def draw_p_noise(self, batch_size, edit_dim):
rand_draw = GPUVariable(torch.randn(batch_size, edit_dim))
rand_draw = rand_draw / torch.norm(rand_draw, p=2, dim=1).expand(
batch_size, edit_dim)
rand_norms = (torch.rand(batch_size, 1) * self.norm_max).expand(
batch_size, edit_dim)
return rand_draw * GPUVariable(rand_norms)
def sample_vMF(self, mu, kappa):
"""vMF sampler in pytorch.
Args:
mu (Tensor): of shape (batch_size, 2*word_dim)
kappa (Float): controls dispersion. kappa of zero is no dispersion.
"""
batch_size, id_dim = mu.size()
result_list = []
for i in range(batch_size):
munorm = mu[i].norm().expand(id_dim)
munoise = self.add_norm_noise(munorm, self.norm_eps)
if float(mu[i].norm().data.cpu().numpy()) > 1e-10:
# sample offset from center (on sphere) with spread kappa
w = self._sample_weight(kappa, id_dim)
wtorch = GPUVariable(w*torch.ones(id_dim))
# sample a point v on the unit sphere that's orthogonal to mu
v = self._sample_orthonormal_to(mu[i]/munorm, id_dim)
# compute new point
scale_factr = torch.sqrt(GPUVariable(torch.ones(id_dim)) - torch.pow(wtorch,2))
orth_term = v * scale_factr
muscale = mu[i] * wtorch / munorm
sampled_vec = (orth_term + muscale)*munoise
else:
rand_draw = GPUVariable(torch.randn(id_dim))
rand_draw = rand_draw / torch.norm(rand_draw, p=2).expand(id_dim)
rand_norms = (torch.rand(1) * self.norm_eps).expand(id_dim)
sampled_vec = rand_draw*GPUVariable(rand_norms)#mu[i]
result_list.append(sampled_vec)
return torch.stack(result_list,0)
def test_split(self):
input_embeds = GPUVariable(torch.LongTensor([
# batch item 1
[
[1, 2], [2, 3], [5, 6]
],
# batch item 2
[
[4, 8], [3, 5], [0, 0]
],
]))
input_mask = GPUVariable(torch.FloatTensor([
[1, 1, 1],
[1, 1, 0],
]))
sb = SequenceBatch(input_embeds, input_mask)
elements = sb.split()
input_list = [e.values for e in elements]
mask_list = [e.mask for e in elements]
assert len(input_list) == 3
assert_tensor_equal(input_list[0], [[1, 2], [4, 8]])
assert_tensor_equal(input_list[1], [[2, 3], [3, 5]])
assert_tensor_equal(input_list[2], [[5, 6], [0, 0]])
assert len(mask_list) == 3
assert_tensor_equal(mask_list[0], [[1], [1]])
assert_tensor_equal(mask_list[1], [[1], [1]])
assert_tensor_equal(mask_list[2], [[1], [0]])
def test_reduce_max(self, some_seq_batch):
with pytest.raises(ValueError):
# should complain about empty sequence
SequenceBatch.reduce_max(some_seq_batch)
values = GPUVariable(
torch.FloatTensor([
[
[1, 2], [4, 5], [4, 4]
], # actual max is in later elements, but shd be suppressed by mask
[[0, -4], [43, -5],
[-1, -20]], # note that all elements in 2nd dim are negative
]))
mask = GPUVariable(torch.FloatTensor([
[1, 0, 0],
[1, 1, 0],
]))
seq_batch = SequenceBatch(values, mask)
result = SequenceBatch.reduce_max(seq_batch)
assert_tensor_equal(result, [
[1, 2],
[43, -4],
])
def from_sequences(cls, sequences, vocab, min_seq_length=0):
"""Convert a batch of sequences into a SequenceBatch.
Args:
sequences (list[list[unicode]])
vocab (WordVocab)
min_seq_length (int): enforce that the Tensor representing the SequenceBatch have at least
this many columns.
Returns:
SequenceBatch
"""
batch_size = len(sequences)
if batch_size == 0:
seq_length = 0
else:
seq_length = max(len(seq)
for seq in sequences) # max seq length in batch
seq_length = max(
seq_length,
min_seq_length) # make sure it is at least min_seq_length
shape = (batch_size, seq_length)
values = np.zeros(shape, dtype=np.int64) # pad with zeros
mask = np.zeros(shape, dtype=np.float32)
for i, seq in enumerate(sequences):
for j, word in enumerate(seq):
values[i, j] = vocab.word2index(word)
mask[i, j] = 1.0
return SequenceBatch(GPUVariable(torch.from_numpy(values)),
GPUVariable(torch.from_numpy(mask)))
def sample_vMF(self, mu, kappa):
"""vMF sampler in pytorch.
Args:
mu (Tensor): of shape (batch_size, 2*word_dim)
kappa (Float): controls dispersion. kappa of inf is no dispersion.
"""
batch_size, id_dim = mu.size()
result_list = []
for i in range(batch_size):
munorm = mu[i].norm().expand(id_dim)
# sample offset from center (on sphere) with spread kappa
w = self._sample_weight(kappa, id_dim)
wtorch = GPUVariable(w * torch.ones(id_dim))
# sample a point v on the unit sphere that's orthogonal to mu
v = self._sample_orthonormal_to(mu[i] / munorm, id_dim)
# compute new point
scale_factr = torch.sqrt(
GPUVariable(torch.ones(id_dim)) - torch.pow(wtorch, 2))
orth_term = v * scale_factr
muscale = mu[i] * wtorch / munorm
sampled_vec = (orth_term + muscale)
result_list.append(sampled_vec)
return torch.stack(result_list, 0)
def encoder_generate_edits(self, encoder_input):
""" Draw uniform random vectors with given norm, and use as edit vector """
source_words = encoder_input.source_words
source_word_embeds = self.editor.encoder.token_embedder.embed_seq_batch(source_words)
insert_embeds = self.editor.encoder.token_embedder.embed_seq_batch(encoder_input.insert_words)
delete_embeds = self.editor.encoder.token_embedder.embed_seq_batch(encoder_input.delete_words)
insert_embeds_exact = self.editor.encoder.token_embedder.embed_seq_batch(encoder_input.insert_exact_words)
delete_embeds_exact = self.editor.encoder.token_embedder.embed_seq_batch(encoder_input.delete_exact_words)
source_encoder_output = self.editor.encoder.source_encoder(source_word_embeds.split())
source_embeds_list = source_encoder_output.combined_states
source_embeds = SequenceBatch.cat(source_embeds_list)
# the final hidden states in both the forward and backward direction, concatenated
source_embeds_final = torch.cat(source_encoder_output.final_states, 1) # (batch_size, hidden_dim)
edit_encoded = self.editor.encoder.edit_encoder(insert_embeds, insert_embeds_exact, delete_embeds,
delete_embeds_exact)
# the random vector is computed as in rand_p_noise (see in edit_encoder)
torch.manual_seed(7)
batch_size, edit_dim = edit_encoded.size()
rand_draw = GPUVariable(torch.randn(batch_size, edit_dim))
rand_draw = rand_draw / torch.norm(rand_draw, p=2, dim=1).expand(batch_size, edit_dim)
rand_norms = (torch.rand(batch_size, 1) * self.editor.encoder.edit_encoder.norm_max).expand(batch_size,
edit_dim)
edit_embed = rand_draw * GPUVariable(rand_norms)
agenda = self.editor.encoder.agenda_maker(source_embeds_final, edit_embed)
return EncoderOutput(source_embeds, insert_embeds_exact, delete_embeds_exact, agenda)
def test_embed_indices(self, embedder):
indices = GPUVariable(torch.LongTensor([
[0, 1],
[2, 2],
[4, 5],
]))
embeds = embedder.embed_indices(indices)
assert_tensor_equal(embeds, [
[[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0]],
[[0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1]],
])
indices = GPUVariable(
torch.LongTensor([
[[0, 1], [1, 0]],
[[2, 2], [3, 2]],
]))
embeds = embedder.embed_indices(indices)
assert_tensor_equal(embeds, [
[[[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0]],
[[0, 1, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0]]],
[[[0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0]],
[[0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0]]],
])
def _drop_seq_batch(self, seq_batch, word_vocab, keep_rate):
batch_sz, max_seq_len = seq_batch.values.size()
keep = torch.rand(batch_sz, max_seq_len) < keep_rate
keep[:,0] = torch.ones(batch_sz, 1) # do not drop start token
kept = seq_batch.values * GPUVariable(torch.ByteTensor.long(keep))
unkd = GPUVariable(torch.ByteTensor.long((1 - keep) * word_vocab.word2index(word_vocab.UNK)))
values = kept + unkd
return SequenceBatch(values, seq_batch.mask)
def clear_cache(self):
# Keep empty tuple cached, for SequenceBatch
self._cache.clear()
self._cache.cache(
[tuple()], [
(GPUVariable(torch.zeros(self._embed_dim)),
SequenceBatchElement(
GPUVariable(torch.zeros(1, self._embed_dim)),
GPUVariable(torch.zeros(1)))
)])
def _query_embeds(self, states, query_entries):
"""Given a batch of states, embed the keys and values of each state's
query.
Args:
states (list[MiniWoBState])
Returns:
entry_embeds (SequenceBatch): batch x num_keys x (2 * embed_dim)
the keys and values concatenated
"""
fields_batch = [state.fields for state in states]
# list[list[list[unicode]]] (batch x num_keys x key length)
values_batch = [[word_tokenize(value) for value in fields.values] for
fields in fields_batch]
keys_batch = [[word_tokenize(key) for key in fields.keys] for fields
in fields_batch]
# Pad
batch_size = len(fields_batch)
max_num_fields = max(len(values) for values in values_batch)
max_num_fields = max(max_num_fields, 1) # Ensure non-empty
mask = torch.ones(batch_size, max_num_fields)
assert len(keys_batch) == len(values_batch) == len(mask)
for keys, values, submask in zip(keys_batch, values_batch, mask):
assert len(keys) == len(values)
if len(keys) < max_num_fields:
submask[len(keys):] = 0.
keys.extend(
[[UtteranceVocab.PAD] for _ in xrange(
max_num_fields - len(keys))])
values.extend(
[[UtteranceVocab.PAD] for _ in xrange(
max_num_fields - len(values))])
# Flatten to list[list[unicode]] (batch * num_keys) x key length
keys_batch = flatten(keys_batch)
values_batch = flatten(values_batch)
# Embed and mask (batch * num_keys) x embed_dim
key_embeds, _ = self._utterance_embedder(keys_batch)
key_embeds = key_embeds.view(
batch_size, max_num_fields, self._utterance_embedder.embed_dim)
value_embeds, _ = self._utterance_embedder(values_batch)
value_embeds = value_embeds.view(
batch_size, max_num_fields, self._utterance_embedder.embed_dim)
key_embeds = SequenceBatch(key_embeds, GPUVariable(mask))
value_embeds = SequenceBatch(value_embeds, GPUVariable(mask))
entry_embed_values = torch.cat(
[key_embeds.values, value_embeds.values], 2)
entry_embeds = SequenceBatch(entry_embed_values, key_embeds.mask)
return entry_embeds
def some_seq_batch(self):
values = GPUVariable(torch.FloatTensor([
[[1, 2], [4, 5], [4, 4]],
[[0, 4], [43, 5], [-1, 20]],
[[-1, 20], [43, 5], [0, 0]],
]))
mask = GPUVariable(torch.FloatTensor([
[1, 1, 0],
[1, 0, 0],
[0, 0, 0],
]))
return SequenceBatch(values, mask)
def forward(self, dom_elements, alignment_fields):
"""Computes the alignments. An element aligns iff elem.text
in utterance and elem.text != ""
Args:
dom_elements (list[list[DOMElement]]): batch of set of DOM
elements (padded to be unragged)
alignment_fields (list[Fields]): batch of fields. Alignments
computed with the values of the fields.
Returns:
Variable[FloatTensor]: batch x num_elems x embed_dim
The aligned embeddings per DOM element
"""
batch_size = len(dom_elements)
assert batch_size > 0
num_dom_elems = len(dom_elements[0])
assert num_dom_elems > 0
# mask batch_size x num_dom_elems x num_buckets
alignments = np.zeros(
(batch_size, num_dom_elems, self._num_buckets)).astype(np.float32)
# Calculate the alignment matrix between elems and fields
for batch_idx in xrange(len(dom_elements)):
for dom_idx, dom in enumerate(dom_elements[batch_idx]):
keys = alignment_fields[batch_idx].keys
vals = alignment_fields[batch_idx].values
for key, val in zip(keys, vals):
if dom.text and dom.text in val:
align_idx = self._keys2index.word2index(key)
alignments[batch_idx, dom_idx, align_idx] = 1.
# Flatten alignments for SequenceBatch
# (batch * num_dom_elems) x num_buckets
alignments = GPUVariable(
torch.from_numpy(
alignments.reshape(
(batch_size * num_dom_elems, self._num_buckets))))
# (batch * num_dom_elems) x num_buckets x embed_dim
expanded_alignment_embeds = self._alignment_embeds.expand(
batch_size * num_dom_elems, self._num_buckets, self.embed_dim)
alignment_seq_batch = SequenceBatch(expanded_alignment_embeds,
alignments,
left_justify=False)
# (batch * num_dom_elems) x alignment_embed_dim
alignment_embeds = SequenceBatch.reduce_sum(alignment_seq_batch)
return alignment_embeds.view(batch_size, num_dom_elems, self.embed_dim)
def from_sequences(cls,
sequences,
vocab_or_vocabs,
min_seq_length=0,
volatile=False):
"""Convert a batch of sequences into a SequenceBatch.
Args:
sequences (list[list[unicode]])
vocab_or_vocabs (WordVocab|list[WordVocab]): either a single vocab, or a list of vocabs, one per sequence
min_seq_length (int): enforce that the Tensor representing the SequenceBatch have at least
this many columns.
volatile (bool): whether to make Variables volatile (don't track grads)
Returns:
SequenceBatch
"""
# determine dimensions
batch_size = len(sequences)
if batch_size == 0:
seq_length = 0
else:
seq_length = max(len(seq)
for seq in sequences) # max seq length in batch
seq_length = max(
seq_length,
min_seq_length) # make sure it is at least min_seq_length
shape = (batch_size, seq_length)
# set up vocabs
if isinstance(vocab_or_vocabs, list):
vocabs = vocab_or_vocabs
assert len(vocabs) == batch_size
else:
# duplicate a single vocab
assert isinstance(vocab_or_vocabs, Vocab)
vocabs = [vocab_or_vocabs] * batch_size
# build arrays
values = np.zeros(shape, dtype=np.int64) # pad with zeros
mask = np.zeros(shape, dtype=np.float32)
for i, (seq, vocab) in enumerate(izip(sequences, vocabs)):
for j, word in enumerate(seq):
values[i, j] = vocab.word2index(word)
mask[i, j] = 1.0
return SequenceBatch(
GPUVariable(torch.from_numpy(values), volatile=volatile),
GPUVariable(torch.from_numpy(mask), volatile=volatile))
def test_gated_update():
h = GPUVariable(torch.FloatTensor([
[1, 2, 3],
[4, 5, 6],
]))
h_new = GPUVariable(torch.FloatTensor([
[-1, 2, 3],
[4, 8, 0],
]))
update = GPUVariable(torch.FloatTensor([[0], [1]
])) # only update the second row
out = gated_update(h, h_new, update)
assert_tensor_equal(out, [[1, 2, 3], [4, 8, 0]])
def add_norm_noise(self, munorm, eps):
"""
KL loss is - log(maxvalue/eps)
cut at maxvalue-eps, and add [0,eps] noise.
"""
trand = torch.rand(1).expand(munorm.size())*eps
return (self.normclip(munorm) + GPUVariable(trand))
def __new__(cls, candidate_selector, candidate_probs, candidates=None):
"""Select candidates.
Args:
candidate_selector (Callable[[Variable[FloatTensor]], list[int]]):
takes candidate_probs and returns a batch of selections (integers)
candidate_probs (Variable[FloatTensor]): of shape (batch_size, num_candidates)
candidates (list[list[object]]): a batch of candidate sets, where each
set is a list of candidates
"""
indices = candidate_selector(candidate_probs)
if candidates is not None:
assert len(candidates) == len(indices)
selected = [thing_list[index] for thing_list, index in zip(
candidates, indices)]
else:
selected = None
indices = GPUVariable(torch.LongTensor(indices)) # (batch_size,)
probs = torch.gather(candidate_probs, 1, torch.unsqueeze(indices, 1)) # (batch_size, 1)
probs = torch.squeeze(probs, 1) # (batch_size)
cls._check_shapes(selected, probs, indices)
self = super(Selection, cls).__new__(cls, selected, probs, indices, candidates, candidate_probs)
return self
def forward(self, memory_cells, query):
"""Performs sentinel attention with a sentinel of 0. Returns the
AttentionOutput where the weights do not include the sentinel weight.
Args:
memory_cells (Variable[FloatTensor]): batch x num_cells x cell_dim
query (Variable[FloatTensor]): batch x query_dim
Returns:
AttentionOutput: weights do not include sentinel weights
"""
batch_size, _, cell_dim = memory_cells.values.size()
sentinel = self._sentinel_embed.expand(batch_size, 1, cell_dim)
sentinel_mask = GPUVariable(torch.ones(batch_size, 1))
cell_values_with_sentinel = torch.cat([memory_cells.values, sentinel],
1)
cell_masks_with_sentinel = torch.cat(
[memory_cells.mask, sentinel_mask], 1)
cells_with_sentinel = SequenceBatch(cell_values_with_sentinel,
cell_masks_with_sentinel,
left_justify=False)
attention_output = super(SentinelAttention,
self).forward(cells_with_sentinel, query)
weights_with_sentinel = attention_output.weights
# TODO: Bring this line in after torch v0.2.0
# weights_without_sentinel = weights_with_sentinel[batch_size, :-1]
# attention_output = AttentionOutput(
# weights=weights_without_sentinel, context=attention_output.context)
return attention_output
def __init__(self, num_embeddings, embedding_dim,
initial_embeddings, **kwargs):
"""Constructs TrainFlagEmbedding with embeddings initialized with
initial_embeddings.
Args:
num_embeddings (int)
embedding_dim (int)
initial_embeddings (np.array): (num_embeddings, embedding_dim)
trainable (bool): if False, weights matrix will not change.
(default True)
kwargs: all other supported keywords in torch.nn.Embeddings.
"""
super(TrainFlagEmbedding, self).__init__()
trainable = kwargs.pop("trainable", True)
self._trainable = trainable
if trainable:
embedding = Embedding(
num_embeddings, embedding_dim, **kwargs)
embedding.weight.data.set_(
torch.from_numpy(initial_embeddings))
self._embedding = embedding
self._weight = embedding.weight
else:
self._weight = GPUVariable(
torch.from_numpy(initial_embeddings))
def forward(self, states):
states = GPUVariable(
torch.FloatTensor(
np.stack(state.goal.all_but_cum_reward for state in states)))
hidden = F.relu(self._layer1(states))
output = F.relu(self._layer2(hidden))
return output
def generate_edits(self, encoder_input, norm):
""" Draw uniform random vectors with given norm, and use as edit vector """
source_words = encoder_input.source_words
source_word_embeds = self.token_embedder.embed_seq_batch(source_words)
insert_embeds = self.token_embedder.embed_seq_batch(
encoder_input.insert_words)
delete_embeds = self.token_embedder.embed_seq_batch(
encoder_input.delete_words)
insert_embeds_exact = self.token_embedder.embed_seq_batch(
encoder_input.insert_exact_words)
delete_embeds_exact = self.token_embedder.embed_seq_batch(
encoder_input.delete_exact_words)
source_encoder_output = self.source_encoder(source_word_embeds.split())
source_embeds_list = source_encoder_output.combined_states
source_embeds = SequenceBatch.cat(source_embeds_list)
# the final hidden states in both the forward and backward direction, concatenated
source_embeds_final = torch.cat(source_encoder_output.final_states,
1) # (batch_size, hidden_dim)
edit_encoded = self.edit_encoder(insert_embeds, delete_embeds)
rand_vec = torch.randn(edit_encoded.shape())
edit_embed = GPUVariable(
rand_vec / torch.norm(rand_vec, 2, dim=1).expand_as(rand_vec) *
norm)
agenda = self.agenda_maker(source_embeds_final, edit_embed)
return EncoderOutput(source_embeds, insert_embeds_exact,
delete_embeds_exact, agenda)
def forward(self, states):
cum_rewards = torch.LongTensor(
np.stack(state.goal.cum_reward for state in states))
cum_rewards = cum_rewards.view(-1, 1)
states = GPUVariable(
torch.FloatTensor(
np.stack(state.goal.all_but_cum_reward for state in states)))
reward_one_hot = torch.FloatTensor(cum_rewards.shape[0], 5)
reward_one_hot.zero_()
reward_one_hot.scatter_(1, cum_rewards, 1)
reward_one_hot = GPUVariable(reward_one_hot)
reward_embed = F.relu(self._reward_embedder(reward_one_hot))
state_embed = F.relu(self._layer1(states))
hidden = torch.cat([state_embed, reward_embed], dim=1)
output = F.relu(self._layer2(hidden))
return output
def forward(self, encoder_input, train_mode=True):
"""Encode.
Args:
encoder_input (EncoderInput)
Returns:
EncoderOutput, cost (0 in this case)
"""
context_agenda, all_channel_embeds = self.ctx_code_out(encoder_input)
if self.use_vae and train_mode:
if self.use_target:
target_agenda = self.target_out(encoder_input)
vae_agenda, vae_loss = self.vae_wrap(
context_agenda + target_agenda, True)
else:
vae_agenda, vae_loss = self.vae_wrap(context_agenda, True)
else:
vae_agenda = context_agenda / torch.sqrt(
torch.sum(context_agenda**2.0,
dim=1)).expand_as(context_agenda)
vae_loss = GPUVariable(torch.zeros(1))
return EncoderOutput(all_channel_embeds, vae_agenda,
encoder_input.token_embedder), vae_loss
def _interpolate_examples(self, ex_a, ex_b):
"""
Args:
[unicode], [unicode]
Returns:
[[unicode]]
"""
examples = [ex_a, ex_b]
enc_input = self.encoder.preprocess(examples)
agenda, _ = self.encoder(enc_input)
agenda_ = agenda.data.cpu().numpy()
agendas = self._interpolate_vectors(agenda_[0, :], agenda_[1, :])
samples = []
for i, ag_ in enumerate(agendas):
ag = GPUVariable(torch.FloatTensor(ag_.reshape(1,
self.agenda_dim)))
# beam, _ = self.sample_decoder.decode(
# [0], ag, beam_size=1, prefix_hints=[[]])
beam, _ = self.beam_decoder.decode([0],
ag,
weighted_value_estimators=[],
beam_size=1,
prefix_hints=[[]],
sibling_penalty=0)
samples.append(beam[0][0])
return samples
def seq_batch_noise(self, seq_batch, draw_noise):
"""
Returns a noisy version of seq_batch, in which every vector is noisy and unit norm.
:param seq_batch(SequenceBatch): a sequence batch of elements
:return: noisy version of seq-batch
"""
values = seq_batch.values
mask = seq_batch.mask
batch_size, max_edits, w_embed_size = values.size()
new_values = GPUVariable(
torch.from_numpy(
np.zeros((batch_size, max_edits, w_embed_size),
dtype=np.float32)))
m_expand = mask.unsqueeze(2).expand(batch_size, max_edits,
w_embed_size)
for max_edit in range(max_edits):
phint = self.sample_vMF(values[:, max_edit, :], self.noise_scaler)
prand = self.draw_p_noise(batch_size, w_embed_size)
new_values[:,
max_edit, :] = phint * m_expand[:,
max_edit, :] + prand * (
1 -
m_expand[:,
max_edit, :]
)
return SequenceBatch(values=new_values * draw_noise, mask=mask)
def test_embed(self):
sequences = [
[],
[1, 2, 3],
[3, 3],
[2]
]
vocab = SimpleVocab([0, 1, 2, 3, 4])
indices = SequenceBatch.from_sequences(sequences, vocab)
embeds = GPUVariable(torch.FloatTensor([
[0, 0],
[2, 2], # 1
[3, 4], # 2
[-10, 1], # 3
[11, -1] # 4
]))
embedded = SequenceBatch.embed(indices, embeds)
correct = np.array([
[[0, 0], [0, 0], [0, 0]],
[[2, 2], [3, 4], [-10, 1]],
[[-10, 1], [-10, 1], [0, 0]],
[[3, 4], [0, 0], [0, 0]]
], dtype=np.float32)
assert_tensor_equal(embedded.values, correct)
def _variable(self, v):
batch_size = int(v.size()[0])
indices = np.expand_dims(np.arange(batch_size), 1) # (batch_size, 1)
dup_indices = np.tile(indices, (1, self.beam_size)) # (batch_size, beam_size)
dup_indices = dup_indices.flatten() # (batch_size * beam_size)
dup_indices = GPUVariable(torch.from_numpy(dup_indices))
return torch.index_select(v, 0, dup_indices)
def _pad_elements(self, dom_elems):
"""Takes a batch of dom element lists. Returns the batch with pads so
that each batch is the same length, and masks.
Args:
dom_elems (list[list[DOMElement]]): unpadded batch
Returns:
list[list[DOMElement]], Variable[FloatTensor]: batch x num_elems
"""
# Pad everything to be the same as longest list
num_elems = max(len(dom_list) for dom_list in dom_elems)
mask = torch.ones(len(dom_elems), num_elems)
for dom_list, submask in zip(dom_elems, mask):
# Avoid empty slice torch errors
if len(dom_list) < num_elems:
submask[len(dom_list): num_elems] = 0.
dom_list.extend(
[DOMElementPAD()] * (num_elems - len(dom_list)))
# TODO: Get rid of these hack.
# TODO(kelvin): WARNING: this hack also means that we cannot ATTEND to these items
for i, elem in enumerate(dom_list):
# never click text elements
if elem.tag == "t":
submask[i] = 0.
return dom_elems, GPUVariable(mask)
def test_log_sum_exp(self):
values = GPUVariable(torch.FloatTensor([
[0, 1, -2, -3],
[-2, -5, 1, 0],
]))
mask = GPUVariable(torch.FloatTensor([
[1, 1, 1, 0],
[1, 1, 0, 0],
]))
seq_batch = SequenceBatch(values, mask, left_justify=False)
result = SequenceBatch.log_sum_exp(seq_batch)
correct = [1.3490122167681864, -1.9514126484262577]
assert_tensor_equal(result, correct)
def forward(self, states):
# Normal embeddings
states = GPUVariable(
torch.FloatTensor(np.stack(state.goal.numpy()
for state in states)))
hidden = F.relu(self._layer1(states))
output = F.relu(self._layer2(hidden))
return output
