Upload configuration_k2_horizon.py to rl-mopd

configuration_k2_horizon.py

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+# 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"]