diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 4ee0d62cb..49e9b7528 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -17,19 +17,18 @@ rocmPackages, vulkan-headers, vulkan-loader, - clblast, + curl, useBlas ? builtins.all (x: !x) [ useCuda useMetalKit - useOpenCL useRocm useVulkan ] && blas.meta.available, useCuda ? config.cudaSupport, - useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL, + useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin, useMpi ? false, # Increases the runtime closure size by ~700M - useOpenCL ? false, useRocm ? config.rocmSupport, + enableCurl ? true, useVulkan ? false, llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake @@ -56,7 +55,6 @@ let ++ lib.optionals useCuda [ "CUDA" ] ++ lib.optionals useMetalKit [ "MetalKit" ] ++ lib.optionals useMpi [ "MPI" ] - ++ lib.optionals useOpenCL [ "OpenCL" ] ++ lib.optionals useRocm [ "ROCm" ] ++ lib.optionals useVulkan [ "Vulkan" ]; @@ -198,19 +196,19 @@ effectiveStdenv.mkDerivation ( optionals effectiveStdenv.isDarwin darwinBuildInputs ++ optionals useCuda cudaBuildInputs ++ optionals useMpi [ mpi ] - ++ optionals useOpenCL [ clblast ] ++ optionals useRocm rocmBuildInputs ++ optionals useBlas [ blas ] - ++ optionals useVulkan vulkanBuildInputs; + ++ optionals useVulkan vulkanBuildInputs + ++ optionals enableCurl [ curl ]; cmakeFlags = [ (cmakeBool "LLAMA_BUILD_SERVER" true) (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic)) (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true) + (cmakeBool "LLAMA_CURL" enableCurl) (cmakeBool "GGML_NATIVE" false) (cmakeBool "GGML_BLAS" useBlas) - (cmakeBool "GGML_CLBLAST" useOpenCL) (cmakeBool "GGML_CUDA" useCuda) (cmakeBool "GGML_HIPBLAS" useRocm) (cmakeBool "GGML_METAL" useMetalKit) @@ -254,7 +252,6 @@ effectiveStdenv.mkDerivation ( useCuda useMetalKit useMpi - useOpenCL useRocm useVulkan ; @@ -281,7 +278,7 @@ effectiveStdenv.mkDerivation ( # Configurations we don't want even the CI to evaluate. Results in the # "unsupported platform" messages. This is mostly a no-op, because # cudaPackages would've refused to evaluate anyway. - badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin; + badPlatforms = optionals useCuda lib.platforms.darwin; # Configurations that are known to result in build failures. Can be # overridden by importing Nixpkgs with `allowBroken = true`. diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index c88134dbb..eb8c4b472 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -9,5 +9,3 @@ contact_links: - name: Want to contribute? url: https://github.com/ggerganov/llama.cpp/wiki/contribute about: Head to the contribution guide page of the wiki for areas you can help with - - diff --git a/.gitignore b/.gitignore index 177e6a8db..4866f6122 100644 --- a/.gitignore +++ b/.gitignore @@ -47,6 +47,7 @@ build* !build-info.cpp.in !build-info.sh !build.zig +!docs/build.md /libllama.so /llama-* android-ndk-* @@ -98,13 +99,14 @@ examples/server/*.mjs.hpp # Python -__pycache__ -.venv -/Pipfile -dist -poetry.lock +/.venv +__pycache__/ +*/poetry.lock poetry.toml +# Nix +/result + # Test binaries /tests/test-backend-ops /tests/test-double-float diff --git a/CMakeLists.txt b/CMakeLists.txt index e3a0cc369..4f6cd6872 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -42,6 +42,10 @@ endif() option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT}) +if (WIN32) + add_compile_definitions(_CRT_SECURE_NO_WARNINGS) +endif() + # # option list # @@ -152,7 +156,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama) install( - FILES convert-hf-to-gguf.py + FILES convert_hf_to_gguf.py PERMISSIONS OWNER_READ OWNER_WRITE diff --git a/CMakePresets.json b/CMakePresets.json index d69bc0344..bdad38952 100644 --- a/CMakePresets.json +++ b/CMakePresets.json @@ -19,6 +19,7 @@ "cacheVariables": { "CMAKE_EXPORT_COMPILE_COMMANDS": "ON", "CMAKE_CXX_COMPILER": "icx", + "CMAKE_C_COMPILER": "cl", "GGML_SYCL": "ON", "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." } diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 991d85e49..9ad516049 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,14 +1,24 @@ -# Contributing Guidelines +# Pull requests -## Checklist +- Always squash-merge the PR before merging +- Use the following format for your final commit: ` : (#)`. For example: `utils : fix typo in utils.py (#1234)` +- Test your changes: + - Using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the GGML library + - Execute [the full CI locally on your machine](ci/README.md) before publishing +- If the pull request contains only documentation changes (e.g., updating READMEs, adding new wiki pages), please add `[no ci]` to the commit title. This will skip unnecessary CI checks and help reduce build times +- Please rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs. + - The PR template has a series of review complexity checkboxes `[ ]` that [you can mark as](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) `[X]` for your conveience -* Make sure your PR follows the [coding guidelines](https://github.com/ggerganov/llama.cpp/blob/master/README.md#coding-guidelines) -* Test your changes using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the GGML library -* Execute [the full CI locally on your machine](ci/README.md) before publishing +# Coding guidelines -## PR formatting +- Avoid adding third-party dependencies, extra files, extra headers, etc. +- Always consider cross-compatibility with other operating systems and architectures +- Avoid fancy looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple +- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit +- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a` +- Naming usually optimizes for common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963) +- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices +- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$ + +![matmul](media/matmul.png) -* Please rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs. - - The PR template has a series of review complexity checkboxes `[ ]` that you can mark as `[X]` for your conveience. Refer to [About task lists](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) for more information. -* If the pull request only contains documentation changes (e.g., updating READMEs, adding new wiki pages), please add `[no ci]` to the commit title. This will skip unnecessary CI checks and help reduce build times. -* When squashing multiple commits on merge, use the following format for your commit title: ` : (#)`. For example: `utils : Fix typo in utils.py (#1234)` diff --git a/Makefile b/Makefile index 8ae4f1dc4..bb6e2f968 100644 --- a/Makefile +++ b/Makefile @@ -14,6 +14,7 @@ BUILD_TARGETS = \ llama-finetune \ llama-gbnf-validator \ llama-gguf \ + llama-gguf-hash \ llama-gguf-split \ llama-gritlm \ llama-imatrix \ @@ -62,6 +63,11 @@ TEST_TARGETS = \ tests/test-tokenizer-1-bpe \ tests/test-tokenizer-1-spm +# Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned +LEGACY_TARGETS = main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ + simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \ + retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm + # Deprecation aliases ifdef LLAMA_CUBLAS $(error LLAMA_CUBLAS is removed. Use GGML_CUDA instead.) @@ -1086,6 +1092,7 @@ clean: rm -vrf ggml/src/ggml-cuda/template-instances/*.o rm -rvf $(BUILD_TARGETS) rm -rvf $(TEST_TARGETS) + rm -rvf $(LEGACY_TARGETS) find examples pocs -type f -name "*.o" -delete # @@ -1172,6 +1179,23 @@ llama-gguf: examples/gguf/gguf.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +examples/gguf-hash/deps/sha1/sha1.o: \ + examples/gguf-hash/deps/sha1/sha1.c + $(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@ + +examples/gguf-hash/deps/xxhash/xxhash.o: \ + examples/gguf-hash/deps/xxhash/xxhash.c + $(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@ + +examples/gguf-hash/deps/sha256/sha256.o: \ + examples/gguf-hash/deps/sha256/sha256.c + $(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@ + +llama-gguf-hash: examples/gguf-hash/gguf-hash.cpp examples/gguf-hash/deps/sha1/sha1.o examples/gguf-hash/deps/xxhash/xxhash.o examples/gguf-hash/deps/sha256/sha256.o\ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -Iexamples/gguf-hash/deps -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + llama-gguf-split: examples/gguf-split/gguf-split.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) diff --git a/README.md b/README.md index 99b16f6e2..aba6a4c59 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) > [!IMPORTANT] [2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809) -### Recent API changes +## Recent API changes - [2024 Jun 26] The source code and CMake build scripts have been restructured https://github.com/ggerganov/llama.cpp/pull/8006 - [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807 @@ -24,9 +24,9 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) - [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849 -### Hot topics +## Hot topics -- **`convert.py` has been deprecated and moved to `examples/convert-legacy-llama.py`, please use `convert-hf-to-gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430 +- **`convert.py` has been deprecated and moved to `examples/convert_legacy_llama.py`, please use `convert_hf_to_gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430 - Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021 - BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920 - MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387 @@ -39,37 +39,6 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ---- -
- Table of Contents -
    -
  1. - Description -
    2. -
  2. - Usage - -
    3. -
  3. Contributing
    4. -
  4. Coding guidelines
    5. -
  5. Docs
    6. -
-
- ## Description The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide @@ -87,14 +56,6 @@ Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomm improved significantly thanks to many contributions. It is the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library. -**Supported platforms:** - -- [X] Mac OS -- [X] Linux -- [X] Windows (via CMake) -- [X] Docker -- [X] FreeBSD - **Supported models:** Typically finetunes of the base models below are supported as well. @@ -108,6 +69,7 @@ Typically finetunes of the base models below are supported as well. - [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon) - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) +- [X] [BERT](https://github.com/ggerganov/llama.cpp/pull/5423) - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/) - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) @@ -149,12 +111,6 @@ Typically finetunes of the base models below are supported as well. - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) -**HTTP server** - -[llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. - -[simplechat](./examples/server/public_simplechat) is a simple chat client, which can be used to chat with the model exposed using above web server (use --path to point to simplechat), from a local web browser. - **Bindings:** - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) @@ -175,6 +131,7 @@ Typically finetunes of the base models below are supported as well. - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig) - Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart) - PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326) +- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp) **UI:** @@ -217,10 +174,16 @@ Unless otherwise noted these projects are open-source with permissive licensing: **Tools:** - [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML +- [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption ---- +**Infrastructure:** -Here is a typical run using LLaMA v2 13B on M2 Ultra: +- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp + +## Demo + +
+Typical run using LLaMA v2 13B on M2 Ultra ``` $ make -j && ./llama-cli -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e @@ -300,454 +263,85 @@ llama_print_timings: eval time = 24513.59 ms / 399 runs ( 61.44 ms llama_print_timings: total time = 25431.49 ms ``` +
+ +
+Demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook + And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook: https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4 +
+ ## Usage Here are the end-to-end binary build and model conversion steps for most supported models. -### Get the Code +### Basic usage + +Firstly, you need to get the binary. There are different methods that you can follow: +- Method 1: Clone this repository and build locally, see [how to build](./docs/build.md) +- Method 2: If you are using MacOS or Linux, you can install llama.cpp via [brew, flox or nix](./docs/install.md) +- Method 3: Use a Docker image, see [documentation for Docker](./docs/docker.md) +- Method 4: Download pre-built binary from [releases](https://github.com/ggerganov/llama.cpp/releases) + +You can run a basic completion using this command: ```bash -git clone https://github.com/ggerganov/llama.cpp -cd llama.cpp +llama-cli -m your_model.gguf -p "I believe the meaning of life is" -n 128 + +# Output: +# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey. ``` -### Build +See [this page](./examples/main/README.md) for a full list of parameters. -In order to build llama.cpp you have four different options. +### Conversation mode -- Using `make`: - - On Linux or MacOS: - - ```bash - make - ``` - - - On Windows: - - 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - 2. Extract `w64devkit` on your pc. - 3. Run `w64devkit.exe`. - 4. Use the `cd` command to reach the `llama.cpp` folder. - 5. From here you can run: - ```bash - make - ``` - - - Notes: - - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel. - - For faster repeated compilation, install [ccache](https://ccache.dev/). - - For debug builds, run `make LLAMA_DEBUG=1` - -- Using `CMake`: - - ```bash - cmake -B build - cmake --build build --config Release - ``` - - **Notes**: - - - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. - - For faster repeated compilation, install [ccache](https://ccache.dev/). - - For debug builds, there are two cases: - - 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): - - ```bash - cmake -B build -DCMAKE_BUILD_TYPE=Debug - cmake --build build - ``` - - 2. Multi-config generators (`-G` param set to Visual Studio, XCode...): - - ```bash - cmake -B build -G "Xcode" - cmake --build build --config Debug - ``` - -- Using `gmake` (FreeBSD): - - 1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics) - 2. Add your user to **video** group - 3. Install compilation dependencies. - - ```bash - sudo pkg install gmake automake autoconf pkgconf llvm15 openblas - - gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 - ``` - -### Homebrew - -On Mac and Linux, the homebrew package manager can be used via -``` -brew install llama.cpp -``` -The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668 - -### Nix - -On Mac and Linux, the Nix package manager can be used via -``` -nix profile install nixpkgs#llama-cpp -``` -For flake enabled installs. - -Or -``` -nix-env --file '' --install --attr llama-cpp -``` -For non-flake enabled installs. - -This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164). - -#### Flox - -On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via -``` -flox install llama-cpp -``` -Flox follows the nixpkgs build of llama.cpp. - -### Metal Build - -On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. -To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option. - -When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line -argument. - -### BLAS Build - -Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use: - -- #### Accelerate Framework: - - This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions. - -- #### OpenBLAS: - - This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine. - - - Using `make`: - - On Linux: - ```bash - make GGML_OPENBLAS=1 - ``` - - - On Windows: - - 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - 2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases). - 3. Extract `w64devkit` on your pc. - 4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`. - 5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`. - 6. Run `w64devkit.exe`. - 7. Use the `cd` command to reach the `llama.cpp` folder. - 8. From here you can run: - - ```bash - make GGML_OPENBLAS=1 - ``` - - - Using `CMake` on Linux: - - ```bash - cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS - cmake --build build --config Release - ``` - -- #### BLIS - - Check [BLIS.md](docs/BLIS.md) for more information. - -- #### SYCL - SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. - - llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). - - For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). - -- #### Intel oneMKL - Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md). - - - Using manual oneAPI installation: - By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: - ```bash - source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation - cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON - cmake --build build --config Release - ``` - - - Using oneAPI docker image: - If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above. - - Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. - -- #### CUDA - - This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). - - For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling. - - - Using `make`: - ```bash - make GGML_CUDA=1 - ``` - - Using `CMake`: - - ```bash - cmake -B build -DGGML_CUDA=ON - cmake --build build --config Release - ``` - - The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance: - - | Option | Legal values | Default | Description | - |-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - | GGML_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | - | GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | - | GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. | - | GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models | - | GGML_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | - | GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | - | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | - | GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | - -- #### hipBLAS - - This provides BLAS acceleration on HIP-supported AMD GPUs. - Make sure to have ROCm installed. - You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick). - - - Using `make`: - ```bash - make GGML_HIPBLAS=1 - ``` - - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): - ```bash - HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ - cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ - && cmake --build build --config Release -- -j 16 - ``` - On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`. - However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). - - Note that if you get the following error: - ``` - clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library - ``` - Try searching for a directory under `HIP_PATH` that contains the file - `oclc_abi_version_400.bc`. Then, add the following to the start of the - command: `HIP_DEVICE_LIB_PATH=`, so something - like: - ```bash - HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \ - HIP_DEVICE_LIB_PATH= \ - cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ - && cmake --build build -- -j 16 - ``` - - - Using `make` (example for target gfx1030, build with 16 CPU threads): - ```bash - make -j16 GGML_HIPBLAS=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030 - ``` - - - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): - ```bash - set PATH=%HIP_PATH%\bin;%PATH% - cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release - cmake --build build - ``` - Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors) - Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`. - - - The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. - If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. - The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): - - | Option | Legal values | Default | Description | - |------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - | GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | - | GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | - -- #### Vulkan - - **With docker**: - - You don't need to install Vulkan SDK. It will be installed inside the container. - - ```sh - # Build the image - docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile . - - # Then, use it: - docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 - ``` - - **Without docker**: - - Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) - - For example, on Ubuntu 22.04 (jammy), use the command below: - - ```bash - wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list - apt update -y - apt-get install -y vulkan-sdk - # To verify the installation, use the command below: - vulkaninfo - ``` - - Alternatively your package manager might be able to provide the appropriate libraries. - For example for Ubuntu 22.04 you can install `libvulkan-dev` instead. - For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages. - - Then, build llama.cpp using the cmake command below: - - ```bash - cmake -B build -DGGML_VULKAN=1 - cmake --build build --config Release - # Test the output binary (with "-ngl 33" to offload all layers to GPU) - ./bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4 - - # You should see in the output, ggml_vulkan detected your GPU. For example: - # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 - ``` - -### Prepare and Quantize - -> [!NOTE] -> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours. - -To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. - -Note: `convert.py` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives. -It does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. +If you want a more ChatGPT-like experience, you can run in conversation mode by passing `-cnv` as a parameter: ```bash -# obtain the official LLaMA model weights and place them in ./models -ls ./models -llama-2-7b tokenizer_checklist.chk tokenizer.model -# [Optional] for models using BPE tokenizers -ls ./models - vocab.json -# [Optional] for PyTorch .bin models like Mistral-7B -ls ./models - +llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv -# install Python dependencies -python3 -m pip install -r requirements.txt - -# convert the model to ggml FP16 format -python3 convert-hf-to-gguf.py models/mymodel/ - -# quantize the model to 4-bits (using Q4_K_M method) -./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M - -# update the gguf filetype to current version if older version is now unsupported -./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY +# Output: +# > hi, who are you? +# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today? +# +# > what is 1+1? +# Easy peasy! The answer to 1+1 is... 2! ``` -### Run the quantized model +By default, the chat template will be taken from the input model. If you want to use another chat template, pass `--chat-template NAME` as a parameter. See the list of [supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) ```bash -# start inference on a gguf model -./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128 +./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml ``` -When running the larger models, make sure you have enough disk space to store all the intermediate files. +You can also use your own template via in-prefix, in-suffix and reverse-prompt parameters: -### Running on Windows with prebuilt binaries - -You will find prebuilt Windows binaries on the release page. - -Simply download and extract the latest zip package of choice: (e.g. `llama-b1380-bin-win-avx2-x64.zip`) - -From the unzipped folder, open a terminal/cmd window here and place a pre-converted `.gguf` model file. Test out the main example like so: - -``` -.\main -m llama-2-7b.Q4_0.gguf -n 128 +```bash +./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:' ``` -### Memory/Disk Requirements +### Web server -As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. +[llama.cpp web server](./examples/server/README.md) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. -| Model | Original size | Quantized size (Q4_0) | -|------:|--------------:|----------------------:| -| 7B | 13 GB | 3.9 GB | -| 13B | 24 GB | 7.8 GB | -| 30B | 60 GB | 19.5 GB | -| 65B | 120 GB | 38.5 GB | +Example usage: -### Quantization +```bash +./llama-server -m your_model.gguf --port 8080 -Several quantization methods are supported. They differ in the resulting model disk size and inference speed. - -*(outdated)* - -| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | -|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:| -| 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 | -| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G | -| 7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 | -| 7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 | -| 7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | -| 13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 | -| 13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G | -| 13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 | -| 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 | -| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | - -- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684) -- recent k-quants improvements and new i-quants - - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707) - - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807) - - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773) - - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856) - - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861) - - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872) - - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897) - - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930) - - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957) - - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969) - - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996) - - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060) - - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196) - - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361) - -### Perplexity (measuring model quality) - -You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better). -For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity). - -The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512. -The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads. - -#### How to run - -1. Download/extract: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip -2. Run `./llama-perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw` -3. Output: +# Basic web UI can be accessed via browser: http://localhost:8080 +# Chat completion endpoint: http://localhost:8080/v1/chat/completions ``` -perplexity : calculating perplexity over 655 chunks -24.43 seconds per pass - ETA 4.45 hours -[1]4.5970,[2]5.1807,[3]6.0382,... -``` -And after 4.45 hours, you will have the final perplexity. ### Interactive mode -If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter. +> [!NOTE] +> If you prefer basic usage, please consider using conversation mode instead of interactive mode + In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`. Here is an example of a few-shot interaction, invoked with the command @@ -798,18 +392,70 @@ The `grammars/` folder contains a handful of sample grammars. To write your own, For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one. -### Obtaining and using the Facebook LLaMA 2 model +## Build -- Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data. -- Alternatively, if you want to save time and space, you can download already converted and quantized models from [TheBloke](https://huggingface.co/TheBloke), including: - - [LLaMA 2 7B base](https://huggingface.co/TheBloke/Llama-2-7B-GGUF) - - [LLaMA 2 13B base](https://huggingface.co/TheBloke/Llama-2-13B-GGUF) - - [LLaMA 2 70B base](https://huggingface.co/TheBloke/Llama-2-70B-GGUF) - - [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGUF) - - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) - - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF) +Please refer to [Build llama.cpp locally](./docs/build.md) -### Seminal papers and background on the models +## Supported backends + +| Backend | Target devices | +| --- | --- | +| [Metal](./docs/build.md#metal-build) | Apple Silicon | +| [BLAS](./docs/build.md#blas-build) | All | +| [BLIS](./docs/backend/BLIS.md) | All | +| [SYCL](./docs/backend/SYCL.md) | Intel and Nvidia GPU | +| [CUDA](./docs/build.md#cuda) | Nvidia GPU | +| [hipBLAS](./docs/build.md#hipblas) | AMD GPU | +| [Vulkan](./docs/build.md#vulkan) | GPU | + +## Tools + +### Prepare and Quantize + +> [!NOTE] +> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours. + +To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. + +Note: `convert.py` has been moved to `examples/convert_legacy_llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives. +It does not support LLaMA 3, you can use `convert_hf_to_gguf.py` with LLaMA 3 downloaded from Hugging Face. + +To learn more about quantizing model, [read this documentation](./examples/quantize/README.md) + +### Perplexity (measuring model quality) + +You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better). +For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity). + +To learn more how to measure perplexity using llama.cpp, [read this documentation](./examples/perplexity/README.md) + +## Contributing + +- Contributors can open PRs +- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch +- Collaborators will be invited based on contributions +- Any help with managing issues and PRs is very appreciated! +- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions +- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information +- Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205) +- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532) + +## Other documentations + +- [main (cli)](./examples/main/README.md) +- [server](./examples/server/README.md) +- [jeopardy](./examples/jeopardy/README.md) +- [GBNF grammars](./grammars/README.md) + +**Development documentations** + +- [How to build](./docs/build.md) +- [Running on Docker](./docs/docker.md) +- [Build on Android](./docs/android.md) +- [Performance troubleshooting](./docs/token_generation_performance_tips.md) +- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks) + +**Seminal papers and background on the models** If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT: - LLaMA: @@ -820,178 +466,3 @@ If your issue is with model generation quality, then please at least scan the fo - GPT-3.5 / InstructGPT / ChatGPT: - [Aligning language models to follow instructions](https://openai.com/research/instruction-following) - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) - -### Android - -#### Build on Android using Termux -[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required). -``` -apt update && apt upgrade -y -apt install git make cmake -``` - -It's recommended to move your model inside the `~/` directory for best performance: -``` -cd storage/downloads -mv model.gguf ~/ -``` - -[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`. - -#### Building the Project using Android NDK -Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake. - -Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux: -``` -$ mkdir build-android -$ cd build-android -$ export NDK= -$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod .. -$ make -``` - -Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice). - -Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission: - -(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`) -``` -$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/ -$cd /data/data/com.termux/files/home/bin -$chmod +x ./* -``` - -Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/` - -``` -$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/ -``` - -Now, you can start chatting: -``` -$cd /data/data/com.termux/files/home/bin -$./llama-cli -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml -``` - -Here's a demo of an interactive session running on Pixel 5 phone: - -https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4 - -### Docker - -#### Prerequisites -* Docker must be installed and running on your system. -* Create a folder to store big models & intermediate files (ex. /llama/models) - -#### Images -We have three Docker images available for this project: - -1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) -2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) -3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`) - -Additionally, there the following images, similar to the above: - -- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) -- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) -- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) - -The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now). - -#### Usage - -The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image. - -Replace `/path/to/models` below with the actual path where you downloaded the models. - -```bash -docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B -``` - -On completion, you are ready to play! - -```bash -docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 -``` - -or with a light image: - -```bash -docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 -``` - -or with a server image: - -```bash -docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 -``` - -### Docker With CUDA - -Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container. - -#### Building Locally - -```bash -docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile . -docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile . -docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile . -``` - -You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture. - -The defaults are: - -- `CUDA_VERSION` set to `11.7.1` -- `CUDA_DOCKER_ARCH` set to `all` - -The resulting images, are essentially the same as the non-CUDA images: - -1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. -2. `local/llama.cpp:light-cuda`: This image only includes the main executable file. -3. `local/llama.cpp:server-cuda`: This image only includes the server executable file. - -#### Usage - -After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag. - -```bash -docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 -docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 -docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 -``` - -### Contributing - -- Contributors can open PRs -- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch -- Collaborators will be invited based on contributions -- Any help with managing issues and PRs is very appreciated! -- Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205) -- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532) - -### Coding guidelines - -- Avoid adding third-party dependencies, extra files, extra headers, etc. -- Always consider cross-compatibility with other operating systems and architectures -- Avoid fancy looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple -- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit -- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a` -- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions -- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices -- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$ - -![matmul](media/matmul.png) - -### Docs - -- [main (cli)](./examples/main/README.md) -- [server](./examples/server/README.md) -- [jeopardy](./examples/jeopardy/README.md) -- [BLIS](./docs/BLIS.md) -- [Performance troubleshooting](./docs/token_generation_performance_tips.md) -- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks) -- [GBNF grammars](./grammars/README.md) diff --git a/ci/run.sh b/ci/run.sh index e0cedb24f..9703b77ce 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -287,7 +287,7 @@ function gg_run_open_llama_7b_v2 { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../examples/convert-legacy-llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" @@ -421,7 +421,7 @@ function gg_run_pythia_1_4b { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" @@ -553,7 +553,7 @@ function gg_run_pythia_2_8b { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" @@ -688,7 +688,7 @@ function gg_run_embd_bge_small { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" diff --git a/common/common.cpp b/common/common.cpp index 6a00d25be..fc0f3b350 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -190,6 +190,12 @@ int32_t cpu_get_num_math() { // CLI argument parsing // +void gpt_params_handle_hf_token(gpt_params & params) { + if (params.hf_token.empty() && std::getenv("HF_TOKEN")) { + params.hf_token = std::getenv("HF_TOKEN"); + } +} + void gpt_params_handle_model_default(gpt_params & params) { if (!params.hf_repo.empty()) { // short-hand to avoid specifying --hf-file -> default it to --model @@ -237,6 +243,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { gpt_params_handle_model_default(params); + gpt_params_handle_hf_token(params); + if (params.escape) { string_process_escapes(params.prompt); string_process_escapes(params.input_prefix); @@ -472,6 +480,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa else { invalid_param = true; } return true; } + if (arg == "--attention") { + CHECK_ARG + std::string value(argv[i]); + /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; } + else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; } + else { invalid_param = true; } + return true; + } if (arg == "--defrag-thold" || arg == "-dt") { CHECK_ARG params.defrag_thold = std::stof(argv[i]); @@ -644,6 +660,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.model_url = argv[i]; return true; } + if (arg == "-hft" || arg == "--hf-token") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.hf_token = argv[i]; + return true; + } if (arg == "-hfr" || arg == "--hf-repo") { CHECK_ARG params.hf_repo = argv[i]; @@ -757,7 +781,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.cache_type_v = argv[++i]; return true; } - if (arg == "--multiline-input") { + if (arg == "-mli" || arg == "--multiline-input") { params.multiline_input = true; return true; } @@ -1014,16 +1038,19 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa } if (arg == "--in-prefix-bos") { params.input_prefix_bos = true; + params.enable_chat_template = false; return true; } if (arg == "--in-prefix") { CHECK_ARG params.input_prefix = argv[i]; + params.enable_chat_template = false; return true; } if (arg == "--in-suffix") { CHECK_ARG params.input_suffix = argv[i]; + params.enable_chat_template = false; return true; } if (arg == "--spm-infill") { @@ -1391,7 +1418,9 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep }); options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks }); options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" }); - options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with (default: '%s')", params.prompt.c_str() }); + options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with\n" + "in conversation mode, this will be used as system prompt\n" + "(default: '%s')", params.prompt.c_str() }); options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" }); options.push_back({ "*", " --in-file FNAME", "an input file (repeat to specify multiple files)" }); options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" }); @@ -1406,7 +1435,9 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param "halt generation at PROMPT, return control in interactive mode\n" "can be specified more than once for multiple prompts" }); options.push_back({ "main", "-sp, --special", "special tokens output enabled (default: %s)", params.special ? "true" : "false" }); - options.push_back({ "main", "-cnv, --conversation", "run in conversation mode (does not print special tokens and suffix/prefix) (default: %s)", params.conversation ? "true" : "false" }); + options.push_back({ "main", "-cnv, --conversation", "run in conversation mode, does not print special tokens and suffix/prefix\n" + "if suffix/prefix are not specified, default chat template will be used\n" + "(default: %s)", params.conversation ? "true" : "false" }); options.push_back({ "main infill", "-i, --interactive", "run in interactive mode (default: %s)", params.interactive ? "true" : "false" }); options.push_back({ "main infill", "-if, --interactive-first", "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" }); options.push_back({ "main infill", "-mli, --multiline-input", "allows you to write or paste multiple lines without ending each in '\\'" }); @@ -1450,6 +1481,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param options.push_back({ "main", " --cfg-scale N", "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale }); options.push_back({ "main", " --chat-template JINJA_TEMPLATE", "set custom jinja chat template (default: template taken from model's metadata)\n" + "if suffix/prefix are specified, template will be disabled\n" "only commonly used templates are accepted:\n" "https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" }); options.push_back({ "grammar" }); @@ -1460,8 +1492,10 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param "For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" }); options.push_back({ "embedding" }); - options.push_back({ "embedding", " --pooling {none,mean,cls}", + options.push_back({ "embedding", " --pooling {none,mean,cls,last}", "pooling type for embeddings, use model default if unspecified" }); + options.push_back({ "embedding", " --attention {causal,non-causal}", + "attention type for embeddings, use model default if unspecified" }); options.push_back({ "context hacking" }); options.push_back({ "*", " --rope-scaling {none,linear,yarn}", @@ -1558,6 +1592,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param options.push_back({ "*", "-mu, --model-url MODEL_URL", "model download url (default: unused)" }); options.push_back({ "*", "-hfr, --hf-repo REPO", "Hugging Face model repository (default: unused)" }); options.push_back({ "*", "-hff, --hf-file FILE", "Hugging Face model file (default: unused)" }); + options.push_back({ "*", "-hft, --hf-token TOKEN", "Hugging Face access token (default: value from HF_TOKEN environment variable)" }); options.push_back({ "retrieval" }); options.push_back({ "retrieval", " --context-file FNAME", "file to load context from (repeat to specify multiple files)" }); @@ -1997,9 +2032,9 @@ std::tuple llama_init_from_gpt_par llama_model * model = nullptr; if (!params.hf_repo.empty() && !params.hf_file.empty()) { - model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams); + model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); } else if (!params.model_url.empty()) { - model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams); + model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); } else { model = llama_load_model_from_file(params.model.c_str(), mparams); } @@ -2067,7 +2102,24 @@ std::tuple llama_init_from_gpt_par if (params.warmup) { LOG("warming up the model with an empty run\n"); - std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; + std::vector tmp; + llama_token bos = llama_token_bos(model); + llama_token eos = llama_token_eos(model); + // some models (e.g. T5) don't have a BOS token + if (bos != -1) { + tmp.push_back(bos); + } + tmp.push_back(eos); + + if (llama_model_has_encoder(model)) { + llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0)); + llama_token decoder_start_token_id = llama_model_decoder_start_token(model); + if (decoder_start_token_id == -1) { + decoder_start_token_id = bos; + } + tmp.clear(); + tmp.push_back(decoder_start_token_id); + } llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); llama_kv_cache_clear(lctx); llama_synchronize(lctx); @@ -2150,6 +2202,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.yarn_orig_ctx = params.yarn_orig_ctx; cparams.pooling_type = params.pooling_type; + cparams.attention_type = params.attention_type; cparams.defrag_thold = params.defrag_thold; cparams.cb_eval = params.cb_eval; cparams.cb_eval_user_data = params.cb_eval_user_data; @@ -2169,7 +2222,7 @@ static bool starts_with(const std::string & str, const std::string & prefix) { return str.rfind(prefix, 0) == 0; } -static bool llama_download_file(const std::string & url, const std::string & path) { +static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) { // Initialize libcurl std::unique_ptr curl(curl_easy_init(), &curl_easy_cleanup); @@ -2184,6 +2237,15 @@ static bool llama_download_file(const std::string & url, const std::string & pat curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L); + // Check if hf-token or bearer-token was specified + if (!hf_token.empty()) { + std::string auth_header = "Authorization: Bearer "; + auth_header += hf_token.c_str(); + struct curl_slist *http_headers = NULL; + http_headers = curl_slist_append(http_headers, auth_header.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers); + } + #if defined(_WIN32) // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of // operating system. Currently implemented under MS-Windows. @@ -2379,6 +2441,7 @@ static bool llama_download_file(const std::string & url, const std::string & pat struct llama_model * llama_load_model_from_url( const char * model_url, const char * path_model, + const char * hf_token, const struct llama_model_params & params) { // Basic validation of the model_url if (!model_url || strlen(model_url) == 0) { @@ -2386,7 +2449,7 @@ struct llama_model * llama_load_model_from_url( return NULL; } - if (!llama_download_file(model_url, path_model)) { + if (!llama_download_file(model_url, path_model, hf_token)) { return NULL; } @@ -2434,14 +2497,14 @@ struct llama_model * llama_load_model_from_url( // Prepare download in parallel std::vector> futures_download; for (int idx = 1; idx < n_split; idx++) { - futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split](int download_idx) -> bool { + futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split, hf_token](int download_idx) -> bool { char split_path[PATH_MAX] = {0}; llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split); char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0}; llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split); - return llama_download_file(split_url, split_path); + return llama_download_file(split_url, split_path, hf_token); }, idx)); } @@ -2460,6 +2523,7 @@ struct llama_model * llama_load_model_from_hf( const char * repo, const char * model, const char * path_model, + const char * hf_token, const struct llama_model_params & params) { // construct hugging face model url: // @@ -2475,7 +2539,7 @@ struct llama_model * llama_load_model_from_hf( model_url += "/resolve/main/"; model_url += model; - return llama_load_model_from_url(model_url.c_str(), path_model, params); + return llama_load_model_from_url(model_url.c_str(), path_model, hf_token, params); } #else @@ -2483,6 +2547,7 @@ struct llama_model * llama_load_model_from_hf( struct llama_model * llama_load_model_from_url( const char * /*model_url*/, const char * /*path_model*/, + const char * /*hf_token*/, const struct llama_model_params & /*params*/) { fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__); return nullptr; @@ -2492,6 +2557,7 @@ struct llama_model * llama_load_model_from_hf( const char * /*repo*/, const char * /*model*/, const char * /*path_model*/, + const char * /*hf_token*/, const struct llama_model_params & /*params*/) { fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__); return nullptr; @@ -2556,51 +2622,35 @@ std::vector llama_tokenize( } std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { - std::vector result(8, 0); - const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); - if (n_tokens < 0) { - result.resize(-n_tokens); - int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); - GGML_ASSERT(check == -n_tokens); - } else { - result.resize(n_tokens); + std::string piece; + piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n' + const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special); + if (n_chars < 0) { + piece.resize(-n_chars); + int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special); + GGML_ASSERT(check == -n_chars); + } + else { + piece.resize(n_chars); } - return std::string(result.data(), result.size()); + return piece; } -std::string llama_detokenize_spm(llama_context * ctx, const std::vector & tokens) { - const llama_token bos_id = llama_token_bos(llama_get_model(ctx)); - - std::string piece; - std::string result; - - for (size_t i = 0; i < tokens.size(); ++i) { - piece = llama_token_to_piece(ctx, tokens[i]); - - // remove the leading space of the first non-BOS token - if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') { - piece = piece.substr(1); - } - - result += piece; +std::string llama_detokenize(llama_context * ctx, const std::vector & tokens, bool special) { + std::string text; + text.resize(std::max(text.capacity(), tokens.size())); + int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + if (n_chars < 0) { + text.resize(-n_chars); + n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization } - return result; -} - -std::string llama_detokenize_bpe(llama_context * ctx, const std::vector & tokens) { - std::string piece; - std::string result; - - for (size_t i = 0; i < tokens.size(); ++i) { - piece = llama_token_to_piece(ctx, tokens[i]); - - result += piece; - } + text.resize(n_chars); // NOTE: the original tokenizer decodes bytes after collecting the pieces. - return result; + return text; } bool llama_should_add_bos_token(const llama_model * model) { @@ -2668,12 +2718,19 @@ std::string llama_chat_format_single(const struct llama_model * model, const std::vector & past_msg, const llama_chat_msg & new_msg, bool add_ass) { + std::ostringstream ss; auto fmt_past_msg = llama_chat_apply_template(model, tmpl, past_msg, false); std::vector chat_new(past_msg); + // if the past_msg ends with a newline, we must preserve it in the formatted version + if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') { + ss << "\n"; + }; + // format chat with new_msg chat_new.push_back(new_msg); auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass); - auto formatted = fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size()); - return formatted; + // get the diff part + ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size()); + return ss.str(); } std::string llama_chat_format_example(const struct llama_model * model, diff --git a/common/common.h b/common/common.h index d6cb814b9..184a53dc0 100644 --- a/common/common.h +++ b/common/common.h @@ -99,6 +99,7 @@ struct gpt_params { enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings + enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings // // sampling parameters struct llama_sampling_params sparams; @@ -107,6 +108,7 @@ struct gpt_params { std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias std::string model_url = ""; // model url to download + std::string hf_token = ""; // HF token std::string hf_repo = ""; // HF repo std::string hf_file = ""; // HF file std::string prompt = ""; @@ -200,6 +202,7 @@ struct gpt_params { std::string public_path = ""; std::string chat_template = ""; std::string system_prompt = ""; + bool enable_chat_template = true; std::vector api_keys; @@ -254,6 +257,7 @@ struct gpt_params { bool spm_infill = false; // suffix/prefix/middle pattern for infill }; +void gpt_params_handle_hf_token(gpt_params & params); void gpt_params_handle_model_default(gpt_params & params); bool gpt_params_parse_ex (int argc, char ** argv, gpt_params & params); @@ -309,8 +313,8 @@ std::tuple llama_init_from_gpt_par struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params); struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params); -struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const struct llama_model_params & params); -struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const struct llama_model_params & params); +struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params); +struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params); // Batch utils @@ -348,21 +352,13 @@ std::string llama_token_to_piece( llama_token token, bool special = true); -// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function -// that takes into account the tokenizer type and decides how to handle the leading space -// // detokenizes a vector of tokens into a string // should work similar to Python's `tokenizer.decode` -// removes the leading space from the first non-BOS token -std::string llama_detokenize_spm( +// optionally renders special/control tokens +std::string llama_detokenize( llama_context * ctx, - const std::vector & tokens); - -// detokenizes a vector of tokens into a string -// should work similar to Python's `tokenizer.decode` -std::string llama_detokenize_bpe( - llama_context * ctx, - const std::vector & tokens); + const std::vector & tokens, + bool special = true); // Uses the value from the model metadata if possible, otherwise // defaults to true when model type is SPM, otherwise false. @@ -458,4 +454,3 @@ void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const cha void yaml_dump_non_result_info( FILE * stream, const gpt_params & params, const llama_context * lctx, const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); - diff --git a/convert-hf-to-gguf.py b/convert_hf_to_gguf.py similarity index 85% rename from convert-hf-to-gguf.py rename to convert_hf_to_gguf.py index 3ef2f69e7..6ee41d3a1 100755 --- a/convert-hf-to-gguf.py +++ b/convert_hf_to_gguf.py @@ -13,7 +13,7 @@ import sys from enum import IntEnum from pathlib import Path from hashlib import sha256 -from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast +from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast import math import numpy as np @@ -404,7 +404,7 @@ class Model: return tokens, toktypes, tokpre - # NOTE: this function is generated by convert-hf-to-gguf-update.py + # NOTE: this function is generated by convert_hf_to_gguf_update.py # do not modify it manually! # ref: https://github.com/ggerganov/llama.cpp/pull/6920 # Marker: Start get_vocab_base_pre @@ -424,7 +424,7 @@ class Model: res = None - # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script + # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script # or pull the latest version of the model from Huggingface # don't edit the hashes manually! if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5": @@ -487,18 +487,24 @@ class Model: if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code res = "jina-v2-code" + if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b": + # ref: https://huggingface.co/THUDM/glm-4-9b-chat + res = "chatglm-bpe" if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee": # ref: https://huggingface.co/LumiOpen/Viking-7B res = "viking" + if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901": + # ref: https://huggingface.co/core42/jais-13b + res = "jais" if res is None: logger.warning("\n") logger.warning("**************************************************************************************") logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!") logger.warning("** There are 2 possible reasons for this:") - logger.warning("** - the model has not been added to convert-hf-to-gguf-update.py yet") + logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet") logger.warning("** - the pre-tokenization config has changed upstream") - logger.warning("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.") + logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.") logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") logger.warning("**") logger.warning(f"** chkhsh: {chkhsh}") @@ -576,7 +582,19 @@ class Model: special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"]) special_vocab.add_to_gguf(self.gguf_writer) - def _set_vocab_sentencepiece(self): + def _set_vocab_sentencepiece(self, add_to_gguf=True): + tokens, scores, toktypes = self._create_vocab_sentencepiece() + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def _create_vocab_sentencepiece(self): from sentencepiece import SentencePieceProcessor tokenizer_path = self.dir_model / 'tokenizer.model' @@ -638,14 +656,7 @@ class Model: scores.append(-1000.0) toktypes.append(SentencePieceTokenTypes.UNUSED) - self.gguf_writer.add_tokenizer_model("llama") - self.gguf_writer.add_tokenizer_pre("default") - self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_scores(scores) - self.gguf_writer.add_token_types(toktypes) - - special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) - special_vocab.add_to_gguf(self.gguf_writer) + return tokens, scores, toktypes def _set_vocab_llama_hf(self): vocab = gguf.LlamaHfVocab(self.dir_model) @@ -669,6 +680,51 @@ class Model: special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) + def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int): + tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf" + logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'") + vocab_reader = gguf.GGUFReader(tokenizer_path, "r") + + default_pre = "mpt" if model_name == "gpt-neox" else "default" + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL) + assert field # tokenizer model + self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8")) + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE) + self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre) + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST) + assert field # token list + self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size]) + + if model_name == "llama-spm": + field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES) + assert field # token scores + self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) + assert field # token types + self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) + + if model_name != "llama-spm": + field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES) + assert field # token merges + self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data]) + + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None: + self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None: + self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None: + self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None: + self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None: + self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None: + self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0]) + @Model.register("GPTNeoXForCausalLM") class GPTNeoXModel(Model): @@ -1934,7 +1990,7 @@ class Phi3MiniModel(Model): if len(rope_scaling_type) == 0: raise KeyError('Missing the required key rope_scaling.type') - if rope_scaling_type == 'su': + if rope_scaling_type == 'su' or rope_scaling_type == 'longrope': attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0 elif rope_scaling_type == 'yarn': attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0 @@ -2308,6 +2364,8 @@ class GemmaModel(Model): special_vocab._set_special_token("eot", 107) special_vocab.add_to_gguf(self.gguf_writer) + self.gguf_writer.add_add_space_prefix(False) + def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] @@ -2345,7 +2403,20 @@ class Gemma2Model(Model): model_arch = gguf.MODEL_ARCH.GEMMA2 def set_vocab(self): - self._set_vocab_llama_hf() + tokens, scores, toktypes = self._create_vocab_sentencepiece() + # hack: This is required so that we can properly use start/end-of-turn for chat template + for i in range(108): + # including , , + toktypes[i] = SentencePieceTokenTypes.CONTROL + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + self.gguf_writer.add_add_space_prefix(False) def set_gguf_parameters(self): @@ -2369,9 +2440,15 @@ class Gemma2Model(Model): self.gguf_writer.add_final_logit_softcapping( self.hparams["final_logit_softcapping"] ) + self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) + + # sanity check + attn_scalar = self.hparams["query_pre_attn_scalar"] + if attn_scalar != hparams["hidden_size"] / hparams["num_attention_heads"]: + raise ValueError("query_pre_attn_scalar must be equal to n_embd / n_head") def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: - del bid # unusem + del bid # unused # lm_head is not used in llama.cpp, while autoawq will include this tensor in model # To prevent errors, skip loading lm_head.weight. @@ -2410,39 +2487,7 @@ class MambaModel(Model): self._set_vocab_sentencepiece() else: # Use the GPT-NeoX tokenizer when no tokenizer files are present - tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf" - logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'") - neox_reader = gguf.GGUFReader(tokenizer_path, "r") - - field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL) - self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8") if field else "gpt2") - - field = neox_reader.get_field(gguf.Keys.Tokenizer.PRE) - self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else "mpt") - - field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST) - assert field - self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size]) - - field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) - assert field - self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) - - field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES) - assert field - self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data]) - - field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID) - self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0] if field else 1) - - field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID) - self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0] if field else 0) - - field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID) - self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0] if field else 0) - - field = neox_reader.get_field(gguf.Keys.Tokenizer.PAD_ID) - self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0] if field else 0) + self._set_vocab_builtin("gpt-neox", vocab_size) def set_gguf_parameters(self): d_model = self.find_hparam(["hidden_size", "d_model"]) @@ -2594,6 +2639,82 @@ class JinaBertV2Model(BertModel): self.gguf_writer.add_add_eos_token(True) +@Model.register("OpenELMForCausalLM") +class OpenELMModel(Model): + model_arch = gguf.MODEL_ARCH.OPENELM + + @staticmethod + def _make_divisible(v: float | int, divisor: int) -> int: + # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38 + new_v = max(divisor, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + ffn_multipliers: list[float] = self.hparams["ffn_multipliers"] + ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"] + self._n_embd: int = self.hparams["model_dim"] + self._num_kv_heads: list[int] = self.hparams["num_kv_heads"] + self._num_query_heads: list[int] = self.hparams["num_query_heads"] + self._ffn_dims: list[int] = [ + OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor) + for multiplier in ffn_multipliers + ] + assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int) + assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int) + + # Uses the tokenizer from meta-llama/Llama-2-7b-hf + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"]) + + def set_gguf_parameters(self): + n_embd = self._n_embd + head_dim = self.hparams["head_dim"] + rot_pct = 1.0 + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_query_heads) + assert self.block_count == len(self._ffn_dims) + + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_context_length(self.hparams["max_context_length"]) + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(self._ffn_dims) + self.gguf_writer.add_head_count(self._num_query_heads) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"]) + # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30 + self.gguf_writer.add_layer_norm_rms_eps(1e-6) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim)) + self.gguf_writer.add_key_length(head_dim) + self.gguf_writer.add_value_length(head_dim) + self.gguf_writer.add_file_type(self.ftype) + + def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any: + if "n_layers" in keys: + return self.hparams["num_transformer_layers"] + + return super().find_hparam(keys, optional) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + + # split ff + if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight": + ff_dim = self._ffn_dims[bid] + yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim]) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:]) + return + + yield (self.map_tensor_name(name), data_torch) + + @Model.register("ArcticForCausalLM") class ArcticModel(Model): model_arch = gguf.MODEL_ARCH.ARCTIC @@ -2824,11 +2945,17 @@ class DeepseekV2Model(Model): raise ValueError(f"Unprocessed experts: {experts}") -@Model.register("T5ForConditionalGeneration") @Model.register("T5WithLMHeadModel") +@Model.register("T5ForConditionalGeneration") +@Model.register("MT5ForConditionalGeneration") +@Model.register("UMT5ForConditionalGeneration") class T5Model(Model): model_arch = gguf.MODEL_ARCH.T5 + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.shared_token_embeddings_found = False + def set_vocab(self): # to avoid TypeError: Descriptors cannot be created directly # exception when importing sentencepiece_model_pb2 @@ -2836,17 +2963,29 @@ class T5Model(Model): from sentencepiece import SentencePieceProcessor from sentencepiece import sentencepiece_model_pb2 as model - tokenizer_path = self.dir_model / 'spiece.model' + tokenizer_path = self.dir_model / 'tokenizer.model' + + # many older models use spiece.model tokenizer model filename + if not tokenizer_path.is_file(): + tokenizer_path = self.dir_model / 'spiece.model' if not tokenizer_path.is_file(): raise FileNotFoundError(f"File not found: {tokenizer_path}") sentencepiece_model = model.ModelProto() sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + + # some models like Pile-T5 family use BPE tokenizer instead of Unigram + if sentencepiece_model.trainer_spec.model_type == 2: # BPE + # assure the tokenizer model file name is correct + assert tokenizer_path.name == 'tokenizer.model' + return self._set_vocab_sentencepiece() + else: + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap - assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM tokenizer = SentencePieceProcessor() tokenizer.LoadFromFile(str(tokenizer_path)) @@ -2916,7 +3055,10 @@ class T5Model(Model): def set_gguf_parameters(self): self.gguf_writer.add_name("T5") - self.gguf_writer.add_context_length(self.hparams["n_positions"]) + if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: + logger.warning("Couldn't find context length in config.json, assuming default value of 512") + n_ctx = 512 + self.gguf_writer.add_context_length(n_ctx) self.gguf_writer.add_embedding_length(self.hparams["d_model"]) self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"]) self.gguf_writer.add_block_count(self.hparams["num_layers"]) @@ -2932,16 +3074,295 @@ class T5Model(Model): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused - # Sometimes T5 and Flan-T5 based models contain "encoder.embed_tokens.weight" tensor or - # "decoder.embed_tokens.weight" tensors that are duplicates of "shared.weight" tensor - # To prevent errors caused by an unnecessary unmapped tensor, skip both of them and use only "shared.weight". - if name == "decoder.embed_tokens.weight" or name == "encoder.embed_tokens.weight": - logger.debug(f"Skipping tensor {name!r} in safetensors so that convert can end normally.") - return [] + # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight", + # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored + # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder + # and decoder and ignore the remaining ones. + if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]: + if not self.shared_token_embeddings_found: + name = "shared.weight" + self.shared_token_embeddings_found = True + else: + logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.") + return [] return [(self.map_tensor_name(name), data_torch)] +@Model.register("JAISLMHeadModel") +class JaisModel(Model): + model_arch = gguf.MODEL_ARCH.JAIS + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # SwigLU activation + assert self.hparams["activation_function"] == "swiglu" + # ALiBi position embedding + assert self.hparams["position_embedding_type"] == "alibi" + + # Embeddings scale + self.embeddings_scale = 1.0 + # note: For some JAIS flavors, output is tied to (same as) wte in original model + self.output_is_wte = False + if 'mup_embeddings_scale' in self.hparams: + self.output_is_wte = True # Hack (?) + self.embeddings_scale = self.hparams['mup_embeddings_scale'] + elif 'embeddings_scale' in self.hparams: + self.embeddings_scale = self.hparams['embeddings_scale'] + else: + assert False + + self.width_scale = 1.0 + if 'mup_output_alpha' in self.hparams: + assert 'mup_width_scale' in self.hparams + self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale'] + elif 'width_scale' in self.hparams: + self.width_scale = self.hparams['width_scale'] + else: + assert False + + self.max_alibi_bias = 8.0 + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"]) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + tensors: list[tuple[str, Tensor]] = [] + + # we don't need these + if name.endswith((".attn.bias")): + return tensors + + if name.endswith(("relative_pe.slopes")): + # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation) + # Some other models has max_alibi_bias spelled out explicitly in the hyperparams, + # but Jais's PyTorch model simply precalculates the slope values and places them + # in relative_pes.slopes + n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"])) + first_val = float(data_torch._data[0]) + self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2) + + return tensors + + if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")): + data_torch = data_torch.transpose(1, 0) + + new_name = self.map_tensor_name(name) + + if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): + tensors.append((new_name, data_torch * self.embeddings_scale)) + if self.output_is_wte: + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch * self.width_scale)) + elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT): + assert not self.output_is_wte + tensors.append((new_name, data_torch * self.width_scale)) + else: + tensors.append((new_name, data_torch)) + + return tensors + + def write_tensors(self): + super().write_tensors() + self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias) + + +@Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration") +class ChatGLMModel(Model): + model_arch = gguf.MODEL_ARCH.CHATGLM + + def set_vocab_chatglm3(self): + dir_model = self.dir_model + hparams = self.hparams + tokens: list[bytearray] = [] + toktypes: list[int] = [] + scores: list[float] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) + vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab())) + assert max(tokenizer.get_vocab().values()) < vocab_size + role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"] + special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens + for token_id in range(vocab_size): + piece = tokenizer._convert_id_to_token(token_id) + if token_id == 0: + piece = "" + elif token_id == 1: + piece = "" + elif token_id == 2: + piece = "" + + text = piece.encode("utf-8") + score = 0.0 + # Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py), + # it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size() + if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size(): + score = tokenizer.tokenizer.sp_model.get_score(token_id) + + if len(piece) == 0: + text = f"[PAD{token_id}]".encode("utf-8") + + if token_id >= tokenizer.tokenizer.sp_model.vocab_size(): + if piece in special_tokens: + # show special tokens in prompt + toktype = SentencePieceTokenTypes.USER_DEFINED + else: + toktype = SentencePieceTokenTypes.UNKNOWN + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + continue + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.tokenizer.sp_model.is_unknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.tokenizer.sp_model.is_control(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.tokenizer.sp_model.is_unused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.tokenizer.sp_model.is_byte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + self.gguf_writer.add_tokenizer_model("llama") + # glm3 needs prefix and suffix formatted as: + # prompt = "[gMASK]sop<|user|>\n" + prompt + "<|assistant|>" + self.gguf_writer.add_tokenizer_pre("chatglm-spm") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + @staticmethod + def token_bytes_to_string(b): + from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode + byte_encoder = bytes_to_unicode() + return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) + + @staticmethod + def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]: + parts = [bytes([b]) for b in token] + while True: + min_idx = None + min_rank = None + for i, pair in enumerate(zip(parts[:-1], parts[1:])): + rank = mergeable_ranks.get(pair[0] + pair[1]) + if rank is not None and (min_rank is None or rank < min_rank): + min_idx = i + min_rank = rank + if min_rank is None or (max_rank is not None and min_rank >= max_rank): + break + assert min_idx is not None + parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:] + return parts + + def set_vocab(self): + if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""): + self.set_vocab_chatglm3() + return + + dir_model = self.dir_model + hparams = self.hparams + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) + vocab_size = hparams["padded_vocab_size"] + assert max(tokenizer.get_vocab().values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + merges = [] + vocab = {} + mergeable_ranks = tokenizer.mergeable_ranks + for token, rank in mergeable_ranks.items(): + vocab[ChatGLMModel.token_bytes_to_string(token)] = rank + if len(token) == 1: + continue + merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank) + assert len(merged) >= 2 and len(merged) <= 7 + merges.append(' '.join(map(ChatGLMModel.token_bytes_to_string, merged))) + + # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined + added_vocab = tokenizer.get_added_vocab() + reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()} + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.USER_DEFINED) + elif reverse_vocab[i] in added_vocab: + tokens.append(reverse_vocab[i]) + if tokenizer.added_tokens_decoder[i].special: + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.NORMAL) + + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(dir_model, load_merges=False) + special_vocab.merges = merges + # only add special tokens when they were not already loaded from config.json + special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) + # this one is usually not in config.json anyway + special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + self.gguf_writer.add_name(self.hparams.get("_name_or_path").split("/")[1]) # THUDM/glm4-9b-chat or THUDM/chatglm3-6b + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_head_kv = self.hparams.get("multi_query_group_num", n_head) + self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) + self.gguf_writer.add_embedding_length(n_embed) + self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", 4 * n_embed)) + self.gguf_writer.add_block_count(self.hparams["num_layers"]) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layernorm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_rope_dimension_count(64) + self.gguf_writer.add_add_bos_token(False) + rope_freq = 10000 + if "rope_ratio" in self.hparams: + rope_freq = rope_freq * self.hparams["rope_ratio"] + self.gguf_writer.add_rope_freq_base(rope_freq) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if name.endswith(".rotary_pos_emb.inv_freq"): + return [] + + name = name.removeprefix("transformer.") + return [(self.map_tensor_name(name), data_torch)] + ###### CONVERSION LOGIC ###### @@ -2991,10 +3412,6 @@ def parse_args() -> argparse.Namespace: "--vocab-only", action="store_true", help="extract only the vocab", ) - parser.add_argument( - "--awq-path", type=Path, default=None, - help="Path to scale awq cache file", - ) parser.add_argument( "--outfile", type=Path, help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", @@ -3072,19 +3489,6 @@ def main() -> None: dir_model = args.model - if args.awq_path: - sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) - from awq.apply_awq import add_scale_weights # type: ignore[import-not-found] - tmp_model_path = args.model / "weighted_model" - dir_model = tmp_model_path - if tmp_model_path.is_dir(): - logger.info(f"{tmp_model_path} exists as a weighted model.") - else: - tmp_model_path.mkdir(parents=True, exist_ok=True) - logger.info("Saving new weighted model ...") - add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) - logger.info(f"Saved weighted model at {tmp_model_path}.") - if not dir_model.is_dir(): logger.error(f'Error: {args.model} is not a directory') sys.exit(1) @@ -3097,7 +3501,8 @@ def main() -> None: "auto": gguf.LlamaFileType.GUESSED, } - if args.use_temp_file and (args.split_max_tensors > 0 or args.split_max_size != "0"): + is_split = args.split_max_tensors > 0 or args.split_max_size != "0" + if args.use_temp_file and is_split: logger.error("Error: Cannot use temp file when splitting") sys.exit(1) @@ -3134,11 +3539,12 @@ def main() -> None: if args.vocab_only: logger.info("Exporting model vocab...") model_instance.write_vocab() - logger.info("Model vocab successfully exported.") + logger.info(f"Model vocab successfully exported to {model_instance.fname_out}") else: logger.info("Exporting model...") model_instance.write() - logger.info("Model successfully exported.") + out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out + logger.info(f"Model successfully exported to {out_path}") if __name__ == '__main__': diff --git a/convert-hf-to-gguf-update.py b/convert_hf_to_gguf_update.py similarity index 87% rename from convert-hf-to-gguf-update.py rename to convert_hf_to_gguf_update.py index 2758214fa..e4165ae2d 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert_hf_to_gguf_update.py @@ -2,7 +2,7 @@ # -*- coding: utf-8 -*- # This script downloads the tokenizer models of the specified models from Huggingface and -# generates the get_vocab_base_pre() function for convert-hf-to-gguf.py +# generates the get_vocab_base_pre() function for convert_hf_to_gguf.py # # This is necessary in order to analyze the type of pre-tokenizer used by the model and # provide the necessary information to llama.cpp via the GGUF header in order to implement @@ -15,9 +15,9 @@ # - Add a new model to the "models" list # - Run the script with your huggingface token: # -# python3 convert-hf-to-gguf-update.py +# python3 convert_hf_to_gguf_update.py # -# - Copy-paste the generated get_vocab_base_pre() function into convert-hf-to-gguf.py +# - Copy-paste the generated get_vocab_base_pre() function into convert_hf_to_gguf.py # - Update llama.cpp with the new pre-tokenizer if necessary # # TODO: generate tokenizer tests for llama.cpp @@ -37,7 +37,7 @@ from enum import IntEnum, auto from transformers import AutoTokenizer logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger("convert-hf-to-gguf-update") +logger = logging.getLogger("convert_hf_to_gguf_update") sess = requests.Session() @@ -45,6 +45,7 @@ class TOKENIZER_TYPE(IntEnum): SPM = auto() BPE = auto() WPM = auto() + UGM = auto() # TODO: this string has to exercise as much pre-tokenizer functionality as possible @@ -55,10 +56,10 @@ if len(sys.argv) == 2: token = sys.argv[1] if not token.startswith("hf_"): logger.info("Huggingface token seems invalid") - logger.info("Usage: python convert-hf-to-gguf-update.py ") + logger.info("Usage: python convert_hf_to_gguf_update.py ") sys.exit(1) else: - logger.info("Usage: python convert-hf-to-gguf-update.py ") + logger.info("Usage: python convert_hf_to_gguf_update.py ") sys.exit(1) # TODO: add models here, base models preferred @@ -86,6 +87,10 @@ models = [ {"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", }, {"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", }, {"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B + {"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", }, + {"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", }, + {"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", }, + {"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", }, ] @@ -107,9 +112,13 @@ def download_model(model): os.makedirs(f"models/tokenizers/{name}", exist_ok=True) files = ["config.json", "tokenizer.json", "tokenizer_config.json"] + if tokt == TOKENIZER_TYPE.SPM: files.append("tokenizer.model") + if tokt == TOKENIZER_TYPE.UGM: + files.append("spiece.model") + for file in files: save_path = f"models/tokenizers/{name}/{file}" if os.path.isfile(save_path): @@ -125,14 +134,14 @@ for model in models: logger.error(f"Failed to download model {model['name']}. Error: {e}") -# generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function: +# generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function: src_ifs = "" for model in models: name = model["name"] tokt = model["tokt"] - if tokt == TOKENIZER_TYPE.SPM: + if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM: continue # Skip if the tokenizer folder does not exist or there are other download issues previously @@ -142,7 +151,10 @@ for model in models: # create the tokenizer try: - tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + if name == "t5": + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False) + else: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") except OSError as e: logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}") continue # Skip to the next model if the tokenizer can't be loaded @@ -189,7 +201,7 @@ src_func = f""" res = None - # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script + # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script # or pull the latest version of the model from Huggingface # don't edit the hashes manually! {src_ifs} @@ -198,9 +210,9 @@ src_func = f""" logger.warning("**************************************************************************************") logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!") logger.warning("** There are 2 possible reasons for this:") - logger.warning("** - the model has not been added to convert-hf-to-gguf-update.py yet") + logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet") logger.warning("** - the pre-tokenization config has changed upstream") - logger.warning("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.") + logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.") logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") logger.warning("**") logger.warning(f"** chkhsh: {{chkhsh}}") @@ -214,7 +226,7 @@ src_func = f""" return res """ -convert_py_pth = pathlib.Path("convert-hf-to-gguf.py") +convert_py_pth = pathlib.Path("convert_hf_to_gguf.py") convert_py = convert_py_pth.read_text(encoding="utf-8") convert_py = re.sub( r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)", @@ -225,7 +237,7 @@ convert_py = re.sub( convert_py_pth.write_text(convert_py, encoding="utf-8") -logger.info("+++ convert-hf-to-gguf.py was updated") +logger.info("+++ convert_hf_to_gguf.py was updated") # generate tests for each tokenizer model @@ -263,6 +275,7 @@ tests = [ "\n =", "' era", "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", + "!!!!!!", "3", "33", "333", @@ -272,7 +285,8 @@ tests = [ "3333333", "33333333", "333333333", - # "Cửa Việt", # llama-bpe fails on this + "Cửa Việt", # llama-bpe fails on this + " discards", chktxt, ] @@ -300,7 +314,10 @@ for model in models: # create the tokenizer try: - tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + if name == "t5": + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False) + else: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") except OSError as e: logger.error(f"Failed to load tokenizer for model {name}. Error: {e}") continue # Skip this model and continue with the next one in the loop @@ -326,6 +343,6 @@ logger.info("\nRun the following commands to generate the vocab files for testin for model in models: name = model["name"] - print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100 + print(f"python3 convert_hf_to_gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100 logger.info("\n") diff --git a/convert-llama-ggml-to-gguf.py b/convert_llama_ggml_to_gguf.py similarity index 100% rename from convert-llama-ggml-to-gguf.py rename to convert_llama_ggml_to_gguf.py diff --git a/docs/android.md b/docs/android.md new file mode 100644 index 000000000..cec4358d9 --- /dev/null +++ b/docs/android.md @@ -0,0 +1,56 @@ + +# Android + +## Build on Android using Termux +[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required). +``` +apt update && apt upgrade -y +apt install git make cmake +``` + +It's recommended to move your model inside the `~/` directory for best performance: +``` +cd storage/downloads +mv model.gguf ~/ +``` + +[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`. + +## Building the Project using Android NDK +Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake. + +Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux: +``` +$ mkdir build-android +$ cd build-android +$ export NDK= +$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod .. +$ make +``` + +Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice). + +Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission: + +(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`) +``` +$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/ +$cd /data/data/com.termux/files/home/bin +$chmod +x ./* +``` + +Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/` + +``` +$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/ +``` + +Now, you can start chatting: +``` +$cd /data/data/com.termux/files/home/bin +$./llama-cli -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml +``` + +Here's a demo of an interactive session running on Pixel 5 phone: + +https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4 diff --git a/docs/BLIS.md b/docs/backend/BLIS.md similarity index 100% rename from docs/BLIS.md rename to docs/backend/BLIS.md diff --git a/README-sycl.md b/docs/backend/SYCL.md similarity index 100% rename from README-sycl.md rename to docs/backend/SYCL.md diff --git a/docs/build.md b/docs/build.md new file mode 100644 index 000000000..bf41bfdf9 --- /dev/null +++ b/docs/build.md @@ -0,0 +1,288 @@ +# Build llama.cpp locally + +**To get the Code:** + +```bash +git clone https://github.com/ggerganov/llama.cpp +cd llama.cpp +``` + +In order to build llama.cpp you have four different options. + +- Using `make`: + - On Linux or MacOS: + + ```bash + make + ``` + + - On Windows: + + 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). + 2. Extract `w64devkit` on your pc. + 3. Run `w64devkit.exe`. + 4. Use the `cd` command to reach the `llama.cpp` folder. + 5. From here you can run: + ```bash + make + ``` + + - Notes: + - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel. + - For faster repeated compilation, install [ccache](https://ccache.dev/). + - For debug builds, run `make LLAMA_DEBUG=1` + +- Using `CMake`: + + ```bash + cmake -B build + cmake --build build --config Release + ``` + + **Notes**: + + - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. + - For faster repeated compilation, install [ccache](https://ccache.dev/). + - For debug builds, there are two cases: + + 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): + + ```bash + cmake -B build -DCMAKE_BUILD_TYPE=Debug + cmake --build build + ``` + + 2. Multi-config generators (`-G` param set to Visual Studio, XCode...): + + ```bash + cmake -B build -G "Xcode" + cmake --build build --config Debug + ``` + +- Using `gmake` (FreeBSD): + + 1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics) + 2. Add your user to **video** group + 3. Install compilation dependencies. + + ```bash + sudo pkg install gmake automake autoconf pkgconf llvm15 openblas + + gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 + ``` + +## Metal Build + +On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. +To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option. + +When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line +argument. + +## BLAS Build + +Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use: + +### Accelerate Framework: + +This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions. + +### OpenBLAS: + +This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine. + +- Using `make`: + - On Linux: + ```bash + make GGML_OPENBLAS=1 + ``` + + - On Windows: + + 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). + 2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases). + 3. Extract `w64devkit` on your pc. + 4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`. + 5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`. + 6. Run `w64devkit.exe`. + 7. Use the `cd` command to reach the `llama.cpp` folder. + 8. From here you can run: + + ```bash + make GGML_OPENBLAS=1 + ``` + +- Using `CMake` on Linux: + + ```bash + cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS + cmake --build build --config Release + ``` + +### BLIS + +Check [BLIS.md](./backend/BLIS.md) for more information. + +### SYCL + +SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + +llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + +For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md). + +### Intel oneMKL + +Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./backend/SYCL.md). + +- Using manual oneAPI installation: + By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: + ```bash + source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation + cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON + cmake --build build --config Release + ``` + +- Using oneAPI docker image: + If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above. + +Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. + +### CUDA + +This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). + +For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling. + +- Using `make`: + ```bash + make GGML_CUDA=1 + ``` +- Using `CMake`: + + ```bash + cmake -B build -DGGML_CUDA=ON + cmake --build build --config Release + ``` + +The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance: + +| Option | Legal values | Default | Description | +|-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| GGML_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | +| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | +| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | +| GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. | +| GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models | +| GGML_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | +| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | +| GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | +| GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | + +### hipBLAS + +This provides BLAS acceleration on HIP-supported AMD GPUs. +Make sure to have ROCm installed. +You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick). + +- Using `make`: + ```bash + make GGML_HIPBLAS=1 + ``` +- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): + ```bash + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ + cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build --config Release -- -j 16 + ``` + On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`. + However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). + + Note that if you get the following error: + ``` + clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library + ``` + Try searching for a directory under `HIP_PATH` that contains the file + `oclc_abi_version_400.bc`. Then, add the following to the start of the + command: `HIP_DEVICE_LIB_PATH=`, so something + like: + ```bash + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \ + HIP_DEVICE_LIB_PATH= \ + cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build -- -j 16 + ``` + +- Using `make` (example for target gfx1030, build with 16 CPU threads): + ```bash + make -j16 GGML_HIPBLAS=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030 + ``` + +- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): + ```bash + set PATH=%HIP_PATH%\bin;%PATH% + cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release + cmake --build build + ``` + Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors) + Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`. + + +The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. +If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. +The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): + +| Option | Legal values | Default | Description | +|------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | +| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | +| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | + +### Vulkan + +**With docker**: + +You don't need to install Vulkan SDK. It will be installed inside the container. + +```sh +# Build the image +docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile . + +# Then, use it: +docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 +``` + +**Without docker**: + +Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) + +For example, on Ubuntu 22.04 (jammy), use the command below: + +```bash +wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - +wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list +apt update -y +apt-get install -y vulkan-sdk +# To verify the installation, use the command below: +vulkaninfo +``` + +Alternatively your package manager might be able to provide the appropriate libraries. +For example for Ubuntu 22.04 you can install `libvulkan-dev` instead. +For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages. + +Then, build llama.cpp using the cmake command below: + +```bash +cmake -B build -DGGML_VULKAN=1 +cmake --build build --config Release +# Test the output binary (with "-ngl 33" to offload all layers to GPU) +./bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4 + +# You should see in the output, ggml_vulkan detected your GPU. For example: +# ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 +``` + +### Android + +To read documentation for how to build on Android, [click here](./android.md) diff --git a/docs/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md similarity index 96% rename from docs/HOWTO-add-model.md rename to docs/development/HOWTO-add-model.md index 3eec077ea..2712b66c1 100644 --- a/docs/HOWTO-add-model.md +++ b/docs/development/HOWTO-add-model.md @@ -1,4 +1,4 @@ -## Add a new model architecture to `llama.cpp` +# Add a new model architecture to `llama.cpp` Adding a model requires few steps: @@ -17,7 +17,7 @@ Also, it is important to check that the examples and main ggml backends (CUDA, M ### 1. Convert the model to GGUF This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library. -Depending on the model architecture, you can use either [convert-hf-to-gguf.py](../convert-hf-to-gguf.py) or [examples/convert-legacy-llama.py](../examples/convert-legacy-llama.py) (for `llama/llama2` models in `.pth` format). +Depending on the model architecture, you can use either [convert_hf_to_gguf.py](../convert_hf_to_gguf.py) or [examples/convert_legacy_llama.py](../examples/convert_legacy_llama.py) (for `llama/llama2` models in `.pth` format). The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors. diff --git a/docs/debugging-tests.md b/docs/development/debugging-tests.md similarity index 100% rename from docs/debugging-tests.md rename to docs/development/debugging-tests.md diff --git a/docs/llama-star/idea-arch.key b/docs/development/llama-star/idea-arch.key similarity index 100% rename from docs/llama-star/idea-arch.key rename to docs/development/llama-star/idea-arch.key diff --git a/docs/llama-star/idea-arch.pdf b/docs/development/llama-star/idea-arch.pdf similarity index 100% rename from docs/llama-star/idea-arch.pdf rename to docs/development/llama-star/idea-arch.pdf diff --git a/docs/token_generation_performance_tips.md b/docs/development/token_generation_performance_tips.md similarity index 100% rename from docs/token_generation_performance_tips.md rename to docs/development/token_generation_performance_tips.md diff --git a/docs/docker.md b/docs/docker.md new file mode 100644 index 000000000..d8922d77d --- /dev/null +++ b/docs/docker.md @@ -0,0 +1,86 @@ +# Docker + +## Prerequisites +* Docker must be installed and running on your system. +* Create a folder to store big models & intermediate files (ex. /llama/models) + +## Images +We have three Docker images available for this project: + +1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) +2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) +3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`) + +Additionally, there the following images, similar to the above: + +- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) + +The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now). + +## Usage + +The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image. + +Replace `/path/to/models` below with the actual path where you downloaded the models. + +```bash +docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B +``` + +On completion, you are ready to play! + +```bash +docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 +``` + +or with a light image: + +```bash +docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 +``` + +or with a server image: + +```bash +docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 +``` + +## Docker With CUDA + +Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container. + +## Building Docker locally + +```bash +docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile . +docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile . +docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile . +``` + +You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture. + +The defaults are: + +- `CUDA_VERSION` set to `11.7.1` +- `CUDA_DOCKER_ARCH` set to `all` + +The resulting images, are essentially the same as the non-CUDA images: + +1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. +2. `local/llama.cpp:light-cuda`: This image only includes the main executable file. +3. `local/llama.cpp:server-cuda`: This image only includes the server executable file. + +## Usage + +After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag. + +```bash +docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 +``` diff --git a/docs/install.md b/docs/install.md new file mode 100644 index 000000000..10a568506 --- /dev/null +++ b/docs/install.md @@ -0,0 +1,39 @@ +# Install pre-built version of llama.cpp + +## Homebrew + +On Mac and Linux, the homebrew package manager can be used via + +```sh +brew install llama.cpp +``` +The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668 + +## Nix + +On Mac and Linux, the Nix package manager can be used via + +```sh +nix profile install nixpkgs#llama-cpp +``` +For flake enabled installs. + +Or + +```sh +nix-env --file '' --install --attr llama-cpp +``` + +For non-flake enabled installs. + +This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164). + +## Flox + +On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via + +```sh +flox install llama-cpp +``` + +Flox follows the nixpkgs build of llama.cpp. diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 7d9ab3457..155743639 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -23,6 +23,7 @@ else() add_subdirectory(export-lora) add_subdirectory(finetune) add_subdirectory(gbnf-validator) + add_subdirectory(gguf-hash) add_subdirectory(gguf-split) add_subdirectory(gguf) add_subdirectory(gritlm) diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift index dbbd06da5..616494d2d 100644 --- a/examples/batched.swift/Sources/main.swift +++ b/examples/batched.swift/Sources/main.swift @@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] { private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? { var result = [CChar](repeating: 0, count: 8) - let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false) + let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), 0, false) if nTokens < 0 { let actualTokensCount = -Int(nTokens) result = .init(repeating: 0, count: actualTokensCount) @@ -238,6 +238,7 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String token, &result, Int32(result.count), + 0, false ) assert(check == actualTokensCount) diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 62d9b144d..2442e954d 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -93,14 +93,34 @@ int main(int argc, char ** argv) { // create a llama_batch // we use this object to submit token data for decoding - llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0, 1); + llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel); + + std::vector seq_ids(n_parallel, 0); + for (int32_t i = 0; i < n_parallel; ++i) { + seq_ids[i] = i; + } // evaluate the initial prompt for (size_t i = 0; i < tokens_list.size(); ++i) { - llama_batch_add(batch, tokens_list[i], i, { 0 }, false); + llama_batch_add(batch, tokens_list[i], i, seq_ids, false); } GGML_ASSERT(batch.n_tokens == (int) tokens_list.size()); + if (llama_model_has_encoder(model)) { + if (llama_encode(ctx, batch)) { + LOG_TEE("%s : failed to eval\n", __func__); + return 1; + } + + llama_token decoder_start_token_id = llama_model_decoder_start_token(model); + if (decoder_start_token_id == -1) { + decoder_start_token_id = llama_token_bos(model); + } + + llama_batch_clear(batch); + llama_batch_add(batch, decoder_start_token_id, 0, seq_ids, false); + } + // llama_decode will output logits only for the last token of the prompt batch.logits[batch.n_tokens - 1] = true; @@ -109,11 +129,11 @@ int main(int argc, char ** argv) { return 1; } - // assign the system KV cache to all parallel sequences - // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them - for (int32_t i = 1; i < n_parallel; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); - } + //// assign the system KV cache to all parallel sequences + //// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them + //for (int32_t i = 1; i < n_parallel; ++i) { + // llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); + //} if (n_parallel > 1) { LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel); diff --git a/examples/convert-legacy-llama.py b/examples/convert_legacy_llama.py similarity index 100% rename from examples/convert-legacy-llama.py rename to examples/convert_legacy_llama.py diff --git a/examples/embedding/README.md b/examples/embedding/README.md index 86df18958..e3705b454 100644 --- a/examples/embedding/README.md +++ b/examples/embedding/README.md @@ -58,4 +58,3 @@ The above command will output space-separated float values. ```powershell embedding.exe -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null ``` - diff --git a/examples/finetune/README.md b/examples/finetune/README.md index a6ae64983..1c27df053 100644 --- a/examples/finetune/README.md +++ b/examples/finetune/README.md @@ -87,4 +87,4 @@ The LORA rank can be configured for each model tensor type separately with these The LORA rank of 'norm' tensors should always be 1. -To see all available options use `finetune --help`. +To see all available options use `llama-finetune --help`. diff --git a/examples/finetune/convert-finetune-checkpoint-to-gguf.py b/examples/finetune/convert_finetune_checkpoint_to_gguf.py similarity index 100% rename from examples/finetune/convert-finetune-checkpoint-to-gguf.py rename to examples/finetune/convert_finetune_checkpoint_to_gguf.py diff --git a/examples/finetune/finetune.sh b/examples/finetune/finetune.sh index d7f2165e5..e3cc7f271 100644 --- a/examples/finetune/finetune.sh +++ b/examples/finetune/finetune.sh @@ -8,7 +8,7 @@ if [[ ! $LLAMA_MODEL_DIR ]]; then LLAMA_MODEL_DIR="./models"; fi if [[ ! $LLAMA_TRAINING_DIR ]]; then LLAMA_TRAINING_DIR="."; fi # MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2-q8_0.gguf" # This is the model the readme uses. -MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2.gguf" # An f16 model. Note in this case with "-g", you get an f32-format .BIN file that isn't yet supported if you use it with "main --lora" with GPU inferencing. +MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2.gguf" # An f16 model. Note in this case with "-g", you get an f32-format .BIN file that isn't yet supported if you use it with "llama-cli --lora" with GPU inferencing. while getopts "dg" opt; do case $opt in diff --git a/examples/gguf-hash/CMakeLists.txt b/examples/gguf-hash/CMakeLists.txt new file mode 100644 index 000000000..633f45535 --- /dev/null +++ b/examples/gguf-hash/CMakeLists.txt @@ -0,0 +1,15 @@ +set(TARGET llama-gguf-hash) +add_executable(${TARGET} gguf-hash.cpp) +install(TARGETS ${TARGET} RUNTIME) + +# clibs dependencies +include_directories(deps/) +add_library(xxhash OBJECT deps/xxhash/xxhash.c deps/xxhash/xxhash.h) +target_link_libraries(${TARGET} PRIVATE xxhash) +add_library(sha1 OBJECT deps/sha1/sha1.c deps/sha1/sha1.h) +target_link_libraries(${TARGET} PRIVATE sha1) +add_library(sha256 OBJECT deps/sha256/sha256.c deps/sha256/sha256.h) +target_link_libraries(${TARGET} PRIVATE sha256) + +target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/gguf-hash/README.md b/examples/gguf-hash/README.md new file mode 100644 index 000000000..2320ffd81 --- /dev/null +++ b/examples/gguf-hash/README.md @@ -0,0 +1,206 @@ + +# llama-gguf-hash + +CLI to hash GGUF files to detect difference on a per model and per tensor level. + +**Command line options:** + +- `--help`: display help message +- `--xxh64`: use xhash 64bit hash mode (default) +- `--sha1`: use sha1 +- `--uuid`: use uuid +- `--sha256`: use sha256 +- `--all`: use all hash +- `--no-layer`: exclude per layer hash +- `--uuid`: generate UUIDv5 ID +- `-c`, `--check `: verify against a manifest + +## About + +While most POSIX systems already have hash checking programs like sha256sum, it +is designed to check entire files. This is not ideal for our purpose if we want +to check for consistency of the tensor data even if the metadata content of the +gguf KV store has been updated. + +This program is designed to hash a gguf tensor payload on a 'per tensor layer' +in addition to a 'entire tensor model' hash. The intent is that the entire +tensor layer can be checked first but if there is any detected inconsistencies, +then the per tensor hash can be used to narrow down the specific tensor layer +that has inconsistencies. + +For Maintainers: +- Detection of tensor inconsistency during development and automated tests + - This is served by xxh64 which is fast + - This is also served by having per tensor layer to assist in narrowing down + the location of the faulty tensor layer + - This is also served by sha1 which is much slower but more widely supported + +For Model Creators: +- Optional consistent UUID generation based on model tensor content + - This is served by UUIDv5 which is useful for databases keys + - llama.cpp UUIDv5 Namespace: `ef001206-dadc-5f6d-a15f-3359e577d4e5` + - Made via UUIDv5 URL namespace of `en.wikipedia.org/wiki/Llama.cpp` + +For Model Users: +- Assurance of tensor layer integrity even if metadata was updated + - This is served by sha256 which is still considered very secure as of 2024 + +### Design Note + +- The default behavior of this program if no arguments is provided is to hash + using xxhash's xxh32 mode because it is very fast and is primarily targeted + towards maintainers who may want to use this in automated tests. +- xxhash support xxh32 and xxh128 for 32bit hash and 128bit hash respectively + however we picked 64bit xxhash as most computers are 64bit as of 2024 and thus + would have a better affinity to calculating hash that is 64bit in size. + +## Compile Example + +```bash +cmake -B build -DCMAKE_BUILD_TYPE=Debug -DLLAMA_FATAL_WARNINGS=ON +make -C build clean +make -C build llama-gguf-hash VERBOSE=1 +./build/bin/llama-gguf-hash test.gguf +./build/bin/llama-gguf-hash --xxh64 test.gguf +./build/bin/llama-gguf-hash --sha1 test.gguf +./build/bin/llama-gguf-hash --uuid test.gguf +./build/bin/llama-gguf-hash --sha256 test.gguf +``` + +## Generation and Verification Example + +To generate we may use this command + +```bash +./llama-gguf-hash --all test.gguf > test.gguf.manifest +``` + +Which would generate a manifest that looks like below, which contains multiple hash type and per tensor layer hashes as well +(This excludes UUID as that is an ID not a hash) + +```bash +xxh64 f66e9cd66a4396a0 test.gguf:tensor_0 +sha1 59f79ecefd8125a996fdf419239051a7e99e5f20 test.gguf:tensor_0 +sha256 c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd test.gguf:tensor_0 +xxh64 7d3a1f9ac04d0537 test.gguf:tensor_1 +sha1 4765f592eacf096df4628ba59476af94d767080a test.gguf:tensor_1 +sha256 8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7 test.gguf:tensor_1 +xxh64 a0af5d700049693b test.gguf:tensor_2 +sha1 25cbfbad4513cc348e2c95ebdee69d6ff2fd8753 test.gguf:tensor_2 +sha256 947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180 test.gguf:tensor_2 +xxh64 e83fddf559d7b6a6 test.gguf:tensor_3 +sha1 a9cba73e2d90f2ee3dae2548caa42bef3fe6a96c test.gguf:tensor_3 +sha256 423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1 test.gguf:tensor_3 +xxh64 1257733306b7992d test.gguf:tensor_4 +sha1 d7bc61db93bb685ce9d598da89717c66729b7543 test.gguf:tensor_4 +sha256 79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf test.gguf:tensor_4 +xxh64 d238d16ba4711e58 test.gguf:tensor_5 +sha1 0706566c198fe1072f37e0a5135b4b5f23654c52 test.gguf:tensor_5 +sha256 60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b test.gguf:tensor_5 +xxh64 3fbc3b65ab8c7f39 test.gguf:tensor_6 +sha1 73922a0727226a409049f6fc3172a52219ca6f00 test.gguf:tensor_6 +sha256 574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0 test.gguf:tensor_6 +xxh64 c22021c29854f093 test.gguf:tensor_7 +sha1 efc39cece6a951188fc41e354c73bbfe6813d447 test.gguf:tensor_7 +sha256 4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75 test.gguf:tensor_7 +xxh64 936df61f5d64261f test.gguf:tensor_8 +sha1 c2490296d789a4f34398a337fed8377d943d9f06 test.gguf:tensor_8 +sha256 c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01 test.gguf:tensor_8 +xxh64 93fd20c64421c081 test.gguf:tensor_9 +sha1 7047ce1e78437a6884337a3751c7ee0421918a65 test.gguf:tensor_9 +sha256 23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514 test.gguf:tensor_9 +xxh64 5a54d3aad816f302 test.gguf +sha1 d15be52c4ff213e823cb6dd13af7ee2f978e7042 test.gguf +sha256 7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4 test.gguf +``` + +We can then use the normal check command which will by default check for the highest security strength hash and verify against that: + +```bash +$ ./llama-gguf-hash --check test.gguf.manifest test.gguf +manifest test.gguf.manifest sha256 sha1 xxh64 +sha256 c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd test.gguf:tensor_0 - Ok +sha256 8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7 test.gguf:tensor_1 - Ok +sha256 947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180 test.gguf:tensor_2 - Ok +sha256 423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1 test.gguf:tensor_3 - Ok +sha256 79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf test.gguf:tensor_4 - Ok +sha256 60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b test.gguf:tensor_5 - Ok +sha256 574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0 test.gguf:tensor_6 - Ok +sha256 4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75 test.gguf:tensor_7 - Ok +sha256 c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01 test.gguf:tensor_8 - Ok +sha256 23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514 test.gguf:tensor_9 - Ok +sha256 7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4 test.gguf - Ok + +Verification results for test.gguf.manifest - Success +``` + +Or we may explicitly ask for a faster hash like: + +```bash +$ ./llama-gguf-hash --check test.gguf.manifest --xxh64 test.gguf +manifest test.gguf.manifest sha256 sha1 xxh64 +xxh64 f66e9cd66a4396a0 test.gguf:tensor_0 - Ok +xxh64 7d3a1f9ac04d0537 test.gguf:tensor_1 - Ok +xxh64 a0af5d700049693b test.gguf:tensor_2 - Ok +xxh64 e83fddf559d7b6a6 test.gguf:tensor_3 - Ok +xxh64 1257733306b7992d test.gguf:tensor_4 - Ok +xxh64 d238d16ba4711e58 test.gguf:tensor_5 - Ok +xxh64 3fbc3b65ab8c7f39 test.gguf:tensor_6 - Ok +xxh64 c22021c29854f093 test.gguf:tensor_7 - Ok +xxh64 936df61f5d64261f test.gguf:tensor_8 - Ok +xxh64 93fd20c64421c081 test.gguf:tensor_9 - Ok +xxh64 5a54d3aad816f302 test.gguf - Ok + +Verification results for test.gguf.manifest - Success +``` + +Or maybe we want to just check that all the hash is valid: + +```bash +$./llama-gguf-hash --check test.gguf.manifest --all test.gguf.manifest +manifest test.gguf.manifest sha256 sha1 xxh64 +xxh64 f66e9cd66a4396a0 test.gguf:tensor_0 - Ok +sha1 59f79ecefd8125a996fdf419239051a7e99e5f20 test.gguf:tensor_0 - Ok +sha256 c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd test.gguf:tensor_0 - Ok +xxh64 7d3a1f9ac04d0537 test.gguf:tensor_1 - Ok +sha1 4765f592eacf096df4628ba59476af94d767080a test.gguf:tensor_1 - Ok +sha256 8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7 test.gguf:tensor_1 - Ok +xxh64 a0af5d700049693b test.gguf:tensor_2 - Ok +sha1 25cbfbad4513cc348e2c95ebdee69d6ff2fd8753 test.gguf:tensor_2 - Ok +sha256 947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180 test.gguf:tensor_2 - Ok +xxh64 e83fddf559d7b6a6 test.gguf:tensor_3 - Ok +sha1 a9cba73e2d90f2ee3dae2548caa42bef3fe6a96c test.gguf:tensor_3 - Ok +sha256 423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1 test.gguf:tensor_3 - Ok +xxh64 1257733306b7992d test.gguf:tensor_4 - Ok +sha1 d7bc61db93bb685ce9d598da89717c66729b7543 test.gguf:tensor_4 - Ok +sha256 79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf test.gguf:tensor_4 - Ok +xxh64 d238d16ba4711e58 test.gguf:tensor_5 - Ok +sha1 0706566c198fe1072f37e0a5135b4b5f23654c52 test.gguf:tensor_5 - Ok +sha256 60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b test.gguf:tensor_5 - Ok +xxh64 3fbc3b65ab8c7f39 test.gguf:tensor_6 - Ok +sha1 73922a0727226a409049f6fc3172a52219ca6f00 test.gguf:tensor_6 - Ok +sha256 574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0 test.gguf:tensor_6 - Ok +xxh64 c22021c29854f093 test.gguf:tensor_7 - Ok +sha1 efc39cece6a951188fc41e354c73bbfe6813d447 test.gguf:tensor_7 - Ok +sha256 4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75 test.gguf:tensor_7 - Ok +xxh64 936df61f5d64261f test.gguf:tensor_8 - Ok +sha1 c2490296d789a4f34398a337fed8377d943d9f06 test.gguf:tensor_8 - Ok +sha256 c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01 test.gguf:tensor_8 - Ok +xxh64 93fd20c64421c081 test.gguf:tensor_9 - Ok +sha1 7047ce1e78437a6884337a3751c7ee0421918a65 test.gguf:tensor_9 - Ok +sha256 23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514 test.gguf:tensor_9 - Ok +xxh64 5a54d3aad816f302 test.gguf - Ok +sha1 d15be52c4ff213e823cb6dd13af7ee2f978e7042 test.gguf - Ok +sha256 7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4 test.gguf - Ok + +Verification results for test.gguf.manifest - Success +``` + + +## Crypto/Hash Libraries Used + +These micro c libraries dependencies was installed via the [clib c package manager](https://github.com/clibs) + +- https://github.com/mofosyne/xxHash (From: https://github.com/Cyan4973/xxHash) +- https://github.com/clibs/sha1/ +- https://github.com/jb55/sha256.c diff --git a/examples/gguf-hash/deps/rotate-bits/package.json b/examples/gguf-hash/deps/rotate-bits/package.json new file mode 100644 index 000000000..74c0bef68 --- /dev/null +++ b/examples/gguf-hash/deps/rotate-bits/package.json @@ -0,0 +1,13 @@ +{ + "name": "rotate-bits", + "version": "0.1.1", + "repo": "jb55/rotate-bits.h", + "description": "rotate bits", + "keywords": ["rotl", "rotr"], + "src": ["rotate-bits.h"], + "license": "Public Domain", + "development": { + "thlorenz/tap.c": "*" + } +} + diff --git a/examples/gguf-hash/deps/rotate-bits/rotate-bits.h b/examples/gguf-hash/deps/rotate-bits/rotate-bits.h new file mode 100644 index 000000000..75c4881fc --- /dev/null +++ b/examples/gguf-hash/deps/rotate-bits/rotate-bits.h @@ -0,0 +1,46 @@ + + +#ifndef __ROTATE_DEFS_H +#define __ROTATE_DEFS_H + +#ifdef _MSC_VER + +#include + +#define ROTL32(v, n) _rotl((v), (n)) +#define ROTL64(v, n) _rotl64((v), (n)) + +#define ROTR32(v, n) _rotr((v), (n)) +#define ROTR64(v, n) _rotr64((v), (n)) + +#else + +#include + +#define U8V(v) ((uint8_t)(v) & 0xFFU) +#define U16V(v) ((uint16_t)(v) & 0xFFFFU) +#define U32V(v) ((uint32_t)(v) & 0xFFFFFFFFU) +#define U64V(v) ((uint64_t)(v) & 0xFFFFFFFFFFFFFFFFU) + +#define ROTL32(v, n) \ + (U32V((uint32_t)(v) << (n)) | ((uint32_t)(v) >> (32 - (n)))) + +// tests fail if we don't have this cast... +#define ROTL64(v, n) \ + (U64V((uint64_t)(v) << (n)) | ((uint64_t)(v) >> (64 - (n)))) + +#define ROTR32(v, n) ROTL32(v, 32 - (n)) +#define ROTR64(v, n) ROTL64(v, 64 - (n)) + +#endif + +#define ROTL8(v, n) \ + (U8V((uint8_t)(v) << (n)) | ((uint8_t)(v) >> (8 - (n)))) + +#define ROTL16(v, n) \ + (U16V((uint16_t)(v) << (n)) | ((uint16_t)(v) >> (16 - (n)))) + +#define ROTR8(v, n) ROTL8(v, 8 - (n)) +#define ROTR16(v, n) ROTL16(v, 16 - (n)) + +#endif diff --git a/examples/gguf-hash/deps/sha1/package.json b/examples/gguf-hash/deps/sha1/package.json new file mode 100644 index 000000000..6a5843dd1 --- /dev/null +++ b/examples/gguf-hash/deps/sha1/package.json @@ -0,0 +1,9 @@ +{ + "name": "sha1", + "version": "0.0.1", + "repo": "clibs/sha1", + "description": "sha1 hash algorithm", + "keywords": ["sha1", "hash"], + "license": "public domain", + "src": ["sha1.c", "sha1.h"] +} diff --git a/examples/gguf-hash/deps/sha1/sha1.c b/examples/gguf-hash/deps/sha1/sha1.c new file mode 100644 index 000000000..76cd6ca33 --- /dev/null +++ b/examples/gguf-hash/deps/sha1/sha1.c @@ -0,0 +1,295 @@ +/* +SHA-1 in C +By Steve Reid +100% Public Domain + +Test Vectors (from FIPS PUB 180-1) +"abc" + A9993E36 4706816A BA3E2571 7850C26C 9CD0D89D +"abcdbcdecdefdefgefghfghighijhijkijkljklmklmnlmnomnopnopq" + 84983E44 1C3BD26E BAAE4AA1 F95129E5 E54670F1 +A million repetitions of "a" + 34AA973C D4C4DAA4 F61EEB2B DBAD2731 6534016F +*/ + +/* #define LITTLE_ENDIAN * This should be #define'd already, if true. */ +/* #define SHA1HANDSOFF * Copies data before messing with it. */ + +#define SHA1HANDSOFF + +#include +#include + +/* for uint32_t */ +#include + +#include "sha1.h" + + +#define rol(value, bits) (((value) << (bits)) | ((value) >> (32 - (bits)))) + +/* blk0() and blk() perform the initial expand. */ +/* I got the idea of expanding during the round function from SSLeay */ +#if BYTE_ORDER == LITTLE_ENDIAN +#define blk0(i) (block->l[i] = (rol(block->l[i],24)&0xFF00FF00) \ + |(rol(block->l[i],8)&0x00FF00FF)) +#elif BYTE_ORDER == BIG_ENDIAN +#define blk0(i) block->l[i] +#else +#error "Endianness not defined!" +#endif +#define blk(i) (block->l[i&15] = rol(block->l[(i+13)&15]^block->l[(i+8)&15] \ + ^block->l[(i+2)&15]^block->l[i&15],1)) + +/* (R0+R1), R2, R3, R4 are the different operations used in SHA1 */ +#define R0(v,w,x,y,z,i) z+=((w&(x^y))^y)+blk0(i)+0x5A827999+rol(v,5);w=rol(w,30); +#define R1(v,w,x,y,z,i) z+=((w&(x^y))^y)+blk(i)+0x5A827999+rol(v,5);w=rol(w,30); +#define R2(v,w,x,y,z,i) z+=(w^x^y)+blk(i)+0x6ED9EBA1+rol(v,5);w=rol(w,30); +#define R3(v,w,x,y,z,i) z+=(((w|x)&y)|(w&x))+blk(i)+0x8F1BBCDC+rol(v,5);w=rol(w,30); +#define R4(v,w,x,y,z,i) z+=(w^x^y)+blk(i)+0xCA62C1D6+rol(v,5);w=rol(w,30); + + +/* Hash a single 512-bit block. This is the core of the algorithm. */ + +void SHA1Transform( + uint32_t state[5], + const unsigned char buffer[64] +) +{ + uint32_t a, b, c, d, e; + + typedef union + { + unsigned char c[64]; + uint32_t l[16]; + } CHAR64LONG16; + +#ifdef SHA1HANDSOFF + CHAR64LONG16 block[1]; /* use array to appear as a pointer */ + + memcpy(block, buffer, 64); +#else + /* The following had better never be used because it causes the + * pointer-to-const buffer to be cast into a pointer to non-const. + * And the result is written through. I threw a "const" in, hoping + * this will cause a diagnostic. + */ + CHAR64LONG16 *block = (const CHAR64LONG16 *) buffer; +#endif + /* Copy context->state[] to working vars */ + a = state[0]; + b = state[1]; + c = state[2]; + d = state[3]; + e = state[4]; + /* 4 rounds of 20 operations each. Loop unrolled. */ + R0(a, b, c, d, e, 0); + R0(e, a, b, c, d, 1); + R0(d, e, a, b, c, 2); + R0(c, d, e, a, b, 3); + R0(b, c, d, e, a, 4); + R0(a, b, c, d, e, 5); + R0(e, a, b, c, d, 6); + R0(d, e, a, b, c, 7); + R0(c, d, e, a, b, 8); + R0(b, c, d, e, a, 9); + R0(a, b, c, d, e, 10); + R0(e, a, b, c, d, 11); + R0(d, e, a, b, c, 12); + R0(c, d, e, a, b, 13); + R0(b, c, d, e, a, 14); + R0(a, b, c, d, e, 15); + R1(e, a, b, c, d, 16); + R1(d, e, a, b, c, 17); + R1(c, d, e, a, b, 18); + R1(b, c, d, e, a, 19); + R2(a, b, c, d, e, 20); + R2(e, a, b, c, d, 21); + R2(d, e, a, b, c, 22); + R2(c, d, e, a, b, 23); + R2(b, c, d, e, a, 24); + R2(a, b, c, d, e, 25); + R2(e, a, b, c, d, 26); + R2(d, e, a, b, c, 27); + R2(c, d, e, a, b, 28); + R2(b, c, d, e, a, 29); + R2(a, b, c, d, e, 30); + R2(e, a, b, c, d, 31); + R2(d, e, a, b, c, 32); + R2(c, d, e, a, b, 33); + R2(b, c, d, e, a, 34); + R2(a, b, c, d, e, 35); + R2(e, a, b, c, d, 36); + R2(d, e, a, b, c, 37); + R2(c, d, e, a, b, 38); + R2(b, c, d, e, a, 39); + R3(a, b, c, d, e, 40); + R3(e, a, b, c, d, 41); + R3(d, e, a, b, c, 42); + R3(c, d, e, a, b, 43); + R3(b, c, d, e, a, 44); + R3(a, b, c, d, e, 45); + R3(e, a, b, c, d, 46); + R3(d, e, a, b, c, 47); + R3(c, d, e, a, b, 48); + R3(b, c, d, e, a, 49); + R3(a, b, c, d, e, 50); + R3(e, a, b, c, d, 51); + R3(d, e, a, b, c, 52); + R3(c, d, e, a, b, 53); + R3(b, c, d, e, a, 54); + R3(a, b, c, d, e, 55); + R3(e, a, b, c, d, 56); + R3(d, e, a, b, c, 57); + R3(c, d, e, a, b, 58); + R3(b, c, d, e, a, 59); + R4(a, b, c, d, e, 60); + R4(e, a, b, c, d, 61); + R4(d, e, a, b, c, 62); + R4(c, d, e, a, b, 63); + R4(b, c, d, e, a, 64); + R4(a, b, c, d, e, 65); + R4(e, a, b, c, d, 66); + R4(d, e, a, b, c, 67); + R4(c, d, e, a, b, 68); + R4(b, c, d, e, a, 69); + R4(a, b, c, d, e, 70); + R4(e, a, b, c, d, 71); + R4(d, e, a, b, c, 72); + R4(c, d, e, a, b, 73); + R4(b, c, d, e, a, 74); + R4(a, b, c, d, e, 75); + R4(e, a, b, c, d, 76); + R4(d, e, a, b, c, 77); + R4(c, d, e, a, b, 78); + R4(b, c, d, e, a, 79); + /* Add the working vars back into context.state[] */ + state[0] += a; + state[1] += b; + state[2] += c; + state[3] += d; + state[4] += e; + /* Wipe variables */ + a = b = c = d = e = 0; +#ifdef SHA1HANDSOFF + memset(block, '\0', sizeof(block)); +#endif +} + + +/* SHA1Init - Initialize new context */ + +void SHA1Init( + SHA1_CTX * context +) +{ + /* SHA1 initialization constants */ + context->state[0] = 0x67452301; + context->state[1] = 0xEFCDAB89; + context->state[2] = 0x98BADCFE; + context->state[3] = 0x10325476; + context->state[4] = 0xC3D2E1F0; + context->count[0] = context->count[1] = 0; +} + + +/* Run your data through this. */ + +void SHA1Update( + SHA1_CTX * context, + const unsigned char *data, + uint32_t len +) +{ + uint32_t i; + + uint32_t j; + + j = context->count[0]; + if ((context->count[0] += len << 3) < j) + context->count[1]++; + context->count[1] += (len >> 29); + j = (j >> 3) & 63; + if ((j + len) > 63) + { + memcpy(&context->buffer[j], data, (i = 64 - j)); + SHA1Transform(context->state, context->buffer); + for (; i + 63 < len; i += 64) + { + SHA1Transform(context->state, &data[i]); + } + j = 0; + } + else + i = 0; + memcpy(&context->buffer[j], &data[i], len - i); +} + + +/* Add padding and return the message digest. */ + +void SHA1Final( + unsigned char digest[20], + SHA1_CTX * context +) +{ + unsigned i; + + unsigned char finalcount[8]; + + unsigned char c; + +#if 0 /* untested "improvement" by DHR */ + /* Convert context->count to a sequence of bytes + * in finalcount. Second element first, but + * big-endian order within element. + * But we do it all backwards. + */ + unsigned char *fcp = &finalcount[8]; + + for (i = 0; i < 2; i++) + { + uint32_t t = context->count[i]; + + int j; + + for (j = 0; j < 4; t >>= 8, j++) + *--fcp = (unsigned char) t} +#else + for (i = 0; i < 8; i++) + { + finalcount[i] = (unsigned char) ((context->count[(i >= 4 ? 0 : 1)] >> ((3 - (i & 3)) * 8)) & 255); /* Endian independent */ + } +#endif + c = 0200; + SHA1Update(context, &c, 1); + while ((context->count[0] & 504) != 448) + { + c = 0000; + SHA1Update(context, &c, 1); + } + SHA1Update(context, finalcount, 8); /* Should cause a SHA1Transform() */ + for (i = 0; i < 20; i++) + { + digest[i] = (unsigned char) + ((context->state[i >> 2] >> ((3 - (i & 3)) * 8)) & 255); + } + /* Wipe variables */ + memset(context, '\0', sizeof(*context)); + memset(&finalcount, '\0', sizeof(finalcount)); +} + +void SHA1( + char *hash_out, + const char *str, + uint32_t len) +{ + SHA1_CTX ctx; + unsigned int ii; + + SHA1Init(&ctx); + for (ii=0; ii + 100% Public Domain + */ + +#include "stdint.h" + +#if defined(__cplusplus) +extern "C" { +#endif + +typedef struct +{ + uint32_t state[5]; + uint32_t count[2]; + unsigned char buffer[64]; +} SHA1_CTX; + +void SHA1Transform( + uint32_t state[5], + const unsigned char buffer[64] + ); + +void SHA1Init( + SHA1_CTX * context + ); + +void SHA1Update( + SHA1_CTX * context, + const unsigned char *data, + uint32_t len + ); + +void SHA1Final( + unsigned char digest[20], + SHA1_CTX * context + ); + +void SHA1( + char *hash_out, + const char *str, + uint32_t len); + +#if defined(__cplusplus) +} +#endif + +#endif /* SHA1_H */ diff --git a/examples/gguf-hash/deps/sha256/package.json b/examples/gguf-hash/deps/sha256/package.json new file mode 100644 index 000000000..b92a04127 --- /dev/null +++ b/examples/gguf-hash/deps/sha256/package.json @@ -0,0 +1,15 @@ +{ + "name": "sha256", + "version": "0.0.2", + "repo": "jb55/sha256.c", + "description": "sha256 in c", + "keywords": ["sha256", "sha2"], + "src": ["sha256.c", "sha256.h"], + "dependencies": { + "jb55/rotate-bits.h": "0.1.1" + }, + "development": { + "thlorenz/tap.c": "*" + } +} + diff --git a/examples/gguf-hash/deps/sha256/sha256.c b/examples/gguf-hash/deps/sha256/sha256.c new file mode 100644 index 000000000..a7a87aeb2 --- /dev/null +++ b/examples/gguf-hash/deps/sha256/sha256.c @@ -0,0 +1,221 @@ +/* Crypto/Sha256.c -- SHA-256 Hash +2010-06-11 : Igor Pavlov : Public domain +This code is based on public domain code from Wei Dai's Crypto++ library. */ + +#include "rotate-bits/rotate-bits.h" +#include "sha256.h" + +/* define it for speed optimization */ +#define _SHA256_UNROLL +#define _SHA256_UNROLL2 + +void +sha256_init(sha256_t *p) +{ + p->state[0] = 0x6a09e667; + p->state[1] = 0xbb67ae85; + p->state[2] = 0x3c6ef372; + p->state[3] = 0xa54ff53a; + p->state[4] = 0x510e527f; + p->state[5] = 0x9b05688c; + p->state[6] = 0x1f83d9ab; + p->state[7] = 0x5be0cd19; + p->count = 0; +} + +#define S0(x) (ROTR32(x, 2) ^ ROTR32(x,13) ^ ROTR32(x, 22)) +#define S1(x) (ROTR32(x, 6) ^ ROTR32(x,11) ^ ROTR32(x, 25)) +#define s0(x) (ROTR32(x, 7) ^ ROTR32(x,18) ^ (x >> 3)) +#define s1(x) (ROTR32(x,17) ^ ROTR32(x,19) ^ (x >> 10)) + +#define blk0(i) (W[i] = data[i]) +#define blk2(i) (W[i&15] += s1(W[(i-2)&15]) + W[(i-7)&15] + s0(W[(i-15)&15])) + +#define Ch(x,y,z) (z^(x&(y^z))) +#define Maj(x,y,z) ((x&y)|(z&(x|y))) + +#define a(i) T[(0-(i))&7] +#define b(i) T[(1-(i))&7] +#define c(i) T[(2-(i))&7] +#define d(i) T[(3-(i))&7] +#define e(i) T[(4-(i))&7] +#define f(i) T[(5-(i))&7] +#define g(i) T[(6-(i))&7] +#define h(i) T[(7-(i))&7] + + +#ifdef _SHA256_UNROLL2 + +#define R(a,b,c,d,e,f,g,h, i) h += S1(e) + Ch(e,f,g) + K[i+j] + (j?blk2(i):blk0(i));\ + d += h; h += S0(a) + Maj(a, b, c) + +#define RX_8(i) \ + R(a,b,c,d,e,f,g,h, i); \ + R(h,a,b,c,d,e,f,g, (i+1)); \ + R(g,h,a,b,c,d,e,f, (i+2)); \ + R(f,g,h,a,b,c,d,e, (i+3)); \ + R(e,f,g,h,a,b,c,d, (i+4)); \ + R(d,e,f,g,h,a,b,c, (i+5)); \ + R(c,d,e,f,g,h,a,b, (i+6)); \ + R(b,c,d,e,f,g,h,a, (i+7)) + +#else + +#define R(i) h(i) += S1(e(i)) + Ch(e(i),f(i),g(i)) + K[i+j] + (j?blk2(i):blk0(i));\ + d(i) += h(i); h(i) += S0(a(i)) + Maj(a(i), b(i), c(i)) + +#ifdef _SHA256_UNROLL + +#define RX_8(i) R(i+0); R(i+1); R(i+2); R(i+3); R(i+4); R(i+5); R(i+6); R(i+7); + +#endif + +#endif + +static const uint32_t K[64] = { + 0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, + 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5, + 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, + 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, + 0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, + 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da, + 0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, + 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967, + 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, + 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, + 0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, + 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070, + 0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, + 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3, + 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, + 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2 +}; + +static void +sha256_transform(uint32_t *state, const uint32_t *data) +{ + uint32_t W[16] = {0}; + unsigned j; + #ifdef _SHA256_UNROLL2 + uint32_t a,b,c,d,e,f,g,h; + a = state[0]; + b = state[1]; + c = state[2]; + d = state[3]; + e = state[4]; + f = state[5]; + g = state[6]; + h = state[7]; + #else + uint32_t T[8]; + for (j = 0; j < 8; j++) + T[j] = state[j]; + #endif + + for (j = 0; j < 64; j += 16) + { + #if defined(_SHA256_UNROLL) || defined(_SHA256_UNROLL2) + RX_8(0); RX_8(8); + #else + unsigned i; + for (i = 0; i < 16; i++) { R(i); } + #endif + } + + #ifdef _SHA256_UNROLL2 + state[0] += a; + state[1] += b; + state[2] += c; + state[3] += d; + state[4] += e; + state[5] += f; + state[6] += g; + state[7] += h; + #else + for (j = 0; j < 8; j++) + state[j] += T[j]; + #endif + + /* Wipe variables */ + /* memset(W, 0, sizeof(W)); */ + /* memset(T, 0, sizeof(T)); */ +} + +#undef S0 +#undef S1 +#undef s0 +#undef s1 + +static void +sha256_write_byte_block(sha256_t *p) +{ + uint32_t data32[16]; + unsigned i; + for (i = 0; i < 16; i++) + data32[i] = + ((uint32_t)(p->buffer[i * 4 ]) << 24) + + ((uint32_t)(p->buffer[i * 4 + 1]) << 16) + + ((uint32_t)(p->buffer[i * 4 + 2]) << 8) + + ((uint32_t)(p->buffer[i * 4 + 3])); + sha256_transform(p->state, data32); +} + + +void +sha256_hash(unsigned char *buf, const unsigned char *data, size_t size) +{ + sha256_t hash; + sha256_init(&hash); + sha256_update(&hash, data, size); + sha256_final(&hash, buf); +} + + +void +sha256_update(sha256_t *p, const unsigned char *data, size_t size) +{ + uint32_t curBufferPos = (uint32_t)p->count & 0x3F; + while (size > 0) + { + p->buffer[curBufferPos++] = *data++; + p->count++; + size--; + if (curBufferPos == 64) + { + curBufferPos = 0; + sha256_write_byte_block(p); + } + } +} + + +void +sha256_final(sha256_t *p, unsigned char *digest) +{ + uint64_t lenInBits = (p->count << 3); + uint32_t curBufferPos = (uint32_t)p->count & 0x3F; + unsigned i; + p->buffer[curBufferPos++] = 0x80; + while (curBufferPos != (64 - 8)) + { + curBufferPos &= 0x3F; + if (curBufferPos == 0) + sha256_write_byte_block(p); + p->buffer[curBufferPos++] = 0; + } + for (i = 0; i < 8; i++) + { + p->buffer[curBufferPos++] = (unsigned char)(lenInBits >> 56); + lenInBits <<= 8; + } + sha256_write_byte_block(p); + + for (i = 0; i < 8; i++) + { + *digest++ = (unsigned char)(p->state[i] >> 24); + *digest++ = (unsigned char)(p->state[i] >> 16); + *digest++ = (unsigned char)(p->state[i] >> 8); + *digest++ = (unsigned char)(p->state[i]); + } + sha256_init(p); +} diff --git a/examples/gguf-hash/deps/sha256/sha256.h b/examples/gguf-hash/deps/sha256/sha256.h new file mode 100644 index 000000000..21657e66b --- /dev/null +++ b/examples/gguf-hash/deps/sha256/sha256.h @@ -0,0 +1,24 @@ +/* Sha256.h -- SHA-256 Hash +2010-06-11 : Igor Pavlov : Public domain */ + +#ifndef __CRYPTO_SHA256_H +#define __CRYPTO_SHA256_H + +#include +#include + +#define SHA256_DIGEST_SIZE 32 + +typedef struct sha256_t +{ + uint32_t state[8]; + uint64_t count; + unsigned char buffer[64]; +} sha256_t; + +void sha256_init(sha256_t *p); +void sha256_update(sha256_t *p, const unsigned char *data, size_t size); +void sha256_final(sha256_t *p, unsigned char *digest); +void sha256_hash(unsigned char *buf, const unsigned char *data, size_t size); + +#endif diff --git a/examples/gguf-hash/deps/xxhash/clib.json b/examples/gguf-hash/deps/xxhash/clib.json new file mode 100644 index 000000000..0048c74dc --- /dev/null +++ b/examples/gguf-hash/deps/xxhash/clib.json @@ -0,0 +1,12 @@ +{ + "name": "xxhash", + "version": "0.8.2", + "repo": "mofosyne/xxhash", + "description": "Extremely fast non-cryptographic hash algorithm", + "keywords": ["xxhash", "hashing"], + "license": "BSD-2-Clause", + "src": [ + "xxhash.c", + "xxhash.h" + ] +} diff --git a/examples/gguf-hash/deps/xxhash/xxhash.c b/examples/gguf-hash/deps/xxhash/xxhash.c new file mode 100644 index 000000000..e60cc37f1 --- /dev/null +++ b/examples/gguf-hash/deps/xxhash/xxhash.c @@ -0,0 +1,42 @@ +/* + * xxHash - Extremely Fast Hash algorithm + * Copyright (C) 2012-2023 Yann Collet + * + * BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php) + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following disclaimer + * in the documentation and/or other materials provided with the + * distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * You can contact the author at: + * - xxHash homepage: https://www.xxhash.com + * - xxHash source repository: https://github.com/Cyan4973/xxHash + */ + +/* + * xxhash.c instantiates functions defined in xxhash.h + */ + +#define XXH_STATIC_LINKING_ONLY /* access advanced declarations */ +#define XXH_IMPLEMENTATION /* access definitions */ + +#include "xxhash.h" diff --git a/examples/gguf-hash/deps/xxhash/xxhash.h b/examples/gguf-hash/deps/xxhash/xxhash.h new file mode 100644 index 000000000..c0fafe20d --- /dev/null +++ b/examples/gguf-hash/deps/xxhash/xxhash.h @@ -0,0 +1,7093 @@ +/* + * xxHash - Extremely Fast Hash algorithm + * Header File + * Copyright (C) 2012-2023 Yann Collet + * + * BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php) + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following disclaimer + * in the documentation and/or other materials provided with the + * distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * You can contact the author at: + * - xxHash homepage: https://www.xxhash.com + * - xxHash source repository: https://github.com/Cyan4973/xxHash + */ + +/*! + * @mainpage xxHash + * + * xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed + * limits. + * + * It is proposed in four flavors, in three families: + * 1. @ref XXH32_family + * - Classic 32-bit hash function. Simple, compact, and runs on almost all + * 32-bit and 64-bit systems. + * 2. @ref XXH64_family + * - Classic 64-bit adaptation of XXH32. Just as simple, and runs well on most + * 64-bit systems (but _not_ 32-bit systems). + * 3. @ref XXH3_family + * - Modern 64-bit and 128-bit hash function family which features improved + * strength and performance across the board, especially on smaller data. + * It benefits greatly from SIMD and 64-bit without requiring it. + * + * Benchmarks + * --- + * The reference system uses an Intel i7-9700K CPU, and runs Ubuntu x64 20.04. + * The open source benchmark program is compiled with clang v10.0 using -O3 flag. + * + * | Hash Name | ISA ext | Width | Large Data Speed | Small Data Velocity | + * | -------------------- | ------- | ----: | ---------------: | ------------------: | + * | XXH3_64bits() | @b AVX2 | 64 | 59.4 GB/s | 133.1 | + * | MeowHash | AES-NI | 128 | 58.2 GB/s | 52.5 | + * | XXH3_128bits() | @b AVX2 | 128 | 57.9 GB/s | 118.1 | + * | CLHash | PCLMUL | 64 | 37.1 GB/s | 58.1 | + * | XXH3_64bits() | @b SSE2 | 64 | 31.5 GB/s | 133.1 | + * | XXH3_128bits() | @b SSE2 | 128 | 29.6 GB/s | 118.1 | + * | RAM sequential read | | N/A | 28.0 GB/s | N/A | + * | ahash | AES-NI | 64 | 22.5 GB/s | 107.2 | + * | City64 | | 64 | 22.0 GB/s | 76.6 | + * | T1ha2 | | 64 | 22.0 GB/s | 99.0 | + * | City128 | | 128 | 21.7 GB/s | 57.7 | + * | FarmHash | AES-NI | 64 | 21.3 GB/s | 71.9 | + * | XXH64() | | 64 | 19.4 GB/s | 71.0 | + * | SpookyHash | | 64 | 19.3 GB/s | 53.2 | + * | Mum | | 64 | 18.0 GB/s | 67.0 | + * | CRC32C | SSE4.2 | 32 | 13.0 GB/s | 57.9 | + * | XXH32() | | 32 | 9.7 GB/s | 71.9 | + * | City32 | | 32 | 9.1 GB/s | 66.0 | + * | Blake3* | @b AVX2 | 256 | 4.4 GB/s | 8.1 | + * | Murmur3 | | 32 | 3.9 GB/s | 56.1 | + * | SipHash* | | 64 | 3.0 GB/s | 43.2 | + * | Blake3* | @b SSE2 | 256 | 2.4 GB/s | 8.1 | + * | HighwayHash | | 64 | 1.4 GB/s | 6.0 | + * | FNV64 | | 64 | 1.2 GB/s | 62.7 | + * | Blake2* | | 256 | 1.1 GB/s | 5.1 | + * | SHA1* | | 160 | 0.8 GB/s | 5.6 | + * | MD5* | | 128 | 0.6 GB/s | 7.8 | + * @note + * - Hashes which require a specific ISA extension are noted. SSE2 is also noted, + * even though it is mandatory on x64. + * - Hashes with an asterisk are cryptographic. Note that MD5 is non-cryptographic + * by modern standards. + * - Small data velocity is a rough average of algorithm's efficiency for small + * data. For more accurate information, see the wiki. + * - More benchmarks and strength tests are found on the wiki: + * https://github.com/Cyan4973/xxHash/wiki + * + * Usage + * ------ + * All xxHash variants use a similar API. Changing the algorithm is a trivial + * substitution. + * + * @pre + * For functions which take an input and length parameter, the following + * requirements are assumed: + * - The range from [`input`, `input + length`) is valid, readable memory. + * - The only exception is if the `length` is `0`, `input` may be `NULL`. + * - For C++, the objects must have the *TriviallyCopyable* property, as the + * functions access bytes directly as if it was an array of `unsigned char`. + * + * @anchor single_shot_example + * **Single Shot** + * + * These functions are stateless functions which hash a contiguous block of memory, + * immediately returning the result. They are the easiest and usually the fastest + * option. + * + * XXH32(), XXH64(), XXH3_64bits(), XXH3_128bits() + * + * @code{.c} + * #include + * #include "xxhash.h" + * + * // Example for a function which hashes a null terminated string with XXH32(). + * XXH32_hash_t hash_string(const char* string, XXH32_hash_t seed) + * { + * // NULL pointers are only valid if the length is zero + * size_t length = (string == NULL) ? 0 : strlen(string); + * return XXH32(string, length, seed); + * } + * @endcode + * + * + * @anchor streaming_example + * **Streaming** + * + * These groups of functions allow incremental hashing of unknown size, even + * more than what would fit in a size_t. + * + * XXH32_reset(), XXH64_reset(), XXH3_64bits_reset(), XXH3_128bits_reset() + * + * @code{.c} + * #include + * #include + * #include "xxhash.h" + * // Example for a function which hashes a FILE incrementally with XXH3_64bits(). + * XXH64_hash_t hashFile(FILE* f) + * { + * // Allocate a state struct. Do not just use malloc() or new. + * XXH3_state_t* state = XXH3_createState(); + * assert(state != NULL && "Out of memory!"); + * // Reset the state to start a new hashing session. + * XXH3_64bits_reset(state); + * char buffer[4096]; + * size_t count; + * // Read the file in chunks + * while ((count = fread(buffer, 1, sizeof(buffer), f)) != 0) { + * // Run update() as many times as necessary to process the data + * XXH3_64bits_update(state, buffer, count); + * } + * // Retrieve the finalized hash. This will not change the state. + * XXH64_hash_t result = XXH3_64bits_digest(state); + * // Free the state. Do not use free(). + * XXH3_freeState(state); + * return result; + * } + * @endcode + * + * Streaming functions generate the xxHash value from an incremental input. + * This method is slower than single-call functions, due to state management. + * For small inputs, prefer `XXH32()` and `XXH64()`, which are better optimized. + * + * An XXH state must first be allocated using `XXH*_createState()`. + * + * Start a new hash by initializing the state with a seed using `XXH*_reset()`. + * + * Then, feed the hash state by calling `XXH*_update()` as many times as necessary. + * + * The function returns an error code, with 0 meaning OK, and any other value + * meaning there is an error. + * + * Finally, a hash value can be produced anytime, by using `XXH*_digest()`. + * This function returns the nn-bits hash as an int or long long. + * + * It's still possible to continue inserting input into the hash state after a + * digest, and generate new hash values later on by invoking `XXH*_digest()`. + * + * When done, release the state using `XXH*_freeState()`. + * + * + * @anchor canonical_representation_example + * **Canonical Representation** + * + * The default return values from XXH functions are unsigned 32, 64 and 128 bit + * integers. + * This the simplest and fastest format for further post-processing. + * + * However, this leaves open the question of what is the order on the byte level, + * since little and big endian conventions will store the same number differently. + * + * The canonical representation settles this issue by mandating big-endian + * convention, the same convention as human-readable numbers (large digits first). + * + * When writing hash values to storage, sending them over a network, or printing + * them, it's highly recommended to use the canonical representation to ensure + * portability across a wider range of systems, present and future. + * + * The following functions allow transformation of hash values to and from + * canonical format. + * + * XXH32_canonicalFromHash(), XXH32_hashFromCanonical(), + * XXH64_canonicalFromHash(), XXH64_hashFromCanonical(), + * XXH128_canonicalFromHash(), XXH128_hashFromCanonical(), + * + * @code{.c} + * #include + * #include "xxhash.h" + * + * // Example for a function which prints XXH32_hash_t in human readable format + * void printXxh32(XXH32_hash_t hash) + * { + * XXH32_canonical_t cano; + * XXH32_canonicalFromHash(&cano, hash); + * size_t i; + * for(i = 0; i < sizeof(cano.digest); ++i) { + * printf("%02x", cano.digest[i]); + * } + * printf("\n"); + * } + * + * // Example for a function which converts XXH32_canonical_t to XXH32_hash_t + * XXH32_hash_t convertCanonicalToXxh32(XXH32_canonical_t cano) + * { + * XXH32_hash_t hash = XXH32_hashFromCanonical(&cano); + * return hash; + * } + * @endcode + * + * + * @file xxhash.h + * xxHash prototypes and implementation + */ + +#if defined (__cplusplus) +extern "C" { +#endif + +/* **************************** + * INLINE mode + ******************************/ +/*! + * @defgroup public Public API + * Contains details on the public xxHash functions. + * @{ + */ +#ifdef XXH_DOXYGEN +/*! + * @brief Gives access to internal state declaration, required for static allocation. + * + * Incompatible with dynamic linking, due to risks of ABI changes. + * + * Usage: + * @code{.c} + * #define XXH_STATIC_LINKING_ONLY + * #include "xxhash.h" + * @endcode + */ +# define XXH_STATIC_LINKING_ONLY +/* Do not undef XXH_STATIC_LINKING_ONLY for Doxygen */ + +/*! + * @brief Gives access to internal definitions. + * + * Usage: + * @code{.c} + * #define XXH_STATIC_LINKING_ONLY + * #define XXH_IMPLEMENTATION + * #include "xxhash.h" + * @endcode + */ +# define XXH_IMPLEMENTATION +/* Do not undef XXH_IMPLEMENTATION for Doxygen */ + +/*! + * @brief Exposes the implementation and marks all functions as `inline`. + * + * Use these build macros to inline xxhash into the target unit. + * Inlining improves performance on small inputs, especially when the length is + * expressed as a compile-time constant: + * + * https://fastcompression.blogspot.com/2018/03/xxhash-for-small-keys-impressive-power.html + * + * It also keeps xxHash symbols private to the unit, so they are not exported. + * + * Usage: + * @code{.c} + * #define XXH_INLINE_ALL + * #include "xxhash.h" + * @endcode + * Do not compile and link xxhash.o as a separate object, as it is not useful. + */ +# define XXH_INLINE_ALL +# undef XXH_INLINE_ALL +/*! + * @brief Exposes the implementation without marking functions as inline. + */ +# define XXH_PRIVATE_API +# undef XXH_PRIVATE_API +/*! + * @brief Emulate a namespace by transparently prefixing all symbols. + * + * If you want to include _and expose_ xxHash functions from within your own + * library, but also want to avoid symbol collisions with other libraries which + * may also include xxHash, you can use @ref XXH_NAMESPACE to automatically prefix + * any public symbol from xxhash library with the value of @ref XXH_NAMESPACE + * (therefore, avoid empty or numeric values). + * + * Note that no change is required within the calling program as long as it + * includes `xxhash.h`: Regular symbol names will be automatically translated + * by this header. + */ +# define XXH_NAMESPACE /* YOUR NAME HERE */ +# undef XXH_NAMESPACE +#endif + +#if (defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API)) \ + && !defined(XXH_INLINE_ALL_31684351384) + /* this section should be traversed only once */ +# define XXH_INLINE_ALL_31684351384 + /* give access to the advanced API, required to compile implementations */ +# undef XXH_STATIC_LINKING_ONLY /* avoid macro redef */ +# define XXH_STATIC_LINKING_ONLY + /* make all functions private */ +# undef XXH_PUBLIC_API +# if defined(__GNUC__) +# define XXH_PUBLIC_API static __inline __attribute__((__unused__)) +# elif defined (__cplusplus) || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) +# define XXH_PUBLIC_API static inline +# elif defined(_MSC_VER) +# define XXH_PUBLIC_API static __inline +# else + /* note: this version may generate warnings for unused static functions */ +# define XXH_PUBLIC_API static +# endif + + /* + * This part deals with the special case where a unit wants to inline xxHash, + * but "xxhash.h" has previously been included without XXH_INLINE_ALL, + * such as part of some previously included *.h header file. + * Without further action, the new include would just be ignored, + * and functions would effectively _not_ be inlined (silent failure). + * The following macros solve this situation by prefixing all inlined names, + * avoiding naming collision with previous inclusions. + */ + /* Before that, we unconditionally #undef all symbols, + * in case they were already defined with XXH_NAMESPACE. + * They will then be redefined for XXH_INLINE_ALL + */ +# undef XXH_versionNumber + /* XXH32 */ +# undef XXH32 +# undef XXH32_createState +# undef XXH32_freeState +# undef XXH32_reset +# undef XXH32_update +# undef XXH32_digest +# undef XXH32_copyState +# undef XXH32_canonicalFromHash +# undef XXH32_hashFromCanonical + /* XXH64 */ +# undef XXH64 +# undef XXH64_createState +# undef XXH64_freeState +# undef XXH64_reset +# undef XXH64_update +# undef XXH64_digest +# undef XXH64_copyState +# undef XXH64_canonicalFromHash +# undef XXH64_hashFromCanonical + /* XXH3_64bits */ +# undef XXH3_64bits +# undef XXH3_64bits_withSecret +# undef XXH3_64bits_withSeed +# undef XXH3_64bits_withSecretandSeed +# undef XXH3_createState +# undef XXH3_freeState +# undef XXH3_copyState +# undef XXH3_64bits_reset +# undef XXH3_64bits_reset_withSeed +# undef XXH3_64bits_reset_withSecret +# undef XXH3_64bits_update +# undef XXH3_64bits_digest +# undef XXH3_generateSecret + /* XXH3_128bits */ +# undef XXH128 +# undef XXH3_128bits +# undef XXH3_128bits_withSeed +# undef XXH3_128bits_withSecret +# undef XXH3_128bits_reset +# undef XXH3_128bits_reset_withSeed +# undef XXH3_128bits_reset_withSecret +# undef XXH3_128bits_reset_withSecretandSeed +# undef XXH3_128bits_update +# undef XXH3_128bits_digest +# undef XXH128_isEqual +# undef XXH128_cmp +# undef XXH128_canonicalFromHash +# undef XXH128_hashFromCanonical + /* Finally, free the namespace itself */ +# undef XXH_NAMESPACE + + /* employ the namespace for XXH_INLINE_ALL */ +# define XXH_NAMESPACE XXH_INLINE_ + /* + * Some identifiers (enums, type names) are not symbols, + * but they must nonetheless be renamed to avoid redeclaration. + * Alternative solution: do not redeclare them. + * However, this requires some #ifdefs, and has a more dispersed impact. + * Meanwhile, renaming can be achieved in a single place. + */ +# define XXH_IPREF(Id) XXH_NAMESPACE ## Id +# define XXH_OK XXH_IPREF(XXH_OK) +# define XXH_ERROR XXH_IPREF(XXH_ERROR) +# define XXH_errorcode XXH_IPREF(XXH_errorcode) +# define XXH32_canonical_t XXH_IPREF(XXH32_canonical_t) +# define XXH64_canonical_t XXH_IPREF(XXH64_canonical_t) +# define XXH128_canonical_t XXH_IPREF(XXH128_canonical_t) +# define XXH32_state_s XXH_IPREF(XXH32_state_s) +# define XXH32_state_t XXH_IPREF(XXH32_state_t) +# define XXH64_state_s XXH_IPREF(XXH64_state_s) +# define XXH64_state_t XXH_IPREF(XXH64_state_t) +# define XXH3_state_s XXH_IPREF(XXH3_state_s) +# define XXH3_state_t XXH_IPREF(XXH3_state_t) +# define XXH128_hash_t XXH_IPREF(XXH128_hash_t) + /* Ensure the header is parsed again, even if it was previously included */ +# undef XXHASH_H_5627135585666179 +# undef XXHASH_H_STATIC_13879238742 +#endif /* XXH_INLINE_ALL || XXH_PRIVATE_API */ + +/* **************************************************************** + * Stable API + *****************************************************************/ +#ifndef XXHASH_H_5627135585666179 +#define XXHASH_H_5627135585666179 1 + +/*! @brief Marks a global symbol. */ +#if !defined(XXH_INLINE_ALL) && !defined(XXH_PRIVATE_API) +# if defined(_WIN32) && defined(_MSC_VER) && (defined(XXH_IMPORT) || defined(XXH_EXPORT)) +# ifdef XXH_EXPORT +# define XXH_PUBLIC_API __declspec(dllexport) +# elif XXH_IMPORT +# define XXH_PUBLIC_API __declspec(dllimport) +# endif +# else +# define XXH_PUBLIC_API /* do nothing */ +# endif +#endif + +#ifdef XXH_NAMESPACE +# define XXH_CAT(A,B) A##B +# define XXH_NAME2(A,B) XXH_CAT(A,B) +# define XXH_versionNumber XXH_NAME2(XXH_NAMESPACE, XXH_versionNumber) +/* XXH32 */ +# define XXH32 XXH_NAME2(XXH_NAMESPACE, XXH32) +# define XXH32_createState XXH_NAME2(XXH_NAMESPACE, XXH32_createState) +# define XXH32_freeState XXH_NAME2(XXH_NAMESPACE, XXH32_freeState) +# define XXH32_reset XXH_NAME2(XXH_NAMESPACE, XXH32_reset) +# define XXH32_update XXH_NAME2(XXH_NAMESPACE, XXH32_update) +# define XXH32_digest XXH_NAME2(XXH_NAMESPACE, XXH32_digest) +# define XXH32_copyState XXH_NAME2(XXH_NAMESPACE, XXH32_copyState) +# define XXH32_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH32_canonicalFromHash) +# define XXH32_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH32_hashFromCanonical) +/* XXH64 */ +# define XXH64 XXH_NAME2(XXH_NAMESPACE, XXH64) +# define XXH64_createState XXH_NAME2(XXH_NAMESPACE, XXH64_createState) +# define XXH64_freeState XXH_NAME2(XXH_NAMESPACE, XXH64_freeState) +# define XXH64_reset XXH_NAME2(XXH_NAMESPACE, XXH64_reset) +# define XXH64_update XXH_NAME2(XXH_NAMESPACE, XXH64_update) +# define XXH64_digest XXH_NAME2(XXH_NAMESPACE, XXH64_digest) +# define XXH64_copyState XXH_NAME2(XXH_NAMESPACE, XXH64_copyState) +# define XXH64_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH64_canonicalFromHash) +# define XXH64_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH64_hashFromCanonical) +/* XXH3_64bits */ +# define XXH3_64bits XXH_NAME2(XXH_NAMESPACE, XXH3_64bits) +# define XXH3_64bits_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSecret) +# define XXH3_64bits_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSeed) +# define XXH3_64bits_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSecretandSeed) +# define XXH3_createState XXH_NAME2(XXH_NAMESPACE, XXH3_createState) +# define XXH3_freeState XXH_NAME2(XXH_NAMESPACE, XXH3_freeState) +# define XXH3_copyState XXH_NAME2(XXH_NAMESPACE, XXH3_copyState) +# define XXH3_64bits_reset XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset) +# define XXH3_64bits_reset_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSeed) +# define XXH3_64bits_reset_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSecret) +# define XXH3_64bits_reset_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSecretandSeed) +# define XXH3_64bits_update XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_update) +# define XXH3_64bits_digest XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_digest) +# define XXH3_generateSecret XXH_NAME2(XXH_NAMESPACE, XXH3_generateSecret) +# define XXH3_generateSecret_fromSeed XXH_NAME2(XXH_NAMESPACE, XXH3_generateSecret_fromSeed) +/* XXH3_128bits */ +# define XXH128 XXH_NAME2(XXH_NAMESPACE, XXH128) +# define XXH3_128bits XXH_NAME2(XXH_NAMESPACE, XXH3_128bits) +# define XXH3_128bits_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSeed) +# define XXH3_128bits_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSecret) +# define XXH3_128bits_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSecretandSeed) +# define XXH3_128bits_reset XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset) +# define XXH3_128bits_reset_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSeed) +# define XXH3_128bits_reset_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSecret) +# define XXH3_128bits_reset_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSecretandSeed) +# define XXH3_128bits_update XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_update) +# define XXH3_128bits_digest XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_digest) +# define XXH128_isEqual XXH_NAME2(XXH_NAMESPACE, XXH128_isEqual) +# define XXH128_cmp XXH_NAME2(XXH_NAMESPACE, XXH128_cmp) +# define XXH128_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH128_canonicalFromHash) +# define XXH128_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH128_hashFromCanonical) +#endif + + +/* ************************************* +* Compiler specifics +***************************************/ + +/* specific declaration modes for Windows */ +#if !defined(XXH_INLINE_ALL) && !defined(XXH_PRIVATE_API) +# if defined(_WIN32) && defined(_MSC_VER) && (defined(XXH_IMPORT) || defined(XXH_EXPORT)) +# ifdef XXH_EXPORT +# define XXH_PUBLIC_API __declspec(dllexport) +# elif XXH_IMPORT +# define XXH_PUBLIC_API __declspec(dllimport) +# endif +# else +# define XXH_PUBLIC_API /* do nothing */ +# endif +#endif + +#if defined (__GNUC__) +# define XXH_CONSTF __attribute__((__const__)) +# define XXH_PUREF __attribute__((__pure__)) +# define XXH_MALLOCF __attribute__((__malloc__)) +#else +# define XXH_CONSTF /* disable */ +# define XXH_PUREF +# define XXH_MALLOCF +#endif + +/* ************************************* +* Version +***************************************/ +#define XXH_VERSION_MAJOR 0 +#define XXH_VERSION_MINOR 8 +#define XXH_VERSION_RELEASE 3 +/*! @brief Version number, encoded as two digits each */ +#define XXH_VERSION_NUMBER (XXH_VERSION_MAJOR *100*100 + XXH_VERSION_MINOR *100 + XXH_VERSION_RELEASE) + +/*! + * @brief Obtains the xxHash version. + * + * This is mostly useful when xxHash is compiled as a shared library, + * since the returned value comes from the library, as opposed to header file. + * + * @return @ref XXH_VERSION_NUMBER of the invoked library. + */ +XXH_PUBLIC_API XXH_CONSTF unsigned XXH_versionNumber (void); + + +/* **************************** +* Common basic types +******************************/ +#include /* size_t */ +/*! + * @brief Exit code for the streaming API. + */ +typedef enum { + XXH_OK = 0, /*!< OK */ + XXH_ERROR /*!< Error */ +} XXH_errorcode; + + +/*-********************************************************************** +* 32-bit hash +************************************************************************/ +#if defined(XXH_DOXYGEN) /* Don't show include */ +/*! + * @brief An unsigned 32-bit integer. + * + * Not necessarily defined to `uint32_t` but functionally equivalent. + */ +typedef uint32_t XXH32_hash_t; + +#elif !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) +# ifdef _AIX +# include +# else +# include +# endif + typedef uint32_t XXH32_hash_t; + +#else +# include +# if UINT_MAX == 0xFFFFFFFFUL + typedef unsigned int XXH32_hash_t; +# elif ULONG_MAX == 0xFFFFFFFFUL + typedef unsigned long XXH32_hash_t; +# else +# error "unsupported platform: need a 32-bit type" +# endif +#endif + +/*! + * @} + * + * @defgroup XXH32_family XXH32 family + * @ingroup public + * Contains functions used in the classic 32-bit xxHash algorithm. + * + * @note + * XXH32 is useful for older platforms, with no or poor 64-bit performance. + * Note that the @ref XXH3_family provides competitive speed for both 32-bit + * and 64-bit systems, and offers true 64/128 bit hash results. + * + * @see @ref XXH64_family, @ref XXH3_family : Other xxHash families + * @see @ref XXH32_impl for implementation details + * @{ + */ + +/*! + * @brief Calculates the 32-bit hash of @p input using xxHash32. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * @param seed The 32-bit seed to alter the hash's output predictably. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 32-bit xxHash32 value. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32 (const void* input, size_t length, XXH32_hash_t seed); + +#ifndef XXH_NO_STREAM +/*! + * @typedef struct XXH32_state_s XXH32_state_t + * @brief The opaque state struct for the XXH32 streaming API. + * + * @see XXH32_state_s for details. + * @see @ref streaming_example "Streaming Example" + */ +typedef struct XXH32_state_s XXH32_state_t; + +/*! + * @brief Allocates an @ref XXH32_state_t. + * + * @return An allocated pointer of @ref XXH32_state_t on success. + * @return `NULL` on failure. + * + * @note Must be freed with XXH32_freeState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_MALLOCF XXH32_state_t* XXH32_createState(void); +/*! + * @brief Frees an @ref XXH32_state_t. + * + * @param statePtr A pointer to an @ref XXH32_state_t allocated with @ref XXH32_createState(). + * + * @return @ref XXH_OK. + * + * @note @p statePtr must be allocated with XXH32_createState(). + * + * @see @ref streaming_example "Streaming Example" + * + */ +XXH_PUBLIC_API XXH_errorcode XXH32_freeState(XXH32_state_t* statePtr); +/*! + * @brief Copies one @ref XXH32_state_t to another. + * + * @param dst_state The state to copy to. + * @param src_state The state to copy from. + * @pre + * @p dst_state and @p src_state must not be `NULL` and must not overlap. + */ +XXH_PUBLIC_API void XXH32_copyState(XXH32_state_t* dst_state, const XXH32_state_t* src_state); + +/*! + * @brief Resets an @ref XXH32_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 32-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note This function resets and seeds a state. Call it before @ref XXH32_update(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH32_reset (XXH32_state_t* statePtr, XXH32_hash_t seed); + +/*! + * @brief Consumes a block of @p input to an @ref XXH32_state_t. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note Call this to incrementally consume blocks of data. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH32_update (XXH32_state_t* statePtr, const void* input, size_t length); + +/*! + * @brief Returns the calculated hash value from an @ref XXH32_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated 32-bit xxHash32 value from that state. + * + * @note + * Calling XXH32_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32_digest (const XXH32_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ + +/******* Canonical representation *******/ + +/*! + * @brief Canonical (big endian) representation of @ref XXH32_hash_t. + */ +typedef struct { + unsigned char digest[4]; /*!< Hash bytes, big endian */ +} XXH32_canonical_t; + +/*! + * @brief Converts an @ref XXH32_hash_t to a big endian @ref XXH32_canonical_t. + * + * @param dst The @ref XXH32_canonical_t pointer to be stored to. + * @param hash The @ref XXH32_hash_t to be converted. + * + * @pre + * @p dst must not be `NULL`. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API void XXH32_canonicalFromHash(XXH32_canonical_t* dst, XXH32_hash_t hash); + +/*! + * @brief Converts an @ref XXH32_canonical_t to a native @ref XXH32_hash_t. + * + * @param src The @ref XXH32_canonical_t to convert. + * + * @pre + * @p src must not be `NULL`. + * + * @return The converted hash. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32_hashFromCanonical(const XXH32_canonical_t* src); + + +/*! @cond Doxygen ignores this part */ +#ifdef __has_attribute +# define XXH_HAS_ATTRIBUTE(x) __has_attribute(x) +#else +# define XXH_HAS_ATTRIBUTE(x) 0 +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* + * C23 __STDC_VERSION__ number hasn't been specified yet. For now + * leave as `201711L` (C17 + 1). + * TODO: Update to correct value when its been specified. + */ +#define XXH_C23_VN 201711L +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* C-language Attributes are added in C23. */ +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= XXH_C23_VN) && defined(__has_c_attribute) +# define XXH_HAS_C_ATTRIBUTE(x) __has_c_attribute(x) +#else +# define XXH_HAS_C_ATTRIBUTE(x) 0 +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +#if defined(__cplusplus) && defined(__has_cpp_attribute) +# define XXH_HAS_CPP_ATTRIBUTE(x) __has_cpp_attribute(x) +#else +# define XXH_HAS_CPP_ATTRIBUTE(x) 0 +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* + * Define XXH_FALLTHROUGH macro for annotating switch case with the 'fallthrough' attribute + * introduced in CPP17 and C23. + * CPP17 : https://en.cppreference.com/w/cpp/language/attributes/fallthrough + * C23 : https://en.cppreference.com/w/c/language/attributes/fallthrough + */ +#if XXH_HAS_C_ATTRIBUTE(fallthrough) || XXH_HAS_CPP_ATTRIBUTE(fallthrough) +# define XXH_FALLTHROUGH [[fallthrough]] +#elif XXH_HAS_ATTRIBUTE(__fallthrough__) +# define XXH_FALLTHROUGH __attribute__ ((__fallthrough__)) +#else +# define XXH_FALLTHROUGH /* fallthrough */ +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* + * Define XXH_NOESCAPE for annotated pointers in public API. + * https://clang.llvm.org/docs/AttributeReference.html#noescape + * As of writing this, only supported by clang. + */ +#if XXH_HAS_ATTRIBUTE(noescape) +# define XXH_NOESCAPE __attribute__((__noescape__)) +#else +# define XXH_NOESCAPE +#endif +/*! @endcond */ + + +/*! + * @} + * @ingroup public + * @{ + */ + +#ifndef XXH_NO_LONG_LONG +/*-********************************************************************** +* 64-bit hash +************************************************************************/ +#if defined(XXH_DOXYGEN) /* don't include */ +/*! + * @brief An unsigned 64-bit integer. + * + * Not necessarily defined to `uint64_t` but functionally equivalent. + */ +typedef uint64_t XXH64_hash_t; +#elif !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) +# ifdef _AIX +# include +# else +# include +# endif + typedef uint64_t XXH64_hash_t; +#else +# include +# if defined(__LP64__) && ULONG_MAX == 0xFFFFFFFFFFFFFFFFULL + /* LP64 ABI says uint64_t is unsigned long */ + typedef unsigned long XXH64_hash_t; +# else + /* the following type must have a width of 64-bit */ + typedef unsigned long long XXH64_hash_t; +# endif +#endif + +/*! + * @} + * + * @defgroup XXH64_family XXH64 family + * @ingroup public + * @{ + * Contains functions used in the classic 64-bit xxHash algorithm. + * + * @note + * XXH3 provides competitive speed for both 32-bit and 64-bit systems, + * and offers true 64/128 bit hash results. + * It provides better speed for systems with vector processing capabilities. + */ + +/*! + * @brief Calculates the 64-bit hash of @p input using xxHash64. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * @param seed The 64-bit seed to alter the hash's output predictably. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 64-bit xxHash64 value. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed); + +/******* Streaming *******/ +#ifndef XXH_NO_STREAM +/*! + * @brief The opaque state struct for the XXH64 streaming API. + * + * @see XXH64_state_s for details. + * @see @ref streaming_example "Streaming Example" + */ +typedef struct XXH64_state_s XXH64_state_t; /* incomplete type */ + +/*! + * @brief Allocates an @ref XXH64_state_t. + * + * @return An allocated pointer of @ref XXH64_state_t on success. + * @return `NULL` on failure. + * + * @note Must be freed with XXH64_freeState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_MALLOCF XXH64_state_t* XXH64_createState(void); + +/*! + * @brief Frees an @ref XXH64_state_t. + * + * @param statePtr A pointer to an @ref XXH64_state_t allocated with @ref XXH64_createState(). + * + * @return @ref XXH_OK. + * + * @note @p statePtr must be allocated with XXH64_createState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH64_freeState(XXH64_state_t* statePtr); + +/*! + * @brief Copies one @ref XXH64_state_t to another. + * + * @param dst_state The state to copy to. + * @param src_state The state to copy from. + * @pre + * @p dst_state and @p src_state must not be `NULL` and must not overlap. + */ +XXH_PUBLIC_API void XXH64_copyState(XXH_NOESCAPE XXH64_state_t* dst_state, const XXH64_state_t* src_state); + +/*! + * @brief Resets an @ref XXH64_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note This function resets and seeds a state. Call it before @ref XXH64_update(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH64_reset (XXH_NOESCAPE XXH64_state_t* statePtr, XXH64_hash_t seed); + +/*! + * @brief Consumes a block of @p input to an @ref XXH64_state_t. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note Call this to incrementally consume blocks of data. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH64_update (XXH_NOESCAPE XXH64_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Returns the calculated hash value from an @ref XXH64_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated 64-bit xxHash64 value from that state. + * + * @note + * Calling XXH64_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64_digest (XXH_NOESCAPE const XXH64_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ +/******* Canonical representation *******/ + +/*! + * @brief Canonical (big endian) representation of @ref XXH64_hash_t. + */ +typedef struct { unsigned char digest[sizeof(XXH64_hash_t)]; } XXH64_canonical_t; + +/*! + * @brief Converts an @ref XXH64_hash_t to a big endian @ref XXH64_canonical_t. + * + * @param dst The @ref XXH64_canonical_t pointer to be stored to. + * @param hash The @ref XXH64_hash_t to be converted. + * + * @pre + * @p dst must not be `NULL`. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API void XXH64_canonicalFromHash(XXH_NOESCAPE XXH64_canonical_t* dst, XXH64_hash_t hash); + +/*! + * @brief Converts an @ref XXH64_canonical_t to a native @ref XXH64_hash_t. + * + * @param src The @ref XXH64_canonical_t to convert. + * + * @pre + * @p src must not be `NULL`. + * + * @return The converted hash. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64_hashFromCanonical(XXH_NOESCAPE const XXH64_canonical_t* src); + +#ifndef XXH_NO_XXH3 + +/*! + * @} + * ************************************************************************ + * @defgroup XXH3_family XXH3 family + * @ingroup public + * @{ + * + * XXH3 is a more recent hash algorithm featuring: + * - Improved speed for both small and large inputs + * - True 64-bit and 128-bit outputs + * - SIMD acceleration + * - Improved 32-bit viability + * + * Speed analysis methodology is explained here: + * + * https://fastcompression.blogspot.com/2019/03/presenting-xxh3.html + * + * Compared to XXH64, expect XXH3 to run approximately + * ~2x faster on large inputs and >3x faster on small ones, + * exact differences vary depending on platform. + * + * XXH3's speed benefits greatly from SIMD and 64-bit arithmetic, + * but does not require it. + * Most 32-bit and 64-bit targets that can run XXH32 smoothly can run XXH3 + * at competitive speeds, even without vector support. Further details are + * explained in the implementation. + * + * XXH3 has a fast scalar implementation, but it also includes accelerated SIMD + * implementations for many common platforms: + * - AVX512 + * - AVX2 + * - SSE2 + * - ARM NEON + * - WebAssembly SIMD128 + * - POWER8 VSX + * - s390x ZVector + * This can be controlled via the @ref XXH_VECTOR macro, but it automatically + * selects the best version according to predefined macros. For the x86 family, an + * automatic runtime dispatcher is included separately in @ref xxh_x86dispatch.c. + * + * XXH3 implementation is portable: + * it has a generic C90 formulation that can be compiled on any platform, + * all implementations generate exactly the same hash value on all platforms. + * Starting from v0.8.0, it's also labelled "stable", meaning that + * any future version will also generate the same hash value. + * + * XXH3 offers 2 variants, _64bits and _128bits. + * + * When only 64 bits are needed, prefer invoking the _64bits variant, as it + * reduces the amount of mixing, resulting in faster speed on small inputs. + * It's also generally simpler to manipulate a scalar return type than a struct. + * + * The API supports one-shot hashing, streaming mode, and custom secrets. + */ +/*-********************************************************************** +* XXH3 64-bit variant +************************************************************************/ + +/*! + * @brief Calculates 64-bit unseeded variant of XXH3 hash of @p input. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 64-bit XXH3 hash value. + * + * @note + * This is equivalent to @ref XXH3_64bits_withSeed() with a seed of `0`, however + * it may have slightly better performance due to constant propagation of the + * defaults. + * + * @see + * XXH3_64bits_withSeed(), XXH3_64bits_withSecret(): other seeding variants + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits(XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Calculates 64-bit seeded variant of XXH3 hash of @p input. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 64-bit XXH3 hash value. + * + * @note + * seed == 0 produces the same results as @ref XXH3_64bits(). + * + * This variant generates a custom secret on the fly based on default secret + * altered using the @p seed value. + * + * While this operation is decently fast, note that it's not completely free. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_withSeed(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed); + +/*! + * The bare minimum size for a custom secret. + * + * @see + * XXH3_64bits_withSecret(), XXH3_64bits_reset_withSecret(), + * XXH3_128bits_withSecret(), XXH3_128bits_reset_withSecret(). + */ +#define XXH3_SECRET_SIZE_MIN 136 + +/*! + * @brief Calculates 64-bit variant of XXH3 with a custom "secret". + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @return The calculated 64-bit XXH3 hash value. + * + * @pre + * The memory between @p data and @p data + @p len must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p data may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * It's possible to provide any blob of bytes as a "secret" to generate the hash. + * This makes it more difficult for an external actor to prepare an intentional collision. + * The main condition is that @p secretSize *must* be large enough (>= @ref XXH3_SECRET_SIZE_MIN). + * However, the quality of the secret impacts the dispersion of the hash algorithm. + * Therefore, the secret _must_ look like a bunch of random bytes. + * Avoid "trivial" or structured data such as repeated sequences or a text document. + * Whenever in doubt about the "randomness" of the blob of bytes, + * consider employing @ref XXH3_generateSecret() instead (see below). + * It will generate a proper high entropy secret derived from the blob of bytes. + * Another advantage of using XXH3_generateSecret() is that + * it guarantees that all bits within the initial blob of bytes + * will impact every bit of the output. + * This is not necessarily the case when using the blob of bytes directly + * because, when hashing _small_ inputs, only a portion of the secret is employed. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_withSecret(XXH_NOESCAPE const void* data, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize); + + +/******* Streaming *******/ +#ifndef XXH_NO_STREAM +/* + * Streaming requires state maintenance. + * This operation costs memory and CPU. + * As a consequence, streaming is slower than one-shot hashing. + * For better performance, prefer one-shot functions whenever applicable. + */ + +/*! + * @brief The opaque state struct for the XXH3 streaming API. + * + * @see XXH3_state_s for details. + * @see @ref streaming_example "Streaming Example" + */ +typedef struct XXH3_state_s XXH3_state_t; +XXH_PUBLIC_API XXH_MALLOCF XXH3_state_t* XXH3_createState(void); +XXH_PUBLIC_API XXH_errorcode XXH3_freeState(XXH3_state_t* statePtr); + +/*! + * @brief Copies one @ref XXH3_state_t to another. + * + * @param dst_state The state to copy to. + * @param src_state The state to copy from. + * @pre + * @p dst_state and @p src_state must not be `NULL` and must not overlap. + */ +XXH_PUBLIC_API void XXH3_copyState(XXH_NOESCAPE XXH3_state_t* dst_state, XXH_NOESCAPE const XXH3_state_t* src_state); + +/*! + * @brief Resets an @ref XXH3_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret with default parameters. + * - Call this function before @ref XXH3_64bits_update(). + * - Digest will be equivalent to `XXH3_64bits()`. + * + * @see @ref streaming_example "Streaming Example" + * + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr); + +/*! + * @brief Resets an @ref XXH3_state_t with 64-bit seed to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret from `seed`. + * - Call this function before @ref XXH3_64bits_update(). + * - Digest will be equivalent to `XXH3_64bits_withSeed()`. + * + * @see @ref streaming_example "Streaming Example" + * + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed); + +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * `secret` is referenced, it _must outlive_ the hash streaming session. + * + * Similar to one-shot API, `secretSize` must be >= @ref XXH3_SECRET_SIZE_MIN, + * and the quality of produced hash values depends on secret's entropy + * (secret's content should look like a bunch of random bytes). + * When in doubt about the randomness of a candidate `secret`, + * consider employing `XXH3_generateSecret()` instead (see below). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize); + +/*! + * @brief Consumes a block of @p input to an @ref XXH3_state_t. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note Call this to incrementally consume blocks of data. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_update (XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Returns the calculated XXH3 64-bit hash value from an @ref XXH3_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated XXH3 64-bit hash value from that state. + * + * @note + * Calling XXH3_64bits_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_digest (XXH_NOESCAPE const XXH3_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ + +/* note : canonical representation of XXH3 is the same as XXH64 + * since they both produce XXH64_hash_t values */ + + +/*-********************************************************************** +* XXH3 128-bit variant +************************************************************************/ + +/*! + * @brief The return value from 128-bit hashes. + * + * Stored in little endian order, although the fields themselves are in native + * endianness. + */ +typedef struct { + XXH64_hash_t low64; /*!< `value & 0xFFFFFFFFFFFFFFFF` */ + XXH64_hash_t high64; /*!< `value >> 64` */ +} XXH128_hash_t; + +/*! + * @brief Calculates 128-bit unseeded variant of XXH3 of @p data. + * + * @param data The block of data to be hashed, at least @p length bytes in size. + * @param len The length of @p data, in bytes. + * + * @return The calculated 128-bit variant of XXH3 value. + * + * The 128-bit variant of XXH3 has more strength, but it has a bit of overhead + * for shorter inputs. + * + * This is equivalent to @ref XXH3_128bits_withSeed() with a seed of `0`, however + * it may have slightly better performance due to constant propagation of the + * defaults. + * + * @see XXH3_128bits_withSeed(), XXH3_128bits_withSecret(): other seeding variants + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits(XXH_NOESCAPE const void* data, size_t len); +/*! @brief Calculates 128-bit seeded variant of XXH3 hash of @p data. + * + * @param data The block of data to be hashed, at least @p length bytes in size. + * @param len The length of @p data, in bytes. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @return The calculated 128-bit variant of XXH3 value. + * + * @note + * seed == 0 produces the same results as @ref XXH3_64bits(). + * + * This variant generates a custom secret on the fly based on default secret + * altered using the @p seed value. + * + * While this operation is decently fast, note that it's not completely free. + * + * @see XXH3_128bits(), XXH3_128bits_withSecret(): other seeding variants + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_withSeed(XXH_NOESCAPE const void* data, size_t len, XXH64_hash_t seed); +/*! + * @brief Calculates 128-bit variant of XXH3 with a custom "secret". + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @return The calculated 128-bit variant of XXH3 value. + * + * It's possible to provide any blob of bytes as a "secret" to generate the hash. + * This makes it more difficult for an external actor to prepare an intentional collision. + * The main condition is that @p secretSize *must* be large enough (>= @ref XXH3_SECRET_SIZE_MIN). + * However, the quality of the secret impacts the dispersion of the hash algorithm. + * Therefore, the secret _must_ look like a bunch of random bytes. + * Avoid "trivial" or structured data such as repeated sequences or a text document. + * Whenever in doubt about the "randomness" of the blob of bytes, + * consider employing @ref XXH3_generateSecret() instead (see below). + * It will generate a proper high entropy secret derived from the blob of bytes. + * Another advantage of using XXH3_generateSecret() is that + * it guarantees that all bits within the initial blob of bytes + * will impact every bit of the output. + * This is not necessarily the case when using the blob of bytes directly + * because, when hashing _small_ inputs, only a portion of the secret is employed. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_withSecret(XXH_NOESCAPE const void* data, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize); + +/******* Streaming *******/ +#ifndef XXH_NO_STREAM +/* + * Streaming requires state maintenance. + * This operation costs memory and CPU. + * As a consequence, streaming is slower than one-shot hashing. + * For better performance, prefer one-shot functions whenever applicable. + * + * XXH3_128bits uses the same XXH3_state_t as XXH3_64bits(). + * Use already declared XXH3_createState() and XXH3_freeState(). + * + * All reset and streaming functions have same meaning as their 64-bit counterpart. + */ + +/*! + * @brief Resets an @ref XXH3_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret with default parameters. + * - Call it before @ref XXH3_128bits_update(). + * - Digest will be equivalent to `XXH3_128bits()`. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr); + +/*! + * @brief Resets an @ref XXH3_state_t with 64-bit seed to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret from `seed`. + * - Call it before @ref XXH3_128bits_update(). + * - Digest will be equivalent to `XXH3_128bits_withSeed()`. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed); +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * `secret` is referenced, it _must outlive_ the hash streaming session. + * Similar to one-shot API, `secretSize` must be >= @ref XXH3_SECRET_SIZE_MIN, + * and the quality of produced hash values depends on secret's entropy + * (secret's content should look like a bunch of random bytes). + * When in doubt about the randomness of a candidate `secret`, + * consider employing `XXH3_generateSecret()` instead (see below). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize); + +/*! + * @brief Consumes a block of @p input to an @ref XXH3_state_t. + * + * Call this to incrementally consume blocks of data. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_update (XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Returns the calculated XXH3 128-bit hash value from an @ref XXH3_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated XXH3 128-bit hash value from that state. + * + * @note + * Calling XXH3_128bits_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_digest (XXH_NOESCAPE const XXH3_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ + +/* Following helper functions make it possible to compare XXH128_hast_t values. + * Since XXH128_hash_t is a structure, this capability is not offered by the language. + * Note: For better performance, these functions can be inlined using XXH_INLINE_ALL */ + +/*! + * @brief Check equality of two XXH128_hash_t values + * + * @param h1 The 128-bit hash value. + * @param h2 Another 128-bit hash value. + * + * @return `1` if `h1` and `h2` are equal. + * @return `0` if they are not. + */ +XXH_PUBLIC_API XXH_PUREF int XXH128_isEqual(XXH128_hash_t h1, XXH128_hash_t h2); + +/*! + * @brief Compares two @ref XXH128_hash_t + * + * This comparator is compatible with stdlib's `qsort()`/`bsearch()`. + * + * @param h128_1 Left-hand side value + * @param h128_2 Right-hand side value + * + * @return >0 if @p h128_1 > @p h128_2 + * @return =0 if @p h128_1 == @p h128_2 + * @return <0 if @p h128_1 < @p h128_2 + */ +XXH_PUBLIC_API XXH_PUREF int XXH128_cmp(XXH_NOESCAPE const void* h128_1, XXH_NOESCAPE const void* h128_2); + + +/******* Canonical representation *******/ +typedef struct { unsigned char digest[sizeof(XXH128_hash_t)]; } XXH128_canonical_t; + + +/*! + * @brief Converts an @ref XXH128_hash_t to a big endian @ref XXH128_canonical_t. + * + * @param dst The @ref XXH128_canonical_t pointer to be stored to. + * @param hash The @ref XXH128_hash_t to be converted. + * + * @pre + * @p dst must not be `NULL`. + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API void XXH128_canonicalFromHash(XXH_NOESCAPE XXH128_canonical_t* dst, XXH128_hash_t hash); + +/*! + * @brief Converts an @ref XXH128_canonical_t to a native @ref XXH128_hash_t. + * + * @param src The @ref XXH128_canonical_t to convert. + * + * @pre + * @p src must not be `NULL`. + * + * @return The converted hash. + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH128_hashFromCanonical(XXH_NOESCAPE const XXH128_canonical_t* src); + + +#endif /* !XXH_NO_XXH3 */ +#endif /* XXH_NO_LONG_LONG */ + +/*! + * @} + */ +#endif /* XXHASH_H_5627135585666179 */ + + + +#if defined(XXH_STATIC_LINKING_ONLY) && !defined(XXHASH_H_STATIC_13879238742) +#define XXHASH_H_STATIC_13879238742 +/* **************************************************************************** + * This section contains declarations which are not guaranteed to remain stable. + * They may change in future versions, becoming incompatible with a different + * version of the library. + * These declarations should only be used with static linking. + * Never use them in association with dynamic linking! + ***************************************************************************** */ + +/* + * These definitions are only present to allow static allocation + * of XXH states, on stack or in a struct, for example. + * Never **ever** access their members directly. + */ + +/*! + * @internal + * @brief Structure for XXH32 streaming API. + * + * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY, + * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. Otherwise it is + * an opaque type. This allows fields to safely be changed. + * + * Typedef'd to @ref XXH32_state_t. + * Do not access the members of this struct directly. + * @see XXH64_state_s, XXH3_state_s + */ +struct XXH32_state_s { + XXH32_hash_t total_len_32; /*!< Total length hashed, modulo 2^32 */ + XXH32_hash_t large_len; /*!< Whether the hash is >= 16 (handles @ref total_len_32 overflow) */ + XXH32_hash_t v[4]; /*!< Accumulator lanes */ + XXH32_hash_t mem32[4]; /*!< Internal buffer for partial reads. Treated as unsigned char[16]. */ + XXH32_hash_t memsize; /*!< Amount of data in @ref mem32 */ + XXH32_hash_t reserved; /*!< Reserved field. Do not read nor write to it. */ +}; /* typedef'd to XXH32_state_t */ + + +#ifndef XXH_NO_LONG_LONG /* defined when there is no 64-bit support */ + +/*! + * @internal + * @brief Structure for XXH64 streaming API. + * + * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY, + * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. Otherwise it is + * an opaque type. This allows fields to safely be changed. + * + * Typedef'd to @ref XXH64_state_t. + * Do not access the members of this struct directly. + * @see XXH32_state_s, XXH3_state_s + */ +struct XXH64_state_s { + XXH64_hash_t total_len; /*!< Total length hashed. This is always 64-bit. */ + XXH64_hash_t v[4]; /*!< Accumulator lanes */ + XXH64_hash_t mem64[4]; /*!< Internal buffer for partial reads. Treated as unsigned char[32]. */ + XXH32_hash_t memsize; /*!< Amount of data in @ref mem64 */ + XXH32_hash_t reserved32; /*!< Reserved field, needed for padding anyways*/ + XXH64_hash_t reserved64; /*!< Reserved field. Do not read or write to it. */ +}; /* typedef'd to XXH64_state_t */ + +#ifndef XXH_NO_XXH3 + +/* Windows SDK under 10.0.22000 is missing stdalign.h so we add a check + before allowing the windows compiler to use the C11 form. + Reference: https://github.com/Cyan4973/xxHash/issues/955 */ +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) \ + && (defined(_MSC_VER) && (_MSC_VER >= 1000) || !defined(_MSC_VER)) /* >= C11 */ +# include +# define XXH_ALIGN(n) alignas(n) +#elif defined(__cplusplus) && (__cplusplus >= 201103L) /* >= C++11 */ +/* In C++ alignas() is a keyword */ +# define XXH_ALIGN(n) alignas(n) +#elif defined(__GNUC__) +# define XXH_ALIGN(n) __attribute__ ((aligned(n))) +#elif defined(_MSC_VER) +# define XXH_ALIGN(n) __declspec(align(n)) +#else +# define XXH_ALIGN(n) /* disabled */ +#endif + +/* Old GCC versions only accept the attribute after the type in structures. */ +#if !(defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L)) /* C11+ */ \ + && ! (defined(__cplusplus) && (__cplusplus >= 201103L)) /* >= C++11 */ \ + && defined(__GNUC__) +# define XXH_ALIGN_MEMBER(align, type) type XXH_ALIGN(align) +#else +# define XXH_ALIGN_MEMBER(align, type) XXH_ALIGN(align) type +#endif + +/*! + * @brief The size of the internal XXH3 buffer. + * + * This is the optimal update size for incremental hashing. + * + * @see XXH3_64b_update(), XXH3_128b_update(). + */ +#define XXH3_INTERNALBUFFER_SIZE 256 + +/*! + * @internal + * @brief Default size of the secret buffer (and @ref XXH3_kSecret). + * + * This is the size used in @ref XXH3_kSecret and the seeded functions. + * + * Not to be confused with @ref XXH3_SECRET_SIZE_MIN. + */ +#define XXH3_SECRET_DEFAULT_SIZE 192 + +/*! + * @internal + * @brief Structure for XXH3 streaming API. + * + * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY, + * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. + * Otherwise it is an opaque type. + * Never use this definition in combination with dynamic library. + * This allows fields to safely be changed in the future. + * + * @note ** This structure has a strict alignment requirement of 64 bytes!! ** + * Do not allocate this with `malloc()` or `new`, + * it will not be sufficiently aligned. + * Use @ref XXH3_createState() and @ref XXH3_freeState(), or stack allocation. + * + * Typedef'd to @ref XXH3_state_t. + * Do never access the members of this struct directly. + * + * @see XXH3_INITSTATE() for stack initialization. + * @see XXH3_createState(), XXH3_freeState(). + * @see XXH32_state_s, XXH64_state_s + */ +struct XXH3_state_s { + XXH_ALIGN_MEMBER(64, XXH64_hash_t acc[8]); + /*!< The 8 accumulators. See @ref XXH32_state_s::v and @ref XXH64_state_s::v */ + XXH_ALIGN_MEMBER(64, unsigned char customSecret[XXH3_SECRET_DEFAULT_SIZE]); + /*!< Used to store a custom secret generated from a seed. */ + XXH_ALIGN_MEMBER(64, unsigned char buffer[XXH3_INTERNALBUFFER_SIZE]); + /*!< The internal buffer. @see XXH32_state_s::mem32 */ + XXH32_hash_t bufferedSize; + /*!< The amount of memory in @ref buffer, @see XXH32_state_s::memsize */ + XXH32_hash_t useSeed; + /*!< Reserved field. Needed for padding on 64-bit. */ + size_t nbStripesSoFar; + /*!< Number or stripes processed. */ + XXH64_hash_t totalLen; + /*!< Total length hashed. 64-bit even on 32-bit targets. */ + size_t nbStripesPerBlock; + /*!< Number of stripes per block. */ + size_t secretLimit; + /*!< Size of @ref customSecret or @ref extSecret */ + XXH64_hash_t seed; + /*!< Seed for _withSeed variants. Must be zero otherwise, @see XXH3_INITSTATE() */ + XXH64_hash_t reserved64; + /*!< Reserved field. */ + const unsigned char* extSecret; + /*!< Reference to an external secret for the _withSecret variants, NULL + * for other variants. */ + /* note: there may be some padding at the end due to alignment on 64 bytes */ +}; /* typedef'd to XXH3_state_t */ + +#undef XXH_ALIGN_MEMBER + +/*! + * @brief Initializes a stack-allocated `XXH3_state_s`. + * + * When the @ref XXH3_state_t structure is merely emplaced on stack, + * it should be initialized with XXH3_INITSTATE() or a memset() + * in case its first reset uses XXH3_NNbits_reset_withSeed(). + * This init can be omitted if the first reset uses default or _withSecret mode. + * This operation isn't necessary when the state is created with XXH3_createState(). + * Note that this doesn't prepare the state for a streaming operation, + * it's still necessary to use XXH3_NNbits_reset*() afterwards. + */ +#define XXH3_INITSTATE(XXH3_state_ptr) \ + do { \ + XXH3_state_t* tmp_xxh3_state_ptr = (XXH3_state_ptr); \ + tmp_xxh3_state_ptr->seed = 0; \ + tmp_xxh3_state_ptr->extSecret = NULL; \ + } while(0) + + +/*! + * @brief Calculates the 128-bit hash of @p data using XXH3. + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param seed The 64-bit seed to alter the hash's output predictably. + * + * @pre + * The memory between @p data and @p data + @p len must be valid, + * readable, contiguous memory. However, if @p len is `0`, @p data may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 128-bit XXH3 value. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH128(XXH_NOESCAPE const void* data, size_t len, XXH64_hash_t seed); + + +/* === Experimental API === */ +/* Symbols defined below must be considered tied to a specific library version. */ + +/*! + * @brief Derive a high-entropy secret from any user-defined content, named customSeed. + * + * @param secretBuffer A writable buffer for derived high-entropy secret data. + * @param secretSize Size of secretBuffer, in bytes. Must be >= XXH3_SECRET_SIZE_MIN. + * @param customSeed A user-defined content. + * @param customSeedSize Size of customSeed, in bytes. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * The generated secret can be used in combination with `*_withSecret()` functions. + * The `_withSecret()` variants are useful to provide a higher level of protection + * than 64-bit seed, as it becomes much more difficult for an external actor to + * guess how to impact the calculation logic. + * + * The function accepts as input a custom seed of any length and any content, + * and derives from it a high-entropy secret of length @p secretSize into an + * already allocated buffer @p secretBuffer. + * + * The generated secret can then be used with any `*_withSecret()` variant. + * The functions @ref XXH3_128bits_withSecret(), @ref XXH3_64bits_withSecret(), + * @ref XXH3_128bits_reset_withSecret() and @ref XXH3_64bits_reset_withSecret() + * are part of this list. They all accept a `secret` parameter + * which must be large enough for implementation reasons (>= @ref XXH3_SECRET_SIZE_MIN) + * _and_ feature very high entropy (consist of random-looking bytes). + * These conditions can be a high bar to meet, so @ref XXH3_generateSecret() can + * be employed to ensure proper quality. + * + * @p customSeed can be anything. It can have any size, even small ones, + * and its content can be anything, even "poor entropy" sources such as a bunch + * of zeroes. The resulting `secret` will nonetheless provide all required qualities. + * + * @pre + * - @p secretSize must be >= @ref XXH3_SECRET_SIZE_MIN + * - When @p customSeedSize > 0, supplying NULL as customSeed is undefined behavior. + * + * Example code: + * @code{.c} + * #include + * #include + * #include + * #define XXH_STATIC_LINKING_ONLY // expose unstable API + * #include "xxhash.h" + * // Hashes argv[2] using the entropy from argv[1]. + * int main(int argc, char* argv[]) + * { + * char secret[XXH3_SECRET_SIZE_MIN]; + * if (argv != 3) { return 1; } + * XXH3_generateSecret(secret, sizeof(secret), argv[1], strlen(argv[1])); + * XXH64_hash_t h = XXH3_64bits_withSecret( + * argv[2], strlen(argv[2]), + * secret, sizeof(secret) + * ); + * printf("%016llx\n", (unsigned long long) h); + * } + * @endcode + */ +XXH_PUBLIC_API XXH_errorcode XXH3_generateSecret(XXH_NOESCAPE void* secretBuffer, size_t secretSize, XXH_NOESCAPE const void* customSeed, size_t customSeedSize); + +/*! + * @brief Generate the same secret as the _withSeed() variants. + * + * @param secretBuffer A writable buffer of @ref XXH3_SECRET_DEFAULT_SIZE bytes + * @param seed The 64-bit seed to alter the hash result predictably. + * + * The generated secret can be used in combination with + *`*_withSecret()` and `_withSecretandSeed()` variants. + * + * Example C++ `std::string` hash class: + * @code{.cpp} + * #include + * #define XXH_STATIC_LINKING_ONLY // expose unstable API + * #include "xxhash.h" + * // Slow, seeds each time + * class HashSlow { + * XXH64_hash_t seed; + * public: + * HashSlow(XXH64_hash_t s) : seed{s} {} + * size_t operator()(const std::string& x) const { + * return size_t{XXH3_64bits_withSeed(x.c_str(), x.length(), seed)}; + * } + * }; + * // Fast, caches the seeded secret for future uses. + * class HashFast { + * unsigned char secret[XXH3_SECRET_DEFAULT_SIZE]; + * public: + * HashFast(XXH64_hash_t s) { + * XXH3_generateSecret_fromSeed(secret, seed); + * } + * size_t operator()(const std::string& x) const { + * return size_t{ + * XXH3_64bits_withSecret(x.c_str(), x.length(), secret, sizeof(secret)) + * }; + * } + * }; + * @endcode + */ +XXH_PUBLIC_API void XXH3_generateSecret_fromSeed(XXH_NOESCAPE void* secretBuffer, XXH64_hash_t seed); + +/*! + * @brief Maximum size of "short" key in bytes. + */ +#define XXH3_MIDSIZE_MAX 240 + +/*! + * @brief Calculates 64/128-bit seeded variant of XXH3 hash of @p data. + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * These variants generate hash values using either: + * - @p seed for "short" keys (< @ref XXH3_MIDSIZE_MAX = 240 bytes) + * - @p secret for "large" keys (>= @ref XXH3_MIDSIZE_MAX). + * + * This generally benefits speed, compared to `_withSeed()` or `_withSecret()`. + * `_withSeed()` has to generate the secret on the fly for "large" keys. + * It's fast, but can be perceptible for "not so large" keys (< 1 KB). + * `_withSecret()` has to generate the masks on the fly for "small" keys, + * which requires more instructions than _withSeed() variants. + * Therefore, _withSecretandSeed variant combines the best of both worlds. + * + * When @p secret has been generated by XXH3_generateSecret_fromSeed(), + * this variant produces *exactly* the same results as `_withSeed()` variant, + * hence offering only a pure speed benefit on "large" input, + * by skipping the need to regenerate the secret for every large input. + * + * Another usage scenario is to hash the secret to a 64-bit hash value, + * for example with XXH3_64bits(), which then becomes the seed, + * and then employ both the seed and the secret in _withSecretandSeed(). + * On top of speed, an added benefit is that each bit in the secret + * has a 50% chance to swap each bit in the output, via its impact to the seed. + * + * This is not guaranteed when using the secret directly in "small data" scenarios, + * because only portions of the secret are employed for small data. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t +XXH3_64bits_withSecretandSeed(XXH_NOESCAPE const void* data, size_t len, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed); + +/*! + * @brief Calculates 128-bit seeded variant of XXH3 hash of @p data. + * + * @param data The memory segment to be hashed, at least @p len bytes in size. + * @param length The length of @p data, in bytes. + * @param secret The secret used to alter hash result predictably. + * @param secretSize The length of @p secret, in bytes (must be >= XXH3_SECRET_SIZE_MIN) + * @param seed64 The 64-bit seed to alter the hash result predictably. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @see XXH3_64bits_withSecretandSeed(): contract is the same. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t +XXH3_128bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t length, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed64); + +#ifndef XXH_NO_STREAM +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState(). + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * @param seed64 The 64-bit seed to alter the hash result predictably. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @see XXH3_64bits_withSecretandSeed(). Contract is identical. + */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed64); + +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState(). + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * @param seed64 The 64-bit seed to alter the hash result predictably. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @see XXH3_64bits_withSecretandSeed(). Contract is identical. + * + * Note: there was a bug in an earlier version of this function (<= v0.8.2) + * that would make it generate an incorrect hash value + * when @p seed == 0 and @p length < XXH3_MIDSIZE_MAX + * and @p secret is different from XXH3_generateSecret_fromSeed(). + * As stated in the contract, the correct hash result must be + * the same as XXH3_128bits_withSeed() when @p length <= XXH3_MIDSIZE_MAX. + * Results generated by this older version are wrong, hence not comparable. + */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed64); + +#endif /* !XXH_NO_STREAM */ + +#endif /* !XXH_NO_XXH3 */ +#endif /* XXH_NO_LONG_LONG */ +#if defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API) +# define XXH_IMPLEMENTATION +#endif + +#endif /* defined(XXH_STATIC_LINKING_ONLY) && !defined(XXHASH_H_STATIC_13879238742) */ + + +/* ======================================================================== */ +/* ======================================================================== */ +/* ======================================================================== */ + + +/*-********************************************************************** + * xxHash implementation + *-********************************************************************** + * xxHash's implementation used to be hosted inside xxhash.c. + * + * However, inlining requires implementation to be visible to the compiler, + * hence be included alongside the header. + * Previously, implementation was hosted inside xxhash.c, + * which was then #included when inlining was activated. + * This construction created issues with a few build and install systems, + * as it required xxhash.c to be stored in /include directory. + * + * xxHash implementation is now directly integrated within xxhash.h. + * As a consequence, xxhash.c is no longer needed in /include. + * + * xxhash.c is still available and is still useful. + * In a "normal" setup, when xxhash is not inlined, + * xxhash.h only exposes the prototypes and public symbols, + * while xxhash.c can be built into an object file xxhash.o + * which can then be linked into the final binary. + ************************************************************************/ + +#if ( defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API) \ + || defined(XXH_IMPLEMENTATION) ) && !defined(XXH_IMPLEM_13a8737387) +# define XXH_IMPLEM_13a8737387 + +/* ************************************* +* Tuning parameters +***************************************/ + +/*! + * @defgroup tuning Tuning parameters + * @{ + * + * Various macros to control xxHash's behavior. + */ +#ifdef XXH_DOXYGEN +/*! + * @brief Define this to disable 64-bit code. + * + * Useful if only using the @ref XXH32_family and you have a strict C90 compiler. + */ +# define XXH_NO_LONG_LONG +# undef XXH_NO_LONG_LONG /* don't actually */ +/*! + * @brief Controls how unaligned memory is accessed. + * + * By default, access to unaligned memory is controlled by `memcpy()`, which is + * safe and portable. + * + * Unfortunately, on some target/compiler combinations, the generated assembly + * is sub-optimal. + * + * The below switch allow selection of a different access method + * in the search for improved performance. + * + * @par Possible options: + * + * - `XXH_FORCE_MEMORY_ACCESS=0` (default): `memcpy` + * @par + * Use `memcpy()`. Safe and portable. Note that most modern compilers will + * eliminate the function call and treat it as an unaligned access. + * + * - `XXH_FORCE_MEMORY_ACCESS=1`: `__attribute__((aligned(1)))` + * @par + * Depends on compiler extensions and is therefore not portable. + * This method is safe _if_ your compiler supports it, + * and *generally* as fast or faster than `memcpy`. + * + * - `XXH_FORCE_MEMORY_ACCESS=2`: Direct cast + * @par + * Casts directly and dereferences. This method doesn't depend on the + * compiler, but it violates the C standard as it directly dereferences an + * unaligned pointer. It can generate buggy code on targets which do not + * support unaligned memory accesses, but in some circumstances, it's the + * only known way to get the most performance. + * + * - `XXH_FORCE_MEMORY_ACCESS=3`: Byteshift + * @par + * Also portable. This can generate the best code on old compilers which don't + * inline small `memcpy()` calls, and it might also be faster on big-endian + * systems which lack a native byteswap instruction. However, some compilers + * will emit literal byteshifts even if the target supports unaligned access. + * + * + * @warning + * Methods 1 and 2 rely on implementation-defined behavior. Use these with + * care, as what works on one compiler/platform/optimization level may cause + * another to read garbage data or even crash. + * + * See https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html for details. + * + * Prefer these methods in priority order (0 > 3 > 1 > 2) + */ +# define XXH_FORCE_MEMORY_ACCESS 0 + +/*! + * @def XXH_SIZE_OPT + * @brief Controls how much xxHash optimizes for size. + * + * xxHash, when compiled, tends to result in a rather large binary size. This + * is mostly due to heavy usage to forced inlining and constant folding of the + * @ref XXH3_family to increase performance. + * + * However, some developers prefer size over speed. This option can + * significantly reduce the size of the generated code. When using the `-Os` + * or `-Oz` options on GCC or Clang, this is defined to 1 by default, + * otherwise it is defined to 0. + * + * Most of these size optimizations can be controlled manually. + * + * This is a number from 0-2. + * - `XXH_SIZE_OPT` == 0: Default. xxHash makes no size optimizations. Speed + * comes first. + * - `XXH_SIZE_OPT` == 1: Default for `-Os` and `-Oz`. xxHash is more + * conservative and disables hacks that increase code size. It implies the + * options @ref XXH_NO_INLINE_HINTS == 1, @ref XXH_FORCE_ALIGN_CHECK == 0, + * and @ref XXH3_NEON_LANES == 8 if they are not already defined. + * - `XXH_SIZE_OPT` == 2: xxHash tries to make itself as small as possible. + * Performance may cry. For example, the single shot functions just use the + * streaming API. + */ +# define XXH_SIZE_OPT 0 + +/*! + * @def XXH_FORCE_ALIGN_CHECK + * @brief If defined to non-zero, adds a special path for aligned inputs (XXH32() + * and XXH64() only). + * + * This is an important performance trick for architectures without decent + * unaligned memory access performance. + * + * It checks for input alignment, and when conditions are met, uses a "fast + * path" employing direct 32-bit/64-bit reads, resulting in _dramatically + * faster_ read speed. + * + * The check costs one initial branch per hash, which is generally negligible, + * but not zero. + * + * Moreover, it's not useful to generate an additional code path if memory + * access uses the same instruction for both aligned and unaligned + * addresses (e.g. x86 and aarch64). + * + * In these cases, the alignment check can be removed by setting this macro to 0. + * Then the code will always use unaligned memory access. + * Align check is automatically disabled on x86, x64, ARM64, and some ARM chips + * which are platforms known to offer good unaligned memory accesses performance. + * + * It is also disabled by default when @ref XXH_SIZE_OPT >= 1. + * + * This option does not affect XXH3 (only XXH32 and XXH64). + */ +# define XXH_FORCE_ALIGN_CHECK 0 + +/*! + * @def XXH_NO_INLINE_HINTS + * @brief When non-zero, sets all functions to `static`. + * + * By default, xxHash tries to force the compiler to inline almost all internal + * functions. + * + * This can usually improve performance due to reduced jumping and improved + * constant folding, but significantly increases the size of the binary which + * might not be favorable. + * + * Additionally, sometimes the forced inlining can be detrimental to performance, + * depending on the architecture. + * + * XXH_NO_INLINE_HINTS marks all internal functions as static, giving the + * compiler full control on whether to inline or not. + * + * When not optimizing (-O0), using `-fno-inline` with GCC or Clang, or if + * @ref XXH_SIZE_OPT >= 1, this will automatically be defined. + */ +# define XXH_NO_INLINE_HINTS 0 + +/*! + * @def XXH3_INLINE_SECRET + * @brief Determines whether to inline the XXH3 withSecret code. + * + * When the secret size is known, the compiler can improve the performance + * of XXH3_64bits_withSecret() and XXH3_128bits_withSecret(). + * + * However, if the secret size is not known, it doesn't have any benefit. This + * happens when xxHash is compiled into a global symbol. Therefore, if + * @ref XXH_INLINE_ALL is *not* defined, this will be defined to 0. + * + * Additionally, this defaults to 0 on GCC 12+, which has an issue with function pointers + * that are *sometimes* force inline on -Og, and it is impossible to automatically + * detect this optimization level. + */ +# define XXH3_INLINE_SECRET 0 + +/*! + * @def XXH32_ENDJMP + * @brief Whether to use a jump for `XXH32_finalize`. + * + * For performance, `XXH32_finalize` uses multiple branches in the finalizer. + * This is generally preferable for performance, + * but depending on exact architecture, a jmp may be preferable. + * + * This setting is only possibly making a difference for very small inputs. + */ +# define XXH32_ENDJMP 0 + +/*! + * @internal + * @brief Redefines old internal names. + * + * For compatibility with code that uses xxHash's internals before the names + * were changed to improve namespacing. There is no other reason to use this. + */ +# define XXH_OLD_NAMES +# undef XXH_OLD_NAMES /* don't actually use, it is ugly. */ + +/*! + * @def XXH_NO_STREAM + * @brief Disables the streaming API. + * + * When xxHash is not inlined and the streaming functions are not used, disabling + * the streaming functions can improve code size significantly, especially with + * the @ref XXH3_family which tends to make constant folded copies of itself. + */ +# define XXH_NO_STREAM +# undef XXH_NO_STREAM /* don't actually */ +#endif /* XXH_DOXYGEN */ +/*! + * @} + */ + +#ifndef XXH_FORCE_MEMORY_ACCESS /* can be defined externally, on command line for example */ + /* prefer __packed__ structures (method 1) for GCC + * < ARMv7 with unaligned access (e.g. Raspbian armhf) still uses byte shifting, so we use memcpy + * which for some reason does unaligned loads. */ +# if defined(__GNUC__) && !(defined(__ARM_ARCH) && __ARM_ARCH < 7 && defined(__ARM_FEATURE_UNALIGNED)) +# define XXH_FORCE_MEMORY_ACCESS 1 +# endif +#endif + +#ifndef XXH_SIZE_OPT + /* default to 1 for -Os or -Oz */ +# if (defined(__GNUC__) || defined(__clang__)) && defined(__OPTIMIZE_SIZE__) +# define XXH_SIZE_OPT 1 +# else +# define XXH_SIZE_OPT 0 +# endif +#endif + +#ifndef XXH_FORCE_ALIGN_CHECK /* can be defined externally */ + /* don't check on sizeopt, x86, aarch64, or arm when unaligned access is available */ +# if XXH_SIZE_OPT >= 1 || \ + defined(__i386) || defined(__x86_64__) || defined(__aarch64__) || defined(__ARM_FEATURE_UNALIGNED) \ + || defined(_M_IX86) || defined(_M_X64) || defined(_M_ARM64) || defined(_M_ARM) /* visual */ +# define XXH_FORCE_ALIGN_CHECK 0 +# else +# define XXH_FORCE_ALIGN_CHECK 1 +# endif +#endif + +#ifndef XXH_NO_INLINE_HINTS +# if XXH_SIZE_OPT >= 1 || defined(__NO_INLINE__) /* -O0, -fno-inline */ +# define XXH_NO_INLINE_HINTS 1 +# else +# define XXH_NO_INLINE_HINTS 0 +# endif +#endif + +#ifndef XXH3_INLINE_SECRET +# if (defined(__GNUC__) && !defined(__clang__) && __GNUC__ >= 12) \ + || !defined(XXH_INLINE_ALL) +# define XXH3_INLINE_SECRET 0 +# else +# define XXH3_INLINE_SECRET 1 +# endif +#endif + +#ifndef XXH32_ENDJMP +/* generally preferable for performance */ +# define XXH32_ENDJMP 0 +#endif + +/*! + * @defgroup impl Implementation + * @{ + */ + + +/* ************************************* +* Includes & Memory related functions +***************************************/ +#if defined(XXH_NO_STREAM) +/* nothing */ +#elif defined(XXH_NO_STDLIB) + +/* When requesting to disable any mention of stdlib, + * the library loses the ability to invoked malloc / free. + * In practice, it means that functions like `XXH*_createState()` + * will always fail, and return NULL. + * This flag is useful in situations where + * xxhash.h is integrated into some kernel, embedded or limited environment + * without access to dynamic allocation. + */ + +static XXH_CONSTF void* XXH_malloc(size_t s) { (void)s; return NULL; } +static void XXH_free(void* p) { (void)p; } + +#else + +/* + * Modify the local functions below should you wish to use + * different memory routines for malloc() and free() + */ +#include + +/*! + * @internal + * @brief Modify this function to use a different routine than malloc(). + */ +static XXH_MALLOCF void* XXH_malloc(size_t s) { return malloc(s); } + +/*! + * @internal + * @brief Modify this function to use a different routine than free(). + */ +static void XXH_free(void* p) { free(p); } + +#endif /* XXH_NO_STDLIB */ + +#include + +/*! + * @internal + * @brief Modify this function to use a different routine than memcpy(). + */ +static void* XXH_memcpy(void* dest, const void* src, size_t size) +{ + return memcpy(dest,src,size); +} + +#include /* ULLONG_MAX */ + + +/* ************************************* +* Compiler Specific Options +***************************************/ +#ifdef _MSC_VER /* Visual Studio warning fix */ +# pragma warning(disable : 4127) /* disable: C4127: conditional expression is constant */ +#endif + +#if XXH_NO_INLINE_HINTS /* disable inlining hints */ +# if defined(__GNUC__) || defined(__clang__) +# define XXH_FORCE_INLINE static __attribute__((__unused__)) +# else +# define XXH_FORCE_INLINE static +# endif +# define XXH_NO_INLINE static +/* enable inlining hints */ +#elif defined(__GNUC__) || defined(__clang__) +# define XXH_FORCE_INLINE static __inline__ __attribute__((__always_inline__, __unused__)) +# define XXH_NO_INLINE static __attribute__((__noinline__)) +#elif defined(_MSC_VER) /* Visual Studio */ +# define XXH_FORCE_INLINE static __forceinline +# define XXH_NO_INLINE static __declspec(noinline) +#elif defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) /* C99 */ +# define XXH_FORCE_INLINE static inline +# define XXH_NO_INLINE static +#else +# define XXH_FORCE_INLINE static +# define XXH_NO_INLINE static +#endif + +#if XXH3_INLINE_SECRET +# define XXH3_WITH_SECRET_INLINE XXH_FORCE_INLINE +#else +# define XXH3_WITH_SECRET_INLINE XXH_NO_INLINE +#endif + + +/* ************************************* +* Debug +***************************************/ +/*! + * @ingroup tuning + * @def XXH_DEBUGLEVEL + * @brief Sets the debugging level. + * + * XXH_DEBUGLEVEL is expected to be defined externally, typically via the + * compiler's command line options. The value must be a number. + */ +#ifndef XXH_DEBUGLEVEL +# ifdef DEBUGLEVEL /* backwards compat */ +# define XXH_DEBUGLEVEL DEBUGLEVEL +# else +# define XXH_DEBUGLEVEL 0 +# endif +#endif + +#if (XXH_DEBUGLEVEL>=1) +# include /* note: can still be disabled with NDEBUG */ +# define XXH_ASSERT(c) assert(c) +#else +# if defined(__INTEL_COMPILER) +# define XXH_ASSERT(c) XXH_ASSUME((unsigned char) (c)) +# else +# define XXH_ASSERT(c) XXH_ASSUME(c) +# endif +#endif + +/* note: use after variable declarations */ +#ifndef XXH_STATIC_ASSERT +# if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) /* C11 */ +# define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { _Static_assert((c),m); } while(0) +# elif defined(__cplusplus) && (__cplusplus >= 201103L) /* C++11 */ +# define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { static_assert((c),m); } while(0) +# else +# define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { struct xxh_sa { char x[(c) ? 1 : -1]; }; } while(0) +# endif +# define XXH_STATIC_ASSERT(c) XXH_STATIC_ASSERT_WITH_MESSAGE((c),#c) +#endif + +/*! + * @internal + * @def XXH_COMPILER_GUARD(var) + * @brief Used to prevent unwanted optimizations for @p var. + * + * It uses an empty GCC inline assembly statement with a register constraint + * which forces @p var into a general purpose register (eg eax, ebx, ecx + * on x86) and marks it as modified. + * + * This is used in a few places to avoid unwanted autovectorization (e.g. + * XXH32_round()). All vectorization we want is explicit via intrinsics, + * and _usually_ isn't wanted elsewhere. + * + * We also use it to prevent unwanted constant folding for AArch64 in + * XXH3_initCustomSecret_scalar(). + */ +#if defined(__GNUC__) || defined(__clang__) +# define XXH_COMPILER_GUARD(var) __asm__("" : "+r" (var)) +#else +# define XXH_COMPILER_GUARD(var) ((void)0) +#endif + +/* Specifically for NEON vectors which use the "w" constraint, on + * Clang. */ +#if defined(__clang__) && defined(__ARM_ARCH) && !defined(__wasm__) +# define XXH_COMPILER_GUARD_CLANG_NEON(var) __asm__("" : "+w" (var)) +#else +# define XXH_COMPILER_GUARD_CLANG_NEON(var) ((void)0) +#endif + +/* ************************************* +* Basic Types +***************************************/ +#if !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) +# ifdef _AIX +# include +# else +# include +# endif + typedef uint8_t xxh_u8; +#else + typedef unsigned char xxh_u8; +#endif +typedef XXH32_hash_t xxh_u32; + +#ifdef XXH_OLD_NAMES +# warning "XXH_OLD_NAMES is planned to be removed starting v0.9. If the program depends on it, consider moving away from it by employing newer type names directly" +# define BYTE xxh_u8 +# define U8 xxh_u8 +# define U32 xxh_u32 +#endif + +/* *** Memory access *** */ + +/*! + * @internal + * @fn xxh_u32 XXH_read32(const void* ptr) + * @brief Reads an unaligned 32-bit integer from @p ptr in native endianness. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * + * @param ptr The pointer to read from. + * @return The 32-bit native endian integer from the bytes at @p ptr. + */ + +/*! + * @internal + * @fn xxh_u32 XXH_readLE32(const void* ptr) + * @brief Reads an unaligned 32-bit little endian integer from @p ptr. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * + * @param ptr The pointer to read from. + * @return The 32-bit little endian integer from the bytes at @p ptr. + */ + +/*! + * @internal + * @fn xxh_u32 XXH_readBE32(const void* ptr) + * @brief Reads an unaligned 32-bit big endian integer from @p ptr. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * + * @param ptr The pointer to read from. + * @return The 32-bit big endian integer from the bytes at @p ptr. + */ + +/*! + * @internal + * @fn xxh_u32 XXH_readLE32_align(const void* ptr, XXH_alignment align) + * @brief Like @ref XXH_readLE32(), but has an option for aligned reads. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * Note that when @ref XXH_FORCE_ALIGN_CHECK == 0, the @p align parameter is + * always @ref XXH_alignment::XXH_unaligned. + * + * @param ptr The pointer to read from. + * @param align Whether @p ptr is aligned. + * @pre + * If @p align == @ref XXH_alignment::XXH_aligned, @p ptr must be 4 byte + * aligned. + * @return The 32-bit little endian integer from the bytes at @p ptr. + */ + +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) +/* + * Manual byteshift. Best for old compilers which don't inline memcpy. + * We actually directly use XXH_readLE32 and XXH_readBE32. + */ +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==2)) + +/* + * Force direct memory access. Only works on CPU which support unaligned memory + * access in hardware. + */ +static xxh_u32 XXH_read32(const void* memPtr) { return *(const xxh_u32*) memPtr; } + +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==1)) + +/* + * __attribute__((aligned(1))) is supported by gcc and clang. Originally the + * documentation claimed that it only increased the alignment, but actually it + * can decrease it on gcc, clang, and icc: + * https://gcc.gnu.org/bugzilla/show_bug.cgi?id=69502, + * https://gcc.godbolt.org/z/xYez1j67Y. + */ +#ifdef XXH_OLD_NAMES +typedef union { xxh_u32 u32; } __attribute__((__packed__)) unalign; +#endif +static xxh_u32 XXH_read32(const void* ptr) +{ + typedef __attribute__((__aligned__(1))) xxh_u32 xxh_unalign32; + return *((const xxh_unalign32*)ptr); +} + +#else + +/* + * Portable and safe solution. Generally efficient. + * see: https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html + */ +static xxh_u32 XXH_read32(const void* memPtr) +{ + xxh_u32 val; + XXH_memcpy(&val, memPtr, sizeof(val)); + return val; +} + +#endif /* XXH_FORCE_DIRECT_MEMORY_ACCESS */ + + +/* *** Endianness *** */ + +/*! + * @ingroup tuning + * @def XXH_CPU_LITTLE_ENDIAN + * @brief Whether the target is little endian. + * + * Defined to 1 if the target is little endian, or 0 if it is big endian. + * It can be defined externally, for example on the compiler command line. + * + * If it is not defined, + * a runtime check (which is usually constant folded) is used instead. + * + * @note + * This is not necessarily defined to an integer constant. + * + * @see XXH_isLittleEndian() for the runtime check. + */ +#ifndef XXH_CPU_LITTLE_ENDIAN +/* + * Try to detect endianness automatically, to avoid the nonstandard behavior + * in `XXH_isLittleEndian()` + */ +# if defined(_WIN32) /* Windows is always little endian */ \ + || defined(__LITTLE_ENDIAN__) \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__) +# define XXH_CPU_LITTLE_ENDIAN 1 +# elif defined(__BIG_ENDIAN__) \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__) +# define XXH_CPU_LITTLE_ENDIAN 0 +# else +/*! + * @internal + * @brief Runtime check for @ref XXH_CPU_LITTLE_ENDIAN. + * + * Most compilers will constant fold this. + */ +static int XXH_isLittleEndian(void) +{ + /* + * Portable and well-defined behavior. + * Don't use static: it is detrimental to performance. + */ + const union { xxh_u32 u; xxh_u8 c[4]; } one = { 1 }; + return one.c[0]; +} +# define XXH_CPU_LITTLE_ENDIAN XXH_isLittleEndian() +# endif +#endif + + + + +/* **************************************** +* Compiler-specific Functions and Macros +******************************************/ +#define XXH_GCC_VERSION (__GNUC__ * 100 + __GNUC_MINOR__) + +#ifdef __has_builtin +# define XXH_HAS_BUILTIN(x) __has_builtin(x) +#else +# define XXH_HAS_BUILTIN(x) 0 +#endif + + + +/* + * C23 and future versions have standard "unreachable()". + * Once it has been implemented reliably we can add it as an + * additional case: + * + * ``` + * #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= XXH_C23_VN) + * # include + * # ifdef unreachable + * # define XXH_UNREACHABLE() unreachable() + * # endif + * #endif + * ``` + * + * Note C++23 also has std::unreachable() which can be detected + * as follows: + * ``` + * #if defined(__cpp_lib_unreachable) && (__cpp_lib_unreachable >= 202202L) + * # include + * # define XXH_UNREACHABLE() std::unreachable() + * #endif + * ``` + * NB: `__cpp_lib_unreachable` is defined in the `` header. + * We don't use that as including `` in `extern "C"` blocks + * doesn't work on GCC12 + */ + +#if XXH_HAS_BUILTIN(__builtin_unreachable) +# define XXH_UNREACHABLE() __builtin_unreachable() + +#elif defined(_MSC_VER) +# define XXH_UNREACHABLE() __assume(0) + +#else +# define XXH_UNREACHABLE() +#endif + +#if XXH_HAS_BUILTIN(__builtin_assume) +# define XXH_ASSUME(c) __builtin_assume(c) +#else +# define XXH_ASSUME(c) if (!(c)) { XXH_UNREACHABLE(); } +#endif + +/*! + * @internal + * @def XXH_rotl32(x,r) + * @brief 32-bit rotate left. + * + * @param x The 32-bit integer to be rotated. + * @param r The number of bits to rotate. + * @pre + * @p r > 0 && @p r < 32 + * @note + * @p x and @p r may be evaluated multiple times. + * @return The rotated result. + */ +#if !defined(NO_CLANG_BUILTIN) && XXH_HAS_BUILTIN(__builtin_rotateleft32) \ + && XXH_HAS_BUILTIN(__builtin_rotateleft64) +# define XXH_rotl32 __builtin_rotateleft32 +# define XXH_rotl64 __builtin_rotateleft64 +/* Note: although _rotl exists for minGW (GCC under windows), performance seems poor */ +#elif defined(_MSC_VER) +# define XXH_rotl32(x,r) _rotl(x,r) +# define XXH_rotl64(x,r) _rotl64(x,r) +#else +# define XXH_rotl32(x,r) (((x) << (r)) | ((x) >> (32 - (r)))) +# define XXH_rotl64(x,r) (((x) << (r)) | ((x) >> (64 - (r)))) +#endif + +/*! + * @internal + * @fn xxh_u32 XXH_swap32(xxh_u32 x) + * @brief A 32-bit byteswap. + * + * @param x The 32-bit integer to byteswap. + * @return @p x, byteswapped. + */ +#if defined(_MSC_VER) /* Visual Studio */ +# define XXH_swap32 _byteswap_ulong +#elif XXH_GCC_VERSION >= 403 +# define XXH_swap32 __builtin_bswap32 +#else +static xxh_u32 XXH_swap32 (xxh_u32 x) +{ + return ((x << 24) & 0xff000000 ) | + ((x << 8) & 0x00ff0000 ) | + ((x >> 8) & 0x0000ff00 ) | + ((x >> 24) & 0x000000ff ); +} +#endif + + +/* *************************** +* Memory reads +*****************************/ + +/*! + * @internal + * @brief Enum to indicate whether a pointer is aligned. + */ +typedef enum { + XXH_aligned, /*!< Aligned */ + XXH_unaligned /*!< Possibly unaligned */ +} XXH_alignment; + +/* + * XXH_FORCE_MEMORY_ACCESS==3 is an endian-independent byteshift load. + * + * This is ideal for older compilers which don't inline memcpy. + */ +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) + +XXH_FORCE_INLINE xxh_u32 XXH_readLE32(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[0] + | ((xxh_u32)bytePtr[1] << 8) + | ((xxh_u32)bytePtr[2] << 16) + | ((xxh_u32)bytePtr[3] << 24); +} + +XXH_FORCE_INLINE xxh_u32 XXH_readBE32(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[3] + | ((xxh_u32)bytePtr[2] << 8) + | ((xxh_u32)bytePtr[1] << 16) + | ((xxh_u32)bytePtr[0] << 24); +} + +#else +XXH_FORCE_INLINE xxh_u32 XXH_readLE32(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_read32(ptr) : XXH_swap32(XXH_read32(ptr)); +} + +static xxh_u32 XXH_readBE32(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_swap32(XXH_read32(ptr)) : XXH_read32(ptr); +} +#endif + +XXH_FORCE_INLINE xxh_u32 +XXH_readLE32_align(const void* ptr, XXH_alignment align) +{ + if (align==XXH_unaligned) { + return XXH_readLE32(ptr); + } else { + return XXH_CPU_LITTLE_ENDIAN ? *(const xxh_u32*)ptr : XXH_swap32(*(const xxh_u32*)ptr); + } +} + + +/* ************************************* +* Misc +***************************************/ +/*! @ingroup public */ +XXH_PUBLIC_API unsigned XXH_versionNumber (void) { return XXH_VERSION_NUMBER; } + + +/* ******************************************************************* +* 32-bit hash functions +*********************************************************************/ +/*! + * @} + * @defgroup XXH32_impl XXH32 implementation + * @ingroup impl + * + * Details on the XXH32 implementation. + * @{ + */ + /* #define instead of static const, to be used as initializers */ +#define XXH_PRIME32_1 0x9E3779B1U /*!< 0b10011110001101110111100110110001 */ +#define XXH_PRIME32_2 0x85EBCA77U /*!< 0b10000101111010111100101001110111 */ +#define XXH_PRIME32_3 0xC2B2AE3DU /*!< 0b11000010101100101010111000111101 */ +#define XXH_PRIME32_4 0x27D4EB2FU /*!< 0b00100111110101001110101100101111 */ +#define XXH_PRIME32_5 0x165667B1U /*!< 0b00010110010101100110011110110001 */ + +#ifdef XXH_OLD_NAMES +# define PRIME32_1 XXH_PRIME32_1 +# define PRIME32_2 XXH_PRIME32_2 +# define PRIME32_3 XXH_PRIME32_3 +# define PRIME32_4 XXH_PRIME32_4 +# define PRIME32_5 XXH_PRIME32_5 +#endif + +/*! + * @internal + * @brief Normal stripe processing routine. + * + * This shuffles the bits so that any bit from @p input impacts several bits in + * @p acc. + * + * @param acc The accumulator lane. + * @param input The stripe of input to mix. + * @return The mixed accumulator lane. + */ +static xxh_u32 XXH32_round(xxh_u32 acc, xxh_u32 input) +{ + acc += input * XXH_PRIME32_2; + acc = XXH_rotl32(acc, 13); + acc *= XXH_PRIME32_1; +#if (defined(__SSE4_1__) || defined(__aarch64__) || defined(__wasm_simd128__)) && !defined(XXH_ENABLE_AUTOVECTORIZE) + /* + * UGLY HACK: + * A compiler fence is used to prevent GCC and Clang from + * autovectorizing the XXH32 loop (pragmas and attributes don't work for some + * reason) without globally disabling SSE4.1. + * + * The reason we want to avoid vectorization is because despite working on + * 4 integers at a time, there are multiple factors slowing XXH32 down on + * SSE4: + * - There's a ridiculous amount of lag from pmulld (10 cycles of latency on + * newer chips!) making it slightly slower to multiply four integers at + * once compared to four integers independently. Even when pmulld was + * fastest, Sandy/Ivy Bridge, it is still not worth it to go into SSE + * just to multiply unless doing a long operation. + * + * - Four instructions are required to rotate, + * movqda tmp, v // not required with VEX encoding + * pslld tmp, 13 // tmp <<= 13 + * psrld v, 19 // x >>= 19 + * por v, tmp // x |= tmp + * compared to one for scalar: + * roll v, 13 // reliably fast across the board + * shldl v, v, 13 // Sandy Bridge and later prefer this for some reason + * + * - Instruction level parallelism is actually more beneficial here because + * the SIMD actually serializes this operation: While v1 is rotating, v2 + * can load data, while v3 can multiply. SSE forces them to operate + * together. + * + * This is also enabled on AArch64, as Clang is *very aggressive* in vectorizing + * the loop. NEON is only faster on the A53, and with the newer cores, it is less + * than half the speed. + * + * Additionally, this is used on WASM SIMD128 because it JITs to the same + * SIMD instructions and has the same issue. + */ + XXH_COMPILER_GUARD(acc); +#endif + return acc; +} + +/*! + * @internal + * @brief Mixes all bits to finalize the hash. + * + * The final mix ensures that all input bits have a chance to impact any bit in + * the output digest, resulting in an unbiased distribution. + * + * @param hash The hash to avalanche. + * @return The avalanched hash. + */ +static xxh_u32 XXH32_avalanche(xxh_u32 hash) +{ + hash ^= hash >> 15; + hash *= XXH_PRIME32_2; + hash ^= hash >> 13; + hash *= XXH_PRIME32_3; + hash ^= hash >> 16; + return hash; +} + +#define XXH_get32bits(p) XXH_readLE32_align(p, align) + +/*! + * @internal + * @brief Processes the last 0-15 bytes of @p ptr. + * + * There may be up to 15 bytes remaining to consume from the input. + * This final stage will digest them to ensure that all input bytes are present + * in the final mix. + * + * @param hash The hash to finalize. + * @param ptr The pointer to the remaining input. + * @param len The remaining length, modulo 16. + * @param align Whether @p ptr is aligned. + * @return The finalized hash. + * @see XXH64_finalize(). + */ +static XXH_PUREF xxh_u32 +XXH32_finalize(xxh_u32 hash, const xxh_u8* ptr, size_t len, XXH_alignment align) +{ +#define XXH_PROCESS1 do { \ + hash += (*ptr++) * XXH_PRIME32_5; \ + hash = XXH_rotl32(hash, 11) * XXH_PRIME32_1; \ +} while (0) + +#define XXH_PROCESS4 do { \ + hash += XXH_get32bits(ptr) * XXH_PRIME32_3; \ + ptr += 4; \ + hash = XXH_rotl32(hash, 17) * XXH_PRIME32_4; \ +} while (0) + + if (ptr==NULL) XXH_ASSERT(len == 0); + + /* Compact rerolled version; generally faster */ + if (!XXH32_ENDJMP) { + len &= 15; + while (len >= 4) { + XXH_PROCESS4; + len -= 4; + } + while (len > 0) { + XXH_PROCESS1; + --len; + } + return XXH32_avalanche(hash); + } else { + switch(len&15) /* or switch(bEnd - p) */ { + case 12: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 8: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 4: XXH_PROCESS4; + return XXH32_avalanche(hash); + + case 13: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 9: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 5: XXH_PROCESS4; + XXH_PROCESS1; + return XXH32_avalanche(hash); + + case 14: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 10: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 6: XXH_PROCESS4; + XXH_PROCESS1; + XXH_PROCESS1; + return XXH32_avalanche(hash); + + case 15: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 11: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 7: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 3: XXH_PROCESS1; + XXH_FALLTHROUGH; /* fallthrough */ + case 2: XXH_PROCESS1; + XXH_FALLTHROUGH; /* fallthrough */ + case 1: XXH_PROCESS1; + XXH_FALLTHROUGH; /* fallthrough */ + case 0: return XXH32_avalanche(hash); + } + XXH_ASSERT(0); + return hash; /* reaching this point is deemed impossible */ + } +} + +#ifdef XXH_OLD_NAMES +# define PROCESS1 XXH_PROCESS1 +# define PROCESS4 XXH_PROCESS4 +#else +# undef XXH_PROCESS1 +# undef XXH_PROCESS4 +#endif + +/*! + * @internal + * @brief The implementation for @ref XXH32(). + * + * @param input , len , seed Directly passed from @ref XXH32(). + * @param align Whether @p input is aligned. + * @return The calculated hash. + */ +XXH_FORCE_INLINE XXH_PUREF xxh_u32 +XXH32_endian_align(const xxh_u8* input, size_t len, xxh_u32 seed, XXH_alignment align) +{ + xxh_u32 h32; + + if (input==NULL) XXH_ASSERT(len == 0); + + if (len>=16) { + const xxh_u8* const bEnd = input + len; + const xxh_u8* const limit = bEnd - 15; + xxh_u32 v1 = seed + XXH_PRIME32_1 + XXH_PRIME32_2; + xxh_u32 v2 = seed + XXH_PRIME32_2; + xxh_u32 v3 = seed + 0; + xxh_u32 v4 = seed - XXH_PRIME32_1; + + do { + v1 = XXH32_round(v1, XXH_get32bits(input)); input += 4; + v2 = XXH32_round(v2, XXH_get32bits(input)); input += 4; + v3 = XXH32_round(v3, XXH_get32bits(input)); input += 4; + v4 = XXH32_round(v4, XXH_get32bits(input)); input += 4; + } while (input < limit); + + h32 = XXH_rotl32(v1, 1) + XXH_rotl32(v2, 7) + + XXH_rotl32(v3, 12) + XXH_rotl32(v4, 18); + } else { + h32 = seed + XXH_PRIME32_5; + } + + h32 += (xxh_u32)len; + + return XXH32_finalize(h32, input, len&15, align); +} + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_hash_t XXH32 (const void* input, size_t len, XXH32_hash_t seed) +{ +#if !defined(XXH_NO_STREAM) && XXH_SIZE_OPT >= 2 + /* Simple version, good for code maintenance, but unfortunately slow for small inputs */ + XXH32_state_t state; + XXH32_reset(&state, seed); + XXH32_update(&state, (const xxh_u8*)input, len); + return XXH32_digest(&state); +#else + if (XXH_FORCE_ALIGN_CHECK) { + if ((((size_t)input) & 3) == 0) { /* Input is 4-bytes aligned, leverage the speed benefit */ + return XXH32_endian_align((const xxh_u8*)input, len, seed, XXH_aligned); + } } + + return XXH32_endian_align((const xxh_u8*)input, len, seed, XXH_unaligned); +#endif +} + + + +/******* Hash streaming *******/ +#ifndef XXH_NO_STREAM +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_state_t* XXH32_createState(void) +{ + return (XXH32_state_t*)XXH_malloc(sizeof(XXH32_state_t)); +} +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH_errorcode XXH32_freeState(XXH32_state_t* statePtr) +{ + XXH_free(statePtr); + return XXH_OK; +} + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API void XXH32_copyState(XXH32_state_t* dstState, const XXH32_state_t* srcState) +{ + XXH_memcpy(dstState, srcState, sizeof(*dstState)); +} + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH_errorcode XXH32_reset(XXH32_state_t* statePtr, XXH32_hash_t seed) +{ + XXH_ASSERT(statePtr != NULL); + memset(statePtr, 0, sizeof(*statePtr)); + statePtr->v[0] = seed + XXH_PRIME32_1 + XXH_PRIME32_2; + statePtr->v[1] = seed + XXH_PRIME32_2; + statePtr->v[2] = seed + 0; + statePtr->v[3] = seed - XXH_PRIME32_1; + return XXH_OK; +} + + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH_errorcode +XXH32_update(XXH32_state_t* state, const void* input, size_t len) +{ + if (input==NULL) { + XXH_ASSERT(len == 0); + return XXH_OK; + } + + { const xxh_u8* p = (const xxh_u8*)input; + const xxh_u8* const bEnd = p + len; + + state->total_len_32 += (XXH32_hash_t)len; + state->large_len |= (XXH32_hash_t)((len>=16) | (state->total_len_32>=16)); + + if (state->memsize + len < 16) { /* fill in tmp buffer */ + XXH_memcpy((xxh_u8*)(state->mem32) + state->memsize, input, len); + state->memsize += (XXH32_hash_t)len; + return XXH_OK; + } + + if (state->memsize) { /* some data left from previous update */ + XXH_memcpy((xxh_u8*)(state->mem32) + state->memsize, input, 16-state->memsize); + { const xxh_u32* p32 = state->mem32; + state->v[0] = XXH32_round(state->v[0], XXH_readLE32(p32)); p32++; + state->v[1] = XXH32_round(state->v[1], XXH_readLE32(p32)); p32++; + state->v[2] = XXH32_round(state->v[2], XXH_readLE32(p32)); p32++; + state->v[3] = XXH32_round(state->v[3], XXH_readLE32(p32)); + } + p += 16-state->memsize; + state->memsize = 0; + } + + if (p <= bEnd-16) { + const xxh_u8* const limit = bEnd - 16; + + do { + state->v[0] = XXH32_round(state->v[0], XXH_readLE32(p)); p+=4; + state->v[1] = XXH32_round(state->v[1], XXH_readLE32(p)); p+=4; + state->v[2] = XXH32_round(state->v[2], XXH_readLE32(p)); p+=4; + state->v[3] = XXH32_round(state->v[3], XXH_readLE32(p)); p+=4; + } while (p<=limit); + + } + + if (p < bEnd) { + XXH_memcpy(state->mem32, p, (size_t)(bEnd-p)); + state->memsize = (unsigned)(bEnd-p); + } + } + + return XXH_OK; +} + + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_hash_t XXH32_digest(const XXH32_state_t* state) +{ + xxh_u32 h32; + + if (state->large_len) { + h32 = XXH_rotl32(state->v[0], 1) + + XXH_rotl32(state->v[1], 7) + + XXH_rotl32(state->v[2], 12) + + XXH_rotl32(state->v[3], 18); + } else { + h32 = state->v[2] /* == seed */ + XXH_PRIME32_5; + } + + h32 += state->total_len_32; + + return XXH32_finalize(h32, (const xxh_u8*)state->mem32, state->memsize, XXH_aligned); +} +#endif /* !XXH_NO_STREAM */ + +/******* Canonical representation *******/ + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API void XXH32_canonicalFromHash(XXH32_canonical_t* dst, XXH32_hash_t hash) +{ + XXH_STATIC_ASSERT(sizeof(XXH32_canonical_t) == sizeof(XXH32_hash_t)); + if (XXH_CPU_LITTLE_ENDIAN) hash = XXH_swap32(hash); + XXH_memcpy(dst, &hash, sizeof(*dst)); +} +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_hash_t XXH32_hashFromCanonical(const XXH32_canonical_t* src) +{ + return XXH_readBE32(src); +} + + +#ifndef XXH_NO_LONG_LONG + +/* ******************************************************************* +* 64-bit hash functions +*********************************************************************/ +/*! + * @} + * @ingroup impl + * @{ + */ +/******* Memory access *******/ + +typedef XXH64_hash_t xxh_u64; + +#ifdef XXH_OLD_NAMES +# define U64 xxh_u64 +#endif + +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) +/* + * Manual byteshift. Best for old compilers which don't inline memcpy. + * We actually directly use XXH_readLE64 and XXH_readBE64. + */ +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==2)) + +/* Force direct memory access. Only works on CPU which support unaligned memory access in hardware */ +static xxh_u64 XXH_read64(const void* memPtr) +{ + return *(const xxh_u64*) memPtr; +} + +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==1)) + +/* + * __attribute__((aligned(1))) is supported by gcc and clang. Originally the + * documentation claimed that it only increased the alignment, but actually it + * can decrease it on gcc, clang, and icc: + * https://gcc.gnu.org/bugzilla/show_bug.cgi?id=69502, + * https://gcc.godbolt.org/z/xYez1j67Y. + */ +#ifdef XXH_OLD_NAMES +typedef union { xxh_u32 u32; xxh_u64 u64; } __attribute__((__packed__)) unalign64; +#endif +static xxh_u64 XXH_read64(const void* ptr) +{ + typedef __attribute__((__aligned__(1))) xxh_u64 xxh_unalign64; + return *((const xxh_unalign64*)ptr); +} + +#else + +/* + * Portable and safe solution. Generally efficient. + * see: https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html + */ +static xxh_u64 XXH_read64(const void* memPtr) +{ + xxh_u64 val; + XXH_memcpy(&val, memPtr, sizeof(val)); + return val; +} + +#endif /* XXH_FORCE_DIRECT_MEMORY_ACCESS */ + +#if defined(_MSC_VER) /* Visual Studio */ +# define XXH_swap64 _byteswap_uint64 +#elif XXH_GCC_VERSION >= 403 +# define XXH_swap64 __builtin_bswap64 +#else +static xxh_u64 XXH_swap64(xxh_u64 x) +{ + return ((x << 56) & 0xff00000000000000ULL) | + ((x << 40) & 0x00ff000000000000ULL) | + ((x << 24) & 0x0000ff0000000000ULL) | + ((x << 8) & 0x000000ff00000000ULL) | + ((x >> 8) & 0x00000000ff000000ULL) | + ((x >> 24) & 0x0000000000ff0000ULL) | + ((x >> 40) & 0x000000000000ff00ULL) | + ((x >> 56) & 0x00000000000000ffULL); +} +#endif + + +/* XXH_FORCE_MEMORY_ACCESS==3 is an endian-independent byteshift load. */ +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) + +XXH_FORCE_INLINE xxh_u64 XXH_readLE64(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[0] + | ((xxh_u64)bytePtr[1] << 8) + | ((xxh_u64)bytePtr[2] << 16) + | ((xxh_u64)bytePtr[3] << 24) + | ((xxh_u64)bytePtr[4] << 32) + | ((xxh_u64)bytePtr[5] << 40) + | ((xxh_u64)bytePtr[6] << 48) + | ((xxh_u64)bytePtr[7] << 56); +} + +XXH_FORCE_INLINE xxh_u64 XXH_readBE64(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[7] + | ((xxh_u64)bytePtr[6] << 8) + | ((xxh_u64)bytePtr[5] << 16) + | ((xxh_u64)bytePtr[4] << 24) + | ((xxh_u64)bytePtr[3] << 32) + | ((xxh_u64)bytePtr[2] << 40) + | ((xxh_u64)bytePtr[1] << 48) + | ((xxh_u64)bytePtr[0] << 56); +} + +#else +XXH_FORCE_INLINE xxh_u64 XXH_readLE64(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_read64(ptr) : XXH_swap64(XXH_read64(ptr)); +} + +static xxh_u64 XXH_readBE64(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_swap64(XXH_read64(ptr)) : XXH_read64(ptr); +} +#endif + +XXH_FORCE_INLINE xxh_u64 +XXH_readLE64_align(const void* ptr, XXH_alignment align) +{ + if (align==XXH_unaligned) + return XXH_readLE64(ptr); + else + return XXH_CPU_LITTLE_ENDIAN ? *(const xxh_u64*)ptr : XXH_swap64(*(const xxh_u64*)ptr); +} + + +/******* xxh64 *******/ +/*! + * @} + * @defgroup XXH64_impl XXH64 implementation + * @ingroup impl + * + * Details on the XXH64 implementation. + * @{ + */ +/* #define rather that static const, to be used as initializers */ +#define XXH_PRIME64_1 0x9E3779B185EBCA87ULL /*!< 0b1001111000110111011110011011000110000101111010111100101010000111 */ +#define XXH_PRIME64_2 0xC2B2AE3D27D4EB4FULL /*!< 0b1100001010110010101011100011110100100111110101001110101101001111 */ +#define XXH_PRIME64_3 0x165667B19E3779F9ULL /*!< 0b0001011001010110011001111011000110011110001101110111100111111001 */ +#define XXH_PRIME64_4 0x85EBCA77C2B2AE63ULL /*!< 0b1000010111101011110010100111011111000010101100101010111001100011 */ +#define XXH_PRIME64_5 0x27D4EB2F165667C5ULL /*!< 0b0010011111010100111010110010111100010110010101100110011111000101 */ + +#ifdef XXH_OLD_NAMES +# define PRIME64_1 XXH_PRIME64_1 +# define PRIME64_2 XXH_PRIME64_2 +# define PRIME64_3 XXH_PRIME64_3 +# define PRIME64_4 XXH_PRIME64_4 +# define PRIME64_5 XXH_PRIME64_5 +#endif + +/*! @copydoc XXH32_round */ +static xxh_u64 XXH64_round(xxh_u64 acc, xxh_u64 input) +{ + acc += input * XXH_PRIME64_2; + acc = XXH_rotl64(acc, 31); + acc *= XXH_PRIME64_1; +#if (defined(__AVX512F__)) && !defined(XXH_ENABLE_AUTOVECTORIZE) + /* + * DISABLE AUTOVECTORIZATION: + * A compiler fence is used to prevent GCC and Clang from + * autovectorizing the XXH64 loop (pragmas and attributes don't work for some + * reason) without globally disabling AVX512. + * + * Autovectorization of XXH64 tends to be detrimental, + * though the exact outcome may change depending on exact cpu and compiler version. + * For information, it has been reported as detrimental for Skylake-X, + * but possibly beneficial for Zen4. + * + * The default is to disable auto-vectorization, + * but you can select to enable it instead using `XXH_ENABLE_AUTOVECTORIZE` build variable. + */ + XXH_COMPILER_GUARD(acc); +#endif + return acc; +} + +static xxh_u64 XXH64_mergeRound(xxh_u64 acc, xxh_u64 val) +{ + val = XXH64_round(0, val); + acc ^= val; + acc = acc * XXH_PRIME64_1 + XXH_PRIME64_4; + return acc; +} + +/*! @copydoc XXH32_avalanche */ +static xxh_u64 XXH64_avalanche(xxh_u64 hash) +{ + hash ^= hash >> 33; + hash *= XXH_PRIME64_2; + hash ^= hash >> 29; + hash *= XXH_PRIME64_3; + hash ^= hash >> 32; + return hash; +} + + +#define XXH_get64bits(p) XXH_readLE64_align(p, align) + +/*! + * @internal + * @brief Processes the last 0-31 bytes of @p ptr. + * + * There may be up to 31 bytes remaining to consume from the input. + * This final stage will digest them to ensure that all input bytes are present + * in the final mix. + * + * @param hash The hash to finalize. + * @param ptr The pointer to the remaining input. + * @param len The remaining length, modulo 32. + * @param align Whether @p ptr is aligned. + * @return The finalized hash + * @see XXH32_finalize(). + */ +static XXH_PUREF xxh_u64 +XXH64_finalize(xxh_u64 hash, const xxh_u8* ptr, size_t len, XXH_alignment align) +{ + if (ptr==NULL) XXH_ASSERT(len == 0); + len &= 31; + while (len >= 8) { + xxh_u64 const k1 = XXH64_round(0, XXH_get64bits(ptr)); + ptr += 8; + hash ^= k1; + hash = XXH_rotl64(hash,27) * XXH_PRIME64_1 + XXH_PRIME64_4; + len -= 8; + } + if (len >= 4) { + hash ^= (xxh_u64)(XXH_get32bits(ptr)) * XXH_PRIME64_1; + ptr += 4; + hash = XXH_rotl64(hash, 23) * XXH_PRIME64_2 + XXH_PRIME64_3; + len -= 4; + } + while (len > 0) { + hash ^= (*ptr++) * XXH_PRIME64_5; + hash = XXH_rotl64(hash, 11) * XXH_PRIME64_1; + --len; + } + return XXH64_avalanche(hash); +} + +#ifdef XXH_OLD_NAMES +# define PROCESS1_64 XXH_PROCESS1_64 +# define PROCESS4_64 XXH_PROCESS4_64 +# define PROCESS8_64 XXH_PROCESS8_64 +#else +# undef XXH_PROCESS1_64 +# undef XXH_PROCESS4_64 +# undef XXH_PROCESS8_64 +#endif + +/*! + * @internal + * @brief The implementation for @ref XXH64(). + * + * @param input , len , seed Directly passed from @ref XXH64(). + * @param align Whether @p input is aligned. + * @return The calculated hash. + */ +XXH_FORCE_INLINE XXH_PUREF xxh_u64 +XXH64_endian_align(const xxh_u8* input, size_t len, xxh_u64 seed, XXH_alignment align) +{ + xxh_u64 h64; + if (input==NULL) XXH_ASSERT(len == 0); + + if (len>=32) { + const xxh_u8* const bEnd = input + len; + const xxh_u8* const limit = bEnd - 31; + xxh_u64 v1 = seed + XXH_PRIME64_1 + XXH_PRIME64_2; + xxh_u64 v2 = seed + XXH_PRIME64_2; + xxh_u64 v3 = seed + 0; + xxh_u64 v4 = seed - XXH_PRIME64_1; + + do { + v1 = XXH64_round(v1, XXH_get64bits(input)); input+=8; + v2 = XXH64_round(v2, XXH_get64bits(input)); input+=8; + v3 = XXH64_round(v3, XXH_get64bits(input)); input+=8; + v4 = XXH64_round(v4, XXH_get64bits(input)); input+=8; + } while (input= 2 + /* Simple version, good for code maintenance, but unfortunately slow for small inputs */ + XXH64_state_t state; + XXH64_reset(&state, seed); + XXH64_update(&state, (const xxh_u8*)input, len); + return XXH64_digest(&state); +#else + if (XXH_FORCE_ALIGN_CHECK) { + if ((((size_t)input) & 7)==0) { /* Input is aligned, let's leverage the speed advantage */ + return XXH64_endian_align((const xxh_u8*)input, len, seed, XXH_aligned); + } } + + return XXH64_endian_align((const xxh_u8*)input, len, seed, XXH_unaligned); + +#endif +} + +/******* Hash Streaming *******/ +#ifndef XXH_NO_STREAM +/*! @ingroup XXH64_family*/ +XXH_PUBLIC_API XXH64_state_t* XXH64_createState(void) +{ + return (XXH64_state_t*)XXH_malloc(sizeof(XXH64_state_t)); +} +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH_errorcode XXH64_freeState(XXH64_state_t* statePtr) +{ + XXH_free(statePtr); + return XXH_OK; +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API void XXH64_copyState(XXH_NOESCAPE XXH64_state_t* dstState, const XXH64_state_t* srcState) +{ + XXH_memcpy(dstState, srcState, sizeof(*dstState)); +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH_errorcode XXH64_reset(XXH_NOESCAPE XXH64_state_t* statePtr, XXH64_hash_t seed) +{ + XXH_ASSERT(statePtr != NULL); + memset(statePtr, 0, sizeof(*statePtr)); + statePtr->v[0] = seed + XXH_PRIME64_1 + XXH_PRIME64_2; + statePtr->v[1] = seed + XXH_PRIME64_2; + statePtr->v[2] = seed + 0; + statePtr->v[3] = seed - XXH_PRIME64_1; + return XXH_OK; +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH_errorcode +XXH64_update (XXH_NOESCAPE XXH64_state_t* state, XXH_NOESCAPE const void* input, size_t len) +{ + if (input==NULL) { + XXH_ASSERT(len == 0); + return XXH_OK; + } + + { const xxh_u8* p = (const xxh_u8*)input; + const xxh_u8* const bEnd = p + len; + + state->total_len += len; + + if (state->memsize + len < 32) { /* fill in tmp buffer */ + XXH_memcpy(((xxh_u8*)state->mem64) + state->memsize, input, len); + state->memsize += (xxh_u32)len; + return XXH_OK; + } + + if (state->memsize) { /* tmp buffer is full */ + XXH_memcpy(((xxh_u8*)state->mem64) + state->memsize, input, 32-state->memsize); + state->v[0] = XXH64_round(state->v[0], XXH_readLE64(state->mem64+0)); + state->v[1] = XXH64_round(state->v[1], XXH_readLE64(state->mem64+1)); + state->v[2] = XXH64_round(state->v[2], XXH_readLE64(state->mem64+2)); + state->v[3] = XXH64_round(state->v[3], XXH_readLE64(state->mem64+3)); + p += 32 - state->memsize; + state->memsize = 0; + } + + if (p+32 <= bEnd) { + const xxh_u8* const limit = bEnd - 32; + + do { + state->v[0] = XXH64_round(state->v[0], XXH_readLE64(p)); p+=8; + state->v[1] = XXH64_round(state->v[1], XXH_readLE64(p)); p+=8; + state->v[2] = XXH64_round(state->v[2], XXH_readLE64(p)); p+=8; + state->v[3] = XXH64_round(state->v[3], XXH_readLE64(p)); p+=8; + } while (p<=limit); + + } + + if (p < bEnd) { + XXH_memcpy(state->mem64, p, (size_t)(bEnd-p)); + state->memsize = (unsigned)(bEnd-p); + } + } + + return XXH_OK; +} + + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH64_hash_t XXH64_digest(XXH_NOESCAPE const XXH64_state_t* state) +{ + xxh_u64 h64; + + if (state->total_len >= 32) { + h64 = XXH_rotl64(state->v[0], 1) + XXH_rotl64(state->v[1], 7) + XXH_rotl64(state->v[2], 12) + XXH_rotl64(state->v[3], 18); + h64 = XXH64_mergeRound(h64, state->v[0]); + h64 = XXH64_mergeRound(h64, state->v[1]); + h64 = XXH64_mergeRound(h64, state->v[2]); + h64 = XXH64_mergeRound(h64, state->v[3]); + } else { + h64 = state->v[2] /*seed*/ + XXH_PRIME64_5; + } + + h64 += (xxh_u64) state->total_len; + + return XXH64_finalize(h64, (const xxh_u8*)state->mem64, (size_t)state->total_len, XXH_aligned); +} +#endif /* !XXH_NO_STREAM */ + +/******* Canonical representation *******/ + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API void XXH64_canonicalFromHash(XXH_NOESCAPE XXH64_canonical_t* dst, XXH64_hash_t hash) +{ + XXH_STATIC_ASSERT(sizeof(XXH64_canonical_t) == sizeof(XXH64_hash_t)); + if (XXH_CPU_LITTLE_ENDIAN) hash = XXH_swap64(hash); + XXH_memcpy(dst, &hash, sizeof(*dst)); +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH64_hash_t XXH64_hashFromCanonical(XXH_NOESCAPE const XXH64_canonical_t* src) +{ + return XXH_readBE64(src); +} + +#ifndef XXH_NO_XXH3 + +/* ********************************************************************* +* XXH3 +* New generation hash designed for speed on small keys and vectorization +************************************************************************ */ +/*! + * @} + * @defgroup XXH3_impl XXH3 implementation + * @ingroup impl + * @{ + */ + +/* === Compiler specifics === */ + +#if ((defined(sun) || defined(__sun)) && __cplusplus) /* Solaris includes __STDC_VERSION__ with C++. Tested with GCC 5.5 */ +# define XXH_RESTRICT /* disable */ +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L /* >= C99 */ +# define XXH_RESTRICT restrict +#elif (defined (__GNUC__) && ((__GNUC__ > 3) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))) \ + || (defined (__clang__)) \ + || (defined (_MSC_VER) && (_MSC_VER >= 1400)) \ + || (defined (__INTEL_COMPILER) && (__INTEL_COMPILER >= 1300)) +/* + * There are a LOT more compilers that recognize __restrict but this + * covers the major ones. + */ +# define XXH_RESTRICT __restrict +#else +# define XXH_RESTRICT /* disable */ +#endif + +#if (defined(__GNUC__) && (__GNUC__ >= 3)) \ + || (defined(__INTEL_COMPILER) && (__INTEL_COMPILER >= 800)) \ + || defined(__clang__) +# define XXH_likely(x) __builtin_expect(x, 1) +# define XXH_unlikely(x) __builtin_expect(x, 0) +#else +# define XXH_likely(x) (x) +# define XXH_unlikely(x) (x) +#endif + +#ifndef XXH_HAS_INCLUDE +# ifdef __has_include +/* + * Not defined as XXH_HAS_INCLUDE(x) (function-like) because + * this causes segfaults in Apple Clang 4.2 (on Mac OS X 10.7 Lion) + */ +# define XXH_HAS_INCLUDE __has_include +# else +# define XXH_HAS_INCLUDE(x) 0 +# endif +#endif + +#if defined(__GNUC__) || defined(__clang__) +# if defined(__ARM_FEATURE_SVE) +# include +# endif +# if defined(__ARM_NEON__) || defined(__ARM_NEON) \ + || (defined(_M_ARM) && _M_ARM >= 7) \ + || defined(_M_ARM64) || defined(_M_ARM64EC) \ + || (defined(__wasm_simd128__) && XXH_HAS_INCLUDE()) /* WASM SIMD128 via SIMDe */ +# define inline __inline__ /* circumvent a clang bug */ +# include +# undef inline +# elif defined(__AVX2__) +# include +# elif defined(__SSE2__) +# include +# endif +#endif + +#if defined(_MSC_VER) +# include +#endif + +/* + * One goal of XXH3 is to make it fast on both 32-bit and 64-bit, while + * remaining a true 64-bit/128-bit hash function. + * + * This is done by prioritizing a subset of 64-bit operations that can be + * emulated without too many steps on the average 32-bit machine. + * + * For example, these two lines seem similar, and run equally fast on 64-bit: + * + * xxh_u64 x; + * x ^= (x >> 47); // good + * x ^= (x >> 13); // bad + * + * However, to a 32-bit machine, there is a major difference. + * + * x ^= (x >> 47) looks like this: + * + * x.lo ^= (x.hi >> (47 - 32)); + * + * while x ^= (x >> 13) looks like this: + * + * // note: funnel shifts are not usually cheap. + * x.lo ^= (x.lo >> 13) | (x.hi << (32 - 13)); + * x.hi ^= (x.hi >> 13); + * + * The first one is significantly faster than the second, simply because the + * shift is larger than 32. This means: + * - All the bits we need are in the upper 32 bits, so we can ignore the lower + * 32 bits in the shift. + * - The shift result will always fit in the lower 32 bits, and therefore, + * we can ignore the upper 32 bits in the xor. + * + * Thanks to this optimization, XXH3 only requires these features to be efficient: + * + * - Usable unaligned access + * - A 32-bit or 64-bit ALU + * - If 32-bit, a decent ADC instruction + * - A 32 or 64-bit multiply with a 64-bit result + * - For the 128-bit variant, a decent byteswap helps short inputs. + * + * The first two are already required by XXH32, and almost all 32-bit and 64-bit + * platforms which can run XXH32 can run XXH3 efficiently. + * + * Thumb-1, the classic 16-bit only subset of ARM's instruction set, is one + * notable exception. + * + * First of all, Thumb-1 lacks support for the UMULL instruction which + * performs the important long multiply. This means numerous __aeabi_lmul + * calls. + * + * Second of all, the 8 functional registers are just not enough. + * Setup for __aeabi_lmul, byteshift loads, pointers, and all arithmetic need + * Lo registers, and this shuffling results in thousands more MOVs than A32. + * + * A32 and T32 don't have this limitation. They can access all 14 registers, + * do a 32->64 multiply with UMULL, and the flexible operand allowing free + * shifts is helpful, too. + * + * Therefore, we do a quick sanity check. + * + * If compiling Thumb-1 for a target which supports ARM instructions, we will + * emit a warning, as it is not a "sane" platform to compile for. + * + * Usually, if this happens, it is because of an accident and you probably need + * to specify -march, as you likely meant to compile for a newer architecture. + * + * Credit: large sections of the vectorial and asm source code paths + * have been contributed by @easyaspi314 + */ +#if defined(__thumb__) && !defined(__thumb2__) && defined(__ARM_ARCH_ISA_ARM) +# warning "XXH3 is highly inefficient without ARM or Thumb-2." +#endif + +/* ========================================== + * Vectorization detection + * ========================================== */ + +#ifdef XXH_DOXYGEN +/*! + * @ingroup tuning + * @brief Overrides the vectorization implementation chosen for XXH3. + * + * Can be defined to 0 to disable SIMD or any of the values mentioned in + * @ref XXH_VECTOR_TYPE. + * + * If this is not defined, it uses predefined macros to determine the best + * implementation. + */ +# define XXH_VECTOR XXH_SCALAR +/*! + * @ingroup tuning + * @brief Possible values for @ref XXH_VECTOR. + * + * Note that these are actually implemented as macros. + * + * If this is not defined, it is detected automatically. + * internal macro XXH_X86DISPATCH overrides this. + */ +enum XXH_VECTOR_TYPE /* fake enum */ { + XXH_SCALAR = 0, /*!< Portable scalar version */ + XXH_SSE2 = 1, /*!< + * SSE2 for Pentium 4, Opteron, all x86_64. + * + * @note SSE2 is also guaranteed on Windows 10, macOS, and + * Android x86. + */ + XXH_AVX2 = 2, /*!< AVX2 for Haswell and Bulldozer */ + XXH_AVX512 = 3, /*!< AVX512 for Skylake and Icelake */ + XXH_NEON = 4, /*!< + * NEON for most ARMv7-A, all AArch64, and WASM SIMD128 + * via the SIMDeverywhere polyfill provided with the + * Emscripten SDK. + */ + XXH_VSX = 5, /*!< VSX and ZVector for POWER8/z13 (64-bit) */ + XXH_SVE = 6, /*!< SVE for some ARMv8-A and ARMv9-A */ +}; +/*! + * @ingroup tuning + * @brief Selects the minimum alignment for XXH3's accumulators. + * + * When using SIMD, this should match the alignment required for said vector + * type, so, for example, 32 for AVX2. + * + * Default: Auto detected. + */ +# define XXH_ACC_ALIGN 8 +#endif + +/* Actual definition */ +#ifndef XXH_DOXYGEN +# define XXH_SCALAR 0 +# define XXH_SSE2 1 +# define XXH_AVX2 2 +# define XXH_AVX512 3 +# define XXH_NEON 4 +# define XXH_VSX 5 +# define XXH_SVE 6 +#endif + +#ifndef XXH_VECTOR /* can be defined on command line */ +# if defined(__ARM_FEATURE_SVE) +# define XXH_VECTOR XXH_SVE +# elif ( \ + defined(__ARM_NEON__) || defined(__ARM_NEON) /* gcc */ \ + || defined(_M_ARM) || defined(_M_ARM64) || defined(_M_ARM64EC) /* msvc */ \ + || (defined(__wasm_simd128__) && XXH_HAS_INCLUDE()) /* wasm simd128 via SIMDe */ \ + ) && ( \ + defined(_WIN32) || defined(__LITTLE_ENDIAN__) /* little endian only */ \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__) \ + ) +# define XXH_VECTOR XXH_NEON +# elif defined(__AVX512F__) +# define XXH_VECTOR XXH_AVX512 +# elif defined(__AVX2__) +# define XXH_VECTOR XXH_AVX2 +# elif defined(__SSE2__) || defined(_M_AMD64) || defined(_M_X64) || (defined(_M_IX86_FP) && (_M_IX86_FP == 2)) +# define XXH_VECTOR XXH_SSE2 +# elif (defined(__PPC64__) && defined(__POWER8_VECTOR__)) \ + || (defined(__s390x__) && defined(__VEC__)) \ + && defined(__GNUC__) /* TODO: IBM XL */ +# define XXH_VECTOR XXH_VSX +# else +# define XXH_VECTOR XXH_SCALAR +# endif +#endif + +/* __ARM_FEATURE_SVE is only supported by GCC & Clang. */ +#if (XXH_VECTOR == XXH_SVE) && !defined(__ARM_FEATURE_SVE) +# ifdef _MSC_VER +# pragma warning(once : 4606) +# else +# warning "__ARM_FEATURE_SVE isn't supported. Use SCALAR instead." +# endif +# undef XXH_VECTOR +# define XXH_VECTOR XXH_SCALAR +#endif + +/* + * Controls the alignment of the accumulator, + * for compatibility with aligned vector loads, which are usually faster. + */ +#ifndef XXH_ACC_ALIGN +# if defined(XXH_X86DISPATCH) +# define XXH_ACC_ALIGN 64 /* for compatibility with avx512 */ +# elif XXH_VECTOR == XXH_SCALAR /* scalar */ +# define XXH_ACC_ALIGN 8 +# elif XXH_VECTOR == XXH_SSE2 /* sse2 */ +# define XXH_ACC_ALIGN 16 +# elif XXH_VECTOR == XXH_AVX2 /* avx2 */ +# define XXH_ACC_ALIGN 32 +# elif XXH_VECTOR == XXH_NEON /* neon */ +# define XXH_ACC_ALIGN 16 +# elif XXH_VECTOR == XXH_VSX /* vsx */ +# define XXH_ACC_ALIGN 16 +# elif XXH_VECTOR == XXH_AVX512 /* avx512 */ +# define XXH_ACC_ALIGN 64 +# elif XXH_VECTOR == XXH_SVE /* sve */ +# define XXH_ACC_ALIGN 64 +# endif +#endif + +#if defined(XXH_X86DISPATCH) || XXH_VECTOR == XXH_SSE2 \ + || XXH_VECTOR == XXH_AVX2 || XXH_VECTOR == XXH_AVX512 +# define XXH_SEC_ALIGN XXH_ACC_ALIGN +#elif XXH_VECTOR == XXH_SVE +# define XXH_SEC_ALIGN XXH_ACC_ALIGN +#else +# define XXH_SEC_ALIGN 8 +#endif + +#if defined(__GNUC__) || defined(__clang__) +# define XXH_ALIASING __attribute__((__may_alias__)) +#else +# define XXH_ALIASING /* nothing */ +#endif + +/* + * UGLY HACK: + * GCC usually generates the best code with -O3 for xxHash. + * + * However, when targeting AVX2, it is overzealous in its unrolling resulting + * in code roughly 3/4 the speed of Clang. + * + * There are other issues, such as GCC splitting _mm256_loadu_si256 into + * _mm_loadu_si128 + _mm256_inserti128_si256. This is an optimization which + * only applies to Sandy and Ivy Bridge... which don't even support AVX2. + * + * That is why when compiling the AVX2 version, it is recommended to use either + * -O2 -mavx2 -march=haswell + * or + * -O2 -mavx2 -mno-avx256-split-unaligned-load + * for decent performance, or to use Clang instead. + * + * Fortunately, we can control the first one with a pragma that forces GCC into + * -O2, but the other one we can't control without "failed to inline always + * inline function due to target mismatch" warnings. + */ +#if XXH_VECTOR == XXH_AVX2 /* AVX2 */ \ + && defined(__GNUC__) && !defined(__clang__) /* GCC, not Clang */ \ + && defined(__OPTIMIZE__) && XXH_SIZE_OPT <= 0 /* respect -O0 and -Os */ +# pragma GCC push_options +# pragma GCC optimize("-O2") +#endif + +#if XXH_VECTOR == XXH_NEON + +/* + * UGLY HACK: While AArch64 GCC on Linux does not seem to care, on macOS, GCC -O3 + * optimizes out the entire hashLong loop because of the aliasing violation. + * + * However, GCC is also inefficient at load-store optimization with vld1q/vst1q, + * so the only option is to mark it as aliasing. + */ +typedef uint64x2_t xxh_aliasing_uint64x2_t XXH_ALIASING; + +/*! + * @internal + * @brief `vld1q_u64` but faster and alignment-safe. + * + * On AArch64, unaligned access is always safe, but on ARMv7-a, it is only + * *conditionally* safe (`vld1` has an alignment bit like `movdq[ua]` in x86). + * + * GCC for AArch64 sees `vld1q_u8` as an intrinsic instead of a load, so it + * prohibits load-store optimizations. Therefore, a direct dereference is used. + * + * Otherwise, `vld1q_u8` is used with `vreinterpretq_u8_u64` to do a safe + * unaligned load. + */ +#if defined(__aarch64__) && defined(__GNUC__) && !defined(__clang__) +XXH_FORCE_INLINE uint64x2_t XXH_vld1q_u64(void const* ptr) /* silence -Wcast-align */ +{ + return *(xxh_aliasing_uint64x2_t const *)ptr; +} +#else +XXH_FORCE_INLINE uint64x2_t XXH_vld1q_u64(void const* ptr) +{ + return vreinterpretq_u64_u8(vld1q_u8((uint8_t const*)ptr)); +} +#endif + +/*! + * @internal + * @brief `vmlal_u32` on low and high halves of a vector. + * + * This is a workaround for AArch64 GCC < 11 which implemented arm_neon.h with + * inline assembly and were therefore incapable of merging the `vget_{low, high}_u32` + * with `vmlal_u32`. + */ +#if defined(__aarch64__) && defined(__GNUC__) && !defined(__clang__) && __GNUC__ < 11 +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_low_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + /* Inline assembly is the only way */ + __asm__("umlal %0.2d, %1.2s, %2.2s" : "+w" (acc) : "w" (lhs), "w" (rhs)); + return acc; +} +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_high_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + /* This intrinsic works as expected */ + return vmlal_high_u32(acc, lhs, rhs); +} +#else +/* Portable intrinsic versions */ +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_low_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + return vmlal_u32(acc, vget_low_u32(lhs), vget_low_u32(rhs)); +} +/*! @copydoc XXH_vmlal_low_u32 + * Assume the compiler converts this to vmlal_high_u32 on aarch64 */ +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_high_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + return vmlal_u32(acc, vget_high_u32(lhs), vget_high_u32(rhs)); +} +#endif + +/*! + * @ingroup tuning + * @brief Controls the NEON to scalar ratio for XXH3 + * + * This can be set to 2, 4, 6, or 8. + * + * ARM Cortex CPUs are _very_ sensitive to how their pipelines are used. + * + * For example, the Cortex-A73 can dispatch 3 micro-ops per cycle, but only 2 of those + * can be NEON. If you are only using NEON instructions, you are only using 2/3 of the CPU + * bandwidth. + * + * This is even more noticeable on the more advanced cores like the Cortex-A76 which + * can dispatch 8 micro-ops per cycle, but still only 2 NEON micro-ops at once. + * + * Therefore, to make the most out of the pipeline, it is beneficial to run 6 NEON lanes + * and 2 scalar lanes, which is chosen by default. + * + * This does not apply to Apple processors or 32-bit processors, which run better with + * full NEON. These will default to 8. Additionally, size-optimized builds run 8 lanes. + * + * This change benefits CPUs with large micro-op buffers without negatively affecting + * most other CPUs: + * + * | Chipset | Dispatch type | NEON only | 6:2 hybrid | Diff. | + * |:----------------------|:--------------------|----------:|-----------:|------:| + * | Snapdragon 730 (A76) | 2 NEON/8 micro-ops | 8.8 GB/s | 10.1 GB/s | ~16% | + * | Snapdragon 835 (A73) | 2 NEON/3 micro-ops | 5.1 GB/s | 5.3 GB/s | ~5% | + * | Marvell PXA1928 (A53) | In-order dual-issue | 1.9 GB/s | 1.9 GB/s | 0% | + * | Apple M1 | 4 NEON/8 micro-ops | 37.3 GB/s | 36.1 GB/s | ~-3% | + * + * It also seems to fix some bad codegen on GCC, making it almost as fast as clang. + * + * When using WASM SIMD128, if this is 2 or 6, SIMDe will scalarize 2 of the lanes meaning + * it effectively becomes worse 4. + * + * @see XXH3_accumulate_512_neon() + */ +# ifndef XXH3_NEON_LANES +# if (defined(__aarch64__) || defined(__arm64__) || defined(_M_ARM64) || defined(_M_ARM64EC)) \ + && !defined(__APPLE__) && XXH_SIZE_OPT <= 0 +# define XXH3_NEON_LANES 6 +# else +# define XXH3_NEON_LANES XXH_ACC_NB +# endif +# endif +#endif /* XXH_VECTOR == XXH_NEON */ + +/* + * VSX and Z Vector helpers. + * + * This is very messy, and any pull requests to clean this up are welcome. + * + * There are a lot of problems with supporting VSX and s390x, due to + * inconsistent intrinsics, spotty coverage, and multiple endiannesses. + */ +#if XXH_VECTOR == XXH_VSX +/* Annoyingly, these headers _may_ define three macros: `bool`, `vector`, + * and `pixel`. This is a problem for obvious reasons. + * + * These keywords are unnecessary; the spec literally says they are + * equivalent to `__bool`, `__vector`, and `__pixel` and may be undef'd + * after including the header. + * + * We use pragma push_macro/pop_macro to keep the namespace clean. */ +# pragma push_macro("bool") +# pragma push_macro("vector") +# pragma push_macro("pixel") +/* silence potential macro redefined warnings */ +# undef bool +# undef vector +# undef pixel + +# if defined(__s390x__) +# include +# else +# include +# endif + +/* Restore the original macro values, if applicable. */ +# pragma pop_macro("pixel") +# pragma pop_macro("vector") +# pragma pop_macro("bool") + +typedef __vector unsigned long long xxh_u64x2; +typedef __vector unsigned char xxh_u8x16; +typedef __vector unsigned xxh_u32x4; + +/* + * UGLY HACK: Similar to aarch64 macOS GCC, s390x GCC has the same aliasing issue. + */ +typedef xxh_u64x2 xxh_aliasing_u64x2 XXH_ALIASING; + +# ifndef XXH_VSX_BE +# if defined(__BIG_ENDIAN__) \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__) +# define XXH_VSX_BE 1 +# elif defined(__VEC_ELEMENT_REG_ORDER__) && __VEC_ELEMENT_REG_ORDER__ == __ORDER_BIG_ENDIAN__ +# warning "-maltivec=be is not recommended. Please use native endianness." +# define XXH_VSX_BE 1 +# else +# define XXH_VSX_BE 0 +# endif +# endif /* !defined(XXH_VSX_BE) */ + +# if XXH_VSX_BE +# if defined(__POWER9_VECTOR__) || (defined(__clang__) && defined(__s390x__)) +# define XXH_vec_revb vec_revb +# else +/*! + * A polyfill for POWER9's vec_revb(). + */ +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_revb(xxh_u64x2 val) +{ + xxh_u8x16 const vByteSwap = { 0x07, 0x06, 0x05, 0x04, 0x03, 0x02, 0x01, 0x00, + 0x0F, 0x0E, 0x0D, 0x0C, 0x0B, 0x0A, 0x09, 0x08 }; + return vec_perm(val, val, vByteSwap); +} +# endif +# endif /* XXH_VSX_BE */ + +/*! + * Performs an unaligned vector load and byte swaps it on big endian. + */ +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_loadu(const void *ptr) +{ + xxh_u64x2 ret; + XXH_memcpy(&ret, ptr, sizeof(xxh_u64x2)); +# if XXH_VSX_BE + ret = XXH_vec_revb(ret); +# endif + return ret; +} + +/* + * vec_mulo and vec_mule are very problematic intrinsics on PowerPC + * + * These intrinsics weren't added until GCC 8, despite existing for a while, + * and they are endian dependent. Also, their meaning swap depending on version. + * */ +# if defined(__s390x__) + /* s390x is always big endian, no issue on this platform */ +# define XXH_vec_mulo vec_mulo +# define XXH_vec_mule vec_mule +# elif defined(__clang__) && XXH_HAS_BUILTIN(__builtin_altivec_vmuleuw) && !defined(__ibmxl__) +/* Clang has a better way to control this, we can just use the builtin which doesn't swap. */ + /* The IBM XL Compiler (which defined __clang__) only implements the vec_* operations */ +# define XXH_vec_mulo __builtin_altivec_vmulouw +# define XXH_vec_mule __builtin_altivec_vmuleuw +# else +/* gcc needs inline assembly */ +/* Adapted from https://github.com/google/highwayhash/blob/master/highwayhash/hh_vsx.h. */ +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_mulo(xxh_u32x4 a, xxh_u32x4 b) +{ + xxh_u64x2 result; + __asm__("vmulouw %0, %1, %2" : "=v" (result) : "v" (a), "v" (b)); + return result; +} +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_mule(xxh_u32x4 a, xxh_u32x4 b) +{ + xxh_u64x2 result; + __asm__("vmuleuw %0, %1, %2" : "=v" (result) : "v" (a), "v" (b)); + return result; +} +# endif /* XXH_vec_mulo, XXH_vec_mule */ +#endif /* XXH_VECTOR == XXH_VSX */ + +#if XXH_VECTOR == XXH_SVE +#define ACCRND(acc, offset) \ +do { \ + svuint64_t input_vec = svld1_u64(mask, xinput + offset); \ + svuint64_t secret_vec = svld1_u64(mask, xsecret + offset); \ + svuint64_t mixed = sveor_u64_x(mask, secret_vec, input_vec); \ + svuint64_t swapped = svtbl_u64(input_vec, kSwap); \ + svuint64_t mixed_lo = svextw_u64_x(mask, mixed); \ + svuint64_t mixed_hi = svlsr_n_u64_x(mask, mixed, 32); \ + svuint64_t mul = svmad_u64_x(mask, mixed_lo, mixed_hi, swapped); \ + acc = svadd_u64_x(mask, acc, mul); \ +} while (0) +#endif /* XXH_VECTOR == XXH_SVE */ + +/* prefetch + * can be disabled, by declaring XXH_NO_PREFETCH build macro */ +#if defined(XXH_NO_PREFETCH) +# define XXH_PREFETCH(ptr) (void)(ptr) /* disabled */ +#else +# if XXH_SIZE_OPT >= 1 +# define XXH_PREFETCH(ptr) (void)(ptr) +# elif defined(_MSC_VER) && (defined(_M_X64) || defined(_M_IX86)) /* _mm_prefetch() not defined outside of x86/x64 */ +# include /* https://msdn.microsoft.com/fr-fr/library/84szxsww(v=vs.90).aspx */ +# define XXH_PREFETCH(ptr) _mm_prefetch((const char*)(ptr), _MM_HINT_T0) +# elif defined(__GNUC__) && ( (__GNUC__ >= 4) || ( (__GNUC__ == 3) && (__GNUC_MINOR__ >= 1) ) ) +# define XXH_PREFETCH(ptr) __builtin_prefetch((ptr), 0 /* rw==read */, 3 /* locality */) +# else +# define XXH_PREFETCH(ptr) (void)(ptr) /* disabled */ +# endif +#endif /* XXH_NO_PREFETCH */ + + +/* ========================================== + * XXH3 default settings + * ========================================== */ + +#define XXH_SECRET_DEFAULT_SIZE 192 /* minimum XXH3_SECRET_SIZE_MIN */ + +#if (XXH_SECRET_DEFAULT_SIZE < XXH3_SECRET_SIZE_MIN) +# error "default keyset is not large enough" +#endif + +/*! Pseudorandom secret taken directly from FARSH. */ +XXH_ALIGN(64) static const xxh_u8 XXH3_kSecret[XXH_SECRET_DEFAULT_SIZE] = { + 0xb8, 0xfe, 0x6c, 0x39, 0x23, 0xa4, 0x4b, 0xbe, 0x7c, 0x01, 0x81, 0x2c, 0xf7, 0x21, 0xad, 0x1c, + 0xde, 0xd4, 0x6d, 0xe9, 0x83, 0x90, 0x97, 0xdb, 0x72, 0x40, 0xa4, 0xa4, 0xb7, 0xb3, 0x67, 0x1f, + 0xcb, 0x79, 0xe6, 0x4e, 0xcc, 0xc0, 0xe5, 0x78, 0x82, 0x5a, 0xd0, 0x7d, 0xcc, 0xff, 0x72, 0x21, + 0xb8, 0x08, 0x46, 0x74, 0xf7, 0x43, 0x24, 0x8e, 0xe0, 0x35, 0x90, 0xe6, 0x81, 0x3a, 0x26, 0x4c, + 0x3c, 0x28, 0x52, 0xbb, 0x91, 0xc3, 0x00, 0xcb, 0x88, 0xd0, 0x65, 0x8b, 0x1b, 0x53, 0x2e, 0xa3, + 0x71, 0x64, 0x48, 0x97, 0xa2, 0x0d, 0xf9, 0x4e, 0x38, 0x19, 0xef, 0x46, 0xa9, 0xde, 0xac, 0xd8, + 0xa8, 0xfa, 0x76, 0x3f, 0xe3, 0x9c, 0x34, 0x3f, 0xf9, 0xdc, 0xbb, 0xc7, 0xc7, 0x0b, 0x4f, 0x1d, + 0x8a, 0x51, 0xe0, 0x4b, 0xcd, 0xb4, 0x59, 0x31, 0xc8, 0x9f, 0x7e, 0xc9, 0xd9, 0x78, 0x73, 0x64, + 0xea, 0xc5, 0xac, 0x83, 0x34, 0xd3, 0xeb, 0xc3, 0xc5, 0x81, 0xa0, 0xff, 0xfa, 0x13, 0x63, 0xeb, + 0x17, 0x0d, 0xdd, 0x51, 0xb7, 0xf0, 0xda, 0x49, 0xd3, 0x16, 0x55, 0x26, 0x29, 0xd4, 0x68, 0x9e, + 0x2b, 0x16, 0xbe, 0x58, 0x7d, 0x47, 0xa1, 0xfc, 0x8f, 0xf8, 0xb8, 0xd1, 0x7a, 0xd0, 0x31, 0xce, + 0x45, 0xcb, 0x3a, 0x8f, 0x95, 0x16, 0x04, 0x28, 0xaf, 0xd7, 0xfb, 0xca, 0xbb, 0x4b, 0x40, 0x7e, +}; + +static const xxh_u64 PRIME_MX1 = 0x165667919E3779F9ULL; /*!< 0b0001011001010110011001111001000110011110001101110111100111111001 */ +static const xxh_u64 PRIME_MX2 = 0x9FB21C651E98DF25ULL; /*!< 0b1001111110110010000111000110010100011110100110001101111100100101 */ + +#ifdef XXH_OLD_NAMES +# define kSecret XXH3_kSecret +#endif + +#ifdef XXH_DOXYGEN +/*! + * @brief Calculates a 32-bit to 64-bit long multiply. + * + * Implemented as a macro. + * + * Wraps `__emulu` on MSVC x86 because it tends to call `__allmul` when it doesn't + * need to (but it shouldn't need to anyways, it is about 7 instructions to do + * a 64x64 multiply...). Since we know that this will _always_ emit `MULL`, we + * use that instead of the normal method. + * + * If you are compiling for platforms like Thumb-1 and don't have a better option, + * you may also want to write your own long multiply routine here. + * + * @param x, y Numbers to be multiplied + * @return 64-bit product of the low 32 bits of @p x and @p y. + */ +XXH_FORCE_INLINE xxh_u64 +XXH_mult32to64(xxh_u64 x, xxh_u64 y) +{ + return (x & 0xFFFFFFFF) * (y & 0xFFFFFFFF); +} +#elif defined(_MSC_VER) && defined(_M_IX86) +# define XXH_mult32to64(x, y) __emulu((unsigned)(x), (unsigned)(y)) +#else +/* + * Downcast + upcast is usually better than masking on older compilers like + * GCC 4.2 (especially 32-bit ones), all without affecting newer compilers. + * + * The other method, (x & 0xFFFFFFFF) * (y & 0xFFFFFFFF), will AND both operands + * and perform a full 64x64 multiply -- entirely redundant on 32-bit. + */ +# define XXH_mult32to64(x, y) ((xxh_u64)(xxh_u32)(x) * (xxh_u64)(xxh_u32)(y)) +#endif + +/*! + * @brief Calculates a 64->128-bit long multiply. + * + * Uses `__uint128_t` and `_umul128` if available, otherwise uses a scalar + * version. + * + * @param lhs , rhs The 64-bit integers to be multiplied + * @return The 128-bit result represented in an @ref XXH128_hash_t. + */ +static XXH128_hash_t +XXH_mult64to128(xxh_u64 lhs, xxh_u64 rhs) +{ + /* + * GCC/Clang __uint128_t method. + * + * On most 64-bit targets, GCC and Clang define a __uint128_t type. + * This is usually the best way as it usually uses a native long 64-bit + * multiply, such as MULQ on x86_64 or MUL + UMULH on aarch64. + * + * Usually. + * + * Despite being a 32-bit platform, Clang (and emscripten) define this type + * despite not having the arithmetic for it. This results in a laggy + * compiler builtin call which calculates a full 128-bit multiply. + * In that case it is best to use the portable one. + * https://github.com/Cyan4973/xxHash/issues/211#issuecomment-515575677 + */ +#if (defined(__GNUC__) || defined(__clang__)) && !defined(__wasm__) \ + && defined(__SIZEOF_INT128__) \ + || (defined(_INTEGRAL_MAX_BITS) && _INTEGRAL_MAX_BITS >= 128) + + __uint128_t const product = (__uint128_t)lhs * (__uint128_t)rhs; + XXH128_hash_t r128; + r128.low64 = (xxh_u64)(product); + r128.high64 = (xxh_u64)(product >> 64); + return r128; + + /* + * MSVC for x64's _umul128 method. + * + * xxh_u64 _umul128(xxh_u64 Multiplier, xxh_u64 Multiplicand, xxh_u64 *HighProduct); + * + * This compiles to single operand MUL on x64. + */ +#elif (defined(_M_X64) || defined(_M_IA64)) && !defined(_M_ARM64EC) + +#ifndef _MSC_VER +# pragma intrinsic(_umul128) +#endif + xxh_u64 product_high; + xxh_u64 const product_low = _umul128(lhs, rhs, &product_high); + XXH128_hash_t r128; + r128.low64 = product_low; + r128.high64 = product_high; + return r128; + + /* + * MSVC for ARM64's __umulh method. + * + * This compiles to the same MUL + UMULH as GCC/Clang's __uint128_t method. + */ +#elif defined(_M_ARM64) || defined(_M_ARM64EC) + +#ifndef _MSC_VER +# pragma intrinsic(__umulh) +#endif + XXH128_hash_t r128; + r128.low64 = lhs * rhs; + r128.high64 = __umulh(lhs, rhs); + return r128; + +#else + /* + * Portable scalar method. Optimized for 32-bit and 64-bit ALUs. + * + * This is a fast and simple grade school multiply, which is shown below + * with base 10 arithmetic instead of base 0x100000000. + * + * 9 3 // D2 lhs = 93 + * x 7 5 // D2 rhs = 75 + * ---------- + * 1 5 // D2 lo_lo = (93 % 10) * (75 % 10) = 15 + * 4 5 | // D2 hi_lo = (93 / 10) * (75 % 10) = 45 + * 2 1 | // D2 lo_hi = (93 % 10) * (75 / 10) = 21 + * + 6 3 | | // D2 hi_hi = (93 / 10) * (75 / 10) = 63 + * --------- + * 2 7 | // D2 cross = (15 / 10) + (45 % 10) + 21 = 27 + * + 6 7 | | // D2 upper = (27 / 10) + (45 / 10) + 63 = 67 + * --------- + * 6 9 7 5 // D4 res = (27 * 10) + (15 % 10) + (67 * 100) = 6975 + * + * The reasons for adding the products like this are: + * 1. It avoids manual carry tracking. Just like how + * (9 * 9) + 9 + 9 = 99, the same applies with this for UINT64_MAX. + * This avoids a lot of complexity. + * + * 2. It hints for, and on Clang, compiles to, the powerful UMAAL + * instruction available in ARM's Digital Signal Processing extension + * in 32-bit ARMv6 and later, which is shown below: + * + * void UMAAL(xxh_u32 *RdLo, xxh_u32 *RdHi, xxh_u32 Rn, xxh_u32 Rm) + * { + * xxh_u64 product = (xxh_u64)*RdLo * (xxh_u64)*RdHi + Rn + Rm; + * *RdLo = (xxh_u32)(product & 0xFFFFFFFF); + * *RdHi = (xxh_u32)(product >> 32); + * } + * + * This instruction was designed for efficient long multiplication, and + * allows this to be calculated in only 4 instructions at speeds + * comparable to some 64-bit ALUs. + * + * 3. It isn't terrible on other platforms. Usually this will be a couple + * of 32-bit ADD/ADCs. + */ + + /* First calculate all of the cross products. */ + xxh_u64 const lo_lo = XXH_mult32to64(lhs & 0xFFFFFFFF, rhs & 0xFFFFFFFF); + xxh_u64 const hi_lo = XXH_mult32to64(lhs >> 32, rhs & 0xFFFFFFFF); + xxh_u64 const lo_hi = XXH_mult32to64(lhs & 0xFFFFFFFF, rhs >> 32); + xxh_u64 const hi_hi = XXH_mult32to64(lhs >> 32, rhs >> 32); + + /* Now add the products together. These will never overflow. */ + xxh_u64 const cross = (lo_lo >> 32) + (hi_lo & 0xFFFFFFFF) + lo_hi; + xxh_u64 const upper = (hi_lo >> 32) + (cross >> 32) + hi_hi; + xxh_u64 const lower = (cross << 32) | (lo_lo & 0xFFFFFFFF); + + XXH128_hash_t r128; + r128.low64 = lower; + r128.high64 = upper; + return r128; +#endif +} + +/*! + * @brief Calculates a 64-bit to 128-bit multiply, then XOR folds it. + * + * The reason for the separate function is to prevent passing too many structs + * around by value. This will hopefully inline the multiply, but we don't force it. + * + * @param lhs , rhs The 64-bit integers to multiply + * @return The low 64 bits of the product XOR'd by the high 64 bits. + * @see XXH_mult64to128() + */ +static xxh_u64 +XXH3_mul128_fold64(xxh_u64 lhs, xxh_u64 rhs) +{ + XXH128_hash_t product = XXH_mult64to128(lhs, rhs); + return product.low64 ^ product.high64; +} + +/*! Seems to produce slightly better code on GCC for some reason. */ +XXH_FORCE_INLINE XXH_CONSTF xxh_u64 XXH_xorshift64(xxh_u64 v64, int shift) +{ + XXH_ASSERT(0 <= shift && shift < 64); + return v64 ^ (v64 >> shift); +} + +/* + * This is a fast avalanche stage, + * suitable when input bits are already partially mixed + */ +static XXH64_hash_t XXH3_avalanche(xxh_u64 h64) +{ + h64 = XXH_xorshift64(h64, 37); + h64 *= PRIME_MX1; + h64 = XXH_xorshift64(h64, 32); + return h64; +} + +/* + * This is a stronger avalanche, + * inspired by Pelle Evensen's rrmxmx + * preferable when input has not been previously mixed + */ +static XXH64_hash_t XXH3_rrmxmx(xxh_u64 h64, xxh_u64 len) +{ + /* this mix is inspired by Pelle Evensen's rrmxmx */ + h64 ^= XXH_rotl64(h64, 49) ^ XXH_rotl64(h64, 24); + h64 *= PRIME_MX2; + h64 ^= (h64 >> 35) + len ; + h64 *= PRIME_MX2; + return XXH_xorshift64(h64, 28); +} + + +/* ========================================== + * Short keys + * ========================================== + * One of the shortcomings of XXH32 and XXH64 was that their performance was + * sub-optimal on short lengths. It used an iterative algorithm which strongly + * favored lengths that were a multiple of 4 or 8. + * + * Instead of iterating over individual inputs, we use a set of single shot + * functions which piece together a range of lengths and operate in constant time. + * + * Additionally, the number of multiplies has been significantly reduced. This + * reduces latency, especially when emulating 64-bit multiplies on 32-bit. + * + * Depending on the platform, this may or may not be faster than XXH32, but it + * is almost guaranteed to be faster than XXH64. + */ + +/* + * At very short lengths, there isn't enough input to fully hide secrets, or use + * the entire secret. + * + * There is also only a limited amount of mixing we can do before significantly + * impacting performance. + * + * Therefore, we use different sections of the secret and always mix two secret + * samples with an XOR. This should have no effect on performance on the + * seedless or withSeed variants because everything _should_ be constant folded + * by modern compilers. + * + * The XOR mixing hides individual parts of the secret and increases entropy. + * + * This adds an extra layer of strength for custom secrets. + */ +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_1to3_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(1 <= len && len <= 3); + XXH_ASSERT(secret != NULL); + /* + * len = 1: combined = { input[0], 0x01, input[0], input[0] } + * len = 2: combined = { input[1], 0x02, input[0], input[1] } + * len = 3: combined = { input[2], 0x03, input[0], input[1] } + */ + { xxh_u8 const c1 = input[0]; + xxh_u8 const c2 = input[len >> 1]; + xxh_u8 const c3 = input[len - 1]; + xxh_u32 const combined = ((xxh_u32)c1 << 16) | ((xxh_u32)c2 << 24) + | ((xxh_u32)c3 << 0) | ((xxh_u32)len << 8); + xxh_u64 const bitflip = (XXH_readLE32(secret) ^ XXH_readLE32(secret+4)) + seed; + xxh_u64 const keyed = (xxh_u64)combined ^ bitflip; + return XXH64_avalanche(keyed); + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_4to8_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(4 <= len && len <= 8); + seed ^= (xxh_u64)XXH_swap32((xxh_u32)seed) << 32; + { xxh_u32 const input1 = XXH_readLE32(input); + xxh_u32 const input2 = XXH_readLE32(input + len - 4); + xxh_u64 const bitflip = (XXH_readLE64(secret+8) ^ XXH_readLE64(secret+16)) - seed; + xxh_u64 const input64 = input2 + (((xxh_u64)input1) << 32); + xxh_u64 const keyed = input64 ^ bitflip; + return XXH3_rrmxmx(keyed, len); + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_9to16_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(9 <= len && len <= 16); + { xxh_u64 const bitflip1 = (XXH_readLE64(secret+24) ^ XXH_readLE64(secret+32)) + seed; + xxh_u64 const bitflip2 = (XXH_readLE64(secret+40) ^ XXH_readLE64(secret+48)) - seed; + xxh_u64 const input_lo = XXH_readLE64(input) ^ bitflip1; + xxh_u64 const input_hi = XXH_readLE64(input + len - 8) ^ bitflip2; + xxh_u64 const acc = len + + XXH_swap64(input_lo) + input_hi + + XXH3_mul128_fold64(input_lo, input_hi); + return XXH3_avalanche(acc); + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_0to16_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(len <= 16); + { if (XXH_likely(len > 8)) return XXH3_len_9to16_64b(input, len, secret, seed); + if (XXH_likely(len >= 4)) return XXH3_len_4to8_64b(input, len, secret, seed); + if (len) return XXH3_len_1to3_64b(input, len, secret, seed); + return XXH64_avalanche(seed ^ (XXH_readLE64(secret+56) ^ XXH_readLE64(secret+64))); + } +} + +/* + * DISCLAIMER: There are known *seed-dependent* multicollisions here due to + * multiplication by zero, affecting hashes of lengths 17 to 240. + * + * However, they are very unlikely. + * + * Keep this in mind when using the unseeded XXH3_64bits() variant: As with all + * unseeded non-cryptographic hashes, it does not attempt to defend itself + * against specially crafted inputs, only random inputs. + * + * Compared to classic UMAC where a 1 in 2^31 chance of 4 consecutive bytes + * cancelling out the secret is taken an arbitrary number of times (addressed + * in XXH3_accumulate_512), this collision is very unlikely with random inputs + * and/or proper seeding: + * + * This only has a 1 in 2^63 chance of 8 consecutive bytes cancelling out, in a + * function that is only called up to 16 times per hash with up to 240 bytes of + * input. + * + * This is not too bad for a non-cryptographic hash function, especially with + * only 64 bit outputs. + * + * The 128-bit variant (which trades some speed for strength) is NOT affected + * by this, although it is always a good idea to use a proper seed if you care + * about strength. + */ +XXH_FORCE_INLINE xxh_u64 XXH3_mix16B(const xxh_u8* XXH_RESTRICT input, + const xxh_u8* XXH_RESTRICT secret, xxh_u64 seed64) +{ +#if defined(__GNUC__) && !defined(__clang__) /* GCC, not Clang */ \ + && defined(__i386__) && defined(__SSE2__) /* x86 + SSE2 */ \ + && !defined(XXH_ENABLE_AUTOVECTORIZE) /* Define to disable like XXH32 hack */ + /* + * UGLY HACK: + * GCC for x86 tends to autovectorize the 128-bit multiply, resulting in + * slower code. + * + * By forcing seed64 into a register, we disrupt the cost model and + * cause it to scalarize. See `XXH32_round()` + * + * FIXME: Clang's output is still _much_ faster -- On an AMD Ryzen 3600, + * XXH3_64bits @ len=240 runs at 4.6 GB/s with Clang 9, but 3.3 GB/s on + * GCC 9.2, despite both emitting scalar code. + * + * GCC generates much better scalar code than Clang for the rest of XXH3, + * which is why finding a more optimal codepath is an interest. + */ + XXH_COMPILER_GUARD(seed64); +#endif + { xxh_u64 const input_lo = XXH_readLE64(input); + xxh_u64 const input_hi = XXH_readLE64(input+8); + return XXH3_mul128_fold64( + input_lo ^ (XXH_readLE64(secret) + seed64), + input_hi ^ (XXH_readLE64(secret+8) - seed64) + ); + } +} + +/* For mid range keys, XXH3 uses a Mum-hash variant. */ +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_17to128_64b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(16 < len && len <= 128); + + { xxh_u64 acc = len * XXH_PRIME64_1; +#if XXH_SIZE_OPT >= 1 + /* Smaller and cleaner, but slightly slower. */ + unsigned int i = (unsigned int)(len - 1) / 32; + do { + acc += XXH3_mix16B(input+16 * i, secret+32*i, seed); + acc += XXH3_mix16B(input+len-16*(i+1), secret+32*i+16, seed); + } while (i-- != 0); +#else + if (len > 32) { + if (len > 64) { + if (len > 96) { + acc += XXH3_mix16B(input+48, secret+96, seed); + acc += XXH3_mix16B(input+len-64, secret+112, seed); + } + acc += XXH3_mix16B(input+32, secret+64, seed); + acc += XXH3_mix16B(input+len-48, secret+80, seed); + } + acc += XXH3_mix16B(input+16, secret+32, seed); + acc += XXH3_mix16B(input+len-32, secret+48, seed); + } + acc += XXH3_mix16B(input+0, secret+0, seed); + acc += XXH3_mix16B(input+len-16, secret+16, seed); +#endif + return XXH3_avalanche(acc); + } +} + +XXH_NO_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_129to240_64b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX); + + #define XXH3_MIDSIZE_STARTOFFSET 3 + #define XXH3_MIDSIZE_LASTOFFSET 17 + + { xxh_u64 acc = len * XXH_PRIME64_1; + xxh_u64 acc_end; + unsigned int const nbRounds = (unsigned int)len / 16; + unsigned int i; + XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX); + for (i=0; i<8; i++) { + acc += XXH3_mix16B(input+(16*i), secret+(16*i), seed); + } + /* last bytes */ + acc_end = XXH3_mix16B(input + len - 16, secret + XXH3_SECRET_SIZE_MIN - XXH3_MIDSIZE_LASTOFFSET, seed); + XXH_ASSERT(nbRounds >= 8); + acc = XXH3_avalanche(acc); +#if defined(__clang__) /* Clang */ \ + && (defined(__ARM_NEON) || defined(__ARM_NEON__)) /* NEON */ \ + && !defined(XXH_ENABLE_AUTOVECTORIZE) /* Define to disable */ + /* + * UGLY HACK: + * Clang for ARMv7-A tries to vectorize this loop, similar to GCC x86. + * In everywhere else, it uses scalar code. + * + * For 64->128-bit multiplies, even if the NEON was 100% optimal, it + * would still be slower than UMAAL (see XXH_mult64to128). + * + * Unfortunately, Clang doesn't handle the long multiplies properly and + * converts them to the nonexistent "vmulq_u64" intrinsic, which is then + * scalarized into an ugly mess of VMOV.32 instructions. + * + * This mess is difficult to avoid without turning autovectorization + * off completely, but they are usually relatively minor and/or not + * worth it to fix. + * + * This loop is the easiest to fix, as unlike XXH32, this pragma + * _actually works_ because it is a loop vectorization instead of an + * SLP vectorization. + */ + #pragma clang loop vectorize(disable) +#endif + for (i=8 ; i < nbRounds; i++) { + /* + * Prevents clang for unrolling the acc loop and interleaving with this one. + */ + XXH_COMPILER_GUARD(acc); + acc_end += XXH3_mix16B(input+(16*i), secret+(16*(i-8)) + XXH3_MIDSIZE_STARTOFFSET, seed); + } + return XXH3_avalanche(acc + acc_end); + } +} + + +/* ======= Long Keys ======= */ + +#define XXH_STRIPE_LEN 64 +#define XXH_SECRET_CONSUME_RATE 8 /* nb of secret bytes consumed at each accumulation */ +#define XXH_ACC_NB (XXH_STRIPE_LEN / sizeof(xxh_u64)) + +#ifdef XXH_OLD_NAMES +# define STRIPE_LEN XXH_STRIPE_LEN +# define ACC_NB XXH_ACC_NB +#endif + +#ifndef XXH_PREFETCH_DIST +# ifdef __clang__ +# define XXH_PREFETCH_DIST 320 +# else +# if (XXH_VECTOR == XXH_AVX512) +# define XXH_PREFETCH_DIST 512 +# else +# define XXH_PREFETCH_DIST 384 +# endif +# endif /* __clang__ */ +#endif /* XXH_PREFETCH_DIST */ + +/* + * These macros are to generate an XXH3_accumulate() function. + * The two arguments select the name suffix and target attribute. + * + * The name of this symbol is XXH3_accumulate_() and it calls + * XXH3_accumulate_512_(). + * + * It may be useful to hand implement this function if the compiler fails to + * optimize the inline function. + */ +#define XXH3_ACCUMULATE_TEMPLATE(name) \ +void \ +XXH3_accumulate_##name(xxh_u64* XXH_RESTRICT acc, \ + const xxh_u8* XXH_RESTRICT input, \ + const xxh_u8* XXH_RESTRICT secret, \ + size_t nbStripes) \ +{ \ + size_t n; \ + for (n = 0; n < nbStripes; n++ ) { \ + const xxh_u8* const in = input + n*XXH_STRIPE_LEN; \ + XXH_PREFETCH(in + XXH_PREFETCH_DIST); \ + XXH3_accumulate_512_##name( \ + acc, \ + in, \ + secret + n*XXH_SECRET_CONSUME_RATE); \ + } \ +} + + +XXH_FORCE_INLINE void XXH_writeLE64(void* dst, xxh_u64 v64) +{ + if (!XXH_CPU_LITTLE_ENDIAN) v64 = XXH_swap64(v64); + XXH_memcpy(dst, &v64, sizeof(v64)); +} + +/* Several intrinsic functions below are supposed to accept __int64 as argument, + * as documented in https://software.intel.com/sites/landingpage/IntrinsicsGuide/ . + * However, several environments do not define __int64 type, + * requiring a workaround. + */ +#if !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) + typedef int64_t xxh_i64; +#else + /* the following type must have a width of 64-bit */ + typedef long long xxh_i64; +#endif + + +/* + * XXH3_accumulate_512 is the tightest loop for long inputs, and it is the most optimized. + * + * It is a hardened version of UMAC, based off of FARSH's implementation. + * + * This was chosen because it adapts quite well to 32-bit, 64-bit, and SIMD + * implementations, and it is ridiculously fast. + * + * We harden it by mixing the original input to the accumulators as well as the product. + * + * This means that in the (relatively likely) case of a multiply by zero, the + * original input is preserved. + * + * On 128-bit inputs, we swap 64-bit pairs when we add the input to improve + * cross-pollination, as otherwise the upper and lower halves would be + * essentially independent. + * + * This doesn't matter on 64-bit hashes since they all get merged together in + * the end, so we skip the extra step. + * + * Both XXH3_64bits and XXH3_128bits use this subroutine. + */ + +#if (XXH_VECTOR == XXH_AVX512) \ + || (defined(XXH_DISPATCH_AVX512) && XXH_DISPATCH_AVX512 != 0) + +#ifndef XXH_TARGET_AVX512 +# define XXH_TARGET_AVX512 /* disable attribute target */ +#endif + +XXH_FORCE_INLINE XXH_TARGET_AVX512 void +XXH3_accumulate_512_avx512(void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + __m512i* const xacc = (__m512i *) acc; + XXH_ASSERT((((size_t)acc) & 63) == 0); + XXH_STATIC_ASSERT(XXH_STRIPE_LEN == sizeof(__m512i)); + + { + /* data_vec = input[0]; */ + __m512i const data_vec = _mm512_loadu_si512 (input); + /* key_vec = secret[0]; */ + __m512i const key_vec = _mm512_loadu_si512 (secret); + /* data_key = data_vec ^ key_vec; */ + __m512i const data_key = _mm512_xor_si512 (data_vec, key_vec); + /* data_key_lo = data_key >> 32; */ + __m512i const data_key_lo = _mm512_srli_epi64 (data_key, 32); + /* product = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */ + __m512i const product = _mm512_mul_epu32 (data_key, data_key_lo); + /* xacc[0] += swap(data_vec); */ + __m512i const data_swap = _mm512_shuffle_epi32(data_vec, (_MM_PERM_ENUM)_MM_SHUFFLE(1, 0, 3, 2)); + __m512i const sum = _mm512_add_epi64(*xacc, data_swap); + /* xacc[0] += product; */ + *xacc = _mm512_add_epi64(product, sum); + } +} +XXH_FORCE_INLINE XXH_TARGET_AVX512 XXH3_ACCUMULATE_TEMPLATE(avx512) + +/* + * XXH3_scrambleAcc: Scrambles the accumulators to improve mixing. + * + * Multiplication isn't perfect, as explained by Google in HighwayHash: + * + * // Multiplication mixes/scrambles bytes 0-7 of the 64-bit result to + * // varying degrees. In descending order of goodness, bytes + * // 3 4 2 5 1 6 0 7 have quality 228 224 164 160 100 96 36 32. + * // As expected, the upper and lower bytes are much worse. + * + * Source: https://github.com/google/highwayhash/blob/0aaf66b/highwayhash/hh_avx2.h#L291 + * + * Since our algorithm uses a pseudorandom secret to add some variance into the + * mix, we don't need to (or want to) mix as often or as much as HighwayHash does. + * + * This isn't as tight as XXH3_accumulate, but still written in SIMD to avoid + * extraction. + * + * Both XXH3_64bits and XXH3_128bits use this subroutine. + */ + +XXH_FORCE_INLINE XXH_TARGET_AVX512 void +XXH3_scrambleAcc_avx512(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 63) == 0); + XXH_STATIC_ASSERT(XXH_STRIPE_LEN == sizeof(__m512i)); + { __m512i* const xacc = (__m512i*) acc; + const __m512i prime32 = _mm512_set1_epi32((int)XXH_PRIME32_1); + + /* xacc[0] ^= (xacc[0] >> 47) */ + __m512i const acc_vec = *xacc; + __m512i const shifted = _mm512_srli_epi64 (acc_vec, 47); + /* xacc[0] ^= secret; */ + __m512i const key_vec = _mm512_loadu_si512 (secret); + __m512i const data_key = _mm512_ternarylogic_epi32(key_vec, acc_vec, shifted, 0x96 /* key_vec ^ acc_vec ^ shifted */); + + /* xacc[0] *= XXH_PRIME32_1; */ + __m512i const data_key_hi = _mm512_srli_epi64 (data_key, 32); + __m512i const prod_lo = _mm512_mul_epu32 (data_key, prime32); + __m512i const prod_hi = _mm512_mul_epu32 (data_key_hi, prime32); + *xacc = _mm512_add_epi64(prod_lo, _mm512_slli_epi64(prod_hi, 32)); + } +} + +XXH_FORCE_INLINE XXH_TARGET_AVX512 void +XXH3_initCustomSecret_avx512(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 63) == 0); + XXH_STATIC_ASSERT(XXH_SEC_ALIGN == 64); + XXH_ASSERT(((size_t)customSecret & 63) == 0); + (void)(&XXH_writeLE64); + { int const nbRounds = XXH_SECRET_DEFAULT_SIZE / sizeof(__m512i); + __m512i const seed_pos = _mm512_set1_epi64((xxh_i64)seed64); + __m512i const seed = _mm512_mask_sub_epi64(seed_pos, 0xAA, _mm512_set1_epi8(0), seed_pos); + + const __m512i* const src = (const __m512i*) ((const void*) XXH3_kSecret); + __m512i* const dest = ( __m512i*) customSecret; + int i; + XXH_ASSERT(((size_t)src & 63) == 0); /* control alignment */ + XXH_ASSERT(((size_t)dest & 63) == 0); + for (i=0; i < nbRounds; ++i) { + dest[i] = _mm512_add_epi64(_mm512_load_si512(src + i), seed); + } } +} + +#endif + +#if (XXH_VECTOR == XXH_AVX2) \ + || (defined(XXH_DISPATCH_AVX2) && XXH_DISPATCH_AVX2 != 0) + +#ifndef XXH_TARGET_AVX2 +# define XXH_TARGET_AVX2 /* disable attribute target */ +#endif + +XXH_FORCE_INLINE XXH_TARGET_AVX2 void +XXH3_accumulate_512_avx2( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 31) == 0); + { __m256i* const xacc = (__m256i *) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */ + const __m256i* const xinput = (const __m256i *) input; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */ + const __m256i* const xsecret = (const __m256i *) secret; + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m256i); i++) { + /* data_vec = xinput[i]; */ + __m256i const data_vec = _mm256_loadu_si256 (xinput+i); + /* key_vec = xsecret[i]; */ + __m256i const key_vec = _mm256_loadu_si256 (xsecret+i); + /* data_key = data_vec ^ key_vec; */ + __m256i const data_key = _mm256_xor_si256 (data_vec, key_vec); + /* data_key_lo = data_key >> 32; */ + __m256i const data_key_lo = _mm256_srli_epi64 (data_key, 32); + /* product = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */ + __m256i const product = _mm256_mul_epu32 (data_key, data_key_lo); + /* xacc[i] += swap(data_vec); */ + __m256i const data_swap = _mm256_shuffle_epi32(data_vec, _MM_SHUFFLE(1, 0, 3, 2)); + __m256i const sum = _mm256_add_epi64(xacc[i], data_swap); + /* xacc[i] += product; */ + xacc[i] = _mm256_add_epi64(product, sum); + } } +} +XXH_FORCE_INLINE XXH_TARGET_AVX2 XXH3_ACCUMULATE_TEMPLATE(avx2) + +XXH_FORCE_INLINE XXH_TARGET_AVX2 void +XXH3_scrambleAcc_avx2(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 31) == 0); + { __m256i* const xacc = (__m256i*) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */ + const __m256i* const xsecret = (const __m256i *) secret; + const __m256i prime32 = _mm256_set1_epi32((int)XXH_PRIME32_1); + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m256i); i++) { + /* xacc[i] ^= (xacc[i] >> 47) */ + __m256i const acc_vec = xacc[i]; + __m256i const shifted = _mm256_srli_epi64 (acc_vec, 47); + __m256i const data_vec = _mm256_xor_si256 (acc_vec, shifted); + /* xacc[i] ^= xsecret; */ + __m256i const key_vec = _mm256_loadu_si256 (xsecret+i); + __m256i const data_key = _mm256_xor_si256 (data_vec, key_vec); + + /* xacc[i] *= XXH_PRIME32_1; */ + __m256i const data_key_hi = _mm256_srli_epi64 (data_key, 32); + __m256i const prod_lo = _mm256_mul_epu32 (data_key, prime32); + __m256i const prod_hi = _mm256_mul_epu32 (data_key_hi, prime32); + xacc[i] = _mm256_add_epi64(prod_lo, _mm256_slli_epi64(prod_hi, 32)); + } + } +} + +XXH_FORCE_INLINE XXH_TARGET_AVX2 void XXH3_initCustomSecret_avx2(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 31) == 0); + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE / sizeof(__m256i)) == 6); + XXH_STATIC_ASSERT(XXH_SEC_ALIGN <= 64); + (void)(&XXH_writeLE64); + XXH_PREFETCH(customSecret); + { __m256i const seed = _mm256_set_epi64x((xxh_i64)(0U - seed64), (xxh_i64)seed64, (xxh_i64)(0U - seed64), (xxh_i64)seed64); + + const __m256i* const src = (const __m256i*) ((const void*) XXH3_kSecret); + __m256i* dest = ( __m256i*) customSecret; + +# if defined(__GNUC__) || defined(__clang__) + /* + * On GCC & Clang, marking 'dest' as modified will cause the compiler: + * - do not extract the secret from sse registers in the internal loop + * - use less common registers, and avoid pushing these reg into stack + */ + XXH_COMPILER_GUARD(dest); +# endif + XXH_ASSERT(((size_t)src & 31) == 0); /* control alignment */ + XXH_ASSERT(((size_t)dest & 31) == 0); + + /* GCC -O2 need unroll loop manually */ + dest[0] = _mm256_add_epi64(_mm256_load_si256(src+0), seed); + dest[1] = _mm256_add_epi64(_mm256_load_si256(src+1), seed); + dest[2] = _mm256_add_epi64(_mm256_load_si256(src+2), seed); + dest[3] = _mm256_add_epi64(_mm256_load_si256(src+3), seed); + dest[4] = _mm256_add_epi64(_mm256_load_si256(src+4), seed); + dest[5] = _mm256_add_epi64(_mm256_load_si256(src+5), seed); + } +} + +#endif + +/* x86dispatch always generates SSE2 */ +#if (XXH_VECTOR == XXH_SSE2) || defined(XXH_X86DISPATCH) + +#ifndef XXH_TARGET_SSE2 +# define XXH_TARGET_SSE2 /* disable attribute target */ +#endif + +XXH_FORCE_INLINE XXH_TARGET_SSE2 void +XXH3_accumulate_512_sse2( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + /* SSE2 is just a half-scale version of the AVX2 version. */ + XXH_ASSERT((((size_t)acc) & 15) == 0); + { __m128i* const xacc = (__m128i *) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */ + const __m128i* const xinput = (const __m128i *) input; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */ + const __m128i* const xsecret = (const __m128i *) secret; + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m128i); i++) { + /* data_vec = xinput[i]; */ + __m128i const data_vec = _mm_loadu_si128 (xinput+i); + /* key_vec = xsecret[i]; */ + __m128i const key_vec = _mm_loadu_si128 (xsecret+i); + /* data_key = data_vec ^ key_vec; */ + __m128i const data_key = _mm_xor_si128 (data_vec, key_vec); + /* data_key_lo = data_key >> 32; */ + __m128i const data_key_lo = _mm_shuffle_epi32 (data_key, _MM_SHUFFLE(0, 3, 0, 1)); + /* product = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */ + __m128i const product = _mm_mul_epu32 (data_key, data_key_lo); + /* xacc[i] += swap(data_vec); */ + __m128i const data_swap = _mm_shuffle_epi32(data_vec, _MM_SHUFFLE(1,0,3,2)); + __m128i const sum = _mm_add_epi64(xacc[i], data_swap); + /* xacc[i] += product; */ + xacc[i] = _mm_add_epi64(product, sum); + } } +} +XXH_FORCE_INLINE XXH_TARGET_SSE2 XXH3_ACCUMULATE_TEMPLATE(sse2) + +XXH_FORCE_INLINE XXH_TARGET_SSE2 void +XXH3_scrambleAcc_sse2(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + { __m128i* const xacc = (__m128i*) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */ + const __m128i* const xsecret = (const __m128i *) secret; + const __m128i prime32 = _mm_set1_epi32((int)XXH_PRIME32_1); + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m128i); i++) { + /* xacc[i] ^= (xacc[i] >> 47) */ + __m128i const acc_vec = xacc[i]; + __m128i const shifted = _mm_srli_epi64 (acc_vec, 47); + __m128i const data_vec = _mm_xor_si128 (acc_vec, shifted); + /* xacc[i] ^= xsecret[i]; */ + __m128i const key_vec = _mm_loadu_si128 (xsecret+i); + __m128i const data_key = _mm_xor_si128 (data_vec, key_vec); + + /* xacc[i] *= XXH_PRIME32_1; */ + __m128i const data_key_hi = _mm_shuffle_epi32 (data_key, _MM_SHUFFLE(0, 3, 0, 1)); + __m128i const prod_lo = _mm_mul_epu32 (data_key, prime32); + __m128i const prod_hi = _mm_mul_epu32 (data_key_hi, prime32); + xacc[i] = _mm_add_epi64(prod_lo, _mm_slli_epi64(prod_hi, 32)); + } + } +} + +XXH_FORCE_INLINE XXH_TARGET_SSE2 void XXH3_initCustomSecret_sse2(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 15) == 0); + (void)(&XXH_writeLE64); + { int const nbRounds = XXH_SECRET_DEFAULT_SIZE / sizeof(__m128i); + +# if defined(_MSC_VER) && defined(_M_IX86) && _MSC_VER < 1900 + /* MSVC 32bit mode does not support _mm_set_epi64x before 2015 */ + XXH_ALIGN(16) const xxh_i64 seed64x2[2] = { (xxh_i64)seed64, (xxh_i64)(0U - seed64) }; + __m128i const seed = _mm_load_si128((__m128i const*)seed64x2); +# else + __m128i const seed = _mm_set_epi64x((xxh_i64)(0U - seed64), (xxh_i64)seed64); +# endif + int i; + + const void* const src16 = XXH3_kSecret; + __m128i* dst16 = (__m128i*) customSecret; +# if defined(__GNUC__) || defined(__clang__) + /* + * On GCC & Clang, marking 'dest' as modified will cause the compiler: + * - do not extract the secret from sse registers in the internal loop + * - use less common registers, and avoid pushing these reg into stack + */ + XXH_COMPILER_GUARD(dst16); +# endif + XXH_ASSERT(((size_t)src16 & 15) == 0); /* control alignment */ + XXH_ASSERT(((size_t)dst16 & 15) == 0); + + for (i=0; i < nbRounds; ++i) { + dst16[i] = _mm_add_epi64(_mm_load_si128((const __m128i *)src16+i), seed); + } } +} + +#endif + +#if (XXH_VECTOR == XXH_NEON) + +/* forward declarations for the scalar routines */ +XXH_FORCE_INLINE void +XXH3_scalarRound(void* XXH_RESTRICT acc, void const* XXH_RESTRICT input, + void const* XXH_RESTRICT secret, size_t lane); + +XXH_FORCE_INLINE void +XXH3_scalarScrambleRound(void* XXH_RESTRICT acc, + void const* XXH_RESTRICT secret, size_t lane); + +/*! + * @internal + * @brief The bulk processing loop for NEON and WASM SIMD128. + * + * The NEON code path is actually partially scalar when running on AArch64. This + * is to optimize the pipelining and can have up to 15% speedup depending on the + * CPU, and it also mitigates some GCC codegen issues. + * + * @see XXH3_NEON_LANES for configuring this and details about this optimization. + * + * NEON's 32-bit to 64-bit long multiply takes a half vector of 32-bit + * integers instead of the other platforms which mask full 64-bit vectors, + * so the setup is more complicated than just shifting right. + * + * Additionally, there is an optimization for 4 lanes at once noted below. + * + * Since, as stated, the most optimal amount of lanes for Cortexes is 6, + * there needs to be *three* versions of the accumulate operation used + * for the remaining 2 lanes. + * + * WASM's SIMD128 uses SIMDe's arm_neon.h polyfill because the intrinsics overlap + * nearly perfectly. + */ + +XXH_FORCE_INLINE void +XXH3_accumulate_512_neon( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + XXH_STATIC_ASSERT(XXH3_NEON_LANES > 0 && XXH3_NEON_LANES <= XXH_ACC_NB && XXH3_NEON_LANES % 2 == 0); + { /* GCC for darwin arm64 does not like aliasing here */ + xxh_aliasing_uint64x2_t* const xacc = (xxh_aliasing_uint64x2_t*) acc; + /* We don't use a uint32x4_t pointer because it causes bus errors on ARMv7. */ + uint8_t const* xinput = (const uint8_t *) input; + uint8_t const* xsecret = (const uint8_t *) secret; + + size_t i; +#ifdef __wasm_simd128__ + /* + * On WASM SIMD128, Clang emits direct address loads when XXH3_kSecret + * is constant propagated, which results in it converting it to this + * inside the loop: + * + * a = v128.load(XXH3_kSecret + 0 + $secret_offset, offset = 0) + * b = v128.load(XXH3_kSecret + 16 + $secret_offset, offset = 0) + * ... + * + * This requires a full 32-bit address immediate (and therefore a 6 byte + * instruction) as well as an add for each offset. + * + * Putting an asm guard prevents it from folding (at the cost of losing + * the alignment hint), and uses the free offset in `v128.load` instead + * of adding secret_offset each time which overall reduces code size by + * about a kilobyte and improves performance. + */ + XXH_COMPILER_GUARD(xsecret); +#endif + /* Scalar lanes use the normal scalarRound routine */ + for (i = XXH3_NEON_LANES; i < XXH_ACC_NB; i++) { + XXH3_scalarRound(acc, input, secret, i); + } + i = 0; + /* 4 NEON lanes at a time. */ + for (; i+1 < XXH3_NEON_LANES / 2; i+=2) { + /* data_vec = xinput[i]; */ + uint64x2_t data_vec_1 = XXH_vld1q_u64(xinput + (i * 16)); + uint64x2_t data_vec_2 = XXH_vld1q_u64(xinput + ((i+1) * 16)); + /* key_vec = xsecret[i]; */ + uint64x2_t key_vec_1 = XXH_vld1q_u64(xsecret + (i * 16)); + uint64x2_t key_vec_2 = XXH_vld1q_u64(xsecret + ((i+1) * 16)); + /* data_swap = swap(data_vec) */ + uint64x2_t data_swap_1 = vextq_u64(data_vec_1, data_vec_1, 1); + uint64x2_t data_swap_2 = vextq_u64(data_vec_2, data_vec_2, 1); + /* data_key = data_vec ^ key_vec; */ + uint64x2_t data_key_1 = veorq_u64(data_vec_1, key_vec_1); + uint64x2_t data_key_2 = veorq_u64(data_vec_2, key_vec_2); + + /* + * If we reinterpret the 64x2 vectors as 32x4 vectors, we can use a + * de-interleave operation for 4 lanes in 1 step with `vuzpq_u32` to + * get one vector with the low 32 bits of each lane, and one vector + * with the high 32 bits of each lane. + * + * The intrinsic returns a double vector because the original ARMv7-a + * instruction modified both arguments in place. AArch64 and SIMD128 emit + * two instructions from this intrinsic. + * + * [ dk11L | dk11H | dk12L | dk12H ] -> [ dk11L | dk12L | dk21L | dk22L ] + * [ dk21L | dk21H | dk22L | dk22H ] -> [ dk11H | dk12H | dk21H | dk22H ] + */ + uint32x4x2_t unzipped = vuzpq_u32( + vreinterpretq_u32_u64(data_key_1), + vreinterpretq_u32_u64(data_key_2) + ); + /* data_key_lo = data_key & 0xFFFFFFFF */ + uint32x4_t data_key_lo = unzipped.val[0]; + /* data_key_hi = data_key >> 32 */ + uint32x4_t data_key_hi = unzipped.val[1]; + /* + * Then, we can split the vectors horizontally and multiply which, as for most + * widening intrinsics, have a variant that works on both high half vectors + * for free on AArch64. A similar instruction is available on SIMD128. + * + * sum = data_swap + (u64x2) data_key_lo * (u64x2) data_key_hi + */ + uint64x2_t sum_1 = XXH_vmlal_low_u32(data_swap_1, data_key_lo, data_key_hi); + uint64x2_t sum_2 = XXH_vmlal_high_u32(data_swap_2, data_key_lo, data_key_hi); + /* + * Clang reorders + * a += b * c; // umlal swap.2d, dkl.2s, dkh.2s + * c += a; // add acc.2d, acc.2d, swap.2d + * to + * c += a; // add acc.2d, acc.2d, swap.2d + * c += b * c; // umlal acc.2d, dkl.2s, dkh.2s + * + * While it would make sense in theory since the addition is faster, + * for reasons likely related to umlal being limited to certain NEON + * pipelines, this is worse. A compiler guard fixes this. + */ + XXH_COMPILER_GUARD_CLANG_NEON(sum_1); + XXH_COMPILER_GUARD_CLANG_NEON(sum_2); + /* xacc[i] = acc_vec + sum; */ + xacc[i] = vaddq_u64(xacc[i], sum_1); + xacc[i+1] = vaddq_u64(xacc[i+1], sum_2); + } + /* Operate on the remaining NEON lanes 2 at a time. */ + for (; i < XXH3_NEON_LANES / 2; i++) { + /* data_vec = xinput[i]; */ + uint64x2_t data_vec = XXH_vld1q_u64(xinput + (i * 16)); + /* key_vec = xsecret[i]; */ + uint64x2_t key_vec = XXH_vld1q_u64(xsecret + (i * 16)); + /* acc_vec_2 = swap(data_vec) */ + uint64x2_t data_swap = vextq_u64(data_vec, data_vec, 1); + /* data_key = data_vec ^ key_vec; */ + uint64x2_t data_key = veorq_u64(data_vec, key_vec); + /* For two lanes, just use VMOVN and VSHRN. */ + /* data_key_lo = data_key & 0xFFFFFFFF; */ + uint32x2_t data_key_lo = vmovn_u64(data_key); + /* data_key_hi = data_key >> 32; */ + uint32x2_t data_key_hi = vshrn_n_u64(data_key, 32); + /* sum = data_swap + (u64x2) data_key_lo * (u64x2) data_key_hi; */ + uint64x2_t sum = vmlal_u32(data_swap, data_key_lo, data_key_hi); + /* Same Clang workaround as before */ + XXH_COMPILER_GUARD_CLANG_NEON(sum); + /* xacc[i] = acc_vec + sum; */ + xacc[i] = vaddq_u64 (xacc[i], sum); + } + } +} +XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(neon) + +XXH_FORCE_INLINE void +XXH3_scrambleAcc_neon(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + + { xxh_aliasing_uint64x2_t* xacc = (xxh_aliasing_uint64x2_t*) acc; + uint8_t const* xsecret = (uint8_t const*) secret; + + size_t i; + /* WASM uses operator overloads and doesn't need these. */ +#ifndef __wasm_simd128__ + /* { prime32_1, prime32_1 } */ + uint32x2_t const kPrimeLo = vdup_n_u32(XXH_PRIME32_1); + /* { 0, prime32_1, 0, prime32_1 } */ + uint32x4_t const kPrimeHi = vreinterpretq_u32_u64(vdupq_n_u64((xxh_u64)XXH_PRIME32_1 << 32)); +#endif + + /* AArch64 uses both scalar and neon at the same time */ + for (i = XXH3_NEON_LANES; i < XXH_ACC_NB; i++) { + XXH3_scalarScrambleRound(acc, secret, i); + } + for (i=0; i < XXH3_NEON_LANES / 2; i++) { + /* xacc[i] ^= (xacc[i] >> 47); */ + uint64x2_t acc_vec = xacc[i]; + uint64x2_t shifted = vshrq_n_u64(acc_vec, 47); + uint64x2_t data_vec = veorq_u64(acc_vec, shifted); + + /* xacc[i] ^= xsecret[i]; */ + uint64x2_t key_vec = XXH_vld1q_u64(xsecret + (i * 16)); + uint64x2_t data_key = veorq_u64(data_vec, key_vec); + /* xacc[i] *= XXH_PRIME32_1 */ +#ifdef __wasm_simd128__ + /* SIMD128 has multiply by u64x2, use it instead of expanding and scalarizing */ + xacc[i] = data_key * XXH_PRIME32_1; +#else + /* + * Expanded version with portable NEON intrinsics + * + * lo(x) * lo(y) + (hi(x) * lo(y) << 32) + * + * prod_hi = hi(data_key) * lo(prime) << 32 + * + * Since we only need 32 bits of this multiply a trick can be used, reinterpreting the vector + * as a uint32x4_t and multiplying by { 0, prime, 0, prime } to cancel out the unwanted bits + * and avoid the shift. + */ + uint32x4_t prod_hi = vmulq_u32 (vreinterpretq_u32_u64(data_key), kPrimeHi); + /* Extract low bits for vmlal_u32 */ + uint32x2_t data_key_lo = vmovn_u64(data_key); + /* xacc[i] = prod_hi + lo(data_key) * XXH_PRIME32_1; */ + xacc[i] = vmlal_u32(vreinterpretq_u64_u32(prod_hi), data_key_lo, kPrimeLo); +#endif + } + } +} +#endif + +#if (XXH_VECTOR == XXH_VSX) + +XXH_FORCE_INLINE void +XXH3_accumulate_512_vsx( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + /* presumed aligned */ + xxh_aliasing_u64x2* const xacc = (xxh_aliasing_u64x2*) acc; + xxh_u8 const* const xinput = (xxh_u8 const*) input; /* no alignment restriction */ + xxh_u8 const* const xsecret = (xxh_u8 const*) secret; /* no alignment restriction */ + xxh_u64x2 const v32 = { 32, 32 }; + size_t i; + for (i = 0; i < XXH_STRIPE_LEN / sizeof(xxh_u64x2); i++) { + /* data_vec = xinput[i]; */ + xxh_u64x2 const data_vec = XXH_vec_loadu(xinput + 16*i); + /* key_vec = xsecret[i]; */ + xxh_u64x2 const key_vec = XXH_vec_loadu(xsecret + 16*i); + xxh_u64x2 const data_key = data_vec ^ key_vec; + /* shuffled = (data_key << 32) | (data_key >> 32); */ + xxh_u32x4 const shuffled = (xxh_u32x4)vec_rl(data_key, v32); + /* product = ((xxh_u64x2)data_key & 0xFFFFFFFF) * ((xxh_u64x2)shuffled & 0xFFFFFFFF); */ + xxh_u64x2 const product = XXH_vec_mulo((xxh_u32x4)data_key, shuffled); + /* acc_vec = xacc[i]; */ + xxh_u64x2 acc_vec = xacc[i]; + acc_vec += product; + + /* swap high and low halves */ +#ifdef __s390x__ + acc_vec += vec_permi(data_vec, data_vec, 2); +#else + acc_vec += vec_xxpermdi(data_vec, data_vec, 2); +#endif + xacc[i] = acc_vec; + } +} +XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(vsx) + +XXH_FORCE_INLINE void +XXH3_scrambleAcc_vsx(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + + { xxh_aliasing_u64x2* const xacc = (xxh_aliasing_u64x2*) acc; + const xxh_u8* const xsecret = (const xxh_u8*) secret; + /* constants */ + xxh_u64x2 const v32 = { 32, 32 }; + xxh_u64x2 const v47 = { 47, 47 }; + xxh_u32x4 const prime = { XXH_PRIME32_1, XXH_PRIME32_1, XXH_PRIME32_1, XXH_PRIME32_1 }; + size_t i; + for (i = 0; i < XXH_STRIPE_LEN / sizeof(xxh_u64x2); i++) { + /* xacc[i] ^= (xacc[i] >> 47); */ + xxh_u64x2 const acc_vec = xacc[i]; + xxh_u64x2 const data_vec = acc_vec ^ (acc_vec >> v47); + + /* xacc[i] ^= xsecret[i]; */ + xxh_u64x2 const key_vec = XXH_vec_loadu(xsecret + 16*i); + xxh_u64x2 const data_key = data_vec ^ key_vec; + + /* xacc[i] *= XXH_PRIME32_1 */ + /* prod_lo = ((xxh_u64x2)data_key & 0xFFFFFFFF) * ((xxh_u64x2)prime & 0xFFFFFFFF); */ + xxh_u64x2 const prod_even = XXH_vec_mule((xxh_u32x4)data_key, prime); + /* prod_hi = ((xxh_u64x2)data_key >> 32) * ((xxh_u64x2)prime >> 32); */ + xxh_u64x2 const prod_odd = XXH_vec_mulo((xxh_u32x4)data_key, prime); + xacc[i] = prod_odd + (prod_even << v32); + } } +} + +#endif + +#if (XXH_VECTOR == XXH_SVE) + +XXH_FORCE_INLINE void +XXH3_accumulate_512_sve( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + uint64_t *xacc = (uint64_t *)acc; + const uint64_t *xinput = (const uint64_t *)(const void *)input; + const uint64_t *xsecret = (const uint64_t *)(const void *)secret; + svuint64_t kSwap = sveor_n_u64_z(svptrue_b64(), svindex_u64(0, 1), 1); + uint64_t element_count = svcntd(); + if (element_count >= 8) { + svbool_t mask = svptrue_pat_b64(SV_VL8); + svuint64_t vacc = svld1_u64(mask, xacc); + ACCRND(vacc, 0); + svst1_u64(mask, xacc, vacc); + } else if (element_count == 2) { /* sve128 */ + svbool_t mask = svptrue_pat_b64(SV_VL2); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 2); + svuint64_t acc2 = svld1_u64(mask, xacc + 4); + svuint64_t acc3 = svld1_u64(mask, xacc + 6); + ACCRND(acc0, 0); + ACCRND(acc1, 2); + ACCRND(acc2, 4); + ACCRND(acc3, 6); + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 2, acc1); + svst1_u64(mask, xacc + 4, acc2); + svst1_u64(mask, xacc + 6, acc3); + } else { + svbool_t mask = svptrue_pat_b64(SV_VL4); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 4); + ACCRND(acc0, 0); + ACCRND(acc1, 4); + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 4, acc1); + } +} + +XXH_FORCE_INLINE void +XXH3_accumulate_sve(xxh_u64* XXH_RESTRICT acc, + const xxh_u8* XXH_RESTRICT input, + const xxh_u8* XXH_RESTRICT secret, + size_t nbStripes) +{ + if (nbStripes != 0) { + uint64_t *xacc = (uint64_t *)acc; + const uint64_t *xinput = (const uint64_t *)(const void *)input; + const uint64_t *xsecret = (const uint64_t *)(const void *)secret; + svuint64_t kSwap = sveor_n_u64_z(svptrue_b64(), svindex_u64(0, 1), 1); + uint64_t element_count = svcntd(); + if (element_count >= 8) { + svbool_t mask = svptrue_pat_b64(SV_VL8); + svuint64_t vacc = svld1_u64(mask, xacc + 0); + do { + /* svprfd(svbool_t, void *, enum svfprop); */ + svprfd(mask, xinput + 128, SV_PLDL1STRM); + ACCRND(vacc, 0); + xinput += 8; + xsecret += 1; + nbStripes--; + } while (nbStripes != 0); + + svst1_u64(mask, xacc + 0, vacc); + } else if (element_count == 2) { /* sve128 */ + svbool_t mask = svptrue_pat_b64(SV_VL2); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 2); + svuint64_t acc2 = svld1_u64(mask, xacc + 4); + svuint64_t acc3 = svld1_u64(mask, xacc + 6); + do { + svprfd(mask, xinput + 128, SV_PLDL1STRM); + ACCRND(acc0, 0); + ACCRND(acc1, 2); + ACCRND(acc2, 4); + ACCRND(acc3, 6); + xinput += 8; + xsecret += 1; + nbStripes--; + } while (nbStripes != 0); + + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 2, acc1); + svst1_u64(mask, xacc + 4, acc2); + svst1_u64(mask, xacc + 6, acc3); + } else { + svbool_t mask = svptrue_pat_b64(SV_VL4); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 4); + do { + svprfd(mask, xinput + 128, SV_PLDL1STRM); + ACCRND(acc0, 0); + ACCRND(acc1, 4); + xinput += 8; + xsecret += 1; + nbStripes--; + } while (nbStripes != 0); + + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 4, acc1); + } + } +} + +#endif + +/* scalar variants - universal */ + +#if defined(__aarch64__) && (defined(__GNUC__) || defined(__clang__)) +/* + * In XXH3_scalarRound(), GCC and Clang have a similar codegen issue, where they + * emit an excess mask and a full 64-bit multiply-add (MADD X-form). + * + * While this might not seem like much, as AArch64 is a 64-bit architecture, only + * big Cortex designs have a full 64-bit multiplier. + * + * On the little cores, the smaller 32-bit multiplier is used, and full 64-bit + * multiplies expand to 2-3 multiplies in microcode. This has a major penalty + * of up to 4 latency cycles and 2 stall cycles in the multiply pipeline. + * + * Thankfully, AArch64 still provides the 32-bit long multiply-add (UMADDL) which does + * not have this penalty and does the mask automatically. + */ +XXH_FORCE_INLINE xxh_u64 +XXH_mult32to64_add64(xxh_u64 lhs, xxh_u64 rhs, xxh_u64 acc) +{ + xxh_u64 ret; + /* note: %x = 64-bit register, %w = 32-bit register */ + __asm__("umaddl %x0, %w1, %w2, %x3" : "=r" (ret) : "r" (lhs), "r" (rhs), "r" (acc)); + return ret; +} +#else +XXH_FORCE_INLINE xxh_u64 +XXH_mult32to64_add64(xxh_u64 lhs, xxh_u64 rhs, xxh_u64 acc) +{ + return XXH_mult32to64((xxh_u32)lhs, (xxh_u32)rhs) + acc; +} +#endif + +/*! + * @internal + * @brief Scalar round for @ref XXH3_accumulate_512_scalar(). + * + * This is extracted to its own function because the NEON path uses a combination + * of NEON and scalar. + */ +XXH_FORCE_INLINE void +XXH3_scalarRound(void* XXH_RESTRICT acc, + void const* XXH_RESTRICT input, + void const* XXH_RESTRICT secret, + size_t lane) +{ + xxh_u64* xacc = (xxh_u64*) acc; + xxh_u8 const* xinput = (xxh_u8 const*) input; + xxh_u8 const* xsecret = (xxh_u8 const*) secret; + XXH_ASSERT(lane < XXH_ACC_NB); + XXH_ASSERT(((size_t)acc & (XXH_ACC_ALIGN-1)) == 0); + { + xxh_u64 const data_val = XXH_readLE64(xinput + lane * 8); + xxh_u64 const data_key = data_val ^ XXH_readLE64(xsecret + lane * 8); + xacc[lane ^ 1] += data_val; /* swap adjacent lanes */ + xacc[lane] = XXH_mult32to64_add64(data_key /* & 0xFFFFFFFF */, data_key >> 32, xacc[lane]); + } +} + +/*! + * @internal + * @brief Processes a 64 byte block of data using the scalar path. + */ +XXH_FORCE_INLINE void +XXH3_accumulate_512_scalar(void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + size_t i; + /* ARM GCC refuses to unroll this loop, resulting in a 24% slowdown on ARMv6. */ +#if defined(__GNUC__) && !defined(__clang__) \ + && (defined(__arm__) || defined(__thumb2__)) \ + && defined(__ARM_FEATURE_UNALIGNED) /* no unaligned access just wastes bytes */ \ + && XXH_SIZE_OPT <= 0 +# pragma GCC unroll 8 +#endif + for (i=0; i < XXH_ACC_NB; i++) { + XXH3_scalarRound(acc, input, secret, i); + } +} +XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(scalar) + +/*! + * @internal + * @brief Scalar scramble step for @ref XXH3_scrambleAcc_scalar(). + * + * This is extracted to its own function because the NEON path uses a combination + * of NEON and scalar. + */ +XXH_FORCE_INLINE void +XXH3_scalarScrambleRound(void* XXH_RESTRICT acc, + void const* XXH_RESTRICT secret, + size_t lane) +{ + xxh_u64* const xacc = (xxh_u64*) acc; /* presumed aligned */ + const xxh_u8* const xsecret = (const xxh_u8*) secret; /* no alignment restriction */ + XXH_ASSERT((((size_t)acc) & (XXH_ACC_ALIGN-1)) == 0); + XXH_ASSERT(lane < XXH_ACC_NB); + { + xxh_u64 const key64 = XXH_readLE64(xsecret + lane * 8); + xxh_u64 acc64 = xacc[lane]; + acc64 = XXH_xorshift64(acc64, 47); + acc64 ^= key64; + acc64 *= XXH_PRIME32_1; + xacc[lane] = acc64; + } +} + +/*! + * @internal + * @brief Scrambles the accumulators after a large chunk has been read + */ +XXH_FORCE_INLINE void +XXH3_scrambleAcc_scalar(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + size_t i; + for (i=0; i < XXH_ACC_NB; i++) { + XXH3_scalarScrambleRound(acc, secret, i); + } +} + +XXH_FORCE_INLINE void +XXH3_initCustomSecret_scalar(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + /* + * We need a separate pointer for the hack below, + * which requires a non-const pointer. + * Any decent compiler will optimize this out otherwise. + */ + const xxh_u8* kSecretPtr = XXH3_kSecret; + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 15) == 0); + +#if defined(__GNUC__) && defined(__aarch64__) + /* + * UGLY HACK: + * GCC and Clang generate a bunch of MOV/MOVK pairs for aarch64, and they are + * placed sequentially, in order, at the top of the unrolled loop. + * + * While MOVK is great for generating constants (2 cycles for a 64-bit + * constant compared to 4 cycles for LDR), it fights for bandwidth with + * the arithmetic instructions. + * + * I L S + * MOVK + * MOVK + * MOVK + * MOVK + * ADD + * SUB STR + * STR + * By forcing loads from memory (as the asm line causes the compiler to assume + * that XXH3_kSecretPtr has been changed), the pipelines are used more + * efficiently: + * I L S + * LDR + * ADD LDR + * SUB STR + * STR + * + * See XXH3_NEON_LANES for details on the pipsline. + * + * XXH3_64bits_withSeed, len == 256, Snapdragon 835 + * without hack: 2654.4 MB/s + * with hack: 3202.9 MB/s + */ + XXH_COMPILER_GUARD(kSecretPtr); +#endif + { int const nbRounds = XXH_SECRET_DEFAULT_SIZE / 16; + int i; + for (i=0; i < nbRounds; i++) { + /* + * The asm hack causes the compiler to assume that kSecretPtr aliases with + * customSecret, and on aarch64, this prevented LDP from merging two + * loads together for free. Putting the loads together before the stores + * properly generates LDP. + */ + xxh_u64 lo = XXH_readLE64(kSecretPtr + 16*i) + seed64; + xxh_u64 hi = XXH_readLE64(kSecretPtr + 16*i + 8) - seed64; + XXH_writeLE64((xxh_u8*)customSecret + 16*i, lo); + XXH_writeLE64((xxh_u8*)customSecret + 16*i + 8, hi); + } } +} + + +typedef void (*XXH3_f_accumulate)(xxh_u64* XXH_RESTRICT, const xxh_u8* XXH_RESTRICT, const xxh_u8* XXH_RESTRICT, size_t); +typedef void (*XXH3_f_scrambleAcc)(void* XXH_RESTRICT, const void*); +typedef void (*XXH3_f_initCustomSecret)(void* XXH_RESTRICT, xxh_u64); + + +#if (XXH_VECTOR == XXH_AVX512) + +#define XXH3_accumulate_512 XXH3_accumulate_512_avx512 +#define XXH3_accumulate XXH3_accumulate_avx512 +#define XXH3_scrambleAcc XXH3_scrambleAcc_avx512 +#define XXH3_initCustomSecret XXH3_initCustomSecret_avx512 + +#elif (XXH_VECTOR == XXH_AVX2) + +#define XXH3_accumulate_512 XXH3_accumulate_512_avx2 +#define XXH3_accumulate XXH3_accumulate_avx2 +#define XXH3_scrambleAcc XXH3_scrambleAcc_avx2 +#define XXH3_initCustomSecret XXH3_initCustomSecret_avx2 + +#elif (XXH_VECTOR == XXH_SSE2) + +#define XXH3_accumulate_512 XXH3_accumulate_512_sse2 +#define XXH3_accumulate XXH3_accumulate_sse2 +#define XXH3_scrambleAcc XXH3_scrambleAcc_sse2 +#define XXH3_initCustomSecret XXH3_initCustomSecret_sse2 + +#elif (XXH_VECTOR == XXH_NEON) + +#define XXH3_accumulate_512 XXH3_accumulate_512_neon +#define XXH3_accumulate XXH3_accumulate_neon +#define XXH3_scrambleAcc XXH3_scrambleAcc_neon +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#elif (XXH_VECTOR == XXH_VSX) + +#define XXH3_accumulate_512 XXH3_accumulate_512_vsx +#define XXH3_accumulate XXH3_accumulate_vsx +#define XXH3_scrambleAcc XXH3_scrambleAcc_vsx +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#elif (XXH_VECTOR == XXH_SVE) +#define XXH3_accumulate_512 XXH3_accumulate_512_sve +#define XXH3_accumulate XXH3_accumulate_sve +#define XXH3_scrambleAcc XXH3_scrambleAcc_scalar +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#else /* scalar */ + +#define XXH3_accumulate_512 XXH3_accumulate_512_scalar +#define XXH3_accumulate XXH3_accumulate_scalar +#define XXH3_scrambleAcc XXH3_scrambleAcc_scalar +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#endif + +#if XXH_SIZE_OPT >= 1 /* don't do SIMD for initialization */ +# undef XXH3_initCustomSecret +# define XXH3_initCustomSecret XXH3_initCustomSecret_scalar +#endif + +XXH_FORCE_INLINE void +XXH3_hashLong_internal_loop(xxh_u64* XXH_RESTRICT acc, + const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + size_t const nbStripesPerBlock = (secretSize - XXH_STRIPE_LEN) / XXH_SECRET_CONSUME_RATE; + size_t const block_len = XXH_STRIPE_LEN * nbStripesPerBlock; + size_t const nb_blocks = (len - 1) / block_len; + + size_t n; + + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); + + for (n = 0; n < nb_blocks; n++) { + f_acc(acc, input + n*block_len, secret, nbStripesPerBlock); + f_scramble(acc, secret + secretSize - XXH_STRIPE_LEN); + } + + /* last partial block */ + XXH_ASSERT(len > XXH_STRIPE_LEN); + { size_t const nbStripes = ((len - 1) - (block_len * nb_blocks)) / XXH_STRIPE_LEN; + XXH_ASSERT(nbStripes <= (secretSize / XXH_SECRET_CONSUME_RATE)); + f_acc(acc, input + nb_blocks*block_len, secret, nbStripes); + + /* last stripe */ + { const xxh_u8* const p = input + len - XXH_STRIPE_LEN; +#define XXH_SECRET_LASTACC_START 7 /* not aligned on 8, last secret is different from acc & scrambler */ + XXH3_accumulate_512(acc, p, secret + secretSize - XXH_STRIPE_LEN - XXH_SECRET_LASTACC_START); + } } +} + +XXH_FORCE_INLINE xxh_u64 +XXH3_mix2Accs(const xxh_u64* XXH_RESTRICT acc, const xxh_u8* XXH_RESTRICT secret) +{ + return XXH3_mul128_fold64( + acc[0] ^ XXH_readLE64(secret), + acc[1] ^ XXH_readLE64(secret+8) ); +} + +static XXH64_hash_t +XXH3_mergeAccs(const xxh_u64* XXH_RESTRICT acc, const xxh_u8* XXH_RESTRICT secret, xxh_u64 start) +{ + xxh_u64 result64 = start; + size_t i = 0; + + for (i = 0; i < 4; i++) { + result64 += XXH3_mix2Accs(acc+2*i, secret + 16*i); +#if defined(__clang__) /* Clang */ \ + && (defined(__arm__) || defined(__thumb__)) /* ARMv7 */ \ + && (defined(__ARM_NEON) || defined(__ARM_NEON__)) /* NEON */ \ + && !defined(XXH_ENABLE_AUTOVECTORIZE) /* Define to disable */ + /* + * UGLY HACK: + * Prevent autovectorization on Clang ARMv7-a. Exact same problem as + * the one in XXH3_len_129to240_64b. Speeds up shorter keys > 240b. + * XXH3_64bits, len == 256, Snapdragon 835: + * without hack: 2063.7 MB/s + * with hack: 2560.7 MB/s + */ + XXH_COMPILER_GUARD(result64); +#endif + } + + return XXH3_avalanche(result64); +} + +#define XXH3_INIT_ACC { XXH_PRIME32_3, XXH_PRIME64_1, XXH_PRIME64_2, XXH_PRIME64_3, \ + XXH_PRIME64_4, XXH_PRIME32_2, XXH_PRIME64_5, XXH_PRIME32_1 } + +XXH_FORCE_INLINE XXH64_hash_t +XXH3_hashLong_64b_internal(const void* XXH_RESTRICT input, size_t len, + const void* XXH_RESTRICT secret, size_t secretSize, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[XXH_ACC_NB] = XXH3_INIT_ACC; + + XXH3_hashLong_internal_loop(acc, (const xxh_u8*)input, len, (const xxh_u8*)secret, secretSize, f_acc, f_scramble); + + /* converge into final hash */ + XXH_STATIC_ASSERT(sizeof(acc) == 64); + /* do not align on 8, so that the secret is different from the accumulator */ +#define XXH_SECRET_MERGEACCS_START 11 + XXH_ASSERT(secretSize >= sizeof(acc) + XXH_SECRET_MERGEACCS_START); + return XXH3_mergeAccs(acc, (const xxh_u8*)secret + XXH_SECRET_MERGEACCS_START, (xxh_u64)len * XXH_PRIME64_1); +} + +/* + * It's important for performance to transmit secret's size (when it's static) + * so that the compiler can properly optimize the vectorized loop. + * This makes a big performance difference for "medium" keys (<1 KB) when using AVX instruction set. + * When the secret size is unknown, or on GCC 12 where the mix of NO_INLINE and FORCE_INLINE + * breaks -Og, this is XXH_NO_INLINE. + */ +XXH3_WITH_SECRET_INLINE XXH64_hash_t +XXH3_hashLong_64b_withSecret(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, const xxh_u8* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; + return XXH3_hashLong_64b_internal(input, len, secret, secretLen, XXH3_accumulate, XXH3_scrambleAcc); +} + +/* + * It's preferable for performance that XXH3_hashLong is not inlined, + * as it results in a smaller function for small data, easier to the instruction cache. + * Note that inside this no_inline function, we do inline the internal loop, + * and provide a statically defined secret size to allow optimization of vector loop. + */ +XXH_NO_INLINE XXH_PUREF XXH64_hash_t +XXH3_hashLong_64b_default(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, const xxh_u8* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; (void)secret; (void)secretLen; + return XXH3_hashLong_64b_internal(input, len, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_accumulate, XXH3_scrambleAcc); +} + +/* + * XXH3_hashLong_64b_withSeed(): + * Generate a custom key based on alteration of default XXH3_kSecret with the seed, + * and then use this key for long mode hashing. + * + * This operation is decently fast but nonetheless costs a little bit of time. + * Try to avoid it whenever possible (typically when seed==0). + * + * It's important for performance that XXH3_hashLong is not inlined. Not sure + * why (uop cache maybe?), but the difference is large and easily measurable. + */ +XXH_FORCE_INLINE XXH64_hash_t +XXH3_hashLong_64b_withSeed_internal(const void* input, size_t len, + XXH64_hash_t seed, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble, + XXH3_f_initCustomSecret f_initSec) +{ +#if XXH_SIZE_OPT <= 0 + if (seed == 0) + return XXH3_hashLong_64b_internal(input, len, + XXH3_kSecret, sizeof(XXH3_kSecret), + f_acc, f_scramble); +#endif + { XXH_ALIGN(XXH_SEC_ALIGN) xxh_u8 secret[XXH_SECRET_DEFAULT_SIZE]; + f_initSec(secret, seed); + return XXH3_hashLong_64b_internal(input, len, secret, sizeof(secret), + f_acc, f_scramble); + } +} + +/* + * It's important for performance that XXH3_hashLong is not inlined. + */ +XXH_NO_INLINE XXH64_hash_t +XXH3_hashLong_64b_withSeed(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed, const xxh_u8* XXH_RESTRICT secret, size_t secretLen) +{ + (void)secret; (void)secretLen; + return XXH3_hashLong_64b_withSeed_internal(input, len, seed, + XXH3_accumulate, XXH3_scrambleAcc, XXH3_initCustomSecret); +} + + +typedef XXH64_hash_t (*XXH3_hashLong64_f)(const void* XXH_RESTRICT, size_t, + XXH64_hash_t, const xxh_u8* XXH_RESTRICT, size_t); + +XXH_FORCE_INLINE XXH64_hash_t +XXH3_64bits_internal(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen, + XXH3_hashLong64_f f_hashLong) +{ + XXH_ASSERT(secretLen >= XXH3_SECRET_SIZE_MIN); + /* + * If an action is to be taken if `secretLen` condition is not respected, + * it should be done here. + * For now, it's a contract pre-condition. + * Adding a check and a branch here would cost performance at every hash. + * Also, note that function signature doesn't offer room to return an error. + */ + if (len <= 16) + return XXH3_len_0to16_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, seed64); + if (len <= 128) + return XXH3_len_17to128_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + if (len <= XXH3_MIDSIZE_MAX) + return XXH3_len_129to240_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + return f_hashLong(input, len, seed64, (const xxh_u8*)secret, secretLen); +} + + +/* === Public entry point === */ + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t XXH3_64bits(XXH_NOESCAPE const void* input, size_t length) +{ + return XXH3_64bits_internal(input, length, 0, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_hashLong_64b_default); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t +XXH3_64bits_withSecret(XXH_NOESCAPE const void* input, size_t length, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + return XXH3_64bits_internal(input, length, 0, secret, secretSize, XXH3_hashLong_64b_withSecret); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t +XXH3_64bits_withSeed(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed) +{ + return XXH3_64bits_internal(input, length, seed, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_hashLong_64b_withSeed); +} + +XXH_PUBLIC_API XXH64_hash_t +XXH3_64bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t length, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed) +{ + if (length <= XXH3_MIDSIZE_MAX) + return XXH3_64bits_internal(input, length, seed, XXH3_kSecret, sizeof(XXH3_kSecret), NULL); + return XXH3_hashLong_64b_withSecret(input, length, seed, (const xxh_u8*)secret, secretSize); +} + + +/* === XXH3 streaming === */ +#ifndef XXH_NO_STREAM +/* + * Malloc's a pointer that is always aligned to align. + * + * This must be freed with `XXH_alignedFree()`. + * + * malloc typically guarantees 16 byte alignment on 64-bit systems and 8 byte + * alignment on 32-bit. This isn't enough for the 32 byte aligned loads in AVX2 + * or on 32-bit, the 16 byte aligned loads in SSE2 and NEON. + * + * This underalignment previously caused a rather obvious crash which went + * completely unnoticed due to XXH3_createState() not actually being tested. + * Credit to RedSpah for noticing this bug. + * + * The alignment is done manually: Functions like posix_memalign or _mm_malloc + * are avoided: To maintain portability, we would have to write a fallback + * like this anyways, and besides, testing for the existence of library + * functions without relying on external build tools is impossible. + * + * The method is simple: Overallocate, manually align, and store the offset + * to the original behind the returned pointer. + * + * Align must be a power of 2 and 8 <= align <= 128. + */ +static XXH_MALLOCF void* XXH_alignedMalloc(size_t s, size_t align) +{ + XXH_ASSERT(align <= 128 && align >= 8); /* range check */ + XXH_ASSERT((align & (align-1)) == 0); /* power of 2 */ + XXH_ASSERT(s != 0 && s < (s + align)); /* empty/overflow */ + { /* Overallocate to make room for manual realignment and an offset byte */ + xxh_u8* base = (xxh_u8*)XXH_malloc(s + align); + if (base != NULL) { + /* + * Get the offset needed to align this pointer. + * + * Even if the returned pointer is aligned, there will always be + * at least one byte to store the offset to the original pointer. + */ + size_t offset = align - ((size_t)base & (align - 1)); /* base % align */ + /* Add the offset for the now-aligned pointer */ + xxh_u8* ptr = base + offset; + + XXH_ASSERT((size_t)ptr % align == 0); + + /* Store the offset immediately before the returned pointer. */ + ptr[-1] = (xxh_u8)offset; + return ptr; + } + return NULL; + } +} +/* + * Frees an aligned pointer allocated by XXH_alignedMalloc(). Don't pass + * normal malloc'd pointers, XXH_alignedMalloc has a specific data layout. + */ +static void XXH_alignedFree(void* p) +{ + if (p != NULL) { + xxh_u8* ptr = (xxh_u8*)p; + /* Get the offset byte we added in XXH_malloc. */ + xxh_u8 offset = ptr[-1]; + /* Free the original malloc'd pointer */ + xxh_u8* base = ptr - offset; + XXH_free(base); + } +} +/*! @ingroup XXH3_family */ +/*! + * @brief Allocate an @ref XXH3_state_t. + * + * @return An allocated pointer of @ref XXH3_state_t on success. + * @return `NULL` on failure. + * + * @note Must be freed with XXH3_freeState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH3_state_t* XXH3_createState(void) +{ + XXH3_state_t* const state = (XXH3_state_t*)XXH_alignedMalloc(sizeof(XXH3_state_t), 64); + if (state==NULL) return NULL; + XXH3_INITSTATE(state); + return state; +} + +/*! @ingroup XXH3_family */ +/*! + * @brief Frees an @ref XXH3_state_t. + * + * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState(). + * + * @return @ref XXH_OK. + * + * @note Must be allocated with XXH3_createState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_freeState(XXH3_state_t* statePtr) +{ + XXH_alignedFree(statePtr); + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API void +XXH3_copyState(XXH_NOESCAPE XXH3_state_t* dst_state, XXH_NOESCAPE const XXH3_state_t* src_state) +{ + XXH_memcpy(dst_state, src_state, sizeof(*dst_state)); +} + +static void +XXH3_reset_internal(XXH3_state_t* statePtr, + XXH64_hash_t seed, + const void* secret, size_t secretSize) +{ + size_t const initStart = offsetof(XXH3_state_t, bufferedSize); + size_t const initLength = offsetof(XXH3_state_t, nbStripesPerBlock) - initStart; + XXH_ASSERT(offsetof(XXH3_state_t, nbStripesPerBlock) > initStart); + XXH_ASSERT(statePtr != NULL); + /* set members from bufferedSize to nbStripesPerBlock (excluded) to 0 */ + memset((char*)statePtr + initStart, 0, initLength); + statePtr->acc[0] = XXH_PRIME32_3; + statePtr->acc[1] = XXH_PRIME64_1; + statePtr->acc[2] = XXH_PRIME64_2; + statePtr->acc[3] = XXH_PRIME64_3; + statePtr->acc[4] = XXH_PRIME64_4; + statePtr->acc[5] = XXH_PRIME32_2; + statePtr->acc[6] = XXH_PRIME64_5; + statePtr->acc[7] = XXH_PRIME32_1; + statePtr->seed = seed; + statePtr->useSeed = (seed != 0); + statePtr->extSecret = (const unsigned char*)secret; + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); + statePtr->secretLimit = secretSize - XXH_STRIPE_LEN; + statePtr->nbStripesPerBlock = statePtr->secretLimit / XXH_SECRET_CONSUME_RATE; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr) +{ + if (statePtr == NULL) return XXH_ERROR; + XXH3_reset_internal(statePtr, 0, XXH3_kSecret, XXH_SECRET_DEFAULT_SIZE); + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + if (statePtr == NULL) return XXH_ERROR; + XXH3_reset_internal(statePtr, 0, secret, secretSize); + if (secret == NULL) return XXH_ERROR; + if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR; + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed) +{ + if (statePtr == NULL) return XXH_ERROR; + if (seed==0) return XXH3_64bits_reset(statePtr); + if ((seed != statePtr->seed) || (statePtr->extSecret != NULL)) + XXH3_initCustomSecret(statePtr->customSecret, seed); + XXH3_reset_internal(statePtr, seed, NULL, XXH_SECRET_DEFAULT_SIZE); + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed64) +{ + if (statePtr == NULL) return XXH_ERROR; + if (secret == NULL) return XXH_ERROR; + if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR; + XXH3_reset_internal(statePtr, seed64, secret, secretSize); + statePtr->useSeed = 1; /* always, even if seed64==0 */ + return XXH_OK; +} + +/*! + * @internal + * @brief Processes a large input for XXH3_update() and XXH3_digest_long(). + * + * Unlike XXH3_hashLong_internal_loop(), this can process data that overlaps a block. + * + * @param acc Pointer to the 8 accumulator lanes + * @param nbStripesSoFarPtr In/out pointer to the number of leftover stripes in the block* + * @param nbStripesPerBlock Number of stripes in a block + * @param input Input pointer + * @param nbStripes Number of stripes to process + * @param secret Secret pointer + * @param secretLimit Offset of the last block in @p secret + * @param f_acc Pointer to an XXH3_accumulate implementation + * @param f_scramble Pointer to an XXH3_scrambleAcc implementation + * @return Pointer past the end of @p input after processing + */ +XXH_FORCE_INLINE const xxh_u8 * +XXH3_consumeStripes(xxh_u64* XXH_RESTRICT acc, + size_t* XXH_RESTRICT nbStripesSoFarPtr, size_t nbStripesPerBlock, + const xxh_u8* XXH_RESTRICT input, size_t nbStripes, + const xxh_u8* XXH_RESTRICT secret, size_t secretLimit, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + const xxh_u8* initialSecret = secret + *nbStripesSoFarPtr * XXH_SECRET_CONSUME_RATE; + /* Process full blocks */ + if (nbStripes >= (nbStripesPerBlock - *nbStripesSoFarPtr)) { + /* Process the initial partial block... */ + size_t nbStripesThisIter = nbStripesPerBlock - *nbStripesSoFarPtr; + + do { + /* Accumulate and scramble */ + f_acc(acc, input, initialSecret, nbStripesThisIter); + f_scramble(acc, secret + secretLimit); + input += nbStripesThisIter * XXH_STRIPE_LEN; + nbStripes -= nbStripesThisIter; + /* Then continue the loop with the full block size */ + nbStripesThisIter = nbStripesPerBlock; + initialSecret = secret; + } while (nbStripes >= nbStripesPerBlock); + *nbStripesSoFarPtr = 0; + } + /* Process a partial block */ + if (nbStripes > 0) { + f_acc(acc, input, initialSecret, nbStripes); + input += nbStripes * XXH_STRIPE_LEN; + *nbStripesSoFarPtr += nbStripes; + } + /* Return end pointer */ + return input; +} + +#ifndef XXH3_STREAM_USE_STACK +# if XXH_SIZE_OPT <= 0 && !defined(__clang__) /* clang doesn't need additional stack space */ +# define XXH3_STREAM_USE_STACK 1 +# endif +#endif +/* + * Both XXH3_64bits_update and XXH3_128bits_update use this routine. + */ +XXH_FORCE_INLINE XXH_errorcode +XXH3_update(XXH3_state_t* XXH_RESTRICT const state, + const xxh_u8* XXH_RESTRICT input, size_t len, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + if (input==NULL) { + XXH_ASSERT(len == 0); + return XXH_OK; + } + + XXH_ASSERT(state != NULL); + { const xxh_u8* const bEnd = input + len; + const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret; +#if defined(XXH3_STREAM_USE_STACK) && XXH3_STREAM_USE_STACK >= 1 + /* For some reason, gcc and MSVC seem to suffer greatly + * when operating accumulators directly into state. + * Operating into stack space seems to enable proper optimization. + * clang, on the other hand, doesn't seem to need this trick */ + XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[8]; + XXH_memcpy(acc, state->acc, sizeof(acc)); +#else + xxh_u64* XXH_RESTRICT const acc = state->acc; +#endif + state->totalLen += len; + XXH_ASSERT(state->bufferedSize <= XXH3_INTERNALBUFFER_SIZE); + + /* small input : just fill in tmp buffer */ + if (len <= XXH3_INTERNALBUFFER_SIZE - state->bufferedSize) { + XXH_memcpy(state->buffer + state->bufferedSize, input, len); + state->bufferedSize += (XXH32_hash_t)len; + return XXH_OK; + } + + /* total input is now > XXH3_INTERNALBUFFER_SIZE */ + #define XXH3_INTERNALBUFFER_STRIPES (XXH3_INTERNALBUFFER_SIZE / XXH_STRIPE_LEN) + XXH_STATIC_ASSERT(XXH3_INTERNALBUFFER_SIZE % XXH_STRIPE_LEN == 0); /* clean multiple */ + + /* + * Internal buffer is partially filled (always, except at beginning) + * Complete it, then consume it. + */ + if (state->bufferedSize) { + size_t const loadSize = XXH3_INTERNALBUFFER_SIZE - state->bufferedSize; + XXH_memcpy(state->buffer + state->bufferedSize, input, loadSize); + input += loadSize; + XXH3_consumeStripes(acc, + &state->nbStripesSoFar, state->nbStripesPerBlock, + state->buffer, XXH3_INTERNALBUFFER_STRIPES, + secret, state->secretLimit, + f_acc, f_scramble); + state->bufferedSize = 0; + } + XXH_ASSERT(input < bEnd); + if (bEnd - input > XXH3_INTERNALBUFFER_SIZE) { + size_t nbStripes = (size_t)(bEnd - 1 - input) / XXH_STRIPE_LEN; + input = XXH3_consumeStripes(acc, + &state->nbStripesSoFar, state->nbStripesPerBlock, + input, nbStripes, + secret, state->secretLimit, + f_acc, f_scramble); + XXH_memcpy(state->buffer + sizeof(state->buffer) - XXH_STRIPE_LEN, input - XXH_STRIPE_LEN, XXH_STRIPE_LEN); + + } + /* Some remaining input (always) : buffer it */ + XXH_ASSERT(input < bEnd); + XXH_ASSERT(bEnd - input <= XXH3_INTERNALBUFFER_SIZE); + XXH_ASSERT(state->bufferedSize == 0); + XXH_memcpy(state->buffer, input, (size_t)(bEnd-input)); + state->bufferedSize = (XXH32_hash_t)(bEnd-input); +#if defined(XXH3_STREAM_USE_STACK) && XXH3_STREAM_USE_STACK >= 1 + /* save stack accumulators into state */ + XXH_memcpy(state->acc, acc, sizeof(acc)); +#endif + } + + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_update(XXH_NOESCAPE XXH3_state_t* state, XXH_NOESCAPE const void* input, size_t len) +{ + return XXH3_update(state, (const xxh_u8*)input, len, + XXH3_accumulate, XXH3_scrambleAcc); +} + + +XXH_FORCE_INLINE void +XXH3_digest_long (XXH64_hash_t* acc, + const XXH3_state_t* state, + const unsigned char* secret) +{ + xxh_u8 lastStripe[XXH_STRIPE_LEN]; + const xxh_u8* lastStripePtr; + + /* + * Digest on a local copy. This way, the state remains unaltered, and it can + * continue ingesting more input afterwards. + */ + XXH_memcpy(acc, state->acc, sizeof(state->acc)); + if (state->bufferedSize >= XXH_STRIPE_LEN) { + /* Consume remaining stripes then point to remaining data in buffer */ + size_t const nbStripes = (state->bufferedSize - 1) / XXH_STRIPE_LEN; + size_t nbStripesSoFar = state->nbStripesSoFar; + XXH3_consumeStripes(acc, + &nbStripesSoFar, state->nbStripesPerBlock, + state->buffer, nbStripes, + secret, state->secretLimit, + XXH3_accumulate, XXH3_scrambleAcc); + lastStripePtr = state->buffer + state->bufferedSize - XXH_STRIPE_LEN; + } else { /* bufferedSize < XXH_STRIPE_LEN */ + /* Copy to temp buffer */ + size_t const catchupSize = XXH_STRIPE_LEN - state->bufferedSize; + XXH_ASSERT(state->bufferedSize > 0); /* there is always some input buffered */ + XXH_memcpy(lastStripe, state->buffer + sizeof(state->buffer) - catchupSize, catchupSize); + XXH_memcpy(lastStripe + catchupSize, state->buffer, state->bufferedSize); + lastStripePtr = lastStripe; + } + /* Last stripe */ + XXH3_accumulate_512(acc, + lastStripePtr, + secret + state->secretLimit - XXH_SECRET_LASTACC_START); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t XXH3_64bits_digest (XXH_NOESCAPE const XXH3_state_t* state) +{ + const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret; + if (state->totalLen > XXH3_MIDSIZE_MAX) { + XXH_ALIGN(XXH_ACC_ALIGN) XXH64_hash_t acc[XXH_ACC_NB]; + XXH3_digest_long(acc, state, secret); + return XXH3_mergeAccs(acc, + secret + XXH_SECRET_MERGEACCS_START, + (xxh_u64)state->totalLen * XXH_PRIME64_1); + } + /* totalLen <= XXH3_MIDSIZE_MAX: digesting a short input */ + if (state->useSeed) + return XXH3_64bits_withSeed(state->buffer, (size_t)state->totalLen, state->seed); + return XXH3_64bits_withSecret(state->buffer, (size_t)(state->totalLen), + secret, state->secretLimit + XXH_STRIPE_LEN); +} +#endif /* !XXH_NO_STREAM */ + + +/* ========================================== + * XXH3 128 bits (a.k.a XXH128) + * ========================================== + * XXH3's 128-bit variant has better mixing and strength than the 64-bit variant, + * even without counting the significantly larger output size. + * + * For example, extra steps are taken to avoid the seed-dependent collisions + * in 17-240 byte inputs (See XXH3_mix16B and XXH128_mix32B). + * + * This strength naturally comes at the cost of some speed, especially on short + * lengths. Note that longer hashes are about as fast as the 64-bit version + * due to it using only a slight modification of the 64-bit loop. + * + * XXH128 is also more oriented towards 64-bit machines. It is still extremely + * fast for a _128-bit_ hash on 32-bit (it usually clears XXH64). + */ + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_1to3_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + /* A doubled version of 1to3_64b with different constants. */ + XXH_ASSERT(input != NULL); + XXH_ASSERT(1 <= len && len <= 3); + XXH_ASSERT(secret != NULL); + /* + * len = 1: combinedl = { input[0], 0x01, input[0], input[0] } + * len = 2: combinedl = { input[1], 0x02, input[0], input[1] } + * len = 3: combinedl = { input[2], 0x03, input[0], input[1] } + */ + { xxh_u8 const c1 = input[0]; + xxh_u8 const c2 = input[len >> 1]; + xxh_u8 const c3 = input[len - 1]; + xxh_u32 const combinedl = ((xxh_u32)c1 <<16) | ((xxh_u32)c2 << 24) + | ((xxh_u32)c3 << 0) | ((xxh_u32)len << 8); + xxh_u32 const combinedh = XXH_rotl32(XXH_swap32(combinedl), 13); + xxh_u64 const bitflipl = (XXH_readLE32(secret) ^ XXH_readLE32(secret+4)) + seed; + xxh_u64 const bitfliph = (XXH_readLE32(secret+8) ^ XXH_readLE32(secret+12)) - seed; + xxh_u64 const keyed_lo = (xxh_u64)combinedl ^ bitflipl; + xxh_u64 const keyed_hi = (xxh_u64)combinedh ^ bitfliph; + XXH128_hash_t h128; + h128.low64 = XXH64_avalanche(keyed_lo); + h128.high64 = XXH64_avalanche(keyed_hi); + return h128; + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_4to8_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(4 <= len && len <= 8); + seed ^= (xxh_u64)XXH_swap32((xxh_u32)seed) << 32; + { xxh_u32 const input_lo = XXH_readLE32(input); + xxh_u32 const input_hi = XXH_readLE32(input + len - 4); + xxh_u64 const input_64 = input_lo + ((xxh_u64)input_hi << 32); + xxh_u64 const bitflip = (XXH_readLE64(secret+16) ^ XXH_readLE64(secret+24)) + seed; + xxh_u64 const keyed = input_64 ^ bitflip; + + /* Shift len to the left to ensure it is even, this avoids even multiplies. */ + XXH128_hash_t m128 = XXH_mult64to128(keyed, XXH_PRIME64_1 + (len << 2)); + + m128.high64 += (m128.low64 << 1); + m128.low64 ^= (m128.high64 >> 3); + + m128.low64 = XXH_xorshift64(m128.low64, 35); + m128.low64 *= PRIME_MX2; + m128.low64 = XXH_xorshift64(m128.low64, 28); + m128.high64 = XXH3_avalanche(m128.high64); + return m128; + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_9to16_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(9 <= len && len <= 16); + { xxh_u64 const bitflipl = (XXH_readLE64(secret+32) ^ XXH_readLE64(secret+40)) - seed; + xxh_u64 const bitfliph = (XXH_readLE64(secret+48) ^ XXH_readLE64(secret+56)) + seed; + xxh_u64 const input_lo = XXH_readLE64(input); + xxh_u64 input_hi = XXH_readLE64(input + len - 8); + XXH128_hash_t m128 = XXH_mult64to128(input_lo ^ input_hi ^ bitflipl, XXH_PRIME64_1); + /* + * Put len in the middle of m128 to ensure that the length gets mixed to + * both the low and high bits in the 128x64 multiply below. + */ + m128.low64 += (xxh_u64)(len - 1) << 54; + input_hi ^= bitfliph; + /* + * Add the high 32 bits of input_hi to the high 32 bits of m128, then + * add the long product of the low 32 bits of input_hi and XXH_PRIME32_2 to + * the high 64 bits of m128. + * + * The best approach to this operation is different on 32-bit and 64-bit. + */ + if (sizeof(void *) < sizeof(xxh_u64)) { /* 32-bit */ + /* + * 32-bit optimized version, which is more readable. + * + * On 32-bit, it removes an ADC and delays a dependency between the two + * halves of m128.high64, but it generates an extra mask on 64-bit. + */ + m128.high64 += (input_hi & 0xFFFFFFFF00000000ULL) + XXH_mult32to64((xxh_u32)input_hi, XXH_PRIME32_2); + } else { + /* + * 64-bit optimized (albeit more confusing) version. + * + * Uses some properties of addition and multiplication to remove the mask: + * + * Let: + * a = input_hi.lo = (input_hi & 0x00000000FFFFFFFF) + * b = input_hi.hi = (input_hi & 0xFFFFFFFF00000000) + * c = XXH_PRIME32_2 + * + * a + (b * c) + * Inverse Property: x + y - x == y + * a + (b * (1 + c - 1)) + * Distributive Property: x * (y + z) == (x * y) + (x * z) + * a + (b * 1) + (b * (c - 1)) + * Identity Property: x * 1 == x + * a + b + (b * (c - 1)) + * + * Substitute a, b, and c: + * input_hi.hi + input_hi.lo + ((xxh_u64)input_hi.lo * (XXH_PRIME32_2 - 1)) + * + * Since input_hi.hi + input_hi.lo == input_hi, we get this: + * input_hi + ((xxh_u64)input_hi.lo * (XXH_PRIME32_2 - 1)) + */ + m128.high64 += input_hi + XXH_mult32to64((xxh_u32)input_hi, XXH_PRIME32_2 - 1); + } + /* m128 ^= XXH_swap64(m128 >> 64); */ + m128.low64 ^= XXH_swap64(m128.high64); + + { /* 128x64 multiply: h128 = m128 * XXH_PRIME64_2; */ + XXH128_hash_t h128 = XXH_mult64to128(m128.low64, XXH_PRIME64_2); + h128.high64 += m128.high64 * XXH_PRIME64_2; + + h128.low64 = XXH3_avalanche(h128.low64); + h128.high64 = XXH3_avalanche(h128.high64); + return h128; + } } +} + +/* + * Assumption: `secret` size is >= XXH3_SECRET_SIZE_MIN + */ +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_0to16_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(len <= 16); + { if (len > 8) return XXH3_len_9to16_128b(input, len, secret, seed); + if (len >= 4) return XXH3_len_4to8_128b(input, len, secret, seed); + if (len) return XXH3_len_1to3_128b(input, len, secret, seed); + { XXH128_hash_t h128; + xxh_u64 const bitflipl = XXH_readLE64(secret+64) ^ XXH_readLE64(secret+72); + xxh_u64 const bitfliph = XXH_readLE64(secret+80) ^ XXH_readLE64(secret+88); + h128.low64 = XXH64_avalanche(seed ^ bitflipl); + h128.high64 = XXH64_avalanche( seed ^ bitfliph); + return h128; + } } +} + +/* + * A bit slower than XXH3_mix16B, but handles multiply by zero better. + */ +XXH_FORCE_INLINE XXH128_hash_t +XXH128_mix32B(XXH128_hash_t acc, const xxh_u8* input_1, const xxh_u8* input_2, + const xxh_u8* secret, XXH64_hash_t seed) +{ + acc.low64 += XXH3_mix16B (input_1, secret+0, seed); + acc.low64 ^= XXH_readLE64(input_2) + XXH_readLE64(input_2 + 8); + acc.high64 += XXH3_mix16B (input_2, secret+16, seed); + acc.high64 ^= XXH_readLE64(input_1) + XXH_readLE64(input_1 + 8); + return acc; +} + + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_17to128_128b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(16 < len && len <= 128); + + { XXH128_hash_t acc; + acc.low64 = len * XXH_PRIME64_1; + acc.high64 = 0; + +#if XXH_SIZE_OPT >= 1 + { + /* Smaller, but slightly slower. */ + unsigned int i = (unsigned int)(len - 1) / 32; + do { + acc = XXH128_mix32B(acc, input+16*i, input+len-16*(i+1), secret+32*i, seed); + } while (i-- != 0); + } +#else + if (len > 32) { + if (len > 64) { + if (len > 96) { + acc = XXH128_mix32B(acc, input+48, input+len-64, secret+96, seed); + } + acc = XXH128_mix32B(acc, input+32, input+len-48, secret+64, seed); + } + acc = XXH128_mix32B(acc, input+16, input+len-32, secret+32, seed); + } + acc = XXH128_mix32B(acc, input, input+len-16, secret, seed); +#endif + { XXH128_hash_t h128; + h128.low64 = acc.low64 + acc.high64; + h128.high64 = (acc.low64 * XXH_PRIME64_1) + + (acc.high64 * XXH_PRIME64_4) + + ((len - seed) * XXH_PRIME64_2); + h128.low64 = XXH3_avalanche(h128.low64); + h128.high64 = (XXH64_hash_t)0 - XXH3_avalanche(h128.high64); + return h128; + } + } +} + +XXH_NO_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_129to240_128b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX); + + { XXH128_hash_t acc; + unsigned i; + acc.low64 = len * XXH_PRIME64_1; + acc.high64 = 0; + /* + * We set as `i` as offset + 32. We do this so that unchanged + * `len` can be used as upper bound. This reaches a sweet spot + * where both x86 and aarch64 get simple agen and good codegen + * for the loop. + */ + for (i = 32; i < 160; i += 32) { + acc = XXH128_mix32B(acc, + input + i - 32, + input + i - 16, + secret + i - 32, + seed); + } + acc.low64 = XXH3_avalanche(acc.low64); + acc.high64 = XXH3_avalanche(acc.high64); + /* + * NB: `i <= len` will duplicate the last 32-bytes if + * len % 32 was zero. This is an unfortunate necessity to keep + * the hash result stable. + */ + for (i=160; i <= len; i += 32) { + acc = XXH128_mix32B(acc, + input + i - 32, + input + i - 16, + secret + XXH3_MIDSIZE_STARTOFFSET + i - 160, + seed); + } + /* last bytes */ + acc = XXH128_mix32B(acc, + input + len - 16, + input + len - 32, + secret + XXH3_SECRET_SIZE_MIN - XXH3_MIDSIZE_LASTOFFSET - 16, + (XXH64_hash_t)0 - seed); + + { XXH128_hash_t h128; + h128.low64 = acc.low64 + acc.high64; + h128.high64 = (acc.low64 * XXH_PRIME64_1) + + (acc.high64 * XXH_PRIME64_4) + + ((len - seed) * XXH_PRIME64_2); + h128.low64 = XXH3_avalanche(h128.low64); + h128.high64 = (XXH64_hash_t)0 - XXH3_avalanche(h128.high64); + return h128; + } + } +} + +XXH_FORCE_INLINE XXH128_hash_t +XXH3_hashLong_128b_internal(const void* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[XXH_ACC_NB] = XXH3_INIT_ACC; + + XXH3_hashLong_internal_loop(acc, (const xxh_u8*)input, len, secret, secretSize, f_acc, f_scramble); + + /* converge into final hash */ + XXH_STATIC_ASSERT(sizeof(acc) == 64); + XXH_ASSERT(secretSize >= sizeof(acc) + XXH_SECRET_MERGEACCS_START); + { XXH128_hash_t h128; + h128.low64 = XXH3_mergeAccs(acc, + secret + XXH_SECRET_MERGEACCS_START, + (xxh_u64)len * XXH_PRIME64_1); + h128.high64 = XXH3_mergeAccs(acc, + secret + secretSize + - sizeof(acc) - XXH_SECRET_MERGEACCS_START, + ~((xxh_u64)len * XXH_PRIME64_2)); + return h128; + } +} + +/* + * It's important for performance that XXH3_hashLong() is not inlined. + */ +XXH_NO_INLINE XXH_PUREF XXH128_hash_t +XXH3_hashLong_128b_default(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, + const void* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; (void)secret; (void)secretLen; + return XXH3_hashLong_128b_internal(input, len, XXH3_kSecret, sizeof(XXH3_kSecret), + XXH3_accumulate, XXH3_scrambleAcc); +} + +/* + * It's important for performance to pass @p secretLen (when it's static) + * to the compiler, so that it can properly optimize the vectorized loop. + * + * When the secret size is unknown, or on GCC 12 where the mix of NO_INLINE and FORCE_INLINE + * breaks -Og, this is XXH_NO_INLINE. + */ +XXH3_WITH_SECRET_INLINE XXH128_hash_t +XXH3_hashLong_128b_withSecret(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, + const void* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; + return XXH3_hashLong_128b_internal(input, len, (const xxh_u8*)secret, secretLen, + XXH3_accumulate, XXH3_scrambleAcc); +} + +XXH_FORCE_INLINE XXH128_hash_t +XXH3_hashLong_128b_withSeed_internal(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble, + XXH3_f_initCustomSecret f_initSec) +{ + if (seed64 == 0) + return XXH3_hashLong_128b_internal(input, len, + XXH3_kSecret, sizeof(XXH3_kSecret), + f_acc, f_scramble); + { XXH_ALIGN(XXH_SEC_ALIGN) xxh_u8 secret[XXH_SECRET_DEFAULT_SIZE]; + f_initSec(secret, seed64); + return XXH3_hashLong_128b_internal(input, len, (const xxh_u8*)secret, sizeof(secret), + f_acc, f_scramble); + } +} + +/* + * It's important for performance that XXH3_hashLong is not inlined. + */ +XXH_NO_INLINE XXH128_hash_t +XXH3_hashLong_128b_withSeed(const void* input, size_t len, + XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen) +{ + (void)secret; (void)secretLen; + return XXH3_hashLong_128b_withSeed_internal(input, len, seed64, + XXH3_accumulate, XXH3_scrambleAcc, XXH3_initCustomSecret); +} + +typedef XXH128_hash_t (*XXH3_hashLong128_f)(const void* XXH_RESTRICT, size_t, + XXH64_hash_t, const void* XXH_RESTRICT, size_t); + +XXH_FORCE_INLINE XXH128_hash_t +XXH3_128bits_internal(const void* input, size_t len, + XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen, + XXH3_hashLong128_f f_hl128) +{ + XXH_ASSERT(secretLen >= XXH3_SECRET_SIZE_MIN); + /* + * If an action is to be taken if `secret` conditions are not respected, + * it should be done here. + * For now, it's a contract pre-condition. + * Adding a check and a branch here would cost performance at every hash. + */ + if (len <= 16) + return XXH3_len_0to16_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, seed64); + if (len <= 128) + return XXH3_len_17to128_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + if (len <= XXH3_MIDSIZE_MAX) + return XXH3_len_129to240_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + return f_hl128(input, len, seed64, secret, secretLen); +} + + +/* === Public XXH128 API === */ + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t XXH3_128bits(XXH_NOESCAPE const void* input, size_t len) +{ + return XXH3_128bits_internal(input, len, 0, + XXH3_kSecret, sizeof(XXH3_kSecret), + XXH3_hashLong_128b_default); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH3_128bits_withSecret(XXH_NOESCAPE const void* input, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + return XXH3_128bits_internal(input, len, 0, + (const xxh_u8*)secret, secretSize, + XXH3_hashLong_128b_withSecret); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH3_128bits_withSeed(XXH_NOESCAPE const void* input, size_t len, XXH64_hash_t seed) +{ + return XXH3_128bits_internal(input, len, seed, + XXH3_kSecret, sizeof(XXH3_kSecret), + XXH3_hashLong_128b_withSeed); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH3_128bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed) +{ + if (len <= XXH3_MIDSIZE_MAX) + return XXH3_128bits_internal(input, len, seed, XXH3_kSecret, sizeof(XXH3_kSecret), NULL); + return XXH3_hashLong_128b_withSecret(input, len, seed, secret, secretSize); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH128(XXH_NOESCAPE const void* input, size_t len, XXH64_hash_t seed) +{ + return XXH3_128bits_withSeed(input, len, seed); +} + + +/* === XXH3 128-bit streaming === */ +#ifndef XXH_NO_STREAM +/* + * All initialization and update functions are identical to 64-bit streaming variant. + * The only difference is the finalization routine. + */ + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr) +{ + return XXH3_64bits_reset(statePtr); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + return XXH3_64bits_reset_withSecret(statePtr, secret, secretSize); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed) +{ + return XXH3_64bits_reset_withSeed(statePtr, seed); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed) +{ + return XXH3_64bits_reset_withSecretandSeed(statePtr, secret, secretSize, seed); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_update(XXH_NOESCAPE XXH3_state_t* state, XXH_NOESCAPE const void* input, size_t len) +{ + return XXH3_64bits_update(state, input, len); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t XXH3_128bits_digest (XXH_NOESCAPE const XXH3_state_t* state) +{ + const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret; + if (state->totalLen > XXH3_MIDSIZE_MAX) { + XXH_ALIGN(XXH_ACC_ALIGN) XXH64_hash_t acc[XXH_ACC_NB]; + XXH3_digest_long(acc, state, secret); + XXH_ASSERT(state->secretLimit + XXH_STRIPE_LEN >= sizeof(acc) + XXH_SECRET_MERGEACCS_START); + { XXH128_hash_t h128; + h128.low64 = XXH3_mergeAccs(acc, + secret + XXH_SECRET_MERGEACCS_START, + (xxh_u64)state->totalLen * XXH_PRIME64_1); + h128.high64 = XXH3_mergeAccs(acc, + secret + state->secretLimit + XXH_STRIPE_LEN + - sizeof(acc) - XXH_SECRET_MERGEACCS_START, + ~((xxh_u64)state->totalLen * XXH_PRIME64_2)); + return h128; + } + } + /* len <= XXH3_MIDSIZE_MAX : short code */ + if (state->useSeed) + return XXH3_128bits_withSeed(state->buffer, (size_t)state->totalLen, state->seed); + return XXH3_128bits_withSecret(state->buffer, (size_t)(state->totalLen), + secret, state->secretLimit + XXH_STRIPE_LEN); +} +#endif /* !XXH_NO_STREAM */ +/* 128-bit utility functions */ + +#include /* memcmp, memcpy */ + +/* return : 1 is equal, 0 if different */ +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API int XXH128_isEqual(XXH128_hash_t h1, XXH128_hash_t h2) +{ + /* note : XXH128_hash_t is compact, it has no padding byte */ + return !(memcmp(&h1, &h2, sizeof(h1))); +} + +/* This prototype is compatible with stdlib's qsort(). + * @return : >0 if *h128_1 > *h128_2 + * <0 if *h128_1 < *h128_2 + * =0 if *h128_1 == *h128_2 */ +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API int XXH128_cmp(XXH_NOESCAPE const void* h128_1, XXH_NOESCAPE const void* h128_2) +{ + XXH128_hash_t const h1 = *(const XXH128_hash_t*)h128_1; + XXH128_hash_t const h2 = *(const XXH128_hash_t*)h128_2; + int const hcmp = (h1.high64 > h2.high64) - (h2.high64 > h1.high64); + /* note : bets that, in most cases, hash values are different */ + if (hcmp) return hcmp; + return (h1.low64 > h2.low64) - (h2.low64 > h1.low64); +} + + +/*====== Canonical representation ======*/ +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API void +XXH128_canonicalFromHash(XXH_NOESCAPE XXH128_canonical_t* dst, XXH128_hash_t hash) +{ + XXH_STATIC_ASSERT(sizeof(XXH128_canonical_t) == sizeof(XXH128_hash_t)); + if (XXH_CPU_LITTLE_ENDIAN) { + hash.high64 = XXH_swap64(hash.high64); + hash.low64 = XXH_swap64(hash.low64); + } + XXH_memcpy(dst, &hash.high64, sizeof(hash.high64)); + XXH_memcpy((char*)dst + sizeof(hash.high64), &hash.low64, sizeof(hash.low64)); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH128_hashFromCanonical(XXH_NOESCAPE const XXH128_canonical_t* src) +{ + XXH128_hash_t h; + h.high64 = XXH_readBE64(src); + h.low64 = XXH_readBE64(src->digest + 8); + return h; +} + + + +/* ========================================== + * Secret generators + * ========================================== + */ +#define XXH_MIN(x, y) (((x) > (y)) ? (y) : (x)) + +XXH_FORCE_INLINE void XXH3_combine16(void* dst, XXH128_hash_t h128) +{ + XXH_writeLE64( dst, XXH_readLE64(dst) ^ h128.low64 ); + XXH_writeLE64( (char*)dst+8, XXH_readLE64((char*)dst+8) ^ h128.high64 ); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_generateSecret(XXH_NOESCAPE void* secretBuffer, size_t secretSize, XXH_NOESCAPE const void* customSeed, size_t customSeedSize) +{ +#if (XXH_DEBUGLEVEL >= 1) + XXH_ASSERT(secretBuffer != NULL); + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); +#else + /* production mode, assert() are disabled */ + if (secretBuffer == NULL) return XXH_ERROR; + if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR; +#endif + + if (customSeedSize == 0) { + customSeed = XXH3_kSecret; + customSeedSize = XXH_SECRET_DEFAULT_SIZE; + } +#if (XXH_DEBUGLEVEL >= 1) + XXH_ASSERT(customSeed != NULL); +#else + if (customSeed == NULL) return XXH_ERROR; +#endif + + /* Fill secretBuffer with a copy of customSeed - repeat as needed */ + { size_t pos = 0; + while (pos < secretSize) { + size_t const toCopy = XXH_MIN((secretSize - pos), customSeedSize); + memcpy((char*)secretBuffer + pos, customSeed, toCopy); + pos += toCopy; + } } + + { size_t const nbSeg16 = secretSize / 16; + size_t n; + XXH128_canonical_t scrambler; + XXH128_canonicalFromHash(&scrambler, XXH128(customSeed, customSeedSize, 0)); + for (n=0; n /* abort() */ +#include +#include +#include +#include +#include +#include + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +#include "xxhash/xxhash.h" +#include "sha1/sha1.h" +#include "sha256/sha256.h" + +#ifdef __cplusplus +} +#endif + + +// uuid.uuid5(uuid.NAMESPACE_URL, 'en.wikipedia.org/wiki/Llama.cpp') +#define UUID_NAMESPACE_LLAMA_CPP "ef001206-dadc-5f6d-a15f-3359e577d4e5" +#define UUID_NAMESPACE_LLAMA_CPP_HEX 0xef, 0x00, 0x12, 0x06, 0xda, 0xdc, 0x5f, 0x6d, 0xa1, 0x5f, 0x33, 0x59, 0xe5, 0x77, 0xd4, 0xe5 + + +#define HASH_TYPE_SHA256_STR "sha256" +#define HASH_TYPE_SHA1_STR "sha1" +#define HASH_TYPE_XXH64_STR "xxh64" +#define HASH_TYPE_UUID_STR "uuid" + + +typedef enum { + HASH_EXIT_SUCCESS = 0, // All hash has been generated or validated + HASH_EXIT_FAILURE = 1, // Generic Failure + HASH_EXIT_MISMATCH = 2, // Hash mismatched during validation + HASH_EXIT_MANIFEST_MISSING_ENTRY = 3, // Hash attempted validation but missing entry in manifest + HASH_EXIT_MANIFEST_UNKNOWN_HASH = 4, // Manifest is present, but we do not know any hash format within it + HASH_EXIT_MANIFEST_FILE_ERROR = 5 // Manifest is either missing or not a known format +} hash_exit_code_t; + + +typedef enum { + HASH_MANIFEST_NOT_FOUND, + HASH_MANIFEST_MISMATCH, + HASH_MANIFEST_OK, +} hash_manifest_result_t; + + +struct hash_params { + std::string input; + bool xxh64 = false; + bool sha1 = false; + bool sha256 = false; + bool uuid = false; + + bool no_layer = false; + + bool manifest_is_usable = false; + std::string manifest_file; +}; + +struct manifest_check_params { + bool xxh64 = false; + bool sha1 = false; + bool sha256 = false; + bool uuid = false; +}; + +static char const * hash_manifest_result_to_str(hash_manifest_result_t value) { + switch (value) { + case HASH_MANIFEST_NOT_FOUND: return "Not Found"; + case HASH_MANIFEST_MISMATCH: return "Mismatch"; + case HASH_MANIFEST_OK: return "Ok"; + } + return "?"; +} + +static char const * hash_exit_code_to_str(hash_exit_code_t value) { + switch (value) { + case HASH_EXIT_SUCCESS: return "Success"; + case HASH_EXIT_FAILURE: return "Failure"; + case HASH_EXIT_MISMATCH: return "Mismatch"; + case HASH_EXIT_MANIFEST_MISSING_ENTRY: return "Manifest Missing Entry"; + case HASH_EXIT_MANIFEST_UNKNOWN_HASH: return "Manifest Unknown Hash"; + case HASH_EXIT_MANIFEST_FILE_ERROR: return "Manifest File Error"; + } + return "?"; +} + +static void hash_print_usage(const char * executable) { + const hash_params default_params; + printf("\n"); + printf("usage: %s [options] GGUF_IN\n", executable); + printf("\n"); + printf("Hash a GGUF file"); + printf("\n"); + printf("options:\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" --xxh64 use xxh64 hash\n"); + printf(" --sha1 use sha1 hash\n"); + printf(" --sha256 use sha256 hash\n"); + printf(" --all use all hash\n"); + printf(" --no-layer exclude per layer hash\n"); + printf(" --uuid generate UUIDv5 ID\n"); + printf(" -c, --check verify against a manifest\n"); + printf("\n"); +} + +static void hash_params_parse_ex(int argc, const char ** argv, hash_params & params) { + std::string arg; + bool invalid_param = false; + const std::string arg_prefix = "--"; + + int arg_idx = 1; + for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { + arg = argv[arg_idx]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + + bool arg_found = false; + if (arg == "-h" || arg == "--help") { + hash_print_usage(argv[0]); + exit(0); + } + + if (arg == "--xxh64") { + arg_found = true; + params.xxh64 = true; + } + + if (arg == "--sha1") { + arg_found = true; + params.sha1 = true; + } + + if (arg == "--uuid") { + arg_found = true; + params.uuid = true; + } + + if (arg == "--sha256") { + arg_found = true; + params.sha256 = true; + } + + if (arg == "--all") { + arg_found = true; + params.sha256 = true; + params.sha1 = true; + params.xxh64 = true; + } + + if (arg == "--no-layer") { + arg_found = true; + params.no_layer = true; + } + + if (arg == "-c" || arg == "--check") { + if (++arg_idx >= argc) { + invalid_param = true; + break; + } + arg_found = true; + params.manifest_file = argv[arg_idx]; + } + + if (!arg_found) { + throw std::invalid_argument("error: unknown argument: " + arg); + } + } + + if (invalid_param) { + throw std::invalid_argument("error: invalid parameter for argument:" + arg); + } + + if (argc - arg_idx < 1) { + throw std::invalid_argument("error: bad arguments"); + } + + params.input = argv[arg_idx++]; +} + +static bool hash_params_parse(int argc, const char ** argv, hash_params & params) { + bool result = true; + try { + hash_params_parse_ex(argc, argv, params); + } + catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); + hash_print_usage(argv[0]); + exit(EXIT_FAILURE); + } + return result; +} + +static bool manifest_type(const std::string & manifest_file, manifest_check_params & manifest_check) { + if (manifest_file.empty()) { + return false; + } + + std::ifstream file(manifest_file); + if (!file.is_open()) { + return false; + } + + std::string manifest_entry_line; + while (getline(file, manifest_entry_line)) { + // hash_type_str hash_str tensor_name + // e.g. 'xxh64 f66e9cd66a4396a0 test.gguf:tensor_0' + std::istringstream line_stream(manifest_entry_line); + std::string file_hash_type; + if (line_stream >> file_hash_type) { + if (file_hash_type == HASH_TYPE_SHA256_STR) { + manifest_check.sha256 = true; + } else if (file_hash_type == HASH_TYPE_SHA1_STR) { + manifest_check.sha1 = true; + } else if (file_hash_type == HASH_TYPE_XXH64_STR) { + manifest_check.xxh64 = true; + } else if (file_hash_type == HASH_TYPE_UUID_STR) { + manifest_check.uuid = true; + } + } + } + + return true; +} + +static hash_manifest_result_t manifest_verify(const std::string& manifest_file, const std::string& hash_type_str, const std::string& hash_str, const std::string& tensor_name) { + if (manifest_file.empty()) { + return HASH_MANIFEST_NOT_FOUND; + } + + std::ifstream file(manifest_file); + if (!file.is_open()) { + return HASH_MANIFEST_NOT_FOUND; + } + + std::string manifest_entry_line; + while (getline(file, manifest_entry_line)) { + std::istringstream line_stream(manifest_entry_line); + std::string file_hash_type; + std::string file_hash; + std::string file_tensor_name; + if (line_stream >> file_hash_type >> file_hash >> file_tensor_name) { + // Line parsed. Check hash validity + + if (file_hash_type != hash_type_str) { + continue; + } + + if (file_tensor_name != tensor_name) { + continue; + } + + return (file_hash == hash_str) ? HASH_MANIFEST_OK : HASH_MANIFEST_MISMATCH; + } + } + + return HASH_MANIFEST_NOT_FOUND; +} + +static void generate_uuidv5(const unsigned char sha1_digest[20], unsigned char uuid[16]) { + // Ref: https://www.rfc-editor.org/rfc/rfc9562.html#section-5.5 + // Assumes that digest was processed correctly with the expected namespace + for (int i = 0; i < 16; i++) { + uuid[i] = sha1_digest[i]; + } + + // Set bits corresponding to UUID ver 5 + uuid[ 6] &= ~(0xF << 4); + uuid[ 6] |= (5 << 4); + + // Set bits corresponding to UUID variant 0b10XX + uuid[ 8] &= ~(0xc << 4); + uuid[ 8] |= (0x8 << 4); +} + +static hash_exit_code_t gguf_hash(const hash_params & hash_params) { + const std::string & fname = hash_params.input; + struct ggml_context * ctx_data = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ false, + /*.ctx = */ &ctx_data, + }; + + // xxh64 init + XXH64_state_t* xxh64_model_hash_state = NULL; + if (hash_params.xxh64) { + xxh64_model_hash_state = XXH64_createState(); + if (xxh64_model_hash_state==NULL) { + abort(); + } + + XXH64_hash_t const seed = 0; + if (XXH64_reset(xxh64_model_hash_state, seed) == XXH_ERROR) { + abort(); + } + } + + // sha1 init + SHA1_CTX sha1_model_hash_ctx; + if (hash_params.sha1) { + SHA1Init(&sha1_model_hash_ctx); + } + + // sha256 init + sha256_t sha256_model_hash_ctx; + if (hash_params.sha256) { + sha256_init(&sha256_model_hash_ctx); + } + + // sha1 for uuid init + SHA1_CTX sha1_for_uuid_ctx; + if (hash_params.uuid) { + unsigned char const uuidv5_namespace[] = {UUID_NAMESPACE_LLAMA_CPP_HEX}; + SHA1Init(&sha1_for_uuid_ctx); + SHA1Update( &sha1_for_uuid_ctx, (unsigned char const *)uuidv5_namespace, sizeof(uuidv5_namespace)); + } + + struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); + const int n_tensors = gguf_get_n_tensors(ctx); + bool tensor_layer_in_manifest = false; + bool model_in_manifest = false; + bool tensor_layer_has_mismatch = false; + bool model_has_mismatch = false; + for (int i = 0; i < n_tensors; ++i) { + const char * name = gguf_get_tensor_name(ctx, i); + struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name); + auto n_bytes = ggml_nbytes(cur); + auto *raw_data = cur->data; + const std::string tensor_layer_name = fname + ":" + name; + + if (hash_params.xxh64) { + + if (!hash_params.no_layer) { + // Per Layer Hash + XXH64_hash_t hash = XXH64(raw_data, n_bytes, 0); + + char hex_result[17]; + for (int offset = 0; offset < 8; offset++) { + unsigned int shift_bits_by = (8 * (8 - offset - 1)); + sprintf( ( hex_result + (2*offset)), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + tensor_layer_in_manifest = true; + tensor_layer_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + tensor_layer_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name.c_str()); + } + } + + // Overall Model Hash + if (XXH64_update(xxh64_model_hash_state, raw_data, n_bytes) == XXH_ERROR) abort(); + } + + if (hash_params.sha1) { + + if (!hash_params.no_layer) { + // Per Layer Hash + char result[21]; // sha1 outputs 20 bytes + SHA1( result, (const char *)raw_data, n_bytes); + + char hex_result[41] = {0}; + for (int offset = 0; offset < 20; offset++) { + sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + tensor_layer_in_manifest = true; + tensor_layer_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + tensor_layer_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name.c_str()); + } + } + + // Overall Model Hash + SHA1Update( &sha1_model_hash_ctx, (unsigned char const *)raw_data, n_bytes); + } + + if (hash_params.sha256) { + + if (!hash_params.no_layer) { + // Per Layer Hash + unsigned char result[SHA256_DIGEST_SIZE]; // sha256 outputs 32 bytes + sha256_hash((unsigned char*) result, (const unsigned char *)raw_data, n_bytes); + + char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0}; + for (int offset = 0; offset < SHA256_DIGEST_SIZE; offset++) { + sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + tensor_layer_in_manifest = true; + tensor_layer_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + tensor_layer_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name.c_str()); + } + } + + // Overall Model Hash + sha256_update( &sha256_model_hash_ctx, (unsigned char const *)raw_data, n_bytes); + } + + if (hash_params.uuid) { + SHA1Update( &sha1_for_uuid_ctx, (unsigned char const *)raw_data, n_bytes); + } + } + + if (hash_params.xxh64) { + XXH64_hash_t const hash = XXH64_digest(xxh64_model_hash_state); + + char hex_result[17]; + for (int offset = 0; offset < 8; offset++) { + unsigned int shift_bits_by = (8 * (8 - offset - 1)); + sprintf( ( hex_result + (2*offset)), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_XXH64_STR, hex_result, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_XXH64_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_XXH64_STR, hex_result, fname.c_str()); + } + } + + if (hash_params.sha1) { + unsigned char result[21]; + SHA1Final(result, &sha1_model_hash_ctx); + + char hex_result[41]; + for (int offset = 0; offset < 20; offset++) { + sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA1_STR, hex_result, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA1_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA1_STR, hex_result, fname.c_str()); + } + } + + if (hash_params.sha256) { + unsigned char result[SHA256_DIGEST_SIZE]; // sha256 outputs 32 bytes + sha256_final( &sha256_model_hash_ctx, result); + + char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0}; + for (int offset = 0; offset < SHA256_DIGEST_SIZE; offset++) { + sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, hex_result, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA256_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA256_STR, hex_result, fname.c_str()); + } + } + + if (hash_params.uuid) { + unsigned char result[21]; + SHA1Final(result, &sha1_for_uuid_ctx); + + unsigned char uuid[16]; + generate_uuidv5(result, uuid); + + char string_buffer[37] = {0}; + sprintf(string_buffer, "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x", + uuid[0], uuid[1], uuid[2], uuid[3], + uuid[4], uuid[5], uuid[6], uuid[7], + uuid[8], uuid[9], uuid[10], uuid[11], + uuid[12], uuid[13], uuid[14], uuid[15]); + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, string_buffer, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_UUID_STR, string_buffer, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_UUID_STR, string_buffer, fname.c_str()); + } + } + + + ggml_free(ctx_data); + gguf_free(ctx); + + + if (hash_params.manifest_is_usable) { + // In hash verification mode + + if (!model_in_manifest) { + // model missing in manifest? + + // Check tensor layer... + if (!tensor_layer_in_manifest) { + // Still missing? Maybe we are reading the wrong manifest. + return HASH_EXIT_MANIFEST_MISSING_ENTRY; + } + + if (tensor_layer_has_mismatch) { + // Per tensor check found error + return HASH_EXIT_FAILURE; + } + + // All per tensor layer checks passed? Sounds good enough. + return HASH_EXIT_SUCCESS; + } + + // Overall model check passed, but let's check per layer just in case + // If missing, we don't care too much as the overall model checked + if (tensor_layer_in_manifest && tensor_layer_has_mismatch) { + return HASH_EXIT_FAILURE; + } + + if (model_has_mismatch) { + // model has failed hash somewhere in the model + return HASH_EXIT_FAILURE; + } + + // All checks appears to be fine + return HASH_EXIT_SUCCESS; + } + + // In hash generation mode + return HASH_EXIT_SUCCESS; +} + +int main(int argc, const char ** argv) { + hash_params params; + manifest_check_params manifest_check; + hash_params_parse(argc, argv, params); + + if (!params.manifest_file.empty()) { + if (!manifest_type(params.manifest_file, manifest_check)) { + printf("ERROR cannot open manifest %s", params.manifest_file.c_str()); + return HASH_EXIT_MANIFEST_FILE_ERROR; + } + + if (!manifest_check.sha256 && !manifest_check.sha1 && !manifest_check.xxh64 && !manifest_check.uuid) { + printf("ERROR manifest does not have any known hash format in %s", params.manifest_file.c_str()); + return HASH_EXIT_MANIFEST_UNKNOWN_HASH; + } + + printf("manifest %s", params.manifest_file.c_str()); + + if (manifest_check.sha256) { + printf(" sha256"); + } + + if (manifest_check.sha1) { + printf(" sha1"); + } + + if (manifest_check.xxh64) { + printf(" xxh64"); + } + + if (manifest_check.uuid) { + printf(" uuid"); + } + + printf("\n"); + + // Autoselect the highest security hash if manifest is provided but + // the user has not specifically defined the hash they care about + if (!params.xxh64 && !params.sha1 && !params.uuid && !params.sha256) { + // User has not selected a specific value, pick most secure hash + if (manifest_check.sha256) { + params.sha256 = true; + } else if (manifest_check.sha1) { + params.sha1 = true; + } else if (manifest_check.xxh64) { + params.xxh64 = true; + } else if (manifest_check.uuid) { + params.uuid = true; + } + } + + params.manifest_is_usable = true; + } + + // By default if no swich argument provided, assume xxh64 + if (!params.xxh64 && !params.sha1 && !params.uuid && !params.sha256) { + params.xxh64 = true; + } + + hash_exit_code_t exit_code = gguf_hash(params); + + if (params.manifest_is_usable) { + printf("\nVerification results for %s - %s\n", params.manifest_file.c_str(), hash_exit_code_to_str(exit_code)); + } + + return exit_code; +} diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index ca71dd687..0e682154d 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -659,4 +659,3 @@ int main(int argc, char ** argv) { return 0; } - diff --git a/examples/json-schema-pydantic-example.py b/examples/json_schema_pydantic_example.py similarity index 98% rename from examples/json-schema-pydantic-example.py rename to examples/json_schema_pydantic_example.py index 2a24f8118..c7ca7b8d9 100644 --- a/examples/json-schema-pydantic-example.py +++ b/examples/json_schema_pydantic_example.py @@ -1,7 +1,7 @@ # Usage: #! ./llama-server -m some-model.gguf & #! pip install pydantic -#! python json-schema-pydantic-example.py +#! python json_schema_pydantic_example.py from pydantic import BaseModel, Extra, TypeAdapter from annotated_types import MinLen diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 737f882fb..2a3f9f758 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -322,7 +322,7 @@ actor LlamaContext { defer { result.deallocate() } - let nTokens = llama_token_to_piece(model, token, result, 8, false) + let nTokens = llama_token_to_piece(model, token, result, 8, 0, false) if nTokens < 0 { let newResult = UnsafeMutablePointer.allocate(capacity: Int(-nTokens)) @@ -330,7 +330,7 @@ actor LlamaContext { defer { newResult.deallocate() } - let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false) + let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, 0, false) let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens)) return Array(bufferPointer) } else { diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index f6c619c87..06a65fba4 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -30,16 +30,16 @@ git clone https://huggingface.co/mtgv/MobileVLM-1.7B git clone https://huggingface.co/openai/clip-vit-large-patch14-336 ``` -2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: +2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh -python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B +python ./examples/llava/llava_surgery.py -m path/to/MobileVLM-1.7B ``` -3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF: +3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF: ```sh -python ./examples/llava/convert-image-encoder-to-gguf \ +python ./examples/llava/convert_image_encoder_to_gguf \ -m path/to/clip-vit-large-patch14-336 \ --llava-projector path/to/MobileVLM-1.7B/llava.projector \ --output-dir path/to/MobileVLM-1.7B \ @@ -47,17 +47,17 @@ python ./examples/llava/convert-image-encoder-to-gguf \ ``` ```sh -python ./examples/llava/convert-image-encoder-to-gguf \ +python ./examples/llava/convert_image_encoder_to_gguf \ -m path/to/clip-vit-large-patch14-336 \ --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \ --output-dir path/to/MobileVLM-1.7B_V2 \ --projector-type ldpv2 ``` -4. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF: +4. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./examples/convert-legacy-llama.py path/to/MobileVLM-1.7B +python ./examples/convert_legacy_llama.py path/to/MobileVLM-1.7B ``` 5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k` diff --git a/examples/llava/README.md b/examples/llava/README.md index f4554de67..012451361 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -38,22 +38,22 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336 pip install -r examples/llava/requirements.txt ``` -3. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: +3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh -python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b +python ./examples/llava/llava_surgery.py -m ../llava-v1.5-7b ``` -4. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF: +4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF: ```sh -python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b +python ./examples/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` -5. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF: +5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./examples/convert-legacy-llama.py ../llava-v1.5-7b --skip-unknown +python ./examples/convert_legacy_llama.py ../llava-v1.5-7b --skip-unknown ``` Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory. @@ -70,9 +70,9 @@ git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b pip install -r examples/llava/requirements.txt ``` -3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: +3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: ```console -python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/ +python examples/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/ ``` - you will find a llava.projector and a llava.clip file in your model directory @@ -86,13 +86,13 @@ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.jso 5) Create the visual gguf model: ```console -python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision +python ./examples/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision ``` - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP 6) Then convert the model to gguf format: ```console -python ./examples/convert-legacy-llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown +python ./examples/convert_legacy_llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown ``` 7) And finally we can run the llava cli using the 1.6 model version: diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert_image_encoder_to_gguf.py similarity index 100% rename from examples/llava/convert-image-encoder-to-gguf.py rename to examples/llava/convert_image_encoder_to_gguf.py diff --git a/examples/llava/llava-surgery.py b/examples/llava/llava_surgery.py similarity index 100% rename from examples/llava/llava-surgery.py rename to examples/llava/llava_surgery.py diff --git a/examples/llava/llava-surgery-v2.py b/examples/llava/llava_surgery_v2.py similarity index 100% rename from examples/llava/llava-surgery-v2.py rename to examples/llava/llava_surgery_v2.py diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt index 17cb4d5e5..fbfd0cdd7 100644 --- a/examples/llava/requirements.txt +++ b/examples/llava/requirements.txt @@ -1,3 +1,4 @@ --r ../../requirements/requirements-convert-legacy-llama.txt +-r ../../requirements/requirements-convert_legacy_llama.txt +--extra-index-url https://download.pytorch.org/whl/cpu pillow~=10.2.0 -torch~=2.1.1 +torch~=2.2.1 diff --git a/examples/lookup/README.md b/examples/lookup/README.md index 5bfb0de93..71c345c03 100644 --- a/examples/lookup/README.md +++ b/examples/lookup/README.md @@ -10,4 +10,3 @@ More info: https://github.com/ggerganov/llama.cpp/pull/4484 https://github.com/ggerganov/llama.cpp/issues/4226 - diff --git a/examples/main-cmake-pkg/.gitignore b/examples/main-cmake-pkg/.gitignore index e32c11c7f..67c01d64c 100644 --- a/examples/main-cmake-pkg/.gitignore +++ b/examples/main-cmake-pkg/.gitignore @@ -48,4 +48,3 @@ build*/ out/ tmp/ - diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt index a97ded365..3b38db292 100644 --- a/examples/main-cmake-pkg/CMakeLists.txt +++ b/examples/main-cmake-pkg/CMakeLists.txt @@ -30,4 +30,3 @@ target_include_directories(${TARGET} PRIVATE ${_common_path}) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) - diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 1114073b8..4ef55c1e6 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -37,7 +37,8 @@ static gpt_params * g_params; static std::vector * g_input_tokens; static std::ostringstream * g_output_ss; static std::vector * g_output_tokens; -static bool is_interacting = false; +static bool is_interacting = false; +static bool need_insert_eot = false; static bool file_exists(const std::string & path) { std::ifstream f(path.c_str()); @@ -99,7 +100,8 @@ static void write_logfile( static void sigint_handler(int signo) { if (signo == SIGINT) { if (!is_interacting && g_params->interactive) { - is_interacting = true; + is_interacting = true; + need_insert_eot = true; } else { console::cleanup(); printf("\n"); @@ -224,7 +226,14 @@ int main(int argc, char ** argv) { __func__, n_ctx_train, n_ctx); } - LOG_TEE("%s: chat template example: %s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str()); + // print chat template example in conversation mode + if (params.conversation) { + if (params.enable_chat_template) { + LOG_TEE("%s: chat template example: %s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str()); + } else { + LOG_TEE("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__); + } + } // print system information { @@ -255,13 +264,15 @@ int main(int argc, char ** argv) { } const bool add_bos = llama_should_add_bos_token(model); - GGML_ASSERT(llama_add_eos_token(model) != 1); + if (!llama_model_has_encoder(model)) { + GGML_ASSERT(llama_add_eos_token(model) != 1); + } LOG("add_bos: %d\n", add_bos); std::vector embd_inp; { - auto prompt = params.conversation + auto prompt = (params.conversation && params.enable_chat_template && !params.prompt.empty()) ? chat_add_and_format(model, chat_msgs, "system", params.prompt) // format the system prompt in conversation mode : params.prompt; if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) { @@ -517,6 +528,24 @@ int main(int argc, char ** argv) { exit(1); } + if (llama_model_has_encoder(model)) { + int enc_input_size = embd_inp.size(); + llama_token * enc_input_buf = embd_inp.data(); + + if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size, 0, 0))) { + LOG_TEE("%s : failed to eval\n", __func__); + return 1; + } + + llama_token decoder_start_token_id = llama_model_decoder_start_token(model); + if (decoder_start_token_id == -1) { + decoder_start_token_id = llama_token_bos(model); + } + + embd_inp.clear(); + embd_inp.push_back(decoder_start_token_id); + } + while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict if (!embd.empty()) { @@ -810,7 +839,9 @@ int main(int argc, char ** argv) { is_antiprompt = true; } - chat_add_and_format(model, chat_msgs, "assistant", assistant_ss.str()); + if (params.enable_chat_template) { + chat_add_and_format(model, chat_msgs, "assistant", assistant_ss.str()); + } is_interacting = true; printf("\n"); } @@ -872,16 +903,24 @@ int main(int argc, char ** argv) { string_process_escapes(buffer); } - std::string user_inp = params.conversation + bool format_chat = params.conversation && params.enable_chat_template; + std::string user_inp = format_chat ? chat_add_and_format(model, chat_msgs, "user", std::move(buffer)) : std::move(buffer); // TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix) const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); - const auto line_inp = ::llama_tokenize(ctx, user_inp, false, params.conversation); + const auto line_inp = ::llama_tokenize(ctx, user_inp, false, format_chat); const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); + // if user stop generation mid-way, we must add EOT to finish model's last response + if (need_insert_eot && format_chat) { + llama_token eot = llama_token_eot(model); + embd_inp.push_back(eot == -1 ? llama_token_eos(model) : eot); + need_insert_eot = false; + } + embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end()); embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end()); diff --git a/examples/passkey/README.md b/examples/passkey/README.md index a48a6283a..2b8e910f9 100644 --- a/examples/passkey/README.md +++ b/examples/passkey/README.md @@ -1,5 +1,8 @@ # llama.cpp/example/passkey +A passkey retrieval task is an evaluation method used to measure a language +models ability to recall information from long contexts. + See the following PRs for more info: - https://github.com/ggerganov/llama.cpp/pull/3856 diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index efde8dfdf..dbe445391 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1991,6 +1991,12 @@ int main(int argc, char ** argv) { params.n_batch = std::min(params.n_batch, n_kv); } else { params.n_batch = std::min(params.n_batch, params.n_ctx); + if (params.kl_divergence) { + params.n_parallel = 1; + } else { + // ensure there's at least enough seq_ids for HellaSwag + params.n_parallel = std::max(4, params.n_parallel); + } } if (params.ppl_stride > 0) { @@ -2015,9 +2021,6 @@ int main(int argc, char ** argv) { llama_model * model; llama_context * ctx; - // ensure there's at least enough seq_ids for HellaSwag - params.n_parallel = std::max(4, params.n_parallel); - // load the model and apply lora adapter, if any std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == NULL) { diff --git a/examples/pydantic-models-to-grammar-examples.py b/examples/pydantic_models_to_grammar_examples.py similarity index 100% rename from examples/pydantic-models-to-grammar-examples.py rename to examples/pydantic_models_to_grammar_examples.py diff --git a/examples/quantize/README.md b/examples/quantize/README.md index b78ece4e7..553c2701b 100644 --- a/examples/quantize/README.md +++ b/examples/quantize/README.md @@ -4,7 +4,89 @@ You can also use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf- Note: It is synced from llama.cpp `main` every 6 hours. -## Llama 2 7B +Example usage: + +```bash +# obtain the official LLaMA model weights and place them in ./models +ls ./models +llama-2-7b tokenizer_checklist.chk tokenizer.model +# [Optional] for models using BPE tokenizers +ls ./models + vocab.json +# [Optional] for PyTorch .bin models like Mistral-7B +ls ./models + + +# install Python dependencies +python3 -m pip install -r requirements.txt + +# convert the model to ggml FP16 format +python3 convert_hf_to_gguf.py models/mymodel/ + +# quantize the model to 4-bits (using Q4_K_M method) +./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M + +# update the gguf filetype to current version if older version is now unsupported +./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY +``` + +Run the quantized model: + +```bash +# start inference on a gguf model +./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128 +``` + +When running the larger models, make sure you have enough disk space to store all the intermediate files. + +## Memory/Disk Requirements + +As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. + +| Model | Original size | Quantized size (Q4_0) | +|------:|--------------:|----------------------:| +| 7B | 13 GB | 3.9 GB | +| 13B | 24 GB | 7.8 GB | +| 30B | 60 GB | 19.5 GB | +| 65B | 120 GB | 38.5 GB | + +## Quantization + +Several quantization methods are supported. They differ in the resulting model disk size and inference speed. + +*(outdated)* + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | +|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:| +| 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 | +| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G | +| 7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 | +| 7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 | +| 7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | +| 13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 | +| 13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G | +| 13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 | +| 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 | +| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | + +- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684) +- recent k-quants improvements and new i-quants + - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707) + - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807) + - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773) + - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856) + - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861) + - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872) + - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897) + - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930) + - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957) + - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969) + - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996) + - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060) + - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196) + - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361) + +**Llama 2 7B** | Quantization | Bits per Weight (BPW) | |--------------|-----------------------| @@ -18,7 +100,8 @@ Note: It is synced from llama.cpp `main` every 6 hours. | Q5_K_M | 5.68 | | Q6_K | 6.56 | -## Llama 2 13B +**Llama 2 13B** + Quantization | Bits per Weight (BPW) -- | -- Q2_K | 3.34 @@ -31,7 +114,7 @@ Q5_K_S | 5.51 Q5_K_M | 5.67 Q6_K | 6.56 -# Llama 2 70B +**Llama 2 70B** Quantization | Bits per Weight (BPW) -- | -- diff --git a/examples/regex-to-grammar.py b/examples/regex_to_grammar.py similarity index 100% rename from examples/regex-to-grammar.py rename to examples/regex_to_grammar.py diff --git a/examples/server/README.md b/examples/server/README.md index 4fab006bb..cb45ee06d 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -366,7 +366,8 @@ Notice that each `probs` is an array of length `n_probs`. "assistant_name": "", "user_name": "", "default_generation_settings": { ... }, - "total_slots": 1 + "total_slots": 1, + "chat_template": "" } ``` @@ -374,8 +375,9 @@ Notice that each `probs` is an array of length `n_probs`. - `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots. - `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint. - `total_slots` - the total number of slots for process requests (defined by `--parallel` option) +- `chat_template` - the model's original Jinja2 prompt template -- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only model with [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, ChatML template will be used. +- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used. *Options:* diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d7fb61812..47bea1591 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2605,7 +2605,7 @@ int main(int argc, char ** argv) { // if a custom chat template is not supplied, we will use the one that comes with the model (if any) if (params.chat_template.empty()) { if (!ctx_server.validate_model_chat_template()) { - LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); + LOG_WARNING("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); params.chat_template = "chatml"; } } @@ -2967,11 +2967,20 @@ int main(int argc, char ** argv) { }; const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) { + std::string template_key = "tokenizer.chat_template", curr_tmpl; + int32_t tlen = llama_model_meta_val_str(ctx_server.model, template_key.c_str(), nullptr, 0); + if (tlen > 0) { + std::vector curr_tmpl_buf(tlen + 1, 0); + if (llama_model_meta_val_str(ctx_server.model, template_key.c_str(), curr_tmpl_buf.data(), curr_tmpl_buf.size()) == tlen) { + curr_tmpl = std::string(curr_tmpl_buf.data(), tlen); + } + } res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "system_prompt", ctx_server.system_prompt.c_str() }, { "default_generation_settings", ctx_server.default_generation_settings_for_props }, - { "total_slots", ctx_server.params.n_parallel } + { "total_slots", ctx_server.params.n_parallel }, + { "chat_template", curr_tmpl.c_str() } }; res.set_content(data.dump(), "application/json; charset=utf-8"); diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature index 1bde7aab8..6a5a84e6a 100644 --- a/examples/server/tests/features/passkey.feature +++ b/examples/server/tests/features/passkey.feature @@ -52,4 +52,3 @@ Feature: Passkey / Self-extend with context shift #| TheBloke/Llama-2-7B-GGUF | llama-2-7b.Q2_K.gguf | 4096 | 3 | 16384 | 512 | 4 | 512 | 500 | 300 | 1234 | 5 | 1234 | #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768 | 2 | 16384 | 512 | 4 | 512 | 500 | 100 | 0987 | 5 | 0 # 987 | - diff --git a/examples/server/themes/buttons-top/index.html b/examples/server/themes/buttons-top/index.html index 6af30d307..8334bcde5 100644 --- a/examples/server/themes/buttons-top/index.html +++ b/examples/server/themes/buttons-top/index.html @@ -1054,4 +1054,3 @@ - diff --git a/examples/server/themes/wild/index.html b/examples/server/themes/wild/index.html index 772e716cd..8361c5774 100644 --- a/examples/server/themes/wild/index.html +++ b/examples/server/themes/wild/index.html @@ -1058,4 +1058,3 @@ - diff --git a/examples/server-embd.py b/examples/server_embd.py similarity index 99% rename from examples/server-embd.py rename to examples/server_embd.py index 118e04271..a9a36a44c 100644 --- a/examples/server-embd.py +++ b/examples/server_embd.py @@ -31,4 +31,3 @@ for i in range(n-1): embedding2 = np.array(result[j]) similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2)) print(f"Similarity between {i} and {j}: {similarity:.2f}") - diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index da0e4aaba..111366fb0 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -34,4 +34,3 @@ fi #use multiple GPUs with same max compute units #ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 - diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat index cdae5a528..17dd1ff5c 100644 --- a/examples/sycl/win-build-sycl.bat +++ b/examples/sycl/win-build-sycl.bat @@ -31,4 +31,3 @@ exit /B 0 :ERROR echo comomand error: %errorlevel% exit /B %errorlevel% - diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat index 1d4d7d2cd..f0385cdf0 100644 --- a/examples/sycl/win-run-llama2.bat +++ b/examples/sycl/win-run-llama2.bat @@ -7,5 +7,3 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" .\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0 - - diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp index 54c9834af..0180c87d8 100644 --- a/examples/tokenize/tokenize.cpp +++ b/examples/tokenize/tokenize.cpp @@ -30,6 +30,7 @@ static void print_usage_information(const char * argv0, FILE * stream) { fprintf(stream, " --stdin read prompt from standard input.\n"); fprintf(stream, " --no-bos do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n"); fprintf(stream, " --log-disable disable logs. Makes stderr quiet when loading the model.\n"); + fprintf(stream, " --show-count print the total number of tokens.\n"); } static void llama_log_callback_null(ggml_log_level level, const char * text, void * user_data) { @@ -195,6 +196,7 @@ int main(int raw_argc, char ** raw_argv) { bool printing_ids = false; bool no_bos = false; bool disable_logging = false; + bool show_token_count = false; const char * model_path = NULL; const char * prompt_path = NULL; const char * prompt_arg = NULL; @@ -249,6 +251,9 @@ int main(int raw_argc, char ** raw_argv) { else if (arg == "--log-disable") { disable_logging = true; } + else if (arg == "--show-count") { + show_token_count = true; + } else { fprintf(stderr, "Error: unknown option '%s'\n", argv[iarg].c_str()); return 1; @@ -384,6 +389,9 @@ int main(int raw_argc, char ** raw_argv) { printf("]\n"); } + if (show_token_count) { + printf("Total number of tokens: %ld\n", tokens.size()); + } // silence valgrind llama_free(ctx); llama_free_model(model); diff --git a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py b/examples/train-text-from-scratch/convert_train_checkpoint_to_gguf.py similarity index 100% rename from examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py rename to examples/train-text-from-scratch/convert_train_checkpoint_to_gguf.py diff --git a/flake.lock b/flake.lock index 79bb3f63f..973ff4e56 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1718895438, - "narHash": "sha256-k3JqJrkdoYwE3fHE6xGDY676AYmyh4U2Zw+0Bwe5DLU=", + "lastModified": 1719506693, + "narHash": "sha256-C8e9S7RzshSdHB7L+v9I51af1gDM5unhJ2xO1ywxNH8=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "d603719ec6e294f034936c0d0dc06f689d91b6c3", + "rev": "b2852eb9365c6de48ffb0dc2c9562591f652242a", "type": "github" }, "original": { diff --git a/ggml/include/ggml-metal.h b/ggml/include/ggml-metal.h index e7543ae79..6c3226c37 100644 --- a/ggml/include/ggml-metal.h +++ b/ggml/include/ggml-metal.h @@ -63,4 +63,3 @@ GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend); #ifdef __cplusplus } #endif - diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index d0f4097d8..c6694df67 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -486,9 +486,11 @@ if (GGML_SYCL) add_compile_options(-I./) #include DPCT set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3") if (GGML_SYCL_TARGET STREQUAL "NVIDIA") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda") + add_compile_definitions(GGML_SYCL_WARP_SIZE=32) + else() + add_compile_definitions(GGML_SYCL_WARP_SIZE=16) endif() file(GLOB GGML_HEADERS_SYCL "ggml-sycl/*.hpp") @@ -1166,9 +1168,12 @@ target_link_libraries(ggml PRIVATE Threads::Threads ${GGML_EXTRA_LIBS}) find_library(MATH_LIBRARY m) if (MATH_LIBRARY) - target_link_libraries(ggml PRIVATE ${MATH_LIBRARY}) + if (NOT WIN32 OR NOT GGML_SYCL) + target_link_libraries(ggml PRIVATE ${MATH_LIBRARY}) + endif() endif() if (BUILD_SHARED_LIBS) set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON) + target_compile_definitions(ggml PRIVATE GGML_SHARED GGML_BUILD) endif() diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h index e8efceb76..c74060cc4 100644 --- a/ggml/src/ggml-common.h +++ b/ggml/src/ggml-common.h @@ -106,19 +106,19 @@ typedef sycl::half2 ggml_half2; #define QR6_K 2 #define QI2_XXS (QK_K / (4*QR2_XXS)) -#define QR2_XXS 8 +#define QR2_XXS 4 #define QI2_XS (QK_K / (4*QR2_XS)) -#define QR2_XS 8 +#define QR2_XS 4 #define QI2_S (QK_K / (4*QR2_S)) -#define QR2_S 8 +#define QR2_S 4 #define QI3_XXS (QK_K / (4*QR3_XXS)) -#define QR3_XXS 8 +#define QR3_XXS 4 #define QI3_XS (QK_K / (4*QR3_XS)) -#define QR3_XS 8 +#define QR3_XS 4 #define QI1_S (QK_K / (4*QR1_S)) #define QR1_S 8 @@ -130,10 +130,10 @@ typedef sycl::half2 ggml_half2; #define QR4_NL 2 #define QI4_XS (QK_K / (4*QR4_XS)) -#define QR4_XS 8 +#define QR4_XS 2 #define QI3_S (QK_K / (4*QR3_S)) -#define QR3_S 8 +#define QR3_S 4 #endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index 0acfda91d..1c9ccc8a1 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -1882,6 +1882,11 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor bool use_mul_mat_q = ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; + // if mmvq is available it's a better choice than dmmv: +#ifndef GGML_CUDA_FORCE_DMMV + use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; +#endif // GGML_CUDA_FORCE_DMMV + bool any_gpus_with_slow_fp16 = false; if (split) { @@ -1894,22 +1899,15 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor } const int cc = ggml_cuda_info().devices[id].cc; - use_mul_mat_vec_q = use_mul_mat_vec_q && cc >= MIN_CC_DP4A; use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); } } else { const int cc = ggml_cuda_info().devices[ctx.device].cc; - use_mul_mat_vec_q = use_mul_mat_vec_q && cc >= MIN_CC_DP4A; use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); } - // if mmvq is available it's a better choice than dmmv: -#ifndef GGML_CUDA_FORCE_DMMV - use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; -#endif // GGML_CUDA_FORCE_DMMV - // debug helpers //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); //printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]); @@ -2713,27 +2711,40 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: { - struct ggml_tensor * a; - struct ggml_tensor * b; + struct ggml_tensor * a = op->src[0]; if (op->op == GGML_OP_MUL_MAT) { - a = op->src[0]; - b = op->src[1]; - } else { - a = op->src[2]; - b = op->src[1]; - } - if (a->ne[3] != b->ne[3]) { - return false; - } - ggml_type a_type = a->type; - if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || - a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S || - a_type == GGML_TYPE_IQ1_M || a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) { - if (b->ne[1] == 1 && ggml_nrows(b) > 1) { + struct ggml_tensor * b = op->src[1]; + if (a->ne[3] != b->ne[3]) { return false; } } - return true; + switch (a->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_Q8_K: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + return true; + default: + return false; + } } break; case GGML_OP_GET_ROWS: { diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 8d00db6c1..4ff06b871 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -3,6 +3,7 @@ #include "ggml.h" #include "ggml-cuda.h" +#include #include #if defined(GGML_USE_HIPBLAS) @@ -226,6 +227,10 @@ typedef float2 dfloat2; #define RDNA2 #endif +#if defined(__gfx1010__) || defined(__gfx1012__) +#define RDNA1 +#endif + #ifndef __has_builtin #define __has_builtin(x) 0 #endif @@ -268,30 +273,15 @@ static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigne return c; } -static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { -#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__) - c = __builtin_amdgcn_sdot4(a, b, c, false); -#elif defined(RDNA3) - c = __builtin_amdgcn_sudot4( true, a, true, b, c, false); -#elif defined(__gfx1010__) || defined(__gfx900__) - int tmp1; - int tmp2; - asm("\n \ - v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \ - v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \ - v_add3_u32 %0, %1, %2, %0 \n \ - v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \ - v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \ - v_add3_u32 %0, %1, %2, %0 \n \ - " - : "+v"(c), "=&v"(tmp1), "=&v"(tmp2) - : "v"(a), "v"(b) - ); -#else - const int8x4_t va = reinterpret_cast(a); - const int8x4_t vb = reinterpret_cast(b); - c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3]; -#endif +static __device__ __forceinline__ unsigned int __vcmpne4(unsigned int a, unsigned int b) { + const uint8x4_t& va = reinterpret_cast(a); + const uint8x4_t& vb = reinterpret_cast(b); + unsigned int c; + uint8x4_t& vc = reinterpret_cast(c); +#pragma unroll + for (int i = 0; i < 4; ++i) { + vc[i] = va[i] == vb[i] ? 0x00 : 0xff; + } return c; } @@ -467,8 +457,48 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half } #endif // CUDART_VERSION < 12000 +static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) { +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__) + c = __builtin_amdgcn_sdot4(a, b, c, false); +#elif defined(RDNA3) + c = __builtin_amdgcn_sudot4( true, a, true, b, c, false); +#elif defined(__gfx1010__) || defined(__gfx900__) + int tmp1; + int tmp2; + asm("\n \ + v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \ + v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \ + v_add3_u32 %0, %1, %2, %0 \n \ + v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \ + v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \ + v_add3_u32 %0, %1, %2, %0 \n \ + " + : "+v"(c), "=&v"(tmp1), "=&v"(tmp2) + : "v"(a), "v"(b) + ); +#else + const int8x4_t va = reinterpret_cast(a); + const int8x4_t vb = reinterpret_cast(b); + c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3]; +#endif + return c; + +#else // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + +#if __CUDA_ARCH__ >= MIN_CC_DP4A + return __dp4a(a, b, c); +#else // __CUDA_ARCH__ >= MIN_CC_DP4A + const int8_t * a8 = (const int8_t *) &a; + const int8_t * b8 = (const int8_t *) &b; + return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3]; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A + +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +} + // TODO: move to ggml-common.h -static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; +static constexpr __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v); diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu index 12d741f01..3db57034b 100644 --- a/ggml/src/ggml-cuda/cpy.cu +++ b/ggml/src/ggml-cuda/cpy.cu @@ -487,4 +487,3 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) { GGML_ASSERT(false); } } - diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index bd7993595..f24312dd0 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -54,12 +54,11 @@ typedef float (*vec_dot_KQ_f32_t)( template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c; GGML_UNUSED(Q_v); - half sum = 0.0f; + T sum = 0.0f; #pragma unroll for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { @@ -69,10 +68,10 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( const int iqs4 = k_KQ % QI4_0; const int shift = k_KQ & (QI8_1/2); - const int v = (get_int_from_uint8(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int v = (get_int_b2(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; const int u = Q_q8[k_KQ_0/WARP_SIZE]; - const int sumi = __dp4a(v, u, 0); + const int sumi = ggml_cuda_dp4a(v, u, 0); #ifdef FP16_AVAILABLE if (std::is_same::value) { @@ -90,19 +89,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( } return sum; -#else - GGML_UNUSED(K_c); - GGML_UNUSED(Q_v); - GGML_UNUSED(Q_q8); - GGML_UNUSED(Q_ds_v); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c; GGML_UNUSED(Q_v); @@ -117,10 +108,10 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( const int iqs4 = k_KQ % QI4_1; const int shift = k_KQ & (QI8_1/2); - const int v = (get_int_from_uint8_aligned(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int v = (get_int_b4(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; const int u = Q_q8[k_KQ_0/WARP_SIZE]; - const int sumi = __dp4a(v, u, 0); + const int sumi = ggml_cuda_dp4a(v, u, 0); #ifdef FP16_AVAILABLE if (std::is_same::value) { @@ -142,19 +133,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( } return sum; -#else - GGML_UNUSED(K_c); - GGML_UNUSED(Q_v); - GGML_UNUSED(Q_q8); - GGML_UNUSED(Q_ds_v); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c; GGML_UNUSED(Q_v); @@ -170,8 +153,8 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( const int iqs8 = k_KQ % QI8_1; const int shift = k_KQ & (QI8_1/2); - int v = (get_int_from_uint8(K_q5_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; - const int vh = get_int_from_uint8(K_q5_0[ib].qh, 0) >> (iqs8 * QI5_0); + int v = (get_int_b2(K_q5_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int vh = get_int_b2(K_q5_0[ib].qh, 0) >> (iqs8 * QI5_0); v |= (vh << 4) & 0x00000010; // 0 -> 4 v |= (vh << 11) & 0x00001000; // 1 -> 12 v |= (vh << 18) & 0x00100000; // 2 -> 20 @@ -179,7 +162,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( const int u = Q_q8[k_KQ_0/WARP_SIZE]; - const int sumi = __dp4a(v, u, 0); + const int sumi = ggml_cuda_dp4a(v, u, 0); #ifdef FP16_AVAILABLE if (std::is_same::value) { @@ -197,19 +180,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( } return sum; -#else - GGML_UNUSED(K_c); - GGML_UNUSED(Q_v); - GGML_UNUSED(Q_q8); - GGML_UNUSED(Q_ds_v); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c; GGML_UNUSED(Q_v); @@ -225,8 +200,8 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( const int iqs8 = k_KQ % QI8_1; const int shift = k_KQ & (QI8_1/2); - int v = (get_int_from_uint8(K_q5_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; - const int vh = get_int_from_uint8(K_q5_1[ib].qh, 0) >> (iqs8 * QI5_1); + int v = (get_int_b2(K_q5_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int vh = get_int_b2(K_q5_1[ib].qh, 0) >> (iqs8 * QI5_1); v |= (vh << 4) & 0x00000010; // 0 -> 4 v |= (vh << 11) & 0x00001000; // 1 -> 12 v |= (vh << 18) & 0x00100000; // 2 -> 20 @@ -234,7 +209,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( const int u = Q_q8[k_KQ_0/WARP_SIZE]; - const int sumi = __dp4a(v, u, 0); + const int sumi = ggml_cuda_dp4a(v, u, 0); #ifdef FP16_AVAILABLE if (std::is_same::value) { @@ -256,19 +231,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( } return sum; -#else - GGML_UNUSED(K_c); - GGML_UNUSED(Q_v); - GGML_UNUSED(Q_q8); - GGML_UNUSED(Q_ds_v); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c; GGML_UNUSED(Q_v); @@ -282,7 +249,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0( const int ib = k_KQ / QI8_0; const int iqs = k_KQ % QI8_0; - const int v = get_int_from_int8(K_q8_0[ib].qs, iqs); + const int v = get_int_b2(K_q8_0[ib].qs, iqs); T Q_d; if (std::is_same::value) { @@ -297,13 +264,6 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0( } return sum; -#else - GGML_UNUSED(K_c); - GGML_UNUSED(Q_v); - GGML_UNUSED(Q_q8); - GGML_UNUSED(Q_ds_v); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template @@ -448,7 +408,7 @@ static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__ const T d = x[ib].d; const int ql0 = x[ib].qs[iqs]; - const int qh0 = get_int_from_uint8(x[ib].qh, 0); + const int qh0 = get_int_b2(x[ib].qh, 0); const int ql = ((ql0 >> (4*shift)) & 0x0F); const int qh = ((qh0 >> idq) << 4) & 0x10; const int q = (ql | qh) - 16; @@ -473,7 +433,7 @@ static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__ const half2 dm = x[ib].dm; const int ql0 = x[ib].qs[iqs]; - const int qh0 = get_int_from_uint8_aligned(x[ib].qh, 0); + const int qh0 = get_int_b4(x[ib].qh, 0); const int ql = ((ql0 >> (4*shift)) & 0x0F); const int qh = ((qh0 >> idq) << 4) & 0x10; const int q = (ql | qh); diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index 0308beacc..3af2eec2f 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -59,6 +59,12 @@ void ggml_cuda_op_mul_mat_q( case GGML_TYPE_Q6_K: mul_mat_q_case(ctx, args, stream); break; + case GGML_TYPE_IQ4_XS: + mul_mat_q_case(ctx, args, stream); + break; + case GGML_TYPE_IQ4_NL: + mul_mat_q_case(ctx, args, stream); + break; default: GGML_ASSERT(false); break; @@ -87,6 +93,8 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) { case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: mmq_supported = true; break; default: diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index 1396e7a75..118e34d28 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -60,12 +60,16 @@ static constexpr __device__ int get_mmq_x_max_device() { } static constexpr int get_mmq_y_host(const int cc) { - return int8_mma_available(cc) || cc >= CC_VOLTA ? 128 : 64; + return cc >= CC_OFFSET_AMD ? (cc == CC_RDNA1 ? 64 : 128) : (cc >= CC_VOLTA ? 128 : 64); } static constexpr __device__ int get_mmq_y_device() { #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA1) + return 64; +#else return 128; +#endif // defined RDNA1 #else #if __CUDA_ARCH__ >= CC_VOLTA return 128; @@ -88,15 +92,17 @@ static constexpr __device__ int get_mmq_y_device() { static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml_type type, int mmq_y) { return type == GGML_TYPE_Q4_0 ? MMQ_DP4A_TXS_Q4_0 : - type == GGML_TYPE_Q4_1 ? MMQ_DP4A_TXS_Q4_1 : - type == GGML_TYPE_Q5_0 ? MMQ_DP4A_TXS_Q5_0 : - type == GGML_TYPE_Q5_1 ? MMQ_DP4A_TXS_Q5_1 : - type == GGML_TYPE_Q8_0 ? MMQ_DP4A_TXS_Q8_0 : - type == GGML_TYPE_Q2_K ? MMQ_DP4A_TXS_Q2_K : - type == GGML_TYPE_Q3_K ? MMQ_DP4A_TXS_Q3_K : - type == GGML_TYPE_Q4_K ? MMQ_DP4A_TXS_Q4_K : - type == GGML_TYPE_Q5_K ? MMQ_DP4A_TXS_Q5_K : - type == GGML_TYPE_Q6_K ? MMQ_DP4A_TXS_Q6_K : + type == GGML_TYPE_Q4_1 ? MMQ_DP4A_TXS_Q4_1 : + type == GGML_TYPE_Q5_0 ? MMQ_DP4A_TXS_Q5_0 : + type == GGML_TYPE_Q5_1 ? MMQ_DP4A_TXS_Q5_1 : + type == GGML_TYPE_Q8_0 ? MMQ_DP4A_TXS_Q8_0 : + type == GGML_TYPE_Q2_K ? MMQ_DP4A_TXS_Q2_K : + type == GGML_TYPE_Q3_K ? MMQ_DP4A_TXS_Q3_K : + type == GGML_TYPE_Q4_K ? MMQ_DP4A_TXS_Q4_K : + type == GGML_TYPE_Q5_K ? MMQ_DP4A_TXS_Q5_K : + type == GGML_TYPE_Q6_K ? MMQ_DP4A_TXS_Q6_K : + type == GGML_TYPE_IQ4_XS ? MMQ_DP4A_TXS_Q5_0 : + type == GGML_TYPE_IQ4_NL ? MMQ_DP4A_TXS_Q5_0 : tile_x_sizes{0, 0, 0}; } @@ -124,15 +130,17 @@ static_assert(MMQ_MMA_TILE_X_K_Q6_K % 8 == 4, "Wrong padding."); static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) { return type == GGML_TYPE_Q4_0 ? MMQ_MMA_TILE_X_K_Q4_0 : - type == GGML_TYPE_Q4_1 ? MMQ_MMA_TILE_X_K_Q4_1 : - type == GGML_TYPE_Q5_0 ? MMQ_MMA_TILE_X_K_Q5_0 : - type == GGML_TYPE_Q5_1 ? MMQ_MMA_TILE_X_K_Q5_1 : - type == GGML_TYPE_Q8_0 ? MMQ_MMA_TILE_X_K_Q8_0 : - type == GGML_TYPE_Q2_K ? MMQ_MMA_TILE_X_K_Q2_K : - type == GGML_TYPE_Q3_K ? MMQ_MMA_TILE_X_K_Q3_K : - type == GGML_TYPE_Q4_K ? MMQ_MMA_TILE_X_K_Q4_K : - type == GGML_TYPE_Q5_K ? MMQ_MMA_TILE_X_K_Q5_K : - type == GGML_TYPE_Q6_K ? MMQ_MMA_TILE_X_K_Q6_K : + type == GGML_TYPE_Q4_1 ? MMQ_MMA_TILE_X_K_Q4_1 : + type == GGML_TYPE_Q5_0 ? MMQ_MMA_TILE_X_K_Q5_0 : + type == GGML_TYPE_Q5_1 ? MMQ_MMA_TILE_X_K_Q5_1 : + type == GGML_TYPE_Q8_0 ? MMQ_MMA_TILE_X_K_Q8_0 : + type == GGML_TYPE_Q2_K ? MMQ_MMA_TILE_X_K_Q2_K : + type == GGML_TYPE_Q3_K ? MMQ_MMA_TILE_X_K_Q3_K : + type == GGML_TYPE_Q4_K ? MMQ_MMA_TILE_X_K_Q4_K : + type == GGML_TYPE_Q5_K ? MMQ_MMA_TILE_X_K_Q5_K : + type == GGML_TYPE_Q6_K ? MMQ_MMA_TILE_X_K_Q6_K : + type == GGML_TYPE_IQ4_XS ? MMQ_MMA_TILE_X_K_Q5_0 : + type == GGML_TYPE_IQ4_NL ? MMQ_MMA_TILE_X_K_Q5_0 : 0; } @@ -181,9 +189,9 @@ template static __device__ __forceinlin const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbx; #ifdef INT8_MMA_AVAILABLE - x_qs[i*MMQ_MMA_TILE_X_K_Q4_0 + threadIdx.x] = get_int_from_uint8(bxi->qs, kqsx); + x_qs[i*MMQ_MMA_TILE_X_K_Q4_0 + threadIdx.x] = get_int_b2(bxi->qs, kqsx); #else - x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8(bxi->qs, kqsx); + x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = get_int_b2(bxi->qs, kqsx); #endif // INT8_MMA_AVAILABLE } @@ -344,9 +352,9 @@ template static __device__ __forceinlin const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbx; #ifdef INT8_MMA_AVAILABLE - x_qs[i*MMQ_MMA_TILE_X_K_Q4_1 + threadIdx.x] = get_int_from_uint8_aligned(bxi->qs, kqsx); + x_qs[i*MMQ_MMA_TILE_X_K_Q4_1 + threadIdx.x] = get_int_b4(bxi->qs, kqsx); #else - x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8_aligned(bxi->qs, kqsx); + x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = get_int_b4(bxi->qs, kqsx); #endif // INT8_MMA_AVAILABLE } @@ -505,8 +513,8 @@ template static __device__ __forceinlin const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbx; - const int ql = get_int_from_uint8(bxi->qs, kqsx); - const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (threadIdx.x % QI5_0)); + const int ql = get_int_b2(bxi->qs, kqsx); + const int qh = get_int_b2(bxi->qh, 0) >> (4 * (threadIdx.x % QI5_0)); int qs0 = (ql >> 0) & 0x0F0F0F0F; qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 @@ -670,8 +678,8 @@ template static __device__ __forceinlin const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbx; - const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); - const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (threadIdx.x % QI5_1)); + const int ql = get_int_b4(bxi->qs, kqsx); + const int qh = get_int_b4(bxi->qh, 0) >> (4 * (threadIdx.x % QI5_1)); int qs0 = (ql >> 0) & 0x0F0F0F0F; qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 @@ -835,9 +843,9 @@ template static __device__ __forceinlin const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx; #ifdef INT8_MMA_AVAILABLE - x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + threadIdx.x] = get_int_from_int8(bxi->qs, kqsx); + x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + threadIdx.x] = get_int_b2(bxi->qs, kqsx); #else - x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = get_int_from_int8(bxi->qs, kqsx); + x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = get_int_b2(bxi->qs, kqsx); #endif // INT8_MMA_AVAILABLE } @@ -980,7 +988,7 @@ template static __device__ __forceinlin const block_q2_K * bxi = (const block_q2_K *) x + kbx0 + i*stride + kbx; - const int x_ql_0 = get_int_from_uint8(bxi->qs, kqsx); + const int x_ql_0 = get_int_b2(bxi->qs, kqsx); #pragma unroll for (int l = 0; l < QR2_K; ++l) { @@ -1162,8 +1170,8 @@ template static __device__ __forceinlin const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + kbx; - const int x_ql_0 = get_int_from_uint8(bxi->qs, kqsx); - const int x_qh_0 = get_int_from_uint8(bxi->hmask, kqsx % (QI3_K/2)) >> (4 * (kqsx / (QI3_K/2))); + const int x_ql_0 = get_int_b2(bxi->qs, kqsx); + const int x_qh_0 = get_int_b2(bxi->hmask, kqsx % (QI3_K/2)) >> (4 * (kqsx / (QI3_K/2))); #pragma unroll for (int l = 0; l < QR3_K; ++l) { @@ -1221,11 +1229,11 @@ template static __device__ __forceinlin const int ksc_low = ksc % (QI3_K/8); const int shift_low = 4 * (ksc / (QI3_K/8)); - const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F; + const int sc_low = (get_int_b2(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F; const int ksc_high = QI3_K/8; const int shift_high = 2 * ksc; - const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030; + const int sc_high = ((get_int_b2(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030; const int sc = __vsubss4(sc_low | sc_high, 0x20202020); @@ -1389,9 +1397,9 @@ template static __device__ __forceinlin const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + kbx; #ifdef INT8_MMA_AVAILABLE - x_qs[i*MMQ_MMA_TILE_X_K_Q4_K + threadIdx.x] = get_int_from_uint8_aligned(bxi->qs, kqsx); + x_qs[i*MMQ_MMA_TILE_X_K_Q4_K + threadIdx.x] = get_int_b4(bxi->qs, kqsx); #else - x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8_aligned(bxi->qs, kqsx); + x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = get_int_b4(bxi->qs, kqsx); #endif // INT8_MMA_AVAILABLE } @@ -1606,11 +1614,11 @@ template static __device__ __forceinlin const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + kbx; const int ky = QR5_K*kqsx; - const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); + const int ql = get_int_b4(bxi->qs, kqsx); const int ql0 = (ql >> 0) & 0x0F0F0F0F; const int ql1 = (ql >> 4) & 0x0F0F0F0F; - const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4)); + const int qh = get_int_b4(bxi->qh, kqsx % (QI5_K/4)); const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010; const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010; @@ -1828,11 +1836,11 @@ template static __device__ __forceinlin const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + kbx; const int ky = QR6_K*kqsx; - const int ql = get_int_from_uint8(bxi->ql, kqsx); + const int ql = get_int_b2(bxi->ql, kqsx); const int ql0 = (ql >> 0) & 0x0F0F0F0F; const int ql1 = (ql >> 4) & 0x0F0F0F0F; - const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4)); + const int qh = get_int_b2(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4)); const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030; const int qh1 = (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) & 0x30303030; @@ -1879,9 +1887,9 @@ template static __device__ __forceinlin const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / 4; #ifdef INT8_MMA_AVAILABLE - x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + threadIdx.x % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, threadIdx.x % (QI6_K/8)); + x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + threadIdx.x % (WARP_SIZE/8)] = get_int_b2(bxi->scales, threadIdx.x % (QI6_K/8)); #else - x_sc[i*(WARP_SIZE/8) + i/8 + threadIdx.x % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, threadIdx.x % (QI6_K/8)); + x_sc[i*(WARP_SIZE/8) + i/8 + threadIdx.x % (WARP_SIZE/8)] = get_int_b2(bxi->scales, threadIdx.x % (QI6_K/8)); #endif // INT8_MMA_AVAILABLE } } @@ -2014,6 +2022,124 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma( #endif // INT8_MMA_AVAILABLE } +template static __device__ __forceinline__ void load_tiles_iq4_nl( + const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + +#ifdef INT8_MMA_AVAILABLE + int * x_qs = (int *) x_tile; + float * x_df = (float *) (x_qs + WARP_SIZE*2); +#else + constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_NL, mmq_y); + int * x_qs = (int *) x_tile; + float * x_df = (float *) (x_qs + txs.qs); +#endif // INT8_MMA_AVAILABLE + + const int kbx = threadIdx.x / QI4_NL; + const int kqsx = threadIdx.x % QI4_NL; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbx; + + const int aux_q4 = get_int_b2(bxi->qs, kqsx); + const int2 v = get_int_from_table_16(aux_q4); + const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4; +#ifdef INT8_MMA_AVAILABLE + x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + k0 + 0] = v.x; + x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + k0 + 4] = v.y; +#else + x_qs[i*(2*WARP_SIZE + 1) + k0 + 0] = v.x; + x_qs[i*(2*WARP_SIZE + 1) + k0 + 4] = v.y; +#endif // INT8_MMA_AVAILABLE + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI4_NL; + const int kbxd = threadIdx.x % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_NL) { + int i = i0 + threadIdx.y * QI4_NL + threadIdx.x / blocks_per_tile_x_row; + + if (need_check) { + i = min(i, i_max); + } + + const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbxd; + +#ifdef INT8_MMA_AVAILABLE + x_df[i*MMQ_MMA_TILE_X_K_Q5_0 + kbxd] = __half2float(bxi->d); +#else + x_df[i*(WARP_SIZE/4) + i/4 + kbxd] = __half2float(bxi->d); +#endif // INT8_MMA_AVAILABLE + } +} + +template static __device__ __forceinline__ void load_tiles_iq4_xs( + const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) { + +#ifdef INT8_MMA_AVAILABLE + int * x_qs = (int *) x_tile; + float * x_df = (float *) (x_qs + WARP_SIZE*2); +#else + constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_XS, mmq_y); + int * x_qs = (int *) x_tile; + float * x_df = (float *) (x_qs + txs.qs); +#endif // INT8_MMA_AVAILABLE + + const int kbx = 0; // threadIdx.x / QI4_XS + const int kqsx = threadIdx.x; // threadIdx.x % QI4_XS + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_iq4_xs * bxi = (const block_iq4_xs *) x + kbx0 + i*stride + kbx; + + const int aux_q4 = get_int_b4(bxi->qs, kqsx); + const int2 v = get_int_from_table_16(aux_q4); + const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4; +#ifdef INT8_MMA_AVAILABLE + x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + k0 + 0] = v.x; + x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + k0 + 4] = v.y; +#else + x_qs[i*(2*WARP_SIZE + 1) + k0 + 0] = v.x; + x_qs[i*(2*WARP_SIZE + 1) + k0 + 4] = v.y; +#endif // INT8_MMA_AVAILABLE + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { + int i = i0 + threadIdx.y * 4 + threadIdx.x / (WARP_SIZE/4); + + if (need_check) { + i = min(i, i_max); + } + + const block_iq4_xs * bxi = (const block_iq4_xs *) x + kbx0 + i*stride; + + const float d = __half2float(bxi->d); + + const int ls = ((bxi->scales_l[(threadIdx.x % 8)/2] >> (4*(threadIdx.x % 2))) & 0x0F) + | (((bxi->scales_h >> (2*(threadIdx.x % 8))) & 0x03) << 4); + +#ifdef INT8_MMA_AVAILABLE + x_df[i*MMQ_MMA_TILE_X_K_Q5_0 + threadIdx.x % 8] = d * (ls - 32); +#else + x_df[i*(WARP_SIZE/4) + i/4 + threadIdx.x % 8] = d * (ls - 32); +#endif // INT8_MMA_AVAILABLE + } +} + template static __device__ __forceinline__ void mmq_write_back_dp4a( const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max) { @@ -2163,6 +2289,22 @@ struct mmq_type_traits { static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q6_K_q8_1_dp4a; }; +template +struct mmq_type_traits { + static constexpr int vdr = VDR_IQ4_NL_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_iq4_nl; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q5_0_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q5_0_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_IQ4_XS_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_iq4_xs; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q5_0_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q5_0_q8_1_dp4a; +}; + static bool mmq_need_sum(const ggml_type type_x) { switch (type_x) { case GGML_TYPE_Q4_0: @@ -2180,6 +2322,8 @@ static bool mmq_need_sum(const ggml_type type_x) { case GGML_TYPE_Q5_K: return true; case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: return false; default: GGML_ASSERT(false); @@ -2301,8 +2445,11 @@ static __global__ void mul_mat_q( const int nty = (ne01 + mmq_y - 1) / mmq_y; // Number of tiles y // kbc == k block continuous, current index in continuous ijk space. - int64_t kbc = GGML_PAD((int64_t) blockIdx.x *blocks_per_ne00*ntx*nty / gridDim.x, blocks_per_warp); - const int64_t kbc_stop = GGML_PAD((int64_t)(blockIdx.x + 1)*blocks_per_ne00*ntx*nty / gridDim.x, blocks_per_warp); + int64_t kbc = (int64_t) blockIdx.x *blocks_per_ne00*ntx*nty / gridDim.x; + int64_t kbc_stop = (int64_t)(blockIdx.x + 1)*blocks_per_ne00*ntx*nty / gridDim.x; + + kbc -= (kbc % blocks_per_ne00) % blocks_per_warp; + kbc_stop -= (kbc_stop % blocks_per_ne00) % blocks_per_warp; // kb0 == k index when doing the matrix multiplication for an output tile. int kb0_start = kbc % blocks_per_ne00; @@ -2358,8 +2505,11 @@ static __global__ void mul_mat_q_stream_k_fixup( const int bidx_stop = (blockIdx.y*nty + blockIdx.x + 1) * block_num_mmq / (gridDim.y*gridDim.x) + 1; for (int bidx = bidx_start; bidx < bidx_stop; ++bidx) { - const int64_t kbc = GGML_PAD((int64_t) bidx *blocks_per_ne00*ntx*nty / block_num_mmq, blocks_per_warp); - const int64_t kbc_stop = GGML_PAD((int64_t)(bidx + 1)*blocks_per_ne00*ntx*nty / block_num_mmq, blocks_per_warp); + int64_t kbc = (int64_t) bidx *blocks_per_ne00*ntx*nty / block_num_mmq; + int64_t kbc_stop = (int64_t)(bidx + 1)*blocks_per_ne00*ntx*nty / block_num_mmq; + + kbc -= (kbc % blocks_per_ne00) % blocks_per_warp; + kbc_stop -= (kbc_stop % blocks_per_ne00) % blocks_per_warp; // Skip fixup tile if the MMQ CUDA block never wrote anything to it: if (kbc == kbc_stop || kbc_stop % blocks_per_ne00 == 0) { @@ -2598,6 +2748,8 @@ extern DECL_MMQ_CASE(GGML_TYPE_Q3_K); extern DECL_MMQ_CASE(GGML_TYPE_Q4_K); extern DECL_MMQ_CASE(GGML_TYPE_Q5_K); extern DECL_MMQ_CASE(GGML_TYPE_Q6_K); +extern DECL_MMQ_CASE(GGML_TYPE_IQ4_NL); +extern DECL_MMQ_CASE(GGML_TYPE_IQ4_XS); // ------------------------------------------------------------------------------------------------------------------------- diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index e8d157169..e22faf69b 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -28,16 +28,22 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type) static constexpr __device__ int get_vdr_mmvq(ggml_type type) { return type == GGML_TYPE_Q4_0 ? VDR_Q4_0_Q8_1_MMVQ : - type == GGML_TYPE_Q4_1 ? VDR_Q4_1_Q8_1_MMVQ : - type == GGML_TYPE_Q5_0 ? VDR_Q5_0_Q8_1_MMVQ : - type == GGML_TYPE_Q5_1 ? VDR_Q5_1_Q8_1_MMVQ : - type == GGML_TYPE_Q8_0 ? VDR_Q8_0_Q8_1_MMVQ : - type == GGML_TYPE_Q2_K ? VDR_Q2_K_Q8_1_MMVQ : - type == GGML_TYPE_Q3_K ? VDR_Q3_K_Q8_1_MMVQ : - type == GGML_TYPE_Q4_K ? VDR_Q4_K_Q8_1_MMVQ : - type == GGML_TYPE_Q5_K ? VDR_Q5_K_Q8_1_MMVQ : - type == GGML_TYPE_Q6_K ? VDR_Q6_K_Q8_1_MMVQ : - type == GGML_TYPE_IQ4_NL ? VDR_Q4_K_Q8_1_MMVQ : + type == GGML_TYPE_Q4_1 ? VDR_Q4_1_Q8_1_MMVQ : + type == GGML_TYPE_Q5_0 ? VDR_Q5_0_Q8_1_MMVQ : + type == GGML_TYPE_Q5_1 ? VDR_Q5_1_Q8_1_MMVQ : + type == GGML_TYPE_Q8_0 ? VDR_Q8_0_Q8_1_MMVQ : + type == GGML_TYPE_Q2_K ? VDR_Q2_K_Q8_1_MMVQ : + type == GGML_TYPE_Q3_K ? VDR_Q3_K_Q8_1_MMVQ : + type == GGML_TYPE_Q4_K ? VDR_Q4_K_Q8_1_MMVQ : + type == GGML_TYPE_Q5_K ? VDR_Q5_K_Q8_1_MMVQ : + type == GGML_TYPE_Q6_K ? VDR_Q6_K_Q8_1_MMVQ : + type == GGML_TYPE_IQ2_XXS ? VDR_IQ2_XXS_Q8_1_MMVQ : + type == GGML_TYPE_IQ2_XS ? VDR_IQ2_XS_Q8_1_MMVQ : + type == GGML_TYPE_IQ2_S ? VDR_IQ2_S_Q8_1_MMVQ : + type == GGML_TYPE_IQ3_XXS ? VDR_IQ3_XXS_Q8_1_MMVQ : + type == GGML_TYPE_IQ3_S ? VDR_IQ3_S_Q8_1_MMVQ : + type == GGML_TYPE_IQ4_NL ? VDR_IQ4_NL_Q8_1_MMVQ : + type == GGML_TYPE_IQ4_XS ? VDR_IQ4_XS_Q8_1_MMVQ : 1; } diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py index ea58d0968..ffeb3c27d 100755 --- a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py +++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py @@ -22,7 +22,8 @@ SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block} TYPES_MMQ = [ "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", - "GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K" + "GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K", + "GGML_TYPE_IQ4_NL", "GGML_TYPE_IQ4_XS" ] SOURCE_MMQ = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_nl.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_nl.cu new file mode 100644 index 000000000..eb02fab00 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_nl.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_IQ4_NL); diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_xs.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_xs.cu new file mode 100644 index 000000000..1eb3b7430 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_xs.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_IQ4_XS); diff --git a/ggml/src/ggml-cuda/vecdotq.cuh b/ggml/src/ggml-cuda/vecdotq.cuh index 3b12d6566..1d510484a 100644 --- a/ggml/src/ggml-cuda/vecdotq.cuh +++ b/ggml/src/ggml-cuda/vecdotq.cuh @@ -1,34 +1,19 @@ #include "common.cuh" +#include -static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) { - const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment +static __device__ __forceinline__ int get_int_b2(const void * x, const int & i32) { + const uint16_t * x16 = (const uint16_t *) x; // assume at least 2 byte alignment - int x32 = 0; - x32 |= x16[0] << 0; - x32 |= x16[1] << 16; + int x32 = x16[2*i32 + 0] << 0; + x32 |= x16[2*i32 + 1] << 16; return x32; } -static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) { - const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment - - int x32 = 0; - x32 |= x16[0] << 0; - x32 |= x16[1] << 16; - - return x32; +static __device__ __forceinline__ int get_int_b4(const void * x, const int & i32) { + return ((const int *) x)[i32]; // assume at least 4 byte alignment } -static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) { - return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment -} - -static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) { - return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment -} - - // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q @@ -38,7 +23,6 @@ static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * template static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl( const int * v, const int * u, const float & d4, const half2 & ds8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; #pragma unroll @@ -47,17 +31,14 @@ template static __device__ __forceinline__ float vec_dot_q4_0_q8_1_imp const int vi1 = (v[i] >> 4) & 0x0F0F0F0F; // SIMD dot product of quantized values - sumi = __dp4a(vi0, u[2*i+0], sumi); - sumi = __dp4a(vi1, u[2*i+1], sumi); + sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi); + sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi); } const float2 ds8f = __half22float2(ds8); // second part effectively subtracts 8 from each quant value return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y); -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } #define VDR_Q4_1_Q8_1_MMVQ 2 @@ -66,7 +47,6 @@ template static __device__ __forceinline__ float vec_dot_q4_0_q8_1_imp template static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl( const int * v, const int * u, const half2 & dm4, const half2 & ds8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; #pragma unroll @@ -75,8 +55,8 @@ template static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp const int vi1 = (v[i] >> 4) & 0x0F0F0F0F; // SIMD dot product of quantized values - sumi = __dp4a(vi0, u[2*i+0], sumi); - sumi = __dp4a(vi1, u[2*i+1], sumi); + sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi); + sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi); } #ifdef GGML_CUDA_F16 @@ -92,9 +72,6 @@ template static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1)); -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } #define VDR_Q5_0_Q8_1_MMVQ 2 @@ -103,7 +80,6 @@ template static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp template static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl( const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; #pragma unroll @@ -113,23 +89,20 @@ template static __device__ __forceinline__ float vec_dot_q5_0_q8_1_imp vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12 vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20 vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28 - sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values + sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4 vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12 vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20 vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28 - sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values + sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values } const float2 ds8f = __half22float2(ds8); // second part effectively subtracts 16 from each quant value return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y); -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } #define VDR_Q5_1_Q8_1_MMVQ 2 @@ -138,7 +111,6 @@ template static __device__ __forceinline__ float vec_dot_q5_0_q8_1_imp template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl( const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; #pragma unroll @@ -148,14 +120,14 @@ template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12 vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20 vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28 - sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values + sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4 vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12 vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20 vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28 - sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values + sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values } #ifdef GGML_CUDA_F16 @@ -171,10 +143,6 @@ template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it return sumi*d5d8 + m5s8 / (QI5_1 / vdr); - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } #define VDR_Q8_0_Q8_1_MMVQ 2 @@ -183,31 +151,26 @@ template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp template static __device__ __forceinline__ T vec_dot_q8_0_q8_1_impl( const int * v, const int * u, const T & d8_0, const T & d8_1) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; #pragma unroll for (int i = 0; i < vdr; ++i) { // SIMD dot product of quantized values - sumi = __dp4a(v[i], u[i], sumi); + sumi = ggml_cuda_dp4a(v[i], u[i], sumi); } return d8_0*d8_1 * ((T) sumi); -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl( const int * v, const int * u, const half2 & dm8, const half2 & ds8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; #pragma unroll for (int i = 0; i < vdr; ++i) { // SIMD dot product of quantized values - sumi = __dp4a(v[i], u[i], sumi); + sumi = ggml_cuda_dp4a(v[i], u[i], sumi); } #ifdef GGML_CUDA_F16 @@ -223,9 +186,6 @@ template static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it return sumi*d8d8 + m8s8 / (QI8_1 / vdr); -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } #define VDR_Q2_K_Q8_1_MMVQ 1 @@ -236,7 +196,6 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq( const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales, const half2 & dm2, const float * __restrict__ d8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics float sumf_d = 0.0f; float sumf_m = 0.0f; @@ -246,28 +205,24 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq( const int vi = (v >> (2*i)) & 0x03030303; - sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product + sumf_d += d8[i] * (ggml_cuda_dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product // fill int with 4x m int m = sc >> 4; m |= m << 8; m |= m << 16; - sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values + sumf_m += d8[i] * ggml_cuda_dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values } const float2 dm2f = __half22float2(dm2); return dm2f.x*sumf_d - dm2f.y*sumf_m; -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } // contiguous u/y values static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq( const int * __restrict__ v, const int * __restrict__ u, const half2 * dm2, const float & d8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics float sumf_d = 0.0f; float sumf_m = 0.0f; @@ -281,8 +236,8 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq( #pragma unroll for (int i = i0; i < i0 + QI8_1/2; ++i) { const int vi = (vi0 >> (2*(i % (QI8_1/2)))) & 0x03030303; - sumi_d = __dp4a(vi, u[i], sumi_d); // SIMD dot product - sumi_m = __dp4a(0x01010101, u[i], sumi_m); + sumi_d = ggml_cuda_dp4a(vi, u[i], sumi_d); // SIMD dot product + sumi_m = ggml_cuda_dp4a(0x01010101, u[i], sumi_m); } sumf_d += dm2f.x * sumi_d; @@ -290,9 +245,6 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq( } return d8*(sumf_d - sumf_m); -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } #define VDR_Q3_K_Q8_1_MMVQ 1 @@ -303,7 +255,6 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq( const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales, const int & scale_offset, const float & d3, const float * __restrict__ d8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics float sumf = 0.0f; #pragma unroll @@ -326,13 +277,10 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq( const int vi = __vsubss4(vil, vih); - sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product + sumf += d8[i] * (ggml_cuda_dp4a(vi, u[i], 0) * sc); // SIMD dot product } return d3 * sumf; -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } // contiguous u/y values @@ -340,7 +288,6 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq( const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales, const float & d3, const float & d8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; #pragma unroll @@ -350,16 +297,13 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq( #pragma unroll for (int i = i0; i < i0 + QI8_1/2; ++i) { const int vi = __vsubss4((v[i/2] >> (4*(i%2))) & 0x0F0F0F0F, 0x04040404); - sumi_sc = __dp4a(vi, u[i], sumi_sc); // SIMD dot product + sumi_sc = ggml_cuda_dp4a(vi, u[i], sumi_sc); // SIMD dot product } sumi += sumi_sc * scales[i0 / (QI8_1/2)]; } return d3*d8 * sumi; -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } #define VDR_Q4_K_Q8_1_MMVQ 2 @@ -370,7 +314,6 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq( const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc, const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics float sumf_d = 0.0f; float sumf_m = 0.0f; @@ -379,8 +322,8 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq( const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F; const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F; - const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product - const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u + const int dot1 = ggml_cuda_dp4a(v1i, u[2*i+1], ggml_cuda_dp4a(v0i, u[2*i+0], 0)); // SIMD dot product + const int dot2 = ggml_cuda_dp4a(0x01010101, u[2*i+1], ggml_cuda_dp4a(0x01010101, u[2*i+0], 0)); // sum of u sumf_d += d8[i] * (dot1 * sc[i]); sumf_m += d8[i] * (dot2 * m[i]); // multiply constant part of q4_K with sum of q8_1 values @@ -389,10 +332,6 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq( const float2 dm4f = __half22float2(dm4); return dm4f.x*sumf_d - dm4f.y*sumf_m; - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } // contiguous u/y values @@ -400,7 +339,6 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq( const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc, const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics float sumf_d = 0.0f; float sumf_m = 0.0f; @@ -410,7 +348,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq( #pragma unroll for (int j = 0; j < QI8_1; ++j) { - sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product + sumi_d = ggml_cuda_dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product } const float2 ds8f = __half22float2(ds8[i]); @@ -422,10 +360,6 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq( const float2 dm4f = __half22float2(dm4); return dm4f.x*sumf_d - dm4f.y*sumf_m; - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } #define VDR_Q5_K_Q8_1_MMVQ 2 @@ -436,7 +370,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq( const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc, const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics float sumf_d = 0.0f; float sumf_m = 0.0f; @@ -451,8 +384,8 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq( const int v0i = vl0i | vh0i; const int v1i = vl1i | vh1i; - const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product - const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u + const int dot1 = ggml_cuda_dp4a(v0i, u[2*i+0], ggml_cuda_dp4a(v1i, u[2*i+1], 0)); // SIMD dot product + const int dot2 = ggml_cuda_dp4a(0x01010101, u[2*i+0], ggml_cuda_dp4a(0x01010101, u[2*i+1], 0)); // sum of u sumf_d += d8[i] * (dot1 * sc[i]); sumf_m += d8[i] * (dot2 * m[i]); @@ -462,10 +395,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq( const float2 dm5f = __half22float2(dm5); return dm5f.x*sumf_d - dm5f.y*sumf_m; - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } // contiguous u/y values @@ -473,7 +402,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq( const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc, const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics float sumf_d = 0.0f; float sumf_m = 0.0f; @@ -483,7 +411,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq( #pragma unroll for (int j = 0; j < QI8_1; ++j) { - sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product + sumi_d = ggml_cuda_dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product } const float2 ds8f = __half22float2(ds8[i]); @@ -495,10 +423,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq( const float2 dm4f = __half22float2(dm4); return dm4f.x*sumf_d - dm4f.y*sumf_m; - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } #define VDR_Q6_K_Q8_1_MMVQ 1 @@ -509,7 +433,6 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq( const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales, const float & d, const float * __restrict__ d8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics float sumf = 0.0f; #pragma unroll @@ -522,13 +445,10 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq( const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32 - sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product + sumf += d8[i] * (ggml_cuda_dp4a(vi, u[i], 0) * sc); // SIMD dot product } return d*sumf; -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } // contiguous u/y values @@ -536,7 +456,6 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq( const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc, const float & d6, const float * __restrict__ d8) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics float sumf_d = 0.0f; #pragma unroll @@ -545,21 +464,17 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq( #pragma unroll for (int i = i0; i < i0 + 2; ++i) { - sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product - sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product + sumi_d.x = ggml_cuda_dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product + sumi_d.x = ggml_cuda_dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product - sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product - sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product + sumi_d.y = ggml_cuda_dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product + sumi_d.y = ggml_cuda_dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product } sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y); } return d6 * sumf_d; - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } static __device__ __forceinline__ float vec_dot_q4_0_q8_1( @@ -572,9 +487,9 @@ static __device__ __forceinline__ float vec_dot_q4_0_q8_1( #pragma unroll for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) { - v[i] = get_int_from_uint8(bq4_0->qs, iqs + i); - u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); - u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0); + v[i] = get_int_b2(bq4_0->qs, iqs + i); + u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI4_0); } return vec_dot_q4_0_q8_1_impl(v, u, bq4_0->d, bq8_1->ds); @@ -591,9 +506,9 @@ static __device__ __forceinline__ float vec_dot_q4_1_q8_1( #pragma unroll for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) { - v[i] = get_int_from_uint8_aligned(bq4_1->qs, iqs + i); - u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); - u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1); + v[i] = get_int_b4(bq4_1->qs, iqs + i); + u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI4_1); } return vec_dot_q4_1_q8_1_impl(v, u, bq4_1->dm, bq8_1->ds); @@ -610,10 +525,10 @@ static __device__ __forceinline__ float vec_dot_q5_0_q8_1( #pragma unroll for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) { - vl[i] = get_int_from_uint8(bq5_0->qs, iqs + i); - vh[i] = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i)); - u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); - u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0); + vl[i] = get_int_b2(bq5_0->qs, iqs + i); + vh[i] = get_int_b2(bq5_0->qh, 0) >> (4 * (iqs + i)); + u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI5_0); } return vec_dot_q5_0_q8_1_impl(vl, vh, u, bq5_0->d, bq8_1->ds); @@ -630,10 +545,10 @@ static __device__ __forceinline__ float vec_dot_q5_1_q8_1( #pragma unroll for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) { - vl[i] = get_int_from_uint8_aligned(bq5_1->qs, iqs + i); - vh[i] = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i)); - u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); - u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1); + vl[i] = get_int_b4(bq5_1->qs, iqs + i); + vh[i] = get_int_b4(bq5_1->qh, 0) >> (4 * (iqs + i)); + u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI5_1); } return vec_dot_q5_1_q8_1_impl(vl, vh, u, bq5_1->dm, bq8_1->ds); @@ -649,8 +564,8 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1( #pragma unroll for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) { - v[i] = get_int_from_int8(bq8_0->qs, iqs + i); - u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + v[i] = get_int_b2(bq8_0->qs, iqs + i); + u[i] = get_int_b4(bq8_1->qs, iqs + i); } return vec_dot_q8_0_q8_1_impl(v, u, bq8_0->d, __low2half(bq8_1->ds)); @@ -666,13 +581,13 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1( const uint8_t * scales = bq2_K->scales + scale_offset; - const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs); + const int v = get_int_b4(bq2_K->qs, iqs); int u[QR2_K]; float d8[QR2_K]; #pragma unroll for (int i = 0; i < QR2_K; ++ i) { - u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); + u[i] = get_int_b4(bq8_1[bq8_offset + i].qs, iqs % QI8_1); d8[i] = __low2float(bq8_1[bq8_offset + i].ds); } @@ -689,17 +604,17 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1( const float d = bq3_K->d; - const int vl = get_int_from_uint8(bq3_K->qs, iqs); + const int vl = get_int_b2(bq3_K->qs, iqs); // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted - const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset; + const int vh = ~get_int_b2(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset; int u[QR3_K]; float d8[QR3_K]; #pragma unroll for (int i = 0; i < QR3_K; ++i) { - u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); + u[i] = get_int_b4(bq8_1[bq8_offset + i].qs, iqs % QI8_1); d8[i] = __low2float(bq8_1[bq8_offset + i].ds); } @@ -807,8 +722,8 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1( const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8); const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4)); - const int vl = get_int_from_uint8(bq6_K->ql, iqs); - const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift; + const int vl = get_int_b2(bq6_K->ql, iqs); + const int vh = get_int_b2(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift; const int8_t * scales = bq6_K->scales + scale_offset; @@ -817,335 +732,355 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1( #pragma unroll for (int i = 0; i < QR6_K; ++i) { - u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1); + u[i] = get_int_b4(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1); d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds); } return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8); } +#define VDR_IQ2_XXS_Q8_1_MMVQ 2 +#define VDR_IQ2_XXS_Q8_1_MMQ 2 + static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { + const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq + kbx; -#if QR2_XXS == 8 - const int ib32 = iqs; - const uint16_t * q2 = bq2->qs + 4*ib32; - const uint8_t * aux8 = (const uint8_t *)q2; - const int8_t * q8 = bq8_1[ib32].qs; - uint32_t aux32 = q2[2] | (q2[3] << 16); + const int q2 = get_int_b2(bq2->qs, iqs); + const uint8_t * aux8 = (const uint8_t *) &q2; + const uint32_t aux32 = get_int_b2(bq2->qs, iqs + 1); + int sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); - const uint8_t signs = ksigns_iq2xs[aux32 & 127]; - for (int j = 0; j < 8; ++j) { - sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - aux32 >>= 7; +#pragma unroll + for (int k0 = 0; k0 < 8; k0 += 2) { + const int * grid_pos = (const int *) (iq2xxs_grid + aux8[k0/2]); + const int signs_packed = ksigns_iq2xs[(aux32 >> (7*k0/2)) & 0x7F]; + + const int signs0 = __vcmpne4(((signs_packed & 0x03) << 7) | ((signs_packed & 0x0C) << 21), 0x00000000); + const int grid0 = __vsub4(grid_pos[0] ^ signs0, signs0); + const int u0 = get_int_b4(bq8_1[iqs/2].qs, k0 + 0); + sumi = ggml_cuda_dp4a(grid0, u0, sumi); + + const int signs1 = __vcmpne4(((signs_packed & 0x30) << 3) | ((signs_packed & 0xC0) << 17), 0x00000000); + const int grid1 = __vsub4(grid_pos[1] ^ signs1, signs1); + const int u1 = get_int_b4(bq8_1[iqs/2].qs, k0 + 1); + sumi = ggml_cuda_dp4a(grid1, u1, sumi); } - const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f; + + const int ls = aux32 >> 28; + sumi = (ls*sumi + sumi/2)/4; + const float d = __half2float(bq2->d) * __low2float(bq8_1[iqs/2].ds); return d * sumi; -#else - // iqs is 0...15 - const int ib32 = iqs/2; - const int il = iqs%2; - const uint16_t * q2 = bq2->qs + 4*ib32; - const uint8_t * aux8 = (const uint8_t *)q2; - const uint8_t * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]); - const uint8_t * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]); - const uint32_t aux32 = q2[2] | (q2[3] << 16); - const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f; - const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127]; - const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127]; - const int8_t * q8 = bq8_1[ib32].qs + 16*il; - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 8; ++j) { - sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1); - sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1); - } - return d * (sumi1 + sumi2); -#endif } +#define VDR_IQ2_XS_Q8_1_MMVQ 2 +#define VDR_IQ2_XS_Q8_1_MMQ 2 + static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq + kbx; - const int ib32 = iqs; - const uint16_t * q2 = bq2->qs + 4*ib32; - const int8_t * q8 = bq8_1[ib32].qs; - const uint8_t ls1 = bq2->scales[ib32] & 0xf; - const uint8_t ls2 = bq2->scales[ib32] >> 4; + const int2 q2_packed = make_int2(get_int_b2(bq2->qs, iqs + 0), get_int_b2(bq2->qs, iqs + 1)); + const uint16_t * q2 = (const uint16_t *) &q2_packed; + const int ls0 = bq2->scales[iqs/2] & 0x0F; + const int ls1 = bq2->scales[iqs/2] >> 4; + + int sumi0 = 0; int sumi1 = 0; - for (int l = 0; l < 2; ++l) { - const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); - const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); - const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]); - const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]); - sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1); - sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1); - q8 += 8; +#pragma unroll + for (int l0 = 0; l0 < 8; l0 += 2) { + const uint32_t * grid_pos = (const uint32_t *)(iq2xs_grid + (q2[l0/2] & 0x000001FF)); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l0/2] >> 9)); + + const int grid_l = __vsub4(grid_pos[0] ^ signs[0], signs[0]); + const int grid_h = __vsub4(grid_pos[1] ^ signs[1], signs[1]); + + const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0); + const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1); + + if (l0 < 4) { + sumi0 = ggml_cuda_dp4a(grid_l, u0, sumi0); + sumi0 = ggml_cuda_dp4a(grid_h, u1, sumi0); + } else { + sumi1 = ggml_cuda_dp4a(grid_l, u0, sumi1); + sumi1 = ggml_cuda_dp4a(grid_h, u1, sumi1); + } } - int sumi2 = 0; - for (int l = 2; l < 4; ++l) { - const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); - const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); - const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]); - const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]); - sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2); - sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2); - q8 += 8; - } - const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; - return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); -#else - GGML_UNUSED(ksigns64); - NO_DEVICE_CODE; -#endif + const int sumi = (sumi0*ls0 + sumi1*ls1 + (sumi0 + sumi1)/2)/4; + const float d = __half2float(bq2->d) * __low2float(bq8_1[iqs/2].ds); + return d * sumi; } -// TODO +#define VDR_IQ2_S_Q8_1_MMVQ 2 +#define VDR_IQ2_S_Q8_1_MMQ 2 + static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + const block_iq2_s * bq2 = (const block_iq2_s *) vbq + kbx; - const int ib32 = iqs; - const int8_t * q8 = bq8_1[ib32].qs; - const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32; - const uint8_t ls1 = bq2->scales[ib32] & 0xf; - const uint8_t ls2 = bq2->scales[ib32] >> 4; + const int qs_packed = get_int_b2(bq2->qs, iqs/2); + const uint8_t * qs = (const uint8_t *) &qs_packed; + + const int qh = bq2->qh[iqs/2]; + + const int signs_packed_32 = get_int_b2(bq2->qs, QK_K/32 + iqs/2); + const uint8_t * signs_packed_8 = (const uint8_t *) &signs_packed_32; + + const int ls0 = bq2->scales[iqs/2] & 0x0F; + const int ls1 = bq2->scales[iqs/2] >> 4; + + int sumi0 = 0; int sumi1 = 0; - for (int l = 0; l < 2; ++l) { - const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); - const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); - const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); - const int grid_l = __vsub4(grid[0] ^ signs0, signs0); - const int grid_h = __vsub4(grid[1] ^ signs1, signs1); - sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1); - sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1); - q8 += 8; +#pragma unroll + for (int l0 = 0; l0 < 8; l0 += 2) { + const int * grid_pos = (const int *)(iq2s_grid + (qs[l0/2] | ((qh << (8-l0)) & 0x300))); + + const int signs0 = __vcmpne4(((signs_packed_8[l0/2] & 0x03) << 7) | ((signs_packed_8[l0/2] & 0x0C) << 21), 0x00000000); + const int signs1 = __vcmpne4(((signs_packed_8[l0/2] & 0x30) << 3) | ((signs_packed_8[l0/2] & 0xC0) << 17), 0x00000000); + + const int grid_l = __vsub4(grid_pos[0] ^ signs0, signs0); + const int grid_h = __vsub4(grid_pos[1] ^ signs1, signs1); + + const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0); + const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1); + + if (l0 < 4) { + sumi0 = ggml_cuda_dp4a(grid_l, u0, sumi0); + sumi0 = ggml_cuda_dp4a(grid_h, u1, sumi0); + } else { + sumi1 = ggml_cuda_dp4a(grid_l, u0, sumi1); + sumi1 = ggml_cuda_dp4a(grid_h, u1, sumi1); + } } - int sumi2 = 0; - for (int l = 2; l < 4; ++l) { - const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); - const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); - const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); - const int grid_l = __vsub4(grid[0] ^ signs0, signs0); - const int grid_h = __vsub4(grid[1] ^ signs1, signs1); - sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2); - sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2); - q8 += 8; - } - const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; - return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); -#else - GGML_UNUSED(ksigns64); - NO_DEVICE_CODE; -#endif + const int sumi = (sumi0*ls0 + sumi1*ls1 + (sumi0 + sumi1)/2)/4; + + const float d = __half2float(bq2->d) * __low2float(bq8_1[iqs/2].ds); + return d * sumi; } +#define VDR_IQ3_XXS_Q8_1_MMVQ 2 +#define VDR_IQ3_XXS_Q8_1_MMQ 2 + static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq + kbx; - const int ib32 = iqs; - const uint8_t * q3 = bq2->qs + 8*ib32; - const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32; - const int8_t * q8 = bq8_1[ib32].qs; - uint32_t aux32 = gas[0] | (gas[1] << 16); + const block_iq3_xxs * bq3 = (const block_iq3_xxs *) vbq + kbx; + + const int2 q3_packed = make_int2(get_int_b2(bq3->qs, iqs), get_int_b2(bq3->qs, iqs+1)); + const uint8_t * q3 = (const uint8_t *) &q3_packed; + const uint32_t aux32 = get_int_b2(bq3->qs, QK_K/16 + iqs/2); + int sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0]; - const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1]; - const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127)); - const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]); - const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]); - sumi = __dp4a(grid_l, *((int *)q8+0), sumi); - sumi = __dp4a(grid_h, *((int *)q8+1), sumi); - q8 += 8; - aux32 >>= 7; +#pragma unroll + for (int l0 = 0; l0 < 8; l0 += 2) { + const int2 grid_pos = make_int2(iq3xxs_grid[q3[l0 + 0]], iq3xxs_grid[q3[l0 + 1]]); + + const int * signs = (const int *)(ksigns64 + ((aux32 >> (7*l0/2)) & 0x7F)); + + const int grid_l = __vsub4(grid_pos.x ^ signs[0], signs[0]); + const int grid_h = __vsub4(grid_pos.y ^ signs[1], signs[1]); + + const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0); + const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1); + + sumi = ggml_cuda_dp4a(grid_l, u0, sumi); + sumi = ggml_cuda_dp4a(grid_h, u1, sumi); } - const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f; + + const int ls = aux32 >> 28; + sumi = (ls*sumi + sumi/2)/2; + const float d = __half2float(bq3->d) * __low2float(bq8_1[iqs/2].ds); return d * sumi; -#else - NO_DEVICE_CODE; -#endif } +#define VDR_IQ3_S_Q8_1_MMVQ 2 +#define VDR_IQ3_S_Q8_1_MMQ 2 + // TODO: don't use lookup table for signs static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq3_s * bq2 = (const block_iq3_s *) vbq + kbx; - const int ib32 = iqs; - const uint8_t * qs = bq2->qs + 8*ib32; - const int8_t * q8 = bq8_1[ib32].qs; + const block_iq3_s * bq3 = (const block_iq3_s *) vbq + kbx; + + const int2 qs_packed = make_int2(get_int_b2(bq3->qs, iqs + 0), get_int_b2(bq3->qs, iqs + 1)); + const uint8_t * qs = (const uint8_t *) &qs_packed; + + const int qh = bq3->qh[iqs/2]; + + const int signs_packed_32 = get_int_b2(bq3->signs, iqs/2); + const uint8_t * signs_packed_8 = (const uint8_t *) &signs_packed_32; + int sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); - const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); - uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); - uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); - const int grid_l = __vsub4(grid1[0] ^ signs0, signs0); - const int grid_h = __vsub4(grid2[0] ^ signs1, signs1); - sumi = __dp4a(grid_l, *((int *)q8+0), sumi); - sumi = __dp4a(grid_h, *((int *)q8+1), sumi); - q8 += 8; +#pragma unroll + for (int l0 = 0; l0 < 8; l0 += 2) { + const int2 grid_pos = make_int2( + iq3s_grid[qs[l0 + 0] | ((qh << (8 - l0)) & 0x100)], + iq3s_grid[qs[l0 + 1] | ((qh << (7 - l0)) & 0x100)]); + + const int signs0 = __vcmpne4(((signs_packed_8[l0/2] & 0x03) << 7) | ((signs_packed_8[l0/2] & 0x0C) << 21), 0x00000000); + const int signs1 = __vcmpne4(((signs_packed_8[l0/2] & 0x30) << 3) | ((signs_packed_8[l0/2] & 0xC0) << 17), 0x00000000); + + const int grid_l = __vsub4(grid_pos.x ^ signs0, signs0); + const int grid_h = __vsub4(grid_pos.y ^ signs1, signs1); + + const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0); + const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1); + + sumi = ggml_cuda_dp4a(grid_l, u0, sumi); + sumi = ggml_cuda_dp4a(grid_h, u1, sumi); } - const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds); + + sumi *= 1 + 2*((bq3->scales[iqs/4] >> ((iqs << 1) & 0x04)) & 0x0F); + + const float d = __half2float(bq3->d) * __low2float(bq8_1[iqs/2].ds); return d * sumi; -#else - NO_DEVICE_CODE; -#endif } +#define VDR_IQ1_S_Q8_1_MMVQ 1 +#define VDR_IQ1_S_Q8_1_MMQ 1 + static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { const block_iq1_s * bq1 = (const block_iq1_s *) vbq + kbx; - const int ib32 = iqs; + const int qs_packed = get_int_b2(bq1->qs, iqs); + const uint8_t * qs = (const uint8_t *) &qs_packed; + + const int qh = bq1->qh[iqs]; + int sumi = 0; -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const int * q8 = (const int *)bq8_1[ib32].qs; - for (int l = 0; l < 4; ++l) { - const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); - int grid0 = grid[0] & 0x0f0f0f0f; - int grid1 = (grid[0] >> 4) & 0x0f0f0f0f; - sumi = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi)); +#pragma unroll + for (int l0 = 0; l0 < 8; l0 += 2) { + const int grid = iq1s_grid_gpu[qs[l0/2] | (((qh >> 3*(l0/2)) & 0x07) << 8)]; + + const int grid0 = (grid >> 0) & 0x0F0F0F0F; + const int grid1 = (grid >> 4) & 0x0F0F0F0F; + + const int u0 = get_int_b4(bq8_1[iqs].qs, l0 + 0); + const int u1 = get_int_b4(bq8_1[iqs].qs, l0 + 1); + + sumi = ggml_cuda_dp4a(grid0, u0, sumi); + sumi = ggml_cuda_dp4a(grid1, u1, sumi); } -#else - const int8_t * q8 = bq8_1[ib32].qs; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); - for (int j = 0; j < 4; ++j) { - sumi += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4); - } - q8 += 8; - } -#endif - const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA; - const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1); - const float d = d1q * __low2float (bq8_1[ib32].ds); - const float m = d1q * __high2float(bq8_1[ib32].ds); - return d * sumi + m * delta; + + const float d1q = __half2float(bq1->d) * (((qh >> 11) & 0x0E) + 1); + const float delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000); + const float2 ds = __half22float2(bq8_1[iqs].ds); + return d1q * (ds.x*sumi + ds.y*delta); } +#define VDR_IQ1_M_Q8_1_MMVQ 1 +#define VDR_IQ1_M_Q8_1_MMQ 1 + static __device__ __forceinline__ float vec_dot_iq1_m_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { + const block_iq1_m * bq1 = (const block_iq1_m *) vbq + kbx; - const int ib32 = iqs; - int sumi[2] = {0, 0}; - float sumf[2] = {0.f, 0.f}; -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const int * q8 = (const int *)bq8_1[ib32].qs; - for (int l = 0; l < 4; ++l) { - const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8))); - int grid0 = grid[0] & 0x0f0f0f0f; - int grid1 = (grid[0] >> 4) & 0x0f0f0f0f; - sumi[l/2] = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi[l/2])); - const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA; - const int sumy = __dp4a(q8[2*l+1], 0x01010101, __dp4a(q8[2*l+0], 0x01010101, 0)); - sumf[l/2] += delta*sumy; - } -#else - const int8_t * q8 = bq8_1[ib32].qs; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8))); + const int qs_packed = get_int_b4(bq1->qs, iqs); + const uint8_t * qs = (const uint8_t *) &qs_packed; + + int sumi[2] = {0}; + float sumf[2] = {0.0f}; +#pragma unroll + for (int l0 = 0; l0 < 8; l0 += 2) { + const int qhl = bq1->qh[2*iqs + l0/4] >> (4 * ((l0/2) % 2)); + + const int grid = iq1s_grid_gpu[qs[l0/2] | ((qhl & 0x07) << 8)]; + + const int grid0 = (grid >> 0) & 0x0F0F0F0F; + const int grid1 = (grid >> 4) & 0x0F0F0F0F; + + const int u0 = get_int_b4(bq8_1[iqs].qs, l0 + 0); + const int u1 = get_int_b4(bq8_1[iqs].qs, l0 + 1); + + sumi[l0/4] = ggml_cuda_dp4a(grid0, u0, sumi[l0/4]); + sumi[l0/4] = ggml_cuda_dp4a(grid1, u1, sumi[l0/4]); + + const float delta = -1.0f + IQ1M_DELTA - (qhl & 0x08) * (2.0f*IQ1M_DELTA/0x08); int sumy = 0; - for (int j = 0; j < 4; ++j) { - sumi[l/2] += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4); - sumy += q8[j] + q8[j+4]; - } - const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA; - sumf[l/2] += delta*sumy; - q8 += 8; + sumy = ggml_cuda_dp4a(u0, 0x01010101, sumy); + sumy = ggml_cuda_dp4a(u1, 0x01010101, sumy); + sumf[l0/4] += delta*sumy; } -#endif + + const uint16_t * sc = (const uint16_t *) bq1->scales; + iq1m_scale_t scale; - const uint16_t * sc = (const uint16_t *)bq1->scales; - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds); - return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1)); + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00F0) | ((sc[2] >> 4) & 0x0F00) | (sc[3] & 0xF000); + const float d = __half2float(scale.f16) * __low2float(bq8_1[iqs].ds); + + const int tmp = sc[iqs/2] >> (6*(iqs%2)); + const int sc0 = 2*((tmp >> 0) & 0x07) + 1; + const int sc1 = 2*((tmp >> 3) & 0x07) + 1; + return d * ((sumi[0] + sumf[0]) * sc0 + (sumi[1] + sumf[1]) * sc1); } -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values, - int & val1, int & val2) { +static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4) { + const int q0_32 = (q4 >> 0) & 0x0F0F0F0F; + const int8_t * q0_8 = (const int8_t *) &q0_32; + const char4 val0_8 = make_char4( + kvalues_iq4nl[q0_8[0]], kvalues_iq4nl[q0_8[1]], kvalues_iq4nl[q0_8[2]], kvalues_iq4nl[q0_8[3]]); - uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32; - aux32 = q4 & 0x0f0f0f0f; - uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8); - uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8); - val1 = v1 | (v2 << 16); - aux32 = (q4 >> 4) & 0x0f0f0f0f; - v1 = values[q8[0]] | (values[q8[1]] << 8); - v2 = values[q8[2]] | (values[q8[3]] << 8); - val2 = v1 | (v2 << 16); + const int q1_32 = (q4 >> 4) & 0x0F0F0F0F; + const int8_t * q1_8 = (const int8_t *) &q1_32; + const char4 val1_8 = make_char4( + kvalues_iq4nl[q1_8[0]], kvalues_iq4nl[q1_8[1]], kvalues_iq4nl[q1_8[2]], kvalues_iq4nl[q1_8[3]]); + + return make_int2(*((const int *) &val0_8), *((const int *) &val1_8)); } -#endif + +#define VDR_IQ4_NL_Q8_1_MMVQ 2 +#define VDR_IQ4_NL_Q8_1_MMQ 4 static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_iq4_nl * bq = (const block_iq4_nl *) vbq + kbx; + const block_iq4_nl * bq4 = (const block_iq4_nl *) vbq + kbx; -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs; - const int32_t * q8 = (const int32_t *)bq8_1->qs + iqs; + const int * q8 = (const int *) bq8_1->qs + iqs; - const uint8_t * values = (const uint8_t *)kvalues_iq4nl; - - int v1, v2; - int sumi1 = 0, sumi2 = 0; + int sumi = 0; +#pragma unroll for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) { - const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16); - get_int_from_table_16(aux, values, v1, v2); - sumi1 = __dp4a(v1, q8[l+0], sumi1); - sumi2 = __dp4a(v2, q8[l+4], sumi2); + const int aux_q4 = get_int_b2(bq4->qs, iqs + l); + const int2 v = get_int_from_table_16(aux_q4); + + sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi); + sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi); } -#else - const uint8_t * q4 = bq->qs + 4*iqs; - const int8_t * q8 = bq8_1->qs + 4*iqs; - - int sumi1 = 0, sumi2 = 0; - for (int l = 0; l < 4*VDR_Q4_0_Q8_1_MMVQ; ++l) { - sumi1 += q8[l+ 0] * kvalues_iq4nl[q4[l] & 0xf]; - sumi2 += q8[l+16] * kvalues_iq4nl[q4[l] >> 4]; - } -#endif - const float d = (float)bq->d * __low2float(bq8_1->ds); - return d * (sumi1 + sumi2); + const float d = __half2float(bq4->d) * __low2float(bq8_1->ds); + return d * sumi; } +#define VDR_IQ4_XS_Q8_1_MMVQ 4 +#define VDR_IQ4_XS_Q8_1_MMQ 4 + static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq + kbx; - const uint8_t * values = (const uint8_t *)kvalues_iq4nl; - // iqs is 0...7 - const int ib32 = iqs; - const int32_t * q8 = (const int *)bq8_1[ib32].qs; - const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32; - const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4); - const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds); - int v1, v2; - int sumi1 = 0, sumi2 = 0; + int sumi = 0; +#pragma unroll for (int j = 0; j < 4; ++j) { - get_int_from_table_16(q4[j], values, v1, v2); - sumi1 = __dp4a(v1, q8[j+0], sumi1); - sumi2 = __dp4a(v2, q8[j+4], sumi2); + const int aux_q4 = get_int_b4(bq4->qs, iqs + j); + const int2 v = get_int_from_table_16(aux_q4); + + const int u0 = get_int_b4(bq8_1[iqs/4].qs, j + 0); + const int u1 = get_int_b4(bq8_1[iqs/4].qs, j + 4); + + sumi = ggml_cuda_dp4a(v.x, u0, sumi); + sumi = ggml_cuda_dp4a(v.y, u1, sumi); } - return d * (sumi1 + sumi2); -#else - return vec_dot_iq4_xs_q8_1(vbq, bq8_1, kbx, iqs); -#endif + + const int ls = ((bq4->scales_l[iqs/8] >> (iqs & 0x04)) & 0x0F) | (((bq4->scales_h >> (iqs/2)) & 0x03) << 4); + sumi *= ls - 32; + + const float d = __half2float(bq4->d) * __low2float(bq8_1[iqs/4].ds); + return d * sumi; } diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal.metal index e2796fd60..c3503479b 100644 --- a/ggml/src/ggml-metal.metal +++ b/ggml/src/ggml-metal.metal @@ -6537,4 +6537,3 @@ template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; - diff --git a/ggml/src/ggml-quants.h b/ggml/src/ggml-quants.h index 4d436a8f0..30983b872 100644 --- a/ggml/src/ggml-quants.h +++ b/ggml/src/ggml-quants.h @@ -130,4 +130,3 @@ void iq3xs_free_impl(int grid_size); #ifdef __cplusplus } #endif - diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl.cpp index 4a668a2c3..21006cd7b 100644 --- a/ggml/src/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl.cpp @@ -49,7 +49,7 @@ bool ggml_backend_is_sycl(ggml_backend_t backend); int ggml_backend_sycl_get_device(ggml_backend_t backend); static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer); static inline int get_sycl_env(const char *env_name, int default_val); -static inline int get_work_group_size(const sycl::device& device); + void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, const void *ptr_src, size_t size) { @@ -74,51 +74,6 @@ typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const gg const float *src1_dd, float *dst_dd, const queue_ptr &main_stream); -static __dpct_inline__ float warp_reduce_sum(float x, - const sycl::nd_item<3> &item_ct1) { -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - /* - DPCT1096:98: The right-most dimension of the work-group used in the SYCL - kernel that calls this function may be less than "32". The function - "dpct::permute_sub_group_by_xor" may return an unexpected result on the - CPU device. Modify the size of the work-group to ensure that the value - of the right-most dimension is a multiple of "32". - */ - x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask); - } - return x; -} - -static __dpct_inline__ sycl::float2 -warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3> &item_ct1) { -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(), - mask); - a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(), - mask); - } - return a; -} - -static __dpct_inline__ float warp_reduce_max(float x, - const sycl::nd_item<3> &item_ct1) { -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - /* - DPCT1096:97: The right-most dimension of the work-group used in the SYCL - kernel that calls this function may be less than "32". The function - "dpct::permute_sub_group_by_xor" may return an unexpected result on the - CPU device. Modify the size of the work-group to ensure that the value - of the right-most dimension is a multiple of "32". - */ - x = sycl::fmax(x, dpct::permute_sub_group_by_xor( - item_ct1.get_sub_group(), x, mask)); - } - return x; -} - static __dpct_inline__ float op_repeat(const float a, const float b) { return b; GGML_UNUSED(a); @@ -336,47 +291,6 @@ static void sqr_f32(const float * x, float * dst, const int k, dst[i] = x[i] * x[i]; } -static void norm_f32(const float * x, float * dst, const int ncols, const float eps, - const sycl::nd_item<3> &item_ct1, sycl::float2 *s_sum, int block_size) { - const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + - item_ct1.get_local_id(1); - const int tid = item_ct1.get_local_id(2); - - sycl::float2 mean_var = sycl::float2(0.f, 0.f); - - for (int col = tid; col < ncols; col += block_size) { - const float xi = x[row*ncols + col]; - mean_var.x() += xi; - mean_var.y() += xi * xi; - } - - // sum up partial sums - mean_var = warp_reduce_sum(mean_var, item_ct1); - if (block_size > WARP_SIZE) { - - int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; - int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; - if (lane_id == 0) { - s_sum[warp_id] = mean_var; - } - /* - DPCT1118:0: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - item_ct1.barrier(sycl::access::fence_space::local_space); - mean_var = s_sum[lane_id]; - mean_var = warp_reduce_sum(mean_var, item_ct1); - } - - const float mean = mean_var.x() / ncols; - const float var = mean_var.y() / ncols - mean * mean; - const float inv_std = sycl::rsqrt(var + eps); - - for (int col = tid; col < ncols; col += block_size) { - dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std; - } -} - static void concat_f32(const float *x,const float *y, float *dst, const int ne0, const int ne02, const sycl::nd_item<3> &item_ct1) { int nidx = item_ct1.get_local_id(2) + @@ -444,126 +358,11 @@ static void pad_f32(const float *x, float *dst, const int ne0, const int ne00, } } -static void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps, - const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) { - int start = item_ct1.get_group(2) * group_size; - int end = start + group_size; - - start += item_ct1.get_local_id(2); - - if (end >= ne_elements) { - end = ne_elements; - } - - float tmp = 0.0f; // partial sum for thread in warp - - for (int j = start; j < end; j += block_size) { - tmp += x[j]; - } - - tmp = warp_reduce_sum(tmp, item_ct1); - if (block_size > WARP_SIZE) { - - int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; - int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; - if (lane_id == 0) { - s_sum[warp_id] = tmp; - } - /* - DPCT1118:1: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:54: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); - tmp = s_sum[lane_id]; - tmp = warp_reduce_sum(tmp, item_ct1); - } - - float mean = tmp / group_size; - tmp = 0.0f; - - for (int j = start; j < end; j += block_size) { - float xi = x[j] - mean; - dst[j] = xi; - tmp += xi * xi; - } - - tmp = warp_reduce_sum(tmp, item_ct1); - if (block_size > WARP_SIZE) { - - int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; - int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; - if (lane_id == 0) { - s_sum[warp_id] = tmp; - } - /* - DPCT1118:2: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:55: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); - tmp = s_sum[lane_id]; - tmp = warp_reduce_sum(tmp, item_ct1); - } - - float variance = tmp / group_size; - float scale = sycl::rsqrt(variance + eps); - for (int j = start; j < end; j += block_size) { - dst[j] *= scale; - } -} - -static void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps, - const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) { - const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + - item_ct1.get_local_id(1); - const int tid = item_ct1.get_local_id(2); - - float tmp = 0.0f; // partial sum for thread in warp - - for (int col = tid; col < ncols; col += block_size) { - const float xi = x[row*ncols + col]; - tmp += xi * xi; - } - - // sum up partial sums - tmp = warp_reduce_sum(tmp, item_ct1); - if (block_size > WARP_SIZE) { - - int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; - int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; - if (lane_id == 0) { - s_sum[warp_id] = tmp; - } - /* - DPCT1118:3: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - item_ct1.barrier(sycl::access::fence_space::local_space); - tmp = s_sum[lane_id]; - tmp = warp_reduce_sum(tmp, item_ct1); - } - - const float mean = tmp / ncols; - const float scale = sycl::rsqrt(mean + eps); - - for (int col = tid; col < ncols; col += block_size) { - dst[row*ncols + col] = scale * x[row*ncols + col]; - } -} - +template static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded, const sycl::nd_item<3> &item_ct1) { - const int ix = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); + const int ix = (item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2)) * QUANT_BLOCK_TILE; if (ix >= kx_padded) { return; @@ -578,23 +377,39 @@ static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int ib = i_padded / QK8_1; // block index const int iqs = i_padded % QK8_1; // quant index - - const float xi = ix < kx ? x[iy*kx + ix] : 0.0f; - float amax = sycl::fabs((float)xi); - float sum = xi; - + typedef sycl::vec TC; + typedef sycl::vec TQ; + TC zeros; + TQ qzeros; #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - amax = sycl::fmax(amax, dpct::permute_sub_group_by_xor( - item_ct1.get_sub_group(), amax, mask)); - sum += - dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), sum, mask); + for (int i = 0; i < QUANT_BLOCK_TILE; i++) + { + zeros[i] = 0.f; + qzeros[i] = 0; } + const TC xi = ix < kx ? *(TC *)&x[iy * kx + ix] : zeros; + float sum = xi[0]; + float amax = sycl::fabs(xi[0]); +#pragma unroll + for (int i = 1; i < QUANT_BLOCK_TILE; i++) + { + sum += xi[i]; + amax = sycl::fmax(sycl::fabs(xi[i]), amax); + } + sum = warp_reduce_sum(sum, item_ct1); + amax = warp_reduce_max(amax, item_ct1); const float d = amax / 127; - const int8_t q = amax == 0.0f ? 0 : sycl::round(xi / d); + TQ q = qzeros; + if (amax != 0.0f) + { +#pragma unroll + for (int i = 0; i < QUANT_BLOCK_TILE; i++) { + q[i] = sycl::round(xi[i] / d); + } + } - y[ib].qs[iqs] = q; + *(TQ *)&y[ib].qs[iqs] = q; if (iqs > 0) { return; @@ -728,7 +543,7 @@ static void mul_mat_p021_f16_f32( // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -781,7 +596,7 @@ static void mul_mat_vec_nc_f16_f32( // nc == non-contiguous // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -978,114 +793,6 @@ static void cpy_f32_q(const char * cx, char * cdst, const int ne, cpy_blck(cx + x_offset, cdst + dst_offset); } -static float rope_yarn_ramp(const float low, const float high, const int i0) { - const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low); - return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y)); -} - -struct rope_corr_dims { - float v[4]; -}; - -// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn -// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. -static void rope_yarn( - float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta -) { - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = freq_scale * theta_extrap; - float theta = theta_interp; - if (ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale); - } - *cos_theta = sycl::cos(theta) * mscale; - *sin_theta = sycl::sin(theta) * mscale; -} - -// rope == RoPE == rotary positional embedding -template -static void rope( - const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, - float ext_factor, float attn_factor, rope_corr_dims corr_dims -, - const sycl::nd_item<3> &item_ct1) { - const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1)); - - if (col >= ncols) { - return; - } - - const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - const int i = row*ncols + col; - const int i2 = row/p_delta_rows; - - const int p = has_pos ? pos[i2] : 0; - const float theta_base = p * dpct::pow(freq_base, -float(col) / ncols); - - float cos_theta, sin_theta; - rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta); - - const float x0 = x[i + 0]; - const float x1 = x[i + 1]; - - dst[i + 0] = x0*cos_theta - x1*sin_theta; - dst[i + 1] = x0*sin_theta + x1*cos_theta; -} - -template -static void rope_neox( - const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, - float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims, - const float * freq_factors, const sycl::nd_item<3> &item_ct1) { - const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1)); - - if (col >= ncols) { - return; - } - - const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - const int ib = col / n_dims; - const int ic = col % n_dims; - - if (ib > 0) { - const int i = row*ncols + ib*n_dims + ic; - - dst[i + 0] = x[i + 0]; - dst[i + 1] = x[i + 1]; - - return; - } - - const int i = row*ncols + ib*n_dims + ic/2; - const int i2 = row/p_delta_rows; - - float cur_rot = inv_ndims * ic - ib; - - const int p = has_pos ? pos[i2] : 0; - const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f; - - const float theta_base = - p * freq_scale * dpct::pow(theta_scale, col / 2.0f)/freq_factor; - - float cos_theta, sin_theta; - rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); - - const float x0 = x[i + 0]; - const float x1 = x[i + n_dims/2]; - - dst[i + 0] = x0*cos_theta - x1*sin_theta; - dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta; -} - static void k_sum_rows_f32(const float * x, float * dst, const int ncols, const sycl::nd_item<3> &item_ct1) { const int row = item_ct1.get_group(1); @@ -1185,117 +892,6 @@ static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, con dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; } - -template -static void soft_max_f32(const float * x, const float * mask, float * dst, const int ncols_par, - const int nrows_y, const float scale, const float max_bias, const float m0, - const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) { - const int ncols = ncols_template == 0 ? ncols_par : ncols_template; - - const int tid = item_ct1.get_local_id(2); - const int rowx = item_ct1.get_group(2); - const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension - - const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template; - - const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; - const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; - - float slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = rowx/nrows_y; // head index - - const float base = h < n_head_log2 ? m0 : m1; - const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = sycl::pow(base, float(exp)); - } - - float * vals = vals_smem ? buf + WARP_SIZE : dst + rowx*ncols; - float max_val = -INFINITY; - - for (int col0 = 0; col0 < ncols; col0 += block_size) { - const int col = col0 + tid; - - if (ncols_template == 0 && col >= ncols) { - break; - } - - const int ix = rowx*ncols + col; - const int iy = rowy*ncols + col; - - const float val = x[ix]*scale + (mask ? slope*mask[iy] : 0.0f); - - vals[col] = val; - max_val = sycl::max(max_val, val); - } - - // find the max value in the block - max_val = warp_reduce_max(max_val, item_ct1); - if (block_size > WARP_SIZE) { - if (warp_id == 0) { - buf[lane_id] = -INFINITY; - } - item_ct1.barrier(sycl::access::fence_space::local_space); - - if (lane_id == 0) { - buf[warp_id] = max_val; - } - item_ct1.barrier(sycl::access::fence_space::local_space); - - max_val = buf[lane_id]; - max_val = warp_reduce_max(max_val, item_ct1); - } - - float tmp = 0.f; - -#pragma unroll - for (int col0 = 0; col0 < ncols; col0 += block_size) { - const int col = col0 + tid; - if (ncols_template == 0 && col >= ncols) { - break; - } - - const float val = sycl::native::exp(vals[col] - max_val); - tmp += val; - vals[col] = val; - } - - // find the sum of exps in the block - tmp = warp_reduce_sum(tmp, item_ct1); - if (block_size > WARP_SIZE) { - item_ct1.barrier(sycl::access::fence_space::local_space); - if (warp_id == 0) { - buf[lane_id] = 0.f; - } - item_ct1.barrier(sycl::access::fence_space::local_space); - - if (lane_id == 0) { - buf[warp_id] = tmp; - } - item_ct1.barrier(sycl::access::fence_space::local_space); - - tmp = buf[lane_id]; - tmp = warp_reduce_sum(tmp, item_ct1); - } - - const float inv_sum = 1.f / tmp; - -#pragma unroll - for (int col0 = 0; col0 < ncols; col0 += block_size) { - const int col = col0 + tid; - - if (ncols_template == 0 && col >= ncols) { - return; - } - - const int idst = rowx*ncols + col; - dst[idst] = vals[col] * inv_sum; - } -} - static void scale_f32(const float * x, float * dst, const float scale, const int k, const sycl::nd_item<3> &item_ct1) { const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + @@ -1751,99 +1347,6 @@ static void sqr_f32_sycl(const float *x, float *dst, const int k, }); } -static void norm_f32_sycl(const float *x, float *dst, const int ncols, - const int nrows, const float eps, - queue_ptr stream) { - GGML_ASSERT(ncols % WARP_SIZE == 0); - if (ncols < 1024) { - const sycl::range<3> block_dims(1, 1, WARP_SIZE); - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor s_sum_acc_ct1( - sycl::range<1>(32), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - norm_f32(x, dst, ncols, eps, item_ct1, - s_sum_acc_ct1.get_pointer(), WARP_SIZE); - }); - }); - } else { - const int work_group_size = get_work_group_size(stream->get_device()); - const sycl::range<3> block_dims(1, 1, work_group_size); - /* - DPCT1049:17: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor s_sum_acc_ct1( - sycl::range<1>(32), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - norm_f32(x, dst, ncols, eps, item_ct1, - s_sum_acc_ct1.get_pointer(), work_group_size); - }); - }); - } -} - -static void group_norm_f32_sycl(const float *x, float *dst, - const int num_groups, const int group_size, - const int ne_elements, queue_ptr stream) { - static const float eps = 1e-6f; - if (group_size < 1024) { - const sycl::range<3> block_dims(1, 1, WARP_SIZE); - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(32), - cgh); - - const float eps_ct4 = eps; - - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - group_norm_f32( - x, dst, group_size, ne_elements, eps_ct4, item_ct1, - s_sum_acc_ct1.get_pointer(), WARP_SIZE); - }); - }); - } else { - const int work_group_size = get_work_group_size(stream->get_device()); - const sycl::range<3> block_dims(1, 1, work_group_size); - /* - DPCT1049:18: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(32), - cgh); - - const float eps_ct4 = eps; - - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - group_norm_f32(x, dst, group_size, ne_elements, - eps_ct4, item_ct1, - s_sum_acc_ct1.get_pointer(), work_group_size); - }); - }); - } -} - static void concat_f32_sycl(const float *x, const float *y, float *dst, const int ne0, int ne1, int ne2, int ne02, queue_ptr stream) { @@ -1885,64 +1388,22 @@ static void pad_f32_sycl(const float *x, float *dst, const int ne00, }); } -static void rms_norm_f32_sycl(const float *x, float *dst, const int ncols, - const int nrows, const float eps, - queue_ptr stream) { - GGML_ASSERT(ncols % WARP_SIZE == 0); - // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE); - if (ncols < 1024) { - const sycl::range<3> block_dims(1, 1, WARP_SIZE); - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(32), - cgh); - - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - rms_norm_f32(x, dst, ncols, eps, item_ct1, - s_sum_acc_ct1.get_pointer(), WARP_SIZE); - }); - }); - } else { - const int work_group_size = get_work_group_size(stream->get_device()); - const sycl::range<3> block_dims(1, 1, work_group_size); - /* - DPCT1049:19: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(32), - cgh); - - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - rms_norm_f32(x, dst, ncols, eps, item_ct1, - s_sum_acc_ct1.get_pointer(), work_group_size); - }); - }); - } -} - static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx, const int ky, const int kx_padded, queue_ptr stream) { const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE; const sycl::range<3> num_blocks(1, ky, block_num_x); - const sycl::range<3> block_size(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE); + int constexpr QUANT_BLOCK_TILE = QK8_1 / WARP_SIZE; + static_assert(QK8_1 % WARP_SIZE == 0); + const sycl::range<3> block_size(1, 1, SYCL_QUANTIZE_BLOCK_SIZE / QUANT_BLOCK_TILE); { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); stream->parallel_for( sycl::nd_range<3>(num_blocks * block_size, block_size), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - quantize_q8_1(x, vy, kx, kx_padded, item_ct1); + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { + quantize_q8_1(x, vy, kx, kx_padded, item_ct1); }); } } @@ -1962,7 +1423,7 @@ static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y, stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_p021_f16_f32(vx, y, dst, ncols_x, nrows_x, nchannels_x, nchannels_y, item_ct1); }); @@ -1982,7 +1443,7 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl( stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_nc_f16_f32(vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y / nchannels_x, item_ct1); @@ -2241,117 +1702,13 @@ static void clamp_f32_sycl(const float *x, float *dst, const float min, }); } -template -static void rope_sycl(const T *x, T *dst, int ncols, int nrows, - const int32_t *pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, - rope_corr_dims corr_dims, queue_ptr stream) { - GGML_ASSERT(ncols % 2 == 0); - const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); - const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); - const sycl::range<3> block_nums(1, num_blocks_x, nrows); - if (pos == nullptr) { - /* - DPCT1049:40: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope(x, dst, ncols, pos, freq_scale, p_delta_rows, - freq_base, ext_factor, attn_factor, corr_dims, - item_ct1); - }); - } else { - /* - DPCT1049:41: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope(x, dst, ncols, pos, freq_scale, p_delta_rows, - freq_base, ext_factor, attn_factor, corr_dims, - item_ct1); - }); - } -} - -template -static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows, - const int32_t *pos, float freq_scale, - int p_delta_rows, float freq_base, float ext_factor, - float attn_factor, rope_corr_dims corr_dims, - const float * freq_factors, queue_ptr stream) { - GGML_ASSERT(ncols % 2 == 0); - const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); - const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); - const sycl::range<3> block_nums(1, num_blocks_x, nrows); - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.0f / n_dims; - - if (pos == nullptr) { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - if (freq_factors == nullptr) { - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ncols, n_dims, pos, freq_scale, - p_delta_rows, ext_factor, attn_factor, - corr_dims, theta_scale, inv_ndims, freq_factors, - item_ct1); - }); - } else { - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ncols, n_dims, pos, freq_scale, - p_delta_rows, ext_factor, attn_factor, - corr_dims, theta_scale, inv_ndims, freq_factors, - item_ct1); - }); - } - } else { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - if (freq_factors == nullptr) { - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ncols, n_dims, pos, freq_scale, - p_delta_rows, ext_factor, attn_factor, - corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1); - }); - } else { - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ncols, n_dims, pos, freq_scale, - p_delta_rows, ext_factor, attn_factor, - corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1); - }); - } - } -} - static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols, const int nrows, queue_ptr stream) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); const sycl::range<3> block_nums(1, nrows, 1); stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { k_sum_rows_f32(x, dst, ncols, item_ct1); }); } @@ -2422,106 +1779,6 @@ static void diag_mask_inf_f32_sycl(const float *x, float *dst, }); } -template -static void soft_max_f32_submitter(const float * x, const float * mask, float * dst, const int ncols_par, - const int nrows_y, const float scale, const float max_bias, const float m0, - const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims, - const size_t n_local_scratch, queue_ptr stream) { - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor local_buf_acc(n_local_scratch, cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - soft_max_f32(x, mask, dst, ncols_par, - nrows_y, scale, max_bias, m0, - m1, n_head_log2, item_ct1, - local_buf_acc.get_pointer()); - }); - }); -} - -static void soft_max_f32_sycl(const float * x, const float * mask, - float * dst, const int ncols_x, const int nrows_x, - const int nrows_y, const float scale, const float max_bias, - queue_ptr stream) { - int nth = WARP_SIZE; - int max_block_size = get_work_group_size(stream->get_device()); - while (nth < ncols_x && nth < max_block_size) nth *= 2; - if (nth>max_block_size) nth = max_block_size; - - const sycl::range<3> block_dims(1, 1, nth); - const sycl::range<3> block_nums(1, 1, nrows_x); - const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE); - - const uint32_t n_head_kv = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - const size_t local_mem_size = stream->get_device().get_info(); - if (n_local_scratch*sizeof(float) < local_mem_size) { - if (ncols_x > max_block_size) { - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - return; - } - switch (ncols_x) { - case 32: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 64: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 128: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 256: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 512: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 1024: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 2048: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - case 4096: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - default: - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, n_local_scratch, stream); - break; - } - } else { - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, WARP_SIZE, stream); - } -} - template static void im2col_sycl(const float *x, T *dst, int IW, int IH, int OW, int OH, int KW, int KH, int IC, @@ -2612,12 +1869,6 @@ static inline int get_sycl_env(const char *env_name, int default_val) { return user_number; } -static inline int get_work_group_size(const sycl::device& device) { - dpct::device_info prop; - dpct::get_device_info(prop, device); - return prop.get_max_work_group_size(); -} - static void ggml_check_sycl() try { static bool initialized = false; @@ -2694,6 +1945,8 @@ static ggml_sycl_device_info ggml_sycl_init() { info.devices[i].cc = 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + + info.max_work_group_sizes[i] = prop.get_max_work_group_size(); } for (int id = 0; id < info.device_count; ++id) { @@ -3176,45 +2429,6 @@ inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor (void) src1_dd; } -inline void ggml_sycl_op_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t nrows = ggml_nrows(src0); - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int num_groups = dst->op_params[0]; - int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); - group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - inline void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -3278,28 +2492,6 @@ inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor (void) src1_dd; } -inline void ggml_sycl_op_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t nrows = ggml_nrows(src0); - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { int64_t min_compute_capability = INT_MAX; int64_t max_compute_capability = INT_MIN; @@ -3461,97 +2653,6 @@ catch (sycl::exception const &exc) { std::exit(1); } -inline void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - const ggml_tensor * src2 = dst->src[2]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); - GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); - GGML_ASSERT(src0->type == dst->type); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t nrows = ggml_nrows(src0); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - //const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - - // RoPE alteration for extended context - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - - const float * freq_factors = nullptr; - const int32_t * pos = nullptr; - if ((mode & 1) == 0) { - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(src1->ne[0] == ne2); - pos = (const int32_t *) src1_dd; - } - - const bool is_neox = mode & 2; - -#pragma message("TODO: update rope NORM mode to match NEOX mode") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7634") - - if (is_neox) { - pos = (const int32_t *) src1_dd; - - if (src2 != nullptr) { - freq_factors = (const float *) src2->data; - } - } else { - GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox"); - } - - rope_corr_dims corr_dims; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v); - - // compute - if (is_neox) { - if (src0->type == GGML_TYPE_F32) { - rope_neox_sycl( - (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, freq_factors, main_stream - ); - } else if (src0->type == GGML_TYPE_F16) { - rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, - ne00, n_dims, nrows, pos, freq_scale, ne01, - freq_base, ext_factor, attn_factor, corr_dims, - freq_factors, main_stream); - } else { - GGML_ASSERT(false); - } - } else { - if (src0->type == GGML_TYPE_F32) { - rope_sycl( - (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, main_stream - ); - } else if (src0->type == GGML_TYPE_F16) { - rope_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, - nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, main_stream); - } else { - GGML_ASSERT(false); - } - } - - (void) src1; - (void) dst; - (void) src1_dd; -} - static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -3697,33 +2798,6 @@ inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const gg (void) src1_dd; } -inline void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - -#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 support") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") - GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional - - const int64_t ne00 = src0->ne[0]; - const int64_t nrows_x = ggml_nrows(src0); - const int64_t nrows_y = src0->ne[1]; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, dst->op_params + 1, sizeof(float)); - - soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, - nrows_x, nrows_y, scale, max_bias, main_stream); -} - inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, @@ -4419,10 +3493,6 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, SYCL_CHECK(ggml_sycl_set_device(ctx.device)); queue_ptr main_stream = ctx.stream();; - bool no_mixed_dtypes = main_stream->get_backend() == sycl::backend::ext_oneapi_cuda || - main_stream->get_backend() == sycl::backend::ext_oneapi_hip; - - void * src0_ddq = src0->data; sycl::half *src0_as_f16 = (sycl::half *)src0_ddq; float * src1_ddf = (float *) src1->data; @@ -4440,15 +3510,10 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf : src1_f16_alloc.get(); - ggml_sycl_pool_alloc dst_f16(ctx.pool()); char * dst_t; dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float; dpct::library_data_t cu_data_type = dpct::library_data_t::real_float; - if (no_mixed_dtypes) { - cu_compute_type = dpct::library_data_t::real_half; - cu_data_type = dpct::library_data_t::real_half; - } // dst strides size_t nbd2 = dst->nb[2]; @@ -4457,26 +3522,10 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, const float alpha_f32 = 1.0f; const float beta_f32 = 0.0f; - const sycl::half alpha_f16 = 1.0f; - const sycl::half beta_f16 = 0.0f; - const void * alpha = &alpha_f32; const void * beta = &beta_f32; - if (no_mixed_dtypes) { - alpha = &alpha_f16; - beta = &beta_f16; - } - - // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway - // when oneMKL open source supports half, half, float, float: datatypes dst_t = (char *) dst_ddf; - if (no_mixed_dtypes) { - dst_t = (char *) dst_f16.alloc(ne_dst); - - nbd2 /= sizeof(float) / sizeof(sycl::half); - nbd3 /= sizeof(float) / sizeof(sycl::half); - } GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -4538,11 +3587,6 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type))); } - - if (no_mixed_dtypes) { - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream); - } } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -4576,7 +3620,6 @@ bool ggml_sycl_supports_dmmv(enum ggml_type type) { static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer); - int64_t min_compute_capability = INT_MAX; if (split) { @@ -6221,7 +5264,8 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons case GGML_OP_CONCAT: { ggml_type src0_type = op->src[0]->type; - return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; + int dim = op->op_params[0]; + return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16 && dim == 2; } break; case GGML_OP_DUP: case GGML_OP_NONE: @@ -6241,7 +5285,9 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons case GGML_OP_CONT: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: + return true; case GGML_OP_ROPE: + return ggml_is_contiguous(op->src[0]); case GGML_OP_IM2COL: case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp index 2d37e271f..2a789edfc 100644 --- a/ggml/src/ggml-sycl/backend.hpp +++ b/ggml/src/ggml-sycl/backend.hpp @@ -19,5 +19,8 @@ #include "dmmv.hpp" #include "mmq.hpp" #include "mmvq.hpp" +#include "rope.hpp" +#include "norm.hpp" +#include "softmax.hpp" #endif // GGML_SYCL_BACKEND_HPP diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index e01f91633..9a1c161b6 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -47,10 +47,6 @@ static int g_ggml_sycl_debug = 0; } \ }() -// #define DEBUG_SYCL_MALLOC - -static int g_work_group_size = 0; -// typedef sycl::half ggml_fp16_t; #define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP #define VER_4VEC 610 // todo for hardward optimize. @@ -193,6 +189,8 @@ struct ggml_sycl_device_info { sycl_device_info devices[GGML_SYCL_MAX_DEVICES] = {}; std::array default_tensor_split = {}; + + int max_work_group_sizes[GGML_SYCL_MAX_DEVICES] = {0}; }; const ggml_sycl_device_info & ggml_sycl_info(); @@ -295,5 +293,57 @@ struct ggml_backend_sycl_context { } }; +// common device functions + +static __dpct_inline__ float warp_reduce_sum(float x, + const sycl::nd_item<3>& item_ct1) { +#pragma unroll + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + /* + DPCT1096:98: The right-most dimension of the work-group used in the SYCL + kernel that calls this function may be less than "32". The function + "dpct::permute_sub_group_by_xor" may return an unexpected result on the + CPU device. Modify the size of the work-group to ensure that the value + of the right-most dimension is a multiple of "32". + */ + x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask); + } + return x; +} + +static __dpct_inline__ sycl::float2 +warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3>& item_ct1) { +#pragma unroll + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(), + mask); + a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(), + mask); + } + return a; +} + +static __dpct_inline__ float warp_reduce_max(float x, + const sycl::nd_item<3>& item_ct1) { +#pragma unroll + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + /* + DPCT1096:97: The right-most dimension of the work-group used in the SYCL + kernel that calls this function may be less than "32". The function + "dpct::permute_sub_group_by_xor" may return an unexpected result on the + CPU device. Modify the size of the work-group to ensure that the value + of the right-most dimension is a multiple of "32". + */ + x = sycl::fmax(x, dpct::permute_sub_group_by_xor( + item_ct1.get_sub_group(), x, mask)); + } + return x; +} + +// Helper for vec loading aligned data +template +inline sycl::vec vec_aligned_load(const Tp* aligned_ptr) { + return *reinterpret_cast*>(aligned_ptr); +} #endif // GGML_SYCL_COMMON_HPP diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index ce9de2b42..a15271b51 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -152,12 +152,15 @@ static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor scale_local_acc(sycl::range<1>(12), cgh); + cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q4_K(vx, y, item_ct1); + dequantize_block_q4_K(vx, y, scale_local_acc.get_pointer(), item_ct1); }); + }); } } diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp index b6080d83a..ed8ad098b 100644 --- a/ggml/src/ggml-sycl/dequantize.hpp +++ b/ggml/src/ggml-sycl/dequantize.hpp @@ -293,7 +293,8 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri #if QK_K == 256 static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { if (j < 4) { - d = q[j] & 63; m = q[j + 4] & 63; + d = q[j] & 63; + m = q[j + 4] & 63; } else { d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); @@ -303,7 +304,7 @@ static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8 template static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy, - const sycl::nd_item<3> &item_ct1) { + uint8_t* scales_local, const sycl::nd_item<3> &item_ct1) { const block_q4_K * x = (const block_q4_K *) vx; const int i = item_ct1.get_group(2); @@ -318,19 +319,26 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri dst_t * y = yy + i*QK_K + 64*il + n*ir; - const float dall = x[i].dm[0]; - const float dmin = x[i].dm[1]; + const sycl::half2 dm = x[i].dm; + const float dall = dm[0]; + const float dmin = dm[1]; - const uint8_t * q = x[i].qs + 32*il + n*ir; + if (tid < 12) + scales_local[tid] = x[i].scales[tid]; + item_ct1.barrier(sycl::access::fence_space::local_space); uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, sc, m); - const float d1 = dall * sc; const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, sc, m); - const float d2 = dall * sc; const float m2 = dmin * m; + get_scale_min_k4(is + 0, scales_local, sc, m); + const float d1 = dall * sc; + const float m1 = dmin * m; + get_scale_min_k4(is + 1, scales_local, sc, m); + const float d2 = dall * sc; + const float m2 = dmin * m; + + sycl::vec q_vec = vec_aligned_load(x[i].qs + 32*il + n*ir); for (int l = 0; l < n; ++l) { - y[l + 0] = d1 * (q[l] & 0xF) - m1; - y[l +32] = d2 * (q[l] >> 4) - m2; + y[l + 0] = d1 * (q_vec[l] & 0xF) - m1; + y[l +32] = d2 * (q_vec[l] >> 4) - m2; } #else const int tid = item_ct1.get_local_id(2); diff --git a/ggml/src/ggml-sycl/dmmv.cpp b/ggml/src/ggml-sycl/dmmv.cpp index 3a87d3ef8..70a94fc16 100644 --- a/ggml/src/ggml-sycl/dmmv.cpp +++ b/ggml/src/ggml-sycl/dmmv.cpp @@ -3,6 +3,7 @@ #include "dequantize.hpp" #include "presets.hpp" + static void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){ const sycl::half *x = (const sycl::half *)vx; @@ -76,7 +77,7 @@ static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -104,7 +105,7 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y, stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols, nrows, item_ct1); }); @@ -227,7 +228,7 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -346,7 +347,7 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -499,7 +500,7 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -633,7 +634,7 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -748,7 +749,7 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -774,7 +775,7 @@ static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y, stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { dequantize_mul_mat_vec( vx, y, dst, ncols, nrows, item_ct1); }); @@ -795,7 +796,7 @@ static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y, stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { dequantize_mul_mat_vec( vx, y, dst, ncols, nrows, item_ct1); }); @@ -816,7 +817,7 @@ static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y, stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { dequantize_mul_mat_vec( vx, y, dst, ncols, nrows, item_ct1); }); @@ -837,7 +838,7 @@ static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y, stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { dequantize_mul_mat_vec( vx, y, dst, ncols, nrows, item_ct1); }); @@ -858,7 +859,7 @@ static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y, stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { dequantize_mul_mat_vec( vx, y, dst, ncols, nrows, item_ct1); }); @@ -873,10 +874,10 @@ static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y, const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2 const int block_num_y = (nrows + ny - 1) / ny; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, ny, 32); + const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE); stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1); }); } @@ -889,10 +890,10 @@ static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y, const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, ny, 32); + const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE); stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1); }); } @@ -905,10 +906,10 @@ static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y, const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, ny, 32); + const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE); stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1); }); } @@ -918,10 +919,10 @@ static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y, const int nrows, dpct::queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); - const sycl::range<3> block_dims(1, 1, 32); + const sycl::range<3> block_dims(1, 1, QK_WARP_SIZE); stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1); }); } @@ -934,10 +935,10 @@ static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y, const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, ny, 32); + const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE); stream->parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1); }); } diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index 1ff297218..31df1cb9e 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -255,7 +255,7 @@ namespace dpct void set_pitch(size_t pitch) { _pitch = pitch; } size_t get_x() { return _x; } - void set_x(size_t x) { _x = x; }; + void set_x(size_t x) { _x = x; } size_t get_y() { return _y; } void set_y(size_t y) { _y = y; } @@ -1056,7 +1056,7 @@ namespace dpct #error "Only support Windows and Linux." #endif next_free = mapped_address_space; - }; + } public: using buffer_id_t = int; @@ -1077,7 +1077,7 @@ namespace dpct #else #error "Only support Windows and Linux." #endif - }; + } mem_mgr(const mem_mgr &) = delete; mem_mgr &operator=(const mem_mgr &) = delete; @@ -2426,6 +2426,7 @@ namespace dpct b, ldb, beta, c, ldc, batch_size); break; } +#endif case detail::get_type_combination_id( library_data_t::real_int8, library_data_t::real_int8, library_data_t::real_int32, library_data_t::real_int32): @@ -2458,7 +2459,6 @@ namespace dpct batch_size); break; } -#endif case detail::get_type_combination_id( library_data_t::real_half, library_data_t::real_half, library_data_t::real_half, library_data_t::real_float): @@ -2595,6 +2595,7 @@ namespace dpct stride_c, batch_size); break; } +#endif case detail::get_type_combination_id( library_data_t::real_int8, library_data_t::real_int8, library_data_t::real_int32, library_data_t::real_int32): @@ -2623,7 +2624,6 @@ namespace dpct beta, c, ldc, stride_c, batch_size); break; } -#endif case detail::get_type_combination_id( library_data_t::real_half, library_data_t::real_half, library_data_t::real_half, library_data_t::real_float): diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp index 23227649e..3fbc4dd60 100644 --- a/ggml/src/ggml-sycl/mmvq.cpp +++ b/ggml/src/ggml-sycl/mmvq.cpp @@ -37,7 +37,7 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -85,7 +85,7 @@ static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -133,7 +133,7 @@ static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -181,7 +181,7 @@ static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -229,7 +229,7 @@ static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -277,7 +277,7 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -325,7 +325,7 @@ static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -373,7 +373,7 @@ static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -421,7 +421,7 @@ static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -470,7 +470,7 @@ static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { + for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -495,7 +495,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -519,7 +519,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -543,7 +543,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -567,7 +567,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -591,7 +591,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -615,7 +615,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -639,7 +639,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -663,7 +663,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -687,7 +687,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -711,7 +711,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -734,8 +734,8 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q_iq2_xxs_q8_1( + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq2_xxs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); }); @@ -759,8 +759,8 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q_iq2_xs_q8_1( + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq2_xs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); }); @@ -784,8 +784,8 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q_iq2_s_q8_1( + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq2_s_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); }); @@ -809,8 +809,8 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q_iq3_xxs_q8_1( + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq3_xxs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); }); @@ -833,8 +833,8 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q_iq3_s_q8_1( + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq3_s_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); }); @@ -858,7 +858,7 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q_iq1_s_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -879,7 +879,7 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q_iq1_m_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -901,7 +901,7 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { + [[intel::reqd_sub_group_size(WARP_SIZE)]] { mul_mat_vec_q_iq4_nl_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -923,8 +923,8 @@ static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(32)]] { - mul_mat_vec_q_iq4_xs_q8_1( + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq4_xs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); }); @@ -936,7 +936,7 @@ void ggml_sycl_op_mul_mat_vec_q( const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, float *dst_dd_i, const int64_t row_low, const int64_t row_high, - const int64_t src1_ncols, const int64_t src1_padded_row_size, + const int64_t src1_ncols, const int64_t src1_padded_col_size, const dpct::queue_ptr &stream) { const int64_t ne10 = src1->ne[0]; @@ -948,77 +948,80 @@ void ggml_sycl_op_mul_mat_vec_q( int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - + const size_t q8_1_ts = sizeof(block_q8_1); + const size_t q8_1_bs = QK8_1; // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the kernel writes into const int64_t nrows_dst = id == ctx.device ? ne00 : row_diff; - - switch (src0->type) { + for (int i = 0; i < src1_ncols; i++) + { + const size_t src1_ddq_i_offset = i * src1_padded_col_size * q8_1_ts / q8_1_bs; + const char* src1_ddq_i_bs = src1_ddq_i + src1_ddq_i_offset; + float* dst_dd_i_bs = dst_dd_i + i * dst->ne[0]; + switch (src0->type) { case GGML_TYPE_Q4_0: - mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_Q4_1: - mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_Q5_0: - mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_Q5_1: - mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_Q8_0: - mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_Q2_K: - mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_Q3_K: - mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_Q4_K: - mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_Q5_K: - mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_Q6_K: - mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_IQ1_S: - mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_IQ1_M: - mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_IQ2_XXS: - mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_IQ2_XS: - mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_IQ2_S: - mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_IQ3_XXS: - mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_IQ3_S: - mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_IQ4_NL: - mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; case GGML_TYPE_IQ4_XS: - mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; default: GGML_ASSERT(false); break; + } } - (void) src1; (void) dst; (void) src1_ddf_i; - (void) src1_ncols; - (void) src1_padded_row_size; } diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp new file mode 100644 index 000000000..e0c5dfeca --- /dev/null +++ b/ggml/src/ggml-sycl/norm.cpp @@ -0,0 +1,374 @@ +#include "norm.hpp" + +static void norm_f32(const float* x, float* dst, const int ncols, const float eps, + const sycl::nd_item<3>& item_ct1, sycl::float2* s_sum, int block_size) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + const int tid = item_ct1.get_local_id(2); + + const int nthreads = item_ct1.get_local_range(2); + const int nwarps = nthreads / WARP_SIZE; + assert(nwarps % WARP_SIZE == 0); + sycl::float2 mean_var = sycl::float2(0.f, 0.f); + + for (int col = tid; col < ncols; col += block_size) { + const float xi = x[row * ncols + col]; + mean_var.x() += xi; + mean_var.y() += xi * xi; + } + + // sum up partial sums + mean_var = warp_reduce_sum(mean_var, item_ct1); + if (block_size > WARP_SIZE) { + + int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = mean_var; + } + /* + DPCT1118:0: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + item_ct1.barrier(sycl::access::fence_space::local_space); + mean_var = 0.f; + int nreduce = nwarps / WARP_SIZE; + for (size_t i = 0; i < nreduce; i += 1) + { + mean_var += s_sum[lane_id + i * WARP_SIZE]; + } + mean_var = warp_reduce_sum(mean_var, item_ct1); + } + + const float mean = mean_var.x() / ncols; + const float var = mean_var.y() / ncols - mean * mean; + const float inv_std = sycl::rsqrt(var + eps); + + for (int col = tid; col < ncols; col += block_size) { + dst[row * ncols + col] = (x[row * ncols + col] - mean) * inv_std; + } +} + +static void group_norm_f32(const float* x, float* dst, const int group_size, const int ne_elements, const float eps, + const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) { + int start = item_ct1.get_group(2) * group_size; + int end = start + group_size; + const int nthreads = item_ct1.get_local_range(2); + const int nwarps = nthreads / WARP_SIZE; + assert(nwarps % WARP_SIZE == 0); + start += item_ct1.get_local_id(2); + int nreduce = nwarps / WARP_SIZE; + + if (end >= ne_elements) { + end = ne_elements; + } + + float tmp = 0.0f; // partial sum for thread in warp + + for (int j = start; j < end; j += block_size) { + tmp += x[j]; + } + + tmp = warp_reduce_sum(tmp, item_ct1); + if (block_size > WARP_SIZE) { + + int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + /* + DPCT1118:1: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:54: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + tmp = 0.f; + for (size_t i = 0; i < nreduce; i += 1) + { + tmp += s_sum[lane_id + i * WARP_SIZE]; + } + tmp = warp_reduce_sum(tmp, item_ct1); + } + + float mean = tmp / group_size; + tmp = 0.0f; + + for (int j = start; j < end; j += block_size) { + float xi = x[j] - mean; + dst[j] = xi; + tmp += xi * xi; + } + + tmp = warp_reduce_sum(tmp, item_ct1); + if (block_size > WARP_SIZE) { + + int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + /* + DPCT1118:2: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:55: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + tmp = 0.f; + for (size_t i = 0; i < nreduce; i += 1) + { + tmp += s_sum[lane_id + i * WARP_SIZE]; + } + tmp = warp_reduce_sum(tmp, item_ct1); + } + + float variance = tmp / group_size; + float scale = sycl::rsqrt(variance + eps); + for (int j = start; j < end; j += block_size) { + dst[j] *= scale; + } +} + +static void rms_norm_f32(const float* x, float* dst, const int ncols, const float eps, + const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + const int tid = item_ct1.get_local_id(2); + const int nthreads = item_ct1.get_local_range(2); + const int nwarps = nthreads / WARP_SIZE; + assert(nwarps % WARP_SIZE == 0); + float tmp = 0.0f; // partial sum for thread in warp + + for (int col = tid; col < ncols; col += block_size) { + const float xi = x[row * ncols + col]; + tmp += xi * xi; + } + + // sum up partial sums + tmp = warp_reduce_sum(tmp, item_ct1); + if (block_size > WARP_SIZE) { + + int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + /* + DPCT1118:3: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + item_ct1.barrier(sycl::access::fence_space::local_space); + int nreduce = nwarps / WARP_SIZE; + tmp = 0.f; + for (size_t i = 0; i < nreduce; i += 1) + { + tmp += s_sum[lane_id + i * WARP_SIZE]; + } + tmp = warp_reduce_sum(tmp, item_ct1); + } + + const float mean = tmp / ncols; + const float scale = sycl::rsqrt(mean + eps); + + for (int col = tid; col < ncols; col += block_size) { + dst[row * ncols + col] = scale * x[row * ncols + col]; + } +} + +static void norm_f32_sycl(const float* x, float* dst, const int ncols, + const int nrows, const float eps, + queue_ptr stream, int device) { + GGML_ASSERT(ncols % WARP_SIZE == 0); + if (ncols < 1024) { + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + stream->submit([&](sycl::handler& cgh) { + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + norm_f32(x, dst, ncols, eps, item_ct1, + nullptr, WARP_SIZE); + }); + }); + } + else { + const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + const sycl::range<3> block_dims(1, 1, work_group_size); + /* + DPCT1049:17: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + stream->submit([&](sycl::handler& cgh) { + sycl::local_accessor s_sum_acc_ct1( + sycl::range<1>(work_group_size / WARP_SIZE), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + norm_f32(x, dst, ncols, eps, item_ct1, + s_sum_acc_ct1.get_pointer(), work_group_size); + }); + }); + } +} + +static void group_norm_f32_sycl(const float* x, float* dst, + const int num_groups, const int group_size, + const int ne_elements, queue_ptr stream, int device) { + static const float eps = 1e-6f; + if (group_size < 1024) { + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + stream->submit([&](sycl::handler& cgh) { + const float eps_ct4 = eps; + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + group_norm_f32( + x, dst, group_size, ne_elements, eps_ct4, item_ct1, + nullptr, WARP_SIZE); + }); + }); + } + else { + const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + const sycl::range<3> block_dims(1, 1, work_group_size); + /* + DPCT1049:18: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + + stream->submit([&](sycl::handler& cgh) { + sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE), + cgh); + + const float eps_ct4 = eps; + + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + group_norm_f32(x, dst, group_size, ne_elements, + eps_ct4, item_ct1, + s_sum_acc_ct1.get_pointer(), work_group_size); + }); + }); + } +} + +static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, + const int nrows, const float eps, + queue_ptr stream, int device) { + GGML_ASSERT(ncols % WARP_SIZE == 0); + // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE); + if (ncols < 1024) { + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + stream->submit([&](sycl::handler& cgh) { + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + rms_norm_f32(x, dst, ncols, eps, item_ct1, + nullptr, WARP_SIZE); + }); + }); + } + else { + const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + const sycl::range<3> block_dims(1, 1, work_group_size); + /* + DPCT1049:19: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + stream->submit([&](sycl::handler& cgh) { + sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE), + cgh); + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(WARP_SIZE)]] { + rms_norm_f32(x, dst, ncols, eps, item_ct1, + s_sum_acc_ct1.get_pointer(), work_group_size); + }); + }); + } +} + +void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, const ggml_tensor* src1, + ggml_tensor* dst, const float* src0_dd, + const float* src1_dd, float* dst_dd, + const queue_ptr& main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream, ctx.device); + + (void)src1; + (void)dst; + (void)src1_dd; +} + +void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst, + const float* src0_dd, const float* src1_dd, + float* dst_dd, + const queue_ptr& main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + int num_groups = dst->op_params[0]; + int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); + group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream, ctx.device); + + (void)src1; + (void)dst; + (void)src1_dd; +} + +void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst, + const float* src0_dd, const float* src1_dd, + float* dst_dd, + const queue_ptr& main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream, ctx.device); + + (void)src1; + (void)dst; + (void)src1_dd; +} diff --git a/ggml/src/ggml-sycl/norm.hpp b/ggml/src/ggml-sycl/norm.hpp new file mode 100644 index 000000000..a9ad9156f --- /dev/null +++ b/ggml/src/ggml-sycl/norm.hpp @@ -0,0 +1,35 @@ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_NORM_HPP +#define GGML_SYCL_NORM_HPP + +#include "common.hpp" + +void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, const ggml_tensor* src1, + ggml_tensor* dst, const float* src0_dd, + const float* src1_dd, float* dst_dd, + const queue_ptr& main_stream); + +void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst, + const float* src0_dd, const float* src1_dd, + float* dst_dd, + const queue_ptr& main_stream); + +void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst, + const float* src0_dd, const float* src1_dd, + float* dst_dd, + const queue_ptr& main_stream); + +#endif // GGML_SYCL_NORM_HPP diff --git a/ggml/src/ggml-sycl/presets.hpp b/ggml/src/ggml-sycl/presets.hpp index fe9d41770..15ddcac1f 100644 --- a/ggml/src/ggml-sycl/presets.hpp +++ b/ggml/src/ggml-sycl/presets.hpp @@ -16,7 +16,7 @@ #define GGML_SYCL_MAX_STREAMS 8 #define GGML_SYCL_MAX_BUFFERS 256 -#define WARP_SIZE 32 +#define WARP_SIZE GGML_SYCL_WARP_SIZE #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses #define SYCL_GELU_BLOCK_SIZE 256 @@ -62,4 +62,5 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA #define MUL_MAT_SRC1_COL_STRIDE 128 +#define QK_WARP_SIZE 32 #endif // GGML_SYCL_PRESETS_HPP diff --git a/ggml/src/ggml-sycl/rope.cpp b/ggml/src/ggml-sycl/rope.cpp new file mode 100644 index 000000000..eabf1693e --- /dev/null +++ b/ggml/src/ggml-sycl/rope.cpp @@ -0,0 +1,275 @@ +#include "rope.hpp" + +struct rope_corr_dims { + float v[2]; +}; + +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low); + return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y)); +} + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static void rope_yarn( + float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale); + } + *cos_theta = sycl::cos(theta) * mscale; + *sin_theta = sycl::sin(theta) * mscale; +} + +template +static void rope_norm( + const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors, + const sycl::nd_item<3> &item_ct1) { + const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); + + if (i0 >= ne0) { + return; + } + + const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i0 >= n_dims) { + const int i = row*ne0 + i0; + + dst[i + 0] = x[i + 0]; + dst[i + 1] = x[i + 1]; + + return; + } + + const int i = row*ne0 + i0; + const int i2 = row/p_delta_rows; + + const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f); + + const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f; + + float cos_theta; + float sin_theta; + + rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + + const float x0 = x[i + 0]; + const float x1 = x[i + 1]; + + dst[i + 0] = x0*cos_theta - x1*sin_theta; + dst[i + 1] = x0*sin_theta + x1*cos_theta; +} + +template +static void rope_neox( + const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors, + const sycl::nd_item<3> &item_ct1) { + const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); + + if (i0 >= ne0) { + return; + } + + const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i0 >= n_dims) { + const int i = row*ne0 + i0; + + dst[i + 0] = x[i + 0]; + dst[i + 1] = x[i + 1]; + + return; + } + + const int i = row*ne0 + i0/2; + const int i2 = row/p_delta_rows; + + const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f); + + const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f; + + float cos_theta; + float sin_theta; + + rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + + const float x0 = x[i + 0]; + const float x1 = x[i + n_dims/2]; + + dst[i + 0] = x0*cos_theta - x1*sin_theta; + dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta; +} + +template +static void rope_norm_sycl( + const T *x, T *dst, int ne0, int n_dims, int nr, const int32_t *pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, queue_ptr stream) { + GGML_ASSERT(ne0 % 2 == 0); + const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); + const int num_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); + const sycl::range<3> block_nums(1, num_blocks_x, nr); + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + if (freq_factors == nullptr) { + /* + DPCT1049:40: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_norm(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, + ext_factor, attn_factor, corr_dims, theta_scale, freq_factors, + item_ct1); + }); + } else { + /* + DPCT1049:41: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_norm(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, + ext_factor, attn_factor, corr_dims, theta_scale, freq_factors, + item_ct1); + }); + } +} + +template +static void rope_neox_sycl( + const T *x, T *dst, int ne0, int n_dims, int nr, const int32_t *pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, queue_ptr stream) { + GGML_ASSERT(ne0 % 2 == 0); + const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); + const int num_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); + const sycl::range<3> block_nums(1, num_blocks_x, nr); + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + if (freq_factors == nullptr) { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ne0, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, freq_factors, + item_ct1); + }); + } else { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ne0, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, freq_factors, + item_ct1); + }); + } +} + +void ggml_sycl_op_rope( + ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, float *dst_dd, const queue_ptr &main_stream) { + const ggml_tensor * src2 = dst->src[2]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); + GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + GGML_ASSERT(src0->type == dst->type); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t nr = ggml_nrows(src0); + + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + //const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + + // RoPE alteration for extended context + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; + + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + + const bool is_neox = mode & 2; + + const int32_t * pos = (const int32_t *) src1_dd; + + const float * freq_factors = nullptr; + if (src2 != nullptr) { + freq_factors = (const float *) src2->data; + } + + rope_corr_dims corr_dims; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v); + + // compute + if (is_neox) { + if (src0->type == GGML_TYPE_F32) { + rope_neox_sycl( + (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, freq_factors, main_stream + ); + } else if (src0->type == GGML_TYPE_F16) { + rope_neox_sycl( + (const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, freq_factors, main_stream + ); + } else { + GGML_ASSERT(false); + } + } else { + if (src0->type == GGML_TYPE_F32) { + rope_norm_sycl( + (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, freq_factors, main_stream + ); + } else if (src0->type == GGML_TYPE_F16) { + rope_norm_sycl( + (const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, freq_factors, main_stream + ); + } else { + GGML_ASSERT(false); + } + } + + (void) src1; + (void) dst; + (void) src1_dd; +} diff --git a/ggml/src/ggml-sycl/rope.hpp b/ggml/src/ggml-sycl/rope.hpp new file mode 100644 index 000000000..00354c313 --- /dev/null +++ b/ggml/src/ggml-sycl/rope.hpp @@ -0,0 +1,22 @@ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_ROPE_HPP +#define GGML_SYCL_ROPE_HPP + +#include "common.hpp" + +void ggml_sycl_op_rope( + ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, float *dst_dd, const queue_ptr &main_stream); + +#endif // GGML_SYCL_ROPE_HPP diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp new file mode 100644 index 000000000..e624b6ba3 --- /dev/null +++ b/ggml/src/ggml-sycl/softmax.cpp @@ -0,0 +1,250 @@ +#include "norm.hpp" + +template +static void soft_max_f32(const float * x, const float * mask, float * dst, const int ncols_par, + const int nrows_y, const float scale, const float max_bias, const float m0, + const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) { + const int ncols = ncols_template == 0 ? ncols_par : ncols_template; + + const int tid = item_ct1.get_local_id(2); + const int rowx = item_ct1.get_group(2); + const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension + + const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template; + + const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + const int nthreads = block_size; + const int nwarps = nthreads / WARP_SIZE; + int nreduce = nwarps / WARP_SIZE; + float slope = 1.0f; + + // ALiBi + if (max_bias > 0.0f) { + const uint32_t h = rowx/nrows_y; // head index + + const float base = h < n_head_log2 ? m0 : m1; + const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = sycl::pow(base, float(exp)); + } + + float *vals = vals_smem ? buf + std::max(nwarps, WARP_SIZE) : dst + rowx * ncols; + float max_val = -INFINITY; + + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + break; + } + + const int ix = rowx*ncols + col; + const int iy = rowy*ncols + col; + + const float val = x[ix]*scale + (mask ? slope*mask[iy] : 0.0f); + + vals[col] = val; + max_val = sycl::max(max_val, val); + } + + // find the max value in the block + max_val = warp_reduce_max(max_val, item_ct1); + if (block_size > WARP_SIZE) { + if (warp_id == 0) { + buf[lane_id] = -INFINITY; + for (size_t i = 1; i < nreduce; i += 1) + buf[lane_id + i * WARP_SIZE] = -INFINITY; + } + item_ct1.barrier(sycl::access::fence_space::local_space); + + if (lane_id == 0) { + buf[warp_id] = max_val; + } + item_ct1.barrier(sycl::access::fence_space::local_space); + max_val = buf[lane_id]; + for (size_t i = 1; i < nreduce; i += 1) + { + max_val = std::max(max_val, buf[lane_id + i * WARP_SIZE]); + } + max_val = warp_reduce_max(max_val, item_ct1); + } + + float tmp = 0.f; +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + if (ncols_template == 0 && col >= ncols) { + break; + } + + const float val = sycl::native::exp(vals[col] - max_val); + tmp += val; + vals[col] = val; + } + + // find the sum of exps in the block + tmp = warp_reduce_sum(tmp, item_ct1); + if (block_size > WARP_SIZE) { + item_ct1.barrier(sycl::access::fence_space::local_space); + if (warp_id == 0) { + buf[lane_id] = 0.f; + for (size_t i = 1; i < nreduce; i += 1) + buf[lane_id + i * WARP_SIZE] = 0.f; + } + item_ct1.barrier(sycl::access::fence_space::local_space); + + if (lane_id == 0) { + buf[warp_id] = tmp; + } + item_ct1.barrier(sycl::access::fence_space::local_space); + + tmp = buf[lane_id]; + for (size_t i = 1; i < nreduce; i += 1) + { + tmp += buf[lane_id + i * WARP_SIZE]; + } + tmp = warp_reduce_sum(tmp, item_ct1); + } + + const float inv_sum = 1.f / tmp; + +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + return; + } + + const int idst = rowx*ncols + col; + dst[idst] = vals[col] * inv_sum; + } +} + +template +static void soft_max_f32_submitter(const float * x, const float * mask, float * dst, const int ncols_par, + const int nrows_y, const float scale, const float max_bias, const float m0, + const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims, + const size_t n_local_scratch, queue_ptr stream) { + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor local_buf_acc(n_local_scratch, cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { + soft_max_f32(x, mask, dst, ncols_par, + nrows_y, scale, max_bias, m0, + m1, n_head_log2, item_ct1, + local_buf_acc.get_pointer()); + }); + }); +} + +static void soft_max_f32_sycl(const float * x, const float * mask, + float * dst, const int ncols_x, const int nrows_x, + const int nrows_y, const float scale, const float max_bias, + queue_ptr stream, int device) { + int nth = WARP_SIZE; + int max_block_size = ggml_sycl_info().max_work_group_sizes[device]; + while (nth < ncols_x && nth < max_block_size) nth *= 2; + if (nth>max_block_size) nth = max_block_size; + + const sycl::range<3> block_dims(1, 1, nth); + const sycl::range<3> block_nums(1, 1, nrows_x); + const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE); + + const uint32_t n_head_kv = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + const size_t local_mem_size = stream->get_device().get_info(); + if (n_local_scratch*sizeof(float) < local_mem_size) { + if (ncols_x > max_block_size) { + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + return; + } + switch (ncols_x) { + case 32: + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 64: + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 128: + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 256: + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 512: + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 1024: + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 2048: + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 4096: + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + default: + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + } + } else { + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, WARP_SIZE, stream); + } +} + +void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + +#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 support") +#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional + + const int64_t ne00 = src0->ne[0]; + const int64_t nrows_x = ggml_nrows(src0); + const int64_t nrows_y = src0->ne[1]; + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, dst->op_params + 1, sizeof(float)); + + soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, + nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device); +} diff --git a/ggml/src/ggml-sycl/softmax.hpp b/ggml/src/ggml-sycl/softmax.hpp new file mode 100644 index 000000000..bdb8f712e --- /dev/null +++ b/ggml/src/ggml-sycl/softmax.hpp @@ -0,0 +1,24 @@ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_SOFTMAX_HPP +#define GGML_SYCL_SOFTMAX_HPP + +#include "common.hpp" + +void ggml_sycl_op_soft_max(ggml_backend_sycl_context &ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream); + +#endif // GGML_SYCL_SOFTMAX_HPP diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp index 5e2e82546..d2dccade2 100644 --- a/ggml/src/ggml-sycl/vecdotq.hpp +++ b/ggml/src/ggml-sycl/vecdotq.hpp @@ -820,7 +820,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, #if QK_K == 256 const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; -#if QR2_XXS == 8 const int ib32 = iqs; const uint16_t * q2 = bq2->qs + 4*ib32; const uint8_t * aux8 = (const uint8_t *)q2; @@ -838,26 +837,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, } const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.25f; return d * sumi; -#else - // iqs is 0...15 - const int ib32 = iqs/2; - const int il = iqs%2; - const uint16_t * q2 = bq2->qs + 4*ib32; - const uint8_t * aux8 = (const uint8_t *)q2; - const uint8_t * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]); - const uint8_t * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]); - const uint32_t aux32 = q2[2] | (q2[3] << 16); - const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * bq8_1[ib32].ds[0] * 0.25f; - const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127]; - const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127]; - const int8_t * q8 = bq8_1[ib32].qs + 16*il; - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 8; ++j) { - sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1); - sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1); - } - return d * (sumi1 + sumi2); -#endif #else assert(false); return 0.f; diff --git a/ggml/src/ggml-vulkan-shaders.hpp b/ggml/src/ggml-vulkan-shaders.hpp index 01ff66f71..f0c4c6baf 100644 --- a/ggml/src/ggml-vulkan-shaders.hpp +++ b/ggml/src/ggml-vulkan-shaders.hpp @@ -144954,4 +144954,3 @@ unsigned char sum_rows_f32_data[] = { }; const uint64_t sum_rows_f32_len = 2112; - diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index f5502afbe..bc91ac3a7 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -5312,7 +5312,7 @@ void ggml_mul_mat_set_prec( as -> [cols, rows, n_expert] ids -> [n_experts_used, n_tokens] (i32) b -> [cols, n_expert_used, n_tokens] - c -> [cols, n_expert_used, n_tokens] + c -> [rows, n_expert_used, n_tokens] in b, n_experts_used can be broadcasted to match the n_expert_used of ids diff --git a/gguf-py/README.md b/gguf-py/README.md index a04c22759..bc46d6e1d 100644 --- a/gguf-py/README.md +++ b/gguf-py/README.md @@ -3,7 +3,7 @@ This is a Python package for writing binary files in the [GGUF](https://github.com/ggerganov/ggml/pull/302) (GGML Universal File) format. -See [convert-llama-hf-to-gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert-hf-to-gguf.py) +See [convert_hf_to_gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py) as an example for its usage. ## Installation @@ -15,13 +15,13 @@ pip install gguf [examples/writer.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model. -[scripts/gguf-dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-dump.py) — Dumps a GGUF file's metadata to the console. +[scripts/gguf_dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_dump.py) — Dumps a GGUF file's metadata to the console. -[scripts/gguf-set-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-set-metadata.py) — Allows changing simple metadata values in a GGUF file by key. +[scripts/gguf_set_metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_set_metadata.py) — Allows changing simple metadata values in a GGUF file by key. -[scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files. +[scripts/gguf_convert_endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_convert_endian.py) — Allows converting the endianness of GGUF files. -[scripts/gguf-new-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-new-metadata.py) — Copies a GGUF file with added/modified/removed metadata values. +[scripts/gguf_new_metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_new_metadata.py) — Copies a GGUF file with added/modified/removed metadata values. ## Development Maintainers who participate in development of this package are advised to install it in editable mode: diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 9bfa891d5..a95a44237 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -66,6 +66,7 @@ class Keys: Q_LORA_RANK = "{arch}.attention.q_lora_rank" KV_LORA_RANK = "{arch}.attention.kv_lora_rank" REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count" + SLIDING_WINDOW = "{arch}.attention.sliding_window" class Rope: DIMENSION_COUNT = "{arch}.rope.dimension_count" @@ -119,7 +120,6 @@ class Keys: MIDDLE_ID = "tokenizer.ggml.middle_token_id" EOT_ID = "tokenizer.ggml.eot_token_id" - # # recommended mapping of model tensor names for storage in gguf # @@ -159,10 +159,13 @@ class MODEL_ARCH(IntEnum): COMMAND_R = auto() DBRX = auto() OLMO = auto() + OPENELM = auto() ARCTIC = auto() DEEPSEEK2 = auto() + CHATGLM = auto() BITNET = auto() T5 = auto() + JAIS = auto() class MODEL_TENSOR(IntEnum): @@ -283,10 +286,13 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.COMMAND_R: "command-r", MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", + MODEL_ARCH.OPENELM: "openelm", MODEL_ARCH.ARCTIC: "arctic", MODEL_ARCH.DEEPSEEK2: "deepseek2", + MODEL_ARCH.CHATGLM: "chatglm", MODEL_ARCH.BITNET: "bitnet", MODEL_ARCH.T5: "t5", + MODEL_ARCH.JAIS: "jais", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -858,6 +864,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.OPENELM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.ARCTIC: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -906,6 +925,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, ], + MODEL_ARCH.CHATGLM : [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.BITNET: [ MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, @@ -953,6 +984,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ENC_FFN_UP, MODEL_TENSOR.ENC_OUTPUT_NORM, ], + MODEL_ARCH.JAIS: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_UP, + ], # TODO } @@ -990,6 +1033,9 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.CHATGLM: [ + MODEL_TENSOR.ROPE_FREQS, + ], } # diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 1aeb0d9b0..cf9554162 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -480,8 +480,11 @@ class GGUFWriter: def add_leading_dense_block_count(self, length: int) -> None: self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length) - def add_feed_forward_length(self, length: int) -> None: - self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + def add_feed_forward_length(self, length: int | Sequence[int]) -> None: + if isinstance(length, int): + self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + else: + self.add_array(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) def add_expert_feed_forward_length(self, length: int) -> None: self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length) @@ -495,11 +498,17 @@ class GGUFWriter: def add_decoder_start_token_id(self, id: int) -> None: self.add_uint32(Keys.LLM.DECODER_START_TOKEN_ID.format(arch=self.arch), id) - def add_head_count(self, count: int) -> None: - self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) + def add_head_count(self, count: int | Sequence[int]) -> None: + if isinstance(count, int): + self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) + else: + self.add_array(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) - def add_head_count_kv(self, count: int) -> None: - self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + def add_head_count_kv(self, count: int | Sequence[int]) -> None: + if isinstance(count, int): + self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + else: + self.add_array(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) def add_key_length(self, length: int) -> None: self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length) @@ -552,6 +561,9 @@ class GGUFWriter: def add_relative_attn_buckets_count(self, value: int) -> None: self.add_uint32(Keys.Attention.REL_BUCKETS_COUNT.format(arch=self.arch), value) + def add_sliding_window(self, value: int) -> None: + self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value) + def add_pooling_type(self, value: PoolingType) -> None: self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 0bed43939..7264240f5 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -10,7 +10,7 @@ class TensorNameMap: # Token embeddings MODEL_TENSOR.TOKEN_EMBD: ( "gpt_neox.embed_in", # gptneox - "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx + "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais "transformer.word_embeddings", # falcon "word_embeddings", # bloom "model.embed_tokens", # llama-hf @@ -24,6 +24,8 @@ class TensorNameMap: "backbone.embedding", # mamba "backbone.embeddings", # mamba-hf "transformer.in_out_embed", # Grok + "embedding.word_embeddings", # chatglm + "transformer.token_embeddings", # openelm "shared", # t5 ), @@ -37,6 +39,7 @@ class TensorNameMap: "word_embeddings_layernorm", # bloom "embeddings.LayerNorm", # bert "emb_ln", # nomic-bert + "transformer.norm", # openelm ), # Position embeddings @@ -49,16 +52,17 @@ class TensorNameMap: # Output MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx + "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 + "output_layer", # chatglm ), # Output norm MODEL_TENSOR.OUTPUT_NORM: ( "gpt_neox.final_layer_norm", # gptneox - "transformer.ln_f", # gpt2 gpt-j falcon + "transformer.ln_f", # gpt2 gpt-j falcon jais "model.norm", # llama-hf baichuan internlm2 "norm", # llama-pth "transformer.norm_f", # mpt dbrx @@ -69,11 +73,14 @@ class TensorNameMap: "model.norm_f", # mamba-qbert "backbone.norm_f", # mamba "transformer.rms_norm", # Grok + "encoder.final_layernorm", # chatglm + "transformer.norm", # openelm ), # Rope frequencies MODEL_TENSOR.ROPE_FREQS: ( "rope.freqs", # llama-pth + "rotary_pos_emb.inv_freq", # chatglm ), } @@ -81,7 +88,7 @@ class TensorNameMap: # Attention norm MODEL_TENSOR.ATTN_NORM: ( "gpt_neox.layers.{bid}.input_layernorm", # gptneox - "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen + "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen jais "transformer.blocks.{bid}.norm_1", # mpt "transformer.h.{bid}.input_layernorm", # falcon7b "h.{bid}.input_layernorm", # bloom @@ -98,6 +105,8 @@ class TensorNameMap: "backbone.layers.{bid}.norm", # mamba "transformer.decoder_layer.{bid}.rms_norm", # Grok "transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx + "encoder.layers.{bid}.input_layernorm", # chatglm + "transformer.layers.{bid}.attn_norm", # openelm ), # Attention norm 2 @@ -109,7 +118,7 @@ class TensorNameMap: # Attention query-key-value MODEL_TENSOR.ATTN_QKV: ( "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox - "transformer.h.{bid}.attn.c_attn", # gpt2 qwen + "transformer.h.{bid}.attn.c_attn", # gpt2 qwen jais "transformer.blocks.{bid}.attn.Wqkv", # mpt "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx "transformer.h.{bid}.self_attention.query_key_value", # falcon @@ -119,7 +128,9 @@ class TensorNameMap: "h.{bid}.attn.c_attn", # gpt2 "transformer.h.{bid}.mixer.Wqkv", # phi2 "encoder.layers.{bid}.attn.Wqkv", # nomic-bert - "model.layers.{bid}.self_attn.qkv_proj" # phi3 + "model.layers.{bid}.self_attn.qkv_proj", # phi3 + "encoder.layers.{bid}.self_attention.query_key_value", # chatglm + "transformer.layers.{bid}.attn.qkv_proj", # openelm ), # Attention query @@ -130,7 +141,7 @@ class TensorNameMap: "transformer.h.{bid}.attn.q_proj", # gpt-j "model.layers.layers.{bid}.self_attn.q_proj", # plamo "model.layers.{bid}.attention.wq", # internlm2 - "transformer.decoder_layer.{bid}.multi_head_attention.query" # Grok + "transformer.decoder_layer.{bid}.multi_head_attention.query",# Grok ), # Attention key @@ -142,7 +153,7 @@ class TensorNameMap: "transformer.h.{bid}.attn.k", # refact "model.layers.layers.{bid}.self_attn.k_proj", # plamo "model.layers.{bid}.attention.wk", # internlm2 - "transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok + "transformer.decoder_layer.{bid}.multi_head_attention.key",# Grok ), # Attention value @@ -160,7 +171,7 @@ class TensorNameMap: # Attention output MODEL_TENSOR.ATTN_OUT: ( "gpt_neox.layers.{bid}.attention.dense", # gptneox - "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen + "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen jais "transformer.blocks.{bid}.attn.out_proj", # mpt "transformer.h.{bid}.self_attention.dense", # falcon "h.{bid}.self_attention.dense", # bloom @@ -177,6 +188,8 @@ class TensorNameMap: "encoder.layers.{bid}.attn.out_proj", # nomic-bert "transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx + "encoder.layers.{bid}.self_attention.dense", # chatglm + "transformer.layers.{bid}.attn.out_proj", # openelm ), # Attention output norm @@ -202,7 +215,7 @@ class TensorNameMap: # Feed-forward norm MODEL_TENSOR.FFN_NORM: ( "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox - "transformer.h.{bid}.ln_2", # gpt2 refact qwen + "transformer.h.{bid}.ln_2", # gpt2 refact qwen jais "h.{bid}.post_attention_layernorm", # bloom "transformer.blocks.{bid}.norm_2", # mpt "model.layers.{bid}.post_attention_layernorm", # llama-hf @@ -212,6 +225,8 @@ class TensorNameMap: "h.{bid}.ln_2", # gpt2 "model.layers.{bid}.ffn_norm", # internlm2 "transformer.decoder_layer.{bid}.rms_norm_2", # Grok + "encoder.layers.{bid}.post_attention_layernorm", # chatglm + "transformer.layers.{bid}.ffn_norm", # openelm ), # Post feed-forward norm @@ -239,7 +254,7 @@ class TensorNameMap: # Feed-forward up MODEL_TENSOR.FFN_UP: ( "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox - "transformer.h.{bid}.mlp.c_fc", # gpt2 + "transformer.h.{bid}.mlp.c_fc", # gpt2 jais "transformer.blocks.{bid}.ffn.up_proj", # mpt "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon "h.{bid}.mlp.dense_h_to_4h", # bloom @@ -261,6 +276,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.c_fc", # starcoder2 "encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2 "model.layers.{bid}.residual_mlp.w3", # arctic + "encoder.layers.{bid}.mlp.dense_h_to_4h", # chatglm ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -285,6 +301,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.gate_proj", # llama-hf refact "layers.{bid}.feed_forward.w1", # llama-pth "transformer.h.{bid}.mlp.w2", # qwen + "transformer.h.{bid}.mlp.c_fc2", # jais "model.layers.layers.{bid}.mlp.gate_proj", # plamo "model.layers.{bid}.feed_forward.w1", # internlm2 "encoder.layers.{bid}.mlp.fc12", # nomic-bert @@ -308,7 +325,7 @@ class TensorNameMap: # Feed-forward down MODEL_TENSOR.FFN_DOWN: ( "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox - "transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen + "transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen jais "transformer.blocks.{bid}.ffn.down_proj", # mpt "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon "h.{bid}.mlp.dense_4h_to_h", # bloom @@ -326,8 +343,10 @@ class TensorNameMap: "encoder.layers.{bid}.mlp.fc2", # nomic-bert "model.layers.{bid}.mlp.c_proj", # starcoder2 "encoder.layer.{bid}.mlp.wo", # jina-bert-v2 + "transformer.layers.{bid}.ffn.proj_2", # openelm "model.layers.{bid}.residual_mlp.w2", # arctic "encoder.layer.{bid}.mlp.down_layer", # jina-bert-v2 + "encoder.layers.{bid}.mlp.dense_4h_to_h", # chatglm ), MODEL_TENSOR.FFN_DOWN_EXP: ( @@ -347,7 +366,8 @@ class TensorNameMap: "model.layers.{bid}.self_attn.q_layernorm", # persimmon "model.layers.{bid}.self_attn.q_norm", # cohere "transformer.blocks.{bid}.attn.q_ln", # sea-lion - "encoder.layer.{bid}.attention.self.layer_norm_q" # jina-bert-v2 + "encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2 + "transformer.layers.{bid}.attn.q_norm", # openelm ), MODEL_TENSOR.ATTN_K_NORM: ( @@ -355,7 +375,8 @@ class TensorNameMap: "model.layers.{bid}.self_attn.k_layernorm", # persimmon "model.layers.{bid}.self_attn.k_norm", # cohere "transformer.blocks.{bid}.attn.k_ln", # sea-lion - "encoder.layer.{bid}.attention.self.layer_norm_k" # jina-bert-v2 + "encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2 + "transformer.layers.{bid}.attn.k_norm", # openelm ), MODEL_TENSOR.ROPE_FREQS: ( diff --git a/gguf-py/scripts/__init__.py b/gguf-py/scripts/__init__.py index 1ad45639a..f9d29cb69 100644 --- a/gguf-py/scripts/__init__.py +++ b/gguf-py/scripts/__init__.py @@ -1,13 +1,4 @@ -import os - -from importlib import import_module - - -os.environ["NO_LOCAL_GGUF"] = "TRUE" - -gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main -gguf_dump_entrypoint = import_module("scripts.gguf-dump").main -gguf_set_metadata_entrypoint = import_module("scripts.gguf-set-metadata").main -gguf_new_metadata_entrypoint = import_module("scripts.gguf-new-metadata").main - -del import_module, os +from .gguf_convert_endian import main as gguf_convert_endian_entrypoint +from .gguf_dump import main as gguf_dump_entrypoint +from .gguf_set_metadata import main as gguf_set_metadata_entrypoint +from .gguf_new_metadata import main as gguf_new_metadata_entrypoint diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf_convert_endian.py similarity index 100% rename from gguf-py/scripts/gguf-convert-endian.py rename to gguf-py/scripts/gguf_convert_endian.py diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf_dump.py similarity index 100% rename from gguf-py/scripts/gguf-dump.py rename to gguf-py/scripts/gguf_dump.py diff --git a/gguf-py/scripts/gguf_hash.py b/gguf-py/scripts/gguf_hash.py new file mode 100755 index 000000000..956775182 --- /dev/null +++ b/gguf-py/scripts/gguf_hash.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import uuid +import hashlib + +import logging +import argparse +import os +import sys +from pathlib import Path + +from tqdm import tqdm + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFReader # noqa: E402 + + +logger = logging.getLogger("gguf-hash") + +# UUID_NAMESPACE_LLAMA_CPP = uuid.uuid5(uuid.NAMESPACE_URL, 'en.wikipedia.org/wiki/Llama.cpp') +UUID_NAMESPACE_LLAMA_CPP = uuid.UUID('ef001206-dadc-5f6d-a15f-3359e577d4e5') + + +# For more information about what field.parts and field.data represent, +# please see the comments in the modify_gguf.py example. +def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar) -> None: + sha1 = hashlib.sha1() + uuidv5_sha1 = hashlib.sha1() + uuidv5_sha1.update(UUID_NAMESPACE_LLAMA_CPP.bytes) + + # Total Weight Calculation For Progress Bar + total_weights = 0 + for n, tensor in enumerate(reader.tensors, 1): + + # We don't need these + if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): + continue + + # Calculate Tensor Volume + sum_weights_in_tensor = 1 + for dim in tensor.shape: + sum_weights_in_tensor *= dim + total_weights += sum_weights_in_tensor + + # Hash Progress Bar + bar = tqdm(desc="Hashing", total=total_weights, unit="weights", unit_scale=True, disable=disable_progress_bar) + + # Hashing Process + for n, tensor in enumerate(reader.tensors, 1): + + # We don't need these + if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): + continue + + # Progressbar + sum_weights_in_tensor = 1 + for dim in tensor.shape: + sum_weights_in_tensor *= dim + bar.update(sum_weights_in_tensor) + + sha1_layer = hashlib.sha1() + sha1_layer.update(tensor.data) + sha1.update(tensor.data) + uuidv5_sha1.update(tensor.data) + print("sha1 {0} {1}:{2}".format(sha1_layer.hexdigest(), filename, tensor.name)) # noqa: NP100 + + # Flush Hash Progress Bar + bar.close() + + # Display Hash Output + print("sha1 {0} {1}".format(sha1.hexdigest(), filename)) # noqa: NP100 + print("UUIDv5 {0} {1}".format(uuid.UUID(bytes=uuidv5_sha1.digest()[:16], version=5), filename)) # noqa: NP100 + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dump GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + parser.add_argument("--progressbar", action="store_true", help="enable progressbar") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + reader = GGUFReader(args.model, 'r') + gguf_hash(reader, args.model, not args.progressbar) + + +if __name__ == '__main__': + main() diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf_new_metadata.py similarity index 100% rename from gguf-py/scripts/gguf-new-metadata.py rename to gguf-py/scripts/gguf_new_metadata.py diff --git a/gguf-py/scripts/gguf-set-metadata.py b/gguf-py/scripts/gguf_set_metadata.py similarity index 100% rename from gguf-py/scripts/gguf-set-metadata.py rename to gguf-py/scripts/gguf_set_metadata.py diff --git a/include/llama.h b/include/llama.h index cafeafb85..bb4b05ba6 100644 --- a/include/llama.h +++ b/include/llama.h @@ -88,7 +88,10 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_DBRX = 13, LLAMA_VOCAB_PRE_TYPE_SMAUG = 14, LLAMA_VOCAB_PRE_TYPE_PORO = 15, - LLAMA_VOCAB_PRE_TYPE_VIKING = 16, + LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16, + LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17, + LLAMA_VOCAB_PRE_TYPE_VIKING = 18, + LLAMA_VOCAB_PRE_TYPE_JAIS = 19, }; // note: these values should be synchronized with ggml_rope @@ -179,6 +182,12 @@ extern "C" { LLAMA_POOLING_TYPE_LAST = 3, }; + enum llama_attention_type { + LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1, + LLAMA_ATTENTION_TYPE_CAUSAL = 0, + LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1, + }; + enum llama_split_mode { LLAMA_SPLIT_MODE_NONE = 0, // single GPU LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs @@ -296,6 +305,7 @@ extern "C" { enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id + enum llama_attention_type attention_type; // attention type to use for embeddings // ref: https://github.com/ggerganov/llama.cpp/pull/2054 float rope_freq_base; // RoPE base frequency, 0 = from model @@ -484,6 +494,13 @@ extern "C" { // Get a llama model tensor LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name); + // Returns true if the model contains an encoder that requires llama_encode() call + LLAMA_API bool llama_model_has_encoder(const struct llama_model * model); + + // For encoder-decoder models, this function returns id of the token that must be provided + // to the decoder to start generating output sequence. For other models, it returns -1. + LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model); + // Returns 0 on success LLAMA_API uint32_t llama_model_quantize( const char * fname_inp, @@ -769,6 +786,14 @@ extern "C" { // Frees a batch of tokens allocated with llama_batch_init() LLAMA_API void llama_batch_free(struct llama_batch batch); + // Processes a batch of tokens with the ecoder part of the encoder-decoder model. + // Stores the encoder output internally for later use by the decoder cross-attention layers. + // 0 - success + // < 0 - error + LLAMA_API int32_t llama_encode( + struct llama_context * ctx, + struct llama_batch batch); + // Positive return values does not mean a fatal error, but rather a warning. // 0 - success // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context) @@ -881,6 +906,7 @@ extern "C" { /// @param tokens The tokens pointer must be large enough to hold the resulting tokens. /// @return Returns the number of tokens on success, no more than n_tokens_max /// @return Returns a negative number on failure - the number of tokens that would have been returned + /// @param add_special Allow to add BOS and EOS tokens if model is configured to do so. /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated /// as plaintext. Does not insert a leading space. LLAMA_API int32_t llama_tokenize( @@ -895,15 +921,31 @@ extern "C" { // Token Id -> Piece. // Uses the vocabulary in the provided context. // Does not write null terminator to the buffer. - // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens. + // User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix') // @param special If true, special tokens are rendered in the output. LLAMA_API int32_t llama_token_to_piece( const struct llama_model * model, llama_token token, char * buf, int32_t length, + int32_t lstrip, bool special); + /// @details Convert the provided tokens into text (inverse of llama_tokenize()). + /// @param text The char pointer must be large enough to hold the resulting text. + /// @return Returns the number of chars/bytes on success, no more than text_len_max. + /// @return Returns a negative number on failure - the number of chars/bytes that would have been returned. + /// @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so. + /// @param unparse_special If true, special tokens are rendered in the output. + LLAMA_API int32_t llama_detokenize( + const struct llama_model * model, + const llama_token * tokens, + int32_t n_tokens, + char * text, + int32_t text_len_max, + bool remove_special, + bool unparse_special); + /// Apply chat template. Inspired by hf apply_chat_template() on python. /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model" /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template diff --git a/models/ggml-vocab-bert-bge.gguf.inp b/models/ggml-vocab-bert-bge.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-bert-bge.gguf.inp +++ b/models/ggml-vocab-bert-bge.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-bert-bge.gguf.out b/models/ggml-vocab-bert-bge.gguf.out index e4a76cdb0..a62566ce7 100644 --- a/models/ggml-vocab-bert-bge.gguf.out +++ b/models/ggml-vocab-bert-bge.gguf.out @@ -31,6 +31,7 @@ 1027 1005 3690 7592 1010 1061 1005 2035 999 2129 2024 2017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995 + 999 999 999 999 999 999 1017 3943 21211 @@ -40,4 +41,6 @@ 21211 22394 22394 21211 22394 22394 2509 21211 22394 22394 22394 + 12731 2050 19710 + 5860 18117 100 1006 3671 1007 100 1006 3674 7861 29147 2483 9530 16280 23854 1007 100 100 1017 3943 21211 21211 2509 21211 22394 21211 22394 2509 21211 22394 22394 21211 22394 22394 2509 1017 1012 1017 1017 1012 1012 1017 1017 1012 1012 1012 1017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995 1011 1011 1011 1011 1011 1011 1027 1027 1027 1027 1027 1027 1027 1192 15290 29754 14150 1192 10260 1181 29755 29436 29741 10260 16856 29747 23925 10325 1005 1005 1005 1005 1005 1005 1036 1036 1036 1036 1036 1036 1036 1000 1000 1000 1000 1012 1012 1012 1012 1012 1012 999 999 999 999 999 999 1029 1029 1029 1029 1029 1029 1045 1005 2310 2042 1005 2409 2002 1005 1055 2045 1010 1005 2128 2017 2469 1029 1005 1049 2025 2469 1045 1005 2222 2191 2009 1010 1005 1040 2017 2066 2070 5572 1029 2057 1005 2310 1037 1005 2222 diff --git a/models/ggml-vocab-command-r.gguf.inp b/models/ggml-vocab-command-r.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-command-r.gguf.inp +++ b/models/ggml-vocab-command-r.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-command-r.gguf.out b/models/ggml-vocab-command-r.gguf.out index cc4277daa..3f6b41888 100644 --- a/models/ggml-vocab-command-r.gguf.out +++ b/models/ggml-vocab-command-r.gguf.out @@ -31,6 +31,7 @@ 206 1857 14 4515 28339 19 1770 14 1954 8 4070 1955 1933 80503 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372 + 57178 10251 26 26 26 26 26 26 @@ -40,4 +41,6 @@ 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 + 42 30719 12584 + 3642 4388 127731 51628 205 57788 18494 97469 126134 206 2226 256 230 1737 18258 16 80503 122 35927 2226 242 112 57462 1737 54457 223165 106230 2096 16 48389 11254 107 255 2226 107 255 228 26 228 26 26 228 26 26 26 228 26 26 26 26 228 26 26 26 26 26 228 26 26 26 26 26 26 228 26 26 26 26 26 26 26 228 26 26 26 26 26 26 26 26 228 26 21 26 228 26 2271 26 228 26 3834 26 182018 230 174833 38111 249 86325 241 38111 245 86325 232 38111 252 38111 123 38111 261 165 24629 38111 261 38111 103 174833 38111 235 188568 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372 8391 158343 3512 40071 2196 3236 8750 1764 37097 41168 29721 32797 25646 3802 4975 4975 116167 57178 10251 154048 27292 1767 5125 2632 2155 91 2378 1919 1914 2782 19 2155 3354 1933 5470 38 2155 52 2068 5470 1767 4961 3059 1894 19 2155 43 1933 3026 2725 23186 38 2930 14 20676 1671 14 83 51 diff --git a/models/ggml-vocab-deepseek-coder.gguf.inp b/models/ggml-vocab-deepseek-coder.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-deepseek-coder.gguf.inp +++ b/models/ggml-vocab-deepseek-coder.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-deepseek-coder.gguf.out b/models/ggml-vocab-deepseek-coder.gguf.out index 9ccc560d6..52c4111a1 100644 --- a/models/ggml-vocab-deepseek-coder.gguf.out +++ b/models/ggml-vocab-deepseek-coder.gguf.out @@ -31,6 +31,7 @@ 185 405 6 2895 17535 11 320 6 435 0 1717 417 340 12394 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239 + 15330 3023 18 18 18 18 18 18 @@ -40,4 +41,6 @@ 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 + 34 155 119 242 64 24297 155 119 216 83 + 1607 2539 185 207 185 185 207 185 185 185 207 12405 459 22758 185 243 185 315 185 251 185 730 185 10047 235 209 334 8760 8 12394 233 114 350 222 10047 221 104 169 116 224 334 4684 3909 992 24330 262 29651 612 8 207 156 237 214 12394 99 234 10047 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 524 18 207 18 1202 18 207 155 239 209 155 239 114 155 239 228 155 240 220 155 239 224 155 240 211 155 239 231 155 239 115 155 239 240 155 240 210 155 239 240 155 239 95 155 239 114 155 239 214 10047 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239 18155 374 17194 28 2861 6478 616 2251 14994 31269 4191 6 4686 4686 10252 3358 3358 3409 524 15330 3023 15031 5668 303 6 312 798 651 83 839 362 6 82 741 11 651 1369 340 2037 30 651 44 441 2037 303 6 642 1098 359 11 651 35 340 833 738 10860 30 998 6 10709 245 6 75 43 diff --git a/models/ggml-vocab-deepseek-llm.gguf.inp b/models/ggml-vocab-deepseek-llm.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-deepseek-llm.gguf.inp +++ b/models/ggml-vocab-deepseek-llm.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-deepseek-llm.gguf.out b/models/ggml-vocab-deepseek-llm.gguf.out index fd94b896d..0191b7a11 100644 --- a/models/ggml-vocab-deepseek-llm.gguf.out +++ b/models/ggml-vocab-deepseek-llm.gguf.out @@ -31,6 +31,7 @@ 185 403 6 2906 17464 11 320 6 436 0 1724 418 340 33701 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 + 15278 3033 18 18 18 18 18 18 @@ -40,4 +41,6 @@ 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 + 34 32555 242 64 23708 32555 216 83 + 1763 2550 185 207 185 185 207 185 185 185 207 11969 486 22504 185 243 185 300 185 251 185 663 185 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 12356 99 234 10044 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 526 18 207 18 1204 18 207 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71899 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 78827 55170 76659 620 91754 31116 36804 4885 4885 10897 4390 4390 41047 15278 3033 14986 5675 304 6 313 803 655 33326 362 6 82 745 11 655 1374 340 2049 30 655 44 441 2049 304 6 647 1099 359 11 655 35 340 837 742 10842 30 1003 6 10699 245 6 75 43 diff --git a/models/ggml-vocab-falcon.gguf.inp b/models/ggml-vocab-falcon.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-falcon.gguf.inp +++ b/models/ggml-vocab-falcon.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-falcon.gguf.out b/models/ggml-vocab-falcon.gguf.out index 209b04cda..64a48d97f 100644 --- a/models/ggml-vocab-falcon.gguf.out +++ b/models/ggml-vocab-falcon.gguf.out @@ -31,6 +31,7 @@ 1212 40 18 4932 9856 23 291 18 436 12 1265 362 299 8196 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236 + 51520 30 3138 22287 @@ -40,4 +41,6 @@ 22287 22287 30 22287 22287 3138 22287 22287 22287 + 46 19768 239 76 9634 19768 213 95 + 1080 1502 1212 4824 1001 1212 192 204 663 49453 2069 742 561 1501 193 2571 232 206 204 19 11003 20 8196 126 283 219 48778 116 13392 204 19 51831 732 63209 1741 7955 522 20 22438 211 3346 111 231 2571 111 231 204 30 204 3138 204 22287 204 22287 30 204 22287 3138 204 22287 22287 204 22287 22287 30 204 22287 22287 3138 204 30 25 30 204 30 513 30 204 30 951 30 27171 236 206 38154 126 38154 225 167 237 217 38154 221 167 237 208 38154 228 38154 127 38154 237 167 237 207 38154 237 38154 107 38154 126 38154 211 20589 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236 204 37057 2228 10666 5052 133 6207 151 215 150 134 5052 133 6279 5052 223 151 216 49679 123 53110 47043 7795 204 7544 7544 7544 8543 8543 17593 3513 3513 12844 51520 17664 4247 295 18 298 650 204 18 95 693 332 18 94 629 23 204 18 1553 299 1310 42 204 18 56 416 1310 295 18 567 717 334 23 204 18 47 299 606 596 6696 42 703 18 16139 241 18 87 55 diff --git a/models/ggml-vocab-gpt-2.gguf.inp b/models/ggml-vocab-gpt-2.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-gpt-2.gguf.inp +++ b/models/ggml-vocab-gpt-2.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-gpt-2.gguf.out b/models/ggml-vocab-gpt-2.gguf.out index 78430f0d3..17a13bdfc 100644 --- a/models/ggml-vocab-gpt-2.gguf.out +++ b/models/ggml-vocab-gpt-2.gguf.out @@ -31,6 +31,7 @@ 198 796 6 6980 15496 11 331 6 439 0 1374 389 345 30325 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252 + 13896 3228 18 2091 20370 @@ -40,4 +41,6 @@ 24840 20370 24840 24840 24840 2091 20370 + 34 157 119 255 64 16049 157 119 229 83 + 1221 1371 198 220 628 220 628 198 220 197 220 197 197 220 197 198 220 220 198 220 220 220 198 220 220 220 220 198 220 220 220 220 220 198 8582 248 222 357 11265 8 30325 114 447 235 8582 234 104 37929 357 48101 795 13210 271 1673 36686 515 8 14519 227 12520 99 247 8582 99 247 513 4747 23460 513 20370 23460 2091 23460 20370 23460 24840 23460 2091 20370 513 13 18 513 492 18 513 986 18 28053 252 222 157 252 114 157 252 241 157 253 233 157 252 237 157 253 224 157 252 244 157 252 115 157 252 253 157 253 223 157 252 253 157 252 95 157 252 114 157 252 227 47249 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252 40103 1421 18604 12466 121 16843 141 231 15166 12466 121 16142 12466 239 141 232 30143 140 111 16142 21169 21727 31583 18849 705 39115 6 33153 15506 63 15931 15931 16317 13896 3228 9805 3548 314 1053 587 705 44040 339 338 612 11 705 2200 345 1654 30 705 44 407 1654 314 1183 787 340 11 705 35 345 588 617 8887 30 775 6 26979 257 6 75 43 diff --git a/models/ggml-vocab-llama-bpe.gguf.inp b/models/ggml-vocab-llama-bpe.gguf.inp index 9380bf355..9baf7d77a 100644 --- a/models/ggml-vocab-llama-bpe.gguf.inp +++ b/models/ggml-vocab-llama-bpe.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ @@ -104,5 +110,3 @@ __ggml_vocab_test__ 🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL __ggml_vocab_test__ - Việt -__ggml_vocab_test__ diff --git a/models/ggml-vocab-llama-bpe.gguf.out b/models/ggml-vocab-llama-bpe.gguf.out index 1f3607fb6..4b35cf93f 100644 --- a/models/ggml-vocab-llama-bpe.gguf.out +++ b/models/ggml-vocab-llama-bpe.gguf.out @@ -31,6 +31,7 @@ 198 284 6 11639 9906 11 379 65948 0 2650 527 499 27623 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 + 17523 3001 18 1644 8765 @@ -40,5 +41,6 @@ 8765 8765 18 8765 8765 1644 8765 8765 8765 + 34 91163 101798 + 2624 2402 198 4815 15073 66597 8004 1602 2355 79772 11187 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 11410 99 247 9468 99 247 220 18 220 1644 220 8765 220 8765 18 220 8765 1644 220 8765 8765 220 8765 8765 18 220 8765 8765 1644 220 18 13 18 220 18 497 18 220 18 1131 18 220 21549 222 98629 241 45358 233 21549 237 45358 224 21549 244 21549 115 21549 253 45358 223 21549 253 21549 95 98629 227 76460 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 56560 54337 19175 102118 13373 64571 34694 3114 112203 80112 3436 106451 14196 14196 74694 3089 3089 29249 17523 3001 27708 7801 358 3077 1027 364 83 820 568 596 1070 11 364 793 499 2771 30 364 44 539 2771 358 3358 1304 433 11 364 35 499 1093 1063 15600 30 1226 6 43712 264 64966 43 - 101798 diff --git a/models/ggml-vocab-llama-spm.gguf.inp b/models/ggml-vocab-llama-spm.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-llama-spm.gguf.inp +++ b/models/ggml-vocab-llama-spm.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-llama-spm.gguf.out b/models/ggml-vocab-llama-spm.gguf.out index 9c3327cb5..93aacf8ba 100644 --- a/models/ggml-vocab-llama-spm.gguf.out +++ b/models/ggml-vocab-llama-spm.gguf.out @@ -31,6 +31,7 @@ 29871 13 353 525 3152 15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 + 1738 6824 21004 29871 29941 29871 29941 29941 29871 29941 29941 29941 @@ -40,4 +41,6 @@ 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29941 + 315 228 190 176 29874 10630 30529 29873 + 29871 2313 3163 29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931 diff --git a/models/ggml-vocab-mpt.gguf.inp b/models/ggml-vocab-mpt.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-mpt.gguf.inp +++ b/models/ggml-vocab-mpt.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-mpt.gguf.out b/models/ggml-vocab-mpt.gguf.out index d8d0fe909..372c751bf 100644 --- a/models/ggml-vocab-mpt.gguf.out +++ b/models/ggml-vocab-mpt.gguf.out @@ -31,6 +31,7 @@ 187 426 8 8685 12092 13 340 8 455 2 1359 403 368 49042 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 + 18963 4672 20 1610 20084 @@ -40,4 +41,6 @@ 26409 20084 26409 26409 26409 1610 20084 + 36 6829 244 66 17721 35177 85 + 1262 2196 586 1744 33525 186 209 623 28910 187 50276 187 50275 187 50274 187 50273 187 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 22692 101 236 14931 101 236 495 5922 30057 495 20084 495 26409 30057 20084 495 26409 1610 495 26409 20084 495 15 20 495 537 20 495 1051 20 209 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 14931 235 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 16081 6877 12880 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 42011 35033 34842 11202 9739 9739 33021 18963 4672 25561 8220 309 1849 644 686 42618 344 434 627 13 686 1848 368 2119 32 686 46 417 2119 309 1833 1056 352 13 686 37 368 751 690 10331 32 844 8 31516 247 8 77 45 diff --git a/models/ggml-vocab-phi-3.gguf.inp b/models/ggml-vocab-phi-3.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-phi-3.gguf.inp +++ b/models/ggml-vocab-phi-3.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-phi-3.gguf.out b/models/ggml-vocab-phi-3.gguf.out index 9c3327cb5..93aacf8ba 100644 --- a/models/ggml-vocab-phi-3.gguf.out +++ b/models/ggml-vocab-phi-3.gguf.out @@ -31,6 +31,7 @@ 29871 13 353 525 3152 15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 + 1738 6824 21004 29871 29941 29871 29941 29941 29871 29941 29941 29941 @@ -40,4 +41,6 @@ 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29941 + 315 228 190 176 29874 10630 30529 29873 + 29871 2313 3163 29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931 diff --git a/models/ggml-vocab-qwen2.gguf.inp b/models/ggml-vocab-qwen2.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-qwen2.gguf.inp +++ b/models/ggml-vocab-qwen2.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-qwen2.gguf.out b/models/ggml-vocab-qwen2.gguf.out index 401a510e8..18b4b45cd 100644 --- a/models/ggml-vocab-qwen2.gguf.out +++ b/models/ggml-vocab-qwen2.gguf.out @@ -31,6 +31,7 @@ 198 284 6 11385 9707 11 379 64848 0 2585 525 498 26525 223 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216 + 17085 2928 18 18 18 18 18 18 @@ -40,4 +41,6 @@ 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 + 34 90063 128324 + 2560 2347 198 4710 14731 65497 7847 1572 2303 78672 10947 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 11162 99 247 149955 220 18 220 18 18 220 18 18 18 220 18 18 18 18 220 18 18 18 18 18 220 18 18 18 18 18 18 220 18 18 18 18 18 18 18 220 18 18 18 18 18 18 18 18 220 18 13 18 220 18 496 18 220 18 1112 18 220 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 144534 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216 55460 53237 18658 14144 1456 13073 63471 33594 3038 133178 79012 3355 4605 4605 13874 13874 73594 3014 3014 28149 17085 2928 26610 7646 358 3003 1012 364 83 813 566 594 1052 11 364 787 498 2704 30 364 44 537 2704 358 3278 1281 432 11 364 35 498 1075 1045 15243 30 1205 6 42612 264 63866 43 diff --git a/models/ggml-vocab-refact.gguf.inp b/models/ggml-vocab-refact.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-refact.gguf.inp +++ b/models/ggml-vocab-refact.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ __ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ 3 __ggml_vocab_test__ 33 @@ -91,6 +93,10 @@ __ggml_vocab_test__ __ggml_vocab_test__ 333333333 __ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ diff --git a/models/ggml-vocab-refact.gguf.out b/models/ggml-vocab-refact.gguf.out index 06b15c090..63d8305c3 100644 --- a/models/ggml-vocab-refact.gguf.out +++ b/models/ggml-vocab-refact.gguf.out @@ -31,6 +31,7 @@ 203 280 25 34666 8279 30 533 25 464 19 4971 884 844 18458 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 + 9163 3202 37 37 37 37 37 37 @@ -40,4 +41,6 @@ 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 + 53 33934 83 33217 17102 102 + 1214 12258 334 719 8878 202 10885 4222 16104 28570 203 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 5945 118 252 3807 118 252 225 37 225 37 37 225 37 37 37 225 37 37 37 37 225 37 37 37 37 37 225 37 37 37 37 37 37 225 37 37 37 37 37 37 37 225 37 37 37 37 37 37 37 37 225 37 32 37 225 37 497 37 225 37 1179 37 225 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 36628 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 20921 16623 13028 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700 5881 9592 13299 914 31753 31359 9163 3202 35472 10397 439 4763 2583 330 102 1455 938 1182 2017 30 330 613 844 3654 49 330 63 646 3654 439 4621 1930 561 30 330 54 844 2124 1629 35993 49 2688 25 7709 312 25 94 62 diff --git a/models/ggml-vocab-starcoder.gguf.inp b/models/ggml-vocab-starcoder.gguf.inp index 0a89107c6..9baf7d77a 100644 --- a/models/ggml-vocab-starcoder.gguf.inp +++ b/models/ggml-vocab-starcoder.gguf.inp @@ -73,6 +73,8 @@ __ggml_vocab_test__ __ggml_vocab_test__ Hello, y'all! 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= ">=3.9" +numpy = "^1.25.0" +sentencepiece = ">=0.1.98,<0.2.0" +transformers = ">=4.35.2,<5.0.0" +protobuf = ">=4.21.0,<5.0.0" +gguf = { path = "./gguf-py" } +torch = { version = "^2.2.0", source = "pytorch" } + +[tool.poetry.dev-dependencies] +pytest = "^5.2" + + +# Force wheel + cpu +# For discussion and context see https://github.com/python-poetry/poetry#6409 +[[tool.poetry.source]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cpu" +priority = "explicit" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.scripts] +llama-convert-hf-to-gguf = "convert_hf_to_gguf:main" +llama-convert-llama-ggml-to-gguf = "convert_llama_ggml_to_gguf:main" +llama-ggml-vk-generate-shaders = "ggml_vk_generate_shaders:main" diff --git a/requirements.txt b/requirements.txt index e5cfbf10b..52456c2e6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,8 +4,8 @@ # Package versions must stay compatible across all top-level python scripts. # --r ./requirements/requirements-convert-legacy-llama.txt +-r ./requirements/requirements-convert_legacy_llama.txt --r ./requirements/requirements-convert-hf-to-gguf.txt --r ./requirements/requirements-convert-hf-to-gguf-update.txt --r ./requirements/requirements-convert-llama-ggml-to-gguf.txt +-r ./requirements/requirements-convert_hf_to_gguf.txt +-r ./requirements/requirements-convert_hf_to_gguf_update.txt +-r ./requirements/requirements-convert_llama_ggml_to_gguf.txt diff --git a/requirements/requirements-convert-hf-to-gguf-update.txt b/requirements/requirements-convert-hf-to-gguf-update.txt deleted file mode 100644 index a7112f396..000000000 --- a/requirements/requirements-convert-hf-to-gguf-update.txt +++ /dev/null @@ -1,2 +0,0 @@ --r ./requirements-convert-legacy-llama.txt -torch~=2.2.1 diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt deleted file mode 100644 index a7112f396..000000000 --- a/requirements/requirements-convert-hf-to-gguf.txt +++ /dev/null @@ -1,2 +0,0 @@ --r ./requirements-convert-legacy-llama.txt -torch~=2.2.1 diff --git a/requirements/requirements-convert-llama-ggml-to-gguf.txt b/requirements/requirements-convert-llama-ggml-to-gguf.txt deleted file mode 100644 index e80c29012..000000000 --- a/requirements/requirements-convert-llama-ggml-to-gguf.txt +++ /dev/null @@ -1 +0,0 @@ --r ./requirements-convert-legacy-llama.txt diff --git a/requirements/requirements-convert_hf_to_gguf.txt b/requirements/requirements-convert_hf_to_gguf.txt new file mode 100644 index 000000000..8cb9c354f --- /dev/null +++ b/requirements/requirements-convert_hf_to_gguf.txt @@ -0,0 +1,3 @@ +-r ./requirements-convert_legacy_llama.txt +--extra-index-url https://download.pytorch.org/whl/cpu +torch~=2.2.1 diff --git a/requirements/requirements-convert_hf_to_gguf_update.txt b/requirements/requirements-convert_hf_to_gguf_update.txt new file mode 100644 index 000000000..8cb9c354f --- /dev/null +++ b/requirements/requirements-convert_hf_to_gguf_update.txt @@ -0,0 +1,3 @@ +-r ./requirements-convert_legacy_llama.txt +--extra-index-url https://download.pytorch.org/whl/cpu +torch~=2.2.1 diff --git a/requirements/requirements-convert-legacy-llama.txt b/requirements/requirements-convert_legacy_llama.txt similarity index 100% rename from requirements/requirements-convert-legacy-llama.txt rename to requirements/requirements-convert_legacy_llama.txt diff --git a/requirements/requirements-convert_llama_ggml_to_gguf.txt b/requirements/requirements-convert_llama_ggml_to_gguf.txt new file mode 100644 index 000000000..afe2747d4 --- /dev/null +++ b/requirements/requirements-convert_llama_ggml_to_gguf.txt @@ -0,0 +1 @@ +-r ./requirements-convert_legacy_llama.txt diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh index 0c6afdd59..48f924c02 100755 --- a/scripts/check-requirements.sh +++ b/scripts/check-requirements.sh @@ -97,9 +97,9 @@ check_requirements() { } check_convert_script() { - local py=$1 # e.g. ./convert-hf-to-gguf.py - local pyname=${py##*/} # e.g. convert-hf-to-gguf.py - pyname=${pyname%.py} # e.g. convert-hf-to-gguf + local py=$1 # e.g. ./convert_hf_to_gguf.py + local pyname=${py##*/} # e.g. convert_hf_to_gguf.py + pyname=${pyname%.py} # e.g. convert_hf_to_gguf info "$py: beginning check" @@ -166,12 +166,12 @@ if (( do_cleanup )); then rm -rf -- "$all_venv" fi -check_convert_script examples/convert-legacy-llama.py -for py in convert-*.py; do - # skip convert-hf-to-gguf-update.py +check_convert_script examples/convert_legacy_llama.py +for py in convert_*.py; do + # skip convert_hf_to_gguf_update.py # TODO: the check is failing for some reason: # https://github.com/ggerganov/llama.cpp/actions/runs/8875330981/job/24364557177?pr=6920 - [[ $py == convert-hf-to-gguf-update.py ]] && continue + [[ $py == convert_hf_to_gguf_update.py ]] && continue check_convert_script "$py" done diff --git a/scripts/convert-gg.sh b/scripts/convert-gg.sh deleted file mode 100755 index 8a0168432..000000000 --- a/scripts/convert-gg.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash - -set -e - -# LLaMA v1 -python3 examples/convert-legacy-llama.py ../llama1/7B --outfile models/llama-7b/ggml-model-f16.gguf --outtype f16 -python3 examples/convert-legacy-llama.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16 -python3 examples/convert-legacy-llama.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16 -python3 examples/convert-legacy-llama.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16 - -# LLaMA v2 -python3 examples/convert-legacy-llama.py ../llama2/llama-2-7b --outfile models/llama-7b-v2/ggml-model-f16.gguf --outtype f16 -python3 examples/convert-legacy-llama.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16 -python3 examples/convert-legacy-llama.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16 - -# Code Llama -python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-7b/ --outfile models/codellama-7b/ggml-model-f16.gguf --outtype f16 -python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16 -python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16 - -# Falcon -python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b 1 -mv -v ../falcon/falcon-7b/ggml-model-f16.gguf models/falcon-7b/ggml-model-f16.gguf - -python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-40b 1 -mv -v ../falcon/falcon-40b/ggml-model-f16.gguf models/falcon-40b/ggml-model-f16.gguf diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh index 586d6ea18..6e56e1ed0 100644 --- a/scripts/pod-llama.sh +++ b/scripts/pod-llama.sh @@ -75,7 +75,7 @@ if [ "$1" -eq "1" ]; then cd /workspace/llama.cpp - python3 examples/convert-legacy-llama.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert_legacy_llama.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16 ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0 ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k @@ -90,7 +90,7 @@ if [ "$1" -eq "2" ]; then cd /workspace/llama.cpp - python3 examples/convert-legacy-llama.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert_legacy_llama.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16 ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0 ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k @@ -105,7 +105,7 @@ if [ "$1" -eq "3" ]; then cd /workspace/llama.cpp - python3 examples/convert-legacy-llama.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert_legacy_llama.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16 ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0 ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k @@ -120,7 +120,7 @@ if [ "$1" -eq "4" ]; then cd /workspace/llama.cpp - python3 examples/convert-legacy-llama.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert_legacy_llama.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16 ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0 ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k @@ -135,7 +135,7 @@ if [ "$1" -eq "5" ]; then cd /workspace/llama.cpp - python3 examples/convert-legacy-llama.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16 + python3 examples/convert_legacy_llama.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16 ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0 ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k @@ -150,7 +150,7 @@ if [ "$1" -eq "6" ]; then cd /workspace/llama.cpp - python3 examples/convert-legacy-llama.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16 + python3 examples/convert_legacy_llama.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16 ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0 ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k @@ -165,7 +165,7 @@ if [ "$1" -eq "7" ]; then cd /workspace/llama.cpp - python3 examples/convert-legacy-llama.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16 + python3 examples/convert_legacy_llama.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16 ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0 ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k @@ -210,4 +210,3 @@ fi # more benches #GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 #GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 - diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index ccb607e56..c2049df79 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -1,7 +1,5 @@ # TODO: should not use this if (WIN32) - add_compile_definitions(_CRT_SECURE_NO_WARNINGS) - if (BUILD_SHARED_LIBS) set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON) endif() diff --git a/src/llama.cpp b/src/llama.cpp index 5082daaae..2e712a9d6 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -104,8 +104,10 @@ #define LLAMA_ATTRIBUTE_FORMAT(...) #endif +// bump if necessary #define LLAMA_MAX_NODES 8192 -#define LLAMA_MAX_EXPERTS 160 +#define LLAMA_MAX_LAYERS 256 +#define LLAMA_MAX_EXPERTS 160 // DeepSeekV2 // // logging @@ -224,10 +226,13 @@ enum llm_arch { LLM_ARCH_COMMAND_R, LLM_ARCH_DBRX, LLM_ARCH_OLMO, + LLM_ARCH_OPENELM, LLM_ARCH_ARCTIC, LLM_ARCH_DEEPSEEK2, + LLM_ARCH_CHATGLM, LLM_ARCH_BITNET, LLM_ARCH_T5, + LLM_ARCH_JAIS, LLM_ARCH_UNKNOWN, }; @@ -265,10 +270,13 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_COMMAND_R, "command-r" }, { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_OLMO, "olmo" }, + { LLM_ARCH_OPENELM, "openelm" }, { LLM_ARCH_ARCTIC, "arctic" }, { LLM_ARCH_DEEPSEEK2, "deepseek2" }, + { LLM_ARCH_CHATGLM, "chatglm" }, { LLM_ARCH_BITNET, "bitnet" }, { LLM_ARCH_T5, "t5" }, + { LLM_ARCH_JAIS, "jais" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -317,6 +325,7 @@ enum llm_kv { LLM_KV_ATTENTION_Q_LORA_RANK, LLM_KV_ATTENTION_KV_LORA_RANK, LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, + LLM_KV_ATTENTION_SLIDING_WINDOW, LLM_KV_ROPE_DIMENSION_COUNT, LLM_KV_ROPE_FREQ_BASE, @@ -409,6 +418,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" }, { LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" }, { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" }, + { LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" }, { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, @@ -1130,6 +1140,22 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_OPENELM, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_ARCTIC, { @@ -1181,6 +1207,21 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, }, }, + { + LLM_ARCH_CHATGLM, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + }, + }, { LLM_ARCH_BITNET, { @@ -1234,6 +1275,21 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_ENC_FFN_UP, "enc.blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_JAIS, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1956,18 +2012,19 @@ using llama_mlocks = std::vector>; // NOTE: avoid ever using this except for building the token_to_piece caches static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) { - std::vector result(8, 0); - const int n_tokens = llama_token_to_piece(model, token, result.data(), result.size(), special); - if (n_tokens < 0) { - result.resize(-n_tokens); - int check = llama_token_to_piece(model, token, result.data(), result.size(), special); - GGML_ASSERT(check == -n_tokens); + std::string piece; + piece.resize(piece.capacity()); // using string internal cache + const int n_chars = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special); + if (n_chars < 0) { + piece.resize(-n_chars); + int check = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special); + GGML_ASSERT(check == -n_chars); } else { - result.resize(n_tokens); + piece.resize(n_chars); } - return std::string(result.data(), result.size()); + return piece; } static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) { @@ -2025,22 +2082,34 @@ enum e_model { MODEL_17M, MODEL_22M, MODEL_33M, + MODEL_60M, MODEL_70M, + MODEL_80M, MODEL_109M, MODEL_137M, MODEL_160M, + MODEL_220M, + MODEL_250M, + MODEL_270M, MODEL_335M, MODEL_410M, + MODEL_450M, + MODEL_770M, + MODEL_780M, MODEL_0_5B, MODEL_1B, + MODEL_1_3B, MODEL_1_4B, MODEL_2B, MODEL_2_8B, MODEL_3B, MODEL_4B, + MODEL_6B, MODEL_6_9B, MODEL_7B, MODEL_8B, + MODEL_9B, + MODEL_11B, MODEL_12B, MODEL_13B, MODEL_14B, @@ -2065,7 +2134,6 @@ enum e_model { MODEL_16x12B, MODEL_10B_128x3_66B, MODEL_57B_A14B, - MODEL_9B, MODEL_27B, }; @@ -2081,16 +2149,19 @@ struct llama_hparams { uint32_t n_vocab; uint32_t n_ctx_train; // context size the model was trained on uint32_t n_embd; - uint32_t n_head; - uint32_t n_head_kv; uint32_t n_layer; uint32_t n_rot; + uint32_t n_swa = 0; // sliding window attention (SWA) uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head - uint32_t n_ff; uint32_t n_expert = 0; uint32_t n_expert_used = 0; uint32_t n_vocab_type = 0; // for BERT-style token types + uint32_t n_rel_attn_bkts = 0; + + std::array n_head_arr; + std::array n_head_kv_arr; + std::array n_ff_arr; uint32_t n_layer_dense_lead = 0; uint32_t n_lora_q = 0; @@ -2126,6 +2197,10 @@ struct llama_hparams { bool use_alibi = false; bool attn_soft_cap = false; + // needed by encoder-decoder models (e.g. T5, FLAN-T5) + // ref: https://github.com/ggerganov/llama.cpp/pull/8141 + llama_token dec_start_token_id = -1; + enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE; enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE; @@ -2135,16 +2210,19 @@ struct llama_hparams { if (this->n_vocab != other.n_vocab) return true; if (this->n_ctx_train != other.n_ctx_train) return true; if (this->n_embd != other.n_embd) return true; - if (this->n_head != other.n_head) return true; - if (this->n_head_kv != other.n_head_kv) return true; if (this->n_layer != other.n_layer) return true; if (this->n_rot != other.n_rot) return true; + if (this->n_swa != other.n_swa) return true; if (this->n_embd_head_k != other.n_embd_head_k) return true; if (this->n_embd_head_v != other.n_embd_head_v) return true; - if (this->n_ff != other.n_ff) return true; if (this->n_expert != other.n_expert) return true; if (this->n_expert_used != other.n_expert_used) return true; + if (this->n_head_arr != other.n_head_arr) return true; + if (this->n_head_kv_arr != other.n_head_kv_arr) return true; + if (this->n_ff_arr != other.n_ff_arr) return true; + + if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true; if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true; if (this->n_lora_q != other.n_lora_q) return true; if (this->n_lora_kv != other.n_lora_kv) return true; @@ -2160,6 +2238,8 @@ struct llama_hparams { if (this->ssm_d_state != other.ssm_d_state) return true; if (this->ssm_dt_rank != other.ssm_dt_rank) return true; + if (this->dec_start_token_id != other.dec_start_token_id) return true; + const float EPSILON = 1e-9f; if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true; @@ -2173,18 +2253,53 @@ struct llama_hparams { return false; } - uint32_t n_gqa() const { + uint32_t n_head(uint32_t il = 0) const { + if (il < n_layer) { + return n_head_arr[il]; + } + + GGML_ASSERT(false); + return 0; + } + + uint32_t n_head_kv(uint32_t il = 0) const { + if (il < n_layer) { + return n_head_kv_arr[il]; + } + + GGML_ASSERT(false); + return 0; + } + + uint32_t n_ff(uint32_t il = 0) const { + if (il < n_layer) { + return n_ff_arr[il]; + } + + GGML_ASSERT(false); + return 0; + } + + uint32_t n_gqa(uint32_t il = 0) const { + const uint32_t n_head = this->n_head(il); + const uint32_t n_head_kv = this->n_head_kv(il); + if (n_head_kv == 0) { return 0; } + return n_head/n_head_kv; } - uint32_t n_embd_k_gqa() const { // dimension of key embeddings across all k-v heads + uint32_t n_embd_k_gqa(uint32_t il = 0) const { // dimension of key embeddings across all k-v heads + const uint32_t n_head_kv = this->n_head_kv(il); + return n_embd_head_k * n_head_kv; } - uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads + uint32_t n_embd_v_gqa(uint32_t il = 0) const { // dimension of value embeddings across all k-v heads + const uint32_t n_head_kv = this->n_head_kv(il); + return n_embd_head_v * n_head_kv; } @@ -2201,6 +2316,8 @@ struct llama_hparams { } }; +static_assert(std::is_trivially_copyable::value, "llama_hparams must be trivially copyable"); + struct llama_cparams { uint32_t n_ctx; // context size used during inference uint32_t n_batch; @@ -2232,6 +2349,7 @@ struct llama_cparams { void * cb_eval_user_data; }; +// TODO: separate into "llama_layer_enc" and "llama_layer_dec" struct llama_layer { // normalization struct ggml_tensor * attn_norm; @@ -2249,6 +2367,8 @@ struct llama_layer { struct ggml_tensor * attn_sub_norm; struct ggml_tensor * attn_post_norm; struct ggml_tensor * ffn_sub_norm; + struct ggml_tensor * attn_norm_cross; + struct ggml_tensor * attn_norm_enc; // attention struct ggml_tensor * wq; @@ -2260,6 +2380,14 @@ struct llama_layer { struct ggml_tensor * wq_b; struct ggml_tensor * wkv_a_mqa; struct ggml_tensor * wkv_b; + struct ggml_tensor * wq_cross; + struct ggml_tensor * wk_cross; + struct ggml_tensor * wv_cross; + struct ggml_tensor * wo_cross; + struct ggml_tensor * wq_enc; + struct ggml_tensor * wk_enc; + struct ggml_tensor * wv_enc; + struct ggml_tensor * wo_enc; // attention bias struct ggml_tensor * bq; @@ -2268,6 +2396,11 @@ struct llama_layer { struct ggml_tensor * bo; struct ggml_tensor * bqkv; + // relative position bias + struct ggml_tensor * attn_rel_b; + struct ggml_tensor * attn_rel_b_enc; + struct ggml_tensor * attn_rel_b_cross; + // normalization struct ggml_tensor * ffn_norm; struct ggml_tensor * ffn_norm_b; @@ -2275,11 +2408,15 @@ struct llama_layer { struct ggml_tensor * layer_out_norm; struct ggml_tensor * layer_out_norm_b; struct ggml_tensor * ffn_norm_exps; + struct ggml_tensor * ffn_norm_enc; // ff struct ggml_tensor * ffn_gate; // w1 struct ggml_tensor * ffn_down; // w2 struct ggml_tensor * ffn_up; // w3 + struct ggml_tensor * ffn_gate_enc; + struct ggml_tensor * ffn_down_enc; + struct ggml_tensor * ffn_up_enc; // ff MoE struct ggml_tensor * ffn_gate_inp; @@ -2468,10 +2605,11 @@ struct llama_vocab { id special_eot_id = -1; // TODO: move above after "eos_id", and here add "file separator" token // tokenizer flags - bool tokenizer_add_space_prefix = true; + bool tokenizer_add_space_prefix = false; bool tokenizer_add_bos = false; bool tokenizer_add_eos = false; bool tokenizer_ignore_merges = false; + bool tokenizer_clean_spaces = false; // clean_up_tokenization_spaces bool tokenizer_remove_extra_whitespaces = false; bool tokenizer_escape_whitespaces = true; bool tokenizer_treat_whitespace_as_suffix = false; @@ -2513,6 +2651,7 @@ struct llama_model { struct ggml_tensor * output_norm_b; struct ggml_tensor * output; struct ggml_tensor * output_b; + struct ggml_tensor * output_norm_enc; std::vector layers; @@ -2641,6 +2780,13 @@ struct llama_context { // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE std::map> embd_seq; + // whether we are computing encoder output or decoder output + bool is_encoding = false; + + // output of the encoder part of the encoder-decoder models + std::vector embd_enc; + std::vector> seq_ids_enc; + // memory buffers used to evaluate the model std::vector buf_compute_meta; ggml_backend_sched_t sched = nullptr; @@ -2649,17 +2795,21 @@ struct llama_context { void * abort_callback_data = nullptr; // input tensors - struct ggml_tensor * inp_tokens; // I32 [n_batch] - struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch] - struct ggml_tensor * inp_pos; // I32 [n_batch] - struct ggml_tensor * inp_out_ids; // I32 [n_outputs] - struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch] - struct ggml_tensor * inp_K_shift; // I32 [kv_size] - struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch] - struct ggml_tensor * inp_cls; // I32 [n_batch] - struct ggml_tensor * inp_s_copy; // I32 [kv_size] - struct ggml_tensor * inp_s_mask; // F32 [1, n_kv] - struct ggml_tensor * inp_s_seq; // I32 [n_kv, n_batch] + struct ggml_tensor * inp_tokens; // I32 [n_batch] + struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch] + struct ggml_tensor * inp_pos; // I32 [n_batch] + struct ggml_tensor * inp_out_ids; // I32 [n_outputs] + struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch] + struct ggml_tensor * inp_KQ_mask_swa; // F32 [kv_size, n_batch] + struct ggml_tensor * inp_K_shift; // I32 [kv_size] + struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch] + struct ggml_tensor * inp_cls; // I32 [n_batch] + struct ggml_tensor * inp_s_copy; // I32 [kv_size] + struct ggml_tensor * inp_s_mask; // F32 [1, n_kv] + struct ggml_tensor * inp_s_seq; // I32 [n_kv, n_batch] + struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch] + struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc] + struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch] // control vectors struct llama_control_vector cvec; @@ -2788,9 +2938,7 @@ static bool llama_kv_cache_init( const struct llama_hparams & hparams = model.hparams; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); - const int64_t n_layer = hparams.n_layer; + const int64_t n_layer = hparams.n_layer; cache.has_shift = false; @@ -2798,13 +2946,6 @@ static bool llama_kv_cache_init( cache.recurrent = model.arch == LLM_ARCH_MAMBA; cache.v_trans = !cparams.flash_attn; - // TODO: support mixed recurrent Transformer architectures - // NOTE: (!a || b) is a logical implication (a -> b) - GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s()); - GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s()); - GGML_ASSERT( cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_gqa()); - GGML_ASSERT( cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_gqa()); - cache.head = 0; cache.size = kv_size; cache.used = 0; @@ -2854,6 +2995,9 @@ static bool llama_kv_cache_init( cache.v_l.reserve(n_layer); for (int i = 0; i < (int) n_layer; i++) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s(); + struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size); @@ -3134,6 +3278,8 @@ static void llama_kv_cache_seq_add( if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + // If there is no range then return early to avoid looping over the cache. + if (p0 == p1) return; if (cache.recurrent) { // for Mamba-like models, only the pos needs to be shifted @@ -3178,6 +3324,8 @@ static void llama_kv_cache_seq_div( int d) { if (p0 < 0) p0 = 0; if (p1 < 0) p1 = std::numeric_limits::max(); + // If there is no range then return early to avoid looping over the cache. + if (p0 == p1) return; if (cache.recurrent) { // for Mamba-like models, only the pos needs to be changed @@ -3729,9 +3877,9 @@ struct llama_model_loader { bool get_arr(const std::string & key, std::vector & result, const bool required = true) { const int kid = gguf_find_key(meta, key.c_str()); - if (kid < 0) { + if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) { if (required) { - throw std::runtime_error(format("key not found in model: %s", key.c_str())); + throw std::runtime_error(format("array key not found in model: %s", key.c_str())); } return false; } @@ -3739,22 +3887,53 @@ struct llama_model_loader { struct GGUFMeta::ArrayInfo arr_info = GGUFMeta::GKV::get_kv(meta, kid); - if (arr_info.gt != GGUF_TYPE_FLOAT32 && arr_info.gt != GGUF_TYPE_INT32) { - throw std::runtime_error(format("%s is not a float32 or int32 array", key.c_str())); + switch (arr_info.gt) { + case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; + case GGUF_TYPE_INT32: GGML_ASSERT( + (std::is_same::value) || + (std::is_same::value)); break; + default: + throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str())); } - // GGML_ASSERT(gguf_type_size(arr_info.gt) == sizeof(T)); - GGML_ASSERT((arr_info.gt != GGUF_TYPE_FLOAT32 || std::is_same::value)); - GGML_ASSERT((arr_info.gt != GGUF_TYPE_INT32 || std::is_same::value)); - result.resize(arr_info.length); result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length); return true; } + template + bool get_arr(const std::string & key, std::array & result, const bool required = true) { + const int kid = gguf_find_key(meta, key.c_str()); + + if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) { + if (required) { + throw std::runtime_error(format("array key not found in model: %s", key.c_str())); + } + return false; + } + + struct GGUFMeta::ArrayInfo arr_info = + GGUFMeta::GKV::get_kv(meta, kid); + + switch (arr_info.gt) { + case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; + case GGUF_TYPE_INT32: GGML_ASSERT( + (std::is_same::value) || + (std::is_same::value)); break; + default: + throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str())); + } + + GGML_ASSERT(arr_info.length <= N_MAX); + + std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin()); + + return true; + } + template - bool get_arr(const enum llm_kv kid, T& result, const bool required = true) { + bool get_arr(const enum llm_kv kid, T & result, const bool required = true) { return get_arr(llm_kv(kid), result, required); } @@ -3779,6 +3958,50 @@ struct llama_model_loader { return get_key(llm_kv(kid), result, required); } + // get array of n <= N_MAX elements, or a single element repeated n times + template + bool get_key_or_arr(const std::string & key, std::array & result, uint32_t n, const bool required = true) { + GGML_ASSERT(n <= N_MAX); + + const int kid = gguf_find_key(meta, key.c_str()); + + if (kid < 0) { + if (required) { + throw std::runtime_error(format("key not found in model: %s", key.c_str())); + } + return false; + } + + if (gguf_get_kv_type(meta, kid) == GGUF_TYPE_ARRAY) { + struct GGUFMeta::ArrayInfo arr_info = + GGUFMeta::GKV::get_kv(meta, kid); + + if (n != arr_info.length) { + throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length)); + } + + return get_arr(key, result, required); + } else { + T value; + + bool ok = get_key(key, value, required); + if (!ok) { + return false; + } + + for (uint32_t i = 0; i < n; i++) { + result[i] = value; + } + + return true; + } + } + + template + bool get_key_or_arr(const enum llm_kv kid, T & result, uint32_t n, const bool required = true) { + return get_key_or_arr(llm_kv(kid), result, n, required); + } + std::string get_arch_name() const { return arch_name; } @@ -4011,7 +4234,7 @@ struct llama_model_loader { #if defined(GGML_USE_CUDA) // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives. // NVMe raid configurations might require more / larger buffers. - constexpr size_t num_buffers = 4; + constexpr size_t n_buffers = 4; constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB std::vector host_buffers; @@ -4037,7 +4260,7 @@ struct llama_model_loader { // If the cuda backend is active create pinned memory buffers and events for synchronisation. if (cuda_backend) { - for (size_t idx = 0; idx < num_buffers; ++idx) { + for (size_t idx = 0; idx < n_buffers; ++idx) { host_buffers.emplace_back(ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buffer_size)); host_ptrs.emplace_back(ggml_backend_buffer_get_base(host_buffers[idx])); events.emplace_back(ggml_backend_event_new(cuda_backend)); @@ -4118,7 +4341,7 @@ struct llama_model_loader { bytes_read += read_iteration; ++buffer_idx; - buffer_idx %= num_buffers; + buffer_idx %= n_buffers; } } else @@ -4141,7 +4364,7 @@ struct llama_model_loader { #if defined(GGML_USE_CUDA) // free temporary resources used for async cuda uploads if (cuda_backend) { - for (size_t idx = 0; idx < num_buffers;++idx) { + for (size_t idx = 0; idx < n_buffers;++idx) { ggml_backend_event_synchronize(events[idx]); ggml_backend_event_free(events[idx]); ggml_backend_buffer_free(host_buffers[idx]); @@ -4263,22 +4486,34 @@ static const char * llama_model_type_name(e_model type) { case MODEL_17M: return "17M"; case MODEL_22M: return "22M"; case MODEL_33M: return "33M"; + case MODEL_60M: return "60M"; case MODEL_70M: return "70M"; + case MODEL_80M: return "80M"; case MODEL_109M: return "109M"; case MODEL_137M: return "137M"; case MODEL_160M: return "160M"; + case MODEL_220M: return "220M"; + case MODEL_250M: return "250M"; + case MODEL_270M: return "270M"; case MODEL_335M: return "335M"; case MODEL_410M: return "410M"; + case MODEL_450M: return "450M"; + case MODEL_770M: return "770M"; + case MODEL_780M: return "780M"; case MODEL_0_5B: return "0.5B"; case MODEL_1B: return "1B"; + case MODEL_1_3B: return "1.3B"; case MODEL_1_4B: return "1.4B"; case MODEL_2B: return "2B"; case MODEL_2_8B: return "2.8B"; case MODEL_3B: return "3B"; case MODEL_4B: return "4B"; + case MODEL_6B: return "6B"; case MODEL_6_9B: return "6.9B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; + case MODEL_9B: return "9B"; + case MODEL_11B: return "11B"; case MODEL_12B: return "12B"; case MODEL_13B: return "13B"; case MODEL_14B: return "14B"; @@ -4303,7 +4538,6 @@ static const char * llama_model_type_name(e_model type) { case MODEL_16x12B: return "16x12B"; case MODEL_10B_128x3_66B: return "10B+128x3.66B"; case MODEL_57B_A14B: return "57B.A14B"; - case MODEL_9B: return "9B"; case MODEL_27B: return "27B"; default: return "?B"; } @@ -4348,20 +4582,18 @@ static void llm_load_hparams( ml.get_key(LLM_KV_GENERAL_NAME, model.name, false); // get hparams kv - ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab); + ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab); // everything past this point is not vocab-related if (hparams.vocab_only) { return; } - ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train); - ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd); - ml.get_key(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff); - ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head); - ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer); - ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false); - ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false); + ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train); + ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd); + ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer); + ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false); + ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false); GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS); GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert); @@ -4371,9 +4603,18 @@ static void llm_load_hparams( GGML_ASSERT(hparams.n_expert_used == 0); } + // zero-out the per-layer hparams + std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0); + std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0); + std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0); + + ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer); + ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer); + // n_head_kv is optional, default to n_head - hparams.n_head_kv = hparams.n_head; - ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv, false); + hparams.n_head_kv_arr = hparams.n_head_arr; + + ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, hparams.n_layer, false); bool rope_finetuned = false; ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false); @@ -4401,27 +4642,33 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false); - // sanity check for n_rot (optional) - { - hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; + // non-transformer models do not have attention heads + if (hparams.n_head() > 0) { + // gpt-neox n_rot = rotary_pct * (n_embd / n_head) + // gpt-j n_rot = rotary_dim + + hparams.n_embd_head_k = hparams.n_embd / hparams.n_head(); + ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false); + + hparams.n_embd_head_v = hparams.n_embd / hparams.n_head(); + ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false); + + // sanity check for n_rot (optional) + hparams.n_rot = hparams.n_embd_head_k; ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false); if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) { - if (hparams.n_rot != hparams.n_embd / hparams.n_head) { - throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head)); + if (hparams.n_rot != hparams.n_embd_head_k) { + throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k)); } } - // gpt-neox n_rot = rotary_pct * (n_embd / n_head) - // gpt-j n_rot = rotary_dim + } else { + hparams.n_rot = 0; + hparams.n_embd_head_k = 0; + hparams.n_embd_head_v = 0; } - hparams.n_embd_head_k = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; - ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false); - - hparams.n_embd_head_v = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; - ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false); - // arch-specific KVs switch (model.arch) { case LLM_ARCH_LLAMA: @@ -4444,7 +4691,7 @@ static void llm_load_hparams( case 40: model.type = e_model::MODEL_13B; break; case 48: model.type = e_model::MODEL_34B; break; case 60: model.type = e_model::MODEL_30B; break; - case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break; + case 80: model.type = hparams.n_head() == hparams.n_head_kv() ? e_model::MODEL_65B : e_model::MODEL_70B; break; default: model.type = e_model::MODEL_UNKNOWN; } } @@ -4613,7 +4860,7 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break; case 32: model.type = e_model::MODEL_7B; break; - case 40: model.type = hparams.n_head == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break; + case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break; case 80: model.type = e_model::MODEL_70B; break; default: model.type = e_model::MODEL_UNKNOWN; } @@ -4709,6 +4956,8 @@ static void llm_load_hparams( } break; case LLM_ARCH_GEMMA2: { + hparams.n_swa = 4096; // default value of gemma 2 + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false); ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false); @@ -4803,46 +5052,58 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_OPENELM: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + switch (hparams.n_layer) { + case 16: model.type = e_model::MODEL_270M; break; + case 20: model.type = e_model::MODEL_450M; break; + case 28: model.type = e_model::MODEL_1B; break; + case 36: model.type = e_model::MODEL_3B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_GPTNEOX: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res); switch (hparams.n_layer) { case 6: - switch (hparams.n_ff) { + switch (hparams.n_ff()) { case 512: model.type = e_model::MODEL_14M; break; case 2048: model.type = e_model::MODEL_70M; break; default: model.type = e_model::MODEL_UNKNOWN; } break; case 12: - switch (hparams.n_ff) { + switch (hparams.n_ff()) { case 3072: model.type = e_model::MODEL_160M; break; default: model.type = e_model::MODEL_UNKNOWN; } break; case 16: - switch (hparams.n_ff) { + switch (hparams.n_ff()) { case 8192: model.type = e_model::MODEL_1B; break; default: model.type = e_model::MODEL_UNKNOWN; } break; case 24: - switch (hparams.n_ff) { + switch (hparams.n_ff()) { case 4096: model.type = e_model::MODEL_410M; break; case 8192: model.type = e_model::MODEL_1_4B; break; default: model.type = e_model::MODEL_UNKNOWN; } break; case 32: - switch (hparams.n_ff) { + switch (hparams.n_ff()) { case 10240: model.type = e_model::MODEL_2_8B; break; case 16384: model.type = e_model::MODEL_6_9B; break; default: model.type = e_model::MODEL_UNKNOWN; } break; case 36: - switch (hparams.n_ff) { + switch (hparams.n_ff()) { case 20480: model.type = e_model::MODEL_12B; break; default: model.type = e_model::MODEL_UNKNOWN; } break; case 44: - switch (hparams.n_ff) { + switch (hparams.n_ff()) { case 24576: model.type = e_model::MODEL_20B; break; default: model.type = e_model::MODEL_UNKNOWN; } break; @@ -4882,6 +5143,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_CHATGLM: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { + case 28: model.type = e_model::MODEL_6B; break; + case 40: model.type = e_model::MODEL_9B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_BITNET: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -4891,6 +5161,50 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_T5: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts); + + uint32_t dec_start_token_id; + if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) { + hparams.dec_start_token_id = dec_start_token_id; + } + + switch (hparams.n_layer) { + case 6: model.type = e_model::MODEL_60M; break; // t5-small + case 8: model.type = e_model::MODEL_80M; break; // flan-t5-small + case 12: + switch (hparams.n_ff()) { + case 3072: model.type = e_model::MODEL_220M; break; // t5-base + case 2048: model.type = e_model::MODEL_250M; break; // flan-t5-base + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 24: + switch (hparams.n_ff()) { + case 4096: model.type = e_model::MODEL_770M; break; // t5-large + case 2816: model.type = e_model::MODEL_780M; break; // flan-t5-large + case 16384: model.type = e_model::MODEL_3B; break; // t5-3b + case 5120: model.type = e_model::MODEL_3B; break; // flan-t5-xl + case 65536: model.type = e_model::MODEL_11B; break; // t5-11b + case 10240: model.type = e_model::MODEL_11B; break; // flan-t5-xxl + default: model.type = e_model::MODEL_UNKNOWN; + } break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; + case LLM_ARCH_JAIS: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias); + + switch (hparams.n_layer) { + case 24: model.type = e_model::MODEL_1_3B; break; + case 40: model.type = e_model::MODEL_13B; break; + /* TODO: add variants */ + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4951,11 +5265,6 @@ static void llm_load_vocab( vocab.special_pad_id = -1; vocab.special_cls_id = -1; vocab.special_mask_id = -1; - - const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); - if (add_space_prefix_keyidx != -1) { - vocab.tokenizer_add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); - } // The default value of add_space_prefix is true. } else if (tokenizer_model == "bert") { vocab.type = LLAMA_VOCAB_TYPE_WPM; @@ -4967,23 +5276,15 @@ static void llm_load_vocab( vocab.special_pad_id = 0; vocab.special_cls_id = 101; vocab.special_mask_id = 103; - vocab.tokenizer_add_space_prefix = false; } else if (tokenizer_model == "gpt2") { vocab.type = LLAMA_VOCAB_TYPE_BPE; - const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); - if (add_space_prefix_keyidx != -1) { - vocab.tokenizer_add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); - } - // read bpe merges and populate bpe ranks const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str()); if (merges_keyidx == -1) { throw std::runtime_error("cannot find tokenizer merges in model file\n"); } - const int n_merges = gguf_get_arr_n(ctx, merges_keyidx); - for (int i = 0; i < n_merges; i++) { const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i); GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0); @@ -5054,6 +5355,8 @@ static void llm_load_vocab( // for now, only BPE models have pre-tokenizers if (vocab.type == LLAMA_VOCAB_TYPE_BPE) { + vocab.tokenizer_add_space_prefix = false; + vocab.tokenizer_clean_spaces = true; if (tokenizer_pre.empty()) { LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__); LLAMA_LOG_WARN("%s: \n", __func__); @@ -5075,9 +5378,11 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "deepseek-llm") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM; + vocab.tokenizer_clean_spaces = false; } else if ( tokenizer_pre == "deepseek-coder") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER; + vocab.tokenizer_clean_spaces = false; } else if ( tokenizer_pre == "falcon") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON; @@ -5089,6 +5394,7 @@ static void llm_load_vocab( vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER; } else if ( tokenizer_pre == "gpt-2" || + tokenizer_pre == "phi-2" || tokenizer_pre == "jina-es" || tokenizer_pre == "jina-de" || tokenizer_pre == "jina-v2-es" || @@ -5104,6 +5410,7 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "qwen2") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2; + vocab.tokenizer_clean_spaces = false; } else if ( tokenizer_pre == "stablelm2") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2; @@ -5119,18 +5426,31 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "poro-chat") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO; + vocab.tokenizer_clean_spaces = false; + } else if ( + tokenizer_pre == "chatglm-bpe") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4; + vocab.special_bos_id = -1; } else if ( tokenizer_pre == "viking") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING; + vocab.tokenizer_clean_spaces = false; + } else if ( + tokenizer_pre == "jais") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } } else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; + vocab.tokenizer_add_space_prefix = true; + vocab.tokenizer_clean_spaces = false; vocab.tokenizer_add_bos = true; vocab.tokenizer_add_eos = false; } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; + vocab.tokenizer_add_space_prefix = false; + vocab.tokenizer_clean_spaces = true; vocab.tokenizer_add_bos = true; vocab.tokenizer_add_eos = false; } else if (vocab.type == LLAMA_VOCAB_TYPE_UGM) { @@ -5140,6 +5460,11 @@ static void llm_load_vocab( } else { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; } + + const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); + if (add_space_prefix_keyidx != -1) { + vocab.tokenizer_add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); + } } const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str()); @@ -5252,7 +5577,6 @@ static void llm_load_vocab( vocab.special_eot_id = 107; } } - try { vocab.linefeed_id = llama_byte_to_token(vocab, '\n'); } catch (const std::exception & e) { @@ -5343,7 +5667,7 @@ static void llm_load_vocab( } } - std::sort( vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(), + std::sort(vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(), [&] (const llama_vocab::id a, const llama_vocab::id b) { return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size(); } @@ -5429,43 +5753,78 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); + auto print_f = [](const std::function & f, uint32_t n) { + bool is_var = false; + + std::vector v; + for (uint32_t i = 0; i < n; ++i) { + v.push_back(f(i)); + if (v[i] != v[0]) { + is_var = true; + } + } + + std::stringstream ss; + + if (is_var) { + ss << "["; + for (uint32_t i = 0; i < n; ++i) { + ss << v[i]; + if (i < n - 1) { + ss << ", "; + } + } + ss << "]"; + } else { + ss << v[0]; + } + + return ss.str(); + }; + // hparams LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver)); LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch)); LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type)); LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab); LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size()); - LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train); - LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd); - LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head); - LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv); - LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer); - LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); - LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k); - LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v); - LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa()); - LLAMA_LOG_INFO("%s: n_embd_k_gqa = %u\n", __func__, hparams.n_embd_k_gqa()); - LLAMA_LOG_INFO("%s: n_embd_v_gqa = %u\n", __func__, hparams.n_embd_v_gqa()); - LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps); - LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps); - LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv); - LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias); - LLAMA_LOG_INFO("%s: f_logit_scale = %.1e\n", __func__, hparams.f_logit_scale); - LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); - LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert); - LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used); - LLAMA_LOG_INFO("%s: causal attn = %d\n", __func__, hparams.causal_attn); - LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type); - LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type); - LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type); - LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); - LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); - LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn); - LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown"); - LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv); - LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner); - LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state); - LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank); + LLAMA_LOG_INFO("%s: vocab_only = %d\n", __func__, hparams.vocab_only); + + if (!hparams.vocab_only) { + LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train); + LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd); + LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer); + LLAMA_LOG_INFO("%s: n_head = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head(il); }, hparams.n_layer).c_str()); + LLAMA_LOG_INFO("%s: n_head_kv = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str()); + LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); + LLAMA_LOG_INFO("%s: n_swa = %u\n", __func__, hparams.n_swa); + LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k); + LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v); + LLAMA_LOG_INFO("%s: n_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il); }, hparams.n_layer).c_str()); + LLAMA_LOG_INFO("%s: n_embd_k_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str()); + LLAMA_LOG_INFO("%s: n_embd_v_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str()); + LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps); + LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps); + LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv); + LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias); + LLAMA_LOG_INFO("%s: f_logit_scale = %.1e\n", __func__, hparams.f_logit_scale); + LLAMA_LOG_INFO("%s: n_ff = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str()); + LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert); + LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used); + LLAMA_LOG_INFO("%s: causal attn = %d\n", __func__, hparams.causal_attn); + LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type); + LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type); + LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type); + LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); + LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); + LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn); + LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown"); + LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv); + LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner); + LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state); + LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank); + } + LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type)); LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str()); if (ml.n_elements >= 1e12) { @@ -5545,8 +5904,8 @@ static bool llm_load_tensors( model.main_gpu = main_gpu; model.n_gpu_layers = n_gpu_layers; - const int64_t n_layer = hparams.n_layer; - const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0); + const int n_layer = hparams.n_layer; + const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0); bool use_mmap_buffer = true; // there is very little benefit to offloading the input layer, so always keep it on the CPU @@ -5556,7 +5915,7 @@ static bool llm_load_tensors( model.buft_layer.resize(n_layer); // assign cpu layers - for (int64_t i = 0; i < i_gpu_start; ++i) { + for (int i = 0; i < i_gpu_start; ++i) { model.buft_layer[i] = llama_default_buffer_type_cpu(true); } @@ -5586,7 +5945,7 @@ static bool llm_load_tensors( // assign the repeating layers to the devices according to the splits int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); - for (int64_t i = i_gpu_start; i < n_layer; ++i) { + for (int i = i_gpu_start; i < n_layer; ++i) { int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin(); model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu); } @@ -5606,7 +5965,7 @@ static bool llm_load_tensors( split_buft = llama_default_buffer_type_offload(model, main_gpu); } // assign the repeating layers - for (int64_t i = i_gpu_start; i < n_layer; ++i) { + for (int i = i_gpu_start; i < n_layer; ++i) { model.buft_layer[i] = { split_buft, llama_default_buffer_type_offload(model, main_gpu) @@ -5629,7 +5988,7 @@ static bool llm_load_tensors( buft_layer_count[model.buft_input.buft_matrix]++; buft_layer_count[model.buft_output.buft]++; buft_layer_count[model.buft_output.buft_matrix]++; - for (int64_t i = 0; i < n_layer; ++i) { + for (int i = 0; i < n_layer; ++i) { buft_layer_count[model.buft_layer[i].buft]++; buft_layer_count[model.buft_layer[i].buft_matrix]++; } @@ -5659,15 +6018,21 @@ static bool llm_load_tensors( // create tensors for the weights { - const int64_t n_embd = hparams.n_embd; - const int64_t n_embd_head = (hparams.n_head == 0) ? 0 : n_embd / hparams.n_head; - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const int64_t n_embd_gqa = n_embd_v_gqa; - const int64_t n_vocab = hparams.n_vocab; - const int64_t n_vocab_type = hparams.n_vocab_type; - const int64_t n_ff = hparams.n_ff; - const int64_t n_expert = hparams.n_expert; + // note: cast to int64_t since we will use these for the tensor dimensions + const int64_t n_head = hparams.n_head(); + const int64_t n_head_kv = hparams.n_head_kv(); + const int64_t n_embd = hparams.n_embd; + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const int64_t n_embd_head_k = hparams.n_embd_head_k; + const int64_t n_embd_head_v = hparams.n_embd_head_v; + const int64_t n_ff = hparams.n_ff(); + const int64_t n_embd_gqa = n_embd_v_gqa; + const int64_t n_vocab = hparams.n_vocab; + const int64_t n_vocab_type = hparams.n_vocab_type; + const int64_t n_expert = hparams.n_expert; + const int64_t n_expert_used = hparams.n_expert_used; + const int64_t n_ctx_train = hparams.n_ctx_train; if (n_expert > 0 && hparams.n_expert_used == 0) { throw std::runtime_error("model has expert layers but no expert layers are used"); @@ -5676,8 +6041,9 @@ static bool llm_load_tensors( ggml_context * ctx_input = ctx_map.at(model.buft_input.buft); ggml_context * ctx_output = ctx_map.at(model.buft_output.buft); ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix); - auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); }; - auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); }; + + auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); }; + auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); }; model.layers.resize(n_layer); @@ -5692,7 +6058,8 @@ static bool llm_load_tensors( // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed if (model.output == NULL) { model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); @@ -5775,6 +6142,7 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed if (model.output == NULL) { model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); @@ -5798,9 +6166,9 @@ static bool llm_load_tensors( layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); + if (layer.ffn_gate_exps) { layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); @@ -5852,12 +6220,12 @@ static bool llm_load_tensors( auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); + layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}); @@ -5899,10 +6267,10 @@ static bool llm_load_tensors( // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } @@ -5930,7 +6298,7 @@ static bool llm_load_tensors( case LLM_ARCH_STARCODER: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); // output { @@ -5956,8 +6324,8 @@ static bool llm_load_tensors( layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); @@ -5965,8 +6333,8 @@ static bool llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; case LLM_ARCH_BERT: @@ -5974,8 +6342,9 @@ static bool llm_load_tensors( { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); + if (model.arch == LLM_ARCH_BERT) { - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); } model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); @@ -5988,31 +6357,30 @@ static bool llm_load_tensors( auto & layer = model.layers[i]; if (model.arch == LLM_ARCH_BERT) { - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); } else { layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); } - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); if (model.arch == LLM_ARCH_BERT) { - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); - - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); } else { layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); } @@ -6023,8 +6391,9 @@ static bool llm_load_tensors( } break; case LLM_ARCH_JINA_BERT_V2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings - model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); //token_type_embeddings + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings + model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); // token_type_embeddings + model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias @@ -6034,38 +6403,38 @@ static bool llm_load_tensors( auto & layer = model.layers[i]; // JinaBertLayer - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens - layer.bo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens + layer.bo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm - layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); + layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); - layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); + layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); + layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); } } break; case LLM_ARCH_BLOOM: @@ -6088,35 +6457,35 @@ static bool llm_load_tensors( auto & layer = model.layers[i]; layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; case LLM_ARCH_MPT: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED); // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } @@ -6187,8 +6556,8 @@ static bool llm_load_tensors( layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional q and k layernorms, present in StableLM 2 12B - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional FFN norm, not present in StableLM 2 12B which uses parallel residual layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); @@ -6299,21 +6668,23 @@ static bool llm_load_tensors( layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - GGML_ASSERT(hparams.n_expert > 0); - GGML_ASSERT(hparams.n_expert_used > 0); + GGML_ASSERT(n_expert > 0); + GGML_ASSERT(n_expert_used > 0); // MoE branch - auto n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / hparams.n_expert_used; + const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used; + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); // Shared expert branch - auto n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff; + const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff; + layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}); - layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}); - layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}); - layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}); + layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp}); + layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}); + layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp}); } } break; case LLM_ARCH_PHI2: @@ -6363,6 +6734,8 @@ static bool llm_load_tensors( } break; case LLM_ARCH_PHI3: { + const int64_t n_embd_head = n_embd / n_head; + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }); // output @@ -6372,8 +6745,8 @@ static bool llm_load_tensors( } for (int i = 0; i < n_layer; ++i) { - ggml_context* ctx_layer = ctx_for_layer(i); - ggml_context* ctx_split = ctx_for_layer_split(i); + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; @@ -6422,7 +6795,7 @@ static bool llm_load_tensors( case LLM_ARCH_GPT2: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); // output { @@ -6559,12 +6932,7 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading - const int64_t n_ff = hparams.n_ff; - const int64_t n_embd_head_k = hparams.n_embd_head_k; - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - - for (uint32_t i = 0; i < n_layer; ++i) { + for (int i = 0; i < n_layer; ++i) { ggml_context * ctx_layer = ctx_for_layer(i); ggml_context * ctx_split = ctx_for_layer_split(i); @@ -6572,10 +6940,10 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * hparams.n_head}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); @@ -6591,12 +6959,7 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading - const int64_t n_ff = hparams.n_ff; - const int64_t n_embd_head_k = hparams.n_embd_head_k; - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - - for (uint32_t i = 0; i < n_layer; ++i) { + for (int i = 0; i < n_layer; ++i) { ggml_context * ctx_layer = ctx_for_layer(i); ggml_context * ctx_split = ctx_for_layer_split(i); @@ -6604,10 +6967,10 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * hparams.n_head}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); layer.attn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); @@ -6671,6 +7034,7 @@ static bool llm_load_tensors( const int64_t d_inner = hparams.ssm_d_inner; const int64_t d_state = hparams.ssm_d_state; const int64_t dt_rank = hparams.ssm_dt_rank; + // only an expansion factor of 2 is supported for now GGML_ASSERT(2 * n_embd == d_inner); @@ -6721,15 +7085,20 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } + for (int i = 0; i < n_layer; ++i) { ggml_context * ctx_layer = ctx_for_layer(i); ggml_context * ctx_split = ctx_for_layer_split(i); + auto & layer = model.layers[i]; + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); @@ -6756,8 +7125,8 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); if (n_layer >= 64){ - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}); } layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); @@ -6793,15 +7162,49 @@ static bool llm_load_tensors( layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_OPENELM: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + // init output from the input tok embed + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); + } + + for (int i = 0; i < n_layer; ++i) { + const int64_t n_head = hparams.n_head(i); + const int64_t n_head_qkv = 2*hparams.n_head_kv(i) + n_head; + const int64_t n_ff = hparams.n_ff(i); + + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; case LLM_ARCH_GPTNEOX: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); @@ -6840,8 +7243,9 @@ static bool llm_load_tensors( // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed if (model.output == NULL) { model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); @@ -6876,13 +7280,16 @@ static bool llm_load_tensors( } break; case LLM_ARCH_DEEPSEEK2: { - bool is_lite = (hparams.n_layer == 27); + const bool is_lite = (hparams.n_layer == 27); - const uint32_t n_embd_head_qk_rope = hparams.n_rot; - const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; - const uint32_t q_lora_rank = hparams.n_lora_q; - const uint32_t kv_lora_rank = hparams.n_lora_kv; - const uint32_t n_ff_exp = hparams.n_ff_exp; + const int64_t n_embd_head_qk_rope = hparams.n_rot; + const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + + const int64_t q_lora_rank = hparams.n_lora_q; + const int64_t kv_lora_rank = hparams.n_lora_kv; + + const int64_t n_ff_exp = hparams.n_ff_exp; + const int64_t n_expert_shared = hparams.n_expert_shared; model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -6902,29 +7309,31 @@ static bool llm_load_tensors( if (!is_lite) { layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}); } + layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}); if (!is_lite) { - layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}); - layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.n_head * hparams.n_embd_head_k}); + layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}); + layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}); } else { - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); } - layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}); - layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, hparams.n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {hparams.n_head * hparams.n_embd_head_v, n_embd}); + + layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}); + layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - if ((uint32_t) i < hparams.n_layer_dense_lead) { + if (i < (int) hparams.n_layer_dense_lead) { layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } else { layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - GGML_ASSERT(hparams.n_expert > 0); - GGML_ASSERT(hparams.n_expert_used > 0); + GGML_ASSERT(n_expert > 0); + GGML_ASSERT(n_expert_used > 0); // MoE branch layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); @@ -6932,9 +7341,9 @@ static bool llm_load_tensors( layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); // Shared expert branch - layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * hparams.n_expert_shared}); - layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * hparams.n_expert_shared, n_embd}); - layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * hparams.n_expert_shared}); + layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}); + layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}); + layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}); } } } break; @@ -6953,27 +7362,156 @@ static bool llm_load_tensors( auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); layer.attn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wq_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wk_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "scale", i), {1}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wv_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "scale", i), {1}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.wo_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wq_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wk_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "scale", i), {1}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wv_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "scale", i), {1}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wo_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); layer.ffn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_gate_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "scale", i), {1}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_gate_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "scale", i), {1}); + } + } break; + case LLM_ARCH_T5: + { + const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts; + + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}); + + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); + } + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + + layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); + layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + + layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}); + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_rel_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + + layer.attn_norm_cross = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd}); + // this tensor seems to be unused in HF transformers implementation + layer.attn_rel_b_cross = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + + layer.wq_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wk_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wv_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); + layer.wo_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; + case LLM_ARCH_JAIS: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // Output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_gate_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + } + } break; + case LLM_ARCH_CHATGLM: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + (hparams.n_embd_head_k << 2)}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + (hparams.n_embd_head_k << 2)}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); } } break; default: @@ -7200,6 +7738,7 @@ enum llm_ffn_op_type { LLM_FFN_GELU, LLM_FFN_RELU, LLM_FFN_RELU_SQR, + LLM_FFN_SWIGLU, }; enum llm_ffn_gate_type { @@ -7254,8 +7793,8 @@ static void llm_build_kv_store( int64_t il) { const int64_t n_ctx = cparams.n_ctx; - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); GGML_ASSERT(kv.size == n_ctx); @@ -7404,6 +7943,19 @@ static struct ggml_tensor * llm_build_ffn( cur = ggml_sqr(ctx, cur); cb(cur, "ffn_sqr(relu)", il); } break; + case LLM_FFN_SWIGLU: + { + // Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + int64_t split_point = cur->ne[0] / 2; + struct ggml_tensor * x0 = ggml_cont(ctx, ggml_view_2d(ctx, cur, split_point, cur->ne[1], cur->nb[1], 0)); + struct ggml_tensor * x1 = ggml_cont(ctx, ggml_view_2d(ctx, cur, split_point, cur->ne[1], cur->nb[1], split_point * ggml_element_size(cur))); + + x0 = ggml_silu(ctx, x0); + cb(cur, "ffn_silu", il); + + cur = ggml_mul(ctx, x0, x1); + cb(cur, "ffn_mul", il); + } break; } if (type_gate == LLM_FFN_PAR) { @@ -7548,12 +8100,12 @@ static struct ggml_tensor * llm_build_kqv( const llm_build_cb & cb, int il) { const int64_t n_ctx = cparams.n_ctx; - const int64_t n_head = hparams.n_head; - const int64_t n_head_kv = hparams.n_head_kv; + const int64_t n_head = hparams.n_head(il); + const int64_t n_head_kv = hparams.n_head_kv(il); const int64_t n_embd_head_k = hparams.n_embd_head_k; - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); const int64_t n_embd_head_v = hparams.n_embd_head_v; - const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3); cb(q, "q", il); @@ -7729,6 +8281,7 @@ struct llm_build_context { const int32_t n_tokens; const int32_t n_kv; // size of KV cache to consider (n_kv <= kv_self.size) const int32_t n_outputs; + const int32_t n_outputs_enc; const int32_t kv_head; // index of where we store new KV data in the cache const int32_t n_ctx_orig; @@ -7759,8 +8312,8 @@ struct llm_build_context { n_layer (hparams.n_layer), n_rot (hparams.n_rot), n_ctx (cparams.n_ctx), - n_head (hparams.n_head), - n_head_kv (hparams.n_head_kv), + n_head (hparams.n_head()), + n_head_kv (hparams.n_head_kv()), n_embd_head_k (hparams.n_embd_head_k), n_embd_k_gqa (hparams.n_embd_k_gqa()), n_embd_head_v (hparams.n_embd_head_v), @@ -7778,6 +8331,7 @@ struct llm_build_context { n_tokens (batch.n_tokens), n_kv (worst_case ? kv_self.size : kv_self.n), n_outputs (worst_case ? n_tokens : lctx.n_outputs), + n_outputs_enc (worst_case ? n_tokens : lctx.embd_enc.size() / hparams.n_embd), kv_head (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head), n_ctx_orig (cparams.n_ctx_orig_yarn), flash_attn (cparams.flash_attn), @@ -7797,17 +8351,21 @@ struct llm_build_context { ctx0 = ggml_init(params); - lctx.inp_tokens = nullptr; - lctx.inp_embd = nullptr; - lctx.inp_pos = nullptr; - lctx.inp_out_ids = nullptr; - lctx.inp_KQ_mask = nullptr; - lctx.inp_K_shift = nullptr; - lctx.inp_mean = nullptr; - lctx.inp_cls = nullptr; - lctx.inp_s_copy = nullptr; - lctx.inp_s_mask = nullptr; - lctx.inp_s_seq = nullptr; + lctx.inp_tokens = nullptr; + lctx.inp_embd = nullptr; + lctx.inp_pos = nullptr; + lctx.inp_out_ids = nullptr; + lctx.inp_KQ_mask = nullptr; + lctx.inp_KQ_mask_swa = nullptr; + lctx.inp_K_shift = nullptr; + lctx.inp_mean = nullptr; + lctx.inp_cls = nullptr; + lctx.inp_s_copy = nullptr; + lctx.inp_s_mask = nullptr; + lctx.inp_s_seq = nullptr; + lctx.inp_pos_bucket = nullptr; + lctx.inp_embd_enc = nullptr; + lctx.inp_KQ_mask_cross = nullptr; } void free() { @@ -7826,8 +8384,9 @@ struct llm_build_context { cb(lctx.inp_K_shift, "K_shift", -1); ggml_set_input(lctx.inp_K_shift); - for (int il = 0; il < n_layer; ++il) { + const int64_t n_head_kv = hparams.n_head_kv(il); + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); struct ggml_tensor * rope_factors = build_rope_factors(il); struct ggml_tensor * tmp = // we rotate only the first n_rot dimensions @@ -7887,6 +8446,9 @@ struct llm_build_context { } for (int il = 0; il < n_layer; ++il) { + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); + ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il], n_embd_k_gqa, nm, ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), @@ -7961,16 +8523,27 @@ struct llm_build_context { } struct ggml_tensor * build_inp_KQ_mask(bool causal = true) { - if (causal) { - lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); - } else { - lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); - } + lctx.inp_KQ_mask = causal + ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)) + : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); cb(lctx.inp_KQ_mask, "KQ_mask", -1); ggml_set_input(lctx.inp_KQ_mask); + return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask; } + struct ggml_tensor * build_inp_KQ_mask_swa(bool causal = true) { + GGML_ASSERT(hparams.n_swa > 0); + + lctx.inp_KQ_mask_swa = causal + ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)) + : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); + cb(lctx.inp_KQ_mask_swa, "KQ_mask_swa", -1); + ggml_set_input(lctx.inp_KQ_mask_swa); + + return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask_swa, GGML_TYPE_F16) : lctx.inp_KQ_mask_swa; + } + struct ggml_tensor * build_inp_mean() { lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens); cb(lctx.inp_mean, "inp_mean", -1); @@ -8050,6 +8623,53 @@ struct llm_build_context { return gf; } + struct ggml_tensor * llm_build_pos_bucket(bool causal) { + if (causal) { + lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens); + } else { + lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_tokens); + } + + ggml_set_input(lctx.inp_pos_bucket); + cb(lctx.inp_pos_bucket, "pos_bucket", -1); + + return lctx.inp_pos_bucket; + } + + struct ggml_tensor * llm_build_pos_bias(struct ggml_tensor * pos_bucket, struct ggml_tensor * attn_rel_b) { + struct ggml_tensor * pos_bucket_1d = ggml_view_1d(ctx0, pos_bucket, pos_bucket->ne[0] * pos_bucket->ne[1], 0); + cb(pos_bucket_1d, "pos_bucket_1d", -1); + + struct ggml_tensor * pos_bias = ggml_get_rows(ctx0, attn_rel_b, pos_bucket_1d); + cb(pos_bias, "pos_bias", -1); + + pos_bias = ggml_view_3d(ctx0, pos_bias, pos_bias->ne[0], lctx.inp_pos_bucket->ne[0], lctx.inp_pos_bucket->ne[1], ggml_element_size(pos_bias) * pos_bias->ne[0], ggml_element_size(pos_bias) * pos_bias->ne[0] * lctx.inp_pos_bucket->ne[0], 0); + cb(pos_bias, "pos_bias", -1); + + pos_bias = ggml_permute(ctx0, pos_bias, 2, 0, 1, 3); + cb(pos_bias, "pos_bias", -1); + + pos_bias = ggml_cont(ctx0, pos_bias); + cb(pos_bias, "pos_bias", -1); + + return pos_bias; + } + + struct ggml_tensor * llm_build_inp_embd_enc() { + const int64_t n_embd = hparams.n_embd; + lctx.inp_embd_enc = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_outputs_enc); + ggml_set_input(lctx.inp_embd_enc); + cb(lctx.inp_embd_enc, "embd_enc", -1); + return lctx.inp_embd_enc; + } + + struct ggml_tensor * llm_build_inp_KQ_mask_cross() { + lctx.inp_KQ_mask_cross = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_outputs_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); + ggml_set_input(lctx.inp_KQ_mask_cross); + cb(lctx.inp_KQ_mask_cross, "KQ_mask_cross", -1); + return lctx.inp_KQ_mask_cross; + } + struct ggml_cgraph * build_llama() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -10184,19 +10804,12 @@ struct llm_build_context { // special-case: the up and gate tensors are merged into a single tensor // TOOD: support into llm_build_ffn { - struct ggml_tensor* up = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur); - cb(up, "ffn_up", il); - - auto g = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), 0)); - auto y = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), up->nb[1] / 2)); - - y = ggml_mul(ctx0, y, ggml_silu(ctx0, g)); - cb(y, "ffn_gate", il); - - auto down = ggml_mul_mat(ctx0, model.layers[il].ffn_down, y); - cb(down, "ffn_down", il); - - cur = down; + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, NULL, + NULL, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -10966,7 +11579,7 @@ struct llm_build_context { Qcur = ggml_rope_ext( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, nullptr, - n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); @@ -10975,7 +11588,7 @@ struct llm_build_context { Kcur = ggml_rope_ext( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr, - n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); @@ -11051,9 +11664,14 @@ struct llm_build_context { struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + // gemma 2 requires different mask for layers using sliding window (SWA) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(true); + struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa(true); for (int il = 0; il < n_layer; ++il) { + // (il % 2) layers use SWA + struct ggml_tensor * KQ_mask_l = (il % 2 == 0) ? KQ_mask_swa : KQ_mask; + // norm cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, @@ -11074,7 +11692,7 @@ struct llm_build_context { Qcur = ggml_rope_ext( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, nullptr, - n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); @@ -11083,13 +11701,13 @@ struct llm_build_context { Kcur = ggml_rope_ext( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr, - n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f, cb, il); } cur = llm_build_norm(ctx0, cur, hparams, @@ -11705,6 +12323,131 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_openelm() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + const int64_t n_head = hparams.n_head(il); + const int64_t n_head_kv = hparams.n_head_kv(il); + const int64_t n_head_qkv = 2*n_head_kv + n_head; + + cur = inpL; + struct ggml_tensor * residual = cur; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, cur->nb[1], cur->nb[2], 0)); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head)); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv))); + cb(Vcur, "Vcur", il); + + Qcur = llm_build_norm(ctx0, Qcur, hparams, + model.layers[il].attn_q_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(Qcur, "Qcur", il); + + Kcur = llm_build_norm(ctx0, Kcur, hparams, + model.layers[il].attn_k_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(Kcur, "Kcur", il); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + Vcur = ggml_reshape_2d(ctx0, Vcur, n_embd_head * n_head_kv, n_tokens); + cb(Qcur, "Vcur", il); + + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + residual = ggml_get_rows(ctx0, residual, inp_out_ids); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cur = lctx.cvec.apply_to(ctx0, cur, il); + cb(cur, "l_out", il); + + inpL = cur; + } + + cur = inpL; + + // norm + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + struct ggml_cgraph * build_gptneox() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -12352,6 +13095,526 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_t5() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + if (lctx.is_encoding) { + struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask_enc = build_inp_KQ_mask(false); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm_enc, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_enc, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_enc, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_enc, cur); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + + struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); + struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3)); + + struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + cb(kq, "kq", il); + + struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc; + struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_enc, attn_rel_b); + struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias); + cb(kq_b, "kq_b", il); + + kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_enc, 1.0f, hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); + + struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens))); + cb(v, "v", il); + + struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq); + cb(kqv, "kqv", il); + + struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); + + cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); + cb(cur, "kqv_merged_cont", il); + + ggml_build_forward_expand(gf, cur); + + cur = ggml_mul_mat(ctx0, model.layers[il].wo_enc, cur); + cb(cur, "kqv_out", il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm_enc, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + // T5 uses relu, flan-T5 uses gelu-gated + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up_enc, NULL, NULL, + model.layers[il].ffn_gate_enc, NULL, NULL, + model.layers[il].ffn_down_enc, NULL, NULL, + NULL, + model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU, + model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ, + cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + cb(cur, "result_embd", -1); + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm_enc, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + } else { + struct ggml_tensor * embd_enc = llm_build_inp_embd_enc(); + struct ggml_tensor * pos_bucket_dec = llm_build_pos_bucket(true); + + struct ggml_tensor * KQ_mask_dec = build_inp_KQ_mask(); + struct ggml_tensor * KQ_mask_cross = llm_build_inp_KQ_mask_cross(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); + + struct ggml_tensor * k = + ggml_view_3d(ctx0, kv_self.k_l[il], + n_embd_head_k, n_kv, n_head_kv, + ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), + ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k), + 0); + cb(k, "k", il); + + struct ggml_tensor * v = + ggml_view_3d(ctx0, kv_self.v_l[il], + n_kv, n_embd_head_v, n_head_kv, + ggml_element_size(kv_self.v_l[il])*n_ctx, + ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v, + 0); + cb(v, "v", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + + struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); + + struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + cb(kq, "kq", il); + + struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b; + struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_dec, attn_rel_b); + struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias); + cb(kq_b, "kq_b", il); + + kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_dec, 1.0f, hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); + + struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq); + cb(kqv, "kqv", il); + + struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); + + cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); + cb(cur, "kqv_merged_cont", il); + + ggml_build_forward_expand(gf, cur); + + cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur); + cb(cur, "kqv_out", il); + } + + cur = ggml_add(ctx0, cur, inpSA); + cb(cur, "cross_inp", il); + + struct ggml_tensor * inpCA = cur; + + // norm + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].attn_norm_cross, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm_cross", il); + + // cross-attention + { + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_cross, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_cross, embd_enc); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_cross, embd_enc); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc); + + struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); + struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3)); + + struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + cb(kq, "kq", il); + + kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); + + struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc))); + cb(v, "v", il); + + struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq); + cb(kqv, "kqv", il); + + struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); + + cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); + cb(cur, "kqv_merged_cont", il); + + ggml_build_forward_expand(gf, cur); + + cur = ggml_mul_mat(ctx0, model.layers[il].wo_cross, cur); + cb(cur, "kqv_out", il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + // T5 uses relu, flan-T5 uses gelu-gated + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU, + model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ, + cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + cb(cur, "result_embd", -1); + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + } + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_jais() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*cur->nb[0]*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/float(n_embd_head), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + + // add the input + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + // FF + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_chatglm() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + struct ggml_tensor * Qcur = nullptr; + struct ggml_tensor * Kcur = nullptr; + struct ggml_tensor * Vcur = nullptr; + + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + //printf("freq_base: %f freq_scale: %f ext_factor: %f attn_factor: %f\n", freq_base, freq_scale, ext_factor, attn_factor); + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur_rope", il); + + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur_rope", il); + + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + // Add the input + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // FF + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, NULL, + NULL, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + + } + + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -12567,6 +13830,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_olmo(); } break; + case LLM_ARCH_OPENELM: + { + result = llm.build_openelm(); + } break; case LLM_ARCH_GPTNEOX: { result = llm.build_gptneox(); @@ -12579,10 +13846,22 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_deepseek2(); } break; + case LLM_ARCH_CHATGLM: + { + result = llm.build_chatglm(); + } break; case LLM_ARCH_BITNET: { result = llm.build_bitnet(); } break; + case LLM_ARCH_T5: + { + result = llm.build_t5(); + } break; + case LLM_ARCH_JAIS: + { + result = llm.build_jais(); + } break; default: GGML_ASSERT(false); } @@ -12621,6 +13900,30 @@ static void llama_set_s_copy(llama_context & lctx) { } } +static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) { + // TODO move to hparams if a T5 variant appears that uses a different value + const int64_t max_distance = 128; + + if (bidirectional) { + n_buckets >>= 1; + } + + const int64_t max_exact = n_buckets >> 1; + + int32_t relative_position = x - y; + int32_t relative_bucket = 0; + if (bidirectional) { + relative_bucket += (relative_position > 0) * n_buckets; + relative_position = abs(relative_position); + } else { + relative_position = -std::min(relative_position, 0); + } + int32_t relative_position_if_large = floorf(max_exact + logf(1.0 * relative_position / max_exact) * (n_buckets - max_exact) / log(1.0 * max_distance / max_exact)); + relative_position_if_large = std::min(relative_position_if_large, n_buckets - 1); + relative_bucket += (relative_position < max_exact ? relative_position : relative_position_if_large); + return relative_bucket; +} + static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { // // set input data @@ -12686,13 +13989,18 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { if (lctx.inp_KQ_mask) { // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache. - if (cparams.causal_attn) { + if (cparams.causal_attn && !lctx.is_encoding) { const int64_t n_kv = kv_self.n; const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); - float * data = (float *) lctx.inp_KQ_mask->data; + float * data = (float *) lctx.inp_KQ_mask->data; + float * data_swa = nullptr; + + if (lctx.inp_KQ_mask_swa) { + data_swa = (float *) lctx.inp_KQ_mask_swa->data; + } // For causal attention, use only the previous KV cells // of the correct sequence for each token of the batch. @@ -12714,6 +14022,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } data[h*(n_kv*n_tokens) + j*n_kv + i] = f; + + // may need to cut off old tokens for sliding window + if (data_swa) { + if (pos - lctx.kv_self.cells[i].pos >= (int32_t)hparams.n_swa) { + f = -INFINITY; + } + data_swa[h*(n_kv*n_tokens) + j*n_kv + i] = f; + } } } @@ -12726,7 +14042,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } else { // when using kv cache, the mask needs to match the kv cache size const int64_t n_tokens = batch.n_tokens; - const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens; + const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); @@ -12760,7 +14076,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { + if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(lctx.inp_mean); @@ -12792,7 +14108,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { + if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(lctx.inp_cls); @@ -12813,7 +14129,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) { + if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(lctx.inp_cls); @@ -12890,6 +14206,70 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } } + + if (lctx.inp_pos_bucket) { + const int64_t n_tokens = batch.n_tokens; + + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer)); + + int32_t * data = (int32_t *) lctx.inp_pos_bucket->data; + + if (!lctx.is_encoding) { + const int64_t n_kv = kv_self.n; + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + for (int i = 0; i < n_kv; ++i) { + data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); + } + } + } + } else { + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + for (int i = 0; i < n_tokens; ++i) { + data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(batch.pos[i], batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); + } + } + } + } + } + + if (!lctx.is_encoding && lctx.inp_embd_enc) { + assert(lctx.inp_embd_enc->type == GGML_TYPE_F32); + assert((size_t) ggml_nelements(lctx.inp_embd_enc) == lctx.embd_enc.size()); + + ggml_backend_tensor_set(lctx.inp_embd_enc, lctx.embd_enc.data(), 0, ggml_nbytes(lctx.inp_embd_enc)); + } + + if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) { + const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd; + const int64_t n_tokens = batch.n_tokens; + + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer)); + + float * data = (float *) lctx.inp_KQ_mask_cross->data; + + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + for (int i = 0; i < n_output_enc; ++i) { + float f = -INFINITY; + for (int s = 0; s < batch.n_seq_id[j]; ++s) { + const llama_seq_id seq_id = batch.seq_id[j][s]; + if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) { + f = 0.0f; + } + } + data[h*(n_output_enc*n_tokens) + j*n_output_enc + i] = f; + } + } + + for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) { + for (int j = 0; j < n_output_enc; ++j) { + data[h*(n_output_enc*n_tokens) + i*n_output_enc + j] = -INFINITY; + } + } + } + } } // Make sure enough space is available for outputs. @@ -12906,7 +14286,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { // TODO: use a per-batch flag for logits presence instead const bool has_logits = !cparams.embeddings; - const bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE); + const bool has_embd = lctx.is_encoding || (cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE)); const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0; const size_t embd_size = has_embd ? n_embd*n_outputs_max : 0; @@ -12998,6 +14378,7 @@ static int llama_decode_internal( llama_context & lctx, llama_batch batch_all) { // TODO: rename back to batch + lctx.is_encoding = false; const uint32_t n_tokens_all = batch_all.n_tokens; if (n_tokens_all == 0) { @@ -13030,19 +14411,21 @@ static int llama_decode_internal( const auto n_ubatch = cparams.n_ubatch; + // TODO: simplify or deprecate std::vector pos; std::vector n_seq_id; std::vector seq_id_arr; std::vector> seq_id; + // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens + const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE; + // count outputs - if (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE) { - n_outputs = n_tokens_all; - } else if (batch_all.logits) { + if (batch_all.logits && !embd_pooled) { for (uint32_t i = 0; i < n_tokens_all; ++i) { n_outputs += batch_all.logits[i] != 0; } - } else if (lctx.logits_all) { + } else if (lctx.logits_all || embd_pooled) { n_outputs = n_tokens_all; } else { // keep last output only @@ -13088,7 +14471,7 @@ static int llama_decode_internal( { int32_t n_outputs_new = 0; - if (u_batch.logits) { + if (u_batch.logits && !embd_pooled) { for (uint32_t i = 0; i < n_tokens; i++) { n_outputs_new += u_batch.logits[i] != 0; } @@ -13293,6 +14676,138 @@ static int llama_decode_internal( return 0; } +// encode a batch of tokens by evaluating the encoder part of the transformer +// +// - lctx: llama context +// - batch: batch to evaluate +// +// return 0 on success +// return positive int on warning +// return negative int on error +// +static int llama_encode_internal( + llama_context & lctx, + llama_batch batch) { + + lctx.is_encoding = true; + + const uint32_t n_tokens = batch.n_tokens; + + if (n_tokens == 0) { + LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__); + return -1; + } + + const auto & model = lctx.model; + const auto & hparams = model.hparams; + const auto & cparams = lctx.cparams; + + GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT + + // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot + GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens"); + + if (lctx.t_compute_start_us == 0) { + lctx.t_compute_start_us = ggml_time_us(); + } + + lctx.n_queued_tokens += n_tokens; + + const int64_t n_embd = hparams.n_embd; + + // TODO: simplify or deprecate + std::vector pos; + std::vector n_seq_id; + std::vector seq_id_arr; + std::vector> seq_id; + + // reserve output buffer + if (llama_output_reserve(lctx, n_tokens) < n_tokens) { + LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens); + return -2; + }; + + for (uint32_t i = 0; i < n_tokens; ++i) { + lctx.output_ids[i] = i; + } + + lctx.inp_embd_enc = NULL; + lctx.n_outputs = n_tokens; + + const int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch; + GGML_ASSERT(n_threads > 0); + + // helpers for smoother batch API transition + // after deprecating the llama_eval calls, these will be removed + if (batch.pos == nullptr) { + pos.resize(n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + pos[i] = batch.all_pos_0 + i*batch.all_pos_1; + } + + batch.pos = pos.data(); + } + + if (batch.seq_id == nullptr) { + n_seq_id.resize(n_tokens); + seq_id.resize(n_tokens); + seq_id_arr.resize(n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + n_seq_id[i] = 1; + seq_id[i].resize(1); + seq_id[i][0] = batch.all_seq_id; + seq_id_arr[i] = seq_id[i].data(); + } + + batch.n_seq_id = n_seq_id.data(); + batch.seq_id = seq_id_arr.data(); + } + + ggml_backend_sched_reset(lctx.sched); + ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); + + ggml_cgraph * gf = llama_build_graph(lctx, batch, false); + + // the output embeddings after the final encoder normalization + struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 1]; + + GGML_ASSERT(strcmp(embd->name, "result_norm") == 0); + + ggml_backend_sched_alloc_graph(lctx.sched, gf); + + llama_set_inputs(lctx, batch); + + llama_graph_compute(lctx, gf, n_threads); + + // extract embeddings + if (embd) { + ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd); + GGML_ASSERT(backend_embd != nullptr); + + // extract token embeddings + GGML_ASSERT(lctx.embd != nullptr); + + lctx.embd_enc.resize(n_tokens*n_embd); + float * embd_out = lctx.embd_enc.data(); + + ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float)); + + // remember the sequence ids used during the encoding - needed for cross attention later + lctx.seq_ids_enc.resize(n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + for (int s = 0; s < batch.n_seq_id[i]; s++) { + llama_seq_id seq_id = batch.seq_id[i][s]; + lctx.seq_ids_enc[i].insert(seq_id); + } + } + } + + // Reset state for the next token before backend sync, to allow the CPU activities in the reset to + // overlap with device computation. + ggml_backend_sched_reset(lctx.sched); + + return 0; +} // find holes from the beginning of the KV cache and fill them by moving data from the end of the cache static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { @@ -13927,6 +15442,7 @@ struct llm_tokenizer_bpe { }; break; case LLAMA_VOCAB_PRE_TYPE_GPT2: + case LLAMA_VOCAB_PRE_TYPE_JAIS: regex_exprs = { "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", }; @@ -13944,6 +15460,11 @@ struct llm_tokenizer_bpe { " ?[^(\\s|.,!?…。，、।۔،)]+", }; break; + case LLAMA_VOCAB_PRE_TYPE_CHATGLM4: + regex_exprs = { + "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + }; + break; case LLAMA_VOCAB_PRE_TYPE_VIKING: regex_exprs = { "\\p{N}", @@ -14363,11 +15884,14 @@ struct llm_tokenizer_ugm { std::string normalized; normalize(text, &normalized); size_t input_len = normalized.size(); + if (input_len == 0) { + return; + } // initialize score_sum to -FLT_MAX so it will be always lower than sums of token scores - std::vector tokenization_results(input_len + 1, {0, 0, -FLT_MAX}); + std::vector tokenization_results(input_len + 1, {vocab.special_unk_id, 0, -FLT_MAX}); // at the beginning tokenization score is zero - tokenization_results[0] = { 0, 0, 0 }; + tokenization_results[0] = { vocab.special_unk_id, 0, 0 }; for (size_t input_offset = 0; input_offset < input_len;) { size_t prefix_offset = input_offset; @@ -14387,7 +15911,7 @@ struct llm_tokenizer_ugm { single_codepoint_token_found = true; } llama_token token_id = node->value; - const auto &token_data = vocab.id_to_token[token_id]; + const auto & token_data = vocab.id_to_token[token_id]; // we set the user-defined token scores to 0 to make them more likely to be selected // (normal token scores are log probabilities, so they are negative) @@ -14597,7 +16121,7 @@ private: size_t prefix_offset = input_offset; unicode_cpt_from_utf8(input, prefix_offset); return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset }; - } catch(std::invalid_argument & ex) { + } catch (std::invalid_argument & /*ex*/) { // if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER return { "\xEF\xBF\xBD", 3, 1 }; } @@ -14797,7 +16321,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & // tokenizer.encode('', add_special_tokens=True) returns [1] // tokenizer.encode('', add_special_tokens=False) returns [] - bool is_prev_special = false; + bool is_prev_special = true; // prefix with space if first token if (add_special && vocab.tokenizer_add_bos) { GGML_ASSERT(vocab.special_bos_id != -1); @@ -14809,10 +16333,9 @@ static std::vector llama_tokenize_internal(const llama_vocab & if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); - if (vocab.tokenizer_add_space_prefix) { - if (!output.size() || is_prev_special) { // prefix with space if first token - raw_text = " " + raw_text; - } + // prefix with space if previous is special + if (vocab.tokenizer_add_space_prefix && is_prev_special) { + raw_text = " " + raw_text; } #ifdef PRETOKENIZERDEBUG @@ -14821,6 +16344,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & llm_tokenizer_spm tokenizer(vocab); llama_escape_whitespace(raw_text); tokenizer.tokenize(raw_text, output); + is_prev_special = false; } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); is_prev_special = true; @@ -14846,7 +16370,6 @@ static std::vector llama_tokenize_internal(const llama_vocab & if (add_special) { tokenizer.append_bos(output); } - for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); @@ -16205,8 +17728,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n const llm_arch arch = qs.model.arch; const auto tn = LLM_TN(arch); - auto use_more_bits = [](int i_layer, int num_layers) -> bool { - return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2; + auto use_more_bits = [](int i_layer, int n_layers) -> bool { + return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2; }; const int n_expert = std::max(1, (int)qs.model.hparams.n_expert); auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) { @@ -16670,10 +18193,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // sanity checks // - // - qs.n_attention_wv == 0 for Mamba models - // - qs.n_attention_wv == model.hparams.n_layer for Transformer models + // - qs.n_attention_wv == 0 for Mamba models + // - qs.n_attention_wv == model.hparams.n_layer for Transformer models + // - qs.n_attention_wv == 3 * model.hparams.n_layer for Encoder-Decoder models // - GGML_ASSERT((qs.n_attention_wv == 0 || qs.n_attention_wv == (int)model.hparams.n_layer) && "n_attention_wv is unexpected"); + GGML_ASSERT((qs.n_attention_wv == 0 || qs.n_attention_wv == (int)model.hparams.n_layer || qs.n_attention_wv == 3 * (int)model.hparams.n_layer) && "n_attention_wv is unexpected"); size_t total_size_org = 0; size_t total_size_new = 0; @@ -16798,6 +18322,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s quantize &= name.find("ssm_x.weight") == std::string::npos; quantize &= name.find("ssm_dt.weight") == std::string::npos; + // do not quantize relative position bias (T5) + quantize &= name.find("attn_rel_b.weight") == std::string::npos; + enum ggml_type new_type; void * new_data; size_t new_size; @@ -17246,6 +18773,7 @@ struct llama_context_params llama_context_default_params() { /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS, /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED, /*.pooling_type =*/ LLAMA_POOLING_TYPE_UNSPECIFIED, + /*.attention_type =*/ LLAMA_ATTENTION_TYPE_UNSPECIFIED, /*.rope_freq_base =*/ 0.0f, /*.rope_freq_scale =*/ 0.0f, /*.yarn_ext_factor =*/ -1.0f, @@ -17498,7 +19026,6 @@ struct llama_context * llama_new_context_with_model( } cparams.yarn_attn_factor *= hparams.rope_attn_factor; - cparams.causal_attn = hparams.causal_attn; if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { @@ -17508,6 +19035,12 @@ struct llama_context * llama_new_context_with_model( } } + if (params.attention_type == LLAMA_ATTENTION_TYPE_UNSPECIFIED) { + cparams.causal_attn = hparams.causal_attn; + } else { + cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL; + } + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } @@ -17810,6 +19343,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_MAMBA: case LLM_ARCH_JINA_BERT_V2: case LLM_ARCH_T5: + case LLM_ARCH_JAIS: return LLAMA_ROPE_TYPE_NONE; // use what we call a normal RoPE, operating on pairs of consecutive head values @@ -17826,6 +19360,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_OLMO: case LLM_ARCH_ARCTIC: case LLM_ARCH_DEEPSEEK2: + case LLM_ARCH_CHATGLM: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 @@ -17844,6 +19379,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_GEMMA: case LLM_ARCH_GEMMA2: case LLM_ARCH_STARCODER2: + case LLM_ARCH_OPENELM: case LLM_ARCH_GPTNEOX: return LLAMA_ROPE_TYPE_NEOX; @@ -17953,6 +19489,17 @@ struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const ch return it->second; } +bool llama_model_has_encoder(const struct llama_model * model) { + switch (model->arch) { + case LLM_ARCH_T5: return true; + default: return false; + } +} + +llama_token llama_model_decoder_start_token(const struct llama_model * model) { + return model->hparams.dec_start_token_id; +} + uint32_t llama_model_quantize( const char * fname_inp, const char * fname_out, @@ -18426,8 +19973,6 @@ static void llama_state_get_data_internal(struct llama_context * ctx, llama_data const auto & hparams = ctx->model.hparams; const uint32_t n_layer = hparams.n_layer; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); // NOTE: kv_size and kv_buf_size are mostly used for sanity checks const uint32_t kv_head = llama_kv_cache_cell_max(kv_self); @@ -18447,6 +19992,9 @@ static void llama_state_get_data_internal(struct llama_context * ctx, llama_data std::vector tmp_buf; for (int il = 0; il < (int) n_layer; ++il) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head); tmp_buf.resize(k_size); @@ -18579,8 +20127,6 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { const auto & hparams = ctx->model.hparams; const uint32_t n_layer = hparams.n_layer; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); size_t kv_buf_size; uint32_t kv_head; @@ -18612,6 +20158,9 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { GGML_ASSERT(kv_self.total_size() >= kv_buf_size); for (int il = 0; il < (int) n_layer; ++il) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head); ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size); @@ -18774,8 +20323,6 @@ size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id) const auto & hparams = ctx->model.hparams; const uint32_t n_layer = hparams.n_layer; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); for (uint32_t i = 0; i < kv_self.size; ++i) { const auto & cell = kv_self.cells[i]; @@ -18786,6 +20333,9 @@ size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id) } for (int il = 0; il < (int)n_layer; ++il) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + // types of keys and values s_cell_data_size += sizeof(int32_t) * 2; // k_size_row and v_size_el values of layer @@ -18860,14 +20410,15 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam const auto & hparams = ctx->model.hparams; const uint32_t n_layer = hparams.n_layer; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); // Write the layer count data_ctx.write(&n_layer, sizeof(n_layer)); - // Write n_embd_v_gqa - data_ctx.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa)); + // Write n_embd_v_gqa (reference value) + { + const uint32_t n_embd_v_gqa_ref = hparams.n_embd_v_gqa() + hparams.n_embd_k_s(); + data_ctx.write(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref)); + } // Iterate the ranges and write all the pos (this is the token position in the prompt) for (const auto & range : cell_ranges) { @@ -18881,6 +20432,8 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam // Get whole range at a time std::vector tmp_buf; for (int il = 0; il < (int)n_layer; ++il) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(); + // Write key type const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type; data_ctx.write(&k_type_i, sizeof(k_type_i)); @@ -18901,6 +20454,8 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam // TODO: simplify, reduce copy-paste if (!kv_self.v_trans) { for (int il = 0; il < (int)n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + // Write value type const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; data_ctx.write(&v_type_i, sizeof(v_type_i)); @@ -18921,6 +20476,8 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam // For the values, they are transposed, so we also need the element size and get the element ranges from each row const uint32_t kv_size = kv_self.size; for (int il = 0; il < (int)n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + // Write value type const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; data_ctx.write(&v_type_i, sizeof(v_type_i)); @@ -18989,14 +20546,14 @@ size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, // Sanity check model compatibility const auto & hparams = ctx->model.hparams; const uint32_t n_layer = hparams.n_layer; - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s(); + if (n_layer != n_layer_ref) { LLAMA_LOG_ERROR("%s: mismatched n_layer (%d != %d)\n", __func__, n_layer, n_layer_ref); return 0; } - if (n_embd_v_gqa != n_embd_v_gqa_ref) { - LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref); + + if (hparams.n_embd_v_gqa() != n_embd_v_gqa_ref) { + LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, hparams.n_embd_v_gqa(), n_embd_v_gqa_ref); return 0; } @@ -19036,6 +20593,8 @@ size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, // For each layer, read the keys for each cell, one row is one cell, read as one contiguous blo for (int il = 0; il < (int)n_layer; ++il) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(); + // Read type of key int32_t k_type_i_ref; memcpy(&k_type_i_ref, inp, sizeof(k_type_i_ref)); @@ -19068,6 +20627,8 @@ size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, // TODO: simplify, reduce copy-paste if (!kv_self.v_trans) { for (int il = 0; il < (int)n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + // Read type of value int32_t v_type_i_ref; memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref)); @@ -19099,6 +20660,8 @@ size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, } else { // For each layer, read the values for each cell (transposed) for (int il = 0; il < (int)n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + // Read type of value int32_t v_type_i_ref; memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref)); @@ -19299,6 +20862,17 @@ void llama_batch_free(struct llama_batch batch) { if (batch.logits) free(batch.logits); } +int32_t llama_encode( + struct llama_context * ctx, + struct llama_batch batch) { + const int ret = llama_encode_internal(*ctx, batch); + if (ret < 0) { + LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret); + } + + return ret; +} + int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch) { @@ -19519,7 +21093,6 @@ int32_t llama_tokenize( bool add_special, bool parse_special) { auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_special, parse_special); - if (n_tokens_max < (int) res.size()) { // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__); return -((int) res.size()); @@ -19540,7 +21113,7 @@ static std::string llama_decode_text(const std::string & text) { const auto utf8 = unicode_cpt_to_utf8(cpt); try { decoded_text += unicode_utf8_to_byte(utf8); - } catch (const std::out_of_range & e) { + } catch (const std::out_of_range & /*e*/) { decoded_text += "[UNK_BYTE_0x"; for (const auto c : utf8) { decoded_text += format("%02x", (uint8_t) c); @@ -19553,85 +21126,66 @@ static std::string llama_decode_text(const std::string & text) { } // does not write null-terminator to buf -int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) { +int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) { // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843 - if (!special && llama_is_control_token(model->vocab, token)) { + static const int attr_special = LLAMA_TOKEN_ATTR_UNKNOWN | LLAMA_TOKEN_ATTR_CONTROL; + const llama_token_attr attr = llama_token_get_attr(model, token); + if (!special && (attr & attr_special)) { return 0; } + // copy piece chars to output text buffer + // skip up to 'lstrip' leading spaces before copying + auto _try_copy = [=] (const char * token, size_t size) -> int32_t { + for (int32_t i = 0; i < lstrip && size && *token == ' '; ++i) { + token++; + size--; + } + if (length < (int32_t)size) { + return (int32_t) -size; + } + memcpy(buf, token, size); + return (int32_t) size; + }; + // if we have a cache - use it { const auto & cache = model->vocab.cache_token_to_piece; if (!cache.empty()) { - const auto & res = cache.at(token); - if (length < (int) res.size()) { - return -(int) res.size(); - } - memcpy(buf, res.c_str(), res.size()); - return res.size(); + const auto & result = cache.at(token); + return _try_copy(result.data(), result.size()); } } if (0 <= token && token < llama_n_vocab(model)) { + const std::string & token_text = model->vocab.id_to_token[token].text; switch (llama_vocab_get_type(model->vocab)) { case LLAMA_VOCAB_TYPE_WPM: case LLAMA_VOCAB_TYPE_SPM: case LLAMA_VOCAB_TYPE_UGM: { // NOTE: we accept all unsupported token types, // suppressing them like CONTROL tokens. - if (llama_is_normal_token(model->vocab, token)) { - std::string result = model->vocab.id_to_token[token].text; + if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) { + return _try_copy(token_text.data(), token_text.size()); + } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) { + std::string result = token_text; llama_unescape_whitespace(result); - if (length < (int) result.length()) { - return -(int) result.length(); - } - memcpy(buf, result.c_str(), result.length()); - return result.length(); - } else if ( - (llama_is_user_defined_token(model->vocab, token)) || - (llama_is_control_token (model->vocab, token) && special)) { - std::string result = model->vocab.id_to_token[token].text; - if (length < (int) result.length()) { - return -(int) result.length(); - } - memcpy(buf, result.c_str(), result.length()); - return result.length(); - } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT - if (length < 3) { - return -3; - } - memcpy(buf, "\xe2\x96\x85", 3); - return 3; - } else if (llama_is_byte_token(model->vocab, token)) { - if (length < 1) { - return -1; - } - buf[0] = llama_token_to_byte(model->vocab, token); - return 1; + return _try_copy(result.data(), result.size()); + } else if (attr & LLAMA_TOKEN_ATTR_BYTE) { + char byte = (char) llama_token_to_byte(model->vocab, token); + return _try_copy((char*) &byte, 1); } break; } case LLAMA_VOCAB_TYPE_BPE: { // NOTE: we accept all unsupported token types, // suppressing them like CONTROL tokens. - if (llama_is_normal_token(model->vocab, token)) { - std::string result = model->vocab.id_to_token[token].text; - result = llama_decode_text(result); - if (length < (int) result.length()) { - return -(int) result.length(); - } - memcpy(buf, result.c_str(), result.length()); - return result.length(); - } else if ( - (llama_is_user_defined_token(model->vocab, token)) || - (llama_is_control_token (model->vocab, token) && special)) { - std::string result = model->vocab.id_to_token[token].text; - if (length < (int) result.length()) { - return -(int) result.length(); - } - memcpy(buf, result.c_str(), result.length()); - return result.length(); + if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) { + return _try_copy(token_text.data(), token_text.size()); + } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) { + std::string result = llama_decode_text(token_text); + return _try_copy(result.data(), result.size()); } break; } @@ -19642,6 +21196,113 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token return 0; } +int32_t llama_detokenize( + const struct llama_model * model, + const llama_token * tokens, + int32_t n_tokens, + char * text, + int32_t text_len_max, + bool remove_special, + bool unparse_special) { + int32_t avail = text_len_max; + int32_t total = 0; + + // remove the leading space + bool remove_space = model->vocab.tokenizer_add_space_prefix; + + if (remove_special && model->vocab.tokenizer_add_bos) { + if (n_tokens > 0 && tokens[0] == model->vocab.special_bos_id) { + remove_space = false; + n_tokens--; + tokens++; + } + } + + if (remove_special && model->vocab.tokenizer_add_eos) { + if (n_tokens > 0 && tokens[n_tokens-1] == model->vocab.special_eos_id) { + n_tokens--; + } + } + + for (int32_t i = 0; i < n_tokens; ++i) { + GGML_ASSERT(avail >= 0); + int32_t n_chars = llama_token_to_piece(model, tokens[i], text, avail, remove_space, unparse_special); + remove_space = false; + if (n_chars < 0) { + avail = 0; + total -= n_chars; + } else if (n_chars > 0) { + avail -= n_chars; + text += n_chars; + total += n_chars; + } + } + + if (total > text_len_max) { + return -total; + } + + if (model->vocab.tokenizer_clean_spaces) { + text -= total; // restart text + + // first pass: characters ?!., //TODO: where do these characters come from? + const int32_t total1 = total; + total = total ? 1 : 0; + for (int32_t i = 1; i < total1; ++i) { + const char x = text[i]; + if (text[i - 1] == ' ') { + if (x == '?' || x == '!' || x == '.' || x == ',') { // " ?", " !", " .", " ," + total--; // remove space + } + } + text[total++] = x; + } + + // second pass: strip single apostrophe between spaces + const int32_t total2 = total; + total = total ? 1 : 0; + for (int32_t i = 1; i < total2; ++i) { + const char x = text[i]; + if (x == '\'' && i + 1 < total2 && text[i - 1] == ' ' && text[i + 1] == ' ') { // " ' " + total--; // remove prev space + text[++i] = '\0'; // remove next space + } + text[total++] = x; + } + + // third pass: apostrophe contractions //NOTE: this makes sense? + const int32_t total3 = total; + total = total ? 1 : 0; + for (int32_t i = 1; i < total3; ++i) { + const char x = text[i]; + if (text[i - 1] == ' ') { + if (x == '\'' && i + 1 < total3) { + const char x1 = text[i + 1]; + if (x1 == 't' || x1 == 'd') { // " 't", " 'd" + //total--; // remove space + } else if (x1 == 's' || x1 == 'm') { // " 's", " 'm" + total--; // remove space + } else if (i + 2 < total3) { + const char x2 = text[i + 2]; + if ((x1 == 'l' && x2 == 'l')) { // " 'll" + //total--; // remove space + } else if ((x1 == 'r' && x2 == 'e') || (x1 == 'v' && x2 == 'e')) { // " 're", " 've" + total--; // remove space + } else { + //total--; // remove space + } + } else { + //total--; // remove space + } + } + } + text[total++] = x; + } + } + + return total <= text_len_max ? total : -total; +} + // trim whitespace from the beginning and end of a string static std::string trim(const std::string & str) { size_t start = 0; @@ -19850,6 +21511,25 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|start_header_id|>assistant<|end_header_id|>\n\n"; } + } else if (tmpl == "chatglm3" || tmpl_contains("[gMASK]sop")) { + // chatglm3-6b + ss << "[gMASK]" << "sop"; + for (auto message : chat) { + std::string role(message->role); + ss << "<|" << role << "|>" << "\n " << message->content; + } + if (add_ass) { + ss << "<|assistant|>"; + } + } else if (tmpl == "chaglm4" || tmpl_contains("[gMASK]")) { + ss << "[gMASK]" << ""; + for (auto message : chat) { + std::string role(message->role); + ss << "<|" << role << "|>" << "\n" << message->content; + } + if (add_ass) { + ss << "<|assistant|>"; + } } else if (tmpl == "minicpm" || tmpl_contains(u8"<用户>")) { // MiniCPM-3B-OpenHermes-2.5-v2-GGUF for (auto message : chat) { diff --git a/src/unicode-data.cpp b/src/unicode-data.cpp index 4a939898b..02bdf7823 100644 --- a/src/unicode-data.cpp +++ b/src/unicode-data.cpp @@ -7030,4 +7030,3 @@ const std::vector unicode_ranges_nfd = { // start, last, nfd {0x02FA1C, 0x02FA1C, 0x009F3B}, {0x02FA1D, 0x02FA1D, 0x02A600}, }; - diff --git a/src/unicode.cpp b/src/unicode.cpp index 8692924b9..51daa15af 100644 --- a/src/unicode.cpp +++ b/src/unicode.cpp @@ -232,8 +232,7 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t }; auto _get_flags = [&] (const size_t pos) -> codepoint_flags { - static const codepoint_flags undef(codepoint_flags::UNDEFINED); - return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef; + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : codepoint_flags{}; }; size_t _prev_end = offset_ini; @@ -295,9 +294,9 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t continue; } // regex: ?[^\s\p{L}\p{N}]+ - if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + if (!(flags2.is_whitespace | flags2.is_letter | flags2.is_number) && flags2.as_uint()) { pos += (cpt == ' '); - while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + while (!(flags2.is_whitespace | flags2.is_letter | flags2.is_number) && flags2.as_uint()) { flags2 = _get_flags(++pos); } _add_token(pos); @@ -351,8 +350,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & }; auto _get_flags = [&] (const size_t pos) -> codepoint_flags { - static const codepoint_flags undef(codepoint_flags::UNDEFINED); - return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef; + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : codepoint_flags{}; }; size_t _prev_end = offset_ini; @@ -394,8 +392,8 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } } - // regex: [^\r\n\p{L}\p{N}]?\p{L}+ //####FIXME: the first \p{L} is correct? - if (!(cpt == '\r' || cpt == '\n' || /*flags.is_letter |*/ flags.is_number)) { + // regex: [^\r\n\p{L}\p{N}]?\p{L}+ + if (!(cpt == '\r' || cpt == '\n' || flags.is_number)) { if (flags.is_letter || _get_flags(pos+1).is_letter) { // one or more letters pos++; while (_get_flags(pos).is_letter) { @@ -421,9 +419,9 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & // regex: ?[^\s\p{L}\p{N}]+[\r\n]* auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags); - if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + if (!(flags2.is_whitespace | flags2.is_letter | flags2.is_number) && flags.as_uint()) { pos += (cpt == ' '); - while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + while (!(flags2.is_whitespace | flags2.is_letter | flags2.is_number) && flags2.as_uint()) { flags2 = _get_flags(++pos); } uint32_t cpt2 = _get_cpt(pos); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index f74c0db47..2bb71ac03 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2052,6 +2052,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M, GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS, + GGML_TYPE_BF16, }; // unary ops diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index b154038b2..6583dd0b2 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -58,6 +58,10 @@ int main(void) { "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}", //Phi-3-vision "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}", + // ChatGLM3 + "{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}", + // ChatGLM4 + u8"[gMASK]{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}", // MiniCPM-3B-OpenHermes-2.5-v2-GGUF u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", // DeepSeek-V2 @@ -98,6 +102,10 @@ int main(void) { "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", //Phi-3-vision "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", + // ChatGLM3 + "[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n I am an assistant <|user|>\n Another question<|assistant|>", + // ChatGLM4 + "[gMASK]<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>", // MiniCPM-3B-OpenHermes-2.5-v2-GGUF u8"You are a helpful assistant<用户>HelloHi there<用户>Who are youI am an assistant<用户>Another question", // DeepSeek-V2 @@ -142,9 +150,9 @@ int main(void) { std::cout << "fmt_single(" << tmpl << ")\n" << output << "\n-------------------------\n"; return output; }; - assert(fmt_single("chatml") == "<|im_start|>user\nHow are you<|im_end|>\n<|im_start|>assistant\n"); + assert(fmt_single("chatml") == "\n<|im_start|>user\nHow are you<|im_end|>\n<|im_start|>assistant\n"); assert(fmt_single("llama2") == "[INST] How are you [/INST]"); - assert(fmt_single("gemma") == "user\nHow are you\nmodel\n"); + assert(fmt_single("gemma") == "\nuser\nHow are you\nmodel\n"); assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"); return 0; diff --git a/tests/test-rope.cpp b/tests/test-rope.cpp index f0895ffaa..8159e276a 100644 --- a/tests/test-rope.cpp +++ b/tests/test-rope.cpp @@ -218,4 +218,3 @@ int main(int /*argc*/, const char ** /*argv*/) { return 0; } - diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp index d478f1041..1f04b6f34 100644 --- a/tests/test-tokenizer-0.cpp +++ b/tests/test-tokenizer-0.cpp @@ -195,11 +195,11 @@ int main(int argc, char **argv) { const bool add_special = false; for (const auto & test_kv : k_tests) { - const std::vector res = llama_tokenize(ctx, test_kv.first, add_special); + const std::vector res = llama_tokenize(ctx, test_kv.first, add_special, true); printf("\n"); printf("src: '%s'\n", test_kv.first.c_str()); - printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str()); + printf("res: '%s'\n", llama_detokenize(ctx, res).c_str()); printf("tok: "); for (const auto & tok : res) { printf("%d ", tok); @@ -216,8 +216,8 @@ int main(int argc, char **argv) { if (!correct) { fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str()); fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__, - llama_detokenize_bpe(ctx, res).c_str(), - llama_detokenize_bpe(ctx, test_kv.second).c_str()); + llama_detokenize(ctx, res).c_str(), + llama_detokenize(ctx, test_kv.second).c_str()); fprintf(stderr, "%s : expected tokens: ", __func__); for (const auto & t : test_kv.second) { fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str()); @@ -253,7 +253,7 @@ int main(int argc, char **argv) { { const auto t_start = ggml_time_us(); - res = llama_tokenize(ctx, text, add_special); + res = llama_tokenize(ctx, text, add_special, true); const auto t_end = ggml_time_us(); @@ -272,7 +272,7 @@ int main(int argc, char **argv) { } for (const auto & tok : res) { - //ofs << tok << " '" << string_strip(llama_detokenize_bpe(ctx, std::vector{tok})) << "'" << std::endl; + //ofs << tok << " '" << string_strip(llama_detokenize(ctx, std::vector{tok})) << "'" << std::endl; ofs << tok << "\n"; } } diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp index 209a04ad6..9498387e0 100644 --- a/tests/test-tokenizer-1-bpe.cpp +++ b/tests/test-tokenizer-1-bpe.cpp @@ -11,6 +11,7 @@ #include #include #include +#include int main(int argc, char **argv) { if (argc < 2 || argc > 3) { @@ -63,7 +64,10 @@ int main(int argc, char **argv) { } } - GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_BPE); + //GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_BPE); + if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_BPE) { + return 99; + } #ifdef _WIN32 // We need this for unicode console support @@ -74,7 +78,7 @@ int main(int argc, char **argv) { const int n_vocab = llama_n_vocab(model); for (int i = 0; i < n_vocab; ++i) { - std::string str = llama_detokenize_bpe(ctx, std::vector(1, i)); + std::string str = llama_detokenize(ctx, std::vector(1, i)); try { auto cps = unicode_cpts_from_utf8(str); std::vector tokens = llama_tokenize(ctx, str, false, true); @@ -90,7 +94,7 @@ int main(int argc, char **argv) { fprintf(stderr, "]\n"); return 2; } - std::string check = llama_detokenize_bpe(ctx, tokens); + std::string check = llama_detokenize(ctx, tokens); if (check != str) { fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n", __func__, i, str.c_str(), str.length(), check.c_str(), check.length()); @@ -108,26 +112,23 @@ int main(int argc, char **argv) { std::vector threads(nthread); + std::atomic_int errcode = {}; + for (int i = 0; i < nthread; ++i) { - threads[i] = std::thread([i, nthread, ctx]() { - for (uint32_t cp = i; cp < 0x0010ffff; cp += nthread) { - if (!( // NOLINT - (cp < 0x03 || cp > 0x05) && cp != 0x0b && cp != 0x11 && - (cp < 0x13 || cp > 0x17) && cp != 0x19 && - (cp < 0x1c || cp > 0x1e) && - (cp < 0xd800 || cp > 0xdfff) && - (cp < 0x00040000 || cp >= 0x000e0000) - )) { + threads[i] = std::thread([i, nthread, ctx, &errcode]() { + for (uint32_t cp = i; !errcode && cp < 0x00110000; cp += nthread) { + if ((0x0000D800 <= cp && cp <= 0x0000DFFF) || // surrogates \p{Cs} + (0x00040000 <= cp && cp <= 0x000E0000)) { // undefined \p{Cn} continue; } std::string str = unicode_cpt_to_utf8(cp); std::vector tokens = llama_tokenize(ctx, str, false); - std::string check = llama_detokenize_bpe(ctx, tokens); + std::string check = llama_detokenize(ctx, tokens); if (cp != 9601 && str != check) { - fprintf(stderr, "error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", + fprintf(stderr, "error: codepoint 0x%x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", cp, check.c_str(), check.length(), str.c_str(), str.length()); - std::exit(3); + errcode = 3; } } }); @@ -136,6 +137,10 @@ int main(int argc, char **argv) { for (auto & t : threads) { t.join(); } + + if (errcode) { + return errcode; + } } llama_free_model(model); diff --git a/tests/test-tokenizer-1-spm.cpp b/tests/test-tokenizer-1-spm.cpp index ac2333dda..7ca9e2ca6 100644 --- a/tests/test-tokenizer-1-spm.cpp +++ b/tests/test-tokenizer-1-spm.cpp @@ -11,6 +11,7 @@ #include #include #include +#include int main(int argc, char ** argv) { if (argc < 2) { @@ -51,7 +52,10 @@ int main(int argc, char ** argv) { } } - GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM); + //GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM); + if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_SPM) { + return 99; + } #ifdef _WIN32 // We need this for unicode console support @@ -62,9 +66,9 @@ int main(int argc, char ** argv) { const int n_vocab = llama_n_vocab(model); for (int i = 0; i < n_vocab; ++i) { - std::string str = llama_detokenize_spm(ctx, std::vector(1, i)); - std::vector tokens = llama_tokenize(ctx, str, false); - std::string check = llama_detokenize_spm(ctx, tokens); + std::string str = llama_detokenize(ctx, std::vector(1, i), true); + std::vector tokens = llama_tokenize(ctx, str, false, true); + std::string check = llama_detokenize(ctx, tokens); if (check != str) { fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n", __func__, i, str.c_str(), str.length(), check.c_str(), check.length()); @@ -78,20 +82,23 @@ int main(int argc, char ** argv) { std::vector threads(nthread); + std::atomic_int errcode = {}; + for (int i = 0; i < nthread; ++i) { - threads[i] = std::thread([i, nthread, ctx]() { - for (uint32_t cp = i; cp < 0x0010ffff; cp += nthread) { - if (cp >= 0xd800 && cp <= 0xdfff) { + threads[i] = std::thread([i, nthread, ctx, &errcode]() { + for (uint32_t cp = i; !errcode && cp < 0x00110000; cp += nthread) { + if ((0x0000D800 <= cp && cp <= 0x0000DFFF) || // surrogates \p{Cs} + (0x00040000 <= cp && cp <= 0x000E0000)) { // undefined \p{Cn} continue; } std::string str = unicode_cpt_to_utf8(cp); - std::vector tokens = llama_tokenize(ctx, str, false); - std::string check = llama_detokenize_spm(ctx, tokens); + std::vector tokens = llama_tokenize(ctx, str, false, true); + std::string check = llama_detokenize(ctx, tokens); if (cp != 9601 && str != check) { - fprintf(stderr, "error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", + fprintf(stderr, "error: codepoint 0x%x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", cp, check.c_str(), check.length(), str.c_str(), str.length()); - std::exit(3); + errcode = 3; } } }); @@ -100,6 +107,10 @@ int main(int argc, char ** argv) { for (auto & t : threads) { t.join(); } + + if(errcode) { + return errcode; + } } llama_free_model(model); diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py index a07c52fb3..48cab8a1e 100644 --- a/tests/test-tokenizer-random.py +++ b/tests/test-tokenizer-random.py @@ -13,7 +13,7 @@ import subprocess import random import unicodedata -from typing import Callable, Iterator +from typing import Iterator import cffi from transformers import AutoTokenizer @@ -24,17 +24,20 @@ logger = logging.getLogger("test-tokenizer-random") class LibLlama: - DEFAULT_PATH_LLAMA_H = "./llama.h" - DEFAULT_PATH_LIBLLAMA = "./build/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON + DEFAULT_PATH_LLAMA_H = "./include/llama.h" + DEFAULT_PATH_INCLUDES = ["./ggml/include/", "./include/"] + DEFAULT_PATH_LIBLLAMA = "./build/src/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON - def __init__(self, path_llama_h: str = None, path_libllama: str = None): + def __init__(self, path_llama_h: str = None, path_includes: list[str] = [], path_libllama: str = None): path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H + path_includes = path_includes or self.DEFAULT_PATH_INCLUDES path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA - (self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_libllama) + (self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_includes, path_libllama) self.lib.llama_backend_init() - def _load_libllama_cffi(self, path_llama_h: str, path_libllama: str): - cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)=", path_llama_h] + def _load_libllama_cffi(self, path_llama_h: str, path_includes: list[str], path_libllama: str): + cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)="] + cmd += ["-I" + path for path in path_includes] + [path_llama_h] res = subprocess.run(cmd, stdout=subprocess.PIPE) assert (res.returncode == 0) source = res.stdout.decode() @@ -79,6 +82,7 @@ class LibLlamaModel: raise RuntimeError("error: failed to create context for model '%s'" % path_model) n_tokens_max = self.lib.llama_n_ctx(self.ctx) self.token_ids = self.ffi.new("llama_token[]", n_tokens_max) + self.text_buff = self.ffi.new("uint8_t[]", 1024) def free(self): if self.ctx: @@ -89,14 +93,78 @@ class LibLlamaModel: self.model = None self.lib = None - def tokenize(self, text: str, n_tokens_max: int = 0, add_special: bool = False, parse_special: bool = False) -> list[int]: - n_tokens_max = n_tokens_max if n_tokens_max > 0 else len(self.token_ids) + def tokenize(self, text: str, add_special: bool = False, parse_special: bool = False) -> list[int]: text = text.encode("utf-8") - num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, n_tokens_max, add_special, parse_special) - if num < 0: - return [] + num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, len(self.token_ids), add_special, parse_special) + while num < 0 and len(self.token_ids) < (16 << 20): + self.token_ids = self.ffi.new("llama_token[]", -2 * num) + num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, len(self.token_ids), add_special, parse_special) return list(self.token_ids[0:num]) + def detokenize(self, ids: list[int], remove_special: bool = False, unparse_special: bool = False) -> str: + if len(self.token_ids) < len(ids): + self.token_ids = self.ffi.new("llama_token[]", 2 * len(ids)) + for i, id in enumerate(ids): + self.token_ids[i] = id + num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special) + while num < 0 and len(self.text_buff) < (16 << 20): + self.text_buff = self.ffi.new("uint8_t[]", -2 * num) + num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special) + return str(self.ffi.buffer(self.text_buff, num), encoding="utf-8", errors="replace") # replace errors with '\uFFFD' + + +class Tokenizer: + + def encode(self, text: str) -> list[int]: + raise NotImplementedError + + def decode(self, ids: list[int]) -> str: + raise NotImplementedError + + +class TokenizerGroundtruth (Tokenizer): + + def __init__(self, dir_tokenizer: str): + self.model = AutoTokenizer.from_pretrained(dir_tokenizer) + # guess BOS and EOS + ids = self.encode("a") + assert 1 <= len(ids) <= 3 + add_bos_token = len(ids) > 1 and self.model.bos_token_id == ids[0] + add_eos_token = len(ids) > 1 and self.model.eos_token_id == ids[-1] + self.add_bos_token = getattr(self.model, "add_bos_token", add_bos_token) + self.add_eos_token = getattr(self.model, "add_eos_token", add_eos_token) + # build vocab + tokens = list(self.model.get_vocab().values()) + self.vocab = self.model.batch_decode(tokens, skip_special_tokens=True) + self.vocab = list(sorted(self.vocab)) + # tokens and lists + self.special_tokens = list(self.model.all_special_tokens) + self.added_tokens = list(self.model.added_tokens_encoder) + self.bos_token = self.model.bos_token + self.eos_token = self.model.eos_token + + def encode(self, text: str) -> list[int]: + return self.model.encode(text, add_special_tokens=True) + + def decode(self, ids: list[int]) -> str: + return self.model.decode(ids, skip_special_tokens=False) + + +class TokenizerLlamaCpp (Tokenizer): + + libllama: LibLlama = None + + def __init__(self, vocab_file: str): + if not self.libllama: + self.libllama = LibLlama() + self.model = LibLlamaModel(self.libllama, vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096)) + + def encode(self, text: str) -> list[int]: + return self.model.tokenize(text, add_special=True, parse_special=True) + + def decode(self, ids: list[int]) -> str: + return self.model.detokenize(ids, remove_special=False, unparse_special=True) + def generator_custom_text() -> Iterator[str]: """General tests""" @@ -165,19 +233,48 @@ def generator_custom_text_edge_cases() -> Iterator[str]: 'a b', # rstrip phi-3 'a b', # lstrip jina-v2 '\xa0aC', # deepseek + '\u2029 \uA3E4', # deepseek-llm + "a ?", + 'å', # mpt + '\U000ac517', # utf-8 encode error, falcon + '\U000522f4', # utf-8 encode error, starcoder + "abcd", + " abcd", ] -def generator_vocab_words(vocab: list[str]) -> Iterator[str]: +def generator_vocab_words(tokenizer: TokenizerGroundtruth) -> Iterator[str]: """Brute force check all vocab words""" - yield from vocab + yield from tokenizer.vocab -def generator_added_lr_strip(tokenizer) -> Iterator[str]: - WHITESPACES = ["", " ", " ", " "] - special_tokens = list(tokenizer.all_special_tokens) - added_tokens = list(tokenizer.added_tokens_encoder) - all_tokens = list(sorted(set(special_tokens + added_tokens))) +def generator_ascii_lr_strip() -> Iterator[str]: + WHITESPACES = ["", " ", " "] + CHARACTERS = list(chr(i) for i in range(1, 0x80)) + [""] + for char1 in CHARACTERS: + for char2 in CHARACTERS: + for lstrip in WHITESPACES: + for rstrip in WHITESPACES: + yield lstrip + char1 + char2 + rstrip + yield lstrip + char1 + rstrip + char2 + yield char1 + lstrip + char2 + rstrip + + +def generator_apostrophe() -> Iterator[str]: + WHITESPACES = ["", " ", " "] + CHARACTERS = list(chr(i) for i in range(1, 0x80)) + [""] + for char1 in CHARACTERS: + for char2 in CHARACTERS: + for lstrip in WHITESPACES: + for rstrip in WHITESPACES: + yield char1 + lstrip + "'" + rstrip + char2 + yield char1 + char2 + lstrip + "'" + rstrip + "z" + yield "a" + lstrip + "'" + rstrip + char1 + char2 + + +def generator_added_lr_strip(tokenizer: TokenizerGroundtruth) -> Iterator[str]: + WHITESPACES = ["", " ", " ", "\n", "\r\n", "\n\n", "\t", "\t\t"] + all_tokens = list(sorted(set(tokenizer.special_tokens + tokenizer.added_tokens))) for token in all_tokens: for lstrip in WHITESPACES: for rstrip in WHITESPACES: @@ -187,11 +284,9 @@ def generator_added_lr_strip(tokenizer) -> Iterator[str]: yield "a" + lstrip + token + rstrip + "z" -def generator_random_added_tokens(tokenizer, iterations=100) -> Iterator[str]: - special_tokens = list(tokenizer.all_special_tokens) - added_tokens = list(tokenizer.added_tokens_encoder) - separations = [" ", "\n", "\t", "-", "!", "one", "1", "", ""] - all_tokens = list(sorted(set(special_tokens + added_tokens + separations))) +def generator_random_added_tokens(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]: + separations = [" ", "\n", "\t", "-", "!", "one", "1", "", ""] + all_tokens = list(sorted(set(tokenizer.special_tokens + tokenizer.added_tokens + separations))) rand = random.Random() for m in range(iterations): rand.seed(m) @@ -242,13 +337,13 @@ def generator_unicodes() -> Iterator[str]: def _valid(cpt): if cpt >= 0x30000: # unassigned and supplement­ary return False - if 0x00D800 <= cpt <= 0x00F8FF: # Surrogates - return False - if unicodedata.category(chr(cpt)) == "Cn": + # if cpt == 0x2029: # deepseek-llm + # return False + if unicodedata.category(chr(cpt)) in ("Cn", "Cs", "Co"): # undefined, surrogates, private return False return True - characters = [chr(cpt) for cpt in range(1, MAX_CODEPOINTS) if _valid(cpt)] + characters = [chr(cpt) for cpt in range(0, MAX_CODEPOINTS) if _valid(cpt)] yield from characters @@ -273,11 +368,11 @@ def generator_random_unicodes(iterations=100) -> Iterator[str]: yield "".join(text) -def generator_random_vocab_chars(vocab: list[str], iterations=100) -> Iterator[str]: +def generator_random_vocab_chars(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]: """Brute force random text with vocab characters""" vocab_chars = set() - for word in vocab: + for word in tokenizer.vocab: vocab_chars.update(word) vocab_chars = list(sorted(vocab_chars)) @@ -288,10 +383,10 @@ def generator_random_vocab_chars(vocab: list[str], iterations=100) -> Iterator[s yield "".join(text) -def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[str]: +def generator_random_vocab_words(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]: """Brute force random text from vocab words""" - vocab = [w.strip() for w in vocab] + vocab = [w.strip() for w in tokenizer.vocab] yield from vocab rand = random.Random() @@ -307,7 +402,7 @@ def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[s yield "".join(text) -def compare_tokenizers(func_tokenize1: Callable, func_tokenize2: Callable, generator: Iterator[str]): +def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLlamaCpp, generator: Iterator[str]): def find_first_mismatch(ids1: list[int], ids2: list[int]): for i, (a, b) in enumerate(zip(ids1, ids2)): @@ -317,34 +412,67 @@ def compare_tokenizers(func_tokenize1: Callable, func_tokenize2: Callable, gener return -1 return min(len(ids1), len(ids2)) - t_tokenizer1 = 0 - t_tokenizer2 = 0 + def check_detokenizer(text: str, text1: str, text2: str) -> bool: + if text1 == text2: # equal to TokenizerGroundtruth? + return True + # equal to source text? + if tokenizer1.add_bos_token: # remove BOS + if text2.startswith(tokenizer1.bos_token): + text2 = text2[len(tokenizer1.bos_token):] + if tokenizer1.add_eos_token: # remove EOS + if text2.endswith(tokenizer1.eos_token): + text2 = text2[:-len(tokenizer1.eos_token)] + return text == text2 + + t_encode1 = 0 + t_encode2 = 0 + t_decode1 = 0 + t_decode2 = 0 t_start = time.perf_counter() - num_errors = 10 + encode_errors = 0 + decode_errors = 0 + MAX_ERRORS = 10 logger.info("%s: %s" % (generator.__name__, "ini")) for text in generator: + # print(repr(text), text.encode()) # print(repr(text), hex(ord(text[0])), text.encode()) t0 = time.perf_counter() - ids1 = func_tokenize1(text) + ids1 = tokenizer1.encode(text) t1 = time.perf_counter() - ids2 = func_tokenize2(text) + ids2 = tokenizer2.encode(text) t2 = time.perf_counter() - t_tokenizer1 += t1 - t0 - t_tokenizer2 += t2 - t1 - if ids1 != ids2: + text1 = tokenizer1.decode(ids1) + t3 = time.perf_counter() + text2 = tokenizer2.decode(ids1) + t4 = time.perf_counter() + t_encode1 += t1 - t0 + t_encode2 += t2 - t1 + t_decode1 += t3 - t2 + t_decode2 += t4 - t3 + if encode_errors < MAX_ERRORS and ids1 != ids2: i = find_first_mismatch(ids1, ids2) ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1] ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1] - logger.error(" TokenIDs: " + str(ids1)) - logger.error(" Expected: " + str(ids2)) + logger.error(" Expected: " + str(ids1)) + logger.error(" Result: " + str(ids2)) + encode_errors += 1 + logger.error(f" {encode_errors=}") + if decode_errors < MAX_ERRORS and not check_detokenizer(text, text1, text2): + i = find_first_mismatch(text1, text2) + text1 = list(text1[max(0, i - 2) : i + 5 + 1]) + text2 = list(text2[max(0, i - 2) : i + 5 + 1]) + logger.error(" Expected: " + " ".join(hex(ord(x)) for x in text1)) + logger.error(" Result: " + " ".join(hex(ord(x)) for x in text2)) + decode_errors += 1 + logger.error(f" {decode_errors=}") + if encode_errors >= MAX_ERRORS and decode_errors >= MAX_ERRORS: + logger.error(f" EXIT: {encode_errors=} {decode_errors=}") # raise Exception() - num_errors += 1 - if num_errors > 10: - break + break t_total = time.perf_counter() - t_start - logger.info("%s: end, tok1: %.3f tok2: %.3f total: %.3f" % (generator.__name__, t_tokenizer1, t_tokenizer2, t_total)) + logger.info(f"{generator.__name__}: end, {t_encode1=:.3f} {t_encode2=:.3f} {t_decode1=:.3f} {t_decode2=:.3f} {t_total=:.3f}") def main(argv: list[str] = None): @@ -357,74 +485,76 @@ def main(argv: list[str] = None): logging.basicConfig(level = logging.DEBUG if args.verbose else logging.INFO) logger.info(f"VOCABFILE: '{args.vocab_file}'") - model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096)) - tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) + tokenizer1 = TokenizerGroundtruth(args.dir_tokenizer) + tokenizer2 = TokenizerLlamaCpp(args.vocab_file) - def func_tokenize1(text: str): - return model.tokenize(text, add_special=True, parse_special=True) + # compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text()) + # compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text_edge_cases()) + compare_tokenizers(tokenizer1, tokenizer2, generator_ascii_lr_strip()) + compare_tokenizers(tokenizer1, tokenizer2, generator_apostrophe()) + compare_tokenizers(tokenizer1, tokenizer2, generator_unicodes()) + compare_tokenizers(tokenizer1, tokenizer2, generator_vocab_words(tokenizer1)) + compare_tokenizers(tokenizer1, tokenizer2, generator_added_lr_strip(tokenizer1)) + # compare_tokenizers(tokenizer1, tokenizer2, generator_random_added_tokens(tokenizer1, 10_000)) + # compare_tokenizers(tokenizer1, tokenizer2, generator_random_chars(10_000)) + # compare_tokenizers(tokenizer1, tokenizer2, generator_random_unicodes(10_000)) + # compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_chars(tokenizer1, 10_000)) + # compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_words(tokenizer1, 5_000)) - def func_tokenize2(text: str): - return tokenizer.encode(text, add_special_tokens=True) - - ids = func_tokenize2("a") - assert 1 <= len(ids) <= 3 - add_bos_token = len(ids) > 1 and tokenizer.bos_token_id == ids[0] - add_eos_token = len(ids) > 1 and tokenizer.eos_token_id == ids[-1] - tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", add_bos_token) - tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", add_eos_token) - - vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True))) - - compare_tokenizers(func_tokenize1, func_tokenize2, generator_custom_text()) - compare_tokenizers(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases()) - compare_tokenizers(func_tokenize1, func_tokenize2, generator_unicodes()) - compare_tokenizers(func_tokenize1, func_tokenize2, generator_vocab_words(vocab)) - compare_tokenizers(func_tokenize1, func_tokenize2, generator_added_lr_strip(tokenizer)) - compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_added_tokens(tokenizer, 10_000)) - compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_chars(10_000)) - compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_unicodes(10_000)) - compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000)) - compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 5_000)) - - model.free() + tokenizer2.model.free() if __name__ == "__main__": # main() + if True: + logging.basicConfig( + level = logging.DEBUG, + format = "%(asctime)s.%(msecs)03d %(name)s %(levelname)s %(message)s", + datefmt = "%Y-%m-%d %H:%M:%S", + filename = logger.name + ".log", + filemode = "a" + ) logging.basicConfig( level = logging.DEBUG, - format = "%(asctime)s.%(msecs)03d %(name)s %(levelname)s %(message)s", - datefmt = "%Y-%m-%d %H:%M:%S", - filename = logger.name + ".log", - filemode = "a" + format = "%(levelname)s %(message)s", ) path_tokenizers = "./models/tokenizers/" path_vocab_format = "./models/ggml-vocab-%s.gguf" - # import os - # tokenizers = os.listdir(path_tokenizers) tokenizers = [ - # "llama-spm", # SPM - # "phi-3", # SPM - # "bert-bge", # WPM - # "jina-v2-en", # WPM - "gpt-2", # BPE + "llama-spm", # SPM + "phi-3", # SPM + "gemma", # SPM + "gemma-2", # SPM + "baichuan", # SPM + "bert-bge", # WPM + "jina-v2-en", # WPM "llama-bpe", # BPE + "phi-2", # BPE + "deepseek-llm", # BPE + "deepseek-coder", # BPE "falcon", # BPE + "mpt", # BPE "starcoder", # BPE + "gpt-2", # BPE + "stablelm2", # BPE + "refact", # BPE + "qwen2", # BPE + "olmo", # BPE "jina-v2-es", # BPE "jina-v2-de", # BPE - "jina-v2-code", # BPE "smaug-bpe", # BPE - "phi-2", # BPE - "deepseek-coder", # BPE - "deepseek-llm", # BPE + "poro-chat", # BPE + "jina-v2-code", # BPE + "viking", # BPE + "jais", # BPE ] + logger.info("=" * 50) for tokenizer in tokenizers: - logger.info("=" * 50) + logger.info("-" * 50) logger.info(f"TOKENIZER: '{tokenizer}'") vocab_file = path_vocab_format % tokenizer dir_tokenizer = path_tokenizers + "/" + tokenizer