from transformers.configuration_utils import PretrainedConfig class HelpingAIConfig(PretrainedConfig): model_type = "helpingai" def __init__( self, vocab_size=50257, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, max_position_embeddings=2048, layer_norm_epsilon=1e-5, hidden_act="gelu", dropout=0.0, attention_dropout=0.0, tie_word_embeddings=True, # Structured output head use_structured_output=True, structured_output_vocab_size=2, # Speech head use_speech_output=False, speech_num_mels=80, speech_head_hidden_dim=1024, speech_upsample_factor=1, speech_loss_type="l1", # Misc initializer_range=0.02, **kwargs, ): super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.max_position_embeddings = max_position_embeddings self.layer_norm_epsilon = layer_norm_epsilon self.hidden_act = hidden_act self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range # Structured self.use_structured_output = use_structured_output self.structured_output_vocab_size = structured_output_vocab_size # Speech self.use_speech_output = use_speech_output self.speech_num_mels = speech_num_mels self.speech_head_hidden_dim = speech_head_hidden_dim self.speech_upsample_factor = speech_upsample_factor self.speech_loss_type = speech_loss_type """HelpingAI model configuration""" from transformers.configuration_utils import PretrainedConfig, layer_type_validation from transformers.modeling_rope_utils import rope_config_validation from transformers.utils import logging logger = logging.get_logger(__name__) class HelpingAIConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`HelpingAIModel`]. It is used to instantiate a HelpingAI model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of HelpingAI-8B [HelpingAI/HelpingAI-8B](https://huggingface.co/HelpingAI/HelpingAI-8B). 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 HelpingAI model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`HelpingAIModel`] 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, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`. 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 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 attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. 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 using full attention. The first `max_window_layers` layers will use full attention, while any additional layer afterwards will use SWA (Sliding Window Attention). layer_types (`list`, *optional*): Attention pattern for each layer. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. use_emotional_reasoning (`bool`, *optional*, defaults to `True`): Whether to enable Semantic Emotion Reasoning (SER) capabilities for emotional understanding and processing. use_perspective_threading (`bool`, *optional*, defaults to `True`): Whether to enable Perspective Emotion Threading (PET) for multi-threaded emotional reasoning. num_emotion_heads (`int`, *optional*, defaults to 4): Number of specialized attention heads dedicated to emotional processing and reasoning. num_thinking_stages (`int`, *optional*, defaults to 3): Number of thinking stages for multi-stage reasoning and reflection processing. emotion_hidden_size (`int`, *optional*, defaults to 512): Hidden size for the emotional reasoning layers and SER processing modules. perspective_threads (`int`, *optional*, defaults to 4): Number of parallel perspective threads for PET processing (relatable, supportive, motivational, analytical). thinking_depth (`int`, *optional*, defaults to 2): Depth of thinking layers for internal reasoning and reflection processes. structured_output_vocab_size (`int`, *optional*, defaults to 100): Additional vocabulary size for structured output tokens like , , , etc. empathy_scaling_factor (`float`, *optional*, defaults to 1.2): Scaling factor for empathy-related attention weights and emotional processing. reasoning_temperature (`float`, *optional*, defaults to 0.8): Temperature parameter for reasoning and thinking processes to balance creativity and coherence. use_speech_output (`bool`, *optional*, defaults to `False`): Whether to enable an additional text-to-speech head that predicts mel-spectrogram frames from hidden states. speech_num_mels (`int`, *optional*, defaults to `80`): Number of mel bins to predict for the speech head. speech_upsample_factor (`int`, *optional*, defaults to `1`): Temporal upsampling factor to expand token-level hidden states to frame-level resolution by simple repetition. speech_loss_type (`str`, *optional*, defaults to `"l1"`): Loss for speech supervision. One of {"l1", "mse"}. speech_head_hidden_dim (`int`, *optional*, defaults to `None`): Hidden dimension for the speech head MLP (hidden_size -> speech_head_hidden_dim -> num_mels). If None, defaults to hidden_size // 2. Increase to scale speech head params (e.g., ~9.6k for ~50M). ```python >>> from transformers import HelpingAIModel, HelpingAIConfig >>> # Initializing a HelpingAI style configuration with advanced reasoning >>> configuration = HelpingAIConfig( ... use_emotional_reasoning=True, ... use_perspective_threading=True, ... num_emotion_heads=4, ... num_thinking_stages=3 ... ) >>> # Initializing a model from the HelpingAI-8B style configuration >>> model = HelpingAIModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "helpingai" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `HelpingAI` 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"]), } def __init__( self, vocab_size=151936, hidden_size=4096, intermediate_size=22016, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=8, # Match num_attention_heads for compatibility head_dim=128, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=False, use_sliding_window=False, sliding_window=4096, max_window_layers=28, layer_types=None, attention_dropout=0.0, # Advanced reasoning parameters use_emotional_reasoning=False, # Disable by default for now use_perspective_threading=True, num_emotion_heads=4, num_thinking_stages=3, emotion_hidden_size=512, perspective_threads=4, thinking_depth=2, structured_output_vocab_size=100, empathy_scaling_factor=1.2, reasoning_temperature=0.8, # Structured head architecture (new) structured_head_type: str = "linear", # one of: linear, mlp_v1 structured_head_hidden_dim: int | None = None, structured_head_activation: str = "gelu", # gelu or relu # Speech output head options use_speech_output=False, speech_num_mels=80, speech_upsample_factor=1, speech_loss_type="l1", speech_head_hidden_dim=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_attention_heads = num_attention_heads self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window if self.use_sliding_window else None self.max_window_layers = max_window_layers # 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 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_bias = attention_bias self.attention_dropout = attention_dropout # Advanced reasoning capabilities self.use_emotional_reasoning = use_emotional_reasoning self.use_perspective_threading = use_perspective_threading self.num_emotion_heads = num_emotion_heads self.num_thinking_stages = num_thinking_stages self.emotion_hidden_size = emotion_hidden_size self.perspective_threads = perspective_threads self.thinking_depth = thinking_depth self.structured_output_vocab_size = structured_output_vocab_size self.empathy_scaling_factor = empathy_scaling_factor self.reasoning_temperature = reasoning_temperature # Structured head architecture spec self.structured_head_type = structured_head_type self.structured_head_hidden_dim = structured_head_hidden_dim self.structured_head_activation = structured_head_activation # Speech head config self.use_speech_output = use_speech_output self.speech_num_mels = speech_num_mels self.speech_upsample_factor = speech_upsample_factor self.speech_loss_type = speech_loss_type self.speech_head_hidden_dim = speech_head_hidden_dim # Validate emotional reasoning parameters if self.use_emotional_reasoning and self.num_emotion_heads > self.num_attention_heads: raise ValueError(f"num_emotion_heads ({self.num_emotion_heads}) cannot exceed num_attention_heads ({self.num_attention_heads})") if self.use_perspective_threading and self.perspective_threads < 2: raise ValueError(f"perspective_threads ({self.perspective_threads}) must be at least 2 for meaningful threading") if self.use_speech_output: if not isinstance(self.speech_num_mels, int) or self.speech_num_mels <= 0: raise ValueError("speech_num_mels must be a positive integer") if not isinstance(self.speech_upsample_factor, int) or self.speech_upsample_factor <= 0: raise ValueError("speech_upsample_factor must be a positive integer") if self.speech_loss_type not in {"l1", "mse"}: raise ValueError("speech_loss_type must be one of {'l1','mse'}") if self.speech_head_hidden_dim is not None: if not isinstance(self.speech_head_hidden_dim, int) or self.speech_head_hidden_dim <= 0: raise ValueError("speech_head_hidden_dim must be a positive integer when provided") # 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) self.layer_types = layer_types if self.layer_types is None: self.layer_types = [ "sliding_attention" if self.sliding_window is not None and i >= self.max_window_layers else "full_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types) super().__init__( tie_word_embeddings=tie_word_embeddings, **kwargs, ) __all__ = ["HelpingAIConfig"]