Merge GPU-only sparse attention support from tzj/minference-exp branch:
**GPU-only mode additions:**
- Add compute_prefill/compute_decode methods to SparsePolicy base class
- Add GPU-only attention routing in attention.py
- Add alloc_policy_metadata() for pre-allocating GQA buffers
- Add XAttention + BSA sparse attention for GPU-only prefill
- Add kvcache_manager to set_context() for policy access
**bench.py enhancements:**
- Add --model argument for configurable model path
- Add --policy argument (full, xattn) for sparse policy selection
- Add --enable-policy flag for FullAttentionPolicy routing
- Add --enforce-eager option to disable CUDA graphs
- Add --gpu-util option for GPU memory utilization
**Documentation:**
- Add gpu_only_xattn_guide.md with performance analysis
- Add gpu_only_sparse_integration.md baseline document
- Add gpu-vram-requirement.md rule for GPU-only mode
Both CPU offload and GPU-only paths are preserved and functional.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement extended CUDA Graph coverage for CPU offload path:
- Add graphed_layers.py with N+2 graph architecture (EmbedGraph, FirstGraph, InterGraphs, LastGraph)
- Support both prefill (seq_len=chunk_size) and decode (seq_len=1) graph modes
- Extend graph coverage to ~70-80% including qkv_proj, rotary, o_proj
- Only attention core remains in eager mode for dynamic offload
Performance: Prefill throughput improved ~5.6% (3782 -> 3995 tok/s at 32K)
Also adds:
- --enforce-eager flag to bench_offload.py for comparison
- Offload mode constraint documentation in CLAUDE.md
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- Add compute_prefill() and compute_decode() GPU-only methods to SparsePolicy base class
- Implement GPU-only methods in FullAttentionPolicy using flash_attn
- Add sparse_policy parameter to GPUOnlyManager
- Update create_kvcache_manager() to create FullAttentionPolicy for GPU-only mode
- Route GPU-only attention through sparse_policy in attention.py
- Pass kvcache_manager to context for policy access
- Add --enable-policy flag to bench.py for testing
- Handle warmup phase when kvcache_manager is not yet allocated
This allows GPU-only mode to use the same policy architecture as CPU offload mode,
enabling future sparse attention implementations (Quest, XAttention) in GPU-only mode.
Performance verified: ~4890 tok/s (unchanged from baseline)
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- Switch from torch.cuda.nvtx to nvtx package for colored range support
- Add color coding: blue for H2D, green for D2H decode, orange for D2H prefill
- Add --num-gpu-blocks parameter to profile_offload.sh
- Include slot count in output filename for easier comparison
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- Add write_to_prefill_buffer() and write_to_decode_buffer() methods
- Add chunk_idx parameter to load_to_slot_layer() for NVTX labeling
- Replace direct copy_() calls with OffloadEngine methods in attention.py
- Update all load_to_slot_layer() calls to pass chunk_idx
- NVTX markers now show chunk info: "H2D: L{layer} Chunk{chunk} CPU[{block}]->Slot[{slot}]"
All KV cache data transfers in chunked offload mode now go through
OffloadEngine, enabling better profiling and consistent management.
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Add statistics tracking to compare block selection between policies:
- XAttentionBSAPolicy: track available/selected blocks per chunk
- FullAttentionPolicy: track total blocks (always 100% density)
- Add reset_stats(), get_density_stats(), print_density_stats() methods
- Use logger.debug for per-chunk density logging
Results on 32K niah_single_1:
- Full: 100% density across all chunks
- XAttn BSA: 90% -> 73% density (saves ~25-30% blocks in later chunks)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Copy compute_chunked_prefill implementation from FullAttentionPolicy
- Set default threshold to 0.95 for accuracy testing
- Remove debug code (sys.exit, verbose prints)
- Use ring buffer pipeline for historical block loading
- Merge with current chunk attention using flash_attn_with_lse
RULER NIAH test passed with 5/5 samples (100% accuracy).
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement XAttention-based block selection for sparse attention:
- Use flat_group_gemm_fuse_reshape to compute Q@K^T attention scores
- Apply softmax_fuse_block_sum to aggregate into block-level attention
- Use find_blocks_chunked for threshold-based block selection
- Handle GQA by aggregating within KV head groups first
- Use majority voting (>50%) across heads instead of any() for better sparsity
- Align block_size with CPU offload block size (1024 tokens / stride = 128)
Test results show ~45% density at chunk 40 (down from 100% with any() aggregation).
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add test_xattn_kernels.py demonstrating flat_group_gemm_fuse_reshape
and softmax_fuse_block_sum Triton kernels with structured data
- Update testing.md with new test code style guidelines
- Update xattn.py and xattn_bsa.py with improvements
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>
Root Cause:
- OffloadEngine.reset() cleared GPU buffers but NOT CPU cache
- Previous request's KV cache data persisted in CPU memory, contaminating subsequent requests
Fixes:
- Add k_cache_cpu.zero_() and v_cache_cpu.zero_() to OffloadEngine.reset()
- Add clear_decode_tracking(seq) call in HybridKVCacheManager.deallocate()
Results:
- niah_single_1 accuracy improved from ~80% to 94% (+14%)
- Remaining ~6% errors are model limitations, not state leakage
Also:
- Update docs/ruler_32k_chunked_offload_issue.md with fix details
- Remove debug planning files (findings.md, progress.md, task_plan.md)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Port XAttention operators from COMPASS project:
- flat_group_gemm_fuse_reshape: stride reshape GEMM kernel
- softmax_fuse_block_sum: fused softmax with block-level summation
- xattn_estimate: main estimation function for block sparse attention
- find_blocks_chunked: cumulative threshold-based block selection
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Create nanovllm/ops/ module for low-level attention operators
- Move chunked_attention.py from kvcache/ to ops/
- Update imports in full_policy.py (3 locations)
- Fix: remove dead code in OffloadEngine.reset() referencing
non-existent layer_k/v_buffer_a/b attributes
Verified with needle test (32K offload): PASSED
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Remove cross-layer pipeline from OffloadEngine (saves ~1GB GPU memory for long sequences)
- Delete layer_k/v_buffer_a/b double buffers
- Remove start_decode_pipeline, get_decode_layer_kv, end_decode_pipeline methods
- Remove pipeline state tracking variables
- Simplify decode to use ring buffer pipeline only (more efficient for long sequences)
- Rename compute_chunked_attention → compute_chunked_prefill for clarity
- Add mandatory needle test requirements: --enable-offload --input-len 32768
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Move decode attention computation from attention.py to SparsePolicy:
- Add compute_chunked_decode abstract method to SparsePolicy base class
- Implement compute_chunked_decode in FullAttentionPolicy with:
- Ring buffer pipeline (_decode_ring_buffer_pipeline)
- Cross-layer pipeline (_decode_with_layer_pipeline)
- Decode buffer handling
- Simplify _chunked_decode_attention to only validate and delegate
- Remove _decode_ring_buffer_pipeline and _decode_with_layer_pipeline from attention.py
- Add supports_decode check for policy validation
This completes the SparsePolicy v5 refactoring where both prefill and
decode paths now delegate all computation to the sparse policy.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Move all chunked prefill attention computation from attention.py to
SparsePolicy.compute_chunked_attention(). This is the v4 architecture
refactoring for sparse attention policies.
Changes:
- Add compute_chunked_attention abstract method to SparsePolicy base
- Add offload_engine parameter to select_blocks for policies needing
KV access during block selection
- Implement compute_chunked_attention in FullAttentionPolicy with
complete ring buffer pipeline logic
- Simplify attention.py to delegate all chunked prefill to policy
- Remove redundant _sync_load_previous_chunks and
_ring_buffer_pipeline_load methods from Attention class
Test: test_needle.py --enable-offload PASSED
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Import os and socket modules
- Add _find_free_port() function for automatic port detection
- Use NANOVLLM_DIST_PORT env var if set, otherwise auto-assign
- Enables running multiple model instances without port conflicts
Co-Authored-By: Claude <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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