)
parser.add_argument("--mode",
choices=["input", "output", "both", "none"],
default="both")
parser.add_argument("--top_layer_only", action="store_true")
parser.add_argument("--no-tfidf",
action='store_true',
help="Don't add TF-IDF negative examples")
args = parser.parse_args()
out = args.output_dir + "-" + datetime.now().strftime("%m%d-%H%M%S")
dim = args.dim
recurrent_layer = CudnnGru(dim, w_init=TruncatedNormal(stddev=0.05))
if args.loss_mode == 'default':
n_epochs = 24
answer_encoder = SingleSpanAnswerEncoder()
predictor = BoundsPredictor(
ChainBiMapper(first_layer=recurrent_layer,
second_layer=recurrent_layer))
batcher = ClusteredBatcher(45, ContextLenKey(), False, False)
data = DocumentQaTrainingData(SquadCorpus(), None, batcher, batcher)
elif args.loss_mode == 'confidence':
if args.no_tfidf:
prepro = SquadDefault()
n_epochs = 15
else:
prepro = SquadTfIdfRanker(NltkPlusStopWords(True), 4, True)
n_epochs = 50
answer_encoder = DenseMultiSpanAnswerEncoder()
predictor = ConfidencePredictor(ChainBiMapper(
first_layer=recurrent_layer,
second_layer=recurrent_layer,
),
AttentionEncoder(),
FullyConnected(80, activation="tanh"),
aggregate="sum")
eval_dataset = RandomParagraphSetDatasetBuilder(
100, 'flatten', True, 0)
train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3), True,
False)
data = PreprocessedData(SquadCorpus(),
prepro,
StratifyParagraphsBuilder(train_batching, 1),
eval_dataset,
eval_on_verified=False)
data.preprocess(1)
params = trainer.TrainParams(trainer.SerializableOptimizer(
"Adadelta", dict(learning_rate=1.0)),
ema=0.999,
max_checkpoints_to_keep=2,
async_encoding=10,
num_epochs=n_epochs,
log_period=30,
eval_period=1200,
save_period=1200,
best_weights=("dev", "b17/text-f1"),
eval_samples=dict(dev=None, train=8000))
lm_reduce = MapperSeq(
ElmoLayer(args.l2,
layer_norm=False,
top_layer_only=args.top_layer_only),
DropoutLayer(0.5),
)
model = AttentionWithElmo(
encoder=DocumentAndQuestionEncoder(answer_encoder),
lm_model=SquadContextConcatSkip(),
append_before_atten=(args.mode == "both" or args.mode == "output"),
append_embed=(args.mode == "both" or args.mode == "input"),
max_batch_size=128,
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d",
word_vec_init_scale=0,
learn_unk=False,
cpu=True),
char_embed=CharWordEmbedder(LearnedCharEmbedder(word_size_th=14,
char_th=49,
char_dim=20,
init_scale=0.05,
force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True),
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
),
lm_reduce=None,
lm_reduce_shared=lm_reduce,
per_sentence=False,
memory_builder=NullBiMapper(),
attention=BiAttention(TriLinear(bias=True), True),
match_encoder=SequenceMapperSeq(
FullyConnected(dim * 2, activation="relu"),
ResidualLayer(
SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
StaticAttentionSelf(TriLinear(bias=True),
ConcatWithProduct()),
FullyConnected(dim * 2, activation="relu"),
)), VariationalDropoutLayer(0.8)),
predictor=predictor)
with open(__file__, "r") as f:
notes = f.read()
notes = str(sorted(args.__dict__.items(),
key=lambda x: x[0])) + "\n" + notes
trainer.start_training(
data, model, params,
[LossEvaluator(),
SpanEvaluator(bound=[17], text_eval="squad")], ModelDir(out), notes)
def get_model(char_th: int, dim: int, mode: str, preprocess: Optional[TextPreprocessor]):
recurrent_layer = CudnnGru(dim, w_init=TruncatedNormal(stddev=0.05))
#pdb.set_trace()
if mode.startswith("shared-norm"):
answer_encoder = GroupedSpanAnswerEncoder()
predictor = BoundsPredictor(
ChainBiMapper(
first_layer=recurrent_layer,
second_layer=recurrent_layer
),
span_predictor=IndependentBoundsGrouped(aggregate="sum")
)
elif mode == "confidence":
answer_encoder = DenseMultiSpanAnswerEncoder()
predictor = ConfidencePredictor(
ChainBiMapper(
first_layer=recurrent_layer,
second_layer=recurrent_layer,
),
AttentionEncoder(),
FullyConnected(80, activation="tanh"),
aggregate="sum"
)
elif mode == "sigmoid":
answer_encoder = DenseMultiSpanAnswerEncoder()
predictor = BoundsPredictor(
ChainBiMapper(
first_layer=recurrent_layer,
second_layer=recurrent_layer
),
span_predictor=IndependentBoundsSigmoidLoss()
)
elif mode == "paragraph" or mode == "merge":
answer_encoder = MultiChoiceAnswerEncoder()
predictor = MultiChoicePredictor(4)
else:
raise NotImplementedError(mode)
return Attention(
encoder=DocumentAndQuestionEncoder(answer_encoder),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=char_th, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
preprocess=preprocess,
word_embed_layer=None,
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
),
question_mapper=None,
context_mapper=None,
memory_builder=NullBiMapper(),
attention=BiAttention(TriLinear(bias=True), True),
match_encoder=SequenceMapperSeq(FullyConnected(dim * 2, activation="relu"),
ResidualLayer(SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
StaticAttentionSelf(TriLinear(bias=True), ConcatWithProduct()),
FullyConnected(dim * 2, activation="relu"),
)),
VariationalDropoutLayer(0.8)),
#templayer = BiLinear(bias = True),
predictor=predictor
)
def build_model(preprocess: Optional[TextPreprocessor], train_config, use_cudnn=False):
if use_cudnn:
print('Using Cuddn:')
recurrent_layer = CudnnGru(train_config.dim, w_init=TruncatedNormal(stddev=train_config.recurrent_stdev))
else:
recurrent_layer = BiRecurrentMapper(CompatGruCellSpec(train_config.dim))
lm_reduce = MapperSeq(
ElmoLayer(
train_config.l2,
layer_norm=train_config.lm_layernorm,
top_layer_only=train_config.top_layer_only
),
DropoutLayer(train_config.elmo_dropout),
)
answer_encoder = GroupedSpanAnswerEncoder()
predictor = BoundsPredictor(
ChainBiMapper(
first_layer=recurrent_layer,
second_layer=recurrent_layer
),
span_predictor=IndependentBoundsGrouped(aggregate="sum")
)
word_embed = FixedWordEmbedder(
vec_name=train_config.word_vectors,
word_vec_init_scale=0,
learn_unk=train_config.learn_unk_vector,
cpu=True
)
char_embed = CharWordEmbedder(
LearnedCharEmbedder(
word_size_th=14,
char_th=train_config.char_th,
char_dim=train_config.char_dim,
init_scale=0.05,
force_cpu=True
),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
)
embed_mapper = SequenceMapperSeq(
VariationalDropoutLayer(train_config.var_dropout),
recurrent_layer,
VariationalDropoutLayer(train_config.var_dropout)
)
attention = BiAttention(TriLinear(bias=True), True)
match_encoder = SequenceMapperSeq(
FullyConnected(train_config.dim * 2, activation="relu"),
ResidualLayer(SequenceMapperSeq(
VariationalDropoutLayer(train_config.var_dropout),
recurrent_layer,
VariationalDropoutLayer(train_config.var_dropout),
StaticAttentionSelf(TriLinear(bias=True), ConcatWithProduct()),
FullyConnected(train_config.dim * 2, activation="relu"),
)),
VariationalDropoutLayer(train_config.var_dropout)
)
lm_model = LanguageModel(LM_VOCAB, LM_OPTIONS, LM_WEIGHTS, LM_TOKEN_WEIGHTS)
model = CapeAttentionWithElmo(
encoder=DocumentAndQuestionEncoder(answer_encoder),
lm_model=lm_model,
max_batch_size=train_config.max_batch_size,
preprocess=preprocess,
per_sentence=False,
append_embed=(train_config.elmo_mode == "both" or train_config.elmo_mode == "input"),
append_before_atten=(train_config.elmo_mode == "both" or train_config.elmo_mode == "output"),
word_embed=word_embed,
char_embed=char_embed,
embed_mapper=embed_mapper,
lm_reduce=None,
lm_reduce_shared=lm_reduce,
memory_builder=NullBiMapper(),
attention=attention,
match_encoder=match_encoder,
predictor=predictor
)
return model
def main():
parser = argparse.ArgumentParser("Train rejection model on SQuAD")
parser.add_argument("--corpus_dir", type=str, default="~/data/document-qa")
parser.add_argument("--output_dir",
type=str,
default="~/model/document-qa/squad")
parser.add_argument("--lm_dir", type=str, default="~/data/lm")
parser.add_argument("--exp_id", type=str, default="rejection")
parser.add_argument("--lr", type=float, default=0.5)
parser.add_argument("--epoch", type=int, default=20)
parser.add_argument("--dim", type=int, default=100)
parser.add_argument("--batch_size", type=int, default=45)
parser.add_argument("--l2", type=float, default=0)
parser.add_argument("--mode",
choices=["input", "output", "both", "none"],
default="both")
parser.add_argument("--top_layer_only", action="store_true")
args = parser.parse_args()
print("Arguments : ", args)
out = args.output_dir + "_" + args.exp_id + "_lr" + str(
args.lr) + "-" + datetime.now().strftime("%m%d-%H%M%S")
dim = args.dim
batch_size = args.batch_size
out = expanduser(out)
lm_dir = expanduser(args.lm_dir)
corpus_dir = expanduser(args.corpus_dir)
print("Make global recurrent_layer...")
recurrent_layer = CudnnGru(
dim, w_init=tf.keras.initializers.TruncatedNormal(stddev=0.05))
params = trainer.TrainParams(trainer.SerializableOptimizer(
"Adadelta", dict(learning_rate=args.lr)),
ema=0.999,
max_checkpoints_to_keep=2,
async_encoding=10,
num_epochs=args.epoch,
log_period=30,
eval_period=1200,
save_period=1200,
best_weights=("dev", "b17/text-f1"),
eval_samples=dict(dev=None, train=8000))
lm_reduce = MapperSeq(
ElmoLayer(args.l2,
layer_norm=False,
top_layer_only=args.top_layer_only),
DropoutLayer(0.5),
)
model = AttentionWithElmo(
encoder=DocumentAndQuestionEncoder(SingleSpanAnswerEncoder()),
lm_model=SquadContextConcatSkip(lm_dir=lm_dir),
append_before_atten=(args.mode == "both" or args.mode == "output"),
append_embed=(args.mode == "both" or args.mode == "input"),
max_batch_size=128,
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d",
word_vec_init_scale=0,
learn_unk=False,
cpu=True),
char_embed=CharWordEmbedder(LearnedCharEmbedder(word_size_th=14,
char_th=49,
char_dim=20,
init_scale=0.05,
force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True),
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
),
lm_reduce=None,
lm_reduce_shared=lm_reduce,
per_sentence=False,
memory_builder=NullBiMapper(),
attention=BiAttention(TriLinear(bias=True), True),
match_encoder=SequenceMapperSeq(
FullyConnected(dim * 2, activation="relu"),
ResidualLayer(
SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
StaticAttentionSelf(TriLinear(bias=True),
ConcatWithProduct()),
FullyConnected(dim * 2, activation="relu"),
)), VariationalDropoutLayer(0.8)),
predictor=BoundsPredictor(
ChainBiMapper(first_layer=recurrent_layer,
second_layer=recurrent_layer)))
batcher = ClusteredBatcher(batch_size, ContextLenKey(), False, False)
data = DocumentQaTrainingData(SquadCorpus(corpus_dir), None, batcher,
batcher)
with open(__file__, "r") as f:
notes = f.read()
notes = str(sorted(args.__dict__.items(),
key=lambda x: x[0])) + "\n" + notes
trainer.start_training(
data, model, params,
[LossEvaluator(),
SpanEvaluator(bound=[17], text_eval="squad")], ModelDir(out), notes)
def __init__(self, num_of_choices, init: str = "glorot_uniform"):
self.num_of_choices = num_of_choices
self.init = init
self.recurrent_layer = CudnnGru(100)