Files
foxhunt/crates/ml/examples/precompute_features.rs
jgrusewski a448e03e64 fix: 6 CUDA OOB bugs + remove 1228 lines of dead training path
Root cause of NaN at step 22: cuBLAS out-of-bounds reads in the GPU
experience collector and training forward pass, caused by buffer
dimension mismatches that produced garbage states → garbage Q-values →
NaN loss.

Bugs fixed:
1. GpuDqnTrainConfig::default() had bottleneck_dim=2, market_dim=42 —
   collector computed s1_input_dim=40 instead of state_dim=80, cuBLAS
   read 2x past W1 weight buffer (10KB OOB per SGEMM)
2. batch_states allocated with state_dim=80 but cuBLAS ldb=128 (CUTLASS
   K-tile alignment) — 48 bf16/f32 values read past end per row
3. Validation state tensor had 59 elements/row (42+3+8+6) instead of
   128 (state_dim_padded) — missing portfolio/MTF features + padding
4. CUTLASS K-tile overread on all hidden activation buffers (K=64 but
   tile=128) — added 128-element padding to 14 bf16 buffers
5. init_from_fxcache never set self.ofi_features — collector OFI buffer
   was 1-element placeholder, state_gather kernel read OOB
6. collect_gpu_experiences_slices missing *2 counterfactual multiplier —
   half of collected experiences never inserted into PER

Dead code removed (~1228 lines):
- train_with_data_full_loop (old Vec<(FeatureVector, Vec<f64>)> loop)
- init_gpu_data, init_gpu_raw_buffers, collect_gpu_experiences,
  run_training_steps (old helpers only called by above)
- train_with_preloaded_data, train_with_shared_data (old hyperopt APIs)
- train() and train_walk_forward() now convert to fixed-size arrays
  and route through init_from_fxcache + train_fold_from_slices

Other fixes:
- precompute_features defaults output-dir to sibling of data-dir
- workspace_root() + feature_cache_dir() helpers for reliable path
  resolution (works from any cwd, with/without CARGO_MANIFEST_DIR)
- Production smoketest uses fxcache (no slow DBN parsing)
- compute-sanitizer: 0 errors (was 2539)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-03 13:22:46 +02:00

417 lines
17 KiB
Rust

#![allow(
clippy::assertions_on_constants,
clippy::assertions_on_result_states,
clippy::clone_on_copy,
clippy::decimal_literal_representation,
clippy::doc_markdown,
clippy::empty_line_after_doc_comments,
clippy::field_reassign_with_default,
clippy::get_unwrap,
clippy::identity_op,
clippy::inconsistent_digit_grouping,
clippy::indexing_slicing,
clippy::integer_division,
clippy::len_zero,
clippy::let_underscore_must_use,
clippy::manual_div_ceil,
clippy::manual_let_else,
clippy::manual_range_contains,
clippy::modulo_arithmetic,
clippy::needless_range_loop,
clippy::non_ascii_literal,
clippy::redundant_clone,
clippy::shadow_reuse,
clippy::shadow_same,
clippy::shadow_unrelated,
clippy::single_match_else,
clippy::str_to_string,
clippy::string_slice,
clippy::tests_outside_test_module,
clippy::too_many_lines,
clippy::unnecessary_wraps,
clippy::unseparated_literal_suffix,
clippy::use_debug,
clippy::useless_vec,
clippy::wildcard_enum_match_arm,
clippy::else_if_without_else,
clippy::expect_used,
clippy::missing_const_for_fn,
clippy::similar_names,
clippy::type_complexity,
clippy::collapsible_else_if,
clippy::doc_lazy_continuation,
clippy::items_after_test_module,
clippy::map_clone,
clippy::multiple_unsafe_ops_per_block,
clippy::unwrap_or_default,
clippy::assign_op_pattern,
clippy::needless_borrow,
clippy::println_empty_string,
clippy::unnecessary_cast,
clippy::used_underscore_binding,
clippy::create_dir,
clippy::implicit_saturating_sub,
clippy::exit,
clippy::expect_fun_call,
clippy::too_many_arguments,
clippy::unnecessary_map_or,
clippy::unwrap_used,
dead_code,
unused_imports,
unused_variables,
clippy::cloned_ref_to_slice_refs,
clippy::neg_multiply,
clippy::while_let_loop,
clippy::bool_assert_comparison,
clippy::excessive_precision,
clippy::trivially_copy_pass_by_ref,
clippy::op_ref,
clippy::redundant_closure,
clippy::unnecessary_lazy_evaluations,
clippy::if_then_some_else_none,
clippy::unnecessary_to_owned,
clippy::single_component_path_imports,
)]
//! Precompute Features — DBN to .fxcache pipeline
//!
//! Reads OHLCV + MBP-10 + trades DBN data, runs the full feature extraction
//! pipeline (standalone, no GPU required), and writes a `.fxcache` binary file for
//! zero-overhead GPU loading during training.
//!
//! Usage:
//! cargo run -p ml --example precompute_features --release -- \
//! --data-dir test_data/futures-baseline \
//! --mbp10-data-dir test_data/futures-baseline-mbp10 \
//! --trades-data-dir test_data/futures-baseline-trades \
//! --output-dir test_data/feature-cache \
//! --symbol ES.FUT \
//! --bf16 \
//! --yes
use std::path::{Path, PathBuf};
use std::time::Instant;
use anyhow::{Context, Result};
use clap::Parser;
use tracing::info;
use ml::trainers::dqn::{
collect_dbn_files_recursive, extract_features_from_bars, extract_ohlcv_bars_from_dbn,
};
use ml::features::extraction::OHLCVBar;
// ---------------------------------------------------------------------------
// CLI
// ---------------------------------------------------------------------------
#[derive(Debug, Parser)]
#[command(
name = "precompute_features",
about = "Precompute features from DBN data and write .fxcache binary"
)]
struct Opts {
/// Directory containing OHLCV .dbn/.dbn.zst files (e.g. test_data/futures-baseline)
#[arg(long)]
data_dir: String,
/// Directory containing MBP-10 .dbn/.dbn.zst files (optional)
#[arg(long)]
mbp10_data_dir: Option<String>,
/// Directory containing trades .dbn/.dbn.zst files (optional)
#[arg(long)]
trades_data_dir: Option<String>,
/// Output directory for .fxcache files (default: sibling of --data-dir)
#[arg(long)]
output_dir: Option<String>,
/// Symbol name (used in output filename)
#[arg(long, default_value = "ES.FUT")]
symbol: String,
/// Write version 2 (bf16) instead of version 1 (f64)
#[arg(long)]
bf16: bool,
/// Skip confirmation prompt
#[arg(long)]
yes: bool,
}
// ---------------------------------------------------------------------------
// Cache key computation
// ---------------------------------------------------------------------------
/// Compute a SHA256 cache key from all DBN files across the given directories.
///
// ---------------------------------------------------------------------------
// Main
// ---------------------------------------------------------------------------
#[tokio::main]
async fn main() -> Result<()> {
// Initialize tracing
tracing_subscriber::fmt()
.with_env_filter(
tracing_subscriber::EnvFilter::try_from_default_env()
.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
)
.init();
let opts = Opts::parse();
let data_dir = PathBuf::from(&opts.data_dir);
// Default output_dir: sibling of data_dir (e.g. test_data/futures-baseline → test_data/feature-cache)
let output_dir = match opts.output_dir {
Some(ref d) => PathBuf::from(d),
None => data_dir.parent().unwrap_or(&data_dir).join("feature-cache"),
};
let mbp10_dir = opts.mbp10_data_dir.as_ref().map(PathBuf::from);
let trades_dir = opts.trades_data_dir.as_ref().map(PathBuf::from);
// ── Validate inputs ──────────────────────────────────────────────────────
if !data_dir.exists() {
anyhow::bail!("Data directory not found: {}", data_dir.display());
}
// ── Print config summary ─────────────────────────────────────────────────
println!("================================================================================");
println!("Precompute Features — DBN to .fxcache Pipeline");
println!("================================================================================");
println!();
println!("Data dir: {}", data_dir.display());
if let Some(ref d) = mbp10_dir {
println!("MBP-10 dir: {}", d.display());
}
if let Some(ref d) = trades_dir {
println!("Trades dir: {}", d.display());
}
println!("Output dir: {}", output_dir.display());
println!("Symbol: {}", opts.symbol);
println!("Format: {}", if opts.bf16 { "bf16 (v2)" } else { "f64 (v1)" });
println!();
// ── Confirmation ─────────────────────────────────────────────────────────
if !opts.yes {
print!("Proceed with feature extraction? (yes/no): ");
std::io::Write::flush(&mut std::io::stdout())?;
let mut input = String::new();
std::io::stdin().read_line(&mut input)?;
let trimmed = input.trim();
if !trimmed.eq_ignore_ascii_case("yes") && !trimmed.eq_ignore_ascii_case("y") {
println!("Cancelled.");
return Ok(());
}
println!();
}
let t0 = Instant::now();
// ── Step 1: Load OHLCV bars from DBN files ──────────────────────────────
info!("Loading OHLCV bars from {}...", data_dir.display());
let mut dbn_files = collect_dbn_files_recursive(&data_dir);
dbn_files.sort();
info!("Found {} DBN files", dbn_files.len());
let mut all_bars: Vec<OHLCVBar> = Vec::new();
for file in &dbn_files {
match extract_ohlcv_bars_from_dbn(file) {
Ok(bars) => all_bars.extend(bars),
Err(e) => tracing::warn!("Failed to load {:?}: {e}", file.file_name()),
}
}
all_bars.sort_by_key(|b| b.timestamp);
info!("Loaded {} OHLCV bars in {:.1}s", all_bars.len(), t0.elapsed().as_secs_f64());
// ── Step 2: Extract 42-dim feature vectors ──────────────────────────────
let t1 = Instant::now();
info!("Extracting features...");
let feature_vectors = extract_features_from_bars(&all_bars)
.context("Feature extraction failed")?;
info!("Extracted {} feature vectors in {:.1}s", feature_vectors.len(), t1.elapsed().as_secs_f64());
// ── Step 3: Build targets [preproc_close, preproc_next, raw_close, raw_next]
const WARMUP: usize = 50;
let n = feature_vectors.len().saturating_sub(1); // need next bar for target
let features: Vec<[f64; 42]> = feature_vectors[..n].to_vec();
let mut targets: Vec<[f64; 4]> = Vec::with_capacity(n);
for i in 0..n {
let raw_curr = all_bars[i + WARMUP].close;
let raw_next = all_bars[i + WARMUP + 1].close;
targets.push([raw_curr, raw_next, raw_curr, raw_next]);
}
// ── Step 3b: Build per-bar timestamps ─────────────────────────────────
let timestamps: Vec<i64> = (0..n)
.map(|i| all_bars[i + WARMUP].timestamp.timestamp_nanos_opt().unwrap_or(0))
.collect();
// ── Step 4: Compute OFI from MBP-10 + trades ────────────────────────────
let t2 = Instant::now();
let ofi: Vec<[f64; 8]> = if let Some(ref mbp10_path) = mbp10_dir {
use ml::features::mbp10_loader::load_ofi_features_parallel;
use ml::features::trades_loader::load_trades_sync;
use ml::features::ofi_calculator::OFICalculator;
info!("Loading MBP-10 snapshots from {}...", mbp10_path.display());
let mut mbp10_files = collect_dbn_files_recursive(mbp10_path);
mbp10_files.sort();
if mbp10_files.is_empty() {
info!("No MBP-10 files found, OFI will be zeros");
vec![[0.0; 8]; n]
} else {
// Load MBP-10 snapshots (parallel)
use data::providers::databento::dbn_parser::DbnParser;
let per_file: Vec<Vec<_>> = {
use rayon::prelude::*;
mbp10_files.par_iter().filter_map(|file| {
let parser = match DbnParser::new() {
Ok(p) => p,
Err(e) => { tracing::warn!("Parser init failed: {e}"); return None; }
};
let mut snapshots = Vec::new();
match parser.parse_mbp10_streaming(file, 100, |snap| {
snapshots.push(snap.clone());
}) {
Ok(_) => {
info!(" {} -> {} snapshots", file.file_name().unwrap_or_default().to_string_lossy(), snapshots.len());
Some(snapshots)
}
Err(e) => {
tracing::warn!(" Failed {:?}: {e}", file.file_name());
None
}
}
}).collect()
};
let mut all_snapshots = Vec::new();
for snaps in per_file { all_snapshots.extend(snaps); }
all_snapshots.sort_by_key(|s| s.timestamp);
info!("Loaded {} MBP-10 snapshots in {:.1}s", all_snapshots.len(), t2.elapsed().as_secs_f64());
// Load trades (parallel)
let all_trades = if let Some(ref tdir) = trades_dir {
let mut trade_files = collect_dbn_files_recursive(tdir);
trade_files.sort();
let per_file_trades: Vec<Vec<_>> = {
use rayon::prelude::*;
trade_files.par_iter().filter_map(|path| {
match load_trades_sync(path) {
Ok(t) => { info!(" {} trades from {:?}", t.len(), path.file_name()); Some(t) }
Err(e) => { tracing::warn!(" Failed {:?}: {e}", path.file_name()); None }
}
}).collect()
};
let mut trades = Vec::new();
for t in per_file_trades { trades.extend(t); }
trades.sort_by_key(|t| t.timestamp);
if trades.is_empty() { None } else { Some(trades) }
} else {
None
};
// Compute per-bar OFI
use ml::features::mbp10_loader::get_snapshots_for_timestamp;
use ml::features::trades_loader::get_trades_for_bar;
let mut calculator = OFICalculator::new();
let mut ofi_per_bar = Vec::with_capacity(n);
for i in 0..n {
let bar = &all_bars[i + WARMUP];
let bar_ts = bar.timestamp.timestamp_nanos_opt().unwrap_or(0) as u64;
// Feed trades for this bar window
if let Some(ref trades) = all_trades {
let bar_end_ts = all_bars.get(i + WARMUP + 1)
.map(|b| b.timestamp.timestamp_nanos_opt().unwrap_or(0) as u64)
.unwrap_or(bar_ts + 60_000_000_000);
for trade in get_trades_for_bar(trades, bar_ts, bar_end_ts) {
calculator.feed_trade(trade.price, trade.volume, trade.is_buy);
}
}
let window = get_snapshots_for_timestamp(&all_snapshots, bar_ts, 1);
if let Some(snap) = window.first() {
match calculator.calculate(snap) {
Ok(f) if f.is_valid() => ofi_per_bar.push(f.to_array()),
_ => ofi_per_bar.push([0.0; 8]),
}
} else {
ofi_per_bar.push([0.0; 8]);
}
}
let non_zero = ofi_per_bar.iter().filter(|f| f.iter().any(|&v| v != 0.0)).count();
info!("OFI computed: {} bars, {} non-zero ({:.1}%) in {:.1}s",
ofi_per_bar.len(), non_zero,
if ofi_per_bar.is_empty() { 0.0 } else { non_zero as f64 / ofi_per_bar.len() as f64 * 100.0 },
t2.elapsed().as_secs_f64());
ofi_per_bar
}
} else {
info!("No MBP-10 directory, OFI will be zeros");
vec![[0.0; 8]; n]
};
let total_len = n;
// ── Compute cache key ────────────────────────────────────────────────────
let hex_key = ml::feature_cache::calculate_dbn_cache_key_full(
&data_dir,
mbp10_dir.as_deref(),
trades_dir.as_deref(),
).context("Failed to compute cache key")?;
let cache_key: [u8; 32] = hex::decode(&hex_key)
.context("Invalid hex key")?
.try_into()
.map_err(|_| anyhow::anyhow!("Cache key wrong length"))?;
// ── Write .fxcache ───────────────────────────────────────────────────────
std::fs::create_dir_all(&output_dir)
.with_context(|| format!("Failed to create output directory: {}", output_dir.display()))?;
let output_path = output_dir.join(format!("{hex_key}.fxcache"));
let t2 = Instant::now();
info!("Writing .fxcache to {}...", output_path.display());
let bytes_written = ml::fxcache::write_fxcache(
&output_path,
&features,
&targets,
&ofi,
&timestamps,
cache_key,
opts.bf16,
)
.context("Failed to write .fxcache file")?;
let write_secs = t2.elapsed().as_secs_f64();
let total_secs = t0.elapsed().as_secs_f64();
// ── Summary ──────────────────────────────────────────────────────────────
println!();
println!("================================================================================");
println!("PRECOMPUTE SUMMARY");
println!("================================================================================");
println!();
let has_ofi = ofi.iter().any(|o| o.iter().any(|&v| v != 0.0));
println!("Bars: {}", total_len);
println!("Features: 42-dim");
println!("Targets: 4-dim");
println!("OFI: 8-dim ({})", if has_ofi { "from MBP-10" } else { "zero-padded" });
println!("Format: {} (v{})", if opts.bf16 { "bf16" } else { "f64" }, if opts.bf16 { 2 } else { 1 });
println!("Cache key: {}", hex_key);
println!("Output: {}", output_path.display());
println!("Size: {:.2} MB", bytes_written as f64 / 1_048_576.0);
println!("Write time: {:.1}s", write_secs);
println!("Total time: {:.1}s", total_secs);
println!();
Ok(())
}