from typing import Any, Literal from transformers.configuration_utils import PretrainedConfig from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLVisionConfig class Helium1CASAConfig(PretrainedConfig): r""" Helium1 Config augmented with CASA options Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Helium1Model`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*): 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 `num_attention_heads`. 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 2048): The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens, Llama 2 up to 4096, CodeLlama up to 16384. 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`. pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. pretraining_tp (`int`, *optional*, defaults to 1): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings 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 attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. 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. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_attention_heads """ model_type = "helium1_casa" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `Helium1Model` 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 = { # pyright: ignore[reportAssignmentType] "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size: int = 32000, hidden_size: int = 4096, intermediate_size: int = 11008, num_hidden_layers: int = 32, num_attention_heads: int = 32, num_key_value_heads: None | int = None, head_dim: None | int = None, hidden_act: str = "silu", attention_dropout: float = 0.0, max_position_embeddings: int = 2048, 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, pad_token_id: int = 3, eos_token_id: int = 2, bos_token_id: int = 1, pretraining_tp: int = 1, rope_scaling: None | dict = None, attention_bias: bool = False, mlp_bias: bool = False, # Our fusion mechanisms # Common to all fusion mechanisms xa_layers: None | tuple = None, xa_order: Literal["ca_first", "parallel", "instead"] = "ca_first", xa_norm_on_images: bool = False, xa_update_image_embeds: bool = False, mask_squash_blockwise: bool = False, # CASA casa_attention: bool = False, casa_delta_w: bool = False, casa_windows: Literal["batch", "squashed", "images", "turn_based"] = "batch", casa_use_asymetric_qkv: bool = True, xa_custom_norm: bool = False, # Qwen2.5-VL vision config vision_config: dict[str, Any] | None = None, **kwargs: Any, ): from transformers.modeling_rope_utils import rope_config_validation 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.head_dim = ( head_dim if head_dim is not None else self.hidden_size // self.num_attention_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.rope_theta = rope_theta self.rope_scaling = rope_scaling self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.mlp_bias = mlp_bias # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, copy it 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) self.head_dim = self.hidden_size // self.num_attention_heads self.xa_layers = xa_layers self.xa_order: Literal["ca_first", "parallel", "instead"] = xa_order self.xa_norm_on_images = xa_norm_on_images self.xa_update_image_embeds = xa_update_image_embeds self.mask_squash_blockwise = mask_squash_blockwise # CASA config self.casa_attention = casa_attention self.casa_delta_w = casa_delta_w self.casa_windows: Literal["batch", "squashed", "images", "turn_based"] = casa_windows self.casa_use_asymetric_qkv = casa_use_asymetric_qkv self.xa_custom_norm = xa_custom_norm if vision_config is None: vision_config = dict() self.vision_config = Qwen2_5_VLVisionConfig(**vision_config) self.vision_config.temporal_patch_size = 1 self.vision_config.image_mean = [0.48145466, 0.4578275, 0.40821073] self.vision_config.image_std = [0.26862954, 0.26130258, 0.27577711] self.vision_config.out_dim = 2048 self.pre_image_tokens = [] self.post_image_tokens = [] super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) if __name__ == "__main__": import argparse from pathlib import Path import rich import yaml from transformers.models.auto.configuration_auto import AutoConfig parser = argparse.ArgumentParser() parser.add_argument("--out_dir", type=str, default="./saved_config/") parser.add_argument( "--ckpt_path", type=str, default="/lustre/scwpod02/client/kyutai/juliette/experiments/finext_casa_896_xtxt_up_b20_64gpu/fdf76e6774", ) args = parser.parse_args() path = Path(args.ckpt_path) / "kyuteye_config.yml" helium_config = AutoConfig.from_pretrained("kyutai/helium-1-2b") vision_config = AutoConfig.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct").vision_config # 3) Create YOUR config by merging both config = Helium1CASAConfig( **helium_config.to_dict(), # all helium parameters vision_config=vision_config.to_dict(), # override or add vision_config ) with open(path) as stream: kconfig = yaml.safe_load(stream) # print keys that are in kconfig and in config for key in set(kconfig.keys()).intersection(set(config.to_dict().keys())): rich.print(f"Overwriting [bold green]{key:>50s}[/]: [bold red]{kconfig[key]}") setattr(config, key, kconfig[key]) # TODO: handle casa_own_norm -> xa_custom_norm print("Configuration successfully loaded.") # Save config to json config.save_pretrained(args.out_dir) print(f"Configuration saved to {args.out_dir}/config.json")