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ollama/llama/ggml.h
Jeffrey Morgan 96efd9052f
Re-introduce the llama package (#5034) 
* Re-introduce the llama package

This PR brings back the llama package, making it possible to call llama.cpp and
ggml APIs from Go directly via CGo. This has a few advantages:

- C APIs can be called directly from Go without needing to use the previous
  "server" REST API
- On macOS and for CPU builds on Linux and Windows, Ollama can be built without
  a go generate ./... step, making it easy to get up and running to hack on
  parts of Ollama that don't require fast inference
- Faster build times for AVX,AVX2,CUDA and ROCM (a full build of all runners
  takes <5 min on a fast CPU)
- No git submodule making it easier to clone and build from source

This is a big PR, but much of it is vendor code except for:

- llama.go CGo bindings
- example/: a simple example of running inference
- runner/: a subprocess server designed to replace the llm/ext_server package
- Makefile an as minimal as possible Makefile to build the runner package for
  different targets (cpu, avx, avx2, cuda, rocm)

Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

* cache: Clear old KV cache entries when evicting a slot

When forking a cache entry, if no empty slots are available we
evict the least recently used one and copy over the KV entries
from the closest match. However, this copy does not overwrite
existing values but only adds new ones. Therefore, we need to
clear the old slot first.

This change fixes two issues:
 - The KV cache fills up and runs out of space even though we think
   we are managing it correctly
 - Performance gets worse over time as we use new cache entries that
   are not hot in the processor caches

* doc: explain golang objc linker warning (#6830)

* llama: gather transitive dependencies for rocm for dist packaging (#6848)

* Refine go server makefiles to be more DRY (#6924)

This breaks up the monolithic Makefile for the Go based runners into a
set of utility files as well as recursive Makefiles for the runners.
Files starting with the name "Makefile" are buildable, while files that
end with ".make" are utilities to include in other Makefiles.  This
reduces the amount of nearly identical targets and helps set a pattern
for future community contributions for new GPU runner architectures.

When we are ready to switch over to the Go runners, these files should
move to the top of the repo, and we should add targets for the main CLI,
as well as a helper "install" (put all the built binaries on the local
system in a runnable state) and "dist" target (generate the various
tar/zip files for distribution) for local developer use.

* llama: don't create extraneous directories (#6988)

* llama: Exercise the new build in CI (#6989)

Wire up some basic sanity testing in CI for the Go runner.  GPU runners are not covered yet.

* llama: Refine developer docs for Go server (#6842)

This enhances the documentation for development focusing on the new Go
server.  After we complete the transition further doc refinements
can remove the "transition" discussion.

* runner.go: Allocate batches for all sequences during init

We should tell the model that we could have full batches for all
sequences. We already do this when we allocate the batches but it was
missed during initialization.

* llama.go: Don't return nil from Tokenize on zero length input

Potentially receiving nil in a non-error condition is surprising to
most callers - it's better to return an empty slice.

* runner.go: Remove stop tokens from cache

If the last token is EOG then we don't return this and it isn't
present in the cache (because it was never submitted to Decode).
This works well for extending the cache entry with a new sequence.

However, for multi-token stop sequences, we won't return any of the
tokens but all but the last one will be in the cache. This means
when the conversation continues the cache will contain tokens that
don't overlap with the new prompt.

This works (we will pick up the portion where there is overlap) but
it causes unnecessary cache thrashing because we will fork the original
cache entry as it is not a perfect match.

By trimming the cache to the tokens that we actually return this
issue can be avoided.

* runner.go: Simplify flushing of pending tokens

* runner.go: Update TODOs

* runner.go: Don't panic when processing sequences

If there is an error processing a sequence, we should return a
clean HTTP error back to Ollama rather than panicing. This will
make us more resilient to transient failures.

Panics can still occur during startup as there is no way to serve
requests if that fails.

Co-authored-by: jmorganca <jmorganca@gmail.com>

* runner.go: More accurately capture timings

Currently prompt processing time doesn't capture the that it takes
to tokenize the input, only decoding time. We should capture the
full process to more accurately reflect reality. This is especially
true once we start processing images where the initial processing
can take significant time. This is also more consistent with the
existing C++ runner.

* runner.go: Support for vision models

In addition to bringing feature parity with the C++ runner, this also
incorporates several improvements:
 - Cache prompting works with images, avoiding the need to re-decode
   embeddings for every message in a conversation
 - Parallelism is supported, avoiding the need to restrict to one
   sequence at a time. (Though for now Ollama will not schedule
   them while we might need to fall back to the old runner.)

Co-authored-by: jmorganca <jmorganca@gmail.com>

* runner.go: Move Unicode checking code and add tests

* runner.go: Export external cache members

Runner and cache are in the same package so the change doesn't
affect anything but it is more internally consistent.

* runner.go: Image embedding cache

Generating embeddings from images can take significant time (on
my machine between 100ms and 8s depending on the model). Although
we already cache the result of decoding these images, the embeddings
need to be regenerated every time. This is not necessary if we get
the same image over and over again, for example, during a conversation.

This currently uses a very small cache with a very simple algorithm
but it is easy to improve as is warranted.

* llama: catch up on patches

Carry forward solar-pro and cli-unicode patches

* runner.go: Don't re-allocate memory for every batch

We can reuse memory allocated from batch to batch since batch
size is fixed. This both saves the cost of reallocation as well
keeps the cache lines hot.

This results in a roughly 1% performance improvement for token
generation with Nvidia GPUs on Linux.

* runner.go: Default to classic input cache policy

The input cache as part of the go runner implemented a cache
policy that aims to maximize hit rate in both single and multi-
user scenarios. When there is a cache hit, the response is
very fast.

However, performance is actually slower when there is an input
cache miss due to worse GPU VRAM locality. This means that
performance is generally better overall for multi-user scenarios
(better input cache hit rate, locality was relatively poor already).
But worse for single users (input cache hit rate is about the same,
locality is now worse).

This defaults the policy back to the old one to avoid a regression
but keeps the new one available through an environment variable
OLLAMA_MULTIUSER_CACHE. This is left undocumented as the goal is
to improve this in the future to get the best of both worlds
without user configuration.

For inputs that result in cache misses, on Nvidia/Linux this
change improves performance by 31% for prompt processing and
13% for token generation.

* runner.go: Increase size of response channel

Generally the CPU can easily keep up with handling reponses that
are generated but there's no reason not to let generation continue
and handle things in larger batches if needed.

* llama: Add CI to verify all vendored changes have patches (#7066)

Make sure we don't accidentally merge changes in the vendored code
that aren't also reflected in the patches.

* llama: adjust clip patch for mingw utf-16 (#7065)

* llama: adjust clip patch for mingw utf-16

* llama: ensure static linking of runtime libs

Avoid runtime dependencies on non-standard libraries

* runner.go: Enable llamafile (all platforms) and BLAS (Mac OS)

These are two features that are shown on llama.cpp's system info
that are currently different between the two runners. On my test
systems the performance difference is very small to negligible
but it is probably still good to equalize the features.

* llm: Don't add BOS/EOS for tokenize requests

This is consistent with what server.cpp currently does. It affects
things like token processing counts for embedding requests.

* runner.go: Don't cache prompts for embeddings

Our integration with server.cpp implicitly disables prompt caching
because it is not part of the JSON object being parsed, this makes
the Go runner behavior similarly.

Prompt caching has been seen to affect the results of text completions
on certain hardware. The results are not wrong either way but they
are non-deterministic. However, embeddings seem to be affected even
on hardware that does not show this behavior for completions. For
now, it is best to maintain consistency with the existing behavior.

* runner.go: Adjust debug log levels

Add system info printed at startup and quiet down noisier logging.

* llama: fix compiler flag differences (#7082)

Adjust the flags for the new Go server to more closely match the
generate flow

* llama: refine developer docs (#7121)

* llama: doc and example clean up (#7122)

* llama: doc and example clean up

* llama: Move new dockerfile into llama dir

Temporary home until we fully transition to the Go server

* llama: runner doc cleanup

* llama.go: Add description for Tokenize error case

---------

Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
2024-10-08 08:53:54 -07:00

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/**
* llama.cpp - commit 8962422b1c6f9b8b15f5aeaea42600bcc2d44177 - do not edit this file
*
* MIT License
*
* Copyright (c) 2023-2024 The ggml authors
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#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_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 + 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
#ifdef GGML_MULTIPLATFORM
# if defined(_WIN32)
# define GGML_CALL
# else
# define GGML_CALL __attribute__((__ms_abi__))
# endif
#else
# define GGML_CALL
#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__)
# define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
# define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#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_CONTEXTS 64
#define GGML_MAX_SRC 10
#ifndef GGML_MAX_NAME
#define GGML_MAX_NAME 64
#define GGML_MAX_N_THREADS 512
#endif
#define GGML_MAX_OP_PARAMS 64
#define GGML_DEFAULT_N_THREADS 4
#define GGML_DEFAULT_GRAPH_SIZE 2048
#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 GGML_ROPE_TYPE_NEOX 2
#define GGUF_MAGIC "GGUF"
#define GGUF_VERSION 3
#define GGUF_DEFAULT_ALIGNMENT 32
#define GGML_UNUSED(x) (void)(x)
#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)->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);
#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_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
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 GGML_CALL 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;
// 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,
GGML_TYPE_Q4_0_4_8 = 32,
GGML_TYPE_Q4_0_8_8 = 33,
GGML_TYPE_COUNT,
};
// precision
enum ggml_prec {
GGML_PREC_DEFAULT,
GGML_PREC_F32,
};
enum ggml_backend_type {
GGML_BACKEND_TYPE_CPU = 0,
GGML_BACKEND_TYPE_GPU = 10,
GGML_BACKEND_TYPE_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
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_Q4_0_4_4 = 25, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0_4_8 = 26, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0_8_8 = 27, // 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_SIN,
GGML_OP_COS,
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_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_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_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
GGML_OP_POOL_2D,
GGML_OP_POOL_2D_BACK,
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_PAD,
GGML_OP_ARANGE,
GGML_OP_TIMESTEP_EMBEDDING,
GGML_OP_ARGSORT,
GGML_OP_LEAKY_RELU,
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_WKV,
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_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_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_ERROR = 2,
GGML_LOG_LEVEL_WARN = 3,
GGML_LOG_LEVEL_INFO = 4,
GGML_LOG_LEVEL_DEBUG = 5
};
enum ggml_tensor_flag {
GGML_TENSOR_FLAG_INPUT = 1,
GGML_TENSOR_FLAG_OUTPUT = 2,
GGML_TENSOR_FLAG_PARAM = 4,
};
// 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;
GGML_DEPRECATED(enum ggml_backend_type backend, "use the buffer type to find the storage location of the tensor");
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 * grad;
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[4];
};
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);
// Scheduling priorities
enum ggml_sched_priority {
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;
// 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;
struct ggml_threadpool * threadpool;
// abort ggml_graph_compute when true
ggml_abort_callback abort_callback;
void * abort_callback_data;
};
enum ggml_cgraph_eval_order {
GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT = 0,
GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT,
GGML_CGRAPH_EVAL_ORDER_COUNT
};
typedef uint32_t ggml_bitset_t;
struct ggml_hash_set {
size_t size;
ggml_bitset_t * used;
struct ggml_tensor ** keys;
};
// computation graph
struct ggml_cgraph {
int size;
int n_nodes;
int n_leafs;
struct ggml_tensor ** nodes;
struct ggml_tensor ** grads;
struct ggml_tensor ** leafs;
struct ggml_hash_set visited_hash_set;
enum ggml_cgraph_eval_order order;
};
// 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
};
// numa strategies
enum ggml_numa_strategy {
GGML_NUMA_STRATEGY_DISABLED = 0,
GGML_NUMA_STRATEGY_DISTRIBUTE = 1,
GGML_NUMA_STRATEGY_ISOLATE = 2,
GGML_NUMA_STRATEGY_NUMACTL = 3,
GGML_NUMA_STRATEGY_MIRROR = 4,
GGML_NUMA_STRATEGY_COUNT
};
//
// 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 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_numa_init(enum ggml_numa_strategy numa); // 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 GGML_CALL int64_t ggml_nelements (const struct ggml_tensor * tensor);
GGML_API GGML_CALL int64_t ggml_nrows (const struct ggml_tensor * tensor);
GGML_API GGML_CALL 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 GGML_CALL int64_t ggml_blck_size(enum ggml_type type);
GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
GGML_API GGML_CALL 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 GGML_CALL const char * ggml_type_name(enum ggml_type type);
GGML_API GGML_CALL 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 GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
GGML_API GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor);
GGML_API GGML_CALL 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 GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_empty (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
GGML_API GGML_CALL bool ggml_is_contiguous (const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous()
GGML_API GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1
GGML_API GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2
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_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, 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);
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);
// 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 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 int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
GGML_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, 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 float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
GGML_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, 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 GGML_CALL 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_ATTRIBUTE_FORMAT(2, 3)
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_add_cast(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
enum ggml_type type);
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_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);
// 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);
// 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);
// 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);
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);
// 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
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);
// 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);
// 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);
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 3D: a->ne[2] == b->ne[1]
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);
// fused soft_max(a*scale + mask*(ALiBi slope))
// mask is optional
// 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_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) - 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);
// 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_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_CALL 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_back(
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);
// 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_depthwise_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_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
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
// 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
// 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);
// nearest interpolate
// multiplies ne0 and ne1 by scale factor
// used in stable-diffusion
GGML_API struct ggml_tensor * ggml_upscale(
struct ggml_context * ctx,
struct ggml_tensor * a,
int scale_factor);
// nearest interpolate
// nearest interpolate to specified dimensions
// used in tortoise.cpp
GGML_API struct ggml_tensor * ggml_upscale_ext(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
int ne1,
int ne2,
int ne3);
// 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);
// 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);
GGML_API struct ggml_tensor * ggml_arange(
struct ggml_context * ctx,
float start,
float stop,
float step);
// top k elements per row
GGML_API struct ggml_tensor * ggml_top_k(
struct ggml_context * ctx,
struct ggml_tensor * a,
int k);
#define GGML_KQ_MASK_PAD 32
// q: [n_embd, n_batch, n_head, 1]
// k: [n_embd, n_kv, n_head_kv, 1]
// v: [n_embd, n_kv, n_head_kv, 1] !! not transposed !!
// mask: [n_kv, n_batch_pad, 1, 1] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !!
// res: [n_embd, n_head, n_batch, 1] !! permuted !!
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);
// 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);
// 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_wkv(
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);
// 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);
// 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);
GGML_API struct ggml_cgraph ggml_graph_view (struct ggml_cgraph * cgraph, int i0, int i1);
GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads
GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);
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_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);
GGML_API struct ggml_threadpool* ggml_threadpool_new (struct ggml_threadpool_params * params);
GGML_API void ggml_threadpool_free (struct ggml_threadpool * threadpool);
GGML_API int ggml_threadpool_get_n_threads(struct ggml_threadpool * threadpool);
GGML_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool);
GGML_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool);
// 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(
const struct ggml_cgraph * cgraph,
int n_threads, /* = GGML_DEFAULT_N_THREADS */
struct ggml_threadpool * threadpool /* = NULL */ );
GGML_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
// 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 enum ggml_status 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);
// build gradient checkpointing backward graph gb for gf using provided checkpoints
// gb_tmp will contain original backward graph with rewritten backward process nodes,
// but without the second forward pass nodes.
GGML_API void ggml_build_backward_gradient_checkpointing(
struct ggml_context * ctx,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb,
struct ggml_cgraph * gb_tmp,
struct ggml_tensor * * checkpoints,
int n_checkpoints);
//
// optimization
//
// optimization methods
enum ggml_opt_type {
GGML_OPT_TYPE_ADAM,
GGML_OPT_TYPE_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_RESULT_OK = 0,
GGML_OPT_RESULT_DID_NOT_CONVERGE,
GGML_OPT_RESULT_NO_CONTEXT,
GGML_OPT_RESULT_INVALID_WOLFE,
GGML_OPT_RESULT_FAIL,
GGML_OPT_RESULT_CANCEL,
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, int accum_step, float * sched, bool * cancel);
typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data);
// optimization parameters
//
// see ggml.c (ggml_opt_default_params) for default values
//
struct ggml_opt_params {
enum ggml_opt_type type;
size_t graph_size;
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;
int n_gradient_accumulation;
// 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 * g; // current gradient
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);
//
// tensor flags
//
GGML_API void ggml_set_input(struct ggml_tensor * tensor);
GGML_API void ggml_set_output(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);
//
// 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 (const struct gguf_context * ctx);
GGML_API size_t gguf_get_alignment (const struct gguf_context * ctx);
GGML_API size_t gguf_get_data_offset(const struct gguf_context * ctx);
GGML_API void * gguf_get_data (const struct gguf_context * ctx);
GGML_API int gguf_get_n_kv(const struct gguf_context * ctx);
GGML_API int gguf_find_key(const struct gguf_context * ctx, const char * key);
GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int key_id);
GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int key_id);
GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int key_id);
// will abort if the wrong type is used for the key
GGML_API uint8_t gguf_get_val_u8 (const struct gguf_context * ctx, int key_id);
GGML_API int8_t gguf_get_val_i8 (const struct gguf_context * ctx, int key_id);
GGML_API uint16_t gguf_get_val_u16 (const struct gguf_context * ctx, int key_id);
GGML_API int16_t gguf_get_val_i16 (const struct gguf_context * ctx, int key_id);
GGML_API uint32_t gguf_get_val_u32 (const struct gguf_context * ctx, int key_id);
GGML_API int32_t gguf_get_val_i32 (const struct gguf_context * ctx, int key_id);
GGML_API float gguf_get_val_f32 (const struct gguf_context * ctx, int key_id);
GGML_API uint64_t gguf_get_val_u64 (const struct gguf_context * ctx, int key_id);
GGML_API int64_t gguf_get_val_i64 (const struct gguf_context * ctx, int key_id);
GGML_API double gguf_get_val_f64 (const struct gguf_context * ctx, int key_id);
GGML_API bool gguf_get_val_bool(const struct gguf_context * ctx, int key_id);
GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int key_id);
GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int key_id);
GGML_API int gguf_get_arr_n (const struct gguf_context * ctx, int key_id);
GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id);
GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i);
GGML_API int gguf_get_n_tensors (const struct gguf_context * ctx);
GGML_API int gguf_find_tensor (const struct gguf_context * ctx, const char * name);
GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i);
GGML_API char * gguf_get_tensor_name (const struct gguf_context * ctx, int i);
GGML_API enum ggml_type gguf_get_tensor_type (const struct gguf_context * ctx, int i);
// removes key if it exists
GGML_API void gguf_remove_key(struct gguf_context * ctx, const char * key);
// 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(const 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(const struct gguf_context * ctx);
GGML_API void gguf_get_meta_data(const struct gguf_context * ctx, void * data);
//
// system info
//
GGML_API int ggml_cpu_has_avx (void);
GGML_API int ggml_cpu_has_avx_vnni (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_avx512_bf16(void);
GGML_API int ggml_cpu_has_fma (void);
GGML_API int ggml_cpu_has_neon (void);
GGML_API int ggml_cpu_has_sve (void);
GGML_API int ggml_cpu_has_arm_fma (void);
GGML_API int ggml_cpu_has_metal (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_cuda (void);
GGML_API int ggml_cpu_has_vulkan (void);
GGML_API int ggml_cpu_has_kompute (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_sycl (void);
GGML_API int ggml_cpu_has_rpc (void);
GGML_API int ggml_cpu_has_vsx (void);
GGML_API int ggml_cpu_has_matmul_int8(void);
GGML_API int ggml_cpu_has_cann (void);
GGML_API int ggml_cpu_has_llamafile (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, int64_t k);
typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
typedef void (*ggml_from_float_to_mat_t)
(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs);
typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
const void * GGML_RESTRICT y, size_t by, int nrc);
typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
const void * GGML_RESTRICT y, int nr, int nc);
typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
const void * GGML_RESTRICT y, int nr, int nc);
typedef struct {
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;
ggml_from_float_t from_float_ref;
ggml_from_float_to_mat_t from_float_to_mat;
ggml_vec_dot_t vec_dot;
enum ggml_type vec_dot_type;
int64_t nrows; // number of rows to process simultaneously
int64_t ncols; // number of columns to process simultaneously
ggml_gemv_t gemv;
ggml_gemm_t gemm;
} ggml_type_traits_t;
GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
#ifdef __cplusplus
}
#endif
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