# coding=utf-8 # Copyright 2025 RWKV team. All rights reserved. # 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. """RwkvHybrid model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.modeling_rope_utils import rope_config_validation from transformers.utils import logging from typing import Optional, Union, List logger = logging.get_logger(__name__) class RwkvHybridConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`RwkvHybridModel`]. It is used to instantiate a RwkvHybrid model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of RwkvHybrid-7B-beta. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 151936): Vocabulary size of the RwkvHybrid model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`RwkvHybridModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 22016): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 32): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*, defaults to 4096): Sliding window attention (SWA) window size. If not specified, will default to `4096`. max_window_layers (`int`, *optional*, defaults to 28): The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. head_size (`int`, *optional*, defaults to 64): Dimensionality of each RWKV attention head. Defines the hidden dimension size for RWKV attention mechanisms. head_size_divisor (`int`, *optional*, defaults to 8): Constraint for head_size initialization, typically set to the square root of head_size. Ensures divisibility between hidden_size and head_size. wkv_version (`int`, *optional*, defaults to 7): Version of RWKV attention implementation. Currently supports: - 6: Original implementation requiring `wkv_has_gate=True` and `wkv_use_vfirst=False` - 7: Improved version requiring `wkv_use_vfirst=True` wkv_has_gate (`bool`, *optional*, defaults to False): Whether to include gating mechanism in RWKV attention. Required for version 6. wkv_has_group_norm (`bool`, *optional*, defaults to True): Whether to apply group normalization in RWKV attention layers. wkv_use_vfirst (`bool`, *optional*, defaults to True): Whether to prioritize value projection in RWKV attention computation. Required for version 7. wkv_layers (`Union[str, List[int]]`, *optional*, defaults to None): Specifies which layers use RWKV attention: - `"full"` or `None`: All layers use RWKV - List of integers: Only specified layers (e.g., `[0,1,2]`) use RWKV attention ```python >>> from transformers import RwkvHybridModel, RwkvHybridConfig >>> # Initializing a RwkvHybrid style configuration >>> configuration = RwkvHybridConfig() >>> # Initializing a model from the RwkvHybrid-7B style configuration >>> model = RwkvHybridModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "rwkv_hybrid" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `RwkvHybrid` base_model_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", } def __init__( self, vocab_size: int = 151936, hidden_size: int = 4096, intermediate_size: int = 22016, num_hidden_layers: int = 32, num_attention_heads: int = 32, num_key_value_heads: int = 32, head_size: int = 64, head_size_divisor: int = 8, 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_theta: float = 10000.0, rope_scaling: Optional[dict] = None, use_sliding_window: bool = False, sliding_window: int = 4096, max_window_layers: int = 28, attention_dropout: float = 0.0, wkv_version: int = 7, wkv_has_gate: bool = False, wkv_has_group_norm: bool = True, wkv_use_vfirst: bool = True, wkv_layers: Optional[Union[str, List[int]]] = 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_wkv_heads = hidden_size // head_size assert hidden_size % head_size == 0, "hidden_size must be divisible by head_size" self.num_attention_heads = num_attention_heads self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window if use_sliding_window else None self.max_window_layers = max_window_layers self.head_size = head_size self.head_size_divisor = head_size_divisor self.wkv_version = wkv_version self.wkv_has_gate = wkv_has_gate self.wkv_has_group_norm = wkv_has_group_norm self.wkv_use_vfirst = wkv_use_vfirst if self.wkv_version == 7: assert self.wkv_use_vfirst, "wkv_use_vfirst must be True for wkv_version 7" elif self.wkv_version == 6: assert self.wkv_has_gate, "wkv_has_gate must be True for wkv_version 6" assert not self.wkv_use_vfirst, "wkv_use_vfirst must be False for wkv_version 6" else: raise NotImplementedError(f"Unsupported wkv_version: {self.wkv_version}, \ wkv_version must be 6 or 7") if wkv_layers == "full" or wkv_layers == None: self.wkv_layers = list(range(num_hidden_layers)) elif isinstance(wkv_layers, list): if all(isinstance(layer, int) for layer in wkv_layers): self.wkv_layers = wkv_layers else: raise ValueError("All elements in wkv_layers must be integers.") else: raise TypeError("wkv_layers must be either 'full', None, or a list of integers.") # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_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.rope_theta = rope_theta self.rope_scaling = rope_scaling self.attention_dropout = attention_dropout # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. 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, )