// An interface allowing to compute ggml_cgraph with Metal // // This is a fully functional interface that extends ggml with GPU support for Apple devices. // A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.) // // How it works? // // As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this // interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you // use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.) // // You only need to make sure that all memory buffers that you used during the graph creation // are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is // used during the graph evaluation to determine the arguments of the compute kernels. // // Synchronization between device and host memory (for example for input and output tensors) // is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions. // #pragma once #include #include // max memory buffers that can be mapped to the device #define GGML_METAL_MAX_BUFFERS 256 struct ggml_tensor; struct ggml_cgraph; #ifdef __cplusplus extern "C" { #endif struct ggml_metal_context; struct ggml_metal_context * ggml_metal_init(void); void ggml_metal_free(struct ggml_metal_context * ctx); // creates a mapping between a host memory buffer and a device memory buffer // - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute // - the mapping is used during computation to determine the arguments of the compute kernels // - you don't need to keep the host memory buffer allocated as it is never accessed by Metal // - max_size specifies the maximum size of a tensor and is used to create shared views such // that it is guaranteed that the tensor will fit in at least one of the views // bool ggml_metal_add_buffer( struct ggml_metal_context * ctx, const char * name, void * data, size_t size, size_t max_size); // set data from host memory into the device void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t); // get data from the device into host memory void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t); // same as ggml_graph_compute but uses Metal // creates gf->n_threads command buffers in parallel void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf); #ifdef __cplusplus } #endif