| | from transformers.utils import logging |
| | from transformers.configuration_utils import PretrainedConfig |
| |
|
| |
|
| | logger = logging.get_logger(__name__) |
| |
|
| | INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {} |
| |
|
| |
|
| | class BufferEmbeddingConfig(PretrainedConfig): |
| | model_type = "buffer_embedding" |
| | _auto_class = "AutoConfig" |
| | keys_to_ignore_at_inference = ["past_key_values"] |
| | attribute_map = { |
| | "num_hidden_layers": "n_layer", |
| | "num_attention_heads": "n_head", |
| | } |
| | def __init__( |
| | self, |
| | vocab_size=250880, |
| | hidden_size=64, |
| | n_layer=2, |
| | n_head=8, |
| | layer_norm_epsilon=1e-5, |
| | initializer_range=0.02, |
| | use_cache=True, |
| | bos_token_id=1, |
| | eos_token_id=2, |
| | apply_residual_connection_post_layernorm=False, |
| | hidden_dropout=0.0, |
| | attention_dropout=0.0, |
| | pretraining_tp=1, |
| | slow_but_exact=False, |
| | **kwargs, |
| | ): |
| | self.vocab_size = vocab_size |
| | |
| | n_embed = kwargs.pop("n_embed", None) |
| | self.hidden_size = hidden_size if n_embed is None else n_embed |
| | self.n_layer = n_layer |
| | self.n_head = n_head |
| | self.layer_norm_epsilon = layer_norm_epsilon |
| | self.initializer_range = initializer_range |
| | self.use_cache = use_cache |
| | self.pretraining_tp = pretraining_tp |
| | self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm |
| | self.hidden_dropout = hidden_dropout |
| | self.attention_dropout = attention_dropout |
| |
|
| | self.bos_token_id = bos_token_id |
| | self.eos_token_id = eos_token_id |
| | self.slow_but_exact = slow_but_exact |
| |
|
| | super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
| |
|
| |
|
