vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Merge branch 'master' of https://github.com/goerch/llama.cpp

Browse source
This commit is contained in:
goerch 2023-09-14 17:08:12 +02:00
commit 5d528ed5be
14 changed files with 1389 additions and 800 deletions
Download patch file Download diff file Expand all files Collapse all files

56
.github/workflows/build.yml vendored
View file

@ -197,6 +197,62 @@ jobs:
cd build
ctest --verbose --timeout 900
macOS-latest-cmake-ios:
runs-on: macos-latest
steps:
- name: Clone
id: checkout
uses: actions/checkout@v1
- name: Dependencies
id: depends
continue-on-error: true
run: |
brew update
- name: Build
id: cmake_build
run: |
sysctl -a
mkdir build
cd build
cmake -G Xcode .. \
-DLLAMA_BUILD_EXAMPLES=OFF \
-DLLAMA_BUILD_TESTS=OFF \
-DLLAMA_BUILD_SERVER=OFF \
-DCMAKE_SYSTEM_NAME=iOS \
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0
cmake --build . --config Release
macOS-latest-cmake-tvos:
runs-on: macos-latest
steps:
- name: Clone
id: checkout
uses: actions/checkout@v1
- name: Dependencies
id: depends
continue-on-error: true
run: |
brew update
- name: Build
id: cmake_build
run: |
sysctl -a
mkdir build
cd build
cmake -G Xcode .. \
-DLLAMA_BUILD_EXAMPLES=OFF \
-DLLAMA_BUILD_TESTS=OFF \
-DLLAMA_BUILD_SERVER=OFF \
-DCMAKE_SYSTEM_NAME=tvOS \
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0
cmake --build . --config Release
windows-latest-cmake:
runs-on: windows-latest

39
CMakeLists.txt
View file

@ -135,6 +135,7 @@ set(CMAKE_C_STANDARD 11)
set(CMAKE_C_STANDARD_REQUIRED true)
set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
include(CheckCXXCompilerFlag)
if (NOT MSVC)
if (LLAMA_SANITIZE_THREAD)
@ -388,7 +389,6 @@ if (LLAMA_HIPBLAS)
target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
target_compile_definitions(ggml-rocm PRIVATE CC_TURING=1000000000)
set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX)
target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas)
@ -461,6 +461,13 @@ endif()
# TODO: probably these flags need to be tweaked on some architectures
# feel free to update the Makefile for your architecture and send a pull request or issue
message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
if (MSVC)
string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR)
message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}")
else ()
set(CMAKE_GENERATOR_PLATFORM_LWR "")
endif ()
if (NOT MSVC)
if (LLAMA_STATIC)
add_link_options(-static)
@ -476,25 +483,33 @@ if (NOT MSVC)
endif()
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64"))
message(STATUS "ARM detected")
if (MSVC)
# TODO: arm msvc?
add_compile_definitions(__ARM_NEON)
add_compile_definitions(__ARM_FEATURE_FMA)
add_compile_definitions(__ARM_FEATURE_DOTPROD)
# add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) # MSVC doesn't support vdupq_n_f16, vld1q_f16, vst1q_f16
add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead
else()
check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E)
if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
add_compile_options(-mfp16-format=ieee)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
# Raspberry Pi 1, Zero
add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access)
add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
# Raspberry Pi 2
add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations)
add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
# Raspberry Pi 3, 4, Zero 2 (32-bit)
add_compile_options(-mfp16-format=ieee -mno-unaligned-access)
add_compile_options(-mno-unaligned-access)
endif()
endif()
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$")
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" )
message(STATUS "x86 detected")
if (MSVC)
if (LLAMA_AVX512)
@ -578,10 +593,12 @@ endif()
# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1,
# and on macOS its availability depends on enabling Darwin extensions
# similarly on DragonFly, enabling BSD extensions is necessary
if (CMAKE_SYSTEM_NAME MATCHES "Darwin")
add_compile_definitions(_DARWIN_C_SOURCE)
endif()
if (CMAKE_SYSTEM_NAME MATCHES "DragonFly")
if (
CMAKE_SYSTEM_NAME MATCHES "Darwin" OR
CMAKE_SYSTEM_NAME MATCHES "iOS" OR
CMAKE_SYSTEM_NAME MATCHES "tvOS" OR
CMAKE_SYSTEM_NAME MATCHES "DragonFly"
)
add_compile_definitions(_DARWIN_C_SOURCE)
endif()

1
Makefile
View file

@ -408,7 +408,6 @@ ifdef LLAMA_HIPBLAS
HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
HIPFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
HIPFLAGS += -DCC_TURING=1000000000
ifdef LLAMA_CUDA_FORCE_DMMV
HIPFLAGS += -DGGML_CUDA_FORCE_DMMV
endif # LLAMA_CUDA_FORCE_DMMV

32
Package.swift
View file

@ -2,8 +2,30 @@
import PackageDescription
#if arch(arm) || arch(arm64)
let platforms: [SupportedPlatform]? = [
.macOS(.v11),
.iOS(.v14),
.watchOS(.v4),
.tvOS(.v14)
]
let exclude: [String] = []
let additionalSources: [String] = ["ggml-metal.m"]
let additionalSettings: [CSetting] = [
.unsafeFlags(["-fno-objc-arc"]),
.define("GGML_SWIFT"),
.define("GGML_USE_METAL")
]
#else
let platforms: [SupportedPlatform]? = nil
let exclude: [String] = ["ggml-metal.metal"]
let additionalSources: [String] = []
let additionalSettings: [CSetting] = []
#endif
let package = Package(
name: "llama",
platforms: platforms,
products: [
.library(name: "llama", targets: ["llama"]),
],
@ -11,23 +33,23 @@ let package = Package(
.target(
name: "llama",
path: ".",
exclude: ["ggml-metal.metal"],
exclude: exclude,
sources: [
"ggml.c",
"llama.cpp",
"ggml-alloc.c",
"k_quants.c"
],
"k_quants.c",
] + additionalSources,
publicHeadersPath: "spm-headers",
cSettings: [
.unsafeFlags(["-Wno-shorten-64-to-32"]),
.define("GGML_USE_K_QUANTS"),
.define("GGML_USE_ACCELERATE")
],
] + additionalSettings,
linkerSettings: [
.linkedFramework("Accelerate")
]
),
)
],
cxxLanguageStandard: .cxx11
)

13
common/common.cpp
View file

@ -374,6 +374,17 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
#else
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
#endif
} else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") {
if (++i >= argc) {
invalid_param = true;
break;
}
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
params.n_gpu_layers_draft = std::stoi(argv[i]);
#else
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
#endif
} else if (arg == "--main-gpu" || arg == "-mg") {
if (++i >= argc) {
@ -664,6 +675,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
printf(" -ngl N, --n-gpu-layers N\n");
printf(" number of layers to store in VRAM\n");
printf(" -ngld N, --n-gpu-layers-draft N\n");
printf(" number of layers to store in VRAM for the draft model\n");
printf(" -ts SPLIT --tensor-split SPLIT\n");
printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");

1
common/common.h
View file

@ -38,6 +38,7 @@ struct gpt_params {
int32_t n_draft = 16; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.

18
convert.py
View file

@ -145,7 +145,6 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
class Params:
n_vocab: int
n_embd: int
n_mult: int
n_layer: int
n_ctx: int
n_ff: int
@ -161,15 +160,6 @@ class Params:
# path to the directory containing the model files
path_model: Path | None = None
@staticmethod
def find_n_mult(n_ff: int, n_embd: int) -> int:
# hardcoded magic range
for n_mult in range(8192, 1, -1):
calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
if calc_ff == n_ff:
return n_mult
raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).")
@staticmethod
def guessed(model: LazyModel) -> Params:
# try transformer naming first
@ -197,7 +187,6 @@ class Params:
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_layer = n_layer,
n_ctx = -1,
n_ff = n_ff,
@ -225,8 +214,6 @@ class Params:
else:
f_rope_scale = None
n_mult = Params.find_n_mult(n_ff, n_embd)
if "max_sequence_length" in config:
n_ctx = config["max_sequence_length"]
elif "max_position_embeddings" in config:
@ -238,7 +225,6 @@ class Params:
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_layer = n_layer,
n_ctx = n_ctx,
n_ff = n_ff,
@ -250,7 +236,7 @@ class Params:
)
# LLaMA v2 70B params.json
# {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1
# {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1}
@staticmethod
def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
config = json.load(open(config_path))
@ -258,7 +244,6 @@ class Params:
n_vocab = config["vocab_size"] if "vocab_size" in config else -1
n_embd = config["dim"]
n_layer = config["n_layers"]
n_mult = config["multiple_of"]
n_ff = -1
n_head = config["n_heads"]
n_head_kv = config["n_kv_heads"] if "n_kv_heads" in config else n_head
@ -285,7 +270,6 @@ class Params:
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_layer = n_layer,
n_ctx = n_ctx,
n_ff = n_ff,

1
examples/speculative/speculative.cpp
View file

@ -42,6 +42,7 @@ int main(int argc, char ** argv) {
// load the draft model
params.model = params.model_draft;
params.n_gpu_layers = params.n_gpu_layers_draft;
std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
// tokenize the prompt

1666
ggml-cuda.cu
View file

File diff suppressed because it is too large Load diff

72
ggml-metal.m
View file

@ -63,7 +63,9 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(relu);
GGML_METAL_DECL_KERNEL(gelu);
GGML_METAL_DECL_KERNEL(soft_max);
GGML_METAL_DECL_KERNEL(soft_max_4);
GGML_METAL_DECL_KERNEL(diag_mask_inf);
GGML_METAL_DECL_KERNEL(diag_mask_inf_8);
GGML_METAL_DECL_KERNEL(get_rows_f16);
GGML_METAL_DECL_KERNEL(get_rows_q4_0);
GGML_METAL_DECL_KERNEL(get_rows_q4_1);
@ -77,6 +79,7 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(norm);
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32_1row);
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32_l4);
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q8_0_f32);
@ -117,14 +120,17 @@ static NSString * const msl_library_source = @"see metal.metal";
struct ggml_metal_context * ggml_metal_init(int n_cb) {
metal_printf("%s: allocating\n", __func__);
// Show all the Metal device instances in the system
NSArray * devices = MTLCopyAllDevices();
id <MTLDevice> device;
NSString * s;
#if TARGET_OS_OSX
// Show all the Metal device instances in the system
NSArray * devices = MTLCopyAllDevices();
for (device in devices) {
s = [device name];
metal_printf("%s: found device: %s\n", __func__, [s UTF8String]);
}
#endif
// Pick and show default Metal device
device = MTLCreateSystemDefaultDevice();
@ -141,12 +147,20 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->d_queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
#if 0
// compile from source string and show compile log
#ifdef GGML_SWIFT
// load the default.metallib file
{
NSError * error = nil;
ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error];
NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
NSString * llamaBundlePath = [bundle pathForResource:@"llama_llama" ofType:@"bundle"];
NSBundle * llamaBundle = [NSBundle bundleWithPath:llamaBundlePath];
NSString * libPath = [llamaBundle pathForResource:@"default" ofType:@"metallib"];
NSURL * libURL = [NSURL fileURLWithPath:libPath];
// Load the metallib file into a Metal library
ctx->library = [ctx->device newLibraryWithURL:libURL error:&error];
if (error) {
metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL;
@ -207,7 +221,9 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(relu);
GGML_METAL_ADD_KERNEL(gelu);
GGML_METAL_ADD_KERNEL(soft_max);
GGML_METAL_ADD_KERNEL(soft_max_4);
GGML_METAL_ADD_KERNEL(diag_mask_inf);
GGML_METAL_ADD_KERNEL(diag_mask_inf_8);
GGML_METAL_ADD_KERNEL(get_rows_f16);
GGML_METAL_ADD_KERNEL(get_rows_q4_0);
GGML_METAL_ADD_KERNEL(get_rows_q4_1);
@ -221,6 +237,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(norm);
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32_1row);
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32_l4);
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q8_0_f32);
@ -247,13 +264,15 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
#undef GGML_METAL_ADD_KERNEL
}
metal_printf("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
metal_printf("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
#if TARGET_OS_OSX
metal_printf("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
if (ctx->device.maxTransferRate != 0) {
metal_printf("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
} else {
metal_printf("%s: maxTransferRate = built-in GPU\n", __func__);
}
#endif
return ctx;
}
@ -273,7 +292,8 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_DEL_KERNEL(relu);
GGML_METAL_DEL_KERNEL(gelu);
GGML_METAL_DEL_KERNEL(soft_max);
GGML_METAL_DEL_KERNEL(diag_mask_inf);
GGML_METAL_DEL_KERNEL(soft_max_4);
GGML_METAL_DEL_KERNEL(diag_mask_inf_8);
GGML_METAL_DEL_KERNEL(get_rows_f16);
GGML_METAL_DEL_KERNEL(get_rows_q4_0);
GGML_METAL_DEL_KERNEL(get_rows_q4_1);
@ -287,6 +307,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_DEL_KERNEL(norm);
GGML_METAL_DEL_KERNEL(mul_mat_f16_f32);
GGML_METAL_DEL_KERNEL(mul_mat_f16_f32_1row);
GGML_METAL_DEL_KERNEL(mul_mat_f16_f32_l4);
GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32);
@ -454,6 +475,7 @@ bool ggml_metal_add_buffer(
}
}
#if TARGET_OS_OSX
metal_printf(", (%8.2f / %8.2f)",
ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
@ -463,6 +485,9 @@ bool ggml_metal_add_buffer(
} else {
metal_printf("\n");
}
#else
metal_printf(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0);
#endif
}
return true;
@ -750,7 +775,7 @@ void ggml_metal_graph_compute(
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&scale length:sizeof(scale) atIndex:2];
const int64_t n = ggml_nelements(dst);
const int64_t n = ggml_nelements(dst)/4;
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
@ -762,7 +787,7 @@ void ggml_metal_graph_compute(
[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);
const int64_t n = ggml_nelements(dst)/4;
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
@ -782,7 +807,7 @@ void ggml_metal_graph_compute(
[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);
const int64_t n = ggml_nelements(dst)/4;
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
@ -796,13 +821,16 @@ void ggml_metal_graph_compute(
{
const int nth = 32;
[encoder setComputePipelineState:ctx->pipeline_soft_max];
if (ne00%4 == 0) {
[encoder setComputePipelineState:ctx->pipeline_soft_max_4];
} else {
[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;
@ -810,14 +838,23 @@ void ggml_metal_graph_compute(
{
const int n_past = ((int32_t *)(dst->op_params))[0];
[encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
if (ne00%8 == 0) {
[encoder setComputePipelineState:ctx->pipeline_diag_mask_inf_8];
} else {
[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)];
if (ne00%8 == 0) {
[encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
}
else {
[encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
}
} break;
case GGML_OP_MUL_MAT:
{
@ -864,6 +901,7 @@ void ggml_metal_graph_compute(
} else {
int nth0 = 32;
int nth1 = 1;
int nrows = 1;
// use custom matrix x vector kernel
switch (src0t) {
@ -873,8 +911,12 @@ void ggml_metal_graph_compute(
nth1 = 1;
if (ne11 * ne12 < 4) {
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_1row];
} else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_l4];
nrows = ne11;
} else {
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
nrows = 4;
}
} break;
case GGML_TYPE_Q4_0:
@ -995,7 +1037,7 @@ void ggml_metal_graph_compute(
else if (src0t == GGML_TYPE_Q6_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
int64_t ny = (ne11 + 3)/4;
int64_t ny = (ne11 + nrows - 1)/nrows;
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
}

281
ggml-metal.metal
View file

@ -63,18 +63,18 @@ kernel void kernel_mul_row(
}
kernel void kernel_scale(
device const float * src0,
device float * dst,
device const float4 * src0,
device float4 * 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,
device const float4 * src0,
device float4 * dst,
uint tpig[[thread_position_in_grid]]) {
float x = src0[tpig];
device const float4 & x = src0[tpig];
dst[tpig] = x / (1.0f + exp(-x));
}
@ -89,10 +89,10 @@ constant float GELU_COEF_A = 0.044715f;
constant float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
kernel void kernel_gelu(
device const float * src0,
device float * dst,
device const float4 * src0,
device float4 * dst,
uint tpig[[thread_position_in_grid]]) {
float x = src0[tpig];
device const float4 & x = src0[tpig];
// BEWARE !!!
// Simply using "tanh" instead of "precise::tanh" will sometimes results in NaNs!
@ -107,7 +107,6 @@ kernel void kernel_soft_max(
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]]) {
@ -119,64 +118,70 @@ kernel void kernel_soft_max(
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]);
float lmax = psrc0[tpitg[0]];
for (int i00 = tpitg[0] + ntg[0]; i00 < ne00; i00 += ntg[0]) {
lmax = MAX(lmax, 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 - not needed. There is a threadgroup barrier above in the last iteration of
// the loop, and when that is done, buf[0] has the correct (synchronized) value
//if (tpitg[0] == 0) {
// buf[0] = buf[0];
//}
//threadgroup_barrier(mem_flags::mem_threadgroup);
const float max = buf[0];
const float max = simd_max(lmax);
// parallel sum
buf[tpitg[0]] = 0.0f;
float lsum = 0.0f;
for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
const float exp_psrc0 = exp(psrc0[i00] - max);
buf[tpitg[0]] += exp_psrc0;
lsum += exp_psrc0;
// Remember the result of exp here. exp is expensive, so we really do not
// whish to compute it twice.
pdst[i00] = exp_psrc0;
}
// 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 - not needed, see above
//// broadcast
//if (tpitg[0] == 0) {
// buf[0] = buf[0];
//}
//threadgroup_barrier(mem_flags::mem_threadgroup);
const float sum = buf[0];
const float sum = simd_sum(lsum);
for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
pdst[i00] /= sum;
}
}
kernel void kernel_soft_max_4(
device const float * src0,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
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 float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
device float4 * pdst4 = (device float4 *)(dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
// parallel max
float4 lmax4 = psrc4[tpitg[0]];
for (int i00 = tpitg[0] + ntg[0]; i00 < ne00/4; i00 += ntg[0]) {
lmax4 = fmax(lmax4, psrc4[i00]);
}
float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
const float max = simd_max(lmax);
// parallel sum
float4 lsum4 = 0.0f;
for (int i00 = tpitg[0]; i00 < ne00/4; i00 += ntg[0]) {
const float4 exp_psrc4 = exp(psrc4[i00] - max);
lsum4 += exp_psrc4;
pdst4[i00] = exp_psrc4;
}
float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3];
const float sum = simd_sum(lsum);
for (int i00 = tpitg[0]; i00 < ne00/4; i00 += ntg[0]) {
pdst4[i00] /= sum;
}
}
kernel void kernel_diag_mask_inf(
device const float * src0,
device float * dst,
@ -192,6 +197,33 @@ kernel void kernel_diag_mask_inf(
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_diag_mask_inf_8(
device const float4 * src0,
device float4 * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int & n_past,
uint3 tpig[[thread_position_in_grid]]) {
const int64_t i = 2*tpig[0];
dst[i+0] = src0[i+0];
dst[i+1] = src0[i+1];
int64_t i4 = 4*i;
const int64_t i02 = i4/(ne00*ne01); i4 -= i02*ne00*ne01;
const int64_t i01 = i4/(ne00); i4 -= i01*ne00;
const int64_t i00 = i4;
for (int k = 3; k >= 0; --k) {
if (i00 + 4 + k <= n_past + i01) {
break;
}
dst[i+1][k] = -INFINITY;
if (i00 + k > n_past + i01) {
dst[i][k] = -INFINITY;
}
}
}
@ -616,6 +648,49 @@ kernel void kernel_mul_mat_f16_f32(
}
}
// Assumes row size (ne00) is a multiple of 4
kernel void kernel_mul_mat_f16_f32_l4(
device const char * src0,
device const char * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]]) {
const int nrows = ne11;
const int64_t r0 = tgpig.x;
const int64_t im = tgpig.z;
device const half4 * x4 = (device const half4 *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02);
for (int r1 = 0; r1 < nrows; ++r1) {
device const float4 * y4 = (device const float4 *) (src1 + r1*nb11 + im*nb12);
float sumf = 0;
for (int i = tiisg; i < ne00/4; i += 32) {
for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
}
float all_sum = simd_sum(sumf);
if (tiisg == 0) {
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
}
}
kernel void kernel_alibi_f32(
device const float * src0,
device float * dst,
@ -1800,29 +1875,34 @@ void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg)
template <typename type4x4>
void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 1);
const half d = il ? (xb->d / 16.h) : xb->d;
const half m = il ? ( -8.h * 16.h) : -8.h;
const float d1 = il ? (xb->d / 16.h) : xb->d;
const float d2 = d1 / 256.f;
const float md = -8.h * xb->d;
const ushort mask0 = il ? 0x00F0 : 0x000F;
const ushort mask1 = il ? 0xF000 : 0x0F00;
const ushort mask1 = mask0 << 8;
for (int i=0;i<8;i++) {
reg[i/2][2*(i%2)] = (((qs[i] & mask0) ) + m) * d;
reg[i/2][2*(i%2)+1] = (((qs[i] & mask1) >> 8) + m) * d;
reg[i/2][2*(i%2)+0] = d1 * (qs[i] & mask0) + md;
reg[i/2][2*(i%2)+1] = d2 * (qs[i] & mask1) + md;
}
}
template <typename type4x4>
void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 2);
const half d = il ? (xb->d / 16.h) : xb->d;
const half m = xb->m;
const float d1 = il ? (xb->d / 16.h) : xb->d;
const float d2 = d1 / 256.f;
const float m = xb->m;
const ushort mask0 = il ? 0x00F0 : 0x000F;
const ushort mask1 = il ? 0xF000 : 0x0F00;
const ushort mask1 = mask0 << 8;
for (int i=0;i<8;i++) {
reg[i/2][2*(i%2)] = (((qs[i] & mask0) ) * d) + m;
reg[i/2][2*(i%2)+1] = (((qs[i] & mask1) >> 8) * d) + m;
reg[i/2][2*(i%2)+0] = ((qs[i] & mask0) * d1) + m;
reg[i/2][2*(i%2)+1] = ((qs[i] & mask1) * d2) + m;
}
}
@ -1858,7 +1938,7 @@ void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg
template <typename type4x4>
void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) {
const float d_all = (float)(xb->d);
const half d_all = xb->d;
device const uint8_t * q = (device const uint8_t *)xb->qs;
device const uint8_t * h = (device const uint8_t *)xb->hmask;
device const int8_t * scales = (device const int8_t *)xb->scales;
@ -1871,17 +1951,20 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg
((il/4)>0 ? 12 : 3);
uint16_t kmask2 = il/8 ? 0xF0 : 0x0F;
uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4];
int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2) : \
(scale_2&kmask2) | ((scale_1&kmask1) << 4);
float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f);
int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2)
: (scale_2&kmask2) | ((scale_1&kmask1) << 4);
half dl = il<8 ? d_all * (dl_int - 32.h) : d_all * (dl_int / 16.h - 32.h);
const half ml = 4.h * dl;
il = (il/2)%4;
float coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
il = (il/2) & 3;
const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
dl *= coef;
for (int i = 0; i < 16; ++i) {
reg[i/4][i%4] = coef * dl * ((q[i] & mask) - ((h[i] & m) ? 0 : 4.f/coef));
reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml);
}
#else
float kcoef = il&1 ? 1.f/16.f : 1.f;
uint16_t kmask = il&1 ? 0xF0 : 0x0F;
@ -1895,31 +1978,37 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg
#endif
}
static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) {
return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)}
: uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))};
}
template <typename type4x4>
void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) {
device const uint8_t * q = xb->qs;
device const uchar * q = xb->qs;
#if QK_K == 256
const float d = (float)(xb->d);
const float min = (float)(xb->dmin);
short is = (il/4) * 2;
q = q + (il/4) * 32 + 16 * (il&1);
il = il%4;
const uchar4 sc = get_scale_min_k4(is, xb->scales);
const float dl = il<2 ? d * sc[0] : d * sc[2]/16.h;
const float ml = il<2 ? min * sc[1] : min * sc[3];
il = il & 3;
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
const half d = il < 2 ? xb->d : xb->d / 16.h;
const half min = xb->dmin;
const half dl = d * sc[0];
const half ml = min * sc[1];
#else
q = q + 16 * (il&1);
device const uint8_t * s = xb->scales;
device const half2 * dh = (device const half2 *)xb->d;
const float2 d = (float2)dh[0];
const float dl = il<2 ? d[0] * (s[0]&0xF) : d[0] * (s[1]&0xF)/16.h;
const float ml = il<2 ? d[1] * (s[0]>>4) : d[1 ]* (s[1]>>4);
const float ml = il<2 ? d[1] * (s[0]>>4) : d[1] * (s[1]>>4);
#endif
const ushort mask = il<2 ? 0x0F : 0xF0;
for (int i = 0; i < 16; ++i) {
reg[i/4][i%4] = dl * (q[i] & mask) - ml;
}
}
template <typename type4x4>
@ -1928,19 +2017,19 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg
device const uint8_t * qh = xb->qh;
#if QK_K == 256
const float d = (float)(xb->d);
const float min = (float)(xb->dmin);
short is = (il/4) * 2;
q = q + 32 * (il/4) + 16 * (il&1);
qh = qh + 16 * (il&1);
uint8_t ul = 1 << (il/2);
il = il%4;
const uchar4 sc = get_scale_min_k4(is, xb->scales);
const float dl = il<2 ? d * sc[0] : d * sc[2]/16.h;
const float ml = il<2 ? min * sc[1] : min * sc[3];
il = il & 3;
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
const half d = il < 2 ? xb->d : xb->d / 16.h;
const half min = xb->dmin;
const half dl = d * sc[0];
const half ml = min * sc[1];
const ushort mask = il<2 ? 0x0F : 0xF0;
const float qh_val = il<2 ? 16.f : 256.f;
const ushort mask = il<2 ? 0x0F : 0xF0;
const half qh_val = il<2 ? 16.h : 256.h;
for (int i = 0; i < 16; ++i) {
reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml;
}
@ -1959,7 +2048,7 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg
template <typename type4x4>
void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) {
const float d_all = (float)(xb->d);
const half d_all = xb->d;
device const uint8_t * ql = (device const uint8_t *)xb->ql;
device const uint8_t * qh = (device const uint8_t *)xb->qh;
device const int8_t * scales = (device const int8_t *)xb->scales;
@ -1967,19 +2056,21 @@ void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg
#if QK_K == 256
ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1);
qh = qh + 32*(il/8) + 16*(il&1);
float sc = scales[(il%2) + 2 * ((il/2))];
il = (il/2)%4;
half sc = scales[(il%2) + 2 * ((il/2))];
il = (il/2) & 3;
#else
ql = ql + 16 * (il&1);
float sc = scales[il];
half sc = scales[il];
#endif
const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F;
const half coef = il>1 ? 1.f/16.h : 1.h;
const half ml = d_all * sc * 32.h;
const half dl = d_all * sc * coef;
for (int i = 0; i < 16; ++i) {
uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
uint16_t kmask2 = il>1 ? 0xF0 : 0x0F;
const float coef = il>1 ? 1.f/16.f : 1.f;
float q = il&1 ? ((ql[i]&kmask2)|((qh[i]&kmask1)<<2)) - 32.f/coef : \
((ql[i]&kmask2)|((qh[i]&kmask1)<<4)) - 32.f/coef;
reg[i/4][i%4] = d_all * sc * q * coef;
const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2))
: ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4));
reg[i/4][i%4] = dl * q - ml;
}
}

2
ggml.c
View file

@ -283,7 +283,7 @@ typedef double ggml_float;
// 16-bit float
// on Arm, we use __fp16
// on x86, we use uint16_t
#ifdef __ARM_NEON
#if defined(__ARM_NEON) && !defined(_MSC_VER)
// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
//

2
ggml.h
View file

@ -270,7 +270,7 @@ extern "C" {
#if defined(__ARM_NEON) && defined(__CUDACC__)
typedef half ggml_fp16_t;
#elif defined(__ARM_NEON)
#elif defined(__ARM_NEON) && !defined(_MSC_VER)
typedef __fp16 ggml_fp16_t;
#else
typedef uint16_t ggml_fp16_t;

5
k_quants.c
View file

@ -2609,7 +2609,10 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
memcpy(utmp, x[i].scales, 12);
const uint32x2_t mins8 = {utmp[1] & kmask1, ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4)};
uint32x2_t mins8 = { 0 };
mins8 = vset_lane_u32(utmp[1] & kmask1, mins8, 0);
mins8 = vset_lane_u32(((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4), mins8, 1);
utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
utmp[0] &= kmask1;