def beam_search_step(self,
input,
state,
cell,
beam_size,
attention_construct_fn=None,
input_text=None):
output, state = cell(input, state)
if hasattr(state, 'alignments'):
tf.add_to_collection('attention_alignments', state.alignments)
tf.add_to_collection('beam_search_alignments',
tf.get_collection('attention_alignments')[-1])
#TODO: this step cause.. attenion decode each step after initalization still need input_text feed
#will this case attention_keys and attention_values to be recompute(means redo encoding process) each step?
#can we avoid this? seems no better method,
#if enocding is slow may be feed attention_keys, attention_values each step
if not FLAGS.decode_use_alignment:
if FLAGS.gen_only:
output_fn = self.output_fn
logits = output_fn(output)
else:
indices = melt.batch_values_to_indices(tf.to_int32(input_text))
batch_size = melt.get_batch_size(input)
if FLAGS.copy_only:
output_fn_ = self.copy_output_fn
else:
output_fn_ = self.gen_copy_output_fn
output_fn = lambda cell_output, cell_state: output_fn_(
indices, batch_size, cell_output, cell_state)
logits = output_fn(output, state)
if FLAGS.gen_copy_switch and FLAGS.switch_after_softmax:
logprobs = tf.log(logits)
else:
logprobs = tf.nn.log_softmax(logits)
if FLAGS.decode_copy:
logprobs = melt.gather_cols(logprobs, tf.to_int32(input_text))
else:
logits = state.alignments
logits = scores[:, :tf.shape(input_text)[-1]]
logprobs = tf.nn.log_softmax(logits)
top_logprobs, top_ids = tf.nn.top_k(logprobs, beam_size)
#------too slow... for transfering large data between py and c++ cost a lot!
#top_logprobs, top_ids = tf.nn.top_k(logprobs, self.vocab_size)
if input_text is not None and FLAGS.decode_copy:
top_ids = tf.nn.embedding_lookup(input_text, top_ids)
if hasattr(state, 'cell_state'):
state = state.cell_state
return output, state, top_logprobs, top_ids
def generate_sequence_greedy(self,
input,
max_words,
initial_state=None,
attention_states=None,
convert_unk=True,
input_text=None,
emb=None):
"""
this one is using greedy search method
for beam search using generate_sequence_by_beam_search with addditional params like beam_size
"""
if emb is None:
emb = self.emb
batch_size = melt.get_batch_size(input)
if attention_states is None:
cell = self.cell
else:
cell = self.prepare_attention(
attention_states,
initial_state=initial_state,
score_as_alignment=self.score_as_alignment)
initial_state = None
state = cell.zero_state(
batch_size, tf.float32) if initial_state is None else initial_state
helper = melt.seq2seq.GreedyEmbeddingHelper(embedding=emb,
first_input=input,
end_token=self.end_id)
if FLAGS.gen_only:
output_fn = self.output_fn
else:
indices = melt.batch_values_to_indices(tf.to_int32(input_text))
if FLAGS.copy_only:
output_fn_ = self.copy_output_fn
else:
output_fn_ = self.gen_copy_output_fn
output_fn = lambda cell_output, cell_state: output_fn_(
indices, batch_size, cell_output, cell_state)
my_decoder = melt.seq2seq.BasicDecoder(cell=cell,
helper=helper,
initial_state=state,
vocab_size=self.vocab_size,
output_fn=output_fn)
outputs, _, _ = melt.seq2seq.dynamic_decode(
my_decoder, maximum_iterations=max_words, scope=self.scope)
generated_sequence = outputs.sample_id
#------like beam search return sequence, score
return generated_sequence, tf.zeros([
batch_size,
])
def words_importance_encode(self, sequence, emb=None, input=None):
#[batch_size, emb_dim]
argmax_values = self.encode(
sequence, emb, input,
output_method=melt.rnn.OutputMethod.argmax)[0]
indices = melt.batch_values_to_indices(tf.to_int32(argmax_values))
updates = tf.ones_like(argmax_values)
shape = tf.shape(sequence)
scores = tf.scatter_nd(indices, updates, shape=shape) * tf.to_int64(
tf.sequence_mask(self.sequence_length, shape[-1]))
return scores
def gen_copy_output_train(time, indices, targets, sampled_values,
batch_size, cell_output, cell_state):
if self.softmax_loss_function is not None:
labels = tf.slice(targets, [0, time], [-1, 1])
sampled, true_expected_count, sampled_expected_count = sampled_values
sampled_values = \
sampled, tf.slice(tf.reshape(true_expected_count, [batch_size, -1]), [0, time], [-1, 1]), sampled_expected_count
sampled_ids, sampled_logits = melt.nn.compute_sampled_ids_and_logits(
weights=self.w_t,
biases=self.v,
labels=labels,
inputs=cell_output,
num_sampled=self.num_sampled,
num_classes=self.vocab_size,
sampled_values=sampled_values,
remove_accidental_hits=False)
gen_indices = melt.batch_values_to_indices(
tf.to_int32(sampled_ids))
gen_logits = tf.scatter_nd(gen_indices,
sampled_logits,
shape=[batch_size, self.vocab_size])
else:
gen_logits = self.output_fn(cell_output)
copy_logits = copy_output(indices, batch_size, cell_output,
cell_state)
if FLAGS.gen_copy_switch:
#gen_copy_switch == True.
gen_probability = cell_state.gen_probability
if FLAGS.switch_after_softmax:
return gen_probability * tf.nn.softmax(gen_logits) + (
1 - gen_probability) * tf.nn.softmax(copy_logits)
else:
return gen_probability * gen_logits + (
1 - gen_probability) * copy_logits
else:
return gen_logits + copy_logits
def decoder_fn(time, cell_state, cell_input, cell_output, context_state):
"""Decoder function used in the `dynamic_rnn_decoder` for training.
Args:
time: positive integer constant reflecting the current timestep.
cell_state: state of RNNCell.
cell_input: input provided by `dynamic_rnn_decoder`.
cell_output: output of RNNCell.
context_state: context state provided by `dynamic_rnn_decoder`.
Returns:
A tuple (done, next state, next input, emit output, next context state)
where:
done: `None`, which is used by the `dynamic_rnn_decoder` to indicate
that `sequence_lengths` in `dynamic_rnn_decoder` should be used.
next state: `cell_state`, this decoder function does not modify the
given state.
next input: `cell_input`, this decoder function does not modify the
given input. The input could be modified when applying e.g. attention.
emit output: `cell_output`, this decoder function does not modify the
given output.
next context state: `context_state`, this decoder function does not
modify the given context state. The context state could be modified when
applying e.g. beam search.
"""
with ops.name_scope(
name, "attention_decoder_fn_train",
[time, cell_state, cell_input, cell_output, context_state]):
#input_text = None
if cell_state is None: # first call, return encoder_state
cell_state = encoder_state
# init attention
attention = init_attention(encoder_state)
if input_text is not None:
#cell_output = array_ops.zeros([vocab_size], dtype=dtypes.float32)
context_state = tensor_array_ops.TensorArray(
dtype=tf.float32,
size=0,
dynamic_size=True,
infer_shape=False)
else:
# construct attention
attention, scores, alignments = attention_construct_fn(
cell_output, attention_keys, attention_values)
#attention, scores, alignments = attention_construct_fn(cell_state.h, attention_keys,
# attention_values)
cell_output = attention
if input_text is not None:
#encoder_info = nest.flatten(encoder_state)[0]
#batch_size = encoder_info.get_shape()[0].value
#if batch_size is None:
# batch_size = array_ops.shape(encoder_info)[0]
##ref = tf.Variable(array_ops.zeros([batch_size, vocab_size], dtype=dtypes.float32))
##https://github.com/tensorflow/tensorflow/issues/8604
#with tf.control_dependencies(None):
##TODO... must fix batch_size right now
# cell_output_ = tf.Variable(array_ops.zeros([256, vocab_size], dtype=dtypes.float32))
#actually should use tf.scatter_nd, anyway this attention is deprecated!
cell_output_ = tf.get_variable(
"cell_output_", [256, vocab_size],
dtype=dtypes.float32,
initializer=tf.zeros_initializer())
#cell_output_ = tf.get_variable("cell_output_", [256, vocab_size], dtype=dtypes.float32, initializer=tf.ones_initializer())
#cell_output_.assign(array_ops.zeros([256, vocab_size]))
cell_output_ = tf.assign(
cell_output_, array_ops.zeros([256, vocab_size]))
indices = melt.batch_values_to_indices(
tf.to_int32(input_text))
updates = scores
#updates = alignments
cell_output_ = tf.scatter_nd_add(cell_output_, indices,
updates)
cell_output_ = tf.stop_gradient(cell_output_)
##print(cell_output, vocab_size)
##cell_output = attention
##cell_output = array_ops.zeros([batch_size, vocab_size], dtype=dtypes.float32)
##cell_output = scores
#cell_output = tf.convert_to_tensor(cell_output)
context_state = context_state.write(time - 1, cell_output_)
#context_state = context_state.write(time - 1, scores)
# combine cell_input and attention
next_input = array_ops.concat([cell_input, attention], 1)
#next_input = cell_input
return (None, cell_state, next_input, cell_output, context_state)
def take_step(self, i, prev, state):
print('-------------i', i)
if self.output_fn is not None:
#[batch_size * beam_size, num_units] -> [batch_size * beam_size, num_classes]
try:
output = self.output_fn(prev)
except Exception:
output = self.output_fn(prev, state)
else:
output = prev
self.output = output
#[batch_size * beam_size, num_classes], here use log sofmax
if self.need_softmax:
logprobs = tf.nn.log_softmax(output)
else:
logprobs = tf.log(tf.maximum(output, 1e-12))
if self.num_classes is None:
self.num_classes = tf.shape(logprobs)[1]
#->[batch_size, beam_size, num_classes]
logprobs_batched = tf.reshape(logprobs,
[-1, self.beam_size, self.num_classes])
logprobs_batched.set_shape((None, self.beam_size, None))
# Note: masking out entries to -inf plays poorly with top_k, so just subtract out a large number.
nondone_mask = tf.reshape(
tf.cast(
tf.equal(tf.range(self.num_classes), self.done_token),
tf.float32) * -1e18,
[1, 1, self.num_classes])
if self.past_logprobs is None:
#[batch_size, beam_size, num_classes] -> [batch_size, num_classes]
#-> past_logprobs[batch_size, beam_size], indices[batch_size, beam_size]
self.past_logprobs, indices = tf.nn.top_k(
(logprobs_batched + nondone_mask)[:, 0, :],
self.beam_size)
step_logprobs = self.past_logprobs
else:
#logprobs_batched [batch_size, beam_size, num_classes] -> [batch_size, beam_size, num_classes]
#past_logprobs [batch_size, beam_size] -> [batch_size, beam_size, 1]
step_logprobs_batched = logprobs_batched
logprobs_batched = logprobs_batched + tf.expand_dims(self.past_logprobs, 2)
#get [batch_size, beam_size] each
self.past_logprobs, indices = tf.nn.top_k(
#[batch_size, beam_size * num_classes]
tf.reshape(logprobs_batched + nondone_mask,
[-1, self.beam_size * self.num_classes]),
self.beam_size)
#get current step logprobs [batch_size, beam_size]
step_logprobs = tf.gather_nd(tf.reshape(step_logprobs_batched,
[-1, self.beam_size * self.num_classes]),
melt.batch_values_to_indices(indices))
# For continuing to the next symbols [batch_size, beam_size]
symbols = indices % self.num_classes
#from wich beam it comes [batch_size, beam_size]
parent_refs = indices // self.num_classes
if self.past_symbols is None:
#here when i == 1, when i==0 will not do take step it just do one rnn() get output and use it for i==1 here
#here will not need to gather state for inital state of each beam is the same
#[batch_size, beam_size] -> [batch_size, beam_size, 1]
self.past_symbols = tf.expand_dims(symbols, 2)
self.past_step_logprobs = tf.expand_dims(step_logprobs, 2)
else:
# NOTE: outputing a zero-length sequence is not supported for simplicity reasons
#hasky/jupter/tensorflow/beam-search2.ipynb below for mergeing path
#here when i >= 2
# tf.reshape(
# (tf.range(3 * 5) // 5) * 5,
# [3, 5]
# ).eval()
# array([[ 0, 0, 0, 0, 0],
# [ 5, 5, 5, 5, 5],
# [10, 10, 10, 10, 10]], dtype=int32)
parent_refs_offsets = tf.reshape(
(tf.range(self.batch_size * self.beam_size)
// self.beam_size) * self.beam_size,
[self.batch_size, self.beam_size])
#self.past_symbols [batch_size, beam_size, i - 1] -> past_symbols_batch_major [batch_size * beam_size, i - 1]
past_symbols_batch_major = tf.reshape(self.past_symbols, [-1, i-1])
past_step_logprobs_batch_major = tf.reshape(self.past_step_logprobs, [-1, i - 1])
#[batch_size, beam_size]
past_indices = parent_refs + parent_refs_offsets
#-> [batch_size, beam_size, i - 1]
beam_past_symbols = tf.gather(past_symbols_batch_major, #[batch_size * beam_size, i - 1]
past_indices #[batch_size, beam_size]
)
beam_past_step_logprobs = tf.gather(past_step_logprobs_batch_major, past_indices)
#we must also choose corresponding past state as new start
past_indices = tf.reshape(past_indices, [-1])
#TODO not support tf.TensorArray right now, can not use aligment_history in attention_wrapper
def try_gather(x, indices):
#if isinstance(x, tf.Tensor) and x.shape.ndims >= 2:
assert isinstance(x, tf.Tensor)
if x.shape.ndims >= 2:
return tf.gather(x, indices)
else:
return x
state = nest.map_structure(lambda x: try_gather(x, past_indices), state)
if hasattr(state, 'alignments'):
attention_size = melt.get_shape(state.alignments, -1)
alignments = tf.reshape(state.alignments, [-1, self.beam_size, attention_size])
print('alignments', alignments)
if not self.fast_greedy:
#[batch_size, beam_size, max_len]
path = tf.concat([self.past_symbols,
tf.ones_like(tf.expand_dims(symbols, 2)) * self.done_token,
tf.tile(tf.ones_like(tf.expand_dims(symbols, 2)) * self.pad_token,
[1, 1, self.max_len - i])], 2)
step_logprobs_path = tf.concat([self.past_step_logprobs,
tf.expand_dims(step_logprobs_batched[:, :, self.done_token], 2),
tf.tile(tf.ones_like(tf.expand_dims(step_logprobs, 2)) * -float('inf'),
[1, 1, self.max_len - i])], 2)
#[batch_size, 1, beam_size, max_len]
path = tf.expand_dims(path, 1)
step_logprobs_path = tf.expand_dims(step_logprobs_path, 1)
self.paths_list.append(path)
self.step_logprobs_list.append(step_logprobs_path)
#[batch_size * beam_size, i - 1] -> [batch_size, bam_size, i] the best beam_size paths until step i
self.past_symbols = tf.concat([beam_past_symbols, tf.expand_dims(symbols, 2)], 2)
self.past_step_logprobs = tf.concat([beam_past_step_logprobs, tf.expand_dims(step_logprobs, 2)], 2)
# For finishing the beam
#[batch_size, beam_size]
logprobs_done = logprobs_batched[:, :, self.done_token]
if not self.fast_greedy:
self.logprobs_list.append(logprobs_done / i ** self.length_normalization_factor)
else:
done_parent_refs = tf.cast(tf.argmax(logprobs_done, 1), tf.int32)
done_parent_refs_offsets = tf.range(self.batch_size) * self.beam_size
done_past_symbols = tf.gather(past_symbols_batch_major,
done_parent_refs + done_parent_refs_offsets)
#[batch_size, max_len]
symbols_done = tf.concat([done_past_symbols,
tf.ones_like(done_past_symbols[:,0:1]) * self.done_token,
tf.tile(tf.zeros_like(done_past_symbols[:,0:1]),
[1, self.max_len - i])
], 1)
#[batch_size, beam_size] -> [batch_size,]
logprobs_done_max = tf.reduce_max(logprobs_done, 1)
if self.length_normalization_factor > 0:
logprobs_done_max /= i ** self.length_normalization_factor
#[batch_size, max_len]
self.finished_beams = tf.where(logprobs_done_max > self.logprobs_finished_beams,
symbols_done,
self.finished_beams)
self.logprobs_finished_beams = tf.maximum(logprobs_done_max, self.logprobs_finished_beams)
#->[batch_size * beam_size,]
symbols_flat = tf.reshape(symbols, [-1])
self.final_state = state
return symbols_flat, state
def sequence_loss(self,
sequence,
initial_state=None,
attention_states=None,
input=None,
input_text=None,
exact_prob=False,
exact_loss=False,
emb=None):
"""
for general seq2seq input is None, sequence will pad <GO>, inital_state is last state from encoder
for img2text/showandtell input is image_embedding, inital_state is None/zero set
TODO since exact_porb and exact_loss same value, may remove exact_prob
NOTICE! assume sequence to be padded by zero and must have one instance full length(no zero!)
"""
if emb is None:
emb = self.emb
is_training = self.is_training
batch_size = tf.shape(sequence)[0]
sequence, sequence_length = melt.pad(sequence,
start_id=self.get_start_id(),
end_id=self.get_end_id())
#TODO different init state as show in ptb_word_lm
state = self.cell.zero_state(
batch_size, tf.float32) if initial_state is None else initial_state
#[batch_size, num_steps - 1, emb_dim], remove last col
inputs = tf.nn.embedding_lookup(emb, sequence[:, :-1])
if is_training and FLAGS.keep_prob < 1:
inputs = tf.nn.dropout(inputs, FLAGS.keep_prob)
#inputs[batch_size, num_steps, emb_dim] input([batch_size, emb_dim] -> [batch_size, 1, emb_dim]) before concat
if input is not None:
#used like showandtell where image_emb is as input, additional to sequence
inputs = tf.concat([tf.expand_dims(input, 1), inputs], 1)
else:
#common usage input is None, sequence as input, notice already pad <GO> before using melt.pad
sequence_length -= 1
sequence = sequence[:, 1:]
if self.is_predict:
#---only need when predict, since train input already dynamic length, NOTICE this will improve speed a lot
num_steps = tf.to_int32(tf.reduce_max(sequence_length))
sequence = sequence[:, :num_steps]
inputs = inputs[:, :num_steps, :]
tf.add_to_collection('sequence', sequence)
tf.add_to_collection('sequence_length', sequence_length)
if attention_states is None:
outputs, state = tf.nn.dynamic_rnn(self.cell,
inputs,
initial_state=state,
sequence_length=sequence_length,
scope=self.scope)
self.final_state = state
else:
attention_keys, attention_values, attention_score_fn, attention_construct_fn = \
self.prepare_attention(attention_states)
decoder_fn_train = melt.seq2seq.attention_decoder_fn_train(
encoder_state=state,
attention_keys=attention_keys,
attention_values=attention_values,
attention_score_fn=attention_score_fn,
attention_construct_fn=attention_construct_fn)
decoder_outputs_train, decoder_state_train, _ = \
melt.seq2seq.dynamic_rnn_decoder(
cell=self.cell,
decoder_fn=decoder_fn_train,
inputs=inputs,
sequence_length=tf.to_int32(sequence_length),
scope=self.scope)
outputs = decoder_outputs_train
self.final_state = decoder_state_train
tf.add_to_collection('outputs', outputs)
#[batch_size, num_steps]
targets = sequence
if FLAGS.copy_only:
#TODO now not work!
attention_scores = tf.get_collection('attention_scores')[-1]
indices = melt.batch_values_to_indices(input_text)
#logits = ;
else:
#TODO: hack here add FLAGS.predict_no_sample just for Seq2seqPredictor exact_predict
softmax_loss_function = self.softmax_loss_function
if self.is_predict and (exact_prob or exact_loss):
softmax_loss_function = None
if softmax_loss_function is None:
#[batch_size, num_steps, num_units] * [num_units, vocab_size]
# -> logits [batch_size, num_steps, vocab_size] (if use exact_predict_loss)
#or [batch_size * num_steps, vocab_size] by default flatten=True
keep_dims = exact_prob or exact_loss
logits = melt.batch_matmul_embedding(
outputs, self.w, keep_dims=keep_dims) + self.v
if not keep_dims:
targets = tf.reshape(targets, [-1])
else:
logits = outputs
mask = tf.cast(tf.sign(targets), dtype=tf.float32)
if self.is_predict and exact_prob:
#generate real prob for sequence
#for 10w vocab textsum seq2seq 20 -> 4 about
loss = melt.seq2seq.exact_predict_loss(logits, targets, mask,
num_steps, batch_size)
elif self.is_predict and exact_loss:
#force no sample softmax loss, the diff with exact_prob is here we just use cross entropy error as result not real prob of seq
#NOTICE using time a bit less 55 to 57(prob), same result with exact prob and exact score
#but 256 vocab sample will use only about 10ms
#TODO check more with softmax loss and sampled somtmax loss, check length normalize
loss = melt.seq2seq.sequence_loss_by_example(logits,
targets,
weights=mask)
else:
#loss [batch_size,]
loss = melt.seq2seq.sequence_loss_by_example(
logits,
targets,
weights=mask,
softmax_loss_function=softmax_loss_function)
#mainly for compat with [bach_size, num_losses]
loss = tf.reshape(loss, [-1, 1])
if self.is_predict:
loss = self.normalize_length(loss, sequence_length, exact_prob)
#loss = tf.squeeze(loss) TODO: later will uncomment this with all models rerun
return loss
def sequence_loss(self,
sequence,
initial_state=None,
attention_states=None,
input=None,
input_text=None,
exact_prob=False,
exact_loss=False,
emb=None):
"""
for general seq2seq input is None, sequence will pad <GO>, inital_state is last state from encoder
for img2text/showandtell input is image_embedding, inital_state is None/zero set
TODO since exact_porb and exact_loss same value, may remove exact_prob
NOTICE! assume sequence to be padded by zero and must have one instance full length(no zero!)
"""
if emb is None:
emb = self.emb
is_training = self.is_training
batch_size = tf.shape(sequence)[0]
sequence, sequence_length = melt.pad(sequence,
start_id=self.get_start_id(),
end_id=self.get_end_id())
#[batch_size, num_steps - 1, emb_dim], remove last col
inputs = tf.nn.embedding_lookup(emb, sequence[:, :-1])
if is_training and FLAGS.keep_prob < 1:
inputs = tf.nn.dropout(inputs, FLAGS.keep_prob)
#inputs[batch_size, num_steps, emb_dim] input([batch_size, emb_dim] -> [batch_size, 1, emb_dim]) before concat
if input is not None:
#used like showandtell where image_emb is as input, additional to sequence
inputs = tf.concat([tf.expand_dims(input, 1), inputs], 1)
else:
#common usage input is None, sequence as input, notice already pad <GO> before using melt.pad
sequence_length -= 1
sequence = sequence[:, 1:]
if self.is_predict:
#---only need when predict, since train input already dynamic length, NOTICE this will improve speed a lot
num_steps = tf.to_int32(tf.reduce_max(sequence_length))
sequence = sequence[:, :num_steps]
inputs = inputs[:, :num_steps, :]
tf.add_to_collection('sequence', sequence)
tf.add_to_collection('sequence_length', sequence_length)
if attention_states is None:
cell = self.cell
else:
cell = self.prepare_attention(attention_states,
initial_state=initial_state)
#initial_state = None
initial_state = cell.zero_state(batch_size, tf.float32)
state = cell.zero_state(
batch_size, tf.float32) if initial_state is None else initial_state
#if attention_states is None:
#-----TODO using attention_wrapper works now with dynamic_rnn but still slower then old attention method...
outputs, state = tf.nn.dynamic_rnn(cell,
inputs,
initial_state=state,
sequence_length=sequence_length,
dtype=tf.float32,
scope=self.scope)
#else:
# #---below is also ok but slower, above 16+ ,below only 13,14 batch/s, may be due to sample id
# #TODO: can we make below code as fast as tf.nn.dyanmic_rnn if not need smaple id remove it ?
# #FIXME... AttentionWrapper is only 1/2 speed comapred to old function based attention, why?
# #helper = tf.contrib.seq2seq.TrainingHelper(inputs, tf.to_int32(sequence_length))
# helper = melt.seq2seq.TrainingHelper(inputs, tf.to_int32(sequence_length))
# #my_decoder = tf.contrib.seq2seq.BasicDecoder(
# my_decoder = melt.seq2seq.BasicTrainingDecoder(
# cell=cell,
# helper=helper,
# initial_state=state)
# outputs, state, _ = tf.contrib.seq2seq.dynamic_decode(my_decoder, scope=self.scope)
# #outputs = outputs.rnn_output
self.final_state = state
tf.add_to_collection('outputs', outputs)
#[batch_size, num_steps]
targets = sequence
if FLAGS.copy_only:
#TODO now not work!
attention_scores = tf.get_collection('attention_scores')[-1]
indices = melt.batch_values_to_indices(input_text)
#logits = ;
else:
#TODO: hack here add FLAGS.predict_no_sample just for Seq2seqPredictor exact_predict
softmax_loss_function = self.softmax_loss_function
if self.is_predict and (exact_prob or exact_loss):
softmax_loss_function = None
if softmax_loss_function is None:
#[batch_size, num_steps, num_units] * [num_units, vocab_size]
# -> logits [batch_size, num_steps, vocab_size] (if use exact_predict_loss)
#or [batch_size * num_steps, vocab_size] by default flatten=True
keep_dims = exact_prob or exact_loss
logits = melt.batch_matmul_embedding(
outputs, self.w, keep_dims=keep_dims) + self.v
if not keep_dims:
targets = tf.reshape(targets, [-1])
else:
logits = outputs
mask = tf.cast(tf.sign(targets), dtype=tf.float32)
if self.is_predict and exact_prob:
#generate real prob for sequence
#for 10w vocab textsum seq2seq 20 -> 4 about
loss = melt.seq2seq.exact_predict_loss(logits, targets, mask,
num_steps, batch_size)
elif self.is_predict and exact_loss:
#force no sample softmax loss, the diff with exact_prob is here we just use cross entropy error as result not real prob of seq
#NOTICE using time a bit less 55 to 57(prob), same result with exact prob and exact score
#but 256 vocab sample will use only about 10ms
#TODO check more with softmax loss and sampled somtmax loss, check length normalize
loss = melt.seq2seq.sequence_loss_by_example(logits,
targets,
weights=mask)
else:
#loss [batch_size,]
loss = melt.seq2seq.sequence_loss_by_example(
logits,
targets,
weights=mask,
softmax_loss_function=softmax_loss_function)
#mainly for compat with [bach_size, num_losses]
loss = tf.reshape(loss, [-1, 1])
if self.is_predict:
loss = self.normalize_length(loss, sequence_length, exact_prob)
#loss = tf.squeeze(loss) TODO: later will uncomment this with all models rerun
return loss
def generate_sequence_beam(self,
input,
max_words,
initial_state=None,
attention_states=None,
beam_size=5,
convert_unk=True,
length_normalization_factor=0.,
input_text=None,
input_text_length=None,
emb=None):
"""
beam dcode means ingraph beam search
return top (path, score)
"""
if emb is None:
emb = self.emb
def loop_function(i, prev, state, decoder):
prev, state = decoder.take_step(i, prev, state)
next_input = tf.nn.embedding_lookup(emb, prev)
return next_input, state
batch_size = melt.get_batch_size(input)
if initial_state is not None:
initial_state = nest.map_structure(
lambda x: tf.contrib.seq2seq.tile_batch(x, beam_size),
initial_state)
if attention_states is None:
cell = self.cell
else:
attention_states = tf.contrib.seq2seq.tile_batch(
attention_states, beam_size)
#print('tiled_attention_states', attention_states, 'tiled_initial_state', initial_state)
cell = self.prepare_attention(
attention_states,
initial_state=initial_state,
score_as_alignment=self.score_as_alignment)
initial_state = None
state = cell.zero_state(batch_size * beam_size, tf.float32) \
if initial_state is None else initial_state
if FLAGS.gen_only:
output_fn = self.output_fn
else:
input_text = tf.contrib.seq2seq.tile_batch(input_text, beam_size)
batch_size = batch_size * beam_size
indices = melt.batch_values_to_indices(tf.to_int32(input_text))
if FLAGS.copy_only:
output_fn_ = self.copy_output_fn
else:
output_fn_ = self.gen_copy_output_fn
output_fn = lambda cell_output, cell_state: output_fn_(
indices, batch_size, cell_output, cell_state)
##TODO to be safe make topn the same as beam size
return melt.seq2seq.beam_decode(
input,
max_words,
state,
cell,
loop_function,
scope=self.scope,
beam_size=beam_size,
done_token=vocabulary.vocab.end_id(),
output_fn=output_fn,
length_normalization_factor=length_normalization_factor,
topn=beam_size)
def sequence_loss(self,
sequence,
initial_state=None,
attention_states=None,
input=None,
input_text=None,
exact_prob=False,
exact_loss=False,
emb=None):
"""
for general seq2seq input is None, sequence will pad <GO>, inital_state is last state from encoder
for img2text/showandtell input is image_embedding, inital_state is None/zero set
TODO since exact_porb and exact_loss same value, may remove exact_prob
NOTICE! assume sequence to be padded by zero and must have one instance full length(no zero!)
"""
if emb is None:
emb = self.emb
is_training = self.is_training
batch_size = melt.get_batch_size(sequence)
sequence, sequence_length = melt.pad(sequence,
start_id=self.get_start_id(),
end_id=self.get_end_id())
#[batch_size, num_steps - 1, emb_dim], remove last col
inputs = tf.nn.embedding_lookup(emb, sequence[:, :-1])
if is_training and FLAGS.keep_prob < 1:
inputs = tf.nn.dropout(inputs, FLAGS.keep_prob)
#inputs[batch_size, num_steps, emb_dim] input([batch_size, emb_dim] -> [batch_size, 1, emb_dim]) before concat
if input is not None:
#used like showandtell where image_emb is as input, additional to sequence
inputs = tf.concat([tf.expand_dims(input, 1), inputs], 1)
else:
#common usage input is None, sequence as input, notice already pad <GO> before using melt.pad
sequence_length -= 1
sequence = sequence[:, 1:]
if self.is_predict:
#---only need when predict, since train input already dynamic length, NOTICE this will improve speed a lot
num_steps = tf.to_int32(tf.reduce_max(sequence_length))
sequence = sequence[:, :num_steps]
inputs = inputs[:, :num_steps, :]
tf.add_to_collection('sequence', sequence)
tf.add_to_collection('sequence_length', sequence_length)
#[batch_size, num_steps]
targets = sequence
if attention_states is None:
cell = self.cell
else:
cell = self.prepare_attention(
attention_states,
initial_state=initial_state,
score_as_alignment=self.score_as_alignment)
initial_state = None
state = cell.zero_state(
batch_size, tf.float32) if initial_state is None else initial_state
if FLAGS.gen_only:
#gen only mode
#for attention wrapper can not use dynamic_rnn if aligments_history=True TODO see pointer_network in application seems ok.. why
outputs, state = tf.nn.dynamic_rnn(cell,
inputs,
initial_state=state,
sequence_length=sequence_length,
dtype=tf.float32,
scope=self.scope)
#--------below is ok but slower then dynamic_rnn 3.4batch -> 3.1 batch/s
#helper = melt.seq2seq.TrainingHelper(inputs, tf.to_int32(sequence_length))
##helper = tf.contrib.seq2seq.TrainingHelper(inputs, tf.to_int32(sequence_length))
#my_decoder = melt.seq2seq.BasicTrainingDecoder(
##my_decoder = tf.contrib.seq2seq.BasicDecoder(
##my_decoder = melt.seq2seq.BasicDecoder(
# cell=cell,
# helper=helper,
# initial_state=state)
##outputs, state, _ = tf.contrib.seq2seq.dynamic_decode(my_decoder, scope=self.scope)
#outputs, state, _ = melt.seq2seq.dynamic_decode(my_decoder, scope=self.scope)
##outputs = outputs.rnn_output
else:
#---copy only or gen copy
helper = melt.seq2seq.TrainingHelper(inputs,
tf.to_int32(sequence_length))
indices = melt.batch_values_to_indices(tf.to_int32(input_text))
if FLAGS.copy_only:
output_fn = lambda cell_output, cell_state: self.copy_output_fn(
indices, batch_size, cell_output, cell_state)
else:
#gen_copy right now, not use switch
sampled_values = None
if self.softmax_loss_function is not None:
sampled_values = tf.nn.log_uniform_candidate_sampler(
true_classes=tf.reshape(targets, [-1, 1]),
num_true=1,
num_sampled=self.num_sampled,
unique=True,
range_max=self.vocab_size)
#TODO since perf of sampled version here is ok not modify now, but actually in addtional to sampled_values
#sampled_w, sampled_b can also be pre embedding lookup, may imporve not much
output_fn = lambda time, cell_output, cell_state: self.gen_copy_output_train_fn(
time, indices, targets, sampled_values, batch_size,
cell_output, cell_state)
my_decoder = melt.seq2seq.BasicTrainingDecoder(
cell=cell,
helper=helper,
initial_state=state,
vocab_size=self.vocab_size,
output_fn=output_fn)
outputs, state, _ = tf.contrib.seq2seq.dynamic_decode(
my_decoder, scope=self.scope)
#outputs, state, _ = melt.seq2seq.dynamic_decode(my_decoder, scope=self.scope)
tf.add_to_collection('outputs', outputs)
#TODO: hack here add FLAGS.predict_no_sample just for Seq2seqPredictor exact_predict
softmax_loss_function = self.softmax_loss_function
if self.is_predict and (exact_prob or exact_loss):
softmax_loss_function = None
if not FLAGS.gen_only:
logits = outputs
softmax_loss_function = None
elif softmax_loss_function is not None:
logits = outputs
else:
#[batch_size, num_steps, num_units] * [num_units, vocab_size]
# -> logits [batch_size, num_steps, vocab_size] (if use exact_predict_loss)
#or [batch_size * num_steps, vocab_size] by default flatten=True
keep_dims = exact_prob or exact_loss
logits = melt.batch_matmul_embedding(
outputs, self.w, keep_dims=keep_dims) + self.v
if not keep_dims:
targets = tf.reshape(targets, [-1])
tf.add_to_collection('logits', logits)
#if input_text is not None:
# logits = outputs
mask = tf.cast(tf.sign(targets), dtype=tf.float32)
if FLAGS.gen_copy_switch:
#TODO why need more gpu mem ? ... do not save logits ? just calc loss in output_fn ?
#batch size 256
#File "/home/gezi/mine/hasky/util/melt/seq2seq/loss.py", line 154, in body
#step_logits = logits[:, i, :]
#ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[256,21,33470]
num_steps = tf.shape(targets)[1]
loss = melt.seq2seq.exact_predict_loss(logits,
targets,
mask,
num_steps,
need_softmax=False,
need_average=True,
batch_size=batch_size)
# loss = melt.seq2seq.sequence_loss_by_example(
# logits,
# targets,
# weights=mask)
elif self.is_predict and exact_prob:
#generate real prob for sequence
#for 10w vocab textsum seq2seq 20 -> 4 about
loss = melt.seq2seq.exact_predict_loss(logits,
targets,
mask,
num_steps,
batch_size=batch_size)
elif self.is_predict and exact_loss:
#force no sample softmax loss, the diff with exact_prob is here we just use cross entropy error as result not real prob of seq
#NOTICE using time a bit less 55 to 57(prob), same result with exact prob and exact score
#but 256 vocab sample will use only about 10ms
loss = melt.seq2seq.sequence_loss_by_example(logits,
targets,
weights=mask)
else:
#loss [batch_size,]
loss = melt.seq2seq.sequence_loss_by_example(
logits,
targets,
weights=mask,
softmax_loss_function=softmax_loss_function)
#mainly for compat with [bach_size, num_losses]
loss = tf.reshape(loss, [-1, 1])
if self.is_predict:
loss = self.normalize_length(loss, sequence_length, exact_prob)
#loss = tf.squeeze(loss) TODO: later will uncomment this with all models rerun
return loss
def generate_sequence(self,
input,
max_words,
initial_state=None,
attention_states=None,
convert_unk=True,
input_text=None,
Helper=None,
emb=None):
"""
this one is using greedy search method
for beam search using generate_sequence_by_beam_search with addditional params like beam_size
"""
if emb is None:
emb = self.emb
batch_size = melt.get_batch_size(input)
if attention_states is None:
cell = self.cell
else:
cell = self.prepare_attention(
attention_states,
initial_state=initial_state,
score_as_alignment=self.score_as_alignment)
initial_state = None
state = cell.zero_state(
batch_size, tf.float32) if initial_state is None else initial_state
need_logprobs = FLAGS.greedy_decode_with_logprobs
if Helper is None:
if not need_logprobs:
helper = melt.seq2seq.GreedyEmbeddingHelper(
embedding=emb, first_input=input, end_token=self.end_id)
else:
helper = melt.seq2seq.LogProbsGreedyEmbeddingHelper(
embedding=emb,
first_input=input,
end_token=self.end_id,
need_softmax=self.need_softmax)
else:
helper = melt.seq2seq.MultinomialEmbeddingHelper(
embedding=emb,
first_input=input,
end_token=self.end_id,
need_softmax=self.need_softmax)
if FLAGS.gen_only:
output_fn = self.output_fn
else:
indices = melt.batch_values_to_indices(tf.to_int32(input_text))
if FLAGS.copy_only:
output_fn_ = self.copy_output_fn
else:
output_fn_ = self.gen_copy_output_fn
output_fn = lambda cell_output, cell_state: output_fn_(
indices, batch_size, cell_output, cell_state)
Decoder = melt.seq2seq.BasicDecoder if not need_logprobs else melt.seq2seq.LogProbsDecoder
my_decoder = Decoder(cell=cell,
helper=helper,
initial_state=state,
vocab_size=self.vocab_size,
output_fn=output_fn)
outputs, final_state, sequence_length = melt.seq2seq.dynamic_decode(
my_decoder,
maximum_iterations=max_words,
#MUST set to True, other wise will not set zero and sumup tokens past done/end token
impute_finished=True,
scope=self.scope)
sequence = outputs.sample_id
if not hasattr(final_state, 'log_probs'):
score = tf.zeros([
batch_size,
])
else:
score = self.normalize_length(final_state.log_probs,
sequence_length,
reshape=False)
##below can be verified to be the same
# num_steps = tf.to_int32(tf.reduce_max(sequence_length))
# score2 = -melt.seq2seq.exact_predict_loss(outputs.rnn_output, sequence, tf.to_float(tf.sign(sequence)),
# num_steps, need_softmax=True, average_across_timesteps=False)
# score2 = self.normalize_length(score2, sequence_length, reshape=False)
# score -= score2
#score = tf.exp(score)
#score = tf.concat([tf.expand_dims(score, 1), outputs.log_probs], 1)
if FLAGS.predict_use_prob:
score = tf.exp(score)
tf.add_to_collection('greedy_log_probs_list', outputs.log_probs)
#------like beam search return sequence, score
return sequence, score
def sequence_loss(self,
sequence,
initial_state=None,
attention_states=None,
input=None,
input_text=None,
exact_prob=False,
exact_loss=False,
emb=None):
"""
for general seq2seq input is None, sequence will pad <GO>, inital_state is last state from encoder
for showandtell input is image_embedding, inital_state is None/zero set, if use im2txt mode set image_as_init_state=True will do as above, need to PAD <GO> !
TODO since exact_porb and exact_loss same value, may remove exact_prob
NOTICE! assume sequence to be padded by zero and must have one instance full length(no zero!)
"""
if emb is None:
emb = self.emb
is_training = self.is_training
batch_size = melt.get_batch_size(sequence)
sequence, sequence_length = melt.pad(sequence,
start_id=self.get_start_id(),
end_id=self.get_end_id())
#[batch_size, num_steps - 1, emb_dim], remove last col
inputs = tf.nn.embedding_lookup(emb, sequence[:, :-1])
if is_training and FLAGS.keep_prob < 1:
inputs = tf.nn.dropout(inputs, FLAGS.keep_prob)
#inputs[batch_size, num_steps, emb_dim] input([batch_size, emb_dim] -> [batch_size, 1, emb_dim]) before concat
if input is not None:
#used like showandtell where image_emb is as input, additional to sequence
inputs = tf.concat([tf.expand_dims(input, 1), inputs], 1)
else:
#common usage input is None, sequence as input, notice already pad <GO> before using melt.pad
sequence_length -= 1
sequence = sequence[:, 1:]
if self.is_predict:
#---only need when predict, since train input already dynamic length, NOTICE this will improve speed a lot
num_steps = tf.to_int32(tf.reduce_max(sequence_length))
sequence = sequence[:, :num_steps]
inputs = inputs[:, :num_steps, :]
tf.add_to_collection('sequence', sequence)
tf.add_to_collection('sequence_length', sequence_length)
#[batch_size, num_steps]
targets = sequence
if attention_states is None:
cell = self.cell
else:
cell = self.prepare_attention(
attention_states,
initial_state=initial_state,
score_as_alignment=self.score_as_alignment)
initial_state = None
state = cell.zero_state(
batch_size, tf.float32) if initial_state is None else initial_state
#TODO: hack here add FLAGS.predict_no_sample just for Seq2seqPredictor exact_predict
softmax_loss_function = self.softmax_loss_function
if self.is_predict and (exact_prob or exact_loss):
softmax_loss_function = None
scheduled_sampling_probability = FLAGS.scheduled_sampling_probability if self.is_training else 0.
if FLAGS.gen_only:
#gen only mode
#for attention wrapper can not use dynamic_rnn if aligments_history=True TODO see pointer_network in application seems ok.. why
if scheduled_sampling_probability > 0.:
helper = melt.seq2seq.ScheduledEmbeddingTrainingHelper(
inputs, tf.to_int32(sequence_length), emb,
tf.constant(FLAGS.scheduled_sampling_probability))
#helper = tf.contrib.seq2seq.TrainingHelper(inputs, tf.to_int32(sequence_length))
my_decoder = melt.seq2seq.BasicDecoder(
#my_decoder = tf.contrib.seq2seq.BasicDecoder(
#my_decoder = melt.seq2seq.BasicDecoder(
cell=cell,
helper=helper,
initial_state=state)
outputs, state, _ = tf.contrib.seq2seq.dynamic_decode(
my_decoder, scope=self.scope)
#outputs, state, _ = melt.seq2seq.dynamic_decode(my_decoder, scope=self.scope)
outputs = outputs.rnn_output
else:
outputs, state = tf.nn.dynamic_rnn(
cell,
inputs,
initial_state=state,
sequence_length=sequence_length,
dtype=tf.float32,
scope=self.scope)
#--------below is ok but slower then dynamic_rnn 3.4batch -> 3.1 batch/s
#helper = melt.seq2seq.TrainingHelper(inputs, tf.to_int32(sequence_length))
##helper = tf.contrib.seq2seq.TrainingHelper(inputs, tf.to_int32(sequence_length))
#my_decoder = melt.seq2seq.BasicTrainingDecoder(
##my_decoder = tf.contrib.seq2seq.BasicDecoder(
##my_decoder = melt.seq2seq.BasicDecoder(
# cell=cell,
# helper=helper,
# initial_state=state)
##outputs, state, _ = tf.contrib.seq2seq.dynamic_decode(my_decoder, scope=self.scope)
#outputs, state, _ = melt.seq2seq.dynamic_decode(my_decoder, scope=self.scope)
##outputs = outputs.rnn_output
else:
#---copy only or gen copy
if scheduled_sampling_probability > 0.:
#not tested yet TODO
helper = melt.seq2seq.ScheduledEmbeddingTrainingHelper(
inputs, tf.to_int32(sequence_length), emb,
tf.constant(FLAGS.scheduled_sampling_probability))
Decoder_ = melt.seq2seq.BasicDecoder
else:
#as before
helper = melt.seq2seq.TrainingHelper(
inputs, tf.to_int32(sequence_length))
Decoder_ = melt.seq2seq.BasicTrainingDecoder
indices = melt.batch_values_to_indices(tf.to_int32(input_text))
if FLAGS.copy_only:
output_fn = lambda cell_output, cell_state: self.copy_output_fn(
indices, batch_size, cell_output, cell_state)
else:
#gen_copy right now, not use switch ? gen_copy and switch?
sampled_values = None
#TODO CHECK this is it ok? why train and predict not equal and score/exact score same? FIXME
#need first debug why score and exact score is same ? score should be the same as train! TODO
#sh ./inference/infrence-score.sh to reproduce
#now just set num_sampled = 0 for safe, may be here train also not correct FIXME
if softmax_loss_function is not None:
sampled_values = tf.nn.log_uniform_candidate_sampler(
true_classes=tf.reshape(targets, [-1, 1]),
num_true=1,
num_sampled=self.num_sampled,
unique=True,
range_max=self.vocab_size)
#TODO since perf of sampled version here is ok not modify now, but actually in addtional to sampled_values
#sampled_w, sampled_b can also be pre embedding lookup, may imporve not much
output_fn = lambda time, cell_output, cell_state: self.gen_copy_output_train_fn(
time, indices, targets, sampled_values, batch_size,
cell_output, cell_state)
my_decoder = Decoder_(cell=cell,
helper=helper,
initial_state=state,
vocab_size=self.vocab_size,
output_fn=output_fn)
outputs, state, _ = tf.contrib.seq2seq.dynamic_decode(
my_decoder, scope=self.scope)
#outputs, state, _ = melt.seq2seq.dynamic_decode(my_decoder, scope=self.scope)
if hasattr(outputs, 'rnn_output'):
outputs = outputs.rnn_output
tf.add_to_collection('outputs', outputs)
if not FLAGS.gen_only:
logits = outputs
softmax_loss_function = None
elif softmax_loss_function is not None:
logits = outputs
else:
#--softmax_loss_function is None means num_sample = 0 or exact_loss or exact_prob
#[batch_size, num_steps, num_units] * [num_units, vocab_size]
# -> logits [batch_size, num_steps, vocab_size] (if use exact_predict_loss)
#or [batch_size * num_steps, vocab_size] by default flatten=True
#this will be fine for train [batch_size * num_steps] but not good for eval since we want
#get score of each instance also not good for predict
#--------only training mode not keep dims, but this will be dangerous, since class call rnn_decoder
#need to manully set rnn_decoder.is_training=False! TODO other wise will show incorrect scores in eval mode
#but not affect the final model!
keep_dims = exact_prob or exact_loss or (not self.is_training)
logits = melt.batch_matmul_embedding(
outputs, self.w, keep_dims=keep_dims) + self.v
if not keep_dims:
targets = tf.reshape(targets, [-1])
tf.add_to_collection('logits', logits)
mask = tf.cast(tf.sign(targets), dtype=tf.float32)
if FLAGS.gen_copy_switch and FLAGS.switch_after_softmax:
#TODO why need more gpu mem ? ... do not save logits ? just calc loss in output_fn ?
#batch size 256
#File "/home/gezi/mine/hasky/util/melt/seq2seq/loss.py", line 154, in body
#step_logits = logits[:, i, :]
#ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[256,21,33470]
num_steps = tf.shape(targets)[1]
loss = melt.seq2seq.exact_predict_loss(
logits,
targets,
mask,
num_steps,
need_softmax=False,
average_across_timesteps=not self.is_predict,
batch_size=batch_size)
elif self.is_predict and exact_prob:
#generate real prob for sequence
#for 10w vocab textsum seq2seq 20 -> 4 about
loss = melt.seq2seq.exact_predict_loss(
logits,
targets,
mask,
num_steps,
batch_size=batch_size,
average_across_timesteps=False)
elif self.is_predict and exact_loss:
#force no sample softmax loss, the diff with exact_prob is here we just use cross entropy error as result not real prob of seq
#NOTICE using time a bit less 55 to 57(prob), same result with exact prob and exact score
#but 256 vocab sample will use only about 10ms
loss = melt.seq2seq.sequence_loss_by_example(
logits, targets, weights=mask, average_across_timesteps=False)
else:
#loss [batch_size,]
loss = melt.seq2seq.sequence_loss_by_example(
logits,
targets,
weights=mask,
average_across_timesteps=not self.
is_predict, #train must average, other wise long sentence big loss..
softmax_loss_function=softmax_loss_function)
#mainly for compat with [bach_size, num_losses] here may be [batch_size * num_steps,] if is_training and not exact loss/prob
loss = tf.reshape(loss, [-1, 1])
self.ori_loss = loss
if self.is_predict:
#note use avg_loss not to change loss pointer, avg_loss is same as average time step=True is length_normalize_fator=1.0
avg_loss = self.normalize_length(loss, sequence_length)
return avg_loss
#if not is_predict loss is averaged per time step else not but avg loss will average it
return loss
