fix: restore all removed bundled skills + fix skills sync system
- Restored 21 skills removed in commits757d012and740dd92: accelerate, audiocraft, code-review, faiss, flash-attention, gguf, grpo-rl-training, guidance, llava, nemo-curator, obliteratus, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, stable-diffusion, tensorrt-llm, torchtitan, trl-fine-tuning, whisper - Rewrote sync_skills() with proper update semantics: * New skills (not in manifest): copied to user dir * Existing skills (in manifest + on disk): updated via hash comparison * User-deleted skills (in manifest, not on disk): respected, not re-added * Stale manifest entries (removed from bundled): cleaned from manifest - Added sync_skills() to CLI startup (cmd_chat) and gateway startup (start_gateway) — previously only ran during 'hermes update' - Updated cmd_update output to show new/updated/cleaned counts - Rewrote tests: 20 tests covering manifest CRUD, dir hashing, fresh install, user deletion respect, update detection, stale cleanup, and name collision handling 75 bundled skills total. 2002 tests pass.
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skills/mlops/flash-attention/references/benchmarks.md
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skills/mlops/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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# HuggingFace Transformers Integration
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## Contents
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- Enabling Flash Attention in Transformers
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- Supported model architectures
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- Configuration examples
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- Performance comparisons
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- Troubleshooting model-specific issues
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## Enabling Flash Attention in Transformers
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HuggingFace Transformers (v4.36+) supports Flash Attention 2 natively.
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**Simple enable for any supported model**:
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```python
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from transformers import AutoModel
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model = AutoModel.from_pretrained(
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"meta-llama/Llama-2-7b-hf",
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attn_implementation="flash_attention_2",
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torch_dtype=torch.float16,
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device_map="auto"
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)
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```
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**Install requirements**:
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```bash
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pip install transformers>=4.36
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pip install flash-attn --no-build-isolation
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```
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## Supported model architectures
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As of Transformers 4.40:
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**Fully supported**:
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- Llama / Llama 2 / Llama 3
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- Mistral / Mixtral
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- Falcon
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- GPT-NeoX
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- Phi / Phi-2 / Phi-3
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- Qwen / Qwen2
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- Gemma
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- Starcoder2
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- GPT-J
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- OPT
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- BLOOM
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**Partially supported** (encoder-decoder):
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- BART
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- T5 / Flan-T5
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- Whisper
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**Check support**:
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```python
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from transformers import AutoConfig
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config = AutoConfig.from_pretrained("model-name")
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print(config._attn_implementation_internal)
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# 'flash_attention_2' if supported
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```
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## Configuration examples
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### Llama 2 with Flash Attention
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "meta-llama/Llama-2-7b-hf"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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attn_implementation="flash_attention_2",
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torch_dtype=torch.float16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# Generate
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inputs = tokenizer("Once upon a time", return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_length=100)
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print(tokenizer.decode(outputs[0]))
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```
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### Mistral with Flash Attention for long context
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```python
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from transformers import AutoModelForCausalLM
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"mistralai/Mistral-7B-v0.1",
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attn_implementation="flash_attention_2",
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torch_dtype=torch.bfloat16, # Better for long context
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device_map="auto",
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max_position_embeddings=32768 # Extended context
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)
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# Process long document (32K tokens)
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long_text = "..." * 10000
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inputs = tokenizer(long_text, return_tensors="pt", truncation=False).to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512)
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```
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### Fine-tuning with Flash Attention
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```python
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from transformers import Trainer, TrainingArguments
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-2-7b-hf",
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attn_implementation="flash_attention_2",
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torch_dtype=torch.float16
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)
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training_args = TrainingArguments(
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output_dir="./results",
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per_device_train_batch_size=4,
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gradient_accumulation_steps=4,
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num_train_epochs=3,
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fp16=True, # Must match model dtype
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optim="adamw_torch_fused" # Fast optimizer
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)
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset
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)
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trainer.train()
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```
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### Multi-GPU training
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```python
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from transformers import AutoModelForCausalLM
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import torch
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# Model parallelism with Flash Attention
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model = AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-2-13b-hf",
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attn_implementation="flash_attention_2",
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torch_dtype=torch.float16,
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device_map="auto", # Automatic multi-GPU placement
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max_memory={0: "20GB", 1: "20GB"} # Limit per GPU
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)
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```
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## Performance comparisons
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### Memory usage (Llama 2 7B, batch=1)
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| Sequence Length | Standard Attention | Flash Attention 2 | Reduction |
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|-----------------|-------------------|-------------------|-----------|
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| 512 | 1.2 GB | 0.9 GB | 25% |
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| 2048 | 3.8 GB | 1.4 GB | 63% |
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| 8192 | 14.2 GB | 3.2 GB | 77% |
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| 32768 | OOM (>24GB) | 10.8 GB | Fits! |
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### Speed (tokens/sec, A100 80GB)
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| Model | Standard | Flash Attn 2 | Speedup |
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|-------|----------|--------------|---------|
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| Llama 2 7B (seq=2048) | 42 | 118 | 2.8x |
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| Llama 2 13B (seq=4096) | 18 | 52 | 2.9x |
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| Llama 2 70B (seq=2048) | 4 | 11 | 2.75x |
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|
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### Training throughput (samples/sec)
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|
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| Model | Batch Size | Standard | Flash Attn 2 | Speedup |
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|-------|------------|----------|--------------|---------|
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| Llama 2 7B | 4 | 1.2 | 3.1 | 2.6x |
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| Llama 2 7B | 8 | 2.1 | 5.8 | 2.8x |
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| Llama 2 13B | 2 | 0.6 | 1.7 | 2.8x |
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## Troubleshooting model-specific issues
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|
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### Issue: Model doesn't support Flash Attention
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|
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Check support list above. If not supported, use PyTorch SDPA as fallback:
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|
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```python
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model = AutoModelForCausalLM.from_pretrained(
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"model-name",
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attn_implementation="sdpa", # PyTorch native (still faster)
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torch_dtype=torch.float16
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)
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```
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### Issue: CUDA out of memory during loading
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Reduce memory footprint:
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|
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```python
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model = AutoModelForCausalLM.from_pretrained(
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"model-name",
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attn_implementation="flash_attention_2",
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torch_dtype=torch.float16,
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device_map="auto",
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max_memory={0: "18GB"}, # Reserve memory for KV cache
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low_cpu_mem_usage=True
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)
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```
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### Issue: Slower inference than expected
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|
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Ensure dtype matches:
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|
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```python
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# Model and inputs must both be float16/bfloat16
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model = model.to(torch.float16)
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inputs = tokenizer(..., return_tensors="pt").to("cuda")
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inputs = {k: v.to(torch.float16) if v.dtype == torch.float32 else v
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for k, v in inputs.items()}
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```
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|
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### Issue: Different outputs vs standard attention
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|
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Flash Attention is numerically equivalent but uses different computation order. Small differences (<1e-3) are normal:
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|
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```python
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# Compare outputs
|
||||
model_standard = AutoModelForCausalLM.from_pretrained("model-name", torch_dtype=torch.float16)
|
||||
model_flash = AutoModelForCausalLM.from_pretrained(
|
||||
"model-name",
|
||||
attn_implementation="flash_attention_2",
|
||||
torch_dtype=torch.float16
|
||||
)
|
||||
|
||||
inputs = tokenizer("Test", return_tensors="pt").to("cuda")
|
||||
|
||||
with torch.no_grad():
|
||||
out_standard = model_standard(**inputs).logits
|
||||
out_flash = model_flash(**inputs).logits
|
||||
|
||||
diff = (out_standard - out_flash).abs().max()
|
||||
print(f"Max diff: {diff:.6f}") # Should be ~1e-3 to 1e-4
|
||||
```
|
||||
|
||||
### Issue: ImportError during model loading
|
||||
|
||||
Install flash-attn:
|
||||
```bash
|
||||
pip install flash-attn --no-build-isolation
|
||||
```
|
||||
|
||||
Or disable Flash Attention:
|
||||
```python
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
"model-name",
|
||||
attn_implementation="eager", # Standard PyTorch
|
||||
torch_dtype=torch.float16
|
||||
)
|
||||
```
|
||||
|
||||
## Best practices
|
||||
|
||||
1. **Always use float16/bfloat16** with Flash Attention (not float32)
|
||||
2. **Set device_map="auto"** for automatic memory management
|
||||
3. **Use bfloat16 for long context** (better numerical stability)
|
||||
4. **Enable gradient checkpointing** for training large models
|
||||
5. **Monitor memory** with `torch.cuda.max_memory_allocated()`
|
||||
|
||||
**Example with all best practices**:
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, TrainingArguments
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
"meta-llama/Llama-2-7b-hf",
|
||||
attn_implementation="flash_attention_2",
|
||||
torch_dtype=torch.bfloat16, # Better for training
|
||||
device_map="auto",
|
||||
low_cpu_mem_usage=True
|
||||
)
|
||||
|
||||
# Enable gradient checkpointing for memory
|
||||
model.gradient_checkpointing_enable()
|
||||
|
||||
# Training with optimizations
|
||||
training_args = TrainingArguments(
|
||||
output_dir="./results",
|
||||
per_device_train_batch_size=8,
|
||||
gradient_accumulation_steps=2,
|
||||
bf16=True, # Match model dtype
|
||||
optim="adamw_torch_fused",
|
||||
gradient_checkpointing=True
|
||||
)
|
||||
```
|
||||
Loading…
Add table
Add a link
Reference in a new issue