def buffered_mask(self, tensor):
dim = tensor.size(-1)
if self._mask is None:
self._mask = torch.triu(utils.fill_with_neg_inf(tensor.new(dim, dim)), 1)
if self._mask.size(0) < dim:
self._mask = torch.triu(utils.fill_with_neg_inf(self._mask.resize_(dim, dim)), 1)
return self._mask[:dim, :dim]
def forward(self, input_features, adj):
#x = self.conv1(input_features, adj)
#x = self.bn1(x)
#x = self.act(x)
#x = self.conv2(x, adj)
#x = self.bn2(x)
# pool over all nodes
#graph_h = self.pool_graph(x)
graph_h = input_features.view(-1, self.max_num_nodes * self.max_num_nodes)
# vae
h_decode, z_mu, z_lsgms = self.vae(graph_h)
out = F.sigmoid(h_decode)
out_tensor = out.cpu().data
recon_adj_lower = self.recover_adj_lower(out_tensor)
recon_adj_tensor = self.recover_full_adj_from_lower(recon_adj_lower)
# set matching features be degree
out_features = torch.sum(recon_adj_tensor, 1)
adj_data = adj.cpu().data[0]
adj_features = torch.sum(adj_data, 1)
S = self.edge_similarity_matrix(adj_data, recon_adj_tensor, adj_features, out_features,
self.deg_feature_similarity)
# initialization strategies
init_corr = 1 / self.max_num_nodes
init_assignment = torch.ones(self.max_num_nodes, self.max_num_nodes) * init_corr
#init_assignment = torch.FloatTensor(4, 4)
#init.uniform(init_assignment)
assignment = self.mpm(init_assignment, S)
#print('Assignment: ', assignment)
# matching
# use negative of the assignment score since the alg finds min cost flow
row_ind, col_ind = scipy.optimize.linear_sum_assignment(-assignment.numpy())
print('row: ', row_ind)
print('col: ', col_ind)
# order row index according to col index
#adj_permuted = self.permute_adj(adj_data, row_ind, col_ind)
adj_permuted = adj_data
adj_vectorized = adj_permuted[torch.triu(torch.ones(self.max_num_nodes,self.max_num_nodes) )== 1].squeeze_()
adj_vectorized_var = Variable(adj_vectorized).cuda()
#print(adj)
#print('permuted: ', adj_permuted)
#print('recon: ', recon_adj_tensor)
adj_recon_loss = self.adj_recon_loss(adj_vectorized_var, out[0])
print('recon: ', adj_recon_loss)
print(adj_vectorized_var)
print(out[0])
loss_kl = -0.5 * torch.sum(1 + z_lsgms - z_mu.pow(2) - z_lsgms.exp())
loss_kl /= self.max_num_nodes * self.max_num_nodes # normalize
print('kl: ', loss_kl)
loss = adj_recon_loss + loss_kl
return loss
def get_subsequent_mask(seq): """ For preventing lookahead """ batch_size,seq_len = seq.size() subsequent_mask = torch.triu(torch.ones((seq_len,seq_len),device=seq.device,dtype=torch.uint8),diagonal=1) subsequent_mask = subsequent_mask.unsqueeze(0).expand(batch_size,-1,-1) # size [batch_size x seq_len x seq_len] return subsequent_mask
def __call__(self, y_pred, y_true=None):
"""
y_pred should be two projections
"""
covar_mat = th.abs(th_matrixcorr(y_pred[0].data, y_pred[1].data))
upper_sum = th.sum(th.triu(covar_mat,1))
lower_sum = th.sum(th.tril(covar_mat,-1))
self.anticorr_sum += upper_sum
self.anticorr_sum += lower_sum
self.total_count += covar_mat.size(0)*(covar_mat.size(1) - 1)
return self.anticorr_sum / self.total_count
def btriunpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True):
r"""Unpacks the data and pivots from a batched LU factorization (btrifact) of a tensor.
Returns a tuple indexed by:
0: The pivots.
1: The L tensor.
2: The U tensor.
Arguments:
LU_data (Tensor): the packed LU factorization data
LU_pivots (Tensor): the packed LU factorization pivots
unpack_data (bool): flag indicating if the data should be unpacked
unpack_pivots (bool): tlag indicating if the pivots should be unpacked
Example::
>>> A = torch.randn(2, 3, 3)
>>> A_LU, pivots = A.btrifact()
>>> P, a_L, a_U = torch.btriunpack(A_LU, pivots)
>>>
>>> # test that (P, A_L, A_U) gives LU factorization
>>> A_ = torch.bmm(P, torch.bmm(A_L, A_U))
>>> assert torch.equal(A_, A) == True # can recover A
"""
nBatch, sz, _ = LU_data.size()
if unpack_data:
I_U = torch.triu(torch.ones(sz, sz)).type_as(LU_data).byte().unsqueeze(0).expand(nBatch, sz, sz)
I_L = 1 - I_U
L = LU_data.new(LU_data.size()).zero_()
U = LU_data.new(LU_data.size()).zero_()
I_diag = torch.eye(sz).type_as(LU_data).byte().unsqueeze(0).expand(nBatch, sz, sz)
L[I_diag] = 1.0
L[I_L] = LU_data[I_L]
U[I_U] = LU_data[I_U]
else:
L = U = None
if unpack_pivots:
P = torch.eye(sz).type_as(LU_data).unsqueeze(0).repeat(nBatch, 1, 1)
for i in range(nBatch):
for j in range(sz):
k = LU_pivots[i, j] - 1
t = P[i, :, j].clone()
P[i, :, j] = P[i, :, k]
P[i, :, k] = t
else:
P = None
return P, L, U
def forward(
self, query, key, value, mask_future_timesteps=False,
key_padding_mask=None, use_scalar_bias=False,
):
"""Input shape: Time x Batch x Channel
Self-attention can be implemented by passing in the same arguments for
query, key and value. Future timesteps can be masked with the
`mask_future_timesteps` argument. Padding elements can be excluded from
the key by passing a binary ByteTensor (`key_padding_mask`) with shape:
batch x src_len, where padding elements are indicated by 1s.
"""
src_len, bsz, out_channels = key.size()
tgt_len = query.size(0)
assert list(query.size()) == [tgt_len, bsz, out_channels]
assert key.size() == value.size()
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if self.downsample:
size = bsz
else:
size = bsz * self.num_heads
k = key
v = value
q = query
if self.project_input:
q = self.in_proj_q(q)
k = self.in_proj_k(k)
v = self.in_proj_v(v)
src_len = k.size()[0]
q *= self.scaling
if not self.downsample:
q = q.view(tgt_len, size, self.head_dim)
k = k.view(src_len, size, self.head_dim)
v = v.view(src_len, size, self.head_dim)
q = q.transpose(0, 1)
k = k.transpose(0, 1)
v = v.transpose(0, 1)
attn_weights = torch.bmm(q, k.transpose(1, 2))
if mask_future_timesteps:
assert query.size() == key.size(), \
'mask_future_timesteps only applies to self-attention'
attn_weights *= torch.tril(
attn_weights.data.new([1]).expand(tgt_len, tgt_len).clone(),
diagonal=-1,
)[:, ::self.head_index + 1 if self.downsample else 1].unsqueeze(0)
attn_weights += torch.triu(
attn_weights.data.new([-math.inf]).expand(tgt_len, tgt_len).clone(),
diagonal=0
)[:, ::self.head_index + 1 if self.downsample else 1].unsqueeze(0)
tgt_size = tgt_len
if use_scalar_bias:
attn_weights = scalar_bias(attn_weights, 2)
v = scalar_bias(v, 1)
tgt_size += 1
if key_padding_mask is not None:
# don't attend to padding symbols
if key_padding_mask.max() > 0:
if self.downsample:
attn_weights = attn_weights.view(bsz, 1, tgt_len, src_len)
else:
attn_weights = attn_weights.view(size, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
-math.inf,
)
attn_weights = attn_weights.view(size, tgt_len, src_len)
attn_weights = F.softmax(attn_weights, dim=-1)
attn_weights = F.dropout(attn_weights, p=self.dropout, training=self.training)
attn = torch.bmm(attn_weights, v)
if self.downsample:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.head_dim)
else:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
attn = self.out_proj(attn)
return attn, attn_weights
def subsequent_mask(size):
attn_shape = (1, size, size)
mask = torch.triu(torch.ones(attn_shape, dtype=torch.float), diagonal=1)
return mask == 0
def _forward(self, dec_inp, mems=None):
qlen, bsz = dec_inp.size()
word_emb = self.word_emb(dec_inp)
mlen = mems[0].size(0) if mems is not None else 0
klen = mlen + qlen
if self.same_length:
all_ones = word_emb.new_ones(qlen, klen)
mask_len = klen - self.mem_len
if mask_len > 0:
mask_shift_len = qlen - mask_len
else:
mask_shift_len = qlen
dec_attn_mask = (
torch.triu(all_ones, 1 + mlen) +
torch.tril(all_ones, -mask_shift_len)).bool()[:, :, None] # -1
else:
dec_attn_mask = torch.triu(word_emb.new_ones(qlen, klen),
diagonal=1 + mlen).bool()[:, :, None]
hids = []
if self.attn_type == 0: # default
pos_seq = torch.arange(klen - 1,
-1,
-1.0,
device=word_emb.device,
dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
core_out = self.drop(word_emb)
pos_emb = self.drop(pos_emb)
hids.append(core_out)
for i, layer in enumerate(self.layers):
mems_i = None if mems is None else mems[i]
core_out = layer(core_out,
pos_emb,
dec_attn_mask=dec_attn_mask,
mems=mems_i)
hids.append(core_out)
elif self.attn_type == 1: # learnable
core_out = self.drop(word_emb)
hids.append(core_out)
for i, layer in enumerate(self.layers):
if self.clamp_len > 0:
r_emb = self.r_emb[i][-self.clamp_len:]
r_bias = self.r_bias[i][-self.clamp_len:]
else:
r_emb, r_bias = self.r_emb[i], self.r_bias[i]
mems_i = None if mems is None else mems[i]
core_out = layer(core_out,
r_emb,
self.r_w_bias[i],
r_bias,
dec_attn_mask=dec_attn_mask,
mems=mems_i)
hids.append(core_out)
elif self.attn_type == 2: # absolute
pos_seq = torch.arange(klen - 1,
-1,
-1.0,
device=word_emb.device,
dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
core_out = self.drop(word_emb + pos_emb[-qlen:])
hids.append(core_out)
for i, layer in enumerate(self.layers):
mems_i = None if mems is None else mems[i]
if mems_i is not None and i == 0:
mems_i += pos_emb[:mlen]
core_out = layer(core_out,
dec_attn_mask=dec_attn_mask,
mems=mems_i)
hids.append(core_out)
elif self.attn_type == 3:
core_out = self.drop(word_emb)
hids.append(core_out)
for i, layer in enumerate(self.layers):
mems_i = None if mems is None else mems[i]
if mems_i is not None and mlen > 0:
cur_emb = self.r_emb[i][:-qlen]
cur_size = cur_emb.size(0)
if cur_size < mlen:
cur_emb_pad = cur_emb[0:1].expand(
mlen - cur_size, -1, -1)
cur_emb = torch.cat([cur_emb_pad, cur_emb], 0)
else:
cur_emb = cur_emb[-mlen:]
mems_i += cur_emb.view(mlen, 1, -1)
core_out += self.r_emb[i][-qlen:].view(qlen, 1, -1)
core_out = layer(core_out,
dec_attn_mask=dec_attn_mask,
mems=mems_i)
hids.append(core_out)
core_out = self.drop(core_out)
new_mems = self._update_mems(hids, mems, mlen, qlen)
return core_out, new_mems
def make_mask(n_head, length):
a = torch.ones(1, n_head, length, length)
b = torch.triu(a, diagonal=0)
return b.transpose(2, 3)
def _generate_square_subsequent_mask(self, sz):
#triu returns the upper triangular part of a matrix (2-D tensor) or batch of matrices (see section below)
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
if not hasattr(self, '_future_mask') or self._future_mask is None or self._future_mask.device != tensor.device or self._future_mask.size(0) < dim:
self._future_mask = torch.triu(utils.fill_with_neg_inf(tensor.new(dim, dim)), 1)
return self._future_mask[:dim, :dim]
def get_mask(self, seq):
mask = (torch.triu(torch.ones(seq.size(1),
seq.size(1))) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(
mask == 1, float(0.0))
return mask.to(self._config.device)
device = src.device
mask = self._generate_square_subsequent_mask(len(src)).to(device)
self.src_mask = mask
src = self.encoder(src) * math.sqrt(self.ninp)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, self.src_mask)
output = self.decoder(output)
return output
"""#### Masking
By passing the mask into the transformer_encoder forward() function, the attention will only be calculated on the earlier positions in the sequence.
"""
#triu returns the upper triangular part of a matrix (2-D tensor) or batch of matrices (see section below)
torch.triu(torch.ones(3, 3))
# Masking
def masking():
sz = 4
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
masking()
"""### Positional Encoding
Transformer architecture takes base architecture of Seq2Seq model (Encoder - Decoder). However, the transformer does not use recurrent model so this means we need a module that captures sequence information of the input/output.
def forward_seq2seq(self, batch, target_masking=None, zero_encoder=False):
"""
Inputs Shapes:
input: (Variable) batch_size x len_tgt (wanna tranpose)
context: (Variable) batch_size x len_src x d_model
mask_src (Tensor) batch_size x len_src
Outputs Shapes:
out: batch_size x len_tgt x d_model
coverage: batch_size x len_tgt x len_src
"""
src = batch.get('source')
tgt = batch.get('target_input')
input = torch.cat([src, tgt], dim=0)
""" Embedding: batch_size x len_tgt x d_model """
# we work with two embeddings at the same time
src_emb = embedded_dropout(self.src_word_lut, src, dropout=self.word_dropout if self.training else 0)
tgt_emb = embedded_dropout(self.tgt_word_lut, tgt, dropout=self.word_dropout if self.training else 0)
# Concatenate the embeddings by time dimension
emb = torch.cat([src_emb, tgt_emb], dim=0)
# Add dropout and scale
emb = self.preprocess_layer(emb)
emb = emb * math.sqrt(self.model_size)
klen, batch_size = emb.size(0), emb.size(1)
# Prepare positional encoding:
pos_seq = torch.arange(klen - 1, -1, -1.0, device=emb.device, dtype=emb.dtype)
# pos_seq = torch.arange(0, klen, device=emb.device, dtype=emb.dtype)
pos_emb = self.preprocess_layer(self.positional_encoder(pos_seq))
if self.use_feature:
raise NotImplementedError # No feature/attributes for the moment
# attention masking
qlen = klen
mlen = 0 # we don't have any memory in this mode
# print(input)
dec_attn_mask = torch.triu(
emb.new_ones(qlen, klen), diagonal=1 + mlen).byte()[:, :, None] # Size T x T ?
pad_mask = input.eq(onmt.Constants.PAD).byte().unsqueeze(1) # Size 1 x T x B
# pad_mask = input.new(*input.size()).zero_()
mask = dec_attn_mask + pad_mask
mask = torch.gt(mask, 0).bool()
# mask = dec_attn_mask
mask = mask.bool()
output = emb
for i, layer in enumerate(self.layer_modules):
output, coverage = layer(output, pos_emb, self.r_w_bias, self.r_r_bias, mask) # batch_size x len_src x d_model
# From Google T2T
# if normalization is done in layer_preprocess, then it should also be done
# on the output, since the output can grow very large, being the sum of
# a whole stack of unnormalized layer outputs.
output = self.postprocess_layer(output)
all_output = output
src_len = src.size(0)
context = output[src_len:, :, :]
tgt_len = tgt.size(0)
tgt_hiddens = output[:tgt_len, :, :]
# output_dict = {'hidden': output, 'coverage': coverage, 'context': context}
output_dict = defaultdict(lambda: None)
output_dict['hidden'] = tgt_hiddens
output_dict['encoder'] = context
output_dict['src_mask'] = mask[src_len:, :, :]
output = tgt_hiddens
# This step removes the padding to reduce the load for the final layer
if target_masking is not None:
output = output.contiguous().view(-1, output.size(-1))
mask = target_masking
""" We remove all positions with PAD """
flattened_mask = mask.view(-1)
non_pad_indices = torch.nonzero(flattened_mask).squeeze(1)
output = output.index_select(0, non_pad_indices)
# final layer: computing softmax
logprobs = self.generator[0](output)
output_dict['logprobs'] = logprobs
# return output, None
return output_dict
def get_subsequent_mask(seq):
''' For masking out the subsequent info. '''
sz_b, len_s = seq.size()
subsequent_mask = (1 - torch.triu(
torch.ones((1, len_s, len_s), device=seq.device), diagonal=1)).bool()
return subsequent_mask
def generate_square_subsequent_mask(size: int):
"""Generate a triangular (size, size) mask."""
mask = (torch.triu(torch.ones(size, size)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float("-inf")).masked_fill(mask == 1, float(0.0))
return mask
def _forward(self,
inputs,
memory_bank,
src_pad_mask,
tgt_pad_mask,
layer_cache=None,
step=None,
future=False):
""" A naive forward pass for transformer decoder.
# T: could be 1 in the case of stepwise decoding or tgt_len
Args:
inputs (FloatTensor): ``(batch_size, T, model_dim)``
memory_bank (FloatTensor): ``(batch_size, src_len, model_dim)``
src_pad_mask (LongTensor): ``(batch_size, 1, src_len)``
tgt_pad_mask (LongTensor): ``(batch_size, 1, T)``
layer_cache (dict or None): cached layer info when stepwise decode
step (int or None): stepwise decoding counter
future (bool): If set True, do not apply future_mask.
Returns:
(FloatTensor, FloatTensor):
* output ``(batch_size, T, model_dim)``
* attns ``(batch_size, head, T, src_len)``
"""
dec_mask = None
if step is None:
tgt_len = tgt_pad_mask.size(-1)
if not future: # apply future_mask, result mask in (B, T, T)
future_mask = torch.ones([tgt_len, tgt_len],
device=tgt_pad_mask.device,
dtype=torch.uint8)
future_mask = torch.triu(future_mask,
1).view(1, tgt_len, tgt_len)
#future_mask = future_mask.triu_(1).view(1, tgt_len, tgt_len)
# BoolTensor was introduced in pytorch 1.2
try:
future_mask = future_mask.bool()
except AttributeError:
pass
dec_mask = torch.gt(tgt_pad_mask + future_mask, 0)
else: # only mask padding, result mask in (B, 1, T)
dec_mask = tgt_pad_mask
input_norm = self.layer_norm_1(inputs)
if isinstance(self.self_attn, MultiHeadedAttention):
query, _ = self.self_attn(input_norm,
input_norm,
input_norm,
mask=dec_mask,
layer_cache=layer_cache,
attn_type="self")
elif isinstance(self.self_attn, AverageAttention):
query, _ = self.self_attn(input_norm,
mask=dec_mask,
layer_cache=layer_cache,
step=step)
query = self.drop(query) + inputs
query_norm = self.layer_norm_2(query)
mid, attns = self.context_attn(memory_bank,
memory_bank,
query_norm,
mask=src_pad_mask,
layer_cache=layer_cache,
attn_type="context")
output = self.feed_forward(self.drop(mid) + query)
return output, attns
def __init__(self, B, L, device="cpu"):
mask_shape = [B, 1, L, L]
with torch.no_grad():
self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
def batch_head_stats(attn_variables, triu_masking=False):
# Retrieve context (shape bsz x nheads x L x dhead), mask (shape bsz x L) and weights (shape bsz x nheads x L x l)
ctx = attn_variables["context"].detach()
in_mask = attn_variables["in_mask"]
out_mask = attn_variables["out_mask"]
p = attn_variables["weights"].detach()
logp = torch.log(p)
device = p.device
# Results
results = {}
# Triu mask for self att
triu_mask = torch.triu(p.new_ones((p.size(2), p.size(3))), 1).byte()
# Reverse mask
if in_mask is not None:
in_mask = torch.eq(in_mask, 0.0).float()
else:
in_mask = torch.ones(p.size(0), p.size(3)).to(ctx.device)
# Reverse mask
if out_mask is not None:
out_mask = torch.eq(out_mask, 0.0).float()
else:
out_mask = torch.ones(ctx.size(0), ctx.size(2)).to(ctx.device)
def reduce_head(x):
return (x * out_mask.unsqueeze(1)).sum(0).sum(-1).detach().cpu()
def reduce_head_pairs(x):
return (
x *
out_mask.unsqueeze(1).unsqueeze(1)).sum(0).sum(-1).detach().cpu()
# p_mask has shape bsz x -1 x -1 x l
p_mask = in_mask.unsqueeze(1).unsqueeze(1)
# Entropy
plogp = p * logp
plogp[p == 0] = 0
if triu_masking:
plogp.masked_fill_(triu_mask.unsqueeze(0).unsqueeze(0), 0)
#plogp.masked_fill_(p_mask.eq(0), 0)
H_p = -plogp.sum(-1)
results["entropy"] = reduce_head(H_p)
# Cross entropy
plogq = torch.einsum("bilk,bjlk->bijlk", [p, logp])
plogq.masked_fill_((p == 0).unsqueeze(1), 0)
if triu_masking:
plogq.masked_fill_(triu_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0), 0)
H_pq = -plogq.sum(-1)
# Avg KL (bsz x nhead x L)
avg_KL = (H_pq - H_p.unsqueeze(2))
results["kl"] = reduce_head_pairs(avg_KL)
if (results["kl"] == float('inf')).any():
print(triu_mask)
print(p)
print(avg_KL)
exit()
# avg output disagreement
out = ctx / (torch.sqrt((ctx**2).sum(-1, keepdim=True)) + 1e-20)
out_dis = torch.einsum("bild,bjld->bijl", [out, out]) / out.size(1)**2
results["out_dis"] = reduce_head_pairs(out_dis)
# avg attn disagreement
attn_dis = torch.einsum("bilk,bjlk->bijl", [p, p]) / p.size(1)**2
results["attn_dis"] = reduce_head_pairs(attn_dis)
# Avg attn offset
self_pos = torch.arange(p.size(2)).to(device).float().view(1, 1, -1)
if triu_masking:
masked_p = torch.where(
triu_mask.unsqueeze(0).unsqueeze(0), -p.new_ones(p.size()), p)
else:
masked_p = p
attn_pos = masked_p.argmax(dim=-1).float()
attn_offset = self_pos - attn_pos
results["attn_pos"] = reduce_head(attn_pos)
results["attn_offset"] = reduce_head(attn_offset)
# Avg attn offset
attn_dist = torch.abs(attn_offset)
results["attn_dist"] = reduce_head(attn_dist)
# Avg squared attn offset
results["attn_offset_sq"] = reduce_head(attn_offset**2)
results["attn_pos_sq"] = reduce_head(attn_pos**2)
# Denominator
denom = out_mask.sum().detach().cpu().data
return results, denom
def create_and_check_xlnet_model_use_mems(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
causal_mask = torch.ones(
input_ids_1.shape[0],
input_ids_1.shape[1],
input_ids_1.shape[1],
dtype=torch.float,
device=torch_device,
)
causal_mask = torch.triu(causal_mask, diagonal=0)
outputs_cache = model(input_ids_1, use_mems=True, perm_mask=causal_mask)
outputs_no_cache = model(input_ids_1, use_mems=False, perm_mask=causal_mask)
outputs_conf = model(input_ids_1)
self.parent.assertTrue(len(outputs_cache) == len(outputs_conf))
self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1)
output, mems = outputs_cache.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1)
# causal mask
causal_mask = torch.ones(
input_ids_1.shape[0],
input_ids_1.shape[1] + 1,
input_ids_1.shape[1] + 1,
dtype=torch.float,
device=torch_device,
)
causal_mask = torch.triu(causal_mask, diagonal=0)
single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device)
# second forward pass
output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"]
output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def forward(
self,
input_ids=None,
past=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
use_cache=True,
):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the last layer of the model.
past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
Examples::
from transformers import CTRLTokenizer, CTRLModel
import torch
tokenizer = CTRLTokenizer.from_pretrained('ctrl')
model = CTRLModel.from_pretrained('ctrl')
input_ids = torch.tensor(tokenizer.encode("Links Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError(
"You have to specify either input_ids or inputs_embeds")
if past is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = past[0][0].size(-2)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length,
input_shape[-1] + past_length,
dtype=torch.long,
device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
assert batch_size > 0, "batch_size has to be defined and > 0"
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(
dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
token_type_embeds = self.w(token_type_ids)
token_type_embeds *= np.sqrt(self.d_model_size)
else:
token_type_embeds = 0
position_ids = position_ids.view(-1, input_shape[-1])
if inputs_embeds is None:
inputs_embeds = self.w(input_ids)
# inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
seq_len = input_shape[-1]
mask = torch.triu(
torch.ones(seq_len + past_length, seq_len + past_length),
1).to(inputs_embeds.device)
inputs_embeds *= np.sqrt(self.d_model_size)
pos_embeds = self.pos_encoding[position_ids, :].to(
inputs_embeds.device)
hidden_states = inputs_embeds + pos_embeds + token_type_embeds
hidden_states = self.dropout(hidden_states)
output_shape = input_shape + (inputs_embeds.size(-1), )
presents = ()
all_hidden_states = ()
all_attentions = []
for i, (h, layer_past) in enumerate(zip(self.h, past)):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states.view(
*output_shape), )
outputs = h(
hidden_states,
mask,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
use_cache=use_cache,
)
hidden_states, present = outputs[:2]
if use_cache is True:
presents = presents + (present, )
if self.output_attentions:
all_attentions.append(outputs[2])
hidden_states = self.layernorm(hidden_states)
hidden_states = hidden_states.view(*output_shape)
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states, )
outputs = (hidden_states, )
if use_cache is True:
outputs = outputs + (presents, )
if self.output_hidden_states:
outputs = outputs + (all_hidden_states, )
if self.output_attentions:
# let the number of heads free (-1) so we can extract attention even after head pruning
attention_output_shape = input_shape[:-1] + (
-1, ) + all_attentions[0].shape[-2:]
all_attentions = tuple(
t.view(*attention_output_shape) for t in all_attentions)
outputs = outputs + (all_attentions, )
return outputs
def conditional_corrcoeff(
density: Any,
limits: Tensor,
condition: Tensor,
subset: Optional[List[int]] = None,
resolution: int = 50,
warn_about_deprecation: bool = True,
) -> Tensor:
r"""
Returns the conditional correlation matrix of a distribution.
To compute the conditional distribution, we condition all but two parameters to
values from `condition`, and then compute the Pearson correlation
coefficient $\rho$ between the remaining two parameters under the distribution
`density`. We do so for any pair of parameters specified in `subset`, thus
creating a matrix containing conditional correlations between any pair of
parameters.
If `condition` is a batch of conditions, this function computes the conditional
correlation matrix for each one of them and returns the mean.
Args:
density: Probability density function with `.log_prob()` function.
limits: Limits within which to evaluate the `density`.
condition: Values to condition the `density` on. If a batch of conditions is
passed, we compute the conditional correlation matrix for each of them and
return the average conditional correlation matrix.
subset: Evaluate the conditional distribution only on a subset of dimensions.
If `None` this function uses all dimensions.
resolution: Number of grid points on which the conditional distribution is
evaluated. A higher value increases the accuracy of the estimated
correlation but also increases the computational cost.
warn_about_deprecation: With sbi v0.15.0, we depracated the import of this
function from `sbi.utils`. Instead, it should be imported from
`sbi.analysis`.
Returns: Average conditional correlation matrix of shape either `(num_dim, num_dim)`
or `(len(subset), len(subset))` if `subset` was specified.
"""
if warn_about_deprecation:
warn(
"Importing `conditional_corrcoeff` from `sbi.utils` is deprecated since "
"sbi v0.15.0. Instead, use "
"`from sbi.analysis import conditional_corrcoeff`."
)
condition = ensure_theta_batched(condition)
if subset is None:
subset = range(condition.shape[1])
correlation_matrices = []
for cond in condition:
correlation_matrices.append(
torch.stack(
[
_compute_corrcoeff(
eval_conditional_density(
density,
cond,
limits,
dim1=dim1,
dim2=dim2,
resolution=resolution,
warn_about_deprecation=False,
),
limits[[dim1, dim2]],
)
for dim1 in subset
for dim2 in subset
if dim1 < dim2
]
)
)
average_correlations = torch.mean(torch.stack(correlation_matrices), dim=0)
# `average_correlations` is still a vector containing the upper triangular entries.
# Below, assemble them into a matrix:
av_correlation_matrix = torch.zeros((len(subset), len(subset)))
triu_indices = torch.triu_indices(row=len(subset), col=len(subset), offset=1)
av_correlation_matrix[triu_indices[0], triu_indices[1]] = average_correlations
# Make the matrix symmetric by copying upper diagonal to lower diagonal.
av_correlation_matrix = torch.triu(av_correlation_matrix) + torch.tril(
av_correlation_matrix.T
)
av_correlation_matrix.fill_diagonal_(1.0)
return av_correlation_matrix
def masking():
sz = 4
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def get_subsequent_mask(seq):
sz_b, len_s = seq.size()
subsequent_mask = torch.triu(torch.ones((1, len_s, len_s)),
diagonal=1).bool()
return subsequent_mask
def get_mask(size):
weights = torch.triu(torch.ones((size, size), dtype = torch.bool), 1)
return weights
def forward(
self,
input_ids=None,
mems=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_tuple=None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (output_hidden_states
if output_hidden_states is not None else
self.config.output_hidden_states)
return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
# the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
# so we transpose here from shape [bsz, len] to shape [len, bsz]
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
input_ids = input_ids.transpose(0, 1).contiguous()
qlen, bsz = input_ids.size()
elif inputs_embeds is not None:
inputs_embeds = inputs_embeds.transpose(0, 1).contiguous()
qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1]
else:
raise ValueError(
"You have to specify either input_ids or inputs_embeds")
if mems is None:
mems = self.init_mems(bsz)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer)
# and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(
0).unsqueeze(0)
head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)
head_mask = head_mask.to(dtype=next(self.parameters(
)).dtype) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.n_layer
if inputs_embeds is not None:
word_emb = inputs_embeds
else:
word_emb = self.word_emb(input_ids)
mlen = mems[0].size(0) if mems is not None else 0
klen = mlen + qlen
if self.same_length:
all_ones = word_emb.new_ones((qlen, klen), dtype=torch.uint8)
mask_len = klen - self.mem_len
if mask_len > 0:
mask_shift_len = qlen - mask_len
else:
mask_shift_len = qlen
dec_attn_mask = (torch.triu(all_ones, 1 + mlen) +
torch.tril(all_ones, -mask_shift_len))[:, :,
None] # -1
else:
dec_attn_mask = torch.triu(word_emb.new_ones((qlen, klen),
dtype=torch.uint8),
diagonal=1 + mlen)[:, :, None]
hids = []
attentions = [] if output_attentions else None
if self.attn_type == 0: # default
pos_seq = torch.arange(klen - 1,
-1,
-1.0,
device=word_emb.device,
dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
core_out = self.drop(word_emb)
pos_emb = self.drop(pos_emb)
for i, layer in enumerate(self.layers):
hids.append(core_out)
mems_i = None if mems is None else mems[i]
layer_outputs = layer(
core_out,
pos_emb,
dec_attn_mask=dec_attn_mask,
mems=mems_i,
head_mask=head_mask[i],
output_attentions=output_attentions,
)
core_out = layer_outputs[0]
if output_attentions:
attentions.append(layer_outputs[1])
else: # learnable embeddings and absolute embeddings
raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint
core_out = self.drop(core_out)
new_mems = self._update_mems(hids, mems, mlen, qlen)
if output_hidden_states:
# Add last layer and transpose to library standard shape [bsz, len, hidden_dim]
hids.append(core_out)
hids = tuple(t.transpose(0, 1).contiguous() for t in hids)
else:
hids = None
if output_attentions:
# Transpose to library standard shape [bsz, n_heads, query_seq_len, key_seq_len]
attentions = tuple(
t.permute(2, 3, 0, 1).contiguous() for t in attentions)
# We transpose back here to shape [bsz, len, hidden_dim]
core_out = core_out.transpose(0, 1).contiguous()
if return_tuple:
return tuple(v for v in [core_out, new_mems, hids, attentions]
if v is not None)
return TransfoXLModelOutput(
last_hidden_state=core_out,
mems=new_mems,
hidden_states=hids,
attentions=attentions,
)
def _assemble_W(self):
""" assemble W from its pieces (P, L, U, S) """
L = torch.tril(self.L, diagonal=-1) + torch.diag(torch.ones(self.dim))
U = torch.triu(self.U, diagonal=1)
W = self.P @ L @ (U + torch.diag(self.S))
return W
def get_att(head, seq_len):
att = torch.triu(torch.ones(seq_len, seq_len).byte(), diagonal=1)
for _ in range(2):
att = torch.unsqueeze(att, dim=0)
return att.repeat(1, head, 1, 1)
def _generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(
mask == 1, float(0.0))
return mask
def recover_adj_lower(self, l):
# NOTE: Assumes 1 per minibatch
adj = torch.zeros(self.max_num_nodes, self.max_num_nodes)
adj[torch.triu(torch.ones(self.max_num_nodes, self.max_num_nodes)) == 1] = l
return adj
def generate_square_subsequent_mask(sz):
return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)
def generate_square_subsequent_mask(self, sz):
mask = torch.triu(torch.ones(sz, sz), 1)
mask = mask.masked_fill(mask == 1, float('-inf'))
return mask
def convert_examples_to_features(examples, seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
features = []
attention_mask = torch.tril(
torch.ones(seq_length, seq_length, dtype=torch.long), -1) + torch.triu(
torch.ones(seq_length, seq_length, dtype=torch.long), 1)
for (ex_index, example) in enumerate(examples):
tokens_a = example.text_a
labels = example.labels
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > seq_length - 2:
tokens_a = tokens_a[0:(seq_length - 2)]
if len(labels) > seq_length - 2:
labels = labels[0:(seq_length - 2)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambigiously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = []
input_type_ids = []
targets = []
tokens.append("[CLS]")
targets.append("[PAD]")
input_type_ids.append(0)
for i, token in enumerate(tokens_a):
tokens.append(token)
input_type_ids.append(0)
targets.append(labels[i])
tokens.append("[SEP]")
targets.append("[PAD]")
input_type_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
input_type_ids.append(1)
tokens.append("[SEP]")
input_type_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
target_ids = tokenizer.convert_tokens_to_ids(targets)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < seq_length:
input_ids.append(0)
input_mask.append(0)
target_ids.append(0)
input_type_ids.append(0)
assert len(input_ids) == seq_length
assert len(input_mask) == seq_length
assert len(target_ids) == seq_length
assert len(input_type_ids) == seq_length
#input_mask=(torch.tensor(input_mask, dtype=torch.long).unsqueeze(0)*attention_mask).tolist()
candidates = example.candidates
if ex_index < 0:
logger.info("*** Example ***")
logger.info("unique_id: %s" % (example.unique_id))
logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
logger.info("input_ids: %s" % " ".join([str(x)
for x in input_ids]))
logger.info("target_ids: %s" %
" ".join([str(x) for x in target_ids]))
logger.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
logger.info("input_type_ids: %s" %
" ".join([str(x) for x in input_type_ids]))
if False:
logger.info("*** Example ***")
logger.info("unique_id: %s" % (example.unique_id))
logger.info("tokens: %s" % " ".join([str(x) for x in labels]))
features.append(
InputFeatures(
unique_id=example.unique_id,
target_ids=target_ids,
input_ids=input_ids,
input_mask=input_mask,
input_type_ids=input_type_ids,
candidates=candidates,
))
return features
def masking(self, weight):
# pdb.set_trace()
mask = torch.triu(torch.ones_like(weight)).transpose(2, 3)
weight[mask == 0] = float('-inf')
return weight
def forward(
self, # type: ignore
token_ids: torch.LongTensor,
type_ids: torch.LongTensor,
offsets: torch.LongTensor,
wordpiece_mask: torch.BoolTensor,
pos_tags: torch.LongTensor,
word_mask: torch.BoolTensor,
subtree_spans: torch.LongTensor = None,
):
""" todo implement docstring
Args:
token_ids: [batch_size, num_word_pieces]
type_ids: [batch_size, num_word_pieces]
offsets: [batch_size, num_words, 2]
wordpiece_mask: [batch_size, num_word_pieces]
pos_tags: [batch_size, num_words]
word_mask: [batch_size, num_words]
subtree_spans: [batch_size, num_words, 2]
Returns:
span_start_logits: [batch_size, num_words, num_words]
span_end_logits: [batch_size, num_words, num_words]
"""
# [bsz, seq_len, hidden]
embedded_text_input = self.get_word_embedding(
token_ids=token_ids,
offsets=offsets,
wordpiece_mask=wordpiece_mask,
type_ids=type_ids,
)
if self.pos_embedding is not None:
embedded_pos_tags = self.pos_embedding(pos_tags)
embedded_text_input = torch.cat(
[embedded_text_input, embedded_pos_tags], -1)
if self.fuse_layer is not None:
embedded_text_input = self.fuse_layer(embedded_text_input)
# todo compare normal dropout with InputVariationalDropout
embedded_text_input = self._dropout(embedded_text_input)
if self.additional_encoder is not None:
if self.config.additional_layer_type == "transformer":
extended_attention_mask = self.bert.get_extended_attention_mask(
word_mask, word_mask.size(), word_mask.device)
encoded_text = self.additional_encoder(
hidden_states=embedded_text_input,
attention_mask=extended_attention_mask)[0]
else:
encoded_text = self.additional_encoder(
inputs=embedded_text_input, mask=word_mask)
else:
encoded_text = embedded_text_input
batch_size, seq_len, encoding_dim = encoded_text.size()
# [bsz, seq_len, dim]
subtree_start_representation = self._dropout(
self.subtree_start_feedforward(encoded_text))
subtree_end_representation = self._dropout(
self.subtree_end_feedforward(encoded_text))
# [bsz, seq_len, seq_len]
span_start_scores = self.subtree_start_attention(
subtree_start_representation, subtree_start_representation)
span_end_scores = self.subtree_end_attention(
subtree_end_representation, subtree_end_representation)
# start of word should less equal to it
start_mask = word_mask.unsqueeze(-1) & (
~torch.triu(span_start_scores.bool(), 1))
# end of word should greater equal to it.
end_mask = word_mask.unsqueeze(-1) & torch.triu(span_end_scores.bool())
minus_inf = -1e8
span_start_scores = span_start_scores + (
~start_mask).float() * minus_inf
span_end_scores = span_end_scores + (~end_mask).float() * minus_inf
output = (F.log_softmax(span_start_scores,
dim=-1), F.log_softmax(span_end_scores,
dim=-1))
if subtree_spans is not None:
start_loss = F.cross_entropy(
span_start_scores.view(batch_size * seq_len, -1),
subtree_spans[:, :, 0].view(-1))
end_loss = F.cross_entropy(
span_end_scores.view(batch_size * seq_len, -1),
subtree_spans[:, :, 1].view(-1))
span_loss = start_loss + end_loss
output = output + (span_loss, )
return output
def half_and_half(a, b):
a = torch.stack([torch.triu(x) for x in a], 0)
b = torch.stack([torch.tril(x, diagonal=-1) for x in b], 0)
return a + b
def sequence_mask(seq):
batch_size, seq_len = seq.size()
mask = torch.triu(torch.ones((seq_len, seq_len), dtype=torch.uint8),
diagonal=1)
mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
return mask
print(torch.rand(5)) # tensor([ 0.4177, 0.4903, 0.5730, 0.1205, 0.1452]); It is a Vector print(torch.randint(10, (2, 2))) # tensor([[3., 3.], [8., 2.]]); 还是实数 print(torch.randint(10, (2, 2), dtype=torch.long)) # tensor([[8, 2], [8, 5]]) # arange & linspace x = torch.arange(1, 8); print(x) # tensor([1, 2, 3, 4, 5, 6, 7]) x = torch.linspace(-1, 1, 10); print(x, x.shape) # torch.Size([10]) x = torch.full((2, 3), 0.1); print(x) # tensor([[ 0.1000, 0.1000, 0.1000], [ 0.1000, 0.1000, 0.1000]]) x = torch.ones(2, 3); print(x) print(x.zero_()) # 将所有的元素都变为 0; 这个会对原始数据进行修改 x = torch.zeros(2, 3); print(x) print(x.random_(2)) # discrete uniform distribution over [from, to - 1]; 这里是 [0, 1] 之间的均匀分布 x = torch.empty(2, 3); print(x) # Returns a tensor filled with uninitialized data ## triu(input, diagonal=0, out=None): 下三角置零 a = torch.randn(3, 3); print(a) print(torch.triu(a)) # diagonal=0, all elements on and below the main diagonal are retained; 对角线不为 0 print(torch.triu(a, diagonal=1)) # a positive value excludes just as many diagonals above the main diagonal; 对角线也为 0 print(torch.triu(a, diagonal=-1)) # a negative value includes just as many diagonals below the main diagonal ## squeeze x = torch.unsqueeze(torch.linspace(-1, 1, 10), dim=0); print(x.shape, x) # torch.Size([1, 10]) x = torch.unsqueeze(torch.linspace(-1, 1, 10), dim=1); print(x.shape, x) # torch.Size([10, 1]); new tensor with a dimension of size one inserted at the specified position. x = torch.unsqueeze(torch.linspace(-1, 1, 10), dim=-1); print(x.shape, x) # torch.Size([10, 1]); x = torch.unsqueeze(torch.rand(2, 4), dim=1); print(x.shape) # torch.Size([2, 1, 4]) x = torch.zeros(2, 1, 2, 1, 2); print(x.shape) # torch.Size([2, 1, 2, 1, 2]) y = torch.squeeze(x); print(y.shape) # torch.Size([2, 2, 2]); 所有维度是 1 的去掉 y = torch.squeeze(x, 0); print(y.shape) # torch.Size([2, 1, 2, 1, 2]); 只对第 0 维操作 y = torch.squeeze(x, 1); print(y.shape) # torch.Size([2, 2, 1, 2]); 只对第 1 维操作 ## Operation; 和 np 的区别就是 torch 需要变量都是 tensor 类型的 # 求和以及按索引求和: torch.sum() torch.Tensor.indexadd()
