hermes-mal 1.8.71

//! Model Architecture Language (MAL) for Hermes LLM
//!
//! A composable DSL for defining LLM model architectures using pest parser.
//!
//! # Example MAL - Minimal inline style
//!
//! ```text
//! model tiny {
//!     vocab_size: 32000
//!     max_seq_len: 2048
//!     hidden_size: 128
//!     num_layers: 4
//!     block: {
//!         attention: { num_heads: 4 }
//!         ffn: { hidden_dim: 512 }
//!     }
//! }
//! ```
//!
//! # Example MAL - Composable style
//!
//! ```text
//! # Define attention mechanism
//! attention gqa {
//!     num_heads: 32
//!     num_kv_heads: 8
//!     head_dim: 128
//!     position_encoding: rope { theta: 10000.0 }
//! }
//!
//! # Define FFN
//! ffn swiglu_mlp {
//!     hidden_dim: 14336
//!     activation: swiglu
//!     bias: false
//! }
//!
//! # Define transformer block
//! block llama_block {
//!     attention: gqa
//!     ffn: swiglu_mlp
//!     norm: rmsnorm { eps: 1e-5 }
//!     norm_position: pre
//! }
//!
//! # Define model using the block
//! model llama_7b {
//!     vocab_size: 32000
//!     max_seq_len: 4096
//!     hidden_size: 4096
//!     block: llama_block
//!     num_layers: 32
//! }
//! ```

use anyhow::{Result, anyhow};
use pest::Parser;
use pest_derive::Parser;
use rust_embed::Embed;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Embedded well-known model definitions
#[derive(Embed)]
#[folder = "well-known/"]
#[include = "*.mal"]
struct WellKnown;

#[derive(Parser)]
#[grammar = "mal.pest"]
pub struct MalParser;

// ============================================================================
// AST Types
// ============================================================================

/// Position encoding type
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PositionEncoding {
    Rope { theta: f64, scaling: Option<f64> },
    Alibi { learned_slopes: bool },
    Learned { max_positions: usize },
    None,
}

impl Default for PositionEncoding {
    fn default() -> Self {
        Self::Rope {
            theta: 10000.0,
            scaling: None,
        }
    }
}

/// Attention mechanism definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionDef {
    pub name: String,
    pub num_heads: Option<usize>,
    pub num_kv_heads: Option<usize>,
    pub head_dim: Option<usize>,
    pub dropout: f64,
    pub bias: bool,
    pub position_encoding: PositionEncoding,
    pub window_size: Option<usize>,
    pub causal: bool,
    /// Per-head RMSNorm on Q and K before RoPE (OLMo2/Gemma-style stabilizer)
    #[serde(default)]
    pub qk_norm: bool,
}

impl Default for AttentionDef {
    fn default() -> Self {
        Self {
            name: "default".to_string(),
            num_heads: None,
            num_kv_heads: None,
            head_dim: None,
            dropout: 0.0,
            bias: false,
            position_encoding: PositionEncoding::default(),
            window_size: None,
            causal: true,
            qk_norm: false,
        }
    }
}

/// Normalization type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Default)]
pub enum NormType {
    #[default]
    RmsNorm,
    LayerNorm,
    None,
}

/// Normalization configuration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct NormConfig {
    pub norm_type: NormType,
    pub eps: f64,
}

/// Activation function
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Default)]
pub enum Activation {
    #[default]
    SwiGLU,
    GELU,
    SiLU,
    ReLU,
    GELUNew,
    GELUTanh,
}

/// Selective state-space (Mamba) mixer definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SsmDef {
    pub name: String,
    /// SSM state dimension N (default 16)
    pub state_dim: usize,
    /// Depthwise causal conv kernel width (default 4)
    pub conv_kernel: usize,
    /// Inner expansion factor: d_inner = expand * hidden_size (default 2)
    pub expand: usize,
    /// Δ projection rank (default ceil(hidden_size / 16))
    pub dt_rank: Option<usize>,
}

impl Default for SsmDef {
    fn default() -> Self {
        Self {
            name: "default".to_string(),
            state_dim: 16,
            conv_kernel: 4,
            expand: 2,
            dt_rank: None,
        }
    }
}

/// Feed-forward network definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FfnDef {
    pub name: String,
    pub hidden_dim: Option<usize>,
    pub activation: Activation,
    pub bias: bool,
    pub dropout: f64,
    pub gate: bool,
    /// Sparse token-choice routing. Omitted for an ordinary dense FFN.
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub moe: Option<MoeDef>,
}

/// Configurable dropless token-choice mixture of experts.
///
/// `experts` are routed experts; `shared_experts` are always active. Router
/// regularization belongs to the architecture config so every training entry
/// point applies the same stable objective.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MoeDef {
    pub experts: usize,
    pub top_k: usize,
    #[serde(default)]
    pub shared_experts: usize,
    #[serde(default)]
    pub load_balance_loss_weight: f64,
    #[serde(default)]
    pub router_z_loss_weight: f64,
}

impl Default for FfnDef {
    fn default() -> Self {
        Self {
            name: "default".to_string(),
            hidden_dim: None,
            activation: Activation::default(),
            bias: false,
            dropout: 0.0,
            gate: true,
            moe: None,
        }
    }
}

/// Transformer block definition
///
/// The mixer is attention by default; when `ssm` is set the block is a
/// Mamba block (attention settings are ignored).
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BlockDef {
    pub name: String,
    pub attention: AttentionDef,
    #[serde(default)]
    pub ssm: Option<SsmDef>,
    pub ffn: FfnDef,
    pub norm: NormConfig,
    pub norm_position: NormPosition,
    pub residual: bool,
    pub dropout: f64,
}

impl BlockDef {
    pub fn is_ssm(&self) -> bool {
        self.ssm.is_some()
    }

    // Per-block computed properties (pattern-aware model construction)

    pub fn num_heads(&self) -> usize {
        self.attention.num_heads.unwrap_or(12)
    }

    pub fn num_kv_heads(&self) -> usize {
        self.attention.num_kv_heads.unwrap_or(self.num_heads())
    }

    pub fn head_dim(&self, hidden_size: usize) -> usize {
        self.attention
            .head_dim
            .unwrap_or(hidden_size / self.num_heads())
    }

    pub fn intermediate_size(&self, hidden_size: usize) -> usize {
        self.ffn.hidden_dim.unwrap_or(hidden_size * 4)
    }

    pub fn norm_eps(&self) -> f64 {
        if self.norm.eps > 0.0 {
            self.norm.eps
        } else {
            1e-5
        }
    }

    pub fn rope_theta(&self) -> f64 {
        match &self.attention.position_encoding {
            PositionEncoding::Rope { theta, .. } => *theta,
            _ => 10000.0,
        }
    }

    pub fn rope_scaling(&self) -> Option<f64> {
        match &self.attention.position_encoding {
            PositionEncoding::Rope { scaling, .. } => *scaling,
            _ => None,
        }
    }
}

/// Normalization position in block
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Default)]
pub enum NormPosition {
    #[default]
    Pre,
    Post,
}

impl Default for BlockDef {
    fn default() -> Self {
        Self {
            name: "default".to_string(),
            attention: AttentionDef::default(),
            ssm: None,
            ffn: FfnDef::default(),
            norm: NormConfig {
                norm_type: NormType::RmsNorm,
                eps: 1e-5,
            },
            norm_position: NormPosition::Pre,
            residual: true,
            dropout: 0.0,
        }
    }
}

/// Embeddings configuration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct EmbeddingsConfig {
    pub tie_weights: bool,
    pub dropout: f64,
    pub scale: Option<f64>,
}

/// Output head configuration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct OutputConfig {
    pub bias: bool,
    pub norm: Option<NormConfig>,
}

/// Parsed model definition from MAL
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelDef {
    pub name: String,
    pub description: Option<String>,
    /// Number of token IDs exposed by the tokenizer and model API. Parameter
    /// storage is padded internally for efficient accelerator kernels.
    pub vocab_size: usize,
    pub max_seq_len: usize,
    pub hidden_size: usize,
    pub num_layers: usize,
    pub block: BlockDef,
    /// Optional heterogeneous layer pattern, repeated cyclically across
    /// num_layers (e.g. [mamba, mamba, attn]). Overrides `block` when set.
    #[serde(default)]
    pub pattern: Option<Vec<BlockDef>>,
    pub embeddings: EmbeddingsConfig,
    pub output: OutputConfig,
}

impl Default for ModelDef {
    fn default() -> Self {
        Self {
            name: "default".to_string(),
            description: None,
            vocab_size: 32000,
            max_seq_len: 2048,
            hidden_size: 768,
            num_layers: 12,
            block: BlockDef::default(),
            pattern: None,
            embeddings: EmbeddingsConfig::default(),
            output: OutputConfig::default(),
        }
    }
}

impl std::fmt::Display for ModelDef {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        writeln!(f, "model {} {{", self.name)?;
        if let Some(desc) = &self.description {
            writeln!(f, "    description: \"{}\"", desc)?;
        }
        writeln!(f, "    vocab_size: {}", self.vocab_size)?;
        writeln!(f, "    max_seq_len: {}", self.max_seq_len)?;
        writeln!(f, "    hidden_size: {}", self.hidden_size)?;
        writeln!(f, "    num_layers: {}", self.num_layers)?;
        writeln!(f, "}}")?;
        writeln!(f)?;

        // Attention
        writeln!(f, "attention {{")?;
        if let Some(h) = self.block.attention.num_heads {
            writeln!(f, "    num_heads: {}", h)?;
        }
        if let Some(kv) = self.block.attention.num_kv_heads {
            writeln!(f, "    num_kv_heads: {}", kv)?;
        }
        if let Some(hd) = self.block.attention.head_dim {
            writeln!(f, "    head_dim: {}", hd)?;
        }
        writeln!(f, "    bias: {}", self.block.attention.bias)?;
        writeln!(f, "}}")?;
        writeln!(f)?;

        // FFN
        writeln!(f, "ffn {{")?;
        if let Some(dim) = self.block.ffn.hidden_dim {
            writeln!(f, "    hidden_dim: {}", dim)?;
        }
        writeln!(f, "    activation: {:?}", self.block.ffn.activation)?;
        writeln!(f, "    bias: {}", self.block.ffn.bias)?;
        writeln!(f, "}}")?;
        writeln!(f)?;

        // Block
        writeln!(f, "block {{")?;
        writeln!(f, "    norm: {:?}", self.block.norm.norm_type)?;
        writeln!(f, "    norm_position: {:?}", self.block.norm_position)?;
        writeln!(f, "    residual: {}", self.block.residual)?;
        writeln!(f, "}}")?;
        writeln!(f)?;

        // Estimated parameters
        let params = self.estimated_params();
        writeln!(
            f,
            "Estimated parameters: {:.2}B",
            params as f64 / 1_000_000_000.0
        )
    }
}

impl ModelDef {
    // ========================================================================
    // Computed properties for model construction
    // ========================================================================

    /// Block definition for layer `i`: cycles through `pattern` when set,
    /// otherwise the homogeneous `block`.
    pub fn block_for_layer(&self, i: usize) -> &BlockDef {
        match &self.pattern {
            Some(p) if !p.is_empty() => &p[i % p.len()],
            _ => &self.block,
        }
    }

    /// Effective Δ rank for an SSM mixer (paper default: ceil(hidden/16))
    pub fn dt_rank(&self, ssm: &SsmDef) -> usize {
        ssm.dt_rank.unwrap_or(self.hidden_size.div_ceil(16))
    }

    pub fn num_heads(&self) -> usize {
        self.block.attention.num_heads.unwrap_or(12)
    }

    pub fn num_kv_heads(&self) -> usize {
        self.block
            .attention
            .num_kv_heads
            .unwrap_or(self.num_heads())
    }

    pub fn head_dim(&self) -> usize {
        self.block
            .attention
            .head_dim
            .unwrap_or(self.hidden_size / self.num_heads())
    }

    pub fn intermediate_size(&self) -> usize {
        self.block.ffn.hidden_dim.unwrap_or(self.hidden_size * 4)
    }

    pub fn norm_eps(&self) -> f64 {
        if self.block.norm.eps > 0.0 {
            self.block.norm.eps
        } else {
            1e-5
        }
    }

    pub fn rope_theta(&self) -> f64 {
        match &self.block.attention.position_encoding {
            PositionEncoding::Rope { theta, .. } => *theta,
            _ => 10000.0,
        }
    }

    /// Vocabulary rows stored by embeddings and the output projection.
    ///
    /// Keeping this derived preserves a single logical vocabulary in MAL and
    /// checkpoint configs while giving GPU kernels an aligned output dimension.
    pub fn padded_vocab_size(&self) -> usize {
        self.vocab_size.next_multiple_of(64)
    }

    /// Count trainable parameters implied by the model definition.
    pub fn estimated_params(&self) -> usize {
        let h = self.hidden_size;
        let stored_vocab_size = self.padded_vocab_size();
        let embed_params = stored_vocab_size * h;
        let norm_params = |norm: &NormConfig| match norm.norm_type {
            NormType::RmsNorm => h,
            NormType::LayerNorm => 2 * h,
            NormType::None => 0,
        };

        let mut layer_params = 0usize;
        for i in 0..self.num_layers {
            let block = self.block_for_layer(i);
            let mixer = match &block.ssm {
                // Mamba: in_proj + out_proj + conv + x_proj + dt_proj + A + D.
                Some(ssm) => {
                    let d_inner = ssm.expand * h;
                    let dt_rank = self.dt_rank(ssm);
                    2 * h * d_inner            // in_proj (x, z)
                        + d_inner * h          // out_proj
                        + d_inner * ssm.conv_kernel
                        + d_inner * (dt_rank + 2 * ssm.state_dim) // x_proj
                        + dt_rank * d_inner + d_inner // dt_proj weight + bias
                        + d_inner * ssm.state_dim // A_log
                        + d_inner // D
                        + d_inner // depthwise conv bias
                }
                // Attention: q/k/v/o. Uses GQA kv width when configured.
                None => {
                    let q = block.num_heads() * block.head_dim(h);
                    let kv = block.num_kv_heads() * block.head_dim(h);
                    let weights = h * q + 2 * h * kv + q * h;
                    let bias = if block.attention.bias {
                        2 * q + 2 * kv
                    } else {
                        0
                    };
                    let qk_norm = if block.attention.qk_norm {
                        2 * block.head_dim(h)
                    } else {
                        0
                    };
                    weights + bias + qk_norm
                }
            };
            let intermediate = block.intermediate_size(h);
            let projections = if block.ffn.gate { 3 } else { 2 };
            let expert_count = block
                .ffn
                .moe
                .as_ref()
                .map_or(1, |moe| moe.experts + moe.shared_experts);
            let ff_weights = expert_count * projections * h * intermediate;
            let ff_bias = if block.ffn.bias {
                expert_count * ((if block.ffn.gate { 2 } else { 1 }) * intermediate + h)
            } else {
                0
            };
            let router = block.ffn.moe.as_ref().map_or(0, |moe| h * moe.experts);
            layer_params += mixer + ff_weights + ff_bias + router + 2 * norm_params(&block.norm);
        }

        let final_norm = self
            .output
            .norm
            .as_ref()
            .unwrap_or(&self.block_for_layer(0).norm);
        let head_weights = (!self.embeddings.tie_weights) as usize * h * stored_vocab_size;
        let head_bias = self.output.bias as usize * stored_vocab_size;
        embed_params + layer_params + norm_params(final_norm) + head_weights + head_bias
    }

    /// Load from JSON file
    pub fn from_json<P: AsRef<std::path::Path>>(path: P) -> Result<Self> {
        let content = std::fs::read_to_string(path)?;
        Ok(serde_json::from_str(&content)?)
    }

    /// Save to JSON file
    pub fn save_json(&self, path: &str) -> Result<()> {
        let content = serde_json::to_string_pretty(self)?;
        std::fs::write(path, content)?;
        Ok(())
    }
}

/// Complete parsed MAL file with all definitions
#[derive(Debug, Clone, Default)]
pub struct MalFile {
    pub attentions: HashMap<String, AttentionDef>,
    pub ssms: HashMap<String, SsmDef>,
    pub ffns: HashMap<String, FfnDef>,
    pub blocks: HashMap<String, BlockDef>,
    pub models: HashMap<String, ModelDef>,
}

// ============================================================================
// Parsing Functions
// ============================================================================

/// Parse activation type from string
fn parse_activation(s: &str) -> Activation {
    match s {
        "swiglu" => Activation::SwiGLU,
        "gelu" => Activation::GELU,
        "silu" => Activation::SiLU,
        "relu" => Activation::ReLU,
        "gelu_new" => Activation::GELUNew,
        "gelu_tanh" => Activation::GELUTanh,
        _ => Activation::SwiGLU,
    }
}

/// Parse a model property (block-based only)
fn parse_model_prop(
    pair: pest::iterators::Pair<Rule>,
    def: &mut ModelDef,
    file: &MalFile,
) -> Result<()> {
    for inner in pair.into_inner() {
        match inner.as_rule() {
            Rule::vocab_size_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.vocab_size = val.as_str().parse()?;
                }
            }
            Rule::max_seq_len_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.max_seq_len = val.as_str().parse()?;
                }
            }
            Rule::hidden_size_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.hidden_size = val.as_str().parse()?;
                }
            }
            Rule::num_layers_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.num_layers = val.as_str().parse()?;
                }
            }
            Rule::block_ref_prop => {
                for child in inner.into_inner() {
                    match child.as_rule() {
                        Rule::identifier => {
                            let name = child.as_str();
                            def.block = file
                                .blocks
                                .get(name)
                                .ok_or_else(|| anyhow!("undefined block '{name}'"))?
                                .clone();
                        }
                        Rule::inline_block => {
                            let mut block = BlockDef::default();
                            for prop in child.into_inner() {
                                if prop.as_rule() == Rule::block_prop {
                                    parse_block_prop(prop, &mut block, file)?;
                                }
                            }
                            def.block = block;
                        }
                        _ => {}
                    }
                }
            }
            Rule::pattern_prop => {
                let mut blocks = Vec::new();
                for child in inner.into_inner() {
                    if child.as_rule() == Rule::identifier {
                        let name = child.as_str();
                        let block = file.blocks.get(name).ok_or_else(|| {
                            anyhow!("pattern references undefined block '{}'", name)
                        })?;
                        blocks.push(block.clone());
                    }
                }
                if !blocks.is_empty() {
                    def.pattern = Some(blocks);
                }
            }
            Rule::embeddings_prop => {
                for param in inner.into_inner() {
                    for child in param.into_inner() {
                        match child.as_rule() {
                            Rule::tie_weights_prop => {
                                if let Some(val) = child.into_inner().next() {
                                    def.embeddings.tie_weights = val.as_str() == "true";
                                }
                            }
                            Rule::dropout_prop => {
                                if let Some(val) = child.into_inner().next() {
                                    def.embeddings.dropout = val.as_str().parse()?;
                                }
                            }
                            Rule::scale_prop => {
                                if let Some(val) = child.into_inner().next() {
                                    def.embeddings.scale = Some(val.as_str().parse()?);
                                }
                            }
                            _ => {}
                        }
                    }
                }
            }
            Rule::output_prop => {
                for param in inner.into_inner() {
                    for child in param.into_inner() {
                        match child.as_rule() {
                            Rule::bias_prop => {
                                if let Some(val) = child.into_inner().next() {
                                    def.output.bias = val.as_str() == "true";
                                }
                            }
                            Rule::norm_prop => {
                                if let Some(cfg) = child.into_inner().next() {
                                    def.output.norm = Some(parse_norm_config(cfg)?);
                                }
                            }
                            _ => {}
                        }
                    }
                }
            }
            Rule::description_prop => {
                if let Some(val) = inner.into_inner().next() {
                    let s = val.as_str();
                    def.description = Some(s[1..s.len() - 1].to_string());
                }
            }
            _ => {}
        }
    }
    Ok(())
}

/// Parse a model definition from pest pair
fn parse_model_def(pair: pest::iterators::Pair<Rule>, file: &MalFile) -> Result<ModelDef> {
    let mut def = ModelDef::default();
    let mut inner = pair.into_inner();

    // Get model name
    if let Some(name) = inner.next() {
        def.name = name.as_str().to_string();
    }

    // Parse properties
    for prop in inner {
        if prop.as_rule() == Rule::model_prop {
            parse_model_prop(prop, &mut def, file)?;
        }
    }

    Ok(def)
}

/// Parse a MAL string containing exactly one model definition.
///
/// Use [`parse_mal_full`] when a source intentionally defines multiple models.
pub fn parse_mal(input: &str) -> Result<ModelDef> {
    let file = parse_mal_full(input)?;
    match file.models.len() {
        0 => Err(anyhow!("no model definition found")),
        1 => Ok(file.models.into_values().next().expect("length checked")),
        _ => {
            let mut names = file.models.keys().cloned().collect::<Vec<_>>();
            names.sort();
            Err(anyhow!(
                "multiple model definitions found ({}); use parse_mal_full to select one",
                names.join(", ")
            ))
        }
    }
}

fn insert_unique<T>(
    definitions: &mut HashMap<String, T>,
    kind: &str,
    name: String,
    value: T,
) -> Result<()> {
    match definitions.entry(name) {
        std::collections::hash_map::Entry::Vacant(entry) => {
            entry.insert(value);
            Ok(())
        }
        std::collections::hash_map::Entry::Occupied(entry) => {
            Err(anyhow!("duplicate {kind} '{}'", entry.key()))
        }
    }
}

/// Parse complete MAL file with all definitions
pub fn parse_mal_full(input: &str) -> Result<MalFile> {
    let pairs = MalParser::parse(Rule::file, input).map_err(|e| anyhow!("Parse error: {}", e))?;

    let mut file = MalFile::default();

    for pair in pairs {
        if pair.as_rule() == Rule::file {
            for inner in pair.into_inner() {
                if inner.as_rule() == Rule::definition {
                    for def in inner.into_inner() {
                        match def.as_rule() {
                            Rule::model_def => {
                                let model = parse_model_def(def, &file)?;
                                insert_unique(
                                    &mut file.models,
                                    "model",
                                    model.name.clone(),
                                    model,
                                )?;
                            }
                            Rule::attention_def => {
                                let attn = parse_attention_def(def)?;
                                insert_unique(
                                    &mut file.attentions,
                                    "attention",
                                    attn.name.clone(),
                                    attn,
                                )?;
                            }
                            Rule::ssm_def => {
                                let ssm = parse_ssm_def(def)?;
                                insert_unique(&mut file.ssms, "ssm", ssm.name.clone(), ssm)?;
                            }
                            Rule::ffn_def => {
                                let ffn = parse_ffn_def(def)?;
                                insert_unique(&mut file.ffns, "ffn", ffn.name.clone(), ffn)?;
                            }
                            Rule::block_def => {
                                let block = parse_block_def(def, &file)?;
                                insert_unique(
                                    &mut file.blocks,
                                    "block",
                                    block.name.clone(),
                                    block,
                                )?;
                            }
                            _ => {}
                        }
                    }
                }
            }
        }
    }

    Ok(file)
}

/// Parse an attention definition
fn parse_attention_def(pair: pest::iterators::Pair<Rule>) -> Result<AttentionDef> {
    let mut def = AttentionDef::default();
    let mut inner = pair.into_inner();

    if let Some(name) = inner.next() {
        def.name = name.as_str().to_string();
    }

    for prop in inner {
        if prop.as_rule() == Rule::attention_prop {
            parse_attention_prop(prop, &mut def)?;
        }
    }

    Ok(def)
}

/// Parse a position-encoding config (rope { theta, scaling } | alibi | learned | none)
fn parse_position_encoding(pair: pest::iterators::Pair<Rule>) -> Result<PositionEncoding> {
    // position_encoding_config contains one of the variant configs; the bare
    // "none" literal produces no inner pair.
    let Some(config) = pair.into_inner().next() else {
        return Ok(PositionEncoding::None);
    };
    match config.as_rule() {
        Rule::rope_config => {
            let mut theta = 10000.0;
            let mut scaling = None;
            for param in config.into_inner() {
                for inner in param.into_inner() {
                    match inner.as_rule() {
                        Rule::rope_theta_prop | Rule::rope_base_prop => {
                            if let Some(val) = inner.into_inner().next() {
                                theta = val.as_str().parse()?;
                            }
                        }
                        Rule::rope_scaling_prop => {
                            if let Some(val) = inner.into_inner().next() {
                                scaling = Some(val.as_str().parse()?);
                            }
                        }
                        _ => {}
                    }
                }
            }
            Ok(PositionEncoding::Rope { theta, scaling })
        }
        Rule::alibi_config => {
            let learned_slopes = config.as_str().contains("learned");
            Ok(PositionEncoding::Alibi { learned_slopes })
        }
        Rule::learned_config => {
            let mut max_positions = 0;
            for param in config.into_inner() {
                for inner in param.into_inner() {
                    if inner.as_rule() == Rule::max_positions_prop
                        && let Some(val) = inner.into_inner().next()
                    {
                        max_positions = val.as_str().parse()?;
                    }
                }
            }
            Ok(PositionEncoding::Learned { max_positions })
        }
        _ => Ok(PositionEncoding::None),
    }
}

/// Parse attention properties
fn parse_attention_prop(pair: pest::iterators::Pair<Rule>, def: &mut AttentionDef) -> Result<()> {
    for inner in pair.into_inner() {
        match inner.as_rule() {
            Rule::num_heads_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.num_heads = Some(val.as_str().parse()?);
                }
            }
            Rule::num_kv_heads_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.num_kv_heads = Some(val.as_str().parse()?);
                }
            }
            Rule::head_dim_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.head_dim = Some(val.as_str().parse()?);
                }
            }
            Rule::dropout_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.dropout = val.as_str().parse()?;
                }
            }
            Rule::bias_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.bias = val.as_str() == "true";
                }
            }
            Rule::causal_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.causal = val.as_str() == "true";
                }
            }
            Rule::window_size_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.window_size = Some(val.as_str().parse()?);
                }
            }
            Rule::position_encoding_prop => {
                if let Some(config) = inner.into_inner().next() {
                    def.position_encoding = parse_position_encoding(config)?;
                }
            }
            Rule::qk_norm_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.qk_norm = val.as_str() == "true";
                }
            }
            _ => {}
        }
    }
    Ok(())
}

/// Parse an SSM (Mamba) definition
fn parse_ssm_def(pair: pest::iterators::Pair<Rule>) -> Result<SsmDef> {
    let mut def = SsmDef::default();
    let mut inner = pair.into_inner();

    if let Some(name) = inner.next() {
        def.name = name.as_str().to_string();
    }

    for prop in inner {
        if prop.as_rule() == Rule::ssm_prop {
            parse_ssm_prop(prop, &mut def)?;
        }
    }

    Ok(def)
}

/// Parse SSM properties
fn parse_ssm_prop(pair: pest::iterators::Pair<Rule>, def: &mut SsmDef) -> Result<()> {
    for inner in pair.into_inner() {
        match inner.as_rule() {
            Rule::state_dim_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.state_dim = val.as_str().parse()?;
                }
            }
            Rule::conv_kernel_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.conv_kernel = val.as_str().parse()?;
                }
            }
            Rule::expand_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.expand = val.as_str().parse()?;
                }
            }
            Rule::dt_rank_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.dt_rank = Some(val.as_str().parse()?);
                }
            }
            _ => {}
        }
    }
    Ok(())
}

/// Parse an FFN definition
/// Parse a `norm_config` (`rmsnorm { eps: … }` | `layernorm { … }` | `none`)
/// into a `NormConfig`. An omitted epsilon is represented as zero and resolved
/// by model construction.
fn parse_norm_config(pair: pest::iterators::Pair<Rule>) -> Result<NormConfig> {
    let mut norm = NormConfig::default();
    match pair.into_inner().next() {
        // Bare `none` literal produces no inner rule.
        None => norm.norm_type = NormType::None,
        Some(cfg) => {
            norm.norm_type = match cfg.as_rule() {
                Rule::rmsnorm_config => NormType::RmsNorm,
                Rule::layernorm_config => NormType::LayerNorm,
                other => anyhow::bail!("unexpected norm config rule: {other:?}"),
            };
            for param in cfg.into_inner() {
                // norm_param -> norm_eps_prop -> number
                if let Some(prop) = param.into_inner().next()
                    && let Some(number) = prop.into_inner().next()
                {
                    norm.eps = number.as_str().parse()?;
                }
            }
        }
    }
    Ok(norm)
}

fn parse_ffn_def(pair: pest::iterators::Pair<Rule>) -> Result<FfnDef> {
    let mut def = FfnDef::default();
    let mut inner = pair.into_inner();

    if let Some(name) = inner.next() {
        def.name = name.as_str().to_string();
    }

    for prop in inner {
        if prop.as_rule() == Rule::ffn_prop {
            parse_ffn_prop(prop, &mut def)?;
        }
    }

    Ok(def)
}

/// Parse FFN properties
fn parse_ffn_prop(pair: pest::iterators::Pair<Rule>, def: &mut FfnDef) -> Result<()> {
    for inner in pair.into_inner() {
        match inner.as_rule() {
            Rule::hidden_dim_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.hidden_dim = Some(val.as_str().parse()?);
                }
            }
            Rule::activation_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.activation = parse_activation(val.as_str());
                }
            }
            Rule::bias_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.bias = val.as_str() == "true";
                }
            }
            Rule::dropout_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.dropout = val.as_str().parse()?;
                }
            }
            Rule::gate_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.gate = val.as_str() == "true";
                }
            }
            Rule::moe_prop => {
                let mut moe = MoeDef {
                    experts: 0,
                    top_k: 0,
                    shared_experts: 0,
                    load_balance_loss_weight: 0.0,
                    router_z_loss_weight: 0.0,
                };
                for param in inner.into_inner() {
                    let Some(prop) = param.into_inner().next() else {
                        continue;
                    };
                    let value = prop
                        .clone()
                        .into_inner()
                        .next()
                        .map(|value| value.as_str())
                        .unwrap_or_default();
                    match prop.as_rule() {
                        Rule::experts_prop => moe.experts = value.parse()?,
                        Rule::top_k_prop => moe.top_k = value.parse()?,
                        Rule::shared_experts_prop => moe.shared_experts = value.parse()?,
                        Rule::load_balance_loss_weight_prop => {
                            moe.load_balance_loss_weight = value.parse()?
                        }
                        Rule::router_z_loss_weight_prop => {
                            moe.router_z_loss_weight = value.parse()?
                        }
                        _ => {}
                    }
                }
                def.moe = Some(moe);
            }
            _ => {}
        }
    }
    Ok(())
}

/// Parse a block definition
fn parse_block_def(pair: pest::iterators::Pair<Rule>, file: &MalFile) -> Result<BlockDef> {
    let mut def = BlockDef::default();
    let mut inner = pair.into_inner();

    if let Some(name) = inner.next() {
        def.name = name.as_str().to_string();
    }

    for prop in inner {
        if prop.as_rule() == Rule::block_prop {
            parse_block_prop(prop, &mut def, file)?;
        }
    }

    Ok(def)
}

/// Parse block properties
fn parse_block_prop(
    pair: pest::iterators::Pair<Rule>,
    def: &mut BlockDef,
    file: &MalFile,
) -> Result<()> {
    for inner in pair.into_inner() {
        match inner.as_rule() {
            Rule::attention_ref_prop => {
                // Can be identifier or inline definition
                for child in inner.into_inner() {
                    match child.as_rule() {
                        Rule::identifier => {
                            let name = child.as_str();
                            def.attention = file
                                .attentions
                                .get(name)
                                .ok_or_else(|| anyhow!("undefined attention '{name}'"))?
                                .clone();
                        }
                        Rule::inline_attention => {
                            let mut attn = AttentionDef::default();
                            for prop in child.into_inner() {
                                if prop.as_rule() == Rule::attention_prop {
                                    parse_attention_prop(prop, &mut attn)?;
                                }
                            }
                            def.attention = attn;
                        }
                        _ => {}
                    }
                }
            }
            Rule::ssm_ref_prop => {
                for child in inner.into_inner() {
                    match child.as_rule() {
                        Rule::identifier => {
                            let name = child.as_str();
                            def.ssm = Some(
                                file.ssms
                                    .get(name)
                                    .ok_or_else(|| anyhow!("undefined ssm '{name}'"))?
                                    .clone(),
                            );
                        }
                        Rule::inline_ssm => {
                            let mut ssm = SsmDef::default();
                            for prop in child.into_inner() {
                                if prop.as_rule() == Rule::ssm_prop {
                                    parse_ssm_prop(prop, &mut ssm)?;
                                }
                            }
                            def.ssm = Some(ssm);
                        }
                        _ => {}
                    }
                }
            }
            Rule::ffn_ref_prop => {
                for child in inner.into_inner() {
                    match child.as_rule() {
                        Rule::identifier => {
                            let name = child.as_str();
                            def.ffn = file
                                .ffns
                                .get(name)
                                .ok_or_else(|| anyhow!("undefined ffn '{name}'"))?
                                .clone();
                        }
                        Rule::inline_ffn => {
                            let mut ffn = FfnDef::default();
                            for prop in child.into_inner() {
                                if prop.as_rule() == Rule::ffn_prop {
                                    parse_ffn_prop(prop, &mut ffn)?;
                                }
                            }
                            def.ffn = ffn;
                        }
                        _ => {}
                    }
                }
            }
            Rule::norm_prop => {
                // norm_prop -> norm_config -> (rmsnorm_config | layernorm_config | "none")
                if let Some(cfg) = inner.into_inner().next() {
                    def.norm = parse_norm_config(cfg)?;
                }
            }
            Rule::norm_position_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.norm_position = match val.as_str() {
                        "pre" => NormPosition::Pre,
                        "post" => NormPosition::Post,
                        _ => NormPosition::Pre,
                    };
                }
            }
            Rule::residual_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.residual = val.as_str() == "true";
                }
            }
            Rule::dropout_prop => {
                if let Some(val) = inner.into_inner().next() {
                    def.dropout = val.as_str().parse()?;
                }
            }
            _ => {}
        }
    }
    Ok(())
}

/// Parse MAL from a file
pub fn parse_mal_file<P: AsRef<std::path::Path>>(path: P) -> Result<ModelDef> {
    let content = std::fs::read_to_string(path)?;
    parse_mal(&content)
}

// ============================================================================
// Built-in model definitions
// ============================================================================

/// Get a well-known model definition by name
///
/// Accepts:
/// - Short names: "nano", "tiny", "gpt2-small", etc.
/// - Well-known paths: "well-known/nano.mal", "well-known/gpt2_small.mal"
/// - Filenames: "nano.mal", "gpt2_small.mal"
pub fn get_builtin_model(name: &str) -> Option<ModelDef> {
    let mal = get_wellknown_mal(name)?;
    parse_mal(&mal).ok()
}

/// Get the raw MAL content for a well-known model
///
/// Dynamically loads from embedded well-known/ directory.
pub fn get_wellknown_mal(name: &str) -> Option<String> {
    // Normalize: strip well-known/ prefix, ensure .mal suffix
    let name = name.strip_prefix("well-known/").unwrap_or(name);
    let filename = if name.ends_with(".mal") {
        name.to_string()
    } else {
        // Convert kebab-case to snake_case for filename
        format!("{}.mal", name.replace('-', "_"))
    };

    WellKnown::get(&filename).map(|f| String::from_utf8_lossy(&f.data).into_owned())
}

/// List all well-known model names (auto-discovered from embedded files)
pub fn list_wellknown_models() -> Vec<String> {
    WellKnown::iter()
        .filter_map(|path| {
            let path: &str = path.as_ref();
            if path.ends_with(".mal") {
                Some(path.strip_suffix(".mal").unwrap().replace('_', "-"))
            } else {
                None
            }
        })
        .collect()
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_parse_simple_model() {
        let mal = r#"
            attention test_attn {
                num_heads: 8
                bias: false
            }

            ffn test_ffn {
                hidden_dim: 2048
                activation: gelu
            }

            block test_block {
                attention: test_attn
                ffn: test_ffn
                norm_position: pre
            }

            model test {
                vocab_size: 32000
                hidden_size: 512
                num_layers: 8
                block: test_block
            }
        "#;

        let def = parse_mal(mal).unwrap();
        assert_eq!(def.name, "test");
        assert_eq!(def.vocab_size, 32000);
        assert_eq!(def.hidden_size, 512);
        assert_eq!(def.num_layers, 8);
    }

    #[test]
    fn test_parse_with_block_props() {
        let mal = r#"
            attention full_attn {
                num_heads: 16
                num_kv_heads: 4
                bias: true
                dropout: 0.1
            }

            ffn full_ffn {
                hidden_dim: 4096
                activation: gelu
                bias: true
                dropout: 0.1
            }

            block full_block {
                attention: full_attn
                ffn: full_ffn
                norm: layernorm { eps: 1e-6 }
                norm_position: pre
                residual: true
            }

            model full_test {
                description: "A test model"
                vocab_size: 50000
                max_seq_len: 4096
                hidden_size: 1024
                num_layers: 12
                block: full_block
            }
        "#;

        let def = parse_mal(mal).unwrap();
        assert_eq!(def.description, Some("A test model".to_string()));
        assert_eq!(def.vocab_size, 50000);
        assert_eq!(def.max_seq_len, 4096);
        assert_eq!(def.block.attention.num_heads, Some(16));
        assert_eq!(def.block.attention.num_kv_heads, Some(4));
        assert_eq!(def.block.ffn.hidden_dim, Some(4096));
        assert!(matches!(def.block.ffn.activation, Activation::GELU));
        // Regression: `norm:` was silently dropped (no Rule::norm_prop arm),
        // building every block as the default RMSNorm regardless of config.
        assert!(matches!(def.block.norm.norm_type, NormType::LayerNorm));
        assert_eq!(def.block.norm.eps, 1e-6);
    }

    #[test]
    fn moe_is_optional_and_fully_configurable() {
        let dense = parse_mal(
            "model d { vocab_size: 64 max_seq_len: 16 hidden_size: 8 num_layers: 1 \
             block: { attention: { num_heads: 1 } ffn: { hidden_dim: 12 } } }",
        )
        .unwrap();
        assert!(dense.block.ffn.moe.is_none());

        let moe = parse_mal(
            r#"
            ffn experts {
                hidden_dim: 12
                activation: swiglu
                moe {
                    experts: 8
                    top_k: 2
                    shared_experts: 1
                    load_balance_loss_weight: 0.01
                    router_z_loss_weight: 0.001
                }
            }
            model m {
                vocab_size: 64 max_seq_len: 16 hidden_size: 8 num_layers: 1
                block: { attention: { num_heads: 1 } ffn: experts }
            }
            "#,
        )
        .unwrap();
        let config = moe.block.ffn.moe.as_ref().unwrap();
        assert_eq!(config.experts, 8);
        assert_eq!(config.top_k, 2);
        assert_eq!(config.shared_experts, 1);
        assert_eq!(config.load_balance_loss_weight, 0.01);
        assert_eq!(config.router_z_loss_weight, 0.001);
        assert!(moe.estimated_params() > dense.estimated_params());
    }

    #[test]
    fn test_norm_none_and_rmsnorm() {
        let base = |norm: &str| {
            format!(
                r#"
                block b {{ attention: {{ num_heads: 4 }} ffn: {{ hidden_dim: 64 }} norm: {norm} }}
                model m {{ vocab_size: 100 max_seq_len: 64 hidden_size: 32 num_layers: 2 block: b }}
                "#
            )
        };
        let d = parse_mal(&base("rmsnorm { eps: 1e-5 }")).unwrap();
        assert!(matches!(d.block.norm.norm_type, NormType::RmsNorm));
        let d = parse_mal(&base("none")).unwrap();
        assert!(matches!(d.block.norm.norm_type, NormType::None));
    }

    #[test]
    fn test_embedding_and_output_configuration() {
        let def = parse_mal(
            r#"
            block b { attention: { num_heads: 4 } ffn: { hidden_dim: 64 } }
            model m {
                vocab_size: 100
                max_seq_len: 64
                hidden_size: 32
                num_layers: 2
                block: b
                embeddings { tie_weights: true dropout: 0.2 scale: 5.5 }
                output { bias: true norm: none }
            }
            "#,
        )
        .unwrap();

        assert!(def.embeddings.tie_weights);
        assert_eq!(def.embeddings.dropout, 0.2);
        assert_eq!(def.embeddings.scale, Some(5.5));
        assert!(def.output.bias);
        assert!(matches!(def.output.norm.unwrap().norm_type, NormType::None));
    }

    #[test]
    fn test_undefined_refs_error() {
        // Undefined block/attention/ffn/ssm references must fail loud, not
        // silently fall back to defaults.
        let cases = [
            "model m { vocab_size: 100 max_seq_len: 64 hidden_size: 32 num_layers: 2 block: nope }",
            "block b { attention: nope ffn: { hidden_dim: 64 } }\n\
             model m { vocab_size: 100 max_seq_len: 64 hidden_size: 32 num_layers: 2 block: b }",
            "block b { attention: { num_heads: 4 } ffn: nope }\n\
             model m { vocab_size: 100 max_seq_len: 64 hidden_size: 32 num_layers: 2 block: b }",
            "block b { ssm: nope ffn: { hidden_dim: 64 } }\n\
             model m { vocab_size: 100 max_seq_len: 64 hidden_size: 32 num_layers: 2 block: b }",
        ];
        for mal in cases {
            let err = parse_mal(mal).unwrap_err().to_string();
            assert!(
                err.contains("undefined"),
                "expected undefined-ref error, got: {err}"
            );
        }
    }

    #[test]
    fn test_parse_mal_rejects_multiple_models() {
        let err = parse_mal(
            r#"
            model alpha { vocab_size: 10 hidden_size: 8 num_layers: 1 block: { attention: { num_heads: 1 } ffn: { hidden_dim: 16 } } }
            model beta { vocab_size: 10 hidden_size: 8 num_layers: 1 block: { attention: { num_heads: 1 } ffn: { hidden_dim: 16 } } }
            "#,
        )
        .unwrap_err()
        .to_string();
        assert!(err.contains("multiple model definitions"), "{err}");
        assert!(err.contains("alpha") && err.contains("beta"), "{err}");
    }

    #[test]
    fn test_parse_mal_full_rejects_duplicate_definitions() {
        let err = parse_mal_full("attention repeated {} attention repeated {}")
            .unwrap_err()
            .to_string();
        assert!(err.contains("duplicate attention 'repeated'"), "{err}");
    }

    #[test]
    fn test_wellknown_models() {
        for name in list_wellknown_models() {
            let def = get_builtin_model(&name).unwrap_or_else(|| panic!("Failed to get {}", name));
            // Verify computed properties work
            assert!(def.num_heads() > 0);
            assert!(def.intermediate_size() > 0);
        }
    }

    #[test]
    fn test_model_properties() {
        let def = get_builtin_model("tiny").unwrap();

        assert_eq!(def.vocab_size, 32000);
        assert_eq!(def.hidden_size, 128);
        assert_eq!(def.num_layers, 4);
        assert_eq!(def.num_heads(), 4);
    }

    #[test]
    fn test_comments() {
        let mal = r#"
            # This is a comment
            attention test_attn {
                # Comment in attention
                num_heads: 2
            }

            ffn test_ffn {
                hidden_dim: 256
            }

            block test_block {
                attention: test_attn
                ffn: test_ffn
            }

            # Comment before model
            model test {
                vocab_size: 1000
                hidden_size: 64
                num_layers: 2
                block: test_block
            }
        "#;

        let def = parse_mal(mal).unwrap();
        assert_eq!(def.vocab_size, 1000);
    }

    #[test]
    fn test_parse_position_encoding_and_tie_weights() {
        let mal = r#"
            attention pe_attn {
                num_heads: 8
                position_encoding: rope { theta: 100000.0 }
            }

            ffn pe_ffn {
                hidden_dim: 1024
            }

            block pe_block {
                attention: pe_attn
                ffn: pe_ffn
            }

            model pe_test {
                vocab_size: 1000
                hidden_size: 256
                num_layers: 2
                block: pe_block
                embeddings {
                    tie_weights: true
                }
            }
        "#;

        let def = parse_mal(mal).unwrap();
        assert_eq!(def.rope_theta(), 100000.0, "theta must not be dropped");

        // qk_norm parses and lands
        let with_qk = parse_mal(
            r#"
            attention qk { num_heads: 4
                           qk_norm: true }
            ffn f { hidden_dim: 64 }
            block b { attention: qk
                      ffn: f }
            model m { vocab_size: 100
                      hidden_size: 64
                      num_layers: 1
                      block: b }
        "#,
        )
        .unwrap();
        assert!(with_qk.block.attention.qk_norm);

        assert!(
            def.embeddings.tie_weights,
            "tie_weights must not be dropped"
        );

        // Alternate spellings and variants
        let mal2 = r#"
            attention a { rope_theta: 500000.0 }
        "#;
        // rope_theta at attention level requires position_encoding wrapper;
        // bare form is not part of the grammar — this should fail to parse
        assert!(
            parse_mal_full(mal2).is_err() || {
                let f = parse_mal_full(mal2).unwrap();
                f.attentions.is_empty()
            }
        );

        let mal3 = r#"
            attention nopos { position_encoding: none }
            ffn f { hidden_dim: 64 }
            block b { attention: nopos
                      ffn: f }
            model m { vocab_size: 100
                      hidden_size: 64
                      num_layers: 1
                      block: b }
        "#;
        let def3 = parse_mal(mal3).unwrap();
        assert!(matches!(
            def3.block.attention.position_encoding,
            PositionEncoding::None
        ));
    }

    #[test]
    fn test_parse_hybrid_ssm_pattern() {
        let mal = r#"
            attention h_attn {
                num_heads: 4
                bias: false
            }

            ssm h_ssm {
                state_dim: 16
                conv_kernel: 4
                expand: 2
            }

            ffn h_ffn {
                hidden_dim: 512
                activation: swiglu
                bias: false
            }

            block attn_block {
                attention: h_attn
                ffn: h_ffn
                norm: rmsnorm { eps: 1e-5 }
                norm_position: pre
            }

            block mamba_block {
                ssm: h_ssm
                ffn: h_ffn
                norm: rmsnorm { eps: 1e-5 }
                norm_position: pre
            }

            model hybrid {
                vocab_size: 1000
                max_seq_len: 128
                hidden_size: 64
                num_layers: 6
                block: attn_block
                pattern: [mamba_block, mamba_block, attn_block]
            }
        "#;

        let def = parse_mal(mal).unwrap();
        assert_eq!(def.num_layers, 6);

        let pattern = def.pattern.as_ref().unwrap();
        assert_eq!(pattern.len(), 3);
        assert!(pattern[0].is_ssm());
        assert!(pattern[1].is_ssm());
        assert!(!pattern[2].is_ssm());

        // Cyclic layer assignment
        assert!(def.block_for_layer(0).is_ssm());
        assert!(!def.block_for_layer(2).is_ssm());
        assert!(def.block_for_layer(3).is_ssm());
        assert!(!def.block_for_layer(5).is_ssm());

        let ssm = pattern[0].ssm.as_ref().unwrap();
        assert_eq!(ssm.state_dim, 16);
        assert_eq!(ssm.conv_kernel, 4);
        assert_eq!(ssm.expand, 2);
        assert_eq!(def.dt_rank(ssm), 4); // ceil(64/16)

        // JSON roundtrip keeps the hybrid structure
        let json = serde_json::to_string(&def).unwrap();
        let back: ModelDef = serde_json::from_str(&json).unwrap();
        assert!(back.pattern.as_ref().unwrap()[0].is_ssm());

        // Attention-only JSON leaves the optional hybrid fields empty.
        let attention_only: ModelDef = serde_json::from_str(
            &serde_json::to_string(&get_builtin_model("tiny").unwrap()).unwrap(),
        )
        .unwrap();
        assert!(attention_only.pattern.is_none());
        assert!(!attention_only.block.is_ssm());
    }

    #[test]
    fn test_composable_architecture() {
        let mal = r#"
            attention my_attn {
                num_heads: 16
                num_kv_heads: 4
                head_dim: 128
                bias: false
            }

            ffn my_ffn {
                hidden_dim: 11008
                activation: swiglu
                bias: false
            }

            block my_block {
                attention: my_attn
                ffn: my_ffn
                norm: rmsnorm { eps: 1e-5 }
                norm_position: pre
                residual: true
            }

            model my_model {
                description: "LLaMA 7B architecture"
                vocab_size: 32000
                max_seq_len: 4096
                hidden_size: 4096
                num_layers: 32
                block: my_block
            }
        "#;

        let file = parse_mal_full(mal).unwrap();

        assert!(file.attentions.contains_key("my_attn"));
        assert!(file.ffns.contains_key("my_ffn"));
        assert!(file.blocks.contains_key("my_block"));
        assert!(file.models.contains_key("my_model"));

        let attn = file.attentions.get("my_attn").unwrap();
        assert_eq!(attn.num_heads, Some(16));
        assert_eq!(attn.num_kv_heads, Some(4));

        let ffn = file.ffns.get("my_ffn").unwrap();
        assert_eq!(ffn.hidden_dim, Some(11008));
        assert!(matches!(ffn.activation, Activation::SwiGLU));

        let block = file.blocks.get("my_block").unwrap();
        assert!(matches!(block.norm_position, NormPosition::Pre));
        assert!(block.residual);
    }

    #[test]
    fn vocabulary_storage_alignment_is_derived() {
        let mut model = ModelDef {
            vocab_size: 50_277,
            ..ModelDef::default()
        };
        assert_eq!(model.padded_vocab_size(), 50_304);

        model.vocab_size = 32_000;
        assert_eq!(model.padded_vocab_size(), 32_000);

        let serialized = serde_json::to_value(&model).unwrap();
        assert_eq!(serialized["vocab_size"], 32_000);
        assert!(serialized.get("padded_vocab_size").is_none());
    }
}