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https://github.com/ggerganov/llama.cpp.git
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1d656d6360
* ggml_graph_compute: deprecate using ggml_context, try resolve issue #287 * rewrite: no longer consider backward compitability; plan and make_plan * minor: rename ctx as plan; const * remove ggml_graph_compute from tests/test-grad0.c, but current change breaks backward * add static ggml_graph_compute_sugar() * minor: update comments * reusable buffers * ggml : more consistent naming + metal fixes * ggml : fix docs * tests : disable grad / opt + minor naming changes * ggml : add ggml_graph_compute_with_ctx() - backwards compatible API - deduplicates a lot of copy-paste * ci : enable test-grad0 * examples : factor out plan allocation into a helper function * llama : factor out plan stuff into a helper function * ci : fix env * llama : fix duplicate symbols + refactor example benchmark * ggml : remove obsolete assert + refactor n_tasks section * ggml : fix indentation in switch * llama : avoid unnecessary bool * ggml : remove comments from source file and match order in header --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
72 lines
2.7 KiB
C
72 lines
2.7 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 <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 16
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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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struct ggml_metal_context;
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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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// 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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// 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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#ifdef __cplusplus
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}
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#endif
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