d232aca5a7
* llama : initial ggml-backend integration * add ggml-metal * cuda backend can be used though ggml-backend with LLAMA_GGML_BACKEND_CUDA_TEST access all tensor data with ggml_backend_tensor_get/set * add ggml_backend_buffer_clear zero-init KV cache buffer * add ggml_backend_buffer_is_hos, used to avoid copies if possible when accesing tensor data * disable gpu backends with ngl 0 * more accurate mlock * unmap offloaded part of the model * use posix_fadvise64(.., POSIX_FADV_SEQUENTIAL) to improve performance with mmap * update quantize and lora * update session copy/set to use ggml-backend ggml-ci * use posix_fadvise instead of posix_fadvise64 * ggml_backend_alloc_ctx_tensors_from_buft : remove old print * llama_mmap::align_offset : use pointers instead of references for out parameters * restore progress_callback behavior * move final progress_callback call to load_all_data * cuda : fix fprintf format string (minor) * do not offload scales * llama_mmap : avoid unmapping the same fragments again in the destructor * remove unnecessary unmap * metal : add default log function that prints to stderr, cleanup code ggml-ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
116 lines
4.2 KiB
C
116 lines
4.2 KiB
C
// An interface allowing to compute ggml_cgraph with Metal
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//
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// This is a fully functional interface that extends ggml with GPU support for Apple devices.
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// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
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//
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// How it works?
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//
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// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
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// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
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// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
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//
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// You only need to make sure that all memory buffers that you used during the graph creation
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// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
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// used during the graph evaluation to determine the arguments of the compute kernels.
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//
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// Synchronization between device and host memory (for example for input and output tensors)
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// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
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//
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#pragma once
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#include "ggml.h"
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#include "ggml-backend.h"
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#include <stddef.h>
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#include <stdbool.h>
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// max memory buffers that can be mapped to the device
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#define GGML_METAL_MAX_BUFFERS 64
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#define GGML_METAL_MAX_COMMAND_BUFFERS 32
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struct ggml_tensor;
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struct ggml_cgraph;
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#ifdef __cplusplus
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extern "C" {
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#endif
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//
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// internal API
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// temporary exposed to user-code
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//
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struct ggml_metal_context;
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void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data);
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// number of command buffers to use
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struct ggml_metal_context * ggml_metal_init(int n_cb);
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void ggml_metal_free(struct ggml_metal_context * ctx);
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void * ggml_metal_host_malloc(size_t n);
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void ggml_metal_host_free (void * data);
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// set the number of command buffers to use
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void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
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// creates a mapping between a host memory buffer and a device memory buffer
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// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
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// - the mapping is used during computation to determine the arguments of the compute kernels
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// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
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// - max_size specifies the maximum size of a tensor and is used to create shared views such
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// that it is guaranteed that the tensor will fit in at least one of the views
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//
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bool ggml_metal_add_buffer(
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struct ggml_metal_context * ctx,
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const char * name,
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void * data,
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size_t size,
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size_t max_size);
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// set data from host memory into the device
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void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
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// get data from the device into host memory
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void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
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// try to find operations that can be run concurrently in the graph
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// you should run it again if the topology of your graph changes
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void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem);
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// if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized
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int ggml_metal_if_optimized(struct ggml_metal_context * ctx);
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// output the concur_list for ggml_alloc
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int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
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// same as ggml_graph_compute but uses Metal
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// creates gf->n_threads command buffers in parallel
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void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
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//
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// backend API
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// user-code should use only these functions
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//
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GGML_API ggml_backend_t ggml_backend_metal_init(void);
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GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
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GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
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GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
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GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
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// helper to check if the device supports a specific family
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// ideally, the user code should be doing these checks
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// ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
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GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family);
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#ifdef __cplusplus
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}
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#endif
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