foxhunt/WAVE_8_11_QUICK_REFERENCE.md

Wave 8.11: TFT Inference Latency Benchmark - Quick Reference

Date: 2025-10-15 Status: ⚠️ NEEDS OPTIMIZATION (P95: 12.78ms, Target: <5ms, Gap: 2.6x)

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🎯 Mission

Benchmark TFT inference latency to ensure P95 <5ms for production HFT.

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📊 Results Summary

Metric	Value	Target	Status
P95 Latency	12.78ms	<5ms	❌ 2.6x slower
Mean Latency	10.75ms	<2ms	❌ 5.4x slower
P50 Latency	10.37ms	N/A	❌
P99 Latency	15.61ms	N/A	❌
Consistency (P99/P50)	1.51x	<2.0	✅ PASS
Memory/Inference	4.67 KB	<10MB	✅ PASS

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🔍 Key Findings

TFT vs Other Models (P95 Comparison)

Model         P95       Status
─────────────────────────────
DQN           2.1ms     ✅ (6.1x faster)
PPO           3.2ms     ✅ (4.0x faster)
MAMBA-2       1.8ms     ✅ (7.1x faster)
TFT          14.1ms     ❌ (2.8x above target)

Batch Size Impact

Batch=1:  14.77ms latency,  68 samples/sec  (HFT use case)
Batch=8:   1.76ms/sample, 570 samples/sec  (throughput mode)

Flash Attention

Standard: 14.64ms P95
Flash:    15.10ms P95  (0.97x speedup - NO benefit for short sequences)

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🚀 Optimization Roadmap

Phase 1: INT8 Quantization (1 week) ⭐⭐⭐⭐

• Expected: 12.78ms → 3.20ms (✅ 36% below 5ms target)
• Action: Implement post-training INT8 quantization
• Risk: <5% accuracy loss
• Priority: HIGHEST - START IMMEDIATELY

Phase 2: FP16 Mixed Precision (3 days)

• Expected: 12.78ms → 6.39ms (still 1.3x above target)
• Action: Convert model to FP16
• Risk: <2% accuracy loss
• Priority: If INT8 insufficient

Phase 3: CUDA Kernel Fusion (2-3 weeks)

• Expected: 12.78ms → 6.39-8.52ms
• Action: Fuse matmul+activation, layernorm+dropout
• Complexity: High (custom CUDA kernels)
• Priority: If INT8 + FP16 insufficient

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✅ What Works

1. Consistency: P99/P50 ratio = 1.51x (✅ <2.0 target)
2. Memory: 4.67 KB/inference (✅ <10MB target)
3. Batch throughput: 570 samples/sec with batch=8
4. CUDA acceleration: All tests run on CUDA (RTX 3050 Ti)

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❌ What Doesn't Work

1. P95 latency: 12.78ms (❌ 2.6x above 5ms target)
2. Flash Attention: 0.97x speedup (❌ expected 2-4x)
3. Model size reduction: Even smallest model (64 hidden, 2 layers) = 13.12ms (❌ still 2.6x above)

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🛠️ Commands

Run Full Benchmark Suite

cargo test -p ml --test tft_inference_latency_benchmark --release -- --nocapture --test-threads=1

Run Single Test (P95)

cargo test -p ml --test tft_inference_latency_benchmark test_tft_inference_latency_p95_target --release -- --nocapture

Run Model Comparison

cargo test -p ml --test tft_inference_latency_benchmark test_tft_latency_comparison_with_other_models --release -- --nocapture

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📁 Files

1. Benchmark Tests: /home/jgrusewski/Work/foxhunt/ml/tests/tft_inference_latency_benchmark.rs (765 lines)
2. Full Report: /home/jgrusewski/Work/foxhunt/WAVE_8_11_TFT_INFERENCE_LATENCY_BENCHMARK.md
3. This File: /home/jgrusewski/Work/foxhunt/WAVE_8_11_QUICK_REFERENCE.md

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🎯 Next Action

IMMEDIATE: Implement INT8 quantization (Phase 1) to reduce P95 from 12.78ms → 3.20ms.

Timeline: 1 week for implementation + validation

Success Criteria: P95 <5ms with <5% accuracy loss

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💡 Decision Tree

Current P95: 12.78ms (❌ 2.6x above 5ms)
    │
    ├─ Apply INT8 quantization (Phase 1)
    │  └─ Expected: 3.20ms
    │     │
    │     ├─ If P95 <5ms → ✅ DEPLOY
    │     │
    │     └─ If P95 >5ms → Apply FP16 (Phase 2)
    │        └─ Expected: 6.39ms
    │           │
    │           ├─ If P95 <5ms → ✅ DEPLOY
    │           │
    │           └─ If P95 >5ms → Apply Kernel Fusion (Phase 3)
    │              └─ Expected: 6.39-8.52ms
    │                 │
    │                 ├─ If P95 <5ms → ✅ DEPLOY
    │                 │
    │                 └─ If P95 >5ms → ⚠️ USE SIMPLER MODELS (DQN/PPO/MAMBA-2)

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📊 Benchmark Test Breakdown

1. test_tft_inference_latency_p95_target (CORE)

   • 100 iterations after warmup
   • Measures P50, P95, P99, consistency
   • Validates P95 <5ms target

2. test_tft_latency_comparison_with_other_models

   • Compares TFT vs DQN/PPO/MAMBA-2
   • Shows 6.7x slowdown vs DQN

3. test_tft_batch_size_latency_tradeoff

   • Tests batch sizes: 1, 2, 4, 8
   • Shows amortization effect

4. test_tft_flash_attention_speedup

   • Compares standard vs Flash Attention
   • Result: 0.97x (NO benefit for short sequences)

5. test_tft_model_size_latency_scaling

   • Tests model sizes: Small (64), Medium (128), Large (256), XL (512)
   • Shows 1.35x scaling from Small → XL

6. test_tft_inference_memory_usage

   • Validates memory <10MB target
   • Result: 4.67 KB (✅ PASS)

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Wave 8.11 Status: ✅ BENCHMARK COMPLETE, ⚠️ OPTIMIZATION REQUIRED