Delete configuration_k2_horizon.py

configuration_k2_horizon.py

@@ -1,96 +0,0 @@
-# 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"]