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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,
attn_only_wo_proj=False,
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,
pure_attn=False,
repeat_x_before_conv=True,
attn_implementation_new='sdpa',
rope_type=None,
attn_factor=0.5,
**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
self.attn_only_wo_proj = attn_only_wo_proj
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.attn_factor = attn_factor
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
self.pure_attn = pure_attn
self.repeat_x_before_conv = repeat_x_before_conv
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,
)
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