| """Quasar Long model configuration""" |
|
|
| from transformers.configuration_utils import PretrainedConfig |
|
|
|
|
| class QuasarLongConfig(PretrainedConfig): |
| model_type = "quasar_long" |
|
|
| def __init__( |
| self, |
| vocab_size=157184, |
| hidden_size=2048, |
| intermediate_size=5120, |
| num_hidden_layers=20, |
| num_attention_heads=16, |
| num_key_value_heads=4, |
| hidden_act="silu", |
| use_qkv_bias=False, |
| use_bias=False, |
| rms_norm_eps=1e-06, |
| tie_word_embeddings=False, |
| embedding_dropout=0.0, |
| attention_dropout=0.0, |
| output_dropout=0.0, |
| initializer_range=0.02, |
| max_position_embeddings=32768, |
| rope_theta=600000.0, |
| use_cache=True, |
| max_window_layers=20, |
| rope_scaling=None, |
| pad_token_id=156892, |
| eos_token_id=156892, |
| num_experts=256, |
| num_shared_experts=1, |
| num_experts_per_tok=8, |
| n_group=8, |
| topk_group=4, |
| moe_intermediate_size=512, |
| first_k_dense_replace=1, |
| head_dim=128, |
| output_router_logits=False, |
| use_qk_norm=True, |
| num_nextn_predict_layers=0, |
| mtp_loss_scaling_factor=0, |
| moe_router_enable_expert_bias=True, |
| routed_scaling_factor=1.0, |
| hybrid_attention_layers=None, |
| hybrid_alpha_init=-15.0, |
| hybrid_gla_expand_k=1.0, |
| hybrid_gla_expand_v=1.0, |
| hybrid_use_short_conv=False, |
| hybrid_quasar_enabled=True, |
| hybrid_gla_enabled=True, |
| hybrid_branch_layout="mixed", |
| hybrid_layerwise_cycle=None, |
| |
| num_loops=1, |
| use_looped_injection=False, |
| |
| |
| engram_layers=None, |
| engram_dim=512, |
| engram_slots=2_000_000, |
| engram_num_heads=8, |
| engram_ngram_orders=None, |
| engram_lr_multiplier=5.0, |
| use_nope=False, |
| long_context_mode="rope_short_nope_long", |
| nope_after_position=512, |
| max_seq_length=None, |
| max_sequence_length=None, |
| **kwargs, |
| ): |
| self.num_hidden_layers = num_hidden_layers |
| self.vocab_size = vocab_size |
| self.hidden_size = hidden_size |
| self.intermediate_size = intermediate_size |
| self.num_attention_heads = num_attention_heads |
| self.num_key_value_heads = num_key_value_heads |
| self.hidden_act = hidden_act |
| self.use_qkv_bias = use_qkv_bias |
| self.use_bias = use_bias |
| self.rms_norm_eps = rms_norm_eps |
| self.embedding_dropout = embedding_dropout |
| self.attention_dropout = attention_dropout |
| self.output_dropout = output_dropout |
| self.num_nextn_predict_layers = num_nextn_predict_layers |
| self.mtp_loss_scaling_factor = mtp_loss_scaling_factor |
| self.initializer_range = initializer_range |
| self.max_position_embeddings = max_position_embeddings |
| self.rope_theta = rope_theta |
| self.use_cache = use_cache |
| self.max_window_layers = max_window_layers |
| self.head_dim = head_dim or self.hidden_size // self.num_attention_heads |
| self.rope_scaling = rope_scaling |
| self.use_qk_norm = use_qk_norm |
| self.moe_router_enable_expert_bias = moe_router_enable_expert_bias |
| self.routed_scaling_factor = routed_scaling_factor |
| self.hybrid_attention_layers = hybrid_attention_layers or [] |
| self.hybrid_alpha_init = hybrid_alpha_init |
| self.hybrid_gla_expand_k = hybrid_gla_expand_k |
| self.hybrid_gla_expand_v = hybrid_gla_expand_v |
| self.hybrid_use_short_conv = hybrid_use_short_conv |
| self.hybrid_quasar_enabled = hybrid_quasar_enabled |
| self.hybrid_gla_enabled = hybrid_gla_enabled |
| self.hybrid_branch_layout = hybrid_branch_layout |
| self.hybrid_layerwise_cycle = list(hybrid_layerwise_cycle) if hybrid_layerwise_cycle is not None else [ |
| "quasar", |
| "raven", |
| "gla", |
| ] |
|
|
| |
| self.num_loops = num_loops |
| self.use_looped_injection = use_looped_injection |
|
|
| |
| self.engram_layers = list(engram_layers) if engram_layers is not None else [] |
| self.engram_dim = engram_dim |
| self.engram_slots = engram_slots |
| self.engram_num_heads = engram_num_heads |
| self.engram_ngram_orders = list(engram_ngram_orders) if engram_ngram_orders is not None else [2, 3] |
| self.engram_lr_multiplier = engram_lr_multiplier |
| self.use_nope = use_nope |
| self.long_context_mode = long_context_mode |
| self.nope_after_position = int(nope_after_position) |
| self.max_seq_length = int(max_seq_length) if max_seq_length is not None else None |
| self.max_sequence_length = int(max_sequence_length) if max_sequence_length is not None else None |
|
|
| |
| self.num_experts = num_experts |
| self.num_shared_experts = num_shared_experts |
| self.num_experts_per_tok = num_experts_per_tok |
| self.n_group = n_group |
| self.topk_group = topk_group |
| self.moe_intermediate_size = moe_intermediate_size |
| self.first_k_dense_replace = first_k_dense_replace |
| self.output_router_logits = output_router_logits |
|
|
| super().__init__(pad_token_id=pad_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs) |
|
|
