- Add --model parameter (default: Llama-3.1-8B-Instruct)
- Add --enable-xattn flag for XAttention BSA sparse prefill
- Add --xattn-threshold and --xattn-stride parameters
- Change default num-gpu-blocks from 6 to 4
- Add benchmark results doc with Full vs XAttn comparison (32K/128K)
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Co-Authored-By: Claude <noreply@anthropic.com>
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- Document kernel gap analysis showing 77-81% CPU scheduling overhead
- Identify GPU utilization at 12.8% with potential to reach 39.5%
- Outline optimization directions: CUDA Graph, Triton fusion, C++ extension
- Add documentation index entry in CLAUDE.md
Generated with [Claude Code](https://claude.ai/code)
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Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>
- Document ring buffer pipeline triggering nsys timestamp bug
- Update profile_offload.sh to use test_ruler.py with options
- Add reference to new doc in CLAUDE.md
Root cause: 4-slot ring buffer pipeline (4 transfer streams +
1 compute stream) triggers event ordering bug in nsys < 2024.2
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add .claude/rules/gpu-monitor.md requiring gpu-monitor agent for all GPU memory monitoring tasks
- Update CLAUDE.md rules index with reference to new rule
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add new sections to xattn_bsa_policy_design.md:
- Performance benchmarks: 128K context comparison (Full vs XAttn BSA)
- Density trend analysis across chunks
- Memory leak issue and fix (64GB -> 4GB reduction)
- Memory monitoring guide with gpu-monitor agent
- Density statistics API documentation
- Known issues and optimization directions
Update CLAUDE.md description to reflect new content.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Document flat_group_gemm_fuse_reshape and softmax_fuse_block_sum kernels
- Explain anti-diagonal sum principle and stride sampling
- Add GPU-specific BLOCK_M/N constraints (RTX 3090 vs A100)
- Show Q/K can have different lengths (chunked prefill support)
- Update CLAUDE.md with doc reference
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add xattn_estimate_chunked function ported from COMPASS
- Support chunked prefill with q_start_pos parameter
- Ensure 100% consistency with standard xattn_estimate when
using matching chunk_size parameter
- Add test and documentation
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add comprehensive documentation analyzing the 32K chunked offload
accuracy issues with proposed solutions covering LSE precision,
ring buffer state management, and position encoding validation.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add docs/block_sparse_attn_interface.md with BSA function signatures
- Update CLAUDE.md documentation index
- Remove obsolete DEBUG_SUMMARY.md and test_report_sparse_policy_refactor.md
- Add notes.md to .gitignore
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Create docs/sparse_policy_implementation_guide.md with comprehensive guide
- Rewrite .claude/rules/sparse-policy.md with mandatory base class requirements
- Add new doc reference to CLAUDE.md documentation index
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Document accuracy degradation issue in 32K context with chunked offload
- Add detailed hypothesis analysis and debugging approach
- Include 4-slot ring buffer experiment results
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add comprehensive documentation for the SparsePolicy abstraction:
- SparsePolicy base class and abstract methods
- FullAttentionPolicy prefill/decode flow
- Ring buffer and cross-layer pipeline modes
- Code conventions and testing guidelines
Update CLAUDE.md documentation index with reference.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Combines two performance optimization features:
- perf_opt-1: Cross-layer pipeline for decode (double-buffered layer cache)
- perf_opt-2: Per-layer prefill buffer for async offload
Both features are complementary and improve CPU offload performance.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add instructions for Claude instances to check GPU availability before
running CUDA operations, preventing conflicts when multiple instances
debug in parallel on a single GPU.
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
