sgl-project--sglang/python/sglang/srt/configs/bailing_hybrid.py

# 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
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"""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
from sglang.srt.runtime_context import get_parallel

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:

        shape = Mamba2StateShape.create(
            tp_world_size=get_parallel().attn_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)