Add ops module ported from tzj/minference branch containing:
- xattn.py: XAttention block importance estimation with Triton kernels
- xattn_estimate(): standard estimation for sparse attention mask
- xattn_estimate_chunked(): chunked prefill compatible version
- flat_group_gemm_fuse_reshape(): fused stride reshape + GEMM kernel
- softmax_fuse_block_sum(): online softmax + block-wise sum kernel
- chunked_attention.py: Flash attention with LSE output for chunk merging
- test_xattn_estimate_chunked.py: verification test (all seq_lens pass)
This prepares the foundation for AttentionPolicy refactoring where
XAttentionPolicy.estimate() will call these ops.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add comprehensive memory analysis for 64k inference on Llama 3.1 8B:
New documentation:
- docs/64k_memory_analysis.md: GPU-only vs offload memory analysis,
OOM root cause (memory fragmentation), RTX 3090 limitations,
theoretical vs actual memory usage breakdown
Test configuration updates:
- tests/test_ruler.py: Add --num-kv-buffers parameter for ring buffer
size tuning (default 4, can reduce to 1 for lower memory)
- Update default data_dir to ruler_64k
- Update default max_model_len to 65664 for 64k support
CLAUDE.md updates:
- Add 64k_memory_analysis.md to documentation index
- Document num_kv_buffers parameter in Configuration section
- Add 64k hardware requirements note to Model Limits
Key findings: 64k inference requires ~26GB (GPU-only) or ~23GB (offload)
due to memory fragmentation on 24GB GPUs, making A100 (40GB+) the
recommended hardware for 64k workloads.
Co-Authored-By: Claude <noreply@anthropic.com>
- Add test_ruler.py supporting all 13 RULER tasks (NIAH, QA, CWE, FWE, VT)
- Implement RULER official evaluation metrics (string_match_all/part)
- Fix max_model_len to 32896 to prevent decode OOM on long inputs
- Add ruler_benchmark_report.md with full test results (92.1% accuracy)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add test_ruler_niah.sh for independent sample testing across multiple GPUs.
Each sample runs in a separate Python process to avoid state accumulation issues.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Auto port allocation with _find_free_port() in model_runner.py
- Resource management refactor with close() + context manager in llm_engine.py
- Add tests/test_port_conflict.py and tests/run_parallel_niah.sh
- Remove docs/torch_distributed_port_issue.md (issue fixed)
- Ignore tests/data/ directory
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
