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@ -1,5 +1,9 @@
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## Overview
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> [!IMPORTANT]
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> This example and the RPC backend are currently in a proof-of-concept development stage. As such, the functionality is fragile and
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> insecure. **Never run the RPC server on an open network or in a sensitive environment!**
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The `rpc-server` allows running `ggml` backend on a remote host.
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The RPC backend communicates with one or several instances of `rpc-server` and offloads computations to them.
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This can be used for distributed LLM inference with `llama.cpp` in the following way:
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@ -16,7 +16,7 @@
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#include <stdio.h>
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struct rpc_server_params {
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std::string host = "0.0.0.0";
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std::string host = "127.0.0.1";
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int port = 50052;
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size_t backend_mem = 0;
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};
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@ -114,6 +114,17 @@ int main(int argc, char * argv[]) {
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fprintf(stderr, "Invalid parameters\n");
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return 1;
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}
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if (params.host != "127.0.0.1") {
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fprintf(stderr, "\n");
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fprintf(stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n");
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fprintf(stderr, "WARNING: Host ('%s') is != '127.0.0.1'\n", params.host.c_str());
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fprintf(stderr, " Never expose the RPC server to an open network!\n");
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fprintf(stderr, " This is an experimental feature and is not secure!\n");
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fprintf(stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n");
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fprintf(stderr, "\n");
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}
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ggml_backend_t backend = create_backend();
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if (!backend) {
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fprintf(stderr, "Failed to create backend\n");
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@ -197,6 +197,10 @@ static std::shared_ptr<socket_t> create_server_socket(const char * host, int por
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fprintf(stderr, "Failed to set SO_REUSEADDR\n");
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return nullptr;
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}
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if (inet_addr(host) == INADDR_NONE) {
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fprintf(stderr, "Invalid host address: %s\n", host);
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return nullptr;
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}
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struct sockaddr_in serv_addr;
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serv_addr.sin_family = AF_INET;
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serv_addr.sin_addr.s_addr = inet_addr(host);
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@ -879,6 +883,14 @@ ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rp
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if (result->buffer && buffers.find(result->buffer) == buffers.end()) {
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return nullptr;
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}
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// require that the tensor data does not go beyond the buffer end
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uint64_t tensor_size = (uint64_t) ggml_nbytes(result);
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uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(result->buffer);
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uint64_t buffer_size = (uint64_t) ggml_backend_buffer_get_size(result->buffer);
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GGML_ASSERT(tensor->data + tensor_size >= tensor->data); // check for overflow
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GGML_ASSERT(tensor->data >= buffer_start && tensor->data + tensor_size <= buffer_start + buffer_size);
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result->op = (ggml_op) tensor->op;
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for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) {
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result->op_params[i] = tensor->op_params[i];
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@ -898,7 +910,7 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
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const rpc_tensor * in_tensor = (const rpc_tensor *)input.data();
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uint64_t offset;
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memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset));
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size_t size = input.size() - sizeof(rpc_tensor) - sizeof(offset);
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const size_t size = input.size() - sizeof(rpc_tensor) - sizeof(offset);
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struct ggml_init_params params {
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/*.mem_size =*/ ggml_tensor_overhead(),
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@ -913,6 +925,17 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
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return false;
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}
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GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu\n", __func__, (void*)tensor->buffer, tensor->data, offset, size);
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// sanitize tensor->data
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{
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const size_t p0 = (size_t) ggml_backend_buffer_get_base(tensor->buffer);
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const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer);
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if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) {
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GGML_ABORT("[%s] tensor->data out of bounds\n", __func__);
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}
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}
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const void * data = input.data() + sizeof(rpc_tensor) + sizeof(offset);
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ggml_backend_tensor_set(tensor, data, offset, size);
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ggml_free(ctx);
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@ -943,6 +966,17 @@ bool rpc_server::get_tensor(const std::vector<uint8_t> & input, std::vector<uint
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return false;
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}
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GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, offset, size);
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// sanitize tensor->data
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{
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const size_t p0 = (size_t) ggml_backend_buffer_get_base(tensor->buffer);
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const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer);
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if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) {
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GGML_ABORT("[%s] tensor->data out of bounds\n", __func__);
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}
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}
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// output serialization format: | data (size bytes) |
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output.resize(size, 0);
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ggml_backend_tensor_get(tensor, output.data(), offset, size);
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@ -3724,7 +3724,8 @@ static struct ggml_tensor * ggml_new_tensor_impl(
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struct ggml_tensor * view_src,
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size_t view_offs) {
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assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS);
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GGML_ASSERT(type >= 0 && type < GGML_TYPE_COUNT);
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GGML_ASSERT(n_dims >= 1 && n_dims <= GGML_MAX_DIMS);
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// find the base tensor and absolute offset
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if (view_src != NULL && view_src->view_src != NULL) {
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