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# Copyright 2026 The XHToken team 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.
from transformers import PretrainedConfig
class Spark2_5Config(PretrainedConfig):
model_type = "spark2_5"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_k_v_proj": "colwise",
"layers.*.self_attn.g_proj": "colwise",
"layers.*.self_attn.out_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embedding": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="gelu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_parameters=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
head_dim=None,
headwise_attn_output_gate=False,
gate_attn_act_mode="sigmoid",
sliding_window=None,
layer_types=None,
**kwargs,
):
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(
f"num_attention_heads ({num_attention_heads}) must be divisible by num_key_value_heads ({num_key_value_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.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
self.headwise_attn_output_gate = headwise_attn_output_gate
self.gate_attn_act_mode = gate_attn_act_mode
self.sliding_window = sliding_window
self.rope_parameters = rope_parameters
if layer_types is None:
layer_types = ["full_attention"] * num_hidden_layers
if len(layer_types) != num_hidden_layers:
raise ValueError(
f"layer_types length ({len(layer_types)}) must match num_hidden_layers ({num_hidden_layers})"
)
self.layer_types = layer_types
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def get_rope_theta(self, layer_type):
params = self.rope_parameters.get(layer_type, {})
return params.get("rope_theta", 10000)
def get_partial_rotary_factor(self, layer_type):
params = self.rope_parameters.get(layer_type, {})
return params.get("partial_rotary_factor", 1.0)
__all__ = ["Spark2_5Config"]
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