aditya/llama.cpp
1
0
Fork 0
mirror of https://git.adityakumar.xyz/llama.cpp.git synced 2024-11-09 15:29:43 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

llama : Metal inference (#1642)

Browse source 
* mtl : export the LLaMA computation graph

* ci : disable temporary

* mtl : adapt the MNIST example as starter

* mtl : no need for mtl-export tool, add cli arg for main instead

* mtl : export just a small part of the graph for now to make it easier

* mtl : move MSL code into separate file for easy editing

* mtl : initial get_rows_q4_0 kernel

* mtl : confirmed get_rows_q4_0 is working correctly

* mtl : add rms_norm kernel + confirm working

* mtl : add mul kernel + confirm working

* mtl : initial mul_mat Q4 kernel (wrong results)

* mtl : mul_mat fixes (still wrong)

* mtl : another mul_mat Q4 (still does not work)

* mtl : working mul_mat q4

* ggml : fix handling of "view" ops in ggml_graph_import()

* mtl : add rope kernel

* mtl : add reshape and transpose handling

* ggml : store offset as opt arg for ggml_view_xd() operators

* mtl : add cpy kernel + handle view ops

* mtl : confirm f16 x f32 attention mul mat

* mtl : add scale kernel

* mtl : add diag_mask_inf kernel

* mtl : fix soft_max kernel

* ggml : update ggml_nbytes() to handle non-contiguous tensors

* mtl : verify V tensor contents

* mtl : add f32 -> f32 cpy kernel

* mtl : add silu kernel

* mtl : add non-broadcast mul kernel

* mtl : full GPU inference of the computation graph

* mtl : optimize rms_norm and soft_max kernels

* mtl : add f16 mat x f32 vec multiplication kernel

* mtl : fix bug in f16 x f32 mul mat + speed-up computation

* mtl : faster mul_mat_q4_0_f32 kernel

* mtl : fix kernel signature + roll inner loop

* mtl : more threads for rms_norm + better timing

* mtl : remove printfs from inner loop

* mtl : simplify implementation

* mtl : add save/load vocab to ggml file

* mtl : plug Metal inference into llama.cpp (very quick-n-dirty)

* mtl : make it work with main example

Lots of hacks but at least now it generates text

* mtl : preparing for merge

* mtl : clean-up ggml mtl interface + suport scratch / inplace

* mtl : remove temp / debug code

* metal : final refactoring and simplification

* Revert "ci : disable temporary"

This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63.

* metal : add comments

* metal : clean-up stuff, fix typos

* readme : add Metal instructions

* readme : add example for main
This commit is contained in:
Georgi Gerganov 2023-06-04 23:34:30 +03:00 committed by GitHub
parent dcb2ed4826
commit ecb217db4f
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
17 changed files with 1677 additions and 94 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

1
.gitignore vendored
View file

@ -17,6 +17,7 @@ build-release/
build-static/
build-cublas/
build-opencl/
build-metal/
build-no-accel/
build-sanitize-addr/
build-sanitize-thread/

46
CMakeLists.txt
View file

@ -71,6 +71,7 @@ option(LLAMA_CUBLAS "llama: use cuBLAS"
set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
option(LLAMA_METAL "llama: use Metal" OFF)
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
@ -183,7 +184,7 @@ if (LLAMA_CUBLAS)
enable_language(CUDA)
set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
add_compile_definitions(GGML_USE_CUBLAS)
add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
@ -200,12 +201,37 @@ if (LLAMA_CUBLAS)
endif()
endif()
if (LLAMA_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
find_library(METALPERFORMANCE_FRAMEWORK MetalPerformanceShaders REQUIRED)
set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
add_compile_definitions(GGML_USE_METAL)
add_compile_definitions(GGML_METAL_NDEBUG)
# get full path to the file
#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
# copy ggml-metal.metal to bin directory
configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
${FOUNDATION_LIBRARY}
${METAL_FRAMEWORK}
${METALKIT_FRAMEWORK}
${METALPERFORMANCE_FRAMEWORK}
)
endif()
if (LLAMA_CLBLAST)
find_package(CLBlast)
if (CLBlast_FOUND)
message(STATUS "CLBlast found")
set(GGML_OPENCL_SOURCES ggml-opencl.cpp ggml-opencl.h)
set(GGML_SOURCES_OPENCL ggml-opencl.cpp ggml-opencl.h)
add_compile_definitions(GGML_USE_CLBLAST)
@ -370,8 +396,10 @@ endif()
add_library(ggml OBJECT
ggml.c
ggml.h
${GGML_CUDA_SOURCES}
${GGML_OPENCL_SOURCES})
${GGML_SOURCES_CUDA}
${GGML_SOURCES_OPENCL}
${GGML_SOURCES_METAL}
)
target_include_directories(ggml PUBLIC .)
target_compile_features(ggml PUBLIC c_std_11) # don't bump
@ -384,18 +412,22 @@ endif()
add_library(llama
llama.cpp
llama.h
llama-util.h)
llama-util.h
)
target_include_directories(llama PUBLIC .)
target_compile_features(llama PUBLIC cxx_std_11) # don't bump
target_link_libraries(llama PRIVATE ggml ${LLAMA_EXTRA_LIBS})
target_link_libraries(llama PRIVATE
ggml
${LLAMA_EXTRA_LIBS}
)
if (BUILD_SHARED_LIBS)
set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD)
endif()
if (GGML_CUDA_SOURCES)
if (GGML_SOURCES_CUDA)
message(STATUS "GGML CUDA sources found, configuring CUDA architecture")
set_property(TARGET ggml PROPERTY CUDA_ARCHITECTURES OFF)
set_property(TARGET ggml PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto")

29
Makefile
View file

@ -105,6 +105,7 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
#CFLAGS += -mfma -mf16c -mavx
#CXXFLAGS += -mfma -mf16c -mavx
endif
ifneq ($(filter ppc64%,$(UNAME_M)),)
POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
ifneq (,$(findstring POWER9,$(POWER9_M)))
@ -116,6 +117,7 @@ ifneq ($(filter ppc64%,$(UNAME_M)),)
CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN
endif
endif
ifndef LLAMA_NO_ACCELERATE
# Mac M1 - include Accelerate framework.
# `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time).
@ -123,7 +125,8 @@ ifndef LLAMA_NO_ACCELERATE
CFLAGS += -DGGML_USE_ACCELERATE
LDFLAGS += -framework Accelerate
endif
endif
endif # LLAMA_NO_ACCELERATE
ifdef LLAMA_OPENBLAS
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -I/usr/include/openblas
ifneq ($(shell grep -e "Arch Linux" -e "ID_LIKE=arch" /etc/os-release 2>/dev/null),)
@ -131,11 +134,13 @@ ifdef LLAMA_OPENBLAS
else
LDFLAGS += -lopenblas
endif
endif
endif # LLAMA_OPENBLAS
ifdef LLAMA_BLIS
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis
LDFLAGS += -lblis -L/usr/local/lib
endif
endif # LLAMA_BLIS
ifdef LLAMA_CUBLAS
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
@ -156,6 +161,7 @@ endif # LLAMA_CUDA_DMMV_Y
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
endif # LLAMA_CUBLAS
ifdef LLAMA_CLBLAST
CFLAGS += -DGGML_USE_CLBLAST
CXXFLAGS += -DGGML_USE_CLBLAST
@ -166,23 +172,38 @@ ifdef LLAMA_CLBLAST
LDFLAGS += -lclblast -lOpenCL
endif
OBJS += ggml-opencl.o
ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
$(CXX) $(CXXFLAGS) -c $< -o $@
endif
endif # LLAMA_CLBLAST
ifdef LLAMA_METAL
CFLAGS += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
CXXFLAGS += -DGGML_USE_METAL
LDFLAGS += -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
OBJS += ggml-metal.o
ggml-metal.o: ggml-metal.m ggml-metal.h
$(CC) $(CFLAGS) -c $< -o $@
endif # LLAMA_METAL
ifneq ($(filter aarch64%,$(UNAME_M)),)
# Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit)
CFLAGS += -mcpu=native
CXXFLAGS += -mcpu=native
endif
ifneq ($(filter armv6%,$(UNAME_M)),)
# Raspberry Pi 1, Zero
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
endif
ifneq ($(filter armv7%,$(UNAME_M)),)
# Raspberry Pi 2
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
endif
ifneq ($(filter armv8%,$(UNAME_M)),)
# Raspberry Pi 3, 4, Zero 2 (32-bit)
CFLAGS += -mfp16-format=ieee -mno-unaligned-access

29
README.md
View file

@ -51,11 +51,10 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quantization on a MacBook
- Plain C/C++ implementation without dependencies
- Apple silicon first-class citizen - optimized via ARM NEON and Accelerate framework
- Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
- AVX, AVX2 and AVX512 support for x86 architectures
- Mixed F16 / F32 precision
- 4-bit, 5-bit and 8-bit integer quantization support
- Runs on the CPU
- Supports OpenBLAS/Apple BLAS/ARM Performance Lib/ATLAS/BLIS/Intel MKL/NVHPC/ACML/SCSL/SGIMATH and [more](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) in BLAS
- cuBLAS and CLBlast support
@ -236,6 +235,32 @@ In order to build llama.cpp you have three different options.
zig build -Drelease-fast
```
### Metal Build
Using Metal allows the computation to be executed on the GPU for Apple devices:
- Using `make`:
```bash
LLAMA_METAL=1 make
```
- Using `CMake`:
```bash
mkdir build-metal
cd build-metal
cmake -DLLAMA_METAL=ON ..
cmake --build . --config Release
```
When built with Metal support, you can enable GPU inference with the `--gpu-layers|-ngl` command-line argument.
Any value larger than 0 will offload the computation to the GPU. For example:
```bash
./main -m ./models/7B/ggml-model-q4_0.bin -n 128 -ngl 1
```
### BLAS Build
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:

5
examples/CMakeLists.txt
View file

@ -37,7 +37,10 @@ else()
add_subdirectory(save-load-state)
add_subdirectory(benchmark)
add_subdirectory(baby-llama)
if(LLAMA_BUILD_SERVER)
if (LLAMA_METAL)
add_subdirectory(metal)
endif()
if (LLAMA_BUILD_SERVER)
add_subdirectory(server)
endif()
endif()

3
examples/common.cpp
View file

@ -299,6 +299,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.use_mmap = false;
} else if (arg == "--mtest") {
params.mem_test = true;
} else if (arg == "--export") {
params.export_cgraph = true;
} else if (arg == "--verbose-prompt") {
params.verbose_prompt = true;
} else if (arg == "-r" || arg == "--reverse-prompt") {
@ -438,6 +440,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stderr, " number of layers to store in VRAM\n");
#endif
fprintf(stderr, " --mtest compute maximum memory usage\n");
fprintf(stderr, " --export export the computation graph to 'llama.ggml'\n");
fprintf(stderr, " --verbose-prompt print prompt before generation\n");
fprintf(stderr, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");

1
examples/common.h
View file

@ -71,6 +71,7 @@ struct gpt_params {
bool use_mmap = true; // use mmap for faster loads
bool use_mlock = false; // use mlock to keep model in memory
bool mem_test = false; // compute maximum memory usage
bool export_cgraph = false; // export the computation graph
bool verbose_prompt = false; // print prompt tokens before generation
};

7
examples/main/main.cpp
View file

@ -134,6 +134,13 @@ int main(int argc, char ** argv) {
return 0;
}
// export the cgraph and exit
if (params.export_cgraph) {
llama_eval_export(ctx, "llama.ggml");
llama_free(ctx);
return 0;
}
std::string path_session = params.path_prompt_cache;
std::vector<llama_token> session_tokens;

3
examples/metal/CMakeLists.txt Normal file
View file

@ -0,0 +1,3 @@
set(TEST_TARGET metal)
add_executable(${TEST_TARGET} metal.cpp)
target_link_libraries(${TEST_TARGET} PRIVATE ggml)

102
examples/metal/metal.cpp Normal file
View file

@ -0,0 +1,102 @@
// Evaluate a statically exported ggml computation graph with Metal
//
// - First, export a LLaMA graph:
//
// $ ./bin/main -m ../models/7B/ggml-model-q4_0.bin --export
//
// - Run this tool to evaluate the exported graph:
//
// $ ./bin/metal llama.ggml
//
// The purpose of this tool is mostly for debugging and demonstration purposes.
// The main limitation of exporting computation graphs is that their sizes are static which often
// can be a problem for real-world applications.
//
#include "ggml.h"
#include "ggml-metal.h"
#include <cstdio>
#include <cstring>
#include <cstdlib>
int main(int argc, char ** argv) {
ggml_time_init();
if (argc != 2) {
fprintf(stderr, "Usage: %s llama.ggml\n", argv[0]);
return -1;
}
const char * fname_cgraph = argv[1];
// load the compute graph
struct ggml_context * ctx_data = NULL;
struct ggml_context * ctx_eval = NULL;
struct ggml_cgraph gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
gf.n_threads = 1;
// this allocates all Metal resources and memory buffers
auto * ctx_metal = ggml_metal_init();
ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data));
ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval));
// main
{
struct ggml_tensor * input = ggml_graph_get_tensor(&gf, "embd");
*(int32_t *) input->data = 1; // BOS
ggml_metal_set_tensor(ctx_metal, input);
// warmup
ggml_metal_graph_compute(ctx_metal, &gf);
const int n_iter = 16;
const int64_t t0 = ggml_time_us();
// the actual inference happens here
for (int i = 0; i < n_iter; ++i) {
ggml_metal_graph_compute(ctx_metal, &gf);
}
const int64_t t1 = ggml_time_us();
printf("time: %.2f ms, %.2f ms/tok\n", (t1 - t0) / 1000.0, (t1 - t0) / 1000.0 / n_iter);
}
// debug output
{
struct ggml_tensor * logits = gf.nodes[gf.n_nodes - 1];
ggml_metal_get_tensor(ctx_metal, logits);
float * ptr = (float *) ggml_get_data(logits);
printf("logits: ");
for (int i = 0; i < 10; i++) {
printf("%8.4f ", ptr[i]);
}
printf("\n");
int imax = 0;
double sum = 0.0;
double vmax = -1e9;
for (int i = 0; i < 32000; i++) {
sum += (double) ptr[i];
if (ptr[i] > vmax) {
vmax = ptr[i];
imax = i;
}
}
printf("sum: %f, imax = %d, vmax = %f\n", sum, imax, vmax);
}
ggml_metal_free(ctx_metal);
ggml_free(ctx_data);
ggml_free(ctx_eval);
return 0;
}

63
ggml-metal.h Normal file
View file

@ -0,0 +1,63 @@
// An interface allowing to compute ggml_cgraph with Metal
//
// This is a fully functional interface that extends ggml with GPU support for Apple devices.
// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
//
// How it works?
//
// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
//
// You only need to make sure that all memory buffers that you used during the graph creation
// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
// used during the graph evaluation to determine the arguments of the compute kernels.
//
// Synchronization between device and host memory (for example for input and output tensors)
// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
//
#pragma once
#include <stddef.h>
#include <stdbool.h>
// max memory buffers that can be mapped to the device
#define GGML_METAL_MAX_BUFFERS 16
struct ggml_tensor;
struct ggml_cgraph;
#ifdef __cplusplus
extern "C" {
#endif
struct ggml_metal_context;
struct ggml_metal_context * ggml_metal_init(void);
void ggml_metal_free(struct ggml_metal_context * ctx);
// creates a mapping between a host memory buffer and a device memory buffer
// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
// - the mapping is used during computation to determine the arguments of the compute kernels
// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
//
bool ggml_metal_add_buffer(
struct ggml_metal_context * ctx,
const char * name,
void * data,
size_t size);
// set data from host memory into the device
void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
// get data from the device into host memory
void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
// same as ggml_graph_compute but uses Metal
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
#ifdef __cplusplus
}
#endif

672
ggml-metal.m Normal file
View file

@ -0,0 +1,672 @@
#import "ggml-metal.h"
#import "ggml.h"
#import <Foundation/Foundation.h>
#import <Metal/Metal.h>
#import <MetalPerformanceShaders/MetalPerformanceShaders.h>
#ifdef GGML_METAL_NDEBUG
#define metal_printf(...)
#else
#define metal_printf(...) fprintf(stderr, __VA_ARGS__)
#endif
#define UNUSED(x) (void)(x)
struct ggml_metal_buffer {
const char * name;
void * data;
size_t size;
id<MTLBuffer> metal;
};
struct ggml_metal_context {
float * logits;
id<MTLDevice> device;
id<MTLCommandQueue> queue;
id<MTLLibrary> library;
int n_buffers;
struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
// custom kernels
#define GGML_METAL_DECL_KERNEL(name) \
id<MTLFunction> function_##name; \
id<MTLComputePipelineState> pipeline_##name
GGML_METAL_DECL_KERNEL(add);
GGML_METAL_DECL_KERNEL(mul);
GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast
GGML_METAL_DECL_KERNEL(scale);
GGML_METAL_DECL_KERNEL(silu);
GGML_METAL_DECL_KERNEL(relu);
GGML_METAL_DECL_KERNEL(soft_max);
GGML_METAL_DECL_KERNEL(diag_mask_inf);
GGML_METAL_DECL_KERNEL(get_rows_q4_0);
GGML_METAL_DECL_KERNEL(rms_norm);
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
GGML_METAL_DECL_KERNEL(rope);
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
GGML_METAL_DECL_KERNEL(cpy_f32_f32);
#undef GGML_METAL_DECL_KERNEL
};
// MSL code
// TODO: move the contents here when ready
// for now it is easier to work in a separate file
static NSString * const msl_library_source = @"see metal.metal";
struct ggml_metal_context * ggml_metal_init(void) {
fprintf(stderr, "%s: allocating\n", __func__);
struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
ctx->device = MTLCreateSystemDefaultDevice();
ctx->queue = [ctx->device newCommandQueue];
// determine if we can use MPS
if (MPSSupportsMTLDevice(ctx->device)) {
fprintf(stderr, "%s: using MPS\n", __func__);
} else {
fprintf(stderr, "%s: not using MPS\n", __func__);
GGML_ASSERT(false && "MPS not supported");
}
#if 0
// compile from source string and show compile log
{
NSError * error = nil;
ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error];
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
exit(1);
}
}
#else
UNUSED(msl_library_source);
// read the source from "ggml-metal.metal" into a string and use newLibraryWithSource
{
NSError * error = nil;
//NSString * path = [[NSBundle mainBundle] pathForResource:@"../../examples/metal/metal" ofType:@"metal"];
NSString * path = [[NSBundle mainBundle] pathForResource:@"ggml-metal" ofType:@"metal"];
fprintf(stderr, "%s: loading '%s'\n", __func__, [path UTF8String]);
NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error];
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
exit(1);
}
ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error];
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
exit(1);
}
}
#endif
// load kernels
{
#define GGML_METAL_ADD_KERNEL(name) \
ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:nil]; \
fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name);
GGML_METAL_ADD_KERNEL(add);
GGML_METAL_ADD_KERNEL(mul);
GGML_METAL_ADD_KERNEL(mul_row);
GGML_METAL_ADD_KERNEL(scale);
GGML_METAL_ADD_KERNEL(silu);
GGML_METAL_ADD_KERNEL(relu);
GGML_METAL_ADD_KERNEL(soft_max);
GGML_METAL_ADD_KERNEL(diag_mask_inf);
GGML_METAL_ADD_KERNEL(get_rows_q4_0);
GGML_METAL_ADD_KERNEL(rms_norm);
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
GGML_METAL_ADD_KERNEL(rope);
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
GGML_METAL_ADD_KERNEL(cpy_f32_f32);
#undef GGML_METAL_ADD_KERNEL
}
return ctx;
}
void ggml_metal_free(struct ggml_metal_context * ctx) {
fprintf(stderr, "%s: deallocating\n", __func__);
free(ctx);
}
// finds the Metal buffer that contains the tensor data on the GPU device
// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
// Metal buffer based on the host memory pointer
//
static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) {
//fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
for (int i = 0; i < ctx->n_buffers; ++i) {
const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data;
if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
*offs = (size_t) ioffs;
//fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
return ctx->buffers[i].metal;
}
}
fprintf(stderr, "%s: error: buffer is nil\n", __func__);
return nil;
}
bool ggml_metal_add_buffer(
struct ggml_metal_context * ctx,
const char * name,
void * data,
size_t size) {
if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
fprintf(stderr, "%s: too many buffers\n", __func__);
return false;
}
if (data) {
// verify that the buffer does not overlap with any of the existing buffers
for (int i = 0; i < ctx->n_buffers; ++i) {
const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data;
if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
fprintf(stderr, "%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
return false;
}
}
ctx->buffers[ctx->n_buffers].name = name;
ctx->buffers[ctx->n_buffers].data = data;
ctx->buffers[ctx->n_buffers].size = size;
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytes:data length:size options:MTLResourceStorageModeShared];
++ctx->n_buffers;
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB\n", __func__, name, size / 1024.0 / 1024.0);
}
return true;
}
void ggml_metal_set_tensor(
struct ggml_metal_context * ctx,
struct ggml_tensor * t) {
metal_printf("%s: set input for tensor '%s'\n", __func__, t->name);
size_t offs;
id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs);
memcpy((void *) ((uint8_t *) id_dst.contents + offs), t->data, ggml_nbytes(t));
}
void ggml_metal_get_tensor(
struct ggml_metal_context * ctx,
struct ggml_tensor * t) {
metal_printf("%s: extract results for tensor '%s'\n", __func__, t->name);
size_t offs;
id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs);
memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t));
}
void ggml_metal_graph_compute(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
metal_printf("%s: evaluating graph\n", __func__);
size_t offs_src0 = 0;
size_t offs_src1 = 0;
size_t offs_dst = 0;
id<MTLCommandBuffer> command_buffer = [ctx->queue commandBuffer];
id<MTLComputeCommandEncoder> encoder = nil;
for (int i = 0; i < gf->n_nodes; ++i) {
//metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
struct ggml_tensor * src0 = gf->nodes[i]->src0;
struct ggml_tensor * src1 = gf->nodes[i]->src1;
struct ggml_tensor * dst = gf->nodes[i];
const int64_t ne00 = src0 ? src0->ne[0] : 0;
const int64_t ne01 = src0 ? src0->ne[1] : 0;
const int64_t ne02 = src0 ? src0->ne[2] : 0;
const int64_t ne03 = src0 ? src0->ne[3] : 0;
const uint64_t nb00 = src0 ? src0->nb[0] : 0;
const uint64_t nb01 = src0 ? src0->nb[1] : 0;
const uint64_t nb02 = src0 ? src0->nb[2] : 0;
const uint64_t nb03 = src0 ? src0->nb[3] : 0;
const int64_t ne10 = src1 ? src1->ne[0] : 0;
const int64_t ne11 = src1 ? src1->ne[1] : 0;
const int64_t ne12 = src1 ? src1->ne[2] : 0;
const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
const uint64_t nb10 = src1 ? src1->nb[0] : 0;
const uint64_t nb11 = src1 ? src1->nb[1] : 0;
const uint64_t nb12 = src1 ? src1->nb[2] : 0;
const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
const int64_t ne0 = dst ? dst->ne[0] : 0;
const int64_t ne1 = dst ? dst->ne[1] : 0;
const int64_t ne2 = dst ? dst->ne[2] : 0;
const int64_t ne3 = dst ? dst->ne[3] : 0;
const uint64_t nb0 = dst ? dst->nb[0] : 0;
const uint64_t nb1 = dst ? dst->nb[1] : 0;
const uint64_t nb2 = dst ? dst->nb[2] : 0;
const uint64_t nb3 = dst ? dst->nb[3] : 0;
const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT;
id<MTLBuffer> id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil;
id<MTLBuffer> id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil;
id<MTLBuffer> id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil;
//metal_printf("%s: op - %s\n", __func__, ggml_op_name(dst->op));
//if (src0) {
// metal_printf("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02,
// ggml_is_contiguous(src0), src0->name);
//}
//if (src1) {
// metal_printf("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12,
// ggml_is_contiguous(src1), src1->name);
//}
//if (dst) {
// metal_printf("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2,
// dst->name);
//}
switch (dst->op) {
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_TRANSPOSE:
case GGML_OP_PERMUTE:
{
// noop
} break;
case GGML_OP_ADD:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_add];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_MUL:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
if (ggml_nelements(src1) == ne10) {
// src1 is a row
[encoder setComputePipelineState:ctx->pipeline_mul_row];
} else {
[encoder setComputePipelineState:ctx->pipeline_mul];
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SCALE:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
const float scale = *(const float *) src1->data;
[encoder setComputePipelineState:ctx->pipeline_scale];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&scale length:sizeof(scale) atIndex:2];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SILU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_silu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_RELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_relu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SOFT_MAX:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
const int nth = 32;
[encoder setComputePipelineState:ctx->pipeline_soft_max];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_DIAG_MASK_INF:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
const int n_past = ((int32_t *)(src1->data))[0];
[encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&n_past length:sizeof(int) atIndex:4];
[encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_MUL_MAT:
{
// TODO: needs to be updated after PR: https://github.com/ggerganov/ggml/pull/224
GGML_ASSERT(ne00 == ne10);
GGML_ASSERT(ne02 == ne12);
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
(src0t == GGML_TYPE_F32 || src0t == GGML_TYPE_F16) && ne11 > 1) {
if (encoder != nil) {
[encoder endEncoding];
encoder = nil;
}
MPSDataType src0dt = src0t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16;
MPSDataType src1dt = src1t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16;
// for F32 x F32 we use MPS
MPSMatrixDescriptor * desc0 = [MPSMatrixDescriptor
matrixDescriptorWithRows:ne01 columns:ne00 rowBytes:src0->nb[1] dataType:src0dt];
MPSMatrixDescriptor * desc1 = [MPSMatrixDescriptor
matrixDescriptorWithRows:ne11 columns:ne10 rowBytes:src1->nb[1] dataType:src1dt];
MPSMatrixDescriptor * desc = [MPSMatrixDescriptor
matrixDescriptorWithRows:ne1 columns:ne0 rowBytes:dst->nb[1] dataType:MPSDataTypeFloat32];
MPSMatrixMultiplication * mul = [[MPSMatrixMultiplication alloc]
initWithDevice:ctx->device transposeLeft:false transposeRight:true
resultRows:ne11 resultColumns:ne01 interiorColumns:ne00 alpha:1.0 beta:0.0];
// we need to do ne02 multiplications
// TODO: is there a way to do this in parallel - currently very slow ..
// TODO: might be possible to offload part of the computation to ANE using Accelerate's CBLAS
for (int64_t i02 = 0; i02 < ne02; ++i02) {
size_t offs_src0_cur = offs_src0 + i02*nb02;
size_t offs_src1_cur = offs_src1 + i02*nb12;
size_t offs_dst_cur = offs_dst + i02*nb2;
MPSMatrix * mat_src0 = [[MPSMatrix alloc] initWithBuffer:id_src0 offset:offs_src0_cur descriptor:desc0];
MPSMatrix * mat_src1 = [[MPSMatrix alloc] initWithBuffer:id_src1 offset:offs_src1_cur descriptor:desc1];
MPSMatrix * mat_dst = [[MPSMatrix alloc] initWithBuffer:id_dst offset:offs_dst_cur descriptor:desc ];
[mul encodeToCommandBuffer:command_buffer leftMatrix:mat_src1 rightMatrix:mat_src0 resultMatrix:mat_dst];
}
} else {
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
int nth0 = 32;
int nth1 = 1;
// use custom matrix x vector kernel
switch (src0t) {
case GGML_TYPE_Q4_0:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 8;
nth1 = 4;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32];
} break;
case GGML_TYPE_F16:
{
GGML_ASSERT(ne02 == ne12);
nth0 = 32;
nth1 = 1;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
} break;
default: GGML_ASSERT(false && "not implemented");
};
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:5];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:6];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:7];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:8];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:9];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14];
if (src0t == GGML_TYPE_Q4_0) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
}
} break;
case GGML_OP_GET_ROWS:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
switch (src0->type) {
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
default: GGML_ASSERT(false && "not implemented");
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&(src0->ne[0]) length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&(src0->nb[1]) length:sizeof(uint64_t) atIndex:4];
[encoder setBytes:&(dst->nb[1]) length:sizeof(uint64_t) atIndex:5];
const int64_t n = ggml_nelements(src1);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_RMS_NORM:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
const float eps = 1e-6f;
const int nth = 256;
[encoder setComputePipelineState:ctx->pipeline_rms_norm];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
[encoder setBytes:&eps length:sizeof( float) atIndex:4];
[encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
const int64_t nrows = ggml_nrows(src0);
[encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ROPE:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
const int n_dims = ((int32_t *) src1->data)[1];
const int mode = ((int32_t *) src1->data)[2];
const int n_past = ((int32_t *)(src1->data))[0];
[encoder setComputePipelineState:ctx->pipeline_rope];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&n_past length:sizeof( int) atIndex:18];
[encoder setBytes:&n_dims length:sizeof( int) atIndex:19];
[encoder setBytes:&mode length:sizeof( int) atIndex:20];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_CPY:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
const int nth = 32;
switch (src0t) {
case GGML_TYPE_F32:
{
switch (dstt) {
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f16]; break;
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32]; break;
default: GGML_ASSERT(false && "not implemented");
};
} break;
default: GGML_ASSERT(false && "not implemented");
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
default:
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false);
}
}
if (encoder != nil) {
[encoder endEncoding];
encoder = nil;
}
[command_buffer commit];
[command_buffer waitUntilCompleted];
{
const double time_elapsed = [command_buffer GPUEndTime] - [command_buffer GPUStartTime];
UNUSED(time_elapsed);
metal_printf("%s: time elapsed = %f ms\n", __func__, time_elapsed * 1000.0);
}
}

489
ggml-metal.metal Normal file
View file

@ -0,0 +1,489 @@
#include <metal_stdlib>
using namespace metal;
#define MAX(x, y) ((x) > (y) ? (x) : (y))
#define QK4_0 32
#define QR4_0 2
typedef struct {
half d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static void dequantize_row_q4_0(device const block_q4_0 * x, device float * y, int k) {
const int qk = QK4_0;
assert(k % qk == 0);
const int nb = k / qk;
for (int i = 0; i < nb; i++) {
const half d = x[i].d;
for (int j = 0; j < qk/2; ++j) {
const int x0 = (x[i].qs[j] & 0x0F) - 8;
const int x1 = (x[i].qs[j] >> 4) - 8;
y[i*qk + j + 0 ] = x0*d;
y[i*qk + j + qk/2] = x1*d;
}
}
}
kernel void kernel_add(
device const float * src0,
device const float * src1,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] + src1[tpig];
}
kernel void kernel_mul(
device const float * src0,
device const float * src1,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] * src1[tpig];
}
// assumption: src1 is a row
// broadcast src1 into src0
kernel void kernel_mul_row(
device const float * src0,
device const float * src1,
device float * dst,
constant int64_t & ne00,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] * src1[tpig % ne00];
}
kernel void kernel_scale(
device const float * src0,
device float * dst,
constant float & scale,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] * scale;
}
kernel void kernel_silu(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
float x = src0[tpig];
dst[tpig] = x / (1.0f + exp(-x));
}
kernel void kernel_relu(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = max(0.0f, src0[tpig]);
}
kernel void kernel_soft_max(
device const float * src0,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
threadgroup float * buf [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig[2];
const int64_t i02 = tgpig[1];
const int64_t i01 = tgpig[0];
device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
device float * pdst = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
// parallel max
buf[tpitg[0]] = -INFINITY;
for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
buf[tpitg[0]] = MAX(buf[tpitg[0]], psrc0[i00]);
}
// reduce
threadgroup_barrier(mem_flags::mem_threadgroup);
for (uint i = ntg[0]/2; i > 0; i /= 2) {
if (tpitg[0] < i) {
buf[tpitg[0]] = MAX(buf[tpitg[0]], buf[tpitg[0] + i]);
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
// broadcast
if (tpitg[0] == 0) {
buf[0] = buf[0];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
const float max = buf[0];
// parallel sum
buf[tpitg[0]] = 0.0f;
for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
buf[tpitg[0]] += exp(psrc0[i00] - max);
}
// reduce
threadgroup_barrier(mem_flags::mem_threadgroup);
for (uint i = ntg[0]/2; i > 0; i /= 2) {
if (tpitg[0] < i) {
buf[tpitg[0]] += buf[tpitg[0] + i];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
// broadcast
if (tpitg[0] == 0) {
buf[0] = buf[0];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
const float sum = buf[0];
for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
pdst[i00] = exp(psrc0[i00] - max) / sum;
}
}
kernel void kernel_diag_mask_inf(
device const float * src0,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int & n_past,
uint3 tpig[[thread_position_in_grid]]) {
const int64_t i02 = tpig[2];
const int64_t i01 = tpig[1];
const int64_t i00 = tpig[0];
if (i00 > n_past + i01) {
dst[i02*ne01*ne00 + i01*ne00 + i00] = -INFINITY;
} else {
dst[i02*ne01*ne00 + i01*ne00 + i00] = src0[i02*ne01*ne00 + i01*ne00 + i00];
}
}
kernel void kernel_get_rows_q4_0(
device const void * src0,
device const int * src1,
device float * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
constant uint64_t & nb1,
uint tpig[[thread_position_in_grid]]) {
const int i = tpig;
const int r = ((device int32_t *) src1)[i];
dequantize_row_q4_0(
(device const block_q4_0 *) ((device char *) src0 + r*nb01),
(device float *) ((device char *) dst + i*nb1), ne00);
}
kernel void kernel_rms_norm(
device const void * src0,
device float * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
constant float & eps,
threadgroup float * sum [[threadgroup(0)]],
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint ntg[[threads_per_threadgroup]]) {
device const float * x = (device const float *) ((device const char *) src0 + tgpig*nb01);
// parallel sum
sum[tpitg] = 0.0f;
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
sum[tpitg] += x[i00] * x[i00];
}
// reduce
threadgroup_barrier(mem_flags::mem_threadgroup);
for (uint i = ntg/2; i > 0; i /= 2) {
if (tpitg < i) {
sum[tpitg] += sum[tpitg + i];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
// broadcast
if (tpitg == 0) {
sum[0] /= ne00;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
const float mean = sum[0];
const float scale = 1.0f/sqrt(mean + eps);
device float * y = dst + tgpig*ne00;
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
y[i00] = x[i00] * scale;
}
}
kernel void kernel_mul_mat_q4_0_f32(
device const void * src0,
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
threadgroup float * sum [[threadgroup(0)]],
uint2 tgpig[[threadgroup_position_in_grid]],
uint2 tpig[[thread_position_in_grid]],
uint2 tpitg[[thread_position_in_threadgroup]],
uint2 tptg[[threads_per_threadgroup]]) {
const int nb = ne00/QK4_0;
const int64_t r0 = tgpig.x;
const int64_t r1 = tgpig.y;
device const block_q4_0 * x = (device const block_q4_0 *) src0 + r0*nb;
device const float * y = (device const float *) src1 + r1*ne10;
const uint nth = tptg.x*tptg.y;
const uint ith = tptg.y*tpitg.x + tpitg.y;
sum[ith] = 0.0f;
for (int i = tpitg.x; i < nb; i += tptg.x) {
device const uchar4 * x0p = (device const uchar4 *) (x + i)->qs;
device const float4 * y0p = (device const float4 *) (y + i*QK4_0);
const float d = (float)((x + i)->d);
const uchar4 x0v = *(x0p + tpitg.y);
const float4 y0v = *(y0p + tpitg.y + 0);
const float4 y1v = *(y0p + tpitg.y + 4);
float acc = 0.0f;
for (int j = 0; j < 4; ++j) {
const int x0 = x0v[j] & 0x0F;
const int x1 = x0v[j] >> 4;
const float y0 = y0v[j];
const float y1 = y1v[j];
acc += (x0 - 8)*y0 + (x1 - 8)*y1;
}
sum[ith] += acc*d;
}
// accumulate the sum from all threads in the threadgroup
threadgroup_barrier(mem_flags::mem_threadgroup);
for (uint i = nth/2; i > 0; i /= 2) {
if (ith < i) {
sum[ith] += sum[ith + i];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
if (ith == 0) {
dst[r1*ne0 + r0] = sum[0];
}
}
kernel void kernel_mul_mat_f16_f32(
device const char * src0,
device const char * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
threadgroup float * sum [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpig[[thread_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 tptg[[threads_per_threadgroup]]) {
const int64_t r0 = tgpig.x;
const int64_t r1 = tgpig.y;
const int64_t im = tgpig.z;
device const half * x = (device const half *) (src0 + r0*nb01 + im*nb02);
device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
sum[tpitg.x] = 0.0f;
for (int i = tpitg.x; i < ne00; i += tptg.x) {
sum[tpitg.x] += (float) x[i] * (float) y[i];
}
// accumulate the sum from all threads in the threadgroup
threadgroup_barrier(mem_flags::mem_threadgroup);
for (uint i = tptg.x/2; i > 0; i /= 2) {
if (tpitg.x < i) {
sum[tpitg.x] += sum[tpitg.x + i];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
if (tpitg.x == 0) {
dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0];
}
}
kernel void kernel_rope(
device const void * src0,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
constant int & n_past,
constant int & n_dims,
constant int & mode,
uint3 tpig[[thread_position_in_grid]]) {
const int64_t i3 = tpig[2];
const int64_t i2 = tpig[1];
const int64_t i1 = tpig[0];
const bool is_neox = mode & 2;
const float theta_scale = pow(10000.0, -2.0f/n_dims);
const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
float theta = (float)p;
if (!is_neox) {
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
const float cos_theta = cos(theta);
const float sin_theta = sin(theta);
theta *= theta_scale;
device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
device float * dst_data = (device float *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
const float x0 = src[0];
const float x1 = src[1];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[1] = x0*sin_theta + x1*cos_theta;
}
} else {
// TODO: implement
}
}
kernel void kernel_cpy_f32_f16(
device const float * src0,
device half * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig[2];
const int64_t i02 = tgpig[1];
const int64_t i01 = tgpig[0];
const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
const int64_t i3 = n / (ne2*ne1*ne0);
const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
device half * dst_data = (device half *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
dst_data[i00] = src[0];
}
}
kernel void kernel_cpy_f32_f32(
device const float * src0,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig[2];
const int64_t i02 = tgpig[1];
const int64_t i01 = tgpig[0];
const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
const int64_t i3 = n / (ne2*ne1*ne0);
const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
device float * dst_data = (device float *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
dst_data[i00] = src[0];
}
}

137
ggml.c
View file

@ -3723,7 +3723,7 @@ int64_t ggml_nelements(const struct ggml_tensor * tensor) {
return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
}
int ggml_nrows(const struct ggml_tensor * tensor) {
int64_t ggml_nrows(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
@ -3732,7 +3732,14 @@ int ggml_nrows(const struct ggml_tensor * tensor) {
size_t ggml_nbytes(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type];
// this should handle cases where the tensor is not contiguous in memory
// probaby just:
//
// return tensor->ne[3]*tensor->nb[3]
//
// is enough, but just in case, adding the second part
return MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]);
}
int ggml_blck_size(enum ggml_type type) {
@ -3814,11 +3821,11 @@ size_t ggml_tensor_overhead(void) {
return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE + 16;
}
static inline bool ggml_is_transposed(const struct ggml_tensor * tensor) {
bool ggml_is_transposed(const struct ggml_tensor * tensor) {
return tensor->nb[0] > tensor->nb[1];
}
static inline bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return
@ -5802,10 +5809,18 @@ struct ggml_tensor * ggml_view_1d(
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset);
ggml_scratch_save(ctx);
struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2);
memcpy(offs->data, &offset, 2*sizeof(int32_t));
ggml_scratch_load(ctx);
result->op = GGML_OP_VIEW;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
result->opt[0] = offs;
if (is_node) {
memcpy(result->padding, &offset, sizeof(offset));
@ -5834,6 +5849,13 @@ struct ggml_tensor * ggml_view_2d(
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset);
ggml_scratch_save(ctx);
struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2);
memcpy(offs->data, &offset, 2*sizeof(int32_t));
ggml_scratch_load(ctx);
result->nb[1] = nb1;
result->nb[2] = result->nb[1]*ne1;
result->nb[3] = result->nb[2];
@ -5842,6 +5864,7 @@ struct ggml_tensor * ggml_view_2d(
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
result->opt[0] = offs;
if (is_node) {
memcpy(result->padding, &offset, sizeof(offset));
@ -5872,6 +5895,13 @@ struct ggml_tensor * ggml_view_3d(
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset);
ggml_scratch_save(ctx);
struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2);
memcpy(offs->data, &offset, 2*sizeof(int32_t));
ggml_scratch_load(ctx);
result->nb[1] = nb1;
result->nb[2] = nb2;
result->nb[3] = result->nb[2]*ne2;
@ -5880,6 +5910,7 @@ struct ggml_tensor * ggml_view_3d(
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
result->opt[0] = offs;
if (is_node) {
memcpy(result->padding, &offset, sizeof(offset));
@ -5912,6 +5943,13 @@ struct ggml_tensor * ggml_view_4d(
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, (char *) a->data + offset);
ggml_scratch_save(ctx);
struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2);
memcpy(offs->data, &offset, 2*sizeof(int32_t));
ggml_scratch_load(ctx);
result->nb[1] = nb1;
result->nb[2] = nb2;
result->nb[3] = nb3;
@ -5920,6 +5958,7 @@ struct ggml_tensor * ggml_view_4d(
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
result->opt[0] = offs;
if (is_node) {
memcpy(result->padding, &offset, sizeof(offset));
@ -9252,7 +9291,7 @@ static void ggml_compute_forward_rms_norm_f32(
sum += (ggml_float)(x[i00] * x[i00]);
}
float mean = sum/ne00;
const float mean = sum/ne00;
float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
@ -14588,7 +14627,7 @@ static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fou
const int64_t * ne = tensor->ne;
const size_t * nb = tensor->nb;
fprintf(fout, "%-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %16p %16s\n",
fprintf(fout, "%-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %16p %32s\n",
ggml_type_name(tensor->type),
ggml_op_name (tensor->op),
tensor->n_dims,
@ -14602,7 +14641,7 @@ static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char
const int64_t * ne = tensor->ne;
const size_t * nb = tensor->nb;
fprintf(fout, "%-6s %-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %8d %16p %16s\n",
fprintf(fout, "%-6s %-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %8d %16p %32s\n",
arg,
ggml_type_name(tensor->type),
ggml_op_name (tensor->op),
@ -14615,8 +14654,8 @@ static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char
}
void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
assert(cgraph->work == NULL);
assert(cgraph->work_size == 0);
//assert(cgraph->work == NULL);
//assert(cgraph->work_size == 0);
uint64_t size_eval = 0;
@ -14837,7 +14876,6 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
// read file into data
{
FILE * fin = fopen(fname, "rb");
if (!fin) {
fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
return result;
@ -14977,6 +15015,8 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
op = *(const uint32_t *) ptr; ptr += sizeof(op);
n_dims = *(const uint32_t *) ptr; ptr += sizeof(n_dims);
enum ggml_op eop = (enum ggml_op) op;
int64_t ne[GGML_MAX_DIMS];
size_t nb[GGML_MAX_DIMS];
@ -14991,42 +15031,77 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
nb[j] = nb_cur;
}
struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne);
uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur); // TODO: not yet used
tensor->op = (enum ggml_op) op;
const char * ptr_name = ptr; ptr += GGML_MAX_NAME;
uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur);
const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += (2 + GGML_MAX_OPT)*sizeof(int32_t);
memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME;
for (int j = 0; j < GGML_MAX_DIMS; ++j) {
tensor->nb[j] = nb[j];
}
struct ggml_tensor * args[2 + GGML_MAX_OPT] = { NULL };
// parse args
{
struct ggml_tensor ** args[2 + GGML_MAX_OPT] = {
&tensor->src0,
&tensor->src1,
};
for (int j = 0; j < GGML_MAX_OPT; ++j) {
args[2 + j] = &tensor->opt[j];
}
for (int j = 0; j < 2 + GGML_MAX_OPT; ++j) {
const int32_t arg_idx = *(const int32_t *) ptr; ptr += sizeof(arg_idx);
const int32_t arg_idx = ptr_arg_idx[j];
if (arg_idx == -1) {
continue;
}
if (arg_idx < GGML_MAX_NODES) {
*args[j] = result.leafs[arg_idx];
args[j] = result.leafs[arg_idx];
} else {
*args[j] = result.nodes[arg_idx - GGML_MAX_NODES];
args[j] = result.nodes[arg_idx - GGML_MAX_NODES];
}
}
// create the tensor
// "view" operations are handled differently
// TODO: handle inplace ops - currently a copy is always made
struct ggml_tensor * tensor = NULL;
switch (eop) {
// TODO: implement other view ops
case GGML_OP_RESHAPE:
{
tensor = ggml_reshape_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3]);
} break;
case GGML_OP_VIEW:
{
tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0);
uint64_t offs;
memcpy(&offs, args[2]->data, sizeof(offs));
tensor->data = ((char *) tensor->data) + offs;
} break;
case GGML_OP_TRANSPOSE:
{
tensor = ggml_transpose(*ctx_eval, args[0]);
} break;
case GGML_OP_PERMUTE:
{
tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0);
} break;
default:
{
tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne);
tensor->op = eop;
} break;
}
memcpy(tensor->name, ptr_name, GGML_MAX_NAME);
for (int j = 0; j < GGML_MAX_DIMS; ++j) {
tensor->nb[j] = nb[j];
}
tensor->src0 = args[0];
tensor->src1 = args[1];
for (int j = 0; j < GGML_MAX_OPT; ++j) {
tensor->opt[j] = args[2 + j];
}
result.nodes[i] = tensor;

4
ggml.h
View file

@ -425,6 +425,7 @@ extern "C" {
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 int ggml_blck_size (enum ggml_type type);
@ -441,6 +442,9 @@ extern "C" {
// 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);
// use this to compute the memory overhead of a tensor
GGML_API size_t ggml_tensor_overhead(void);

112
llama.cpp
View file

@ -16,6 +16,10 @@
#include "ggml-opencl.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include <array>
#include <ctime>
#include <cinttypes>
@ -243,6 +247,10 @@ struct llama_context {
llama_ctx_buffer buf_compute;
llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
#ifdef GGML_USE_METAL
ggml_metal_context * ctx_metal = NULL;
#endif
int buf_last = 0;
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
@ -1088,7 +1096,7 @@ static void llama_model_load_internal(
mmapped_size - vram_total + // weights in VRAM not in memory
MEM_REQ_SCRATCH0().at(model.type) +
MEM_REQ_SCRATCH1().at(model.type) +
MEM_REQ_EVAL().at(model.type);
MEM_REQ_EVAL().at (model.type);
// this is the memory required by one llama_state
const size_t mem_required_state =
@ -1199,13 +1207,15 @@ static bool llama_model_load(
// - tokens: new batch of tokens to process
// - n_past: the context size so far
// - n_threads: number of threads to use
// - cgraph_fname: filename of the exported computation graph
//
static bool llama_eval_internal(
llama_context & lctx,
const llama_token * tokens,
const int n_tokens,
const int n_past,
const int n_threads) {
const int n_threads,
const char * cgraph_fname) {
// enforce that the first token is BOS
if (n_past == 0 && tokens[0] != llama_token_bos()) {
@ -1251,13 +1261,18 @@ static bool llama_eval_internal(
ggml_set_name(embd, "embd");
memcpy(embd->data, tokens, N*ggml_element_size(embd));
#ifdef GGML_USE_METAL
if (lctx.ctx_metal && N == 1) {
ggml_metal_set_tensor(lctx.ctx_metal, embd);
}
#endif
struct ggml_tensor * cur;
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
struct ggml_tensor * cur;
lctx.use_buf(ctx0, 0);
// norm
@ -1271,6 +1286,7 @@ static bool llama_eval_internal(
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
ggml_set_name(Qcur, "Qcur");
@ -1280,6 +1296,7 @@ static bool llama_eval_internal(
{
// compute the transposed [N, n_embd] V matrix
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), n_embd, N));
ggml_set_name(Vcur, "Vcur");
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
@ -1325,7 +1342,6 @@ static bool llama_eval_internal(
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
ggml_set_name(KQ_soft_max, "KQ_soft_max");
// split cached V into n_head heads
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
@ -1407,26 +1423,53 @@ static bool llama_eval_internal(
// norm
{
cur = ggml_rms_norm(ctx0, inpL);
inpL = ggml_rms_norm(ctx0, inpL);
// cur = cur*norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.norm);
// inpL = inpL*norm(broadcasted)
inpL = ggml_mul(ctx0, inpL, model.norm);
embeddings = inpL;
embeddings = cur;
}
// lm_head
inpL = ggml_mul_mat(ctx0, model.output, inpL);
cur = ggml_mul_mat(ctx0, model.output, cur);
lctx.use_buf(ctx0, -1);
// logits -> probs
//inpL = ggml_soft_max_inplace(ctx0, inpL);
//cur = ggml_soft_max_inplace(ctx0, cur);
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
ggml_build_forward_expand(&gf, cur);
#ifdef GGML_USE_METAL
if (lctx.ctx_metal && N == 1) {
ggml_metal_graph_compute(lctx.ctx_metal, &gf);
ggml_metal_get_tensor (lctx.ctx_metal, cur);
} else {
// IMPORTANT:
// Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
// ggml_graph_compute(). It uses Apple's Accelerate CBLAS API which takes advantage of the ANE or the AMX
// coprocessor.
//
// When we implement Matrix x Matrix Metal multiplication, we can avoid this branch.
// But for now, we have focused only on Matrix x Vector Metal multiplication.
//
ggml_graph_compute(ctx0, &gf);
if (lctx.ctx_metal) {
// We need to sync the CPU KV cache with the GPU KV cache
ggml_metal_set_tensor(lctx.ctx_metal, kv_self.k);
ggml_metal_set_tensor(lctx.ctx_metal, kv_self.v);
}
}
#else
ggml_graph_compute(ctx0, &gf);
#endif
if (cgraph_fname) {
ggml_graph_export(&gf, cgraph_fname);
}
#ifdef GGML_PERF
// print timing information per ggml operation (for debugging purposes)
@ -1440,7 +1483,7 @@ static bool llama_eval_internal(
//}
//embd_w.resize(n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(cur), sizeof(float)*n_vocab*N);
// update kv token count
lctx.model.kv_self.n = n_past + N;
@ -1451,11 +1494,11 @@ static bool llama_eval_internal(
if (lctx.logits_all) {
logits_out.resize(n_vocab * N);
memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
memcpy(logits_out.data(), (float *) ggml_get_data(cur), sizeof(float)*n_vocab*N);
} else {
// return result for just the last token
logits_out.resize(n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(cur) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
}
}
@ -2290,6 +2333,25 @@ struct llama_context * llama_init_from_file(
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
}
#ifdef GGML_USE_METAL
if (params.n_gpu_layers > 0) {
// this allocates all Metal resources and memory buffers
ctx->ctx_metal = ggml_metal_init();
if (params.use_mmap) {
ggml_metal_add_buffer(ctx->ctx_metal, "data", ctx->model.mapping->addr, ctx->model.mapping->size);
ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size);
} else {
ggml_metal_add_buffer(ctx->ctx_metal, "data", ggml_get_mem_buffer(ctx->model.ctx), ggml_get_mem_size(ctx->model.ctx));
ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size);
}
ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size);
ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size);
ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size);
}
#endif
return ctx;
}
@ -2905,7 +2967,7 @@ int llama_eval(
int n_tokens,
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads)) {
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
@ -2920,6 +2982,20 @@ int llama_eval(
return 0;
}
int llama_eval_export(struct llama_context * ctx, const char * fname) {
const int n_batch = 1;
const int n_ctx = 512 - n_batch;
const std::vector<llama_token> tmp(n_batch, llama_token_bos());
if (!llama_eval_internal(*ctx, tmp.data(), tmp.size(), n_ctx, 1, fname)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
return 0;
}
int llama_tokenize(
struct llama_context * ctx,
const char * text,

8
llama.h
View file

@ -31,7 +31,7 @@
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 1
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
#define LLAMA_SUPPORTS_GPU_OFFLOAD
#endif
@ -173,6 +173,12 @@ extern "C" {
int n_past,
int n_threads);
// Export a static computation graph for context of 511 and batch size of 1
// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
// parameters here to keep things simple
// IMPORTANT: do not use for anything else other than debugging and testing!
LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
// Convert the provided text into tokens.
// The tokens pointer must be large enough to hold the resulting tokens.
// Returns the number of tokens on success, no more than n_max_tokens