11cc14d707
Sovereign backup of all Hermes Agent configuration and data. Excludes: secrets, auth tokens, sessions, caches, code (separate repo). Tracked: - config.yaml (model, fallback chain, toolsets, display prefs) - SOUL.md (Timmy personality charter) - memories/ (persistent MEMORY.md + USER.md) - skills/ (371 files — full skill library) - cron/jobs.json (scheduled tasks) - channel_directory.json (platform channels) - hooks/ (custom hooks)
216 lines
7.0 KiB
Markdown
216 lines
7.0 KiB
Markdown
# Performance Benchmarks
|
||
|
||
## Contents
|
||
- Speed comparisons across GPUs
|
||
- Memory usage analysis
|
||
- Scaling with sequence length
|
||
- Training vs inference performance
|
||
- Flash Attention versions comparison
|
||
|
||
## Speed comparisons across GPUs
|
||
|
||
### A100 80GB (Ampere)
|
||
|
||
**Forward pass time** (milliseconds, batch=8, heads=32, dim=64):
|
||
|
||
| Seq Length | Standard | Flash Attn 2 | Flash Attn 3 | Speedup (FA2) |
|
||
|------------|----------|--------------|--------------|---------------|
|
||
| 512 | 1.2 | 0.9 | N/A | 1.3x |
|
||
| 1024 | 3.8 | 1.4 | N/A | 2.7x |
|
||
| 2048 | 14.2 | 4.8 | N/A | 3.0x |
|
||
| 4096 | 55.1 | 17.3 | N/A | 3.2x |
|
||
| 8192 | 218.5 | 66.2 | N/A | 3.3x |
|
||
|
||
### H100 80GB (Hopper)
|
||
|
||
**Forward pass time** (milliseconds, same config):
|
||
|
||
| Seq Length | Standard | Flash Attn 2 | Flash Attn 3 (FP16) | Flash Attn 3 (FP8) | Best Speedup |
|
||
|------------|----------|--------------|---------------------|--------------------|--------------|
|
||
| 512 | 0.8 | 0.6 | 0.4 | 0.3 | 2.7x |
|
||
| 1024 | 2.6 | 1.0 | 0.6 | 0.4 | 6.5x |
|
||
| 2048 | 9.8 | 3.4 | 2.0 | 1.3 | 7.5x |
|
||
| 4096 | 38.2 | 12.5 | 7.2 | 4.8 | 8.0x |
|
||
| 8192 | 151.4 | 47.8 | 27.1 | 18.2 | 8.3x |
|
||
|
||
**Key insight**: Flash Attention 3 on H100 with FP8 achieves ~1.2 PFLOPS (75% of theoretical max).
|
||
|
||
### A10G 24GB (Ampere)
|
||
|
||
**Forward pass time** (milliseconds, batch=4):
|
||
|
||
| Seq Length | Standard | Flash Attn 2 | Speedup |
|
||
|------------|----------|--------------|---------|
|
||
| 512 | 2.1 | 1.6 | 1.3x |
|
||
| 1024 | 6.8 | 2.8 | 2.4x |
|
||
| 2048 | 25.9 | 9.4 | 2.8x |
|
||
| 4096 | 102.1 | 35.2 | 2.9x |
|
||
|
||
## Memory usage analysis
|
||
|
||
### GPU memory consumption (batch=8, heads=32, dim=64)
|
||
|
||
**Standard attention memory**:
|
||
|
||
| Seq Length | Attention Matrix | KV Cache | Total | Notes |
|
||
|------------|------------------|----------|-------|-------|
|
||
| 512 | 8 MB | 32 MB | 40 MB | Manageable |
|
||
| 2048 | 128 MB | 128 MB | 256 MB | Growing |
|
||
| 8192 | 2048 MB (2 GB) | 512 MB | 2.5 GB | Large |
|
||
| 32768 | 32768 MB (32 GB) | 2048 MB | 34 GB | OOM on 24GB GPUs |
|
||
|
||
**Flash Attention 2 memory**:
|
||
|
||
| Seq Length | Attention (on-chip) | KV Cache | Total | Reduction |
|
||
|------------|---------------------|----------|-------|-----------|
|
||
| 512 | 0 MB (recomputed) | 32 MB | 32 MB | 20% |
|
||
| 2048 | 0 MB | 128 MB | 128 MB | 50% |
|
||
| 8192 | 0 MB | 512 MB | 512 MB | 80% |
|
||
| 32768 | 0 MB | 2048 MB | 2 GB | 94% |
|
||
|
||
**Key insight**: Flash Attention doesn't materialize attention matrix, saving O(N²) memory.
|
||
|
||
### Memory scaling comparison
|
||
|
||
**Llama 2 7B model memory** (float16, batch=1):
|
||
|
||
| Context Length | Standard Attention | Flash Attention 2 | Can Fit 24GB GPU? |
|
||
|----------------|-------------------|-------------------|-------------------|
|
||
| 2K | 3.2 GB | 2.1 GB | Both: Yes |
|
||
| 4K | 5.8 GB | 2.8 GB | Both: Yes |
|
||
| 8K | 12.1 GB | 4.2 GB | Both: Yes |
|
||
| 16K | 26.3 GB (OOM) | 7.8 GB | Only Flash: Yes |
|
||
| 32K | OOM | 14.2 GB | Only Flash: Yes |
|
||
|
||
### Training memory (Llama 2 7B, batch=4)
|
||
|
||
| Context | Standard (GB) | Flash Attn (GB) | Reduction |
|
||
|---------|---------------|-----------------|-----------|
|
||
| 2K | 18.2 | 12.4 | 32% |
|
||
| 4K | 34.8 | 16.8 | 52% |
|
||
| 8K | OOM (>40GB) | 26.2 | Fits! |
|
||
|
||
## Scaling with sequence length
|
||
|
||
### Computational complexity
|
||
|
||
**Standard attention**:
|
||
- Time: O(N² × d)
|
||
- Memory: O(N² + N × d)
|
||
|
||
**Flash Attention**:
|
||
- Time: O(N² × d) (same, but with better constants)
|
||
- Memory: O(N × d) (linear!)
|
||
|
||
### Empirical scaling (A100, batch=1, heads=32, dim=64)
|
||
|
||
**Time per token (milliseconds)**:
|
||
|
||
| Sequence | 512 | 1K | 2K | 4K | 8K | 16K |
|
||
|----------|-----|-----|-----|-----|-----|------|
|
||
| Standard | 0.15 | 0.37 | 1.11 | 3.44 | 13.4 | 52.8 |
|
||
| Flash Attn 2 | 0.11 | 0.14 | 0.24 | 0.43 | 0.83 | 1.64 |
|
||
| Speedup | 1.4x | 2.6x | 4.6x | 8.0x | 16.1x | 32.2x |
|
||
|
||
**Observation**: Speedup increases quadratically with sequence length!
|
||
|
||
### Memory per token (MB)
|
||
|
||
| Sequence | 512 | 1K | 2K | 4K | 8K | 16K |
|
||
|----------|-----|-----|-----|-----|-----|------|
|
||
| Standard | 0.08 | 0.13 | 0.25 | 0.64 | 2.05 | 8.13 |
|
||
| Flash Attn 2 | 0.06 | 0.06 | 0.06 | 0.06 | 0.06 | 0.06 |
|
||
|
||
**Observation**: Flash Attention memory per token is constant!
|
||
|
||
## Training vs inference performance
|
||
|
||
### Training (forward + backward, Llama 2 7B, A100)
|
||
|
||
| Batch × Seq | Standard (samples/sec) | Flash Attn (samples/sec) | Speedup |
|
||
|-------------|------------------------|--------------------------|---------|
|
||
| 4 × 2K | 1.2 | 3.1 | 2.6x |
|
||
| 8 × 2K | 2.1 | 5.8 | 2.8x |
|
||
| 4 × 4K | 0.4 | 1.3 | 3.3x |
|
||
| 8 × 4K | OOM | 2.4 | Enabled |
|
||
| 2 × 8K | 0.1 | 0.4 | 4.0x |
|
||
|
||
### Inference (generation, Llama 2 7B, A100)
|
||
|
||
| Context Length | Standard (tokens/sec) | Flash Attn (tokens/sec) | Speedup |
|
||
|----------------|----------------------|-------------------------|---------|
|
||
| 512 | 48 | 52 | 1.1x |
|
||
| 2K | 42 | 62 | 1.5x |
|
||
| 4K | 31 | 58 | 1.9x |
|
||
| 8K | 18 | 51 | 2.8x |
|
||
| 16K | OOM | 42 | Enabled |
|
||
|
||
**Note**: Inference speedup less dramatic than training because generation is memory-bound (KV cache accesses).
|
||
|
||
## Flash Attention versions comparison
|
||
|
||
### Flash Attention 1 vs 2 vs 3 (H100, seq=4096, batch=8)
|
||
|
||
| Metric | FA1 | FA2 | FA3 (FP16) | FA3 (FP8) |
|
||
|--------|-----|-----|------------|-----------|
|
||
| Forward time (ms) | 28.4 | 12.5 | 7.2 | 4.8 |
|
||
| Memory (GB) | 4.8 | 4.2 | 4.2 | 2.8 |
|
||
| TFLOPS | 180 | 420 | 740 | 1150 |
|
||
| GPU util % | 35% | 55% | 75% | 82% |
|
||
|
||
**Key improvements**:
|
||
- FA2: 2.3x faster than FA1 (better parallelism)
|
||
- FA3 (FP16): 1.7x faster than FA2 (H100 async optimizations)
|
||
- FA3 (FP8): 2.6x faster than FA2 (low precision)
|
||
|
||
### Features by version
|
||
|
||
| Feature | FA1 | FA2 | FA3 |
|
||
|---------|-----|-----|-----|
|
||
| Basic attention | ✅ | ✅ | ✅ |
|
||
| Causal masking | ✅ | ✅ | ✅ |
|
||
| Multi-query attention | ❌ | ✅ | ✅ |
|
||
| Sliding window | ❌ | ✅ | ✅ |
|
||
| Paged KV cache | ❌ | ✅ | ✅ |
|
||
| FP8 support | ❌ | ❌ | ✅ (H100 only) |
|
||
| Work partitioning | Basic | Advanced | Optimal |
|
||
|
||
## Real-world model benchmarks
|
||
|
||
### Llama 2 models (A100 80GB, batch=4, seq=2048)
|
||
|
||
| Model | Params | Standard (samples/sec) | Flash Attn (samples/sec) | Speedup |
|
||
|-------|--------|------------------------|--------------------------|---------|
|
||
| Llama 2 7B | 7B | 1.2 | 3.1 | 2.6x |
|
||
| Llama 2 13B | 13B | 0.6 | 1.7 | 2.8x |
|
||
| Llama 2 70B | 70B | 0.12 | 0.34 | 2.8x |
|
||
|
||
### GPT-style models (seq=1024)
|
||
|
||
| Model | Standard (tokens/sec) | Flash Attn (tokens/sec) | Speedup |
|
||
|-------|----------------------|-------------------------|---------|
|
||
| GPT-2 (124M) | 520 | 680 | 1.3x |
|
||
| GPT-J (6B) | 42 | 98 | 2.3x |
|
||
| GPT-NeoX (20B) | 8 | 22 | 2.75x |
|
||
|
||
## Recommendations by use case
|
||
|
||
**Training large models (>7B parameters)**:
|
||
- Use Flash Attention 2 on A100
|
||
- Use Flash Attention 3 FP8 on H100 for maximum speed
|
||
- Expected: 2.5-3x speedup
|
||
|
||
**Long context inference (>4K tokens)**:
|
||
- Flash Attention essential (enables contexts standard attention can't handle)
|
||
- Expected: 2-4x speedup, 5-10x memory reduction
|
||
|
||
**Short sequences (<512 tokens)**:
|
||
- Flash Attention provides 1.2-1.5x speedup
|
||
- Minimal memory benefit
|
||
- Still worth enabling (no downside)
|
||
|
||
**Multi-user serving**:
|
||
- Flash Attention reduces per-request memory
|
||
- Allows higher concurrent batch sizes
|
||
- Can serve 2-3x more users on same hardware
|