ferrum_types/

auto_config.rs

//! Startup auto-configuration and selector decision trace types.
//!
//! This is the typed control-plane surface for gradually replacing M3 shell
//! env bundles with validated model/hardware/workload driven selections.

use crate::{
    parse_bool_env_value, parse_usize_env_value, RuntimeConfigEffect, RuntimeConfigEntry,
    RuntimeConfigSnapshot, RuntimeConfigSource,
};
use serde::{Deserialize, Serialize};
use std::collections::BTreeMap;
use thiserror::Error;

pub const M3_QWEN3_30B_A3B_INT4_PRESET: &str = "m3_qwen3_30b_a3b_int4";
const DEFAULT_KV_BLOCK_SIZE_TOKENS: usize = 16;
const DEFAULT_KV_BLOCKS: usize = 2048;
const GIB: u64 = 1024 * 1024 * 1024;

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub struct ModelCapabilities {
    pub architecture: String,
    pub quantization: Option<String>,
    pub moe: Option<MoeCapabilities>,
    pub max_context_len: Option<usize>,
    pub num_hidden_layers: Option<usize>,
    pub head_dim: Option<usize>,
    pub kv_heads: Option<usize>,
    pub estimated_weight_bytes: Option<u64>,
    pub supported_dtypes: Vec<String>,
    pub graph_safe_moe: bool,
}

impl ModelCapabilities {
    pub fn unknown() -> Self {
        Self {
            architecture: "unknown".to_string(),
            quantization: None,
            moe: None,
            max_context_len: None,
            num_hidden_layers: None,
            head_dim: None,
            kv_heads: None,
            estimated_weight_bytes: None,
            supported_dtypes: vec!["fp16".to_string(), "fp32".to_string()],
            graph_safe_moe: false,
        }
    }

    pub fn qwen3_30b_a3b_gptq_int4() -> Self {
        Self {
            architecture: "qwen3_moe".to_string(),
            quantization: Some("gptq_int4".to_string()),
            moe: Some(MoeCapabilities {
                num_experts: 128,
                experts_per_token: 8,
                moe_intermediate_size: Some(768),
            }),
            max_context_len: Some(40960),
            num_hidden_layers: Some(48),
            head_dim: Some(128),
            kv_heads: Some(4),
            // Conservative GPTQ int4 weight footprint including quant scales
            // and loader/runtime overhead. This keeps the RTX 4090 M3 preset
            // at the historical 2048 KV blocks while still allowing smaller
            // GPUs to be downgraded before startup allocation.
            estimated_weight_bytes: Some(18 * GIB),
            supported_dtypes: vec!["fp16".to_string()],
            graph_safe_moe: true,
        }
    }
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub struct MoeCapabilities {
    pub num_experts: usize,
    pub experts_per_token: usize,
    pub moe_intermediate_size: Option<usize>,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub struct HardwareCapabilities {
    pub backend: String,
    pub cuda_runtime: Option<String>,
    pub compute_capability: Option<String>,
    pub vram_bytes: Option<u64>,
    pub sm_count: Option<u32>,
    pub supported_dtypes: Vec<String>,
    pub supported_kv_dtypes: Vec<String>,
    pub graph_support: bool,
    pub compiled_features: CompiledKernelFeatures,
}

impl HardwareCapabilities {
    pub fn unknown() -> Self {
        Self {
            backend: "unknown".to_string(),
            cuda_runtime: None,
            compute_capability: None,
            vram_bytes: None,
            sm_count: None,
            supported_dtypes: vec!["fp16".to_string(), "fp32".to_string()],
            supported_kv_dtypes: vec!["fp16".to_string()],
            graph_support: false,
            compiled_features: CompiledKernelFeatures::default(),
        }
    }

    pub fn rtx4090_cuda(features: CompiledKernelFeatures) -> Self {
        Self {
            backend: "cuda".to_string(),
            cuda_runtime: None,
            compute_capability: Some("8.9".to_string()),
            vram_bytes: Some(24 * 1024 * 1024 * 1024),
            sm_count: Some(128),
            supported_dtypes: vec!["fp16".to_string(), "fp32".to_string()],
            supported_kv_dtypes: vec!["fp16".to_string(), "int8".to_string()],
            graph_support: true,
            compiled_features: features,
        }
    }
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub struct CompiledKernelFeatures {
    pub cuda: bool,
    pub vllm_paged_attn: bool,
    pub vllm_moe_marlin: bool,
    pub cuda_graph: bool,
    pub greedy_argmax: bool,
    pub fa2_source: bool,
    pub fa2_direct_ffi: bool,
}

impl Default for CompiledKernelFeatures {
    fn default() -> Self {
        Self {
            cuda: false,
            vllm_paged_attn: false,
            vllm_moe_marlin: false,
            cuda_graph: false,
            greedy_argmax: false,
            fa2_source: false,
            fa2_direct_ffi: false,
        }
    }
}

impl CompiledKernelFeatures {
    pub fn m3_fast_path_without_fa2() -> Self {
        Self {
            cuda: true,
            vllm_paged_attn: true,
            vllm_moe_marlin: true,
            cuda_graph: true,
            greedy_argmax: true,
            fa2_source: false,
            fa2_direct_ffi: false,
        }
    }

    pub fn m3_fast_path_with_source_fa2() -> Self {
        Self {
            fa2_source: true,
            ..Self::m3_fast_path_without_fa2()
        }
    }
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub struct WorkloadProfile {
    pub preset: Option<String>,
    pub serving_mode: String,
    pub target_concurrency: usize,
    pub prompt_length_class: String,
    pub output_length_class: String,
    pub priority: WorkloadPriority,
}

impl WorkloadProfile {
    pub fn serving_default() -> Self {
        Self {
            preset: None,
            serving_mode: "openai_chat".to_string(),
            target_concurrency: 1,
            prompt_length_class: "unknown".to_string(),
            output_length_class: "unknown".to_string(),
            priority: WorkloadPriority::Balanced,
        }
    }

    pub fn m3_qwen3_30b_a3b_int4() -> Self {
        Self {
            preset: Some(M3_QWEN3_30B_A3B_INT4_PRESET.to_string()),
            serving_mode: "bench_serve".to_string(),
            target_concurrency: 32,
            prompt_length_class: "random_256".to_string(),
            output_length_class: "random_128".to_string(),
            priority: WorkloadPriority::Throughput,
        }
    }

    fn is_m3_preset(&self) -> bool {
        self.preset.as_deref() == Some(M3_QWEN3_30B_A3B_INT4_PRESET)
    }
}

impl Default for WorkloadProfile {
    fn default() -> Self {
        Self::serving_default()
    }
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
#[serde(rename_all = "snake_case")]
pub enum WorkloadPriority {
    Latency,
    Throughput,
    Balanced,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub struct ResolvedFerrumConfig {
    pub schema_version: u32,
    pub preset: Option<String>,
    pub runtime_config: RuntimeConfigSnapshot,
    pub model_capabilities: ModelCapabilities,
    pub hardware_capabilities: HardwareCapabilities,
    pub workload_profile: WorkloadProfile,
    pub decisions: Vec<AutoConfigDecision>,
}

impl ResolvedFerrumConfig {
    pub fn effective_config_document(&self) -> serde_json::Value {
        serde_json::json!({
            "schema_version": 1,
            "preset": self.preset,
            "env_hash": self.runtime_env_hash(),
            "entries": self.runtime_config.entries,
            "model_capabilities": self.model_capabilities,
            "hardware_capabilities": self.hardware_capabilities,
            "workload_profile": self.workload_profile,
            "decisions": self.decisions,
        })
    }

    pub fn decision_trace_jsonl(&self) -> Result<String, serde_json::Error> {
        let mut out = String::new();
        for decision in &self.decisions {
            out.push_str(&serde_json::to_string(decision)?);
            out.push('\n');
        }
        Ok(out)
    }

    pub fn runtime_env_hash(&self) -> String {
        use sha2::{Digest, Sha256};

        let bytes = serde_json::to_vec(&self.runtime_config.entries).unwrap_or_default();
        let digest = Sha256::digest(bytes);
        format!("sha256:{digest:x}")
    }
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub struct AutoConfigDecision {
    pub schema_version: u32,
    pub selection: String,
    pub selected: String,
    pub source: AutoConfigSource,
    pub source_key: Option<String>,
    pub candidates: Vec<String>,
    pub rejected: Vec<RejectedCandidate>,
    pub affects: Vec<RuntimeConfigEffect>,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub struct RejectedCandidate {
    pub value: String,
    pub reason: String,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
#[serde(rename_all = "snake_case")]
pub enum AutoConfigSource {
    Default,
    Cli,
    ConfigFile,
    Env,
    ScriptCase,
    ModelMetadata,
    HardwareCapability,
    MemoryProfile,
    WorkloadPreset,
    CompiledFeature,
}

#[derive(Debug, Clone, PartialEq, Eq, Error)]
pub enum AutoConfigError {
    #[error("{key}: invalid override: {reason}")]
    InvalidOverride { key: String, reason: String },
    #[error("{selection}: unsupported combination: {reason}")]
    UnsupportedCombination { selection: String, reason: String },
}

pub struct FerrumConfigBuilder {
    runtime_config: RuntimeConfigSnapshot,
    model: ModelCapabilities,
    hardware: HardwareCapabilities,
    workload: WorkloadProfile,
}

impl FerrumConfigBuilder {
    pub fn new(runtime_config: RuntimeConfigSnapshot) -> Self {
        Self {
            runtime_config,
            model: ModelCapabilities::unknown(),
            hardware: HardwareCapabilities::unknown(),
            workload: WorkloadProfile::default(),
        }
    }

    pub fn m3_qwen3_30b_a3b_int4(runtime_config: RuntimeConfigSnapshot) -> Self {
        Self::new(runtime_config)
            .with_model_capabilities(ModelCapabilities::qwen3_30b_a3b_gptq_int4())
            .with_hardware_capabilities(HardwareCapabilities::rtx4090_cuda(
                CompiledKernelFeatures::m3_fast_path_without_fa2(),
            ))
            .with_workload_profile(WorkloadProfile::m3_qwen3_30b_a3b_int4())
    }

    pub fn with_model_capabilities(mut self, model: ModelCapabilities) -> Self {
        self.model = model;
        self
    }

    pub fn with_hardware_capabilities(mut self, hardware: HardwareCapabilities) -> Self {
        self.hardware = hardware;
        self
    }

    pub fn with_workload_profile(mut self, workload: WorkloadProfile) -> Self {
        self.workload = workload;
        self
    }

    pub fn resolve(self) -> Result<ResolvedFerrumConfig, AutoConfigError> {
        let mut decisions = Vec::new();
        let cuda_backend = self.is_cuda_backend();
        let use_vllm_paged_attn = self.bool_value(
            "FERRUM_USE_VLLM_PAGED_ATTN",
            self.workload.is_m3_preset()
                && cuda_backend
                && self.hardware.compiled_features.vllm_paged_attn,
            AutoConfigSource::WorkloadPreset,
        )?;
        let fa_layout =
            self.bool_value("FERRUM_FA_LAYOUT_VARLEN", false, AutoConfigSource::Default)?;
        let fa2_source = self.bool_value("FERRUM_FA2_SOURCE", false, AutoConfigSource::Default)?;
        let shim_present = self.raw("FERRUM_FA2_DIRECT_FFI_SHIM").is_some();
        let fa2_direct_ffi = self.bool_value(
            "FERRUM_FA2_DIRECT_FFI",
            shim_present,
            if shim_present {
                AutoConfigSource::Env
            } else {
                AutoConfigSource::Default
            },
        )?;
        let vllm_v1_short = self.bool_value(
            "FERRUM_VLLM_PAGED_ATTN_V1_SHORT",
            use_vllm_paged_attn.value,
            AutoConfigSource::Default,
        )?;
        let vllm_moe = self.bool_value(
            "FERRUM_VLLM_MOE",
            self.workload.is_m3_preset()
                && cuda_backend
                && self.hardware.compiled_features.vllm_moe_marlin,
            AutoConfigSource::WorkloadPreset,
        )?;
        let device_route = self.bool_value(
            "FERRUM_MOE_DEVICE_ROUTE",
            self.workload.is_m3_preset() && vllm_moe.value,
            AutoConfigSource::WorkloadPreset,
        )?;
        let pair_ids = self.bool_value(
            "FERRUM_VLLM_MOE_PAIR_IDS",
            vllm_moe.value,
            AutoConfigSource::WorkloadPreset,
        )?;
        let graph = self.bool_value(
            "FERRUM_MOE_GRAPH",
            self.workload.is_m3_preset()
                && vllm_moe.value
                && self.hardware.graph_support
                && self.hardware.compiled_features.cuda_graph,
            AutoConfigSource::WorkloadPreset,
        )?;
        let greedy = self.bool_value(
            "FERRUM_GREEDY_ARGMAX",
            self.workload.is_m3_preset()
                && cuda_backend
                && self.hardware.compiled_features.greedy_argmax,
            AutoConfigSource::WorkloadPreset,
        )?;
        let prefix_cache = self.bool_value(
            "FERRUM_PREFIX_CACHE",
            false,
            if self.workload.is_m3_preset() {
                AutoConfigSource::WorkloadPreset
            } else {
                AutoConfigSource::Default
            },
        )?;
        let default_max_sequences = self.default_max_sequences();
        let max_sequences = self.usize_value(
            "FERRUM_PAGED_MAX_SEQS",
            default_max_sequences.value,
            default_max_sequences.source,
        )?;
        let default_kv_blocks = self.default_kv_blocks(&max_sequences);
        let kv_blocks = self.usize_value(
            "FERRUM_KV_MAX_BLOCKS",
            default_kv_blocks.value,
            default_kv_blocks.source,
        )?;
        let default_max_batched_tokens =
            self.default_max_batched_tokens(&max_sequences, &kv_blocks);
        let max_batched_tokens = self.usize_value(
            "FERRUM_MAX_BATCHED_TOKENS",
            default_max_batched_tokens.value,
            default_max_batched_tokens.source,
        )?;
        let max_model_len = self.optional_usize_value("FERRUM_MAX_MODEL_LEN")?;

        self.validate_attention(
            use_vllm_paged_attn.value,
            fa_layout.value,
            fa2_source.value,
            fa2_direct_ffi.value,
            shim_present,
            vllm_v1_short.value,
        )?;
        self.validate_moe(
            vllm_moe.value,
            device_route.value,
            pair_ids.value,
            graph.value,
        )?;
        self.validate_memory(
            kv_blocks.value,
            max_sequences.value,
            max_batched_tokens.value,
            max_model_len.as_ref().map(|value| value.value),
        )?;
        self.validate_dtypes()?;
        self.validate_sampling(greedy.value)?;

        decisions.push(self.attention_prefill_decision(
            use_vllm_paged_attn.clone(),
            fa_layout,
            fa2_source,
            fa2_direct_ffi,
        ));
        decisions.push(self.attention_decode_decision(use_vllm_paged_attn, vllm_v1_short));
        decisions.push(self.moe_decision(vllm_moe, device_route, pair_ids));
        decisions.push(self.graph_decision(graph));
        decisions.push(self.scalar_decision(
            "kv_block_count",
            kv_blocks,
            RuntimeConfigEffect::Memory,
        ));
        decisions.push(self.scalar_decision(
            "max_sequences",
            max_sequences,
            RuntimeConfigEffect::Memory,
        ));
        decisions.push(self.scalar_decision(
            "max_batched_tokens",
            max_batched_tokens,
            RuntimeConfigEffect::Performance,
        ));
        if let Some(max_model_len) = max_model_len {
            decisions.push(self.scalar_decision(
                "max_model_len",
                max_model_len,
                RuntimeConfigEffect::Memory,
            ));
        }
        decisions.push(self.prefix_cache_decision(prefix_cache));
        decisions.push(self.scheduler_decision()?);
        decisions.push(self.sampling_decision(greedy));

        Ok(ResolvedFerrumConfig {
            schema_version: 1,
            preset: self.workload.preset.clone(),
            runtime_config: self.runtime_config.clone(),
            model_capabilities: self.model.clone(),
            hardware_capabilities: self.hardware.clone(),
            workload_profile: self.workload.clone(),
            decisions,
        })
    }

    fn entries(&self) -> BTreeMap<&str, &str> {
        self.runtime_config
            .entries
            .iter()
            .map(|entry| (entry.key.as_str(), entry.effective_value.as_str()))
            .collect()
    }

    fn raw(&self, key: &str) -> Option<&str> {
        self.entry(key).map(|entry| entry.effective_value.as_str())
    }

    fn entry(&self, key: &str) -> Option<&RuntimeConfigEntry> {
        self.runtime_config
            .entries
            .iter()
            .find(|entry| entry.key == key)
    }

    fn source_for_key(&self, key: &str, default_source: AutoConfigSource) -> AutoConfigSource {
        self.entry(key)
            .map(|entry| auto_config_source_from_runtime(entry.source))
            .unwrap_or(default_source)
    }

    fn is_cuda_backend(&self) -> bool {
        self.hardware.backend.eq_ignore_ascii_case("cuda")
    }

    fn cuda_compute_capability_at_least(&self, major: u32, minor: u32) -> Option<bool> {
        let (actual_major, actual_minor) =
            parse_compute_capability(self.hardware.compute_capability.as_deref()?)?;
        Some((actual_major, actual_minor) >= (major, minor))
    }

    fn default_max_sequences(&self) -> ResolvedValue<usize> {
        let target = self.workload.target_concurrency.max(1);
        let mut selected = target;
        if self.workload.is_m3_preset() {
            if let Some(sm_count) = self.hardware.sm_count {
                // The M3 throughput preset assumes a large GPU. On smaller
                // known GPUs, avoid auto-selecting a c32-sized admission
                // window before memory profiling has a chance to refine KV.
                selected = selected.min((sm_count as usize / 4).max(1));
            }
            if let Some(vram_bytes) = self.hardware.vram_bytes {
                selected = selected.min(vram_default_max_sequences(vram_bytes));
            }
        }
        ResolvedValue {
            value: selected.max(1),
            source: if selected < target {
                AutoConfigSource::HardwareCapability
            } else {
                AutoConfigSource::WorkloadPreset
            },
            source_key: None,
        }
    }

    fn default_max_batched_tokens(
        &self,
        max_sequences: &ResolvedValue<usize>,
        kv_blocks: &ResolvedValue<usize>,
    ) -> ResolvedValue<usize> {
        let kv_token_capacity = kv_blocks
            .value
            .saturating_mul(DEFAULT_KV_BLOCK_SIZE_TOKENS)
            .max(max_sequences.value.max(1));
        let value = max_sequences
            .value
            .max(1)
            .saturating_mul(64)
            .min(kv_token_capacity)
            .max(max_sequences.value.max(1));
        ResolvedValue {
            value,
            source: if max_sequences.source == AutoConfigSource::HardwareCapability
                || kv_blocks.source == AutoConfigSource::HardwareCapability
            {
                AutoConfigSource::HardwareCapability
            } else {
                AutoConfigSource::WorkloadPreset
            },
            source_key: None,
        }
    }

    fn default_kv_blocks(&self, max_sequences: &ResolvedValue<usize>) -> ResolvedValue<usize> {
        let min_blocks = ceil_div(max_sequences.value.max(1), DEFAULT_KV_BLOCK_SIZE_TOKENS);
        let target = DEFAULT_KV_BLOCKS.max(min_blocks);
        let selected = match (
            self.hardware.vram_bytes,
            self.model.estimated_weight_bytes,
            self.kv_cache_bytes_per_token(),
        ) {
            (Some(vram_bytes), Some(weight_bytes), Some(kv_bytes_per_token))
                if kv_bytes_per_token > 0 =>
            {
                let headroom = (vram_bytes / 10).max(2 * GIB);
                let available = vram_bytes.saturating_sub(weight_bytes.saturating_add(headroom));
                let kv_token_budget = (available / kv_bytes_per_token) as usize;
                let block_budget = kv_token_budget / DEFAULT_KV_BLOCK_SIZE_TOKENS;
                target.min(block_budget.max(min_blocks))
            }
            _ => target,
        };
        ResolvedValue {
            value: selected.max(1),
            source: if selected < target {
                AutoConfigSource::HardwareCapability
            } else {
                AutoConfigSource::WorkloadPreset
            },
            source_key: None,
        }
    }

    fn kv_cache_bytes_per_token(&self) -> Option<u64> {
        let layers = self.model.num_hidden_layers? as u64;
        let kv_heads = self.model.kv_heads? as u64;
        let head_dim = self.model.head_dim? as u64;
        layers
            .checked_mul(2)?
            .checked_mul(kv_heads)?
            .checked_mul(head_dim)?
            .checked_mul(2)
    }

    fn bool_value(
        &self,
        key: &str,
        default: bool,
        default_source: AutoConfigSource,
    ) -> Result<ResolvedValue<bool>, AutoConfigError> {
        match self.entry(key) {
            Some(entry) => Ok(ResolvedValue {
                value: parse_bool_env_value(&entry.effective_value).map_err(|reason| {
                    AutoConfigError::InvalidOverride {
                        key: key.to_string(),
                        reason,
                    }
                })?,
                source: auto_config_source_from_runtime(entry.source),
                source_key: Some(key.to_string()),
            }),
            None => Ok(ResolvedValue {
                value: default,
                source: default_source,
                source_key: None,
            }),
        }
    }

    fn usize_value(
        &self,
        key: &str,
        default: usize,
        default_source: AutoConfigSource,
    ) -> Result<ResolvedValue<usize>, AutoConfigError> {
        match self.entry(key) {
            Some(entry) => Ok(ResolvedValue {
                value: parse_usize_env_value(&entry.effective_value).map_err(|reason| {
                    AutoConfigError::InvalidOverride {
                        key: key.to_string(),
                        reason,
                    }
                })?,
                source: auto_config_source_from_runtime(entry.source),
                source_key: Some(key.to_string()),
            }),
            None => Ok(ResolvedValue {
                value: default,
                source: default_source,
                source_key: None,
            }),
        }
    }

    fn optional_usize_value(
        &self,
        key: &str,
    ) -> Result<Option<ResolvedValue<usize>>, AutoConfigError> {
        match self.entry(key) {
            Some(entry) => Ok(Some(ResolvedValue {
                value: parse_usize_env_value(&entry.effective_value).map_err(|reason| {
                    AutoConfigError::InvalidOverride {
                        key: key.to_string(),
                        reason,
                    }
                })?,
                source: auto_config_source_from_runtime(entry.source),
                source_key: Some(key.to_string()),
            })),
            None => Ok(None),
        }
    }

    fn validate_attention(
        &self,
        use_vllm_paged_attn: bool,
        fa_layout: bool,
        fa2_source: bool,
        fa2_direct_ffi: bool,
        shim_present: bool,
        vllm_v1_short: bool,
    ) -> Result<(), AutoConfigError> {
        if use_vllm_paged_attn && !self.hardware.compiled_features.vllm_paged_attn {
            return self.invalid(
                "FERRUM_USE_VLLM_PAGED_ATTN",
                "vLLM paged attention is not compiled",
            );
        }
        if use_vllm_paged_attn && !self.is_cuda_backend() {
            return self.invalid(
                "FERRUM_USE_VLLM_PAGED_ATTN",
                "vLLM paged attention requires CUDA backend",
            );
        }
        if fa_layout && !use_vllm_paged_attn {
            return self.invalid(
                "FERRUM_FA_LAYOUT_VARLEN",
                "FA layout requires vLLM paged attention layout",
            );
        }
        if fa2_source && !self.hardware.compiled_features.fa2_source {
            return self.invalid(
                "FERRUM_FA2_SOURCE",
                "source-built FA2 support is not compiled",
            );
        }
        if fa2_source && !self.is_cuda_backend() {
            return self.invalid(
                "FERRUM_FA2_SOURCE",
                "source-built FA2 requires CUDA backend",
            );
        }
        if fa2_source && self.cuda_compute_capability_at_least(8, 0) == Some(false) {
            return self.invalid(
                "FERRUM_FA2_SOURCE",
                "source-built FA2 requires CUDA compute capability >= 8.0",
            );
        }
        if fa2_direct_ffi && !self.hardware.compiled_features.fa2_direct_ffi {
            return self.invalid(
                "FERRUM_FA2_DIRECT_FFI",
                "direct FA2 FFI shim support is not compiled",
            );
        }
        if fa2_direct_ffi && !self.is_cuda_backend() {
            return self.invalid(
                "FERRUM_FA2_DIRECT_FFI",
                "direct FA2 FFI shim requires CUDA backend",
            );
        }
        if fa2_direct_ffi && self.cuda_compute_capability_at_least(8, 0) == Some(false) {
            return self.invalid(
                "FERRUM_FA2_DIRECT_FFI",
                "direct FA2 FFI shim requires CUDA compute capability >= 8.0",
            );
        }
        if fa2_direct_ffi && !shim_present {
            return self.invalid(
                "FERRUM_FA2_DIRECT_FFI",
                "requires FERRUM_FA2_DIRECT_FFI_SHIM",
            );
        }
        if fa2_source && fa2_direct_ffi {
            return self.unsupported(
                "attention_prefill_mixed_backend",
                "FA2 source and direct FFI shim cannot both own the prefill path",
            );
        }
        if vllm_v1_short && !use_vllm_paged_attn {
            return self.invalid(
                "FERRUM_VLLM_PAGED_ATTN_V1_SHORT",
                "short-context v1 requires vLLM paged attention",
            );
        }
        Ok(())
    }

    fn validate_moe(
        &self,
        vllm_moe: bool,
        device_route: bool,
        pair_ids: bool,
        graph: bool,
    ) -> Result<(), AutoConfigError> {
        if vllm_moe && !self.hardware.compiled_features.vllm_moe_marlin {
            return self.invalid("FERRUM_VLLM_MOE", "vLLM Marlin MoE is not compiled");
        }
        if vllm_moe && !self.is_cuda_backend() {
            return self.invalid("FERRUM_VLLM_MOE", "vLLM Marlin MoE requires CUDA backend");
        }
        if device_route && !vllm_moe {
            return self.invalid(
                "FERRUM_MOE_DEVICE_ROUTE",
                "device route currently requires vLLM MoE",
            );
        }
        if pair_ids && !vllm_moe {
            return self.invalid(
                "FERRUM_VLLM_MOE_PAIR_IDS",
                "pair-id routing requires vLLM MoE",
            );
        }
        let graph_relevant = self.model.moe.is_some() || self.workload.is_m3_preset();
        if graph && graph_relevant && !self.hardware.graph_support {
            return self.invalid(
                "FERRUM_MOE_GRAPH",
                "hardware/backend does not support CUDA graph replay",
            );
        }
        if graph && graph_relevant && !self.hardware.compiled_features.cuda_graph {
            return self.invalid("FERRUM_MOE_GRAPH", "CUDA graph support is not compiled");
        }
        if graph && graph_relevant && !vllm_moe {
            return self.invalid(
                "FERRUM_MOE_GRAPH",
                "graph decode requires the graph-clean vLLM MoE path",
            );
        }
        if graph && graph_relevant && self.model.moe.is_some() && !self.model.graph_safe_moe {
            return self.unsupported(
                "moe_graph_policy",
                "model MoE path is not marked graph-safe",
            );
        }
        Ok(())
    }

    fn validate_sampling(&self, greedy: bool) -> Result<(), AutoConfigError> {
        if greedy && !self.hardware.compiled_features.greedy_argmax {
            return self.invalid("FERRUM_GREEDY_ARGMAX", "GPU argmax is not compiled");
        }
        if greedy
            && !(self.is_cuda_backend() || self.hardware.backend.eq_ignore_ascii_case("metal"))
        {
            return self.invalid(
                "FERRUM_GREEDY_ARGMAX",
                "greedy argmax requires CUDA or Metal backend",
            );
        }
        Ok(())
    }

    fn validate_memory(
        &self,
        kv_blocks: usize,
        max_sequences: usize,
        max_batched_tokens: usize,
        requested_max_model_len: Option<usize>,
    ) -> Result<(), AutoConfigError> {
        if kv_blocks == 0 {
            return self.invalid("FERRUM_KV_MAX_BLOCKS", "must be greater than zero");
        }
        if max_sequences == 0 {
            return self.invalid("FERRUM_PAGED_MAX_SEQS", "must be greater than zero");
        }
        if max_batched_tokens < max_sequences {
            return self.invalid(
                "FERRUM_MAX_BATCHED_TOKENS",
                "must be at least FERRUM_PAGED_MAX_SEQS",
            );
        }
        let kv_token_capacity = kv_blocks.saturating_mul(DEFAULT_KV_BLOCK_SIZE_TOKENS);
        if max_batched_tokens > kv_token_capacity {
            return self.invalid(
                "FERRUM_MAX_BATCHED_TOKENS",
                "exceeds KV cache token capacity",
            );
        }
        if let Some(max_model_len) = requested_max_model_len {
            if max_model_len == 0 {
                return self.invalid("FERRUM_MAX_MODEL_LEN", "must be greater than zero");
            }
            if let Some(model_max) = self.model.max_context_len {
                if max_model_len > model_max {
                    return self.invalid(
                        "FERRUM_MAX_MODEL_LEN",
                        "exceeds model metadata max context length",
                    );
                }
            }
            if max_model_len > kv_token_capacity {
                return self.invalid(
                    "FERRUM_KV_MAX_BLOCKS",
                    "KV cache token capacity is smaller than FERRUM_MAX_MODEL_LEN",
                );
            }
        }
        Ok(())
    }

    fn validate_dtypes(&self) -> Result<(), AutoConfigError> {
        if let Some(dtype) = self.raw("FERRUM_DTYPE") {
            let dtype = dtype.to_ascii_lowercase();
            if !self.hardware.supported_dtypes.iter().any(|d| d == &dtype) {
                return self.invalid("FERRUM_DTYPE", "dtype is not supported by hardware profile");
            }
        }
        if let Some(dtype) = self.raw("FERRUM_KV_DTYPE") {
            let dtype = dtype.to_ascii_lowercase();
            if !self
                .hardware
                .supported_kv_dtypes
                .iter()
                .any(|d| d == &dtype)
            {
                return self.invalid(
                    "FERRUM_KV_DTYPE",
                    "KV dtype is not supported by hardware profile",
                );
            }
        }
        Ok(())
    }

    fn attention_prefill_decision(
        &self,
        use_vllm_paged_attn: ResolvedValue<bool>,
        fa_layout: ResolvedValue<bool>,
        fa2_source: ResolvedValue<bool>,
        fa2_direct_ffi: ResolvedValue<bool>,
    ) -> AutoConfigDecision {
        let (selected, source, source_key) = if fa2_source.value {
            ("fa2_source", fa2_source.source, fa2_source.source_key)
        } else if fa2_direct_ffi.value {
            (
                "fa2_direct_ffi",
                fa2_direct_ffi.source,
                fa2_direct_ffi.source_key,
            )
        } else if fa_layout.value {
            ("fa_layout_varlen", fa_layout.source, fa_layout.source_key)
        } else if use_vllm_paged_attn.value {
            (
                "vllm_paged_varlen",
                use_vllm_paged_attn.source,
                use_vllm_paged_attn.source_key,
            )
        } else {
            ("legacy_paged_varlen", AutoConfigSource::Default, None)
        };
        self.decision(
            "attention_prefill_mixed_backend",
            selected,
            source,
            source_key,
            [
                "fa2_source",
                "fa2_direct_ffi",
                "fa_layout_varlen",
                "vllm_paged_varlen",
                "legacy_paged_varlen",
            ],
            self.rejected_except(
                selected,
                [
                    ("fa2_source", "source-built FA2 not selected"),
                    ("fa2_direct_ffi", "diagnostic direct FFI shim not selected"),
                    ("fa_layout_varlen", "FA-compatible layout not selected"),
                    ("vllm_paged_varlen", "vLLM paged varlen bridge not selected"),
                    (
                        "legacy_paged_varlen",
                        "a higher-priority attention path was selected",
                    ),
                ],
            ),
            vec![
                RuntimeConfigEffect::Performance,
                RuntimeConfigEffect::Memory,
            ],
        )
    }

    fn attention_decode_decision(
        &self,
        use_vllm_paged_attn: ResolvedValue<bool>,
        vllm_v1_short: ResolvedValue<bool>,
    ) -> AutoConfigDecision {
        let (selected, source, source_key) = if use_vllm_paged_attn.value {
            if vllm_v1_short.value {
                (
                    "vllm_paged_attn_v1_short",
                    vllm_v1_short.source,
                    vllm_v1_short.source_key,
                )
            } else {
                (
                    "vllm_paged_attn_v2",
                    vllm_v1_short.source,
                    vllm_v1_short.source_key,
                )
            }
        } else {
            ("legacy_paged_decode", use_vllm_paged_attn.source, None)
        };
        self.decision(
            "attention_decode_backend",
            selected,
            source,
            source_key,
            [
                "vllm_paged_attn_v1_short",
                "vllm_paged_attn_v2",
                "legacy_paged_decode",
            ],
            self.rejected_except(
                selected,
                [
                    (
                        "vllm_paged_attn_v1_short",
                        "short-context v1 decode not selected",
                    ),
                    ("vllm_paged_attn_v2", "v2 decode not selected"),
                    ("legacy_paged_decode", "legacy decode not selected"),
                ],
            ),
            vec![RuntimeConfigEffect::Performance],
        )
    }

    fn moe_decision(
        &self,
        vllm_moe: ResolvedValue<bool>,
        device_route: ResolvedValue<bool>,
        pair_ids: ResolvedValue<bool>,
    ) -> AutoConfigDecision {
        let selected = if vllm_moe.value && device_route.value && pair_ids.value {
            "vllm_marlin_moe_device_route_pair_ids"
        } else if vllm_moe.value && device_route.value {
            "vllm_marlin_moe_device_route"
        } else if vllm_moe.value {
            "vllm_marlin_moe"
        } else {
            "legacy_moe"
        };
        self.decision(
            "moe_implementation",
            selected,
            vllm_moe.source,
            vllm_moe.source_key,
            [
                "vllm_marlin_moe_device_route_pair_ids",
                "vllm_marlin_moe_device_route",
                "vllm_marlin_moe",
                "legacy_moe",
            ],
            self.rejected_except(
                selected,
                [
                    (
                        "vllm_marlin_moe_device_route_pair_ids",
                        "pair-id device route not selected",
                    ),
                    (
                        "vllm_marlin_moe_device_route",
                        "device-route MoE not selected",
                    ),
                    ("vllm_marlin_moe", "vLLM Marlin MoE not selected"),
                    ("legacy_moe", "legacy MoE not selected"),
                ],
            ),
            vec![RuntimeConfigEffect::Performance],
        )
    }

    fn graph_decision(&self, graph: ResolvedValue<bool>) -> AutoConfigDecision {
        let selected = if graph.value {
            "graph_clean_decode"
        } else {
            "graph_disabled"
        };
        self.decision(
            "moe_graph_policy",
            selected,
            graph.source,
            graph.source_key,
            ["graph_clean_decode", "graph_disabled"],
            self.rejected_except(
                selected,
                [
                    ("graph_clean_decode", "graph decode not selected"),
                    ("graph_disabled", "graph decode selected"),
                ],
            ),
            vec![
                RuntimeConfigEffect::Performance,
                RuntimeConfigEffect::Correctness,
            ],
        )
    }

    fn scalar_decision(
        &self,
        selection: &str,
        value: ResolvedValue<usize>,
        effect: RuntimeConfigEffect,
    ) -> AutoConfigDecision {
        self.decision(
            selection,
            &value.value.to_string(),
            value.source,
            value.source_key,
            [value.value.to_string()],
            Vec::new(),
            vec![effect],
        )
    }

    fn scheduler_decision(&self) -> Result<AutoConfigDecision, AutoConfigError> {
        let entries = self.entries();
        let mut selected = "continuous_default".to_string();
        let mut source_key = None;
        if let Some(chunk) = entries.get("FERRUM_ACTIVE_DECODE_PREFILL_CHUNK") {
            parse_usize_env_value(chunk).map_err(|reason| AutoConfigError::InvalidOverride {
                key: "FERRUM_ACTIVE_DECODE_PREFILL_CHUNK".to_string(),
                reason,
            })?;
            selected = format!("active_decode_prefill_chunk:{chunk}");
            source_key = Some("FERRUM_ACTIVE_DECODE_PREFILL_CHUNK".to_string());
        } else if let Some(until) = entries.get("FERRUM_SCHED_PREFILL_FIRST_UNTIL_ACTIVE") {
            parse_usize_env_value(until).map_err(|reason| AutoConfigError::InvalidOverride {
                key: "FERRUM_SCHED_PREFILL_FIRST_UNTIL_ACTIVE".to_string(),
                reason,
            })?;
            selected = format!("prefill_first_until_active:{until}");
            source_key = Some("FERRUM_SCHED_PREFILL_FIRST_UNTIL_ACTIVE".to_string());
        } else if self
            .bool_value(
                "FERRUM_SCHED_PROMPT_TOKEN_ESTIMATE",
                false,
                AutoConfigSource::Default,
            )?
            .value
        {
            selected = "prompt_token_estimate".to_string();
            source_key = Some("FERRUM_SCHED_PROMPT_TOKEN_ESTIMATE".to_string());
        }
        self.unsupported_if(
            source_key.as_deref() == Some("FERRUM_ACTIVE_DECODE_PREFILL_CHUNK")
                && selected.ends_with(":0"),
            "scheduler_admission_policy",
            "active decode prefill chunk must be greater than zero",
        )?;
        Ok(self.decision(
            "scheduler_admission_policy",
            &selected,
            source_key
                .as_deref()
                .map(|key| self.source_for_key(key, AutoConfigSource::Default))
                .unwrap_or(AutoConfigSource::Default),
            source_key,
            [
                "continuous_default",
                "prompt_token_estimate",
                "prefill_first_until_active",
                "active_decode_prefill_chunk",
            ],
            Vec::new(),
            vec![RuntimeConfigEffect::Performance],
        ))
    }

    fn prefix_cache_decision(&self, prefix_cache: ResolvedValue<bool>) -> AutoConfigDecision {
        let selected = if prefix_cache.value {
            "prefix_cache_enabled"
        } else {
            "prefix_cache_disabled"
        };
        self.decision(
            "prefix_cache_policy",
            selected,
            prefix_cache.source,
            prefix_cache.source_key,
            ["prefix_cache_enabled", "prefix_cache_disabled"],
            self.rejected_except(
                selected,
                [
                    ("prefix_cache_enabled", "prefix cache not selected"),
                    ("prefix_cache_disabled", "prefix cache enabled"),
                ],
            ),
            vec![
                RuntimeConfigEffect::Correctness,
                RuntimeConfigEffect::Performance,
                RuntimeConfigEffect::Memory,
            ],
        )
    }

    fn sampling_decision(&self, greedy: ResolvedValue<bool>) -> AutoConfigDecision {
        let selected = if greedy.value {
            "gpu_greedy_argmax"
        } else {
            "logits_readback"
        };
        self.decision(
            "sampling_readback_path",
            selected,
            greedy.source,
            greedy.source_key,
            ["gpu_greedy_argmax", "logits_readback"],
            self.rejected_except(
                selected,
                [
                    ("gpu_greedy_argmax", "GPU argmax not selected"),
                    ("logits_readback", "logits readback not selected"),
                ],
            ),
            vec![
                RuntimeConfigEffect::Performance,
                RuntimeConfigEffect::Correctness,
            ],
        )
    }

    fn decision<I, C>(
        &self,
        selection: &str,
        selected: &str,
        source: AutoConfigSource,
        source_key: Option<String>,
        candidates: I,
        rejected: Vec<RejectedCandidate>,
        affects: Vec<RuntimeConfigEffect>,
    ) -> AutoConfigDecision
    where
        I: IntoIterator<Item = C>,
        C: Into<String>,
    {
        AutoConfigDecision {
            schema_version: 1,
            selection: selection.to_string(),
            selected: selected.to_string(),
            source,
            source_key,
            candidates: candidates.into_iter().map(Into::into).collect(),
            rejected,
            affects,
        }
    }

    fn rejected_except<I>(&self, selected: &str, candidates: I) -> Vec<RejectedCandidate>
    where
        I: IntoIterator<Item = (&'static str, &'static str)>,
    {
        candidates
            .into_iter()
            .filter(|(value, _)| *value != selected)
            .map(|(value, reason)| RejectedCandidate {
                value: value.to_string(),
                reason: reason.to_string(),
            })
            .collect()
    }

    fn invalid<T>(&self, key: &str, reason: &str) -> Result<T, AutoConfigError> {
        Err(AutoConfigError::InvalidOverride {
            key: key.to_string(),
            reason: reason.to_string(),
        })
    }

    fn unsupported<T>(&self, selection: &str, reason: &str) -> Result<T, AutoConfigError> {
        Err(AutoConfigError::UnsupportedCombination {
            selection: selection.to_string(),
            reason: reason.to_string(),
        })
    }

    fn unsupported_if(
        &self,
        condition: bool,
        selection: &str,
        reason: &str,
    ) -> Result<(), AutoConfigError> {
        if condition {
            self.unsupported(selection, reason)
        } else {
            Ok(())
        }
    }
}

#[derive(Debug, Clone, PartialEq, Eq)]
struct ResolvedValue<T> {
    value: T,
    source: AutoConfigSource,
    source_key: Option<String>,
}

fn parse_compute_capability(value: &str) -> Option<(u32, u32)> {
    let value = value.trim();
    if value.is_empty() {
        return None;
    }
    let (major, minor) = value.split_once('.').unwrap_or((value, "0"));
    Some((major.trim().parse().ok()?, minor.trim().parse().ok()?))
}

fn vram_default_max_sequences(vram_bytes: u64) -> usize {
    match vram_bytes {
        bytes if bytes >= 20 * GIB => 32,
        bytes if bytes >= 12 * GIB => 16,
        bytes if bytes >= 8 * GIB => 8,
        _ => 4,
    }
}

fn ceil_div(value: usize, divisor: usize) -> usize {
    value.div_ceil(divisor)
}

fn auto_config_source_from_runtime(source: RuntimeConfigSource) -> AutoConfigSource {
    match source {
        RuntimeConfigSource::Default => AutoConfigSource::Default,
        RuntimeConfigSource::ConfigFile => AutoConfigSource::ConfigFile,
        RuntimeConfigSource::Cli => AutoConfigSource::Cli,
        RuntimeConfigSource::Env => AutoConfigSource::Env,
        RuntimeConfigSource::ScriptCase => AutoConfigSource::ScriptCase,
        RuntimeConfigSource::MemoryProfile => AutoConfigSource::MemoryProfile,
    }
}

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

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

    fn snapshot_with_sources(vars: &[(&str, &str, RuntimeConfigSource)]) -> RuntimeConfigSnapshot {
        let mut entries: Vec<_> = vars
            .iter()
            .map(|(key, effective_value, source)| RuntimeConfigEntry {
                key: (*key).to_string(),
                effective_value: (*effective_value).to_string(),
                source: *source,
                affects: vec![RuntimeConfigEffect::Performance],
            })
            .collect();
        entries.sort_by(|a, b| a.key.cmp(&b.key));
        RuntimeConfigSnapshot { entries }
    }

    fn m3(vars: &[(&str, &str)], features: CompiledKernelFeatures) -> FerrumConfigBuilder {
        FerrumConfigBuilder::new(snapshot(vars))
            .with_model_capabilities(ModelCapabilities::qwen3_30b_a3b_gptq_int4())
            .with_hardware_capabilities(HardwareCapabilities::rtx4090_cuda(features))
            .with_workload_profile(WorkloadProfile::m3_qwen3_30b_a3b_int4())
    }

    fn m3_with_hardware(
        vars: &[(&str, &str)],
        hardware: HardwareCapabilities,
    ) -> FerrumConfigBuilder {
        FerrumConfigBuilder::new(snapshot(vars))
            .with_model_capabilities(ModelCapabilities::qwen3_30b_a3b_gptq_int4())
            .with_hardware_capabilities(hardware)
            .with_workload_profile(WorkloadProfile::m3_qwen3_30b_a3b_int4())
    }

    fn expect_invalid_key(vars: &[(&str, &str)], key: &str) {
        expect_invalid_key_with_features(
            vars,
            key,
            CompiledKernelFeatures::m3_fast_path_without_fa2(),
        );
    }

    fn expect_invalid_key_with_features(
        vars: &[(&str, &str)],
        key: &str,
        features: CompiledKernelFeatures,
    ) {
        expect_invalid_key_with_hardware(vars, key, HardwareCapabilities::rtx4090_cuda(features));
    }

    fn expect_invalid_key_with_hardware(
        vars: &[(&str, &str)],
        key: &str,
        hardware: HardwareCapabilities,
    ) {
        let err = m3_with_hardware(vars, hardware)
            .resolve()
            .expect_err("override should fail");
        match err {
            AutoConfigError::InvalidOverride { key: actual, .. } => assert_eq!(actual, key),
            other => panic!("expected invalid override for {key}, got {other:?}"),
        }
    }

    fn cpu_hardware_with_features(features: CompiledKernelFeatures) -> HardwareCapabilities {
        HardwareCapabilities {
            backend: "cpu".to_string(),
            supported_dtypes: vec!["fp32".to_string()],
            supported_kv_dtypes: vec!["fp16".to_string()],
            compiled_features: features,
            ..HardwareCapabilities::unknown()
        }
    }

    #[test]
    fn m3_preset_selects_current_safe_fast_path_without_fa2() {
        let resolved = m3(&[], CompiledKernelFeatures::m3_fast_path_without_fa2())
            .resolve()
            .unwrap();
        let decisions: BTreeMap<_, _> = resolved
            .decisions
            .iter()
            .map(|decision| (decision.selection.as_str(), decision.selected.as_str()))
            .collect();
        assert_eq!(
            decisions["attention_prefill_mixed_backend"],
            "vllm_paged_varlen"
        );
        assert_eq!(
            decisions["attention_decode_backend"],
            "vllm_paged_attn_v1_short"
        );
        assert_eq!(
            decisions["moe_implementation"],
            "vllm_marlin_moe_device_route_pair_ids"
        );
        assert_eq!(decisions["moe_graph_policy"], "graph_clean_decode");
        assert_eq!(decisions["prefix_cache_policy"], "prefix_cache_disabled");
        assert_eq!(decisions["sampling_readback_path"], "gpu_greedy_argmax");
        assert_eq!(
            resolved.preset.as_deref(),
            Some(M3_QWEN3_30B_A3B_INT4_PRESET)
        );
    }

    #[test]
    fn source_fa2_selects_only_when_compiled() {
        let resolved = m3(
            &[("FERRUM_FA2_SOURCE", "1")],
            CompiledKernelFeatures::m3_fast_path_with_source_fa2(),
        )
        .resolve()
        .unwrap();
        let prefill = resolved
            .decisions
            .iter()
            .find(|decision| decision.selection == "attention_prefill_mixed_backend")
            .unwrap();
        assert_eq!(prefill.selected, "fa2_source");
        expect_invalid_key(&[("FERRUM_FA2_SOURCE", "1")], "FERRUM_FA2_SOURCE");
    }

    #[test]
    fn hardware_capabilities_keep_m3_preset_on_compatible_backend_paths() {
        let resolved = m3_with_hardware(
            &[],
            cpu_hardware_with_features(CompiledKernelFeatures::m3_fast_path_with_source_fa2()),
        )
        .resolve()
        .unwrap();
        let decisions: BTreeMap<_, _> = resolved
            .decisions
            .iter()
            .map(|decision| (decision.selection.as_str(), decision.selected.as_str()))
            .collect();

        assert_eq!(
            decisions["attention_prefill_mixed_backend"],
            "legacy_paged_varlen"
        );
        assert_eq!(decisions["attention_decode_backend"], "legacy_paged_decode");
        assert_eq!(decisions["moe_implementation"], "legacy_moe");
        assert_eq!(decisions["moe_graph_policy"], "graph_disabled");
        assert_eq!(decisions["sampling_readback_path"], "logits_readback");
    }

    #[test]
    fn hardware_incompatible_attention_and_sampling_overrides_are_rejected() {
        let cpu =
            cpu_hardware_with_features(CompiledKernelFeatures::m3_fast_path_with_source_fa2());
        expect_invalid_key_with_hardware(
            &[("FERRUM_USE_VLLM_PAGED_ATTN", "1")],
            "FERRUM_USE_VLLM_PAGED_ATTN",
            cpu.clone(),
        );
        expect_invalid_key_with_hardware(
            &[("FERRUM_VLLM_MOE", "1")],
            "FERRUM_VLLM_MOE",
            cpu.clone(),
        );
        expect_invalid_key_with_hardware(
            &[("FERRUM_GREEDY_ARGMAX", "1")],
            "FERRUM_GREEDY_ARGMAX",
            cpu.clone(),
        );
        expect_invalid_key_with_hardware(&[("FERRUM_FA2_SOURCE", "1")], "FERRUM_FA2_SOURCE", cpu);

        let mut old_cuda = HardwareCapabilities::rtx4090_cuda(
            CompiledKernelFeatures::m3_fast_path_with_source_fa2(),
        );
        old_cuda.compute_capability = Some("7.5".to_string());
        expect_invalid_key_with_hardware(
            &[("FERRUM_FA2_SOURCE", "1")],
            "FERRUM_FA2_SOURCE",
            old_cuda,
        );
    }

    #[test]
    fn hardware_capacity_sizes_default_sequence_budget_without_overriding_user_values() {
        let mut small_gpu =
            HardwareCapabilities::rtx4090_cuda(CompiledKernelFeatures::m3_fast_path_without_fa2());
        small_gpu.sm_count = Some(16);
        small_gpu.vram_bytes = Some(24 * 1024 * 1024 * 1024);

        let resolved = m3_with_hardware(&[], small_gpu.clone()).resolve().unwrap();
        let decision = |selection: &str| {
            resolved
                .decisions
                .iter()
                .find(|decision| decision.selection == selection)
                .unwrap()
        };
        let max_sequences = decision("max_sequences");
        assert_eq!(max_sequences.selected, "4");
        assert_eq!(max_sequences.source, AutoConfigSource::HardwareCapability);
        let max_batched_tokens = decision("max_batched_tokens");
        assert_eq!(max_batched_tokens.selected, "256");
        assert_eq!(
            max_batched_tokens.source,
            AutoConfigSource::HardwareCapability
        );

        let resolved = m3_with_hardware(&[("FERRUM_PAGED_MAX_SEQS", "16")], small_gpu)
            .resolve()
            .unwrap();
        let max_sequences = resolved
            .decisions
            .iter()
            .find(|decision| decision.selection == "max_sequences")
            .unwrap();
        assert_eq!(max_sequences.selected, "16");
        assert_eq!(max_sequences.source, AutoConfigSource::Env);
        assert_eq!(
            max_sequences.source_key.as_deref(),
            Some("FERRUM_PAGED_MAX_SEQS")
        );
    }

    #[test]
    fn vram_capacity_caps_m3_default_sequence_budget() {
        let mut low_vram_gpu =
            HardwareCapabilities::rtx4090_cuda(CompiledKernelFeatures::m3_fast_path_without_fa2());
        low_vram_gpu.sm_count = Some(128);
        low_vram_gpu.vram_bytes = Some(7 * 1024 * 1024 * 1024);

        let resolved = m3_with_hardware(&[], low_vram_gpu).resolve().unwrap();
        let max_sequences = resolved
            .decisions
            .iter()
            .find(|decision| decision.selection == "max_sequences")
            .unwrap();
        assert_eq!(max_sequences.selected, "4");
        assert_eq!(max_sequences.source, AutoConfigSource::HardwareCapability);
    }

    #[test]
    fn memory_budget_keeps_rtx4090_m3_kv_blocks_but_caps_constrained_vram() {
        let resolved = m3(&[], CompiledKernelFeatures::m3_fast_path_without_fa2())
            .resolve()
            .unwrap();
        let decision = |selection: &str| {
            resolved
                .decisions
                .iter()
                .find(|decision| decision.selection == selection)
                .unwrap()
        };
        assert_eq!(decision("kv_block_count").selected, "2048");
        assert_eq!(
            decision("kv_block_count").source,
            AutoConfigSource::WorkloadPreset
        );

        let mut constrained =
            HardwareCapabilities::rtx4090_cuda(CompiledKernelFeatures::m3_fast_path_without_fa2());
        constrained.vram_bytes = Some(20 * 1024 * 1024 * 1024);
        let resolved = m3_with_hardware(&[], constrained).resolve().unwrap();
        let decision = |selection: &str| {
            resolved
                .decisions
                .iter()
                .find(|decision| decision.selection == selection)
                .unwrap()
        };
        assert_eq!(decision("kv_block_count").selected, "2");
        assert_eq!(
            decision("kv_block_count").source,
            AutoConfigSource::HardwareCapability
        );
        assert_eq!(decision("max_batched_tokens").selected, "32");
        assert_eq!(
            decision("max_batched_tokens").source,
            AutoConfigSource::HardwareCapability
        );
    }

    #[test]
    fn compute_capability_parser_accepts_major_minor_and_major_only() {
        assert_eq!(parse_compute_capability("8.9"), Some((8, 9)));
        assert_eq!(parse_compute_capability("9"), Some((9, 0)));
        assert_eq!(parse_compute_capability("N/A"), None);
    }

    #[test]
    fn vram_capacity_tiers_are_monotonic() {
        assert_eq!(vram_default_max_sequences(24 * 1024 * 1024 * 1024), 32);
        assert_eq!(vram_default_max_sequences(16 * 1024 * 1024 * 1024), 16);
        assert_eq!(vram_default_max_sequences(8 * 1024 * 1024 * 1024), 8);
        assert_eq!(vram_default_max_sequences(6 * 1024 * 1024 * 1024), 4);
    }

    #[test]
    fn validates_invalid_override_matrix() {
        expect_invalid_key(
            &[("FERRUM_USE_VLLM_PAGED_ATTN", "maybe")],
            "FERRUM_USE_VLLM_PAGED_ATTN",
        );
        expect_invalid_key(&[("FERRUM_PREFIX_CACHE", "maybe")], "FERRUM_PREFIX_CACHE");
        expect_invalid_key(
            &[
                ("FERRUM_FA_LAYOUT_VARLEN", "1"),
                ("FERRUM_USE_VLLM_PAGED_ATTN", "0"),
            ],
            "FERRUM_FA_LAYOUT_VARLEN",
        );
        expect_invalid_key(&[("FERRUM_FA2_DIRECT_FFI", "1")], "FERRUM_FA2_DIRECT_FFI");
        expect_invalid_key_with_features(
            &[("FERRUM_VLLM_MOE", "1")],
            "FERRUM_VLLM_MOE",
            CompiledKernelFeatures::default(),
        );
        expect_invalid_key(
            &[("FERRUM_MOE_DEVICE_ROUTE", "1"), ("FERRUM_VLLM_MOE", "0")],
            "FERRUM_MOE_DEVICE_ROUTE",
        );
        expect_invalid_key(
            &[("FERRUM_VLLM_MOE_PAIR_IDS", "1"), ("FERRUM_VLLM_MOE", "0")],
            "FERRUM_VLLM_MOE_PAIR_IDS",
        );
        expect_invalid_key(
            &[("FERRUM_MOE_GRAPH", "1"), ("FERRUM_VLLM_MOE", "0")],
            "FERRUM_MOE_GRAPH",
        );
        expect_invalid_key(&[("FERRUM_KV_MAX_BLOCKS", "0")], "FERRUM_KV_MAX_BLOCKS");
        expect_invalid_key(&[("FERRUM_PAGED_MAX_SEQS", "0")], "FERRUM_PAGED_MAX_SEQS");
        expect_invalid_key(
            &[
                ("FERRUM_PAGED_MAX_SEQS", "32"),
                ("FERRUM_MAX_BATCHED_TOKENS", "16"),
            ],
            "FERRUM_MAX_BATCHED_TOKENS",
        );
        expect_invalid_key(
            &[
                ("FERRUM_KV_MAX_BLOCKS", "16"),
                ("FERRUM_MAX_BATCHED_TOKENS", "512"),
            ],
            "FERRUM_MAX_BATCHED_TOKENS",
        );
        expect_invalid_key(&[("FERRUM_MAX_MODEL_LEN", "0")], "FERRUM_MAX_MODEL_LEN");
        expect_invalid_key(&[("FERRUM_MAX_MODEL_LEN", "50000")], "FERRUM_MAX_MODEL_LEN");
        expect_invalid_key(
            &[
                ("FERRUM_KV_MAX_BLOCKS", "16"),
                ("FERRUM_MAX_MODEL_LEN", "1024"),
            ],
            "FERRUM_KV_MAX_BLOCKS",
        );
        expect_invalid_key(&[("FERRUM_DTYPE", "bf16")], "FERRUM_DTYPE");
        expect_invalid_key(&[("FERRUM_KV_DTYPE", "fp8")], "FERRUM_KV_DTYPE");
        expect_invalid_key(
            &[
                ("FERRUM_VLLM_PAGED_ATTN_V1_SHORT", "1"),
                ("FERRUM_USE_VLLM_PAGED_ATTN", "0"),
            ],
            "FERRUM_VLLM_PAGED_ATTN_V1_SHORT",
        );
    }

    #[test]
    fn requested_max_model_len_is_optional_and_reflected_when_valid() {
        let default_resolved = m3(&[], CompiledKernelFeatures::m3_fast_path_without_fa2())
            .resolve()
            .unwrap();
        assert!(!default_resolved
            .decisions
            .iter()
            .any(|decision| decision.selection == "max_model_len"));

        let resolved = m3(
            &[
                ("FERRUM_KV_MAX_BLOCKS", "64"),
                ("FERRUM_MAX_MODEL_LEN", "1024"),
            ],
            CompiledKernelFeatures::m3_fast_path_without_fa2(),
        )
        .resolve()
        .unwrap();
        let max_model_len = resolved
            .decisions
            .iter()
            .find(|decision| decision.selection == "max_model_len")
            .unwrap();
        assert_eq!(max_model_len.selected, "1024");
        assert_eq!(
            max_model_len.source_key.as_deref(),
            Some("FERRUM_MAX_MODEL_LEN")
        );
    }

    #[test]
    fn graph_enabled_with_graph_unsafe_moe_is_rejected() {
        let mut model = ModelCapabilities::qwen3_30b_a3b_gptq_int4();
        model.graph_safe_moe = false;
        let err = FerrumConfigBuilder::new(snapshot(&[("FERRUM_MOE_GRAPH", "1")]))
            .with_model_capabilities(model)
            .with_hardware_capabilities(HardwareCapabilities::rtx4090_cuda(
                CompiledKernelFeatures::m3_fast_path_without_fa2(),
            ))
            .with_workload_profile(WorkloadProfile::m3_qwen3_30b_a3b_int4())
            .resolve()
            .expect_err("graph unsafe MoE must fail");
        assert!(matches!(
            err,
            AutoConfigError::UnsupportedCombination {
                selection,
                ..
            } if selection == "moe_graph_policy"
        ));
    }

    #[test]
    fn scheduler_override_is_reflected_in_decision_trace() {
        let resolved = m3(
            &[("FERRUM_ACTIVE_DECODE_PREFILL_CHUNK", "64")],
            CompiledKernelFeatures::m3_fast_path_without_fa2(),
        )
        .resolve()
        .unwrap();
        let scheduler = resolved
            .decisions
            .iter()
            .find(|decision| decision.selection == "scheduler_admission_policy")
            .unwrap();
        assert_eq!(scheduler.selected, "active_decode_prefill_chunk:64");
        assert_eq!(
            scheduler.source_key.as_deref(),
            Some("FERRUM_ACTIVE_DECODE_PREFILL_CHUNK")
        );
    }

    #[test]
    fn prefix_cache_override_is_reflected_in_decision_trace() {
        let resolved = m3(
            &[("FERRUM_PREFIX_CACHE", "1")],
            CompiledKernelFeatures::m3_fast_path_without_fa2(),
        )
        .resolve()
        .unwrap();
        let prefix_cache = resolved
            .decisions
            .iter()
            .find(|decision| decision.selection == "prefix_cache_policy")
            .unwrap();
        assert_eq!(prefix_cache.selected, "prefix_cache_enabled");
        assert_eq!(
            prefix_cache.source_key.as_deref(),
            Some("FERRUM_PREFIX_CACHE")
        );
    }

    #[test]
    fn non_env_runtime_sources_are_preserved_in_decision_trace() {
        let runtime_config = snapshot_with_sources(&[
            (
                "FERRUM_FA_LAYOUT_VARLEN",
                "1",
                RuntimeConfigSource::ConfigFile,
            ),
            ("FERRUM_PAGED_MAX_SEQS", "48", RuntimeConfigSource::Cli),
            (
                "FERRUM_SCHED_PREFILL_FIRST_UNTIL_ACTIVE",
                "32",
                RuntimeConfigSource::ScriptCase,
            ),
        ]);
        let resolved = FerrumConfigBuilder::new(runtime_config)
            .with_model_capabilities(ModelCapabilities::qwen3_30b_a3b_gptq_int4())
            .with_hardware_capabilities(HardwareCapabilities::rtx4090_cuda(
                CompiledKernelFeatures::m3_fast_path_without_fa2(),
            ))
            .with_workload_profile(WorkloadProfile::m3_qwen3_30b_a3b_int4())
            .resolve()
            .unwrap();

        let decision = |selection: &str| {
            resolved
                .decisions
                .iter()
                .find(|decision| decision.selection == selection)
                .unwrap()
        };
        let attention = decision("attention_prefill_mixed_backend");
        assert_eq!(attention.selected, "fa_layout_varlen");
        assert_eq!(attention.source, AutoConfigSource::ConfigFile);
        assert_eq!(
            attention.source_key.as_deref(),
            Some("FERRUM_FA_LAYOUT_VARLEN")
        );

        let max_sequences = decision("max_sequences");
        assert_eq!(max_sequences.selected, "48");
        assert_eq!(max_sequences.source, AutoConfigSource::Cli);
        assert_eq!(
            max_sequences.source_key.as_deref(),
            Some("FERRUM_PAGED_MAX_SEQS")
        );

        let scheduler = decision("scheduler_admission_policy");
        assert_eq!(scheduler.selected, "prefill_first_until_active:32");
        assert_eq!(scheduler.source, AutoConfigSource::ScriptCase);
        assert_eq!(
            scheduler.source_key.as_deref(),
            Some("FERRUM_SCHED_PREFILL_FIRST_UNTIL_ACTIVE")
        );
    }

    #[test]
    fn renders_effective_config_and_decision_trace_artifacts() {
        let resolved = m3(&[], CompiledKernelFeatures::m3_fast_path_without_fa2())
            .resolve()
            .unwrap();
        let effective = resolved.effective_config_document();
        assert_eq!(effective["schema_version"], 1);
        assert!(effective["env_hash"]
            .as_str()
            .unwrap()
            .starts_with("sha256:"));
        assert!(effective["entries"].is_array());
        assert_eq!(effective["model_capabilities"]["architecture"], "qwen3_moe");
        assert_eq!(effective["hardware_capabilities"]["backend"], "cuda");
        assert_eq!(
            effective["workload_profile"]["preset"],
            M3_QWEN3_30B_A3B_INT4_PRESET
        );
        assert_eq!(
            effective["decisions"].as_array().unwrap().len(),
            resolved.decisions.len()
        );
        let trace = resolved.decision_trace_jsonl().unwrap();
        assert_eq!(trace.lines().count(), resolved.decisions.len());
        assert!(trace.contains("\"attention_prefill_mixed_backend\""));
    }

    #[test]
    fn auto_config_artifacts_match_locked_schema_shape() {
        let resolved = FerrumConfigBuilder::m3_qwen3_30b_a3b_int4(snapshot_with_sources(&[
            (
                "FERRUM_FA_LAYOUT_VARLEN",
                "1",
                RuntimeConfigSource::ScriptCase,
            ),
            ("FERRUM_PAGED_MAX_SEQS", "32", RuntimeConfigSource::Cli),
        ]))
        .resolve()
        .unwrap();

        let effective = resolved.effective_config_document();
        assert_eq!(effective["schema_version"], 1);
        assert!(effective["env_hash"]
            .as_str()
            .unwrap()
            .starts_with("sha256:"));

        let entries = effective["entries"].as_array().unwrap();
        let keys: Vec<_> = entries
            .iter()
            .map(|entry| entry["key"].as_str().unwrap())
            .collect();
        let mut sorted_keys = keys.clone();
        sorted_keys.sort_unstable();
        assert_eq!(keys, sorted_keys);
        for entry in entries {
            assert!(entry["key"].as_str().unwrap().starts_with("FERRUM_"));
            assert!(entry["effective_value"].is_string());
            assert!(matches!(
                entry["source"].as_str().unwrap(),
                "default" | "config_file" | "cli" | "env" | "script_case" | "memory_profile"
            ));
            assert!(!entry["affects"].as_array().unwrap().is_empty());
        }
        assert_eq!(
            effective["model_capabilities"]["quantization"].as_str(),
            Some("gptq_int4")
        );
        assert_eq!(
            effective["model_capabilities"]["moe"]["experts_per_token"].as_u64(),
            Some(8)
        );
        assert_eq!(
            effective["hardware_capabilities"]["compute_capability"].as_str(),
            Some("8.9")
        );
        assert_eq!(
            effective["hardware_capabilities"]["compiled_features"]["vllm_moe_marlin"].as_bool(),
            Some(true)
        );
        assert_eq!(
            effective["workload_profile"]["target_concurrency"].as_u64(),
            Some(32)
        );
        assert_eq!(
            effective["workload_profile"]["priority"].as_str(),
            Some("throughput")
        );

        let trace = resolved.decision_trace_jsonl().unwrap();
        let trace_decisions: Vec<AutoConfigDecision> = trace
            .lines()
            .map(|line| serde_json::from_str(line).unwrap())
            .collect();
        assert_eq!(trace_decisions, resolved.decisions);
        assert_eq!(
            serde_json::from_value::<Vec<AutoConfigDecision>>(effective["decisions"].clone())
                .unwrap(),
            trace_decisions
        );

        for decision in &trace_decisions {
            assert_eq!(decision.schema_version, 1);
            assert!(!decision.selection.trim().is_empty());
            assert!(!decision.selected.trim().is_empty());
            assert!(!decision.candidates.is_empty());
            assert!(!decision.affects.is_empty());
            if let Some(source_key) = &decision.source_key {
                assert!(source_key.starts_with("FERRUM_"));
            }
            for rejected in &decision.rejected {
                assert!(!rejected.value.trim().is_empty());
                assert!(!rejected.reason.trim().is_empty());
            }
        }
    }
}