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40a27ad ae62f19 40a27ad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | # Copyright 2025 Antgroup 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.
"""LLaDA2 MoE model configuration."""
from transformers.configuration_utils import PretrainedConfig
class LLaDA2MoeConfig(PretrainedConfig):
r"""
Configuration class for the LLaDA2 MoE model (discrete-token multimodal LLM).
This config covers the LLM backbone only. Images are represented as discrete VQ tokens
in the extended vocabulary — no vision encoder config is needed.
```python
>>> from configuration_llada2uni_moe import LLaDA2MoeConfig
>>> config = LLaDA2MoeConfig()
```
"""
model_type = "llada2_moe"
def __init__(
self,
vocab_size=30592,
hidden_size=1024,
intermediate_size=None,
num_hidden_layers=24,
num_attention_heads=16,
num_key_value_heads=0,
head_dim=None,
hidden_act="silu",
use_qkv_bias=False,
use_qk_norm=False,
use_bias=True,
rms_norm_eps=1e-05,
tie_word_embeddings=False,
attention_dropout=0.1,
initializer_range=0.02,
max_position_embeddings=16384,
rope_theta=10000.0,
rope_parameters=None,
partial_rotary_factor=0.5,
use_cache=True,
sliding_window=None,
pad_token_id=126081,
# Image
image_token_offset=157184,
# MoE
num_experts=16,
num_shared_experts=0,
num_experts_per_tok=2,
n_group=8,
topk_group=4,
routed_scaling_factor=2.5,
moe_intermediate_size=None,
first_k_dense_replace=0,
output_router_logits=False,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_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.head_dim = head_dim or hidden_size // num_attention_heads
self.hidden_act = hidden_act
self.use_qkv_bias = use_qkv_bias
self.use_qk_norm = use_qk_norm
self.use_bias = use_bias
self.rms_norm_eps = rms_norm_eps
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.partial_rotary_factor = partial_rotary_factor
self.use_cache = use_cache
self.sliding_window = sliding_window
# Image token offset: VQ codebook indices are shifted by this amount in the vocabulary
self.image_token_offset = image_token_offset
# RoPE parameters dict — used by LLaDA2MoeRotaryEmbedding
if rope_parameters is None:
rope_parameters = {
"rope_type": "default",
"rope_theta": rope_theta,
"partial_rotary_factor": partial_rotary_factor,
}
self.rope_parameters = rope_parameters
# MoE
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.routed_scaling_factor = routed_scaling_factor
self.moe_intermediate_size = moe_intermediate_size
self.first_k_dense_replace = first_k_dense_replace
self.output_router_logits = output_router_logits
# FP8 quantization flag — set to True to use FP8Linear for experts
self.use_fp8_experts = kwargs.pop("use_fp8_experts", False)
super().__init__(
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
__all__ = ["LLaDA2MoeConfig"]
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