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from typing import List
from transformers import PretrainedConfig
class TimeMoeConfig(PretrainedConfig):
model_type = "time_moe"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
input_size: int = 1,
hidden_size: int = 4096,
intermediate_size: int = 22016,
horizon_lengths: List[int] = 1,
num_hidden_layers: int = 32,
num_attention_heads: int = 32,
num_key_value_heads: int = None,
hidden_act: str = "silu",
num_experts_per_tok: int = 2,
num_experts: int = 1,
max_position_embeddings: int = 32768,
initializer_range: float = 0.02,
rms_norm_eps: float = 1e-6,
use_cache: bool = True,
use_dense: bool = False,
rope_theta: int = 10000,
attention_dropout: float = 0.0,
apply_aux_loss: bool = True,
router_aux_loss_factor: float = 0.02,
tie_word_embeddings: bool = False,
**kwargs,
):
self.input_size = input_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.max_position_embeddings = max_position_embeddings
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
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
if isinstance(horizon_lengths, int):
horizon_lengths = [horizon_lengths]
self.horizon_lengths = horizon_lengths # Predict horizon length for each prediction.
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.use_dense = use_dense
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.apply_aux_loss = apply_aux_loss
self.router_aux_loss_factor = router_aux_loss_factor
assert self.use_dense ^ self.apply_aux_loss, 'Both use_dense and apply_aux_loss cannot be set to True or False at the same time.'
kwargs.pop('tie_word_embeddings', None)
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
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