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transformer_utils.py
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import tensorflow as tf
from tensorflow import keras
#from transformer_helpers.Models import get_non_pad_mask
from models import get_non_pad_mask
import transformer_helpers.Constants as Constants
def compute_event(event, non_pad_mask):
""" Log-likelihood of events. """
non_pad_mask_not = tf.cast(non_pad_mask==Constants.PAD, tf.float32)
# add 1e-9 in case some events have 0 likelihood
event += math.pow(10, -9)
#event.masked_fill_(~non_pad_mask.bool(), 1.0)
event = tf.where(event==non_pad_mask_not, 1., event)
result = tf.math.log(event)
return result
def compute_integral_biased(all_lambda, time, non_pad_mask):
""" Log-likelihood of non-events, using linear interpolation. """
diff_time = (time[:, 1:] - time[:, :-1]) * non_pad_mask[:, 1:]
diff_lambda = (all_lambda[:, 1:] + all_lambda[:, :-1]) * non_pad_mask[:, 1:]
biased_integral = diff_lambda * diff_time
result = 0.5 * biased_integral
return result
def compute_integral_unbiased(model, data, time, non_pad_mask, type_mask):
""" Log-likelihood of non-events, using Monte Carlo integration. """
num_samples = 100
#diff_time = (time[:, 1:] - time[:, :-1]) * non_pad_mask[:, 1:]
diff_time = (time) * non_pad_mask
#temp_time = diff_time.unsqueeze(2) * \
# torch.rand([*diff_time.size(), num_samples], device=data.device)
temp_time = tf.expand_dims(diff_time, axis=2) * \
tf.random.uniform([*(diff_time.shape.as_list()), num_samples])
#temp_time /= (time[:, :-1] + 1).unsqueeze(2)
#temp_time /= tf.expand_dims((time[:, :-1] + 1), axis=2)
temp_time /= tf.expand_dims((tf.cumsum(time, axis=-1) + 1), axis=2)
temp_hid = model.linear(data)
#temp_hid = torch.sum(temp_hid * type_mask[:, 1:, :], dim=2, keepdim=True)
temp_hid = tf.reduce_sum(temp_hid * type_mask, axis=2, keepdims=True)
#all_lambda = F.softplus(temp_hid + model.alpha * temp_time, threshold=10)
# No threshold parameter for tf.nn.softplus is available, (or not required).
all_lambda = tf.nn.softplus(temp_hid + model.alpha * temp_time)
#all_lambda = torch.sum(all_lambda, dim=2) / num_samples
all_lambda = tf.reduce_sum(all_lambda, axis=2) / num_samples
unbiased_integral = all_lambda * diff_time
return unbiased_integral
def log_likelihood(model, data, time, types):
""" Log-likelihood of sequence. """
#non_pad_mask = get_non_pad_mask(types).squeeze(2)
non_pad_mask = get_non_pad_mask(types)
#type_mask = torch.zeros([*types.size(), model.num_types], device=data.device)
#type_mask = tf.zeros([*(types.shape.as_list()), model.num_types])
#for i in range(model.num_types):
# #type_mask[:, :, i] = (types == i + 1).bool().to(data.device)
# type_mask[:, :, i] = tf.cast((types == i + 1), tf.bool)
type_ids = tf.expand_dims(tf.expand_dims(tf.range(1, model.num_types+1, dtype=tf.float32), axis=0), axis=1)
type_mask = tf.cast((tf.expand_dims(types, axis=-1) == type_ids), tf.float32)
all_hid = model.linear(data)
#all_lambda = F.softplus(all_hid, threshold=10)
all_lambda = tf.nn.softplus(all_hid)
#type_lambda = torch.sum(all_lambda * type_mask, dim=2)
type_lambda = tf.reduce_sum(all_lambda * type_mask, axis=2)
# event log-likelihood
event_ll = compute_event(type_lambda, non_pad_mask)
#event_ll = torch.sum(event_ll, dim=-1)
event_ll = tf.reduce_sum(event_ll, axis=-1)
# non-event log-likelihood, either numerical integration or MC integration
# non_event_ll = compute_integral_biased(type_lambda, time, non_pad_mask)
non_event_ll = compute_integral_unbiased(model, data, time, non_pad_mask, type_mask)
#non_event_ll = torch.sum(non_event_ll, dim=-1)
non_event_ll = tf.reduce_sum(non_event_ll, axis=-1)
return event_ll, non_event_ll
def type_loss(prediction, types, loss_func):
""" Event prediction loss, cross entropy or label smoothing. """
# convert [1,2,3] based types to [0,1,2]; also convert padding events to -1
truth = types[:, 1:] - 1
prediction = prediction[:, :-1, :]
#pred_type = torch.max(prediction, dim=-1)[1]
pred_type = tf.cast(tf.argmax(prediction, axis=-1), tf.float32)
#correct_num = torch.sum(pred_type == truth)
correct_num = tf.reduce_sum(tf.cast(pred_type == truth, tf.float32))
# compute cross entropy loss
if isinstance(loss_func, LabelSmoothingLoss):
loss = loss_func(prediction, truth)
else:
#loss = loss_func(prediction.transpose(1, 2), truth)
loss = loss_func(truth, prediction)
#loss = torch.sum(loss)
loss = tf.reduce_sum(loss)
return loss, correct_num
def time_loss(prediction, event_time):
""" Time prediction loss. """
#prediction.squeeze_(-1)
#tf.squeeze(prediction, axis=-1)
#true = event_time[:, 1:] - event_time[:, :-1]
true = event_time
#prediction = prediction[:, :-1]
# event time gap prediction
diff = prediction - true
#se = torch.sum(diff * diff)
se = tf.reduce_sum(diff * diff)
return se
class LabelSmoothingLoss(tf.keras.losses.Loss):
"""
With label smoothing,
KL-divergence between q_{smoothed ground truth prob.}(w)
and p_{prob. computed by model}(w) is minimized.
"""
def __init__(
self, label_smoothing, tgt_vocab_size, ignore_index=-100,
reduction=keras.losses.Reduction.AUTO,
name='LabelSmoothingLoss'):
assert 0.0 < label_smoothing <= 1.0
super(LabelSmoothingLoss, self).__init__(reduction=reduction, name=name)
self.eps = label_smoothing
self.num_classes = tgt_vocab_size
self.ignore_index = ignore_index
def call(self, output, target):
"""
output (FloatTensor): (batch_size) x n_classes
target (LongTensor): batch_size
"""
non_pad_mask = tf.cast((target != tf.cast((self.ignore_index), tf.float32)), tf.float32)
#target[target.eq(self.ignore_index)] = 0
#target[target == (self.ignore_index)] = 0
target = tf.where(target==self.ignore_index, 0, target)
#one_hot = F.one_hot(target, num_classes=self.num_classes).float()
one_hot = tf.cast(tf.one_hot(tf.cast(target, tf.int64), depth=self.num_classes), tf.float32)
one_hot = one_hot * (1 - self.eps) + (1 - one_hot) * self.eps / self.num_classes
#log_prb = F.log_softmax(output, dim=-1)
log_prb = tf.nn.log_softmax(output, axis=-1)
#loss = -(one_hot * log_prb).sum(dim=-1)
loss = tf.reduce_sum(-(one_hot * log_prb), axis=-1)
loss = loss * non_pad_mask
return loss