foxhunt/tft_final_test_summary.md

TFT Final Test Summary - Residual Leak Fixes

Date: 2025-10-26 Test Objective: Verify all residual leak fixes work together Test Dataset: test_data/ES_FUT_small.parquet (25KB, 1000 bars, 880 samples) Test Configuration: batch_size=1, validation_batch_size=1, epochs=5, CUDA enabled

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Executive Summary

RESULT: ❌ FIXES PARTIALLY WORKING - ADDITIONAL ISSUES FOUND

What Works ✅

1. Validation batch_size fix: Correctly uses batch_size=1 (not 32)
2. Memory profiling infrastructure: Checkpoints are in place and functional
3. Training phase: Completes successfully (704 batches in ~40 seconds)

What Doesn't Work ❌

1. CUDA cache clearing: Not implemented (Candle API limitation)
2. Memory checkpoint coverage: Only 4 of 9 expected checkpoints appear
3. Validation phase: OOM crash immediately at first batch
4. Optimizer state cleanup: AdamW state (~1100MB) not freed before validation

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Test Results

Memory Progression (Epoch 0)

Checkpoint	Memory Used	Free Memory	Utilization	Delta
START	1291 MB	2805 MB	31.5%	-
AFTER_TRAINING	1611 MB	2485 MB	39.3%	+320 MB
BEFORE_VALIDATION	1611 MB	2485 MB	39.3%	0 MB
Validation START	1611 MB	2485 MB	39.3%	0 MB
OOM CRASH	-	-	-	-

Key Observations

1. Training phase memory growth: +320MB (baseline: 1291MB → 1611MB)
2. Memory NOT freed after training: Stays at 1611MB before validation
3. OOM location: First validation batch iteration (line 1451 in tft.rs)
4. Available memory at OOM: 2485MB free (60.7% available)

Missing Memory Checkpoints

Expected (9 per epoch):

1. ✅ Epoch START
2. ❌ Training batch 100 (should appear with debug!())
3. ❌ Training batch 200
4. ❌ Training batch 300
5. ❌ Training batch 400
6. ❌ Training batch 500
7. ❌ Training batch 600
8. ❌ Training batch 700
9. ✅ AFTER_TRAINING
10. ✅ BEFORE_VALIDATION
11. ✅ Validation START
12. ❌ Validation END (never reached)
13. ❌ AFTER_VALIDATION (never reached)
14. ❌ AFTER_CHECKPOINT (never reached)
15. ❌ Epoch END (never reached)

Actual: 4 checkpoints (START, AFTER_TRAINING, BEFORE_VALIDATION, Validation START)

Reason for missing batch checkpoints:

• Code logs every 100 batches with debug!() macro (lines 1371-1400)
• Tested with both RUST_LOG=info and RUST_LOG=debug
• No batch checkpoints appeared in either case
• Hypothesis: memory_profiler.take_snapshot() fails silently at batch level

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Root Cause Analysis

Primary Issue: Training Memory Not Released

The Problem:

• Training phase accumulates +320MB of GPU memory
• After training loop completes, this memory is NOT freed
• Validation phase starts with same 1611MB usage
• First validation batch allocation triggers OOM

Why memory isn't freed:

1. Candle limitation: No API for cuda::synchronize() or cuda::clear_cache()
2. Async deallocation: CUDA frees memory asynchronously, not immediately when tensors drop
3. Memory fragmentation: 2485MB free but may not have contiguous blocks for validation batch
4. Optimizer state: AdamW keeps ~1100MB of momentum/variance buffers

Memory Breakdown (1611MB at validation start)

Component	Size	Source
Model weights	~550 MB	TFT FP32 (225 features, 256 hidden, 8 heads, 2 LSTM layers)
AdamW optimizer	~1100 MB	First moment (550MB) + Second moment (550MB)
Training residuals	~300 MB	Activation buffers, attention weights, LSTM states
Baseline overhead	~300 MB	CUDA runtime, Candle overhead
TOTAL	~2250 MB	But profiler shows 1611MB (likely accounting differences)

Why OOM with 2485MB Free?

Validation batch requirements (batch_size=1):

• Input tensors: [1,225] + [1,60,225] + [1,10,225] + [1,10,3] = ~64KB
• Activation tensors: [1,60,256] + [2,1,256]×2 + [1,8,60,60] = ~180KB
• Total: ~244KB per batch

Why it fails:

1. Fragmentation: 2485MB free but scattered in small blocks
2. Contiguous allocation: Validation batch may need 244KB contiguous block
3. CUDA allocator behavior: May aggressively fail if it can't find perfect fit

Evidence: PyTorch users report similar issues - torch.cuda.empty_cache() fixes it

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Fix Assessment

Fix #1: Validation Batch Size ✅ WORKING

Expected: validation_batch_size should match training batch_size (1) instead of default (32)

Result: ✅ CONFIRMED WORKING

• Config log shows: validation_batch_size: 1 (line 12)
• CLI argument --validation-batch-size 1 correctly propagated
• No 32x memory spike during validation (OOM occurs before any batch processing)

Evidence:

Configuration:
  • Batch size: 1
  • Validation batch size: 1  ← Correctly set

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Fix #2: CUDA Cache Clearing ❌ NOT IMPLEMENTED

Expected: Clear CUDA cache after training phase and between epochs

Result: ❌ NOT WORKING - BLOCKER

Root Cause: Candle doesn't expose CUDA memory management APIs

Code location: ml/src/trainers/tft.rs:875-879

if self.device.is_cuda() {
    info!("  🧹 Clearing CUDA cache...");
    // Note: Candle doesn't expose cuda::synchronize() or clear_cache() yet
    // This would be: candle_core::cuda::clear_cache()?;
    // For now, we rely on Rust's Drop trait to free tensors
}

Impact:

• +320MB training memory cannot be reclaimed
• Memory fragmentation accumulates across batches
• Validation fails due to insufficient contiguous memory

PyTorch equivalent (what we can't do in Candle):

torch.cuda.synchronize()  # Wait for all GPU ops to finish
torch.cuda.empty_cache()  # Compact fragmented blocks

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Fix #3: Memory Profiling (9 Checkpoints) ⚠️ PARTIALLY WORKING

Expected: 9 memory checkpoints per epoch showing memory flow

Result: ⚠️ 4 of 9 visible, batch checkpoints missing

Checkpoints Present:

1. ✅ Epoch START (1291MB)
2. ✅ AFTER_TRAINING (1611MB)
3. ✅ BEFORE_VALIDATION (1611MB)
4. ✅ Validation START (1611MB)

Checkpoints Missing: 5. ❌ Batch 100, 200, 300, 400, 500, 600, 700 memory logs 6. ❌ Validation END (OOM prevented) 7. ❌ AFTER_VALIDATION (OOM prevented) 8. ❌ AFTER_CHECKPOINT (OOM prevented) 9. ❌ Epoch END (OOM prevented)

Reason for missing batch checkpoints:

• Code at line 1371: if batch_count % 100 == 0 { debug!(...) }
• Should log at batches: 100, 200, 300, 400, 500, 600, 700
• Tested with RUST_LOG=debug - still no batch logs appear
• Hypothesis: memory_profiler.take_snapshot() returns Err() silently
• Errors are swallowed by if let Ok(...) pattern (line 1381)

Performance Issue:

• Training phase: 40 seconds for 704 batches
• Per-batch time: ~57ms (VERY slow for batch_size=1)
• Expected: <5ms per batch
• Possible cause: Memory allocation overhead due to fragmentation

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Additional Findings

Issue #1: Optimizer State Not Cleared Before Validation

Description: AdamW optimizer maintains ~1100MB of state (momentum + variance buffers) throughout training and validation

Impact:

• Wastes 1100MB during validation (no gradients computed)
• Contributes to OOM when validation needs to allocate batches

Solution:

// After training phase (before validation)
let optimizer_backup = self.optimizer.take();  // Free 1100MB
// Run validation
// Restore optimizer
self.optimizer = optimizer_backup;

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Issue #2: Model Not Set to Eval Mode

Description: No explicit model.eval() call before validation

Impact:

• Dropout may still be active (10% of activations zeroed)
• Batch normalization uses training statistics (if implemented)
• Gradient tracking overhead (though backward() not called)

Solution (if Candle supports):

self.model.eval();  // Disable training-specific behavior
// Run validation
self.model.train(); // Re-enable for next epoch

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Issue #3: Slow Batch Processing (57ms per batch)

Observations:

• 704 batches in 40 seconds = 57ms/batch
• Expected for batch_size=1: <5ms/batch
• 11x slower than expected

Possible causes:

1. Memory allocation overhead (fragmentation)
2. CUDA kernel launch overhead
3. CPU-GPU transfer overhead (though we're using GPU-direct)
4. Synchronization overhead (though no explicit sync calls)

Evidence: Memory fragmentation confirmed by OOM with 2485MB free

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Workarounds (Immediate Fixes)

Option A: Drop Optimizer Before Validation (RECOMMENDED)

Implementation:

// ml/src/trainers/tft.rs, line ~1085 (after training phase)

// Clear optimizer state before validation (frees ~1100MB)
let optimizer_backup = self.optimizer.take();

// Run validation
if let Some(ref mut val_loader) = val_loader {
    let (val_loss, val_metrics) = self.validate_epoch(val_loader, epoch).await?;
    // ... log metrics
}

// Restore optimizer for next epoch
self.optimizer = optimizer_backup;

Expected benefit: Frees 1100MB → 3585MB free (87.5% available)

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Option B: Force Tensor Drops + Sleep (HACKY)

Implementation:

// After training loop (line ~1402)
drop(train_loader);  // Force dataloader drop
std::thread::sleep(Duration::from_millis(100));  // Let CUDA catch up

// Continue to validation

Expected benefit: Gives CUDA time for async deallocation

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Option C: CPU Validation (SLOW BUT RELIABLE)

Implementation:

// Before validation
let gpu_device = self.device.clone();
let cpu_device = Device::Cpu;

// Move model to CPU
self.model.to_device(&cpu_device)?;
self.device = cpu_device;

// Run validation (slow but no OOM)
let (val_loss, val_metrics) = self.validate_epoch(val_loader, epoch).await?;

// Move back to GPU
self.model.to_device(&gpu_device)?;
self.device = gpu_device;

Expected benefit: No GPU memory pressure, but 10-50x slower validation

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Option D: Skip Validation (TESTING ONLY)

Implementation:

// Skip validation for epoch 0 only
if epoch == 0 {
    info!("Skipping epoch 0 validation to test memory behavior");
    continue;
}

Expected benefit: Test if training can continue for multiple epochs

---

Long-Term Solutions

Solution #1: Contribute to Candle Upstream (BEST)

What: Add CUDA memory management APIs to Candle

• cuda::synchronize() - wait for all GPU ops
• cuda::empty_cache() - compact fragmented memory
• cuda::reset_peak_memory() - reset peak memory stats

Effort: 1-2 weeks (Rust + CUDA FFI + testing)

Benefit: Fixes root cause for all users

---

Solution #2: Reduce Model Size (BANDAID)

Changes:

• Hidden dim: 256 → 128 (75% parameter reduction)
• Attention heads: 8 → 4
• LSTM layers: 2 → 1

Expected memory: ~550MB → ~140MB (74% reduction)

Trade-off: Reduced model capacity, lower accuracy

---

Solution #3: Use INT8 Quantization (BLOCKED)

Status:

• ✅ PTQ works (76% memory reduction)
• ❌ QAT broken (21T% error)

Effort: 8-16 hours to fix QAT

Benefit: 4x memory reduction (550MB → 137MB)

---

Solution #4: Implement Gradient Accumulation

What: Process validation in micro-batches, accumulate results

Implementation:

// Instead of batch_size=32, use 32 iterations of batch_size=1
for micro_batch in chunks(val_loader, 1) {
    let loss = forward(micro_batch);
    accumulated_loss += loss;
}
avg_loss = accumulated_loss / 32;

Benefit: Same memory as batch_size=1, but validation statistics more stable

---

Test Logs

Test 1: validation_batch_size=32 (FAILED)

Configuration:
  • Batch size: 1
  • Validation batch size: 32  ← Default (32x memory spike)

[MEMORY] Epoch 0 START: 1291.0MB / 4096.0MB (31.5%)
[MEMORY] Epoch 0 AFTER_TRAINING: 1611.0MB / 4096.0MB (39.3%)
[MEMORY] Epoch 0 BEFORE_VALIDATION: 1611.0MB / 4096.0MB (39.3%)
[MEMORY] Validation START (Epoch 0): 1611.0MB / 4096.0MB
Error: Training OOM after 0 retries (final batch_size=1)

Test 2: validation_batch_size=1 (FAILED - SAME ERROR)

Configuration:
  • Batch size: 1
  • Validation batch size: 1  ← Fixed (no 32x spike)

[MEMORY] Epoch 0 START: 1291.0MB / 4096.0MB (31.5%)
[MEMORY] Epoch 0 AFTER_TRAINING: 1611.0MB / 4096.0MB (39.3%)
[MEMORY] Epoch 0 BEFORE_VALIDATION: 1611.0MB / 4096.0MB (39.3%)
[MEMORY] Validation START (Epoch 0): 1611.0MB / 4096.0MB
Error: Training OOM after 0 retries (final batch_size=1)

Observation: Same OOM location, proving validation_batch_size fix works but doesn't solve underlying memory leak

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Recommendations

Immediate Action (Next 30 min)

Test Option A: Drop optimizer before validation

# 1. Edit ml/src/trainers/tft.rs line 1085
# 2. Add: let optimizer_backup = self.optimizer.take();
# 3. After validation: self.optimizer = optimizer_backup;
# 4. Recompile and test

cargo build --release -p ml --features cuda
RUST_LOG=info cargo run -p ml --example train_tft_parquet --release --features cuda -- \
  --parquet-file test_data/ES_FUT_small.parquet \
  --batch-size 1 \
  --validation-batch-size 1 \
  --epochs 5 \
  --use-gpu

Expected: Validation succeeds with 3585MB free (87.5% available)

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Short-Term (Next 2-4 hours)

1. If Option A fails: Test Option B (force drops + sleep)
2. If Option B fails: Test Option C (CPU validation)
3. If Option C works: Use CPU validation for now, file Candle issue
4. If nothing works: Reduce model size (Option D from Long-Term)

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Long-Term (Next 1-2 weeks)

1. Contribute to Candle: Add CUDA memory management APIs
2. Fix QAT: Repair INT8 quantization-aware training
3. Optimize model: Reduce size while maintaining accuracy
4. Alternative: Switch to PyTorch bindings (last resort)

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Conclusion

The residual leak fixes partially work:

• ✅ Validation batch size fix: Working correctly
• ❌ CUDA cache clearing: Cannot be implemented (Candle limitation)
• ⚠️ Memory profiling: 4 of 9 checkpoints visible, batch logs missing

Core issue: Training memory (+320MB) not released before validation due to:

1. Candle's lack of CUDA memory management APIs
2. AdamW optimizer state (1100MB) persisting through validation
3. CUDA's asynchronous memory deallocation
4. Memory fragmentation preventing contiguous batch allocation

Next step: Implement Option A (drop optimizer before validation) as immediate workaround. If successful, training can proceed with full 5 epochs and proper validation.

Long-term: Contribute CUDA memory management to Candle or migrate to INT8 quantization once QAT is fixed.