def _create_model(params, is_train):
"""Creates transformer model."""
encdec_kwargs = dict(num_layers=params["num_hidden_layers"],
num_attention_heads=params["num_heads"],
intermediate_size=params["filter_size"],
activation="relu",
dropout_rate=params["relu_dropout"],
attention_dropout_rate=params["attention_dropout"],
use_bias=False,
norm_first=True,
norm_epsilon=1e-6,
intermediate_dropout=params["relu_dropout"])
encoder_layer = models.TransformerEncoder(**encdec_kwargs)
decoder_layer = models.TransformerDecoder(**encdec_kwargs)
model_kwargs = dict(vocab_size=params["vocab_size"],
embedding_width=params["hidden_size"],
dropout_rate=params["layer_postprocess_dropout"],
padded_decode=params["padded_decode"],
decode_max_length=params["decode_max_length"],
dtype=params["dtype"],
extra_decode_length=params["extra_decode_length"],
beam_size=params["beam_size"],
alpha=params["alpha"],
encoder_layer=encoder_layer,
decoder_layer=decoder_layer,
name="transformer_v2")
if is_train:
inputs = tf.keras.layers.Input((None, ), dtype="int64", name="inputs")
targets = tf.keras.layers.Input((None, ),
dtype="int64",
name="targets")
internal_model = models.Seq2SeqTransformer(**model_kwargs)
logits = internal_model(dict(inputs=inputs, targets=targets),
training=is_train)
vocab_size = params["vocab_size"]
label_smoothing = params["label_smoothing"]
if params["enable_metrics_in_training"]:
logits = metrics.MetricLayer(vocab_size)([logits, targets])
logits = tf.keras.layers.Lambda(lambda x: x,
name="logits",
dtype=tf.float32)(logits)
model = tf.keras.Model([inputs, targets], logits)
loss = metrics.transformer_loss(logits, targets, label_smoothing,
vocab_size)
model.add_loss(loss)
return model
batch_size = params["decode_batch_size"] if params[
"padded_decode"] else None
inputs = tf.keras.layers.Input((None, ),
batch_size=batch_size,
dtype="int64",
name="inputs")
internal_model = models.Seq2SeqTransformer(**model_kwargs)
ret = internal_model(dict(inputs=inputs), training=is_train)
outputs, scores = ret["outputs"], ret["scores"]
return tf.keras.Model(inputs, [outputs, scores])
def build_model(self, params) -> tf.keras.Model:
"""Creates model architecture."""
model_cfg = params or self.task_config.model
encoder_kwargs = model_cfg.encoder.as_dict()
encoder_layer = models.TransformerEncoder(**encoder_kwargs)
decoder_kwargs = model_cfg.decoder.as_dict()
decoder_layer = models.TransformerDecoder(**decoder_kwargs)
return models.Seq2SeqTransformer(
vocab_size=self._vocab_size,
embedding_width=model_cfg.embedding_width,
dropout_rate=model_cfg.dropout_rate,
padded_decode=model_cfg.padded_decode,
decode_max_length=model_cfg.decode_max_length,
beam_size=model_cfg.beam_size,
alpha=model_cfg.alpha,
encoder_layer=encoder_layer,
decoder_layer=decoder_layer)
