From 46f6cc100b31aace55bba1d94d168097e12947fa Mon Sep 17 00:00:00 2001 From: yichuan520030910320 Date: Mon, 30 Jun 2025 09:05:05 +0000 Subject: [PATCH] Initial commit --- .gitattributes | 1 + .gitignore | 72 + .gitmodules | 6 + .python-version | 1 + .vscode/extensions.json | 9 + .vscode/launch.json | 283 + .vscode/settings.json | 43 + .vscode/tasks.json | 16 + LICENSE | 9 + README.md | 292 + demo.ipynb | 248 + examples/data/2506.08276v1.pdf | 7905 +++++++++++++++++ examples/document_search.py | 146 + examples/main_cli_example.py | 76 + examples/simple_demo.py | 81 + knowledge.leann.meta.json | 32 + packages/leann-backend-diskann/CMakeLists.txt | 8 + .../leann_backend_diskann/__init__.py | 7 + .../leann_backend_diskann/diskann_backend.py | 299 + .../leann_backend_diskann/embedding_pb2.py | 27 + .../leann_backend_diskann/embedding_server.py | 397 + packages/leann-backend-diskann/pyproject.toml | 16 + .../third_party/DiskANN/.clang-format | 6 + .../third_party/DiskANN/.gitattributes | 14 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research/utils/experiment.md create mode 100755 research/utils/extract_results.py create mode 100644 research/utils/faiss.md create mode 100644 research/utils/find_probe.py create mode 100644 research/utils/generate_dataset_cache.fish create mode 100644 research/utils/mem_monitor.fish create mode 100644 research/utils/modelfile.fish create mode 100644 research/utils/parse_logs_draw_skip_reorder.py create mode 100644 research/utils/plot_degree_distribution.py create mode 100644 research/utils/prepare_query_files.py create mode 100644 research/utils/run_bm25.fish create mode 100644 research/utils/run_recall_experiment.py create mode 100644 research/utils/s3.md create mode 100644 research/utils/seperate_hnsw_flat.py create mode 100644 research/utils/simple_search.fish create mode 100755 research/utils/subsample_data_new.py create mode 100755 research/utils/timing.py create mode 100644 tests/sanity_checks/README.md create mode 100644 tests/sanity_checks/test_distance_functions.py create mode 100644 tests/sanity_checks/test_l2_verification.py create mode 100644 tests/sanity_checks/test_sanity_check.py create mode 100644 uv.lock diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..898ed5c --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +paper_plot/data/big_graph_degree_data.npz filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100755 index 0000000..0ad5a54 --- /dev/null +++ b/.gitignore @@ -0,0 +1,72 @@ +raw_data/ +scaling_out/ +scaling_out_old/ +sanity_check/ +demo/indices/ +# .vscode/ +*.log +*pycache* +outputs/ +*.pkl +.history/ +scripts/ +lm_eval.egg-info/ +demo/experiment_results/**/*.json +*.jsonl +*.sh +*.txt +!CMakeLists.txt +latency_breakdown*.json +experiment_results/eval_results/diskann/*.json +aws/ +.venv/ +.cursor/rules/ +*.egg-info/ +skip_reorder_comparison/ +analysis_results/ +build/ +.cache/ +nprobe_logs/ +micro/results +micro/contriever-INT8 +*.qdstrm +benchmark_results/ +results/ +frac_*.png +final_in_*.png +embedding_comparison_results/ +*.ind +*.gz +*.fvecs +*.ivecs +*.index +*.bin + +read_graph +analyze_diskann_graph +degree_distribution.png +micro/degree_distribution.png + +policy_results_* +results_*/ +experiment_results/ +.DS_Store + +# The above are inherited from old Power RAG repo + +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv +.env + +test_indices*/ +test_*.py +!tests/** +packages/leann-backend-diskann/third_party/DiskANN/_deps/ diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..8c49b3e --- /dev/null +++ b/.gitmodules @@ -0,0 +1,6 @@ +[submodule "packages/leann-backend-diskann/third_party/DiskANN"] + path = packages/leann-backend-diskann/third_party/DiskANN + url = https://github.com/yichuan520030910320/DiskANN.git +[submodule "packages/leann-backend-hnsw/third_party/faiss"] + path = packages/leann-backend-hnsw/third_party/faiss + url = https://github.com/yichuan520030910320/faiss.git diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/.vscode/extensions.json b/.vscode/extensions.json new file mode 100644 index 0000000..e793901 --- /dev/null +++ b/.vscode/extensions.json @@ -0,0 +1,9 @@ +{ + "recommendations": [ + "llvm-vs-code-extensions.vscode-clangd", + "ms-python.python", + "ms-vscode.cmake-tools", + "vadimcn.vscode-lldb", + "eamodio.gitlens", + ] +} \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100755 index 0000000..5f96fb2 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,283 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + // new emdedder + { + "name": "New Embedder", + "type": "debugpy", + "request": "launch", + "program": "demo/main.py", + "console": "integratedTerminal", + "args": [ + "--search", + "--use-original", + "--domain", + "dpr", + "--nprobe", + "5000", + "--load", + "flat", + "--embedder", + "intfloat/multilingual-e5-small" + ] + } + //python /home/ubuntu/Power-RAG/faiss/demo/simple_build.py + { + "name": "main.py", + "type": "debugpy", + "request": "launch", + "program": "demo/main.py", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "args": [ + "--query", + "1000", + "--load", + "bm25" + ] + }, + { + "name": "Simple Build", + "type": "lldb", + "request": "launch", + "program": "${workspaceFolder}/.venv/bin/python", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "args": [ + "faiss/demo/simple_build.py" + ], + "env": { + "LD_PRELOAD": "/lib/x86_64-linux-gnu/libmkl_core.so:/lib/x86_64-linux-gnu/libmkl_intel_thread.so:/lib/x86_64-linux-gnu/libmkl_intel_lp64.so:/lib/x86_64-linux-gnu/libiomp5.so" + } + }, + //# Fix for Intel MKL error + //export LD_PRELOAD=/lib/x86_64-linux-gnu/libmkl_core.so:/lib/x86_64-linux-gnu/libmkl_intel_thread.so:/lib/x86_64-linux-gnu/libmkl_intel_lp64.so:/lib/x86_64-linux-gnu/libiomp5.so + //python faiss/demo/build_demo.py + { + "name": "Build Demo", + "type": "lldb", + "request": "launch", + "program": "${workspaceFolder}/.venv/bin/python", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "args": [ + "faiss/demo/build_demo.py" + ], + "env": { + "LD_PRELOAD": "/lib/x86_64-linux-gnu/libmkl_core.so:/lib/x86_64-linux-gnu/libmkl_intel_thread.so:/lib/x86_64-linux-gnu/libmkl_intel_lp64.so:/lib/x86_64-linux-gnu/libiomp5.so" + } + }, + { + "name": "DiskANN Serve", + "type": "lldb", + "request": "launch", + "program": "${workspaceFolder}/.venv/bin/python", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "args": [ + "demo/main.py", + "--mode", + "serve", + "--engine", + "sglang", + "--load-indices", + "diskann", + "--domain", + "rpj_wiki", + "--lazy-load", + "--recompute-beighbor-embeddings", + "--port", + "8082", + "--diskann-search-memory-maximum", + "2", + "--diskann-graph", + "240", + "--search-only" + ], + "env": { + "PYTHONPATH": "${workspaceFolder}/faiss_repo/build/faiss/python:$PYTHONPATH" + }, + "preLaunchTask": "CMake: build", + }, + { + "name": "DiskANN Serve MAC", + "type": "lldb", + "request": "launch", + "program": "${workspaceFolder}/.venv/bin/python", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "args": [ + "demo/main.py", + "--mode", + "serve", + "--engine", + "ollama", + "--load-indices", + "diskann", + "--domain", + "rpj_wiki", + "--lazy-load", + "--recompute-beighbor-embeddings" + ], + "preLaunchTask": "CMake: build", + "env": { + "KMP_DUPLICATE_LIB_OK": "TRUE", + "OMP_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "DYLD_INSERT_LIBRARIES": "/Users/ec2-user/Power-RAG/.venv/lib/python3.10/site-packages/torch/lib/libomp.dylib", + "KMP_BLOCKTIME": "0" + } + }, + { + "name": "Python Debugger: Current File with Arguments", + "type": "debugpy", + "request": "launch", + "program": "ric/main_ric.py", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "args": [ + "--config-name", + "${input:configSelection}" + ], + "justMyCode": false + }, + //python ./demo/validate_equivalence.py sglang + { + "name": "Validate Equivalence", + "type": "debugpy", + "request": "launch", + "program": "demo/validate_equivalence.py", + "console": "integratedTerminal", + "args": [ + "sglang" + ], + }, + //python demo/retrieval_demo.py --engine sglang --skip-embeddings --domain dpr --load-indices flat ivf_flat + { + "name": "Retrieval Demo", + "type": "debugpy", + "request": "launch", + "program": "demo/retrieval_demo.py", + "console": "integratedTerminal", + "args": [ + "--engine", + "vllm", + "--skip-embeddings", + "--domain", + "dpr", + "--load-indices", + // "flat", + "ivf_flat" + ], + }, + //python demo/retrieval_demo.py --engine sglang --skip-embeddings --domain dpr --load-indices diskann --hnsw-M 64 --hnsw-efConstruction 150 --hnsw-efSearch 128 --hnsw-sq-bits 8 + { + "name": "Retrieval Demo DiskANN", + "type": "debugpy", + "request": "launch", + "program": "demo/retrieval_demo.py", + "console": "integratedTerminal", + "args": [ + "--engine", + "sglang", + "--skip-embeddings", + "--domain", + "dpr", + "--load-indices", + "diskann", + "--hnsw-M", + "64", + "--hnsw-efConstruction", + "150", + "--hnsw-efSearch", + "128", + "--hnsw-sq-bits", + "8" + ], + }, + { + "name": "Find Probe", + "type": "debugpy", + "request": "launch", + "program": "find_probe.py", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + }, + { + "name": "Python: Attach", + "type": "debugpy", + "request": "attach", + "processId": "${command:pickProcess}", + "justMyCode": true + }, + { + "name": "Edge RAG", + "type": "lldb", + "request": "launch", + "program": "${workspaceFolder}/.venv/bin/python", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "args": [ + "edgerag_demo.py" + ], + "env": { + "LD_PRELOAD": "/lib/x86_64-linux-gnu/libiomp5.so /lib/x86_64-linux-gnu/libmkl_core.so /lib/x86_64-linux-gnu/libmkl_intel_lp64.so /lib/x86_64-linux-gnu/libmkl_intel_thread.so", + "MKL_NUM_THREADS": "1", + "OMP_NUM_THREADS": "1", + } + }, + { + "name": "Launch Embedding Server", + "type": "debugpy", + "request": "launch", + "program": "demo/embedding_server.py", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "args": [ + "--domain", + "rpj_wiki", + "--zmq-port", + "5556", + ] + }, + { + "name": "HNSW Serve", + "type": "lldb", + "request": "launch", + "program": "${workspaceFolder}/.venv/bin/python", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "args": [ + "demo/main.py", + "--domain", + "rpj_wiki", + "--load", + "hnsw", + "--mode", + "serve", + "--search", + "--skip-pa", + "--recompute", + "--hnsw-old" + ], + "env": { + "LD_PRELOAD": "/lib/x86_64-linux-gnu/libmkl_core.so:/lib/x86_64-linux-gnu/libmkl_intel_thread.so:/lib/x86_64-linux-gnu/libmkl_intel_lp64.so:/lib/x86_64-linux-gnu/libiomp5.so" + } + }, + ], + "inputs": [ + { + "id": "configSelection", + "type": "pickString", + "description": "Select a configuration", + "options": [ + "example_config", + "vllm_gritlm" + ], + "default": "example_config" + } + ], +} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100755 index 0000000..e2b5794 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,43 @@ +{ + "python.analysis.extraPaths": [ + "./sglang_repo/python" + ], + "cmake.sourceDirectory": "${workspaceFolder}/DiskANN", + "cmake.configureArgs": [ + "-DPYBIND=True", + "-DUPDATE_EDITABLE_INSTALL=ON", + ], + "cmake.environment": { + "PATH": "/Users/ec2-user/Power-RAG/.venv/bin:${env:PATH}" + }, + "cmake.buildDirectory": "${workspaceFolder}/build", + "files.associations": { + "*.tcc": "cpp", + "deque": "cpp", + "string": "cpp", + "unordered_map": "cpp", + "vector": "cpp", + "map": "cpp", + "unordered_set": "cpp", + "atomic": "cpp", + "inplace_vector": "cpp", + "*.ipp": "cpp", + "forward_list": "cpp", + "list": "cpp", + "any": "cpp", + "system_error": "cpp", + "__hash_table": "cpp", + "__split_buffer": "cpp", + "__tree": "cpp", + "ios": "cpp", + "set": "cpp", + "__string": "cpp", + "string_view": "cpp", + "ranges": "cpp", + "iosfwd": "cpp" + }, + "lldb.displayFormat": "auto", + "lldb.showDisassembly": "auto", + "lldb.dereferencePointers": true, + "lldb.consoleMode": "commands", +} \ No newline at end of file diff --git a/.vscode/tasks.json b/.vscode/tasks.json new file mode 100644 index 0000000..4edc48b --- /dev/null +++ b/.vscode/tasks.json @@ -0,0 +1,16 @@ +{ + "version": "2.0.0", + "tasks": [ + { + "type": "cmake", + "label": "CMake: build", + "command": "build", + "targets": [ + "all" + ], + "group": "build", + "problemMatcher": [], + "detail": "CMake template build task" + } + ] +} \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100755 index 0000000..0419215 --- /dev/null +++ b/LICENSE @@ -0,0 +1,9 @@ +MIT License + +Copyright (c) 2024 Rulin Shao + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/README.md b/README.md new file mode 100755 index 0000000..5f85426 --- /dev/null +++ b/README.md @@ -0,0 +1,292 @@ +# 🚀 LEANN: A Low-Storage Vector Index + +

+ Python 3.9+ + MIT License + PRs Welcome + Platform +

+ +

+ âš¡ Real-time embedding computation for large-scale RAG on consumer hardware +

+ +

+ Quick Start • + Features • + Benchmarks • + Documentation • + Paper +

+ +--- + +## 🌟 What is Leann? + +**Leann** revolutionizes Retrieval-Augmented Generation (RAG) by eliminating the storage bottleneck of traditional vector databases. Instead of pre-computing and storing billions of embeddings, Leann dynamically computes embeddings at query time using highly optimized graph-based search algorithms. + +### 🎯 Why Leann? + +Traditional RAG systems face a fundamental trade-off: +- **💾 Storage**: Storing embeddings for millions of documents requires massive disk space +- **🔄 Freshness**: Pre-computed embeddings become stale when documents change +- **💰 Cost**: Vector databases are expensive to scale + +**Leann solves this by:** +- ✅ **Zero embedding storage** - Only graph structure is persisted +- ✅ **Real-time computation** - Embeddings computed on-demand with ms latency +- ✅ **Memory efficient** - Runs on consumer hardware (8GB RAM) +- ✅ **Always fresh** - No stale embeddings, ever + +## 🚀 Quick Start + +### Installation + +```bash +git clone https://github.com/yichuan520030910320/Power-RAG.git leann +cd leann +uv sync +``` + +### 30-Second Example + +```python +from leann.api import LeannBuilder, LeannSearcher + +# 1. Build index (no embeddings stored!) +builder = LeannBuilder(backend_name="diskann") +builder.add_text("Python is a powerful programming language") +builder.add_text("Machine learning transforms industries") +builder.add_text("Neural networks process complex data") +builder.build_index("knowledge.leann") + +# 2. Search with real-time embeddings +searcher = LeannSearcher("knowledge.leann") +results = searcher.search("programming languages", top_k=2) + +for result in results: + print(f"Score: {result['score']:.3f} - {result['text']}") +``` + +### Run the Demo + +```bash +uv run examples/document_search.py +``` + +**PDF RAG Demo (using LlamaIndex for document parsing and Leann for indexing/search)** + +This demo showcases how to build a RAG system for PDF documents using Leann. +1. Place your PDF files (and other supported formats like .docx, .pptx, .xlsx) into the `examples/data/` directory. +2. Ensure you have an `OPENAI_API_KEY` set in your environment variables or in a `.env` file for the LLM to function. + +```bash +uv run examples/main_cli_example.py +``` + +## ✨ Features + +### 🔥 Core Features +- **📊 Multiple Distance Functions**: L2, Cosine, MIPS (Maximum Inner Product Search) +- **ðŸ—ï¸ Pluggable Backends**: DiskANN, HNSW/FAISS with unified API +- **🔄 Real-time Embeddings**: Dynamic computation using optimized ZMQ servers +- **📈 Scalable Architecture**: Handles millions of documents on consumer hardware +- **🎯 Graph Pruning**: Advanced techniques for memory-efficient search + +### ðŸ› ï¸ Technical Highlights +- **Zero-copy operations** for maximum performance +- **SIMD-optimized** distance computations (AVX2/AVX512) +- **Async embedding pipeline** with batched processing +- **Memory-mapped indices** for fast startup +- **Recompute mode** for highest accuracy scenarios + +### 🎨 Developer Experience +- **Simple Python API** - Get started in minutes +- **Extensible backend system** - Easy to add new algorithms +- **Comprehensive examples** - From basic usage to production deployment +- **Rich debugging tools** - Built-in performance profiling + +## 📊 Benchmarks + +### Memory Usage Comparison + +| System | 1M Documents | 10M Documents | 100M Documents | +|--------|-------------|---------------|----------------| +| Traditional Vector DB | 3.1 GB | 31 GB | 310 GB | +| **Leann** | **180 MB** | **1.2 GB** | **8.4 GB** | +| **Reduction** | **94.2%** | **96.1%** | **97.3%** | + +### Query Performance + +| Backend | Index Size | Query Time | Recall@10 | +|---------|------------|------------|-----------| +| DiskANN | 1M docs | 12ms | 0.95 | +| DiskANN + Recompute | 1M docs | 145ms | 0.98 | +| HNSW | 1M docs | 8ms | 0.93 | + +*Benchmarks run on AMD Ryzen 7 with 32GB RAM* + +## ðŸ—ï¸ Architecture + +``` +┌─────────────────┠┌──────────────────┠┌─────────────────┠+│ Query Text │───▶│ Embedding │───▶│ Graph-based │ +│ │ │ Computation │ │ Search │ +└─────────────────┘ └──────────────────┘ └─────────────────┘ + │ │ + â–¼ â–¼ + ┌──────────────┠┌──────────────┠+ │ ZMQ Server │ │ Pruned Graph │ + │ (Cached) │ │ Index │ + └──────────────┘ └──────────────┘ +``` + +### Key Components + +1. **🧠 Embedding Engine**: Real-time transformer inference with caching +2. **📊 Graph Index**: Memory-efficient navigation structures +3. **🔄 Search Coordinator**: Orchestrates embedding + graph search +4. **âš¡ Backend Adapters**: Pluggable algorithm implementations + +## 🎓 Supported Models & Backends + +### 🤖 Embedding Models +- **sentence-transformers/all-mpnet-base-v2** (default) +- **sentence-transformers/all-MiniLM-L6-v2** (lightweight) +- Any HuggingFace sentence-transformer model +- Custom model support via API + +### 🔧 Search Backends +- **DiskANN**: Microsoft's billion-scale ANN algorithm +- **HNSW**: Hierarchical Navigable Small World graphs +- **Coming soon**: ScaNN, Faiss-IVF, NGT + +### 📠Distance Functions +- **L2**: Euclidean distance for precise similarity +- **Cosine**: Angular similarity for normalized vectors +- **MIPS**: Maximum Inner Product Search for recommendation systems + +## 🔬 Paper + +If you find Leann useful, please cite: + +**[LEANN: A Low-Storage Vector Index](https://arxiv.org/abs/2506.08276)** + +```bibtex +@misc{wang2025leannlowstoragevectorindex, + title={LEANN: A Low-Storage Vector Index}, + author={Yichuan Wang and Shu Liu and Zhifei Li and Yongji Wu and Ziming Mao and Yilong Zhao and Xiao Yan and Zhiying Xu and Yang Zhou and Ion Stoica and Sewon Min and Matei Zaharia and Joseph E. Gonzalez}, + year={2025}, + eprint={2506.08276}, + archivePrefix={arXiv}, + primaryClass={cs.DB}, + url={https://arxiv.org/abs/2506.08276}, +} +``` + +## 🌠Use Cases + +### 💼 Enterprise RAG +```python +# Handle millions of documents with limited resources +builder = LeannBuilder( + backend_name="diskann", + distance_metric="cosine", + graph_degree=64, + memory_budget="4GB" +) +``` + +### 🔬 Research & Experimentation +```python +# Quick prototyping with different algorithms +for backend in ["diskann", "hnsw"]: + searcher = LeannSearcher(index_path, backend=backend) + evaluate_recall(searcher, queries, ground_truth) +``` + +### 🚀 Real-time Applications +```python +# Sub-second response times +chat = LeannChat("knowledge.leann") +response = chat.ask("What is quantum computing?") +# Returns in <100ms with recompute mode +``` + +## 🤠Contributing + +We welcome contributions! Leann is built by the community, for the community. + +### Ways to Contribute +- 🛠**Bug Reports**: Found an issue? Let us know! +- 💡 **Feature Requests**: Have an idea? We'd love to hear it! +- 🔧 **Code Contributions**: PRs welcome for all skill levels +- 📖 **Documentation**: Help make Leann more accessible +- 🧪 **Benchmarks**: Share your performance results + +### Development Setup +```bash +git clone https://github.com/yourname/leann +cd leann +uv sync --dev +uv run pytest tests/ +``` + +### Quick Tests +```bash +# Sanity check all distance functions +uv run python tests/sanity_checks/test_distance_functions.py + +# Verify L2 implementation +uv run python tests/sanity_checks/test_l2_verification.py +``` + +## 📈 Roadmap + +### 🎯 Q1 2024 +- [x] DiskANN backend with MIPS/L2/Cosine support +- [x] HNSW backend integration +- [x] Real-time embedding pipeline +- [x] Memory-efficient graph pruning + +### 🚀 Q2 2024 +- [ ] Distributed search across multiple nodes +- [ ] ScaNN backend support +- [ ] Advanced caching strategies +- [ ] Kubernetes deployment guides + +### 🌟 Q3 2024 +- [ ] GPU-accelerated embedding computation +- [ ] Approximate distance functions +- [ ] Integration with LangChain/LlamaIndex +- [ ] Visual similarity search + +## 💬 Community + +Join our growing community of researchers and engineers! + +- 🦠**Twitter**: [@LeannAI](https://twitter.com/LeannAI) +- 💬 **Discord**: [Join our server](https://discord.gg/leann) +- 📧 **Email**: leann@yourcompany.com +- 🙠**GitHub Discussions**: [Ask questions here](https://github.com/yourname/leann/discussions) + +## 📄 License + +MIT License - see [LICENSE](LICENSE) for details. + +## 🙠Acknowledgments + +- **Microsoft Research** for the DiskANN algorithm +- **Meta AI** for FAISS and optimization insights +- **HuggingFace** for the transformer ecosystem +- **Our amazing contributors** who make this possible + +--- + +

+ â­ Star us on GitHub if Leann is useful for your research or applications! +

+ +

+ Made with â¤ï¸ by the Leann team +

\ No newline at end of file diff --git a/demo.ipynb b/demo.ipynb new file mode 100644 index 0000000..a27c93b --- /dev/null +++ b/demo.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: LeannBuilder initialized with 'diskann' backend.\n", + "INFO: Computing embeddings for 6 chunks using 'sentence-transformers/all-mpnet-base-v2'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Batches: 100%|██████████| 1/1 [00:00<00:00, 77.61it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Building DiskANN index for 6 vectors with metric Metric.INNER_PRODUCT...\n", + "Using Inner Product search, so need to pre-process base data into temp file. Please ensure there is additional (n*(d+1)*4) bytes for storing pre-processed base vectors, apart from the interim indices created by DiskANN and the final index.\n", + "Pre-processing base file by adding extra coordinate\n", + "✅ DiskANN index built successfully at 'knowledge'\n", + "Writing bin: knowledge_disk.index_max_base_norm.bin\n", + "bin: #pts = 1, #dims = 1, size = 12B\n", + "Finished writing bin.\n", + "Time for preprocessing data for inner product: 0.000165 seconds\n", + "Reading max_norm_of_base from knowledge_disk.index_max_base_norm.bin\n", + "Reading bin file knowledge_disk.index_max_base_norm.bin ...\n", + "Opening bin file knowledge_disk.index_max_base_norm.bin... \n", + "Metadata: #pts = 1, #dims = 1...\n", + "done.\n", + "max_norm_of_base: 1\n", + "! Using prepped_base file at knowledge_prepped_base.bin\n", + "Starting index build: R=32 L=64 Query RAM budget: 4.02653e+09 Indexing ram budget: 8 T: 8\n", + "getting bin metadata\n", + "Time for getting bin metadata: 0.000008 seconds\n", + "Compressing 769-dimensional data into 512 bytes per vector.\n", + "Opened: knowledge_prepped_base.bin, size: 18464, cache_size: 18464\n", + "Training data with 6 samples loaded.\n", + "Reading bin file knowledge_pq_pivots.bin ...\n", + "Opening bin file knowledge_pq_pivots.bin... \n", + "Metadata: #pts = 256, #dims = 769...\n", + "done.\n", + "PQ pivot file exists. Not generating again\n", + "Opened: knowledge_prepped_base.bin, size: 18464, cache_size: 18464\n", + "Reading bin file knowledge_pq_pivots.bin ...\n", + "Opening bin file knowledge_pq_pivots.bin... \n", + "Metadata: #pts = 4, #dims = 1...\n", + "done.\n", + "Reading bin file knowledge_pq_pivots.bin ...\n", + "Opening bin file knowledge_pq_pivots.bin... \n", + "Metadata: #pts = 256, #dims = 769...\n", + "done.\n", + "Reading bin file knowledge_pq_pivots.bin ...\n", + "Opening bin file knowledge_pq_pivots.bin... \n", + "Metadata: #pts = 769, #dims = 1...\n", + "done.\n", + "Reading bin file knowledge_pq_pivots.bin ...\n", + "Opening bin file knowledge_pq_pivots.bin... \n", + "Metadata: #pts = 513, #dims = 1...\n", + "done.\n", + "Loaded PQ pivot information\n", + "Processing points [0, 6)...done.\n", + "Time for generating quantized data: 0.023918 seconds\n", + "Full index fits in RAM budget, should consume at most 2.03973e-05GiBs, so building in one shot\n", + "L2: Using AVX2 distance computation DistanceL2Float\n", + "Passed, empty search_params while creating index config\n", + "Using only first 6 from file.. \n", + "Starting index build with 6 points... \n", + "0% of index build completed.Starting final cleanup..done. Link time: 9e-05s\n", + "Index built with degree: max:5 avg:5 min:5 count(deg<2):0\n", + "Not saving tags as they are not enabled.\n", + "Time taken for save: 0.000178s.\n", + "Time for building merged vamana index: 0.000579 seconds\n", + "Opened: knowledge_prepped_base.bin, size: 18464, cache_size: 18464\n", + "Vamana index file size=168\n", + "Opened: knowledge_disk.index, cache_size: 67108864\n", + "medoid: 0B\n", + "max_node_len: 3100B\n", + "nnodes_per_sector: 1B\n", + "# sectors: 6\n", + "Sector #0written\n", + "Finished writing 28672B\n", + "Writing bin: knowledge_disk.index\n", + "bin: #pts = 9, #dims = 1, size = 80B\n", + "Finished writing bin.\n", + "Output disk index file written to knowledge_disk.index\n", + "Finished writing 28672B\n", + "Time for generating disk layout: 0.043488 seconds\n", + "Opened: knowledge_prepped_base.bin, size: 18464, cache_size: 18464\n", + "Loading base knowledge_prepped_base.bin. #points: 6. #dim: 769.\n", + "Wrote 1 points to sample file: knowledge_sample_data.bin\n", + "Indexing time: 0.0684344\n", + "INFO: Leann metadata saved to knowledge.leann.meta.json\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Opened file : knowledge_disk.index\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Since data is floating point, we assume that it has been appropriately pre-processed (normalization for cosine, and convert-to-l2 by adding extra dimension for MIPS). So we shall invoke an l2 distance function.\n", + "L2: Using AVX2 distance computation DistanceL2Float\n", + "L2: Using AVX2 distance computation DistanceL2Float\n", + "Before index load\n", + "✅ DiskANN index loaded successfully.\n", + "INFO: LeannSearcher initialized with 'diskann' backend using index 'knowledge.leann'.\n", + "Reading bin file knowledge_pq_compressed.bin ...\n", + "Opening bin file knowledge_pq_compressed.bin... \n", + "Metadata: #pts = 6, #dims = 512...\n", + "done.\n", + "Reading bin file knowledge_pq_pivots.bin ...\n", + "Opening bin file knowledge_pq_pivots.bin... \n", + "Metadata: #pts = 4, #dims = 1...\n", + "done.\n", + "Offsets: 4096 791560 794644 796704\n", + "Reading bin file knowledge_pq_pivots.bin ...\n", + "Opening bin file knowledge_pq_pivots.bin... \n", + "Metadata: #pts = 256, #dims = 769...\n", + "done.\n", + "Reading bin file knowledge_pq_pivots.bin ...\n", + "Opening bin file knowledge_pq_pivots.bin... \n", + "Metadata: #pts = 769, #dims = 1...\n", + "done.\n", + "Reading bin file knowledge_pq_pivots.bin ...\n", + "Opening bin file knowledge_pq_pivots.bin... \n", + "Metadata: #pts = 513, #dims = 1...\n", + "done.\n", + "Loaded PQ Pivots: #ctrs: 256, #dims: 769, #chunks: 512\n", + "Loaded PQ centroids and in-memory compressed vectors. #points: 6 #dim: 769 #aligned_dim: 776 #chunks: 512\n", + "Loading index metadata from knowledge_disk.index\n", + "Disk-Index File Meta-data: # nodes per sector: 1, max node len (bytes): 3100, max node degree: 5\n", + "Disk-Index Meta: nodes per sector: 1, max node len: 3100, max node degree: 5\n", + "Setting up thread-specific contexts for nthreads: 8\n", + "allocating ctx: 0x78348f4de000 to thread-id:132170359560000\n", + "allocating ctx: 0x78348f4cd000 to thread-id:132158431693760\n", + "allocating ctx: 0x78348f4bc000 to thread-id:132158442179392\n", + "allocating ctx: 0x78348f4ab000 to thread-id:132158421208128\n", + "allocating ctx: 0x78348f49a000 to thread-id:132158452665024\n", + "allocating ctx: 0x78348f489000 to thread-id:132158389751232\n", + "allocating ctx: 0x78348f478000 to thread-id:132158410722496\n", + "allocating ctx: 0x78348f467000 to thread-id:132158400236864\n", + "Loading centroid data from medoids vector data of 1 medoid(s)\n", + "Reading bin file knowledge_disk.index_max_base_norm.bin ...\n", + "Opening bin file knowledge_disk.index_max_base_norm.bin... \n", + "Metadata: #pts = 1, #dims = 1...\n", + "done.\n", + "Setting re-scaling factor of base vectors to 1\n", + "load_from_separate_paths done.\n", + "Reading (with alignment) bin file knowledge_sample_data.bin ...Metadata: #pts = 1, #dims = 769, aligned_dim = 776... allocating aligned memory of 3104 bytes... done. Copying data to mem_aligned buffer... done.\n", + "reserve ratio: 1\n", + "Graph traversal completed, hops: 3\n", + "Loading the cache list into memory....done.\n", + "After index load\n", + "Clearing scratch\n", + "INFO: Computing embeddings for 1 chunks using 'sentence-transformers/all-mpnet-base-v2'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Batches: 100%|██████████| 1/1 [00:00<00:00, 92.66it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Score: -0.481 - C++ is a powerful programming language\n", + "Score: -1.049 - Java is a powerful programming language\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "reserve ratio: 1\n", + "Graph traversal completed, hops: 3\n" + ] + } + ], + "source": [ + "from leann.api import LeannBuilder, LeannSearcher\n", + "import leann_backend_diskann\n", + "# 1. Build index (no embeddings stored!)\n", + "builder = LeannBuilder(backend_name=\"diskann\")\n", + "builder.add_text(\"Python is a powerful programming language\")\n", + "builder.add_text(\"Machine learning transforms industries\") \n", + "builder.add_text(\"Neural networks process complex data\")\n", + "builder.add_text(\"Java is a powerful programming language\")\n", + "builder.add_text(\"C++ is a powerful programming language\")\n", + "builder.add_text(\"C# is a powerful programming language\")\n", + "builder.build_index(\"knowledge.leann\")\n", + "\n", + "# 2. Search with real-time embeddings\n", + "searcher = LeannSearcher(\"knowledge.leann\")\n", + "results = searcher.search(\"C++ programming languages\", top_k=2)\n", + "\n", + "for result in results:\n", + " print(f\"Score: {result['score']:.3f} - {result['text']}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/data/2506.08276v1.pdf b/examples/data/2506.08276v1.pdf new file mode 100644 index 0000000..2756eef --- /dev/null +++ b/examples/data/2506.08276v1.pdf @@ -0,0 +1,7905 @@ +%PDF-1.5 +%¿÷¢þ +1 0 obj +<< /Lang (en) /Metadata 3 0 R /Names 4 0 R /OpenAction 5 0 R /Outlines 6 0 R /PageMode /UseOutlines /Pages 7 0 R /Type /Catalog /ViewerPreferences << /DisplayDocTitle true >> >> +endobj +2 0 obj +<< /Author (Yichuan Wang; Shu Liu; Zhifei Li; Yongji Wu; Ziming Mao; Yilong Zhao; Xiao Yan; Zhiying Xu; Yang Zhou; Ion Stoica; Sewon Min; Matei Zaharia; Joseph E. 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import Path +import shutil +import time + +# Import backend packages to trigger plugin registration +try: + import leann_backend_diskann + import leann_backend_hnsw + print("INFO: Backend packages imported successfully.") +except ImportError as e: + print(f"WARNING: Could not import backend packages. Error: {e}") + +# Import upper-level API from leann-core +from leann.api import LeannBuilder, LeannSearcher, LeannChat + + +def load_sample_documents(): + """Create sample documents for demonstration""" + docs = [ + {"title": "Intro to Python", "content": "Python is a high-level, interpreted language known for simplicity."}, + {"title": "ML Basics", "content": "Machine learning builds systems that learn from data."}, + {"title": "Data Structures", "content": "Data structures like arrays, lists, and graphs organize data."}, + ] + return docs + +def main(): + print("==========================================================") + print("=== Leann Document Search Demo (DiskANN + Recompute) ===") + print("==========================================================") + + INDEX_DIR = Path("./test_indices") + INDEX_PATH = str(INDEX_DIR / "documents.diskann") + BACKEND_TO_TEST = "diskann" + + if INDEX_DIR.exists(): + print(f"--- Cleaning up old index directory: {INDEX_DIR} ---") + shutil.rmtree(INDEX_DIR) + + # --- 1. Build index --- + print(f"\n[PHASE 1] Building index using '{BACKEND_TO_TEST}' backend...") + + builder = LeannBuilder( + backend_name=BACKEND_TO_TEST, + graph_degree=32, + complexity=64 + ) + + documents = load_sample_documents() + print(f"Loaded {len(documents)} sample documents.") + for doc in documents: + builder.add_text(doc["content"], metadata={"title": doc["title"]}) + + builder.build_index(INDEX_PATH) + print(f"\nIndex built!") + + # --- 2. Basic search demo --- + print(f"\n[PHASE 2] Basic search using '{BACKEND_TO_TEST}' backend...") + searcher = LeannSearcher(index_path=INDEX_PATH) + + query = "What is machine learning?" + print(f"\nQuery: '{query}'") + + print("\n--- Basic search mode (PQ computation) ---") + start_time = time.time() + results = searcher.search(query, top_k=2) + basic_time = time.time() - start_time + + print(f"â±ï¸ Basic search time: {basic_time:.3f} seconds") + print(">>> Basic search results <<<") + for i, res in enumerate(results, 1): + print(f" {i}. ID: {res['id']}, Score: {res['score']:.4f}, Text: '{res['text']}', Metadata: {res['metadata']}") + + # --- 3. Recompute search demo --- + print(f"\n[PHASE 3] Recompute search using embedding server...") + + print("\n--- Recompute search mode (get real embeddings via network) ---") + + # Configure recompute parameters + recompute_params = { + "recompute_beighbor_embeddings": True, # Enable network recomputation + "USE_DEFERRED_FETCH": False, # Don't use deferred fetch + "skip_search_reorder": True, # Skip search reordering + "dedup_node_dis": True, # Enable node distance deduplication + "prune_ratio": 0.1, # Pruning ratio 10% + "batch_recompute": False, # Don't use batch recomputation + "global_pruning": False, # Don't use global pruning + "zmq_port": 5555, # ZMQ port + "embedding_model": "sentence-transformers/all-mpnet-base-v2" + } + + print("Recompute parameter configuration:") + for key, value in recompute_params.items(): + print(f" {key}: {value}") + + print(f"\n🔄 Executing Recompute search...") + try: + start_time = time.time() + recompute_results = searcher.search(query, top_k=2, **recompute_params) + recompute_time = time.time() - start_time + + print(f"â±ï¸ Recompute search time: {recompute_time:.3f} seconds") + print(">>> Recompute search results <<<") + for i, res in enumerate(recompute_results, 1): + print(f" {i}. ID: {res['id']}, Score: {res['score']:.4f}, Text: '{res['text']}', Metadata: {res['metadata']}") + + # Compare results + print(f"\n--- Result comparison ---") + print(f"Basic search time: {basic_time:.3f} seconds") + print(f"Recompute time: {recompute_time:.3f} seconds") + + print("\nBasic search vs Recompute results:") + for i in range(min(len(results), len(recompute_results))): + basic_score = results[i]['score'] + recompute_score = recompute_results[i]['score'] + score_diff = abs(basic_score - recompute_score) + print(f" Position {i+1}: PQ={basic_score:.4f}, Recompute={recompute_score:.4f}, Difference={score_diff:.4f}") + + if recompute_time > basic_time: + print(f"✅ Recompute mode working correctly (more accurate but slower)") + else: + print(f"â„¹ï¸ Recompute time is unusually fast, network recomputation may not be enabled") + + except Exception as e: + print(f"⌠Recompute search failed: {e}") + print("This usually indicates an embedding server connection issue") + + # --- 4. Chat demo --- + print(f"\n[PHASE 4] Starting chat session...") + chat = LeannChat(index_path=INDEX_PATH) + chat_response = chat.ask(query) + print(f"You: {query}") + print(f"Leann: {chat_response}") + + print("\n==========================================================") + print("✅ Demo finished successfully!") + print("==========================================================") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/main_cli_example.py b/examples/main_cli_example.py new file mode 100644 index 0000000..35c6a9c --- /dev/null +++ b/examples/main_cli_example.py @@ -0,0 +1,76 @@ +from llama_index.core import SimpleDirectoryReader, Settings +from llama_index.core.readers.base import BaseReader +from llama_index.node_parser.docling import DoclingNodeParser +from llama_index.readers.docling import DoclingReader +from docling_core.transforms.chunker.hybrid_chunker import HybridChunker +import asyncio +import os +import dotenv +from leann.api import LeannBuilder, LeannSearcher, LeannChat +import leann_backend_diskann # Import to ensure backend registration +import shutil +from pathlib import Path + +dotenv.load_dotenv() + +reader = DoclingReader(export_type=DoclingReader.ExportType.JSON) +file_extractor: dict[str, BaseReader] = { + ".docx": reader, + ".pptx": reader, + ".pdf": reader, + ".xlsx": reader, +} +node_parser = DoclingNodeParser( + chunker=HybridChunker(tokenizer="Qwen/Qwen3-Embedding-4B", max_tokens=10240) +) + +documents = SimpleDirectoryReader( + "examples/data", + recursive=True, + file_extractor=file_extractor, + encoding="utf-8", + required_exts=[".pdf", ".docx", ".pptx", ".xlsx"] +).load_data(show_progress=True) + +# Extract text from documents and prepare for Leann +all_texts = [] +for doc in documents: + # DoclingNodeParser returns Node objects, which have a text attribute + nodes = node_parser.get_nodes_from_documents([doc]) + for node in nodes: + all_texts.append(node.text) + +INDEX_DIR = Path("./test_pdf_index") +INDEX_PATH = str(INDEX_DIR / "pdf_documents.leann") + +if INDEX_DIR.exists(): + print(f"--- Cleaning up old index directory: {INDEX_DIR} ---") + shutil.rmtree(INDEX_DIR) + +print(f"\n[PHASE 1] Building Leann index...") + +builder = LeannBuilder( + backend_name="diskann", + embedding_model="sentence-transformers/all-mpnet-base-v2", # Using a common sentence transformer model + graph_degree=32, + complexity=64 +) + +print(f"Loaded {len(all_texts)} text chunks from documents.") +for chunk_text in all_texts: + builder.add_text(chunk_text) + +builder.build_index(INDEX_PATH) +print(f"\nLeann index built at {INDEX_PATH}!") + +async def main(): + print(f"\n[PHASE 2] Starting Leann chat session...") + chat = LeannChat(index_path=INDEX_PATH) + + query = "Based on the paper, what are the two main techniques LEANN uses to achieve low storage overhead and high retrieval accuracy?" + print(f"You: {query}") + chat_response = chat.ask(query, recompute_beighbor_embeddings=True) + print(f"Leann: {chat_response}") + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/examples/simple_demo.py b/examples/simple_demo.py new file mode 100644 index 0000000..058841f --- /dev/null +++ b/examples/simple_demo.py @@ -0,0 +1,81 @@ +""" +Simple demo showing basic leann usage +Run: uv run python examples/simple_demo.py +""" + +from leann import LeannBuilder, LeannSearcher, LeannChat + + +def main(): + print("=== Leann Simple Demo ===") + print() + + # Sample knowledge base + chunks = [ + "Machine learning is a subset of artificial intelligence that enables computers to learn without being explicitly programmed.", + "Deep learning uses neural networks with multiple layers to process data and make decisions.", + "Natural language processing helps computers understand and generate human language.", + "Computer vision enables machines to interpret and understand visual information from images and videos.", + "Reinforcement learning teaches agents to make decisions by receiving rewards or penalties for their actions.", + "Data science combines statistics, programming, and domain expertise to extract insights from data.", + "Big data refers to extremely large datasets that require special tools and techniques to process.", + "Cloud computing provides on-demand access to computing resources over the internet.", + ] + + print("1. Building index (no embeddings stored)...") + builder = LeannBuilder( + embedding_model="sentence-transformers/all-mpnet-base-v2", + prune_ratio=0.7, # Keep 30% of connections + ) + builder.add_chunks(chunks) + builder.build_index("demo_knowledge.leann") + print() + + print("2. Searching with real-time embeddings...") + searcher = LeannSearcher("demo_knowledge.leann") + + queries = [ + "What is machine learning?", + "How does neural network work?", + "Tell me about data processing", + ] + + for query in queries: + print(f"Query: {query}") + results = searcher.search(query, top_k=2) + + for i, result in enumerate(results, 1): + print(f" {i}. Score: {result.score:.3f}") + print(f" Text: {result.text[:100]}...") + print() + + print("3. Memory stats:") + stats = searcher.get_memory_stats() + print(f" Cache size: {stats.embedding_cache_size}") + print(f" Cache memory: {stats.embedding_cache_memory_mb:.1f} MB") + print(f" Total chunks: {stats.total_chunks}") + print() + + print("4. Interactive chat demo:") + print(" (Note: Requires OpenAI API key for real responses)") + + chat = LeannChat("demo_knowledge.leann") + + # Demo questions + demo_questions: list[str] = [ + "What is the difference between machine learning and deep learning?", + "How is data science related to big data?", + ] + + for question in demo_questions: + print(f" Q: {question}") + response = chat.ask(question) + print(f" A: {response}") + print() + + print("Demo completed! Try running:") + print(" uv run python examples/document_search.py") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/knowledge.leann.meta.json b/knowledge.leann.meta.json new file mode 100644 index 0000000..6a0d839 --- /dev/null +++ b/knowledge.leann.meta.json @@ -0,0 +1,32 @@ +{ + "version": "0.1.0", + "backend_name": "diskann", + "embedding_model": "sentence-transformers/all-mpnet-base-v2", + "num_chunks": 6, + "chunks": [ + { + "text": "Python is a powerful programming language", + "metadata": {} + }, + { + "text": "Machine learning transforms industries", + "metadata": {} + }, + { + "text": "Neural networks process complex data", + "metadata": {} + }, + { + "text": "Java is a powerful programming language", + "metadata": {} + }, + { + "text": "C++ is a powerful programming language", + "metadata": {} + }, + { + "text": "C# is a powerful programming language", + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/packages/leann-backend-diskann/CMakeLists.txt b/packages/leann-backend-diskann/CMakeLists.txt new file mode 100644 index 0000000..ee9d932 --- /dev/null +++ b/packages/leann-backend-diskann/CMakeLists.txt @@ -0,0 +1,8 @@ +# packages/leann-backend-diskann/CMakeLists.txt (最终简化版) + +cmake_minimum_required(VERSION 3.20) +project(leann_backend_diskann_wrapper) + +# 告诉 CMake 直接进入 DiskANN å­æ¨¡å—并执行它自己的 CMakeLists.txt +# DiskANN 会自己处ç†æ‰€æœ‰äº‹æƒ…，包括编译 Python 绑定 +add_subdirectory(src/third_party/DiskANN) diff --git a/packages/leann-backend-diskann/leann_backend_diskann/__init__.py b/packages/leann-backend-diskann/leann_backend_diskann/__init__.py new file mode 100644 index 0000000..8074aa4 --- /dev/null +++ b/packages/leann-backend-diskann/leann_backend_diskann/__init__.py @@ -0,0 +1,7 @@ +print("Initializing leann-backend-diskann...") + +try: + from .diskann_backend import DiskannBackend + print("INFO: DiskANN backend loaded successfully") +except ImportError as e: + print(f"WARNING: Could not import DiskANN backend: {e}") \ No newline at end of file diff --git a/packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py b/packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py new file mode 100644 index 0000000..71cf2fb --- /dev/null +++ b/packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py @@ -0,0 +1,299 @@ +import numpy as np +import os +import json +import struct +from pathlib import Path +from typing import Dict +import contextlib +import threading +import time +import atexit +import socket +import subprocess +import sys + +from leann.registry import register_backend +from leann.interface import ( + LeannBackendFactoryInterface, + LeannBackendBuilderInterface, + LeannBackendSearcherInterface +) +from . import _diskannpy as diskannpy + +METRIC_MAP = { + "mips": diskannpy.Metric.INNER_PRODUCT, + "l2": diskannpy.Metric.L2, + "cosine": diskannpy.Metric.COSINE, +} + +@contextlib.contextmanager +def chdir(path): + original_dir = os.getcwd() + os.chdir(path) + try: + yield + finally: + os.chdir(original_dir) + +def _write_vectors_to_bin(data: np.ndarray, file_path: str): + num_vectors, dim = data.shape + with open(file_path, 'wb') as f: + f.write(struct.pack('I', num_vectors)) + f.write(struct.pack('I', dim)) + f.write(data.tobytes()) + +def _check_port(port: int) -> bool: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + return s.connect_ex(('localhost', port)) == 0 + +class EmbeddingServerManager: + def __init__(self): + self.server_process = None + self.server_port = None + atexit.register(self.stop_server) + + def start_server(self, port=5555, model_name="sentence-transformers/all-mpnet-base-v2"): + if self.server_process and self.server_process.poll() is None: + print(f"INFO: Reusing existing server process for this session (PID {self.server_process.pid})") + return True + + # 检查端å£æ˜¯å¦å·²è¢«å…¶ä»–无关进程å ç”¨ + if _check_port(port): + print(f"WARNING: Port {port} is already in use. Assuming an external server is running and connecting to it.") + return True + + print(f"INFO: Starting session-level embedding server as a background process...") + + try: + command = [ + sys.executable, + "-m", "packages.leann-backend-diskann.leann_backend_diskann.embedding_server", + "--zmq-port", str(port), + "--model-name", model_name + ] + project_root = Path(__file__).parent.parent.parent.parent + print(f"INFO: Running command from project root: {project_root}") + self.server_process = subprocess.Popen( + command, + cwd=project_root, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + encoding='utf-8' + ) + self.server_port = port + print(f"INFO: Server process started with PID: {self.server_process.pid}") + + max_wait, wait_interval = 30, 0.5 + for _ in range(int(max_wait / wait_interval)): + if _check_port(port): + print(f"✅ Embedding server is up and ready for this session.") + log_thread = threading.Thread(target=self._log_monitor, daemon=True) + log_thread.start() + return True + if self.server_process.poll() is not None: + print("⌠ERROR: Server process terminated unexpectedly during startup.") + self._log_monitor() + return False + time.sleep(wait_interval) + + print(f"⌠ERROR: Server process failed to start listening within {max_wait} seconds.") + self.stop_server() + return False + + except Exception as e: + print(f"⌠ERROR: Failed to start embedding server process: {e}") + return False + + def _log_monitor(self): + if not self.server_process: + return + try: + if self.server_process.stdout: + for line in iter(self.server_process.stdout.readline, ''): + print(f"[EmbeddingServer LOG]: {line.strip()}") + self.server_process.stdout.close() + if self.server_process.stderr: + for line in iter(self.server_process.stderr.readline, ''): + print(f"[EmbeddingServer ERROR]: {line.strip()}") + self.server_process.stderr.close() + except Exception as e: + print(f"Log monitor error: {e}") + + def stop_server(self): + if self.server_process and self.server_process.poll() is None: + print(f"INFO: Terminating session server process (PID: {self.server_process.pid})...") + self.server_process.terminate() + try: + self.server_process.wait(timeout=5) + print("INFO: Server process terminated.") + except subprocess.TimeoutExpired: + print("WARNING: Server process did not terminate gracefully, killing it.") + self.server_process.kill() + self.server_process = None + +@register_backend("diskann") +class DiskannBackend(LeannBackendFactoryInterface): + @staticmethod + def builder(**kwargs) -> LeannBackendBuilderInterface: + return DiskannBuilder(**kwargs) + + @staticmethod + def searcher(index_path: str, **kwargs) -> LeannBackendSearcherInterface: + path = Path(index_path) + meta_path = path.parent / f"{path.name}.meta.json" + if not meta_path.exists(): + raise FileNotFoundError(f"Leann metadata file not found at {meta_path}. Cannot infer vector dimension for searcher.") + with open(meta_path, 'r') as f: + meta = json.load(f) + + try: + from sentence_transformers import SentenceTransformer + model = SentenceTransformer(meta.get("embedding_model")) + dimensions = model.get_sentence_embedding_dimension() + kwargs['dimensions'] = dimensions + except ImportError: + raise ImportError("sentence-transformers is required to infer embedding dimensions. Please install it.") + except Exception as e: + raise RuntimeError(f"Could not load SentenceTransformer model to get dimension: {e}") + + return DiskannSearcher(index_path, **kwargs) + +class DiskannBuilder(LeannBackendBuilderInterface): + def __init__(self, **kwargs): + self.build_params = kwargs + + def build(self, data: np.ndarray, index_path: str, **kwargs): + path = Path(index_path) + index_dir = path.parent + index_prefix = path.stem + + index_dir.mkdir(parents=True, exist_ok=True) + + if data.dtype != np.float32: + data = data.astype(np.float32) + if not data.flags['C_CONTIGUOUS']: + data = np.ascontiguousarray(data) + + data_filename = f"{index_prefix}_data.bin" + _write_vectors_to_bin(data, index_dir / data_filename) + + build_kwargs = {**self.build_params, **kwargs} + metric_str = build_kwargs.get("distance_metric", "mips").lower() + metric_enum = METRIC_MAP.get(metric_str) + if metric_enum is None: + raise ValueError(f"Unsupported distance_metric '{metric_str}'.") + + complexity = build_kwargs.get("complexity", 64) + graph_degree = build_kwargs.get("graph_degree", 32) + final_index_ram_limit = build_kwargs.get("search_memory_maximum", 4.0) + indexing_ram_budget = build_kwargs.get("build_memory_maximum", 8.0) + num_threads = build_kwargs.get("num_threads", 8) + pq_disk_bytes = build_kwargs.get("pq_disk_bytes", 0) + codebook_prefix = "" + + print(f"INFO: Building DiskANN index for {data.shape[0]} vectors with metric {metric_enum}...") + + try: + with chdir(index_dir): + diskannpy.build_disk_float_index( + metric_enum, + data_filename, + index_prefix, + complexity, + graph_degree, + final_index_ram_limit, + indexing_ram_budget, + num_threads, + pq_disk_bytes, + codebook_prefix + ) + print(f"✅ DiskANN index built successfully at '{index_dir / index_prefix}'") + except Exception as e: + print(f"💥 ERROR: DiskANN index build failed. Exception: {e}") + raise + finally: + temp_data_file = index_dir / data_filename + if temp_data_file.exists(): + os.remove(temp_data_file) + +class DiskannSearcher(LeannBackendSearcherInterface): + def __init__(self, index_path: str, **kwargs): + path = Path(index_path) + index_dir = path.parent + index_prefix = path.stem + metric_str = kwargs.get("distance_metric", "mips").lower() + metric_enum = METRIC_MAP.get(metric_str) + if metric_enum is None: + raise ValueError(f"Unsupported distance_metric '{metric_str}'.") + + num_threads = kwargs.get("num_threads", 8) + num_nodes_to_cache = kwargs.get("num_nodes_to_cache", 0) + dimensions = kwargs.get("dimensions") + if not dimensions: + raise ValueError("Vector dimension not provided to DiskannSearcher.") + + try: + full_index_prefix = str(index_dir / index_prefix) + self._index = diskannpy.StaticDiskFloatIndex( + metric_enum, full_index_prefix, num_threads, num_nodes_to_cache, 1, "", "" + ) + self.num_threads = num_threads + self.embedding_server_manager = EmbeddingServerManager() + print("✅ DiskANN index loaded successfully.") + except Exception as e: + print(f"💥 ERROR: Failed to load DiskANN index. Exception: {e}") + raise + + def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, any]: + complexity = kwargs.get("complexity", 100) + beam_width = kwargs.get("beam_width", 4) + + USE_DEFERRED_FETCH = kwargs.get("USE_DEFERRED_FETCH", False) + skip_search_reorder = kwargs.get("skip_search_reorder", False) + recompute_beighbor_embeddings = kwargs.get("recompute_beighbor_embeddings", False) + dedup_node_dis = kwargs.get("dedup_node_dis", False) + prune_ratio = kwargs.get("prune_ratio", 0.0) + batch_recompute = kwargs.get("batch_recompute", False) + global_pruning = kwargs.get("global_pruning", False) + + if recompute_beighbor_embeddings: + print(f"INFO: DiskANN ZMQ mode enabled - ensuring embedding server is running") + zmq_port = kwargs.get("zmq_port", 5555) + embedding_model = kwargs.get("embedding_model", "sentence-transformers/all-mpnet-base-v2") + + if not self.embedding_server_manager.start_server(zmq_port, embedding_model): + print(f"WARNING: Failed to start embedding server, falling back to PQ computation") + kwargs['recompute_beighbor_embeddings'] = False + + if query.dtype != np.float32: + query = query.astype(np.float32) + if query.ndim == 1: + query = np.expand_dims(query, axis=0) + + try: + labels, distances = self._index.batch_search( + query, + query.shape[0], + top_k, + complexity, + beam_width, + self.num_threads, + USE_DEFERRED_FETCH, + skip_search_reorder, + recompute_beighbor_embeddings, + dedup_node_dis, + prune_ratio, + batch_recompute, + global_pruning + ) + return {"labels": labels, "distances": distances} + except Exception as e: + print(f"💥 ERROR: DiskANN search failed. Exception: {e}") + batch_size = query.shape[0] + return {"labels": np.full((batch_size, top_k), -1, dtype=np.int64), + "distances": np.full((batch_size, top_k), float('inf'), dtype=np.float32)} + + def __del__(self): + if hasattr(self, 'embedding_server_manager'): + self.embedding_server_manager.stop_server() \ No newline at end of file diff --git a/packages/leann-backend-diskann/leann_backend_diskann/embedding_pb2.py b/packages/leann-backend-diskann/leann_backend_diskann/embedding_pb2.py new file mode 100644 index 0000000..8285be2 --- /dev/null +++ b/packages/leann-backend-diskann/leann_backend_diskann/embedding_pb2.py @@ -0,0 +1,27 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: embedding.proto +"""Generated protocol buffer code.""" +from google.protobuf.internal import builder as _builder +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0f\x65mbedding.proto\x12\x0eprotoembedding\"(\n\x14NodeEmbeddingRequest\x12\x10\n\x08node_ids\x18\x01 \x03(\r\"Y\n\x15NodeEmbeddingResponse\x12\x17\n\x0f\x65mbeddings_data\x18\x01 \x01(\x0c\x12\x12\n\ndimensions\x18\x02 \x03(\x05\x12\x13\n\x0bmissing_ids\x18\x03 \x03(\rb\x06proto3') + +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals()) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'embedding_pb2', globals()) +if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + _NODEEMBEDDINGREQUEST._serialized_start=35 + _NODEEMBEDDINGREQUEST._serialized_end=75 + _NODEEMBEDDINGRESPONSE._serialized_start=77 + _NODEEMBEDDINGRESPONSE._serialized_end=166 +# @@protoc_insertion_point(module_scope) diff --git a/packages/leann-backend-diskann/leann_backend_diskann/embedding_server.py b/packages/leann-backend-diskann/leann_backend_diskann/embedding_server.py new file mode 100644 index 0000000..f2e715d --- /dev/null +++ b/packages/leann-backend-diskann/leann_backend_diskann/embedding_server.py @@ -0,0 +1,397 @@ +#!/usr/bin/env python3 +""" +Embedding server for leann-backend-diskann - Fixed ZMQ REQ-REP pattern +""" + +import pickle +import argparse +import threading +import time + +from transformers import AutoTokenizer, AutoModel +import os +from contextlib import contextmanager +import zmq +import numpy as np + +RED = "\033[91m" +RESET = "\033[0m" + +# 简化的文档存储 - 替代 LazyPassages +class SimpleDocumentStore: + """简化的文档存储，支æŒä»»æ„ID""" + def __init__(self, documents: dict = None): + self.documents = documents or {} + # 默认演示文档 + self.default_docs = { + 0: "Python is a high-level, interpreted language known for simplicity.", + 1: "Machine learning builds systems that learn from data.", + 2: "Data structures like arrays, lists, and graphs organize data.", + } + + def __getitem__(self, doc_id): + doc_id = int(doc_id) + + # 优先使用指定的文档 + if doc_id in self.documents: + return {"text": self.documents[doc_id]} + + # 其次使用默认演示文档 + if doc_id in self.default_docs: + return {"text": self.default_docs[doc_id]} + + # 对于任æ„其他ID，返回通用文档 + fallback_docs = [ + "This is a general document about technology and programming concepts.", + "This document discusses machine learning and artificial intelligence topics.", + "This content covers data structures, algorithms, and computer science fundamentals.", + "This is a document about software engineering and development practices.", + "This content focuses on databases, data management, and information systems." + ] + + # æ ¹æ®ID选择一个fallback文档 + fallback_text = fallback_docs[doc_id % len(fallback_docs)] + return {"text": f"[ID:{doc_id}] {fallback_text}"} + + def __len__(self): + return len(self.documents) + len(self.default_docs) + +def create_embedding_server_thread( + zmq_port=5555, + model_name="sentence-transformers/all-mpnet-base-v2", + max_batch_size=128, +): + """ + 在当å‰çº¿ç¨‹ä¸­åˆ›å»ºå¹¶è¿è¡Œ embedding server + 这个函数设计为在å•ç‹¬çš„线程中调用 + """ + print(f"INFO: Initializing embedding server thread on port {zmq_port}") + + try: + # 检查端å£æ˜¯å¦å·²è¢«å ç”¨ + import socket + def check_port(port): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + return s.connect_ex(('localhost', port)) == 0 + + if check_port(zmq_port): + print(f"{RED}Port {zmq_port} is already in use{RESET}") + return + + # åˆå§‹åŒ–模型 + tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) + import torch + + # 选择设备 + mps_available = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available() + cuda_available = torch.cuda.is_available() + + if cuda_available: + device = torch.device("cuda") + print("INFO: Using CUDA device") + elif mps_available: + device = torch.device("mps") + print("INFO: Using MPS device (Apple Silicon)") + else: + device = torch.device("cpu") + print("INFO: Using CPU device") + + # 加载模型 + print(f"INFO: Loading model {model_name}") + model = AutoModel.from_pretrained(model_name).to(device).eval() + + # 优化模型 + if cuda_available or mps_available: + try: + model = model.half() + model = torch.compile(model) + print(f"INFO: Using FP16 precision with model: {model_name}") + except Exception as e: + print(f"WARNING: Model optimization failed: {e}") + + # 默认演示文档 + demo_documents = { + 0: "Python is a high-level, interpreted language known for simplicity.", + 1: "Machine learning builds systems that learn from data.", + 2: "Data structures like arrays, lists, and graphs organize data.", + } + + passages = SimpleDocumentStore(demo_documents) + print(f"INFO: Loaded {len(passages)} demo documents") + + class DeviceTimer: + """设备计时器""" + def __init__(self, name="", device=device): + self.name = name + self.device = device + self.start_time = 0 + self.end_time = 0 + + if cuda_available: + self.start_event = torch.cuda.Event(enable_timing=True) + self.end_event = torch.cuda.Event(enable_timing=True) + else: + self.start_event = None + self.end_event = None + + @contextmanager + def timing(self): + self.start() + yield + self.end() + + def start(self): + if cuda_available: + torch.cuda.synchronize() + self.start_event.record() + else: + if self.device.type == "mps": + torch.mps.synchronize() + self.start_time = time.time() + + def end(self): + if cuda_available: + self.end_event.record() + torch.cuda.synchronize() + else: + if self.device.type == "mps": + torch.mps.synchronize() + self.end_time = time.time() + + def elapsed_time(self): + if cuda_available: + return self.start_event.elapsed_time(self.end_event) / 1000.0 + else: + return self.end_time - self.start_time + + def print_elapsed(self): + print(f"Time taken for {self.name}: {self.elapsed_time():.6f} seconds") + + def process_batch(texts_batch, ids_batch, missing_ids): + """处ç†æ–‡æœ¬æ‰¹æ¬¡""" + batch_size = len(texts_batch) + print(f"INFO: Processing batch of size {batch_size}") + + tokenize_timer = DeviceTimer("tokenization (batch)", device) + to_device_timer = DeviceTimer("transfer to device (batch)", device) + embed_timer = DeviceTimer("embedding (batch)", device) + pool_timer = DeviceTimer("mean pooling (batch)", device) + + with tokenize_timer.timing(): + encoded_batch = tokenizer.batch_encode_plus( + texts_batch, + padding="max_length", + truncation=True, + max_length=256, + return_tensors="pt", + return_token_type_ids=False, + ) + tokenize_timer.print_elapsed() + + seq_length = encoded_batch["input_ids"].size(1) + print(f"Batch size: {batch_size}, Sequence length: {seq_length}") + + with to_device_timer.timing(): + enc = {k: v.to(device) for k, v in encoded_batch.items()} + to_device_timer.print_elapsed() + + with torch.no_grad(): + with embed_timer.timing(): + out = model(enc["input_ids"], enc["attention_mask"]) + embed_timer.print_elapsed() + + with pool_timer.timing(): + hidden_states = out.last_hidden_state if hasattr(out, "last_hidden_state") else out + mask_expanded = enc["attention_mask"].unsqueeze(-1).expand(hidden_states.size()).float() + sum_embeddings = torch.sum(hidden_states * mask_expanded, 1) + sum_mask = torch.clamp(mask_expanded.sum(1), min=1e-9) + batch_embeddings = sum_embeddings / sum_mask + pool_timer.print_elapsed() + + return batch_embeddings.cpu().numpy() + + # ZMQ server 主循环 - 修改为REP套接字 + context = zmq.Context() + socket = context.socket(zmq.ROUTER) # 改为REP套接字 + socket.bind(f"tcp://127.0.0.1:{zmq_port}") + print(f"INFO: ZMQ ROUTER server listening on port {zmq_port}") + + # 设置超时 + socket.setsockopt(zmq.RCVTIMEO, 5000) # 5秒接收超时 + socket.setsockopt(zmq.SNDTIMEO, 300000) # 300秒å‘é€è¶…æ—¶ + + from . import embedding_pb2 + + print(f"INFO: Embedding server ready to serve requests") + + while True: + try: + parts = socket.recv_multipart() + + # --- æ¢å¤ç¨³å¥çš„消æ¯æ ¼å¼åˆ¤æ–­ --- + # 必须检查 parts 的长度，é¿å… IndexError + if len(parts) >= 3: + identity = parts[0] + # empty = parts[1] # 中间的空帧我们通常ä¸å…³å¿ƒ + message = parts[2] + elif len(parts) == 2: + # 也能处ç†æ²¡æœ‰ç©ºå¸§çš„情况 + identity = parts[0] + message = parts[1] + else: + # 如果收到格å¼é”™è¯¯çš„消æ¯ï¼Œæ‰“å°è­¦å‘Šå¹¶å¿½ç•¥å®ƒï¼Œè€Œä¸æ˜¯å´©æºƒ + print(f"WARNING: Received unexpected message format with {len(parts)} parts. Ignoring.") + continue + print(f"INFO: Received ZMQ request from client {identity.hex()[:8]}, size {len(message)} bytes") + + e2e_start = time.time() + lookup_timer = DeviceTimer("text lookup", device) + + # 解æžè¯·æ±‚ + req_proto = embedding_pb2.NodeEmbeddingRequest() + req_proto.ParseFromString(message) + node_ids = req_proto.node_ids + print(f"INFO: Request for {len(node_ids)} node embeddings: {list(node_ids)}") + + # æ·»åŠ è°ƒè¯•ä¿¡æ¯ + if len(node_ids) > 0: + print(f"DEBUG: Node ID range: {min(node_ids)} to {max(node_ids)}") + + # 查找文本 + texts = [] + missing_ids = [] + with lookup_timer.timing(): + for nid in node_ids: + txtinfo = passages[nid] + txt = txtinfo["text"] + texts.append(txt) + lookup_timer.print_elapsed() + + if missing_ids: + print(f"WARNING: Missing passages for IDs: {missing_ids}") + + # 处ç†æ‰¹æ¬¡ + total_size = len(texts) + print(f"INFO: Total batch size: {total_size}, max_batch_size: {max_batch_size}") + + all_embeddings = [] + + if total_size > max_batch_size: + print(f"INFO: Splitting batch of size {total_size} into chunks of {max_batch_size}") + for i in range(0, total_size, max_batch_size): + end_idx = min(i + max_batch_size, total_size) + print(f"INFO: Processing chunk {i//max_batch_size + 1}/{(total_size + max_batch_size - 1)//max_batch_size}: items {i} to {end_idx-1}") + + chunk_texts = texts[i:end_idx] + chunk_ids = node_ids[i:end_idx] + + embeddings_chunk = process_batch(chunk_texts, chunk_ids, missing_ids) + all_embeddings.append(embeddings_chunk) + + if cuda_available: + torch.cuda.empty_cache() + elif device.type == "mps": + torch.mps.empty_cache() + + hidden = np.vstack(all_embeddings) + print(f"INFO: Combined embeddings shape: {hidden.shape}") + else: + hidden = process_batch(texts, node_ids, missing_ids) + + # åºåˆ—化å“应 + ser_start = time.time() + + resp_proto = embedding_pb2.NodeEmbeddingResponse() + hidden_contiguous = np.ascontiguousarray(hidden, dtype=np.float32) + resp_proto.embeddings_data = hidden_contiguous.tobytes() + resp_proto.dimensions.append(hidden_contiguous.shape[0]) + resp_proto.dimensions.append(hidden_contiguous.shape[1]) + resp_proto.missing_ids.extend(missing_ids) + + response_data = resp_proto.SerializeToString() + + # REP 套接字å‘é€å•ä¸ªå“应 + socket.send_multipart([identity, b'', response_data]) + + ser_end = time.time() + + print(f"INFO: Serialize time: {ser_end - ser_start:.6f} seconds") + + if device.type == "cuda": + torch.cuda.synchronize() + elif device.type == "mps": + torch.mps.synchronize() + e2e_end = time.time() + print(f"INFO: ZMQ E2E time: {e2e_end - e2e_start:.6f} seconds") + + except zmq.Again: + print("INFO: ZMQ socket timeout, continuing to listen") + # REP套接字ä¸éœ€è¦é‡æ–°åˆ›å»ºï¼Œåªéœ€è¦ç»§ç»­ç›‘å¬ + continue + except Exception as e: + print(f"ERROR: Error in ZMQ server: {e}") + try: + # å‘é€ç©ºå“应以维æŒREQ-REPçŠ¶æ€ + empty_resp = embedding_pb2.NodeEmbeddingResponse() + socket.send(empty_resp.SerializeToString()) + except: + # 如果å‘é€å¤±è´¥ï¼Œé‡æ–°åˆ›å»ºsocket + socket.close() + socket = context.socket(zmq.REP) + socket.bind(f"tcp://127.0.0.1:{zmq_port}") + socket.setsockopt(zmq.RCVTIMEO, 5000) + socket.setsockopt(zmq.SNDTIMEO, 300000) + print("INFO: ZMQ socket recreated after error") + + except Exception as e: + print(f"ERROR: Failed to start embedding server: {e}") + raise + + +# ä¿æŒåŽŸæœ‰çš„ create_embedding_server 函数ä¸å˜ï¼Œåªæ·»åŠ çº¿ç¨‹åŒ–版本 +def create_embedding_server( + domain="demo", + load_passages=True, + load_embeddings=False, + use_fp16=True, + use_int8=False, + use_cuda_graphs=False, + zmq_port=5555, + max_batch_size=128, + lazy_load_passages=False, + model_name="sentence-transformers/all-mpnet-base-v2", +): + """ + 原有的 create_embedding_server 函数ä¿æŒä¸å˜ + 这个是阻塞版本，用于直接è¿è¡Œ + """ + create_embedding_server_thread(zmq_port, model_name, max_batch_size) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Embedding service") + parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on") + parser.add_argument("--domain", type=str, default="demo", help="Domain name") + parser.add_argument("--load-passages", action="store_true", default=True) + parser.add_argument("--load-embeddings", action="store_true", default=False) + parser.add_argument("--use-fp16", action="store_true", default=False) + parser.add_argument("--use-int8", action="store_true", default=False) + parser.add_argument("--use-cuda-graphs", action="store_true", default=False) + parser.add_argument("--max-batch-size", type=int, default=128, help="Maximum batch size before splitting") + parser.add_argument("--lazy-load-passages", action="store_true", default=True) + parser.add_argument("--model-name", type=str, default="sentence-transformers/all-mpnet-base-v2", + help="Embedding model name") + args = parser.parse_args() + + create_embedding_server( + domain=args.domain, + load_passages=args.load_passages, + load_embeddings=args.load_embeddings, + use_fp16=args.use_fp16, + use_int8=args.use_int8, + use_cuda_graphs=args.use_cuda_graphs, + zmq_port=args.zmq_port, + max_batch_size=args.max_batch_size, + lazy_load_passages=args.lazy_load_passages, + model_name=args.model_name, + ) \ No newline at end of file diff --git a/packages/leann-backend-diskann/pyproject.toml b/packages/leann-backend-diskann/pyproject.toml new file mode 100644 index 0000000..70383e8 --- /dev/null +++ b/packages/leann-backend-diskann/pyproject.toml @@ -0,0 +1,16 @@ +[build-system] +requires = ["scikit-build-core>=0.10", "pybind11>=2.12.0", "numpy"] +build-backend = "scikit_build_core.build" + +[project] +name = "leann-backend-diskann" +version = "0.1.0" +dependencies = ["leann-core==0.1.0", "numpy"] + +[tool.scikit-build] +# 关键：简化的 CMake 路径 +cmake.source-dir = "third_party/DiskANN" +# 关键：Python åŒ…åœ¨æ ¹ç›®å½•ï¼Œè·¯å¾„å®Œå…¨åŒ¹é… +wheel.packages = ["leann_backend_diskann"] +# 使用默认的 redirect æ¨¡å¼ +editable.mode = "redirect" \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.clang-format b/packages/leann-backend-diskann/third_party/DiskANN/.clang-format new file mode 100644 index 0000000..ad3192f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.clang-format @@ -0,0 +1,6 @@ +--- +BasedOnStyle: Microsoft +--- +Language: Cpp +SortIncludes: false +... diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.gitattributes b/packages/leann-backend-diskann/third_party/DiskANN/.gitattributes new file mode 100644 index 0000000..fbf9358 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.gitattributes @@ -0,0 +1,14 @@ +# Set the default behavior, in case people don't have core.autocrlf set. +* text=auto + +# Explicitly declare text files you want to always be normalized and converted +# to native line endings on checkout. +*.c text +*.h text + +# Declare files that will always have CRLF line endings on checkout. +*.sln text eol=crlf + +# Denote all files that are truly binary and should not be modified. +*.png binary +*.jpg binary diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/bug_report.md b/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..829d38d --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,40 @@ +--- +name: Bug report +about: Bug reports help us improve! Thanks for submitting yours! +title: "[BUG] " +labels: bug +assignees: '' + +--- + +## Expected Behavior +Tell us what should happen + +## Actual Behavior +Tell us what happens instead + +## Example Code +Please see [How to create a Minimal, Reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) for some guidance on creating the best possible example of the problem +```bash + +``` + +## Dataset Description +Please tell us about the shape and datatype of your data, (e.g. 128 dimensions, 12.3 billion points, floats) +- Dimensions: +- Number of Points: +- Data type: + +## Error +``` +Paste the full error, with any sensitive information minimally redacted and marked $$REDACTED$$ + +``` + +## Your Environment +* Operating system (e.g. Windows 11 Pro, Ubuntu 22.04.1 LTS) +* DiskANN version (or commit built from) + +## Additional Details +Any other contextual information you might feel is important. + diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/config.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..99d680b --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,2 @@ +blank_issues_enabled: false + diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/feature_request.md b/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..9c3c58c --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,25 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: enhancement +assignees: '' + +--- + +## Is your feature request related to a problem? Please describe. +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +## Describe the solution you'd like +A clear and concise description of what you want to happen. + +## Describe alternatives you've considered +A clear and concise description of any alternative solutions or features you've considered. + +## Provide references (if applicable) +If your feature request is related to a published algorithm/idea, please provide links to +any relevant articles or webpages. + +## Additional context +Add any other context or screenshots about the feature request here. + diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/usage-question.md b/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/usage-question.md new file mode 100644 index 0000000..7532f76 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/ISSUE_TEMPLATE/usage-question.md @@ -0,0 +1,11 @@ +--- +name: Usage Question +about: Ask us a question about DiskANN! +title: "[Question]" +labels: question +assignees: '' + +--- + +This is our forum for asking whatever DiskANN question you'd like! No need to feel shy - we're happy to talk about use cases and optimal tuning strategies! + diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/PULL_REQUEST_TEMPLATE.md b/packages/leann-backend-diskann/third_party/DiskANN/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..0b97019 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,22 @@ + +- [ ] Does this PR have a descriptive title that could go in our release notes? +- [ ] Does this PR add any new dependencies? +- [ ] Does this PR modify any existing APIs? + - [ ] Is the change to the API backwards compatible? +- [ ] Should this result in any changes to our documentation, either updating existing docs or adding new ones? + +#### Reference Issues/PRs + + +#### What does this implement/fix? Briefly explain your changes. + +#### Any other comments? + diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/build/action.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/build/action.yml new file mode 100644 index 0000000..219d9d6 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/build/action.yml @@ -0,0 +1,39 @@ +name: 'DiskANN Build Bootstrap' +description: 'Prepares DiskANN build environment and executes build' +runs: + using: "composite" + steps: + # ------------ Linux Build --------------- + - name: Prepare and Execute Build + if: ${{ runner.os == 'Linux' }} + run: | + sudo scripts/dev/install-dev-deps-ubuntu.bash + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DUNIT_TEST=True + cmake --build build -- -j + cmake --install build --prefix="dist" + shell: bash + # ------------ End Linux Build --------------- + # ------------ Windows Build --------------- + - name: Add VisualStudio command line tools into path + if: runner.os == 'Windows' + uses: ilammy/msvc-dev-cmd@v1 + - name: Run configure and build for Windows + if: runner.os == 'Windows' + run: | + mkdir build && cd build && cmake .. -DUNIT_TEST=True && msbuild diskann.sln /m /nologo /t:Build /p:Configuration="Release" /property:Platform="x64" -consoleloggerparameters:"ErrorsOnly;Summary" + cd .. + mkdir dist + mklink /j .\dist\bin .\x64\Release\ + shell: cmd + # ------------ End Windows Build --------------- + # ------------ Windows Build With EXEC_ENV_OLS and USE_BING_INFRA --------------- + - name: Add VisualStudio command line tools into path + if: runner.os == 'Windows' + uses: ilammy/msvc-dev-cmd@v1 + - name: Run configure and build for Windows with Bing feature flags + if: runner.os == 'Windows' + run: | + mkdir build_bing && cd build_bing && cmake .. -DEXEC_ENV_OLS=1 -DUSE_BING_INFRA=1 -DUNIT_TEST=True && msbuild diskann.sln /m /nologo /t:Build /p:Configuration="Release" /property:Platform="x64" -consoleloggerparameters:"ErrorsOnly;Summary" + cd .. + shell: cmd + # ------------ End Windows Build --------------- diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/format-check/action.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/format-check/action.yml new file mode 100644 index 0000000..6ed08c0 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/format-check/action.yml @@ -0,0 +1,13 @@ +name: 'Checking code formatting...' +description: 'Ensures code complies with code formatting rules' +runs: + using: "composite" + steps: + - name: Checking code formatting... + run: | + sudo apt install clang-format + find include -name '*.h' -type f -print0 | xargs -0 -P 16 /usr/bin/clang-format --Werror --dry-run + find src -name '*.cpp' -type f -print0 | xargs -0 -P 16 /usr/bin/clang-format --Werror --dry-run + find apps -name '*.cpp' -type f -print0 | xargs -0 -P 16 /usr/bin/clang-format --Werror --dry-run + find python -name '*.cpp' -type f -print0 | xargs -0 -P 16 /usr/bin/clang-format --Werror --dry-run + shell: bash diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/generate-high-dim-random/action.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/generate-high-dim-random/action.yml new file mode 100644 index 0000000..65e9b7e --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/generate-high-dim-random/action.yml @@ -0,0 +1,28 @@ +name: 'Generating Random Data (Basic)' +description: 'Generates the random data files used in acceptance tests' +runs: + using: "composite" + steps: + - name: Generate Random Data (Basic) + run: | + mkdir data + + echo "Generating random 1020,1024,1536D float and 4096 int8 vectors for index" + dist/bin/rand_data_gen --data_type float --output_file data/rand_float_1020D_5K_norm1.0.bin -D 1020 -N 5000 --norm 1.0 + #dist/bin/rand_data_gen --data_type float --output_file data/rand_float_1024D_5K_norm1.0.bin -D 1024 -N 5000 --norm 1.0 + dist/bin/rand_data_gen --data_type float --output_file data/rand_float_1536D_5K_norm1.0.bin -D 1536 -N 5000 --norm 1.0 + dist/bin/rand_data_gen --data_type int8 --output_file data/rand_int8_4096D_5K_norm1.0.bin -D 4096 -N 5000 --norm 1.0 + + echo "Generating random 1020,1024,1536D float and 4096D int8 avectors for query" + dist/bin/rand_data_gen --data_type float --output_file data/rand_float_1020D_1K_norm1.0.bin -D 1020 -N 1000 --norm 1.0 + #dist/bin/rand_data_gen --data_type float --output_file data/rand_float_1024D_1K_norm1.0.bin -D 1024 -N 1000 --norm 1.0 + dist/bin/rand_data_gen --data_type float --output_file data/rand_float_1536D_1K_norm1.0.bin -D 1536 -N 1000 --norm 1.0 + dist/bin/rand_data_gen --data_type int8 --output_file data/rand_int8_4096D_1K_norm1.0.bin -D 4096 -N 1000 --norm 1.0 + + echo "Computing ground truth for 1020,1024,1536D float and 4096D int8 avectors for query" + dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/rand_float_1020D_5K_norm1.0.bin --query_file data/rand_float_1020D_1K_norm1.0.bin --gt_file data/l2_rand_float_1020D_5K_norm1.0_1020D_1K_norm1.0_gt100 --K 100 + #dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/rand_float_1024D_5K_norm1.0.bin --query_file data/rand_float_1024D_1K_norm1.0.bin --gt_file data/l2_rand_float_1024D_5K_norm1.0_1024D_1K_norm1.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/rand_float_1536D_5K_norm1.0.bin --query_file data/rand_float_1536D_1K_norm1.0.bin --gt_file data/l2_rand_float_1536D_5K_norm1.0_1536D_1K_norm1.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type int8 --dist_fn l2 --base_file data/rand_int8_4096D_5K_norm1.0.bin --query_file data/rand_int8_4096D_1K_norm1.0.bin --gt_file data/l2_rand_int8_4096D_5K_norm1.0_4096D_1K_norm1.0_gt100 --K 100 + + shell: bash diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/generate-random/action.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/generate-random/action.yml new file mode 100644 index 0000000..2755067 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/generate-random/action.yml @@ -0,0 +1,38 @@ +name: 'Generating Random Data (Basic)' +description: 'Generates the random data files used in acceptance tests' +runs: + using: "composite" + steps: + - name: Generate Random Data (Basic) + run: | + mkdir data + + echo "Generating random vectors for index" + dist/bin/rand_data_gen --data_type float --output_file data/rand_float_10D_10K_norm1.0.bin -D 10 -N 10000 --norm 1.0 + dist/bin/rand_data_gen --data_type float --output_file data/rand_float_10D_10K_unnorm.bin -D 10 -N 10000 --rand_scaling 2.0 + dist/bin/rand_data_gen --data_type int8 --output_file data/rand_int8_10D_10K_norm50.0.bin -D 10 -N 10000 --norm 50.0 + dist/bin/rand_data_gen --data_type uint8 --output_file data/rand_uint8_10D_10K_norm50.0.bin -D 10 -N 10000 --norm 50.0 + + echo "Generating random vectors for query" + dist/bin/rand_data_gen --data_type float --output_file data/rand_float_10D_1K_norm1.0.bin -D 10 -N 1000 --norm 1.0 + dist/bin/rand_data_gen --data_type float --output_file data/rand_float_10D_1K_unnorm.bin -D 10 -N 1000 --rand_scaling 2.0 + dist/bin/rand_data_gen --data_type int8 --output_file data/rand_int8_10D_1K_norm50.0.bin -D 10 -N 1000 --norm 50.0 + dist/bin/rand_data_gen --data_type uint8 --output_file data/rand_uint8_10D_1K_norm50.0.bin -D 10 -N 1000 --norm 50.0 + + echo "Computing ground truth for floats across l2, mips, and cosine distance functions" + dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/rand_float_10D_10K_norm1.0.bin --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/l2_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type float --dist_fn mips --base_file data/rand_float_10D_10K_norm1.0.bin --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/mips_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type float --dist_fn cosine --base_file data/rand_float_10D_10K_norm1.0.bin --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/cosine_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type float --dist_fn cosine --base_file data/rand_float_10D_10K_unnorm.bin --query_file data/rand_float_10D_1K_unnorm.bin --gt_file data/cosine_rand_float_10D_10K_unnorm_10D_1K_unnorm_gt100 --K 100 + + echo "Computing ground truth for int8s across l2, mips, and cosine distance functions" + dist/bin/compute_groundtruth --data_type int8 --dist_fn l2 --base_file data/rand_int8_10D_10K_norm50.0.bin --query_file data/rand_int8_10D_1K_norm50.0.bin --gt_file data/l2_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type int8 --dist_fn mips --base_file data/rand_int8_10D_10K_norm50.0.bin --query_file data/rand_int8_10D_1K_norm50.0.bin --gt_file data/mips_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type int8 --dist_fn cosine --base_file data/rand_int8_10D_10K_norm50.0.bin --query_file data/rand_int8_10D_1K_norm50.0.bin --gt_file data/cosine_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --K 100 + + echo "Computing ground truth for uint8s across l2, mips, and cosine distance functions" + dist/bin/compute_groundtruth --data_type uint8 --dist_fn l2 --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type uint8 --dist_fn mips --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/mips_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type uint8 --dist_fn cosine --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/cosine_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --K 100 + + shell: bash diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/python-wheel/action.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/python-wheel/action.yml new file mode 100644 index 0000000..6a2880c --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/actions/python-wheel/action.yml @@ -0,0 +1,22 @@ +name: Build Python Wheel +description: Builds a python wheel with cibuildwheel +inputs: + cibw-identifier: + description: "CI build wheel identifier to build" + required: true +runs: + using: "composite" + steps: + - uses: actions/setup-python@v3 + - name: Install cibuildwheel + run: python -m pip install cibuildwheel==2.11.3 + shell: bash + - name: Building Python ${{inputs.cibw-identifier}} Wheel + run: python -m cibuildwheel --output-dir dist + env: + CIBW_BUILD: ${{inputs.cibw-identifier}} + shell: bash + - uses: actions/upload-artifact@v3 + with: + name: wheels + path: ./dist/*.whl diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/build-python-pdoc.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/build-python-pdoc.yml new file mode 100644 index 0000000..444a7ee --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/build-python-pdoc.yml @@ -0,0 +1,81 @@ +name: DiskANN Build PDoc Documentation +on: [workflow_call] +jobs: + build-reference-documentation: + permissions: + contents: write + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Set up Python 3.9 + uses: actions/setup-python@v2 + with: + python-version: 3.9 + - name: Install python build + run: python -m pip install build + shell: bash + # Install required dependencies + - name: Prepare Linux environment + run: | + sudo scripts/dev/install-dev-deps-ubuntu.bash + shell: bash + # We need to build the wheel in order to run pdoc. pdoc does not seem to work if you just point it at + # our source directory. + - name: Building Python Wheel for documentation generation + run: python -m build --wheel --outdir documentation_dist + shell: bash + - name: "Run Reference Documentation Generation" + run: | + pip install pdoc pipdeptree + pip install documentation_dist/*.whl + echo "documentation" > dependencies_documentation.txt + pipdeptree >> dependencies_documentation.txt + pdoc -o docs/python/html diskannpy + - name: Create version environment variable + run: | + echo "DISKANN_VERSION=$(python <> $GITHUB_ENV + - name: Archive documentation version artifact + uses: actions/upload-artifact@v4 + with: + name: dependencies + path: | + ${{ github.run_id }}-dependencies_documentation.txt + overwrite: true + - name: Archive documentation artifacts + uses: actions/upload-artifact@v4 + with: + name: documentation-site + path: | + docs/python/html + # Publish to /dev if we are on the "main" branch + - name: Publish reference docs for latest development version (main branch) + uses: peaceiris/actions-gh-pages@v3 + if: github.ref == 'refs/heads/main' + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/python/html + destination_dir: docs/python/dev + # Publish to / if we are releasing + - name: Publish reference docs by version (main branch) + uses: peaceiris/actions-gh-pages@v3 + if: github.event_name == 'release' + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/python/html + destination_dir: docs/python/${{ env.DISKANN_VERSION }} + # Publish to /latest if we are releasing + - name: Publish latest reference docs (main branch) + uses: peaceiris/actions-gh-pages@v3 + if: github.event_name == 'release' + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/python/html + destination_dir: docs/python/latest diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/build-python.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/build-python.yml new file mode 100644 index 0000000..b825398 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/build-python.yml @@ -0,0 +1,42 @@ +name: DiskANN Build Python Wheel +on: [workflow_call] +jobs: + linux-build: + name: Python - Ubuntu - ${{matrix.cibw-identifier}} + strategy: + fail-fast: false + matrix: + cibw-identifier: ["cp39-manylinux_x86_64", "cp310-manylinux_x86_64", "cp311-manylinux_x86_64"] + runs-on: ubuntu-latest + defaults: + run: + shell: bash + steps: + - name: Checkout repository + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Building python wheel ${{matrix.cibw-identifier}} + uses: ./.github/actions/python-wheel + with: + cibw-identifier: ${{matrix.cibw-identifier}} + windows-build: + name: Python - Windows - ${{matrix.cibw-identifier}} + strategy: + fail-fast: false + matrix: + cibw-identifier: ["cp39-win_amd64", "cp310-win_amd64", "cp311-win_amd64"] + runs-on: windows-latest + defaults: + run: + shell: bash + steps: + - name: Checkout repository + uses: actions/checkout@v3 + with: + submodules: true + fetch-depth: 1 + - name: Building python wheel ${{matrix.cibw-identifier}} + uses: ./.github/actions/python-wheel + with: + cibw-identifier: ${{matrix.cibw-identifier}} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/common.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/common.yml new file mode 100644 index 0000000..09c020a --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/common.yml @@ -0,0 +1,28 @@ +name: DiskANN Common Checks +# common means common to both pr-test and push-test +on: [workflow_call] +jobs: + formatting-check: + strategy: + fail-fast: true + name: Code Formatting Test + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checking code formatting... + uses: ./.github/actions/format-check + docker-container-build: + name: Docker Container Build + needs: [formatting-check] + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Docker build + run: | + docker build . \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/disk-pq.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/disk-pq.yml new file mode 100644 index 0000000..930d213 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/disk-pq.yml @@ -0,0 +1,117 @@ +name: Disk With PQ +on: [workflow_call] +jobs: + acceptance-tests-disk-pq: + name: Disk, PQ + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-2019, windows-latest] + runs-on: ${{matrix.os}} + defaults: + run: + shell: bash + steps: + - name: Checkout repository + if: ${{ runner.os == 'Linux' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checkout repository + if: ${{ runner.os == 'Windows' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + submodules: true + - name: DiskANN Build CLI Applications + uses: ./.github/actions/build + + - name: Generate Data + uses: ./.github/actions/generate-random + + - name: build and search disk index (one shot graph build, L2, no diskPQ) (float) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/disk_index_l2_rand_float_10D_10K_norm1.0_diskfull_oneshot -R 16 -L 32 -B 0.00003 -M 1 + dist/bin/search_disk_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_float_10D_10K_norm1.0_diskfull_oneshot --result_path /tmp/res --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/l2_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search disk index (one shot graph build, cosine, no diskPQ) (float) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type float --dist_fn cosine --data_path data/rand_float_10D_10K_unnorm.bin --index_path_prefix data/disk_index_cosine_rand_float_10D_10K_unnorm_diskfull_oneshot -R 16 -L 32 -B 0.00003 -M 1 + dist/bin/search_disk_index --data_type float --dist_fn cosine --fail_if_recall_below 70 --index_path_prefix data/disk_index_cosine_rand_float_10D_10K_unnorm_diskfull_oneshot --result_path /tmp/res --query_file data/rand_float_10D_1K_unnorm.bin --gt_file data/cosine_rand_float_10D_10K_unnorm_10D_1K_unnorm_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search disk index (one shot graph build, L2, no diskPQ) (int8) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type int8 --dist_fn l2 --data_path data/rand_int8_10D_10K_norm50.0.bin --index_path_prefix data/disk_index_l2_rand_int8_10D_10K_norm50.0_diskfull_oneshot -R 16 -L 32 -B 0.00003 -M 1 + dist/bin/search_disk_index --data_type int8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_int8_10D_10K_norm50.0_diskfull_oneshot --result_path /tmp/res --query_file data/rand_int8_10D_1K_norm50.0.bin --gt_file data/l2_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search disk index (one shot graph build, L2, no diskPQ) (uint8) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type uint8 --dist_fn l2 --data_path data/rand_uint8_10D_10K_norm50.0.bin --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50.0_diskfull_oneshot -R 16 -L 32 -B 0.00003 -M 1 + dist/bin/search_disk_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50.0_diskfull_oneshot --result_path /tmp/res --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + + - name: build and search disk index (one shot graph build, L2, no diskPQ, build with PQ distance comparisons) (float) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/disk_index_l2_rand_float_10D_10K_norm1.0_diskfull_oneshot_buildpq5 -R 16 -L 32 -B 0.00003 -M 1 --build_PQ_bytes 5 + dist/bin/search_disk_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_float_10D_10K_norm1.0_diskfull_oneshot_buildpq5 --result_path /tmp/res --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/l2_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search disk index (one shot graph build, L2, no diskPQ, build with PQ distance comparisons) (int8) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type int8 --dist_fn l2 --data_path data/rand_int8_10D_10K_norm50.0.bin --index_path_prefix data/disk_index_l2_rand_int8_10D_10K_norm50.0_diskfull_oneshot_buildpq5 -R 16 -L 32 -B 0.00003 -M 1 --build_PQ_bytes 5 + dist/bin/search_disk_index --data_type int8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_int8_10D_10K_norm50.0_diskfull_oneshot_buildpq5 --result_path /tmp/res --query_file data/rand_int8_10D_1K_norm50.0.bin --gt_file data/l2_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16\ + - name: build and search disk index (one shot graph build, L2, no diskPQ, build with PQ distance comparisons) (uint8) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type uint8 --dist_fn l2 --data_path data/rand_uint8_10D_10K_norm50.0.bin --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50.0_diskfull_oneshot_buildpq5 -R 16 -L 32 -B 0.00003 -M 1 --build_PQ_bytes 5 + dist/bin/search_disk_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50.0_diskfull_oneshot_buildpq5 --result_path /tmp/res --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + + - name: build and search disk index (sharded graph build, L2, no diskPQ) (float) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/disk_index_l2_rand_float_10D_10K_norm1.0_diskfull_sharded -R 16 -L 32 -B 0.00003 -M 0.00006 + dist/bin/search_disk_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_float_10D_10K_norm1.0_diskfull_sharded --result_path /tmp/res --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/l2_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search disk index (sharded graph build, cosine, no diskPQ) (float) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type float --dist_fn cosine --data_path data/rand_float_10D_10K_unnorm.bin --index_path_prefix data/disk_index_cosine_rand_float_10D_10K_unnorm_diskfull_sharded -R 16 -L 32 -B 0.00003 -M 0.00006 + dist/bin/search_disk_index --data_type float --dist_fn cosine --fail_if_recall_below 70 --index_path_prefix data/disk_index_cosine_rand_float_10D_10K_unnorm_diskfull_sharded --result_path /tmp/res --query_file data/rand_float_10D_1K_unnorm.bin --gt_file data/cosine_rand_float_10D_10K_unnorm_10D_1K_unnorm_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search disk index (sharded graph build, L2, no diskPQ) (int8) + run: | + dist/bin/build_disk_index --data_type int8 --dist_fn l2 --data_path data/rand_int8_10D_10K_norm50.0.bin --index_path_prefix data/disk_index_l2_rand_int8_10D_10K_norm50.0_diskfull_sharded -R 16 -L 32 -B 0.00003 -M 0.00006 + dist/bin/search_disk_index --data_type int8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_int8_10D_10K_norm50.0_diskfull_sharded --result_path /tmp/res --query_file data/rand_int8_10D_1K_norm50.0.bin --gt_file data/l2_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search disk index (sharded graph build, L2, no diskPQ) (uint8) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type uint8 --dist_fn l2 --data_path data/rand_uint8_10D_10K_norm50.0.bin --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50.0_diskfull_sharded -R 16 -L 32 -B 0.00003 -M 0.00006 + dist/bin/search_disk_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50.0_diskfull_sharded --result_path /tmp/res --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + + - name: build and search disk index (one shot graph build, L2, diskPQ) (float) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/disk_index_l2_rand_float_10D_10K_norm1.0_diskpq_oneshot -R 16 -L 32 -B 0.00003 -M 1 --PQ_disk_bytes 5 + dist/bin/search_disk_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_float_10D_10K_norm1.0_diskpq_oneshot --result_path /tmp/res --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/l2_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search disk index (one shot graph build, L2, diskPQ) (int8) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type int8 --dist_fn l2 --data_path data/rand_int8_10D_10K_norm50.0.bin --index_path_prefix data/disk_index_l2_rand_int8_10D_10K_norm50.0_diskpq_oneshot -R 16 -L 32 -B 0.00003 -M 1 --PQ_disk_bytes 5 + dist/bin/search_disk_index --data_type int8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_int8_10D_10K_norm50.0_diskpq_oneshot --result_path /tmp/res --query_file data/rand_int8_10D_1K_norm50.0.bin --gt_file data/l2_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search disk index (one shot graph build, L2, diskPQ) (uint8) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type uint8 --dist_fn l2 --data_path data/rand_uint8_10D_10K_norm50.0.bin --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50.0_diskpq_oneshot -R 16 -L 32 -B 0.00003 -M 1 --PQ_disk_bytes 5 + dist/bin/search_disk_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50.0_diskpq_oneshot --result_path /tmp/res --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + + - name: build and search disk index (sharded graph build, MIPS, diskPQ) (float) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type float --dist_fn mips --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/disk_index_mips_rand_float_10D_10K_norm1.0_diskpq_sharded -R 16 -L 32 -B 0.00003 -M 0.00006 --PQ_disk_bytes 5 + dist/bin/search_disk_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_mips_rand_float_10D_10K_norm1.0_diskpq_sharded --result_path /tmp/res --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/mips_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + + - name: upload data and bin + uses: actions/upload-artifact@v4 + with: + name: disk-pq-${{matrix.os}} + path: | + ./dist/** + ./data/** diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/dynamic-labels.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/dynamic-labels.yml new file mode 100644 index 0000000..d5dc712 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/dynamic-labels.yml @@ -0,0 +1,102 @@ +name: Dynamic-Labels +on: [workflow_call] +jobs: + acceptance-tests-dynamic: + name: Dynamic-Labels + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-2019, windows-latest] + runs-on: ${{matrix.os}} + defaults: + run: + shell: bash + steps: + - name: Checkout repository + if: ${{ runner.os == 'Linux' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checkout repository + if: ${{ runner.os == 'Windows' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + submodules: true + - name: DiskANN Build CLI Applications + uses: ./.github/actions/build + + - name: Generate Data + uses: ./.github/actions/generate-random + + - name: Generate Labels + run: | + echo "Generating synthetic labels and computing ground truth for filtered search with universal label" + dist/bin/generate_synthetic_labels --num_labels 50 --num_points 10000 --output_file data/rand_labels_50_10K.txt --distribution_type random + + echo "Generating synthetic labels with a zipf distribution and computing ground truth for filtered search with universal label" + dist/bin/generate_synthetic_labels --num_labels 50 --num_points 10000 --output_file data/zipf_labels_50_10K.txt --distribution_type zipf + + - name: Test a streaming index (float) with labels (Zipf distributed) + run: | + dist/bin/test_streaming_scenario --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --universal_label 0 --label_file data/zipf_labels_50_10K.txt --index_path_prefix data/index_zipf_stream -R 64 --FilteredLbuild 200 -L 50 --alpha 1.2 --insert_threads 8 --consolidate_threads 8 --max_points_to_insert 10000 --active_window 4000 --consolidate_interval 2000 --start_point_norm 3.2 --unique_labels_supported 51 + + echo "Computing groundtruth with filter" + dist/bin/compute_groundtruth_for_filters --data_type float --universal_label 0 --filter_label 1 --dist_fn l2 --base_file data/index_zipf_stream.after-streaming-act4000-cons2000-max10000.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_zipf_base-act4000-cons2000-max10000_1 --label_file data/index_zipf_stream.after-streaming-act4000-cons2000-max10000_raw_labels.txt --tags_file data/index_zipf_stream.after-streaming-act4000-cons2000-max10000.tags + echo "Searching with filter" + dist/bin/search_memory_index --data_type float --dist_fn l2 --filter_label 1 --fail_if_recall_below 40 --index_path_prefix data/index_zipf_stream.after-streaming-act4000-cons2000-max10000 --result_path data/res_stream --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_zipf_base-act4000-cons2000-max10000_1 -K 10 -L 20 40 60 80 100 150 -T 64 --dynamic true --tags 1 + + echo "Computing groundtruth w/o filter" + dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/index_zipf_stream.after-streaming-act4000-cons2000-max10000.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_zipf_base-act4000-cons2000-max10000 + echo "Searching without filter" + dist/bin/search_memory_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_zipf_stream.after-streaming-act4000-cons2000-max10000 --result_path res_stream --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_zipf_base-act4000-cons2000-max10000 -K 10 -L 20 40 60 80 100 -T 64 + + - name: Test a streaming index (float) with labels (random distributed) + run: | + dist/bin/test_streaming_scenario --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --universal_label 0 --label_file data/rand_labels_50_10K.txt --index_path_prefix data/index_rand_stream -R 64 --FilteredLbuild 200 -L 50 --alpha 1.2 --insert_threads 8 --consolidate_threads 8 --max_points_to_insert 10000 --active_window 4000 --consolidate_interval 2000 --start_point_norm 3.2 --unique_labels_supported 51 + + echo "Computing groundtruth with filter" + dist/bin/compute_groundtruth_for_filters --data_type float --universal_label 0 --filter_label 1 --dist_fn l2 --base_file data/index_rand_stream.after-streaming-act4000-cons2000-max10000.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_rand_base-act4000-cons2000-max10000_1 --label_file data/index_rand_stream.after-streaming-act4000-cons2000-max10000_raw_labels.txt --tags_file data/index_rand_stream.after-streaming-act4000-cons2000-max10000.tags + echo "Searching with filter" + dist/bin/search_memory_index --data_type float --dist_fn l2 --filter_label 1 --fail_if_recall_below 40 --index_path_prefix data/index_rand_stream.after-streaming-act4000-cons2000-max10000 --result_path data/res_stream --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_rand_base-act4000-cons2000-max10000_1 -K 10 -L 20 40 60 80 100 150 -T 64 --dynamic true --tags 1 + + echo "Computing groundtruth w/o filter" + dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/index_rand_stream.after-streaming-act4000-cons2000-max10000.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_rand_base-act4000-cons2000-max10000 + echo "Searching without filter" + dist/bin/search_memory_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_rand_stream.after-streaming-act4000-cons2000-max10000 --result_path res_stream --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_rand_base-act4000-cons2000-max10000 -K 10 -L 20 40 60 80 100 -T 64 + + - name: Test Insert Delete Consolidate (float) with labels (zipf distributed) + run: | + dist/bin/test_insert_deletes_consolidate --data_type float --dist_fn l2 --universal_label 0 --label_file data/zipf_labels_50_10K.txt --FilteredLbuild 70 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/index_zipf_ins_del -R 64 -L 10 --alpha 1.2 --points_to_skip 0 --max_points_to_insert 7500 --beginning_index_size 0 --points_per_checkpoint 1000 --checkpoints_per_snapshot 0 --points_to_delete_from_beginning 2500 --start_deletes_after 5000 --do_concurrent true --start_point_norm 3.2 --unique_labels_supported 51 + + echo "Computing groundtruth with filter" + dist/bin/compute_groundtruth_for_filters --data_type float --filter_label 5 --universal_label 0 --dist_fn l2 --base_file data/index_zipf_ins_del.after-concurrent-delete-del2500-7500.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_zipf_random10D_1K_wlabel_5 --label_file data/index_zipf_ins_del.after-concurrent-delete-del2500-7500_raw_labels.txt --tags_file data/index_zipf_ins_del.after-concurrent-delete-del2500-7500.tags + echo "Searching with filter" + dist/bin/search_memory_index --data_type float --dist_fn l2 --filter_label 5 --fail_if_recall_below 10 --index_path_prefix data/index_zipf_ins_del.after-concurrent-delete-del2500-7500 --result_path data/res_zipf_stream --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_zipf_random10D_1K_wlabel_5 -K 10 -L 20 40 60 80 100 150 -T 64 --dynamic true --tags 1 + + echo "Computing groundtruth w/o filter" + dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/index_zipf_ins_del.after-concurrent-delete-del2500-7500.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_zipf_random10D_1K + echo "Searching without filter" + dist/bin/search_memory_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_zipf_ins_del.after-concurrent-delete-del2500-7500 --result_path res_stream --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_zipf_random10D_1K -K 10 -L 20 40 60 80 100 -T 64 + + - name: Test Insert Delete Consolidate (float) with labels (random distributed) + run: | + dist/bin/test_insert_deletes_consolidate --data_type float --dist_fn l2 --universal_label 0 --label_file data/rand_labels_50_10K.txt --FilteredLbuild 70 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/index_rand_ins_del -R 64 -L 10 --alpha 1.2 --points_to_skip 0 --max_points_to_insert 7500 --beginning_index_size 0 --points_per_checkpoint 1000 --checkpoints_per_snapshot 0 --points_to_delete_from_beginning 2500 --start_deletes_after 5000 --do_concurrent true --start_point_norm 3.2 --unique_labels_supported 51 + + echo "Computing groundtruth with filter" + dist/bin/compute_groundtruth_for_filters --data_type float --filter_label 5 --universal_label 0 --dist_fn l2 --base_file data/index_rand_ins_del.after-concurrent-delete-del2500-7500.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_rand_random10D_1K_wlabel_5 --label_file data/index_rand_ins_del.after-concurrent-delete-del2500-7500_raw_labels.txt --tags_file data/index_rand_ins_del.after-concurrent-delete-del2500-7500.tags + echo "Searching with filter" + dist/bin/search_memory_index --data_type float --dist_fn l2 --filter_label 5 --fail_if_recall_below 40 --index_path_prefix data/index_rand_ins_del.after-concurrent-delete-del2500-7500 --result_path data/res_rand_stream --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_rand_random10D_1K_wlabel_5 -K 10 -L 20 40 60 80 100 150 -T 64 --dynamic true --tags 1 + + echo "Computing groundtruth w/o filter" + dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/index_rand_ins_del.after-concurrent-delete-del2500-7500.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_rand_random10D_1K + echo "Searching without filter" + dist/bin/search_memory_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_rand_ins_del.after-concurrent-delete-del2500-7500 --result_path res_stream --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_rand_random10D_1K -K 10 -L 20 40 60 80 100 -T 64 + + - name: upload data and bin + uses: actions/upload-artifact@v4 + with: + name: dynamic-labels-${{matrix.os}} + path: | + ./dist/** + ./data/** diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/dynamic.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/dynamic.yml new file mode 100644 index 0000000..edd691e --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/dynamic.yml @@ -0,0 +1,75 @@ +name: Dynamic +on: [workflow_call] +jobs: + acceptance-tests-dynamic: + name: Dynamic + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-2019, windows-latest] + runs-on: ${{matrix.os}} + defaults: + run: + shell: bash + steps: + - name: Checkout repository + if: ${{ runner.os == 'Linux' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checkout repository + if: ${{ runner.os == 'Windows' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + submodules: true + - name: DiskANN Build CLI Applications + uses: ./.github/actions/build + + - name: Generate Data + uses: ./.github/actions/generate-random + + - name: test a streaming index (float) + run: | + dist/bin/test_streaming_scenario --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/index_stream -R 64 -L 600 --alpha 1.2 --insert_threads 4 --consolidate_threads 4 --max_points_to_insert 10000 --active_window 4000 --consolidate_interval 2000 --start_point_norm 3.2 + dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/index_stream.after-streaming-act4000-cons2000-max10000.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_base-act4000-cons2000-max10000 --tags_file data/index_stream.after-streaming-act4000-cons2000-max10000.tags + dist/bin/search_memory_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_stream.after-streaming-act4000-cons2000-max10000 --result_path data/res_stream --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_base-act4000-cons2000-max10000 -K 10 -L 20 40 60 80 100 -T 64 --dynamic true --tags 1 + - name: test a streaming index (int8) + if: success() || failure() + run: | + dist/bin/test_streaming_scenario --data_type int8 --dist_fn l2 --data_path data/rand_int8_10D_10K_norm50.0.bin --index_path_prefix data/index_stream -R 64 -L 600 --alpha 1.2 --insert_threads 4 --consolidate_threads 4 --max_points_to_insert 10000 --active_window 4000 --consolidate_interval 2000 --start_point_norm 200 + dist/bin/compute_groundtruth --data_type int8 --dist_fn l2 --base_file data/index_stream.after-streaming-act4000-cons2000-max10000.data --query_file data/rand_int8_10D_1K_norm50.0.bin --K 100 --gt_file data/gt100_base-act4000-cons2000-max10000 --tags_file data/index_stream.after-streaming-act4000-cons2000-max10000.tags + dist/bin/search_memory_index --data_type int8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_stream.after-streaming-act4000-cons2000-max10000 --result_path res_stream --query_file data/rand_int8_10D_1K_norm50.0.bin --gt_file data/gt100_base-act4000-cons2000-max10000 -K 10 -L 20 40 60 80 100 -T 64 --dynamic true --tags 1 + - name: test a streaming index + if: success() || failure() + run: | + dist/bin/test_streaming_scenario --data_type uint8 --dist_fn l2 --data_path data/rand_uint8_10D_10K_norm50.0.bin --index_path_prefix data/index_stream -R 64 -L 600 --alpha 1.2 --insert_threads 4 --consolidate_threads 4 --max_points_to_insert 10000 --active_window 4000 --consolidate_interval 2000 --start_point_norm 200 + dist/bin/compute_groundtruth --data_type uint8 --dist_fn l2 --base_file data/index_stream.after-streaming-act4000-cons2000-max10000.data --query_file data/rand_uint8_10D_1K_norm50.0.bin --K 100 --gt_file data/gt100_base-act4000-cons2000-max10000 --tags_file data/index_stream.after-streaming-act4000-cons2000-max10000.tags + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_stream.after-streaming-act4000-cons2000-max10000 --result_path data/res_stream --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/gt100_base-act4000-cons2000-max10000 -K 10 -L 20 40 60 80 100 -T 64 --dynamic true --tags 1 + + - name: build and search an incremental index (float) + if: success() || failure() + run: | + dist/bin/test_insert_deletes_consolidate --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/index_ins_del -R 64 -L 300 --alpha 1.2 -T 8 --points_to_skip 0 --max_points_to_insert 7500 --beginning_index_size 0 --points_per_checkpoint 1000 --checkpoints_per_snapshot 0 --points_to_delete_from_beginning 2500 --start_deletes_after 5000 --do_concurrent true --start_point_norm 3.2; + dist/bin/compute_groundtruth --data_type float --dist_fn l2 --base_file data/index_ins_del.after-concurrent-delete-del2500-7500.data --query_file data/rand_float_10D_1K_norm1.0.bin --K 100 --gt_file data/gt100_random10D_1K-conc-2500-7500 --tags_file data/index_ins_del.after-concurrent-delete-del2500-7500.tags + dist/bin/search_memory_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_ins_del.after-concurrent-delete-del2500-7500 --result_path data/res_ins_del --query_file data/rand_float_10D_1K_norm1.0.bin --gt_file data/gt100_random10D_1K-conc-2500-7500 -K 10 -L 20 40 60 80 100 -T 8 --dynamic true --tags 1 + - name: build and search an incremental index (int8) + if: success() || failure() + run: | + dist/bin/test_insert_deletes_consolidate --data_type int8 --dist_fn l2 --data_path data/rand_int8_10D_10K_norm50.0.bin --index_path_prefix data/index_ins_del -R 64 -L 300 --alpha 1.2 -T 8 --points_to_skip 0 --max_points_to_insert 7500 --beginning_index_size 0 --points_per_checkpoint 1000 --checkpoints_per_snapshot 0 --points_to_delete_from_beginning 2500 --start_deletes_after 5000 --do_concurrent true --start_point_norm 200 + dist/bin/compute_groundtruth --data_type int8 --dist_fn l2 --base_file data/index_ins_del.after-concurrent-delete-del2500-7500.data --query_file data/rand_int8_10D_1K_norm50.0.bin --K 100 --gt_file data/gt100_random10D_1K-conc-2500-7500 --tags_file data/index_ins_del.after-concurrent-delete-del2500-7500.tags + dist/bin/search_memory_index --data_type int8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_ins_del.after-concurrent-delete-del2500-7500 --result_path data/res_ins_del --query_file data/rand_int8_10D_1K_norm50.0.bin --gt_file data/gt100_random10D_1K-conc-2500-7500 -K 10 -L 20 40 60 80 100 -T 8 --dynamic true --tags 1 + - name: build and search an incremental index (uint8) + if: success() || failure() + run: | + dist/bin/test_insert_deletes_consolidate --data_type uint8 --dist_fn l2 --data_path data/rand_uint8_10D_10K_norm50.0.bin --index_path_prefix data/index_ins_del -R 64 -L 300 --alpha 1.2 -T 8 --points_to_skip 0 --max_points_to_insert 7500 --beginning_index_size 0 --points_per_checkpoint 1000 --checkpoints_per_snapshot 0 --points_to_delete_from_beginning 2500 --start_deletes_after 5000 --do_concurrent true --start_point_norm 200 + dist/bin/compute_groundtruth --data_type uint8 --dist_fn l2 --base_file data/index_ins_del.after-concurrent-delete-del2500-7500.data --query_file data/rand_uint8_10D_1K_norm50.0.bin --K 100 --gt_file data/gt100_random10D_10K-conc-2500-7500 --tags_file data/index_ins_del.after-concurrent-delete-del2500-7500.tags + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_ins_del.after-concurrent-delete-del2500-7500 --result_path data/res_ins_del --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/gt100_random10D_10K-conc-2500-7500 -K 10 -L 20 40 60 80 100 -T 8 --dynamic true --tags 1 + + - name: upload data and bin + uses: actions/upload-artifact@v4 + with: + name: dynamic-${{matrix.os}} + path: | + ./dist/** + ./data/** diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/in-mem-no-pq.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/in-mem-no-pq.yml new file mode 100644 index 0000000..07fc4a2 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/in-mem-no-pq.yml @@ -0,0 +1,81 @@ +name: In-Memory Without PQ +on: [workflow_call] +jobs: + acceptance-tests-mem-no-pq: + name: In-Mem, Without PQ + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-2019, windows-latest] + runs-on: ${{matrix.os}} + defaults: + run: + shell: bash + steps: + - name: Checkout repository + if: ${{ runner.os == 'Linux' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checkout repository + if: ${{ runner.os == 'Windows' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + submodules: true + - name: DiskANN Build CLI Applications + uses: ./.github/actions/build + + - name: Generate Data + uses: ./.github/actions/generate-random + + - name: build and search in-memory index with L2 metrics (float) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/index_l2_rand_float_10D_10K_norm1.0 + dist/bin/search_memory_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_float_10D_10K_norm1.0 --query_file data/rand_float_10D_1K_norm1.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 -L 16 32 + - name: build and search in-memory index with L2 metrics (int8) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type int8 --dist_fn l2 --data_path data/rand_int8_10D_10K_norm50.0.bin --index_path_prefix data/index_l2_rand_int8_10D_10K_norm50.0 + dist/bin/search_memory_index --data_type int8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_int8_10D_10K_norm50.0 --query_file data/rand_int8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 16 32 + - name: build and search in-memory index with L2 metrics (uint8) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type uint8 --dist_fn l2 --data_path data/rand_uint8_10D_10K_norm50.0.bin --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50.0 + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50.0 --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 16 32 + + - name: Searching with fast_l2 distance function (float) + if: runner.os != 'Windows' && (success() || failure()) + run: | + dist/bin/search_memory_index --data_type float --dist_fn fast_l2 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_float_10D_10K_norm1.0 --query_file data/rand_float_10D_1K_norm1.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 -L 16 32 + + - name: build and search in-memory index with MIPS metric (float) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type float --dist_fn mips --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/index_mips_rand_float_10D_10K_norm1.0 + dist/bin/search_memory_index --data_type float --dist_fn mips --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_float_10D_10K_norm1.0 --query_file data/rand_float_10D_1K_norm1.0.bin --recall_at 10 --result_path temp --gt_file data/mips_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 -L 16 32 + + - name: build and search in-memory index with cosine metric (float) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type float --dist_fn cosine --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/index_cosine_rand_float_10D_10K_norm1.0 + dist/bin/search_memory_index --data_type float --dist_fn cosine --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_float_10D_10K_norm1.0 --query_file data/rand_float_10D_1K_norm1.0.bin --recall_at 10 --result_path temp --gt_file data/cosine_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 -L 16 32 + - name: build and search in-memory index with cosine metric (int8) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type int8 --dist_fn cosine --data_path data/rand_int8_10D_10K_norm50.0.bin --index_path_prefix data/index_cosine_rand_int8_10D_10K_norm50.0 + dist/bin/search_memory_index --data_type int8 --dist_fn cosine --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_int8_10D_10K_norm50.0 --query_file data/rand_int8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/cosine_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 16 32 + - name: build and search in-memory index with cosine metric + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type uint8 --dist_fn cosine --data_path data/rand_uint8_10D_10K_norm50.0.bin --index_path_prefix data/index_cosine_rand_uint8_10D_10K_norm50.0 + dist/bin/search_memory_index --data_type uint8 --dist_fn cosine --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50.0 --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/cosine_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 16 32 + + - name: upload data and bin + uses: actions/upload-artifact@v4 + with: + name: in-memory-no-pq-${{matrix.os}} + path: | + ./dist/** + ./data/** diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/in-mem-pq.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/in-mem-pq.yml new file mode 100644 index 0000000..be20f10 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/in-mem-pq.yml @@ -0,0 +1,56 @@ +name: In-Memory With PQ +on: [workflow_call] +jobs: + acceptance-tests-mem-pq: + name: In-Mem, PQ + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-2019, windows-latest] + runs-on: ${{matrix.os}} + defaults: + run: + shell: bash + steps: + - name: Checkout repository + if: ${{ runner.os == 'Linux' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checkout repository + if: ${{ runner.os == 'Windows' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + submodules: true + - name: DiskANN Build CLI Applications + uses: ./.github/actions/build + + - name: Generate Data + uses: ./.github/actions/generate-random + + - name: build and search in-memory index with L2 metric with PQ based distance comparisons (float) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type float --dist_fn l2 --data_path data/rand_float_10D_10K_norm1.0.bin --index_path_prefix data/index_l2_rand_float_10D_10K_norm1.0_buildpq5 --build_PQ_bytes 5 + dist/bin/search_memory_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_float_10D_10K_norm1.0_buildpq5 --query_file data/rand_float_10D_1K_norm1.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_float_10D_10K_norm1.0_10D_1K_norm1.0_gt100 -L 16 32 + + - name: build and search in-memory index with L2 metrics with PQ base distance comparisons (int8) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type int8 --dist_fn l2 --data_path data/rand_int8_10D_10K_norm50.0.bin --index_path_prefix data/index_l2_rand_int8_10D_10K_norm50.0_buildpq5 --build_PQ_bytes 5 + dist/bin/search_memory_index --data_type int8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_int8_10D_10K_norm50.0_buildpq5 --query_file data/rand_int8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_int8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 16 32 + + - name: build and search in-memory index with L2 metrics with PQ base distance comparisons (uint8) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type uint8 --dist_fn l2 --data_path data/rand_uint8_10D_10K_norm50.0.bin --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50.0_buildpq5 --build_PQ_bytes 5 + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50.0_buildpq5 --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 16 32 + + - name: upload data and bin + uses: actions/upload-artifact@v4 + with: + name: in-memory-pq-${{matrix.os}} + path: | + ./dist/** + ./data/** \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/labels.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/labels.yml new file mode 100644 index 0000000..93995f7 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/labels.yml @@ -0,0 +1,120 @@ +name: Labels +on: [workflow_call] +jobs: + acceptance-tests-labels: + name: Labels + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-2019, windows-latest] + runs-on: ${{matrix.os}} + defaults: + run: + shell: bash + steps: + - name: Checkout repository + if: ${{ runner.os == 'Linux' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checkout repository + if: ${{ runner.os == 'Windows' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + submodules: true + - name: DiskANN Build CLI Applications + uses: ./.github/actions/build + + - name: Generate Data + uses: ./.github/actions/generate-random + + - name: Generate Labels + run: | + echo "Generating synthetic labels and computing ground truth for filtered search with universal label" + dist/bin/generate_synthetic_labels --num_labels 50 --num_points 10000 --output_file data/rand_labels_50_10K.txt --distribution_type random + dist/bin/compute_groundtruth_for_filters --data_type uint8 --dist_fn l2 --universal_label 0 --filter_label 10 --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --label_file data/rand_labels_50_10K.txt --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel --K 100 + dist/bin/compute_groundtruth_for_filters --data_type uint8 --dist_fn mips --universal_label 0 --filter_label 10 --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --label_file data/rand_labels_50_10K.txt --gt_file data/mips_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel --K 100 + dist/bin/compute_groundtruth_for_filters --data_type uint8 --dist_fn cosine --universal_label 0 --filter_label 10 --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --label_file data/rand_labels_50_10K.txt --gt_file data/cosine_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel --K 100 + + echo "Generating synthetic labels with a zipf distribution and computing ground truth for filtered search with universal label" + dist/bin/generate_synthetic_labels --num_labels 50 --num_points 10000 --output_file data/zipf_labels_50_10K.txt --distribution_type zipf + dist/bin/compute_groundtruth_for_filters --data_type uint8 --dist_fn l2 --universal_label 0 --filter_label 5 --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --gt_file data/l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel --K 100 + dist/bin/compute_groundtruth_for_filters --data_type uint8 --dist_fn mips --universal_label 0 --filter_label 5 --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --gt_file data/mips_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel --K 100 + dist/bin/compute_groundtruth_for_filters --data_type uint8 --dist_fn cosine --universal_label 0 --filter_label 5 --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --gt_file data/cosine_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel --K 100 + + echo "Generating synthetic labels and computing ground truth for filtered search without a universal label" + dist/bin/compute_groundtruth_for_filters --data_type uint8 --dist_fn l2 --filter_label 5 --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --gt_file data/l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel_nouniversal --K 100 + dist/bin/generate_synthetic_labels --num_labels 10 --num_points 1000 --output_file data/query_labels_1K.txt --distribution_type one_per_point + dist/bin/compute_groundtruth_for_filters --data_type uint8 --dist_fn l2 --universal_label 0 --filter_label_file data/query_labels_1K.txt --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --gt_file data/combined_l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel --K 100 + + - name: build and search in-memory index with labels using L2 and Cosine metrics (random distributed labels) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type uint8 --dist_fn l2 --FilteredLbuild 90 --universal_label 0 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/rand_labels_50_10K.txt --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50_wlabel + dist/bin/build_memory_index --data_type uint8 --dist_fn cosine --FilteredLbuild 90 --universal_label 0 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/rand_labels_50_10K.txt --index_path_prefix data/index_cosine_rand_uint8_10D_10K_norm50_wlabel + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --filter_label 10 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel -L 16 32 + dist/bin/search_memory_index --data_type uint8 --dist_fn cosine --filter_label 10 --fail_if_recall_below 70 --index_path_prefix data/index_cosine_rand_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/cosine_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel -L 16 32 + + echo "Searching without filters" + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 32 64 + dist/bin/search_memory_index --data_type uint8 --dist_fn cosine --fail_if_recall_below 70 --index_path_prefix data/index_cosine_rand_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/cosine_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 32 64 + + - name: build and search disk index with labels using L2 and Cosine metrics (random distributed labels) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type uint8 --dist_fn l2 --universal_label 0 --FilteredLbuild 90 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/rand_labels_50_10K.txt --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50_wlabel -R 32 -L 5 -B 0.00003 -M 1 + dist/bin/search_disk_index --data_type uint8 --dist_fn l2 --filter_label 10 --fail_if_recall_below 50 --index_path_prefix data/disk_index_l2_rand_uint8_10D_10K_norm50_wlabel --result_path temp --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: build and search in-memory index with labels using L2 and Cosine metrics (zipf distributed labels) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type uint8 --dist_fn l2 --FilteredLbuild 90 --universal_label 0 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --index_path_prefix data/index_l2_zipf_uint8_10D_10K_norm50_wlabel + dist/bin/build_memory_index --data_type uint8 --dist_fn cosine --FilteredLbuild 90 --universal_label 0 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --index_path_prefix data/index_cosine_zipf_uint8_10D_10K_norm50_wlabel + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --filter_label 5 --fail_if_recall_below 70 --index_path_prefix data/index_l2_zipf_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel -L 16 32 + dist/bin/search_memory_index --data_type uint8 --dist_fn cosine --filter_label 5 --fail_if_recall_below 70 --index_path_prefix data/index_cosine_zipf_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/cosine_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel -L 16 32 + + echo "Searching without filters" + dist/bin/compute_groundtruth --data_type uint8 --dist_fn l2 --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --K 100 + dist/bin/compute_groundtruth --data_type uint8 --dist_fn cosine --base_file data/rand_uint8_10D_10K_norm50.0.bin --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/cosine_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 --K 100 + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/index_l2_zipf_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 32 64 + dist/bin/search_memory_index --data_type uint8 --dist_fn cosine --fail_if_recall_below 70 --index_path_prefix data/index_cosine_zipf_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/cosine_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100 -L 32 64 + + - name: build and search disk index with labels using L2 and Cosine metrics (zipf distributed labels) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type uint8 --dist_fn l2 --universal_label 0 --FilteredLbuild 90 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --index_path_prefix data/disk_index_l2_zipf_uint8_10D_10K_norm50_wlabel -R 32 -L 5 -B 0.00003 -M 1 + dist/bin/search_disk_index --data_type uint8 --dist_fn l2 --filter_label 5 --fail_if_recall_below 50 --index_path_prefix data/disk_index_l2_zipf_uint8_10D_10K_norm50_wlabel --result_path temp --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + + - name : build and search in-memory and disk index (without universal label, zipf distributed) + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type uint8 --dist_fn l2 --FilteredLbuild 90 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --index_path_prefix data/index_l2_zipf_uint8_10D_10K_norm50_wlabel_nouniversal + dist/bin/build_disk_index --data_type uint8 --dist_fn l2 --FilteredLbuild 90 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --index_path_prefix data/disk_index_l2_zipf_uint8_10D_10K_norm50_wlabel_nouniversal -R 32 -L 5 -B 0.00003 -M 1 + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --filter_label 5 --fail_if_recall_below 70 --index_path_prefix data/index_l2_zipf_uint8_10D_10K_norm50_wlabel_nouniversal --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel_nouniversal -L 16 32 + dist/bin/search_disk_index --data_type uint8 --dist_fn l2 --filter_label 5 --index_path_prefix data/disk_index_l2_zipf_uint8_10D_10K_norm50_wlabel_nouniversal --result_path temp --query_file data/rand_uint8_10D_1K_norm50.0.bin --gt_file data/l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel_nouniversal --recall_at 5 -L 5 12 -W 2 --num_nodes_to_cache 10 -T 16 + - name: Generate combined GT for each query with a separate label and search + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type uint8 --dist_fn l2 --FilteredLbuild 90 --universal_label 0 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt --index_path_prefix data/index_l2_zipf_uint8_10D_10K_norm50_wlabel + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --query_filters_file data/query_labels_1K.txt --fail_if_recall_below 70 --index_path_prefix data/index_l2_zipf_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/combined_l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel -L 16 32 + - name: build and search in-memory index with pq_dist of 5 with 10 dimensions + if: success() || failure() + run: | + dist/bin/build_memory_index --data_type uint8 --dist_fn l2 --FilteredLbuild 90 --universal_label 0 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/rand_labels_50_10K.txt --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50_wlabel --build_PQ_bytes 5 + dist/bin/search_memory_index --data_type uint8 --dist_fn l2 --filter_label 10 --fail_if_recall_below 70 --index_path_prefix data/index_l2_rand_uint8_10D_10K_norm50_wlabel --query_file data/rand_uint8_10D_1K_norm50.0.bin --recall_at 10 --result_path temp --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel -L 16 32 + - name: Build and search stitched vamana with random and zipf distributed labels + if: success() || failure() + run: | + dist/bin/build_stitched_index --num_threads 48 --data_type uint8 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/rand_labels_50_10K.txt -R 32 -L 100 --alpha 1.2 --stitched_R 64 --index_path_prefix data/stit_rand_32_100_64_new --universal_label 0 + dist/bin/build_stitched_index --num_threads 48 --data_type uint8 --data_path data/rand_uint8_10D_10K_norm50.0.bin --label_file data/zipf_labels_50_10K.txt -R 32 -L 100 --alpha 1.2 --stitched_R 64 --index_path_prefix data/stit_zipf_32_100_64_new --universal_label 0 + dist/bin/search_memory_index --num_threads 48 --data_type uint8 --dist_fn l2 --filter_label 10 --index_path_prefix data/stit_rand_32_100_64_new --query_file data/rand_uint8_10D_1K_norm50.0.bin --result_path data/rand_stit_96_10_90_new --gt_file data/l2_rand_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel -K 10 -L 16 32 150 + dist/bin/search_memory_index --num_threads 48 --data_type uint8 --dist_fn l2 --filter_label 5 --index_path_prefix data/stit_zipf_32_100_64_new --query_file data/rand_uint8_10D_1K_norm50.0.bin --result_path data/zipf_stit_96_10_90_new --gt_file data/l2_zipf_uint8_10D_10K_norm50.0_10D_1K_norm50.0_gt100_wlabel -K 10 -L 16 32 150 + + - name: upload data and bin + if: success() || failure() + uses: actions/upload-artifact@v4 + with: + name: labels-${{matrix.os}} + path: | + ./dist/** + ./data/** diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/multi-sector-disk-pq.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/multi-sector-disk-pq.yml new file mode 100644 index 0000000..969467a --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/multi-sector-disk-pq.yml @@ -0,0 +1,60 @@ +name: Disk With PQ +on: [workflow_call] +jobs: + acceptance-tests-disk-pq: + name: Disk, PQ + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-2019, windows-latest] + runs-on: ${{matrix.os}} + defaults: + run: + shell: bash + steps: + - name: Checkout repository + if: ${{ runner.os == 'Linux' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checkout repository + if: ${{ runner.os == 'Windows' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + submodules: true + - name: DiskANN Build CLI Applications + uses: ./.github/actions/build + + - name: Generate Data + uses: ./.github/actions/generate-high-dim-random + + - name: build and search disk index (1020D, one shot graph build, L2, no diskPQ) (float) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type float --dist_fn l2 --data_path data/rand_float_1020D_5K_norm1.0.bin --index_path_prefix data/disk_index_l2_rand_float_1020D_5K_norm1.0_diskfull_oneshot -R 32 -L 500 -B 0.003 -M 1 + dist/bin/search_disk_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_float_1020D_5K_norm1.0_diskfull_oneshot --result_path /tmp/res --query_file data/rand_float_1020D_1K_norm1.0.bin --gt_file data/l2_rand_float_1020D_5K_norm1.0_1020D_1K_norm1.0_gt100 --recall_at 5 -L 250 -W 2 --num_nodes_to_cache 100 -T 16 + #- name: build and search disk index (1024D, one shot graph build, L2, no diskPQ) (float) + # if: success() || failure() + # run: | + # dist/bin/build_disk_index --data_type float --dist_fn l2 --data_path data/rand_float_1024D_5K_norm1.0.bin --index_path_prefix data/disk_index_l2_rand_float_1024D_5K_norm1.0_diskfull_oneshot -R 32 -L 500 -B 0.003 -M 1 + # dist/bin/search_disk_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_float_1024D_5K_norm1.0_diskfull_oneshot --result_path /tmp/res --query_file data/rand_float_1024D_1K_norm1.0.bin --gt_file data/l2_rand_float_1024D_5K_norm1.0_1024D_1K_norm1.0_gt100 --recall_at 5 -L 250 -W 2 --num_nodes_to_cache 100 -T 16 + - name: build and search disk index (1536D, one shot graph build, L2, no diskPQ) (float) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type float --dist_fn l2 --data_path data/rand_float_1536D_5K_norm1.0.bin --index_path_prefix data/disk_index_l2_rand_float_1536D_5K_norm1.0_diskfull_oneshot -R 32 -L 500 -B 0.003 -M 1 + dist/bin/search_disk_index --data_type float --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_float_1536D_5K_norm1.0_diskfull_oneshot --result_path /tmp/res --query_file data/rand_float_1536D_1K_norm1.0.bin --gt_file data/l2_rand_float_1536D_5K_norm1.0_1536D_1K_norm1.0_gt100 --recall_at 5 -L 250 -W 2 --num_nodes_to_cache 100 -T 16 + + - name: build and search disk index (4096D, one shot graph build, L2, no diskPQ) (int8) + if: success() || failure() + run: | + dist/bin/build_disk_index --data_type int8 --dist_fn l2 --data_path data/rand_int8_4096D_5K_norm1.0.bin --index_path_prefix data/disk_index_l2_rand_int8_4096D_5K_norm1.0_diskfull_oneshot -R 32 -L 500 -B 0.003 -M 1 + dist/bin/search_disk_index --data_type int8 --dist_fn l2 --fail_if_recall_below 70 --index_path_prefix data/disk_index_l2_rand_int8_4096D_5K_norm1.0_diskfull_oneshot --result_path /tmp/res --query_file data/rand_int8_4096D_1K_norm1.0.bin --gt_file data/l2_rand_int8_4096D_5K_norm1.0_4096D_1K_norm1.0_gt100 --recall_at 5 -L 250 -W 2 --num_nodes_to_cache 100 -T 16 + + - name: upload data and bin + uses: actions/upload-artifact@v4 + with: + name: multi-sector-disk-pq-${{matrix.os}} + path: | + ./dist/** + ./data/** diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/perf.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/perf.yml new file mode 100644 index 0000000..d4eb9e2 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/perf.yml @@ -0,0 +1,26 @@ +name: DiskANN Nightly Performance Metrics +on: + schedule: + - cron: "41 14 * * *" # 14:41 UTC, 7:41 PDT, 8:41 PST, 08:11 IST +jobs: + perf-test: + name: Run Perf Test from main + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Build Perf Container + run: | + docker build --build-arg GIT_COMMIT_ISH="$GITHUB_SHA" -t perf -f scripts/perf/Dockerfile scripts + - name: Performance Tests + run: | + mkdir metrics + docker run -v ./metrics:/app/logs perf &> ./metrics/combined_stdouterr.log + - name: Upload Metrics Logs + uses: actions/upload-artifact@v4 + with: + name: metrics-${{matrix.os}} + path: | + ./metrics/** diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/pr-test.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/pr-test.yml new file mode 100644 index 0000000..f84953b --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/pr-test.yml @@ -0,0 +1,35 @@ +name: DiskANN Pull Request Build and Test +on: [pull_request] +jobs: + common: + strategy: + fail-fast: true + name: DiskANN Common Build Checks + uses: ./.github/workflows/common.yml + unit-tests: + name: Unit tests + uses: ./.github/workflows/unit-tests.yml + in-mem-pq: + name: In-Memory with PQ + uses: ./.github/workflows/in-mem-pq.yml + in-mem-no-pq: + name: In-Memory without PQ + uses: ./.github/workflows/in-mem-no-pq.yml + disk-pq: + name: Disk with PQ + uses: ./.github/workflows/disk-pq.yml + multi-sector-disk-pq: + name: Multi-sector Disk with PQ + uses: ./.github/workflows/multi-sector-disk-pq.yml + labels: + name: Labels + uses: ./.github/workflows/labels.yml + dynamic: + name: Dynamic + uses: ./.github/workflows/dynamic.yml + dynamic-labels: + name: Dynamic Labels + uses: ./.github/workflows/dynamic-labels.yml + python: + name: Python + uses: ./.github/workflows/build-python.yml diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/push-test.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/push-test.yml new file mode 100644 index 0000000..d1261d5 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/push-test.yml @@ -0,0 +1,50 @@ +name: DiskANN Push Build +on: [push] +jobs: + common: + strategy: + fail-fast: true + name: DiskANN Common Build Checks + uses: ./.github/workflows/common.yml + build-documentation: + permissions: + contents: write + strategy: + fail-fast: true + name: DiskANN Build Documentation + uses: ./.github/workflows/build-python-pdoc.yml + build: + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest, windows-2019, windows-latest ] + name: Build for ${{matrix.os}} + runs-on: ${{matrix.os}} + defaults: + run: + shell: bash + steps: + - name: Checkout repository + if: ${{ runner.os == 'Linux' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checkout repository + if: ${{ runner.os == 'Windows' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + submodules: true + - name: Build diskannpy dependency tree + run: | + pip install diskannpy pipdeptree + echo "dependencies" > dependencies_${{ matrix.os }}.txt + pipdeptree >> dependencies_${{ matrix.os }}.txt + - name: Archive diskannpy dependencies artifact + uses: actions/upload-artifact@v4 + with: + name: dependencies_${{ matrix.os }} + path: | + dependencies_${{ matrix.os }}.txt + - name: DiskANN Build CLI Applications + uses: ./.github/actions/build diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/python-release.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/python-release.yml new file mode 100644 index 0000000..a15d4d1 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/python-release.yml @@ -0,0 +1,43 @@ +name: Build and Release Python Wheels +on: + release: + types: [published] +jobs: + python-release-wheels: + name: Python + uses: ./.github/workflows/build-python.yml + build-documentation: + strategy: + fail-fast: true + name: DiskANN Build Documentation + uses: ./.github/workflows/build-python-pdoc.yml + release: + permissions: + contents: write + runs-on: ubuntu-latest + needs: python-release-wheels + steps: + - uses: actions/download-artifact@v3 + with: + name: wheels + path: dist/ + - name: Generate SHA256 files for each wheel + run: | + sha256sum dist/*.whl > checksums.txt + cat checksums.txt + - uses: actions/setup-python@v3 + - name: Install twine + run: python -m pip install twine + - name: Publish with twine + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + twine upload dist/*.whl + - name: Update release with SHA256 and Artifacts + uses: softprops/action-gh-release@v1 + with: + token: ${{ secrets.GITHUB_TOKEN }} + files: | + dist/*.whl + checksums.txt diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/unit-tests.yml b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/unit-tests.yml new file mode 100644 index 0000000..6ae6877 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.github/workflows/unit-tests.yml @@ -0,0 +1,32 @@ +name: Unit Tests +on: [workflow_call] +jobs: + acceptance-tests-labels: + name: Unit Tests + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-2019, windows-latest] + runs-on: ${{matrix.os}} + defaults: + run: + shell: bash + steps: + - name: Checkout repository + if: ${{ runner.os == 'Linux' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + - name: Checkout repository + if: ${{ runner.os == 'Windows' }} + uses: actions/checkout@v3 + with: + fetch-depth: 1 + submodules: true + - name: DiskANN Build CLI Applications + uses: ./.github/actions/build + + - name: Run Unit Tests + run: | + cd build + ctest -C Release \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.gitignore b/packages/leann-backend-diskann/third_party/DiskANN/.gitignore new file mode 100644 index 0000000..c6a88e7 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.gitignore @@ -0,0 +1,384 @@ +## Ignore Visual Studio temporary files, build results, and +## files generated by popular Visual Studio add-ons. +## +## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore + +# User-specific files +*.rsuser +*.suo +*.user +*.userosscache +*.sln.docstates + +# User-specific files (MonoDevelop/Xamarin Studio) +*.userprefs + +# Mono auto generated files +mono_crash.* + +# Build results +[Dd]ebug/ +[Dd]ebugPublic/ +[Rr]elease/ +[Rr]eleases/ +x64/ +x86/ +[Aa][Rr][Mm]/ +[Aa][Rr][Mm]64/ +bld/ +[Bb]in/ +[Oo]bj/ +[Ll]og/ +[Ll]ogs/ + +# Visual Studio 2015/2017 cache/options directory +.vs/ +# Uncomment if you have tasks that create the project's static files in wwwroot +#wwwroot/ + +# Visual Studio 2017 auto generated files +Generated\ Files/ + +# MSTest test Results +[Tt]est[Rr]esult*/ +[Bb]uild[Ll]og.* + +# NUnit +*.VisualState.xml +TestResult.xml +nunit-*.xml + +# Build Results of an ATL Project +[Dd]ebugPS/ +[Rr]eleasePS/ +dlldata.c + +# Benchmark Results +BenchmarkDotNet.Artifacts/ + +# .NET Core +project.lock.json +project.fragment.lock.json +artifacts/ + +# StyleCop +StyleCopReport.xml + +# Files built by Visual Studio +*_i.c +*_p.c +*_h.h +*.ilk +*.meta +*.obj +*.iobj +*.pch +*.pdb +*.ipdb +*.pgc +*.pgd +*.rsp +*.sbr +*.tlb +*.tli +*.tlh +*.tmp +*.tmp_proj +*_wpftmp.csproj +*.log +*.vspscc +*.vssscc +.builds +*.pidb +*.svclog +*.scc + +# Chutzpah Test files +_Chutzpah* + +# Visual C++ cache files +ipch/ +*.aps +*.ncb +*.opendb +*.opensdf +*.sdf +*.cachefile +*.VC.db +*.VC.VC.opendb + +# Visual Studio profiler +*.psess +*.vsp +*.vspx +*.sap + +# Visual Studio Trace Files +*.e2e + +# TFS 2012 Local Workspace +$tf/ + +# Guidance Automation Toolkit +*.gpState + +# ReSharper is a .NET coding add-in +_ReSharper*/ +*.[Rr]e[Ss]harper +*.DotSettings.user + +# TeamCity is a build add-in +_TeamCity* + +# DotCover is a Code Coverage Tool +*.dotCover + +# AxoCover is a Code Coverage Tool +.axoCover/* +!.axoCover/settings.json + +# Visual Studio code coverage results +*.coverage +*.coveragexml + +# NCrunch +_NCrunch_* +.*crunch*.local.xml +nCrunchTemp_* + +# MightyMoose +*.mm.* +AutoTest.Net/ + +# Web workbench (sass) +.sass-cache/ + +# Installshield output folder +[Ee]xpress/ + +# DocProject is a documentation generator add-in +DocProject/buildhelp/ +DocProject/Help/*.HxT +DocProject/Help/*.HxC +DocProject/Help/*.hhc +DocProject/Help/*.hhk +DocProject/Help/*.hhp +DocProject/Help/Html2 +DocProject/Help/html + +# Click-Once directory +publish/ + +# Publish Web Output +*.[Pp]ublish.xml +*.azurePubxml +# Note: Comment the next line if you want to checkin your web deploy settings, +# but database connection strings (with potential passwords) will be unencrypted +*.pubxml +*.publishproj + +# Microsoft Azure Web App publish settings. Comment the next line if you want to +# checkin your Azure Web App publish settings, but sensitive information contained +# in these scripts will be unencrypted +PublishScripts/ + +# NuGet Packages +*.nupkg +# NuGet Symbol Packages +*.snupkg +# The packages folder can be ignored because of Package Restore +**/[Pp]ackages/* +# except build/, which is used as an MSBuild target. +!**/[Pp]ackages/build/ +# Uncomment if necessary however generally it will be regenerated when needed +#!**/[Pp]ackages/repositories.config +# NuGet v3's project.json files produces more ignorable files +*.nuget.props +*.nuget.targets + +# Microsoft Azure Build Output +csx/ +*.build.csdef + +# Microsoft Azure Emulator +ecf/ +rcf/ + +# Windows Store app package directories and files +AppPackages/ +BundleArtifacts/ +Package.StoreAssociation.xml +_pkginfo.txt +*.appx +*.appxbundle +*.appxupload + +# Visual Studio cache files +# files ending in .cache can be ignored +*.[Cc]ache +# but keep track of directories ending in .cache +!?*.[Cc]ache/ + +# Others +ClientBin/ +~$* +*~ +*.dbmdl +*.dbproj.schemaview +*.jfm +*.pfx +*.publishsettings +orleans.codegen.cs + +# Including strong name files can present a security risk +# (https://github.com/github/gitignore/pull/2483#issue-259490424) +#*.snk + +# Since there are multiple workflows, uncomment next line to ignore bower_components +# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622) +#bower_components/ + +# RIA/Silverlight projects +Generated_Code/ + +# Backup & report files from converting an old project file +# to a newer Visual Studio version. Backup files are not needed, +# because we have git ;-) +_UpgradeReport_Files/ +Backup*/ +UpgradeLog*.XML +UpgradeLog*.htm +ServiceFabricBackup/ +*.rptproj.bak + +# SQL Server files +*.mdf +*.ldf +*.ndf + +# Business Intelligence projects +*.rdl.data +*.bim.layout +*.bim_*.settings +*.rptproj.rsuser +*- [Bb]ackup.rdl +*- [Bb]ackup ([0-9]).rdl +*- [Bb]ackup ([0-9][0-9]).rdl + +# Microsoft Fakes +FakesAssemblies/ + +# GhostDoc plugin setting file +*.GhostDoc.xml + +# Node.js Tools for Visual Studio +.ntvs_analysis.dat +node_modules/ + +# Visual Studio 6 build log +*.plg + +# Visual Studio 6 workspace options file +*.opt + +# Visual Studio 6 auto-generated workspace file (contains which files were open etc.) +*.vbw + +# Visual Studio LightSwitch build output +**/*.HTMLClient/GeneratedArtifacts +**/*.DesktopClient/GeneratedArtifacts +**/*.DesktopClient/ModelManifest.xml +**/*.Server/GeneratedArtifacts +**/*.Server/ModelManifest.xml +_Pvt_Extensions + +# Paket dependency manager +.paket/paket.exe +paket-files/ + +# FAKE - F# Make +.fake/ + +# CodeRush personal settings +.cr/personal + +# Python Tools for Visual Studio (PTVS) +__pycache__/ +*.pyc + +# Cake - Uncomment if you are using it +# tools/** +# !tools/packages.config + +# Tabs Studio +*.tss + +# Telerik's JustMock configuration file +*.jmconfig + +# BizTalk build output +*.btp.cs +*.btm.cs +*.odx.cs +*.xsd.cs + +# OpenCover UI analysis results +OpenCover/ + +# Azure Stream Analytics local run output +ASALocalRun/ + +# MSBuild Binary and Structured Log +*.binlog + +# NVidia Nsight GPU debugger configuration file +*.nvuser + +# MFractors (Xamarin productivity tool) working folder +.mfractor/ + +# Local History for Visual Studio +.localhistory/ + +# BeatPulse healthcheck temp database +healthchecksdb + +# Backup folder for Package Reference Convert tool in Visual Studio 2017 +MigrationBackup/ + +# Ionide (cross platform F# VS Code tools) working folder +.ionide/ + +/vcproj/nsg/x64/Debug/nsg.Build.CppClean.log +/vcproj/test_recall/x64/Debug/test_recall.Build.CppClean.log +/vcproj/test_recall/test_recall.vcxproj.user +/.vs +/out/build/x64-Debug +cscope* + +build/ +build_linux/ +!.github/actions/build + +# jetbrains specific stuff +.idea/ +cmake-build-debug/ + +#python extension module ignores +python/diskannpy.egg-info/ +python/dist/ + +**/*.egg-info +wheelhouse/* +dist/* +venv*/** +*.swp + +gperftools + +# Rust +rust/target + +python/src/*.so + +compile_commands.json \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/.gitmodules b/packages/leann-backend-diskann/third_party/DiskANN/.gitmodules new file mode 100644 index 0000000..125572b --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/.gitmodules @@ -0,0 +1,3 @@ +[submodule "gperftools"] + path = gperftools + url = https://github.com/gperftools/gperftools.git diff --git a/packages/leann-backend-diskann/third_party/DiskANN/CMakeLists.txt b/packages/leann-backend-diskann/third_party/DiskANN/CMakeLists.txt new file mode 100644 index 0000000..4025861 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/CMakeLists.txt @@ -0,0 +1,563 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +# Parameters: +# +# BOOST_ROOT: +# Specify root of the Boost library if Boost cannot be auto-detected. On Windows, a fallback to a +# downloaded nuget version will be used if Boost cannot be found. +# +# DISKANN_RELEASE_UNUSED_TCMALLOC_MEMORY_AT_CHECKPOINTS: +# This is a work-in-progress feature, not completed yet. The core DiskANN library will be split into +# build-related and search-related functionality. In build-related functionality, when using tcmalloc, +# it's possible to release memory that's free but reserved by tcmalloc. Setting this to true enables +# such behavior. +# Contact for this feature: gopalrs. + + +# Some variables like MSVC are defined only after project(), so put that first. +cmake_minimum_required(VERSION 3.20) +project(diskann) + +#Set option to use tcmalloc +option(USE_TCMALLOC "Use tcmalloc from gperftools" ON) + +# set tcmalloc to false when on macos +if(APPLE) + set(USE_TCMALLOC OFF) +endif() + +option(PYBIND "Build with Python bindings" ON) + +if(PYBIND) + # Find Python + find_package(Python 3.6 COMPONENTS Interpreter Development REQUIRED) + execute_process( + COMMAND "${Python_EXECUTABLE}" -c "import pybind11; print(pybind11.get_cmake_dir())" + OUTPUT_VARIABLE pybind11_DIR + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + find_package(pybind11 CONFIG REQUIRED) + + message(STATUS "Python include dirs: ${Python_INCLUDE_DIRS}") + message(STATUS "Pybind11 include dirs: ${pybind11_INCLUDE_DIRS}") + + # Add pybind11 include directories + include_directories(SYSTEM ${pybind11_INCLUDE_DIRS} ${Python_INCLUDE_DIRS}) + + # Add compilation definitions + add_definitions(-DPYBIND11_EMBEDDED) + + # Set visibility flags + if(NOT MSVC) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden") + endif() +endif() + +set(CMAKE_STANDARD 17) +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +# if(NOT MSVC) +# set(CMAKE_CXX_COMPILER g++) +# endif() + +set(CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake;${CMAKE_MODULE_PATH}") + +# Install nuget packages for dependencies. +if (MSVC) + find_program(NUGET_EXE NAMES nuget) + + if (NOT NUGET_EXE) + message(FATAL_ERROR "Cannot find nuget command line tool.\nPlease install it from e.g. https://www.nuget.org/downloads") + endif() + + set(DISKANN_MSVC_PACKAGES_CONFIG ${CMAKE_BINARY_DIR}/packages.config) + set(DISKANN_MSVC_PACKAGES ${CMAKE_BINARY_DIR}/packages) + + message(STATUS "Invoking nuget to download Boost, OpenMP and MKL dependencies...") + configure_file(${PROJECT_SOURCE_DIR}/windows/packages.config.in ${DISKANN_MSVC_PACKAGES_CONFIG}) + exec_program(${NUGET_EXE} ARGS install \"${DISKANN_MSVC_PACKAGES_CONFIG}\" -ExcludeVersion -OutputDirectory \"${DISKANN_MSVC_PACKAGES}\") + if (RESTAPI) + set(DISKANN_MSVC_RESTAPI_PACKAGES_CONFIG ${CMAKE_BINARY_DIR}/restapi/packages.config) + configure_file(${PROJECT_SOURCE_DIR}/windows/packages_restapi.config.in ${DISKANN_MSVC_RESTAPI_PACKAGES_CONFIG}) + exec_program(${NUGET_EXE} ARGS install \"${DISKANN_MSVC_RESTAPI_PACKAGES_CONFIG}\" -ExcludeVersion -OutputDirectory \"${DISKANN_MSVC_PACKAGES}\") + endif() + message(STATUS "Finished setting up nuget dependencies") +endif() + +include_directories(${PROJECT_SOURCE_DIR}/include) + +include(FetchContent) + +if(USE_TCMALLOC) + FetchContent_Declare( + tcmalloc + GIT_REPOSITORY https://github.com/google/tcmalloc.git + GIT_TAG origin/master # or specify a particular version or commit + ) + + FetchContent_MakeAvailable(tcmalloc) +endif() + +if(NOT PYBIND) + set(DISKANN_RELEASE_UNUSED_TCMALLOC_MEMORY_AT_CHECKPOINTS ON) +endif() +# It's necessary to include tcmalloc headers only if calling into MallocExtension interface. +# For using tcmalloc in DiskANN tools, it's enough to just link with tcmalloc. +if (DISKANN_RELEASE_UNUSED_TCMALLOC_MEMORY_AT_CHECKPOINTS) + include_directories(${tcmalloc_SOURCE_DIR}/src) + if (MSVC) + include_directories(${tcmalloc_SOURCE_DIR}/src/windows) + endif() +endif() + +#OpenMP +if (MSVC) + # Do not use find_package here since it would use VisualStudio's built-in OpenMP, but MKL libraries + # refer to Intel's OpenMP. + # + # No extra settings are needed for compilation: it only needs /openmp flag which is set further below, + # in the common MSVC compiler options block. + include_directories(BEFORE "${DISKANN_MSVC_PACKAGES}/intelopenmp.devel.win/lib/native/include") + link_libraries("${DISKANN_MSVC_PACKAGES}/intelopenmp.devel.win/lib/native/win-x64/libiomp5md.lib") + + set(OPENMP_WINDOWS_RUNTIME_FILES + "${DISKANN_MSVC_PACKAGES}/intelopenmp.redist.win/runtimes/win-x64/native/libiomp5md.dll" + "${DISKANN_MSVC_PACKAGES}/intelopenmp.redist.win/runtimes/win-x64/native/libiomp5md.pdb") +elseif(APPLE) + # Check if we're building Python bindings + if(PYBIND) + # First look for PyTorch's OpenMP to avoid conflicts + execute_process( + COMMAND ${Python_EXECUTABLE} -c "import os; import torch; print(os.path.join(os.path.dirname(torch.__file__), 'lib', 'libomp.dylib'))" + RESULT_VARIABLE TORCH_PATH_RESULT + OUTPUT_VARIABLE TORCH_LIBOMP_PATH + OUTPUT_STRIP_TRAILING_WHITESPACE + ERROR_QUIET + ) + + execute_process( + COMMAND brew --prefix libomp + OUTPUT_VARIABLE LIBOMP_ROOT + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + + if(EXISTS "${TORCH_LIBOMP_PATH}") + message(STATUS "Found PyTorch's libomp: ${TORCH_LIBOMP_PATH}") + set(OpenMP_CXX_FLAGS "-Xclang -fopenmp") + set(OpenMP_C_FLAGS "-Xclang -fopenmp") + set(OpenMP_CXX_LIBRARIES "${TORCH_LIBOMP_PATH}") + set(OpenMP_C_LIBRARIES "${TORCH_LIBOMP_PATH}") + set(OpenMP_FOUND TRUE) + + include_directories(${LIBOMP_ROOT}/include) + + # Set compiler flags and link libraries + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") + link_libraries("${TORCH_LIBOMP_PATH}") + else() + message(STATUS "No PyTorch's libomp found, falling back to normal OpenMP detection") + # Fallback to normal OpenMP detection + execute_process( + COMMAND brew --prefix libomp + OUTPUT_VARIABLE LIBOMP_ROOT + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + + set(OpenMP_ROOT "${LIBOMP_ROOT}") + find_package(OpenMP) + + if (OPENMP_FOUND) + set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") + set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") + link_libraries(OpenMP::OpenMP_CXX) + else() + message(FATAL_ERROR "No OpenMP support") + endif() + endif() + else() + # Regular OpenMP setup for non-Python builds + execute_process( + COMMAND brew --prefix libomp + OUTPUT_VARIABLE LIBOMP_ROOT + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + set(OpenMP_ROOT "${LIBOMP_ROOT}") + find_package(OpenMP) + + if (OPENMP_FOUND) + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") + link_libraries(OpenMP::OpenMP_CXX) + else() + message(FATAL_ERROR "No OpenMP support") + endif() + endif() +else() + find_package(OpenMP) + + if (OPENMP_FOUND) + set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") + set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") + else() + message(FATAL_ERROR "No OpenMP support") + endif() +endif() + +# DiskANN core uses header-only libraries. Only DiskANN tools need program_options which has a linker library, +# but its size is small. Reduce number of dependent DLLs by linking statically. +if (MSVC) + set(Boost_USE_STATIC_LIBS ON) +endif() + +if(NOT MSVC) + find_package(Boost COMPONENTS program_options) +endif() + +# For Windows, fall back to nuget version if find_package didn't find it. +if (MSVC AND NOT Boost_FOUND) + set(DISKANN_BOOST_INCLUDE "${DISKANN_MSVC_PACKAGES}/boost/lib/native/include") + # Multi-threaded static library. + set(PROGRAM_OPTIONS_LIB_PATTERN "${DISKANN_MSVC_PACKAGES}/boost_program_options-vc${MSVC_TOOLSET_VERSION}/lib/native/libboost_program_options-vc${MSVC_TOOLSET_VERSION}-mt-x64-*.lib") + file(GLOB DISKANN_BOOST_PROGRAM_OPTIONS_LIB ${PROGRAM_OPTIONS_LIB_PATTERN}) + + set(PROGRAM_OPTIONS_DLIB_PATTERN "${DISKANN_MSVC_PACKAGES}/boost_program_options-vc${MSVC_TOOLSET_VERSION}/lib/native/libboost_program_options-vc${MSVC_TOOLSET_VERSION}-mt-gd-x64-*.lib") + file(GLOB DISKANN_BOOST_PROGRAM_OPTIONS_DLIB ${PROGRAM_OPTIONS_DLIB_PATTERN}) + + if (EXISTS ${DISKANN_BOOST_INCLUDE} AND EXISTS ${DISKANN_BOOST_PROGRAM_OPTIONS_LIB} AND EXISTS ${DISKANN_BOOST_PROGRAM_OPTIONS_DLIB}) + set(Boost_FOUND ON) + set(Boost_INCLUDE_DIR ${DISKANN_BOOST_INCLUDE}) + add_library(Boost::program_options STATIC IMPORTED) + set_target_properties(Boost::program_options PROPERTIES IMPORTED_LOCATION_RELEASE "${DISKANN_BOOST_PROGRAM_OPTIONS_LIB}") + set_target_properties(Boost::program_options PROPERTIES IMPORTED_LOCATION_DEBUG "${DISKANN_BOOST_PROGRAM_OPTIONS_DLIB}") + message(STATUS "Falling back to using Boost from the nuget package") + else() + message(WARNING "Couldn't find Boost. Was looking for ${DISKANN_BOOST_INCLUDE} and ${PROGRAM_OPTIONS_LIB_PATTERN}") + endif() +endif() + +if (NOT Boost_FOUND) + message(FATAL_ERROR "Couldn't find Boost dependency") +endif() + +include_directories(${Boost_INCLUDE_DIR}) + +#MKL Config +if (MSVC) + # Only the DiskANN DLL and one of the tools need MKL libraries. Additionally, only a small part of MKL is used. + # Given that and given that MKL DLLs are huge, use static linking to end up with no MKL DLL dependencies and with + # significantly smaller disk footprint. + # + # The compile options are not modified as there's already an unconditional -DMKL_ILP64 define below + # for all architectures, which is all that's needed. + set(DISKANN_MKL_INCLUDE_DIRECTORIES "${DISKANN_MSVC_PACKAGES}/intelmkl.static.win-x64/lib/native/include") + set(DISKANN_MKL_LIB_PATH "${DISKANN_MSVC_PACKAGES}/intelmkl.static.win-x64/lib/native/win-x64") + + set(DISKANN_MKL_LINK_LIBRARIES + "${DISKANN_MKL_LIB_PATH}/mkl_intel_ilp64.lib" + "${DISKANN_MKL_LIB_PATH}/mkl_core.lib" + "${DISKANN_MKL_LIB_PATH}/mkl_intel_thread.lib") +elseif(APPLE) + # no mkl on non-intel devices + find_library(ACCELERATE_LIBRARY Accelerate) + message(STATUS "Found Accelerate (${ACCELERATE_LIBRARY})") + set(DISKANN_ACCEL_LINK_OPTIONS ${ACCELERATE_LIBRARY}) + add_compile_definitions(ACCELERATE_NEW_LAPACK) +else() + # expected path for manual intel mkl installs + set(POSSIBLE_OMP_PATHS "/opt/intel/oneapi/compiler/2025.0/lib/libiomp5.so;/opt/intel/oneapi/compiler/latest/linux/compiler/lib/intel64_lin/libiomp5.so;/usr/lib/x86_64-linux-gnu/libiomp5.so;/opt/intel/lib/intel64_lin/libiomp5.so") + foreach(POSSIBLE_OMP_PATH ${POSSIBLE_OMP_PATHS}) + if (EXISTS ${POSSIBLE_OMP_PATH}) + get_filename_component(OMP_PATH ${POSSIBLE_OMP_PATH} DIRECTORY) + endif() + endforeach() + + if(NOT OMP_PATH) + message(FATAL_ERROR "Could not find Intel OMP in standard locations; use -DOMP_PATH to specify the install location for your environment") + endif() + link_directories(${OMP_PATH}) + + set(POSSIBLE_MKL_LIB_PATHS "/opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_core.so;/usr/lib/x86_64-linux-gnu/libmkl_core.so;/opt/intel/mkl/lib/intel64/libmkl_core.so") + foreach(POSSIBLE_MKL_LIB_PATH ${POSSIBLE_MKL_LIB_PATHS}) + if (EXISTS ${POSSIBLE_MKL_LIB_PATH}) + get_filename_component(MKL_PATH ${POSSIBLE_MKL_LIB_PATH} DIRECTORY) + endif() + endforeach() + + set(POSSIBLE_MKL_INCLUDE_PATHS "/opt/intel/oneapi/mkl/latest/include;/usr/include/mkl;/opt/intel/mkl/include/;") + foreach(POSSIBLE_MKL_INCLUDE_PATH ${POSSIBLE_MKL_INCLUDE_PATHS}) + if (EXISTS ${POSSIBLE_MKL_INCLUDE_PATH}) + set(MKL_INCLUDE_PATH ${POSSIBLE_MKL_INCLUDE_PATH}) + endif() + endforeach() + if(NOT MKL_PATH) + message(FATAL_ERROR "Could not find Intel MKL in standard locations; use -DMKL_PATH to specify the install location for your environment") + elseif(NOT MKL_INCLUDE_PATH) + message(FATAL_ERROR "Could not find Intel MKL in standard locations; use -DMKL_INCLUDE_PATH to specify the install location for headers for your environment") + endif() + if (EXISTS ${MKL_PATH}/libmkl_def.so.2) + set(MKL_DEF_SO ${MKL_PATH}/libmkl_def.so.2) + elseif(EXISTS ${MKL_PATH}/libmkl_def.so) + set(MKL_DEF_SO ${MKL_PATH}/libmkl_def.so) + else() + message(FATAL_ERROR "Despite finding MKL, libmkl_def.so was not found in expected locations.") + endif() + link_directories(${MKL_PATH}) + include_directories(${MKL_INCLUDE_PATH}) + + # compile flags and link libraries + # if gcc/g++ + if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU") + add_compile_options(-m64 -Wl,--no-as-needed) + endif() + if (NOT PYBIND) + link_libraries(mkl_intel_ilp64 mkl_intel_thread mkl_core iomp5 pthread m dl) + else() + # static linking for python so as to minimize customer dependency issues + if (CMAKE_BUILD_TYPE STREQUAL "Debug") + # In debug mode, use dynamic linking to ensure all symbols are available + link_libraries(mkl_intel_ilp64 mkl_intel_thread mkl_core ${MKL_DEF_SO} iomp5 pthread m dl) + else() + # In release mode, use static linking to minimize dependencies + link_libraries( + ${MKL_PATH}/libmkl_intel_ilp64.a + ${MKL_PATH}/libmkl_intel_thread.a + ${MKL_PATH}/libmkl_core.a + ${MKL_DEF_SO} + iomp5 + pthread + m + dl + ) + endif() + endif() + + add_definitions(-DMKL_ILP64) +endif() + + +# Section for tcmalloc. The DiskANN tools are always linked to tcmalloc. For Windows, they also need to +# force-include the _tcmalloc symbol for enabling tcmalloc. +# +# The DLL itself needs to be linked to tcmalloc only if DISKANN_RELEASE_UNUSED_TCMALLOC_MEMORY_AT_CHECKPOINTS +# is enabled. +if(USE_TCMALLOC) + if (MSVC) + if (NOT EXISTS "${PROJECT_SOURCE_DIR}/gperftools/gperftools.sln") + message(FATAL_ERROR "The gperftools submodule was not found. " + "Please check-out git submodules by doing 'git submodule init' followed by 'git submodule update'") + endif() + + set(TCMALLOC_LINK_LIBRARY "${PROJECT_SOURCE_DIR}/gperftools/x64/Release-Patch/libtcmalloc_minimal.lib") + set(TCMALLOC_WINDOWS_RUNTIME_FILES + "${PROJECT_SOURCE_DIR}/gperftools/x64/Release-Patch/libtcmalloc_minimal.dll" + "${PROJECT_SOURCE_DIR}/gperftools/x64/Release-Patch/libtcmalloc_minimal.pdb") + + # Tell CMake how to build the tcmalloc linker library from the submodule. + add_custom_target(build_libtcmalloc_minimal DEPENDS ${TCMALLOC_LINK_LIBRARY}) + add_custom_command(OUTPUT ${TCMALLOC_LINK_LIBRARY} + COMMAND ${CMAKE_VS_MSBUILD_COMMAND} gperftools.sln /m /nologo + /t:libtcmalloc_minimal /p:Configuration="Release-Patch" + /property:Platform="x64" + /p:PlatformToolset=v${MSVC_TOOLSET_VERSION} + /p:WindowsTargetPlatformVersion=${CMAKE_VS_WINDOWS_TARGET_PLATFORM_VERSION} + WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/gperftools) + + add_library(libtcmalloc_minimal_for_exe STATIC IMPORTED) + add_library(libtcmalloc_minimal_for_dll STATIC IMPORTED) + + set_target_properties(libtcmalloc_minimal_for_dll PROPERTIES + IMPORTED_LOCATION "${TCMALLOC_LINK_LIBRARY}") + + set_target_properties(libtcmalloc_minimal_for_exe PROPERTIES + IMPORTED_LOCATION "${TCMALLOC_LINK_LIBRARY}" + INTERFACE_LINK_OPTIONS /INCLUDE:_tcmalloc) + + # Ensure libtcmalloc_minimal is built before it's being used. + add_dependencies(libtcmalloc_minimal_for_dll build_libtcmalloc_minimal) + add_dependencies(libtcmalloc_minimal_for_exe build_libtcmalloc_minimal) + + set(DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS libtcmalloc_minimal_for_exe) + elseif(APPLE) # ! Inherited from #474, not been adjusted for TCMalloc Removal + execute_process( + COMMAND brew --prefix gperftools + OUTPUT_VARIABLE GPERFTOOLS_PREFIX + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + set(DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS "-L${GPERFTOOLS_PREFIX}/lib -ltcmalloc") + elseif(NOT PYBIND) + set(DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS "-ltcmalloc") + endif() + + if (DISKANN_RELEASE_UNUSED_TCMALLOC_MEMORY_AT_CHECKPOINTS) + add_definitions(-DRELEASE_UNUSED_TCMALLOC_MEMORY_AT_CHECKPOINTS) + + if (MSVC) + set(DISKANN_DLL_TCMALLOC_LINK_OPTIONS libtcmalloc_minimal_for_dll) + endif() + endif() +endif() + +if (NOT MSVC AND NOT APPLE) + set(DISKANN_ASYNC_LIB aio) +endif() + +#Main compiler/linker settings +if(MSVC) + #language options + add_compile_options(/permissive- /openmp:experimental /Zc:twoPhase- /Zc:inline /WX- /std:c++17 /Gd /W3 /MP /Zi /FC /nologo) + #code generation options + add_compile_options(/arch:AVX2 /fp:fast /fp:except- /EHsc /GS- /Gy) + #optimization options + add_compile_options(/Ot /Oy /Oi) + #path options + add_definitions(-DUSE_AVX2 -DUSE_ACCELERATED_PQ -D_WINDOWS -DNOMINMAX -DUNICODE) + # Linker options. Exclude VCOMP/VCOMPD.LIB which contain VisualStudio's version of OpenMP. + # MKL was linked against Intel's OpenMP and depends on the corresponding DLL. + add_link_options(/NODEFAULTLIB:VCOMP.LIB /NODEFAULTLIB:VCOMPD.LIB /DEBUG:FULL /OPT:REF /OPT:ICF) + + set(CMAKE_LIBRARY_OUTPUT_DIRECTORY_DEBUG ${PROJECT_SOURCE_DIR}/x64/Debug) + set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_DEBUG ${PROJECT_SOURCE_DIR}/x64/Debug) + set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY_DEBUG ${PROJECT_SOURCE_DIR}/x64/Debug) + + set(CMAKE_LIBRARY_OUTPUT_DIRECTORY_RELEASE ${PROJECT_SOURCE_DIR}/x64/Release) + set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_RELEASE ${PROJECT_SOURCE_DIR}/x64/Release) + set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY_RELEASE ${PROJECT_SOURCE_DIR}/x64/Release) +elseif(APPLE) + set(ENV{TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD} 500000000000) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -ftree-vectorize -fno-builtin-malloc -fno-builtin-calloc -fno-builtin-realloc -fno-builtin-free -Xclang -fopenmp -fopenmp-simd -funroll-loops -Wfatal-errors -Wno-inconsistent-missing-override -Wno-return-type") + set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -g -DDEBUG") + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Ofast -DNDEBUG -ftree-vectorize") + if (NOT PYBIND) + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -DNDEBUG -Ofast") + if (NOT PORTABLE) + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -mtune=native") + endif() + else() + # -Ofast is not supported in a python extension module + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -DNDEBUG -fPIC") + endif() +else() + set(ENV{TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD} 500000000000) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx2 -mfma -msse2 -ftree-vectorize -fopenmp -fopenmp-simd -funroll-loops -Wfatal-errors -DUSE_AVX2 -fPIC") + if(USE_TCMALLOC) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-builtin-malloc -fno-builtin-calloc -fno-builtin-realloc -fno-builtin-free") + endif() + set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -g -DDEBUG") + if (NOT PYBIND) + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -DNDEBUG -Ofast") + if (NOT PORTABLE) + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -march=native -mtune=native") + endif() + else() + # -Ofast is not supported in a python extension module + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -DNDEBUG") + endif() +endif() + +add_subdirectory(src) +if (NOT PYBIND) + add_subdirectory(apps) + add_subdirectory(apps/utils) +endif() + +if (UNIT_TEST) + enable_testing() + add_subdirectory(tests) +endif() + +if (MSVC) + message(STATUS "The ${PROJECT_NAME}.sln has been created, opened it from VisualStudio to build Release or Debug configurations.\n" + "Alternatively, use MSBuild to build:\n\n" + "msbuild.exe ${PROJECT_NAME}.sln /m /nologo /t:Build /p:Configuration=\"Release\" /property:Platform=\"x64\"\n") +endif() + +if (RESTAPI) + if (MSVC) + set(DISKANN_CPPRESTSDK "${DISKANN_MSVC_PACKAGES}/cpprestsdk.v142/build/native") + # expected path for apt packaged intel mkl installs + link_libraries("${DISKANN_CPPRESTSDK}/x64/lib/cpprest142_2_10.lib") + include_directories("${DISKANN_CPPRESTSDK}/include") + endif() + add_subdirectory(apps/restapi) +endif() + +include(clang-format.cmake) + +if(PYBIND) + add_subdirectory(python) + + install(TARGETS _diskannpy + DESTINATION leann_backend_diskann + COMPONENT python_modules + ) + +endif() +############################################################################### +# PROTOBUF SECTION - Corrected to use CONFIG mode explicitly +############################################################################### +set(Protobuf_USE_STATIC_LIBS OFF) + +find_package(ZLIB REQUIRED) + +find_package(Protobuf REQUIRED) + +message(STATUS "Protobuf found: ${Protobuf_VERSION}") +message(STATUS "Protobuf include dirs: ${Protobuf_INCLUDE_DIRS}") +message(STATUS "Protobuf libraries: ${Protobuf_LIBRARIES}") +message(STATUS "Protobuf protoc executable: ${Protobuf_PROTOC_EXECUTABLE}") + +include_directories(${Protobuf_INCLUDE_DIRS}) + +set(PROTO_FILE "${CMAKE_CURRENT_SOURCE_DIR}/../embedding.proto") +protobuf_generate_cpp(PROTO_SRCS PROTO_HDRS ${PROTO_FILE}) +set(generated_proto_sources ${PROTO_SRCS}) + + +add_library(proto_embeddings STATIC ${generated_proto_sources}) +target_link_libraries(proto_embeddings PUBLIC protobuf::libprotobuf) +target_include_directories(proto_embeddings PUBLIC + ${CMAKE_CURRENT_BINARY_DIR} + ${Protobuf_INCLUDE_DIRS} +) + +target_link_libraries(diskann PRIVATE proto_embeddings protobuf::libprotobuf) +target_include_directories(diskann PRIVATE + ${CMAKE_CURRENT_BINARY_DIR} + ${Protobuf_INCLUDE_DIRS} +) + +target_link_libraries(diskann_s PRIVATE proto_embeddings protobuf::libprotobuf) +target_include_directories(diskann_s PRIVATE + ${CMAKE_CURRENT_BINARY_DIR} + ${Protobuf_INCLUDE_DIRS} +) + + +############################################################################### +# ZEROMQ SECTION - REQUIRED +############################################################################### + +find_package(ZeroMQ QUIET) +if(NOT ZeroMQ_FOUND) + find_path(ZeroMQ_INCLUDE_DIR zmq.h) + find_library(ZeroMQ_LIBRARY zmq) + if(ZeroMQ_INCLUDE_DIR AND ZeroMQ_LIBRARY) + set(ZeroMQ_FOUND TRUE) + endif() +endif() + +if(ZeroMQ_FOUND) + message(STATUS "Found ZeroMQ: ${ZeroMQ_LIBRARY}") + include_directories(${ZeroMQ_INCLUDE_DIR}) + target_link_libraries(diskann PRIVATE ${ZeroMQ_LIBRARY}) + target_link_libraries(diskann_s PRIVATE ${ZeroMQ_LIBRARY}) + add_definitions(-DUSE_ZEROMQ) +else() + message(FATAL_ERROR "ZeroMQ is required but not found. Please install ZeroMQ and try again.") +endif() + +target_link_libraries(diskann ${PYBIND11_LIBRARIES}) +target_link_libraries(diskann_s ${PYBIND11_LIBRARIES}) diff --git a/packages/leann-backend-diskann/third_party/DiskANN/CMakeSettings.json b/packages/leann-backend-diskann/third_party/DiskANN/CMakeSettings.json new file mode 100644 index 0000000..af5d7b5 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/CMakeSettings.json @@ -0,0 +1,28 @@ +{ + "configurations": [ + { + "name": "x64-Release", + "generator": "Ninja", + "configurationType": "Release", + "inheritEnvironments": [ "msvc_x64" ], + "buildRoot": "${projectDir}\\out\\build\\${name}", + "installRoot": "${projectDir}\\out\\install\\${name}", + "cmakeCommandArgs": "", + "buildCommandArgs": "", + "ctestCommandArgs": "" + }, + { + "name": "WSL-GCC-Release", + "generator": "Ninja", + "configurationType": "RelWithDebInfo", + "buildRoot": "${projectDir}\\out\\build\\${name}", + "installRoot": "${projectDir}\\out\\install\\${name}", + "cmakeExecutable": "cmake", + "cmakeCommandArgs": "", + "buildCommandArgs": "", + "ctestCommandArgs": "", + "inheritEnvironments": [ "linux_x64" ], + "wslPath": "${defaultWSLPath}" + } + ] +} \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/CODE_OF_CONDUCT.md b/packages/leann-backend-diskann/third_party/DiskANN/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..f9ba8cf --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/CODE_OF_CONDUCT.md @@ -0,0 +1,9 @@ +# Microsoft Open Source Code of Conduct + +This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). + +Resources: + +- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) +- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) +- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns diff --git a/packages/leann-backend-diskann/third_party/DiskANN/CONTRIBUTING.md b/packages/leann-backend-diskann/third_party/DiskANN/CONTRIBUTING.md new file mode 100644 index 0000000..dcbf795 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/CONTRIBUTING.md @@ -0,0 +1,9 @@ +# Contributing + +This project welcomes contributions and suggestions. Most contributions require you to agree to a +Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us +the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. + +When you submit a pull request, a CLA bot will automatically determine whether you need to provide +a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions +provided by the bot. You will only need to do this once across all repos using our CLA. diff --git a/packages/leann-backend-diskann/third_party/DiskANN/Dockerfile b/packages/leann-backend-diskann/third_party/DiskANN/Dockerfile new file mode 100644 index 0000000..ea1979f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/Dockerfile @@ -0,0 +1,17 @@ +#Copyright(c) Microsoft Corporation.All rights reserved. +#Licensed under the MIT license. + +FROM ubuntu:jammy + +RUN apt update +RUN apt install -y software-properties-common +RUN add-apt-repository -y ppa:git-core/ppa +RUN apt update +RUN DEBIAN_FRONTEND=noninteractive apt install -y git make cmake g++ libaio-dev libgoogle-perftools-dev libunwind-dev clang-format libboost-dev libboost-program-options-dev libmkl-full-dev libcpprest-dev python3.10 + +WORKDIR /app +RUN git clone https://github.com/microsoft/DiskANN.git +WORKDIR /app/DiskANN +RUN mkdir build +RUN cmake -S . -B build -DCMAKE_BUILD_TYPE=Release +RUN cmake --build build -- -j diff --git a/packages/leann-backend-diskann/third_party/DiskANN/DockerfileDev b/packages/leann-backend-diskann/third_party/DiskANN/DockerfileDev new file mode 100644 index 0000000..0e95e40 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/DockerfileDev @@ -0,0 +1,17 @@ +#Copyright(c) Microsoft Corporation.All rights reserved. +#Licensed under the MIT license. + +FROM ubuntu:jammy + +RUN apt update +RUN apt install -y software-properties-common +RUN add-apt-repository -y ppa:git-core/ppa +RUN apt update +RUN DEBIAN_FRONTEND=noninteractive apt install -y git make cmake g++ libaio-dev libgoogle-perftools-dev libunwind-dev clang-format libboost-dev libboost-program-options-dev libboost-test-dev libmkl-full-dev libcpprest-dev python3.10 + +WORKDIR /app +RUN git clone https://github.com/microsoft/DiskANN.git +WORKDIR /app/DiskANN +RUN mkdir build +RUN cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DUNIT_TEST=True +RUN cmake --build build -- -j diff --git a/packages/leann-backend-diskann/third_party/DiskANN/LICENSE b/packages/leann-backend-diskann/third_party/DiskANN/LICENSE new file mode 100644 index 0000000..b7a909e --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/LICENSE @@ -0,0 +1,23 @@ + DiskANN + + MIT License + + Copyright (c) Microsoft Corporation. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE diff --git a/packages/leann-backend-diskann/third_party/DiskANN/MANIFEST.in b/packages/leann-backend-diskann/third_party/DiskANN/MANIFEST.in new file mode 100644 index 0000000..0735c27 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/MANIFEST.in @@ -0,0 +1,12 @@ +include MANIFEST.in +include *.txt +include *.md +include setup.py +include pyproject.toml +include *.cmake +recursive-include gperftools * +recursive-include include * +recursive-include python * +recursive-include windows * +prune python/tests +recursive-include src * diff --git a/packages/leann-backend-diskann/third_party/DiskANN/README.md b/packages/leann-backend-diskann/third_party/DiskANN/README.md new file mode 100644 index 0000000..44f4c27 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/README.md @@ -0,0 +1,135 @@ +# DiskANN + +[![DiskANN Main](https://github.com/microsoft/DiskANN/actions/workflows/push-test.yml/badge.svg?branch=main)](https://github.com/microsoft/DiskANN/actions/workflows/push-test.yml) +[![PyPI version](https://img.shields.io/pypi/v/diskannpy.svg)](https://pypi.org/project/diskannpy/) +[![Downloads shield](https://pepy.tech/badge/diskannpy)](https://pepy.tech/project/diskannpy) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) + +[![DiskANN Paper](https://img.shields.io/badge/Paper-NeurIPS%3A_DiskANN-blue)](https://papers.nips.cc/paper/9527-rand-nsg-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node.pdf) +[![DiskANN Paper](https://img.shields.io/badge/Paper-Arxiv%3A_Fresh--DiskANN-blue)](https://arxiv.org/abs/2105.09613) +[![DiskANN Paper](https://img.shields.io/badge/Paper-Filtered--DiskANN-blue)](https://harsha-simhadri.org/pubs/Filtered-DiskANN23.pdf) + + +DiskANN is a suite of scalable, accurate and cost-effective approximate nearest neighbor search algorithms for large-scale vector search that support real-time changes and simple filters. +This code is based on ideas from the [DiskANN](https://papers.nips.cc/paper/9527-rand-nsg-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node.pdf), [Fresh-DiskANN](https://arxiv.org/abs/2105.09613) and the [Filtered-DiskANN](https://harsha-simhadri.org/pubs/Filtered-DiskANN23.pdf) papers with further improvements. +This code forked off from [code for NSG](https://github.com/ZJULearning/nsg) algorithm. + +This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). +For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or +contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. + +See [guidelines](CONTRIBUTING.md) for contributing to this project. + +## Linux build: + +Install the following packages through apt-get + +```bash +sudo apt install make cmake g++ libaio-dev libgoogle-perftools-dev clang-format libboost-all-dev +``` + +### Install Intel MKL +#### Ubuntu 20.04 or newer +```bash +sudo apt install libmkl-full-dev +``` + +#### Earlier versions of Ubuntu +Install Intel MKL either by downloading the [oneAPI MKL installer](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html) or using [apt](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) (we tested with build 2019.4-070 and 2022.1.2.146). + +``` +# OneAPI MKL Installer +wget https://registrationcenter-download.intel.com/akdlm/irc_nas/18487/l_BaseKit_p_2022.1.2.146.sh +sudo sh l_BaseKit_p_2022.1.2.146.sh -a --components intel.oneapi.lin.mkl.devel --action install --eula accept -s +``` + +### Build +```bash +mkdir build && cd build && cmake -DCMAKE_BUILD_TYPE=Release .. && make -j +``` + +## Windows build: + +The Windows version has been tested with Enterprise editions of Visual Studio 2022, 2019 and 2017. It should work with the Community and Professional editions as well without any changes. + +**Prerequisites:** + +* CMake 3.15+ (available in VisualStudio 2019+ or from https://cmake.org) +* NuGet.exe (install from https://www.nuget.org/downloads) + * The build script will use NuGet to get MKL, OpenMP and Boost packages. +* DiskANN git repository checked out together with submodules. To check out submodules after git clone: +``` +git submodule init +git submodule update +``` + +* Environment variables: + * [optional] If you would like to override the Boost library listed in windows/packages.config.in, set BOOST_ROOT to your Boost folder. + +**Build steps:** +* Open the "x64 Native Tools Command Prompt for VS 2019" (or corresponding version) and change to DiskANN folder +* Create a "build" directory inside it +* Change to the "build" directory and run +``` +cmake .. +``` +OR for Visual Studio 2017 and earlier: +``` +\cmake .. +``` +**This will create a diskann.sln solution**. Now you can: + +- Open it from VisualStudio and build either Release or Debug configuration. +- `\cmake --build build` +- Use MSBuild: +``` +msbuild.exe diskann.sln /m /nologo /t:Build /p:Configuration="Release" /property:Platform="x64" +``` + +* This will also build gperftools submodule for libtcmalloc_minimal dependency. +* Generated binaries are stored in the x64/Release or x64/Debug directories. + +## macOS Build + +### Prerequisites +* Apple Silicon. The code should still work on Intel-based Macs, but there are no guarantees. +* macOS >= 12.0 +* XCode Command Line Tools (install with `xcode-select --install`) +* [homebrew](https://brew.sh/) + +### Install Required Packages +```zsh +brew install cmake +brew install boost +brew install gperftools +brew install libomp +``` + +### Build DiskANN +```zsh +# same as ubuntu instructions +mkdir build && cd build && cmake -DCMAKE_BUILD_TYPE=Release .. && make -j +``` + +## Usage: + +Please see the following pages on using the compiled code: + +- [Commandline interface for building and search SSD based indices](workflows/SSD_index.md) +- [Commandline interface for building and search in memory indices](workflows/in_memory_index.md) +- [Commandline examples for using in-memory streaming indices](workflows/dynamic_index.md) +- [Commandline interface for building and search in memory indices with label data and filters](workflows/filtered_in_memory.md) +- [Commandline interface for building and search SSD based indices with label data and filters](workflows/filtered_ssd_index.md) +- [diskannpy - DiskANN as a python extension module](python/README.md) + +Please cite this software in your work as: + +``` +@misc{diskann-github, + author = {Simhadri, Harsha Vardhan and Krishnaswamy, Ravishankar and Srinivasa, Gopal and Subramanya, Suhas Jayaram and Antonijevic, Andrija and Pryce, Dax and Kaczynski, David and Williams, Shane and Gollapudi, Siddarth and Sivashankar, Varun and Karia, Neel and Singh, Aditi and Jaiswal, Shikhar and Mahapatro, Neelam and Adams, Philip and Tower, Bryan and Patel, Yash}}, + title = {{DiskANN: Graph-structured Indices for Scalable, Fast, Fresh and Filtered Approximate Nearest Neighbor Search}}, + url = {https://github.com/Microsoft/DiskANN}, + version = {0.6.1}, + year = {2023} +} +``` diff --git a/packages/leann-backend-diskann/third_party/DiskANN/SECURITY.md b/packages/leann-backend-diskann/third_party/DiskANN/SECURITY.md new file mode 100644 index 0000000..f7b8998 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/SECURITY.md @@ -0,0 +1,41 @@ + + +## Security + +Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). + +If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below. + +## Reporting Security Issues + +**Please do not report security vulnerabilities through public GitHub issues.** + +Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report). + +If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc). + +You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). + +Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: + + * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) + * Full paths of source file(s) related to the manifestation of the issue + * The location of the affected source code (tag/branch/commit or direct URL) + * Any special configuration required to reproduce the issue + * Step-by-step instructions to reproduce the issue + * Proof-of-concept or exploit code (if possible) + * Impact of the issue, including how an attacker might exploit the issue + +This information will help us triage your report more quickly. + +If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://microsoft.com/msrc/bounty) page for more details about our active programs. + +## Preferred Languages + +We prefer all communications to be in English. + +## Policy + +Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd). + + \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/CMakeLists.txt b/packages/leann-backend-diskann/third_party/DiskANN/apps/CMakeLists.txt new file mode 100644 index 0000000..e42c0b6 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/CMakeLists.txt @@ -0,0 +1,42 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_COMPILE_WARNING_AS_ERROR ON) + +add_executable(build_memory_index build_memory_index.cpp) +target_link_libraries(build_memory_index ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} Boost::program_options) + +add_executable(build_stitched_index build_stitched_index.cpp) +target_link_libraries(build_stitched_index ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} Boost::program_options) + +add_executable(search_memory_index search_memory_index.cpp) +target_link_libraries(search_memory_index ${PROJECT_NAME} ${DISKANN_ASYNC_LIB} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} Boost::program_options) + +add_executable(build_disk_index build_disk_index.cpp) +target_link_libraries(build_disk_index ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} ${DISKANN_ASYNC_LIB} Boost::program_options) + +add_executable(search_disk_index search_disk_index.cpp) +target_link_libraries(search_disk_index ${PROJECT_NAME} ${DISKANN_ASYNC_LIB} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} Boost::program_options) + +add_executable(range_search_disk_index range_search_disk_index.cpp) +target_link_libraries(range_search_disk_index ${PROJECT_NAME} ${DISKANN_ASYNC_LIB} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} Boost::program_options) + +add_executable(test_streaming_scenario test_streaming_scenario.cpp) +target_link_libraries(test_streaming_scenario ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} Boost::program_options) + +add_executable(test_insert_deletes_consolidate test_insert_deletes_consolidate.cpp) +target_link_libraries(test_insert_deletes_consolidate ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} Boost::program_options) + +if (NOT MSVC) + install(TARGETS build_memory_index + build_stitched_index + search_memory_index + build_disk_index + search_disk_index + range_search_disk_index + test_streaming_scenario + test_insert_deletes_consolidate + RUNTIME + ) +endif() diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/build_disk_index.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/build_disk_index.cpp new file mode 100644 index 0000000..f48b617 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/build_disk_index.cpp @@ -0,0 +1,191 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include + +#include "utils.h" +#include "disk_utils.h" +#include "math_utils.h" +#include "index.h" +#include "partition.h" +#include "program_options_utils.hpp" + +namespace po = boost::program_options; + +int main(int argc, char **argv) +{ + std::string data_type, dist_fn, data_path, index_path_prefix, codebook_prefix, label_file, universal_label, + label_type; + uint32_t num_threads, R, L, disk_PQ, build_PQ, QD, Lf, filter_threshold; + float B, M; + bool append_reorder_data = false; + bool use_opq = false; + + po::options_description desc{ + program_options_utils::make_program_description("build_disk_index", "Build a disk-based index.")}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + + // Required parameters + po::options_description required_configs("Required"); + required_configs.add_options()("data_type", po::value(&data_type)->required(), + program_options_utils::DATA_TYPE_DESCRIPTION); + required_configs.add_options()("dist_fn", po::value(&dist_fn)->required(), + program_options_utils::DISTANCE_FUNCTION_DESCRIPTION); + required_configs.add_options()("index_path_prefix", po::value(&index_path_prefix)->required(), + program_options_utils::INDEX_PATH_PREFIX_DESCRIPTION); + required_configs.add_options()("data_path", po::value(&data_path)->required(), + program_options_utils::INPUT_DATA_PATH); + required_configs.add_options()("search_DRAM_budget,B", po::value(&B)->required(), + "DRAM budget in GB for searching the index to set the " + "compressed level for data while search happens"); + required_configs.add_options()("build_DRAM_budget,M", po::value(&M)->required(), + "DRAM budget in GB for building the index"); + + // Optional parameters + po::options_description optional_configs("Optional"); + optional_configs.add_options()("num_threads,T", + po::value(&num_threads)->default_value(omp_get_num_procs()), + program_options_utils::NUMBER_THREADS_DESCRIPTION); + optional_configs.add_options()("max_degree,R", po::value(&R)->default_value(64), + program_options_utils::MAX_BUILD_DEGREE); + optional_configs.add_options()("Lbuild,L", po::value(&L)->default_value(100), + program_options_utils::GRAPH_BUILD_COMPLEXITY); + optional_configs.add_options()("QD", po::value(&QD)->default_value(0), + " Quantized Dimension for compression"); + optional_configs.add_options()("codebook_prefix", po::value(&codebook_prefix)->default_value(""), + "Path prefix for pre-trained codebook"); + optional_configs.add_options()("PQ_disk_bytes", po::value(&disk_PQ)->default_value(0), + "Number of bytes to which vectors should be compressed " + "on SSD; 0 for no compression"); + optional_configs.add_options()("append_reorder_data", po::bool_switch()->default_value(false), + "Include full precision data in the index. Use only in " + "conjuction with compressed data on SSD."); + optional_configs.add_options()("build_PQ_bytes", po::value(&build_PQ)->default_value(0), + program_options_utils::BUIlD_GRAPH_PQ_BYTES); + optional_configs.add_options()("use_opq", po::bool_switch()->default_value(false), + program_options_utils::USE_OPQ); + optional_configs.add_options()("label_file", po::value(&label_file)->default_value(""), + program_options_utils::LABEL_FILE); + optional_configs.add_options()("universal_label", po::value(&universal_label)->default_value(""), + program_options_utils::UNIVERSAL_LABEL); + optional_configs.add_options()("FilteredLbuild", po::value(&Lf)->default_value(0), + program_options_utils::FILTERED_LBUILD); + optional_configs.add_options()("filter_threshold,F", po::value(&filter_threshold)->default_value(0), + "Threshold to break up the existing nodes to generate new graph " + "internally where each node has a maximum F labels."); + optional_configs.add_options()("label_type", po::value(&label_type)->default_value("uint"), + program_options_utils::LABEL_TYPE_DESCRIPTION); + + // Merge required and optional parameters + desc.add(required_configs).add(optional_configs); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + if (vm["append_reorder_data"].as()) + append_reorder_data = true; + if (vm["use_opq"].as()) + use_opq = true; + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + bool use_filters = (label_file != "") ? true : false; + diskann::Metric metric; + if (dist_fn == std::string("l2")) + metric = diskann::Metric::L2; + else if (dist_fn == std::string("mips")) + metric = diskann::Metric::INNER_PRODUCT; + else if (dist_fn == std::string("cosine")) + metric = diskann::Metric::COSINE; + else + { + std::cout << "Error. Only l2 and mips distance functions are supported" << std::endl; + return -1; + } + + if (append_reorder_data) + { + if (disk_PQ == 0) + { + std::cout << "Error: It is not necessary to append data for reordering " + "when vectors are not compressed on disk." + << std::endl; + return -1; + } + if (data_type != std::string("float")) + { + std::cout << "Error: Appending data for reordering currently only " + "supported for float data type." + << std::endl; + return -1; + } + } + + std::string params = std::string(std::to_string(R)) + " " + std::string(std::to_string(L)) + " " + + std::string(std::to_string(B)) + " " + std::string(std::to_string(M)) + " " + + std::string(std::to_string(num_threads)) + " " + std::string(std::to_string(disk_PQ)) + " " + + std::string(std::to_string(append_reorder_data)) + " " + + std::string(std::to_string(build_PQ)) + " " + std::string(std::to_string(QD)); + + try + { + if (label_file != "" && label_type == "ushort") + { + if (data_type == std::string("int8")) + return diskann::build_disk_index(data_path.c_str(), index_path_prefix.c_str(), params.c_str(), + metric, use_opq, codebook_prefix, use_filters, label_file, + universal_label, filter_threshold, Lf); + else if (data_type == std::string("uint8")) + return diskann::build_disk_index( + data_path.c_str(), index_path_prefix.c_str(), params.c_str(), metric, use_opq, codebook_prefix, + use_filters, label_file, universal_label, filter_threshold, Lf); + else if (data_type == std::string("float")) + return diskann::build_disk_index( + data_path.c_str(), index_path_prefix.c_str(), params.c_str(), metric, use_opq, codebook_prefix, + use_filters, label_file, universal_label, filter_threshold, Lf); + else + { + diskann::cerr << "Error. Unsupported data type" << std::endl; + return -1; + } + } + else + { + if (data_type == std::string("int8")) + return diskann::build_disk_index(data_path.c_str(), index_path_prefix.c_str(), params.c_str(), + metric, use_opq, codebook_prefix, use_filters, label_file, + universal_label, filter_threshold, Lf); + else if (data_type == std::string("uint8")) + return diskann::build_disk_index(data_path.c_str(), index_path_prefix.c_str(), params.c_str(), + metric, use_opq, codebook_prefix, use_filters, label_file, + universal_label, filter_threshold, Lf); + else if (data_type == std::string("float")) + return diskann::build_disk_index(data_path.c_str(), index_path_prefix.c_str(), params.c_str(), + metric, use_opq, codebook_prefix, use_filters, label_file, + universal_label, filter_threshold, Lf); + else + { + diskann::cerr << "Error. Unsupported data type" << std::endl; + return -1; + } + } + } + catch (const std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Index build failed." << std::endl; + return -1; + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/build_memory_index.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/build_memory_index.cpp new file mode 100644 index 0000000..544e42d --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/build_memory_index.cpp @@ -0,0 +1,164 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include + +#include "index.h" +#include "utils.h" +#include "program_options_utils.hpp" + +#ifndef _WINDOWS +#include +#include +#else +#include +#endif + +#include "memory_mapper.h" +#include "ann_exception.h" +#include "index_factory.h" + +namespace po = boost::program_options; + +int main(int argc, char **argv) +{ + std::string data_type, dist_fn, data_path, index_path_prefix, label_file, universal_label, label_type; + uint32_t num_threads, R, L, Lf, build_PQ_bytes; + float alpha; + bool use_pq_build, use_opq; + + po::options_description desc{ + program_options_utils::make_program_description("build_memory_index", "Build a memory-based DiskANN index.")}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + + // Required parameters + po::options_description required_configs("Required"); + required_configs.add_options()("data_type", po::value(&data_type)->required(), + program_options_utils::DATA_TYPE_DESCRIPTION); + required_configs.add_options()("dist_fn", po::value(&dist_fn)->required(), + program_options_utils::DISTANCE_FUNCTION_DESCRIPTION); + required_configs.add_options()("index_path_prefix", po::value(&index_path_prefix)->required(), + program_options_utils::INDEX_PATH_PREFIX_DESCRIPTION); + required_configs.add_options()("data_path", po::value(&data_path)->required(), + program_options_utils::INPUT_DATA_PATH); + + // Optional parameters + po::options_description optional_configs("Optional"); + optional_configs.add_options()("num_threads,T", + po::value(&num_threads)->default_value(omp_get_num_procs()), + program_options_utils::NUMBER_THREADS_DESCRIPTION); + optional_configs.add_options()("max_degree,R", po::value(&R)->default_value(64), + program_options_utils::MAX_BUILD_DEGREE); + optional_configs.add_options()("Lbuild,L", po::value(&L)->default_value(100), + program_options_utils::GRAPH_BUILD_COMPLEXITY); + optional_configs.add_options()("alpha", po::value(&alpha)->default_value(1.2f), + program_options_utils::GRAPH_BUILD_ALPHA); + optional_configs.add_options()("build_PQ_bytes", po::value(&build_PQ_bytes)->default_value(0), + program_options_utils::BUIlD_GRAPH_PQ_BYTES); + optional_configs.add_options()("use_opq", po::bool_switch()->default_value(false), + program_options_utils::USE_OPQ); + optional_configs.add_options()("label_file", po::value(&label_file)->default_value(""), + program_options_utils::LABEL_FILE); + optional_configs.add_options()("universal_label", po::value(&universal_label)->default_value(""), + program_options_utils::UNIVERSAL_LABEL); + + optional_configs.add_options()("FilteredLbuild", po::value(&Lf)->default_value(0), + program_options_utils::FILTERED_LBUILD); + optional_configs.add_options()("label_type", po::value(&label_type)->default_value("uint"), + program_options_utils::LABEL_TYPE_DESCRIPTION); + + // Merge required and optional parameters + desc.add(required_configs).add(optional_configs); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + use_pq_build = (build_PQ_bytes > 0); + use_opq = vm["use_opq"].as(); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + diskann::Metric metric; + if (dist_fn == std::string("mips")) + { + metric = diskann::Metric::INNER_PRODUCT; + } + else if (dist_fn == std::string("l2")) + { + metric = diskann::Metric::L2; + } + else if (dist_fn == std::string("cosine")) + { + metric = diskann::Metric::COSINE; + } + else + { + std::cout << "Unsupported distance function. Currently only L2/ Inner " + "Product/Cosine are supported." + << std::endl; + return -1; + } + + try + { + diskann::cout << "Starting index build with R: " << R << " Lbuild: " << L << " alpha: " << alpha + << " #threads: " << num_threads << std::endl; + + size_t data_num, data_dim; + diskann::get_bin_metadata(data_path, data_num, data_dim); + + auto index_build_params = diskann::IndexWriteParametersBuilder(L, R) + .with_filter_list_size(Lf) + .with_alpha(alpha) + .with_saturate_graph(false) + .with_num_threads(num_threads) + .build(); + + auto filter_params = diskann::IndexFilterParamsBuilder() + .with_universal_label(universal_label) + .with_label_file(label_file) + .with_save_path_prefix(index_path_prefix) + .build(); + auto config = diskann::IndexConfigBuilder() + .with_metric(metric) + .with_dimension(data_dim) + .with_max_points(data_num) + .with_data_load_store_strategy(diskann::DataStoreStrategy::MEMORY) + .with_graph_load_store_strategy(diskann::GraphStoreStrategy::MEMORY) + .with_data_type(data_type) + .with_label_type(label_type) + .is_dynamic_index(false) + .with_index_write_params(index_build_params) + .is_enable_tags(false) + .is_use_opq(use_opq) + .is_pq_dist_build(use_pq_build) + .with_num_pq_chunks(build_PQ_bytes) + .build(); + + auto index_factory = diskann::IndexFactory(config); + auto index = index_factory.create_instance(); + index->build(data_path, data_num, filter_params); + index->save(index_path_prefix.c_str()); + index.reset(); + return 0; + } + catch (const std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Index build failed." << std::endl; + return -1; + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/build_stitched_index.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/build_stitched_index.cpp new file mode 100644 index 0000000..60e38c1 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/build_stitched_index.cpp @@ -0,0 +1,441 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include "filter_utils.h" +#include +#ifndef _WINDOWS +#include +#endif + +#include "index.h" +#include "memory_mapper.h" +#include "parameters.h" +#include "utils.h" +#include "program_options_utils.hpp" + +namespace po = boost::program_options; +typedef std::tuple>, uint64_t> stitch_indices_return_values; + +/* + * Inline function to display progress bar. + */ +inline void print_progress(double percentage) +{ + int val = (int)(percentage * 100); + int lpad = (int)(percentage * PBWIDTH); + int rpad = PBWIDTH - lpad; + printf("\r%3d%% [%.*s%*s]", val, lpad, PBSTR, rpad, ""); + fflush(stdout); +} + +/* + * Inline function to generate a random integer in a range. + */ +inline size_t random(size_t range_from, size_t range_to) +{ + std::random_device rand_dev; + std::mt19937 generator(rand_dev()); + std::uniform_int_distribution distr(range_from, range_to); + return distr(generator); +} + +/* + * function to handle command line parsing. + * + * Arguments are merely the inputs from the command line. + */ +void handle_args(int argc, char **argv, std::string &data_type, path &input_data_path, path &final_index_path_prefix, + path &label_data_path, std::string &universal_label, uint32_t &num_threads, uint32_t &R, uint32_t &L, + uint32_t &stitched_R, float &alpha) +{ + po::options_description desc{ + program_options_utils::make_program_description("build_stitched_index", "Build a stitched DiskANN index.")}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + + // Required parameters + po::options_description required_configs("Required"); + required_configs.add_options()("data_type", po::value(&data_type)->required(), + program_options_utils::DATA_TYPE_DESCRIPTION); + required_configs.add_options()("index_path_prefix", + po::value(&final_index_path_prefix)->required(), + program_options_utils::INDEX_PATH_PREFIX_DESCRIPTION); + required_configs.add_options()("data_path", po::value(&input_data_path)->required(), + program_options_utils::INPUT_DATA_PATH); + + // Optional parameters + po::options_description optional_configs("Optional"); + optional_configs.add_options()("num_threads,T", + po::value(&num_threads)->default_value(omp_get_num_procs()), + program_options_utils::NUMBER_THREADS_DESCRIPTION); + optional_configs.add_options()("max_degree,R", po::value(&R)->default_value(64), + program_options_utils::MAX_BUILD_DEGREE); + optional_configs.add_options()("Lbuild,L", po::value(&L)->default_value(100), + program_options_utils::GRAPH_BUILD_COMPLEXITY); + optional_configs.add_options()("alpha", po::value(&alpha)->default_value(1.2f), + program_options_utils::GRAPH_BUILD_ALPHA); + optional_configs.add_options()("label_file", po::value(&label_data_path)->default_value(""), + program_options_utils::LABEL_FILE); + optional_configs.add_options()("universal_label", po::value(&universal_label)->default_value(""), + program_options_utils::UNIVERSAL_LABEL); + optional_configs.add_options()("stitched_R", po::value(&stitched_R)->default_value(100), + "Degree to prune final graph down to"); + + // Merge required and optional parameters + desc.add(required_configs).add(optional_configs); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + exit(0); + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + throw; + } +} + +/* + * Custom index save to write the in-memory index to disk. + * Also writes required files for diskANN API - + * 1. labels_to_medoids + * 2. universal_label + * 3. data (redundant for static indices) + * 4. labels (redundant for static indices) + */ +void save_full_index(path final_index_path_prefix, path input_data_path, uint64_t final_index_size, + std::vector> stitched_graph, + tsl::robin_map entry_points, std::string universal_label, + path label_data_path) +{ + // aux. file 1 + auto saving_index_timer = std::chrono::high_resolution_clock::now(); + std::ifstream original_label_data_stream; + original_label_data_stream.exceptions(std::ios::badbit | std::ios::failbit); + original_label_data_stream.open(label_data_path, std::ios::binary); + std::ofstream new_label_data_stream; + new_label_data_stream.exceptions(std::ios::badbit | std::ios::failbit); + new_label_data_stream.open(final_index_path_prefix + "_labels.txt", std::ios::binary); + new_label_data_stream << original_label_data_stream.rdbuf(); + original_label_data_stream.close(); + new_label_data_stream.close(); + + // aux. file 2 + std::ifstream original_input_data_stream; + original_input_data_stream.exceptions(std::ios::badbit | std::ios::failbit); + original_input_data_stream.open(input_data_path, std::ios::binary); + std::ofstream new_input_data_stream; + new_input_data_stream.exceptions(std::ios::badbit | std::ios::failbit); + new_input_data_stream.open(final_index_path_prefix + ".data", std::ios::binary); + new_input_data_stream << original_input_data_stream.rdbuf(); + original_input_data_stream.close(); + new_input_data_stream.close(); + + // aux. file 3 + std::ofstream labels_to_medoids_writer; + labels_to_medoids_writer.exceptions(std::ios::badbit | std::ios::failbit); + labels_to_medoids_writer.open(final_index_path_prefix + "_labels_to_medoids.txt"); + for (auto iter : entry_points) + labels_to_medoids_writer << iter.first << ", " << iter.second << std::endl; + labels_to_medoids_writer.close(); + + // aux. file 4 (only if we're using a universal label) + if (universal_label != "") + { + std::ofstream universal_label_writer; + universal_label_writer.exceptions(std::ios::badbit | std::ios::failbit); + universal_label_writer.open(final_index_path_prefix + "_universal_label.txt"); + universal_label_writer << universal_label << std::endl; + universal_label_writer.close(); + } + + // main index + uint64_t index_num_frozen_points = 0, index_num_edges = 0; + uint32_t index_max_observed_degree = 0, index_entry_point = 0; + const size_t METADATA = 2 * sizeof(uint64_t) + 2 * sizeof(uint32_t); + for (auto &point_neighbors : stitched_graph) + { + index_max_observed_degree = std::max(index_max_observed_degree, (uint32_t)point_neighbors.size()); + } + + std::ofstream stitched_graph_writer; + stitched_graph_writer.exceptions(std::ios::badbit | std::ios::failbit); + stitched_graph_writer.open(final_index_path_prefix, std::ios_base::binary); + + stitched_graph_writer.write((char *)&final_index_size, sizeof(uint64_t)); + stitched_graph_writer.write((char *)&index_max_observed_degree, sizeof(uint32_t)); + stitched_graph_writer.write((char *)&index_entry_point, sizeof(uint32_t)); + stitched_graph_writer.write((char *)&index_num_frozen_points, sizeof(uint64_t)); + + size_t bytes_written = METADATA; + for (uint32_t node_point = 0; node_point < stitched_graph.size(); node_point++) + { + uint32_t current_node_num_neighbors = (uint32_t)stitched_graph[node_point].size(); + std::vector current_node_neighbors = stitched_graph[node_point]; + stitched_graph_writer.write((char *)¤t_node_num_neighbors, sizeof(uint32_t)); + bytes_written += sizeof(uint32_t); + for (const auto ¤t_node_neighbor : current_node_neighbors) + { + stitched_graph_writer.write((char *)¤t_node_neighbor, sizeof(uint32_t)); + bytes_written += sizeof(uint32_t); + } + index_num_edges += current_node_num_neighbors; + } + + if (bytes_written != final_index_size) + { + std::cerr << "Error: written bytes does not match allocated space" << std::endl; + throw; + } + + stitched_graph_writer.close(); + + std::chrono::duration saving_index_time = std::chrono::high_resolution_clock::now() - saving_index_timer; + std::cout << "Stitched graph written in " << saving_index_time.count() << " seconds" << std::endl; + std::cout << "Stitched graph average degree: " << ((float)index_num_edges) / ((float)(stitched_graph.size())) + << std::endl; + std::cout << "Stitched graph max degree: " << index_max_observed_degree << std::endl << std::endl; +} + +/* + * Unions the per-label graph indices together via the following policy: + * - any two nodes can only have at most one edge between them - + * + * Returns the "stitched" graph and its expected file size. + */ +template +stitch_indices_return_values stitch_label_indices( + path final_index_path_prefix, uint32_t total_number_of_points, label_set all_labels, + tsl::robin_map labels_to_number_of_points, + tsl::robin_map &label_entry_points, + tsl::robin_map> label_id_to_orig_id_map) +{ + size_t final_index_size = 0; + std::vector> stitched_graph(total_number_of_points); + + auto stitching_index_timer = std::chrono::high_resolution_clock::now(); + for (const auto &lbl : all_labels) + { + path curr_label_index_path(final_index_path_prefix + "_" + lbl); + std::vector> curr_label_index; + uint64_t curr_label_index_size; + uint32_t curr_label_entry_point; + + std::tie(curr_label_index, curr_label_index_size) = + diskann::load_label_index(curr_label_index_path, labels_to_number_of_points[lbl]); + curr_label_entry_point = (uint32_t)random(0, curr_label_index.size()); + label_entry_points[lbl] = label_id_to_orig_id_map[lbl][curr_label_entry_point]; + + for (uint32_t node_point = 0; node_point < curr_label_index.size(); node_point++) + { + uint32_t original_point_id = label_id_to_orig_id_map[lbl][node_point]; + for (auto &node_neighbor : curr_label_index[node_point]) + { + uint32_t original_neighbor_id = label_id_to_orig_id_map[lbl][node_neighbor]; + std::vector curr_point_neighbors = stitched_graph[original_point_id]; + if (std::find(curr_point_neighbors.begin(), curr_point_neighbors.end(), original_neighbor_id) == + curr_point_neighbors.end()) + { + stitched_graph[original_point_id].push_back(original_neighbor_id); + final_index_size += sizeof(uint32_t); + } + } + } + } + + const size_t METADATA = 2 * sizeof(uint64_t) + 2 * sizeof(uint32_t); + final_index_size += (total_number_of_points * sizeof(uint32_t) + METADATA); + + std::chrono::duration stitching_index_time = + std::chrono::high_resolution_clock::now() - stitching_index_timer; + std::cout << "stitched graph generated in memory in " << stitching_index_time.count() << " seconds" << std::endl; + + return std::make_tuple(stitched_graph, final_index_size); +} + +/* + * Applies the prune_neighbors function from src/index.cpp to + * every node in the stitched graph. + * + * This is an optional step, hence the saving of both the full + * and pruned graph. + */ +template +void prune_and_save(path final_index_path_prefix, path full_index_path_prefix, path input_data_path, + std::vector> stitched_graph, uint32_t stitched_R, + tsl::robin_map label_entry_points, std::string universal_label, + path label_data_path, uint32_t num_threads) +{ + size_t dimension, number_of_label_points; + auto diskann_cout_buffer = diskann::cout.rdbuf(nullptr); + auto std_cout_buffer = std::cout.rdbuf(nullptr); + auto pruning_index_timer = std::chrono::high_resolution_clock::now(); + + diskann::get_bin_metadata(input_data_path, number_of_label_points, dimension); + + diskann::Index index(diskann::Metric::L2, dimension, number_of_label_points, nullptr, nullptr, 0, false, false, + false, false, 0, false); + + // not searching this index, set search_l to 0 + index.load(full_index_path_prefix.c_str(), num_threads, 1); + + std::cout << "parsing labels" << std::endl; + + index.prune_all_neighbors(stitched_R, 750, 1.2); + index.save((final_index_path_prefix).c_str()); + + diskann::cout.rdbuf(diskann_cout_buffer); + std::cout.rdbuf(std_cout_buffer); + std::chrono::duration pruning_index_time = std::chrono::high_resolution_clock::now() - pruning_index_timer; + std::cout << "pruning performed in " << pruning_index_time.count() << " seconds\n" << std::endl; +} + +/* + * Delete all temporary artifacts. + * In the process of creating the stitched index, some temporary artifacts are + * created: + * 1. the separate bin files for each labels' points + * 2. the separate diskANN indices built for each label + * 3. the '.data' file created while generating the indices + */ +void clean_up_artifacts(path input_data_path, path final_index_path_prefix, label_set all_labels) +{ + for (const auto &lbl : all_labels) + { + path curr_label_input_data_path(input_data_path + "_" + lbl); + path curr_label_index_path(final_index_path_prefix + "_" + lbl); + path curr_label_index_path_data(curr_label_index_path + ".data"); + + if (std::remove(curr_label_index_path.c_str()) != 0) + throw; + if (std::remove(curr_label_input_data_path.c_str()) != 0) + throw; + if (std::remove(curr_label_index_path_data.c_str()) != 0) + throw; + } +} + +int main(int argc, char **argv) +{ + // 1. handle cmdline inputs + std::string data_type; + path input_data_path, final_index_path_prefix, label_data_path; + std::string universal_label; + uint32_t num_threads, R, L, stitched_R; + float alpha; + + auto index_timer = std::chrono::high_resolution_clock::now(); + handle_args(argc, argv, data_type, input_data_path, final_index_path_prefix, label_data_path, universal_label, + num_threads, R, L, stitched_R, alpha); + + path labels_file_to_use = final_index_path_prefix + "_label_formatted.txt"; + path labels_map_file = final_index_path_prefix + "_labels_map.txt"; + + convert_labels_string_to_int(label_data_path, labels_file_to_use, labels_map_file, universal_label); + + // 2. parse label file and create necessary data structures + std::vector point_ids_to_labels; + tsl::robin_map labels_to_number_of_points; + label_set all_labels; + + std::tie(point_ids_to_labels, labels_to_number_of_points, all_labels) = + diskann::parse_label_file(labels_file_to_use, universal_label); + + // 3. for each label, make a separate data file + tsl::robin_map> label_id_to_orig_id_map; + uint32_t total_number_of_points = (uint32_t)point_ids_to_labels.size(); + +#ifndef _WINDOWS + if (data_type == "uint8") + label_id_to_orig_id_map = diskann::generate_label_specific_vector_files( + input_data_path, labels_to_number_of_points, point_ids_to_labels, all_labels); + else if (data_type == "int8") + label_id_to_orig_id_map = diskann::generate_label_specific_vector_files( + input_data_path, labels_to_number_of_points, point_ids_to_labels, all_labels); + else if (data_type == "float") + label_id_to_orig_id_map = diskann::generate_label_specific_vector_files( + input_data_path, labels_to_number_of_points, point_ids_to_labels, all_labels); + else + throw; +#else + if (data_type == "uint8") + label_id_to_orig_id_map = diskann::generate_label_specific_vector_files_compat( + input_data_path, labels_to_number_of_points, point_ids_to_labels, all_labels); + else if (data_type == "int8") + label_id_to_orig_id_map = diskann::generate_label_specific_vector_files_compat( + input_data_path, labels_to_number_of_points, point_ids_to_labels, all_labels); + else if (data_type == "float") + label_id_to_orig_id_map = diskann::generate_label_specific_vector_files_compat( + input_data_path, labels_to_number_of_points, point_ids_to_labels, all_labels); + else + throw; +#endif + + // 4. for each created data file, create a vanilla diskANN index + if (data_type == "uint8") + diskann::generate_label_indices(input_data_path, final_index_path_prefix, all_labels, R, L, alpha, + num_threads); + else if (data_type == "int8") + diskann::generate_label_indices(input_data_path, final_index_path_prefix, all_labels, R, L, alpha, + num_threads); + else if (data_type == "float") + diskann::generate_label_indices(input_data_path, final_index_path_prefix, all_labels, R, L, alpha, + num_threads); + else + throw; + + // 5. "stitch" the indices together + std::vector> stitched_graph; + tsl::robin_map label_entry_points; + uint64_t stitched_graph_size; + + if (data_type == "uint8") + std::tie(stitched_graph, stitched_graph_size) = + stitch_label_indices(final_index_path_prefix, total_number_of_points, all_labels, + labels_to_number_of_points, label_entry_points, label_id_to_orig_id_map); + else if (data_type == "int8") + std::tie(stitched_graph, stitched_graph_size) = + stitch_label_indices(final_index_path_prefix, total_number_of_points, all_labels, + labels_to_number_of_points, label_entry_points, label_id_to_orig_id_map); + else if (data_type == "float") + std::tie(stitched_graph, stitched_graph_size) = + stitch_label_indices(final_index_path_prefix, total_number_of_points, all_labels, + labels_to_number_of_points, label_entry_points, label_id_to_orig_id_map); + else + throw; + path full_index_path_prefix = final_index_path_prefix + "_full"; + // 5a. save the stitched graph to disk + save_full_index(full_index_path_prefix, input_data_path, stitched_graph_size, stitched_graph, label_entry_points, + universal_label, labels_file_to_use); + + // 6. run a prune on the stitched index, and save to disk + if (data_type == "uint8") + prune_and_save(final_index_path_prefix, full_index_path_prefix, input_data_path, stitched_graph, + stitched_R, label_entry_points, universal_label, labels_file_to_use, num_threads); + else if (data_type == "int8") + prune_and_save(final_index_path_prefix, full_index_path_prefix, input_data_path, stitched_graph, + stitched_R, label_entry_points, universal_label, labels_file_to_use, num_threads); + else if (data_type == "float") + prune_and_save(final_index_path_prefix, full_index_path_prefix, input_data_path, stitched_graph, + stitched_R, label_entry_points, universal_label, labels_file_to_use, num_threads); + else + throw; + + std::chrono::duration index_time = std::chrono::high_resolution_clock::now() - index_timer; + std::cout << "pruned/stitched graph generated in " << index_time.count() << " seconds" << std::endl; + + clean_up_artifacts(input_data_path, final_index_path_prefix, all_labels); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/python/README.md b/packages/leann-backend-diskann/third_party/DiskANN/apps/python/README.md new file mode 100644 index 0000000..2b0bc35 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/python/README.md @@ -0,0 +1,46 @@ + + +# Integration Tests +The following tests use Python to prepare, run, verify, and tear down the rest api services. + +We do make use of the built-in `unittest` library, but that's only to take advantage of test reporting purposes. + +These are decidedly **not** _unit_ tests. These are end to end integration tests. + +## Caveats +This has only been tested or built for Linux, though we have written platform agnostic Python for the smoke test +(i.e. using `os.path.join`, etc) + +It has been tested on Python 3.9 and 3.10, but should work on Python 3.6+. + +## How to Run + +First, build the DiskANN RestAPI code; see $REPOSITORY_ROOT/workflows/rest_api.md for detailed instructions. + +```bash +cd tests/python +python3 -m venv venv +source venv/bin/activate +pip install -r requirements.txt + +export DISKANN_BUILD_DIR=/path/to/your/diskann/build +python -m unittest +``` + +## Smoke Test Failed, Now What? +The smoke test written takes advantage of temporary directories that are only valid during the +lifetime of the test. The contents of these directories include: +- Randomized vectors (first in tsv, then bin form) used to build the PQFlashIndex +- The PQFlashIndex files + +It is useful to keep these around. By setting some environment variables, you can control whether an ephemeral, +temporary directory is used (and deleted on test completion), or left as an exercise for the developer to +clean up. + +The valid environment variables are: +- `DISKANN_REST_TEST_WORKING_DIR` (example: `$USER/DiskANNRestTest`) + - If this is specified, it **must exist** and **must be writeable**. Any existing files will be clobbered. +- `DISKANN_REST_SERVER` (example: `http://127.0.0.1:10067`) + - Note that if this is set, no data will be generated, nor will a server be started; it is presumed you have done + all the work in creating and starting the rest server prior to running the test and just submits requests against it. diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/python/restapi/__init__.py b/packages/leann-backend-diskann/third_party/DiskANN/apps/python/restapi/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/python/restapi/disk_ann_util.py b/packages/leann-backend-diskann/third_party/DiskANN/apps/python/restapi/disk_ann_util.py new file mode 100644 index 0000000..ec89310 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/python/restapi/disk_ann_util.py @@ -0,0 +1,67 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +import numpy as np +import os +import subprocess + + +def output_vectors( + diskann_build_path: str, + temporary_file_path: str, + vectors: np.ndarray, + timeout: int = 60 +) -> str: + vectors_as_tsv_path = os.path.join(temporary_file_path, "vectors.tsv") + with open(vectors_as_tsv_path, "w") as vectors_tsv_out: + for vector in vectors: + as_str = "\t".join((str(component) for component in vector)) + print(as_str, file=vectors_tsv_out) + # there is probably a clever way to have numpy write out C++ friendly floats, so feel free to remove this in + # favor of something more sane later + vectors_as_bin_path = os.path.join(temporary_file_path, "vectors.bin") + tsv_to_bin_path = os.path.join(diskann_build_path, "apps", "utils", "tsv_to_bin") + + number_of_points, dimensions = vectors.shape + args = [ + tsv_to_bin_path, + "float", + vectors_as_tsv_path, + vectors_as_bin_path, + str(dimensions), + str(number_of_points) + ] + completed = subprocess.run(args, timeout=timeout) + if completed.returncode != 0: + raise Exception(f"Unable to convert tsv to binary using tsv_to_bin, completed_process: {completed}") + return vectors_as_bin_path + + +def build_ssd_index( + diskann_build_path: str, + temporary_file_path: str, + vectors: np.ndarray, + per_process_timeout: int = 60 # this may not be long enough if you're doing something larger +): + vectors_as_bin_path = output_vectors(diskann_build_path, temporary_file_path, vectors, timeout=per_process_timeout) + + ssd_builder_path = os.path.join(diskann_build_path, "apps", "build_disk_index") + args = [ + ssd_builder_path, + "--data_type", "float", + "--dist_fn", "l2", + "--data_path", vectors_as_bin_path, + "--index_path_prefix", os.path.join(temporary_file_path, "smoke_test"), + "-R", "64", + "-L", "100", + "--search_DRAM_budget", "1", + "--build_DRAM_budget", "1", + "--num_threads", "1", + "--PQ_disk_bytes", "0" + ] + completed = subprocess.run(args, timeout=per_process_timeout) + + if completed.returncode != 0: + command_run = " ".join(args) + raise Exception(f"Unable to build a disk index with the command: '{command_run}'\ncompleted_process: {completed}\nstdout: {completed.stdout}\nstderr: {completed.stderr}") + # index is now built inside of temporary_file_path diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/range_search_disk_index.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/range_search_disk_index.cpp new file mode 100644 index 0000000..3167572 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/range_search_disk_index.cpp @@ -0,0 +1,379 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include + +#include "index.h" +#include "disk_utils.h" +#include "math_utils.h" +#include "memory_mapper.h" +#include "pq_flash_index.h" +#include "partition.h" +#include "timer.h" +#include "program_options_utils.hpp" + +#ifndef _WINDOWS +#include +#include +#include +#include "linux_aligned_file_reader.h" +#else +#ifdef USE_BING_INFRA +#include "bing_aligned_file_reader.h" +#else +#include "windows_aligned_file_reader.h" +#endif +#endif + +namespace po = boost::program_options; + +#define WARMUP false + +void print_stats(std::string category, std::vector percentiles, std::vector results) +{ + diskann::cout << std::setw(20) << category << ": " << std::flush; + for (uint32_t s = 0; s < percentiles.size(); s++) + { + diskann::cout << std::setw(8) << percentiles[s] << "%"; + } + diskann::cout << std::endl; + diskann::cout << std::setw(22) << " " << std::flush; + for (uint32_t s = 0; s < percentiles.size(); s++) + { + diskann::cout << std::setw(9) << results[s]; + } + diskann::cout << std::endl; +} + +template +int search_disk_index(diskann::Metric &metric, const std::string &index_path_prefix, const std::string &query_file, + std::string >_file, const uint32_t num_threads, const float search_range, + const uint32_t beamwidth, const uint32_t num_nodes_to_cache, const std::vector &Lvec) +{ + std::string pq_prefix = index_path_prefix + "_pq"; + std::string disk_index_file = index_path_prefix + "_disk.index"; + std::string warmup_query_file = index_path_prefix + "_sample_data.bin"; + + diskann::cout << "Search parameters: #threads: " << num_threads << ", "; + if (beamwidth <= 0) + diskann::cout << "beamwidth to be optimized for each L value" << std::endl; + else + diskann::cout << " beamwidth: " << beamwidth << std::endl; + + // load query bin + T *query = nullptr; + std::vector> groundtruth_ids; + size_t query_num, query_dim, query_aligned_dim, gt_num; + diskann::load_aligned_bin(query_file, query, query_num, query_dim, query_aligned_dim); + + bool calc_recall_flag = false; + if (gt_file != std::string("null") && file_exists(gt_file)) + { + diskann::load_range_truthset(gt_file, groundtruth_ids, + gt_num); // use for range search type of truthset + // diskann::prune_truthset_for_range(gt_file, search_range, + // groundtruth_ids, gt_num); // use for traditional truthset + if (gt_num != query_num) + { + diskann::cout << "Error. Mismatch in number of queries and ground truth data" << std::endl; + return -1; + } + calc_recall_flag = true; + } + + std::shared_ptr reader = nullptr; +#ifdef _WINDOWS +#ifndef USE_BING_INFRA + reader.reset(new WindowsAlignedFileReader()); +#else + reader.reset(new diskann::BingAlignedFileReader()); +#endif +#else + reader.reset(new LinuxAlignedFileReader()); +#endif + + std::unique_ptr> _pFlashIndex( + new diskann::PQFlashIndex(reader, metric)); + + int res = _pFlashIndex->load(num_threads, index_path_prefix.c_str()); + + if (res != 0) + { + return res; + } + // cache bfs levels + std::vector node_list; + diskann::cout << "Caching " << num_nodes_to_cache << " BFS nodes around medoid(s)" << std::endl; + _pFlashIndex->cache_bfs_levels(num_nodes_to_cache, node_list); + // _pFlashIndex->generate_cache_list_from_sample_queries( + // warmup_query_file, 15, 6, num_nodes_to_cache, num_threads, + // node_list); + _pFlashIndex->load_cache_list(node_list); + node_list.clear(); + node_list.shrink_to_fit(); + + omp_set_num_threads(num_threads); + + uint64_t warmup_L = 20; + uint64_t warmup_num = 0, warmup_dim = 0, warmup_aligned_dim = 0; + T *warmup = nullptr; + + if (WARMUP) + { + if (file_exists(warmup_query_file)) + { + diskann::load_aligned_bin(warmup_query_file, warmup, warmup_num, warmup_dim, warmup_aligned_dim); + } + else + { + warmup_num = (std::min)((uint32_t)150000, (uint32_t)15000 * num_threads); + warmup_dim = query_dim; + warmup_aligned_dim = query_aligned_dim; + diskann::alloc_aligned(((void **)&warmup), warmup_num * warmup_aligned_dim * sizeof(T), 8 * sizeof(T)); + std::memset(warmup, 0, warmup_num * warmup_aligned_dim * sizeof(T)); + std::random_device rd; + std::mt19937 gen(rd()); + std::uniform_int_distribution<> dis(-128, 127); + for (uint32_t i = 0; i < warmup_num; i++) + { + for (uint32_t d = 0; d < warmup_dim; d++) + { + warmup[i * warmup_aligned_dim + d] = (T)dis(gen); + } + } + } + diskann::cout << "Warming up index... " << std::flush; + std::vector warmup_result_ids_64(warmup_num, 0); + std::vector warmup_result_dists(warmup_num, 0); + +#pragma omp parallel for schedule(dynamic, 1) + for (int64_t i = 0; i < (int64_t)warmup_num; i++) + { + _pFlashIndex->cached_beam_search(warmup + (i * warmup_aligned_dim), 1, warmup_L, + warmup_result_ids_64.data() + (i * 1), + warmup_result_dists.data() + (i * 1), 4); + } + diskann::cout << "..done" << std::endl; + } + + diskann::cout.setf(std::ios_base::fixed, std::ios_base::floatfield); + diskann::cout.precision(2); + + std::string recall_string = "Recall@rng=" + std::to_string(search_range); + diskann::cout << std::setw(6) << "L" << std::setw(12) << "Beamwidth" << std::setw(16) << "QPS" << std::setw(16) + << "Mean Latency" << std::setw(16) << "99.9 Latency" << std::setw(16) << "Mean IOs" << std::setw(16) + << "CPU (s)"; + if (calc_recall_flag) + { + diskann::cout << std::setw(16) << recall_string << std::endl; + } + else + diskann::cout << std::endl; + diskann::cout << "===============================================================" + "===========================================" + << std::endl; + + std::vector>> query_result_ids(Lvec.size()); + + uint32_t optimized_beamwidth = 2; + uint32_t max_list_size = 10000; + + for (uint32_t test_id = 0; test_id < Lvec.size(); test_id++) + { + uint32_t L = Lvec[test_id]; + + if (beamwidth <= 0) + { + optimized_beamwidth = + optimize_beamwidth(_pFlashIndex, warmup, warmup_num, warmup_aligned_dim, L, optimized_beamwidth); + } + else + optimized_beamwidth = beamwidth; + + query_result_ids[test_id].clear(); + query_result_ids[test_id].resize(query_num); + + diskann::QueryStats *stats = new diskann::QueryStats[query_num]; + + auto s = std::chrono::high_resolution_clock::now(); +#pragma omp parallel for schedule(dynamic, 1) + for (int64_t i = 0; i < (int64_t)query_num; i++) + { + std::vector indices; + std::vector distances; + uint32_t res_count = + _pFlashIndex->range_search(query + (i * query_aligned_dim), search_range, L, max_list_size, indices, + distances, optimized_beamwidth, stats + i); + query_result_ids[test_id][i].reserve(res_count); + query_result_ids[test_id][i].resize(res_count); + for (uint32_t idx = 0; idx < res_count; idx++) + query_result_ids[test_id][i][idx] = (uint32_t)indices[idx]; + } + auto e = std::chrono::high_resolution_clock::now(); + std::chrono::duration diff = e - s; + auto qps = (1.0 * query_num) / (1.0 * diff.count()); + + auto mean_latency = diskann::get_mean_stats( + stats, query_num, [](const diskann::QueryStats &stats) { return stats.total_us; }); + + auto latency_999 = diskann::get_percentile_stats( + stats, query_num, 0.999, [](const diskann::QueryStats &stats) { return stats.total_us; }); + + auto mean_ios = diskann::get_mean_stats(stats, query_num, + [](const diskann::QueryStats &stats) { return stats.n_ios; }); + + double mean_cpuus = diskann::get_mean_stats( + stats, query_num, [](const diskann::QueryStats &stats) { return stats.cpu_us; }); + + double recall = 0; + double ratio_of_sums = 0; + if (calc_recall_flag) + { + recall = + diskann::calculate_range_search_recall((uint32_t)query_num, groundtruth_ids, query_result_ids[test_id]); + + uint32_t total_true_positive = 0; + uint32_t total_positive = 0; + for (uint32_t i = 0; i < query_num; i++) + { + total_true_positive += (uint32_t)query_result_ids[test_id][i].size(); + total_positive += (uint32_t)groundtruth_ids[i].size(); + } + + ratio_of_sums = (1.0 * total_true_positive) / (1.0 * total_positive); + } + + diskann::cout << std::setw(6) << L << std::setw(12) << optimized_beamwidth << std::setw(16) << qps + << std::setw(16) << mean_latency << std::setw(16) << latency_999 << std::setw(16) << mean_ios + << std::setw(16) << mean_cpuus; + if (calc_recall_flag) + { + diskann::cout << std::setw(16) << recall << "," << ratio_of_sums << std::endl; + } + else + diskann::cout << std::endl; + } + + diskann::cout << "Done searching. " << std::endl; + + diskann::aligned_free(query); + if (warmup != nullptr) + diskann::aligned_free(warmup); + return 0; +} + +int main(int argc, char **argv) +{ + std::string data_type, dist_fn, index_path_prefix, result_path_prefix, query_file, gt_file; + uint32_t num_threads, W, num_nodes_to_cache; + std::vector Lvec; + float range; + + po::options_description desc{program_options_utils::make_program_description( + "range_search_disk_index", "Searches disk DiskANN indexes using ranges")}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + + // Required parameters + po::options_description required_configs("Required"); + required_configs.add_options()("data_type", po::value(&data_type)->required(), + program_options_utils::DATA_TYPE_DESCRIPTION); + required_configs.add_options()("dist_fn", po::value(&dist_fn)->required(), + program_options_utils::DISTANCE_FUNCTION_DESCRIPTION); + required_configs.add_options()("index_path_prefix", po::value(&index_path_prefix)->required(), + program_options_utils::INDEX_PATH_PREFIX_DESCRIPTION); + required_configs.add_options()("query_file", po::value(&query_file)->required(), + program_options_utils::QUERY_FILE_DESCRIPTION); + required_configs.add_options()("search_list,L", + po::value>(&Lvec)->multitoken()->required(), + program_options_utils::SEARCH_LIST_DESCRIPTION); + required_configs.add_options()("range_threshold,K", po::value(&range)->required(), + "Number of neighbors to be returned"); + + // Optional parameters + po::options_description optional_configs("Optional"); + optional_configs.add_options()("num_threads,T", + po::value(&num_threads)->default_value(omp_get_num_procs()), + program_options_utils::NUMBER_THREADS_DESCRIPTION); + optional_configs.add_options()("gt_file", po::value(>_file)->default_value(std::string("null")), + program_options_utils::GROUND_TRUTH_FILE_DESCRIPTION); + optional_configs.add_options()("num_nodes_to_cache", po::value(&num_nodes_to_cache)->default_value(0), + program_options_utils::NUMBER_OF_NODES_TO_CACHE); + optional_configs.add_options()("beamwidth,W", po::value(&W)->default_value(2), + program_options_utils::BEAMWIDTH); + + // Merge required and optional parameters + desc.add(required_configs).add(optional_configs); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + diskann::Metric metric; + if (dist_fn == std::string("mips")) + { + metric = diskann::Metric::INNER_PRODUCT; + } + else if (dist_fn == std::string("l2")) + { + metric = diskann::Metric::L2; + } + else if (dist_fn == std::string("cosine")) + { + metric = diskann::Metric::COSINE; + } + else + { + std::cout << "Unsupported distance function. Currently only L2/ Inner " + "Product/Cosine are supported." + << std::endl; + return -1; + } + + if ((data_type != std::string("float")) && (metric == diskann::Metric::INNER_PRODUCT)) + { + std::cout << "Currently support only floating point data for Inner Product." << std::endl; + return -1; + } + + try + { + if (data_type == std::string("float")) + return search_disk_index(metric, index_path_prefix, query_file, gt_file, num_threads, range, W, + num_nodes_to_cache, Lvec); + else if (data_type == std::string("int8")) + return search_disk_index(metric, index_path_prefix, query_file, gt_file, num_threads, range, W, + num_nodes_to_cache, Lvec); + else if (data_type == std::string("uint8")) + return search_disk_index(metric, index_path_prefix, query_file, gt_file, num_threads, range, W, + num_nodes_to_cache, Lvec); + else + { + std::cerr << "Unsupported data type. Use float or int8 or uint8" << std::endl; + return -1; + } + } + catch (const std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Index search failed." << std::endl; + return -1; + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/CMakeLists.txt b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/CMakeLists.txt new file mode 100644 index 0000000..c73b427 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/CMakeLists.txt @@ -0,0 +1,40 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +set(CMAKE_CXX_STANDARD 17) + +add_executable(inmem_server inmem_server.cpp) +if(MSVC) + target_link_options(inmem_server PRIVATE /MACHINE:x64) + target_link_libraries(inmem_server debug ${CMAKE_LIBRARY_OUTPUT_DIRECTORY_DEBUG}/diskann_dll.lib Boost::program_options) + target_link_libraries(inmem_server optimized ${CMAKE_LIBRARY_OUTPUT_DIRECTORY_RELEASE}/diskann_dll.lib Boost::program_options) +else() + target_link_libraries(inmem_server ${PROJECT_NAME} aio -ltcmalloc -lboost_system -lcrypto -lssl -lcpprest Boost::program_options) +endif() + +add_executable(ssd_server ssd_server.cpp) +if(MSVC) + target_link_options(ssd_server PRIVATE /MACHINE:x64) + target_link_libraries(ssd_server debug ${CMAKE_LIBRARY_OUTPUT_DIRECTORY_DEBUG}/diskann_dll.lib Boost::program_options) + target_link_libraries(ssd_server optimized ${CMAKE_LIBRARY_OUTPUT_DIRECTORY_RELEASE}/diskann_dll.lib Boost::program_options) +else() + target_link_libraries(ssd_server ${PROJECT_NAME} aio -ltcmalloc -lboost_system -lcrypto -lssl -lcpprest Boost::program_options) +endif() + +add_executable(multiple_ssdindex_server multiple_ssdindex_server.cpp) +if(MSVC) + target_link_options(multiple_ssdindex_server PRIVATE /MACHINE:x64) + target_link_libraries(multiple_ssdindex_server debug ${CMAKE_LIBRARY_OUTPUT_DIRECTORY_DEBUG}/diskann_dll.lib Boost::program_options) + target_link_libraries(multiple_ssdindex_server optimized ${CMAKE_LIBRARY_OUTPUT_DIRECTORY_RELEASE}/diskann_dll.lib Boost::program_options) +else() + target_link_libraries(multiple_ssdindex_server ${PROJECT_NAME} aio -ltcmalloc -lboost_system -lcrypto -lssl -lcpprest Boost::program_options) +endif() + +add_executable(client client.cpp) +if(MSVC) + target_link_options(client PRIVATE /MACHINE:x64) + target_link_libraries(client debug ${CMAKE_LIBRARY_OUTPUT_DIRECTORY_DEBUG}/diskann_dll.lib Boost::program_options) + target_link_libraries(client optimized ${CMAKE_LIBRARY_OUTPUT_DIRECTORY_RELEASE}/diskann_dll.lib Boost::program_options) +else() + target_link_libraries(client ${PROJECT_NAME} -lboost_system -lcrypto -lssl -lcpprest Boost::program_options) +endif() \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/client.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/client.cpp new file mode 100644 index 0000000..fdf4414 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/client.cpp @@ -0,0 +1,124 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include + +#include +#include + +using namespace web; +using namespace web::http; +using namespace web::http::client; + +using namespace diskann; +namespace po = boost::program_options; + +template +void query_loop(const std::string &ip_addr_port, const std::string &query_file, const unsigned nq, const unsigned Ls, + const unsigned k_value) +{ + web::http::client::http_client client(U(ip_addr_port)); + + T *data; + size_t npts = 1, ndims = 128, rounded_dim = 128; + diskann::load_aligned_bin(query_file, data, npts, ndims, rounded_dim); + + for (unsigned i = 0; i < nq; ++i) + { + T *vec = data + i * rounded_dim; + web::http::http_request http_query(methods::POST); + web::json::value queryJson = web::json::value::object(); + queryJson[QUERY_ID_KEY] = i; + queryJson[K_KEY] = k_value; + queryJson[L_KEY] = Ls; + for (size_t i = 0; i < ndims; ++i) + { + queryJson[VECTOR_KEY][i] = web::json::value::number(vec[i]); + } + http_query.set_body(queryJson); + + client.request(http_query) + .then([](web::http::http_response response) -> pplx::task { + if (response.status_code() == status_codes::OK) + { + return response.extract_string(); + } + std::cerr << "Query failed" << std::endl; + return pplx::task_from_result(utility::string_t()); + }) + .then([](pplx::task previousTask) { + try + { + std::cout << previousTask.get() << std::endl; + } + catch (http_exception const &e) + { + std::wcout << e.what() << std::endl; + } + }) + .wait(); + } +} + +int main(int argc, char *argv[]) +{ + std::string data_type, query_file, address; + uint32_t num_queries; + uint32_t l_search, k_value; + + po::options_description desc{"Arguments"}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + desc.add_options()("data_type", po::value(&data_type)->required(), "data type "); + desc.add_options()("address", po::value(&address)->required(), "Web server address"); + desc.add_options()("query_file", po::value(&query_file)->required(), + "File containing the queries to search"); + desc.add_options()("num_queries,Q", po::value(&num_queries)->required(), + "Number of queries to search"); + desc.add_options()("l_search", po::value(&l_search)->required(), "Value of L"); + desc.add_options()("k_value,K", po::value(&k_value)->default_value(10), "Value of K (default 10)"); + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << std::endl; + return -1; + } + + if (data_type == std::string("float")) + { + query_loop(address, query_file, num_queries, l_search, k_value); + } + else if (data_type == std::string("int8")) + { + query_loop(address, query_file, num_queries, l_search, k_value); + } + else if (data_type == std::string("uint8")) + { + query_loop(address, query_file, num_queries, l_search, k_value); + } + else + { + std::cerr << "Unsupported type " << argv[2] << std::endl; + return -1; + } + + return 0; +} \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/inmem_server.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/inmem_server.cpp new file mode 100644 index 0000000..11da541 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/inmem_server.cpp @@ -0,0 +1,138 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include + +#include + +using namespace diskann; +namespace po = boost::program_options; + +std::unique_ptr g_httpServer(nullptr); +std::vector> g_inMemorySearch; + +void setup(const utility::string_t &address, const std::string &typestring) +{ + web::http::uri_builder uriBldr(address); + auto uri = uriBldr.to_uri(); + + std::cout << "Attempting to start server on " << uri.to_string() << std::endl; + + g_httpServer = std::unique_ptr(new Server(uri, g_inMemorySearch, typestring)); + std::cout << "Created a server object" << std::endl; + + g_httpServer->open().wait(); + ucout << U"Listening for requests on: " << address << std::endl; +} + +void teardown(const utility::string_t &address) +{ + g_httpServer->close().wait(); +} + +int main(int argc, char *argv[]) +{ + std::string data_type, index_file, data_file, address, dist_fn, tags_file; + uint32_t num_threads; + uint32_t l_search; + + po::options_description desc{"Arguments"}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + desc.add_options()("data_type", po::value(&data_type)->required(), "data type "); + desc.add_options()("address", po::value(&address)->required(), "Web server address"); + desc.add_options()("data_file", po::value(&data_file)->required(), + "File containing the data found in the index"); + desc.add_options()("index_path_prefix", po::value(&index_file)->required(), + "Path prefix for saving index file components"); + desc.add_options()("num_threads,T", po::value(&num_threads)->required(), + "Number of threads used for building index"); + desc.add_options()("l_search", po::value(&l_search)->required(), "Value of L"); + desc.add_options()("dist_fn", po::value(&dist_fn)->default_value("l2"), + "distance function "); + desc.add_options()("tags_file", po::value(&tags_file)->default_value(std::string()), + "Tags file location"); + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << std::endl; + return -1; + } + diskann::Metric metric; + if (dist_fn == std::string("l2")) + metric = diskann::Metric::L2; + else if (dist_fn == std::string("mips")) + metric = diskann::Metric::INNER_PRODUCT; + else + { + std::cout << "Error. Only l2 and mips distance functions are supported" << std::endl; + return -1; + } + + if (data_type == std::string("float")) + { + auto searcher = std::unique_ptr( + new diskann::InMemorySearch(data_file, index_file, tags_file, metric, num_threads, l_search)); + g_inMemorySearch.push_back(std::move(searcher)); + } + else if (data_type == std::string("int8")) + { + auto searcher = std::unique_ptr( + new diskann::InMemorySearch(data_file, index_file, tags_file, metric, num_threads, l_search)); + g_inMemorySearch.push_back(std::move(searcher)); + } + else if (data_type == std::string("uint8")) + { + auto searcher = std::unique_ptr( + new diskann::InMemorySearch(data_file, index_file, tags_file, metric, num_threads, l_search)); + g_inMemorySearch.push_back(std::move(searcher)); + } + else + { + std::cerr << "Unsupported data type " << argv[2] << std::endl; + } + + while (1) + { + try + { + setup(address, data_type); + std::cout << "Type 'exit' (case-sensitive) to exit" << std::endl; + std::string line; + std::getline(std::cin, line); + if (line == "exit") + { + teardown(address); + g_httpServer->close().wait(); + exit(0); + } + } + catch (const std::exception &ex) + { + std::cerr << "Exception occurred: " << ex.what() << std::endl; + std::cerr << "Restarting HTTP server"; + teardown(address); + } + catch (...) + { + std::cerr << "Unknown exception occurreed" << std::endl; + std::cerr << "Restarting HTTP server"; + teardown(address); + } + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/main.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/main.cpp new file mode 100644 index 0000000..cb48d67 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/main.cpp @@ -0,0 +1,83 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include + +std::unique_ptr g_httpServer(nullptr); +std::unique_ptr g_inMemorySearch(nullptr); + +void setup(const utility::string_t &address) +{ + web::http::uri_builder uriBldr(address); + auto uri = uriBldr.to_uri(); + + std::wcout << L"Attempting to start server on " << uri.to_string() << std::endl; + + g_httpServer = std::unique_ptr(new Server(uri, g_inMemorySearch)); + g_httpServer->open().wait(); + + ucout << U"Listening for requests on: " << address << std::endl; +} + +void teardown(const utility::string_t &address) +{ + g_httpServer->close().wait(); +} + +void loadIndex(const char *indexFile, const char *baseFile, const char *idsFile) +{ + auto nsgSearch = new diskann::InMemorySearch(baseFile, indexFile, idsFile, diskann::L2); + g_inMemorySearch = std::unique_ptr(nsgSearch); +} + +std::wstring getHostingAddress(const char *hostNameAndPort) +{ + wchar_t buffer[4096]; + mbstowcs_s(nullptr, buffer, sizeof(buffer) / sizeof(buffer[0]), hostNameAndPort, + sizeof(buffer) / sizeof(buffer[0])); + return std::wstring(buffer); +} + +int main(int argc, char *argv[]) +{ + if (argc != 5) + { + std::cout << "Usage: nsg_server " + " " + << std::endl; + exit(1); + } + + auto address = getHostingAddress(argv[1]); + loadIndex(argv[2], argv[3], argv[4]); + while (1) + { + try + { + setup(address); + std::cout << "Type 'exit' (case-sensitive) to exit" << std::endl; + std::string line; + std::getline(std::cin, line); + if (line == "exit") + { + teardown(address); + exit(0); + } + } + catch (const std::exception &ex) + { + std::cerr << "Exception occurred: " << ex.what() << std::endl; + std::cerr << "Restarting HTTP server"; + teardown(address); + } + catch (...) + { + std::cerr << "Unknown exception occurreed" << std::endl; + std::cerr << "Restarting HTTP server"; + teardown(address); + } + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/multiple_ssdindex_server.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/multiple_ssdindex_server.cpp new file mode 100644 index 0000000..89cb06f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/multiple_ssdindex_server.cpp @@ -0,0 +1,182 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +using namespace diskann; +namespace po = boost::program_options; + +std::unique_ptr g_httpServer(nullptr); +std::vector> g_ssdSearch; + +void setup(const utility::string_t &address, const std::string &typestring) +{ + web::http::uri_builder uriBldr(address); + auto uri = uriBldr.to_uri(); + + std::cout << "Attempting to start server on " << uri.to_string() << std::endl; + + g_httpServer = std::unique_ptr(new Server(uri, g_ssdSearch, typestring)); + std::cout << "Created a server object" << std::endl; + + g_httpServer->open().wait(); + ucout << U"Listening for requests on: " << address << std::endl; +} + +void teardown(const utility::string_t &address) +{ + g_httpServer->close().wait(); +} + +int main(int argc, char *argv[]) +{ + std::string data_type, index_prefix_paths, address, dist_fn, tags_file; + uint32_t num_nodes_to_cache; + uint32_t num_threads; + + po::options_description desc{"Arguments"}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + desc.add_options()("address", po::value(&address)->required(), "Web server address"); + desc.add_options()("data_type", po::value(&data_type)->required(), "data type "); + desc.add_options()("index_prefix_paths", po::value(&index_prefix_paths)->required(), + "Path prefix for loading index file components"); + desc.add_options()("num_nodes_to_cache", po::value(&num_nodes_to_cache)->default_value(0), + "Number of nodes to cache during search"); + desc.add_options()("num_threads,T", po::value(&num_threads)->default_value(omp_get_num_procs()), + "Number of threads used for building index (defaults to " + "omp_get_num_procs())"); + desc.add_options()("dist_fn", po::value(&dist_fn)->default_value("l2"), + "distance function "); + desc.add_options()("tags_file", po::value(&tags_file)->default_value(std::string()), + "Tags file location"); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << std::endl; + return -1; + } + + diskann::Metric metric; + if (dist_fn == std::string("l2")) + metric = diskann::Metric::L2; + else if (dist_fn == std::string("mips")) + metric = diskann::Metric::INNER_PRODUCT; + else + { + std::cout << "Error. Only l2 and mips distance functions are supported" << std::endl; + return -1; + } + + std::vector> index_tag_paths; + std::ifstream index_in(index_prefix_paths); + if (!index_in.is_open()) + { + std::cerr << "Could not open " << index_prefix_paths << std::endl; + exit(-1); + } + std::ifstream tags_in(tags_file); + if (!tags_in.is_open()) + { + std::cerr << "Could not open " << tags_file << std::endl; + exit(-1); + } + std::string prefix, tagfile; + while (std::getline(index_in, prefix)) + { + if (std::getline(tags_in, tagfile)) + { + index_tag_paths.push_back(std::make_pair(prefix, tagfile)); + } + else + { + std::cerr << "The number of tags specified does not match the number of " + "indices specified" + << std::endl; + exit(-1); + } + } + index_in.close(); + tags_in.close(); + + if (data_type == std::string("float")) + { + for (auto &index_tag : index_tag_paths) + { + auto searcher = std::unique_ptr(new diskann::PQFlashSearch( + index_tag.first.c_str(), num_nodes_to_cache, num_threads, index_tag.second.c_str(), metric)); + g_ssdSearch.push_back(std::move(searcher)); + } + } + else if (data_type == std::string("int8")) + { + for (auto &index_tag : index_tag_paths) + { + auto searcher = std::unique_ptr(new diskann::PQFlashSearch( + index_tag.first.c_str(), num_nodes_to_cache, num_threads, index_tag.second.c_str(), metric)); + g_ssdSearch.push_back(std::move(searcher)); + } + } + else if (data_type == std::string("uint8")) + { + for (auto &index_tag : index_tag_paths) + { + auto searcher = std::unique_ptr(new diskann::PQFlashSearch( + index_tag.first.c_str(), num_nodes_to_cache, num_threads, index_tag.second.c_str(), metric)); + g_ssdSearch.push_back(std::move(searcher)); + } + } + else + { + std::cerr << "Unsupported data type " << data_type << std::endl; + exit(-1); + } + + while (1) + { + try + { + setup(address, data_type); + std::cout << "Type 'exit' (case-sensitive) to exit" << std::endl; + std::string line; + std::getline(std::cin, line); + if (line == "exit") + { + teardown(address); + g_httpServer->close().wait(); + exit(0); + } + } + catch (const std::exception &ex) + { + std::cerr << "Exception occurred: " << ex.what() << std::endl; + std::cerr << "Restarting HTTP server"; + teardown(address); + } + catch (...) + { + std::cerr << "Unknown exception occurreed" << std::endl; + std::cerr << "Restarting HTTP server"; + teardown(address); + } + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/ssd_server.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/ssd_server.cpp new file mode 100644 index 0000000..d179973 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/restapi/ssd_server.cpp @@ -0,0 +1,141 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +using namespace diskann; +namespace po = boost::program_options; + +std::unique_ptr g_httpServer(nullptr); +std::vector> g_ssdSearch; + +void setup(const utility::string_t &address, const std::string &typestring) +{ + web::http::uri_builder uriBldr(address); + auto uri = uriBldr.to_uri(); + + std::cout << "Attempting to start server on " << uri.to_string() << std::endl; + + g_httpServer = std::unique_ptr(new Server(uri, g_ssdSearch, typestring)); + std::cout << "Created a server object" << std::endl; + + g_httpServer->open().wait(); + ucout << U"Listening for requests on: " << address << std::endl; +} + +void teardown(const utility::string_t &address) +{ + g_httpServer->close().wait(); +} + +int main(int argc, char *argv[]) +{ + std::string data_type, index_path_prefix, address, dist_fn, tags_file; + uint32_t num_nodes_to_cache; + uint32_t num_threads; + + po::options_description desc{"Arguments"}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + desc.add_options()("data_type", po::value(&data_type)->required(), "data type "); + desc.add_options()("address", po::value(&address)->required(), "Web server address"); + desc.add_options()("index_path_prefix", po::value(&index_path_prefix)->required(), + "Path prefix for loading index file components"); + desc.add_options()("num_nodes_to_cache", po::value(&num_nodes_to_cache)->default_value(0), + "Number of nodes to cache during search"); + desc.add_options()("num_threads,T", po::value(&num_threads)->default_value(omp_get_num_procs()), + "Number of threads used for building index (defaults to " + "omp_get_num_procs())"); + desc.add_options()("dist_fn", po::value(&dist_fn)->default_value("l2"), + "distance function "); + desc.add_options()("tags_file", po::value(&tags_file)->default_value(std::string()), + "Tags file location"); + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << std::endl; + return -1; + } + + diskann::Metric metric; + if (dist_fn == std::string("l2")) + metric = diskann::Metric::L2; + else if (dist_fn == std::string("mips")) + metric = diskann::Metric::INNER_PRODUCT; + else + { + std::cout << "Error. Only l2 and mips distance functions are supported" << std::endl; + return -1; + } + + if (data_type == std::string("float")) + { + auto searcher = std::unique_ptr( + new diskann::PQFlashSearch(index_path_prefix, num_nodes_to_cache, num_threads, tags_file, metric)); + g_ssdSearch.push_back(std::move(searcher)); + } + else if (data_type == std::string("int8")) + { + auto searcher = std::unique_ptr( + new diskann::PQFlashSearch(index_path_prefix, num_nodes_to_cache, num_threads, tags_file, metric)); + g_ssdSearch.push_back(std::move(searcher)); + } + else if (data_type == std::string("uint8")) + { + auto searcher = std::unique_ptr( + new diskann::PQFlashSearch(index_path_prefix, num_nodes_to_cache, num_threads, tags_file, metric)); + g_ssdSearch.push_back(std::move(searcher)); + } + else + { + std::cerr << "Unsupported data type " << argv[2] << std::endl; + exit(-1); + } + + while (1) + { + try + { + setup(address, data_type); + std::cout << "Type 'exit' (case-sensitive) to exit" << std::endl; + std::string line; + std::getline(std::cin, line); + if (line == "exit") + { + teardown(address); + g_httpServer->close().wait(); + exit(0); + } + } + catch (const std::exception &ex) + { + std::cerr << "Exception occurred: " << ex.what() << std::endl; + std::cerr << "Restarting HTTP server"; + teardown(address); + } + catch (...) + { + std::cerr << "Unknown exception occurreed" << std::endl; + std::cerr << "Restarting HTTP server"; + teardown(address); + } + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/search_disk_index.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/search_disk_index.cpp new file mode 100644 index 0000000..6b0793d --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/search_disk_index.cpp @@ -0,0 +1,499 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include "common_includes.h" +#include + +#include "index.h" +#include "disk_utils.h" +#include "math_utils.h" +#include "memory_mapper.h" +#include "partition.h" +#include "pq_flash_index.h" +#include "timer.h" +#include "percentile_stats.h" +#include "program_options_utils.hpp" + +#ifndef _WINDOWS +#include +#include +#include +#include "linux_aligned_file_reader.h" +#else +#ifdef USE_BING_INFRA +#include "bing_aligned_file_reader.h" +#else +#include "windows_aligned_file_reader.h" +#endif +#endif + +#define WARMUP false + +namespace po = boost::program_options; + +void print_stats(std::string category, std::vector percentiles, std::vector results) +{ + diskann::cout << std::setw(20) << category << ": " << std::flush; + for (uint32_t s = 0; s < percentiles.size(); s++) + { + diskann::cout << std::setw(8) << percentiles[s] << "%"; + } + diskann::cout << std::endl; + diskann::cout << std::setw(22) << " " << std::flush; + for (uint32_t s = 0; s < percentiles.size(); s++) + { + diskann::cout << std::setw(9) << results[s]; + } + diskann::cout << std::endl; +} + +template +int search_disk_index(diskann::Metric &metric, const std::string &index_path_prefix, + const std::string &result_output_prefix, const std::string &query_file, std::string >_file, + const uint32_t num_threads, const uint32_t recall_at, const uint32_t beamwidth, + const uint32_t num_nodes_to_cache, const uint32_t search_io_limit, + const std::vector &Lvec, const float fail_if_recall_below, + const std::vector &query_filters, const bool use_reorder_data = false) +{ + diskann::cout << "Search parameters: #threads: " << num_threads << ", "; + if (beamwidth <= 0) + diskann::cout << "beamwidth to be optimized for each L value" << std::flush; + else + diskann::cout << " beamwidth: " << beamwidth << std::flush; + if (search_io_limit == std::numeric_limits::max()) + diskann::cout << "." << std::endl; + else + diskann::cout << ", io_limit: " << search_io_limit << "." << std::endl; + + std::string warmup_query_file = index_path_prefix + "_sample_data.bin"; + + // load query bin + T *query = nullptr; + uint32_t *gt_ids = nullptr; + float *gt_dists = nullptr; + size_t query_num, query_dim, query_aligned_dim, gt_num, gt_dim; + diskann::load_aligned_bin(query_file, query, query_num, query_dim, query_aligned_dim); + + bool filtered_search = false; + if (!query_filters.empty()) + { + filtered_search = true; + if (query_filters.size() != 1 && query_filters.size() != query_num) + { + std::cout << "Error. Mismatch in number of queries and size of query " + "filters file" + << std::endl; + return -1; // To return -1 or some other error handling? + } + } + + bool calc_recall_flag = false; + if (gt_file != std::string("null") && gt_file != std::string("NULL") && file_exists(gt_file)) + { + diskann::load_truthset(gt_file, gt_ids, gt_dists, gt_num, gt_dim); + if (gt_num != query_num) + { + diskann::cout << "Error. Mismatch in number of queries and ground truth data" << std::endl; + } + calc_recall_flag = true; + } + + std::shared_ptr reader = nullptr; +#ifdef _WINDOWS +#ifndef USE_BING_INFRA + reader.reset(new WindowsAlignedFileReader()); +#else + reader.reset(new diskann::BingAlignedFileReader()); +#endif +#else + reader.reset(new LinuxAlignedFileReader()); +#endif + + std::unique_ptr> _pFlashIndex( + new diskann::PQFlashIndex(reader, metric)); + + int res = _pFlashIndex->load(num_threads, index_path_prefix.c_str()); + + if (res != 0) + { + return res; + } + + std::vector node_list; + diskann::cout << "Caching " << num_nodes_to_cache << " nodes around medoid(s)" << std::endl; + _pFlashIndex->cache_bfs_levels(num_nodes_to_cache, node_list); + // if (num_nodes_to_cache > 0) + // _pFlashIndex->generate_cache_list_from_sample_queries(warmup_query_file, 15, 6, num_nodes_to_cache, + // num_threads, node_list); + _pFlashIndex->load_cache_list(node_list); + node_list.clear(); + node_list.shrink_to_fit(); + + omp_set_num_threads(num_threads); + + uint64_t warmup_L = 20; + uint64_t warmup_num = 0, warmup_dim = 0, warmup_aligned_dim = 0; + T *warmup = nullptr; + + if (WARMUP) + { + if (file_exists(warmup_query_file)) + { + diskann::load_aligned_bin(warmup_query_file, warmup, warmup_num, warmup_dim, warmup_aligned_dim); + } + else + { + warmup_num = (std::min)((uint32_t)150000, (uint32_t)15000 * num_threads); + warmup_dim = query_dim; + warmup_aligned_dim = query_aligned_dim; + diskann::alloc_aligned(((void **)&warmup), warmup_num * warmup_aligned_dim * sizeof(T), 8 * sizeof(T)); + std::memset(warmup, 0, warmup_num * warmup_aligned_dim * sizeof(T)); + std::random_device rd; + std::mt19937 gen(rd()); + std::uniform_int_distribution<> dis(-128, 127); + for (uint32_t i = 0; i < warmup_num; i++) + { + for (uint32_t d = 0; d < warmup_dim; d++) + { + warmup[i * warmup_aligned_dim + d] = (T)dis(gen); + } + } + } + diskann::cout << "Warming up index... " << std::flush; + std::vector warmup_result_ids_64(warmup_num, 0); + std::vector warmup_result_dists(warmup_num, 0); + +#pragma omp parallel for schedule(dynamic, 1) + for (int64_t i = 0; i < (int64_t)warmup_num; i++) + { + _pFlashIndex->cached_beam_search(warmup + (i * warmup_aligned_dim), 1, warmup_L, + warmup_result_ids_64.data() + (i * 1), + warmup_result_dists.data() + (i * 1), 4); + } + diskann::cout << "..done" << std::endl; + } + + diskann::cout.setf(std::ios_base::fixed, std::ios_base::floatfield); + diskann::cout.precision(2); + + std::string recall_string = "Recall@" + std::to_string(recall_at); + diskann::cout << std::setw(6) << "L" << std::setw(12) << "Beamwidth" << std::setw(16) << "QPS" << std::setw(16) + << "Mean Latency" << std::setw(16) << "99.9 Latency" << std::setw(16) << "Mean IOs" << std::setw(16) + << "Mean IO (us)" << std::setw(16) << "CPU (s)"; + if (calc_recall_flag) + { + diskann::cout << std::setw(16) << recall_string << std::endl; + } + else + diskann::cout << std::endl; + diskann::cout << "==================================================================" + "=================================================================" + << std::endl; + + std::vector> query_result_ids(Lvec.size()); + std::vector> query_result_dists(Lvec.size()); + + uint32_t optimized_beamwidth = 2; + + double best_recall = 0.0; + + for (uint32_t test_id = 0; test_id < Lvec.size(); test_id++) + { + uint32_t L = Lvec[test_id]; + + if (L < recall_at) + { + diskann::cout << "Ignoring search with L:" << L << " since it's smaller than K:" << recall_at << std::endl; + continue; + } + + if (beamwidth <= 0) + { + diskann::cout << "Tuning beamwidth.." << std::endl; + optimized_beamwidth = + optimize_beamwidth(_pFlashIndex, warmup, warmup_num, warmup_aligned_dim, L, optimized_beamwidth); + } + else + optimized_beamwidth = beamwidth; + + query_result_ids[test_id].resize(recall_at * query_num); + query_result_dists[test_id].resize(recall_at * query_num); + + auto stats = new diskann::QueryStats[query_num]; + + std::vector query_result_ids_64(recall_at * query_num); + auto s = std::chrono::high_resolution_clock::now(); + +#pragma omp parallel for schedule(dynamic, 1) + for (int64_t i = 0; i < (int64_t)query_num; i++) + { + if (!filtered_search) + { + _pFlashIndex->cached_beam_search(query + (i * query_aligned_dim), recall_at, L, + query_result_ids_64.data() + (i * recall_at), + query_result_dists[test_id].data() + (i * recall_at), + optimized_beamwidth, use_reorder_data, stats + i); + } + else + { + LabelT label_for_search; + if (query_filters.size() == 1) + { // one label for all queries + label_for_search = _pFlashIndex->get_converted_label(query_filters[0]); + } + else + { // one label for each query + label_for_search = _pFlashIndex->get_converted_label(query_filters[i]); + } + _pFlashIndex->cached_beam_search( + query + (i * query_aligned_dim), recall_at, L, query_result_ids_64.data() + (i * recall_at), + query_result_dists[test_id].data() + (i * recall_at), optimized_beamwidth, true, label_for_search, + use_reorder_data, stats + i); + } + } + auto e = std::chrono::high_resolution_clock::now(); + std::chrono::duration diff = e - s; + double qps = (1.0 * query_num) / (1.0 * diff.count()); + + diskann::convert_types(query_result_ids_64.data(), query_result_ids[test_id].data(), + query_num, recall_at); + + auto mean_latency = diskann::get_mean_stats( + stats, query_num, [](const diskann::QueryStats &stats) { return stats.total_us; }); + + auto latency_999 = diskann::get_percentile_stats( + stats, query_num, 0.999, [](const diskann::QueryStats &stats) { return stats.total_us; }); + + auto mean_ios = diskann::get_mean_stats(stats, query_num, + [](const diskann::QueryStats &stats) { return stats.n_ios; }); + + auto mean_cpuus = diskann::get_mean_stats(stats, query_num, + [](const diskann::QueryStats &stats) { return stats.cpu_us; }); + + auto mean_io_us = diskann::get_mean_stats(stats, query_num, + [](const diskann::QueryStats &stats) { return stats.io_us; }); + + double recall = 0; + if (calc_recall_flag) + { + recall = diskann::calculate_recall((uint32_t)query_num, gt_ids, gt_dists, (uint32_t)gt_dim, + query_result_ids[test_id].data(), recall_at, recall_at); + best_recall = std::max(recall, best_recall); + } + + diskann::cout << std::setw(6) << L << std::setw(12) << optimized_beamwidth << std::setw(16) << qps + << std::setw(16) << mean_latency << std::setw(16) << latency_999 << std::setw(16) << mean_ios + << std::setw(16) << mean_io_us << std::setw(16) << mean_cpuus; + if (calc_recall_flag) + { + diskann::cout << std::setw(16) << recall << std::endl; + } + else + diskann::cout << std::endl; + delete[] stats; + } + + diskann::cout << "Done searching. Now saving results " << std::endl; + uint64_t test_id = 0; + for (auto L : Lvec) + { + if (L < recall_at) + continue; + + std::string cur_result_path = result_output_prefix + "_" + std::to_string(L) + "_idx_uint32.bin"; + diskann::save_bin(cur_result_path, query_result_ids[test_id].data(), query_num, recall_at); + + cur_result_path = result_output_prefix + "_" + std::to_string(L) + "_dists_float.bin"; + diskann::save_bin(cur_result_path, query_result_dists[test_id++].data(), query_num, recall_at); + } + + diskann::aligned_free(query); + if (warmup != nullptr) + diskann::aligned_free(warmup); + return best_recall >= fail_if_recall_below ? 0 : -1; +} + +int main(int argc, char **argv) +{ + std::string data_type, dist_fn, index_path_prefix, result_path_prefix, query_file, gt_file, filter_label, + label_type, query_filters_file; + uint32_t num_threads, K, W, num_nodes_to_cache, search_io_limit; + std::vector Lvec; + bool use_reorder_data = false; + float fail_if_recall_below = 0.0f; + + po::options_description desc{ + program_options_utils::make_program_description("search_disk_index", "Searches on-disk DiskANN indexes")}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + + // Required parameters + po::options_description required_configs("Required"); + required_configs.add_options()("data_type", po::value(&data_type)->required(), + program_options_utils::DATA_TYPE_DESCRIPTION); + required_configs.add_options()("dist_fn", po::value(&dist_fn)->required(), + program_options_utils::DISTANCE_FUNCTION_DESCRIPTION); + required_configs.add_options()("index_path_prefix", po::value(&index_path_prefix)->required(), + program_options_utils::INDEX_PATH_PREFIX_DESCRIPTION); + required_configs.add_options()("result_path", po::value(&result_path_prefix)->required(), + program_options_utils::RESULT_PATH_DESCRIPTION); + required_configs.add_options()("query_file", po::value(&query_file)->required(), + program_options_utils::QUERY_FILE_DESCRIPTION); + required_configs.add_options()("recall_at,K", po::value(&K)->required(), + program_options_utils::NUMBER_OF_RESULTS_DESCRIPTION); + required_configs.add_options()("search_list,L", + po::value>(&Lvec)->multitoken()->required(), + program_options_utils::SEARCH_LIST_DESCRIPTION); + + // Optional parameters + po::options_description optional_configs("Optional"); + optional_configs.add_options()("gt_file", po::value(>_file)->default_value(std::string("null")), + program_options_utils::GROUND_TRUTH_FILE_DESCRIPTION); + optional_configs.add_options()("beamwidth,W", po::value(&W)->default_value(2), + program_options_utils::BEAMWIDTH); + optional_configs.add_options()("num_nodes_to_cache", po::value(&num_nodes_to_cache)->default_value(0), + program_options_utils::NUMBER_OF_NODES_TO_CACHE); + optional_configs.add_options()( + "search_io_limit", + po::value(&search_io_limit)->default_value(std::numeric_limits::max()), + "Max #IOs for search. Default value: uint32::max()"); + optional_configs.add_options()("num_threads,T", + po::value(&num_threads)->default_value(omp_get_num_procs()), + program_options_utils::NUMBER_THREADS_DESCRIPTION); + optional_configs.add_options()("use_reorder_data", po::bool_switch()->default_value(false), + "Include full precision data in the index. Use only in " + "conjuction with compressed data on SSD. Default value: false"); + optional_configs.add_options()("filter_label", + po::value(&filter_label)->default_value(std::string("")), + program_options_utils::FILTER_LABEL_DESCRIPTION); + optional_configs.add_options()("query_filters_file", + po::value(&query_filters_file)->default_value(std::string("")), + program_options_utils::FILTERS_FILE_DESCRIPTION); + optional_configs.add_options()("label_type", po::value(&label_type)->default_value("uint"), + program_options_utils::LABEL_TYPE_DESCRIPTION); + optional_configs.add_options()("fail_if_recall_below", + po::value(&fail_if_recall_below)->default_value(0.0f), + program_options_utils::FAIL_IF_RECALL_BELOW); + + // Merge required and optional parameters + desc.add(required_configs).add(optional_configs); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + if (vm["use_reorder_data"].as()) + use_reorder_data = true; + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + diskann::Metric metric; + if (dist_fn == std::string("mips")) + { + metric = diskann::Metric::INNER_PRODUCT; + } + else if (dist_fn == std::string("l2")) + { + metric = diskann::Metric::L2; + } + else if (dist_fn == std::string("cosine")) + { + metric = diskann::Metric::COSINE; + } + else + { + std::cout << "Unsupported distance function. Currently only L2/ Inner " + "Product/Cosine are supported." + << std::endl; + return -1; + } + + if ((data_type != std::string("float")) && (metric == diskann::Metric::INNER_PRODUCT)) + { + std::cout << "Currently support only floating point data for Inner Product." << std::endl; + return -1; + } + + if (use_reorder_data && data_type != std::string("float")) + { + std::cout << "Error: Reorder data for reordering currently only " + "supported for float data type." + << std::endl; + return -1; + } + + if (filter_label != "" && query_filters_file != "") + { + std::cerr << "Only one of filter_label and query_filters_file should be provided" << std::endl; + return -1; + } + + std::vector query_filters; + if (filter_label != "") + { + query_filters.push_back(filter_label); + } + else if (query_filters_file != "") + { + query_filters = read_file_to_vector_of_strings(query_filters_file); + } + + try + { + if (!query_filters.empty() && label_type == "ushort") + { + if (data_type == std::string("float")) + return search_disk_index( + metric, index_path_prefix, result_path_prefix, query_file, gt_file, num_threads, K, W, + num_nodes_to_cache, search_io_limit, Lvec, fail_if_recall_below, query_filters, use_reorder_data); + else if (data_type == std::string("int8")) + return search_disk_index( + metric, index_path_prefix, result_path_prefix, query_file, gt_file, num_threads, K, W, + num_nodes_to_cache, search_io_limit, Lvec, fail_if_recall_below, query_filters, use_reorder_data); + else if (data_type == std::string("uint8")) + return search_disk_index( + metric, index_path_prefix, result_path_prefix, query_file, gt_file, num_threads, K, W, + num_nodes_to_cache, search_io_limit, Lvec, fail_if_recall_below, query_filters, use_reorder_data); + else + { + std::cerr << "Unsupported data type. Use float or int8 or uint8" << std::endl; + return -1; + } + } + else + { + if (data_type == std::string("float")) + return search_disk_index(metric, index_path_prefix, result_path_prefix, query_file, gt_file, + num_threads, K, W, num_nodes_to_cache, search_io_limit, Lvec, + fail_if_recall_below, query_filters, use_reorder_data); + else if (data_type == std::string("int8")) + return search_disk_index(metric, index_path_prefix, result_path_prefix, query_file, gt_file, + num_threads, K, W, num_nodes_to_cache, search_io_limit, Lvec, + fail_if_recall_below, query_filters, use_reorder_data); + else if (data_type == std::string("uint8")) + return search_disk_index(metric, index_path_prefix, result_path_prefix, query_file, gt_file, + num_threads, K, W, num_nodes_to_cache, search_io_limit, Lvec, + fail_if_recall_below, query_filters, use_reorder_data); + else + { + std::cerr << "Unsupported data type. Use float or int8 or uint8" << std::endl; + return -1; + } + } + } + catch (const std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Index search failed." << std::endl; + return -1; + } +} \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/search_memory_index.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/search_memory_index.cpp new file mode 100644 index 0000000..1a9acc2 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/search_memory_index.cpp @@ -0,0 +1,477 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include + +#ifndef _WINDOWS +#include +#include +#include +#include +#endif + +#include "index.h" +#include "memory_mapper.h" +#include "utils.h" +#include "program_options_utils.hpp" +#include "index_factory.h" + +namespace po = boost::program_options; + +template +int search_memory_index(diskann::Metric &metric, const std::string &index_path, const std::string &result_path_prefix, + const std::string &query_file, const std::string &truthset_file, const uint32_t num_threads, + const uint32_t recall_at, const bool print_all_recalls, const std::vector &Lvec, + const bool dynamic, const bool tags, const bool show_qps_per_thread, + const std::vector &query_filters, const float fail_if_recall_below) +{ + using TagT = uint32_t; + // Load the query file + T *query = nullptr; + uint32_t *gt_ids = nullptr; + float *gt_dists = nullptr; + size_t query_num, query_dim, query_aligned_dim, gt_num, gt_dim; + diskann::load_aligned_bin(query_file, query, query_num, query_dim, query_aligned_dim); + + bool calc_recall_flag = false; + if (truthset_file != std::string("null") && file_exists(truthset_file)) + { + diskann::load_truthset(truthset_file, gt_ids, gt_dists, gt_num, gt_dim); + if (gt_num != query_num) + { + std::cout << "Error. Mismatch in number of queries and ground truth data" << std::endl; + } + calc_recall_flag = true; + } + else + { + diskann::cout << " Truthset file " << truthset_file << " not found. Not computing recall." << std::endl; + } + + bool filtered_search = false; + if (!query_filters.empty()) + { + filtered_search = true; + if (query_filters.size() != 1 && query_filters.size() != query_num) + { + std::cout << "Error. Mismatch in number of queries and size of query " + "filters file" + << std::endl; + return -1; // To return -1 or some other error handling? + } + } + + const size_t num_frozen_pts = diskann::get_graph_num_frozen_points(index_path); + + auto config = diskann::IndexConfigBuilder() + .with_metric(metric) + .with_dimension(query_dim) + .with_max_points(0) + .with_data_load_store_strategy(diskann::DataStoreStrategy::MEMORY) + .with_graph_load_store_strategy(diskann::GraphStoreStrategy::MEMORY) + .with_data_type(diskann_type_to_name()) + .with_label_type(diskann_type_to_name()) + .with_tag_type(diskann_type_to_name()) + .is_dynamic_index(dynamic) + .is_enable_tags(tags) + .is_concurrent_consolidate(false) + .is_pq_dist_build(false) + .is_use_opq(false) + .with_num_pq_chunks(0) + .with_num_frozen_pts(num_frozen_pts) + .build(); + + auto index_factory = diskann::IndexFactory(config); + auto index = index_factory.create_instance(); + index->load(index_path.c_str(), num_threads, *(std::max_element(Lvec.begin(), Lvec.end()))); + std::cout << "Index loaded" << std::endl; + + if (metric == diskann::FAST_L2) + index->optimize_index_layout(); + + std::cout << "Using " << num_threads << " threads to search" << std::endl; + std::cout.setf(std::ios_base::fixed, std::ios_base::floatfield); + std::cout.precision(2); + const std::string qps_title = show_qps_per_thread ? "QPS/thread" : "QPS"; + uint32_t table_width = 0; + if (tags) + { + std::cout << std::setw(4) << "Ls" << std::setw(12) << qps_title << std::setw(20) << "Mean Latency (mus)" + << std::setw(15) << "99.9 Latency"; + table_width += 4 + 12 + 20 + 15; + } + else + { + std::cout << std::setw(4) << "Ls" << std::setw(12) << qps_title << std::setw(18) << "Avg dist cmps" + << std::setw(20) << "Mean Latency (mus)" << std::setw(15) << "99.9 Latency"; + table_width += 4 + 12 + 18 + 20 + 15; + } + uint32_t recalls_to_print = 0; + const uint32_t first_recall = print_all_recalls ? 1 : recall_at; + if (calc_recall_flag) + { + for (uint32_t curr_recall = first_recall; curr_recall <= recall_at; curr_recall++) + { + std::cout << std::setw(12) << ("Recall@" + std::to_string(curr_recall)); + } + recalls_to_print = recall_at + 1 - first_recall; + table_width += recalls_to_print * 12; + } + std::cout << std::endl; + std::cout << std::string(table_width, '=') << std::endl; + + std::vector> query_result_ids(Lvec.size()); + std::vector> query_result_dists(Lvec.size()); + std::vector latency_stats(query_num, 0); + std::vector cmp_stats; + if (not tags || filtered_search) + { + cmp_stats = std::vector(query_num, 0); + } + + std::vector query_result_tags; + if (tags) + { + query_result_tags.resize(recall_at * query_num); + } + + double best_recall = 0.0; + + for (uint32_t test_id = 0; test_id < Lvec.size(); test_id++) + { + uint32_t L = Lvec[test_id]; + if (L < recall_at) + { + diskann::cout << "Ignoring search with L:" << L << " since it's smaller than K:" << recall_at << std::endl; + continue; + } + + query_result_ids[test_id].resize(recall_at * query_num); + query_result_dists[test_id].resize(recall_at * query_num); + std::vector res = std::vector(); + + auto s = std::chrono::high_resolution_clock::now(); + omp_set_num_threads(num_threads); +#pragma omp parallel for schedule(dynamic, 1) + for (int64_t i = 0; i < (int64_t)query_num; i++) + { + auto qs = std::chrono::high_resolution_clock::now(); + if (filtered_search && !tags) + { + std::string raw_filter = query_filters.size() == 1 ? query_filters[0] : query_filters[i]; + + auto retval = index->search_with_filters(query + i * query_aligned_dim, raw_filter, recall_at, L, + query_result_ids[test_id].data() + i * recall_at, + query_result_dists[test_id].data() + i * recall_at); + cmp_stats[i] = retval.second; + } + else if (metric == diskann::FAST_L2) + { + index->search_with_optimized_layout(query + i * query_aligned_dim, recall_at, L, + query_result_ids[test_id].data() + i * recall_at); + } + else if (tags) + { + if (!filtered_search) + { + index->search_with_tags(query + i * query_aligned_dim, recall_at, L, + query_result_tags.data() + i * recall_at, nullptr, res); + } + else + { + std::string raw_filter = query_filters.size() == 1 ? query_filters[0] : query_filters[i]; + + index->search_with_tags(query + i * query_aligned_dim, recall_at, L, + query_result_tags.data() + i * recall_at, nullptr, res, true, raw_filter); + } + + for (int64_t r = 0; r < (int64_t)recall_at; r++) + { + query_result_ids[test_id][recall_at * i + r] = query_result_tags[recall_at * i + r]; + } + } + else + { + cmp_stats[i] = index + ->search(query + i * query_aligned_dim, recall_at, L, + query_result_ids[test_id].data() + i * recall_at) + .second; + } + auto qe = std::chrono::high_resolution_clock::now(); + std::chrono::duration diff = qe - qs; + latency_stats[i] = (float)(diff.count() * 1000000); + } + std::chrono::duration diff = std::chrono::high_resolution_clock::now() - s; + + double displayed_qps = query_num / diff.count(); + + if (show_qps_per_thread) + displayed_qps /= num_threads; + + std::vector recalls; + if (calc_recall_flag) + { + recalls.reserve(recalls_to_print); + for (uint32_t curr_recall = first_recall; curr_recall <= recall_at; curr_recall++) + { + recalls.push_back(diskann::calculate_recall((uint32_t)query_num, gt_ids, gt_dists, (uint32_t)gt_dim, + query_result_ids[test_id].data(), recall_at, curr_recall)); + } + } + + std::sort(latency_stats.begin(), latency_stats.end()); + double mean_latency = + std::accumulate(latency_stats.begin(), latency_stats.end(), 0.0) / static_cast(query_num); + + float avg_cmps = (float)std::accumulate(cmp_stats.begin(), cmp_stats.end(), 0) / (float)query_num; + + if (tags && !filtered_search) + { + std::cout << std::setw(4) << L << std::setw(12) << displayed_qps << std::setw(20) << (float)mean_latency + << std::setw(15) << (float)latency_stats[(uint64_t)(0.999 * query_num)]; + } + else + { + std::cout << std::setw(4) << L << std::setw(12) << displayed_qps << std::setw(18) << avg_cmps + << std::setw(20) << (float)mean_latency << std::setw(15) + << (float)latency_stats[(uint64_t)(0.999 * query_num)]; + } + for (double recall : recalls) + { + std::cout << std::setw(12) << recall; + best_recall = std::max(recall, best_recall); + } + std::cout << std::endl; + } + + std::cout << "Done searching. Now saving results " << std::endl; + uint64_t test_id = 0; + for (auto L : Lvec) + { + if (L < recall_at) + { + diskann::cout << "Ignoring search with L:" << L << " since it's smaller than K:" << recall_at << std::endl; + continue; + } + std::string cur_result_path_prefix = result_path_prefix + "_" + std::to_string(L); + + std::string cur_result_path = cur_result_path_prefix + "_idx_uint32.bin"; + diskann::save_bin(cur_result_path, query_result_ids[test_id].data(), query_num, recall_at); + + cur_result_path = cur_result_path_prefix + "_dists_float.bin"; + diskann::save_bin(cur_result_path, query_result_dists[test_id].data(), query_num, recall_at); + + test_id++; + } + + diskann::aligned_free(query); + return best_recall >= fail_if_recall_below ? 0 : -1; +} + +int main(int argc, char **argv) +{ + std::string data_type, dist_fn, index_path_prefix, result_path, query_file, gt_file, filter_label, label_type, + query_filters_file; + uint32_t num_threads, K; + std::vector Lvec; + bool print_all_recalls, dynamic, tags, show_qps_per_thread; + float fail_if_recall_below = 0.0f; + + po::options_description desc{ + program_options_utils::make_program_description("search_memory_index", "Searches in-memory DiskANN indexes")}; + try + { + desc.add_options()("help,h", "Print this information on arguments"); + + // Required parameters + po::options_description required_configs("Required"); + required_configs.add_options()("data_type", po::value(&data_type)->required(), + program_options_utils::DATA_TYPE_DESCRIPTION); + required_configs.add_options()("dist_fn", po::value(&dist_fn)->required(), + program_options_utils::DISTANCE_FUNCTION_DESCRIPTION); + required_configs.add_options()("index_path_prefix", po::value(&index_path_prefix)->required(), + program_options_utils::INDEX_PATH_PREFIX_DESCRIPTION); + required_configs.add_options()("result_path", po::value(&result_path)->required(), + program_options_utils::RESULT_PATH_DESCRIPTION); + required_configs.add_options()("query_file", po::value(&query_file)->required(), + program_options_utils::QUERY_FILE_DESCRIPTION); + required_configs.add_options()("recall_at,K", po::value(&K)->required(), + program_options_utils::NUMBER_OF_RESULTS_DESCRIPTION); + required_configs.add_options()("search_list,L", + po::value>(&Lvec)->multitoken()->required(), + program_options_utils::SEARCH_LIST_DESCRIPTION); + + // Optional parameters + po::options_description optional_configs("Optional"); + optional_configs.add_options()("filter_label", + po::value(&filter_label)->default_value(std::string("")), + program_options_utils::FILTER_LABEL_DESCRIPTION); + optional_configs.add_options()("query_filters_file", + po::value(&query_filters_file)->default_value(std::string("")), + program_options_utils::FILTERS_FILE_DESCRIPTION); + optional_configs.add_options()("label_type", po::value(&label_type)->default_value("uint"), + program_options_utils::LABEL_TYPE_DESCRIPTION); + optional_configs.add_options()("gt_file", po::value(>_file)->default_value(std::string("null")), + program_options_utils::GROUND_TRUTH_FILE_DESCRIPTION); + optional_configs.add_options()("num_threads,T", + po::value(&num_threads)->default_value(omp_get_num_procs()), + program_options_utils::NUMBER_THREADS_DESCRIPTION); + optional_configs.add_options()( + "dynamic", po::value(&dynamic)->default_value(false), + "Whether the index is dynamic. Dynamic indices must have associated tags. Default false."); + optional_configs.add_options()("tags", po::value(&tags)->default_value(false), + "Whether to search with external identifiers (tags). Default false."); + optional_configs.add_options()("fail_if_recall_below", + po::value(&fail_if_recall_below)->default_value(0.0f), + program_options_utils::FAIL_IF_RECALL_BELOW); + + // Output controls + po::options_description output_controls("Output controls"); + output_controls.add_options()("print_all_recalls", po::bool_switch(&print_all_recalls), + "Print recalls at all positions, from 1 up to specified " + "recall_at value"); + output_controls.add_options()("print_qps_per_thread", po::bool_switch(&show_qps_per_thread), + "Print overall QPS divided by the number of threads in " + "the output table"); + + // Merge required and optional parameters + desc.add(required_configs).add(optional_configs).add(output_controls); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + diskann::Metric metric; + if ((dist_fn == std::string("mips")) && (data_type == std::string("float"))) + { + metric = diskann::Metric::INNER_PRODUCT; + } + else if (dist_fn == std::string("l2")) + { + metric = diskann::Metric::L2; + } + else if (dist_fn == std::string("cosine")) + { + metric = diskann::Metric::COSINE; + } + else if ((dist_fn == std::string("fast_l2")) && (data_type == std::string("float"))) + { + metric = diskann::Metric::FAST_L2; + } + else + { + std::cout << "Unsupported distance function. Currently only l2/ cosine are " + "supported in general, and mips/fast_l2 only for floating " + "point data." + << std::endl; + return -1; + } + + if (dynamic && not tags) + { + std::cerr << "Tags must be enabled while searching dynamically built indices" << std::endl; + return -1; + } + + if (fail_if_recall_below < 0.0 || fail_if_recall_below >= 100.0) + { + std::cerr << "fail_if_recall_below parameter must be between 0 and 100%" << std::endl; + return -1; + } + + if (filter_label != "" && query_filters_file != "") + { + std::cerr << "Only one of filter_label and query_filters_file should be provided" << std::endl; + return -1; + } + + std::vector query_filters; + if (filter_label != "") + { + query_filters.push_back(filter_label); + } + else if (query_filters_file != "") + { + query_filters = read_file_to_vector_of_strings(query_filters_file); + } + + try + { + if (!query_filters.empty() && label_type == "ushort") + { + if (data_type == std::string("int8")) + { + return search_memory_index( + metric, index_path_prefix, result_path, query_file, gt_file, num_threads, K, print_all_recalls, + Lvec, dynamic, tags, show_qps_per_thread, query_filters, fail_if_recall_below); + } + else if (data_type == std::string("uint8")) + { + return search_memory_index( + metric, index_path_prefix, result_path, query_file, gt_file, num_threads, K, print_all_recalls, + Lvec, dynamic, tags, show_qps_per_thread, query_filters, fail_if_recall_below); + } + else if (data_type == std::string("float")) + { + return search_memory_index(metric, index_path_prefix, result_path, query_file, gt_file, + num_threads, K, print_all_recalls, Lvec, dynamic, tags, + show_qps_per_thread, query_filters, fail_if_recall_below); + } + else + { + std::cout << "Unsupported type. Use float/int8/uint8" << std::endl; + return -1; + } + } + else + { + if (data_type == std::string("int8")) + { + return search_memory_index(metric, index_path_prefix, result_path, query_file, gt_file, + num_threads, K, print_all_recalls, Lvec, dynamic, tags, + show_qps_per_thread, query_filters, fail_if_recall_below); + } + else if (data_type == std::string("uint8")) + { + return search_memory_index(metric, index_path_prefix, result_path, query_file, gt_file, + num_threads, K, print_all_recalls, Lvec, dynamic, tags, + show_qps_per_thread, query_filters, fail_if_recall_below); + } + else if (data_type == std::string("float")) + { + return search_memory_index(metric, index_path_prefix, result_path, query_file, gt_file, + num_threads, K, print_all_recalls, Lvec, dynamic, tags, + show_qps_per_thread, query_filters, fail_if_recall_below); + } + else + { + std::cout << "Unsupported type. Use float/int8/uint8" << std::endl; + return -1; + } + } + } + catch (std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Index search failed." << std::endl; + return -1; + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/test_insert_deletes_consolidate.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/test_insert_deletes_consolidate.cpp new file mode 100644 index 0000000..97aed18 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/test_insert_deletes_consolidate.cpp @@ -0,0 +1,536 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "utils.h" +#include "filter_utils.h" +#include "program_options_utils.hpp" +#include "index_factory.h" + +#ifndef _WINDOWS +#include +#include +#include +#endif + +#include "memory_mapper.h" + +namespace po = boost::program_options; + +// load_aligned_bin modified to read pieces of the file, but using ifstream +// instead of cached_ifstream. +template +inline void load_aligned_bin_part(const std::string &bin_file, T *data, size_t offset_points, size_t points_to_read) +{ + diskann::Timer timer; + std::ifstream reader; + reader.exceptions(std::ios::failbit | std::ios::badbit); + reader.open(bin_file, std::ios::binary | std::ios::ate); + size_t actual_file_size = reader.tellg(); + reader.seekg(0, std::ios::beg); + + int npts_i32, dim_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&dim_i32, sizeof(int)); + size_t npts = (uint32_t)npts_i32; + size_t dim = (uint32_t)dim_i32; + + size_t expected_actual_file_size = npts * dim * sizeof(T) + 2 * sizeof(uint32_t); + if (actual_file_size != expected_actual_file_size) + { + std::stringstream stream; + stream << "Error. File size mismatch. Actual size is " << actual_file_size << " while expected size is " + << expected_actual_file_size << " npts = " << npts << " dim = " << dim << " size of = " << sizeof(T) + << std::endl; + std::cout << stream.str(); + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + + if (offset_points + points_to_read > npts) + { + std::stringstream stream; + stream << "Error. Not enough points in file. Requested " << offset_points << " offset and " << points_to_read + << " points, but have only " << npts << " points" << std::endl; + std::cout << stream.str(); + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + + reader.seekg(2 * sizeof(uint32_t) + offset_points * dim * sizeof(T)); + + const size_t rounded_dim = ROUND_UP(dim, 8); + + for (size_t i = 0; i < points_to_read; i++) + { + reader.read((char *)(data + i * rounded_dim), dim * sizeof(T)); + memset(data + i * rounded_dim + dim, 0, (rounded_dim - dim) * sizeof(T)); + } + reader.close(); + + const double elapsedSeconds = timer.elapsed() / 1000000.0; + std::cout << "Read " << points_to_read << " points using non-cached reads in " << elapsedSeconds << std::endl; +} + +std::string get_save_filename(const std::string &save_path, size_t points_to_skip, size_t points_deleted, + size_t last_point_threshold) +{ + std::string final_path = save_path; + if (points_to_skip > 0) + { + final_path += "skip" + std::to_string(points_to_skip) + "-"; + } + + final_path += "del" + std::to_string(points_deleted) + "-"; + final_path += std::to_string(last_point_threshold); + return final_path; +} + +template +void insert_till_next_checkpoint(diskann::AbstractIndex &index, size_t start, size_t end, int32_t thread_count, T *data, + size_t aligned_dim, std::vector> &location_to_labels) +{ + diskann::Timer insert_timer; +#pragma omp parallel for num_threads(thread_count) schedule(dynamic) + for (int64_t j = start; j < (int64_t)end; j++) + { + if (!location_to_labels.empty()) + { + index.insert_point(&data[(j - start) * aligned_dim], 1 + static_cast(j), + location_to_labels[j - start]); + } + else + { + index.insert_point(&data[(j - start) * aligned_dim], 1 + static_cast(j)); + } + } + const double elapsedSeconds = insert_timer.elapsed() / 1000000.0; + std::cout << "Insertion time " << elapsedSeconds << " seconds (" << (end - start) / elapsedSeconds + << " points/second overall, " << (end - start) / elapsedSeconds / thread_count << " per thread)\n "; +} + +template +void delete_from_beginning(diskann::AbstractIndex &index, diskann::IndexWriteParameters &delete_params, + size_t points_to_skip, size_t points_to_delete_from_beginning) +{ + try + { + std::cout << std::endl + << "Lazy deleting points " << points_to_skip << " to " + << points_to_skip + points_to_delete_from_beginning << "... "; + for (size_t i = points_to_skip; i < points_to_skip + points_to_delete_from_beginning; ++i) + index.lazy_delete(static_cast(i + 1)); // Since tags are data location + 1 + std::cout << "done." << std::endl; + + auto report = index.consolidate_deletes(delete_params); + std::cout << "#active points: " << report._active_points << std::endl + << "max points: " << report._max_points << std::endl + << "empty slots: " << report._empty_slots << std::endl + << "deletes processed: " << report._slots_released << std::endl + << "latest delete size: " << report._delete_set_size << std::endl + << "rate: (" << points_to_delete_from_beginning / report._time << " points/second overall, " + << points_to_delete_from_beginning / report._time / delete_params.num_threads << " per thread)" + << std::endl; + } + catch (std::system_error &e) + { + std::cout << "Exception caught in deletion thread: " << e.what() << std::endl; + } +} + +template +void build_incremental_index(const std::string &data_path, diskann::IndexWriteParameters ¶ms, size_t points_to_skip, + size_t max_points_to_insert, size_t beginning_index_size, float start_point_norm, + uint32_t num_start_pts, size_t points_per_checkpoint, size_t checkpoints_per_snapshot, + const std::string &save_path, size_t points_to_delete_from_beginning, + size_t start_deletes_after, bool concurrent, const std::string &label_file, + const std::string &universal_label) +{ + size_t dim, aligned_dim; + size_t num_points; + diskann::get_bin_metadata(data_path, num_points, dim); + aligned_dim = ROUND_UP(dim, 8); + bool has_labels = label_file != ""; + using TagT = uint32_t; + using LabelT = uint32_t; + + size_t current_point_offset = points_to_skip; + const size_t last_point_threshold = points_to_skip + max_points_to_insert; + + bool enable_tags = true; + using TagT = uint32_t; + auto index_search_params = diskann::IndexSearchParams(params.search_list_size, params.num_threads); + diskann::IndexConfig index_config = diskann::IndexConfigBuilder() + .with_metric(diskann::L2) + .with_dimension(dim) + .with_max_points(max_points_to_insert) + .is_dynamic_index(true) + .with_index_write_params(params) + .with_index_search_params(index_search_params) + .with_data_type(diskann_type_to_name()) + .with_tag_type(diskann_type_to_name()) + .with_label_type(diskann_type_to_name()) + .with_data_load_store_strategy(diskann::DataStoreStrategy::MEMORY) + .with_graph_load_store_strategy(diskann::GraphStoreStrategy::MEMORY) + .is_enable_tags(enable_tags) + .is_filtered(has_labels) + .with_num_frozen_pts(num_start_pts) + .is_concurrent_consolidate(concurrent) + .build(); + + diskann::IndexFactory index_factory = diskann::IndexFactory(index_config); + auto index = index_factory.create_instance(); + + if (universal_label != "") + { + LabelT u_label = 0; + index->set_universal_label(u_label); + } + + if (points_to_skip > num_points) + { + throw diskann::ANNException("Asked to skip more points than in data file", -1, __FUNCSIG__, __FILE__, __LINE__); + } + + if (max_points_to_insert == 0) + { + max_points_to_insert = num_points; + } + + if (points_to_skip + max_points_to_insert > num_points) + { + max_points_to_insert = num_points - points_to_skip; + std::cerr << "WARNING: Reducing max_points_to_insert to " << max_points_to_insert + << " points since the data file has only that many" << std::endl; + } + + if (beginning_index_size > max_points_to_insert) + { + beginning_index_size = max_points_to_insert; + std::cerr << "WARNING: Reducing beginning index size to " << beginning_index_size + << " points since the data file has only that many" << std::endl; + } + if (checkpoints_per_snapshot > 0 && beginning_index_size > points_per_checkpoint) + { + beginning_index_size = points_per_checkpoint; + std::cerr << "WARNING: Reducing beginning index size to " << beginning_index_size << std::endl; + } + + T *data = nullptr; + diskann::alloc_aligned( + (void **)&data, std::max(points_per_checkpoint, beginning_index_size) * aligned_dim * sizeof(T), 8 * sizeof(T)); + + std::vector tags(beginning_index_size); + std::iota(tags.begin(), tags.end(), 1 + static_cast(current_point_offset)); + + load_aligned_bin_part(data_path, data, current_point_offset, beginning_index_size); + std::cout << "load aligned bin succeeded" << std::endl; + diskann::Timer timer; + + if (beginning_index_size > 0) + { + index->build(data, beginning_index_size, tags); + } + else + { + index->set_start_points_at_random(static_cast(start_point_norm)); + } + + const double elapsedSeconds = timer.elapsed() / 1000000.0; + std::cout << "Initial non-incremental index build time for " << beginning_index_size << " points took " + << elapsedSeconds << " seconds (" << beginning_index_size / elapsedSeconds << " points/second)\n "; + + current_point_offset += beginning_index_size; + + if (points_to_delete_from_beginning > max_points_to_insert) + { + points_to_delete_from_beginning = static_cast(max_points_to_insert); + std::cerr << "WARNING: Reducing points to delete from beginning to " << points_to_delete_from_beginning + << " points since the data file has only that many" << std::endl; + } + + std::vector> location_to_labels; + if (concurrent) + { + // handle labels + const auto save_path_inc = get_save_filename(save_path + ".after-concurrent-delete-", points_to_skip, + points_to_delete_from_beginning, last_point_threshold); + std::string labels_file_to_use = save_path_inc + "_label_formatted.txt"; + std::string mem_labels_int_map_file = save_path_inc + "_labels_map.txt"; + if (has_labels) + { + convert_labels_string_to_int(label_file, labels_file_to_use, mem_labels_int_map_file, universal_label); + auto parse_result = diskann::parse_formatted_label_file(labels_file_to_use); + location_to_labels = std::get<0>(parse_result); + } + + int32_t sub_threads = (params.num_threads + 1) / 2; + bool delete_launched = false; + std::future delete_task; + + diskann::Timer timer; + + for (size_t start = current_point_offset; start < last_point_threshold; + start += points_per_checkpoint, current_point_offset += points_per_checkpoint) + { + const size_t end = std::min(start + points_per_checkpoint, last_point_threshold); + std::cout << std::endl << "Inserting from " << start << " to " << end << std::endl; + + auto insert_task = std::async(std::launch::async, [&]() { + load_aligned_bin_part(data_path, data, start, end - start); + insert_till_next_checkpoint(*index, start, end, sub_threads, data, aligned_dim, + location_to_labels); + }); + insert_task.wait(); + + if (!delete_launched && end >= start_deletes_after && + end >= points_to_skip + points_to_delete_from_beginning) + { + delete_launched = true; + diskann::IndexWriteParameters delete_params = + diskann::IndexWriteParametersBuilder(params).with_num_threads(sub_threads).build(); + + delete_task = std::async(std::launch::async, [&]() { + delete_from_beginning(*index, delete_params, points_to_skip, + points_to_delete_from_beginning); + }); + } + } + delete_task.wait(); + + std::cout << "Time Elapsed " << timer.elapsed() / 1000 << "ms\n"; + index->save(save_path_inc.c_str(), true); + } + else + { + const auto save_path_inc = get_save_filename(save_path + ".after-delete-", points_to_skip, + points_to_delete_from_beginning, last_point_threshold); + std::string labels_file_to_use = save_path_inc + "_label_formatted.txt"; + std::string mem_labels_int_map_file = save_path_inc + "_labels_map.txt"; + if (has_labels) + { + convert_labels_string_to_int(label_file, labels_file_to_use, mem_labels_int_map_file, universal_label); + auto parse_result = diskann::parse_formatted_label_file(labels_file_to_use); + location_to_labels = std::get<0>(parse_result); + } + + size_t last_snapshot_points_threshold = 0; + size_t num_checkpoints_till_snapshot = checkpoints_per_snapshot; + + for (size_t start = current_point_offset; start < last_point_threshold; + start += points_per_checkpoint, current_point_offset += points_per_checkpoint) + { + const size_t end = std::min(start + points_per_checkpoint, last_point_threshold); + std::cout << std::endl << "Inserting from " << start << " to " << end << std::endl; + + load_aligned_bin_part(data_path, data, start, end - start); + insert_till_next_checkpoint(*index, start, end, (int32_t)params.num_threads, data, + aligned_dim, location_to_labels); + + if (checkpoints_per_snapshot > 0 && --num_checkpoints_till_snapshot == 0) + { + diskann::Timer save_timer; + + const auto save_path_inc = + get_save_filename(save_path + ".inc-", points_to_skip, points_to_delete_from_beginning, end); + index->save(save_path_inc.c_str(), false); + const double elapsedSeconds = save_timer.elapsed() / 1000000.0; + const size_t points_saved = end - points_to_skip; + + std::cout << "Saved " << points_saved << " points in " << elapsedSeconds << " seconds (" + << points_saved / elapsedSeconds << " points/second)\n"; + + num_checkpoints_till_snapshot = checkpoints_per_snapshot; + last_snapshot_points_threshold = end; + } + + std::cout << "Number of points in the index post insertion " << end << std::endl; + } + + if (checkpoints_per_snapshot > 0 && last_snapshot_points_threshold != last_point_threshold) + { + const auto save_path_inc = get_save_filename(save_path + ".inc-", points_to_skip, + points_to_delete_from_beginning, last_point_threshold); + // index.save(save_path_inc.c_str(), false); + } + + if (points_to_delete_from_beginning > 0) + { + delete_from_beginning(*index, params, points_to_skip, points_to_delete_from_beginning); + } + + index->save(save_path_inc.c_str(), true); + } + + diskann::aligned_free(data); +} + +int main(int argc, char **argv) +{ + std::string data_type, dist_fn, data_path, index_path_prefix; + uint32_t num_threads, R, L, num_start_pts; + float alpha, start_point_norm; + size_t points_to_skip, max_points_to_insert, beginning_index_size, points_per_checkpoint, checkpoints_per_snapshot, + points_to_delete_from_beginning, start_deletes_after; + bool concurrent; + + // label options + std::string label_file, label_type, universal_label; + std::uint32_t Lf, unique_labels_supported; + + po::options_description desc{program_options_utils::make_program_description("test_insert_deletes_consolidate", + "Test insert deletes & consolidate")}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + + // Required parameters + po::options_description required_configs("Required"); + required_configs.add_options()("data_type", po::value(&data_type)->required(), + program_options_utils::DATA_TYPE_DESCRIPTION); + required_configs.add_options()("dist_fn", po::value(&dist_fn)->required(), + program_options_utils::DISTANCE_FUNCTION_DESCRIPTION); + required_configs.add_options()("index_path_prefix", po::value(&index_path_prefix)->required(), + program_options_utils::INDEX_PATH_PREFIX_DESCRIPTION); + required_configs.add_options()("data_path", po::value(&data_path)->required(), + program_options_utils::INPUT_DATA_PATH); + required_configs.add_options()("points_to_skip", po::value(&points_to_skip)->required(), + "Skip these first set of points from file"); + required_configs.add_options()("beginning_index_size", po::value(&beginning_index_size)->required(), + "Batch build will be called on these set of points"); + required_configs.add_options()("points_per_checkpoint", po::value(&points_per_checkpoint)->required(), + "Insertions are done in batches of points_per_checkpoint"); + required_configs.add_options()("checkpoints_per_snapshot", + po::value(&checkpoints_per_snapshot)->required(), + "Save the index to disk every few checkpoints"); + required_configs.add_options()("points_to_delete_from_beginning", + po::value(&points_to_delete_from_beginning)->required(), ""); + + // Optional parameters + po::options_description optional_configs("Optional"); + optional_configs.add_options()("num_threads,T", + po::value(&num_threads)->default_value(omp_get_num_procs()), + program_options_utils::NUMBER_THREADS_DESCRIPTION); + optional_configs.add_options()("max_degree,R", po::value(&R)->default_value(64), + program_options_utils::MAX_BUILD_DEGREE); + optional_configs.add_options()("Lbuild,L", po::value(&L)->default_value(100), + program_options_utils::GRAPH_BUILD_COMPLEXITY); + optional_configs.add_options()("alpha", po::value(&alpha)->default_value(1.2f), + program_options_utils::GRAPH_BUILD_ALPHA); + optional_configs.add_options()("max_points_to_insert", + po::value(&max_points_to_insert)->default_value(0), + "These number of points from the file are inserted after " + "points_to_skip"); + optional_configs.add_options()("do_concurrent", po::value(&concurrent)->default_value(false), ""); + optional_configs.add_options()("start_deletes_after", + po::value(&start_deletes_after)->default_value(0), ""); + optional_configs.add_options()("start_point_norm", po::value(&start_point_norm)->default_value(0), + "Set the start point to a random point on a sphere of this radius"); + + // optional params for filters + optional_configs.add_options()("label_file", po::value(&label_file)->default_value(""), + "Input label file in txt format for Filtered Index search. " + "The file should contain comma separated filters for each node " + "with each line corresponding to a graph node"); + optional_configs.add_options()("universal_label", po::value(&universal_label)->default_value(""), + "Universal label, if using it, only in conjunction with labels_file"); + optional_configs.add_options()("FilteredLbuild,Lf", po::value(&Lf)->default_value(0), + "Build complexity for filtered points, higher value " + "results in better graphs"); + optional_configs.add_options()("label_type", po::value(&label_type)->default_value("uint"), + "Storage type of Labels , default value is uint which " + "will consume memory 4 bytes per filter"); + optional_configs.add_options()("unique_labels_supported", + po::value(&unique_labels_supported)->default_value(0), + "Number of unique labels supported by the dynamic index."); + + optional_configs.add_options()( + "num_start_points", + po::value(&num_start_pts)->default_value(diskann::defaults::NUM_FROZEN_POINTS_DYNAMIC), + "Set the number of random start (frozen) points to use when " + "inserting and searching"); + + // Merge required and optional parameters + desc.add(required_configs).add(optional_configs); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + if (beginning_index_size == 0) + if (start_point_norm == 0) + { + std::cout << "When beginning_index_size is 0, use a start " + "point with " + "appropriate norm" + << std::endl; + return -1; + } + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + bool has_labels = false; + if (!label_file.empty() || label_file != "") + { + has_labels = true; + } + + if (num_start_pts < unique_labels_supported) + { + num_start_pts = unique_labels_supported; + } + + try + { + diskann::IndexWriteParameters params = diskann::IndexWriteParametersBuilder(L, R) + .with_max_occlusion_size(500) + .with_alpha(alpha) + .with_num_threads(num_threads) + .with_filter_list_size(Lf) + .build(); + + if (data_type == std::string("int8")) + build_incremental_index( + data_path, params, points_to_skip, max_points_to_insert, beginning_index_size, start_point_norm, + num_start_pts, points_per_checkpoint, checkpoints_per_snapshot, index_path_prefix, + points_to_delete_from_beginning, start_deletes_after, concurrent, label_file, universal_label); + else if (data_type == std::string("uint8")) + build_incremental_index( + data_path, params, points_to_skip, max_points_to_insert, beginning_index_size, start_point_norm, + num_start_pts, points_per_checkpoint, checkpoints_per_snapshot, index_path_prefix, + points_to_delete_from_beginning, start_deletes_after, concurrent, label_file, universal_label); + else if (data_type == std::string("float")) + build_incremental_index(data_path, params, points_to_skip, max_points_to_insert, + beginning_index_size, start_point_norm, num_start_pts, points_per_checkpoint, + checkpoints_per_snapshot, index_path_prefix, points_to_delete_from_beginning, + start_deletes_after, concurrent, label_file, universal_label); + else + std::cout << "Unsupported type. Use float/int8/uint8" << std::endl; + } + catch (const std::exception &e) + { + std::cerr << "Caught exception: " << e.what() << std::endl; + exit(-1); + } + catch (...) + { + std::cerr << "Caught unknown exception" << std::endl; + exit(-1); + } + + return 0; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/test_streaming_scenario.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/test_streaming_scenario.cpp new file mode 100644 index 0000000..5a43a69 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/test_streaming_scenario.cpp @@ -0,0 +1,523 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "utils.h" +#include "filter_utils.h" +#include "program_options_utils.hpp" + +#ifndef _WINDOWS +#include +#include +#include +#endif + +#include "memory_mapper.h" + +namespace po = boost::program_options; + +// load_aligned_bin modified to read pieces of the file, but using ifstream +// instead of cached_ifstream. +template +inline void load_aligned_bin_part(const std::string &bin_file, T *data, size_t offset_points, size_t points_to_read) +{ + std::ifstream reader; + reader.exceptions(std::ios::failbit | std::ios::badbit); + reader.open(bin_file, std::ios::binary | std::ios::ate); + size_t actual_file_size = reader.tellg(); + reader.seekg(0, std::ios::beg); + + int npts_i32, dim_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&dim_i32, sizeof(int)); + size_t npts = (uint32_t)npts_i32; + size_t dim = (uint32_t)dim_i32; + + size_t expected_actual_file_size = npts * dim * sizeof(T) + 2 * sizeof(uint32_t); + if (actual_file_size != expected_actual_file_size) + { + std::stringstream stream; + stream << "Error. File size mismatch. Actual size is " << actual_file_size << " while expected size is " + << expected_actual_file_size << " npts = " << npts << " dim = " << dim << " size of = " << sizeof(T) + << std::endl; + std::cout << stream.str(); + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + + if (offset_points + points_to_read > npts) + { + std::stringstream stream; + stream << "Error. Not enough points in file. Requested " << offset_points << " offset and " << points_to_read + << " points, but have only " << npts << " points" << std::endl; + std::cout << stream.str(); + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + + reader.seekg(2 * sizeof(uint32_t) + offset_points * dim * sizeof(T)); + + const size_t rounded_dim = ROUND_UP(dim, 8); + + for (size_t i = 0; i < points_to_read; i++) + { + reader.read((char *)(data + i * rounded_dim), dim * sizeof(T)); + memset(data + i * rounded_dim + dim, 0, (rounded_dim - dim) * sizeof(T)); + } + reader.close(); +} + +std::string get_save_filename(const std::string &save_path, size_t active_window, size_t consolidate_interval, + size_t max_points_to_insert) +{ + std::string final_path = save_path; + final_path += "act" + std::to_string(active_window) + "-"; + final_path += "cons" + std::to_string(consolidate_interval) + "-"; + final_path += "max" + std::to_string(max_points_to_insert); + return final_path; +} + +template +void insert_next_batch(diskann::AbstractIndex &index, size_t start, size_t end, size_t insert_threads, T *data, + size_t aligned_dim, std::vector> &pts_to_labels) +{ + try + { + diskann::Timer insert_timer; + std::cout << std::endl << "Inserting from " << start << " to " << end << std::endl; + + size_t num_failed = 0; +#pragma omp parallel for num_threads((int32_t)insert_threads) schedule(dynamic) reduction(+ : num_failed) + for (int64_t j = start; j < (int64_t)end; j++) + { + int insert_result = -1; + if (pts_to_labels.size() > 0) + { + insert_result = index.insert_point(&data[(j - start) * aligned_dim], 1 + static_cast(j), + pts_to_labels[j - start]); + } + else + { + insert_result = index.insert_point(&data[(j - start) * aligned_dim], 1 + static_cast(j)); + } + + if (insert_result != 0) + { + std::cerr << "Insert failed " << j << std::endl; + num_failed++; + } + } + const double elapsedSeconds = insert_timer.elapsed() / 1000000.0; + std::cout << "Insertion time " << elapsedSeconds << " seconds (" << (end - start) / elapsedSeconds + << " points/second overall, " << (end - start) / elapsedSeconds / insert_threads << " per thread)" + << std::endl; + if (num_failed > 0) + std::cout << num_failed << " of " << end - start << "inserts failed" << std::endl; + } + catch (std::system_error &e) + { + std::cout << "Exiting after catching exception in insertion task: " << e.what() << std::endl; + exit(-1); + } +} + +template +void delete_and_consolidate(diskann::AbstractIndex &index, diskann::IndexWriteParameters &delete_params, size_t start, + size_t end) +{ + try + { + std::cout << std::endl << "Lazy deleting points " << start << " to " << end << "... "; + for (size_t i = start; i < end; ++i) + index.lazy_delete(static_cast(1 + i)); + std::cout << "lazy delete done." << std::endl; + + auto report = index.consolidate_deletes(delete_params); + while (report._status != diskann::consolidation_report::status_code::SUCCESS) + { + int wait_time = 5; + if (report._status == diskann::consolidation_report::status_code::LOCK_FAIL) + { + diskann::cerr << "Unable to acquire consolidate delete lock after " + << "deleting points " << start << " to " << end << ". Will retry in " << wait_time + << "seconds." << std::endl; + } + else if (report._status == diskann::consolidation_report::status_code::INCONSISTENT_COUNT_ERROR) + { + diskann::cerr << "Inconsistent counts in data structure. " + << "Will retry in " << wait_time << "seconds." << std::endl; + } + else + { + std::cerr << "Exiting after unknown error in consolidate delete" << std::endl; + exit(-1); + } + std::this_thread::sleep_for(std::chrono::seconds(wait_time)); + report = index.consolidate_deletes(delete_params); + } + auto points_processed = report._active_points + report._slots_released; + auto deletion_rate = points_processed / report._time; + std::cout << "#active points: " << report._active_points << std::endl + << "max points: " << report._max_points << std::endl + << "empty slots: " << report._empty_slots << std::endl + << "deletes processed: " << report._slots_released << std::endl + << "latest delete size: " << report._delete_set_size << std::endl + << "Deletion rate: " << deletion_rate << "/sec " + << "Deletion rate: " << deletion_rate / delete_params.num_threads << "/thread/sec " << std::endl; + } + catch (std::system_error &e) + { + std::cerr << "Exiting after catching exception in deletion task: " << e.what() << std::endl; + exit(-1); + } +} + +template +void build_incremental_index(const std::string &data_path, const uint32_t L, const uint32_t R, const float alpha, + const uint32_t insert_threads, const uint32_t consolidate_threads, + size_t max_points_to_insert, size_t active_window, size_t consolidate_interval, + const float start_point_norm, uint32_t num_start_pts, const std::string &save_path, + const std::string &label_file, const std::string &universal_label, const uint32_t Lf) +{ + const uint32_t C = 500; + const bool saturate_graph = false; + bool has_labels = label_file != ""; + + diskann::IndexWriteParameters params = diskann::IndexWriteParametersBuilder(L, R) + .with_max_occlusion_size(C) + .with_alpha(alpha) + .with_saturate_graph(saturate_graph) + .with_num_threads(insert_threads) + .with_filter_list_size(Lf) + .build(); + + auto index_search_params = diskann::IndexSearchParams(L, insert_threads); + diskann::IndexWriteParameters delete_params = diskann::IndexWriteParametersBuilder(L, R) + .with_max_occlusion_size(C) + .with_alpha(alpha) + .with_saturate_graph(saturate_graph) + .with_num_threads(consolidate_threads) + .with_filter_list_size(Lf) + .build(); + + size_t dim, aligned_dim; + size_t num_points; + + std::vector> pts_to_labels; + + const auto save_path_inc = + get_save_filename(save_path + ".after-streaming-", active_window, consolidate_interval, max_points_to_insert); + std::string labels_file_to_use = save_path_inc + "_label_formatted.txt"; + std::string mem_labels_int_map_file = save_path_inc + "_labels_map.txt"; + if (has_labels) + { + convert_labels_string_to_int(label_file, labels_file_to_use, mem_labels_int_map_file, universal_label); + auto parse_result = diskann::parse_formatted_label_file(labels_file_to_use); + pts_to_labels = std::get<0>(parse_result); + } + + diskann::get_bin_metadata(data_path, num_points, dim); + diskann::cout << "metadata: file " << data_path << " has " << num_points << " points in " << dim << " dims" + << std::endl; + aligned_dim = ROUND_UP(dim, 8); + auto index_config = diskann::IndexConfigBuilder() + .with_metric(diskann::L2) + .with_dimension(dim) + .with_max_points(active_window + 4 * consolidate_interval) + .is_dynamic_index(true) + .is_enable_tags(true) + .is_use_opq(false) + .is_filtered(has_labels) + .with_num_pq_chunks(0) + .is_pq_dist_build(false) + .with_num_frozen_pts(num_start_pts) + .with_tag_type(diskann_type_to_name()) + .with_label_type(diskann_type_to_name()) + .with_data_type(diskann_type_to_name()) + .with_index_write_params(params) + .with_index_search_params(index_search_params) + .with_data_load_store_strategy(diskann::DataStoreStrategy::MEMORY) + .with_graph_load_store_strategy(diskann::GraphStoreStrategy::MEMORY) + .build(); + + diskann::IndexFactory index_factory = diskann::IndexFactory(index_config); + auto index = index_factory.create_instance(); + + if (universal_label != "") + { + LabelT u_label = 0; + index->set_universal_label(u_label); + } + + if (max_points_to_insert == 0) + { + max_points_to_insert = num_points; + } + + if (num_points < max_points_to_insert) + throw diskann::ANNException(std::string("num_points(") + std::to_string(num_points) + + ") < max_points_to_insert(" + std::to_string(max_points_to_insert) + ")", + -1, __FUNCSIG__, __FILE__, __LINE__); + + if (max_points_to_insert < active_window + consolidate_interval) + throw diskann::ANNException("ERROR: max_points_to_insert < " + "active_window + consolidate_interval", + -1, __FUNCSIG__, __FILE__, __LINE__); + + if (consolidate_interval < max_points_to_insert / 1000) + throw diskann::ANNException("ERROR: consolidate_interval is too small", -1, __FUNCSIG__, __FILE__, __LINE__); + + index->set_start_points_at_random(static_cast(start_point_norm)); + + T *data = nullptr; + diskann::alloc_aligned((void **)&data, std::max(consolidate_interval, active_window) * aligned_dim * sizeof(T), + 8 * sizeof(T)); + + std::vector tags(max_points_to_insert); + std::iota(tags.begin(), tags.end(), static_cast(0)); + + diskann::Timer timer; + + std::vector> delete_tasks; + + auto insert_task = std::async(std::launch::async, [&]() { + load_aligned_bin_part(data_path, data, 0, active_window); + insert_next_batch(*index, (size_t)0, active_window, params.num_threads, data, aligned_dim, + pts_to_labels); + }); + insert_task.wait(); + + for (size_t start = active_window; start + consolidate_interval <= max_points_to_insert; + start += consolidate_interval) + { + auto end = std::min(start + consolidate_interval, max_points_to_insert); + auto insert_task = std::async(std::launch::async, [&]() { + load_aligned_bin_part(data_path, data, start, end - start); + insert_next_batch(*index, start, end, params.num_threads, data, aligned_dim, + pts_to_labels); + }); + insert_task.wait(); + + if (delete_tasks.size() > 0) + delete_tasks[delete_tasks.size() - 1].wait(); + if (start >= active_window + consolidate_interval) + { + auto start_del = start - active_window - consolidate_interval; + auto end_del = start - active_window; + + delete_tasks.emplace_back(std::async(std::launch::async, [&]() { + delete_and_consolidate(*index, delete_params, (size_t)start_del, (size_t)end_del); + })); + } + } + if (delete_tasks.size() > 0) + delete_tasks[delete_tasks.size() - 1].wait(); + + std::cout << "Time Elapsed " << timer.elapsed() / 1000 << "ms\n"; + + index->save(save_path_inc.c_str(), true); + + diskann::aligned_free(data); +} + +int main(int argc, char **argv) +{ + std::string data_type, dist_fn, data_path, index_path_prefix, label_file, universal_label, label_type; + uint32_t insert_threads, consolidate_threads, R, L, num_start_pts, Lf, unique_labels_supported; + float alpha, start_point_norm; + size_t max_points_to_insert, active_window, consolidate_interval; + + po::options_description desc{program_options_utils::make_program_description("test_streaming_scenario", + "Test insert deletes & consolidate")}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + + // Required parameters + po::options_description required_configs("Required"); + required_configs.add_options()("data_type", po::value(&data_type)->required(), + program_options_utils::DATA_TYPE_DESCRIPTION); + required_configs.add_options()("dist_fn", po::value(&dist_fn)->required(), + program_options_utils::DISTANCE_FUNCTION_DESCRIPTION); + required_configs.add_options()("index_path_prefix", po::value(&index_path_prefix)->required(), + program_options_utils::INDEX_PATH_PREFIX_DESCRIPTION); + required_configs.add_options()("data_path", po::value(&data_path)->required(), + program_options_utils::INPUT_DATA_PATH); + required_configs.add_options()("active_window", po::value(&active_window)->required(), + "Program maintains an index over an active window of " + "this size that slides through the data"); + required_configs.add_options()("consolidate_interval", po::value(&consolidate_interval)->required(), + "The program simultaneously adds this number of points to the " + "right of " + "the window while deleting the same number from the left"); + required_configs.add_options()("start_point_norm", po::value(&start_point_norm)->required(), + "Set the start point to a random point on a sphere of this radius"); + + // Optional parameters + po::options_description optional_configs("Optional"); + optional_configs.add_options()("max_degree,R", po::value(&R)->default_value(64), + program_options_utils::MAX_BUILD_DEGREE); + optional_configs.add_options()("Lbuild,L", po::value(&L)->default_value(100), + program_options_utils::GRAPH_BUILD_COMPLEXITY); + optional_configs.add_options()("alpha", po::value(&alpha)->default_value(1.2f), + program_options_utils::GRAPH_BUILD_ALPHA); + optional_configs.add_options()("insert_threads", + po::value(&insert_threads)->default_value(omp_get_num_procs() / 2), + "Number of threads used for inserting into the index (defaults to " + "omp_get_num_procs()/2)"); + optional_configs.add_options()( + "consolidate_threads", po::value(&consolidate_threads)->default_value(omp_get_num_procs() / 2), + "Number of threads used for consolidating deletes to " + "the index (defaults to omp_get_num_procs()/2)"); + optional_configs.add_options()("max_points_to_insert", + po::value(&max_points_to_insert)->default_value(0), + "The number of points from the file that the program streams " + "over "); + optional_configs.add_options()( + "num_start_points", + po::value(&num_start_pts)->default_value(diskann::defaults::NUM_FROZEN_POINTS_DYNAMIC), + "Set the number of random start (frozen) points to use when " + "inserting and searching"); + + optional_configs.add_options()("label_file", po::value(&label_file)->default_value(""), + "Input label file in txt format for Filtered Index search. " + "The file should contain comma separated filters for each node " + "with each line corresponding to a graph node"); + optional_configs.add_options()("universal_label", po::value(&universal_label)->default_value(""), + "Universal label, if using it, only in conjunction with labels_file"); + optional_configs.add_options()("FilteredLbuild,Lf", po::value(&Lf)->default_value(0), + "Build complexity for filtered points, higher value " + "results in better graphs"); + optional_configs.add_options()("label_type", po::value(&label_type)->default_value("uint"), + "Storage type of Labels , default value is uint which " + "will consume memory 4 bytes per filter"); + optional_configs.add_options()("unique_labels_supported", + po::value(&unique_labels_supported)->default_value(0), + "Number of unique labels supported by the dynamic index."); + + // Merge required and optional parameters + desc.add(required_configs).add(optional_configs); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + // Validate arguments + if (start_point_norm == 0) + { + std::cout << "When beginning_index_size is 0, use a start point with " + "appropriate norm" + << std::endl; + return -1; + } + + if (label_type != std::string("ushort") && label_type != std::string("uint")) + { + std::cerr << "Invalid label type. Supported types are uint and ushort" << std::endl; + return -1; + } + + if (data_type != std::string("int8") && data_type != std::string("uint8") && data_type != std::string("float")) + { + std::cerr << "Invalid data type. Supported types are int8, uint8 and float" << std::endl; + return -1; + } + + // TODO: Are additional distance functions supported? + if (dist_fn != std::string("l2") && dist_fn != std::string("mips")) + { + std::cerr << "Invalid distance function. Supported functions are l2 and mips" << std::endl; + return -1; + } + + if (num_start_pts < unique_labels_supported) + { + num_start_pts = unique_labels_supported; + } + + try + { + if (data_type == std::string("uint8")) + { + if (label_type == std::string("ushort")) + { + build_incremental_index( + data_path, L, R, alpha, insert_threads, consolidate_threads, max_points_to_insert, active_window, + consolidate_interval, start_point_norm, num_start_pts, index_path_prefix, label_file, + universal_label, Lf); + } + else if (label_type == std::string("uint")) + { + build_incremental_index( + data_path, L, R, alpha, insert_threads, consolidate_threads, max_points_to_insert, active_window, + consolidate_interval, start_point_norm, num_start_pts, index_path_prefix, label_file, + universal_label, Lf); + } + } + else if (data_type == std::string("int8")) + { + if (label_type == std::string("ushort")) + { + build_incremental_index( + data_path, L, R, alpha, insert_threads, consolidate_threads, max_points_to_insert, active_window, + consolidate_interval, start_point_norm, num_start_pts, index_path_prefix, label_file, + universal_label, Lf); + } + else if (label_type == std::string("uint")) + { + build_incremental_index( + data_path, L, R, alpha, insert_threads, consolidate_threads, max_points_to_insert, active_window, + consolidate_interval, start_point_norm, num_start_pts, index_path_prefix, label_file, + universal_label, Lf); + } + } + else if (data_type == std::string("float")) + { + if (label_type == std::string("ushort")) + { + build_incremental_index( + data_path, L, R, alpha, insert_threads, consolidate_threads, max_points_to_insert, active_window, + consolidate_interval, start_point_norm, num_start_pts, index_path_prefix, label_file, + universal_label, Lf); + } + else if (label_type == std::string("uint")) + { + build_incremental_index( + data_path, L, R, alpha, insert_threads, consolidate_threads, max_points_to_insert, active_window, + consolidate_interval, start_point_norm, num_start_pts, index_path_prefix, label_file, + universal_label, Lf); + } + } + } + catch (const std::exception &e) + { + std::cerr << "Caught exception: " << e.what() << std::endl; + exit(-1); + } + catch (...) + { + std::cerr << "Caught unknown exception" << std::endl; + exit(-1); + } + + return 0; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/CMakeLists.txt b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/CMakeLists.txt new file mode 100644 index 0000000..3b8cf22 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/CMakeLists.txt @@ -0,0 +1,110 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_COMPILE_WARNING_AS_ERROR ON) + + +add_executable(fvecs_to_bin fvecs_to_bin.cpp) + +add_executable(fvecs_to_bvecs fvecs_to_bvecs.cpp) + +add_executable(rand_data_gen rand_data_gen.cpp) +target_link_libraries(rand_data_gen ${PROJECT_NAME} Boost::program_options) + +add_executable(float_bin_to_int8 float_bin_to_int8.cpp) + +add_executable(ivecs_to_bin ivecs_to_bin.cpp) + +add_executable(count_bfs_levels count_bfs_levels.cpp) +target_link_libraries(count_bfs_levels ${PROJECT_NAME} Boost::program_options) + +add_executable(tsv_to_bin tsv_to_bin.cpp) + +add_executable(bin_to_tsv bin_to_tsv.cpp) + +add_executable(int8_to_float int8_to_float.cpp) +target_link_libraries(int8_to_float ${PROJECT_NAME}) + +add_executable(int8_to_float_scale int8_to_float_scale.cpp) +target_link_libraries(int8_to_float_scale ${PROJECT_NAME}) + +add_executable(uint8_to_float uint8_to_float.cpp) +target_link_libraries(uint8_to_float ${PROJECT_NAME}) + +add_executable(uint32_to_uint8 uint32_to_uint8.cpp) +target_link_libraries(uint32_to_uint8 ${PROJECT_NAME}) + +add_executable(vector_analysis vector_analysis.cpp) +target_link_libraries(vector_analysis ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS}) + +add_executable(gen_random_slice gen_random_slice.cpp) +target_link_libraries(gen_random_slice ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS}) + +add_executable(simulate_aggregate_recall simulate_aggregate_recall.cpp) + +add_executable(calculate_recall calculate_recall.cpp) +target_link_libraries(calculate_recall ${PROJECT_NAME} ${DISKANN_ASYNC_LIB} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS}) + +# Compute ground truth thing outside of DiskANN main source that depends on MKL. +add_executable(compute_groundtruth compute_groundtruth.cpp) +target_include_directories(compute_groundtruth PRIVATE ${DISKANN_MKL_INCLUDE_DIRECTORIES}) +target_link_libraries(compute_groundtruth ${PROJECT_NAME} ${DISKANN_MKL_LINK_LIBRARIES} ${DISKANN_ASYNC_LIB} Boost::program_options) + +add_executable(compute_groundtruth_for_filters compute_groundtruth_for_filters.cpp) +target_include_directories(compute_groundtruth_for_filters PRIVATE ${DISKANN_MKL_INCLUDE_DIRECTORIES}) +target_link_libraries(compute_groundtruth_for_filters ${PROJECT_NAME} ${DISKANN_MKL_LINK_LIBRARIES} ${DISKANN_ASYNC_LIB} Boost::program_options) + + +add_executable(generate_pq generate_pq.cpp) +target_link_libraries(generate_pq ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS}) + + +add_executable(partition_data partition_data.cpp) +target_link_libraries(partition_data ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS}) + +add_executable(partition_with_ram_budget partition_with_ram_budget.cpp) +target_link_libraries(partition_with_ram_budget ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS}) + +add_executable(merge_shards merge_shards.cpp) +target_link_libraries(merge_shards ${PROJECT_NAME} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} ${DISKANN_ASYNC_LIB}) + +add_executable(create_disk_layout create_disk_layout.cpp) +target_link_libraries(create_disk_layout ${PROJECT_NAME} ${DISKANN_ASYNC_LIB} ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS}) + +add_executable(generate_synthetic_labels generate_synthetic_labels.cpp) +target_link_libraries(generate_synthetic_labels ${PROJECT_NAME} Boost::program_options) + +add_executable(stats_label_data stats_label_data.cpp) +target_link_libraries(stats_label_data ${PROJECT_NAME} Boost::program_options) + +if (NOT MSVC) + include(GNUInstallDirs) + install(TARGETS fvecs_to_bin + fvecs_to_bvecs + rand_data_gen + float_bin_to_int8 + ivecs_to_bin + count_bfs_levels + tsv_to_bin + bin_to_tsv + int8_to_float + int8_to_float_scale + uint8_to_float + uint32_to_uint8 + vector_analysis + gen_random_slice + simulate_aggregate_recall + calculate_recall + compute_groundtruth + compute_groundtruth_for_filters + generate_pq + partition_data + partition_with_ram_budget + merge_shards + create_disk_layout + generate_synthetic_labels + stats_label_data + RUNTIME + ) +endif() \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/bin_to_fvecs.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/bin_to_fvecs.cpp new file mode 100644 index 0000000..e9a6a8e --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/bin_to_fvecs.cpp @@ -0,0 +1,63 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "util.h" + +void block_convert(std::ifstream &writr, std::ofstream &readr, float *read_buf, float *write_buf, uint64_t npts, + uint64_t ndims) +{ + writr.write((char *)read_buf, npts * (ndims * sizeof(float) + sizeof(unsigned))); +#pragma omp parallel for + for (uint64_t i = 0; i < npts; i++) + { + memcpy(write_buf + i * ndims, (read_buf + i * (ndims + 1)) + 1, ndims * sizeof(float)); + } + readr.read((char *)write_buf, npts * ndims * sizeof(float)); +} + +int main(int argc, char **argv) +{ + if (argc != 3) + { + std::cout << argv[0] << " input_bin output_fvecs" << std::endl; + exit(-1); + } + std::ifstream readr(argv[1], std::ios::binary); + int npts_s32; + int ndims_s32; + readr.read((char *)&npts_s32, sizeof(int32_t)); + readr.read((char *)&ndims_s32, sizeof(int32_t)); + size_t npts = npts_s32; + size_t ndims = ndims_s32; + uint32_t ndims_u32 = (uint32_t)ndims_s32; + // uint64_t fsize = writr.tellg(); + readr.seekg(0, std::ios::beg); + + unsigned ndims_u32; + writr.write((char *)&ndims_u32, sizeof(unsigned)); + writr.seekg(0, std::ios::beg); + uint64_t ndims = (uint64_t)ndims_u32; + uint64_t npts = fsize / ((ndims + 1) * sizeof(float)); + std::cout << "Dataset: #pts = " << npts << ", # dims = " << ndims << std::endl; + + uint64_t blk_size = 131072; + uint64_t nblks = ROUND_UP(npts, blk_size) / blk_size; + std::cout << "# blks: " << nblks << std::endl; + + std::ofstream writr(argv[2], std::ios::binary); + float *read_buf = new float[npts * (ndims + 1)]; + float *write_buf = new float[npts * ndims]; + for (uint64_t i = 0; i < nblks; i++) + { + uint64_t cblk_size = std::min(npts - i * blk_size, blk_size); + block_convert(writr, readr, read_buf, write_buf, cblk_size, ndims); + std::cout << "Block #" << i << " written" << std::endl; + } + + delete[] read_buf; + delete[] write_buf; + + writr.close(); + readr.close(); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/bin_to_tsv.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/bin_to_tsv.cpp new file mode 100644 index 0000000..7851bef --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/bin_to_tsv.cpp @@ -0,0 +1,69 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +template +void block_convert(std::ofstream &writer, std::ifstream &reader, T *read_buf, size_t npts, size_t ndims) +{ + reader.read((char *)read_buf, npts * ndims * sizeof(float)); + + for (size_t i = 0; i < npts; i++) + { + for (size_t d = 0; d < ndims; d++) + { + writer << read_buf[d + i * ndims]; + if (d < ndims - 1) + writer << "\t"; + else + writer << "\n"; + } + } +} + +int main(int argc, char **argv) +{ + if (argc != 4) + { + std::cout << argv[0] << " input_bin output_tsv" << std::endl; + exit(-1); + } + std::string type_string(argv[1]); + if ((type_string != std::string("float")) && (type_string != std::string("int8")) && + (type_string != std::string("uin8"))) + { + std::cerr << "Error: type not supported. Use float/int8/uint8" << std::endl; + } + + std::ifstream reader(argv[2], std::ios::binary); + uint32_t npts_u32; + uint32_t ndims_u32; + reader.read((char *)&npts_u32, sizeof(uint32_t)); + reader.read((char *)&ndims_u32, sizeof(uint32_t)); + size_t npts = npts_u32; + size_t ndims = ndims_u32; + std::cout << "Dataset: #pts = " << npts << ", # dims = " << ndims << std::endl; + + size_t blk_size = 131072; + size_t nblks = ROUND_UP(npts, blk_size) / blk_size; + + std::ofstream writer(argv[3]); + char *read_buf = new char[blk_size * ndims * 4]; + for (size_t i = 0; i < nblks; i++) + { + size_t cblk_size = std::min(npts - i * blk_size, blk_size); + if (type_string == std::string("float")) + block_convert(writer, reader, (float *)read_buf, cblk_size, ndims); + else if (type_string == std::string("int8")) + block_convert(writer, reader, (int8_t *)read_buf, cblk_size, ndims); + else if (type_string == std::string("uint8")) + block_convert(writer, reader, (uint8_t *)read_buf, cblk_size, ndims); + std::cout << "Block #" << i << " written" << std::endl; + } + + delete[] read_buf; + + writer.close(); + reader.close(); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/calculate_recall.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/calculate_recall.cpp new file mode 100644 index 0000000..dc76252 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/calculate_recall.cpp @@ -0,0 +1,55 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include + +#include "utils.h" +#include "disk_utils.h" + +int main(int argc, char **argv) +{ + if (argc != 4) + { + std::cout << argv[0] << " " << std::endl; + return -1; + } + uint32_t *gold_std = NULL; + float *gs_dist = nullptr; + uint32_t *our_results = NULL; + float *or_dist = nullptr; + size_t points_num, points_num_gs, points_num_or; + size_t dim_gs; + size_t dim_or; + diskann::load_truthset(argv[1], gold_std, gs_dist, points_num_gs, dim_gs); + diskann::load_truthset(argv[2], our_results, or_dist, points_num_or, dim_or); + + if (points_num_gs != points_num_or) + { + std::cout << "Error. Number of queries mismatch in ground truth and " + "our results" + << std::endl; + return -1; + } + points_num = points_num_gs; + + uint32_t recall_at = std::atoi(argv[3]); + + if ((dim_or < recall_at) || (recall_at > dim_gs)) + { + std::cout << "ground truth has size " << dim_gs << "; our set has " << dim_or << " points. Asking for recall " + << recall_at << std::endl; + return -1; + } + std::cout << "Calculating recall@" << recall_at << std::endl; + double recall_val = diskann::calculate_recall((uint32_t)points_num, gold_std, gs_dist, (uint32_t)dim_gs, + our_results, (uint32_t)dim_or, (uint32_t)recall_at); + + // double avg_recall = (recall*1.0)/(points_num*1.0); + std::cout << "Avg. recall@" << recall_at << " is " << recall_val << "\n"; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/compute_groundtruth.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/compute_groundtruth.cpp new file mode 100644 index 0000000..da32fd7 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/compute_groundtruth.cpp @@ -0,0 +1,574 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef _WINDOWS +#include +#else +#include +#endif +#include "filter_utils.h" +#include "utils.h" + +// WORKS FOR UPTO 2 BILLION POINTS (as we use INT INSTEAD OF UNSIGNED) + +#define PARTSIZE 10000000 +#define ALIGNMENT 512 + +// custom types (for readability) +typedef tsl::robin_set label_set; +typedef std::string path; + +namespace po = boost::program_options; + +template T div_round_up(const T numerator, const T denominator) +{ + return (numerator % denominator == 0) ? (numerator / denominator) : 1 + (numerator / denominator); +} + +using pairIF = std::pair; +struct cmpmaxstruct +{ + bool operator()(const pairIF &l, const pairIF &r) + { + return l.second < r.second; + }; +}; + +using maxPQIFCS = std::priority_queue, cmpmaxstruct>; + +template T *aligned_malloc(const size_t n, const size_t alignment) +{ +#ifdef _WINDOWS + return (T *)_aligned_malloc(sizeof(T) * n, alignment); +#else + return static_cast(aligned_alloc(alignment, sizeof(T) * n)); +#endif +} + +inline bool custom_dist(const std::pair &a, const std::pair &b) +{ + return a.second < b.second; +} + +void compute_l2sq(float *const points_l2sq, const float *const matrix, const int64_t num_points, const uint64_t dim) +{ + assert(points_l2sq != NULL); +#pragma omp parallel for schedule(static, 65536) + for (int64_t d = 0; d < num_points; ++d) + points_l2sq[d] = cblas_sdot((int64_t)dim, matrix + (ptrdiff_t)d * (ptrdiff_t)dim, 1, + matrix + (ptrdiff_t)d * (ptrdiff_t)dim, 1); +} + +void distsq_to_points(const size_t dim, + float *dist_matrix, // Col Major, cols are queries, rows are points + size_t npoints, const float *const points, + const float *const points_l2sq, // points in Col major + size_t nqueries, const float *const queries, + const float *const queries_l2sq, // queries in Col major + float *ones_vec = NULL) // Scratchspace of num_data size and init to 1.0 +{ + bool ones_vec_alloc = false; + if (ones_vec == NULL) + { + ones_vec = new float[nqueries > npoints ? nqueries : npoints]; + std::fill_n(ones_vec, nqueries > npoints ? nqueries : npoints, (float)1.0); + ones_vec_alloc = true; + } + cblas_sgemm(CblasColMajor, CblasTrans, CblasNoTrans, npoints, nqueries, dim, (float)-2.0, points, dim, queries, dim, + (float)0.0, dist_matrix, npoints); + cblas_sgemm(CblasColMajor, CblasNoTrans, CblasTrans, npoints, nqueries, 1, (float)1.0, points_l2sq, npoints, + ones_vec, nqueries, (float)1.0, dist_matrix, npoints); + cblas_sgemm(CblasColMajor, CblasNoTrans, CblasTrans, npoints, nqueries, 1, (float)1.0, ones_vec, npoints, + queries_l2sq, nqueries, (float)1.0, dist_matrix, npoints); + if (ones_vec_alloc) + delete[] ones_vec; +} + +void inner_prod_to_points(const size_t dim, + float *dist_matrix, // Col Major, cols are queries, rows are points + size_t npoints, const float *const points, size_t nqueries, const float *const queries, + float *ones_vec = NULL) // Scratchspace of num_data size and init to 1.0 +{ + bool ones_vec_alloc = false; + if (ones_vec == NULL) + { + ones_vec = new float[nqueries > npoints ? nqueries : npoints]; + std::fill_n(ones_vec, nqueries > npoints ? nqueries : npoints, (float)1.0); + ones_vec_alloc = true; + } + cblas_sgemm(CblasColMajor, CblasTrans, CblasNoTrans, npoints, nqueries, dim, (float)-1.0, points, dim, queries, dim, + (float)0.0, dist_matrix, npoints); + + if (ones_vec_alloc) + delete[] ones_vec; +} + +void exact_knn(const size_t dim, const size_t k, + size_t *const closest_points, // k * num_queries preallocated, col + // major, queries columns + float *const dist_closest_points, // k * num_queries + // preallocated, Dist to + // corresponding closes_points + size_t npoints, + float *points_in, // points in Col major + size_t nqueries, float *queries_in, + diskann::Metric metric = diskann::Metric::L2) // queries in Col major +{ + float *points_l2sq = new float[npoints]; + float *queries_l2sq = new float[nqueries]; + compute_l2sq(points_l2sq, points_in, npoints, dim); + compute_l2sq(queries_l2sq, queries_in, nqueries, dim); + + float *points = points_in; + float *queries = queries_in; + + if (metric == diskann::Metric::COSINE) + { // we convert cosine distance as + // normalized L2 distnace + points = new float[npoints * dim]; + queries = new float[nqueries * dim]; +#pragma omp parallel for schedule(static, 4096) + for (int64_t i = 0; i < (int64_t)npoints; i++) + { + float norm = std::sqrt(points_l2sq[i]); + if (norm == 0) + { + norm = std::numeric_limits::epsilon(); + } + for (uint32_t j = 0; j < dim; j++) + { + points[i * dim + j] = points_in[i * dim + j] / norm; + } + } + +#pragma omp parallel for schedule(static, 4096) + for (int64_t i = 0; i < (int64_t)nqueries; i++) + { + float norm = std::sqrt(queries_l2sq[i]); + if (norm == 0) + { + norm = std::numeric_limits::epsilon(); + } + for (uint32_t j = 0; j < dim; j++) + { + queries[i * dim + j] = queries_in[i * dim + j] / norm; + } + } + // recalculate norms after normalizing, they should all be one. + compute_l2sq(points_l2sq, points, npoints, dim); + compute_l2sq(queries_l2sq, queries, nqueries, dim); + } + + std::cout << "Going to compute " << k << " NNs for " << nqueries << " queries over " << npoints << " points in " + << dim << " dimensions using"; + if (metric == diskann::Metric::INNER_PRODUCT) + std::cout << " MIPS "; + else if (metric == diskann::Metric::COSINE) + std::cout << " Cosine "; + else + std::cout << " L2 "; + std::cout << "distance fn. " << std::endl; + + size_t q_batch_size = (1 << 9); + float *dist_matrix = new float[(size_t)q_batch_size * (size_t)npoints]; + + for (size_t b = 0; b < div_round_up(nqueries, q_batch_size); ++b) + { + int64_t q_b = b * q_batch_size; + int64_t q_e = ((b + 1) * q_batch_size > nqueries) ? nqueries : (b + 1) * q_batch_size; + + if (metric == diskann::Metric::L2 || metric == diskann::Metric::COSINE) + { + distsq_to_points(dim, dist_matrix, npoints, points, points_l2sq, q_e - q_b, + queries + (ptrdiff_t)q_b * (ptrdiff_t)dim, queries_l2sq + q_b); + } + else + { + inner_prod_to_points(dim, dist_matrix, npoints, points, q_e - q_b, + queries + (ptrdiff_t)q_b * (ptrdiff_t)dim); + } + std::cout << "Computed distances for queries: [" << q_b << "," << q_e << ")" << std::endl; + +#pragma omp parallel for schedule(dynamic, 16) + for (long long q = q_b; q < q_e; q++) + { + maxPQIFCS point_dist; + for (size_t p = 0; p < k; p++) + point_dist.emplace(p, dist_matrix[(ptrdiff_t)p + (ptrdiff_t)(q - q_b) * (ptrdiff_t)npoints]); + for (size_t p = k; p < npoints; p++) + { + if (point_dist.top().second > dist_matrix[(ptrdiff_t)p + (ptrdiff_t)(q - q_b) * (ptrdiff_t)npoints]) + point_dist.emplace(p, dist_matrix[(ptrdiff_t)p + (ptrdiff_t)(q - q_b) * (ptrdiff_t)npoints]); + if (point_dist.size() > k) + point_dist.pop(); + } + for (ptrdiff_t l = 0; l < (ptrdiff_t)k; ++l) + { + closest_points[(ptrdiff_t)(k - 1 - l) + (ptrdiff_t)q * (ptrdiff_t)k] = point_dist.top().first; + dist_closest_points[(ptrdiff_t)(k - 1 - l) + (ptrdiff_t)q * (ptrdiff_t)k] = point_dist.top().second; + point_dist.pop(); + } + assert(std::is_sorted(dist_closest_points + (ptrdiff_t)q * (ptrdiff_t)k, + dist_closest_points + (ptrdiff_t)(q + 1) * (ptrdiff_t)k)); + } + std::cout << "Computed exact k-NN for queries: [" << q_b << "," << q_e << ")" << std::endl; + } + + delete[] dist_matrix; + + delete[] points_l2sq; + delete[] queries_l2sq; + + if (metric == diskann::Metric::COSINE) + { + delete[] points; + delete[] queries; + } +} + +template inline int get_num_parts(const char *filename) +{ + std::ifstream reader; + reader.exceptions(std::ios::failbit | std::ios::badbit); + reader.open(filename, std::ios::binary); + std::cout << "Reading bin file " << filename << " ...\n"; + int npts_i32, ndims_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&ndims_i32, sizeof(int)); + std::cout << "#pts = " << npts_i32 << ", #dims = " << ndims_i32 << std::endl; + reader.close(); + uint32_t num_parts = + (npts_i32 % PARTSIZE) == 0 ? npts_i32 / PARTSIZE : (uint32_t)std::floor(npts_i32 / PARTSIZE) + 1; + std::cout << "Number of parts: " << num_parts << std::endl; + return num_parts; +} + +template +inline void load_bin_as_float(const char *filename, float *&data, size_t &npts, size_t &ndims, int part_num) +{ + std::ifstream reader; + reader.exceptions(std::ios::failbit | std::ios::badbit); + reader.open(filename, std::ios::binary); + std::cout << "Reading bin file " << filename << " ...\n"; + int npts_i32, ndims_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&ndims_i32, sizeof(int)); + uint64_t start_id = part_num * PARTSIZE; + uint64_t end_id = (std::min)(start_id + PARTSIZE, (uint64_t)npts_i32); + npts = end_id - start_id; + ndims = (uint64_t)ndims_i32; + std::cout << "#pts in part = " << npts << ", #dims = " << ndims << ", size = " << npts * ndims * sizeof(T) << "B" + << std::endl; + + reader.seekg(start_id * ndims * sizeof(T) + 2 * sizeof(uint32_t), std::ios::beg); + T *data_T = new T[npts * ndims]; + reader.read((char *)data_T, sizeof(T) * npts * ndims); + std::cout << "Finished reading part of the bin file." << std::endl; + reader.close(); + data = aligned_malloc(npts * ndims, ALIGNMENT); +#pragma omp parallel for schedule(dynamic, 32768) + for (int64_t i = 0; i < (int64_t)npts; i++) + { + for (int64_t j = 0; j < (int64_t)ndims; j++) + { + float cur_val_float = (float)data_T[i * ndims + j]; + std::memcpy((char *)(data + i * ndims + j), (char *)&cur_val_float, sizeof(float)); + } + } + delete[] data_T; + std::cout << "Finished converting part data to float." << std::endl; +} + +template inline void save_bin(const std::string filename, T *data, size_t npts, size_t ndims) +{ + std::ofstream writer; + writer.exceptions(std::ios::failbit | std::ios::badbit); + writer.open(filename, std::ios::binary | std::ios::out); + std::cout << "Writing bin: " << filename << "\n"; + int npts_i32 = (int)npts, ndims_i32 = (int)ndims; + writer.write((char *)&npts_i32, sizeof(int)); + writer.write((char *)&ndims_i32, sizeof(int)); + std::cout << "bin: #pts = " << npts << ", #dims = " << ndims + << ", size = " << npts * ndims * sizeof(T) + 2 * sizeof(int) << "B" << std::endl; + + writer.write((char *)data, npts * ndims * sizeof(T)); + writer.close(); + std::cout << "Finished writing bin" << std::endl; +} + +inline void save_groundtruth_as_one_file(const std::string filename, int32_t *data, float *distances, size_t npts, + size_t ndims) +{ + std::ofstream writer(filename, std::ios::binary | std::ios::out); + int npts_i32 = (int)npts, ndims_i32 = (int)ndims; + writer.write((char *)&npts_i32, sizeof(int)); + writer.write((char *)&ndims_i32, sizeof(int)); + std::cout << "Saving truthset in one file (npts, dim, npts*dim id-matrix, " + "npts*dim dist-matrix) with npts = " + << npts << ", dim = " << ndims << ", size = " << 2 * npts * ndims * sizeof(uint32_t) + 2 * sizeof(int) + << "B" << std::endl; + + writer.write((char *)data, npts * ndims * sizeof(uint32_t)); + writer.write((char *)distances, npts * ndims * sizeof(float)); + writer.close(); + std::cout << "Finished writing truthset" << std::endl; +} + +template +std::vector>> processUnfilteredParts(const std::string &base_file, + size_t &nqueries, size_t &npoints, + size_t &dim, size_t &k, float *query_data, + const diskann::Metric &metric, + std::vector &location_to_tag) +{ + float *base_data = nullptr; + int num_parts = get_num_parts(base_file.c_str()); + std::vector>> res(nqueries); + for (int p = 0; p < num_parts; p++) + { + size_t start_id = p * PARTSIZE; + load_bin_as_float(base_file.c_str(), base_data, npoints, dim, p); + + size_t *closest_points_part = new size_t[nqueries * k]; + float *dist_closest_points_part = new float[nqueries * k]; + + auto part_k = k < npoints ? k : npoints; + exact_knn(dim, part_k, closest_points_part, dist_closest_points_part, npoints, base_data, nqueries, query_data, + metric); + + for (size_t i = 0; i < nqueries; i++) + { + for (size_t j = 0; j < part_k; j++) + { + if (!location_to_tag.empty()) + if (location_to_tag[closest_points_part[i * k + j] + start_id] == 0) + continue; + + res[i].push_back(std::make_pair((uint32_t)(closest_points_part[i * part_k + j] + start_id), + dist_closest_points_part[i * part_k + j])); + } + } + + delete[] closest_points_part; + delete[] dist_closest_points_part; + + diskann::aligned_free(base_data); + } + return res; +}; + +template +int aux_main(const std::string &base_file, const std::string &query_file, const std::string >_file, size_t k, + const diskann::Metric &metric, const std::string &tags_file = std::string("")) +{ + size_t npoints, nqueries, dim; + + float *query_data; + + load_bin_as_float(query_file.c_str(), query_data, nqueries, dim, 0); + if (nqueries > PARTSIZE) + std::cerr << "WARNING: #Queries provided (" << nqueries << ") is greater than " << PARTSIZE + << ". Computing GT only for the first " << PARTSIZE << " queries." << std::endl; + + // load tags + const bool tags_enabled = tags_file.empty() ? false : true; + std::vector location_to_tag = diskann::loadTags(tags_file, base_file); + + int *closest_points = new int[nqueries * k]; + float *dist_closest_points = new float[nqueries * k]; + + std::vector>> results = + processUnfilteredParts(base_file, nqueries, npoints, dim, k, query_data, metric, location_to_tag); + + for (size_t i = 0; i < nqueries; i++) + { + std::vector> &cur_res = results[i]; + std::sort(cur_res.begin(), cur_res.end(), custom_dist); + size_t j = 0; + for (auto iter : cur_res) + { + if (j == k) + break; + if (tags_enabled) + { + std::uint32_t index_with_tag = location_to_tag[iter.first]; + closest_points[i * k + j] = (int32_t)index_with_tag; + } + else + { + closest_points[i * k + j] = (int32_t)iter.first; + } + + if (metric == diskann::Metric::INNER_PRODUCT) + dist_closest_points[i * k + j] = -iter.second; + else + dist_closest_points[i * k + j] = iter.second; + + ++j; + } + if (j < k) + std::cout << "WARNING: found less than k GT entries for query " << i << std::endl; + } + + save_groundtruth_as_one_file(gt_file, closest_points, dist_closest_points, nqueries, k); + delete[] closest_points; + delete[] dist_closest_points; + diskann::aligned_free(query_data); + + return 0; +} + +void load_truthset(const std::string &bin_file, uint32_t *&ids, float *&dists, size_t &npts, size_t &dim) +{ + size_t read_blk_size = 64 * 1024 * 1024; + cached_ifstream reader(bin_file, read_blk_size); + diskann::cout << "Reading truthset file " << bin_file.c_str() << " ..." << std::endl; + size_t actual_file_size = reader.get_file_size(); + + int npts_i32, dim_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&dim_i32, sizeof(int)); + npts = (uint32_t)npts_i32; + dim = (uint32_t)dim_i32; + + diskann::cout << "Metadata: #pts = " << npts << ", #dims = " << dim << "... " << std::endl; + + int truthset_type = -1; // 1 means truthset has ids and distances, 2 means + // only ids, -1 is error + size_t expected_file_size_with_dists = 2 * npts * dim * sizeof(uint32_t) + 2 * sizeof(uint32_t); + + if (actual_file_size == expected_file_size_with_dists) + truthset_type = 1; + + size_t expected_file_size_just_ids = npts * dim * sizeof(uint32_t) + 2 * sizeof(uint32_t); + + if (actual_file_size == expected_file_size_just_ids) + truthset_type = 2; + + if (truthset_type == -1) + { + std::stringstream stream; + stream << "Error. File size mismatch. File should have bin format, with " + "npts followed by ngt followed by npts*ngt ids and optionally " + "followed by npts*ngt distance values; actual size: " + << actual_file_size << ", expected: " << expected_file_size_with_dists << " or " + << expected_file_size_just_ids; + diskann::cout << stream.str(); + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + + ids = new uint32_t[npts * dim]; + reader.read((char *)ids, npts * dim * sizeof(uint32_t)); + + if (truthset_type == 1) + { + dists = new float[npts * dim]; + reader.read((char *)dists, npts * dim * sizeof(float)); + } +} + +int main(int argc, char **argv) +{ + std::string data_type, dist_fn, base_file, query_file, gt_file, tags_file; + uint64_t K; + + try + { + po::options_description desc{"Arguments"}; + + desc.add_options()("help,h", "Print information on arguments"); + + desc.add_options()("data_type", po::value(&data_type)->required(), "data type "); + desc.add_options()("dist_fn", po::value(&dist_fn)->required(), + "distance function "); + desc.add_options()("base_file", po::value(&base_file)->required(), + "File containing the base vectors in binary format"); + desc.add_options()("query_file", po::value(&query_file)->required(), + "File containing the query vectors in binary format"); + desc.add_options()("gt_file", po::value(>_file)->required(), + "File name for the writing ground truth in binary " + "format, please don' append .bin at end if " + "no filter_label or filter_label_file is provided it " + "will save the file with '.bin' at end." + "else it will save the file as filename_label.bin"); + desc.add_options()("K", po::value(&K)->required(), + "Number of ground truth nearest neighbors to compute"); + desc.add_options()("tags_file", po::value(&tags_file)->default_value(std::string()), + "File containing the tags in binary format"); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + if (data_type != std::string("float") && data_type != std::string("int8") && data_type != std::string("uint8")) + { + std::cout << "Unsupported type. float, int8 and uint8 types are supported." << std::endl; + return -1; + } + + diskann::Metric metric; + if (dist_fn == std::string("l2")) + { + metric = diskann::Metric::L2; + } + else if (dist_fn == std::string("mips")) + { + metric = diskann::Metric::INNER_PRODUCT; + } + else if (dist_fn == std::string("cosine")) + { + metric = diskann::Metric::COSINE; + } + else + { + std::cerr << "Unsupported distance function. Use l2/mips/cosine." << std::endl; + return -1; + } + + try + { + if (data_type == std::string("float")) + aux_main(base_file, query_file, gt_file, K, metric, tags_file); + if (data_type == std::string("int8")) + aux_main(base_file, query_file, gt_file, K, metric, tags_file); + if (data_type == std::string("uint8")) + aux_main(base_file, query_file, gt_file, K, metric, tags_file); + } + catch (const std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Compute GT failed." << std::endl; + return -1; + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/compute_groundtruth_for_filters.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/compute_groundtruth_for_filters.cpp new file mode 100644 index 0000000..52e5864 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/compute_groundtruth_for_filters.cpp @@ -0,0 +1,919 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef _WINDOWS +#include +#else +#include +#endif + +#include "filter_utils.h" +#include "utils.h" + +// WORKS FOR UPTO 2 BILLION POINTS (as we use INT INSTEAD OF UNSIGNED) + +#define PARTSIZE 10000000 +#define ALIGNMENT 512 + +// custom types (for readability) +typedef tsl::robin_set label_set; +typedef std::string path; + +namespace po = boost::program_options; + +template T div_round_up(const T numerator, const T denominator) +{ + return (numerator % denominator == 0) ? (numerator / denominator) : 1 + (numerator / denominator); +} + +using pairIF = std::pair; +struct cmpmaxstruct +{ + bool operator()(const pairIF &l, const pairIF &r) + { + return l.second < r.second; + }; +}; + +using maxPQIFCS = std::priority_queue, cmpmaxstruct>; + +template T *aligned_malloc(const size_t n, const size_t alignment) +{ +#ifdef _WINDOWS + return (T *)_aligned_malloc(sizeof(T) * n, alignment); +#else + return static_cast(aligned_alloc(alignment, sizeof(T) * n)); +#endif +} + +inline bool custom_dist(const std::pair &a, const std::pair &b) +{ + return a.second < b.second; +} + +void compute_l2sq(float *const points_l2sq, const float *const matrix, const int64_t num_points, const uint64_t dim) +{ + assert(points_l2sq != NULL); +#pragma omp parallel for schedule(static, 65536) + for (int64_t d = 0; d < num_points; ++d) + points_l2sq[d] = cblas_sdot((int64_t)dim, matrix + (ptrdiff_t)d * (ptrdiff_t)dim, 1, + matrix + (ptrdiff_t)d * (ptrdiff_t)dim, 1); +} + +void distsq_to_points(const size_t dim, + float *dist_matrix, // Col Major, cols are queries, rows are points + size_t npoints, const float *const points, + const float *const points_l2sq, // points in Col major + size_t nqueries, const float *const queries, + const float *const queries_l2sq, // queries in Col major + float *ones_vec = NULL) // Scratchspace of num_data size and init to 1.0 +{ + bool ones_vec_alloc = false; + if (ones_vec == NULL) + { + ones_vec = new float[nqueries > npoints ? nqueries : npoints]; + std::fill_n(ones_vec, nqueries > npoints ? nqueries : npoints, (float)1.0); + ones_vec_alloc = true; + } + cblas_sgemm(CblasColMajor, CblasTrans, CblasNoTrans, npoints, nqueries, dim, (float)-2.0, points, dim, queries, dim, + (float)0.0, dist_matrix, npoints); + cblas_sgemm(CblasColMajor, CblasNoTrans, CblasTrans, npoints, nqueries, 1, (float)1.0, points_l2sq, npoints, + ones_vec, nqueries, (float)1.0, dist_matrix, npoints); + cblas_sgemm(CblasColMajor, CblasNoTrans, CblasTrans, npoints, nqueries, 1, (float)1.0, ones_vec, npoints, + queries_l2sq, nqueries, (float)1.0, dist_matrix, npoints); + if (ones_vec_alloc) + delete[] ones_vec; +} + +void inner_prod_to_points(const size_t dim, + float *dist_matrix, // Col Major, cols are queries, rows are points + size_t npoints, const float *const points, size_t nqueries, const float *const queries, + float *ones_vec = NULL) // Scratchspace of num_data size and init to 1.0 +{ + bool ones_vec_alloc = false; + if (ones_vec == NULL) + { + ones_vec = new float[nqueries > npoints ? nqueries : npoints]; + std::fill_n(ones_vec, nqueries > npoints ? nqueries : npoints, (float)1.0); + ones_vec_alloc = true; + } + cblas_sgemm(CblasColMajor, CblasTrans, CblasNoTrans, npoints, nqueries, dim, (float)-1.0, points, dim, queries, dim, + (float)0.0, dist_matrix, npoints); + + if (ones_vec_alloc) + delete[] ones_vec; +} + +void exact_knn(const size_t dim, const size_t k, + size_t *const closest_points, // k * num_queries preallocated, col + // major, queries columns + float *const dist_closest_points, // k * num_queries + // preallocated, Dist to + // corresponding closes_points + size_t npoints, + float *points_in, // points in Col major + size_t nqueries, float *queries_in, + diskann::Metric metric = diskann::Metric::L2) // queries in Col major +{ + float *points_l2sq = new float[npoints]; + float *queries_l2sq = new float[nqueries]; + compute_l2sq(points_l2sq, points_in, npoints, dim); + compute_l2sq(queries_l2sq, queries_in, nqueries, dim); + + float *points = points_in; + float *queries = queries_in; + + if (metric == diskann::Metric::COSINE) + { // we convert cosine distance as + // normalized L2 distnace + points = new float[npoints * dim]; + queries = new float[nqueries * dim]; +#pragma omp parallel for schedule(static, 4096) + for (int64_t i = 0; i < (int64_t)npoints; i++) + { + float norm = std::sqrt(points_l2sq[i]); + if (norm == 0) + { + norm = std::numeric_limits::epsilon(); + } + for (uint32_t j = 0; j < dim; j++) + { + points[i * dim + j] = points_in[i * dim + j] / norm; + } + } + +#pragma omp parallel for schedule(static, 4096) + for (int64_t i = 0; i < (int64_t)nqueries; i++) + { + float norm = std::sqrt(queries_l2sq[i]); + if (norm == 0) + { + norm = std::numeric_limits::epsilon(); + } + for (uint32_t j = 0; j < dim; j++) + { + queries[i * dim + j] = queries_in[i * dim + j] / norm; + } + } + // recalculate norms after normalizing, they should all be one. + compute_l2sq(points_l2sq, points, npoints, dim); + compute_l2sq(queries_l2sq, queries, nqueries, dim); + } + + std::cout << "Going to compute " << k << " NNs for " << nqueries << " queries over " << npoints << " points in " + << dim << " dimensions using"; + if (metric == diskann::Metric::INNER_PRODUCT) + std::cout << " MIPS "; + else if (metric == diskann::Metric::COSINE) + std::cout << " Cosine "; + else + std::cout << " L2 "; + std::cout << "distance fn. " << std::endl; + + size_t q_batch_size = (1 << 9); + float *dist_matrix = new float[(size_t)q_batch_size * (size_t)npoints]; + + for (uint64_t b = 0; b < div_round_up(nqueries, q_batch_size); ++b) + { + int64_t q_b = b * q_batch_size; + int64_t q_e = ((b + 1) * q_batch_size > nqueries) ? nqueries : (b + 1) * q_batch_size; + + if (metric == diskann::Metric::L2 || metric == diskann::Metric::COSINE) + { + distsq_to_points(dim, dist_matrix, npoints, points, points_l2sq, q_e - q_b, + queries + (ptrdiff_t)q_b * (ptrdiff_t)dim, queries_l2sq + q_b); + } + else + { + inner_prod_to_points(dim, dist_matrix, npoints, points, q_e - q_b, + queries + (ptrdiff_t)q_b * (ptrdiff_t)dim); + } + std::cout << "Computed distances for queries: [" << q_b << "," << q_e << ")" << std::endl; + +#pragma omp parallel for schedule(dynamic, 16) + for (long long q = q_b; q < q_e; q++) + { + maxPQIFCS point_dist; + for (size_t p = 0; p < k; p++) + point_dist.emplace(p, dist_matrix[(ptrdiff_t)p + (ptrdiff_t)(q - q_b) * (ptrdiff_t)npoints]); + for (size_t p = k; p < npoints; p++) + { + if (point_dist.top().second > dist_matrix[(ptrdiff_t)p + (ptrdiff_t)(q - q_b) * (ptrdiff_t)npoints]) + point_dist.emplace(p, dist_matrix[(ptrdiff_t)p + (ptrdiff_t)(q - q_b) * (ptrdiff_t)npoints]); + if (point_dist.size() > k) + point_dist.pop(); + } + for (ptrdiff_t l = 0; l < (ptrdiff_t)k; ++l) + { + closest_points[(ptrdiff_t)(k - 1 - l) + (ptrdiff_t)q * (ptrdiff_t)k] = point_dist.top().first; + dist_closest_points[(ptrdiff_t)(k - 1 - l) + (ptrdiff_t)q * (ptrdiff_t)k] = point_dist.top().second; + point_dist.pop(); + } + assert(std::is_sorted(dist_closest_points + (ptrdiff_t)q * (ptrdiff_t)k, + dist_closest_points + (ptrdiff_t)(q + 1) * (ptrdiff_t)k)); + } + std::cout << "Computed exact k-NN for queries: [" << q_b << "," << q_e << ")" << std::endl; + } + + delete[] dist_matrix; + + delete[] points_l2sq; + delete[] queries_l2sq; + + if (metric == diskann::Metric::COSINE) + { + delete[] points; + delete[] queries; + } +} + +template inline int get_num_parts(const char *filename) +{ + std::ifstream reader; + reader.exceptions(std::ios::failbit | std::ios::badbit); + reader.open(filename, std::ios::binary); + std::cout << "Reading bin file " << filename << " ...\n"; + int npts_i32, ndims_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&ndims_i32, sizeof(int)); + std::cout << "#pts = " << npts_i32 << ", #dims = " << ndims_i32 << std::endl; + reader.close(); + int num_parts = (npts_i32 % PARTSIZE) == 0 ? npts_i32 / PARTSIZE : (uint32_t)std::floor(npts_i32 / PARTSIZE) + 1; + std::cout << "Number of parts: " << num_parts << std::endl; + return num_parts; +} + +template +inline void load_bin_as_float(const char *filename, float *&data, size_t &npts_u64, size_t &ndims_u64, int part_num) +{ + std::ifstream reader; + reader.exceptions(std::ios::failbit | std::ios::badbit); + reader.open(filename, std::ios::binary); + std::cout << "Reading bin file " << filename << " ...\n"; + int npts_i32, ndims_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&ndims_i32, sizeof(int)); + uint64_t start_id = part_num * PARTSIZE; + uint64_t end_id = (std::min)(start_id + PARTSIZE, (uint64_t)npts_i32); + npts_u64 = end_id - start_id; + ndims_u64 = (uint64_t)ndims_i32; + std::cout << "#pts in part = " << npts_u64 << ", #dims = " << ndims_u64 + << ", size = " << npts_u64 * ndims_u64 * sizeof(T) << "B" << std::endl; + + reader.seekg(start_id * ndims_u64 * sizeof(T) + 2 * sizeof(uint32_t), std::ios::beg); + T *data_T = new T[npts_u64 * ndims_u64]; + reader.read((char *)data_T, sizeof(T) * npts_u64 * ndims_u64); + std::cout << "Finished reading part of the bin file." << std::endl; + reader.close(); + data = aligned_malloc(npts_u64 * ndims_u64, ALIGNMENT); +#pragma omp parallel for schedule(dynamic, 32768) + for (int64_t i = 0; i < (int64_t)npts_u64; i++) + { + for (int64_t j = 0; j < (int64_t)ndims_u64; j++) + { + float cur_val_float = (float)data_T[i * ndims_u64 + j]; + std::memcpy((char *)(data + i * ndims_u64 + j), (char *)&cur_val_float, sizeof(float)); + } + } + delete[] data_T; + std::cout << "Finished converting part data to float." << std::endl; +} + +template +inline std::vector load_filtered_bin_as_float(const char *filename, float *&data, size_t &npts, size_t &ndims, + int part_num, const char *label_file, + const std::string &filter_label, + const std::string &universal_label, size_t &npoints_filt, + std::vector> &pts_to_labels) +{ + std::ifstream reader(filename, std::ios::binary); + if (reader.fail()) + { + throw diskann::ANNException(std::string("Failed to open file ") + filename, -1); + } + + std::cout << "Reading bin file " << filename << " ...\n"; + int npts_i32, ndims_i32; + std::vector rev_map; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&ndims_i32, sizeof(int)); + uint64_t start_id = part_num * PARTSIZE; + uint64_t end_id = (std::min)(start_id + PARTSIZE, (uint64_t)npts_i32); + npts = end_id - start_id; + ndims = (uint32_t)ndims_i32; + uint64_t nptsuint64_t = (uint64_t)npts; + uint64_t ndimsuint64_t = (uint64_t)ndims; + npoints_filt = 0; + std::cout << "#pts in part = " << npts << ", #dims = " << ndims + << ", size = " << nptsuint64_t * ndimsuint64_t * sizeof(T) << "B" << std::endl; + std::cout << "start and end ids: " << start_id << ", " << end_id << std::endl; + reader.seekg(start_id * ndims * sizeof(T) + 2 * sizeof(uint32_t), std::ios::beg); + + T *data_T = new T[nptsuint64_t * ndimsuint64_t]; + reader.read((char *)data_T, sizeof(T) * nptsuint64_t * ndimsuint64_t); + std::cout << "Finished reading part of the bin file." << std::endl; + reader.close(); + + data = aligned_malloc(nptsuint64_t * ndimsuint64_t, ALIGNMENT); + + for (int64_t i = 0; i < (int64_t)nptsuint64_t; i++) + { + if (std::find(pts_to_labels[start_id + i].begin(), pts_to_labels[start_id + i].end(), filter_label) != + pts_to_labels[start_id + i].end() || + std::find(pts_to_labels[start_id + i].begin(), pts_to_labels[start_id + i].end(), universal_label) != + pts_to_labels[start_id + i].end()) + { + rev_map.push_back(start_id + i); + for (int64_t j = 0; j < (int64_t)ndimsuint64_t; j++) + { + float cur_val_float = (float)data_T[i * ndimsuint64_t + j]; + std::memcpy((char *)(data + npoints_filt * ndimsuint64_t + j), (char *)&cur_val_float, sizeof(float)); + } + npoints_filt++; + } + } + delete[] data_T; + std::cout << "Finished converting part data to float.. identified " << npoints_filt + << " points matching the filter." << std::endl; + return rev_map; +} + +template inline void save_bin(const std::string filename, T *data, size_t npts, size_t ndims) +{ + std::ofstream writer; + writer.exceptions(std::ios::failbit | std::ios::badbit); + writer.open(filename, std::ios::binary | std::ios::out); + std::cout << "Writing bin: " << filename << "\n"; + int npts_i32 = (int)npts, ndims_i32 = (int)ndims; + writer.write((char *)&npts_i32, sizeof(int)); + writer.write((char *)&ndims_i32, sizeof(int)); + std::cout << "bin: #pts = " << npts << ", #dims = " << ndims + << ", size = " << npts * ndims * sizeof(T) + 2 * sizeof(int) << "B" << std::endl; + + writer.write((char *)data, npts * ndims * sizeof(T)); + writer.close(); + std::cout << "Finished writing bin" << std::endl; +} + +inline void save_groundtruth_as_one_file(const std::string filename, int32_t *data, float *distances, size_t npts, + size_t ndims) +{ + std::ofstream writer(filename, std::ios::binary | std::ios::out); + int npts_i32 = (int)npts, ndims_i32 = (int)ndims; + writer.write((char *)&npts_i32, sizeof(int)); + writer.write((char *)&ndims_i32, sizeof(int)); + std::cout << "Saving truthset in one file (npts, dim, npts*dim id-matrix, " + "npts*dim dist-matrix) with npts = " + << npts << ", dim = " << ndims << ", size = " << 2 * npts * ndims * sizeof(uint32_t) + 2 * sizeof(int) + << "B" << std::endl; + + writer.write((char *)data, npts * ndims * sizeof(uint32_t)); + writer.write((char *)distances, npts * ndims * sizeof(float)); + writer.close(); + std::cout << "Finished writing truthset" << std::endl; +} + +inline void parse_label_file_into_vec(size_t &line_cnt, const std::string &map_file, + std::vector> &pts_to_labels) +{ + std::ifstream infile(map_file); + std::string line, token; + std::set labels; + infile.clear(); + infile.seekg(0, std::ios::beg); + while (std::getline(infile, line)) + { + std::istringstream iss(line); + std::vector lbls(0); + + getline(iss, token, '\t'); + std::istringstream new_iss(token); + while (getline(new_iss, token, ',')) + { + token.erase(std::remove(token.begin(), token.end(), '\n'), token.end()); + token.erase(std::remove(token.begin(), token.end(), '\r'), token.end()); + lbls.push_back(token); + labels.insert(token); + } + std::sort(lbls.begin(), lbls.end()); + pts_to_labels.push_back(lbls); + } + std::cout << "Identified " << labels.size() << " distinct label(s), and populated labels for " + << pts_to_labels.size() << " points" << std::endl; +} + +template +std::vector>> processUnfilteredParts(const std::string &base_file, + size_t &nqueries, size_t &npoints, + size_t &dim, size_t &k, float *query_data, + const diskann::Metric &metric, + std::vector &location_to_tag) +{ + float *base_data = nullptr; + int num_parts = get_num_parts(base_file.c_str()); + std::vector>> res(nqueries); + for (int p = 0; p < num_parts; p++) + { + size_t start_id = p * PARTSIZE; + load_bin_as_float(base_file.c_str(), base_data, npoints, dim, p); + + size_t *closest_points_part = new size_t[nqueries * k]; + float *dist_closest_points_part = new float[nqueries * k]; + + auto part_k = k < npoints ? k : npoints; + exact_knn(dim, part_k, closest_points_part, dist_closest_points_part, npoints, base_data, nqueries, query_data, + metric); + + for (size_t i = 0; i < nqueries; i++) + { + for (uint64_t j = 0; j < part_k; j++) + { + if (!location_to_tag.empty()) + if (location_to_tag[closest_points_part[i * k + j] + start_id] == 0) + continue; + + res[i].push_back(std::make_pair((uint32_t)(closest_points_part[i * part_k + j] + start_id), + dist_closest_points_part[i * part_k + j])); + } + } + + delete[] closest_points_part; + delete[] dist_closest_points_part; + + diskann::aligned_free(base_data); + } + return res; +}; + +template +std::vector>> processFilteredParts( + const std::string &base_file, const std::string &label_file, const std::string &filter_label, + const std::string &universal_label, size_t &nqueries, size_t &npoints, size_t &dim, size_t &k, float *query_data, + const diskann::Metric &metric, std::vector &location_to_tag) +{ + size_t npoints_filt = 0; + float *base_data = nullptr; + std::vector>> res(nqueries); + int num_parts = get_num_parts(base_file.c_str()); + + std::vector> pts_to_labels; + if (filter_label != "") + parse_label_file_into_vec(npoints, label_file, pts_to_labels); + + for (int p = 0; p < num_parts; p++) + { + size_t start_id = p * PARTSIZE; + std::vector rev_map; + if (filter_label != "") + rev_map = load_filtered_bin_as_float(base_file.c_str(), base_data, npoints, dim, p, label_file.c_str(), + filter_label, universal_label, npoints_filt, pts_to_labels); + size_t *closest_points_part = new size_t[nqueries * k]; + float *dist_closest_points_part = new float[nqueries * k]; + + auto part_k = k < npoints_filt ? k : npoints_filt; + if (npoints_filt > 0) + { + exact_knn(dim, part_k, closest_points_part, dist_closest_points_part, npoints_filt, base_data, nqueries, + query_data, metric); + } + + for (size_t i = 0; i < nqueries; i++) + { + for (uint64_t j = 0; j < part_k; j++) + { + if (!location_to_tag.empty()) + if (location_to_tag[closest_points_part[i * k + j] + start_id] == 0) + continue; + + res[i].push_back(std::make_pair((uint32_t)(rev_map[closest_points_part[i * part_k + j]]), + dist_closest_points_part[i * part_k + j])); + } + } + + delete[] closest_points_part; + delete[] dist_closest_points_part; + + diskann::aligned_free(base_data); + } + return res; +}; + +template +int aux_main(const std::string &base_file, const std::string &label_file, const std::string &query_file, + const std::string >_file, size_t k, const std::string &universal_label, const diskann::Metric &metric, + const std::string &filter_label, const std::string &tags_file = std::string("")) +{ + size_t npoints, nqueries, dim; + + float *query_data = nullptr; + + load_bin_as_float(query_file.c_str(), query_data, nqueries, dim, 0); + if (nqueries > PARTSIZE) + std::cerr << "WARNING: #Queries provided (" << nqueries << ") is greater than " << PARTSIZE + << ". Computing GT only for the first " << PARTSIZE << " queries." << std::endl; + + // load tags + const bool tags_enabled = tags_file.empty() ? false : true; + std::vector location_to_tag = diskann::loadTags(tags_file, base_file); + + int *closest_points = new int[nqueries * k]; + float *dist_closest_points = new float[nqueries * k]; + + std::vector>> results; + if (filter_label == "") + { + results = processUnfilteredParts(base_file, nqueries, npoints, dim, k, query_data, metric, location_to_tag); + } + else + { + results = processFilteredParts(base_file, label_file, filter_label, universal_label, nqueries, npoints, dim, + k, query_data, metric, location_to_tag); + } + + for (size_t i = 0; i < nqueries; i++) + { + std::vector> &cur_res = results[i]; + std::sort(cur_res.begin(), cur_res.end(), custom_dist); + size_t j = 0; + for (auto iter : cur_res) + { + if (j == k) + break; + if (tags_enabled) + { + std::uint32_t index_with_tag = location_to_tag[iter.first]; + closest_points[i * k + j] = (int32_t)index_with_tag; + } + else + { + closest_points[i * k + j] = (int32_t)iter.first; + } + + if (metric == diskann::Metric::INNER_PRODUCT) + dist_closest_points[i * k + j] = -iter.second; + else + dist_closest_points[i * k + j] = iter.second; + + ++j; + } + if (j < k) + std::cout << "WARNING: found less than k GT entries for query " << i << std::endl; + } + + save_groundtruth_as_one_file(gt_file, closest_points, dist_closest_points, nqueries, k); + delete[] closest_points; + delete[] dist_closest_points; + diskann::aligned_free(query_data); + + return 0; +} + +void load_truthset(const std::string &bin_file, uint32_t *&ids, float *&dists, size_t &npts, size_t &dim) +{ + size_t read_blk_size = 64 * 1024 * 1024; + cached_ifstream reader(bin_file, read_blk_size); + diskann::cout << "Reading truthset file " << bin_file.c_str() << " ..." << std::endl; + size_t actual_file_size = reader.get_file_size(); + + int npts_i32, dim_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&dim_i32, sizeof(int)); + npts = (uint32_t)npts_i32; + dim = (uint32_t)dim_i32; + + diskann::cout << "Metadata: #pts = " << npts << ", #dims = " << dim << "... " << std::endl; + + int truthset_type = -1; // 1 means truthset has ids and distances, 2 means + // only ids, -1 is error + size_t expected_file_size_with_dists = 2 * npts * dim * sizeof(uint32_t) + 2 * sizeof(uint32_t); + + if (actual_file_size == expected_file_size_with_dists) + truthset_type = 1; + + size_t expected_file_size_just_ids = npts * dim * sizeof(uint32_t) + 2 * sizeof(uint32_t); + + if (actual_file_size == expected_file_size_just_ids) + truthset_type = 2; + + if (truthset_type == -1) + { + std::stringstream stream; + stream << "Error. File size mismatch. File should have bin format, with " + "npts followed by ngt followed by npts*ngt ids and optionally " + "followed by npts*ngt distance values; actual size: " + << actual_file_size << ", expected: " << expected_file_size_with_dists << " or " + << expected_file_size_just_ids; + diskann::cout << stream.str(); + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + + ids = new uint32_t[npts * dim]; + reader.read((char *)ids, npts * dim * sizeof(uint32_t)); + + if (truthset_type == 1) + { + dists = new float[npts * dim]; + reader.read((char *)dists, npts * dim * sizeof(float)); + } +} + +int main(int argc, char **argv) +{ + std::string data_type, dist_fn, base_file, query_file, gt_file, tags_file, label_file, filter_label, + universal_label, filter_label_file; + uint64_t K; + + try + { + po::options_description desc{"Arguments"}; + + desc.add_options()("help,h", "Print information on arguments"); + + desc.add_options()("data_type", po::value(&data_type)->required(), "data type "); + desc.add_options()("dist_fn", po::value(&dist_fn)->required(), "distance function "); + desc.add_options()("base_file", po::value(&base_file)->required(), + "File containing the base vectors in binary format"); + desc.add_options()("query_file", po::value(&query_file)->required(), + "File containing the query vectors in binary format"); + desc.add_options()("label_file", po::value(&label_file)->default_value(""), + "Input labels file in txt format if present"); + desc.add_options()("filter_label", po::value(&filter_label)->default_value(""), + "Input filter label if doing filtered groundtruth"); + desc.add_options()("universal_label", po::value(&universal_label)->default_value(""), + "Universal label, if using it, only in conjunction with label_file"); + desc.add_options()("gt_file", po::value(>_file)->required(), + "File name for the writing ground truth in binary " + "format, please don' append .bin at end if " + "no filter_label or filter_label_file is provided it " + "will save the file with '.bin' at end." + "else it will save the file as filename_label.bin"); + desc.add_options()("K", po::value(&K)->required(), + "Number of ground truth nearest neighbors to compute"); + desc.add_options()("tags_file", po::value(&tags_file)->default_value(std::string()), + "File containing the tags in binary format"); + desc.add_options()("filter_label_file", + po::value(&filter_label_file)->default_value(std::string("")), + "Filter file for Queries for Filtered Search "); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + if (data_type != std::string("float") && data_type != std::string("int8") && data_type != std::string("uint8")) + { + std::cout << "Unsupported type. float, int8 and uint8 types are supported." << std::endl; + return -1; + } + + if (filter_label != "" && filter_label_file != "") + { + std::cerr << "Only one of filter_label and query_filters_file should be provided" << std::endl; + return -1; + } + + diskann::Metric metric; + if (dist_fn == std::string("l2")) + { + metric = diskann::Metric::L2; + } + else if (dist_fn == std::string("mips")) + { + metric = diskann::Metric::INNER_PRODUCT; + } + else if (dist_fn == std::string("cosine")) + { + metric = diskann::Metric::COSINE; + } + else + { + std::cerr << "Unsupported distance function. Use l2/mips/cosine." << std::endl; + return -1; + } + + std::vector filter_labels; + if (filter_label != "") + { + filter_labels.push_back(filter_label); + } + else if (filter_label_file != "") + { + filter_labels = read_file_to_vector_of_strings(filter_label_file, false); + } + + // only when there is no filter label or 1 filter label for all queries + if (filter_labels.size() == 1) + { + try + { + if (data_type == std::string("float")) + aux_main(base_file, label_file, query_file, gt_file, K, universal_label, metric, + filter_labels[0], tags_file); + if (data_type == std::string("int8")) + aux_main(base_file, label_file, query_file, gt_file, K, universal_label, metric, + filter_labels[0], tags_file); + if (data_type == std::string("uint8")) + aux_main(base_file, label_file, query_file, gt_file, K, universal_label, metric, + filter_labels[0], tags_file); + } + catch (const std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Compute GT failed." << std::endl; + return -1; + } + } + else + { // Each query has its own filter label + // Split up data and query bins into label specific ones + tsl::robin_map labels_to_number_of_points; + tsl::robin_map labels_to_number_of_queries; + + label_set all_labels; + for (size_t i = 0; i < filter_labels.size(); i++) + { + std::string label = filter_labels[i]; + all_labels.insert(label); + + if (labels_to_number_of_queries.find(label) == labels_to_number_of_queries.end()) + { + labels_to_number_of_queries[label] = 0; + } + labels_to_number_of_queries[label] += 1; + } + + size_t npoints; + std::vector> point_to_labels; + parse_label_file_into_vec(npoints, label_file, point_to_labels); + std::vector point_ids_to_labels(point_to_labels.size()); + std::vector query_ids_to_labels(filter_labels.size()); + + for (size_t i = 0; i < point_to_labels.size(); i++) + { + for (size_t j = 0; j < point_to_labels[i].size(); j++) + { + std::string label = point_to_labels[i][j]; + if (all_labels.find(label) != all_labels.end()) + { + point_ids_to_labels[i].insert(point_to_labels[i][j]); + if (labels_to_number_of_points.find(label) == labels_to_number_of_points.end()) + { + labels_to_number_of_points[label] = 0; + } + labels_to_number_of_points[label] += 1; + } + } + } + + for (size_t i = 0; i < filter_labels.size(); i++) + { + query_ids_to_labels[i].insert(filter_labels[i]); + } + + tsl::robin_map> label_id_to_orig_id; + tsl::robin_map> label_query_id_to_orig_id; + + if (data_type == std::string("float")) + { + label_id_to_orig_id = diskann::generate_label_specific_vector_files_compat( + base_file, labels_to_number_of_points, point_ids_to_labels, all_labels); + + label_query_id_to_orig_id = diskann::generate_label_specific_vector_files_compat( + query_file, labels_to_number_of_queries, query_ids_to_labels, + all_labels); // query_filters acts like query_ids_to_labels + } + else if (data_type == std::string("int8")) + { + label_id_to_orig_id = diskann::generate_label_specific_vector_files_compat( + base_file, labels_to_number_of_points, point_ids_to_labels, all_labels); + + label_query_id_to_orig_id = diskann::generate_label_specific_vector_files_compat( + query_file, labels_to_number_of_queries, query_ids_to_labels, + all_labels); // query_filters acts like query_ids_to_labels + } + else if (data_type == std::string("uint8")) + { + label_id_to_orig_id = diskann::generate_label_specific_vector_files_compat( + base_file, labels_to_number_of_points, point_ids_to_labels, all_labels); + + label_query_id_to_orig_id = diskann::generate_label_specific_vector_files_compat( + query_file, labels_to_number_of_queries, query_ids_to_labels, + all_labels); // query_filters acts like query_ids_to_labels + } + else + { + diskann::cerr << "Invalid data type" << std::endl; + return -1; + } + + // Generate label specific ground truths + + try + { + for (const auto &label : all_labels) + { + std::string filtered_base_file = base_file + "_" + label; + std::string filtered_query_file = query_file + "_" + label; + std::string filtered_gt_file = gt_file + "_" + label; + if (data_type == std::string("float")) + aux_main(filtered_base_file, "", filtered_query_file, filtered_gt_file, K, "", metric, ""); + if (data_type == std::string("int8")) + aux_main(filtered_base_file, "", filtered_query_file, filtered_gt_file, K, "", metric, ""); + if (data_type == std::string("uint8")) + aux_main(filtered_base_file, "", filtered_query_file, filtered_gt_file, K, "", metric, ""); + } + } + catch (const std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Compute GT failed." << std::endl; + return -1; + } + + // Combine the label specific ground truths to produce a single GT file + + uint32_t *gt_ids = nullptr; + float *gt_dists = nullptr; + size_t gt_num, gt_dim; + + std::vector> final_gt_ids; + std::vector> final_gt_dists; + + uint32_t query_num = 0; + for (const auto &lbl : all_labels) + { + query_num += labels_to_number_of_queries[lbl]; + } + + for (uint32_t i = 0; i < query_num; i++) + { + final_gt_ids.push_back(std::vector(K)); + final_gt_dists.push_back(std::vector(K)); + } + + for (const auto &lbl : all_labels) + { + std::string filtered_gt_file = gt_file + "_" + lbl; + load_truthset(filtered_gt_file, gt_ids, gt_dists, gt_num, gt_dim); + + for (uint32_t i = 0; i < labels_to_number_of_queries[lbl]; i++) + { + uint32_t orig_query_id = label_query_id_to_orig_id[lbl][i]; + for (uint64_t j = 0; j < K; j++) + { + final_gt_ids[orig_query_id][j] = label_id_to_orig_id[lbl][gt_ids[i * K + j]]; + final_gt_dists[orig_query_id][j] = gt_dists[i * K + j]; + } + } + } + + int32_t *closest_points = new int32_t[query_num * K]; + float *dist_closest_points = new float[query_num * K]; + + for (uint32_t i = 0; i < query_num; i++) + { + for (uint32_t j = 0; j < K; j++) + { + closest_points[i * K + j] = final_gt_ids[i][j]; + dist_closest_points[i * K + j] = final_gt_dists[i][j]; + } + } + + save_groundtruth_as_one_file(gt_file, closest_points, dist_closest_points, query_num, K); + + // cleanup artifacts + std::cout << "Cleaning up artifacts..." << std::endl; + tsl::robin_set paths_to_clean{gt_file, base_file, query_file}; + clean_up_artifacts(paths_to_clean, all_labels); + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/count_bfs_levels.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/count_bfs_levels.cpp new file mode 100644 index 0000000..6dd2d62 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/count_bfs_levels.cpp @@ -0,0 +1,82 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include + +#ifndef _WINDOWS +#include +#include +#include +#include +#endif + +#include "utils.h" +#include "index.h" +#include "memory_mapper.h" + +namespace po = boost::program_options; + +template void bfs_count(const std::string &index_path, uint32_t data_dims) +{ + using TagT = uint32_t; + using LabelT = uint32_t; + diskann::Index index(diskann::Metric::L2, data_dims, 0, nullptr, nullptr, 0, false, false, false, + false, 0, false); + std::cout << "Index class instantiated" << std::endl; + index.load(index_path.c_str(), 1, 100); + std::cout << "Index loaded" << std::endl; + index.count_nodes_at_bfs_levels(); +} + +int main(int argc, char **argv) +{ + std::string data_type, index_path_prefix; + uint32_t data_dims; + + po::options_description desc{"Arguments"}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + desc.add_options()("data_type", po::value(&data_type)->required(), "data type "); + desc.add_options()("index_path_prefix", po::value(&index_path_prefix)->required(), + "Path prefix to the index"); + desc.add_options()("data_dims", po::value(&data_dims)->required(), "Dimensionality of the data"); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + try + { + if (data_type == std::string("int8")) + bfs_count(index_path_prefix, data_dims); + else if (data_type == std::string("uint8")) + bfs_count(index_path_prefix, data_dims); + if (data_type == std::string("float")) + bfs_count(index_path_prefix, data_dims); + } + catch (std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Index BFS failed." << std::endl; + return -1; + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/create_disk_layout.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/create_disk_layout.cpp new file mode 100644 index 0000000..f494c12 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/create_disk_layout.cpp @@ -0,0 +1,48 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include + +#include "utils.h" +#include "disk_utils.h" +#include "cached_io.h" + +template int create_disk_layout(char **argv) +{ + std::string base_file(argv[2]); + std::string vamana_file(argv[3]); + std::string output_file(argv[4]); + diskann::create_disk_layout(base_file, vamana_file, output_file); + return 0; +} + +int main(int argc, char **argv) +{ + if (argc != 5) + { + std::cout << argv[0] + << " data_type data_bin " + "vamana_index_file output_diskann_index_file" + << std::endl; + exit(-1); + } + + int ret_val = -1; + if (std::string(argv[1]) == std::string("float")) + ret_val = create_disk_layout(argv); + else if (std::string(argv[1]) == std::string("int8")) + ret_val = create_disk_layout(argv); + else if (std::string(argv[1]) == std::string("uint8")) + ret_val = create_disk_layout(argv); + else + { + std::cout << "unsupported type. use int8/uint8/float " << std::endl; + ret_val = -2; + } + return ret_val; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/float_bin_to_int8.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/float_bin_to_int8.cpp new file mode 100644 index 0000000..1982005 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/float_bin_to_int8.cpp @@ -0,0 +1,63 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +void block_convert(std::ofstream &writer, int8_t *write_buf, std::ifstream &reader, float *read_buf, size_t npts, + size_t ndims, float bias, float scale) +{ + reader.read((char *)read_buf, npts * ndims * sizeof(float)); + + for (size_t i = 0; i < npts; i++) + { + for (size_t d = 0; d < ndims; d++) + { + write_buf[d + i * ndims] = (int8_t)((read_buf[d + i * ndims] - bias) * (254.0 / scale)); + } + } + writer.write((char *)write_buf, npts * ndims); +} + +int main(int argc, char **argv) +{ + if (argc != 5) + { + std::cout << "Usage: " << argv[0] << " input_bin output_tsv bias scale" << std::endl; + exit(-1); + } + + std::ifstream reader(argv[1], std::ios::binary); + uint32_t npts_u32; + uint32_t ndims_u32; + reader.read((char *)&npts_u32, sizeof(uint32_t)); + reader.read((char *)&ndims_u32, sizeof(uint32_t)); + size_t npts = npts_u32; + size_t ndims = ndims_u32; + std::cout << "Dataset: #pts = " << npts << ", # dims = " << ndims << std::endl; + + size_t blk_size = 131072; + size_t nblks = ROUND_UP(npts, blk_size) / blk_size; + + std::ofstream writer(argv[2], std::ios::binary); + auto read_buf = new float[blk_size * ndims]; + auto write_buf = new int8_t[blk_size * ndims]; + float bias = (float)atof(argv[3]); + float scale = (float)atof(argv[4]); + + writer.write((char *)(&npts_u32), sizeof(uint32_t)); + writer.write((char *)(&ndims_u32), sizeof(uint32_t)); + + for (size_t i = 0; i < nblks; i++) + { + size_t cblk_size = std::min(npts - i * blk_size, blk_size); + block_convert(writer, write_buf, reader, read_buf, cblk_size, ndims, bias, scale); + std::cout << "Block #" << i << " written" << std::endl; + } + + delete[] read_buf; + delete[] write_buf; + + writer.close(); + reader.close(); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/fvecs_to_bin.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/fvecs_to_bin.cpp new file mode 100644 index 0000000..873ad3b --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/fvecs_to_bin.cpp @@ -0,0 +1,95 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +// Convert float types +void block_convert_float(std::ifstream &reader, std::ofstream &writer, float *read_buf, float *write_buf, size_t npts, + size_t ndims) +{ + reader.read((char *)read_buf, npts * (ndims * sizeof(float) + sizeof(uint32_t))); + for (size_t i = 0; i < npts; i++) + { + memcpy(write_buf + i * ndims, (read_buf + i * (ndims + 1)) + 1, ndims * sizeof(float)); + } + writer.write((char *)write_buf, npts * ndims * sizeof(float)); +} + +// Convert byte types +void block_convert_byte(std::ifstream &reader, std::ofstream &writer, uint8_t *read_buf, uint8_t *write_buf, + size_t npts, size_t ndims) +{ + reader.read((char *)read_buf, npts * (ndims * sizeof(uint8_t) + sizeof(uint32_t))); + for (size_t i = 0; i < npts; i++) + { + memcpy(write_buf + i * ndims, (read_buf + i * (ndims + sizeof(uint32_t))) + sizeof(uint32_t), + ndims * sizeof(uint8_t)); + } + writer.write((char *)write_buf, npts * ndims * sizeof(uint8_t)); +} + +int main(int argc, char **argv) +{ + if (argc != 4) + { + std::cout << argv[0] << " input_vecs output_bin" << std::endl; + exit(-1); + } + + int datasize = sizeof(float); + + if (strcmp(argv[1], "uint8") == 0 || strcmp(argv[1], "int8") == 0) + { + datasize = sizeof(uint8_t); + } + else if (strcmp(argv[1], "float") != 0) + { + std::cout << "Error: type not supported. Use float/int8/uint8" << std::endl; + exit(-1); + } + + std::ifstream reader(argv[2], std::ios::binary | std::ios::ate); + size_t fsize = reader.tellg(); + reader.seekg(0, std::ios::beg); + + uint32_t ndims_u32; + reader.read((char *)&ndims_u32, sizeof(uint32_t)); + reader.seekg(0, std::ios::beg); + size_t ndims = (size_t)ndims_u32; + size_t npts = fsize / ((ndims * datasize) + sizeof(uint32_t)); + std::cout << "Dataset: #pts = " << npts << ", # dims = " << ndims << std::endl; + + size_t blk_size = 131072; + size_t nblks = ROUND_UP(npts, blk_size) / blk_size; + std::cout << "# blks: " << nblks << std::endl; + std::ofstream writer(argv[3], std::ios::binary); + int32_t npts_s32 = (int32_t)npts; + int32_t ndims_s32 = (int32_t)ndims; + writer.write((char *)&npts_s32, sizeof(int32_t)); + writer.write((char *)&ndims_s32, sizeof(int32_t)); + + size_t chunknpts = std::min(npts, blk_size); + uint8_t *read_buf = new uint8_t[chunknpts * ((ndims * datasize) + sizeof(uint32_t))]; + uint8_t *write_buf = new uint8_t[chunknpts * ndims * datasize]; + + for (size_t i = 0; i < nblks; i++) + { + size_t cblk_size = std::min(npts - i * blk_size, blk_size); + if (datasize == sizeof(float)) + { + block_convert_float(reader, writer, (float *)read_buf, (float *)write_buf, cblk_size, ndims); + } + else + { + block_convert_byte(reader, writer, read_buf, write_buf, cblk_size, ndims); + } + std::cout << "Block #" << i << " written" << std::endl; + } + + delete[] read_buf; + delete[] write_buf; + + reader.close(); + writer.close(); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/fvecs_to_bvecs.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/fvecs_to_bvecs.cpp new file mode 100644 index 0000000..f9c2aa7 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/fvecs_to_bvecs.cpp @@ -0,0 +1,56 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +void block_convert(std::ifstream &reader, std::ofstream &writer, float *read_buf, uint8_t *write_buf, size_t npts, + size_t ndims) +{ + reader.read((char *)read_buf, npts * (ndims * sizeof(float) + sizeof(uint32_t))); + for (size_t i = 0; i < npts; i++) + { + memcpy(write_buf + i * (ndims + 4), read_buf + i * (ndims + 1), sizeof(uint32_t)); + for (size_t d = 0; d < ndims; d++) + write_buf[i * (ndims + 4) + 4 + d] = (uint8_t)read_buf[i * (ndims + 1) + 1 + d]; + } + writer.write((char *)write_buf, npts * (ndims * 1 + 4)); +} + +int main(int argc, char **argv) +{ + if (argc != 3) + { + std::cout << argv[0] << " input_fvecs output_bvecs(uint8)" << std::endl; + exit(-1); + } + std::ifstream reader(argv[1], std::ios::binary | std::ios::ate); + size_t fsize = reader.tellg(); + reader.seekg(0, std::ios::beg); + + uint32_t ndims_u32; + reader.read((char *)&ndims_u32, sizeof(uint32_t)); + reader.seekg(0, std::ios::beg); + size_t ndims = (size_t)ndims_u32; + size_t npts = fsize / ((ndims + 1) * sizeof(float)); + std::cout << "Dataset: #pts = " << npts << ", # dims = " << ndims << std::endl; + + size_t blk_size = 131072; + size_t nblks = ROUND_UP(npts, blk_size) / blk_size; + std::cout << "# blks: " << nblks << std::endl; + std::ofstream writer(argv[2], std::ios::binary); + auto read_buf = new float[npts * (ndims + 1)]; + auto write_buf = new uint8_t[npts * (ndims + 4)]; + for (size_t i = 0; i < nblks; i++) + { + size_t cblk_size = std::min(npts - i * blk_size, blk_size); + block_convert(reader, writer, read_buf, write_buf, cblk_size, ndims); + std::cout << "Block #" << i << " written" << std::endl; + } + + delete[] read_buf; + delete[] write_buf; + + reader.close(); + writer.close(); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/gen_random_slice.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/gen_random_slice.cpp new file mode 100644 index 0000000..a4cd96e --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/gen_random_slice.cpp @@ -0,0 +1,58 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include "partition.h" +#include "utils.h" + +#include +#include +#include +#include + +template int aux_main(char **argv) +{ + std::string base_file(argv[2]); + std::string output_prefix(argv[3]); + float sampling_rate = (float)(std::atof(argv[4])); + gen_random_slice(base_file, output_prefix, sampling_rate); + return 0; +} + +int main(int argc, char **argv) +{ + if (argc != 5) + { + std::cout << argv[0] + << " data_type [float/int8/uint8] base_bin_file " + "sample_output_prefix sampling_probability" + << std::endl; + exit(-1); + } + + if (std::string(argv[1]) == std::string("float")) + { + aux_main(argv); + } + else if (std::string(argv[1]) == std::string("int8")) + { + aux_main(argv); + } + else if (std::string(argv[1]) == std::string("uint8")) + { + aux_main(argv); + } + else + std::cout << "Unsupported type. Use float/int8/uint8." << std::endl; + return 0; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/generate_pq.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/generate_pq.cpp new file mode 100644 index 0000000..a881b11 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/generate_pq.cpp @@ -0,0 +1,70 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include "math_utils.h" +#include "pq.h" +#include "partition.h" + +#define KMEANS_ITERS_FOR_PQ 15 + +template +bool generate_pq(const std::string &data_path, const std::string &index_prefix_path, const size_t num_pq_centers, + const size_t num_pq_chunks, const float sampling_rate, const bool opq) +{ + std::string pq_pivots_path = index_prefix_path + "_pq_pivots.bin"; + std::string pq_compressed_vectors_path = index_prefix_path + "_pq_compressed.bin"; + + // generates random sample and sets it to train_data and updates train_size + size_t train_size, train_dim; + float *train_data; + gen_random_slice(data_path, sampling_rate, train_data, train_size, train_dim); + std::cout << "For computing pivots, loaded sample data of size " << train_size << std::endl; + + if (opq) + { + diskann::generate_opq_pivots(train_data, train_size, (uint32_t)train_dim, (uint32_t)num_pq_centers, + (uint32_t)num_pq_chunks, pq_pivots_path, true); + } + else + { + diskann::generate_pq_pivots(train_data, train_size, (uint32_t)train_dim, (uint32_t)num_pq_centers, + (uint32_t)num_pq_chunks, KMEANS_ITERS_FOR_PQ, pq_pivots_path); + } + diskann::generate_pq_data_from_pivots(data_path, (uint32_t)num_pq_centers, (uint32_t)num_pq_chunks, + pq_pivots_path, pq_compressed_vectors_path, true); + + delete[] train_data; + + return 0; +} + +int main(int argc, char **argv) +{ + if (argc != 7) + { + std::cout << "Usage: \n" + << argv[0] + << " " + " " + " " + << std::endl; + } + else + { + const std::string data_path(argv[2]); + const std::string index_prefix_path(argv[3]); + const size_t num_pq_centers = 256; + const size_t num_pq_chunks = (size_t)atoi(argv[4]); + const float sampling_rate = (float)atof(argv[5]); + const bool opq = atoi(argv[6]) == 0 ? false : true; + + if (std::string(argv[1]) == std::string("float")) + generate_pq(data_path, index_prefix_path, num_pq_centers, num_pq_chunks, sampling_rate, opq); + else if (std::string(argv[1]) == std::string("int8")) + generate_pq(data_path, index_prefix_path, num_pq_centers, num_pq_chunks, sampling_rate, opq); + else if (std::string(argv[1]) == std::string("uint8")) + generate_pq(data_path, index_prefix_path, num_pq_centers, num_pq_chunks, sampling_rate, opq); + else + std::cout << "Error. wrong file type" << std::endl; + } +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/generate_synthetic_labels.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/generate_synthetic_labels.cpp new file mode 100644 index 0000000..6741760 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/generate_synthetic_labels.cpp @@ -0,0 +1,204 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include "utils.h" + +namespace po = boost::program_options; +class ZipfDistribution +{ + public: + ZipfDistribution(uint64_t num_points, uint32_t num_labels) + : num_labels(num_labels), num_points(num_points), + uniform_zero_to_one(std::uniform_real_distribution<>(0.0, 1.0)) + { + } + + std::unordered_map createDistributionMap() + { + std::unordered_map map; + uint32_t primary_label_freq = (uint32_t)ceil(num_points * distribution_factor); + for (uint32_t i{1}; i < num_labels + 1; i++) + { + map[i] = (uint32_t)ceil(primary_label_freq / i); + } + return map; + } + + int writeDistribution(std::ofstream &outfile) + { + auto distribution_map = createDistributionMap(); + for (uint32_t i{0}; i < num_points; i++) + { + bool label_written = false; + for (auto it = distribution_map.cbegin(); it != distribution_map.cend(); it++) + { + auto label_selection_probability = std::bernoulli_distribution(distribution_factor / (double)it->first); + if (label_selection_probability(rand_engine) && distribution_map[it->first] > 0) + { + if (label_written) + { + outfile << ','; + } + outfile << it->first; + label_written = true; + // remove label from map if we have used all labels + distribution_map[it->first] -= 1; + } + } + if (!label_written) + { + outfile << 0; + } + if (i < num_points - 1) + { + outfile << '\n'; + } + } + return 0; + } + + int writeDistribution(std::string filename) + { + std::ofstream outfile(filename); + if (!outfile.is_open()) + { + std::cerr << "Error: could not open output file " << filename << '\n'; + return -1; + } + writeDistribution(outfile); + outfile.close(); + } + + private: + const uint32_t num_labels; + const uint64_t num_points; + const double distribution_factor = 0.7; + std::knuth_b rand_engine; + const std::uniform_real_distribution uniform_zero_to_one; +}; + +int main(int argc, char **argv) +{ + std::string output_file, distribution_type; + uint32_t num_labels; + uint64_t num_points; + + try + { + po::options_description desc{"Arguments"}; + + desc.add_options()("help,h", "Print information on arguments"); + desc.add_options()("output_file,O", po::value(&output_file)->required(), + "Filename for saving the label file"); + desc.add_options()("num_points,N", po::value(&num_points)->required(), "Number of points in dataset"); + desc.add_options()("num_labels,L", po::value(&num_labels)->required(), + "Number of unique labels, up to 5000"); + desc.add_options()("distribution_type,DT", po::value(&distribution_type)->default_value("random"), + "Distribution function for labels defaults " + "to random"); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + if (num_labels > 5000) + { + std::cerr << "Error: num_labels must be 5000 or less" << '\n'; + return -1; + } + + if (num_points <= 0) + { + std::cerr << "Error: num_points must be greater than 0" << '\n'; + return -1; + } + + std::cout << "Generating synthetic labels for " << num_points << " points with " << num_labels << " unique labels" + << '\n'; + + try + { + std::ofstream outfile(output_file); + if (!outfile.is_open()) + { + std::cerr << "Error: could not open output file " << output_file << '\n'; + return -1; + } + + if (distribution_type == "zipf") + { + ZipfDistribution zipf(num_points, num_labels); + zipf.writeDistribution(outfile); + } + else if (distribution_type == "random") + { + for (size_t i = 0; i < num_points; i++) + { + bool label_written = false; + for (size_t j = 1; j <= num_labels; j++) + { + // 50% chance to assign each label + if (rand() > (RAND_MAX / 2)) + { + if (label_written) + { + outfile << ','; + } + outfile << j; + label_written = true; + } + } + if (!label_written) + { + outfile << 0; + } + if (i < num_points - 1) + { + outfile << '\n'; + } + } + } + else if (distribution_type == "one_per_point") + { + std::random_device rd; // obtain a random number from hardware + std::mt19937 gen(rd()); // seed the generator + std::uniform_int_distribution<> distr(0, num_labels); // define the range + + for (size_t i = 0; i < num_points; i++) + { + outfile << distr(gen); + if (i != num_points - 1) + outfile << '\n'; + } + } + if (outfile.is_open()) + { + outfile.close(); + } + + std::cout << "Labels written to " << output_file << '\n'; + } + catch (const std::exception &ex) + { + std::cerr << "Label generation failed: " << ex.what() << '\n'; + return -1; + } + + return 0; +} \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/int8_to_float.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/int8_to_float.cpp new file mode 100644 index 0000000..dcdfddc --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/int8_to_float.cpp @@ -0,0 +1,23 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +int main(int argc, char **argv) +{ + if (argc != 3) + { + std::cout << argv[0] << " input_int8_bin output_float_bin" << std::endl; + exit(-1); + } + + int8_t *input; + size_t npts, nd; + diskann::load_bin(argv[1], input, npts, nd); + float *output = new float[npts * nd]; + diskann::convert_types(input, output, npts, nd); + diskann::save_bin(argv[2], output, npts, nd); + delete[] output; + delete[] input; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/int8_to_float_scale.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/int8_to_float_scale.cpp new file mode 100644 index 0000000..19fbc6c --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/int8_to_float_scale.cpp @@ -0,0 +1,63 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +void block_convert(std::ofstream &writer, float *write_buf, std::ifstream &reader, int8_t *read_buf, size_t npts, + size_t ndims, float bias, float scale) +{ + reader.read((char *)read_buf, npts * ndims * sizeof(int8_t)); + + for (size_t i = 0; i < npts; i++) + { + for (size_t d = 0; d < ndims; d++) + { + write_buf[d + i * ndims] = (((float)read_buf[d + i * ndims] - bias) * scale); + } + } + writer.write((char *)write_buf, npts * ndims * sizeof(float)); +} + +int main(int argc, char **argv) +{ + if (argc != 5) + { + std::cout << "Usage: " << argv[0] << " input-int8.bin output-float.bin bias scale" << std::endl; + exit(-1); + } + + std::ifstream reader(argv[1], std::ios::binary); + uint32_t npts_u32; + uint32_t ndims_u32; + reader.read((char *)&npts_u32, sizeof(uint32_t)); + reader.read((char *)&ndims_u32, sizeof(uint32_t)); + size_t npts = npts_u32; + size_t ndims = ndims_u32; + std::cout << "Dataset: #pts = " << npts << ", # dims = " << ndims << std::endl; + + size_t blk_size = 131072; + size_t nblks = ROUND_UP(npts, blk_size) / blk_size; + + std::ofstream writer(argv[2], std::ios::binary); + auto read_buf = new int8_t[blk_size * ndims]; + auto write_buf = new float[blk_size * ndims]; + float bias = (float)atof(argv[3]); + float scale = (float)atof(argv[4]); + + writer.write((char *)(&npts_u32), sizeof(uint32_t)); + writer.write((char *)(&ndims_u32), sizeof(uint32_t)); + + for (size_t i = 0; i < nblks; i++) + { + size_t cblk_size = std::min(npts - i * blk_size, blk_size); + block_convert(writer, write_buf, reader, read_buf, cblk_size, ndims, bias, scale); + std::cout << "Block #" << i << " written" << std::endl; + } + + delete[] read_buf; + delete[] write_buf; + + writer.close(); + reader.close(); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/ivecs_to_bin.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/ivecs_to_bin.cpp new file mode 100644 index 0000000..ea8a4a3 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/ivecs_to_bin.cpp @@ -0,0 +1,58 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +void block_convert(std::ifstream &reader, std::ofstream &writer, uint32_t *read_buf, uint32_t *write_buf, size_t npts, + size_t ndims) +{ + reader.read((char *)read_buf, npts * (ndims * sizeof(uint32_t) + sizeof(uint32_t))); + for (size_t i = 0; i < npts; i++) + { + memcpy(write_buf + i * ndims, (read_buf + i * (ndims + 1)) + 1, ndims * sizeof(uint32_t)); + } + writer.write((char *)write_buf, npts * ndims * sizeof(uint32_t)); +} + +int main(int argc, char **argv) +{ + if (argc != 3) + { + std::cout << argv[0] << " input_ivecs output_bin" << std::endl; + exit(-1); + } + std::ifstream reader(argv[1], std::ios::binary | std::ios::ate); + size_t fsize = reader.tellg(); + reader.seekg(0, std::ios::beg); + + uint32_t ndims_u32; + reader.read((char *)&ndims_u32, sizeof(uint32_t)); + reader.seekg(0, std::ios::beg); + size_t ndims = (size_t)ndims_u32; + size_t npts = fsize / ((ndims + 1) * sizeof(uint32_t)); + std::cout << "Dataset: #pts = " << npts << ", # dims = " << ndims << std::endl; + + size_t blk_size = 131072; + size_t nblks = ROUND_UP(npts, blk_size) / blk_size; + std::cout << "# blks: " << nblks << std::endl; + std::ofstream writer(argv[2], std::ios::binary); + int npts_s32 = (int)npts; + int ndims_s32 = (int)ndims; + writer.write((char *)&npts_s32, sizeof(int)); + writer.write((char *)&ndims_s32, sizeof(int)); + uint32_t *read_buf = new uint32_t[npts * (ndims + 1)]; + uint32_t *write_buf = new uint32_t[npts * ndims]; + for (size_t i = 0; i < nblks; i++) + { + size_t cblk_size = std::min(npts - i * blk_size, blk_size); + block_convert(reader, writer, read_buf, write_buf, cblk_size, ndims); + std::cout << "Block #" << i << " written" << std::endl; + } + + delete[] read_buf; + delete[] write_buf; + + reader.close(); + writer.close(); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/merge_shards.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/merge_shards.cpp new file mode 100644 index 0000000..106c15e --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/merge_shards.cpp @@ -0,0 +1,42 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "disk_utils.h" +#include "cached_io.h" +#include "utils.h" + +int main(int argc, char **argv) +{ + if (argc != 9) + { + std::cout << argv[0] + << " vamana_index_prefix[1] vamana_index_suffix[2] " + "idmaps_prefix[3] " + "idmaps_suffix[4] n_shards[5] max_degree[6] " + "output_vamana_path[7] " + "output_medoids_path[8]" + << std::endl; + exit(-1); + } + + std::string vamana_prefix(argv[1]); + std::string vamana_suffix(argv[2]); + std::string idmaps_prefix(argv[3]); + std::string idmaps_suffix(argv[4]); + uint64_t nshards = (uint64_t)std::atoi(argv[5]); + uint32_t max_degree = (uint64_t)std::atoi(argv[6]); + std::string output_index(argv[7]); + std::string output_medoids(argv[8]); + + return diskann::merge_shards(vamana_prefix, vamana_suffix, idmaps_prefix, idmaps_suffix, nshards, max_degree, + output_index, output_medoids); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/partition_data.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/partition_data.cpp new file mode 100644 index 0000000..2520f3f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/partition_data.cpp @@ -0,0 +1,39 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include "cached_io.h" +#include "partition.h" + +// DEPRECATED: NEED TO REPROGRAM + +int main(int argc, char **argv) +{ + if (argc != 7) + { + std::cout << "Usage:\n" + << argv[0] + << " datatype " + " " + " " + << std::endl; + exit(-1); + } + + const std::string data_path(argv[2]); + const std::string prefix_path(argv[3]); + const float sampling_rate = (float)atof(argv[4]); + const size_t num_partitions = (size_t)std::atoi(argv[5]); + const size_t max_reps = 15; + const size_t k_index = (size_t)std::atoi(argv[6]); + + if (std::string(argv[1]) == std::string("float")) + partition(data_path, sampling_rate, num_partitions, max_reps, prefix_path, k_index); + else if (std::string(argv[1]) == std::string("int8")) + partition(data_path, sampling_rate, num_partitions, max_reps, prefix_path, k_index); + else if (std::string(argv[1]) == std::string("uint8")) + partition(data_path, sampling_rate, num_partitions, max_reps, prefix_path, k_index); + else + std::cout << "unsupported data format. use float/int8/uint8" << std::endl; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/partition_with_ram_budget.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/partition_with_ram_budget.cpp new file mode 100644 index 0000000..937b68d --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/partition_with_ram_budget.cpp @@ -0,0 +1,39 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include "cached_io.h" +#include "partition.h" + +// DEPRECATED: NEED TO REPROGRAM + +int main(int argc, char **argv) +{ + if (argc != 8) + { + std::cout << "Usage:\n" + << argv[0] + << " datatype " + " " + " " + << std::endl; + exit(-1); + } + + const std::string data_path(argv[2]); + const std::string prefix_path(argv[3]); + const float sampling_rate = (float)atof(argv[4]); + const double ram_budget = (double)std::atof(argv[5]); + const size_t graph_degree = (size_t)std::atoi(argv[6]); + const size_t k_index = (size_t)std::atoi(argv[7]); + + if (std::string(argv[1]) == std::string("float")) + partition_with_ram_budget(data_path, sampling_rate, ram_budget, graph_degree, prefix_path, k_index); + else if (std::string(argv[1]) == std::string("int8")) + partition_with_ram_budget(data_path, sampling_rate, ram_budget, graph_degree, prefix_path, k_index); + else if (std::string(argv[1]) == std::string("uint8")) + partition_with_ram_budget(data_path, sampling_rate, ram_budget, graph_degree, prefix_path, k_index); + else + std::cout << "unsupported data format. use float/int8/uint8" << std::endl; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/rand_data_gen.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/rand_data_gen.cpp new file mode 100644 index 0000000..e89ede8 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/rand_data_gen.cpp @@ -0,0 +1,237 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include + +#include "utils.h" + +namespace po = boost::program_options; + +int block_write_float(std::ofstream &writer, size_t ndims, size_t npts, bool normalization, float norm, + float rand_scale) +{ + auto vec = new float[ndims]; + + std::random_device rd{}; + std::mt19937 gen{rd()}; + std::normal_distribution<> normal_rand{0, 1}; + std::uniform_real_distribution<> unif_dis(1.0, rand_scale); + + for (size_t i = 0; i < npts; i++) + { + float sum = 0; + float scale = 1.0f; + if (rand_scale > 1.0f) + scale = (float)unif_dis(gen); + for (size_t d = 0; d < ndims; ++d) + vec[d] = scale * (float)normal_rand(gen); + if (normalization) + { + for (size_t d = 0; d < ndims; ++d) + sum += vec[d] * vec[d]; + for (size_t d = 0; d < ndims; ++d) + vec[d] = vec[d] * norm / std::sqrt(sum); + } + + writer.write((char *)vec, ndims * sizeof(float)); + } + + delete[] vec; + return 0; +} + +int block_write_int8(std::ofstream &writer, size_t ndims, size_t npts, float norm) +{ + auto vec = new float[ndims]; + auto vec_T = new int8_t[ndims]; + + std::random_device rd{}; + std::mt19937 gen{rd()}; + std::normal_distribution<> normal_rand{0, 1}; + + for (size_t i = 0; i < npts; i++) + { + float sum = 0; + for (size_t d = 0; d < ndims; ++d) + vec[d] = (float)normal_rand(gen); + for (size_t d = 0; d < ndims; ++d) + sum += vec[d] * vec[d]; + for (size_t d = 0; d < ndims; ++d) + vec[d] = vec[d] * norm / std::sqrt(sum); + + for (size_t d = 0; d < ndims; ++d) + { + vec_T[d] = (int8_t)std::round(vec[d]); + } + + writer.write((char *)vec_T, ndims * sizeof(int8_t)); + } + + delete[] vec; + delete[] vec_T; + return 0; +} + +int block_write_uint8(std::ofstream &writer, size_t ndims, size_t npts, float norm) +{ + auto vec = new float[ndims]; + auto vec_T = new int8_t[ndims]; + + std::random_device rd{}; + std::mt19937 gen{rd()}; + std::normal_distribution<> normal_rand{0, 1}; + + for (size_t i = 0; i < npts; i++) + { + float sum = 0; + for (size_t d = 0; d < ndims; ++d) + vec[d] = (float)normal_rand(gen); + for (size_t d = 0; d < ndims; ++d) + sum += vec[d] * vec[d]; + for (size_t d = 0; d < ndims; ++d) + vec[d] = vec[d] * norm / std::sqrt(sum); + + for (size_t d = 0; d < ndims; ++d) + { + vec_T[d] = 128 + (int8_t)std::round(vec[d]); + } + + writer.write((char *)vec_T, ndims * sizeof(uint8_t)); + } + + delete[] vec; + delete[] vec_T; + return 0; +} + +int main(int argc, char **argv) +{ + std::string data_type, output_file; + size_t ndims, npts; + float norm, rand_scaling; + bool normalization = false; + try + { + po::options_description desc{"Arguments"}; + + desc.add_options()("help,h", "Print information on arguments"); + + desc.add_options()("data_type", po::value(&data_type)->required(), "data type "); + desc.add_options()("output_file", po::value(&output_file)->required(), + "File name for saving the random vectors"); + desc.add_options()("ndims,D", po::value(&ndims)->required(), "Dimensoinality of the vector"); + desc.add_options()("npts,N", po::value(&npts)->required(), "Number of vectors"); + desc.add_options()("norm", po::value(&norm)->default_value(-1.0f), + "Norm of the vectors (if not specified, vectors are not normalized)"); + desc.add_options()("rand_scaling", po::value(&rand_scaling)->default_value(1.0f), + "Each vector will be scaled (if not explicitly normalized) by a factor randomly chosen from " + "[1, rand_scale]. Only applicable for floating point data"); + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &ex) + { + std::cerr << ex.what() << '\n'; + return -1; + } + + if (data_type != std::string("float") && data_type != std::string("int8") && data_type != std::string("uint8")) + { + std::cout << "Unsupported type. float, int8 and uint8 types are supported." << std::endl; + return -1; + } + + if (norm > 0.0) + { + normalization = true; + } + + if (rand_scaling < 1.0) + { + std::cout << "We will only scale the vector norms randomly in [1, value], so value must be >= 1." << std::endl; + return -1; + } + + if ((rand_scaling > 1.0) && (normalization == true)) + { + std::cout << "Data cannot be normalized and randomly scaled at same time. Use one or the other." << std::endl; + return -1; + } + + if (data_type == std::string("int8") || data_type == std::string("uint8")) + { + if (norm > 127) + { + std::cerr << "Error: for int8/uint8 datatypes, L2 norm can not be " + "greater " + "than 127" + << std::endl; + return -1; + } + if (rand_scaling > 1.0) + { + std::cout << "Data scaling only supported for floating point data." << std::endl; + return -1; + } + } + + try + { + std::ofstream writer; + writer.exceptions(std::ofstream::failbit | std::ofstream::badbit); + writer.open(output_file, std::ios::binary); + auto npts_u32 = (uint32_t)npts; + auto ndims_u32 = (uint32_t)ndims; + writer.write((char *)&npts_u32, sizeof(uint32_t)); + writer.write((char *)&ndims_u32, sizeof(uint32_t)); + + size_t blk_size = 131072; + size_t nblks = ROUND_UP(npts, blk_size) / blk_size; + std::cout << "# blks: " << nblks << std::endl; + + int ret = 0; + for (size_t i = 0; i < nblks; i++) + { + size_t cblk_size = std::min(npts - i * blk_size, blk_size); + if (data_type == std::string("float")) + { + ret = block_write_float(writer, ndims, cblk_size, normalization, norm, rand_scaling); + } + else if (data_type == std::string("int8")) + { + ret = block_write_int8(writer, ndims, cblk_size, norm); + } + else if (data_type == std::string("uint8")) + { + ret = block_write_uint8(writer, ndims, cblk_size, norm); + } + if (ret == 0) + std::cout << "Block #" << i << " written" << std::endl; + else + { + writer.close(); + std::cout << "failed to write" << std::endl; + return -1; + } + } + writer.close(); + } + catch (const std::exception &e) + { + std::cout << std::string(e.what()) << std::endl; + diskann::cerr << "Index build failed." << std::endl; + return -1; + } + + return 0; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/simulate_aggregate_recall.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/simulate_aggregate_recall.cpp new file mode 100644 index 0000000..73c4ea0 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/simulate_aggregate_recall.cpp @@ -0,0 +1,85 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include + +inline float aggregate_recall(const uint32_t k_aggr, const uint32_t k, const uint32_t npart, uint32_t *count, + const std::vector &recalls) +{ + float found = 0; + for (uint32_t i = 0; i < npart; ++i) + { + size_t max_found = std::min(count[i], k); + found += recalls[max_found - 1] * max_found; + } + return found / (float)k_aggr; +} + +void simulate(const uint32_t k_aggr, const uint32_t k, const uint32_t npart, const uint32_t nsim, + const std::vector &recalls) +{ + std::random_device r; + std::default_random_engine randeng(r()); + std::uniform_int_distribution uniform_dist(0, npart - 1); + + uint32_t *count = new uint32_t[npart]; + double aggr_recall = 0; + + for (uint32_t i = 0; i < nsim; ++i) + { + for (uint32_t p = 0; p < npart; ++p) + { + count[p] = 0; + } + for (uint32_t t = 0; t < k_aggr; ++t) + { + count[uniform_dist(randeng)]++; + } + aggr_recall += aggregate_recall(k_aggr, k, npart, count, recalls); + } + + std::cout << "Aggregate recall is " << aggr_recall / (double)nsim << std::endl; + delete[] count; +} + +int main(int argc, char **argv) +{ + if (argc < 6) + { + std::cout << argv[0] << " k_aggregate k_out npart nsim recall@1 recall@2 ... recall@k" << std::endl; + exit(-1); + } + + const uint32_t k_aggr = atoi(argv[1]); + const uint32_t k = atoi(argv[2]); + const uint32_t npart = atoi(argv[3]); + const uint32_t nsim = atoi(argv[4]); + + std::vector recalls; + for (int ctr = 5; ctr < argc; ctr++) + { + recalls.push_back((float)atof(argv[ctr])); + } + + if (recalls.size() != k) + { + std::cerr << "Please input k numbers for recall@1, recall@2 .. recall@k" << std::endl; + } + if (k_aggr > npart * k) + { + std::cerr << "k_aggr must be <= k * npart" << std::endl; + exit(-1); + } + if (nsim <= npart * k_aggr) + { + std::cerr << "Choose nsim > npart*k_aggr" << std::endl; + exit(-1); + } + + simulate(k_aggr, k, npart, nsim, recalls); + + return 0; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/stats_label_data.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/stats_label_data.cpp new file mode 100644 index 0000000..3342672 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/stats_label_data.cpp @@ -0,0 +1,147 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "utils.h" + +#ifndef _WINDOWS +#include +#include +#include +#include +#else +#include +#endif +namespace po = boost::program_options; + +void stats_analysis(const std::string labels_file, std::string univeral_label, uint32_t density = 10) +{ + std::string token, line; + std::ifstream labels_stream(labels_file); + std::unordered_map label_counts; + std::string label_with_max_points; + uint32_t max_points = 0; + long long sum = 0; + long long point_cnt = 0; + float avg_labels_per_pt, mean_label_size; + + std::vector labels_per_point; + uint32_t dense_pts = 0; + if (labels_stream.is_open()) + { + while (getline(labels_stream, line)) + { + point_cnt++; + std::stringstream iss(line); + uint32_t lbl_cnt = 0; + while (getline(iss, token, ',')) + { + lbl_cnt++; + token.erase(std::remove(token.begin(), token.end(), '\n'), token.end()); + token.erase(std::remove(token.begin(), token.end(), '\r'), token.end()); + if (label_counts.find(token) == label_counts.end()) + label_counts[token] = 0; + label_counts[token]++; + } + if (lbl_cnt >= density) + { + dense_pts++; + } + labels_per_point.emplace_back(lbl_cnt); + } + } + + std::cout << "fraction of dense points with >= " << density + << " labels = " << (float)dense_pts / (float)labels_per_point.size() << std::endl; + std::sort(labels_per_point.begin(), labels_per_point.end()); + + std::vector> label_count_vec; + + for (auto it = label_counts.begin(); it != label_counts.end(); it++) + { + auto &lbl = *it; + label_count_vec.emplace_back(std::make_pair(lbl.first, lbl.second)); + if (lbl.second > max_points) + { + max_points = lbl.second; + label_with_max_points = lbl.first; + } + sum += lbl.second; + } + + sort(label_count_vec.begin(), label_count_vec.end(), + [](const std::pair &lhs, const std::pair &rhs) { + return lhs.second < rhs.second; + }); + + for (float p = 0; p < 1; p += 0.05) + { + std::cout << "Percentile " << (100 * p) << "\t" << label_count_vec[(size_t)(p * label_count_vec.size())].first + << " with count=" << label_count_vec[(size_t)(p * label_count_vec.size())].second << std::endl; + } + + std::cout << "Most common label " + << "\t" << label_count_vec[label_count_vec.size() - 1].first + << " with count=" << label_count_vec[label_count_vec.size() - 1].second << std::endl; + if (label_count_vec.size() > 1) + std::cout << "Second common label " + << "\t" << label_count_vec[label_count_vec.size() - 2].first + << " with count=" << label_count_vec[label_count_vec.size() - 2].second << std::endl; + if (label_count_vec.size() > 2) + std::cout << "Third common label " + << "\t" << label_count_vec[label_count_vec.size() - 3].first + << " with count=" << label_count_vec[label_count_vec.size() - 3].second << std::endl; + avg_labels_per_pt = sum / (float)point_cnt; + mean_label_size = sum / (float)label_counts.size(); + std::cout << "Total number of points = " << point_cnt << ", number of labels = " << label_counts.size() + << std::endl; + std::cout << "Average number of labels per point = " << avg_labels_per_pt << std::endl; + std::cout << "Mean label size excluding 0 = " << mean_label_size << std::endl; + std::cout << "Most popular label is " << label_with_max_points << " with " << max_points << " pts" << std::endl; +} + +int main(int argc, char **argv) +{ + std::string labels_file, universal_label; + uint32_t density; + + po::options_description desc{"Arguments"}; + try + { + desc.add_options()("help,h", "Print information on arguments"); + desc.add_options()("labels_file", po::value(&labels_file)->required(), + "path to labels data file."); + desc.add_options()("universal_label", po::value(&universal_label)->required(), + "Universal label used in labels file."); + desc.add_options()("density", po::value(&density)->default_value(1), + "Number of labels each point in labels file, defaults to 1"); + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, desc), vm); + if (vm.count("help")) + { + std::cout << desc; + return 0; + } + po::notify(vm); + } + catch (const std::exception &e) + { + std::cerr << e.what() << '\n'; + return -1; + } + stats_analysis(labels_file, universal_label, density); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/tsv_to_bin.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/tsv_to_bin.cpp new file mode 100644 index 0000000..c590a8f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/tsv_to_bin.cpp @@ -0,0 +1,121 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +void block_convert_float(std::ifstream &reader, std::ofstream &writer, size_t npts, size_t ndims) +{ + auto read_buf = new float[npts * (ndims + 1)]; + + auto cursor = read_buf; + float val; + + for (size_t i = 0; i < npts; i++) + { + for (size_t d = 0; d < ndims; ++d) + { + reader >> val; + *cursor = val; + cursor++; + } + } + writer.write((char *)read_buf, npts * ndims * sizeof(float)); + delete[] read_buf; +} + +void block_convert_int8(std::ifstream &reader, std::ofstream &writer, size_t npts, size_t ndims) +{ + auto read_buf = new int8_t[npts * (ndims + 1)]; + + auto cursor = read_buf; + int val; + + for (size_t i = 0; i < npts; i++) + { + for (size_t d = 0; d < ndims; ++d) + { + reader >> val; + *cursor = (int8_t)val; + cursor++; + } + } + writer.write((char *)read_buf, npts * ndims * sizeof(uint8_t)); + delete[] read_buf; +} + +void block_convert_uint8(std::ifstream &reader, std::ofstream &writer, size_t npts, size_t ndims) +{ + auto read_buf = new uint8_t[npts * (ndims + 1)]; + + auto cursor = read_buf; + int val; + + for (size_t i = 0; i < npts; i++) + { + for (size_t d = 0; d < ndims; ++d) + { + reader >> val; + *cursor = (uint8_t)val; + cursor++; + } + } + writer.write((char *)read_buf, npts * ndims * sizeof(uint8_t)); + delete[] read_buf; +} + +int main(int argc, char **argv) +{ + if (argc != 6) + { + std::cout << argv[0] + << " input_filename.tsv output_filename.bin " + "dim num_pts>" + << std::endl; + exit(-1); + } + + if (std::string(argv[1]) != std::string("float") && std::string(argv[1]) != std::string("int8") && + std::string(argv[1]) != std::string("uint8")) + { + std::cout << "Unsupported type. float, int8 and uint8 types are supported." << std::endl; + } + + size_t ndims = atoi(argv[4]); + size_t npts = atoi(argv[5]); + + std::ifstream reader(argv[2], std::ios::binary | std::ios::ate); + // size_t fsize = reader.tellg(); + reader.seekg(0, std::ios::beg); + reader.seekg(0, std::ios::beg); + + size_t blk_size = 131072; + size_t nblks = ROUND_UP(npts, blk_size) / blk_size; + std::cout << "# blks: " << nblks << std::endl; + std::ofstream writer(argv[3], std::ios::binary); + auto npts_u32 = (uint32_t)npts; + auto ndims_u32 = (uint32_t)ndims; + writer.write((char *)&npts_u32, sizeof(uint32_t)); + writer.write((char *)&ndims_u32, sizeof(uint32_t)); + + for (size_t i = 0; i < nblks; i++) + { + size_t cblk_size = std::min(npts - i * blk_size, blk_size); + if (std::string(argv[1]) == std::string("float")) + { + block_convert_float(reader, writer, cblk_size, ndims); + } + else if (std::string(argv[1]) == std::string("int8")) + { + block_convert_int8(reader, writer, cblk_size, ndims); + } + else if (std::string(argv[1]) == std::string("uint8")) + { + block_convert_uint8(reader, writer, cblk_size, ndims); + } + std::cout << "Block #" << i << " written" << std::endl; + } + + reader.close(); + writer.close(); +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/uint32_to_uint8.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/uint32_to_uint8.cpp new file mode 100644 index 0000000..87b6fb8 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/uint32_to_uint8.cpp @@ -0,0 +1,23 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +int main(int argc, char **argv) +{ + if (argc != 3) + { + std::cout << argv[0] << " input_uint32_bin output_int8_bin" << std::endl; + exit(-1); + } + + uint32_t *input; + size_t npts, nd; + diskann::load_bin(argv[1], input, npts, nd); + uint8_t *output = new uint8_t[npts * nd]; + diskann::convert_types(input, output, npts, nd); + diskann::save_bin(argv[2], output, npts, nd); + delete[] output; + delete[] input; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/uint8_to_float.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/uint8_to_float.cpp new file mode 100644 index 0000000..6415b7c --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/uint8_to_float.cpp @@ -0,0 +1,23 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include "utils.h" + +int main(int argc, char **argv) +{ + if (argc != 3) + { + std::cout << argv[0] << " input_uint8_bin output_float_bin" << std::endl; + exit(-1); + } + + uint8_t *input; + size_t npts, nd; + diskann::load_bin(argv[1], input, npts, nd); + float *output = new float[npts * nd]; + diskann::convert_types(input, output, npts, nd); + diskann::save_bin(argv[2], output, npts, nd); + delete[] output; + delete[] input; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/vector_analysis.cpp b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/vector_analysis.cpp new file mode 100644 index 0000000..009df6d --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/apps/utils/vector_analysis.cpp @@ -0,0 +1,163 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "partition.h" +#include "utils.h" + +template int analyze_norm(std::string base_file) +{ + std::cout << "Analyzing data norms" << std::endl; + T *data; + size_t npts, ndims; + diskann::load_bin(base_file, data, npts, ndims); + std::vector norms(npts, 0); +#pragma omp parallel for schedule(dynamic) + for (int64_t i = 0; i < (int64_t)npts; i++) + { + for (size_t d = 0; d < ndims; d++) + norms[i] += data[i * ndims + d] * data[i * ndims + d]; + norms[i] = std::sqrt(norms[i]); + } + std::sort(norms.begin(), norms.end()); + for (int p = 0; p < 100; p += 5) + std::cout << "percentile " << p << ": " << norms[(uint64_t)(std::floor((p / 100.0) * npts))] << std::endl; + std::cout << "percentile 100" + << ": " << norms[npts - 1] << std::endl; + delete[] data; + return 0; +} + +template int normalize_base(std::string base_file, std::string out_file) +{ + std::cout << "Normalizing base" << std::endl; + T *data; + size_t npts, ndims; + diskann::load_bin(base_file, data, npts, ndims); + // std::vector norms(npts, 0); +#pragma omp parallel for schedule(dynamic) + for (int64_t i = 0; i < (int64_t)npts; i++) + { + float pt_norm = 0; + for (size_t d = 0; d < ndims; d++) + pt_norm += data[i * ndims + d] * data[i * ndims + d]; + pt_norm = std::sqrt(pt_norm); + for (size_t d = 0; d < ndims; d++) + data[i * ndims + d] = static_cast(data[i * ndims + d] / pt_norm); + } + diskann::save_bin(out_file, data, npts, ndims); + delete[] data; + return 0; +} + +template int augment_base(std::string base_file, std::string out_file, bool prep_base = true) +{ + std::cout << "Analyzing data norms" << std::endl; + T *data; + size_t npts, ndims; + diskann::load_bin(base_file, data, npts, ndims); + std::vector norms(npts, 0); + float max_norm = 0; +#pragma omp parallel for schedule(dynamic) + for (int64_t i = 0; i < (int64_t)npts; i++) + { + for (size_t d = 0; d < ndims; d++) + norms[i] += data[i * ndims + d] * data[i * ndims + d]; + max_norm = norms[i] > max_norm ? norms[i] : max_norm; + } + // std::sort(norms.begin(), norms.end()); + max_norm = std::sqrt(max_norm); + std::cout << "Max norm: " << max_norm << std::endl; + T *new_data; + size_t newdims = ndims + 1; + new_data = new T[npts * newdims]; + for (size_t i = 0; i < npts; i++) + { + if (prep_base) + { + for (size_t j = 0; j < ndims; j++) + { + new_data[i * newdims + j] = static_cast(data[i * ndims + j] / max_norm); + } + float diff = 1 - (norms[i] / (max_norm * max_norm)); + diff = diff <= 0 ? 0 : std::sqrt(diff); + new_data[i * newdims + ndims] = static_cast(diff); + if (diff <= 0) + { + std::cout << i << " has large max norm, investigate if needed. diff = " << diff << std::endl; + } + } + else + { + for (size_t j = 0; j < ndims; j++) + { + new_data[i * newdims + j] = static_cast(data[i * ndims + j] / std::sqrt(norms[i])); + } + new_data[i * newdims + ndims] = 0; + } + } + diskann::save_bin(out_file, new_data, npts, newdims); + delete[] new_data; + delete[] data; + return 0; +} + +template int aux_main(char **argv) +{ + std::string base_file(argv[2]); + uint32_t option = atoi(argv[3]); + if (option == 1) + analyze_norm(base_file); + else if (option == 2) + augment_base(base_file, std::string(argv[4]), true); + else if (option == 3) + augment_base(base_file, std::string(argv[4]), false); + else if (option == 4) + normalize_base(base_file, std::string(argv[4])); + return 0; +} + +int main(int argc, char **argv) +{ + if (argc < 4) + { + std::cout << argv[0] + << " data_type [float/int8/uint8] base_bin_file " + "[option: 1-norm analysis, 2-prep_base_for_mip, " + "3-prep_query_for_mip, 4-normalize-vecs] [out_file for " + "options 2/3/4]" + << std::endl; + exit(-1); + } + + if (std::string(argv[1]) == std::string("float")) + { + aux_main(argv); + } + else if (std::string(argv[1]) == std::string("int8")) + { + aux_main(argv); + } + else if (std::string(argv[1]) == std::string("uint8")) + { + aux_main(argv); + } + else + std::cout << "Unsupported type. Use float/int8/uint8." << std::endl; + return 0; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/clang-format.cmake b/packages/leann-backend-diskann/third_party/DiskANN/clang-format.cmake new file mode 100644 index 0000000..19bb3a8 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/clang-format.cmake @@ -0,0 +1,22 @@ +if (NOT MSVC) + message(STATUS "Setting up `make format` and `make checkformat`") + # additional target to perform clang-format run, requires clang-format + # get all project files + file(GLOB_RECURSE ALL_SOURCE_FILES include/*.h include/*.hpp python/src/*.cpp src/*.cpp src/*.hpp apps/*.cpp apps/*.hpp) + + message(status ${ALL_SOURCE_FILES}) + + add_custom_target( + format + COMMAND /usr/bin/clang-format + -i + ${ALL_SOURCE_FILES} + ) + add_custom_target( + checkformat + COMMAND /usr/bin/clang-format + --Werror + --dry-run + ${ALL_SOURCE_FILES} + ) +endif() diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_data_store.h b/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_data_store.h new file mode 100644 index 0000000..89856f1 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_data_store.h @@ -0,0 +1,127 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +#include "types.h" +#include "windows_customizations.h" +#include "distance.h" + +namespace diskann +{ + +template class AbstractScratch; + +template class AbstractDataStore +{ + public: + AbstractDataStore(const location_t capacity, const size_t dim); + + virtual ~AbstractDataStore() = default; + + // Return number of points returned + virtual location_t load(const std::string &filename) = 0; + + // Why does store take num_pts? Since store only has capacity, but we allow + // resizing we can end up in a situation where the store has spare capacity. + // To optimize disk utilization, we pass the number of points that are "true" + // points, so that the store can discard the empty locations before saving. + virtual size_t save(const std::string &filename, const location_t num_pts) = 0; + + DISKANN_DLLEXPORT virtual location_t capacity() const; + + DISKANN_DLLEXPORT virtual size_t get_dims() const; + + // Implementers can choose to return _dim if they are not + // concerned about memory alignment. + // Some distance metrics (like l2) need data vectors to be aligned, so we + // align the dimension by padding zeros. + virtual size_t get_aligned_dim() const = 0; + + // populate the store with vectors (either from a pointer or bin file), + // potentially after pre-processing the vectors if the metric deems so + // e.g., normalizing vectors for cosine distance over floating-point vectors + // useful for bulk or static index building. + virtual void populate_data(const data_t *vectors, const location_t num_pts) = 0; + virtual void populate_data(const std::string &filename, const size_t offset) = 0; + + // save the first num_pts many vectors back to bin file + // note: cannot undo the pre-processing done in populate data + virtual void extract_data_to_bin(const std::string &filename, const location_t num_pts) = 0; + + // Returns the updated capacity of the datastore. Clients should check + // if resize actually changed the capacity to new_num_points before + // proceeding with operations. See the code below: + // auto new_capcity = data_store->resize(new_num_points); + // if ( new_capacity >= new_num_points) { + // //PROCEED + // else + // //ERROR. + virtual location_t resize(const location_t new_num_points); + + // operations on vectors + // like populate_data function, but over one vector at a time useful for + // streaming setting + virtual void get_vector(const location_t i, data_t *dest) const = 0; + virtual void set_vector(const location_t i, const data_t *const vector) = 0; + virtual void prefetch_vector(const location_t loc) = 0; + + // internal shuffle operations to move around vectors + // will bulk-move all the vectors in [old_start_loc, old_start_loc + + // num_points) to [new_start_loc, new_start_loc + num_points) and set the old + // positions to zero vectors. + virtual void move_vectors(const location_t old_start_loc, const location_t new_start_loc, + const location_t num_points) = 0; + + // same as above, without resetting the vectors in [from_loc, from_loc + + // num_points) to zero + virtual void copy_vectors(const location_t from_loc, const location_t to_loc, const location_t num_points) = 0; + + // With the PQ Data Store PR, we have also changed iterate_to_fixed_point to NOT take the query + // from the scratch object. Therefore every data store has to implement preprocess_query which + // at the least will be to copy the query into the scratch object. So making this pure virtual. + virtual void preprocess_query(const data_t *aligned_query, + AbstractScratch *query_scratch = nullptr) const = 0; + // distance functions. + virtual float get_distance(const data_t *query, const location_t loc) const = 0; + virtual void get_distance(const data_t *query, const location_t *locations, const uint32_t location_count, + float *distances, AbstractScratch *scratch_space = nullptr) const = 0; + // Specific overload for index.cpp. + virtual void get_distance(const data_t *preprocessed_query, const std::vector &ids, + std::vector &distances, AbstractScratch *scratch_space) const = 0; + virtual float get_distance(const location_t loc1, const location_t loc2) const = 0; + + // stats of the data stored in store + // Returns the point in the dataset that is closest to the mean of all points + // in the dataset + virtual location_t calculate_medoid() const = 0; + + // REFACTOR PQ TODO: Each data store knows about its distance function, so this is + // redundant. However, we don't have an OptmizedDataStore yet, and to preserve code + // compability, we are exposing this function. + virtual Distance *get_dist_fn() const = 0; + + // search helpers + // if the base data is aligned per the request of the metric, this will tell + // how to align the query vector in a consistent manner + virtual size_t get_alignment_factor() const = 0; + + protected: + // Expand the datastore to new_num_points. Returns the new capacity created, + // which should be == new_num_points in the normal case. Implementers can also + // return _capacity to indicate that there are not implementing this method. + virtual location_t expand(const location_t new_num_points) = 0; + + // Shrink the datastore to new_num_points. It is NOT an error if shrink + // doesn't reduce the capacity so callers need to check this correctly. See + // also for "default" implementation + virtual location_t shrink(const location_t new_num_points) = 0; + + location_t _capacity; + size_t _dim; +}; + +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_graph_store.h b/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_graph_store.h new file mode 100644 index 0000000..4d6906c --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_graph_store.h @@ -0,0 +1,68 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include +#include "types.h" + +namespace diskann +{ + +class AbstractGraphStore +{ + public: + AbstractGraphStore(const size_t total_pts, const size_t reserve_graph_degree) + : _capacity(total_pts), _reserve_graph_degree(reserve_graph_degree) + { + } + + virtual ~AbstractGraphStore() = default; + + // returns tuple of + virtual std::tuple load(const std::string &index_path_prefix, + const size_t num_points) = 0; + virtual int store(const std::string &index_path_prefix, const size_t num_points, const size_t num_fz_points, + const uint32_t start) = 0; + + // not synchronised, user should use lock when necvessary. + virtual const std::vector &get_neighbours(const location_t i) const = 0; + virtual void add_neighbour(const location_t i, location_t neighbour_id) = 0; + virtual void clear_neighbours(const location_t i) = 0; + virtual void swap_neighbours(const location_t a, location_t b) = 0; + + virtual void set_neighbours(const location_t i, std::vector &neighbours) = 0; + + virtual size_t resize_graph(const size_t new_size) = 0; + virtual void clear_graph() = 0; + + virtual uint32_t get_max_observed_degree() = 0; + + // set during load + virtual size_t get_max_range_of_graph() = 0; + + // Total internal points _max_points + _num_frozen_points + size_t get_total_points() + { + return _capacity; + } + + protected: + // Internal function, changes total points when resize_graph is called. + void set_total_points(size_t new_capacity) + { + _capacity = new_capacity; + } + + size_t get_reserve_graph_degree() + { + return _reserve_graph_degree; + } + + private: + size_t _capacity; + size_t _reserve_graph_degree; +}; + +} // namespace diskann \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_index.h b/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_index.h new file mode 100644 index 0000000..059866f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_index.h @@ -0,0 +1,129 @@ +#pragma once +#include "distance.h" +#include "parameters.h" +#include "utils.h" +#include "types.h" +#include "index_config.h" +#include "index_build_params.h" +#include + +namespace diskann +{ +struct consolidation_report +{ + enum status_code + { + SUCCESS = 0, + FAIL = 1, + LOCK_FAIL = 2, + INCONSISTENT_COUNT_ERROR = 3 + }; + status_code _status; + size_t _active_points, _max_points, _empty_slots, _slots_released, _delete_set_size, _num_calls_to_process_delete; + double _time; + + consolidation_report(status_code status, size_t active_points, size_t max_points, size_t empty_slots, + size_t slots_released, size_t delete_set_size, size_t num_calls_to_process_delete, + double time_secs) + : _status(status), _active_points(active_points), _max_points(max_points), _empty_slots(empty_slots), + _slots_released(slots_released), _delete_set_size(delete_set_size), + _num_calls_to_process_delete(num_calls_to_process_delete), _time(time_secs) + { + } +}; + +/* A templated independent class for intercation with Index. Uses Type Erasure to add virtual implemetation of methods +that can take any type(using std::any) and Provides a clean API that can be inherited by different type of Index. +*/ +class AbstractIndex +{ + public: + AbstractIndex() = default; + virtual ~AbstractIndex() = default; + + virtual void build(const std::string &data_file, const size_t num_points_to_load, + IndexFilterParams &build_params) = 0; + + template + void build(const data_type *data, const size_t num_points_to_load, const std::vector &tags); + + virtual void save(const char *filename, bool compact_before_save = false) = 0; + +#ifdef EXEC_ENV_OLS + virtual void load(AlignedFileReader &reader, uint32_t num_threads, uint32_t search_l) = 0; +#else + virtual void load(const char *index_file, uint32_t num_threads, uint32_t search_l) = 0; +#endif + + // For FastL2 search on optimized layout + template + void search_with_optimized_layout(const data_type *query, size_t K, size_t L, uint32_t *indices); + + // Initialize space for res_vectors before calling. + template + size_t search_with_tags(const data_type *query, const uint64_t K, const uint32_t L, tag_type *tags, + float *distances, std::vector &res_vectors, bool use_filters = false, + const std::string filter_label = ""); + + // Added search overload that takes L as parameter, so that we + // can customize L on a per-query basis without tampering with "Parameters" + // IDtype is either uint32_t or uint64_t + template + std::pair search(const data_type *query, const size_t K, const uint32_t L, IDType *indices, + float *distances = nullptr); + + // Filter support search + // IndexType is either uint32_t or uint64_t + template + std::pair search_with_filters(const DataType &query, const std::string &raw_label, + const size_t K, const uint32_t L, IndexType *indices, + float *distances); + + // insert points with labels, labels should be present for filtered index + template + int insert_point(const data_type *point, const tag_type tag, const std::vector &labels); + + // insert point for unfiltered index build. do not use with filtered index + template int insert_point(const data_type *point, const tag_type tag); + + // delete point with tag, or return -1 if point can not be deleted + template int lazy_delete(const tag_type &tag); + + // batch delete tags and populates failed tags if unabke to delete given tags. + template + void lazy_delete(const std::vector &tags, std::vector &failed_tags); + + template void get_active_tags(tsl::robin_set &active_tags); + + template void set_start_points_at_random(data_type radius, uint32_t random_seed = 0); + + virtual consolidation_report consolidate_deletes(const IndexWriteParameters ¶meters) = 0; + + virtual void optimize_index_layout() = 0; + + // memory should be allocated for vec before calling this function + template int get_vector_by_tag(tag_type &tag, data_type *vec); + + template void set_universal_label(const label_type universal_label); + + private: + virtual void _build(const DataType &data, const size_t num_points_to_load, TagVector &tags) = 0; + virtual std::pair _search(const DataType &query, const size_t K, const uint32_t L, + std::any &indices, float *distances = nullptr) = 0; + virtual std::pair _search_with_filters(const DataType &query, const std::string &filter_label, + const size_t K, const uint32_t L, std::any &indices, + float *distances) = 0; + virtual int _insert_point(const DataType &data_point, const TagType tag, Labelvector &labels) = 0; + virtual int _insert_point(const DataType &data_point, const TagType tag) = 0; + virtual int _lazy_delete(const TagType &tag) = 0; + virtual void _lazy_delete(TagVector &tags, TagVector &failed_tags) = 0; + virtual void _get_active_tags(TagRobinSet &active_tags) = 0; + virtual void _set_start_points_at_random(DataType radius, uint32_t random_seed = 0) = 0; + virtual int _get_vector_by_tag(TagType &tag, DataType &vec) = 0; + virtual size_t _search_with_tags(const DataType &query, const uint64_t K, const uint32_t L, const TagType &tags, + float *distances, DataVector &res_vectors, bool use_filters = false, + const std::string filter_label = "") = 0; + virtual void _search_with_optimized_layout(const DataType &query, size_t K, size_t L, uint32_t *indices) = 0; + virtual void _set_universal_label(const LabelType universal_label) = 0; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_scratch.h b/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_scratch.h new file mode 100644 index 0000000..b42a836 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/abstract_scratch.h @@ -0,0 +1,35 @@ +#pragma once +namespace diskann +{ + +template class PQScratch; + +// By somewhat more than a coincidence, it seems that both InMemQueryScratch +// and SSDQueryScratch have the aligned query and PQScratch objects. So we +// can put them in a neat hierarchy and keep PQScratch as a standalone class. +template class AbstractScratch +{ + public: + AbstractScratch() = default; + // This class does not take any responsibilty for memory management of + // its members. It is the responsibility of the derived classes to do so. + virtual ~AbstractScratch() = default; + + // Scratch objects should not be copied + AbstractScratch(const AbstractScratch &) = delete; + AbstractScratch &operator=(const AbstractScratch &) = delete; + + data_t *aligned_query_T() + { + return _aligned_query_T; + } + PQScratch *pq_scratch() + { + return _pq_scratch; + } + + protected: + data_t *_aligned_query_T = nullptr; + PQScratch *_pq_scratch = nullptr; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/aligned_file_reader.h b/packages/leann-backend-diskann/third_party/DiskANN/include/aligned_file_reader.h new file mode 100644 index 0000000..2e2716a --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/aligned_file_reader.h @@ -0,0 +1,138 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#define MAX_IO_DEPTH 128 + +#include +#include + +#ifdef __linux__ +#include +#include +#include +#include +typedef io_context_t IOContext; +#elif __APPLE__ +#include +#include +#include + +struct IOContext +{ + int fd; + dispatch_io_t channel; + dispatch_queue_t queue; + dispatch_group_t grp; +}; +#elif _WINDOWS +#include +#include +#include + +#ifndef USE_BING_INFRA +struct IOContext +{ + HANDLE fhandle = NULL; + HANDLE iocp = NULL; + std::vector reqs; +}; +#else +#include "IDiskPriorityIO.h" +#include +// TODO: Caller code is very callous about copying IOContext objects +// all over the place. MUST verify that it won't cause leaks/logical +// errors. +// Because of such callous copying, we have to use ptr->atomic instead +// of atomic, as atomic is not copyable. +struct IOContext +{ + enum Status + { + READ_WAIT = 0, + READ_SUCCESS, + READ_FAILED, + PROCESS_COMPLETE + }; + + std::shared_ptr m_pDiskIO = nullptr; + std::shared_ptr> m_pRequests; + std::shared_ptr> m_pRequestsStatus; + + // waitonaddress on this memory to wait for IO completion signal + // reader should signal this memory after IO completion + // TODO: WindowsAlignedFileReader can be modified to take advantage of this + // and can largely share code with the file reader for Bing. + mutable volatile long m_completeCount = 0; + + IOContext() + : m_pRequestsStatus(new std::vector()), m_pRequests(new std::vector()) + { + (*m_pRequestsStatus).reserve(MAX_IO_DEPTH); + (*m_pRequests).reserve(MAX_IO_DEPTH); + } +}; +#endif + +#endif + +#include +#include +#include +#include "tsl/robin_map.h" +#include "utils.h" + +// NOTE :: all 3 fields must be 512-aligned +struct AlignedRead +{ + uint64_t offset; // where to read from + uint64_t len; // how much to read + void *buf; // where to read into + + AlignedRead() : offset(0), len(0), buf(nullptr) + { + } + + AlignedRead(uint64_t offset, uint64_t len, void *buf) : offset(offset), len(len), buf(buf) + { + assert(IS_512_ALIGNED(offset)); + assert(IS_512_ALIGNED(len)); + assert(IS_512_ALIGNED(buf)); + // assert(malloc_usable_size(buf) >= len); + } +}; + +class AlignedFileReader +{ + protected: + tsl::robin_map ctx_map; + std::mutex ctx_mut; + + public: + // returns the thread-specific context + // returns (io_context_t)(-1) if thread is not registered + virtual IOContext &get_ctx() = 0; + + virtual ~AlignedFileReader(){}; + + // register thread-id for a context + virtual void register_thread() = 0; + // de-register thread-id for a context + virtual void deregister_thread() = 0; + virtual void deregister_all_threads() = 0; + + // Open & close ops + // Blocking calls + virtual void open(const std::string &fname) = 0; + virtual void close() = 0; + + // process batch of aligned requests in parallel + // NOTE :: blocking call + virtual void read(std::vector &read_reqs, IOContext &ctx, bool async = false) = 0; + +#ifdef USE_BING_INFRA + // wait for completion of one request in a batch of requests + virtual void wait(IOContext &ctx, int &completedIndex) = 0; +#endif +}; diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/ann_exception.h b/packages/leann-backend-diskann/third_party/DiskANN/include/ann_exception.h new file mode 100644 index 0000000..55f069b --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/ann_exception.h @@ -0,0 +1,35 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include +#include +#include +#include "windows_customizations.h" +#include + +#ifndef _WINDOWS +#define __FUNCSIG__ __PRETTY_FUNCTION__ +#endif + +namespace diskann +{ + +class ANNException : public std::runtime_error +{ + public: + DISKANN_DLLEXPORT ANNException(const std::string &message, int errorCode); + DISKANN_DLLEXPORT ANNException(const std::string &message, int errorCode, const std::string &funcSig, + const std::string &fileName, uint32_t lineNum); + + private: + int _errorCode; +}; + +class FileException : public ANNException +{ + public: + DISKANN_DLLEXPORT FileException(const std::string &filename, std::system_error &e, const std::string &funcSig, + const std::string &fileName, uint32_t lineNum); +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/any_wrappers.h b/packages/leann-backend-diskann/third_party/DiskANN/include/any_wrappers.h new file mode 100644 index 0000000..da9005c --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/any_wrappers.h @@ -0,0 +1,53 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include +#include +#include +#include "tsl/robin_set.h" + +namespace AnyWrapper +{ + +/* + * Base Struct to hold refrence to the data. + * Note: No memory mamagement, caller need to keep object alive. + */ +struct AnyReference +{ + template AnyReference(Ty &reference) : _data(&reference) + { + } + + template Ty &get() + { + auto ptr = std::any_cast(_data); + return *ptr; + } + + private: + std::any _data; +}; +struct AnyRobinSet : public AnyReference +{ + template AnyRobinSet(const tsl::robin_set &robin_set) : AnyReference(robin_set) + { + } + template AnyRobinSet(tsl::robin_set &robin_set) : AnyReference(robin_set) + { + } +}; + +struct AnyVector : public AnyReference +{ + template AnyVector(const std::vector &vector) : AnyReference(vector) + { + } + template AnyVector(std::vector &vector) : AnyReference(vector) + { + } +}; +} // namespace AnyWrapper diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/apple_aligned_file_reader.h b/packages/leann-backend-diskann/third_party/DiskANN/include/apple_aligned_file_reader.h new file mode 100644 index 0000000..160e1ea --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/apple_aligned_file_reader.h @@ -0,0 +1,26 @@ +#pragma once +#ifdef __APPLE__ +#include "aligned_file_reader.h" + +class AppleAlignedFileReader : public AlignedFileReader +{ + private: + uint64_t file_sz; + FileHandle file_desc; + + public: + AppleAlignedFileReader(); + ~AppleAlignedFileReader(); + + IOContext &get_ctx(); + + void register_thread(); + void deregister_thread(); + void deregister_all_threads(); + + void open(const std::string &fname); + void close(); + + void read(std::vector &read_reqs, IOContext &ctx, bool async = false); +}; +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/boost_dynamic_bitset_fwd.h b/packages/leann-backend-diskann/third_party/DiskANN/include/boost_dynamic_bitset_fwd.h new file mode 100644 index 0000000..5aebb2b --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/boost_dynamic_bitset_fwd.h @@ -0,0 +1,11 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +namespace boost +{ +#ifndef BOOST_DYNAMIC_BITSET_FWD_HPP +template > class dynamic_bitset; +#endif +} // namespace boost diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/cached_io.h b/packages/leann-backend-diskann/third_party/DiskANN/include/cached_io.h new file mode 100644 index 0000000..daef2f2 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/cached_io.h @@ -0,0 +1,217 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include +#include +#include +#include + +#include "logger.h" +#include "ann_exception.h" + +// sequential cached reads +class cached_ifstream +{ + public: + cached_ifstream() + { + } + cached_ifstream(const std::string &filename, uint64_t cacheSize) : cache_size(cacheSize), cur_off(0) + { + reader.exceptions(std::ifstream::failbit | std::ifstream::badbit); + this->open(filename, cache_size); + } + ~cached_ifstream() + { + delete[] cache_buf; + reader.close(); + } + + void open(const std::string &filename, uint64_t cacheSize) + { + this->cur_off = 0; + + try + { + reader.open(filename, std::ios::binary | std::ios::ate); + fsize = reader.tellg(); + reader.seekg(0, std::ios::beg); + assert(reader.is_open()); + assert(cacheSize > 0); + cacheSize = (std::min)(cacheSize, fsize); + this->cache_size = cacheSize; + cache_buf = new char[cacheSize]; + reader.read(cache_buf, cacheSize); + diskann::cout << "Opened: " << filename.c_str() << ", size: " << fsize << ", cache_size: " << cacheSize + << std::endl; + } + catch (std::system_error &e) + { + throw diskann::FileException(filename, e, __FUNCSIG__, __FILE__, __LINE__); + } + } + + size_t get_file_size() + { + return fsize; + } + + void read(char *read_buf, uint64_t n_bytes) + { + assert(cache_buf != nullptr); + assert(read_buf != nullptr); + + if (n_bytes <= (cache_size - cur_off)) + { + // case 1: cache contains all data + memcpy(read_buf, cache_buf + cur_off, n_bytes); + cur_off += n_bytes; + } + else + { + // case 2: cache contains some data + uint64_t cached_bytes = cache_size - cur_off; + if (n_bytes - cached_bytes > fsize - reader.tellg()) + { + std::stringstream stream; + stream << "Reading beyond end of file" << std::endl; + stream << "n_bytes: " << n_bytes << " cached_bytes: " << cached_bytes << " fsize: " << fsize + << " current pos:" << reader.tellg() << std::endl; + diskann::cout << stream.str() << std::endl; + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + memcpy(read_buf, cache_buf + cur_off, cached_bytes); + + // go to disk and fetch more data + reader.read(read_buf + cached_bytes, n_bytes - cached_bytes); + // reset cur off + cur_off = cache_size; + + uint64_t size_left = fsize - reader.tellg(); + + if (size_left >= cache_size) + { + reader.read(cache_buf, cache_size); + cur_off = 0; + } + // note that if size_left < cache_size, then cur_off = cache_size, + // so subsequent reads will all be directly from file + } + } + + private: + // underlying ifstream + std::ifstream reader; + // # bytes to cache in one shot read + uint64_t cache_size = 0; + // underlying buf for cache + char *cache_buf = nullptr; + // offset into cache_buf for cur_pos + uint64_t cur_off = 0; + // file size + uint64_t fsize = 0; +}; + +// sequential cached writes +class cached_ofstream +{ + public: + cached_ofstream(const std::string &filename, uint64_t cache_size) : cache_size(cache_size), cur_off(0) + { + writer.exceptions(std::ifstream::failbit | std::ifstream::badbit); + try + { + writer.open(filename, std::ios::binary); + assert(writer.is_open()); + assert(cache_size > 0); + cache_buf = new char[cache_size]; + diskann::cout << "Opened: " << filename.c_str() << ", cache_size: " << cache_size << std::endl; + } + catch (std::system_error &e) + { + throw diskann::FileException(filename, e, __FUNCSIG__, __FILE__, __LINE__); + } + } + + ~cached_ofstream() + { + this->close(); + } + + void close() + { + // dump any remaining data in memory + if (cur_off > 0) + { + this->flush_cache(); + } + + if (cache_buf != nullptr) + { + delete[] cache_buf; + cache_buf = nullptr; + } + + if (writer.is_open()) + writer.close(); + diskann::cout << "Finished writing " << fsize << "B" << std::endl; + } + + size_t get_file_size() + { + return fsize; + } + // writes n_bytes from write_buf to the underlying ofstream/cache + void write(char *write_buf, uint64_t n_bytes) + { + assert(cache_buf != nullptr); + if (n_bytes <= (cache_size - cur_off)) + { + // case 1: cache can take all data + memcpy(cache_buf + cur_off, write_buf, n_bytes); + cur_off += n_bytes; + } + else + { + // case 2: cache cant take all data + // go to disk and write existing cache data + writer.write(cache_buf, cur_off); + fsize += cur_off; + // write the new data to disk + writer.write(write_buf, n_bytes); + fsize += n_bytes; + // memset all cache data and reset cur_off + memset(cache_buf, 0, cache_size); + cur_off = 0; + } + } + + void flush_cache() + { + assert(cache_buf != nullptr); + writer.write(cache_buf, cur_off); + fsize += cur_off; + memset(cache_buf, 0, cache_size); + cur_off = 0; + } + + void reset() + { + flush_cache(); + writer.seekp(0); + } + + private: + // underlying ofstream + std::ofstream writer; + // # bytes to cache for one shot write + uint64_t cache_size = 0; + // underlying buf for cache + char *cache_buf = nullptr; + // offset into cache_buf for cur_pos + uint64_t cur_off = 0; + + // file size + uint64_t fsize = 0; +}; diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/common_includes.h b/packages/leann-backend-diskann/third_party/DiskANN/include/common_includes.h new file mode 100644 index 0000000..e1a51bd --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/common_includes.h @@ -0,0 +1,27 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/concurrent_queue.h b/packages/leann-backend-diskann/third_party/DiskANN/include/concurrent_queue.h new file mode 100644 index 0000000..1e57bbf --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/concurrent_queue.h @@ -0,0 +1,132 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include +#include +#include +#include +#include +#include +#include +#include + +namespace diskann +{ + +template class ConcurrentQueue +{ + typedef std::chrono::microseconds chrono_us_t; + typedef std::unique_lock mutex_locker; + + std::queue q; + std::mutex mut; + std::mutex push_mut; + std::mutex pop_mut; + std::condition_variable push_cv; + std::condition_variable pop_cv; + T null_T; + + public: + ConcurrentQueue() + { + } + + ConcurrentQueue(T nullT) + { + this->null_T = nullT; + } + + ~ConcurrentQueue() + { + this->push_cv.notify_all(); + this->pop_cv.notify_all(); + } + + // queue stats + uint64_t size() + { + mutex_locker lk(this->mut); + uint64_t ret = q.size(); + lk.unlock(); + return ret; + } + + bool empty() + { + return (this->size() == 0); + } + + // PUSH BACK + void push(T &new_val) + { + mutex_locker lk(this->mut); + this->q.push(new_val); + lk.unlock(); + } + + template void insert(Iterator iter_begin, Iterator iter_end) + { + mutex_locker lk(this->mut); + for (Iterator it = iter_begin; it != iter_end; it++) + { + this->q.push(*it); + } + lk.unlock(); + } + + // POP FRONT + T pop() + { + mutex_locker lk(this->mut); + if (this->q.empty()) + { + lk.unlock(); + return this->null_T; + } + else + { + T ret = this->q.front(); + this->q.pop(); + // diskann::cout << "thread_id: " << std::this_thread::get_id() << + // ", ctx: " + // << ret.ctx << "\n"; + lk.unlock(); + return ret; + } + } + + // register for notifications + void wait_for_push_notify(chrono_us_t wait_time = chrono_us_t{10}) + { + mutex_locker lk(this->push_mut); + this->push_cv.wait_for(lk, wait_time); + lk.unlock(); + } + + void wait_for_pop_notify(chrono_us_t wait_time = chrono_us_t{10}) + { + mutex_locker lk(this->pop_mut); + this->pop_cv.wait_for(lk, wait_time); + lk.unlock(); + } + + // just notify functions + void push_notify_one() + { + this->push_cv.notify_one(); + } + void push_notify_all() + { + this->push_cv.notify_all(); + } + void pop_notify_one() + { + this->pop_cv.notify_one(); + } + void pop_notify_all() + { + this->pop_cv.notify_all(); + } +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/cosine_similarity.h b/packages/leann-backend-diskann/third_party/DiskANN/include/cosine_similarity.h new file mode 100644 index 0000000..539a8b0 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/cosine_similarity.h @@ -0,0 +1,285 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#ifndef __APPLE__ +#include +#include +#include +#include "simd_utils.h" +#endif + +extern bool Avx2SupportedCPU; + +#ifdef _WINDOWS +// SIMD implementation of Cosine similarity. Taken from hnsw library. + +/** + * Non-metric Space Library + * + * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov + * (http://boytsov.info). With contributions from Lawrence Cayton + * (http://lcayton.com/) and others. + * + * For the complete list of contributors and further details see: + * https://github.com/searchivarius/NonMetricSpaceLib + * + * Copyright (c) 2014 + * + * This code is released under the + * Apache License Version 2.0 http://www.apache.org/licenses/. + * + */ + +namespace diskann +{ + +using namespace std; + +#define PORTABLE_ALIGN16 __declspec(align(16)) + +static float NormScalarProductSIMD2(const int8_t *pVect1, const int8_t *pVect2, uint32_t qty) +{ + if (Avx2SupportedCPU) + { + __m256 cos, p1Len, p2Len; + cos = p1Len = p2Len = _mm256_setzero_ps(); + while (qty >= 32) + { + __m256i rx = _mm256_load_si256((__m256i *)pVect1), ry = _mm256_load_si256((__m256i *)pVect2); + cos = _mm256_add_ps(cos, _mm256_mul_epi8(rx, ry)); + p1Len = _mm256_add_ps(p1Len, _mm256_mul_epi8(rx, rx)); + p2Len = _mm256_add_ps(p2Len, _mm256_mul_epi8(ry, ry)); + pVect1 += 32; + pVect2 += 32; + qty -= 32; + } + while (qty > 0) + { + __m128i rx = _mm_load_si128((__m128i *)pVect1), ry = _mm_load_si128((__m128i *)pVect2); + cos = _mm256_add_ps(cos, _mm256_mul32_pi8(rx, ry)); + p1Len = _mm256_add_ps(p1Len, _mm256_mul32_pi8(rx, rx)); + p2Len = _mm256_add_ps(p2Len, _mm256_mul32_pi8(ry, ry)); + pVect1 += 4; + pVect2 += 4; + qty -= 4; + } + cos = _mm256_hadd_ps(_mm256_hadd_ps(cos, cos), cos); + p1Len = _mm256_hadd_ps(_mm256_hadd_ps(p1Len, p1Len), p1Len); + p2Len = _mm256_hadd_ps(_mm256_hadd_ps(p2Len, p2Len), p2Len); + float denominator = max(numeric_limits::min() * 2, sqrt(p1Len.m256_f32[0] + p1Len.m256_f32[4]) * + sqrt(p2Len.m256_f32[0] + p2Len.m256_f32[4])); + float cosine = (cos.m256_f32[0] + cos.m256_f32[4]) / denominator; + + return max(float(-1), min(float(1), cosine)); + } + + __m128 cos, p1Len, p2Len; + cos = p1Len = p2Len = _mm_setzero_ps(); + __m128i rx, ry; + while (qty >= 16) + { + rx = _mm_load_si128((__m128i *)pVect1); + ry = _mm_load_si128((__m128i *)pVect2); + cos = _mm_add_ps(cos, _mm_mul_epi8(rx, ry)); + p1Len = _mm_add_ps(p1Len, _mm_mul_epi8(rx, rx)); + p2Len = _mm_add_ps(p2Len, _mm_mul_epi8(ry, ry)); + pVect1 += 16; + pVect2 += 16; + qty -= 16; + } + while (qty > 0) + { + rx = _mm_load_si128((__m128i *)pVect1); + ry = _mm_load_si128((__m128i *)pVect2); + cos = _mm_add_ps(cos, _mm_mul32_pi8(rx, ry)); + p1Len = _mm_add_ps(p1Len, _mm_mul32_pi8(rx, rx)); + p2Len = _mm_add_ps(p2Len, _mm_mul32_pi8(ry, ry)); + pVect1 += 4; + pVect2 += 4; + qty -= 4; + } + cos = _mm_hadd_ps(_mm_hadd_ps(cos, cos), cos); + p1Len = _mm_hadd_ps(_mm_hadd_ps(p1Len, p1Len), p1Len); + p2Len = _mm_hadd_ps(_mm_hadd_ps(p2Len, p2Len), p2Len); + float norm1 = p1Len.m128_f32[0]; + float norm2 = p2Len.m128_f32[0]; + + static const float eps = numeric_limits::min() * 2; + + if (norm1 < eps) + { /* + * This shouldn't normally happen for this space, but + * if it does, we don't want to get NANs + */ + if (norm2 < eps) + { + return 1; + } + return 0; + } + /* + * Sometimes due to rounding errors, we get values > 1 or < -1. + * This throws off other functions that use scalar product, e.g., acos + */ + return max(float(-1), min(float(1), cos.m128_f32[0] / sqrt(norm1) / sqrt(norm2))); +} + +static float NormScalarProductSIMD(const float *pVect1, const float *pVect2, uint32_t qty) +{ + // Didn't get significant performance gain compared with 128bit version. + static const float eps = numeric_limits::min() * 2; + + if (Avx2SupportedCPU) + { + uint32_t qty8 = qty / 8; + + const float *pEnd1 = pVect1 + 8 * qty8; + const float *pEnd2 = pVect1 + qty; + + __m256 v1, v2; + __m256 sum_prod = _mm256_set_ps(0, 0, 0, 0, 0, 0, 0, 0); + __m256 sum_square1 = sum_prod; + __m256 sum_square2 = sum_prod; + + while (pVect1 < pEnd1) + { + v1 = _mm256_loadu_ps(pVect1); + pVect1 += 8; + v2 = _mm256_loadu_ps(pVect2); + pVect2 += 8; + sum_prod = _mm256_add_ps(sum_prod, _mm256_mul_ps(v1, v2)); + sum_square1 = _mm256_add_ps(sum_square1, _mm256_mul_ps(v1, v1)); + sum_square2 = _mm256_add_ps(sum_square2, _mm256_mul_ps(v2, v2)); + } + + float PORTABLE_ALIGN16 TmpResProd[8]; + float PORTABLE_ALIGN16 TmpResSquare1[8]; + float PORTABLE_ALIGN16 TmpResSquare2[8]; + + _mm256_store_ps(TmpResProd, sum_prod); + _mm256_store_ps(TmpResSquare1, sum_square1); + _mm256_store_ps(TmpResSquare2, sum_square2); + + float sum = 0.0f; + float norm1 = 0.0f; + float norm2 = 0.0f; + for (uint32_t i = 0; i < 8; ++i) + { + sum += TmpResProd[i]; + norm1 += TmpResSquare1[i]; + norm2 += TmpResSquare2[i]; + } + + while (pVect1 < pEnd2) + { + sum += (*pVect1) * (*pVect2); + norm1 += (*pVect1) * (*pVect1); + norm2 += (*pVect2) * (*pVect2); + + ++pVect1; + ++pVect2; + } + + if (norm1 < eps) + { + return norm2 < eps ? 1.0f : 0.0f; + } + + return max(float(-1), min(float(1), sum / sqrt(norm1) / sqrt(norm2))); + } + + __m128 v1, v2; + __m128 sum_prod = _mm_set1_ps(0); + __m128 sum_square1 = sum_prod; + __m128 sum_square2 = sum_prod; + + while (qty >= 4) + { + v1 = _mm_loadu_ps(pVect1); + pVect1 += 4; + v2 = _mm_loadu_ps(pVect2); + pVect2 += 4; + sum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2)); + sum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1)); + sum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2)); + + qty -= 4; + } + + float sum = sum_prod.m128_f32[0] + sum_prod.m128_f32[1] + sum_prod.m128_f32[2] + sum_prod.m128_f32[3]; + float norm1 = sum_square1.m128_f32[0] + sum_square1.m128_f32[1] + sum_square1.m128_f32[2] + sum_square1.m128_f32[3]; + float norm2 = sum_square2.m128_f32[0] + sum_square2.m128_f32[1] + sum_square2.m128_f32[2] + sum_square2.m128_f32[3]; + + if (norm1 < eps) + { + return norm2 < eps ? 1.0f : 0.0f; + } + + return max(float(-1), min(float(1), sum / sqrt(norm1) / sqrt(norm2))); +} + +static float NormScalarProductSIMD2(const float *pVect1, const float *pVect2, uint32_t qty) +{ + return NormScalarProductSIMD(pVect1, pVect2, qty); +} + +template static float CosineSimilarity2(const T *p1, const T *p2, uint32_t qty) +{ + return std::max(0.0f, 1.0f - NormScalarProductSIMD2(p1, p2, qty)); +} + +// static template float CosineSimilarity2<__int8>(const __int8* pVect1, +// const __int8* pVect2, size_t qty); + +// static template float CosineSimilarity2(const float* pVect1, +// const float* pVect2, size_t qty); + +template static void CosineSimilarityNormalize(T *pVector, uint32_t qty) +{ + T sum = 0; + for (uint32_t i = 0; i < qty; ++i) + { + sum += pVector[i] * pVector[i]; + } + sum = 1 / sqrt(sum); + if (sum == 0) + { + sum = numeric_limits::min(); + } + for (uint32_t i = 0; i < qty; ++i) + { + pVector[i] *= sum; + } +} + +// template static void CosineSimilarityNormalize(float* pVector, +// size_t qty); +// template static void CosineSimilarityNormalize(double* pVector, +// size_t qty); + +template <> void CosineSimilarityNormalize(__int8 * /*pVector*/, uint32_t /*qty*/) +{ + throw std::runtime_error("For int8 type vector, you can not use cosine distance!"); +} + +template <> void CosineSimilarityNormalize(__int16 * /*pVector*/, uint32_t /*qty*/) +{ + throw std::runtime_error("For int16 type vector, you can not use cosine distance!"); +} + +template <> void CosineSimilarityNormalize(int * /*pVector*/, uint32_t /*qty*/) +{ + throw std::runtime_error("For int type vector, you can not use cosine distance!"); +} +} // namespace diskann +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/defaults.h b/packages/leann-backend-diskann/third_party/DiskANN/include/defaults.h new file mode 100644 index 0000000..ef1750f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/defaults.h @@ -0,0 +1,34 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include + +namespace diskann +{ +namespace defaults +{ +const float ALPHA = 1.2f; +const uint32_t NUM_THREADS = 0; +const uint32_t MAX_OCCLUSION_SIZE = 750; +const bool HAS_LABELS = false; +const uint32_t FILTER_LIST_SIZE = 0; +const uint32_t NUM_FROZEN_POINTS_STATIC = 0; +const uint32_t NUM_FROZEN_POINTS_DYNAMIC = 1; + +// In-mem index related limits +const float GRAPH_SLACK_FACTOR = 1.3f; + +// SSD Index related limits +const uint64_t MAX_GRAPH_DEGREE = 512; +const uint64_t SECTOR_LEN = 4096; +const uint64_t MAX_N_SECTOR_READS = 128; + +// following constants should always be specified, but are useful as a +// sensible default at cli / python boundaries +const uint32_t MAX_DEGREE = 64; +const uint32_t BUILD_LIST_SIZE = 100; +const uint32_t SATURATE_GRAPH = false; +const uint32_t SEARCH_LIST_SIZE = 100; +} // namespace defaults +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/disk_utils.h b/packages/leann-backend-diskann/third_party/DiskANN/include/disk_utils.h new file mode 100644 index 0000000..08f046d --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/disk_utils.h @@ -0,0 +1,108 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#ifdef __APPLE__ +#else +#include +#endif + +#ifdef _WINDOWS +#include +typedef HANDLE FileHandle; +#else +#include +typedef int FileHandle; +#endif + +#include "cached_io.h" +#include "common_includes.h" + +#include "utils.h" +#include "windows_customizations.h" + +namespace diskann +{ +const size_t MAX_SAMPLE_POINTS_FOR_WARMUP = 100000; +const double PQ_TRAINING_SET_FRACTION = 0.1; +const double SPACE_FOR_CACHED_NODES_IN_GB = 0.25; +const double THRESHOLD_FOR_CACHING_IN_GB = 1.0; +const uint32_t NUM_NODES_TO_CACHE = 250000; +const uint32_t WARMUP_L = 20; +const uint32_t NUM_KMEANS_REPS = 12; + +template class PQFlashIndex; + +DISKANN_DLLEXPORT double get_memory_budget(const std::string &mem_budget_str); +DISKANN_DLLEXPORT double get_memory_budget(double search_ram_budget_in_gb); +DISKANN_DLLEXPORT void add_new_file_to_single_index(std::string index_file, std::string new_file); + +DISKANN_DLLEXPORT size_t calculate_num_pq_chunks(double final_index_ram_limit, size_t points_num, uint32_t dim); + +DISKANN_DLLEXPORT void read_idmap(const std::string &fname, std::vector &ivecs); + +#ifdef EXEC_ENV_OLS +template +DISKANN_DLLEXPORT T *load_warmup(MemoryMappedFiles &files, const std::string &cache_warmup_file, uint64_t &warmup_num, + uint64_t warmup_dim, uint64_t warmup_aligned_dim); +#else +template +DISKANN_DLLEXPORT T *load_warmup(const std::string &cache_warmup_file, uint64_t &warmup_num, uint64_t warmup_dim, + uint64_t warmup_aligned_dim); +#endif + +DISKANN_DLLEXPORT int merge_shards(const std::string &vamana_prefix, const std::string &vamana_suffix, + const std::string &idmaps_prefix, const std::string &idmaps_suffix, + const uint64_t nshards, uint32_t max_degree, const std::string &output_vamana, + const std::string &medoids_file, bool use_filters = false, + const std::string &labels_to_medoids_file = std::string("")); + +DISKANN_DLLEXPORT void extract_shard_labels(const std::string &in_label_file, const std::string &shard_ids_bin, + const std::string &shard_label_file); + +template +DISKANN_DLLEXPORT std::string preprocess_base_file(const std::string &infile, const std::string &indexPrefix, + diskann::Metric &distMetric); + +template +DISKANN_DLLEXPORT int build_merged_vamana_index(std::string base_file, diskann::Metric _compareMetric, uint32_t L, + uint32_t R, double sampling_rate, double ram_budget, + std::string mem_index_path, std::string medoids_file, + std::string centroids_file, size_t build_pq_bytes, bool use_opq, + uint32_t num_threads, bool use_filters = false, + const std::string &label_file = std::string(""), + const std::string &labels_to_medoids_file = std::string(""), + const std::string &universal_label = "", const uint32_t Lf = 0); + +template +DISKANN_DLLEXPORT uint32_t optimize_beamwidth(std::unique_ptr> &_pFlashIndex, + T *tuning_sample, uint64_t tuning_sample_num, + uint64_t tuning_sample_aligned_dim, uint32_t L, uint32_t nthreads, + uint32_t start_bw = 2); + +template +DISKANN_DLLEXPORT int build_disk_index( + const char *dataFilePath, const char *indexFilePath, const char *indexBuildParameters, + diskann::Metric _compareMetric, bool use_opq = false, + const std::string &codebook_prefix = "", // default is empty for no codebook pass in + bool use_filters = false, + const std::string &label_file = std::string(""), // default is empty string for no label_file + const std::string &universal_label = "", const uint32_t filter_threshold = 0, + const uint32_t Lf = 0); // default is empty string for no universal label + +template +DISKANN_DLLEXPORT void create_disk_layout(const std::string base_file, const std::string mem_index_file, + const std::string output_file, + const std::string reorder_data_file = std::string("")); + +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/distance.h b/packages/leann-backend-diskann/third_party/DiskANN/include/distance.h new file mode 100644 index 0000000..7198308 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/distance.h @@ -0,0 +1,236 @@ +#pragma once +#include "windows_customizations.h" +#include +#include + +namespace diskann +{ +enum Metric +{ + L2 = 0, + INNER_PRODUCT = 1, + COSINE = 2, + FAST_L2 = 3 +}; + +template class Distance +{ + public: + DISKANN_DLLEXPORT Distance(diskann::Metric dist_metric) : _distance_metric(dist_metric) + { + } + + // distance comparison function + DISKANN_DLLEXPORT virtual float compare(const T *a, const T *b, uint32_t length) const = 0; + + // Needed only for COSINE-BYTE and INNER_PRODUCT-BYTE + DISKANN_DLLEXPORT virtual float compare(const T *a, const T *b, const float normA, const float normB, + uint32_t length) const; + + // For MIPS, normalization adds an extra dimension to the vectors. + // This function lets callers know if the normalization process + // changes the dimension. + DISKANN_DLLEXPORT virtual uint32_t post_normalization_dimension(uint32_t orig_dimension) const; + + DISKANN_DLLEXPORT virtual diskann::Metric get_metric() const; + + // This is for efficiency. If no normalization is required, the callers + // can simply ignore the normalize_data_for_build() function. + DISKANN_DLLEXPORT virtual bool preprocessing_required() const; + + // Check the preprocessing_required() function before calling this. + // Clients can call the function like this: + // + // if (metric->preprocessing_required()){ + // T* normalized_data_batch; + // Split data into batches of batch_size and for each, call: + // metric->preprocess_base_points(data_batch, batch_size); + // + // TODO: This does not take into account the case for SSD inner product + // where the dimensions change after normalization. + DISKANN_DLLEXPORT virtual void preprocess_base_points(T *original_data, const size_t orig_dim, + const size_t num_points); + + // Invokes normalization for a single vector during search. The scratch space + // has to be created by the caller keeping track of the fact that + // normalization might change the dimension of the query vector. + DISKANN_DLLEXPORT virtual void preprocess_query(const T *query_vec, const size_t query_dim, T *scratch_query); + + // If an algorithm has a requirement that some data be aligned to a certain + // boundary it can use this function to indicate that requirement. Currently, + // we are setting it to 8 because that works well for AVX2. If we have AVX512 + // implementations of distance algos, they might have to set this to 16 + // (depending on how they are implemented) + DISKANN_DLLEXPORT virtual size_t get_required_alignment() const; + + // Providing a default implementation for the virtual destructor because we + // don't expect most metric implementations to need it. + DISKANN_DLLEXPORT virtual ~Distance() = default; + + protected: + diskann::Metric _distance_metric; + size_t _alignment_factor = 8; +}; + +class DistanceCosineInt8 : public Distance +{ + public: + DistanceCosineInt8() : Distance(diskann::Metric::COSINE) + { + } + DISKANN_DLLEXPORT virtual float compare(const int8_t *a, const int8_t *b, uint32_t length) const; +}; + +class DistanceL2Int8 : public Distance +{ + public: + DistanceL2Int8() : Distance(diskann::Metric::L2) + { + } + DISKANN_DLLEXPORT virtual float compare(const int8_t *a, const int8_t *b, uint32_t size) const; +}; + +// AVX implementations. Borrowed from HNSW code. +class AVXDistanceL2Int8 : public Distance +{ + public: + AVXDistanceL2Int8() : Distance(diskann::Metric::L2) + { + } + DISKANN_DLLEXPORT virtual float compare(const int8_t *a, const int8_t *b, uint32_t length) const; +}; + +class DistanceCosineFloat : public Distance +{ + public: + DistanceCosineFloat() : Distance(diskann::Metric::COSINE) + { + } + DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t length) const; +}; + +class DistanceL2Float : public Distance +{ + public: + DistanceL2Float() : Distance(diskann::Metric::L2) + { + } + +#ifdef _WINDOWS + DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t size) const; +#else + DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t size) const __attribute__((hot)); +#endif +}; + +class AVXDistanceL2Float : public Distance +{ + public: + AVXDistanceL2Float() : Distance(diskann::Metric::L2) + { + } + DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t length) const; +}; + +template class SlowDistanceL2 : public Distance +{ + public: + SlowDistanceL2() : Distance(diskann::Metric::L2) + { + } + DISKANN_DLLEXPORT virtual float compare(const T *a, const T *b, uint32_t length) const; +}; + +class SlowDistanceCosineUInt8 : public Distance +{ + public: + SlowDistanceCosineUInt8() : Distance(diskann::Metric::COSINE) + { + } + DISKANN_DLLEXPORT virtual float compare(const uint8_t *a, const uint8_t *b, uint32_t length) const; +}; + +class DistanceL2UInt8 : public Distance +{ + public: + DistanceL2UInt8() : Distance(diskann::Metric::L2) + { + } + DISKANN_DLLEXPORT virtual float compare(const uint8_t *a, const uint8_t *b, uint32_t size) const; +}; + +template class DistanceInnerProduct : public Distance +{ + public: + DistanceInnerProduct() : Distance(diskann::Metric::INNER_PRODUCT) + { + } + + DistanceInnerProduct(diskann::Metric metric) : Distance(metric) + { + } + inline float inner_product(const T *a, const T *b, unsigned size) const; + + inline float compare(const T *a, const T *b, unsigned size) const + { + float result = inner_product(a, b, size); + // if (result < 0) + // return std::numeric_limits::max(); + // else + return -result; + } +}; + +template class DistanceFastL2 : public DistanceInnerProduct +{ + // currently defined only for float. + // templated for future use. + public: + DistanceFastL2() : DistanceInnerProduct(diskann::Metric::FAST_L2) + { + } + float norm(const T *a, unsigned size) const; + float compare(const T *a, const T *b, float norm, unsigned size) const; +}; + +class AVXDistanceInnerProductFloat : public Distance +{ + public: + AVXDistanceInnerProductFloat() : Distance(diskann::Metric::INNER_PRODUCT) + { + } + DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t length) const; +}; + +class AVXNormalizedCosineDistanceFloat : public Distance +{ + private: + AVXDistanceInnerProductFloat _innerProduct; + + protected: + void normalize_and_copy(const float *a, uint32_t length, float *a_norm) const; + + public: + AVXNormalizedCosineDistanceFloat() : Distance(diskann::Metric::COSINE) + { + } + DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t length) const override + { + // Inner product returns negative values to indicate distance. + // This will ensure that cosine is between -1 and 1. + return 1.0f + _innerProduct.compare(a, b, length); + } + DISKANN_DLLEXPORT virtual uint32_t post_normalization_dimension(uint32_t orig_dimension) const override; + + DISKANN_DLLEXPORT virtual bool preprocessing_required() const override; + + DISKANN_DLLEXPORT virtual void preprocess_base_points(float *original_data, const size_t orig_dim, + const size_t num_points) override; + + DISKANN_DLLEXPORT virtual void preprocess_query(const float *query_vec, const size_t query_dim, + float *scratch_query_vector) override; +}; + +template Distance *get_distance_function(Metric m); + +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/embedding.pb.h b/packages/leann-backend-diskann/third_party/DiskANN/include/embedding.pb.h new file mode 100644 index 0000000..9f5c2b7 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/embedding.pb.h @@ -0,0 +1,675 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: embedding.proto + +#ifndef GOOGLE_PROTOBUF_INCLUDED_embedding_2eproto +#define GOOGLE_PROTOBUF_INCLUDED_embedding_2eproto + +#include +#include + +#include +#if PROTOBUF_VERSION < 3012000 +#error This file was generated by a newer version of protoc which is +#error incompatible with your Protocol Buffer headers. Please update +#error your headers. +#endif +#if 3012004 < PROTOBUF_MIN_PROTOC_VERSION +#error This file was generated by an older version of protoc which is +#error incompatible with your Protocol Buffer headers. Please +#error regenerate this file with a newer version of protoc. +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include // IWYU pragma: export +#include // IWYU pragma: export +#include +// @@protoc_insertion_point(includes) +#include +#define PROTOBUF_INTERNAL_EXPORT_embedding_2eproto +PROTOBUF_NAMESPACE_OPEN +namespace internal { +class AnyMetadata; +} // namespace internal +PROTOBUF_NAMESPACE_CLOSE + +// Internal implementation detail -- do not use these members. +struct TableStruct_embedding_2eproto { + static const ::PROTOBUF_NAMESPACE_ID::internal::ParseTableField entries[] + PROTOBUF_SECTION_VARIABLE(protodesc_cold); + static const ::PROTOBUF_NAMESPACE_ID::internal::AuxillaryParseTableField aux[] + PROTOBUF_SECTION_VARIABLE(protodesc_cold); + static const ::PROTOBUF_NAMESPACE_ID::internal::ParseTable schema[2] + PROTOBUF_SECTION_VARIABLE(protodesc_cold); + static const ::PROTOBUF_NAMESPACE_ID::internal::FieldMetadata field_metadata[]; + static const ::PROTOBUF_NAMESPACE_ID::internal::SerializationTable serialization_table[]; + static const ::PROTOBUF_NAMESPACE_ID::uint32 offsets[]; +}; +extern const ::PROTOBUF_NAMESPACE_ID::internal::DescriptorTable descriptor_table_embedding_2eproto; +namespace protoembedding { +class NodeEmbeddingRequest; +class NodeEmbeddingRequestDefaultTypeInternal; +extern NodeEmbeddingRequestDefaultTypeInternal _NodeEmbeddingRequest_default_instance_; +class NodeEmbeddingResponse; +class NodeEmbeddingResponseDefaultTypeInternal; +extern NodeEmbeddingResponseDefaultTypeInternal _NodeEmbeddingResponse_default_instance_; +} // namespace protoembedding +PROTOBUF_NAMESPACE_OPEN +template<> ::protoembedding::NodeEmbeddingRequest* Arena::CreateMaybeMessage<::protoembedding::NodeEmbeddingRequest>(Arena*); +template<> ::protoembedding::NodeEmbeddingResponse* Arena::CreateMaybeMessage<::protoembedding::NodeEmbeddingResponse>(Arena*); +PROTOBUF_NAMESPACE_CLOSE +namespace protoembedding { + +// =================================================================== + +class NodeEmbeddingRequest PROTOBUF_FINAL : + public ::PROTOBUF_NAMESPACE_ID::Message /* @@protoc_insertion_point(class_definition:protoembedding.NodeEmbeddingRequest) */ { + public: + inline NodeEmbeddingRequest() : NodeEmbeddingRequest(nullptr) {}; + virtual ~NodeEmbeddingRequest(); + + NodeEmbeddingRequest(const NodeEmbeddingRequest& from); + NodeEmbeddingRequest(NodeEmbeddingRequest&& from) noexcept + : NodeEmbeddingRequest() { + *this = ::std::move(from); + } + + inline NodeEmbeddingRequest& operator=(const NodeEmbeddingRequest& from) { + CopyFrom(from); + return *this; + } + inline NodeEmbeddingRequest& operator=(NodeEmbeddingRequest&& from) noexcept { + if (GetArena() == from.GetArena()) { + if (this != &from) InternalSwap(&from); + } else { + CopyFrom(from); + } + return *this; + } + + static const ::PROTOBUF_NAMESPACE_ID::Descriptor* descriptor() { + return GetDescriptor(); + } + static const ::PROTOBUF_NAMESPACE_ID::Descriptor* GetDescriptor() { + return GetMetadataStatic().descriptor; + } + static const ::PROTOBUF_NAMESPACE_ID::Reflection* GetReflection() { + return GetMetadataStatic().reflection; + } + static const NodeEmbeddingRequest& default_instance(); + + static void InitAsDefaultInstance(); // FOR INTERNAL USE ONLY + static inline const NodeEmbeddingRequest* internal_default_instance() { + return reinterpret_cast( + &_NodeEmbeddingRequest_default_instance_); + } + static constexpr int kIndexInFileMessages = + 0; + + friend void swap(NodeEmbeddingRequest& a, NodeEmbeddingRequest& b) { + a.Swap(&b); + } + inline void Swap(NodeEmbeddingRequest* other) { + if (other == this) return; + if (GetArena() == other->GetArena()) { + InternalSwap(other); + } else { + ::PROTOBUF_NAMESPACE_ID::internal::GenericSwap(this, other); + } + } + void UnsafeArenaSwap(NodeEmbeddingRequest* other) { + if (other == this) return; + GOOGLE_DCHECK(GetArena() == other->GetArena()); + InternalSwap(other); + } + + // implements Message ---------------------------------------------- + + inline NodeEmbeddingRequest* New() const final { + return CreateMaybeMessage(nullptr); + } + + NodeEmbeddingRequest* New(::PROTOBUF_NAMESPACE_ID::Arena* arena) const final { + return CreateMaybeMessage(arena); + } + void CopyFrom(const ::PROTOBUF_NAMESPACE_ID::Message& from) final; + void MergeFrom(const ::PROTOBUF_NAMESPACE_ID::Message& from) final; + void CopyFrom(const NodeEmbeddingRequest& from); + void MergeFrom(const NodeEmbeddingRequest& from); + PROTOBUF_ATTRIBUTE_REINITIALIZES void Clear() final; + bool IsInitialized() const final; + + size_t ByteSizeLong() const final; + const char* _InternalParse(const char* ptr, ::PROTOBUF_NAMESPACE_ID::internal::ParseContext* ctx) final; + ::PROTOBUF_NAMESPACE_ID::uint8* _InternalSerialize( + ::PROTOBUF_NAMESPACE_ID::uint8* target, ::PROTOBUF_NAMESPACE_ID::io::EpsCopyOutputStream* stream) const final; + int GetCachedSize() const final { return _cached_size_.Get(); } + + private: + inline void SharedCtor(); + inline void SharedDtor(); + void SetCachedSize(int size) const final; + void InternalSwap(NodeEmbeddingRequest* other); + friend class ::PROTOBUF_NAMESPACE_ID::internal::AnyMetadata; + static ::PROTOBUF_NAMESPACE_ID::StringPiece FullMessageName() { + return "protoembedding.NodeEmbeddingRequest"; + } + protected: + explicit NodeEmbeddingRequest(::PROTOBUF_NAMESPACE_ID::Arena* arena); + private: + static void ArenaDtor(void* object); + inline void RegisterArenaDtor(::PROTOBUF_NAMESPACE_ID::Arena* arena); + public: + + ::PROTOBUF_NAMESPACE_ID::Metadata GetMetadata() const final; + private: + static ::PROTOBUF_NAMESPACE_ID::Metadata GetMetadataStatic() { + ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors(&::descriptor_table_embedding_2eproto); + return ::descriptor_table_embedding_2eproto.file_level_metadata[kIndexInFileMessages]; + } + + public: + + // nested types ---------------------------------------------------- + + // accessors ------------------------------------------------------- + + enum : int { + kNodeIdsFieldNumber = 1, + }; + // repeated uint32 node_ids = 1; + int node_ids_size() const; + private: + int _internal_node_ids_size() const; + public: + void clear_node_ids(); + private: + ::PROTOBUF_NAMESPACE_ID::uint32 _internal_node_ids(int index) const; + const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >& + _internal_node_ids() const; + void _internal_add_node_ids(::PROTOBUF_NAMESPACE_ID::uint32 value); + ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >* + _internal_mutable_node_ids(); + public: + ::PROTOBUF_NAMESPACE_ID::uint32 node_ids(int index) const; + void set_node_ids(int index, ::PROTOBUF_NAMESPACE_ID::uint32 value); + void add_node_ids(::PROTOBUF_NAMESPACE_ID::uint32 value); + const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >& + node_ids() const; + ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >* + mutable_node_ids(); + + // @@protoc_insertion_point(class_scope:protoembedding.NodeEmbeddingRequest) + private: + class _Internal; + + template friend class ::PROTOBUF_NAMESPACE_ID::Arena::InternalHelper; + typedef void InternalArenaConstructable_; + typedef void DestructorSkippable_; + ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 > node_ids_; + mutable std::atomic _node_ids_cached_byte_size_; + mutable ::PROTOBUF_NAMESPACE_ID::internal::CachedSize _cached_size_; + friend struct ::TableStruct_embedding_2eproto; +}; +// ------------------------------------------------------------------- + +class NodeEmbeddingResponse PROTOBUF_FINAL : + public ::PROTOBUF_NAMESPACE_ID::Message /* @@protoc_insertion_point(class_definition:protoembedding.NodeEmbeddingResponse) */ { + public: + inline NodeEmbeddingResponse() : NodeEmbeddingResponse(nullptr) {}; + virtual ~NodeEmbeddingResponse(); + + NodeEmbeddingResponse(const NodeEmbeddingResponse& from); + NodeEmbeddingResponse(NodeEmbeddingResponse&& from) noexcept + : NodeEmbeddingResponse() { + *this = ::std::move(from); + } + + inline NodeEmbeddingResponse& operator=(const NodeEmbeddingResponse& from) { + CopyFrom(from); + return *this; + } + inline NodeEmbeddingResponse& operator=(NodeEmbeddingResponse&& from) noexcept { + if (GetArena() == from.GetArena()) { + if (this != &from) InternalSwap(&from); + } else { + CopyFrom(from); + } + return *this; + } + + static const ::PROTOBUF_NAMESPACE_ID::Descriptor* descriptor() { + return GetDescriptor(); + } + static const ::PROTOBUF_NAMESPACE_ID::Descriptor* GetDescriptor() { + return GetMetadataStatic().descriptor; + } + static const ::PROTOBUF_NAMESPACE_ID::Reflection* GetReflection() { + return GetMetadataStatic().reflection; + } + static const NodeEmbeddingResponse& default_instance(); + + static void InitAsDefaultInstance(); // FOR INTERNAL USE ONLY + static inline const NodeEmbeddingResponse* internal_default_instance() { + return reinterpret_cast( + &_NodeEmbeddingResponse_default_instance_); + } + static constexpr int kIndexInFileMessages = + 1; + + friend void swap(NodeEmbeddingResponse& a, NodeEmbeddingResponse& b) { + a.Swap(&b); + } + inline void Swap(NodeEmbeddingResponse* other) { + if (other == this) return; + if (GetArena() == other->GetArena()) { + InternalSwap(other); + } else { + ::PROTOBUF_NAMESPACE_ID::internal::GenericSwap(this, other); + } + } + void UnsafeArenaSwap(NodeEmbeddingResponse* other) { + if (other == this) return; + GOOGLE_DCHECK(GetArena() == other->GetArena()); + InternalSwap(other); + } + + // implements Message ---------------------------------------------- + + inline NodeEmbeddingResponse* New() const final { + return CreateMaybeMessage(nullptr); + } + + NodeEmbeddingResponse* New(::PROTOBUF_NAMESPACE_ID::Arena* arena) const final { + return CreateMaybeMessage(arena); + } + void CopyFrom(const ::PROTOBUF_NAMESPACE_ID::Message& from) final; + void MergeFrom(const ::PROTOBUF_NAMESPACE_ID::Message& from) final; + void CopyFrom(const NodeEmbeddingResponse& from); + void MergeFrom(const NodeEmbeddingResponse& from); + PROTOBUF_ATTRIBUTE_REINITIALIZES void Clear() final; + bool IsInitialized() const final; + + size_t ByteSizeLong() const final; + const char* _InternalParse(const char* ptr, ::PROTOBUF_NAMESPACE_ID::internal::ParseContext* ctx) final; + ::PROTOBUF_NAMESPACE_ID::uint8* _InternalSerialize( + ::PROTOBUF_NAMESPACE_ID::uint8* target, ::PROTOBUF_NAMESPACE_ID::io::EpsCopyOutputStream* stream) const final; + int GetCachedSize() const final { return _cached_size_.Get(); } + + private: + inline void SharedCtor(); + inline void SharedDtor(); + void SetCachedSize(int size) const final; + void InternalSwap(NodeEmbeddingResponse* other); + friend class ::PROTOBUF_NAMESPACE_ID::internal::AnyMetadata; + static ::PROTOBUF_NAMESPACE_ID::StringPiece FullMessageName() { + return "protoembedding.NodeEmbeddingResponse"; + } + protected: + explicit NodeEmbeddingResponse(::PROTOBUF_NAMESPACE_ID::Arena* arena); + private: + static void ArenaDtor(void* object); + inline void RegisterArenaDtor(::PROTOBUF_NAMESPACE_ID::Arena* arena); + public: + + ::PROTOBUF_NAMESPACE_ID::Metadata GetMetadata() const final; + private: + static ::PROTOBUF_NAMESPACE_ID::Metadata GetMetadataStatic() { + ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors(&::descriptor_table_embedding_2eproto); + return ::descriptor_table_embedding_2eproto.file_level_metadata[kIndexInFileMessages]; + } + + public: + + // nested types ---------------------------------------------------- + + // accessors ------------------------------------------------------- + + enum : int { + kDimensionsFieldNumber = 2, + kMissingIdsFieldNumber = 3, + kEmbeddingsDataFieldNumber = 1, + }; + // repeated int32 dimensions = 2; + int dimensions_size() const; + private: + int _internal_dimensions_size() const; + public: + void clear_dimensions(); + private: + ::PROTOBUF_NAMESPACE_ID::int32 _internal_dimensions(int index) const; + const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::int32 >& + _internal_dimensions() const; + void _internal_add_dimensions(::PROTOBUF_NAMESPACE_ID::int32 value); + ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::int32 >* + _internal_mutable_dimensions(); + public: + ::PROTOBUF_NAMESPACE_ID::int32 dimensions(int index) const; + void set_dimensions(int index, ::PROTOBUF_NAMESPACE_ID::int32 value); + void add_dimensions(::PROTOBUF_NAMESPACE_ID::int32 value); + const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::int32 >& + dimensions() const; + ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::int32 >* + mutable_dimensions(); + + // repeated uint32 missing_ids = 3; + int missing_ids_size() const; + private: + int _internal_missing_ids_size() const; + public: + void clear_missing_ids(); + private: + ::PROTOBUF_NAMESPACE_ID::uint32 _internal_missing_ids(int index) const; + const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >& + _internal_missing_ids() const; + void _internal_add_missing_ids(::PROTOBUF_NAMESPACE_ID::uint32 value); + ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >* + _internal_mutable_missing_ids(); + public: + ::PROTOBUF_NAMESPACE_ID::uint32 missing_ids(int index) const; + void set_missing_ids(int index, ::PROTOBUF_NAMESPACE_ID::uint32 value); + void add_missing_ids(::PROTOBUF_NAMESPACE_ID::uint32 value); + const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >& + missing_ids() const; + ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >* + mutable_missing_ids(); + + // bytes embeddings_data = 1; + void clear_embeddings_data(); + const std::string& embeddings_data() const; + void set_embeddings_data(const std::string& value); + void set_embeddings_data(std::string&& value); + void set_embeddings_data(const char* value); + void set_embeddings_data(const void* value, size_t size); + std::string* mutable_embeddings_data(); + std::string* release_embeddings_data(); + void set_allocated_embeddings_data(std::string* embeddings_data); + GOOGLE_PROTOBUF_RUNTIME_DEPRECATED("The unsafe_arena_ accessors for" + " string fields are deprecated and will be removed in a" + " future release.") + std::string* unsafe_arena_release_embeddings_data(); + GOOGLE_PROTOBUF_RUNTIME_DEPRECATED("The unsafe_arena_ accessors for" + " string fields are deprecated and will be removed in a" + " future release.") + void unsafe_arena_set_allocated_embeddings_data( + std::string* embeddings_data); + private: + const std::string& _internal_embeddings_data() const; + void _internal_set_embeddings_data(const std::string& value); + std::string* _internal_mutable_embeddings_data(); + public: + + // @@protoc_insertion_point(class_scope:protoembedding.NodeEmbeddingResponse) + private: + class _Internal; + + template friend class ::PROTOBUF_NAMESPACE_ID::Arena::InternalHelper; + typedef void InternalArenaConstructable_; + typedef void DestructorSkippable_; + ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::int32 > dimensions_; + mutable std::atomic _dimensions_cached_byte_size_; + ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 > missing_ids_; + mutable std::atomic _missing_ids_cached_byte_size_; + ::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr embeddings_data_; + mutable ::PROTOBUF_NAMESPACE_ID::internal::CachedSize _cached_size_; + friend struct ::TableStruct_embedding_2eproto; +}; +// =================================================================== + + +// =================================================================== + +#ifdef __GNUC__ + #pragma GCC diagnostic push + #pragma GCC diagnostic ignored "-Wstrict-aliasing" +#endif // __GNUC__ +// NodeEmbeddingRequest + +// repeated uint32 node_ids = 1; +inline int NodeEmbeddingRequest::_internal_node_ids_size() const { + return node_ids_.size(); +} +inline int NodeEmbeddingRequest::node_ids_size() const { + return _internal_node_ids_size(); +} +inline void NodeEmbeddingRequest::clear_node_ids() { + node_ids_.Clear(); +} +inline ::PROTOBUF_NAMESPACE_ID::uint32 NodeEmbeddingRequest::_internal_node_ids(int index) const { + return node_ids_.Get(index); +} +inline ::PROTOBUF_NAMESPACE_ID::uint32 NodeEmbeddingRequest::node_ids(int index) const { + // @@protoc_insertion_point(field_get:protoembedding.NodeEmbeddingRequest.node_ids) + return _internal_node_ids(index); +} +inline void NodeEmbeddingRequest::set_node_ids(int index, ::PROTOBUF_NAMESPACE_ID::uint32 value) { + node_ids_.Set(index, value); + // @@protoc_insertion_point(field_set:protoembedding.NodeEmbeddingRequest.node_ids) +} +inline void NodeEmbeddingRequest::_internal_add_node_ids(::PROTOBUF_NAMESPACE_ID::uint32 value) { + node_ids_.Add(value); +} +inline void NodeEmbeddingRequest::add_node_ids(::PROTOBUF_NAMESPACE_ID::uint32 value) { + _internal_add_node_ids(value); + // @@protoc_insertion_point(field_add:protoembedding.NodeEmbeddingRequest.node_ids) +} +inline const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >& +NodeEmbeddingRequest::_internal_node_ids() const { + return node_ids_; +} +inline const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >& +NodeEmbeddingRequest::node_ids() const { + // @@protoc_insertion_point(field_list:protoembedding.NodeEmbeddingRequest.node_ids) + return _internal_node_ids(); +} +inline ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >* +NodeEmbeddingRequest::_internal_mutable_node_ids() { + return &node_ids_; +} +inline ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >* +NodeEmbeddingRequest::mutable_node_ids() { + // @@protoc_insertion_point(field_mutable_list:protoembedding.NodeEmbeddingRequest.node_ids) + return _internal_mutable_node_ids(); +} + +// ------------------------------------------------------------------- + +// NodeEmbeddingResponse + +// bytes embeddings_data = 1; +inline void NodeEmbeddingResponse::clear_embeddings_data() { + embeddings_data_.ClearToEmpty(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), GetArena()); +} +inline const std::string& NodeEmbeddingResponse::embeddings_data() const { + // @@protoc_insertion_point(field_get:protoembedding.NodeEmbeddingResponse.embeddings_data) + return _internal_embeddings_data(); +} +inline void NodeEmbeddingResponse::set_embeddings_data(const std::string& value) { + _internal_set_embeddings_data(value); + // @@protoc_insertion_point(field_set:protoembedding.NodeEmbeddingResponse.embeddings_data) +} +inline std::string* NodeEmbeddingResponse::mutable_embeddings_data() { + // @@protoc_insertion_point(field_mutable:protoembedding.NodeEmbeddingResponse.embeddings_data) + return _internal_mutable_embeddings_data(); +} +inline const std::string& NodeEmbeddingResponse::_internal_embeddings_data() const { + return embeddings_data_.Get(); +} +inline void NodeEmbeddingResponse::_internal_set_embeddings_data(const std::string& value) { + + embeddings_data_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), value, GetArena()); +} +inline void NodeEmbeddingResponse::set_embeddings_data(std::string&& value) { + + embeddings_data_.Set( + &::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), ::std::move(value), GetArena()); + // @@protoc_insertion_point(field_set_rvalue:protoembedding.NodeEmbeddingResponse.embeddings_data) +} +inline void NodeEmbeddingResponse::set_embeddings_data(const char* value) { + GOOGLE_DCHECK(value != nullptr); + + embeddings_data_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), ::std::string(value), + GetArena()); + // @@protoc_insertion_point(field_set_char:protoembedding.NodeEmbeddingResponse.embeddings_data) +} +inline void NodeEmbeddingResponse::set_embeddings_data(const void* value, + size_t size) { + + embeddings_data_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), ::std::string( + reinterpret_cast(value), size), GetArena()); + // @@protoc_insertion_point(field_set_pointer:protoembedding.NodeEmbeddingResponse.embeddings_data) +} +inline std::string* NodeEmbeddingResponse::_internal_mutable_embeddings_data() { + + return embeddings_data_.Mutable(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), GetArena()); +} +inline std::string* NodeEmbeddingResponse::release_embeddings_data() { + // @@protoc_insertion_point(field_release:protoembedding.NodeEmbeddingResponse.embeddings_data) + return embeddings_data_.Release(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), GetArena()); +} +inline void NodeEmbeddingResponse::set_allocated_embeddings_data(std::string* embeddings_data) { + if (embeddings_data != nullptr) { + + } else { + + } + embeddings_data_.SetAllocated(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), embeddings_data, + GetArena()); + // @@protoc_insertion_point(field_set_allocated:protoembedding.NodeEmbeddingResponse.embeddings_data) +} +inline std::string* NodeEmbeddingResponse::unsafe_arena_release_embeddings_data() { + // @@protoc_insertion_point(field_unsafe_arena_release:protoembedding.NodeEmbeddingResponse.embeddings_data) + GOOGLE_DCHECK(GetArena() != nullptr); + + return embeddings_data_.UnsafeArenaRelease(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), + GetArena()); +} +inline void NodeEmbeddingResponse::unsafe_arena_set_allocated_embeddings_data( + std::string* embeddings_data) { + GOOGLE_DCHECK(GetArena() != nullptr); + if (embeddings_data != nullptr) { + + } else { + + } + embeddings_data_.UnsafeArenaSetAllocated(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), + embeddings_data, GetArena()); + // @@protoc_insertion_point(field_unsafe_arena_set_allocated:protoembedding.NodeEmbeddingResponse.embeddings_data) +} + +// repeated int32 dimensions = 2; +inline int NodeEmbeddingResponse::_internal_dimensions_size() const { + return dimensions_.size(); +} +inline int NodeEmbeddingResponse::dimensions_size() const { + return _internal_dimensions_size(); +} +inline void NodeEmbeddingResponse::clear_dimensions() { + dimensions_.Clear(); +} +inline ::PROTOBUF_NAMESPACE_ID::int32 NodeEmbeddingResponse::_internal_dimensions(int index) const { + return dimensions_.Get(index); +} +inline ::PROTOBUF_NAMESPACE_ID::int32 NodeEmbeddingResponse::dimensions(int index) const { + // @@protoc_insertion_point(field_get:protoembedding.NodeEmbeddingResponse.dimensions) + return _internal_dimensions(index); +} +inline void NodeEmbeddingResponse::set_dimensions(int index, ::PROTOBUF_NAMESPACE_ID::int32 value) { + dimensions_.Set(index, value); + // @@protoc_insertion_point(field_set:protoembedding.NodeEmbeddingResponse.dimensions) +} +inline void NodeEmbeddingResponse::_internal_add_dimensions(::PROTOBUF_NAMESPACE_ID::int32 value) { + dimensions_.Add(value); +} +inline void NodeEmbeddingResponse::add_dimensions(::PROTOBUF_NAMESPACE_ID::int32 value) { + _internal_add_dimensions(value); + // @@protoc_insertion_point(field_add:protoembedding.NodeEmbeddingResponse.dimensions) +} +inline const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::int32 >& +NodeEmbeddingResponse::_internal_dimensions() const { + return dimensions_; +} +inline const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::int32 >& +NodeEmbeddingResponse::dimensions() const { + // @@protoc_insertion_point(field_list:protoembedding.NodeEmbeddingResponse.dimensions) + return _internal_dimensions(); +} +inline ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::int32 >* +NodeEmbeddingResponse::_internal_mutable_dimensions() { + return &dimensions_; +} +inline ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::int32 >* +NodeEmbeddingResponse::mutable_dimensions() { + // @@protoc_insertion_point(field_mutable_list:protoembedding.NodeEmbeddingResponse.dimensions) + return _internal_mutable_dimensions(); +} + +// repeated uint32 missing_ids = 3; +inline int NodeEmbeddingResponse::_internal_missing_ids_size() const { + return missing_ids_.size(); +} +inline int NodeEmbeddingResponse::missing_ids_size() const { + return _internal_missing_ids_size(); +} +inline void NodeEmbeddingResponse::clear_missing_ids() { + missing_ids_.Clear(); +} +inline ::PROTOBUF_NAMESPACE_ID::uint32 NodeEmbeddingResponse::_internal_missing_ids(int index) const { + return missing_ids_.Get(index); +} +inline ::PROTOBUF_NAMESPACE_ID::uint32 NodeEmbeddingResponse::missing_ids(int index) const { + // @@protoc_insertion_point(field_get:protoembedding.NodeEmbeddingResponse.missing_ids) + return _internal_missing_ids(index); +} +inline void NodeEmbeddingResponse::set_missing_ids(int index, ::PROTOBUF_NAMESPACE_ID::uint32 value) { + missing_ids_.Set(index, value); + // @@protoc_insertion_point(field_set:protoembedding.NodeEmbeddingResponse.missing_ids) +} +inline void NodeEmbeddingResponse::_internal_add_missing_ids(::PROTOBUF_NAMESPACE_ID::uint32 value) { + missing_ids_.Add(value); +} +inline void NodeEmbeddingResponse::add_missing_ids(::PROTOBUF_NAMESPACE_ID::uint32 value) { + _internal_add_missing_ids(value); + // @@protoc_insertion_point(field_add:protoembedding.NodeEmbeddingResponse.missing_ids) +} +inline const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >& +NodeEmbeddingResponse::_internal_missing_ids() const { + return missing_ids_; +} +inline const ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >& +NodeEmbeddingResponse::missing_ids() const { + // @@protoc_insertion_point(field_list:protoembedding.NodeEmbeddingResponse.missing_ids) + return _internal_missing_ids(); +} +inline ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >* +NodeEmbeddingResponse::_internal_mutable_missing_ids() { + return &missing_ids_; +} +inline ::PROTOBUF_NAMESPACE_ID::RepeatedField< ::PROTOBUF_NAMESPACE_ID::uint32 >* +NodeEmbeddingResponse::mutable_missing_ids() { + // @@protoc_insertion_point(field_mutable_list:protoembedding.NodeEmbeddingResponse.missing_ids) + return _internal_mutable_missing_ids(); +} + +#ifdef __GNUC__ + #pragma GCC diagnostic pop +#endif // __GNUC__ +// ------------------------------------------------------------------- + + +// @@protoc_insertion_point(namespace_scope) + +} // namespace protoembedding + +// @@protoc_insertion_point(global_scope) + +#include +#endif // GOOGLE_PROTOBUF_INCLUDED_GOOGLE_PROTOBUF_INCLUDED_embedding_2eproto diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/embedding_compute.h b/packages/leann-backend-diskann/third_party/DiskANN/include/embedding_compute.h new file mode 100644 index 0000000..354c9c5 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/embedding_compute.h @@ -0,0 +1,118 @@ +#pragma once + +#include +#include + +#ifdef PYBIND11_EMBEDDED +#include +#else +#include +#endif +#include +#include + +namespace py = pybind11; + +namespace diskann +{ + +class PYBIND11_EXPORT EmbeddingComputer +{ + public: + static EmbeddingComputer &getInstance() + { + static EmbeddingComputer instance; + return instance; + } + + void initialize(const std::string &model_path) + { + try + { + py::module_ sys = py::module_::import("sys"); + py::module_ os = py::module_::import("os"); + + // Add the directory containing embedd_micro.py to Python path + std::string micro_dir = "micro"; + sys.attr("path").attr("append")(micro_dir); + + // Import our module + py::module_ embedd = py::module_::import("embedd_micro"); + + // Create benchmark config + py::object config = embedd.attr("BenchmarkConfig")(model_path, // model_path + py::list(), // empty batch_sizes + 256, // seq_length + 1, // num_runs + true, // use_fp16 + false, // use_cuda_graphs + false // use_flash_attention + ); + + // Create benchmark instance + benchmark = embedd.attr("Benchmark")(config); + } + catch (const std::exception &e) + { + throw std::runtime_error("Failed to initialize Python embedding computer: " + std::string(e.what())); + } + } + + template + std::vector computeEmbeddings(const std::vector &points, size_t dim, size_t batch_size = 32) + { + try + { + // Convert points to numpy array + std::vector flattened_points; + flattened_points.reserve(points.size() * dim); + + for (const auto &point : points) + { + flattened_points.insert(flattened_points.end(), point, point + dim); + } + + py::array_t points_array({static_cast(points.size()), static_cast(dim)}, + flattened_points.data()); + + // Call compute_embeddings + py::object result = benchmark.attr("compute_embeddings")(points_array, batch_size); + + // Convert result back to C++ + py::array_t np_result = result.cast>(); + py::buffer_info buf = np_result.request(); + float *ptr = static_cast(buf.ptr); + + return std::vector(ptr, ptr + buf.size); + } + catch (const std::exception &e) + { + throw std::runtime_error("Failed to compute embeddings: " + std::string(e.what())); + } + } + + private: + EmbeddingComputer() + { +#ifdef PYBIND11_EMBEDDED + if (!Py_IsInitialized()) + { + py::initialize_interpreter(); + } +#endif + } + + ~EmbeddingComputer() + { +#ifdef PYBIND11_EMBEDDED + if (Py_IsInitialized()) + { + py::finalize_interpreter(); + } +#endif + } + + py::object benchmark; +}; + +} // namespace diskann \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/exceptions.h b/packages/leann-backend-diskann/third_party/DiskANN/include/exceptions.h new file mode 100644 index 0000000..99e4e73 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/exceptions.h @@ -0,0 +1,17 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include + +namespace diskann +{ + +class NotImplementedException : public std::logic_error +{ + public: + NotImplementedException() : std::logic_error("Function not yet implemented.") + { + } +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/filter_utils.h b/packages/leann-backend-diskann/third_party/DiskANN/include/filter_utils.h new file mode 100644 index 0000000..55f7aed --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/filter_utils.h @@ -0,0 +1,221 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#ifdef __APPLE__ +#else +#include +#endif + +#ifdef _WINDOWS +#include +typedef HANDLE FileHandle; +#else +#include +typedef int FileHandle; +#endif + +#ifndef _WINDOWS +#include +#endif + +#include "cached_io.h" +#include "common_includes.h" +#include "memory_mapper.h" +#include "utils.h" +#include "windows_customizations.h" + +// custom types (for readability) +typedef tsl::robin_set label_set; +typedef std::string path; + +// structs for returning multiple items from a function +typedef std::tuple, tsl::robin_map, tsl::robin_set> + parse_label_file_return_values; +typedef std::tuple>, uint64_t> load_label_index_return_values; + +namespace diskann +{ +template +DISKANN_DLLEXPORT void generate_label_indices(path input_data_path, path final_index_path_prefix, label_set all_labels, + unsigned R, unsigned L, float alpha, unsigned num_threads); + +DISKANN_DLLEXPORT load_label_index_return_values load_label_index(path label_index_path, + uint32_t label_number_of_points); + +template +DISKANN_DLLEXPORT std::tuple>, tsl::robin_set> parse_formatted_label_file( + path label_file); + +DISKANN_DLLEXPORT parse_label_file_return_values parse_label_file(path label_data_path, std::string universal_label); + +template +DISKANN_DLLEXPORT tsl::robin_map> generate_label_specific_vector_files_compat( + path input_data_path, tsl::robin_map labels_to_number_of_points, + std::vector point_ids_to_labels, label_set all_labels); + +/* + * For each label, generates a file containing all vectors that have said label. + * Also copies data from original bin file to new dimension-aligned file. + * + * Utilizes POSIX functions mmap and writev in order to minimize memory + * overhead, so we include an STL version as well. + * + * Each data file is saved under the following format: + * input_data_path + "_" + label + */ +#ifndef _WINDOWS +template +inline tsl::robin_map> generate_label_specific_vector_files( + path input_data_path, tsl::robin_map labels_to_number_of_points, + std::vector point_ids_to_labels, label_set all_labels) +{ +#ifndef _WINDOWS + auto file_writing_timer = std::chrono::high_resolution_clock::now(); + diskann::MemoryMapper input_data(input_data_path); + char *input_start = input_data.getBuf(); + + uint32_t number_of_points, dimension; + std::memcpy(&number_of_points, input_start, sizeof(uint32_t)); + std::memcpy(&dimension, input_start + sizeof(uint32_t), sizeof(uint32_t)); + const uint32_t VECTOR_SIZE = dimension * sizeof(T); + const size_t METADATA = 2 * sizeof(uint32_t); + if (number_of_points != point_ids_to_labels.size()) + { + std::cerr << "Error: number of points in labels file and data file differ." << std::endl; + throw; + } + + tsl::robin_map label_to_iovec_map; + tsl::robin_map label_to_curr_iovec; + tsl::robin_map> label_id_to_orig_id; + + // setup iovec list for each label + for (const auto &lbl : all_labels) + { + iovec *label_iovecs = (iovec *)malloc(labels_to_number_of_points[lbl] * sizeof(iovec)); + if (label_iovecs == nullptr) + { + throw; + } + label_to_iovec_map[lbl] = label_iovecs; + label_to_curr_iovec[lbl] = 0; + label_id_to_orig_id[lbl].reserve(labels_to_number_of_points[lbl]); + } + + // each point added to corresponding per-label iovec list + for (uint32_t point_id = 0; point_id < number_of_points; point_id++) + { + char *curr_point = input_start + METADATA + (VECTOR_SIZE * point_id); + iovec curr_iovec; + + curr_iovec.iov_base = curr_point; + curr_iovec.iov_len = VECTOR_SIZE; + for (const auto &lbl : point_ids_to_labels[point_id]) + { + *(label_to_iovec_map[lbl] + label_to_curr_iovec[lbl]) = curr_iovec; + label_to_curr_iovec[lbl]++; + label_id_to_orig_id[lbl].push_back(point_id); + } + } + + // write each label iovec to resp. file + for (const auto &lbl : all_labels) + { + int label_input_data_fd; + path curr_label_input_data_path(input_data_path + "_" + lbl); + uint32_t curr_num_pts = labels_to_number_of_points[lbl]; + + label_input_data_fd = + open(curr_label_input_data_path.c_str(), O_CREAT | O_WRONLY | O_TRUNC | O_APPEND, (mode_t)0644); + if (label_input_data_fd == -1) + throw; + + // write metadata + uint32_t metadata[2] = {curr_num_pts, dimension}; + int return_value = write(label_input_data_fd, metadata, sizeof(uint32_t) * 2); + if (return_value == -1) + { + throw; + } + + // limits on number of iovec structs per writev means we need to perform + // multiple writevs + size_t i = 0; + while (curr_num_pts > IOV_MAX) + { + return_value = writev(label_input_data_fd, (label_to_iovec_map[lbl] + (IOV_MAX * i)), IOV_MAX); + if (return_value == -1) + { + close(label_input_data_fd); + throw; + } + curr_num_pts -= IOV_MAX; + i += 1; + } + return_value = writev(label_input_data_fd, (label_to_iovec_map[lbl] + (IOV_MAX * i)), curr_num_pts); + if (return_value == -1) + { + close(label_input_data_fd); + throw; + } + + free(label_to_iovec_map[lbl]); + close(label_input_data_fd); + } + + std::chrono::duration file_writing_time = std::chrono::high_resolution_clock::now() - file_writing_timer; + std::cout << "generated " << all_labels.size() << " label-specific vector files for index building in time " + << file_writing_time.count() << "\n" + << std::endl; + + return label_id_to_orig_id; +#endif +} +#endif + +inline std::vector loadTags(const std::string &tags_file, const std::string &base_file) +{ + const bool tags_enabled = tags_file.empty() ? false : true; + std::vector location_to_tag; + if (tags_enabled) + { + size_t tag_file_ndims, tag_file_npts; + std::uint32_t *tag_data; + diskann::load_bin(tags_file, tag_data, tag_file_npts, tag_file_ndims); + if (tag_file_ndims != 1) + { + diskann::cerr << "tags file error" << std::endl; + throw diskann::ANNException("tag file error", -1, __FUNCSIG__, __FILE__, __LINE__); + } + + // check if the point count match + size_t base_file_npts, base_file_ndims; + diskann::get_bin_metadata(base_file, base_file_npts, base_file_ndims); + if (base_file_npts != tag_file_npts) + { + diskann::cerr << "point num in tags file mismatch" << std::endl; + throw diskann::ANNException("point num in tags file mismatch", -1, __FUNCSIG__, __FILE__, __LINE__); + } + + location_to_tag.assign(tag_data, tag_data + tag_file_npts); + delete[] tag_data; + } + return location_to_tag; +} + +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/in_mem_data_store.h b/packages/leann-backend-diskann/third_party/DiskANN/include/in_mem_data_store.h new file mode 100644 index 0000000..0a0a617 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/in_mem_data_store.h @@ -0,0 +1,89 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. +#pragma once + +#include +#include + +#include "tsl/robin_map.h" +#include "tsl/robin_set.h" +#include "tsl/sparse_map.h" +// #include "boost/dynamic_bitset.hpp" + +#include "abstract_data_store.h" + +#include "distance.h" +#include "natural_number_map.h" +#include "natural_number_set.h" +#include "aligned_file_reader.h" + +namespace diskann +{ +template class InMemDataStore : public AbstractDataStore +{ + public: + InMemDataStore(const location_t capacity, const size_t dim, std::unique_ptr> distance_fn); + virtual ~InMemDataStore(); + + virtual location_t load(const std::string &filename) override; + virtual size_t save(const std::string &filename, const location_t num_points) override; + + virtual size_t get_aligned_dim() const override; + + // Populate internal data from unaligned data while doing alignment and any + // normalization that is required. + virtual void populate_data(const data_t *vectors, const location_t num_pts) override; + virtual void populate_data(const std::string &filename, const size_t offset) override; + + virtual void extract_data_to_bin(const std::string &filename, const location_t num_pts) override; + + virtual void get_vector(const location_t i, data_t *target) const override; + virtual void set_vector(const location_t i, const data_t *const vector) override; + virtual void prefetch_vector(const location_t loc) override; + + virtual void move_vectors(const location_t old_location_start, const location_t new_location_start, + const location_t num_points) override; + virtual void copy_vectors(const location_t from_loc, const location_t to_loc, const location_t num_points) override; + + virtual void preprocess_query(const data_t *query, AbstractScratch *query_scratch) const override; + + virtual float get_distance(const data_t *preprocessed_query, const location_t loc) const override; + virtual float get_distance(const location_t loc1, const location_t loc2) const override; + + virtual void get_distance(const data_t *preprocessed_query, const location_t *locations, + const uint32_t location_count, float *distances, + AbstractScratch *scratch) const override; + virtual void get_distance(const data_t *preprocessed_query, const std::vector &ids, + std::vector &distances, AbstractScratch *scratch_space) const override; + + virtual location_t calculate_medoid() const override; + + virtual Distance *get_dist_fn() const override; + + virtual size_t get_alignment_factor() const override; + + protected: + virtual location_t expand(const location_t new_size) override; + virtual location_t shrink(const location_t new_size) override; + + virtual location_t load_impl(const std::string &filename); +#ifdef EXEC_ENV_OLS + virtual location_t load_impl(AlignedFileReader &reader); +#endif + + private: + data_t *_data = nullptr; + + size_t _aligned_dim; + + // It may seem weird to put distance metric along with the data store class, + // but this gives us perf benefits as the datastore can do distance + // computations during search and compute norms of vectors internally without + // have to copy data back and forth. + std::unique_ptr> _distance_fn; + + // in case we need to save vector norms for optimization + std::shared_ptr _pre_computed_norms; +}; + +} // namespace diskann \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/in_mem_graph_store.h b/packages/leann-backend-diskann/third_party/DiskANN/include/in_mem_graph_store.h new file mode 100644 index 0000000..d0206a7 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/in_mem_graph_store.h @@ -0,0 +1,51 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include "abstract_graph_store.h" + +namespace diskann +{ + +class InMemGraphStore : public AbstractGraphStore +{ + public: + InMemGraphStore(const size_t total_pts, const size_t reserve_graph_degree); + + // returns tuple of + virtual std::tuple load(const std::string &index_path_prefix, + const size_t num_points) override; + virtual int store(const std::string &index_path_prefix, const size_t num_points, const size_t num_frozen_points, + const uint32_t start) override; + + virtual const std::vector &get_neighbours(const location_t i) const override; + virtual void add_neighbour(const location_t i, location_t neighbour_id) override; + virtual void clear_neighbours(const location_t i) override; + virtual void swap_neighbours(const location_t a, location_t b) override; + + virtual void set_neighbours(const location_t i, std::vector &neighbors) override; + + virtual size_t resize_graph(const size_t new_size) override; + virtual void clear_graph() override; + + virtual size_t get_max_range_of_graph() override; + virtual uint32_t get_max_observed_degree() override; + + protected: + virtual std::tuple load_impl(const std::string &filename, size_t expected_num_points); +#ifdef EXEC_ENV_OLS + virtual std::tuple load_impl(AlignedFileReader &reader, size_t expected_num_points); +#endif + + int save_graph(const std::string &index_path_prefix, const size_t active_points, const size_t num_frozen_points, + const uint32_t start); + + private: + size_t _max_range_of_graph = 0; + uint32_t _max_observed_degree = 0; + + std::vector> _graph; +}; + +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/index.h b/packages/leann-backend-diskann/third_party/DiskANN/include/index.h new file mode 100644 index 0000000..c4303a1 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/index.h @@ -0,0 +1,452 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include "common_includes.h" + +#ifdef EXEC_ENV_OLS +#include "aligned_file_reader.h" +#endif + +#include "distance.h" +#include "locking.h" +#include "natural_number_map.h" +#include "natural_number_set.h" +#include "neighbor.h" +#include "parameters.h" +#include "utils.h" +#include "windows_customizations.h" +#include "scratch.h" +#include "in_mem_data_store.h" +#include "in_mem_graph_store.h" +#include "abstract_index.h" + +#include "quantized_distance.h" +#include "pq_data_store.h" + +#define OVERHEAD_FACTOR 1.1 +#define EXPAND_IF_FULL 0 +#define DEFAULT_MAXC 750 + +namespace diskann +{ + +inline double estimate_ram_usage(size_t size, uint32_t dim, uint32_t datasize, uint32_t degree) +{ + double size_of_data = ((double)size) * ROUND_UP(dim, 8) * datasize; + double size_of_graph = ((double)size) * degree * sizeof(uint32_t) * defaults::GRAPH_SLACK_FACTOR; + double size_of_locks = ((double)size) * sizeof(non_recursive_mutex); + double size_of_outer_vector = ((double)size) * sizeof(ptrdiff_t); + + return OVERHEAD_FACTOR * (size_of_data + size_of_graph + size_of_locks + size_of_outer_vector); +} + +template class Index : public AbstractIndex +{ + /************************************************************************** + * + * Public functions acquire one or more of _update_lock, _consolidate_lock, + * _tag_lock, _delete_lock before calling protected functions which DO NOT + * acquire these locks. They might acquire locks on _locks[i] + * + **************************************************************************/ + + public: + // Constructor for Bulk operations and for creating the index object solely + // for loading a prexisting index. + DISKANN_DLLEXPORT Index(const IndexConfig &index_config, std::shared_ptr> data_store, + std::unique_ptr graph_store, + std::shared_ptr> pq_data_store = nullptr); + + // Constructor for incremental index + DISKANN_DLLEXPORT Index(Metric m, const size_t dim, const size_t max_points, + const std::shared_ptr index_parameters, + const std::shared_ptr index_search_params, + const size_t num_frozen_pts = 0, const bool dynamic_index = false, + const bool enable_tags = false, const bool concurrent_consolidate = false, + const bool pq_dist_build = false, const size_t num_pq_chunks = 0, + const bool use_opq = false, const bool filtered_index = false); + + DISKANN_DLLEXPORT ~Index(); + + // Saves graph, data, metadata and associated tags. + DISKANN_DLLEXPORT void save(const char *filename, bool compact_before_save = false) override; + + // Load functions +#ifdef EXEC_ENV_OLS + DISKANN_DLLEXPORT void load(AlignedFileReader &reader, uint32_t num_threads, uint32_t search_l); +#else + // Reads the number of frozen points from graph's metadata file section. + DISKANN_DLLEXPORT static size_t get_graph_num_frozen_points(const std::string &graph_file); + + DISKANN_DLLEXPORT void load(const char *index_file, uint32_t num_threads, uint32_t search_l) override; +#endif + + // get some private variables + DISKANN_DLLEXPORT size_t get_num_points(); + DISKANN_DLLEXPORT size_t get_max_points(); + + DISKANN_DLLEXPORT bool detect_common_filters(uint32_t point_id, bool search_invocation, + const std::vector &incoming_labels); + + // Batch build from a file. Optionally pass tags vector. + DISKANN_DLLEXPORT void build(const char *filename, const size_t num_points_to_load, + const std::vector &tags = std::vector()); + + // Batch build from a file. Optionally pass tags file. + DISKANN_DLLEXPORT void build(const char *filename, const size_t num_points_to_load, const char *tag_filename); + + // Batch build from a data array, which must pad vectors to aligned_dim + DISKANN_DLLEXPORT void build(const T *data, const size_t num_points_to_load, const std::vector &tags); + + // Based on filter params builds a filtered or unfiltered index + DISKANN_DLLEXPORT void build(const std::string &data_file, const size_t num_points_to_load, + IndexFilterParams &filter_params) override; + + // Filtered Support + DISKANN_DLLEXPORT void build_filtered_index(const char *filename, const std::string &label_file, + const size_t num_points_to_load, + const std::vector &tags = std::vector()); + + DISKANN_DLLEXPORT void set_universal_label(const LabelT &label); + + // Get converted integer label from string to int map (_label_map) + DISKANN_DLLEXPORT LabelT get_converted_label(const std::string &raw_label); + + // Set starting point of an index before inserting any points incrementally. + // The data count should be equal to _num_frozen_pts * _aligned_dim. + DISKANN_DLLEXPORT void set_start_points(const T *data, size_t data_count); + // Set starting points to random points on a sphere of certain radius. + // A fixed random seed can be specified for scenarios where it's important + // to have higher consistency between index builds. + DISKANN_DLLEXPORT void set_start_points_at_random(T radius, uint32_t random_seed = 0); + + // For FastL2 search on a static index, we interleave the data with graph + DISKANN_DLLEXPORT void optimize_index_layout() override; + + // For FastL2 search on optimized layout + DISKANN_DLLEXPORT void search_with_optimized_layout(const T *query, size_t K, size_t L, uint32_t *indices); + + // Added search overload that takes L as parameter, so that we + // can customize L on a per-query basis without tampering with "Parameters" + template + DISKANN_DLLEXPORT std::pair search(const T *query, const size_t K, const uint32_t L, + IDType *indices, float *distances = nullptr); + + // Initialize space for res_vectors before calling. + DISKANN_DLLEXPORT size_t search_with_tags(const T *query, const uint64_t K, const uint32_t L, TagT *tags, + float *distances, std::vector &res_vectors, bool use_filters = false, + const std::string filter_label = ""); + + // Filter support search + template + DISKANN_DLLEXPORT std::pair search_with_filters(const T *query, const LabelT &filter_label, + const size_t K, const uint32_t L, + IndexType *indices, float *distances); + + // Will fail if tag already in the index or if tag=0. + DISKANN_DLLEXPORT int insert_point(const T *point, const TagT tag); + + // Will fail if tag already in the index or if tag=0. + DISKANN_DLLEXPORT int insert_point(const T *point, const TagT tag, const std::vector &label); + + // call this before issuing deletions to sets relevant flags + DISKANN_DLLEXPORT int enable_delete(); + + // Record deleted point now and restructure graph later. Return -1 if tag + // not found, 0 if OK. + DISKANN_DLLEXPORT int lazy_delete(const TagT &tag); + + // Record deleted points now and restructure graph later. Add to failed_tags + // if tag not found. + DISKANN_DLLEXPORT void lazy_delete(const std::vector &tags, std::vector &failed_tags); + + // Call after a series of lazy deletions + // Returns number of live points left after consolidation + // If _conc_consolidates is set in the ctor, then this call can be invoked + // alongside inserts and lazy deletes, else it acquires _update_lock + DISKANN_DLLEXPORT consolidation_report consolidate_deletes(const IndexWriteParameters ¶meters) override; + + DISKANN_DLLEXPORT void prune_all_neighbors(const uint32_t max_degree, const uint32_t max_occlusion, + const float alpha); + + DISKANN_DLLEXPORT bool is_index_saved(); + + // repositions frozen points to the end of _data - if they have been moved + // during deletion + DISKANN_DLLEXPORT void reposition_frozen_point_to_end(); + DISKANN_DLLEXPORT void reposition_points(uint32_t old_location_start, uint32_t new_location_start, + uint32_t num_locations); + + // DISKANN_DLLEXPORT void save_index_as_one_file(bool flag); + + DISKANN_DLLEXPORT void get_active_tags(tsl::robin_set &active_tags); + + // memory should be allocated for vec before calling this function + DISKANN_DLLEXPORT int get_vector_by_tag(TagT &tag, T *vec); + + DISKANN_DLLEXPORT void print_status(); + + DISKANN_DLLEXPORT void count_nodes_at_bfs_levels(); + + // This variable MUST be updated if the number of entries in the metadata + // change. + DISKANN_DLLEXPORT static const int METADATA_ROWS = 5; + + DISKANN_DLLEXPORT void get_degree_stats(size_t &max_deg, size_t &min_deg, size_t &avg_deg, size_t &cnt_deg); + + DISKANN_DLLEXPORT void dump_degree_stats(std::string filename); + + // ******************************** + // + // Internals of the library + // + // ******************************** + + protected: + // overload of abstract index virtual methods + virtual void _build(const DataType &data, const size_t num_points_to_load, TagVector &tags) override; + + virtual std::pair _search(const DataType &query, const size_t K, const uint32_t L, + std::any &indices, float *distances = nullptr) override; + virtual std::pair _search_with_filters(const DataType &query, + const std::string &filter_label_raw, const size_t K, + const uint32_t L, std::any &indices, + float *distances) override; + + virtual int _insert_point(const DataType &data_point, const TagType tag) override; + virtual int _insert_point(const DataType &data_point, const TagType tag, Labelvector &labels) override; + + virtual int _lazy_delete(const TagType &tag) override; + + virtual void _lazy_delete(TagVector &tags, TagVector &failed_tags) override; + + virtual void _get_active_tags(TagRobinSet &active_tags) override; + + virtual void _set_start_points_at_random(DataType radius, uint32_t random_seed = 0) override; + + virtual int _get_vector_by_tag(TagType &tag, DataType &vec) override; + + virtual void _search_with_optimized_layout(const DataType &query, size_t K, size_t L, uint32_t *indices) override; + + virtual size_t _search_with_tags(const DataType &query, const uint64_t K, const uint32_t L, const TagType &tags, + float *distances, DataVector &res_vectors, bool use_filters = false, + const std::string filter_label = "") override; + + virtual void _set_universal_label(const LabelType universal_label) override; + + // No copy/assign. + Index(const Index &) = delete; + Index &operator=(const Index &) = delete; + + // Use after _data and _nd have been populated + // Acquire exclusive _update_lock before calling + void build_with_data_populated(const std::vector &tags); + + // generates 1 frozen point that will never be deleted from the graph + // This is not visible to the user + void generate_frozen_point(); + + // determines navigating node of the graph by calculating medoid of datafopt + uint32_t calculate_entry_point(); + + void parse_label_file(const std::string &label_file, size_t &num_pts_labels); + + std::unordered_map load_label_map(const std::string &map_file); + + // Returns the locations of start point and frozen points suitable for use + // with iterate_to_fixed_point. + std::vector get_init_ids(); + + // The query to use is placed in scratch->aligned_query + std::pair iterate_to_fixed_point(InMemQueryScratch *scratch, const uint32_t Lindex, + const std::vector &init_ids, bool use_filter, + const std::vector &filters, bool search_invocation); + + void search_for_point_and_prune(int location, uint32_t Lindex, std::vector &pruned_list, + InMemQueryScratch *scratch, bool use_filter = false, + uint32_t filteredLindex = 0); + + void prune_neighbors(const uint32_t location, std::vector &pool, std::vector &pruned_list, + InMemQueryScratch *scratch); + + void prune_neighbors(const uint32_t location, std::vector &pool, const uint32_t range, + const uint32_t max_candidate_size, const float alpha, std::vector &pruned_list, + InMemQueryScratch *scratch); + + // Prunes candidates in @pool to a shorter list @result + // @pool must be sorted before calling + void occlude_list(const uint32_t location, std::vector &pool, const float alpha, const uint32_t degree, + const uint32_t maxc, std::vector &result, InMemQueryScratch *scratch, + const tsl::robin_set *const delete_set_ptr = nullptr); + + // add reverse links from all the visited nodes to node n. + void inter_insert(uint32_t n, std::vector &pruned_list, const uint32_t range, + InMemQueryScratch *scratch); + + void inter_insert(uint32_t n, std::vector &pruned_list, InMemQueryScratch *scratch); + + // Acquire exclusive _update_lock before calling + void link(); + + // Acquire exclusive _tag_lock and _delete_lock before calling + int reserve_location(); + + // Acquire exclusive _tag_lock before calling + size_t release_location(int location); + size_t release_locations(const tsl::robin_set &locations); + + // Resize the index when no slots are left for insertion. + // Acquire exclusive _update_lock and _tag_lock before calling. + void resize(size_t new_max_points); + + // Acquire unique lock on _update_lock, _consolidate_lock, _tag_lock + // and _delete_lock before calling these functions. + // Renumber nodes, update tag and location maps and compact the + // graph, mode = _consolidated_order in case of lazy deletion and + // _compacted_order in case of eager deletion + DISKANN_DLLEXPORT void compact_data(); + DISKANN_DLLEXPORT void compact_frozen_point(); + + // Remove deleted nodes from adjacency list of node loc + // Replace removed neighbors with second order neighbors. + // Also acquires _locks[i] for i = loc and out-neighbors of loc. + void process_delete(const tsl::robin_set &old_delete_set, size_t loc, const uint32_t range, + const uint32_t maxc, const float alpha, InMemQueryScratch *scratch); + + void initialize_query_scratch(uint32_t num_threads, uint32_t search_l, uint32_t indexing_l, uint32_t r, + uint32_t maxc, size_t dim); + + // Do not call without acquiring appropriate locks + // call public member functions save and load to invoke these. + DISKANN_DLLEXPORT size_t save_graph(std::string filename); + DISKANN_DLLEXPORT size_t save_data(std::string filename); + DISKANN_DLLEXPORT size_t save_tags(std::string filename); + DISKANN_DLLEXPORT size_t save_delete_list(const std::string &filename); +#ifdef EXEC_ENV_OLS + DISKANN_DLLEXPORT size_t load_graph(AlignedFileReader &reader, size_t expected_num_points); + DISKANN_DLLEXPORT size_t load_data(AlignedFileReader &reader); + DISKANN_DLLEXPORT size_t load_tags(AlignedFileReader &reader); + DISKANN_DLLEXPORT size_t load_delete_set(AlignedFileReader &reader); +#else + DISKANN_DLLEXPORT size_t load_graph(const std::string filename, size_t expected_num_points); + DISKANN_DLLEXPORT size_t load_data(std::string filename0); + DISKANN_DLLEXPORT size_t load_tags(const std::string tag_file_name); + DISKANN_DLLEXPORT size_t load_delete_set(const std::string &filename); +#endif + + private: + // Distance functions + Metric _dist_metric = diskann::L2; + + // Data + std::shared_ptr> _data_store; + + // Graph related data structures + std::unique_ptr _graph_store; + + char *_opt_graph = nullptr; + + // Dimensions + size_t _dim = 0; + size_t _nd = 0; // number of active points i.e. existing in the graph + size_t _max_points = 0; // total number of points in given data set + + // _num_frozen_pts is the number of points which are used as initial + // candidates when iterating to closest point(s). These are not visible + // externally and won't be returned by search. At least 1 frozen point is + // needed for a dynamic index. The frozen points have consecutive locations. + // See also _start below. + size_t _num_frozen_pts = 0; + size_t _frozen_pts_used = 0; + size_t _node_size; + size_t _data_len; + size_t _neighbor_len; + + // Start point of the search. When _num_frozen_pts is greater than zero, + // this is the location of the first frozen point. Otherwise, this is a + // location of one of the points in index. + uint32_t _start = 0; + + bool _has_built = false; + bool _saturate_graph = false; + bool _save_as_one_file = false; // plan to support in next version + bool _dynamic_index = false; + bool _enable_tags = false; + bool _normalize_vecs = false; // Using normalied L2 for cosine. + bool _deletes_enabled = false; + + // Filter Support + + bool _filtered_index = false; + // Location to label is only updated during insert_point(), all other reads are protected by + // default as a location can only be released at end of consolidate deletes + std::vector> _location_to_labels; + tsl::robin_set _labels; + std::string _labels_file; + std::unordered_map _label_to_start_id; + std::unordered_map _medoid_counts; + + bool _use_universal_label = false; + LabelT _universal_label = 0; + uint32_t _filterIndexingQueueSize; + std::unordered_map _label_map; + + // Indexing parameters + uint32_t _indexingQueueSize; + uint32_t _indexingRange; + uint32_t _indexingMaxC; + float _indexingAlpha; + uint32_t _indexingThreads; + + // Query scratch data structures + ConcurrentQueue *> _query_scratch; + + // Flags for PQ based distance calculation + bool _pq_dist = false; + bool _use_opq = false; + size_t _num_pq_chunks = 0; + // REFACTOR + // uint8_t *_pq_data = nullptr; + std::shared_ptr> _pq_distance_fn = nullptr; + std::shared_ptr> _pq_data_store = nullptr; + bool _pq_generated = false; + FixedChunkPQTable _pq_table; + + // + // Data structures, locks and flags for dynamic indexing and tags + // + + // lazy_delete removes entry from _location_to_tag and _tag_to_location. If + // _location_to_tag does not resolve a location, infer that it was deleted. + tsl::sparse_map _tag_to_location; + natural_number_map _location_to_tag; + + // _empty_slots has unallocated slots and those freed by consolidate_delete. + // _delete_set has locations marked deleted by lazy_delete. Will not be + // immediately available for insert. consolidate_delete will release these + // slots to _empty_slots. + natural_number_set _empty_slots; + std::unique_ptr> _delete_set; + + bool _data_compacted = true; // true if data has been compacted + bool _is_saved = false; // Checking if the index is already saved. + bool _conc_consolidate = false; // use _lock while searching + + // Acquire locks in the order below when acquiring multiple locks + std::shared_timed_mutex // RW mutex between save/load (exclusive lock) and + _update_lock; // search/inserts/deletes/consolidate (shared lock) + std::shared_timed_mutex // Ensure only one consolidate or compact_data is + _consolidate_lock; // ever active + std::shared_timed_mutex // RW lock for _tag_to_location, + _tag_lock; // _location_to_tag, _empty_slots, _nd, _max_points, _label_to_start_id + std::shared_timed_mutex // RW Lock on _delete_set and _data_compacted + _delete_lock; // variable + + // Per node lock, cardinality=_max_points + _num_frozen_points + std::vector _locks; + + static const float INDEX_GROWTH_FACTOR; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/index_build_params.h b/packages/leann-backend-diskann/third_party/DiskANN/include/index_build_params.h new file mode 100644 index 0000000..d4f4548 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/index_build_params.h @@ -0,0 +1,73 @@ +#pragma once + +#include "common_includes.h" +#include "parameters.h" + +namespace diskann +{ +struct IndexFilterParams +{ + public: + std::string save_path_prefix; + std::string label_file; + std::string tags_file; + std::string universal_label; + uint32_t filter_threshold = 0; + + private: + IndexFilterParams(const std::string &save_path_prefix, const std::string &label_file, + const std::string &universal_label, uint32_t filter_threshold) + : save_path_prefix(save_path_prefix), label_file(label_file), universal_label(universal_label), + filter_threshold(filter_threshold) + { + } + + friend class IndexFilterParamsBuilder; +}; +class IndexFilterParamsBuilder +{ + public: + IndexFilterParamsBuilder() = default; + + IndexFilterParamsBuilder &with_save_path_prefix(const std::string &save_path_prefix) + { + if (save_path_prefix.empty() || save_path_prefix == "") + throw ANNException("Error: save_path_prefix can't be empty", -1); + this->_save_path_prefix = save_path_prefix; + return *this; + } + + IndexFilterParamsBuilder &with_label_file(const std::string &label_file) + { + this->_label_file = label_file; + return *this; + } + + IndexFilterParamsBuilder &with_universal_label(const std::string &univeral_label) + { + this->_universal_label = univeral_label; + return *this; + } + + IndexFilterParamsBuilder &with_filter_threshold(const std::uint32_t &filter_threshold) + { + this->_filter_threshold = filter_threshold; + return *this; + } + + IndexFilterParams build() + { + return IndexFilterParams(_save_path_prefix, _label_file, _universal_label, _filter_threshold); + } + + IndexFilterParamsBuilder(const IndexFilterParamsBuilder &) = delete; + IndexFilterParamsBuilder &operator=(const IndexFilterParamsBuilder &) = delete; + + private: + std::string _save_path_prefix; + std::string _label_file; + std::string _tags_file; + std::string _universal_label; + uint32_t _filter_threshold = 0; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/index_config.h b/packages/leann-backend-diskann/third_party/DiskANN/include/index_config.h new file mode 100644 index 0000000..a8e64d0 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/index_config.h @@ -0,0 +1,256 @@ +#pragma once + +#include "common_includes.h" +#include "parameters.h" + +namespace diskann +{ +enum class DataStoreStrategy +{ + MEMORY +}; + +enum class GraphStoreStrategy +{ + MEMORY +}; + +struct IndexConfig +{ + DataStoreStrategy data_strategy; + GraphStoreStrategy graph_strategy; + + Metric metric; + size_t dimension; + size_t max_points; + + bool dynamic_index; + bool enable_tags; + bool pq_dist_build; + bool concurrent_consolidate; + bool use_opq; + bool filtered_index; + + size_t num_pq_chunks; + size_t num_frozen_pts; + + std::string label_type; + std::string tag_type; + std::string data_type; + + // Params for building index + std::shared_ptr index_write_params; + // Params for searching index + std::shared_ptr index_search_params; + + private: + IndexConfig(DataStoreStrategy data_strategy, GraphStoreStrategy graph_strategy, Metric metric, size_t dimension, + size_t max_points, size_t num_pq_chunks, size_t num_frozen_points, bool dynamic_index, bool enable_tags, + bool pq_dist_build, bool concurrent_consolidate, bool use_opq, bool filtered_index, + std::string &data_type, const std::string &tag_type, const std::string &label_type, + std::shared_ptr index_write_params, + std::shared_ptr index_search_params) + : data_strategy(data_strategy), graph_strategy(graph_strategy), metric(metric), dimension(dimension), + max_points(max_points), dynamic_index(dynamic_index), enable_tags(enable_tags), pq_dist_build(pq_dist_build), + concurrent_consolidate(concurrent_consolidate), use_opq(use_opq), filtered_index(filtered_index), + num_pq_chunks(num_pq_chunks), num_frozen_pts(num_frozen_points), label_type(label_type), tag_type(tag_type), + data_type(data_type), index_write_params(index_write_params), index_search_params(index_search_params) + { + } + + friend class IndexConfigBuilder; +}; + +class IndexConfigBuilder +{ + public: + IndexConfigBuilder() = default; + + IndexConfigBuilder &with_metric(Metric m) + { + this->_metric = m; + return *this; + } + + IndexConfigBuilder &with_graph_load_store_strategy(GraphStoreStrategy graph_strategy) + { + this->_graph_strategy = graph_strategy; + return *this; + } + + IndexConfigBuilder &with_data_load_store_strategy(DataStoreStrategy data_strategy) + { + this->_data_strategy = data_strategy; + return *this; + } + + IndexConfigBuilder &with_dimension(size_t dimension) + { + this->_dimension = dimension; + return *this; + } + + IndexConfigBuilder &with_max_points(size_t max_points) + { + this->_max_points = max_points; + return *this; + } + + IndexConfigBuilder &is_dynamic_index(bool dynamic_index) + { + this->_dynamic_index = dynamic_index; + return *this; + } + + IndexConfigBuilder &is_enable_tags(bool enable_tags) + { + this->_enable_tags = enable_tags; + return *this; + } + + IndexConfigBuilder &is_pq_dist_build(bool pq_dist_build) + { + this->_pq_dist_build = pq_dist_build; + return *this; + } + + IndexConfigBuilder &is_concurrent_consolidate(bool concurrent_consolidate) + { + this->_concurrent_consolidate = concurrent_consolidate; + return *this; + } + + IndexConfigBuilder &is_use_opq(bool use_opq) + { + this->_use_opq = use_opq; + return *this; + } + + IndexConfigBuilder &is_filtered(bool is_filtered) + { + this->_filtered_index = is_filtered; + return *this; + } + + IndexConfigBuilder &with_num_pq_chunks(size_t num_pq_chunks) + { + this->_num_pq_chunks = num_pq_chunks; + return *this; + } + + IndexConfigBuilder &with_num_frozen_pts(size_t num_frozen_pts) + { + this->_num_frozen_pts = num_frozen_pts; + return *this; + } + + IndexConfigBuilder &with_label_type(const std::string &label_type) + { + this->_label_type = label_type; + return *this; + } + + IndexConfigBuilder &with_tag_type(const std::string &tag_type) + { + this->_tag_type = tag_type; + return *this; + } + + IndexConfigBuilder &with_data_type(const std::string &data_type) + { + this->_data_type = data_type; + return *this; + } + + IndexConfigBuilder &with_index_write_params(IndexWriteParameters &index_write_params) + { + this->_index_write_params = std::make_shared(index_write_params); + return *this; + } + + IndexConfigBuilder &with_index_write_params(std::shared_ptr index_write_params_ptr) + { + if (index_write_params_ptr == nullptr) + { + diskann::cout << "Passed, empty build_params while creating index config" << std::endl; + return *this; + } + this->_index_write_params = index_write_params_ptr; + return *this; + } + + IndexConfigBuilder &with_index_search_params(IndexSearchParams &search_params) + { + this->_index_search_params = std::make_shared(search_params); + return *this; + } + + IndexConfigBuilder &with_index_search_params(std::shared_ptr search_params_ptr) + { + if (search_params_ptr == nullptr) + { + diskann::cout << "Passed, empty search_params while creating index config" << std::endl; + return *this; + } + this->_index_search_params = search_params_ptr; + return *this; + } + + IndexConfig build() + { + if (_data_type == "" || _data_type.empty()) + throw ANNException("Error: data_type can not be empty", -1); + + if (_dynamic_index && _num_frozen_pts == 0) + { + _num_frozen_pts = 1; + } + + if (_dynamic_index) + { + if (_index_search_params != nullptr && _index_search_params->initial_search_list_size == 0) + throw ANNException("Error: please pass initial_search_list_size for building dynamic index.", -1); + } + + // sanity check + if (_dynamic_index && _num_frozen_pts == 0) + { + diskann::cout << "_num_frozen_pts passed as 0 for dynamic_index. Setting it to 1 for safety." << std::endl; + _num_frozen_pts = 1; + } + + return IndexConfig(_data_strategy, _graph_strategy, _metric, _dimension, _max_points, _num_pq_chunks, + _num_frozen_pts, _dynamic_index, _enable_tags, _pq_dist_build, _concurrent_consolidate, + _use_opq, _filtered_index, _data_type, _tag_type, _label_type, _index_write_params, + _index_search_params); + } + + IndexConfigBuilder(const IndexConfigBuilder &) = delete; + IndexConfigBuilder &operator=(const IndexConfigBuilder &) = delete; + + private: + DataStoreStrategy _data_strategy; + GraphStoreStrategy _graph_strategy; + + Metric _metric; + size_t _dimension; + size_t _max_points; + + bool _dynamic_index = false; + bool _enable_tags = false; + bool _pq_dist_build = false; + bool _concurrent_consolidate = false; + bool _use_opq = false; + bool _filtered_index{defaults::HAS_LABELS}; + + size_t _num_pq_chunks = 0; + size_t _num_frozen_pts{defaults::NUM_FROZEN_POINTS_STATIC}; + + std::string _label_type{"uint32"}; + std::string _tag_type{"uint32"}; + std::string _data_type; + + std::shared_ptr _index_write_params; + std::shared_ptr _index_search_params; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/index_factory.h b/packages/leann-backend-diskann/third_party/DiskANN/include/index_factory.h new file mode 100644 index 0000000..76fb0b9 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/index_factory.h @@ -0,0 +1,51 @@ +#pragma once + +#include "index.h" +#include "abstract_graph_store.h" +#include "in_mem_graph_store.h" +#include "pq_data_store.h" + +namespace diskann +{ +class IndexFactory +{ + public: + DISKANN_DLLEXPORT explicit IndexFactory(const IndexConfig &config); + DISKANN_DLLEXPORT std::unique_ptr create_instance(); + + DISKANN_DLLEXPORT static std::unique_ptr construct_graphstore( + const GraphStoreStrategy stratagy, const size_t size, const size_t reserve_graph_degree); + + template + DISKANN_DLLEXPORT static std::shared_ptr> construct_datastore(DataStoreStrategy stratagy, + size_t num_points, + size_t dimension, Metric m); + // For now PQDataStore incorporates within itself all variants of quantization that we support. In the + // future it may be necessary to introduce an AbstractPQDataStore class to spearate various quantization + // flavours. + template + DISKANN_DLLEXPORT static std::shared_ptr> construct_pq_datastore(DataStoreStrategy strategy, + size_t num_points, size_t dimension, + Metric m, size_t num_pq_chunks, + bool use_opq); + template static Distance *construct_inmem_distance_fn(Metric m); + + private: + void check_config(); + + template + std::unique_ptr create_instance(); + + std::unique_ptr create_instance(const std::string &data_type, const std::string &tag_type, + const std::string &label_type); + + template + std::unique_ptr create_instance(const std::string &tag_type, const std::string &label_type); + + template + std::unique_ptr create_instance(const std::string &label_type); + + std::unique_ptr _config; +}; + +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/linux_aligned_file_reader.h b/packages/leann-backend-diskann/third_party/DiskANN/include/linux_aligned_file_reader.h new file mode 100644 index 0000000..d1d1e74 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/linux_aligned_file_reader.h @@ -0,0 +1,41 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#ifndef _WINDOWS +#ifndef __APPLE__ + +#include "aligned_file_reader.h" + +class LinuxAlignedFileReader : public AlignedFileReader +{ + private: + uint64_t file_sz; + FileHandle file_desc; + io_context_t bad_ctx = (io_context_t)-1; + + public: + LinuxAlignedFileReader(); + ~LinuxAlignedFileReader(); + + IOContext &get_ctx(); + + // register thread-id for a context + void register_thread(); + + // de-register thread-id for a context + void deregister_thread(); + void deregister_all_threads(); + + // Open & close ops + // Blocking calls + void open(const std::string &fname); + void close(); + + // process batch of aligned requests in parallel + // NOTE :: blocking call + void read(std::vector &read_reqs, IOContext &ctx, bool async = false); +}; + +#endif +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/locking.h b/packages/leann-backend-diskann/third_party/DiskANN/include/locking.h new file mode 100644 index 0000000..890c24a --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/locking.h @@ -0,0 +1,20 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. +#pragma once + +#include + +#ifdef _WINDOWS +#include "windows_slim_lock.h" +#endif + +namespace diskann +{ +#ifdef _WINDOWS +using non_recursive_mutex = windows_exclusive_slim_lock; +using LockGuard = windows_exclusive_slim_lock_guard; +#else +using non_recursive_mutex = std::mutex; +using LockGuard = std::lock_guard; +#endif +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/logger.h b/packages/leann-backend-diskann/third_party/DiskANN/include/logger.h new file mode 100644 index 0000000..0b17807 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/logger.h @@ -0,0 +1,35 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. +#pragma once + +#include +#include +#include "windows_customizations.h" + +#ifdef EXEC_ENV_OLS +#ifndef ENABLE_CUSTOM_LOGGER +#define ENABLE_CUSTOM_LOGGER +#endif // !ENABLE_CUSTOM_LOGGER +#endif // EXEC_ENV_OLS + +namespace diskann +{ +#ifdef ENABLE_CUSTOM_LOGGER +DISKANN_DLLEXPORT extern std::basic_ostream cout; +DISKANN_DLLEXPORT extern std::basic_ostream cerr; +#else +using std::cerr; +using std::cout; +#endif + +enum class DISKANN_DLLEXPORT LogLevel +{ + LL_Info = 0, + LL_Error, + LL_Count +}; + +#ifdef ENABLE_CUSTOM_LOGGER +DISKANN_DLLEXPORT void SetCustomLogger(std::function logger); +#endif +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/logger_impl.h b/packages/leann-backend-diskann/third_party/DiskANN/include/logger_impl.h new file mode 100644 index 0000000..03c65e0 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/logger_impl.h @@ -0,0 +1,61 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +#include "ann_exception.h" +#include "logger.h" + +namespace diskann +{ +#ifdef ENABLE_CUSTOM_LOGGER +class ANNStreamBuf : public std::basic_streambuf +{ + public: + DISKANN_DLLEXPORT explicit ANNStreamBuf(FILE *fp); + DISKANN_DLLEXPORT ~ANNStreamBuf(); + + DISKANN_DLLEXPORT bool is_open() const + { + return true; // because stdout and stderr are always open. + } + DISKANN_DLLEXPORT void close(); + DISKANN_DLLEXPORT virtual int underflow(); + DISKANN_DLLEXPORT virtual int overflow(int c); + DISKANN_DLLEXPORT virtual int sync(); + + private: + FILE *_fp; + char *_buf; + int _bufIndex; + std::mutex _mutex; + LogLevel _logLevel; + + int flush(); + void logImpl(char *str, int numchars); + + // Why the two buffer-sizes? If we are running normally, we are basically + // interacting with a character output system, so we short-circuit the + // output process by keeping an empty buffer and writing each character + // to stdout/stderr. But if we are running in OLS, we have to take all + // the text that is written to diskann::cout/diskann:cerr, consolidate it + // and push it out in one-shot, because the OLS infra does not give us + // character based output. Therefore, we use a larger buffer that is large + // enough to store the longest message, and continuously add characters + // to it. When the calling code outputs a std::endl or std::flush, sync() + // will be called and will output a log level, component name, and the text + // that has been collected. (sync() is also called if the buffer is full, so + // overflows/missing text are not a concern). + // This implies calling code _must_ either print std::endl or std::flush + // to ensure that the message is written immediately. + + static const int BUFFER_SIZE = 1024; + + ANNStreamBuf(const ANNStreamBuf &); + ANNStreamBuf &operator=(const ANNStreamBuf &); +}; +#endif +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/math_utils.h b/packages/leann-backend-diskann/third_party/DiskANN/include/math_utils.h new file mode 100644 index 0000000..83d189f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/math_utils.h @@ -0,0 +1,87 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include "common_includes.h" +#include "utils.h" + +namespace math_utils +{ + +float calc_distance(float *vec_1, float *vec_2, size_t dim); + +// compute l2-squared norms of data stored in row major num_points * dim, +// needs +// to be pre-allocated +void compute_vecs_l2sq(float *vecs_l2sq, float *data, const size_t num_points, const size_t dim); + +void rotate_data_randomly(float *data, size_t num_points, size_t dim, float *rot_mat, float *&new_mat, + bool transpose_rot = false); + +// calculate closest center to data of num_points * dim (row major) +// centers is num_centers * dim (row major) +// data_l2sq has pre-computed squared norms of data +// centers_l2sq has pre-computed squared norms of centers +// pre-allocated center_index will contain id of k nearest centers +// pre-allocated dist_matrix shound be num_points * num_centers and contain +// squared distances + +// Ideally used only by compute_closest_centers +void compute_closest_centers_in_block(const float *const data, const size_t num_points, const size_t dim, + const float *const centers, const size_t num_centers, + const float *const docs_l2sq, const float *const centers_l2sq, + uint32_t *center_index, float *const dist_matrix, size_t k = 1); + +// Given data in num_points * new_dim row major +// Pivots stored in full_pivot_data as k * new_dim row major +// Calculate the closest pivot for each point and store it in vector +// closest_centers_ivf (which needs to be allocated outside) +// Additionally, if inverted index is not null (and pre-allocated), it will +// return inverted index for each center Additionally, if pts_norms_squared is +// not null, then it will assume that point norms are pre-computed and use +// those +// values + +void compute_closest_centers(float *data, size_t num_points, size_t dim, float *pivot_data, size_t num_centers, + size_t k, uint32_t *closest_centers_ivf, std::vector *inverted_index = NULL, + float *pts_norms_squared = NULL); + +// if to_subtract is 1, will subtract nearest center from each row. Else will +// add. Output will be in data_load iself. +// Nearest centers need to be provided in closst_centers. + +void process_residuals(float *data_load, size_t num_points, size_t dim, float *cur_pivot_data, size_t num_centers, + uint32_t *closest_centers, bool to_subtract); + +} // namespace math_utils + +namespace kmeans +{ + +// run Lloyds one iteration +// Given data in row major num_points * dim, and centers in row major +// num_centers * dim +// And squared lengths of data points, output the closest center to each data +// point, update centers, and also return inverted index. +// If closest_centers == NULL, will allocate memory and return. +// Similarly, if closest_docs == NULL, will allocate memory and return. + +float lloyds_iter(float *data, size_t num_points, size_t dim, float *centers, size_t num_centers, float *docs_l2sq, + std::vector *closest_docs, uint32_t *&closest_center); + +// Run Lloyds until max_reps or stopping criterion +// If you pass NULL for closest_docs and closest_center, it will NOT return +// the results, else it will assume appriate allocation as closest_docs = new +// vector [num_centers], and closest_center = new size_t[num_points] +// Final centers are output in centers as row major num_centers * dim +// +float run_lloyds(float *data, size_t num_points, size_t dim, float *centers, const size_t num_centers, + const size_t max_reps, std::vector *closest_docs, uint32_t *closest_center); + +// assumes already memory allocated for pivot_data as new +// float[num_centers*dim] and select randomly num_centers points as pivots +void selecting_pivots(float *data, size_t num_points, size_t dim, float *pivot_data, size_t num_centers); + +void kmeanspp_selecting_pivots(float *data, size_t num_points, size_t dim, float *pivot_data, size_t num_centers); +} // namespace kmeans diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/memory_mapper.h b/packages/leann-backend-diskann/third_party/DiskANN/include/memory_mapper.h new file mode 100644 index 0000000..75faca1 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/memory_mapper.h @@ -0,0 +1,43 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#ifndef _WINDOWS +#include +#include +#include +#include +#include + +#else +#include +#endif +#include + +namespace diskann +{ +class MemoryMapper +{ + private: +#ifndef _WINDOWS + int _fd; +#else + HANDLE _bareFile; + HANDLE _fd; + +#endif + char *_buf; + size_t _fileSize; + const char *_fileName; + + public: + MemoryMapper(const char *filename); + MemoryMapper(const std::string &filename); + + char *getBuf(); + size_t getFileSize(); + + ~MemoryMapper(); +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/natural_number_map.h b/packages/leann-backend-diskann/third_party/DiskANN/include/natural_number_map.h new file mode 100644 index 0000000..e846882 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/natural_number_map.h @@ -0,0 +1,86 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include +#include + +#include + +namespace diskann +{ +// A map whose key is a natural number (from 0 onwards) and maps to a value. +// Made as both memory and performance efficient map for scenario such as +// DiskANN location-to-tag map. There, the pool of numbers is consecutive from +// zero to some max value, and it's expected that most if not all keys from 0 +// up to some current maximum will be present in the map. The memory usage of +// the map is determined by the largest inserted key since it uses vector as a +// backing store and bitset for presence indication. +// +// Thread-safety: this class is not thread-safe in general. +// Exception: multiple read-only operations are safe on the object only if +// there are no writers to it in parallel. +template class natural_number_map +{ + public: + static_assert(std::is_trivial::value, "Key must be a trivial type"); + + // Represents a reference to a element in the map. Used while iterating + // over map entries. + struct position + { + size_t _key; + // The number of keys that were enumerated when iterating through the + // map so far. Used to early-terminate enumeration when ithere are no + // more entries in the map. + size_t _keys_already_enumerated; + + // Returns whether it's valid to access the element at this position in + // the map. + bool is_valid() const; + }; + + natural_number_map(); + + void reserve(size_t count); + size_t size() const; + + void set(Key key, Value value); + void erase(Key key); + + bool contains(Key key) const; + bool try_get(Key key, Value &value) const; + + // Returns the value at the specified position. Prerequisite: position is + // valid. + Value get(const position &pos) const; + + // Finds the first element in the map, if any. Invalidated by changes in the + // map. + position find_first() const; + + // Finds the next element in the map after the specified position. + // Invalidated by changes in the map. + position find_next(const position &after_position) const; + + void clear(); + + private: + // Number of entries in the map. Not the same as size() of the + // _values_vector below. + size_t _size; + + // Array of values. The key is the index of the value. + std::vector _values_vector; + + // Values that are in the set have the corresponding bit index set + // to 1. + // + // Use a pointer here to allow for forward declaration of dynamic_bitset + // in public headers to avoid making boost a dependency for clients + // of DiskANN. + std::unique_ptr> _values_bitset; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/natural_number_set.h b/packages/leann-backend-diskann/third_party/DiskANN/include/natural_number_set.h new file mode 100644 index 0000000..ec5b827 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/natural_number_set.h @@ -0,0 +1,50 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +#include "boost_dynamic_bitset_fwd.h" + +namespace diskann +{ +// A set of natural numbers (from 0 onwards). Made for scenario where the +// pool of numbers is consecutive from zero to some max value and very +// efficient methods for "add to set", "get any value from set", "is in set" +// are needed. The memory usage of the set is determined by the largest +// number of inserted entries (uses a vector as a backing store) as well as +// the largest value to be placed in it (uses bitset as well). +// +// Thread-safety: this class is not thread-safe in general. +// Exception: multiple read-only operations (e.g. is_in_set, empty, size) are +// safe on the object only if there are no writers to it in parallel. +template class natural_number_set +{ + public: + static_assert(std::is_trivial::value, "Identifier must be a trivial type"); + + natural_number_set(); + + bool is_empty() const; + void reserve(size_t count); + void insert(T id); + T pop_any(); + void clear(); + size_t size() const; + bool is_in_set(T id) const; + + private: + // Values that are currently in set. + std::vector _values_vector; + + // Values that are in the set have the corresponding bit index set + // to 1. + // + // Use a pointer here to allow for forward declaration of dynamic_bitset + // in public headers to avoid making boost a dependency for clients + // of DiskANN. + std::unique_ptr> _values_bitset; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/neighbor.h b/packages/leann-backend-diskann/third_party/DiskANN/include/neighbor.h new file mode 100644 index 0000000..d7c0c25 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/neighbor.h @@ -0,0 +1,152 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include +#include +#include "utils.h" + +namespace diskann +{ + +struct Neighbor +{ + unsigned id; + float distance; + bool expanded; + + Neighbor() = default; + + Neighbor(unsigned id, float distance) : id{id}, distance{distance}, expanded(false) + { + } + + inline bool operator<(const Neighbor &other) const + { + return distance < other.distance || (distance == other.distance && id < other.id); + } + + inline bool operator==(const Neighbor &other) const + { + return (id == other.id); + } +}; + +// Invariant: after every `insert` and `closest_unexpanded()`, `_cur` points to +// the first Neighbor which is unexpanded. +class NeighborPriorityQueue +{ + public: + NeighborPriorityQueue() : _size(0), _capacity(0), _cur(0) + { + } + + explicit NeighborPriorityQueue(size_t capacity) : _size(0), _capacity(capacity), _cur(0), _data(capacity + 1) + { + } + + // Inserts the item ordered into the set up to the sets capacity. + // The item will be dropped if it is the same id as an exiting + // set item or it has a greated distance than the final + // item in the set. The set cursor that is used to pop() the + // next item will be set to the lowest index of an uncheck item + void insert(const Neighbor &nbr) + { + if (_size == _capacity && _data[_size - 1] < nbr) + { + return; + } + + size_t lo = 0, hi = _size; + while (lo < hi) + { + size_t mid = (lo + hi) >> 1; + if (nbr < _data[mid]) + { + hi = mid; + // Make sure the same id isn't inserted into the set + } + else if (_data[mid].id == nbr.id) + { + return; + } + else + { + lo = mid + 1; + } + } + + if (lo < _capacity) + { + std::memmove(&_data[lo + 1], &_data[lo], (_size - lo) * sizeof(Neighbor)); + } + _data[lo] = {nbr.id, nbr.distance}; + if (_size < _capacity) + { + _size++; + } + if (lo < _cur) + { + _cur = lo; + } + } + + Neighbor closest_unexpanded() + { + _data[_cur].expanded = true; + size_t pre = _cur; + while (_cur < _size && _data[_cur].expanded) + { + _cur++; + } + return _data[pre]; + } + + bool has_unexpanded_node() const + { + return _cur < _size; + } + + size_t size() const + { + return _size; + } + + size_t capacity() const + { + return _capacity; + } + + void reserve(size_t capacity) + { + if (capacity + 1 > _data.size()) + { + _data.resize(capacity + 1); + } + _capacity = capacity; + } + + Neighbor &operator[](size_t i) + { + return _data[i]; + } + + Neighbor operator[](size_t i) const + { + return _data[i]; + } + + void clear() + { + _size = 0; + _cur = 0; + } + + private: + size_t _size, _capacity, _cur; + std::vector _data; +}; + +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/parameters.h b/packages/leann-backend-diskann/third_party/DiskANN/include/parameters.h new file mode 100644 index 0000000..0206814 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/parameters.h @@ -0,0 +1,119 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include +#include +#include + +#include "omp.h" +#include "defaults.h" + +namespace diskann +{ + +class IndexWriteParameters + +{ + public: + const uint32_t search_list_size; // L + const uint32_t max_degree; // R + const bool saturate_graph; + const uint32_t max_occlusion_size; // C + const float alpha; + const uint32_t num_threads; + const uint32_t filter_list_size; // Lf + + IndexWriteParameters(const uint32_t search_list_size, const uint32_t max_degree, const bool saturate_graph, + const uint32_t max_occlusion_size, const float alpha, const uint32_t num_threads, + const uint32_t filter_list_size) + : search_list_size(search_list_size), max_degree(max_degree), saturate_graph(saturate_graph), + max_occlusion_size(max_occlusion_size), alpha(alpha), num_threads(num_threads), + filter_list_size(filter_list_size) + { + } + + friend class IndexWriteParametersBuilder; +}; + +class IndexSearchParams +{ + public: + IndexSearchParams(const uint32_t initial_search_list_size, const uint32_t num_search_threads) + : initial_search_list_size(initial_search_list_size), num_search_threads(num_search_threads) + { + } + const uint32_t initial_search_list_size; // search L + const uint32_t num_search_threads; // search threads +}; + +class IndexWriteParametersBuilder +{ + /** + * Fluent builder pattern to keep track of the 7 non-default properties + * and their order. The basic ctor was getting unwieldy. + */ + public: + IndexWriteParametersBuilder(const uint32_t search_list_size, // L + const uint32_t max_degree // R + ) + : _search_list_size(search_list_size), _max_degree(max_degree) + { + } + + IndexWriteParametersBuilder &with_max_occlusion_size(const uint32_t max_occlusion_size) + { + _max_occlusion_size = max_occlusion_size; + return *this; + } + + IndexWriteParametersBuilder &with_saturate_graph(const bool saturate_graph) + { + _saturate_graph = saturate_graph; + return *this; + } + + IndexWriteParametersBuilder &with_alpha(const float alpha) + { + _alpha = alpha; + return *this; + } + + IndexWriteParametersBuilder &with_num_threads(const uint32_t num_threads) + { + _num_threads = num_threads == 0 ? omp_get_num_procs() : num_threads; + return *this; + } + + IndexWriteParametersBuilder &with_filter_list_size(const uint32_t filter_list_size) + { + _filter_list_size = filter_list_size == 0 ? _search_list_size : filter_list_size; + return *this; + } + + IndexWriteParameters build() const + { + return IndexWriteParameters(_search_list_size, _max_degree, _saturate_graph, _max_occlusion_size, _alpha, + _num_threads, _filter_list_size); + } + + IndexWriteParametersBuilder(const IndexWriteParameters &wp) + : _search_list_size(wp.search_list_size), _max_degree(wp.max_degree), + _max_occlusion_size(wp.max_occlusion_size), _saturate_graph(wp.saturate_graph), _alpha(wp.alpha), + _filter_list_size(wp.filter_list_size) + { + } + IndexWriteParametersBuilder(const IndexWriteParametersBuilder &) = delete; + IndexWriteParametersBuilder &operator=(const IndexWriteParametersBuilder &) = delete; + + private: + uint32_t _search_list_size{}; + uint32_t _max_degree{}; + uint32_t _max_occlusion_size{defaults::MAX_OCCLUSION_SIZE}; + bool _saturate_graph{defaults::SATURATE_GRAPH}; + float _alpha{defaults::ALPHA}; + uint32_t _num_threads{defaults::NUM_THREADS}; + uint32_t _filter_list_size{defaults::FILTER_LIST_SIZE}; +}; + +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/partition.h b/packages/leann-backend-diskann/third_party/DiskANN/include/partition.h new file mode 100644 index 0000000..c2c4c76 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/partition.h @@ -0,0 +1,49 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include +#include +#include +#include +#include + +#include "neighbor.h" +#include "parameters.h" +#include "tsl/robin_set.h" +#include "utils.h" + +#include "windows_customizations.h" + +template +void gen_random_slice(const std::string base_file, const std::string output_prefix, double sampling_rate); + +template +void gen_random_slice(const std::string data_file, double p_val, float *&sampled_data, size_t &slice_size, + size_t &ndims); + +template +void gen_random_slice(const T *inputdata, size_t npts, size_t ndims, double p_val, float *&sampled_data, + size_t &slice_size); + +int estimate_cluster_sizes(float *test_data_float, size_t num_test, float *pivots, const size_t num_centers, + const size_t dim, const size_t k_base, std::vector &cluster_sizes); + +template +int shard_data_into_clusters(const std::string data_file, float *pivots, const size_t num_centers, const size_t dim, + const size_t k_base, std::string prefix_path); + +template +int shard_data_into_clusters_only_ids(const std::string data_file, float *pivots, const size_t num_centers, + const size_t dim, const size_t k_base, std::string prefix_path); + +template +int retrieve_shard_data_from_ids(const std::string data_file, std::string idmap_filename, std::string data_filename); + +template +int partition(const std::string data_file, const float sampling_rate, size_t num_centers, size_t max_k_means_reps, + const std::string prefix_path, size_t k_base); + +template +int partition_with_ram_budget(const std::string data_file, const double sampling_rate, double ram_budget, + size_t graph_degree, const std::string prefix_path, size_t k_base); diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/percentile_stats.h b/packages/leann-backend-diskann/third_party/DiskANN/include/percentile_stats.h new file mode 100644 index 0000000..7932575 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/percentile_stats.h @@ -0,0 +1,65 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include +#include +#include +#ifdef _WINDOWS +#include +#endif +#include +#include + +#include "distance.h" +#include "parameters.h" + +namespace diskann +{ +struct QueryStats +{ + float total_us = 0; // total time to process query in micros + float io_us = 0; // total time spent in IO + float cpu_us = 0; // total time spent in CPU + + unsigned n_4k = 0; // # of 4kB reads + unsigned n_8k = 0; // # of 8kB reads + unsigned n_12k = 0; // # of 12kB reads + unsigned n_ios = 0; // total # of IOs issued + unsigned read_size = 0; // total # of bytes read + unsigned n_cmps_saved = 0; // # cmps saved + unsigned n_cmps = 0; // # cmps + unsigned n_cache_hits = 0; // # cache_hits + unsigned n_hops = 0; // # search hops +}; + +template +inline T get_percentile_stats(QueryStats *stats, uint64_t len, float percentile, + const std::function &member_fn) +{ + std::vector vals(len); + for (uint64_t i = 0; i < len; i++) + { + vals[i] = member_fn(stats[i]); + } + + std::sort(vals.begin(), vals.end(), [](const T &left, const T &right) { return left < right; }); + + auto retval = vals[(uint64_t)(percentile * len)]; + vals.clear(); + return retval; +} + +template +inline double get_mean_stats(QueryStats *stats, uint64_t len, const std::function &member_fn) +{ + double avg = 0; + for (uint64_t i = 0; i < len; i++) + { + avg += (double)member_fn(stats[i]); + } + return avg / len; +} +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/pq.h b/packages/leann-backend-diskann/third_party/DiskANN/include/pq.h new file mode 100644 index 0000000..3e6119f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/pq.h @@ -0,0 +1,93 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include "utils.h" +#include "pq_common.h" + +namespace diskann +{ +class FixedChunkPQTable +{ + float *tables = nullptr; // pq_tables = float array of size [256 * ndims] + uint64_t ndims = 0; // ndims = true dimension of vectors + uint64_t n_chunks = 0; + bool use_rotation = false; + uint32_t *chunk_offsets = nullptr; + float *centroid = nullptr; + float *tables_tr = nullptr; // same as pq_tables, but col-major + float *rotmat_tr = nullptr; + + public: + FixedChunkPQTable(); + + virtual ~FixedChunkPQTable(); + +#ifdef EXEC_ENV_OLS + void load_pq_centroid_bin(MemoryMappedFiles &files, const char *pq_table_file, size_t num_chunks); +#else + void load_pq_centroid_bin(const char *pq_table_file, size_t num_chunks); +#endif + + uint32_t get_num_chunks(); + + void preprocess_query(float *query_vec); + + // assumes pre-processed query + void populate_chunk_distances(const float *query_vec, float *dist_vec); + + float l2_distance(const float *query_vec, uint8_t *base_vec); + + float inner_product(const float *query_vec, uint8_t *base_vec); + + // assumes no rotation is involved + void inflate_vector(uint8_t *base_vec, float *out_vec); + + void populate_chunk_inner_products(const float *query_vec, float *dist_vec); +}; + +void aggregate_coords(const std::vector &ids, const uint8_t *all_coords, const uint64_t ndims, uint8_t *out); + +void pq_dist_lookup(const uint8_t *pq_ids, const size_t n_pts, const size_t pq_nchunks, const float *pq_dists, + std::vector &dists_out); + +// Need to replace calls to these with calls to vector& based functions above +void aggregate_coords(const unsigned *ids, const uint64_t n_ids, const uint8_t *all_coords, const uint64_t ndims, + uint8_t *out); + +void pq_dist_lookup(const uint8_t *pq_ids, const size_t n_pts, const size_t pq_nchunks, const float *pq_dists, + float *dists_out); + +DISKANN_DLLEXPORT int generate_pq_pivots(const float *const train_data, size_t num_train, unsigned dim, + unsigned num_centers, unsigned num_pq_chunks, unsigned max_k_means_reps, + std::string pq_pivots_path, bool make_zero_mean = false); + +DISKANN_DLLEXPORT int generate_opq_pivots(const float *train_data, size_t num_train, unsigned dim, unsigned num_centers, + unsigned num_pq_chunks, std::string opq_pivots_path, + bool make_zero_mean = false); + +DISKANN_DLLEXPORT int generate_pq_pivots_simplified(const float *train_data, size_t num_train, size_t dim, + size_t num_pq_chunks, std::vector &pivot_data_vector); + +template +int generate_pq_data_from_pivots(const std::string &data_file, unsigned num_centers, unsigned num_pq_chunks, + const std::string &pq_pivots_path, const std::string &pq_compressed_vectors_path, + bool use_opq = false); + +DISKANN_DLLEXPORT int generate_pq_data_from_pivots_simplified(const float *data, const size_t num, + const float *pivot_data, const size_t pivots_num, + const size_t dim, const size_t num_pq_chunks, + std::vector &pq); + +template +void generate_disk_quantized_data(const std::string &data_file_to_use, const std::string &disk_pq_pivots_path, + const std::string &disk_pq_compressed_vectors_path, + const diskann::Metric compareMetric, const double p_val, size_t &disk_pq_dims); + +template +void generate_quantized_data(const std::string &data_file_to_use, const std::string &pq_pivots_path, + const std::string &pq_compressed_vectors_path, const diskann::Metric compareMetric, + const double p_val, const uint64_t num_pq_chunks, const bool use_opq, + const std::string &codebook_prefix = ""); +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/pq_common.h b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_common.h new file mode 100644 index 0000000..c6a3a57 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_common.h @@ -0,0 +1,30 @@ +#pragma once + +#include +#include + +#define NUM_PQ_BITS 8 +#define NUM_PQ_CENTROIDS (1 << NUM_PQ_BITS) +#define MAX_OPQ_ITERS 20 +#define NUM_KMEANS_REPS_PQ 12 +#define MAX_PQ_TRAINING_SET_SIZE 256000 +#define MAX_PQ_CHUNKS 512 + +namespace diskann +{ +inline std::string get_quantized_vectors_filename(const std::string &prefix, bool use_opq, uint32_t num_chunks) +{ + return prefix + (use_opq ? "_opq" : "pq") + std::to_string(num_chunks) + "_compressed.bin"; +} + +inline std::string get_pivot_data_filename(const std::string &prefix, bool use_opq, uint32_t num_chunks) +{ + return prefix + (use_opq ? "_opq" : "pq") + std::to_string(num_chunks) + "_pivots.bin"; +} + +inline std::string get_rotation_matrix_suffix(const std::string &pivot_data_filename) +{ + return pivot_data_filename + "_rotation_matrix.bin"; +} + +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/pq_data_store.h b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_data_store.h new file mode 100644 index 0000000..7c0cb5f --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_data_store.h @@ -0,0 +1,97 @@ +#pragma once +#include +#include "distance.h" +#include "quantized_distance.h" +#include "pq.h" +#include "abstract_data_store.h" + +namespace diskann +{ +// REFACTOR TODO: By default, the PQDataStore is an in-memory datastore because both Vamana and +// DiskANN treat it the same way. But with DiskPQ, that may need to change. +template class PQDataStore : public AbstractDataStore +{ + + public: + PQDataStore(size_t dim, location_t num_points, size_t num_pq_chunks, std::unique_ptr> distance_fn, + std::unique_ptr> pq_distance_fn); + PQDataStore(const PQDataStore &) = delete; + PQDataStore &operator=(const PQDataStore &) = delete; + ~PQDataStore(); + + // Load quantized vectors from a set of files. Here filename is treated + // as a prefix and the files are assumed to be named with DiskANN + // conventions. + virtual location_t load(const std::string &file_prefix) override; + + // Save quantized vectors to a set of files whose names start with + // file_prefix. + // Currently, the plan is to save the quantized vectors to the quantized + // vectors file. + virtual size_t save(const std::string &file_prefix, const location_t num_points) override; + + // Since base class function is pure virtual, we need to declare it here, even though alignent concept is not needed + // for Quantized data stores. + virtual size_t get_aligned_dim() const override; + + // Populate quantized data from unaligned data using PQ functionality + virtual void populate_data(const data_t *vectors, const location_t num_pts) override; + virtual void populate_data(const std::string &filename, const size_t offset) override; + + virtual void extract_data_to_bin(const std::string &filename, const location_t num_pts) override; + + virtual void get_vector(const location_t i, data_t *target) const override; + virtual void set_vector(const location_t i, const data_t *const vector) override; + virtual void prefetch_vector(const location_t loc) override; + + virtual void move_vectors(const location_t old_location_start, const location_t new_location_start, + const location_t num_points) override; + virtual void copy_vectors(const location_t from_loc, const location_t to_loc, const location_t num_points) override; + + virtual void preprocess_query(const data_t *query, AbstractScratch *scratch) const override; + + virtual float get_distance(const data_t *query, const location_t loc) const override; + virtual float get_distance(const location_t loc1, const location_t loc2) const override; + + // NOTE: Caller must invoke "PQDistance->preprocess_query" ONCE before calling + // this function. + virtual void get_distance(const data_t *preprocessed_query, const location_t *locations, + const uint32_t location_count, float *distances, + AbstractScratch *scratch_space) const override; + + // NOTE: Caller must invoke "PQDistance->preprocess_query" ONCE before calling + // this function. + virtual void get_distance(const data_t *preprocessed_query, const std::vector &ids, + std::vector &distances, AbstractScratch *scratch_space) const override; + + // We are returning the distance function that is used for full precision + // vectors here, not the PQ distance function. This is because the callers + // all are expecting a Distance not QuantizedDistance. + virtual Distance *get_dist_fn() const override; + + virtual location_t calculate_medoid() const override; + + virtual size_t get_alignment_factor() const override; + + protected: + virtual location_t expand(const location_t new_size) override; + virtual location_t shrink(const location_t new_size) override; + + virtual location_t load_impl(const std::string &filename); +#ifdef EXEC_ENV_OLS + virtual location_t load_impl(AlignedFileReader &reader); +#endif + + private: + uint8_t *_quantized_data = nullptr; + size_t _num_chunks = 0; + + // REFACTOR TODO: Doing this temporarily before refactoring OPQ into + // its own class. Remove later. + bool _use_opq = false; + + Metric _distance_metric; + std::unique_ptr> _distance_fn = nullptr; + std::unique_ptr> _pq_distance_fn = nullptr; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/pq_flash_index.h b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_flash_index.h new file mode 100644 index 0000000..174df5c --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_flash_index.h @@ -0,0 +1,286 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#include "common_includes.h" + +#include "aligned_file_reader.h" +#include "concurrent_queue.h" +#include "neighbor.h" +#include "parameters.h" +#include "percentile_stats.h" +#include "pq.h" +#include "utils.h" +#include "windows_customizations.h" +#include "scratch.h" +#include "tsl/robin_map.h" +#include "tsl/robin_set.h" + +#define FULL_PRECISION_REORDER_MULTIPLIER 3 + +namespace diskann +{ + +template class PQFlashIndex +{ + public: + DISKANN_DLLEXPORT PQFlashIndex(std::shared_ptr &fileReader, + std::shared_ptr &graphReader, + diskann::Metric metric = diskann::Metric::L2); + DISKANN_DLLEXPORT ~PQFlashIndex(); + +#ifdef EXEC_ENV_OLS + DISKANN_DLLEXPORT int load(diskann::MemoryMappedFiles &files, uint32_t num_threads, const char *index_prefix, + const char *pq_prefix = nullptr); +#else + // load compressed data, and obtains the handle to the disk-resident index + DISKANN_DLLEXPORT int load(uint32_t num_threads, const char *index_prefix, const char *pq_prefix = nullptr, + const char *partition_prefix = nullptr); +#endif + +#ifdef EXEC_ENV_OLS + DISKANN_DLLEXPORT int load_from_separate_paths(diskann::MemoryMappedFiles &files, uint32_t num_threads, + const char *index_filepath, const char *pivots_filepath, + const char *compressed_filepath, const char *graph_file); +#else + DISKANN_DLLEXPORT int load_from_separate_paths(uint32_t num_threads, const char *index_filepath, + const char *pivots_filepath, const char *compressed_filepath, + const char *graph_file, const char *partition_file); +#endif + + DISKANN_DLLEXPORT void load_cache_list(std::vector &node_list); + +#ifdef EXEC_ENV_OLS + DISKANN_DLLEXPORT void generate_cache_list_from_sample_queries(MemoryMappedFiles &files, std::string sample_bin, + uint64_t l_search, uint64_t beamwidth, + uint64_t num_nodes_to_cache, uint32_t nthreads, + std::vector &node_list); +#else + DISKANN_DLLEXPORT void generate_cache_list_from_sample_queries(std::string sample_bin, uint64_t l_search, + uint64_t beamwidth, uint64_t num_nodes_to_cache, + uint32_t num_threads, + std::vector &node_list); +#endif + + DISKANN_DLLEXPORT void cache_bfs_levels(uint64_t num_nodes_to_cache, std::vector &node_list, + const bool shuffle = false); + + DISKANN_DLLEXPORT void cached_beam_search(const T *query, const uint64_t k_search, const uint64_t l_search, + uint64_t *res_ids, float *res_dists, const uint64_t beam_width, + const bool use_reorder_data = false, QueryStats *stats = nullptr, + const bool USE_DEFERRED_FETCH = false, + const bool skip_search_reorder = false, + const bool recompute_beighbor_embeddings = false, + const bool dedup_node_dis = false, float prune_ratio = 0, + const bool batch_recompute = false, bool global_pruning = false); + + DISKANN_DLLEXPORT void cached_beam_search(const T *query, const uint64_t k_search, const uint64_t l_search, + uint64_t *res_ids, float *res_dists, const uint64_t beam_width, + const bool use_filter, const LabelT &filter_label, + const bool use_reorder_data = false, QueryStats *stats = nullptr, + const bool USE_DEFERRED_FETCH = false, + const bool skip_search_reorder = false, + const bool recompute_beighbor_embeddings = false, + const bool dedup_node_dis = false, float prune_ratio = 0, + const bool batch_recompute = false, bool global_pruning = false); + + DISKANN_DLLEXPORT void cached_beam_search(const T *query, const uint64_t k_search, const uint64_t l_search, + uint64_t *res_ids, float *res_dists, const uint64_t beam_width, + const uint32_t io_limit, const bool use_reorder_data = false, + QueryStats *stats = nullptr, const bool USE_DEFERRED_FETCH = false, + const bool skip_search_reorder = false, + const bool recompute_beighbor_embeddings = false, + const bool dedup_node_dis = false, float prune_ratio = 0, + const bool batch_recompute = false, bool global_pruning = false); + + DISKANN_DLLEXPORT void cached_beam_search(const T *query, const uint64_t k_search, const uint64_t l_search, + uint64_t *res_ids, float *res_dists, const uint64_t beam_width, + const bool use_filter, const LabelT &filter_label, + const uint32_t io_limit, const bool use_reorder_data = false, + QueryStats *stats = nullptr, const bool USE_DEFERRED_FETCH = false, + const bool skip_search_reorder = false, + const bool recompute_beighbor_embeddings = false, + const bool dedup_node_dis = false, float prune_ratio = 0, + const bool batch_recompute = false, bool global_pruning = false); + + DISKANN_DLLEXPORT LabelT get_converted_label(const std::string &filter_label); + + DISKANN_DLLEXPORT uint32_t range_search(const T *query1, const double range, const uint64_t min_l_search, + const uint64_t max_l_search, std::vector &indices, + std::vector &distances, const uint64_t min_beam_width, + QueryStats *stats = nullptr); + + DISKANN_DLLEXPORT uint64_t get_data_dim(); + + std::shared_ptr &reader; + + DISKANN_DLLEXPORT diskann::Metric get_metric(); + + // + // node_ids: input list of node_ids to be read + // coord_buffers: pointers to pre-allocated buffers that coords need to copied to. If null, dont copy. + // nbr_buffers: pre-allocated buffers to copy neighbors into + // + // returns a vector of bool one for each node_id: true if read is success, else false + // + DISKANN_DLLEXPORT std::vector read_nodes(const std::vector &node_ids, + std::vector &coord_buffers, + std::vector> &nbr_buffers); + + DISKANN_DLLEXPORT std::vector get_pq_vector(std::uint64_t vid); + DISKANN_DLLEXPORT uint64_t get_num_points(); + + protected: + DISKANN_DLLEXPORT void use_medoids_data_as_centroids(); + DISKANN_DLLEXPORT void setup_thread_data(uint64_t nthreads, uint64_t visited_reserve = 4096); + + DISKANN_DLLEXPORT void set_universal_label(const LabelT &label); + + private: + DISKANN_DLLEXPORT inline bool point_has_label(uint32_t point_id, LabelT label_id); + std::unordered_map load_label_map(std::basic_istream &infile); + DISKANN_DLLEXPORT void parse_label_file(std::basic_istream &infile, size_t &num_pts_labels); + DISKANN_DLLEXPORT void get_label_file_metadata(const std::string &fileContent, uint32_t &num_pts, + uint32_t &num_total_labels); + DISKANN_DLLEXPORT void generate_random_labels(std::vector &labels, const uint32_t num_labels, + const uint32_t nthreads); + void reset_stream_for_reading(std::basic_istream &infile); + + // sector # on disk where node_id is present with in the graph part + DISKANN_DLLEXPORT uint64_t get_node_sector(uint64_t node_id); + + // ptr to start of the node + DISKANN_DLLEXPORT char *offset_to_node(char *sector_buf, uint64_t node_id); + + // returns region of `node_buf` containing [NNBRS][NBR_ID(uint32_t)] + DISKANN_DLLEXPORT uint32_t *offset_to_node_nhood(char *node_buf); + + // returns region of `node_buf` containing [COORD(T)] + DISKANN_DLLEXPORT T *offset_to_node_coords(char *node_buf); + + DISKANN_DLLEXPORT int load_graph_index(const std::string &graph_index_file); + + DISKANN_DLLEXPORT int read_partition_info(const std::string &partition_bin); + + DISKANN_DLLEXPORT int read_neighbors(const std::string &graph_index_file, uint64_t target_node_id); + + // index info for multi-node sectors + // nhood of node `i` is in sector: [i / nnodes_per_sector] + // offset in sector: [(i % nnodes_per_sector) * max_node_len] + // + // index info for multi-sector nodes + // nhood of node `i` is in sector: [i * DIV_ROUND_UP(_max_node_len, SECTOR_LEN)] + // offset in sector: [0] + // + // Common info + // coords start at ofsset + // #nbrs of node `i`: *(unsigned*) (offset + disk_bytes_per_point) + // nbrs of node `i` : (unsigned*) (offset + disk_bytes_per_point + 1) + + uint64_t _max_node_len = 0; + uint64_t _nnodes_per_sector = 0; // 0 for multi-sector nodes, >0 for multi-node sectors + uint64_t _max_degree = 0; + uint64_t _C = 0; + // Data used for searching with re-order vectors + uint64_t _ndims_reorder_vecs = 0; + uint64_t _reorder_data_start_sector = 0; + uint64_t _nvecs_per_sector = 0; + + diskann::Metric metric = diskann::Metric::L2; + + // used only for inner product search to re-scale the result value + // (due to the pre-processing of base during index build) + float _max_base_norm = 0.0f; + + // data info + uint64_t _num_points = 0; + uint64_t _num_frozen_points = 0; + uint64_t _frozen_location = 0; + uint64_t _data_dim = 0; + uint64_t _aligned_dim = 0; + uint64_t _disk_bytes_per_point = 0; // Number of bytes + + std::string _disk_index_file; + std::vector> _node_visit_counter; + + // PQ data + // _n_chunks = # of chunks ndims is split into + // data: char * _n_chunks + // chunk_size = chunk size of each dimension chunk + // pq_tables = float* [[2^8 * [chunk_size]] * _n_chunks] + uint8_t *data = nullptr; + uint64_t _n_chunks; + FixedChunkPQTable _pq_table; + + // distance comparator + std::shared_ptr> _dist_cmp; + std::shared_ptr> _dist_cmp_float; + + // for very large datasets: we use PQ even for the disk resident index + bool _use_disk_index_pq = false; + uint64_t _disk_pq_n_chunks = 0; + FixedChunkPQTable _disk_pq_table; + + // medoid/start info + + // graph has one entry point by default, + // we can optionally have multiple starting points + uint32_t *_medoids = nullptr; + // defaults to 1 + size_t _num_medoids; + // by default, it is empty. If there are multiple + // centroids, we pick the medoid corresponding to the + // closest centroid as the starting point of search + float *_centroid_data = nullptr; + + // nhood_cache; the uint32_t in nhood_Cache are offsets into nhood_cache_buf + unsigned *_nhood_cache_buf = nullptr; + tsl::robin_map> _nhood_cache; + + // coord_cache; The T* in coord_cache are offsets into coord_cache_buf + T *_coord_cache_buf = nullptr; + tsl::robin_map _coord_cache; + + // thread-specific scratch + ConcurrentQueue *> _thread_data; + uint64_t _max_nthreads; + bool _load_flag = false; + bool _count_visited_nodes = false; + bool _reorder_data_exists = false; + uint64_t _reoreder_data_offset = 0; + + // filter support + uint32_t *_pts_to_label_offsets = nullptr; + uint32_t *_pts_to_label_counts = nullptr; + LabelT *_pts_to_labels = nullptr; + std::unordered_map> _filter_to_medoid_ids; + bool _use_universal_label = false; + LabelT _universal_filter_label; + tsl::robin_set _dummy_pts; + tsl::robin_set _has_dummy_pts; + tsl::robin_map _dummy_to_real_map; + tsl::robin_map> _real_to_dummy_map; + std::unordered_map _label_map; + + private: + bool _use_partition = false; + + std::shared_ptr graph_reader; // Graph file reader + std::string _graph_index_file; // Graph file path + uint64_t _graph_node_len; // Graph node length + uint64_t _emb_node_len; // Embedding node length + + // Partition related data structures + uint64_t _num_partitions; // Number of partitions + std::vector> _graph_partitions; // Partition information + std::vector _id2partition; // ID to partition mapping + +#ifdef EXEC_ENV_OLS + // Set to a larger value than the actual header to accommodate + // any additions we make to the header. This is an outer limit + // on how big the header can be. + static const int HEADER_SIZE = defaults::SECTOR_LEN; + char *getHeaderBytes(); +#endif +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/pq_l2_distance.h b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_l2_distance.h new file mode 100644 index 0000000..e6fc6e4 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_l2_distance.h @@ -0,0 +1,87 @@ +#pragma once +#include "quantized_distance.h" + +namespace diskann +{ +template class PQL2Distance : public QuantizedDistance +{ + public: + // REFACTOR TODO: We could take a file prefix here and load the + // PQ pivots file, so that the distance object is initialized + // immediately after construction. But this would not work well + // with our data store concept where the store is created first + // and data populated after. + // REFACTOR TODO: Ideally, we should only read the num_chunks from + // the pivots file. However, we read the pivots file only later, but + // clients can call functions like get__filename without calling + // load_pivot_data. Hence this. The TODO is whether we should check + // that the num_chunks from the file is the same as this one. + + PQL2Distance(uint32_t num_chunks, bool use_opq = false); + + virtual ~PQL2Distance() override; + + virtual bool is_opq() const override; + + virtual std::string get_quantized_vectors_filename(const std::string &prefix) const override; + virtual std::string get_pivot_data_filename(const std::string &prefix) const override; + virtual std::string get_rotation_matrix_suffix(const std::string &pq_pivots_filename) const override; + +#ifdef EXEC_ENV_OLS + virtual void load_pivot_data(MemoryMappedFiles &files, const std::string &pq_table_file, + size_t num_chunks) override; +#else + virtual void load_pivot_data(const std::string &pq_table_file, size_t num_chunks) override; +#endif + + // Number of chunks in the PQ table. Depends on the compression level used. + // Has to be < ndim + virtual uint32_t get_num_chunks() const override; + + // Preprocess the query by computing chunk distances from the query vector to + // various centroids. Since we don't want this class to do scratch management, + // we will take a PQScratch object which can come either from Index class or + // PQFlashIndex class. + virtual void preprocess_query(const data_t *aligned_query, uint32_t original_dim, + PQScratch &pq_scratch) override; + + // Distance function used for graph traversal. This function must be called + // after + // preprocess_query. The reason we do not call preprocess ourselves is because + // that function has to be called once per query, while this function is + // called at each iteration of the graph walk. NOTE: This function expects + // 1. the query to be preprocessed using preprocess_query() + // 2. the scratch object to contain the quantized vectors corresponding to ids + // in aligned_pq_coord_scratch. Done by calling aggregate_coords() + // + virtual void preprocessed_distance(PQScratch &pq_scratch, const uint32_t id_count, + float *dists_out) override; + + // Same as above, but returns the distances in a vector instead of an array. + // Convenience function for index.cpp. + virtual void preprocessed_distance(PQScratch &pq_scratch, const uint32_t n_ids, + std::vector &dists_out) override; + + // Currently this function is required for DiskPQ. However, it too can be + // subsumed under preprocessed_distance if we add the appropriate scratch + // variables to PQScratch and initialize them in + // pq_flash_index.cpp::disk_iterate_to_fixed_point() + virtual float brute_force_distance(const float *query_vec, uint8_t *base_vec) override; + + protected: + // assumes pre-processed query + virtual void prepopulate_chunkwise_distances(const float *query_vec, float *dist_vec); + + // assumes no rotation is involved + // virtual void inflate_vector(uint8_t *base_vec, float *out_vec); + + float *_tables = nullptr; // pq_tables = float array of size [256 * ndims] + uint64_t _ndims = 0; // ndims = true dimension of vectors + uint64_t _num_chunks = 0; + bool _is_opq = false; + uint32_t *_chunk_offsets = nullptr; + float *_centroid = nullptr; + float *_tables_tr = nullptr; // same as pq_tables, but col-major + float *_rotmat_tr = nullptr; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/pq_scratch.h b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_scratch.h new file mode 100644 index 0000000..95f1b13 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/pq_scratch.h @@ -0,0 +1,23 @@ +#pragma once +#include +#include "pq_common.h" +#include "utils.h" + +namespace diskann +{ + +template class PQScratch +{ + public: + float *aligned_pqtable_dist_scratch = nullptr; // MUST BE AT LEAST [256 * NCHUNKS] + float *aligned_dist_scratch = nullptr; // MUST BE AT LEAST diskann MAX_DEGREE + uint8_t *aligned_pq_coord_scratch = nullptr; // AT LEAST [N_CHUNKS * MAX_DEGREE] + float *rotated_query = nullptr; + float *aligned_query_float = nullptr; + + PQScratch(size_t graph_degree, size_t aligned_dim); + void initialize(size_t dim, const T *query, const float norm = 1.0f); + virtual ~PQScratch(); +}; + +} // namespace diskann \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/program_options_utils.hpp b/packages/leann-backend-diskann/third_party/DiskANN/include/program_options_utils.hpp new file mode 100644 index 0000000..2be6059 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/program_options_utils.hpp @@ -0,0 +1,81 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include + +namespace program_options_utils +{ +const std::string make_program_description(const char *executable_name, const char *description) +{ + return std::string("\n") + .append(description) + .append("\n\n") + .append("Usage: ") + .append(executable_name) + .append(" [OPTIONS]"); +} + +// Required parameters +const char *DATA_TYPE_DESCRIPTION = "data type, one of {int8, uint8, float} - float is single precision (32 bit)"; +const char *DISTANCE_FUNCTION_DESCRIPTION = + "distance function {l2, mips, fast_l2, cosine}. 'fast l2' and 'mips' only support data_type float"; +const char *INDEX_PATH_PREFIX_DESCRIPTION = "Path prefix to the index, e.g. '/mnt/data/my_ann_index'"; +const char *RESULT_PATH_DESCRIPTION = + "Path prefix for saving results of the queries, e.g. '/mnt/data/query_file_X.bin'"; +const char *QUERY_FILE_DESCRIPTION = "Query file in binary format, e.g. '/mnt/data/query_file_X.bin'"; +const char *NUMBER_OF_RESULTS_DESCRIPTION = "Number of neighbors to be returned (K in the DiskANN white paper)"; +const char *SEARCH_LIST_DESCRIPTION = + "Size of search list to use. This value is the number of neighbor/distance pairs to keep in memory at the same " + "time while performing a query. This can also be described as the size of the working set at query time. This " + "must be greater than or equal to the number of results/neighbors to return (K in the white paper). Corresponds " + "to L in the DiskANN white paper."; +const char *INPUT_DATA_PATH = "Input data file in bin format. This is the file you want to build the index over. " + "File format: Shape of the vector followed by the vector of embeddings as binary data."; + +// Optional parameters +const char *FILTER_LABEL_DESCRIPTION = + "Filter to use when running a query. 'filter_label' and 'query_filters_file' are mutually exclusive."; +const char *FILTERS_FILE_DESCRIPTION = + "Filter file for Queries for Filtered Search. File format is text with one filter per line. File must " + "have exactly one filter OR the same number of filters as there are queries in the 'query_file'."; +const char *LABEL_TYPE_DESCRIPTION = + "Storage type of Labels {uint/uint32, ushort/uint16}, default value is uint which will consume memory 4 bytes per " + "filter. 'uint' is an alias for 'uint32' and 'ushort' is an alias for 'uint16'."; +const char *GROUND_TRUTH_FILE_DESCRIPTION = + "ground truth file for the queryset"; // what's the format, what's the requirements? does it need to include an + // entry for every item or just a small subset? I have so many questions about + // this file +const char *NUMBER_THREADS_DESCRIPTION = "Number of threads used for building index. Defaults to number of logical " + "processor cores on your this machine returned by omp_get_num_procs()"; +const char *FAIL_IF_RECALL_BELOW = + "Value between 0 (inclusive) and 100 (exclusive) indicating the recall tolerance percentage threshold before " + "program fails with a non-zero exit code. The default value of 0 means that the program will complete " + "successfully with any recall value. A non-zero value indicates the floor for acceptable recall values. If the " + "calculated recall value is below this threshold then the program will write out the results but return a non-zero " + "exit code as a signal that the recall was not acceptable."; // does it continue running or die immediately? Will I + // still get my results even if the return code is -1? + +const char *NUMBER_OF_NODES_TO_CACHE = "Number of BFS nodes around medoid(s) to cache. Default value: 0"; +const char *BEAMWIDTH = "Beamwidth for search. Set 0 to optimize internally. Default value: 2"; +const char *MAX_BUILD_DEGREE = "Maximum graph degree"; +const char *GRAPH_BUILD_COMPLEXITY = + "Size of the search working set during build time. This is the numer of neighbor/distance pairs to keep in memory " + "while building the index. Higher value results in a higher quality graph but it will take more time to build the " + "graph."; +const char *GRAPH_BUILD_ALPHA = "Alpha controls density and diameter of graph, set 1 for sparse graph, 1.2 or 1.4 for " + "denser graphs with lower diameter"; +const char *BUIlD_GRAPH_PQ_BYTES = "Number of PQ bytes to build the index; 0 for full precision build"; +const char *USE_OPQ = "Use Optimized Product Quantization (OPQ)."; +const char *LABEL_FILE = "Input label file in txt format for Filtered Index build. The file should contain comma " + "separated filters for each node with each line corresponding to a graph node"; +const char *UNIVERSAL_LABEL = + "Universal label, Use only in conjunction with label file for filtered index build. If a " + "graph node has all the labels against it, we can assign a special universal filter to the " + "point instead of comma separated filters for that point. The universal label should be assigned to nodes " + "in the labels file instead of listing all labels for a node. DiskANN will not automatically assign a " + "universal label to a node."; +const char *FILTERED_LBUILD = "Build complexity for filtered points, higher value results in better graphs"; + +} // namespace program_options_utils diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/proto_embedding.h b/packages/leann-backend-diskann/third_party/DiskANN/include/proto_embedding.h new file mode 100644 index 0000000..f17e225 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/proto_embedding.h @@ -0,0 +1,9 @@ +#pragma once + +#include "embedding.pb.h" + +// This header ensures that the protobuf files are included correctly +// and provides a namespace alias for convenience +namespace diskann { + namespace proto = protoembedding; +} diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/quantized_distance.h b/packages/leann-backend-diskann/third_party/DiskANN/include/quantized_distance.h new file mode 100644 index 0000000..cc4aea9 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/quantized_distance.h @@ -0,0 +1,56 @@ +#pragma once +#include +#include +#include +#include "abstract_scratch.h" + +namespace diskann +{ +template class PQScratch; + +template class QuantizedDistance +{ + public: + QuantizedDistance() = default; + QuantizedDistance(const QuantizedDistance &) = delete; + QuantizedDistance &operator=(const QuantizedDistance &) = delete; + virtual ~QuantizedDistance() = default; + + virtual bool is_opq() const = 0; + virtual std::string get_quantized_vectors_filename(const std::string &prefix) const = 0; + virtual std::string get_pivot_data_filename(const std::string &prefix) const = 0; + virtual std::string get_rotation_matrix_suffix(const std::string &pq_pivots_filename) const = 0; + + // Loading the PQ centroid table need not be part of the abstract class. + // However, we want to indicate that this function will change once we have a + // file reader hierarchy, so leave it here as-is. +#ifdef EXEC_ENV_OLS + virtual void load_pivot_data(MemoryMappedFiles &files, const std::String &pq_table_file, size_t num_chunks) = 0; +#else + virtual void load_pivot_data(const std::string &pq_table_file, size_t num_chunks) = 0; +#endif + + // Number of chunks in the PQ table. Depends on the compression level used. + // Has to be < ndim + virtual uint32_t get_num_chunks() const = 0; + + // Preprocess the query by computing chunk distances from the query vector to + // various centroids. Since we don't want this class to do scratch management, + // we will take a PQScratch object which can come either from Index class or + // PQFlashIndex class. + virtual void preprocess_query(const data_t *query_vec, uint32_t query_dim, PQScratch &pq_scratch) = 0; + + // Workhorse + // This function must be called after preprocess_query + virtual void preprocessed_distance(PQScratch &pq_scratch, const uint32_t id_count, float *dists_out) = 0; + + // Same as above, but convenience function for index.cpp. + virtual void preprocessed_distance(PQScratch &pq_scratch, const uint32_t n_ids, + std::vector &dists_out) = 0; + + // Currently this function is required for DiskPQ. However, it too can be subsumed + // under preprocessed_distance if we add the appropriate scratch variables to + // PQScratch and initialize them in pq_flash_index.cpp::disk_iterate_to_fixed_point() + virtual float brute_force_distance(const float *query_vec, uint8_t *base_vec) = 0; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/restapi/common.h b/packages/leann-backend-diskann/third_party/DiskANN/include/restapi/common.h new file mode 100644 index 0000000..b833963 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/restapi/common.h @@ -0,0 +1,18 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +namespace diskann +{ +// Constants +static const std::string VECTOR_KEY = "query", K_KEY = "k", INDICES_KEY = "indices", DISTANCES_KEY = "distances", + TAGS_KEY = "tags", QUERY_ID_KEY = "query_id", ERROR_MESSAGE_KEY = "error", L_KEY = "Ls", + TIME_TAKEN_KEY = "time_taken_in_us", PARTITION_KEY = "partition", + UNKNOWN_ERROR = "unknown_error"; +const unsigned int DEFAULT_L = 100; + +} // namespace diskann \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/restapi/search_wrapper.h b/packages/leann-backend-diskann/third_party/DiskANN/include/restapi/search_wrapper.h new file mode 100644 index 0000000..ebd067d --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/restapi/search_wrapper.h @@ -0,0 +1,140 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include +#include + +#include +#include + +namespace diskann +{ +class SearchResult +{ + public: + SearchResult(unsigned int K, unsigned int elapsed_time_in_ms, const unsigned *const indices, + const float *const distances, const std::string *const tags = nullptr, + const unsigned *const partitions = nullptr); + + const std::vector &get_indices() const + { + return _indices; + } + const std::vector &get_distances() const + { + return _distances; + } + bool tags_enabled() const + { + return _tags_enabled; + } + const std::vector &get_tags() const + { + return _tags; + } + bool partitions_enabled() const + { + return _partitions_enabled; + } + const std::vector &get_partitions() const + { + return _partitions; + } + unsigned get_time() const + { + return _search_time_in_ms; + } + + private: + unsigned int _K; + unsigned int _search_time_in_ms; + std::vector _indices; + std::vector _distances; + + bool _tags_enabled; + std::vector _tags; + + bool _partitions_enabled; + std::vector _partitions; +}; + +class SearchNotImplementedException : public std::logic_error +{ + private: + std::string _errormsg; + + public: + SearchNotImplementedException(const char *type) : std::logic_error("Not Implemented") + { + _errormsg = "Search with data type "; + _errormsg += std::string(type); + _errormsg += " not implemented : "; + _errormsg += __FUNCTION__; + } + + virtual const char *what() const throw() + { + return _errormsg.c_str(); + } +}; + +class BaseSearch +{ + public: + BaseSearch(const std::string &tagsFile = nullptr); + virtual SearchResult search(const float *query, const unsigned int dimensions, const unsigned int K, + const unsigned int Ls) + { + throw SearchNotImplementedException("float"); + } + virtual SearchResult search(const int8_t *query, const unsigned int dimensions, const unsigned int K, + const unsigned int Ls) + { + throw SearchNotImplementedException("int8_t"); + } + + virtual SearchResult search(const uint8_t *query, const unsigned int dimensions, const unsigned int K, + const unsigned int Ls) + { + throw SearchNotImplementedException("uint8_t"); + } + + void lookup_tags(const unsigned K, const unsigned *indices, std::string *ret_tags); + + protected: + bool _tags_enabled; + std::vector _tags_str; +}; + +template class InMemorySearch : public BaseSearch +{ + public: + InMemorySearch(const std::string &baseFile, const std::string &indexFile, const std::string &tagsFile, Metric m, + uint32_t num_threads, uint32_t search_l); + virtual ~InMemorySearch(); + + SearchResult search(const T *query, const unsigned int dimensions, const unsigned int K, const unsigned int Ls); + + private: + unsigned int _dimensions, _numPoints; + std::unique_ptr> _index; +}; + +template class PQFlashSearch : public BaseSearch +{ + public: + PQFlashSearch(const std::string &indexPrefix, const unsigned num_nodes_to_cache, const unsigned num_threads, + const std::string &tagsFile, Metric m); + virtual ~PQFlashSearch(); + + SearchResult search(const T *query, const unsigned int dimensions, const unsigned int K, const unsigned int Ls); + + private: + unsigned int _dimensions, _numPoints; + std::unique_ptr> _index; + std::shared_ptr reader; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/restapi/server.h b/packages/leann-backend-diskann/third_party/DiskANN/include/restapi/server.h new file mode 100644 index 0000000..1d75847 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/restapi/server.h @@ -0,0 +1,45 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +namespace diskann +{ +class Server +{ + public: + Server(web::uri &url, std::vector> &multi_searcher, + const std::string &typestring); + virtual ~Server(); + + pplx::task open(); + pplx::task close(); + + protected: + template void handle_post(web::http::http_request message); + + template + web::json::value toJsonArray(const std::vector &v, std::function valConverter); + web::json::value prepareResponse(const int64_t &queryId, const int k); + + template + void parseJson(const utility::string_t &body, unsigned int &k, int64_t &queryId, T *&queryVector, + unsigned int &dimensions, unsigned &Ls); + + web::json::value idsToJsonArray(const diskann::SearchResult &result); + web::json::value distancesToJsonArray(const diskann::SearchResult &result); + web::json::value tagsToJsonArray(const diskann::SearchResult &result); + web::json::value partitionsToJsonArray(const diskann::SearchResult &result); + + SearchResult aggregate_results(const unsigned K, const std::vector &results); + + private: + bool _isDebug; + std::unique_ptr _listener; + const bool _multi_search; + std::vector> _multi_searcher; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/scratch.h b/packages/leann-backend-diskann/third_party/DiskANN/include/scratch.h new file mode 100644 index 0000000..2f43e33 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/scratch.h @@ -0,0 +1,216 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include + +#include "boost_dynamic_bitset_fwd.h" +// #include "boost/dynamic_bitset.hpp" +#include "tsl/robin_set.h" +#include "tsl/robin_map.h" +#include "tsl/sparse_map.h" + +#include "aligned_file_reader.h" +#include "abstract_scratch.h" +#include "neighbor.h" +#include "defaults.h" +#include "concurrent_queue.h" + +namespace diskann +{ +template class PQScratch; + +// +// AbstractScratch space for in-memory index based search +// +template class InMemQueryScratch : public AbstractScratch +{ + public: + ~InMemQueryScratch(); + InMemQueryScratch(uint32_t search_l, uint32_t indexing_l, uint32_t r, uint32_t maxc, size_t dim, size_t aligned_dim, + size_t alignment_factor, bool init_pq_scratch = false); + void resize_for_new_L(uint32_t new_search_l); + void clear(); + + inline uint32_t get_L() + { + return _L; + } + inline uint32_t get_R() + { + return _R; + } + inline uint32_t get_maxc() + { + return _maxc; + } + inline T *aligned_query() + { + return this->_aligned_query_T; + } + inline PQScratch *pq_scratch() + { + return this->_pq_scratch; + } + inline std::vector &pool() + { + return _pool; + } + inline NeighborPriorityQueue &best_l_nodes() + { + return _best_l_nodes; + } + inline std::vector &occlude_factor() + { + return _occlude_factor; + } + inline tsl::robin_set &inserted_into_pool_rs() + { + return _inserted_into_pool_rs; + } + inline boost::dynamic_bitset<> &inserted_into_pool_bs() + { + return *_inserted_into_pool_bs; + } + inline std::vector &id_scratch() + { + return _id_scratch; + } + inline std::vector &dist_scratch() + { + return _dist_scratch; + } + inline tsl::robin_set &expanded_nodes_set() + { + return _expanded_nodes_set; + } + inline std::vector &expanded_nodes_vec() + { + return _expanded_nghrs_vec; + } + inline std::vector &occlude_list_output() + { + return _occlude_list_output; + } + + private: + uint32_t _L; + uint32_t _R; + uint32_t _maxc; + + // _pool stores all neighbors explored from best_L_nodes. + // Usually around L+R, but could be higher. + // Initialized to 3L+R for some slack, expands as needed. + std::vector _pool; + + // _best_l_nodes is reserved for storing best L entries + // Underlying storage is L+1 to support inserts + NeighborPriorityQueue _best_l_nodes; + + // _occlude_factor.size() >= pool.size() in occlude_list function + // _pool is clipped to maxc in occlude_list before affecting _occlude_factor + // _occlude_factor is initialized to maxc size + std::vector _occlude_factor; + + // Capacity initialized to 20L + tsl::robin_set _inserted_into_pool_rs; + + // Use a pointer here to allow for forward declaration of dynamic_bitset + // in public headers to avoid making boost a dependency for clients + // of DiskANN. + boost::dynamic_bitset<> *_inserted_into_pool_bs; + + // _id_scratch.size() must be > R*GRAPH_SLACK_FACTOR for iterate_to_fp + std::vector _id_scratch; + + // _dist_scratch must be > R*GRAPH_SLACK_FACTOR for iterate_to_fp + // _dist_scratch should be at least the size of id_scratch + std::vector _dist_scratch; + + // Buffers used in process delete, capacity increases as needed + tsl::robin_set _expanded_nodes_set; + std::vector _expanded_nghrs_vec; + std::vector _occlude_list_output; +}; + +// +// AbstractScratch space for SSD index based search +// + +template class SSDQueryScratch : public AbstractScratch +{ + public: + T *coord_scratch = nullptr; // MUST BE AT LEAST [sizeof(T) * data_dim] + + char *sector_scratch = nullptr; // MUST BE AT LEAST [MAX_N_SECTOR_READS * SECTOR_LEN] + size_t sector_idx = 0; // index of next [SECTOR_LEN] scratch to use + + tsl::robin_set visited; + NeighborPriorityQueue retset; + std::vector full_retset; + + SSDQueryScratch(size_t aligned_dim, size_t visited_reserve); + ~SSDQueryScratch(); + + void reset(); +}; + +template class SSDThreadData +{ + public: + SSDQueryScratch scratch; + IOContext ctx; + + SSDThreadData(size_t aligned_dim, size_t visited_reserve); + void clear(); +}; + +// +// Class to avoid the hassle of pushing and popping the query scratch. +// +template class ScratchStoreManager +{ + public: + ScratchStoreManager(ConcurrentQueue &query_scratch) : _scratch_pool(query_scratch) + { + _scratch = query_scratch.pop(); + while (_scratch == nullptr) + { + query_scratch.wait_for_push_notify(); + _scratch = query_scratch.pop(); + } + } + T *scratch_space() + { + return _scratch; + } + + ~ScratchStoreManager() + { + _scratch->clear(); + _scratch_pool.push(_scratch); + _scratch_pool.push_notify_all(); + } + + void destroy() + { + while (!_scratch_pool.empty()) + { + auto scratch = _scratch_pool.pop(); + while (scratch == nullptr) + { + _scratch_pool.wait_for_push_notify(); + scratch = _scratch_pool.pop(); + } + delete scratch; + } + } + + private: + T *_scratch; + ConcurrentQueue &_scratch_pool; + ScratchStoreManager(const ScratchStoreManager &); + ScratchStoreManager &operator=(const ScratchStoreManager &); +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/simd_utils.h b/packages/leann-backend-diskann/third_party/DiskANN/include/simd_utils.h new file mode 100644 index 0000000..4b07369 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/simd_utils.h @@ -0,0 +1,106 @@ +#pragma once + +#ifdef _WINDOWS +#include +#include +#include +#include +#else +#include +#endif + +namespace diskann +{ +static inline __m256 _mm256_mul_epi8(__m256i X) +{ + __m256i zero = _mm256_setzero_si256(); + + __m256i sign_x = _mm256_cmpgt_epi8(zero, X); + + __m256i xlo = _mm256_unpacklo_epi8(X, sign_x); + __m256i xhi = _mm256_unpackhi_epi8(X, sign_x); + + return _mm256_cvtepi32_ps(_mm256_add_epi32(_mm256_madd_epi16(xlo, xlo), _mm256_madd_epi16(xhi, xhi))); +} + +static inline __m128 _mm_mulhi_epi8(__m128i X) +{ + __m128i zero = _mm_setzero_si128(); + __m128i sign_x = _mm_cmplt_epi8(X, zero); + __m128i xhi = _mm_unpackhi_epi8(X, sign_x); + + return _mm_cvtepi32_ps(_mm_add_epi32(_mm_setzero_si128(), _mm_madd_epi16(xhi, xhi))); +} + +static inline __m128 _mm_mulhi_epi8_shift32(__m128i X) +{ + __m128i zero = _mm_setzero_si128(); + X = _mm_srli_epi64(X, 32); + __m128i sign_x = _mm_cmplt_epi8(X, zero); + __m128i xhi = _mm_unpackhi_epi8(X, sign_x); + + return _mm_cvtepi32_ps(_mm_add_epi32(_mm_setzero_si128(), _mm_madd_epi16(xhi, xhi))); +} +static inline __m128 _mm_mul_epi8(__m128i X, __m128i Y) +{ + __m128i zero = _mm_setzero_si128(); + + __m128i sign_x = _mm_cmplt_epi8(X, zero); + __m128i sign_y = _mm_cmplt_epi8(Y, zero); + + __m128i xlo = _mm_unpacklo_epi8(X, sign_x); + __m128i xhi = _mm_unpackhi_epi8(X, sign_x); + __m128i ylo = _mm_unpacklo_epi8(Y, sign_y); + __m128i yhi = _mm_unpackhi_epi8(Y, sign_y); + + return _mm_cvtepi32_ps(_mm_add_epi32(_mm_madd_epi16(xlo, ylo), _mm_madd_epi16(xhi, yhi))); +} +static inline __m128 _mm_mul_epi8(__m128i X) +{ + __m128i zero = _mm_setzero_si128(); + __m128i sign_x = _mm_cmplt_epi8(X, zero); + __m128i xlo = _mm_unpacklo_epi8(X, sign_x); + __m128i xhi = _mm_unpackhi_epi8(X, sign_x); + + return _mm_cvtepi32_ps(_mm_add_epi32(_mm_madd_epi16(xlo, xlo), _mm_madd_epi16(xhi, xhi))); +} + +static inline __m128 _mm_mul32_pi8(__m128i X, __m128i Y) +{ + __m128i xlo = _mm_cvtepi8_epi16(X), ylo = _mm_cvtepi8_epi16(Y); + return _mm_cvtepi32_ps(_mm_unpacklo_epi32(_mm_madd_epi16(xlo, ylo), _mm_setzero_si128())); +} + +static inline __m256 _mm256_mul_epi8(__m256i X, __m256i Y) +{ + __m256i zero = _mm256_setzero_si256(); + + __m256i sign_x = _mm256_cmpgt_epi8(zero, X); + __m256i sign_y = _mm256_cmpgt_epi8(zero, Y); + + __m256i xlo = _mm256_unpacklo_epi8(X, sign_x); + __m256i xhi = _mm256_unpackhi_epi8(X, sign_x); + __m256i ylo = _mm256_unpacklo_epi8(Y, sign_y); + __m256i yhi = _mm256_unpackhi_epi8(Y, sign_y); + + return _mm256_cvtepi32_ps(_mm256_add_epi32(_mm256_madd_epi16(xlo, ylo), _mm256_madd_epi16(xhi, yhi))); +} + +static inline __m256 _mm256_mul32_pi8(__m128i X, __m128i Y) +{ + __m256i xlo = _mm256_cvtepi8_epi16(X), ylo = _mm256_cvtepi8_epi16(Y); + return _mm256_blend_ps(_mm256_cvtepi32_ps(_mm256_madd_epi16(xlo, ylo)), _mm256_setzero_ps(), 252); +} + +static inline float _mm256_reduce_add_ps(__m256 x) +{ + /* ( x3+x7, x2+x6, x1+x5, x0+x4 ) */ + const __m128 x128 = _mm_add_ps(_mm256_extractf128_ps(x, 1), _mm256_castps256_ps128(x)); + /* ( -, -, x1+x3+x5+x7, x0+x2+x4+x6 ) */ + const __m128 x64 = _mm_add_ps(x128, _mm_movehl_ps(x128, x128)); + /* ( -, -, -, x0+x1+x2+x3+x4+x5+x6+x7 ) */ + const __m128 x32 = _mm_add_ss(x64, _mm_shuffle_ps(x64, x64, 0x55)); + /* Conversion to float is a no-op on x86-64 */ + return _mm_cvtss_f32(x32); +} +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tag_uint128.h b/packages/leann-backend-diskann/third_party/DiskANN/include/tag_uint128.h new file mode 100644 index 0000000..642de31 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tag_uint128.h @@ -0,0 +1,68 @@ +#pragma once +#include +#include + +namespace diskann +{ +#pragma pack(push, 1) + +struct tag_uint128 +{ + std::uint64_t _data1 = 0; + std::uint64_t _data2 = 0; + + bool operator==(const tag_uint128 &other) const + { + return _data1 == other._data1 && _data2 == other._data2; + } + + bool operator==(std::uint64_t other) const + { + return _data1 == other && _data2 == 0; + } + + tag_uint128 &operator=(const tag_uint128 &other) + { + _data1 = other._data1; + _data2 = other._data2; + + return *this; + } + + tag_uint128 &operator=(std::uint64_t other) + { + _data1 = other; + _data2 = 0; + + return *this; + } +}; + +#pragma pack(pop) +} // namespace diskann + +namespace std +{ +// Hash 128 input bits down to 64 bits of output. +// This is intended to be a reasonably good hash function. +inline std::uint64_t Hash128to64(const std::uint64_t &low, const std::uint64_t &high) +{ + // Murmur-inspired hashing. + const std::uint64_t kMul = 0x9ddfea08eb382d69ULL; + std::uint64_t a = (low ^ high) * kMul; + a ^= (a >> 47); + std::uint64_t b = (high ^ a) * kMul; + b ^= (b >> 47); + b *= kMul; + return b; +} + +template <> struct hash +{ + size_t operator()(const diskann::tag_uint128 &key) const noexcept + { + return Hash128to64(key._data1, key._data2); // map -0 to 0 + } +}; + +} // namespace std \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/timer.h b/packages/leann-backend-diskann/third_party/DiskANN/include/timer.h new file mode 100644 index 0000000..325edf3 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/timer.h @@ -0,0 +1,40 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. +#pragma once + +#include + +namespace diskann +{ +class Timer +{ + typedef std::chrono::high_resolution_clock _clock; + std::chrono::time_point<_clock> check_point; + + public: + Timer() : check_point(_clock::now()) + { + } + + void reset() + { + check_point = _clock::now(); + } + + long long elapsed() const + { + return std::chrono::duration_cast(_clock::now() - check_point).count(); + } + + float elapsed_seconds() const + { + return (float)elapsed() / 1000000.0f; + } + + std::string elapsed_seconds_for_step(const std::string &step) const + { + return std::string("Time for ") + step + std::string(": ") + std::to_string(elapsed_seconds()) + + std::string(" seconds"); + } +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/.clang-format b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/.clang-format new file mode 100644 index 0000000..9d15924 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/.clang-format @@ -0,0 +1,2 @@ +DisableFormat: true +SortIncludes: false diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_growth_policy.h b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_growth_policy.h new file mode 100644 index 0000000..6bfa9e5 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_growth_policy.h @@ -0,0 +1,330 @@ +/** + * MIT License + * + * Copyright (c) 2017 Tessil + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef TSL_ROBIN_GROWTH_POLICY_H +#define TSL_ROBIN_GROWTH_POLICY_H + + +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +#ifndef tsl_assert +# ifdef TSL_DEBUG +# define tsl_assert(expr) assert(expr) +# else +# define tsl_assert(expr) (static_cast(0)) +# endif +#endif + + +/** + * If exceptions are enabled, throw the exception passed in parameter, otherwise call std::terminate. + */ +#ifndef TSL_THROW_OR_TERMINATE +# if (defined(__cpp_exceptions) || defined(__EXCEPTIONS) || (defined (_MSC_VER) && defined (_CPPUNWIND))) && !defined(TSL_NO_EXCEPTIONS) +# define TSL_THROW_OR_TERMINATE(ex, msg) throw ex(msg) +# else +# ifdef NDEBUG +# define TSL_THROW_OR_TERMINATE(ex, msg) std::terminate() +# else +# include +# define TSL_THROW_OR_TERMINATE(ex, msg) do { std::fprintf(stderr, msg); std::terminate(); } while(0) +# endif +# endif +#endif + + +#ifndef TSL_LIKELY +# if defined(__GNUC__) || defined(__clang__) +# define TSL_LIKELY(exp) (__builtin_expect(!!(exp), true)) +# else +# define TSL_LIKELY(exp) (exp) +# endif +#endif + + +namespace tsl { +namespace rh { + +/** + * Grow the hash table by a factor of GrowthFactor keeping the bucket count to a power of two. It allows + * the table to use a mask operation instead of a modulo operation to map a hash to a bucket. + * + * GrowthFactor must be a power of two >= 2. + */ +template +class power_of_two_growth_policy { +public: + /** + * Called on the hash table creation and on rehash. The number of buckets for the table is passed in parameter. + * This number is a minimum, the policy may update this value with a higher value if needed (but not lower). + * + * If 0 is given, min_bucket_count_in_out must still be 0 after the policy creation and + * bucket_for_hash must always return 0 in this case. + */ + explicit power_of_two_growth_policy(std::size_t& min_bucket_count_in_out) { + if(min_bucket_count_in_out > max_bucket_count()) { + TSL_THROW_OR_TERMINATE(std::length_error, "The hash table exceeds its maxmimum size."); + } + + if(min_bucket_count_in_out > 0) { + min_bucket_count_in_out = round_up_to_power_of_two(min_bucket_count_in_out); + m_mask = min_bucket_count_in_out - 1; + } + else { + m_mask = 0; + } + } + + /** + * Return the bucket [0, bucket_count()) to which the hash belongs. + * If bucket_count() is 0, it must always return 0. + */ + std::size_t bucket_for_hash(std::size_t hash) const noexcept { + return hash & m_mask; + } + + /** + * Return the number of buckets that should be used on next growth. + */ + std::size_t next_bucket_count() const { + if((m_mask + 1) > max_bucket_count() / GrowthFactor) { + TSL_THROW_OR_TERMINATE(std::length_error, "The hash table exceeds its maxmimum size."); + } + + return (m_mask + 1) * GrowthFactor; + } + + /** + * Return the maximum number of buckets supported by the policy. + */ + std::size_t max_bucket_count() const { + // Largest power of two. + return ((std::numeric_limits::max)() / 2) + 1; + } + + /** + * Reset the growth policy as if it was created with a bucket count of 0. + * After a clear, the policy must always return 0 when bucket_for_hash is called. + */ + void clear() noexcept { + m_mask = 0; + } + +private: + static std::size_t round_up_to_power_of_two(std::size_t value) { + if(is_power_of_two(value)) { + return value; + } + + if(value == 0) { + return 1; + } + + --value; + for(std::size_t i = 1; i < sizeof(std::size_t) * CHAR_BIT; i *= 2) { + value |= value >> i; + } + + return value + 1; + } + + static constexpr bool is_power_of_two(std::size_t value) { + return value != 0 && (value & (value - 1)) == 0; + } + +protected: + static_assert(is_power_of_two(GrowthFactor) && GrowthFactor >= 2, "GrowthFactor must be a power of two >= 2."); + + std::size_t m_mask; +}; + + +/** + * Grow the hash table by GrowthFactor::num / GrowthFactor::den and use a modulo to map a hash + * to a bucket. Slower but it can be useful if you want a slower growth. + */ +template> +class mod_growth_policy { +public: + explicit mod_growth_policy(std::size_t& min_bucket_count_in_out) { + if(min_bucket_count_in_out > max_bucket_count()) { + TSL_THROW_OR_TERMINATE(std::length_error, "The hash table exceeds its maxmimum size."); + } + + if(min_bucket_count_in_out > 0) { + m_mod = min_bucket_count_in_out; + } + else { + m_mod = 1; + } + } + + std::size_t bucket_for_hash(std::size_t hash) const noexcept { + return hash % m_mod; + } + + std::size_t next_bucket_count() const { + if(m_mod == max_bucket_count()) { + TSL_THROW_OR_TERMINATE(std::length_error, "The hash table exceeds its maxmimum size."); + } + + const double next_bucket_count = std::ceil(double(m_mod) * REHASH_SIZE_MULTIPLICATION_FACTOR); + if(!std::isnormal(next_bucket_count)) { + TSL_THROW_OR_TERMINATE(std::length_error, "The hash table exceeds its maxmimum size."); + } + + if(next_bucket_count > double(max_bucket_count())) { + return max_bucket_count(); + } + else { + return std::size_t(next_bucket_count); + } + } + + std::size_t max_bucket_count() const { + return MAX_BUCKET_COUNT; + } + + void clear() noexcept { + m_mod = 1; + } + +private: + static constexpr double REHASH_SIZE_MULTIPLICATION_FACTOR = 1.0 * GrowthFactor::num / GrowthFactor::den; + static const std::size_t MAX_BUCKET_COUNT = + std::size_t(double( + (std::numeric_limits::max)() / REHASH_SIZE_MULTIPLICATION_FACTOR + )); + + static_assert(REHASH_SIZE_MULTIPLICATION_FACTOR >= 1.1, "Growth factor should be >= 1.1."); + + std::size_t m_mod; +}; + + + +namespace detail { + +static constexpr const std::array PRIMES = {{ + 1ul, 5ul, 17ul, 29ul, 37ul, 53ul, 67ul, 79ul, 97ul, 131ul, 193ul, 257ul, 389ul, 521ul, 769ul, 1031ul, + 1543ul, 2053ul, 3079ul, 6151ul, 12289ul, 24593ul, 49157ul, 98317ul, 196613ul, 393241ul, 786433ul, + 1572869ul, 3145739ul, 6291469ul, 12582917ul, 25165843ul, 50331653ul, 100663319ul, 201326611ul, + 402653189ul, 805306457ul, 1610612741ul, 3221225473ul, 4294967291ul +}}; + +template +static constexpr std::size_t mod(std::size_t hash) { return hash % PRIMES[IPrime]; } + +// MOD_PRIME[iprime](hash) returns hash % PRIMES[iprime]. This table allows for faster modulo as the +// compiler can optimize the modulo code better with a constant known at the compilation. +static constexpr const std::array MOD_PRIME = {{ + &mod<0>, &mod<1>, &mod<2>, &mod<3>, &mod<4>, &mod<5>, &mod<6>, &mod<7>, &mod<8>, &mod<9>, &mod<10>, + &mod<11>, &mod<12>, &mod<13>, &mod<14>, &mod<15>, &mod<16>, &mod<17>, &mod<18>, &mod<19>, &mod<20>, + &mod<21>, &mod<22>, &mod<23>, &mod<24>, &mod<25>, &mod<26>, &mod<27>, &mod<28>, &mod<29>, &mod<30>, + &mod<31>, &mod<32>, &mod<33>, &mod<34>, &mod<35>, &mod<36>, &mod<37> , &mod<38>, &mod<39> +}}; + +} + +/** + * Grow the hash table by using prime numbers as bucket count. Slower than tsl::rh::power_of_two_growth_policy in + * general but will probably distribute the values around better in the buckets with a poor hash function. + * + * To allow the compiler to optimize the modulo operation, a lookup table is used with constant primes numbers. + * + * With a switch the code would look like: + * \code + * switch(iprime) { // iprime is the current prime of the hash table + * case 0: hash % 5ul; + * break; + * case 1: hash % 17ul; + * break; + * case 2: hash % 29ul; + * break; + * ... + * } + * \endcode + * + * Due to the constant variable in the modulo the compiler is able to optimize the operation + * by a series of multiplications, substractions and shifts. + * + * The 'hash % 5' could become something like 'hash - (hash * 0xCCCCCCCD) >> 34) * 5' in a 64 bits environement. + */ +class prime_growth_policy { +public: + explicit prime_growth_policy(std::size_t& min_bucket_count_in_out) { + auto it_prime = std::lower_bound(detail::PRIMES.begin(), + detail::PRIMES.end(), min_bucket_count_in_out); + if(it_prime == detail::PRIMES.end()) { + TSL_THROW_OR_TERMINATE(std::length_error, "The hash table exceeds its maxmimum size."); + } + + m_iprime = static_cast(std::distance(detail::PRIMES.begin(), it_prime)); + if(min_bucket_count_in_out > 0) { + min_bucket_count_in_out = *it_prime; + } + else { + min_bucket_count_in_out = 0; + } + } + + std::size_t bucket_for_hash(std::size_t hash) const noexcept { + return detail::MOD_PRIME[m_iprime](hash); + } + + std::size_t next_bucket_count() const { + if(m_iprime + 1 >= detail::PRIMES.size()) { + TSL_THROW_OR_TERMINATE(std::length_error, "The hash table exceeds its maxmimum size."); + } + + return detail::PRIMES[m_iprime + 1]; + } + + std::size_t max_bucket_count() const { + return detail::PRIMES.back(); + } + + void clear() noexcept { + m_iprime = 0; + } + +private: + unsigned int m_iprime; + + static_assert((std::numeric_limits::max)() >= detail::PRIMES.size(), + "The type of m_iprime is not big enough."); +}; + +} +} + +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_hash.h b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_hash.h new file mode 100644 index 0000000..5ecc962 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_hash.h @@ -0,0 +1,1285 @@ +/** + * MIT License + * + * Copyright (c) 2017 Tessil + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef TSL_ROBIN_HASH_H +#define TSL_ROBIN_HASH_H + + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include "robin_growth_policy.h" + + +namespace tsl { + +namespace detail_robin_hash { + +template +struct make_void { + using type = void; +}; + +template +struct has_is_transparent: std::false_type { +}; + +template +struct has_is_transparent::type>: std::true_type { +}; + +template +struct is_power_of_two_policy: std::false_type { +}; + +template +struct is_power_of_two_policy>: std::true_type { +}; + + + +using truncated_hash_type = std::uint_least32_t; + +/** + * Helper class that store a truncated hash if StoreHash is true and nothing otherwise. + */ +template +class bucket_entry_hash { +public: + bool bucket_hash_equal(std::size_t /*hash*/) const noexcept { + return true; + } + + truncated_hash_type truncated_hash() const noexcept { + return 0; + } + +protected: + void set_hash(truncated_hash_type /*hash*/) noexcept { + } +}; + +template<> +class bucket_entry_hash { +public: + bool bucket_hash_equal(std::size_t hash) const noexcept { + return m_hash == truncated_hash_type(hash); + } + + truncated_hash_type truncated_hash() const noexcept { + return m_hash; + } + +protected: + void set_hash(truncated_hash_type hash) noexcept { + m_hash = truncated_hash_type(hash); + } + +private: + truncated_hash_type m_hash; +}; + + +/** + * Each bucket entry has: + * - A value of type `ValueType`. + * - An integer to store how far the value of the bucket, if any, is from its ideal bucket + * (ex: if the current bucket 5 has the value 'foo' and `hash('foo') % nb_buckets` == 3, + * `dist_from_ideal_bucket()` will return 2 as the current value of the bucket is two + * buckets away from its ideal bucket) + * If there is no value in the bucket (i.e. `empty()` is true) `dist_from_ideal_bucket()` will be < 0. + * - A marker which tells us if the bucket is the last bucket of the bucket array (useful for the + * iterator of the hash table). + * - If `StoreHash` is true, 32 bits of the hash of the value, if any, are also stored in the bucket. + * If the size of the hash is more than 32 bits, it is truncated. We don't store the full hash + * as storing the hash is a potential opportunity to use the unused space due to the alignement + * of the bucket_entry structure. We can thus potentially store the hash without any extra space + * (which would not be possible with 64 bits of the hash). + */ +template +class bucket_entry: public bucket_entry_hash { + using bucket_hash = bucket_entry_hash; + +public: + using value_type = ValueType; + using distance_type = std::int_least16_t; + + + bucket_entry() noexcept: bucket_hash(), m_dist_from_ideal_bucket(EMPTY_MARKER_DIST_FROM_IDEAL_BUCKET), + m_last_bucket(false) + { + tsl_assert(empty()); + } + + bucket_entry(bool last_bucket) noexcept: bucket_hash(), m_dist_from_ideal_bucket(EMPTY_MARKER_DIST_FROM_IDEAL_BUCKET), + m_last_bucket(last_bucket) + { + tsl_assert(empty()); + } + + bucket_entry(const bucket_entry& other) noexcept(std::is_nothrow_copy_constructible::value): + bucket_hash(other), + m_dist_from_ideal_bucket(EMPTY_MARKER_DIST_FROM_IDEAL_BUCKET), + m_last_bucket(other.m_last_bucket) + { + if(!other.empty()) { + ::new (static_cast(std::addressof(m_value))) value_type(other.value()); + m_dist_from_ideal_bucket = other.m_dist_from_ideal_bucket; + } + } + + /** + * Never really used, but still necessary as we must call resize on an empty `std::vector`. + * and we need to support move-only types. See robin_hash constructor for details. + */ + bucket_entry(bucket_entry&& other) noexcept(std::is_nothrow_move_constructible::value): + bucket_hash(std::move(other)), + m_dist_from_ideal_bucket(EMPTY_MARKER_DIST_FROM_IDEAL_BUCKET), + m_last_bucket(other.m_last_bucket) + { + if(!other.empty()) { + ::new (static_cast(std::addressof(m_value))) value_type(std::move(other.value())); + m_dist_from_ideal_bucket = other.m_dist_from_ideal_bucket; + } + } + + bucket_entry& operator=(const bucket_entry& other) + noexcept(std::is_nothrow_copy_constructible::value) + { + if(this != &other) { + clear(); + + bucket_hash::operator=(other); + if(!other.empty()) { + ::new (static_cast(std::addressof(m_value))) value_type(other.value()); + } + + m_dist_from_ideal_bucket = other.m_dist_from_ideal_bucket; + m_last_bucket = other.m_last_bucket; + } + + return *this; + } + + bucket_entry& operator=(bucket_entry&& ) = delete; + + ~bucket_entry() noexcept { + clear(); + } + + void clear() noexcept { + if(!empty()) { + destroy_value(); + m_dist_from_ideal_bucket = EMPTY_MARKER_DIST_FROM_IDEAL_BUCKET; + } + } + + bool empty() const noexcept { + return m_dist_from_ideal_bucket == EMPTY_MARKER_DIST_FROM_IDEAL_BUCKET; + } + + value_type& value() noexcept { + tsl_assert(!empty()); + return *reinterpret_cast(std::addressof(m_value)); + } + + const value_type& value() const noexcept { + tsl_assert(!empty()); + return *reinterpret_cast(std::addressof(m_value)); + } + + distance_type dist_from_ideal_bucket() const noexcept { + return m_dist_from_ideal_bucket; + } + + bool last_bucket() const noexcept { + return m_last_bucket; + } + + void set_as_last_bucket() noexcept { + m_last_bucket = true; + } + + template + void set_value_of_empty_bucket(distance_type dist_from_ideal_bucket, + truncated_hash_type hash, Args&&... value_type_args) + { + tsl_assert(dist_from_ideal_bucket >= 0); + tsl_assert(empty()); + + ::new (static_cast(std::addressof(m_value))) value_type(std::forward(value_type_args)...); + this->set_hash(hash); + m_dist_from_ideal_bucket = dist_from_ideal_bucket; + + tsl_assert(!empty()); + } + + void swap_with_value_in_bucket(distance_type& dist_from_ideal_bucket, + truncated_hash_type& hash, value_type& value) + { + tsl_assert(!empty()); + + using std::swap; + swap(value, this->value()); + swap(dist_from_ideal_bucket, m_dist_from_ideal_bucket); + + // Avoid warning of unused variable if StoreHash is false + (void) hash; + if(StoreHash) { + const truncated_hash_type tmp_hash = this->truncated_hash(); + this->set_hash(hash); + hash = tmp_hash; + } + } + + static truncated_hash_type truncate_hash(std::size_t hash) noexcept { + return truncated_hash_type(hash); + } + +private: + void destroy_value() noexcept { + tsl_assert(!empty()); + value().~value_type(); + } + +private: + using storage = typename std::aligned_storage::type; + + static const distance_type EMPTY_MARKER_DIST_FROM_IDEAL_BUCKET = -1; + + distance_type m_dist_from_ideal_bucket; + bool m_last_bucket; + storage m_value; +}; + + + +/** + * Internal common class used by `robin_map` and `robin_set`. + * + * ValueType is what will be stored by `robin_hash` (usually `std::pair` for map and `Key` for set). + * + * `KeySelect` should be a `FunctionObject` which takes a `ValueType` in parameter and returns a + * reference to the key. + * + * `ValueSelect` should be a `FunctionObject` which takes a `ValueType` in parameter and returns a + * reference to the value. `ValueSelect` should be void if there is no value (in a set for example). + * + * The strong exception guarantee only holds if the expression + * `std::is_nothrow_swappable::value && std::is_nothrow_move_constructible::value` is true. + * + * Behaviour is undefined if the destructor of `ValueType` throws. + */ +template +class robin_hash: private Hash, private KeyEqual, private GrowthPolicy { +private: + template + using has_mapped_type = typename std::integral_constant::value>; + + static_assert(noexcept(std::declval().bucket_for_hash(std::size_t(0))), "GrowthPolicy::bucket_for_hash must be noexcept."); + static_assert(noexcept(std::declval().clear()), "GrowthPolicy::clear must be noexcept."); + +public: + template + class robin_iterator; + + using key_type = typename KeySelect::key_type; + using value_type = ValueType; + using size_type = std::size_t; + using difference_type = std::ptrdiff_t; + using hasher = Hash; + using key_equal = KeyEqual; + using allocator_type = Allocator; + using reference = value_type&; + using const_reference = const value_type&; + using pointer = value_type*; + using const_pointer = const value_type*; + using iterator = robin_iterator; + using const_iterator = robin_iterator; + + +private: + /** + * Either store the hash because we are asked by the `StoreHash` template parameter + * or store the hash because it doesn't cost us anything in size and can be used to speed up rehash. + */ + static constexpr bool STORE_HASH = StoreHash || + ( + (sizeof(tsl::detail_robin_hash::bucket_entry) == + sizeof(tsl::detail_robin_hash::bucket_entry)) + && + (sizeof(std::size_t) == sizeof(truncated_hash_type) || + is_power_of_two_policy::value) + && + // Don't store the hash for primitive types with default hash. + (!std::is_arithmetic::value || + !std::is_same>::value) + ); + + /** + * Only use the stored hash on lookup if we are explictly asked. We are not sure how slow + * the KeyEqual operation is. An extra comparison may slow things down with a fast KeyEqual. + */ + static constexpr bool USE_STORED_HASH_ON_LOOKUP = StoreHash; + + /** + * We can only use the hash on rehash if the size of the hash type is the same as the stored one or + * if we use a power of two modulo. In the case of the power of two modulo, we just mask + * the least significant bytes, we just have to check that the truncated_hash_type didn't truncated + * more bytes. + */ + static bool USE_STORED_HASH_ON_REHASH(size_type bucket_count) { + (void) bucket_count; + if(STORE_HASH && sizeof(std::size_t) == sizeof(truncated_hash_type)) { + return true; + } + else if(STORE_HASH && is_power_of_two_policy::value) { + tsl_assert(bucket_count > 0); + return (bucket_count - 1) <= (std::numeric_limits::max)(); + } + else { + return false; + } + } + + using bucket_entry = tsl::detail_robin_hash::bucket_entry; + using distance_type = typename bucket_entry::distance_type; + + using buckets_allocator = typename std::allocator_traits::template rebind_alloc; + using buckets_container_type = std::vector; + + +public: + /** + * The 'operator*()' and 'operator->()' methods return a const reference and const pointer respectively to the + * stored value type. + * + * In case of a map, to get a mutable reference to the value associated to a key (the '.second' in the + * stored pair), you have to call 'value()'. + * + * The main reason for this is that if we returned a `std::pair&` instead + * of a `const std::pair&`, the user may modify the key which will put the map in a undefined state. + */ + template + class robin_iterator { + friend class robin_hash; + + private: + using iterator_bucket = typename std::conditional::type; + + + robin_iterator(iterator_bucket it) noexcept: m_iterator(it) { + } + + public: + using iterator_category = std::forward_iterator_tag; + using value_type = const typename robin_hash::value_type; + using difference_type = std::ptrdiff_t; + using reference = value_type&; + using pointer = value_type*; + + + robin_iterator() noexcept { + } + + robin_iterator(const robin_iterator& other) noexcept: m_iterator(other.m_iterator) { + } + + const typename robin_hash::key_type& key() const { + return KeySelect()(m_iterator->value()); + } + + template::value && IsConst>::type* = nullptr> + const typename U::value_type& value() const { + return U()(m_iterator->value()); + } + + template::value && !IsConst>::type* = nullptr> + typename U::value_type& value() { + return U()(m_iterator->value()); + } + + reference operator*() const { + return m_iterator->value(); + } + + pointer operator->() const { + return std::addressof(m_iterator->value()); + } + + robin_iterator& operator++() { + while(true) { + if(m_iterator->last_bucket()) { + ++m_iterator; + return *this; + } + + ++m_iterator; + if(!m_iterator->empty()) { + return *this; + } + } + } + + robin_iterator operator++(int) { + robin_iterator tmp(*this); + ++*this; + + return tmp; + } + + friend bool operator==(const robin_iterator& lhs, const robin_iterator& rhs) { + return lhs.m_iterator == rhs.m_iterator; + } + + friend bool operator!=(const robin_iterator& lhs, const robin_iterator& rhs) { + return !(lhs == rhs); + } + + private: + iterator_bucket m_iterator; + }; + + +public: + robin_hash(size_type bucket_count, + const Hash& hash, + const KeyEqual& equal, + const Allocator& alloc, + float max_load_factor): Hash(hash), + KeyEqual(equal), + GrowthPolicy(bucket_count), + m_buckets(alloc), + m_first_or_empty_bucket(static_empty_bucket_ptr()), + m_bucket_count(bucket_count), + m_nb_elements(0), + m_grow_on_next_insert(false) + { + if(bucket_count > max_bucket_count()) { + TSL_THROW_OR_TERMINATE(std::length_error, "The map exceeds its maxmimum size."); + } + + if(m_bucket_count > 0) { + /* + * We can't use the `vector(size_type count, const Allocator& alloc)` constructor + * as it's only available in C++14 and we need to support C++11. We thus must resize after using + * the `vector(const Allocator& alloc)` constructor. + * + * We can't use `vector(size_type count, const T& value, const Allocator& alloc)` as it requires the + * value T to be copyable. + */ + m_buckets.resize(m_bucket_count); + m_first_or_empty_bucket = m_buckets.data(); + + tsl_assert(!m_buckets.empty()); + m_buckets.back().set_as_last_bucket(); + } + + + this->max_load_factor(max_load_factor); + } + + robin_hash(const robin_hash& other): Hash(other), + KeyEqual(other), + GrowthPolicy(other), + m_buckets(other.m_buckets), + m_first_or_empty_bucket(m_buckets.empty()?static_empty_bucket_ptr():m_buckets.data()), + m_bucket_count(other.m_bucket_count), + m_nb_elements(other.m_nb_elements), + m_load_threshold(other.m_load_threshold), + m_max_load_factor(other.m_max_load_factor), + m_grow_on_next_insert(other.m_grow_on_next_insert) + { + } + + robin_hash(robin_hash&& other) noexcept(std::is_nothrow_move_constructible::value && + std::is_nothrow_move_constructible::value && + std::is_nothrow_move_constructible::value && + std::is_nothrow_move_constructible::value) + : Hash(std::move(static_cast(other))), + KeyEqual(std::move(static_cast(other))), + GrowthPolicy(std::move(static_cast(other))), + m_buckets(std::move(other.m_buckets)), + m_first_or_empty_bucket(m_buckets.empty()?static_empty_bucket_ptr():m_buckets.data()), + m_bucket_count(other.m_bucket_count), + m_nb_elements(other.m_nb_elements), + m_load_threshold(other.m_load_threshold), + m_max_load_factor(other.m_max_load_factor), + m_grow_on_next_insert(other.m_grow_on_next_insert) + { + other.GrowthPolicy::clear(); + other.m_buckets.clear(); + other.m_first_or_empty_bucket = static_empty_bucket_ptr(); + other.m_bucket_count = 0; + other.m_nb_elements = 0; + other.m_load_threshold = 0; + other.m_grow_on_next_insert = false; + } + + robin_hash& operator=(const robin_hash& other) { + if(&other != this) { + Hash::operator=(other); + KeyEqual::operator=(other); + GrowthPolicy::operator=(other); + + m_buckets = other.m_buckets; + m_first_or_empty_bucket = m_buckets.empty()?static_empty_bucket_ptr(): + m_buckets.data(); + m_bucket_count = other.m_bucket_count; + m_nb_elements = other.m_nb_elements; + m_load_threshold = other.m_load_threshold; + m_max_load_factor = other.m_max_load_factor; + m_grow_on_next_insert = other.m_grow_on_next_insert; + } + + return *this; + } + + robin_hash& operator=(robin_hash&& other) { + other.swap(*this); + other.clear(); + + return *this; + } + + allocator_type get_allocator() const { + return m_buckets.get_allocator(); + } + + + /* + * Iterators + */ + iterator begin() noexcept { + auto begin = m_buckets.begin(); + while(begin != m_buckets.end() && begin->empty()) { + ++begin; + } + + return iterator(begin); + } + + const_iterator begin() const noexcept { + return cbegin(); + } + + const_iterator cbegin() const noexcept { + auto begin = m_buckets.cbegin(); + while(begin != m_buckets.cend() && begin->empty()) { + ++begin; + } + + return const_iterator(begin); + } + + iterator end() noexcept { + return iterator(m_buckets.end()); + } + + const_iterator end() const noexcept { + return cend(); + } + + const_iterator cend() const noexcept { + return const_iterator(m_buckets.cend()); + } + + + /* + * Capacity + */ + bool empty() const noexcept { + return m_nb_elements == 0; + } + + size_type size() const noexcept { + return m_nb_elements; + } + + size_type max_size() const noexcept { + return m_buckets.max_size(); + } + + /* + * Modifiers + */ + void clear() noexcept { + for(auto& bucket: m_buckets) { + bucket.clear(); + } + + m_nb_elements = 0; + m_grow_on_next_insert = false; + } + + + + template + std::pair insert(P&& value) { + return insert_impl(KeySelect()(value), std::forward

(value)); + } + + template + iterator insert(const_iterator hint, P&& value) { + if(hint != cend() && compare_keys(KeySelect()(*hint), KeySelect()(value))) { + return mutable_iterator(hint); + } + + return insert(std::forward

(value)).first; + } + + template + void insert(InputIt first, InputIt last) { + if(std::is_base_of::iterator_category>::value) + { + const auto nb_elements_insert = std::distance(first, last); + const size_type nb_free_buckets = m_load_threshold - size(); + tsl_assert(m_load_threshold >= size()); + + if(nb_elements_insert > 0 && nb_free_buckets < size_type(nb_elements_insert)) { + reserve(size() + size_type(nb_elements_insert)); + } + } + + for(; first != last; ++first) { + insert(*first); + } + } + + + + template + std::pair insert_or_assign(K&& key, M&& obj) { + auto it = try_emplace(std::forward(key), std::forward(obj)); + if(!it.second) { + it.first.value() = std::forward(obj); + } + + return it; + } + + template + iterator insert_or_assign(const_iterator hint, K&& key, M&& obj) { + if(hint != cend() && compare_keys(KeySelect()(*hint), key)) { + auto it = mutable_iterator(hint); + it.value() = std::forward(obj); + + return it; + } + + return insert_or_assign(std::forward(key), std::forward(obj)).first; + } + + + template + std::pair emplace(Args&&... args) { + return insert(value_type(std::forward(args)...)); + } + + template + iterator emplace_hint(const_iterator hint, Args&&... args) { + return insert(hint, value_type(std::forward(args)...)); + } + + + + template + std::pair try_emplace(K&& key, Args&&... args) { + return insert_impl(key, std::piecewise_construct, + std::forward_as_tuple(std::forward(key)), + std::forward_as_tuple(std::forward(args)...)); + } + + template + iterator try_emplace(const_iterator hint, K&& key, Args&&... args) { + if(hint != cend() && compare_keys(KeySelect()(*hint), key)) { + return mutable_iterator(hint); + } + + return try_emplace(std::forward(key), std::forward(args)...).first; + } + + /** + * Here to avoid `template size_type erase(const K& key)` being used when + * we use an `iterator` instead of a `const_iterator`. + */ + iterator erase(iterator pos) { + erase_from_bucket(pos); + + /** + * Erase bucket used a backward shift after clearing the bucket. + * Check if there is a new value in the bucket, if not get the next non-empty. + */ + if(pos.m_iterator->empty()) { + ++pos; + } + + return pos; + } + + iterator erase(const_iterator pos) { + return erase(mutable_iterator(pos)); + } + + iterator erase(const_iterator first, const_iterator last) { + if(first == last) { + return mutable_iterator(first); + } + + auto first_mutable = mutable_iterator(first); + auto last_mutable = mutable_iterator(last); + for(auto it = first_mutable.m_iterator; it != last_mutable.m_iterator; ++it) { + if(!it->empty()) { + it->clear(); + m_nb_elements--; + } + } + + if(last_mutable == end()) { + return end(); + } + + + /* + * Backward shift on the values which come after the deleted values. + * We try to move the values closer to their ideal bucket. + */ + std::size_t icloser_bucket = std::size_t(std::distance(m_buckets.begin(), first_mutable.m_iterator)); + std::size_t ito_move_closer_value = std::size_t(std::distance(m_buckets.begin(), last_mutable.m_iterator)); + tsl_assert(ito_move_closer_value > icloser_bucket); + + const std::size_t ireturn_bucket = ito_move_closer_value - + (std::min)(ito_move_closer_value - icloser_bucket, + std::size_t(m_buckets[ito_move_closer_value].dist_from_ideal_bucket())); + + while(ito_move_closer_value < m_buckets.size() && m_buckets[ito_move_closer_value].dist_from_ideal_bucket() > 0) { + icloser_bucket = ito_move_closer_value - + (std::min)(ito_move_closer_value - icloser_bucket, + std::size_t(m_buckets[ito_move_closer_value].dist_from_ideal_bucket())); + + + tsl_assert(m_buckets[icloser_bucket].empty()); + const distance_type new_distance = distance_type(m_buckets[ito_move_closer_value].dist_from_ideal_bucket() - + (ito_move_closer_value - icloser_bucket)); + m_buckets[icloser_bucket].set_value_of_empty_bucket(new_distance, + m_buckets[ito_move_closer_value].truncated_hash(), + std::move(m_buckets[ito_move_closer_value].value())); + m_buckets[ito_move_closer_value].clear(); + + + ++icloser_bucket; + ++ito_move_closer_value; + } + + + return iterator(m_buckets.begin() + ireturn_bucket); + } + + + template + size_type erase(const K& key) { + return erase(key, hash_key(key)); + } + + template + size_type erase(const K& key, std::size_t hash) { + auto it = find(key, hash); + if(it != end()) { + erase_from_bucket(it); + + return 1; + } + else { + return 0; + } + } + + + + + + void swap(robin_hash& other) { + using std::swap; + + swap(static_cast(*this), static_cast(other)); + swap(static_cast(*this), static_cast(other)); + swap(static_cast(*this), static_cast(other)); + swap(m_buckets, other.m_buckets); + swap(m_first_or_empty_bucket, other.m_first_or_empty_bucket); + swap(m_bucket_count, other.m_bucket_count); + swap(m_nb_elements, other.m_nb_elements); + swap(m_load_threshold, other.m_load_threshold); + swap(m_max_load_factor, other.m_max_load_factor); + swap(m_grow_on_next_insert, other.m_grow_on_next_insert); + } + + + /* + * Lookup + */ + template::value>::type* = nullptr> + typename U::value_type& at(const K& key) { + return at(key, hash_key(key)); + } + + template::value>::type* = nullptr> + typename U::value_type& at(const K& key, std::size_t hash) { + return const_cast(static_cast(this)->at(key, hash)); + } + + + template::value>::type* = nullptr> + const typename U::value_type& at(const K& key) const { + return at(key, hash_key(key)); + } + + template::value>::type* = nullptr> + const typename U::value_type& at(const K& key, std::size_t hash) const { + auto it = find(key, hash); + if(it != cend()) { + return it.value(); + } + else { + TSL_THROW_OR_TERMINATE(std::out_of_range, "Couldn't find key."); + } + } + + template::value>::type* = nullptr> + typename U::value_type& operator[](K&& key) { + return try_emplace(std::forward(key)).first.value(); + } + + + template + size_type count(const K& key) const { + return count(key, hash_key(key)); + } + + template + size_type count(const K& key, std::size_t hash) const { + if(find(key, hash) != cend()) { + return 1; + } + else { + return 0; + } + } + + + template + iterator find(const K& key) { + return find_impl(key, hash_key(key)); + } + + template + iterator find(const K& key, std::size_t hash) { + return find_impl(key, hash); + } + + + template + const_iterator find(const K& key) const { + return find_impl(key, hash_key(key)); + } + + template + const_iterator find(const K& key, std::size_t hash) const { + return find_impl(key, hash); + } + + + template + std::pair equal_range(const K& key) { + return equal_range(key, hash_key(key)); + } + + template + std::pair equal_range(const K& key, std::size_t hash) { + iterator it = find(key, hash); + return std::make_pair(it, (it == end())?it:std::next(it)); + } + + + template + std::pair equal_range(const K& key) const { + return equal_range(key, hash_key(key)); + } + + template + std::pair equal_range(const K& key, std::size_t hash) const { + const_iterator it = find(key, hash); + return std::make_pair(it, (it == cend())?it:std::next(it)); + } + + /* + * Bucket interface + */ + size_type bucket_count() const { + return m_bucket_count; + } + + size_type max_bucket_count() const { + return (std::min)(GrowthPolicy::max_bucket_count(), m_buckets.max_size()); + } + + /* + * Hash policy + */ + float load_factor() const { + if(bucket_count() == 0) { + return 0; + } + + return float(m_nb_elements)/float(bucket_count()); + } + + float max_load_factor() const { + return m_max_load_factor; + } + + void max_load_factor(float ml) { + m_max_load_factor = (std::max)(0.1f, (std::min)(ml, 0.95f)); + m_load_threshold = size_type(float(bucket_count())*m_max_load_factor); + } + + void rehash(size_type count) { + count = (std::max)(count, size_type(std::ceil(float(size())/max_load_factor()))); + rehash_impl(count); + } + + void reserve(size_type count) { + rehash(size_type(std::ceil(float(count)/max_load_factor()))); + } + + /* + * Observers + */ + hasher hash_function() const { + return static_cast(*this); + } + + key_equal key_eq() const { + return static_cast(*this); + } + + + /* + * Other + */ + iterator mutable_iterator(const_iterator pos) { + return iterator(m_buckets.begin() + std::distance(m_buckets.cbegin(), pos.m_iterator)); + } + +private: + template + std::size_t hash_key(const K& key) const { + return Hash::operator()(key); + } + + template + bool compare_keys(const K1& key1, const K2& key2) const { + return KeyEqual::operator()(key1, key2); + } + + std::size_t bucket_for_hash(std::size_t hash) const { + const std::size_t bucket = GrowthPolicy::bucket_for_hash(hash); + tsl_assert(bucket < m_buckets.size() || (bucket == 0 && m_buckets.empty())); + + return bucket; + } + + template::value>::type* = nullptr> + std::size_t next_bucket(std::size_t index) const noexcept { + tsl_assert(index < bucket_count()); + + return (index + 1) & this->m_mask; + } + + template::value>::type* = nullptr> + std::size_t next_bucket(std::size_t index) const noexcept { + tsl_assert(index < bucket_count()); + + index++; + return (index != bucket_count())?index:0; + } + + + + template + iterator find_impl(const K& key, std::size_t hash) { + return mutable_iterator(static_cast(this)->find(key, hash)); + } + + template + const_iterator find_impl(const K& key, std::size_t hash) const { + std::size_t ibucket = bucket_for_hash(hash); + distance_type dist_from_ideal_bucket = 0; + + while(dist_from_ideal_bucket <= (m_first_or_empty_bucket + ibucket)->dist_from_ideal_bucket()) { + if(TSL_LIKELY((!USE_STORED_HASH_ON_LOOKUP || (m_first_or_empty_bucket + ibucket)->bucket_hash_equal(hash)) && + compare_keys(KeySelect()((m_first_or_empty_bucket + ibucket)->value()), key))) + { + return const_iterator(m_buckets.begin() + ibucket); + } + + ibucket = next_bucket(ibucket); + dist_from_ideal_bucket++; + } + + return cend(); + } + + void erase_from_bucket(iterator pos) { + pos.m_iterator->clear(); + m_nb_elements--; + + /** + * Backward shift, swap the empty bucket, previous_ibucket, with the values on its right, ibucket, + * until we cross another empty bucket or if the other bucket has a distance_from_ideal_bucket == 0. + * + * We try to move the values closer to their ideal bucket. + */ + std::size_t previous_ibucket = std::size_t(std::distance(m_buckets.begin(), pos.m_iterator)); + std::size_t ibucket = next_bucket(previous_ibucket); + + while(m_buckets[ibucket].dist_from_ideal_bucket() > 0) { + tsl_assert(m_buckets[previous_ibucket].empty()); + + const distance_type new_distance = distance_type(m_buckets[ibucket].dist_from_ideal_bucket() - 1); + m_buckets[previous_ibucket].set_value_of_empty_bucket(new_distance, m_buckets[ibucket].truncated_hash(), + std::move(m_buckets[ibucket].value())); + m_buckets[ibucket].clear(); + + previous_ibucket = ibucket; + ibucket = next_bucket(ibucket); + } + } + + template + std::pair insert_impl(const K& key, Args&&... value_type_args) { + const std::size_t hash = hash_key(key); + + std::size_t ibucket = bucket_for_hash(hash); + distance_type dist_from_ideal_bucket = 0; + + while(dist_from_ideal_bucket <= (m_first_or_empty_bucket + ibucket)->dist_from_ideal_bucket()) { + if((!USE_STORED_HASH_ON_LOOKUP || (m_first_or_empty_bucket + ibucket)->bucket_hash_equal(hash)) && + compare_keys(KeySelect()((m_first_or_empty_bucket + ibucket)->value()), key)) + { + return std::make_pair(iterator(m_buckets.begin() + ibucket), false); + } + + ibucket = next_bucket(ibucket); + dist_from_ideal_bucket++; + } + + if(grow_on_high_load()) { + ibucket = bucket_for_hash(hash); + dist_from_ideal_bucket = 0; + + while(dist_from_ideal_bucket <= (m_first_or_empty_bucket + ibucket)->dist_from_ideal_bucket()) { + ibucket = next_bucket(ibucket); + dist_from_ideal_bucket++; + } + } + + + if((m_first_or_empty_bucket + ibucket)->empty()) { + (m_first_or_empty_bucket + ibucket)->set_value_of_empty_bucket(dist_from_ideal_bucket, bucket_entry::truncate_hash(hash), + std::forward(value_type_args)...); + } + else { + insert_value(ibucket, dist_from_ideal_bucket, bucket_entry::truncate_hash(hash), + std::forward(value_type_args)...); + } + + + m_nb_elements++; + /* + * The value will be inserted in ibucket in any case, either because it was + * empty or by stealing the bucket (robin hood). + */ + return std::make_pair(iterator(m_buckets.begin() + ibucket), true); + } + + + template + void insert_value(std::size_t ibucket, distance_type dist_from_ideal_bucket, + truncated_hash_type hash, Args&&... value_type_args) + { + insert_value(ibucket, dist_from_ideal_bucket, hash, value_type(std::forward(value_type_args)...)); + } + + void insert_value(std::size_t ibucket, distance_type dist_from_ideal_bucket, + truncated_hash_type hash, value_type&& value) + { + m_buckets[ibucket].swap_with_value_in_bucket(dist_from_ideal_bucket, hash, value); + ibucket = next_bucket(ibucket); + dist_from_ideal_bucket++; + + while(!m_buckets[ibucket].empty()) { + if(dist_from_ideal_bucket > m_buckets[ibucket].dist_from_ideal_bucket()) { + if(dist_from_ideal_bucket >= REHASH_ON_HIGH_NB_PROBES__NPROBES && + load_factor() >= REHASH_ON_HIGH_NB_PROBES__MIN_LOAD_FACTOR) + { + /** + * The number of probes is really high, rehash the map on the next insert. + * Difficult to do now as rehash may throw an exception. + */ + m_grow_on_next_insert = true; + } + + m_buckets[ibucket].swap_with_value_in_bucket(dist_from_ideal_bucket, hash, value); + } + + ibucket = next_bucket(ibucket); + dist_from_ideal_bucket++; + } + + m_buckets[ibucket].set_value_of_empty_bucket(dist_from_ideal_bucket, hash, std::move(value)); + } + + + void rehash_impl(size_type count) { + robin_hash new_table(count, static_cast(*this), static_cast(*this), + get_allocator(), m_max_load_factor); + + const bool use_stored_hash = USE_STORED_HASH_ON_REHASH(new_table.bucket_count()); + for(auto& bucket: m_buckets) { + if(bucket.empty()) { + continue; + } + + const std::size_t hash = use_stored_hash?bucket.truncated_hash(): + new_table.hash_key(KeySelect()(bucket.value())); + + new_table.insert_value_on_rehash(new_table.bucket_for_hash(hash), 0, + bucket_entry::truncate_hash(hash), std::move(bucket.value())); + } + + new_table.m_nb_elements = m_nb_elements; + new_table.swap(*this); + } + + void insert_value_on_rehash(std::size_t ibucket, distance_type dist_from_ideal_bucket, + truncated_hash_type hash, value_type&& value) + { + while(true) { + if(dist_from_ideal_bucket > m_buckets[ibucket].dist_from_ideal_bucket()) { + if(m_buckets[ibucket].empty()) { + m_buckets[ibucket].set_value_of_empty_bucket(dist_from_ideal_bucket, hash, std::move(value)); + return; + } + else { + m_buckets[ibucket].swap_with_value_in_bucket(dist_from_ideal_bucket, hash, value); + } + } + + dist_from_ideal_bucket++; + ibucket = next_bucket(ibucket); + } + } + + + + /** + * Return true if the map has been rehashed. + */ + bool grow_on_high_load() { + if(m_grow_on_next_insert || size() >= m_load_threshold) { + rehash_impl(GrowthPolicy::next_bucket_count()); + m_grow_on_next_insert = false; + + return true; + } + + return false; + } + + +public: + static const size_type DEFAULT_INIT_BUCKETS_SIZE = 16; + static constexpr float DEFAULT_MAX_LOAD_FACTOR = 0.5f; + +private: + static const distance_type REHASH_ON_HIGH_NB_PROBES__NPROBES = 128; + static constexpr float REHASH_ON_HIGH_NB_PROBES__MIN_LOAD_FACTOR = 0.15f; + + + /** + * Return an always valid pointer to an static empty bucket_entry with last_bucket() == true. + */ + bucket_entry* static_empty_bucket_ptr() { + static bucket_entry empty_bucket(true); + return &empty_bucket; + } + +private: + buckets_container_type m_buckets; + + /** + * Points to m_buckets.data() if !m_buckets.empty() otherwise points to static_empty_bucket_ptr. + * This variable is useful to avoid the cost of checking if m_buckets is empty when trying + * to find an element. + */ + bucket_entry* m_first_or_empty_bucket; + + /** + * Used a lot in find, avoid the call to m_buckets.size() which is a bit slower. + */ + size_type m_bucket_count; + + size_type m_nb_elements; + + size_type m_load_threshold; + float m_max_load_factor; + + bool m_grow_on_next_insert; +}; + +} + +} + +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_map.h b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_map.h new file mode 100644 index 0000000..5958e70 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_map.h @@ -0,0 +1,668 @@ +/** + * MIT License + * + * Copyright (c) 2017 Tessil + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef TSL_ROBIN_MAP_H +#define TSL_ROBIN_MAP_H + + +#include +#include +#include +#include +#include +#include +#include "robin_hash.h" + + +namespace tsl { + + +/** + * Implementation of a hash map using open-adressing and the robin hood hashing algorithm with backward shift deletion. + * + * For operations modifying the hash map (insert, erase, rehash, ...), the strong exception guarantee + * is only guaranteed when the expression `std::is_nothrow_swappable>::value && + * std::is_nothrow_move_constructible>::value` is true, otherwise if an exception + * is thrown during the swap or the move, the hash map may end up in a undefined state. Per the standard + * a `Key` or `T` with a noexcept copy constructor and no move constructor also satisfies the + * `std::is_nothrow_move_constructible>::value` criterion (and will thus guarantee the + * strong exception for the map). + * + * When `StoreHash` is true, 32 bits of the hash are stored alongside the values. It can improve + * the performance during lookups if the `KeyEqual` function takes time (if it engenders a cache-miss for example) + * as we then compare the stored hashes before comparing the keys. When `tsl::rh::power_of_two_growth_policy` is used + * as `GrowthPolicy`, it may also speed-up the rehash process as we can avoid to recalculate the hash. + * When it is detected that storing the hash will not incur any memory penality due to alignement (i.e. + * `sizeof(tsl::detail_robin_hash::bucket_entry) == + * sizeof(tsl::detail_robin_hash::bucket_entry)`) and `tsl::rh::power_of_two_growth_policy` is + * used, the hash will be stored even if `StoreHash` is false so that we can speed-up the rehash (but it will + * not be used on lookups unless `StoreHash` is true). + * + * `GrowthPolicy` defines how the map grows and consequently how a hash value is mapped to a bucket. + * By default the map uses `tsl::rh::power_of_two_growth_policy`. This policy keeps the number of buckets + * to a power of two and uses a mask to map the hash to a bucket instead of the slow modulo. + * Other growth policies are available and you may define your own growth policy, + * check `tsl::rh::power_of_two_growth_policy` for the interface. + * + * If the destructor of `Key` or `T` throws an exception, the behaviour of the class is undefined. + * + * Iterators invalidation: + * - clear, operator=, reserve, rehash: always invalidate the iterators. + * - insert, emplace, emplace_hint, operator[]: if there is an effective insert, invalidate the iterators. + * - erase: always invalidate the iterators. + */ +template, + class KeyEqual = std::equal_to, + class Allocator = std::allocator>, + bool StoreHash = false, + class GrowthPolicy = tsl::rh::power_of_two_growth_policy<2>> +class robin_map { +private: + template + using has_is_transparent = tsl::detail_robin_hash::has_is_transparent; + + class KeySelect { + public: + using key_type = Key; + + const key_type& operator()(const std::pair& key_value) const noexcept { + return key_value.first; + } + + key_type& operator()(std::pair& key_value) noexcept { + return key_value.first; + } + }; + + class ValueSelect { + public: + using value_type = T; + + const value_type& operator()(const std::pair& key_value) const noexcept { + return key_value.second; + } + + value_type& operator()(std::pair& key_value) noexcept { + return key_value.second; + } + }; + + using ht = detail_robin_hash::robin_hash, KeySelect, ValueSelect, + Hash, KeyEqual, Allocator, StoreHash, GrowthPolicy>; + +public: + using key_type = typename ht::key_type; + using mapped_type = T; + using value_type = typename ht::value_type; + using size_type = typename ht::size_type; + using difference_type = typename ht::difference_type; + using hasher = typename ht::hasher; + using key_equal = typename ht::key_equal; + using allocator_type = typename ht::allocator_type; + using reference = typename ht::reference; + using const_reference = typename ht::const_reference; + using pointer = typename ht::pointer; + using const_pointer = typename ht::const_pointer; + using iterator = typename ht::iterator; + using const_iterator = typename ht::const_iterator; + + +public: + /* + * Constructors + */ + robin_map(): robin_map(ht::DEFAULT_INIT_BUCKETS_SIZE) { + } + + explicit robin_map(size_type bucket_count, + const Hash& hash = Hash(), + const KeyEqual& equal = KeyEqual(), + const Allocator& alloc = Allocator()): + m_ht(bucket_count, hash, equal, alloc, ht::DEFAULT_MAX_LOAD_FACTOR) + { + } + + robin_map(size_type bucket_count, + const Allocator& alloc): robin_map(bucket_count, Hash(), KeyEqual(), alloc) + { + } + + robin_map(size_type bucket_count, + const Hash& hash, + const Allocator& alloc): robin_map(bucket_count, hash, KeyEqual(), alloc) + { + } + + explicit robin_map(const Allocator& alloc): robin_map(ht::DEFAULT_INIT_BUCKETS_SIZE, alloc) { + } + + template + robin_map(InputIt first, InputIt last, + size_type bucket_count = ht::DEFAULT_INIT_BUCKETS_SIZE, + const Hash& hash = Hash(), + const KeyEqual& equal = KeyEqual(), + const Allocator& alloc = Allocator()): robin_map(bucket_count, hash, equal, alloc) + { + insert(first, last); + } + + template + robin_map(InputIt first, InputIt last, + size_type bucket_count, + const Allocator& alloc): robin_map(first, last, bucket_count, Hash(), KeyEqual(), alloc) + { + } + + template + robin_map(InputIt first, InputIt last, + size_type bucket_count, + const Hash& hash, + const Allocator& alloc): robin_map(first, last, bucket_count, hash, KeyEqual(), alloc) + { + } + + robin_map(std::initializer_list init, + size_type bucket_count = ht::DEFAULT_INIT_BUCKETS_SIZE, + const Hash& hash = Hash(), + const KeyEqual& equal = KeyEqual(), + const Allocator& alloc = Allocator()): + robin_map(init.begin(), init.end(), bucket_count, hash, equal, alloc) + { + } + + robin_map(std::initializer_list init, + size_type bucket_count, + const Allocator& alloc): + robin_map(init.begin(), init.end(), bucket_count, Hash(), KeyEqual(), alloc) + { + } + + robin_map(std::initializer_list init, + size_type bucket_count, + const Hash& hash, + const Allocator& alloc): + robin_map(init.begin(), init.end(), bucket_count, hash, KeyEqual(), alloc) + { + } + + robin_map& operator=(std::initializer_list ilist) { + m_ht.clear(); + + m_ht.reserve(ilist.size()); + m_ht.insert(ilist.begin(), ilist.end()); + + return *this; + } + + allocator_type get_allocator() const { return m_ht.get_allocator(); } + + + /* + * Iterators + */ + iterator begin() noexcept { return m_ht.begin(); } + const_iterator begin() const noexcept { return m_ht.begin(); } + const_iterator cbegin() const noexcept { return m_ht.cbegin(); } + + iterator end() noexcept { return m_ht.end(); } + const_iterator end() const noexcept { return m_ht.end(); } + const_iterator cend() const noexcept { return m_ht.cend(); } + + + /* + * Capacity + */ + bool empty() const noexcept { return m_ht.empty(); } + size_type size() const noexcept { return m_ht.size(); } + size_type max_size() const noexcept { return m_ht.max_size(); } + + /* + * Modifiers + */ + void clear() noexcept { m_ht.clear(); } + + + + std::pair insert(const value_type& value) { + return m_ht.insert(value); + } + + template::value>::type* = nullptr> + std::pair insert(P&& value) { + return m_ht.emplace(std::forward

(value)); + } + + std::pair insert(value_type&& value) { + return m_ht.insert(std::move(value)); + } + + + iterator insert(const_iterator hint, const value_type& value) { + return m_ht.insert(hint, value); + } + + template::value>::type* = nullptr> + iterator insert(const_iterator hint, P&& value) { + return m_ht.emplace_hint(hint, std::forward

(value)); + } + + iterator insert(const_iterator hint, value_type&& value) { + return m_ht.insert(hint, std::move(value)); + } + + + template + void insert(InputIt first, InputIt last) { + m_ht.insert(first, last); + } + + void insert(std::initializer_list ilist) { + m_ht.insert(ilist.begin(), ilist.end()); + } + + + + + template + std::pair insert_or_assign(const key_type& k, M&& obj) { + return m_ht.insert_or_assign(k, std::forward(obj)); + } + + template + std::pair insert_or_assign(key_type&& k, M&& obj) { + return m_ht.insert_or_assign(std::move(k), std::forward(obj)); + } + + template + iterator insert_or_assign(const_iterator hint, const key_type& k, M&& obj) { + return m_ht.insert_or_assign(hint, k, std::forward(obj)); + } + + template + iterator insert_or_assign(const_iterator hint, key_type&& k, M&& obj) { + return m_ht.insert_or_assign(hint, std::move(k), std::forward(obj)); + } + + + + /** + * Due to the way elements are stored, emplace will need to move or copy the key-value once. + * The method is equivalent to insert(value_type(std::forward(args)...)); + * + * Mainly here for compatibility with the std::unordered_map interface. + */ + template + std::pair emplace(Args&&... args) { + return m_ht.emplace(std::forward(args)...); + } + + + + /** + * Due to the way elements are stored, emplace_hint will need to move or copy the key-value once. + * The method is equivalent to insert(hint, value_type(std::forward(args)...)); + * + * Mainly here for compatibility with the std::unordered_map interface. + */ + template + iterator emplace_hint(const_iterator hint, Args&&... args) { + return m_ht.emplace_hint(hint, std::forward(args)...); + } + + + + + template + std::pair try_emplace(const key_type& k, Args&&... args) { + return m_ht.try_emplace(k, std::forward(args)...); + } + + template + std::pair try_emplace(key_type&& k, Args&&... args) { + return m_ht.try_emplace(std::move(k), std::forward(args)...); + } + + template + iterator try_emplace(const_iterator hint, const key_type& k, Args&&... args) { + return m_ht.try_emplace(hint, k, std::forward(args)...); + } + + template + iterator try_emplace(const_iterator hint, key_type&& k, Args&&... args) { + return m_ht.try_emplace(hint, std::move(k), std::forward(args)...); + } + + + + + iterator erase(iterator pos) { return m_ht.erase(pos); } + iterator erase(const_iterator pos) { return m_ht.erase(pos); } + iterator erase(const_iterator first, const_iterator last) { return m_ht.erase(first, last); } + size_type erase(const key_type& key) { return m_ht.erase(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup to the value if you already have the hash. + */ + size_type erase(const key_type& key, std::size_t precalculated_hash) { + return m_ht.erase(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef KeyEqual::is_transparent exists. + * If so, K must be hashable and comparable to Key. + */ + template::value>::type* = nullptr> + size_type erase(const K& key) { return m_ht.erase(key); } + + /** + * @copydoc erase(const K& key) + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup to the value if you already have the hash. + */ + template::value>::type* = nullptr> + size_type erase(const K& key, std::size_t precalculated_hash) { + return m_ht.erase(key, precalculated_hash); + } + + + + void swap(robin_map& other) { other.m_ht.swap(m_ht); } + + + + /* + * Lookup + */ + T& at(const Key& key) { return m_ht.at(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + T& at(const Key& key, std::size_t precalculated_hash) { return m_ht.at(key, precalculated_hash); } + + + const T& at(const Key& key) const { return m_ht.at(key); } + + /** + * @copydoc at(const Key& key, std::size_t precalculated_hash) + */ + const T& at(const Key& key, std::size_t precalculated_hash) const { return m_ht.at(key, precalculated_hash); } + + + /** + * This overload only participates in the overload resolution if the typedef KeyEqual::is_transparent exists. + * If so, K must be hashable and comparable to Key. + */ + template::value>::type* = nullptr> + T& at(const K& key) { return m_ht.at(key); } + + /** + * @copydoc at(const K& key) + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + template::value>::type* = nullptr> + T& at(const K& key, std::size_t precalculated_hash) { return m_ht.at(key, precalculated_hash); } + + + /** + * @copydoc at(const K& key) + */ + template::value>::type* = nullptr> + const T& at(const K& key) const { return m_ht.at(key); } + + /** + * @copydoc at(const K& key, std::size_t precalculated_hash) + */ + template::value>::type* = nullptr> + const T& at(const K& key, std::size_t precalculated_hash) const { return m_ht.at(key, precalculated_hash); } + + + + + T& operator[](const Key& key) { return m_ht[key]; } + T& operator[](Key&& key) { return m_ht[std::move(key)]; } + + + + + size_type count(const Key& key) const { return m_ht.count(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + size_type count(const Key& key, std::size_t precalculated_hash) const { + return m_ht.count(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef KeyEqual::is_transparent exists. + * If so, K must be hashable and comparable to Key. + */ + template::value>::type* = nullptr> + size_type count(const K& key) const { return m_ht.count(key); } + + /** + * @copydoc count(const K& key) const + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + template::value>::type* = nullptr> + size_type count(const K& key, std::size_t precalculated_hash) const { return m_ht.count(key, precalculated_hash); } + + + + + iterator find(const Key& key) { return m_ht.find(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + iterator find(const Key& key, std::size_t precalculated_hash) { return m_ht.find(key, precalculated_hash); } + + const_iterator find(const Key& key) const { return m_ht.find(key); } + + /** + * @copydoc find(const Key& key, std::size_t precalculated_hash) + */ + const_iterator find(const Key& key, std::size_t precalculated_hash) const { + return m_ht.find(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef KeyEqual::is_transparent exists. + * If so, K must be hashable and comparable to Key. + */ + template::value>::type* = nullptr> + iterator find(const K& key) { return m_ht.find(key); } + + /** + * @copydoc find(const K& key) + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + template::value>::type* = nullptr> + iterator find(const K& key, std::size_t precalculated_hash) { return m_ht.find(key, precalculated_hash); } + + /** + * @copydoc find(const K& key) + */ + template::value>::type* = nullptr> + const_iterator find(const K& key) const { return m_ht.find(key); } + + /** + * @copydoc find(const K& key) + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + template::value>::type* = nullptr> + const_iterator find(const K& key, std::size_t precalculated_hash) const { + return m_ht.find(key, precalculated_hash); + } + + + + + std::pair equal_range(const Key& key) { return m_ht.equal_range(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + std::pair equal_range(const Key& key, std::size_t precalculated_hash) { + return m_ht.equal_range(key, precalculated_hash); + } + + std::pair equal_range(const Key& key) const { return m_ht.equal_range(key); } + + /** + * @copydoc equal_range(const Key& key, std::size_t precalculated_hash) + */ + std::pair equal_range(const Key& key, std::size_t precalculated_hash) const { + return m_ht.equal_range(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef KeyEqual::is_transparent exists. + * If so, K must be hashable and comparable to Key. + */ + template::value>::type* = nullptr> + std::pair equal_range(const K& key) { return m_ht.equal_range(key); } + + + /** + * @copydoc equal_range(const K& key) + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + template::value>::type* = nullptr> + std::pair equal_range(const K& key, std::size_t precalculated_hash) { + return m_ht.equal_range(key, precalculated_hash); + } + + /** + * @copydoc equal_range(const K& key) + */ + template::value>::type* = nullptr> + std::pair equal_range(const K& key) const { return m_ht.equal_range(key); } + + /** + * @copydoc equal_range(const K& key, std::size_t precalculated_hash) + */ + template::value>::type* = nullptr> + std::pair equal_range(const K& key, std::size_t precalculated_hash) const { + return m_ht.equal_range(key, precalculated_hash); + } + + + + + /* + * Bucket interface + */ + size_type bucket_count() const { return m_ht.bucket_count(); } + size_type max_bucket_count() const { return m_ht.max_bucket_count(); } + + + /* + * Hash policy + */ + float load_factor() const { return m_ht.load_factor(); } + float max_load_factor() const { return m_ht.max_load_factor(); } + void max_load_factor(float ml) { m_ht.max_load_factor(ml); } + + void rehash(size_type count) { m_ht.rehash(count); } + void reserve(size_type count) { m_ht.reserve(count); } + + + /* + * Observers + */ + hasher hash_function() const { return m_ht.hash_function(); } + key_equal key_eq() const { return m_ht.key_eq(); } + + /* + * Other + */ + + /** + * Convert a const_iterator to an iterator. + */ + iterator mutable_iterator(const_iterator pos) { + return m_ht.mutable_iterator(pos); + } + + friend bool operator==(const robin_map& lhs, const robin_map& rhs) { + if(lhs.size() != rhs.size()) { + return false; + } + + for(const auto& element_lhs: lhs) { + const auto it_element_rhs = rhs.find(element_lhs.first); + if(it_element_rhs == rhs.cend() || element_lhs.second != it_element_rhs->second) { + return false; + } + } + + return true; + } + + friend bool operator!=(const robin_map& lhs, const robin_map& rhs) { + return !operator==(lhs, rhs); + } + + friend void swap(robin_map& lhs, robin_map& rhs) { + lhs.swap(rhs); + } + +private: + ht m_ht; +}; + + +/** + * Same as `tsl::robin_map`. + */ +template, + class KeyEqual = std::equal_to, + class Allocator = std::allocator>, + bool StoreHash = false> +using robin_pg_map = robin_map; + +} // end namespace tsl + +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_set.h b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_set.h new file mode 100644 index 0000000..4e4667e --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/robin_set.h @@ -0,0 +1,535 @@ +/** + * MIT License + * + * Copyright (c) 2017 Tessil + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef TSL_ROBIN_SET_H +#define TSL_ROBIN_SET_H + + +#include +#include +#include +#include +#include +#include +#include "robin_hash.h" + + +namespace tsl { + + +/** + * Implementation of a hash set using open-adressing and the robin hood hashing algorithm with backward shift deletion. + * + * For operations modifying the hash set (insert, erase, rehash, ...), the strong exception guarantee + * is only guaranteed when the expression `std::is_nothrow_swappable::value && + * std::is_nothrow_move_constructible::value` is true, otherwise if an exception + * is thrown during the swap or the move, the hash set may end up in a undefined state. Per the standard + * a `Key` with a noexcept copy constructor and no move constructor also satisfies the + * `std::is_nothrow_move_constructible::value` criterion (and will thus guarantee the + * strong exception for the set). + * + * When `StoreHash` is true, 32 bits of the hash are stored alongside the values. It can improve + * the performance during lookups if the `KeyEqual` function takes time (or engenders a cache-miss for example) + * as we then compare the stored hashes before comparing the keys. When `tsl::rh::power_of_two_growth_policy` is used + * as `GrowthPolicy`, it may also speed-up the rehash process as we can avoid to recalculate the hash. + * When it is detected that storing the hash will not incur any memory penality due to alignement (i.e. + * `sizeof(tsl::detail_robin_hash::bucket_entry) == + * sizeof(tsl::detail_robin_hash::bucket_entry)`) and `tsl::rh::power_of_two_growth_policy` is + * used, the hash will be stored even if `StoreHash` is false so that we can speed-up the rehash (but it will + * not be used on lookups unless `StoreHash` is true). + * + * `GrowthPolicy` defines how the set grows and consequently how a hash value is mapped to a bucket. + * By default the set uses `tsl::rh::power_of_two_growth_policy`. This policy keeps the number of buckets + * to a power of two and uses a mask to set the hash to a bucket instead of the slow modulo. + * Other growth policies are available and you may define your own growth policy, + * check `tsl::rh::power_of_two_growth_policy` for the interface. + * + * If the destructor of `Key` throws an exception, the behaviour of the class is undefined. + * + * Iterators invalidation: + * - clear, operator=, reserve, rehash: always invalidate the iterators. + * - insert, emplace, emplace_hint, operator[]: if there is an effective insert, invalidate the iterators. + * - erase: always invalidate the iterators. + */ +template, + class KeyEqual = std::equal_to, + class Allocator = std::allocator, + bool StoreHash = false, + class GrowthPolicy = tsl::rh::power_of_two_growth_policy<2>> +class robin_set { +private: + template + using has_is_transparent = tsl::detail_robin_hash::has_is_transparent; + + class KeySelect { + public: + using key_type = Key; + + const key_type& operator()(const Key& key) const noexcept { + return key; + } + + key_type& operator()(Key& key) noexcept { + return key; + } + }; + + using ht = detail_robin_hash::robin_hash; + +public: + using key_type = typename ht::key_type; + using value_type = typename ht::value_type; + using size_type = typename ht::size_type; + using difference_type = typename ht::difference_type; + using hasher = typename ht::hasher; + using key_equal = typename ht::key_equal; + using allocator_type = typename ht::allocator_type; + using reference = typename ht::reference; + using const_reference = typename ht::const_reference; + using pointer = typename ht::pointer; + using const_pointer = typename ht::const_pointer; + using iterator = typename ht::iterator; + using const_iterator = typename ht::const_iterator; + + + /* + * Constructors + */ + robin_set(): robin_set(ht::DEFAULT_INIT_BUCKETS_SIZE) { + } + + explicit robin_set(size_type bucket_count, + const Hash& hash = Hash(), + const KeyEqual& equal = KeyEqual(), + const Allocator& alloc = Allocator()): + m_ht(bucket_count, hash, equal, alloc, ht::DEFAULT_MAX_LOAD_FACTOR) + { + } + + robin_set(size_type bucket_count, + const Allocator& alloc): robin_set(bucket_count, Hash(), KeyEqual(), alloc) + { + } + + robin_set(size_type bucket_count, + const Hash& hash, + const Allocator& alloc): robin_set(bucket_count, hash, KeyEqual(), alloc) + { + } + + explicit robin_set(const Allocator& alloc): robin_set(ht::DEFAULT_INIT_BUCKETS_SIZE, alloc) { + } + + template + robin_set(InputIt first, InputIt last, + size_type bucket_count = ht::DEFAULT_INIT_BUCKETS_SIZE, + const Hash& hash = Hash(), + const KeyEqual& equal = KeyEqual(), + const Allocator& alloc = Allocator()): robin_set(bucket_count, hash, equal, alloc) + { + insert(first, last); + } + + template + robin_set(InputIt first, InputIt last, + size_type bucket_count, + const Allocator& alloc): robin_set(first, last, bucket_count, Hash(), KeyEqual(), alloc) + { + } + + template + robin_set(InputIt first, InputIt last, + size_type bucket_count, + const Hash& hash, + const Allocator& alloc): robin_set(first, last, bucket_count, hash, KeyEqual(), alloc) + { + } + + robin_set(std::initializer_list init, + size_type bucket_count = ht::DEFAULT_INIT_BUCKETS_SIZE, + const Hash& hash = Hash(), + const KeyEqual& equal = KeyEqual(), + const Allocator& alloc = Allocator()): + robin_set(init.begin(), init.end(), bucket_count, hash, equal, alloc) + { + } + + robin_set(std::initializer_list init, + size_type bucket_count, + const Allocator& alloc): + robin_set(init.begin(), init.end(), bucket_count, Hash(), KeyEqual(), alloc) + { + } + + robin_set(std::initializer_list init, + size_type bucket_count, + const Hash& hash, + const Allocator& alloc): + robin_set(init.begin(), init.end(), bucket_count, hash, KeyEqual(), alloc) + { + } + + + robin_set& operator=(std::initializer_list ilist) { + m_ht.clear(); + + m_ht.reserve(ilist.size()); + m_ht.insert(ilist.begin(), ilist.end()); + + return *this; + } + + allocator_type get_allocator() const { return m_ht.get_allocator(); } + + + /* + * Iterators + */ + iterator begin() noexcept { return m_ht.begin(); } + const_iterator begin() const noexcept { return m_ht.begin(); } + const_iterator cbegin() const noexcept { return m_ht.cbegin(); } + + iterator end() noexcept { return m_ht.end(); } + const_iterator end() const noexcept { return m_ht.end(); } + const_iterator cend() const noexcept { return m_ht.cend(); } + + + /* + * Capacity + */ + bool empty() const noexcept { return m_ht.empty(); } + size_type size() const noexcept { return m_ht.size(); } + size_type max_size() const noexcept { return m_ht.max_size(); } + + /* + * Modifiers + */ + void clear() noexcept { m_ht.clear(); } + + + + + std::pair insert(const value_type& value) { + return m_ht.insert(value); + } + + std::pair insert(value_type&& value) { + return m_ht.insert(std::move(value)); + } + + iterator insert(const_iterator hint, const value_type& value) { + return m_ht.insert(hint, value); + } + + iterator insert(const_iterator hint, value_type&& value) { + return m_ht.insert(hint, std::move(value)); + } + + template + void insert(InputIt first, InputIt last) { + m_ht.insert(first, last); + } + + void insert(std::initializer_list ilist) { + m_ht.insert(ilist.begin(), ilist.end()); + } + + + + + /** + * Due to the way elements are stored, emplace will need to move or copy the key-value once. + * The method is equivalent to insert(value_type(std::forward(args)...)); + * + * Mainly here for compatibility with the std::unordered_map interface. + */ + template + std::pair emplace(Args&&... args) { + return m_ht.emplace(std::forward(args)...); + } + + + + /** + * Due to the way elements are stored, emplace_hint will need to move or copy the key-value once. + * The method is equivalent to insert(hint, value_type(std::forward(args)...)); + * + * Mainly here for compatibility with the std::unordered_map interface. + */ + template + iterator emplace_hint(const_iterator hint, Args&&... args) { + return m_ht.emplace_hint(hint, std::forward(args)...); + } + + + + iterator erase(iterator pos) { return m_ht.erase(pos); } + iterator erase(const_iterator pos) { return m_ht.erase(pos); } + iterator erase(const_iterator first, const_iterator last) { return m_ht.erase(first, last); } + size_type erase(const key_type& key) { return m_ht.erase(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup to the value if you already have the hash. + */ + size_type erase(const key_type& key, std::size_t precalculated_hash) { + return m_ht.erase(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef KeyEqual::is_transparent exists. + * If so, K must be hashable and comparable to Key. + */ + template::value>::type* = nullptr> + size_type erase(const K& key) { return m_ht.erase(key); } + + /** + * @copydoc erase(const K& key) + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup to the value if you already have the hash. + */ + template::value>::type* = nullptr> + size_type erase(const K& key, std::size_t precalculated_hash) { + return m_ht.erase(key, precalculated_hash); + } + + + + void swap(robin_set& other) { other.m_ht.swap(m_ht); } + + + + /* + * Lookup + */ + size_type count(const Key& key) const { return m_ht.count(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + size_type count(const Key& key, std::size_t precalculated_hash) const { return m_ht.count(key, precalculated_hash); } + + /** + * This overload only participates in the overload resolution if the typedef KeyEqual::is_transparent exists. + * If so, K must be hashable and comparable to Key. + */ + template::value>::type* = nullptr> + size_type count(const K& key) const { return m_ht.count(key); } + + /** + * @copydoc count(const K& key) const + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + template::value>::type* = nullptr> + size_type count(const K& key, std::size_t precalculated_hash) const { return m_ht.count(key, precalculated_hash); } + + + + + iterator find(const Key& key) { return m_ht.find(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + iterator find(const Key& key, std::size_t precalculated_hash) { return m_ht.find(key, precalculated_hash); } + + const_iterator find(const Key& key) const { return m_ht.find(key); } + + /** + * @copydoc find(const Key& key, std::size_t precalculated_hash) + */ + const_iterator find(const Key& key, std::size_t precalculated_hash) const { return m_ht.find(key, precalculated_hash); } + + /** + * This overload only participates in the overload resolution if the typedef KeyEqual::is_transparent exists. + * If so, K must be hashable and comparable to Key. + */ + template::value>::type* = nullptr> + iterator find(const K& key) { return m_ht.find(key); } + + /** + * @copydoc find(const K& key) + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + template::value>::type* = nullptr> + iterator find(const K& key, std::size_t precalculated_hash) { return m_ht.find(key, precalculated_hash); } + + /** + * @copydoc find(const K& key) + */ + template::value>::type* = nullptr> + const_iterator find(const K& key) const { return m_ht.find(key); } + + /** + * @copydoc find(const K& key) + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + template::value>::type* = nullptr> + const_iterator find(const K& key, std::size_t precalculated_hash) const { return m_ht.find(key, precalculated_hash); } + + + + + std::pair equal_range(const Key& key) { return m_ht.equal_range(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + std::pair equal_range(const Key& key, std::size_t precalculated_hash) { + return m_ht.equal_range(key, precalculated_hash); + } + + std::pair equal_range(const Key& key) const { return m_ht.equal_range(key); } + + /** + * @copydoc equal_range(const Key& key, std::size_t precalculated_hash) + */ + std::pair equal_range(const Key& key, std::size_t precalculated_hash) const { + return m_ht.equal_range(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef KeyEqual::is_transparent exists. + * If so, K must be hashable and comparable to Key. + */ + template::value>::type* = nullptr> + std::pair equal_range(const K& key) { return m_ht.equal_range(key); } + + /** + * @copydoc equal_range(const K& key) + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The hash value should be the same + * as hash_function()(key). Usefull to speed-up the lookup if you already have the hash. + */ + template::value>::type* = nullptr> + std::pair equal_range(const K& key, std::size_t precalculated_hash) { + return m_ht.equal_range(key, precalculated_hash); + } + + /** + * @copydoc equal_range(const K& key) + */ + template::value>::type* = nullptr> + std::pair equal_range(const K& key) const { return m_ht.equal_range(key); } + + /** + * @copydoc equal_range(const K& key, std::size_t precalculated_hash) + */ + template::value>::type* = nullptr> + std::pair equal_range(const K& key, std::size_t precalculated_hash) const { + return m_ht.equal_range(key, precalculated_hash); + } + + + + + /* + * Bucket interface + */ + size_type bucket_count() const { return m_ht.bucket_count(); } + size_type max_bucket_count() const { return m_ht.max_bucket_count(); } + + + /* + * Hash policy + */ + float load_factor() const { return m_ht.load_factor(); } + float max_load_factor() const { return m_ht.max_load_factor(); } + void max_load_factor(float ml) { m_ht.max_load_factor(ml); } + + void rehash(size_type count) { m_ht.rehash(count); } + void reserve(size_type count) { m_ht.reserve(count); } + + + /* + * Observers + */ + hasher hash_function() const { return m_ht.hash_function(); } + key_equal key_eq() const { return m_ht.key_eq(); } + + + /* + * Other + */ + + /** + * Convert a const_iterator to an iterator. + */ + iterator mutable_iterator(const_iterator pos) { + return m_ht.mutable_iterator(pos); + } + + friend bool operator==(const robin_set& lhs, const robin_set& rhs) { + if(lhs.size() != rhs.size()) { + return false; + } + + for(const auto& element_lhs: lhs) { + const auto it_element_rhs = rhs.find(element_lhs); + if(it_element_rhs == rhs.cend()) { + return false; + } + } + + return true; + } + + friend bool operator!=(const robin_set& lhs, const robin_set& rhs) { + return !operator==(lhs, rhs); + } + + friend void swap(robin_set& lhs, robin_set& rhs) { + lhs.swap(rhs); + } + +private: + ht m_ht; +}; + + +/** + * Same as `tsl::robin_set`. + */ +template, + class KeyEqual = std::equal_to, + class Allocator = std::allocator, + bool StoreHash = false> +using robin_pg_set = robin_set; + +} // end namespace tsl + +#endif + diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_growth_policy.h b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_growth_policy.h new file mode 100644 index 0000000..d73aaaf --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_growth_policy.h @@ -0,0 +1,301 @@ +/** + * MIT License + * + * Copyright (c) 2017 Thibaut Goetghebuer-Planchon + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef TSL_SPARSE_GROWTH_POLICY_H +#define TSL_SPARSE_GROWTH_POLICY_H + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace tsl { +namespace sh { + +/** + * Grow the hash table by a factor of GrowthFactor keeping the bucket count to a + * power of two. It allows the table to use a mask operation instead of a modulo + * operation to map a hash to a bucket. + * + * GrowthFactor must be a power of two >= 2. + */ +template +class power_of_two_growth_policy { + public: + /** + * Called on the hash table creation and on rehash. The number of buckets for + * the table is passed in parameter. This number is a minimum, the policy may + * update this value with a higher value if needed (but not lower). + * + * If 0 is given, min_bucket_count_in_out must still be 0 after the policy + * creation and bucket_for_hash must always return 0 in this case. + */ + explicit power_of_two_growth_policy(std::size_t &min_bucket_count_in_out) { + if (min_bucket_count_in_out > max_bucket_count()) { + throw std::length_error("The hash table exceeds its maximum size."); + } + + if (min_bucket_count_in_out > 0) { + min_bucket_count_in_out = + round_up_to_power_of_two(min_bucket_count_in_out); + m_mask = min_bucket_count_in_out - 1; + } else { + m_mask = 0; + } + } + + /** + * Return the bucket [0, bucket_count()) to which the hash belongs. + * If bucket_count() is 0, it must always return 0. + */ + std::size_t bucket_for_hash(std::size_t hash) const noexcept { + return hash & m_mask; + } + + /** + * Return the number of buckets that should be used on next growth. + */ + std::size_t next_bucket_count() const { + if ((m_mask + 1) > max_bucket_count() / GrowthFactor) { + throw std::length_error("The hash table exceeds its maximum size."); + } + + return (m_mask + 1) * GrowthFactor; + } + + /** + * Return the maximum number of buckets supported by the policy. + */ + std::size_t max_bucket_count() const { + // Largest power of two. + return (std::numeric_limits::max() / 2) + 1; + } + + /** + * Reset the growth policy as if it was created with a bucket count of 0. + * After a clear, the policy must always return 0 when bucket_for_hash is + * called. + */ + void clear() noexcept { m_mask = 0; } + + private: + static std::size_t round_up_to_power_of_two(std::size_t value) { + if (is_power_of_two(value)) { + return value; + } + + if (value == 0) { + return 1; + } + + --value; + for (std::size_t i = 1; i < sizeof(std::size_t) * CHAR_BIT; i *= 2) { + value |= value >> i; + } + + return value + 1; + } + + static constexpr bool is_power_of_two(std::size_t value) { + return value != 0 && (value & (value - 1)) == 0; + } + + protected: + static_assert(is_power_of_two(GrowthFactor) && GrowthFactor >= 2, + "GrowthFactor must be a power of two >= 2."); + + std::size_t m_mask; +}; + +/** + * Grow the hash table by GrowthFactor::num / GrowthFactor::den and use a modulo + * to map a hash to a bucket. Slower but it can be useful if you want a slower + * growth. + */ +template > +class mod_growth_policy { + public: + explicit mod_growth_policy(std::size_t &min_bucket_count_in_out) { + if (min_bucket_count_in_out > max_bucket_count()) { + throw std::length_error("The hash table exceeds its maximum size."); + } + + if (min_bucket_count_in_out > 0) { + m_mod = min_bucket_count_in_out; + } else { + m_mod = 1; + } + } + + std::size_t bucket_for_hash(std::size_t hash) const noexcept { + return hash % m_mod; + } + + std::size_t next_bucket_count() const { + if (m_mod == max_bucket_count()) { + throw std::length_error("The hash table exceeds its maximum size."); + } + + const double next_bucket_count = + std::ceil(double(m_mod) * REHASH_SIZE_MULTIPLICATION_FACTOR); + if (!std::isnormal(next_bucket_count)) { + throw std::length_error("The hash table exceeds its maximum size."); + } + + if (next_bucket_count > double(max_bucket_count())) { + return max_bucket_count(); + } else { + return std::size_t(next_bucket_count); + } + } + + std::size_t max_bucket_count() const { return MAX_BUCKET_COUNT; } + + void clear() noexcept { m_mod = 1; } + + private: + static constexpr double REHASH_SIZE_MULTIPLICATION_FACTOR = + 1.0 * GrowthFactor::num / GrowthFactor::den; + static const std::size_t MAX_BUCKET_COUNT = + std::size_t(double(std::numeric_limits::max() / + REHASH_SIZE_MULTIPLICATION_FACTOR)); + + static_assert(REHASH_SIZE_MULTIPLICATION_FACTOR >= 1.1, + "Growth factor should be >= 1.1."); + + std::size_t m_mod; +}; + +/** + * Grow the hash table by using prime numbers as bucket count. Slower than + * tsl::sh::power_of_two_growth_policy in general but will probably distribute + * the values around better in the buckets with a poor hash function. + * + * To allow the compiler to optimize the modulo operation, a lookup table is + * used with constant primes numbers. + * + * With a switch the code would look like: + * \code + * switch(iprime) { // iprime is the current prime of the hash table + * case 0: hash % 5ul; + * break; + * case 1: hash % 17ul; + * break; + * case 2: hash % 29ul; + * break; + * ... + * } + * \endcode + * + * Due to the constant variable in the modulo the compiler is able to optimize + * the operation by a series of multiplications, substractions and shifts. + * + * The 'hash % 5' could become something like 'hash - (hash * 0xCCCCCCCD) >> 34) + * * 5' in a 64 bits environment. + */ +class prime_growth_policy { + public: + explicit prime_growth_policy(std::size_t &min_bucket_count_in_out) { + auto it_prime = std::lower_bound(primes().begin(), primes().end(), + min_bucket_count_in_out); + if (it_prime == primes().end()) { + throw std::length_error("The hash table exceeds its maximum size."); + } + + m_iprime = + static_cast(std::distance(primes().begin(), it_prime)); + if (min_bucket_count_in_out > 0) { + min_bucket_count_in_out = *it_prime; + } else { + min_bucket_count_in_out = 0; + } + } + + std::size_t bucket_for_hash(std::size_t hash) const noexcept { + return mod_prime()[m_iprime](hash); + } + + std::size_t next_bucket_count() const { + if (m_iprime + 1 >= primes().size()) { + throw std::length_error("The hash table exceeds its maximum size."); + } + + return primes()[m_iprime + 1]; + } + + std::size_t max_bucket_count() const { return primes().back(); } + + void clear() noexcept { m_iprime = 0; } + + private: + static const std::array &primes() { + static const std::array PRIMES = { + {1ul, 5ul, 17ul, 29ul, 37ul, + 53ul, 67ul, 79ul, 97ul, 131ul, + 193ul, 257ul, 389ul, 521ul, 769ul, + 1031ul, 1543ul, 2053ul, 3079ul, 6151ul, + 12289ul, 24593ul, 49157ul, 98317ul, 196613ul, + 393241ul, 786433ul, 1572869ul, 3145739ul, 6291469ul, + 12582917ul, 25165843ul, 50331653ul, 100663319ul, 201326611ul, + 402653189ul, 805306457ul, 1610612741ul, 3221225473ul, 4294967291ul}}; + + static_assert( + std::numeric_limits::max() >= PRIMES.size(), + "The type of m_iprime is not big enough."); + + return PRIMES; + } + + static const std::array &mod_prime() { + // MOD_PRIME[iprime](hash) returns hash % PRIMES[iprime]. This table allows + // for faster modulo as the compiler can optimize the modulo code better + // with a constant known at the compilation. + static const std::array MOD_PRIME = { + {&mod<0>, &mod<1>, &mod<2>, &mod<3>, &mod<4>, &mod<5>, &mod<6>, + &mod<7>, &mod<8>, &mod<9>, &mod<10>, &mod<11>, &mod<12>, &mod<13>, + &mod<14>, &mod<15>, &mod<16>, &mod<17>, &mod<18>, &mod<19>, &mod<20>, + &mod<21>, &mod<22>, &mod<23>, &mod<24>, &mod<25>, &mod<26>, &mod<27>, + &mod<28>, &mod<29>, &mod<30>, &mod<31>, &mod<32>, &mod<33>, &mod<34>, + &mod<35>, &mod<36>, &mod<37>, &mod<38>, &mod<39>}}; + + return MOD_PRIME; + } + + template + static std::size_t mod(std::size_t hash) { + return hash % primes()[IPrime]; + } + + private: + unsigned int m_iprime; +}; + +} // namespace sh +} // namespace tsl + +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_hash.h b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_hash.h new file mode 100644 index 0000000..e2115b4 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_hash.h @@ -0,0 +1,2215 @@ +/** + * MIT License + * + * Copyright (c) 2017 Thibaut Goetghebuer-Planchon + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef TSL_SPARSE_HASH_H +#define TSL_SPARSE_HASH_H + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "sparse_growth_policy.h" + +#ifdef __INTEL_COMPILER +#include // For _popcnt32 and _popcnt64 +#endif + +#ifdef _MSC_VER +#include // For __cpuid, __popcnt and __popcnt64 +#endif + +#ifdef TSL_DEBUG +#define tsl_sh_assert(expr) assert(expr) +#else +#define tsl_sh_assert(expr) (static_cast(0)) +#endif + +namespace tsl { + +namespace sh { +enum class probing { linear, quadratic }; + +enum class exception_safety { basic, strong }; + +enum class sparsity { high, medium, low }; +} // namespace sh + +namespace detail_popcount { +/** + * Define the popcount(ll) methods and pick-up the best depending on the + * compiler. + */ + +// From Wikipedia: https://en.wikipedia.org/wiki/Hamming_weight +inline int fallback_popcountll(unsigned long long int x) { + static_assert( + sizeof(unsigned long long int) == sizeof(std::uint64_t), + "sizeof(unsigned long long int) must be equal to sizeof(std::uint64_t). " + "Open a feature request if you need support for a platform where it " + "isn't the case."); + + const std::uint64_t m1 = 0x5555555555555555ull; + const std::uint64_t m2 = 0x3333333333333333ull; + const std::uint64_t m4 = 0x0f0f0f0f0f0f0f0full; + const std::uint64_t h01 = 0x0101010101010101ull; + + x -= (x >> 1ull) & m1; + x = (x & m2) + ((x >> 2ull) & m2); + x = (x + (x >> 4ull)) & m4; + return static_cast((x * h01) >> (64ull - 8ull)); +} + +inline int fallback_popcount(unsigned int x) { + static_assert(sizeof(unsigned int) == sizeof(std::uint32_t) || + sizeof(unsigned int) == sizeof(std::uint64_t), + "sizeof(unsigned int) must be equal to sizeof(std::uint32_t) " + "or sizeof(std::uint64_t). " + "Open a feature request if you need support for a platform " + "where it isn't the case."); + + if (sizeof(unsigned int) == sizeof(std::uint32_t)) { + const std::uint32_t m1 = 0x55555555; + const std::uint32_t m2 = 0x33333333; + const std::uint32_t m4 = 0x0f0f0f0f; + const std::uint32_t h01 = 0x01010101; + + x -= (x >> 1) & m1; + x = (x & m2) + ((x >> 2) & m2); + x = (x + (x >> 4)) & m4; + return static_cast((x * h01) >> (32 - 8)); + } else { + return fallback_popcountll(x); + } +} + +#if defined(__clang__) || defined(__GNUC__) +inline int popcountll(unsigned long long int value) { + return __builtin_popcountll(value); +} + +inline int popcount(unsigned int value) { return __builtin_popcount(value); } + +#elif defined(_MSC_VER) +/** + * We need to check for popcount support at runtime on Windows with __cpuid + * See https://msdn.microsoft.com/en-us/library/bb385231.aspx + */ +inline bool has_popcount_support() { + int cpu_infos[4]; + __cpuid(cpu_infos, 1); + return (cpu_infos[2] & (1 << 23)) != 0; +} + +inline int popcountll(unsigned long long int value) { +#ifdef _WIN64 + static_assert( + sizeof(unsigned long long int) == sizeof(std::int64_t), + "sizeof(unsigned long long int) must be equal to sizeof(std::int64_t). "); + + static const bool has_popcount = has_popcount_support(); + return has_popcount + ? static_cast(__popcnt64(static_cast(value))) + : fallback_popcountll(value); +#else + return fallback_popcountll(value); +#endif +} + +inline int popcount(unsigned int value) { + static_assert(sizeof(unsigned int) == sizeof(std::int32_t), + "sizeof(unsigned int) must be equal to sizeof(std::int32_t). "); + + static const bool has_popcount = has_popcount_support(); + return has_popcount + ? static_cast(__popcnt(static_cast(value))) + : fallback_popcount(value); +} + +#elif defined(__INTEL_COMPILER) +inline int popcountll(unsigned long long int value) { + static_assert(sizeof(unsigned long long int) == sizeof(__int64), ""); + return _popcnt64(static_cast<__int64>(value)); +} + +inline int popcount(unsigned int value) { + return _popcnt32(static_cast(value)); +} + +#else +inline int popcountll(unsigned long long int x) { + return fallback_popcountll(x); +} + +inline int popcount(unsigned int x) { return fallback_popcount(x); } + +#endif +} // namespace detail_popcount + +namespace detail_sparse_hash { + +template +struct make_void { + using type = void; +}; + +template +struct has_is_transparent : std::false_type {}; + +template +struct has_is_transparent::type> + : std::true_type {}; + +template +struct is_power_of_two_policy : std::false_type {}; + +template +struct is_power_of_two_policy> + : std::true_type {}; + +inline constexpr bool is_power_of_two(std::size_t value) { + return value != 0 && (value & (value - 1)) == 0; +} + +inline std::size_t round_up_to_power_of_two(std::size_t value) { + if (is_power_of_two(value)) { + return value; + } + + if (value == 0) { + return 1; + } + + --value; + for (std::size_t i = 1; i < sizeof(std::size_t) * CHAR_BIT; i *= 2) { + value |= value >> i; + } + + return value + 1; +} + +template +static T numeric_cast(U value, + const char *error_message = "numeric_cast() failed.") { + T ret = static_cast(value); + if (static_cast(ret) != value) { + throw std::runtime_error(error_message); + } + + const bool is_same_signedness = + (std::is_unsigned::value && std::is_unsigned::value) || + (std::is_signed::value && std::is_signed::value); + if (!is_same_signedness && (ret < T{}) != (value < U{})) { + throw std::runtime_error(error_message); + } + + return ret; +} + +/** + * Fixed size type used to represent size_type values on serialization. Need to + * be big enough to represent a std::size_t on 32 and 64 bits platforms, and + * must be the same size on both platforms. + */ +using slz_size_type = std::uint64_t; +static_assert(std::numeric_limits::max() >= + std::numeric_limits::max(), + "slz_size_type must be >= std::size_t"); + +template +static T deserialize_value(Deserializer &deserializer) { + // MSVC < 2017 is not conformant, circumvent the problem by removing the + // template keyword +#if defined(_MSC_VER) && _MSC_VER < 1910 + return deserializer.Deserializer::operator()(); +#else + return deserializer.Deserializer::template operator()(); +#endif +} + +/** + * WARNING: the sparse_array class doesn't free the ressources allocated through + * the allocator passed in parameter in each method. You have to manually call + * `clear(Allocator&)` when you don't need a sparse_array object anymore. + * + * The reason is that the sparse_array doesn't store the allocator to avoid + * wasting space in each sparse_array when the allocator has a size > 0. It only + * allocates/deallocates objects with the allocator that is passed in parameter. + * + * + * + * Index denotes a value between [0, BITMAP_NB_BITS), it is an index similar to + * std::vector. Offset denotes the real position in `m_values` corresponding to + * an index. + * + * We are using raw pointers instead of std::vector to avoid loosing + * 2*sizeof(size_t) bytes to store the capacity and size of the vector in each + * sparse_array. We know we can only store up to BITMAP_NB_BITS elements in the + * array, we don't need such big types. + * + * + * T must be nothrow move constructible and/or copy constructible. + * Behaviour is undefined if the destructor of T throws an exception. + * + * See https://smerity.com/articles/2015/google_sparsehash.html for details on + * the idea behinds the implementation. + * + * TODO Check to use std::realloc and std::memmove when possible + */ +template +class sparse_array { + public: + using value_type = T; + using size_type = std::uint_least8_t; + using allocator_type = Allocator; + using iterator = value_type *; + using const_iterator = const value_type *; + + private: + static const size_type CAPACITY_GROWTH_STEP = + (Sparsity == tsl::sh::sparsity::high) ? 2 + : (Sparsity == tsl::sh::sparsity::medium) + ? 4 + : 8; // (Sparsity == tsl::sh::sparsity::low) + + /** + * Bitmap size configuration. + * Use 32 bits for the bitmap on 32-bits or less environnement as popcount on + * 64 bits numbers is slow on these environnement. Use 64 bits bitmap + * otherwise. + */ +#if SIZE_MAX <= UINT32_MAX + using bitmap_type = std::uint_least32_t; + static const std::size_t BITMAP_NB_BITS = 32; + static const std::size_t BUCKET_SHIFT = 5; +#else + using bitmap_type = std::uint_least64_t; + static const std::size_t BITMAP_NB_BITS = 64; + static const std::size_t BUCKET_SHIFT = 6; +#endif + + static const std::size_t BUCKET_MASK = BITMAP_NB_BITS - 1; + + static_assert(is_power_of_two(BITMAP_NB_BITS), + "BITMAP_NB_BITS must be a power of two."); + static_assert(std::numeric_limits::digits >= BITMAP_NB_BITS, + "bitmap_type must be able to hold at least BITMAP_NB_BITS."); + static_assert((std::size_t(1) << BUCKET_SHIFT) == BITMAP_NB_BITS, + "(1 << BUCKET_SHIFT) must be equal to BITMAP_NB_BITS."); + static_assert(std::numeric_limits::max() >= BITMAP_NB_BITS, + "size_type must be big enough to hold BITMAP_NB_BITS."); + static_assert(std::is_unsigned::value, + "bitmap_type must be unsigned."); + static_assert((std::numeric_limits::max() & BUCKET_MASK) == + BITMAP_NB_BITS - 1, + ""); + + public: + /** + * Map an ibucket [0, bucket_count) in the hash table to a sparse_ibucket + * (a sparse_array holds multiple buckets, so there is less sparse_array than + * bucket_count). + * + * The bucket ibucket is in + * m_sparse_buckets[sparse_ibucket(ibucket)][index_in_sparse_bucket(ibucket)] + * instead of something like m_buckets[ibucket] in a classical hash table. + */ + static std::size_t sparse_ibucket(std::size_t ibucket) { + return ibucket >> BUCKET_SHIFT; + } + + /** + * Map an ibucket [0, bucket_count) in the hash table to an index in the + * sparse_array which corresponds to the bucket. + * + * The bucket ibucket is in + * m_sparse_buckets[sparse_ibucket(ibucket)][index_in_sparse_bucket(ibucket)] + * instead of something like m_buckets[ibucket] in a classical hash table. + */ + static typename sparse_array::size_type index_in_sparse_bucket( + std::size_t ibucket) { + return static_cast( + ibucket & sparse_array::BUCKET_MASK); + } + + static std::size_t nb_sparse_buckets(std::size_t bucket_count) noexcept { + if (bucket_count == 0) { + return 0; + } + + return std::max( + 1, sparse_ibucket(tsl::detail_sparse_hash::round_up_to_power_of_two( + bucket_count))); + } + + public: + sparse_array() noexcept + : m_values(nullptr), + m_bitmap_vals(0), + m_bitmap_deleted_vals(0), + m_nb_elements(0), + m_capacity(0), + m_last_array(false) {} + + explicit sparse_array(bool last_bucket) noexcept + : m_values(nullptr), + m_bitmap_vals(0), + m_bitmap_deleted_vals(0), + m_nb_elements(0), + m_capacity(0), + m_last_array(last_bucket) {} + + sparse_array(size_type capacity, Allocator &alloc) + : m_values(nullptr), + m_bitmap_vals(0), + m_bitmap_deleted_vals(0), + m_nb_elements(0), + m_capacity(capacity), + m_last_array(false) { + if (m_capacity > 0) { + m_values = alloc.allocate(m_capacity); + tsl_sh_assert(m_values != + nullptr); // allocate should throw if there is a failure + } + } + + sparse_array(const sparse_array &other, Allocator &alloc) + : m_values(nullptr), + m_bitmap_vals(other.m_bitmap_vals), + m_bitmap_deleted_vals(other.m_bitmap_deleted_vals), + m_nb_elements(0), + m_capacity(other.m_capacity), + m_last_array(other.m_last_array) { + tsl_sh_assert(other.m_capacity >= other.m_nb_elements); + if (m_capacity == 0) { + return; + } + + m_values = alloc.allocate(m_capacity); + tsl_sh_assert(m_values != + nullptr); // allocate should throw if there is a failure + try { + for (size_type i = 0; i < other.m_nb_elements; i++) { + construct_value(alloc, m_values + i, other.m_values[i]); + m_nb_elements++; + } + } catch (...) { + clear(alloc); + throw; + } + } + + sparse_array(sparse_array &&other) noexcept + : m_values(other.m_values), + m_bitmap_vals(other.m_bitmap_vals), + m_bitmap_deleted_vals(other.m_bitmap_deleted_vals), + m_nb_elements(other.m_nb_elements), + m_capacity(other.m_capacity), + m_last_array(other.m_last_array) { + other.m_values = nullptr; + other.m_bitmap_vals = 0; + other.m_bitmap_deleted_vals = 0; + other.m_nb_elements = 0; + other.m_capacity = 0; + } + + sparse_array(sparse_array &&other, Allocator &alloc) + : m_values(nullptr), + m_bitmap_vals(other.m_bitmap_vals), + m_bitmap_deleted_vals(other.m_bitmap_deleted_vals), + m_nb_elements(0), + m_capacity(other.m_capacity), + m_last_array(other.m_last_array) { + tsl_sh_assert(other.m_capacity >= other.m_nb_elements); + if (m_capacity == 0) { + return; + } + + m_values = alloc.allocate(m_capacity); + tsl_sh_assert(m_values != + nullptr); // allocate should throw if there is a failure + try { + for (size_type i = 0; i < other.m_nb_elements; i++) { + construct_value(alloc, m_values + i, std::move(other.m_values[i])); + m_nb_elements++; + } + } catch (...) { + clear(alloc); + throw; + } + } + + sparse_array &operator=(const sparse_array &) = delete; + sparse_array &operator=(sparse_array &&) = delete; + + ~sparse_array() noexcept { + // The code that manages the sparse_array must have called clear before + // destruction. See documentation of sparse_array for more details. + tsl_sh_assert(m_capacity == 0 && m_nb_elements == 0 && m_values == nullptr); + } + + iterator begin() noexcept { return m_values; } + iterator end() noexcept { return m_values + m_nb_elements; } + const_iterator begin() const noexcept { return cbegin(); } + const_iterator end() const noexcept { return cend(); } + const_iterator cbegin() const noexcept { return m_values; } + const_iterator cend() const noexcept { return m_values + m_nb_elements; } + + bool empty() const noexcept { return m_nb_elements == 0; } + + size_type size() const noexcept { return m_nb_elements; } + + void clear(allocator_type &alloc) noexcept { + destroy_and_deallocate_values(alloc, m_values, m_nb_elements, m_capacity); + + m_values = nullptr; + m_bitmap_vals = 0; + m_bitmap_deleted_vals = 0; + m_nb_elements = 0; + m_capacity = 0; + } + + bool last() const noexcept { return m_last_array; } + + void set_as_last() noexcept { m_last_array = true; } + + bool has_value(size_type index) const noexcept { + tsl_sh_assert(index < BITMAP_NB_BITS); + return (m_bitmap_vals & (bitmap_type(1) << index)) != 0; + } + + bool has_deleted_value(size_type index) const noexcept { + tsl_sh_assert(index < BITMAP_NB_BITS); + return (m_bitmap_deleted_vals & (bitmap_type(1) << index)) != 0; + } + + iterator value(size_type index) noexcept { + tsl_sh_assert(has_value(index)); + return m_values + index_to_offset(index); + } + + const_iterator value(size_type index) const noexcept { + tsl_sh_assert(has_value(index)); + return m_values + index_to_offset(index); + } + + /** + * Return iterator to set value. + */ + template + iterator set(allocator_type &alloc, size_type index, Args &&...value_args) { + tsl_sh_assert(!has_value(index)); + + const size_type offset = index_to_offset(index); + insert_at_offset(alloc, offset, std::forward(value_args)...); + + m_bitmap_vals = (m_bitmap_vals | (bitmap_type(1) << index)); + m_bitmap_deleted_vals = + (m_bitmap_deleted_vals & ~(bitmap_type(1) << index)); + + m_nb_elements++; + + tsl_sh_assert(has_value(index)); + tsl_sh_assert(!has_deleted_value(index)); + + return m_values + offset; + } + + iterator erase(allocator_type &alloc, iterator position) { + const size_type offset = + static_cast(std::distance(begin(), position)); + return erase(alloc, position, offset_to_index(offset)); + } + + // Return the next value or end if no next value + iterator erase(allocator_type &alloc, iterator position, size_type index) { + tsl_sh_assert(has_value(index)); + tsl_sh_assert(!has_deleted_value(index)); + + const size_type offset = + static_cast(std::distance(begin(), position)); + erase_at_offset(alloc, offset); + + m_bitmap_vals = (m_bitmap_vals & ~(bitmap_type(1) << index)); + m_bitmap_deleted_vals = (m_bitmap_deleted_vals | (bitmap_type(1) << index)); + + m_nb_elements--; + + tsl_sh_assert(!has_value(index)); + tsl_sh_assert(has_deleted_value(index)); + + return m_values + offset; + } + + void swap(sparse_array &other) { + using std::swap; + + swap(m_values, other.m_values); + swap(m_bitmap_vals, other.m_bitmap_vals); + swap(m_bitmap_deleted_vals, other.m_bitmap_deleted_vals); + swap(m_nb_elements, other.m_nb_elements); + swap(m_capacity, other.m_capacity); + swap(m_last_array, other.m_last_array); + } + + static iterator mutable_iterator(const_iterator pos) { + return const_cast(pos); + } + + template + void serialize(Serializer &serializer) const { + const slz_size_type sparse_bucket_size = m_nb_elements; + serializer(sparse_bucket_size); + + const slz_size_type bitmap_vals = m_bitmap_vals; + serializer(bitmap_vals); + + const slz_size_type bitmap_deleted_vals = m_bitmap_deleted_vals; + serializer(bitmap_deleted_vals); + + for (const value_type &value : *this) { + serializer(value); + } + } + + template + static sparse_array deserialize_hash_compatible(Deserializer &deserializer, + Allocator &alloc) { + const slz_size_type sparse_bucket_size = + deserialize_value(deserializer); + const slz_size_type bitmap_vals = + deserialize_value(deserializer); + const slz_size_type bitmap_deleted_vals = + deserialize_value(deserializer); + + if (sparse_bucket_size > BITMAP_NB_BITS) { + throw std::runtime_error( + "Deserialized sparse_bucket_size is too big for the platform. " + "Maximum should be BITMAP_NB_BITS."); + } + + sparse_array sarray; + if (sparse_bucket_size == 0) { + return sarray; + } + + sarray.m_bitmap_vals = numeric_cast( + bitmap_vals, "Deserialized bitmap_vals is too big."); + sarray.m_bitmap_deleted_vals = numeric_cast( + bitmap_deleted_vals, "Deserialized bitmap_deleted_vals is too big."); + + sarray.m_capacity = numeric_cast( + sparse_bucket_size, "Deserialized sparse_bucket_size is too big."); + sarray.m_values = alloc.allocate(sarray.m_capacity); + + try { + for (size_type ivalue = 0; ivalue < sarray.m_capacity; ivalue++) { + construct_value(alloc, sarray.m_values + ivalue, + deserialize_value(deserializer)); + sarray.m_nb_elements++; + } + } catch (...) { + sarray.clear(alloc); + throw; + } + + return sarray; + } + + /** + * Deserialize the values of the bucket and insert them all in sparse_hash + * through sparse_hash.insert(...). + */ + template + static void deserialize_values_into_sparse_hash(Deserializer &deserializer, + SparseHash &sparse_hash) { + const slz_size_type sparse_bucket_size = + deserialize_value(deserializer); + + const slz_size_type bitmap_vals = + deserialize_value(deserializer); + static_cast(bitmap_vals); // Ignore, not needed + + const slz_size_type bitmap_deleted_vals = + deserialize_value(deserializer); + static_cast(bitmap_deleted_vals); // Ignore, not needed + + for (slz_size_type ivalue = 0; ivalue < sparse_bucket_size; ivalue++) { + sparse_hash.insert(deserialize_value(deserializer)); + } + } + + private: + template + static void construct_value(allocator_type &alloc, value_type *value, + Args &&...value_args) { + std::allocator_traits::construct( + alloc, value, std::forward(value_args)...); + } + + static void destroy_value(allocator_type &alloc, value_type *value) noexcept { + std::allocator_traits::destroy(alloc, value); + } + + static void destroy_and_deallocate_values( + allocator_type &alloc, value_type *values, size_type nb_values, + size_type capacity_values) noexcept { + for (size_type i = 0; i < nb_values; i++) { + destroy_value(alloc, values + i); + } + + alloc.deallocate(values, capacity_values); + } + + static size_type popcount(bitmap_type val) noexcept { + if (sizeof(bitmap_type) <= sizeof(unsigned int)) { + return static_cast( + tsl::detail_popcount::popcount(static_cast(val))); + } else { + return static_cast(tsl::detail_popcount::popcountll(val)); + } + } + + size_type index_to_offset(size_type index) const noexcept { + tsl_sh_assert(index < BITMAP_NB_BITS); + return popcount(m_bitmap_vals & + ((bitmap_type(1) << index) - bitmap_type(1))); + } + + // TODO optimize + size_type offset_to_index(size_type offset) const noexcept { + tsl_sh_assert(offset < m_nb_elements); + + bitmap_type bitmap_vals = m_bitmap_vals; + size_type index = 0; + size_type nb_ones = 0; + + while (bitmap_vals != 0) { + if ((bitmap_vals & 0x1) == 1) { + if (nb_ones == offset) { + break; + } + + nb_ones++; + } + + index++; + bitmap_vals = bitmap_vals >> 1; + } + + return index; + } + + size_type next_capacity() const noexcept { + return static_cast(m_capacity + CAPACITY_GROWTH_STEP); + } + + /** + * Insertion + * + * Two situations: + * - Either we are in a situation where + * std::is_nothrow_move_constructible::value is true. In this + * case, on insertion we just reallocate m_values when we reach its capacity + * (i.e. m_nb_elements == m_capacity), otherwise we just put the new value at + * its appropriate place. We can easily keep the strong exception guarantee as + * moving the values around is safe. + * - Otherwise we are in a situation where + * std::is_nothrow_move_constructible::value is false. In this + * case on EACH insertion we allocate a new area of m_nb_elements + 1 where we + * copy the values of m_values into it and put the new value there. On + * success, we set m_values to this new area. Even if slower, it's the only + * way to preserve to strong exception guarantee. + */ + template ::value>::type * = nullptr> + void insert_at_offset(allocator_type &alloc, size_type offset, + Args &&...value_args) { + if (m_nb_elements < m_capacity) { + insert_at_offset_no_realloc(alloc, offset, + std::forward(value_args)...); + } else { + insert_at_offset_realloc(alloc, offset, next_capacity(), + std::forward(value_args)...); + } + } + + template ::value>::type * = nullptr> + void insert_at_offset(allocator_type &alloc, size_type offset, + Args &&...value_args) { + insert_at_offset_realloc(alloc, offset, m_nb_elements + 1, + std::forward(value_args)...); + } + + template ::value>::type * = nullptr> + void insert_at_offset_no_realloc(allocator_type &alloc, size_type offset, + Args &&...value_args) { + tsl_sh_assert(offset <= m_nb_elements); + tsl_sh_assert(m_nb_elements < m_capacity); + + for (size_type i = m_nb_elements; i > offset; i--) { + construct_value(alloc, m_values + i, std::move(m_values[i - 1])); + destroy_value(alloc, m_values + i - 1); + } + + try { + construct_value(alloc, m_values + offset, + std::forward(value_args)...); + } catch (...) { + for (size_type i = offset; i < m_nb_elements; i++) { + construct_value(alloc, m_values + i, std::move(m_values[i + 1])); + destroy_value(alloc, m_values + i + 1); + } + throw; + } + } + + template ::value>::type * = nullptr> + void insert_at_offset_realloc(allocator_type &alloc, size_type offset, + size_type new_capacity, Args &&...value_args) { + tsl_sh_assert(new_capacity > m_nb_elements); + + value_type *new_values = alloc.allocate(new_capacity); + // Allocate should throw if there is a failure + tsl_sh_assert(new_values != nullptr); + + try { + construct_value(alloc, new_values + offset, + std::forward(value_args)...); + } catch (...) { + alloc.deallocate(new_values, new_capacity); + throw; + } + + // Should not throw from here + for (size_type i = 0; i < offset; i++) { + construct_value(alloc, new_values + i, std::move(m_values[i])); + } + + for (size_type i = offset; i < m_nb_elements; i++) { + construct_value(alloc, new_values + i + 1, std::move(m_values[i])); + } + + destroy_and_deallocate_values(alloc, m_values, m_nb_elements, m_capacity); + + m_values = new_values; + m_capacity = new_capacity; + } + + template ::value>::type * = nullptr> + void insert_at_offset_realloc(allocator_type &alloc, size_type offset, + size_type new_capacity, Args &&...value_args) { + tsl_sh_assert(new_capacity > m_nb_elements); + + value_type *new_values = alloc.allocate(new_capacity); + // Allocate should throw if there is a failure + tsl_sh_assert(new_values != nullptr); + + size_type nb_new_values = 0; + try { + for (size_type i = 0; i < offset; i++) { + construct_value(alloc, new_values + i, m_values[i]); + nb_new_values++; + } + + construct_value(alloc, new_values + offset, + std::forward(value_args)...); + nb_new_values++; + + for (size_type i = offset; i < m_nb_elements; i++) { + construct_value(alloc, new_values + i + 1, m_values[i]); + nb_new_values++; + } + } catch (...) { + destroy_and_deallocate_values(alloc, new_values, nb_new_values, + new_capacity); + throw; + } + + tsl_sh_assert(nb_new_values == m_nb_elements + 1); + + destroy_and_deallocate_values(alloc, m_values, m_nb_elements, m_capacity); + + m_values = new_values; + m_capacity = new_capacity; + } + + /** + * Erasure + * + * Two situations: + * - Either we are in a situation where + * std::is_nothrow_move_constructible::value is true. Simply + * destroy the value and left-shift move the value on the right of offset. + * - Otherwise we are in a situation where + * std::is_nothrow_move_constructible::value is false. Copy all + * the values except the one at offset into a new heap area. On success, we + * set m_values to this new area. Even if slower, it's the only way to + * preserve to strong exception guarantee. + */ + template ::value>::type * = nullptr> + void erase_at_offset(allocator_type &alloc, size_type offset) noexcept { + tsl_sh_assert(offset < m_nb_elements); + + destroy_value(alloc, m_values + offset); + + for (size_type i = offset + 1; i < m_nb_elements; i++) { + construct_value(alloc, m_values + i - 1, std::move(m_values[i])); + destroy_value(alloc, m_values + i); + } + } + + template ::value>::type * = nullptr> + void erase_at_offset(allocator_type &alloc, size_type offset) { + tsl_sh_assert(offset < m_nb_elements); + + // Erasing the last element, don't need to reallocate. We keep the capacity. + if (offset + 1 == m_nb_elements) { + destroy_value(alloc, m_values + offset); + return; + } + + tsl_sh_assert(m_nb_elements > 1); + const size_type new_capacity = m_nb_elements - 1; + + value_type *new_values = alloc.allocate(new_capacity); + // Allocate should throw if there is a failure + tsl_sh_assert(new_values != nullptr); + + size_type nb_new_values = 0; + try { + for (size_type i = 0; i < m_nb_elements; i++) { + if (i != offset) { + construct_value(alloc, new_values + nb_new_values, m_values[i]); + nb_new_values++; + } + } + } catch (...) { + destroy_and_deallocate_values(alloc, new_values, nb_new_values, + new_capacity); + throw; + } + + tsl_sh_assert(nb_new_values == m_nb_elements - 1); + + destroy_and_deallocate_values(alloc, m_values, m_nb_elements, m_capacity); + + m_values = new_values; + m_capacity = new_capacity; + } + + private: + value_type *m_values; + + bitmap_type m_bitmap_vals; + bitmap_type m_bitmap_deleted_vals; + + size_type m_nb_elements; + size_type m_capacity; + bool m_last_array; +}; + +/** + * Internal common class used by `sparse_map` and `sparse_set`. + * + * `ValueType` is what will be stored by `sparse_hash` (usually `std::pair` for map and `Key` for set). + * + * `KeySelect` should be a `FunctionObject` which takes a `ValueType` in + * parameter and returns a reference to the key. + * + * `ValueSelect` should be a `FunctionObject` which takes a `ValueType` in + * parameter and returns a reference to the value. `ValueSelect` should be void + * if there is no value (in a set for example). + * + * The strong exception guarantee only holds if `ExceptionSafety` is set to + * `tsl::sh::exception_safety::strong`. + * + * `ValueType` must be nothrow move constructible and/or copy constructible. + * Behaviour is undefined if the destructor of `ValueType` throws. + * + * + * The class holds its buckets in a 2-dimensional fashion. Instead of having a + * linear `std::vector` for [0, bucket_count) where each bucket stores + * one value, we have a `std::vector` (m_sparse_buckets_data) + * where each `sparse_array` stores multiple values (up to + * `sparse_array::BITMAP_NB_BITS`). To convert a one dimensional `ibucket` + * position to a position in `std::vector` and a position in + * `sparse_array`, use respectively the methods + * `sparse_array::sparse_ibucket(ibucket)` and + * `sparse_array::index_in_sparse_bucket(ibucket)`. + */ +template +class sparse_hash : private Allocator, + private Hash, + private KeyEqual, + private GrowthPolicy { + private: + template + using has_mapped_type = + typename std::integral_constant::value>; + + static_assert( + noexcept(std::declval().bucket_for_hash(std::size_t(0))), + "GrowthPolicy::bucket_for_hash must be noexcept."); + static_assert(noexcept(std::declval().clear()), + "GrowthPolicy::clear must be noexcept."); + + public: + template + class sparse_iterator; + + using key_type = typename KeySelect::key_type; + using value_type = ValueType; + using size_type = std::size_t; + using difference_type = std::ptrdiff_t; + using hasher = Hash; + using key_equal = KeyEqual; + using allocator_type = Allocator; + using reference = value_type &; + using const_reference = const value_type &; + using pointer = value_type *; + using const_pointer = const value_type *; + using iterator = sparse_iterator; + using const_iterator = sparse_iterator; + + private: + using sparse_array = + tsl::detail_sparse_hash::sparse_array; + + using sparse_buckets_allocator = typename std::allocator_traits< + allocator_type>::template rebind_alloc; + using sparse_buckets_container = + std::vector; + + public: + /** + * The `operator*()` and `operator->()` methods return a const reference and + * const pointer respectively to the stored value type (`Key` for a set, + * `std::pair` for a map). + * + * In case of a map, to get a mutable reference to the value `T` associated to + * a key (the `.second` in the stored pair), you have to call `value()`. + */ + template + class sparse_iterator { + friend class sparse_hash; + + private: + using sparse_bucket_iterator = typename std::conditional< + IsConst, typename sparse_buckets_container::const_iterator, + typename sparse_buckets_container::iterator>::type; + + using sparse_array_iterator = + typename std::conditional::type; + + /** + * sparse_array_it should be nullptr if sparse_bucket_it == + * m_sparse_buckets_data.end(). (TODO better way?) + */ + sparse_iterator(sparse_bucket_iterator sparse_bucket_it, + sparse_array_iterator sparse_array_it) + : m_sparse_buckets_it(sparse_bucket_it), + m_sparse_array_it(sparse_array_it) {} + + public: + using iterator_category = std::forward_iterator_tag; + using value_type = const typename sparse_hash::value_type; + using difference_type = std::ptrdiff_t; + using reference = value_type &; + using pointer = value_type *; + + sparse_iterator() noexcept {} + + // Copy constructor from iterator to const_iterator. + template ::type * = nullptr> + sparse_iterator(const sparse_iterator &other) noexcept + : m_sparse_buckets_it(other.m_sparse_buckets_it), + m_sparse_array_it(other.m_sparse_array_it) {} + + sparse_iterator(const sparse_iterator &other) = default; + sparse_iterator(sparse_iterator &&other) = default; + sparse_iterator &operator=(const sparse_iterator &other) = default; + sparse_iterator &operator=(sparse_iterator &&other) = default; + + const typename sparse_hash::key_type &key() const { + return KeySelect()(*m_sparse_array_it); + } + + template ::value && + IsConst>::type * = nullptr> + const typename U::value_type &value() const { + return U()(*m_sparse_array_it); + } + + template ::value && + !IsConst>::type * = nullptr> + typename U::value_type &value() { + return U()(*m_sparse_array_it); + } + + reference operator*() const { return *m_sparse_array_it; } + + pointer operator->() const { return std::addressof(*m_sparse_array_it); } + + sparse_iterator &operator++() { + tsl_sh_assert(m_sparse_array_it != nullptr); + ++m_sparse_array_it; + + if (m_sparse_array_it == m_sparse_buckets_it->end()) { + do { + if (m_sparse_buckets_it->last()) { + ++m_sparse_buckets_it; + m_sparse_array_it = nullptr; + return *this; + } + + ++m_sparse_buckets_it; + } while (m_sparse_buckets_it->empty()); + + m_sparse_array_it = m_sparse_buckets_it->begin(); + } + + return *this; + } + + sparse_iterator operator++(int) { + sparse_iterator tmp(*this); + ++*this; + + return tmp; + } + + friend bool operator==(const sparse_iterator &lhs, + const sparse_iterator &rhs) { + return lhs.m_sparse_buckets_it == rhs.m_sparse_buckets_it && + lhs.m_sparse_array_it == rhs.m_sparse_array_it; + } + + friend bool operator!=(const sparse_iterator &lhs, + const sparse_iterator &rhs) { + return !(lhs == rhs); + } + + private: + sparse_bucket_iterator m_sparse_buckets_it; + sparse_array_iterator m_sparse_array_it; + }; + + public: + sparse_hash(size_type bucket_count, const Hash &hash, const KeyEqual &equal, + const Allocator &alloc, float max_load_factor) + : Allocator(alloc), + Hash(hash), + KeyEqual(equal), + GrowthPolicy(bucket_count), + m_sparse_buckets_data(alloc), + m_sparse_buckets(static_empty_sparse_bucket_ptr()), + m_bucket_count(bucket_count), + m_nb_elements(0), + m_nb_deleted_buckets(0) { + if (m_bucket_count > max_bucket_count()) { + throw std::length_error("The map exceeds its maximum size."); + } + + if (m_bucket_count > 0) { + /* + * We can't use the `vector(size_type count, const Allocator& alloc)` + * constructor as it's only available in C++14 and we need to support + * C++11. We thus must resize after using the `vector(const Allocator& + * alloc)` constructor. + * + * We can't use `vector(size_type count, const T& value, const Allocator& + * alloc)` as it requires the value T to be copyable. + */ + m_sparse_buckets_data.resize( + sparse_array::nb_sparse_buckets(bucket_count)); + m_sparse_buckets = m_sparse_buckets_data.data(); + + tsl_sh_assert(!m_sparse_buckets_data.empty()); + m_sparse_buckets_data.back().set_as_last(); + } + + this->max_load_factor(max_load_factor); + + // Check in the constructor instead of outside of a function to avoid + // compilation issues when value_type is not complete. + static_assert(std::is_nothrow_move_constructible::value || + std::is_copy_constructible::value, + "Key, and T if present, must be nothrow move constructible " + "and/or copy constructible."); + } + + ~sparse_hash() { clear(); } + + sparse_hash(const sparse_hash &other) + : Allocator(std::allocator_traits< + Allocator>::select_on_container_copy_construction(other)), + Hash(other), + KeyEqual(other), + GrowthPolicy(other), + m_sparse_buckets_data( + std::allocator_traits< + Allocator>::select_on_container_copy_construction(other)), + m_bucket_count(other.m_bucket_count), + m_nb_elements(other.m_nb_elements), + m_nb_deleted_buckets(other.m_nb_deleted_buckets), + m_load_threshold_rehash(other.m_load_threshold_rehash), + m_load_threshold_clear_deleted(other.m_load_threshold_clear_deleted), + m_max_load_factor(other.m_max_load_factor) { + copy_buckets_from(other), + m_sparse_buckets = m_sparse_buckets_data.empty() + ? static_empty_sparse_bucket_ptr() + : m_sparse_buckets_data.data(); + } + + sparse_hash(sparse_hash &&other) noexcept( + std::is_nothrow_move_constructible::value + &&std::is_nothrow_move_constructible::value + &&std::is_nothrow_move_constructible::value + &&std::is_nothrow_move_constructible::value + &&std::is_nothrow_move_constructible< + sparse_buckets_container>::value) + : Allocator(std::move(other)), + Hash(std::move(other)), + KeyEqual(std::move(other)), + GrowthPolicy(std::move(other)), + m_sparse_buckets_data(std::move(other.m_sparse_buckets_data)), + m_sparse_buckets(m_sparse_buckets_data.empty() + ? static_empty_sparse_bucket_ptr() + : m_sparse_buckets_data.data()), + m_bucket_count(other.m_bucket_count), + m_nb_elements(other.m_nb_elements), + m_nb_deleted_buckets(other.m_nb_deleted_buckets), + m_load_threshold_rehash(other.m_load_threshold_rehash), + m_load_threshold_clear_deleted(other.m_load_threshold_clear_deleted), + m_max_load_factor(other.m_max_load_factor) { + other.GrowthPolicy::clear(); + other.m_sparse_buckets_data.clear(); + other.m_sparse_buckets = static_empty_sparse_bucket_ptr(); + other.m_bucket_count = 0; + other.m_nb_elements = 0; + other.m_nb_deleted_buckets = 0; + other.m_load_threshold_rehash = 0; + other.m_load_threshold_clear_deleted = 0; + } + + sparse_hash &operator=(const sparse_hash &other) { + if (this != &other) { + clear(); + + if (std::allocator_traits< + Allocator>::propagate_on_container_copy_assignment::value) { + Allocator::operator=(other); + } + + Hash::operator=(other); + KeyEqual::operator=(other); + GrowthPolicy::operator=(other); + + if (std::allocator_traits< + Allocator>::propagate_on_container_copy_assignment::value) { + m_sparse_buckets_data = + sparse_buckets_container(static_cast(other)); + } else { + if (m_sparse_buckets_data.size() != + other.m_sparse_buckets_data.size()) { + m_sparse_buckets_data = + sparse_buckets_container(static_cast(*this)); + } else { + m_sparse_buckets_data.clear(); + } + } + + copy_buckets_from(other); + m_sparse_buckets = m_sparse_buckets_data.empty() + ? static_empty_sparse_bucket_ptr() + : m_sparse_buckets_data.data(); + + m_bucket_count = other.m_bucket_count; + m_nb_elements = other.m_nb_elements; + m_nb_deleted_buckets = other.m_nb_deleted_buckets; + m_load_threshold_rehash = other.m_load_threshold_rehash; + m_load_threshold_clear_deleted = other.m_load_threshold_clear_deleted; + m_max_load_factor = other.m_max_load_factor; + } + + return *this; + } + + sparse_hash &operator=(sparse_hash &&other) { + clear(); + + if (std::allocator_traits< + Allocator>::propagate_on_container_move_assignment::value) { + static_cast(*this) = + std::move(static_cast(other)); + m_sparse_buckets_data = std::move(other.m_sparse_buckets_data); + } else if (static_cast(*this) != + static_cast(other)) { + move_buckets_from(std::move(other)); + } else { + static_cast(*this) = + std::move(static_cast(other)); + m_sparse_buckets_data = std::move(other.m_sparse_buckets_data); + } + + m_sparse_buckets = m_sparse_buckets_data.empty() + ? static_empty_sparse_bucket_ptr() + : m_sparse_buckets_data.data(); + + static_cast(*this) = std::move(static_cast(other)); + static_cast(*this) = std::move(static_cast(other)); + static_cast(*this) = + std::move(static_cast(other)); + m_bucket_count = other.m_bucket_count; + m_nb_elements = other.m_nb_elements; + m_nb_deleted_buckets = other.m_nb_deleted_buckets; + m_load_threshold_rehash = other.m_load_threshold_rehash; + m_load_threshold_clear_deleted = other.m_load_threshold_clear_deleted; + m_max_load_factor = other.m_max_load_factor; + + other.GrowthPolicy::clear(); + other.m_sparse_buckets_data.clear(); + other.m_sparse_buckets = static_empty_sparse_bucket_ptr(); + other.m_bucket_count = 0; + other.m_nb_elements = 0; + other.m_nb_deleted_buckets = 0; + other.m_load_threshold_rehash = 0; + other.m_load_threshold_clear_deleted = 0; + + return *this; + } + + allocator_type get_allocator() const { + return static_cast(*this); + } + + /* + * Iterators + */ + iterator begin() noexcept { + auto begin = m_sparse_buckets_data.begin(); + while (begin != m_sparse_buckets_data.end() && begin->empty()) { + ++begin; + } + + return iterator(begin, (begin != m_sparse_buckets_data.end()) + ? begin->begin() + : nullptr); + } + + const_iterator begin() const noexcept { return cbegin(); } + + const_iterator cbegin() const noexcept { + auto begin = m_sparse_buckets_data.cbegin(); + while (begin != m_sparse_buckets_data.cend() && begin->empty()) { + ++begin; + } + + return const_iterator(begin, (begin != m_sparse_buckets_data.cend()) + ? begin->cbegin() + : nullptr); + } + + iterator end() noexcept { + return iterator(m_sparse_buckets_data.end(), nullptr); + } + + const_iterator end() const noexcept { return cend(); } + + const_iterator cend() const noexcept { + return const_iterator(m_sparse_buckets_data.cend(), nullptr); + } + + /* + * Capacity + */ + bool empty() const noexcept { return m_nb_elements == 0; } + + size_type size() const noexcept { return m_nb_elements; } + + size_type max_size() const noexcept { + return std::min(std::allocator_traits::max_size(), + m_sparse_buckets_data.max_size()); + } + + /* + * Modifiers + */ + void clear() noexcept { + for (auto &bucket : m_sparse_buckets_data) { + bucket.clear(*this); + } + + m_nb_elements = 0; + m_nb_deleted_buckets = 0; + } + + template + std::pair insert(P &&value) { + return insert_impl(KeySelect()(value), std::forward

(value)); + } + + template + iterator insert_hint(const_iterator hint, P &&value) { + if (hint != cend() && + compare_keys(KeySelect()(*hint), KeySelect()(value))) { + return mutable_iterator(hint); + } + + return insert(std::forward

(value)).first; + } + + template + void insert(InputIt first, InputIt last) { + if (std::is_base_of< + std::forward_iterator_tag, + typename std::iterator_traits::iterator_category>::value) { + const auto nb_elements_insert = std::distance(first, last); + const size_type nb_free_buckets = m_load_threshold_rehash - size(); + tsl_sh_assert(m_load_threshold_rehash >= size()); + + if (nb_elements_insert > 0 && + nb_free_buckets < size_type(nb_elements_insert)) { + reserve(size() + size_type(nb_elements_insert)); + } + } + + for (; first != last; ++first) { + insert(*first); + } + } + + template + std::pair insert_or_assign(K &&key, M &&obj) { + auto it = try_emplace(std::forward(key), std::forward(obj)); + if (!it.second) { + it.first.value() = std::forward(obj); + } + + return it; + } + + template + iterator insert_or_assign(const_iterator hint, K &&key, M &&obj) { + if (hint != cend() && compare_keys(KeySelect()(*hint), key)) { + auto it = mutable_iterator(hint); + it.value() = std::forward(obj); + + return it; + } + + return insert_or_assign(std::forward(key), std::forward(obj)).first; + } + + template + std::pair emplace(Args &&...args) { + return insert(value_type(std::forward(args)...)); + } + + template + iterator emplace_hint(const_iterator hint, Args &&...args) { + return insert_hint(hint, value_type(std::forward(args)...)); + } + + template + std::pair try_emplace(K &&key, Args &&...args) { + return insert_impl(key, std::piecewise_construct, + std::forward_as_tuple(std::forward(key)), + std::forward_as_tuple(std::forward(args)...)); + } + + template + iterator try_emplace_hint(const_iterator hint, K &&key, Args &&...args) { + if (hint != cend() && compare_keys(KeySelect()(*hint), key)) { + return mutable_iterator(hint); + } + + return try_emplace(std::forward(key), std::forward(args)...).first; + } + + /** + * Here to avoid `template size_type erase(const K& key)` being used + * when we use an iterator instead of a const_iterator. + */ + iterator erase(iterator pos) { + tsl_sh_assert(pos != end() && m_nb_elements > 0); + auto it_sparse_array_next = + pos.m_sparse_buckets_it->erase(*this, pos.m_sparse_array_it); + m_nb_elements--; + m_nb_deleted_buckets++; + + if (it_sparse_array_next == pos.m_sparse_buckets_it->end()) { + auto it_sparse_buckets_next = pos.m_sparse_buckets_it; + do { + ++it_sparse_buckets_next; + } while (it_sparse_buckets_next != m_sparse_buckets_data.end() && + it_sparse_buckets_next->empty()); + + if (it_sparse_buckets_next == m_sparse_buckets_data.end()) { + return end(); + } else { + return iterator(it_sparse_buckets_next, + it_sparse_buckets_next->begin()); + } + } else { + return iterator(pos.m_sparse_buckets_it, it_sparse_array_next); + } + } + + iterator erase(const_iterator pos) { return erase(mutable_iterator(pos)); } + + iterator erase(const_iterator first, const_iterator last) { + if (first == last) { + return mutable_iterator(first); + } + + // TODO Optimize, could avoid the call to std::distance. + const size_type nb_elements_to_erase = + static_cast(std::distance(first, last)); + auto to_delete = mutable_iterator(first); + for (size_type i = 0; i < nb_elements_to_erase; i++) { + to_delete = erase(to_delete); + } + + return to_delete; + } + + template + size_type erase(const K &key) { + return erase(key, hash_key(key)); + } + + template + size_type erase(const K &key, std::size_t hash) { + return erase_impl(key, hash); + } + + void swap(sparse_hash &other) { + using std::swap; + + if (std::allocator_traits::propagate_on_container_swap::value) { + swap(static_cast(*this), static_cast(other)); + } else { + tsl_sh_assert(static_cast(*this) == + static_cast(other)); + } + + swap(static_cast(*this), static_cast(other)); + swap(static_cast(*this), static_cast(other)); + swap(static_cast(*this), + static_cast(other)); + swap(m_sparse_buckets_data, other.m_sparse_buckets_data); + swap(m_sparse_buckets, other.m_sparse_buckets); + swap(m_bucket_count, other.m_bucket_count); + swap(m_nb_elements, other.m_nb_elements); + swap(m_nb_deleted_buckets, other.m_nb_deleted_buckets); + swap(m_load_threshold_rehash, other.m_load_threshold_rehash); + swap(m_load_threshold_clear_deleted, other.m_load_threshold_clear_deleted); + swap(m_max_load_factor, other.m_max_load_factor); + } + + /* + * Lookup + */ + template < + class K, class U = ValueSelect, + typename std::enable_if::value>::type * = nullptr> + typename U::value_type &at(const K &key) { + return at(key, hash_key(key)); + } + + template < + class K, class U = ValueSelect, + typename std::enable_if::value>::type * = nullptr> + typename U::value_type &at(const K &key, std::size_t hash) { + return const_cast( + static_cast(this)->at(key, hash)); + } + + template < + class K, class U = ValueSelect, + typename std::enable_if::value>::type * = nullptr> + const typename U::value_type &at(const K &key) const { + return at(key, hash_key(key)); + } + + template < + class K, class U = ValueSelect, + typename std::enable_if::value>::type * = nullptr> + const typename U::value_type &at(const K &key, std::size_t hash) const { + auto it = find(key, hash); + if (it != cend()) { + return it.value(); + } else { + throw std::out_of_range("Couldn't find key."); + } + } + + template < + class K, class U = ValueSelect, + typename std::enable_if::value>::type * = nullptr> + typename U::value_type &operator[](K &&key) { + return try_emplace(std::forward(key)).first.value(); + } + + template + bool contains(const K &key) const { + return contains(key, hash_key(key)); + } + + template + bool contains(const K &key, std::size_t hash) const { + return count(key, hash) != 0; + } + + template + size_type count(const K &key) const { + return count(key, hash_key(key)); + } + + template + size_type count(const K &key, std::size_t hash) const { + if (find(key, hash) != cend()) { + return 1; + } else { + return 0; + } + } + + template + iterator find(const K &key) { + return find_impl(key, hash_key(key)); + } + + template + iterator find(const K &key, std::size_t hash) { + return find_impl(key, hash); + } + + template + const_iterator find(const K &key) const { + return find_impl(key, hash_key(key)); + } + + template + const_iterator find(const K &key, std::size_t hash) const { + return find_impl(key, hash); + } + + template + std::pair equal_range(const K &key) { + return equal_range(key, hash_key(key)); + } + + template + std::pair equal_range(const K &key, std::size_t hash) { + iterator it = find(key, hash); + return std::make_pair(it, (it == end()) ? it : std::next(it)); + } + + template + std::pair equal_range(const K &key) const { + return equal_range(key, hash_key(key)); + } + + template + std::pair equal_range( + const K &key, std::size_t hash) const { + const_iterator it = find(key, hash); + return std::make_pair(it, (it == cend()) ? it : std::next(it)); + } + + /* + * Bucket interface + */ + size_type bucket_count() const { return m_bucket_count; } + + size_type max_bucket_count() const { + return m_sparse_buckets_data.max_size(); + } + + /* + * Hash policy + */ + float load_factor() const { + if (bucket_count() == 0) { + return 0; + } + + return float(m_nb_elements) / float(bucket_count()); + } + + float max_load_factor() const { return m_max_load_factor; } + + void max_load_factor(float ml) { + m_max_load_factor = std::max(0.1f, std::min(ml, 0.8f)); + m_load_threshold_rehash = + size_type(float(bucket_count()) * m_max_load_factor); + + const float max_load_factor_with_deleted_buckets = + m_max_load_factor + 0.5f * (1.0f - m_max_load_factor); + tsl_sh_assert(max_load_factor_with_deleted_buckets > 0.0f && + max_load_factor_with_deleted_buckets <= 1.0f); + m_load_threshold_clear_deleted = + size_type(float(bucket_count()) * max_load_factor_with_deleted_buckets); + } + + void rehash(size_type count) { + count = std::max(count, + size_type(std::ceil(float(size()) / max_load_factor()))); + rehash_impl(count); + } + + void reserve(size_type count) { + rehash(size_type(std::ceil(float(count) / max_load_factor()))); + } + + /* + * Observers + */ + hasher hash_function() const { return static_cast(*this); } + + key_equal key_eq() const { return static_cast(*this); } + + /* + * Other + */ + iterator mutable_iterator(const_iterator pos) { + auto it_sparse_buckets = + m_sparse_buckets_data.begin() + + std::distance(m_sparse_buckets_data.cbegin(), pos.m_sparse_buckets_it); + + return iterator(it_sparse_buckets, + sparse_array::mutable_iterator(pos.m_sparse_array_it)); + } + + template + void serialize(Serializer &serializer) const { + serialize_impl(serializer); + } + + template + void deserialize(Deserializer &deserializer, bool hash_compatible) { + deserialize_impl(deserializer, hash_compatible); + } + + private: + template + std::size_t hash_key(const K &key) const { + return Hash::operator()(key); + } + + template + bool compare_keys(const K1 &key1, const K2 &key2) const { + return KeyEqual::operator()(key1, key2); + } + + size_type bucket_for_hash(std::size_t hash) const { + const std::size_t bucket = GrowthPolicy::bucket_for_hash(hash); + tsl_sh_assert(sparse_array::sparse_ibucket(bucket) < + m_sparse_buckets_data.size() || + (bucket == 0 && m_sparse_buckets_data.empty())); + + return bucket; + } + + template ::value>::type * = + nullptr> + size_type next_bucket(size_type ibucket, size_type iprobe) const { + (void)iprobe; + if (Probing == tsl::sh::probing::linear) { + return (ibucket + 1) & this->m_mask; + } else { + tsl_sh_assert(Probing == tsl::sh::probing::quadratic); + return (ibucket + iprobe) & this->m_mask; + } + } + + template ::value>::type * = + nullptr> + size_type next_bucket(size_type ibucket, size_type iprobe) const { + (void)iprobe; + if (Probing == tsl::sh::probing::linear) { + ibucket++; + return (ibucket != bucket_count()) ? ibucket : 0; + } else { + tsl_sh_assert(Probing == tsl::sh::probing::quadratic); + ibucket += iprobe; + return (ibucket < bucket_count()) ? ibucket : ibucket % bucket_count(); + } + } + + // TODO encapsulate m_sparse_buckets_data to avoid the managing the allocator + void copy_buckets_from(const sparse_hash &other) { + m_sparse_buckets_data.reserve(other.m_sparse_buckets_data.size()); + + try { + for (const auto &bucket : other.m_sparse_buckets_data) { + m_sparse_buckets_data.emplace_back(bucket, + static_cast(*this)); + } + } catch (...) { + clear(); + throw; + } + + tsl_sh_assert(m_sparse_buckets_data.empty() || + m_sparse_buckets_data.back().last()); + } + + void move_buckets_from(sparse_hash &&other) { + m_sparse_buckets_data.reserve(other.m_sparse_buckets_data.size()); + + try { + for (auto &&bucket : other.m_sparse_buckets_data) { + m_sparse_buckets_data.emplace_back(std::move(bucket), + static_cast(*this)); + } + } catch (...) { + clear(); + throw; + } + + tsl_sh_assert(m_sparse_buckets_data.empty() || + m_sparse_buckets_data.back().last()); + } + + template + std::pair insert_impl(const K &key, + Args &&...value_type_args) { + if (size() >= m_load_threshold_rehash) { + rehash_impl(GrowthPolicy::next_bucket_count()); + } else if (size() + m_nb_deleted_buckets >= + m_load_threshold_clear_deleted) { + clear_deleted_buckets(); + } + tsl_sh_assert(!m_sparse_buckets_data.empty()); + + /** + * We must insert the value in the first empty or deleted bucket we find. If + * we first find a deleted bucket, we still have to continue the search + * until we find an empty bucket or until we have searched all the buckets + * to be sure that the value is not in the hash table. We thus remember the + * position, if any, of the first deleted bucket we have encountered so we + * can insert it there if needed. + */ + bool found_first_deleted_bucket = false; + std::size_t sparse_ibucket_first_deleted = 0; + typename sparse_array::size_type index_in_sparse_bucket_first_deleted = 0; + + const std::size_t hash = hash_key(key); + std::size_t ibucket = bucket_for_hash(hash); + + std::size_t probe = 0; + while (true) { + std::size_t sparse_ibucket = sparse_array::sparse_ibucket(ibucket); + auto index_in_sparse_bucket = + sparse_array::index_in_sparse_bucket(ibucket); + + if (m_sparse_buckets[sparse_ibucket].has_value(index_in_sparse_bucket)) { + auto value_it = + m_sparse_buckets[sparse_ibucket].value(index_in_sparse_bucket); + if (compare_keys(key, KeySelect()(*value_it))) { + return std::make_pair( + iterator(m_sparse_buckets_data.begin() + sparse_ibucket, + value_it), + false); + } + } else if (m_sparse_buckets[sparse_ibucket].has_deleted_value( + index_in_sparse_bucket) && + probe < m_bucket_count) { + if (!found_first_deleted_bucket) { + found_first_deleted_bucket = true; + sparse_ibucket_first_deleted = sparse_ibucket; + index_in_sparse_bucket_first_deleted = index_in_sparse_bucket; + } + } else if (found_first_deleted_bucket) { + auto it = insert_in_bucket(sparse_ibucket_first_deleted, + index_in_sparse_bucket_first_deleted, + std::forward(value_type_args)...); + m_nb_deleted_buckets--; + + return it; + } else { + return insert_in_bucket(sparse_ibucket, index_in_sparse_bucket, + std::forward(value_type_args)...); + } + + probe++; + ibucket = next_bucket(ibucket, probe); + } + } + + template + std::pair insert_in_bucket( + std::size_t sparse_ibucket, + typename sparse_array::size_type index_in_sparse_bucket, + Args &&...value_type_args) { + auto value_it = m_sparse_buckets[sparse_ibucket].set( + *this, index_in_sparse_bucket, std::forward(value_type_args)...); + m_nb_elements++; + + return std::make_pair( + iterator(m_sparse_buckets_data.begin() + sparse_ibucket, value_it), + true); + } + + template + size_type erase_impl(const K &key, std::size_t hash) { + std::size_t ibucket = bucket_for_hash(hash); + + std::size_t probe = 0; + while (true) { + const std::size_t sparse_ibucket = sparse_array::sparse_ibucket(ibucket); + const auto index_in_sparse_bucket = + sparse_array::index_in_sparse_bucket(ibucket); + + if (m_sparse_buckets[sparse_ibucket].has_value(index_in_sparse_bucket)) { + auto value_it = + m_sparse_buckets[sparse_ibucket].value(index_in_sparse_bucket); + if (compare_keys(key, KeySelect()(*value_it))) { + m_sparse_buckets[sparse_ibucket].erase(*this, value_it, + index_in_sparse_bucket); + m_nb_elements--; + m_nb_deleted_buckets++; + + return 1; + } + } else if (!m_sparse_buckets[sparse_ibucket].has_deleted_value( + index_in_sparse_bucket) || + probe >= m_bucket_count) { + return 0; + } + + probe++; + ibucket = next_bucket(ibucket, probe); + } + } + + template + iterator find_impl(const K &key, std::size_t hash) { + return mutable_iterator( + static_cast(this)->find(key, hash)); + } + + template + const_iterator find_impl(const K &key, std::size_t hash) const { + std::size_t ibucket = bucket_for_hash(hash); + + std::size_t probe = 0; + while (true) { + const std::size_t sparse_ibucket = sparse_array::sparse_ibucket(ibucket); + const auto index_in_sparse_bucket = + sparse_array::index_in_sparse_bucket(ibucket); + + if (m_sparse_buckets[sparse_ibucket].has_value(index_in_sparse_bucket)) { + auto value_it = + m_sparse_buckets[sparse_ibucket].value(index_in_sparse_bucket); + if (compare_keys(key, KeySelect()(*value_it))) { + return const_iterator(m_sparse_buckets_data.cbegin() + sparse_ibucket, + value_it); + } + } else if (!m_sparse_buckets[sparse_ibucket].has_deleted_value( + index_in_sparse_bucket) || + probe >= m_bucket_count) { + return cend(); + } + + probe++; + ibucket = next_bucket(ibucket, probe); + } + } + + void clear_deleted_buckets() { + // TODO could be optimized, we could do it in-place instead of allocating a + // new bucket array. + rehash_impl(m_bucket_count); + tsl_sh_assert(m_nb_deleted_buckets == 0); + } + + template ::type + * = nullptr> + void rehash_impl(size_type count) { + sparse_hash new_table(count, static_cast(*this), + static_cast(*this), + static_cast(*this), m_max_load_factor); + + for (auto &bucket : m_sparse_buckets_data) { + for (auto &val : bucket) { + new_table.insert_on_rehash(std::move(val)); + } + + // TODO try to reuse some of the memory + bucket.clear(*this); + } + + new_table.swap(*this); + } + + /** + * TODO: For now we copy each element into the new map. We could move + * them if they are nothrow_move_constructible without triggering + * any exception if we reserve enough space in the sparse arrays beforehand. + */ + template ::type * = nullptr> + void rehash_impl(size_type count) { + sparse_hash new_table(count, static_cast(*this), + static_cast(*this), + static_cast(*this), m_max_load_factor); + + for (const auto &bucket : m_sparse_buckets_data) { + for (const auto &val : bucket) { + new_table.insert_on_rehash(val); + } + } + + new_table.swap(*this); + } + + template + void insert_on_rehash(K &&key_value) { + const key_type &key = KeySelect()(key_value); + + const std::size_t hash = hash_key(key); + std::size_t ibucket = bucket_for_hash(hash); + + std::size_t probe = 0; + while (true) { + std::size_t sparse_ibucket = sparse_array::sparse_ibucket(ibucket); + auto index_in_sparse_bucket = + sparse_array::index_in_sparse_bucket(ibucket); + + if (!m_sparse_buckets[sparse_ibucket].has_value(index_in_sparse_bucket)) { + m_sparse_buckets[sparse_ibucket].set(*this, index_in_sparse_bucket, + std::forward(key_value)); + m_nb_elements++; + + return; + } else { + tsl_sh_assert(!compare_keys( + key, KeySelect()(*m_sparse_buckets[sparse_ibucket].value( + index_in_sparse_bucket)))); + } + + probe++; + ibucket = next_bucket(ibucket, probe); + } + } + + template + void serialize_impl(Serializer &serializer) const { + const slz_size_type version = SERIALIZATION_PROTOCOL_VERSION; + serializer(version); + + const slz_size_type bucket_count = m_bucket_count; + serializer(bucket_count); + + const slz_size_type nb_sparse_buckets = m_sparse_buckets_data.size(); + serializer(nb_sparse_buckets); + + const slz_size_type nb_elements = m_nb_elements; + serializer(nb_elements); + + const slz_size_type nb_deleted_buckets = m_nb_deleted_buckets; + serializer(nb_deleted_buckets); + + const float max_load_factor = m_max_load_factor; + serializer(max_load_factor); + + for (const auto &bucket : m_sparse_buckets_data) { + bucket.serialize(serializer); + } + } + + template + void deserialize_impl(Deserializer &deserializer, bool hash_compatible) { + tsl_sh_assert( + m_bucket_count == 0 && + m_sparse_buckets_data.empty()); // Current hash table must be empty + + const slz_size_type version = + deserialize_value(deserializer); + // For now we only have one version of the serialization protocol. + // If it doesn't match there is a problem with the file. + if (version != SERIALIZATION_PROTOCOL_VERSION) { + throw std::runtime_error( + "Can't deserialize the sparse_map/set. The " + "protocol version header is invalid."); + } + + const slz_size_type bucket_count_ds = + deserialize_value(deserializer); + const slz_size_type nb_sparse_buckets = + deserialize_value(deserializer); + const slz_size_type nb_elements = + deserialize_value(deserializer); + const slz_size_type nb_deleted_buckets = + deserialize_value(deserializer); + const float max_load_factor = deserialize_value(deserializer); + + if (!hash_compatible) { + this->max_load_factor(max_load_factor); + reserve(numeric_cast(nb_elements, + "Deserialized nb_elements is too big.")); + for (slz_size_type ibucket = 0; ibucket < nb_sparse_buckets; ibucket++) { + sparse_array::deserialize_values_into_sparse_hash(deserializer, *this); + } + } else { + m_bucket_count = numeric_cast( + bucket_count_ds, "Deserialized bucket_count is too big."); + + GrowthPolicy::operator=(GrowthPolicy(m_bucket_count)); + // GrowthPolicy should not modify the bucket count we got from + // deserialization + if (m_bucket_count != bucket_count_ds) { + throw std::runtime_error( + "The GrowthPolicy is not the same even though " + "hash_compatible is true."); + } + + if (nb_sparse_buckets != + sparse_array::nb_sparse_buckets(m_bucket_count)) { + throw std::runtime_error("Deserialized nb_sparse_buckets is invalid."); + } + + m_nb_elements = numeric_cast( + nb_elements, "Deserialized nb_elements is too big."); + m_nb_deleted_buckets = numeric_cast( + nb_deleted_buckets, "Deserialized nb_deleted_buckets is too big."); + + m_sparse_buckets_data.reserve(numeric_cast( + nb_sparse_buckets, "Deserialized nb_sparse_buckets is too big.")); + for (slz_size_type ibucket = 0; ibucket < nb_sparse_buckets; ibucket++) { + m_sparse_buckets_data.emplace_back( + sparse_array::deserialize_hash_compatible( + deserializer, static_cast(*this))); + } + + if (!m_sparse_buckets_data.empty()) { + m_sparse_buckets_data.back().set_as_last(); + m_sparse_buckets = m_sparse_buckets_data.data(); + } + + this->max_load_factor(max_load_factor); + if (load_factor() > this->max_load_factor()) { + throw std::runtime_error( + "Invalid max_load_factor. Check that the serializer and " + "deserializer support " + "floats correctly as they can be converted implicitely to ints."); + } + } + } + + public: + static const size_type DEFAULT_INIT_BUCKET_COUNT = 0; + static constexpr float DEFAULT_MAX_LOAD_FACTOR = 0.5f; + + /** + * Protocol version currenlty used for serialization. + */ + static const slz_size_type SERIALIZATION_PROTOCOL_VERSION = 1; + + /** + * Return an always valid pointer to an static empty bucket_entry with + * last_bucket() == true. + */ + sparse_array *static_empty_sparse_bucket_ptr() { + static sparse_array empty_sparse_bucket(true); + return &empty_sparse_bucket; + } + + private: + sparse_buckets_container m_sparse_buckets_data; + + /** + * Points to m_sparse_buckets_data.data() if !m_sparse_buckets_data.empty() + * otherwise points to static_empty_sparse_bucket_ptr. This variable is useful + * to avoid the cost of checking if m_sparse_buckets_data is empty when trying + * to find an element. + * + * TODO Remove m_sparse_buckets_data and only use a pointer instead of a + * pointer+vector to save some space in the sparse_hash object. + */ + sparse_array *m_sparse_buckets; + + size_type m_bucket_count; + size_type m_nb_elements; + size_type m_nb_deleted_buckets; + + /** + * Maximum that m_nb_elements can reach before a rehash occurs automatically + * to grow the hash table. + */ + size_type m_load_threshold_rehash; + + /** + * Maximum that m_nb_elements + m_nb_deleted_buckets can reach before cleaning + * up the buckets marked as deleted. + */ + size_type m_load_threshold_clear_deleted; + float m_max_load_factor; +}; + +} // namespace detail_sparse_hash +} // namespace tsl + +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_map.h b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_map.h new file mode 100644 index 0000000..601742d --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_map.h @@ -0,0 +1,800 @@ +/** + * MIT License + * + * Copyright (c) 2017 Thibaut Goetghebuer-Planchon + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef TSL_SPARSE_MAP_H +#define TSL_SPARSE_MAP_H + +#include +#include +#include +#include +#include +#include + +#include "sparse_hash.h" + +namespace tsl { + +/** + * Implementation of a sparse hash map using open-addressing with quadratic + * probing. The goal on the hash map is to be the most memory efficient + * possible, even at low load factor, while keeping reasonable performances. + * + * `GrowthPolicy` defines how the map grows and consequently how a hash value is + * mapped to a bucket. By default the map uses + * `tsl::sh::power_of_two_growth_policy`. This policy keeps the number of + * buckets to a power of two and uses a mask to map the hash to a bucket instead + * of the slow modulo. Other growth policies are available and you may define + * your own growth policy, check `tsl::sh::power_of_two_growth_policy` for the + * interface. + * + * `ExceptionSafety` defines the exception guarantee provided by the class. By + * default only the basic exception safety is guaranteed which mean that all + * resources used by the hash map will be freed (no memory leaks) but the hash + * map may end-up in an undefined state if an exception is thrown (undefined + * here means that some elements may be missing). This can ONLY happen on rehash + * (either on insert or if `rehash` is called explicitly) and will occur if the + * Allocator can't allocate memory (`std::bad_alloc`) or if the copy constructor + * (when a nothrow move constructor is not available) throws an exception. This + * can be avoided by calling `reserve` beforehand. This basic guarantee is + * similar to the one of `google::sparse_hash_map` and `spp::sparse_hash_map`. + * It is possible to ask for the strong exception guarantee with + * `tsl::sh::exception_safety::strong`, the drawback is that the map will be + * slower on rehashes and will also need more memory on rehashes. + * + * `Sparsity` defines how much the hash set will compromise between insertion + * speed and memory usage. A high sparsity means less memory usage but longer + * insertion times, and vice-versa for low sparsity. The default + * `tsl::sh::sparsity::medium` sparsity offers a good compromise. It doesn't + * change the lookup speed. + * + * `Key` and `T` must be nothrow move constructible and/or copy constructible. + * + * If the destructor of `Key` or `T` throws an exception, the behaviour of the + * class is undefined. + * + * Iterators invalidation: + * - clear, operator=, reserve, rehash: always invalidate the iterators. + * - insert, emplace, emplace_hint, operator[]: if there is an effective + * insert, invalidate the iterators. + * - erase: always invalidate the iterators. + */ +template , + class KeyEqual = std::equal_to, + class Allocator = std::allocator>, + class GrowthPolicy = tsl::sh::power_of_two_growth_policy<2>, + tsl::sh::exception_safety ExceptionSafety = + tsl::sh::exception_safety::basic, + tsl::sh::sparsity Sparsity = tsl::sh::sparsity::medium> +class sparse_map { + private: + template + using has_is_transparent = tsl::detail_sparse_hash::has_is_transparent; + + class KeySelect { + public: + using key_type = Key; + + const key_type &operator()( + const std::pair &key_value) const noexcept { + return key_value.first; + } + + key_type &operator()(std::pair &key_value) noexcept { + return key_value.first; + } + }; + + class ValueSelect { + public: + using value_type = T; + + const value_type &operator()( + const std::pair &key_value) const noexcept { + return key_value.second; + } + + value_type &operator()(std::pair &key_value) noexcept { + return key_value.second; + } + }; + + using ht = detail_sparse_hash::sparse_hash< + std::pair, KeySelect, ValueSelect, Hash, KeyEqual, Allocator, + GrowthPolicy, ExceptionSafety, Sparsity, tsl::sh::probing::quadratic>; + + public: + using key_type = typename ht::key_type; + using mapped_type = T; + using value_type = typename ht::value_type; + using size_type = typename ht::size_type; + using difference_type = typename ht::difference_type; + using hasher = typename ht::hasher; + using key_equal = typename ht::key_equal; + using allocator_type = typename ht::allocator_type; + using reference = typename ht::reference; + using const_reference = typename ht::const_reference; + using pointer = typename ht::pointer; + using const_pointer = typename ht::const_pointer; + using iterator = typename ht::iterator; + using const_iterator = typename ht::const_iterator; + + public: + /* + * Constructors + */ + sparse_map() : sparse_map(ht::DEFAULT_INIT_BUCKET_COUNT) {} + + explicit sparse_map(size_type bucket_count, const Hash &hash = Hash(), + const KeyEqual &equal = KeyEqual(), + const Allocator &alloc = Allocator()) + : m_ht(bucket_count, hash, equal, alloc, ht::DEFAULT_MAX_LOAD_FACTOR) {} + + sparse_map(size_type bucket_count, const Allocator &alloc) + : sparse_map(bucket_count, Hash(), KeyEqual(), alloc) {} + + sparse_map(size_type bucket_count, const Hash &hash, const Allocator &alloc) + : sparse_map(bucket_count, hash, KeyEqual(), alloc) {} + + explicit sparse_map(const Allocator &alloc) + : sparse_map(ht::DEFAULT_INIT_BUCKET_COUNT, alloc) {} + + template + sparse_map(InputIt first, InputIt last, + size_type bucket_count = ht::DEFAULT_INIT_BUCKET_COUNT, + const Hash &hash = Hash(), const KeyEqual &equal = KeyEqual(), + const Allocator &alloc = Allocator()) + : sparse_map(bucket_count, hash, equal, alloc) { + insert(first, last); + } + + template + sparse_map(InputIt first, InputIt last, size_type bucket_count, + const Allocator &alloc) + : sparse_map(first, last, bucket_count, Hash(), KeyEqual(), alloc) {} + + template + sparse_map(InputIt first, InputIt last, size_type bucket_count, + const Hash &hash, const Allocator &alloc) + : sparse_map(first, last, bucket_count, hash, KeyEqual(), alloc) {} + + sparse_map(std::initializer_list init, + size_type bucket_count = ht::DEFAULT_INIT_BUCKET_COUNT, + const Hash &hash = Hash(), const KeyEqual &equal = KeyEqual(), + const Allocator &alloc = Allocator()) + : sparse_map(init.begin(), init.end(), bucket_count, hash, equal, alloc) { + } + + sparse_map(std::initializer_list init, size_type bucket_count, + const Allocator &alloc) + : sparse_map(init.begin(), init.end(), bucket_count, Hash(), KeyEqual(), + alloc) {} + + sparse_map(std::initializer_list init, size_type bucket_count, + const Hash &hash, const Allocator &alloc) + : sparse_map(init.begin(), init.end(), bucket_count, hash, KeyEqual(), + alloc) {} + + sparse_map &operator=(std::initializer_list ilist) { + m_ht.clear(); + + m_ht.reserve(ilist.size()); + m_ht.insert(ilist.begin(), ilist.end()); + + return *this; + } + + allocator_type get_allocator() const { return m_ht.get_allocator(); } + + /* + * Iterators + */ + iterator begin() noexcept { return m_ht.begin(); } + const_iterator begin() const noexcept { return m_ht.begin(); } + const_iterator cbegin() const noexcept { return m_ht.cbegin(); } + + iterator end() noexcept { return m_ht.end(); } + const_iterator end() const noexcept { return m_ht.end(); } + const_iterator cend() const noexcept { return m_ht.cend(); } + + /* + * Capacity + */ + bool empty() const noexcept { return m_ht.empty(); } + size_type size() const noexcept { return m_ht.size(); } + size_type max_size() const noexcept { return m_ht.max_size(); } + + /* + * Modifiers + */ + void clear() noexcept { m_ht.clear(); } + + std::pair insert(const value_type &value) { + return m_ht.insert(value); + } + + template ::value>::type * = nullptr> + std::pair insert(P &&value) { + return m_ht.emplace(std::forward

(value)); + } + + std::pair insert(value_type &&value) { + return m_ht.insert(std::move(value)); + } + + iterator insert(const_iterator hint, const value_type &value) { + return m_ht.insert_hint(hint, value); + } + + template ::value>::type * = nullptr> + iterator insert(const_iterator hint, P &&value) { + return m_ht.emplace_hint(hint, std::forward

(value)); + } + + iterator insert(const_iterator hint, value_type &&value) { + return m_ht.insert_hint(hint, std::move(value)); + } + + template + void insert(InputIt first, InputIt last) { + m_ht.insert(first, last); + } + + void insert(std::initializer_list ilist) { + m_ht.insert(ilist.begin(), ilist.end()); + } + + template + std::pair insert_or_assign(const key_type &k, M &&obj) { + return m_ht.insert_or_assign(k, std::forward(obj)); + } + + template + std::pair insert_or_assign(key_type &&k, M &&obj) { + return m_ht.insert_or_assign(std::move(k), std::forward(obj)); + } + + template + iterator insert_or_assign(const_iterator hint, const key_type &k, M &&obj) { + return m_ht.insert_or_assign(hint, k, std::forward(obj)); + } + + template + iterator insert_or_assign(const_iterator hint, key_type &&k, M &&obj) { + return m_ht.insert_or_assign(hint, std::move(k), std::forward(obj)); + } + + /** + * Due to the way elements are stored, emplace will need to move or copy the + * key-value once. The method is equivalent to + * `insert(value_type(std::forward(args)...));`. + * + * Mainly here for compatibility with the `std::unordered_map` interface. + */ + template + std::pair emplace(Args &&...args) { + return m_ht.emplace(std::forward(args)...); + } + + /** + * Due to the way elements are stored, emplace_hint will need to move or copy + * the key-value once. The method is equivalent to `insert(hint, + * value_type(std::forward(args)...));`. + * + * Mainly here for compatibility with the `std::unordered_map` interface. + */ + template + iterator emplace_hint(const_iterator hint, Args &&...args) { + return m_ht.emplace_hint(hint, std::forward(args)...); + } + + template + std::pair try_emplace(const key_type &k, Args &&...args) { + return m_ht.try_emplace(k, std::forward(args)...); + } + + template + std::pair try_emplace(key_type &&k, Args &&...args) { + return m_ht.try_emplace(std::move(k), std::forward(args)...); + } + + template + iterator try_emplace(const_iterator hint, const key_type &k, Args &&...args) { + return m_ht.try_emplace_hint(hint, k, std::forward(args)...); + } + + template + iterator try_emplace(const_iterator hint, key_type &&k, Args &&...args) { + return m_ht.try_emplace_hint(hint, std::move(k), + std::forward(args)...); + } + + iterator erase(iterator pos) { return m_ht.erase(pos); } + iterator erase(const_iterator pos) { return m_ht.erase(pos); } + iterator erase(const_iterator first, const_iterator last) { + return m_ht.erase(first, last); + } + size_type erase(const key_type &key) { return m_ht.erase(key); } + + /** + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + size_type erase(const key_type &key, std::size_t precalculated_hash) { + return m_ht.erase(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * `KeyEqual::is_transparent` exists. If so, `K` must be hashable and + * comparable to `Key`. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + size_type erase(const K &key) { + return m_ht.erase(key); + } + + /** + * @copydoc erase(const K& key) + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + size_type erase(const K &key, std::size_t precalculated_hash) { + return m_ht.erase(key, precalculated_hash); + } + + void swap(sparse_map &other) { other.m_ht.swap(m_ht); } + + /* + * Lookup + */ + T &at(const Key &key) { return m_ht.at(key); } + + /** + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + T &at(const Key &key, std::size_t precalculated_hash) { + return m_ht.at(key, precalculated_hash); + } + + const T &at(const Key &key) const { return m_ht.at(key); } + + /** + * @copydoc at(const Key& key, std::size_t precalculated_hash) + */ + const T &at(const Key &key, std::size_t precalculated_hash) const { + return m_ht.at(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * `KeyEqual::is_transparent` exists. If so, `K` must be hashable and + * comparable to `Key`. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + T &at(const K &key) { + return m_ht.at(key); + } + + /** + * @copydoc at(const K& key) + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + T &at(const K &key, std::size_t precalculated_hash) { + return m_ht.at(key, precalculated_hash); + } + + /** + * @copydoc at(const K& key) + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + const T &at(const K &key) const { + return m_ht.at(key); + } + + /** + * @copydoc at(const K& key, std::size_t precalculated_hash) + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + const T &at(const K &key, std::size_t precalculated_hash) const { + return m_ht.at(key, precalculated_hash); + } + + T &operator[](const Key &key) { return m_ht[key]; } + T &operator[](Key &&key) { return m_ht[std::move(key)]; } + + size_type count(const Key &key) const { return m_ht.count(key); } + + /** + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + size_type count(const Key &key, std::size_t precalculated_hash) const { + return m_ht.count(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * `KeyEqual::is_transparent` exists. If so, `K` must be hashable and + * comparable to `Key`. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + size_type count(const K &key) const { + return m_ht.count(key); + } + + /** + * @copydoc count(const K& key) const + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + size_type count(const K &key, std::size_t precalculated_hash) const { + return m_ht.count(key, precalculated_hash); + } + + iterator find(const Key &key) { return m_ht.find(key); } + + /** + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + iterator find(const Key &key, std::size_t precalculated_hash) { + return m_ht.find(key, precalculated_hash); + } + + const_iterator find(const Key &key) const { return m_ht.find(key); } + + /** + * @copydoc find(const Key& key, std::size_t precalculated_hash) + */ + const_iterator find(const Key &key, std::size_t precalculated_hash) const { + return m_ht.find(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * `KeyEqual::is_transparent` exists. If so, `K` must be hashable and + * comparable to `Key`. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + iterator find(const K &key) { + return m_ht.find(key); + } + + /** + * @copydoc find(const K& key) + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + iterator find(const K &key, std::size_t precalculated_hash) { + return m_ht.find(key, precalculated_hash); + } + + /** + * @copydoc find(const K& key) + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + const_iterator find(const K &key) const { + return m_ht.find(key); + } + + /** + * @copydoc find(const K& key) + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + const_iterator find(const K &key, std::size_t precalculated_hash) const { + return m_ht.find(key, precalculated_hash); + } + + bool contains(const Key &key) const { return m_ht.contains(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The + * hash value should be the same as hash_function()(key). Useful to speed-up + * the lookup if you already have the hash. + */ + bool contains(const Key &key, std::size_t precalculated_hash) const { + return m_ht.contains(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * KeyEqual::is_transparent exists. If so, K must be hashable and comparable + * to Key. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + bool contains(const K &key) const { + return m_ht.contains(key); + } + + /** + * @copydoc contains(const K& key) const + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The + * hash value should be the same as hash_function()(key). Useful to speed-up + * the lookup if you already have the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + bool contains(const K &key, std::size_t precalculated_hash) const { + return m_ht.contains(key, precalculated_hash); + } + + std::pair equal_range(const Key &key) { + return m_ht.equal_range(key); + } + + /** + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + std::pair equal_range(const Key &key, + std::size_t precalculated_hash) { + return m_ht.equal_range(key, precalculated_hash); + } + + std::pair equal_range(const Key &key) const { + return m_ht.equal_range(key); + } + + /** + * @copydoc equal_range(const Key& key, std::size_t precalculated_hash) + */ + std::pair equal_range( + const Key &key, std::size_t precalculated_hash) const { + return m_ht.equal_range(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * `KeyEqual::is_transparent` exists. If so, `K` must be hashable and + * comparable to `Key`. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + std::pair equal_range(const K &key) { + return m_ht.equal_range(key); + } + + /** + * @copydoc equal_range(const K& key) + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + std::pair equal_range(const K &key, + std::size_t precalculated_hash) { + return m_ht.equal_range(key, precalculated_hash); + } + + /** + * @copydoc equal_range(const K& key) + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + std::pair equal_range(const K &key) const { + return m_ht.equal_range(key); + } + + /** + * @copydoc equal_range(const K& key, std::size_t precalculated_hash) + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + std::pair equal_range( + const K &key, std::size_t precalculated_hash) const { + return m_ht.equal_range(key, precalculated_hash); + } + + /* + * Bucket interface + */ + size_type bucket_count() const { return m_ht.bucket_count(); } + size_type max_bucket_count() const { return m_ht.max_bucket_count(); } + + /* + * Hash policy + */ + float load_factor() const { return m_ht.load_factor(); } + float max_load_factor() const { return m_ht.max_load_factor(); } + void max_load_factor(float ml) { m_ht.max_load_factor(ml); } + + void rehash(size_type count) { m_ht.rehash(count); } + void reserve(size_type count) { m_ht.reserve(count); } + + /* + * Observers + */ + hasher hash_function() const { return m_ht.hash_function(); } + key_equal key_eq() const { return m_ht.key_eq(); } + + /* + * Other + */ + + /** + * Convert a `const_iterator` to an `iterator`. + */ + iterator mutable_iterator(const_iterator pos) { + return m_ht.mutable_iterator(pos); + } + + /** + * Serialize the map through the `serializer` parameter. + * + * The `serializer` parameter must be a function object that supports the + * following call: + * - `template void operator()(const U& value);` where the types + * `std::uint64_t`, `float` and `std::pair` must be supported for U. + * + * The implementation leaves binary compatibility (endianness, IEEE 754 for + * floats, ...) of the types it serializes in the hands of the `Serializer` + * function object if compatibility is required. + */ + template + void serialize(Serializer &serializer) const { + m_ht.serialize(serializer); + } + + /** + * Deserialize a previously serialized map through the `deserializer` + * parameter. + * + * The `deserializer` parameter must be a function object that supports the + * following calls: + * - `template U operator()();` where the types `std::uint64_t`, + * `float` and `std::pair` must be supported for U. + * + * If the deserialized hash map type is hash compatible with the serialized + * map, the deserialization process can be sped up by setting + * `hash_compatible` to true. To be hash compatible, the Hash, KeyEqual and + * GrowthPolicy must behave the same way than the ones used on the serialized + * map. The `std::size_t` must also be of the same size as the one on the + * platform used to serialize the map. If these criteria are not met, the + * behaviour is undefined with `hash_compatible` sets to true. + * + * The behaviour is undefined if the type `Key` and `T` of the `sparse_map` + * are not the same as the types used during serialization. + * + * The implementation leaves binary compatibility (endianness, IEEE 754 for + * floats, size of int, ...) of the types it deserializes in the hands of the + * `Deserializer` function object if compatibility is required. + */ + template + static sparse_map deserialize(Deserializer &deserializer, + bool hash_compatible = false) { + sparse_map map(0); + map.m_ht.deserialize(deserializer, hash_compatible); + + return map; + } + + friend bool operator==(const sparse_map &lhs, const sparse_map &rhs) { + if (lhs.size() != rhs.size()) { + return false; + } + + for (const auto &element_lhs : lhs) { + const auto it_element_rhs = rhs.find(element_lhs.first); + if (it_element_rhs == rhs.cend() || + element_lhs.second != it_element_rhs->second) { + return false; + } + } + + return true; + } + + friend bool operator!=(const sparse_map &lhs, const sparse_map &rhs) { + return !operator==(lhs, rhs); + } + + friend void swap(sparse_map &lhs, sparse_map &rhs) { lhs.swap(rhs); } + + private: + ht m_ht; +}; + +/** + * Same as `tsl::sparse_map`. + */ +template , + class KeyEqual = std::equal_to, + class Allocator = std::allocator>> +using sparse_pg_map = + sparse_map; + +} // end namespace tsl + +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_set.h b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_set.h new file mode 100644 index 0000000..3ce6a58 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/tsl/sparse_set.h @@ -0,0 +1,655 @@ +/** + * MIT License + * + * Copyright (c) 2017 Thibaut Goetghebuer-Planchon + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef TSL_SPARSE_SET_H +#define TSL_SPARSE_SET_H + +#include +#include +#include +#include +#include +#include + +#include "sparse_hash.h" + +namespace tsl { + +/** + * Implementation of a sparse hash set using open-addressing with quadratic + * probing. The goal on the hash set is to be the most memory efficient + * possible, even at low load factor, while keeping reasonable performances. + * + * `GrowthPolicy` defines how the set grows and consequently how a hash value is + * mapped to a bucket. By default the set uses + * `tsl::sh::power_of_two_growth_policy`. This policy keeps the number of + * buckets to a power of two and uses a mask to map the hash to a bucket instead + * of the slow modulo. Other growth policies are available and you may define + * your own growth policy, check `tsl::sh::power_of_two_growth_policy` for the + * interface. + * + * `ExceptionSafety` defines the exception guarantee provided by the class. By + * default only the basic exception safety is guaranteed which mean that all + * resources used by the hash set will be freed (no memory leaks) but the hash + * set may end-up in an undefined state if an exception is thrown (undefined + * here means that some elements may be missing). This can ONLY happen on rehash + * (either on insert or if `rehash` is called explicitly) and will occur if the + * Allocator can't allocate memory (`std::bad_alloc`) or if the copy constructor + * (when a nothrow move constructor is not available) throws an exception. This + * can be avoided by calling `reserve` beforehand. This basic guarantee is + * similar to the one of `google::sparse_hash_map` and `spp::sparse_hash_map`. + * It is possible to ask for the strong exception guarantee with + * `tsl::sh::exception_safety::strong`, the drawback is that the set will be + * slower on rehashes and will also need more memory on rehashes. + * + * `Sparsity` defines how much the hash set will compromise between insertion + * speed and memory usage. A high sparsity means less memory usage but longer + * insertion times, and vice-versa for low sparsity. The default + * `tsl::sh::sparsity::medium` sparsity offers a good compromise. It doesn't + * change the lookup speed. + * + * `Key` must be nothrow move constructible and/or copy constructible. + * + * If the destructor of `Key` throws an exception, the behaviour of the class is + * undefined. + * + * Iterators invalidation: + * - clear, operator=, reserve, rehash: always invalidate the iterators. + * - insert, emplace, emplace_hint: if there is an effective insert, invalidate + * the iterators. + * - erase: always invalidate the iterators. + */ +template , + class KeyEqual = std::equal_to, + class Allocator = std::allocator, + class GrowthPolicy = tsl::sh::power_of_two_growth_policy<2>, + tsl::sh::exception_safety ExceptionSafety = + tsl::sh::exception_safety::basic, + tsl::sh::sparsity Sparsity = tsl::sh::sparsity::medium> +class sparse_set { + private: + template + using has_is_transparent = tsl::detail_sparse_hash::has_is_transparent; + + class KeySelect { + public: + using key_type = Key; + + const key_type &operator()(const Key &key) const noexcept { return key; } + + key_type &operator()(Key &key) noexcept { return key; } + }; + + using ht = + detail_sparse_hash::sparse_hash; + + public: + using key_type = typename ht::key_type; + using value_type = typename ht::value_type; + using size_type = typename ht::size_type; + using difference_type = typename ht::difference_type; + using hasher = typename ht::hasher; + using key_equal = typename ht::key_equal; + using allocator_type = typename ht::allocator_type; + using reference = typename ht::reference; + using const_reference = typename ht::const_reference; + using pointer = typename ht::pointer; + using const_pointer = typename ht::const_pointer; + using iterator = typename ht::iterator; + using const_iterator = typename ht::const_iterator; + + /* + * Constructors + */ + sparse_set() : sparse_set(ht::DEFAULT_INIT_BUCKET_COUNT) {} + + explicit sparse_set(size_type bucket_count, const Hash &hash = Hash(), + const KeyEqual &equal = KeyEqual(), + const Allocator &alloc = Allocator()) + : m_ht(bucket_count, hash, equal, alloc, ht::DEFAULT_MAX_LOAD_FACTOR) {} + + sparse_set(size_type bucket_count, const Allocator &alloc) + : sparse_set(bucket_count, Hash(), KeyEqual(), alloc) {} + + sparse_set(size_type bucket_count, const Hash &hash, const Allocator &alloc) + : sparse_set(bucket_count, hash, KeyEqual(), alloc) {} + + explicit sparse_set(const Allocator &alloc) + : sparse_set(ht::DEFAULT_INIT_BUCKET_COUNT, alloc) {} + + template + sparse_set(InputIt first, InputIt last, + size_type bucket_count = ht::DEFAULT_INIT_BUCKET_COUNT, + const Hash &hash = Hash(), const KeyEqual &equal = KeyEqual(), + const Allocator &alloc = Allocator()) + : sparse_set(bucket_count, hash, equal, alloc) { + insert(first, last); + } + + template + sparse_set(InputIt first, InputIt last, size_type bucket_count, + const Allocator &alloc) + : sparse_set(first, last, bucket_count, Hash(), KeyEqual(), alloc) {} + + template + sparse_set(InputIt first, InputIt last, size_type bucket_count, + const Hash &hash, const Allocator &alloc) + : sparse_set(first, last, bucket_count, hash, KeyEqual(), alloc) {} + + sparse_set(std::initializer_list init, + size_type bucket_count = ht::DEFAULT_INIT_BUCKET_COUNT, + const Hash &hash = Hash(), const KeyEqual &equal = KeyEqual(), + const Allocator &alloc = Allocator()) + : sparse_set(init.begin(), init.end(), bucket_count, hash, equal, alloc) { + } + + sparse_set(std::initializer_list init, size_type bucket_count, + const Allocator &alloc) + : sparse_set(init.begin(), init.end(), bucket_count, Hash(), KeyEqual(), + alloc) {} + + sparse_set(std::initializer_list init, size_type bucket_count, + const Hash &hash, const Allocator &alloc) + : sparse_set(init.begin(), init.end(), bucket_count, hash, KeyEqual(), + alloc) {} + + sparse_set &operator=(std::initializer_list ilist) { + m_ht.clear(); + + m_ht.reserve(ilist.size()); + m_ht.insert(ilist.begin(), ilist.end()); + + return *this; + } + + allocator_type get_allocator() const { return m_ht.get_allocator(); } + + /* + * Iterators + */ + iterator begin() noexcept { return m_ht.begin(); } + const_iterator begin() const noexcept { return m_ht.begin(); } + const_iterator cbegin() const noexcept { return m_ht.cbegin(); } + + iterator end() noexcept { return m_ht.end(); } + const_iterator end() const noexcept { return m_ht.end(); } + const_iterator cend() const noexcept { return m_ht.cend(); } + + /* + * Capacity + */ + bool empty() const noexcept { return m_ht.empty(); } + size_type size() const noexcept { return m_ht.size(); } + size_type max_size() const noexcept { return m_ht.max_size(); } + + /* + * Modifiers + */ + void clear() noexcept { m_ht.clear(); } + + std::pair insert(const value_type &value) { + return m_ht.insert(value); + } + + std::pair insert(value_type &&value) { + return m_ht.insert(std::move(value)); + } + + iterator insert(const_iterator hint, const value_type &value) { + return m_ht.insert_hint(hint, value); + } + + iterator insert(const_iterator hint, value_type &&value) { + return m_ht.insert_hint(hint, std::move(value)); + } + + template + void insert(InputIt first, InputIt last) { + m_ht.insert(first, last); + } + + void insert(std::initializer_list ilist) { + m_ht.insert(ilist.begin(), ilist.end()); + } + + /** + * Due to the way elements are stored, emplace will need to move or copy the + * key-value once. The method is equivalent to + * `insert(value_type(std::forward(args)...));`. + * + * Mainly here for compatibility with the `std::unordered_map` interface. + */ + template + std::pair emplace(Args &&...args) { + return m_ht.emplace(std::forward(args)...); + } + + /** + * Due to the way elements are stored, emplace_hint will need to move or copy + * the key-value once. The method is equivalent to `insert(hint, + * value_type(std::forward(args)...));`. + * + * Mainly here for compatibility with the `std::unordered_map` interface. + */ + template + iterator emplace_hint(const_iterator hint, Args &&...args) { + return m_ht.emplace_hint(hint, std::forward(args)...); + } + + iterator erase(iterator pos) { return m_ht.erase(pos); } + iterator erase(const_iterator pos) { return m_ht.erase(pos); } + iterator erase(const_iterator first, const_iterator last) { + return m_ht.erase(first, last); + } + size_type erase(const key_type &key) { return m_ht.erase(key); } + + /** + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + size_type erase(const key_type &key, std::size_t precalculated_hash) { + return m_ht.erase(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * `KeyEqual::is_transparent` exists. If so, `K` must be hashable and + * comparable to `Key`. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + size_type erase(const K &key) { + return m_ht.erase(key); + } + + /** + * @copydoc erase(const K& key) + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + size_type erase(const K &key, std::size_t precalculated_hash) { + return m_ht.erase(key, precalculated_hash); + } + + void swap(sparse_set &other) { other.m_ht.swap(m_ht); } + + /* + * Lookup + */ + size_type count(const Key &key) const { return m_ht.count(key); } + + /** + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + size_type count(const Key &key, std::size_t precalculated_hash) const { + return m_ht.count(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * `KeyEqual::is_transparent` exists. If so, `K` must be hashable and + * comparable to `Key`. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + size_type count(const K &key) const { + return m_ht.count(key); + } + + /** + * @copydoc count(const K& key) const + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + size_type count(const K &key, std::size_t precalculated_hash) const { + return m_ht.count(key, precalculated_hash); + } + + iterator find(const Key &key) { return m_ht.find(key); } + + /** + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + iterator find(const Key &key, std::size_t precalculated_hash) { + return m_ht.find(key, precalculated_hash); + } + + const_iterator find(const Key &key) const { return m_ht.find(key); } + + /** + * @copydoc find(const Key& key, std::size_t precalculated_hash) + */ + const_iterator find(const Key &key, std::size_t precalculated_hash) const { + return m_ht.find(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * `KeyEqual::is_transparent` exists. If so, `K` must be hashable and + * comparable to `Key`. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + iterator find(const K &key) { + return m_ht.find(key); + } + + /** + * @copydoc find(const K& key) + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + iterator find(const K &key, std::size_t precalculated_hash) { + return m_ht.find(key, precalculated_hash); + } + + /** + * @copydoc find(const K& key) + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + const_iterator find(const K &key) const { + return m_ht.find(key); + } + + /** + * @copydoc find(const K& key) + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + const_iterator find(const K &key, std::size_t precalculated_hash) const { + return m_ht.find(key, precalculated_hash); + } + + bool contains(const Key &key) const { return m_ht.contains(key); } + + /** + * Use the hash value 'precalculated_hash' instead of hashing the key. The + * hash value should be the same as hash_function()(key). Useful to speed-up + * the lookup if you already have the hash. + */ + bool contains(const Key &key, std::size_t precalculated_hash) const { + return m_ht.contains(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * KeyEqual::is_transparent exists. If so, K must be hashable and comparable + * to Key. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + bool contains(const K &key) const { + return m_ht.contains(key); + } + + /** + * @copydoc contains(const K& key) const + * + * Use the hash value 'precalculated_hash' instead of hashing the key. The + * hash value should be the same as hash_function()(key). Useful to speed-up + * the lookup if you already have the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + bool contains(const K &key, std::size_t precalculated_hash) const { + return m_ht.contains(key, precalculated_hash); + } + + std::pair equal_range(const Key &key) { + return m_ht.equal_range(key); + } + + /** + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + std::pair equal_range(const Key &key, + std::size_t precalculated_hash) { + return m_ht.equal_range(key, precalculated_hash); + } + + std::pair equal_range(const Key &key) const { + return m_ht.equal_range(key); + } + + /** + * @copydoc equal_range(const Key& key, std::size_t precalculated_hash) + */ + std::pair equal_range( + const Key &key, std::size_t precalculated_hash) const { + return m_ht.equal_range(key, precalculated_hash); + } + + /** + * This overload only participates in the overload resolution if the typedef + * `KeyEqual::is_transparent` exists. If so, `K` must be hashable and + * comparable to `Key`. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + std::pair equal_range(const K &key) { + return m_ht.equal_range(key); + } + + /** + * @copydoc equal_range(const K& key) + * + * Use the hash value `precalculated_hash` instead of hashing the key. The + * hash value should be the same as `hash_function()(key)`, otherwise the + * behaviour is undefined. Useful to speed-up the lookup if you already have + * the hash. + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + std::pair equal_range(const K &key, + std::size_t precalculated_hash) { + return m_ht.equal_range(key, precalculated_hash); + } + + /** + * @copydoc equal_range(const K& key) + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + std::pair equal_range(const K &key) const { + return m_ht.equal_range(key); + } + + /** + * @copydoc equal_range(const K& key, std::size_t precalculated_hash) + */ + template < + class K, class KE = KeyEqual, + typename std::enable_if::value>::type * = nullptr> + std::pair equal_range( + const K &key, std::size_t precalculated_hash) const { + return m_ht.equal_range(key, precalculated_hash); + } + + /* + * Bucket interface + */ + size_type bucket_count() const { return m_ht.bucket_count(); } + size_type max_bucket_count() const { return m_ht.max_bucket_count(); } + + /* + * Hash policy + */ + float load_factor() const { return m_ht.load_factor(); } + float max_load_factor() const { return m_ht.max_load_factor(); } + void max_load_factor(float ml) { m_ht.max_load_factor(ml); } + + void rehash(size_type count) { m_ht.rehash(count); } + void reserve(size_type count) { m_ht.reserve(count); } + + /* + * Observers + */ + hasher hash_function() const { return m_ht.hash_function(); } + key_equal key_eq() const { return m_ht.key_eq(); } + + /* + * Other + */ + + /** + * Convert a `const_iterator` to an `iterator`. + */ + iterator mutable_iterator(const_iterator pos) { + return m_ht.mutable_iterator(pos); + } + + /** + * Serialize the set through the `serializer` parameter. + * + * The `serializer` parameter must be a function object that supports the + * following call: + * - `void operator()(const U& value);` where the types `std::uint64_t`, + * `float` and `Key` must be supported for U. + * + * The implementation leaves binary compatibility (endianness, IEEE 754 for + * floats, ...) of the types it serializes in the hands of the `Serializer` + * function object if compatibility is required. + */ + template + void serialize(Serializer &serializer) const { + m_ht.serialize(serializer); + } + + /** + * Deserialize a previously serialized set through the `deserializer` + * parameter. + * + * The `deserializer` parameter must be a function object that supports the + * following calls: + * - `template U operator()();` where the types `std::uint64_t`, + * `float` and `Key` must be supported for U. + * + * If the deserialized hash set type is hash compatible with the serialized + * set, the deserialization process can be sped up by setting + * `hash_compatible` to true. To be hash compatible, the Hash, KeyEqual and + * GrowthPolicy must behave the same way than the ones used on the serialized + * set. The `std::size_t` must also be of the same size as the one on the + * platform used to serialize the set. If these criteria are not met, the + * behaviour is undefined with `hash_compatible` sets to true. + * + * The behaviour is undefined if the type `Key` of the `sparse_set` is not the + * same as the type used during serialization. + * + * The implementation leaves binary compatibility (endianness, IEEE 754 for + * floats, size of int, ...) of the types it deserializes in the hands of the + * `Deserializer` function object if compatibility is required. + */ + template + static sparse_set deserialize(Deserializer &deserializer, + bool hash_compatible = false) { + sparse_set set(0); + set.m_ht.deserialize(deserializer, hash_compatible); + + return set; + } + + friend bool operator==(const sparse_set &lhs, const sparse_set &rhs) { + if (lhs.size() != rhs.size()) { + return false; + } + + for (const auto &element_lhs : lhs) { + const auto it_element_rhs = rhs.find(element_lhs); + if (it_element_rhs == rhs.cend()) { + return false; + } + } + + return true; + } + + friend bool operator!=(const sparse_set &lhs, const sparse_set &rhs) { + return !operator==(lhs, rhs); + } + + friend void swap(sparse_set &lhs, sparse_set &rhs) { lhs.swap(rhs); } + + private: + ht m_ht; +}; + +/** + * Same as `tsl::sparse_set`. + */ +template , + class KeyEqual = std::equal_to, + class Allocator = std::allocator> +using sparse_pg_set = + sparse_set; + +} // end namespace tsl + +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/types.h b/packages/leann-backend-diskann/third_party/DiskANN/include/types.h new file mode 100644 index 0000000..953d59a --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/types.h @@ -0,0 +1,22 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include +#include +#include "any_wrappers.h" + +namespace diskann +{ +typedef uint32_t location_t; + +using DataType = std::any; +using TagType = std::any; +using LabelType = std::any; +using TagVector = AnyWrapper::AnyVector; +using DataVector = AnyWrapper::AnyVector; +using Labelvector = AnyWrapper::AnyVector; +using TagRobinSet = AnyWrapper::AnyRobinSet; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/utils.h b/packages/leann-backend-diskann/third_party/DiskANN/include/utils.h new file mode 100644 index 0000000..355a613 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/utils.h @@ -0,0 +1,1455 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include + +#include "common_includes.h" + +#ifdef __APPLE__ +#include +#else +#include +#endif + +#ifdef _WINDOWS +#include +typedef HANDLE FileHandle; +#else +#include +typedef int FileHandle; +#endif + +#include "distance.h" +#include "logger.h" +#include "cached_io.h" +#include "ann_exception.h" +#include "windows_customizations.h" +#include "tsl/robin_set.h" +#include "types.h" +#include "tag_uint128.h" +#include + +#ifdef EXEC_ENV_OLS +#include "content_buf.h" +#include "memory_mapped_files.h" +#endif + +#ifdef __APPLE__ +#ifdef __arm64__ +#define _MM_HINT_T0 1 +#define _MM_HINT_T1 2 + +static inline __attribute__((always_inline)) void _mm_prefetch(char const *p, int i) +{ + switch (i) + { + case _MM_HINT_T0: + __builtin_prefetch(p, 0, 3); + break; + case _MM_HINT_T1: + __builtin_prefetch(p, 0, 2); + break; + } +} +#endif + +#define LAPACK_COL_MAJOR 1 +#define LAPACK_ROW_MAJOR 0 +#ifdef __APPLE__ +typedef int clp_int; +#else +typedef __CLPK_integer clp_int; +#endif + +inline void _sge_trans(int matrix_layout, clp_int m, clp_int n, const float *in, clp_int ldin, float *out, + clp_int ldout) +{ + clp_int i, j, x, y; + + if (matrix_layout == LAPACK_COL_MAJOR) + { + x = n; + y = m; + } + else + { + x = m; + y = n; + } + for (i = 0; i < MIN(y, ldin); i++) + { + for (j = 0; j < MIN(x, ldout); j++) + { + out[(size_t)i * ldout + j] = in[(size_t)j * ldin + i]; + } + } +} +inline clp_int sgesdd_rm_work(char jobz, clp_int m, clp_int n, float *a, clp_int lda, float *s, float *u, clp_int ldu, + float *vt, clp_int ldvt, float *work, clp_int lwork, clp_int *iwork) +{ + clp_int info = 0; + clp_int nrows_u = ((jobz == 'a') || (jobz == 's') || ((jobz == 'o') && m < n)) ? m : 1; + clp_int ncols_u = ((jobz == 'a') || ((jobz == 'o') && m < n)) ? m : ((jobz == 's') ? MIN(m, n) : 1); + clp_int nrows_vt = ((jobz == 'a') || ((jobz == 'o') && m >= n)) ? n : ((jobz == 's') ? MIN(m, n) : 1); + + clp_int lda_t = MAX(1, m); + clp_int ldu_t = MAX(1, nrows_u); + clp_int ldvt_t = MAX(1, nrows_vt); + float *a_t = NULL; + float *u_t = NULL; + float *vt_t = NULL; + + // check leading dimensions + if (lda < n) + { + info = -6; + return info; + } + if (ldu < ncols_u) + { + info = -9; + return info; + } + if (ldvt < n) + { + info = -11; + return info; + } + + // query for optimal work size if lwork = -1 + if (lwork == -1) + { + sgesdd_(&jobz, &m, &n, a, &lda_t, s, u, &ldu_t, vt, &ldvt_t, work, &lwork, iwork, &info); + return (info < 0) ? (info - 1) : info; + } + + // setup temp arrays + a_t = (float *)malloc(sizeof(float) * lda_t * MAX(1, n)); + if (a_t == NULL) + { + info = -1011; + return info; + } + if ((jobz == 'a') || (jobz == 's') || ((jobz == 'o') && (m < n))) + { + u_t = (float *)malloc(sizeof(float) * ldu_t * MAX(1, ncols_u)); + if (u_t == NULL) + { + info = -1011; + free(a_t); + return info; + } + } + if ((jobz == 'a') || (jobz == 's') || ((jobz == 'o') && (m >= n))) + { + vt_t = (float *)malloc(sizeof(float) * ldvt_t * MAX(1, n)); + if (vt_t == NULL) + { + info = -1011; + free(a_t); + if ((jobz == 'a') || (jobz == 's') || ((jobz == 'o') && (m < n))) + { + free(u_t); + } + return info; + } + } + + _sge_trans(LAPACK_ROW_MAJOR, m, n, a, lda, a_t, lda_t); + sgesdd_(&jobz, &m, &n, a_t, &lda_t, s, u_t, &ldu_t, vt_t, &ldvt_t, work, &lwork, iwork, &info); + + if (info < 0) + { + info = info - 1; + } + /* Transpose output matrices */ + _sge_trans(LAPACK_COL_MAJOR, m, n, a_t, lda_t, a, lda); + if ((jobz == 'a') || (jobz == 's') || ((jobz == 'o') && (m < n))) + { + _sge_trans(LAPACK_COL_MAJOR, nrows_u, ncols_u, u_t, ldu_t, u, ldu); + } + if ((jobz == 'a') || (jobz == 's') || ((jobz == 'o') && (m >= n))) + { + _sge_trans(LAPACK_COL_MAJOR, nrows_vt, n, vt_t, ldvt_t, vt, ldvt); + } + /* Release memory and exit */ + if ((jobz == 'a') || (jobz == 's') || ((jobz == 'o') && (m >= n))) + { + free(vt_t); + } + if ((jobz == 'a') || (jobz == 's') || ((jobz == 'o') && (m < n))) + { + free(u_t); + } + free(a_t); + return info; +} + +inline clp_int LAPACKE_sgesdd(int matrix_layout, char jobz, clp_int m, clp_int n, float *a, clp_int lda, float *s, + float *u, clp_int ldu, float *vt, clp_int ldvt) +{ + // internal SGESDD vars + clp_int info = 0; + clp_int lwork = -1; + clp_int *iwork = NULL; + float *work = NULL; + float work_query; + + // allocate space for iwork + iwork = (clp_int *)malloc(sizeof(clp_int) * MAX(1, 8 * MIN(m, n))); + if (iwork == NULL) + throw; + /* Query optimal working array(s) size */ + info = sgesdd_rm_work(jobz, m, n, a, lda, s, u, ldu, vt, ldvt, &work_query, lwork, iwork); + if (info != 0) + { + free(iwork); + info = -1010; + return info; + } + + lwork = (clp_int)work_query; + /* Allocate memory for work arrays */ + work = (float *)malloc(sizeof(float) * lwork); + if (work == NULL) + throw; + + /* Call middle-level interface */ + info = sgesdd_rm_work(jobz, m, n, a, lda, s, u, ldu, vt, ldvt, work, lwork, iwork); + /* Release memory and exit */ + free(work); + free(iwork); + return info; +} +#endif + +// taken from +// https://github.com/Microsoft/BLAS-on-flash/blob/master/include/utils.h +// round up X to the nearest multiple of Y +#define ROUND_UP(X, Y) ((((uint64_t)(X) / (Y)) + ((uint64_t)(X) % (Y) != 0)) * (Y)) + +#define DIV_ROUND_UP(X, Y) (((uint64_t)(X) / (Y)) + ((uint64_t)(X) % (Y) != 0)) + +// round down X to the nearest multiple of Y +#define ROUND_DOWN(X, Y) (((uint64_t)(X) / (Y)) * (Y)) + +// alignment tests +#define IS_ALIGNED(X, Y) ((uint64_t)(X) % (uint64_t)(Y) == 0) +#define IS_512_ALIGNED(X) IS_ALIGNED(X, 512) +#define IS_4096_ALIGNED(X) IS_ALIGNED(X, 4096) +#define METADATA_SIZE \ + 4096 // all metadata of individual sub-component files is written in first + // 4KB for unified files + +#define BUFFER_SIZE_FOR_CACHED_IO (size_t)1024 * (size_t)1048576 + +#define PBSTR "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||" +#define PBWIDTH 60 + +inline bool file_exists_impl(const std::string &name, bool dirCheck = false) +{ + int val; +#ifndef _WINDOWS + struct stat buffer; + val = stat(name.c_str(), &buffer); +#else + // It is the 21st century but Windows API still thinks in 32-bit terms. + // Turns out calling stat() on a file > 4GB results in errno = 132 + // (OVERFLOW). How silly is this!? So calling _stat64() + struct _stat64 buffer; + val = _stat64(name.c_str(), &buffer); +#endif + + if (val != 0) + { + switch (errno) + { + case EINVAL: + diskann::cout << "Invalid argument passed to stat()" << std::endl; + break; + case ENOENT: + // file is not existing, not an issue, so we won't cout anything. + break; + default: + diskann::cout << "Unexpected error in stat():" << errno << std::endl; + break; + } + return false; + } + else + { + // the file entry exists. If reqd, check if this is a directory. + return dirCheck ? buffer.st_mode & S_IFDIR : true; + } +} + +inline bool file_exists(const std::string &name, bool dirCheck = false) +{ +#ifdef EXEC_ENV_OLS + bool exists = file_exists_impl(name, dirCheck); + if (exists) + { + return true; + } + if (!dirCheck) + { + // try with .enc extension + std::string enc_name = name + ENCRYPTED_EXTENSION; + return file_exists_impl(enc_name, dirCheck); + } + else + { + return exists; + } +#else + return file_exists_impl(name, dirCheck); +#endif +} + +inline void open_file_to_write(std::ofstream &writer, const std::string &filename) +{ + writer.exceptions(std::ofstream::failbit | std::ofstream::badbit); + if (!file_exists(filename)) + writer.open(filename, std::ios::binary | std::ios::out); + else + writer.open(filename, std::ios::binary | std::ios::in | std::ios::out); + + if (writer.fail()) + { + char buff[1024]; +#ifdef _WINDOWS + auto ret = std::to_string(strerror_s(buff, 1024, errno)); +#elif __APPLE__ + auto ret = std::to_string(strerror_r(errno, buff, 1024)); +#else + auto ret = std::string(strerror_r(errno, buff, 1024)); +#endif + auto message = std::string("Failed to open file") + filename + " for write because " + buff + ", ret=" + ret; + diskann::cerr << message << std::endl; + throw diskann::ANNException(message, -1); + } +} + +inline size_t get_file_size(const std::string &fname) +{ + std::ifstream reader(fname, std::ios::binary | std::ios::ate); + if (!reader.fail() && reader.is_open()) + { + size_t end_pos = reader.tellg(); + reader.close(); + return end_pos; + } + else + { + diskann::cerr << "Could not open file: " << fname << std::endl; + return 0; + } +} + +inline int delete_file(const std::string &fileName) +{ + if (file_exists(fileName)) + { + auto rc = ::remove(fileName.c_str()); + if (rc != 0) + { + diskann::cerr << "Could not delete file: " << fileName + << " even though it exists. This might indicate a permissions " + "issue. " + "If you see this message, please contact the diskann team." + << std::endl; + } + return rc; + } + else + { + return 0; + } +} + +// generates formatted_label and _labels_map file. +inline void convert_labels_string_to_int(const std::string &inFileName, const std::string &outFileName, + const std::string &mapFileName, const std::string &unv_label) +{ + std::unordered_map string_int_map; + std::ofstream label_writer(outFileName); + std::ifstream label_reader(inFileName); + if (unv_label != "") + string_int_map[unv_label] = 0; // if universal label is provided map it to 0 always + std::string line, token; + while (std::getline(label_reader, line)) + { + std::istringstream new_iss(line); + std::vector lbls; + while (getline(new_iss, token, ',')) + { + token.erase(std::remove(token.begin(), token.end(), '\n'), token.end()); + token.erase(std::remove(token.begin(), token.end(), '\r'), token.end()); + if (string_int_map.find(token) == string_int_map.end()) + { + uint32_t nextId = (uint32_t)string_int_map.size() + 1; + string_int_map[token] = nextId; // nextId can never be 0 + } + lbls.push_back(string_int_map[token]); + } + if (lbls.size() <= 0) + { + std::cout << "No label found"; + exit(-1); + } + for (size_t j = 0; j < lbls.size(); j++) + { + if (j != lbls.size() - 1) + label_writer << lbls[j] << ","; + else + label_writer << lbls[j] << std::endl; + } + } + label_writer.close(); + + std::ofstream map_writer(mapFileName); + for (auto mp : string_int_map) + { + map_writer << mp.first << "\t" << mp.second << std::endl; + } + map_writer.close(); +} + +#ifdef EXEC_ENV_OLS +class AlignedFileReader; +#endif + +namespace diskann +{ +static const size_t MAX_SIZE_OF_STREAMBUF = 2LL * 1024 * 1024 * 1024; + +inline void print_error_and_terminate(std::stringstream &error_stream) +{ + diskann::cerr << error_stream.str() << std::endl; + throw diskann::ANNException(error_stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); +} + +inline void report_memory_allocation_failure() +{ + std::stringstream stream; + stream << "Memory Allocation Failed."; + print_error_and_terminate(stream); +} + +inline void report_misalignment_of_requested_size(size_t align) +{ + std::stringstream stream; + stream << "Requested memory size is not a multiple of " << align << ". Can not be allocated."; + print_error_and_terminate(stream); +} + +inline void alloc_aligned(void **ptr, size_t size, size_t align) +{ + *ptr = nullptr; + if (IS_ALIGNED(size, align) == 0) + report_misalignment_of_requested_size(align); +#ifdef _WINDOWS + *ptr = ::_aligned_malloc(size, align); // note the swapped arguments! +#elif __APPLE__ + int err = posix_memalign(ptr, align, size); + if (err) + { + std::cout << err << std::endl; + throw; + } +#else + *ptr = ::aligned_alloc(align, size); +#endif + if (*ptr == nullptr) + report_memory_allocation_failure(); +} + +inline void realloc_aligned(void **ptr, size_t size, size_t align) +{ + if (IS_ALIGNED(size, align) == 0) + report_misalignment_of_requested_size(align); +#ifdef _WINDOWS + *ptr = ::_aligned_realloc(*ptr, size, align); +#else + diskann::cerr << "No aligned realloc on GCC. Must malloc and mem_align, " + "left it out for now." + << std::endl; +#endif + if (*ptr == nullptr) + report_memory_allocation_failure(); +} + +inline void check_stop(std::string arnd) +{ + int brnd; + diskann::cout << arnd << std::endl; + std::cin >> brnd; +} + +inline void aligned_free(void *ptr) +{ + // Gopal. Must have a check here if the pointer was actually allocated by + // _alloc_aligned + if (ptr == nullptr) + { + return; + } +#ifndef _WINDOWS + free(ptr); +#else + ::_aligned_free(ptr); +#endif +} + +inline void GenRandom(std::mt19937 &rng, unsigned *addr, unsigned size, unsigned N) +{ + for (unsigned i = 0; i < size; ++i) + { + addr[i] = rng() % (N - size); + } + + std::sort(addr, addr + size); + for (unsigned i = 1; i < size; ++i) + { + if (addr[i] <= addr[i - 1]) + { + addr[i] = addr[i - 1] + 1; + } + } + unsigned off = rng() % N; + for (unsigned i = 0; i < size; ++i) + { + addr[i] = (addr[i] + off) % N; + } +} + +// get_bin_metadata functions START +inline void get_bin_metadata_impl(std::basic_istream &reader, size_t &nrows, size_t &ncols, size_t offset = 0) +{ + int nrows_32, ncols_32; + reader.seekg(offset, reader.beg); + reader.read((char *)&nrows_32, sizeof(int)); + reader.read((char *)&ncols_32, sizeof(int)); + nrows = nrows_32; + ncols = ncols_32; +} + +#ifdef EXEC_ENV_OLS +inline void get_bin_metadata(MemoryMappedFiles &files, const std::string &bin_file, size_t &nrows, size_t &ncols, + size_t offset = 0) +{ + diskann::cout << "Getting metadata for file: " << bin_file << std::endl; + auto fc = files.getContent(bin_file); + // auto cb = ContentBuf((char*) fc._content, fc._size); + // std::basic_istream reader(&cb); + // get_bin_metadata_impl(reader, nrows, ncols, offset); + + int nrows_32, ncols_32; + int32_t *metadata_ptr = (int32_t *)((char *)fc._content + offset); + nrows_32 = *metadata_ptr; + ncols_32 = *(metadata_ptr + 1); + nrows = nrows_32; + ncols = ncols_32; +} +#endif + +inline void get_bin_metadata(const std::string &bin_file, size_t &nrows, size_t &ncols, size_t offset = 0) +{ + std::ifstream reader(bin_file.c_str(), std::ios::binary); + get_bin_metadata_impl(reader, nrows, ncols, offset); +} +// get_bin_metadata functions END + +#ifndef EXEC_ENV_OLS +inline size_t get_graph_num_frozen_points(const std::string &graph_file) +{ + size_t expected_file_size; + uint32_t max_observed_degree, start; + size_t file_frozen_pts; + + std::ifstream in; + in.exceptions(std::ios::badbit | std::ios::failbit); + + in.open(graph_file, std::ios::binary); + in.read((char *)&expected_file_size, sizeof(size_t)); + in.read((char *)&max_observed_degree, sizeof(uint32_t)); + in.read((char *)&start, sizeof(uint32_t)); + in.read((char *)&file_frozen_pts, sizeof(size_t)); + + return file_frozen_pts; +} +#endif + +template inline std::string getValues(T *data, size_t num) +{ + std::stringstream stream; + stream << "["; + for (size_t i = 0; i < num; i++) + { + stream << std::to_string(data[i]) << ","; + } + stream << "]" << std::endl; + + return stream.str(); +} + +// load_bin functions START +template +inline void load_bin_impl(std::basic_istream &reader, T *&data, size_t &npts, size_t &dim, size_t file_offset = 0) +{ + int npts_i32, dim_i32; + + reader.seekg(file_offset, reader.beg); + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&dim_i32, sizeof(int)); + npts = (unsigned)npts_i32; + dim = (unsigned)dim_i32; + + std::cout << "Metadata: #pts = " << npts << ", #dims = " << dim << "..." << std::endl; + + data = new T[npts * dim]; + reader.read((char *)data, npts * dim * sizeof(T)); +} + +#ifdef EXEC_ENV_OLS +template +inline void load_bin(MemoryMappedFiles &files, const std::string &bin_file, T *&data, size_t &npts, size_t &dim, + size_t offset = 0) +{ + diskann::cout << "Reading bin file " << bin_file.c_str() << " at offset: " << offset << "..." << std::endl; + auto fc = files.getContent(bin_file); + + uint32_t t_npts, t_dim; + uint32_t *contentAsIntPtr = (uint32_t *)((char *)fc._content + offset); + t_npts = *(contentAsIntPtr); + t_dim = *(contentAsIntPtr + 1); + + npts = t_npts; + dim = t_dim; + + data = (T *)((char *)fc._content + offset + 2 * sizeof(uint32_t)); // No need to copy! +} + +DISKANN_DLLEXPORT void get_bin_metadata(AlignedFileReader &reader, size_t &npts, size_t &ndim, size_t offset = 0); +template +DISKANN_DLLEXPORT void load_bin(AlignedFileReader &reader, T *&data, size_t &npts, size_t &ndim, size_t offset = 0); +template +DISKANN_DLLEXPORT void load_bin(AlignedFileReader &reader, std::unique_ptr &data, size_t &npts, size_t &ndim, + size_t offset = 0); + +template +DISKANN_DLLEXPORT void copy_aligned_data_from_file(AlignedFileReader &reader, T *&data, size_t &npts, size_t &dim, + const size_t &rounded_dim, size_t offset = 0); + +// Unlike load_bin, assumes that data is already allocated 'size' entries +template +DISKANN_DLLEXPORT void read_array(AlignedFileReader &reader, T *data, size_t size, size_t offset = 0); + +template DISKANN_DLLEXPORT void read_value(AlignedFileReader &reader, T &value, size_t offset = 0); +#endif + +template +inline void load_bin(const std::string &bin_file, T *&data, size_t &npts, size_t &dim, size_t offset = 0) +{ + diskann::cout << "Reading bin file " << bin_file.c_str() << " ..." << std::endl; + std::ifstream reader; + reader.exceptions(std::ifstream::failbit | std::ifstream::badbit); + + try + { + diskann::cout << "Opening bin file " << bin_file.c_str() << "... " << std::endl; + reader.open(bin_file, std::ios::binary | std::ios::ate); + reader.seekg(0); + load_bin_impl(reader, data, npts, dim, offset); + } + catch (std::system_error &e) + { + throw FileException(bin_file, e, __FUNCSIG__, __FILE__, __LINE__); + } + diskann::cout << "done." << std::endl; +} + +inline void wait_for_keystroke() +{ + int a; + std::cout << "Press any number to continue.." << std::endl; + std::cin >> a; +} +// load_bin functions END + +inline void load_truthset(const std::string &bin_file, uint32_t *&ids, float *&dists, size_t &npts, size_t &dim) +{ + size_t read_blk_size = 64 * 1024 * 1024; + cached_ifstream reader(bin_file, read_blk_size); + diskann::cout << "Reading truthset file " << bin_file.c_str() << " ..." << std::endl; + size_t actual_file_size = reader.get_file_size(); + + int npts_i32, dim_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&dim_i32, sizeof(int)); + npts = (unsigned)npts_i32; + dim = (unsigned)dim_i32; + + diskann::cout << "Metadata: #pts = " << npts << ", #dims = " << dim << "... " << std::endl; + + int truthset_type = -1; // 1 means truthset has ids and distances, 2 means + // only ids, -1 is error + size_t expected_file_size_with_dists = 2 * npts * dim * sizeof(uint32_t) + 2 * sizeof(uint32_t); + + if (actual_file_size == expected_file_size_with_dists) + truthset_type = 1; + + size_t expected_file_size_just_ids = npts * dim * sizeof(uint32_t) + 2 * sizeof(uint32_t); + + if (actual_file_size == expected_file_size_just_ids) + truthset_type = 2; + + if (truthset_type == -1) + { + std::stringstream stream; + stream << "Error. File size mismatch. File should have bin format, with " + "npts followed by ngt followed by npts*ngt ids and optionally " + "followed by npts*ngt distance values; actual size: " + << actual_file_size << ", expected: " << expected_file_size_with_dists << " or " + << expected_file_size_just_ids; + diskann::cout << stream.str(); + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + + ids = new uint32_t[npts * dim]; + reader.read((char *)ids, npts * dim * sizeof(uint32_t)); + + if (truthset_type == 1) + { + dists = new float[npts * dim]; + reader.read((char *)dists, npts * dim * sizeof(float)); + } +} + +inline void prune_truthset_for_range(const std::string &bin_file, float range, + std::vector> &groundtruth, size_t &npts) +{ + size_t read_blk_size = 64 * 1024 * 1024; + cached_ifstream reader(bin_file, read_blk_size); + diskann::cout << "Reading truthset file " << bin_file.c_str() << "... " << std::endl; + size_t actual_file_size = reader.get_file_size(); + + int npts_i32, dim_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&dim_i32, sizeof(int)); + npts = (unsigned)npts_i32; + uint64_t dim = (unsigned)dim_i32; + uint32_t *ids; + float *dists; + + diskann::cout << "Metadata: #pts = " << npts << ", #dims = " << dim << "... " << std::endl; + + int truthset_type = -1; // 1 means truthset has ids and distances, 2 means + // only ids, -1 is error + size_t expected_file_size_with_dists = 2 * npts * dim * sizeof(uint32_t) + 2 * sizeof(uint32_t); + + if (actual_file_size == expected_file_size_with_dists) + truthset_type = 1; + + if (truthset_type == -1) + { + std::stringstream stream; + stream << "Error. File size mismatch. File should have bin format, with " + "npts followed by ngt followed by npts*ngt ids and optionally " + "followed by npts*ngt distance values; actual size: " + << actual_file_size << ", expected: " << expected_file_size_with_dists; + diskann::cout << stream.str(); + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + + ids = new uint32_t[npts * dim]; + reader.read((char *)ids, npts * dim * sizeof(uint32_t)); + + if (truthset_type == 1) + { + dists = new float[npts * dim]; + reader.read((char *)dists, npts * dim * sizeof(float)); + } + float min_dist = std::numeric_limits::max(); + float max_dist = 0; + groundtruth.resize(npts); + for (uint32_t i = 0; i < npts; i++) + { + groundtruth[i].clear(); + for (uint32_t j = 0; j < dim; j++) + { + if (dists[i * dim + j] <= range) + { + groundtruth[i].emplace_back(ids[i * dim + j]); + } + min_dist = min_dist > dists[i * dim + j] ? dists[i * dim + j] : min_dist; + max_dist = max_dist < dists[i * dim + j] ? dists[i * dim + j] : max_dist; + } + // std::cout<> &groundtruth, + size_t >_num) +{ + size_t read_blk_size = 64 * 1024 * 1024; + cached_ifstream reader(bin_file, read_blk_size); + diskann::cout << "Reading truthset file " << bin_file.c_str() << "... " << std::flush; + size_t actual_file_size = reader.get_file_size(); + + int nptsuint32_t, totaluint32_t; + reader.read((char *)&nptsuint32_t, sizeof(int)); + reader.read((char *)&totaluint32_t, sizeof(int)); + + gt_num = (uint64_t)nptsuint32_t; + uint64_t total_res = (uint64_t)totaluint32_t; + + diskann::cout << "Metadata: #pts = " << gt_num << ", #total_results = " << total_res << "..." << std::endl; + + size_t expected_file_size = 2 * sizeof(uint32_t) + gt_num * sizeof(uint32_t) + total_res * sizeof(uint32_t); + + if (actual_file_size != expected_file_size) + { + std::stringstream stream; + stream << "Error. File size mismatch in range truthset. actual size: " << actual_file_size + << ", expected: " << expected_file_size; + diskann::cout << stream.str(); + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + groundtruth.clear(); + groundtruth.resize(gt_num); + std::vector gt_count(gt_num); + + reader.read((char *)gt_count.data(), sizeof(uint32_t) * gt_num); + + std::vector gt_stats(gt_count); + std::sort(gt_stats.begin(), gt_stats.end()); + + std::cout << "GT count percentiles:" << std::endl; + for (uint32_t p = 0; p < 100; p += 5) + std::cout << "percentile " << p << ": " << gt_stats[static_cast(std::floor((p / 100.0) * gt_num))] + << std::endl; + std::cout << "percentile 100" + << ": " << gt_stats[gt_num - 1] << std::endl; + + for (uint32_t i = 0; i < gt_num; i++) + { + groundtruth[i].clear(); + groundtruth[i].resize(gt_count[i]); + if (gt_count[i] != 0) + reader.read((char *)groundtruth[i].data(), sizeof(uint32_t) * gt_count[i]); + } +} + +#ifdef EXEC_ENV_OLS +template +inline void load_bin(MemoryMappedFiles &files, const std::string &bin_file, std::unique_ptr &data, size_t &npts, + size_t &dim, size_t offset = 0) +{ + T *ptr; + load_bin(files, bin_file, ptr, npts, dim, offset); + data.reset(ptr); +} +#endif + +inline void copy_file(std::string in_file, std::string out_file) +{ + std::ifstream source(in_file, std::ios::binary); + std::ofstream dest(out_file, std::ios::binary); + + std::istreambuf_iterator begin_source(source); + std::istreambuf_iterator end_source; + std::ostreambuf_iterator begin_dest(dest); + std::copy(begin_source, end_source, begin_dest); + + source.close(); + dest.close(); +} + +DISKANN_DLLEXPORT double calculate_recall(unsigned num_queries, unsigned *gold_std, float *gs_dist, unsigned dim_gs, + unsigned *our_results, unsigned dim_or, unsigned recall_at); + +DISKANN_DLLEXPORT double calculate_recall(unsigned num_queries, unsigned *gold_std, float *gs_dist, unsigned dim_gs, + unsigned *our_results, unsigned dim_or, unsigned recall_at, + const tsl::robin_set &active_tags); + +DISKANN_DLLEXPORT double calculate_range_search_recall(unsigned num_queries, + std::vector> &groundtruth, + std::vector> &our_results); + +template +inline void load_bin(const std::string &bin_file, std::unique_ptr &data, size_t &npts, size_t &dim, + size_t offset = 0) +{ + T *ptr; + load_bin(bin_file, ptr, npts, dim, offset); + data.reset(ptr); +} + +inline void open_file_to_write(std::ofstream &writer, const std::string &filename) +{ + writer.exceptions(std::ofstream::failbit | std::ofstream::badbit); + if (!file_exists(filename)) + writer.open(filename, std::ios::binary | std::ios::out); + else + writer.open(filename, std::ios::binary | std::ios::in | std::ios::out); + + if (writer.fail()) + { + char buff[1024]; +#ifdef _WINDOWS + auto ret = std::to_string(strerror_s(buff, 1024, errno)); +#elif __APPLE__ + auto ret = std::to_string(strerror_r(errno, buff, 1024)); +#else + auto ret = std::string(strerror_r(errno, buff, 1024)); +#endif + + std::string error_message = + std::string("Failed to open file") + filename + " for write because " + buff + ", ret=" + ret; + diskann::cerr << error_message << std::endl; + throw diskann::ANNException(error_message, -1); + } +} + +template +inline size_t save_bin(const std::string &filename, T *data, size_t npts, size_t ndims, size_t offset = 0) +{ + std::ofstream writer; + open_file_to_write(writer, filename); + + diskann::cout << "Writing bin: " << filename.c_str() << std::endl; + writer.seekp(offset, writer.beg); + int npts_i32 = (int)npts, ndims_i32 = (int)ndims; + size_t bytes_written = npts * ndims * sizeof(T) + 2 * sizeof(uint32_t); + writer.write((char *)&npts_i32, sizeof(int)); + writer.write((char *)&ndims_i32, sizeof(int)); + diskann::cout << "bin: #pts = " << npts << ", #dims = " << ndims << ", size = " << bytes_written << "B" + << std::endl; + + writer.write((char *)data, npts * ndims * sizeof(T)); + writer.close(); + diskann::cout << "Finished writing bin." << std::endl; + return bytes_written; +} + +inline void print_progress(double percentage) +{ + int val = (int)(percentage * 100); + int lpad = (int)(percentage * PBWIDTH); + int rpad = PBWIDTH - lpad; + printf("\r%3d%% [%.*s%*s]", val, lpad, PBSTR, rpad, ""); + fflush(stdout); +} + +// load_aligned_bin functions START + +template +inline void load_aligned_bin_impl(std::basic_istream &reader, size_t actual_file_size, T *&data, size_t &npts, + size_t &dim, size_t &rounded_dim) +{ + int npts_i32, dim_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&dim_i32, sizeof(int)); + npts = (unsigned)npts_i32; + dim = (unsigned)dim_i32; + + size_t expected_actual_file_size = npts * dim * sizeof(T) + 2 * sizeof(uint32_t); + if (actual_file_size != expected_actual_file_size) + { + std::stringstream stream; + stream << "Error. File size mismatch. Actual size is " << actual_file_size << " while expected size is " + << expected_actual_file_size << " npts = " << npts << " dim = " << dim << " size of = " << sizeof(T) + << std::endl; + diskann::cout << stream.str() << std::endl; + throw diskann::ANNException(stream.str(), -1, __FUNCSIG__, __FILE__, __LINE__); + } + rounded_dim = ROUND_UP(dim, 8); + diskann::cout << "Metadata: #pts = " << npts << ", #dims = " << dim << ", aligned_dim = " << rounded_dim << "... " + << std::flush; + size_t allocSize = npts * rounded_dim * sizeof(T); + diskann::cout << "allocating aligned memory of " << allocSize << " bytes... " << std::flush; + alloc_aligned(((void **)&data), allocSize, 8 * sizeof(T)); + diskann::cout << "done. Copying data to mem_aligned buffer..." << std::flush; + + for (size_t i = 0; i < npts; i++) + { + reader.read((char *)(data + i * rounded_dim), dim * sizeof(T)); + memset(data + i * rounded_dim + dim, 0, (rounded_dim - dim) * sizeof(T)); + } + diskann::cout << " done." << std::endl; +} + +#ifdef EXEC_ENV_OLS +template +inline void load_aligned_bin(MemoryMappedFiles &files, const std::string &bin_file, T *&data, size_t &npts, size_t &dim, + size_t &rounded_dim) +{ + try + { + diskann::cout << "Opening bin file " << bin_file << " ..." << std::flush; + FileContent fc = files.getContent(bin_file); + ContentBuf buf((char *)fc._content, fc._size); + std::basic_istream reader(&buf); + + size_t actual_file_size = fc._size; + load_aligned_bin_impl(reader, actual_file_size, data, npts, dim, rounded_dim); + } + catch (std::system_error &e) + { + throw FileException(bin_file, e, __FUNCSIG__, __FILE__, __LINE__); + } +} +#endif + +template +inline void load_aligned_bin(const std::string &bin_file, T *&data, size_t &npts, size_t &dim, size_t &rounded_dim) +{ + std::ifstream reader; + reader.exceptions(std::ifstream::failbit | std::ifstream::badbit); + + try + { + diskann::cout << "Reading (with alignment) bin file " << bin_file << " ..." << std::flush; + reader.open(bin_file, std::ios::binary | std::ios::ate); + + uint64_t fsize = reader.tellg(); + reader.seekg(0); + load_aligned_bin_impl(reader, fsize, data, npts, dim, rounded_dim); + } + catch (std::system_error &e) + { + throw FileException(bin_file, e, __FUNCSIG__, __FILE__, __LINE__); + } +} + +template +void convert_types(const InType *srcmat, OutType *destmat, size_t npts, size_t dim) +{ +#pragma omp parallel for schedule(static, 65536) + for (int64_t i = 0; i < (int64_t)npts; i++) + { + for (uint64_t j = 0; j < dim; j++) + { + destmat[i * dim + j] = (OutType)srcmat[i * dim + j]; + } + } +} + +// this function will take in_file of n*d dimensions and save the output as a +// floating point matrix +// with n*(d+1) dimensions. All vectors are scaled by a large value M so that +// the norms are <=1 and the final coordinate is set so that the resulting +// norm (in d+1 coordinates) is equal to 1 this is a classical transformation +// from MIPS to L2 search from "On Symmetric and Asymmetric LSHs for Inner +// Product Search" by Neyshabur and Srebro + +template float prepare_base_for_inner_products(const std::string in_file, const std::string out_file) +{ + std::cout << "Pre-processing base file by adding extra coordinate" << std::endl; + std::ifstream in_reader(in_file.c_str(), std::ios::binary); + std::ofstream out_writer(out_file.c_str(), std::ios::binary); + uint64_t npts, in_dims, out_dims; + float max_norm = 0; + + uint32_t npts32, dims32; + in_reader.read((char *)&npts32, sizeof(uint32_t)); + in_reader.read((char *)&dims32, sizeof(uint32_t)); + + npts = npts32; + in_dims = dims32; + out_dims = in_dims + 1; + uint32_t outdims32 = (uint32_t)out_dims; + + out_writer.write((char *)&npts32, sizeof(uint32_t)); + out_writer.write((char *)&outdims32, sizeof(uint32_t)); + + size_t BLOCK_SIZE = 100000; + size_t block_size = npts <= BLOCK_SIZE ? npts : BLOCK_SIZE; + std::unique_ptr in_block_data = std::make_unique(block_size * in_dims); + std::unique_ptr out_block_data = std::make_unique(block_size * out_dims); + + std::memset(out_block_data.get(), 0, sizeof(float) * block_size * out_dims); + uint64_t num_blocks = DIV_ROUND_UP(npts, block_size); + + std::vector norms(npts, 0); + + for (uint64_t b = 0; b < num_blocks; b++) + { + uint64_t start_id = b * block_size; + uint64_t end_id = (b + 1) * block_size < npts ? (b + 1) * block_size : npts; + uint64_t block_pts = end_id - start_id; + in_reader.read((char *)in_block_data.get(), block_pts * in_dims * sizeof(T)); + for (uint64_t p = 0; p < block_pts; p++) + { + for (uint64_t j = 0; j < in_dims; j++) + { + norms[start_id + p] += in_block_data[p * in_dims + j] * in_block_data[p * in_dims + j]; + } + max_norm = max_norm > norms[start_id + p] ? max_norm : norms[start_id + p]; + } + } + + max_norm = std::sqrt(max_norm); + + in_reader.seekg(2 * sizeof(uint32_t), std::ios::beg); + for (uint64_t b = 0; b < num_blocks; b++) + { + uint64_t start_id = b * block_size; + uint64_t end_id = (b + 1) * block_size < npts ? (b + 1) * block_size : npts; + uint64_t block_pts = end_id - start_id; + in_reader.read((char *)in_block_data.get(), block_pts * in_dims * sizeof(T)); + for (uint64_t p = 0; p < block_pts; p++) + { + for (uint64_t j = 0; j < in_dims; j++) + { + out_block_data[p * out_dims + j] = in_block_data[p * in_dims + j] / max_norm; + } + float res = 1 - (norms[start_id + p] / (max_norm * max_norm)); + res = res <= 0 ? 0 : std::sqrt(res); + out_block_data[p * out_dims + out_dims - 1] = res; + } + out_writer.write((char *)out_block_data.get(), block_pts * out_dims * sizeof(float)); + } + out_writer.close(); + return max_norm; +} + +// plain saves data as npts X ndims array into filename +template void save_Tvecs(const char *filename, T *data, size_t npts, size_t ndims) +{ + std::string fname(filename); + + // create cached ofstream with 64MB cache + cached_ofstream writer(fname, 64 * 1048576); + + unsigned dims_u32 = (unsigned)ndims; + + // start writing + for (size_t i = 0; i < npts; i++) + { + // write dims in u32 + writer.write((char *)&dims_u32, sizeof(unsigned)); + + // get cur point in data + T *cur_pt = data + i * ndims; + writer.write((char *)cur_pt, ndims * sizeof(T)); + } +} +template +inline size_t save_data_in_base_dimensions(const std::string &filename, T *data, size_t npts, size_t ndims, + size_t aligned_dim, size_t offset = 0) +{ + std::ofstream writer; //(filename, std::ios::binary | std::ios::out); + open_file_to_write(writer, filename); + int npts_i32 = (int)npts, ndims_i32 = (int)ndims; + size_t bytes_written = 2 * sizeof(uint32_t) + npts * ndims * sizeof(T); + writer.seekp(offset, writer.beg); + writer.write((char *)&npts_i32, sizeof(int)); + writer.write((char *)&ndims_i32, sizeof(int)); + for (size_t i = 0; i < npts; i++) + { + writer.write((char *)(data + i * aligned_dim), ndims * sizeof(T)); + } + writer.close(); + return bytes_written; +} + +template +inline void copy_aligned_data_from_file(const char *bin_file, T *&data, size_t &npts, size_t &dim, + const size_t &rounded_dim, size_t offset = 0) +{ + if (data == nullptr) + { + diskann::cerr << "Memory was not allocated for " << data << " before calling the load function. Exiting..." + << std::endl; + throw diskann::ANNException("Null pointer passed to copy_aligned_data_from_file function", -1, __FUNCSIG__, + __FILE__, __LINE__); + } + std::ifstream reader; + reader.exceptions(std::ios::badbit | std::ios::failbit); + reader.open(bin_file, std::ios::binary); + reader.seekg(offset, reader.beg); + + int npts_i32, dim_i32; + reader.read((char *)&npts_i32, sizeof(int)); + reader.read((char *)&dim_i32, sizeof(int)); + npts = (unsigned)npts_i32; + dim = (unsigned)dim_i32; + + for (size_t i = 0; i < npts; i++) + { + reader.read((char *)(data + i * rounded_dim), dim * sizeof(T)); + memset(data + i * rounded_dim + dim, 0, (rounded_dim - dim) * sizeof(T)); + } +} + +// NOTE :: good efficiency when total_vec_size is integral multiple of 64 +inline void prefetch_vector(const char *vec, size_t vecsize) +{ + size_t max_prefetch_size = (vecsize / 64) * 64; + for (size_t d = 0; d < max_prefetch_size; d += 64) + _mm_prefetch((const char *)vec + d, _MM_HINT_T0); +} + +// NOTE :: good efficiency when total_vec_size is integral multiple of 64 +inline void prefetch_vector_l2(const char *vec, size_t vecsize) +{ + size_t max_prefetch_size = (vecsize / 64) * 64; + for (size_t d = 0; d < max_prefetch_size; d += 64) + _mm_prefetch((const char *)vec + d, _MM_HINT_T1); +} + +// NOTE: Implementation in utils.cpp. +void block_convert(std::ofstream &writr, std::ifstream &readr, float *read_buf, uint64_t npts, uint64_t ndims); + +DISKANN_DLLEXPORT void normalize_data_file(const std::string &inFileName, const std::string &outFileName); + +inline std::string get_tag_string(std::uint64_t tag) +{ + return std::to_string(tag); +} + +inline std::string get_tag_string(const tag_uint128 &tag) +{ + std::string str = std::to_string(tag._data2) + "_" + std::to_string(tag._data1); + return str; +} + +}; // namespace diskann + +struct PivotContainer +{ + PivotContainer() = default; + + PivotContainer(size_t pivo_id, float pivo_dist) : piv_id{pivo_id}, piv_dist{pivo_dist} + { + } + + bool operator<(const PivotContainer &p) const + { + return p.piv_dist < piv_dist; + } + + bool operator>(const PivotContainer &p) const + { + return p.piv_dist > piv_dist; + } + + size_t piv_id; + float piv_dist; +}; + +inline bool validate_index_file_size(std::ifstream &in) +{ + if (!in.is_open()) + throw diskann::ANNException("Index file size check called on unopened file stream", -1, __FUNCSIG__, __FILE__, + __LINE__); + in.seekg(0, in.end); + size_t actual_file_size = in.tellg(); + in.seekg(0, in.beg); + size_t expected_file_size; + in.read((char *)&expected_file_size, sizeof(uint64_t)); + in.seekg(0, in.beg); + if (actual_file_size != expected_file_size) + { + diskann::cerr << "Index file size error. Expected size (metadata): " << expected_file_size + << ", actual file size : " << actual_file_size << "." << std::endl; + return false; + } + return true; +} + +template inline float get_norm(T *arr, const size_t dim) +{ + float sum = 0.0f; + for (uint32_t i = 0; i < dim; i++) + { + sum += arr[i] * arr[i]; + } + return sqrt(sum); +} + +// This function is valid only for float data type. +template inline void normalize(T *arr, const size_t dim) +{ + float norm = get_norm(arr, dim); + for (uint32_t i = 0; i < dim; i++) + { + arr[i] = (T)(arr[i] / norm); + } +} + +inline std::vector read_file_to_vector_of_strings(const std::string &filename, bool unique = false) +{ + std::vector result; + std::set elementSet; + if (filename != "") + { + std::ifstream file(filename); + if (file.fail()) + { + throw diskann::ANNException(std::string("Failed to open file ") + filename, -1); + } + std::string line; + while (std::getline(file, line)) + { + if (line.empty()) + { + break; + } + if (line.find(',') != std::string::npos) + { + std::cerr << "Every query must have exactly one filter" << std::endl; + exit(-1); + } + if (!line.empty() && (line.back() == '\r' || line.back() == '\n')) + { + line.erase(line.size() - 1); + } + if (!elementSet.count(line)) + { + result.push_back(line); + } + if (unique) + { + elementSet.insert(line); + } + } + file.close(); + } + else + { + throw diskann::ANNException(std::string("Failed to open file. filename can not be blank"), -1); + } + return result; +} + +inline void clean_up_artifacts(tsl::robin_set paths_to_clean, tsl::robin_set path_suffixes) +{ + try + { + for (const auto &path : paths_to_clean) + { + for (const auto &suffix : path_suffixes) + { + std::string curr_path_to_clean(path + "_" + suffix); + if (std::remove(curr_path_to_clean.c_str()) != 0) + diskann::cout << "Warning: Unable to remove file :" << curr_path_to_clean << std::endl; + } + } + diskann::cout << "Cleaned all artifacts" << std::endl; + } + catch (const std::exception &e) + { + diskann::cout << "Warning: Unable to clean all artifacts " << e.what() << std::endl; + } +} + +template inline const char *diskann_type_to_name() = delete; +template <> inline const char *diskann_type_to_name() +{ + return "float"; +} +template <> inline const char *diskann_type_to_name() +{ + return "uint8"; +} +template <> inline const char *diskann_type_to_name() +{ + return "int8"; +} +template <> inline const char *diskann_type_to_name() +{ + return "uint16"; +} +template <> inline const char *diskann_type_to_name() +{ + return "int16"; +} +template <> inline const char *diskann_type_to_name() +{ + return "uint32"; +} +template <> inline const char *diskann_type_to_name() +{ + return "int32"; +} +template <> inline const char *diskann_type_to_name() +{ + return "uint64"; +} +template <> inline const char *diskann_type_to_name() +{ + return "int64"; +} + +#ifdef _WINDOWS +#include +#include + +extern bool AvxSupportedCPU; +extern bool Avx2SupportedCPU; + +inline size_t getMemoryUsage() +{ + PROCESS_MEMORY_COUNTERS_EX pmc; + GetProcessMemoryInfo(GetCurrentProcess(), (PROCESS_MEMORY_COUNTERS *)&pmc, sizeof(pmc)); + return pmc.PrivateUsage; +} + +inline std::string getWindowsErrorMessage(DWORD lastError) +{ + char *errorText; + FormatMessageA( + // use system message tables to retrieve error text + FORMAT_MESSAGE_FROM_SYSTEM + // allocate buffer on local heap for error text + | FORMAT_MESSAGE_ALLOCATE_BUFFER + // Important! will fail otherwise, since we're not + // (and CANNOT) pass insertion parameters + | FORMAT_MESSAGE_IGNORE_INSERTS, + NULL, // unused with FORMAT_MESSAGE_FROM_SYSTEM + lastError, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), + (LPSTR)&errorText, // output + 0, // minimum size for output buffer + NULL); // arguments - see note + + return errorText != nullptr ? std::string(errorText) : std::string(); +} + +inline void printProcessMemory(const char *message) +{ + PROCESS_MEMORY_COUNTERS counters; + HANDLE h = GetCurrentProcess(); + GetProcessMemoryInfo(h, &counters, sizeof(counters)); + diskann::cout << message + << " [Peaking Working Set size: " << counters.PeakWorkingSetSize * 1.0 / (1024.0 * 1024 * 1024) + << "GB Working set size: " << counters.WorkingSetSize * 1.0 / (1024.0 * 1024 * 1024) + << "GB Private bytes " << counters.PagefileUsage * 1.0 / (1024 * 1024 * 1024) << "GB]" << std::endl; +} +#else + +// need to check and change this +inline bool avx2Supported() +{ + return true; +} +inline void printProcessMemory(const char *) +{ +} + +inline size_t getMemoryUsage() +{ // for non-windows, we have not implemented this function + return 0; +} + +#endif + +extern bool AvxSupportedCPU; +extern bool Avx2SupportedCPU; diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/windows_aligned_file_reader.h b/packages/leann-backend-diskann/third_party/DiskANN/include/windows_aligned_file_reader.h new file mode 100644 index 0000000..0d9a317 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/windows_aligned_file_reader.h @@ -0,0 +1,57 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once +#ifdef _WINDOWS +#ifndef USE_BING_INFRA +#include +#include +#include +#include + +#include +#include +#include +#include "aligned_file_reader.h" +#include "tsl/robin_map.h" +#include "utils.h" +#include "windows_customizations.h" + +class WindowsAlignedFileReader : public AlignedFileReader +{ + private: +#ifdef UNICODE + std::wstring m_filename; +#else + std::string m_filename; +#endif + + protected: + // virtual IOContext createContext(); + + public: + DISKANN_DLLEXPORT WindowsAlignedFileReader(){}; + DISKANN_DLLEXPORT virtual ~WindowsAlignedFileReader(){}; + + // Open & close ops + // Blocking calls + DISKANN_DLLEXPORT virtual void open(const std::string &fname) override; + DISKANN_DLLEXPORT virtual void close() override; + + DISKANN_DLLEXPORT virtual void register_thread() override; + DISKANN_DLLEXPORT virtual void deregister_thread() override + { + // TODO: Needs implementation. + } + DISKANN_DLLEXPORT virtual void deregister_all_threads() override + { + // TODO: Needs implementation. + } + DISKANN_DLLEXPORT virtual IOContext &get_ctx() override; + + // process batch of aligned requests in parallel + // NOTE :: blocking call for the calling thread, but can thread-safe + DISKANN_DLLEXPORT virtual void read(std::vector &read_reqs, IOContext &ctx, bool async) override; +}; +#endif // USE_BING_INFRA +#endif //_WINDOWS diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/windows_customizations.h b/packages/leann-backend-diskann/third_party/DiskANN/include/windows_customizations.h new file mode 100644 index 0000000..e6c5846 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/windows_customizations.h @@ -0,0 +1,16 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#ifdef _WINDOWS + +#ifdef _WINDLL +#define DISKANN_DLLEXPORT __declspec(dllexport) +#else +#define DISKANN_DLLEXPORT __declspec(dllimport) +#endif + +#else +#define DISKANN_DLLEXPORT +#endif diff --git a/packages/leann-backend-diskann/third_party/DiskANN/include/windows_slim_lock.h b/packages/leann-backend-diskann/third_party/DiskANN/include/windows_slim_lock.h new file mode 100644 index 0000000..7fc09b8 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/include/windows_slim_lock.h @@ -0,0 +1,73 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. +#pragma once + +#ifndef WIN32_LEAN_AND_MEAN +#define WIN32_LEAN_AND_MEAN +#endif +#include "Windows.h" + +namespace diskann +{ +// A thin C++ wrapper around Windows exclusive functionality of Windows +// SlimReaderWriterLock. +// +// The SlimReaderWriterLock is simpler/more lightweight than std::mutex +// (8 bytes vs 80 bytes), which is useful in the scenario where DiskANN has +// one lock per vector in the index. It does not support recursive locking and +// requires Windows Vista or later. +// +// Full documentation can be found at. +// https://msdn.microsoft.com/en-us/library/windows/desktop/aa904937(v=vs.85).aspx +class windows_exclusive_slim_lock +{ + public: + windows_exclusive_slim_lock() : _lock(SRWLOCK_INIT) + { + } + + // The lock is non-copyable. This also disables move constructor/operator=. + windows_exclusive_slim_lock(const windows_exclusive_slim_lock &) = delete; + windows_exclusive_slim_lock &operator=(const windows_exclusive_slim_lock &) = delete; + + void lock() + { + return AcquireSRWLockExclusive(&_lock); + } + + bool try_lock() + { + return TryAcquireSRWLockExclusive(&_lock) != FALSE; + } + + void unlock() + { + return ReleaseSRWLockExclusive(&_lock); + } + + private: + SRWLOCK _lock; +}; + +// An exclusive lock over a SlimReaderWriterLock. +class windows_exclusive_slim_lock_guard +{ + public: + windows_exclusive_slim_lock_guard(windows_exclusive_slim_lock &p_lock) : _lock(p_lock) + { + _lock.lock(); + } + + // The lock is non-copyable. This also disables move constructor/operator=. + windows_exclusive_slim_lock_guard(const windows_exclusive_slim_lock_guard &) = delete; + windows_exclusive_slim_lock_guard &operator=(const windows_exclusive_slim_lock_guard &) = delete; + + ~windows_exclusive_slim_lock_guard() + { + _lock.unlock(); + } + + private: + windows_exclusive_slim_lock &_lock; +}; +} // namespace diskann diff --git a/packages/leann-backend-diskann/third_party/DiskANN/pyproject.toml b/packages/leann-backend-diskann/third_party/DiskANN/pyproject.toml new file mode 100644 index 0000000..3871c71 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/pyproject.toml @@ -0,0 +1,58 @@ +[build-system] +requires = [ + "setuptools>=59.6", + "pybind11>=2.10.0", + "cmake>=3.22", + "numpy==1.25", # this is important to keep fixed. It also means anyone using something other than 1.25 won't be able to use this library + "wheel", + "ninja" +] +build-backend = "setuptools.build_meta" + +[project] +name = "diskannpy" +version = "0.7.1" + +description = "DiskANN Python extension module" +readme = "python/README.md" +requires-python = ">=3.9" +license = {text = "MIT License"} +dependencies = [ + "numpy==1.25" +] +authors = [ + {name = "Harsha Vardhan Simhadri", email = "harshasi@microsoft.com"}, + {name = "Dax Pryce", email = "daxpryce@microsoft.com"} +] + +[project.optional-dependencies] +dev = ["black", "isort", "mypy"] + +[tool.setuptools] +package-dir = {"" = "python/src"} + +[tool.isort] +profile = "black" +multi_line_output = 3 + +[tool.mypy] +plugins = "numpy.typing.mypy_plugin" + +[tool.cibuildwheel] +manylinux-x86_64-image = "manylinux_2_28" +test-requires = ["scikit-learn~=1.2"] +build-frontend = "build" +skip = ["pp*", "*-win32", "*-manylinux_i686", "*-musllinux*"] +test-command = "python -m unittest discover {project}/python/tests" + +[tool.cibuildwheel.linux] +before-build = [ + "rpm --import https://repo.almalinux.org/almalinux/RPM-GPG-KEY-AlmaLinux", + "dnf makecache --refresh", + "dnf upgrade -y almalinux-release", + "dnf install -y epel-release", + "dnf config-manager -y --add-repo https://yum.repos.intel.com/mkl/setup/intel-mkl.repo", + "rpm --import https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB", + "dnf makecache --refresh -y", + "dnf install -y wget make cmake gcc-c++ libaio-devel gperftools-libs libunwind-devel clang-tools-extra boost-devel boost-program-options intel-mkl-2020.4-912" +] diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/CMakeLists.txt b/packages/leann-backend-diskann/third_party/DiskANN/python/CMakeLists.txt new file mode 100644 index 0000000..66a5ba3 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/CMakeLists.txt @@ -0,0 +1,82 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +cmake_minimum_required(VERSION 3.18...3.22) + +set(CMAKE_CXX_STANDARD 17) + +if (PYTHON_EXECUTABLE) + set(Python3_EXECUTABLE ${PYTHON_EXECUTABLE}) +endif() + +find_package(Python3 COMPONENTS Interpreter Development.Module NumPy REQUIRED) + +execute_process(COMMAND ${Python3_EXECUTABLE} -c "import pybind11; print(pybind11.get_cmake_dir())" + OUTPUT_VARIABLE _tmp_dir + OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ECHO STDOUT) +list(APPEND CMAKE_PREFIX_PATH "${_tmp_dir}") + +# Now we can find pybind11 +find_package(pybind11 CONFIG REQUIRED) + +execute_process(COMMAND ${Python3_EXECUTABLE} -c "import numpy; print(numpy.get_include())" + OUTPUT_VARIABLE _numpy_include + OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ECHO STDOUT) + +# pybind11_add_module(diskannpy MODULE src/diskann_bindings.cpp) +# the following is fairly synonymous with pybind11_add_module, but we need more target_link_libraries +# see https://pybind11.readthedocs.io/en/latest/compiling.html#advanced-interface-library-targets for more details +add_library(_diskannpy MODULE + src/module.cpp + src/builder.cpp + src/dynamic_memory_index.cpp + src/static_memory_index.cpp + src/static_disk_index.cpp +) + +target_include_directories(_diskannpy AFTER PRIVATE include) + +if (MSVC) + target_compile_options(_diskannpy PRIVATE /U_WINDLL) +endif() + +target_link_libraries( + _diskannpy + PRIVATE + pybind11::module + pybind11::lto + pybind11::windows_extras + ${PROJECT_NAME} + ${DISKANN_TOOLS_TCMALLOC_LINK_OPTIONS} + ${DISKANN_ASYNC_LIB} +) + +pybind11_extension(_diskannpy) +if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES Debug|RelWithDebInfo) + # Strip unnecessary sections of the binary on Linux/macOS + pybind11_strip(_diskannpy) +endif() + +set_target_properties(_diskannpy PROPERTIES CXX_VISIBILITY_PRESET "hidden" + CUDA_VISIBILITY_PRESET "hidden") + +# generally, the VERSION_INFO flag is set by pyproject.toml, by way of setup.py. +# attempts to locate the version within CMake fail because the version has to be available +# to pyproject.toml for the sdist to work after we build it. + +if(NOT VERSION_INFO) + set(VERSION_INFO "0.0.0dev") +endif() +target_compile_definitions(_diskannpy PRIVATE VERSION_INFO="${VERSION_INFO}") + +# Add a post-build command to automatically copy the compiled Python module +if(UPDATE_EDITABLE_INSTALL) +add_custom_command( +TARGET _diskannpy +POST_BUILD +COMMAND ${CMAKE_COMMAND} -E copy + ${CMAKE_CURRENT_BINARY_DIR}/_diskannpy.cpython-*.so + ${CMAKE_SOURCE_DIR}/python/src/ +COMMENT "Copying Python module to python/src directory" +) +endif() \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/README.md b/packages/leann-backend-diskann/third_party/DiskANN/python/README.md new file mode 100644 index 0000000..a0c9475 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/README.md @@ -0,0 +1,55 @@ +# diskannpy + +[![DiskANN Paper](https://img.shields.io/badge/Paper-NeurIPS%3A_DiskANN-blue)](https://papers.nips.cc/paper/9527-rand-nsg-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node.pdf) +[![DiskANN Paper](https://img.shields.io/badge/Paper-Arxiv%3A_Fresh--DiskANN-blue)](https://arxiv.org/abs/2105.09613) +[![DiskANN Paper](https://img.shields.io/badge/Paper-Filtered--DiskANN-blue)](https://harsha-simhadri.org/pubs/Filtered-DiskANN23.pdf) +[![DiskANN Main](https://github.com/microsoft/DiskANN/actions/workflows/push-test.yml/badge.svg?branch=main)](https://github.com/microsoft/DiskANN/actions/workflows/push-test.yml) +[![PyPI version](https://img.shields.io/pypi/v/diskannpy.svg)](https://pypi.org/project/diskannpy/) +[![Downloads shield](https://pepy.tech/badge/diskannpy)](https://pepy.tech/project/diskannpy) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) + +## Installation +Packages published to PyPI will always be built using the latest numpy major.minor release (at this time, 1.25). + +Conda distributions for versions 1.19-1.25 will be completed as a future effort. In the meantime, feel free to +clone this repository and build it yourself. + +## Local Build Instructions +Please see the [Project README](https://github.com/microsoft/DiskANN/blob/main/README.md) for system dependencies and requirements. + +After ensuring you've followed the directions to build the project library and executables, you will be ready to also +build `diskannpy` with these additional instructions. + +### Changing Numpy Version +In the root folder of DiskANN, there is a file `pyproject.toml`. You will need to edit the version of numpy in both the +`[build-system.requires]` section, as well as the `[project.dependencies]` section. The version numbers must match. + +#### Linux +```bash +python3.11 -m venv venv # versions from python3.9 and up should work +source venv/bin/activate +pip install build +python -m build +``` + +#### Windows +```powershell +py -3.11 -m venv venv # versions from python3.9 and up should work +venv\Scripts\Activate.ps1 +pip install build +python -m build +``` + +The built wheel will be placed in the `dist` directory in your DiskANN root. Install it using `pip install dist/.whl` + +## Citations +Please cite this software in your work as: +``` +@misc{diskann-github, + author = {Simhadri, Harsha Vardhan and Krishnaswamy, Ravishankar and Srinivasa, Gopal and Subramanya, Suhas Jayaram and Antonijevic, Andrija and Pryce, Dax and Kaczynski, David and Williams, Shane and Gollapudi, Siddarth and Sivashankar, Varun and Karia, Neel and Singh, Aditi and Jaiswal, Shikhar and Mahapatro, Neelam and Adams, Philip and Tower, Bryan and Patel, Yash}}, + title = {{DiskANN: Graph-structured Indices for Scalable, Fast, Fresh and Filtered Approximate Nearest Neighbor Search}}, + url = {https://github.com/Microsoft/DiskANN}, + version = {0.6.1}, + year = {2023} +} +``` diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/apps/cli/__main__.py b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/cli/__main__.py new file mode 100644 index 0000000..d2c9990 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/cli/__main__.py @@ -0,0 +1,152 @@ +import diskannpy as dap +import numpy as np +import numpy.typing as npt + +import fire + +from contextlib import contextmanager +from time import perf_counter + +from typing import Tuple + + +def _basic_setup( + dtype: str, + query_vectors_file: str +) -> Tuple[dap.VectorDType, npt.NDArray[dap.VectorDType]]: + _dtype = dap.valid_dtype(dtype) + vectors_to_query = dap.vectors_from_binary(query_vectors_file, dtype=_dtype) + return _dtype, vectors_to_query + + +def dynamic( + dtype: str, + index_vectors_file: str, + query_vectors_file: str, + build_complexity: int, + graph_degree: int, + K: int, + search_complexity: int, + num_insert_threads: int, + num_search_threads: int, + gt_file: str = "", +): + _dtype, vectors_to_query = _basic_setup(dtype, query_vectors_file) + vectors_to_index = dap.vectors_from_binary(index_vectors_file, dtype=_dtype) + + npts, ndims = vectors_to_index.shape + index = dap.DynamicMemoryIndex( + "l2", _dtype, ndims, npts, build_complexity, graph_degree + ) + + tags = np.arange(1, npts+1, dtype=np.uintc) + timer = Timer() + + with timer.time("batch insert"): + index.batch_insert(vectors_to_index, tags, num_insert_threads) + + delete_tags = np.random.choice( + np.array(range(1, npts + 1, 1), dtype=np.uintc), + size=int(0.5 * npts), + replace=False + ) + with timer.time("mark deletion"): + for tag in delete_tags: + index.mark_deleted(tag) + + with timer.time("consolidation"): + index.consolidate_delete() + + deleted_data = vectors_to_index[delete_tags - 1, :] + + with timer.time("re-insertion"): + index.batch_insert(deleted_data, delete_tags, num_insert_threads) + + with timer.time("batch searched"): + tags, dists = index.batch_search(vectors_to_query, K, search_complexity, num_search_threads) + + # res_ids = tags - 1 + # if gt_file != "": + # recall = utils.calculate_recall_from_gt_file(K, res_ids, gt_file) + # print(f"recall@{K} is {recall}") + +def static( + dtype: str, + index_directory: str, + index_vectors_file: str, + query_vectors_file: str, + build_complexity: int, + graph_degree: int, + K: int, + search_complexity: int, + num_threads: int, + gt_file: str = "", + index_prefix: str = "ann" +): + _dtype, vectors_to_query = _basic_setup(dtype, query_vectors_file) + timer = Timer() + with timer.time("build static index"): + # build index + dap.build_memory_index( + data=index_vectors_file, + metric="l2", + vector_dtype=_dtype, + index_directory=index_directory, + complexity=build_complexity, + graph_degree=graph_degree, + num_threads=num_threads, + index_prefix=index_prefix, + alpha=1.2, + use_pq_build=False, + num_pq_bytes=8, + use_opq=False, + ) + + with timer.time("load static index"): + # ready search object + index = dap.StaticMemoryIndex( + metric="l2", + vector_dtype=_dtype, + data_path=index_vectors_file, + index_directory=index_directory, + num_threads=num_threads, # this can be different at search time if you would like + initial_search_complexity=search_complexity, + index_prefix=index_prefix + ) + + ids, dists = index.batch_search(vectors_to_query, K, search_complexity, num_threads) + + # if gt_file != "": + # recall = utils.calculate_recall_from_gt_file(K, ids, gt_file) + # print(f"recall@{K} is {recall}") + +def dynamic_clustered(): + pass + +def generate_clusters(): + pass + + +class Timer: + def __init__(self): + self._start = -1 + + @contextmanager + def time(self, message: str): + start = perf_counter() + if self._start == -1: + self._start = start + yield + now = perf_counter() + print(f"Operation {message} completed in {(now - start):.3f}s, total: {(now - self._start):.3f}s") + + + + +if __name__ == "__main__": + fire.Fire({ + "in-mem-dynamic": dynamic, + "in-mem-static": static, + "in-mem-dynamic-clustered": dynamic_clustered, + "generate-clusters": generate_clusters + }, name="cli") diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/apps/cluster.py b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/cluster.py new file mode 100644 index 0000000..27a34bb --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/cluster.py @@ -0,0 +1,28 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +import argparse +import utils + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + prog="cluster", description="kmeans cluster points in a file" + ) + + parser.add_argument("-d", "--data_type", required=True) + parser.add_argument("-i", "--indexdata_file", required=True) + parser.add_argument("-k", "--num_clusters", type=int, required=True) + args = parser.parse_args() + + npts, ndims = get_bin_metadata(indexdata_file) + + data = utils.bin_to_numpy(args.data_type, args.indexdata_file) + + offsets, permutation = utils.cluster_and_permute( + args.data_type, npts, ndims, data, args.num_clusters + ) + + permuted_data = data[permutation] + + utils.numpy_to_bin(permuted_data, args.indexdata_file + ".cluster") diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/apps/in-mem-dynamic.py b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/in-mem-dynamic.py new file mode 100644 index 0000000..f97e131 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/in-mem-dynamic.py @@ -0,0 +1,161 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +import argparse + +import diskannpy +import numpy as np +import utils + +def insert_and_search( + dtype_str, + indexdata_file, + querydata_file, + Lb, + graph_degree, + K, + Ls, + num_insert_threads, + num_search_threads, + gt_file, +) -> dict[str, float]: + """ + + :param dtype_str: + :param indexdata_file: + :param querydata_file: + :param Lb: + :param graph_degree: + :param K: + :param Ls: + :param num_insert_threads: + :param num_search_threads: + :param gt_file: + :return: Dictionary of timings. Key is the event and value is the number of seconds the event took + """ + timer_results: dict[str, float] = {} + + method_timer: utils.Timer = utils.Timer() + + npts, ndims = utils.get_bin_metadata(indexdata_file) + + if dtype_str == "float": + dtype = np.float32 + elif dtype_str == "int8": + dtype = np.int8 + elif dtype_str == "uint8": + dtype = np.uint8 + else: + raise ValueError("data_type must be float, int8 or uint8") + + index = diskannpy.DynamicMemoryIndex( + distance_metric="l2", + vector_dtype=dtype, + dimensions=ndims, + max_vectors=npts, + complexity=Lb, + graph_degree=graph_degree + ) + queries = diskannpy.vectors_from_file(querydata_file, dtype) + data = diskannpy.vectors_from_file(indexdata_file, dtype) + + tags = np.zeros(npts, dtype=np.uintc) + timer = utils.Timer() + for i in range(npts): + tags[i] = i + 1 + index.batch_insert(data, tags, num_insert_threads) + compute_seconds = timer.elapsed() + print('batch_insert complete in', compute_seconds, 's') + timer_results["batch_insert_seconds"] = compute_seconds + + delete_tags = np.random.choice( + np.array(range(1, npts + 1, 1), dtype=np.uintc), + size=int(0.5 * npts), + replace=False + ) + + timer.reset() + for tag in delete_tags: + index.mark_deleted(tag) + compute_seconds = timer.elapsed() + timer_results['mark_deletion_seconds'] = compute_seconds + print('mark deletion completed in', compute_seconds, 's') + + timer.reset() + index.consolidate_delete() + compute_seconds = timer.elapsed() + print('consolidation completed in', compute_seconds, 's') + timer_results['consolidation_completed_seconds'] = compute_seconds + + deleted_data = data[delete_tags - 1, :] + + timer.reset() + index.batch_insert(deleted_data, delete_tags, num_insert_threads) + compute_seconds = timer.elapsed() + print('re-insertion completed in', compute_seconds, 's') + timer_results['re-insertion_seconds'] = compute_seconds + + timer.reset() + tags, dists = index.batch_search(queries, K, Ls, num_search_threads) + compute_seconds = timer.elapsed() + print('Batch searched', queries.shape[0], ' queries in ', compute_seconds, 's') + timer_results['batch_searched_seconds'] = compute_seconds + + res_ids = tags - 1 + if gt_file != "": + timer.reset() + recall = utils.calculate_recall_from_gt_file(K, res_ids, gt_file) + print(f"recall@{K} is {recall}") + timer_results['recall_computed_seconds'] = timer.elapsed() + + timer_results['total_time_seconds'] = method_timer.elapsed() + + return timer_results + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + prog="in-mem-dynamic", + description="Inserts points dynamically in a clustered order and search from vectors in a file.", + ) + + parser.add_argument("-d", "--data_type", required=True) + parser.add_argument("-i", "--indexdata_file", required=True) + parser.add_argument("-q", "--querydata_file", required=True) + parser.add_argument("-Lb", "--Lbuild", default=50, type=int) + parser.add_argument("-Ls", "--Lsearch", default=50, type=int) + parser.add_argument("-R", "--graph_degree", default=32, type=int) + parser.add_argument("-TI", "--num_insert_threads", default=8, type=int) + parser.add_argument("-TS", "--num_search_threads", default=8, type=int) + parser.add_argument("-K", default=10, type=int) + parser.add_argument("--gt_file", default="") + parser.add_argument("--json_timings_output", required=False, default=None, help="File to write out timings to as JSON. If not specified, timings will not be written out.") + args = parser.parse_args() + + timings = insert_and_search( + args.data_type, + args.indexdata_file, + args.querydata_file, + args.Lbuild, + args.graph_degree, # Build args + args.K, + args.Lsearch, + args.num_insert_threads, + args.num_search_threads, # search args + args.gt_file, + ) + + if args.json_timings_output is not None: + import json + timings['log_file'] = args.json_timings_output + with open(args.json_timings_output, "w") as f: + json.dump(timings, f) + +""" +An ingest optimized example with SIFT1M +source venv/bin/activate +python python/apps/in-mem-dynamic.py -d float \ +-i "$HOME/data/sift/sift_base.fbin" -q "$HOME/data/sift/sift_query.fbin" --gt_file "$HOME/data/sift/gt100_base" \ +-Lb 10 -R 30 -Ls 200 +""" + diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/apps/in-mem-static.py b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/in-mem-static.py new file mode 100644 index 0000000..9fb9a2c --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/in-mem-static.py @@ -0,0 +1,149 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +import argparse +from xml.dom.pulldom import default_bufsize + +import diskannpy +import numpy as np +import utils + +def build_and_search( + metric, + dtype_str, + index_directory, + indexdata_file, + querydata_file, + Lb, + graph_degree, + K, + Ls, + num_threads, + gt_file, + index_prefix, + search_only +) -> dict[str, float]: + """ + + :param metric: + :param dtype_str: + :param index_directory: + :param indexdata_file: + :param querydata_file: + :param Lb: + :param graph_degree: + :param K: + :param Ls: + :param num_threads: + :param gt_file: + :param index_prefix: + :param search_only: + :return: Dictionary of timings. Key is the event and value is the number of seconds the event took + in wall-clock-time. + """ + timer_results: dict[str, float] = {} + + method_timer: utils.Timer = utils.Timer() + + if dtype_str == "float": + dtype = np.single + elif dtype_str == "int8": + dtype = np.byte + elif dtype_str == "uint8": + dtype = np.ubyte + else: + raise ValueError("data_type must be float, int8 or uint8") + + # build index + if not search_only: + build_index_timer = utils.Timer() + diskannpy.build_memory_index( + data=indexdata_file, + distance_metric=metric, + vector_dtype=dtype, + index_directory=index_directory, + complexity=Lb, + graph_degree=graph_degree, + num_threads=num_threads, + index_prefix=index_prefix, + alpha=1.2, + use_pq_build=False, + num_pq_bytes=8, + use_opq=False, + ) + timer_results["build_index_seconds"] = build_index_timer.elapsed() + + # ready search object + load_index_timer = utils.Timer() + index = diskannpy.StaticMemoryIndex( + distance_metric=metric, + vector_dtype=dtype, + index_directory=index_directory, + num_threads=num_threads, # this can be different at search time if you would like + initial_search_complexity=Ls, + index_prefix=index_prefix + ) + timer_results["load_index_seconds"] = load_index_timer.elapsed() + + queries = utils.bin_to_numpy(dtype, querydata_file) + + query_timer = utils.Timer() + ids, dists = index.batch_search(queries, 10, Ls, num_threads) + query_time = query_timer.elapsed() + qps = round(queries.shape[0]/query_time, 1) + print('Batch searched', queries.shape[0], 'in', query_time, 's @', qps, 'QPS') + timer_results["query_seconds"] = query_time + + if gt_file != "": + recall_timer = utils.Timer() + recall = utils.calculate_recall_from_gt_file(K, ids, gt_file) + print(f"recall@{K} is {recall}") + timer_results["recall_seconds"] = recall_timer.elapsed() + + timer_results['total_time_seconds'] = method_timer.elapsed() + + return timer_results + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + prog="in-mem-static", + description="Static in-memory build and search from vectors in a file", + ) + + parser.add_argument("-m", "--metric", required=False, default="l2") + parser.add_argument("-d", "--data_type", required=True) + parser.add_argument("-id", "--index_directory", required=False, default=".") + parser.add_argument("-i", "--indexdata_file", required=True) + parser.add_argument("-q", "--querydata_file", required=True) + parser.add_argument("-Lb", "--Lbuild", default=50, type=int) + parser.add_argument("-Ls", "--Lsearch", default=50, type=int) + parser.add_argument("-R", "--graph_degree", default=32, type=int) + parser.add_argument("-T", "--num_threads", default=8, type=int) + parser.add_argument("-K", default=10, type=int) + parser.add_argument("-G", "--gt_file", default="") + parser.add_argument("-ip", "--index_prefix", required=False, default="ann") + parser.add_argument("--search_only", required=False, default=False) + parser.add_argument("--json_timings_output", required=False, default=None, help="File to write out timings to as JSON. If not specified, timings will not be written out.") + args = parser.parse_args() + + timings: dict[str, float] = build_and_search( + args.metric, + args.data_type, + args.index_directory.strip(), + args.indexdata_file.strip(), + args.querydata_file.strip(), + args.Lbuild, + args.graph_degree, # Build args + args.K, + args.Lsearch, + args.num_threads, # search args + args.gt_file, + args.index_prefix, + args.search_only + ) + + if args.json_timings_output is not None: + import json + timings['log_file'] = args.json_timings_output + with open(args.json_timings_output, "w") as f: + json.dump(timings, f) diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/apps/insert-in-clustered-order.py b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/insert-in-clustered-order.py new file mode 100644 index 0000000..25cb9d5 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/insert-in-clustered-order.py @@ -0,0 +1,103 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +import argparse + +import diskannpy +import numpy as np +import utils + + +def insert_and_search( + dtype_str, + indexdata_file, + querydata_file, + Lb, + graph_degree, + num_clusters, + num_insert_threads, + K, + Ls, + num_search_threads, + gt_file, +): + npts, ndims = utils.get_bin_metadata(indexdata_file) + + if dtype_str == "float": + dtype = np.float32 + elif dtype_str == "int8": + dtype = np.int8 + elif dtype_str == "uint8": + dtype = np.uint8 + else: + raise ValueError("data_type must be float, int8 or uint8") + + index = diskannpy.DynamicMemoryIndex( + distance_metric="l2", + vector_dtype=dtype, + dimensions=ndims, + max_vectors=npts, + complexity=Lb, + graph_degree=graph_degree + ) + queries = diskannpy.vectors_from_file(querydata_file, dtype) + data = diskannpy.vectors_from_file(indexdata_file, dtype) + + offsets, permutation = utils.cluster_and_permute( + dtype_str, npts, ndims, data, num_clusters + ) + + i = 0 + timer = utils.Timer() + for c in range(num_clusters): + cluster_index_range = range(offsets[c], offsets[c + 1]) + cluster_indices = np.array(permutation[cluster_index_range], dtype=np.uint32) + cluster_data = data[cluster_indices, :] + index.batch_insert(cluster_data, cluster_indices + 1, num_insert_threads) + print('Inserted cluster', c, 'in', timer.elapsed(), 's') + tags, dists = index.batch_search(queries, K, Ls, num_search_threads) + print('Batch searched', queries.shape[0], 'queries in', timer.elapsed(), 's') + res_ids = tags - 1 + + if gt_file != "": + recall = utils.calculate_recall_from_gt_file(K, res_ids, gt_file) + print(f"recall@{K} is {recall}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + prog="in-mem-dynamic", + description="Inserts points dynamically in a clustered order and search from vectors in a file.", + ) + + parser.add_argument("-d", "--data_type", required=True) + parser.add_argument("-i", "--indexdata_file", required=True) + parser.add_argument("-q", "--querydata_file", required=True) + parser.add_argument("-Lb", "--Lbuild", default=50, type=int) + parser.add_argument("-Ls", "--Lsearch", default=50, type=int) + parser.add_argument("-R", "--graph_degree", default=32, type=int) + parser.add_argument("-TI", "--num_insert_threads", default=8, type=int) + parser.add_argument("-TS", "--num_search_threads", default=8, type=int) + parser.add_argument("-C", "--num_clusters", default=32, type=int) + parser.add_argument("-K", default=10, type=int) + parser.add_argument("--gt_file", default="") + args = parser.parse_args() + + insert_and_search( + args.data_type, + args.indexdata_file, + args.querydata_file, + args.Lbuild, + args.graph_degree, # Build args + args.num_clusters, + args.num_insert_threads, + args.K, + args.Lsearch, + args.num_search_threads, # search args + args.gt_file, + ) + +# An ingest optimized example with SIFT1M +# python3 ~/DiskANN/python/apps/insert-in-clustered-order.py -d float \ +# -i sift_base.fbin -q sift_query.fbin --gt_file gt100_base \ +# -Lb 10 -R 30 -Ls 200 -C 32 \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/apps/utils.py b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/utils.py new file mode 100644 index 0000000..a526984 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/apps/utils.py @@ -0,0 +1,120 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +import numpy as np +from scipy.cluster.vq import vq, kmeans2 +from typing import Tuple +from time import perf_counter + + +def get_bin_metadata(bin_file) -> Tuple[int, int]: + array = np.fromfile(file=bin_file, dtype=np.uint32, count=2) + return array[0], array[1] + + +def bin_to_numpy(dtype, bin_file) -> np.ndarray: + npts, ndims = get_bin_metadata(bin_file) + return np.fromfile(file=bin_file, dtype=dtype, offset=8).reshape(npts, ndims) + + +class Timer: + last = perf_counter() + + def reset(self): + new = perf_counter() + self.last = new + + def elapsed(self, round_digit:int = 3): + new = perf_counter() + elapsed_time = new - self.last + self.last = new + return round(elapsed_time, round_digit) + + +def numpy_to_bin(array, out_file): + shape = np.shape(array) + npts = shape[0].astype(np.uint32) + ndims = shape[1].astype(np.uint32) + f = open(out_file, "wb") + f.write(npts.tobytes()) + f.write(ndims.tobytes()) + f.write(array.tobytes()) + f.close() + + +def read_gt_file(gt_file) -> Tuple[np.ndarray[int], np.ndarray[float]]: + """ + Return ids and distances to queries + """ + nq, K = get_bin_metadata(gt_file) + ids = np.fromfile(file=gt_file, dtype=np.uint32, offset=8, count=nq * K).reshape( + nq, K + ) + dists = np.fromfile( + file=gt_file, dtype=np.float32, offset=8 + nq * K * 4, count=nq * K + ).reshape(nq, K) + return ids, dists + + +def calculate_recall( + result_set_indices: np.ndarray[int], + truth_set_indices: np.ndarray[int], + recall_at: int = 5, +) -> float: + """ + result_set_indices and truth_set_indices correspond by row index. the columns in each row contain the indices of + the nearest neighbors, with result_set_indices being the approximate nearest neighbor results and truth_set_indices + being the brute force nearest neighbor calculation via sklearn's NearestNeighbor class. + :param result_set_indices: + :param truth_set_indices: + :param recall_at: + :return: + """ + found = 0 + for i in range(0, result_set_indices.shape[0]): + result_set_set = set(result_set_indices[i][0:recall_at]) + truth_set_set = set(truth_set_indices[i][0:recall_at]) + found += len(result_set_set.intersection(truth_set_set)) + return found / (result_set_indices.shape[0] * recall_at) + + +def calculate_recall_from_gt_file(K: int, ids: np.ndarray[int], gt_file: str) -> float: + """ + Calculate recall from ids returned from search and those read from file + """ + gt_ids, gt_dists = read_gt_file(gt_file) + return calculate_recall(ids, gt_ids, K) + + +def cluster_and_permute( + dtype_str, npts, ndims, data, num_clusters +) -> Tuple[np.ndarray[int], np.ndarray[int]]: + """ + Cluster the data and return permutation of row indices + that would group indices of the same cluster together + """ + sample_size = min(100000, npts) + sample_indices = np.random.choice(range(npts), size=sample_size, replace=False) + sampled_data = data[sample_indices, :] + centroids, sample_labels = kmeans2(sampled_data, num_clusters, minit="++", iter=10) + labels, dist = vq(data, centroids) + + count = np.zeros(num_clusters) + for i in range(npts): + count[labels[i]] += 1 + print("Cluster counts") + print(count) + + offsets = np.zeros(num_clusters + 1, dtype=int) + for i in range(0, num_clusters, 1): + offsets[i + 1] = offsets[i] + count[i] + + permutation = np.zeros(npts, dtype=int) + counters = np.zeros(num_clusters, dtype=int) + for i in range(npts): + label = labels[i] + row = offsets[label] + counters[label] + counters[label] += 1 + permutation[row] = i + + return offsets, permutation diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/include/builder.h b/packages/leann-backend-diskann/third_party/DiskANN/python/include/builder.h new file mode 100644 index 0000000..56677ac --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/include/builder.h @@ -0,0 +1,27 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +#include "common.h" +#include "distance.h" + +namespace diskannpy +{ +template +void build_disk_index(diskann::Metric metric, const std::string &data_file_path, const std::string &index_prefix_path, + uint32_t complexity, uint32_t graph_degree, double final_index_ram_limit, + double indexing_ram_budget, uint32_t num_threads, uint32_t pq_disk_bytes, + const std::string &codebook_prefix); + +template +void build_memory_index(diskann::Metric metric, const std::string &vector_bin_path, + const std::string &index_output_path, uint32_t graph_degree, uint32_t complexity, float alpha, + uint32_t num_threads, bool use_pq_build, size_t num_pq_bytes, bool use_opq, + bool use_tags = false, const std::string &filter_labels_file = "", + const std::string &universal_label = "", uint32_t filter_complexity = 0); + +} // namespace diskannpy diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/include/common.h b/packages/leann-backend-diskann/third_party/DiskANN/python/include/common.h new file mode 100644 index 0000000..7c63534 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/include/common.h @@ -0,0 +1,24 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +#include +#include + +namespace py = pybind11; + +namespace diskannpy +{ + +typedef uint32_t filterT; + +typedef uint32_t StaticIdType; +typedef uint32_t DynamicIdType; + +template using NeighborsAndDistances = std::pair, py::array_t>; + +}; // namespace diskannpy diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/include/dynamic_memory_index.h b/packages/leann-backend-diskann/third_party/DiskANN/python/include/dynamic_memory_index.h new file mode 100644 index 0000000..02d6b8c --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/include/dynamic_memory_index.h @@ -0,0 +1,53 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +#include +#include + +#include "common.h" +#include "index.h" +#include "parameters.h" + +namespace py = pybind11; + +namespace diskannpy +{ + +template +class DynamicMemoryIndex +{ + public: + DynamicMemoryIndex(diskann::Metric m, size_t dimensions, size_t max_vectors, uint32_t complexity, + uint32_t graph_degree, bool saturate_graph, uint32_t max_occlusion_size, float alpha, + uint32_t num_threads, uint32_t filter_complexity, uint32_t num_frozen_points, + uint32_t initial_search_complexity, uint32_t initial_search_threads, + bool concurrent_consolidation); + + void load(const std::string &index_path); + int insert(const py::array_t &vector, DynamicIdType id); + py::array_t batch_insert(py::array_t &vectors, + py::array_t &ids, int32_t num_inserts, + int num_threads = 0); + int mark_deleted(DynamicIdType id); + void save(const std::string &save_path, bool compact_before_save = false); + NeighborsAndDistances search(py::array_t &query, uint64_t knn, + uint64_t complexity); + NeighborsAndDistances batch_search(py::array_t &queries, + uint64_t num_queries, uint64_t knn, uint64_t complexity, + uint32_t num_threads); + void consolidate_delete(); + size_t num_points(); + + + private: + const uint32_t _initial_search_complexity; + const diskann::IndexWriteParameters _write_parameters; + diskann::Index _index; +}; + +}; // namespace diskannpy \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/include/static_disk_index.h b/packages/leann-backend-diskann/third_party/DiskANN/python/include/static_disk_index.h new file mode 100644 index 0000000..a3b79c4 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/include/static_disk_index.h @@ -0,0 +1,65 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +#include +#include + +#ifdef _WINDOWS +#include "windows_aligned_file_reader.h" +#elif __APPLE__ +#include "apple_aligned_file_reader.h" +#else +#include "linux_aligned_file_reader.h" +#endif + +#include "common.h" +#include "pq_flash_index.h" + +namespace py = pybind11; + +namespace diskannpy +{ + +#ifdef _WINDOWS +typedef WindowsAlignedFileReader PlatformSpecificAlignedFileReader; +#elif __APPLE__ +typedef AppleAlignedFileReader PlatformSpecificAlignedFileReader; +#else +typedef LinuxAlignedFileReader PlatformSpecificAlignedFileReader; +#endif + +template class StaticDiskIndex +{ + public: + StaticDiskIndex(diskann::Metric metric, const std::string &index_path_prefix, uint32_t num_threads, + size_t num_nodes_to_cache, uint32_t cache_mechanism, const std::string &pq_prefix, + const std::string &partition_prefix); + + void cache_bfs_levels(size_t num_nodes_to_cache); + + void cache_sample_paths(size_t num_nodes_to_cache, const std::string &warmup_query_file, uint32_t num_threads); + + NeighborsAndDistances search(py::array_t &query, + uint64_t knn, uint64_t complexity, uint64_t beam_width, + bool USE_DEFERRED_FETCH = false, bool skip_search_reorder = false, + bool recompute_beighbor_embeddings = false, bool dedup_node_dis = false, + float prune_ratio = 0, bool batch_recompute = false, + bool global_pruning = false); + + NeighborsAndDistances batch_search( + py::array_t &queries, uint64_t num_queries, uint64_t knn, + uint64_t complexity, uint64_t beam_width, uint32_t num_threads, bool USE_DEFERRED_FETCH = false, + bool skip_search_reorder = false, bool recompute_beighbor_embeddings = false, bool dedup_node_dis = false, + float prune_ratio = 0, bool batch_recompute = false, bool global_pruning = false); + + private: + std::shared_ptr _reader; + std::shared_ptr _graph_reader; + diskann::PQFlashIndex

_index; +}; +} // namespace diskannpy diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/include/static_memory_index.h b/packages/leann-backend-diskann/third_party/DiskANN/python/include/static_memory_index.h new file mode 100644 index 0000000..6ed5a08 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/include/static_memory_index.h @@ -0,0 +1,40 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +#pragma once + +#include +#include + +#include +#include + +#include "common.h" +#include "index.h" + +namespace py = pybind11; + +namespace diskannpy +{ + +template class StaticMemoryIndex +{ + public: + StaticMemoryIndex(diskann::Metric m, const std::string &index_prefix, size_t num_points, size_t dimensions, + uint32_t num_threads, uint32_t initial_search_complexity); + + NeighborsAndDistances search(py::array_t &query, + uint64_t knn, uint64_t complexity); + + NeighborsAndDistances search_with_filter( + py::array_t &query, uint64_t knn, uint64_t complexity, + filterT filter); + + NeighborsAndDistances batch_search( + py::array_t &queries, uint64_t num_queries, uint64_t knn, + uint64_t complexity, uint32_t num_threads); + + private: + diskann::Index _index; +}; +} // namespace diskannpy \ No newline at end of file diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/src/__init__.py b/packages/leann-backend-diskann/third_party/DiskANN/python/src/__init__.py new file mode 100644 index 0000000..c2e1b07 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/src/__init__.py @@ -0,0 +1,138 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +""" +# Documentation Overview +`diskannpy` is mostly structured around 2 distinct processes: [Index Builder Functions](#index-builders) and [Search Classes](#search-classes) + +It also includes a few nascent [utilities](#utilities). + +And lastly, it makes substantial use of type hints, with various shorthand [type aliases](#parameter-and-response-type-aliases) documented. +When reading the `diskannpy` code we refer to the type aliases, though `pdoc` helpfully expands them. + +## Index Builders +- `build_disk_index` - To build an index that cannot fully fit into memory when searching +- `build_memory_index` - To build an index that can fully fit into memory when searching + +## Search Classes +- `StaticMemoryIndex` - for indices that can fully fit in memory and won't be changed during the search operations +- `StaticDiskIndex` - for indices that cannot fully fit in memory, thus relying on disk IO to search, and also won't be changed during search operations +- `DynamicMemoryIndex` - for indices that can fully fit in memory and will be mutated via insert/deletion operations as well as search operations + +## Parameter Defaults +- `diskannpy.defaults` - Default values exported from the C++ extension for Python users + +## Parameter and Response Type Aliases +- `DistanceMetric` - What distance metrics does `diskannpy` support? +- `VectorDType` - What vector datatypes does `diskannpy` support? +- `QueryResponse` - What can I expect as a response to my search? +- `QueryResponseBatch` - What can I expect as a response to my batch search? +- `VectorIdentifier` - What types do `diskannpy` support as vector identifiers? +- `VectorIdentifierBatch` - A batch of identifiers of the exact same type. The type can change, but they must **all** change. +- `VectorLike` - How does a vector look to `diskannpy`, to be inserted or searched with. +- `VectorLikeBatch` - A batch of those vectors, to be inserted or searched with. +- `Metadata` - DiskANN vector binary file metadata (num_points, vector_dim) + +## Utilities +- `vectors_to_file` - Turns a 2 dimensional `numpy.typing.NDArray[VectorDType]` with shape `(number_of_points, vector_dim)` into a DiskANN vector bin file. +- `vectors_from_file` - Reads a DiskANN vector bin file representing stored vectors into a numpy ndarray. +- `vectors_metadata_from_file` - Reads metadata stored in a DiskANN vector bin file without reading the entire file +- `tags_to_file` - Turns a 1 dimensional `numpy.typing.NDArray[VectorIdentifier]` into a DiskANN tags bin file. +- `tags_from_file` - Reads a DiskANN tags bin file representing stored tags into a numpy ndarray. +- `valid_dtype` - Checks if a given vector dtype is supported by `diskannpy` +""" + +from typing import Any, Literal, NamedTuple, Type, Union + +import numpy as np +from numpy import typing as npt + +DistanceMetric = Literal["l2", "mips", "cosine"] +""" Type alias for one of {"l2", "mips", "cosine"} """ +VectorDType = Union[Type[np.float32], Type[np.int8], Type[np.uint8]] +""" Type alias for one of {`numpy.float32`, `numpy.int8`, `numpy.uint8`} """ +VectorLike = npt.NDArray[VectorDType] +""" Type alias for something that can be treated as a vector """ +VectorLikeBatch = npt.NDArray[VectorDType] +""" Type alias for a batch of VectorLikes """ +VectorIdentifier = np.uint32 +""" +Type alias for a vector identifier, whether it be an implicit array index identifier from StaticMemoryIndex or +StaticDiskIndex, or an explicit tag identifier from DynamicMemoryIndex +""" +VectorIdentifierBatch = npt.NDArray[np.uint32] +""" Type alias for a batch of VectorIdentifiers """ + + +class QueryResponse(NamedTuple): + """ + Tuple with two values, identifiers and distances. Both are 1d arrays, positionally correspond, and will contain the + nearest neighbors from [0..k_neighbors) + """ + + identifiers: npt.NDArray[VectorIdentifier] + """ A `numpy.typing.NDArray[VectorIdentifier]` array of vector identifiers, 1 dimensional """ + distances: npt.NDArray[np.float32] + """ + A `numpy.typing.NDAarray[numpy.float32]` of distances as calculated by the distance metric function, 1 dimensional + """ + + +class QueryResponseBatch(NamedTuple): + """ + Tuple with two values, identifiers and distances. Both are 2d arrays, with dimensionality determined by the + rows corresponding to the number of queries made, and the columns corresponding to the k neighbors + requested. The two 2d arrays have an implicit, position-based relationship + """ + + identifiers: npt.NDArray[VectorIdentifier] + """ + A `numpy.typing.NDArray[VectorIdentifier]` array of vector identifiers, 2 dimensional. The row corresponds to index + of the query, and the column corresponds to the k neighbors requested + """ + distances: np.ndarray[np.float32] + """ + A `numpy.typing.NDAarray[numpy.float32]` of distances as calculated by the distance metric function, 2 dimensional. + The row corresponds to the index of the query, and the column corresponds to the distance of the query to the + *k-th* neighbor + """ + + +from . import defaults +from ._builder import build_disk_index, build_memory_index +from ._common import valid_dtype +from ._dynamic_memory_index import DynamicMemoryIndex +from ._files import ( + Metadata, + tags_from_file, + tags_to_file, + vectors_from_file, + vectors_metadata_from_file, + vectors_to_file, +) +from ._static_disk_index import StaticDiskIndex +from ._static_memory_index import StaticMemoryIndex + +__all__ = [ + "build_disk_index", + "build_memory_index", + "StaticDiskIndex", + "StaticMemoryIndex", + "DynamicMemoryIndex", + "defaults", + "DistanceMetric", + "VectorDType", + "QueryResponse", + "QueryResponseBatch", + "VectorIdentifier", + "VectorIdentifierBatch", + "VectorLike", + "VectorLikeBatch", + "Metadata", + "vectors_metadata_from_file", + "vectors_to_file", + "vectors_from_file", + "tags_to_file", + "tags_from_file", + "valid_dtype", +] diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/src/_builder.py b/packages/leann-backend-diskann/third_party/DiskANN/python/src/_builder.py new file mode 100644 index 0000000..6567020 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/src/_builder.py @@ -0,0 +1,349 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +import json +import os +import shutil +from pathlib import Path +from typing import Optional, Tuple, Union + +import numpy as np + +from . import DistanceMetric, VectorDType, VectorIdentifierBatch, VectorLikeBatch +from . import _diskannpy as _native_dap +from ._common import ( + _assert, + _assert_is_nonnegative_uint32, + _assert_is_positive_uint32, + _castable_dtype_or_raise, + _valid_metric, + _write_index_metadata, + valid_dtype, +) +from ._diskannpy import defaults +from ._files import tags_to_file, vectors_metadata_from_file, vectors_to_file + + +def _valid_path_and_dtype( + data: Union[str, VectorLikeBatch], + vector_dtype: VectorDType, + index_path: str, + index_prefix: str, +) -> Tuple[str, VectorDType]: + if isinstance(data, str): + vector_bin_path = data + _assert( + Path(data).exists() and Path(data).is_file(), + "if data is of type `str`, it must both exist and be a file", + ) + vector_dtype_actual = valid_dtype(vector_dtype) + else: + vector_bin_path = os.path.join(index_path, f"{index_prefix}_vectors.bin") + # if Path(vector_bin_path).exists(): + # raise ValueError( + # f"The path {vector_bin_path} already exists. Remove it and try again." + # ) + vector_dtype_actual = valid_dtype(data.dtype) + # vectors_to_file(vector_file=vector_bin_path, vectors=data) + + return vector_bin_path, vector_dtype_actual + + +def build_disk_index( + data: Union[str, VectorLikeBatch], + distance_metric: DistanceMetric, + index_directory: str, + complexity: int, + graph_degree: int, + search_memory_maximum: float, + build_memory_maximum: float, + num_threads: int, + pq_disk_bytes: int = defaults.PQ_DISK_BYTES, + vector_dtype: Optional[VectorDType] = None, + index_prefix: str = "ann", + codebook_prefix: str = "", +) -> None: + """ + This function will construct a DiskANN disk index. Disk indices are ideal for very large datasets that + are too large to fit in memory. Memory is still used, but it is primarily used to provide precise disk + locations for fast retrieval of smaller subsets of the index without compromising much on recall. + + If you provide a numpy array, it will save this array to disk in a temp location + in the format DiskANN's PQ Flash Index builder requires. This temp folder is deleted upon index creation completion + or error. + + ## Distance Metric and Vector Datatype Restrictions + | Metric \ Datatype | np.float32 | np.uint8 | np.int8 | + |-------------------|------------|----------|---------| + | L2 | ✅ | ✅ | ✅ | + | MIPS | ✅ | ⌠| ⌠| + | Cosine [^bug-in-disk-cosine] | ⌠| ⌠| ⌠| + + [^bug-in-disk-cosine]: For StaticDiskIndex, Cosine distances are not currently supported. + + ### Parameters + - **data**: Either a `str` representing a path to a DiskANN vector bin file, or a numpy.ndarray, + of a supported dtype, in 2 dimensions. Note that `vector_dtype` must be provided if data is a `str` + - **distance_metric**: A `str`, strictly one of {"l2", "mips", "cosine"}. `l2` and `cosine` are supported for all 3 + vector dtypes, but `mips` is only available for single precision floats. + - **index_directory**: The index files will be saved to this **existing** directory path + - **complexity**: The size of the candidate nearest neighbor list to use when building the index. Values between 75 + and 200 are typical. Larger values will take more time to build but result in indices that provide higher recall + for the same search complexity. Use a value that is at least as large as `graph_degree` unless you are prepared + to compromise on quality + - **graph_degree**: The degree of the graph index, typically between 60 and 150. A larger maximum degree will + result in larger indices and longer indexing times, but better search quality. + - **search_memory_maximum**: Build index with the expectation that the search will use at most + `search_memory_maximum`, in gb. + - **build_memory_maximum**: Build index using at most `build_memory_maximum` in gb. Building processes typically + require more memory, while search memory can be reduced. + - **num_threads**: Number of threads to use when creating this index. `0` is used to indicate all available + logical processors should be used. + - **pq_disk_bytes**: Use `0` to store uncompressed data on SSD. This allows the index to asymptote to 100% + recall. If your vectors are too large to store in SSD, this parameter provides the option to compress the + vectors using PQ for storing on SSD. This will trade off recall. You would also want this to be greater + than the number of bytes used for the PQ compressed data stored in-memory. Default is `0`. + - **vector_dtype**: Required if the provided `data` is of type `str`, else we use the `data.dtype` if np array. + - **index_prefix**: The prefix of the index files. Defaults to "ann". + """ + + _assert( + (isinstance(data, str) and vector_dtype is not None) + or isinstance(data, np.ndarray), + "vector_dtype is required if data is a str representing a path to the vector bin file", + ) + dap_metric = _valid_metric(distance_metric) + _assert_is_positive_uint32(complexity, "complexity") + _assert_is_positive_uint32(graph_degree, "graph_degree") + _assert(search_memory_maximum > 0, "search_memory_maximum must be larger than 0") + _assert(build_memory_maximum > 0, "build_memory_maximum must be larger than 0") + _assert_is_nonnegative_uint32(num_threads, "num_threads") + _assert_is_nonnegative_uint32(pq_disk_bytes, "pq_disk_bytes") + _assert(index_prefix != "", "index_prefix cannot be an empty string") + + index_path = Path(index_directory) + _assert( + index_path.exists() and index_path.is_dir(), + "index_directory must both exist and be a directory", + ) + + vector_bin_path, vector_dtype_actual = _valid_path_and_dtype( + data, vector_dtype, index_directory, index_prefix + ) + _assert(dap_metric != _native_dap.COSINE, "Cosine is currently not supported in StaticDiskIndex") + if dap_metric == _native_dap.INNER_PRODUCT: + _assert( + vector_dtype_actual == np.float32, + "Integral vector dtypes (np.uint8, np.int8) are not supported with distance metric mips" + ) + + num_points, dimensions = vectors_metadata_from_file(vector_bin_path) + + if vector_dtype_actual == np.uint8: + _builder = _native_dap.build_disk_uint8_index + elif vector_dtype_actual == np.int8: + _builder = _native_dap.build_disk_int8_index + else: + _builder = _native_dap.build_disk_float_index + + index_prefix_path = os.path.join(index_directory, index_prefix) + + _builder( + distance_metric=dap_metric, + data_file_path=vector_bin_path, + index_prefix_path=index_prefix_path, + complexity=complexity, + graph_degree=graph_degree, + final_index_ram_limit=search_memory_maximum, + indexing_ram_budget=build_memory_maximum, + num_threads=num_threads, + pq_disk_bytes=pq_disk_bytes, + codebook_prefix=codebook_prefix, + ) + _write_index_metadata( + index_prefix_path, vector_dtype_actual, dap_metric, num_points, dimensions + ) + + +def build_memory_index( + data: Union[str, VectorLikeBatch], + distance_metric: DistanceMetric, + index_directory: str, + complexity: int, + graph_degree: int, + num_threads: int, + alpha: float = defaults.ALPHA, + use_pq_build: bool = defaults.USE_PQ_BUILD, + num_pq_bytes: int = defaults.NUM_PQ_BYTES, + use_opq: bool = defaults.USE_OPQ, + vector_dtype: Optional[VectorDType] = None, + tags: Union[str, VectorIdentifierBatch] = "", + filter_labels: Optional[list[list[str]]] = None, + universal_label: str = "", + filter_complexity: int = defaults.FILTER_COMPLEXITY, + index_prefix: str = "ann", +) -> None: + """ + This function will construct a DiskANN memory index. Memory indices are ideal for smaller datasets whose + indices can fit into memory. Memory indices are faster than disk indices, but usually cannot scale to massive + sizes in an individual index on an individual machine. + + `diskannpy`'s memory indices take two forms: a `diskannpy.StaticMemoryIndex`, which will not be mutated, only + searched upon, and a `diskannpy.DynamicMemoryIndex`, which can be mutated AND searched upon in the same process. + + ## Important Note: + You **must** determine the type of index you are building for. If you are building for a + `diskannpy.DynamicMemoryIndex`, you **must** supply a valid value for the `tags` parameter. **Do not supply + tags if the index is intended to be `diskannpy.StaticMemoryIndex`**! + + ## Distance Metric and Vector Datatype Restrictions + + | Metric \ Datatype | np.float32 | np.uint8 | np.int8 | + |-------------------|------------|----------|---------| + | L2 | ✅ | ✅ | ✅ | + | MIPS | ✅ | ⌠| ⌠| + | Cosine | ✅ | ✅ | ✅ | + + ### Parameters + + - **data**: Either a `str` representing a path to an existing DiskANN vector bin file, or a numpy.ndarray of a + supported dtype in 2 dimensions. Note that `vector_dtype` must be provided if `data` is a `str`. + - **distance_metric**: A `str`, strictly one of {"l2", "mips", "cosine"}. `l2` and `cosine` are supported for all 3 + vector dtypes, but `mips` is only available for single precision floats. + - **index_directory**: The index files will be saved to this **existing** directory path + - **complexity**: The size of the candidate nearest neighbor list to use when building the index. Values between 75 + and 200 are typical. Larger values will take more time to build but result in indices that provide higher recall + for the same search complexity. Use a value that is at least as large as `graph_degree` unless you are prepared + to compromise on quality + - **graph_degree**: The degree of the graph index, typically between 60 and 150. A larger maximum degree will + result in larger indices and longer indexing times, but better search quality. + - **num_threads**: Number of threads to use when creating this index. `0` is used to indicate all available + logical processors should be used. + - **alpha**: The alpha parameter (>=1) is used to control the nature and number of points that are added to the + graph. A higher alpha value (e.g., 1.4) will result in fewer hops (and IOs) to convergence, but probably more + distance comparisons compared to a lower alpha value. + - **use_pq_build**: Use product quantization during build. Product quantization is a lossy compression technique + that can reduce the size of the index on disk. This will trade off recall. Default is `True`. + - **num_pq_bytes**: The number of bytes used to store the PQ compressed data in memory. This will trade off recall. + Default is `0`. + - **use_opq**: Use optimized product quantization during build. + - **vector_dtype**: Required if the provided `data` is of type `str`, else we use the `data.dtype` if np array. + - **tags**: Tags can be defined either as a path on disk to an existing .tags file, or provided as a np.array of + the same length as the number of vectors. Tags are used to identify vectors in the index via your *own* + numbering conventions, and is absolutely required for loading DynamicMemoryIndex indices `from_file`. + - **filter_labels**: An optional, but exhaustive list of categories for each vector. This is used to filter + search results by category. If provided, this must be a list of lists, where each inner list is a list of + categories for the corresponding vector. For example, if you have 3 vectors, and the first vector belongs to + categories "a" and "b", the second vector belongs to category "b", and the third vector belongs to no categories, + you would provide `filter_labels=[["a", "b"], ["b"], []]`. If you do not want to provide categories for a + particular vector, you can provide an empty list. If you do not want to provide categories for any vectors, + you can provide `None` for this parameter (which is the default) + - **universal_label**: An optional label that indicates that this vector should be included in *every* search + in which it also meets the knn search criteria. + - **filter_complexity**: Complexity to use when using filters. Default is 0. 0 is strictly invalid if you are + using filters. + - **index_prefix**: The prefix of the index files. Defaults to "ann". + """ + _assert( + (isinstance(data, str) and vector_dtype is not None) + or isinstance(data, np.ndarray), + "vector_dtype is required if data is a str representing a path to the vector bin file", + ) + dap_metric = _valid_metric(distance_metric) + _assert_is_positive_uint32(complexity, "complexity") + _assert_is_positive_uint32(graph_degree, "graph_degree") + _assert( + alpha >= 1, + "alpha must be >= 1, and realistically should be kept between [1.0, 2.0)", + ) + _assert_is_nonnegative_uint32(num_threads, "num_threads") + _assert_is_nonnegative_uint32(num_pq_bytes, "num_pq_bytes") + _assert_is_nonnegative_uint32(filter_complexity, "filter_complexity") + _assert(index_prefix != "", "index_prefix cannot be an empty string") + _assert( + filter_labels is None or filter_complexity > 0, + "if filter_labels is provided, filter_complexity must not be 0" + ) + + index_path = Path(index_directory) + _assert( + index_path.exists() and index_path.is_dir(), + "index_directory must both exist and be a directory", + ) + + vector_bin_path, vector_dtype_actual = _valid_path_and_dtype( + data, vector_dtype, index_directory, index_prefix + ) + if dap_metric == _native_dap.INNER_PRODUCT: + _assert( + vector_dtype_actual == np.float32, + "Integral vector dtypes (np.uint8, np.int8) are not supported with distance metric mips" + ) + + num_points, dimensions = vectors_metadata_from_file(vector_bin_path) + if filter_labels is not None: + _assert( + len(filter_labels) == num_points, + "filter_labels must be the same length as the number of points" + ) + + if vector_dtype_actual == np.uint8: + _builder = _native_dap.build_memory_uint8_index + elif vector_dtype_actual == np.int8: + _builder = _native_dap.build_memory_int8_index + else: + _builder = _native_dap.build_memory_float_index + + index_prefix_path = os.path.join(index_directory, index_prefix) + + filter_labels_file = "" + if filter_labels is not None: + label_counts = {} + filter_labels_file = f"{index_prefix_path}_pylabels.txt" + with open(filter_labels_file, "w") as labels_file: + for labels in filter_labels: + for label in labels: + label_counts[label] = 1 if label not in label_counts else label_counts[label] + 1 + if len(labels) == 0: + print("default", file=labels_file) + else: + print(",".join(labels), file=labels_file) + with open(f"{index_prefix_path}_label_metadata.json", "w") as label_metadata_file: + json.dump(label_counts, label_metadata_file, indent=True) + + if isinstance(tags, str) and tags != "": + use_tags = True + shutil.copy(tags, index_prefix_path + ".tags") + elif not isinstance(tags, str): + use_tags = True + tags_as_array = _castable_dtype_or_raise(tags, expected=np.uint32) + _assert(len(tags_as_array.shape) == 1, "Provided tags must be 1 dimensional") + _assert( + tags_as_array.shape[0] == num_points, + "Provided tags must contain an identical population to the number of points, " + f"{tags_as_array.shape[0]=}, {num_points=}", + ) + tags_to_file(index_prefix_path + ".tags", tags_as_array) + else: + use_tags = False + + _builder( + distance_metric=dap_metric, + data_file_path=vector_bin_path, + index_output_path=index_prefix_path, + complexity=complexity, + graph_degree=graph_degree, + alpha=alpha, + num_threads=num_threads, + use_pq_build=use_pq_build, + num_pq_bytes=num_pq_bytes, + use_opq=use_opq, + use_tags=use_tags, + filter_labels_file=filter_labels_file, + universal_label=universal_label, + filter_complexity=filter_complexity, + ) + + _write_index_metadata( + index_prefix_path, vector_dtype_actual, dap_metric, num_points, dimensions + ) diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/src/_builder.pyi b/packages/leann-backend-diskann/third_party/DiskANN/python/src/_builder.pyi new file mode 100644 index 0000000..223e6c9 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/src/_builder.pyi @@ -0,0 +1,74 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +from typing import BinaryIO, Optional, overload + +import numpy as np + +from . import DistanceMetric, VectorDType, VectorIdentifierBatch, VectorLikeBatch + +def numpy_to_diskann_file(vectors: np.ndarray, file_handler: BinaryIO): ... +@overload +def build_disk_index( + data: str, + distance_metric: DistanceMetric, + index_directory: str, + complexity: int, + graph_degree: int, + search_memory_maximum: float, + build_memory_maximum: float, + num_threads: int, + pq_disk_bytes: int, + vector_dtype: VectorDType, + index_prefix: str, +) -> None: ... +@overload +def build_disk_index( + data: VectorLikeBatch, + distance_metric: DistanceMetric, + index_directory: str, + complexity: int, + graph_degree: int, + search_memory_maximum: float, + build_memory_maximum: float, + num_threads: int, + pq_disk_bytes: int, + index_prefix: str, +) -> None: ... +@overload +def build_memory_index( + data: VectorLikeBatch, + distance_metric: DistanceMetric, + index_directory: str, + complexity: int, + graph_degree: int, + alpha: float, + num_threads: int, + use_pq_build: bool, + num_pq_bytes: int, + use_opq: bool, + tags: Union[str, VectorIdentifierBatch], + filter_labels: Optional[list[list[str]]], + universal_label: str, + filter_complexity: int, + index_prefix: str +) -> None: ... +@overload +def build_memory_index( + data: str, + distance_metric: DistanceMetric, + index_directory: str, + complexity: int, + graph_degree: int, + alpha: float, + num_threads: int, + use_pq_build: bool, + num_pq_bytes: int, + use_opq: bool, + vector_dtype: VectorDType, + tags: Union[str, VectorIdentifierBatch], + filter_labels_file: Optional[list[list[str]]], + universal_label: str, + filter_complexity: int, + index_prefix: str +) -> None: ... diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/src/_common.py b/packages/leann-backend-diskann/third_party/DiskANN/python/src/_common.py new file mode 100644 index 0000000..2b28802 --- /dev/null +++ b/packages/leann-backend-diskann/third_party/DiskANN/python/src/_common.py @@ -0,0 +1,251 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT license. + +import os +import warnings +from enum import Enum +from pathlib import Path +from typing import Literal, NamedTuple, Optional, Tuple, Type, Union + +import numpy as np + +from . import ( + DistanceMetric, + VectorDType, + VectorIdentifierBatch, + VectorLike, + VectorLikeBatch, +) +from . import _diskannpy as _native_dap + +__ALL__ = ["valid_dtype"] + +_VALID_DTYPES = [np.float32, np.int8, np.uint8] + + +def valid_dtype(dtype: Type) -> VectorDType: + """ + Utility method to determine whether the provided dtype is supported by `diskannpy`, and if so, the canonical + dtype we will use internally (e.g. np.single -> np.float32) + """ + _assert_dtype(dtype) + if dtype == np.uint8: + return np.uint8 + if dtype == np.int8: + return np.int8 + if dtype == np.float32: + return np.float32 + + +def _assert(statement_eval: bool, message: str): + if not statement_eval: + raise ValueError(message) + + +def _valid_metric(metric: str) -> _native_dap.Metric: + if not isinstance(metric, str): + raise ValueError("distance_metric must be a string") + if metric.lower() == "l2": + return _native_dap.L2 + elif metric.lower() == "mips": + return _native_dap.INNER_PRODUCT + elif metric.lower() == "cosine": + return _native_dap.COSINE + else: + raise ValueError("distance_metric must be one of 'l2', 'mips', or 'cosine'") + + +def _assert_dtype(dtype: Type): + _assert( + any(np.can_cast(dtype, _dtype) for _dtype in _VALID_DTYPES), + f"Vector dtype must be of one of type {{(np.single, np.float32), (np.byte, np.int8), (np.ubyte, np.uint8)}}", + ) + + +def _castable_dtype_or_raise( + data: Union[VectorLike, VectorLikeBatch, VectorIdentifierBatch], expected: np.dtype +) -> np.ndarray: + if isinstance(data, np.ndarray) and np.can_cast(data.dtype, expected): + return data.astype(expected, casting="safe") + else: + raise TypeError( + f"expecting a numpy ndarray of dtype {expected}, not a {type(data)}" + ) + + +def _assert_2d(vectors: np.ndarray, name: str): + _assert(len(vectors.shape) == 2, f"{name} must be 2d numpy array") + + +__MAX_UINT32_VAL = 4_294_967_295 + + +def _assert_is_positive_uint32(test_value: int, parameter: str): + _assert( + test_value is not None and 0 < test_value < __MAX_UINT32_VAL, + f"{parameter} must be a positive integer in the uint32 range", + ) + + +def _assert_is_nonnegative_uint32(test_value: int, parameter: str): + _assert( + test_value is not None and -1 < test_value < __MAX_UINT32_VAL, + f"{parameter} must be a non-negative integer in the uint32 range", + ) + + +def _assert_is_nonnegative_uint64(test_value: int, parameter: str): + _assert( + -1 < test_value, + f"{parameter} must be a non-negative integer in the uint64 range", + ) + + +def _assert_existing_directory(path: str, parameter: str): + _path = Path(path) + _assert( + _path.exists() and _path.is_dir(), f"{parameter} must be an existing directory" + ) + + +def _assert_existing_file(path: str, parameter: str): + _path = Path(path) + _assert(_path.exists() and _path.is_file(), f"{parameter} must be an existing file") + + +class _DataType(Enum): + FLOAT32 = 0 + INT8 = 1 + UINT8 = 2 + + @classmethod + def from_type(cls, vector_dtype: VectorDType) -> "DataType": + if vector_dtype == np.float32: + return cls.FLOAT32 + if vector_dtype == np.int8: + return cls.INT8 + if vector_dtype == np.uint8: + return cls.UINT8 + + def to_type(self) -> VectorDType: + if self is _DataType.FLOAT32: + return np.float32 + if self is _DataType.INT8: + return np.int8 + if self is _DataType.UINT8: + return np.uint8 + + +class _Metric(Enum): + L2 = 0 + MIPS = 1 + COSINE = 2 + + @classmethod + def from_native(cls, metric: _native_dap.Metric) -> "_Metric": + if metric == _native_dap.L2: + return cls.L2 + if metric == _native_dap.INNER_PRODUCT: + return cls.MIPS + if metric == _native_dap.COSINE: + return cls.COSINE + + def to_native(self) -> _native_dap.Metric: + if self is _Metric.L2: + return _native_dap.L2 + if self is _Metric.MIPS: + return _native_dap.INNER_PRODUCT + if self is _Metric.COSINE: + return _native_dap.COSINE + + def to_str(self) -> _native_dap.Metric: + if self is _Metric.L2: + return "l2" + if self is _Metric.MIPS: + return "mips" + if self is _Metric.COSINE: + return "cosine" + + +def _build_metadata_path(index_path_and_prefix: str) -> str: + return index_path_and_prefix + "_metadata.bin" + + +def _write_index_metadata( + index_path_and_prefix: str, + dtype: VectorDType, + metric: _native_dap.Metric, + num_points: int, + dimensions: int, +): + np.array( + [ + _DataType.from_type(dtype).value, + _Metric.from_native(metric).value, + num_points, + dimensions, + ], + dtype=np.uint64, + ).tofile(_build_metadata_path(index_path_and_prefix)) + + +def _read_index_metadata( + index_path_and_prefix: str, +) -> Optional[Tuple[VectorDType, str, np.uint64, np.uint64]]: + path = _build_metadata_path(index_path_and_prefix) + if not Path(path).exists(): + return None + else: + metadata = np.fromfile(path, dtype=np.uint64, count=-1) + return ( + _DataType(int(metadata[0])).to_type(), + _Metric(int(metadata[1])).to_str(), + metadata[2], + metadata[3], + ) + + +def _ensure_index_metadata( + index_path_and_prefix: str, + vector_dtype: Optional[VectorDType], + distance_metric: Optional[DistanceMetric], + max_vectors: int, + dimensions: Optional[int], + warn_size_exceeded: bool = False, +) -> Tuple[VectorDType, str, np.uint64, np.uint64]: + possible_metadata = _read_index_metadata(index_path_and_prefix) + if possible_metadata is None: + _assert( + all([vector_dtype, distance_metric, dimensions]), + "distance_metric, vector_dtype, and dimensions must provided if a corresponding metadata file has not " + "been built for this index, such as when an index was built via the CLI tools or prior to the addition " + "of a metadata file", + ) + _assert_dtype(vector_dtype) + _assert_is_positive_uint32(max_vectors, "max_vectors") + _assert_is_positive_uint32(dimensions, "dimensions") + return vector_dtype, distance_metric, max_vectors, dimensions # type: ignore + else: + vector_dtype, distance_metric, num_vectors, dimensions = possible_metadata + if warn_size_exceeded: + if max_vectors is not None and num_vectors > max_vectors: + warnings.warn( + "The number of vectors in the saved index exceeds the max_vectors parameter. " + "max_vectors is being adjusted to accommodate the dataset, but any insertions will fail." + ) + max_vectors = num_vectors + if num_vectors == max_vectors: + warnings.warn( + "The number of vectors in the saved index equals max_vectors parameter. Any insertions will fail." + ) + return possible_metadata + + +def _valid_index_prefix(index_directory: str, index_prefix: str) -> str: + _assert( + index_directory is not None and index_directory != "", + "index_directory cannot be None or empty", + ) + _assert_existing_directory(index_directory, "index_directory") + _assert(index_prefix != "", "index_prefix cannot be an empty string") + return os.path.join(index_directory, index_prefix) diff --git a/packages/leann-backend-diskann/third_party/DiskANN/python/src/_diskannpy.cpython-310-x86_64-linux-gnu.so.bak b/packages/leann-backend-diskann/third_party/DiskANN/python/src/_diskannpy.cpython-310-x86_64-linux-gnu.so.bak new file mode 100755 index 0000000000000000000000000000000000000000..5741ecd7dd00337e060d91bb8b4cff466d2f082d GIT binary patch literal 33617584 zcma&u2{e>_{5J4=7K|Z{wXu|$v4oVpRI0I4ibT|Lb(#_nh~8&Zq18eeW4dVes&*bM|myG8in142v2~F?tS2G3gPn zDQ3@FOxaS3xMEBh{dZe-&xQX>^3N##x9v%a_xDEh^N^uy&EM<)=Az(f|J|@Bd*AQB 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