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transformer_model.py
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transformer_model.py
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import tensorflow as tf
import tensorflow_probability as tfp
import losses
from layers import MultiHeadSelfAttLayer, FCReluBN
class ReRanker:
def __init__(self, seed, learning_rate, n_heads, num_features, loss_fn, list_size, coll_name, det_model=False,
norm_labels=False, max_label_value=4, n=32, consider_raw_rj_dists=False):
tf.set_random_seed(seed)
self.global_step = tf.Variable(0, trainable=False)
self.training = tf.placeholder(tf.bool, None)
self.list_size_train = list_size
out_size = 1
self.relevance_judgments = tf.placeholder(tf.float32, (None, list_size), name='relevance_judgments')
self.rl_lengths_mask = tf.placeholder(tf.float32, (None, list_size), name='rl_lengths_mask')
self.n_heads = n_heads
self.n = n
self.hidden_size = int(num_features / self.n_heads)
self.input_docs = tf.placeholder(tf.float32, (None, list_size, num_features), name='raw_features')
with tf.variable_scope('encoder1'):
# self.fcrelubn_layer = FCReluBN(num_features, num_features, seed=0)
self.multi_head_satt0 = MultiHeadSelfAttLayer(self.n_heads, self.input_docs.shape[-1], self.hidden_size, 0)
with tf.variable_scope('encoder0'):
self.multi_head_satt1 = MultiHeadSelfAttLayer(self.n_heads, self.input_docs.shape[-1], self.hidden_size, 0)
self.output_layer = tf.keras.layers.Dense(out_size, activation=None)
###########################################
# model application
# self.input_docs = tf.math.log1p(self.input_docs) + tf.random.normal(tf.shape(self.input_docs))
# left_side = self.fcrelubn_layer(self.input_docs)
_, self_att_0_out = self.multi_head_satt0(self.input_docs, self.training)
_, self_att_1_out = self.multi_head_satt1(self_att_0_out, self.training)
# joint = tf.multiply(left_side, right_side)
self.logits = tf.squeeze(self.output_layer(self_att_1_out), axis=-1)
self.logits = tf.sigmoid(self.logits)
self.logits = tf.einsum('bs, bs->bs', self.rl_lengths_mask, self.logits)
self.relevance_judgments = tf.multiply(self.relevance_judgments, self.rl_lengths_mask)
###########################################
if loss_fn == 'ApproxNDCG':
# Approx NDCG loss
self.relevance_judgments += tf.cast(tf.less_equal(self.rl_lengths_mask, 0), tf.float32) * (-1)
self.ranking_loss, _ = losses.compute_approxNDCG_unreduced_loss(self.logits, self.relevance_judgments)
self.ranking_loss = tf.einsum('bs, bs->bs', self.rl_lengths_mask, self.ranking_loss)
self.ranking_loss = tf.reduce_mean(self.ranking_loss, axis=-1) # leave only batch dimension
elif loss_fn == 'ApproxNDCG_G':
self.relevance_judgments += tf.cast(tf.less_equal(self.rl_lengths_mask, 0), tf.float32) * (-1)
self.ranking_loss = losses.compute_approxNDCG_gumbel(self.logits, self.relevance_judgments)
elif loss_fn == 'KL_G_H':
self.relevance_judgments = self.relevance_judgments + tf.cast(
tf.less_equal(self.rl_lengths_mask, 0), tf.float32) * (-1)
self.pairs, self.weights = losses.compute_pairwise_kl_g_loss(self.logits, self.relevance_judgments)
self.ranking_loss = tf.reduce_mean(tf.multiply(self.pairs, self.weights))
elif loss_fn == 'KL_B_H':
self.relevance_judgments = self.relevance_judgments + tf.cast(
tf.less_equal(self.rl_lengths_mask, 0), tf.float32) * (-1)
self.pairs, self.weights = losses.compute_pairwise_kl_bin_loss(self.logits, self.relevance_judgments)
self.ranking_loss = tf.reduce_mean(tf.multiply(self.pairs, self.weights))
elif loss_fn == 'KL_G':
# KL div loss
# self.ranking_loss = self.compute_kl_multivariate_gaussian(self.logits, self.relevance_judgments) + self.compute_kl_multivariate_gaussian(self.relevance_judgments, self.logits)
# self.ranking_loss = tf.reduce_mean(tf.reduce_sum(self.ranking_loss, axis=-1) / tf.reduce_sum(self.rl_lengths_mask, axis=-1))
# print('tf prob klg loss')
self.ranking_loss_unred = self.compute_kl_multivariate_gaussian_simm(self.logits, self.relevance_judgments)
self.ranking_loss_unred = tf.multiply(self.ranking_loss_unred, self.rl_lengths_mask)
thr = 0.2
mask_n_rel = tf.cast(tf.less_equal(thr, self.relevance_judgments), tf.float32) * self.rl_lengths_mask
mask_rel = tf.cast(tf.greater(thr, self.relevance_judgments), tf.float32) * self.rl_lengths_mask
weight_non_rel = (1 + 1e-6) / (1e-6 + tf.reduce_sum(mask_n_rel, axis=-1))
weight_rel = (1 + 1e-6) / (1e-6 + tf.reduce_sum(mask_rel, axis=-1))
self.wnr = tf.multiply(mask_n_rel,
tf.einsum('bs, b->bs', tf.ones_like(self.ranking_loss_unred), weight_non_rel))
self.wr = tf.multiply(mask_rel, tf.einsum('bs, b->bs', tf.ones_like(self.ranking_loss_unred), weight_rel))
self.w = self.wnr + self.wr
self.ranking_loss_unred = tf.multiply(self.w, self.ranking_loss_unred)
self.ranking_loss = tf.reduce_mean(
tf.reduce_sum(self.ranking_loss_unred, axis=-1) / tf.reduce_sum(self.rl_lengths_mask, axis=-1))
elif loss_fn == 'KL_B':
# KL div loss
# self.ranking_loss = tf.reduce_mean(
# compute_kl_div_loss_bin_simm(self.logits, self.relevance_judgments, self.n), axis=-1)
self.ranking_loss_unred = compute_kl_div_loss_bin_simm(self.logits, self.relevance_judgments, self.n)
self.ranking_loss_unred = tf.multiply(self.ranking_loss_unred, self.rl_lengths_mask)
thr = 0.2
mask_n_rel = tf.cast(tf.less_equal(thr, self.relevance_judgments), tf.float32) * self.rl_lengths_mask
mask_rel = tf.cast(tf.greater(thr, self.relevance_judgments), tf.float32) * self.rl_lengths_mask
weight_non_rel = (1 + 1e-6) / (1e-6 + tf.reduce_sum(mask_n_rel, axis=-1))
weight_rel = (1 + 1e-6) / (1e-6 + tf.reduce_sum(mask_rel, axis=-1))
self.wnr = tf.multiply(mask_n_rel,
tf.einsum('bs, b->bs', tf.ones_like(self.ranking_loss_unred), weight_non_rel))
self.wr = tf.multiply(mask_rel, tf.einsum('bs, b->bs', tf.ones_like(self.ranking_loss_unred), weight_rel))
self.w = self.wnr + self.wr
self.ranking_loss_unred = tf.multiply(self.w, self.ranking_loss_unred)
self.ranking_loss = tf.reduce_mean(
tf.reduce_sum(self.ranking_loss_unred, axis=-1) / tf.reduce_sum(self.rl_lengths_mask, axis=-1))
elif loss_fn == 'MSE':
self.mse = tf.reduce_mean(
tf.reduce_sum(tf.square(self.logits - self.relevance_judgments), axis=-1) / tf.reduce_sum(
self.rl_lengths_mask,
axis=-1, keepdims=True))
self.ranking_loss = self.mse
elif loss_fn == 'Hinge':
self.relevance_judgments = self.relevance_judgments + tf.cast(
tf.less_equal(self.rl_lengths_mask, 0), tf.float32) * (-1)
self.ranking_loss_unred, weights = losses.compute_pairwise_hinge_loss(self.logits, self.relevance_judgments)
self.ranking_loss = tf.reduce_mean(tf.multiply(self.ranking_loss_unred, weights))
reg_losses = tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
reg_coeff = 0.0001
print('reg term coeff: {}'.format(reg_coeff))
self.loss = tf.reduce_mean(self.ranking_loss) + reg_losses * reg_coeff
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = optimizer.minimize(self.loss, global_step=self.global_step)
self.init_op = tf.group(tf.compat.v1.global_variables_initializer(),
tf.compat.v1.local_variables_initializer())
self.train_op = tf.group([train_op, update_ops])
# self.train_op = tf.group([qdiff_train_op, rerank_train_op, update_ops])
self.saver = tf.train.Saver(max_to_keep=None)
def compute_kl_multivariate_gaussian(self, logits, labels):
res = 0.5 * ((logits - labels) * (1 / tf.ones(self.list_size_train) * 0.25) * (logits - labels))
return res
@staticmethod
def compute_reg_term_for_att_matrices(attention_coeffs):
reg_values = []
att_vs = tf.unstack(attention_coeffs, axis=-1)
for i in range(attention_coeffs.shape[-1]):
for j in range(i + 1, attention_coeffs.shape[-1]):
reg_term = tf.reduce_mean(tf.abs(tf.subtract(att_vs[i], att_vs[j])), axis=-1)
reg_values.append(reg_term)
reg_values = tf.stack(reg_values)
return tf.reduce_mean(reg_values)
def compute_kl_multivariate_gaussian_simm(self, logits, labels):
return self.compute_kl_multivariate_gaussian(logits, labels) + self.compute_kl_multivariate_gaussian(labels,
logits)
def compute_kl_div_loss(self, logits, labels, normalize_labels=False, max_label_value=4,
consider_raw_rj_dists=False):
if not consider_raw_rj_dists:
if normalize_labels:
d1 = tfp.distributions.MultivariateNormalDiag(loc=logits,
scale_diag=tf.ones(
shape=logits.shape[1]) / max_label_value)
d2 = tfp.distributions.MultivariateNormalDiag(loc=labels,
scale_diag=tf.ones(
shape=labels.shape[1]) / max_label_value)
else:
d1 = tfp.distributions.MultivariateNormalDiag(loc=logits, scale_diag=tf.ones(
self.list_size_train) / max_label_value)
d2 = tfp.distributions.MultivariateNormalDiag(loc=labels, scale_diag=tf.ones(
self.list_size_train) / max_label_value)
# d2 = tfp.distributions.MultivariateNormalDiag(loc=labels, scale_diag=tf.ones(self.labels.shape[1]))
else:
d1 = tfp.distributions.Normal(loc=logits, scale=tf.nn.softmax(tf.ones_like(logits) / max_label_value))
d2 = tfp.distributions.Normal(loc=labels, scale=tf.nn.softmax(tf.ones_like(logits) / max_label_value))
return tfp.distributions.kl_divergence(d1, d2) + tfp.distributions.kl_divergence(d2, d1)
def compute_kl_div_loss_bin_simm(logits, labels, n=6):
return compute_kl_div_loss_bin(logits, labels, n) + compute_kl_div_loss_bin(labels, logits, n)
def compute_kl_div_loss_bin(logits, labels, n=6):
loss = tf.log((1e-6 + labels) / (1e-6 + logits)) * n * labels + tf.log(
(1e-6 + 1 - labels) / (1e-6 + 1 - logits)) * n * (1 - labels)
return loss
def smooth_max(sequence, gamma=10, axis=-1):
return tf.log(tf.reduce_sum(tf.exp(gamma * (sequence + 1e-6)), axis=axis)) / (gamma + 1e-6)