From c262beddf29f3f3be5bbbf167b56029a19876956 Mon Sep 17 00:00:00 2001 From: Gaurav Garg <52341457+gaugarg-nv@users.noreply.github.com> Date: Thu, 3 Apr 2025 21:50:29 +0530 Subject: [PATCH 01/99] CUDA: Prefer vector flash decoding kernel for Gemma models (#12738) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Prefer vector flash decoding kernel for Gemma models Vector flash decoding kernel was not being picked for models with head dimension 256. Gemma models are in this category. Removing this limit improves e2e performance by upto 12% in gen phase throughput for Gemm models. * Update ggml/src/ggml-cuda/fattn.cu Co-authored-by: Johannes Gäßler --------- Co-authored-by: Johannes Gäßler --- ggml/src/ggml-cuda/fattn.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 8edc12649a..7a2d1e4536 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -299,7 +299,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst const bool gqa_opt_applies = ((Q->ne[2] / K->ne[2]) % 2 == 0) && mask; // The mma-based kernels have GQA-specific optimizations const bool mma_needs_data_conversion = K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16; const bool mma_faster_for_bs1 = new_mma_available(cc) && gqa_opt_applies && cc < GGML_CUDA_CC_ADA_LOVELACE && !mma_needs_data_conversion; - const bool can_use_vector_kernel = (Q->ne[0] % (2*warp_size) == 0) && (prec == GGML_PREC_DEFAULT || Q->ne[0] <= 128); + const bool can_use_vector_kernel = Q->ne[0] % (2*warp_size) == 0; if (Q->ne[1] == 1 && can_use_vector_kernel && !mma_faster_for_bs1) { if (prec == GGML_PREC_DEFAULT) { ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); From 7d7b1bafa7980603a61cb102879fe38a33f66c08 Mon Sep 17 00:00:00 2001 From: lhez Date: Thu, 3 Apr 2025 22:18:17 -0700 Subject: [PATCH 02/99] opencl: update doc for OpenCL (#12702) * opencl: add OpenCL to build.md * opencl: remove fixed issue/TODO * opencl: add link to OPENCL.md * opencl: update doc - refine tools requirement for Windows 11 arm64 --- docs/backend/OPENCL.md | 10 +++-- docs/build.md | 90 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 97 insertions(+), 3 deletions(-) diff --git a/docs/backend/OPENCL.md b/docs/backend/OPENCL.md index 2a946dc8df..07146f7102 100644 --- a/docs/backend/OPENCL.md +++ b/docs/backend/OPENCL.md @@ -145,8 +145,13 @@ A Snapdragon X Elite device with Windows 11 Arm64 is used. Make sure the followi * Clang 19 * Ninja * Visual Studio 2022 +* Powershell 7 -Powershell is used for the following instructions. +Visual Studio provides necessary headers and libraries although it is not directly used for building. +Alternatively, Visual Studio Build Tools can be installed instead of the full Visual Studio. + +Powershell 7 is used for the following commands. +If an older version of Powershell is used, these commands may not work as they are. ### I. Setup Environment @@ -196,10 +201,9 @@ ninja ## Known Issues -- Qwen2.5 0.5B model produces gibberish output with Adreno kernels. +- Currently OpenCL backend does not work on Adreno 6xx GPUs. ## TODO -- Fix Qwen2.5 0.5B - Optimization for Q6_K - Support and optimization for Q4_K diff --git a/docs/build.md b/docs/build.md index 9c1314a294..3f1b043992 100644 --- a/docs/build.md +++ b/docs/build.md @@ -456,6 +456,96 @@ KleidiAI's microkernels implement optimized tensor operations using Arm CPU feat Depending on your build target, other higher priority backends may be enabled by default. To ensure the CPU backend is used, you must disable the higher priority backends either at compile time, e.g. -DGGML_METAL=OFF, or during run-time using the command line option `--device none`. +## OpenCL + +This provides GPU acceleration through OpenCL on recent Adreno GPU. +More information about OpenCL backend can be found in [OPENCL.md](./backend/OPENCL.md) for more information. + +### Android + +Assume NDK is available in `$ANDROID_NDK`. First, install OpenCL headers and ICD loader library if not available, + +```sh +mkdir -p ~/dev/llm +cd ~/dev/llm + +git clone https://github.com/KhronosGroup/OpenCL-Headers && \ +cd OpenCL-Headers && \ +cp -r CL $ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include + +cd ~/dev/llm + +git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && \ +cd OpenCL-ICD-Loader && \ +mkdir build_ndk && cd build_ndk && \ +cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ + -DOPENCL_ICD_LOADER_HEADERS_DIR=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=24 \ + -DANDROID_STL=c++_shared && \ +ninja && \ +cp libOpenCL.so $ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android +``` + +Then build llama.cpp with OpenCL enabled, + +```sh +cd ~/dev/llm + +git clone https://github.com/ggml-org/llama.cpp && \ +cd llama.cpp && \ +mkdir build-android && cd build-android + +cmake .. -G Ninja \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=android-28 \ + -DBUILD_SHARED_LIBS=OFF \ + -DGGML_OPENCL=ON + +ninja +``` + +### Windows Arm64 + +First, install OpenCL headers and ICD loader library if not available, + +```powershell +mkdir -p ~/dev/llm + +cd ~/dev/llm +git clone https://github.com/KhronosGroup/OpenCL-Headers && cd OpenCL-Headers +mkdir build && cd build +cmake .. -G Ninja ` + -DBUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF ` + -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl" +cmake --build . --target install + +cd ~/dev/llm +git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && cd OpenCL-ICD-Loader +mkdir build && cd build +cmake .. -G Ninja ` + -DCMAKE_BUILD_TYPE=Release ` + -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" ` + -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl" +cmake --build . --target install +``` + +Then build llama.cpp with OpenCL enabled, + +```powershell +cmake .. -G Ninja ` + -DCMAKE_TOOLCHAIN_FILE="$HOME/dev/llm/llama.cpp/cmake/arm64-windows-llvm.cmake" ` + -DCMAKE_BUILD_TYPE=Release ` + -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" ` + -DBUILD_SHARED_LIBS=OFF ` + -DGGML_OPENCL=ON +ninja +``` + ## Android To read documentation for how to build on Android, [click here](./android.md) From 35e592eb30832187412360912ab8f2f5b7984df1 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Fri, 4 Apr 2025 00:53:20 -0500 Subject: [PATCH 03/99] vulkan: set cmake minimum and project name in vulkan-shaders (#12744) --- ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt index e993941607..d6e0b2a5a5 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt +++ b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt @@ -1,3 +1,6 @@ +cmake_minimum_required(VERSION 3.19) +project("vulkan-shaders-gen" C CXX) + find_package (Threads REQUIRED) if (GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) From 74d4f5b041ad837153b0e90fc864b8290e01d8d5 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Fri, 4 Apr 2025 00:54:35 -0500 Subject: [PATCH 04/99] vulkan: Hybrid waitForFences/getFenceStatus to reduce fence latency (#12630) There seems to be a bubble waking up from waitForFences, which costs a few percent performance and also increased variance in performance. This change inserts an "almost_ready" fence when the graph is about 80% complete and we waitForFences for the almost_ready fence and then spin (with _mm_pauses) waiting for the final fence to be signaled. --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 93 +++++++++++++++++++++++----- 1 file changed, 78 insertions(+), 15 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index f6cc286034..189b3892e9 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -24,6 +24,28 @@ #include #include +#if defined(_MSC_VER) +# define NOMINMAX 1 +# include +# define YIELD() YieldProcessor() +#elif defined(__clang__) || defined(__GNUC__) +# if defined(__x86_64__) ||defined(__i386__) +# include +# define YIELD() _mm_pause() +# elif defined(__arm__) || defined(__aarch64__) +# if defined(__clang__) +# include +# define YIELD() __yield() +# else +# define YIELD() asm volatile("yield") +# endif +# endif +#endif + +#if !defined(YIELD) +#define YIELD() +#endif + #include "ggml-impl.h" #include "ggml-backend-impl.h" @@ -787,7 +809,8 @@ struct ggml_backend_vk_context { ggml_vk_garbage_collector gc; size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k; vk_buffer prealloc_x, prealloc_y, prealloc_split_k; - vk::Fence fence; + vk::Fence fence, almost_ready_fence; + bool almost_ready_fence_pending {}; vk_buffer buffer_pool[MAX_VK_BUFFERS]; @@ -878,6 +901,39 @@ typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context& subctx static void ggml_backend_vk_free(ggml_backend_t backend); +// Wait for ctx->fence to be signaled. +static void ggml_vk_wait_for_fence(ggml_backend_vk_context * ctx) { + // Use waitForFences while most of the graph executes. Hopefully the CPU can sleep + // during this wait. + if (ctx->almost_ready_fence_pending) { + VK_CHECK(ctx->device->device.waitForFences({ ctx->almost_ready_fence }, true, UINT64_MAX), "almost_ready_fence"); + ctx->device->device.resetFences({ ctx->almost_ready_fence }); + ctx->almost_ready_fence_pending = false; + } + + // Spin (w/pause) waiting for the graph to finish executing. + vk::Result result; + while ((result = ctx->device->device.getFenceStatus(ctx->fence)) != vk::Result::eSuccess) { + if (result != vk::Result::eNotReady) { + fprintf(stderr, "ggml_vulkan: error %s at %s:%d\n", to_string(result).c_str(), __FILE__, __LINE__); + exit(1); + } + for (uint32_t i = 0; i < 100; ++i) { + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + } + } + ctx->device->device.resetFences({ ctx->fence }); +} + // variables to track number of compiles in progress static uint32_t compile_count = 0; static std::mutex compile_count_mutex; @@ -3355,6 +3411,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->prealloc_size_split_k = 0; ctx->fence = ctx->device->device.createFence({}); + ctx->almost_ready_fence = ctx->device->device.createFence({}); #ifdef GGML_VULKAN_CHECK_RESULTS const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS"); @@ -7959,11 +8016,11 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { } } -static bool ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_tensor* tensor, int tensor_idx, bool use_fence); +static bool ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_tensor* tensor, int tensor_idx, bool use_fence, bool almost_ready); // Returns true if node has enqueued work into the queue, false otherwise // If submit is true the current all operations queued so far are being submitted to Vulkan to overlap cmdlist creation and GPU execution. -static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, ggml_tensor *node_begin, int node_idx_begin, bool dryrun, bool last_node, bool submit){ +static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, ggml_tensor *node_begin, int node_idx_begin, bool dryrun, bool last_node, bool almost_ready, bool submit){ if (ggml_is_empty(node) || !node->buffer) { return false; } @@ -8335,7 +8392,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod ctx->compute_ctx.reset(); - bool ok = ggml_vk_compute_forward(ctx, node_begin, node_idx_begin, false); + bool ok = ggml_vk_compute_forward(ctx, node_begin, node_idx_begin, false, almost_ready); if (!ok) { if (node->op == GGML_OP_UNARY) { std::cerr << __func__ << ": error: op not supported UNARY " << node->name << " (" << ggml_unary_op_name(static_cast(node->op_params[0])) << ")" << std::endl; @@ -8349,7 +8406,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod return true; } -static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * tensor, int tensor_idx, bool use_fence = true){ +static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * tensor, int tensor_idx, bool use_fence = true, bool almost_ready = false) { ggml_backend_buffer * buf = nullptr; switch (tensor->op) { @@ -8452,12 +8509,15 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(subctx, use_fence ? ctx->fence : vk::Fence{}); + if (almost_ready && !ctx->almost_ready_fence_pending && !use_fence) { + ggml_vk_submit(subctx, ctx->almost_ready_fence); + ctx->almost_ready_fence_pending = true; + } else { + ggml_vk_submit(subctx, use_fence ? ctx->fence : vk::Fence{}); + } if (use_fence) { - VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - - ctx->device->device.resetFences({ ctx->fence }); + ggml_vk_wait_for_fence(ctx); } #ifdef GGML_VULKAN_CHECK_RESULTS ggml_vk_check_results_1(tensor); @@ -8543,6 +8603,7 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { ctx->gc.events.clear(); ctx->device->device.destroyFence(ctx->fence); + ctx->device->device.destroyFence(ctx->almost_ready_fence); } static int ggml_vk_get_device_count() { @@ -8889,8 +8950,7 @@ static void ggml_backend_vk_synchronize(ggml_backend_t backend) { } ggml_vk_submit(transfer_ctx, ctx->fence); - VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); - ctx->device->device.resetFences({ ctx->fence }); + ggml_vk_wait_for_fence(ctx); for (auto& cpy : transfer_ctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); @@ -8909,7 +8969,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg uint64_t total_mat_mul_bytes = 0; for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(ctx, cgraph->nodes[i], i, nullptr, 0, true, false, false); + ggml_vk_build_graph(ctx, cgraph->nodes[i], i, nullptr, 0, true, false, false, false); if (cgraph->nodes[i]->op == GGML_OP_MUL_MAT || cgraph->nodes[i]->op == GGML_OP_MUL_MAT_ID) { total_mat_mul_bytes += ggml_nbytes(cgraph->nodes[i]->src[0]); } @@ -8951,11 +9011,14 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg mul_mat_bytes += ggml_nbytes(cgraph->nodes[i]->src[0]); } + // Signal the almost_ready fence when the graph is mostly complete (< 20% remaining) + bool almost_ready = (cgraph->n_nodes - i) < cgraph->n_nodes / 5; bool submit = (submitted_nodes >= nodes_per_submit) || (mul_mat_bytes >= mul_mat_bytes_per_submit) || - (i == last_node); + (i == last_node) || + (almost_ready && !ctx->almost_ready_fence_pending); - bool enqueued = ggml_vk_build_graph(ctx, cgraph->nodes[i], i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i == last_node, submit); + bool enqueued = ggml_vk_build_graph(ctx, cgraph->nodes[i], i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i == last_node, almost_ready, submit); if (enqueued) { ++submitted_nodes; @@ -8967,7 +9030,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg #endif } - if (submit) { + if (submit && enqueued) { first_node_in_batch = true; submitted_nodes = 0; mul_mat_bytes = 0; From 348888e0dc469a476f9e9eccb394893c513daab8 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 4 Apr 2025 10:24:12 +0200 Subject: [PATCH 05/99] docs : add XCFramework section to README.md [no ci] (#12746) This commit adds a new section to the README.md file, detailing the usage of the XCFramework. The motivation for this is that it might not be immediately clear to users how to use the XCFramework in their projects and hopefully this will help. --- README.md | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/README.md b/README.md index f8ed423c4e..95a05e6ed7 100644 --- a/README.md +++ b/README.md @@ -530,6 +530,35 @@ If your issue is with model generation quality, then please at least scan the fo - [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) +## XCFramework +The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, +and macOS. It can be used in Swift projects without the need to compile the +library from source. For example: +```swift +// swift-tools-version: 5.10 +// The swift-tools-version declares the minimum version of Swift required to build this package. + +import PackageDescription + +let package = Package( + name: "MyLlamaPackage", + targets: [ + .executableTarget( + name: "MyLlamaPackage", + dependencies: [ + "LlamaFramework" + ]), + .binaryTarget( + name: "LlamaFramework", + url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip", + checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab" + ) + ] +) +``` +The above example is using an intermediate build `b5046` of the library. This can be modified +to use a different version by changing the URL and checksum. + ## Completions Command-line completion is available for some environments. From 9ac4d611d05b03f961e627f4f399e4377bdb4ad3 Mon Sep 17 00:00:00 2001 From: Ronny Brendel Date: Fri, 4 Apr 2025 15:12:40 +0200 Subject: [PATCH 06/99] cmake: fix ggml-shaders-gen compiler paths containing spaces (#12747) fixes error for compiler paths with spaces --- ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in b/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in index b6af747a50..2d8a85696d 100644 --- a/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in +++ b/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in @@ -4,8 +4,8 @@ set(CMAKE_CXX_FLAGS -O2) set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY NEVER) set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE NEVER) -set(CMAKE_C_COMPILER @HOST_C_COMPILER@) -set(CMAKE_CXX_COMPILER @HOST_CXX_COMPILER@) +set(CMAKE_C_COMPILER "@HOST_C_COMPILER@") +set(CMAKE_CXX_COMPILER "@HOST_CXX_COMPILER@") set(CMAKE_RUNTIME_OUTPUT_DIRECTORY @CMAKE_RUNTIME_OUTPUT_DIRECTORY@) if("@CMAKE_C_COMPILER_ID@" STREQUAL "MSVC") From 94148ba330968bbfb8d9ecc67751bdc2218486cd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nicol=C3=B2=20Scipione?= Date: Fri, 4 Apr 2025 16:00:46 +0200 Subject: [PATCH 07/99] sycl: allow ggml-sycl configuration and compilation using Visual Studio project/solution (#12625) --- docs/backend/SYCL.md | 87 +++++++++++++++++++++++++++++-- ggml/src/ggml-sycl/CMakeLists.txt | 9 ++++ 2 files changed, 91 insertions(+), 5 deletions(-) diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index f1204dded0..cb29075b10 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -475,6 +475,12 @@ b. Enable oneAPI running environment: "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 ``` +- if you are using Powershell, enable the runtime environment with the following: + +``` +cmd.exe "/K" '"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" && powershell' +``` + c. Verify installation In the oneAPI command line, run the following to print the available SYCL devices: @@ -505,13 +511,13 @@ You could download the release package for Windows directly, which including bin Choose one of following methods to build from source code. -1. Script +#### 1. Script ```sh .\examples\sycl\win-build-sycl.bat ``` -2. CMake +#### 2. CMake On the oneAPI command line window, step into the llama.cpp main directory and run the following: @@ -540,13 +546,84 @@ cmake --preset x64-windows-sycl-debug cmake --build build-x64-windows-sycl-debug -j --target llama-cli ``` -3. Visual Studio +#### 3. Visual Studio -You can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project. +You have two options to use Visual Studio to build llama.cpp: +- As CMake Project using CMake presets. +- Creating a Visual Studio solution to handle the project. + +**Note**: + +All following commands are executed in PowerShell. + +##### - Open as a CMake Project + +You can use Visual Studio to open the `llama.cpp` folder directly as a CMake project. Before compiling, select one of the SYCL CMake presets: + +- `x64-windows-sycl-release` + +- `x64-windows-sycl-debug` *Notes:* +- For a minimal experimental setup, you can build only the inference executable using: -- In case of a minimal experimental setup, the user can build the inference executable only through `cmake --build build --config Release -j --target llama-cli`. + ```Powershell + cmake --build build --config Release -j --target llama-cli + ``` + +##### - Generating a Visual Studio Solution + +You can use Visual Studio solution to build and work on llama.cpp on Windows. You need to convert the CMake Project into a `.sln` file. + +If you want to use the Intel C++ Compiler for the entire `llama.cpp` project, run the following command: + +```Powershell +cmake -B build -G "Visual Studio 17 2022" -T "Intel C++ Compiler 2025" -A x64 -DGGML_SYCL=ON -DCMAKE_BUILD_TYPE=Release +``` + +If you prefer to use the Intel C++ Compiler only for `ggml-sycl`, ensure that `ggml` and its backend libraries are built as shared libraries ( i.e. `-DBUILD_SHARED_LIBRARIES=ON`, this is default behaviour): + +```Powershell +cmake -B build -G "Visual Studio 17 2022" -A x64 -DGGML_SYCL=ON -DCMAKE_BUILD_TYPE=Release \ + -DSYCL_INCLUDE_DIR="C:\Program Files (x86)\Intel\oneAPI\compiler\latest\include" \ + -DSYCL_LIBRARY_DIR="C:\Program Files (x86)\Intel\oneAPI\compiler\latest\lib" +``` + +If successful the build files have been written to: *path/to/llama.cpp/build* +Open the project file **build/llama.cpp.sln** with Visual Studio. + +Once the Visual Studio solution is created, follow these steps: + +1. Open the solution in Visual Studio. + +2. Right-click on `ggml-sycl` and select **Properties**. + +3. In the left column, expand **C/C++** and select **DPC++**. + +4. In the right panel, find **Enable SYCL Offload** and set it to `Yes`. + +5. Apply the changes and save. + + +*Navigation Path:* + +``` +Properties -> C/C++ -> DPC++ -> Enable SYCL Offload (Yes) +``` + +Now, you can build `llama.cpp` with the SYCL backend as a Visual Studio project. +To do it from menu: `Build -> Build Solution`. +Once it is completed, final results will be in **build/Release/bin** + +*Additional Note* + +- You can avoid specifying `SYCL_INCLUDE_DIR` and `SYCL_LIBRARY_DIR` in the CMake command by setting the environment variables: + + - `SYCL_INCLUDE_DIR_HINT` + + - `SYCL_LIBRARY_DIR_HINT` + +- Above instruction has been tested with Visual Studio 17 Community edition and oneAPI 2025.0. We expect them to work also with future version if the instructions are adapted accordingly. ### III. Run the inference diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt index 6747fd8836..6699b70bad 100644 --- a/ggml/src/ggml-sycl/CMakeLists.txt +++ b/ggml/src/ggml-sycl/CMakeLists.txt @@ -27,6 +27,15 @@ file(GLOB GGML_HEADERS_SYCL "*.hpp") file(GLOB GGML_SOURCES_SYCL "*.cpp") target_sources(ggml-sycl PRIVATE ${GGML_HEADERS_SYCL} ${GGML_SOURCES_SYCL}) +if (WIN32) + # To generate a Visual Studio solution, using Intel C++ Compiler for ggml-sycl is mandatory + if( ${CMAKE_GENERATOR} MATCHES "Visual Studio" AND NOT (${CMAKE_GENERATOR_TOOLSET} MATCHES "Intel C")) + set_target_properties(ggml-sycl PROPERTIES VS_PLATFORM_TOOLSET "Intel C++ Compiler 2025") + set(CMAKE_CXX_COMPILER "icx") + set(CMAKE_CXX_COMPILER_ID "IntelLLVM") + endif() +endif() + find_package(IntelSYCL) if (IntelSYCL_FOUND) # Use oneAPI CMake when possible From 23106f94ea2bc3da929afb7330655fd5515d08dc Mon Sep 17 00:00:00 2001 From: nick huang Date: Fri, 4 Apr 2025 22:09:12 +0800 Subject: [PATCH 08/99] gguf-split : --merge now respects --dry-run option (#12681) * gguf-split now respects dry-run option * removing trailing space --- examples/gguf-split/gguf-split.cpp | 31 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp index ef3ceb686f..30e771564e 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -408,8 +408,6 @@ static void gguf_merge(const split_params & split_params) { exit(EXIT_FAILURE); } - std::ofstream fout(split_params.output.c_str(), std::ios::binary); - fout.exceptions(std::ofstream::failbit); // fail fast on write errors auto * ctx_out = gguf_init_empty(); @@ -453,7 +451,6 @@ static void gguf_merge(const split_params & split_params) { gguf_free(ctx_gguf); ggml_free(ctx_meta); gguf_free(ctx_out); - fout.close(); exit(EXIT_FAILURE); } @@ -466,7 +463,6 @@ static void gguf_merge(const split_params & split_params) { gguf_free(ctx_gguf); ggml_free(ctx_meta); gguf_free(ctx_out); - fout.close(); exit(EXIT_FAILURE); } @@ -479,7 +475,6 @@ static void gguf_merge(const split_params & split_params) { gguf_free(ctx_gguf); ggml_free(ctx_meta); gguf_free(ctx_out); - fout.close(); exit(EXIT_FAILURE); } @@ -500,9 +495,11 @@ static void gguf_merge(const split_params & split_params) { fprintf(stderr, "\033[3Ddone\n"); } - - // placeholder for the meta data - { + std::ofstream fout; + if (!split_params.dry_run) { + fout.open(split_params.output.c_str(), std::ios::binary); + fout.exceptions(std::ofstream::failbit); // fail fast on write errors + // placeholder for the meta data auto meta_size = gguf_get_meta_size(ctx_out); ::zeros(fout, meta_size); } @@ -518,7 +515,9 @@ static void gguf_merge(const split_params & split_params) { ggml_free(ctx_metas[i]); } gguf_free(ctx_out); - fout.close(); + if (!split_params.dry_run) { + fout.close(); + } exit(EXIT_FAILURE); } fprintf(stderr, "%s: writing tensors %s ...", __func__, split_path); @@ -540,10 +539,11 @@ static void gguf_merge(const split_params & split_params) { auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor); f_input.seekg(offset); f_input.read((char *)read_data.data(), n_bytes); - - // write tensor data + padding - fout.write((const char *)read_data.data(), n_bytes); - zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes); + if (!split_params.dry_run) { + // write tensor data + padding + fout.write((const char *)read_data.data(), n_bytes); + zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes); + } } gguf_free(ctx_gguf); @@ -552,16 +552,15 @@ static void gguf_merge(const split_params & split_params) { fprintf(stderr, "\033[3Ddone\n"); } - { + if (!split_params.dry_run) { // go back to beginning of file and write the updated metadata fout.seekp(0); std::vector data(gguf_get_meta_size(ctx_out)); gguf_get_meta_data(ctx_out, data.data()); fout.write((const char *)data.data(), data.size()); - fout.close(); - gguf_free(ctx_out); } + gguf_free(ctx_out); fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n", __func__, split_params.output.c_str(), n_split, total_tensors); From b7723942970b70412793f1926a35cfb93d5c157c Mon Sep 17 00:00:00 2001 From: Nauful Shaikh Date: Fri, 4 Apr 2025 09:09:52 -0500 Subject: [PATCH 09/99] server : webui : Upgrade daisyui, tailwindcss. (#12735) * Upgrade daisyui, tailwindcss. * Switch to all themes. * Revert a change. * Update formatting. * Install packages before npm build. * Revert "Install packages before npm build." This reverts commit 336c5147e614e60993162794ba9d9d4629a916f8. * Add index.html.gz * run build --------- Co-authored-by: Xuan Son Nguyen --- examples/server/public/index.html.gz | Bin 1260624 -> 1263128 bytes examples/server/webui/package-lock.json | 1436 ++++++----------- examples/server/webui/package.json | 6 +- examples/server/webui/postcss.config.js | 3 +- examples/server/webui/src/App.tsx | 2 +- examples/server/webui/src/Config.ts | 2 +- .../server/webui/src/components/Header.tsx | 3 +- examples/server/webui/src/index.scss | 11 +- 8 files changed, 554 insertions(+), 909 deletions(-) diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz index d0e6da8e4a1e0646ad83f40bceea6462506ffc8a..941815c22efb0b6dcbbe50f0bd2d7d193dad1c7d 100644 GIT binary patch delta 1246812 zcmV(?K-a&}_)M6LO@AMY2mk;800062+`U^@+sf7`_`ZLI(8=(THf6^Ka+9g}<6=m5 z0?C4q%VN{)!Y(W=WXY>kG8pXrKl-WP`uFuu=rLziNhR5ktbM+R?lT4htM2z%^IqF) 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"normalize-path": "^3.0.0", - "object-hash": "^3.0.0", - "picocolors": "^1.1.1", - "postcss": "^8.4.47", - "postcss-import": "^15.1.0", - "postcss-js": "^4.0.1", - "postcss-load-config": "^4.0.2", - "postcss-nested": "^6.2.0", - "postcss-selector-parser": "^6.1.2", - "resolve": "^1.22.8", - "sucrase": "^3.35.0" - }, - "bin": { - "tailwind": "lib/cli.js", - "tailwindcss": "lib/cli.js" - }, "engines": { - "node": ">=14.0.0" + "node": ">=6" } }, "node_modules/textlinestream": { @@ -6007,27 +5765,6 @@ "integrity": "sha512-iBHbi7BQxrFmwZUQJsT0SjNzlLLsXhvW/kg7EyOMVMBIrlnj/qYofwo1LVLZi+3GbUEo96Iu2eqToI2+lZoAEQ==", "license": "MIT" }, - "node_modules/thenify": { - "version": "3.3.1", - "resolved": "https://registry.npmjs.org/thenify/-/thenify-3.3.1.tgz", - "integrity": "sha512-RVZSIV5IG10Hk3enotrhvz0T9em6cyHBLkH/YAZuKqd8hRkKhSfCGIcP2KUY0EPxndzANBmNllzWPwak+bheSw==", - "license": "MIT", - "dependencies": { - "any-promise": "^1.0.0" - } - }, - "node_modules/thenify-all": { - "version": "1.6.0", - "resolved": "https://registry.npmjs.org/thenify-all/-/thenify-all-1.6.0.tgz", - "integrity": "sha512-RNxQH/qI8/t3thXJDwcstUO4zeqo64+Uy/+sNVRBx4Xn2OX+OZ9oP+iJnNFqplFra2ZUVeKCSa2oVWi3T4uVmA==", - "license": "MIT", - "dependencies": { - "thenify": ">= 3.1.0 < 4" - }, - "engines": { - "node": ">=0.8" - } - }, "node_modules/to-regex-range": { "version": "5.0.1", "resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz", @@ -6073,12 +5810,6 @@ "typescript": ">=4.8.4" } }, - "node_modules/ts-interface-checker": { - "version": "0.1.13", - "resolved": "https://registry.npmjs.org/ts-interface-checker/-/ts-interface-checker-0.1.13.tgz", - "integrity": "sha512-Y/arvbn+rrz3JCKl9C4kVNfTfSm2/mEp5FSz5EsZSANGPSlQrpRI5M4PKF+mJnE52jOO90PnPSc3Ur3bTQw0gA==", - "license": "Apache-2.0" - }, "node_modules/tslib": { "version": "2.8.1", "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz", @@ -6304,12 +6035,6 @@ "punycode": "^2.1.0" } }, - "node_modules/util-deprecate": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", - "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==", - "license": "MIT" - }, "node_modules/varint": { "version": "6.0.0", "resolved": "https://registry.npmjs.org/varint/-/varint-6.0.0.tgz", @@ -6460,6 +6185,7 @@ "version": "2.0.2", "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz", "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==", + "dev": true, "license": "ISC", "dependencies": { "isexe": "^2.0.0" @@ -6481,94 +6207,6 @@ "node": ">=0.10.0" } }, - "node_modules/wrap-ansi": { - "version": "8.1.0", - "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz", - "integrity": "sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==", - "license": "MIT", - "dependencies": { - "ansi-styles": "^6.1.0", - "string-width": "^5.0.1", - "strip-ansi": "^7.0.1" - }, - "engines": { - "node": ">=12" - }, - "funding": { - "url": "https://github.com/chalk/wrap-ansi?sponsor=1" - } - }, - "node_modules/wrap-ansi-cjs": { - "name": "wrap-ansi", - "version": "7.0.0", - "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz", - "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==", - "license": "MIT", - "dependencies": { - "ansi-styles": "^4.0.0", - "string-width": "^4.1.0", - "strip-ansi": "^6.0.0" - }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/chalk/wrap-ansi?sponsor=1" - } - }, - "node_modules/wrap-ansi-cjs/node_modules/ansi-regex": { - "version": "5.0.1", - "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz", - "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==", - "license": "MIT", - "engines": { - "node": ">=8" - } - }, - "node_modules/wrap-ansi-cjs/node_modules/emoji-regex": { - "version": "8.0.0", - "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", - "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==", - "license": "MIT" - }, - "node_modules/wrap-ansi-cjs/node_modules/string-width": { - "version": "4.2.3", - "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", - "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", - "license": "MIT", - "dependencies": { - "emoji-regex": "^8.0.0", - "is-fullwidth-code-point": "^3.0.0", - "strip-ansi": "^6.0.1" - }, - "engines": { - "node": ">=8" - } - }, - "node_modules/wrap-ansi-cjs/node_modules/strip-ansi": { - "version": "6.0.1", - "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz", - "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==", - "license": "MIT", - "dependencies": { - "ansi-regex": "^5.0.1" - }, - "engines": { - "node": ">=8" - } - }, - "node_modules/wrap-ansi/node_modules/ansi-styles": { - "version": "6.2.1", - "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-6.2.1.tgz", - "integrity": "sha512-bN798gFfQX+viw3R7yrGWRqnrN2oRkEkUjjl4JNn4E8GxxbjtG3FbrEIIY3l8/hrwUwIeCZvi4QuOTP4MErVug==", - "license": "MIT", - "engines": { - "node": ">=12" - }, - "funding": { - "url": "https://github.com/chalk/ansi-styles?sponsor=1" - } - }, "node_modules/yallist": { "version": "3.1.1", "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz", @@ -6581,6 +6219,8 @@ "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.7.0.tgz", "integrity": "sha512-+hSoy/QHluxmC9kCIJyL/uyFmLmc+e5CFR5Wa+bpIhIj85LVb9ZH2nVnqrHoSvKogwODv0ClqZkmiSSaIH5LTA==", "license": "ISC", + "optional": true, + "peer": true, "bin": { "yaml": "bin.mjs" }, diff --git a/examples/server/webui/package.json b/examples/server/webui/package.json index 8c833d0241..6ac06b1a49 100644 --- a/examples/server/webui/package.json +++ b/examples/server/webui/package.json @@ -13,9 +13,11 @@ "dependencies": { "@heroicons/react": "^2.2.0", "@sec-ant/readable-stream": "^0.6.0", + "@tailwindcss/postcss": "^4.1.1", + "@tailwindcss/vite": "^4.1.1", "@vscode/markdown-it-katex": "^1.1.1", "autoprefixer": "^10.4.20", - "daisyui": "^4.12.14", + "daisyui": "^5.0.12", "dexie": "^4.0.11", "highlight.js": "^11.10.0", "katex": "^0.16.15", @@ -29,7 +31,7 @@ "remark-breaks": "^4.0.0", "remark-gfm": "^4.0.0", "remark-math": "^6.0.0", - "tailwindcss": "^3.4.15", + "tailwindcss": "^4.1.1", "textlinestream": "^1.1.1", "vite-plugin-singlefile": "^2.0.3" }, diff --git a/examples/server/webui/postcss.config.js b/examples/server/webui/postcss.config.js index 2e7af2b7f1..fb05b5692b 100644 --- a/examples/server/webui/postcss.config.js +++ b/examples/server/webui/postcss.config.js @@ -1,6 +1,5 @@ export default { plugins: { - tailwindcss: {}, - autoprefixer: {}, + "@tailwindcss/postcss": {}, }, } diff --git a/examples/server/webui/src/App.tsx b/examples/server/webui/src/App.tsx index 2ce734682c..cc4659e152 100644 --- a/examples/server/webui/src/App.tsx +++ b/examples/server/webui/src/App.tsx @@ -28,7 +28,7 @@ function AppLayout() { <> {/* chat input */} -
+
+ {isGenerating(currConvId ?? '') ? (
diff --git a/examples/server/webui/src/Config.ts b/examples/server/webui/src/Config.ts index 779ed9bf78..dd1cc0e100 100644 --- a/examples/server/webui/src/Config.ts +++ b/examples/server/webui/src/Config.ts @@ -1,4 +1,4 @@ -import daisyuiThemes from 'daisyui/src/theming/themes'; +import daisyuiThemes from 'daisyui/theme/object'; import { isNumeric } from './utils/misc'; export const isDev = import.meta.env.MODE === 'development'; diff --git a/examples/server/webui/src/components/Header.tsx b/examples/server/webui/src/components/Header.tsx index cbee394ba2..4c6b291e61 100644 --- a/examples/server/webui/src/components/Header.tsx +++ b/examples/server/webui/src/components/Header.tsx @@ -2,7 +2,7 @@ import { useEffect, useState } from 'react'; import StorageUtils from '../utils/storage'; import { useAppContext } from '../utils/app.context'; import { classNames } from '../utils/misc'; -import daisyuiThemes from 'daisyui/src/theming/themes'; +import daisyuiThemes from 'daisyui/theme/object'; import { THEMES } from '../Config'; import { useNavigate } from 'react-router'; @@ -20,7 +20,6 @@ export default function Header() { document.body.setAttribute('data-theme', selectedTheme); document.body.setAttribute( 'data-color-scheme', - // @ts-expect-error daisyuiThemes complains about index type, but it should work daisyuiThemes[selectedTheme]?.['color-scheme'] ?? 'auto' ); }, [selectedTheme]); diff --git a/examples/server/webui/src/index.scss b/examples/server/webui/src/index.scss index 314b682322..a18f094542 100644 --- a/examples/server/webui/src/index.scss +++ b/examples/server/webui/src/index.scss @@ -1,8 +1,13 @@ @use 'sass:meta'; +@use 'tailwindcss'; -@tailwind base; -@tailwind components; -@tailwind utilities; +@plugin 'daisyui' { + themes: all; +} + +html { + scrollbar-gutter: auto; +} .markdown { h1, From 1be76e4620ddc99b3cbff0f206436117ae561047 Mon Sep 17 00:00:00 2001 From: bandoti <141645996+bandoti@users.noreply.github.com> Date: Fri, 4 Apr 2025 14:05:12 -0300 Subject: [PATCH 10/99] ci: add Linux cross-compile build (#12428) --- .github/workflows/build-linux-cross.yml | 121 ++++++++++++++++++++++++ .github/workflows/build.yml | 5 +- 2 files changed, 125 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/build-linux-cross.yml diff --git a/.github/workflows/build-linux-cross.yml b/.github/workflows/build-linux-cross.yml new file mode 100644 index 0000000000..d4176fef9c --- /dev/null +++ b/.github/workflows/build-linux-cross.yml @@ -0,0 +1,121 @@ +name: Build on Linux using cross-compiler +on: + workflow_dispatch: + workflow_call: + +jobs: + ubuntu-latest-riscv64-cpu-cross: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + - name: Setup Riscv + run: | + sudo dpkg --add-architecture riscv64 + sudo sed -i 's|http://azure.archive.ubuntu.com/ubuntu|http://ports.ubuntu.com/ubuntu-ports|g' \ + /etc/apt/sources.list /etc/apt/apt-mirrors.txt + sudo apt-get clean + sudo apt-get update + sudo apt-get install -y --no-install-recommends \ + build-essential \ + gcc-14-riscv64-linux-gnu \ + g++-14-riscv64-linux-gnu + + - name: Build + run: | + cmake -B build -DCMAKE_BUILD_TYPE=Release \ + -DGGML_OPENMP=OFF \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TESTS=OFF \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=riscv64 \ + -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ + -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \ + -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \ + -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + cmake --build build --config Release -j $(nproc) + + ubuntu-latest-riscv64-vulkan-cross: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Setup Riscv + run: | + sudo dpkg --add-architecture riscv64 + sudo sed -i 's|http://azure.archive.ubuntu.com/ubuntu|http://ports.ubuntu.com/ubuntu-ports|g' \ + /etc/apt/sources.list /etc/apt/apt-mirrors.txt + sudo apt-get clean + sudo apt-get update + sudo apt-get install -y --no-install-recommends \ + build-essential \ + glslc \ + gcc-14-riscv64-linux-gnu \ + g++-14-riscv64-linux-gnu \ + libvulkan-dev:riscv64 + + - name: Build + run: | + cmake -B build -DCMAKE_BUILD_TYPE=Release \ + -DGGML_VULKAN=ON \ + -DGGML_OPENMP=OFF \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TESTS=OFF \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=riscv64 \ + -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ + -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \ + -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \ + -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + cmake --build build --config Release -j $(nproc) + + ubuntu-latest-arm64-vulkan-cross: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Setup Arm64 + run: | + sudo dpkg --add-architecture arm64 + sudo sed -i 's|http://azure.archive.ubuntu.com/ubuntu|http://ports.ubuntu.com/ubuntu-ports|g' \ + /etc/apt/sources.list /etc/apt/apt-mirrors.txt + sudo apt-get clean + sudo apt-get update + sudo apt-get install -y --no-install-recommends \ + build-essential \ + glslc \ + crossbuild-essential-arm64 \ + libvulkan-dev:arm64 + + - name: Build + run: | + cmake -B build -DCMAKE_BUILD_TYPE=Release \ + -DGGML_VULKAN=ON \ + -DGGML_OPENMP=OFF \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TESTS=OFF \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=aarch64 \ + -DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \ + -DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++ \ + -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ + -DCMAKE_FIND_ROOT_PATH=/usr/lib/aarch64-linux-gnu \ + -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \ + -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \ + -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH + + cmake --build build --config Release -j $(nproc) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 9b955f905b..dc2a4c0566 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -10,7 +10,7 @@ on: push: branches: - master - paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp'] + paths: ['.github/workflows/build.yml', '.github/workflows/build-linux-cross.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp'] pull_request: types: [opened, synchronize, reopened] paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp'] @@ -606,6 +606,9 @@ jobs: -DGGML_SYCL_F16=ON cmake --build build --config Release -j $(nproc) + build-linux-cross: + uses: ./.github/workflows/build-linux-cross.yml + macOS-latest-cmake-ios: runs-on: macos-latest From 3e1d29348b5d77269f6931500dd1c1a729d429c8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 4 Apr 2025 21:48:10 +0300 Subject: [PATCH 11/99] kv-cache : simplify + fix warning for recurrent models (#12756) ggml-ci --- src/llama-context.cpp | 77 ++++++++++++++++++++++---- src/llama-kv-cache.cpp | 122 +++-------------------------------------- src/llama-kv-cache.h | 52 ++---------------- src/llama-memory.h | 2 +- 4 files changed, 80 insertions(+), 173 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 3927079432..4735e98ea0 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -2474,7 +2474,12 @@ int32_t llama_get_kv_cache_token_count(const llama_context * ctx) { } int32_t llama_kv_self_n_tokens(const llama_context * ctx) { - return llama_kv_cache_n_tokens(ctx->get_kv_self()); + const auto * kv = ctx->get_kv_self(); + if (!kv) { + return 0; + } + + return kv->get_n_tokens(); } // deprecated @@ -2483,7 +2488,12 @@ int32_t llama_get_kv_cache_used_cells(const llama_context * ctx) { } int32_t llama_kv_self_used_cells(const llama_context * ctx) { - return llama_kv_cache_used_cells(ctx->get_kv_self()); + const auto * kv = ctx->get_kv_self(); + if (!kv) { + return 0; + } + + return kv->get_used_cells(); } // deprecated @@ -2492,7 +2502,12 @@ void llama_kv_cache_clear(llama_context * ctx) { } void llama_kv_self_clear(llama_context * ctx) { - llama_kv_cache_clear(ctx->get_kv_self()); + auto * kv = ctx->get_kv_self(); + if (!kv) { + return; + } + + kv->clear(); } // deprecated @@ -2509,7 +2524,12 @@ bool llama_kv_self_seq_rm( llama_seq_id seq_id, llama_pos p0, llama_pos p1) { - return llama_kv_cache_seq_rm(ctx->get_kv_self(), seq_id, p0, p1); + auto * kv = ctx->get_kv_self(); + if (!kv) { + return true; + } + + return kv->seq_rm(seq_id, p0, p1); } // deprecated @@ -2528,7 +2548,12 @@ void llama_kv_self_seq_cp( llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { - return llama_kv_cache_seq_cp(ctx->get_kv_self(), seq_id_src, seq_id_dst, p0, p1); + auto * kv = ctx->get_kv_self(); + if (!kv) { + return; + } + + return kv->seq_cp(seq_id_src, seq_id_dst, p0, p1); } // deprecated @@ -2539,7 +2564,12 @@ void llama_kv_cache_seq_keep( } void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) { - return llama_kv_cache_seq_keep(ctx->get_kv_self(), seq_id); + auto * kv = ctx->get_kv_self(); + if (!kv) { + return; + } + + return kv->seq_keep(seq_id); } // deprecated @@ -2558,7 +2588,12 @@ void llama_kv_self_seq_add( llama_pos p0, llama_pos p1, llama_pos delta) { - return llama_kv_cache_seq_add(ctx->get_kv_self(), seq_id, p0, p1, delta); + auto * kv = ctx->get_kv_self(); + if (!kv) { + return; + } + + return kv->seq_add(seq_id, p0, p1, delta); } // deprecated @@ -2577,7 +2612,12 @@ void llama_kv_self_seq_div( llama_pos p0, llama_pos p1, int d) { - return llama_kv_cache_seq_div(ctx->get_kv_self(), seq_id, p0, p1, d); + auto * kv = ctx->get_kv_self(); + if (!kv) { + return; + } + + return kv->seq_div(seq_id, p0, p1, d); } // deprecated @@ -2586,7 +2626,12 @@ llama_pos llama_kv_cache_seq_pos_max(llama_context * ctx, llama_seq_id seq_id) { } llama_pos llama_kv_self_seq_pos_max(llama_context * ctx, llama_seq_id seq_id) { - return llama_kv_cache_seq_pos_max(ctx->get_kv_self(), seq_id); + const auto * kv = ctx->get_kv_self(); + if (!kv) { + return 0; + } + + return kv->seq_pos_max(seq_id); } // deprecated @@ -2595,7 +2640,12 @@ void llama_kv_cache_defrag(llama_context * ctx) { } void llama_kv_self_defrag(llama_context * ctx) { - llama_kv_cache_defrag(ctx->get_kv_self()); + auto * kv = ctx->get_kv_self(); + if (!kv) { + return; + } + + return kv->defrag(); } // deprecated @@ -2604,7 +2654,12 @@ bool llama_kv_cache_can_shift(const llama_context * ctx) { } bool llama_kv_self_can_shift(const llama_context * ctx) { - return llama_kv_cache_can_shift(ctx->get_kv_self()); + const auto * kv = ctx->get_kv_self(); + if (!kv) { + return false; + } + + return kv->get_can_shift(); } // deprecated diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index 7ba546c10f..dbf5f1187d 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -131,7 +131,7 @@ int32_t llama_kv_cache_unified::get_n_tokens() const { return result; } -uint32_t llama_kv_cache_unified::get_used_cells() const { +int32_t llama_kv_cache_unified::get_used_cells() const { return used; } @@ -428,7 +428,7 @@ void llama_kv_cache_unified::seq_div(llama_seq_id seq_id, llama_pos p0, llama_po } } -llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) { +llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const { llama_pos result = 0; for (uint32_t i = 0; i < size; ++i) { @@ -481,6 +481,11 @@ void llama_kv_cache_unified::restore() { } void llama_kv_cache_unified::commit() { + // TODO: tmp - move to llama_kv_cache_recurrent + if (recurrent) { + return; + } + if (pending.ranges.empty()) { LLAMA_LOG_WARN("%s: no pending KV cache updates to commit - might indicate a bug (ref: %s)\n", __func__, "https://github.com/ggml-org/llama.cpp/pull/12695"); @@ -1273,117 +1278,6 @@ bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t cell return true; } -// -// interface implementation -// - -int32_t llama_kv_cache_n_tokens(const llama_kv_cache * kv) { - if (!kv) { - return 0; - } - - return kv->get_n_tokens(); -} - -int32_t llama_kv_cache_used_cells(const llama_kv_cache * kv) { - if (!kv) { - return 0; - } - - return kv->get_used_cells(); -} - -void llama_kv_cache_clear(llama_kv_cache * kv) { - if (!kv) { - return; - } - - kv->clear(); -} - -bool llama_kv_cache_seq_rm( - llama_kv_cache * kv, - llama_seq_id seq_id, - llama_pos p0, - llama_pos p1) { - if (!kv) { - return true; - } - - return kv->seq_rm(seq_id, p0, p1); -} - -void llama_kv_cache_seq_cp( - llama_kv_cache * kv, - llama_seq_id seq_id_src, - llama_seq_id seq_id_dst, - llama_pos p0, - llama_pos p1) { - if (!kv) { - return; - } - - kv->seq_cp(seq_id_src, seq_id_dst, p0, p1); -} - -void llama_kv_cache_seq_keep(llama_kv_cache * kv, llama_seq_id seq_id) { - if (!kv) { - return; - } - - kv->seq_keep(seq_id); -} - -void llama_kv_cache_seq_add( - llama_kv_cache * kv, - llama_seq_id seq_id, - llama_pos p0, - llama_pos p1, - llama_pos delta) { - if (!kv) { - return; - } - - kv->seq_add(seq_id, p0, p1, delta); -} - -void llama_kv_cache_seq_div( - llama_kv_cache * kv, - llama_seq_id seq_id, - llama_pos p0, - llama_pos p1, - int d) { - if (!kv) { - return; - } - - kv->seq_div(seq_id, p0, p1, d); -} - -llama_pos llama_kv_cache_seq_pos_max(llama_kv_cache * kv, llama_seq_id seq_id) { - if (!kv) { - return 0; - } - - return kv->seq_pos_max(seq_id); -} - -void llama_kv_cache_defrag(llama_kv_cache * kv) { - if (!kv) { - return; - } - - kv->defrag(); -} - -bool llama_kv_cache_can_shift(const llama_kv_cache * kv) { - if (!kv) { - return false; - } - - return kv->get_can_shift(); -} - // // kv cache view // @@ -1393,7 +1287,7 @@ llama_kv_cache_view llama_kv_cache_view_init(const llama_kv_cache & kv, int32_t /*.n_cells = */ 0, /*.n_seq_max = */ n_seq_max, /*.token_count = */ 0, - /*.used_cells = */ llama_kv_cache_used_cells(&kv), + /*.used_cells = */ kv.get_used_cells(), /*.max_contiguous = */ 0, /*.max_contiguous_idx = */ -1, /*.cells = */ nullptr, diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h index ff0ba3540d..56c74035ae 100644 --- a/src/llama-kv-cache.h +++ b/src/llama-kv-cache.h @@ -20,8 +20,8 @@ struct llama_kv_cache : public llama_memory_i { virtual void restore() = 0; // call if batch processing fails - restores the cache state virtual void commit() = 0; // call after successful batch processing - clears any pending state - virtual int32_t get_n_tokens() const = 0; - virtual uint32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache + virtual int32_t get_n_tokens() const = 0; + virtual int32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache virtual bool get_can_shift() const = 0; @@ -89,8 +89,8 @@ public: uint32_t kv_size, bool offload); - int32_t get_n_tokens() const override; - uint32_t get_used_cells() const override; + int32_t get_n_tokens() const override; + int32_t get_used_cells() const override; size_t total_size() const; @@ -109,7 +109,7 @@ public: void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) override; void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override; - llama_pos seq_pos_max(llama_seq_id seq_id) override; + llama_pos seq_pos_max(llama_seq_id seq_id) const override; bool get_can_shift() const override; @@ -204,48 +204,6 @@ private: // using llama_kv_cache_unified::llama_kv_cache_unified; //}; -// TODO: maybe become part of the public llama_kv_cache in the future -int32_t llama_kv_cache_n_tokens(const llama_kv_cache * kv); - -int32_t llama_kv_cache_used_cells(const llama_kv_cache * kv); - -void llama_kv_cache_clear(llama_kv_cache * kv); - -bool llama_kv_cache_seq_rm( - llama_kv_cache * kv, - llama_seq_id seq_id, - llama_pos p0, - llama_pos p1); - -void llama_kv_cache_seq_cp( - llama_kv_cache * kv, - llama_seq_id seq_id_src, - llama_seq_id seq_id_dst, - llama_pos p0, - llama_pos p1); - -void llama_kv_cache_seq_keep(llama_kv_cache * kv, llama_seq_id seq_id); - -void llama_kv_cache_seq_add( - llama_kv_cache * kv, - llama_seq_id seq_id, - llama_pos p0, - llama_pos p1, - llama_pos delta); - -void llama_kv_cache_seq_div( - llama_kv_cache * kv, - llama_seq_id seq_id, - llama_pos p0, - llama_pos p1, - int d); - -llama_pos llama_kv_cache_seq_pos_max(llama_kv_cache * kv, llama_seq_id seq_id); - -void llama_kv_cache_defrag(llama_kv_cache * kv); - -bool llama_kv_cache_can_shift(const llama_kv_cache * kv); - // // kv cache view // diff --git a/src/llama-memory.h b/src/llama-memory.h index 69e6e34ca4..dfa8c4e90f 100644 --- a/src/llama-memory.h +++ b/src/llama-memory.h @@ -15,7 +15,7 @@ public: virtual void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) = 0; virtual void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) = 0; - virtual llama_pos seq_pos_max(llama_seq_id seq_id) = 0; + virtual llama_pos seq_pos_max(llama_seq_id seq_id) const = 0; virtual bool get_can_edit() const = 0; }; From 7a84777f42a9b3ba47db5d20b7662f8ddf92f652 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Fri, 4 Apr 2025 13:16:39 -0700 Subject: [PATCH 12/99] sync: minja (#12739) * sync: minja https://github.com/google/minja/pull/57 * fix json include --- common/minja/chat-template.hpp | 22 ++-- common/minja/minja.hpp | 194 ++++++++++++++++++--------------- 2 files changed, 123 insertions(+), 93 deletions(-) diff --git a/common/minja/chat-template.hpp b/common/minja/chat-template.hpp index 882ba41bd1..237a7625cd 100644 --- a/common/minja/chat-template.hpp +++ b/common/minja/chat-template.hpp @@ -9,10 +9,19 @@ #pragma once #include "minja.hpp" -#include + +#include +#include +#include +#include +#include +#include +#include #include #include +#include + using json = nlohmann::ordered_json; namespace minja { @@ -425,7 +434,7 @@ class chat_template { auto obj = json { {"tool_calls", tool_calls}, }; - if (!content.is_null() && content != "") { + if (!content.is_null() && !content.empty()) { obj["content"] = content; } message["content"] = obj.dump(2); @@ -435,13 +444,12 @@ class chat_template { if (polyfill_tool_responses && role == "tool") { message["role"] = "user"; auto obj = json { - {"tool_response", { - {"content", message.at("content")}, - }}, + {"tool_response", json::object()}, }; if (message.contains("name")) { - obj["tool_response"]["name"] = message.at("name"); + obj["tool_response"]["tool"] = message.at("name"); } + obj["tool_response"]["content"] = message.at("content"); if (message.contains("tool_call_id")) { obj["tool_response"]["tool_call_id"] = message.at("tool_call_id"); } @@ -510,7 +518,7 @@ class chat_template { static nlohmann::ordered_json add_system(const nlohmann::ordered_json & messages, const std::string & system_prompt) { json messages_with_system = messages; - if (messages_with_system.size() > 0 && messages_with_system[0].at("role") == "system") { + if (!messages_with_system.empty() && messages_with_system[0].at("role") == "system") { std::string existing_system = messages_with_system.at(0).at("content"); messages_with_system[0] = json { {"role", "system"}, diff --git a/common/minja/minja.hpp b/common/minja/minja.hpp index c4f40b17e6..e52e792d84 100644 --- a/common/minja/minja.hpp +++ b/common/minja/minja.hpp @@ -8,14 +8,26 @@ // SPDX-License-Identifier: MIT #pragma once +#include +#include +#include +#include +#include +#include #include -#include -#include -#include +#include +#include +#include #include -#include +#include #include +#include +#include +#include #include +#include +#include + #include using json = nlohmann::ordered_json; @@ -240,7 +252,7 @@ public: auto index = key.get(); return array_->at(index < 0 ? array_->size() + index : index); } else if (object_) { - if (!key.is_hashable()) throw std::runtime_error("Unhashable type: " + dump()); + if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump()); auto it = object_->find(key.primitive_); if (it == object_->end()) return Value(); return it->second; @@ -249,7 +261,7 @@ public: } void set(const Value& key, const Value& value) { if (!object_) throw std::runtime_error("Value is not an object: " + dump()); - if (!key.is_hashable()) throw std::runtime_error("Unhashable type: " + dump()); + if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump()); (*object_)[key.primitive_] = value; } Value call(const std::shared_ptr & context, ArgumentsValue & args) const { @@ -731,51 +743,51 @@ public: struct TextTemplateToken : public TemplateToken { std::string text; - TextTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Text, location, pre, post), text(t) {} + TextTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Text, loc, pre, post), text(t) {} }; struct ExpressionTemplateToken : public TemplateToken { std::shared_ptr expr; - ExpressionTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && e) : TemplateToken(Type::Expression, location, pre, post), expr(std::move(e)) {} + ExpressionTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, std::shared_ptr && e) : TemplateToken(Type::Expression, loc, pre, post), expr(std::move(e)) {} }; struct IfTemplateToken : public TemplateToken { std::shared_ptr condition; - IfTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && c) : TemplateToken(Type::If, location, pre, post), condition(std::move(c)) {} + IfTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, std::shared_ptr && c) : TemplateToken(Type::If, loc, pre, post), condition(std::move(c)) {} }; struct ElifTemplateToken : public TemplateToken { std::shared_ptr condition; - ElifTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && c) : TemplateToken(Type::Elif, location, pre, post), condition(std::move(c)) {} + ElifTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, std::shared_ptr && c) : TemplateToken(Type::Elif, loc, pre, post), condition(std::move(c)) {} }; struct ElseTemplateToken : public TemplateToken { - ElseTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Else, location, pre, post) {} + ElseTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Else, loc, pre, post) {} }; struct EndIfTemplateToken : public TemplateToken { - EndIfTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndIf, location, pre, post) {} + EndIfTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndIf, loc, pre, post) {} }; struct MacroTemplateToken : public TemplateToken { std::shared_ptr name; Expression::Parameters params; - MacroTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && n, Expression::Parameters && p) - : TemplateToken(Type::Macro, location, pre, post), name(std::move(n)), params(std::move(p)) {} + MacroTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, std::shared_ptr && n, Expression::Parameters && p) + : TemplateToken(Type::Macro, loc, pre, post), name(std::move(n)), params(std::move(p)) {} }; struct EndMacroTemplateToken : public TemplateToken { - EndMacroTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndMacro, location, pre, post) {} + EndMacroTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndMacro, loc, pre, post) {} }; struct FilterTemplateToken : public TemplateToken { std::shared_ptr filter; - FilterTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && filter) - : TemplateToken(Type::Filter, location, pre, post), filter(std::move(filter)) {} + FilterTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, std::shared_ptr && filter) + : TemplateToken(Type::Filter, loc, pre, post), filter(std::move(filter)) {} }; struct EndFilterTemplateToken : public TemplateToken { - EndFilterTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFilter, location, pre, post) {} + EndFilterTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFilter, loc, pre, post) {} }; struct ForTemplateToken : public TemplateToken { @@ -783,38 +795,38 @@ struct ForTemplateToken : public TemplateToken { std::shared_ptr iterable; std::shared_ptr condition; bool recursive; - ForTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::vector & vns, std::shared_ptr && iter, + ForTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, const std::vector & vns, std::shared_ptr && iter, std::shared_ptr && c, bool r) - : TemplateToken(Type::For, location, pre, post), var_names(vns), iterable(std::move(iter)), condition(std::move(c)), recursive(r) {} + : TemplateToken(Type::For, loc, pre, post), var_names(vns), iterable(std::move(iter)), condition(std::move(c)), recursive(r) {} }; struct EndForTemplateToken : public TemplateToken { - EndForTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFor, location, pre, post) {} + EndForTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFor, loc, pre, post) {} }; struct GenerationTemplateToken : public TemplateToken { - GenerationTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Generation, location, pre, post) {} + GenerationTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Generation, loc, pre, post) {} }; struct EndGenerationTemplateToken : public TemplateToken { - EndGenerationTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndGeneration, location, pre, post) {} + EndGenerationTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndGeneration, loc, pre, post) {} }; struct SetTemplateToken : public TemplateToken { std::string ns; std::vector var_names; std::shared_ptr value; - SetTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string & ns, const std::vector & vns, std::shared_ptr && v) - : TemplateToken(Type::Set, location, pre, post), ns(ns), var_names(vns), value(std::move(v)) {} + SetTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, const std::string & ns, const std::vector & vns, std::shared_ptr && v) + : TemplateToken(Type::Set, loc, pre, post), ns(ns), var_names(vns), value(std::move(v)) {} }; struct EndSetTemplateToken : public TemplateToken { - EndSetTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndSet, location, pre, post) {} + EndSetTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndSet, loc, pre, post) {} }; struct CommentTemplateToken : public TemplateToken { std::string text; - CommentTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Comment, location, pre, post), text(t) {} + CommentTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Comment, loc, pre, post), text(t) {} }; enum class LoopControlType { Break, Continue }; @@ -830,7 +842,7 @@ public: struct LoopControlTemplateToken : public TemplateToken { LoopControlType control_type; - LoopControlTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, LoopControlType control_type) : TemplateToken(Type::Break, location, pre, post), control_type(control_type) {} + LoopControlTemplateToken(const Location & loc, SpaceHandling pre, SpaceHandling post, LoopControlType control_type) : TemplateToken(Type::Break, loc, pre, post), control_type(control_type) {} }; class TemplateNode { @@ -868,8 +880,8 @@ public: class SequenceNode : public TemplateNode { std::vector> children; public: - SequenceNode(const Location & location, std::vector> && c) - : TemplateNode(location), children(std::move(c)) {} + SequenceNode(const Location & loc, std::vector> && c) + : TemplateNode(loc), children(std::move(c)) {} void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { for (const auto& child : children) child->render(out, context); } @@ -878,7 +890,7 @@ public: class TextNode : public TemplateNode { std::string text; public: - TextNode(const Location & location, const std::string& t) : TemplateNode(location), text(t) {} + TextNode(const Location & loc, const std::string& t) : TemplateNode(loc), text(t) {} void do_render(std::ostringstream & out, const std::shared_ptr &) const override { out << text; } @@ -887,7 +899,7 @@ public: class ExpressionNode : public TemplateNode { std::shared_ptr expr; public: - ExpressionNode(const Location & location, std::shared_ptr && e) : TemplateNode(location), expr(std::move(e)) {} + ExpressionNode(const Location & loc, std::shared_ptr && e) : TemplateNode(loc), expr(std::move(e)) {} void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { if (!expr) throw std::runtime_error("ExpressionNode.expr is null"); auto result = expr->evaluate(context); @@ -904,8 +916,8 @@ public: class IfNode : public TemplateNode { std::vector, std::shared_ptr>> cascade; public: - IfNode(const Location & location, std::vector, std::shared_ptr>> && c) - : TemplateNode(location), cascade(std::move(c)) {} + IfNode(const Location & loc, std::vector, std::shared_ptr>> && c) + : TemplateNode(loc), cascade(std::move(c)) {} void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { for (const auto& branch : cascade) { auto enter_branch = true; @@ -924,7 +936,7 @@ public: class LoopControlNode : public TemplateNode { LoopControlType control_type_; public: - LoopControlNode(const Location & location, LoopControlType control_type) : TemplateNode(location), control_type_(control_type) {} + LoopControlNode(const Location & loc, LoopControlType control_type) : TemplateNode(loc), control_type_(control_type) {} void do_render(std::ostringstream &, const std::shared_ptr &) const override { throw LoopControlException(control_type_); } @@ -938,9 +950,9 @@ class ForNode : public TemplateNode { bool recursive; std::shared_ptr else_body; public: - ForNode(const Location & location, std::vector && var_names, std::shared_ptr && iterable, + ForNode(const Location & loc, std::vector && var_names, std::shared_ptr && iterable, std::shared_ptr && condition, std::shared_ptr && body, bool recursive, std::shared_ptr && else_body) - : TemplateNode(location), var_names(var_names), iterable(std::move(iterable)), condition(std::move(condition)), body(std::move(body)), recursive(recursive), else_body(std::move(else_body)) {} + : TemplateNode(loc), var_names(var_names), iterable(std::move(iterable)), condition(std::move(condition)), body(std::move(body)), recursive(recursive), else_body(std::move(else_body)) {} void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { // https://jinja.palletsprojects.com/en/3.0.x/templates/#for @@ -1025,8 +1037,8 @@ class MacroNode : public TemplateNode { std::shared_ptr body; std::unordered_map named_param_positions; public: - MacroNode(const Location & location, std::shared_ptr && n, Expression::Parameters && p, std::shared_ptr && b) - : TemplateNode(location), name(std::move(n)), params(std::move(p)), body(std::move(b)) { + MacroNode(const Location & loc, std::shared_ptr && n, Expression::Parameters && p, std::shared_ptr && b) + : TemplateNode(loc), name(std::move(n)), params(std::move(p)), body(std::move(b)) { for (size_t i = 0; i < params.size(); ++i) { const auto & name = params[i].first; if (!name.empty()) { @@ -1072,8 +1084,8 @@ class FilterNode : public TemplateNode { std::shared_ptr body; public: - FilterNode(const Location & location, std::shared_ptr && f, std::shared_ptr && b) - : TemplateNode(location), filter(std::move(f)), body(std::move(b)) {} + FilterNode(const Location & loc, std::shared_ptr && f, std::shared_ptr && b) + : TemplateNode(loc), filter(std::move(f)), body(std::move(b)) {} void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { if (!filter) throw std::runtime_error("FilterNode.filter is null"); @@ -1095,8 +1107,8 @@ class SetNode : public TemplateNode { std::vector var_names; std::shared_ptr value; public: - SetNode(const Location & location, const std::string & ns, const std::vector & vns, std::shared_ptr && v) - : TemplateNode(location), ns(ns), var_names(vns), value(std::move(v)) {} + SetNode(const Location & loc, const std::string & ns, const std::vector & vns, std::shared_ptr && v) + : TemplateNode(loc), ns(ns), var_names(vns), value(std::move(v)) {} void do_render(std::ostringstream &, const std::shared_ptr & context) const override { if (!value) throw std::runtime_error("SetNode.value is null"); if (!ns.empty()) { @@ -1118,8 +1130,8 @@ class SetTemplateNode : public TemplateNode { std::string name; std::shared_ptr template_value; public: - SetTemplateNode(const Location & location, const std::string & name, std::shared_ptr && tv) - : TemplateNode(location), name(name), template_value(std::move(tv)) {} + SetTemplateNode(const Location & loc, const std::string & name, std::shared_ptr && tv) + : TemplateNode(loc), name(name), template_value(std::move(tv)) {} void do_render(std::ostringstream &, const std::shared_ptr & context) const override { if (!template_value) throw std::runtime_error("SetTemplateNode.template_value is null"); Value value { template_value->render(context) }; @@ -1132,8 +1144,8 @@ class IfExpr : public Expression { std::shared_ptr then_expr; std::shared_ptr else_expr; public: - IfExpr(const Location & location, std::shared_ptr && c, std::shared_ptr && t, std::shared_ptr && e) - : Expression(location), condition(std::move(c)), then_expr(std::move(t)), else_expr(std::move(e)) {} + IfExpr(const Location & loc, std::shared_ptr && c, std::shared_ptr && t, std::shared_ptr && e) + : Expression(loc), condition(std::move(c)), then_expr(std::move(t)), else_expr(std::move(e)) {} Value do_evaluate(const std::shared_ptr & context) const override { if (!condition) throw std::runtime_error("IfExpr.condition is null"); if (!then_expr) throw std::runtime_error("IfExpr.then_expr is null"); @@ -1150,16 +1162,16 @@ public: class LiteralExpr : public Expression { Value value; public: - LiteralExpr(const Location & location, const Value& v) - : Expression(location), value(v) {} + LiteralExpr(const Location & loc, const Value& v) + : Expression(loc), value(v) {} Value do_evaluate(const std::shared_ptr &) const override { return value; } }; class ArrayExpr : public Expression { std::vector> elements; public: - ArrayExpr(const Location & location, std::vector> && e) - : Expression(location), elements(std::move(e)) {} + ArrayExpr(const Location & loc, std::vector> && e) + : Expression(loc), elements(std::move(e)) {} Value do_evaluate(const std::shared_ptr & context) const override { auto result = Value::array(); for (const auto& e : elements) { @@ -1173,8 +1185,8 @@ public: class DictExpr : public Expression { std::vector, std::shared_ptr>> elements; public: - DictExpr(const Location & location, std::vector, std::shared_ptr>> && e) - : Expression(location), elements(std::move(e)) {} + DictExpr(const Location & loc, std::vector, std::shared_ptr>> && e) + : Expression(loc), elements(std::move(e)) {} Value do_evaluate(const std::shared_ptr & context) const override { auto result = Value::object(); for (const auto& [key, value] : elements) { @@ -1189,8 +1201,8 @@ public: class SliceExpr : public Expression { public: std::shared_ptr start, end; - SliceExpr(const Location & location, std::shared_ptr && s, std::shared_ptr && e) - : Expression(location), start(std::move(s)), end(std::move(e)) {} + SliceExpr(const Location & loc, std::shared_ptr && s, std::shared_ptr && e) + : Expression(loc), start(std::move(s)), end(std::move(e)) {} Value do_evaluate(const std::shared_ptr &) const override { throw std::runtime_error("SliceExpr not implemented"); } @@ -1200,8 +1212,8 @@ class SubscriptExpr : public Expression { std::shared_ptr base; std::shared_ptr index; public: - SubscriptExpr(const Location & location, std::shared_ptr && b, std::shared_ptr && i) - : Expression(location), base(std::move(b)), index(std::move(i)) {} + SubscriptExpr(const Location & loc, std::shared_ptr && b, std::shared_ptr && i) + : Expression(loc), base(std::move(b)), index(std::move(i)) {} Value do_evaluate(const std::shared_ptr & context) const override { if (!base) throw std::runtime_error("SubscriptExpr.base is null"); if (!index) throw std::runtime_error("SubscriptExpr.index is null"); @@ -1243,8 +1255,8 @@ public: enum class Op { Plus, Minus, LogicalNot, Expansion, ExpansionDict }; std::shared_ptr expr; Op op; - UnaryOpExpr(const Location & location, std::shared_ptr && e, Op o) - : Expression(location), expr(std::move(e)), op(o) {} + UnaryOpExpr(const Location & loc, std::shared_ptr && e, Op o) + : Expression(loc), expr(std::move(e)), op(o) {} Value do_evaluate(const std::shared_ptr & context) const override { if (!expr) throw std::runtime_error("UnaryOpExpr.expr is null"); auto e = expr->evaluate(context); @@ -1269,8 +1281,8 @@ private: std::shared_ptr right; Op op; public: - BinaryOpExpr(const Location & location, std::shared_ptr && l, std::shared_ptr && r, Op o) - : Expression(location), left(std::move(l)), right(std::move(r)), op(o) {} + BinaryOpExpr(const Location & loc, std::shared_ptr && l, std::shared_ptr && r, Op o) + : Expression(loc), left(std::move(l)), right(std::move(r)), op(o) {} Value do_evaluate(const std::shared_ptr & context) const override { if (!left) throw std::runtime_error("BinaryOpExpr.left is null"); if (!right) throw std::runtime_error("BinaryOpExpr.right is null"); @@ -1427,8 +1439,8 @@ class MethodCallExpr : public Expression { std::shared_ptr method; ArgumentsExpression args; public: - MethodCallExpr(const Location & location, std::shared_ptr && obj, std::shared_ptr && m, ArgumentsExpression && a) - : Expression(location), object(std::move(obj)), method(std::move(m)), args(std::move(a)) {} + MethodCallExpr(const Location & loc, std::shared_ptr && obj, std::shared_ptr && m, ArgumentsExpression && a) + : Expression(loc), object(std::move(obj)), method(std::move(m)), args(std::move(a)) {} Value do_evaluate(const std::shared_ptr & context) const override { if (!object) throw std::runtime_error("MethodCallExpr.object is null"); if (!method) throw std::runtime_error("MethodCallExpr.method is null"); @@ -1526,8 +1538,8 @@ class CallExpr : public Expression { public: std::shared_ptr object; ArgumentsExpression args; - CallExpr(const Location & location, std::shared_ptr && obj, ArgumentsExpression && a) - : Expression(location), object(std::move(obj)), args(std::move(a)) {} + CallExpr(const Location & loc, std::shared_ptr && obj, ArgumentsExpression && a) + : Expression(loc), object(std::move(obj)), args(std::move(a)) {} Value do_evaluate(const std::shared_ptr & context) const override { if (!object) throw std::runtime_error("CallExpr.object is null"); auto obj = object->evaluate(context); @@ -1542,8 +1554,8 @@ public: class FilterExpr : public Expression { std::vector> parts; public: - FilterExpr(const Location & location, std::vector> && p) - : Expression(location), parts(std::move(p)) {} + FilterExpr(const Location & loc, std::vector> && p) + : Expression(loc), parts(std::move(p)) {} Value do_evaluate(const std::shared_ptr & context) const override { Value result; bool first = true; @@ -2460,7 +2472,7 @@ private: static std::regex leading_space_regex(R"(^\s+)"); text = std::regex_replace(text, leading_space_regex, ""); } else if (options.trim_blocks && (it - 1) != begin && !dynamic_cast((*(it - 2)).get())) { - if (text.length() > 0 && text[0] == '\n') { + if (!text.empty() && text[0] == '\n') { text.erase(0, 1); } } @@ -2538,7 +2550,7 @@ public: TemplateTokenIterator begin = tokens.begin(); auto it = begin; TemplateTokenIterator end = tokens.end(); - return parser.parseTemplate(begin, it, end, /* full= */ true); + return parser.parseTemplate(begin, it, end, /* fully= */ true); } }; @@ -2577,7 +2589,7 @@ inline std::shared_ptr Context::builtins() { throw std::runtime_error(args.at("message").get()); })); globals.set("tojson", simple_function("tojson", { "value", "indent" }, [](const std::shared_ptr &, Value & args) { - return Value(args.at("value").dump(args.get("indent", -1), /* tojson= */ true)); + return Value(args.at("value").dump(args.get("indent", -1), /* to_json= */ true)); })); globals.set("items", simple_function("items", { "object" }, [](const std::shared_ptr &, Value & args) { auto items = Value::array(); @@ -2599,7 +2611,7 @@ inline std::shared_ptr Context::builtins() { globals.set("last", simple_function("last", { "items" }, [](const std::shared_ptr &, Value & args) { auto items = args.at("items"); if (!items.is_array()) throw std::runtime_error("object is not a list"); - if (items.size() == 0) return Value(); + if (items.empty()) return Value(); return items.at(items.size() - 1); })); globals.set("trim", simple_function("trim", { "text" }, [](const std::shared_ptr &, Value & args) { @@ -2747,12 +2759,17 @@ inline std::shared_ptr Context::builtins() { return Value::callable([=](const std::shared_ptr & context, ArgumentsValue & args) { args.expectArgs(is_select ? "select" : "reject", {2, (std::numeric_limits::max)()}, {0, 0}); auto & items = args.args[0]; - if (items.is_null()) + if (items.is_null()) { return Value::array(); - if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump()); + } + if (!items.is_array()) { + throw std::runtime_error("object is not iterable: " + items.dump()); + } auto filter_fn = context->get(args.args[1]); - if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump()); + if (filter_fn.is_null()) { + throw std::runtime_error("Undefined filter: " + args.args[1].dump()); + } auto filter_args = Value::array(); for (size_t i = 2, n = args.args.size(); i < n; i++) { @@ -2874,20 +2891,25 @@ inline std::shared_ptr Context::builtins() { auto v = arg.get(); startEndStep[i] = v; param_set[i] = true; - } } - for (auto & [name, value] : args.kwargs) { - size_t i; - if (name == "start") i = 0; - else if (name == "end") i = 1; - else if (name == "step") i = 2; - else throw std::runtime_error("Unknown argument " + name + " for function range"); + } + for (auto & [name, value] : args.kwargs) { + size_t i; + if (name == "start") { + i = 0; + } else if (name == "end") { + i = 1; + } else if (name == "step") { + i = 2; + } else { + throw std::runtime_error("Unknown argument " + name + " for function range"); + } - if (param_set[i]) { - throw std::runtime_error("Duplicate argument " + name + " for function range"); - } - startEndStep[i] = value.get(); - param_set[i] = true; + if (param_set[i]) { + throw std::runtime_error("Duplicate argument " + name + " for function range"); + } + startEndStep[i] = value.get(); + param_set[i] = true; } if (!param_set[1]) { throw std::runtime_error("Missing required argument 'end' for function range"); From c6ff5d2a8da2587cc78c9ede9171dfc3f076c757 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E3=82=A8=E3=82=B7=E3=83=A5=E3=83=8A=E3=83=B4=E3=82=A1?= =?UTF-8?q?=E3=83=AA=E3=82=B7=E3=82=A2?= <148695646+eternaphia@users.noreply.github.com> Date: Sat, 5 Apr 2025 21:31:42 +0800 Subject: [PATCH 13/99] common: custom hf endpoint support (#12769) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * common: custom hf endpoint support Add support for custom huggingface endpoints via HF_ENDPOINT environment variable You can now specify a custom huggingface endpoint using the HF_ENDPOINT environment variable when using the --hf-repo flag, which works similarly to huggingface-cli's endpoint configuration. Example usage: HF_ENDPOINT=https://hf-mirror.com/ ./bin/llama-cli --hf-repo Qwen/Qwen1.5-0.5B-Chat-GGUF --hf-file qwen1_5-0_5b-chat-q2_k.gguf -p "The meaning to life and the universe is" The trailing slash in the URL is optional: HF_ENDPOINT=https://hf-mirror.com ./bin/llama-cli --hf-repo Qwen/Qwen1.5-0.5B-Chat-GGUF --hf-file qwen1_5-0_5b-chat-q2_k.gguf -p "The meaning to life and the universe is" * Update common/arg.cpp readability Improvement Co-authored-by: Xuan-Son Nguyen * Apply suggestions from code review --------- Co-authored-by: ベアトリーチェ <148695646+MakiSonomura@users.noreply.github.com> Co-authored-by: Xuan-Son Nguyen --- common/arg.cpp | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index fa22e86cd1..96facce67a 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -656,9 +656,13 @@ static void common_params_handle_model( } } - // TODO: allow custom host - model.url = "https://huggingface.co/" + model.hf_repo + "/resolve/main/" + model.hf_file; - + std::string hf_endpoint = "https://huggingface.co/"; + const char * hf_endpoint_env = getenv("HF_ENDPOINT"); + if (hf_endpoint_env) { + hf_endpoint = hf_endpoint_env; + if (hf_endpoint.back() != '/') hf_endpoint += '/'; + } + model.url = hf_endpoint + model.hf_repo + "/resolve/main/" + model.hf_file; // make sure model path is present (for caching purposes) if (model.path.empty()) { // this is to avoid different repo having same file name, or same file name in different subdirs From 0364178ca2452bd241a4cdd4350d0d7a6ff8c4b4 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 5 Apr 2025 17:17:40 +0200 Subject: [PATCH 14/99] clip : refactor clip_init, add tests (#12757) * refactor clip_init * fix loading file * fix style * test ok * better test with report * add missing headers * clarify * add KEY_MM_PATCH_MERGE_TYPE * remove bool has_* pattern * Apply suggestions from code review Co-authored-by: Georgi Gerganov * Update examples/llava/clip.cpp Co-authored-by: Georgi Gerganov * use ggml_soft_max_ext * refactor logging system * add minicpm-v-o 2.6 for testing * use nullptr everywhere * fix Yi-VL model --------- Co-authored-by: Georgi Gerganov --- examples/llava/clip-impl.h | 273 ++++++ examples/llava/clip.cpp | 1434 ++++++++++++------------------- examples/llava/clip.h | 3 +- examples/llava/gemma3-cli.cpp | 6 +- examples/llava/llava-cli.cpp | 2 +- examples/llava/minicpmv-cli.cpp | 2 +- examples/llava/qwen2vl-cli.cpp | 2 +- examples/llava/test-1.jpeg | Bin 0 -> 124071 bytes examples/llava/tests.sh | 81 ++ 9 files changed, 916 insertions(+), 887 deletions(-) create mode 100644 examples/llava/clip-impl.h create mode 100644 examples/llava/test-1.jpeg create mode 100755 examples/llava/tests.sh diff --git a/examples/llava/clip-impl.h b/examples/llava/clip-impl.h new file mode 100644 index 0000000000..685d6e7e09 --- /dev/null +++ b/examples/llava/clip-impl.h @@ -0,0 +1,273 @@ +#include "ggml.h" +#include "gguf.h" + +#include +#include +#include +#include +#include +#include + +// Internal header for clip.cpp + +#define KEY_FTYPE "general.file_type" +#define KEY_NAME "general.name" +#define KEY_DESCRIPTION "general.description" +#define KEY_HAS_TEXT_ENC "clip.has_text_encoder" +#define KEY_HAS_VIS_ENC "clip.has_vision_encoder" +#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector" +#define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector" +#define KEY_HAS_GLM_PROJ "clip.has_glm_projector" +#define KEY_MINICPMV_VERSION "clip.minicpmv_version" +#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger" +#define KEY_USE_GELU "clip.use_gelu" +#define KEY_USE_SILU "clip.use_silu" +#define KEY_N_EMBD "clip.%s.embedding_length" +#define KEY_N_FF "clip.%s.feed_forward_length" +#define KEY_N_BLOCK "clip.%s.block_count" +#define KEY_N_HEAD "clip.%s.attention.head_count" +#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" +#define KEY_PROJ_DIM "clip.%s.projection_dim" +#define KEY_TOKENS "tokenizer.ggml.tokens" +#define KEY_N_POSITIONS "clip.text.context_length" +#define KEY_IMAGE_SIZE "clip.vision.image_size" +#define KEY_PATCH_SIZE "clip.vision.patch_size" +#define KEY_IMAGE_MEAN "clip.vision.image_mean" +#define KEY_IMAGE_STD "clip.vision.image_std" +#define KEY_PROJ_TYPE "clip.projector_type" +#define KEY_FEATURE_LAYER "clip.vision.feature_layer" + +#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type" +#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints" +#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution" + + +// +// tensor name constants +// + +#define TN_TOKEN_EMBD "%s.token_embd.weight" +#define TN_POS_EMBD "%s.position_embd.weight" +#define TN_CLASS_EMBD "v.class_embd" +#define TN_PATCH_EMBD "v.patch_embd.weight" // not rename tensor with ".0" postfix for backwrad compat +#define TN_PATCH_EMBD_1 "v.patch_embd.weight.1" +#define TN_PATCH_BIAS "v.patch_embd.bias" +#define TN_ATTN_K "%s.blk.%d.attn_k.%s" +#define TN_ATTN_Q "%s.blk.%d.attn_q.%s" +#define TN_ATTN_V "%s.blk.%d.attn_v.%s" +#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s" +#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s" +#define TN_FFN_UP "%s.blk.%d.ffn_up.%s" +#define TN_LN_1 "%s.blk.%d.ln1.%s" +#define TN_LN_2 "%s.blk.%d.ln2.%s" +#define TN_LN_PRE "%s.pre_ln.%s" +#define TN_LN_POST "%s.post_ln.%s" +#define TN_TEXT_PROJ "text_projection.weight" +#define TN_VIS_PROJ "visual_projection.weight" +#define TN_LLAVA_PROJ "mm.%d.%s" +#define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s" +#define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s" +#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s" +#define TN_IMAGE_NEWLINE "model.image_newline" +#define TN_MM_INP_PROJ "mm.input_projection.weight" // gemma3 +#define TN_MM_SOFT_EMB_N "mm.soft_emb_norm.weight" // gemma3 + +// mimicpmv +#define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k" +#define TN_MINICPMV_QUERY "resampler.query" +#define TN_MINICPMV_PROJ "resampler.proj.weight" +#define TN_MINICPMV_KV_PROJ "resampler.kv.weight" +#define TN_MINICPMV_ATTN "resampler.attn.%s.%s" +#define TN_MINICPMV_LN "resampler.ln_%s.%s" + +#define TN_GLM_ADAPER_CONV "adapter.conv.%s" +#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s" +#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s" +#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s" +#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s" +#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s" +#define TN_GLM_BOI_W "adapter.boi" +#define TN_GLM_EOI_W "adapter.eoi" + +enum projector_type { + PROJECTOR_TYPE_MLP, + PROJECTOR_TYPE_MLP_NORM, + PROJECTOR_TYPE_LDP, + PROJECTOR_TYPE_LDPV2, + PROJECTOR_TYPE_RESAMPLER, + PROJECTOR_TYPE_GLM_EDGE, + PROJECTOR_TYPE_MERGER, + PROJECTOR_TYPE_GEMMA3, + PROJECTOR_TYPE_UNKNOWN, +}; + +static std::map PROJECTOR_TYPE_NAMES = { + { PROJECTOR_TYPE_MLP, "mlp" }, + { PROJECTOR_TYPE_LDP, "ldp" }, + { PROJECTOR_TYPE_LDPV2, "ldpv2"}, + { PROJECTOR_TYPE_RESAMPLER, "resampler"}, + { PROJECTOR_TYPE_GLM_EDGE, "adapter"}, + { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"}, + { PROJECTOR_TYPE_GEMMA3, "gemma3"}, +}; + +static projector_type clip_projector_type_from_string(const std::string & str) { + for (const auto & pair : PROJECTOR_TYPE_NAMES) { + if (pair.second == str) { + return pair.first; + } + } + return PROJECTOR_TYPE_UNKNOWN; +} + +// +// logging +// + +static void clip_log_callback_default(enum ggml_log_level level, const char * text, void * user_data) { + (void) level; + (void) user_data; + fputs(text, stderr); + fflush(stderr); +} + +struct clip_logger_state { + ggml_log_level verbosity_thold; + ggml_log_callback log_callback; + void * log_callback_user_data; +}; + +extern struct clip_logger_state g_logger_state; + +static void clip_log_internal_v(enum ggml_log_level level, const char * format, va_list args) { + if (format == NULL) { + return; + } + va_list args_copy; + va_copy(args_copy, args); + char buffer[128]; + int len = vsnprintf(buffer, 128, format, args); + if (len < 128) { + g_logger_state.log_callback(level, buffer, g_logger_state.log_callback_user_data); + } else { + char * buffer2 = (char *) calloc(len + 1, sizeof(char)); + vsnprintf(buffer2, len + 1, format, args_copy); + buffer2[len] = 0; + g_logger_state.log_callback(level, buffer2, g_logger_state.log_callback_user_data); + free(buffer2); + } + va_end(args_copy); +} + +static void clip_log_internal(enum ggml_log_level level, const char * format, ...) { + va_list args; + va_start(args, format); + clip_log_internal_v(level, format, args); + va_end(args); +} + +#define LOG_TMPL(level, ...) \ + do { \ + if ((level) >= g_logger_state.verbosity_thold) { \ + clip_log_internal((level), __VA_ARGS__); \ + } \ + } while (0) +#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO, __VA_ARGS__) +#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN, __VA_ARGS__) +#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, __VA_ARGS__) +#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__) +#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT, __VA_ARGS__) + +// +// common utils +// + +static std::string string_format(const char * fmt, ...) { + va_list ap; + va_list ap2; + va_start(ap, fmt); + va_copy(ap2, ap); + int size = vsnprintf(NULL, 0, fmt, ap); + GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT + std::vector buf(size + 1); + int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2); + GGML_ASSERT(size2 == size); + va_end(ap2); + va_end(ap); + return std::string(buf.data(), buf.size()); +} + +static void string_replace_all(std::string & s, const std::string & search, const std::string & replace) { + if (search.empty()) { + return; + } + std::string builder; + builder.reserve(s.length()); + size_t pos = 0; + size_t last_pos = 0; + while ((pos = s.find(search, last_pos)) != std::string::npos) { + builder.append(s, last_pos, pos - last_pos); + builder.append(replace); + last_pos = pos + search.length(); + } + builder.append(s, last_pos, std::string::npos); + s = std::move(builder); +} + +// +// gguf utils +// + +static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) { + switch (type) { + case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]); + case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]); + case GGUF_TYPE_UINT16: return std::to_string(((const uint16_t *)data)[i]); + case GGUF_TYPE_INT16: return std::to_string(((const int16_t *)data)[i]); + case GGUF_TYPE_UINT32: return std::to_string(((const uint32_t *)data)[i]); + case GGUF_TYPE_INT32: return std::to_string(((const int32_t *)data)[i]); + case GGUF_TYPE_UINT64: return std::to_string(((const uint64_t *)data)[i]); + case GGUF_TYPE_INT64: return std::to_string(((const int64_t *)data)[i]); + case GGUF_TYPE_FLOAT32: return std::to_string(((const float *)data)[i]); + case GGUF_TYPE_FLOAT64: return std::to_string(((const double *)data)[i]); + case GGUF_TYPE_BOOL: return ((const bool *)data)[i] ? "true" : "false"; + default: return string_format("unknown type %d", type); + } +} + +static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { + const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i); + + switch (type) { + case GGUF_TYPE_STRING: + return gguf_get_val_str(ctx_gguf, i); + case GGUF_TYPE_ARRAY: + { + const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i); + int arr_n = gguf_get_arr_n(ctx_gguf, i); + const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i); + std::stringstream ss; + ss << "["; + for (int j = 0; j < arr_n; j++) { + if (arr_type == GGUF_TYPE_STRING) { + std::string val = gguf_get_arr_str(ctx_gguf, i, j); + // escape quotes + string_replace_all(val, "\\", "\\\\"); + string_replace_all(val, "\"", "\\\""); + ss << '"' << val << '"'; + } else if (arr_type == GGUF_TYPE_ARRAY) { + ss << "???"; + } else { + ss << gguf_data_to_str(arr_type, data, j); + } + if (j < arr_n - 1) { + ss << ", "; + } + } + ss << "]"; + return ss.str(); + } + default: + return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0); + } +} diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index e315ef571f..1145c816c8 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -3,6 +3,7 @@ // I'll gradually clean and extend it // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch #include "clip.h" +#include "clip-impl.h" #include "ggml.h" #include "ggml-cpp.h" #include "ggml-cpu.h" @@ -27,17 +28,7 @@ #include #include -#if defined(LLAVA_LOG_OFF) -# define LOG_INF(...) -# define LOG_WRN(...) -# define LOG_ERR(...) -# define LOG_DBG(...) -#else // defined(LLAVA_LOG_OFF) -# define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -# define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -# define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -#endif // defined(LLAVA_LOG_OFF) +struct clip_logger_state g_logger_state = {GGML_LOG_LEVEL_CONT, clip_log_callback_default, NULL}; //#define CLIP_DEBUG_FUNCTIONS @@ -58,253 +49,6 @@ struct clip_image_f32 { std::vector buf; }; -static std::string format(const char * fmt, ...) { - va_list ap; - va_list ap2; - va_start(ap, fmt); - va_copy(ap2, ap); - int size = vsnprintf(NULL, 0, fmt, ap); - GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT - std::vector buf(size + 1); - int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2); - GGML_ASSERT(size2 == size); - va_end(ap2); - va_end(ap); - return std::string(buf.data(), buf.size()); -} - -// -// key constants -// - -#define KEY_FTYPE "general.file_type" -#define KEY_NAME "general.name" -#define KEY_DESCRIPTION "general.description" -#define KEY_HAS_TEXT_ENC "clip.has_text_encoder" -#define KEY_HAS_VIS_ENC "clip.has_vision_encoder" -#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector" -#define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector" -#define KEY_HAS_GLM_PROJ "clip.has_glm_projector" -#define KEY_MINICPMV_VERSION "clip.minicpmv_version" -#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger" -#define KEY_USE_GELU "clip.use_gelu" -#define KEY_USE_SILU "clip.use_silu" -#define KEY_N_EMBD "clip.%s.embedding_length" -#define KEY_N_FF "clip.%s.feed_forward_length" -#define KEY_N_BLOCK "clip.%s.block_count" -#define KEY_N_HEAD "clip.%s.attention.head_count" -#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" -#define KEY_PROJ_DIM "clip.%s.projection_dim" -#define KEY_TOKENS "tokenizer.ggml.tokens" -#define KEY_N_POSITIONS "clip.text.context_length" -#define KEY_IMAGE_SIZE "clip.vision.image_size" -#define KEY_PATCH_SIZE "clip.vision.patch_size" -#define KEY_IMAGE_MEAN "clip.vision.image_mean" -#define KEY_IMAGE_STD "clip.vision.image_std" -#define KEY_PROJ_TYPE "clip.projector_type" -#define KEY_FEATURE_LAYER "clip.vision.feature_layer" - -#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type" -#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints" -#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution" - - -// -// tensor name constants -// - -#define TN_TOKEN_EMBD "%s.token_embd.weight" -#define TN_POS_EMBD "%s.position_embd.weight" -#define TN_CLASS_EMBD "v.class_embd" -#define TN_PATCH_EMBD "v.patch_embd.weight" // not rename tensor with ".0" postfix for backwrad compat -#define TN_PATCH_EMBD_1 "v.patch_embd.weight.1" -#define TN_PATCH_BIAS "v.patch_embd.bias" -#define TN_ATTN_K "%s.blk.%d.attn_k.%s" -#define TN_ATTN_Q "%s.blk.%d.attn_q.%s" -#define TN_ATTN_V "%s.blk.%d.attn_v.%s" -#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s" -#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s" -#define TN_FFN_UP "%s.blk.%d.ffn_up.%s" -#define TN_LN_1 "%s.blk.%d.ln1.%s" -#define TN_LN_2 "%s.blk.%d.ln2.%s" -#define TN_LN_PRE "%s.pre_ln.%s" -#define TN_LN_POST "%s.post_ln.%s" -#define TN_TEXT_PROJ "text_projection.weight" -#define TN_VIS_PROJ "visual_projection.weight" -#define TN_LLAVA_PROJ "mm.%d.%s" -#define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s" -#define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s" -#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s" -#define TN_IMAGE_NEWLINE "model.image_newline" -#define TN_MM_INP_PROJ "mm.input_projection.weight" // gemma3 -#define TN_MM_SOFT_EMB_N "mm.soft_emb_norm.weight" // gemma3 - -#define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k" -#define TN_MINICPMV_QUERY "resampler.query" -#define TN_MINICPMV_PROJ "resampler.proj.weight" -#define TN_MINICPMV_KV_PROJ "resampler.kv.weight" -#define TN_MINICPMV_ATTN "resampler.attn.%s.%s" -#define TN_MINICPMV_LN "resampler.ln_%s.%s" - -#define TN_GLM_ADAPER_CONV "adapter.conv.%s" -#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s" -#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s" -#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s" -#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s" -#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s" -#define TN_GLM_BOI_W "adapter.boi" -#define TN_GLM_EOI_W "adapter.eoi" - - -enum projector_type { - PROJECTOR_TYPE_MLP, - PROJECTOR_TYPE_MLP_NORM, - PROJECTOR_TYPE_LDP, - PROJECTOR_TYPE_LDPV2, - PROJECTOR_TYPE_RESAMPLER, - PROJECTOR_TYPE_GLM_EDGE, - PROJECTOR_TYPE_MERGER, - PROJECTOR_TYPE_GEMMA3, - PROJECTOR_TYPE_UNKNOWN, -}; - -static std::map PROJECTOR_TYPE_NAMES = { - { PROJECTOR_TYPE_MLP, "mlp" }, - { PROJECTOR_TYPE_LDP, "ldp" }, - { PROJECTOR_TYPE_LDPV2, "ldpv2"}, - { PROJECTOR_TYPE_RESAMPLER, "resampler"}, - { PROJECTOR_TYPE_GLM_EDGE, "adapter"}, - { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"}, - { PROJECTOR_TYPE_GEMMA3, "gemma3"}, -}; - - -// -// utilities to get data from a gguf file -// - -static int get_key_idx(const gguf_context * ctx, const char * key) { - int i = gguf_find_key(ctx, key); - if (i == -1) { - LOG_ERR("key %s not found in file\n", key); - throw std::runtime_error(format("Missing required key: %s", key)); - } - - return i; -} - -static uint32_t get_u32(const gguf_context * ctx, const std::string & key) { - const int i = get_key_idx(ctx, key.c_str()); - - return gguf_get_val_u32(ctx, i); -} - -static float get_f32(const gguf_context * ctx, const std::string & key) { - const int i = get_key_idx(ctx, key.c_str()); - - return gguf_get_val_f32(ctx, i); -} - -static struct ggml_tensor * get_tensor(struct ggml_context * ctx, const std::string & name) { - struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str()); - if (!cur) { - throw std::runtime_error(format("%s: unable to find tensor %s\n", __func__, name.c_str())); - } - - return cur; -} - -static std::string get_ftype(int ftype) { - return ggml_type_name(static_cast(ftype)); -} - -static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) { - switch (type) { - case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]); - case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]); - case GGUF_TYPE_UINT16: return std::to_string(((const uint16_t *)data)[i]); - case GGUF_TYPE_INT16: return std::to_string(((const int16_t *)data)[i]); - case GGUF_TYPE_UINT32: return std::to_string(((const uint32_t *)data)[i]); - case GGUF_TYPE_INT32: return std::to_string(((const int32_t *)data)[i]); - case GGUF_TYPE_UINT64: return std::to_string(((const uint64_t *)data)[i]); - case GGUF_TYPE_INT64: return std::to_string(((const int64_t *)data)[i]); - case GGUF_TYPE_FLOAT32: return std::to_string(((const float *)data)[i]); - case GGUF_TYPE_FLOAT64: return std::to_string(((const double *)data)[i]); - case GGUF_TYPE_BOOL: return ((const bool *)data)[i] ? "true" : "false"; - default: return format("unknown type %d", type); - } -} - -static void replace_all(std::string & s, const std::string & search, const std::string & replace) { - if (search.empty()) { - return; - } - std::string builder; - builder.reserve(s.length()); - size_t pos = 0; - size_t last_pos = 0; - while ((pos = s.find(search, last_pos)) != std::string::npos) { - builder.append(s, last_pos, pos - last_pos); - builder.append(replace); - last_pos = pos + search.length(); - } - builder.append(s, last_pos, std::string::npos); - s = std::move(builder); -} - -static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { - const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i); - - switch (type) { - case GGUF_TYPE_STRING: - return gguf_get_val_str(ctx_gguf, i); - case GGUF_TYPE_ARRAY: - { - const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i); - int arr_n = gguf_get_arr_n(ctx_gguf, i); - const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i); - std::stringstream ss; - ss << "["; - for (int j = 0; j < arr_n; j++) { - if (arr_type == GGUF_TYPE_STRING) { - std::string val = gguf_get_arr_str(ctx_gguf, i, j); - // escape quotes - replace_all(val, "\\", "\\\\"); - replace_all(val, "\"", "\\\""); - ss << '"' << val << '"'; - } else if (arr_type == GGUF_TYPE_ARRAY) { - ss << "???"; - } else { - ss << gguf_data_to_str(arr_type, data, j); - } - if (j < arr_n - 1) { - ss << ", "; - } - } - ss << "]"; - return ss.str(); - } - default: - return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0); - } -} - -static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") { - size_t tensor_size = ggml_nbytes(tensor); - LOG_INF("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n", - prefix, ggml_n_dims(tensor), tensor->name, tensor_size, - tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type)); -} - -static projector_type clip_projector_type_from_string(const std::string & name) { - for (const auto & kv : PROJECTOR_TYPE_NAMES) { // NOLINT - if (kv.second == name) { - return kv.first; - } - } - throw std::runtime_error(format("Unknown projector type: %s", name.c_str())); -} - #ifdef CLIP_DEBUG_FUNCTIONS static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) { std::ofstream file(filename, std::ios::binary); @@ -418,6 +162,11 @@ static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u // clip layers // +enum patch_merge_type { + PATCH_MERGE_FLAT, + PATCH_MERGE_SPATIAL_UNPAD, +}; + struct clip_hparams { int32_t image_size; int32_t patch_size; @@ -427,9 +176,9 @@ struct clip_hparams { int32_t n_head; int32_t n_layer; - float eps; + patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT; - char mm_patch_merge_type[32] = "flat"; // spatial_unpad or flat (default) + float eps; std::vector image_grid_pinpoints; int32_t image_crop_resolution; @@ -438,44 +187,44 @@ struct clip_hparams { struct clip_layer { // attention - struct ggml_tensor * k_w; - struct ggml_tensor * k_b; - struct ggml_tensor * q_w; - struct ggml_tensor * q_b; - struct ggml_tensor * v_w; - struct ggml_tensor * v_b; + struct ggml_tensor * k_w = nullptr; + struct ggml_tensor * k_b = nullptr; + struct ggml_tensor * q_w = nullptr; + struct ggml_tensor * q_b = nullptr; + struct ggml_tensor * v_w = nullptr; + struct ggml_tensor * v_b = nullptr; - struct ggml_tensor * o_w; - struct ggml_tensor * o_b; + struct ggml_tensor * o_w = nullptr; + struct ggml_tensor * o_b = nullptr; // layernorm 1 - struct ggml_tensor * ln_1_w; - struct ggml_tensor * ln_1_b; + struct ggml_tensor * ln_1_w = nullptr; + struct ggml_tensor * ln_1_b = nullptr; // ff - struct ggml_tensor * ff_i_w; - struct ggml_tensor * ff_i_b; + struct ggml_tensor * ff_i_w = nullptr; + struct ggml_tensor * ff_i_b = nullptr; - struct ggml_tensor * ff_o_w; - struct ggml_tensor * ff_o_b; + struct ggml_tensor * ff_o_w = nullptr; + struct ggml_tensor * ff_o_b = nullptr; // layernorm 2 - struct ggml_tensor * ln_2_w; - struct ggml_tensor * ln_2_b; + struct ggml_tensor * ln_2_w = nullptr; + struct ggml_tensor * ln_2_b = nullptr; }; struct clip_vision_model { struct clip_hparams hparams; // embeddings - struct ggml_tensor * class_embedding; - struct ggml_tensor * patch_embeddings_0; - struct ggml_tensor * patch_embeddings_1; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) - struct ggml_tensor * patch_bias; - struct ggml_tensor * position_embeddings; + struct ggml_tensor * class_embedding = nullptr; + struct ggml_tensor * patch_embeddings_0 = nullptr; + struct ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) + struct ggml_tensor * patch_bias = nullptr; + struct ggml_tensor * position_embeddings = nullptr; - struct ggml_tensor * pre_ln_w; - struct ggml_tensor * pre_ln_b; + struct ggml_tensor * pre_ln_w = nullptr; + struct ggml_tensor * pre_ln_b = nullptr; std::vector layers; @@ -485,84 +234,84 @@ struct clip_vision_model { struct ggml_tensor * projection; // LLaVA projection - struct ggml_tensor * mm_0_w = NULL; - struct ggml_tensor * mm_0_b = NULL; - struct ggml_tensor * mm_2_w = NULL; - struct ggml_tensor * mm_2_b = NULL; + struct ggml_tensor * mm_0_w = nullptr; + struct ggml_tensor * mm_0_b = nullptr; + struct ggml_tensor * mm_2_w = nullptr; + struct ggml_tensor * mm_2_b = nullptr; - struct ggml_tensor * image_newline = NULL; + struct ggml_tensor * image_newline = nullptr; // Yi type models with mlp+normalization projection - struct ggml_tensor * mm_1_w = NULL; // Yi type models have 0, 1, 3, 4 - struct ggml_tensor * mm_1_b = NULL; - struct ggml_tensor * mm_3_w = NULL; - struct ggml_tensor * mm_3_b = NULL; - struct ggml_tensor * mm_4_w = NULL; - struct ggml_tensor * mm_4_b = NULL; + struct ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4 + struct ggml_tensor * mm_1_b = nullptr; + struct ggml_tensor * mm_3_w = nullptr; + struct ggml_tensor * mm_3_b = nullptr; + struct ggml_tensor * mm_4_w = nullptr; + struct ggml_tensor * mm_4_b = nullptr; //GLMV-Edge projection - struct ggml_tensor * mm_model_adapter_conv_w; - struct ggml_tensor * mm_model_adapter_conv_b; - struct ggml_tensor * boi_w; - struct ggml_tensor * eoi_w; + struct ggml_tensor * mm_model_adapter_conv_w = nullptr; + struct ggml_tensor * mm_model_adapter_conv_b = nullptr; + struct ggml_tensor * boi_w = nullptr; + struct ggml_tensor * eoi_w = nullptr; // MobileVLM projection - struct ggml_tensor * mm_model_mlp_1_w; - struct ggml_tensor * mm_model_mlp_1_b; - struct ggml_tensor * mm_model_mlp_3_w; - struct ggml_tensor * mm_model_mlp_3_b; - struct ggml_tensor * mm_model_block_1_block_0_0_w; - struct ggml_tensor * mm_model_block_1_block_0_1_w; - struct ggml_tensor * mm_model_block_1_block_0_1_b; - struct ggml_tensor * mm_model_block_1_block_1_fc1_w; - struct ggml_tensor * mm_model_block_1_block_1_fc1_b; - struct ggml_tensor * mm_model_block_1_block_1_fc2_w; - struct ggml_tensor * mm_model_block_1_block_1_fc2_b; - struct ggml_tensor * mm_model_block_1_block_2_0_w; - struct ggml_tensor * mm_model_block_1_block_2_1_w; - struct ggml_tensor * mm_model_block_1_block_2_1_b; - struct ggml_tensor * mm_model_block_2_block_0_0_w; - struct ggml_tensor * mm_model_block_2_block_0_1_w; - struct ggml_tensor * mm_model_block_2_block_0_1_b; - struct ggml_tensor * mm_model_block_2_block_1_fc1_w; - struct ggml_tensor * mm_model_block_2_block_1_fc1_b; - struct ggml_tensor * mm_model_block_2_block_1_fc2_w; - struct ggml_tensor * mm_model_block_2_block_1_fc2_b; - struct ggml_tensor * mm_model_block_2_block_2_0_w; - struct ggml_tensor * mm_model_block_2_block_2_1_w; - struct ggml_tensor * mm_model_block_2_block_2_1_b; + struct ggml_tensor * mm_model_mlp_1_w = nullptr; + struct ggml_tensor * mm_model_mlp_1_b = nullptr; + struct ggml_tensor * mm_model_mlp_3_w = nullptr; + struct ggml_tensor * mm_model_mlp_3_b = nullptr; + struct ggml_tensor * mm_model_block_1_block_0_0_w = nullptr; + struct ggml_tensor * mm_model_block_1_block_0_1_w = nullptr; + struct ggml_tensor * mm_model_block_1_block_0_1_b = nullptr; + struct ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr; + struct ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr; + struct ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr; + struct ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr; + struct ggml_tensor * mm_model_block_1_block_2_0_w = nullptr; + struct ggml_tensor * mm_model_block_1_block_2_1_w = nullptr; + struct ggml_tensor * mm_model_block_1_block_2_1_b = nullptr; + struct ggml_tensor * mm_model_block_2_block_0_0_w = nullptr; + struct ggml_tensor * mm_model_block_2_block_0_1_w = nullptr; + struct ggml_tensor * mm_model_block_2_block_0_1_b = nullptr; + struct ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr; + struct ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr; + struct ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr; + struct ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr; + struct ggml_tensor * mm_model_block_2_block_2_0_w = nullptr; + struct ggml_tensor * mm_model_block_2_block_2_1_w = nullptr; + struct ggml_tensor * mm_model_block_2_block_2_1_b = nullptr; // MobileVLM_V2 projection - struct ggml_tensor * mm_model_mlp_0_w; - struct ggml_tensor * mm_model_mlp_0_b; - struct ggml_tensor * mm_model_mlp_2_w; - struct ggml_tensor * mm_model_mlp_2_b; - struct ggml_tensor * mm_model_peg_0_w; - struct ggml_tensor * mm_model_peg_0_b; + struct ggml_tensor * mm_model_mlp_0_w = nullptr; + struct ggml_tensor * mm_model_mlp_0_b = nullptr; + struct ggml_tensor * mm_model_mlp_2_w = nullptr; + struct ggml_tensor * mm_model_mlp_2_b = nullptr; + struct ggml_tensor * mm_model_peg_0_w = nullptr; + struct ggml_tensor * mm_model_peg_0_b = nullptr; // MINICPMV projection - struct ggml_tensor * mm_model_pos_embed_k; - struct ggml_tensor * mm_model_query; - struct ggml_tensor * mm_model_proj; - struct ggml_tensor * mm_model_kv_proj; - struct ggml_tensor * mm_model_attn_q_w; - struct ggml_tensor * mm_model_attn_q_b; - struct ggml_tensor * mm_model_attn_k_w; - struct ggml_tensor * mm_model_attn_k_b; - struct ggml_tensor * mm_model_attn_v_w; - struct ggml_tensor * mm_model_attn_v_b; - struct ggml_tensor * mm_model_attn_o_w; - struct ggml_tensor * mm_model_attn_o_b; - struct ggml_tensor * mm_model_ln_q_w; - struct ggml_tensor * mm_model_ln_q_b; - struct ggml_tensor * mm_model_ln_kv_w; - struct ggml_tensor * mm_model_ln_kv_b; - struct ggml_tensor * mm_model_ln_post_w; - struct ggml_tensor * mm_model_ln_post_b; + struct ggml_tensor * mm_model_pos_embed_k = nullptr; + struct ggml_tensor * mm_model_query = nullptr; + struct ggml_tensor * mm_model_proj = nullptr; + struct ggml_tensor * mm_model_kv_proj = nullptr; + struct ggml_tensor * mm_model_attn_q_w = nullptr; + struct ggml_tensor * mm_model_attn_q_b = nullptr; + struct ggml_tensor * mm_model_attn_k_w = nullptr; + struct ggml_tensor * mm_model_attn_k_b = nullptr; + struct ggml_tensor * mm_model_attn_v_w = nullptr; + struct ggml_tensor * mm_model_attn_v_b = nullptr; + struct ggml_tensor * mm_model_attn_o_w = nullptr; + struct ggml_tensor * mm_model_attn_o_b = nullptr; + struct ggml_tensor * mm_model_ln_q_w = nullptr; + struct ggml_tensor * mm_model_ln_q_b = nullptr; + struct ggml_tensor * mm_model_ln_kv_w = nullptr; + struct ggml_tensor * mm_model_ln_kv_b = nullptr; + struct ggml_tensor * mm_model_ln_post_w = nullptr; + struct ggml_tensor * mm_model_ln_post_b = nullptr; // gemma3 - struct ggml_tensor * mm_input_proj_w; - struct ggml_tensor * mm_soft_emb_norm_w; + struct ggml_tensor * mm_input_proj_w = nullptr; + struct ggml_tensor * mm_soft_emb_norm_w = nullptr; }; struct clip_ctx { @@ -584,11 +333,6 @@ struct clip_ctx { bool use_silu = false; int32_t ftype = 1; - bool has_class_embedding = true; - bool has_pre_norm = true; - bool has_post_norm = false; - bool has_patch_bias = false; - struct gguf_context * ctx_gguf = nullptr; struct ggml_context * ctx_data = nullptr; @@ -711,8 +455,7 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - KQ = ggml_scale_inplace(ctx0, KQ, 1.0f / sqrtf((float)d_head)); - KQ = ggml_soft_max_inplace(ctx0, KQ); + KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); KQV = ggml_reshape_3d(ctx0, KQV, d_head, num_patches, n_head); @@ -751,7 +494,7 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im } // post-layernorm - if (ctx->has_post_norm) { + if (model.post_ln_w) { embeddings = ggml_norm(ctx0, embeddings, eps); ggml_set_name(embeddings, "post_ln"); @@ -827,7 +570,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); const int patches_w = image_size_width / patch_size; const int patches_h = image_size_height / patch_size; - const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); + const int num_positions = num_patches + (model.class_embedding ? 1 : 0); const int num_position_ids = ctx->has_qwen2vl_merger ? num_positions * 4 : num_positions; const int hidden_size = hparams.hidden_size; const int n_head = hparams.n_head; @@ -879,7 +622,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); } - if (ctx->has_patch_bias) { + if (model.patch_bias) { // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); inp = ggml_add(ctx0, inp, model.patch_bias); } @@ -888,7 +631,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im if (ctx->has_llava_projector) { // concat class_embeddings and patch_embeddings - if (ctx->has_class_embedding) { + if (model.class_embedding) { embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); ggml_set_name(embeddings, "embeddings"); ggml_set_input(embeddings); @@ -925,7 +668,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im } // pre-layernorm - if (ctx->has_pre_norm) { + if (model.pre_ln_w) { embeddings = ggml_norm(ctx0, embeddings, eps); ggml_set_name(embeddings, "pre_ln"); @@ -967,7 +710,6 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im ctx0, Q, positions, nullptr, d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); } - Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size); @@ -991,7 +733,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - KQ = ggml_soft_max_inplace(ctx0, KQ); + KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size); KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); @@ -1035,7 +777,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im } // post-layernorm - if (ctx->has_post_norm) { + if (model.post_ln_w) { embeddings = ggml_norm(ctx0, embeddings, eps); ggml_set_name(embeddings, "post_ln"); @@ -1075,8 +817,10 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); embeddings = ggml_gelu(ctx0, embeddings); - embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); + if (model.mm_2_w) { + embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); + } } else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); @@ -1279,7 +1023,6 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im } struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b); - Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); struct ggml_tensor * K = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), model.mm_model_attn_k_b); struct ggml_tensor * V = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), model.mm_model_attn_v_b); // permute @@ -1293,7 +1036,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - KQ = ggml_soft_max_inplace(ctx0, KQ); + KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_query, n_head, batch_size); KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); @@ -1369,549 +1112,395 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 } } -// read and create ggml_context containing the tensors and their data -struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { - return clip_init(fname, clip_context_params{ - /* use_gpu */ true, - /* verbosity */ verbosity, - }); -} +struct clip_model_loader { + ggml_context_ptr ctx_meta; + gguf_context_ptr ctx_gguf; -struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params) { - int verbosity = ctx_params.verbosity; - struct ggml_context * meta = NULL; + clip_ctx & ctx_clip; + std::string fname; - struct gguf_init_params params = { - /*.no_alloc = */ true, - /*.ctx = */ &meta, - }; + size_t model_size; // in bytes - struct gguf_context * ctx = gguf_init_from_file(fname, params); - if (!ctx) { - throw std::runtime_error(format("%s: failed to load CLIP model from %s. Does this file exist?\n", __func__, fname)); - } + // TODO @ngxson : we should not pass clip_ctx here, it should be clip_vision_model + clip_model_loader(const char * fname, clip_ctx & ctx_clip) : ctx_clip(ctx_clip), fname(fname) { + struct ggml_context * meta = nullptr; - if (verbosity >= 1) { - const int n_tensors = gguf_get_n_tensors(ctx); - const int n_kv = gguf_get_n_kv(ctx); - const int ftype = get_u32(ctx, KEY_FTYPE); - const std::string ftype_str = get_ftype(ftype); - const int idx_name = gguf_find_key(ctx, KEY_NAME); - if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug - const std::string name = gguf_get_val_str(ctx, idx_name); - LOG_INF("%s: model name: %s\n", __func__, name.c_str()); - } - const int idx_desc = gguf_find_key(ctx, KEY_DESCRIPTION); - if (idx_desc != -1) { // ditto - const std::string description = gguf_get_val_str(ctx, idx_desc); - LOG_INF("%s: description: %s\n", __func__, description.c_str()); - } - LOG_INF("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx)); - LOG_INF("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); - LOG_INF("%s: n_tensors: %d\n", __func__, n_tensors); - LOG_INF("%s: n_kv: %d\n", __func__, n_kv); - LOG_INF("%s: ftype: %s\n", __func__, ftype_str.c_str()); - LOG_INF("\n"); - } - const int n_tensors = gguf_get_n_tensors(ctx); + struct gguf_init_params params = { + /*.no_alloc = */ true, + /*.ctx = */ &meta, + }; - // kv - const int n_kv = gguf_get_n_kv(ctx); - LOG_INF("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n", - __func__, n_kv, n_tensors, fname); - { - std::map n_type; - - for (int i = 0; i < n_tensors; i++) { - enum ggml_type type = gguf_get_tensor_type(ctx, i); - - n_type[type]++; + ctx_gguf = gguf_context_ptr(gguf_init_from_file(fname, params)); + if (!ctx_gguf.get()) { + throw std::runtime_error(string_format("%s: failed to load CLIP model from %s. Does this file exist?\n", __func__, fname)); } - LOG_INF("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); - for (int i = 0; i < n_kv; i++) { - const char * name = gguf_get_key(ctx, i); - const enum gguf_type type = gguf_get_kv_type(ctx, i); - const std::string type_name = - type == GGUF_TYPE_ARRAY - ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx, i)), gguf_get_arr_n(ctx, i)) - : gguf_type_name(type); + ctx_meta.reset(meta); - std::string value = gguf_kv_to_str(ctx, i); - const size_t MAX_VALUE_LEN = 40; - if (value.size() > MAX_VALUE_LEN) { - value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()); - } - replace_all(value, "\n", "\\n"); + const int n_tensors = gguf_get_n_tensors(ctx_gguf.get()); - LOG_INF("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str()); + // print gguf info + { + int ftype = -1; + get_u32(KEY_FTYPE, ftype, false); + const std::string ftype_str = ggml_type_name(static_cast(ftype)); + std::string name; + get_string(KEY_NAME, name, false); + std::string description; + get_string(KEY_DESCRIPTION, description, false); + LOG_INF("%s: model name: %s\n", __func__, name.c_str()); + LOG_INF("%s: description: %s\n", __func__, description.c_str()); + LOG_INF("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx_gguf.get())); + LOG_INF("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx_gguf.get())); + LOG_INF("%s: n_tensors: %d\n", __func__, n_tensors); + LOG_INF("%s: n_kv: %d\n", __func__, (int)gguf_get_n_kv(ctx_gguf.get())); + LOG_INF("%s: ftype: %s\n", __func__, ftype_str.c_str()); + LOG_INF("\n"); } - // print type counts - for (auto & kv : n_type) { - if (kv.second == 0) { - continue; - } - - LOG_INF("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second); - } - } - - // data - size_t model_size = 0; - { - for (int i = 0; i < n_tensors; ++i) { - const char * name = gguf_get_tensor_name(ctx, i); - const size_t offset = gguf_get_tensor_offset(ctx, i); - enum ggml_type type = gguf_get_tensor_type(ctx, i); - struct ggml_tensor * cur = ggml_get_tensor(meta, name); - size_t tensor_size = ggml_nbytes(cur); - model_size += tensor_size; - if (verbosity >= 3) { - LOG_INF("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", - __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type)); + // tensors + { + for (int i = 0; i < n_tensors; ++i) { + const char * name = gguf_get_tensor_name(ctx_gguf.get(), i); + const size_t offset = gguf_get_tensor_offset(ctx_gguf.get(), i); + enum ggml_type type = gguf_get_tensor_type(ctx_gguf.get(), i); + struct ggml_tensor * cur = ggml_get_tensor(meta, name); + size_t tensor_size = ggml_nbytes(cur); + model_size += tensor_size; + LOG_DBG("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", + __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type)); } } } - clip_ctx * new_clip = new clip_ctx(ctx_params); - - // update projector type - { - int idx = gguf_find_key(ctx, KEY_PROJ_TYPE); - if (idx != -1) { - const std::string proj_type = gguf_get_val_str(ctx, idx); - new_clip->proj_type = clip_projector_type_from_string(proj_type); - } else { - new_clip->proj_type = PROJECTOR_TYPE_MLP; - } - - if (new_clip->proj_type == PROJECTOR_TYPE_MLP) { - if (gguf_find_tensor(ctx, format(TN_LLAVA_PROJ, 3, "weight").c_str()) != -1) { - new_clip->proj_type = PROJECTOR_TYPE_MLP_NORM; + void load_hparams() { + // projector type + { + std::string proj_type; + get_string(KEY_PROJ_TYPE, proj_type, false); + if (!proj_type.empty()) { + ctx_clip.proj_type = clip_projector_type_from_string(proj_type); + } + if (ctx_clip.proj_type == PROJECTOR_TYPE_UNKNOWN) { + throw std::runtime_error(string_format("%s: unknown projector type: %s\n", __func__, proj_type.c_str())); } } - } - // model size and capabilities - { - int idx = get_key_idx(ctx, KEY_HAS_TEXT_ENC); - new_clip->has_text_encoder = gguf_get_val_bool(ctx, idx); + // other hparams + { + get_bool(KEY_HAS_TEXT_ENC, ctx_clip.has_text_encoder, false); + get_bool(KEY_HAS_VIS_ENC, ctx_clip.has_vision_encoder, false); + GGML_ASSERT(ctx_clip.has_vision_encoder); + GGML_ASSERT(!ctx_clip.has_text_encoder); - idx = get_key_idx(ctx, KEY_HAS_VIS_ENC); - new_clip->has_vision_encoder = gguf_get_val_bool(ctx, idx); + // legacy keys, use KEY_PROJ_TYPE instead + get_bool(KEY_HAS_LLAVA_PROJ, ctx_clip.has_llava_projector, false); + get_bool(KEY_HAS_MINICPMV_PROJ, ctx_clip.has_minicpmv_projector, false); + get_i32(KEY_MINICPMV_VERSION, ctx_clip.minicpmv_version, false); + get_bool(KEY_HAS_GLM_PROJ, ctx_clip.has_glm_projector, false); + get_bool(KEY_HAS_QWEN2VL_MERGER, ctx_clip.has_qwen2vl_merger, false); + // !!! do NOT extend the list above, use KEY_PROJ_TYPE instead - idx = gguf_find_key(ctx, KEY_HAS_LLAVA_PROJ); - if (idx != -1) { - new_clip->has_llava_projector = gguf_get_val_bool(ctx, idx); - } + get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false); + get_bool(KEY_USE_SILU, ctx_clip.use_silu, false); - idx = gguf_find_key(ctx, KEY_HAS_MINICPMV_PROJ); - if (idx != -1) { - new_clip->has_minicpmv_projector = gguf_get_val_bool(ctx, idx); - } + auto & hparams = ctx_clip.vision_model.hparams; + get_u32(string_format(KEY_N_EMBD, "vision"), hparams.hidden_size); + get_u32(string_format(KEY_N_HEAD, "vision"), hparams.n_head); + get_u32(string_format(KEY_N_FF, "vision"), hparams.n_intermediate); + get_u32(string_format(KEY_N_BLOCK, "vision"), hparams.n_layer); + get_u32(string_format(KEY_PROJ_DIM, "vision"), hparams.projection_dim); + get_f32(string_format(KEY_LAYER_NORM_EPS, "vision"), hparams.eps); + get_u32(KEY_IMAGE_SIZE, hparams.image_size); + get_u32(KEY_PATCH_SIZE, hparams.patch_size); + get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false); + get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false); - idx = gguf_find_key(ctx, KEY_MINICPMV_VERSION); - if (idx != -1) { - new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx); - } + { + std::string mm_patch_merge_type; + get_string(KEY_MM_PATCH_MERGE_TYPE, mm_patch_merge_type, false); + if (mm_patch_merge_type == "spatial_unpad") { + hparams.mm_patch_merge_type = PATCH_MERGE_SPATIAL_UNPAD; + } + } - idx = gguf_find_key(ctx, KEY_HAS_GLM_PROJ); - if (idx != -1) { - new_clip->has_glm_projector = gguf_get_val_bool(ctx, idx); - } + { + int idx_mean = gguf_find_key(ctx_gguf.get(), KEY_IMAGE_MEAN); + int idx_std = gguf_find_key(ctx_gguf.get(), KEY_IMAGE_STD); + GGML_ASSERT(idx_mean >= 0 && "image_mean not found"); + GGML_ASSERT(idx_std >= 0 && "image_std not found"); + const float * mean_data = (const float *) gguf_get_arr_data(ctx_gguf.get(), idx_mean); + const float * std_data = (const float *) gguf_get_arr_data(ctx_gguf.get(), idx_std); + for (int i = 0; i < 3; ++i) { + ctx_clip.image_mean[i] = mean_data[i]; + ctx_clip.image_std[i] = std_data[i]; + } + } - idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER); - if (idx != -1) { - new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx); - } - // GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search + // Load the vision feature layer indices if they are explicitly provided; + // if multiple vision feature layers are present, the values will be concatenated + // to form the final visual features. + // NOTE: gguf conversions should standardize the values of the vision feature layer to + // be non-negative, since we use -1 to mark values as unset here. + std::vector vision_feature_layer; + get_arr_int(KEY_FEATURE_LAYER, vision_feature_layer, false); + // convert std::vector to std::unordered_set + for (auto & layer : vision_feature_layer) { + hparams.vision_feature_layer.insert(layer); + } + // Calculate the deepest feature layer based on hparams and projector type + ctx_clip.max_feature_layer = get_deepest_feature_layer(&ctx_clip); - GGML_ASSERT(new_clip->has_vision_encoder); - GGML_ASSERT(!new_clip->has_text_encoder); - - try { - idx = get_key_idx(ctx, KEY_USE_GELU); - new_clip->use_gelu = gguf_get_val_bool(ctx, idx); - } catch (std::runtime_error & /*e*/) { - new_clip->use_gelu = false; - } - - try { - idx = get_key_idx(ctx, KEY_USE_SILU); - new_clip->use_silu = gguf_get_val_bool(ctx, idx); - } catch (std::runtime_error & /*e*/) { - new_clip->use_silu = false; - } - - if (verbosity >= 1) { - LOG_INF("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); - LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); - LOG_INF("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); - LOG_INF("%s: minicpmv_projector: %d\n", __func__, new_clip->has_minicpmv_projector); - LOG_INF("%s: minicpmv_version: %d\n", __func__, new_clip->minicpmv_version); - LOG_INF("%s: glm_projector: %d\n", __func__, new_clip->has_glm_projector); - LOG_INF("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0); - LOG_INF("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); + LOG_INF("%s: text_encoder: %d\n", __func__, ctx_clip.has_text_encoder); + LOG_INF("%s: vision_encoder: %d\n", __func__, ctx_clip.has_vision_encoder); + LOG_INF("%s: llava_projector: %d\n", __func__, ctx_clip.has_llava_projector); + LOG_INF("%s: minicpmv_projector: %d\n", __func__, ctx_clip.has_minicpmv_projector); + LOG_INF("%s: minicpmv_version: %d\n", __func__, ctx_clip.minicpmv_version); + LOG_INF("%s: glm_projector: %d\n", __func__, ctx_clip.has_glm_projector); + LOG_INF("%s: model size: %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0); + LOG_INF("%s: metadata size: %.2f MiB\n", __func__, ggml_get_mem_size(ctx_meta.get()) / 1024.0 / 1024.0); } } - LOG_INF("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors); + void load_tensors() { + std::map tensor_offset; + std::vector tensors_to_load; - // load tensors - { - std::vector read_buf; + // get offsets + for (int64_t i = 0; i < gguf_get_n_tensors(ctx_gguf.get()); ++i) { + const char * name = gguf_get_tensor_name(ctx_gguf.get(), i); + tensor_offset[name] = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), i); + } + + // create data context struct ggml_init_params params = { - /*.mem_size =*/ (n_tensors + 1) * ggml_tensor_overhead(), + /*.mem_size =*/ (gguf_get_n_tensors(ctx_gguf.get()) + 1) * ggml_tensor_overhead(), /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; - - new_clip->ctx_data = ggml_init(params); - if (!new_clip->ctx_data) { - LOG_ERR("%s: ggml_init() failed\n", __func__); - clip_free(new_clip); - gguf_free(ctx); - return nullptr; + ctx_clip.ctx_data = ggml_init(params); + if (!ctx_clip.ctx_data) { + throw std::runtime_error(string_format("%s: failed to init ggml context\n", __func__)); } - auto fin = std::ifstream(fname, std::ios::binary); - if (!fin) { - LOG_ERR("cannot open model file for loading tensors\n"); - clip_free(new_clip); - gguf_free(ctx); - return nullptr; - } - - // add tensors to context - for (int i = 0; i < n_tensors; ++i) { - const char * name = gguf_get_tensor_name(ctx, i); - struct ggml_tensor * t = ggml_get_tensor(meta, name); - struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx_data, t); - ggml_set_name(cur, name); - } - - // alloc memory and offload data - ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(new_clip->backend); - new_clip->buf = ggml_backend_alloc_ctx_tensors_from_buft(new_clip->ctx_data, buft); - ggml_backend_buffer_set_usage(new_clip->buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); - for (int i = 0; i < n_tensors; ++i) { - const char * name = gguf_get_tensor_name(ctx, i); - struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name); - const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i); - fin.seekg(offset, std::ios::beg); - if (!fin) { - LOG_ERR("%s: failed to seek for tensor %s\n", __func__, name); - clip_free(new_clip); - gguf_free(ctx); - return nullptr; + // helper function + auto get_tensor = [&](const std::string & name, bool required = true) { + struct ggml_tensor * cur = ggml_get_tensor(ctx_meta.get(), name.c_str()); + if (!cur && required) { + throw std::runtime_error(string_format("%s: unable to find tensor %s\n", __func__, name.c_str())); } - int num_bytes = ggml_nbytes(cur); - if (ggml_backend_buft_is_host(buft)) { - // for the CPU and Metal backend, we can read directly into the tensor - fin.read(reinterpret_cast(cur->data), num_bytes); - } else { - // read into a temporary buffer first, then copy to device memory - read_buf.resize(num_bytes); - fin.read(reinterpret_cast(read_buf.data()), num_bytes); - ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes); + if (cur) { + tensors_to_load.push_back(cur); + // add tensors to context + struct ggml_tensor * data_tensor = ggml_dup_tensor(ctx_clip.ctx_data, cur); + ggml_set_name(data_tensor, cur->name); + cur = data_tensor; } - } - fin.close(); - } + return cur; + }; - // vision model - if (new_clip->has_vision_encoder) { - // load vision model - auto & vision_model = new_clip->vision_model; - auto & hparams = vision_model.hparams; - hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "vision")); - hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "vision")); - hparams.n_intermediate = get_u32(ctx, format(KEY_N_FF, "vision")); - hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "vision")); - hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE); - hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE); - hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision")); - hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision")); + auto & vision_model = ctx_clip.vision_model; - try { - int idx = get_key_idx(ctx, KEY_IMAGE_GRID_PINPOINTS); - int n = gguf_get_arr_n(ctx, idx); - const int32_t * pinpoints = (const int32_t *)gguf_get_arr_data(ctx, idx); - for (int i = 0; i < n; ++i) { - hparams.image_grid_pinpoints.push_back(pinpoints[i]); - } - } catch (std::runtime_error & /*e*/) { } + vision_model.class_embedding = get_tensor(TN_CLASS_EMBD, false); - // Load the vision feature layer indices if they are explicitly provided; - // if multiple vision feature layers are present, the values will be concatenated - // to form the final visual features. - // NOTE: gguf conversions should standardize the values of the vision feature layer to - // be non-negative, since we use -1 to mark values as unset here. - try { - int idx = get_key_idx(ctx, KEY_FEATURE_LAYER); - int n = gguf_get_arr_n(ctx, idx); + vision_model.pre_ln_w = get_tensor(string_format(TN_LN_PRE, "v", "weight"), false); + vision_model.pre_ln_b = get_tensor(string_format(TN_LN_PRE, "v", "bias"), false); - const int32_t * vision_feature_layer = (const int32_t *)gguf_get_arr_data(ctx, idx); + vision_model.post_ln_w = get_tensor(string_format(TN_LN_POST, "v", "weight"), false); + vision_model.post_ln_b = get_tensor(string_format(TN_LN_POST, "v", "bias"), false); - for (int i = 0; i < n; ++i) { - hparams.vision_feature_layer.insert(vision_feature_layer[i]); - } - } catch (std::runtime_error & /*e*/) { } - - try { - int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE); - strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx)); - } catch (std::runtime_error & /*e*/) { - strcpy(hparams.mm_patch_merge_type, "flat"); + vision_model.patch_bias = get_tensor(TN_PATCH_BIAS, false); + vision_model.patch_embeddings_0 = get_tensor(TN_PATCH_EMBD, false); + vision_model.patch_embeddings_1 = get_tensor(TN_PATCH_EMBD_1, false); + if (vision_model.patch_embeddings_1 == nullptr) { + ctx_clip.has_qwen2vl_merger = false; } - try { - hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6 - } catch(const std::exception& /*e*/) { - hparams.image_crop_resolution = hparams.image_size; - } + vision_model.position_embeddings = get_tensor(string_format(TN_POS_EMBD, "v"), false); - int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN); - int idx_std = get_key_idx(ctx, KEY_IMAGE_STD); - - const float * mean_data = (const float *)gguf_get_arr_data(ctx, idx_mean); - const float * std_data = (const float *)gguf_get_arr_data(ctx, idx_std); - - for (int i = 0; i < 3; ++i) { - new_clip->image_mean[i] = mean_data[i]; - new_clip->image_std[i] = std_data[i]; - } - - // Calculate the deepest feature layer based on hparams and projector type - new_clip->max_feature_layer = get_deepest_feature_layer(new_clip); - - if (verbosity >= 2) { - LOG_INF("\n%s: vision model hparams\n", __func__); - LOG_INF("image_size %d\n", hparams.image_size); - LOG_INF("patch_size %d\n", hparams.patch_size); - LOG_INF("v_hidden_size %d\n", hparams.hidden_size); - LOG_INF("v_n_intermediate %d\n", hparams.n_intermediate); - LOG_INF("v_projection_dim %d\n", hparams.projection_dim); - LOG_INF("v_n_head %d\n", hparams.n_head); - LOG_INF("v_n_layer %d\n", hparams.n_layer); - LOG_INF("v_eps %f\n", hparams.eps); - LOG_INF("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); - LOG_INF("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); - LOG_INF("v_image_grid_pinpoints: "); - for (const auto & pp : hparams.image_grid_pinpoints) { - LOG_INF("%d ", pp); - } - LOG_INF("\n"); - LOG_INF("v_vision_feature_layer: "); - for (const auto & feature_layer: hparams.vision_feature_layer) { - LOG_INF("%d ", feature_layer); - } - LOG_INF("\n"); - LOG_INF("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type); - - } - - try { - vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); - new_clip->has_class_embedding = true; - } catch (const std::exception& /*e*/) { - new_clip->has_class_embedding = false; - } - - try { - vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); - vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); - new_clip->has_pre_norm = true; - } catch (std::exception & /*e*/) { - new_clip->has_pre_norm = false; - } - - try { - vision_model.post_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight")); - vision_model.post_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias")); - new_clip->has_post_norm = true; - } catch (std::exception & /*e*/) { - new_clip->has_post_norm = false; - } - - try { - vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS); - new_clip->has_patch_bias = true; - } catch (std::exception & /*e*/) { - new_clip->has_patch_bias = false; - } - - try { - vision_model.patch_embeddings_0 = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); - } catch(const std::exception& /*e*/) { - vision_model.patch_embeddings_0 = nullptr; - } - - try { - vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); - } catch(const std::exception& /*e*/) { - vision_model.position_embeddings = nullptr; - } - - try { - vision_model.patch_embeddings_1 = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD_1); - } catch(const std::exception& /*e*/) { - new_clip->has_qwen2vl_merger = false; - } - - // LLaVA projection - if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) { - vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight")); - vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias")); - try { - // Yi-type llava - vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight")); - vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias")); - } catch (std::runtime_error & /*e*/) { } - try { - // missing in Yi-type llava - vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); - vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); - } catch (std::runtime_error & /*e*/) { } - try { - // Yi-type llava - vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight")); - vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias")); - } catch (std::runtime_error & /*e*/) { } - try { - // Yi-type llava - vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight")); - vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias")); - } catch (std::runtime_error & /*e*/) { } - try { - vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE); - // LOG_INF("%s: image_newline tensor (llava-1.6) found\n", __func__); - } catch (std::runtime_error & /*e*/) { } - } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) { - // MobileVLM projection - vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight")); - vision_model.mm_model_mlp_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "bias")); - vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "weight")); - vision_model.mm_model_mlp_3_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "bias")); - vision_model.mm_model_block_1_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "0.weight")); - vision_model.mm_model_block_1_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.weight")); - vision_model.mm_model_block_1_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.bias")); - vision_model.mm_model_block_1_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.weight")); - vision_model.mm_model_block_1_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.bias")); - vision_model.mm_model_block_1_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.weight")); - vision_model.mm_model_block_1_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.bias")); - vision_model.mm_model_block_1_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "0.weight")); - vision_model.mm_model_block_1_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.weight")); - vision_model.mm_model_block_1_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.bias")); - vision_model.mm_model_block_2_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "0.weight")); - vision_model.mm_model_block_2_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.weight")); - vision_model.mm_model_block_2_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.bias")); - vision_model.mm_model_block_2_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.weight")); - vision_model.mm_model_block_2_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.bias")); - vision_model.mm_model_block_2_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.weight")); - vision_model.mm_model_block_2_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.bias")); - vision_model.mm_model_block_2_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight")); - vision_model.mm_model_block_2_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight")); - vision_model.mm_model_block_2_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias")); - } - else if (new_clip->proj_type == PROJECTOR_TYPE_LDPV2) - { - // MobilVLM_V2 projection - vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "weight")); - vision_model.mm_model_mlp_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "bias")); - vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "weight")); - vision_model.mm_model_mlp_2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "bias")); - vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight")); - vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias")); - } - else if (new_clip->proj_type == PROJECTOR_TYPE_RESAMPLER) { - // vision_model.mm_model_pos_embed = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD); - vision_model.mm_model_pos_embed_k = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD_K); - vision_model.mm_model_query = get_tensor(new_clip->ctx_data, TN_MINICPMV_QUERY); - vision_model.mm_model_proj = get_tensor(new_clip->ctx_data, TN_MINICPMV_PROJ); - vision_model.mm_model_kv_proj = get_tensor(new_clip->ctx_data, TN_MINICPMV_KV_PROJ); - vision_model.mm_model_attn_q_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "q", "weight")); - vision_model.mm_model_attn_k_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "k", "weight")); - vision_model.mm_model_attn_v_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "v", "weight")); - vision_model.mm_model_attn_q_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "q", "bias")); - vision_model.mm_model_attn_k_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "k", "bias")); - vision_model.mm_model_attn_v_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "v", "bias")); - vision_model.mm_model_attn_o_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "out", "weight")); - vision_model.mm_model_attn_o_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "out", "bias")); - vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "q", "weight")); - vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "q", "bias")); - vision_model.mm_model_ln_kv_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "kv", "weight")); - vision_model.mm_model_ln_kv_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "kv", "bias")); - vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight")); - vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias")); - } - else if (new_clip->proj_type == PROJECTOR_TYPE_GLM_EDGE) { - vision_model.mm_model_adapter_conv_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "weight")); - vision_model.mm_model_adapter_conv_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "bias")); - vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_LINEAR,"weight")); - vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"weight")); - vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"bias")); - vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_H_2_4H,"weight")); - vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_GATE,"weight")); - vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_4H_2_H,"weight")); - vision_model.boi_w = get_tensor(new_clip->ctx_data, TN_GLM_BOI_W); - vision_model.eoi_w = get_tensor(new_clip->ctx_data, TN_GLM_EOI_W); - } - else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) { - vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight")); - vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias")); - vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); - vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); - } - else if (new_clip->proj_type == PROJECTOR_TYPE_GEMMA3) { - vision_model.mm_input_proj_w = get_tensor(new_clip->ctx_data, TN_MM_INP_PROJ); - vision_model.mm_soft_emb_norm_w = get_tensor(new_clip->ctx_data, TN_MM_SOFT_EMB_N); - } - else { - std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type]; - throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); - } - - vision_model.layers.resize(hparams.n_layer); - - for (int il = 0; il < hparams.n_layer; ++il) { + // layers + vision_model.layers.resize(vision_model.hparams.n_layer); + for (int il = 0; il < vision_model.hparams.n_layer; ++il) { auto & layer = vision_model.layers[il]; - layer.k_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "weight")); - layer.q_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "weight")); - layer.v_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "weight")); - layer.o_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "weight")); - layer.ln_1_w = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "weight")); - layer.ln_2_w = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "weight")); - layer.ff_i_w = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "weight")); - layer.ff_o_w = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "weight")); - layer.k_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "bias")); - layer.q_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "bias")); - layer.v_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "bias")); - layer.o_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "bias")); - layer.ln_1_b = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "bias")); - layer.ln_2_b = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "bias")); - layer.ff_i_b = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "bias")); - layer.ff_o_b = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "bias")); + layer.k_w = get_tensor(string_format(TN_ATTN_K, "v", il, "weight")); + layer.q_w = get_tensor(string_format(TN_ATTN_Q, "v", il, "weight")); + layer.v_w = get_tensor(string_format(TN_ATTN_V, "v", il, "weight")); + layer.o_w = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "weight")); + layer.ln_1_w = get_tensor(string_format(TN_LN_1, "v", il, "weight"), false); + layer.ln_2_w = get_tensor(string_format(TN_LN_2, "v", il, "weight"), false); + layer.ff_i_w = get_tensor(string_format(TN_FFN_DOWN, "v", il, "weight")); + layer.ff_o_w = get_tensor(string_format(TN_FFN_UP, "v", il, "weight")); + layer.k_b = get_tensor(string_format(TN_ATTN_K, "v", il, "bias"), false); + layer.q_b = get_tensor(string_format(TN_ATTN_Q, "v", il, "bias"), false); + layer.v_b = get_tensor(string_format(TN_ATTN_V, "v", il, "bias"), false); + layer.o_b = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "bias"), false); + layer.ln_1_b = get_tensor(string_format(TN_LN_1, "v", il, "bias"), false); + layer.ln_2_b = get_tensor(string_format(TN_LN_2, "v", il, "bias"), false); + layer.ff_i_b = get_tensor(string_format(TN_FFN_DOWN, "v", il, "bias"), false); + layer.ff_o_b = get_tensor(string_format(TN_FFN_UP, "v", il, "bias"), false); + } + + switch (ctx_clip.proj_type) { + case PROJECTOR_TYPE_MLP: + case PROJECTOR_TYPE_MLP_NORM: + { + // LLaVA projection + vision_model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"), false); + vision_model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"), false); + // Yi-type llava + vision_model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"), false); + vision_model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"), false); + // missing in Yi-type llava + vision_model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"), false); + vision_model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false); + // Yi-type llava + vision_model.mm_3_w = get_tensor(string_format(TN_LLAVA_PROJ, 3, "weight"), false); + vision_model.mm_3_b = get_tensor(string_format(TN_LLAVA_PROJ, 3, "bias"), false); + vision_model.mm_4_w = get_tensor(string_format(TN_LLAVA_PROJ, 4, "weight"), false); + vision_model.mm_4_b = get_tensor(string_format(TN_LLAVA_PROJ, 4, "bias"), false); + if (vision_model.mm_3_w) { + // TODO: this is a hack to support Yi-type llava + ctx_clip.proj_type = PROJECTOR_TYPE_MLP_NORM; + } + vision_model.image_newline = get_tensor(TN_IMAGE_NEWLINE, false); + } break; + case PROJECTOR_TYPE_LDP: + { + // MobileVLM projection + vision_model.mm_model_mlp_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight")); + vision_model.mm_model_mlp_1_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "bias")); + vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight")); + vision_model.mm_model_mlp_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias")); + vision_model.mm_model_block_1_block_0_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 0, "0.weight")); + vision_model.mm_model_block_1_block_0_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.weight")); + vision_model.mm_model_block_1_block_0_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.bias")); + vision_model.mm_model_block_1_block_1_fc1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.weight")); + vision_model.mm_model_block_1_block_1_fc1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.bias")); + vision_model.mm_model_block_1_block_1_fc2_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.weight")); + vision_model.mm_model_block_1_block_1_fc2_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.bias")); + vision_model.mm_model_block_1_block_2_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 2, "0.weight")); + vision_model.mm_model_block_1_block_2_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.weight")); + vision_model.mm_model_block_1_block_2_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.bias")); + vision_model.mm_model_block_2_block_0_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 0, "0.weight")); + vision_model.mm_model_block_2_block_0_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.weight")); + vision_model.mm_model_block_2_block_0_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.bias")); + vision_model.mm_model_block_2_block_1_fc1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.weight")); + vision_model.mm_model_block_2_block_1_fc1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.bias")); + vision_model.mm_model_block_2_block_1_fc2_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.weight")); + vision_model.mm_model_block_2_block_1_fc2_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.bias")); + vision_model.mm_model_block_2_block_2_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight")); + vision_model.mm_model_block_2_block_2_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight")); + vision_model.mm_model_block_2_block_2_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias")); + } break; + case PROJECTOR_TYPE_LDPV2: + { + // MobilVLM_V2 projection + vision_model.mm_model_mlp_0_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight")); + vision_model.mm_model_mlp_0_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "bias")); + vision_model.mm_model_mlp_2_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 2, "weight")); + vision_model.mm_model_mlp_2_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 2, "bias")); + vision_model.mm_model_peg_0_w = get_tensor(string_format(TN_MVLM_PROJ_PEG, 0, "weight")); + vision_model.mm_model_peg_0_b = get_tensor(string_format(TN_MVLM_PROJ_PEG, 0, "bias")); + } break; + case PROJECTOR_TYPE_RESAMPLER: + { + // vision_model.mm_model_pos_embed = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD); + vision_model.mm_model_pos_embed_k = get_tensor(TN_MINICPMV_POS_EMBD_K); + vision_model.mm_model_query = get_tensor(TN_MINICPMV_QUERY); + vision_model.mm_model_proj = get_tensor(TN_MINICPMV_PROJ); + vision_model.mm_model_kv_proj = get_tensor(TN_MINICPMV_KV_PROJ); + vision_model.mm_model_attn_q_w = get_tensor(string_format(TN_MINICPMV_ATTN, "q", "weight")); + vision_model.mm_model_attn_k_w = get_tensor(string_format(TN_MINICPMV_ATTN, "k", "weight")); + vision_model.mm_model_attn_v_w = get_tensor(string_format(TN_MINICPMV_ATTN, "v", "weight")); + vision_model.mm_model_attn_q_b = get_tensor(string_format(TN_MINICPMV_ATTN, "q", "bias")); + vision_model.mm_model_attn_k_b = get_tensor(string_format(TN_MINICPMV_ATTN, "k", "bias")); + vision_model.mm_model_attn_v_b = get_tensor(string_format(TN_MINICPMV_ATTN, "v", "bias")); + vision_model.mm_model_attn_o_w = get_tensor(string_format(TN_MINICPMV_ATTN, "out", "weight")); + vision_model.mm_model_attn_o_b = get_tensor(string_format(TN_MINICPMV_ATTN, "out", "bias")); + vision_model.mm_model_ln_q_w = get_tensor(string_format(TN_MINICPMV_LN, "q", "weight")); + vision_model.mm_model_ln_q_b = get_tensor(string_format(TN_MINICPMV_LN, "q", "bias")); + vision_model.mm_model_ln_kv_w = get_tensor(string_format(TN_MINICPMV_LN, "kv", "weight")); + vision_model.mm_model_ln_kv_b = get_tensor(string_format(TN_MINICPMV_LN, "kv", "bias")); + vision_model.mm_model_ln_post_w = get_tensor(string_format(TN_MINICPMV_LN, "post", "weight")); + vision_model.mm_model_ln_post_b = get_tensor(string_format(TN_MINICPMV_LN, "post", "bias")); + } break; + case PROJECTOR_TYPE_GLM_EDGE: + { + vision_model.mm_model_adapter_conv_w = get_tensor(string_format(TN_GLM_ADAPER_CONV, "weight")); + vision_model.mm_model_adapter_conv_b = get_tensor(string_format(TN_GLM_ADAPER_CONV, "bias")); + vision_model.mm_model_mlp_0_w = get_tensor(string_format(TN_GLM_ADAPTER_LINEAR,"weight")); + vision_model.mm_model_ln_q_w = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1,"weight")); + vision_model.mm_model_ln_q_b = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1,"bias")); + vision_model.mm_model_mlp_1_w = get_tensor(string_format(TN_GLM_ADAPTER_D_H_2_4H,"weight")); + vision_model.mm_model_mlp_2_w = get_tensor(string_format(TN_GLM_ADAPTER_GATE,"weight")); + vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H,"weight")); + vision_model.boi_w = get_tensor(TN_GLM_BOI_W); + vision_model.eoi_w = get_tensor(TN_GLM_EOI_W); + } break; + case PROJECTOR_TYPE_MERGER: + { + vision_model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); + vision_model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias")); + vision_model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); + vision_model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias")); + } break; + case PROJECTOR_TYPE_GEMMA3: + { + vision_model.mm_input_proj_w = get_tensor(TN_MM_INP_PROJ); + vision_model.mm_soft_emb_norm_w = get_tensor(TN_MM_SOFT_EMB_N); + } break; + default: + GGML_ASSERT(false && "unknown projector type"); + } + + // load data + { + std::vector read_buf; + + auto fin = std::ifstream(fname, std::ios::binary); + if (!fin) { + throw std::runtime_error(string_format("%s: failed to open %s\n", __func__, fname.c_str())); + } + + // alloc memory and offload data + ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(ctx_clip.backend); + ctx_clip.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx_clip.ctx_data, buft); + ggml_backend_buffer_set_usage(ctx_clip.buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); + for (auto & t : tensors_to_load) { + struct ggml_tensor * cur = ggml_get_tensor(ctx_clip.ctx_data, t->name); + const size_t offset = tensor_offset[t->name]; + fin.seekg(offset, std::ios::beg); + if (!fin) { + throw std::runtime_error(string_format("%s: failed to seek for tensor %s\n", __func__, t->name)); + } + size_t num_bytes = ggml_nbytes(cur); + if (ggml_backend_buft_is_host(buft)) { + // for the CPU and Metal backend, we can read directly into the tensor + fin.read(reinterpret_cast(cur->data), num_bytes); + } else { + // read into a temporary buffer first, then copy to device memory + read_buf.resize(num_bytes); + fin.read(reinterpret_cast(read_buf.data()), num_bytes); + ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes); + } + } + fin.close(); + + LOG_DBG("%s: loaded %zu tensors from %s\n", __func__, tensors_to_load.size(), fname.c_str()); } } - ggml_free(meta); - - new_clip->ctx_gguf = ctx; - - // measure mem requirement and allocate - { - new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead()); + void alloc_compute_meta() { + ctx_clip.buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead()); clip_image_f32_batch batch; batch.size = 1; batch.data = nullptr; - ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, nullptr, false); - ggml_backend_sched_reserve(new_clip->sched.get(), gf); - for (size_t i = 0; i < new_clip->backend_ptrs.size(); ++i) { - ggml_backend_t backend = new_clip->backend_ptrs[i]; - ggml_backend_buffer_type_t buft = new_clip->backend_buft[i]; - size_t size = ggml_backend_sched_get_buffer_size(new_clip->sched.get(), backend); + ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, &batch, nullptr, false); + ggml_backend_sched_reserve(ctx_clip.sched.get(), gf); + for (size_t i = 0; i < ctx_clip.backend_ptrs.size(); ++i) { + ggml_backend_t backend = ctx_clip.backend_ptrs[i]; + ggml_backend_buffer_type_t buft = ctx_clip.backend_buft[i]; + size_t size = ggml_backend_sched_get_buffer_size(ctx_clip.sched.get(), backend); if (size > 1) { LOG_INF("%s: %10s compute buffer size = %8.2f MiB\n", __func__, ggml_backend_buft_name(buft), @@ -1920,7 +1509,90 @@ struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_p } } - return new_clip; + void get_bool(const std::string & key, bool & output, bool required = true) { + const int i = gguf_find_key(ctx_gguf.get(), key.c_str()); + if (i < 0) { + if (required) throw std::runtime_error("Key not found: " + key); + return; + } + output = gguf_get_val_bool(ctx_gguf.get(), i); + } + + void get_i32(const std::string & key, int & output, bool required = true) { + const int i = gguf_find_key(ctx_gguf.get(), key.c_str()); + if (i < 0) { + if (required) throw std::runtime_error("Key not found: " + key); + return; + } + output = gguf_get_val_i32(ctx_gguf.get(), i); + } + + void get_u32(const std::string & key, int & output, bool required = true) { + const int i = gguf_find_key(ctx_gguf.get(), key.c_str()); + if (i < 0) { + if (required) throw std::runtime_error("Key not found: " + key); + return; + } + output = gguf_get_val_u32(ctx_gguf.get(), i); + } + + void get_f32(const std::string & key, float & output, bool required = true) { + const int i = gguf_find_key(ctx_gguf.get(), key.c_str()); + if (i < 0) { + if (required) throw std::runtime_error("Key not found: " + key); + return; + } + output = gguf_get_val_f32(ctx_gguf.get(), i); + } + + void get_string(const std::string & key, std::string & output, bool required = true) { + const int i = gguf_find_key(ctx_gguf.get(), key.c_str()); + if (i < 0) { + if (required) throw std::runtime_error("Key not found: " + key); + return; + } + output = std::string(gguf_get_val_str(ctx_gguf.get(), i)); + } + + void get_arr_int(const std::string & key, std::vector & output, bool required = true) { + const int i = gguf_find_key(ctx_gguf.get(), key.c_str()); + if (i < 0) { + if (required) throw std::runtime_error("Key not found: " + key); + return; + } + int n = gguf_get_arr_n(ctx_gguf.get(), i); + output.resize(n); + const int32_t * values = (const int32_t *)gguf_get_arr_data(ctx_gguf.get(), i); + for (int i = 0; i < n; ++i) { + output[i] = values[i]; + } + } +}; + +// read and create ggml_context containing the tensors and their data +struct clip_ctx * clip_model_load(const char * fname, const int verbosity) { + return clip_init(fname, clip_context_params{ + /* use_gpu */ true, + /* verbosity */ static_cast(verbosity), + }); +} + +struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params) { + g_logger_state.verbosity_thold = ctx_params.verbosity; + clip_ctx * ctx_clip = new clip_ctx(ctx_params); + + try { + clip_model_loader loader(fname, *ctx_clip); + loader.load_hparams(); + loader.load_tensors(); + loader.alloc_compute_meta(); + } catch (const std::exception & e) { + LOG_ERR("%s: failed to load model '%s': %s\n", __func__, fname, e.what()); + delete ctx_clip; + return nullptr; + } + + return ctx_clip; } void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size) { @@ -2292,7 +1964,7 @@ static std::vector> uhd_slice_image(const clip_imag const int multiple = fmin(ceil(ratio), max_slice_nums); std::vector> images; - LOG_INF("%s: multiple %d\n", __func__, multiple); + LOG_DBG("%s: multiple %d\n", __func__, multiple); images.push_back(std::vector()); if (multiple <= 1) { @@ -2307,17 +1979,17 @@ static std::vector> uhd_slice_image(const clip_imag clip_image_u8 * source_image = clip_image_u8_init(); bicubic_resize(*img, *source_image, best_size.first, best_size.second); // source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC) - LOG_INF("%s: image_size: %d %d; source_image size: %d %d\n", __func__, img->nx, img->ny, best_size.first, best_size.second); + LOG_DBG("%s: image_size: %d %d; source_image size: %d %d\n", __func__, img->nx, img->ny, best_size.first, best_size.second); images[images.size()-1].push_back(source_image); std::pair best_grid = uhd_best_grid(max_slice_nums, multiple, log_ratio); - LOG_INF("%s: image_size: %d %d; best_grid: %d %d\n", __func__, img->nx, img->ny, best_grid.first, best_grid.second); + LOG_DBG("%s: image_size: %d %d; best_grid: %d %d\n", __func__, img->nx, img->ny, best_grid.first, best_grid.second); auto refine_size = uhd_get_refine_size(original_size, best_grid, scale_resolution, patch_size, true); clip_image_u8 * refine_image = clip_image_u8_init(); bicubic_resize(*img, *refine_image, refine_size.first, refine_size.second); - LOG_INF("%s: refine_image_size: %d %d; refine_size: %d %d\n", __func__, refine_image->nx, refine_image->ny, refine_size.first, refine_size.second); + LOG_DBG("%s: refine_image_size: %d %d; refine_size: %d %d\n", __func__, refine_image->nx, refine_image->ny, refine_size.first, refine_size.second); // split_to_patches int width = refine_image->nx; @@ -2425,12 +2097,12 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli bool pad_to_square = true; if (!ctx->has_vision_encoder) { - LOG_ERR("This gguf file seems to have no vision encoder\n"); + LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__); return false; } auto & params = ctx->vision_model.hparams; // The model config actually contains all we need to decide on how to preprocess, here we automatically switch to the new llava-1.6 preprocessing - if (strcmp(params.mm_patch_merge_type, "spatial_unpad") == 0) { + if (params.mm_patch_merge_type == PATCH_MERGE_SPATIAL_UNPAD) { pad_to_square = false; } // free the previous res_imgs if any set @@ -2626,7 +2298,7 @@ int32_t clip_hidden_size(const struct clip_ctx * ctx) { } const char * clip_patch_merge_type(const struct clip_ctx * ctx) { - return ctx->vision_model.hparams.mm_patch_merge_type; + return ctx->vision_model.hparams.mm_patch_merge_type == PATCH_MERGE_SPATIAL_UNPAD ? "spatial_unpad" : "flat"; } const int32_t * clip_image_grid(const struct clip_ctx * ctx) { @@ -2762,7 +2434,7 @@ static std::vector> get_2d_sincos_pos_embed(int embed_dim, co bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { if (!ctx->has_vision_encoder) { - LOG_ERR("This gguf file seems to have no vision encoder\n"); + LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__); return false; } @@ -2774,7 +2446,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3 bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) { if (!ctx->has_vision_encoder) { - LOG_ERR("This gguf file seems to have no vision encoder\n"); + LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__); return false; } @@ -2810,7 +2482,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } const int patch_size = hparams.patch_size; const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); - const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); + const int num_positions = num_patches + (model.class_embedding ? 1 : 0); if(ctx->load_image_size==nullptr){ ctx->load_image_size= clip_image_size_init(); } @@ -2895,16 +2567,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima free(pos_embed_data); } } - else{ - { - if (ctx->has_class_embedding) { - struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); + else { + if (model.class_embedding) { + struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); - void* zero_mem = malloc(ggml_nbytes(embeddings)); - memset(zero_mem, 0, ggml_nbytes(embeddings)); - ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); - free(zero_mem); - } + void* zero_mem = malloc(ggml_nbytes(embeddings)); + memset(zero_mem, 0, ggml_nbytes(embeddings)); + ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); + free(zero_mem); } if (ctx->has_qwen2vl_merger) { @@ -2952,7 +2622,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima // The patches vector is used to get rows to index into the embeds with; // we should skip dim 0 only if we have CLS to avoid going out of bounds // when retrieving the rows. - int patch_offset = ctx->has_class_embedding ? 1 : 0; + int patch_offset = model.class_embedding ? 1 : 0; int* patches_data = (int*)malloc(ggml_nbytes(patches)); for (int i = 0; i < num_patches; i++) { patches_data[i] = i + patch_offset; @@ -2993,7 +2663,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i auto * ctx_clip = clip_init(fname_inp, clip_context_params{ /* use_gpu */ false, - /* verbosity */ 2, + /* verbosity */ GGML_LOG_LEVEL_ERROR, }); const auto & ctx_src = ctx_clip->ctx_gguf; @@ -3071,7 +2741,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i f32_data = (float *)conv_buf.data(); break; default: - LOG_ERR("Please use an input file in f32 or f16\n"); + LOG_ERR("%s: Please use an input file in f32 or f16\n", __func__); gguf_free(ctx_out); return false; } @@ -3157,7 +2827,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { } std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type]; - throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); + throw std::runtime_error(string_format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); } int clip_is_minicpmv(const struct clip_ctx * ctx) { diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 47059ca1b9..783bdca3e0 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -1,6 +1,7 @@ #ifndef CLIP_H #define CLIP_H +#include "ggml.h" #include #include @@ -41,7 +42,7 @@ struct clip_image_f32_batch { struct clip_context_params { bool use_gpu; - int verbosity; + ggml_log_level verbosity; }; // deprecated, use clip_init diff --git a/examples/llava/gemma3-cli.cpp b/examples/llava/gemma3-cli.cpp index 7813ac19f5..fe577510ef 100644 --- a/examples/llava/gemma3-cli.cpp +++ b/examples/llava/gemma3-cli.cpp @@ -79,7 +79,11 @@ struct gemma3_context { void init_clip_model(common_params & params) { const char * clip_path = params.mmproj.path.c_str(); - ctx_clip = clip_model_load(clip_path, params.verbosity > 1); + ctx_clip = clip_model_load(clip_path, GGML_LOG_LEVEL_INFO); + if (!ctx_clip) { + LOG_ERR("Failed to load CLIP model from %s\n", clip_path); + exit(1); + } } ~gemma3_context() { diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index a151313436..0fe0e333a5 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -241,7 +241,7 @@ static struct llava_context * llava_init_context(common_params * params, llama_m prompt = "describe the image in detail."; } - auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); + auto ctx_clip = clip_model_load(clip_path, GGML_LOG_LEVEL_INFO); llama_context_params ctx_params = common_context_params_to_llama(*params); ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp index 48fddeaa4d..5ad970c220 100644 --- a/examples/llava/minicpmv-cli.cpp +++ b/examples/llava/minicpmv-cli.cpp @@ -88,7 +88,7 @@ static struct clip_ctx * clip_init_context(common_params * params) { } struct clip_context_params clip_params = { /* use_gpu */ params->n_gpu_layers != 0, - /* verbosity */ params->verbosity, + /* verbosity */ GGML_LOG_LEVEL_INFO, // TODO: make this configurable }; auto * ctx_clip = clip_init(clip_path, clip_params); return ctx_clip; diff --git a/examples/llava/qwen2vl-cli.cpp b/examples/llava/qwen2vl-cli.cpp index c6481e482a..eca7b7f10b 100644 --- a/examples/llava/qwen2vl-cli.cpp +++ b/examples/llava/qwen2vl-cli.cpp @@ -330,7 +330,7 @@ static struct llava_context * llava_init_context(common_params * params, llama_m prompt = "describe the image in detail."; } - auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); + auto ctx_clip = clip_model_load(clip_path, GGML_LOG_LEVEL_INFO); llama_context_params ctx_params = common_context_params_to_llama(*params); ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings diff --git a/examples/llava/test-1.jpeg b/examples/llava/test-1.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..7fdcaaf04b24bd162d9ac5163a458a4ac3bd08df GIT binary patch literal 124071 zcmbTdWmH>1+cp{qG`K@?D51rnxND0SE$%H=+=>>r76@LT#ogU0uE8np8l-rF20uK{ zd%o}cbAFt2uARLzYt3Y4u357)_kGPhPYX{Q00JdBML7Tp3IL$+d;y*|0eZ4tHdX+D ziVA=g003YBP*I2gz~>ao^9A^B13>$48UT3n{0#sAvr+y}ZZ_)w%0&L7)!%D?{< zeCh%aVxas(4FsYv08j~0fP^SdeE_=WdeKq-i~kh*4^U8nXy`9M7?@bt&jlI?o;v^p zqM`xO(b1lpM)7;j2cQw66TRY=enG793B=$+!V?sqg8`PQ=_b{jgfsGfb`8eFB6~?r zLHYU(6Eh1dAHRU0kg&+R_p);G3W`b}wX}6~_4EzQEiA39zu4HieRcQn^z!xz2@MPX z{v#qXA@OHYa>}pYskwRi1%*Y$C8f1>^$m?p%`Ja=di(kZpo2rhQ`0lEbMp&}OPgD; 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zM+AHHgBSupY}P4ZPd+}GacL@O&>=~5;k8I+V(efdD_{ym{e)Q&4Mxw6^mjE;7+9ru zq%&WFIoEBBP(v6A{5SWe?}U?b&)>*c*XDf(44{3eNIb=IFkFT^Lx#ozrF79f?w|(k zC_Gh+^D&TIJPB!|K2C3-`#TGs_O6}}RPNqOx-#27m&2U(%fO37p=>F4+DnXAW#Kko zKfDiCr%=9n6&ASw8$?gk8zARCX&*VOvdHw11b<*qp5%Eess`U z{CVfA6|d}Q*A0N{$2aE(t4Z+R^?9PZ;Z-TyU*`B(QAwrMyE4H++P?!v&+k(!S(%Zk zCa~`u+1m>5XCnLkP$rGT>|4Yli3%{ZyiN@_uYME6`Shx~!FOd{+1zuT!FMdU2TtGU z|8ir@@Zh2_OCGQ05){U#2p%!J@N*}yd3@k|gQ2US-A@Wxq%q$5c7($X^|>FT*k~O7$|nTxj?!!+SgJf<0MG ZbiybzB5&sYub~5cP7?Y5y2x%8{|9NW@Y4VQ literal 0 HcmV?d00001 diff --git a/examples/llava/tests.sh b/examples/llava/tests.sh new file mode 100755 index 0000000000..cc9bda8769 --- /dev/null +++ b/examples/llava/tests.sh @@ -0,0 +1,81 @@ +#!/bin/bash + +# make sure we are in the right directory +SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) +cd $SCRIPT_DIR + +#export LLAMA_CACHE="$SCRIPT_DIR/tmp" + +set -eux + +mkdir -p $SCRIPT_DIR/output + +PROJ_ROOT="$SCRIPT_DIR/../.." +cd $PROJ_ROOT + +############### + +arr_bin=() +arr_hf=() + +add_test() { + local bin=$1 + local hf=$2 + arr_bin+=("$bin") + arr_hf+=("$hf") +} + +add_test "llama-gemma3-cli" "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M" +add_test "llama-llava-cli" "cmp-nct/Yi-VL-6B-GGUF:Q5_K" +add_test "llama-llava-cli" "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" +add_test "llama-llava-cli" "THUDM/glm-edge-v-5b-gguf:Q4_K_M" +add_test "llama-llava-cli" "second-state/Llava-v1.5-7B-GGUF:Q2_K" +add_test "llama-llava-cli" "cjpais/llava-1.6-mistral-7b-gguf:Q3_K" +add_test "llama-llava-cli" "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M" +add_test "llama-minicpmv-cli" "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K" # model from openbmb is corrupted +add_test "llama-minicpmv-cli" "openbmb/MiniCPM-V-2_6-gguf:Q2_K" +add_test "llama-minicpmv-cli" "openbmb/MiniCPM-o-2_6-gguf:Q4_0" +add_test "llama-qwen2vl-cli" "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" + +############### + +cmake --build build -j --target "${arr_bin[@]}" + +arr_res=() + +for i in "${!arr_bin[@]}"; do + bin="${arr_bin[$i]}" + hf="${arr_hf[$i]}" + + echo "Running test with binary: $bin and HF model: $hf" + echo "" + echo "" + + output=$("$PROJ_ROOT/build/bin/$bin" -hf "$hf" --image $SCRIPT_DIR/test-1.jpeg -p "what is the publisher name of the newspaper?" --temp 0 2>&1 | tee /dev/tty) + + echo "$output" > $SCRIPT_DIR/output/$bin-$(echo "$hf" | tr '/' '-').log + + if echo "$output" | grep -iq "new york"; then + result="\033[32mOK\033[0m: $bin $hf" + else + result="\033[31mFAIL\033[0m: $bin $hf" + fi + echo -e "$result" + arr_res+=("$result") + + echo "" + echo "" + echo "" + echo "#################################################" + echo "#################################################" + echo "" + echo "" +done + +set +x + +for i in "${!arr_res[@]}"; do + echo -e "${arr_res[$i]}" +done +echo "" +echo "Output logs are saved in $SCRIPT_DIR/output" From f1e3eb4249db68d97be352c0adf16eef7ae53795 Mon Sep 17 00:00:00 2001 From: Sergey Fedorov Date: Sat, 5 Apr 2025 23:46:00 +0800 Subject: [PATCH 15/99] common : fix includes in arg.cpp and gemma3-cli.cpp (#12766) * arg.cpp: add a missing include * gemma3-cli.cpp: fix cinttypes include --- common/arg.cpp | 1 + examples/llava/gemma3-cli.cpp | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/common/arg.cpp b/common/arg.cpp index 96facce67a..cdf8970254 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -18,6 +18,7 @@ #include #include #include +#include #include #include #include diff --git a/examples/llava/gemma3-cli.cpp b/examples/llava/gemma3-cli.cpp index fe577510ef..4f89c0e15b 100644 --- a/examples/llava/gemma3-cli.cpp +++ b/examples/llava/gemma3-cli.cpp @@ -10,7 +10,7 @@ #include #include -#include +#include #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) #include From 6bf28f0111ff9f21b3c1b1eace20c590281e7ba6 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Sat, 5 Apr 2025 18:04:03 +0200 Subject: [PATCH 16/99] Vulkan: Tune Vulkan mmq int dot shader for performance (#12767) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 18 ++++-------------- 1 file changed, 4 insertions(+), 14 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 189b3892e9..f3c24e503d 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -1736,19 +1736,9 @@ static void ggml_vk_load_shaders(vk_device& device) { m_warptile_mmq = { 128, 64, 64, 32, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 }; s_warptile_mmq = { subgroup_size_32, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, subgroup_size_8 }; - const uint32_t tm_int_l = device->coopmat_int_support ? device->coopmat_int_m : 4; - const uint32_t tm_int_m = device->coopmat_int_support ? device->coopmat_int_m : 4; - const uint32_t tm_int_s = device->coopmat_int_support ? device->coopmat_int_m : 2; - const uint32_t tn_int_l = device->coopmat_int_support ? device->coopmat_int_n : 4; - const uint32_t tn_int_m = device->coopmat_int_support ? device->coopmat_int_n : 2; - const uint32_t tn_int_s = device->coopmat_int_support ? device->coopmat_int_n : 2; - const uint32_t tk_int_l = device->coopmat_int_support ? device->coopmat_int_k : 1; - const uint32_t tk_int_m = device->coopmat_int_support ? device->coopmat_int_k : 1; - const uint32_t tk_int_s = device->coopmat_int_support ? device->coopmat_int_k : 1; - - l_warptile_mmq_int = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 2, tm_int_l, tn_int_l, tk_int_l, subgroup_size_8 }; - m_warptile_mmq_int = { 128, 64, 64, 32, subgroup_size_8, 32, 2, tm_int_m, tn_int_m, tk_int_m, subgroup_size_8 }; - s_warptile_mmq_int = { subgroup_size_32, 32, 32, 32, 32, 32, 2, tm_int_s, tn_int_s, tk_int_s, subgroup_size_8 }; + l_warptile_mmq_int = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 2, 4, 4, 1, subgroup_size_8 }; + m_warptile_mmq_int = { 128, 64, 64, 32, subgroup_size_8, 32, 2, 2, 2, 1, subgroup_size_8 }; + s_warptile_mmq_int = { subgroup_size_32, 32, 32, 32, 32, 32, 2, 2, 1, 1, subgroup_size_8 }; l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 }; m_mmq_wg_denoms = m_wg_denoms = { 64, 64, 1 }; @@ -7922,7 +7912,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { 128, 49, 49, 4096, 49, 4096, }; - const size_t num_it = 1; + const size_t num_it = 100; ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 0, GGML_TYPE_Q4_0); ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 1, GGML_TYPE_Q4_0); From 80b717d493a9a5bae7167ad2384c12c60bb2ef20 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sun, 6 Apr 2025 03:47:13 -0500 Subject: [PATCH 17/99] vulkan: Use unclamped loads for flash attention mask (#12720) nem1 must be a multiple of GGML_KQ_MASK_PAD, and GGML_KQ_MASK_PAD is a multiple of the number of rows in the matrix. The KV dim is a multiple of the number of columns for the aligned shader. --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 5 +++++ ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index f3c24e503d..705a6135a6 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -1833,6 +1833,8 @@ static void ggml_vk_load_shaders(vk_device& device) { // can't use 256 for D==80. uint32_t wg_size = (small_rows && (D % 32) == 0) ? 256 : 128; auto rows_cols = fa_rows_cols(D, clamp, type, small_rows); + // mask dim1 is padded to 64, we rely on this to avoid clamping mask loads + GGML_ASSERT((GGML_KQ_MASK_PAD % rows_cols[0]) == 0); return {wg_size, rows_cols[0], rows_cols[1], (D), clamp}; }; @@ -5511,6 +5513,9 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx // the "aligned" shader variant will forcibly align strides, for performance (q_stride & 7) == 0 && (k_stride & 7) == 0 && (v_stride & 7) == 0; + // mask dim1 is padded to 64, we rely on this to avoid clamping mask loads + GGML_ASSERT((nem1 % GGML_KQ_MASK_PAD) == 0); + vk_pipeline pipeline = pipelines[aligned]; assert(pipeline); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp index d78092000d..eedbc6f8b0 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp @@ -256,7 +256,7 @@ void main() { } if (p.mask != 0) { - tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutM = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutNV<2, Clamp> tensorLayoutM = createTensorLayoutNV(2, Clamp); tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV); // When using grouped query attention, all rows use the same mask. if (p.gqa_ratio > 1) { From 0c74b04376b0b9efc096480fe10f866afc8d7c1c Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sun, 6 Apr 2025 04:03:47 -0500 Subject: [PATCH 18/99] vulkan: fix NaN issue in flash attention shader (#12776) Use -FLT_MAX/2 rather than -inf as the initial value for computing the maximum. --- ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp index eedbc6f8b0..8ddadb8a15 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp @@ -227,8 +227,11 @@ void main() { coopmat L, M; + // Use -FLT_MAX/2 rather than -inf to reduce the possibility of NaNs, e.g. when computing Mold-M. + const float NEG_FLT_MAX_OVER_2 = uintBitsToFloat(0xFEFFFFFF); + L = coopmat(0); - M = coopmat(-1.0/0.0); + M = coopmat(NEG_FLT_MAX_OVER_2); coopmat slopeMat = coopmat(1.0); @@ -278,7 +281,7 @@ void main() { uint R = ((i + 1) * Br > N) ? (N % Br) : Br; uint C = ((j + 1) * Bc > KV) ? (KV % Bc) : Bc; - coopMatPerElementNV(S, S, replacePadding, ACC_TYPE(-1.0/0.0), R, C); + coopMatPerElementNV(S, S, replacePadding, ACC_TYPE(NEG_FLT_MAX_OVER_2), R, C); } coopmat rowmax, P, rowsum, eM; From 916c83bfe7f8b08ada609c3b8e583cf5301e594b Mon Sep 17 00:00:00 2001 From: R0CKSTAR Date: Sun, 6 Apr 2025 21:23:54 +0800 Subject: [PATCH 19/99] musa: fix compilation warnings in mp_22/31 (#12780) Signed-off-by: Xiaodong Ye --- ggml/src/ggml-cuda/cpy.cu | 3 +++ ggml/src/ggml-cuda/fattn-common.cuh | 10 +++++----- ggml/src/ggml-cuda/fattn-tile-f32.cu | 12 ++++++++++++ ggml/src/ggml-cuda/fattn-vec-f32.cuh | 18 +++++++++++++++--- 4 files changed, 35 insertions(+), 8 deletions(-) diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu index 1f3151e756..ed853ee6c1 100644 --- a/ggml/src/ggml-cuda/cpy.cu +++ b/ggml/src/ggml-cuda/cpy.cu @@ -360,6 +360,9 @@ void ggml_cuda_cpy_dest_ptrs_copy(ggml_cuda_graph * cuda_graph, char ** host_des // copy destination pointers to GPU CUDA_CHECK(cudaMemcpyAsync(cuda_graph->dest_ptrs_d, host_dest_ptrs, host_dest_ptrs_size*sizeof(char *), cudaMemcpyHostToDevice, stream)); cuda_graph->graph_cpynode_index = 0; // reset index +#else + GGML_UNUSED(cuda_graph); GGML_UNUSED(host_dest_ptrs); + GGML_UNUSED(host_dest_ptrs_size); GGML_UNUSED(stream); #endif } diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 3fe22092f2..56121705bd 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -62,7 +62,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( T sum = 0.0f; #pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += warp_size) { + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) { const int k_KQ = k_KQ_0 + threadIdx.x; const int ib = k_KQ / QI8_1; @@ -102,7 +102,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( T sum = 0.0f; #pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += warp_size) { + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) { const int k_KQ = k_KQ_0 + threadIdx.x; const int ib = k_KQ / QI8_1; @@ -146,7 +146,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( T sum = 0.0f; #pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += warp_size) { + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) { const int k_KQ = k_KQ_0 + threadIdx.x; const int ib = k_KQ / QI8_1; @@ -193,7 +193,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( T sum = 0.0f; #pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += warp_size) { + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) { const int k_KQ = k_KQ_0 + threadIdx.x; const int ib = k_KQ / QI8_1; @@ -244,7 +244,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0( T sum = 0.0f; #pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += warp_size) { + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += warp_size) { const int k_KQ = k_KQ_0 + threadIdx.x; const int ib = k_KQ / QI8_0; diff --git a/ggml/src/ggml-cuda/fattn-tile-f32.cu b/ggml/src/ggml-cuda/fattn-tile-f32.cu index 81290c9013..fcb6f848fe 100644 --- a/ggml/src/ggml-cuda/fattn-tile-f32.cu +++ b/ggml/src/ggml-cuda/fattn-tile-f32.cu @@ -52,6 +52,18 @@ static __global__ void flash_attn_tile_ext_f32( return; #endif // FP16_MMA_AVAILABLE if (use_logit_softcap && !(D == 128 || D == 256)) { + GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); + GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale); + GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1); + GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap); + GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); + GGML_UNUSED(ne03); GGML_UNUSED(ne10); GGML_UNUSED(ne11); + GGML_UNUSED(ne12); GGML_UNUSED(ne13); GGML_UNUSED(ne31); + GGML_UNUSED(nb31); GGML_UNUSED(nb01); GGML_UNUSED(nb02); + GGML_UNUSED(nb03); GGML_UNUSED(nb11); GGML_UNUSED(nb12); + GGML_UNUSED(nb13); GGML_UNUSED(nb21); GGML_UNUSED(nb22); + GGML_UNUSED(nb23); GGML_UNUSED(ne0); GGML_UNUSED(ne1); + GGML_UNUSED(ne2); GGML_UNUSED(ne3); NO_DEVICE_CODE; return; } diff --git a/ggml/src/ggml-cuda/fattn-vec-f32.cuh b/ggml/src/ggml-cuda/fattn-vec-f32.cuh index 7048748551..d42ddca49f 100644 --- a/ggml/src/ggml-cuda/fattn-vec-f32.cuh +++ b/ggml/src/ggml-cuda/fattn-vec-f32.cuh @@ -45,6 +45,18 @@ static __global__ void flash_attn_vec_ext_f32( // Skip unused kernel variants for faster compilation: if (use_logit_softcap && !(D == 128 || D == 256)) { + GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); + GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale); + GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1); + GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap); + GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); + GGML_UNUSED(ne03); GGML_UNUSED(ne10); GGML_UNUSED(ne11); + GGML_UNUSED(ne12); GGML_UNUSED(ne13); GGML_UNUSED(ne31); + GGML_UNUSED(nb31); GGML_UNUSED(nb01); GGML_UNUSED(nb02); + GGML_UNUSED(nb03); GGML_UNUSED(nb11); GGML_UNUSED(nb12); + GGML_UNUSED(nb13); GGML_UNUSED(nb21); GGML_UNUSED(nb22); + GGML_UNUSED(nb23); GGML_UNUSED(ne0); GGML_UNUSED(ne1); + GGML_UNUSED(ne2); GGML_UNUSED(ne3); NO_DEVICE_CODE; return; } @@ -114,7 +126,7 @@ static __global__ void flash_attn_vec_ext_f32( // Set memory to zero if out of bounds: if (ncols > 2 && ic0 + j >= ne01) { #pragma unroll - for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += WARP_SIZE) { const int i = i0 + threadIdx.x; tmp_q_i32[i] = 0; @@ -127,7 +139,7 @@ static __global__ void flash_attn_vec_ext_f32( const float * Q_f = (const float *) (Q + j*nb01); #pragma unroll - for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += WARP_SIZE) { quantize_q8_1_to_shared(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds); } } @@ -140,7 +152,7 @@ static __global__ void flash_attn_vec_ext_f32( float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int)); #pragma unroll - for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += WARP_SIZE) { const int i = i0 + threadIdx.x; Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i]; From d0d5b2232b3826cac2c6e62d0244a474ba79860f Mon Sep 17 00:00:00 2001 From: hipudding Date: Mon, 7 Apr 2025 17:10:36 +0800 Subject: [PATCH 20/99] CANN: Refactor to reduce duplicate code (#12731) * CANN: Refactor to reduce duplicate code * CANN: fix review comment --- ggml/src/ggml-cann/aclnn_ops.cpp | 1297 +++++------------------------- ggml/src/ggml-cann/aclnn_ops.h | 356 +++++--- ggml/src/ggml-cann/ggml-cann.cpp | 74 +- 3 files changed, 482 insertions(+), 1245 deletions(-) diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index f5734cbabb..fee66aea91 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -28,7 +28,6 @@ #include #include #include -#include #include #include #include @@ -45,13 +44,19 @@ #include #include #include -#include #include #include #include #include #include #include +#include +#include +#include +#include +#include +#include +#include #include #include @@ -65,6 +70,22 @@ #include "../ggml-common.h" +void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclTensor ** acl_src0, + aclTensor ** acl_src1, aclTensor ** acl_dst) { + GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_can_repeat(src1, src0)); + // Need bcast + if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) { + BCAST_SHAPE(src0, src1) + *acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0)); + *acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1)); + *acl_dst = ggml_cann_create_tensor(dst, BCAST_PARAM(src0)); + } else { + *acl_src0 = ggml_cann_create_tensor(src0); + *acl_src1 = ggml_cann_create_tensor(src1); + *acl_dst = ggml_cann_create_tensor(dst); + } +} + /** * @brief Repeats elements of a tensor along each dimension according to the * specified repeat array. @@ -80,26 +101,28 @@ static void aclnn_repeat(ggml_backend_cann_context& ctx, aclTensor* acl_src, // repeat tensor along each dim with repeat_array aclIntArray* repeats = aclCreateIntArray(repeat_array, GGML_MAX_DIMS); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnRepeatGetWorkspaceSize(acl_src, repeats, acl_dst, - &workspaceSize, &executor)); - - if (workspaceSize > 0) { - // Memory from allocator will "free" immediately, and this memory - // will be alloced to other pointers, but it won't access before - // this async task end because all tasks in same stream will execute - // in queue. - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - ACL_CHECK( - aclnnRepeat(workspaceAddr, workspaceSize, executor, ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(Repeat, acl_src, repeats, acl_dst); ACL_CHECK(aclDestroyIntArray(repeats)); } +/** + * @brief Casts the data type of a source tensor to a destination tensor. + * + * This function casts the data type of the source tensor `acl_src` to the + * specified data type `cast_data_type` and stores the result in the destination + * tensor `acl_dst`. + * + * @param ctx The context for the CANN backend operations. + * @param acl_src The source tensor whose data type will be casted. + * @param acl_dst The destination tensor where the casted result will be stored. + * @param cast_data_type The target data type to which the source tensor will be + * casted. + */ +static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_dst, aclDataType cast_data_type) { + GGML_CANN_CALL_ACLNN_OP(Cast, acl_src, cast_data_type, acl_dst); +} + /** * @brief Casts the elements of a tensor to a specified data type using the CANN backend. * @@ -114,19 +137,7 @@ static void aclnn_repeat(ggml_backend_cann_context& ctx, aclTensor* acl_src, */ static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst, ggml_tensor* dst) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - ACL_CHECK(aclnnCastGetWorkspaceSize(acl_src, - ggml_cann_type_mapping(dst->type), - acl_dst, &workspaceSize, &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnCast(workspaceAddr, workspaceSize, executor, ctx.stream())); + aclnn_cast(ctx, acl_src, acl_dst, ggml_cann_type_mapping(dst->type)); } void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { @@ -144,69 +155,75 @@ void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ACL_CHECK(aclDestroyTensor(acl_dst)); } -/** - * @brief Adds two tensors element-wise and stores the result in a destination - * tensor. - * - * This function performs the operation: - * \f[ - * dst = acl\_src0 + alpha \times acl\_src1 - * \f] - * where alpha is a scalar value and defaults to 1.0f. - * - * @param ctx The context for the CANN backend operations. - * @param acl_src0 The first source tensor. - * @param acl_src1 The second source tensor. - * @param acl_dst The destination tensor where the result will be stored. - */ -static void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0, +void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0, aclTensor* acl_src1, aclTensor* acl_dst) { - aclScalar* alpha = nullptr; float alphaValue = 1.0f; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnAddGetWorkspaceSize(acl_src0, acl_src1, alpha, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnAdd(workspaceAddr, workspaceSize, executor, ctx.stream())); - + aclScalar* alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + if (acl_dst != nullptr) + GGML_CANN_CALL_ACLNN_OP(Add, acl_src0, acl_src1, alpha, acl_dst); + else + GGML_CANN_CALL_ACLNN_OP(InplaceAdd, acl_src0, acl_src1, alpha); ACL_CHECK(aclDestroyScalar(alpha)); } -void ggml_cann_add(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); +void aclnn_sub(ggml_backend_cann_context& ctx, aclTensor* acl_src0, + aclTensor* acl_src1, aclTensor* acl_dst) { + float alphaValue = 1.0f; + aclScalar* alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + if (acl_dst != nullptr) + GGML_CANN_CALL_ACLNN_OP(Sub, acl_src0, acl_src1, alpha, acl_dst); + else + GGML_CANN_CALL_ACLNN_OP(InplaceSub, acl_src0, acl_src1, alpha); + ACL_CHECK(aclDestroyScalar(alpha)); +} - aclTensor* acl_src0; - aclTensor* acl_src1; - aclTensor* acl_dst; +void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_other, aclTensor* acl_dst) { + if (acl_dst != nullptr) + GGML_CANN_CALL_ACLNN_OP(Mul, acl_src, acl_other, acl_dst); + else + GGML_CANN_CALL_ACLNN_OP(InplaceMul, acl_src, acl_other); +} - // Need bcast - if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) { - BCAST_SHAPE(src0, src1) - acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0)); - acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1)); - acl_dst = ggml_cann_create_tensor(dst, BCAST_PARAM(src0)); +void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_other, aclTensor* acl_dst) { + if (acl_dst != nullptr) + GGML_CANN_CALL_ACLNN_OP(Div, acl_src, acl_other, acl_dst); + else + GGML_CANN_CALL_ACLNN_OP(InplaceDiv, acl_src, acl_other); +} + +/** + * @brief Multiplies elements of a tensor by a scalar value, optionally + * in-place. + * + * This function multiplies each element of the source tensor `acl_src` by the + * scalar `scale` and stores the result in the destination tensor `acl_dst`. If + * `inplace` is true, `acl_dst` will not be used and the operation is performed + * in-place on `acl_src`. + * The operation is defined as: + * \f[ + * \text {acl_dst }_i=\text {acl_src }_i \times \text {scale} + * \f] + * + * @param ctx The context for the CANN backend operations. + * @param acl_src The source tensor whose elements will be multiplied. + * @param scale The scalar value by which each element of `acl_src` will be + * multiplied. + * @param acl_dst The destination tensor where the result will be stored if + * `inplace` is false. + * @param inplace Flag indicating whether to perform the operation in-place on + * `acl_src`. + */ +static void aclnn_muls(ggml_backend_cann_context& ctx, aclTensor* acl_src, + float scale, aclTensor* acl_dst, bool inplace) { + aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); + if (inplace) { + GGML_CANN_CALL_ACLNN_OP(InplaceMuls, acl_src, acl_scale); } else { - acl_src0 = ggml_cann_create_tensor(src0); - acl_src1 = ggml_cann_create_tensor(src1); - acl_dst = ggml_cann_create_tensor(dst); + GGML_CANN_CALL_ACLNN_OP(Muls, acl_src, acl_scale, acl_dst); } - - aclnn_add(ctx, acl_src0, acl_src1, acl_dst); - - ACL_CHECK(aclDestroyTensor(acl_src0)); - ACL_CHECK(aclDestroyTensor(acl_src1)); - ACL_CHECK(aclDestroyTensor(acl_dst)); + ACL_CHECK(aclDestroyScalar(acl_scale)); } void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) { @@ -223,19 +240,7 @@ void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclScalar* acl_negative_slope = aclCreateScalar(&negative_slope, aclDataType::ACL_FLOAT); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnLeakyReluGetWorkspaceSize( - acl_src, acl_negative_slope, acl_dst, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnLeakyRelu(workspaceAddr, workspaceSize, executor, ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(LeakyRelu, acl_src, acl_negative_slope, acl_dst); ACL_CHECK(aclDestroyScalar(acl_negative_slope)); ACL_CHECK(aclDestroyTensor(acl_src)); @@ -255,18 +260,7 @@ void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) { static void aclnn_concat(ggml_backend_cann_context& ctx, aclTensorList* tensorList, aclTensor* acl_dst, int64_t concat_dim) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnCatGetWorkspaceSize(tensorList, concat_dim, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnCat(workspaceAddr, workspaceSize, executor, ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(Cat, tensorList, concat_dim, acl_dst); } void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { @@ -312,24 +306,11 @@ static void aclnn_arange(ggml_backend_cann_context& ctx, aclTensor* acl_dst, int64_t steps = (int64_t)std::ceil((stop - start) / step); GGML_ASSERT(n_elements == steps); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - aclScalar* acl_start = aclCreateScalar(&start, aclDataType::ACL_FLOAT); aclScalar* acl_end = aclCreateScalar(&stop, aclDataType::ACL_FLOAT); aclScalar* acl_step = aclCreateScalar(&step, aclDataType::ACL_FLOAT); - ACL_CHECK(aclnnArangeGetWorkspaceSize(acl_start, acl_end, acl_step, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnArange(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(Arange, acl_start, acl_end, acl_step, acl_dst); ACL_CHECK(aclDestroyScalar(acl_start)); ACL_CHECK(aclDestroyScalar(acl_end)); ACL_CHECK(aclDestroyScalar(acl_step)); @@ -352,11 +333,6 @@ void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ACL_CHECK(aclDestroyTensor(acl_dst)); } -void ggml_cann_sqr(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - dst->src[1] = dst->src[0]; - ggml_cann_mul_div(ctx, dst); -} - void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src = dst->src[0]; @@ -371,19 +347,7 @@ void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclScalar* acl_min = aclCreateScalar(&min, aclDataType::ACL_FLOAT); aclScalar* acl_max = aclCreateScalar(&max, aclDataType::ACL_FLOAT); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnClampGetWorkspaceSize(acl_src, acl_min, acl_max, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnClamp(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(Clamp, acl_src, acl_min, acl_max, acl_dst); ACL_CHECK(aclDestroyScalar(acl_min)); ACL_CHECK(aclDestroyScalar(acl_max)); ACL_CHECK(aclDestroyTensor(acl_src)); @@ -401,19 +365,7 @@ void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclTensor* acl_src = ggml_cann_create_tensor(src); aclTensor* acl_dst = ggml_cann_create_tensor(dst); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnMulsGetWorkspaceSize(acl_src, scale, acl_dst, &workspaceSize, - &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnMuls(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(Muls, acl_src, scale, acl_dst); ACL_CHECK(aclDestroyScalar(scale)); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); @@ -431,33 +383,9 @@ void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclTensor* tmp_tensor = ggml_cann_create_tensor(buffer, ACL_INT64, ggml_type_size(dst->type), dst->ne, dst->nb, GGML_MAX_DIMS); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnArgsortGetWorkspaceSize( - acl_src, -1, (order == GGML_SORT_ORDER_DESC ? true : false), tmp_tensor, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnArgsort(workspaceAddr, workspaceSize, executor, ctx.stream())); - - workspaceSize = 0; - ACL_CHECK(aclnnCastGetWorkspaceSize(tmp_tensor, - ggml_cann_type_mapping(dst->type), - acl_dst, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnCast(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(Argsort, acl_src, -1, (order == GGML_SORT_ORDER_DESC ? true : false), + tmp_tensor); + GGML_CANN_CALL_ACLNN_OP(Cast, tmp_tensor, ggml_cann_type_mapping(dst->type), acl_dst); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(tmp_tensor)); ACL_CHECK(aclDestroyTensor(acl_dst)); @@ -472,24 +400,10 @@ void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { float eps; memcpy(&eps, dst->op_params, sizeof(float)); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - std::vector normData = {dst->ne[0]}; aclIntArray* norm = aclCreateIntArray(normData.data(), normData.size()); - ACL_CHECK(aclnnLayerNormGetWorkspaceSize(acl_src, norm, nullptr, nullptr, - eps, acl_dst, nullptr, nullptr, - &workspaceSize, &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnLayerNorm(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(LayerNorm, acl_src, norm, nullptr, nullptr, + eps, acl_dst, nullptr, nullptr); ACL_CHECK(aclDestroyIntArray(norm)); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); @@ -506,10 +420,6 @@ void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { float eps; memcpy(&eps, dst->op_params + 1, sizeof(float)); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - int64_t N = src->ne[3]; int64_t C = src->ne[2]; int64_t HxW = src->ne[1] * src->ne[0]; @@ -526,18 +436,8 @@ void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclTensor* acl_rstd_out = ggml_cann_create_tensor( (char*)buffer + n_bytes, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); - ACL_CHECK(aclnnGroupNormGetWorkspaceSize( - acl_src, nullptr, nullptr, N, C, HxW, n_groups, eps, acl_dst, - acl_mean_out, acl_rstd_out, &workspaceSize, &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnGroupNorm(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(GroupNorm, acl_src, nullptr, nullptr, N, C, HxW, n_groups, eps, + acl_dst, acl_mean_out, acl_rstd_out); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); ACL_CHECK(aclDestroyTensor(acl_mean_out)); @@ -564,68 +464,57 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) { float alphaValue = 1.0f; alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - if (!inplace) { size_t cpy_size = ggml_nbytes(dst); ACL_CHECK(aclrtMemcpyAsync(dst->data, cpy_size, src0->data, cpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream())); aclTensor* acl_src0 = ggml_cann_create_tensor( src0, src1->ne, src0->nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); - ACL_CHECK(aclnnAddGetWorkspaceSize(acl_src0, acl_src1, alpha, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - ACL_CHECK( - aclnnAdd(workspaceAddr, workspaceSize, executor, ctx.stream())); + + GGML_CANN_CALL_ACLNN_OP(Add, acl_src0, acl_src1, alpha, acl_dst); ACL_CHECK(aclDestroyTensor(acl_src0)); } else { - ACL_CHECK(aclnnInplaceAddGetWorkspaceSize(acl_dst, acl_src1, alpha, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - ACL_CHECK(aclnnInplaceAdd(workspaceAddr, workspaceSize, executor, - ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(InplaceAdd, acl_dst, acl_src1, alpha); } ACL_CHECK(aclDestroyTensor(acl_src1)); ACL_CHECK(aclDestroyTensor(acl_dst)); } -void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; - - aclTensor* acl_src = ggml_cann_create_tensor(src); +/** + * @brief Performs sum reduction on a given tensor along specified dimensions. + * + * This function reduces the input tensor by summing along the specified dimensions. + * + * @param ctx The context for the CANN backend operations. + * @param dst The destination tensor where the reduced result will be stored. + * @param dim An array of dimension indices. + * @param dim_size The number of dimensions. + */ +static void aclnn_reduce_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst, + int64_t* dim, size_t dim_size) { GGML_ASSERT(dst->ne[0] == 1); + ggml_tensor* src = dst->src[0]; + aclTensor* acl_src = ggml_cann_create_tensor(src); aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclIntArray* reduce_dims = aclCreateIntArray(dim, dim_size); - int64_t reduce_dims_host[] = {3}; - aclIntArray* reduce_dims = aclCreateIntArray(reduce_dims_host, 1); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnReduceSumGetWorkspaceSize( - acl_src, reduce_dims, true, ggml_cann_type_mapping(src->type), acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnReduceSum(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(ReduceSum, acl_src, reduce_dims, true, + ggml_cann_type_mapping(dst->type), acl_dst); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); + ACL_CHECK(aclDestroyIntArray(reduce_dims)); +} + +void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { + int64_t reduce_dims[] = {3}; + aclnn_reduce_sum(ctx, dst, reduce_dims, 1); +} + +void ggml_cann_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst) { + int64_t reduce_dims[] = {0, 1, 2, 3}; + aclnn_reduce_sum(ctx, dst, reduce_dims, 4); } void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, @@ -639,20 +528,7 @@ void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, std::vector output_size{dst->ne[1], dst->ne[0]}; auto output_size_array = aclCreateIntArray(output_size.data(), 2); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnUpsampleNearest2dGetWorkspaceSize( - acl_src, output_size_array, acl_dst, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnUpsampleNearest2d(workspaceAddr, workspaceSize, executor, - ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(UpsampleNearest2d, acl_src, output_size_array, acl_dst); ACL_CHECK(aclDestroyIntArray(output_size_array)); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); @@ -678,21 +554,7 @@ static void aclnn_pad(ggml_backend_cann_context& ctx, aclTensor* acl_src, aclIntArray* acl_pad = aclCreateIntArray(paddings, GGML_MAX_DIMS * 2); aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnConstantPadNdGetWorkspaceSize( - acl_src, acl_pad, acl_value, acl_dst, &workspaceSize, &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnConstantPadNd(workspaceAddr, workspaceSize, executor, - ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(ConstantPadNd, acl_src, acl_pad, acl_value, acl_dst); ACL_CHECK(aclDestroyIntArray(acl_pad)); ACL_CHECK(aclDestroyScalar(acl_value)); } @@ -758,23 +620,9 @@ static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx, bool count_include_pad = true; int64_t divisor_override = 0; int8_t cube_math_type = 0; - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnAvgPool2dGetWorkspaceSize( - acl_src, kernel_size, strides, paddings_avg, ceil_mode, - count_include_pad, divisor_override, cube_math_type, acl_dst, - &workspaceSize, &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - ACL_CHECK( - aclnnAvgPool2d(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(AvgPool2d, acl_src, kernel_size, strides, paddings_avg, + ceil_mode, count_include_pad, divisor_override, + cube_math_type, acl_dst); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); ACL_CHECK(aclDestroyIntArray(kernel_size)); @@ -847,22 +695,8 @@ static void ggml_cann_max_pool2d(ggml_backend_cann_context& ctx, bool ceil_mode = false; int64_t auto_pads = 0; - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnMaxPoolGetWorkspaceSize( - tmp_tensor, kernel_size, strides, auto_pads, paddings_max, dilations, - ceil_mode, acl_dst, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnMaxPool(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(MaxPool, tmp_tensor, kernel_size, strides, auto_pads, + paddings_max, dilations, ceil_mode, acl_dst); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); ACL_CHECK(aclDestroyTensor(tmp_tensor)); @@ -900,20 +734,7 @@ void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst) { */ static void cann_copy(ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnInplaceCopyGetWorkspaceSize(acl_dst, acl_src, &workspaceSize, - &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnInplaceCopy(workspaceAddr, workspaceSize, executor, ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(InplaceCopy, acl_dst, acl_src); } void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { @@ -989,21 +810,6 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ACL_CHECK(aclDestroyTensor(acl_dst)); } -#ifdef __cplusplus -extern "C" { -#endif -aclnnStatus aclnnRmsNormGetWorkspaceSize(const aclTensor* x, - const aclTensor* gamma, double epsilon, - const aclTensor* yOut, - const aclTensor* rstdOout, - uint64_t* workspaceSize, - aclOpExecutor** executor); -aclnnStatus aclnnRmsNorm(void* workspace, uint64_t workspaceSize, - aclOpExecutor* executor, aclrtStream stream); -#ifdef __cplusplus -} -#endif - /** * @brief Creates an ACL tensor initialized with zeros using a provided buffer. * @@ -1062,21 +868,7 @@ static aclTensor* aclnn_values(ggml_backend_cann_context& ctx, void* buffer, float alpha_host = 1.0f; aclScalar* alpha = aclCreateScalar(&alpha_host, aclDataType::ACL_FLOAT); aclScalar* other = aclCreateScalar(&value, aclDataType::ACL_FLOAT); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnInplaceAddsGetWorkspaceSize(acl_tensor, other, alpha, - &workspaceSize, &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - ACL_CHECK( - aclnnInplaceAdds(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(InplaceAdds, acl_tensor, other, alpha); return acl_tensor; } @@ -1088,11 +880,6 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { float eps; memcpy(&eps, dst->op_params, sizeof(float)); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - size_t one_tensor_n_bytes = src->ne[0] * ggml_element_size(src); ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes); @@ -1107,18 +894,7 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclnn_zero(ctx, zero_tensor_allocator.get(), zero_tensor_n_bytes, src->ne, GGML_MAX_DIMS, ggml_cann_type_mapping(src->type), ggml_element_size(src)); - - ACL_CHECK(aclnnRmsNormGetWorkspaceSize( - acl_src, acl_gamma, eps, acl_dst, acl_rstd, &workspaceSize, &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnRmsNorm(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(RmsNorm, acl_src, acl_gamma, eps, acl_dst, acl_rstd); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); ACL_CHECK(aclDestroyTensor(acl_gamma)); @@ -1144,77 +920,19 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, src->ne, GGML_MAX_DIMS, ggml_cann_type_mapping(src->type), ggml_element_size(src), value); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnInplaceTriuGetWorkspaceSize(mask_tensor, n_past + 1, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnInplaceTriu(workspaceAddr, workspaceSize, executor, ctx.stream())); - - ACL_CHECK(aclnnTrilGetWorkspaceSize(acl_src, n_past + 1, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnTril(workspaceAddr, workspaceSize, executor, ctx.stream())); - aclScalar* alpha = nullptr; float alphaValue = 1.0f; alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - ACL_CHECK(aclnnInplaceAddGetWorkspaceSize(acl_dst, mask_tensor, alpha, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - ACL_CHECK( - aclnnInplaceAdd(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(InplaceTriu, mask_tensor, n_past + 1); + GGML_CANN_CALL_ACLNN_OP(Tril, acl_src, n_past + 1, acl_dst); + GGML_CANN_CALL_ACLNN_OP(InplaceAdd, acl_dst, mask_tensor, alpha); ACL_CHECK(aclDestroyScalar(alpha)); ACL_CHECK(aclDestroyTensor(mask_tensor)); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); } -/** - * @brief Casts the data type of a source tensor to a destination tensor. - * - * This function casts the data type of the source tensor `acl_src` to the - * specified data type `cast_data_type` and stores the result in the destination - * tensor `acl_dst`. - * - * @param ctx The context for the CANN backend operations. - * @param acl_src The source tensor whose data type will be casted. - * @param acl_dst The destination tensor where the casted result will be stored. - * @param cast_data_type The target data type to which the source tensor will be - * casted. - */ -static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, aclDataType cast_data_type) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnCastGetWorkspaceSize(acl_src, cast_data_type, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnCast(workspaceAddr, workspaceSize, executor, ctx.stream())); -} - /** * @brief Permutes the dimensions of a tensor according to a specified order. * @@ -1233,40 +951,9 @@ static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src, static void aclnn_permute(ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst, int64_t* new_dim, uint64_t dims) { aclIntArray* acl_dims = aclCreateIntArray(new_dim, dims); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnPermuteGetWorkspaceSize(acl_src, acl_dims, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnPermute(workspaceAddr, workspaceSize, executor, ctx.stream())); - - ACL_CHECK(aclDestroyIntArray(acl_dims)); + GGML_CANN_CALL_ACLNN_OP(Permute, acl_src, acl_dims, acl_dst); } -#ifdef __cplusplus -extern "C" { -#endif -aclnnStatus aclnnIm2colGetWorkspaceSize(const aclTensor* self, - const aclIntArray* kernelSize, - const aclIntArray* dilation, - const aclIntArray* padding, - const aclIntArray* stride, - aclTensor* out, uint64_t* workspaceSize, - aclOpExecutor** executor); -aclnnStatus aclnnIm2col(void* workspace, uint64_t workspaceSize, - aclOpExecutor* executor, aclrtStream stream); -#ifdef __cplusplus -} -#endif - static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_tensor* src1, @@ -1430,23 +1117,8 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { auto* dilations = aclCreateIntArray(dilation_size.data(), 2); auto* paddings = aclCreateIntArray(padding_dims.data(), 2); auto* strides = aclCreateIntArray(stride_dims.data(), 2); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnIm2colGetWorkspaceSize(acl_src1, kernel_size, dilations, - paddings, strides, tmp_im2col_tensor, - &workspaceSize, &executor)); - - ggml_cann_pool_alloc workspace_allocator(ctx.pool()); - if (workspaceSize > 0) { - workspace_allocator.alloc(workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnIm2col(workspaceAddr, workspaceSize, executor, ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(Im2col, acl_src1, kernel_size, dilations, + paddings, strides, tmp_im2col_tensor); // Cast if dst is f16. aclTensor* tmp_cast_tensor = nullptr; @@ -1465,8 +1137,7 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { tmp_cast_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), tmp_im2col_ne, temp_cast_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND); - aclnn_cast(ctx, tmp_im2col_tensor, tmp_cast_tensor, - ggml_cann_type_mapping(dst->type)); + aclnn_cast(ctx, tmp_im2col_tensor, tmp_cast_tensor, dst); } // post-processing @@ -1504,285 +1175,17 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param acl_src The tensor on which the exponential function will be applied. */ static void aclnn_exp(ggml_backend_cann_context& ctx, aclTensor* acl_src) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK( - aclnnInplaceExpGetWorkspaceSize(acl_src, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnInplaceExp(workspaceAddr, workspaceSize, executor, ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(InplaceExp, acl_src); } -/** - * @brief Multiplies elements of a tensor by a scalar value, optionally - * in-place. - * - * This function multiplies each element of the source tensor `acl_src` by the - * scalar `scale` and stores the result in the destination tensor `acl_dst`. If - * `inplace` is true, `acl_dst` will not be used and the operation is performed - * in-place on `acl_src`. - * The operation is defined as: - * \f[ - * \text {acl_dst }_i=\text {acl_src }_i \times \text {scale} - * \f] - * - * @param ctx The context for the CANN backend operations. - * @param acl_src The source tensor whose elements will be multiplied. - * @param scale The scalar value by which each element of `acl_src` will be - * multiplied. - * @param acl_dst The destination tensor where the result will be stored if - * `inplace` is false. - * @param inplace Flag indicating whether to perform the operation in-place on - * `acl_src`. - */ -static void aclnn_muls(ggml_backend_cann_context& ctx, aclTensor* acl_src, - float scale, aclTensor* acl_dst, bool inplace) { - aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - if (inplace) { - ACL_CHECK(aclnnInplaceMulsGetWorkspaceSize(acl_src, acl_scale, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnInplaceMuls(workspaceAddr, workspaceSize, executor, - ctx.stream())); - } else { - ACL_CHECK(aclnnMulsGetWorkspaceSize(acl_src, acl_scale, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnMuls(workspaceAddr, workspaceSize, executor, ctx.stream())); - } - - ACL_CHECK(aclDestroyScalar(acl_scale)); -} - -/** - * @brief Performs an in-place element-wise multiplication of two tensors. - * - * This function performs an element-wise multiplication of the tensors - * `acl_src` and `acl_other` and stores the result in `acl_src`. - * The operation is defined as: - * \f[ - * \text {acl_src }_i=\text {acl_src }_i \times \text {acl_other }_i - * \f] - * - * @param ctx The context for the CANN backend operations. - * @param acl_src The source tensor where the multiplication result will be - * stored. - * @param acl_other The tensor whose elements will be multiplied with `acl_src`. - */ -static void aclnn_inplace_mul(ggml_backend_cann_context& ctx, - aclTensor* acl_src, aclTensor* acl_other) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnInplaceMulGetWorkspaceSize(acl_src, acl_other, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnInplaceMul(workspaceAddr, workspaceSize, executor, ctx.stream())); -} - -/** - * @brief Performs element-wise multiplication of two tensors and stores the - * result in a destination tensor. - * - * This function performs element-wise multiplication of the tensors `acl_src` - * and `acl_other` and stores the result in the destination tensor `acl_dst`. - * The operation is defined as: - * \f[ - * \text {acl_dst }_i=\text {acl_src }_i \times \text {acl_other }_i - * \f] - * - * @param ctx The context for the CANN backend operations. - * @param acl_src The first tensor for element-wise multiplication. - * @param acl_other The second tensor for element-wise multiplication. - * @param acl_dst The destination tensor where the result will be stored. - */ -static void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnMulGetWorkspaceSize(acl_src, acl_other, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnMul(workspaceAddr, workspaceSize, executor, ctx.stream())); -} - -/** - * @brief Applies element-wise cosine function to the elements of a tensor. - * - * This function computes the cosine of each element in the source tensor - * `acl_src` and stores the result in the destination tensor `acl_dst`. The - * operation is defined as: \f[ \text {acl_dst }_i=\cos \left(\text {acl_src - * }_i\right) \f] - * - * @param ctx The context for the CANN backend operations. - * @param acl_src The source tensor on which the cosine function will be - * applied. - * @param acl_dst The destination tensor where the cosine results will be - * stored. - */ -static void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, +void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK( - aclnnCosGetWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnCos(workspaceAddr, workspaceSize, executor, ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(Cos, acl_src, acl_dst); } -/** - * @brief Applies element-wise sine function to the elements of a tensor. - * - * This function computes the sine of each element in the source tensor - `acl_src` - * and stores the result in the destination tensor `acl_dst`. - * The operation is defined as: - * \f[ - * \text {acl_dst }_i=\sin \left(\text {acl_src }_i\right) - * \f] - - * @param ctx The context for the CANN backend operations. - * @param acl_src The source tensor on which the sine function will be applied. - * @param acl_dst The destination tensor where the sine results will be stored. - */ -static void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, +void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK( - aclnnSinGetWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnSin(workspaceAddr, workspaceSize, executor, ctx.stream())); -} - -/** - * @brief Performs element-wise division of tensor1 by tensor2 , multiplies the - result by the scalar value and adds it to self . - * - * Performs element-wise division of tensor1 by tensor2, - * multiplies the result by the scalar value and adds it to self . - * The operation is defined as: - * \f[ - * \text{out}_i = \text{selft}_i + \text{value} \times - \frac{\text{tensor1}_i}{\text{tensor2}_i} - * \f] - - * @param ctx The context for the CANN backend operations. - * @param acl_self The source tensor on which the addcdiv function will be - applied. - * @param tensor1 Numerator tensor. - * @param tensor2 Denominator tensor. - * @param value The value to be used for coefficient. - */ -static void aclnn_inplace_addcdiv(ggml_backend_cann_context& ctx, - aclTensor* acl_self, aclTensor* tensor1, - aclTensor* tensor2, float value) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); - - ACL_CHECK(aclnnInplaceAddcdivGetWorkspaceSize( - acl_self, tensor1, tensor2, acl_value, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnInplaceAddcdiv(workspaceAddr, workspaceSize, executor, - ctx.stream())); -} - -/** - * @brief Matrix division, optionally in-place. - * - * This function division each element of the source tensor `acl_src` by the - * tensor `acl_other` and stores the result in the destination tensor `acl_dst`. - * If `inplace` is true, `acl_dst` will not be used and the operation is - * performed in-place on `acl_src`. The operation is defined as: \f[ - * \text{dst}_i = \frac{\text{acl_src}_i}{\text{acl_other}_i} - * \f] - * - * @param ctx The context for the CANN backend operations. - * @param acl_src Numerator tensor.. - * @param acl_other Denominator tensor. - * @param acl_dst The destination tensor where the result will be stored if - * `inplace` is false. - * @param inplace Flag indicating whether to perform the operation in-place on - * `acl_src`. - */ -static void aclnn_div_tensor(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst, - bool inplace) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - if (inplace) { - ACL_CHECK(aclnnInplaceDivGetWorkspaceSize(acl_src, acl_other, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnInplaceDiv(workspaceAddr, workspaceSize, executor, - ctx.stream())); - } else { - ACL_CHECK(aclnnDivGetWorkspaceSize(acl_src, acl_other, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnDiv(workspaceAddr, workspaceSize, executor, ctx.stream())); - } + GGML_CANN_CALL_ACLNN_OP(Sin, acl_src, acl_dst); } void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, @@ -1912,20 +1315,7 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar, aclTensor* acl_dst) { auto acl_scalar = aclCreateScalar(&scalar, aclDataType::ACL_FLOAT); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnInplaceFillScalarGetWorkspaceSize( - acl_dst, acl_scalar, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnInplaceFillScalar(workspaceAddr, workspaceSize, executor, - ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(InplaceFillScalar, acl_dst, acl_scalar); ACL_CHECK(aclDestroyScalar(acl_scalar)); } @@ -1947,19 +1337,7 @@ static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar, */ static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx, aclTensor* acl_dst, aclTensor* acl_exp) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnInplacePowTensorTensorGetWorkspaceSize( - acl_dst, acl_exp, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnInplacePowTensorTensor(workspaceAddr, workspaceSize, - executor, ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(InplacePowTensorTensor, acl_dst, acl_exp); } /** @@ -2126,41 +1504,6 @@ void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_dup(ctx, dst); } -/** - * @brief Performs element-wise addition of two tensors in place. - * - * This function adds the source tensor `acl_src` to the destination tensor - * `acl_dst` element-wise and stores the result in the destination tensor - * `acl_dst`. - * - * @param ctx The context for the CANN backend operations. - * @param acl_src The source tensor to be added. - * @param acl_dst The destination tensor which will hold the result of the - * addition. - */ -static void aclnn_inplace_add(ggml_backend_cann_context& ctx, - aclTensor* acl_src, aclTensor* acl_dst) { - aclScalar* alpha = nullptr; - float alphaValue = 1.0f; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnInplaceAddGetWorkspaceSize(acl_dst, acl_src, alpha, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnInplaceAdd(workspaceAddr, workspaceSize, executor, ctx.stream())); - - ACL_CHECK(aclDestroyScalar(alpha)); -} - /** * @brief Applies the softmax function to a tensor along a specified dimension. * @@ -2177,20 +1520,7 @@ static void aclnn_inplace_add(ggml_backend_cann_context& ctx, */ static void aclnn_softmax(ggml_backend_cann_context& ctx, aclTensor* acl_src, int64_t dim, aclTensor* acl_dst) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnSoftmaxGetWorkspaceSize(acl_src, dim, acl_dst, - &workspaceSize, &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - aclrtStream stream = ctx.stream(); - ACL_CHECK(aclnnSoftmax(workspaceAddr, workspaceSize, executor, stream)); + GGML_CANN_CALL_ACLNN_OP(Softmax, acl_src, dim, acl_dst); } void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) { @@ -2351,23 +1681,7 @@ static void aclnn_embedding_4d(ggml_backend_cann_context& ctx, void* src_buffer, (char*)dst->data + i * dst->nb[3] + j * dst->nb[2], ggml_cann_type_mapping(dst->type), ggml_element_size(dst), acl_out_ne, acl_out_nb, 2); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnEmbeddingGetWorkspaceSize( - acl_src_tensor, acl_index, acl_out, &workspaceSize, &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), - workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnEmbedding(workspaceAddr, workspaceSize, executor, - ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(Embedding, acl_src_tensor, acl_index, acl_out); ACL_CHECK(aclDestroyTensor(acl_src_tensor)); ACL_CHECK(aclDestroyTensor(acl_index)); ACL_CHECK(aclDestroyTensor(acl_out)); @@ -2492,133 +1806,8 @@ static void aclnn_repeat_interleave(ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst, int64_t dim, int64_t repeats, int64_t output_size) { - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnRepeatInterleaveIntWithDimGetWorkspaceSize( - acl_src, repeats, dim, output_size, acl_dst, &workspaceSize, - &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnRepeatInterleaveIntWithDim(workspaceAddr, workspaceSize, - executor, ctx.stream())); -} - -/** - * @brief Performs matrix multiplication of two tensors. - * - * This function computes the matrix multiplication of the input tensor - * `acl_input` and the weight tensor `acl_weight`, and stores the result in the - * destination tensor `acl_dst`. - * The operation is defined as: - * \f[ - * \text {acl_dst}=\text {acl_input@acl_weight} - * \f] - * - * @param ctx The context for the CANN backend operations. - * @param acl_input The input tensor for the matrix multiplication. - * @param acl_weight The weight tensor for the matrix multiplication. - * @param acl_dst The destination tensor where the result of the matrix - * multiplication will be stored. - */ -static void aclnn_mat_mul(ggml_backend_cann_context& ctx, aclTensor* acl_input, - aclTensor* acl_weight, aclTensor* acl_dst) { - int8_t cube_math_type = 1; // ALLOW_FP32_DOWN_PRECISION, when input is - // fp32, atlas a2 will transpose it to HFLOAT32. - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnMatmulGetWorkspaceSize(acl_input, acl_weight, acl_dst, - cube_math_type, &workspaceSize, - &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnMatmul(workspaceAddr, workspaceSize, executor, ctx.stream())); -} - -/** - * @brief Performs matrix multiplication of two 2D tensors. - * - * This function computes the matrix multiplication of the input tensor - * `acl_input` and the weight tensor `acl_weight`, and stores the result in the - * destination tensor `acl_dst`. - * The operation is defined as: - * \f[ - * \text {acl_dst}=\text {acl_input@acl_weight} - * \f] - * - * @param ctx The context for the CANN backend operations. - * @param acl_input The input tensor for the matrix multiplication. - * @param acl_weight The weight tensor for the matrix multiplication. - * @param acl_dst The destination tensor where the result of the matrix - * multiplication will be stored. - */ -static void aclnn_mat_mul_2d(ggml_backend_cann_context& ctx, - aclTensor* acl_input, aclTensor* acl_weight, - aclTensor* acl_dst) { - int8_t cube_math_type = 2; - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnMmGetWorkspaceSize(acl_input, acl_weight, acl_dst, - cube_math_type, &workspaceSize, - &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnMm(workspaceAddr, workspaceSize, executor, ctx.stream())); -} - -/** - * @brief Performs matrix multiplication of two 3D tensors. - * - * This function computes the matrix multiplication of the input tensor - * `acl_input` and the weight tensor `acl_weight`, and stores the result in the - * destination tensor `acl_dst`. - * The operation is defined as: - * \f[ - * \text {acl_dst}=\text {acl_input@acl_weight} - * \f] - * - * @param ctx The context for the CANN backend operations. - * @param acl_input The input tensor for the matrix multiplication. - * @param acl_weight The weight tensor for the matrix multiplication. - * @param acl_dst The destination tensor where the result of the matrix - * multiplication will be stored. - */ -static void aclnn_mat_mul_3d(ggml_backend_cann_context& ctx, - aclTensor* acl_input, aclTensor* acl_weight, - aclTensor* acl_dst) { - int8_t cube_math_type = 2; - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnBatchMatMulGetWorkspaceSize(acl_input, acl_weight, acl_dst, - cube_math_type, &workspaceSize, - &executor)); - - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK( - aclnnBatchMatMul(workspaceAddr, workspaceSize, executor, ctx.stream())); + GGML_CANN_CALL_ACLNN_OP(RepeatInterleaveIntWithDim, acl_src, repeats, dim, + output_size, acl_dst); } /** @@ -2666,13 +1855,15 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, switch (n_dims) { case 2: - aclnn_mat_mul_2d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + GGML_CANN_CALL_ACLNN_OP(Mm, acl_input_tensor, acl_weight_tensor, acl_dst, 2); break; case 3: - aclnn_mat_mul_3d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + GGML_CANN_CALL_ACLNN_OP(BatchMatMul, acl_input_tensor, acl_weight_tensor, acl_dst, 2); break; default: - aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + // ALLOW_FP32_DOWN_PRECISION, when input is + // fp32, atlas a2 will transpose it to HFLOAT32. + GGML_CANN_CALL_ACLNN_OP(Matmul, acl_input_tensor, acl_weight_tensor, acl_dst, 1); break; } @@ -2765,9 +1956,6 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, int64_t max_elem_size = 65535; int64_t split_size = (src0->ne[1] / max_elem_size) + 1; ggml_cann_pool_alloc workspace_allocator(ctx.pool()); - aclOpExecutor* executor = nullptr; - uint64_t workspaceSize = 0; - void* workspaceAddr = nullptr; for (int64_t n1 = 0; n1 < src1->ne[3]; n1++) { for (int64_t c1 = 0; c1 < src1->ne[2]; c1++) { int64_t n0 = n1 / (src1->ne[3] / src0->ne[3]); @@ -2806,17 +1994,10 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, if (src0->ne[0] > QK8_0) { antiquantGroupSize = QK8_0; } - - ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize( - acl_input_tensor, acl_weight_tensor, acl_scale_tensor, nullptr, - nullptr, nullptr, nullptr, antiquantGroupSize, - acl_output_tensor, &workspaceSize, &executor)); - if (workspaceAddr == nullptr) { - workspaceAddr = workspace_allocator.alloc(workspaceSize); - } - ACL_CHECK(aclnnWeightQuantBatchMatmulV2( - workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(WeightQuantBatchMatmulV2, acl_input_tensor, + acl_weight_tensor, acl_scale_tensor, nullptr, + nullptr, nullptr, nullptr, antiquantGroupSize, + acl_output_tensor); ACL_CHECK(aclDestroyTensor(acl_weight_tensor)); ACL_CHECK(aclDestroyTensor(acl_scale_tensor)); ACL_CHECK(aclDestroyTensor(acl_output_tensor)); @@ -2846,14 +2027,10 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, output_ne_offset); - - ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize( - acl_input_tensor, acl_weight_tensor, acl_scale_tensor, - nullptr, nullptr, nullptr, nullptr, antiquantGroupSize, - acl_output_tensor, &workspaceSize, &executor)); - ACL_CHECK(aclnnWeightQuantBatchMatmulV2( - workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(WeightQuantBatchMatmulV2, acl_input_tensor, + acl_weight_tensor, acl_scale_tensor, nullptr, + nullptr, nullptr, nullptr, antiquantGroupSize, + acl_output_tensor); ACL_CHECK(aclDestroyTensor(acl_weight_tensor)); ACL_CHECK(aclDestroyTensor(acl_scale_tensor)); ACL_CHECK(aclDestroyTensor(acl_output_tensor)); @@ -2876,8 +2053,7 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, output_buffer, ACL_FLOAT16, output_elem_size, output_cast_ne, output_cast_nb, GGML_MAX_DIMS); aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst); - aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, - ggml_cann_type_mapping(dst->type)); + aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, dst); ACL_CHECK(aclDestroyTensor(acl_output_tensor)); ACL_CHECK(aclDestroyTensor(acl_dst_tensor)); @@ -2896,7 +2072,7 @@ void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_mul_mat_quant(ctx, dst, type); break; default: - GGML_ABORT("fatal error"); + GGML_ABORT("Unsupported type for mul_mat"); break; } } @@ -2921,20 +2097,7 @@ static void aclnn_roll(ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst, int64_t* shifts, int64_t* dims) { aclIntArray* acl_shifts = aclCreateIntArray(shifts, 1); aclIntArray* acl_dims = aclCreateIntArray(dims, 1); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnRollGetWorkspaceSize(acl_src, acl_shifts, acl_dims, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnRoll(workspaceAddr, workspaceSize, executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(Roll, acl_src, acl_shifts, acl_dims, acl_dst); ACL_CHECK(aclDestroyIntArray(acl_shifts)); ACL_CHECK(aclDestroyIntArray(acl_dims)); } @@ -2958,21 +2121,7 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx, float value) { aclIntArray* acl_index = aclCreateIntArray(index, index_num); aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnInplaceIndexFillTensorGetWorkspaceSize( - acl_src, dim, acl_index, acl_value, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnInplaceIndexFillTensor(workspaceAddr, workspaceSize, - executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(InplaceIndexFillTensor, acl_src, dim, acl_index, acl_value); ACL_CHECK(aclDestroyIntArray(acl_index)); ACL_CHECK(aclDestroyScalar(acl_value)); } @@ -3031,8 +2180,7 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor( src2->data, ggml_cann_type_mapping(src2->type), ggml_type_size(src2->type), arange_ne, arange_nb, GGML_MAX_DIMS); - aclnn_div_tensor(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, - nullptr, true); + aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor); ACL_CHECK(aclDestroyTensor(acl_freq_factors_tensor)); } @@ -3331,8 +2479,8 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { // output void* output_fp32_buffer; if (src0->type == GGML_TYPE_F32) { - aclnn_inplace_mul(ctx, acl_src, acl_cos_reshape_tensor); - aclnn_inplace_mul(ctx, acl_input_roll_mul_scale_tensor, + aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor); + aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor); aclnn_add(ctx, acl_src, acl_input_roll_mul_scale_tensor, acl_dst); // TODO: ne0 != n_dims in mode2 @@ -3405,37 +2553,21 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_type_size(src0->type), sin_final_ne, sin_final_nb, GGML_MAX_DIMS); - aclnn_cast(ctx, acl_sin_reshape_tensor, acl_sin_final_tensor, - ggml_cann_type_mapping(src0->type)); - aclnn_cast(ctx, acl_cos_reshape_tensor, acl_cos_final_tensor, - ggml_cann_type_mapping(src0->type)); + aclnn_cast(ctx, acl_sin_reshape_tensor, acl_sin_final_tensor, dst); + aclnn_cast(ctx, acl_cos_reshape_tensor, acl_cos_final_tensor, dst); ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor)); ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); acl_sin_reshape_tensor = acl_sin_final_tensor; acl_cos_reshape_tensor = acl_cos_final_tensor; } - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - - void* workspaceAddr = nullptr; - int acl_mode = mode; if (mode == 0) { acl_mode = 1; } - ACL_CHECK(aclnnRotaryPositionEmbeddingGetWorkspaceSize( - acl_src, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, - acl_dst, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - ACL_CHECK(aclnnRotaryPositionEmbedding(workspaceAddr, workspaceSize, - executor, ctx.stream())); - + GGML_CANN_CALL_ACLNN_OP(RotaryPositionEmbedding, acl_src, acl_cos_reshape_tensor, + acl_sin_reshape_tensor, acl_mode, acl_dst); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor)); ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); @@ -3449,38 +2581,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclTensor* acl_src = ggml_cann_create_tensor(src0); aclTensor* acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(aclnnArgMaxGetWorkspaceSize(acl_src, 3, false, acl_dst, - &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - ACL_CHECK(aclnnArgMax(workspaceAddr, workspaceSize, executor, ctx.stream())); - - ACL_CHECK(aclDestroyTensor(acl_src)); - ACL_CHECK(aclDestroyTensor(acl_dst)); -} - -void ggml_cann_cos(ggml_backend_cann_context& ctx, ggml_tensor* dst){ - ggml_tensor * src0 = dst->src[0]; - - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - aclnn_cos(ctx, acl_src, acl_dst); - ACL_CHECK(aclDestroyTensor(acl_src)); - ACL_CHECK(aclDestroyTensor(acl_dst)); -} - -void ggml_cann_sin(ggml_backend_cann_context& ctx, ggml_tensor* dst){ - ggml_tensor * src0 = dst->src[0]; - - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - aclnn_sin(ctx, acl_src, acl_dst); + GGML_CANN_CALL_ACLNN_OP(ArgMax, acl_src, 3, false, acl_dst); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); } diff --git a/ggml/src/ggml-cann/aclnn_ops.h b/ggml/src/ggml-cann/aclnn_ops.h index 1327905032..116ddf0fb3 100644 --- a/ggml/src/ggml-cann/aclnn_ops.h +++ b/ggml/src/ggml-cann/aclnn_ops.h @@ -31,20 +31,25 @@ * IN THE SOFTWARE. */ -#include +#include +#include +#include #include #include #include #include -#include #include +#include +#include #include #include #include -#include #include #include #include +#include +#include +#include #include "acl_tensor.h" #include "common.h" @@ -63,23 +68,6 @@ */ void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst); -/** - * @brief Adds two ggml tensors using the CANN backend. - * - * @details This function performs an element-wise addition of two tensors. In - * case the tensors do not have the same shape, one or both tensors - * will be broadcasted to match the shape of the other before the - * addition is performed.The formula for the operation is given by: - * \f[ - * \text{dst} = \text{acl_src0} + \alpha \cdot \text{acl_src1} - * \f] - * - * @param ctx The CANN context used for operations. - * @param dst The ggml tensor representing the destination, result of the - * addition is stored at dst->data, and dst->op is `GGML_OP_ADD` - */ -void ggml_cann_add(ggml_backend_cann_context& ctx, ggml_tensor* dst); - /** * @brief Applies the Leaky ReLU activation function to a tensor using the CANN * backend. @@ -131,19 +119,6 @@ void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst); */ void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst); -/** - * @brief Computes the square of the elements of a ggml tensor using the CANN - * backend. - * @details The function sets the second source tensor of the destination - * tensor `dst` to be equal to the first source tensor. This is - * effectively squaring the elements since the multiplication becomes - * `element * element`. - * @param ctx The CANN context used for operations. - * @param dst The destination tensor where the squared values will be stored， - * which dst->op is `GGML_OP_SQR`. - */ -void ggml_cann_sqr(ggml_backend_cann_context& ctx, ggml_tensor* dst); - /** * @brief Applies a clamp operation to the elements of a ggml tensor using the * CANN backend. @@ -275,6 +250,20 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst); */ void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); +/** + * @brief Computes the sum of elements in a ggml tensor. + * + * @details This function performs a reduction sum operation along the last + * dimension of the input tensor `src`. The result of the sum is stored + * in the destination tensor `dst`. + * + * @param ctx The CANN context used for operations. + * @param dst The destination tensor where the reduced values will be stored。 + * + */ + +void ggml_cann_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst); + /** * @brief Upsamples a ggml tensor using nearest neighbor interpolation using * the CANN backend. @@ -500,128 +489,247 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst); void ggml_cann_argmax(ggml_backend_cann_context& ctx, ggml_tensor* dst); /** - * @brief Computes the cosine of each element in a ggml tensor using the CANN backend. + * @brief Adds two tensors element-wise and stores the result in a destination + * tensor. * - * @details This function applies the cosine function element-wise to the input tensor. - * The computed cosine values are stored in the destination tensor `dst`. - * The operation is optimized using the CANN backend for improved performance. + * This function performs the operation: + * \f[ + * dst = acl\_src0 + alpha \times acl\_src1 + * \f] + * where alpha is a scalar value and defaults to 1.0f. * - * @param ctx The CANN context used for operations. - * @param dst The destination tensor where the cosine values will be stored. - * dst->op is `GGML_OP_COS`. + * @param ctx The context for the CANN backend operations. + * @param acl_src0 The first source tensor. + * @param acl_src1 The second source tensor. + * @param acl_dst The destination tensor where the result will be stored. */ -void ggml_cann_cos(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0, + aclTensor* acl_src1, aclTensor* acl_dst = nullptr); /** - * @brief Computes the sine of each element in a ggml tensor using the CANN backend. + * @brief Sub two tensors element-wise and stores the result in a destination + * tensor. * - * @details This function applies the sine function element-wise to the input tensor. - * The computed sine values are stored in the destination tensor `dst`. - * The operation is optimized using the CANN backend for improved performance. + * This function performs the operation: + * \f[ + * dst = acl\_src0 - alpha \times acl\_src1 + * \f] + * where alpha is a scalar value and defaults to 1.0f. * - * @param ctx The CANN context used for operations. - * @param dst The destination tensor where the sine values will be stored. - * dst->op is `GGML_OP_SIN`. + * @param ctx The context for the CANN backend operations. + * @param acl_src0 The first source tensor. + * @param acl_src1 The second source tensor. + * @param acl_dst The destination tensor where the result will be stored. */ -void ggml_cann_sin(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void aclnn_sub(ggml_backend_cann_context& ctx, aclTensor* acl_src0, + aclTensor* acl_src1, aclTensor* acl_dst = nullptr); -template -void ggml_cann_mul_div(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +/** + * @brief Performs element-wise multiplication of two tensors and stores the + * result in a destination tensor. + * + * This function performs element-wise multiplication of the tensors `acl_src` + * and `acl_other` and stores the result in the destination tensor `acl_dst`. + * The operation is defined as: + * \f[ + * \text {acl_dst }_i=\text {acl_src }_i \times \text {acl_other }_i + * \f] + * + * @param ctx The context for the CANN backend operations. + * @param acl_src The first tensor for element-wise multiplication. + * @param acl_other The second tensor for element-wise multiplication. + * @param acl_dst The destination tensor where the result will be stored. + */ +void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_other, aclTensor* acl_dst = nullptr); + +/** + * @brief Matrix division, optionally in-place. + * + * This function division each element of the source tensor `acl_src` by the + * tensor `acl_other` and stores the result in the destination tensor `acl_dst`. + * If `inplace` is true, `acl_dst` will not be used and the operation is + * performed in-place on `acl_src`. The operation is defined as: \f[ + * \text{dst}_i = \frac{\text{acl_src}_i}{\text{acl_other}_i} + * \f] + * + * @param ctx The context for the CANN backend operations. + * @param acl_src Numerator tensor.. + * @param acl_other Denominator tensor. + * @param acl_dst The destination tensor where the result will be stored if + * `inplace` is false. + * @param inplace Flag indicating whether to perform the operation in-place on + * `acl_src`. + */ +void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_other, aclTensor* acl_dst = nullptr); + +/** + * @brief Applies element-wise cosine function to the elements of a tensor. + * + * This function computes the cosine of each element in the source tensor + * `acl_src` and stores the result in the destination tensor `acl_dst`. The + * operation is defined as: \f[ \text {acl_dst }_i=\cos \left(\text {acl_src + * }_i\right) \f] + * + * @param ctx The context for the CANN backend operations. + * @param acl_src The source tensor on which the cosine function will be + * applied. + * @param acl_dst The destination tensor where the cosine results will be + * stored. + */ +void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_dst); + +/** + * @brief Applies element-wise sine function to the elements of a tensor. + * + * This function computes the sine of each element in the source tensor + `acl_src` + * and stores the result in the destination tensor `acl_dst`. + * The operation is defined as: + * \f[ + * \text {acl_dst }_i=\sin \left(\text {acl_src }_i\right) + * \f] + + * @param ctx The context for the CANN backend operations. + * @param acl_src The source tensor on which the sine function will be applied. + * @param acl_dst The destination tensor where the sine results will be stored. + */ +void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_dst); + +/** + * @brief Launches an asynchronous task using the memory allocator. + * + * This macro submit an asynchronous task on the specified stream. + * The task uses memory allocated by the allocator. It is guaranteed + * that the memory will not be accessed by other tasks until this task + * completes, due to the sequential execution order within the same stream. + * + * @param OP_NAME aclnn operator name. + * @param args Additional arguments required by the task. + * + * @note + * Memory from the allocator will be "freed" immediately and can be + * reallocated to other pointers. However, it won't be accessed by any + * other task before this asynchronous task ends, because all tasks in the + * same stream are executed in queue order. + */ +#define GGML_CANN_CALL_ACLNN_OP(OP_NAME, ...) \ + do { \ + uint64_t workspaceSize = 0; \ + aclOpExecutor * executor; \ + void * workspaceAddr = nullptr; \ + \ + ACL_CHECK(aclnn##OP_NAME##GetWorkspaceSize(__VA_ARGS__, &workspaceSize, &executor)); \ + \ + if (workspaceSize > 0) { \ + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); \ + workspaceAddr = workspace_allocator.get(); \ + } \ + ACL_CHECK(aclnn##OP_NAME(workspaceAddr, workspaceSize, executor, ctx.stream())); \ + } while (0) + + +/** + * @brief Prepares broadcast-compatible ACL tensors for two input tensors and one output tensor. + * + * This function checks whether broadcasting is needed between `src0` and `src1`. + * If broadcasting is required, it calculates the proper shapes and creates + * ACL tensors with broadcast parameters. Otherwise, it directly creates ACL tensors + * based on the original tensor shapes. + * + * @param src0 The first input tensor (reference shape). + * @param src1 The second input tensor (possibly broadcasted). + * @param dst The destination/output tensor. + * @param acl_src0 Output pointer to the created ACL tensor corresponding to src0. + * @param acl_src1 Output pointer to the created ACL tensor corresponding to src1. + * @param acl_dst Output pointer to the created ACL tensor corresponding to dst. + */ +void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclTensor ** acl_src0, + aclTensor ** acl_src1, aclTensor ** acl_dst); + +/** + * @brief Applies a element-wise operation to two input tensors using the CANN backend. + * + * This templated function takes a binary operator and applies it to two source tensors + * associated with the destination tensor. The function handles broadcasting as needed. + * + * @tparam binary_op A callable object (e.g., lambda or function pointer) representing + * the binary operation to be performed. It must take three arguments: + * (ggml_backend_cann_context&, aclTensor*, aclTensor*, aclTensor*). + * + * @param ctx The CANN backend context used to manage execution and resources. + * @param dst The destination tensor. + */ +template +void ggml_cann_binary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src0 = dst->src[0]; ggml_tensor* src1 = dst->src[1]; - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); aclTensor* acl_src0; aclTensor* acl_src1; aclTensor* acl_dst; // Need bcast - if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) { - BCAST_SHAPE(src0, src1) - acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0)); - acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1)); - acl_dst = ggml_cann_create_tensor(dst, BCAST_PARAM(src0)); - } else { - acl_src0 = ggml_cann_create_tensor(src0); - acl_src1 = ggml_cann_create_tensor(src1); - acl_dst = ggml_cann_create_tensor(dst); - } - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(getWorkspaceSize(acl_src0, acl_src1, acl_dst, &workspaceSize, - &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - aclrtStream main_stream = ctx.stream(); - ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream)); + bcast_shape(src0, src1, dst, &acl_src0, &acl_src1, &acl_dst); + binary_op(ctx, acl_src0, acl_src1, acl_dst); ACL_CHECK(aclDestroyTensor(acl_src0)); ACL_CHECK(aclDestroyTensor(acl_src1)); ACL_CHECK(aclDestroyTensor(acl_dst)); } -// Activation functions template. -template -void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +/** + * @brief Applies a unary operation to an input tensor using the CANN backend. + * + * This templated function applies a unary operator to the source tensor of `dst` + * and stores the result in the destination tensor. + * + * @tparam unary_op A callable with the signature: + * void(ggml_backend_cann_context&, aclTensor*, aclTensor*) + * where the first aclTensor is the source and the second is the destination. + * + * @param ctx The CANN backend context for managing resources and execution. + * @param dst The destination tensor. Its src[0] is treated as the input tensor. + */ +template + void ggml_cann_unary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src = dst->src[0]; aclTensor* acl_src = ggml_cann_create_tensor(src); aclTensor* acl_dst = ggml_cann_create_tensor(dst); - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - aclrtStream main_stream = ctx.stream(); - ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream)); - + unary_op(ctx, acl_src, acl_dst); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); } -// Activation functions template for const aclTensors. -template -void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; - - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - - uint64_t workspaceSize = 0; - aclOpExecutor* executor; - void* workspaceAddr = nullptr; - - ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor)); - if (workspaceSize > 0) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); - workspaceAddr = workspace_allocator.get(); - } - - aclrtStream main_stream = ctx.stream(); - ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream)); - - ACL_CHECK(aclDestroyTensor(acl_src)); - ACL_CHECK(aclDestroyTensor(acl_dst)); -} +/** + * @brief Helper macro to invoke a unary ACL operation using ggml_cann_unary_op. + * + * This macro defines an inline lambda wrapping a specific ACL operation name, + * and passes it to the templated ggml_cann_unary_op function. It simplifies + * calling unary ops by hiding the lambda boilerplate. + * + * Internally, the lambda will call: + * @code + * GGML_CANN_CALL_ACLNN_OP(OP_NAME, acl_src, acl_dst); + * @endcode + * + * @param OP_NAME The name of the ACL unary operator to invoke via GGML_CANN_CALL_ACLNN_OP. + * + * @see ggml_cann_unary_op + * @see GGML_CANN_CALL_ACLNN_OP + */ +#define GGML_CANN_CALL_UNARY_OP(OP_NAME) \ + do { \ + auto lambda = [](auto ctx, auto acl_src, auto acl_dst) { \ + GGML_CANN_CALL_ACLNN_OP(OP_NAME, acl_src, acl_dst); \ + }; \ + ggml_cann_unary_op(ctx, dst); \ + } \ + while (0) #endif // CANN_ACLNN_OPS diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index 5e790f05fb..bcd05c9ae7 100644 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -1300,47 +1300,59 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, ggml_cann_dup(ctx, dst); break; case GGML_OP_ADD: - ggml_cann_add(ctx, dst); + case GGML_OP_ADD1: + ggml_cann_binary_op(ctx, dst); + break; + case GGML_OP_SUB: + ggml_cann_binary_op(ctx, dst); break; case GGML_OP_ACC: ggml_cann_acc(ctx, dst); break; case GGML_OP_MUL: - ggml_cann_mul_div(ctx, dst); + ggml_cann_binary_op(ctx, dst); break; case GGML_OP_DIV: - ggml_cann_mul_div(ctx, dst); + ggml_cann_binary_op(ctx, dst); break; case GGML_OP_UNARY: switch (ggml_get_unary_op(dst)) { + case GGML_UNARY_OP_ABS: + GGML_CANN_CALL_UNARY_OP(Abs); + break; + case GGML_UNARY_OP_NEG: + GGML_CANN_CALL_UNARY_OP(Neg); + break; case GGML_UNARY_OP_GELU: - ggml_cann_activation( - ctx, dst); + GGML_CANN_CALL_UNARY_OP(Gelu); break; case GGML_UNARY_OP_SILU: - ggml_cann_activation( - ctx, dst); + GGML_CANN_CALL_UNARY_OP(Silu); break; - // TODO: Use faster gelu?? - case GGML_UNARY_OP_GELU_QUICK: - ggml_cann_activation( - ctx, dst); + case GGML_UNARY_OP_GELU_QUICK: { + auto lambda = [](auto ctx, auto acl_src, auto acl_dst) { + GGML_CANN_CALL_ACLNN_OP(GeluV2, acl_src, 0, acl_dst); + }; + ggml_cann_unary_op(ctx, dst); + } break; case GGML_UNARY_OP_TANH: - ggml_cann_activation( - ctx, dst); + GGML_CANN_CALL_UNARY_OP(Tanh); break; case GGML_UNARY_OP_RELU: - ggml_cann_activation( - ctx, dst); + GGML_CANN_CALL_UNARY_OP(Relu); + break; + case GGML_UNARY_OP_SIGMOID: + GGML_CANN_CALL_UNARY_OP(Sigmoid); break; case GGML_UNARY_OP_HARDSIGMOID: - ggml_cann_activation(ctx, dst); + GGML_CANN_CALL_UNARY_OP(Hardsigmoid); break; case GGML_UNARY_OP_HARDSWISH: - ggml_cann_activation(ctx, dst); + GGML_CANN_CALL_UNARY_OP(Hardswish); + break; + case GGML_UNARY_OP_EXP: + GGML_CANN_CALL_UNARY_OP(Exp); break; default: return false; @@ -1382,7 +1394,12 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, ggml_cann_scale(ctx, dst); break; case GGML_OP_SQR: - ggml_cann_sqr(ctx, dst); + GGML_ASSERT(dst->src[1] == nullptr); + dst->src[1] = dst->src[0]; + ggml_cann_binary_op(ctx, dst); + break; + case GGML_OP_SQRT: + GGML_CANN_CALL_UNARY_OP(Sqrt); break; case GGML_OP_CLAMP: ggml_cann_clamp(ctx, dst); @@ -1414,6 +1431,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, case GGML_OP_POOL_2D: ggml_cann_pool2d(ctx, dst); break; + case GGML_OP_SUM: + ggml_cann_sum(ctx, dst); + break; case GGML_OP_SUM_ROWS: ggml_cann_sum_rows(ctx, dst); break; @@ -1424,11 +1444,11 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, ggml_cann_argmax(ctx, dst); break; case GGML_OP_COS: - ggml_cann_cos(ctx, dst); + ggml_cann_unary_op(ctx, dst); break; case GGML_OP_SIN: - ggml_cann_sin(ctx, dst); - break; + ggml_cann_unary_op(ctx, dst); + break; default: return false; } @@ -1679,13 +1699,17 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_NEG: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_HARDSIGMOID: case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_EXP: return true; default: return false; @@ -1784,6 +1808,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, // value of paddingW should be at most half of kernelW return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2)); } + case GGML_OP_SUM: case GGML_OP_DUP: case GGML_OP_IM2COL: case GGML_OP_CONCAT: @@ -1795,11 +1820,14 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, case GGML_OP_TRANSPOSE: case GGML_OP_NORM: case GGML_OP_ADD: + case GGML_OP_ADD1: + case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_RMS_NORM: case GGML_OP_SCALE: case GGML_OP_SQR: + case GGML_OP_SQRT: case GGML_OP_CLAMP: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: From 52b3d71f128c1eeab39b918adf1f7a8f5e256619 Mon Sep 17 00:00:00 2001 From: zhouwg Date: Mon, 7 Apr 2025 19:34:14 +0800 Subject: [PATCH 21/99] CANN: fix typo in ggml-cann (#12733) --- ggml/src/ggml-cann/ggml-cann.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index bcd05c9ae7..326f9d298b 100644 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -803,7 +803,7 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor( return GGML_STATUS_SUCCESS; } - // TODO: can backend doesn't support quantized yet. Just leave the code + // TODO: cann backend doesn't support quantized yet. Just leave the code // here. if (ggml_is_quantized(tensor->type)) { // Initialize padding to 0 to avoid possible NaN values From bd3f59f81289b920bcc597a208c14f55e39ed37e Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 7 Apr 2025 13:35:19 +0200 Subject: [PATCH 22/99] cmake : enable curl by default (#12761) * cmake : enable curl by default * no curl if no examples * fix build * fix build-linux-cross * add windows-setup-curl * fix * shell * fix path * fix windows-latest-cmake* * run: include_directories * LLAMA_RUN_EXTRA_LIBS * sycl: no llama_curl * no test-arg-parser on windows * clarification * try riscv64 / arm64 * windows: include libcurl inside release binary * add msg * fix mac / ios / android build * will this fix xcode? * try clearing the cache * add bunch of licenses * revert clear cache * fix xcode * fix xcode (2) * fix typo --- .devops/cpu.Dockerfile | 4 +- .devops/cuda.Dockerfile | 2 +- .devops/intel.Dockerfile | 2 +- .devops/musa.Dockerfile | 2 +- .devops/rocm.Dockerfile | 2 +- .github/actions/windows-setup-curl/action.yml | 25 ++++++ .github/workflows/bench.yml.disabled | 1 - .github/workflows/build-linux-cross.yml | 9 ++- .github/workflows/build.yml | 80 ++++++++++++++----- .github/workflows/server.yml | 18 ++--- CMakeLists.txt | 24 +++++- build-xcframework.sh | 7 ++ common/CMakeLists.txt | 5 +- examples/llama.android/llama/build.gradle.kts | 1 + examples/run/CMakeLists.txt | 13 ++- examples/server/tests/README.md | 2 +- examples/sycl/win-build-sycl.bat | 4 +- licenses/LICENSE-curl | 9 +++ licenses/LICENSE-httplib | 21 +++++ licenses/LICENSE-jsonhpp | 21 +++++ .../LICENSE => licenses/LICENSE-linenoise | 0 tests/CMakeLists.txt | 6 +- 22 files changed, 209 insertions(+), 49 deletions(-) create mode 100644 .github/actions/windows-setup-curl/action.yml create mode 100644 licenses/LICENSE-curl create mode 100644 licenses/LICENSE-httplib create mode 100644 licenses/LICENSE-jsonhpp rename examples/run/linenoise.cpp/LICENSE => licenses/LICENSE-linenoise (100%) diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile index 522ee8147d..aa2aa03120 100644 --- a/.devops/cpu.Dockerfile +++ b/.devops/cpu.Dockerfile @@ -14,9 +14,9 @@ WORKDIR /app COPY . . RUN if [ "$TARGETARCH" = "amd64" ]; then \ - cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \ elif [ "$TARGETARCH" = "arm64" ]; then \ - cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \ else \ echo "Unsupported architecture"; \ exit 1; \ diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile index 0bc4e7ee13..a196111e61 100644 --- a/.devops/cuda.Dockerfile +++ b/.devops/cuda.Dockerfile @@ -21,7 +21,7 @@ COPY . . RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ cmake --build build --config Release -j$(nproc) RUN mkdir -p /app/lib && \ diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile index af783f5e99..e2b381766f 100644 --- a/.devops/intel.Dockerfile +++ b/.devops/intel.Dockerfile @@ -17,7 +17,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ fi && \ echo "Building with dynamic libs" && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ cmake --build build --config Release -j$(nproc) RUN mkdir -p /app/lib && \ diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile index 1e87737abf..e8297c6948 100644 --- a/.devops/musa.Dockerfile +++ b/.devops/musa.Dockerfile @@ -35,7 +35,7 @@ COPY . . RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ cmake --build build --config Release -j$(nproc) RUN mkdir -p /app/lib && \ diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile index 48e7e6aaa5..66687a25ba 100644 --- a/.devops/rocm.Dockerfile +++ b/.devops/rocm.Dockerfile @@ -40,7 +40,7 @@ WORKDIR /app COPY . . RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ - cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DCMAKE_BUILD_TYPE=Release \ && cmake --build build --config Release -j$(nproc) RUN mkdir -p /app/lib \ diff --git a/.github/actions/windows-setup-curl/action.yml b/.github/actions/windows-setup-curl/action.yml new file mode 100644 index 0000000000..5d76da3d79 --- /dev/null +++ b/.github/actions/windows-setup-curl/action.yml @@ -0,0 +1,25 @@ +name: 'Windows - Setup CURL' +description: 'Composite action, to be reused in other workflow' +inputs: + curl_version: + description: 'CURL version' + required: false + default: '8.6.0_6' +outputs: + curl_path: + description: "Path to the downloaded libcurl" + value: ${{ steps.get_libcurl.outputs.curl_path }} + +runs: + using: "composite" + steps: + - name: libCURL + id: get_libcurl + shell: powershell + env: + CURL_VERSION: ${{ inputs.curl_version }} + run: | + curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip" + mkdir $env:RUNNER_TEMP/libcurl + tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl + echo "curl_path=$env:RUNNER_TEMP/libcurl" >> $env:GITHUB_OUTPUT diff --git a/.github/workflows/bench.yml.disabled b/.github/workflows/bench.yml.disabled index 0370c8943f..75d2714792 100644 --- a/.github/workflows/bench.yml.disabled +++ b/.github/workflows/bench.yml.disabled @@ -104,7 +104,6 @@ jobs: cmake -B build \ -DGGML_NATIVE=OFF \ -DLLAMA_BUILD_SERVER=ON \ - -DLLAMA_CURL=ON \ -DLLAMA_CUBLAS=ON \ -DCUDAToolkit_ROOT=/usr/local/cuda \ -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \ diff --git a/.github/workflows/build-linux-cross.yml b/.github/workflows/build-linux-cross.yml index d4176fef9c..e8639913ea 100644 --- a/.github/workflows/build-linux-cross.yml +++ b/.github/workflows/build-linux-cross.yml @@ -19,7 +19,8 @@ jobs: sudo apt-get install -y --no-install-recommends \ build-essential \ gcc-14-riscv64-linux-gnu \ - g++-14-riscv64-linux-gnu + g++-14-riscv64-linux-gnu \ + libcurl4-openssl-dev:riscv64 - name: Build run: | @@ -59,7 +60,8 @@ jobs: glslc \ gcc-14-riscv64-linux-gnu \ g++-14-riscv64-linux-gnu \ - libvulkan-dev:riscv64 + libvulkan-dev:riscv64 \ + libcurl4-openssl-dev:riscv64 - name: Build run: | @@ -99,7 +101,8 @@ jobs: build-essential \ glslc \ crossbuild-essential-arm64 \ - libvulkan-dev:arm64 + libvulkan-dev:arm64 \ + libcurl4-openssl-dev:arm64 - name: Build run: | diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index dc2a4c0566..33f6a4fb48 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -54,6 +54,7 @@ jobs: continue-on-error: true run: | brew update + brew install curl - name: Build id: cmake_build @@ -62,7 +63,6 @@ jobs: cmake -B build \ -DCMAKE_BUILD_RPATH="@loader_path" \ -DLLAMA_FATAL_WARNINGS=ON \ - -DLLAMA_CURL=ON \ -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ -DGGML_RPC=ON @@ -92,7 +92,6 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | cp LICENSE ./build/bin/ - cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/* - name: Upload artifacts @@ -123,6 +122,7 @@ jobs: continue-on-error: true run: | brew update + brew install curl - name: Build id: cmake_build @@ -133,7 +133,6 @@ jobs: cmake -B build \ -DCMAKE_BUILD_RPATH="@loader_path" \ -DLLAMA_FATAL_WARNINGS=ON \ - -DLLAMA_CURL=ON \ -DGGML_METAL=OFF \ -DGGML_RPC=ON cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) @@ -162,7 +161,6 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | cp LICENSE ./build/bin/ - cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/* - name: Upload artifacts @@ -207,7 +205,6 @@ jobs: run: | cmake -B build \ -DLLAMA_FATAL_WARNINGS=ON \ - -DLLAMA_CURL=ON \ -DGGML_RPC=ON cmake --build build --config Release -j $(nproc) @@ -246,7 +243,6 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | cp LICENSE ./build/bin/ - cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip ./build/bin/* - name: Upload artifacts @@ -281,7 +277,7 @@ jobs: id: depends run: | sudo apt-get update - sudo apt-get install build-essential + sudo apt-get install build-essential libcurl4-openssl-dev - name: Build id: cmake_build @@ -322,7 +318,7 @@ jobs: id: depends run: | sudo apt-get update - sudo apt-get install build-essential + sudo apt-get install build-essential libcurl4-openssl-dev - name: Build id: cmake_build @@ -360,7 +356,7 @@ jobs: id: depends run: | sudo apt-get update - sudo apt-get install build-essential + sudo apt-get install build-essential libcurl4-openssl-dev - name: Build id: cmake_build @@ -397,7 +393,7 @@ jobs: wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add - sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list sudo apt-get update -y - sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk + sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libcurl4-openssl-dev - name: Build id: cmake_build @@ -431,7 +427,6 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | cp LICENSE ./build/bin/ - cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip ./build/bin/* - name: Upload artifacts @@ -454,7 +449,7 @@ jobs: id: depends run: | sudo apt-get update - sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev + sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libcurl4-openssl-dev - name: ccache uses: hendrikmuhs/ccache-action@v1.2.16 @@ -530,7 +525,7 @@ jobs: shell: bash run: | sudo apt update - sudo apt install intel-oneapi-compiler-dpcpp-cpp + sudo apt install intel-oneapi-compiler-dpcpp-cpp libcurl4-openssl-dev - name: install oneAPI MKL library shell: bash @@ -578,7 +573,7 @@ jobs: shell: bash run: | sudo apt update - sudo apt install intel-oneapi-compiler-dpcpp-cpp + sudo apt install intel-oneapi-compiler-dpcpp-cpp libcurl4-openssl-dev - name: install oneAPI MKL library shell: bash @@ -636,6 +631,7 @@ jobs: cmake -B build -G Xcode \ -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_COMMON=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ @@ -671,6 +667,7 @@ jobs: cmake -B build -G Xcode \ -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_COMMON=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ @@ -700,6 +697,7 @@ jobs: cmake -B build -G Xcode \ -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_COMMON=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ @@ -739,6 +737,7 @@ jobs: cmake -B build -G Xcode \ -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_CURL=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ @@ -899,10 +898,17 @@ jobs: -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release" cmake --build build-arm64-release --target install --config release + - name: libCURL + id: get_libcurl + uses: ./.github/actions/windows-setup-curl + - name: Build id: cmake_build + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | - cmake -S . -B build ${{ matrix.defines }} + cmake -S . -B build ${{ matrix.defines }} ` + -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} - name: Add libopenblas.dll @@ -962,9 +968,10 @@ jobs: - name: Pack artifacts id: pack_artifacts if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | - Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt - Copy-Item .\examples\run\linenoise.cpp\LICENSE .\build\bin\Release\linenoise.cpp.txt + Copy-Item $env:CURL_PATH\bin\libcurl-x64.dll .\build\bin\Release\libcurl-x64.dll 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip .\build\bin\Release\* - name: Upload artifacts @@ -990,7 +997,7 @@ jobs: DEBIAN_FRONTEND: noninteractive run: | apt update - apt install -y cmake build-essential ninja-build libgomp1 git + apt install -y cmake build-essential ninja-build libgomp1 git libcurl4-openssl-dev - name: ccache uses: hendrikmuhs/ccache-action@v1.2.16 @@ -1092,16 +1099,23 @@ jobs: run: | choco install ninja + - name: libCURL + id: get_libcurl + uses: ./.github/actions/windows-setup-curl + - name: Build id: cmake_build shell: cmd + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat" cmake -S . -B build -G "Ninja Multi-Config" ^ -DLLAMA_BUILD_SERVER=ON ^ -DGGML_NATIVE=OFF ^ -DGGML_CUDA=ON ^ - -DGGML_RPC=ON + -DGGML_RPC=ON ^ + -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1 cmake --build build --config Release -j %NINJA_JOBS% -t ggml cmake --build build --config Release @@ -1122,7 +1136,10 @@ jobs: - name: Pack artifacts id: pack_artifacts if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | + cp $env:CURL_PATH\bin\libcurl-x64.dll .\build\bin\Release\libcurl-x64.dll 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip .\build\bin\Release\* - name: Upload artifacts @@ -1177,6 +1194,8 @@ jobs: run: | scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL + # TODO: add libcurl support ; we will also need to modify win-build-sycl.bat to accept user-specified args + - name: Build id: cmake_build run: examples/sycl/win-build-sycl.bat @@ -1262,8 +1281,14 @@ jobs: key: ${{ github.job }} evict-old-files: 1d + - name: libCURL + id: get_libcurl + uses: ./.github/actions/windows-setup-curl + - name: Build id: cmake_build + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" @@ -1274,9 +1299,11 @@ jobs: -DCMAKE_BUILD_TYPE=Release ` -DGGML_HIP=ON ` -DGGML_HIP_ROCWMMA_FATTN=ON ` - -DGGML_RPC=ON + -DGGML_RPC=ON ` + -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" cmake --build build -j ${env:NUMBER_OF_PROCESSORS} + # TODO: reuse windows-latest-cmake-hip instead of duplicating this job windows-latest-cmake-hip-release: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} runs-on: windows-latest @@ -1318,8 +1345,14 @@ jobs: run: | & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version + - name: libCURL + id: get_libcurl + uses: ./.github/actions/windows-setup-curl + - name: Build id: cmake_build + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" @@ -1331,7 +1364,8 @@ jobs: -DAMDGPU_TARGETS=${{ matrix.gpu_target }} ` -DGGML_HIP_ROCWMMA_FATTN=ON ` -DGGML_HIP=ON ` - -DGGML_RPC=ON + -DGGML_RPC=ON ` + -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" cmake --build build -j ${env:NUMBER_OF_PROCESSORS} md "build\bin\rocblas\library\" cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\" @@ -1353,7 +1387,10 @@ jobs: - name: Pack artifacts id: pack_artifacts + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | + cp $env:CURL_PATH\bin\libcurl-x64.dll .\build\bin\libcurl-x64.dll 7z a llama-${{ steps.tag.outputs.name }}-bin-win-hip-x64-${{ matrix.gpu_target }}.zip .\build\bin\* - name: Upload artifacts @@ -1378,6 +1415,7 @@ jobs: cmake -B build -G Xcode \ -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_CURL=OFF \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 092c928bdf..6c9b513227 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -129,7 +129,6 @@ jobs: cmake -B build \ -DGGML_NATIVE=OFF \ -DLLAMA_BUILD_SERVER=ON \ - -DLLAMA_CURL=ON \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ -DGGML_OPENMP=OFF ; @@ -142,7 +141,6 @@ jobs: cmake -B build \ -DGGML_NATIVE=OFF \ -DLLAMA_BUILD_SERVER=ON \ - -DLLAMA_CURL=ON \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server @@ -154,7 +152,6 @@ jobs: cmake -B build \ -DGGML_NATIVE=OFF \ -DLLAMA_BUILD_SERVER=ON \ - -DLLAMA_CURL=ON \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ; cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server @@ -195,17 +192,14 @@ jobs: - name: libCURL id: get_libcurl - env: - CURL_VERSION: 8.6.0_6 - run: | - curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip" - mkdir $env:RUNNER_TEMP/libcurl - tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl + uses: ./.github/actions/windows-setup-curl - name: Build id: cmake_build + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | - cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include" + cmake -B build -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server - name: Python setup @@ -221,8 +215,10 @@ jobs: - name: Copy Libcurl id: prepare_libcurl + env: + CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | - cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll + cp $env:CURL_PATH/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll - name: Tests id: server_integration_tests diff --git a/CMakeLists.txt b/CMakeLists.txt index 23cfbce5ae..de51c0a17b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -81,7 +81,7 @@ option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_SERVER "llama: build server example" ${LLAMA_STANDALONE}) # 3rd party libs -option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF) +option(LLAMA_CURL "llama: use libcurl to download model from an URL" ON) option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF) # Required for relocatable CMake package @@ -168,6 +168,11 @@ add_subdirectory(src) # utils, programs, examples and tests # +if (NOT LLAMA_BUILD_COMMON) + message(STATUS "LLAMA_BUILD_COMMON is OFF, disabling LLAMA_CURL") + set(LLAMA_CURL OFF) +endif() + if (LLAMA_BUILD_COMMON) add_subdirectory(common) endif() @@ -242,3 +247,20 @@ configure_file(cmake/llama.pc.in install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig) + +# +# copy the license files +# + +# Check if running in GitHub Actions +if(DEFINED ENV{GITHUB_ACTIONS} AND "$ENV{GITHUB_ACTIONS}" STREQUAL "true") + message(STATUS "Running inside GitHub Actions - copying license files") + + # Copy all files from licenses/ to build/bin/ + file(GLOB LICENSE_FILES "${CMAKE_SOURCE_DIR}/licenses/*") + foreach(LICENSE_FILE ${LICENSE_FILES}) + get_filename_component(FILENAME ${LICENSE_FILE} NAME) + configure_file(${LICENSE_FILE} "${CMAKE_BINARY_DIR}/bin/${FILENAME}" COPYONLY) + endforeach() +endif() + diff --git a/build-xcframework.sh b/build-xcframework.sh index 2ce3939c43..1b9091d288 100755 --- a/build-xcframework.sh +++ b/build-xcframework.sh @@ -399,6 +399,7 @@ cmake -B build-ios-sim -G Xcode \ -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphonesimulator \ -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ -S . cmake --build build-ios-sim --config Release -- -quiet @@ -411,6 +412,7 @@ cmake -B build-ios-device -G Xcode \ -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \ -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ -S . cmake --build build-ios-device --config Release -- -quiet @@ -421,6 +423,7 @@ cmake -B build-macos -G Xcode \ -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \ -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ -S . cmake --build build-macos --config Release -- -quiet @@ -434,6 +437,7 @@ cmake -B build-visionos -G Xcode \ -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xros \ -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \ -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ -S . cmake --build build-visionos --config Release -- -quiet @@ -447,6 +451,7 @@ cmake -B build-visionos-sim -G Xcode \ -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xrsimulator \ -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \ -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ -S . cmake --build build-visionos-sim --config Release -- -quiet @@ -462,6 +467,7 @@ cmake -B build-tvos-sim -G Xcode \ -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \ -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ -S . cmake --build build-tvos-sim --config Release -- -quiet @@ -476,6 +482,7 @@ cmake -B build-tvos-device -G Xcode \ -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvos \ -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \ -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \ + -DLLAMA_CURL=OFF \ -S . cmake --build build-tvos-device --config Release -- -quiet diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 829eb5b723..43533fc86a 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -85,7 +85,10 @@ set(LLAMA_COMMON_EXTRA_LIBS build_info) # Use curl to download model url if (LLAMA_CURL) - find_package(CURL REQUIRED) + find_package(CURL) + if (NOT CURL_FOUND) + message(FATAL_ERROR "Could NOT find CURL. Hint: to disable this feature, set -DLLAMA_CURL=OFF") + endif() target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL) include_directories(${CURL_INCLUDE_DIRS}) find_library(CURL_LIBRARY curl REQUIRED) diff --git a/examples/llama.android/llama/build.gradle.kts b/examples/llama.android/llama/build.gradle.kts index 28dbc19048..5bb6478022 100644 --- a/examples/llama.android/llama/build.gradle.kts +++ b/examples/llama.android/llama/build.gradle.kts @@ -18,6 +18,7 @@ android { } externalNativeBuild { cmake { + arguments += "-DLLAMA_CURL=OFF" arguments += "-DLLAMA_BUILD_COMMON=ON" arguments += "-DGGML_LLAMAFILE=OFF" arguments += "-DCMAKE_BUILD_TYPE=Release" diff --git a/examples/run/CMakeLists.txt b/examples/run/CMakeLists.txt index cd6b0520e0..7cff188ca6 100644 --- a/examples/run/CMakeLists.txt +++ b/examples/run/CMakeLists.txt @@ -1,5 +1,16 @@ set(TARGET llama-run) add_executable(${TARGET} run.cpp linenoise.cpp/linenoise.cpp) + +# TODO: avoid copying this code block from common/CMakeLists.txt +set(LLAMA_RUN_EXTRA_LIBS "") +if (LLAMA_CURL) + find_package(CURL REQUIRED) + target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL) + include_directories(${CURL_INCLUDE_DIRS}) + find_library(CURL_LIBRARY curl REQUIRED) + set(LLAMA_RUN_EXTRA_LIBS ${LLAMA_RUN_EXTRA_LIBS} ${CURL_LIBRARY}) +endif () + install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT} ${LLAMA_RUN_EXTRA_LIBS}) target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index 12816bfa87..652dea0382 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -17,7 +17,7 @@ To mitigate it, you can increase values in `n_predict`, `kv_size`. ```shell cd ../../.. -cmake -B build -DLLAMA_CURL=ON +cmake -B build cmake --build build --target llama-server ``` diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat index 17dd1ff5c1..6fc897b148 100644 --- a/examples/sycl/win-build-sycl.bat +++ b/examples/sycl/win-build-sycl.bat @@ -13,10 +13,10 @@ if %errorlevel% neq 0 goto ERROR :: for FP16 :: faster for long-prompt inference -:: cmake -G "MinGW Makefiles" .. -DGGML_SYCL=ON -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON +:: cmake -G "MinGW Makefiles" .. -DLLAMA_CURL=OFF -DGGML_SYCL=ON -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON :: for FP32 -cmake -G "Ninja" .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release +cmake -G "Ninja" .. -DLLAMA_CURL=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release if %errorlevel% neq 0 goto ERROR :: build example/main only :: make main diff --git a/licenses/LICENSE-curl b/licenses/LICENSE-curl new file mode 100644 index 0000000000..da9c038253 --- /dev/null +++ b/licenses/LICENSE-curl @@ -0,0 +1,9 @@ +Copyright (c) 1996 - 2025, Daniel Stenberg, daniel@haxx.se, and many contributors, see the THANKS file. + +All rights reserved. + +Permission to use, copy, modify, and distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +Except as contained in this notice, the name of a copyright holder shall not be used in advertising or otherwise to promote the sale, use or other dealings in this Software without prior written authorization of the copyright holder. diff --git a/licenses/LICENSE-httplib b/licenses/LICENSE-httplib new file mode 100644 index 0000000000..47c418e072 --- /dev/null +++ b/licenses/LICENSE-httplib @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2017 yhirose + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/licenses/LICENSE-jsonhpp b/licenses/LICENSE-jsonhpp new file mode 100644 index 0000000000..b5a10275c1 --- /dev/null +++ b/licenses/LICENSE-jsonhpp @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2013-2025 Niels Lohmann + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/examples/run/linenoise.cpp/LICENSE b/licenses/LICENSE-linenoise similarity index 100% rename from examples/run/linenoise.cpp/LICENSE rename to licenses/LICENSE-linenoise diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 7a158d6024..2bb210702a 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -131,9 +131,13 @@ if (NOT WIN32) endif() llama_target_and_test(test-log.cpp) -llama_target_and_test(test-arg-parser.cpp) llama_target_and_test(test-chat-template.cpp) +# this fails on windows (github hosted runner) due to curl DLL not found (exit code 0xc0000135) +if (NOT WIN32) + llama_target_and_test(test-arg-parser.cpp) +endif() + # llama_target_and_test(test-opt.cpp) # SLOW llama_target_and_test(test-gguf.cpp) llama_target_and_test(test-backend-ops.cpp) From e391d3ee8ddae86be70c034de1082ad51c55e211 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 7 Apr 2025 14:37:28 +0200 Subject: [PATCH 23/99] ci : no curl on ggml-ci (#12796) --- ci/run.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci/run.sh b/ci/run.sh index ebd662b38b..f463d7a8b2 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -39,7 +39,7 @@ sd=`dirname $0` cd $sd/../ SRC=`pwd` -CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON" +CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=OFF" if [ ! -z ${GG_BUILD_METAL} ]; then CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON" From 518a01480eb3a7c80a4951b430db9dee55428310 Mon Sep 17 00:00:00 2001 From: zhouwg Date: Mon, 7 Apr 2025 23:22:57 +0800 Subject: [PATCH 24/99] sycl: remove redundant memcopy in function ggml_backend_sycl_buffer_set_tensor (#12734) --- ggml/src/ggml-sycl/ggml-sycl.cpp | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index dff9f8d4c4..f6f288d990 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -372,12 +372,9 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue()); SYCL_CHECK( CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); - char* host_buf = (char*)malloc(size); - memcpy(host_buf, data, size); SYCL_CHECK( - CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size) + CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, data, size) .wait())); - free(host_buf); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ From 995083e4ed24933e6a289472f9de0f0b53ca5eca Mon Sep 17 00:00:00 2001 From: cmdr2 Date: Wed, 2 Apr 2025 17:46:16 +0530 Subject: [PATCH 25/99] cpu: move all the operators into a separate c++ file (except mul_mat) (ggml/1167) * cpu: refactor SIMD mappings and vectorized op functions into separate files * Fix warning for ggml_float to float * Fix warnings * cpu: move all the operations (except mul_mat) to a separate c++ file * fix whitespace * Update ggml/src/ggml-cpu/vec.h Co-authored-by: Diego Devesa * Fix PR comments - use GGML_UNUSED, use cassert in ops.cpp * Reverse the order of import for ops.h and vec.h, to match what was present in ggml-cpu.c previously --------- Co-authored-by: Diego Devesa --- ggml/src/ggml-cpu/CMakeLists.txt | 5 + ggml/src/ggml-cpu/ggml-cpu.c | 10628 +--------------------------- ggml/src/ggml-cpu/ops.cpp | 8719 +++++++++++++++++++++++ ggml/src/ggml-cpu/ops.h | 128 + ggml/src/ggml-cpu/simd-mappings.h | 884 +++ ggml/src/ggml-cpu/vec.cpp | 258 + ggml/src/ggml-cpu/vec.h | 802 +++ 7 files changed, 10800 insertions(+), 10624 deletions(-) create mode 100644 ggml/src/ggml-cpu/ops.cpp create mode 100644 ggml/src/ggml-cpu/ops.h create mode 100644 ggml/src/ggml-cpu/simd-mappings.h create mode 100644 ggml/src/ggml-cpu/vec.cpp create mode 100644 ggml/src/ggml-cpu/vec.h diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index c8cc32fa5a..e73a3b69b5 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -28,6 +28,11 @@ function(ggml_add_cpu_backend_variant_impl tag_name) ggml-cpu/binary-ops.cpp ggml-cpu/unary-ops.h ggml-cpu/unary-ops.cpp + ggml-cpu/simd-mappings.h + ggml-cpu/vec.h + ggml-cpu/vec.cpp + ggml-cpu/ops.h + ggml-cpu/ops.cpp ) target_compile_features(${GGML_CPU_NAME} PRIVATE c_std_11 cxx_std_17) diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index f3925e17a2..34618c27aa 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -9,8 +9,10 @@ #include "ggml-impl.h" #include "ggml-cpu-quants.h" #include "ggml-threading.h" -#include "ggml-cpu/unary-ops.h" -#include "ggml-cpu/binary-ops.h" +#include "unary-ops.h" +#include "binary-ops.h" +#include "vec.h" +#include "ops.h" #include "ggml.h" #if defined(_MSC_VER) || defined(__MINGW32__) @@ -83,30 +85,6 @@ #define UNUSED GGML_UNUSED #define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0) -#if defined(GGML_USE_ACCELERATE) -#include -#endif - -// floating point type used to accumulate sums -typedef double ggml_float; - -#define GGML_GELU_FP16 -#define GGML_GELU_QUICK_FP16 - -#define GGML_SOFT_MAX_UNROLL 4 -#define GGML_VEC_DOT_UNROLL 2 -#define GGML_VEC_MAD_UNROLL 32 - -// -// global data -// - -// precomputed gelu table for f16 (128 KB) -static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; - -// precomputed quick gelu table for f16 (128 KB) -static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; - #if defined(__ARM_ARCH) struct ggml_arm_arch_features_type { int has_neon; @@ -230,29 +208,6 @@ typedef pthread_t ggml_thread_t; #include #endif -// -// cache line -// - -#if defined(__cpp_lib_hardware_interference_size) -#define CACHE_LINE_SIZE hardware_destructive_interference_size -#else -#if defined(__POWER9_VECTOR__) -#define CACHE_LINE_SIZE 128 -#elif defined(__VXE__) || defined(__VXE2__) -#define CACHE_LINE_SIZE 256 -#else -#define CACHE_LINE_SIZE 64 -#endif -#endif - -static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); - - -static void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * GGML_RESTRICT x, size_t bx, const float * GGML_RESTRICT y, size_t by, int nrc); -static void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * GGML_RESTRICT x, size_t bx, ggml_fp16_t * GGML_RESTRICT y, size_t by, int nrc); -static void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t * GGML_RESTRICT x, size_t bx, ggml_bf16_t * GGML_RESTRICT y, size_t by, int nrc); - static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = { [GGML_TYPE_F32] = { .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, @@ -424,887 +379,6 @@ const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type return &type_traits_cpu[type]; } -// -// simd mappings -// - -// we define a common set of C macros which map to specific intrinsics based on the current architecture -// we then implement the fundamental computation operations below using only these macros -// adding support for new architectures requires to define the corresponding SIMD macros -// -// GGML_F32_STEP / GGML_F16_STEP -// number of elements to process in a single step -// -// GGML_F32_EPR / GGML_F16_EPR -// number of elements to fit in a single register -// - -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) - -#define GGML_SIMD - -// F32 NEON - -#define GGML_F32_STEP 16 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 float32x4_t -#define GGML_F32x4_ZERO vdupq_n_f32(0.0f) -#define GGML_F32x4_SET1(x) vdupq_n_f32(x) -#define GGML_F32x4_LOAD vld1q_f32 -#define GGML_F32x4_STORE vst1q_f32 -#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) -#define GGML_F32x4_ADD vaddq_f32 -#define GGML_F32x4_MUL vmulq_f32 -#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - (res) = (ggml_float) GGML_F32x4_REDUCE_ONE((x)[0]); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 NEON - -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - #define GGML_F16_STEP 32 - #define GGML_F16_EPR 8 - - #define GGML_F16x8 float16x8_t - #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) - #define GGML_F16x8_SET1(x) vdupq_n_f16(x) - #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) - #define GGML_F16x8_STORE vst1q_f16 - #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) - #define GGML_F16x8_ADD vaddq_f16 - #define GGML_F16x8_MUL vmulq_f16 - #define GGML_F16x8_REDUCE(res, x) \ - do { \ - int offset = GGML_F16_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \ - const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \ - (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ - } while (0) - - #define GGML_F16_VEC GGML_F16x8 - #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO - #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 - #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), (r)[i]) - #define GGML_F16_VEC_FMA GGML_F16x8_FMA - #define GGML_F16_VEC_ADD GGML_F16x8_ADD - #define GGML_F16_VEC_MUL GGML_F16x8_MUL - #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE -#else - // if FP16 vector arithmetic is not supported, we use FP32 instead - // and take advantage of the vcvt_ functions to convert to/from FP16 - - #define GGML_F16_STEP 16 - #define GGML_F16_EPR 4 - - #define GGML_F32Cx4 float32x4_t - #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) - #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) - #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x))) - #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) - #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) - #define GGML_F32Cx4_ADD vaddq_f32 - #define GGML_F32Cx4_MUL vmulq_f32 - #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - - #define GGML_F16_VEC GGML_F32Cx4 - #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO - #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 - #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i]) - #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA - #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD - #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL - #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE -#endif - -#elif defined(__AVX512F__) - -#define GGML_SIMD - -// F32 AVX512 - -#define GGML_F32_STEP 64 -#define GGML_F32_EPR 16 - -#define GGML_F32x16 __m512 -#define GGML_F32x16_ZERO _mm512_setzero_ps() -#define GGML_F32x16_SET1(x) _mm512_set1_ps(x) -#define GGML_F32x16_LOAD _mm512_loadu_ps -#define GGML_F32x16_STORE _mm512_storeu_ps -// _mm512_fmadd_ps is defined in AVX512F so no guard is required -#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) -#define GGML_F32x16_ADD _mm512_add_ps -#define GGML_F32x16_MUL _mm512_mul_ps -#define GGML_F32x16_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - res = (ggml_float) _mm512_reduce_add_ps(x[0]); \ -} while (0) - -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x16 -#define GGML_F32_VEC_ZERO GGML_F32x16_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x16_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x16_LOAD -#define GGML_F32_VEC_STORE GGML_F32x16_STORE -#define GGML_F32_VEC_FMA GGML_F32x16_FMA -#define GGML_F32_VEC_ADD GGML_F32x16_ADD -#define GGML_F32_VEC_MUL GGML_F32x16_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE - -// F16 AVX512 - -// F16 AVX - -#define GGML_F16_STEP 64 -#define GGML_F16_EPR 16 - -// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead - -#define GGML_F32Cx16 __m512 -#define GGML_F32Cx16_ZERO _mm512_setzero_ps() -#define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) - -// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F -// so F16C guard isn't required -#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x))) -#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) - -#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) -#define GGML_F32Cx16_ADD _mm512_add_ps -#define GGML_F32Cx16_MUL _mm512_mul_ps -#define GGML_F32Cx16_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - res = (ggml_float) _mm512_reduce_add_ps(x[0]); \ -} while (0) - -#define GGML_F16_VEC GGML_F32Cx16 -#define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx16_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx16_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx16_MUL - -#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE -#elif defined(__AVX__) - -#define GGML_SIMD - -// F32 AVX - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 8 - -#define GGML_F32x8 __m256 -#define GGML_F32x8_ZERO _mm256_setzero_ps() -#define GGML_F32x8_SET1(x) _mm256_set1_ps(x) -#define GGML_F32x8_LOAD _mm256_loadu_ps -#define GGML_F32x8_STORE _mm256_storeu_ps -#if defined(__FMA__) - #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) -#else - #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) -#endif -#define GGML_F32x8_ADD _mm256_add_ps -#define GGML_F32x8_MUL _mm256_mul_ps -#define GGML_F32x8_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ - _mm256_extractf128_ps(x[0], 1)); \ - const __m128 t1 = _mm_hadd_ps(t0, t0); \ - res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ -} while (0) -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x8 -#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD -#define GGML_F32_VEC_STORE GGML_F32x8_STORE -#define GGML_F32_VEC_FMA GGML_F32x8_FMA -#define GGML_F32_VEC_ADD GGML_F32x8_ADD -#define GGML_F32_VEC_MUL GGML_F32x8_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE - -// F16 AVX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 8 - -// F16 arithmetic is not supported by AVX, so we use F32 instead - -#define GGML_F32Cx8 __m256 -#define GGML_F32Cx8_ZERO _mm256_setzero_ps() -#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) - -#if defined(__F16C__) -// the _mm256_cvt intrinsics require F16C -#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) -#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) -#else -static inline __m256 __avx_f32cx8_load(const ggml_fp16_t * x) { - float tmp[8]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return _mm256_loadu_ps(tmp); -} -static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { - float arr[8]; - - _mm256_storeu_ps(arr, y); - - for (int i = 0; i < 8; i++) - x[i] = GGML_FP32_TO_FP16(arr[i]); -} -#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) -#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) -#endif - -#define GGML_F32Cx8_FMA GGML_F32x8_FMA -#define GGML_F32Cx8_ADD _mm256_add_ps -#define GGML_F32Cx8_MUL _mm256_mul_ps -#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE - -#define GGML_F16_VEC GGML_F32Cx8 -#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE - -#elif defined(__POWER9_VECTOR__) - -#define GGML_SIMD - -// F32 POWER9 - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 vector float -#define GGML_F32x4_ZERO 0.0f -#define GGML_F32x4_SET1 vec_splats -#define GGML_F32x4_LOAD(p) vec_xl(0, p) -#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) -#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) -#define GGML_F32x4_ADD vec_add -#define GGML_F32x4_MUL vec_mul -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - res = vec_extract(x[0], 0) + \ - vec_extract(x[0], 1) + \ - vec_extract(x[0], 2) + \ - vec_extract(x[0], 3); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 POWER9 -#define GGML_F16_STEP GGML_F32_STEP -#define GGML_F16_EPR GGML_F32_EPR -#define GGML_F16_VEC GGML_F32x4 -#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F16_VEC_FMA GGML_F32x4_FMA -#define GGML_F16_VEC_ADD GGML_F32x4_ADD -#define GGML_F16_VEC_MUL GGML_F32x4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE -// Use vec_xl, not vec_ld, in case the load address is not aligned. -#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ - vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ - vec_extract_fp32_from_shortl(vec_xl(0, p)) -#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] -#define GGML_F16_VEC_STORE(p, r, i) \ - if (i & 0x1) \ - vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ - r[i - GGML_ENDIAN_BYTE(0)]), \ - 0, p - GGML_F16_EPR) - -#elif defined(__wasm_simd128__) - -#define GGML_SIMD - -// F32 WASM - -#define GGML_F32_STEP 16 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 v128_t -#define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) -#define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) -#define GGML_F32x4_LOAD wasm_v128_load -#define GGML_F32x4_STORE wasm_v128_store -#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) -#define GGML_F32x4_ADD wasm_f32x4_add -#define GGML_F32x4_MUL wasm_f32x4_mul -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - res = wasm_f32x4_extract_lane(x[0], 0) + \ - wasm_f32x4_extract_lane(x[0], 1) + \ - wasm_f32x4_extract_lane(x[0], 2) + \ - wasm_f32x4_extract_lane(x[0], 3); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 WASM - -#define GGML_F16_STEP 16 -#define GGML_F16_EPR 4 - -inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(p[0]); - tmp[1] = GGML_FP16_TO_FP32(p[1]); - tmp[2] = GGML_FP16_TO_FP32(p[2]); - tmp[3] = GGML_FP16_TO_FP32(p[3]); - - return wasm_v128_load(tmp); -} - -inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { - float tmp[4]; - - wasm_v128_store(tmp, x); - - p[0] = GGML_FP32_TO_FP16(tmp[0]); - p[1] = GGML_FP32_TO_FP16(tmp[1]); - p[2] = GGML_FP32_TO_FP16(tmp[2]); - p[3] = GGML_FP32_TO_FP16(tmp[3]); -} - -#define GGML_F16x4 v128_t -#define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) -#define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) -#define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) -#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) -#define GGML_F16x4_FMA GGML_F32x4_FMA -#define GGML_F16x4_ADD wasm_f32x4_add -#define GGML_F16x4_MUL wasm_f32x4_mul -#define GGML_F16x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F16_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - res = (ggml_float) (wasm_f32x4_extract_lane(x[0], 0) + \ - wasm_f32x4_extract_lane(x[0], 1) + \ - wasm_f32x4_extract_lane(x[0], 2) + \ - wasm_f32x4_extract_lane(x[0], 3)); \ -} - -#define GGML_F16_VEC GGML_F16x4 -#define GGML_F16_VEC_ZERO GGML_F16x4_ZERO -#define GGML_F16_VEC_SET1 GGML_F16x4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F16x4_FMA -#define GGML_F16_VEC_ADD GGML_F16x4_ADD -#define GGML_F16_VEC_MUL GGML_F16x4_MUL -#define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE - -#elif defined(__SSE3__) - -#define GGML_SIMD - -// F32 SSE - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 __m128 -#define GGML_F32x4_ZERO _mm_setzero_ps() -#define GGML_F32x4_SET1(x) _mm_set1_ps(x) -#define GGML_F32x4_LOAD _mm_loadu_ps -#define GGML_F32x4_STORE _mm_storeu_ps -#if defined(__FMA__) - // TODO: Does this work? - #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) -#else - #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) -#endif -#define GGML_F32x4_ADD _mm_add_ps -#define GGML_F32x4_MUL _mm_mul_ps -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ - res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ -} -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 SSE - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 4 - -static inline __m128 __sse_f16x4_load(const ggml_fp16_t * x) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(x[0]); - tmp[1] = GGML_FP16_TO_FP32(x[1]); - tmp[2] = GGML_FP16_TO_FP32(x[2]); - tmp[3] = GGML_FP16_TO_FP32(x[3]); - - return _mm_loadu_ps(tmp); -} - -static inline void __sse_f16x4_store(ggml_fp16_t * x, __m128 y) { - float arr[4]; - - _mm_storeu_ps(arr, y); - - x[0] = GGML_FP32_TO_FP16(arr[0]); - x[1] = GGML_FP32_TO_FP16(arr[1]); - x[2] = GGML_FP32_TO_FP16(arr[2]); - x[3] = GGML_FP32_TO_FP16(arr[3]); -} - -#define GGML_F32Cx4 __m128 -#define GGML_F32Cx4_ZERO _mm_setzero_ps() -#define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) -#define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) -#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) -#define GGML_F32Cx4_FMA GGML_F32x4_FMA -#define GGML_F32Cx4_ADD _mm_add_ps -#define GGML_F32Cx4_MUL _mm_mul_ps -#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - -#define GGML_F16_VEC GGML_F32Cx4 -#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE - -#elif defined(__loongarch_asx) - -#define GGML_SIMD - -// F32 LASX -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 8 - -#define GGML_F32x8 __m256 -#define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) -#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) -#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) -#define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) -#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) -#define GGML_F32x8_ADD __lasx_xvfadd_s -#define GGML_F32x8_MUL __lasx_xvfmul_s -#define GGML_F32x8_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - float *tmp_p = (float *)&x[0]; \ - res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ -} while (0) -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x8 -#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD -#define GGML_F32_VEC_STORE GGML_F32x8_STORE -#define GGML_F32_VEC_FMA GGML_F32x8_FMA -#define GGML_F32_VEC_ADD GGML_F32x8_ADD -#define GGML_F32_VEC_MUL GGML_F32x8_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE - -// F16 LASX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 8 - -// F16 arithmetic is not supported by LASX, so we use F32 instead - -#define GGML_F32Cx8 __m256 -#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) -#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) - -static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { - __m256i a; - memcpy(&a, x, sizeof(ggml_fp16_t) * 8); - a = __lasx_xvpermi_d(a, 0 | (1 << 4)); - return __lasx_xvfcvtl_s_h(a); -} - -static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { - __m256i a = __lasx_xvfcvt_h_s(y, y); - a = __lasx_xvpermi_d(a, 0 | (2 << 2)); - memcpy(x, &a, sizeof(ggml_fp16_t) * 8); -} -#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) -#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) - -#define GGML_F32Cx8_FMA GGML_F32x8_FMA -#define GGML_F32Cx8_ADD __lasx_xvfadd_s -#define GGML_F32Cx8_MUL __lasx_xvfmul_s -#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE - -#define GGML_F16_VEC GGML_F32Cx8 -#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE - -#elif defined(__loongarch_sx) - -#define GGML_SIMD - -// F32 LSX - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 __m128 -#define GGML_F32x4_ZERO __lsx_vldi(0) -#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) -#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) -#define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) -#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) -#define GGML_F32x4_ADD __lsx_vfadd_s -#define GGML_F32x4_MUL __lsx_vfmul_s -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ - } \ - __m128i tmp = __lsx_vsrli_d((__m128i) x[0], 32); \ - tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, x[0]); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ - tmp = __lsx_vsrli_d((__m128i) t0, 32); \ - tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, t0); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 LSX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 4 - -static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(x[0]); - tmp[1] = GGML_FP16_TO_FP32(x[1]); - tmp[2] = GGML_FP16_TO_FP32(x[2]); - tmp[3] = GGML_FP16_TO_FP32(x[3]); - - return __lsx_vld(tmp, 0); -} - -static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { - float arr[4]; - - __lsx_vst(y, arr, 0); - - x[0] = GGML_FP32_TO_FP16(arr[0]); - x[1] = GGML_FP32_TO_FP16(arr[1]); - x[2] = GGML_FP32_TO_FP16(arr[2]); - x[3] = GGML_FP32_TO_FP16(arr[3]); -} - -#define GGML_F32Cx4 __m128 -#define GGML_F32Cx4_ZERO __lsx_vldi(0) -#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) -#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) -#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) -#define GGML_F32Cx4_FMA GGML_F32x4_FMA -#define GGML_F32Cx4_ADD __lsx_vfadd_s -#define GGML_F32Cx4_MUL __lsx_vfmul_s -#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - -#define GGML_F16_VEC GGML_F32Cx4 -#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE - -#elif defined(__VXE__) || defined(__VXE2__) - -#define GGML_SIMD - -// F32 s390x - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 __vector float -#define GGML_F32x4_ZERO vec_splats(0.0f) -#define GGML_F32x4_SET1 vec_splats -#define GGML_F32x4_LOAD(p) vec_xl(0, p) -#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) -#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) -#define GGML_F32x4_ADD vec_add -#define GGML_F32x4_MUL vec_mul -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset + i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset + i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset + i]); \ - } \ - res = vec_extract(x[0], 0) + \ - vec_extract(x[0], 1) + \ - vec_extract(x[0], 2) + \ - vec_extract(x[0], 3); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 s390x -#define GGML_F16_STEP GGML_F32_STEP -#define GGML_F16_EPR GGML_F32_EPR - -static inline __vector float __lzs_f16cx4_load(const ggml_fp16_t * x) { - float tmp[4]; - - for (int i = 0; i < 4; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return vec_xl(0, tmp); -} - -static inline void __lzs_f16cx4_store(ggml_fp16_t * x, __vector float y) { - float arr[4]; - - vec_xst(y, 0, arr); - - for (int i = 0; i < 4; i++) { - x[i] = GGML_FP32_TO_FP16(arr[i]); - } -} - -#define GGML_F16_VEC GGML_F32x4 -#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F16_VEC_LOAD(p, i) __lzs_f16cx4_load(p) -#define GGML_F16_VEC_STORE(p, r, i) __lzs_f16cx4_store(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32x4_FMA -#define GGML_F16_VEC_ADD GGML_F32x4_ADD -#define GGML_F16_VEC_MUL GGML_F32x4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE - -#endif - -// GGML_F32_ARR / GGML_F16_ARR -// number of registers to use per step -#ifdef GGML_SIMD -#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) -#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) -#endif - // // Threading defs // @@ -1404,1004 +478,6 @@ struct ggml_compute_state { int ith; }; -// -// fundamental operations -// - -inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } -inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } -inline static void ggml_vec_cpy_i32(const int n, int32_t * y, const int32_t * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } - -inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } -inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } -inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } -inline static void ggml_vec_add_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) { - for (int i = 0; i < n; ++i) { - z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) + GGML_FP16_TO_FP32(y[i])); - } -} -inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } -inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } -inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } -inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } -inline static void ggml_vec_sub_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) { - for (int i = 0; i < n; ++i) { - z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) - GGML_FP16_TO_FP32(y[i])); - } -} -inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } -inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } -inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } -inline static void ggml_vec_neg_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(-GGML_FP16_TO_FP32(x[i])); - } -} - -inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } -inline static void ggml_vec_mul_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) { - for (int i = 0; i < n; ++i) { - z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) * GGML_FP16_TO_FP32(y[i])); - } -} -inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } -inline static void ggml_vec_div_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) { - for (int i = 0; i < n; ++i) { - z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) / GGML_FP16_TO_FP32(y[i])); - } -} - -static void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * GGML_RESTRICT x, size_t bx, const float * GGML_RESTRICT y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - -#if defined(GGML_SIMD) - float sumf = 0.0f; - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; - - GGML_F32_VEC ax[GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - - sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); - } - } - - // reduce sum0..sum3 to sum0 - GGML_F32_VEC_REDUCE(sumf, sum); - - // leftovers - for (int i = np; i < n; ++i) { - sumf += x[i]*y[i]; - } -#else - // scalar - ggml_float sumf = 0.0; - for (int i = 0; i < n; ++i) { - sumf += (ggml_float)(x[i]*y[i]); - } -#endif - - *s = sumf; -} - -static void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t * GGML_RESTRICT x, size_t bx, ggml_bf16_t * GGML_RESTRICT y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - int i = 0; - ggml_float sumf = 0; - -#if defined(__AVX512BF16__) - __m512 c1 = _mm512_setzero_ps(); - __m512 c2 = _mm512_setzero_ps(); - for (; i + 64 <= n; i += 64) { - c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), - m512bh(_mm512_loadu_si512((y + i)))); - c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), - m512bh(_mm512_loadu_si512((y + i + 32)))); - } - sumf += (ggml_float)_mm512_reduce_add_ps(c1); - sumf += (ggml_float)_mm512_reduce_add_ps(c2); - -#elif defined(__AVX512F__) -#define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16)) - __m512 c1 = _mm512_setzero_ps(); - __m512 c2 = _mm512_setzero_ps(); - for (; i + 32 <= n; i += 32) { - c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1); - c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2); - } - sumf += (ggml_float)_mm512_reduce_add_ps(c1); - sumf += (ggml_float)_mm512_reduce_add_ps(c2); - -#undef LOAD -#elif defined(__AVX2__) || defined(__AVX__) -#if defined(__AVX2__) -#define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)) -#else -#define LOAD(p) _mm256_castsi256_ps(_mm256_insertf128_si256(_mm256_castsi128_si256(_mm_slli_epi32(_mm_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)), (_mm_slli_epi32(_mm_cvtepu16_epi32(_mm_bsrli_si128(_mm_loadu_si128((const __m128i *)(p)), 8)), 16)), 1)) -#endif - __m256 c1 = _mm256_setzero_ps(); - __m256 c2 = _mm256_setzero_ps(); - __m256 c3 = _mm256_setzero_ps(); - __m256 c4 = _mm256_setzero_ps(); - for (; i + 32 <= n; i += 32) { - c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1); - c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2); - c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3); - c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4); - } - __m128 g; - c1 = _mm256_add_ps(_mm256_add_ps(c1, c3), - _mm256_add_ps(c2, c4)); - g = _mm_add_ps(_mm256_extractf128_ps(c1, 1), - _mm256_castps256_ps128(c1)); - g = _mm_add_ps(g, _mm_movehl_ps(g, g)); - g = _mm_add_ss(g, _mm_movehdup_ps(g)); - sumf += (ggml_float)_mm_cvtss_f32(g); - -#undef LOAD -#endif - - for (; i < n; ++i) { - sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) * - GGML_BF16_TO_FP32(y[i])); - } - *s = sumf; -} - -static void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * GGML_RESTRICT x, size_t bx, ggml_fp16_t * GGML_RESTRICT y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - ggml_float sumf = 0.0; - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - - sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); - } - } - - // reduce sum0..sum3 to sum0 - GGML_F16_VEC_REDUCE(sumf, sum); - - // leftovers - for (int i = np; i < n; ++i) { - sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); - } -#else - for (int i = 0; i < n; ++i) { - sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); - } -#endif - - *s = sumf; -} - -// compute GGML_VEC_DOT_UNROLL dot products at once -// xs - x row stride in bytes -inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GGML_RESTRICT s, void * GGML_RESTRICT xv, ggml_fp16_t * GGML_RESTRICT y) { - ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; - - ggml_fp16_t * GGML_RESTRICT x[GGML_VEC_DOT_UNROLL]; - - for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { - x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); - } - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - - for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { - ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); - - sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); - } - } - } - - // reduce sum0..sum3 to sum0 - for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { - GGML_F16_VEC_REDUCE(sumf[k], sum[k]); - } - - // leftovers - for (int i = np; i < n; ++i) { - for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); - } - } -#else - for (int i = 0; i < n; ++i) { - for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); - } - } -#endif - - for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { - s[i] = sumf[i]; - } -} - -inline static void ggml_vec_mad_f32(const int n, float * GGML_RESTRICT y, const float * GGML_RESTRICT x, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); - - GGML_F32_VEC ax[GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] += x[i]*v; - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] += x[i]*v; - } -#endif -} - -inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y, const ggml_fp16_t * GGML_RESTRICT x, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx); - - GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); - } -#endif -} - -// xs and vs are byte strides of x and v -inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * GGML_RESTRICT y, const float * GGML_RESTRICT xv, const float * GGML_RESTRICT vv) { - - const float * GGML_RESTRICT x[GGML_VEC_MAD_UNROLL]; - const float * GGML_RESTRICT v[GGML_VEC_MAD_UNROLL]; - - for (int i = 0; i < GGML_VEC_MAD_UNROLL; ++i) { - x[i] = (const float *) ((const char *) xv + i*xs); - v[i] = (const float *) ((const char *) vv + i*vs); - } - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL]; - - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - vx[k] = GGML_F32_VEC_SET1(v[k][0]); - } - - GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]); - } - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - for (int i = np; i < n; ++i) { - y[i] += x[k][i]*v[k][0]; - } - } -#else - // scalar - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - for (int i = 0; i < n; ++i) { - y[i] += x[k][i]*v[k][0]; - } - } -#endif -} - -//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } -inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { -#if defined(GGML_USE_ACCELERATE) - vDSP_vsmul(y, 1, &v, y, 1, n); -#elif defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); - - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_MUL(ay[j], vx); - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] *= v; - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] *= v; - } -#endif -} - -inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); - - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_MUL(ay[j], vx); - - GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); - } -#endif -} - -inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } -inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } -inline static void ggml_vec_sqr_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - float v = GGML_FP16_TO_FP32(x[i]); - y[i] = GGML_FP32_TO_FP16(v*v); - } -} -inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } -inline static void ggml_vec_sqrt_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(sqrtf(GGML_FP16_TO_FP32(x[i]))); - } -} -inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } -inline static void ggml_vec_log_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(logf(GGML_FP16_TO_FP32(x[i]))); - } -} -inline static void ggml_vec_sin_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sinf(x[i]); } -inline static void ggml_vec_sin_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(sinf(GGML_FP16_TO_FP32(x[i]))); - } -} -inline static void ggml_vec_cos_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = cosf(x[i]); } -inline static void ggml_vec_cos_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(cosf(GGML_FP16_TO_FP32(x[i]))); - } -} -inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } -inline static void ggml_vec_abs_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(fabsf(GGML_FP16_TO_FP32(x[i]))); - } -} -inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } -inline static void ggml_vec_sgn_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - float v = GGML_FP16_TO_FP32(x[i]); - y[i] = GGML_FP32_TO_FP16((v > 0.f) ? 1.f : ((v < 0.f) ? -1.f : 0.f)); - } -} -inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } -inline static void ggml_vec_step_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16((GGML_FP16_TO_FP32(x[i]) > 0.f) ? 1.f : 0.f); - } -} -inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } -inline static void ggml_vec_tanh_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(tanhf(GGML_FP16_TO_FP32(x[i]))); - } -} -inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } -inline static void ggml_vec_elu_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(expm1f(GGML_FP16_TO_FP32(x[i]))); - } -} -inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } -inline static void ggml_vec_relu_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - float v = GGML_FP16_TO_FP32(x[i]); - y[i] = GGML_FP32_TO_FP16((v > 0.f) ? v : 0.f); - } -} -inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } -inline static void ggml_vec_leaky_relu_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x, const float ns) { - for (int i = 0; i < n; ++i) { - float v = GGML_FP16_TO_FP32(x[i]); - y[i] = GGML_FP32_TO_FP16(((v > 0.f) ? v : 0.f) + ns * ((v < 0.0f) ? v : 0.f)); - } -} -inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } -inline static void ggml_vec_sigmoid_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(1.f / (1.f + expf(-GGML_FP16_TO_FP32(x[i])))); - } -} -// TODO: optimize performance -inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } -inline static void ggml_vec_hardswish_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - float v = GGML_FP16_TO_FP32(x[i]); - y[i] = GGML_FP32_TO_FP16(v * fminf(1.0f, fmaxf(0.0f, (v + 3.0f) / 6.0f))); - } -} -inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } -inline static void ggml_vec_hardsigmoid_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(fminf(1.0f, fmaxf(0.0f, (GGML_FP16_TO_FP32(x[i]) + 3.0f) / 6.0f))); - } -} -inline static void ggml_vec_exp_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = expf(x[i]); } -inline static void ggml_vec_exp_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(expf(GGML_FP16_TO_FP32(x[i]))); - } -} - -static const float GELU_COEF_A = 0.044715f; -static const float GELU_QUICK_COEF = -1.702f; -static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - -inline static float ggml_gelu_f32(float x) { - return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); -} - -inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - const uint16_t * i16 = (const uint16_t *) x; - for (int i = 0; i < n; ++i) { - y[i] = ggml_table_gelu_f16[i16[i]]; - } -} - -#ifdef GGML_GELU_FP16 -inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - if (x[i] <= -10.0f) { - y[i] = 0.0f; - } else if (x[i] >= 10.0f) { - y[i] = x[i]; - } else { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); - } - } -} -#else -inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_f32(x[i]); - } -} -#endif - -inline static float ggml_gelu_quick_f32(float x) { - return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); -} - -//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { -// const uint16_t * i16 = (const uint16_t *) x; -// for (int i = 0; i < n; ++i) { -// y[i] = ggml_table_gelu_quick_f16[i16[i]]; -// } -//} - -#ifdef GGML_GELU_QUICK_FP16 -inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]); - } -} -#else -inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_quick_f32(x[i]); - } -} -#endif - -inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - float v = GGML_FP16_TO_FP32(x[i]); - y[i] = GGML_FP32_TO_FP16(v*(1.0f/(1.0f+expf(GELU_QUICK_COEF*v)))); - } -} - -// Sigmoid Linear Unit (SiLU) function -inline static float ggml_silu_f32(float x) { - return x/(1.0f + expf(-x)); -} -inline static ggml_fp16_t ggml_silu_f16(ggml_fp16_t x) { - float v = GGML_FP16_TO_FP32(x); - return GGML_FP32_TO_FP16(v/(1.0f + expf(-v))); -} - -#if __FINITE_MATH_ONLY__ -#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" -#error "ref: https://github.com/ggml-org/llama.cpp/pull/7154#issuecomment-2143844461" -#endif - -#if defined(__ARM_NEON) && defined(__aarch64__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static float32x4_t ggml_v_expf(float32x4_t x) { - const float32x4_t r = vdupq_n_f32(0x1.8p23f); - const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); - const float32x4_t n = vsubq_f32(z, r); - const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, - vdupq_n_f32(0x1.7f7d1cp-20f)); - const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); - const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); - const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); - const float32x4_t u = vmulq_f32(b, b); - const float32x4_t j = vfmaq_f32( - vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), - vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), - vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); - if (!vpaddd_u64(vreinterpretq_u64_u32(c))) - return vfmaq_f32(k, j, k); - const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); - const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); - const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); - return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), - vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static float32x4_t ggml_v_silu(float32x4_t x) { - const float32x4_t one = vdupq_n_f32(1.0f); - const float32x4_t zero = vdupq_n_f32(0.0f); - const float32x4_t neg_x = vsubq_f32(zero, x); - const float32x4_t exp_neg_x = ggml_v_expf(neg_x); - const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); - return vdivq_f32(x, one_plus_exp_neg_x); -} - -#elif defined(__AVX512F__) && defined(__AVX512DQ__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m512 ggml_v_expf(__m512 x) { - const __m512 r = _mm512_set1_ps(0x1.8p23f); - const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); - const __m512 n = _mm512_sub_ps(z, r); - const __m512 b = - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); - const __mmask16 d = - _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); - const __m512 u = _mm512_mul_ps(b, b); - const __m512 j = _mm512_fmadd_ps( - _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, - _mm512_set1_ps(0x1.573e2ep-5f)), - u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, - _mm512_set1_ps(0x1.fffdb6p-2f))), - u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); - const __m512 res = _mm512_scalef_ps(j, n); - if (_mm512_kortestz(d, d)) - return res; - const __m512 zero = _mm512_setzero_ps(); - const __m512 alt = _mm512_mask_blend_ps( - _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); - return _mm512_mask_blend_ps(d, res, alt); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m512 ggml_v_silu(__m512 x) { - const __m512 one = _mm512_set1_ps(1); - const __m512 zero = _mm512_setzero_ps(); - const __m512 neg_x = _mm512_sub_ps(zero, x); - const __m512 exp_neg_x = ggml_v_expf(neg_x); - const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); - return _mm512_div_ps(x, one_plus_exp_neg_x); -} - -#elif defined(__AVX2__) && defined(__FMA__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m256 ggml_v_expf(__m256 x) { - const __m256 r = _mm256_set1_ps(0x1.8p23f); - const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); - const __m256 n = _mm256_sub_ps(z, r); - const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), - _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); - const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); - const __m256 k = _mm256_castsi256_ps( - _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); - const __m256i c = _mm256_castps_si256( - _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), - _mm256_set1_ps(126), _CMP_GT_OQ)); - const __m256 u = _mm256_mul_ps(b, b); - const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, - _mm256_set1_ps(0x1.573e2ep-5f)), u, - _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, - _mm256_set1_ps(0x1.fffdb6p-2f))), - u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); - if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) - return _mm256_fmadd_ps(j, k, k); - const __m256i g = _mm256_and_si256( - _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), - _mm256_set1_epi32(0x82000000u)); - const __m256 s1 = - _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); - const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); - const __m256i d = _mm256_castps_si256( - _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), - _mm256_set1_ps(192), _CMP_GT_OQ)); - return _mm256_or_ps( - _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), - _mm256_andnot_ps( - _mm256_castsi256_ps(d), - _mm256_or_ps( - _mm256_and_ps(_mm256_castsi256_ps(c), - _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), - _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m256 ggml_v_silu(__m256 x) { - const __m256 one = _mm256_set1_ps(1); - const __m256 zero = _mm256_setzero_ps(); - const __m256 neg_x = _mm256_sub_ps(zero, x); - const __m256 exp_neg_x = ggml_v_expf(neg_x); - const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); - return _mm256_div_ps(x, one_plus_exp_neg_x); -} - -#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON - -#if defined(__FMA__) -#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) -#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) -#else -#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) -#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) -#endif - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m128 ggml_v_expf(__m128 x) { - const __m128 r = _mm_set1_ps(0x1.8p23f); - const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); - const __m128 n = _mm_sub_ps(z, r); - const __m128 b = - NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); - const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); - const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); - const __m128i c = - _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); - const __m128 u = _mm_mul_ps(b, b); - const __m128 j = - MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, - MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), - u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); - if (!_mm_movemask_epi8(c)) - return MADD128(j, k, k); - const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), - _mm_set1_epi32(0x82000000u)); - const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); - const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); - const __m128i d = - _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); - return _mm_or_ps( - _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), - _mm_andnot_ps(_mm_castsi128_ps(d), - _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), - _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m128 ggml_v_silu(__m128 x) { - const __m128 one = _mm_set1_ps(1); - const __m128 zero = _mm_setzero_ps(); - const __m128 neg_x = _mm_sub_ps(zero, x); - const __m128 exp_neg_x = ggml_v_expf(neg_x); - const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); - return _mm_div_ps(x, one_plus_exp_neg_x); -} - -#endif // __ARM_NEON / __AVX2__ / __SSE2__ - -static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - int i = 0; -#if defined(__AVX512F__) && defined(__AVX512DQ__) - for (; i + 15 < n; i += 16) { - _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); - } -#elif defined(__AVX2__) && defined(__FMA__) - for (; i + 7 < n; i += 8) { - _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); - } -#elif defined(__SSE2__) - for (; i + 3 < n; i += 4) { - _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); - } -#elif defined(__ARM_NEON) && defined(__aarch64__) - for (; i + 3 < n; i += 4) { - vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); - } -#endif - for (; i < n; ++i) { - y[i] = ggml_silu_f32(x[i]); - } -} - -inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_silu_f16(x[i]); - } -} - -static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { - int i = 0; - ggml_float sum = 0; -#if defined(__AVX512F__) && defined(__AVX512DQ__) - for (; i + 15 < n; i += 16) { - __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), - _mm512_set1_ps(max))); - _mm512_storeu_ps(y + i, val); - sum += (ggml_float)_mm512_reduce_add_ps(val); - } -#elif defined(__AVX2__) && defined(__FMA__) - for (; i + 7 < n; i += 8) { - __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), - _mm256_set1_ps(max))); - _mm256_storeu_ps(y + i, val); - __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), - _mm256_castps256_ps128(val)); - val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); - val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); - sum += (ggml_float)_mm_cvtss_f32(val2); - } -#elif defined(__SSE2__) - for (; i + 3 < n; i += 4) { - __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), - _mm_set1_ps(max))); - _mm_storeu_ps(y + i, val); -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) - val = _mm_add_ps(val, _mm_movehl_ps(val, val)); - val = _mm_add_ss(val, _mm_movehdup_ps(val)); -#else - __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); - val = _mm_add_ps(val, tmp); - tmp = _mm_movehl_ps(tmp, val); - val = _mm_add_ss(val, tmp); -#endif - sum += (ggml_float)_mm_cvtss_f32(val); - } -#elif defined(__ARM_NEON) && defined(__aarch64__) - for (; i + 3 < n; i += 4) { - float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), - vdupq_n_f32(max))); - vst1q_f32(y + i, val); - sum += (ggml_float)vaddvq_f32(val); - } -#endif - for (; i < n; ++i) { - float val = expf(x[i] - max); - sum += (ggml_float)val; - y[i] = val; - } - return sum; -} - -static ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max) { - // log(soft_max) = log(soft_max_i / soft_max_sum) = log(soft_max_i) - log(soft_max_sum) = (logit_i - max) - log(soft_max_i) - - int i = 0; - ggml_float sum = 0; - for (; i < n; ++i) { - float val = x[i] - max; - y[i] = val; - sum += (ggml_float)expf(val); - } - return sum = (ggml_float)logf(sum); -} - -inline static float ggml_silu_backward_f32(float x, float dy) { - const float s = 1.0f/(1.0f + expf(-x)); - return dy*s*(1.0f + x*(1.0f - s)); -} - -inline static ggml_fp16_t ggml_silu_backward_f16(ggml_fp16_t x, ggml_fp16_t dy) { - const float v = GGML_FP16_TO_FP32(x); - const float s = 1.0f/(1.0f + expf(-v)); - return GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(dy)*s*(1.0f + v*(1.0f - s))); -} - -inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { - for (int i = 0; i < n; ++i) { - dx[i] = ggml_silu_backward_f32(x[i], dy[i]); - } -} - -inline static void ggml_vec_silu_backward_f16(const int n, ggml_fp16_t * dx, const ggml_fp16_t * x, const ggml_fp16_t * dy) { - for (int i = 0; i < n; ++i) { - dx[i] = ggml_silu_backward_f16(x[i], dy[i]); - } -} - -inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { -#ifndef GGML_USE_ACCELERATE - ggml_float sum = 0.0; - for (int i = 0; i < n; ++i) { - sum += (ggml_float)x[i]; - } - *s = sum; -#else - vDSP_sve(x, 1, s, n); -#endif -} - -inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) { - ggml_float sum = 0.0; - for (int i = 0; i < n; ++i) { - sum += (ggml_float)x[i]; - } - *s = sum; -} - -inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) { - float sum = 0.0f; - for (int i = 0; i < n; ++i) { - sum += GGML_FP16_TO_FP32(x[i]); - } - *s = sum; -} - -inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) { - float sum = 0.0f; - for (int i = 0; i < n; ++i) { - sum += GGML_BF16_TO_FP32(x[i]); - } - *s = sum; -} - -inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { -#ifndef GGML_USE_ACCELERATE - float max = -INFINITY; - for (int i = 0; i < n; ++i) { - max = MAX(max, x[i]); - } - *s = max; -#else - vDSP_maxv(x, 1, s, n); -#endif -} - -inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { - ggml_vec_norm_f32(n, s, x); - *s = 1.f/(*s); -} - -inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { - float max = -INFINITY; - int idx = 0; - for (int i = 0; i < n; ++i) { - max = MAX(max, x[i]); - if (max == x[i]) { idx = i; } - } - *s = idx; -} - // Helpers for polling loops #if defined(__aarch64__) && ( defined(__clang__) || defined(__GNUC__) ) static inline void ggml_thread_cpu_relax(void) { @@ -3095,3561 +1171,6 @@ void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, //////////////////////////////////////////////////////////////////////////////// -// ggml_compute_forward_dup - -static void ggml_compute_forward_dup_same_cont( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - GGML_ASSERT(src0->type == dst->type); - - const size_t nb0 = ggml_type_size(src0->type); - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by blocks - const int nk = ggml_nelements(src0)/ggml_blck_size(src0->type); - const int dr = (nk + nth - 1) / nth; - const int k0 = dr * ith; - const int k1 = MIN(k0 + dr, nk); - - if (k0 < k1) { - memcpy( - ((char *) dst->data + k0*nb0), - ((char *) src0->data + k0*nb0), - (k1 - k0) * nb0); - } -} - -static void ggml_compute_forward_dup_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy - - if (ggml_is_contiguous(dst)) { - if (nb00 == sizeof(ggml_fp16_t)) { - if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { - ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; - float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - for (int i00 = 0; i00 < ne00; i00++) { - src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); - } - - quantize_row_q(src0_f32, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - return; - } - - // dst counters - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); - - if (++i10 == ne00) { - i10 = 0; - if (++i11 == ne01) { - i11 = 0; - if (++i12 == ne02) { - i12 = 0; - if (++i13 == ne03) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -static void ggml_compute_forward_dup_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy - - if (ggml_is_contiguous(dst)) { - if (nb00 == sizeof(ggml_bf16_t)) { - if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00])); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { - ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; - float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - for (int i00 = 0; i00 < ne00; i00++) { - src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]); - } - - quantize_row_q(src0_f32, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr)); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - return; - } - - // dst counters - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_BF16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t)); - - if (++i10 == ne00) { - i10 = 0; - if (++i11 == ne01) { - i11 = 0; - if (++i12 == ne02) { - i12 = 0; - if (++i13 == ne03) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr)); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -static void ggml_compute_forward_dup_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - if (ggml_is_contiguous(dst)) { - // TODO: simplify - if (nb00 == sizeof(float)) { - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { - ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - quantize_row_q(src0_ptr, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - - return; - } - - // dst counters - - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(float)); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_BF16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. -static void ggml_compute_forward_dup_bytes( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(src0->type == dst->type); - - GGML_TENSOR_UNARY_OP_LOCALS; - - if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { - ggml_compute_forward_dup_same_cont(params, dst); - return; - } - - const size_t type_size = ggml_type_size(src0->type); - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ggml_are_same_shape(src0, dst) && - nb00 == type_size && nb0 == type_size) { - // copy by rows - const size_t rs = ggml_row_size(src0->type, ne00); - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - if (ggml_is_contiguous(dst)) { - size_t id = 0; - char * dst_ptr = (char *) dst->data; - const size_t rs = ne00 * type_size; - - if (nb00 == type_size) { - // src0 is contigous on first dimension, copy by rows - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int64_t i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, type_size); - - id += type_size; - } - } - id += rs * (ne01 - ir1); - } - } - } - - return; - } - - // dst counters - int64_t k10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - // number of blocks in a row - const int64_t nk00 = ne00 / ggml_blck_size(src0->type); - const int64_t nk0 = ne0 / ggml_blck_size(dst->type); - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - k10 += nk00 * ir0; - while (k10 >= nk0) { - k10 -= nk0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t k00 = 0; k00 < nk00; k00++) { - const char * src0_ptr = ((char *) src0->data + k00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + k10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, type_size); - - if (++k10 == nk0) { - k10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - k10 += nk00 * (ne01 - ir1); - while (k10 >= nk0) { - k10 -= nk0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } -} - -static void ggml_compute_forward_dup_q( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; - - size_t qk = ggml_blck_size(type); - const int64_t nr = ggml_nelements(src1) / qk; - - // destination must be contiguous in the first dimension - GGML_ASSERT(nb10 == ggml_type_size(dst->type)); - // must either have first dimension large enough to hold a row, or fully contiguous - GGML_ASSERT((ne10 % qk) == 0 || ggml_is_contiguous(dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t ir = ir0; ir < ir1; ++ir) { - - uint32_t i = ir * qk; - - const int64_t i03 = i/(ne00 * ne01 * ne02); - const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); - const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; - const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; - const int64_t x_offset = (i00/qk)*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - - const int64_t i13 = i/(ne10 * ne11 * ne12); - const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); - const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; - const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; - const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13; - - dequantize_row_q( - (const void *) ((char *) src0->data + x_offset), - (float *) ((char *) dst->data + dst_offset), qk); - } -} - -static void ggml_compute_forward_dup( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (src0->type == dst->type) { - ggml_compute_forward_dup_bytes(params, dst); - return; - } - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_dup_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_dup_bf16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_dup_f32(params, dst); - } break; - default: - { - if (ggml_is_quantized(src0->type) && dst->type == GGML_TYPE_F32) { - ggml_compute_forward_dup_q(params, dst); - break; - } - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add - -static void ggml_compute_forward_add_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - const enum ggml_type dtype = dst->type; - ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; - ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dtype)->from_float; - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == sizeof(float)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ggml_is_quantized(src0->type)); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 indices - const int i03 = ir/(ne02*ne01); - const int i02 = (ir - i03*ne02*ne01)/ne01; - const int i01 = (ir - i03*ne02*ne01 - i02*ne01); - - // src1 and dst are same shape as src0 => same indices - const int i13 = i03; - const int i12 = i02; - const int i11 = i01; - - const int i3 = i03; - const int i2 = i02; - const int i1 = i01; - - void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); - float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); - void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - assert(ne00 % 32 == 0); - - // unquantize row from src0 to temp buffer - dequantize_row_q(src0_row, wdata, ne00); - // add src1 - ggml_vec_acc_f32(ne00, wdata, src1_row); - // quantize row to dst - if (quantize_row_q != NULL) { - quantize_row_q(wdata, dst_row, ne00); - } else { - memcpy(dst_row, wdata, ne0*nb0); - } - } -} - -static void ggml_compute_forward_add( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - { - ggml_compute_forward_add_non_quantized(params, dst); - } break; - 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_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - { - ggml_compute_forward_add_q_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add1 - -static void ggml_compute_forward_add1_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_add1_f32); - - vDSP_vadd( - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, - (float *) ((char *) src1->data), 0, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, - ne0); -#else - ggml_vec_add1_f32(ne0, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), - *(float *) src1->data); -#endif - } -} - -static void ggml_compute_forward_add1_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_f16_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; - ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(type)->from_float; - - // we don't support permuted src0 - GGML_ASSERT(nb00 == ggml_type_size(type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ggml_is_quantized(src0->type)); - GGML_ASSERT(dst->type == src0->type); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); - void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); - - assert(ne0 % 32 == 0); - - // unquantize row from src0 to temp buffer - dequantize_row_q(src0_row, wdata, ne0); - // add src1 - ggml_vec_acc1_f32(ne0, wdata, v); - // quantize row to dst - quantize_row_q(wdata, dst_row, ne0); - } -} - -static void ggml_compute_forward_add1_bf16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_bf16_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_BF16); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_add1_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - if (src1->type == GGML_TYPE_F16) { - ggml_compute_forward_add1_f16_f16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add1_f16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_BF16: - { - if (src1->type == GGML_TYPE_BF16) { - ggml_compute_forward_add1_bf16_bf16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add1_bf16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - 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_Q8_1: - 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_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - { - ggml_compute_forward_add1_q_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_acc - -static void ggml_compute_forward_acc_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - - // view src0 and dst with these strides and data offset inbytes during acc - // nb0 is implicitly element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) dst->op_params)[0]; - size_t nb2 = ((int32_t *) dst->op_params)[1]; - size_t nb3 = ((int32_t *) dst->op_params)[2]; - size_t offset = ((int32_t *) dst->op_params)[3]; - bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - if (params->ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src1); - const int nc = src1->ne[0]; - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) - GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) - - // src0 and dst as viewed during acc - const size_t nb0 = ggml_element_size(src0); - - const size_t nb00 = nb0; - const size_t nb01 = nb1; - const size_t nb02 = nb2; - const size_t nb03 = nb3; - - GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); - GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); - - GGML_ASSERT(nb10 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are viewed with shape of src1 and offset - // => same indices - const int i3 = ir/(ne12*ne11); - const int i2 = (ir - i3*ne12*ne11)/ne11; - const int i1 = (ir - i3*ne12*ne11 - i2*ne11); - -#ifdef GGML_USE_ACCELERATE - vDSP_vadd( - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); -#else - ggml_vec_add_f32(nc, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); -#endif - } -} - -static void ggml_compute_forward_acc( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_acc_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - 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_Q8_1: - 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_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sum - -static void ggml_compute_forward_sum_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - assert(src0->nb[0] == sizeof(float)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - ggml_float sum = 0; - ggml_float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f32_ggf(ne00, - &row_sum, - (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); - sum += row_sum; - } - } - } - ((float *) dst->data)[0] = sum; -} - -static void ggml_compute_forward_sum_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - - assert(src0->nb[0] == sizeof(ggml_fp16_t)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - float sum = 0; - float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f16_ggf(ne00, - &row_sum, - (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); - sum += row_sum; - } - } - } - ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); -} - -static void ggml_compute_forward_sum_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - - assert(src0->nb[0] == sizeof(ggml_bf16_t)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - float sum = 0; - float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_bf16_ggf(ne00, - &row_sum, - (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); - sum += row_sum; - } - } - } - ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum); -} - -static void ggml_compute_forward_sum( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sum_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_sum_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_sum_bf16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sum_rows - -static void ggml_compute_forward_sum_rows_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(dst->nb[0] == sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(ne0 == 1); - GGML_ASSERT(ne1 == ne01); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - for (int64_t i3 = 0; i3 < ne03; i3++) { - for (int64_t i2 = 0; i2 < ne02; i2++) { - for (int64_t i1 = 0; i1 < ne01; i1++) { - float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); - float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); - float row_sum = 0; - ggml_vec_sum_f32(ne00, &row_sum, src_row); - dst_row[0] = row_sum; - } - } - } -} - -static void ggml_compute_forward_sum_rows( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sum_rows_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mean - -static void ggml_compute_forward_mean_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(src0->nb[0] == sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - assert(ne0 == 1); - assert(ne1 == ne01); - assert(ne2 == ne02); - assert(ne3 == ne03); - - UNUSED(ne0); - UNUSED(ne1); - UNUSED(ne2); - UNUSED(ne3); - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f32(ne00, - (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); - - *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; - } - } - } -} - -static void ggml_compute_forward_mean( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_mean_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_argmax - -static void ggml_compute_forward_argmax_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(src0->nb[0] == sizeof(float)); - assert(dst->nb[0] == sizeof(float)); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - - const size_t nb01 = src0->nb[1]; - const size_t nb0 = dst->nb[0]; - - for (int64_t i1 = 0; i1 < ne01; i1++) { - float * src = (float *) ((char *) src0->data + i1*nb01); - int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); - int v = 0; - ggml_vec_argmax_f32(ne00, &v, src); - dst_[0] = v; - } -} - -static void ggml_compute_forward_argmax( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_argmax_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_count_equal - -static void ggml_compute_forward_count_equal_i32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS; - - GGML_ASSERT(src0->type == GGML_TYPE_I32); - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_are_same_shape(src0, src1)); - GGML_ASSERT(ggml_is_scalar(dst)); - GGML_ASSERT(dst->type == GGML_TYPE_I64); - - const int64_t nr = ggml_nrows(src0); - - const int ith = params->ith; - const int nth = params->nth; - - int64_t * sums = (int64_t *) params->wdata; - int64_t sum_thread = 0; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - for (int64_t ir = ir0; ir < ir1; ++ir) { - const int64_t i03 = ir / (ne02*ne01); - const int64_t i02 = (ir - i03*ne03) / ne01; - const int64_t i01 = ir - i03*ne03 - i02*ne02; - - const char * data0 = (const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01; - const char * data1 = (const char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11; - - for (int64_t i00 = 0; i00 < ne00; ++i00) { - const int32_t val0 = *((const int32_t *) (data0 + i00*nb00)); - const int32_t val1 = *((const int32_t *) (data1 + i00*nb10)); - - sum_thread += val0 == val1; - } - } - if (ith != 0) { - sums[ith] = sum_thread; - } - ggml_barrier(params->threadpool); - - if (ith != 0) { - return; - } - - for (int ith_other = 1; ith_other < nth; ++ith_other) { - sum_thread += sums[ith_other]; - } - *((int64_t *) dst->data) = sum_thread; -} - -static void ggml_compute_forward_count_equal( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_I32: - { - ggml_compute_forward_count_equal_i32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_repeat - -static void ggml_compute_forward_repeat_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(src0, dst)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne0/ne00); - const int nr1 = (int)(ne1/ne01); - const int nr2 = (int)(ne2/ne02); - const int nr3 = (int)(ne3/ne03); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne03; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne02; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne01; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_vec_cpy_f32(ne00, - (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), - (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(src0, dst)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne0/ne00); - const int nr1 = (int)(ne1/ne01); - const int nr2 = (int)(ne2/ne02); - const int nr3 = (int)(ne3/ne03); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne03; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne02; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne01; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_fp16_t * y = (ggml_fp16_t *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0); - ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01); - // ggml_vec_cpy_f16(ne00, y, x) - for (int i = 0; i < ne00; ++i) { - y[i] = x[i]; - } - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_I16: - { - ggml_compute_forward_repeat_f16(params, dst); - } break; - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_repeat_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_repeat_back - -static void ggml_compute_forward_repeat_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(dst, src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne00/ne0); - const int nr1 = (int)(ne01/ne1); - const int nr2 = (int)(ne02/ne2); - const int nr3 = (int)(ne03/ne3); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (ggml_is_contiguous(dst)) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } else { - for (int k3 = 0; k3 < ne3; k3++) { - for (int k2 = 0; k2 < ne2; k2++) { - for (int k1 = 0; k1 < ne1; k1++) { - ggml_vec_set_f32(ne0, - (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), - 0); - } - } - } - } - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne3; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne2; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne1; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_vec_acc_f32(ne0, - (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), - (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_repeat_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_concat - -static void ggml_compute_forward_concat_any( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - const size_t len = ggml_type_size(src0->type); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int32_t dim = ggml_get_op_params_i32(dst, 0); - - GGML_ASSERT(dim >= 0 && dim < 4); - - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = src0->ne[dim]; - - const char * x; - - // TODO: smarter multi-theading - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = ith; i2 < ne2; i2 += nth) { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03; - } else { - x = (const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13; - } - - char * y = (char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3; - - memcpy(y, x, len); - } - } - } - } -} - -static void ggml_compute_forward_concat_i8( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_type_size(src0->type) == sizeof(int8_t)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int32_t dim = ggml_get_op_params_i32(dst, 0); - - GGML_ASSERT(dim >= 0 && dim < 4); - - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = src0->ne[dim]; - - const int8_t * x; - - // TODO: smarter multi-theading - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = ith; i2 < ne2; i2 += nth) { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (const int8_t *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); - } else { - x = (const int8_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); - } - - int8_t * y = (int8_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_concat_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_type_size(src0->type) == sizeof(ggml_fp16_t)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int32_t dim = ggml_get_op_params_i32(dst, 0); - - GGML_ASSERT(dim >= 0 && dim < 4); - - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = src0->ne[dim]; - - const ggml_fp16_t * x; - - // TODO: smarter multi-theading - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = ith; i2 < ne2; i2 += nth) { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (const ggml_fp16_t *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); - } else { - x = (const ggml_fp16_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); - } - - ggml_fp16_t * y = (ggml_fp16_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_concat_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_type_size(src0->type) == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int32_t dim = ggml_get_op_params_i32(dst, 0); - - GGML_ASSERT(dim >= 0 && dim < 4); - - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = src0->ne[dim]; - - const float * x; - - // TODO: smarter multi-theading - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = ith; i2 < ne2; i2 += nth) { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); - } else { - x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); - } - - float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_concat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_I16: - { - ggml_compute_forward_concat_f16(params, dst); - } break; - case GGML_TYPE_I8: - { - ggml_compute_forward_concat_i8(params, dst); - } break; - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_concat_f32(params, dst); - } break; - default: - { - ggml_compute_forward_concat_any(params, dst); - } - } -} - -// ggml_compute_forward_gelu - -static void ggml_compute_forward_gelu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_gelu_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_f16(nc, - (ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])), - (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - const float v = GGML_FP16_TO_FP32(x); - UNUSED(v); - assert(!isnan(v)); - assert(!isinf(v)); - } -#endif - } -} - -static void ggml_compute_forward_gelu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_gelu_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_gelu_f16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_gelu_quick - -static void ggml_compute_forward_gelu_quick_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_quick_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_gelu_quick_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_quick_f16(nc, - (ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])), - (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - const float v = GGML_FP16_TO_FP32(x); - UNUSED(v); - assert(!isnan(v)); - assert(!isinf(v)); - } -#endif - } -} - -static void ggml_compute_forward_gelu_quick( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_gelu_quick_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_gelu_quick_f16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_silu - -static void ggml_compute_forward_silu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_silu_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_f16(nc, - (ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])), - (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*(dst->nb[1])))[k]; - const float v = GGML_FP16_TO_FP32(x); - UNUSED(v); - assert(!isnan(v)); - assert(!isinf(v)); - } -#endif - } -} - -static void ggml_compute_forward_silu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_silu_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_silu_f16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} -// ggml_compute_forward_leaky_relu - -static void ggml_compute_forward_leaky_relu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - float negative_slope; - memcpy(&negative_slope, dst->op_params, sizeof(float)); - - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_leaky_relu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); - } -} - -static void ggml_compute_forward_leaky_relu_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - float negative_slope; - memcpy(&negative_slope, dst->op_params, sizeof(float)); - - assert(dst->nb[0] == sizeof(ggml_fp16_t)); - assert(src0->nb[0] == sizeof(ggml_fp16_t)); - - for (int i = 0; i < n; i++) { - ggml_vec_leaky_relu_f16(nc, - (ggml_fp16_t *) ((char *) dst->data + i*( dst->nb[1])), - (ggml_fp16_t *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); - } -} - -static void ggml_compute_forward_leaky_relu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_leaky_relu_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_leaky_relu_f16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_silu_back - -static void ggml_compute_forward_silu_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * grad = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - assert(ggml_is_contiguous_1(grad)); - assert(ggml_is_contiguous_1(src1)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src1, dst)); - assert(ggml_are_same_shape(src1, grad)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src1->ne[0]; - const int nr = ggml_nrows(src1); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_backward_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src1->data + i1*(src1->nb[1])), - (float *) ((char *) grad->data + i1*(grad->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_silu_back_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * grad = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - assert(ggml_is_contiguous_1(grad)); - assert(ggml_is_contiguous_1(src1)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src1, dst)); - assert(ggml_are_same_shape(src1, grad)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src1->ne[0]; - const int nr = ggml_nrows(src1); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_backward_f16(nc, - (ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])), - (ggml_fp16_t *) ((char *) src1->data + i1*(src1->nb[1])), - (ggml_fp16_t *) ((char *) grad->data + i1*(grad->nb[1]))); - - #ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - const float v = GGML_FP16_TO_FP32(x); - UNUSED(v); - assert(!isnan(v)); - assert(!isinf(v)); - } - #endif - } -} - -static void ggml_compute_forward_silu_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_silu_back_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_silu_back_f16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_norm - -static void ggml_compute_forward_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - GGML_ASSERT(eps >= 0.0f); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)x[i00]; - } - - float mean = sum/ne00; - - float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - ggml_float sum2 = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - float v = x[i00] - mean; - y[i00] = v; - sum2 += (ggml_float)(v*v); - } - - float variance = sum2/ne00; - const float scale = 1.0f/sqrtf(variance + eps); - - ggml_vec_scale_f32(ne00, y, scale); - } - } - } -} - -static void ggml_compute_forward_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_group_rms_norm - -static void ggml_compute_forward_rms_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - GGML_ASSERT(eps >= 0.0f); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)(x[i00] * x[i00]); - } - - const float mean = sum/ne00; - - float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - memcpy(y, x, ne00 * sizeof(float)); - // for (int i00 = 0; i00 < ne00; i00++) { - // y[i00] = x[i00]; - // } - - const float scale = 1.0f/sqrtf(mean + eps); - - ggml_vec_scale_f32(ne00, y, scale); - } - } - } -} - -static void ggml_compute_forward_rms_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rms_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_rms_norm_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; // gradients from forward pass output - const struct ggml_tensor * src1 = dst->src[1]; // src1 from forward pass - - GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(src1->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - // src1 is same shape as src0 => same indices - const int64_t i11 = i01; - const int64_t i12 = i02; - const int64_t i13 = i03; - - const float * dz = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - const float * x = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13); - - ggml_float sum_xx = 0.0; - ggml_float sum_xdz = 0.0; - - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum_xx += (ggml_float)(x[i00] * x[i00]); - sum_xdz += (ggml_float)(x[i00] * dz[i00]); - } - - //const float mean = (float)(sum_xx)/ne00; - const float mean_eps = (float)(sum_xx)/ne00 + eps; - const float sum_eps = (float)(sum_xx) + eps*ne00; - //const float mean_xdz = (float)(sum_xdz)/ne00; - // we could cache rms from forward pass to improve performance. - // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms. - //const float rms = sqrtf(mean_eps); - const float rrms = 1.0f / sqrtf(mean_eps); - //const float scale = -rrms/(ne00 * mean_eps); // -1/(n*rms**3) - - { - // z = rms_norm(x) - // - // rms_norm(src1) = - // scale( - // src1, - // div( - // 1, - // sqrt( - // add( - // scale( - // sum( - // sqr( - // src1)), - // (1.0/N)), - // eps)))); - - // postorder: - // ## op args grad - // 00 param src1 grad[#00] - // 01 const 1 - // 02 sqr (#00) grad[#02] - // 03 sum (#02) grad[#03] - // 04 const 1/N - // 05 scale (#03, #04) grad[#05] - // 06 const eps - // 07 add (#05, #06) grad[#07] - // 08 sqrt (#07) grad[#08] - // 09 div (#01,#08) grad[#09] - // 10 scale (#00,#09) grad[#10] - // - // backward pass, given grad[#10] - // #10: scale - // grad[#00] += scale(grad[#10],#09) - // grad[#09] += sum(mul(grad[#10],#00)) - // #09: div - // grad[#08] += neg(mul(grad[#09], div(#09,#08))) - // #08: sqrt - // grad[#07] += mul(grad[#08], div(0.5, #08)) - // #07: add - // grad[#05] += grad[#07] - // #05: scale - // grad[#03] += scale(grad[#05],#04) - // #03: sum - // grad[#02] += repeat(grad[#03], #02) - // #02: - // grad[#00] += scale(mul(#00, grad[#02]), 2.0) - // - // substitute and simplify: - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) - // grad[#02] = repeat(grad[#03], #02) - // grad[#02] = repeat(scale(grad[#05],#04), #02) - // grad[#02] = repeat(scale(grad[#07],#04), #02) - // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02) - // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02) - // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02) - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) - // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) - // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps)) - // a = b*c + d*e - // a = b*c*f/f + d*e*f/f - // a = (b*c*f + d*e*f)*(1/f) - // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c)) - // a = (b + d*e/c)*c - // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) - // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms - // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms - // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms - // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms - // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms - // a = (dz + x*div(-mean_xdz,mean_eps))*rrms - // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) - // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - } - // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - // post-order: - // dx := x - // dx := scale(dx,-mean_xdz/mean_eps) - // dx := add(dx, dz) - // dx := scale(dx, rrms) - float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - // dx[i00] = (x*(-sum_xdz/sum_eps) + dz) / sqrtf(mean_eps) - ggml_vec_cpy_f32 (ne00, dx, x); - // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps); - ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps); - ggml_vec_acc_f32 (ne00, dx, dz); - ggml_vec_scale_f32(ne00, dx, rrms); - } - } - } -} - -static void ggml_compute_forward_rms_norm_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rms_norm_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_group_norm - -static void ggml_compute_forward_group_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - // TODO: optimize - - float eps; - memcpy(&eps, dst->op_params + 1, sizeof(float)); - - int n_channels = src0->ne[2]; - int n_groups = dst->op_params[0]; - int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; - for (int i = ith; i < n_groups; i += nth) { - int start = i * n_channels_per_group; - int end = start + n_channels_per_group; - if (end > n_channels) { - end = n_channels; - } - int step = end - start; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - ggml_float sum = 0.0; - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); - - ggml_float sumr = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sumr += (ggml_float)x[i00]; - } - sum += sumr; - } - } - const float mean = sum / (ne00 * ne01 * step); - - ggml_float sum2 = 0.0; - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); - - float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); - - ggml_float sumr = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - float v = x[i00] - mean; - y[i00] = v; - sumr += (ggml_float)(v * v); - } - sum2 += sumr; - } - } - const float variance = sum2 / (ne00 * ne01 * step); - const float scale = 1.0f / sqrtf(variance + eps); - - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); - ggml_vec_scale_f32(ne00, y, scale); - } - } - } - } -} - -static void ggml_compute_forward_group_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_group_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_l2_norm - -static void ggml_compute_forward_l2_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - GGML_ASSERT(eps >= 0.0f); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)(x[i00] * x[i00]); - } - - float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - memcpy(y, x, ne00 * sizeof(float)); - - const float scale = 1.0f/fmaxf(sqrtf(sum), eps); - - ggml_vec_scale_f32(ne00, y, scale); - } - } - } -} - -static void ggml_compute_forward_l2_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_l2_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - // ggml_compute_forward_mul_mat static void ggml_compute_forward_mul_mat_one_chunk( @@ -7196,5147 +1717,6 @@ static void ggml_compute_forward_mul_mat_id( } } -// ggml_compute_forward_out_prod - -static void ggml_compute_forward_out_prod_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - GGML_ASSERT(ne2 % ne02 == 0); - GGML_ASSERT(ne3 % ne03 == 0); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == sizeof(float)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - // GGML_ASSERT(nb0 <= nb1); - // GGML_ASSERT(nb1 <= nb2); - // GGML_ASSERT(nb2 <= nb3); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - - if (ith == 0) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } - ggml_barrier(params->threadpool); - - // dst[:,:,:,:] = 0 - // for i2,i3: - // for i1: - // for i01: - // for i0: - // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] - - // parallelize by last three dimensions - - // total rows in dst - const int64_t nr = ne1*ne2*ne3; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - // block-tiling attempt - const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32); - const int64_t blck_1 = 16; - - // dps == dst per src0, used for group query attention - const int64_t dps2 = ne2 / ne02; - const int64_t dps3 = ne3 / ne03; - - for (int64_t bir = ir0; bir < ir1; bir += blck_1) { - const int64_t bir1 = MIN(bir + blck_1, ir1); - for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) { - const int64_t bne01 = MIN(bi01 + blck_0, ne01); - for (int64_t ir = bir; ir < bir1; ++ir) { - // dst indices - const int64_t i3 = ir/(ne2*ne1); - const int64_t i2 = (ir - i3*ne2*ne1)/ne1; - const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); - - const int64_t i02 = i2 / dps2; - const int64_t i03 = i3 / dps3; - - //const int64_t i10 = i1; - const int64_t i12 = i2; - const int64_t i13 = i3; - -#if GGML_VEC_MAD_UNROLL > 2 - const int64_t bne01_unroll = bne01 - (bne01 % GGML_VEC_MAD_UNROLL); - for (int64_t i01 = bi01; i01 < bne01_unroll; i01 += GGML_VEC_MAD_UNROLL) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1); - } - for (int64_t i01 = bne01_unroll; i01 < bne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32(ne0, d, s0, *s1); - } -#else - for (int64_t i01 = bi01; i01 < bne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32(ne0, d, s0, *s1); - } -#endif - } - } - } -} - -static void ggml_compute_forward_out_prod_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; - - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne03 == ne13); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // we don't support permuted src0 dim0 - GGML_ASSERT(nb00 == ggml_type_size(type)); - - // dst dim0 cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - // GGML_ASSERT(nb0 <= nb1); - // GGML_ASSERT(nb1 <= nb2); - // GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - - if (ith == 0) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } - ggml_barrier(params->threadpool); - - // parallelize by last three dimensions - - // total rows in dst - const int64_t nr = ne1*ne2*ne3; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - // dst[:,:,:,:] = 0 - // for i2,i3: - // for i1: - // for i01: - // for i0: - // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] - - float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; - - for (int64_t ir = ir0; ir < ir1; ++ir) { - // dst indices - const int64_t i3 = ir/(ne2*ne1); - const int64_t i2 = (ir - i3*ne2*ne1)/ne1; - const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); - - const int64_t i02 = i2; - const int64_t i03 = i3; - - //const int64_t i10 = i1; - const int64_t i12 = i2; - const int64_t i13 = i3; - - for (int64_t i01 = 0; i01 < ne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - dequantize_row_q(s0, wdata, ne0); - ggml_vec_mad_f32(ne0, d, wdata, *s1); - } - } -} - -static void ggml_compute_forward_out_prod( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - 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_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - { - ggml_compute_forward_out_prod_q_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - GGML_ABORT("fatal error"); // todo - // ggml_compute_forward_out_prod_f16_f32(params, dst); - } - case GGML_TYPE_F32: - { - ggml_compute_forward_out_prod_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_scale - -static void ggml_compute_forward_scale_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - // scale factor - float v; - memcpy(&v, dst->op_params, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const size_t nb01 = src0->nb[1]; - - const size_t nb1 = dst->nb[1]; - - for (int i1 = ir0; i1 < ir1; i1++) { - if (dst->data != src0->data) { - // src0 is same shape as dst => same indices - memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float)); - } - ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); - } -} - -static void ggml_compute_forward_scale( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_scale_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_set - -static void ggml_compute_forward_set_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - - // view src0 and dst with these strides and data offset inbytes during set - // nb0 is implicitly element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) dst->op_params)[0]; - size_t nb2 = ((int32_t *) dst->op_params)[1]; - size_t nb3 = ((int32_t *) dst->op_params)[2]; - size_t offset = ((int32_t *) dst->op_params)[3]; - bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - if (params->ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src1); - const int nc = src1->ne[0]; - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) - GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) - - // src0 and dst as viewed during set - const size_t nb0 = ggml_element_size(src0); - - const int im0 = (ne10 == 0 ? 0 : ne10-1); - const int im1 = (ne11 == 0 ? 0 : ne11-1); - const int im2 = (ne12 == 0 ? 0 : ne12-1); - const int im3 = (ne13 == 0 ? 0 : ne13-1); - - GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); - - GGML_ASSERT(nb10 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are viewed with shape of src1 and offset - // => same indices - const int i3 = ir/(ne12*ne11); - const int i2 = (ir - i3*ne12*ne11)/ne11; - const int i1 = (ir - i3*ne12*ne11 - i2*ne11); - - ggml_vec_cpy_f32(nc, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); - } -} - -static void ggml_compute_forward_set_i32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - - // view src0 and dst with these strides and data offset inbytes during set - // nb0 is implicitly element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) dst->op_params)[0]; - size_t nb2 = ((int32_t *) dst->op_params)[1]; - size_t nb3 = ((int32_t *) dst->op_params)[2]; - size_t offset = ((int32_t *) dst->op_params)[3]; - bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - if (params->ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src1); - const int nc = src1->ne[0]; - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) - GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) - - // src0 and dst as viewed during set - const size_t nb0 = ggml_element_size(src0); - - const int im0 = (ne10 == 0 ? 0 : ne10-1); - const int im1 = (ne11 == 0 ? 0 : ne11-1); - const int im2 = (ne12 == 0 ? 0 : ne12-1); - const int im3 = (ne13 == 0 ? 0 : ne13-1); - - GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); - - GGML_ASSERT(nb10 == sizeof(int32_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are viewed with shape of src1 and offset - // => same indices - const int i3 = ir/(ne12*ne11); - const int i2 = (ir - i3*ne12*ne11)/ne11; - const int i1 = (ir - i3*ne12*ne11 - i2*ne11); - - ggml_vec_cpy_i32(nc, - (int32_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), - (int32_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); - } -} - -static void ggml_compute_forward_set( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_set_f32(params, dst); - } break; - case GGML_TYPE_I32: - { - ggml_compute_forward_set_i32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - 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_Q8_1: - 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_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cpy - -static void ggml_compute_forward_cpy( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, dst); -} - -// ggml_compute_forward_cont - -static void ggml_compute_forward_cont( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, dst); -} - -// ggml_compute_forward_reshape - -static void ggml_compute_forward_reshape( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_view - -static void ggml_compute_forward_view( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_permute - -static void ggml_compute_forward_permute( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_transpose - -static void ggml_compute_forward_transpose( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_get_rows - -static void ggml_compute_forward_get_rows_q( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == ggml_type_size(type)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - dequantize_row_q( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(ggml_fp16_t)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_fp16_to_fp32_row( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(ggml_bf16_t)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_bf16_to_fp32_row( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(float)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_vec_cpy_f32(nc, - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), - (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); - } -} - -static void ggml_compute_forward_get_rows( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - 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_Q8_1: - 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_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - { - ggml_compute_forward_get_rows_q(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_get_rows_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_get_rows_bf16(params, dst); - } break; - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_get_rows_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - //static bool first = true; - //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - //if (first) { - // first = false; - //} else { - // for (int k = 0; k < dst->ne[1]; ++k) { - // for (int j = 0; j < dst->ne[0]/16; ++j) { - // for (int i = 0; i < 16; ++i) { - // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); - // } - // printf("\n"); - // } - // printf("\n"); - // } - // printf("\n"); - // exit(0); - //} -} - -// ggml_compute_forward_get_rows_back - -static void ggml_compute_forward_get_rows_back_f32_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_is_contiguous(dst)); - - // ggml_compute_forward_dup_same_cont(params, opt0, dst); - - memset(dst->data, 0, ggml_nbytes(dst)); - - const int nc = src0->ne[0]; - const int nr = ggml_nelements(src1); - - GGML_ASSERT( dst->ne[0] == nc); - GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); - - for (int i = 0; i < nr; ++i) { - const int r = ((int32_t *) src1->data)[i]; - - for (int j = 0; j < nc; ++j) { - ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; - ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); - } - } -} - -static void ggml_compute_forward_get_rows_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_is_contiguous(dst)); - - // ggml_compute_forward_dup_same_cont(params, opt0, dst); - - memset(dst->data, 0, ggml_nbytes(dst)); - - const int nc = src0->ne[0]; - const int nr = ggml_nelements(src1); - - GGML_ASSERT( dst->ne[0] == nc); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < nr; ++i) { - const int r = ((int32_t *) src1->data)[i]; - - ggml_vec_add_f32(nc, - (float *) ((char *) dst->data + r*dst->nb[1]), - (float *) ((char *) dst->data + r*dst->nb[1]), - (float *) ((char *) src0->data + i*src0->nb[1])); - } -} - -static void ggml_compute_forward_get_rows_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_get_rows_back_f32_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_get_rows_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - //static bool first = true; - //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - //if (first) { - // first = false; - //} else { - // for (int k = 0; k < dst->ne[1]; ++k) { - // for (int j = 0; j < dst->ne[0]/16; ++j) { - // for (int i = 0; i < 16; ++i) { - // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); - // } - // printf("\n"); - // } - // printf("\n"); - // } - // printf("\n"); - // exit(0); - //} -} - -// ggml_compute_forward_diag - -static void ggml_compute_forward_diag_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - // TODO: handle transposed/permuted matrices - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(ne00 == ne0); - GGML_ASSERT(ne00 == ne1); - GGML_ASSERT(ne01 == 1); - GGML_ASSERT(ne02 == ne2); - GGML_ASSERT(ne03 == ne3); - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb0 == sizeof(float)); - - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = 0; i2 < ne2; i2++) { - for (int i1 = 0; i1 < ne1; i1++) { - float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); - for (int i0 = 0; i0 < i1; i0++) { - d[i0] = 0; - } - d[i1] = s[i1]; - for (int i0 = i1+1; i0 < ne0; i0++) { - d[i0] = 0; - } - } - } - } -} - -static void ggml_compute_forward_diag( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_diag_mask_inf - -static void ggml_compute_forward_diag_mask_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const float value) { - - const struct ggml_tensor * src0 = dst->src[0]; - - const int ith = params->ith; - const int nth = params->nth; - - const int n_past = ((int32_t *) dst->op_params)[0]; - const bool inplace = src0->data == dst->data; - - GGML_ASSERT(n_past >= 0); - - if (!inplace) { - if (ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - // TODO: handle transposed/permuted matrices - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - const int nr = src0->ne[1]; - const int nz = n/nr; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int k = 0; k < nz; k++) { - for (int j = ith; j < nr; j += nth) { - for (int i = n_past; i < nc; i++) { - if (i > n_past + j) { - *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; - } - } - } - } -} - -static void ggml_compute_forward_diag_mask_inf( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_diag_mask_zero( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_mask_f32(params, dst, 0); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_soft_max - -static void ggml_compute_forward_soft_max_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - assert(ggml_is_contiguous(dst)); - assert(ggml_are_same_shape(src0, dst)); - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - - // TODO: handle transposed/permuted matrices - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - //const int64_t ne11 = src1 ? src1->ne[1] : 1; - - // TODO: is this supposed to be ceil instead of floor? - // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 - const uint32_t n_head = ne02; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); - - 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 int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); - - for (int i1 = ir0; i1 < ir1; i1++) { - // ALiBi - const uint32_t h = (i1/ne01)%ne02; // head - const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - - float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); - float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); - - // broadcast the mask across rows - ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; - float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; - - ggml_vec_cpy_f32 (nc, wp, sp); - ggml_vec_scale_f32(nc, wp, scale); - if (mp_f32) { - if (use_f16) { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); - } - } else { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*mp_f32[i]; - } - } - } - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(wp[i])); - } -#endif - - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, wp); - - ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(nc, dp, sum); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - assert(!isnan(dp[i])); - assert(!isinf(dp[i])); - } -#endif - } -} - -static void ggml_compute_forward_soft_max( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_soft_max_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_soft_max_ext_back - -static void ggml_compute_forward_soft_max_ext_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_are_same_shape(src1, dst)); - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float)); - - GGML_ASSERT(max_bias == 0.0f); - - // TODO: handle transposed/permuted matrices - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); - float *y = (float *)((char *) src1->data + i1*src1->nb[1]); - float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(dy[i])); - assert(!isnan(y[i])); - } -#endif - // Jii = yi - yi*yi - // Jij = -yi*yj - // J = diag(y)-y.T*y - // dx = J * dy - // dxk = sum_i(Jki * dyi) - // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk - // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk - // dxk = sum_i(-yk*yi * dyi) + yk*dyk - // dxk = -yk * sum_i(yi * dyi) + yk*dyk - // dxk = -yk * dot(y, dy) + yk*dyk - // dxk = yk * (- dot(y, dy) + dyk) - // dxk = yk * (dyk - dot(y, dy)) - // - // post-order: - // dot_y_dy := dot(y, dy) - // dx := dy - // dx := dx - dot_y_dy - // dx := dx * y - - // linear runtime, no additional memory - float dot_y_dy = 0; - ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1); - ggml_vec_cpy_f32 (nc, dx, dy); - ggml_vec_acc1_f32 (nc, dx, -dot_y_dy); - ggml_vec_mul_f32 (nc, dx, dx, y); - ggml_vec_scale_f32(nc, dx, scale); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - assert(!isnan(dx[i])); - assert(!isinf(dx[i])); - } -#endif - } -} - -static void ggml_compute_forward_soft_max_ext_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_soft_max_ext_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_clamp - -static void ggml_compute_forward_clamp_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - float min; - float max; - memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - for (int j = ith; j < n; j += nth) { - float * dst_ptr = (float *) ((char *) dst->data + j*nb1); - float * src0_ptr = (float *) ((char *) src0->data + j*nb01); - - for (int i = 0; i < nc; i++) { - dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); - } - } -} - -static void ggml_compute_forward_clamp_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - float min; - float max; - memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - for (int j = ith; j < n; j += nth) { - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + j*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + j*nb01); - - for (int i = 0; i < nc; i++) { - float v = GGML_FP16_TO_FP32(src0_ptr[i]); - dst_ptr[i] = GGML_FP32_TO_FP16(MAX(MIN(v, max), min)); - } - } -} - -static void ggml_compute_forward_clamp( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_clamp_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_clamp_f16(params, dst); - } break; - case GGML_TYPE_BF16: - 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_Q8_1: - 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_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q8_K: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_I64: - case GGML_TYPE_F64: - case GGML_TYPE_COUNT: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rope - -static float rope_yarn_ramp(const float low, const float high, const int i0) { - const float y = (i0 / 2 - low) / MAX(0.001f, high - low); - return 1 - MIN(1, MAX(0, 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, float corr_dims[2], 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[0], corr_dims[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 * logf(1.0f / freq_scale); - } - *cos_theta = cosf(theta) * mscale; - *sin_theta = sinf(theta) * mscale; -} - -static void ggml_rope_cache_init( - float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, - float * cache, float sin_sign, float theta_scale) { - // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py - float theta = theta_base; - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; - rope_yarn( - theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] - ); - cache[i0 + 1] *= sin_sign; - - theta *= theta_scale; - } -} - -static void ggml_mrope_cache_init( - float theta_base_t, float theta_base_h, float theta_base_w, float theta_base_e, int sections[4], bool indep_sects, - float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, - float * cache, float sin_sign, float theta_scale) { - // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py - float theta_t = theta_base_t; - float theta_h = theta_base_h; - float theta_w = theta_base_w; - float theta_e = theta_base_e; // extra position id for vision encoder - int sect_dims = sections[0] + sections[1] + sections[2] + sections[3]; - int sec_w = sections[1] + sections[0]; - int sec_e = sections[2] + sec_w; - GGML_ASSERT(sect_dims <= ne0); - - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; - - int sector = (i0 / 2) % sect_dims; - if (indep_sects) { - // compute theta independently for each dim sections - // (i.e. reset corresponding theta when `i0` go from one section to another) - if (sector == 0) { - theta_t = theta_base_t; - } - else if (sector == sections[0]) { - theta_h = theta_base_h;; - } - else if (sector == sec_w) { - theta_w = theta_base_w; - } - else if (sector == sec_e) { - theta_e = theta_base_e; - } - } - - float theta = theta_t; - if (sector >= sections[0] && sector < sec_w) { - theta = theta_h; - } - else if (sector >= sec_w && sector < sec_w + sections[2]) { - theta = theta_w; - } - else if (sector >= sec_w + sections[2]) { - theta = theta_e; - } - - rope_yarn( - theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] - ); - cache[i0 + 1] *= sin_sign; - - theta_t *= theta_scale; - theta_w *= theta_scale; - theta_h *= theta_scale; - theta_e *= theta_scale; - } -} - -static void ggml_compute_forward_rope_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const bool forward) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - int sections[4]; - - //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]; - - 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)); - memcpy(§ions, (int32_t *) dst->op_params + 11, sizeof(int)*4); - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - GGML_ASSERT(nb00 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - GGML_ASSERT(n_dims <= ne0); - GGML_ASSERT(n_dims % 2 == 0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE; // ggml_rope_multi, multimodal rotary position embedding - const bool is_vision = mode == GGML_ROPE_TYPE_VISION; - - if (is_mrope) { - GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0); - } - - if (is_vision) { - GGML_ASSERT(n_dims == ne0/2); - } - - const float * freq_factors = NULL; - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - - // backward process uses inverse rotation by cos and sin. - // cos and sin build a rotation matrix, where the inverse is the transpose. - // this essentially just switches the sign of sin. - const float sin_sign = forward ? 1.0f : -1.0f; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { // batch - for (int64_t i2 = 0; i2 < ne2; i2++) { // seq-len - - float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - if (!is_mrope) { - const int64_t p = pos[i2]; - ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - } - else { - const int64_t p_t = pos[i2]; - const int64_t p_h = pos[i2 + ne2]; - const int64_t p_w = pos[i2 + ne2 * 2]; - const int64_t p_e = pos[i2 + ne2 * 3]; - ggml_mrope_cache_init( - p_t, p_h, p_w, p_e, sections, is_vision, - freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - } - - for (int64_t i1 = 0; i1 < ne1; i1++) { // attn-heads - if (ir++ < ir0) continue; - if (ir > ir1) break; - - if (is_neox || is_mrope) { - if (is_vision){ - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = src[0]; - const float x1 = src[n_dims]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims] = x0*sin_theta + x1*cos_theta; - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = src[0]; - const float x1 = src[n_dims/2]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; - } - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = src[0]; - const float x1 = src[1]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[1] = x0*sin_theta + x1*cos_theta; - } - } - - if (is_vision) { - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = src[0]; - const float x1 = src[n_dims]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims] = x0*sin_theta + x1*cos_theta; - } - } else { - // fill the remain channels with data from src tensor - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } - } - } - } -} - -// TODO: deduplicate f16/f32 code -static void ggml_compute_forward_rope_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const bool forward) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - int sections[4]; - - //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]; - 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)); - memcpy(§ions, (int32_t *) dst->op_params + 11, sizeof(int)*4); - - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - GGML_ASSERT(n_dims <= ne0); - GGML_ASSERT(n_dims % 2 == 0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE; - const bool is_vision = mode == GGML_ROPE_TYPE_VISION; - - if (is_mrope) { - GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0); - } - - if (is_vision) { - GGML_ASSERT(n_dims == ne0/2); - } - - const float * freq_factors = NULL; - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - - // backward process uses inverse rotation by cos and sin. - // cos and sin build a rotation matrix, where the inverse is the transpose. - // this essentially just switches the sign of sin. - const float sin_sign = forward ? 1.0f : -1.0f; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - - float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - if (!is_mrope) { - const int64_t p = pos[i2]; - ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - } - else { - const int64_t p_t = pos[i2]; - const int64_t p_h = pos[i2 + ne2]; - const int64_t p_w = pos[i2 + ne2 * 2]; - const int64_t p_e = pos[i2 + ne2 * 3]; - ggml_mrope_cache_init( - p_t, p_h, p_w, p_e, sections, is_vision, - freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - } - - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - if (is_neox || is_mrope) { - if (is_vision) { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[n_dims]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[n_dims] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[1]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } - - if (is_vision) { - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[n_dims]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[n_dims] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } else { - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } - } - } - } -} - -static void ggml_compute_forward_rope( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_rope_f16(params, dst, true); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_rope_f32(params, dst, true); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rope_back - -static void ggml_compute_forward_rope_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_rope_f16(params, dst, false); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_rope_f32(params, dst, false); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_conv_transpose_1d - -static void ggml_compute_forward_conv_transpose_1d_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); - ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // permute source data (src1) from (L x Cin) to (Cin x L) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; - ggml_fp16_t * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f16(ne02, &v, 0, - (ggml_fp16_t *) wdata_src + i1n, 0, - (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - float * const wdata = (float *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); - float * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // prepare source data (src1) - { - float * const wdata = (float *) params->wdata + nk; - float * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = src[i10]; - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * const wdata = (float *) params->wdata + 0; - float * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - float * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f32(ne02, &v, 0, - wdata_src + i1n, 0, - wdata_kernel + i00*ne02, 0, 1); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_conv_transpose_1d_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_im2col_f32 -// src0: kernel [OC, IC, KH, KW] -// src1: image [N, IC, IH, IW] -// dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_im2col_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne13 : ne12; - const int64_t IC = is_2D ? ne12 : ne11; - const int64_t IH = is_2D ? ne11 : 1; - const int64_t IW = ne10; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne2 : 1; - const int64_t OW = ne1; - - int ofs0 = is_2D ? nb13 : nb12; - int ofs1 = is_2D ? nb12 : nb11; - - GGML_ASSERT(nb10 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - float * const wdata = (float *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 - for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - - // micro kernel - float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - - for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 - for (int64_t ikw = 0; ikw < KW; ikw++) { - const int64_t iiw = iow*s0 + ikw*d0 - p0; - const int64_t iih = ioh*s1 + ikh*d1 - p1; - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; - } else { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } -} - - -// ggml_compute_forward_im2col_f16 -// src0: kernel [OC, IC, KH, KW] -// src1: image [N, IC, IH, IW] -// dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_im2col_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne13 : ne12; - const int64_t IC = is_2D ? ne12 : ne11; - const int64_t IH = is_2D ? ne11 : 1; - const int64_t IW = ne10; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne2 : 1; - const int64_t OW = ne1; - - int ofs0 = is_2D ? nb13 : nb12; - int ofs1 = is_2D ? nb12 : nb11; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 - for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - - // micro kernel - ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - - for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 - for (int64_t ikw = 0; ikw < KW; ikw++) { - const int64_t iiw = iow*s0 + ikw*d0 - p0; - const int64_t iih = ioh*s1 + ikh*d1 - p1; - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; - } else { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_im2col( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_im2col_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_im2col_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_im2col_back_f32 - -static void ggml_compute_forward_im2col_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; // gradients of forward pass output - const struct ggml_tensor * src1 = dst->src[1]; // convolution kernel - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne3 : ne2; - const int64_t IC = is_2D ? ne2 : ne1; - const int64_t IH = is_2D ? ne1 : 1; - const int64_t IW = ne0; - - const int64_t KH = is_2D ? ne11 : 1; - const int64_t KW = ne10; - - const int64_t OH = is_2D ? ne02 : 1; - const int64_t OW = ne01; - - int ofs0 = is_2D ? nb3 : nb2; - int ofs1 = is_2D ? nb2 : nb1; - - GGML_ASSERT(nb0 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - float * const wdata = (float *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - for (int64_t iih = 0; iih < IH; iih++) { - for (int64_t iiw = 0; iiw < IW; iiw++) { - - // micro kernel - float grad = 0.0f; - for (int64_t ikh = 0; ikh < KH; ikh++) { - for (int64_t ikw = 0; ikw < KW; ikw++) { - // For s0 > 1 some values were skipped over in the forward pass. - // These values have tmpw % s0 != 0 and need to be skipped in the backwards pass as well. - const int64_t tmpw = (iiw + p0 - ikw*d0); - if (tmpw % s0 != 0) { - continue; - } - const int64_t iow = tmpw / s0; - - // Equivalent logic as above except for s1. - int64_t ioh; - if (is_2D) { - const int64_t tmph = iih + p1 - ikh*d1; - - if (tmph % s1 != 0) { - continue; - } - - ioh = tmph / s1; - } else { - ioh = 0; - } - - if (iow < 0 || iow >= OW || ioh < 0 || ioh >= OH) { - continue; - } - - const float * const grad_in = (const float *) src0->data - + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - grad += grad_in[iic*(KH*KW) + ikh*KW + ikw]; - } - } - float * dst_data = (float *)((char *) wdata + (in*ofs0 + iic*ofs1)); // [IH, IW] - dst_data[iih*IW + iiw] = grad; - } - } - } - } - } -} - -// ggml_compute_forward_conv_transpose_2d - -static void ggml_compute_forward_conv_transpose_2d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02*ne03; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02); - ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03; - for (int64_t i01 = 0; i01 < ne01; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00]; - } - } - } - } - } - - // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; - for (int i12 = 0; i12 < ne12; i12++) { - for (int i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11); - ggml_fp16_t * dst_data = wdata + i11*ne10*ne12; - for (int i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne12 + i12] = GGML_FP32_TO_FP16(src[i10]); - } - } - } - } - - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t stride = ggml_get_op_params_i32(dst, 0); - - // total patches in dst - const int np = ne2; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - const int ip0 = dp*ith; - const int ip1 = MIN(ip0 + dp, np); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = wdata + nk; - - for (int i2 = ip0; i2 < ip1; i2++) { // Cout - float * dst_data = (float *)((char *) dst->data + i2*nb2); - ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03; - for (int i11 = 0; i11 < ne11; i11++) { - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i11*ne10*ne12 + i10*ne12; - for (int i01 = 0; i01 < ne01; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f16(ne03, &v, 0, - wdata_src + i1n, 0, - wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1); - dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; - } - } - } - } - } -} - -// ggml_compute_forward_pool_1d_sk_p0 - -static void ggml_compute_forward_pool_1d_sk_p0( - const struct ggml_compute_params * params, - const enum ggml_op_pool op, - const int k, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - - assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const char * cdata = (const char *)src->data; - const char * const data_end = cdata + ggml_nbytes(src); - float * drow = (float *)dst->data; - - const int64_t rs = dst->ne[0]; - - while (cdata < data_end) { - const void * srow = (const void *)cdata; - int j = 0; - for (int64_t i = 0; i < rs; ++i) { - switch (op) { - case GGML_OP_POOL_AVG: drow[i] = 0; break; - case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - for (int ki = 0; ki < k; ++ki) { - const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); - switch (op) { - case GGML_OP_POOL_AVG: drow[i] += srow_j; break; - case GGML_OP_POOL_MAX: if (srow_j > drow[i]) drow[i] = srow_j; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - ++j; - } - switch (op) { - case GGML_OP_POOL_AVG: drow[i] /= k; break; - case GGML_OP_POOL_MAX: break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - - cdata += src->nb[1]; - drow += rs; - } -} - -// ggml_compute_forward_pool_1d - -static void ggml_compute_forward_pool_1d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int s0 = opts[2]; - const int p0 = opts[3]; - GGML_ASSERT(p0 == 0); // padding not supported - GGML_ASSERT(k0 == s0); // only s = k supported - - ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst); -} - -// ggml_compute_forward_pool_2d - -static void ggml_compute_forward_pool_2d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - - assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - const char * cdata = (const char*)src->data; - const char * const data_end = cdata + ggml_nbytes(src); - - const int64_t px = dst->ne[0]; - const int64_t py = dst->ne[1]; - const int64_t pa = px * py; - - float * dplane = (float *)dst->data; - - const int ka = k0 * k1; - const int offset0 = -p0; - const int offset1 = -p1; - - while (cdata < data_end) { - for (int oy = 0; oy < py; ++oy) { - float * const drow = dplane + oy * px; - for (int ox = 0; ox < px; ++ox) { - float * const out = drow + ox; - switch (op) { - case GGML_OP_POOL_AVG: *out = 0; break; - case GGML_OP_POOL_MAX: *out = -FLT_MAX; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - - const int ix = offset0 + ox * s0; - const int iy = offset1 + oy * s1; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; - const void * srow = (const void *)(cdata + src->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= src->ne[0]) continue; - const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); - switch (op) { - case GGML_OP_POOL_AVG: *out += srow_j; break; - case GGML_OP_POOL_MAX: if (srow_j > *out) *out = srow_j; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - } - switch (op) { - case GGML_OP_POOL_AVG: *out /= ka; break; - case GGML_OP_POOL_MAX: break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - } - - cdata += src->nb[2]; - dplane += pa; - } -} - -// ggml_compute_forward_pool_2d_back - -static void ggml_compute_forward_pool_2d_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - const struct ggml_tensor * dstf = dst->src[1]; // forward tensor of dst - - assert(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - - char * cdata = (char *) dst->data; - const char * cdataf = (const char *) dstf->data; - const char * const data_end = cdata + ggml_nbytes(dst); - - GGML_ASSERT(params->ith == 0); - memset(cdata, 0, ggml_nbytes(dst)); - - const int64_t px = src->ne[0]; - const int64_t py = src->ne[1]; - const int64_t pa = px * py; - - const float * splane = (const float *) src->data; - - const int ka = k0 * k1; - const int offset0 = -p0; - const int offset1 = -p1; - - while (cdata < data_end) { - for (int oy = 0; oy < py; ++oy) { - const float * const srow = splane + oy * px; - for (int ox = 0; ox < px; ++ox) { - const float grad0 = srow[ox]; - - const int ix = offset0 + ox * s0; - const int iy = offset1 + oy * s1; - - if (op == GGML_OP_POOL_MAX) { - float maxval = -FLT_MAX; - int kxmax = -1; - int kymax = -1; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= dst->ne[1]) { - continue; - } - const void * drowf = (const void *)(cdataf + dst->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= dst->ne[0]) { - continue; - } - - const float val = dst->type == GGML_TYPE_F32 ? - ((const float *) drowf)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t *) drowf)[j]); - if (val <= maxval) { - continue; - } - - maxval = val; - kxmax = kx; - kymax = ky; - } - } - - if (kxmax == -1 || kymax == -1) { - continue; - } - - void * drow = (void *)(cdata + dst->nb[1] * (iy + kymax)); - const int j = ix + kxmax; - if (dst->type == GGML_TYPE_F32) { - ((float *) drow)[j] += grad0; - } else { - ((ggml_fp16_t *) drow)[j] = GGML_FP32_TO_FP16(grad0 + GGML_FP16_TO_FP32(((const ggml_fp16_t *) drow)[j])); - } - } else if (op == GGML_OP_POOL_AVG) { - const float grad = grad0 / ka; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= dst->ne[1]) { - continue; - } - void * drow = (void *)(cdata + dst->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= dst->ne[0]) { - continue; - } - - if (dst->type == GGML_TYPE_F32) { - ((float *) drow)[j] += grad; - } else { - ((ggml_fp16_t *) drow)[j] += GGML_FP32_TO_FP16(grad); - } - } - } - } else { - GGML_ASSERT(false); - } - } - } - - cdata += dst->nb[2]; - cdataf += dst->nb[2]; - splane += pa; - } -} - -// ggml_compute_forward_upscale - -static void ggml_compute_forward_upscale_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - const float sf0 = (float)ne0/src0->ne[0]; - const float sf1 = (float)ne1/src0->ne[1]; - const float sf2 = (float)ne2/src0->ne[2]; - const float sf3 = (float)ne3/src0->ne[3]; - - // TODO: optimize - - for (int64_t i3 = 0; i3 < ne3; i3++) { - const int64_t i03 = i3 / sf3; - for (int64_t i2 = ith; i2 < ne2; i2 += nth) { - const int64_t i02 = i2 / sf2; - for (int64_t i1 = 0; i1 < ne1; i1++) { - const int64_t i01 = i1 / sf1; - for (int64_t i0 = 0; i0 < ne0; i0++) { - const int64_t i00 = i0 / sf0; - - const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_upscale( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_upscale_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_pad - -static void ggml_compute_forward_pad_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT( dst->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float * dst_ptr = (float *) dst->data; - - // TODO: optimize - - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = ith; i1 < ne1; i1 += nth) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - for (int64_t i3 = 0; i3 < ne3; ++i3) { - const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; - - const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - dst_ptr[dst_idx] = *src_ptr; - } else { - dst_ptr[dst_idx] = 0; - } - } - } - } - } -} - -static void ggml_compute_forward_pad( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_pad_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_pad_reflect_1d - -static void ggml_compute_forward_pad_reflect_1d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int ith = params->ith; - const int nth = params->nth; - - const int32_t * opts = (const int32_t *) dst->op_params; - const int p0 = opts[0]; - const int p1 = opts[1]; - - GGML_TENSOR_UNARY_OP_LOCALS - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - for (int64_t i1 = ith; i1 < ne1; i1 += nth) { - float * left = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + p0*nb0); - float * right = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (ne0-p1-1)*nb0); - - ggml_vec_cpy_f32(ne00, left, (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01)); - - for (int i0 = 1; i0 <= p0; i0++) { left[-i0] = left[i0]; } - for (int i0 = 1; i0 <= p1; i0++) { right[i0] = right[-i0]; } - } - } - } -} - -// ggml_compute_forward_arange - -static void ggml_compute_forward_arange_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - GGML_ASSERT(dst->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const float start = ggml_get_op_params_f32(dst, 0); - const float stop = ggml_get_op_params_f32(dst, 1); - const float step = ggml_get_op_params_f32(dst, 2); - - const int64_t steps = (int64_t) ceilf((stop - start) / step); - - GGML_ASSERT(ggml_nelements(dst) == steps); - - for (int64_t i = ith; i < steps; i+= nth) { - float value = start + step * i; - ((float *)dst->data)[i] = value; - } -} - -static void ggml_compute_forward_arange( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_arange_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_timestep_embedding_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - const int dim = ggml_get_op_params_i32(dst, 0); - const int max_period = ggml_get_op_params_i32(dst, 1); - - int half = dim / 2; - - for (int64_t i = 0; i < ne00; i++) { - float * embed_data = (float *)((char *) dst->data + i*nb1); - for (int64_t j = ith; j < half; j += nth) { - float timestep = ((float *)src0->data)[i]; - float freq = (float)expf(-logf(max_period) * j / half); - float arg = timestep * freq; - embed_data[j] = cosf(arg); - embed_data[j + half] = sinf(arg); - } - if (dim % 2 != 0 && ith == 0) { - embed_data[dim] = 0.f; - } - } -} - -static void ggml_compute_forward_timestep_embedding( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_timestep_embedding_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_argsort - -static void ggml_compute_forward_argsort_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(nb0 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0); - - for (int64_t i = ith; i < nr; i += nth) { - int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); - const float * src_data = (float *)((char *) src0->data + i*nb01); - - for (int64_t j = 0; j < ne0; j++) { - dst_data[j] = j; - } - - // C doesn't have a functional sort, so we do a bubble sort instead - for (int64_t j = 0; j < ne0; j++) { - for (int64_t k = j + 1; k < ne0; k++) { - if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || - (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { - int32_t tmp = dst_data[j]; - dst_data[j] = dst_data[k]; - dst_data[k] = tmp; - } - } - } - } -} - -static void ggml_compute_forward_argsort( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_argsort_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_flash_attn_ext - -static void ggml_compute_forward_flash_attn_ext_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * mask, - struct ggml_tensor * dst) { - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t DK = nek0; - const int64_t DV = nev0; - const int64_t N = neq1; - - GGML_ASSERT(ne0 == DV); - GGML_ASSERT(ne2 == N); - - // input tensor rows must be contiguous - GGML_ASSERT(nbq0 == ggml_type_size(q->type)); - GGML_ASSERT(nbk0 == ggml_type_size(k->type)); - GGML_ASSERT(nbv0 == ggml_type_size(v->type)); - - GGML_ASSERT(neq0 == DK); - GGML_ASSERT(nek0 == DK); - GGML_ASSERT(nev0 == DV); - - GGML_ASSERT(neq1 == N); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // broadcast factors - const int64_t rk2 = neq2/nek2; - const int64_t rk3 = neq3/nek3; - - const int64_t rv2 = neq2/nev2; - const int64_t rv3 = neq3/nev3; - - // parallelize by q rows using ggml_vec_dot_f32 - - // total rows in q - const int nr = neq1*neq2*neq3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float scale = 1.0f; - float max_bias = 0.0f; - float logit_softcap = 0.0f; - - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float)); - - if (logit_softcap != 0) { - scale /= logit_softcap; - } - - const uint32_t n_head = neq2; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - enum ggml_type const k_vec_dot_type = type_traits_cpu[k->type].vec_dot_type; - ggml_from_float_t const q_to_vec_dot = type_traits_cpu[k_vec_dot_type].from_float; - ggml_vec_dot_t const kq_vec_dot = type_traits_cpu[k->type].vec_dot; - ggml_to_float_t const v_to_float = ggml_get_type_traits(v->type)->to_float; - - GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type"); - GGML_ASSERT(v_to_float && "fattn: unsupported V-type"); - - // loop over n_batch and n_head - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int iq3 = ir/(neq2*neq1); - const int iq2 = (ir - iq3*neq2*neq1)/neq1; - const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - - const uint32_t h = iq2; // head index - const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - - float S = 0.0f; // sum - float M = -INFINITY; // maximum KQ value - - float * VKQ32 = (float *) params->wdata + ith*(1*DK + 2*DV + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator - float * V32 = (VKQ32 + 1*DV); // (temporary) FP32 V buffer - ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*DV); // (temporary) FP16 VKQ accumulator - ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*DV); // (temporary) buffer for Q converted to quantized/FP16 - - if (v->type == GGML_TYPE_F16) { - memset(VKQ16, 0, DV*sizeof(ggml_fp16_t)); - } else { - memset(VKQ32, 0, DV*sizeof(float)); - } - - const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; - - // k indices - const int ik3 = iq3 / rk3; - const int ik2 = iq2 / rk2; - - // v indices - const int iv3 = iq3 / rv3; - const int iv2 = iq2 / rv2; - - const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); - q_to_vec_dot(pq, Q_q, DK); - - // online softmax / attention - // loop over n_kv and n_head_kv - // ref: https://arxiv.org/pdf/2112.05682.pdf - for (int64_t ic = 0; ic < nek1; ++ic) { - const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; - if (mv == -INFINITY) { - continue; - } - - float s; // KQ value - - const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); - kq_vec_dot(DK, &s, 0, k_data, 0, Q_q, 0, 1); - - s = s*scale; // scale KQ value - - if (logit_softcap != 0.0f) { - s = logit_softcap*tanhf(s); - } - - s += mv; // apply mask - - const float Mold = M; - - float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value - float vs = 1.0f; // post-softmax KQ value, expf(s - M) - - const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); - - if (v->type == GGML_TYPE_F16) { - if (s > M) { - // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f - M = s; - ms = expf(Mold - M); - - // V = V*expf(Mold - M) - ggml_vec_scale_f16(DV, VKQ16, ms); - } else { - // no new maximum, ms == 1.0f, vs != 1.0f - vs = expf(s - M); - } - - // V += v*expf(s - M) - ggml_vec_mad_f16(DV, VKQ16, (const ggml_fp16_t *) v_data, vs); - } else { - if (s > M) { - // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f - M = s; - ms = expf(Mold - M); - - // V = V*expf(Mold - M) - ggml_vec_scale_f32(DV, VKQ32, ms); - } else { - // no new maximum, ms == 1.0f, vs != 1.0f - vs = expf(s - M); - } - - v_to_float(v_data, V32, DV); - - // V += v*expf(s - M) - ggml_vec_mad_f32(DV, VKQ32, V32, vs); - } - - S = S*ms + vs; // scale and increment sum with partial sum - } - - if (v->type == GGML_TYPE_F16) { - for (int64_t d = 0; d < DV; ++d) { - VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); - } - } - - // V /= S - const float S_inv = 1.0f/S; - ggml_vec_scale_f32(DV, VKQ32, S_inv); - - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // original - //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); - - // permute(0, 2, 1, 3) - memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); - } -} - -static void ggml_compute_forward_flash_attn_ext( - const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * mask, - struct ggml_tensor * dst) { - switch (dst->op_params[3]) { - case GGML_PREC_DEFAULT: - case GGML_PREC_F32: - { - // uses F32 accumulators - ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_flash_attn_back - -static void ggml_compute_forward_flash_attn_back_f32( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - const struct ggml_tensor * d = dst->src[3]; - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ned, d, ne) - GGML_TENSOR_LOCALS(size_t, nbd, d, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - const int mxDM = MAX(D, Mup); - - // GGML_ASSERT(ne0 == D); - // GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(float)); - GGML_ASSERT(nbv0 == sizeof(float)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - GGML_ASSERT(ned0 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - GGML_ASSERT(ned1 == N); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (ith == 0) { - memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); - } - ggml_barrier(params->threadpool); - - const int64_t elem_q = ggml_nelements(q); - const int64_t elem_k = ggml_nelements(k); - - enum ggml_type result_type = dst->type; - GGML_ASSERT(ggml_blck_size(result_type) == 1); - const size_t tsize = ggml_type_size(result_type); - - const size_t offs_q = 0; - const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); - const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); - - void * grad_q = (char *) dst->data; - void * grad_k = (char *) dst->data + offs_k; - void * grad_v = (char *) dst->data + offs_v; - - const size_t nbgq1 = nb0*neq0; - const size_t nbgq2 = nb0*neq0*neq1; - const size_t nbgq3 = nb0*neq0*neq1*neq2; - - const size_t nbgk1 = nb0*nek0; - const size_t nbgk2 = nb0*nek0*nek1; - const size_t nbgk3 = nb0*nek0*nek1*neq2; - - const size_t nbgv1 = nb0*nev0; - const size_t nbgv2 = nb0*nev0*nev1; - const size_t nbgv3 = nb0*nev0*nev1*neq2; - - // parallelize by k rows using ggml_vec_dot_f32 - - // total rows in k - const int nr = nek2*nek3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - // how often k2 (and v2) is repeated in q2 - int nrep = neq2/nek2; - - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int ik3 = ir/(nek2); - const int ik2 = ir - ik3*nek2; - - const int iq3 = ik3; - const int id3 = ik3; - const int iv3 = ik3; - const int iv2 = ik2; - - for (int irep = 0; irep < nrep; ++irep) { - const int iq2 = ik2 + irep*nek2; - const int id2 = iq2; - - // (ik2 + irep*nek2) % nek2 == ik2 - for (int iq1 = 0; iq1 < neq1; ++iq1) { - const int id1 = iq1; - - // not sure about CACHE_LINE_SIZE_F32.. - // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset? - float * S = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32); - float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - const int64_t masked_begin = masked ? (P + iq1 + 1) : M; - for (int64_t ic = 0; ic < masked_begin; ++ic) { - // k indices - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f32(neq0, - S + i1, 0, - (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - - // scale - ggml_vec_scale_f32(masked_begin, S, scale); - - for (int64_t i = masked_begin; i < M; i++) { - S[i] = -INFINITY; - } - - // softmax - // exclude known -INF S[..] values from max and loop - // dont forget to set their SM values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(masked_begin, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(SM, 1, &max, SM, 1, Mup); - vvexpf(SM, SM, &Mup); - ggml_vec_sum_f32(Mup, &sum, SM); -#else - sum = ggml_vec_soft_max_f32(Mup, SM, S, max); -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(masked_begin, SM, sum); - - } - - // step-by-step explanation - { - // forward-process shape grads from backward process - // parallel_for ik2,ik3: - // for irep: - // iq2 = ik2 + irep*nek2 - // k[:D,:M,:,:] [D,M,:,:] grad[k][:D,:M,ik2,ik3] += grad[kcur] - // q[:D,:N,:,:] [D,N,:,:] grad[q][:D,iq1,iq2,iq3] += grad[qcur] - // v[:M,:D,:,:] [M,D,:,:] grad[v][:M,:D,iv2,iv3] += grad[vcur] - // for iq1: - // kcur = k[:D,:M,ik2,ik3] [D,M,1,1] grad[kcur] = grad[S1].T @ qcur - // qcur = q[:D,iq1,iq2,iq3] [D,1,1,1] grad[qcur] = grad[S1] @ kcur - // vcur = v[:M,:D,iv2,iv3] [M,D,1,1] grad[vcur] = grad[S5].T @ S4 - // S0 = -Inf [D,1,1,1] - // ~S1[i] = dot(kcur[:D,i], qcur) - // S1 = qcur @ kcur.T [M,1,1,1] grad[S1] = grad[S2] * scale - // S2 = S1 * scale [M,1,1,1] grad[S2] = diag_mask_zero(grad[S3], P) - // S3 = diag_mask_inf(S2, P) [M,1,1,1] grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // S4 = softmax(S3) [M,1,1,1] grad[S4] = grad[S5] @ vcur - // ~S5[i] = dot(vcur[:,i], S4) - // S5 = S4 @ vcur.T [D,1,1,1] grad[S5] = d[:D,id1,id2,id3] - // ~dst[i,iq1,iq2,iq3] = S5[i] ^ - // dst[:D,iq1,iq2,iq3] = S5 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,id1,id2,id3] - // dst backward-/ grad[dst] = d - // - // output gradients with their dependencies: - // - // grad[kcur] = grad[S1].T @ qcur - // grad[S1] = diag_mask_zero(grad[S3], P) * scale - // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // grad[S4] = grad[S5] @ vcur - // grad[S4] = d[:D,id1,id2,id3] @ vcur - // grad[qcur] = grad[S1] @ kcur - // grad[vcur] = grad[S5].T @ S4 - // grad[vcur] = d[:D,id1,id2,id3].T @ S4 - // - // in post-order: - // - // S1 = qcur @ kcur.T - // S2 = S1 * scale - // S3 = diag_mask_inf(S2, P) - // S4 = softmax(S3) - // grad[S4] = d[:D,id1,id2,id3] @ vcur - // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // grad[S1] = diag_mask_zero(grad[S3], P) * scale - // grad[qcur] = grad[S1] @ kcur - // grad[kcur] = grad[S1].T @ qcur - // grad[vcur] = d[:D,id1,id2,id3].T @ S4 - // - // using less variables (SM=S4): - // - // S = diag_mask_inf(qcur @ kcur.T * scale, P) - // SM = softmax(S) - // S = d[:D,iq1,iq2,iq3] @ vcur - // dot_SM_gradSM = dot(SM, S) - // S = SM * (S - dot(SM, S)) - // S = diag_mask_zero(S, P) * scale - // - // grad[q][:D,iq1,iq2,iq3] += S @ kcur - // grad[k][:D,:M,ik2,ik3] += S.T @ qcur - // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM - } - - // S = gradSM = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] - // S = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] - // for ic: - // S[:M] += vcur[:M,ic,iv2,iv3] * d[ic,id1,id2,id3] - // exclude known future zero S[..] values from operation - ggml_vec_set_f32(masked_begin, S, 0); - for (int64_t ic = 0; ic < D; ++ic) { - ggml_vec_mad_f32(masked_begin, - S, - (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), - *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); - } - - // S = SM * (S - dot(SM, S)) - float dot_SM_gradSM = 0; - ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1); - ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); - ggml_vec_mul_f32 (masked_begin, S, S, SM); - - // S = diag_mask_zero(S, P) * scale - // already done by above ggml_vec_set_f32 - - // exclude known zero S[..] values from operation - ggml_vec_scale_f32(masked_begin, S, scale); - - // S shape [M,1] - // SM shape [M,1] - // kcur shape [D,M] - // qcur shape [D,1] - // vcur shape [M,D] - - // grad[q][:D,iq1,iq2,iq3] += S @ kcur - // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M] - // for ic: - // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic,ik2,ik3] - // exclude known zero S[..] values from loop - for (int64_t ic = 0; ic < masked_begin; ++ic) { - ggml_vec_mad_f32(D, - (float *) ((char *) grad_q + (iq1*nbgq1 + iq2*nbgq2 + iq3*nbgq3)), - (float *) ((char *) k->data + (ic*nbk1 + ik2*nbk2 + ik3*nbk3)), - S[ic]); - } - - // grad[k][:D,:M,iq2,iq3] += S.T @ qcur - // for ic: - // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0] - // grad[k][:D,ic,iq2,iq3] += S[ic] * qcur[:D,0] - // exclude known zero S[..] values from loop - for (int64_t ic = 0; ic < masked_begin; ++ic) { - ggml_vec_mad_f32(D, - (float *) ((char *) grad_k + (ic*nbgk1 + ik2*nbgk2 + ik3*nbgk3)), - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), - S[ic]); - } - - // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM - // for ic: - // grad[v][:M,ic,iv2,iv3] += d[:D,id1,id2,id3].T[0,ic] * SM[:M] - // grad[v][:M,ic,iv2,iv3] += d[ic,id1,id2,id3] * SM[:M] - // exclude known zero SM[..] values from mad - for (int64_t ic = 0; ic < D; ++ic) { - ggml_vec_mad_f32(masked_begin, - (float *) ((char *) grad_v + ( ic*nbgv1 + iv2*nbgv2 + iv3*nbgv3)), - SM, - *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); - } - } - } - } -} - -static void ggml_compute_forward_flash_attn_back( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - - switch (q->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_flash_attn_back_f32(params, masked, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_ssm_conv - -static void ggml_compute_forward_ssm_conv_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; // conv_x - const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src1->ne[0]; // d_conv - const int ncs = src0->ne[0]; // d_conv - 1 + n_t - const int nr = src0->ne[1]; // d_inner - const int n_t = dst->ne[1]; // tokens per sequence - const int n_s = dst->ne[2]; // number of sequences in the batch - - GGML_ASSERT( dst->ne[0] == nr); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - const int ir = ir1 - ir0; - - for (int i3 = 0; i3 < n_s; ++i3) { - for (int i2 = 0; i2 < n_t; ++i2) { - // {d_conv - 1 + n_t, d_inner, n_seqs} - // sliding window - const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s} - const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner} - float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s} - - // TODO: transpose the output for smaller strides for big batches? - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // rowwise dot product - // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision - float sumf = 0.0f; - - // d_conv - for (int i0 = 0; i0 < nc; ++i0) { - sumf += s[i0 + i1*ncs] * c[i0 + i1*nc]; - } - x[i1] = sumf; - } - } - } -} - -static void ggml_compute_forward_ssm_conv( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->src[0]->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_ssm_conv_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_ssm_scan - -static void ggml_compute_forward_ssm_scan_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; // s - const struct ggml_tensor * src1 = dst->src[1]; // x - const struct ggml_tensor * src2 = dst->src[2]; // dt - const struct ggml_tensor * src3 = dst->src[3]; // A - const struct ggml_tensor * src4 = dst->src[4]; // B - const struct ggml_tensor * src5 = dst->src[5]; // C - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nc = src0->ne[0]; // d_state - const int64_t nr = src0->ne[1]; // d_inner - const int64_t n_t = src1->ne[1]; // number of tokens per sequence - const int64_t n_s = src0->ne[2]; // number of sequences in the batch - - GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src2->nb[0] == sizeof(float)); - GGML_ASSERT(src3->nb[0] == sizeof(float)); - GGML_ASSERT(src4->nb[0] == sizeof(float)); - GGML_ASSERT(src5->nb[0] == sizeof(float)); - // required for the dot product between s and C - GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); - // required for per-sequence offsets for states - GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); - // required to get correct offset for state destination (i.e. src1->nb[3]) - GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - const int ir = ir1 - ir0; - - for (int i3 = 0; i3 < n_s; ++i3) { - for (int i2 = 0; i2 < n_t; ++i2) { - const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s} - const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} - const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s} - const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} - const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s} - const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s} - float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} - float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s} - - // use the output as the source for the next token-wise iterations - if (i2 > 0) { s0 = s; } - - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 - float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; - float x_dt = x[i1] * dt_soft_plus; - float sumf = 0.0f; - // d_state - for (int i0 = 0; i0 < nc; ++i0) { - int i = i0 + i1*nc; - // state = prev_state * dA + dB * x - float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); - // y = rowwise_dotprod(state, C) - sumf += state * C[i0]; - s[i] = state; - } - y[i1] = sumf; - } - } - } -} - -static void ggml_compute_forward_ssm_scan( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->src[0]->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_ssm_scan_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_win_part - -static void ggml_compute_forward_win_part_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - - const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t w = ((const int32_t *)(dst->op_params))[2]; - - assert(ne00 == ne0); - assert(ne3 == nep0*nep1); - - // TODO: optimize / multi-thread - for (int py = 0; py < nep1; ++py) { - for (int px = 0; px < nep0; ++px) { - const int64_t i3 = py*nep0 + px; - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i02 = py*w + i2; - const int64_t i01 = px*w + i1; - const int64_t i00 = i0; - - const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; - const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; - - if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { - ((float *) dst->data)[i] = 0.0f; - } else { - ((float *) dst->data)[i] = ((float *) src0->data)[j]; - } - } - } - } - } - } -} - -static void ggml_compute_forward_win_part( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_win_part_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_win_unpart - -static void ggml_compute_forward_win_unpart_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - - const int32_t w = ((const int32_t *)(dst->op_params))[0]; - - // padding - const int px = (w - ne1%w)%w; - //const int py = (w - ne2%w)%w; - - const int npx = (px + ne1)/w; - //const int npy = (py + ne2)/w; - - assert(ne0 == ne00); - - // TODO: optimize / multi-thread - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int ip2 = i2/w; - const int ip1 = i1/w; - - const int64_t i02 = i2%w; - const int64_t i01 = i1%w; - const int64_t i00 = i0; - - const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; - const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; - - ((float *) dst->data)[j] = ((float *) src0->data)[i]; - } - } - } -} - -static void ggml_compute_forward_win_unpart( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_win_unpart_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -//gmml_compute_forward_unary - -static void ggml_compute_forward_unary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const enum ggml_unary_op op = ggml_get_unary_op(dst); - - switch (op) { - case GGML_UNARY_OP_ABS: - { - ggml_compute_forward_abs(params, dst); - } break; - case GGML_UNARY_OP_SGN: - { - ggml_compute_forward_sgn(params, dst); - } break; - case GGML_UNARY_OP_NEG: - { - ggml_compute_forward_neg(params, dst); - } break; - case GGML_UNARY_OP_STEP: - { - ggml_compute_forward_step(params, dst); - } break; - case GGML_UNARY_OP_TANH: - { - ggml_compute_forward_tanh(params, dst); - } break; - case GGML_UNARY_OP_ELU: - { - ggml_compute_forward_elu(params, dst); - } break; - case GGML_UNARY_OP_RELU: - { - ggml_compute_forward_relu(params, dst); - } break; - case GGML_UNARY_OP_SIGMOID: - { - ggml_compute_forward_sigmoid(params, dst); - } break; - case GGML_UNARY_OP_GELU: - { - ggml_compute_forward_gelu(params, dst); - } break; - case GGML_UNARY_OP_GELU_QUICK: - { - ggml_compute_forward_gelu_quick(params, dst); - } break; - case GGML_UNARY_OP_SILU: - { - ggml_compute_forward_silu(params, dst); - } break; - case GGML_UNARY_OP_HARDSWISH: - { - ggml_compute_forward_hardswish(params, dst); - } break; - case GGML_UNARY_OP_HARDSIGMOID: - { - ggml_compute_forward_hardsigmoid(params, dst); - } break; - case GGML_UNARY_OP_EXP: - { - ggml_compute_forward_exp(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_get_rel_pos - -static void ggml_compute_forward_get_rel_pos_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322 - - GGML_TENSOR_UNARY_OP_LOCALS - - const int64_t w = ne1; - - ggml_fp16_t * src0_data = (ggml_fp16_t *) src0->data; - ggml_fp16_t * dst_data = (ggml_fp16_t *) dst->data; - - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - const int64_t pos = (w - i1 - 1) + i2; - for (int64_t i0 = 0; i0 < ne0; ++i0) { - dst_data[i2*ne1*ne0 + i1*ne0 + i0] = src0_data[pos*ne00 + i0]; - } - } - } -} - -static void ggml_compute_forward_get_rel_pos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - { - ggml_compute_forward_get_rel_pos_f16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add_rel_pos - -static void ggml_compute_forward_add_rel_pos_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; - if (!inplace) { - if (params->ith == 0) { - memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359 - - float * src1_data = (float *) src1->data; - float * src2_data = (float *) src2->data; - float * dst_data = (float *) dst->data; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int ith = params->ith; - const int nth = params->nth; - - // total patches in dst - const int np = ne13; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - const int ip0 = dp*ith; - const int ip1 = MIN(ip0 + dp, np); - - for (int64_t i13 = ip0; i13 < ip1; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - const int64_t jp1 = i13*ne12*ne11*ne10 + i12*ne11*ne10 + i11*ne10; - for (int64_t i10 = 0; i10 < ne10; ++i10) { - const int64_t jp0 = jp1 + i10; - const float src1_e = src1_data[jp0]; - const float src2_e = src2_data[jp0]; - - const int64_t jdh = jp0 * ne10; - const int64_t jdw = jdh - (ne10 - 1) * i10; - - for (int64_t j = 0; j < ne10; ++j) { - dst_data[jdh + j ] += src2_e; - dst_data[jdw + j*ne10] += src1_e; - } - } - } - } - } -} - -static void ggml_compute_forward_add_rel_pos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_add_rel_pos_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rwkv_wkv6 - -static void ggml_compute_forward_rwkv_wkv6_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const int64_t T = dst->src[1]->ne[2]; - const int64_t C = dst->ne[0]; - const int64_t HEADS = dst->src[1]->ne[1]; - const int64_t n_seqs = dst->src[5]->ne[1]; - const int64_t head_size = C / HEADS; - - float * dst_data = (float *) dst->data; - float * state = ((float *) dst->data) + C * T; - - const int ith = params->ith; - const int nth = params->nth; - - if (ith >= HEADS) { - return; - } - - const int h_start = (HEADS * ith) / nth; - const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? - (HEADS * (ith + 1)) / nth : HEADS; - - float * k = (float *) dst->src[0]->data; - float * v = (float *) dst->src[1]->data; - float * r = (float *) dst->src[2]->data; - float * time_faaaa = (float *) dst->src[3]->data; - float * time_decay = (float *) dst->src[4]->data; - - size_t t_stride = HEADS * head_size; // Same to C - - size_t h_stride = C / HEADS; - GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS - size_t h_stride_2d = head_size * head_size; - - if (ith == 0) { - memset(dst_data, 0, T * C * sizeof(float)); - } - ggml_barrier(params->threadpool); - - - #if defined(__AVX__) && !defined(__AVX512F__) - #define GGML_F32X GGML_F32x8 - #define GGML_F32X_SET1 GGML_F32x8_SET1 - #define GGML_F32X_LOAD GGML_F32x8_LOAD - #define GGML_F32X_STORE GGML_F32x8_STORE - #define GGML_F32X_MUL GGML_F32x8_MUL - #define GGML_F32X_FMA GGML_F32x8_FMA - #define WKV_VECTOR_SIZE 8 - #elif defined(__AVX512F__) - #define GGML_F32X GGML_F32x16 - #define GGML_F32X_SET1 GGML_F32x16_SET1 - #define GGML_F32X_LOAD GGML_F32x16_LOAD - #define GGML_F32X_STORE GGML_F32x16_STORE - #define GGML_F32X_MUL GGML_F32x16_MUL - #define GGML_F32X_FMA GGML_F32x16_FMA - #define WKV_VECTOR_SIZE 16 - #elif defined(__ARM_NEON) && defined(__aarch64__) - #define GGML_F32X GGML_F32x4 - #define GGML_F32X_SET1 GGML_F32x4_SET1 - #define GGML_F32X_LOAD GGML_F32x4_LOAD - #define GGML_F32X_STORE GGML_F32x4_STORE - #define GGML_F32X_MUL GGML_F32x4_MUL - #define GGML_F32X_FMA GGML_F32x4_FMA - #define WKV_VECTOR_SIZE 4 - #endif - - #ifdef WKV_VECTOR_SIZE - const int64_t vec_count = head_size / WKV_VECTOR_SIZE; - - for (int64_t t = 0; t < T; t++) { - size_t t_offset = t * t_stride; - size_t state_offset = head_size * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; - - for (int64_t h = h_start; h < h_end; h++) { - size_t h_offset = h * h_stride; - size_t t_h_offset = t_offset + h_offset; - size_t h_2d_offset = h * h_stride_2d; - - for (int64_t i = 0; i < head_size; i++) { - size_t t_h_i_offset = t_h_offset + i; - size_t h_i_offset = h_offset + i; - size_t h_2d_i_offset = h_2d_offset + i * h_stride; - - float k_val = k[t_h_i_offset]; - float r_val = r[t_h_i_offset]; - float time_faaaa_val = time_faaaa[h_i_offset]; - float time_decay_val = time_decay[t_h_i_offset]; - - // Broadcast scalar values to vectors - GGML_F32X k_vec = GGML_F32X_SET1(k_val); - GGML_F32X r_vec = GGML_F32X_SET1(r_val); - GGML_F32X time_faaaa_vec = GGML_F32X_SET1(time_faaaa_val); - GGML_F32X time_decay_vec = GGML_F32X_SET1(time_decay_val); - - for (int64_t j = 0; j < vec_count; j++) { - size_t base_j = j * WKV_VECTOR_SIZE; - size_t t_h_j_offset = t_h_offset + base_j; - size_t h_2d_i_j_offset = h_2d_i_offset + base_j; - - // Load x elements at once - GGML_F32X v_vec = GGML_F32X_LOAD(&v[t_h_j_offset]); - GGML_F32X prev_state_vec = GGML_F32X_LOAD(&state_prev[h_2d_i_j_offset]); - GGML_F32X dst_vec = GGML_F32X_LOAD(&dst_data[t_h_j_offset]); - - // Compute kv = v * k - GGML_F32X kv_vec = GGML_F32X_MUL(v_vec, k_vec); - - // Compute temp = kv * time_faaaa + prev_state - GGML_F32X temp_vec = GGML_F32X_FMA(prev_state_vec, kv_vec, time_faaaa_vec); - - // Update dst: dst += temp * r - dst_vec = GGML_F32X_FMA(dst_vec, temp_vec, r_vec); - GGML_F32X_STORE(&dst_data[t_h_j_offset], dst_vec); - - // Update state: state = prev_state * time_decay + kv - GGML_F32X new_state_vec = GGML_F32X_FMA(kv_vec, prev_state_vec, time_decay_vec); - GGML_F32X_STORE(&state_cur[h_2d_i_j_offset], new_state_vec); - } - - // Handle remaining elements, this will not be used. - for (int64_t j = vec_count * WKV_VECTOR_SIZE; j < head_size; j++) { - size_t t_h_j_offset = t_h_offset + j; - size_t h_2d_i_j_offset = h_2d_i_offset + j; - float v_val = v[t_h_j_offset]; - float kv_val = v_val * k_val; - float prev_state_val = state_prev[h_2d_i_j_offset]; - float temp_val = kv_val * time_faaaa_val + prev_state_val; - dst_data[t_h_j_offset] += temp_val * r_val; - state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; - } - } - } - } - - #else - // basically fused operations: - // dst = r @ (time_faaaa * (k @ v) + state), - // state = time_decay * state + (k @ v), - // recursive through each token - for (int64_t t = 0; t < T; t++) { - size_t t_offset = t * t_stride; - size_t state_offset = head_size * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; - - for (int64_t h = h_start; h < h_end; h++) { - size_t h_offset = h * h_stride; - size_t t_h_offset = t_offset + h_offset; - size_t h_2d_offset = h * h_stride_2d; - - for (int64_t i = 0; i < head_size; i++) { - size_t t_h_i_offset = t_h_offset + i; - size_t h_i_offset = h_offset + i; - size_t h_2d_i_offset = h_2d_offset + i * h_stride; - - float k_val = k[t_h_i_offset]; - float r_val = r[t_h_i_offset]; - float time_faaaa_val = time_faaaa[h_i_offset]; - // RWKV v6: different time_decay for each token. - float time_decay_val = time_decay[t_h_i_offset]; - - for (int64_t j = 0; j < head_size; j++) { - size_t t_h_j_offset = t_h_offset + j; - size_t h_2d_i_j_offset = h_2d_i_offset + j; - - float v_val = v[t_h_j_offset]; - float kv_val = v_val * k_val; - float prev_state_val = state_prev[h_2d_i_j_offset]; - float temp_val = kv_val * time_faaaa_val + prev_state_val; - dst_data[t_h_j_offset] += temp_val * r_val; - state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; - } - } - } - } - #endif -} - - -static void ggml_compute_forward_rwkv_wkv6( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rwkv_wkv6_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_gla - -static void ggml_compute_forward_gla_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const int64_t T = dst->src[1]->ne[2]; - const int64_t C = dst->ne[0]; - const int64_t HEADS = dst->src[1]->ne[1]; - const int64_t n_seqs = dst->src[4]->ne[1]; - const int64_t head_size = C / HEADS; - const float scale = ggml_get_op_params_f32(dst, 0); - - float * dst_data = (float *) dst->data; - float * state = ((float *) dst->data) + C * T; - - const int ith = params->ith; - const int nth = params->nth; - - if (ith >= HEADS) { - return; - } - - const int h_start = (HEADS * ith) / nth; - const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? - (HEADS * (ith + 1)) / nth : HEADS; - - float * k = (float *) dst->src[0]->data; - float * v = (float *) dst->src[1]->data; - float * q = (float *) dst->src[2]->data; - float * g = (float *) dst->src[3]->data; - - size_t t_stride = HEADS * head_size; // Same to C - - size_t h_stride = C / HEADS; - GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS - size_t h_stride_2d = head_size * head_size; - - if (ith == 0) { - memset(dst_data, 0, T * C * sizeof(float)); - } - ggml_barrier(params->threadpool); - - - #if defined(__AVX__) && !defined(__AVX512F__) - #define GGML_F32X GGML_F32x8 - #define GGML_F32X_SET1 GGML_F32x8_SET1 - #define GGML_F32X_LOAD GGML_F32x8_LOAD - #define GGML_F32X_STORE GGML_F32x8_STORE - #define GGML_F32X_MUL GGML_F32x8_MUL - #define GGML_F32X_FMA GGML_F32x8_FMA - #define GLA_VECTOR_SIZE 8 - #elif defined(__AVX512F__) - #define GGML_F32X GGML_F32x16 - #define GGML_F32X_SET1 GGML_F32x16_SET1 - #define GGML_F32X_LOAD GGML_F32x16_LOAD - #define GGML_F32X_STORE GGML_F32x16_STORE - #define GGML_F32X_MUL GGML_F32x16_MUL - #define GGML_F32X_FMA GGML_F32x16_FMA - #define GLA_VECTOR_SIZE 16 - #elif defined(__ARM_NEON) && defined(__aarch64__) - #define GGML_F32X GGML_F32x4 - #define GGML_F32X_SET1 GGML_F32x4_SET1 - #define GGML_F32X_LOAD GGML_F32x4_LOAD - #define GGML_F32X_STORE GGML_F32x4_STORE - #define GGML_F32X_MUL GGML_F32x4_MUL - #define GGML_F32X_FMA GGML_F32x4_FMA - #define GLA_VECTOR_SIZE 4 - #endif - - #ifdef GLA_VECTOR_SIZE - const int64_t vec_count = head_size / GLA_VECTOR_SIZE; - - for (int64_t t = 0; t < T; t++) { - size_t t_offset = t * t_stride; - size_t state_offset = head_size * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[4]->data + state_offset; - - for (int64_t h = h_start; h < h_end; h++) { - size_t h_offset = h * h_stride; - size_t t_h_offset = t_offset + h_offset; - size_t h_2d_offset = h * h_stride_2d; - - for (int64_t i = 0; i < head_size; i++) { - size_t t_h_i_offset = t_h_offset + i; - size_t h_2d_i_offset = h_2d_offset + i * h_stride; - - float k_val = k[t_h_i_offset]; - float q_val = q[t_h_i_offset] * scale; - float g_val = g[t_h_i_offset]; - - // Broadcast scalar values to vectors - GGML_F32X k_vec = GGML_F32X_SET1(k_val); - GGML_F32X q_vec = GGML_F32X_SET1(q_val); - GGML_F32X g_vec = GGML_F32X_SET1(g_val); - - for (int64_t j = 0; j < vec_count; j++) { - size_t base_j = j * GLA_VECTOR_SIZE; - size_t t_h_j_offset = t_h_offset + base_j; - size_t h_2d_i_j_offset = h_2d_i_offset + base_j; - - // Load x elements at once - GGML_F32X v_vec = GGML_F32X_LOAD(&v[t_h_j_offset]); - GGML_F32X prev_state_vec = GGML_F32X_LOAD(&state_prev[h_2d_i_j_offset]); - GGML_F32X dst_vec = GGML_F32X_LOAD(&dst_data[t_h_j_offset]); - - // Compute kv = v * k - GGML_F32X kv_vec = GGML_F32X_MUL(v_vec, k_vec); - - // Compute temp = prev_state * g + kv - GGML_F32X temp_vec = GGML_F32X_FMA(kv_vec, prev_state_vec, g_vec); - - // Update dst: dst += temp * q - dst_vec = GGML_F32X_FMA(dst_vec, temp_vec, q_vec); - GGML_F32X_STORE(&dst_data[t_h_j_offset], dst_vec); - - // Update state - GGML_F32X_STORE(&state_cur[h_2d_i_j_offset], temp_vec); - } - - // Handle remaining elements, this will not be used. - for (int64_t j = vec_count * GLA_VECTOR_SIZE; j < head_size; j++) { - size_t t_h_j_offset = t_h_offset + j; - size_t h_2d_i_j_offset = h_2d_i_offset + j; - float v_val = v[t_h_j_offset]; - float kv_val = v_val * k_val; - float prev_state_val = state_prev[h_2d_i_j_offset]; - float temp_val = kv_val + prev_state_val * g_val; - dst_data[t_h_j_offset] += temp_val * q_val; - state_cur[h_2d_i_j_offset] = temp_val; - } - } - } - } - - #else - for (int64_t t = 0; t < T; t++) { - size_t t_offset = t * t_stride; - size_t state_offset = head_size * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[4]->data + state_offset; - - for (int64_t h = h_start; h < h_end; h++) { - size_t h_offset = h * h_stride; - size_t t_h_offset = t_offset + h_offset; - size_t h_2d_offset = h * h_stride_2d; - - for (int64_t i = 0; i < head_size; i++) { - size_t t_h_i_offset = t_h_offset + i; - size_t h_2d_i_offset = h_2d_offset + i * h_stride; - - float k_val = k[t_h_i_offset]; - float q_val = q[t_h_i_offset] * scale; - float g_val = g[t_h_i_offset]; - - for (int64_t j = 0; j < head_size; j++) { - size_t t_h_j_offset = t_h_offset + j; - size_t h_2d_i_j_offset = h_2d_i_offset + j; - - float v_val = v[t_h_j_offset]; - float kv_val = v_val * k_val; - float prev_state_val = state_prev[h_2d_i_j_offset]; - float temp_val = prev_state_val * g_val + kv_val; - dst_data[t_h_j_offset] += temp_val * q_val; - state_cur[h_2d_i_j_offset] = temp_val; - } - } - } - } - #endif -} - - -static void ggml_compute_forward_gla( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_gla_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rwkv_wkv7 - -static void ggml_compute_forward_rwkv_wkv7_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const int64_t T = dst->src[1]->ne[2]; - const int64_t C = dst->ne[0]; - const int64_t HEADS = dst->src[1]->ne[1]; - const int64_t n_seqs = dst->src[6]->ne[1]; - const int64_t head_size = C / HEADS; - - float * dst_data = (float *) dst->data; - float * state = ((float *) dst->data) + C * T; - - const int ith = params->ith; - const int nth = params->nth; - - if (ith >= HEADS) { - return; - } - - const int h_start = (HEADS * ith) / nth; - const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? - (HEADS * (ith + 1)) / nth : HEADS; - - float * r = (float *) dst->src[0]->data; - float * w = (float *) dst->src[1]->data; - float * k = (float *) dst->src[2]->data; - float * v = (float *) dst->src[3]->data; - float * a = (float *) dst->src[4]->data; - float * b = (float *) dst->src[5]->data; - - int64_t t_stride = HEADS * head_size; // Same to C - - int64_t h_stride = C / HEADS; - GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS - int64_t h_stride_2d = head_size * head_size; - - #if defined(GGML_SIMD) - for (int64_t t = 0; t < T; t++) { - int64_t t_offset = t * t_stride; - int64_t state_offset = head_size * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset; - - for (int64_t h = h_start; h < h_end; h++) { - int64_t h_offset = h * h_stride; - int64_t t_h_offset = t_offset + h_offset; - int64_t h_2d_offset = h * h_stride_2d; - - for (int64_t ii = 0; ii < head_size; ii++) { - int64_t t_h_i_offset = t_h_offset + ii; - int64_t h_2d_i_offset = h_2d_offset + ii * h_stride; - - GGML_F32_VEC v_vec = GGML_F32_VEC_SET1(v[t_h_i_offset]); - - float sa = 0; - { - GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; - GGML_F32_VEC ax[GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - for (int64_t j = 0; j < head_size; j += GGML_F32_STEP) { - for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) { - ax[kk] = GGML_F32_VEC_LOAD(&a[t_h_offset + j + kk * GGML_F32_EPR]); - ay[kk] = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_offset + j + kk * GGML_F32_EPR]); - sum[kk] = GGML_F32_VEC_FMA(sum[kk], ax[kk], ay[kk]); - } - } - GGML_F32_VEC_REDUCE(sa, sum); - } - - GGML_F32_VEC sa_vec = GGML_F32_VEC_SET1(sa); - - int64_t j = 0; - GGML_F32_VEC result_vec[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; - for (; j < head_size; j += GGML_F32_STEP) { - for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) { - int64_t t_h_j_offset = t_h_offset + j + kk * GGML_F32_EPR; - int64_t h_2d_i_j_offset = h_2d_i_offset + j + kk * GGML_F32_EPR; - - GGML_F32_VEC r_vec = GGML_F32_VEC_LOAD(&r[t_h_j_offset]); - GGML_F32_VEC w_vec = GGML_F32_VEC_LOAD(&w[t_h_j_offset]); - GGML_F32_VEC k_vec = GGML_F32_VEC_LOAD(&k[t_h_j_offset]); - GGML_F32_VEC b_vec = GGML_F32_VEC_LOAD(&b[t_h_j_offset]); - - k_vec = GGML_F32_VEC_MUL(v_vec, k_vec); - - GGML_F32_VEC state_vec = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_j_offset]); - // kv + s * decay + sa * b - state_vec = GGML_F32_VEC_FMA(k_vec, state_vec, w_vec); - state_vec = GGML_F32_VEC_FMA(state_vec, sa_vec, b_vec); - GGML_F32_VEC_STORE(&state_cur[h_2d_i_j_offset], state_vec); - - result_vec[kk] = GGML_F32_VEC_FMA(result_vec[kk], state_vec, r_vec); - } - } - GGML_F32_VEC_REDUCE(dst_data[t_h_i_offset], result_vec); - - // There shouldn't be left-overs though. - for (; j < head_size; j++) { - int64_t t_h_j_offset = t_h_offset + j; - int64_t h_2d_i_j_offset = h_2d_i_offset + j; - - float r_val = r[t_h_j_offset]; - float w_val = w[t_h_j_offset]; - float k_val = k[t_h_j_offset]; - float b_val = b[t_h_j_offset]; - float kv_val = v[t_h_i_offset] * k_val; - - float prev_state_val = state_prev[h_2d_i_j_offset]; - state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val; - dst_data[t_h_i_offset] += state_cur[h_2d_i_j_offset] * r_val; - } - } - } - } - #else - for (int64_t t = 0; t < T; t++) { - int64_t t_offset = t * t_stride; - int64_t state_offset = head_size * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset; - - for (int64_t h = h_start; h < h_end; h++) { - int64_t h_offset = h * h_stride; - int64_t t_h_offset = t_offset + h_offset; - int64_t h_2d_offset = h * h_stride_2d; - - for (int64_t i = 0; i < head_size; i++) { - int64_t t_h_i_offset = t_h_offset + i; - int64_t h_2d_i_offset = h_2d_offset + i * h_stride; - - float v_val = v[t_h_i_offset]; - - float sa = 0, result = 0; - for (int64_t j = 0; j < head_size; j++) { - sa += a[t_h_offset + j] * state_prev[h_2d_i_offset + j]; - } - - for (int64_t j = 0; j < head_size; j++) { - int64_t t_h_j_offset = t_h_offset + j; - int64_t h_2d_i_j_offset = h_2d_i_offset + j; - - float r_val = r[t_h_j_offset]; - float w_val = w[t_h_j_offset]; - float k_val = k[t_h_j_offset]; - float b_val = b[t_h_j_offset]; - float kv_val = v_val * k_val; - float prev_state_val = state_prev[h_2d_i_j_offset]; - state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val; - result += state_cur[h_2d_i_j_offset] * r_val; - } - dst_data[t_h_i_offset] = result; - } - } - } - #endif -} - - -static void ggml_compute_forward_rwkv_wkv7( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rwkv_wkv7_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_unary - -static void ggml_compute_forward_map_unary_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_unary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - fun(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_map_unary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_unary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_map_unary_f32(params, dst, fun); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_binary - -static void ggml_compute_forward_map_binary_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_binary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(src1)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - fun(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1])), - (float *) ((char *) src1->data + i*(src1->nb[1]))); - } -} - -static void ggml_compute_forward_map_binary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_binary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_map_binary_f32(params, dst, fun); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_custom1 - -static void ggml_compute_forward_map_custom1_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom1_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - - if (params->ith != 0) { - return; - } - - fun(dst, a); -} - -// ggml_compute_forward_map_custom2 - -static void ggml_compute_forward_map_custom2_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom2_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - - if (params->ith != 0) { - return; - } - - fun(dst, a, b); -} - -// ggml_compute_forward_map_custom3 - -static void ggml_compute_forward_map_custom3_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom3_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - const struct ggml_tensor * c = dst->src[1]; - - if (params->ith != 0) { - return; - } - - fun(dst, a, b, c); -} - -// ggml_compute_forward_map_custom1 - -static void ggml_compute_forward_map_custom1( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - - struct ggml_map_custom1_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_map_custom2 - -static void ggml_compute_forward_map_custom2( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - - struct ggml_map_custom2_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, b, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_map_custom3 - -static void ggml_compute_forward_map_custom3( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - const struct ggml_tensor * c = dst->src[2]; - - struct ggml_map_custom3_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_cross_entropy_loss - -static void ggml_compute_forward_cross_entropy_loss_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); - GGML_ASSERT(ggml_are_same_shape(src0, src1)); - GGML_ASSERT(ggml_is_scalar(dst)); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - // TODO: handle transposed/permuted matrices - const int64_t nc = src0->ne[0]; - const int64_t nr = ggml_nrows(src0); - - const int ith = params->ith; - const int nth = params->nth; - - float * sums = (float *) params->wdata; - float * st = ((float *) params->wdata) + nth + ith*nc; - float sum_thread = 0.0f; - - GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - for (int64_t i1 = ir0; i1 < ir1; ++i1) { - const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]); - const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(s0[i])); - assert(!isnan(s1[i])); - } -#endif - - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max); - assert(sum_softmax >= 0.0); - - ggml_vec_add1_f32(nc, st, st, -sum_softmax); - ggml_vec_mul_f32(nc, st, st, s1); - - float sum_st = 0.0f; - ggml_vec_sum_f32(nc, &sum_st, st); - sum_thread += sum_st; - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - assert(!isnan(st[i])); - assert(!isinf(st[i])); - } -#endif - } - sums[ith] = sum_thread; - ggml_barrier(params->threadpool); - - if (ith == 0) { - float * dp = (float *) dst->data; - ggml_vec_sum_f32(nth, dp, sums); - dp[0] *= -1.0f / (float) nr; - } -} - -static void ggml_compute_forward_cross_entropy_loss( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cross_entropy_loss_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cross_entropy_loss_back - -static void ggml_compute_forward_cross_entropy_loss_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * grad = dst->src[0]; // gradient of forward pass output - const struct ggml_tensor * src0f = dst->src[1]; // src0 of forward pass - const struct ggml_tensor * src1f = dst->src[2]; // src1 of forward pass - - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_is_contiguous(src0f)); - GGML_ASSERT(ggml_is_contiguous(src1f)); - GGML_ASSERT(ggml_is_contiguous(grad)); - GGML_ASSERT(ggml_are_same_shape(src0f, src1f) && ggml_are_same_shape(src0f, dst)); - - const int64_t ith = params->ith; - const int64_t nth = params->nth; - - // TODO: handle transposed/permuted matrices - const int64_t nc = src0f->ne[0]; - const int64_t nr = ggml_nrows(src0f); - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - const float d_by_nr = ((const float *) grad->data)[0] / (float) nr; - - for (int64_t i1 = ir0; i1 < ir1; i1++) { - float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); - const float * s0 = (const float *)((const char *) src0f->data + i1*src0f->nb[1]); - const float * s1 = (const float *)((const char *) src1f->data + i1*src1f->nb[1]); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(s0[i])); - assert(!isnan(s1[i])); - } -#endif - - // soft_max - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - const ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); - assert(sum > 0.0); - ggml_vec_scale_f32(nc, ds0, 1.0/sum); - - // grad(src0f) = (softmax(src0f) - src1f) * grad(cross_entropy_loss(src0f, src1f)) / nr - ggml_vec_sub_f32(nc, ds0, ds0, s1); - ggml_vec_scale_f32(nc, ds0, d_by_nr); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - assert(!isnan(ds0[i])); - assert(!isinf(ds0[i])); - } -#endif - } -} - -static void ggml_compute_forward_cross_entropy_loss_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cross_entropy_loss_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_opt_step_adamw_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src0_grad = dst->src[1]; - const struct ggml_tensor * src0_grad_m = dst->src[2]; - const struct ggml_tensor * src0_grad_v = dst->src[3]; - const struct ggml_tensor * adamw_params = dst->src[4]; - - GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); - GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m)); - GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v)); - GGML_ASSERT(ggml_nelements(adamw_params) == 7); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const float * adamw_params_ptr = ggml_get_data_f32(adamw_params); - const float alpha = adamw_params_ptr[0]; - const float beta1 = adamw_params_ptr[1]; - const float beta2 = adamw_params_ptr[2]; - const float eps = adamw_params_ptr[3]; - const float wd = adamw_params_ptr[4]; - const float beta1h = adamw_params_ptr[5]; - const float beta2h = adamw_params_ptr[6]; - - for (int ir = ir0; ir < ir1; ++ir) { - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const size_t offset = i03*nb03 + i02*nb02 + i01*nb01; - - float * w = (float *) ((char *) src0->data + offset); // weight - const float * g = (const float *) ((const char *) src0_grad->data + offset); // grad - float * m = (float *) ((char *) src0_grad_m->data + offset); - float * v = (float *) ((char *) src0_grad_v->data + offset); - - for (int i00 = 0; i00 < ne00; ++i00) { - m[i00] = m[i00]*beta1 + g[i00]*(1.0f - beta1); - v[i00] = v[i00]*beta2 + g[i00]*g[i00]*(1.0f - beta2); - - const float mh = m[i00]*beta1h; - const float vh = sqrtf(v[i00]*beta2h) + eps; - - // The weight decay is applied independently of the Adam momenta m and v. - // This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss. - // See: https://arxiv.org/pdf/1711.05101v3.pdf - w[i00] = w[i00]*(1.0f - alpha*wd) - alpha*mh/vh; - } - } -} - -static void ggml_compute_forward_opt_step_adamw( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_opt_step_adamw_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} ///////////////////////////////// static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp new file mode 100644 index 0000000000..7a8d5ac6fd --- /dev/null +++ b/ggml/src/ggml-cpu/ops.cpp @@ -0,0 +1,8719 @@ +#include "ops.h" + +#include "ggml-cpu.h" +#include "ggml-impl.h" +#include "binary-ops.h" +#include "unary-ops.h" +#include "vec.h" + +#include + +#if defined(_MSC_VER) +// disable "possible loss of data" to avoid hundreds of casts +// we should just be careful :) +#pragma warning(disable: 4244 4267) + +// disable POSIX deprecation warnings +// these functions are never going away, anyway +#pragma warning(disable: 4996) + +// unreachable code because of multiple instances of code after GGML_ABORT +#pragma warning(disable: 4702) +#endif + +// ggml_compute_forward_dup + +static void ggml_compute_forward_dup_same_cont( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == dst->type); + + const size_t nb0 = ggml_type_size(src0->type); + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by blocks + const int nk = ggml_nelements(src0)/ggml_blck_size(src0->type); + const int dr = (nk + nth - 1) / nth; + const int k0 = dr * ith; + const int k1 = MIN(k0 + dr, nk); + + if (k0 < k1) { + memcpy( + ((char *) dst->data + k0*nb0), + ((char *) src0->data + k0*nb0), + (k1 - k0) * nb0); + } +} + +static void ggml_compute_forward_dup_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy + + if (ggml_is_contiguous(dst)) { + if (nb00 == sizeof(ggml_fp16_t)) { + if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; + float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + for (int i00 = 0; i00 < ne00; i00++) { + src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); + } + + quantize_row_q(src0_f32, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } + return; + } + + // dst counters + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); + + if (++i10 == ne00) { + i10 = 0; + if (++i11 == ne01) { + i11 = 0; + if (++i12 == ne02) { + i12 = 0; + if (++i13 == ne03) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } +} + +static void ggml_compute_forward_dup_bf16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy + + if (ggml_is_contiguous(dst)) { + if (nb00 == sizeof(ggml_bf16_t)) { + if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00])); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; + float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + for (int i00 = 0; i00 < ne00; i00++) { + src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]); + } + + quantize_row_q(src0_f32, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr)); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } + return; + } + + // dst counters + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_BF16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t)); + + if (++i10 == ne00) { + i10 = 0; + if (++i11 == ne01) { + i11 = 0; + if (++i12 == ne02) { + i12 = 0; + if (++i13 == ne03) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr)); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } +} + +static void ggml_compute_forward_dup_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + if (ggml_is_contiguous(dst)) { + // TODO: simplify + if (nb00 == sizeof(float)) { + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + quantize_row_q(src0_ptr, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } + + return; + } + + // dst counters + + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(float)); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_BF16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } +} + +// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. +static void ggml_compute_forward_dup_bytes( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(src0->type == dst->type); + + GGML_TENSOR_UNARY_OP_LOCALS; + + if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { + ggml_compute_forward_dup_same_cont(params, dst); + return; + } + + const size_t type_size = ggml_type_size(src0->type); + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ggml_are_same_shape(src0, dst) && + nb00 == type_size && nb0 == type_size) { + // copy by rows + const size_t rs = ggml_row_size(src0->type, ne00); + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + if (ggml_is_contiguous(dst)) { + size_t id = 0; + char * dst_ptr = (char *) dst->data; + const size_t rs = ne00 * type_size; + + if (nb00 == type_size) { + // src0 is contigous on first dimension, copy by rows + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int64_t i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, type_size); + + id += type_size; + } + } + id += rs * (ne01 - ir1); + } + } + } + + return; + } + + // dst counters + int64_t k10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + // number of blocks in a row + const int64_t nk00 = ne00 / ggml_blck_size(src0->type); + const int64_t nk0 = ne0 / ggml_blck_size(dst->type); + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + k10 += nk00 * ir0; + while (k10 >= nk0) { + k10 -= nk0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t k00 = 0; k00 < nk00; k00++) { + const char * src0_ptr = ((char *) src0->data + k00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + k10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, type_size); + + if (++k10 == nk0) { + k10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + k10 += nk00 * (ne01 - ir1); + while (k10 >= nk0) { + k10 -= nk0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } +} + +static void ggml_compute_forward_dup_q( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + + size_t qk = ggml_blck_size(type); + const int64_t nr = ggml_nelements(src1) / qk; + + // destination must be contiguous in the first dimension + GGML_ASSERT(nb10 == ggml_type_size(dst->type)); + // must either have first dimension large enough to hold a row, or fully contiguous + GGML_ASSERT((ne10 % qk) == 0 || ggml_is_contiguous(dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t ir = ir0; ir < ir1; ++ir) { + + uint32_t i = ir * qk; + + const int64_t i03 = i/(ne00 * ne01 * ne02); + const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int64_t x_offset = (i00/qk)*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; + + const int64_t i13 = i/(ne10 * ne11 * ne12); + const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13; + + dequantize_row_q( + (const void *) ((char *) src0->data + x_offset), + (float *) ((char *) dst->data + dst_offset), qk); + } +} + +void ggml_compute_forward_dup( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (src0->type == dst->type) { + ggml_compute_forward_dup_bytes(params, dst); + return; + } + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_dup_f16(params, dst); + } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_dup_bf16(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_dup_f32(params, dst); + } break; + default: + { + if (ggml_is_quantized(src0->type) && dst->type == GGML_TYPE_F32) { + ggml_compute_forward_dup_q(params, dst); + break; + } + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_add + +static void ggml_compute_forward_add_q_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const ggml_type type = src0->type; + const ggml_type dtype = dst->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dtype)->from_float; + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == sizeof(float)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ggml_is_quantized(src0->type)); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 indices + const int i03 = ir/(ne02*ne01); + const int i02 = (ir - i03*ne02*ne01)/ne01; + const int i01 = (ir - i03*ne02*ne01 - i02*ne01); + + // src1 and dst are same shape as src0 => same indices + const int i13 = i03; + const int i12 = i02; + const int i11 = i01; + + const int i3 = i03; + const int i2 = i02; + const int i1 = i01; + + void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); + float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); + void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + assert(ne00 % 32 == 0); + + // unquantize row from src0 to temp buffer + dequantize_row_q(src0_row, wdata, ne00); + // add src1 + ggml_vec_acc_f32(ne00, wdata, src1_row); + // quantize row to dst + if (quantize_row_q != NULL) { + quantize_row_q(wdata, dst_row, ne00); + } else { + memcpy(dst_row, wdata, ne0*nb0); + } + } +} + +void ggml_compute_forward_add( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + { + ggml_compute_forward_add_non_quantized(params, dst); + } break; + 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_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + { + ggml_compute_forward_add_q_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_add1 + +static void ggml_compute_forward_add1_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + +#ifdef GGML_USE_ACCELERATE + GGML_UNUSED(ggml_vec_add1_f32); + + vDSP_vadd( + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, + (float *) ((char *) src1->data), 0, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, + ne0); +#else + ggml_vec_add1_f32(ne0, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), + *(float *) src1->data); +#endif + } +} + +static void ggml_compute_forward_add1_f16_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_f16_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_q_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + const ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(type)->from_float; + + // we don't support permuted src0 + GGML_ASSERT(nb00 == ggml_type_size(type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ggml_is_quantized(src0->type)); + GGML_ASSERT(dst->type == src0->type); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); + void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); + + assert(ne0 % 32 == 0); + + // unquantize row from src0 to temp buffer + dequantize_row_q(src0_row, wdata, ne0); + // add src1 + ggml_vec_acc1_f32(ne0, wdata, v); + // quantize row to dst + quantize_row_q(wdata, dst_row, ne0); + } +} + +static void ggml_compute_forward_add1_bf16_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_bf16_bf16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_BF16); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +void ggml_compute_forward_add1( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_add1_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + if (src1->type == GGML_TYPE_F16) { + ggml_compute_forward_add1_f16_f16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add1_f16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_BF16: + { + if (src1->type == GGML_TYPE_BF16) { + ggml_compute_forward_add1_bf16_bf16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add1_bf16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + 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_Q8_1: + 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_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + { + ggml_compute_forward_add1_q_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_acc + +static void ggml_compute_forward_acc_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + + // view src0 and dst with these strides and data offset inbytes during acc + // nb0 is implicitly element_size because src0 and dst are contiguous + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; + + if (!inplace) { + if (params->ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src1); + const int nc = src1->ne[0]; + + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) + + // src0 and dst as viewed during acc + const size_t nb0 = ggml_element_size(src0); + + const size_t nb00 = nb0; + const size_t nb01 = nb1; + const size_t nb02 = nb2; + const size_t nb03 = nb3; + + GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); + GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); + + GGML_ASSERT(nb10 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are viewed with shape of src1 and offset + // => same indices + const int i3 = ir/(ne12*ne11); + const int i2 = (ir - i3*ne12*ne11)/ne11; + const int i1 = (ir - i3*ne12*ne11 - i2*ne11); + +#ifdef GGML_USE_ACCELERATE + vDSP_vadd( + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, + (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); +#else + ggml_vec_add_f32(nc, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), + (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); +#endif + } +} + +void ggml_compute_forward_acc( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_acc_f32(params, dst); + } break; + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + 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_Q8_1: + 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_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sum + +static void ggml_compute_forward_sum_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_scalar(dst)); + assert(src0->nb[0] == sizeof(float)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + ggml_float sum = 0; + ggml_float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_f32_ggf(ne00, + &row_sum, + (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); + sum += row_sum; + } + } + } + ((float *) dst->data)[0] = sum; +} + +static void ggml_compute_forward_sum_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_scalar(dst)); + + assert(src0->nb[0] == sizeof(ggml_fp16_t)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + float sum = 0; + float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_f16_ggf(ne00, + &row_sum, + (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); + sum += row_sum; + } + } + } + ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); +} + +static void ggml_compute_forward_sum_bf16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_scalar(dst)); + + assert(src0->nb[0] == sizeof(ggml_bf16_t)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + float sum = 0; + float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_bf16_ggf(ne00, + &row_sum, + (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); + sum += row_sum; + } + } + } + ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum); +} + +void ggml_compute_forward_sum( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sum_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_sum_f16(params, dst); + } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_sum_bf16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sum_rows + +static void ggml_compute_forward_sum_rows_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(dst->nb[0] == sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(ne0 == 1); + GGML_ASSERT(ne1 == ne01); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne3 == ne03); + + for (int64_t i3 = 0; i3 < ne03; i3++) { + for (int64_t i2 = 0; i2 < ne02; i2++) { + for (int64_t i1 = 0; i1 < ne01; i1++) { + float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); + float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); + float row_sum = 0; + ggml_vec_sum_f32(ne00, &row_sum, src_row); + dst_row[0] = row_sum; + } + } + } +} + +void ggml_compute_forward_sum_rows( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sum_rows_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_mean + +static void ggml_compute_forward_mean_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(src0->nb[0] == sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + assert(ne0 == 1); + assert(ne1 == ne01); + assert(ne2 == ne02); + assert(ne3 == ne03); + + GGML_UNUSED(ne0); + GGML_UNUSED(ne1); + GGML_UNUSED(ne2); + GGML_UNUSED(ne3); + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_f32(ne00, + (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); + + *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; + } + } + } +} + +void ggml_compute_forward_mean( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_mean_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_argmax + +static void ggml_compute_forward_argmax_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(src0->nb[0] == sizeof(float)); + assert(dst->nb[0] == sizeof(float)); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + + const size_t nb01 = src0->nb[1]; + const size_t nb0 = dst->nb[0]; + + for (int64_t i1 = 0; i1 < ne01; i1++) { + float * src = (float *) ((char *) src0->data + i1*nb01); + int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); + int v = 0; + ggml_vec_argmax_f32(ne00, &v, src); + dst_[0] = v; + } +} + +void ggml_compute_forward_argmax( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_argmax_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_count_equal + +static void ggml_compute_forward_count_equal_i32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS; + + GGML_ASSERT(src0->type == GGML_TYPE_I32); + GGML_ASSERT(src1->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_are_same_shape(src0, src1)); + GGML_ASSERT(ggml_is_scalar(dst)); + GGML_ASSERT(dst->type == GGML_TYPE_I64); + + const int64_t nr = ggml_nrows(src0); + + const int ith = params->ith; + const int nth = params->nth; + + int64_t * sums = (int64_t *) params->wdata; + int64_t sum_thread = 0; + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t i03 = ir / (ne02*ne01); + const int64_t i02 = (ir - i03*ne03) / ne01; + const int64_t i01 = ir - i03*ne03 - i02*ne02; + + const char * data0 = (const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01; + const char * data1 = (const char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11; + + for (int64_t i00 = 0; i00 < ne00; ++i00) { + const int32_t val0 = *((const int32_t *) (data0 + i00*nb00)); + const int32_t val1 = *((const int32_t *) (data1 + i00*nb10)); + + sum_thread += val0 == val1; + } + } + if (ith != 0) { + sums[ith] = sum_thread; + } + ggml_barrier(params->threadpool); + + if (ith != 0) { + return; + } + + for (int ith_other = 1; ith_other < nth; ++ith_other) { + sum_thread += sums[ith_other]; + } + *((int64_t *) dst->data) = sum_thread; +} + +void ggml_compute_forward_count_equal( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_I32: + { + ggml_compute_forward_count_equal_i32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_repeat + +static void ggml_compute_forward_repeat_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_can_repeat(src0, dst)); + + GGML_TENSOR_UNARY_OP_LOCALS + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne0/ne00); + const int nr1 = (int)(ne1/ne01); + const int nr2 = (int)(ne2/ne02); + const int nr3 = (int)(ne3/ne03); + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // TODO: maybe this is not optimal? + for (int i3 = 0; i3 < nr3; i3++) { + for (int k3 = 0; k3 < ne03; k3++) { + for (int i2 = 0; i2 < nr2; i2++) { + for (int k2 = 0; k2 < ne02; k2++) { + for (int i1 = 0; i1 < nr1; i1++) { + for (int k1 = 0; k1 < ne01; k1++) { + for (int i0 = 0; i0 < nr0; i0++) { + ggml_vec_cpy_f32(ne00, + (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), + (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); + } + } + } + } + } + } + } +} + +static void ggml_compute_forward_repeat_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_can_repeat(src0, dst)); + + GGML_TENSOR_UNARY_OP_LOCALS + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne0/ne00); + const int nr1 = (int)(ne1/ne01); + const int nr2 = (int)(ne2/ne02); + const int nr3 = (int)(ne3/ne03); + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // TODO: maybe this is not optimal? + for (int i3 = 0; i3 < nr3; i3++) { + for (int k3 = 0; k3 < ne03; k3++) { + for (int i2 = 0; i2 < nr2; i2++) { + for (int k2 = 0; k2 < ne02; k2++) { + for (int i1 = 0; i1 < nr1; i1++) { + for (int k1 = 0; k1 < ne01; k1++) { + for (int i0 = 0; i0 < nr0; i0++) { + ggml_fp16_t * y = (ggml_fp16_t *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0); + ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01); + // ggml_vec_cpy_f16(ne00, y, x) + for (int i = 0; i < ne00; ++i) { + y[i] = x[i]; + } + } + } + } + } + } + } + } +} + +void ggml_compute_forward_repeat( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_I16: + { + ggml_compute_forward_repeat_f16(params, dst); + } break; + case GGML_TYPE_F32: + case GGML_TYPE_I32: + { + ggml_compute_forward_repeat_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_repeat_back + +static void ggml_compute_forward_repeat_back_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_can_repeat(dst, src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne00/ne0); + const int nr1 = (int)(ne01/ne1); + const int nr2 = (int)(ne02/ne2); + const int nr3 = (int)(ne03/ne3); + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + if (ggml_is_contiguous(dst)) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, (float *)dst->data, 0); + } else { + for (int k3 = 0; k3 < ne3; k3++) { + for (int k2 = 0; k2 < ne2; k2++) { + for (int k1 = 0; k1 < ne1; k1++) { + ggml_vec_set_f32(ne0, + (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), + 0); + } + } + } + } + + // TODO: maybe this is not optimal? + for (int i3 = 0; i3 < nr3; i3++) { + for (int k3 = 0; k3 < ne3; k3++) { + for (int i2 = 0; i2 < nr2; i2++) { + for (int k2 = 0; k2 < ne2; k2++) { + for (int i1 = 0; i1 < nr1; i1++) { + for (int k1 = 0; k1 < ne1; k1++) { + for (int i0 = 0; i0 < nr0; i0++) { + ggml_vec_acc_f32(ne0, + (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), + (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); + } + } + } + } + } + } + } +} + +void ggml_compute_forward_repeat_back( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_repeat_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_concat + +static void ggml_compute_forward_concat_any( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + const size_t len = ggml_type_size(src0->type); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = src0->ne[dim]; + + const char * x; + + // TODO: smarter multi-theading + for (int i3 = 0; i3 < ne3; i3++) { + for (int i2 = ith; i2 < ne2; i2 += nth) { + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03; + } else { + x = (const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13; + } + + char * y = (char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3; + + memcpy(y, x, len); + } + } + } + } +} + +static void ggml_compute_forward_concat_i8( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_type_size(src0->type) == sizeof(int8_t)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = src0->ne[dim]; + + const int8_t * x; + + // TODO: smarter multi-theading + for (int i3 = 0; i3 < ne3; i3++) { + for (int i2 = ith; i2 < ne2; i2 += nth) { + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const int8_t *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); + } else { + x = (const int8_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); + } + + int8_t * y = (int8_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; + } + } + } + } +} + +static void ggml_compute_forward_concat_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_type_size(src0->type) == sizeof(ggml_fp16_t)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = src0->ne[dim]; + + const ggml_fp16_t * x; + + // TODO: smarter multi-theading + for (int i3 = 0; i3 < ne3; i3++) { + for (int i2 = ith; i2 < ne2; i2 += nth) { + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const ggml_fp16_t *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); + } else { + x = (const ggml_fp16_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); + } + + ggml_fp16_t * y = (ggml_fp16_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; + } + } + } + } +} + +static void ggml_compute_forward_concat_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_type_size(src0->type) == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = src0->ne[dim]; + + const float * x; + + // TODO: smarter multi-theading + for (int i3 = 0; i3 < ne3; i3++) { + for (int i2 = ith; i2 < ne2; i2 += nth) { + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); + } else { + x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); + } + + float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; + } + } + } + } +} + +void ggml_compute_forward_concat( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_I16: + { + ggml_compute_forward_concat_f16(params, dst); + } break; + case GGML_TYPE_I8: + { + ggml_compute_forward_concat_i8(params, dst); + } break; + case GGML_TYPE_F32: + case GGML_TYPE_I32: + { + ggml_compute_forward_concat_f32(params, dst); + } break; + default: + { + ggml_compute_forward_concat_any(params, dst); + } + } +} + +// ggml_compute_forward_gelu + +static void ggml_compute_forward_gelu_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_gelu_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + GGML_UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_gelu_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_gelu_f16(nc, + (ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])), + (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + const float v = GGML_FP16_TO_FP32(x); + GGML_UNUSED(v); + assert(!isnan(v)); + assert(!isinf(v)); + } +#endif + } +} + +static void ggml_compute_forward_gelu( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_gelu_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_gelu_f16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_gelu_quick + +static void ggml_compute_forward_gelu_quick_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_gelu_quick_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + GGML_UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_gelu_quick_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_gelu_quick_f16(nc, + (ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])), + (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + const float v = GGML_FP16_TO_FP32(x); + GGML_UNUSED(v); + assert(!isnan(v)); + assert(!isinf(v)); + } +#endif + } +} + +static void ggml_compute_forward_gelu_quick( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_gelu_quick_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_gelu_quick_f16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_silu + +static void ggml_compute_forward_silu_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_silu_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; + GGML_UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_silu_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_silu_f16(nc, + (ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])), + (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*(dst->nb[1])))[k]; + const float v = GGML_FP16_TO_FP32(x); + GGML_UNUSED(v); + assert(!isnan(v)); + assert(!isinf(v)); + } +#endif + } +} + +static void ggml_compute_forward_silu( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_silu_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_silu_f16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} +// ggml_compute_forward_leaky_relu + +static void ggml_compute_forward_leaky_relu_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + float negative_slope; + memcpy(&negative_slope, dst->op_params, sizeof(float)); + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_leaky_relu_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); + } +} + +static void ggml_compute_forward_leaky_relu_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + float negative_slope; + memcpy(&negative_slope, dst->op_params, sizeof(float)); + + assert(dst->nb[0] == sizeof(ggml_fp16_t)); + assert(src0->nb[0] == sizeof(ggml_fp16_t)); + + for (int i = 0; i < n; i++) { + ggml_vec_leaky_relu_f16(nc, + (ggml_fp16_t *) ((char *) dst->data + i*( dst->nb[1])), + (ggml_fp16_t *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); + } +} + +void ggml_compute_forward_leaky_relu( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_leaky_relu_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_leaky_relu_f16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_silu_back + +static void ggml_compute_forward_silu_back_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * grad = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + assert(ggml_is_contiguous_1(grad)); + assert(ggml_is_contiguous_1(src1)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src1, dst)); + assert(ggml_are_same_shape(src1, grad)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src1->ne[0]; + const int nr = ggml_nrows(src1); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_silu_backward_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src1->data + i1*(src1->nb[1])), + (float *) ((char *) grad->data + i1*(grad->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + GGML_UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_silu_back_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * grad = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + assert(ggml_is_contiguous_1(grad)); + assert(ggml_is_contiguous_1(src1)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src1, dst)); + assert(ggml_are_same_shape(src1, grad)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src1->ne[0]; + const int nr = ggml_nrows(src1); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_silu_backward_f16(nc, + (ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])), + (ggml_fp16_t *) ((char *) src1->data + i1*(src1->nb[1])), + (ggml_fp16_t *) ((char *) grad->data + i1*(grad->nb[1]))); + + #ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + const float v = GGML_FP16_TO_FP32(x); + GGML_UNUSED(v); + assert(!isnan(v)); + assert(!isinf(v)); + } + #endif + } +} + +void ggml_compute_forward_silu_back( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_silu_back_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_silu_back_f16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_norm + +static void ggml_compute_forward_norm_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + GGML_ASSERT(eps >= 0.0f); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + ggml_float sum = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum += (ggml_float)x[i00]; + } + + float mean = sum/ne00; + + float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + ggml_float sum2 = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + float v = x[i00] - mean; + y[i00] = v; + sum2 += (ggml_float)(v*v); + } + + float variance = sum2/ne00; + const float scale = 1.0f/sqrtf(variance + eps); + + ggml_vec_scale_f32(ne00, y, scale); + } + } + } +} + +void ggml_compute_forward_norm( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_group_rms_norm + +static void ggml_compute_forward_rms_norm_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + GGML_ASSERT(eps >= 0.0f); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + ggml_float sum = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum += (ggml_float)(x[i00] * x[i00]); + } + + const float mean = sum/ne00; + + float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + memcpy(y, x, ne00 * sizeof(float)); + // for (int i00 = 0; i00 < ne00; i00++) { + // y[i00] = x[i00]; + // } + + const float scale = 1.0f/sqrtf(mean + eps); + + ggml_vec_scale_f32(ne00, y, scale); + } + } + } +} + +void ggml_compute_forward_rms_norm( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rms_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_rms_norm_back_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; // gradients from forward pass output + const ggml_tensor * src1 = dst->src[1]; // src1 from forward pass + + GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_BINARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + // src1 is same shape as src0 => same indices + const int64_t i11 = i01; + const int64_t i12 = i02; + const int64_t i13 = i03; + + const float * dz = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + const float * x = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13); + + ggml_float sum_xx = 0.0; + ggml_float sum_xdz = 0.0; + + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum_xx += (ggml_float)(x[i00] * x[i00]); + sum_xdz += (ggml_float)(x[i00] * dz[i00]); + } + + //const float mean = (float)(sum_xx)/ne00; + const float mean_eps = (float)(sum_xx)/ne00 + eps; + const float sum_eps = (float)(sum_xx) + eps*ne00; + //const float mean_xdz = (float)(sum_xdz)/ne00; + // we could cache rms from forward pass to improve performance. + // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms. + //const float rms = sqrtf(mean_eps); + const float rrms = 1.0f / sqrtf(mean_eps); + //const float scale = -rrms/(ne00 * mean_eps); // -1/(n*rms**3) + + { + // z = rms_norm(x) + // + // rms_norm(src1) = + // scale( + // src1, + // div( + // 1, + // sqrt( + // add( + // scale( + // sum( + // sqr( + // src1)), + // (1.0/N)), + // eps)))); + + // postorder: + // ## op args grad + // 00 param src1 grad[#00] + // 01 const 1 + // 02 sqr (#00) grad[#02] + // 03 sum (#02) grad[#03] + // 04 const 1/N + // 05 scale (#03, #04) grad[#05] + // 06 const eps + // 07 add (#05, #06) grad[#07] + // 08 sqrt (#07) grad[#08] + // 09 div (#01,#08) grad[#09] + // 10 scale (#00,#09) grad[#10] + // + // backward pass, given grad[#10] + // #10: scale + // grad[#00] += scale(grad[#10],#09) + // grad[#09] += sum(mul(grad[#10],#00)) + // #09: div + // grad[#08] += neg(mul(grad[#09], div(#09,#08))) + // #08: sqrt + // grad[#07] += mul(grad[#08], div(0.5, #08)) + // #07: add + // grad[#05] += grad[#07] + // #05: scale + // grad[#03] += scale(grad[#05],#04) + // #03: sum + // grad[#02] += repeat(grad[#03], #02) + // #02: + // grad[#00] += scale(mul(#00, grad[#02]), 2.0) + // + // substitute and simplify: + // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) + // grad[#02] = repeat(grad[#03], #02) + // grad[#02] = repeat(scale(grad[#05],#04), #02) + // grad[#02] = repeat(scale(grad[#07],#04), #02) + // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02) + // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02) + // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02) + // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) + // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0) + // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) + // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) + // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) + // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps)) + // a = b*c + d*e + // a = b*c*f/f + d*e*f/f + // a = (b*c*f + d*e*f)*(1/f) + // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c)) + // a = (b + d*e/c)*c + // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) + // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms + // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms + // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms + // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms + // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms + // a = (dz + x*div(-mean_xdz,mean_eps))*rrms + // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) + // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) + // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) + } + // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) + // post-order: + // dx := x + // dx := scale(dx,-mean_xdz/mean_eps) + // dx := add(dx, dz) + // dx := scale(dx, rrms) + float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + // dx[i00] = (x*(-sum_xdz/sum_eps) + dz) / sqrtf(mean_eps) + ggml_vec_cpy_f32 (ne00, dx, x); + // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps); + ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps); + ggml_vec_acc_f32 (ne00, dx, dz); + ggml_vec_scale_f32(ne00, dx, rrms); + } + } + } +} + +void ggml_compute_forward_rms_norm_back( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rms_norm_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_group_norm + +static void ggml_compute_forward_group_norm_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + // TODO: optimize + + float eps; + memcpy(&eps, dst->op_params + 1, sizeof(float)); + + int n_channels = src0->ne[2]; + int n_groups = dst->op_params[0]; + int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; + for (int i = ith; i < n_groups; i += nth) { + int start = i * n_channels_per_group; + int end = start + n_channels_per_group; + if (end > n_channels) { + end = n_channels; + } + int step = end - start; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + ggml_float sum = 0.0; + for (int64_t i02 = start; i02 < end; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); + + ggml_float sumr = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sumr += (ggml_float)x[i00]; + } + sum += sumr; + } + } + const float mean = sum / (ne00 * ne01 * step); + + ggml_float sum2 = 0.0; + for (int64_t i02 = start; i02 < end; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); + + float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); + + ggml_float sumr = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + float v = x[i00] - mean; + y[i00] = v; + sumr += (ggml_float)(v * v); + } + sum2 += sumr; + } + } + const float variance = sum2 / (ne00 * ne01 * step); + const float scale = 1.0f / sqrtf(variance + eps); + + for (int64_t i02 = start; i02 < end; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); + ggml_vec_scale_f32(ne00, y, scale); + } + } + } + } +} + +void ggml_compute_forward_group_norm( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_group_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_l2_norm + +static void ggml_compute_forward_l2_norm_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + GGML_ASSERT(eps >= 0.0f); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + ggml_float sum = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum += (ggml_float)(x[i00] * x[i00]); + } + + float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + memcpy(y, x, ne00 * sizeof(float)); + + const float scale = 1.0f/fmaxf(sqrtf(sum), eps); + + ggml_vec_scale_f32(ne00, y, scale); + } + } + } +} + +void ggml_compute_forward_l2_norm( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_l2_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_out_prod + +static void ggml_compute_forward_out_prod_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + GGML_ASSERT(ne2 % ne02 == 0); + GGML_ASSERT(ne3 % ne03 == 0); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == sizeof(float)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + // GGML_ASSERT(nb0 <= nb1); + // GGML_ASSERT(nb1 <= nb2); + // GGML_ASSERT(nb2 <= nb3); + + // nb01 >= nb00 - src0 is not transposed + // compute by src0 rows + + if (ith == 0) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, (float *)dst->data, 0); + } + ggml_barrier(params->threadpool); + + // dst[:,:,:,:] = 0 + // for i2,i3: + // for i1: + // for i01: + // for i0: + // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] + + // parallelize by last three dimensions + + // total rows in dst + const int64_t nr = ne1*ne2*ne3; + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + // block-tiling attempt + const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32); + const int64_t blck_1 = 16; + + // dps == dst per src0, used for group query attention + const int64_t dps2 = ne2 / ne02; + const int64_t dps3 = ne3 / ne03; + + for (int64_t bir = ir0; bir < ir1; bir += blck_1) { + const int64_t bir1 = MIN(bir + blck_1, ir1); + for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) { + const int64_t bne01 = MIN(bi01 + blck_0, ne01); + for (int64_t ir = bir; ir < bir1; ++ir) { + // dst indices + const int64_t i3 = ir/(ne2*ne1); + const int64_t i2 = (ir - i3*ne2*ne1)/ne1; + const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); + + const int64_t i02 = i2 / dps2; + const int64_t i03 = i3 / dps3; + + //const int64_t i10 = i1; + const int64_t i12 = i2; + const int64_t i13 = i3; + +#if GGML_VEC_MAD_UNROLL > 2 + const int64_t bne01_unroll = bne01 - (bne01 % GGML_VEC_MAD_UNROLL); + for (int64_t i01 = bi01; i01 < bne01_unroll; i01 += GGML_VEC_MAD_UNROLL) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1); + } + for (int64_t i01 = bne01_unroll; i01 < bne01; ++i01) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + ggml_vec_mad_f32(ne0, d, s0, *s1); + } +#else + for (int64_t i01 = bi01; i01 < bne01; ++i01) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + ggml_vec_mad_f32(ne0, d, s0, *s1); + } +#endif + } + } + } +} + +static void ggml_compute_forward_out_prod_q_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int ith = params->ith; + const int nth = params->nth; + + const ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + + GGML_ASSERT(ne02 == ne12); + GGML_ASSERT(ne03 == ne13); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + // we don't support permuted src0 dim0 + GGML_ASSERT(nb00 == ggml_type_size(type)); + + // dst dim0 cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + // GGML_ASSERT(nb0 <= nb1); + // GGML_ASSERT(nb1 <= nb2); + // GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne3 == ne03); + + // nb01 >= nb00 - src0 is not transposed + // compute by src0 rows + + if (ith == 0) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, (float *)dst->data, 0); + } + ggml_barrier(params->threadpool); + + // parallelize by last three dimensions + + // total rows in dst + const int64_t nr = ne1*ne2*ne3; + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + // dst[:,:,:,:] = 0 + // for i2,i3: + // for i1: + // for i01: + // for i0: + // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] + + float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; + + for (int64_t ir = ir0; ir < ir1; ++ir) { + // dst indices + const int64_t i3 = ir/(ne2*ne1); + const int64_t i2 = (ir - i3*ne2*ne1)/ne1; + const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); + + const int64_t i02 = i2; + const int64_t i03 = i3; + + //const int64_t i10 = i1; + const int64_t i12 = i2; + const int64_t i13 = i3; + + for (int64_t i01 = 0; i01 < ne01; ++i01) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + dequantize_row_q(s0, wdata, ne0); + ggml_vec_mad_f32(ne0, d, wdata, *s1); + } + } +} + +void ggml_compute_forward_out_prod( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + 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_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + { + ggml_compute_forward_out_prod_q_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + GGML_ABORT("fatal error"); // todo + // ggml_compute_forward_out_prod_f16_f32(params, dst); + } + case GGML_TYPE_F32: + { + ggml_compute_forward_out_prod_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_scale + +static void ggml_compute_forward_scale_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + // scale factor + float v; + memcpy(&v, dst->op_params, sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + const size_t nb01 = src0->nb[1]; + + const size_t nb1 = dst->nb[1]; + + for (int i1 = ir0; i1 < ir1; i1++) { + if (dst->data != src0->data) { + // src0 is same shape as dst => same indices + memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float)); + } + ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); + } +} + +void ggml_compute_forward_scale( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_scale_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_set + +static void ggml_compute_forward_set_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + + // view src0 and dst with these strides and data offset inbytes during set + // nb0 is implicitly element_size because src0 and dst are contiguous + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; + + if (!inplace) { + if (params->ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src1); + const int nc = src1->ne[0]; + + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) + + // src0 and dst as viewed during set + const size_t nb0 = ggml_element_size(src0); + + const int im0 = (ne10 == 0 ? 0 : ne10-1); + const int im1 = (ne11 == 0 ? 0 : ne11-1); + const int im2 = (ne12 == 0 ? 0 : ne12-1); + const int im3 = (ne13 == 0 ? 0 : ne13-1); + + GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); + + GGML_ASSERT(nb10 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are viewed with shape of src1 and offset + // => same indices + const int i3 = ir/(ne12*ne11); + const int i2 = (ir - i3*ne12*ne11)/ne11; + const int i1 = (ir - i3*ne12*ne11 - i2*ne11); + + ggml_vec_cpy_f32(nc, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), + (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); + } +} + +static void ggml_compute_forward_set_i32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + + // view src0 and dst with these strides and data offset inbytes during set + // nb0 is implicitly element_size because src0 and dst are contiguous + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; + + if (!inplace) { + if (params->ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src1); + const int nc = src1->ne[0]; + + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) + + // src0 and dst as viewed during set + const size_t nb0 = ggml_element_size(src0); + + const int im0 = (ne10 == 0 ? 0 : ne10-1); + const int im1 = (ne11 == 0 ? 0 : ne11-1); + const int im2 = (ne12 == 0 ? 0 : ne12-1); + const int im3 = (ne13 == 0 ? 0 : ne13-1); + + GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); + + GGML_ASSERT(nb10 == sizeof(int32_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are viewed with shape of src1 and offset + // => same indices + const int i3 = ir/(ne12*ne11); + const int i2 = (ir - i3*ne12*ne11)/ne11; + const int i1 = (ir - i3*ne12*ne11 - i2*ne11); + + ggml_vec_cpy_i32(nc, + (int32_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), + (int32_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); + } +} + +void ggml_compute_forward_set( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_set_f32(params, dst); + } break; + case GGML_TYPE_I32: + { + ggml_compute_forward_set_i32(params, dst); + } break; + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + 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_Q8_1: + 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_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_cpy + +void ggml_compute_forward_cpy( + const ggml_compute_params * params, + ggml_tensor * dst) { + ggml_compute_forward_dup(params, dst); +} + +// ggml_compute_forward_cont + +void ggml_compute_forward_cont( + const ggml_compute_params * params, + ggml_tensor * dst) { + ggml_compute_forward_dup(params, dst); +} + +// ggml_compute_forward_reshape + +void ggml_compute_forward_reshape( + const ggml_compute_params * params, + ggml_tensor * dst) { + // NOP + GGML_UNUSED(params); + GGML_UNUSED(dst); +} + +// ggml_compute_forward_view + +void ggml_compute_forward_view( + const ggml_compute_params * params, + ggml_tensor * dst) { + // NOP + GGML_UNUSED(params); + GGML_UNUSED(dst); +} + +// ggml_compute_forward_permute + +void ggml_compute_forward_permute( + const ggml_compute_params * params, + ggml_tensor * dst) { + // NOP + GGML_UNUSED(params); + GGML_UNUSED(dst); +} + +// ggml_compute_forward_transpose + +void ggml_compute_forward_transpose( + const ggml_compute_params * params, + ggml_tensor * dst) { + // NOP + GGML_UNUSED(params); + GGML_UNUSED(dst); +} + +// ggml_compute_forward_get_rows + +static void ggml_compute_forward_get_rows_q( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + const ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == ggml_type_size(type)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + dequantize_row_q( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + +static void ggml_compute_forward_get_rows_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(ggml_fp16_t)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + ggml_fp16_to_fp32_row( + (const ggml_fp16_t*) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + +static void ggml_compute_forward_get_rows_bf16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(ggml_bf16_t)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + ggml_bf16_to_fp32_row( + (const ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + +static void ggml_compute_forward_get_rows_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(float)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + ggml_vec_cpy_f32(nc, + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), + (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); + } +} + +void ggml_compute_forward_get_rows( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + 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_Q8_1: + 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_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + { + ggml_compute_forward_get_rows_q(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_get_rows_f16(params, dst); + } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_get_rows_bf16(params, dst); + } break; + case GGML_TYPE_F32: + case GGML_TYPE_I32: + { + ggml_compute_forward_get_rows_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + //static bool first = true; + //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); + //if (first) { + // first = false; + //} else { + // for (int k = 0; k < dst->ne[1]; ++k) { + // for (int j = 0; j < dst->ne[0]/16; ++j) { + // for (int i = 0; i < 16; ++i) { + // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); + // } + // printf("\n"); + // } + // printf("\n"); + // } + // printf("\n"); + // exit(0); + //} +} + +// ggml_compute_forward_get_rows_back + +static void ggml_compute_forward_get_rows_back_f32_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_is_contiguous(dst)); + + // ggml_compute_forward_dup_same_cont(params, opt0, dst); + + memset(dst->data, 0, ggml_nbytes(dst)); + + const int nc = src0->ne[0]; + const int nr = ggml_nelements(src1); + + GGML_ASSERT( dst->ne[0] == nc); + GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); + + for (int i = 0; i < nr; ++i) { + const int r = ((int32_t *) src1->data)[i]; + + for (int j = 0; j < nc; ++j) { + ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; + ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); + } + } +} + +static void ggml_compute_forward_get_rows_back_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_is_contiguous(dst)); + + // ggml_compute_forward_dup_same_cont(params, opt0, dst); + + memset(dst->data, 0, ggml_nbytes(dst)); + + const int nc = src0->ne[0]; + const int nr = ggml_nelements(src1); + + GGML_ASSERT( dst->ne[0] == nc); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < nr; ++i) { + const int r = ((int32_t *) src1->data)[i]; + + ggml_vec_add_f32(nc, + (float *) ((char *) dst->data + r*dst->nb[1]), + (float *) ((char *) dst->data + r*dst->nb[1]), + (float *) ((char *) src0->data + i*src0->nb[1])); + } +} + +void ggml_compute_forward_get_rows_back( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_get_rows_back_f32_f16(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_get_rows_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + //static bool first = true; + //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); + //if (first) { + // first = false; + //} else { + // for (int k = 0; k < dst->ne[1]; ++k) { + // for (int j = 0; j < dst->ne[0]/16; ++j) { + // for (int i = 0; i < 16; ++i) { + // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); + // } + // printf("\n"); + // } + // printf("\n"); + // } + // printf("\n"); + // exit(0); + //} +} + +// ggml_compute_forward_diag + +static void ggml_compute_forward_diag_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + // TODO: handle transposed/permuted matrices + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(ne00 == ne0); + GGML_ASSERT(ne00 == ne1); + GGML_ASSERT(ne01 == 1); + GGML_ASSERT(ne02 == ne2); + GGML_ASSERT(ne03 == ne3); + + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(nb0 == sizeof(float)); + + for (int i3 = 0; i3 < ne3; i3++) { + for (int i2 = 0; i2 < ne2; i2++) { + for (int i1 = 0; i1 < ne1; i1++) { + float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); + for (int i0 = 0; i0 < i1; i0++) { + d[i0] = 0; + } + d[i1] = s[i1]; + for (int i0 = i1+1; i0 < ne0; i0++) { + d[i0] = 0; + } + } + } + } +} + +void ggml_compute_forward_diag( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_diag_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_diag_mask_inf + +static void ggml_compute_forward_diag_mask_f32( + const ggml_compute_params * params, + ggml_tensor * dst, + const float value) { + + const ggml_tensor * src0 = dst->src[0]; + + const int ith = params->ith; + const int nth = params->nth; + + const int n_past = ((int32_t *) dst->op_params)[0]; + const bool inplace = src0->data == dst->data; + + GGML_ASSERT(n_past >= 0); + + if (!inplace) { + if (ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + // TODO: handle transposed/permuted matrices + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + const int nr = src0->ne[1]; + const int nz = n/nr; + + GGML_ASSERT( dst->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int k = 0; k < nz; k++) { + for (int j = ith; j < nr; j += nth) { + for (int i = n_past; i < nc; i++) { + if (i > n_past + j) { + *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; + } + } + } + } +} + +void ggml_compute_forward_diag_mask_inf( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +void ggml_compute_forward_diag_mask_zero( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_diag_mask_f32(params, dst, 0); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_soft_max + +static void ggml_compute_forward_soft_max_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + assert(ggml_is_contiguous(dst)); + assert(ggml_are_same_shape(src0, dst)); + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); + + // TODO: handle transposed/permuted matrices + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + //const int64_t ne11 = src1 ? src1->ne[1] : 1; + + // TODO: is this supposed to be ceil instead of floor? + // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 + const uint32_t n_head = ne02; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + 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 int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; + + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); + + for (int i1 = ir0; i1 < ir1; i1++) { + // ALiBi + const uint32_t h = (i1/ne01)%ne02; // head + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + + float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); + float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); + + // broadcast the mask across rows + ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; + float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; + + ggml_vec_cpy_f32 (nc, wp, sp); + ggml_vec_scale_f32(nc, wp, scale); + if (mp_f32) { + if (use_f16) { + for (int i = 0; i < nc; ++i) { + wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); + } + } else { + for (int i = 0; i < nc; ++i) { + wp[i] += slope*mp_f32[i]; + } + } + } + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(wp[i])); + } +#endif + + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, wp); + + ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); + assert(sum > 0.0); + + sum = 1.0/sum; + ggml_vec_scale_f32(nc, dp, sum); + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + assert(!isnan(dp[i])); + assert(!isinf(dp[i])); + } +#endif + } +} + +void ggml_compute_forward_soft_max( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_soft_max_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +// ggml_compute_forward_soft_max_ext_back + +static void ggml_compute_forward_soft_max_ext_back_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_are_same_shape(src1, dst)); + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float)); + + GGML_ASSERT(max_bias == 0.0f); + + // TODO: handle transposed/permuted matrices + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); + float *y = (float *)((char *) src1->data + i1*src1->nb[1]); + float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(dy[i])); + assert(!isnan(y[i])); + } +#endif + // Jii = yi - yi*yi + // Jij = -yi*yj + // J = diag(y)-y.T*y + // dx = J * dy + // dxk = sum_i(Jki * dyi) + // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk + // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk + // dxk = sum_i(-yk*yi * dyi) + yk*dyk + // dxk = -yk * sum_i(yi * dyi) + yk*dyk + // dxk = -yk * dot(y, dy) + yk*dyk + // dxk = yk * (- dot(y, dy) + dyk) + // dxk = yk * (dyk - dot(y, dy)) + // + // post-order: + // dot_y_dy := dot(y, dy) + // dx := dy + // dx := dx - dot_y_dy + // dx := dx * y + + // linear runtime, no additional memory + float dot_y_dy = 0; + ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1); + ggml_vec_cpy_f32 (nc, dx, dy); + ggml_vec_acc1_f32 (nc, dx, -dot_y_dy); + ggml_vec_mul_f32 (nc, dx, dx, y); + ggml_vec_scale_f32(nc, dx, scale); + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + assert(!isnan(dx[i])); + assert(!isinf(dx[i])); + } +#endif + } +} + +void ggml_compute_forward_soft_max_ext_back( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_soft_max_ext_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_clamp + +static void ggml_compute_forward_clamp_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + float min; + float max; + memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + const size_t nb00 = src0->nb[0]; + const size_t nb01 = src0->nb[1]; + + const size_t nb0 = dst->nb[0]; + const size_t nb1 = dst->nb[1]; + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + for (int j = ith; j < n; j += nth) { + float * dst_ptr = (float *) ((char *) dst->data + j*nb1); + float * src0_ptr = (float *) ((char *) src0->data + j*nb01); + + for (int i = 0; i < nc; i++) { + dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); + } + } +} + +static void ggml_compute_forward_clamp_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + float min; + float max; + memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + const size_t nb00 = src0->nb[0]; + const size_t nb01 = src0->nb[1]; + + const size_t nb0 = dst->nb[0]; + const size_t nb1 = dst->nb[1]; + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + for (int j = ith; j < n; j += nth) { + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + j*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + j*nb01); + + for (int i = 0; i < nc; i++) { + float v = GGML_FP16_TO_FP32(src0_ptr[i]); + dst_ptr[i] = GGML_FP32_TO_FP16(MAX(MIN(v, max), min)); + } + } +} + +void ggml_compute_forward_clamp( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_clamp_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_clamp_f16(params, dst); + } break; + case GGML_TYPE_BF16: + 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_Q8_1: + 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_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q8_K: + case GGML_TYPE_I8: + case GGML_TYPE_I16: + case GGML_TYPE_I32: + case GGML_TYPE_I64: + case GGML_TYPE_F64: + case GGML_TYPE_COUNT: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rope + +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / MAX(0.001f, high - low); + return 1 - MIN(1, MAX(0, 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, float corr_dims[2], 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[0], corr_dims[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 * logf(1.0f / freq_scale); + } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} + +static void ggml_rope_cache_init( + float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, + float * cache, float sin_sign, float theta_scale) { + // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py + float theta = theta_base; + for (int64_t i0 = 0; i0 < ne0; i0 += 2) { + const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; + rope_yarn( + theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] + ); + cache[i0 + 1] *= sin_sign; + + theta *= theta_scale; + } +} + +static void ggml_mrope_cache_init( + float theta_base_t, float theta_base_h, float theta_base_w, float theta_base_e, int sections[4], bool indep_sects, + float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, + float * cache, float sin_sign, float theta_scale) { + // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py + float theta_t = theta_base_t; + float theta_h = theta_base_h; + float theta_w = theta_base_w; + float theta_e = theta_base_e; // extra position id for vision encoder + int sect_dims = sections[0] + sections[1] + sections[2] + sections[3]; + int sec_w = sections[1] + sections[0]; + int sec_e = sections[2] + sec_w; + GGML_ASSERT(sect_dims <= ne0); + + for (int64_t i0 = 0; i0 < ne0; i0 += 2) { + const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; + + int sector = (i0 / 2) % sect_dims; + if (indep_sects) { + // compute theta independently for each dim sections + // (i.e. reset corresponding theta when `i0` go from one section to another) + if (sector == 0) { + theta_t = theta_base_t; + } + else if (sector == sections[0]) { + theta_h = theta_base_h;; + } + else if (sector == sec_w) { + theta_w = theta_base_w; + } + else if (sector == sec_e) { + theta_e = theta_base_e; + } + } + + float theta = theta_t; + if (sector >= sections[0] && sector < sec_w) { + theta = theta_h; + } + else if (sector >= sec_w && sector < sec_w + sections[2]) { + theta = theta_w; + } + else if (sector >= sec_w + sections[2]) { + theta = theta_e; + } + + rope_yarn( + theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] + ); + cache[i0 + 1] *= sin_sign; + + theta_t *= theta_scale; + theta_w *= theta_scale; + theta_h *= theta_scale; + theta_e *= theta_scale; + } +} + +static void ggml_compute_forward_rope_f32( + const ggml_compute_params * params, + ggml_tensor * dst, + const bool forward) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + const ggml_tensor * src2 = dst->src[2]; + + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + int sections[4]; + + //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]; + + 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)); + memcpy(§ions, (int32_t *) dst->op_params + 11, sizeof(int)*4); + + GGML_TENSOR_UNARY_OP_LOCALS + + //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); + //printf("n_past = %d, ne2 = %d\n", n_past, ne2); + + GGML_ASSERT(nb00 == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(dst); + + GGML_ASSERT(n_dims <= ne0); + GGML_ASSERT(n_dims % 2 == 0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + // row index used to determine which thread to use + int ir = 0; + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE; // ggml_rope_multi, multimodal rotary position embedding + const bool is_vision = mode == GGML_ROPE_TYPE_VISION; + + if (is_mrope) { + GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0); + } + + if (is_vision) { + GGML_ASSERT(n_dims == ne0/2); + } + + const float * freq_factors = NULL; + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + + const int32_t * pos = (const int32_t *) src1->data; + + for (int64_t i3 = 0; i3 < ne3; i3++) { // batch + for (int64_t i2 = 0; i2 < ne2; i2++) { // seq-len + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + if (!is_mrope) { + const int64_t p = pos[i2]; + ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + else { + const int64_t p_t = pos[i2]; + const int64_t p_h = pos[i2 + ne2]; + const int64_t p_w = pos[i2 + ne2 * 2]; + const int64_t p_e = pos[i2 + ne2 * 3]; + ggml_mrope_cache_init( + p_t, p_h, p_w, p_e, sections, is_vision, + freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + + for (int64_t i1 = 0; i1 < ne1; i1++) { // attn-heads + if (ir++ < ir0) continue; + if (ir > ir1) break; + + if (is_neox || is_mrope) { + if (is_vision){ + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = src[0]; + const float x1 = src[n_dims]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[n_dims] = x0*sin_theta + x1*cos_theta; + } + } else { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = src[0]; + const float x1 = src[n_dims/2]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + } + } + } else { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + const float x0 = src[0]; + const float x1 = src[1]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[1] = x0*sin_theta + x1*cos_theta; + } + } + + if (is_vision) { + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = src[0]; + const float x1 = src[n_dims]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[n_dims] = x0*sin_theta + x1*cos_theta; + } + } else { + // fill the remain channels with data from src tensor + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; + } + } + } + } + } +} + +// TODO: deduplicate f16/f32 code +static void ggml_compute_forward_rope_f16( + const ggml_compute_params * params, + ggml_tensor * dst, + const bool forward) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + const ggml_tensor * src2 = dst->src[2]; + + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + int sections[4]; + + //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]; + 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)); + memcpy(§ions, (int32_t *) dst->op_params + 11, sizeof(int)*4); + + + GGML_TENSOR_UNARY_OP_LOCALS + + //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); + //printf("n_past = %d, ne2 = %d\n", n_past, ne2); + + GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(dst); + + GGML_ASSERT(n_dims <= ne0); + GGML_ASSERT(n_dims % 2 == 0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + // row index used to determine which thread to use + int ir = 0; + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE; + const bool is_vision = mode == GGML_ROPE_TYPE_VISION; + + if (is_mrope) { + GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0); + } + + if (is_vision) { + GGML_ASSERT(n_dims == ne0/2); + } + + const float * freq_factors = NULL; + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + + const int32_t * pos = (const int32_t *) src1->data; + + for (int64_t i3 = 0; i3 < ne3; i3++) { + for (int64_t i2 = 0; i2 < ne2; i2++) { + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + if (!is_mrope) { + const int64_t p = pos[i2]; + ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + else { + const int64_t p_t = pos[i2]; + const int64_t p_h = pos[i2 + ne2]; + const int64_t p_w = pos[i2 + ne2 * 2]; + const int64_t p_e = pos[i2 + ne2 * 3]; + ggml_mrope_cache_init( + p_t, p_h, p_w, p_e, sections, is_vision, + freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + + for (int64_t i1 = 0; i1 < ne1; i1++) { + if (ir++ < ir0) continue; + if (ir > ir1) break; + + if (is_neox || is_mrope) { + if (is_vision) { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[n_dims]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[n_dims] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } + } else { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } + } + } else { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[1]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } + } + + if (is_vision) { + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[n_dims]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[n_dims] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } + } else { + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; + } + } + } + } + } +} + +void ggml_compute_forward_rope( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_rope_f16(params, dst, true); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_rope_f32(params, dst, true); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rope_back + +void ggml_compute_forward_rope_back( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_rope_f16(params, dst, false); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_rope_f32(params, dst, false); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_conv_transpose_1d + +static void ggml_compute_forward_conv_transpose_1d_f16_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const int nk = ne00*ne01*ne02; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (ith == 0) { + memset(params->wdata, 0, params->wsize); + + // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); + ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i00*ne02 + i02] = src[i00]; + } + } + } + } + + // permute source data (src1) from (L x Cin) to (Cin x L) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; + ggml_fp16_t * dst_data = wdata; + + for (int64_t i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i11*nb11); + for (int64_t i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); + } + } + } + + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + + // total rows in dst + const int nr = ne1; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + ggml_fp16_t * const wdata_src = wdata + nk; + + for (int i1 = ir0; i1 < ir1; i1++) { + float * dst_data = (float *)((char *) dst->data + i1*nb1); + ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i10*ne11; + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f16(ne02, &v, 0, + (ggml_fp16_t *) wdata_src + i1n, 0, + (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1); + dst_data[i10*s0 + i00] += v; + } + } + } +} + +static void ggml_compute_forward_conv_transpose_1d_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const int nk = ne00*ne01*ne02; + + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (ith == 0) { + memset(params->wdata, 0, params->wsize); + + // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) + { + float * const wdata = (float *) params->wdata + 0; + + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); + float * dst_data = wdata + i01*ne00*ne02; + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i00*ne02 + i02] = src[i00]; + } + } + } + } + + // prepare source data (src1) + { + float * const wdata = (float *) params->wdata + nk; + float * dst_data = wdata; + + for (int64_t i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i11*nb11); + for (int64_t i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne11 + i11] = src[i10]; + } + } + } + + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + + // total rows in dst + const int nr = ne1; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * const wdata = (float *) params->wdata + 0; + float * const wdata_src = wdata + nk; + + for (int i1 = ir0; i1 < ir1; i1++) { + float * dst_data = (float *)((char *) dst->data + i1*nb1); + float * wdata_kernel = wdata + i1*ne02*ne00; + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i10*ne11; + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f32(ne02, &v, 0, + wdata_src + i1n, 0, + wdata_kernel + i00*ne02, 0, 1); + dst_data[i10*s0 + i00] += v; + } + } + } +} + +void ggml_compute_forward_conv_transpose_1d( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_conv_transpose_1d_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_im2col_f32 +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; + + GGML_ASSERT(nb10 == sizeof(float)); + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + float * const wdata = (float *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + + // micro kernel + float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] + + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 + for (int64_t ikw = 0; ikw < KW; ikw++) { + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + + +// ggml_compute_forward_im2col_f16 +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + + // micro kernel + ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] + + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 + for (int64_t ikw = 0; ikw < KW; ikw++) { + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + +void ggml_compute_forward_im2col( + const ggml_compute_params * params, + ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_im2col_f16(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_im2col_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_im2col_back_f32 + +void ggml_compute_forward_im2col_back_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; // gradients of forward pass output + const ggml_tensor * src1 = dst->src[1]; // convolution kernel + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne3 : ne2; + const int64_t IC = is_2D ? ne2 : ne1; + const int64_t IH = is_2D ? ne1 : 1; + const int64_t IW = ne0; + + const int64_t KH = is_2D ? ne11 : 1; + const int64_t KW = ne10; + + const int64_t OH = is_2D ? ne02 : 1; + const int64_t OW = ne01; + + int ofs0 = is_2D ? nb3 : nb2; + int ofs1 = is_2D ? nb2 : nb1; + + GGML_ASSERT(nb0 == sizeof(float)); + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + float * const wdata = (float *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + for (int64_t iih = 0; iih < IH; iih++) { + for (int64_t iiw = 0; iiw < IW; iiw++) { + + // micro kernel + float grad = 0.0f; + for (int64_t ikh = 0; ikh < KH; ikh++) { + for (int64_t ikw = 0; ikw < KW; ikw++) { + // For s0 > 1 some values were skipped over in the forward pass. + // These values have tmpw % s0 != 0 and need to be skipped in the backwards pass as well. + const int64_t tmpw = (iiw + p0 - ikw*d0); + if (tmpw % s0 != 0) { + continue; + } + const int64_t iow = tmpw / s0; + + // Equivalent logic as above except for s1. + int64_t ioh; + if (is_2D) { + const int64_t tmph = iih + p1 - ikh*d1; + + if (tmph % s1 != 0) { + continue; + } + + ioh = tmph / s1; + } else { + ioh = 0; + } + + if (iow < 0 || iow >= OW || ioh < 0 || ioh >= OH) { + continue; + } + + const float * const grad_in = (const float *) src0->data + + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + grad += grad_in[iic*(KH*KW) + ikh*KW + ikw]; + } + } + float * dst_data = (float *)((char *) wdata + (in*ofs0 + iic*ofs1)); // [IH, IW] + dst_data[iih*IW + iiw] = grad; + } + } + } + } + } +} + +// ggml_compute_forward_conv_transpose_2d + +void ggml_compute_forward_conv_transpose_2d( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const int nk = ne00*ne01*ne02*ne03; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (ith == 0) { + memset(params->wdata, 0, params->wsize); + + // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02); + ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03; + for (int64_t i01 = 0; i01 < ne01; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00]; + } + } + } + } + } + + // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; + for (int i12 = 0; i12 < ne12; i12++) { + for (int i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11); + ggml_fp16_t * dst_data = wdata + i11*ne10*ne12; + for (int i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne12 + i12] = GGML_FP32_TO_FP16(src[i10]); + } + } + } + } + + memset(dst->data, 0, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + + const int32_t stride = ggml_get_op_params_i32(dst, 0); + + // total patches in dst + const int np = ne2; + + // patches per thread + const int dp = (np + nth - 1)/nth; + + // patch range for this thread + const int ip0 = dp*ith; + const int ip1 = MIN(ip0 + dp, np); + + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + ggml_fp16_t * const wdata_src = wdata + nk; + + for (int i2 = ip0; i2 < ip1; i2++) { // Cout + float * dst_data = (float *)((char *) dst->data + i2*nb2); + ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03; + for (int i11 = 0; i11 < ne11; i11++) { + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i11*ne10*ne12 + i10*ne12; + for (int i01 = 0; i01 < ne01; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f16(ne03, &v, 0, + wdata_src + i1n, 0, + wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1); + dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; + } + } + } + } + } +} + +// ggml_compute_forward_pool_1d_sk_p0 + +static void ggml_compute_forward_pool_1d_sk_p0( + const ggml_compute_params * params, + const ggml_op_pool op, + const int k, + ggml_tensor * dst) { + + const ggml_tensor * src = dst->src[0]; + + assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); + + if (params->ith != 0) { + return; + } + + const char * cdata = (const char *)src->data; + const char * const data_end = cdata + ggml_nbytes(src); + float * drow = (float *)dst->data; + + const int64_t rs = dst->ne[0]; + + while (cdata < data_end) { + const void * srow = (const void *)cdata; + int j = 0; + for (int64_t i = 0; i < rs; ++i) { + switch (op) { + case GGML_OP_POOL_AVG: drow[i] = 0; break; + case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + for (int ki = 0; ki < k; ++ki) { + const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); + switch (op) { + case GGML_OP_POOL_AVG: drow[i] += srow_j; break; + case GGML_OP_POOL_MAX: if (srow_j > drow[i]) drow[i] = srow_j; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + ++j; + } + switch (op) { + case GGML_OP_POOL_AVG: drow[i] /= k; break; + case GGML_OP_POOL_MAX: break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + } + + cdata += src->nb[1]; + drow += rs; + } +} + +// ggml_compute_forward_pool_1d + +void ggml_compute_forward_pool_1d( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const int32_t * opts = (const int32_t *)dst->op_params; + ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int s0 = opts[2]; + const int p0 = opts[3]; + GGML_ASSERT(p0 == 0); // padding not supported + GGML_ASSERT(k0 == s0); // only s = k supported + + ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst); +} + +// ggml_compute_forward_pool_2d + +void ggml_compute_forward_pool_2d( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src = dst->src[0]; + + assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); + + if (params->ith != 0) { + return; + } + + const int32_t * opts = (const int32_t *)dst->op_params; + ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + const char * cdata = (const char*)src->data; + const char * const data_end = cdata + ggml_nbytes(src); + + const int64_t px = dst->ne[0]; + const int64_t py = dst->ne[1]; + const int64_t pa = px * py; + + float * dplane = (float *)dst->data; + + const int ka = k0 * k1; + const int offset0 = -p0; + const int offset1 = -p1; + + while (cdata < data_end) { + for (int oy = 0; oy < py; ++oy) { + float * const drow = dplane + oy * px; + for (int ox = 0; ox < px; ++ox) { + float * const out = drow + ox; + switch (op) { + case GGML_OP_POOL_AVG: *out = 0; break; + case GGML_OP_POOL_MAX: *out = -FLT_MAX; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + + const int ix = offset0 + ox * s0; + const int iy = offset1 + oy * s1; + + for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; + const void * srow = (const void *)(cdata + src->nb[1] * (iy + ky)); + for (int kx = 0; kx < k0; ++kx) { + int j = ix + kx; + if (j < 0 || j >= src->ne[0]) continue; + const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); + switch (op) { + case GGML_OP_POOL_AVG: *out += srow_j; break; + case GGML_OP_POOL_MAX: if (srow_j > *out) *out = srow_j; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + } + } + switch (op) { + case GGML_OP_POOL_AVG: *out /= ka; break; + case GGML_OP_POOL_MAX: break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + } + } + + cdata += src->nb[2]; + dplane += pa; + } +} + +// ggml_compute_forward_pool_2d_back + +void ggml_compute_forward_pool_2d_back( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src = dst->src[0]; + const ggml_tensor * dstf = dst->src[1]; // forward tensor of dst + + assert(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + + if (params->ith != 0) { + return; + } + + const int32_t * opts = (const int32_t *)dst->op_params; + ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + char * cdata = (char *) dst->data; + const char * cdataf = (const char *) dstf->data; + const char * const data_end = cdata + ggml_nbytes(dst); + + GGML_ASSERT(params->ith == 0); + memset(cdata, 0, ggml_nbytes(dst)); + + const int64_t px = src->ne[0]; + const int64_t py = src->ne[1]; + const int64_t pa = px * py; + + const float * splane = (const float *) src->data; + + const int ka = k0 * k1; + const int offset0 = -p0; + const int offset1 = -p1; + + while (cdata < data_end) { + for (int oy = 0; oy < py; ++oy) { + const float * const srow = splane + oy * px; + for (int ox = 0; ox < px; ++ox) { + const float grad0 = srow[ox]; + + const int ix = offset0 + ox * s0; + const int iy = offset1 + oy * s1; + + if (op == GGML_OP_POOL_MAX) { + float maxval = -FLT_MAX; + int kxmax = -1; + int kymax = -1; + + for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= dst->ne[1]) { + continue; + } + const void * drowf = (const void *)(cdataf + dst->nb[1] * (iy + ky)); + for (int kx = 0; kx < k0; ++kx) { + int j = ix + kx; + if (j < 0 || j >= dst->ne[0]) { + continue; + } + + const float val = dst->type == GGML_TYPE_F32 ? + ((const float *) drowf)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t *) drowf)[j]); + if (val <= maxval) { + continue; + } + + maxval = val; + kxmax = kx; + kymax = ky; + } + } + + if (kxmax == -1 || kymax == -1) { + continue; + } + + void * drow = (void *)(cdata + dst->nb[1] * (iy + kymax)); + const int j = ix + kxmax; + if (dst->type == GGML_TYPE_F32) { + ((float *) drow)[j] += grad0; + } else { + ((ggml_fp16_t *) drow)[j] = GGML_FP32_TO_FP16(grad0 + GGML_FP16_TO_FP32(((const ggml_fp16_t *) drow)[j])); + } + } else if (op == GGML_OP_POOL_AVG) { + const float grad = grad0 / ka; + + for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= dst->ne[1]) { + continue; + } + void * drow = (void *)(cdata + dst->nb[1] * (iy + ky)); + for (int kx = 0; kx < k0; ++kx) { + int j = ix + kx; + if (j < 0 || j >= dst->ne[0]) { + continue; + } + + if (dst->type == GGML_TYPE_F32) { + ((float *) drow)[j] += grad; + } else { + ((ggml_fp16_t *) drow)[j] += GGML_FP32_TO_FP16(grad); + } + } + } + } else { + GGML_ASSERT(false); + } + } + } + + cdata += dst->nb[2]; + cdataf += dst->nb[2]; + splane += pa; + } +} + +// ggml_compute_forward_upscale + +static void ggml_compute_forward_upscale_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; + + // TODO: optimize + + for (int64_t i3 = 0; i3 < ne3; i3++) { + const int64_t i03 = i3 / sf3; + for (int64_t i2 = ith; i2 < ne2; i2 += nth) { + const int64_t i02 = i2 / sf2; + for (int64_t i1 = 0; i1 < ne1; i1++) { + const int64_t i01 = i1 / sf1; + for (int64_t i0 = 0; i0 < ne0; i0++) { + const int64_t i00 = i0 / sf0; + + const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; + } + } + } + } +} + +void ggml_compute_forward_upscale( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_upscale_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +// ggml_compute_forward_pad + +static void ggml_compute_forward_pad_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT( dst->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float * dst_ptr = (float *) dst->data; + + // TODO: optimize + + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = ith; i1 < ne1; i1 += nth) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + for (int64_t i3 = 0; i3 < ne3; ++i3) { + const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; + + const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + dst_ptr[dst_idx] = *src_ptr; + } else { + dst_ptr[dst_idx] = 0; + } + } + } + } + } +} + +void ggml_compute_forward_pad( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_pad_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_pad_reflect_1d + +void ggml_compute_forward_pad_reflect_1d( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int ith = params->ith; + const int nth = params->nth; + + const int32_t * opts = (const int32_t *) dst->op_params; + const int p0 = opts[0]; + const int p1 = opts[1]; + + GGML_TENSOR_UNARY_OP_LOCALS + + for (int64_t i3 = 0; i3 < ne3; i3++) { + for (int64_t i2 = 0; i2 < ne2; i2++) { + for (int64_t i1 = ith; i1 < ne1; i1 += nth) { + float * left = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + p0*nb0); + float * right = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (ne0-p1-1)*nb0); + + ggml_vec_cpy_f32(ne00, left, (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01)); + + for (int i0 = 1; i0 <= p0; i0++) { left[-i0] = left[i0]; } + for (int i0 = 1; i0 <= p1; i0++) { right[i0] = right[-i0]; } + } + } + } +} + +// ggml_compute_forward_arange + +static void ggml_compute_forward_arange_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + GGML_ASSERT(dst->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const float start = ggml_get_op_params_f32(dst, 0); + const float stop = ggml_get_op_params_f32(dst, 1); + const float step = ggml_get_op_params_f32(dst, 2); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + GGML_ASSERT(ggml_nelements(dst) == steps); + + for (int64_t i = ith; i < steps; i+= nth) { + float value = start + step * i; + ((float *)dst->data)[i] = value; + } +} + +void ggml_compute_forward_arange( + const ggml_compute_params * params, + ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_arange_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_timestep_embedding_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + const int dim = ggml_get_op_params_i32(dst, 0); + const int max_period = ggml_get_op_params_i32(dst, 1); + + int half = dim / 2; + + for (int64_t i = 0; i < ne00; i++) { + float * embed_data = (float *)((char *) dst->data + i*nb1); + for (int64_t j = ith; j < half; j += nth) { + float timestep = ((float *)src0->data)[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); + } + if (dim % 2 != 0 && ith == 0) { + embed_data[dim] = 0.f; + } + } +} + +void ggml_compute_forward_timestep_embedding( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_timestep_embedding_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_argsort + +static void ggml_compute_forward_argsort_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(nb0 == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = ggml_nrows(src0); + + ggml_sort_order order = (ggml_sort_order) ggml_get_op_params_i32(dst, 0); + + for (int64_t i = ith; i < nr; i += nth) { + int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); + const float * src_data = (float *)((char *) src0->data + i*nb01); + + for (int64_t j = 0; j < ne0; j++) { + dst_data[j] = j; + } + + // C doesn't have a functional sort, so we do a bubble sort instead + for (int64_t j = 0; j < ne0; j++) { + for (int64_t k = j + 1; k < ne0; k++) { + if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || + (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { + int32_t tmp = dst_data[j]; + dst_data[j] = dst_data[k]; + dst_data[k] = tmp; + } + } + } + } +} + +void ggml_compute_forward_argsort( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_argsort_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_flash_attn_ext + +static void ggml_compute_forward_flash_attn_ext_f16( + const ggml_compute_params * params, + const ggml_tensor * q, + const ggml_tensor * k, + const ggml_tensor * v, + const ggml_tensor * mask, + ggml_tensor * dst) { + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t DK = nek0; + const int64_t DV = nev0; + const int64_t N = neq1; + + GGML_ASSERT(ne0 == DV); + GGML_ASSERT(ne2 == N); + + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbv0 == ggml_type_size(v->type)); + + GGML_ASSERT(neq0 == DK); + GGML_ASSERT(nek0 == DK); + GGML_ASSERT(nev0 == DV); + + GGML_ASSERT(neq1 == N); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // broadcast factors + const int64_t rk2 = neq2/nek2; + const int64_t rk3 = neq3/nek3; + + const int64_t rv2 = neq2/nev2; + const int64_t rv3 = neq3/nev3; + + // parallelize by q rows using ggml_vec_dot_f32 + + // total rows in q + const int nr = neq1*neq2*neq3; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float scale = 1.0f; + float max_bias = 0.0f; + float logit_softcap = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float)); + + if (logit_softcap != 0) { + scale /= logit_softcap; + } + + const uint32_t n_head = neq2; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + ggml_type const k_vec_dot_type = ggml_get_type_traits_cpu(k->type)->vec_dot_type; + ggml_from_float_t const q_to_vec_dot = ggml_get_type_traits_cpu(k_vec_dot_type)->from_float; + ggml_vec_dot_t const kq_vec_dot = ggml_get_type_traits_cpu(k->type)->vec_dot; + ggml_to_float_t const v_to_float = ggml_get_type_traits(v->type)->to_float; + + GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type"); + GGML_ASSERT(v_to_float && "fattn: unsupported V-type"); + + // loop over n_batch and n_head + for (int ir = ir0; ir < ir1; ++ir) { + // q indices + const int iq3 = ir/(neq2*neq1); + const int iq2 = (ir - iq3*neq2*neq1)/neq1; + const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); + + const uint32_t h = iq2; // head index + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + + float S = 0.0f; // sum + float M = -INFINITY; // maximum KQ value + + float * VKQ32 = (float *) params->wdata + ith*(1*DK + 2*DV + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator + float * V32 = (VKQ32 + 1*DV); // (temporary) FP32 V buffer + ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*DV); // (temporary) FP16 VKQ accumulator + ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*DV); // (temporary) buffer for Q converted to quantized/FP16 + + if (v->type == GGML_TYPE_F16) { + memset(VKQ16, 0, DV*sizeof(ggml_fp16_t)); + } else { + memset(VKQ32, 0, DV*sizeof(float)); + } + + const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; + + // k indices + const int ik3 = iq3 / rk3; + const int ik2 = iq2 / rk2; + + // v indices + const int iv3 = iq3 / rv3; + const int iv2 = iq2 / rv2; + + const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + q_to_vec_dot(pq, Q_q, DK); + + // online softmax / attention + // loop over n_kv and n_head_kv + // ref: https://arxiv.org/pdf/2112.05682.pdf + for (int64_t ic = 0; ic < nek1; ++ic) { + const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; + if (mv == -INFINITY) { + continue; + } + + float s; // KQ value + + const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); + kq_vec_dot(DK, &s, 0, k_data, 0, Q_q, 0, 1); + + s = s*scale; // scale KQ value + + if (logit_softcap != 0.0f) { + s = logit_softcap*tanhf(s); + } + + s += mv; // apply mask + + const float Mold = M; + + float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value + float vs = 1.0f; // post-softmax KQ value, expf(s - M) + + const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); + + if (v->type == GGML_TYPE_F16) { + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f16(DV, VKQ16, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + // V += v*expf(s - M) + ggml_vec_mad_f16(DV, VKQ16, (const ggml_fp16_t *) v_data, vs); + } else { + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f32(DV, VKQ32, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + v_to_float(v_data, V32, DV); + + // V += v*expf(s - M) + ggml_vec_mad_f32(DV, VKQ32, V32, vs); + } + + S = S*ms + vs; // scale and increment sum with partial sum + } + + if (v->type == GGML_TYPE_F16) { + for (int64_t d = 0; d < DV; ++d) { + VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); + } + } + + // V /= S + const float S_inv = 1.0f/S; + ggml_vec_scale_f32(DV, VKQ32, S_inv); + + // dst indices + const int i1 = iq1; + const int i2 = iq2; + const int i3 = iq3; + + // original + //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); + + // permute(0, 2, 1, 3) + memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); + } +} + +void ggml_compute_forward_flash_attn_ext( + const ggml_compute_params * params, + const ggml_tensor * q, + const ggml_tensor * k, + const ggml_tensor * v, + const ggml_tensor * mask, + ggml_tensor * dst) { + switch (dst->op_params[3]) { + case GGML_PREC_DEFAULT: + case GGML_PREC_F32: + { + // uses F32 accumulators + ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_flash_attn_back + +static void ggml_compute_forward_flash_attn_back_f32( + const ggml_compute_params * params, + const bool masked, + ggml_tensor * dst) { + + const ggml_tensor * q = dst->src[0]; + const ggml_tensor * k = dst->src[1]; + const ggml_tensor * v = dst->src[2]; + const ggml_tensor * d = dst->src[3]; + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ned, d, ne) + GGML_TENSOR_LOCALS(size_t, nbd, d, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t D = neq0; + const int64_t N = neq1; + const int64_t P = nek1 - N; + const int64_t M = P + N; + + const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); + const int mxDM = MAX(D, Mup); + + // GGML_ASSERT(ne0 == D); + // GGML_ASSERT(ne1 == N); + GGML_ASSERT(P >= 0); + + GGML_ASSERT(nbq0 == sizeof(float)); + GGML_ASSERT(nbk0 == sizeof(float)); + GGML_ASSERT(nbv0 == sizeof(float)); + + GGML_ASSERT(neq0 == D); + GGML_ASSERT(nek0 == D); + GGML_ASSERT(nev1 == D); + GGML_ASSERT(ned0 == D); + + GGML_ASSERT(neq1 == N); + GGML_ASSERT(nek1 == N + P); + GGML_ASSERT(nev1 == D); + GGML_ASSERT(ned1 == N); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + if (ith == 0) { + memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); + } + ggml_barrier(params->threadpool); + + const int64_t elem_q = ggml_nelements(q); + const int64_t elem_k = ggml_nelements(k); + + ggml_type result_type = dst->type; + GGML_ASSERT(ggml_blck_size(result_type) == 1); + const size_t tsize = ggml_type_size(result_type); + + const size_t offs_q = 0; + const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); + const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); + + void * grad_q = (char *) dst->data; + void * grad_k = (char *) dst->data + offs_k; + void * grad_v = (char *) dst->data + offs_v; + + const size_t nbgq1 = nb0*neq0; + const size_t nbgq2 = nb0*neq0*neq1; + const size_t nbgq3 = nb0*neq0*neq1*neq2; + + const size_t nbgk1 = nb0*nek0; + const size_t nbgk2 = nb0*nek0*nek1; + const size_t nbgk3 = nb0*nek0*nek1*neq2; + + const size_t nbgv1 = nb0*nev0; + const size_t nbgv2 = nb0*nev0*nev1; + const size_t nbgv3 = nb0*nev0*nev1*neq2; + + // parallelize by k rows using ggml_vec_dot_f32 + + // total rows in k + const int nr = nek2*nek3; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + const float scale = 1.0f/sqrtf(D); + + //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); + + // how often k2 (and v2) is repeated in q2 + int nrep = neq2/nek2; + + for (int ir = ir0; ir < ir1; ++ir) { + // q indices + const int ik3 = ir/(nek2); + const int ik2 = ir - ik3*nek2; + + const int iq3 = ik3; + const int id3 = ik3; + const int iv3 = ik3; + const int iv2 = ik2; + + for (int irep = 0; irep < nrep; ++irep) { + const int iq2 = ik2 + irep*nek2; + const int id2 = iq2; + + // (ik2 + irep*nek2) % nek2 == ik2 + for (int iq1 = 0; iq1 < neq1; ++iq1) { + const int id1 = iq1; + + // not sure about CACHE_LINE_SIZE_F32.. + // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset? + float * S = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32); + float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32); + + for (int i = M; i < Mup; ++i) { + S[i] = -INFINITY; + } + + const int64_t masked_begin = masked ? (P + iq1 + 1) : M; + for (int64_t ic = 0; ic < masked_begin; ++ic) { + // k indices + const int ik1 = ic; + + // S indices + const int i1 = ik1; + + ggml_vec_dot_f32(neq0, + S + i1, 0, + (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, + (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); + } + + // scale + ggml_vec_scale_f32(masked_begin, S, scale); + + for (int64_t i = masked_begin; i < M; i++) { + S[i] = -INFINITY; + } + + // softmax + // exclude known -INF S[..] values from max and loop + // dont forget to set their SM values to zero + { + float max = -INFINITY; + ggml_vec_max_f32(masked_begin, &max, S); + + ggml_float sum = 0.0; + { +#ifdef GGML_SOFT_MAX_ACCELERATE + max = -max; + vDSP_vsadd(SM, 1, &max, SM, 1, Mup); + vvexpf(SM, SM, &Mup); + ggml_vec_sum_f32(Mup, &sum, SM); +#else + sum = ggml_vec_soft_max_f32(Mup, SM, S, max); +#endif + } + + assert(sum > 0.0); + + sum = 1.0/sum; + ggml_vec_scale_f32(masked_begin, SM, sum); + + } + + // step-by-step explanation + { + // forward-process shape grads from backward process + // parallel_for ik2,ik3: + // for irep: + // iq2 = ik2 + irep*nek2 + // k[:D,:M,:,:] [D,M,:,:] grad[k][:D,:M,ik2,ik3] += grad[kcur] + // q[:D,:N,:,:] [D,N,:,:] grad[q][:D,iq1,iq2,iq3] += grad[qcur] + // v[:M,:D,:,:] [M,D,:,:] grad[v][:M,:D,iv2,iv3] += grad[vcur] + // for iq1: + // kcur = k[:D,:M,ik2,ik3] [D,M,1,1] grad[kcur] = grad[S1].T @ qcur + // qcur = q[:D,iq1,iq2,iq3] [D,1,1,1] grad[qcur] = grad[S1] @ kcur + // vcur = v[:M,:D,iv2,iv3] [M,D,1,1] grad[vcur] = grad[S5].T @ S4 + // S0 = -Inf [D,1,1,1] + // ~S1[i] = dot(kcur[:D,i], qcur) + // S1 = qcur @ kcur.T [M,1,1,1] grad[S1] = grad[S2] * scale + // S2 = S1 * scale [M,1,1,1] grad[S2] = diag_mask_zero(grad[S3], P) + // S3 = diag_mask_inf(S2, P) [M,1,1,1] grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) + // S4 = softmax(S3) [M,1,1,1] grad[S4] = grad[S5] @ vcur + // ~S5[i] = dot(vcur[:,i], S4) + // S5 = S4 @ vcur.T [D,1,1,1] grad[S5] = d[:D,id1,id2,id3] + // ~dst[i,iq1,iq2,iq3] = S5[i] ^ + // dst[:D,iq1,iq2,iq3] = S5 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,id1,id2,id3] + // dst backward-/ grad[dst] = d + // + // output gradients with their dependencies: + // + // grad[kcur] = grad[S1].T @ qcur + // grad[S1] = diag_mask_zero(grad[S3], P) * scale + // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) + // grad[S4] = grad[S5] @ vcur + // grad[S4] = d[:D,id1,id2,id3] @ vcur + // grad[qcur] = grad[S1] @ kcur + // grad[vcur] = grad[S5].T @ S4 + // grad[vcur] = d[:D,id1,id2,id3].T @ S4 + // + // in post-order: + // + // S1 = qcur @ kcur.T + // S2 = S1 * scale + // S3 = diag_mask_inf(S2, P) + // S4 = softmax(S3) + // grad[S4] = d[:D,id1,id2,id3] @ vcur + // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) + // grad[S1] = diag_mask_zero(grad[S3], P) * scale + // grad[qcur] = grad[S1] @ kcur + // grad[kcur] = grad[S1].T @ qcur + // grad[vcur] = d[:D,id1,id2,id3].T @ S4 + // + // using less variables (SM=S4): + // + // S = diag_mask_inf(qcur @ kcur.T * scale, P) + // SM = softmax(S) + // S = d[:D,iq1,iq2,iq3] @ vcur + // dot_SM_gradSM = dot(SM, S) + // S = SM * (S - dot(SM, S)) + // S = diag_mask_zero(S, P) * scale + // + // grad[q][:D,iq1,iq2,iq3] += S @ kcur + // grad[k][:D,:M,ik2,ik3] += S.T @ qcur + // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM + } + + // S = gradSM = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] + // S = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] + // for ic: + // S[:M] += vcur[:M,ic,iv2,iv3] * d[ic,id1,id2,id3] + // exclude known future zero S[..] values from operation + ggml_vec_set_f32(masked_begin, S, 0); + for (int64_t ic = 0; ic < D; ++ic) { + ggml_vec_mad_f32(masked_begin, + S, + (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), + *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); + } + + // S = SM * (S - dot(SM, S)) + float dot_SM_gradSM = 0; + ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1); + ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); + ggml_vec_mul_f32 (masked_begin, S, S, SM); + + // S = diag_mask_zero(S, P) * scale + // already done by above ggml_vec_set_f32 + + // exclude known zero S[..] values from operation + ggml_vec_scale_f32(masked_begin, S, scale); + + // S shape [M,1] + // SM shape [M,1] + // kcur shape [D,M] + // qcur shape [D,1] + // vcur shape [M,D] + + // grad[q][:D,iq1,iq2,iq3] += S @ kcur + // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M] + // for ic: + // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic,ik2,ik3] + // exclude known zero S[..] values from loop + for (int64_t ic = 0; ic < masked_begin; ++ic) { + ggml_vec_mad_f32(D, + (float *) ((char *) grad_q + (iq1*nbgq1 + iq2*nbgq2 + iq3*nbgq3)), + (float *) ((char *) k->data + (ic*nbk1 + ik2*nbk2 + ik3*nbk3)), + S[ic]); + } + + // grad[k][:D,:M,iq2,iq3] += S.T @ qcur + // for ic: + // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0] + // grad[k][:D,ic,iq2,iq3] += S[ic] * qcur[:D,0] + // exclude known zero S[..] values from loop + for (int64_t ic = 0; ic < masked_begin; ++ic) { + ggml_vec_mad_f32(D, + (float *) ((char *) grad_k + (ic*nbgk1 + ik2*nbgk2 + ik3*nbgk3)), + (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), + S[ic]); + } + + // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM + // for ic: + // grad[v][:M,ic,iv2,iv3] += d[:D,id1,id2,id3].T[0,ic] * SM[:M] + // grad[v][:M,ic,iv2,iv3] += d[ic,id1,id2,id3] * SM[:M] + // exclude known zero SM[..] values from mad + for (int64_t ic = 0; ic < D; ++ic) { + ggml_vec_mad_f32(masked_begin, + (float *) ((char *) grad_v + ( ic*nbgv1 + iv2*nbgv2 + iv3*nbgv3)), + SM, + *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); + } + } + } + } +} + +void ggml_compute_forward_flash_attn_back( + const ggml_compute_params * params, + const bool masked, + ggml_tensor * dst) { + + const ggml_tensor * q = dst->src[0]; + + switch (q->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_flash_attn_back_f32(params, masked, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_ssm_conv + +static void ggml_compute_forward_ssm_conv_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; // conv_x + const ggml_tensor * src1 = dst->src[1]; // conv1d.weight + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src1->ne[0]; // d_conv + const int ncs = src0->ne[0]; // d_conv - 1 + n_t + const int nr = src0->ne[1]; // d_inner + const int n_t = dst->ne[1]; // tokens per sequence + const int n_s = dst->ne[2]; // number of sequences in the batch + + GGML_ASSERT( dst->ne[0] == nr); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + for (int i3 = 0; i3 < n_s; ++i3) { + for (int i2 = 0; i2 < n_t; ++i2) { + // {d_conv - 1 + n_t, d_inner, n_seqs} + // sliding window + const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s} + const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner} + float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s} + + // TODO: transpose the output for smaller strides for big batches? + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // rowwise dot product + // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision + float sumf = 0.0f; + + // d_conv + for (int i0 = 0; i0 < nc; ++i0) { + sumf += s[i0 + i1*ncs] * c[i0 + i1*nc]; + } + x[i1] = sumf; + } + } + } +} + +void ggml_compute_forward_ssm_conv( + const ggml_compute_params * params, + ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_conv_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_ssm_scan + +static void ggml_compute_forward_ssm_scan_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; // s + const ggml_tensor * src1 = dst->src[1]; // x + const ggml_tensor * src2 = dst->src[2]; // dt + const ggml_tensor * src3 = dst->src[3]; // A + const ggml_tensor * src4 = dst->src[4]; // B + const ggml_tensor * src5 = dst->src[5]; // C + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nc = src0->ne[0]; // d_state + const int64_t nr = src0->ne[1]; // d_inner + const int64_t n_t = src1->ne[1]; // number of tokens per sequence + const int64_t n_s = src0->ne[2]; // number of sequences in the batch + + GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src2->nb[0] == sizeof(float)); + GGML_ASSERT(src3->nb[0] == sizeof(float)); + GGML_ASSERT(src4->nb[0] == sizeof(float)); + GGML_ASSERT(src5->nb[0] == sizeof(float)); + // required for the dot product between s and C + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + // required for per-sequence offsets for states + GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); + // required to get correct offset for state destination (i.e. src1->nb[3]) + GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + for (int i3 = 0; i3 < n_s; ++i3) { + for (int i2 = 0; i2 < n_t; ++i2) { + const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s} + const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} + const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s} + const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} + const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s} + const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s} + float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} + float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s} + + // use the output as the source for the next token-wise iterations + if (i2 > 0) { s0 = s; } + + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 + float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; + float x_dt = x[i1] * dt_soft_plus; + float sumf = 0.0f; + // d_state + for (int i0 = 0; i0 < nc; ++i0) { + int i = i0 + i1*nc; + // state = prev_state * dA + dB * x + float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); + // y = rowwise_dotprod(state, C) + sumf += state * C[i0]; + s[i] = state; + } + y[i1] = sumf; + } + } + } +} + +void ggml_compute_forward_ssm_scan( + const ggml_compute_params * params, + ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_scan_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_win_part + +static void ggml_compute_forward_win_part_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + GGML_UNUSED(params); + + const ggml_tensor * src0 = dst->src[0]; + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + + const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t w = ((const int32_t *)(dst->op_params))[2]; + + assert(ne00 == ne0); + assert(ne3 == nep0*nep1); + + // TODO: optimize / multi-thread + for (int py = 0; py < nep1; ++py) { + for (int px = 0; px < nep0; ++px) { + const int64_t i3 = py*nep0 + px; + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int64_t i02 = py*w + i2; + const int64_t i01 = px*w + i1; + const int64_t i00 = i0; + + const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; + const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; + + if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { + ((float *) dst->data)[i] = 0.0f; + } else { + ((float *) dst->data)[i] = ((float *) src0->data)[j]; + } + } + } + } + } + } +} + +void ggml_compute_forward_win_part( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_win_part_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_win_unpart + +static void ggml_compute_forward_win_unpart_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + GGML_UNUSED(params); + + const ggml_tensor * src0 = dst->src[0]; + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + + const int32_t w = ((const int32_t *)(dst->op_params))[0]; + + // padding + const int px = (w - ne1%w)%w; + //const int py = (w - ne2%w)%w; + + const int npx = (px + ne1)/w; + //const int npy = (py + ne2)/w; + + assert(ne0 == ne00); + + // TODO: optimize / multi-thread + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int ip2 = i2/w; + const int ip1 = i1/w; + + const int64_t i02 = i2%w; + const int64_t i01 = i1%w; + const int64_t i00 = i0; + + const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; + const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; + + ((float *) dst->data)[j] = ((float *) src0->data)[i]; + } + } + } +} + +void ggml_compute_forward_win_unpart( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_win_unpart_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +//gmml_compute_forward_unary + +void ggml_compute_forward_unary( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_unary_op op = ggml_get_unary_op(dst); + + switch (op) { + case GGML_UNARY_OP_ABS: + { + ggml_compute_forward_abs(params, dst); + } break; + case GGML_UNARY_OP_SGN: + { + ggml_compute_forward_sgn(params, dst); + } break; + case GGML_UNARY_OP_NEG: + { + ggml_compute_forward_neg(params, dst); + } break; + case GGML_UNARY_OP_STEP: + { + ggml_compute_forward_step(params, dst); + } break; + case GGML_UNARY_OP_TANH: + { + ggml_compute_forward_tanh(params, dst); + } break; + case GGML_UNARY_OP_ELU: + { + ggml_compute_forward_elu(params, dst); + } break; + case GGML_UNARY_OP_RELU: + { + ggml_compute_forward_relu(params, dst); + } break; + case GGML_UNARY_OP_SIGMOID: + { + ggml_compute_forward_sigmoid(params, dst); + } break; + case GGML_UNARY_OP_GELU: + { + ggml_compute_forward_gelu(params, dst); + } break; + case GGML_UNARY_OP_GELU_QUICK: + { + ggml_compute_forward_gelu_quick(params, dst); + } break; + case GGML_UNARY_OP_SILU: + { + ggml_compute_forward_silu(params, dst); + } break; + case GGML_UNARY_OP_HARDSWISH: + { + ggml_compute_forward_hardswish(params, dst); + } break; + case GGML_UNARY_OP_HARDSIGMOID: + { + ggml_compute_forward_hardsigmoid(params, dst); + } break; + case GGML_UNARY_OP_EXP: + { + ggml_compute_forward_exp(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_get_rel_pos + +static void ggml_compute_forward_get_rel_pos_f16( + const ggml_compute_params * params, + ggml_tensor * dst) { + GGML_UNUSED(params); + + const ggml_tensor * src0 = dst->src[0]; + + // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322 + + GGML_TENSOR_UNARY_OP_LOCALS + + const int64_t w = ne1; + + ggml_fp16_t * src0_data = (ggml_fp16_t *) src0->data; + ggml_fp16_t * dst_data = (ggml_fp16_t *) dst->data; + + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + const int64_t pos = (w - i1 - 1) + i2; + for (int64_t i0 = 0; i0 < ne0; ++i0) { + dst_data[i2*ne1*ne0 + i1*ne0 + i0] = src0_data[pos*ne00 + i0]; + } + } + } +} + +void ggml_compute_forward_get_rel_pos( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + { + ggml_compute_forward_get_rel_pos_f16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_add_rel_pos + +static void ggml_compute_forward_add_rel_pos_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + const ggml_tensor * src2 = dst->src[2]; + + const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; + if (!inplace) { + if (params->ith == 0) { + memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359 + + float * src1_data = (float *) src1->data; + float * src2_data = (float *) src2->data; + float * dst_data = (float *) dst->data; + + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + const int64_t ne12 = src1->ne[2]; + const int64_t ne13 = src1->ne[3]; + + const int ith = params->ith; + const int nth = params->nth; + + // total patches in dst + const int np = ne13; + + // patches per thread + const int dp = (np + nth - 1)/nth; + + // patch range for this thread + const int ip0 = dp*ith; + const int ip1 = MIN(ip0 + dp, np); + + for (int64_t i13 = ip0; i13 < ip1; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + for (int64_t i11 = 0; i11 < ne11; ++i11) { + const int64_t jp1 = i13*ne12*ne11*ne10 + i12*ne11*ne10 + i11*ne10; + for (int64_t i10 = 0; i10 < ne10; ++i10) { + const int64_t jp0 = jp1 + i10; + const float src1_e = src1_data[jp0]; + const float src2_e = src2_data[jp0]; + + const int64_t jdh = jp0 * ne10; + const int64_t jdw = jdh - (ne10 - 1) * i10; + + for (int64_t j = 0; j < ne10; ++j) { + dst_data[jdh + j ] += src2_e; + dst_data[jdw + j*ne10] += src1_e; + } + } + } + } + } +} + +void ggml_compute_forward_add_rel_pos( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_add_rel_pos_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rwkv_wkv6 + +static void ggml_compute_forward_rwkv_wkv6_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const int64_t T = dst->src[1]->ne[2]; + const int64_t C = dst->ne[0]; + const int64_t HEADS = dst->src[1]->ne[1]; + const int64_t n_seqs = dst->src[5]->ne[1]; + const int64_t head_size = C / HEADS; + + float * dst_data = (float *) dst->data; + float * state = ((float *) dst->data) + C * T; + + const int ith = params->ith; + const int nth = params->nth; + + if (ith >= HEADS) { + return; + } + + const int h_start = (HEADS * ith) / nth; + const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? + (HEADS * (ith + 1)) / nth : HEADS; + + float * k = (float *) dst->src[0]->data; + float * v = (float *) dst->src[1]->data; + float * r = (float *) dst->src[2]->data; + float * time_faaaa = (float *) dst->src[3]->data; + float * time_decay = (float *) dst->src[4]->data; + + size_t t_stride = HEADS * head_size; // Same to C + + size_t h_stride = C / HEADS; + GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS + size_t h_stride_2d = head_size * head_size; + + if (ith == 0) { + memset(dst_data, 0, T * C * sizeof(float)); + } + ggml_barrier(params->threadpool); + + + #if defined(__AVX__) && !defined(__AVX512F__) + #define GGML_F32X GGML_F32x8 + #define GGML_F32X_SET1 GGML_F32x8_SET1 + #define GGML_F32X_LOAD GGML_F32x8_LOAD + #define GGML_F32X_STORE GGML_F32x8_STORE + #define GGML_F32X_MUL GGML_F32x8_MUL + #define GGML_F32X_FMA GGML_F32x8_FMA + #define WKV_VECTOR_SIZE 8 + #elif defined(__AVX512F__) + #define GGML_F32X GGML_F32x16 + #define GGML_F32X_SET1 GGML_F32x16_SET1 + #define GGML_F32X_LOAD GGML_F32x16_LOAD + #define GGML_F32X_STORE GGML_F32x16_STORE + #define GGML_F32X_MUL GGML_F32x16_MUL + #define GGML_F32X_FMA GGML_F32x16_FMA + #define WKV_VECTOR_SIZE 16 + #elif defined(__ARM_NEON) && defined(__aarch64__) + #define GGML_F32X GGML_F32x4 + #define GGML_F32X_SET1 GGML_F32x4_SET1 + #define GGML_F32X_LOAD GGML_F32x4_LOAD + #define GGML_F32X_STORE GGML_F32x4_STORE + #define GGML_F32X_MUL GGML_F32x4_MUL + #define GGML_F32X_FMA GGML_F32x4_FMA + #define WKV_VECTOR_SIZE 4 + #endif + + #ifdef WKV_VECTOR_SIZE + const int64_t vec_count = head_size / WKV_VECTOR_SIZE; + + for (int64_t t = 0; t < T; t++) { + size_t t_offset = t * t_stride; + size_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + size_t h_offset = h * h_stride; + size_t t_h_offset = t_offset + h_offset; + size_t h_2d_offset = h * h_stride_2d; + + for (int64_t i = 0; i < head_size; i++) { + size_t t_h_i_offset = t_h_offset + i; + size_t h_i_offset = h_offset + i; + size_t h_2d_i_offset = h_2d_offset + i * h_stride; + + float k_val = k[t_h_i_offset]; + float r_val = r[t_h_i_offset]; + float time_faaaa_val = time_faaaa[h_i_offset]; + float time_decay_val = time_decay[t_h_i_offset]; + + // Broadcast scalar values to vectors + GGML_F32X k_vec = GGML_F32X_SET1(k_val); + GGML_F32X r_vec = GGML_F32X_SET1(r_val); + GGML_F32X time_faaaa_vec = GGML_F32X_SET1(time_faaaa_val); + GGML_F32X time_decay_vec = GGML_F32X_SET1(time_decay_val); + + for (int64_t j = 0; j < vec_count; j++) { + size_t base_j = j * WKV_VECTOR_SIZE; + size_t t_h_j_offset = t_h_offset + base_j; + size_t h_2d_i_j_offset = h_2d_i_offset + base_j; + + // Load x elements at once + GGML_F32X v_vec = GGML_F32X_LOAD(&v[t_h_j_offset]); + GGML_F32X prev_state_vec = GGML_F32X_LOAD(&state_prev[h_2d_i_j_offset]); + GGML_F32X dst_vec = GGML_F32X_LOAD(&dst_data[t_h_j_offset]); + + // Compute kv = v * k + GGML_F32X kv_vec = GGML_F32X_MUL(v_vec, k_vec); + + // Compute temp = kv * time_faaaa + prev_state + GGML_F32X temp_vec = GGML_F32X_FMA(prev_state_vec, kv_vec, time_faaaa_vec); + + // Update dst: dst += temp * r + dst_vec = GGML_F32X_FMA(dst_vec, temp_vec, r_vec); + GGML_F32X_STORE(&dst_data[t_h_j_offset], dst_vec); + + // Update state: state = prev_state * time_decay + kv + GGML_F32X new_state_vec = GGML_F32X_FMA(kv_vec, prev_state_vec, time_decay_vec); + GGML_F32X_STORE(&state_cur[h_2d_i_j_offset], new_state_vec); + } + + // Handle remaining elements, this will not be used. + for (int64_t j = vec_count * WKV_VECTOR_SIZE; j < head_size; j++) { + size_t t_h_j_offset = t_h_offset + j; + size_t h_2d_i_j_offset = h_2d_i_offset + j; + float v_val = v[t_h_j_offset]; + float kv_val = v_val * k_val; + float prev_state_val = state_prev[h_2d_i_j_offset]; + float temp_val = kv_val * time_faaaa_val + prev_state_val; + dst_data[t_h_j_offset] += temp_val * r_val; + state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; + } + } + } + } + + #else + // basically fused operations: + // dst = r @ (time_faaaa * (k @ v) + state), + // state = time_decay * state + (k @ v), + // recursive through each token + for (int64_t t = 0; t < T; t++) { + size_t t_offset = t * t_stride; + size_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + size_t h_offset = h * h_stride; + size_t t_h_offset = t_offset + h_offset; + size_t h_2d_offset = h * h_stride_2d; + + for (int64_t i = 0; i < head_size; i++) { + size_t t_h_i_offset = t_h_offset + i; + size_t h_i_offset = h_offset + i; + size_t h_2d_i_offset = h_2d_offset + i * h_stride; + + float k_val = k[t_h_i_offset]; + float r_val = r[t_h_i_offset]; + float time_faaaa_val = time_faaaa[h_i_offset]; + // RWKV v6: different time_decay for each token. + float time_decay_val = time_decay[t_h_i_offset]; + + for (int64_t j = 0; j < head_size; j++) { + size_t t_h_j_offset = t_h_offset + j; + size_t h_2d_i_j_offset = h_2d_i_offset + j; + + float v_val = v[t_h_j_offset]; + float kv_val = v_val * k_val; + float prev_state_val = state_prev[h_2d_i_j_offset]; + float temp_val = kv_val * time_faaaa_val + prev_state_val; + dst_data[t_h_j_offset] += temp_val * r_val; + state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; + } + } + } + } + #endif +} + + +void ggml_compute_forward_rwkv_wkv6( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rwkv_wkv6_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_gla + +static void ggml_compute_forward_gla_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const int64_t T = dst->src[1]->ne[2]; + const int64_t C = dst->ne[0]; + const int64_t HEADS = dst->src[1]->ne[1]; + const int64_t n_seqs = dst->src[4]->ne[1]; + const int64_t head_size = C / HEADS; + const float scale = ggml_get_op_params_f32(dst, 0); + + float * dst_data = (float *) dst->data; + float * state = ((float *) dst->data) + C * T; + + const int ith = params->ith; + const int nth = params->nth; + + if (ith >= HEADS) { + return; + } + + const int h_start = (HEADS * ith) / nth; + const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? + (HEADS * (ith + 1)) / nth : HEADS; + + float * k = (float *) dst->src[0]->data; + float * v = (float *) dst->src[1]->data; + float * q = (float *) dst->src[2]->data; + float * g = (float *) dst->src[3]->data; + + size_t t_stride = HEADS * head_size; // Same to C + + size_t h_stride = C / HEADS; + GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS + size_t h_stride_2d = head_size * head_size; + + if (ith == 0) { + memset(dst_data, 0, T * C * sizeof(float)); + } + ggml_barrier(params->threadpool); + + + #if defined(__AVX__) && !defined(__AVX512F__) + #define GGML_F32X GGML_F32x8 + #define GGML_F32X_SET1 GGML_F32x8_SET1 + #define GGML_F32X_LOAD GGML_F32x8_LOAD + #define GGML_F32X_STORE GGML_F32x8_STORE + #define GGML_F32X_MUL GGML_F32x8_MUL + #define GGML_F32X_FMA GGML_F32x8_FMA + #define GLA_VECTOR_SIZE 8 + #elif defined(__AVX512F__) + #define GGML_F32X GGML_F32x16 + #define GGML_F32X_SET1 GGML_F32x16_SET1 + #define GGML_F32X_LOAD GGML_F32x16_LOAD + #define GGML_F32X_STORE GGML_F32x16_STORE + #define GGML_F32X_MUL GGML_F32x16_MUL + #define GGML_F32X_FMA GGML_F32x16_FMA + #define GLA_VECTOR_SIZE 16 + #elif defined(__ARM_NEON) && defined(__aarch64__) + #define GGML_F32X GGML_F32x4 + #define GGML_F32X_SET1 GGML_F32x4_SET1 + #define GGML_F32X_LOAD GGML_F32x4_LOAD + #define GGML_F32X_STORE GGML_F32x4_STORE + #define GGML_F32X_MUL GGML_F32x4_MUL + #define GGML_F32X_FMA GGML_F32x4_FMA + #define GLA_VECTOR_SIZE 4 + #endif + + #ifdef GLA_VECTOR_SIZE + const int64_t vec_count = head_size / GLA_VECTOR_SIZE; + + for (int64_t t = 0; t < T; t++) { + size_t t_offset = t * t_stride; + size_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[4]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + size_t h_offset = h * h_stride; + size_t t_h_offset = t_offset + h_offset; + size_t h_2d_offset = h * h_stride_2d; + + for (int64_t i = 0; i < head_size; i++) { + size_t t_h_i_offset = t_h_offset + i; + size_t h_2d_i_offset = h_2d_offset + i * h_stride; + + float k_val = k[t_h_i_offset]; + float q_val = q[t_h_i_offset] * scale; + float g_val = g[t_h_i_offset]; + + // Broadcast scalar values to vectors + GGML_F32X k_vec = GGML_F32X_SET1(k_val); + GGML_F32X q_vec = GGML_F32X_SET1(q_val); + GGML_F32X g_vec = GGML_F32X_SET1(g_val); + + for (int64_t j = 0; j < vec_count; j++) { + size_t base_j = j * GLA_VECTOR_SIZE; + size_t t_h_j_offset = t_h_offset + base_j; + size_t h_2d_i_j_offset = h_2d_i_offset + base_j; + + // Load x elements at once + GGML_F32X v_vec = GGML_F32X_LOAD(&v[t_h_j_offset]); + GGML_F32X prev_state_vec = GGML_F32X_LOAD(&state_prev[h_2d_i_j_offset]); + GGML_F32X dst_vec = GGML_F32X_LOAD(&dst_data[t_h_j_offset]); + + // Compute kv = v * k + GGML_F32X kv_vec = GGML_F32X_MUL(v_vec, k_vec); + + // Compute temp = prev_state * g + kv + GGML_F32X temp_vec = GGML_F32X_FMA(kv_vec, prev_state_vec, g_vec); + + // Update dst: dst += temp * q + dst_vec = GGML_F32X_FMA(dst_vec, temp_vec, q_vec); + GGML_F32X_STORE(&dst_data[t_h_j_offset], dst_vec); + + // Update state + GGML_F32X_STORE(&state_cur[h_2d_i_j_offset], temp_vec); + } + + // Handle remaining elements, this will not be used. + for (int64_t j = vec_count * GLA_VECTOR_SIZE; j < head_size; j++) { + size_t t_h_j_offset = t_h_offset + j; + size_t h_2d_i_j_offset = h_2d_i_offset + j; + float v_val = v[t_h_j_offset]; + float kv_val = v_val * k_val; + float prev_state_val = state_prev[h_2d_i_j_offset]; + float temp_val = kv_val + prev_state_val * g_val; + dst_data[t_h_j_offset] += temp_val * q_val; + state_cur[h_2d_i_j_offset] = temp_val; + } + } + } + } + + #else + for (int64_t t = 0; t < T; t++) { + size_t t_offset = t * t_stride; + size_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[4]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + size_t h_offset = h * h_stride; + size_t t_h_offset = t_offset + h_offset; + size_t h_2d_offset = h * h_stride_2d; + + for (int64_t i = 0; i < head_size; i++) { + size_t t_h_i_offset = t_h_offset + i; + size_t h_2d_i_offset = h_2d_offset + i * h_stride; + + float k_val = k[t_h_i_offset]; + float q_val = q[t_h_i_offset] * scale; + float g_val = g[t_h_i_offset]; + + for (int64_t j = 0; j < head_size; j++) { + size_t t_h_j_offset = t_h_offset + j; + size_t h_2d_i_j_offset = h_2d_i_offset + j; + + float v_val = v[t_h_j_offset]; + float kv_val = v_val * k_val; + float prev_state_val = state_prev[h_2d_i_j_offset]; + float temp_val = prev_state_val * g_val + kv_val; + dst_data[t_h_j_offset] += temp_val * q_val; + state_cur[h_2d_i_j_offset] = temp_val; + } + } + } + } + #endif +} + + +void ggml_compute_forward_gla( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_gla_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rwkv_wkv7 + +static void ggml_compute_forward_rwkv_wkv7_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const int64_t T = dst->src[1]->ne[2]; + const int64_t C = dst->ne[0]; + const int64_t HEADS = dst->src[1]->ne[1]; + const int64_t n_seqs = dst->src[6]->ne[1]; + const int64_t head_size = C / HEADS; + + float * dst_data = (float *) dst->data; + float * state = ((float *) dst->data) + C * T; + + const int ith = params->ith; + const int nth = params->nth; + + if (ith >= HEADS) { + return; + } + + const int h_start = (HEADS * ith) / nth; + const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? + (HEADS * (ith + 1)) / nth : HEADS; + + float * r = (float *) dst->src[0]->data; + float * w = (float *) dst->src[1]->data; + float * k = (float *) dst->src[2]->data; + float * v = (float *) dst->src[3]->data; + float * a = (float *) dst->src[4]->data; + float * b = (float *) dst->src[5]->data; + + int64_t t_stride = HEADS * head_size; // Same to C + + int64_t h_stride = C / HEADS; + GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS + int64_t h_stride_2d = head_size * head_size; + + #if defined(GGML_SIMD) + for (int64_t t = 0; t < T; t++) { + int64_t t_offset = t * t_stride; + int64_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + int64_t h_offset = h * h_stride; + int64_t t_h_offset = t_offset + h_offset; + int64_t h_2d_offset = h * h_stride_2d; + + for (int64_t ii = 0; ii < head_size; ii++) { + int64_t t_h_i_offset = t_h_offset + ii; + int64_t h_2d_i_offset = h_2d_offset + ii * h_stride; + + GGML_F32_VEC v_vec = GGML_F32_VEC_SET1(v[t_h_i_offset]); + + float sa = 0; + { + GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; + GGML_F32_VEC ax[GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + for (int64_t j = 0; j < head_size; j += GGML_F32_STEP) { + for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) { + ax[kk] = GGML_F32_VEC_LOAD(&a[t_h_offset + j + kk * GGML_F32_EPR]); + ay[kk] = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_offset + j + kk * GGML_F32_EPR]); + sum[kk] = GGML_F32_VEC_FMA(sum[kk], ax[kk], ay[kk]); + } + } + GGML_F32_VEC_REDUCE(sa, sum); + } + + GGML_F32_VEC sa_vec = GGML_F32_VEC_SET1(sa); + + int64_t j = 0; + GGML_F32_VEC result_vec[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; + for (; j < head_size; j += GGML_F32_STEP) { + for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) { + int64_t t_h_j_offset = t_h_offset + j + kk * GGML_F32_EPR; + int64_t h_2d_i_j_offset = h_2d_i_offset + j + kk * GGML_F32_EPR; + + GGML_F32_VEC r_vec = GGML_F32_VEC_LOAD(&r[t_h_j_offset]); + GGML_F32_VEC w_vec = GGML_F32_VEC_LOAD(&w[t_h_j_offset]); + GGML_F32_VEC k_vec = GGML_F32_VEC_LOAD(&k[t_h_j_offset]); + GGML_F32_VEC b_vec = GGML_F32_VEC_LOAD(&b[t_h_j_offset]); + + k_vec = GGML_F32_VEC_MUL(v_vec, k_vec); + + GGML_F32_VEC state_vec = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_j_offset]); + // kv + s * decay + sa * b + state_vec = GGML_F32_VEC_FMA(k_vec, state_vec, w_vec); + state_vec = GGML_F32_VEC_FMA(state_vec, sa_vec, b_vec); + GGML_F32_VEC_STORE(&state_cur[h_2d_i_j_offset], state_vec); + + result_vec[kk] = GGML_F32_VEC_FMA(result_vec[kk], state_vec, r_vec); + } + } + GGML_F32_VEC_REDUCE(dst_data[t_h_i_offset], result_vec); + + // There shouldn't be left-overs though. + for (; j < head_size; j++) { + int64_t t_h_j_offset = t_h_offset + j; + int64_t h_2d_i_j_offset = h_2d_i_offset + j; + + float r_val = r[t_h_j_offset]; + float w_val = w[t_h_j_offset]; + float k_val = k[t_h_j_offset]; + float b_val = b[t_h_j_offset]; + float kv_val = v[t_h_i_offset] * k_val; + + float prev_state_val = state_prev[h_2d_i_j_offset]; + state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val; + dst_data[t_h_i_offset] += state_cur[h_2d_i_j_offset] * r_val; + } + } + } + } + #else + for (int64_t t = 0; t < T; t++) { + int64_t t_offset = t * t_stride; + int64_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + int64_t h_offset = h * h_stride; + int64_t t_h_offset = t_offset + h_offset; + int64_t h_2d_offset = h * h_stride_2d; + + for (int64_t i = 0; i < head_size; i++) { + int64_t t_h_i_offset = t_h_offset + i; + int64_t h_2d_i_offset = h_2d_offset + i * h_stride; + + float v_val = v[t_h_i_offset]; + + float sa = 0, result = 0; + for (int64_t j = 0; j < head_size; j++) { + sa += a[t_h_offset + j] * state_prev[h_2d_i_offset + j]; + } + + for (int64_t j = 0; j < head_size; j++) { + int64_t t_h_j_offset = t_h_offset + j; + int64_t h_2d_i_j_offset = h_2d_i_offset + j; + + float r_val = r[t_h_j_offset]; + float w_val = w[t_h_j_offset]; + float k_val = k[t_h_j_offset]; + float b_val = b[t_h_j_offset]; + float kv_val = v_val * k_val; + float prev_state_val = state_prev[h_2d_i_j_offset]; + state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val; + result += state_cur[h_2d_i_j_offset] * r_val; + } + dst_data[t_h_i_offset] = result; + } + } + } + #endif +} + + +void ggml_compute_forward_rwkv_wkv7( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rwkv_wkv7_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_map_unary + +static void ggml_compute_forward_map_unary_f32( + const ggml_compute_params * params, + ggml_tensor * dst, + const ggml_unary_op_f32_t fun) { + + const ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + fun(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +void ggml_compute_forward_map_unary( + const ggml_compute_params * params, + ggml_tensor * dst, + const ggml_unary_op_f32_t fun) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_unary_f32(params, dst, fun); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_map_binary + +static void ggml_compute_forward_map_binary_f32( + const ggml_compute_params * params, + ggml_tensor * dst, + const ggml_binary_op_f32_t fun) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(src1)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + fun(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1])), + (float *) ((char *) src1->data + i*(src1->nb[1]))); + } +} + +void ggml_compute_forward_map_binary( + const ggml_compute_params * params, + ggml_tensor * dst, + const ggml_binary_op_f32_t fun) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_binary_f32(params, dst, fun); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_map_custom1 + +void ggml_compute_forward_map_custom1_f32( + const ggml_compute_params * params, + ggml_tensor * dst, + const ggml_custom1_op_f32_t fun) { + + const ggml_tensor * a = dst->src[0]; + + if (params->ith != 0) { + return; + } + + fun(dst, a); +} + +// ggml_compute_forward_map_custom2 + +void ggml_compute_forward_map_custom2_f32( + const ggml_compute_params * params, + ggml_tensor * dst, + const ggml_custom2_op_f32_t fun) { + + const ggml_tensor * a = dst->src[0]; + const ggml_tensor * b = dst->src[1]; + + if (params->ith != 0) { + return; + } + + fun(dst, a, b); +} + +// ggml_compute_forward_map_custom3 + +void ggml_compute_forward_map_custom3_f32( + const ggml_compute_params * params, + ggml_tensor * dst, + const ggml_custom3_op_f32_t fun) { + + const ggml_tensor * a = dst->src[0]; + const ggml_tensor * b = dst->src[1]; + const ggml_tensor * c = dst->src[1]; + + if (params->ith != 0) { + return; + } + + fun(dst, a, b, c); +} + +// ggml_compute_forward_map_custom1 + +void ggml_compute_forward_map_custom1( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * a = dst->src[0]; + + struct ggml_map_custom1_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); + + p.fun(dst, a, params->ith, params->nth, p.userdata); +} + +// ggml_compute_forward_map_custom2 + +void ggml_compute_forward_map_custom2( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * a = dst->src[0]; + const ggml_tensor * b = dst->src[1]; + + struct ggml_map_custom2_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); + + p.fun(dst, a, b, params->ith, params->nth, p.userdata); +} + +// ggml_compute_forward_map_custom3 + +void ggml_compute_forward_map_custom3( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * a = dst->src[0]; + const ggml_tensor * b = dst->src[1]; + const ggml_tensor * c = dst->src[2]; + + struct ggml_map_custom3_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); + + p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); +} + +// ggml_compute_forward_cross_entropy_loss + +static void ggml_compute_forward_cross_entropy_loss_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); + GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); + GGML_ASSERT(ggml_are_same_shape(src0, src1)); + GGML_ASSERT(ggml_is_scalar(dst)); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + // TODO: handle transposed/permuted matrices + const int64_t nc = src0->ne[0]; + const int64_t nr = ggml_nrows(src0); + + const int ith = params->ith; + const int nth = params->nth; + + float * sums = (float *) params->wdata; + float * st = ((float *) params->wdata) + nth + ith*nc; + float sum_thread = 0.0f; + + GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t i1 = ir0; i1 < ir1; ++i1) { + const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]); + const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]); + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(s0[i])); + assert(!isnan(s1[i])); + } +#endif + + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max); + assert(sum_softmax >= 0.0); + + ggml_vec_add1_f32(nc, st, st, -sum_softmax); + ggml_vec_mul_f32(nc, st, st, s1); + + float sum_st = 0.0f; + ggml_vec_sum_f32(nc, &sum_st, st); + sum_thread += sum_st; + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + assert(!isnan(st[i])); + assert(!isinf(st[i])); + } +#endif + } + sums[ith] = sum_thread; + ggml_barrier(params->threadpool); + + if (ith == 0) { + float * dp = (float *) dst->data; + ggml_vec_sum_f32(nth, dp, sums); + dp[0] *= -1.0f / (float) nr; + } +} + +void ggml_compute_forward_cross_entropy_loss( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_cross_entropy_loss_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_cross_entropy_loss_back + +static void ggml_compute_forward_cross_entropy_loss_back_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * grad = dst->src[0]; // gradient of forward pass output + const ggml_tensor * src0f = dst->src[1]; // src0 of forward pass + const ggml_tensor * src1f = dst->src[2]; // src1 of forward pass + + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_is_contiguous(src0f)); + GGML_ASSERT(ggml_is_contiguous(src1f)); + GGML_ASSERT(ggml_is_contiguous(grad)); + GGML_ASSERT(ggml_are_same_shape(src0f, src1f) && ggml_are_same_shape(src0f, dst)); + + const int64_t ith = params->ith; + const int64_t nth = params->nth; + + // TODO: handle transposed/permuted matrices + const int64_t nc = src0f->ne[0]; + const int64_t nr = ggml_nrows(src0f); + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + const float d_by_nr = ((const float *) grad->data)[0] / (float) nr; + + for (int64_t i1 = ir0; i1 < ir1; i1++) { + float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); + const float * s0 = (const float *)((const char *) src0f->data + i1*src0f->nb[1]); + const float * s1 = (const float *)((const char *) src1f->data + i1*src1f->nb[1]); + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(s0[i])); + assert(!isnan(s1[i])); + } +#endif + + // soft_max + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + const ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); + assert(sum > 0.0); + ggml_vec_scale_f32(nc, ds0, 1.0/sum); + + // grad(src0f) = (softmax(src0f) - src1f) * grad(cross_entropy_loss(src0f, src1f)) / nr + ggml_vec_sub_f32(nc, ds0, ds0, s1); + ggml_vec_scale_f32(nc, ds0, d_by_nr); + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + assert(!isnan(ds0[i])); + assert(!isinf(ds0[i])); + } +#endif + } +} + +void ggml_compute_forward_cross_entropy_loss_back( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_cross_entropy_loss_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_opt_step_adamw_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src0_grad = dst->src[1]; + const ggml_tensor * src0_grad_m = dst->src[2]; + const ggml_tensor * src0_grad_v = dst->src[3]; + const ggml_tensor * adamw_params = dst->src[4]; + + GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); + GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m)); + GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v)); + GGML_ASSERT(ggml_nelements(adamw_params) == 7); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + const float * adamw_params_ptr = ggml_get_data_f32(adamw_params); + const float alpha = adamw_params_ptr[0]; + const float beta1 = adamw_params_ptr[1]; + const float beta2 = adamw_params_ptr[2]; + const float eps = adamw_params_ptr[3]; + const float wd = adamw_params_ptr[4]; + const float beta1h = adamw_params_ptr[5]; + const float beta2h = adamw_params_ptr[6]; + + for (int ir = ir0; ir < ir1; ++ir) { + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const size_t offset = i03*nb03 + i02*nb02 + i01*nb01; + + float * w = (float *) ((char *) src0->data + offset); // weight + const float * g = (const float *) ((const char *) src0_grad->data + offset); // grad + float * m = (float *) ((char *) src0_grad_m->data + offset); + float * v = (float *) ((char *) src0_grad_v->data + offset); + + for (int i00 = 0; i00 < ne00; ++i00) { + m[i00] = m[i00]*beta1 + g[i00]*(1.0f - beta1); + v[i00] = v[i00]*beta2 + g[i00]*g[i00]*(1.0f - beta2); + + const float mh = m[i00]*beta1h; + const float vh = sqrtf(v[i00]*beta2h) + eps; + + // The weight decay is applied independently of the Adam momenta m and v. + // This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss. + // See: https://arxiv.org/pdf/1711.05101v3.pdf + w[i00] = w[i00]*(1.0f - alpha*wd) - alpha*mh/vh; + } + } +} + +void ggml_compute_forward_opt_step_adamw( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_opt_step_adamw_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h new file mode 100644 index 0000000000..d43fbc1fc4 --- /dev/null +++ b/ggml/src/ggml-cpu/ops.h @@ -0,0 +1,128 @@ +#pragma once + +#include "ggml.h" + +// +// cache line +// + +#if defined(__cpp_lib_hardware_interference_size) +#define CACHE_LINE_SIZE std::hardware_destructive_interference_size +#else +#if defined(__POWER9_VECTOR__) +#define CACHE_LINE_SIZE 128 +#elif defined(__VXE__) || defined(__VXE2__) +#define CACHE_LINE_SIZE 256 +#else +#define CACHE_LINE_SIZE 64 +#endif +#endif + +static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); + +#ifdef __cplusplus +extern "C" { +#endif + +void ggml_compute_forward_dup(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_add(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_add1(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_acc(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_sum(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_sum_rows(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_mean(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_argmax(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_count_equal(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_repeat(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_repeat_back(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_concat(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_silu_back(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_rms_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_rms_norm_back(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_group_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_l2_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_out_prod(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_scale(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_set(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_cpy(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_cont(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_reshape(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_view(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_permute(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_transpose(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_get_rows(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_get_rows_back(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_diag(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_diag_mask_inf(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_diag_mask_zero(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_soft_max(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_soft_max_ext_back(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_rope(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_rope_back(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_clamp(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_conv_transpose_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_im2col(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_im2col_back_f32(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_conv_transpose_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_pool_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_pool_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_pool_2d_back(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_upscale(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_pad(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_pad_reflect_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_arange(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_leaky_relu(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_flash_attn_ext( + const struct ggml_compute_params * params, + const struct ggml_tensor * q, + const struct ggml_tensor * k, + const struct ggml_tensor * v, + const struct ggml_tensor * mask, + struct ggml_tensor * dst); +void ggml_compute_forward_flash_attn_back( + const struct ggml_compute_params * params, + const bool masked, + struct ggml_tensor * dst); +void ggml_compute_forward_ssm_conv(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_ssm_scan(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_win_part(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_win_unpart(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_unary(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_get_rel_pos(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_add_rel_pos(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_rwkv_wkv6(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_rwkv_wkv7(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_gla(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_map_unary( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_unary_op_f32_t fun); +void ggml_compute_forward_map_binary( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_binary_op_f32_t fun); +void ggml_compute_forward_map_custom1_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_custom1_op_f32_t fun); +void ggml_compute_forward_map_custom2_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_custom2_op_f32_t fun); +void ggml_compute_forward_map_custom3_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_custom3_op_f32_t fun); +void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_map_custom3(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_cross_entropy_loss(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_cross_entropy_loss_back(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_opt_step_adamw(const struct ggml_compute_params * params, struct ggml_tensor * dst); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h new file mode 100644 index 0000000000..28aaa1b718 --- /dev/null +++ b/ggml/src/ggml-cpu/simd-mappings.h @@ -0,0 +1,884 @@ +#pragma once + +#include "ggml-cpu-impl.h" + +// +// simd mappings +// + +// we define a common set of C macros which map to specific intrinsics based on the current architecture +// we then implement the fundamental computation operations below using only these macros +// adding support for new architectures requires to define the corresponding SIMD macros +// +// GGML_F32_STEP / GGML_F16_STEP +// number of elements to process in a single step +// +// GGML_F32_EPR / GGML_F16_EPR +// number of elements to fit in a single register +// + +#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) + +#define GGML_SIMD + +// F32 NEON + +#define GGML_F32_STEP 16 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 float32x4_t +#define GGML_F32x4_ZERO vdupq_n_f32(0.0f) +#define GGML_F32x4_SET1(x) vdupq_n_f32(x) +#define GGML_F32x4_LOAD vld1q_f32 +#define GGML_F32x4_STORE vst1q_f32 +#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) +#define GGML_F32x4_ADD vaddq_f32 +#define GGML_F32x4_MUL vmulq_f32 +#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ + } \ + (res) = (ggml_float) GGML_F32x4_REDUCE_ONE((x)[0]); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 NEON + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + #define GGML_F16_STEP 32 + #define GGML_F16_EPR 8 + + #define GGML_F16x8 float16x8_t + #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) + #define GGML_F16x8_SET1(x) vdupq_n_f16(x) + #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) + #define GGML_F16x8_STORE vst1q_f16 + #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) + #define GGML_F16x8_ADD vaddq_f16 + #define GGML_F16x8_MUL vmulq_f16 + #define GGML_F16x8_REDUCE(res, x) \ + do { \ + int offset = GGML_F16_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ + } \ + const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \ + const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \ + (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ + } while (0) + + #define GGML_F16_VEC GGML_F16x8 + #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO + #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 + #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), (r)[i]) + #define GGML_F16_VEC_FMA GGML_F16x8_FMA + #define GGML_F16_VEC_ADD GGML_F16x8_ADD + #define GGML_F16_VEC_MUL GGML_F16x8_MUL + #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE +#else + // if FP16 vector arithmetic is not supported, we use FP32 instead + // and take advantage of the vcvt_ functions to convert to/from FP16 + + #define GGML_F16_STEP 16 + #define GGML_F16_EPR 4 + + #define GGML_F32Cx4 float32x4_t + #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) + #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) + #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x))) + #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) + #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) + #define GGML_F32Cx4_ADD vaddq_f32 + #define GGML_F32Cx4_MUL vmulq_f32 + #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + + #define GGML_F16_VEC GGML_F32Cx4 + #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO + #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 + #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i]) + #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA + #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD + #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL + #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE +#endif + +#elif defined(__AVX512F__) + +#define GGML_SIMD + +// F32 AVX512 + +#define GGML_F32_STEP 64 +#define GGML_F32_EPR 16 + +#define GGML_F32x16 __m512 +#define GGML_F32x16_ZERO _mm512_setzero_ps() +#define GGML_F32x16_SET1(x) _mm512_set1_ps(x) +#define GGML_F32x16_LOAD _mm512_loadu_ps +#define GGML_F32x16_STORE _mm512_storeu_ps +// _mm512_fmadd_ps is defined in AVX512F so no guard is required +#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) +#define GGML_F32x16_ADD _mm512_add_ps +#define GGML_F32x16_MUL _mm512_mul_ps +#define GGML_F32x16_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + res = (ggml_float) _mm512_reduce_add_ps(x[0]); \ +} while (0) + +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x16 +#define GGML_F32_VEC_ZERO GGML_F32x16_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x16_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x16_LOAD +#define GGML_F32_VEC_STORE GGML_F32x16_STORE +#define GGML_F32_VEC_FMA GGML_F32x16_FMA +#define GGML_F32_VEC_ADD GGML_F32x16_ADD +#define GGML_F32_VEC_MUL GGML_F32x16_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE + +// F16 AVX512 + +// F16 AVX + +#define GGML_F16_STEP 64 +#define GGML_F16_EPR 16 + +// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead + +#define GGML_F32Cx16 __m512 +#define GGML_F32Cx16_ZERO _mm512_setzero_ps() +#define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) + +// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F +// so F16C guard isn't required +#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x))) +#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) + +#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) +#define GGML_F32Cx16_ADD _mm512_add_ps +#define GGML_F32Cx16_MUL _mm512_mul_ps +#define GGML_F32Cx16_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + res = (ggml_float) _mm512_reduce_add_ps(x[0]); \ +} while (0) + +#define GGML_F16_VEC GGML_F32Cx16 +#define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx16_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx16_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx16_MUL + +#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE +#elif defined(__AVX__) + +#define GGML_SIMD + +// F32 AVX + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 8 + +#define GGML_F32x8 __m256 +#define GGML_F32x8_ZERO _mm256_setzero_ps() +#define GGML_F32x8_SET1(x) _mm256_set1_ps(x) +#define GGML_F32x8_LOAD _mm256_loadu_ps +#define GGML_F32x8_STORE _mm256_storeu_ps +#if defined(__FMA__) + #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) +#else + #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) +#endif +#define GGML_F32x8_ADD _mm256_add_ps +#define GGML_F32x8_MUL _mm256_mul_ps +#define GGML_F32x8_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ + } \ + const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ + _mm256_extractf128_ps(x[0], 1)); \ + const __m128 t1 = _mm_hadd_ps(t0, t0); \ + res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ +} while (0) +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x8 +#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD +#define GGML_F32_VEC_STORE GGML_F32x8_STORE +#define GGML_F32_VEC_FMA GGML_F32x8_FMA +#define GGML_F32_VEC_ADD GGML_F32x8_ADD +#define GGML_F32_VEC_MUL GGML_F32x8_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE + +// F16 AVX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 8 + +// F16 arithmetic is not supported by AVX, so we use F32 instead + +#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8_ZERO _mm256_setzero_ps() +#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) + +#if defined(__F16C__) +// the _mm256_cvt intrinsics require F16C +#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) +#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) +#else +static inline __m256 __avx_f32cx8_load(const ggml_fp16_t * x) { + float tmp[8]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return _mm256_loadu_ps(tmp); +} +static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { + float arr[8]; + + _mm256_storeu_ps(arr, y); + + for (int i = 0; i < 8; i++) + x[i] = GGML_FP32_TO_FP16(arr[i]); +} +#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) +#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) +#endif + +#define GGML_F32Cx8_FMA GGML_F32x8_FMA +#define GGML_F32Cx8_ADD _mm256_add_ps +#define GGML_F32Cx8_MUL _mm256_mul_ps +#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE + +#define GGML_F16_VEC GGML_F32Cx8 +#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE + +#elif defined(__POWER9_VECTOR__) + +#define GGML_SIMD + +// F32 POWER9 + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 vector float +#define GGML_F32x4_ZERO 0.0f +#define GGML_F32x4_SET1 vec_splats +#define GGML_F32x4_LOAD(p) vec_xl(0, p) +#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) +#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) +#define GGML_F32x4_ADD vec_add +#define GGML_F32x4_MUL vec_mul +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ + } \ + res = vec_extract(x[0], 0) + \ + vec_extract(x[0], 1) + \ + vec_extract(x[0], 2) + \ + vec_extract(x[0], 3); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 POWER9 +#define GGML_F16_STEP GGML_F32_STEP +#define GGML_F16_EPR GGML_F32_EPR +#define GGML_F16_VEC GGML_F32x4 +#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F16_VEC_FMA GGML_F32x4_FMA +#define GGML_F16_VEC_ADD GGML_F32x4_ADD +#define GGML_F16_VEC_MUL GGML_F32x4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE +// Use vec_xl, not vec_ld, in case the load address is not aligned. +#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ + vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ + vec_extract_fp32_from_shortl(vec_xl(0, p)) +#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] +#define GGML_F16_VEC_STORE(p, r, i) \ + if (i & 0x1) \ + vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ + r[i - GGML_ENDIAN_BYTE(0)]), \ + 0, p - GGML_F16_EPR) + +#elif defined(__wasm_simd128__) + +#define GGML_SIMD + +// F32 WASM + +#define GGML_F32_STEP 16 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 v128_t +#define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) +#define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) +#define GGML_F32x4_LOAD wasm_v128_load +#define GGML_F32x4_STORE wasm_v128_store +#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) +#define GGML_F32x4_ADD wasm_f32x4_add +#define GGML_F32x4_MUL wasm_f32x4_mul +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + res = wasm_f32x4_extract_lane(x[0], 0) + \ + wasm_f32x4_extract_lane(x[0], 1) + \ + wasm_f32x4_extract_lane(x[0], 2) + \ + wasm_f32x4_extract_lane(x[0], 3); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 WASM + +#define GGML_F16_STEP 16 +#define GGML_F16_EPR 4 + +inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(p[0]); + tmp[1] = GGML_FP16_TO_FP32(p[1]); + tmp[2] = GGML_FP16_TO_FP32(p[2]); + tmp[3] = GGML_FP16_TO_FP32(p[3]); + + return wasm_v128_load(tmp); +} + +inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { + float tmp[4]; + + wasm_v128_store(tmp, x); + + p[0] = GGML_FP32_TO_FP16(tmp[0]); + p[1] = GGML_FP32_TO_FP16(tmp[1]); + p[2] = GGML_FP32_TO_FP16(tmp[2]); + p[3] = GGML_FP32_TO_FP16(tmp[3]); +} + +#define GGML_F16x4 v128_t +#define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) +#define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) +#define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) +#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) +#define GGML_F16x4_FMA GGML_F32x4_FMA +#define GGML_F16x4_ADD wasm_f32x4_add +#define GGML_F16x4_MUL wasm_f32x4_mul +#define GGML_F16x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F16_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + res = (ggml_float) (wasm_f32x4_extract_lane(x[0], 0) + \ + wasm_f32x4_extract_lane(x[0], 1) + \ + wasm_f32x4_extract_lane(x[0], 2) + \ + wasm_f32x4_extract_lane(x[0], 3)); \ +} + +#define GGML_F16_VEC GGML_F16x4 +#define GGML_F16_VEC_ZERO GGML_F16x4_ZERO +#define GGML_F16_VEC_SET1 GGML_F16x4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F16x4_FMA +#define GGML_F16_VEC_ADD GGML_F16x4_ADD +#define GGML_F16_VEC_MUL GGML_F16x4_MUL +#define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE + +#elif defined(__SSE3__) + +#define GGML_SIMD + +// F32 SSE + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 __m128 +#define GGML_F32x4_ZERO _mm_setzero_ps() +#define GGML_F32x4_SET1(x) _mm_set1_ps(x) +#define GGML_F32x4_LOAD _mm_loadu_ps +#define GGML_F32x4_STORE _mm_storeu_ps +#if defined(__FMA__) + // TODO: Does this work? + #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) +#else + #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) +#endif +#define GGML_F32x4_ADD _mm_add_ps +#define GGML_F32x4_MUL _mm_mul_ps +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ + } \ + const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ + res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ +} +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 SSE + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 4 + +static inline __m128 __sse_f16x4_load(const ggml_fp16_t * x) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(x[0]); + tmp[1] = GGML_FP16_TO_FP32(x[1]); + tmp[2] = GGML_FP16_TO_FP32(x[2]); + tmp[3] = GGML_FP16_TO_FP32(x[3]); + + return _mm_loadu_ps(tmp); +} + +static inline void __sse_f16x4_store(ggml_fp16_t * x, __m128 y) { + float arr[4]; + + _mm_storeu_ps(arr, y); + + x[0] = GGML_FP32_TO_FP16(arr[0]); + x[1] = GGML_FP32_TO_FP16(arr[1]); + x[2] = GGML_FP32_TO_FP16(arr[2]); + x[3] = GGML_FP32_TO_FP16(arr[3]); +} + +#define GGML_F32Cx4 __m128 +#define GGML_F32Cx4_ZERO _mm_setzero_ps() +#define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) +#define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) +#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) +#define GGML_F32Cx4_FMA GGML_F32x4_FMA +#define GGML_F32Cx4_ADD _mm_add_ps +#define GGML_F32Cx4_MUL _mm_mul_ps +#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + +#define GGML_F16_VEC GGML_F32Cx4 +#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE + +#elif defined(__loongarch_asx) + +#define GGML_SIMD + +// F32 LASX +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 8 + +#define GGML_F32x8 __m256 +#define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) +#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) +#define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) +#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) +#define GGML_F32x8_ADD __lasx_xvfadd_s +#define GGML_F32x8_MUL __lasx_xvfmul_s +#define GGML_F32x8_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + float *tmp_p = (float *)&x[0]; \ + res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ +} while (0) +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x8 +#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD +#define GGML_F32_VEC_STORE GGML_F32x8_STORE +#define GGML_F32_VEC_FMA GGML_F32x8_FMA +#define GGML_F32_VEC_ADD GGML_F32x8_ADD +#define GGML_F32_VEC_MUL GGML_F32x8_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE + +// F16 LASX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 8 + +// F16 arithmetic is not supported by LASX, so we use F32 instead + +#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) + +static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { + __m256i a; + memcpy(&a, x, sizeof(ggml_fp16_t) * 8); + a = __lasx_xvpermi_d(a, 0 | (1 << 4)); + return __lasx_xvfcvtl_s_h(a); +} + +static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { + __m256i a = __lasx_xvfcvt_h_s(y, y); + a = __lasx_xvpermi_d(a, 0 | (2 << 2)); + memcpy(x, &a, sizeof(ggml_fp16_t) * 8); +} +#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) +#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) + +#define GGML_F32Cx8_FMA GGML_F32x8_FMA +#define GGML_F32Cx8_ADD __lasx_xvfadd_s +#define GGML_F32Cx8_MUL __lasx_xvfmul_s +#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE + +#define GGML_F16_VEC GGML_F32Cx8 +#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE + +#elif defined(__loongarch_sx) + +#define GGML_SIMD + +// F32 LSX + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 __m128 +#define GGML_F32x4_ZERO __lsx_vldi(0) +#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) +#define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) +#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) +#define GGML_F32x4_ADD __lsx_vfadd_s +#define GGML_F32x4_MUL __lsx_vfmul_s +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ + } \ + __m128i tmp = __lsx_vsrli_d((__m128i) x[0], 32); \ + tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, x[0]); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ + tmp = __lsx_vsrli_d((__m128i) t0, 32); \ + tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, t0); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 LSX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 4 + +static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(x[0]); + tmp[1] = GGML_FP16_TO_FP32(x[1]); + tmp[2] = GGML_FP16_TO_FP32(x[2]); + tmp[3] = GGML_FP16_TO_FP32(x[3]); + + return __lsx_vld(tmp, 0); +} + +static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { + float arr[4]; + + __lsx_vst(y, arr, 0); + + x[0] = GGML_FP32_TO_FP16(arr[0]); + x[1] = GGML_FP32_TO_FP16(arr[1]); + x[2] = GGML_FP32_TO_FP16(arr[2]); + x[3] = GGML_FP32_TO_FP16(arr[3]); +} + +#define GGML_F32Cx4 __m128 +#define GGML_F32Cx4_ZERO __lsx_vldi(0) +#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) +#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) +#define GGML_F32Cx4_FMA GGML_F32x4_FMA +#define GGML_F32Cx4_ADD __lsx_vfadd_s +#define GGML_F32Cx4_MUL __lsx_vfmul_s +#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + +#define GGML_F16_VEC GGML_F32Cx4 +#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE + +#elif defined(__VXE__) || defined(__VXE2__) + +#define GGML_SIMD + +// F32 s390x + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 __vector float +#define GGML_F32x4_ZERO vec_splats(0.0f) +#define GGML_F32x4_SET1 vec_splats +#define GGML_F32x4_LOAD(p) vec_xl(0, p) +#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) +#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) +#define GGML_F32x4_ADD vec_add +#define GGML_F32x4_MUL vec_mul +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset + i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset + i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset + i]); \ + } \ + res = vec_extract(x[0], 0) + \ + vec_extract(x[0], 1) + \ + vec_extract(x[0], 2) + \ + vec_extract(x[0], 3); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 s390x +#define GGML_F16_STEP GGML_F32_STEP +#define GGML_F16_EPR GGML_F32_EPR + +static inline __vector float __lzs_f16cx4_load(const ggml_fp16_t * x) { + float tmp[4]; + + for (int i = 0; i < 4; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return vec_xl(0, tmp); +} + +static inline void __lzs_f16cx4_store(ggml_fp16_t * x, __vector float y) { + float arr[4]; + + vec_xst(y, 0, arr); + + for (int i = 0; i < 4; i++) { + x[i] = GGML_FP32_TO_FP16(arr[i]); + } +} + +#define GGML_F16_VEC GGML_F32x4 +#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F16_VEC_LOAD(p, i) __lzs_f16cx4_load(p) +#define GGML_F16_VEC_STORE(p, r, i) __lzs_f16cx4_store(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32x4_FMA +#define GGML_F16_VEC_ADD GGML_F32x4_ADD +#define GGML_F16_VEC_MUL GGML_F32x4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE + +#endif + +// GGML_F32_ARR / GGML_F16_ARR +// number of registers to use per step +#ifdef GGML_SIMD +#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) +#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) +#endif diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp new file mode 100644 index 0000000000..dfe2218e30 --- /dev/null +++ b/ggml/src/ggml-cpu/vec.cpp @@ -0,0 +1,258 @@ +#include "vec.h" + +#include + +#if defined(_MSC_VER) +// disable "possible loss of data" to avoid hundreds of casts +// we should just be careful :) +#pragma warning(disable: 4244 4267) +#endif + +// precomputed gelu table for f16 (128 KB) +ggml_fp16_t ggml_table_gelu_f16[1 << 16]; + +// precomputed quick gelu table for f16 (128 KB) +ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; + +void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * GGML_RESTRICT x, size_t bx, const float * GGML_RESTRICT y, size_t by, int nrc) { + assert(nrc == 1); + GGML_UNUSED(nrc); + GGML_UNUSED(bx); + GGML_UNUSED(by); + GGML_UNUSED(bs); + +#if defined(GGML_SIMD) + float sumf = 0.0f; + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; + + GGML_F32_VEC ax[GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + + sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); + } + } + + // reduce sum0..sum3 to sum0 + GGML_F32_VEC_REDUCE(sumf, sum); + + // leftovers + for (int i = np; i < n; ++i) { + sumf += x[i]*y[i]; + } +#else + // scalar + ggml_float sumf = 0.0; + for (int i = 0; i < n; ++i) { + sumf += (ggml_float)(x[i]*y[i]); + } +#endif + + *s = sumf; +} + +void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t * GGML_RESTRICT x, size_t bx, ggml_bf16_t * GGML_RESTRICT y, size_t by, int nrc) { + assert(nrc == 1); + GGML_UNUSED(nrc); + GGML_UNUSED(bx); + GGML_UNUSED(by); + GGML_UNUSED(bs); + int i = 0; + ggml_float sumf = 0; + +#if defined(__AVX512BF16__) + __m512 c1 = _mm512_setzero_ps(); + __m512 c2 = _mm512_setzero_ps(); + for (; i + 64 <= n; i += 64) { + c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), + m512bh(_mm512_loadu_si512((y + i)))); + c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), + m512bh(_mm512_loadu_si512((y + i + 32)))); + } + sumf += (ggml_float)_mm512_reduce_add_ps(c1); + sumf += (ggml_float)_mm512_reduce_add_ps(c2); + +#elif defined(__AVX512F__) +#define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16)) + __m512 c1 = _mm512_setzero_ps(); + __m512 c2 = _mm512_setzero_ps(); + for (; i + 32 <= n; i += 32) { + c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1); + c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2); + } + sumf += (ggml_float)_mm512_reduce_add_ps(c1); + sumf += (ggml_float)_mm512_reduce_add_ps(c2); + +#undef LOAD +#elif defined(__AVX2__) || defined(__AVX__) +#if defined(__AVX2__) +#define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)) +#else +#define LOAD(p) _mm256_castsi256_ps(_mm256_insertf128_si256(_mm256_castsi128_si256(_mm_slli_epi32(_mm_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)), (_mm_slli_epi32(_mm_cvtepu16_epi32(_mm_bsrli_si128(_mm_loadu_si128((const __m128i *)(p)), 8)), 16)), 1)) +#endif + __m256 c1 = _mm256_setzero_ps(); + __m256 c2 = _mm256_setzero_ps(); + __m256 c3 = _mm256_setzero_ps(); + __m256 c4 = _mm256_setzero_ps(); + for (; i + 32 <= n; i += 32) { + c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1); + c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2); + c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3); + c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4); + } + __m128 g; + c1 = _mm256_add_ps(_mm256_add_ps(c1, c3), + _mm256_add_ps(c2, c4)); + g = _mm_add_ps(_mm256_extractf128_ps(c1, 1), + _mm256_castps256_ps128(c1)); + g = _mm_add_ps(g, _mm_movehl_ps(g, g)); + g = _mm_add_ss(g, _mm_movehdup_ps(g)); + sumf += (ggml_float)_mm_cvtss_f32(g); + +#undef LOAD +#endif + + for (; i < n; ++i) { + sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) * + GGML_BF16_TO_FP32(y[i])); + } + *s = sumf; +} + +void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * GGML_RESTRICT x, size_t bx, ggml_fp16_t * GGML_RESTRICT y, size_t by, int nrc) { + assert(nrc == 1); + GGML_UNUSED(nrc); + GGML_UNUSED(bx); + GGML_UNUSED(by); + GGML_UNUSED(bs); + + ggml_float sumf = 0.0; + +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + + sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); + } + } + + // reduce sum0..sum3 to sum0 + GGML_F16_VEC_REDUCE(sumf, sum); + + // leftovers + for (int i = np; i < n; ++i) { + sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); + } +#else + for (int i = 0; i < n; ++i) { + sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); + } +#endif + + *s = sumf; +} + +void ggml_vec_silu_f32(const int n, float * y, const float * x) { + int i = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); + } +#endif + for (; i < n; ++i) { + y[i] = ggml_silu_f32(x[i]); + } +} + +ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { + int i = 0; + ggml_float sum = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), + _mm512_set1_ps(max))); + _mm512_storeu_ps(y + i, val); + sum += (ggml_float)_mm512_reduce_add_ps(val); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), + _mm256_set1_ps(max))); + _mm256_storeu_ps(y + i, val); + __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), + _mm256_castps256_ps128(val)); + val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); + val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); + sum += (ggml_float)_mm_cvtss_f32(val2); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), + _mm_set1_ps(max))); + _mm_storeu_ps(y + i, val); +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) + val = _mm_add_ps(val, _mm_movehl_ps(val, val)); + val = _mm_add_ss(val, _mm_movehdup_ps(val)); +#else + __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); + val = _mm_add_ps(val, tmp); + tmp = _mm_movehl_ps(tmp, val); + val = _mm_add_ss(val, tmp); +#endif + sum += (ggml_float)_mm_cvtss_f32(val); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), + vdupq_n_f32(max))); + vst1q_f32(y + i, val); + sum += (ggml_float)vaddvq_f32(val); + } +#endif + for (; i < n; ++i) { + float val = expf(x[i] - max); + sum += (ggml_float)val; + y[i] = val; + } + return sum; +} + +ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max) { + // log(soft_max) = log(soft_max_i / soft_max_sum) = log(soft_max_i) - log(soft_max_sum) = (logit_i - max) - log(soft_max_i) + + int i = 0; + ggml_float sum = 0; + for (; i < n; ++i) { + float val = x[i] - max; + y[i] = val; + sum += (ggml_float)expf(val); + } + return sum = (ggml_float)logf(sum); +} diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h new file mode 100644 index 0000000000..23cbb3051f --- /dev/null +++ b/ggml/src/ggml-cpu/vec.h @@ -0,0 +1,802 @@ +// Vectorized functions for fundamental operations + +#pragma once + +#include "ggml-impl.h" +#include "simd-mappings.h" +#include "ggml.h" + +#if defined(GGML_USE_ACCELERATE) +#include +#endif + +// floating point type used to accumulate sums +typedef double ggml_float; + +#define GGML_GELU_FP16 +#define GGML_GELU_QUICK_FP16 + +#define GGML_SOFT_MAX_UNROLL 4 +#define GGML_VEC_DOT_UNROLL 2 +#define GGML_VEC_MAD_UNROLL 32 + +#ifdef __cplusplus +extern "C" { +#endif + +// +// global data +// + +// precomputed gelu table for f16 (128 KB) +extern ggml_fp16_t ggml_table_gelu_f16[1 << 16]; + +// precomputed quick gelu table for f16 (128 KB) +extern ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; + +// +// fundamental operations +// + +void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * GGML_RESTRICT x, size_t bx, const float * GGML_RESTRICT y, size_t by, int nrc); +void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t * GGML_RESTRICT x, size_t bx, ggml_bf16_t * GGML_RESTRICT y, size_t by, int nrc); +void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * GGML_RESTRICT x, size_t bx, ggml_fp16_t * GGML_RESTRICT y, size_t by, int nrc); + +void ggml_vec_silu_f32(const int n, float * y, const float * x); +ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max); +ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max); + +inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } + +inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_cpy_i32(const int n, int32_t * y, const int32_t * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } + +inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const ggml_fp16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } +inline static void ggml_vec_add_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) { + for (int i = 0; i < n; ++i) { + z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) + GGML_FP16_TO_FP32(y[i])); + } +} +inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } +inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } +inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } +inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } +inline static void ggml_vec_sub_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) { + for (int i = 0; i < n; ++i) { + z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) - GGML_FP16_TO_FP32(y[i])); + } +} +inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } +inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } +inline static void ggml_vec_neg_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(-GGML_FP16_TO_FP32(x[i])); + } +} + +inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } +inline static void ggml_vec_mul_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) { + for (int i = 0; i < n; ++i) { + z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) * GGML_FP16_TO_FP32(y[i])); + } +} +inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } +inline static void ggml_vec_div_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) { + for (int i = 0; i < n; ++i) { + z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) / GGML_FP16_TO_FP32(y[i])); + } +} + +// compute GGML_VEC_DOT_UNROLL dot products at once +// xs - x row stride in bytes +inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GGML_RESTRICT s, void * GGML_RESTRICT xv, ggml_fp16_t * GGML_RESTRICT y) { + ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; + + ggml_fp16_t * GGML_RESTRICT x[GGML_VEC_DOT_UNROLL]; + + for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { + x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); + } + +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + + for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { + ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); + + sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); + } + } + } + + // reduce sum0..sum3 to sum0 + for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { + GGML_F16_VEC_REDUCE(sumf[k], sum[k]); + } + + // leftovers + for (int i = np; i < n; ++i) { + for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { + sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); + } + } +#else + for (int i = 0; i < n; ++i) { + for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { + sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); + } + } +#endif + + for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { + s[i] = (float)sumf[i]; + } +} + +inline static void ggml_vec_mad_f32(const int n, float * GGML_RESTRICT y, const float * GGML_RESTRICT x, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); + + GGML_F32_VEC ax[GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); + + GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] += x[i]*v; + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] += x[i]*v; + } +#endif +} + +inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y, const ggml_fp16_t * GGML_RESTRICT x, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx); + + GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); + } +#endif +} + +// xs and vs are byte strides of x and v +inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * GGML_RESTRICT y, const float * GGML_RESTRICT xv, const float * GGML_RESTRICT vv) { + + const float * GGML_RESTRICT x[GGML_VEC_MAD_UNROLL]; + const float * GGML_RESTRICT v[GGML_VEC_MAD_UNROLL]; + + for (int i = 0; i < GGML_VEC_MAD_UNROLL; ++i) { + x[i] = (const float *) ((const char *) xv + i*xs); + v[i] = (const float *) ((const char *) vv + i*vs); + } + +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL]; + + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + vx[k] = GGML_F32_VEC_SET1(v[k][0]); + } + + GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]); + } + + GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); + } + } + + // leftovers + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + for (int i = np; i < n; ++i) { + y[i] += x[k][i]*v[k][0]; + } + } +#else + // scalar + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + for (int i = 0; i < n; ++i) { + y[i] += x[k][i]*v[k][0]; + } + } +#endif +} + +//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } +inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { +#if defined(GGML_USE_ACCELERATE) + vDSP_vsmul(y, 1, &v, y, 1, n); +#elif defined(GGML_SIMD) + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); + + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_MUL(ay[j], vx); + + GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] *= v; + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] *= v; + } +#endif +} + +inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); + + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_MUL(ay[j], vx); + + GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); + } +#endif +} + +inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } +inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } +inline static void ggml_vec_sqr_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + float v = GGML_FP16_TO_FP32(x[i]); + y[i] = GGML_FP32_TO_FP16(v*v); + } +} +inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } +inline static void ggml_vec_sqrt_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(sqrtf(GGML_FP16_TO_FP32(x[i]))); + } +} +inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } +inline static void ggml_vec_log_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(logf(GGML_FP16_TO_FP32(x[i]))); + } +} +inline static void ggml_vec_sin_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sinf(x[i]); } +inline static void ggml_vec_sin_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(sinf(GGML_FP16_TO_FP32(x[i]))); + } +} +inline static void ggml_vec_cos_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = cosf(x[i]); } +inline static void ggml_vec_cos_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(cosf(GGML_FP16_TO_FP32(x[i]))); + } +} +inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } +inline static void ggml_vec_abs_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(fabsf(GGML_FP16_TO_FP32(x[i]))); + } +} +inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } +inline static void ggml_vec_sgn_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + float v = GGML_FP16_TO_FP32(x[i]); + y[i] = GGML_FP32_TO_FP16((v > 0.f) ? 1.f : ((v < 0.f) ? -1.f : 0.f)); + } +} +inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } +inline static void ggml_vec_step_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16((GGML_FP16_TO_FP32(x[i]) > 0.f) ? 1.f : 0.f); + } +} +inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } +inline static void ggml_vec_tanh_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(tanhf(GGML_FP16_TO_FP32(x[i]))); + } +} +inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } +inline static void ggml_vec_elu_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(expm1f(GGML_FP16_TO_FP32(x[i]))); + } +} +inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } +inline static void ggml_vec_relu_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + float v = GGML_FP16_TO_FP32(x[i]); + y[i] = GGML_FP32_TO_FP16((v > 0.f) ? v : 0.f); + } +} +inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } +inline static void ggml_vec_leaky_relu_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x, const float ns) { + for (int i = 0; i < n; ++i) { + float v = GGML_FP16_TO_FP32(x[i]); + y[i] = GGML_FP32_TO_FP16(((v > 0.f) ? v : 0.f) + ns * ((v < 0.0f) ? v : 0.f)); + } +} +inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } +inline static void ggml_vec_sigmoid_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(1.f / (1.f + expf(-GGML_FP16_TO_FP32(x[i])))); + } +} +// TODO: optimize performance +inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } +inline static void ggml_vec_hardswish_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + float v = GGML_FP16_TO_FP32(x[i]); + y[i] = GGML_FP32_TO_FP16(v * fminf(1.0f, fmaxf(0.0f, (v + 3.0f) / 6.0f))); + } +} +inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } +inline static void ggml_vec_hardsigmoid_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(fminf(1.0f, fmaxf(0.0f, (GGML_FP16_TO_FP32(x[i]) + 3.0f) / 6.0f))); + } +} +inline static void ggml_vec_exp_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = expf(x[i]); } +inline static void ggml_vec_exp_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(expf(GGML_FP16_TO_FP32(x[i]))); + } +} + +static const float GELU_COEF_A = 0.044715f; +static const float GELU_QUICK_COEF = -1.702f; +static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + +inline static float ggml_gelu_f32(float x) { + return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); +} + +inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + const uint16_t * i16 = (const uint16_t *) x; + for (int i = 0; i < n; ++i) { + y[i] = ggml_table_gelu_f16[i16[i]]; + } +} + +#ifdef GGML_GELU_FP16 +inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { + uint16_t t; + for (int i = 0; i < n; ++i) { + if (x[i] <= -10.0f) { + y[i] = 0.0f; + } else if (x[i] >= 10.0f) { + y[i] = x[i]; + } else { + ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); + memcpy(&t, &fp16, sizeof(uint16_t)); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); + } + } +} +#else +inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { + for (int i = 0; i < n; ++i) { + y[i] = ggml_gelu_f32(x[i]); + } +} +#endif + +inline static float ggml_gelu_quick_f32(float x) { + return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); +} + +//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { +// const uint16_t * i16 = (const uint16_t *) x; +// for (int i = 0; i < n; ++i) { +// y[i] = ggml_table_gelu_quick_f16[i16[i]]; +// } +//} + +#ifdef GGML_GELU_QUICK_FP16 +inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { + uint16_t t; + for (int i = 0; i < n; ++i) { + ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); + memcpy(&t, &fp16, sizeof(uint16_t)); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]); + } +} +#else +inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { + for (int i = 0; i < n; ++i) { + y[i] = ggml_gelu_quick_f32(x[i]); + } +} +#endif + +inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + float v = GGML_FP16_TO_FP32(x[i]); + y[i] = GGML_FP32_TO_FP16(v*(1.0f/(1.0f+expf(GELU_QUICK_COEF*v)))); + } +} + +// Sigmoid Linear Unit (SiLU) function +inline static float ggml_silu_f32(float x) { + return x/(1.0f + expf(-x)); +} +inline static ggml_fp16_t ggml_silu_f16(ggml_fp16_t x) { + float v = GGML_FP16_TO_FP32(x); + return GGML_FP32_TO_FP16(v/(1.0f + expf(-v))); +} + +#if __FINITE_MATH_ONLY__ +#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" +#error "ref: https://github.com/ggml-org/llama.cpp/pull/7154#issuecomment-2143844461" +#endif + +#if defined(__ARM_NEON) && defined(__aarch64__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static float32x4_t ggml_v_expf(float32x4_t x) { + const float32x4_t r = vdupq_n_f32(0x1.8p23f); + const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); + const float32x4_t n = vsubq_f32(z, r); + const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, + vdupq_n_f32(0x1.7f7d1cp-20f)); + const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); + const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); + const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); + const float32x4_t u = vmulq_f32(b, b); + const float32x4_t j = vfmaq_f32( + vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), + vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), + vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); + if (!vpaddd_u64(vreinterpretq_u64_u32(c))) + return vfmaq_f32(k, j, k); + const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); + const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); + const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); + return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), + vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static float32x4_t ggml_v_silu(float32x4_t x) { + const float32x4_t one = vdupq_n_f32(1.0f); + const float32x4_t zero = vdupq_n_f32(0.0f); + const float32x4_t neg_x = vsubq_f32(zero, x); + const float32x4_t exp_neg_x = ggml_v_expf(neg_x); + const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); + return vdivq_f32(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX512F__) && defined(__AVX512DQ__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m512 ggml_v_expf(__m512 x) { + const __m512 r = _mm512_set1_ps(0x1.8p23f); + const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); + const __m512 n = _mm512_sub_ps(z, r); + const __m512 b = + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); + const __mmask16 d = + _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); + const __m512 u = _mm512_mul_ps(b, b); + const __m512 j = _mm512_fmadd_ps( + _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, + _mm512_set1_ps(0x1.573e2ep-5f)), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, + _mm512_set1_ps(0x1.fffdb6p-2f))), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); + const __m512 res = _mm512_scalef_ps(j, n); + if (_mm512_kortestz(d, d)) + return res; + const __m512 zero = _mm512_setzero_ps(); + const __m512 alt = _mm512_mask_blend_ps( + _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); + return _mm512_mask_blend_ps(d, res, alt); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m512 ggml_v_silu(__m512 x) { + const __m512 one = _mm512_set1_ps(1); + const __m512 zero = _mm512_setzero_ps(); + const __m512 neg_x = _mm512_sub_ps(zero, x); + const __m512 exp_neg_x = ggml_v_expf(neg_x); + const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); + return _mm512_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX2__) && defined(__FMA__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m256 ggml_v_expf(__m256 x) { + const __m256 r = _mm256_set1_ps(0x1.8p23f); + const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); + const __m256 n = _mm256_sub_ps(z, r); + const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), + _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); + const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); + const __m256 k = _mm256_castsi256_ps( + _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); + const __m256i c = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(126), _CMP_GT_OQ)); + const __m256 u = _mm256_mul_ps(b, b); + const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, + _mm256_set1_ps(0x1.573e2ep-5f)), u, + _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, + _mm256_set1_ps(0x1.fffdb6p-2f))), + u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) + return _mm256_fmadd_ps(j, k, k); + const __m256i g = _mm256_and_si256( + _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), + _mm256_set1_epi32(0x82000000u)); + const __m256 s1 = + _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); + const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); + const __m256i d = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(192), _CMP_GT_OQ)); + return _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), + _mm256_andnot_ps( + _mm256_castsi256_ps(d), + _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(c), + _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), + _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m256 ggml_v_silu(__m256 x) { + const __m256 one = _mm256_set1_ps(1); + const __m256 zero = _mm256_setzero_ps(); + const __m256 neg_x = _mm256_sub_ps(zero, x); + const __m256 exp_neg_x = ggml_v_expf(neg_x); + const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); + return _mm256_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON + +#if defined(__FMA__) +#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) +#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) +#else +#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) +#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) +#endif + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m128 ggml_v_expf(__m128 x) { + const __m128 r = _mm_set1_ps(0x1.8p23f); + const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); + const __m128 n = _mm_sub_ps(z, r); + const __m128 b = + NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); + const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); + const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); + const __m128i c = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); + const __m128 u = _mm_mul_ps(b, b); + const __m128 j = + MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, + MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), + u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm_movemask_epi8(c)) + return MADD128(j, k, k); + const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), + _mm_set1_epi32(0x82000000u)); + const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); + const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); + const __m128i d = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); + return _mm_or_ps( + _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), + _mm_andnot_ps(_mm_castsi128_ps(d), + _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), + _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m128 ggml_v_silu(__m128 x) { + const __m128 one = _mm_set1_ps(1); + const __m128 zero = _mm_setzero_ps(); + const __m128 neg_x = _mm_sub_ps(zero, x); + const __m128 exp_neg_x = ggml_v_expf(neg_x); + const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); + return _mm_div_ps(x, one_plus_exp_neg_x); +} + +#endif // __ARM_NEON / __AVX2__ / __SSE2__ + +inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + for (int i = 0; i < n; ++i) { + y[i] = ggml_silu_f16(x[i]); + } +} + +inline static float ggml_silu_backward_f32(float x, float dy) { + const float s = 1.0f/(1.0f + expf(-x)); + return dy*s*(1.0f + x*(1.0f - s)); +} + +inline static ggml_fp16_t ggml_silu_backward_f16(ggml_fp16_t x, ggml_fp16_t dy) { + const float v = GGML_FP16_TO_FP32(x); + const float s = 1.0f/(1.0f + expf(-v)); + return GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(dy)*s*(1.0f + v*(1.0f - s))); +} + +inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { + for (int i = 0; i < n; ++i) { + dx[i] = ggml_silu_backward_f32(x[i], dy[i]); + } +} + +inline static void ggml_vec_silu_backward_f16(const int n, ggml_fp16_t * dx, const ggml_fp16_t * x, const ggml_fp16_t * dy) { + for (int i = 0; i < n; ++i) { + dx[i] = ggml_silu_backward_f16(x[i], dy[i]); + } +} + +inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { +#ifndef GGML_USE_ACCELERATE + ggml_float sum = 0.0; + for (int i = 0; i < n; ++i) { + sum += (ggml_float)x[i]; + } + *s = (float)sum; +#else + vDSP_sve(x, 1, s, n); +#endif +} + +inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) { + ggml_float sum = 0.0; + for (int i = 0; i < n; ++i) { + sum += (ggml_float)x[i]; + } + *s = sum; +} + +inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) { + float sum = 0.0f; + for (int i = 0; i < n; ++i) { + sum += GGML_FP16_TO_FP32(x[i]); + } + *s = sum; +} + +inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) { + float sum = 0.0f; + for (int i = 0; i < n; ++i) { + sum += GGML_BF16_TO_FP32(x[i]); + } + *s = sum; +} + +inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { +#ifndef GGML_USE_ACCELERATE + float max = -INFINITY; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + } + *s = max; +#else + vDSP_maxv(x, 1, s, n); +#endif +} + +inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { + ggml_vec_norm_f32(n, s, x); + *s = 1.f/(*s); +} + +inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { + float max = -INFINITY; + int idx = 0; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + if (max == x[i]) { idx = i; } + } + *s = idx; +} + +#ifdef __cplusplus +} +#endif From 36ca8b362885e4b4984d72e348ba403568864a95 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Fri, 4 Apr 2025 21:05:12 +0200 Subject: [PATCH 26/99] CUDA: don't convert BF16 weights to FP32 (ggml/1174) * add bf16 support * use convert_from_bf16_cuda instead of convert_unary_cuda for f32 * revert 7ec5085 * move functionality into convert_unary with constexpr --- ggml/src/ggml-cuda/convert.cu | 21 ++++++++++++++++++++- ggml/src/ggml-cuda/convert.cuh | 3 +++ ggml/src/ggml-cuda/ggml-cuda.cu | 30 +++++++++++++++++++++++++++++- 3 files changed, 52 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu index 2997e2b4d5..2681c42ebe 100644 --- a/ggml/src/ggml-cuda/convert.cu +++ b/ggml/src/ggml-cuda/convert.cu @@ -579,7 +579,13 @@ static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __res const src_t * x = (const src_t *) vx; - y[i] = x[i]; + if constexpr (std::is_same_v) { + y[i] = __bfloat162float(x[i]); + } else if constexpr (std::is_same_v && std::is_same_v) { + y[i] = (float)x[i]; + } else { + y[i] = x[i]; + } } template @@ -588,6 +594,17 @@ static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict_ convert_unary<<>>(vx, y, k); } +to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type) { + switch (type) { + case GGML_TYPE_F32: + return convert_unary_cuda; + case GGML_TYPE_F16: + return convert_unary_cuda; + default: + return nullptr; + } +} + to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: @@ -633,6 +650,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_iq3_s_cuda; case GGML_TYPE_F32: return convert_unary_cuda; + case GGML_TYPE_BF16: + return convert_unary_cuda; default: return nullptr; } diff --git a/ggml/src/ggml-cuda/convert.cuh b/ggml/src/ggml-cuda/convert.cuh index 5394be9f16..411a13cf12 100644 --- a/ggml/src/ggml-cuda/convert.cuh +++ b/ggml/src/ggml-cuda/convert.cuh @@ -7,7 +7,10 @@ using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, in typedef to_t_cuda_t to_fp32_cuda_t; typedef to_t_cuda_t to_fp16_cuda_t; +typedef to_t_cuda_t to_bf16_cuda_t; to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type); +to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type); + to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type); diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 57319bafdb..78717df1a6 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -1194,7 +1194,35 @@ static void ggml_cuda_op_mul_mat_cublas( const bool use_fp16 = (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT; - if (((GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_VOLTA) || GGML_CUDA_CC_IS_AMD(cc)) && use_fp16) { + if (src0->type == GGML_TYPE_BF16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1]) { + ggml_cuda_pool_alloc src1_as_bf16(ctx.pool(id)); + if (src1->type != GGML_TYPE_BF16) { + const to_bf16_cuda_t to_bf16_cuda = ggml_get_to_bf16_cuda(src1->type); + GGML_ASSERT(to_bf16_cuda != nullptr); + size_t ne = src1_ncols*ne10; + src1_as_bf16.alloc(ne); + to_bf16_cuda(src1_ddf_i, src1_as_bf16.get(), ne, stream); + } + const nv_bfloat16 * src1_ptr = src1->type == GGML_TYPE_BF16 ? (const nv_bfloat16 *) src1_ddf_i : src1_as_bf16.get(); + const nv_bfloat16 * src0_ptr = (const nv_bfloat16 *)src0_dd_i; + ggml_cuda_pool_alloc dst_bf16(ctx.pool(id), row_diff*src1_ncols); + + const float alpha_f32 = 1.0f; + const float beta_f32 = 0.0f; + + CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream)); + CUBLAS_CHECK( + cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N, + row_diff, src1_ncols, ne10, + &alpha_f32, src0_ptr, CUDA_R_16BF, ne00, + src1_ptr, CUDA_R_16BF, ne10, + &beta_f32, dst_bf16.get(), CUDA_R_16BF, ldc, + CUBLAS_COMPUTE_32F, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + + const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_BF16); + to_fp32_cuda(dst_bf16.get(), dst_dd_i, row_diff*src1_ncols, stream); + } else if (((GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_VOLTA) || GGML_CUDA_CC_IS_AMD(cc)) && use_fp16) { // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 ggml_cuda_pool_alloc src0_as_f16(ctx.pool(id)); if (src0->type != GGML_TYPE_F16) { From ff067dbcb9e6ca4ed464d3db999ff8e9c503498b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 7 Apr 2025 12:25:15 +0300 Subject: [PATCH 27/99] ggml : simplify Arm fp16 CPU logic (ggml/1177) * ggml : simlpify Arm fp16 CPU logic ggml-ci * cont : bring back CUDA/MUSA checks ggml-ci --- ggml/src/ggml-cpu/ggml-cpu-impl.h | 21 ++---------------- ggml/src/ggml-cpu/simd-mappings.h | 8 +++---- ggml/src/ggml-impl.h | 36 +++++++++++++++---------------- 3 files changed, 23 insertions(+), 42 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h index 7f7d210cbe..8eed9bb57c 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-impl.h +++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h @@ -4,13 +4,13 @@ #include "ggml.h" #include "ggml-impl.h" + #include // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/ //#include #include #include // memcpy #include // fabsf - #ifdef __cplusplus extern "C" { #endif @@ -69,33 +69,16 @@ struct ggml_compute_params { #endif #if defined(__ARM_FEATURE_SVE) -#include #include #endif -// 16-bit float -// on Arm, we use __fp16 -// on x86, we use uint16_t #if defined(__ARM_NEON) -// if YCM cannot find , make a symbolic link to it, for example: -// -// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/ -// -#include - +// ref: https://github.com/ggml-org/llama.cpp/pull/5404 #ifdef _MSC_VER - -typedef uint16_t ggml_fp16_internal_t; - #define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) } - #else - -typedef __fp16 ggml_fp16_internal_t; - #define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) } - #endif // _MSC_VER #if !defined(__aarch64__) diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h index 28aaa1b718..e0b5fc38dd 100644 --- a/ggml/src/ggml-cpu/simd-mappings.h +++ b/ggml/src/ggml-cpu/simd-mappings.h @@ -71,7 +71,7 @@ #define GGML_F16x8 float16x8_t #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) #define GGML_F16x8_SET1(x) vdupq_n_f16(x) - #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) + #define GGML_F16x8_LOAD(x) vld1q_f16((const __fp16 *)(x)) #define GGML_F16x8_STORE vst1q_f16 #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) #define GGML_F16x8_ADD vaddq_f16 @@ -99,7 +99,7 @@ #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), (r)[i]) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((__fp16 *)(p), (r)[i]) #define GGML_F16_VEC_FMA GGML_F16x8_FMA #define GGML_F16_VEC_ADD GGML_F16x8_ADD #define GGML_F16_VEC_MUL GGML_F16x8_MUL @@ -114,7 +114,7 @@ #define GGML_F32Cx4 float32x4_t #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) - #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x))) + #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const __fp16 *)(x))) #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) #define GGML_F32Cx4_ADD vaddq_f32 @@ -125,7 +125,7 @@ #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i]) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((__fp16 *)(p), r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h index be2e3fc915..606175fb92 100644 --- a/ggml/src/ggml-impl.h +++ b/ggml/src/ggml-impl.h @@ -16,14 +16,6 @@ #include #endif // __ARM_FEATURE_SVE -#if defined(__ARM_NEON) && !defined(__CUDACC__) && !defined(__MUSACC__) -// if YCM cannot find , make a symbolic link to it, for example: -// -// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/ -// -#include -#endif - #if defined(__F16C__) #include #endif @@ -311,29 +303,35 @@ GGML_API void ggml_aligned_free(void * ptr, size_t size); // FP16 to FP32 conversion -#if defined(__ARM_NEON) - #if defined(_MSC_VER) || (defined(__CUDACC__) && __CUDACC_VER_MAJOR__ <= 11) - typedef uint16_t ggml_fp16_internal_t; - #else - typedef __fp16 ggml_fp16_internal_t; - #endif -#endif +// 16-bit float +// on Arm, we use __fp16 +// on x86, we use uint16_t +// +// for old CUDA compilers (<= 11), we use uint16_t: ref https://github.com/ggml-org/llama.cpp/pull/10616 +// for MUSA compilers , we use uint16_t: ref https://github.com/ggml-org/llama.cpp/pull/11843 +// +#if defined(__ARM_NEON) && !(defined(__CUDACC__) && __CUDACC_VER_MAJOR__ <= 11) && !defined(__MUSACC__) + + // if YCM cannot find , make a symbolic link to it, for example: + // + // $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/ + // + #include -#if defined(__ARM_NEON) && !defined(_MSC_VER) && !(defined(__CUDACC__) && __CUDACC_VER_MAJOR__ <= 11) #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) #define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - ggml_fp16_internal_t tmp; + __fp16 tmp; memcpy(&tmp, &h, sizeof(ggml_fp16_t)); return (float)tmp; } static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { ggml_fp16_t res; - ggml_fp16_internal_t tmp = f; + __fp16 tmp = f; memcpy(&res, &tmp, sizeof(ggml_fp16_t)); return res; } @@ -485,7 +483,7 @@ GGML_API void ggml_aligned_free(void * ptr, size_t size); #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) -#endif // defined(__ARM_NEON) && (!defined(__MSC_VER) +#endif // defined(__ARM_NEON) && !(defined(__CUDACC__) && __CUDACC_VER_MAJOR__ <= 11) && !defined(__MUSACC__) // precomputed f32 table for f16 (256 KB) // defined in ggml.c, initialized in ggml_init() From a4e46e28f99d7f64f38d8328cdb6bee5a3a1cd03 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 7 Apr 2025 12:32:39 +0300 Subject: [PATCH 28/99] sync : ggml ggml-ci --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 88b3d0eed7..e096778bfd 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -f06264eda2e2bf6e814db5a32bbf42e0b2b1ed98 +70e85f61f1fdcd1064a1e032ff564d5b5e67560c From 1a1ab7e7a4a9b6e6440c1d9965d2b9d1b7e7dafb Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 7 Apr 2025 13:18:07 +0300 Subject: [PATCH 29/99] cuda : fix HIP and MUSA BF16 (#0) ggml-ci --- ggml/src/ggml-cuda/convert.cu | 8 +------- ggml/src/ggml-cuda/vendors/hip.h | 1 + ggml/src/ggml-cuda/vendors/musa.h | 1 + 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu index 2681c42ebe..a224ec0e12 100644 --- a/ggml/src/ggml-cuda/convert.cu +++ b/ggml/src/ggml-cuda/convert.cu @@ -579,13 +579,7 @@ static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __res const src_t * x = (const src_t *) vx; - if constexpr (std::is_same_v) { - y[i] = __bfloat162float(x[i]); - } else if constexpr (std::is_same_v && std::is_same_v) { - y[i] = (float)x[i]; - } else { - y[i] = x[i]; - } + y[i] = float(x[i]); } template diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h index 3983ce5b42..420b41b8d6 100644 --- a/ggml/src/ggml-cuda/vendors/hip.h +++ b/ggml/src/ggml-cuda/vendors/hip.h @@ -20,6 +20,7 @@ #define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS #define CUBLAS_TF32_TENSOR_OP_MATH 0 #define CUDA_R_16F HIPBLAS_R_16F +#define CUDA_R_16BF HIPBLAS_R_16B #define CUDA_R_32F HIPBLAS_R_32F #define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED hipDeviceAttributeVirtualMemoryManagementSupported #define CU_MEM_ALLOC_GRANULARITY_RECOMMENDED hipMemAllocationGranularityRecommended diff --git a/ggml/src/ggml-cuda/vendors/musa.h b/ggml/src/ggml-cuda/vendors/musa.h index f2d55796e7..937779a90a 100644 --- a/ggml/src/ggml-cuda/vendors/musa.h +++ b/ggml/src/ggml-cuda/vendors/musa.h @@ -15,6 +15,7 @@ #define CUBLAS_STATUS_SUCCESS MUBLAS_STATUS_SUCCESS #define CUBLAS_TF32_TENSOR_OP_MATH MUBLAS_MATH_MODE_DEFAULT #define CUDA_R_16F MUSA_R_16F +#define CUDA_R_16BF MUSA_R_16BF #define CUDA_R_32F MUSA_R_32F #define cublasComputeType_t cudaDataType_t #define cublasCreate mublasCreate From 4ccea213bc629c4eef7b520f7f6c59ce9bbdaca0 Mon Sep 17 00:00:00 2001 From: stduhpf Date: Mon, 7 Apr 2025 17:47:08 +0200 Subject: [PATCH 30/99] hellaswag: display estimated score confidence interval (#12797) --- examples/perplexity/perplexity.cpp | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 8c413f7d66..175f2804b5 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -851,7 +851,7 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) { LOG_INF("%s : calculating hellaswag score over selected tasks.\n", __func__); - LOG("\ntask\tacc_norm\n"); + LOG("\ntask\tacc_norm\t95%% confidence interval\n"); double acc = 0.0f; @@ -985,8 +985,22 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) { acc += 1.0; } - // Print the accumulated accuracy mean x 100 - LOG("%zu\t%.8lf\n", i + 1, acc/double(i + 1)*100.0); + double freq = acc / double(i + 1); + + const double za = 1.95996398454; + + // // Wald normal approx + // double conf =za*sqrt(freq*(1-freq)/double(i + 1)); + // LOG("%zu\t%.8lf +/- %.8lf\n", i + 1, freq*100.0, conf*100.0); + + // Wilson score interval, more accurate + double z = za * za / double(i + 1); + double cnf = z * sqrt(double(i + 1) * (4.0 * freq * (1 - freq) + z)) / (za + za); + double a = (freq + z * 0.5 - cnf) / (1.0 + z); + double b = (freq + z * 0.5 + cnf) / (1.0 + z); + + // Print the accumulated accuracy mean x 100 and confidence interval + LOG("%zu\t%3.8lf%%\t[%3.4lf%%, %3.4lf%%]\n", i + 1, freq * 100.0, a * 100.0, b * 100.0); } i0 = i1 - 1; From 82974011f312057b446c27267105bd7ad3810599 Mon Sep 17 00:00:00 2001 From: lhez Date: Mon, 7 Apr 2025 13:22:54 -0700 Subject: [PATCH 31/99] opencl: better identify Adreno GPU (#12760) --- ggml/src/ggml-opencl/ggml-opencl.cpp | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 6806c13974..b8b5cbd3e2 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -415,6 +415,7 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) { unsigned number; cl_device_type type; char name[128]; + char version[128]; }; enum { NPLAT = 16, NDEV = 16 }; @@ -455,6 +456,7 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) { d->platform = p; CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL)); CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL)); + CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_VERSION, sizeof(d->version), &d->version, NULL)); if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) { p->default_device = d; @@ -547,7 +549,7 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) { } GGML_LOG_INFO("ggml_opencl: selecting platform: '%s'\n", default_device->platform->name); - GGML_LOG_INFO("ggml_opencl: selecting device: '%s'\n", default_device->name); + GGML_LOG_INFO("ggml_opencl: selecting device: '%s (%s)'\n", default_device->name, default_device->version); if (default_device->type != CL_DEVICE_TYPE_GPU) { GGML_LOG_WARN("ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name); } @@ -556,9 +558,15 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) { dev_ctx->device = default_device->id; backend_ctx->device = default_device->id; - if (strstr(default_device->name, "Adreno")) { + if (strstr(default_device->name, "Adreno") || + strstr(default_device->name, "Qualcomm") || + strstr(default_device->version, "Adreno")) { backend_ctx->gpu_family = GPU_FAMILY::ADRENO; - backend_ctx->adreno_gen = get_adreno_gpu_gen(default_device->name); + // Usually device version contains the detailed device name + backend_ctx->adreno_gen = get_adreno_gpu_gen(default_device->version); + if (backend_ctx->adreno_gen == ADRENO_GPU_GEN::ADRENO_UNKNOWN) { + backend_ctx->adreno_gen = get_adreno_gpu_gen(default_device->name); + } // Use wave size of 64 for all Adreno GPUs. backend_ctx->adreno_wave_size = 64; From 1466621e738779eefe1bb672e17dc55d63d166bb Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 7 Apr 2025 23:06:44 +0200 Subject: [PATCH 32/99] llama : Support llama 4 text-only (#12791) * llama4 conversion * initial support, no chat template * clean up a bit * fix tokenizer conversion * correct hparams * try this * fix shexp * ffn_inp_normed * chat template * clean up model conversion * add_bos * add scale_before_ffn * fix order * weight_before_ffn * llm_graph_input_attn_temp * add chunk attn mask * build_inp_attn_scale() * add comment about ggml_repeat * clarify comments * fix build --- convert_hf_to_gguf.py | 68 +++++++++++++- convert_hf_to_gguf_update.py | 1 + gguf-py/gguf/constants.py | 26 ++++++ gguf-py/gguf/gguf_writer.py | 3 + include/llama.h | 1 + models/ggml-vocab-llama4.gguf.inp | 112 ++++++++++++++++++++++ models/ggml-vocab-llama4.gguf.out | 46 +++++++++ src/llama-arch.cpp | 31 ++++++ src/llama-arch.h | 2 + src/llama-chat.cpp | 14 ++- src/llama-chat.h | 1 + src/llama-graph.cpp | 68 +++++++++++++- src/llama-graph.h | 18 ++++ src/llama-hparams.h | 8 ++ src/llama-model.cpp | 150 +++++++++++++++++++++++++++--- src/llama-model.h | 2 + src/llama-vocab.cpp | 3 +- 17 files changed, 532 insertions(+), 22 deletions(-) create mode 100644 models/ggml-vocab-llama4.gguf.inp create mode 100644 models/ggml-vocab-llama4.gguf.out diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index cfe94deaf7..9549900206 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -714,6 +714,9 @@ class Model: if chkhsh == "96a5f08be6259352137b512d4157e333e21df7edd3fcd152990608735a65b224": # ref: https://huggingface.co/inclusionAI/Ling-lite res = "bailingmoe" + if chkhsh == "d353350c764d8c3b39c763113960e4fb4919bea5fbf208a0e3b22e8469dc7406": + # ref: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct + res = "llama4" if res is None: logger.warning("\n") @@ -1608,6 +1611,7 @@ class StableLMModel(Model): @Model.register("LLaMAForCausalLM", "LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM") class LlamaModel(Model): model_arch = gguf.MODEL_ARCH.LLAMA + undo_permute = True def set_vocab(self): try: @@ -1672,10 +1676,11 @@ class LlamaModel(Model): n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") - if name.endswith(("q_proj.weight", "q_proj.bias")): - data_torch = LlamaModel.permute(data_torch, n_head, n_head) - if name.endswith(("k_proj.weight", "k_proj.bias")): - data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + if self.undo_permute: + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) # process the experts separately if name.find("block_sparse_moe.experts") != -1: @@ -1752,6 +1757,61 @@ class LlamaModel(Model): raise ValueError(f"Unprocessed experts: {experts}") +@Model.register("Llama4ForConditionalGeneration") +class Llama4Model(LlamaModel): + model_arch = gguf.MODEL_ARCH.LLAMA4 + has_vision: bool = False + undo_permute = False + + # TODO @ngxson : avoid duplicate this code everywhere by at least support "text_config" + # same with llama, but we need to merge the text_config into the root level of hparams + def __init__(self, *args, **kwargs): + hparams = kwargs["hparams"] if "hparams" in kwargs else Model.load_hparams(args[0]) + if "text_config" in hparams: + hparams = {**hparams, **hparams["text_config"]} + kwargs["hparams"] = hparams + super().__init__(*args, **kwargs) + if "vision_config" in hparams: + logger.info("Has vision encoder, but it will be ignored") + self.has_vision = True + # IMPORTANT: the normal "intermediate_size" is renamed to "intermediate_size_mlp", we need to undo this + self.hparams["intermediate_size_moe"] = self.hparams["intermediate_size"] + self.hparams["intermediate_size"] = self.hparams["intermediate_size_mlp"] + + def set_vocab(self): + self._set_vocab_gpt2() + self.gguf_writer.add_add_bos_token(True) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_interleave_moe_layer_step(self.hparams["interleave_moe_layer_step"]) + self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size_moe"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): + name = name.replace("language_model.", "") + name = name.replace("feed_forward.", "mlp.") # a bit hacky for now + name = name.replace(".router.weight", ".gate.weight") # a bit hacky for now + + # split the gate_up into gate and up + if "gate_up_proj" in name: + name_up = name.replace("gate_up_proj", "up_proj.weight") + name_gate = name.replace("gate_up_proj", "gate_proj.weight") + dim_half = data_torch.shape[-1] // 2 + gate_proj_weight, up_proj_weight = data_torch.transpose(-1, -2).split(dim_half, dim=-2) + return [ + (self.map_tensor_name(name_gate), gate_proj_weight), + (self.map_tensor_name(name_up), up_proj_weight) + ] + + if name.endswith("down_proj"): + name += ".weight" + data_torch = data_torch.transpose(-1, -2) + + if "multi_modal_projector" in name or "vision_model" in name: + return [] + return super().modify_tensors(data_torch, name, bid) + + @Model.register("Mistral3ForConditionalGeneration") class Mistral3Model(LlamaModel): model_arch = gguf.MODEL_ARCH.LLAMA diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 1b86f4c90a..ce6104da4a 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -113,6 +113,7 @@ models = [ {"name": "superbpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k", }, {"name": "trillion", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/trillionlabs/Trillion-7B-preview", }, {"name": "bailingmoe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-lite", }, + {"name": "llama4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", }, ] diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 3a52cfd1e3..d4f4e11799 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -116,6 +116,7 @@ class Keys: RESIDUAL_SCALE = "{arch}.residual_scale" EMBEDDING_SCALE = "{arch}.embedding_scale" TOKEN_SHIFT_COUNT = "{arch}.token_shift_count" + INTERLEAVE_MOE_LAYER_STEP = "{arch}.interleave_moe_layer_step" class Attention: HEAD_COUNT = "{arch}.attention.head_count" @@ -227,6 +228,7 @@ class GGUFType: class MODEL_ARCH(IntEnum): LLAMA = auto() + LLAMA4 = auto() DECI = auto() FALCON = auto() BAICHUAN = auto() @@ -431,6 +433,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.LLAMA: "llama", + MODEL_ARCH.LLAMA4: "llama4", MODEL_ARCH.DECI: "deci", MODEL_ARCH.FALCON: "falcon", MODEL_ARCH.BAICHUAN: "baichuan", @@ -654,6 +657,29 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], + MODEL_ARCH.LLAMA4: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], MODEL_ARCH.DECI: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index af8b388dfa..485550aad6 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -746,6 +746,9 @@ class GGUFWriter: def add_token_shift_count(self, count: int) -> None: self.add_uint32(Keys.LLM.TOKEN_SHIFT_COUNT.format(arch=self.arch), count) + def add_interleave_moe_layer_step(self, value: int) -> None: + self.add_uint32(Keys.LLM.INTERLEAVE_MOE_LAYER_STEP.format(arch=self.arch), value) + def add_layer_norm_eps(self, value: float) -> None: self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) diff --git a/include/llama.h b/include/llama.h index fca2b034ba..5fd99f4c8b 100644 --- a/include/llama.h +++ b/include/llama.h @@ -110,6 +110,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30, LLAMA_VOCAB_PRE_TYPE_TRILLION = 31, LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32, + LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33, }; enum llama_rope_type { diff --git a/models/ggml-vocab-llama4.gguf.inp b/models/ggml-vocab-llama4.gguf.inp new file mode 100644 index 0000000000..9baf7d77ae --- /dev/null +++ b/models/ggml-vocab-llama4.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__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 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__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__ diff --git a/models/ggml-vocab-llama4.gguf.out b/models/ggml-vocab-llama4.gguf.out new file mode 100644 index 0000000000..7ca46ce597 --- /dev/null +++ b/models/ggml-vocab-llama4.gguf.out @@ -0,0 +1,46 @@ + 1190 220 32 220 18215 7112 + 50 16800 258 + + 220 + 256 + 277 + 197 + 198 + 368 + 2946 + 3271 + 19873 3817 + 39715 3817 + 19873 7353 + 39715 7353 + 39715 7353 13 + 19873 24 3817 13 + 39715 24 3817 13 + 544 373 9522 112 247 26 36315 + 99 39923 220 35 9607 21498 21470 3679 9433 + 1595 7653 633 79829 34051 1636 + 8755 102595 115960 21125 148305 96819 102816 39048 14105 22528 160234 + 114590 222 330 14879 21 51358 127 12817 93293 117 24204 330 68239 881 120327 170428 21 89101 330 7384 88230 511 947 1492 3742 7233 21 + 19873 + 39715 + 220 39715 + 256 39715 + 277 39715 + 277 39715 198 277 39715 + 330 + 198 319 + 19 7359 + 19873 24 386 87799 13 2403 583 650 51358 223 1663 155736 1522 42056 7544 13336 28785 29 4412 20645 + 17931 4959 + 31 + 1922 + 12325 + 12325 31 + 12325 1922 + 12325 12325 + 12325 12325 31 + 12325 12325 1922 + 12325 12325 12325 + 47 19811 12077 + 3260 3579 + 198 7283 51499 191231 20192 3271 3322 9287 2143 17860 114590 222 330 14879 21 51358 127 12817 93293 117 24204 330 68239 881 120327 170428 21 89101 9522 112 247 172394 247 220 31 220 1922 220 12325 220 12325 31 220 12325 1922 220 12325 12325 220 12325 12325 31 220 12325 12325 1922 220 31 26 31 220 31 396 31 220 31 1043 31 117131 102595 115960 21125 148305 96819 102816 80883 223 1663 155736 1522 42056 7544 13336 28785 29 4412 20645 79745 150278 117079 633 79829 34051 1636 25611 41990 109428 1488 91054 24072 17931 4959 29795 9296 16517 1806 481 96 1386 36633 1609 24 481 1109 650 5074 43 481 57 702 5074 27088 2170 536 24 481 48 650 1933 1696 30262 43 1665 19 32818 262 27236 56 diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 047782e7d0..ac997b963c 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -6,6 +6,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA, "llama" }, + { LLM_ARCH_LLAMA4, "llama4" }, { LLM_ARCH_DECI, "deci" }, { LLM_ARCH_FALCON, "falcon" }, { LLM_ARCH_GROK, "grok" }, @@ -114,6 +115,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_RESIDUAL_SCALE, "%s.residual_scale" }, { LLM_KV_EMBEDDING_SCALE, "%s.embedding_scale" }, { LLM_KV_TOKEN_SHIFT_COUNT, "%s.token_shift_count" }, + { LLM_KV_INTERLEAVE_MOE_LAYER_STEP, "%s.interleave_moe_layer_step" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -233,6 +235,35 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, }, }, + { + LLM_ARCH_LLAMA4, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { 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_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" }, + { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" }, + { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, + { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, + { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, + }, + }, { LLM_ARCH_DECI, { diff --git a/src/llama-arch.h b/src/llama-arch.h index 297cfa4dae..42e4a3ef95 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -10,6 +10,7 @@ enum llm_arch { LLM_ARCH_LLAMA, + LLM_ARCH_LLAMA4, LLM_ARCH_DECI, LLM_ARCH_FALCON, LLM_ARCH_BAICHUAN, @@ -118,6 +119,7 @@ enum llm_kv { LLM_KV_RESIDUAL_SCALE, LLM_KV_EMBEDDING_SCALE, LLM_KV_TOKEN_SHIFT_COUNT, + LLM_KV_INTERLEAVE_MOE_LAYER_STEP, LLM_KV_ATTENTION_HEAD_COUNT, LLM_KV_ATTENTION_HEAD_COUNT_KV, diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index dd27a38142..721faa4e81 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -61,6 +61,7 @@ static const std::map LLM_CHAT_TEMPLATES = { { "megrez", LLM_CHAT_TEMPLATE_MEGREZ }, { "yandex", LLM_CHAT_TEMPLATE_YANDEX }, { "bailing", LLM_CHAT_TEMPLATE_BAILING }, + { "llama4", LLM_CHAT_TEMPLATE_LLAMA4 }, }; llm_chat_template llm_chat_template_from_str(const std::string & name) { @@ -174,6 +175,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) { return LLM_CHAT_TEMPLATE_YANDEX; } else if (tmpl_contains("ASSISTANT") && tmpl_contains("'HUMAN'")) { return LLM_CHAT_TEMPLATE_BAILING; + } else if (tmpl_contains("<|header_start|>") && tmpl_contains("<|header_end|>")) { + return LLM_CHAT_TEMPLATE_LLAMA4; } return LLM_CHAT_TEMPLATE_UNKNOWN; } @@ -608,7 +611,16 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "ASSISTANT"; } - } else { + } else if (tmpl == LLM_CHAT_TEMPLATE_LLAMA4) { + // Llama 4 + for (auto message : chat) { + std::string role(message->role); + ss << "<|header_start|>" << role << "<|header_end|>\n\n" << trim(message->content) << "<|eot|>"; + } + if (add_ass) { + ss << "<|header_start|>assistant<|header_end|>\n\n"; + } + } else { // template not supported return -1; } diff --git a/src/llama-chat.h b/src/llama-chat.h index 0e0bd772c2..34537ca21e 100644 --- a/src/llama-chat.h +++ b/src/llama-chat.h @@ -40,6 +40,7 @@ enum llm_chat_template { LLM_CHAT_TEMPLATE_MEGREZ, LLM_CHAT_TEMPLATE_YANDEX, LLM_CHAT_TEMPLATE_BAILING, + LLM_CHAT_TEMPLATE_LLAMA4, LLM_CHAT_TEMPLATE_UNKNOWN, }; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index cec203df49..c3469177e0 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -59,6 +59,22 @@ void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) { } } +void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) { + if (ubatch->pos && attn_scale) { + const int64_t n_tokens = ubatch->n_tokens; + + std::vector attn_scale_data(n_tokens, 0.0f); + for (int i = 0; i < n_tokens; ++i) { + const float pos = ubatch->pos[i]; + attn_scale_data[i] = std::log( + std::floor((pos + 1.0f) / n_attn_temp_floor_scale) + 1.0 + ) * f_attn_temp_scale + 1.0; + } + + ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*n_pos_per_token*ggml_element_size(attn_scale)); + } +} + void llm_graph_input_pos_bucket::set_input(const llama_ubatch * ubatch) { if (pos_bucket) { const int64_t n_tokens = ubatch->n_tokens; @@ -458,9 +474,17 @@ void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) { } // may need to cut off old tokens for sliding window + // TODO @ngxson : we are currently re-using the swa logic to store the chunked mask, we should rename SWA to something more generic like "aux mask" if (data_swa) { - if (pos - kv_self->cells[i].pos >= (int32_t)hparams.n_swa) { - f = -INFINITY; + if (hparams.n_attn_chunk) { + llama_pos pos_chunk_start = (pos / hparams.n_attn_chunk) * hparams.n_attn_chunk; + if (kv_self->cells[i].pos < pos_chunk_start || pos < pos_chunk_start) { + f = -INFINITY; + } + } else { + if (pos - kv_self->cells[i].pos >= (int32_t)hparams.n_swa) { + f = -INFINITY; + } } data_swa[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f; } @@ -812,8 +836,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn( float w_scale, llama_expert_gating_func_type gating_op, int il) const { - int64_t n_embd = cur->ne[0]; - int64_t n_tokens = cur->ne[1]; + const int64_t n_embd = cur->ne[0]; + const int64_t n_tokens = cur->ne[1]; + const bool weight_before_ffn = arch == LLM_ARCH_LLAMA4; // for llama4, we apply the sigmoid-ed weights before the FFN ggml_tensor * logits = build_lora_mm(gate_inp, cur); // [n_expert, n_tokens] cb(logits, "ffn_moe_logits", il); @@ -841,6 +866,12 @@ ggml_tensor * llm_graph_context::build_moe_ffn( cb(selection_probs, "ffn_moe_probs_biased", il); } + // llama4 doesn't have exp_probs_b, and sigmoid is only used after top_k + // see: https://github.com/meta-llama/llama-models/blob/699a02993512fb36936b1b0741e13c06790bcf98/models/llama4/moe.py#L183-L198 + if (arch == LLM_ARCH_LLAMA4) { + selection_probs = logits; + } + // select experts ggml_tensor * selected_experts = ggml_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens] cb(selected_experts->src[0], "ffn_moe_argsort", il); @@ -867,6 +898,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn( } cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, n_tokens); + + if (weight_before_ffn) { + // TODO: this is a workaround as we don't yet have a repeat op that takes custom dim (ggml_repeat_4d) + ggml_tensor * repeated = ggml_new_tensor_3d(ctx0, cur->type, n_embd, n_expert_used, n_tokens); + repeated = ggml_repeat(ctx0, cur, repeated); // [n_embd, n_expert_used, n_tokens] + cur = ggml_mul(ctx0, repeated, weights); + cb(cur, "ffn_moe_weighted", il); + } + ggml_tensor * up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] cb(up, "ffn_moe_up", il); @@ -894,7 +934,10 @@ ggml_tensor * llm_graph_context::build_moe_ffn( ggml_tensor * experts = build_lora_mm_id(down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens] cb(experts, "ffn_moe_down", il); - experts = ggml_mul(ctx0, experts, weights); + if (!weight_before_ffn) { + experts = ggml_mul(ctx0, experts, weights); + cb(cur, "ffn_moe_weighted", il); + } // aggregate experts ggml_tensor * moe_out = nullptr; @@ -914,6 +957,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn( moe_out = ggml_cont(ctx0, moe_out); } + cb(moe_out, "ffn_moe_out", il); + return moe_out; } @@ -981,6 +1026,19 @@ ggml_tensor * llm_graph_context::build_inp_pos() const { return cur; } +ggml_tensor * llm_graph_context::build_inp_attn_scale() const { + auto inp = std::make_unique(n_pos_per_token(), hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale); + + auto & cur = inp->attn_scale; + + cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens*n_pos_per_token()); + ggml_set_input(cur); + + res->add_input(std::move(inp)); + + return cur; +} + ggml_tensor * llm_graph_context::build_inp_out_ids() const { auto inp = std::make_unique(hparams, cparams, n_outputs); diff --git a/src/llama-graph.h b/src/llama-graph.h index bdf19ed015..5b6618f9e5 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -100,6 +100,23 @@ public: const int64_t n_pos_per_token = 1; }; +// temperature tuning, used by llama4 +class llm_graph_input_attn_temp : public llm_graph_input_i { +public: + llm_graph_input_attn_temp(int64_t n_pos_per_token, uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale) + : n_pos_per_token(n_pos_per_token), n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {} + virtual ~llm_graph_input_attn_temp() = default; + + void set_input(const llama_ubatch * ubatch) override; + + ggml_tensor * attn_scale = nullptr; // F32 [n_batch] + + const int64_t n_pos_per_token = 1; + + const uint32_t n_attn_temp_floor_scale; + const float f_attn_temp_scale; +}; + class llm_graph_input_pos_bucket : public llm_graph_input_i { public: llm_graph_input_pos_bucket(const llama_hparams & hparams) : hparams(hparams) {} @@ -470,6 +487,7 @@ struct llm_graph_context { ggml_tensor * build_inp_embd(ggml_tensor * tok_embd) const; ggml_tensor * build_inp_pos() const; + ggml_tensor * build_inp_attn_scale() const; ggml_tensor * build_inp_out_ids() const; ggml_tensor * build_inp_mean() const; ggml_tensor * build_inp_cls() const; diff --git a/src/llama-hparams.h b/src/llama-hparams.h index bb17ba86dc..4e0b57190a 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -112,6 +112,14 @@ struct llama_hparams { bool use_alibi = false; bool attn_soft_cap = false; + uint32_t n_moe_layer_step = 0; + bool use_kq_norm = true; + uint32_t n_attn_chunk = 0; + // values below seems to be fixed on llama4 + uint32_t n_no_rope_layer_step = 4; + uint32_t n_attn_temp_floor_scale = 8192; + float f_attn_temp_scale = 0.1; + // 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 = LLAMA_TOKEN_NULL; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index ca6e3ab2ca..4546e9cf9b 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -90,6 +90,8 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_57B_A14B: return "57B.A14B"; case LLM_TYPE_27B: return "27B"; case LLM_TYPE_290B: return "290B"; + case LLM_TYPE_17B_16E: return "17Bx16E (Scout)"; + case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)"; default: return "?B"; } } @@ -550,6 +552,25 @@ void llama_model::load_hparams(llama_model_loader & ml) { } } } break; + case LLM_ARCH_LLAMA4: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step); + hparams.n_swa_pattern = 4; // pattern: 3 chunked - 1 full + hparams.n_attn_chunk = 8192; // should this be a gguf kv? currently it's the same for Scout and Maverick + hparams.n_swa = 1; // TODO @ngxson : this is added to trigger the SWA branch (we store the chunked attn mask in the SWA tensor), will need to clean this up later + + switch (hparams.n_expert) { + case 16: type = LLM_TYPE_17B_16E; break; + case 128: type = LLM_TYPE_17B_128E; break; + default: type = LLM_TYPE_UNKNOWN; + } + + if (type == LLM_TYPE_17B_128E) { + hparams.use_kq_norm = false; + } + } break; case LLM_ARCH_DECI: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -1690,6 +1711,56 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } } } break; + case LLM_ARCH_LLAMA4: + { + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); + + // if output is NULL, init from the input tok embed + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } + + GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Llama 4 requires n_moe_layer_step > 0"); + for (int i = 0; i < n_layer; ++i) { + bool is_moe_layer = (i + 1) % hparams.n_moe_layer_step == 0; + + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); + + if (is_moe_layer) { + int n_ff_exp = hparams.n_ff_exp; + + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + + // Shared expert + const int64_t n_ff_shexp = n_ff_exp; + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd }, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0); + } else { + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } + } + } break; case LLM_ARCH_DECI: { tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -4203,12 +4274,22 @@ struct llm_build_llama : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); + // temperature tuning + ggml_tensor * inp_attn_scale = nullptr; + if (arch == LLM_ARCH_LLAMA4) { + inp_attn_scale = build_inp_attn_scale(); + } + auto * inp_attn = build_attn_inp_kv_unified(); const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; + bool use_rope = arch == LLM_ARCH_LLAMA4 + ? (il + 1) % hparams.n_no_rope_layer_step != 0 + : true; + // norm cur = build_norm(inpL, model.layers[il].attn_norm, NULL, @@ -4246,25 +4327,38 @@ struct llm_build_llama : public llm_graph_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); - Qcur = ggml_rope_ext( - ctx0, Qcur, inp_pos, rope_factors, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); + if (use_rope) { + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); - Kcur = ggml_rope_ext( - ctx0, Kcur, inp_pos, rope_factors, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + } else if (inp_attn_scale) { + Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale); + } cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); + if (arch == LLM_ARCH_LLAMA4 && use_rope && hparams.use_kq_norm) { + // Llama4TextL2Norm + Qcur = ggml_rms_norm(ctx0, Qcur, 1e-6); + Kcur = ggml_rms_norm(ctx0, Kcur, 1e-6); + cb(Qcur, "Qcur_normed", il); + cb(Kcur, "Kcur_normed", il); + } + cur = build_attn(inp_attn, gf, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); } if (il == n_layer - 1) { @@ -4282,7 +4376,7 @@ struct llm_build_llama : public llm_graph_context { ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cb(ffn_inp, "ffn_inp", il); - // feed-forward network + // feed-forward network (non-MoE) if (model.layers[il].ffn_gate_inp == nullptr) { cur = build_norm(ffn_inp, @@ -4297,6 +4391,38 @@ struct llm_build_llama : public llm_graph_context { NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); + + } else if (arch == LLM_ARCH_LLAMA4) { + // llama4 MoE + ggml_tensor * ffn_inp_normed = build_norm(ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + ggml_tensor * moe_out = build_moe_ffn(ffn_inp_normed, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + nullptr, + n_expert, n_expert_used, + LLM_FFN_SILU, false, + false, 0.0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, + il); + + // Shared experts + ggml_tensor * shexp_out = build_ffn(ffn_inp_normed, + model.layers[il].ffn_up_shexp, NULL, NULL, + model.layers[il].ffn_gate_shexp, NULL, NULL, + model.layers[il].ffn_down_shexp, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(shexp_out, "ffn_moe_shexp", il); + + cur = ggml_add(ctx0, moe_out, shexp_out); + cb(cur, "ffn_moe_out_merged", il); + } else { // MoE branch cur = build_norm(ffn_inp, @@ -12091,6 +12217,7 @@ llm_graph_result_ptr llama_model::build_graph( switch (arch) { case LLM_ARCH_LLAMA: + case LLM_ARCH_LLAMA4: case LLM_ARCH_MINICPM: case LLM_ARCH_GRANITE: case LLM_ARCH_GRANITE_MOE: @@ -12440,6 +12567,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { // use what we call a normal RoPE, operating on pairs of consecutive head values case LLM_ARCH_LLAMA: + case LLM_ARCH_LLAMA4: case LLM_ARCH_DECI: case LLM_ARCH_BAICHUAN: case LLM_ARCH_STARCODER: diff --git a/src/llama-model.h b/src/llama-model.h index 91e6e8725a..0f18dac167 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -86,6 +86,8 @@ enum llm_type { LLM_TYPE_57B_A14B, LLM_TYPE_27B, LLM_TYPE_290B, + LLM_TYPE_17B_16E, // llama4 Scout + LLM_TYPE_17B_128E, // llama4 Maverick }; struct llama_layer_posnet { diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 5d5cafbea1..0feabd95aa 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1616,7 +1616,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "megrez") { pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2; } else if ( - tokenizer_pre == "gpt-4o") { + tokenizer_pre == "gpt-4o" || + tokenizer_pre == "llama4") { pre_type = LLAMA_VOCAB_PRE_TYPE_GPT4O; clean_spaces = false; } else if ( From a226bc7a9ac50551f9f113808de0f0046837f188 Mon Sep 17 00:00:00 2001 From: compilade Date: Tue, 8 Apr 2025 03:03:07 -0400 Subject: [PATCH 33/99] gguf-py : support lazy tensor splitting (#12809) * gguf-py : support lazy tensor splitting Splitting usually involves returning tuples of tensors, which need to be handled properly to avoid early eager evaluation. * gguf-py : fix flake8 lint --- gguf-py/gguf/lazy.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/gguf-py/gguf/lazy.py b/gguf-py/gguf/lazy.py index 8d4fece2dc..f9bcadae02 100644 --- a/gguf-py/gguf/lazy.py +++ b/gguf-py/gguf/lazy.py @@ -139,6 +139,16 @@ class LazyBase(ABC, metaclass=LazyMeta): if isinstance(res, cls._tensor_type): return cls(meta=cls.eager_to_meta(res), args=args, kwargs=kwargs, func=fn) + elif isinstance(res, tuple) and all(isinstance(t, cls._tensor_type) for t in res): + # share the evaluation between lazy tuple elements + shared_args: list = [args, None] + + def eager_tuple_element(a: list[Any], i: int = 0, /, **kw) -> LazyBase: + assert len(a) == 2 + if a[1] is None: + a[1] = fn(*a[0], **kw) + return a[1][i] + return tuple(cls(meta=cls.eager_to_meta(res[i]), args=(shared_args, i), kwargs=kwargs, func=eager_tuple_element) for i in range(len(res))) else: del res # not needed # non-tensor return likely relies on the contents of the args From 656babd6c21a3b9b3622324cfcc80a2ab78da25b Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Tue, 8 Apr 2025 15:03:21 +0800 Subject: [PATCH 34/99] Revert "sycl:remove redundant memcopy in function ggml_backend_sycl_buffer_set_tensor" (#12812) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Revert "sycl: remove redundant memcopy in function ggml_backend_sycl_buffer_s…" This reverts commit 518a01480eb3a7c80a4951b430db9dee55428310. * Update ggml/src/ggml-sycl/ggml-sycl.cpp * Update ggml/src/ggml-sycl/ggml-sycl.cpp * rm tail space --- ggml/src/ggml-sycl/ggml-sycl.cpp | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index f6f288d990..89715eaea0 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -372,9 +372,14 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue()); SYCL_CHECK( CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); + // Note: Use host buffer to save the data from mmap(), then copy to device. It's workaround for mmap() issue on PVC GPU. + // This function will be called during load model from disk. Use memory buffer replace dynamic won't save more time and brings potential memory leak risk here. + char* host_buf = (char*)malloc(size); + memcpy(host_buf, data, size); SYCL_CHECK( - CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, data, size) + CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size) .wait())); + free(host_buf); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ From 8ca6e1c3a4deb6bb27ee294bfd5706098d94ae88 Mon Sep 17 00:00:00 2001 From: characharm <123120856+characharm@users.noreply.github.com> Date: Tue, 8 Apr 2025 14:14:59 +0500 Subject: [PATCH 35/99] server : webui : Improve Chat Input with Auto-Sizing Textarea (#12785) * Update ChatScreen.tsx * useAutosizeTextarea.ts useAutosizeTextarea to encapsulate the logic. * Implement responsive auto-sizing chat textarea Replaces the manual textarea resizing with an automatic height adjustment based on content. - `useChatTextarea` hook to manage textarea state and auto-sizing logic via refs, preserving the optimization - Textarea now grows vertically up to a maximum height (`lg:max-h-48`) on large screens (lg breakpoint and up). - Disables auto-sizing and enables manual vertical resizing (`resize-vertical`) on smaller screens for better mobile usability. - Aligns the "Send" button to the bottom of the textarea (`items-end`) for consistent positioning during resize. * -update compressed index.html.gz after npm run build -refactor: replace OptimizedTextareaValue with AutosizeTextareaApi in VSCode context hook * chore: normalize line endings to LF refactor: AutosizeTextareaApi -> chatTextareaApi * refactor: Rename interface to PascalCase --------- Co-authored-by: Xuan Son Nguyen --- examples/server/public/index.html.gz | Bin 1263128 -> 1263380 bytes .../webui/src/components/ChatScreen.tsx | 58 +++-------- .../webui/src/components/useChatTextarea.ts | 96 ++++++++++++++++++ .../server/webui/src/utils/llama-vscode.ts | 4 +- 4 files changed, 111 insertions(+), 47 deletions(-) create mode 100644 examples/server/webui/src/components/useChatTextarea.ts diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz index 941815c22efb0b6dcbbe50f0bd2d7d193dad1c7d..674e227571e2d38be246c0ebde8bf275d973865b 100644 GIT binary patch delta 1198829 zcmV(#K;*xej7^k|O@AMY2mk;800062+`Zd&+qkwU_`be^=Bc4WE<=iBJ2%RRwlA_P zmDox7l1j?u_JSo?WSJsWAShe5wEm-?`mMjOKcUB*07#IMozyz}q5F*CVG;NH#JppB zt=asCc?@mLx0YO@ZmVyV=fuAAu=whdwzD%eB(MVtVH~BoP>=zb*B-(o1{XzI(C5b zAgOy74P{7&d>RdS69dGFl1N4@Ze{}(bJ`)!nB7O?d~70AC3ai7l|dS(qL~dsKIU%O zMjVYo=?s~;Eqf><8S{7l&;Rv*|DR4AvoUK8CKIO#16z{YFI1w+m<>cUkq%4;b;s#2 zkD+RjW?3qtQIx!!GU5C+rkuWel%YuPoU_@4_k~D>$NfbYl7Aagj}u7}pT3i=kYyhC z=kveDp1a2qSWb66@iFIqQs(r28I*=WdCmQP@VX7;zI7knakoU)IFfEqi-eQYROjC%@uHZ2 z1z6F?=*CN1d}8(reABgd*Gvw~RQHC14d)$B2i^dxb-nzQt*%5A# zM&r}LnE z{=IZE6A0`7$&(>mogsIQ-k+Ih=O)deI=nccijVL2`p2hzbrtLTRHUiY*K#Um>i#bqf&&B{ zcmZ?s^SE4xpI&;nfqowA?c*HE?$2Y|!Og+x>1=$R#w|GVu9^<6;F{|bxgr(jmc;-h zS0vsDHqvFI70HZW)k+#VzZ-XR?|8iyC7%lZ89!u_xRG8MX)#FU}bHMP5cy~6;?`FJ$$ z>0^`~rFkOxFtF}1)%sdzL3r|4&U1c{iUB{L44LG1z9RjEPhvLUr}C)`r(7;4F329$ z=bL3&G)l-ZwIRd4cSy#Z?D~(efniN289gKz&?`vCT!jU{$>_2A&atC{?#}1;i+u}r zL>V6Fy>vWD6Tr_N7sO6~&vY+o(M#w6o{Hu9Z#1m9VtyNq!!=woUtwel8*@#~UaQse z14pu3o;iT&U|n!VGe~x(OhoKD<{6+ZY8*}~9Oe|0au-H}&`~S&d=)L%@7nXxs;z|w zi3F#(=%4FXXXK__cxzJhg4F7@Hi@a)waUA`wX`R&{wHNa)M~VU%=PiVV*|iOKcFA2 zwgB%r=^nK3S`;~eN&1oOc?b5;eR9x>vd_8TwLP=eMu5mT-4TM#T2Y3-B7ZX@^&{9Ik(1*yIeVgt5QPRTJZ<^*~%lH>-raV&Aykz@4> zxaFAZoe1D(1jw|1Gv+W)Ym`pYXy~*X4euCc5F&jt=B-;kvlC-UU3Xo@L%JVxb>WUV z8A-pi1QYJ=-)@7}jCnFg)H=v)ll2Y37Z)6LRJ;4*Y z+vRb_o#@8vY~M;B5mKmQS5LZkD}C}+(suW&+`g6FuX25V-QVRGU*%=l$i-K=zH!;D z)XNvqjd$2+92z76`kh%bbSr5!joeCKkH(rA9dj$y_|$QH{OKnJ5(18}H#j7)xUs~= zJ(dJFaBw5ZReG0j5qxRs?XW2ExO=XF&Qo&3bt3ZQm>xTdZgm?P6RrIeR7oN}lFmSfE6qf6burEQnE0eoz@1_5_}a@V12cr$n} zE@*K1VnhlcvRm|G%rAB?7wSg3;`GHIFV?P7u+X`oA& zOQULWIF@&$D!G7Nek=_hcP!~4fQcIkk>rq$u={S$ZMo}r+&h+}?FS=C824_Z-}82< z>k@r`k2MJPV-#pWq4{OWGzQX^%J< z$#pkyVX%k3Zy?Jp0Fzs}eW*cG_ZC3PV=U=^;l-`I>}u#Uk}zSh1gOwGmh1EkLpTAu z9JbHGK(1X*0hKdY*ehR>JS4=liW;n((e4OB&18l*KtG z)h?{B`($M&3h=2ms@^>sB^h(7&%vQjG=8FPuFPSABa?5+;%MkF;3(qt=O74mJfuH= z0>@qV6kUIzu@6=L((UQCoV!Sd&Mlv1&ZE1o??niDhkonNG)g>|IIwgJHyCq&-Su0} z`x|GL=FXV2L<0IFIFdlj8KiNnS%&G2V}Lnv@{9ul#Nb9iRuN}IKzfR8Umo(4^4e7A z5|5?9vb*Xkr5BfQOxmUbgbX7IJKNrW+_AJkms-DF5(Dg5U-uV28CmTn5@01X*}4Ms zEYB#V%}xP64_jA)XL&5UV@}81!#ylRo_IE(tdwrg=e5cJ{1h=AQq)Oz{RR0?>)EK* zagqD?My!*SNN=k!s3p=K)Y zew0m^9E4m114-+1`D*k%hNQ$(gJ#aEWHZr?Fq`1AS2<$|P1TrC>icx!-N zR4E@d8D{(~DCO|`2_L`=;wc~SXv#~`g@QMlx1dhDb*K-KSJNud@qGv$Xm$ckAoN-D z255?*T<|jcM~FH__QFv*eR|HJAR9>f03U-mO$^tUF0HTyfL6?uT~3S0JEMb;#}tv;pc>9z&~==57%9oI1StOyvGI6=%Ei_4)G#__YL5W zfFx*au|hmNVxUeo#D;8tfN}*IyNZUmsn8G#dj&KN>7xn~uCL)0Nyzlx<%)u_iI82s znh3#oZzL~vIT9i8Q}*dr79H%}VxBe}YhfE1Y)x(G!HqF83dAr6y2zx!*Oo zg&|aSxgu0<JERLl*CQAOWr%C0173chrmJMX&O0VM= zz%_!yYIp-8WC#+WJ%l=K@>z4)5F(SkG|BR@30>(0RCTE+#S#XYfQI3REmMWnkCQYQkn9E(sp>)fzNAL|>dR zQ&%9o3=+Ncz=N#wvFdeB44FDe#}g)afJmJ_8OUB7b5&a=nUH726})PSuX(Gyqq~(5 zaP_2^Jsx`g;wDN!-uzh2VA{z+;CCJKs{)qh8Q)`Ze9Z=bx2uQa0MJxRV`o-*7N&}) zk3g-s%IF;yMfdr2KEhp0E1XS}ih`kGEyGjQSF&4;^lxtXK>pAGRo3e8-Vc}eYpvM( zp;u-fPk>1rP5I9(8OD71j#^zst(eJORGVa6KsA43G_^9-VSQw?G?#y)HoHKzs-q{5 zL#+zPSS@;g!iV{QiyxdMox46at6{`a*&Az5)LGF_6jHogtJo|V^zS7X35!o

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} from '../utils/types'; @@ -6,6 +6,7 @@ import { classNames, cleanCurrentUrl, throttle } from '../utils/misc'; import CanvasPyInterpreter from './CanvasPyInterpreter'; import StorageUtils from '../utils/storage'; import { useVSCodeContext } from '../utils/llama-vscode'; +import { useChatTextarea, ChatTextareaApi } from './useChatTextarea.ts'; /** * A message display is a message node with additional information for rendering. @@ -99,7 +100,8 @@ export default function ChatScreen() { canvasData, replaceMessageAndGenerate, } = useAppContext(); - const textarea = useOptimizedTextarea(prefilledMsg.content()); + + const textarea: ChatTextareaApi = useChatTextarea(prefilledMsg.content()); const { extraContext, clearExtraContext } = useVSCodeContext(textarea); // TODO: improve this when we have "upload file" feature @@ -248,14 +250,16 @@ export default function ChatScreen() {

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