From d359f30921a9f62a0fd299c412ff3f270286fea6 Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 20 May 2024 01:17:03 +0200 Subject: [PATCH] llama : remove MPI backend (#7395) --- .devops/nix/package.nix | 1 - .github/workflows/build.yml | 34 ------ CMakeLists.txt | 33 +----- Makefile | 12 -- README.md | 39 ------- ggml-mpi.c | 216 ----------------------------------- ggml-mpi.h | 39 ------- llama.cpp | 48 +------- scripts/LlamaConfig.cmake.in | 5 - 9 files changed, 2 insertions(+), 425 deletions(-) delete mode 100644 ggml-mpi.c delete mode 100644 ggml-mpi.h diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 1c9633cdf..e8d5b0bd9 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -214,7 +214,6 @@ effectiveStdenv.mkDerivation ( (cmakeBool "LLAMA_CUDA" useCuda) (cmakeBool "LLAMA_HIPBLAS" useRocm) (cmakeBool "LLAMA_METAL" useMetalKit) - (cmakeBool "LLAMA_MPI" useMpi) (cmakeBool "LLAMA_VULKAN" useVulkan) (cmakeBool "LLAMA_STATIC" enableStatic) ] diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 53e61b80f..7b616281b 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -306,40 +306,6 @@ jobs: cd build ctest -L main --verbose --timeout 900 - ubuntu-latest-cmake-mpi: - runs-on: ubuntu-latest - - continue-on-error: true - - strategy: - matrix: - mpi_library: [mpich, libopenmpi-dev] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential ${{ matrix.mpi_library }} - - - name: Build - id: cmake_build - run: | - mkdir build - cd build - cmake -DLLAMA_MPI=ON .. - cmake --build . --config Release -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose - ubuntu-latest-cmake-rpc: runs-on: ubuntu-latest diff --git a/CMakeLists.txt b/CMakeLists.txt index cbeb2ee37..616698c7f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -122,7 +122,6 @@ set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING "llama: metal minimum macOS version") set(LLAMA_METAL_STD "" CACHE STRING "llama: metal standard version (-std flag)") option(LLAMA_KOMPUTE "llama: use Kompute" OFF) -option(LLAMA_MPI "llama: use MPI" OFF) option(LLAMA_RPC "llama: use RPC" OFF) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_SYCL "llama: use SYCL" OFF) @@ -466,35 +465,6 @@ if (LLAMA_CUDA) endif() endif() -if (LLAMA_MPI) - cmake_minimum_required(VERSION 3.10) - find_package(MPI) - if (MPI_C_FOUND) - message(STATUS "MPI found") - - set(GGML_HEADERS_MPI ggml-mpi.h) - set(GGML_SOURCES_MPI ggml-mpi.c) - - add_compile_definitions(GGML_USE_MPI) - add_compile_definitions(${MPI_C_COMPILE_DEFINITIONS}) - - if (NOT MSVC) - add_compile_options(-Wno-cast-qual) - endif() - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_C_LIBRARIES}) - set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${MPI_C_INCLUDE_DIRS}) - - # Even if you're only using the C header, C++ programs may bring in MPI - # C++ functions, so more linkage is needed - if (MPI_CXX_FOUND) - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_CXX_LIBRARIES}) - endif() - else() - message(WARNING "MPI not found") - endif() -endif() - if (LLAMA_RPC) add_compile_definitions(GGML_USE_RPC) @@ -1218,7 +1188,6 @@ add_library(ggml OBJECT ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} - ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} ${GGML_SOURCES_RPC} ${GGML_HEADERS_RPC} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} @@ -1306,7 +1275,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" - "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") + "${GGML_HEADERS_METAL}" "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") install(TARGETS ggml PUBLIC_HEADER) diff --git a/Makefile b/Makefile index 22d521856..9a26aec50 100644 --- a/Makefile +++ b/Makefile @@ -399,13 +399,6 @@ ifndef LLAMA_NO_ACCELERATE endif endif # LLAMA_NO_ACCELERATE -ifdef LLAMA_MPI - MK_CPPFLAGS += -DGGML_USE_MPI - MK_CFLAGS += -Wno-cast-qual - MK_CXXFLAGS += -Wno-cast-qual - OBJS += ggml-mpi.o -endif # LLAMA_MPI - ifdef LLAMA_OPENBLAS MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas) MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) @@ -629,11 +622,6 @@ ggml-metal-embed.o: ggml-metal.metal ggml-common.h endif endif # LLAMA_METAL -ifdef LLAMA_MPI -ggml-mpi.o: ggml-mpi.c ggml-mpi.h - $(CC) $(CFLAGS) -c $< -o $@ -endif # LLAMA_MPI - ifndef LLAMA_NO_LLAMAFILE sgemm.o: sgemm.cpp sgemm.h ggml.h $(CXX) $(CXXFLAGS) -c $< -o $@ diff --git a/README.md b/README.md index 7dd6fc0eb..4abfd6d7e 100644 --- a/README.md +++ b/README.md @@ -382,45 +382,6 @@ To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or th When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line argument. -### MPI Build - -MPI lets you distribute the computation over a cluster of machines. Because of the serial nature of LLM prediction, this won't yield any end-to-end speed-ups, but it will let you run larger models than would otherwise fit into RAM on a single machine. - -First you will need MPI libraries installed on your system. The two most popular (only?) options are [MPICH](https://www.mpich.org) and [OpenMPI](https://www.open-mpi.org). Either can be installed with a package manager (`apt`, Homebrew, MacPorts, etc). - -Next you will need to build the project with `LLAMA_MPI` set to true on all machines; if you're building with `make`, you will also need to specify an MPI-capable compiler (when building with CMake, this is configured automatically): - -- Using `make`: - - ```bash - make CC=mpicc CXX=mpicxx LLAMA_MPI=1 - ``` - -- Using `CMake`: - - ```bash - cmake -S . -B build -DLLAMA_MPI=ON - ``` - -Once the programs are built, download/convert the weights on all of the machines in your cluster. The paths to the weights and programs should be identical on all machines. - -Next, ensure password-less SSH access to each machine from the primary host, and create a `hostfile` with a list of the hostnames and their relative "weights" (slots). If you want to use localhost for computation, use its local subnet IP address rather than the loopback address or "localhost". - -Here is an example hostfile: - -``` -192.168.0.1:2 -malvolio.local:1 -``` - -The above will distribute the computation across 2 processes on the first host and 1 process on the second host. Each process will use roughly an equal amount of RAM. Try to keep these numbers small, as inter-process (intra-host) communication is expensive. - -Finally, you're ready to run a computation using `mpirun`: - -```bash -mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 -``` - ### BLAS Build Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use: diff --git a/ggml-mpi.c b/ggml-mpi.c deleted file mode 100644 index ae176d707..000000000 --- a/ggml-mpi.c +++ /dev/null @@ -1,216 +0,0 @@ -#include "ggml-mpi.h" - -#include "ggml.h" - -#include - -#include -#include - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) - -#define UNUSED GGML_UNUSED - -struct ggml_mpi_context { - int rank; - int size; -}; - -void ggml_mpi_backend_init(void) { - MPI_Init(NULL, NULL); -} - -void ggml_mpi_backend_free(void) { - MPI_Finalize(); -} - -struct ggml_mpi_context * ggml_mpi_init(void) { - struct ggml_mpi_context * ctx = calloc(1, sizeof(struct ggml_mpi_context)); - - MPI_Comm_rank(MPI_COMM_WORLD, &ctx->rank); - MPI_Comm_size(MPI_COMM_WORLD, &ctx->size); - - return ctx; -} - -void ggml_mpi_free(struct ggml_mpi_context * ctx) { - free(ctx); -} - -int ggml_mpi_rank(struct ggml_mpi_context * ctx) { - return ctx->rank; -} - -void ggml_mpi_eval_init( - struct ggml_mpi_context * ctx_mpi, - int * n_tokens, - int * n_past, - int * n_threads) { - UNUSED(ctx_mpi); - - // synchronize the worker node parameters with the root node - MPI_Barrier(MPI_COMM_WORLD); - - MPI_Bcast(n_tokens, 1, MPI_INT, 0, MPI_COMM_WORLD); - MPI_Bcast(n_past, 1, MPI_INT, 0, MPI_COMM_WORLD); - MPI_Bcast(n_threads, 1, MPI_INT, 0, MPI_COMM_WORLD); -} - -static int ggml_graph_get_node_idx(struct ggml_cgraph * gf, const char * name) { - struct ggml_tensor * t = ggml_graph_get_tensor(gf, name); - if (t == NULL) { - fprintf(stderr, "%s: tensor %s not found\n", __func__, name); - return -1; - } - - for (int i = 0; i < gf->n_nodes; i++) { - if (gf->nodes[i] == t) { - return i; - } - } - - fprintf(stderr, "%s: tensor %s not found in graph (should not happen)\n", __func__, name); - return -1; -} - -static void ggml_mpi_tensor_send(struct ggml_tensor * t, int mpi_rank_dst) { - MPI_Datatype mpi_type; - - switch (t->type) { - case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break; - case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break; - default: GGML_ASSERT(false && "not implemented"); - } - - const int retval = MPI_Send(t->data, ggml_nelements(t), mpi_type, mpi_rank_dst, 0, MPI_COMM_WORLD); - GGML_ASSERT(retval == MPI_SUCCESS); -} - -static void ggml_mpi_tensor_recv(struct ggml_tensor * t, int mpi_rank_src) { - MPI_Datatype mpi_type; - - switch (t->type) { - case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break; - case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break; - default: GGML_ASSERT(false && "not implemented"); - } - - MPI_Status status; UNUSED(status); - - const int retval = MPI_Recv(t->data, ggml_nelements(t), mpi_type, mpi_rank_src, MPI_ANY_TAG, MPI_COMM_WORLD, &status); - GGML_ASSERT(retval == MPI_SUCCESS); -} - -// TODO: there are many improvements that can be done to this implementation -void ggml_mpi_graph_compute_pre( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers) { - const int mpi_rank = ctx_mpi->rank; - const int mpi_size = ctx_mpi->size; - - struct ggml_tensor * inp_tokens = ggml_graph_get_tensor(gf, "inp_tokens"); - if (inp_tokens == NULL) { - fprintf(stderr, "%s: tensor 'inp_tokens' not found\n", __func__); - return; - } - - struct ggml_tensor * inp0 = ggml_graph_get_tensor(gf, "layer_inp_0"); - if (inp0 == NULL) { - fprintf(stderr, "%s: tensor 'inp0' not found\n", __func__); - return; - } - - GGML_ASSERT(inp0 == gf->nodes[0]); - - // distribute the compute graph into slices across the MPI nodes - // - // the main node (0) processes the last layers + the remainder of the compute graph - // and is responsible to pass the input tokens to the first node (1) - // - // node 1: [( 0) * n_per_node, ( 1) * n_per_node) - // node 2: [( 1) * n_per_node, ( 2) * n_per_node) - // ... - // node n-1: [(n-2) * n_per_node, (n-1) * n_per_node) - // node 0: [(n-1) * n_per_node, n_nodes) - // - if (mpi_rank > 0) { - if (mpi_rank == 1) { - // the first node (1) receives the input tokens from the main node (0) - ggml_mpi_tensor_recv(inp_tokens, 0); - } else { - // recv input data for each node into the "inp0" tensor (i.e. the first node in the compute graph) - ggml_mpi_tensor_recv(inp0, mpi_rank - 1); - } - } else if (mpi_size > 1) { - // node 0 sends the input tokens to node 1 - ggml_mpi_tensor_send(inp_tokens, 1); - - // recv the output data from the last node - ggml_mpi_tensor_recv(inp0, mpi_size - 1); - } - - { - const int n_per_node = (n_layers + (mpi_size - 1)) / mpi_size; - - const int mpi_idx = mpi_rank > 0 ? mpi_rank - 1 : mpi_size - 1; - - const int il0 = (mpi_idx + 0) * n_per_node; - const int il1 = MIN(n_layers, (mpi_idx + 1) * n_per_node); - - char name_l0[GGML_MAX_NAME]; - char name_l1[GGML_MAX_NAME]; - - snprintf(name_l0, sizeof(name_l0), "layer_inp_%d", il0); - snprintf(name_l1, sizeof(name_l1), "layer_inp_%d", il1); - - const int idx_l0 = ggml_graph_get_node_idx(gf, name_l0); - const int idx_l1 = mpi_rank > 0 ? ggml_graph_get_node_idx(gf, name_l1) + 1 : gf->n_nodes; - - if (idx_l0 < 0 || idx_l1 < 0) { - fprintf(stderr, "%s: layer input nodes not found\n", __func__); - return; - } - - // attach the input data to all nodes that need it - // TODO: not great - should be able to do this without modifying the compute graph (see next TODO below) - for (int i = idx_l0; i < idx_l1; i++) { - if (gf->nodes[i]->src[0] == gf->nodes[idx_l0]) { - gf->nodes[i]->src[0] = inp0; - } - if (gf->nodes[i]->src[1] == gf->nodes[idx_l0]) { - gf->nodes[i]->src[1] = inp0; - } - } - - // TODO: instead of rearranging the nodes, we should be able to execute a subset of the compute graph - for (int i = 1; i < idx_l1 - idx_l0; i++) { - gf->nodes[i] = gf->nodes[idx_l0 + i]; - gf->grads[i] = gf->grads[idx_l0 + i]; - } - - // the first node performs the "get_rows" operation, the rest of the nodes get the data from the previous node - if (mpi_idx != 0) { - gf->nodes[0]->op = GGML_OP_NONE; - } - - gf->n_nodes = idx_l1 - idx_l0; - - //fprintf(stderr, "%s: node %d: processing %d nodes [%d, %d)\n", __func__, mpi_rank, gf->n_nodes, il0, il1); - } -} - -void ggml_mpi_graph_compute_post( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers) { - UNUSED(n_layers); - - const int mpi_rank = ctx_mpi->rank; - const int mpi_size = ctx_mpi->size; - - // send the output data to the next node - if (mpi_rank > 0) { - ggml_mpi_tensor_send(gf->nodes[gf->n_nodes - 1], (mpi_rank + 1) % mpi_size); - } -} diff --git a/ggml-mpi.h b/ggml-mpi.h deleted file mode 100644 index eda119d44..000000000 --- a/ggml-mpi.h +++ /dev/null @@ -1,39 +0,0 @@ -#pragma once - -struct ggml_context; -struct ggml_tensor; -struct ggml_cgraph; - -#ifdef __cplusplus -extern "C" { -#endif - -struct ggml_mpi_context; - -void ggml_mpi_backend_init(void); -void ggml_mpi_backend_free(void); - -struct ggml_mpi_context * ggml_mpi_init(void); -void ggml_mpi_free(struct ggml_mpi_context * ctx); - -int ggml_mpi_rank(struct ggml_mpi_context * ctx); - -void ggml_mpi_eval_init( - struct ggml_mpi_context * ctx_mpi, - int * n_tokens, - int * n_past, - int * n_threads); - -void ggml_mpi_graph_compute_pre( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers); - -void ggml_mpi_graph_compute_post( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers); - -#ifdef __cplusplus -} -#endif diff --git a/llama.cpp b/llama.cpp index 06ff4da61..102bc2020 100644 --- a/llama.cpp +++ b/llama.cpp @@ -26,9 +26,6 @@ #ifdef GGML_USE_METAL # include "ggml-metal.h" #endif -#ifdef GGML_USE_MPI -# include "ggml-mpi.h" -#endif #ifndef QK_K # ifdef GGML_QKK_64 # define QK_K 64 @@ -2270,10 +2267,6 @@ struct llama_context { // control vectors struct llama_control_vector cvec; - -#ifdef GGML_USE_MPI - ggml_mpi_context * ctx_mpi = NULL; -#endif }; static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) { @@ -6336,10 +6329,7 @@ static struct ggml_tensor * llm_build_inp_embd( inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens); } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); + lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); inpL = lctx.inp_embd; ggml_set_input(lctx.inp_embd); } @@ -11351,11 +11341,6 @@ static void llama_graph_compute( llama_context & lctx, ggml_cgraph * gf, int n_threads) { -#ifdef GGML_USE_MPI - const int64_t n_layer = lctx.model.hparams.n_layer; - ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); -#endif - #ifdef GGML_USE_METAL if (ggml_backend_is_metal(lctx.backend_metal)) { ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads); @@ -11370,10 +11355,6 @@ static void llama_graph_compute( ggml_backend_sched_graph_compute_async(lctx.sched, gf); // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); - -#ifdef GGML_USE_MPI - ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer); -#endif } // decode a batch of tokens by evaluating the transformer @@ -11411,12 +11392,6 @@ static int llama_decode_internal( } lctx.n_queued_tokens += n_tokens_all; -#ifdef GGML_USE_MPI - // TODO: needs fix after #3228 - GGML_ASSERT(false && "not implemented"); - //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads); -#endif - auto & kv_self = lctx.kv_self; const int64_t n_embd = hparams.n_embd; @@ -15546,10 +15521,6 @@ void llama_backend_init(void) { struct ggml_context * ctx = ggml_init(params); ggml_free(ctx); } - -#ifdef GGML_USE_MPI - ggml_mpi_backend_init(); -#endif } void llama_numa_init(enum ggml_numa_strategy numa) { @@ -15559,9 +15530,6 @@ void llama_numa_init(enum ggml_numa_strategy numa) { } void llama_backend_free(void) { -#ifdef GGML_USE_MPI - ggml_mpi_backend_free(); -#endif ggml_quantize_free(); } @@ -15962,20 +15930,6 @@ struct llama_context * llama_new_context_with_model( } } -#ifdef GGML_USE_MPI - ctx->ctx_mpi = ggml_mpi_init(); - - if (ggml_mpi_rank(ctx->ctx_mpi) > 0) { - // Enter a blocking eval loop with dummy input, letting rank=0 drive the process - // TODO: needs fix after #3228 - GGML_ASSERT(false && "not implemented"); - //const std::vector tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx)); - //while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {}; - llama_backend_free(); - exit(1); - } -#endif - return ctx; } diff --git a/scripts/LlamaConfig.cmake.in b/scripts/LlamaConfig.cmake.in index f842c7137..92e39708b 100644 --- a/scripts/LlamaConfig.cmake.in +++ b/scripts/LlamaConfig.cmake.in @@ -5,7 +5,6 @@ set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) set(LLAMA_BLAS @LLAMA_BLAS@) set(LLAMA_CUDA @LLAMA_CUDA@) set(LLAMA_METAL @LLAMA_METAL@) -set(LLAMA_MPI @LLAMA_MPI@) set(LLAMA_CLBLAST @LLAMA_CLBLAST@) set(LLAMA_HIPBLAS @LLAMA_HIPBLAS@) set(LLAMA_ACCELERATE @LLAMA_ACCELERATE@) @@ -37,10 +36,6 @@ if (LLAMA_METAL) find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) endif() -if (LLAMA_MPI) - find_package(MPI REQUIRED) -endif() - if (LLAMA_CLBLAST) find_package(CLBlast REQUIRED) endif()