# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/aria/modular_aria.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_aria.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2024 The Rhymes-AI Teams Authors 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. from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters from ..auto import CONFIG_MAPPING, AutoConfig class AriaTextConfig(PreTrainedConfig): r""" This class handles the configuration for the text component of the Aria model. Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria [rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture. This class extends the LlamaConfig to include additional parameters specific to the Mixture of Experts (MoE) architecture. 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 [`LlamaModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 4096): The size 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, check out [this paper](https://huggingface.co/papers/2305.13245). 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*, defaults to 2): 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_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`. 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_heads moe_num_experts (`int`, *optional*, defaults to 8): The number of experts in the MoE layer. moe_topk (`int`, *optional*, defaults to 2): The number of top experts to route to for each token. moe_num_shared_experts (`int`, *optional*, defaults to 2): The number of shared experts. """ model_type = "aria_text" keys_to_ignore_at_inference = ["past_key_values"] 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.shared_experts.gate_proj": "colwise", "layers.*.mlp.shared_experts.up_proj": "colwise", "layers.*.mlp.shared_experts.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"]), } base_config_key = "text_config" def __init__( self, vocab_size: int | None = 32000, hidden_size: int | None = 4096, intermediate_size: int = 4096, num_hidden_layers: int | None = 32, num_attention_heads: int | None = 32, num_key_value_heads: int | None = None, hidden_act: str | None = "silu", max_position_embeddings: int | None = 2048, initializer_range: float | None = 0.02, rms_norm_eps: int | None = 1e-6, use_cache: bool | None = True, pad_token_id=2, bos_token_id: int | None = 1, eos_token_id: int | None = 2, pretraining_tp: int | None = 1, tie_word_embeddings: bool | None = False, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, attention_bias: bool | None = False, attention_dropout: float | None = 0.0, mlp_bias: bool | None = False, head_dim: int | None = None, moe_num_experts: int = 8, moe_topk: int = 2, moe_num_shared_experts: int = 2, **kwargs, ): self.intermediate_size = intermediate_size self.moe_num_experts = moe_num_experts self.moe_topk = moe_topk self.moe_num_shared_experts = moe_num_shared_experts 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_bias = attention_bias 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) class AriaConfig(PreTrainedConfig): r""" This class handles the configuration for both vision and text components of the Aria model, as well as additional parameters for image token handling and projector mapping. Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria [rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vision_config (`AriaVisionConfig` or `dict`, *optional*): Configuration for the vision component. vision_feature_layer (`int`, *optional*, defaults to -1): The index of the layer to select the vision feature. text_config (`AriaTextConfig` or `dict`, *optional*): Configuration for the text component. projector_patch_to_query_dict (`dict`, *optional*): Mapping of patch sizes to query dimensions. image_token_index (`int`, *optional*, defaults to 9): Index used to represent image tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated normal initializer for initializing all weight matrices. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings """ model_type = "aria" attribute_map = { "image_token_id": "image_token_index", } sub_configs = {"text_config": AriaTextConfig, "vision_config": AutoConfig} def __init__( self, vision_config=None, vision_feature_layer: int = -1, text_config: AriaTextConfig = None, projector_patch_to_query_dict: dict | None = None, image_token_index: int | None = 9, initializer_range: float | None = 0.02, tie_word_embeddings: bool | None = False, **kwargs, ): self.image_token_index = image_token_index # Convert the keys and values of projector_patch_to_query_dict to integers # This ensures consistency even if they were provided as strings if projector_patch_to_query_dict is None: projector_patch_to_query_dict = { 1225: 128, 4900: 256, } self.projector_patch_to_query_dict = {int(k): int(v) for k, v in projector_patch_to_query_dict.items()} self.max_value_projector_patch_to_query_dict = max(self.projector_patch_to_query_dict.values()) self.vision_feature_layer = vision_feature_layer if isinstance(vision_config, dict): vision_config["model_type"] = "idefics3_vision" vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config) elif vision_config is None: vision_config = CONFIG_MAPPING["idefics3_vision"]() self.vision_config = vision_config self.initializer_range = initializer_range if isinstance(text_config, dict) and "model_type" in text_config: text_config = AriaTextConfig(**text_config) elif text_config is None: text_config = AriaTextConfig() self.text_config = text_config self.tie_word_embeddings = tie_word_embeddings super().__init__(**kwargs) __all__ = ["AriaConfig", "AriaTextConfig"]