44c117f41e
* fix track_max_mem in forward_batch_wo_cache_flash_attn_train * remove unnecessary Adam(W) optimizer tensors. reduces optimizer memory overhead from 7*modelsize to 2*modelsize. additionally allows to optimize models with more than 2^31 parameters by replacing int with int64_t. bumps training checkpoint file version, but old checkpoints can still be read. new version with less tensors is saved. * add gradient clipping to AdamW * Fix reset of unused g->nodes and g->grads to NULL * implement gradient checkpointing for training reduces memory overhead from O(n_layer) to O(sqrt(n_layer)) as explained in readme of https://github.com/cybertronai/gradient-checkpointing * remove unused compute buffer 3 * add and use function ggml_build_backward_expand to avoid stack overflows with large maximum number of nodes GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep); * change AdamW decay parameter to work like the torch AdamW decay parameter It is now relative to Adam learning rate `alpha*sched`. Before that it was relative to `sched` only. `alpha` being the maximum learning rate and `sched` being a scaling parameter in [0..1] * change default AdamW weight decay parameter used in training to 0.1 as used in nanoGPT * change default AdamW weight decay parameter defined in ggml to 0.0, making Adam default instead of AdamW btw: the default weight decay parameter for torch.optim.AdamW is 0.01 * bug fixes for cross entropy loss ggml_cross_entropy_loss: sums where not correctly added in workload of each thread ggml_cross_entropy_loss_back: simplify backward process, reducing numerical issues guard usage of exp f16 lookup in cross entropy by #define GGML_CROSS_ENTROPY_EXP_FP16 cross entropy loss is only used once during training, but it is quite sensitive to numerical errors introduced by exp-f16-lookup. so exp-f16-lookup for cross entropy loss is disabled by default, trading better gradients for very slightly worse runtime performance. * fix test-grad0 for cross_entropy_loss the second argument to cross_entropy_loss must sum up to 1 for each row * fix test-grad0 for soft_max dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0) * improve finite differences of test-grad0 by using double instead of float * change cross_entropy_loss to output average over all rows this helps keeping the loss and gradients in a sane range * improve gradient checkpointing sqrt(n_layers) is only the best checkpoint step when mem size of checkpoints and mem size of layers are equal. since layers require more memory than the single-tensor-checkpoint we use, the optimal values are compute different: ``` given: n, u, v objective: minimize(a*u+b*v) where a*b=n, a>0, b>0 b=n/a minimize(a*u+v*n/a) diff(a*u+v*n/a, a) = u - (v*n/a)/a diff(a*u+v*n/a, a) == 0 u - (v*n/a)/a == 0 u == v*n/(a*a) u*a*a = v*n a*a = v*n/u a = sqrt(n*v/u) ``` this change results in more checkpoints, requiring less layers to store between checkpoints, overall improving memory usage. * disable gradient checkpointing debug output * llama : fix rope usage in train-text-from-scratch after ChatGLM change * add more training parameters: --enable-restart N Only for Adam optimizer. Enable restarts of cos-decay --disable-restart N Only for Adam optimizer. Disable restarts of cos-decay --opt-past N Number of optimization iterations to track for delta convergence test. Disabled when zero. --opt-delta N Maximum delta for delta convergence test. Disabled when <= zero. --opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero. --adam-epsf N AdamW epsilon for convergence test. Disabled when <= zero. --adam-min-alpha N Adam minimum learning rate alpha, usually 0.1 * alpha * replace memcpy with reshape operation so that the graph is not cut at the input this makes it possible to store other values into the input tensor and then simply recompute the graph without rebuilding it * remove unused function argument from get_example_targets_batch * measure and print total training time * add optimization callback to ggml_opt_resume_g this callback is called before each iteration with custom data and pointer to learning schedule parameter (only used in Adam(W)). can be used for dynamic learning schedule and setting input data for batches before each iteration * use optimization callback in training allows dynamic learning schedule and different batch data for each iteration without relying on low n_iter and high n_examples parameters reduces runtime by avoiding restart of optimization function and improves training convergence by providing a different batch for each iteration * add minimum number of tensor dimensions to apply weight decay (default 2) this allows to not apply weight decay to bias parameters * rename training parameter cos-decay-alpha to cos-decay-min and clarify that adam-min-alpha also applies to warmup * fix increase of model.train_samples and model.train_tokens now that each optimizer iteration gets its own batch we need to multiply by number of opt iterations * change sampling parameters for prediction after training to defaults of common.h and clarify what is context for prediction and what are generated tokens * tighten abs error bounds for cross_entropy_loss in test-grad0 * add conditional compilation of using F16 exp in flash attention uncomment `// #define GGML_FLASH_ATTN_EXP_FP16` to enable usage of f16 exp in flash attention * tighten abs error bounds for flash_attn in test-grad0 * tighten abs error bounds for sqrt in test-grad0 * remove out-commented vectorized code of opt_adam the vectorized code might be bit faster for low number of parameters, but it had a big memory usage overhead * ggml : update ggml_rms_norm_back with configurable eps * llama training : fix ggml_rms_norm_back calls to pass configurable eps * remove trailing whitespace * add train function using automatic gradient checkpointing backward pass and allocator * in train function replace add_inplace by regular add because using add_inplace seems to result in different gradients * don't use allocate hash_map on context because the context has no_alloc=True when using memory allocator resulting in NULL data pointers * correctly clone reshape and permute operations by also cloning tensor->nb values * fix variable name and add missing type cast * terminate recursive tensor cloning when reaching tensor without src tensors * correctly clone view tensors by setting data pointers without this the checkpointing would only work when being used together with memory allocator * fix variable names * swap arguments to commutative ops to be the same as in `forward_batch_wo_cache_flash_attn` * add input tensors as checkpoints so that recursive tensor cloning of gradient checkpointing terminates on input tensors * fix variable name and add missing boolean negation * make sure some tensors are not reallocated by inserting new temporary nodes depending on them: output and parameter gradient tensors need to be available at the end of the graph execution parameter gradient tensors also need to be available before the graph execution because they are set to zero before each optimizer iteration checkpoint tensors are allocated all together to reduce memory allocator fragmentation afterwards, in addition to the temporary nodes, we also need to reset the temporary leafs * fix ASSERT to work with zero layers * add training options whether to use allocator and/or unified training function * integrate unified training function which may use memory allocator the unified training function also supports arguments whether to use flash attention and/or gradient checkpointing * format name of cloned tensors with " (clone)" suffix * set names for tensors in unified train function for easier debugging * allocate graph on context using ggml_new_graph * remove handwritten training functions * remove unused training parameters "use_scratch" and "use_unified" * remove trailing whitespace * remove unused train params: mem_compute1_gb & mem_compute2_gb mem_compute_gb is used for compute when automatic memory allocator is not enabled, otherwise it can be very small to only hold the tensor definitions mem_compute0_gb is used for automatic memory allocator (as long as measurement of max required size is not implemented) * remove unused forward_batch function * add debug asserts in ggml_allocr_alloc to some common pitfalls when using this function directly * only use ggml_allocr_alloc when tensor has NULL data and is no view * fix test when to create temporary backward graph temporary backward graph is only necessary when using checkpointing * fix memory "leak" in optimizers each iteration a new cplan with new memory for work data was allocated. now cplan creation only happens at the start of optimization, with each iteration reusing the cplan and its work data. * reverse order of for loop in ggml_build_backward_expand to save memory when using gradient checkpointing and allocator with this loop order gradient checkpointing with allocator on 16 layer model saves 13% memory; 2 layer memory it saves 2% memory. the computation results are the same * add missing lctx argument to get_example_targets_batch * implement llama model file saving using gguf checkpoint loading and saving disabled, to be replaced by loading and saving via gguf * implement loading/saving of checkpointing files using GGUF * bug fixes * add checkpoint file version for future compatibility * update readme with gguf filenames * save & load opt->just_initialized value * add first draft for checkpoint conversion script * add gguf arch and ftype * save opt parameter counter as uint64 * add gguf key and tensor names for optimizer and training * add layer_norm_rms_eps to checkpoint convert script * use same GGUF_GET_KEY macro as in llama.cpp * use norm_rms_eps, and rope parameters and command line options to set them * fix memory corruption bug in gguf ctx->kv and ctx->infos was reallocated using not-aligned realloc, but freed with aligned free. to fix this a GGML_ALIGNED_REALLOC was added, but there is no posix_memalign_realloc function. so on non-windows and non-mingw32 platforms we fall back to aligned malloc, followed by copying and freeing the old data. * add gguf example cmake file * bug fixes in tokenize_file * bug fixes in load_llama_model_gguf * bug fix: init model when no checkpoint was loaded * bug fix in read_tensor_by_name * bug fix in load_opt_context_gguf * avoid printing lots of spaced on the unusual case that loss gets nan * set name of tensors with empty name from what was read from gguf * remove trailing whitespace * print data checksums before saving and after loading to verify correctness * bug fixes for convert-train-checkpoint-to-gguf * temporarily add code to write old checkpoint files used to verify that old checkpoint files are correctly converted to gguf * bug fixes for convert-train-checkpoint-to-gguf.py loading checkpoints with opt_version=0 * remove code used to verify correctness of checkpoint file conversion * remove trailing whitespace * remove prediction related code use main for prediction, it is better optimized * update train-text-from-scratch README.md * fix non-windows GGML_ALIGNED_REALLOC * add missing blank line at end of file * remove GGML_ALIGNED_REALLOC and use normal malloc/realloc/free for gguf ctx->kv & ctx->infos * train : fix compile warnings --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2003 lines
70 KiB
C
2003 lines
70 KiB
C
#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/ggerganov/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 = 16*1024*1024,
|
|
// .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_build_forward(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 + y*a->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)
|
|
# else
|
|
# define GGML_API __declspec(dllimport)
|
|
# endif
|
|
# else
|
|
# define GGML_API __attribute__ ((visibility ("default")))
|
|
# endif
|
|
#else
|
|
# define GGML_API
|
|
#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
|
|
|
|
#include <stdint.h>
|
|
#include <stddef.h>
|
|
#include <stdbool.h>
|
|
|
|
#define GGML_FILE_MAGIC 0x67676d6c // "ggml"
|
|
#define GGML_FILE_VERSION 1
|
|
|
|
#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_NODES 4096
|
|
#define GGML_MAX_PARAMS 256
|
|
#define GGML_MAX_CONTEXTS 64
|
|
#define GGML_MAX_SRC 6
|
|
#define GGML_MAX_NAME 64
|
|
#define GGML_MAX_OP_PARAMS 32
|
|
#define GGML_DEFAULT_N_THREADS 4
|
|
|
|
#if UINTPTR_MAX == 0xFFFFFFFF
|
|
#define GGML_MEM_ALIGN 4
|
|
#else
|
|
#define GGML_MEM_ALIGN 16
|
|
#endif
|
|
|
|
#define GGML_EXIT_SUCCESS 0
|
|
#define GGML_EXIT_ABORTED 1
|
|
|
|
#define GGUF_MAGIC 0x46554747 // "GGUF"
|
|
#define GGUF_VERSION 2
|
|
|
|
#define GGUF_DEFAULT_ALIGNMENT 32
|
|
|
|
#define GGML_UNUSED(x) (void)(x)
|
|
|
|
#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
|
|
|
|
#define GGML_ASSERT(x) \
|
|
do { \
|
|
if (!(x)) { \
|
|
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
|
|
abort(); \
|
|
} \
|
|
} while (0)
|
|
|
|
// 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)->array[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)->array[1]; \
|
|
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)->array[2]; \
|
|
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)->array[3]; \
|
|
GGML_UNUSED(prefix##3);
|
|
|
|
#ifdef __cplusplus
|
|
extern "C" {
|
|
#endif
|
|
|
|
#if defined(__ARM_NEON) && defined(__CUDACC__)
|
|
typedef half ggml_fp16_t;
|
|
#elif defined(__ARM_NEON)
|
|
typedef __fp16 ggml_fp16_t;
|
|
#else
|
|
typedef uint16_t ggml_fp16_t;
|
|
#endif
|
|
|
|
// convert FP16 <-> FP32
|
|
GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x);
|
|
GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
|
|
|
|
GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);
|
|
GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);
|
|
|
|
struct ggml_object;
|
|
struct ggml_context;
|
|
|
|
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,
|
|
// k-quantizations
|
|
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_I8,
|
|
GGML_TYPE_I16,
|
|
GGML_TYPE_I32,
|
|
GGML_TYPE_COUNT,
|
|
};
|
|
|
|
enum ggml_backend {
|
|
GGML_BACKEND_CPU = 0,
|
|
GGML_BACKEND_GPU = 10,
|
|
GGML_BACKEND_GPU_SPLIT = 20,
|
|
};
|
|
|
|
// 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
|
|
};
|
|
|
|
// available tensor operations:
|
|
enum ggml_op {
|
|
GGML_OP_NONE = 0,
|
|
|
|
GGML_OP_DUP,
|
|
GGML_OP_ADD,
|
|
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_SUM,
|
|
GGML_OP_SUM_ROWS,
|
|
GGML_OP_MEAN,
|
|
GGML_OP_ARGMAX,
|
|
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_MUL_MAT,
|
|
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_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_ALIBI,
|
|
GGML_OP_CLAMP,
|
|
GGML_OP_CONV_1D,
|
|
GGML_OP_CONV_2D,
|
|
GGML_OP_CONV_TRANSPOSE_2D,
|
|
GGML_OP_POOL_1D,
|
|
GGML_OP_POOL_2D,
|
|
|
|
GGML_OP_UPSCALE, // nearest interpolate
|
|
|
|
GGML_OP_FLASH_ATTN,
|
|
GGML_OP_FLASH_FF,
|
|
GGML_OP_FLASH_ATTN_BACK,
|
|
GGML_OP_WIN_PART,
|
|
GGML_OP_WIN_UNPART,
|
|
GGML_OP_GET_REL_POS,
|
|
GGML_OP_ADD_REL_POS,
|
|
|
|
GGML_OP_UNARY,
|
|
|
|
GGML_OP_MAP_UNARY,
|
|
GGML_OP_MAP_BINARY,
|
|
|
|
GGML_OP_MAP_CUSTOM1_F32,
|
|
GGML_OP_MAP_CUSTOM2_F32,
|
|
GGML_OP_MAP_CUSTOM3_F32,
|
|
|
|
GGML_OP_MAP_CUSTOM1,
|
|
GGML_OP_MAP_CUSTOM2,
|
|
GGML_OP_MAP_CUSTOM3,
|
|
|
|
GGML_OP_CROSS_ENTROPY_LOSS,
|
|
GGML_OP_CROSS_ENTROPY_LOSS_BACK,
|
|
|
|
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_GELU,
|
|
GGML_UNARY_OP_GELU_QUICK,
|
|
GGML_UNARY_OP_SILU,
|
|
};
|
|
|
|
enum ggml_object_type {
|
|
GGML_OBJECT_TENSOR,
|
|
GGML_OBJECT_GRAPH,
|
|
GGML_OBJECT_WORK_BUFFER
|
|
};
|
|
|
|
// ggml object
|
|
struct ggml_object {
|
|
size_t offs;
|
|
size_t size;
|
|
|
|
struct ggml_object * next;
|
|
|
|
enum ggml_object_type type;
|
|
|
|
char padding[4];
|
|
};
|
|
|
|
static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
|
|
|
|
// n-dimensional tensor
|
|
struct ggml_tensor {
|
|
enum ggml_type type;
|
|
enum ggml_backend backend;
|
|
|
|
int n_dims;
|
|
int64_t ne[GGML_MAX_DIMS]; // number of elements
|
|
size_t nb[GGML_MAX_DIMS]; // stride in bytes:
|
|
// nb[0] = sizeof(type)
|
|
// nb[1] = nb[0] * ne[0] + 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)];
|
|
|
|
bool is_param;
|
|
|
|
struct ggml_tensor * grad;
|
|
struct ggml_tensor * src[GGML_MAX_SRC];
|
|
|
|
// performance
|
|
int perf_runs;
|
|
int64_t perf_cycles;
|
|
int64_t perf_time_us;
|
|
|
|
void * data;
|
|
|
|
char name[GGML_MAX_NAME];
|
|
|
|
void * extra; // extra things e.g. for ggml-cuda.cu
|
|
|
|
char padding[4];
|
|
};
|
|
|
|
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
|
|
|
|
// the compute plan that needs to be prepared for ggml_graph_compute()
|
|
// since https://github.com/ggerganov/ggml/issues/287
|
|
struct ggml_cplan {
|
|
size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()`
|
|
uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`
|
|
|
|
int n_threads;
|
|
|
|
// the `n_tasks` of nodes, 1:1 mapping to cgraph nodes
|
|
int n_tasks[GGML_MAX_NODES];
|
|
|
|
// abort ggml_graph_compute when true
|
|
bool (*abort_callback)(void * data);
|
|
void * abort_callback_data;
|
|
};
|
|
|
|
// next prime after GGML_MAX_NODES
|
|
// #define GGML_GRAPH_HASHTABLE_SIZE 4099
|
|
// next prime after GGML_MAX_NODES * 2 (nodes + leafs)
|
|
#define GGML_GRAPH_HASHTABLE_SIZE 8273
|
|
|
|
// computation graph
|
|
struct ggml_cgraph {
|
|
int n_nodes;
|
|
int n_leafs;
|
|
|
|
struct ggml_tensor * nodes[GGML_MAX_NODES];
|
|
struct ggml_tensor * grads[GGML_MAX_NODES];
|
|
struct ggml_tensor * leafs[GGML_MAX_NODES];
|
|
|
|
void * visited_hash_table[GGML_GRAPH_HASHTABLE_SIZE];
|
|
|
|
// performance
|
|
int perf_runs;
|
|
int64_t perf_cycles;
|
|
int64_t perf_time_us;
|
|
};
|
|
|
|
static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph);
|
|
|
|
// scratch buffer
|
|
struct ggml_scratch {
|
|
size_t offs;
|
|
size_t size;
|
|
void * data;
|
|
};
|
|
|
|
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
|
|
};
|
|
|
|
|
|
// compute types
|
|
|
|
// NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
|
|
// This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
|
|
enum ggml_task_type {
|
|
GGML_TASK_INIT = 0,
|
|
GGML_TASK_COMPUTE,
|
|
GGML_TASK_FINALIZE,
|
|
};
|
|
|
|
struct ggml_compute_params {
|
|
enum ggml_task_type type;
|
|
|
|
// ith = thread index, nth = number of threads
|
|
int ith, nth;
|
|
|
|
// work buffer for all threads
|
|
size_t wsize;
|
|
void * wdata;
|
|
};
|
|
|
|
// misc
|
|
|
|
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);
|
|
|
|
GGML_API void ggml_numa_init(void); // call once for better performance on NUMA systems
|
|
GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node
|
|
|
|
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 size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
|
|
|
|
GGML_API int 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 float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
|
|
|
|
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 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_contiguous(const struct ggml_tensor * tensor);
|
|
GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
|
|
|
|
GGML_API bool ggml_are_same_shape(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);
|
|
|
|
// main
|
|
|
|
GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);
|
|
GGML_API void ggml_free(struct ggml_context * ctx);
|
|
|
|
GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);
|
|
|
|
GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);
|
|
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 struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
|
|
GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
|
|
|
|
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, const struct ggml_tensor * src);
|
|
|
|
GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
|
|
|
|
GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
|
|
GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
|
|
GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
|
|
|
|
GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
|
|
GGML_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
|
|
|
|
GGML_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
|
|
GGML_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
|
|
|
|
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 enum ggml_unary_op ggml_get_unary_op(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_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...);
|
|
|
|
//
|
|
// 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_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);
|
|
|
|
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);
|
|
|
|
// 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);
|
|
|
|
// 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);
|
|
|
|
// 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);
|
|
|
|
GGML_API struct ggml_tensor * ggml_repeat_back(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b);
|
|
|
|
// concat a and b on dim 2
|
|
// used in stable-diffusion
|
|
GGML_API struct ggml_tensor * ggml_concat(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b);
|
|
|
|
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_relu_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a);
|
|
|
|
// TODO: double-check this computation is correct
|
|
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);
|
|
|
|
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);
|
|
|
|
// 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 ne0*ne1*n_groups
|
|
// used in stable-diffusion
|
|
// TODO: eps is hardcoded to 1e-6 for now
|
|
GGML_API struct ggml_tensor * ggml_group_norm(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_groups);
|
|
|
|
GGML_API struct ggml_tensor * ggml_group_norm_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_groups);
|
|
|
|
// 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: n columns, m rows
|
|
// B: n columns, p rows (i.e. we transpose it internally)
|
|
// result is m columns, p rows
|
|
GGML_API struct ggml_tensor * ggml_mul_mat(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b);
|
|
|
|
// 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,
|
|
struct ggml_tensor * b);
|
|
|
|
// in-place, returns view(a)
|
|
GGML_API struct ggml_tensor * ggml_scale_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * 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);
|
|
|
|
// 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);
|
|
|
|
GGML_API struct ggml_tensor * ggml_set_1d(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
size_t offset);
|
|
|
|
GGML_API struct ggml_tensor * ggml_set_1d_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
size_t offset);
|
|
|
|
// 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);
|
|
|
|
// 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);
|
|
|
|
|
|
// a -> b, return view(b)
|
|
GGML_API struct ggml_tensor * ggml_cpy(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b);
|
|
|
|
// a -> b, in-place, return view(b)
|
|
GGML_API struct ggml_tensor * ggml_cpy_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b);
|
|
|
|
// make contiguous
|
|
GGML_API struct ggml_tensor * ggml_cont(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a);
|
|
|
|
// make contiguous, in-place
|
|
GGML_API struct ggml_tensor * ggml_cont_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a);
|
|
|
|
// 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);
|
|
|
|
GGML_API struct ggml_tensor * ggml_get_rows(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b);
|
|
|
|
GGML_API struct ggml_tensor * ggml_get_rows_back(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
struct ggml_tensor * c);
|
|
|
|
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);
|
|
|
|
GGML_API struct ggml_tensor * ggml_soft_max_back(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b);
|
|
|
|
// in-place, returns view(a)
|
|
GGML_API struct ggml_tensor * ggml_soft_max_back_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b);
|
|
|
|
// rotary position embedding
|
|
// if mode & 1 == 1, skip n_past elements
|
|
// if mode & 2 == 1, GPT-NeoX style
|
|
// if mode & 4 == 1, ChatGLM style
|
|
// TODO: avoid creating a new tensor every time
|
|
GGML_API struct ggml_tensor * ggml_rope(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_past,
|
|
int n_dims,
|
|
int mode,
|
|
int n_ctx);
|
|
|
|
// in-place, returns view(a)
|
|
GGML_API struct ggml_tensor * ggml_rope_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_past,
|
|
int n_dims,
|
|
int mode,
|
|
int n_ctx);
|
|
|
|
// custom RoPE
|
|
GGML_API struct ggml_tensor * ggml_rope_custom(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_past,
|
|
int n_dims,
|
|
int mode,
|
|
int n_ctx,
|
|
float freq_base,
|
|
float freq_scale);
|
|
|
|
// in-place, returns view(a)
|
|
GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_past,
|
|
int n_dims,
|
|
int mode,
|
|
int n_ctx,
|
|
float freq_base,
|
|
float freq_scale);
|
|
|
|
// xPos RoPE, in-place, returns view(a)
|
|
GGML_API struct ggml_tensor * ggml_rope_xpos_inplace(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_past,
|
|
int n_dims,
|
|
float base,
|
|
bool down);
|
|
|
|
// rotary position embedding backward, i.e compute dx from dy
|
|
// a - dy
|
|
GGML_API struct ggml_tensor * ggml_rope_back(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_past,
|
|
int n_dims,
|
|
int mode,
|
|
int n_ctx,
|
|
float freq_base,
|
|
float freq_scale,
|
|
float xpos_base,
|
|
bool xpos_down);
|
|
|
|
// alibi position embedding
|
|
// in-place, returns view(a)
|
|
struct ggml_tensor * ggml_alibi(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int n_past,
|
|
int n_head,
|
|
float bias_max);
|
|
|
|
// clamp
|
|
// in-place, returns view(a)
|
|
struct ggml_tensor * ggml_clamp(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
float min,
|
|
float max);
|
|
|
|
GGML_API struct ggml_tensor * ggml_conv_1d(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
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,
|
|
struct ggml_tensor * b,
|
|
int s,
|
|
int d);
|
|
|
|
GGML_API struct ggml_tensor * ggml_conv_2d(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
int s0,
|
|
int s1,
|
|
int p0,
|
|
int p1,
|
|
int d0,
|
|
int d1);
|
|
|
|
|
|
// 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);
|
|
|
|
GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
int stride);
|
|
|
|
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
|
|
|
|
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,
|
|
int p0,
|
|
int p1);
|
|
|
|
// nearest interpolate
|
|
// used in stable-diffusion
|
|
GGML_API struct ggml_tensor * ggml_upscale(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
int scale_factor);
|
|
|
|
GGML_API struct ggml_tensor * ggml_flash_attn(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * q,
|
|
struct ggml_tensor * k,
|
|
struct ggml_tensor * v,
|
|
bool masked);
|
|
|
|
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_flash_ff(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b0,
|
|
struct ggml_tensor * b1,
|
|
struct ggml_tensor * c0,
|
|
struct ggml_tensor * c1);
|
|
|
|
// 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);
|
|
|
|
// custom operators
|
|
|
|
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
|
|
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
|
|
|
|
typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
|
|
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
|
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
ggml_unary_op_f32_t fun),
|
|
"use ggml_map_custom1 instead");
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
ggml_unary_op_f32_t fun),
|
|
"use ggml_map_custom1_inplace instead");
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
ggml_binary_op_f32_t fun),
|
|
"use ggml_map_custom2 instead");
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
ggml_binary_op_f32_t fun),
|
|
"use ggml_map_custom2_inplace instead");
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
ggml_custom1_op_f32_t fun),
|
|
"use ggml_map_custom1 instead");
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
ggml_custom1_op_f32_t fun),
|
|
"use ggml_map_custom1_inplace instead");
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
ggml_custom2_op_f32_t fun),
|
|
"use ggml_map_custom2 instead");
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
ggml_custom2_op_f32_t fun),
|
|
"use ggml_map_custom2_inplace instead");
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
struct ggml_tensor * c,
|
|
ggml_custom3_op_f32_t fun),
|
|
"use ggml_map_custom3 instead");
|
|
|
|
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
struct ggml_tensor * c,
|
|
ggml_custom3_op_f32_t fun),
|
|
"use ggml_map_custom3_inplace instead");
|
|
|
|
// custom operators v2
|
|
|
|
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
|
|
|
|
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);
|
|
|
|
// loss function
|
|
|
|
GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b);
|
|
|
|
GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * a,
|
|
struct ggml_tensor * b,
|
|
struct ggml_tensor * c);
|
|
|
|
//
|
|
// automatic differentiation
|
|
//
|
|
|
|
GGML_API void ggml_set_param(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * tensor);
|
|
|
|
|
|
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, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
|
|
|
|
GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
|
|
GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
|
|
|
|
// graph allocation in a context
|
|
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx);
|
|
GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);
|
|
GGML_API size_t ggml_graph_overhead(void);
|
|
|
|
// ggml_graph_plan() has to be called before ggml_graph_compute()
|
|
// when plan.work_size > 0, caller must allocate memory for plan.work_data
|
|
GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
|
|
GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
|
|
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);
|
|
|
|
// same as ggml_graph_compute() but the work data is allocated as a part of the context
|
|
// note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
|
|
GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
|
|
|
|
GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
|
|
|
|
GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname);
|
|
GGML_API struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval);
|
|
|
|
// 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 * gf, const char * filename);
|
|
|
|
//
|
|
// optimization
|
|
//
|
|
|
|
// optimization methods
|
|
enum ggml_opt_type {
|
|
GGML_OPT_ADAM,
|
|
GGML_OPT_LBFGS,
|
|
};
|
|
|
|
// linesearch methods
|
|
enum ggml_linesearch {
|
|
GGML_LINESEARCH_DEFAULT = 1,
|
|
|
|
GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0,
|
|
GGML_LINESEARCH_BACKTRACKING_WOLFE = 1,
|
|
GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2,
|
|
};
|
|
|
|
// optimization return values
|
|
enum ggml_opt_result {
|
|
GGML_OPT_OK = 0,
|
|
GGML_OPT_DID_NOT_CONVERGE,
|
|
GGML_OPT_NO_CONTEXT,
|
|
GGML_OPT_INVALID_WOLFE,
|
|
GGML_OPT_FAIL,
|
|
|
|
GGML_LINESEARCH_FAIL = -128,
|
|
GGML_LINESEARCH_MINIMUM_STEP,
|
|
GGML_LINESEARCH_MAXIMUM_STEP,
|
|
GGML_LINESEARCH_MAXIMUM_ITERATIONS,
|
|
GGML_LINESEARCH_INVALID_PARAMETERS,
|
|
};
|
|
|
|
typedef void (*ggml_opt_callback)(void * data, float * sched);
|
|
|
|
// optimization parameters
|
|
//
|
|
// see ggml.c (ggml_opt_default_params) for default values
|
|
//
|
|
struct ggml_opt_params {
|
|
enum ggml_opt_type type;
|
|
|
|
int n_threads;
|
|
|
|
// delta-based convergence test
|
|
//
|
|
// if past == 0 - disabled
|
|
// if past > 0:
|
|
// stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|)
|
|
//
|
|
int past;
|
|
float delta;
|
|
|
|
// maximum number of iterations without improvement
|
|
//
|
|
// if 0 - disabled
|
|
// if > 0:
|
|
// assume convergence if no cost improvement in this number of iterations
|
|
//
|
|
int max_no_improvement;
|
|
|
|
bool print_forward_graph;
|
|
bool print_backward_graph;
|
|
|
|
// ADAM parameters
|
|
struct {
|
|
int n_iter;
|
|
|
|
float sched; // schedule multiplier (fixed, decay or warmup)
|
|
float decay; // weight decay for AdamW, use 0.0f to disable
|
|
int decay_min_ndim; // minimum number of tensor dimension to apply weight decay
|
|
float alpha; // learning rate
|
|
float beta1;
|
|
float beta2;
|
|
float eps; // epsilon for numerical stability
|
|
float eps_f; // epsilon for convergence test
|
|
float eps_g; // epsilon for convergence test
|
|
float gclip; // gradient clipping
|
|
} adam;
|
|
|
|
// LBFGS parameters
|
|
struct {
|
|
int m; // number of corrections to approximate the inv. Hessian
|
|
int n_iter;
|
|
int max_linesearch;
|
|
|
|
float eps; // convergence tolerance
|
|
float ftol; // line search tolerance
|
|
float wolfe;
|
|
float min_step;
|
|
float max_step;
|
|
|
|
enum ggml_linesearch linesearch;
|
|
} lbfgs;
|
|
};
|
|
|
|
struct ggml_opt_context {
|
|
struct ggml_context * ctx;
|
|
struct ggml_opt_params params;
|
|
|
|
int iter;
|
|
int64_t nx; // number of parameter elements
|
|
|
|
bool just_initialized;
|
|
|
|
float loss_before;
|
|
float loss_after;
|
|
|
|
struct {
|
|
struct ggml_tensor * m; // first moment
|
|
struct ggml_tensor * v; // second moment
|
|
struct ggml_tensor * pf; // past function values
|
|
float fx_best;
|
|
float fx_prev;
|
|
int n_no_improvement;
|
|
} adam;
|
|
|
|
struct {
|
|
struct ggml_tensor * x; // current parameters
|
|
struct ggml_tensor * xp; // previous parameters
|
|
struct ggml_tensor * g; // current gradient
|
|
struct ggml_tensor * gp; // previous gradient
|
|
struct ggml_tensor * d; // search direction
|
|
struct ggml_tensor * pf; // past function values
|
|
struct ggml_tensor * lmal; // the L-BFGS memory alpha
|
|
struct ggml_tensor * lmys; // the L-BFGS memory ys
|
|
struct ggml_tensor * lms; // the L-BFGS memory s
|
|
struct ggml_tensor * lmy; // the L-BFGS memory y
|
|
float fx_best;
|
|
float step;
|
|
int j;
|
|
int k;
|
|
int end;
|
|
int n_no_improvement;
|
|
} lbfgs;
|
|
};
|
|
|
|
GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
|
|
|
|
// optimize the function defined by the tensor f
|
|
GGML_API enum ggml_opt_result ggml_opt(
|
|
struct ggml_context * ctx,
|
|
struct ggml_opt_params params,
|
|
struct ggml_tensor * f);
|
|
|
|
// initialize optimizer context
|
|
GGML_API void ggml_opt_init(
|
|
struct ggml_context * ctx,
|
|
struct ggml_opt_context * opt,
|
|
struct ggml_opt_params params,
|
|
int64_t nx);
|
|
|
|
// continue optimizing the function defined by the tensor f
|
|
GGML_API enum ggml_opt_result ggml_opt_resume(
|
|
struct ggml_context * ctx,
|
|
struct ggml_opt_context * opt,
|
|
struct ggml_tensor * f);
|
|
|
|
// continue optimizing the function defined by the tensor f
|
|
GGML_API enum ggml_opt_result ggml_opt_resume_g(
|
|
struct ggml_context * ctx,
|
|
struct ggml_opt_context * opt,
|
|
struct ggml_tensor * f,
|
|
struct ggml_cgraph * gf,
|
|
struct ggml_cgraph * gb,
|
|
ggml_opt_callback callback,
|
|
void * callback_data);
|
|
|
|
//
|
|
// quantization
|
|
//
|
|
|
|
GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
|
GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
|
|
GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
|
GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
|
|
GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
|
|
|
GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
|
|
|
|
//
|
|
// gguf
|
|
//
|
|
|
|
enum gguf_type {
|
|
GGUF_TYPE_UINT8 = 0,
|
|
GGUF_TYPE_INT8 = 1,
|
|
GGUF_TYPE_UINT16 = 2,
|
|
GGUF_TYPE_INT16 = 3,
|
|
GGUF_TYPE_UINT32 = 4,
|
|
GGUF_TYPE_INT32 = 5,
|
|
GGUF_TYPE_FLOAT32 = 6,
|
|
GGUF_TYPE_BOOL = 7,
|
|
GGUF_TYPE_STRING = 8,
|
|
GGUF_TYPE_ARRAY = 9,
|
|
GGUF_TYPE_UINT64 = 10,
|
|
GGUF_TYPE_INT64 = 11,
|
|
GGUF_TYPE_FLOAT64 = 12,
|
|
GGUF_TYPE_COUNT, // marks the end of the enum
|
|
};
|
|
|
|
struct gguf_context;
|
|
|
|
struct gguf_init_params {
|
|
bool no_alloc;
|
|
|
|
// if not NULL, create a ggml_context and allocate the tensor data in it
|
|
struct ggml_context ** ctx;
|
|
};
|
|
|
|
GGML_API struct gguf_context * gguf_init_empty(void);
|
|
GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
|
|
//GGML_API struct gguf_context * gguf_init_from_buffer(..);
|
|
|
|
GGML_API void gguf_free(struct gguf_context * ctx);
|
|
|
|
GGML_API const char * gguf_type_name(enum gguf_type type);
|
|
|
|
GGML_API int gguf_get_version (struct gguf_context * ctx);
|
|
GGML_API size_t gguf_get_alignment (struct gguf_context * ctx);
|
|
GGML_API size_t gguf_get_data_offset(struct gguf_context * ctx);
|
|
GGML_API void * gguf_get_data (struct gguf_context * ctx);
|
|
|
|
GGML_API int gguf_get_n_kv(struct gguf_context * ctx);
|
|
GGML_API int gguf_find_key(struct gguf_context * ctx, const char * key);
|
|
GGML_API const char * gguf_get_key (struct gguf_context * ctx, int i);
|
|
|
|
GGML_API enum gguf_type gguf_get_kv_type (struct gguf_context * ctx, int i);
|
|
GGML_API enum gguf_type gguf_get_arr_type(struct gguf_context * ctx, int i);
|
|
|
|
// results are undefined if the wrong type is used for the key
|
|
GGML_API uint8_t gguf_get_val_u8 (struct gguf_context * ctx, int i);
|
|
GGML_API int8_t gguf_get_val_i8 (struct gguf_context * ctx, int i);
|
|
GGML_API uint16_t gguf_get_val_u16 (struct gguf_context * ctx, int i);
|
|
GGML_API int16_t gguf_get_val_i16 (struct gguf_context * ctx, int i);
|
|
GGML_API uint32_t gguf_get_val_u32 (struct gguf_context * ctx, int i);
|
|
GGML_API int32_t gguf_get_val_i32 (struct gguf_context * ctx, int i);
|
|
GGML_API float gguf_get_val_f32 (struct gguf_context * ctx, int i);
|
|
GGML_API uint64_t gguf_get_val_u64 (struct gguf_context * ctx, int i);
|
|
GGML_API int64_t gguf_get_val_i64 (struct gguf_context * ctx, int i);
|
|
GGML_API double gguf_get_val_f64 (struct gguf_context * ctx, int i);
|
|
GGML_API bool gguf_get_val_bool(struct gguf_context * ctx, int i);
|
|
GGML_API const char * gguf_get_val_str (struct gguf_context * ctx, int i);
|
|
GGML_API int gguf_get_arr_n (struct gguf_context * ctx, int i);
|
|
GGML_API const void * gguf_get_arr_data(struct gguf_context * ctx, int i);
|
|
GGML_API const char * gguf_get_arr_str (struct gguf_context * ctx, int key_id, int i);
|
|
|
|
GGML_API int gguf_get_n_tensors (struct gguf_context * ctx);
|
|
GGML_API int gguf_find_tensor (struct gguf_context * ctx, const char * name);
|
|
GGML_API size_t gguf_get_tensor_offset(struct gguf_context * ctx, int i);
|
|
GGML_API char * gguf_get_tensor_name (struct gguf_context * ctx, int i);
|
|
|
|
// overrides existing values or adds a new one
|
|
GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val);
|
|
GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val);
|
|
GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);
|
|
GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t val);
|
|
GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
|
|
GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val);
|
|
GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val);
|
|
GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
|
|
GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t val);
|
|
GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double val);
|
|
GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val);
|
|
GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
|
|
GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
|
|
GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, int n);
|
|
|
|
// set or add KV pairs from another context
|
|
GGML_API void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src);
|
|
|
|
// manage tensor info
|
|
GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
|
|
GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
|
|
GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);
|
|
|
|
// writing gguf files can be done in 2 ways:
|
|
//
|
|
// - write the entire gguf_context to a binary file in a single pass:
|
|
//
|
|
// gguf_write_to_file(ctx, fname);
|
|
//
|
|
// - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
|
|
//
|
|
// FILE * f = fopen(fname, "wb");
|
|
// fseek(f, gguf_get_meta_size(ctx), SEEK_SET);
|
|
// fwrite(f, ...);
|
|
// void * data = gguf_meta_get_meta_data(ctx);
|
|
// fseek(f, 0, SEEK_SET);
|
|
// fwrite(f, data, gguf_get_meta_size(ctx));
|
|
// free(data);
|
|
// fclose(f);
|
|
//
|
|
|
|
// write the entire context to a binary file
|
|
GGML_API void gguf_write_to_file(struct gguf_context * ctx, const char * fname, bool only_meta);
|
|
|
|
// get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
|
|
GGML_API size_t gguf_get_meta_size(struct gguf_context * ctx);
|
|
GGML_API void gguf_get_meta_data(struct gguf_context * ctx, void * data);
|
|
|
|
//
|
|
// system info
|
|
//
|
|
|
|
GGML_API int ggml_cpu_has_avx (void);
|
|
GGML_API int ggml_cpu_has_avx2 (void);
|
|
GGML_API int ggml_cpu_has_avx512 (void);
|
|
GGML_API int ggml_cpu_has_avx512_vbmi(void);
|
|
GGML_API int ggml_cpu_has_avx512_vnni(void);
|
|
GGML_API int ggml_cpu_has_fma (void);
|
|
GGML_API int ggml_cpu_has_neon (void);
|
|
GGML_API int ggml_cpu_has_arm_fma (void);
|
|
GGML_API int ggml_cpu_has_f16c (void);
|
|
GGML_API int ggml_cpu_has_fp16_va (void);
|
|
GGML_API int ggml_cpu_has_wasm_simd (void);
|
|
GGML_API int ggml_cpu_has_blas (void);
|
|
GGML_API int ggml_cpu_has_cublas (void);
|
|
GGML_API int ggml_cpu_has_clblast (void);
|
|
GGML_API int ggml_cpu_has_gpublas (void);
|
|
GGML_API int ggml_cpu_has_sse3 (void);
|
|
GGML_API int ggml_cpu_has_ssse3 (void);
|
|
GGML_API int ggml_cpu_has_vsx (void);
|
|
|
|
//
|
|
// Internal types and functions exposed for tests and benchmarks
|
|
//
|
|
|
|
#ifdef __cplusplus
|
|
// restrict not standard in C++
|
|
#define GGML_RESTRICT
|
|
#else
|
|
#define GGML_RESTRICT restrict
|
|
#endif
|
|
typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
|
|
typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
|
|
typedef void (*ggml_vec_dot_t) (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
|
|
|
|
typedef struct {
|
|
const char * type_name;
|
|
int blck_size;
|
|
size_t type_size;
|
|
bool is_quantized;
|
|
ggml_to_float_t to_float;
|
|
ggml_from_float_t from_float;
|
|
ggml_from_float_t from_float_reference;
|
|
ggml_vec_dot_t vec_dot;
|
|
enum ggml_type vec_dot_type;
|
|
} ggml_type_traits_t;
|
|
|
|
ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
|
|
|
|
#ifdef __cplusplus
|
|
}
|
|
#endif
|