# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/cwm/modular_cwm.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_cwm.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 # # 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 ...configuration_utils import PreTrainedConfig, layer_type_validation class CwmConfig(PreTrainedConfig): """ Configuration for Code World Model (CWM). This is an inherited Llama3-compatible configuration with layer-interleaved sliding-window attention. Configures a `CwmModel`. Designed to yield a configuration mirroring the model in the [facebook/cwm](https://huggingface.co/facebook/cwm) architecture by default. Other models include: - [facebook/cwm-sft](https://huggingface.co/facebook/cwm-sft) - [facebook/cwm-pretrain](https://huggingface.co/facebook/cwm-pretrain) Args: vocab_size (`int`, *optional*, defaults to 128256): Vocabulary size of the CWM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CwmModel`] hidden_size (`int`, *optional*, defaults to 6144): Dimension of the hidden representations intermediate_size (`int`, *optional*, defaults to 21504): Dimension of the MLP representations num_hidden_layers (`int`, *optional*, defaults to 64): Number of hidden layers in the Transformer decoder num_attention_heads (`int`, *optional*, defaults to 48): Number of attention heads for each attention layer in the Transformer decoder num_key_value_heads (`int`, *optional*, defaults to 8): This is the number of key_value heads that should be used to implement Grouped Query Attention (GQA). If it is not specified, will default to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 128): The attention head dimension. 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 131072): The maximum sequence length that this model might ever be used with. CWM's attention allows sequence lengths up to 131072 tokens. 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-05): 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`. pad_token_id (`int`, *optional*): Padding token id. eos_token_id (`int` or `list[int]`, *optional*, defaults to `[128001, 128008, 128009]`): The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens. bos_token_id (`int`, *optional*, defaults to 128000): The id of the *beginning-of-sequence* token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. pretraining_tp (`int`, *optional*, defaults to 1): Tensor parallelism degree used during pretraining. See [this document](https://huggingface.co/docs/transformers/parallelism) and [this issue](https://github.com/pytorch/pytorch/issues/76232). mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. sliding_window (`int`, *optional*, defaults to 8192): Sliding window attention window size. layer_types (`List[str]`, *optional*): List of layer types for each layer. Each element should be either "full_attention" or "sliding_attention". If not specified, will default to alternating pattern based on the provided window pattern. """ model_type = "cwm" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `CwmModel` 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", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } default_theta = 1_000_000.0 def __init__( self, vocab_size: int = 128256, hidden_size: int = 6144, intermediate_size: int = 21504, num_hidden_layers: int = 64, num_attention_heads: int = 48, num_key_value_heads: int = 8, head_dim: int = 128, hidden_act: str = "silu", max_position_embeddings: int = 131072, initializer_range: float = 0.02, rms_norm_eps: float = 1e-5, use_cache: bool = True, pad_token_id: int | None = None, eos_token_id=[128001, 128008, 128009], bos_token_id: int = 128000, tie_word_embeddings: bool = False, attention_dropout: float = 0.0, pretraining_tp: int = 1, mlp_bias: bool = False, rope_parameters: dict | None = None, # CWM interleaved sliding window fields sliding_window: int = 8192, layer_types: list[str] | None = None, # ["full_attention"|"sliding_attention"] per layer **kwargs, ): if rope_parameters is None: rope_parameters = { "rope_theta": 1_000_000.0, "factor": 16.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3", } if layer_types is None: # Default pattern: every 4th layer uses full attention, others use sliding attention window_pattern = 4 layer_types = [ ("full_attention" if (i % window_pattern == 0) else "sliding_attention") for i in range(num_hidden_layers) ] else: layer_type_validation(layer_types, num_hidden_layers) self.sliding_window = int(sliding_window) if sliding_window else None self.layer_types = list(layer_types) 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 # 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.pretraining_tp = pretraining_tp self.use_cache = use_cache 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.rope_parameters = rope_parameters self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(**kwargs) __all__ = ["CwmConfig"]