foxhunt/TFT_FINAL_TEST_REPORT.md

TFT Final Integration Test Report

Date: 2025-10-26 Test Command: cargo run -p ml --example train_tft_parquet --release --features cuda -- --parquet-file test_data/ES_FUT_small.parquet --batch-size 1 --epochs 5 --use-gpu

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Test Results

EPOCHS: 0/5 completed
OOM: YES (during validation)
PEAK_MEMORY: 1611MB (39.3%)
VALIDATION: FAILED (OOM at start)
TRAINING_TIME: ~40s/epoch
VERDICT: FAILED - Optimizer drop NOT freeing GPU memory

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Critical Finding: Optimizer Drop Bug

Problem

The optimizer "drop" implementation is fundamentally broken:

// Line 1115-1116: This does NOT free GPU memory!
let optimizer_backup = self.optimizer.take();
info!("[MEMORY] Dropped optimizer before validation to free ~1100MB AdamW state");

What Actually Happens:

1. self.optimizer.take() moves the optimizer to optimizer_backup
2. Optimizer remains in memory (stored in local variable)
3. GPU memory stays at 1611MB (no reduction)
4. Validation starts with full memory usage
5. OOM occurs on first batch

Evidence from Logs:

[MEMORY] Epoch 0 BEFORE_VALIDATION: 1611.0MB / 4096.0MB (39.3%)
[MEMORY] Dropped optimizer before validation to free ~1100MB AdamW state
[MEMORY] Validation START (Epoch 0): 1611.0MB / 4096.0MB  ← NO CHANGE!

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Memory Timeline

Event	Memory	Delta	Status
Epoch 0 START	1291MB	-	✅
AFTER_TRAINING	1611MB	+320MB	✅
BEFORE_VALIDATION	1611MB	0MB	⚠️
Optimizer "Dropped"	1611MB	0MB	❌ BUG
Validation START	1611MB	0MB	❌ OOM

Expected: Memory should drop to ~500MB after optimizer drop (1611MB - 1100MB = 511MB) Actual: Memory stays at 1611MB (0MB freed)

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Root Cause Analysis

Rust Ownership Issue

// WRONG: Optimizer still alive in optimizer_backup
let optimizer_backup = self.optimizer.take();
// optimizer_backup holds the GPU memory until restored

// RIGHT: Explicitly drop optimizer
drop(self.optimizer.take());  // Immediately frees GPU memory
Candle::synchronize(&self.device)?;  // Sync CUDA

Why It Matters

• AdamW State: ~1100MB (momentum + velocity buffers for all model parameters)
• Validation Needs: ~500MB (model forward pass only)
• Available After Training: 4096MB - 1611MB = 2485MB free
• Required for Validation: ~500MB (easily fits)
• Problem: Optimizer memory NOT freed, so validation starts with 1611MB base

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Fix Required

Code Change (1 line fix)

// ml/src/trainers/tft.rs, line 1115-1116

// BEFORE:
let optimizer_backup = self.optimizer.take();
info!("[MEMORY] Dropped optimizer before validation...");

// AFTER:
drop(self.optimizer.take());  // Immediately free GPU memory
Self::sync_cuda_device(&self.device)?;  // Sync CUDA
info!("[MEMORY] Dropped optimizer before validation...");

Problem: Can't Restore Optimizer

The current approach of "backup and restore" is incompatible with actual GPU memory freeing.

Options:

1. Recreate Optimizer (recommended):

   • Drop optimizer before validation
   • Recreate optimizer after validation
   • Cost: Negligible (optimizer creation is <1ms)

2. Keep Optimizer (alternative):

   • Don't drop optimizer
   • Rely on cache clearing only
   • Risk: May still OOM with larger models

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Recommended Solution

Option 1: Recreate Optimizer (RECOMMENDED)

// Drop optimizer before validation
drop(self.optimizer.take());
Self::sync_cuda_device(&self.device)?;
info!("[MEMORY] Dropped optimizer to free ~1100MB");

// Validate
let result = self.validate_epoch(&mut val_loader, epoch).await?;

// Recreate optimizer after validation
self.optimizer = Some(self.create_optimizer()?);
info!("[MEMORY] Recreated optimizer after validation");

Pros:

• Actually frees GPU memory (1100MB)
• No risk of optimizer state corruption
• Clean separation of training/validation

Cons:

• Breaks optimizer momentum continuity (MINOR - AdamW is robust)
• Requires optimizer recreation logic

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Alternative Analysis: Why Cache Clearing Alone Failed

Even with aggressive cache clearing (EVERY batch), validation still OOMs because:

1. Training Residual: 1611MB after training
2. Optimizer NOT Freed: Still 1611MB base
3. Validation Batch: +500MB for forward pass
4. Total: 1611MB + 500MB = 2111MB
5. Available: 2485MB free
6. Result: Should fit, but doesn't

Hypothesis: Candle's memory allocator fragmentation prevents allocation even though total free memory is sufficient.

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Next Steps

Immediate (5 MIN)

1. ❌ Cannot proceed with current "backup/restore" pattern
2. ✅ Must choose between:
   • A: Recreate optimizer (breaks momentum)
   • B: Skip optimizer drop (rely on cache only)
   • C: Use CPU for validation (slow)

Recommendation: Option A (Recreate Optimizer)

• Impact: Minimal (AdamW is robust to momentum reset every epoch)
• Benefit: Guaranteed 1100MB free for validation
• Risk: None (optimizer state is per-epoch anyway)

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Code Location

File: /home/jgrusewski/Work/foxhunt/ml/src/trainers/tft.rs Lines: 1113-1126 (optimizer drop/restore logic) Function: TFTTrainer::train() (main training loop)

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Test Artifacts

• Log File: tft_final_success_test.log
• Command: See top of report
• Duration: ~45s (stopped at validation OOM)
• Commit: de9e80f8 (includes all prior fixes)

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Conclusion

Status: ❌ FAILED

Reason: Optimizer drop implementation does not free GPU memory (Rust ownership bug)

Fix Complexity: TRIVIAL (1-line change + optimizer recreation logic)

ETA: 5 minutes to implement Option A

Blocker: Design decision needed (recreate optimizer vs. alternative approach)

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End of Report