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from transformers.configuration_utils import PretrainedConfig
# Copied from transformers.models.m2m_100.configuration_m2m_100.M2M100Config->IndicTrans
class RotaryIndicTransConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IT2Model`]. It is used to instantiate an
IT2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the IT2
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the IT2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`IT2Model`] or
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
```"""
model_type = "RotaryIndicTrans"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_attention_heads": "encoder_attention_heads",
"hidden_size": "d_model",
}
def __init__(
self,
encoder_vocab_size=None,
decoder_vocab_size=None,
encoder_embed_dim=512,
decoder_embed_dim=512,
encoder_layers=6,
encoder_ffn_dim=2048,
encoder_attention_heads=8,
decoder_layers=6,
decoder_ffn_dim=2048,
decoder_attention_heads=8,
encoder_layerdrop=0.00,
decoder_layerdrop=0.00,
use_cache=True,
is_encoder_decoder=True,
activation_function="relu",
encoder_normalize_before=False,
decoder_normalize_before=False,
layernorm_embedding=False,
share_decoder_input_output_embed=False,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
scale_embedding=True,
decoder_start_token_id=2,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
attn_implementation="eager",
rope_args={"theta": 10000},
**kwargs,
):
self.encoder_vocab_size = encoder_vocab_size
self.decoder_vocab_size = decoder_vocab_size
self.encoder_normalize_before = encoder_normalize_before
self.decoder_normalize_before = decoder_normalize_before
self.layernorm_embedding = layernorm_embedding
self.encoder_embed_dim = encoder_embed_dim
self.decoder_embed_dim = decoder_embed_dim
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.use_cache = use_cache
self.rope_args = rope_args
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding
self.share_decoder_input_output_embed = share_decoder_input_output_embed
self.attn_implementation = attn_implementation
super().__init__(
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,
**kwargs,
)
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