"""IQuestPLTCoder model configuration. Extends the IQuestCoder configuration with PLT (Parallel Loop Transformer) specific parameters. PLT reuses the same physical transformer layers across multiple loops, with cross-loop processing (CLP) and mixed attention (global full-attention + local sliding-window attention gated per head) in loop 1+. Reference: https://arxiv.org/abs/2510.24824 """ from typing import Dict, List, Optional, Union from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class IQuestPLTCoderConfig(PretrainedConfig): r""" Configuration class for [`IQuestPLTCoderModel`]. This is a PLT (Parallel Loop Transformer) variant of IQuestCoder. The model has `num_hidden_layers` physical transformer layers that are executed `plt_num_loops` times. Weights are shared across loops; each loop adds cross-loop processing and mixed attention via a learned per-head gate. Args: vocab_size (`int`, *optional*, defaults to 75904): Vocabulary size of the model (padded to be divisible by 128). hidden_size (`int`, *optional*, defaults to 5120): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 27648): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 14): Number of physical transformer layers (shared across all loops). num_attention_heads (`int`, *optional*, defaults to 40): Number of attention heads for each attention layer. num_key_value_heads (`int`, *optional*, defaults to 8): Number of key_value heads for Grouped Query Attention (GQA). head_dim (`int`, *optional*, defaults to 128): The dimension of each attention head. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function in the decoder (SwiGLU uses SiLU). max_position_embeddings (`int`, *optional*, defaults to 131072): 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-05): The epsilon used by the RMS normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether the model should return the last key/values attentions. 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` or `list`, *optional*, defaults to `[2, 75864, 75869]`): End of stream token id(s). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie input embedding and output projection weights. rope_theta (`float`, *optional*, defaults to 500000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Supports "linear", "dynamic", "yarn", "longrope", "llama3". attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the Q, K, V and output projection layers. 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 the MLP gate/up/down projection layers. plt_num_loops (`int`, *optional*, defaults to 2): Number of times the physical transformer layers are executed. Loop 0 runs standard causal attention and stores KV caches. Loops 1+ run mixed attention with cross-loop processing. plt_window_size (`list` of `int`, *optional*, defaults to `[64, 0]`): Sliding window size `[left, right]` for the local attention in loop 1+. `[64, 0]` means a left-context window of 64 tokens with causal masking (right=0). plt_normalize_per_loop (`bool`, *optional*, defaults to `True`): When True, apply final_layernorm (shared weights) to hidden states at the end of each non-last loop before cross-loop processing. plt_emb_scale (`float`, *optional*, defaults to `None`): Scaling factor for the embedding in CLP: `a * E + b * shift(H)`. `None` means 1.0 (no scaling). plt_hidden_scale (`float`, *optional*, defaults to `None`): Scaling factor for the shifted hidden state in CLP: `a * E + b * shift(H)`. `None` means 1.0 (no scaling). plt_gate_use_hidden_states (`bool`, *optional*, defaults to `False`): Gate input mode. When `False`, the gate is computed as `sigmoid(einsum(Q, W_gate) + b_gate)` per head on the post-RoPE query tensor. When `True`, gate uses `sigmoid(Linear(RMSNorm(hidden_states)))` (OLMo-style) instead. Example: ```python >>> from configuration_iquestpltcoder import IQuestPLTCoderConfig >>> from modeling_iquestpltcoder import IQuestPLTCoderModel >>> configuration = IQuestPLTCoderConfig() >>> model = IQuestPLTCoderModel(configuration) >>> configuration = model.config ``` """ model_type = "iquestpltcoder" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=75904, hidden_size=5120, intermediate_size=27648, num_hidden_layers=14, num_attention_heads=40, num_key_value_heads=8, head_dim=128, hidden_act="silu", max_position_embeddings=131072, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=None, tie_word_embeddings=False, rope_theta=500000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, mlp_bias=False, # PLT specific plt_num_loops=2, plt_window_size=None, plt_normalize_per_loop=True, plt_emb_scale=None, plt_hidden_scale=None, plt_gate_use_hidden_states=False, **kwargs, ): if eos_token_id is None: eos_token_id = [2, 75864, 75869] if plt_window_size is None: plt_window_size = [64, 0] 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.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 self.mlp_bias = mlp_bias # PLT specific self.plt_num_loops = plt_num_loops self.plt_window_size = plt_window_size self.plt_normalize_per_loop = plt_normalize_per_loop self.plt_emb_scale = plt_emb_scale self.plt_hidden_scale = plt_hidden_scale self.plt_gate_use_hidden_states = plt_gate_use_hidden_states self._rope_scaling_validation() 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, ) def _rope_scaling_validation(self): """Validate the `rope_scaling` configuration.""" if self.rope_scaling is None: return if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) < 1: raise ValueError( "`rope_scaling` must be a dictionary with a minimum of one field, " "`type` or `rope_type`." ) rope_scaling_type = self.rope_scaling.get("type", None) or self.rope_scaling.get( "rope_type", None ) if rope_scaling_type is None: raise ValueError("`rope_scaling` must have a `type` or `rope_type` field.") valid_rope_types = ["linear", "dynamic", "yarn", "longrope", "llama3"] if rope_scaling_type not in valid_rope_types: raise ValueError( f"`rope_scaling`'s type field must be one of {valid_rope_types}, " f"got {rope_scaling_type}" ) __all__ = ["IQuestPLTCoderConfig"]