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# coding=utf-8
# Copyright (c) Huawei Technologies Co., Ltd. 2025. All rights reserved.
# 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.
""" PanguProMoE model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class PanguProMoEConfig(PretrainedConfig):
model_type = "PanguProMoE"
_auto_class = "AutoConfig"
def __init__(
self,
vocab_size=153376,
hidden_size=4608,
intermediate_size=10240,
num_hidden_layers=50,
num_attention_heads=64,
num_key_value_heads=4,
mlp_only_layers=[0,1,2,3],
hidden_act="silu",
max_position_embeddings=8192,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
tie_word_embeddings=False,
rope_theta=100000,
moe_intermediate_size=1280,
shared_expert_intermediate_size=2560,
num_experts_per_tok=8,
num_experts=80,
norm_topk_prob=True,
router_enable_expert_bias=True,
output_router_logits=False,
routed_scaling_factor=2.5,
qk_nope_dim = 128,
qk_rope_dim = 64,
v_channels = 128,
sandwich_norm=True,
param_sink_number = 128,
param_sink_with_value=True,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_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.rope_theta = rope_theta
self.mlp_only_layers = mlp_only_layers
self.intermediate_size = intermediate_size
# MoE arguments
self.moe_intermediate_size = moe_intermediate_size
self.shared_expert_intermediate_size = shared_expert_intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.norm_topk_prob = norm_topk_prob
self.output_router_logits = output_router_logits
self.router_enable_expert_bias = router_enable_expert_bias
self.routed_scaling_factor = routed_scaling_factor
self.qk_nope_dim = qk_nope_dim
self.qk_rope_dim = qk_rope_dim
self.v_channels = v_channels
self.sandwich_norm = sandwich_norm
self.param_sink_number = param_sink_number
self.param_sink_with_value = param_sink_with_value
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
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