# coding=utf-8 # Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """BailingHybrid model configuration""" import enum from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from sglang.srt.configs.mamba_utils import Mamba2CacheParams, Mamba2StateShape logger = logging.get_logger(__name__) class HybridLayerType(enum.Enum): full_attention = "attention" linear_attention = "linear_attention" class BailingHybridConfig(PretrainedConfig): model_type = "bailing_hybrid" keys_to_ignore_at_inference = ["past_key_values"] 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, # bailing only use_bias=False, # bailing only rms_norm_eps=1e-06, tie_word_embeddings=False, # PretrainedConfig key, here change default value. 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, layer_group_size=1, group_norm_size=1, linear_silu=False, kv_lora_rank=512, q_lora_rank=None, qk_rope_head_dim=64, v_head_dim=128, qk_nope_head_dim=128, rope_interleave=True, **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 # MoE configs 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 # Linear configs self.layer_group_size = layer_group_size self.group_norm_size = group_norm_size self.linear_silu = linear_silu self.num_linear_key_value_heads = num_attention_heads # mla self.kv_lora_rank = kv_lora_rank self.q_lora_rank = q_lora_rank self.qk_rope_head_dim = qk_rope_head_dim self.v_head_dim = v_head_dim self.qk_nope_head_dim = qk_nope_head_dim self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim self.rope_interleave = rope_interleave self.for_nextn_model = False super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) @property def layers_block_type(self): if self.for_nextn_model: return [HybridLayerType.full_attention.value] layer_type_list = [] for l in range(self.num_hidden_layers): if (l + 1) % self.layer_group_size == 0: layer_type_list.append(HybridLayerType.full_attention.value) else: layer_type_list.append(HybridLayerType.linear_attention.value) return layer_type_list @property def linear_layer_ids(self): return [ i for i, type_value in enumerate(self.layers_block_type) if type_value == HybridLayerType.linear_attention.value ] @property def full_attention_layer_ids(self): return [ i for i, type_value in enumerate(self.layers_block_type) if type_value == HybridLayerType.full_attention.value ] @property def mamba2_cache_params(self) -> Mamba2CacheParams: from sglang.srt.layers.dp_attention import get_attention_tp_size shape = Mamba2StateShape.create( tp_world_size=get_attention_tp_size(), intermediate_size=0, n_groups=0, num_heads=self.num_linear_key_value_heads, head_dim=self.head_dim, state_size=self.head_dim, conv_kernel=1, ) return Mamba2CacheParams(shape=shape, layers=self.linear_layer_ids)