Datasets:

echodict
/

llama.cpp

	#pragma once

	//
	// GGML Tensor Library
	//
	// This documentation is still a work in progress.
	// If you wish some specific topics to be covered, feel free to drop a comment:
	//
	// https://github.com/ggml-org/whisper.cpp/issues/40
	//
	// ## Overview
	//
	// This library implements:
	//
	// - a set of tensor operations
	// - automatic differentiation
	// - basic optimization algorithms
	//
	// The aim of this library is to provide a minimalistic approach for various machine learning tasks. This includes,
	// but is not limited to, the following:
	//
	// - linear regression
	// - support vector machines
	// - neural networks
	//
	// The library allows the user to define a certain function using the available tensor operations. This function
	// definition is represented internally via a computation graph. Each tensor operation in the function definition
	// corresponds to a node in the graph. Having the computation graph defined, the user can choose to compute the
	// function's value and/or its gradient with respect to the input variables. Optionally, the function can be optimized
	// using one of the available optimization algorithms.
	//
	// For example, here we define the function: f(x) = a*x^2 + b
	//
	// {
	// struct ggml_init_params params = {
	// .mem_size = 1610241024,
	// .mem_buffer = NULL,
	// };
	//
	// // memory allocation happens here
	// struct ggml_context * ctx = ggml_init(params);
	//
	// struct ggml_tensor * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
	//
	// ggml_set_param(ctx, x); // x is an input variable
	//
	// struct ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
	// struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
	// struct ggml_tensor * x2 = ggml_mul(ctx, x, x);
	// struct ggml_tensor * f = ggml_add(ctx, ggml_mul(ctx, a, x2), b);
	//
	// ...
	// }
	//
	// Notice that the function definition above does not involve any actual computation. The computation is performed only
	// when the user explicitly requests it. For example, to compute the function's value at x = 2.0:
	//
	// {
	// ...
	//
	// struct ggml_cgraph * gf = ggml_new_graph(ctx);
	// ggml_build_forward_expand(gf, f);
	//
	// // set the input variable and parameter values
	// ggml_set_f32(x, 2.0f);
	// ggml_set_f32(a, 3.0f);
	// ggml_set_f32(b, 4.0f);
	//
	// ggml_graph_compute_with_ctx(ctx, &gf, n_threads);
	//
	// printf("f = %f\n", ggml_get_f32_1d(f, 0));
	//
	// ...
	// }
	//
	// The actual computation is performed in the ggml_graph_compute() function.
	//
	// The ggml_new_tensor_...() functions create new tensors. They are allocated in the memory buffer provided to the
	// ggml_init() function. You have to be careful not to exceed the memory buffer size. Therefore, you have to know
	// in advance how much memory you need for your computation. Alternatively, you can allocate a large enough memory
	// and after defining the computation graph, call the ggml_used_mem() function to find out how much memory was
	// actually needed.
	//
	// The ggml_set_param() function marks a tensor as an input variable. This is used by the automatic
	// differentiation and optimization algorithms.
	//
	// The described approach allows to define the function graph once and then compute its forward or backward graphs
	// multiple times. All computations will use the same memory buffer allocated in the ggml_init() function. This way
	// the user can avoid the memory allocation overhead at runtime.
	//
	// The library supports multi-dimensional tensors - up to 4 dimensions. The FP16 and FP32 data types are first class
	// citizens, but in theory the library can be extended to support FP8 and integer data types.
	//
	// Each tensor operation produces a new tensor. Initially the library was envisioned to support only the use of unary
	// and binary operations. Most of the available operations fall into one of these two categories. With time, it became
	// clear that the library needs to support more complex operations. The way to support these operations is not clear
	// yet, but a few examples are demonstrated in the following operations:
	//
	// - ggml_permute()
	// - ggml_conv_1d_1s()
	// - ggml_conv_1d_2s()
	//
	// For each tensor operator, the library implements a forward and backward computation function. The forward function
	// computes the output tensor value given the input tensor values. The backward function computes the adjoint of the
	// input tensors given the adjoint of the output tensor. For a detailed explanation of what this means, take a
	// calculus class, or watch the following video:
	//
	// What is Automatic Differentiation?
	// https://www.youtube.com/watch?v=wG_nF1awSSY
	//
	//
	// ## Tensor data (struct ggml_tensor)
	//
	// The tensors are stored in memory via the ggml_tensor struct. The structure provides information about the size of
	// the tensor, the data type, and the memory buffer where the tensor data is stored. Additionally, it contains
	// pointers to the "source" tensors - i.e. the tensors that were used to compute the current tensor. For example:
	//
	// {
	// struct ggml_tensor * c = ggml_add(ctx, a, b);
	//
	// assert(c->src[0] == a);
	// assert(c->src[1] == b);
	// }
	//
	// The multi-dimensional tensors are stored in row-major order. The ggml_tensor struct contains fields for the
	// number of elements in each dimension ("ne") as well as the number of bytes ("nb", a.k.a. stride). This allows
	// to store tensors that are not contiguous in memory, which is useful for operations such as transposition and
	// permutation. All tensor operations have to take the stride into account and not assume that the tensor is
	// contiguous in memory.
	//
	// The data of the tensor is accessed via the "data" pointer. For example:
	//
	// {
	// const int nx = 2;
	// const int ny = 3;
	//
	// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny);
	//
	// for (int y = 0; y < ny; y++) {
	// for (int x = 0; x < nx; x++) {
	// (float ) ((char ) a->data + ya->nb[1] + x*a->nb[0]) = x + y;
	// }
	// }
	//
	// ...
	// }
	//
	// Alternatively, there are helper functions, such as ggml_get_f32_1d() and ggml_set_f32_1d() that can be used.
	//
	// ## The matrix multiplication operator (ggml_mul_mat)
	//
	// TODO
	//
	//
	// ## Multi-threading
	//
	// TODO
	//
	//
	// ## Overview of ggml.c
	//
	// TODO
	//
	//
	// ## SIMD optimizations
	//
	// TODO
	//
	//
	// ## Debugging ggml
	//
	// TODO
	//
	//

	#ifdef GGML_SHARED
	# if defined(_WIN32) && !defined(__MINGW32__)
	# ifdef GGML_BUILD
	# define GGML_API __declspec(dllexport) extern
	# else
	# define GGML_API __declspec(dllimport) extern
	# endif
	# else
	# define GGML_API __attribute__ ((visibility ("default"))) extern
	# endif
	#else
	# define GGML_API extern
	#endif

	// TODO: support for clang
	#ifdef __GNUC__
	# define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
	#elif defined(_MSC_VER)
	# define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func
	#else
	# define GGML_DEPRECATED(func, hint) func
	#endif

	#ifndef __GNUC__
	# define GGML_ATTRIBUTE_FORMAT(...)
	#elif defined(__MINGW32__) && !defined(__clang__)
	# define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
	#else
	# define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
	#endif

	#if defined(_WIN32) && !defined(_WIN32_WINNT)
	# define _WIN32_WINNT 0x0A00
	#endif

	#include <stdbool.h>
	#include <stddef.h>
	#include <stdint.h>
	#include <stdio.h>

	#define GGML_FILE_MAGIC 0x67676d6c // "ggml"
	#define GGML_FILE_VERSION 2

	#define GGML_QNT_VERSION 2 // bump this on quantization format changes
	#define GGML_QNT_VERSION_FACTOR 1000 // do not change this

	#define GGML_MAX_DIMS 4
	#define GGML_MAX_PARAMS 2048
	#define GGML_MAX_SRC 10
	#define GGML_MAX_N_THREADS 512
	#define GGML_MAX_OP_PARAMS 64

	#ifndef GGML_MAX_NAME
	# define GGML_MAX_NAME 64
	#endif

	#define GGML_DEFAULT_N_THREADS 4
	#define GGML_DEFAULT_GRAPH_SIZE 2048

	#if UINTPTR_MAX == 0xFFFFFFFF
	#define GGML_MEM_ALIGN 4
	#elif defined(__EMSCRIPTEN__)
	// emscripten uses max_align_t == 8, so we need GGML_MEM_ALIGN == 8 for 64-bit wasm.
	// (for 32-bit wasm, the first conditional is true and GGML_MEM_ALIGN stays 4.)
	// ref: https://github.com/ggml-org/llama.cpp/pull/18628
	#define GGML_MEM_ALIGN 8
	#else
	#define GGML_MEM_ALIGN 16
	#endif

	#define GGML_EXIT_SUCCESS 0
	#define GGML_EXIT_ABORTED 1

	// TODO: convert to enum https://github.com/ggml-org/llama.cpp/pull/16187#discussion_r2388538726
	#define GGML_ROPE_TYPE_NORMAL 0
	#define GGML_ROPE_TYPE_NEOX 2
	#define GGML_ROPE_TYPE_MROPE 8
	#define GGML_ROPE_TYPE_VISION 24
	#define GGML_ROPE_TYPE_IMROPE 40 // binary: 101000

	#define GGML_MROPE_SECTIONS 4

	#define GGML_UNUSED(x) (void)(x)
	#ifdef __CUDACC__
	template<typename... Args>
	__host__ __device__ constexpr inline void ggml_unused_vars_impl(Args&&...) noexcept {}
	#define GGML_UNUSED_VARS(...) ggml_unused_vars_impl(__VA_ARGS__)
	#else
	#define GGML_UNUSED_VARS(...) do { (void)sizeof((__VA_ARGS__, 0)); } while(0)
	#endif // __CUDACC__

	#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))

	#ifndef NDEBUG
	# define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0)
	#elif defined(__GNUC__)
	# define GGML_UNREACHABLE() __builtin_unreachable()
	#elif defined(_MSC_VER)
	# define GGML_UNREACHABLE() __assume(0)
	#else
	# define GGML_UNREACHABLE() ((void) 0)
	#endif

	#ifdef __cplusplus
	# define GGML_NORETURN [[noreturn]]
	#elif defined(_MSC_VER)
	# define GGML_NORETURN __declspec(noreturn)
	#else
	# define GGML_NORETURN _Noreturn
	#endif

	#define GGML_ABORT(...) ggml_abort(__FILE__, __LINE__, __VA_ARGS__)
	#define GGML_ASSERT(x) if (!(x)) GGML_ABORT("GGML_ASSERT(%s) failed", #x)

	// used to copy the number of elements and stride in bytes of tensors into local variables.
	// main purpose is to reduce code duplication and improve readability.
	//
	// example:
	//
	// GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
	// GGML_TENSOR_LOCALS(size_t, nb1, src1, nb);
	//
	#define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \
	const type prefix##0 = (pointer) ? (pointer)->array[0] : 0; \
	GGML_UNUSED(prefix##0);
	#define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \
	GGML_TENSOR_LOCALS_1 (type, prefix, pointer, array) \
	const type prefix##1 = (pointer) ? (pointer)->array[1] : 0; \
	GGML_UNUSED(prefix##1);
	#define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \
	GGML_TENSOR_LOCALS_2 (type, prefix, pointer, array) \
	const type prefix##2 = (pointer) ? (pointer)->array[2] : 0; \
	GGML_UNUSED(prefix##2);
	#define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \
	GGML_TENSOR_LOCALS_3 (type, prefix, pointer, array) \
	const type prefix##3 = (pointer) ? (pointer)->array[3] : 0; \
	GGML_UNUSED(prefix##3);

	#define GGML_TENSOR_UNARY_OP_LOCALS \
	GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
	GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
	GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
	GGML_TENSOR_LOCALS(size_t, nb, dst, nb)

	#define GGML_TENSOR_BINARY_OP_LOCALS \
	GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
	GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
	GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
	GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) \
	GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
	GGML_TENSOR_LOCALS(size_t, nb, dst, nb)

	#define GGML_TENSOR_TERNARY_OP_LOCALS \
	GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
	GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
	GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
	GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) \
	GGML_TENSOR_LOCALS(int64_t, ne2, src2, ne) \
	GGML_TENSOR_LOCALS(size_t, nb2, src2, nb) \
	GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
	GGML_TENSOR_LOCALS(size_t, nb, dst, nb)

	#define GGML_TENSOR_BINARY_OP_LOCALS01 \
	GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
	GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
	GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
	GGML_TENSOR_LOCALS(size_t, nb1, src1, nb)

	#ifdef __cplusplus
	extern "C" {
	#endif

	// Function type used in fatal error callbacks
	typedef void (ggml_abort_callback_t)(const char error_message);

	// Set the abort callback (passing null will restore original abort functionality: printing a message to stdout)
	// Returns the old callback for chaining
	GGML_API ggml_abort_callback_t ggml_set_abort_callback(ggml_abort_callback_t callback);

	GGML_NORETURN GGML_ATTRIBUTE_FORMAT(3, 4)
	GGML_API void ggml_abort(const char * file, int line, const char * fmt, ...);

	enum ggml_status {
	GGML_STATUS_ALLOC_FAILED = -2,
	GGML_STATUS_FAILED = -1,
	GGML_STATUS_SUCCESS = 0,
	GGML_STATUS_ABORTED = 1,
	};

	// get ggml_status name string
	GGML_API const char * ggml_status_to_string(enum ggml_status status);

	// ieee 754-2008 half-precision float16
	// todo: make this not an integral type
	typedef uint16_t ggml_fp16_t;
	GGML_API float ggml_fp16_to_fp32(ggml_fp16_t);
	GGML_API ggml_fp16_t ggml_fp32_to_fp16(float);
	GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t , float , int64_t);
	GGML_API void ggml_fp32_to_fp16_row(const float , ggml_fp16_t , int64_t);

	// google brain half-precision bfloat16
	typedef struct { uint16_t bits; } ggml_bf16_t;
	GGML_API ggml_bf16_t ggml_fp32_to_bf16(float);
	GGML_API float ggml_bf16_to_fp32(ggml_bf16_t); // consider just doing << 16
	GGML_API void ggml_bf16_to_fp32_row(const ggml_bf16_t , float , int64_t);
	GGML_API void ggml_fp32_to_bf16_row_ref(const float , ggml_bf16_t , int64_t);
	GGML_API void ggml_fp32_to_bf16_row(const float , ggml_bf16_t , int64_t);

	struct ggml_object;
	struct ggml_context;
	struct ggml_cgraph;

	// NOTE: always add types at the end of the enum to keep backward compatibility
	enum ggml_type {
	GGML_TYPE_F32 = 0,
	GGML_TYPE_F16 = 1,
	GGML_TYPE_Q4_0 = 2,
	GGML_TYPE_Q4_1 = 3,
	// GGML_TYPE_Q4_2 = 4, support has been removed
	// GGML_TYPE_Q4_3 = 5, support has been removed
	GGML_TYPE_Q5_0 = 6,
	GGML_TYPE_Q5_1 = 7,
	GGML_TYPE_Q8_0 = 8,
	GGML_TYPE_Q8_1 = 9,
	GGML_TYPE_Q2_K = 10,
	GGML_TYPE_Q3_K = 11,
	GGML_TYPE_Q4_K = 12,
	GGML_TYPE_Q5_K = 13,
	GGML_TYPE_Q6_K = 14,
	GGML_TYPE_Q8_K = 15,
	GGML_TYPE_IQ2_XXS = 16,
	GGML_TYPE_IQ2_XS = 17,
	GGML_TYPE_IQ3_XXS = 18,
	GGML_TYPE_IQ1_S = 19,
	GGML_TYPE_IQ4_NL = 20,
	GGML_TYPE_IQ3_S = 21,
	GGML_TYPE_IQ2_S = 22,
	GGML_TYPE_IQ4_XS = 23,
	GGML_TYPE_I8 = 24,
	GGML_TYPE_I16 = 25,
	GGML_TYPE_I32 = 26,
	GGML_TYPE_I64 = 27,
	GGML_TYPE_F64 = 28,
	GGML_TYPE_IQ1_M = 29,
	GGML_TYPE_BF16 = 30,
	// GGML_TYPE_Q4_0_4_4 = 31, support has been removed from gguf files
	// GGML_TYPE_Q4_0_4_8 = 32,
	// GGML_TYPE_Q4_0_8_8 = 33,
	GGML_TYPE_TQ1_0 = 34,
	GGML_TYPE_TQ2_0 = 35,
	// GGML_TYPE_IQ4_NL_4_4 = 36,
	// GGML_TYPE_IQ4_NL_4_8 = 37,
	// GGML_TYPE_IQ4_NL_8_8 = 38,
	GGML_TYPE_MXFP4 = 39, // MXFP4 (1 block)
	GGML_TYPE_NVFP4 = 40, // NVFP4 (4 blocks, E4M3 scale)
	GGML_TYPE_COUNT = 41,
	};

	// precision
	enum ggml_prec {
	GGML_PREC_DEFAULT = 0, // stored as ggml_tensor.op_params, 0 by default
	GGML_PREC_F32 = 10,
	};

	// model file types
	enum ggml_ftype {
	GGML_FTYPE_UNKNOWN = -1,
	GGML_FTYPE_ALL_F32 = 0,
	GGML_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
	GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
	GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
	GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors
	GGML_FTYPE_MOSTLY_IQ2_XS = 16, // except 1d tensors
	GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors
	GGML_FTYPE_MOSTLY_IQ1_S = 18, // except 1d tensors
	GGML_FTYPE_MOSTLY_IQ4_NL = 19, // except 1d tensors
	GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors
	GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors
	GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors
	GGML_FTYPE_MOSTLY_IQ1_M = 23, // except 1d tensors
	GGML_FTYPE_MOSTLY_BF16 = 24, // except 1d tensors
	GGML_FTYPE_MOSTLY_MXFP4 = 25, // except 1d tensors
	GGML_FTYPE_MOSTLY_NVFP4 = 26, // except 1d tensors
	};

	// available tensor operations:
	enum ggml_op {
	GGML_OP_NONE = 0,

	GGML_OP_DUP,
	GGML_OP_ADD,
	GGML_OP_ADD_ID,
	GGML_OP_ADD1,
	GGML_OP_ACC,
	GGML_OP_SUB,
	GGML_OP_MUL,
	GGML_OP_DIV,
	GGML_OP_SQR,
	GGML_OP_SQRT,
	GGML_OP_LOG,
	GGML_OP_SIN,
	GGML_OP_COS,
	GGML_OP_SUM,
	GGML_OP_SUM_ROWS,
	GGML_OP_CUMSUM,
	GGML_OP_MEAN,
	GGML_OP_ARGMAX,
	GGML_OP_COUNT_EQUAL,
	GGML_OP_REPEAT,
	GGML_OP_REPEAT_BACK,
	GGML_OP_CONCAT,
	GGML_OP_SILU_BACK,
	GGML_OP_NORM, // normalize
	GGML_OP_RMS_NORM,
	GGML_OP_RMS_NORM_BACK,
	GGML_OP_GROUP_NORM,
	GGML_OP_L2_NORM,

	GGML_OP_MUL_MAT,
	GGML_OP_MUL_MAT_ID,
	GGML_OP_OUT_PROD,

	GGML_OP_SCALE,
	GGML_OP_SET,
	GGML_OP_CPY,
	GGML_OP_CONT,
	GGML_OP_RESHAPE,
	GGML_OP_VIEW,
	GGML_OP_PERMUTE,
	GGML_OP_TRANSPOSE,
	GGML_OP_GET_ROWS,
	GGML_OP_GET_ROWS_BACK,
	GGML_OP_SET_ROWS,
	GGML_OP_DIAG,
	GGML_OP_DIAG_MASK_INF,
	GGML_OP_DIAG_MASK_ZERO,
	GGML_OP_SOFT_MAX,
	GGML_OP_SOFT_MAX_BACK,
	GGML_OP_ROPE,
	GGML_OP_ROPE_BACK,
	GGML_OP_CLAMP,
	GGML_OP_CONV_TRANSPOSE_1D,
	GGML_OP_IM2COL,
	GGML_OP_IM2COL_BACK,
	GGML_OP_IM2COL_3D,
	GGML_OP_CONV_2D,
	GGML_OP_CONV_3D,
	GGML_OP_CONV_2D_DW,
	GGML_OP_CONV_TRANSPOSE_2D,
	GGML_OP_POOL_1D,
	GGML_OP_POOL_2D,
	GGML_OP_POOL_2D_BACK,
	GGML_OP_UPSCALE,
	GGML_OP_PAD,
	GGML_OP_PAD_REFLECT_1D,
	GGML_OP_ROLL,
	GGML_OP_ARANGE,
	GGML_OP_TIMESTEP_EMBEDDING,
	GGML_OP_ARGSORT,
	GGML_OP_TOP_K,
	GGML_OP_LEAKY_RELU,
	GGML_OP_TRI,
	GGML_OP_FILL,

	GGML_OP_FLASH_ATTN_EXT,
	GGML_OP_FLASH_ATTN_BACK,
	GGML_OP_SSM_CONV,
	GGML_OP_SSM_SCAN,
	GGML_OP_WIN_PART,
	GGML_OP_WIN_UNPART,
	GGML_OP_GET_REL_POS,
	GGML_OP_ADD_REL_POS,
	GGML_OP_RWKV_WKV6,
	GGML_OP_GATED_LINEAR_ATTN,
	GGML_OP_RWKV_WKV7,
	GGML_OP_SOLVE_TRI,
	GGML_OP_GATED_DELTA_NET,

	GGML_OP_UNARY,

	GGML_OP_MAP_CUSTOM1,
	GGML_OP_MAP_CUSTOM2,
	GGML_OP_MAP_CUSTOM3,

	GGML_OP_CUSTOM,

	GGML_OP_CROSS_ENTROPY_LOSS,
	GGML_OP_CROSS_ENTROPY_LOSS_BACK,
	GGML_OP_OPT_STEP_ADAMW,
	GGML_OP_OPT_STEP_SGD,

	GGML_OP_GLU,

	GGML_OP_COUNT,
	};

	enum ggml_unary_op {
	GGML_UNARY_OP_ABS,
	GGML_UNARY_OP_SGN,
	GGML_UNARY_OP_NEG,
	GGML_UNARY_OP_STEP,
	GGML_UNARY_OP_TANH,
	GGML_UNARY_OP_ELU,
	GGML_UNARY_OP_RELU,
	GGML_UNARY_OP_SIGMOID,
	GGML_UNARY_OP_GELU,
	GGML_UNARY_OP_GELU_QUICK,
	GGML_UNARY_OP_SILU,
	GGML_UNARY_OP_HARDSWISH,
	GGML_UNARY_OP_HARDSIGMOID,
	GGML_UNARY_OP_EXP,
	GGML_UNARY_OP_EXPM1,
	GGML_UNARY_OP_SOFTPLUS,
	GGML_UNARY_OP_GELU_ERF,
	GGML_UNARY_OP_XIELU,
	GGML_UNARY_OP_FLOOR,
	GGML_UNARY_OP_CEIL,
	GGML_UNARY_OP_ROUND,
	GGML_UNARY_OP_TRUNC,

	GGML_UNARY_OP_COUNT,
	};

	enum ggml_glu_op {
	GGML_GLU_OP_REGLU,
	GGML_GLU_OP_GEGLU,
	GGML_GLU_OP_SWIGLU,
	GGML_GLU_OP_SWIGLU_OAI,
	GGML_GLU_OP_GEGLU_ERF,
	GGML_GLU_OP_GEGLU_QUICK,

	GGML_GLU_OP_COUNT,
	};

	enum ggml_object_type {
	GGML_OBJECT_TYPE_TENSOR,
	GGML_OBJECT_TYPE_GRAPH,
	GGML_OBJECT_TYPE_WORK_BUFFER
	};

	enum ggml_log_level {
	GGML_LOG_LEVEL_NONE = 0,
	GGML_LOG_LEVEL_DEBUG = 1,
	GGML_LOG_LEVEL_INFO = 2,
	GGML_LOG_LEVEL_WARN = 3,
	GGML_LOG_LEVEL_ERROR = 4,
	GGML_LOG_LEVEL_CONT = 5, // continue previous log
	};

	// this tensor...
	enum ggml_tensor_flag {
	GGML_TENSOR_FLAG_INPUT = 1, // ...is an input for the GGML compute graph
	GGML_TENSOR_FLAG_OUTPUT = 2, // ...is an output for the GGML compute graph
	GGML_TENSOR_FLAG_PARAM = 4, // ...contains trainable parameters
	GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up)
	GGML_TENSOR_FLAG_COMPUTE = 16, // ...must be computed
	};

	enum ggml_tri_type {
	GGML_TRI_TYPE_UPPER_DIAG = 0,
	GGML_TRI_TYPE_UPPER = 1,
	GGML_TRI_TYPE_LOWER_DIAG = 2,
	GGML_TRI_TYPE_LOWER = 3
	};

	struct ggml_init_params {
	// memory pool
	size_t mem_size; // bytes
	void * mem_buffer; // if NULL, memory will be allocated internally
	bool no_alloc; // don't allocate memory for the tensor data
	};

	// n-dimensional tensor
	struct ggml_tensor {
	enum ggml_type type;

	struct ggml_backend_buffer * buffer;

	int64_t ne[GGML_MAX_DIMS]; // number of elements
	size_t nb[GGML_MAX_DIMS]; // stride in bytes:
	// nb[0] = ggml_type_size(type)
	// nb[1] = nb[0] * (ne[0] / ggml_blck_size(type)) + padding
	// nb[i] = nb[i-1] * ne[i-1]

	// compute data
	enum ggml_op op;

	// op params - allocated as int32_t for alignment
	int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];

	int32_t flags;

	struct ggml_tensor * src[GGML_MAX_SRC];

	// source tensor and offset for views
	struct ggml_tensor * view_src;
	size_t view_offs;

	void * data;

	char name[GGML_MAX_NAME];

	void * extra; // extra things e.g. for ggml-cuda.cu

	char padding[8];
	};

	static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);

	// Abort callback
	// If not NULL, called before ggml computation
	// If it returns true, the computation is aborted
	typedef bool (ggml_abort_callback)(void data);


	//
	// GUID
	//

	// GUID types
	typedef uint8_t ggml_guid[16];
	typedef ggml_guid * ggml_guid_t;

	GGML_API bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b);

	// misc

	GGML_API const char * ggml_version(void);
	GGML_API const char * ggml_commit(void);

	GGML_API void ggml_time_init(void); // call this once at the beginning of the program
	GGML_API int64_t ggml_time_ms(void);
	GGML_API int64_t ggml_time_us(void);
	GGML_API int64_t ggml_cycles(void);
	GGML_API int64_t ggml_cycles_per_ms(void);

	// accepts a UTF-8 path, even on Windows
	GGML_API FILE * ggml_fopen(const char * fname, const char * mode);

	GGML_API void ggml_print_object (const struct ggml_object * obj);
	GGML_API void ggml_print_objects(const struct ggml_context * ctx);

	GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);
	GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);
	GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
	GGML_API size_t ggml_nbytes_pad(const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN

	GGML_API int64_t ggml_blck_size(enum ggml_type type);
	GGML_API size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
	GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row

	GGML_DEPRECATED(
	GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
	"use ggml_row_size() instead");

	GGML_API const char * ggml_type_name(enum ggml_type type);
	GGML_API const char * ggml_op_name (enum ggml_op op);
	GGML_API const char * ggml_op_symbol(enum ggml_op op);

	GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
	GGML_API const char * ggml_glu_op_name(enum ggml_glu_op op);
	GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name

	GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);

	GGML_API bool ggml_is_quantized(enum ggml_type type);

	// TODO: temporary until model loading of ggml examples is refactored
	GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);

	GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
	GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
	GGML_API bool ggml_is_empty (const struct ggml_tensor * tensor);
	GGML_API bool ggml_is_view (const struct ggml_tensor * tensor);
	GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor);
	GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor);
	GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor);
	GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor);
	GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars

	// returns whether the tensor elements can be iterated over with a flattened index (no gaps, no permutation)
	GGML_API bool ggml_is_contiguous (const struct ggml_tensor * tensor);
	GGML_API bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous()
	GGML_API bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1
	GGML_API bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2

	// returns whether the tensor elements are allocated as one contiguous block of memory (no gaps, but permutation ok)
	GGML_API bool ggml_is_contiguously_allocated(const struct ggml_tensor * tensor);

	// true for tensor that is stored in memory as CxWxHxN and has been permuted to WxHxCxN
	GGML_API bool ggml_is_contiguous_channels(const struct ggml_tensor * tensor);

	// true if the elements in dimension 0 are contiguous, or there is just 1 block of elements
	GGML_API bool ggml_is_contiguous_rows(const struct ggml_tensor * tensor);

	GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1);
	GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1);

	GGML_API bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1);

	// use this to compute the memory overhead of a tensor
	GGML_API size_t ggml_tensor_overhead(void);

	GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes);

	// main

	GGML_API struct ggml_context * ggml_init (struct ggml_init_params params);
	GGML_API void ggml_reset(struct ggml_context * ctx);
	GGML_API void ggml_free (struct ggml_context * ctx);

	GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);

	GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx);
	GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);

	GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx);
	GGML_API size_t ggml_get_mem_size (const struct ggml_context * ctx);
	GGML_API size_t ggml_get_max_tensor_size(const struct ggml_context * ctx);

	GGML_API struct ggml_tensor * ggml_new_tensor(
	struct ggml_context * ctx,
	enum ggml_type type,
	int n_dims,
	const int64_t *ne);

	GGML_API struct ggml_tensor * ggml_new_tensor_1d(
	struct ggml_context * ctx,
	enum ggml_type type,
	int64_t ne0);

	GGML_API struct ggml_tensor * ggml_new_tensor_2d(
	struct ggml_context * ctx,
	enum ggml_type type,
	int64_t ne0,
	int64_t ne1);

	GGML_API struct ggml_tensor * ggml_new_tensor_3d(
	struct ggml_context * ctx,
	enum ggml_type type,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2);

	GGML_API struct ggml_tensor * ggml_new_tensor_4d(
	struct ggml_context * ctx,
	enum ggml_type type,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2,
	int64_t ne3);

	GGML_API void * ggml_new_buffer(struct ggml_context * ctx, size_t nbytes);

	GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
	GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);

	// Context tensor enumeration and lookup
	GGML_API struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx);
	GGML_API struct ggml_tensor * ggml_get_next_tensor (const struct ggml_context * ctx, struct ggml_tensor * tensor);
	GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);

	// Converts a flat index into coordinates
	GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3);

	GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
	GGML_API enum ggml_glu_op ggml_get_glu_op(const struct ggml_tensor * tensor);

	GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
	GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);

	GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor);
	GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name);
	GGML_ATTRIBUTE_FORMAT(2, 3)
	GGML_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...);

	// Tensor flags
	GGML_API void ggml_set_input(struct ggml_tensor * tensor);
	GGML_API void ggml_set_output(struct ggml_tensor * tensor);
	GGML_API void ggml_set_param(struct ggml_tensor * tensor);
	GGML_API void ggml_set_loss(struct ggml_tensor * tensor);

	//
	// operations on tensors with backpropagation
	//

	GGML_API struct ggml_tensor * ggml_dup(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// in-place, returns view(a)
	GGML_API struct ggml_tensor * ggml_dup_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_add(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_add_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_add_cast(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	enum ggml_type type);

	// dst[i0, i1, i2] = a[i0, i1, i2] + b[i0, ids[i1, i2]]
	GGML_API struct ggml_tensor * ggml_add_id(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	struct ggml_tensor * ids);

	GGML_API struct ggml_tensor * ggml_add1(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_add1_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	// dst = a
	// view(dst, nb1, nb2, nb3, offset) += b
	// return dst
	GGML_API struct ggml_tensor * ggml_acc(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	size_t nb1,
	size_t nb2,
	size_t nb3,
	size_t offset);

	GGML_API struct ggml_tensor * ggml_acc_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	size_t nb1,
	size_t nb2,
	size_t nb3,
	size_t offset);

	GGML_API struct ggml_tensor * ggml_sub(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_sub_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_mul(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_mul_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_div(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_div_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_sqr(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_sqr_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_sqrt(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_sqrt_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_log(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_log_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_expm1(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_expm1_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_softplus(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_softplus_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_sin(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_sin_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_cos(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_cos_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// return scalar
	GGML_API struct ggml_tensor * ggml_sum(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// sums along rows, with input shape [a,b,c,d] return shape [1,b,c,d]
	GGML_API struct ggml_tensor * ggml_sum_rows(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_cumsum(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// mean along rows
	GGML_API struct ggml_tensor * ggml_mean(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// argmax along rows
	GGML_API struct ggml_tensor * ggml_argmax(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// count number of equal elements in a and b
	GGML_API struct ggml_tensor * ggml_count_equal(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	// if a is the same shape as b, and a is not parameter, return a
	// otherwise, return a new tensor: repeat(a) to fit in b
	GGML_API struct ggml_tensor * ggml_repeat(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	// repeat a to the specified shape
	GGML_API struct ggml_tensor * ggml_repeat_4d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2,
	int64_t ne3);

	// sums repetitions in a into shape of b
	GGML_API struct ggml_tensor * ggml_repeat_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b); // sum up values that are adjacent in dims > 0 instead of repeated with same stride

	// concat a and b along dim
	// used in stable-diffusion
	GGML_API struct ggml_tensor * ggml_concat(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	int dim);

	GGML_API struct ggml_tensor * ggml_abs(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_abs_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_sgn(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_sgn_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_neg(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_neg_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_step(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_step_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_tanh(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_tanh_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_elu(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_elu_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_relu(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_leaky_relu(
	struct ggml_context * ctx,
	struct ggml_tensor * a, float negative_slope, bool inplace);

	GGML_API struct ggml_tensor * ggml_relu_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_sigmoid(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_sigmoid_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_gelu(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_gelu_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// GELU using erf (error function) when possible
	// some backends may fallback to approximation based on Abramowitz and Stegun formula
	GGML_API struct ggml_tensor * ggml_gelu_erf(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_gelu_erf_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_gelu_quick(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_gelu_quick_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_silu(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_silu_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// a - x
	// b - dy
	GGML_API struct ggml_tensor * ggml_silu_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	// hardswish(x) = x * relu6(x + 3) / 6
	GGML_API struct ggml_tensor * ggml_hardswish(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// hardsigmoid(x) = relu6(x + 3) / 6
	GGML_API struct ggml_tensor * ggml_hardsigmoid(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_exp(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_exp_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_floor(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_floor_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_ceil(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_ceil_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_round(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_round_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	/**
	* Truncates the fractional part of each element in the tensor (towards zero).
	* For example: trunc(3.7) = 3.0, trunc(-2.9) = -2.0
	* Similar to std::trunc in C/C++.
	*/

	GGML_API struct ggml_tensor * ggml_trunc(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_trunc_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);



	// xIELU activation function
	// x = x * (c_a(alpha_n) + c_b(alpha_p, beta) * sigmoid(beta * x)) + eps * (x > 0)
	// where c_a = softplus and c_b(a, b) = softplus(a) + b are constraining functions
	// that constrain the positive and negative source alpha values respectively
	GGML_API struct ggml_tensor * ggml_xielu(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float alpha_n,
	float alpha_p,
	float beta,
	float eps);

	// gated linear unit ops
	// A: n columns, r rows,
	// result is n / 2 columns, r rows,
	// expects gate in second half of row, unless swapped is true
	GGML_API struct ggml_tensor * ggml_glu(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	enum ggml_glu_op op,
	bool swapped);

	GGML_API struct ggml_tensor * ggml_reglu(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_reglu_swapped(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_geglu(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_geglu_swapped(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_swiglu(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_swiglu_swapped(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_geglu_erf(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_geglu_erf_swapped(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_geglu_quick(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	GGML_API struct ggml_tensor * ggml_geglu_quick_swapped(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// A: n columns, r rows,
	// B: n columns, r rows,
	GGML_API struct ggml_tensor * ggml_glu_split(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	enum ggml_glu_op op);

	GGML_API struct ggml_tensor * ggml_reglu_split(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_geglu_split(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_swiglu_split(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_geglu_erf_split(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_geglu_quick_split(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	GGML_API struct ggml_tensor * ggml_swiglu_oai(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	float alpha,
	float limit);

	// normalize along rows
	GGML_API struct ggml_tensor * ggml_norm(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float eps);

	GGML_API struct ggml_tensor * ggml_norm_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float eps);

	GGML_API struct ggml_tensor * ggml_rms_norm(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float eps);

	GGML_API struct ggml_tensor * ggml_rms_norm_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float eps);

	// group normalize along ne0ne1n_groups
	// used in stable-diffusion
	GGML_API struct ggml_tensor * ggml_group_norm(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int n_groups,
	float eps);

	GGML_API struct ggml_tensor * ggml_group_norm_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int n_groups,
	float eps);

	// l2 normalize along rows
	// used in rwkv v7
	GGML_API struct ggml_tensor * ggml_l2_norm(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float eps);

	GGML_API struct ggml_tensor * ggml_l2_norm_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float eps);

	// a - x
	// b - dy
	GGML_API struct ggml_tensor * ggml_rms_norm_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	float eps);

	// A: k columns, n rows => [ne03, ne02, n, k]
	// B: k columns, m rows (i.e. we transpose it internally) => [ne03 * x, ne02 * y, m, k]
	// result is n columns, m rows => [ne03 * x, ne02 * y, m, n]
	GGML_API struct ggml_tensor * ggml_mul_mat(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	// change the precision of a matrix multiplication
	// set to GGML_PREC_F32 for higher precision (useful for phi-2)
	GGML_API void ggml_mul_mat_set_prec(
	struct ggml_tensor * a,
	enum ggml_prec prec);

	// indirect matrix multiplication
	GGML_API struct ggml_tensor * ggml_mul_mat_id(
	struct ggml_context * ctx,
	struct ggml_tensor * as,
	struct ggml_tensor * b,
	struct ggml_tensor * ids);

	// A: m columns, n rows,
	// B: p columns, n rows,
	// result is m columns, p rows
	GGML_API struct ggml_tensor * ggml_out_prod(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	//
	// operations on tensors without backpropagation
	//

	GGML_API struct ggml_tensor * ggml_scale(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float s);

	// in-place, returns view(a)
	GGML_API struct ggml_tensor * ggml_scale_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float s);

	// x = s * a + b
	GGML_API struct ggml_tensor * ggml_scale_bias(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float s,
	float b);

	GGML_API struct ggml_tensor * ggml_scale_bias_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float s,
	float b);

	// b -> view(a,offset,nb1,nb2,3), return modified a
	GGML_API struct ggml_tensor * ggml_set(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	size_t nb1,
	size_t nb2,
	size_t nb3,
	size_t offset); // in bytes

	// b -> view(a,offset,nb1,nb2,3), return view(a)
	GGML_API struct ggml_tensor * ggml_set_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	size_t nb1,
	size_t nb2,
	size_t nb3,
	size_t offset); // in bytes

	GGML_API struct ggml_tensor * ggml_set_1d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	size_t offset); // in bytes

	GGML_API struct ggml_tensor * ggml_set_1d_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	size_t offset); // in bytes

	// b -> view(a,offset,nb1,nb2,3), return modified a
	GGML_API struct ggml_tensor * ggml_set_2d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	size_t nb1,
	size_t offset); // in bytes

	// b -> view(a,offset,nb1,nb2,3), return view(a)
	GGML_API struct ggml_tensor * ggml_set_2d_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	size_t nb1,
	size_t offset); // in bytes

	// a -> b, return view(b)
	GGML_API struct ggml_tensor * ggml_cpy(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	// note: casting from f32 to i32 will discard the fractional part
	GGML_API struct ggml_tensor * ggml_cast(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	enum ggml_type type);

	// make contiguous
	GGML_API struct ggml_tensor * ggml_cont(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// make contiguous, with new shape
	GGML_API struct ggml_tensor * ggml_cont_1d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0);

	GGML_API struct ggml_tensor * ggml_cont_2d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1);

	GGML_API struct ggml_tensor * ggml_cont_3d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2);

	GGML_API struct ggml_tensor * ggml_cont_4d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2,
	int64_t ne3);

	// return view(a), b specifies the new shape
	// TODO: when we start computing gradient, make a copy instead of view
	GGML_API struct ggml_tensor * ggml_reshape(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	// return view(a)
	// TODO: when we start computing gradient, make a copy instead of view
	GGML_API struct ggml_tensor * ggml_reshape_1d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0);

	GGML_API struct ggml_tensor * ggml_reshape_2d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1);

	// return view(a)
	// TODO: when we start computing gradient, make a copy instead of view
	GGML_API struct ggml_tensor * ggml_reshape_3d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2);

	GGML_API struct ggml_tensor * ggml_reshape_4d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2,
	int64_t ne3);

	// offset in bytes
	GGML_API struct ggml_tensor * ggml_view_1d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	size_t offset);

	GGML_API struct ggml_tensor * ggml_view_2d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1,
	size_t nb1, // row stride in bytes
	size_t offset);

	GGML_API struct ggml_tensor * ggml_view_3d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2,
	size_t nb1, // row stride in bytes
	size_t nb2, // slice stride in bytes
	size_t offset);

	GGML_API struct ggml_tensor * ggml_view_4d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2,
	int64_t ne3,
	size_t nb1, // row stride in bytes
	size_t nb2, // slice stride in bytes
	size_t nb3,
	size_t offset);

	GGML_API struct ggml_tensor * ggml_permute(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int axis0,
	int axis1,
	int axis2,
	int axis3);

	// alias for ggml_permute(ctx, a, 1, 0, 2, 3)
	GGML_API struct ggml_tensor * ggml_transpose(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// supports 4D a:
	// a [n_embd, ne1, ne2, ne3]
	// b I32 [n_rows, ne2, ne3, 1]
	//
	// return [n_embd, n_rows, ne2, ne3]
	GGML_API struct ggml_tensor * ggml_get_rows(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // data
	struct ggml_tensor * b); // row indices

	GGML_API struct ggml_tensor * ggml_get_rows_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // gradients of ggml_get_rows result
	struct ggml_tensor * b, // row indices
	struct ggml_tensor * c); // data for ggml_get_rows, only used for its shape

	// a TD [n_embd, ne1, ne2, ne3]
	// b TS [n_embd, n_rows, ne02, ne03] \| ne02 == ne2, ne03 == ne3
	// c I64 [n_rows, ne11, ne12, 1] \| c[i] in [0, ne1)
	//
	// undefined behavior if destination rows overlap
	//
	// broadcast:
	// ne2 % ne11 == 0
	// ne3 % ne12 == 0
	//
	// return view(a)
	GGML_API struct ggml_tensor * ggml_set_rows(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // destination
	struct ggml_tensor * b, // source
	struct ggml_tensor * c); // row indices

	GGML_API struct ggml_tensor * ggml_diag(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// set elements above the diagonal to -INF
	GGML_API struct ggml_tensor * ggml_diag_mask_inf(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int n_past);

	// in-place, returns view(a)
	GGML_API struct ggml_tensor * ggml_diag_mask_inf_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int n_past);

	// set elements above the diagonal to 0
	GGML_API struct ggml_tensor * ggml_diag_mask_zero(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int n_past);

	// in-place, returns view(a)
	GGML_API struct ggml_tensor * ggml_diag_mask_zero_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int n_past);

	GGML_API struct ggml_tensor * ggml_soft_max(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// in-place, returns view(a)
	GGML_API struct ggml_tensor * ggml_soft_max_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a);

	// a [ne0, ne01, ne02, ne03]
	// mask [ne0, ne11, ne12, ne13] \| ne11 >= ne01, F16 or F32, optional
	//
	// broadcast:
	// ne02 % ne12 == 0
	// ne03 % ne13 == 0
	//
	// fused soft_max(ascale + mask(ALiBi slope))
	// max_bias = 0.0f for no ALiBi
	GGML_API struct ggml_tensor * ggml_soft_max_ext(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * mask,
	float scale,
	float max_bias);

	GGML_API struct ggml_tensor * ggml_soft_max_ext_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * mask,
	float scale,
	float max_bias);

	GGML_API void ggml_soft_max_add_sinks(
	struct ggml_tensor * a,
	struct ggml_tensor * sinks);

	GGML_API struct ggml_tensor * ggml_soft_max_ext_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	float scale,
	float max_bias);

	// in-place, returns view(a)
	GGML_API struct ggml_tensor * ggml_soft_max_ext_back_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	float scale,
	float max_bias);

	// rotary position embedding
	// if (mode & 1) - skip n_past elements (NOT SUPPORTED)
	// if (mode & GGML_ROPE_TYPE_NEOX) - GPT-NeoX style
	//
	// b is an int32 vector with size a->ne[2], it contains the positions
	GGML_API struct ggml_tensor * ggml_rope(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	int n_dims,
	int mode);

	// in-place, returns view(a)
	GGML_API struct ggml_tensor * ggml_rope_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	int n_dims,
	int mode);

	// custom RoPE
	// c is freq factors (e.g. phi3-128k), (optional)
	GGML_API struct ggml_tensor * ggml_rope_ext(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	struct ggml_tensor * c,
	int n_dims,
	int mode,
	int n_ctx_orig,
	float freq_base,
	float freq_scale,
	float ext_factor,
	float attn_factor,
	float beta_fast,
	float beta_slow);

	GGML_API struct ggml_tensor * ggml_rope_multi(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	struct ggml_tensor * c,
	int n_dims,
	int sections[GGML_MROPE_SECTIONS],
	int mode,
	int n_ctx_orig,
	float freq_base,
	float freq_scale,
	float ext_factor,
	float attn_factor,
	float beta_fast,
	float beta_slow);

	// in-place, returns view(a)
	GGML_API struct ggml_tensor * ggml_rope_ext_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	struct ggml_tensor * c,
	int n_dims,
	int mode,
	int n_ctx_orig,
	float freq_base,
	float freq_scale,
	float ext_factor,
	float attn_factor,
	float beta_fast,
	float beta_slow);

	GGML_API struct ggml_tensor * ggml_rope_multi_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	struct ggml_tensor * c,
	int n_dims,
	int sections[GGML_MROPE_SECTIONS],
	int mode,
	int n_ctx_orig,
	float freq_base,
	float freq_scale,
	float ext_factor,
	float attn_factor,
	float beta_fast,
	float beta_slow);

	GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	int n_dims,
	int mode,
	int n_ctx_orig,
	float freq_base,
	float freq_scale,
	float ext_factor,
	float attn_factor,
	float beta_fast,
	float beta_slow),
	"use ggml_rope_ext instead");

	GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	int n_dims,
	int mode,
	int n_ctx_orig,
	float freq_base,
	float freq_scale,
	float ext_factor,
	float attn_factor,
	float beta_fast,
	float beta_slow),
	"use ggml_rope_ext_inplace instead");

	// compute correction dims for YaRN RoPE scaling
	GGML_API void ggml_rope_yarn_corr_dims(
	int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]);

	// rotary position embedding backward, i.e compute dx from dy
	// a - dy
	GGML_API struct ggml_tensor * ggml_rope_ext_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // gradients of ggml_rope result
	struct ggml_tensor * b, // positions
	struct ggml_tensor * c, // freq factors
	int n_dims,
	int mode,
	int n_ctx_orig,
	float freq_base,
	float freq_scale,
	float ext_factor,
	float attn_factor,
	float beta_fast,
	float beta_slow);

	GGML_API struct ggml_tensor * ggml_rope_multi_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	struct ggml_tensor * c,
	int n_dims,
	int sections[4],
	int mode,
	int n_ctx_orig,
	float freq_base,
	float freq_scale,
	float ext_factor,
	float attn_factor,
	float beta_fast,
	float beta_slow);


	// clamp
	// in-place, returns view(a)
	GGML_API struct ggml_tensor * ggml_clamp(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float min,
	float max);

	// im2col
	// converts data into a format that effectively results in a convolution when combined with matrix multiplication
	GGML_API struct ggml_tensor * ggml_im2col(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel
	struct ggml_tensor * b, // data
	int s0, // stride dimension 0
	int s1, // stride dimension 1
	int p0, // padding dimension 0
	int p1, // padding dimension 1
	int d0, // dilation dimension 0
	int d1, // dilation dimension 1
	bool is_2D,
	enum ggml_type dst_type);

	GGML_API struct ggml_tensor * ggml_im2col_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel
	struct ggml_tensor * b, // gradient of im2col output
	int64_t * ne, // shape of im2col input
	int s0, // stride dimension 0
	int s1, // stride dimension 1
	int p0, // padding dimension 0
	int p1, // padding dimension 1
	int d0, // dilation dimension 0
	int d1, // dilation dimension 1
	bool is_2D);

	GGML_API struct ggml_tensor * ggml_conv_1d(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel
	struct ggml_tensor * b, // data
	int s0, // stride
	int p0, // padding
	int d0); // dilation

	// conv_1d with padding = half
	// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
	GGML_API struct ggml_tensor* ggml_conv_1d_ph(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel
	struct ggml_tensor * b, // data
	int s, // stride
	int d); // dilation

	// depthwise
	// TODO: this is very likely wrong for some cases! - needs more testing
	GGML_API struct ggml_tensor * ggml_conv_1d_dw(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel
	struct ggml_tensor * b, // data
	int s0, // stride
	int p0, // padding
	int d0); // dilation

	GGML_API struct ggml_tensor * ggml_conv_1d_dw_ph(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel
	struct ggml_tensor * b, // data
	int s0, // stride
	int d0); // dilation

	GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel
	struct ggml_tensor * b, // data
	int s0, // stride
	int p0, // padding
	int d0); // dilation

	GGML_API struct ggml_tensor * ggml_conv_2d(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel
	struct ggml_tensor * b, // data
	int s0, // stride dimension 0
	int s1, // stride dimension 1
	int p0, // padding dimension 0
	int p1, // padding dimension 1
	int d0, // dilation dimension 0
	int d1); // dilation dimension 1

	GGML_API struct ggml_tensor * ggml_im2col_3d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	int64_t IC,
	int s0, // stride width
	int s1, // stride height
	int s2, // stride depth
	int p0, // padding width
	int p1, // padding height
	int p2, // padding depth
	int d0, // dilation width
	int d1, // dilation height
	int d2, // dilation depth
	enum ggml_type dst_type);

	// a: [OC*IC, KD, KH, KW]
	// b: [N*IC, ID, IH, IW]
	// result: [N*OC, OD, OH, OW]
	GGML_API struct ggml_tensor * ggml_conv_3d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	int64_t IC,
	int s0, // stride width
	int s1, // stride height
	int s2, // stride depth
	int p0, // padding width
	int p1, // padding height
	int p2, // padding depth
	int d0, // dilation width
	int d1, // dilation height
	int d2 // dilation depth
	);

	// kernel size is a->ne[0] x a->ne[1]
	// stride is equal to kernel size
	// padding is zero
	// example:
	// a: 16 16 3 768
	// b: 1024 1024 3 1
	// res: 64 64 768 1
	// used in sam
	GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	// kernel size is a->ne[0] x a->ne[1]
	// stride is 1
	// padding is half
	// example:
	// a: 3 3 256 256
	// b: 64 64 256 1
	// res: 64 64 256 1
	// used in sam
	GGML_API struct ggml_tensor * ggml_conv_2d_s1_ph(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b);

	// depthwise (via im2col and mul_mat)
	GGML_API struct ggml_tensor * ggml_conv_2d_dw(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel
	struct ggml_tensor * b, // data
	int s0, // stride dimension 0
	int s1, // stride dimension 1
	int p0, // padding dimension 0
	int p1, // padding dimension 1
	int d0, // dilation dimension 0
	int d1); // dilation dimension 1

	// Depthwise 2D convolution
	// may be faster than ggml_conv_2d_dw, but not available in all backends
	// a: KW KH 1 C convolution kernel
	// b: W H C N input data
	// res: W_out H_out C N
	GGML_API struct ggml_tensor * ggml_conv_2d_dw_direct(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	int stride0,
	int stride1,
	int pad0,
	int pad1,
	int dilation0,
	int dilation1);

	GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	int stride);

	GGML_API struct ggml_tensor * ggml_conv_2d_direct(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // convolution kernel [KW, KH, IC, OC]
	struct ggml_tensor * b, // input data [W, H, C, N]
	int s0, // stride dimension 0
	int s1, // stride dimension 1
	int p0, // padding dimension 0
	int p1, // padding dimension 1
	int d0, // dilation dimension 0
	int d1); // dilation dimension 1

	GGML_API struct ggml_tensor * ggml_conv_3d_direct(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // kernel [KW, KH, KD, IC * OC]
	struct ggml_tensor * b, // input [W, H, D, C * N]
	int s0, // stride
	int s1,
	int s2,
	int p0, // padding
	int p1,
	int p2,
	int d0, // dilation
	int d1,
	int d2,
	int n_channels,
	int n_batch,
	int n_channels_out);

	enum ggml_op_pool {
	GGML_OP_POOL_MAX,
	GGML_OP_POOL_AVG,
	GGML_OP_POOL_COUNT,
	};

	GGML_API struct ggml_tensor * ggml_pool_1d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	enum ggml_op_pool op,
	int k0, // kernel size
	int s0, // stride
	int p0); // padding

	// the result will have 2*p0 padding for the first dimension
	// and 2*p1 padding for the second dimension
	GGML_API struct ggml_tensor * ggml_pool_2d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	enum ggml_op_pool op,
	int k0,
	int k1,
	int s0,
	int s1,
	float p0,
	float p1);

	GGML_API struct ggml_tensor * ggml_pool_2d_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * af, // "a"/input used in forward pass
	enum ggml_op_pool op,
	int k0,
	int k1,
	int s0,
	int s1,
	float p0,
	float p1);

	enum ggml_scale_mode {
	GGML_SCALE_MODE_NEAREST = 0,
	GGML_SCALE_MODE_BILINEAR = 1,
	GGML_SCALE_MODE_BICUBIC = 2,

	GGML_SCALE_MODE_COUNT
	};

	enum ggml_scale_flag {
	GGML_SCALE_FLAG_ALIGN_CORNERS = (1 << 8),
	GGML_SCALE_FLAG_ANTIALIAS = (1 << 9),
	};

	// interpolate
	// multiplies ne0 and ne1 by scale factor
	GGML_API struct ggml_tensor * ggml_upscale(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int scale_factor,
	enum ggml_scale_mode mode);

	// interpolate
	// interpolate scale to specified dimensions
	GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_upscale_ext(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int ne0,
	int ne1,
	int ne2,
	int ne3,
	enum ggml_scale_mode mode),
	"use ggml_interpolate instead");

	// Up- or downsamples the input to the specified size.
	// 2D scale modes (eg. bilinear) are applied to the first two dimensions.
	GGML_API struct ggml_tensor * ggml_interpolate(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2,
	int64_t ne3,
	uint32_t mode); // ggml_scale_mode [ \| ggml_scale_flag...]

	// pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0]
	GGML_API struct ggml_tensor * ggml_pad(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int p0,
	int p1,
	int p2,
	int p3);

	// pad each dimension with values on the other side of the torus (looping around)
	GGML_API struct ggml_tensor * ggml_pad_circular(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int p0,
	int p1,
	int p2,
	int p3);

	GGML_API struct ggml_tensor * ggml_pad_ext(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int lp0,
	int rp0,
	int lp1,
	int rp1,
	int lp2,
	int rp2,
	int lp3,
	int rp3
	);

	// pad each dimension with values on the other side of the torus (looping around)
	GGML_API struct ggml_tensor * ggml_pad_ext_circular(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int lp0,
	int rp0,
	int lp1,
	int rp1,
	int lp2,
	int rp2,
	int lp3,
	int rp3);

	// pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c]
	GGML_API struct ggml_tensor * ggml_pad_reflect_1d(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int p0,
	int p1);

	// Move tensor elements by an offset given for each dimension. Elements that
	// are shifted beyond the last position are wrapped around to the beginning.
	GGML_API struct ggml_tensor * ggml_roll(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int shift0,
	int shift1,
	int shift2,
	int shift3);

	// Convert matrix into a triangular one (upper, strict upper, lower or strict lower) by writing
	// zeroes everywhere outside the masked area
	GGML_API struct ggml_tensor * ggml_tri(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	enum ggml_tri_type type);

	// Fill tensor a with constant c
	GGML_API struct ggml_tensor * ggml_fill(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float c);

	GGML_API struct ggml_tensor * ggml_fill_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	float c);

	// Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
	// timesteps: [N,]
	// return: [N, dim]
	GGML_API struct ggml_tensor * ggml_timestep_embedding(
	struct ggml_context * ctx,
	struct ggml_tensor * timesteps,
	int dim,
	int max_period);

	// sort rows
	enum ggml_sort_order {
	GGML_SORT_ORDER_ASC,
	GGML_SORT_ORDER_DESC,
	};

	GGML_API struct ggml_tensor * ggml_argsort(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	enum ggml_sort_order order);

	// similar to ggml_top_k but implemented as `argsort` + `view`
	GGML_API struct ggml_tensor * ggml_argsort_top_k(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int k);

	// top k elements per row
	// note: the resulting top k indices are in no particular order
	GGML_API struct ggml_tensor * ggml_top_k(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int k);

	GGML_API struct ggml_tensor * ggml_arange(
	struct ggml_context * ctx,
	float start,
	float stop,
	float step);

	// q: [n_embd_k, n_batch, n_head, ne3 ]
	// k: [n_embd_k, n_kv, n_head_kv, ne3 ]
	// v: [n_embd_v, n_kv, n_head_kv, ne3 ] !! not transposed !!
	// mask: [n_kv, n_batch, ne32, ne33]
	// res: [n_embd_v, n_head, n_batch, ne3 ] !! permuted !!
	//
	// broadcast:
	// n_head % n_head_kv == 0
	// n_head % ne32 == 0
	// ne3 % ne33 == 0
	//
	GGML_API struct ggml_tensor * ggml_flash_attn_ext(
	struct ggml_context * ctx,
	struct ggml_tensor * q,
	struct ggml_tensor * k,
	struct ggml_tensor * v,
	struct ggml_tensor * mask,
	float scale,
	float max_bias,
	float logit_softcap);

	GGML_API void ggml_flash_attn_ext_set_prec(
	struct ggml_tensor * a,
	enum ggml_prec prec);

	GGML_API enum ggml_prec ggml_flash_attn_ext_get_prec(
	const struct ggml_tensor * a);

	GGML_API void ggml_flash_attn_ext_add_sinks(
	struct ggml_tensor * a,
	struct ggml_tensor * sinks);

	// TODO: needs to be adapted to ggml_flash_attn_ext
	GGML_API struct ggml_tensor * ggml_flash_attn_back(
	struct ggml_context * ctx,
	struct ggml_tensor * q,
	struct ggml_tensor * k,
	struct ggml_tensor * v,
	struct ggml_tensor * d,
	bool masked);

	GGML_API struct ggml_tensor * ggml_ssm_conv(
	struct ggml_context * ctx,
	struct ggml_tensor * sx,
	struct ggml_tensor * c);

	GGML_API struct ggml_tensor * ggml_ssm_scan(
	struct ggml_context * ctx,
	struct ggml_tensor * s,
	struct ggml_tensor * x,
	struct ggml_tensor * dt,
	struct ggml_tensor * A,
	struct ggml_tensor * B,
	struct ggml_tensor * C,
	struct ggml_tensor * ids);

	// partition into non-overlapping windows with padding if needed
	// example:
	// a: 768 64 64 1
	// w: 14
	// res: 768 14 14 25
	// used in sam
	GGML_API struct ggml_tensor * ggml_win_part(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int w);

	// reverse of ggml_win_part
	// used in sam
	GGML_API struct ggml_tensor * ggml_win_unpart(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int w0,
	int h0,
	int w);

	GGML_API struct ggml_tensor * ggml_unary(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	enum ggml_unary_op op);

	GGML_API struct ggml_tensor * ggml_unary_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	enum ggml_unary_op op);

	// used in sam
	GGML_API struct ggml_tensor * ggml_get_rel_pos(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	int qh,
	int kh);

	// used in sam
	GGML_API struct ggml_tensor * ggml_add_rel_pos(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * pw,
	struct ggml_tensor * ph);

	GGML_API struct ggml_tensor * ggml_add_rel_pos_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * pw,
	struct ggml_tensor * ph);

	GGML_API struct ggml_tensor * ggml_rwkv_wkv6(
	struct ggml_context * ctx,
	struct ggml_tensor * k,
	struct ggml_tensor * v,
	struct ggml_tensor * r,
	struct ggml_tensor * tf,
	struct ggml_tensor * td,
	struct ggml_tensor * state);

	GGML_API struct ggml_tensor * ggml_gated_linear_attn(
	struct ggml_context * ctx,
	struct ggml_tensor * k,
	struct ggml_tensor * v,
	struct ggml_tensor * q,
	struct ggml_tensor * g,
	struct ggml_tensor * state,
	float scale);

	GGML_API struct ggml_tensor * ggml_rwkv_wkv7(
	struct ggml_context * ctx,
	struct ggml_tensor * r,
	struct ggml_tensor * w,
	struct ggml_tensor * k,
	struct ggml_tensor * v,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	struct ggml_tensor * state);

	/* Solves a specific equation of the form Ax=B, where A is a triangular matrix
	* without zeroes on the diagonal (i.e. invertible).
	* B can have any number of columns, but must have the same number of rows as A
	* If A is [n, n] and B is [n, m], then the result will be [n, m] as well
	* Has O(n^3) complexity (unlike most matrix ops out there), so use on cases
	* where n > 100 sparingly, pre-chunk if necessary.
	*
	* If left = false, solves xA=B instead
	* If lower = false, assumes upper triangular instead
	* If uni = true, assumes diagonal of A to be all ones (will override actual values)
	*
	* TODO: currently only lower, right, non-unitriangular variant is implemented
	*/
	GGML_API struct ggml_tensor * ggml_solve_tri(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	bool left,
	bool lower,
	bool uni);

	// TODO: add ggml_gated_delta_net_set_bcast() to be able to configure Q, K broadcast type: tiled vs interleaved [TAG_GGML_GDN_BCAST]
	// ref: https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
	GGML_API struct ggml_tensor * ggml_gated_delta_net(
	struct ggml_context * ctx,
	struct ggml_tensor * q,
	struct ggml_tensor * k,
	struct ggml_tensor * v,
	struct ggml_tensor * g,
	struct ggml_tensor * beta,
	struct ggml_tensor * state);

	// custom operators

	typedef void (ggml_custom1_op_t)(struct ggml_tensor dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
	typedef void (ggml_custom2_op_t)(struct ggml_tensor dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
	typedef void (ggml_custom3_op_t)(struct ggml_tensor dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);

	#define GGML_N_TASKS_MAX (-1)
	// n_tasks == GGML_N_TASKS_MAX means to use max number of tasks

	GGML_API struct ggml_tensor * ggml_map_custom1(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	ggml_custom1_op_t fun,
	int n_tasks,
	void * userdata);

	GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	ggml_custom1_op_t fun,
	int n_tasks,
	void * userdata);

	GGML_API struct ggml_tensor * ggml_map_custom2(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	ggml_custom2_op_t fun,
	int n_tasks,
	void * userdata);

	GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	ggml_custom2_op_t fun,
	int n_tasks,
	void * userdata);

	GGML_API struct ggml_tensor * ggml_map_custom3(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	struct ggml_tensor * c,
	ggml_custom3_op_t fun,
	int n_tasks,
	void * userdata);

	GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * b,
	struct ggml_tensor * c,
	ggml_custom3_op_t fun,
	int n_tasks,
	void * userdata);

	typedef void (ggml_custom_op_t)(struct ggml_tensor dst , int ith, int nth, void * userdata);

	GGML_API struct ggml_tensor * ggml_custom_4d(
	struct ggml_context * ctx,
	enum ggml_type type,
	int64_t ne0,
	int64_t ne1,
	int64_t ne2,
	int64_t ne3,
	struct ggml_tensor ** args,
	int n_args,
	ggml_custom_op_t fun,
	int n_tasks,
	void * userdata);

	GGML_API struct ggml_tensor * ggml_custom_inplace(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor ** args,
	int n_args,
	ggml_custom_op_t fun,
	int n_tasks,
	void * userdata);

	// loss function

	GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // logits
	struct ggml_tensor * b); // labels

	GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
	struct ggml_context * ctx,
	struct ggml_tensor * a, // logits
	struct ggml_tensor * b, // labels
	struct ggml_tensor * c); // gradients of cross_entropy_loss result

	// AdamW optimizer step
	// Paper: https://arxiv.org/pdf/1711.05101v3.pdf
	// PyTorch: https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html
	GGML_API struct ggml_tensor * ggml_opt_step_adamw(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * grad,
	struct ggml_tensor * m,
	struct ggml_tensor * v,
	struct ggml_tensor * adamw_params); // parameters such as the learning rate

	// stochastic gradient descent step (with weight decay)
	GGML_API struct ggml_tensor * ggml_opt_step_sgd(
	struct ggml_context * ctx,
	struct ggml_tensor * a,
	struct ggml_tensor * grad,
	struct ggml_tensor * sgd_params); // alpha, weight decay

	// build forward multiple tensors and select one of them for computing
	// this is useful for creating graphs that have constant topology but compute different things based on the input
	// ref: https://github.com/ggml-org/llama.cpp/pull/18550
	//
	// nodes:
	// \| - build forward into the graph but do not compute
	// c - build forward into the graph and compute
	//
	// \| \| ... c ... \|
	// \| \| ... c ... \|
	// \| \| ... c ... \|
	// [0 1 ... idx ... n-1] <-- ggml_build_forward_select(..., n, idx)
	// c
	// c
	//
	// example:
	// struct ggml_tensor * curs[3];
	//
	// curs[0] = compute0(...);
	// curs[1] = compute1(...);
	// curs[2] = compute2(...);
	//
	// int idx = select_branch(some_input);
	//
	// struct ggml_tensor * out = ggml_build_forward_select(cgraph, curs, 3, idx);
	//
	GGML_API struct ggml_tensor * ggml_build_forward_select(
	struct ggml_cgraph * cgraph,
	struct ggml_tensor ** tensors,
	int n_tensors,
	int idx);

	GGML_API void ggml_build_forward_expand(
	struct ggml_cgraph * cgraph,
	struct ggml_tensor * tensor);

	GGML_API void ggml_build_backward_expand(
	struct ggml_context * ctx, // context for gradient computation
	struct ggml_cgraph * cgraph,
	struct ggml_tensor ** grad_accs);

	// graph allocation in a context
	GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
	GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads);
	GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads);
	GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
	GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1
	GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);

	GGML_API int ggml_graph_size (struct ggml_cgraph * cgraph);
	GGML_API struct ggml_tensor * ggml_graph_node (struct ggml_cgraph * cgraph, int i); // if i < 0, returns nodes[n_nodes + i]
	GGML_API struct ggml_tensor ** ggml_graph_nodes (struct ggml_cgraph * cgraph);
	GGML_API int ggml_graph_n_nodes(struct ggml_cgraph * cgraph);

	GGML_API void ggml_graph_add_node(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);

	GGML_API size_t ggml_graph_overhead(void);
	GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads);

	GGML_API struct ggml_tensor * ggml_graph_get_tensor (const struct ggml_cgraph * cgraph, const char * name);
	GGML_API struct ggml_tensor * ggml_graph_get_grad (const struct ggml_cgraph * cgraph, const struct ggml_tensor * node);
	GGML_API struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node);

	// print info and performance information for the graph
	GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);

	// dump the graph into a file using the dot format
	GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * cgraph, const char * filename);

	// TODO these functions were sandwiched in the old optimization interface, is there a better place for them?
	typedef void (ggml_log_callback)(enum ggml_log_level level, const char text, void * user_data);

	// Set callback for all future logging events.
	// If this is not called, or NULL is supplied, everything is output on stderr.
	GGML_API void ggml_log_get(ggml_log_callback * log_callback, void ** user_data);
	GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data);

	GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);

	//
	// quantization
	//

	// - ggml_quantize_init can be called multiple times with the same type
	// it will only initialize the quantization tables for the first call or after ggml_quantize_free
	// automatically called by ggml_quantize_chunk for convenience
	//
	// - ggml_quantize_free will free any memory allocated by ggml_quantize_init
	// call this at the end of the program to avoid memory leaks
	//
	// note: these are thread-safe
	//
	GGML_API void ggml_quantize_init(enum ggml_type type);
	GGML_API void ggml_quantize_free(void);

	// some quantization type cannot be used without an importance matrix
	GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type);

	// calls ggml_quantize_init internally (i.e. can allocate memory)
	GGML_API size_t ggml_quantize_chunk(
	enum ggml_type type,
	const float * src,
	void * dst,
	int64_t start,
	int64_t nrows,
	int64_t n_per_row,
	const float * imatrix);

	#ifdef __cplusplus
	// restrict not standard in C++
	# if defined(__GNUC__)
	# define GGML_RESTRICT __restrict__
	# elif defined(__clang__)
	# define GGML_RESTRICT __restrict
	# elif defined(_MSC_VER)
	# define GGML_RESTRICT __restrict
	# else
	# define GGML_RESTRICT
	# endif
	#else
	# if defined (_MSC_VER) && (__STDC_VERSION__ < 201112L)
	# define GGML_RESTRICT __restrict
	# else
	# define GGML_RESTRICT restrict
	# endif
	#endif
	typedef void (ggml_to_float_t) (const void GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
	typedef void (ggml_from_float_t)(const float GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);

	struct ggml_type_traits {
	const char * type_name;
	int64_t blck_size;
	int64_t blck_size_interleave; // interleave elements in blocks
	size_t type_size;
	bool is_quantized;
	ggml_to_float_t to_float;
	ggml_from_float_t from_float_ref;
	};

	GGML_API const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type);

	// ggml threadpool
	// TODO: currently, only a few functions are in the base ggml API, while the rest are in the CPU backend
	// the goal should be to create an API that other backends can use move everything to the ggml base

	// scheduling priorities
	enum ggml_sched_priority {
	GGML_SCHED_PRIO_LOW = -1,
	GGML_SCHED_PRIO_NORMAL,
	GGML_SCHED_PRIO_MEDIUM,
	GGML_SCHED_PRIO_HIGH,
	GGML_SCHED_PRIO_REALTIME
	};

	// threadpool params
	// Use ggml_threadpool_params_default() or ggml_threadpool_params_init() to populate the defaults
	struct ggml_threadpool_params {
	bool cpumask[GGML_MAX_N_THREADS]; // mask of cpu cores (all-zeros means use default affinity settings)
	int n_threads; // number of threads
	enum ggml_sched_priority prio; // thread priority
	uint32_t poll; // polling level (0 - no polling, 100 - aggressive polling)
	bool strict_cpu; // strict cpu placement
	bool paused; // start in paused state
	};

	struct ggml_threadpool; // forward declaration, see ggml.c

	typedef struct ggml_threadpool * ggml_threadpool_t;

	GGML_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads);
	GGML_API void ggml_threadpool_params_init (struct ggml_threadpool_params * p, int n_threads);
	GGML_API bool ggml_threadpool_params_match (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1);

	#ifdef __cplusplus
	}
	#endif