def broadcast_factor(mask: batchers.Mask,
tensor_expr: dy.Expression) -> numbers.Integral:
"""
returns product(tensor_expr dims) / product(mask dims)
"""
tensor_expr_size = tensor_expr.dim()[1]
for d in tensor_expr.dim()[0]:
tensor_expr_size *= d
return tensor_expr_size / mask.np_arr.size
def backward(self, loss: dy.Expression, dynet_profiling: int) -> None:
"""
Perform backward pass to accumulate gradients.
Args:
loss: Result of self.training_step(...)
dynet_profiling: if > 0, print the computation graph
"""
if dynet_profiling and dynet_profiling > 0:
dy.print_text_graphviz()
loss.backward()
def transform(self, input_expr: dy.Expression, mask: Optional[batchers.Mask]=None):
"""
Apply batch norm.
Args:
input_expr: input
mask: compute statistics only over unmasked parts of the input expression
"""
dim_in = input_expr.dim()
param_bn_gamma = dy.parameter(self.gamma)
param_bn_beta = dy.parameter(self.beta)
if self.train:
num_unmasked = 0
if mask is not None:
input_expr = set_masked_to_mean(mask, input_expr, self.time_first)
num_unmasked = (mask.np_arr.size - np.count_nonzero(mask.np_arr)) * broadcast_factor(mask, input_expr)
bn_mean = dy.moment_dim(input_expr, self.get_stat_dimensions(), 1, True, num_unmasked)
neg_bn_mean_reshaped = -dy.reshape(-bn_mean, self.get_normalizer_dimensionality())
self.population_running_mean += (-BN_MOMENTUM) * self.population_running_mean + BN_MOMENTUM * bn_mean.npvalue()
bn_std = dy.std_dim(input_expr, self.get_stat_dimensions(), True, num_unmasked)
self.population_running_std += (-BN_MOMENTUM) * self.population_running_std + BN_MOMENTUM * bn_std.npvalue()
else:
neg_bn_mean_reshaped = -dy.reshape(dy.inputVector(self.population_running_mean), self.get_normalizer_dimensionality())
bn_std = dy.inputVector(self.population_running_std)
bn_numerator = input_expr + neg_bn_mean_reshaped
bn_xhat = dy.cdiv(bn_numerator, dy.reshape(bn_std, self.get_normalizer_dimensionality()) + BN_EPS)
bn_y = dy.cmult(param_bn_gamma, bn_xhat) + param_bn_beta # y = gamma * xhat + beta
dim_out = bn_y.dim()
self.save_processed_arg("population_running_mean", self.population_running_mean)
self.save_processed_arg("population_running_std", self.population_running_std)
assert dim_out == dim_in
return bn_y
def calc_attention(self, state: dy.Expression) -> dy.Expression:
scores = self.I * state
if self.scale:
scores /= math.sqrt(state.dim()[0][0])
if self.curr_sent.mask is not None:
scores = self.curr_sent.mask.add_to_tensor_expr(scores, multiplicator = -100.0)
normalized = dy.softmax(scores)
self.attention_vecs.append(normalized)
return normalized
def set_masked_to_mean(mask: batchers.Mask,
tensor_expr: dy.Expression,
time_first: bool = False) -> dy.Expression:
"""
Set masked parts of the tensor expr to the mean of the unmasked parts.
"""
if np.count_nonzero(mask.np_arr) == 0:
return tensor_expr
else:
dim_before = tensor_expr.dim()
reshape_size = mask_reshape_size(mask, tensor_expr.dim(), time_first)
inv_mask_expr = dy.inputTensor(
1.0 - np.reshape(mask.np_arr.transpose(), reshape_size),
batched=True)
unmasked = dy.cmult(tensor_expr, inv_mask_expr)
unmasked_mean = unmasked
while sum(
unmasked_mean.dim()[0]
) > 1: # loop because mean_dim only supports reducing up to 2 dimensions at a time
unmasked_mean = dy.mean_dim(
unmasked_mean,
list(range(min(2, len(unmasked_mean.dim()[0])))),
unmasked_mean.dim()[1] > 1,
n=1) # this is mean without normalization == sum
unmasked_mean = dy.cdiv(
unmasked_mean,
dy.inputTensor(np.asarray([
(mask.np_arr.size - np.count_nonzero(mask.np_arr)) *
broadcast_factor(mask, tensor_expr)
]),
batched=False))
mask_expr = dy.cmult(
dy.inputTensor(np.reshape(mask.np_arr.transpose(), reshape_size),
batched=True), unmasked_mean)
ret = unmasked + mask_expr
assert ret.dim() == dim_before
return ret
def __call__(self, x: dy.Expression) -> dy.Expression:
"""
Move the time-dimension of an input expression into the batch dimension via a reshape.
Args:
x: expression of dimensions ((hidden, timesteps), batch_size)
Returns:
expression of dimensions ((hidden,), timesteps*batch_size)
"""
batch_size = x[0].dim()[1]
model_dim = x[0].dim()[0][0]
seq_len = len(x)
total_words = seq_len * batch_size
input_tensor = x.as_tensor()
return dy.reshape(input_tensor, (model_dim, ), batch_size=total_words)
def __call__(self, x: dy.Expression, att_mask: np.ndarray,
batch_mask: np.ndarray, p: numbers.Real):
"""
x: expression of dimensions (input_dim, time) x batch
att_mask: numpy array of dimensions (time, time); pre-transposed
batch_mask: numpy array of dimensions (batch, time)
p: dropout prob
"""
sent_len = x.dim()[0][1]
batch_size = x[0].dim()[1]
if self.downsample_factor > 1:
if sent_len % self.downsample_factor != 0:
raise ValueError(
"For 'reshape' downsampling, sequence lengths must be multiples of the downsampling factor. "
"Configure batcher accordingly.")
if batch_mask is not None:
batch_mask = batch_mask[:, ::self.downsample_factor]
sent_len_out = sent_len // self.downsample_factor
sent_len = sent_len_out
out_mask = x.mask
if self.downsample_factor > 1 and out_mask is not None:
out_mask = out_mask.lin_subsampled(
reduce_factor=self.downsample_factor)
x = ExpressionSequence(expr_tensor=dy.reshape(
x.as_tensor(), (x.dim()[0][0] * self.downsample_factor,
x.dim()[0][1] / self.downsample_factor),
batch_size=batch_size),
mask=out_mask)
residual = SAAMTimeDistributed()(x)
else:
residual = SAAMTimeDistributed()(x)
sent_len_out = sent_len
if self.model_dim != self.input_dim * self.downsample_factor:
residual = self.res_shortcut.transform(residual)
# Concatenate all the words together for doing vectorized affine transform
if self.kq_pos_encoding_type is None:
kvq_lin = self.linear_kvq.transform(SAAMTimeDistributed()(x))
key_up = self.shape_projection(
dy.pick_range(kvq_lin, 0, self.head_count * self.dim_per_head),
batch_size)
value_up = self.shape_projection(
dy.pick_range(kvq_lin, self.head_count * self.dim_per_head,
2 * self.head_count * self.dim_per_head),
batch_size)
query_up = self.shape_projection(
dy.pick_range(kvq_lin, 2 * self.head_count * self.dim_per_head,
3 * self.head_count * self.dim_per_head),
batch_size)
else:
assert self.kq_pos_encoding_type == "embedding"
encoding = self.kq_positional_embedder.embed_sent(
sent_len).as_tensor()
kq_lin = self.linear_kq.transform(SAAMTimeDistributed()(
ExpressionSequence(
expr_tensor=dy.concatenate([x.as_tensor(), encoding]))))
key_up = self.shape_projection(
dy.pick_range(kq_lin, 0, self.head_count * self.dim_per_head),
batch_size)
query_up = self.shape_projection(
dy.pick_range(kq_lin, self.head_count * self.dim_per_head,
2 * self.head_count * self.dim_per_head),
batch_size)
v_lin = self.linear_v.transform(SAAMTimeDistributed()(x))
value_up = self.shape_projection(v_lin, batch_size)
if self.cross_pos_encoding_type:
assert self.cross_pos_encoding_type == "embedding"
emb1 = dy.pick_range(dy.parameter(self.cross_pos_emb_p1), 0,
sent_len)
emb2 = dy.pick_range(dy.parameter(self.cross_pos_emb_p2), 0,
sent_len)
key_up = dy.reshape(key_up,
(sent_len, self.dim_per_head, self.head_count),
batch_size=batch_size)
key_up = dy.concatenate_cols(
[dy.cmult(key_up, emb1),
dy.cmult(key_up, emb2)])
key_up = dy.reshape(key_up, (sent_len, self.dim_per_head * 2),
batch_size=self.head_count * batch_size)
query_up = dy.reshape(
query_up, (sent_len, self.dim_per_head, self.head_count),
batch_size=batch_size)
query_up = dy.concatenate_cols(
[dy.cmult(query_up, emb2),
dy.cmult(query_up, -emb1)])
query_up = dy.reshape(query_up, (sent_len, self.dim_per_head * 2),
batch_size=self.head_count * batch_size)
scaled = query_up * dy.transpose(
key_up / math.sqrt(self.dim_per_head)
) # scale before the matrix multiplication to save memory
# Apply Mask here
if not self.ignore_masks:
if att_mask is not None:
att_mask_inp = att_mask * -100.0
if self.downsample_factor > 1:
att_mask_inp = att_mask_inp[::self.downsample_factor, ::
self.downsample_factor]
scaled += dy.inputTensor(att_mask_inp)
if batch_mask is not None:
# reshape (batch, time) -> (time, head_count*batch), then *-100
inp = np.resize(np.broadcast_to(batch_mask.T[:, np.newaxis, :],
(sent_len, self.head_count, batch_size)),
(1, sent_len, self.head_count * batch_size)) \
* -100
mask_expr = dy.inputTensor(inp, batched=True)
scaled += mask_expr
if self.diag_gauss_mask:
diag_growing = np.zeros((sent_len, sent_len, self.head_count))
for i in range(sent_len):
for j in range(sent_len):
diag_growing[i, j, :] = -(i - j)**2 / 2.0
e_diag_gauss_mask = dy.inputTensor(diag_growing)
e_sigma = dy.parameter(self.diag_gauss_mask_sigma)
if self.square_mask_std:
e_sigma = dy.square(e_sigma)
e_sigma_sq_inv = dy.cdiv(
dy.ones(e_sigma.dim()[0], batch_size=batch_size),
dy.square(e_sigma))
e_diag_gauss_mask_final = dy.cmult(e_diag_gauss_mask,
e_sigma_sq_inv)
scaled += dy.reshape(e_diag_gauss_mask_final,
(sent_len, sent_len),
batch_size=batch_size * self.head_count)
# Computing Softmax here.
attn = dy.softmax(scaled, d=1)
if LOG_ATTENTION:
yaml_logger.info({
"key": "selfatt_mat_ax0",
"value": np.average(attn.value(), axis=0).dumps(),
"desc": self.desc
})
yaml_logger.info({
"key": "selfatt_mat_ax1",
"value": np.average(attn.value(), axis=1).dumps(),
"desc": self.desc
})
yaml_logger.info({
"key": "selfatt_mat_ax0_ent",
"value": entropy(attn.value()).dumps(),
"desc": self.desc
})
yaml_logger.info({
"key": "selfatt_mat_ax1_ent",
"value": entropy(attn.value().transpose()).dumps(),
"desc": self.desc
})
self.select_att_head = 0
if self.select_att_head is not None:
attn = dy.reshape(attn, (sent_len, sent_len, self.head_count),
batch_size=batch_size)
sel_mask = np.zeros((1, 1, self.head_count))
sel_mask[0, 0, self.select_att_head] = 1.0
attn = dy.cmult(attn, dy.inputTensor(sel_mask))
attn = dy.reshape(attn, (sent_len, sent_len),
batch_size=self.head_count * batch_size)
# Applying dropout to attention
if p > 0.0:
drop_attn = dy.dropout(attn, p)
else:
drop_attn = attn
# Computing weighted attention score
attn_prod = drop_attn * value_up
# Reshaping the attn_prod to input query dimensions
out = dy.reshape(attn_prod,
(sent_len_out, self.dim_per_head * self.head_count),
batch_size=batch_size)
out = dy.transpose(out)
out = dy.reshape(out, (self.model_dim, ),
batch_size=batch_size * sent_len_out)
# out = dy.reshape_transpose_reshape(attn_prod, (sent_len_out, self.dim_per_head * self.head_count), (self.model_dim,), pre_batch_size=batch_size, post_batch_size=batch_size*sent_len_out)
if self.plot_attention:
from sklearn.metrics.pairwise import cosine_similarity
assert batch_size == 1
mats = []
for i in range(attn.dim()[1]):
mats.append(dy.pick_batch_elem(attn, i).npvalue())
self.plot_att_mat(
mats[-1], "{}.sent_{}.head_{}.png".format(
self.plot_attention, self.plot_attention_counter, i),
300)
avg_mat = np.average(mats, axis=0)
self.plot_att_mat(
avg_mat,
"{}.sent_{}.head_avg.png".format(self.plot_attention,
self.plot_attention_counter),
300)
cosim_before = cosine_similarity(x.as_tensor().npvalue().T)
self.plot_att_mat(
cosim_before, "{}.sent_{}.cosim_before.png".format(
self.plot_attention, self.plot_attention_counter), 600)
cosim_after = cosine_similarity(out.npvalue().T)
self.plot_att_mat(
cosim_after, "{}.sent_{}.cosim_after.png".format(
self.plot_attention, self.plot_attention_counter), 600)
self.plot_attention_counter += 1
# Adding dropout and layer normalization
if p > 0.0:
res = dy.dropout(out, p) + residual
else:
res = out + residual
ret = self.layer_norm.transform(res)
return ret