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9588df1 | 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 | # 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.
"""K2Horizon model configuration"""
from huggingface_hub.dataclasses import strict
from transformers.configuration_utils import PreTrainedConfig
from transformers.modeling_rope_utils import RopeParameters
@strict
class K2HorizonConfig(PreTrainedConfig):
r"""
decoder_sparse_step (`int`, *optional*, defaults to 1):
The frequency of the MoE layer.
mlp_only_layers (`list[int]`, *optional*, defaults to `[]`):
Indicate which layers use K2HorizonMLP rather than K2HorizonSparseMoeBlock
The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
```python
>>> from transformers import K2HorizonModel, K2HorizonConfig
>>> # Initializing a K2Horizon style configuration
>>> configuration = K2HorizonConfig()
>>> model = K2HorizonModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "k2_horizon"
keys_to_ignore_at_inference = ["past_key_values"]
vocab_size: int = 151936
hidden_size: int = 2048
intermediate_size: int = 6144
num_hidden_layers: int = 24
num_attention_heads: int = 32
num_key_value_heads: int = 4
hidden_act: str = "silu"
max_position_embeddings: int = 32768
initializer_range: float = 0.02
rms_norm_eps: float = 1e-6
use_cache: bool = True
tie_word_embeddings: bool = False
rope_parameters: RopeParameters | dict | None = None
attention_bias: bool = False
use_sliding_window: bool = False
sliding_window: int | None = 4096
attention_dropout: float | int = 0.0
decoder_sparse_step: int = 1
moe_intermediate_size: int = 768
num_experts_per_tok: int = 8
num_experts: int = 128
norm_topk_prob: bool = False
output_router_logits: bool = False
router_aux_loss_coef: float = 0.001
mlp_only_layers: list[int] | None = None
pad_token_id: int | None = None
bos_token_id: int | None = None
eos_token_id: int | list[int] | None = None
head_dim: int = 128
query_key_norm: bool = True
moe_gate_bias: bool = False
layernorm_num_groups: int = 1
num_shared_experts: int = 0
router_score_func: str = "softmax"
router_scaling_factor: float | None = 1.0
rope_head_dim: int | None = None
attention_gate_func: str | None = None
mova_num_experts: int = 0
mova_num_experts_per_tok: int = 0
def __post_init__(self, **kwargs):
self.sliding_window = self.sliding_window if self.use_sliding_window else None
self.mlp_only_layers = [] if self.mlp_only_layers is None else self.mlp_only_layers
if self.router_scaling_factor is None:
self.router_scaling_factor = 1.0
super().__post_init__(**kwargs)
__all__ = ["K2HorizonConfig"] |