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#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" deltalm model configuration"""
import warnings
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class DeltalmConfig(PretrainedConfig):
model_type = "Deltalm"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=250001,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=3072,
encoder_attention_heads=12,
decoder_layers=6,
decoder_ffn_dim=3072,
decoder_attention_heads=12,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
activation_function="gelu",
d_model=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
classifier_dropout=0.0,
scale_embedding=False,
use_cache=True,
num_labels=3,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
is_encoder_decoder=True,
decoder_start_token_id=0,
forced_eos_token_id=2,
label_smoothing=0.1,
length_penalty=1.0,
encoder_normalize_before=False,
**kwargs
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.label_smoothing = label_smoothing
self.encoder_normalize_before = encoder_normalize_before
super().__init__(
num_labels=num_labels,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
length_penalty=length_penalty,
**kwargs,
)
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
self.forced_bos_token_id = self.bos_token_id
warnings.warn(
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
"The config can simply be saved and uploaded again to be fixed."
)
@property
def num_attention_heads(self) -> int:
return self.encoder_attention_heads
@property
def hidden_size(self) -> int:
return self.d_model |