def beam_search(decoder: Decoder,
size: int,
bos_index: int,
eos_index: int,
pad_index: int,
encoder_output: Tensor,
encoder_hidden: Tensor,
src_mask: Tensor,
max_output_length: int,
alpha: float,
embed: Embeddings,
n_best: int = 1) -> (np.array, np.array):
"""
Beam search with size k.
Inspired by OpenNMT-py, adapted for Transformer.
In each decoding step, find the k most likely partial hypotheses.
:param decoder:
:param size: size of the beam
:param bos_index:
:param eos_index:
:param pad_index:
:param encoder_output:
:param encoder_hidden:
:param src_mask:
:param max_output_length:
:param alpha: `alpha` factor for length penalty
:param embed:
:param n_best: return this many hypotheses, <= beam (currently only 1)
:return:
- stacked_output: output hypotheses (2d array of indices),
- stacked_attention_scores: attention scores (3d array)
"""
assert size > 0, 'Beam size must be >0.'
assert n_best <= size, 'Can only return {} best hypotheses.'.format(size)
# init
transformer = isinstance(decoder, TransformerDecoder)
batch_size = src_mask.size(0)
att_vectors = None # not used for Transformer
# Recurrent models only: initialize RNN hidden state
# pylint: disable=protected-access
if not transformer:
hidden = decoder._init_hidden(encoder_hidden)
else:
hidden = None
# tile encoder states and decoder initial states beam_size times
if hidden is not None:
hidden = tile(hidden, size,
dim=1) # layers x batch*k x dec_hidden_size
encoder_output = tile(encoder_output.contiguous(), size,
dim=0) # batch*k x src_len x enc_hidden_size
src_mask = tile(src_mask, size, dim=0) # batch*k x 1 x src_len
# Transformer only: create target mask
if transformer:
trg_mask = src_mask.new_ones([1, 1, 1]) # transformer only
else:
trg_mask = None
# numbering elements in the batch
batch_offset = torch.arange(batch_size,
dtype=torch.long,
device=encoder_output.device)
# numbering elements in the extended batch, i.e. beam size copies of each
# batch element
beam_offset = torch.arange(0,
batch_size * size,
step=size,
dtype=torch.long,
device=encoder_output.device)
# keeps track of the top beam size hypotheses to expand for each element
# in the batch to be further decoded (that are still "alive")
alive_seq = torch.full([batch_size * size, 1],
bos_index,
dtype=torch.long,
device=encoder_output.device)
# Give full probability to the first beam on the first step.
# pylint: disable=not-callable
topk_log_probs = (torch.tensor(
[0.0] + [float("-inf")] * (size - 1),
device=encoder_output.device).repeat(batch_size))
# Structure that holds finished hypotheses.
hypotheses = [[] for _ in range(batch_size)]
results = {}
results["predictions"] = [[] for _ in range(batch_size)]
results["scores"] = [[] for _ in range(batch_size)]
results["gold_score"] = [0] * batch_size
for step in range(max_output_length):
# This decides which part of the predicted sentence we feed to the
# decoder to make the next prediction.
# For Transformer, we feed the complete predicted sentence so far.
# For Recurrent models, only feed the previous target word prediction
if transformer: # Transformer
decoder_input = alive_seq # complete prediction so far
else: # Recurrent
decoder_input = alive_seq[:, -1].view(-1, 1) # only the last word
# expand current hypotheses
# decode one single step
# logits: logits for final softmax
# pylint: disable=unused-variable
trg_embed = embed(decoder_input)
logits, hidden, att_scores, att_vectors = decoder(
encoder_output=encoder_output,
encoder_hidden=encoder_hidden,
src_mask=src_mask,
trg_embed=trg_embed,
hidden=hidden,
prev_att_vector=att_vectors,
unroll_steps=1,
trg_mask=trg_mask # subsequent mask for Transformer only
)
# For the Transformer we made predictions for all time steps up to
# this point, so we only want to know about the last time step.
if transformer:
logits = logits[:, -1] # keep only the last time step
hidden = None # we don't need to keep it for transformer
# batch*k x trg_vocab
log_probs = F.log_softmax(logits, dim=-1).squeeze(1)
# multiply probs by the beam probability (=add logprobs)
log_probs += topk_log_probs.view(-1).unsqueeze(1)
curr_scores = log_probs
# compute length penalty
if alpha > -1:
length_penalty = ((5.0 + (step + 1)) / 6.0)**alpha
curr_scores /= length_penalty
# flatten log_probs into a list of possibilities
curr_scores = curr_scores.reshape(-1, size * decoder.output_size)
# pick currently best top k hypotheses (flattened order)
topk_scores, topk_ids = curr_scores.topk(size, dim=-1)
if alpha > -1:
# recover original log probs
topk_log_probs = topk_scores * length_penalty
# reconstruct beam origin and true word ids from flattened order
topk_beam_index = topk_ids.div(decoder.output_size)
topk_ids = topk_ids.fmod(decoder.output_size)
# map beam_index to batch_index in the flat representation
batch_index = (topk_beam_index +
beam_offset[:topk_beam_index.size(0)].unsqueeze(1))
select_indices = batch_index.view(-1)
# append latest prediction
alive_seq = torch.cat(
[alive_seq.index_select(0, select_indices),
topk_ids.view(-1, 1)], -1) # batch_size*k x hyp_len
is_finished = topk_ids.eq(eos_index)
if step + 1 == max_output_length:
is_finished.fill_(1)
# end condition is whether the top beam is finished
end_condition = is_finished[:, 0].eq(1)
# save finished hypotheses
if is_finished.any():
predictions = alive_seq.view(-1, size, alive_seq.size(-1))
for i in range(is_finished.size(0)):
b = batch_offset[i]
if end_condition[i]:
is_finished[i].fill_(1)
finished_hyp = is_finished[i].nonzero().view(-1)
# store finished hypotheses for this batch
for j in finished_hyp:
hypotheses[b].append(
(topk_scores[i,
j], predictions[i, j,
1:]) # ignore start_token
)
# if the batch reached the end, save the n_best hypotheses
if end_condition[i]:
best_hyp = sorted(hypotheses[b],
key=lambda x: x[0],
reverse=True)
for n, (score, pred) in enumerate(best_hyp):
if n >= n_best:
break
results["scores"][b].append(score)
results["predictions"][b].append(pred)
non_finished = end_condition.eq(0).nonzero().view(-1)
# if all sentences are translated, no need to go further
# pylint: disable=len-as-condition
if len(non_finished) == 0:
break
# remove finished batches for the next step
topk_log_probs = topk_log_probs.index_select(0, non_finished)
batch_index = batch_index.index_select(0, non_finished)
batch_offset = batch_offset.index_select(0, non_finished)
alive_seq = predictions.index_select(0, non_finished) \
.view(-1, alive_seq.size(-1))
# reorder indices, outputs and masks
select_indices = batch_index.view(-1)
encoder_output = encoder_output.index_select(0, select_indices)
src_mask = src_mask.index_select(0, select_indices)
if hidden is not None and not transformer:
if isinstance(hidden, tuple):
# for LSTMs, states are tuples of tensors
h, c = hidden
h = h.index_select(1, select_indices)
c = c.index_select(1, select_indices)
hidden = (h, c)
else:
# for GRUs, states are single tensors
hidden = hidden.index_select(1, select_indices)
if att_vectors is not None:
att_vectors = att_vectors.index_select(0, select_indices)
def pad_and_stack_hyps(hyps, pad_value):
filled = np.ones(
(len(hyps), max([h.shape[0]
for h in hyps])), dtype=int) * pad_value
for j, h in enumerate(hyps):
for k, i in enumerate(h):
filled[j, k] = i
return filled
# from results to stacked outputs
assert n_best == 1
# only works for n_best=1 for now
final_outputs = pad_and_stack_hyps(
[r[0].cpu().numpy() for r in results["predictions"]],
pad_value=pad_index)
return final_outputs, None
def beam_search(decoder: Decoder,
size: int,
bos_index: int,
eos_index: int,
pad_index: int,
encoder_output: Tensor,
encoder_hidden: Tensor,
src_mask: Tensor,
max_output_length: int,
alpha: float,
embed: Embeddings,
n_best: int = 1):
"""
Beam search with size k. Follows OpenNMT-py implementation.
In each decoding step, find the k most likely partial hypotheses.
:param decoder:
:param size: size of the beam
:param bos_index:
:param eos_index:
:param pad_index:
:param encoder_output:
:param encoder_hidden:
:param src_mask:
:param max_output_length:
:param alpha: `alpha` factor for length penalty
:param embed:
:param n_best: return this many hypotheses, <= beam
:return:
"""
# init
batch_size = src_mask.size(0)
# pylint: disable=protected-access
hidden = decoder._init_hidden(encoder_hidden)
# tile hidden decoder states and encoder output beam_size times
hidden = tile(hidden, size, dim=1) # layers x batch*k x dec_hidden_size
att_vectors = None
encoder_output = tile(encoder_output.contiguous(), size,
dim=0) # batch*k x src_len x enc_hidden_size
src_mask = tile(src_mask, size, dim=0) # batch*k x 1 x src_len
batch_offset = torch.arange(batch_size,
dtype=torch.long,
device=encoder_output.device)
beam_offset = torch.arange(0,
batch_size * size,
step=size,
dtype=torch.long,
device=encoder_output.device)
alive_seq = torch.full([batch_size * size, 1],
bos_index,
dtype=torch.long,
device=encoder_output.device)
# Give full probability to the first beam on the first step.
topk_log_probs = (torch.Tensor(
[0.0] + [float("-inf")] * (size - 1),
device=encoder_output.device).repeat(batch_size))
# Structure that holds finished hypotheses.
hypotheses = [[] for _ in range(batch_size)]
results = {}
results["predictions"] = [[] for _ in range(batch_size)]
results["scores"] = [[] for _ in range(batch_size)]
results["gold_score"] = [0] * batch_size
for step in range(max_output_length):
decoder_input = alive_seq[:, -1].view(-1, 1)
# expand current hypotheses
# decode one single step
# out: logits for final softmax
# pylint: disable=unused-variable
out, hidden, att_scores, att_vectors = decoder(
encoder_output=encoder_output,
encoder_hidden=encoder_hidden,
src_mask=src_mask,
trg_embed=embed(decoder_input),
hidden=hidden,
prev_att_vector=att_vectors,
unrol_steps=1)
log_probs = F.log_softmax(out,
dim=-1).squeeze(1) # batch*k x trg_vocab
# multiply probs by the beam probability (=add logprobs)
log_probs += topk_log_probs.view(-1).unsqueeze(1)
curr_scores = log_probs
# compute length penalty
if alpha > -1:
length_penalty = ((5.0 + (step + 1)) / 6.0)**alpha
curr_scores /= length_penalty
# flatten log_probs into a list of possibilities
curr_scores = curr_scores.reshape(-1, size * decoder.output_size)
# pick currently best top k hypotheses (flattened order)
topk_scores, topk_ids = curr_scores.topk(size, dim=-1)
if alpha > -1:
# recover original log probs
topk_log_probs = topk_scores * length_penalty
# reconstruct beam origin and true word ids from flattened order
topk_beam_index = topk_ids.div(decoder.output_size)
topk_ids = topk_ids.fmod(decoder.output_size)
# map beam_index to batch_index in the flat representation
batch_index = (topk_beam_index +
beam_offset[:topk_beam_index.size(0)].unsqueeze(1))
select_indices = batch_index.view(-1)
# append latest prediction
alive_seq = torch.cat(
[alive_seq.index_select(0, select_indices),
topk_ids.view(-1, 1)], -1) # batch_size*k x hyp_len
is_finished = topk_ids.eq(eos_index)
if step + 1 == max_output_length:
is_finished.fill_(1)
# end condition is whether the top beam is finished
end_condition = is_finished[:, 0].eq(1)
# save finished hypotheses
if is_finished.any():
predictions = alive_seq.view(-1, size, alive_seq.size(-1))
for i in range(is_finished.size(0)):
b = batch_offset[i]
if end_condition[i]:
is_finished[i].fill_(1)
finished_hyp = is_finished[i].nonzero().view(-1)
# store finished hypotheses for this batch
for j in finished_hyp:
hypotheses[b].append(
(topk_scores[i,
j], predictions[i, j,
1:]) # ignore start_token
)
# if the batch reached the end, save the n_best hypotheses
if end_condition[i]:
best_hyp = sorted(hypotheses[b],
key=lambda x: x[0],
reverse=True)
for n, (score, pred) in enumerate(best_hyp):
if n >= n_best:
break
results["scores"][b].append(score)
results["predictions"][b].append(pred)
non_finished = end_condition.eq(0).nonzero().view(-1)
# if all sentences are translated, no need to go further
# pylint: disable=len-as-condition
if len(non_finished) == 0:
break
# remove finished batches for the next step
topk_log_probs = topk_log_probs.index_select(0, non_finished)
batch_index = batch_index.index_select(0, non_finished)
batch_offset = batch_offset.index_select(0, non_finished)
alive_seq = predictions.index_select(0, non_finished) \
.view(-1, alive_seq.size(-1))
# reorder indices, outputs and masks
select_indices = batch_index.view(-1)
encoder_output = encoder_output.index_select(0, select_indices)
src_mask = src_mask.index_select(0, select_indices)
if isinstance(hidden, tuple):
# for LSTMs, states are tuples of tensors
h, c = hidden
h = h.index_select(1, select_indices)
c = c.index_select(1, select_indices)
hidden = (h, c)
else:
# for GRUs, states are single tensors
hidden = hidden.index_select(1, select_indices)
att_vectors = att_vectors.index_select(0, select_indices)
def pad_and_stack_hyps(hyps, pad_value):
filled = np.ones(
(len(hyps), max([h.shape[0]
for h in hyps])), dtype=int) * pad_value
for j, h in enumerate(hyps):
for k, i in enumerate(h):
filled[j, k] = i
return filled
# from results to stacked outputs
assert n_best == 1
# only works for n_best=1 for now
final_outputs = pad_and_stack_hyps(
[r[0].cpu().numpy() for r in results["predictions"]],
pad_value=pad_index)
# TODO also return attention scores and probabilities
return final_outputs, None
def beam_search(decoder: Decoder,
generator: Gen,
size: int,
bos_index: int,
eos_index: int,
pad_index: int,
encoder_output: Tensor,
encoder_hidden: Tensor,
src_mask: Tensor,
max_output_length: int,
alpha: float,
embed: Embeddings,
n_best: int = 1,
knowledgebase: Tuple = None) -> (np.array, np.array, np.array):
"""
Beam search with size k.
Inspired by OpenNMT-py, adapted for Transformer.
In each decoding step, find the k most likely partial hypotheses.
:param decoder:
:param generator:
:param size: size of the beam
:param bos_index:
:param eos_index:
:param pad_index:
:param encoder_output:
:param encoder_hidden:
:param src_mask:
:param max_output_length:
:param alpha: `alpha` factor for length penalty
:param embed:
:param n_best: return this many hypotheses, <= beam
:param knowledgebase: knowledgebase tuple containing keys, values and true values for decoding
:return:
- stacked_output: output hypotheses (2d array of indices),
- stacked_attention_scores: attention scores (3d array)
- stacked_kb_att_scores: kb attention scores (3d array)
"""
with torch.no_grad():
# initializations and so on, this should keep weird cuda errors from happening
# init
transformer = isinstance(decoder, TransformerDecoder)
batch_size = src_mask.size(0)
att_vectors = None # not used for Transformer
# Recurrent models only: initialize RNN hidden state
# pylint: disable=protected-access
if not transformer:
hidden = decoder._init_hidden(encoder_hidden)
else:
hidden = None
# tile encoder states and decoder initial states beam_size times
if hidden is not None:
hidden = tile(hidden, size,
dim=1) # layers x batch*k x dec_hidden_size
encoder_output = tile(encoder_output.contiguous(), size,
dim=0) # batch*k x src_len x enc_hidden_size
src_mask = tile(src_mask, size, dim=0) # batch*k x 1 x src_len
# Transformer only: create target mask
if transformer:
trg_mask = src_mask.new_ones([1, 1, 1]) # transformer only
else:
trg_mask = None
# numbering elements in the batch
batch_offset = torch.arange(batch_size,
dtype=torch.long,
device=encoder_output.device)
# numbering elements in the extended batch, i.e. beam size copies of each
# batch element
beam_offset = torch.arange(0,
batch_size * size,
step=size,
dtype=torch.long,
device=encoder_output.device)
# keeps track of the top beam size hypotheses to expand for each element
# in the batch to be further decoded (that are still "alive")
alive_seq = torch.full([batch_size * size, 1],
bos_index,
dtype=torch.long,
device=encoder_output.device)
# Give full probability to the first beam on the first step.
# pylint: disable=not-callable
topk_log_probs = torch.zeros(batch_size,
size,
device=encoder_output.device)
topk_log_probs[:, 1:] = float("-inf")
# Structure that holds finished hypotheses in order of completion.
hypotheses = [[] for _ in range(batch_size)]
results = {}
results["predictions"] = [[] for _ in range(batch_size)]
results["scores"] = [[] for _ in range(batch_size)]
results["att_scores"] = [[] for _ in range(batch_size)]
results["kb_att_scores"] = [[] for _ in range(batch_size)]
# kb task: also tile kb tensors along batch dimension as done with other inputs above
if knowledgebase != None:
kb_values = tile(knowledgebase[1], size, dim=0)
kb_mask = tile(knowledgebase[-1], size, dim=0)
kb_values_embed = tile(knowledgebase[2], size, dim=0)
kb_size = kb_values.size(1)
kb_keys = knowledgebase[0]
if isinstance(kb_keys, tuple):
kb_keys = tuple(
[tile(key_dim, size, dim=0) for key_dim in kb_keys])
else:
kb_keys = tile(kb_keys, size, dim=0)
att_alive = torch.Tensor( # batch * k x src x time
[[[] for _ in range(encoder_output.size(1))]
for _ in range(batch_size * size)
]).to(dtype=torch.float32, device=encoder_output.device)
kb_att_alive = torch.Tensor( # batch*k x KB x time
[[[] for _ in range(kb_size)] for _ in range(batch_size * size)
]).to(dtype=torch.float32, device=encoder_output.device)
debug_tnsrs = (kb_values, kb_mask, kb_values_embed,
(kb_keys if isinstance(kb_keys, torch.Tensor) else
kb_keys[0]), alive_seq)
assert set([t.size(0) for t in debug_tnsrs
]) == set([batch_size * size
]), [t.shape for t in debug_tnsrs]
stacked_attention_scores = [[] for _ in range(batch_size)]
stacked_kb_att_scores = [[] for _ in range(batch_size)]
util_dims_cache = None
kb_feed_hidden_cache = None
else:
kb_keys, kb_values, kb_mask = None, None, None
kb_size = None
att_alive = None
kb_att_alive = None
stacked_attention_scores, stacked_kb_att_scores = None, None
for step in range(max_output_length):
# This decides which part of the predicted sentence we feed to the
# decoder to make the next prediction.
# For Transformer, we feed the complete predicted sentence so far.
# For Recurrent models, only feed the previous target word prediction
if transformer: # Transformer
decoder_input = alive_seq # complete prediction so far
else: # Recurrent
decoder_input = alive_seq[:, -1].view(-1, 1) # only the last word
# expand current hypotheses
# decode one single step
# pylint: disable=unused-variable
trg_embed = embed(decoder_input)
hidden, att_scores, att_vectors, kb_scores, util_dims_cache, kb_feed_hidden_cache = decoder(
encoder_output=encoder_output,
encoder_hidden=encoder_hidden,
src_mask=src_mask,
trg_embed=trg_embed,
hidden=hidden,
prev_att_vector=att_vectors,
unroll_steps=1,
trg_mask=trg_mask, # subsequent mask for Transformer only
kb_keys=kb_keys, # None by default
kb_mask=kb_mask,
kb_values_embed=kb_values_embed,
util_dims_cache=util_dims_cache,
kb_feed_hidden_cache=kb_feed_hidden_cache)
try:
# generator applies output layer, biases towards KB values, then applies log_softmax
log_probs = generator(att_vectors,
kb_values=kb_values,
kb_probs=kb_scores)
except Exception as e:
print(kb_scores.shape)
print(kb_mask_before_index)
print(kb_mask_after_index)
raise e
# hidden = ?? x batch*k x dec hidden #FIXME why 3 ??????
# att_scores = batch*k x 1 x src_len #TODO Find correct beam in dim 0 at every timestep.
# att_vectors = batch*k x 1 x dec hidden
# kb_scores = batch*k x 1 x KB #TODO find correct beam in dim 0 at every timestep
# log_probs = batch*k x 1 x trg_voc
# For the Transformer we made predictions for all time steps up to
# this point, so we only want to know about the last time step.
if transformer:
log_probs = log_probs[:, -1] # keep only the last time step
hidden = None # we don't need to keep it for transformer
# batch * k x trg_vocab
log_probs = log_probs.squeeze(1)
# multiply probs by the probability of each beam thus far ( = add logprobs)
try:
log_probs += topk_log_probs.view(-1).unsqueeze(1)
except Exception as e:
dbg_tnsrs = [
hidden, att_scores, att_vectors, kb_scores, util_dims_cache,
kb_feed_hidden_cache
]
print([t.shape for t in dbg_tnsrs if isinstance(t, torch.Tensor)])
print(
[t.size(0) for t in dbg_tnsrs if isinstance(t, torch.Tensor)])
print(step)
print(encoder_output.shape)
print(select_indices)
print(batch_index)
print(non_finished)
print(non_finished.shape)
print(batch_size * size)
raise e
curr_scores = log_probs
# compute length penalty
if alpha > -1:
length_penalty = ((5.0 + (step + 1)) / 6.0)**alpha
curr_scores /= length_penalty
# flatten log_probs into a list of possibilities
curr_scores = curr_scores.reshape(
-1, size * generator.output_size) # batch x k * voc FIXME
# pick currently best top k hypotheses (flattened order)
topk_scores, topk_ids = curr_scores.topk(size,
dim=-1) # each: batch x k
if alpha > -1:
# recover original log probs
topk_log_probs = topk_scores * length_penalty # b x k
# reconstruct beam origin and true word ids from flattened order
topk_beam_index = (topk_ids // generator.output_size).to(
dtype=torch.int64
) # NOTE why divide by voc size?? this should always be 0
topk_ids = topk_ids.fmod(
generator.output_size
) # NOTE why mod voc size? isnt every entry < voc size?
# map beam_index to batch_index in the flat representation
batch_index = (topk_beam_index +
beam_offset[:topk_beam_index.size(0)].unsqueeze(1))
select_indices = batch_index.view(-1) # batch * k
# append latest prediction
alive_seq = torch.cat(
[
alive_seq.index_select(
0, select_indices
), # index first dim (batch * k) with the beams we want to continue this step
topk_ids.view(-1, 1)
],
-1) # batch_size*k x hyp_len
if knowledgebase is not None:
# print(f"kb_att_alive.shape: {kb_att_alive.shape}")
# print(f"kb_size: {kb_size}")
# print(kb_att_alive.index_select(0,select_indices).shape)
# print(kb_scores.transpose(1,2).index_select(0,select_indices).shape)
if att_scores is not None:
# FIXME sometimes this way sometimes the other idk
try:
att_alive = torch.cat( # batch * k x src len x time
[
att_alive.index_select(0, select_indices),
att_scores.transpose(1, 2).index_select(
0, select_indices).contiguous()
], -1)
except Exception as e:
print(f"step: {step}")
print(select_indices)
print(f"att_alive.shape: {att_alive.shape}")
print(f"encoder steps: {encoder_output.size(1)}")
print(
att_scores.transpose(1, 2).index_select(
0, select_indices).shape)
raise e
kb_att_alive = torch.cat( # batch * k x KB x time
[
kb_att_alive.index_select(0, select_indices),
kb_scores.transpose(1, 2).index_select(
0, select_indices).contiguous()
], -1)
# which batches are finished?
is_finished = topk_ids.eq(eos_index) # batch x k
if step + 1 == max_output_length:
# force finish
is_finished.fill_(True)
# end condition is whether the top beam of given batch is finished
end_condition = is_finished[:, 0].eq(True)
# save finished hypotheses if any of the batches finished
if is_finished.any():
predictions = alive_seq.view(
-1, size, alive_seq.size(-1)) # batch x k x time
for i in range(is_finished.size(0)): # iter over batches
b = batch_offset[i]
if end_condition[i]:
# this batch finished
is_finished[i].fill_(True)
finished_hyp = is_finished[i].nonzero(as_tuple=False).view(
-1) # k
# store finished hypotheses for this batch
# (that doesnt mean the batch is completely finished,
# hence the list 'hypotheses' is maintained outside the unroll loop)
for j in finished_hyp: # iter over finished beams
# first time EOS appears in this beam, save it as hypothesis
# (also save attentions here)
if (predictions[i, j, 1:]
== eos_index).nonzero(as_tuple=False).numel() < 2:
hypotheses[b].append((
topk_scores[
i, j], # for sorting beams by prob (below)
predictions[i, j, 1:]) # ignore BOS token
)
if knowledgebase is not None:
# batch x k x src len x time
if 0 not in att_alive.shape:
# at least one attention matrix has been inserted
attentions = att_alive.view(
-1, size, att_alive.size(-2),
att_alive.size(-1))
stacked_attention_scores[b].append(
attentions[i, j].cpu().numpy())
else:
attentions = None
# batch x k x KB x time
kb_attentions = kb_att_alive.view(
-1, size, kb_att_alive.size(-2),
kb_att_alive.size(-1))
stacked_kb_att_scores[b].append(
kb_attentions[i, j].cpu().numpy())
# if the batch reached the end, save the n best hypotheses (and their attentions and kb attentions)
if end_condition[i]:
# (hypotheses[b] is list of the completed hypotheses of this batch in order of completion => find out which is best)
# (stacked_attention_scores[b] and stacked_kb_att_scores[b] are also in order of completion)
# which beam is best?
best_hyps_descending = sorted(hypotheses[b],
key=lambda x: x[0],
reverse=True)
dbg = np.array(
[hyp[1].cpu().numpy() for hyp in best_hyps_descending])
print(dbg.shape, dbg[0])
if knowledgebase is not None:
print(hypotheses[b][0], type(hypotheses[b][0]))
scores, hyps = zip(*hypotheses[b])
sort_key = np.array(scores)
hyps = np.array([hyp.cpu().numpy() for hyp in hyps])
# indices that would sort hyp[b] in descending order of beam score
best_hyps_idx = np.argsort(sort_key)[::-1].copy()
best_hyps_d_ = hyps[best_hyps_idx]
# sanity check implementation
try:
assert set([(t1 == t2).all()
for t1, t2 in zip(best_hyps_d_, dbg)
]) == {True}
except Exception as e:
print(best_hyps_d_.dtype)
print(dbg.dtype)
print([[t.dtype for t in tup]
for tup in (best_hyps_d_, dbg)])
raise e
assert n_best == 1, f"This is a massive clutch: Currently indexing only top 1 beam while saving attentions"
# FIXME TODO NOTE XXX
if 0 not in att_alive.shape:
best_atts_d_ = [
stacked_attention_scores[b][best_hyps_idx[0]]
]
else:
best_atts_d_ = None
best_kb_atts_d_ = [
stacked_kb_att_scores[b][best_hyps_idx[0]]
]
# TODO replace best_hyps_descending with best_hyps_d_ FIXME XXX (after cluster beam test)
for n, (score, pred) in enumerate(best_hyps_descending):
if n >= n_best:
break
results["scores"][b].append(score)
results["predictions"][b].append(pred)
if knowledgebase is not None:
if best_atts_d_ is not None:
results["att_scores"][b].append(
best_atts_d_[n])
results["kb_att_scores"][b].append(
best_kb_atts_d_[n])
non_finished = end_condition.eq(False).nonzero(
as_tuple=False).view(-1) # batch
# if all sentences are translated, no need to go further
# pylint: disable=len-as-condition
if len(non_finished) == 0:
break
# remove finished batches for the next step
batch_index = batch_index.index_select(0, non_finished)
batch_offset = batch_offset.index_select(0, non_finished)
topk_log_probs = topk_log_probs.index_select(0, non_finished)
alive_seq = predictions.index_select(0, non_finished) \
.view(-1, alive_seq.size(-1))
if knowledgebase is not None:
# briefly go to
# batch x k x time x att
# to easily index_select finished batches in batch dimension 0
# afterwards reshape to
# batch * k x time x att
# where att = src_len for alive attentions, and att = kb_size for kb_attentions alive
if 0 not in att_alive.shape:
att_alive = att_alive.view(-1, size, att_alive.size(-2), att_alive.size(-1)) \
.index_select(0, non_finished)
att_alive = att_alive.view(-1, att_alive.size(-2),
att_alive.size(-1))
kb_att_alive = kb_att_alive.view(-1, size, kb_att_alive.size(-2), kb_att_alive.size(-1)) \
.index_select(0, non_finished)
kb_att_alive = kb_att_alive.view(-1, kb_att_alive.size(-2),
kb_att_alive.size(-1))
# reorder indices, outputs and masks using this
select_indices = batch_index.view(-1)
encoder_output = encoder_output.index_select(0, select_indices)
src_mask = src_mask.index_select(0, select_indices) # for transformer
if hidden is not None and not transformer:
# reshape hidden to correct shape for next step
if isinstance(hidden, tuple):
# for LSTMs, states are tuples of tensors
h, c = hidden
h = h.index_select(1, select_indices)
c = c.index_select(1, select_indices)
hidden = (h, c)
else:
# for GRUs, states are single tensors
hidden = hidden.index_select(1, select_indices)
if att_vectors is not None:
if isinstance(att_vectors, tuple):
att_vectors = tuple([
att_v.index_select(0, select_indices)
for att_v in att_vectors
])
else:
att_vectors = att_vectors.index_select(0, select_indices)
if knowledgebase is not None:
kb_values = kb_values.index_select(0, select_indices)
if isinstance(kb_keys, tuple):
kb_keys = tuple([
key_dim.index_select(0, select_indices)
for key_dim in kb_keys
])
else:
kb_keys = kb_keys.index_select(0, select_indices)
if util_dims_cache is not None:
util_dims_cache = [
utils.index_select(0, select_indices)
for utils in util_dims_cache if utils is not None
]
if kb_feed_hidden_cache is not None:
try:
kb_feed_hidden_cache = [
kbf_hidden.index_select(0, select_indices)
for kbf_hidden in kb_feed_hidden_cache
if kbf_hidden is not None
]
except IndexError as IE:
print(hidden[0].shape)
print([t.shape for t in kb_feed_hidden_cache])
print(select_indices)
print(select_indices.shape)
print(size)
print(generator.output_size)
raise IE
kb_mask_before_index = kb_mask.shape
kb_mask = kb_mask.index_select(0, select_indices)
kb_mask_after_index = kb_mask.shape
def pad_and_stack_hyps(hyps, pad_value):
# hyps is arrays of hypotheses
filled = np.ones(
(len(hyps), max([h.shape[0]
for h in hyps])), dtype=int) * pad_value
for j, h in enumerate(hyps):
for k, i in enumerate(h):
filled[j, k] = i
return filled
def pad_and_stack_attention_matrices(atts, pad_value=float("-inf")):
assert len(list(set([att.shape[1] for att in atts]))) == 1, \
f"attention matrices have differing attention key bag dimension: {[att.shape[1] for att in atts]}"
# atts is array of attention matrices, each of dims time x att_dim, where time dims may vary from matrix to matrix
# NOTE pad_value is used in model.postprocess to recover original part of matrix
try:
filled = np.ones(
(len(atts), max([att.shape[-2]
for att in atts]), atts[0].shape[-1]),
dtype=atts[0].dtype)
filled = filled * pad_value
except Exception as e:
print(atts[0].shape)
raise e
for batch_element_index, attention_matrix in enumerate(atts):
for t, attentions_at_decoding_step in enumerate(attention_matrix):
for attention_key, score in enumerate(
attentions_at_decoding_step):
filled[batch_element_index, t, attention_key] = score
return filled # b x time x attention keys
# from results to stacked outputs
assert n_best == 1
# only works for n_best=1 for now
# final_outputs = batch x time
final_outputs = pad_and_stack_hyps(
[r[0].cpu().numpy() for r in results["predictions"]],
pad_value=pad_index)
if knowledgebase is not None:
# TODO FIXME confirm this implementation
# stacked_attention_scores: batch x max output len x src len
if len(results["att_scores"][0]):
stacked_attention_scores = pad_and_stack_attention_matrices(
[atts[0].T for atts in results["att_scores"]])
else:
stacked_attention_scores = None
# stacked_kb_att_scores: batch x max output len x kb
stacked_kb_att_scores = pad_and_stack_attention_matrices(
[kb_atts[0].T for kb_atts in results["kb_att_scores"]])
return final_outputs, stacked_attention_scores, stacked_kb_att_scores