configuration_aethermind.py

# AetherMind — configuration_aethermind.py
# Copyright 2026 AetherMind Project. Apache License 2.0.
"""AetherMind model configuration.

AetherMind is a fully open, trainable family of decoder-only Transformer
language models with a modern architecture:

    * RMSNorm pre-normalization
    * Rotary Position Embeddings (RoPE, with optional linear scaling)
    * SwiGLU feed-forward blocks
    * Grouped-Query Attention (GQA)
    * Scaled-dot-product / Flash attention (via torch sdpa on CUDA)
    * Dynamic KV cache for fast generation
    * Optional Mixture-of-Experts (MoE) feed-forward for very large configs
    * Optional gradient checkpointing (enable via ``model.gradient_checkpointing_enable()``)

The same code path scales from the CPU-trainable ``tiny`` prototype up to
70B-class configs; every hyper-parameter lives here.
"""

from __future__ import annotations

from typing import Any, Dict, Optional

from transformers import PretrainedConfig


# ----------------------------------------------------------------------
# Named size presets (scaling path: tiny -> 70B)
# ----------------------------------------------------------------------
SIZE_PRESETS: Dict[str, Dict[str, Any]] = {
    # CPU-trainable prototype used to validate the full pipeline.
    "tiny": dict(
        hidden_size=256, intermediate_size=768, num_hidden_layers=6,
        num_attention_heads=8, num_key_value_heads=4, head_dim=32,
        max_position_embeddings=1024, tie_word_embeddings=True,
    ),
    "1B": dict(
        hidden_size=2048, intermediate_size=5632, num_hidden_layers=24,
        num_attention_heads=16, num_key_value_heads=4, head_dim=128,
        max_position_embeddings=32768, vocab_size=65536,
    ),
    "3B": dict(
        hidden_size=3200, intermediate_size=8704, num_hidden_layers=32,
        num_attention_heads=25, num_key_value_heads=5, head_dim=128,
        max_position_embeddings=32768, vocab_size=65536,
    ),
    "7B": dict(
        hidden_size=4096, intermediate_size=11008, num_hidden_layers=32,
        num_attention_heads=32, num_key_value_heads=8, head_dim=128,
        max_position_embeddings=32768, vocab_size=65536,
    ),
    "14B": dict(
        hidden_size=5120, intermediate_size=13824, num_hidden_layers=40,
        num_attention_heads=40, num_key_value_heads=8, head_dim=128,
        max_position_embeddings=32768, vocab_size=65536,
    ),
    # From here up, sparse MoE feed-forward keeps FLOPs per token bounded.
    "32B": dict(
        hidden_size=5120, intermediate_size=13824, num_hidden_layers=48,
        num_attention_heads=40, num_key_value_heads=8, head_dim=128,
        max_position_embeddings=32768, vocab_size=65536,
        use_moe=True, num_experts=64, num_experts_per_tok=6,
        moe_intermediate_size=1408, moe_layers_freq=1,
    ),
    "70B": dict(
        hidden_size=8192, intermediate_size=28672, num_hidden_layers=80,
        num_attention_heads=64, num_key_value_heads=8, head_dim=128,
        max_position_embeddings=32768, vocab_size=65536,
        use_moe=True, num_experts=128, num_experts_per_tok=8,
        moe_intermediate_size=2048, moe_layers_freq=1,
        rope_scaling={"type": "linear", "factor": 4.0},  # 32K -> 128K effective
    ),
}


class AetherMindConfig(PretrainedConfig):
    r"""Configuration class for :class:`~model.modeling_aethermind.AetherMindModel`."""

    model_type = "aethermind"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size: int = 32768,
        hidden_size: int = 512,
        intermediate_size: int = 1376,
        num_hidden_layers: int = 8,
        num_attention_heads: int = 8,
        num_key_value_heads: int = 4,
        head_dim: Optional[int] = None,
        hidden_act: str = "silu",
        max_position_embeddings: int = 4096,
        initializer_range: float = 0.02,
        rms_norm_eps: float = 1e-6,
        use_cache: bool = True,
        pad_token_id: Optional[int] = None,
        bos_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        tie_word_embeddings: bool = False,
        rope_theta: float = 10000.0,
        rope_scaling: Optional[Dict[str, Any]] = None,
        attention_bias: bool = False,
        attention_dropout: float = 0.0,
        # ---- Mixture of Experts (for large-scale configs) ----
        use_moe: bool = False,
        num_experts: int = 8,
        num_experts_per_tok: int = 2,
        moe_intermediate_size: Optional[int] = None,
        moe_layers_freq: int = 1,
        moe_aux_loss_coeff: float = 0.01,
        **kwargs,
    ) -> None:
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        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 hidden_size // num_attention_heads
        self.hidden_act = hidden_act
        self.max_position_embeddings = max_position_embeddings
        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
        if rope_scaling is not None and rope_scaling.get("type", rope_scaling.get("rope_type")) not in (None, "linear"):
            raise ValueError("AetherMind currently supports rope_scaling type='linear' (or None).")
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout

        self.use_moe = use_moe
        self.num_experts = num_experts
        self.num_experts_per_tok = num_experts_per_tok
        self.moe_intermediate_size = moe_intermediate_size or intermediate_size
        self.moe_layers_freq = moe_layers_freq
        self.moe_aux_loss_coeff = moe_aux_loss_coeff

        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,
        )

    @classmethod
    def from_preset(
        cls,
        name: str = "tiny",
        vocab_size: Optional[int] = None,
        **overrides: Any,
    ) -> "AetherMindConfig":
        """Build a config from a named preset (tiny, 1B, 3B, 7B, 14B, 32B, 70B)."""
        if name not in SIZE_PRESETS:
            raise KeyError(f"Unknown preset '{name}'. Available: {sorted(SIZE_PRESETS)}")
        params: Dict[str, Any] = dict(SIZE_PRESETS[name])
        if vocab_size is not None:
            params["vocab_size"] = vocab_size
        params.update(overrides)
        return cls(**params)


__all__ = ["AetherMindConfig", "SIZE_PRESETS"]