| import warnings | |
| from transformers.configuration_utils import PretrainedConfig | |
| class ABCConfig(PretrainedConfig): | |
| model_type = 'abc' | |
| keys_to_ignore_at_inference = ['past_key_values'] | |
| def __init__( | |
| self, | |
| hidden_size: int = 2048, | |
| gate_low_rank_dim: int = 16, | |
| clamp_min: float = -32, | |
| clamp_max: float = 32, | |
| hidden_ratio: int | None = 4, | |
| intermediate_size: int | None = None, | |
| num_hidden_layers: int = 24, | |
| num_heads: int = 4, | |
| num_slots: int | None = 64, | |
| use_short_conv: bool = False, | |
| conv_size: int = 4, | |
| exapnd_k: float = 0.5, | |
| exapnd_v: float = 1, | |
| hidden_act: str = "swish", | |
| max_position_embeddings: int = 2048, | |
| elementwise_affine: bool | None = True, | |
| norm_eps: float = 1e-6, | |
| use_rope: bool = True, | |
| attn: dict | None = None, | |
| use_cache: bool = True, | |
| pad_token_id: int | None = 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_swiglu: bool = True, | |
| fuse_cross_entropy: bool = True, | |
| fuse_linear_cross_entropy: bool = False, | |
| use_l2warp: bool = False, | |
| vocab_size: int = 32000, | |
| **kwargs, | |
| ): | |
| self.hidden_size = hidden_size | |
| self.gate_low_rank_dim = gate_low_rank_dim | |
| self.clamp_min = clamp_min | |
| self.clamp_max = clamp_max | |
| self.hidden_ratio = hidden_ratio | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_heads = num_heads | |
| self.num_slots = num_slots | |
| self.use_short_conv = use_short_conv | |
| self.conv_size = conv_size | |
| self.expand_k = exapnd_k | |
| self.expand_v = exapnd_v | |
| self.hidden_act = hidden_act | |
| self.max_position_embeddings = max_position_embeddings | |
| self.elementwise_affine = elementwise_affine | |
| self.norm_eps = norm_eps | |
| self.use_rope = use_rope | |
| self.attn = attn | |
| self.use_cache = use_cache | |
| self.initializer_range = initializer_range | |
| self.fuse_norm = fuse_norm | |
| self.fuse_swiglu = fuse_swiglu | |
| self.fuse_cross_entropy = fuse_cross_entropy | |
| self.fuse_linear_cross_entropy = fuse_linear_cross_entropy | |
| self.use_l2warp = use_l2warp | |
| self.vocab_size = vocab_size | |
| if fuse_cross_entropy and fuse_linear_cross_entropy: | |
| raise ValueError( | |
| "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", | |
| ) | |
| if fuse_linear_cross_entropy: | |
| warnings.warn( | |
| "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " | |
| "at the potential cost of reduced precision. " | |
| "If you observe issues like loss divergence, consider disabling this setting.", | |
| ) | |
| if attn is not None: | |
| if not isinstance(attn, dict): | |
| raise ValueError("attn must be a dictionary") | |
| if 'layers' not in attn: | |
| raise ValueError("Layer indices must be provided to initialize hybrid attention layers") | |
| if 'num_heads' not in attn: | |
| raise ValueError("Number of heads must be provided to initialize hybrid attention layers") | |
| attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) | |
| attn['qkv_bias'] = attn.get('qkv_bias', False) | |
| attn['window_size'] = attn.get('window_size', None) | |
| attn['rope_theta'] = attn.get('rope_theta', 10000.) | |
| 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, | |
| ) | |