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

Merge branch 'master' into concedo_experimental

Browse source 
# Conflicts:
#	.devops/nix/package.nix
#	CMakeLists.txt
#	README.md
#	ggml-metal.m
#	ggml.c
This commit is contained in:
Concedo 2024-01-08 14:18:49 +08:00
commit f04b6e7287
18 changed files with 195 additions and 191 deletions
Download patch file Download diff file Expand all files Collapse all files

61
examples/base-translate.sh Executable file
View file

@ -0,0 +1,61 @@
#!/bin/bash
#
# Few-shot translation example.
# Requires a base model (i.e. no fine-tuned or instruct models).
#
# Usage:
#
# cd llama.cpp
# make -j
#
# ./examples/base-translate.sh <model-base> "<text>" [extra-main-args]
#
if [ $# -lt 2 ]; then
echo "Usage: ./base-translate.sh <model-base> \"<text>\" [extra-main-args]"
exit 1
fi
eargs=""
if [ $# -gt 2 ]; then
eargs="${@:3}"
fi
ftmp="__llama.cpp_example_tmp__.txt"
trap "rm -f $ftmp" EXIT
echo "Translate from English to French:
===
sea otter, peppermint, plush girafe:
sea otter => loutre de mer
peppermint => menthe poivrée
plush girafe => girafe peluche
===
violin
violin => violon
===
phone, computer, mouse, keyboard:
phone => téléphone
computer => ordinateur
mouse => souris
keyboard => clavier
===
" > $ftmp
echo "$2
" >> $ftmp
model=$1
# generate the most likely continuation until the string "===" is found
./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs

34
examples/llama-bench/llama-bench.cpp
View file

@ -139,6 +139,7 @@ struct cmd_params {
std::vector<int> n_threads;
std::vector<int> n_gpu_layers;
std::vector<int> main_gpu;
std::vector<bool> no_kv_offload;
std::vector<bool> mul_mat_q;
std::vector<std::array<float, LLAMA_MAX_DEVICES>> tensor_split;
int reps;
@ -156,6 +157,7 @@ static const cmd_params cmd_params_defaults = {
/* n_threads */ {get_num_physical_cores()},
/* n_gpu_layers */ {99},
/* main_gpu */ {0},
/* no_kv_offload */ {false},
/* mul_mat_q */ {true},
/* tensor_split */ {{}},
/* reps */ 5,
@ -177,6 +179,7 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
printf(" -ts, --tensor_split <ts0/ts1/..> \n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
@ -310,6 +313,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
break;
}
params.main_gpu = split<int>(argv[i], split_delim);
} else if (arg == "-nkvo" || arg == "--no-kv-offload") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
} else if (arg == "-mmq" || arg == "--mul-mat-q") {
if (++i >= argc) {
invalid_param = true;
@ -384,6 +394,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; }
if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }
@ -401,6 +412,7 @@ struct cmd_params_instance {
int n_threads;
int n_gpu_layers;
int main_gpu;
bool no_kv_offload;
bool mul_mat_q;
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
@ -429,6 +441,7 @@ struct cmd_params_instance {
cparams.type_k = type_k;
cparams.type_v = type_v;
cparams.mul_mat_q = mul_mat_q;
cparams.offload_kqv = !no_kv_offload;
return cparams;
}
@ -445,6 +458,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nkvo : params.no_kv_offload)
for (const auto & nt : params.n_threads) {
cmd_params_instance instance = {
/* .model = */ m,
@ -456,6 +470,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
};
@ -477,6 +492,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nkvo : params.no_kv_offload)
for (const auto & nt : params.n_threads) {
for (const auto & n_prompt : params.n_prompt) {
if (n_prompt == 0) {
@ -492,6 +508,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
};
@ -512,6 +529,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
};
@ -560,6 +578,7 @@ struct test {
ggml_type type_v;
int n_gpu_layers;
int main_gpu;
bool no_kv_offload;
bool mul_mat_q;
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
int n_prompt;
@ -580,6 +599,7 @@ struct test {
type_v = inst.type_v;
n_gpu_layers = inst.n_gpu_layers;
main_gpu = inst.main_gpu;
no_kv_offload = inst.no_kv_offload;
mul_mat_q = inst.mul_mat_q;
tensor_split = inst.tensor_split;
n_prompt = inst.n_prompt;
@ -641,7 +661,8 @@ struct test {
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_threads", "type_k", "type_v",
"n_gpu_layers", "main_gpu", "mul_mat_q", "tensor_split",
"n_gpu_layers", "main_gpu", "no_kv_offload",
"mul_mat_q", "tensor_split",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
"avg_ts", "stddev_ts"
@ -660,7 +681,7 @@ struct test {
return INT;
}
if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" ||
field == "f16_kv" || field == "mul_mat_q") {
field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") {
return BOOL;
}
if (field == "avg_ts" || field == "stddev_ts") {
@ -691,7 +712,8 @@ struct test {
cpu_info, gpu_info,
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), tensor_split_str,
std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(no_kv_offload),
std::to_string(mul_mat_q), tensor_split_str,
std::to_string(n_prompt), std::to_string(n_gen), test_time,
std::to_string(avg_ns()), std::to_string(stdev_ns()),
std::to_string(avg_ts()), std::to_string(stdev_ts())
@ -852,6 +874,9 @@ struct markdown_printer : public printer {
if (field == "mul_mat_q") {
return "mmq";
}
if (field == "no_kv_offload") {
return "nkvo";
}
if (field == "tensor_split") {
return "ts";
}
@ -886,6 +911,9 @@ struct markdown_printer : public printer {
if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
fields.push_back("mul_mat_q");
}
if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
fields.push_back("no_kv_offload");
}
if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
fields.push_back("tensor_split");
}

7
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -1,8 +1,5 @@
import Foundation
// To use this in your own project, add llama.cpp as a swift package dependency
// and uncomment this import line.
// import llama
import llama
enum LlamaError: Error {
case couldNotInitializeContext
@ -161,7 +158,7 @@ actor LlamaContext {
new_token_id = llama_sample_token_greedy(context, &candidates_p)
}
if new_token_id == llama_token_eos(context) || n_cur == n_len {
if new_token_id == llama_token_eos(model) || n_cur == n_len {
print("\n")
let new_token_str = String(cString: temporary_invalid_cchars + [0])
temporary_invalid_cchars.removeAll()

5
examples/llama.swiftui/llama.cpp.swift/bridging-header.h
View file

@ -1,5 +0,0 @@
//
// Use this file to import your target's public headers that you would like to expose to Swift.
//
#import "llama.h"

105
examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
View file

@ -7,68 +7,31 @@
objects = {
/* Begin PBXBuildFile section */
542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 542376072B0D9BFB008E6A1C /* ggml-quants.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09B2AC8723900A8AEE9 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_USE_K_QUANTS -O3"; }; };
542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 542EA0A12AC8729100A8AEE9 /* llama.cpp */; settings = {COMPILER_FLAGS = "-DGGML_USE_K_QUANTS -DGGML_USE_METAL -O3"; }; };
549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; };
549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */ = {isa = PBXBuildFile; fileRef = 549479C52AC9E0F200E0F78B /* ggml-metal.m */; settings = {COMPILER_FLAGS = "-fno-objc-arc -DGGML_SWIFT -DGGML_USE_METAL -O3"; }; };
7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; };
8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */; };
8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83782AC328BD0096AF73 /* ContentView.swift */; };
8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837A2AC328BE0096AF73 /* Assets.xcassets */; };
8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */; };
8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 8A39BE092AC7601000BFEB40 /* Accelerate.framework */; };
8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
DF810E132B4A5BA200301144 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = DF810E122B4A5BA200301144 /* llama */; };
F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; };
F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
/* End PBXBuildFile section */
/* Begin PBXBuildRule section */
F1FE20DB2B465C2100B45541 /* PBXBuildRule */ = {
isa = PBXBuildRule;
compilerSpec = com.apple.compilers.proxy.script;
fileType = sourcecode.metal;
inputFiles = (
);
isEditable = 1;
outputFiles = (
"${DERIVED_FILES_DIR}/ggml-metal.air",
"${DERIVED_FILES_DIR}/ggml.metallib",
);
script = "# metal\nxcrun metal -c \"${INPUT_FILE_PATH}\" -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\nxcrun metallib -o \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE%-metal}.metallib\" \"${DERIVED_FILES_DIR}/${INPUT_FILE_BASE}.air\"\n";
};
/* End PBXBuildRule section */
/* Begin PBXFileReference section */
542376062B0D9BEA008E6A1C /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../ggml-quants.h"; sourceTree = "<group>"; };
542376072B0D9BFB008E6A1C /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../ggml-quants.c"; sourceTree = "<group>"; };
542376092B0D9C40008E6A1C /* ggml-backend.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; name = "ggml-backend.h"; path = "../../ggml-backend.h"; sourceTree = "<group>"; };
5423760A2B0D9C4B008E6A1C /* ggml-backend.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-backend.c"; path = "../../ggml-backend.c"; sourceTree = "<group>"; };
542EA09B2AC8723900A8AEE9 /* ggml.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = ggml.c; path = ../../ggml.c; sourceTree = "<group>"; };
542EA09C2AC8723900A8AEE9 /* ggml.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = ggml.h; path = ../../ggml.h; sourceTree = "<group>"; };
542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-alloc.h"; path = "../../ggml-alloc.h"; sourceTree = "<group>"; };
542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-alloc.c"; path = "../../ggml-alloc.c"; sourceTree = "<group>"; };
542EA0A12AC8729100A8AEE9 /* llama.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = llama.cpp; path = ../../llama.cpp; sourceTree = "<group>"; };
542EA0A22AC8729100A8AEE9 /* llama.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = llama.h; path = ../../llama.h; sourceTree = "<group>"; };
549479C52AC9E0F200E0F78B /* ggml-metal.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; name = "ggml-metal.m"; path = "../../ggml-metal.m"; sourceTree = "<group>"; };
549479C62AC9E0F200E0F78B /* ggml-metal.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-metal.h"; path = "../../ggml-metal.h"; sourceTree = "<group>"; };
549479C82AC9E10B00E0F78B /* ggml-metal.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; name = "ggml-metal.metal"; path = "../../ggml-metal.metal"; sourceTree = "<group>"; };
549479CA2AC9E16000E0F78B /* Metal.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Metal.framework; path = System/Library/Frameworks/Metal.framework; sourceTree = SDKROOT; };
7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = DownloadButton.swift; sourceTree = "<group>"; };
8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = "bridging-header.h"; sourceTree = "<group>"; };
8A1C83732AC328BD0096AF73 /* llama.swiftui.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = llama.swiftui.app; sourceTree = BUILT_PRODUCTS_DIR; };
8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = llama_swiftuiApp.swift; sourceTree = "<group>"; };
8A1C83782AC328BD0096AF73 /* ContentView.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ContentView.swift; sourceTree = "<group>"; };
8A1C837A2AC328BE0096AF73 /* Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = Assets.xcassets; sourceTree = "<group>"; };
8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = "Preview Assets.xcassets"; sourceTree = "<group>"; };
8A39BE092AC7601000BFEB40 /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
8A3F84232AC4C891005E2EE8 /* models */ = {isa = PBXFileReference; lastKnownFileType = folder; name = models; path = llama.swiftui/Resources/models; sourceTree = "<group>"; };
8A907F322AC7134E006146EA /* LibLlama.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LibLlama.swift; sourceTree = "<group>"; };
8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LlamaState.swift; sourceTree = "<group>"; };
DF2D2FE72B4A59BE00FCB72D /* llama.cpp */ = {isa = PBXFileReference; lastKnownFileType = wrapper; name = llama.cpp; path = ../..; sourceTree = "<group>"; };
F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LoadCustomButton.swift; sourceTree = "<group>"; };
/* End PBXFileReference section */
@ -77,6 +40,7 @@
isa = PBXFrameworksBuildPhase;
buildActionMask = 2147483647;
files = (
DF810E132B4A5BA200301144 /* llama in Frameworks */,
549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */,
8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */,
);
@ -85,30 +49,10 @@
/* End PBXFrameworksBuildPhase section */
/* Begin PBXGroup section */
8A08D1F62AC7383900FE6CD4 /* llama.cpp */ = {
isa = PBXGroup;
children = (
5423760A2B0D9C4B008E6A1C /* ggml-backend.c */,
542376092B0D9C40008E6A1C /* ggml-backend.h */,
542376062B0D9BEA008E6A1C /* ggml-quants.h */,
542376072B0D9BFB008E6A1C /* ggml-quants.c */,
549479C82AC9E10B00E0F78B /* ggml-metal.metal */,
549479C62AC9E0F200E0F78B /* ggml-metal.h */,
549479C52AC9E0F200E0F78B /* ggml-metal.m */,
542EA09B2AC8723900A8AEE9 /* ggml.c */,
542EA09C2AC8723900A8AEE9 /* ggml.h */,
542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */,
542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */,
542EA0A12AC8729100A8AEE9 /* llama.cpp */,
542EA0A22AC8729100A8AEE9 /* llama.h */,
);
name = llama.cpp;
sourceTree = "<group>";
};
8A1C836A2AC328BD0096AF73 = {
isa = PBXGroup;
children = (
8A08D1F62AC7383900FE6CD4 /* llama.cpp */,
DF2D2FE72B4A59BE00FCB72D /* llama.cpp */,
8A907F312AC7134E006146EA /* llama.cpp.swift */,
8A3F84232AC4C891005E2EE8 /* models */,
8A1C83752AC328BD0096AF73 /* llama.swiftui */,
@ -133,19 +77,10 @@
8A9F7C4A2AC332BF008AE1EA /* UI */,
8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */,
8A1C837A2AC328BE0096AF73 /* Assets.xcassets */,
8A1C837C2AC328BE0096AF73 /* Preview Content */,
);
path = llama.swiftui;
sourceTree = "<group>";
};
8A1C837C2AC328BE0096AF73 /* Preview Content */ = {
isa = PBXGroup;
children = (
8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */,
);
path = "Preview Content";
sourceTree = "<group>";
};
8A39BE082AC7601000BFEB40 /* Frameworks */ = {
isa = PBXGroup;
children = (
@ -173,7 +108,6 @@
8A907F312AC7134E006146EA /* llama.cpp.swift */ = {
isa = PBXGroup;
children = (
8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */,
8A907F322AC7134E006146EA /* LibLlama.swift */,
);
path = llama.cpp.swift;
@ -209,12 +143,12 @@
8A1C83712AC328BD0096AF73 /* Resources */,
);
buildRules = (
F1FE20DB2B465C2100B45541 /* PBXBuildRule */,
);
dependencies = (
);
name = llama.swiftui;
packageProductDependencies = (
DF810E122B4A5BA200301144 /* llama */,
);
productName = llama.swiftui;
productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */;
@ -261,9 +195,7 @@
isa = PBXResourcesBuildPhase;
buildActionMask = 2147483647;
files = (
F1FE20DC2B465C4500B45541 /* ggml-metal.metal in Resources */,
8A3F84242AC4C891005E2EE8 /* models in Resources */,
8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */,
8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */,
);
runOnlyForDeploymentPostprocessing = 0;
@ -275,18 +207,12 @@
isa = PBXSourcesBuildPhase;
buildActionMask = 2147483647;
files = (
542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */,
549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */,
F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */,
542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */,
8A907F332AC7138A006146EA /* LibLlama.swift in Sources */,
542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */,
8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */,
8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */,
8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */,
7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */,
542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */,
5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */,
);
runOnlyForDeploymentPostprocessing = 0;
};
@ -416,11 +342,9 @@
isa = XCBuildConfiguration;
buildSettings = {
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
CLANG_ENABLE_MODULES = YES;
CODE_SIGN_STYLE = Automatic;
CURRENT_PROJECT_VERSION = 1;
DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
DEVELOPMENT_TEAM = STLSG3FG8Q;
ENABLE_PREVIEWS = YES;
GENERATE_INFOPLIST_FILE = YES;
@ -437,11 +361,12 @@
MARKETING_VERSION = 1.0;
PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
PRODUCT_NAME = "$(TARGET_NAME)";
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
SWIFT_EMIT_LOC_STRINGS = YES;
SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
SWIFT_OPTIMIZATION_LEVEL = "-Onone";
SWIFT_VERSION = 5.0;
TARGETED_DEVICE_FAMILY = "1,2";
TARGETED_DEVICE_FAMILY = "1,2,7";
};
name = Debug;
};
@ -449,11 +374,9 @@
isa = XCBuildConfiguration;
buildSettings = {
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
CLANG_ENABLE_MODULES = YES;
CODE_SIGN_STYLE = Automatic;
CURRENT_PROJECT_VERSION = 1;
DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
DEVELOPMENT_TEAM = STLSG3FG8Q;
ENABLE_PREVIEWS = YES;
GENERATE_INFOPLIST_FILE = YES;
@ -470,10 +393,11 @@
MARKETING_VERSION = 1.0;
PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
PRODUCT_NAME = "$(TARGET_NAME)";
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
SWIFT_EMIT_LOC_STRINGS = YES;
SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
SWIFT_VERSION = 5.0;
TARGETED_DEVICE_FAMILY = "1,2";
TARGETED_DEVICE_FAMILY = "1,2,7";
};
name = Release;
};
@ -499,6 +423,13 @@
defaultConfigurationName = Release;
};
/* End XCConfigurationList section */
/* Begin XCSwiftPackageProductDependency section */
DF810E122B4A5BA200301144 /* llama */ = {
isa = XCSwiftPackageProductDependency;
productName = llama;
};
/* End XCSwiftPackageProductDependency section */
};
rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
}

11
examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json
View file

@ -1,11 +0,0 @@
{
"colors" : [
{
"idiom" : "universal"
}
],
"info" : {
"author" : "xcode",
"version" : 1
}
}

6
examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json
View file

@ -1,6 +0,0 @@
{
"info" : {
"author" : "xcode",
"version" : 1
}
}

13
examples/server/server.cpp
View file

@ -448,7 +448,13 @@ struct llama_client_slot
}
bool has_budget(gpt_params &global_params) {
if (params.n_predict == -1 && global_params.n_predict == -1)
{
return true; // limitless
}
n_remaining = -1;
if (params.n_predict != -1)
{
n_remaining = params.n_predict - n_decoded;
@ -457,7 +463,8 @@ struct llama_client_slot
{
n_remaining = global_params.n_predict - n_decoded;
}
return n_remaining > 0 || n_remaining == -1; // no budget || limitless
return n_remaining > 0; // no budget
}
bool available() const {
@ -1103,7 +1110,7 @@ struct llama_server_context
}
// check the limits
if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
{
slot.stopped_limit = true;
slot.has_next_token = false;
@ -1704,7 +1711,6 @@ struct llama_server_context
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
slot.n_decoded += 1;
slot.n_past += 1;
}
@ -1922,6 +1928,7 @@ struct llama_server_context
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
slot.n_decoded += 1;
if (slot.n_decoded == 1)
{
slot.t_start_genereration = ggml_time_us();

2
ggml-backend-impl.h
View file

@ -90,7 +90,7 @@ extern "C" {
void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// compute graph without a plan
void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
// check if the backend supports an operation
bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);

10
ggml-backend.c
View file

@ -195,11 +195,14 @@ void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_
ggml_backend_synchronize(backend);
}
void ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
backend->iface.graph_compute(backend, cgraph);
bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
if (!backend->iface.graph_compute(backend, cgraph)) {
return false;
}
// TODO: optional sync
ggml_backend_synchronize(backend);
return true;
}
bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
@ -597,7 +600,7 @@ static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_bac
GGML_UNUSED(backend);
}
static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
@ -611,6 +614,7 @@ static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c
cplan.work_data = cpu_ctx->work_data;
ggml_graph_compute(cgraph, &cplan);
return true;
}
static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {

2
ggml-backend.h
View file

@ -58,7 +58,7 @@ extern "C" {
GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API void ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API bool ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API bool ggml_backend_supports_op (ggml_backend_t backend, const struct ggml_tensor * op);
// tensor copy between different backends

41
ggml-cuda.cu
View file

@ -183,7 +183,7 @@ static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
c = __builtin_amdgcn_sdot4(a, b, c, false);
#elif defined(__gfx1100__)
#elif defined(RDNA3)
c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
#elif defined(__gfx1010__) || defined(__gfx900__)
int tmp1;
@ -1873,14 +1873,6 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs,
v.y = x[ib + iqs + 1];
}
static __device__ void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){
const float * x = (const float *) vx;
// automatic half -> float type cast if dfloat == float
v.x = x[ib + iqs + 0];
v.y = x[ib + iqs + 1];
}
static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
const int ix = blockDim.x*blockIdx.x + threadIdx.x;
@ -1984,7 +1976,7 @@ static __global__ void k_get_rows_float(
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x);
if (i >= k) {
return;
@ -2003,6 +1995,19 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __
y[iybs + iqs + y_offset] = v.y;
}
template <typename src_t, typename dst_t>
static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
const src_t * x = (src_t *) vx;
y[i] = x[i];
}
// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
@ -5610,7 +5615,7 @@ static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, con
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}
@ -5660,6 +5665,12 @@ static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cu
#endif
}
template <typename src_t, typename dst_t>
static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
}
static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
@ -5683,7 +5694,7 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
case GGML_TYPE_Q6_K:
return dequantize_row_q6_K_cuda;
case GGML_TYPE_F32:
return dequantize_block_cuda<1, 1, convert_f32>;
return convert_unary_cuda<float>;
default:
return nullptr;
}
@ -5712,7 +5723,7 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
case GGML_TYPE_Q6_K:
return dequantize_row_q6_K_cuda;
case GGML_TYPE_F16:
return dequantize_block_cuda<1, 1, convert_f16>;
return convert_unary_cuda<half>;
default:
return nullptr;
}
@ -9911,7 +9922,7 @@ static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_ba
UNUSED(plan);
}
static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
ggml_cuda_set_main_device(cuda_ctx->device);
@ -9968,6 +9979,8 @@ static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
}
UNUSED(backend);
return true;
}
static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {

2
ggml-metal.h
View file

@ -87,7 +87,7 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
// same as ggml_graph_compute but uses Metal
// creates gf->n_threads command buffers in parallel
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
bool ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
//
// backend API

15
ggml-metal.m
View file

@ -258,14 +258,14 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
#endif
NSError * error = nil;
NSString * libPath = [bundle pathForResource:@"ggml" ofType:@"metallib"];
NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
if (libPath != nil) {
// pre-compiled library found
NSURL * libURL = [NSURL fileURLWithPath:libPath];
GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
ctx->library = [ctx->device newLibraryWithURL:libURL error:&error];
} else {
GGML_METAL_LOG_INFO("%s: ggml.metallib not found, loading from source\n", __func__);
GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
NSString * sourcePath;
NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
@ -295,7 +295,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
#endif
// try to disable fast-math
// NOTE: this seems to have no effect whatsoever
// instead, in order to disable fast-math, we have to build ggml.metallib from the command line
// instead, in order to disable fast-math, we have to build default.metallib from the command line
// using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
// and go through the "pre-compiled library found" path above
//[options setFastMathEnabled:false];
@ -977,7 +977,7 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
return false;
}
}
void ggml_metal_graph_compute(
bool ggml_metal_graph_compute(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
@autoreleasepool {
@ -2420,10 +2420,11 @@ preferably one under the recommended max working set size, or else fall back to
} else {
GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
}
GGML_ASSERT(false);
return false;
}
}
return true;
}
}
@ -2703,10 +2704,10 @@ static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggm
UNUSED(backend);
}
static void ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
ggml_metal_graph_compute(metal_ctx, cgraph);
return ggml_metal_graph_compute(metal_ctx, cgraph);
}
static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {

2
ggml-quants.h
View file

@ -70,7 +70,7 @@ static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block s
// 2-bit quantization
// weight is represented as x = a * q + b
// 16 blocks of 16 elements each
// Effectively 2.5625 bits per weight
// Effectively 2.625 bits per weight
typedef struct {
uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
uint8_t qs[QK_K/4]; // quants

48
ggml.c
View file

@ -9704,10 +9704,10 @@ static void ggml_compute_forward_group_norm(
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
// helper function to determine if it is better to use BLAS or not
// for large matrices, BLAS is faster
static bool ggml_compute_forward_mul_mat_use_blas(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
//const int64_t ne00 = src0->ne[0];
//const int64_t ne01 = src0->ne[1];
@ -9787,7 +9787,7 @@ static void ggml_compute_forward_mul_mat(
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
if (ggml_compute_forward_mul_mat_use_blas(dst)) {
if (params->ith != 0) {
return;
}
@ -16301,24 +16301,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
//n_tasks = MIN(n_threads, MAX(1, nr0/128));
//printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks);
#if defined(GGML_USE_CUBLAS)
if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) {
n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
}
#elif defined(GGML_USE_CLBLAST)
if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) {
n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
}
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) {
n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
}
#endif
} break;
case GGML_OP_MUL_MAT_ID:
{
@ -16491,6 +16473,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
state->shared->node_n += 1;
return (thread_ret_t) GGML_EXIT_ABORTED;
}
if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
// all other threads are finished and spinning
// do finalize and init here so we don't have synchronize again
@ -16556,12 +16539,19 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
} else {
// wait for other threads to finish
const int last = node_n;
do {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_METAL)
//apple does nothing
#else
const bool do_yield = last < 0 || cgraph->nodes[last]->op == GGML_OP_MUL_MAT;
while (true) {
// TODO: this sched_yield can have significant impact on the performance - either positive or negative
// depending on the workload and the operating system.
// since it is not clear what is the best approach, it should potentially become user-configurable
// ref: https://github.com/ggerganov/ggml/issues/291
// UPD: adding the do_yield flag seems to resolve the issue universally
if (do_yield) {
sched_yield();
#endif
}
node_n = atomic_load(&state->shared->node_n);
} while (node_n == last);
}
@ -16638,7 +16628,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
} else
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) {
if (ggml_compute_forward_mul_mat_use_blas(node)) {
if (node->src[0]->type != GGML_TYPE_F32) {
// here we need memory just for single 2D matrix from src0
cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);

16
llama.cpp
View file

@ -2191,7 +2191,11 @@ struct llama_model_loader {
type_max = type;
}
// LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str());
// TODO: make runtime configurable
#if 0
struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str());
#endif
}
switch (type_max) {
@ -4801,7 +4805,6 @@ struct llm_build_context {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_gqa == n_embd);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -4925,7 +4928,6 @@ struct llm_build_context {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_gqa == n_embd);
struct ggml_tensor * cur;
struct ggml_tensor * pos;
@ -5024,9 +5026,7 @@ struct llm_build_context {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_gqa == n_embd);
const int64_t n_rot = n_embd_head_k / 2;
@ -5238,9 +5238,7 @@ struct llm_build_context {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_gqa == n_embd);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -5333,7 +5331,6 @@ struct llm_build_context {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_gqa == n_embd);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -5429,7 +5426,6 @@ struct llm_build_context {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_gqa == n_embd);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -5756,7 +5752,6 @@ struct llm_build_context {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_gqa == n_embd);
struct ggml_tensor * cur;
struct ggml_tensor * attn_norm_output;
@ -5980,7 +5975,6 @@ struct llm_build_context {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_gqa == n_embd);
struct ggml_tensor * cur;
struct ggml_tensor * pos;

2
scripts/sync-ggml.last
View file

@ -1 +1 @@
3fd01e00e40583ccd4b393a7c6502d6a4455a1d5
f96711108d55bdbbd277e6be07204dce6a94fb93