# -*- coding: utf-8 -*- from typing import Optional from transformers.configuration_utils import PretrainedConfig class GLAConfig(PretrainedConfig): model_type = 'gla' keys_to_ignore_at_inference = ['past_key_values'] def __init__( self, vocab_size: int = 32000, hidden_size: int = 2048, expand_k: int = 0.5, expand_v: int = 1, hidden_ratio: Optional[int] = 4, intermediate_size: Optional[int] = None, num_hidden_layers: int = 24, num_heads: int = 4, num_kv_heads: Optional[int] = None, feature_map: Optional[str] = None, attn_mode: str = "chunk", use_short_conv: bool = False, conv_size: int = 4, share_conv_kernel: bool = True, use_output_gate: bool = True, clamp_min: Optional[float] = None, hidden_act: str = "swish", max_position_embeddings: int = 2048, elementwise_affine: Optional[bool] = True, norm_eps: float = 1e-6, use_gk: bool = True, use_gv: bool = False, use_cache: bool = True, pad_token_id: int = None, bos_token_id: int = 1, eos_token_id: int = 2, tie_word_embeddings: bool = False, initializer_range: float = 0.02, fuse_norm: bool = True, fuse_cross_entropy: bool = True, **kwargs ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.expand_k = expand_k self.expand_v = expand_v self.hidden_ratio = hidden_ratio self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_heads = num_heads self.num_kv_heads = num_kv_heads self.feature_map = feature_map self.attn_mode = attn_mode self.clamp_min = clamp_min self.hidden_act = hidden_act self.elementwise_affine = elementwise_affine self.norm_eps = norm_eps self.use_gk = use_gk self.use_gv = use_gv self.use_cache = use_cache self.initializer_range = initializer_range self.fuse_norm = fuse_norm self.fuse_cross_entropy = fuse_cross_entropy self.use_short_conv = use_short_conv self.conv_size = conv_size self.share_conv_kernel = share_conv_kernel self.use_output_gate = use_output_gate super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, )