ferrum_cli/

gpu_mem_autosize.rs

//! GPU memory auto-tuning for the KV pool.
//!
//! Reads model config + on-disk weight file sizes + nvidia-smi reported
//! GPU total, then sets `FERRUM_KV_MAX_BLOCKS` so the KV pool fits inside
//! `total_mem * gpu_memory_utilization` after weights and a scratch reserve.
//! Mirrors vLLM's `gpu_memory_utilization` knob (default 0.9).
//!
//! Skipped when:
//! - nvidia-smi missing (Mac / CPU-only): keep static defaults.
//! - `config.json` not parseable: keep static defaults.
//! - User explicitly set `FERRUM_KV_MAX_BLOCKS`: respect their override.

use ferrum_types::{RuntimeConfigEntry, RuntimeConfigSnapshot, RuntimeConfigSource};
use std::path::Path;

/// Bytes reserved for everything that's NOT weights or KV pool: cuBLAS
/// workspace, Marlin gather scratch, unified path scratch, embedding,
/// lm_head logits buffer, runtime allocator overhead. 4 GB is the
/// observed worst case at c=32 + chunked-prefill mixed batches.
const SCRATCH_RESERVE_BYTES: u64 = 4 * 1024 * 1024 * 1024;

/// PagedKvPool block_size — must match `PAGED_BLOCK_SIZE` in
/// `llama_family.rs`.
const PAGED_BLOCK_SIZE: u64 = 16;
const DEFAULT_MAX_BATCHED_TOKENS: usize = 2048;
// Tight recurrent-state models carry large non-KV decode state and can be
// allocator-fragile near the end of the memory budget. Keep aggregate prefill
// conservative by default; widening this to 1024 was shown to OOM the W3 c16
// product-path diagnostic even though the KV block floor itself still fit.
const TIGHT_RECURRENT_STATE_MAX_BATCHED_TOKENS: usize = 192;
const TIGHT_RECURRENT_STATE_KV_BLOCK_FLOOR: usize = 256;

/// Bytes per element of the KV cache. ferrum currently always uses FP16
/// for KV regardless of weight dtype (Marlin INT4 weights → FP16 KV).
const KV_DTYPE_BYTES: u64 = 2;

#[derive(Debug)]
pub struct AutoSizeResult {
    pub total_gpu_bytes: u64,
    pub free_gpu_bytes: u64,
    pub weight_bytes: u64,
    pub budgeted_weight_bytes: u64,
    pub weight_budget_shards: u64,
    pub budgeted_layer_count: u64,
    pub kv_block_bytes: u64,
    pub kv_pool_copies: u64,
    pub estimated_budget_blocks: usize,
    pub requested_min_blocks: usize,
    pub max_blocks: usize,
    pub reserved_for_scratch: u64,
}

impl AutoSizeResult {
    pub fn print_summary(&self) {
        let gb = |b: u64| (b as f64) / 1024.0 / 1024.0 / 1024.0;
        eprintln!(
            "[auto-size] gpu={:.1} GB total / {:.1} GB free | weights={:.1} GB budget / {:.1} GB total | layers={} budget | scratch reserve={:.1} GB | KV pool budget {:.1} GB → max_blocks={}",
            gb(self.total_gpu_bytes),
            gb(self.free_gpu_bytes),
            gb(self.budgeted_weight_bytes),
            gb(self.weight_bytes),
            self.budgeted_layer_count,
            gb(self.reserved_for_scratch),
            gb((self.max_blocks as u64) * self.kv_block_bytes * self.kv_pool_copies),
            self.max_blocks,
        );
        if self.weight_budget_shards > 1 {
            eprintln!(
                "[auto-size] weight budget shards={} (distributed strategy)",
                self.weight_budget_shards
            );
        }
        if self.kv_pool_copies > 1 {
            eprintln!(
                "[auto-size] KV pool copies={} (FA-compatible attention path)",
                self.kv_pool_copies
            );
        }
        if self.requested_min_blocks > self.estimated_budget_blocks {
            eprintln!(
                "[auto-size] requested runtime token floor requires KV_MAX_BLOCKS={} above estimated budget {}; honoring explicit runtime limits",
                self.requested_min_blocks, self.estimated_budget_blocks
            );
        }
    }
}

/// Compute target `FERRUM_KV_MAX_BLOCKS` from `gpu_memory_utilization`.
///
/// Returns None when any input is unavailable — caller leaves the
/// static default in place.
pub fn auto_size_kv_blocks(model_dir: &Path, gpu_util: f32) -> Option<AutoSizeResult> {
    auto_size_kv_blocks_with_pool_copies(model_dir, gpu_util, 1)
}

pub fn auto_size_kv_blocks_with_pool_copies(
    model_dir: &Path,
    gpu_util: f32,
    kv_pool_copies: u64,
) -> Option<AutoSizeResult> {
    let current = RuntimeConfigSnapshot::capture_current();
    auto_size_kv_blocks_with_pool_copies_for_snapshot(model_dir, gpu_util, kv_pool_copies, &current)
}

fn auto_size_kv_blocks_with_pool_copies_for_snapshot(
    model_dir: &Path,
    gpu_util: f32,
    kv_pool_copies: u64,
    runtime_config: &RuntimeConfigSnapshot,
) -> Option<AutoSizeResult> {
    let gpu_util = gpu_util.clamp(0.1, 1.0);
    let kv_pool_copies = kv_pool_copies.max(1);

    // 1. Query GPU total + free via nvidia-smi (most portable across
    //    cudarc versions and works pre-cuda-driver-load).
    let nvsmi = std::process::Command::new("nvidia-smi")
        .args([
            "--query-gpu=memory.total,memory.free",
            "--format=csv,noheader,nounits",
        ])
        .output()
        .ok()?;
    if !nvsmi.status.success() {
        return None;
    }
    let s = String::from_utf8(nvsmi.stdout).ok()?;
    let line = s.lines().next()?.trim();
    let parts: Vec<&str> = line.split(',').map(str::trim).collect();
    let total_mb: u64 = parts.first()?.parse().ok()?;
    let free_mb: u64 = parts.get(1)?.parse().ok()?;
    let total_bytes = total_mb * 1024 * 1024;
    let free_bytes = free_mb * 1024 * 1024;

    // 2. Parse config.json for model dims.
    let config_path = model_dir.join("config.json");
    let config: serde_json::Value =
        serde_json::from_str(&std::fs::read_to_string(&config_path).ok()?).ok()?;
    let num_layers = config_or_text_u64(&config, "num_hidden_layers")
        .or_else(|| config_or_text_u64(&config, "num_layers"))?;
    let hidden_size = config_or_text_u64(&config, "hidden_size")?;
    let num_attn_heads = config_or_text_u64(&config, "num_attention_heads")?;
    let num_kv_heads = config_or_text_u64(&config, "num_key_value_heads").unwrap_or(num_attn_heads);
    let head_dim = config_or_text_u64(&config, "head_dim")
        .unwrap_or_else(|| hidden_size / num_attn_heads.max(1));

    // 3. Sum .safetensors / .bin file sizes for weight estimate.
    let mut weight_bytes: u64 = 0;
    if let Ok(entries) = std::fs::read_dir(model_dir) {
        for entry in entries.flatten() {
            let p = entry.path();
            let is_weight = p
                .extension()
                .and_then(|s| s.to_str())
                .map(|ext| ext == "safetensors" || ext == "bin")
                .unwrap_or(false);
            if is_weight {
                // HuggingFace snapshot files are commonly symlinks into the
                // blob cache. `DirEntry::metadata` reports the symlink itself;
                // `std::fs::metadata` follows it and gives the real shard size.
                if let Ok(meta) = std::fs::metadata(&p) {
                    weight_bytes += meta.len();
                }
            }
        }
    }
    if weight_bytes == 0 {
        // Couldn't find weights — bail to static defaults.
        return None;
    }
    let weight_budget_shards = weight_budget_shard_count(runtime_config);
    let budgeted_weight_bytes = ceil_div_u64(weight_bytes, weight_budget_shards);
    let budgeted_layer_count = layer_count_for_memory_budget(num_layers, runtime_config);

    // 4. Compute KV budget. Reserve `(1 - util)` of total mem as host-
    //    bookkeeping margin, plus a fixed scratch reserve covering all
    //    transient buffers (cuBLAS workspace, Marlin scratch, unified
    //    forward intermediates, embedding, lm_head, etc).
    let target_used = (total_bytes as f64 * gpu_util as f64) as u64;
    let avail_for_kv = target_used
        .saturating_sub(budgeted_weight_bytes)
        .saturating_sub(SCRATCH_RESERVE_BYTES);

    // KV per block: num_layers × num_kv_heads × block_size × head_dim
    //              × 2 (K and V) × dtype_bytes
    let block_bytes =
        budgeted_layer_count * num_kv_heads * PAGED_BLOCK_SIZE * head_dim * 2 * KV_DTYPE_BYTES;
    if block_bytes == 0 {
        return None;
    }
    let estimated_budget_blocks = (avail_for_kv / (block_bytes * kv_pool_copies)) as usize;
    let requested_min_blocks = requested_min_kv_blocks_from_snapshot(runtime_config);
    let max_blocks = estimated_budget_blocks.max(requested_min_blocks);

    Some(AutoSizeResult {
        total_gpu_bytes: total_bytes,
        free_gpu_bytes: free_bytes,
        weight_bytes,
        budgeted_weight_bytes,
        weight_budget_shards,
        budgeted_layer_count,
        kv_block_bytes: block_bytes,
        kv_pool_copies,
        estimated_budget_blocks,
        requested_min_blocks,
        max_blocks,
        reserved_for_scratch: SCRATCH_RESERVE_BYTES,
    })
}

/// CLI usage profile: which presets the autosizer should consider.
#[derive(Clone, Copy, Debug, PartialEq, Eq)]
pub enum AutoSizeProfile {
    /// `ferrum serve` — many concurrent requests. Admission width and
    /// per-request logical context are resolved independently over the shared
    /// physical KV block pool. Bench scripts also use this profile.
    Server,
    /// `ferrum run` — single interactive user, multi-turn chat. Trade
    /// max_seqs for KV_CAPACITY so a long conversation doesn't crash
    /// after a few turns. With KV_CAPACITY=512 (server default), Qwen3
    /// thinking-mode replies hit overflow at the 4th turn.
    Chat,
}

#[derive(Clone, Copy, Debug, Default, PartialEq, Eq)]
enum ModelAutoSizeClass {
    #[default]
    Generic,
    TightRecurrentState,
}

#[derive(Clone, Copy, Debug, Default, PartialEq, Eq)]
struct ModelAutoSizeHints {
    has_recurrent_linear_attention_state: bool,
}

#[derive(Clone, Copy, Debug)]
struct ModelAutoSizeDefaults {
    max_batched_tokens: usize,
    max_sequences: usize,
    max_sequence_tokens: usize,
    kv_block_floor: usize,
}

/// Apply auto-sizing: read CLI flag, query nvidia-smi, set env vars.
/// Sets `FERRUM_KV_MAX_BLOCKS` (global physical paged-KV block budget),
/// `FERRUM_PAGED_MAX_SEQS` (scheduler/model concurrency shape), and
/// `FERRUM_KV_CAPACITY` (per-sequence logical table stride). The model
/// allocates the GPU KV pool from `KV_MAX_BLOCKS`; `PAGED_MAX_SEQS *
/// KV_CAPACITY` no longer reserves physical KV blocks up front.
///
/// Idempotent — caller invokes once per CLI invocation before engine
/// init. Respects user overrides (no clobber if env already set).
///
/// Defaults to `AutoSizeProfile::Server`. Use `apply_auto_size_with_profile`
/// for chat (`ferrum run`) — it picks longer per-seq context.
pub fn apply_auto_size(model_dir: &Path, gpu_util: f32) {
    apply_auto_size_with_profile(model_dir, gpu_util, AutoSizeProfile::Server);
}

/// Apply auto-sizing with explicit usage profile. The chat profile
/// flips priority — long context per seq beats wide batch — because
/// the CLI REPL only ever has one active sequence and multi-turn
/// dialogues blow past the default 512-token cap fast.
pub fn apply_auto_size_with_profile(model_dir: &Path, gpu_util: f32, profile: AutoSizeProfile) {
    let current = RuntimeConfigSnapshot::capture_current();
    let entries = auto_size_runtime_entries(model_dir, gpu_util, profile, &current);
    crate::runtime_env::materialize_runtime_env_defaults(&entries);
}

/// Legacy pool sizing against the caller's typed request. Product startup
/// keeps these inferred entries separate from the original user environment.
pub fn auto_size_runtime_entries(
    model_dir: &Path,
    gpu_util: f32,
    profile: AutoSizeProfile,
    current: &RuntimeConfigSnapshot,
) -> Vec<RuntimeConfigEntry> {
    let kv_overridden = snapshot_value(&current, "FERRUM_KV_MAX_BLOCKS").is_some();
    let max_seqs_overridden = snapshot_value(&current, "FERRUM_PAGED_MAX_SEQS").is_some();
    let max_batched_tokens_overridden =
        snapshot_value(&current, "FERRUM_MAX_BATCHED_TOKENS").is_some();
    let model_hints = model_auto_size_hints(model_dir);
    let mut entries = Vec::new();
    // ALL three knobs covered by the user — nothing to set.
    if kv_overridden && max_seqs_overridden && max_batched_tokens_overridden {
        return entries;
    }
    let kv_pool_copies = kv_pool_copies_from_snapshot(&current);
    let preliminary_result = auto_size_kv_blocks_with_pool_copies_for_snapshot(
        model_dir,
        gpu_util,
        kv_pool_copies,
        &current,
    );
    let model_class =
        model_auto_size_class_from_hints_and_budget(model_hints, preliminary_result.as_ref());
    let defaults = model_auto_size_defaults(model_class, profile);
    // Set MAX_BATCHED_TOKENS first so it lands even when the user overrode
    // FERRUM_KV_MAX_BLOCKS + FERRUM_PAGED_MAX_SEQS (apples bench does both,
    // which used to silently skip the Phase 3 scratch budget alongside).
    if !max_batched_tokens_overridden {
        // 2048 is the safe generic default across smaller dense and MoE GPTQ
        // profiles. Tight recurrent-state models only use the smaller scratch
        // profile when the measured weight/VRAM budget cannot cover that
        // generic aggregate-prefill floor.
        let mbt = defaults.max_batched_tokens;
        entries.push(RuntimeConfigEntry::new(
            "FERRUM_MAX_BATCHED_TOKENS",
            mbt.to_string(),
            RuntimeConfigSource::MemoryProfile,
        ));
        eprintln!(
            "[auto-size] MAX_BATCHED_TOKENS={} (profile={:?} model={:?})",
            mbt, profile, model_class
        );
    }
    if kv_overridden && max_seqs_overridden {
        return entries;
    }
    let mut budget_snapshot = current.clone();
    for entry in &entries {
        budget_snapshot.upsert_entry(entry.clone());
    }
    let result = if entries.is_empty() {
        preliminary_result
    } else {
        auto_size_kv_blocks_with_pool_copies_for_snapshot(
            model_dir,
            gpu_util,
            kv_pool_copies,
            &budget_snapshot,
        )
    };
    let Some(result) = result else {
        return entries;
    };
    result.print_summary();
    let max_blocks = result.max_blocks.max(defaults.kv_block_floor);

    // `KV_MAX_BLOCKS` is the physical authority. Logical sequence capacity
    // does not reserve that many blocks per admitted sequence; requests borrow
    // blocks on demand and admission applies backpressure when the shared pool
    // cannot satisfy the next step.
    let (max_seqs_clamped, kv_capacity) = select_dynamic_paged_pool_shape(
        defaults.max_sequences,
        defaults.max_sequence_tokens,
        max_blocks,
    );

    let kv_capacity_overridden = snapshot_value(&current, "FERRUM_KV_CAPACITY").is_some();
    // MAX_BATCHED_TOKENS already set above (it's independent of the KV pool
    // sizing logic, runs even when the user overrode KV_MAX_BLOCKS + SEQS).
    // FERRUM_MOE_GRAPH is resolved as a typed CLI startup default and
    // materialized outside the autosizer so it lands even when this function
    // early-returns on full-override.
    if !kv_overridden {
        entries.push(RuntimeConfigEntry::new(
            "FERRUM_KV_MAX_BLOCKS",
            max_blocks.to_string(),
            RuntimeConfigSource::MemoryProfile,
        ));
    }
    if !max_seqs_overridden {
        entries.push(RuntimeConfigEntry::new(
            "FERRUM_PAGED_MAX_SEQS",
            max_seqs_clamped.to_string(),
            RuntimeConfigSource::MemoryProfile,
        ));
    }
    if kv_capacity > 0 && !kv_capacity_overridden {
        entries.push(RuntimeConfigEntry::new(
            "FERRUM_KV_CAPACITY",
            kv_capacity.to_string(),
            RuntimeConfigSource::MemoryProfile,
        ));
    }
    eprintln!(
        "[auto-size] KV_MAX_BLOCKS={} PAGED_MAX_SEQS={} KV_CAPACITY={}",
        if kv_overridden {
            "<user>".to_string()
        } else {
            max_blocks.to_string()
        },
        if max_seqs_overridden {
            "<user>".to_string()
        } else {
            max_seqs_clamped.to_string()
        },
        if kv_capacity_overridden {
            "<user>".to_string()
        } else if kv_capacity > 0 {
            kv_capacity.to_string()
        } else {
            "<default>".to_string()
        },
    );
    entries
}

const MAX_AUTOSIZED_SEQUENCE_TOKENS: usize = 16_384;
const DEFAULT_SERVER_MAX_SEQUENCES: usize = 32;
const TIGHT_RECURRENT_STATE_SERVER_MAX_SEQUENCES: usize = 16;
const CHAT_MAX_SEQUENCES: usize = 2;

fn model_auto_size_defaults(
    model_class: ModelAutoSizeClass,
    profile: AutoSizeProfile,
) -> ModelAutoSizeDefaults {
    match (model_class, profile) {
        (ModelAutoSizeClass::TightRecurrentState, AutoSizeProfile::Server) => {
            ModelAutoSizeDefaults {
                max_batched_tokens: TIGHT_RECURRENT_STATE_MAX_BATCHED_TOKENS,
                max_sequences: TIGHT_RECURRENT_STATE_SERVER_MAX_SEQUENCES,
                max_sequence_tokens: MAX_AUTOSIZED_SEQUENCE_TOKENS,
                kv_block_floor: TIGHT_RECURRENT_STATE_KV_BLOCK_FLOOR,
            }
        }
        (ModelAutoSizeClass::TightRecurrentState, AutoSizeProfile::Chat) => ModelAutoSizeDefaults {
            max_batched_tokens: TIGHT_RECURRENT_STATE_MAX_BATCHED_TOKENS,
            max_sequences: CHAT_MAX_SEQUENCES,
            max_sequence_tokens: MAX_AUTOSIZED_SEQUENCE_TOKENS,
            kv_block_floor: TIGHT_RECURRENT_STATE_KV_BLOCK_FLOOR,
        },
        (ModelAutoSizeClass::Generic, AutoSizeProfile::Server) => ModelAutoSizeDefaults {
            max_batched_tokens: DEFAULT_MAX_BATCHED_TOKENS,
            max_sequences: DEFAULT_SERVER_MAX_SEQUENCES,
            max_sequence_tokens: MAX_AUTOSIZED_SEQUENCE_TOKENS,
            kv_block_floor: 0,
        },
        (ModelAutoSizeClass::Generic, AutoSizeProfile::Chat) => ModelAutoSizeDefaults {
            max_batched_tokens: DEFAULT_MAX_BATCHED_TOKENS,
            max_sequences: CHAT_MAX_SEQUENCES,
            max_sequence_tokens: MAX_AUTOSIZED_SEQUENCE_TOKENS,
            kv_block_floor: 0,
        },
    }
}

fn model_auto_size_hints(model_dir: &Path) -> ModelAutoSizeHints {
    let Ok(config_text) = std::fs::read_to_string(model_dir.join("config.json")) else {
        return ModelAutoSizeHints::default();
    };
    let Ok(config) = serde_json::from_str::<serde_json::Value>(&config_text) else {
        return ModelAutoSizeHints::default();
    };
    model_auto_size_hints_from_config(&config)
}

fn model_auto_size_hints_from_config(config: &serde_json::Value) -> ModelAutoSizeHints {
    ModelAutoSizeHints {
        has_recurrent_linear_attention_state: has_recurrent_linear_attention_state(config),
    }
}

fn model_auto_size_class_from_hints_and_budget(
    hints: ModelAutoSizeHints,
    budget: Option<&AutoSizeResult>,
) -> ModelAutoSizeClass {
    if !hints.has_recurrent_linear_attention_state {
        return ModelAutoSizeClass::Generic;
    }
    let Some(budget) = budget else {
        return ModelAutoSizeClass::Generic;
    };
    let generic_prefill_blocks =
        ceil_div_usize(DEFAULT_MAX_BATCHED_TOKENS, PAGED_BLOCK_SIZE as usize);
    if budget.estimated_budget_blocks < generic_prefill_blocks {
        ModelAutoSizeClass::TightRecurrentState
    } else {
        ModelAutoSizeClass::Generic
    }
}

fn has_recurrent_linear_attention_state(config: &serde_json::Value) -> bool {
    let text = config.get("text_config").unwrap_or(config);
    let has_linear_layers = text
        .get("layer_types")
        .and_then(|value| value.as_array())
        .is_some_and(|layers| {
            layers.iter().any(|layer| {
                layer
                    .as_str()
                    .is_some_and(|name| name.eq_ignore_ascii_case("linear_attention"))
            })
        });
    let has_linear_state_dims = [
        "linear_conv_kernel_dim",
        "linear_key_head_dim",
        "linear_num_key_heads",
        "linear_num_value_heads",
        "linear_value_head_dim",
    ]
    .iter()
    .all(|key| text.get(*key).and_then(|value| value.as_u64()).is_some());
    has_linear_layers && has_linear_state_dims
}

fn config_or_text_u64(config: &serde_json::Value, key: &str) -> Option<u64> {
    config
        .get(key)
        .and_then(|value| value.as_u64())
        .or_else(|| {
            config
                .get("text_config")
                .and_then(|text| text.get(key))
                .and_then(|value| value.as_u64())
        })
}

fn ceil_div_u64(value: u64, divisor: u64) -> u64 {
    if divisor == 0 {
        return value;
    }
    value.div_ceil(divisor)
}

fn ceil_div_usize(value: usize, divisor: usize) -> usize {
    if divisor == 0 {
        return value;
    }
    value.div_ceil(divisor)
}

fn select_dynamic_paged_pool_shape(
    max_sequences: usize,
    max_sequence_tokens: usize,
    max_blocks: usize,
) -> (usize, usize) {
    let physical_blocks = max_blocks.max(1);
    let sequences = max_sequences.max(1).min(physical_blocks);
    let physical_tokens = physical_blocks.saturating_mul(PAGED_BLOCK_SIZE as usize);
    let sequence_tokens = max_sequence_tokens
        .max(PAGED_BLOCK_SIZE as usize)
        .min(physical_tokens);
    (sequences, sequence_tokens)
}

fn weight_budget_shard_count(snapshot: &RuntimeConfigSnapshot) -> u64 {
    match snapshot_value(
        snapshot,
        crate::gpu_devices::SELECTED_DISTRIBUTED_STRATEGY_KEY,
    ) {
        Some("layer_split") => selected_gpu_device_count(snapshot).max(1) as u64,
        // Tensor-parallel support should extend this match with its own
        // weight/KV placement rules instead of treating all multi-GPU
        // strategies as identical.
        _ => 1,
    }
}

fn layer_count_for_memory_budget(num_layers: u64, snapshot: &RuntimeConfigSnapshot) -> u64 {
    match snapshot_value(
        snapshot,
        crate::gpu_devices::SELECTED_DISTRIBUTED_STRATEGY_KEY,
    ) {
        Some("layer_split") => {
            let shards = selected_gpu_device_count(snapshot).max(1) as u64;
            ceil_div_u64(num_layers, shards).max(1)
        }
        _ => num_layers,
    }
}

fn selected_gpu_device_count(snapshot: &RuntimeConfigSnapshot) -> usize {
    snapshot_value(snapshot, crate::gpu_devices::SELECTED_GPU_DEVICES_KEY)
        .map(|value| {
            value
                .split(',')
                .filter(|part| !part.trim().is_empty())
                .count()
        })
        .unwrap_or(1)
}

fn requested_min_kv_blocks_from_snapshot(snapshot: &RuntimeConfigSnapshot) -> usize {
    let max_model_len_blocks = snapshot_usize(snapshot, "FERRUM_MAX_MODEL_LEN")
        .map(|value| ceil_div_usize(value, PAGED_BLOCK_SIZE as usize))
        .unwrap_or(0);
    let max_batched_token_blocks = snapshot_usize(snapshot, "FERRUM_MAX_BATCHED_TOKENS")
        .map(|value| ceil_div_usize(value, PAGED_BLOCK_SIZE as usize))
        .unwrap_or(0);

    max_model_len_blocks.max(max_batched_token_blocks)
}

fn snapshot_value<'a>(snapshot: &'a RuntimeConfigSnapshot, key: &str) -> Option<&'a str> {
    snapshot
        .entries
        .iter()
        .find(|entry| entry.key == key)
        .map(|entry| entry.effective_value.as_str())
}

fn snapshot_usize(snapshot: &RuntimeConfigSnapshot, key: &str) -> Option<usize> {
    snapshot_value(snapshot, key).and_then(|value| value.parse::<usize>().ok())
}

fn snapshot_bool(snapshot: &RuntimeConfigSnapshot, key: &str) -> Option<bool> {
    snapshot_value(snapshot, key).map(|value| matches!(value, "1" | "true" | "TRUE" | "on" | "ON"))
}

fn kv_pool_copies_from_snapshot(snapshot: &RuntimeConfigSnapshot) -> u64 {
    let fa_layout = snapshot_bool(snapshot, "FERRUM_FA_LAYOUT_VARLEN").unwrap_or(false);
    let fa2_source = snapshot_bool(snapshot, "FERRUM_FA2_SOURCE").unwrap_or(false);
    let fa2_direct_ffi = snapshot_bool(snapshot, "FERRUM_FA2_DIRECT_FFI")
        .unwrap_or_else(|| snapshot_value(snapshot, "FERRUM_FA2_DIRECT_FFI_SHIM").is_some());

    if fa_layout || fa2_source || fa2_direct_ffi {
        2
    } else {
        1
    }
}

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

    fn snapshot(vars: &[(&str, &str)]) -> RuntimeConfigSnapshot {
        RuntimeConfigSnapshot::from_env_vars(vars.iter().copied())
    }

    fn budget_with_estimated_blocks(estimated_budget_blocks: usize) -> AutoSizeResult {
        AutoSizeResult {
            total_gpu_bytes: 24 * 1024 * 1024 * 1024,
            free_gpu_bytes: 20 * 1024 * 1024 * 1024,
            weight_bytes: 18 * 1024 * 1024 * 1024,
            budgeted_weight_bytes: 18 * 1024 * 1024 * 1024,
            weight_budget_shards: 1,
            budgeted_layer_count: 40,
            kv_block_bytes: 4 * 1024 * 1024,
            kv_pool_copies: 1,
            estimated_budget_blocks,
            requested_min_blocks: 0,
            max_blocks: estimated_budget_blocks,
            reserved_for_scratch: SCRATCH_RESERVE_BYTES,
        }
    }

    #[test]
    fn fa_compatible_attention_paths_count_two_kv_pool_copies() {
        assert_eq!(kv_pool_copies_from_snapshot(&snapshot(&[])), 1);
        assert_eq!(
            kv_pool_copies_from_snapshot(&snapshot(&[("FERRUM_FA_LAYOUT_VARLEN", "1")])),
            2
        );
        assert_eq!(
            kv_pool_copies_from_snapshot(&snapshot(&[("FERRUM_FA2_SOURCE", "1")])),
            2
        );
        assert_eq!(
            kv_pool_copies_from_snapshot(&snapshot(&[("FERRUM_FA2_DIRECT_FFI_SHIM", "/tmp/x.so")])),
            2
        );
        assert_eq!(
            kv_pool_copies_from_snapshot(&snapshot(&[
                ("FERRUM_FA2_DIRECT_FFI", "0"),
                ("FERRUM_FA2_DIRECT_FFI_SHIM", "/tmp/x.so"),
            ])),
            1
        );
    }

    #[test]
    fn layer_split_scopes_weight_and_layer_budget_to_selected_devices() {
        let snapshot = snapshot(&[
            (
                crate::gpu_devices::SELECTED_DISTRIBUTED_STRATEGY_KEY,
                "layer_split",
            ),
            (crate::gpu_devices::SELECTED_GPU_DEVICES_KEY, "0,1"),
        ]);

        assert_eq!(weight_budget_shard_count(&snapshot), 2);
        assert_eq!(layer_count_for_memory_budget(80, &snapshot), 40);
        assert_eq!(ceil_div_u64(37, weight_budget_shard_count(&snapshot)), 19);
    }

    #[test]
    fn unknown_multi_gpu_strategy_keeps_single_device_budget_until_wired() {
        let snapshot = snapshot(&[
            (
                crate::gpu_devices::SELECTED_DISTRIBUTED_STRATEGY_KEY,
                "tensor_parallel",
            ),
            (crate::gpu_devices::SELECTED_GPU_DEVICES_KEY, "0,1"),
        ]);

        assert_eq!(weight_budget_shard_count(&snapshot), 1);
        assert_eq!(layer_count_for_memory_budget(80, &snapshot), 80);
    }

    #[test]
    fn requested_runtime_token_limits_define_kv_block_floor() {
        let snapshot = snapshot(&[
            ("FERRUM_MAX_MODEL_LEN", "8192"),
            ("FERRUM_MAX_BATCHED_TOKENS", "1024"),
            ("FERRUM_PAGED_MAX_SEQS", "8"),
            ("FERRUM_KV_CAPACITY", "2048"),
        ]);

        assert_eq!(requested_min_kv_blocks_from_snapshot(&snapshot), 512);
    }

    #[test]
    fn paged_pool_shape_decouples_admission_width_from_sequence_capacity() {
        assert_eq!(
            select_dynamic_paged_pool_shape(32, 16_384, 338),
            (32, 5_408)
        );
        assert_eq!(
            select_dynamic_paged_pool_shape(32, 16_384, 2_048),
            (32, 16_384)
        );
        assert_eq!(select_dynamic_paged_pool_shape(32, 16_384, 8), (8, 128));
    }

    #[test]
    fn recurrent_linear_attention_budget_pressure_selects_tight_memory_profile() {
        let config = serde_json::json!({
            "architectures": ["SyntheticRecurrentStateModel"],
            "model_type": "synthetic_recurrent_state",
            "text_config": {
                "model_type": "synthetic_recurrent_state_text",
                "layer_types": ["linear_attention", "full_attention"],
                "linear_conv_kernel_dim": 4,
                "mamba_ssm_dtype": "float32",
                "linear_key_head_dim": 128,
                "linear_num_key_heads": 16,
                "linear_num_value_heads": 16,
                "linear_value_head_dim": 128
            }
        });

        let hints = model_auto_size_hints_from_config(&config);
        assert!(hints.has_recurrent_linear_attention_state);
        let class = model_auto_size_class_from_hints_and_budget(
            hints,
            Some(&budget_with_estimated_blocks(127)),
        );
        assert_eq!(class, ModelAutoSizeClass::TightRecurrentState);
        let server = model_auto_size_defaults(class, AutoSizeProfile::Server);
        assert_eq!(
            server.max_batched_tokens,
            TIGHT_RECURRENT_STATE_MAX_BATCHED_TOKENS
        );
        assert_eq!(
            server
                .max_batched_tokens
                .div_ceil(PAGED_BLOCK_SIZE as usize),
            12
        );
        assert!(
            server
                .max_batched_tokens
                .div_ceil(PAGED_BLOCK_SIZE as usize)
                <= server.kv_block_floor
        );
        assert_eq!(server.kv_block_floor, TIGHT_RECURRENT_STATE_KV_BLOCK_FLOOR);
        assert_eq!(
            select_dynamic_paged_pool_shape(
                server.max_sequences,
                server.max_sequence_tokens,
                server.kv_block_floor,
            ),
            (16, 4096)
        );

        let chat = model_auto_size_defaults(class, AutoSizeProfile::Chat);
        assert_eq!(
            chat.max_batched_tokens,
            TIGHT_RECURRENT_STATE_MAX_BATCHED_TOKENS
        );
        assert_eq!(chat.kv_block_floor, TIGHT_RECURRENT_STATE_KV_BLOCK_FLOOR);
        assert_eq!(
            select_dynamic_paged_pool_shape(
                chat.max_sequences,
                chat.max_sequence_tokens,
                chat.kv_block_floor,
            ),
            (2, 4096)
        );
    }

    #[test]
    fn recurrent_linear_attention_memory_profile_requires_budget_pressure() {
        let recurrent = serde_json::json!({
            "model_type": "synthetic_recurrent_state",
            "text_config": {
                "layer_types": ["linear_attention", "full_attention"],
                "linear_conv_kernel_dim": 4,
                "mamba_ssm_dtype": "float32",
                "linear_key_head_dim": 128,
                "linear_num_key_heads": 16,
                "linear_num_value_heads": 16,
                "linear_value_head_dim": 128
            }
        });
        let hints = model_auto_size_hints_from_config(&recurrent);
        assert_eq!(
            model_auto_size_class_from_hints_and_budget(
                hints,
                Some(&budget_with_estimated_blocks(128)),
            ),
            ModelAutoSizeClass::Generic
        );
        assert_eq!(
            model_auto_size_class_from_hints_and_budget(hints, None),
            ModelAutoSizeClass::Generic
        );

        let dense = serde_json::json!({
            "model_type": "dense",
            "text_config": {
                "layer_types": ["full_attention", "full_attention"]
            }
        });
        assert_eq!(
            model_auto_size_class_from_hints_and_budget(
                model_auto_size_hints_from_config(&dense),
                Some(&budget_with_estimated_blocks(0)),
            ),
            ModelAutoSizeClass::Generic
        );

        let generic =
            model_auto_size_defaults(ModelAutoSizeClass::Generic, AutoSizeProfile::Server);
        assert_eq!(generic.max_batched_tokens, DEFAULT_MAX_BATCHED_TOKENS);
        assert_eq!(generic.max_sequences, DEFAULT_SERVER_MAX_SEQUENCES);
        assert_eq!(generic.max_sequence_tokens, MAX_AUTOSIZED_SEQUENCE_TOKENS);
        assert_eq!(generic.kv_block_floor, 0);
    }

    #[test]
    fn autosize_dimension_lookup_falls_back_to_text_config() {
        let config = serde_json::json!({
            "model_type": "synthetic_text_wrapped_model",
            "text_config": {
                "hidden_size": 2048,
                "num_hidden_layers": 40,
                "num_attention_heads": 16,
                "num_key_value_heads": 2,
                "head_dim": 256
            }
        });

        assert_eq!(config_or_text_u64(&config, "hidden_size"), Some(2048));
        assert_eq!(config_or_text_u64(&config, "num_hidden_layers"), Some(40));
        assert_eq!(config_or_text_u64(&config, "num_key_value_heads"), Some(2));
        assert_eq!(config_or_text_u64(&config, "head_dim"), Some(256));
    }
}