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refactor: reorganize skills into sub-categories
The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
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skills/mlops/training/flash-attention/references/benchmarks.md
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skills/mlops/training/flash-attention/references/benchmarks.md
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# Performance Benchmarks
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## Contents
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- Speed comparisons across GPUs
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- Memory usage analysis
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- Scaling with sequence length
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- Training vs inference performance
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- Flash Attention versions comparison
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## Speed comparisons across GPUs
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### A100 80GB (Ampere)
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**Forward pass time** (milliseconds, batch=8, heads=32, dim=64):
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| Seq Length | Standard | Flash Attn 2 | Flash Attn 3 | Speedup (FA2) |
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|------------|----------|--------------|--------------|---------------|
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| 512 | 1.2 | 0.9 | N/A | 1.3x |
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| 1024 | 3.8 | 1.4 | N/A | 2.7x |
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| 2048 | 14.2 | 4.8 | N/A | 3.0x |
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| 4096 | 55.1 | 17.3 | N/A | 3.2x |
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| 8192 | 218.5 | 66.2 | N/A | 3.3x |
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### H100 80GB (Hopper)
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**Forward pass time** (milliseconds, same config):
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| Seq Length | Standard | Flash Attn 2 | Flash Attn 3 (FP16) | Flash Attn 3 (FP8) | Best Speedup |
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|------------|----------|--------------|---------------------|--------------------|--------------|
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| 512 | 0.8 | 0.6 | 0.4 | 0.3 | 2.7x |
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| 1024 | 2.6 | 1.0 | 0.6 | 0.4 | 6.5x |
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| 2048 | 9.8 | 3.4 | 2.0 | 1.3 | 7.5x |
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| 4096 | 38.2 | 12.5 | 7.2 | 4.8 | 8.0x |
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| 8192 | 151.4 | 47.8 | 27.1 | 18.2 | 8.3x |
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**Key insight**: Flash Attention 3 on H100 with FP8 achieves ~1.2 PFLOPS (75% of theoretical max).
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### A10G 24GB (Ampere)
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**Forward pass time** (milliseconds, batch=4):
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| Seq Length | Standard | Flash Attn 2 | Speedup |
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|------------|----------|--------------|---------|
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| 512 | 2.1 | 1.6 | 1.3x |
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| 1024 | 6.8 | 2.8 | 2.4x |
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| 2048 | 25.9 | 9.4 | 2.8x |
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| 4096 | 102.1 | 35.2 | 2.9x |
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## Memory usage analysis
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### GPU memory consumption (batch=8, heads=32, dim=64)
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**Standard attention memory**:
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| Seq Length | Attention Matrix | KV Cache | Total | Notes |
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|------------|------------------|----------|-------|-------|
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| 512 | 8 MB | 32 MB | 40 MB | Manageable |
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| 2048 | 128 MB | 128 MB | 256 MB | Growing |
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| 8192 | 2048 MB (2 GB) | 512 MB | 2.5 GB | Large |
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| 32768 | 32768 MB (32 GB) | 2048 MB | 34 GB | OOM on 24GB GPUs |
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**Flash Attention 2 memory**:
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| Seq Length | Attention (on-chip) | KV Cache | Total | Reduction |
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|------------|---------------------|----------|-------|-----------|
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| 512 | 0 MB (recomputed) | 32 MB | 32 MB | 20% |
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| 2048 | 0 MB | 128 MB | 128 MB | 50% |
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| 8192 | 0 MB | 512 MB | 512 MB | 80% |
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| 32768 | 0 MB | 2048 MB | 2 GB | 94% |
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**Key insight**: Flash Attention doesn't materialize attention matrix, saving O(N²) memory.
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### Memory scaling comparison
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**Llama 2 7B model memory** (float16, batch=1):
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| Context Length | Standard Attention | Flash Attention 2 | Can Fit 24GB GPU? |
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|----------------|-------------------|-------------------|-------------------|
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| 2K | 3.2 GB | 2.1 GB | Both: Yes |
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| 4K | 5.8 GB | 2.8 GB | Both: Yes |
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| 8K | 12.1 GB | 4.2 GB | Both: Yes |
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| 16K | 26.3 GB (OOM) | 7.8 GB | Only Flash: Yes |
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| 32K | OOM | 14.2 GB | Only Flash: Yes |
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### Training memory (Llama 2 7B, batch=4)
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| Context | Standard (GB) | Flash Attn (GB) | Reduction |
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|---------|---------------|-----------------|-----------|
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| 2K | 18.2 | 12.4 | 32% |
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| 4K | 34.8 | 16.8 | 52% |
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| 8K | OOM (>40GB) | 26.2 | Fits! |
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## Scaling with sequence length
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### Computational complexity
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**Standard attention**:
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- Time: O(N² × d)
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- Memory: O(N² + N × d)
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**Flash Attention**:
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- Time: O(N² × d) (same, but with better constants)
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- Memory: O(N × d) (linear!)
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### Empirical scaling (A100, batch=1, heads=32, dim=64)
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**Time per token (milliseconds)**:
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| Sequence | 512 | 1K | 2K | 4K | 8K | 16K |
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|----------|-----|-----|-----|-----|-----|------|
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| Standard | 0.15 | 0.37 | 1.11 | 3.44 | 13.4 | 52.8 |
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| Flash Attn 2 | 0.11 | 0.14 | 0.24 | 0.43 | 0.83 | 1.64 |
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| Speedup | 1.4x | 2.6x | 4.6x | 8.0x | 16.1x | 32.2x |
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**Observation**: Speedup increases quadratically with sequence length!
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### Memory per token (MB)
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| Sequence | 512 | 1K | 2K | 4K | 8K | 16K |
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|----------|-----|-----|-----|-----|-----|------|
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| Standard | 0.08 | 0.13 | 0.25 | 0.64 | 2.05 | 8.13 |
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| Flash Attn 2 | 0.06 | 0.06 | 0.06 | 0.06 | 0.06 | 0.06 |
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**Observation**: Flash Attention memory per token is constant!
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## Training vs inference performance
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### Training (forward + backward, Llama 2 7B, A100)
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| Batch × Seq | Standard (samples/sec) | Flash Attn (samples/sec) | Speedup |
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|-------------|------------------------|--------------------------|---------|
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| 4 × 2K | 1.2 | 3.1 | 2.6x |
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| 8 × 2K | 2.1 | 5.8 | 2.8x |
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| 4 × 4K | 0.4 | 1.3 | 3.3x |
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| 8 × 4K | OOM | 2.4 | Enabled |
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| 2 × 8K | 0.1 | 0.4 | 4.0x |
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### Inference (generation, Llama 2 7B, A100)
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| Context Length | Standard (tokens/sec) | Flash Attn (tokens/sec) | Speedup |
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|----------------|----------------------|-------------------------|---------|
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| 512 | 48 | 52 | 1.1x |
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| 2K | 42 | 62 | 1.5x |
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| 4K | 31 | 58 | 1.9x |
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| 8K | 18 | 51 | 2.8x |
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| 16K | OOM | 42 | Enabled |
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**Note**: Inference speedup less dramatic than training because generation is memory-bound (KV cache accesses).
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## Flash Attention versions comparison
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### Flash Attention 1 vs 2 vs 3 (H100, seq=4096, batch=8)
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| Metric | FA1 | FA2 | FA3 (FP16) | FA3 (FP8) |
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|--------|-----|-----|------------|-----------|
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| Forward time (ms) | 28.4 | 12.5 | 7.2 | 4.8 |
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| Memory (GB) | 4.8 | 4.2 | 4.2 | 2.8 |
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| TFLOPS | 180 | 420 | 740 | 1150 |
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| GPU util % | 35% | 55% | 75% | 82% |
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**Key improvements**:
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- FA2: 2.3x faster than FA1 (better parallelism)
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- FA3 (FP16): 1.7x faster than FA2 (H100 async optimizations)
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- FA3 (FP8): 2.6x faster than FA2 (low precision)
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### Features by version
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| Feature | FA1 | FA2 | FA3 |
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|---------|-----|-----|-----|
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| Basic attention | ✅ | ✅ | ✅ |
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| Causal masking | ✅ | ✅ | ✅ |
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| Multi-query attention | ❌ | ✅ | ✅ |
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| Sliding window | ❌ | ✅ | ✅ |
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| Paged KV cache | ❌ | ✅ | ✅ |
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| FP8 support | ❌ | ❌ | ✅ (H100 only) |
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| Work partitioning | Basic | Advanced | Optimal |
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## Real-world model benchmarks
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### Llama 2 models (A100 80GB, batch=4, seq=2048)
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| Model | Params | Standard (samples/sec) | Flash Attn (samples/sec) | Speedup |
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|-------|--------|------------------------|--------------------------|---------|
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| Llama 2 7B | 7B | 1.2 | 3.1 | 2.6x |
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| Llama 2 13B | 13B | 0.6 | 1.7 | 2.8x |
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| Llama 2 70B | 70B | 0.12 | 0.34 | 2.8x |
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### GPT-style models (seq=1024)
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| Model | Standard (tokens/sec) | Flash Attn (tokens/sec) | Speedup |
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|-------|----------------------|-------------------------|---------|
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| GPT-2 (124M) | 520 | 680 | 1.3x |
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| GPT-J (6B) | 42 | 98 | 2.3x |
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| GPT-NeoX (20B) | 8 | 22 | 2.75x |
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## Recommendations by use case
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**Training large models (>7B parameters)**:
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- Use Flash Attention 2 on A100
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- Use Flash Attention 3 FP8 on H100 for maximum speed
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- Expected: 2.5-3x speedup
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**Long context inference (>4K tokens)**:
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- Flash Attention essential (enables contexts standard attention can't handle)
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- Expected: 2-4x speedup, 5-10x memory reduction
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**Short sequences (<512 tokens)**:
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- Flash Attention provides 1.2-1.5x speedup
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- Minimal memory benefit
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- Still worth enabling (no downside)
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**Multi-user serving**:
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- Flash Attention reduces per-request memory
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- Allows higher concurrent batch sizes
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- Can serve 2-3x more users on same hardware
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