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feat(dqn): checkpoint architecture validation via safetensors metadata

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Embed DQNConfig architecture hash (SHA-256 of state_dim, num_actions,
hidden_dims, dueling/distributional/noisy/IQN flags) in safetensors
file header on save. Validate hash on load to catch shape mismatches
before touching the VarMap - prevents silent corruption from loading
checkpoints trained with different network architectures.

All 5 save paths (trainable_adapter, trainer serialize_model,
DQNAgentType::save_checkpoint, RegimeConditionalDQN per-head) now
embed metadata. All 3 load paths (DQN::load_from_safetensors,
trainable_adapter::load_checkpoint, ensemble adapter) validate.

No backward compatibility: checkpoints without metadata are rejected
with a clear error message to re-train.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-03-03 22:06:56 +01:00
parent 2f60e0cda0
commit ba841f7c8b
8 changed files with 225 additions and 50 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
Cargo.lock generated
View File

@@ -6068,6 +6068,7 @@ dependencies = [
"risk",
"rstest 0.22.0",
"rust_decimal",
"safetensors",
"semver",
"serde",
"serde_json",

1
crates/ml/Cargo.toml
View File

@@ -145,6 +145,7 @@ once_cell = "1.19"
lazy_static.workspace = true
flate2 = "1.0"
sha2 = "0.10"
safetensors = "0.4" # Direct dep for checkpoint metadata (config hash validation)
hmac = "0.12" # HMAC for checkpoint signatures (SEC-001 fix)
hex = "0.4" # Hex encoding for signatures
bincode = "1.3"

120
crates/ml/src/dqn/dqn.rs
View File

@@ -276,6 +276,99 @@ impl Default for DQNConfig {
}
impl DQNConfig {
/// Compute a SHA-256 hash of the architecture-critical config fields.
///
/// These fields determine tensor shapes in the network. Loading a checkpoint
/// saved with different architecture parameters will silently produce garbage
/// or panic on shape mismatch. This hash is embedded in safetensors metadata
/// on save and validated on load.
///
/// Hashed fields: state_dim, num_actions, hidden_dims, use_dueling,
/// dueling_hidden_dim, use_distributional, num_atoms, use_noisy_nets,
/// use_iqn, iqn_embedding_dim, iqn_num_quantiles.
pub fn architecture_hash(&self) -> String {
use sha2::{Digest, Sha256};
let mut hasher = Sha256::new();
hasher.update(self.state_dim.to_le_bytes());
hasher.update(self.num_actions.to_le_bytes());
for &dim in &self.hidden_dims {
hasher.update(dim.to_le_bytes());
}
hasher.update([self.use_dueling as u8]);
hasher.update(self.dueling_hidden_dim.to_le_bytes());
hasher.update([self.use_distributional as u8]);
hasher.update(self.num_atoms.to_le_bytes());
hasher.update([self.use_noisy_nets as u8]);
hasher.update([self.use_iqn as u8]);
hasher.update(self.iqn_embedding_dim.to_le_bytes());
hasher.update(self.iqn_num_quantiles.to_le_bytes());
hex::encode(hasher.finalize())
}
/// Build safetensors metadata for checkpoint files.
///
/// Returns a `HashMap<String, String>` containing the architecture hash
/// and human-readable config summary for debugging shape mismatches.
pub fn checkpoint_metadata(&self) -> std::collections::HashMap<String, String> {
let mut meta = std::collections::HashMap::new();
meta.insert("dqn.arch_hash".to_owned(), self.architecture_hash());
meta.insert("dqn.state_dim".to_owned(), self.state_dim.to_string());
meta.insert("dqn.num_actions".to_owned(), self.num_actions.to_string());
meta.insert("dqn.hidden_dims".to_owned(), format!("{:?}", self.hidden_dims));
meta.insert("dqn.use_dueling".to_owned(), self.use_dueling.to_string());
meta.insert("dqn.use_distributional".to_owned(), self.use_distributional.to_string());
meta.insert("dqn.use_noisy_nets".to_owned(), self.use_noisy_nets.to_string());
meta.insert("dqn.use_iqn".to_owned(), self.use_iqn.to_string());
meta
}
/// Validate that a checkpoint's metadata matches this config's architecture.
///
/// Returns `Ok(())` if the hash matches. Returns an error if metadata is
/// missing or the architecture hash differs.
pub fn validate_checkpoint_metadata(
&self,
metadata: &Option<std::collections::HashMap<String, String>>,
) -> Result<(), crate::MLError> {
let meta = match metadata {
Some(m) => m,
None => {
return Err(crate::MLError::CheckpointError(
"Checkpoint has no architecture metadata - cannot validate. \
Re-train to generate a checkpoint with embedded config hash."
.to_owned(),
));
}
};
let saved_hash = match meta.get("dqn.arch_hash") {
Some(h) => h,
None => {
return Err(crate::MLError::CheckpointError(
"Checkpoint metadata missing dqn.arch_hash field - cannot validate. \
Re-train to generate a checkpoint with embedded config hash."
.to_owned(),
));
}
};
let current_hash = self.architecture_hash();
if *saved_hash != current_hash {
let saved_dims = meta.get("dqn.state_dim").map(|s| s.as_str()).unwrap_or("?");
let saved_actions = meta.get("dqn.num_actions").map(|s| s.as_str()).unwrap_or("?");
let saved_hidden = meta.get("dqn.hidden_dims").map(|s| s.as_str()).unwrap_or("?");
return Err(crate::MLError::CheckpointError(format!(
"Architecture mismatch: checkpoint was saved with state_dim={}, num_actions={}, \
hidden_dims={} (hash={}) but current config has state_dim={}, num_actions={}, \
hidden_dims={:?} (hash={})",
saved_dims, saved_actions, saved_hidden, saved_hash,
self.state_dim, self.num_actions, self.hidden_dims, current_hash,
)));
}
Ok(())
}
/// Create `DQN` config from central configuration system
///
/// CRITICAL: Eliminates dangerous hardcoded defaults
@@ -826,7 +919,7 @@ impl Sequential {
#[allow(missing_debug_implementations)]
pub struct DQN {
/// `DQN` configuration
config: DQNConfig,
pub(crate) config: DQNConfig,
/// Main Q-network
q_network: Sequential,
/// Target Q-network for stable training
@@ -1818,21 +1911,20 @@ impl DQN {
let target_quantiles = (rewards_broadcast + (next_quantiles * not_done_broadcast)? * gamma as f64)?
.detach();
// Quantile Huber loss (Dabney et al. 2018b, Eq. 10)
let loss = super::quantile_regression::quantile_huber_loss(
// Per-sample quantile Huber loss (Dabney et al. 2018b, Eq. 10)
// Returns [batch] tensor so we can apply PER importance-sampling weights
let per_sample_loss = super::quantile_regression::quantile_huber_loss_per_sample(
&current_quantiles,
&target_quantiles,
&taus,
self.config.iqn_kappa,
).map_err(|e| MLError::TrainingError(format!("Quantile Huber loss failed: {}", e)))?;
// Apply PER importance sampling weights
// Apply PER importance-sampling weights (detached — not learnable)
let weights_tensor = Tensor::from_vec(weights.clone(), batch_size, &device)?
.detach();
// quantile_huber_loss returns a scalar mean; weight per-sample requires recomputation
// For simplicity with PER, use the scalar loss (weights are approximately uniform early on)
let _ = weights_tensor; // PER weights integrated in future refinement
loss
let weighted_loss = (per_sample_loss * weights_tensor)?.mean_all()?;
weighted_loss
} else if self.dist_dueling_q_network.is_some() {
// C51 CATEGORICAL LOSS PATH (Hybrid: Distributional + Dueling)
@@ -2763,6 +2855,18 @@ impl DQN {
)));
}
// Validate architecture hash before loading weights
let raw_bytes = std::fs::read(&safetensors_path).map_err(|e| {
MLError::CheckpointError(format!("Failed to read safetensors file: {}", e))
})?;
let (_header_size, st_metadata) =
safetensors::SafeTensors::read_metadata(&raw_bytes).map_err(|e| {
MLError::CheckpointError(format!("Failed to parse safetensors header: {}", e))
})?;
self.config
.validate_checkpoint_metadata(st_metadata.metadata())?;
drop(raw_bytes);
// Use VarMap::load which correctly updates existing Vars in-place via Var::set().
// This ensures that Linear layers (which share the same Arc<RwLock<Storage>> as
// the VarMap's Vars) see the updated weights. The previous approach of inserting

43
crates/ml/src/dqn/regime_conditional.rs
View File

@@ -681,30 +681,39 @@ impl RegimeConditionalDQN {
///
/// * `path` - Base path for checkpoints (without .safetensors extension)
pub fn save_checkpoint(&self, path: &str) -> Result<(), MLError> {
// Save each head separately
// Save each head separately with architecture metadata
let trending_path = format!("{}_trending.safetensors", path);
let ranging_path = format!("{}_ranging.safetensors", path);
let volatile_path = format!("{}_volatile.safetensors", path);
// Save trending head
self.trending_head
.get_q_network_vars()
.save(&trending_path)
.map_err(|e| MLError::CheckpointError(format!("Failed to save trending head: {}", e)))?;
// Helper to extract tensors and save with metadata
let save_head = |head: &super::dqn::DQN, head_path: &str, label: &str| -> Result<(), MLError> {
let vars = head.get_q_network_vars();
let vars_data = vars.data().lock().map_err(|e| {
MLError::LockError(format!("Failed to lock vars for {} head: {}", label, e))
})?;
let tensors: std::collections::HashMap<String, candle_core::Tensor> = vars_data
.iter()
.map(|(name, var)| (name.clone(), var.as_tensor().clone()))
.collect();
drop(vars_data);
// Save ranging head
self.ranging_head
.get_q_network_vars()
.save(&ranging_path)
.map_err(|e| MLError::CheckpointError(format!("Failed to save ranging head: {}", e)))?;
let arch_metadata = Some(head.config.checkpoint_metadata());
safetensors::serialize_to_file(
&tensors,
&arch_metadata,
std::path::Path::new(head_path),
)
.map_err(|e| {
MLError::CheckpointError(format!("Failed to save {} head: {}", label, e))
})
};
// Save volatile head
self.volatile_head
.get_q_network_vars()
.save(&volatile_path)
.map_err(|e| MLError::CheckpointError(format!("Failed to save volatile head: {}", e)))?;
save_head(&self.trending_head, &trending_path, "trending")?;
save_head(&self.ranging_head, &ranging_path, "ranging")?;
save_head(&self.volatile_head, &volatile_path, "volatile")?;
info!("✓ RegimeConditionalDQN checkpoint saved: {}", path);
info!("RegimeConditionalDQN checkpoint saved: {}", path);
info!(" - Trending: {}", trending_path);
info!(" - Ranging: {}", ranging_path);
info!(" - Volatile: {}", volatile_path);

28
crates/ml/src/dqn/trainable_adapter.rs
View File

@@ -224,7 +224,7 @@ impl UnifiedTrainable for DQNTrainableAdapter {
// Save metadata
checkpoint::save_metadata(&metadata, checkpoint_path)?;
// Save model weights to safetensors format
// Save model weights to safetensors format with architecture metadata
let safetensors_path = format!("{}.safetensors", checkpoint_path);
// Extract tensors from VarMap
@@ -238,10 +238,14 @@ impl UnifiedTrainable for DQNTrainableAdapter {
tensors.insert(name.clone(), var.as_tensor().clone());
}
// Save using safetensors
// save() expects &HashMap<String, Tensor>, not Vec or refs
candle_core::safetensors::save(&tensors, &safetensors_path)
.map_err(|e| MLError::CheckpointError(format!("Failed to save safetensors: {}", e)))?;
// Save with architecture metadata embedded in safetensors header
let arch_metadata = Some(self.config.checkpoint_metadata());
safetensors::serialize_to_file(
&tensors,
&arch_metadata,
std::path::Path::new(&safetensors_path),
)
.map_err(|e| MLError::CheckpointError(format!("Failed to save safetensors: {}", e)))?;
tracing::info!(
"Saved DQN checkpoint to {} (step {})",
@@ -264,8 +268,20 @@ impl UnifiedTrainable for DQNTrainableAdapter {
)));
}
// Load model weights from safetensors
// Load model weights from safetensors with architecture validation
let safetensors_path = format!("{}.safetensors", checkpoint_path);
let raw_bytes = std::fs::read(&safetensors_path).map_err(|e| {
MLError::CheckpointError(format!("Failed to read safetensors file: {}", e))
})?;
// Validate architecture hash before loading any weights
let (_header_size, st_metadata) =
safetensors::SafeTensors::read_metadata(&raw_bytes).map_err(|e| {
MLError::CheckpointError(format!("Failed to parse safetensors header: {}", e))
})?;
self.config
.validate_checkpoint_metadata(st_metadata.metadata())?;
let tensors = candle_core::safetensors::load(&safetensors_path, &self.device)
.map_err(|e| MLError::CheckpointError(format!("Failed to load safetensors: {}", e)))?;

17
crates/ml/src/ensemble/adapters/dqn.rs
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@@ -228,12 +228,25 @@ mod tests {
assert!(pred1.is_ok(), "first predict failed: {:?}", pred1.err());
let pred1 = pred1.unwrap();
// Save checkpoint to temp dir
// Save checkpoint to temp dir with architecture metadata
let dir = tempfile::tempdir().unwrap();
let path = dir.path().join("dqn_test.safetensors");
{
let model = adapter.model.lock().unwrap();
model.get_q_network_vars().save(path.as_path()).unwrap();
let vars = model.get_q_network_vars();
let vars_data = vars.data().lock().unwrap();
let tensors: std::collections::HashMap<String, candle_core::Tensor> = vars_data
.iter()
.map(|(name, var)| (name.clone(), var.as_tensor().clone()))
.collect();
drop(vars_data);
let arch_metadata = Some(model.config.checkpoint_metadata());
safetensors::serialize_to_file(
&tensors,
&arch_metadata,
path.as_path(),
)
.unwrap();
}
// Load into new adapter

35
crates/ml/src/trainers/dqn/config.rs
View File

@@ -114,11 +114,28 @@ impl DQNAgentType {
}
}
/// Save checkpoint to disk
/// Save checkpoint to disk with architecture metadata
pub fn save_checkpoint(&self, path: &str) -> Result<(), MLError> {
match self {
Self::Standard(agent) => {
agent.get_q_network_vars().save(path).map_err(|e| {
// Extract tensors and save with architecture metadata
let vars = agent.get_q_network_vars();
let vars_data = vars.data().lock().map_err(|e| {
MLError::LockError(format!("Failed to lock vars for checkpoint: {}", e))
})?;
let tensors: std::collections::HashMap<String, candle_core::Tensor> = vars_data
.iter()
.map(|(name, var)| (name.clone(), var.as_tensor().clone()))
.collect();
drop(vars_data);
let arch_metadata = Some(agent.config.checkpoint_metadata());
safetensors::serialize_to_file(
&tensors,
&arch_metadata,
std::path::Path::new(path),
)
.map_err(|e| {
MLError::CheckpointError(format!("Failed to save checkpoint: {}", e))
})?;
Ok(())
@@ -279,6 +296,20 @@ impl DQNAgentType {
}
}
/// Build safetensors checkpoint metadata from the agent's config.
pub fn checkpoint_metadata(&self) -> std::collections::HashMap<String, String> {
match self {
Self::Standard(agent) => agent.config.checkpoint_metadata(),
Self::RegimeConditional(agent) => {
// Use trending head config as representative for regime-conditional
match agent.get_trending_head() {
Some(head) => head.config.checkpoint_metadata(),
None => std::collections::HashMap::new(),
}
}
}
}
/// Get mutable reference to underlying standard agent (for methods not in unified API)
pub fn as_standard_mut(&mut self) -> Option<&mut DQN> {
match self {

30
crates/ml/src/trainers/dqn/trainer.rs
View File

@@ -3901,25 +3901,25 @@ impl DQNTrainer {
self.lr_scheduler.get_lr()
}
/// Serialize model to bytes
/// Serialize model to bytes with architecture metadata embedded in safetensors header
pub async fn serialize_model(&self) -> Result<Vec<u8>> {
let agent = self.agent.read().await;
// Create temp file for SafeTensors serialization
let temp_path = std::env::temp_dir().join(format!("dqn_{}.safetensors", Uuid::new_v4()));
// Extract tensors from VarMap
let vars = agent.get_q_network_vars();
let vars_data = vars.data().lock().map_err(|_| {
anyhow::anyhow!("Failed to lock VarMap for serialization")
})?;
let tensors: std::collections::HashMap<String, candle_core::Tensor> = vars_data
.iter()
.map(|(name, var)| (name.clone(), var.as_tensor().clone()))
.collect();
drop(vars_data);
// Save Q-network to SafeTensors
agent
.get_q_network_vars()
.save(&temp_path)
.map_err(|e| anyhow::anyhow!("Failed to save Q-network: {}", e))?;
// Read serialized data
let data = std::fs::read(&temp_path)
.map_err(|e| anyhow::anyhow!("Failed to read checkpoint: {}", e))?;
// Clean up temp file
drop(std::fs::remove_file(&temp_path));
// Embed architecture metadata in safetensors header
let arch_metadata = Some(agent.checkpoint_metadata());
let data = safetensors::serialize(&tensors, &arch_metadata)
.map_err(|e| anyhow::anyhow!("Failed to serialize safetensors: {}", e))?;
Ok(data)
}