def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
if isinstance(features, dict):
if not labels and "labels" in features:
labels = features["labels"]
features = features["input_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = modeling.BertModel(bert_config)
headl = ClassifierLossLayer(
bert_config["num_labels"], bert_config["hidden_dropout_prob"],
utils.create_initializer(bert_config["initializer_range"]),
name=bert_config["scope"]+"/classifier")
_, pooled_output = model(features, training=is_training)
total_loss, log_probs = headl(pooled_output, labels, is_training)
tvars = tf.compat.v1.trainable_variables()
utils.log_variables(tvars, bert_config["ckpt_var_list"])
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
learning_rate = optimization.get_linear_warmup_linear_decay_lr(
init_lr=bert_config["learning_rate"],
num_train_steps=bert_config["num_train_steps"],
num_warmup_steps=bert_config["num_warmup_steps"])
optimizer = optimization.get_optimizer(bert_config, learning_rate)
global_step = tf.compat.v1.train.get_or_create_global_step()
gradients = optimizer.compute_gradients(total_loss, tvars)
train_op = optimizer.apply_gradients(gradients, global_step=global_step)
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
host_call=utils.add_scalars_to_summary(
bert_config["output_dir"], {"learning_rate": learning_rate}))
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(loss_value, label_ids, log_probs):
loss = tf.compat.v1.metrics.mean(values=loss_value)
predictions = tf.argmax(log_probs, axis=-1, output_type=tf.int32)
accuracy = tf.compat.v1.metrics.accuracy(
labels=label_ids, predictions=predictions)
p1, p1_op = tf.compat.v1.metrics.precision_at_k(
labels=tf.cast(label_ids, tf.int64), predictions=log_probs, k=1)
r1, r1_op = tf.compat.v1.metrics.recall_at_k(
labels=tf.cast(label_ids, tf.int64), predictions=log_probs, k=1)
f11 = tf.math.divide_no_nan(2*p1*r1, p1+r1)
metric_dict = {
"P@1": (p1, p1_op),
"R@1": (r1, r1_op),
"f1@1": (f11, tf.no_op()),
"classification_accuracy": accuracy,
"classification_loss": loss,
}
return metric_dict
eval_metrics = (metric_fn,
[tf.expand_dims(total_loss, 0), labels, log_probs])
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics)
else:
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
predictions={"log-probabilities": log_probs})
return output_spec
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = modeling.BertModel(bert_config)
masked_lm = MaskedLMLayer(bert_config["hidden_size"],
bert_config["vocab_size"],
model.embeder,
initializer=utils.create_initializer(
bert_config["initializer_range"]),
activation_fn=utils.get_activation(
bert_config["hidden_act"]))
next_sentence = NSPLayer(bert_config["hidden_size"],
initializer=utils.create_initializer(
bert_config["initializer_range"]))
sequence_output, pooled_output = model(
features["input_ids"],
training=is_training,
token_type_ids=features.get("segment_ids"))
masked_lm_loss, masked_lm_log_probs = masked_lm(
sequence_output,
label_ids=features.get("masked_lm_ids"),
label_weights=features.get("masked_lm_weights"),
masked_lm_positions=features.get("masked_lm_positions"))
next_sentence_loss, next_sentence_log_probs = next_sentence(
pooled_output, features.get("next_sentence_labels"))
total_loss = masked_lm_loss
if bert_config["use_nsp"]:
total_loss += next_sentence_loss
tvars = tf.compat.v1.trainable_variables()
utils.log_variables(tvars, bert_config["ckpt_var_list"])
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
learning_rate = optimization.get_linear_warmup_linear_decay_lr(
init_lr=bert_config["learning_rate"],
num_train_steps=bert_config["num_train_steps"],
num_warmup_steps=bert_config["num_warmup_steps"])
optimizer = optimization.get_optimizer(bert_config, learning_rate)
global_step = tf.compat.v1.train.get_global_step()
gradients = optimizer.compute_gradients(total_loss, tvars)
train_op = optimizer.apply_gradients(gradients,
global_step=global_step)
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
host_call=utils.add_scalars_to_summary(
bert_config["output_dir"],
{"learning_rate": learning_rate}))
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(masked_lm_loss_value, masked_lm_log_probs,
masked_lm_ids, masked_lm_weights,
next_sentence_loss_value, next_sentence_log_probs,
next_sentence_labels):
"""Computes the loss and accuracy of the model."""
masked_lm_predictions = tf.argmax(masked_lm_log_probs,
axis=-1,
output_type=tf.int32)
masked_lm_accuracy = tf.compat.v1.metrics.accuracy(
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = tf.compat.v1.metrics.mean(
values=masked_lm_loss_value)
next_sentence_predictions = tf.argmax(next_sentence_log_probs,
axis=-1,
output_type=tf.int32)
next_sentence_accuracy = tf.compat.v1.metrics.accuracy(
labels=next_sentence_labels,
predictions=next_sentence_predictions)
next_sentence_mean_loss = tf.compat.v1.metrics.mean(
values=next_sentence_loss_value)
return {
"masked_lm_accuracy": masked_lm_accuracy,
"masked_lm_loss": masked_lm_mean_loss,
"next_sentence_accuracy": next_sentence_accuracy,
"next_sentence_loss": next_sentence_mean_loss,
}
eval_metrics = (metric_fn, [
masked_lm_loss, masked_lm_log_probs, features["masked_lm_ids"],
features["masked_lm_weights"], next_sentence_loss,
next_sentence_log_probs, features["next_sentence_labels"]
])
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode, loss=total_loss, eval_metrics=eval_metrics)
else:
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
predictions={
"log-probabilities": masked_lm_log_probs,
"seq-embeddings": sequence_output
})
return output_spec
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
if isinstance(features, dict):
if not labels and "target_ids" in features:
labels = features["target_ids"]
features = features["input_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = modeling.TransformerModel(transformer_config)
(llh, logits, pred_ids), _ = model(features,
target_ids=labels,
training=is_training)
total_loss = padded_cross_entropy_loss(
logits, labels, transformer_config["label_smoothing"],
transformer_config["vocab_size"])
tvars = tf.compat.v1.trainable_variables()
utils.log_variables(tvars, transformer_config["ckpt_var_list"])
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
learning_rate = optimization.get_linear_warmup_rsqrt_decay_lr(
init_lr=transformer_config["learning_rate"],
hidden_size=transformer_config["hidden_size"],
num_warmup_steps=transformer_config["num_warmup_steps"])
optimizer = optimization.get_optimizer(transformer_config,
learning_rate)
global_step = tf.compat.v1.train.get_global_step()
if not transformer_config["use_bias"]:
logging.info("Fixing position embedding, i.e. not trainable.")
posemb = "pegasus/embeddings/position_embeddings"
tvars = list(
filter(lambda v: v.name.split(":")[0] != posemb, tvars))
gradients = optimizer.compute_gradients(total_loss, tvars)
train_op = optimizer.apply_gradients(gradients,
global_step=global_step)
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
host_call=utils.add_scalars_to_summary(
transformer_config["output_dir"],
{"learning_rate": learning_rate}))
elif mode == tf.estimator.ModeKeys.EVAL:
tokenizer = tft.SentencepieceTokenizer(model=tf.io.gfile.GFile(
transformer_config["vocab_model_file"], "rb").read())
def rouge_py_func(label_sent, pred_sent):
"""Approximate ROUGE scores, always run externally for final scores."""
scorer = rouge_scorer.RougeScorer(
["rouge1", "rouge2", "rougeLsum"], use_stemmer=True)
r1, r2, rl = 0.0, 0.0, 0.0
for ls, ps in zip(label_sent, pred_sent):
score = scorer.score(ls.decode("utf-8"),
ps.decode("utf-8"))
r1 += score["rouge1"].fmeasure
r2 += score["rouge2"].fmeasure
rl += score["rougeLsum"].fmeasure
return r1 / len(label_sent), r2 / len(label_sent), rl / len(
label_sent)
def metric_fn(loss, log_probs, label_ids, pred_ids):
loss = tf.compat.v1.metrics.mean(values=loss)
log_probs = tf.compat.v1.metrics.mean(
values=log_probs,
weights=tf.cast(tf.not_equal(label_ids, 0), tf.float32))
metric_dict = {
"prediction_loss": loss,
"log_likelihood": log_probs,
}
if not transformer_config["use_tpu"]:
# Approximate ROUGE scores if not running on tpus.
# Always run externally for final scores.
label_sent = tokenizer.detokenize(label_ids)
label_sent = tf.strings.regex_replace(
label_sent, r"([<\[]\S+[>\]])", b" \\1")
pred_sent = tokenizer.detokenize(pred_ids)
pred_sent = tf.strings.regex_replace(
pred_sent, r"([<\[]\S+[>\]])", b" \\1")
if transformer_config["substitute_newline"]:
label_sent = tf.strings.regex_replace(
label_sent,
transformer_config["substitute_newline"], "\n")
pred_sent = tf.strings.regex_replace(
pred_sent,
transformer_config["substitute_newline"], "\n")
rouge_value = tf.compat.v1.py_func(
func=rouge_py_func,
inp=[label_sent, pred_sent],
Tout=[tf.float64, tf.float64, tf.float64],
stateful=False)
rouge_value = tf.cast(rouge_value, tf.float32)
rouge1 = tf.compat.v1.metrics.mean(values=rouge_value[0])
rouge2 = tf.compat.v1.metrics.mean(values=rouge_value[1])
rougeL = tf.compat.v1.metrics.mean(values=rouge_value[2]) # pylint: disable=invalid-name
metric_dict.update({
"eval/Rouge-1": rouge1,
"eval/Rouge-2": rouge2,
"eval/Rouge-L": rougeL,
})
return metric_dict
eval_metrics = (metric_fn, [total_loss, llh, labels, pred_ids])
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode, loss=total_loss, eval_metrics=eval_metrics)
else:
prediction_dict = {"pred_ids": pred_ids}
if not transformer_config["use_tpu"]:
tokenizer = tft.SentencepieceTokenizer(model=tf.io.gfile.GFile(
transformer_config["vocab_model_file"], "rb").read())
pred_sent = tokenizer.detokenize(pred_ids)
# Add a space before special tokens.
pred_sent = tf.strings.regex_replace(pred_sent,
r"([<\[]\S+[>\]])",
b" \\1")
if transformer_config["substitute_newline"]:
pred_sent = tf.strings.regex_replace(
pred_sent, transformer_config["substitute_newline"],
"\n")
prediction_dict.update({"pred_sent": pred_sent})
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode, predictions=prediction_dict)
return output_spec
