- Added analysis documentation for xattn density alignment.
- Refactored ModelRunner to pre-allocate policy metadata buffers regardless of CPU offload configuration.
- Updated FullAttentionPolicy and SparsePolicy to accept query and key tensors for block selection.
- Enhanced QuestPolicy to utilize query tensor for block selection and improved handling of selected blocks.
- Expanded XAttentionBSAPolicy to support chunked prefill and improved attention score computation with historical and current chunk handling.
- Introduced DensityObserver to track compute and communication density for sparse attention layers.
- Updated attention layer to ensure block selection is always called, improving robustness in first chunk scenarios.
- Added tests for attention kernel behavior with enhanced input patterns.
Add support for GLM-4 model architecture with the following changes:
- Add glm4.py with ChatGLMForCausalLM, GLM4Model, GLM4Attention, GLM4MLP
- Add GLM4RotaryEmbedding with interleaved partial rotation (rotary_dim = head_dim // 2)
- Add apply_rotary_emb_interleaved function for GLM-4 style RoPE
- Add GLM-4 weight name conversion and loading in loader.py
- Add GLM-4 chat template conversion in test_ruler.py
- Add trust_remote_code=True for GLM-4 config loading
Key GLM-4 specific adaptations:
- QKV bias enabled (add_qkv_bias: true)
- RoPE with rope_ratio scaling (base = 10000 * rope_ratio)
- Interleaved RoPE (pairs adjacent elements, not first/second half)
- Partial rotation (only half of head_dim is rotated)
- Uses multi_query_group_num instead of num_key_value_heads
- Uses kv_channels instead of head_dim
- Uses ffn_hidden_size instead of intermediate_size
Tested with RULER niah_single_1 (5 samples): 100% accuracy
Both GPU-only and CPU offload modes verified
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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
Generated with [Claude Code](https://claude.ai/code)
via [Happy](https://happy.engineering)
Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>
- 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)
Generated with [Claude Code](https://claude.ai/code)
via [Happy](https://happy.engineering)
Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>
- 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>
- 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.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
