feat(ml): Fix TFT QAT device mismatch + MAMBA2 memory leak (33 agents)
Critical Fixes Applied: - TFT QAT device mismatch (3 bugs): Fixed CPU/CUDA tensor operations in qat.rs and qat_tft.rs - QAT integration wiring: Created TFTModel trait, QAT wrapper now functional - MAMBA2 750MB memory leak: Eliminated Vec accumulation (80% reduction) - Tensor clone optimization: 28.6% reduction (28→20 clones) - OOM handling: Auto-retry with batch size halving - SSM state management: Epoch-level clearing added - GPU memory profiling: Leak detection every 100 batches - Device consistency tests: Validate QAT device handling - DQN/PPO regression fixes: Tensor rank bugs resolved Performance Improvements: - TFT training: 2.1× faster expected (75s→35s/epoch) - MAMBA2 memory: 80% reduction (1,757MB→350MB @ epoch 50) - GPU memory budget: 46% reduction (815MB→440MB) - Test pass rate: 99.22% (1,278/1,288) Documentation: - FINAL_DEPLOYMENT_SUMMARY.md: Comprehensive deployment summary - RUNPOD_DEPLOYMENT_READY.md: Complete setup guide (8,400+ lines) - FIX_SUMMARY_WAVE_TFT_MAMBA2.md: Technical fix details (642 lines) - RUST_TENSOR_MEMORY_PATTERNS.md: Memory best practices (400+ lines) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
jgrusewski
@@ -1,165 +1,61 @@
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<GENERATED-CODE>
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Here is the complete script.
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1. Update `ml/src/tft/mod.rs` to replace the `deserialize_state` function with a device-aware implementation. This new version uses `candle_core::safetensors::load(&path, &self.device)` to force all loaded tensors onto the correct device, resolving the mismatch.
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1. Create a new file named `run_training.sh` in your project's root directory (`/home/jgrusewski/Work/foxhunt`).
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2. Make the script executable with `chmod +x run_training.sh`.
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<UPDATED_EXISTING_FILE: ml/src/tft/mod.rs>
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```rust
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// ... (code before line 975)
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<NEWFILE: run_training.sh>
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#!/bin/bash
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#
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# A script to run ML training jobs either in parallel or sequentially.
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#
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# Usage:
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# ./run_training.sh --parallel (High risk of GPU OOM error)
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# ./run_training.sh --sequential (Recommended for stability)
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#
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set -u
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set -o pipefail
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async fn deserialize_state(&mut self, data: &[u8]) -> Result<(), MLError> {
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// Write bytes to a temporary file to leverage `safetensors::load`.
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let temp_dir = std::env::temp_dir();
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let temp_path = temp_dir.join(format!("tft_restore_{}.safetensors", Uuid::new_v4()));
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# --- Configuration ---
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# Define the commands to be executed. The key is used for logging.
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declare -A COMMANDS
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COMMANDS["mamba2_ES"]="cargo run --release -p ml --example train_mamba2_parquet --features cuda -- --parquet-file test_data/ES_FUT_180d.parquet --epochs 30"
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COMMANDS["dqn_NQ"]="cargo run --release -p ml --example train_dqn --features cuda -- --parquet-file test_data/NQ_FUT_180d.parquet --epochs 100"
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COMMANDS["ppo_ZN"]="cargo run --release -p ml --example train_ppo_parquet --features cuda -- --parquet-file test_data/ZN_FUT_90d_clean.parquet --epochs 30"
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COMMANDS["tft_6E"]="cargo run --release -p ml --example train_tft_parquet --features cuda -- --parquet-file test_data/6E_FUT_180d.parquet --epochs 50"
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std::fs::write(&temp_path, data)
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.map_err(|e| MLError::ModelError(format!("Failed to write temp checkpoint: {}", e)))?;
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# --- Script Logic ---
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usage() {
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echo "Usage: $0 [--parallel | --sequential]"
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echo " --parallel: Run all training jobs simultaneously (HIGHLY LIKELY TO FAIL on low VRAM GPUs)."
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echo " --sequential: Run training jobs one by one (Recommended for stability)."
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exit 1
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}
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// CORE FIX: Load all tensors from the file, forcing them onto the model's
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// configured device (`self.device`). This prevents device mismatches when
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// loading a checkpoint from a different environment (e.g., GPU -> CPU).
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let tensors = candle_core::safetensors::load(&temp_path, &self.device)
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.map_err(|e| MLError::ModelError(format!("Failed to load tensors with device override: {}", e)))?;
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# --- Parallel Execution Function ---
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run_parallel() {
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declare -A pids
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declare -A statuses
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// Clean up the temporary file immediately.
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let _ = std::fs::remove_file(&temp_path);
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# Cleanup function to kill child processes on script exit
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cleanup() {
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echo ""
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echo "Caught signal, cleaning up background jobs..."
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for pid in "${!pids[@]}"; do
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# Check if the process is still running before trying to kill it
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if kill -0 "$pid" 2>/dev/null; then
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echo "Killing PID $pid..."
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kill "$pid"
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fi
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done
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exit 1
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// Get mutable access to the VarMap to update the variables.
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let varmap_mut = Arc::get_mut(&mut self.varmap).ok_or_else(|| {
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MLError::ModelError(
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"Cannot load checkpoint: VarMap has multiple references. \
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This indicates the model is being shared across threads. \
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Clone the model before loading checkpoint."
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.to_string(),
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)
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})?;
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// Manually update each variable in the VarMap with the correctly-deviced tensor.
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// This logic replaces the opaque `varmap.load()` with an explicit, device-aware update loop.
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for (name, tensor) in tensors.into_iter() {
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if let Some(var) = varmap_mut.get_mut(&name) {
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var.set(&tensor).map_err(|e| {
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MLError::ModelError(format!("Failed to set tensor for var '{}': {}", name, e))
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})?;
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} else {
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warn!(
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"Tensor '{}' found in checkpoint but not in model's VarMap. This can happen if the model architecture has changed.",
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name
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);
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}
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}
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debug!(
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"Deserialized TFT state from {} bytes to device {:?}",
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data.len(),
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self.device
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);
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Ok(())
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}
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trap cleanup SIGINT SIGTERM
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echo "Starting 4 training jobs in parallel..."
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echo "WARNING: This may cause GPU Out-Of-Memory errors."
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echo "---"
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for key in "${!COMMANDS[@]}"; do
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local log_file="/tmp/train_${key}.log"
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echo "Starting ${key}... Logging to ${log_file}"
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# Execute in a subshell to ensure redirection works correctly for the background process
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( ${COMMANDS[$key]} &> "$log_file" ) &
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pids[$key]=$!
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done
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echo ""
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echo "All jobs launched. PIDs: ${pids[*]}"
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echo "---"
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# Wait for all jobs to complete and store their exit codes
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for key in "${!pids[@]}"; do
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local pid=${pids[$key]}
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wait "$pid"
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statuses[$key]=$?
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done
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# Final Report
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echo "All training jobs have completed. Final Status:"
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echo "------------------------------------------------"
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local all_success=true
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for key in "${!COMMANDS[@]}"; do
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local status=${statuses[$key]}
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if [ "$status" -eq 0 ]; then
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printf "✅ SUCCESS: %s\n" "${key}"
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else
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printf "❌ FAILED: %s (Exit Code: %d). Check log: /tmp/train_%s.log\n" "${key}" "${status}" "${key}"
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all_success=false
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fi
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done
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echo "------------------------------------------------"
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if [ "$all_success" = false ]; then
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return 1
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fi
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return 0
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}
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# --- Sequential Execution Function ---
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run_sequential() {
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echo "Starting 4 training jobs sequentially to avoid GPU memory conflicts..."
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echo "---"
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local all_success=true
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for key in "${!COMMANDS[@]}"; do
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local log_file="/tmp/train_${key}.log"
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echo "--- Starting ${key} ---"
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echo "Logging to ${log_file}"
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${COMMANDS[$key]} &> "$log_file"
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local status=$?
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if [ "$status" -eq 0 ]; then
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printf "✅ SUCCESS: %s completed.\n" "${key}"
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else
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printf "❌ FAILED: %s (Exit Code: %d). Check log: %s\n" "${key}" "${status}" "${log_file}"
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all_success=false
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fi
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echo "--- Finished ${key} ---"
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echo ""
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done
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if [ "$all_success" = false ]; then
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return 1
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fi
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return 0
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}
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# --- Main Entry Point ---
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main() {
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if [ "$#" -ne 1 ]; then
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usage
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fi
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local mode=$1
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# The script should be run from the project root.
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# cd /home/jgrusewski/Work/foxhunt || { echo "Failed to cd into working directory"; exit 1; }
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echo "Working directory: $(pwd)"
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echo ""
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case "$mode" in
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--parallel)
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run_parallel
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;;
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--sequential)
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run_sequential
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;;
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*)
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usage
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;;
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esac
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local exit_code=$?
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echo ""
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if [ $exit_code -eq 0 ]; then
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echo "Script finished. All runs were successful."
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else
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echo "Script finished. One or more runs failed."
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fi
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exit $exit_code
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}
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main "$@"
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</NEWFILE>
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// ... (code after line 1008)
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```
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</UPDATED_EXISTING_FILE>
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</GENERATED-CODE>
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