From: Christian Kastner Date: Sat, 27 Jun 2026 07:31:29 +0000 (+0200) Subject: binaries : Improve rpc-server and export-graph-ops names. (#25045) X-Git-Tag: upstream/0.0.10438~614 X-Git-Url: https://git.djapps.eu/?a=commitdiff_plain;h=c299a92c38b6de6a1139617652b66081828648db;p=pkg%2Fggml%2Fsources%2Fllama.cpp binaries : Improve rpc-server and export-graph-ops names. (#25045) Tests are generally prefixed with -test, so rename export-graph-ops accordingly. rpc-server is probably too generic a name for /usr/bin. Because it should work with any ggml application, it is renamed to ggml-rpc-server. --- diff --git a/SECURITY.md b/SECURITY.md index a98b8e70b..0e704e328 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -80,7 +80,7 @@ To protect sensitive data from potential leaks or unauthorized access, it is cru ### Untrusted environments or networks If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions: -* Do not use the RPC backend, [rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) functionality (see https://github.com/ggml-org/llama.cpp/pull/13061). +* Do not use the RPC backend, [ggml-rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) functionality (see https://github.com/ggml-org/llama.cpp/pull/13061). * Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value. * Encrypt your data if sending it over the network. diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 0dd1d7b16..24592a279 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -302,9 +302,9 @@ target_link_libraries(${TEST_TARGET} PRIVATE llama) llama_build_and_test(test-alloc.cpp) target_include_directories(test-alloc PRIVATE ${PROJECT_SOURCE_DIR}/ggml/src) -llama_build(export-graph-ops.cpp) -target_include_directories(export-graph-ops PRIVATE ${PROJECT_SOURCE_DIR}/ggml/src) +llama_build(test-export-graph-ops.cpp) +target_include_directories(test-export-graph-ops PRIVATE ${PROJECT_SOURCE_DIR}/ggml/src) if (TARGET gguf-model-data) - target_link_libraries(export-graph-ops PRIVATE gguf-model-data) - target_compile_definitions(export-graph-ops PRIVATE LLAMA_HF_FETCH) + target_link_libraries(test-export-graph-ops PRIVATE gguf-model-data) + target_compile_definitions(test-export-graph-ops PRIVATE LLAMA_HF_FETCH) endif() diff --git a/tests/export-graph-ops.cpp b/tests/export-graph-ops.cpp deleted file mode 100644 index 64cf6dcea..000000000 --- a/tests/export-graph-ops.cpp +++ /dev/null @@ -1,226 +0,0 @@ -#include "arg.h" -#include "common.h" -#include "log.h" -#include "llama-cpp.h" -#include "../src/llama-ext.h" -#include "ggml.h" -#include "gguf-model-data.h" -#include "gguf.h" -#include "ggml-backend.h" -#include "download.h" - -#include -#include -#include -#include -#include -#include - -// Noop because weights are not needed -static void set_tensor_data(struct ggml_tensor * tensor, void * userdata) { - GGML_UNUSED(tensor); - GGML_UNUSED(userdata); -} - -struct input_tensor { - ggml_type type; - std::array ne; - std::array nb; - - input_tensor(ggml_type type, int64_t * ne, size_t * nb): type(type) { - memcpy(this->ne.data(), ne, 4 * sizeof(int64_t)); - memcpy(this->nb.data(), nb, 4 * sizeof(size_t)); - } - - bool operator<(const input_tensor &b) const { - return std::tie(type, ne, nb) < - std::tie(b.type, b.ne, b.nb); - } - - void serialize(std::ostream& out) const { - out << type << ' '; - for (size_t i = 0; i < 4; i++) { - out << ne[i] << ' '; - } - for (size_t i = 0; i < 4; i++) { - out << nb[i] << ' '; - } - } -}; - -struct test_object { - ggml_op op; - ggml_type type; - std::array ne; - std::vector op_params; - std::vector sources; - std::string name; - - void serialize(std::ostream& out) const { - out << op << ' ' << type << ' '; - for (size_t i = 0; i < 4; i++) { - out << ne[i] << ' '; - } - - out << op_params.size() << ' '; - for (size_t i = 0; i < op_params.size(); i++) { - out << op_params[i] << ' '; - } - - out << sources.size() << ' '; - for (size_t s = 0; s < sources.size(); s++) { - sources[s].serialize(out); - } - - if (!name.empty()) { - out << name; - } else { - out << '-'; - } - - out << '\n'; - } - - bool operator<(const test_object &b) const { - return std::tie(op, type, ne, op_params, sources) < - std::tie(b.op, b.type, b.ne, b.op_params, b.sources); - } -}; - -static void extract_graph_ops(ggml_cgraph * cgraph, const char * label, std::set & tests) { - int n_nodes = ggml_graph_n_nodes(cgraph); - int n_skipped = 0; - int n_before = (int) tests.size(); - for (int i = 0; i < n_nodes; i++) { - ggml_tensor * node = ggml_graph_node(cgraph, i); - - if (node->op == GGML_OP_NONE || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE) { - n_skipped++; - continue; - } - - test_object test; - - test.op = node->op; - test.type = node->type; - memcpy(&test.ne, node->ne, 4 * sizeof(int64_t)); - - test.op_params.resize(GGML_MAX_OP_PARAMS / sizeof(int32_t)); - memcpy(test.op_params.data(), node->op_params, GGML_MAX_OP_PARAMS); - - for (size_t s = 0; s < GGML_MAX_SRC; s++) { - if (node->src[s] == nullptr) { - break; - } - - test.sources.emplace_back(node->src[s]->type, node->src[s]->ne, node->src[s]->nb); - } - - test.name = node->name; - tests.insert(test); - } - - int n_new = (int) tests.size() - n_before; - LOG_INF("%s: %d unique ops, %d total nodes, %d skipped (view ops)\n", - label, n_new, n_nodes, n_skipped); -} - -int main(int argc, char ** argv) { - common_params params; - params.out_file = "tests.txt"; - - common_init(); - - if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_GRAPH_OPS)) { - return 1; - } - - // Load CPU-only - ggml_backend_dev_t cpu_device = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); - params.devices = { cpu_device, nullptr }; - params.fit_params = false; - params.n_gpu_layers = 0; - - params.warmup = false; - - llama_context * ctx; - common_init_result_ptr init_result; - llama_context_ptr ctx2; - llama_model_ptr model; - - if (params.model.hf_repo.empty()) { - init_result = common_init_from_params(params); - - ctx = init_result->context(); - } else { -#ifdef LLAMA_HF_FETCH - auto [hf_repo, hf_quant] = common_download_split_repo_tag(params.model.hf_repo); - if (hf_quant.empty() || hf_quant == "latest") { - hf_quant = "Q4_K_M"; - } - - gguf_context_ptr gguf_ctx = gguf_fetch_gguf_ctx(hf_repo, hf_quant); - if (!gguf_ctx) { - LOG_ERR("failed to fetch GGUF metadata from %s\n", hf_repo.c_str()); - return 1; - } - - llama_model_params model_params = llama_model_default_params(); - model_params.devices = params.devices.data(); - model_params.no_alloc = true; - - model.reset(llama_model_init_from_user(gguf_ctx.get(), set_tensor_data, nullptr, model_params)); - - if (!model) { - LOG_ERR("failed to create llama_model from %s\n", hf_repo.c_str()); - return 1; - } - - llama_context_params ctx_params = llama_context_default_params(); - ctx2.reset(llama_init_from_model(model.get(), ctx_params)); - ctx = ctx2.get(); - - if (!ctx) { - LOG_ERR("failed to create llama_context\n"); - return 1; - } -#else - LOG_ERR("export-graph-ops compiled without HF fetch support\n"); - return 1; -#endif - } - - const uint32_t n_seqs = llama_n_seq_max(ctx); - const uint32_t n_tokens = std::min(llama_n_ctx(ctx), llama_n_ubatch(ctx)); - - std::set tests; - - auto * gf_pp = llama_graph_reserve(ctx, n_tokens, n_seqs, n_tokens); - if (!gf_pp) { - LOG_ERR("failed to reserve prompt processing graph\n"); - return 1; - } - extract_graph_ops(gf_pp, "pp", tests); - - auto * gf_tg = llama_graph_reserve(ctx, n_seqs, n_seqs, n_seqs); - if (!gf_tg) { - LOG_ERR("failed to reserve token generation graph\n"); - return 1; - } - extract_graph_ops(gf_tg, "tg", tests); - - LOG_INF("%d unique ops total\n", (int) tests.size()); - - std::ofstream f(params.out_file); - - if (!f.is_open()) { - LOG_ERR("unable to open output file: %s\n", params.out_file.c_str()); - return 1; - } - - for (const auto& test : tests) { - test.serialize(f); - } - - return 0; -} diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 0830dbf57..09ac62a75 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -9943,7 +9943,7 @@ static void usage(char ** argv) { printf(" --output specifies output format (default: console, options: console, sql, csv)\n"); printf(" --list-ops lists all available GGML operations\n"); printf(" --show-coverage shows test coverage\n"); - printf(" --test-file reads test operators from a test file generated by llama-export-graph-ops\n"); + printf(" --test-file reads test operators from a test file generated by test-export-graph-ops\n"); printf(" -j runs tests using parallel worker threads (default: 1, test mode only)\n"); } diff --git a/tests/test-export-graph-ops.cpp b/tests/test-export-graph-ops.cpp new file mode 100644 index 000000000..7d8118dcd --- /dev/null +++ b/tests/test-export-graph-ops.cpp @@ -0,0 +1,226 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama-cpp.h" +#include "../src/llama-ext.h" +#include "ggml.h" +#include "gguf-model-data.h" +#include "gguf.h" +#include "ggml-backend.h" +#include "download.h" + +#include +#include +#include +#include +#include +#include + +// Noop because weights are not needed +static void set_tensor_data(struct ggml_tensor * tensor, void * userdata) { + GGML_UNUSED(tensor); + GGML_UNUSED(userdata); +} + +struct input_tensor { + ggml_type type; + std::array ne; + std::array nb; + + input_tensor(ggml_type type, int64_t * ne, size_t * nb): type(type) { + memcpy(this->ne.data(), ne, 4 * sizeof(int64_t)); + memcpy(this->nb.data(), nb, 4 * sizeof(size_t)); + } + + bool operator<(const input_tensor &b) const { + return std::tie(type, ne, nb) < + std::tie(b.type, b.ne, b.nb); + } + + void serialize(std::ostream& out) const { + out << type << ' '; + for (size_t i = 0; i < 4; i++) { + out << ne[i] << ' '; + } + for (size_t i = 0; i < 4; i++) { + out << nb[i] << ' '; + } + } +}; + +struct test_object { + ggml_op op; + ggml_type type; + std::array ne; + std::vector op_params; + std::vector sources; + std::string name; + + void serialize(std::ostream& out) const { + out << op << ' ' << type << ' '; + for (size_t i = 0; i < 4; i++) { + out << ne[i] << ' '; + } + + out << op_params.size() << ' '; + for (size_t i = 0; i < op_params.size(); i++) { + out << op_params[i] << ' '; + } + + out << sources.size() << ' '; + for (size_t s = 0; s < sources.size(); s++) { + sources[s].serialize(out); + } + + if (!name.empty()) { + out << name; + } else { + out << '-'; + } + + out << '\n'; + } + + bool operator<(const test_object &b) const { + return std::tie(op, type, ne, op_params, sources) < + std::tie(b.op, b.type, b.ne, b.op_params, b.sources); + } +}; + +static void extract_graph_ops(ggml_cgraph * cgraph, const char * label, std::set & tests) { + int n_nodes = ggml_graph_n_nodes(cgraph); + int n_skipped = 0; + int n_before = (int) tests.size(); + for (int i = 0; i < n_nodes; i++) { + ggml_tensor * node = ggml_graph_node(cgraph, i); + + if (node->op == GGML_OP_NONE || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE) { + n_skipped++; + continue; + } + + test_object test; + + test.op = node->op; + test.type = node->type; + memcpy(&test.ne, node->ne, 4 * sizeof(int64_t)); + + test.op_params.resize(GGML_MAX_OP_PARAMS / sizeof(int32_t)); + memcpy(test.op_params.data(), node->op_params, GGML_MAX_OP_PARAMS); + + for (size_t s = 0; s < GGML_MAX_SRC; s++) { + if (node->src[s] == nullptr) { + break; + } + + test.sources.emplace_back(node->src[s]->type, node->src[s]->ne, node->src[s]->nb); + } + + test.name = node->name; + tests.insert(test); + } + + int n_new = (int) tests.size() - n_before; + LOG_INF("%s: %d unique ops, %d total nodes, %d skipped (view ops)\n", + label, n_new, n_nodes, n_skipped); +} + +int main(int argc, char ** argv) { + common_params params; + params.out_file = "tests.txt"; + + common_init(); + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_GRAPH_OPS)) { + return 1; + } + + // Load CPU-only + ggml_backend_dev_t cpu_device = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + params.devices = { cpu_device, nullptr }; + params.fit_params = false; + params.n_gpu_layers = 0; + + params.warmup = false; + + llama_context * ctx; + common_init_result_ptr init_result; + llama_context_ptr ctx2; + llama_model_ptr model; + + if (params.model.hf_repo.empty()) { + init_result = common_init_from_params(params); + + ctx = init_result->context(); + } else { +#ifdef LLAMA_HF_FETCH + auto [hf_repo, hf_quant] = common_download_split_repo_tag(params.model.hf_repo); + if (hf_quant.empty() || hf_quant == "latest") { + hf_quant = "Q4_K_M"; + } + + gguf_context_ptr gguf_ctx = gguf_fetch_gguf_ctx(hf_repo, hf_quant); + if (!gguf_ctx) { + LOG_ERR("failed to fetch GGUF metadata from %s\n", hf_repo.c_str()); + return 1; + } + + llama_model_params model_params = llama_model_default_params(); + model_params.devices = params.devices.data(); + model_params.no_alloc = true; + + model.reset(llama_model_init_from_user(gguf_ctx.get(), set_tensor_data, nullptr, model_params)); + + if (!model) { + LOG_ERR("failed to create llama_model from %s\n", hf_repo.c_str()); + return 1; + } + + llama_context_params ctx_params = llama_context_default_params(); + ctx2.reset(llama_init_from_model(model.get(), ctx_params)); + ctx = ctx2.get(); + + if (!ctx) { + LOG_ERR("failed to create llama_context\n"); + return 1; + } +#else + LOG_ERR("test-export-graph-ops compiled without HF fetch support\n"); + return 1; +#endif + } + + const uint32_t n_seqs = llama_n_seq_max(ctx); + const uint32_t n_tokens = std::min(llama_n_ctx(ctx), llama_n_ubatch(ctx)); + + std::set tests; + + auto * gf_pp = llama_graph_reserve(ctx, n_tokens, n_seqs, n_tokens); + if (!gf_pp) { + LOG_ERR("failed to reserve prompt processing graph\n"); + return 1; + } + extract_graph_ops(gf_pp, "pp", tests); + + auto * gf_tg = llama_graph_reserve(ctx, n_seqs, n_seqs, n_seqs); + if (!gf_tg) { + LOG_ERR("failed to reserve token generation graph\n"); + return 1; + } + extract_graph_ops(gf_tg, "tg", tests); + + LOG_INF("%d unique ops total\n", (int) tests.size()); + + std::ofstream f(params.out_file); + + if (!f.is_open()) { + LOG_ERR("unable to open output file: %s\n", params.out_file.c_str()); + return 1; + } + + for (const auto& test : tests) { + test.serialize(f); + } + + return 0; +} diff --git a/tools/rpc/CMakeLists.txt b/tools/rpc/CMakeLists.txt index 20f114ad9..0eee9a922 100644 --- a/tools/rpc/CMakeLists.txt +++ b/tools/rpc/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET rpc-server) +set(TARGET ggml-rpc-server) add_executable(${TARGET} rpc-server.cpp) target_link_libraries(${TARGET} PRIVATE ggml) target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/tools/rpc/README.md b/tools/rpc/README.md index 05b7292c0..655b65347 100644 --- a/tools/rpc/README.md +++ b/tools/rpc/README.md @@ -4,8 +4,8 @@ > This example and the RPC backend are currently in a proof-of-concept development stage. As such, the functionality is fragile and > insecure. **Never run the RPC server on an open network or in a sensitive environment!** -The `rpc-server` allows exposing `ggml` devices on a remote host. -The RPC backend communicates with one or several instances of `rpc-server` and offloads computations to them. +The `ggml-rpc-server` allows exposing `ggml` devices on a remote host. +The RPC backend communicates with one or several instances of `ggml-rpc-server` and offloads computations to them. This can be used for distributed LLM inference with `llama.cpp` in the following way: ```mermaid @@ -14,15 +14,15 @@ flowchart TD rpcb<-->|TCP|srvb rpcb<-.->|TCP|srvn subgraph hostn[Host N] - srvn[rpc-server]<-.->dev4["CUDA0"] - srvn[rpc-server]<-.->dev5["CPU"] + srvn[ggml-rpc-server]<-.->dev4["CUDA0"] + srvn[ggml-rpc-server]<-.->dev5["CPU"] end subgraph hostb[Host B] - srvb[rpc-server]<-->dev3["Metal"] + srvb[ggml-rpc-server]<-->dev3["Metal"] end subgraph hosta[Host A] - srva[rpc-server]<-->dev["CUDA0"] - srva[rpc-server]<-->dev2["CUDA1"] + srva[ggml-rpc-server]<-->dev["CUDA0"] + srva[ggml-rpc-server]<-->dev2["CUDA1"] end subgraph host[Main Host] local["Local devices"]<-->ggml[llama-cli] @@ -33,7 +33,7 @@ flowchart TD class local,dev,dev2,dev3,dev4,dev5 devcls ``` -By default, `rpc-server` exposes all available accelerator devices on the host. +By default, `ggml-rpc-server` exposes all available accelerator devices on the host. If there are no accelerators, it exposes a single `CPU` device. ## Usage @@ -41,7 +41,7 @@ If there are no accelerators, it exposes a single `CPU` device. ### Remote hosts On each remote host, build the backends for each accelerator by adding `-DGGML_RPC=ON` to the build options. -For example, to build the `rpc-server` with support for CUDA accelerators: +For example, to build the `ggml-rpc-server` with support for CUDA accelerators: ```bash mkdir build-rpc-cuda @@ -50,10 +50,10 @@ cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON cmake --build . --config Release ``` -When started, the `rpc-server` will detect and expose all available `CUDA` devices: +When started, the `ggml-rpc-server` will detect and expose all available `CUDA` devices: ```bash -$ bin/rpc-server +$ bin/ggml-rpc-server ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: @@ -67,14 +67,14 @@ Devices: You can control the set of exposed CUDA devices with the `CUDA_VISIBLE_DEVICES` environment variable or the `--device` command line option. The following two commands have the same effect: ```bash -$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052 -$ bin/rpc-server --device CUDA0 -p 50052 +$ CUDA_VISIBLE_DEVICES=0 bin/ggml-rpc-server -p 50052 +$ bin/ggml-rpc-server --device CUDA0 -p 50052 ``` ### Main host On the main host build `llama.cpp` with the backends for the local devices and add `-DGGML_RPC=ON` to the build options. -Finally, when running `llama-cli` or `llama-server`, use the `--rpc` option to specify the host and port of each `rpc-server`: +Finally, when running `llama-cli` or `llama-server`, use the `--rpc` option to specify the host and port of each `ggml-rpc-server`: ```bash $ llama-cli -hf ggml-org/gemma-3-1b-it-GGUF -ngl 99 --rpc 192.168.88.10:50052,192.168.88.11:50052 @@ -90,7 +90,7 @@ This can speed up model loading significantly, especially when using large model To enable the cache, use the `-c` option: ```bash -$ bin/rpc-server -c +$ bin/ggml-rpc-server -c ``` By default, the cache is stored in the `$HOME/.cache/llama.cpp/rpc` directory and can be controlled via the `LLAMA_CACHE` environment variable. @@ -103,8 +103,8 @@ RDMA is enabled by default when `libibverbs` is found at build time. ### Troubleshooting -Use the `GGML_RPC_DEBUG` environment variable to enable debug messages from `rpc-server`: +Use the `GGML_RPC_DEBUG` environment variable to enable debug messages from `ggml-rpc-server`: ```bash -$ GGML_RPC_DEBUG=1 bin/rpc-server +$ GGML_RPC_DEBUG=1 bin/ggml-rpc-server ```