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#
# Copyright 2026 Xiaomi Corporation.
# Copyright 2026 The HuggingFace Inc. team.
#
# 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.
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
_MIMOV2_ATTENTION_PROJECTION_LAYOUTS = {"split", "fused_qkv"}
_MIMOV2_SPLIT_TP_PLAN = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
_MIMOV2_FUSED_QKV_TP_PLAN = {
"layers.*.self_attn.qkv_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
_MIMOV2_PP_PLAN = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
class MiMoV2Config(PretrainedConfig):
model_type = "mimo_v2"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = _MIMOV2_SPLIT_TP_PLAN
base_model_pp_plan = _MIMOV2_PP_PLAN
attribute_map = {
"num_local_experts": "n_routed_experts",
}
def __init__(
self,
vocab_size=151936,
hidden_size=4096,
intermediate_size=22016,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=32,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
layernorm_epsilon=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_dropout=0.0,
attention_bias=False,
attention_value_scale=None,
head_dim=None,
v_head_dim=None,
swa_num_attention_heads=None,
swa_num_key_value_heads=None,
swa_head_dim=None,
swa_v_head_dim=None,
swa_rope_theta=None,
sliding_window=None,
sliding_window_size=None,
add_full_attention_sink_bias=False,
add_swa_attention_sink_bias=False,
hybrid_block_size=None,
hybrid_layer_pattern=None,
partial_rotary_factor=1.0,
n_routed_experts=None,
moe_intermediate_size=None,
num_experts_per_tok=None,
routed_scaling_factor=None,
scoring_func="sigmoid",
topk_method="noaux_tc",
n_group=None,
topk_group=None,
norm_topk_prob=True,
moe_layer_freq=None,
attention_projection_layout="split",
**kwargs,
):
rope_parameters = kwargs.pop("rope_parameters", None)
if rope_scaling is None and rope_parameters is not None:
rope_scaling = rope_parameters
if attention_projection_layout is None:
attention_projection_layout = "split"
if attention_projection_layout not in _MIMOV2_ATTENTION_PROJECTION_LAYOUTS:
raise ValueError(f"Unsupported MiMoV2 attention projection layout: {attention_projection_layout}")
self.attention_projection_layout = attention_projection_layout
self.base_model_tp_plan = (
_MIMOV2_FUSED_QKV_TP_PLAN.copy()
if attention_projection_layout == "fused_qkv"
else _MIMOV2_SPLIT_TP_PLAN.copy()
)
self.base_model_pp_plan = _MIMOV2_PP_PLAN.copy()
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
if num_attention_heads % num_key_value_heads != 0:
raise ValueError("num_attention_heads must be divisible by num_key_value_heads")
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.layernorm_epsilon = layernorm_epsilon
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_dropout = attention_dropout
self.attention_bias = attention_bias
self.attention_value_scale = attention_value_scale
self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads
self.v_head_dim = v_head_dim if v_head_dim is not None else self.head_dim
self.swa_num_attention_heads = (
swa_num_attention_heads if swa_num_attention_heads is not None else num_attention_heads
)
self.swa_num_key_value_heads = (
swa_num_key_value_heads if swa_num_key_value_heads is not None else num_key_value_heads
)
if self.swa_num_attention_heads % self.swa_num_key_value_heads != 0:
raise ValueError("swa_num_attention_heads must be divisible by swa_num_key_value_heads")
self.swa_head_dim = swa_head_dim if swa_head_dim is not None else self.head_dim
self.swa_v_head_dim = swa_v_head_dim if swa_v_head_dim is not None else self.swa_head_dim
self.swa_rope_theta = swa_rope_theta if swa_rope_theta is not None else rope_theta
if sliding_window is None:
sliding_window = sliding_window_size
self.sliding_window = sliding_window
self.sliding_window_size = sliding_window_size if sliding_window_size is not None else sliding_window
self.add_full_attention_sink_bias = add_full_attention_sink_bias
self.add_swa_attention_sink_bias = add_swa_attention_sink_bias
if hybrid_block_size is not None and hybrid_layer_pattern is None:
hybrid_layer_pattern = [0 if ((i + 1) % hybrid_block_size == 0) else 1 for i in range(num_hidden_layers)]
elif hybrid_layer_pattern is None:
hybrid_layer_pattern = [0] * num_hidden_layers
if len(hybrid_layer_pattern) != num_hidden_layers:
raise ValueError("hybrid_layer_pattern length must match num_hidden_layers")
self.hybrid_block_size = hybrid_block_size
self.hybrid_layer_pattern = hybrid_layer_pattern
self.partial_rotary_factor = partial_rotary_factor
self.n_routed_experts = n_routed_experts
self.moe_intermediate_size = moe_intermediate_size if moe_intermediate_size is not None else intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.routed_scaling_factor = routed_scaling_factor
self.scoring_func = scoring_func
self.topk_method = topk_method
self.n_group = n_group
self.topk_group = topk_group
self.norm_topk_prob = norm_topk_prob
if isinstance(moe_layer_freq, int):
moe_layer_freq = [moe_layer_freq > 0 and i % moe_layer_freq == 0 for i in range(num_hidden_layers)]
elif moe_layer_freq is None:
moe_layer_freq = [False] * num_hidden_layers
if len(moe_layer_freq) != num_hidden_layers:
raise ValueError("moe_layer_freq length must match num_hidden_layers")
self.moe_layer_freq = moe_layer_freq
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
__all__ = ["MiMoV2Config"]
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