| import math | |
| from transformers.configuration_utils import PretrainedConfig | |
| class HymbaConfig(PretrainedConfig): | |
| model_type = "hymba" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| def __init__( | |
| self, | |
| vocab_size=65536, | |
| tie_word_embeddings=False, | |
| hidden_size=4096, | |
| intermediate_size=14336, | |
| num_hidden_layers=32, | |
| num_attention_heads=32, | |
| num_key_value_heads=8, | |
| hidden_act="silu", | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-6, | |
| use_cache=True, | |
| calc_logits_for_entire_prompt=False, | |
| output_router_logits=False, | |
| router_aux_loss_coef=0.001, | |
| pad_token_id=0, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| sliding_window=None, | |
| max_position_embeddings=262144, | |
| orig_max_position_embeddings=None, | |
| attention_dropout=0.0, | |
| num_experts_per_tok=2, | |
| num_experts=16, | |
| use_mamba_kernels=True, | |
| mamba_d_state=16, | |
| mamba_d_conv=4, | |
| mamba_expand=2, | |
| mamba_dt_rank="auto", | |
| mamba_conv_bias=True, | |
| mamba_proj_bias=False, | |
| mamba_inner_layernorms=True, | |
| kv_reuse_every_i_layer=-1, | |
| kv_reuse_group=None, | |
| kv_weight_reuse=False, | |
| global_attn_idx=None, | |
| num_mamba=1, | |
| attn_implementation_new='sdpa', | |
| rope_type=None, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.tie_word_embeddings = tie_word_embeddings | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.sliding_window = sliding_window | |
| self.max_position_embeddings = max_position_embeddings | |
| self.orig_max_position_embeddings = orig_max_position_embeddings | |
| self.attention_dropout = attention_dropout | |
| if num_key_value_heads is None: | |
| num_key_value_heads = num_attention_heads | |
| self.num_key_value_heads = num_key_value_heads | |
| self.hidden_act = hidden_act | |
| self.initializer_range = initializer_range | |
| self.rms_norm_eps = rms_norm_eps | |
| self.use_cache = use_cache | |
| self.calc_logits_for_entire_prompt = calc_logits_for_entire_prompt | |
| self.output_router_logits = output_router_logits | |
| self.router_aux_loss_coef = router_aux_loss_coef | |
| self.num_experts_per_tok = num_experts_per_tok | |
| self.num_experts = num_experts | |
| self.use_mamba_kernels = use_mamba_kernels | |
| self.mamba_d_state = mamba_d_state | |
| self.mamba_d_conv = mamba_d_conv | |
| self.mamba_expand = mamba_expand | |
| self.mamba_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank | |
| self.mamba_conv_bias = mamba_conv_bias | |
| self.mamba_proj_bias = mamba_proj_bias | |
| self.mamba_inner_layernorms = mamba_inner_layernorms | |
| self.attn_hidden_size = kwargs.pop("attn_hidden_size", -1) | |
| self.kq_head_dim = kwargs.pop("kq_head_dim", -1) | |
| self.v_head_dim = kwargs.pop("v_head_dim", -1) | |
| self.kq_norm = kwargs.pop("kq_norm", None) | |
| self.rope = kwargs.pop("rope", False) | |
| self.rope_theta = kwargs.pop("rope_theta", 10000.0) | |
| self.num_memory_tokens = kwargs.pop("num_memory_tokens", 0) | |
| self.memory_tokens_interspersed_every = kwargs.pop("memory_tokens_interspersed_every", 0) | |
| self.kv_reuse_every_i_layer = kv_reuse_every_i_layer | |
| self.kv_reuse_group = kv_reuse_group | |
| self.kv_weight_reuse = kv_weight_reuse | |
| self.global_attn_idx = global_attn_idx | |
| self.num_mamba = num_mamba | |
| self.attn_implementation_new = attn_implementation_new | |
| self.rope_type = rope_type | |
| 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, | |
| ) | |