mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 11:24:35 +00:00
Merge branch 'master' into gg/imatrix-gpu-4931
ggml-ci
This commit is contained in:
Georgi Gerganov
commit
2917e6b528
25
.github/workflows/build.yml
vendored
25
.github/workflows/build.yml
vendored
@ -515,6 +515,31 @@ jobs:
|
||||
- name: Build Xcode project
|
||||
run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
|
||||
|
||||
android-build:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- name: Clone
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Set up JDK
|
||||
uses: actions/setup-java@v3
|
||||
with:
|
||||
java-version: 17
|
||||
distribution: zulu
|
||||
|
||||
- name: Setup Android SDK
|
||||
uses: android-actions/setup-android@v3
|
||||
with:
|
||||
log-accepted-android-sdk-licenses: false
|
||||
|
||||
- name: Build
|
||||
run: |
|
||||
cd examples/llama.android
|
||||
|
||||
# Skip armeabi-v7a for now (https://github.com/llvm/llvm-project/issues/65820).
|
||||
./gradlew build --no-daemon -Pskip-armeabi-v7a
|
||||
|
||||
# freeBSD-latest:
|
||||
# runs-on: macos-12
|
||||
# steps:
|
||||
|
||||
1
.gitignore
vendored
1
.gitignore
vendored
@ -105,3 +105,4 @@ poetry.toml
|
||||
/tests/test-tokenizer-1-bpe
|
||||
/tests/test-rope
|
||||
/tests/test-backend-ops
|
||||
/tests/test-autorelease
|
||||
|
||||
@ -594,6 +594,13 @@ if (NOT MSVC)
|
||||
endif()
|
||||
endif()
|
||||
|
||||
function(add_compile_option_cpp ARG)
|
||||
# Adds a compile option to C/C++ only, but not for Cuda.
|
||||
# Use, e.g., for CPU-architecture flags.
|
||||
add_compile_options($<$<COMPILE_LANGUAGE:CXX>:${ARG}>)
|
||||
add_compile_options($<$<COMPILE_LANGUAGE:C>:${ARG}>)
|
||||
endfunction()
|
||||
|
||||
if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64"))
|
||||
message(STATUS "ARM detected")
|
||||
if (MSVC)
|
||||
@ -628,8 +635,7 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE
|
||||
include(cmake/FindSIMD.cmake)
|
||||
endif ()
|
||||
if (LLAMA_AVX512)
|
||||
add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX512>)
|
||||
add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX512>)
|
||||
add_compile_option_cpp(/arch:AVX512)
|
||||
# MSVC has no compile-time flags enabling specific
|
||||
# AVX512 extensions, neither it defines the
|
||||
# macros corresponding to the extensions.
|
||||
@ -643,37 +649,35 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE
|
||||
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VNNI__>)
|
||||
endif()
|
||||
elseif (LLAMA_AVX2)
|
||||
add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX2>)
|
||||
add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX2>)
|
||||
add_compile_option_cpp(/arch:AVX2)
|
||||
elseif (LLAMA_AVX)
|
||||
add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX>)
|
||||
add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX>)
|
||||
add_compile_option_cpp(/arch:AVX)
|
||||
endif()
|
||||
else()
|
||||
if (LLAMA_NATIVE)
|
||||
add_compile_options(-march=native)
|
||||
add_compile_option_cpp(-march=native)
|
||||
endif()
|
||||
if (LLAMA_F16C)
|
||||
add_compile_options(-mf16c)
|
||||
add_compile_option_cpp(-mf16c)
|
||||
endif()
|
||||
if (LLAMA_FMA)
|
||||
add_compile_options(-mfma)
|
||||
add_compile_option_cpp(-mfma)
|
||||
endif()
|
||||
if (LLAMA_AVX)
|
||||
add_compile_options(-mavx)
|
||||
add_compile_option_cpp(-mavx)
|
||||
endif()
|
||||
if (LLAMA_AVX2)
|
||||
add_compile_options(-mavx2)
|
||||
add_compile_option_cpp(-mavx2)
|
||||
endif()
|
||||
if (LLAMA_AVX512)
|
||||
add_compile_options(-mavx512f)
|
||||
add_compile_options(-mavx512bw)
|
||||
add_compile_option_cpp(-mavx512f)
|
||||
add_compile_option_cpp(-mavx512bw)
|
||||
endif()
|
||||
if (LLAMA_AVX512_VBMI)
|
||||
add_compile_options(-mavx512vbmi)
|
||||
add_compile_option_cpp(-mavx512vbmi)
|
||||
endif()
|
||||
if (LLAMA_AVX512_VNNI)
|
||||
add_compile_options(-mavx512vnni)
|
||||
add_compile_option_cpp(-mavx512vnni)
|
||||
endif()
|
||||
endif()
|
||||
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64")
|
||||
|
||||
5
Makefile
5
Makefile
@ -9,7 +9,7 @@ TEST_TARGETS = \
|
||||
tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
|
||||
tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama \
|
||||
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope \
|
||||
tests/test-backend-ops
|
||||
tests/test-backend-ops tests/test-autorelease
|
||||
|
||||
# Code coverage output files
|
||||
COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
|
||||
@ -747,3 +747,6 @@ tests/test-c.o: tests/test-c.c llama.h
|
||||
|
||||
tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
@ -179,6 +179,8 @@ function gg_run_open_llama_3b_v2 {
|
||||
|
||||
wiki_test_60="${path_wiki}/wiki.test-60.raw"
|
||||
|
||||
./bin/test-autorelease ${model_f16}
|
||||
|
||||
./bin/quantize ${model_f16} ${model_q8_0} q8_0
|
||||
./bin/quantize ${model_f16} ${model_q4_0} q4_0
|
||||
./bin/quantize ${model_f16} ${model_q4_1} q4_1
|
||||
|
||||
@ -167,6 +167,24 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
|
||||
if (params.n_threads_batch <= 0) {
|
||||
params.n_threads_batch = std::thread::hardware_concurrency();
|
||||
}
|
||||
} else if (arg == "-td" || arg == "--threads-draft") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.n_threads_draft = std::stoi(argv[i]);
|
||||
if (params.n_threads_draft <= 0) {
|
||||
params.n_threads_draft = std::thread::hardware_concurrency();
|
||||
}
|
||||
} else if (arg == "-tbd" || arg == "--threads-batch-draft") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.n_threads_batch_draft = std::stoi(argv[i]);
|
||||
if (params.n_threads_batch_draft <= 0) {
|
||||
params.n_threads_batch_draft = std::thread::hardware_concurrency();
|
||||
}
|
||||
} else if (arg == "-p" || arg == "--prompt") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
@ -845,6 +863,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
||||
printf(" -t N, --threads N number of threads to use during generation (default: %d)\n", params.n_threads);
|
||||
printf(" -tb N, --threads-batch N\n");
|
||||
printf(" number of threads to use during batch and prompt processing (default: same as --threads)\n");
|
||||
printf(" -td N, --threads-draft N");
|
||||
printf(" number of threads to use during generation (default: same as --threads)");
|
||||
printf(" -tbd N, --threads-batch-draft N\n");
|
||||
printf(" number of threads to use during batch and prompt processing (default: same as --threads-draft)\n");
|
||||
printf(" -p PROMPT, --prompt PROMPT\n");
|
||||
printf(" prompt to start generation with (default: empty)\n");
|
||||
printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
|
||||
|
||||
@ -46,7 +46,9 @@ struct gpt_params {
|
||||
uint32_t seed = -1; // RNG seed
|
||||
|
||||
int32_t n_threads = get_num_physical_cores();
|
||||
int32_t n_threads_draft = -1;
|
||||
int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
|
||||
int32_t n_threads_batch_draft = -1;
|
||||
int32_t n_predict = -1; // new tokens to predict
|
||||
int32_t n_ctx = 512; // context size
|
||||
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
|
||||
|
||||
@ -17,7 +17,7 @@ typedef struct llama_sampling_params {
|
||||
float min_p = 0.05f; // 0.0 = disabled
|
||||
float tfs_z = 1.00f; // 1.0 = disabled
|
||||
float typical_p = 1.00f; // 1.0 = disabled
|
||||
float temp = 0.80f; // 1.0 = disabled
|
||||
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
|
||||
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
|
||||
float penalty_repeat = 1.10f; // 1.0 = disabled
|
||||
float penalty_freq = 0.00f; // 0.0 = disabled
|
||||
|
||||
@ -266,11 +266,10 @@ class Model:
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
elif reverse_vocab[i] in added_vocab:
|
||||
tokens.append(reverse_vocab[i])
|
||||
if hasattr(tokenizer, "added_tokens_decoder"):
|
||||
if tokenizer.added_tokens_decoder[i].special:
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
if tokenizer.added_tokens_decoder[i].special:
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
else:
|
||||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
24
convert.py
24
convert.py
@ -387,6 +387,7 @@ class BpeVocab: # GPT
|
||||
self.bpe_tokenizer = json.loads(
|
||||
open(str(fname_tokenizer), encoding="utf-8").read()
|
||||
)
|
||||
self.vocab = self.bpe_tokenizer["model"]["vocab"]
|
||||
added_tokens: dict[str, int]
|
||||
if fname_added_tokens is not None:
|
||||
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
|
||||
@ -405,7 +406,7 @@ class BpeVocab: # GPT
|
||||
if item["content"] not in self.bpe_tokenizer
|
||||
)
|
||||
|
||||
vocab_size: int = len(self.bpe_tokenizer)
|
||||
vocab_size: int = len(self.vocab)
|
||||
expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
|
||||
actual_ids = sorted(added_tokens.values())
|
||||
if expected_ids != actual_ids:
|
||||
@ -415,6 +416,7 @@ class BpeVocab: # GPT
|
||||
)
|
||||
|
||||
items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
|
||||
self.added_tokens_dict = added_tokens
|
||||
self.added_tokens_list = [text for (text, idx) in items]
|
||||
self.vocab_size_base: int = vocab_size
|
||||
self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
|
||||
@ -422,10 +424,9 @@ class BpeVocab: # GPT
|
||||
self.fname_added_tokens = fname_added_tokens
|
||||
|
||||
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
||||
tokenizer = self.bpe_tokenizer
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
|
||||
|
||||
for i, _ in enumerate(tokenizer):
|
||||
for i, _ in enumerate(self.vocab):
|
||||
yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
|
||||
|
||||
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
||||
@ -466,6 +467,7 @@ class SentencePieceVocab: # LlaMa
|
||||
)
|
||||
|
||||
# Token pieces that were added to the base vocabulary.
|
||||
self.added_tokens_dict = added_tokens
|
||||
self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]
|
||||
self.vocab_size_base = vocab_size
|
||||
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
|
||||
@ -1006,6 +1008,7 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
|
||||
)
|
||||
for i in range(1, pad_count + 1):
|
||||
vocab.added_tokens_dict[f"<dummy{i:05}>"] = -1
|
||||
vocab.added_tokens_list.append(f"<dummy{i:05}>")
|
||||
vocab.vocab_size = params.n_vocab
|
||||
return
|
||||
|
||||
@ -1097,6 +1100,8 @@ class OutputFile:
|
||||
scores.append(score)
|
||||
toktypes.append(toktype)
|
||||
|
||||
assert len(tokens) == vocab.vocab_size
|
||||
|
||||
return tokens, scores, toktypes
|
||||
|
||||
def add_meta_vocab(self, vocab: Vocab) -> None:
|
||||
@ -1373,15 +1378,14 @@ class VocabFactory:
|
||||
self.files[file] = file_path
|
||||
elif parent_file_path.exists():
|
||||
self.files[file] = parent_file_path
|
||||
print(f"Found vocab files: {self.files}")
|
||||
|
||||
def _select_file(self, vocabtype: Optional[str]) -> Path:
|
||||
if vocabtype in ["spm", "bpe"]:
|
||||
# For SentencePiece and BPE, return specific files as before
|
||||
file_key = "tokenizer.model" if vocabtype == "spm" else "vocab.json"
|
||||
if self.files[file_key]:
|
||||
return self.files[file_key]
|
||||
else:
|
||||
raise FileNotFoundError(f"{vocabtype} {file_key} not found.")
|
||||
for file_key in self.files.keys():
|
||||
if self.files[file_key]:
|
||||
return self.files[file_key]
|
||||
raise FileNotFoundError(f"{vocabtype} vocab not found.")
|
||||
elif vocabtype == "hfft":
|
||||
# For Hugging Face Fast Tokenizer, return the directory path instead of a specific file
|
||||
return self.path
|
||||
|
||||
@ -1138,9 +1138,8 @@ static void save_as_llama_lora(const char * filename, struct my_llama_lora * lor
|
||||
return tn_buf.data();
|
||||
};
|
||||
|
||||
uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla'
|
||||
// write_magic
|
||||
file.write_u32(LLAMA_FILE_MAGIC_LORA); // magic
|
||||
file.write_u32(LLAMA_FILE_MAGIC_GGLA); // magic
|
||||
file.write_u32(1); // version
|
||||
// write_hparams
|
||||
file.write_u32(lora->hparams.lora_r);
|
||||
@ -1800,7 +1799,7 @@ int main(int argc, char ** argv) {
|
||||
std::vector<llama_token> train_tokens;
|
||||
std::vector<size_t> train_samples_begin;
|
||||
std::vector<size_t> train_samples_size;
|
||||
printf("%s: tokenize training data\n", __func__);
|
||||
printf("%s: tokenize training data from %s\n", __func__, params.common.fn_train_data);
|
||||
tokenize_file(lctx,
|
||||
params.common.fn_train_data,
|
||||
params.common.sample_start,
|
||||
|
||||
33
examples/llama.android/.gitignore
vendored
Normal file
33
examples/llama.android/.gitignore
vendored
Normal file
@ -0,0 +1,33 @@
|
||||
# Gradle files
|
||||
.gradle/
|
||||
build/
|
||||
|
||||
# Local configuration file (sdk path, etc)
|
||||
local.properties
|
||||
|
||||
# Log/OS Files
|
||||
*.log
|
||||
|
||||
# Android Studio generated files and folders
|
||||
captures/
|
||||
.externalNativeBuild/
|
||||
.cxx/
|
||||
*.apk
|
||||
output.json
|
||||
|
||||
# IntelliJ
|
||||
*.iml
|
||||
.idea/
|
||||
misc.xml
|
||||
deploymentTargetDropDown.xml
|
||||
render.experimental.xml
|
||||
|
||||
# Keystore files
|
||||
*.jks
|
||||
*.keystore
|
||||
|
||||
# Google Services (e.g. APIs or Firebase)
|
||||
google-services.json
|
||||
|
||||
# Android Profiling
|
||||
*.hprof
|
||||
0
examples/llama.android/README.md
Normal file
0
examples/llama.android/README.md
Normal file
1
examples/llama.android/app/.gitignore
vendored
Normal file
1
examples/llama.android/app/.gitignore
vendored
Normal file
@ -0,0 +1 @@
|
||||
/build
|
||||
91
examples/llama.android/app/build.gradle.kts
Normal file
91
examples/llama.android/app/build.gradle.kts
Normal file
@ -0,0 +1,91 @@
|
||||
plugins {
|
||||
id("com.android.application")
|
||||
id("org.jetbrains.kotlin.android")
|
||||
}
|
||||
|
||||
android {
|
||||
namespace = "com.example.llama"
|
||||
compileSdk = 34
|
||||
|
||||
ndkVersion = "26.1.10909125"
|
||||
|
||||
defaultConfig {
|
||||
applicationId = "com.example.llama"
|
||||
minSdk = 33
|
||||
targetSdk = 34
|
||||
versionCode = 1
|
||||
versionName = "1.0"
|
||||
|
||||
testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
|
||||
vectorDrawables {
|
||||
useSupportLibrary = true
|
||||
}
|
||||
ndk {
|
||||
// Workaround for https://github.com/llvm/llvm-project/issues/65820
|
||||
// affecting armeabi-v7a. Skip armeabi-v7a when invoked with
|
||||
// -Pskip-armeabi-v7a (e.g., ./gradlew build -Pskip-armeabi-v7a).
|
||||
if (project.hasProperty("skip-armeabi-v7a")) {
|
||||
abiFilters += listOf("arm64-v8a", "x86_64", "x86")
|
||||
}
|
||||
}
|
||||
externalNativeBuild {
|
||||
cmake {
|
||||
cppFlags += listOf()
|
||||
arguments += listOf()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
buildTypes {
|
||||
release {
|
||||
isMinifyEnabled = false
|
||||
proguardFiles(
|
||||
getDefaultProguardFile("proguard-android-optimize.txt"),
|
||||
"proguard-rules.pro"
|
||||
)
|
||||
}
|
||||
}
|
||||
compileOptions {
|
||||
sourceCompatibility = JavaVersion.VERSION_1_8
|
||||
targetCompatibility = JavaVersion.VERSION_1_8
|
||||
}
|
||||
kotlinOptions {
|
||||
jvmTarget = "1.8"
|
||||
}
|
||||
buildFeatures {
|
||||
compose = true
|
||||
}
|
||||
composeOptions {
|
||||
kotlinCompilerExtensionVersion = "1.5.1"
|
||||
}
|
||||
packaging {
|
||||
resources {
|
||||
excludes += "/META-INF/{AL2.0,LGPL2.1}"
|
||||
}
|
||||
}
|
||||
externalNativeBuild {
|
||||
cmake {
|
||||
path = file("src/main/cpp/CMakeLists.txt")
|
||||
version = "3.22.1"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
dependencies {
|
||||
|
||||
implementation("androidx.core:core-ktx:1.12.0")
|
||||
implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.6.2")
|
||||
implementation("androidx.activity:activity-compose:1.8.2")
|
||||
implementation(platform("androidx.compose:compose-bom:2023.08.00"))
|
||||
implementation("androidx.compose.ui:ui")
|
||||
implementation("androidx.compose.ui:ui-graphics")
|
||||
implementation("androidx.compose.ui:ui-tooling-preview")
|
||||
implementation("androidx.compose.material3:material3")
|
||||
testImplementation("junit:junit:4.13.2")
|
||||
androidTestImplementation("androidx.test.ext:junit:1.1.5")
|
||||
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
|
||||
androidTestImplementation(platform("androidx.compose:compose-bom:2023.08.00"))
|
||||
androidTestImplementation("androidx.compose.ui:ui-test-junit4")
|
||||
debugImplementation("androidx.compose.ui:ui-tooling")
|
||||
debugImplementation("androidx.compose.ui:ui-test-manifest")
|
||||
}
|
||||
21
examples/llama.android/app/proguard-rules.pro
vendored
Normal file
21
examples/llama.android/app/proguard-rules.pro
vendored
Normal file
@ -0,0 +1,21 @@
|
||||
# Add project specific ProGuard rules here.
|
||||
# You can control the set of applied configuration files using the
|
||||
# proguardFiles setting in build.gradle.
|
||||
#
|
||||
# For more details, see
|
||||
# http://developer.android.com/guide/developing/tools/proguard.html
|
||||
|
||||
# If your project uses WebView with JS, uncomment the following
|
||||
# and specify the fully qualified class name to the JavaScript interface
|
||||
# class:
|
||||
#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
|
||||
# public *;
|
||||
#}
|
||||
|
||||
# Uncomment this to preserve the line number information for
|
||||
# debugging stack traces.
|
||||
#-keepattributes SourceFile,LineNumberTable
|
||||
|
||||
# If you keep the line number information, uncomment this to
|
||||
# hide the original source file name.
|
||||
#-renamesourcefileattribute SourceFile
|
||||
30
examples/llama.android/app/src/main/AndroidManifest.xml
Normal file
30
examples/llama.android/app/src/main/AndroidManifest.xml
Normal file
@ -0,0 +1,30 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
xmlns:tools="http://schemas.android.com/tools">
|
||||
|
||||
<uses-permission android:name="android.permission.INTERNET" />
|
||||
|
||||
<application
|
||||
android:allowBackup="true"
|
||||
android:dataExtractionRules="@xml/data_extraction_rules"
|
||||
android:fullBackupContent="@xml/backup_rules"
|
||||
android:icon="@mipmap/ic_launcher"
|
||||
android:label="@string/app_name"
|
||||
android:roundIcon="@mipmap/ic_launcher_round"
|
||||
android:supportsRtl="true"
|
||||
android:theme="@style/Theme.LlamaAndroid"
|
||||
>
|
||||
|
||||
<activity
|
||||
android:name=".MainActivity"
|
||||
android:exported="true"
|
||||
android:theme="@style/Theme.LlamaAndroid">
|
||||
<intent-filter>
|
||||
<action android:name="android.intent.action.MAIN" />
|
||||
|
||||
<category android:name="android.intent.category.LAUNCHER" />
|
||||
</intent-filter>
|
||||
</activity>
|
||||
</application>
|
||||
|
||||
</manifest>
|
||||
50
examples/llama.android/app/src/main/cpp/CMakeLists.txt
Normal file
50
examples/llama.android/app/src/main/cpp/CMakeLists.txt
Normal file
@ -0,0 +1,50 @@
|
||||
|
||||
# For more information about using CMake with Android Studio, read the
|
||||
# documentation: https://d.android.com/studio/projects/add-native-code.html.
|
||||
# For more examples on how to use CMake, see https://github.com/android/ndk-samples.
|
||||
|
||||
# Sets the minimum CMake version required for this project.
|
||||
cmake_minimum_required(VERSION 3.22.1)
|
||||
|
||||
# Declares the project name. The project name can be accessed via ${ PROJECT_NAME},
|
||||
# Since this is the top level CMakeLists.txt, the project name is also accessible
|
||||
# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level
|
||||
# build script scope).
|
||||
project("llama-android")
|
||||
|
||||
include(FetchContent)
|
||||
FetchContent_Declare(
|
||||
llama
|
||||
GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
|
||||
GIT_TAG master
|
||||
)
|
||||
|
||||
# Also provides "common"
|
||||
FetchContent_MakeAvailable(llama)
|
||||
|
||||
# Creates and names a library, sets it as either STATIC
|
||||
# or SHARED, and provides the relative paths to its source code.
|
||||
# You can define multiple libraries, and CMake builds them for you.
|
||||
# Gradle automatically packages shared libraries with your APK.
|
||||
#
|
||||
# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define
|
||||
# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
|
||||
# is preferred for the same purpose.
|
||||
#
|
||||
# In order to load a library into your app from Java/Kotlin, you must call
|
||||
# System.loadLibrary() and pass the name of the library defined here;
|
||||
# for GameActivity/NativeActivity derived applications, the same library name must be
|
||||
# used in the AndroidManifest.xml file.
|
||||
add_library(${CMAKE_PROJECT_NAME} SHARED
|
||||
# List C/C++ source files with relative paths to this CMakeLists.txt.
|
||||
llama-android.cpp)
|
||||
|
||||
# Specifies libraries CMake should link to your target library. You
|
||||
# can link libraries from various origins, such as libraries defined in this
|
||||
# build script, prebuilt third-party libraries, or Android system libraries.
|
||||
target_link_libraries(${CMAKE_PROJECT_NAME}
|
||||
# List libraries link to the target library
|
||||
llama
|
||||
common
|
||||
android
|
||||
log)
|
||||
394
examples/llama.android/app/src/main/cpp/llama-android.cpp
Normal file
394
examples/llama.android/app/src/main/cpp/llama-android.cpp
Normal file
@ -0,0 +1,394 @@
|
||||
#include <android/log.h>
|
||||
#include <jni.h>
|
||||
#include <iomanip>
|
||||
#include <math.h>
|
||||
#include <string>
|
||||
#include <unistd.h>
|
||||
#include "llama.h"
|
||||
#include "common/common.h"
|
||||
|
||||
// Write C++ code here.
|
||||
//
|
||||
// Do not forget to dynamically load the C++ library into your application.
|
||||
//
|
||||
// For instance,
|
||||
//
|
||||
// In MainActivity.java:
|
||||
// static {
|
||||
// System.loadLibrary("llama-android");
|
||||
// }
|
||||
//
|
||||
// Or, in MainActivity.kt:
|
||||
// companion object {
|
||||
// init {
|
||||
// System.loadLibrary("llama-android")
|
||||
// }
|
||||
// }
|
||||
|
||||
#define TAG "llama-android.cpp"
|
||||
#define LOGi(...) __android_log_print(ANDROID_LOG_INFO, TAG, __VA_ARGS__)
|
||||
#define LOGe(...) __android_log_print(ANDROID_LOG_ERROR, TAG, __VA_ARGS__)
|
||||
|
||||
jclass la_int_var;
|
||||
jmethodID la_int_var_value;
|
||||
jmethodID la_int_var_inc;
|
||||
|
||||
static void log_callback(ggml_log_level level, const char * fmt, void * data) {
|
||||
if (level == GGML_LOG_LEVEL_ERROR) __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data);
|
||||
else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data);
|
||||
else if (level == GGML_LOG_LEVEL_WARN) __android_log_print(ANDROID_LOG_WARN, TAG, fmt, data);
|
||||
else __android_log_print(ANDROID_LOG_DEFAULT, TAG, fmt, data);
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT jlong JNICALL
|
||||
Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
|
||||
llama_model_params model_params = llama_model_default_params();
|
||||
|
||||
auto path_to_model = env->GetStringUTFChars(filename, 0);
|
||||
LOGi("Loading model from %s", path_to_model);
|
||||
|
||||
auto model = llama_load_model_from_file(path_to_model, model_params);
|
||||
env->ReleaseStringUTFChars(filename, path_to_model);
|
||||
|
||||
if (!model) {
|
||||
LOGe("load_model() failed");
|
||||
env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), "load_model() failed");
|
||||
return 0;
|
||||
}
|
||||
|
||||
return reinterpret_cast<jlong>(model);
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) {
|
||||
llama_free_model(reinterpret_cast<llama_model *>(model));
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT jlong JNICALL
|
||||
Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
|
||||
auto model = reinterpret_cast<llama_model *>(jmodel);
|
||||
|
||||
if (!model) {
|
||||
LOGe("new_context(): model cannot be null");
|
||||
env->ThrowNew(env->FindClass("java/lang/IllegalArgumentException"), "Model cannot be null");
|
||||
return 0;
|
||||
}
|
||||
|
||||
int n_threads = std::max(1, std::min(8, (int) sysconf(_SC_NPROCESSORS_ONLN) - 2));
|
||||
LOGi("Using %d threads", n_threads);
|
||||
|
||||
llama_context_params ctx_params = llama_context_default_params();
|
||||
ctx_params.seed = 1234;
|
||||
ctx_params.n_ctx = 2048;
|
||||
ctx_params.n_threads = n_threads;
|
||||
ctx_params.n_threads_batch = n_threads;
|
||||
|
||||
llama_context * context = llama_new_context_with_model(model, ctx_params);
|
||||
|
||||
if (!context) {
|
||||
LOGe("llama_new_context_with_model() returned null)");
|
||||
env->ThrowNew(env->FindClass("java/lang/IllegalStateException"),
|
||||
"llama_new_context_with_model() returned null)");
|
||||
return 0;
|
||||
}
|
||||
|
||||
return reinterpret_cast<jlong>(context);
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) {
|
||||
llama_free(reinterpret_cast<llama_context *>(context));
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) {
|
||||
llama_backend_free();
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) {
|
||||
llama_log_set(log_callback, NULL);
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT jstring JNICALL
|
||||
Java_com_example_llama_Llm_bench_1model(
|
||||
JNIEnv *env,
|
||||
jobject,
|
||||
jlong context_pointer,
|
||||
jlong model_pointer,
|
||||
jlong batch_pointer,
|
||||
jint pp,
|
||||
jint tg,
|
||||
jint pl,
|
||||
jint nr
|
||||
) {
|
||||
auto pp_avg = 0.0;
|
||||
auto tg_avg = 0.0;
|
||||
auto pp_std = 0.0;
|
||||
auto tg_std = 0.0;
|
||||
|
||||
const auto context = reinterpret_cast<llama_context *>(context_pointer);
|
||||
const auto model = reinterpret_cast<llama_model *>(model_pointer);
|
||||
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
|
||||
|
||||
const int n_ctx = llama_n_ctx(context);
|
||||
|
||||
LOGi("n_ctx = %d", n_ctx);
|
||||
|
||||
int i, j;
|
||||
int nri;
|
||||
for (nri = 0; nri < nr; nri++) {
|
||||
LOGi("Benchmark prompt processing (pp)");
|
||||
|
||||
llama_batch_clear(*batch);
|
||||
|
||||
const int n_tokens = pp;
|
||||
for (i = 0; i < n_tokens; i++) {
|
||||
llama_batch_add(*batch, 0, i, { 0 }, false);
|
||||
}
|
||||
|
||||
batch->logits[batch->n_tokens - 1] = true;
|
||||
llama_kv_cache_clear(context);
|
||||
|
||||
const auto t_pp_start = ggml_time_us();
|
||||
if (llama_decode(context, *batch) != 0) {
|
||||
LOGi("llama_decode() failed during prompt processing");
|
||||
}
|
||||
const auto t_pp_end = ggml_time_us();
|
||||
|
||||
// bench text generation
|
||||
|
||||
LOGi("Benchmark text generation (tg)");
|
||||
|
||||
llama_kv_cache_clear(context);
|
||||
const auto t_tg_start = ggml_time_us();
|
||||
for (i = 0; i < tg; i++) {
|
||||
|
||||
llama_batch_clear(*batch);
|
||||
for (j = 0; j < pl; j++) {
|
||||
llama_batch_add(*batch, 0, i, { j }, true);
|
||||
}
|
||||
|
||||
LOGi("llama_decode() text generation: %d", i);
|
||||
if (llama_decode(context, *batch) != 0) {
|
||||
LOGi("llama_decode() failed during text generation");
|
||||
}
|
||||
}
|
||||
|
||||
const auto t_tg_end = ggml_time_us();
|
||||
|
||||
llama_kv_cache_clear(context);
|
||||
|
||||
const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
|
||||
const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
|
||||
|
||||
const auto speed_pp = double(pp) / t_pp;
|
||||
const auto speed_tg = double(pl * tg) / t_tg;
|
||||
|
||||
pp_avg += speed_pp;
|
||||
tg_avg += speed_tg;
|
||||
|
||||
pp_std += speed_pp * speed_pp;
|
||||
tg_std += speed_tg * speed_tg;
|
||||
|
||||
LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg);
|
||||
}
|
||||
|
||||
pp_avg /= double(nr);
|
||||
tg_avg /= double(nr);
|
||||
|
||||
if (nr > 1) {
|
||||
pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1));
|
||||
tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1));
|
||||
} else {
|
||||
pp_std = 0;
|
||||
tg_std = 0;
|
||||
}
|
||||
|
||||
char model_desc[128];
|
||||
llama_model_desc(model, model_desc, sizeof(model_desc));
|
||||
|
||||
const auto model_size = double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0;
|
||||
const auto model_n_params = double(llama_model_n_params(model)) / 1e9;
|
||||
|
||||
const auto backend = "(Android)"; // TODO: What should this be?
|
||||
|
||||
std::stringstream result;
|
||||
result << std::setprecision(2);
|
||||
result << "| model | size | params | backend | test | t/s |\n";
|
||||
result << "| --- | --- | --- | --- | --- | --- |\n";
|
||||
result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n";
|
||||
result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n";
|
||||
|
||||
return env->NewStringUTF(result.str().c_str());
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
|
||||
llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT jlong JNICALL
|
||||
Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
|
||||
|
||||
// Source: Copy of llama.cpp:llama_batch_init but heap-allocated.
|
||||
|
||||
llama_batch *batch = new llama_batch {
|
||||
0,
|
||||
nullptr,
|
||||
nullptr,
|
||||
nullptr,
|
||||
nullptr,
|
||||
nullptr,
|
||||
nullptr,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
};
|
||||
|
||||
if (embd) {
|
||||
batch->embd = (float *) malloc(sizeof(float) * n_tokens * embd);
|
||||
} else {
|
||||
batch->token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
|
||||
}
|
||||
|
||||
batch->pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens);
|
||||
batch->n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens);
|
||||
batch->seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
|
||||
for (int i = 0; i < n_tokens; ++i) {
|
||||
batch->seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
|
||||
}
|
||||
batch->logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens);
|
||||
|
||||
return reinterpret_cast<jlong>(batch);
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject, jboolean numa) {
|
||||
llama_backend_init(numa);
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT jstring JNICALL
|
||||
Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) {
|
||||
return env->NewStringUTF(llama_print_system_info());
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT jint JNICALL
|
||||
Java_com_example_llama_Llm_completion_1init(
|
||||
JNIEnv *env,
|
||||
jobject,
|
||||
jlong context_pointer,
|
||||
jlong batch_pointer,
|
||||
jstring jtext,
|
||||
jint n_len
|
||||
) {
|
||||
|
||||
const auto text = env->GetStringUTFChars(jtext, 0);
|
||||
const auto context = reinterpret_cast<llama_context *>(context_pointer);
|
||||
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
|
||||
|
||||
const auto tokens_list = llama_tokenize(context, text, 1);
|
||||
|
||||
auto n_ctx = llama_n_ctx(context);
|
||||
auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
|
||||
|
||||
LOGi("n_len = %d, n_ctx = %d, n_kv_req = %d", n_len, n_ctx, n_kv_req);
|
||||
|
||||
if (n_kv_req > n_ctx) {
|
||||
LOGe("error: n_kv_req > n_ctx, the required KV cache size is not big enough");
|
||||
}
|
||||
|
||||
for (auto id : tokens_list) {
|
||||
LOGi("%s", llama_token_to_piece(context, id).c_str());
|
||||
}
|
||||
|
||||
llama_batch_clear(*batch);
|
||||
|
||||
// evaluate the initial prompt
|
||||
for (auto i = 0; i < tokens_list.size(); i++) {
|
||||
llama_batch_add(*batch, tokens_list[i], i, { 0 }, false);
|
||||
}
|
||||
|
||||
// llama_decode will output logits only for the last token of the prompt
|
||||
batch->logits[batch->n_tokens - 1] = true;
|
||||
|
||||
if (llama_decode(context, *batch) != 0) {
|
||||
LOGe("llama_decode() failed");
|
||||
}
|
||||
|
||||
env->ReleaseStringUTFChars(jtext, text);
|
||||
|
||||
return batch->n_tokens;
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT jstring JNICALL
|
||||
Java_com_example_llama_Llm_completion_1loop(
|
||||
JNIEnv * env,
|
||||
jobject,
|
||||
jlong context_pointer,
|
||||
jlong batch_pointer,
|
||||
jint n_len,
|
||||
jobject intvar_ncur
|
||||
) {
|
||||
const auto context = reinterpret_cast<llama_context *>(context_pointer);
|
||||
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
|
||||
const auto model = llama_get_model(context);
|
||||
|
||||
if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur);
|
||||
if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I");
|
||||
if (!la_int_var_inc) la_int_var_inc = env->GetMethodID(la_int_var, "inc", "()V");
|
||||
|
||||
auto n_vocab = llama_n_vocab(model);
|
||||
auto logits = llama_get_logits_ith(context, batch->n_tokens - 1);
|
||||
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
|
||||
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
||||
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
|
||||
// sample the most likely token
|
||||
const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
|
||||
|
||||
const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
|
||||
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
|
||||
return env->NewStringUTF("");
|
||||
}
|
||||
|
||||
auto new_token_chars = llama_token_to_piece(context, new_token_id);
|
||||
LOGi("new_token_chars: `%s`", new_token_chars.c_str());
|
||||
auto new_token = env->NewStringUTF(new_token_chars.c_str());
|
||||
|
||||
llama_batch_clear(*batch);
|
||||
llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
|
||||
|
||||
env->CallVoidMethod(intvar_ncur, la_int_var_inc);
|
||||
|
||||
if (llama_decode(context, *batch) != 0) {
|
||||
LOGe("llama_decode() returned null");
|
||||
}
|
||||
|
||||
return new_token;
|
||||
}
|
||||
|
||||
extern "C"
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
|
||||
llama_kv_cache_clear(reinterpret_cast<llama_context *>(context));
|
||||
}
|
||||
@ -0,0 +1,119 @@
|
||||
package com.example.llama
|
||||
|
||||
import android.app.DownloadManager
|
||||
import android.net.Uri
|
||||
import android.util.Log
|
||||
import androidx.compose.material3.Button
|
||||
import androidx.compose.material3.Text
|
||||
import androidx.compose.runtime.Composable
|
||||
import androidx.compose.runtime.getValue
|
||||
import androidx.compose.runtime.mutableDoubleStateOf
|
||||
import androidx.compose.runtime.mutableStateOf
|
||||
import androidx.compose.runtime.remember
|
||||
import androidx.compose.runtime.rememberCoroutineScope
|
||||
import androidx.compose.runtime.setValue
|
||||
import androidx.core.database.getLongOrNull
|
||||
import androidx.core.net.toUri
|
||||
import kotlinx.coroutines.delay
|
||||
import kotlinx.coroutines.launch
|
||||
import java.io.File
|
||||
|
||||
data class Downloadable(val name: String, val source: Uri, val destination: File) {
|
||||
companion object {
|
||||
@JvmStatic
|
||||
private val tag: String? = this::class.qualifiedName
|
||||
|
||||
sealed interface State
|
||||
data object Ready: State
|
||||
data class Downloading(val id: Long): State
|
||||
data class Downloaded(val downloadable: Downloadable): State
|
||||
data class Error(val message: String): State
|
||||
|
||||
@JvmStatic
|
||||
@Composable
|
||||
fun Button(viewModel: MainViewModel, dm: DownloadManager, item: Downloadable) {
|
||||
var status: State by remember {
|
||||
mutableStateOf(
|
||||
if (item.destination.exists()) Downloaded(item)
|
||||
else Ready
|
||||
)
|
||||
}
|
||||
var progress by remember { mutableDoubleStateOf(0.0) }
|
||||
|
||||
val coroutineScope = rememberCoroutineScope()
|
||||
|
||||
suspend fun waitForDownload(result: Downloading, item: Downloadable): State {
|
||||
while (true) {
|
||||
val cursor = dm.query(DownloadManager.Query().setFilterById(result.id))
|
||||
|
||||
if (cursor == null) {
|
||||
Log.e(tag, "dm.query() returned null")
|
||||
return Error("dm.query() returned null")
|
||||
}
|
||||
|
||||
if (!cursor.moveToFirst() || cursor.count < 1) {
|
||||
cursor.close()
|
||||
Log.i(tag, "cursor.moveToFirst() returned false or cursor.count < 1, download canceled?")
|
||||
return Ready
|
||||
}
|
||||
|
||||
val pix = cursor.getColumnIndex(DownloadManager.COLUMN_BYTES_DOWNLOADED_SO_FAR)
|
||||
val tix = cursor.getColumnIndex(DownloadManager.COLUMN_TOTAL_SIZE_BYTES)
|
||||
val sofar = cursor.getLongOrNull(pix) ?: 0
|
||||
val total = cursor.getLongOrNull(tix) ?: 1
|
||||
cursor.close()
|
||||
|
||||
if (sofar == total) {
|
||||
return Downloaded(item)
|
||||
}
|
||||
|
||||
progress = (sofar * 1.0) / total
|
||||
|
||||
delay(1000L)
|
||||
}
|
||||
}
|
||||
|
||||
fun onClick() {
|
||||
when (val s = status) {
|
||||
is Downloaded -> {
|
||||
viewModel.load(item.destination.path)
|
||||
}
|
||||
|
||||
is Downloading -> {
|
||||
coroutineScope.launch {
|
||||
status = waitForDownload(s, item)
|
||||
}
|
||||
}
|
||||
|
||||
else -> {
|
||||
item.destination.delete()
|
||||
|
||||
val request = DownloadManager.Request(item.source).apply {
|
||||
setTitle("Downloading model")
|
||||
setDescription("Downloading model: ${item.name}")
|
||||
setAllowedNetworkTypes(DownloadManager.Request.NETWORK_WIFI)
|
||||
setDestinationUri(item.destination.toUri())
|
||||
}
|
||||
|
||||
viewModel.log("Saving ${item.name} to ${item.destination.path}")
|
||||
Log.i(tag, "Saving ${item.name} to ${item.destination.path}")
|
||||
|
||||
val id = dm.enqueue(request)
|
||||
status = Downloading(id)
|
||||
onClick()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Button(onClick = { onClick() }, enabled = status !is Downloading) {
|
||||
when (status) {
|
||||
is Downloading -> Text(text = "Downloading ${(progress * 100).toInt()}%")
|
||||
is Downloaded -> Text("Load ${item.name}")
|
||||
is Ready -> Text("Download ${item.name}")
|
||||
is Error -> Text("Download ${item.name}")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,172 @@
|
||||
package com.example.llama
|
||||
|
||||
import android.util.Log
|
||||
import kotlinx.coroutines.CoroutineDispatcher
|
||||
import kotlinx.coroutines.asCoroutineDispatcher
|
||||
import kotlinx.coroutines.flow.Flow
|
||||
import kotlinx.coroutines.flow.flow
|
||||
import kotlinx.coroutines.flow.flowOn
|
||||
import kotlinx.coroutines.withContext
|
||||
import java.util.concurrent.Executors
|
||||
import kotlin.concurrent.thread
|
||||
|
||||
class Llm {
|
||||
private val tag: String? = this::class.simpleName
|
||||
|
||||
private val threadLocalState: ThreadLocal<State> = ThreadLocal.withInitial { State.Idle }
|
||||
|
||||
private val runLoop: CoroutineDispatcher = Executors.newSingleThreadExecutor {
|
||||
thread(start = false, name = "Llm-RunLoop") {
|
||||
Log.d(tag, "Dedicated thread for native code: ${Thread.currentThread().name}")
|
||||
|
||||
// No-op if called more than once.
|
||||
System.loadLibrary("llama-android")
|
||||
|
||||
// Set llama log handler to Android
|
||||
log_to_android()
|
||||
backend_init(false)
|
||||
|
||||
Log.d(tag, system_info())
|
||||
|
||||
it.run()
|
||||
}.apply {
|
||||
uncaughtExceptionHandler = Thread.UncaughtExceptionHandler { _, exception: Throwable ->
|
||||
Log.e(tag, "Unhandled exception", exception)
|
||||
}
|
||||
}
|
||||
}.asCoroutineDispatcher()
|
||||
|
||||
private val nlen: Int = 64
|
||||
|
||||
private external fun log_to_android()
|
||||
private external fun load_model(filename: String): Long
|
||||
private external fun free_model(model: Long)
|
||||
private external fun new_context(model: Long): Long
|
||||
private external fun free_context(context: Long)
|
||||
private external fun backend_init(numa: Boolean)
|
||||
private external fun backend_free()
|
||||
private external fun free_batch(batch: Long)
|
||||
private external fun new_batch(nTokens: Int, embd: Int, nSeqMax: Int): Long
|
||||
private external fun bench_model(
|
||||
context: Long,
|
||||
model: Long,
|
||||
batch: Long,
|
||||
pp: Int,
|
||||
tg: Int,
|
||||
pl: Int,
|
||||
nr: Int
|
||||
): String
|
||||
|
||||
private external fun system_info(): String
|
||||
|
||||
private external fun completion_init(
|
||||
context: Long,
|
||||
batch: Long,
|
||||
text: String,
|
||||
nLen: Int
|
||||
): Int
|
||||
|
||||
private external fun completion_loop(
|
||||
context: Long,
|
||||
batch: Long,
|
||||
nLen: Int,
|
||||
ncur: IntVar
|
||||
): String
|
||||
|
||||
private external fun kv_cache_clear(context: Long)
|
||||
|
||||
suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String {
|
||||
return withContext(runLoop) {
|
||||
when (val state = threadLocalState.get()) {
|
||||
is State.Loaded -> {
|
||||
Log.d(tag, "bench(): $state")
|
||||
bench_model(state.context, state.model, state.batch, pp, tg, pl, nr)
|
||||
}
|
||||
|
||||
else -> throw IllegalStateException("No model loaded")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
suspend fun load(pathToModel: String) {
|
||||
withContext(runLoop) {
|
||||
when (threadLocalState.get()) {
|
||||
is State.Idle -> {
|
||||
val model = load_model(pathToModel)
|
||||
if (model == 0L) throw IllegalStateException("load_model() failed")
|
||||
|
||||
val context = new_context(model)
|
||||
if (context == 0L) throw IllegalStateException("new_context() failed")
|
||||
|
||||
val batch = new_batch(512, 0, 1)
|
||||
if (batch == 0L) throw IllegalStateException("new_batch() failed")
|
||||
|
||||
Log.i(tag, "Loaded model $pathToModel")
|
||||
threadLocalState.set(State.Loaded(model, context, batch))
|
||||
}
|
||||
else -> throw IllegalStateException("Model already loaded")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fun send(message: String): Flow<String> = flow {
|
||||
when (val state = threadLocalState.get()) {
|
||||
is State.Loaded -> {
|
||||
val ncur = IntVar(completion_init(state.context, state.batch, message, nlen))
|
||||
while (ncur.value <= nlen) {
|
||||
val str = completion_loop(state.context, state.batch, nlen, ncur)
|
||||
if (str.isEmpty()) {
|
||||
break
|
||||
}
|
||||
emit(str)
|
||||
}
|
||||
kv_cache_clear(state.context)
|
||||
}
|
||||
else -> {}
|
||||
}
|
||||
}.flowOn(runLoop)
|
||||
|
||||
/**
|
||||
* Unloads the model and frees resources.
|
||||
*
|
||||
* This is a no-op if there's no model loaded.
|
||||
*/
|
||||
suspend fun unload() {
|
||||
withContext(runLoop) {
|
||||
when (val state = threadLocalState.get()) {
|
||||
is State.Loaded -> {
|
||||
free_context(state.context)
|
||||
free_model(state.model)
|
||||
free_batch(state.batch)
|
||||
|
||||
threadLocalState.set(State.Idle)
|
||||
}
|
||||
else -> {}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
companion object {
|
||||
private class IntVar(value: Int) {
|
||||
@Volatile
|
||||
var value: Int = value
|
||||
private set
|
||||
|
||||
fun inc() {
|
||||
synchronized(this) {
|
||||
value += 1
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private sealed interface State {
|
||||
data object Idle: State
|
||||
data class Loaded(val model: Long, val context: Long, val batch: Long): State
|
||||
}
|
||||
|
||||
// Enforce only one instance of Llm.
|
||||
private val _instance: Llm = Llm()
|
||||
|
||||
fun instance(): Llm = _instance
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,154 @@
|
||||
package com.example.llama
|
||||
|
||||
import android.app.ActivityManager
|
||||
import android.app.DownloadManager
|
||||
import android.content.ClipData
|
||||
import android.content.ClipboardManager
|
||||
import android.net.Uri
|
||||
import android.os.Bundle
|
||||
import android.os.StrictMode
|
||||
import android.os.StrictMode.VmPolicy
|
||||
import android.text.format.Formatter
|
||||
import androidx.activity.ComponentActivity
|
||||
import androidx.activity.compose.setContent
|
||||
import androidx.activity.viewModels
|
||||
import androidx.compose.foundation.layout.Box
|
||||
import androidx.compose.foundation.layout.Column
|
||||
import androidx.compose.foundation.layout.Row
|
||||
import androidx.compose.foundation.layout.fillMaxSize
|
||||
import androidx.compose.foundation.layout.padding
|
||||
import androidx.compose.foundation.lazy.LazyColumn
|
||||
import androidx.compose.foundation.lazy.items
|
||||
import androidx.compose.foundation.lazy.rememberLazyListState
|
||||
import androidx.compose.material3.Button
|
||||
import androidx.compose.material3.LocalContentColor
|
||||
import androidx.compose.material3.MaterialTheme
|
||||
import androidx.compose.material3.OutlinedTextField
|
||||
import androidx.compose.material3.Surface
|
||||
import androidx.compose.material3.Text
|
||||
import androidx.compose.runtime.Composable
|
||||
import androidx.compose.ui.Modifier
|
||||
import androidx.compose.ui.unit.dp
|
||||
import androidx.core.content.getSystemService
|
||||
import com.example.llama.ui.theme.LlamaAndroidTheme
|
||||
import java.io.File
|
||||
|
||||
class MainActivity(
|
||||
activityManager: ActivityManager? = null,
|
||||
downloadManager: DownloadManager? = null,
|
||||
clipboardManager: ClipboardManager? = null,
|
||||
): ComponentActivity() {
|
||||
private val tag: String? = this::class.simpleName
|
||||
|
||||
private val activityManager by lazy { activityManager ?: getSystemService<ActivityManager>()!! }
|
||||
private val downloadManager by lazy { downloadManager ?: getSystemService<DownloadManager>()!! }
|
||||
private val clipboardManager by lazy { clipboardManager ?: getSystemService<ClipboardManager>()!! }
|
||||
|
||||
private val viewModel: MainViewModel by viewModels()
|
||||
|
||||
// Get a MemoryInfo object for the device's current memory status.
|
||||
private fun availableMemory(): ActivityManager.MemoryInfo {
|
||||
return ActivityManager.MemoryInfo().also { memoryInfo ->
|
||||
activityManager.getMemoryInfo(memoryInfo)
|
||||
}
|
||||
}
|
||||
|
||||
override fun onCreate(savedInstanceState: Bundle?) {
|
||||
super.onCreate(savedInstanceState)
|
||||
|
||||
StrictMode.setVmPolicy(
|
||||
VmPolicy.Builder(StrictMode.getVmPolicy())
|
||||
.detectLeakedClosableObjects()
|
||||
.build()
|
||||
)
|
||||
|
||||
val free = Formatter.formatFileSize(this, availableMemory().availMem)
|
||||
val total = Formatter.formatFileSize(this, availableMemory().totalMem)
|
||||
|
||||
viewModel.log("Current memory: $free / $total")
|
||||
viewModel.log("Downloads directory: ${getExternalFilesDir(null)}")
|
||||
|
||||
val extFilesDir = getExternalFilesDir(null)
|
||||
|
||||
val models = listOf(
|
||||
Downloadable(
|
||||
"Phi-2 7B (Q4_0, 1.6 GiB)",
|
||||
Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true"),
|
||||
File(extFilesDir, "phi-2-q4_0.gguf"),
|
||||
),
|
||||
Downloadable(
|
||||
"TinyLlama 1.1B (f16, 2.2 GiB)",
|
||||
Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true"),
|
||||
File(extFilesDir, "tinyllama-1.1-f16.gguf"),
|
||||
),
|
||||
Downloadable(
|
||||
"Phi 2 DPO (Q3_K_M, 1.48 GiB)",
|
||||
Uri.parse("https://huggingface.co/TheBloke/phi-2-dpo-GGUF/resolve/main/phi-2-dpo.Q3_K_M.gguf?download=true"),
|
||||
File(extFilesDir, "phi-2-dpo.Q3_K_M.gguf")
|
||||
),
|
||||
)
|
||||
|
||||
setContent {
|
||||
LlamaAndroidTheme {
|
||||
// A surface container using the 'background' color from the theme
|
||||
Surface(
|
||||
modifier = Modifier.fillMaxSize(),
|
||||
color = MaterialTheme.colorScheme.background
|
||||
) {
|
||||
MainCompose(
|
||||
viewModel,
|
||||
clipboardManager,
|
||||
downloadManager,
|
||||
models,
|
||||
)
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@Composable
|
||||
fun MainCompose(
|
||||
viewModel: MainViewModel,
|
||||
clipboard: ClipboardManager,
|
||||
dm: DownloadManager,
|
||||
models: List<Downloadable>
|
||||
) {
|
||||
Column {
|
||||
val scrollState = rememberLazyListState()
|
||||
|
||||
Box(modifier = Modifier.weight(1f)) {
|
||||
LazyColumn(state = scrollState) {
|
||||
items(viewModel.messages) {
|
||||
Text(
|
||||
it,
|
||||
style = MaterialTheme.typography.bodyLarge.copy(color = LocalContentColor.current),
|
||||
modifier = Modifier.padding(16.dp)
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
OutlinedTextField(
|
||||
value = viewModel.message,
|
||||
onValueChange = { viewModel.updateMessage(it) },
|
||||
label = { Text("Message") },
|
||||
)
|
||||
Row {
|
||||
Button({ viewModel.send() }) { Text("Send") }
|
||||
Button({ viewModel.bench(8, 4, 1) }) { Text("Bench") }
|
||||
Button({ viewModel.clear() }) { Text("Clear") }
|
||||
Button({
|
||||
viewModel.messages.joinToString("\n").let {
|
||||
clipboard.setPrimaryClip(ClipData.newPlainText("", it))
|
||||
}
|
||||
}) { Text("Copy") }
|
||||
}
|
||||
|
||||
Column {
|
||||
for (model in models) {
|
||||
Downloadable.Button(viewModel, dm, model)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,104 @@
|
||||
package com.example.llama
|
||||
|
||||
import android.util.Log
|
||||
import androidx.compose.runtime.getValue
|
||||
import androidx.compose.runtime.mutableStateOf
|
||||
import androidx.compose.runtime.setValue
|
||||
import androidx.lifecycle.ViewModel
|
||||
import androidx.lifecycle.viewModelScope
|
||||
import kotlinx.coroutines.flow.catch
|
||||
import kotlinx.coroutines.launch
|
||||
|
||||
class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
|
||||
companion object {
|
||||
@JvmStatic
|
||||
private val NanosPerSecond = 1_000_000_000.0
|
||||
}
|
||||
|
||||
private val tag: String? = this::class.simpleName
|
||||
|
||||
var messages by mutableStateOf(listOf("Initializing..."))
|
||||
private set
|
||||
|
||||
var message by mutableStateOf("")
|
||||
private set
|
||||
|
||||
override fun onCleared() {
|
||||
super.onCleared()
|
||||
|
||||
viewModelScope.launch {
|
||||
try {
|
||||
llm.unload()
|
||||
} catch (exc: IllegalStateException) {
|
||||
messages += exc.message!!
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fun send() {
|
||||
val text = message
|
||||
message = ""
|
||||
|
||||
// Add to messages console.
|
||||
messages += text
|
||||
messages += ""
|
||||
|
||||
viewModelScope.launch {
|
||||
llm.send(text)
|
||||
.catch {
|
||||
Log.e(tag, "send() failed", it)
|
||||
messages += it.message!!
|
||||
}
|
||||
.collect { messages = messages.dropLast(1) + (messages.last() + it) }
|
||||
}
|
||||
}
|
||||
|
||||
fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) {
|
||||
viewModelScope.launch {
|
||||
try {
|
||||
val start = System.nanoTime()
|
||||
val warmupResult = llm.bench(pp, tg, pl, nr)
|
||||
val end = System.nanoTime()
|
||||
|
||||
messages += warmupResult
|
||||
|
||||
val warmup = (end - start).toDouble() / NanosPerSecond
|
||||
messages += "Warm up time: $warmup seconds, please wait..."
|
||||
|
||||
if (warmup > 5.0) {
|
||||
messages += "Warm up took too long, aborting benchmark"
|
||||
return@launch
|
||||
}
|
||||
|
||||
messages += llm.bench(512, 128, 1, 3)
|
||||
} catch (exc: IllegalStateException) {
|
||||
Log.e(tag, "bench() failed", exc)
|
||||
messages += exc.message!!
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fun load(pathToModel: String) {
|
||||
viewModelScope.launch {
|
||||
try {
|
||||
llm.load(pathToModel)
|
||||
messages += "Loaded $pathToModel"
|
||||
} catch (exc: IllegalStateException) {
|
||||
Log.e(tag, "load() failed", exc)
|
||||
messages += exc.message!!
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fun updateMessage(newMessage: String) {
|
||||
message = newMessage
|
||||
}
|
||||
|
||||
fun clear() {
|
||||
messages = listOf()
|
||||
}
|
||||
|
||||
fun log(message: String) {
|
||||
messages += message
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,11 @@
|
||||
package com.example.llama.ui.theme
|
||||
|
||||
import androidx.compose.ui.graphics.Color
|
||||
|
||||
val Purple80 = Color(0xFFD0BCFF)
|
||||
val PurpleGrey80 = Color(0xFFCCC2DC)
|
||||
val Pink80 = Color(0xFFEFB8C8)
|
||||
|
||||
val Purple40 = Color(0xFF6650a4)
|
||||
val PurpleGrey40 = Color(0xFF625b71)
|
||||
val Pink40 = Color(0xFF7D5260)
|
||||
@ -0,0 +1,70 @@
|
||||
package com.example.llama.ui.theme
|
||||
|
||||
import android.app.Activity
|
||||
import android.os.Build
|
||||
import androidx.compose.foundation.isSystemInDarkTheme
|
||||
import androidx.compose.material3.MaterialTheme
|
||||
import androidx.compose.material3.darkColorScheme
|
||||
import androidx.compose.material3.dynamicDarkColorScheme
|
||||
import androidx.compose.material3.dynamicLightColorScheme
|
||||
import androidx.compose.material3.lightColorScheme
|
||||
import androidx.compose.runtime.Composable
|
||||
import androidx.compose.runtime.SideEffect
|
||||
import androidx.compose.ui.graphics.toArgb
|
||||
import androidx.compose.ui.platform.LocalContext
|
||||
import androidx.compose.ui.platform.LocalView
|
||||
import androidx.core.view.WindowCompat
|
||||
|
||||
private val DarkColorScheme = darkColorScheme(
|
||||
primary = Purple80,
|
||||
secondary = PurpleGrey80,
|
||||
tertiary = Pink80
|
||||
)
|
||||
|
||||
private val LightColorScheme = lightColorScheme(
|
||||
primary = Purple40,
|
||||
secondary = PurpleGrey40,
|
||||
tertiary = Pink40
|
||||
|
||||
/* Other default colors to override
|
||||
background = Color(0xFFFFFBFE),
|
||||
surface = Color(0xFFFFFBFE),
|
||||
onPrimary = Color.White,
|
||||
onSecondary = Color.White,
|
||||
onTertiary = Color.White,
|
||||
onBackground = Color(0xFF1C1B1F),
|
||||
onSurface = Color(0xFF1C1B1F),
|
||||
*/
|
||||
)
|
||||
|
||||
@Composable
|
||||
fun LlamaAndroidTheme(
|
||||
darkTheme: Boolean = isSystemInDarkTheme(),
|
||||
// Dynamic color is available on Android 12+
|
||||
dynamicColor: Boolean = true,
|
||||
content: @Composable () -> Unit
|
||||
) {
|
||||
val colorScheme = when {
|
||||
dynamicColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.S -> {
|
||||
val context = LocalContext.current
|
||||
if (darkTheme) dynamicDarkColorScheme(context) else dynamicLightColorScheme(context)
|
||||
}
|
||||
|
||||
darkTheme -> DarkColorScheme
|
||||
else -> LightColorScheme
|
||||
}
|
||||
val view = LocalView.current
|
||||
if (!view.isInEditMode) {
|
||||
SideEffect {
|
||||
val window = (view.context as Activity).window
|
||||
window.statusBarColor = colorScheme.primary.toArgb()
|
||||
WindowCompat.getInsetsController(window, view).isAppearanceLightStatusBars = darkTheme
|
||||
}
|
||||
}
|
||||
|
||||
MaterialTheme(
|
||||
colorScheme = colorScheme,
|
||||
typography = Typography,
|
||||
content = content
|
||||
)
|
||||
}
|
||||
@ -0,0 +1,34 @@
|
||||
package com.example.llama.ui.theme
|
||||
|
||||
import androidx.compose.material3.Typography
|
||||
import androidx.compose.ui.text.TextStyle
|
||||
import androidx.compose.ui.text.font.FontFamily
|
||||
import androidx.compose.ui.text.font.FontWeight
|
||||
import androidx.compose.ui.unit.sp
|
||||
|
||||
// Set of Material typography styles to start with
|
||||
val Typography = Typography(
|
||||
bodyLarge = TextStyle(
|
||||
fontFamily = FontFamily.Default,
|
||||
fontWeight = FontWeight.Normal,
|
||||
fontSize = 16.sp,
|
||||
lineHeight = 24.sp,
|
||||
letterSpacing = 0.5.sp
|
||||
)
|
||||
/* Other default text styles to override
|
||||
titleLarge = TextStyle(
|
||||
fontFamily = FontFamily.Default,
|
||||
fontWeight = FontWeight.Normal,
|
||||
fontSize = 22.sp,
|
||||
lineHeight = 28.sp,
|
||||
letterSpacing = 0.sp
|
||||
),
|
||||
labelSmall = TextStyle(
|
||||
fontFamily = FontFamily.Default,
|
||||
fontWeight = FontWeight.Medium,
|
||||
fontSize = 11.sp,
|
||||
lineHeight = 16.sp,
|
||||
letterSpacing = 0.5.sp
|
||||
)
|
||||
*/
|
||||
)
|
||||
@ -0,0 +1,170 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<vector xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
android:width="108dp"
|
||||
android:height="108dp"
|
||||
android:viewportWidth="108"
|
||||
android:viewportHeight="108">
|
||||
<path
|
||||
android:fillColor="#3DDC84"
|
||||
android:pathData="M0,0h108v108h-108z" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M9,0L9,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M19,0L19,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M29,0L29,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M39,0L39,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M49,0L49,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M59,0L59,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M69,0L69,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M79,0L79,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M89,0L89,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M99,0L99,108"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,9L108,9"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,19L108,19"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,29L108,29"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,39L108,39"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,49L108,49"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,59L108,59"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,69L108,69"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,79L108,79"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,89L108,89"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M0,99L108,99"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M19,29L89,29"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M19,39L89,39"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M19,49L89,49"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M19,59L89,59"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M19,69L89,69"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M19,79L89,79"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M29,19L29,89"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M39,19L39,89"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M49,19L49,89"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M59,19L59,89"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M69,19L69,89"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
<path
|
||||
android:fillColor="#00000000"
|
||||
android:pathData="M79,19L79,89"
|
||||
android:strokeWidth="0.8"
|
||||
android:strokeColor="#33FFFFFF" />
|
||||
</vector>
|
||||
@ -0,0 +1,30 @@
|
||||
<vector xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
xmlns:aapt="http://schemas.android.com/aapt"
|
||||
android:width="108dp"
|
||||
android:height="108dp"
|
||||
android:viewportWidth="108"
|
||||
android:viewportHeight="108">
|
||||
<path android:pathData="M31,63.928c0,0 6.4,-11 12.1,-13.1c7.2,-2.6 26,-1.4 26,-1.4l38.1,38.1L107,108.928l-32,-1L31,63.928z">
|
||||
<aapt:attr name="android:fillColor">
|
||||
<gradient
|
||||
android:endX="85.84757"
|
||||
android:endY="92.4963"
|
||||
android:startX="42.9492"
|
||||
android:startY="49.59793"
|
||||
android:type="linear">
|
||||
<item
|
||||
android:color="#44000000"
|
||||
android:offset="0.0" />
|
||||
<item
|
||||
android:color="#00000000"
|
||||
android:offset="1.0" />
|
||||
</gradient>
|
||||
</aapt:attr>
|
||||
</path>
|
||||
<path
|
||||
android:fillColor="#FFFFFF"
|
||||
android:fillType="nonZero"
|
||||
android:pathData="M65.3,45.828l3.8,-6.6c0.2,-0.4 0.1,-0.9 -0.3,-1.1c-0.4,-0.2 -0.9,-0.1 -1.1,0.3l-3.9,6.7c-6.3,-2.8 -13.4,-2.8 -19.7,0l-3.9,-6.7c-0.2,-0.4 -0.7,-0.5 -1.1,-0.3C38.8,38.328 38.7,38.828 38.9,39.228l3.8,6.6C36.2,49.428 31.7,56.028 31,63.928h46C76.3,56.028 71.8,49.428 65.3,45.828zM43.4,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2c-0.3,-0.7 -0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C45.3,56.528 44.5,57.328 43.4,57.328L43.4,57.328zM64.6,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2s-0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C66.5,56.528 65.6,57.328 64.6,57.328L64.6,57.328z"
|
||||
android:strokeWidth="1"
|
||||
android:strokeColor="#00000000" />
|
||||
</vector>
|
||||
@ -0,0 +1,6 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
|
||||
<background android:drawable="@drawable/ic_launcher_background" />
|
||||
<foreground android:drawable="@drawable/ic_launcher_foreground" />
|
||||
<monochrome android:drawable="@drawable/ic_launcher_foreground" />
|
||||
</adaptive-icon>
|
||||
@ -0,0 +1,6 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
|
||||
<background android:drawable="@drawable/ic_launcher_background" />
|
||||
<foreground android:drawable="@drawable/ic_launcher_foreground" />
|
||||
<monochrome android:drawable="@drawable/ic_launcher_foreground" />
|
||||
</adaptive-icon>
|
||||
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|
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|
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|
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|
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|
After Width: | Height: | Size: 1.9 KiB |
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|
After Width: | Height: | Size: 3.8 KiB |
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|
After Width: | Height: | Size: 2.8 KiB |
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|
After Width: | Height: | Size: 5.8 KiB |
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|
After Width: | Height: | Size: 3.8 KiB |
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|
After Width: | Height: | Size: 7.6 KiB |
10
examples/llama.android/app/src/main/res/values/colors.xml
Normal file
10
examples/llama.android/app/src/main/res/values/colors.xml
Normal file
@ -0,0 +1,10 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<resources>
|
||||
<color name="purple_200">#FFBB86FC</color>
|
||||
<color name="purple_500">#FF6200EE</color>
|
||||
<color name="purple_700">#FF3700B3</color>
|
||||
<color name="teal_200">#FF03DAC5</color>
|
||||
<color name="teal_700">#FF018786</color>
|
||||
<color name="black">#FF000000</color>
|
||||
<color name="white">#FFFFFFFF</color>
|
||||
</resources>
|
||||
@ -0,0 +1,3 @@
|
||||
<resources>
|
||||
<string name="app_name">LlamaAndroid</string>
|
||||
</resources>
|
||||
@ -0,0 +1,5 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<resources>
|
||||
|
||||
<style name="Theme.LlamaAndroid" parent="android:Theme.Material.Light.NoActionBar" />
|
||||
</resources>
|
||||
13
examples/llama.android/app/src/main/res/xml/backup_rules.xml
Normal file
13
examples/llama.android/app/src/main/res/xml/backup_rules.xml
Normal file
@ -0,0 +1,13 @@
|
||||
<?xml version="1.0" encoding="utf-8"?><!--
|
||||
Sample backup rules file; uncomment and customize as necessary.
|
||||
See https://developer.android.com/guide/topics/data/autobackup
|
||||
for details.
|
||||
Note: This file is ignored for devices older that API 31
|
||||
See https://developer.android.com/about/versions/12/backup-restore
|
||||
-->
|
||||
<full-backup-content>
|
||||
<!--
|
||||
<include domain="sharedpref" path="."/>
|
||||
<exclude domain="sharedpref" path="device.xml"/>
|
||||
-->
|
||||
</full-backup-content>
|
||||
@ -0,0 +1,19 @@
|
||||
<?xml version="1.0" encoding="utf-8"?><!--
|
||||
Sample data extraction rules file; uncomment and customize as necessary.
|
||||
See https://developer.android.com/about/versions/12/backup-restore#xml-changes
|
||||
for details.
|
||||
-->
|
||||
<data-extraction-rules>
|
||||
<cloud-backup>
|
||||
<!-- TODO: Use <include> and <exclude> to control what is backed up.
|
||||
<include .../>
|
||||
<exclude .../>
|
||||
-->
|
||||
</cloud-backup>
|
||||
<!--
|
||||
<device-transfer>
|
||||
<include .../>
|
||||
<exclude .../>
|
||||
</device-transfer>
|
||||
-->
|
||||
</data-extraction-rules>
|
||||
5
examples/llama.android/build.gradle.kts
Normal file
5
examples/llama.android/build.gradle.kts
Normal file
@ -0,0 +1,5 @@
|
||||
// Top-level build file where you can add configuration options common to all sub-projects/modules.
|
||||
plugins {
|
||||
id("com.android.application") version "8.2.0" apply false
|
||||
id("org.jetbrains.kotlin.android") version "1.9.0" apply false
|
||||
}
|
||||
23
examples/llama.android/gradle.properties
Normal file
23
examples/llama.android/gradle.properties
Normal file
@ -0,0 +1,23 @@
|
||||
# Project-wide Gradle settings.
|
||||
# IDE (e.g. Android Studio) users:
|
||||
# Gradle settings configured through the IDE *will override*
|
||||
# any settings specified in this file.
|
||||
# For more details on how to configure your build environment visit
|
||||
# http://www.gradle.org/docs/current/userguide/build_environment.html
|
||||
# Specifies the JVM arguments used for the daemon process.
|
||||
# The setting is particularly useful for tweaking memory settings.
|
||||
org.gradle.jvmargs=-Xmx2048m -Dfile.encoding=UTF-8
|
||||
# When configured, Gradle will run in incubating parallel mode.
|
||||
# This option should only be used with decoupled projects. More details, visit
|
||||
# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
|
||||
# org.gradle.parallel=true
|
||||
# AndroidX package structure to make it clearer which packages are bundled with the
|
||||
# Android operating system, and which are packaged with your app's APK
|
||||
# https://developer.android.com/topic/libraries/support-library/androidx-rn
|
||||
android.useAndroidX=true
|
||||
# Kotlin code style for this project: "official" or "obsolete":
|
||||
kotlin.code.style=official
|
||||
# Enables namespacing of each library's R class so that its R class includes only the
|
||||
# resources declared in the library itself and none from the library's dependencies,
|
||||
# thereby reducing the size of the R class for that library
|
||||
android.nonTransitiveRClass=true
|
||||
BIN
examples/llama.android/gradle/wrapper/gradle-wrapper.jar
vendored
Normal file
BIN
examples/llama.android/gradle/wrapper/gradle-wrapper.jar
vendored
Normal file
Binary file not shown.
6
examples/llama.android/gradle/wrapper/gradle-wrapper.properties
vendored
Normal file
6
examples/llama.android/gradle/wrapper/gradle-wrapper.properties
vendored
Normal file
@ -0,0 +1,6 @@
|
||||
#Thu Dec 21 14:31:09 AEDT 2023
|
||||
distributionBase=GRADLE_USER_HOME
|
||||
distributionPath=wrapper/dists
|
||||
distributionUrl=https\://services.gradle.org/distributions/gradle-8.2-bin.zip
|
||||
zipStoreBase=GRADLE_USER_HOME
|
||||
zipStorePath=wrapper/dists
|
||||
185
examples/llama.android/gradlew
vendored
Executable file
185
examples/llama.android/gradlew
vendored
Executable file
@ -0,0 +1,185 @@
|
||||
#!/usr/bin/env sh
|
||||
|
||||
#
|
||||
# Copyright 2015 the original author or authors.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# https://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
##############################################################################
|
||||
##
|
||||
## Gradle start up script for UN*X
|
||||
##
|
||||
##############################################################################
|
||||
|
||||
# Attempt to set APP_HOME
|
||||
# Resolve links: $0 may be a link
|
||||
PRG="$0"
|
||||
# Need this for relative symlinks.
|
||||
while [ -h "$PRG" ] ; do
|
||||
ls=`ls -ld "$PRG"`
|
||||
link=`expr "$ls" : '.*-> \(.*\)$'`
|
||||
if expr "$link" : '/.*' > /dev/null; then
|
||||
PRG="$link"
|
||||
else
|
||||
PRG=`dirname "$PRG"`"/$link"
|
||||
fi
|
||||
done
|
||||
SAVED="`pwd`"
|
||||
cd "`dirname \"$PRG\"`/" >/dev/null
|
||||
APP_HOME="`pwd -P`"
|
||||
cd "$SAVED" >/dev/null
|
||||
|
||||
APP_NAME="Gradle"
|
||||
APP_BASE_NAME=`basename "$0"`
|
||||
|
||||
# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
|
||||
DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
|
||||
|
||||
# Use the maximum available, or set MAX_FD != -1 to use that value.
|
||||
MAX_FD="maximum"
|
||||
|
||||
warn () {
|
||||
echo "$*"
|
||||
}
|
||||
|
||||
die () {
|
||||
echo
|
||||
echo "$*"
|
||||
echo
|
||||
exit 1
|
||||
}
|
||||
|
||||
# OS specific support (must be 'true' or 'false').
|
||||
cygwin=false
|
||||
msys=false
|
||||
darwin=false
|
||||
nonstop=false
|
||||
case "`uname`" in
|
||||
CYGWIN* )
|
||||
cygwin=true
|
||||
;;
|
||||
Darwin* )
|
||||
darwin=true
|
||||
;;
|
||||
MINGW* )
|
||||
msys=true
|
||||
;;
|
||||
NONSTOP* )
|
||||
nonstop=true
|
||||
;;
|
||||
esac
|
||||
|
||||
CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
|
||||
|
||||
|
||||
# Determine the Java command to use to start the JVM.
|
||||
if [ -n "$JAVA_HOME" ] ; then
|
||||
if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
|
||||
# IBM's JDK on AIX uses strange locations for the executables
|
||||
JAVACMD="$JAVA_HOME/jre/sh/java"
|
||||
else
|
||||
JAVACMD="$JAVA_HOME/bin/java"
|
||||
fi
|
||||
if [ ! -x "$JAVACMD" ] ; then
|
||||
die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
|
||||
|
||||
Please set the JAVA_HOME variable in your environment to match the
|
||||
location of your Java installation."
|
||||
fi
|
||||
else
|
||||
JAVACMD="java"
|
||||
which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
|
||||
|
||||
Please set the JAVA_HOME variable in your environment to match the
|
||||
location of your Java installation."
|
||||
fi
|
||||
|
||||
# Increase the maximum file descriptors if we can.
|
||||
if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
|
||||
MAX_FD_LIMIT=`ulimit -H -n`
|
||||
if [ $? -eq 0 ] ; then
|
||||
if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
|
||||
MAX_FD="$MAX_FD_LIMIT"
|
||||
fi
|
||||
ulimit -n $MAX_FD
|
||||
if [ $? -ne 0 ] ; then
|
||||
warn "Could not set maximum file descriptor limit: $MAX_FD"
|
||||
fi
|
||||
else
|
||||
warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
|
||||
fi
|
||||
fi
|
||||
|
||||
# For Darwin, add options to specify how the application appears in the dock
|
||||
if $darwin; then
|
||||
GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
|
||||
fi
|
||||
|
||||
# For Cygwin or MSYS, switch paths to Windows format before running java
|
||||
if [ "$cygwin" = "true" -o "$msys" = "true" ] ; then
|
||||
APP_HOME=`cygpath --path --mixed "$APP_HOME"`
|
||||
CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
|
||||
|
||||
JAVACMD=`cygpath --unix "$JAVACMD"`
|
||||
|
||||
# We build the pattern for arguments to be converted via cygpath
|
||||
ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
|
||||
SEP=""
|
||||
for dir in $ROOTDIRSRAW ; do
|
||||
ROOTDIRS="$ROOTDIRS$SEP$dir"
|
||||
SEP="|"
|
||||
done
|
||||
OURCYGPATTERN="(^($ROOTDIRS))"
|
||||
# Add a user-defined pattern to the cygpath arguments
|
||||
if [ "$GRADLE_CYGPATTERN" != "" ] ; then
|
||||
OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
|
||||
fi
|
||||
# Now convert the arguments - kludge to limit ourselves to /bin/sh
|
||||
i=0
|
||||
for arg in "$@" ; do
|
||||
CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
|
||||
CHECK2=`echo "$arg"|egrep -c "^-"` ### Determine if an option
|
||||
|
||||
if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then ### Added a condition
|
||||
eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
|
||||
else
|
||||
eval `echo args$i`="\"$arg\""
|
||||
fi
|
||||
i=`expr $i + 1`
|
||||
done
|
||||
case $i in
|
||||
0) set -- ;;
|
||||
1) set -- "$args0" ;;
|
||||
2) set -- "$args0" "$args1" ;;
|
||||
3) set -- "$args0" "$args1" "$args2" ;;
|
||||
4) set -- "$args0" "$args1" "$args2" "$args3" ;;
|
||||
5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
|
||||
6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
|
||||
7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
|
||||
8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
|
||||
9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
|
||||
esac
|
||||
fi
|
||||
|
||||
# Escape application args
|
||||
save () {
|
||||
for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
|
||||
echo " "
|
||||
}
|
||||
APP_ARGS=`save "$@"`
|
||||
|
||||
# Collect all arguments for the java command, following the shell quoting and substitution rules
|
||||
eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
|
||||
|
||||
exec "$JAVACMD" "$@"
|
||||
17
examples/llama.android/settings.gradle.kts
Normal file
17
examples/llama.android/settings.gradle.kts
Normal file
@ -0,0 +1,17 @@
|
||||
pluginManagement {
|
||||
repositories {
|
||||
google()
|
||||
mavenCentral()
|
||||
gradlePluginPortal()
|
||||
}
|
||||
}
|
||||
dependencyResolutionManagement {
|
||||
repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
|
||||
repositories {
|
||||
google()
|
||||
mavenCentral()
|
||||
}
|
||||
}
|
||||
|
||||
rootProject.name = "LlamaAndroid"
|
||||
include(":app")
|
||||
@ -428,6 +428,7 @@ static std::vector<float> hellaswag_evaluate_tokens(
|
||||
for (size_t i_chunk = 0; i_chunk < n_chunk; ++i_chunk) {
|
||||
size_t n_tokens = tokens.size() - i_chunk * n_batch;
|
||||
n_tokens = std::min(n_tokens, size_t(n_batch));
|
||||
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
|
||||
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + i_chunk * n_batch, n_tokens, n_past, 0))) {
|
||||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
return {};
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
# Function calling example using pydantic models.
|
||||
|
||||
import datetime
|
||||
import json
|
||||
from enum import Enum
|
||||
from typing import Union, Optional
|
||||
@ -8,7 +8,8 @@ import requests
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
import importlib
|
||||
from pydantic_models_to_grammar import generate_gbnf_grammar_and_documentation
|
||||
from pydantic_models_to_grammar import generate_gbnf_grammar_and_documentation, convert_dictionary_to_pydantic_model, add_run_method_to_dynamic_model, create_dynamic_model_from_function
|
||||
|
||||
|
||||
# Function to get completion on the llama.cpp server with grammar.
|
||||
def create_completion(prompt, grammar):
|
||||
@ -134,3 +135,121 @@ text = create_completion(prompt=prompt, grammar=gbnf_grammar)
|
||||
json_data = json.loads(text)
|
||||
|
||||
print(Book(**json_data))
|
||||
# An example for parallel function calling with a Python function, a pydantic function model and an OpenAI like function definition.
|
||||
|
||||
def get_current_datetime(output_format: Optional[str] = None):
|
||||
"""
|
||||
Get the current date and time in the given format.
|
||||
Args:
|
||||
output_format: formatting string for the date and time, defaults to '%Y-%m-%d %H:%M:%S'
|
||||
"""
|
||||
if output_format is None:
|
||||
output_format = '%Y-%m-%d %H:%M:%S'
|
||||
return datetime.datetime.now().strftime(output_format)
|
||||
|
||||
|
||||
# Enum for the calculator tool.
|
||||
class MathOperation(Enum):
|
||||
ADD = "add"
|
||||
SUBTRACT = "subtract"
|
||||
MULTIPLY = "multiply"
|
||||
DIVIDE = "divide"
|
||||
|
||||
|
||||
|
||||
# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt.
|
||||
class Calculator(BaseModel):
|
||||
"""
|
||||
Perform a math operation on two numbers.
|
||||
"""
|
||||
number_one: Union[int, float] = Field(..., description="First number.")
|
||||
operation: MathOperation = Field(..., description="Math operation to perform.")
|
||||
number_two: Union[int, float] = Field(..., description="Second number.")
|
||||
|
||||
def run(self):
|
||||
if self.operation == MathOperation.ADD:
|
||||
return self.number_one + self.number_two
|
||||
elif self.operation == MathOperation.SUBTRACT:
|
||||
return self.number_one - self.number_two
|
||||
elif self.operation == MathOperation.MULTIPLY:
|
||||
return self.number_one * self.number_two
|
||||
elif self.operation == MathOperation.DIVIDE:
|
||||
return self.number_one / self.number_two
|
||||
else:
|
||||
raise ValueError("Unknown operation.")
|
||||
|
||||
|
||||
# Example function to get the weather
|
||||
def get_current_weather(location, unit):
|
||||
"""Get the current weather in a given location"""
|
||||
if "London" in location:
|
||||
return json.dumps({"location": "London", "temperature": "42", "unit": unit.value})
|
||||
elif "New York" in location:
|
||||
return json.dumps({"location": "New York", "temperature": "24", "unit": unit.value})
|
||||
elif "North Pole" in location:
|
||||
return json.dumps({"location": "North Pole", "temperature": "-42", "unit": unit.value})
|
||||
else:
|
||||
return json.dumps({"location": location, "temperature": "unknown"})
|
||||
|
||||
|
||||
# Here is a function definition in OpenAI style
|
||||
current_weather_tool = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
# Convert OpenAI function definition into pydantic model
|
||||
current_weather_tool_model = convert_dictionary_to_pydantic_model(current_weather_tool)
|
||||
# Add the actual function to a pydantic model
|
||||
current_weather_tool_model = add_run_method_to_dynamic_model(current_weather_tool_model, get_current_weather)
|
||||
|
||||
# Convert normal Python function to a pydantic model
|
||||
current_datetime_model = create_dynamic_model_from_function(get_current_datetime)
|
||||
|
||||
tool_list = [SendMessageToUser, Calculator, current_datetime_model, current_weather_tool_model]
|
||||
|
||||
|
||||
gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
|
||||
pydantic_model_list=tool_list, outer_object_name="function",
|
||||
outer_object_content="params", model_prefix="Function", fields_prefix="Parameters", list_of_outputs=True)
|
||||
|
||||
system_message = "You are an advanced AI assistant. You are interacting with the user and with your environment by calling functions. You call functions by writing JSON objects, which represent specific function calls.\nBelow is a list of your available function calls:\n\n" + documentation
|
||||
|
||||
|
||||
text = """Get the date and time, get the current weather in celsius in London and solve the following calculation: 42 * 42"""
|
||||
prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
|
||||
|
||||
text = create_completion(prompt=prompt, grammar=gbnf_grammar)
|
||||
|
||||
json_data = json.loads(text)
|
||||
|
||||
print(json_data)
|
||||
# Should output something like this:
|
||||
# [{'function': 'get_current_datetime', 'params': {'output_format': '%Y-%m-%d %H:%M:%S'}}, {'function': 'get_current_weather', 'params': {'location': 'London', 'unit': 'celsius'}}, {'function': 'Calculator', 'params': {'number_one': 42, 'operation': 'multiply', 'number_two': 42}}]
|
||||
|
||||
|
||||
for call in json_data:
|
||||
if call["function"] == "Calculator":
|
||||
print(Calculator(**call["params"]).run())
|
||||
elif call["function"] == "get_current_datetime":
|
||||
print(current_datetime_model(**call["params"]).run())
|
||||
elif call["function"] == "get_current_weather":
|
||||
print(current_weather_tool_model(**call["params"]).run())
|
||||
# Should output something like this:
|
||||
# 2024-01-14 13:36:06
|
||||
# {"location": "London", "temperature": "42", "unit": "celsius"}
|
||||
# 1764
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -6,49 +6,11 @@
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
// a function that can be called for every computed node during graph evaluation
|
||||
// the user can choose to whether to observe the data of the node depending on the tensor parameters
|
||||
static bool observe_compute(struct ggml_tensor * t, bool ask, void * user_data) {
|
||||
GGML_UNUSED(user_data);
|
||||
|
||||
// the scheduler is asking us if we want to observe this node
|
||||
if (ask) {
|
||||
// check if name contains soft_max (customize to your needs)
|
||||
return strstr(t->name, "soft_max") != 0;
|
||||
}
|
||||
|
||||
// print the node info
|
||||
printf("%s: t->name = %32s, t->op = %12s, [%5d, %5d, %5d, %5d]\n",
|
||||
__func__, t->name, ggml_op_name(t->op), (int) t->ne[0], (int) t->ne[1], (int) t->ne[2], (int) t->ne[3]);
|
||||
|
||||
// this will copy the data to host memory (if needed)
|
||||
static std::vector<float> t_data;
|
||||
|
||||
const bool is_host = ggml_backend_buffer_is_host(t->buffer);
|
||||
|
||||
if (!is_host || !ggml_is_contiguous(t)) {
|
||||
t_data.resize(ggml_nelements(t));
|
||||
ggml_backend_tensor_get(t, t_data.data(), 0, ggml_nbytes(t));
|
||||
}
|
||||
|
||||
const float * data = is_host ? (const float *) t->data : t_data.data();
|
||||
|
||||
// print first row
|
||||
for (int i = 0; i < t->ne[0]; i++) {
|
||||
printf("%8.4f ", data[i]);
|
||||
}
|
||||
printf("\n");
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
gpt_params params;
|
||||
|
||||
bool observe = false;
|
||||
|
||||
if (argc == 1 || argv[1][0] == '-') {
|
||||
printf("usage: %s MODEL_PATH [PROMPT] [OBSERV]\n" , argv[0]);
|
||||
printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
|
||||
return 1 ;
|
||||
}
|
||||
|
||||
@ -60,10 +22,6 @@ int main(int argc, char ** argv) {
|
||||
params.prompt = argv[2];
|
||||
}
|
||||
|
||||
if (argc >= 4) {
|
||||
observe = atoi(argv[3]);
|
||||
}
|
||||
|
||||
if (params.prompt.empty()) {
|
||||
params.prompt = "Hello my name is";
|
||||
}
|
||||
@ -79,7 +37,7 @@ int main(int argc, char ** argv) {
|
||||
|
||||
llama_model_params model_params = llama_model_default_params();
|
||||
|
||||
model_params.n_gpu_layers = 99; // offload all layers to the GPU
|
||||
// model_params.n_gpu_layers = 99; // offload all layers to the GPU
|
||||
|
||||
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
|
||||
|
||||
@ -97,9 +55,6 @@ int main(int argc, char ** argv) {
|
||||
ctx_params.n_threads = params.n_threads;
|
||||
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
|
||||
|
||||
ctx_params.cb_eval = observe ? observe_compute : NULL;
|
||||
ctx_params.cb_eval_user_data = NULL;
|
||||
|
||||
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
|
||||
|
||||
if (ctx == NULL) {
|
||||
|
||||
@ -65,6 +65,10 @@ int main(int argc, char ** argv) {
|
||||
// load the draft model
|
||||
params.model = params.model_draft;
|
||||
params.n_gpu_layers = params.n_gpu_layers_draft;
|
||||
if (params.n_threads_draft > 0) {
|
||||
params.n_threads = params.n_threads_draft;
|
||||
}
|
||||
params.n_threads_batch = params.n_threads_batch_draft;
|
||||
std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
|
||||
|
||||
{
|
||||
|
||||
18
flake.lock
18
flake.lock
@ -5,11 +5,11 @@
|
||||
"nixpkgs-lib": "nixpkgs-lib"
|
||||
},
|
||||
"locked": {
|
||||
"lastModified": 1701473968,
|
||||
"narHash": "sha256-YcVE5emp1qQ8ieHUnxt1wCZCC3ZfAS+SRRWZ2TMda7E=",
|
||||
"lastModified": 1704982712,
|
||||
"narHash": "sha256-2Ptt+9h8dczgle2Oo6z5ni5rt/uLMG47UFTR1ry/wgg=",
|
||||
"owner": "hercules-ci",
|
||||
"repo": "flake-parts",
|
||||
"rev": "34fed993f1674c8d06d58b37ce1e0fe5eebcb9f5",
|
||||
"rev": "07f6395285469419cf9d078f59b5b49993198c00",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
@ -20,11 +20,11 @@
|
||||
},
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1703637592,
|
||||
"narHash": "sha256-8MXjxU0RfFfzl57Zy3OfXCITS0qWDNLzlBAdwxGZwfY=",
|
||||
"lastModified": 1705133751,
|
||||
"narHash": "sha256-rCIsyE80jgiOU78gCWN3A0wE0tR2GI5nH6MlS+HaaSQ=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "cfc3698c31b1fb9cdcf10f36c9643460264d0ca8",
|
||||
"rev": "9b19f5e77dd906cb52dade0b7bd280339d2a1f3d",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
@ -37,11 +37,11 @@
|
||||
"nixpkgs-lib": {
|
||||
"locked": {
|
||||
"dir": "lib",
|
||||
"lastModified": 1701253981,
|
||||
"narHash": "sha256-ztaDIyZ7HrTAfEEUt9AtTDNoCYxUdSd6NrRHaYOIxtk=",
|
||||
"lastModified": 1703961334,
|
||||
"narHash": "sha256-M1mV/Cq+pgjk0rt6VxoyyD+O8cOUiai8t9Q6Yyq4noY=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "e92039b55bcd58469325ded85d4f58dd5a4eaf58",
|
||||
"rev": "b0d36bd0a420ecee3bc916c91886caca87c894e9",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
||||
57
flake.nix
57
flake.nix
@ -6,28 +6,41 @@
|
||||
flake-parts.url = "github:hercules-ci/flake-parts";
|
||||
};
|
||||
|
||||
# Optional binary cache
|
||||
nixConfig = {
|
||||
extra-substituters = [
|
||||
# Populated by the CI in ggerganov/llama.cpp
|
||||
"https://llama-cpp.cachix.org"
|
||||
|
||||
# A development cache for nixpkgs imported with `config.cudaSupport = true`.
|
||||
# Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci.
|
||||
# This lets one skip building e.g. the CUDA-enabled openmpi.
|
||||
# TODO: Replace once nix-community obtains an official one.
|
||||
"https://cuda-maintainers.cachix.org"
|
||||
];
|
||||
|
||||
# Verify these are the same keys as published on
|
||||
# - https://app.cachix.org/cache/llama-cpp
|
||||
# - https://app.cachix.org/cache/cuda-maintainers
|
||||
extra-trusted-public-keys = [
|
||||
"llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc="
|
||||
"cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E="
|
||||
];
|
||||
};
|
||||
|
||||
# There's an optional binary cache available. The details are below, but they're commented out.
|
||||
#
|
||||
# Why? The terrible experience of being prompted to accept them on every single Nix command run.
|
||||
# Plus, there are warnings shown about not being a trusted user on a default Nix install
|
||||
# if you *do* say yes to the prompts.
|
||||
#
|
||||
# This experience makes having `nixConfig` in a flake a persistent UX problem.
|
||||
#
|
||||
# To make use of the binary cache, please add the relevant settings to your `nix.conf`.
|
||||
# It's located at `/etc/nix/nix.conf` on non-NixOS systems. On NixOS, adjust the `nix.settings`
|
||||
# option in your NixOS configuration to add `extra-substituters` and `extra-trusted-public-keys`,
|
||||
# as shown below.
|
||||
#
|
||||
# ```
|
||||
# nixConfig = {
|
||||
# extra-substituters = [
|
||||
# # Populated by the CI in ggerganov/llama.cpp
|
||||
# "https://llama-cpp.cachix.org"
|
||||
#
|
||||
# # A development cache for nixpkgs imported with `config.cudaSupport = true`.
|
||||
# # Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci.
|
||||
# # This lets one skip building e.g. the CUDA-enabled openmpi.
|
||||
# # TODO: Replace once nix-community obtains an official one.
|
||||
# "https://cuda-maintainers.cachix.org"
|
||||
# ];
|
||||
#
|
||||
# # Verify these are the same keys as published on
|
||||
# # - https://app.cachix.org/cache/llama-cpp
|
||||
# # - https://app.cachix.org/cache/cuda-maintainers
|
||||
# extra-trusted-public-keys = [
|
||||
# "llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc="
|
||||
# "cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E="
|
||||
# ];
|
||||
# };
|
||||
# ```
|
||||
|
||||
# For inspection, use `nix flake show github:ggerganov/llama.cpp` or the nix repl:
|
||||
#
|
||||
|
||||
@ -16,14 +16,14 @@ extern "C" {
|
||||
typedef void * ggml_backend_buffer_type_context_t;
|
||||
|
||||
struct ggml_backend_buffer_type_i {
|
||||
const char * (*get_name) (ggml_backend_buffer_type_t buft);
|
||||
ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
|
||||
size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
|
||||
size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
|
||||
bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
|
||||
const char * (*GGML_CALL get_name) (ggml_backend_buffer_type_t buft);
|
||||
ggml_backend_buffer_t (*GGML_CALL alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
|
||||
size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
|
||||
size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
|
||||
bool (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
|
||||
// check if tensor data is in host memory
|
||||
// should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
|
||||
bool (*is_host) (ggml_backend_buffer_type_t buft);
|
||||
bool (*GGML_CALL is_host) (ggml_backend_buffer_type_t buft);
|
||||
};
|
||||
|
||||
struct ggml_backend_buffer_type {
|
||||
@ -35,15 +35,15 @@ extern "C" {
|
||||
typedef void * ggml_backend_buffer_context_t;
|
||||
|
||||
struct ggml_backend_buffer_i {
|
||||
const char * (*get_name) (ggml_backend_buffer_t buffer);
|
||||
void (*free_buffer)(ggml_backend_buffer_t buffer);
|
||||
void * (*get_base) (ggml_backend_buffer_t buffer);
|
||||
void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
|
||||
void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
bool (*cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
|
||||
void (*clear) (ggml_backend_buffer_t buffer, uint8_t value);
|
||||
void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
|
||||
const char * (*GGML_CALL get_name) (ggml_backend_buffer_t buffer);
|
||||
void (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer);
|
||||
void * (*GGML_CALL get_base) (ggml_backend_buffer_t buffer);
|
||||
void (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
|
||||
void (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
void (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
bool (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
|
||||
void (*GGML_CALL clear) (ggml_backend_buffer_t buffer, uint8_t value);
|
||||
void (*GGML_CALL reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
|
||||
};
|
||||
|
||||
struct ggml_backend_buffer {
|
||||
@ -54,7 +54,7 @@ extern "C" {
|
||||
enum ggml_backend_buffer_usage usage;
|
||||
};
|
||||
|
||||
ggml_backend_buffer_t ggml_backend_buffer_init(
|
||||
GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
|
||||
ggml_backend_buffer_type_t buft,
|
||||
struct ggml_backend_buffer_i iface,
|
||||
ggml_backend_buffer_context_t context,
|
||||
@ -70,31 +70,31 @@ extern "C" {
|
||||
typedef void * ggml_backend_context_t;
|
||||
|
||||
struct ggml_backend_i {
|
||||
const char * (*get_name)(ggml_backend_t backend);
|
||||
const char * (*GGML_CALL get_name)(ggml_backend_t backend);
|
||||
|
||||
void (*free)(ggml_backend_t backend);
|
||||
void (*GGML_CALL free)(ggml_backend_t backend);
|
||||
|
||||
// buffer allocation
|
||||
ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
|
||||
ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend);
|
||||
|
||||
// (optional) asynchronous tensor data access
|
||||
void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
bool (*cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
|
||||
void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
|
||||
|
||||
// (optional) complete all pending operations
|
||||
void (*synchronize)(ggml_backend_t backend);
|
||||
void (*GGML_CALL synchronize)(ggml_backend_t backend);
|
||||
|
||||
// compute graph with a plan
|
||||
ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
|
||||
void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
|
||||
void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
|
||||
ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
|
||||
void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
|
||||
void (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
|
||||
|
||||
// compute graph without a plan (async)
|
||||
bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
|
||||
bool (*GGML_CALL 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);
|
||||
bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
|
||||
};
|
||||
|
||||
struct ggml_backend {
|
||||
@ -107,9 +107,9 @@ extern "C" {
|
||||
// Backend registry
|
||||
//
|
||||
|
||||
typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data);
|
||||
typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data);
|
||||
|
||||
void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
|
||||
GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
||||
@ -19,7 +19,7 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
|
||||
return buft->iface.get_name(buft);
|
||||
}
|
||||
|
||||
ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
return buft->iface.alloc_buffer(buft, size);
|
||||
}
|
||||
|
||||
@ -27,7 +27,7 @@ size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
return buft->iface.get_alignment(buft);
|
||||
}
|
||||
|
||||
size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
|
||||
GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
|
||||
// get_alloc_size is optional, defaults to ggml_nbytes
|
||||
if (buft->iface.get_alloc_size) {
|
||||
return buft->iface.get_alloc_size(buft, tensor);
|
||||
@ -48,7 +48,7 @@ bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
|
||||
|
||||
// backend buffer
|
||||
|
||||
ggml_backend_buffer_t ggml_backend_buffer_init(
|
||||
GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
|
||||
ggml_backend_buffer_type_t buft,
|
||||
struct ggml_backend_buffer_i iface,
|
||||
ggml_backend_buffer_context_t context,
|
||||
@ -95,7 +95,7 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
return base;
|
||||
}
|
||||
|
||||
void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
|
||||
GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
|
||||
// init_tensor is optional
|
||||
if (buffer->iface.init_tensor) {
|
||||
buffer->iface.init_tensor(buffer, tensor);
|
||||
@ -191,7 +191,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
|
||||
|
||||
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
||||
@ -201,7 +201,7 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz
|
||||
tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size);
|
||||
}
|
||||
|
||||
void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
|
||||
|
||||
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
||||
@ -318,9 +318,9 @@ struct ggml_backend_reg {
|
||||
static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG];
|
||||
static size_t ggml_backend_registry_count = 0;
|
||||
|
||||
static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
|
||||
GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
|
||||
|
||||
static void ggml_backend_registry_init(void) {
|
||||
GGML_CALL static void ggml_backend_registry_init(void) {
|
||||
static bool initialized = false;
|
||||
|
||||
if (initialized) {
|
||||
@ -333,18 +333,18 @@ static void ggml_backend_registry_init(void) {
|
||||
|
||||
// add forward decls here to avoid including the backend headers
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
extern void ggml_backend_cuda_reg_devices(void);
|
||||
extern GGML_CALL void ggml_backend_cuda_reg_devices(void);
|
||||
ggml_backend_cuda_reg_devices();
|
||||
#endif
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
extern ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
|
||||
extern ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
|
||||
extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
|
||||
extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
|
||||
ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL);
|
||||
#endif
|
||||
}
|
||||
|
||||
void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
|
||||
GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
|
||||
GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG);
|
||||
|
||||
size_t id = ggml_backend_registry_count;
|
||||
@ -439,33 +439,33 @@ ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) {
|
||||
|
||||
// backend CPU
|
||||
|
||||
static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
|
||||
return "CPU";
|
||||
|
||||
GGML_UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
return (void *)buffer->context;
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
free(buffer->context);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
memcpy((char *)tensor->data + offset, data, size);
|
||||
|
||||
GGML_UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
memcpy(data, (const char *)tensor->data + offset, size);
|
||||
|
||||
GGML_UNUSED(buffer);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
|
||||
GGML_CALL static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
|
||||
if (ggml_backend_buffer_is_host(src->buffer)) {
|
||||
memcpy(dst->data, src->data, ggml_nbytes(src));
|
||||
return true;
|
||||
@ -475,7 +475,7 @@ static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con
|
||||
GGML_UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
memset(buffer->context, value, buffer->size);
|
||||
}
|
||||
|
||||
@ -506,13 +506,13 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
|
||||
|
||||
static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512
|
||||
|
||||
static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
|
||||
return "CPU";
|
||||
|
||||
GGML_UNUSED(buft);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned
|
||||
void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC?
|
||||
|
||||
@ -521,25 +521,25 @@ static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_back
|
||||
return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
return TENSOR_ALIGNMENT;
|
||||
|
||||
GGML_UNUSED(buft);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
return ggml_backend_is_cpu(backend);
|
||||
|
||||
GGML_UNUSED(buft);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
|
||||
return true;
|
||||
|
||||
GGML_UNUSED(buft);
|
||||
}
|
||||
|
||||
ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
|
||||
GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
|
||||
static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
|
||||
/* .iface = */ {
|
||||
/* .get_name = */ ggml_backend_cpu_buffer_type_get_name,
|
||||
@ -561,23 +561,23 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
|
||||
|
||||
#include <hbwmalloc.h>
|
||||
|
||||
static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
|
||||
return "CPU_HBM";
|
||||
|
||||
GGML_UNUSED(buft);
|
||||
}
|
||||
|
||||
static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
|
||||
GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
|
||||
return "CPU_HBM";
|
||||
|
||||
GGML_UNUSED(buf);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
hbw_free(buffer->context);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
//void * ptr = hbw_malloc(size);
|
||||
void * ptr;
|
||||
int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
|
||||
@ -617,20 +617,20 @@ struct ggml_backend_cpu_context {
|
||||
size_t work_size;
|
||||
};
|
||||
|
||||
static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
|
||||
GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
|
||||
return "CPU";
|
||||
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_free(ggml_backend_t backend) {
|
||||
GGML_CALL static void ggml_backend_cpu_free(ggml_backend_t backend) {
|
||||
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
|
||||
free(cpu_ctx->work_data);
|
||||
free(cpu_ctx);
|
||||
free(backend);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
|
||||
GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
|
||||
return ggml_backend_cpu_buffer_type();
|
||||
|
||||
GGML_UNUSED(backend);
|
||||
@ -641,7 +641,7 @@ struct ggml_backend_plan_cpu {
|
||||
struct ggml_cgraph cgraph;
|
||||
};
|
||||
|
||||
static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) {
|
||||
GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) {
|
||||
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
|
||||
|
||||
struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
|
||||
@ -656,7 +656,7 @@ static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend
|
||||
return cpu_plan;
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
|
||||
|
||||
free(cpu_plan->cplan.work_data);
|
||||
@ -665,7 +665,7 @@ static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backen
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
|
||||
|
||||
ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
|
||||
@ -673,7 +673,7 @@ static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_bac
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
GGML_CALL 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);
|
||||
@ -690,7 +690,7 @@ static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
switch (op->op) {
|
||||
case GGML_OP_MUL_MAT:
|
||||
return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
|
||||
@ -732,7 +732,7 @@ ggml_backend_t ggml_backend_cpu_init(void) {
|
||||
return cpu_backend;
|
||||
}
|
||||
|
||||
bool ggml_backend_is_cpu(ggml_backend_t backend) {
|
||||
GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) {
|
||||
return backend && backend->iface.get_name == ggml_backend_cpu_name;
|
||||
}
|
||||
|
||||
@ -743,11 +743,11 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
|
||||
ctx->n_threads = n_threads;
|
||||
}
|
||||
|
||||
ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
|
||||
GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
|
||||
return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size);
|
||||
}
|
||||
|
||||
static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
|
||||
GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
|
||||
return ggml_backend_cpu_init();
|
||||
|
||||
GGML_UNUSED(params);
|
||||
@ -1337,24 +1337,22 @@ static void sched_compute_splits(ggml_backend_sched_t sched) {
|
||||
for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
|
||||
struct ggml_tensor * t = split->graph.nodes[j0];
|
||||
|
||||
// check if the user needs data from this node
|
||||
bool need = sched->callback_eval(t, true, sched->callback_eval_user_data);
|
||||
|
||||
int j1 = j0;
|
||||
|
||||
// determine the range [j0, j1] of nodes that can be computed together
|
||||
while (j1 < split->graph.n_nodes - 1) {
|
||||
// check if the user needs data from this node
|
||||
if (sched->callback_eval(t, true, sched->callback_eval_user_data)) {
|
||||
break;
|
||||
}
|
||||
|
||||
while (!need && j1 < split->graph.n_nodes - 1) {
|
||||
t = split->graph.nodes[++j1];
|
||||
need = sched->callback_eval(t, true, sched->callback_eval_user_data);
|
||||
}
|
||||
|
||||
struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
|
||||
|
||||
ggml_backend_graph_compute(split_backend, &gv);
|
||||
|
||||
if (sched->callback_eval(t, true, sched->callback_eval_user_data) && // ask
|
||||
!sched->callback_eval(t, false, sched->callback_eval_user_data)) { // eval
|
||||
if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
|
||||
break;
|
||||
}
|
||||
|
||||
@ -1386,10 +1384,6 @@ static void sched_reset(ggml_backend_sched_t sched) {
|
||||
memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size);
|
||||
memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size);
|
||||
|
||||
// TODO: should we clear the callbacks?
|
||||
//sched->callback_eval = NULL;
|
||||
//sched->callback_eval_user_data = NULL;
|
||||
|
||||
sched->is_reset = true;
|
||||
}
|
||||
|
||||
|
||||
@ -17,12 +17,12 @@ extern "C" {
|
||||
//
|
||||
|
||||
// buffer type
|
||||
GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft);
|
||||
GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size);
|
||||
GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
|
||||
GGML_API size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
|
||||
GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft);
|
||||
GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size);
|
||||
GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
|
||||
GGML_API GGML_CALL size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
|
||||
GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft);
|
||||
|
||||
// buffer
|
||||
enum ggml_backend_buffer_usage {
|
||||
@ -30,18 +30,18 @@ extern "C" {
|
||||
GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
|
||||
};
|
||||
|
||||
GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer);
|
||||
GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
|
||||
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
|
||||
GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
|
||||
GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
|
||||
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value);
|
||||
GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer);
|
||||
GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer);
|
||||
GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer);
|
||||
GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer);
|
||||
GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
|
||||
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
|
||||
GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
|
||||
GGML_API GGML_CALL void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
|
||||
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value);
|
||||
GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer);
|
||||
GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer);
|
||||
GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer);
|
||||
|
||||
//
|
||||
// Backend
|
||||
@ -58,8 +58,8 @@ extern "C" {
|
||||
GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
|
||||
GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
GGML_API GGML_CALL void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
|
||||
GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
|
||||
|
||||
@ -80,13 +80,13 @@ extern "C" {
|
||||
|
||||
GGML_API ggml_backend_t ggml_backend_cpu_init(void);
|
||||
|
||||
GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend);
|
||||
GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
|
||||
GGML_API GGML_CALL bool ggml_backend_is_cpu (ggml_backend_t backend);
|
||||
GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
|
||||
|
||||
// Create a backend buffer from an existing pointer
|
||||
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
|
||||
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
|
||||
|
||||
#ifdef GGML_USE_CPU_HBM
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
|
||||
@ -194,7 +194,7 @@ extern "C" {
|
||||
GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
|
||||
GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
|
||||
|
||||
typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
|
||||
typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
|
||||
|
||||
// Compare the output of two backends
|
||||
GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
|
||||
|
||||
121
ggml-cuda.cu
121
ggml-cuda.cu
@ -7615,11 +7615,11 @@ struct cuda_pool_alloc {
|
||||
|
||||
static bool g_cublas_loaded = false;
|
||||
|
||||
bool ggml_cublas_loaded(void) {
|
||||
GGML_CALL bool ggml_cublas_loaded(void) {
|
||||
return g_cublas_loaded;
|
||||
}
|
||||
|
||||
void ggml_init_cublas() {
|
||||
GGML_CALL void ggml_init_cublas() {
|
||||
static bool initialized = false;
|
||||
|
||||
if (!initialized) {
|
||||
@ -7707,7 +7707,7 @@ void ggml_init_cublas() {
|
||||
}
|
||||
}
|
||||
|
||||
void * ggml_cuda_host_malloc(size_t size) {
|
||||
GGML_CALL void * ggml_cuda_host_malloc(size_t size) {
|
||||
if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
|
||||
return nullptr;
|
||||
}
|
||||
@ -7725,7 +7725,7 @@ void * ggml_cuda_host_malloc(size_t size) {
|
||||
return ptr;
|
||||
}
|
||||
|
||||
void ggml_cuda_host_free(void * ptr) {
|
||||
GGML_CALL void ggml_cuda_host_free(void * ptr) {
|
||||
CUDA_CHECK(cudaFreeHost(ptr));
|
||||
}
|
||||
|
||||
@ -9242,7 +9242,7 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src
|
||||
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm);
|
||||
}
|
||||
|
||||
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
||||
GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
||||
if (!g_cublas_loaded) return false;
|
||||
|
||||
const int64_t ne10 = src1->ne[0];
|
||||
@ -10013,7 +10013,7 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl
|
||||
return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
|
||||
}
|
||||
|
||||
static void ggml_cuda_set_main_device(const int main_device) {
|
||||
GGML_CALL static void ggml_cuda_set_main_device(const int main_device) {
|
||||
if (main_device >= g_device_count) {
|
||||
fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
|
||||
main_device, g_device_count, g_main_device);
|
||||
@ -10028,7 +10028,7 @@ static void ggml_cuda_set_main_device(const int main_device) {
|
||||
}
|
||||
}
|
||||
|
||||
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
||||
GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
||||
if (!g_cublas_loaded) return false;
|
||||
|
||||
ggml_cuda_func_t func;
|
||||
@ -10186,7 +10186,7 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
|
||||
return true;
|
||||
}
|
||||
|
||||
int ggml_cuda_get_device_count() {
|
||||
GGML_CALL int ggml_cuda_get_device_count() {
|
||||
int device_count;
|
||||
if (cudaGetDeviceCount(&device_count) != cudaSuccess) {
|
||||
return 0;
|
||||
@ -10194,7 +10194,7 @@ int ggml_cuda_get_device_count() {
|
||||
return device_count;
|
||||
}
|
||||
|
||||
void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
|
||||
GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
|
||||
cudaDeviceProp prop;
|
||||
CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
|
||||
snprintf(description, description_size, "%s", prop.name);
|
||||
@ -10244,27 +10244,27 @@ struct ggml_backend_cuda_buffer_context {
|
||||
}
|
||||
};
|
||||
|
||||
static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
return ctx->name.c_str();
|
||||
}
|
||||
|
||||
static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
|
||||
return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
CUDA_CHECK(cudaFree(ctx->dev_ptr));
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
return ctx->dev_ptr;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
|
||||
if (tensor->view_src != NULL && tensor->view_offs == 0) {
|
||||
@ -10296,7 +10296,7 @@ static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, g
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
|
||||
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
@ -10307,7 +10307,7 @@ static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, gg
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
|
||||
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
@ -10318,7 +10318,7 @@ static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, co
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
if (ggml_backend_buffer_is_cuda(src->buffer)) {
|
||||
ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context;
|
||||
ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
@ -10335,7 +10335,7 @@ static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, co
|
||||
return false;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
|
||||
ggml_cuda_set_device(ctx->device);
|
||||
@ -10357,19 +10357,18 @@ static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
|
||||
};
|
||||
|
||||
// cuda buffer type
|
||||
|
||||
struct ggml_backend_cuda_buffer_type_context {
|
||||
int device;
|
||||
std::string name;
|
||||
};
|
||||
|
||||
static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
|
||||
|
||||
return ctx->name.c_str();
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
|
||||
|
||||
ggml_cuda_set_device(buft_ctx->device);
|
||||
@ -10388,13 +10387,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac
|
||||
return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
return 128;
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
||||
GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
||||
int64_t row_low = 0;
|
||||
int64_t row_high = ggml_nrows(tensor);
|
||||
int64_t nrows_split = row_high - row_low;
|
||||
@ -10414,7 +10413,7 @@ static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_t
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
if (!ggml_backend_is_cuda(backend)) {
|
||||
return false;
|
||||
}
|
||||
@ -10434,7 +10433,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = {
|
||||
/* .is_host = */ NULL,
|
||||
};
|
||||
|
||||
ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
|
||||
GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
|
||||
// FIXME: this is not thread safe
|
||||
if (device >= ggml_backend_cuda_get_device_count()) {
|
||||
return nullptr;
|
||||
@ -10479,7 +10478,7 @@ struct ggml_backend_cuda_split_buffer_context {
|
||||
std::vector<ggml_tensor_extra_gpu *> tensor_extras;
|
||||
};
|
||||
|
||||
static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
return GGML_CUDA_NAME "_Split";
|
||||
|
||||
UNUSED(buffer);
|
||||
@ -10490,19 +10489,19 @@ static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_
|
||||
// return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name;
|
||||
//}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
// the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
|
||||
return (void *)0x1000;
|
||||
|
||||
UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
||||
GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
|
||||
|
||||
ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
|
||||
@ -10552,7 +10551,7 @@ static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buf
|
||||
tensor->extra = extra;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
// split tensors must always be set in their entirety at once
|
||||
GGML_ASSERT(offset == 0);
|
||||
GGML_ASSERT(size == ggml_nbytes(tensor));
|
||||
@ -10586,7 +10585,7 @@ static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buff
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
// split tensors must always be set in their entirety at once
|
||||
GGML_ASSERT(offset == 0);
|
||||
GGML_ASSERT(size == ggml_nbytes(tensor));
|
||||
@ -10620,7 +10619,7 @@ static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buff
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
UNUSED(buffer);
|
||||
UNUSED(value);
|
||||
}
|
||||
@ -10639,13 +10638,13 @@ static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
|
||||
|
||||
// cuda split buffer type
|
||||
|
||||
static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
return GGML_CUDA_NAME "_Split";
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
// since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
|
||||
// instead, we allocate them for each tensor separately in init_tensor
|
||||
// however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
|
||||
@ -10655,13 +10654,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(gg
|
||||
return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
return 128;
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
||||
GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
||||
ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context;
|
||||
|
||||
size_t total_size = 0;
|
||||
@ -10688,13 +10687,13 @@ static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_bu
|
||||
return total_size;
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
return ggml_backend_is_cuda(backend);
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
|
||||
return false;
|
||||
|
||||
UNUSED(buft);
|
||||
@ -10709,7 +10708,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface
|
||||
/* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host,
|
||||
};
|
||||
|
||||
ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
|
||||
GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
|
||||
// FIXME: this is not thread safe
|
||||
static std::map<std::array<float, GGML_CUDA_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
|
||||
|
||||
@ -10745,23 +10744,23 @@ ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * ten
|
||||
|
||||
// host buffer type
|
||||
|
||||
static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
return GGML_CUDA_NAME "_Host";
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
|
||||
return GGML_CUDA_NAME "_Host";
|
||||
|
||||
UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
ggml_cuda_host_free(buffer->context);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
void * ptr = ggml_cuda_host_malloc(size);
|
||||
|
||||
if (ptr == nullptr) {
|
||||
@ -10777,7 +10776,7 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm
|
||||
return buffer;
|
||||
}
|
||||
|
||||
ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
|
||||
GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
|
||||
static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = {
|
||||
/* .iface = */ {
|
||||
/* .get_name = */ ggml_backend_cuda_host_buffer_type_name,
|
||||
@ -10795,26 +10794,26 @@ ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
|
||||
|
||||
// backend
|
||||
|
||||
static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
return cuda_ctx->name.c_str();
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_free(ggml_backend_t backend) {
|
||||
GGML_CALL static void ggml_backend_cuda_free(ggml_backend_t backend) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
delete cuda_ctx;
|
||||
delete backend;
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
|
||||
GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
return ggml_backend_cuda_buffer_type(cuda_ctx->device);
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
||||
@ -10823,7 +10822,7 @@ static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tens
|
||||
CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0]));
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
||||
@ -10832,7 +10831,7 @@ static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggm
|
||||
CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]));
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) {
|
||||
@ -10843,7 +10842,7 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggm
|
||||
return false;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
|
||||
GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0]));
|
||||
@ -10851,7 +10850,7 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
|
||||
UNUSED(backend);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
||||
GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
ggml_cuda_set_main_device(cuda_ctx->device);
|
||||
@ -10890,7 +10889,7 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
|
||||
GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
|
||||
switch (op->op) {
|
||||
case GGML_OP_UNARY:
|
||||
switch (ggml_get_unary_op(op)) {
|
||||
@ -11016,7 +11015,7 @@ static ggml_backend_i ggml_backend_cuda_interface = {
|
||||
/* .supports_op = */ ggml_backend_cuda_supports_op,
|
||||
};
|
||||
|
||||
ggml_backend_t ggml_backend_cuda_init(int device) {
|
||||
GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
|
||||
ggml_init_cublas(); // TODO: remove from ggml.c
|
||||
|
||||
if (device < 0 || device >= ggml_cuda_get_device_count()) {
|
||||
@ -11040,35 +11039,35 @@ ggml_backend_t ggml_backend_cuda_init(int device) {
|
||||
return cuda_backend;
|
||||
}
|
||||
|
||||
bool ggml_backend_is_cuda(ggml_backend_t backend) {
|
||||
GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) {
|
||||
return backend && backend->iface.get_name == ggml_backend_cuda_name;
|
||||
}
|
||||
|
||||
int ggml_backend_cuda_get_device_count() {
|
||||
GGML_CALL int ggml_backend_cuda_get_device_count() {
|
||||
return ggml_cuda_get_device_count();
|
||||
}
|
||||
|
||||
void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) {
|
||||
GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) {
|
||||
ggml_cuda_get_device_description(device, description, description_size);
|
||||
}
|
||||
|
||||
void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) {
|
||||
GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) {
|
||||
ggml_cuda_set_device(device);
|
||||
|
||||
CUDA_CHECK(cudaMemGetInfo(free, total));
|
||||
}
|
||||
|
||||
// backend registry
|
||||
static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
|
||||
GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
|
||||
ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data);
|
||||
return cuda_backend;
|
||||
|
||||
UNUSED(params);
|
||||
}
|
||||
|
||||
extern "C" int ggml_backend_cuda_reg_devices();
|
||||
extern "C" GGML_CALL int ggml_backend_cuda_reg_devices();
|
||||
|
||||
int ggml_backend_cuda_reg_devices() {
|
||||
GGML_CALL int ggml_backend_cuda_reg_devices() {
|
||||
int device_count = ggml_cuda_get_device_count();
|
||||
//int device_count = 1; // DEBUG: some tools require delaying CUDA initialization
|
||||
for (int i = 0; i < device_count; i++) {
|
||||
|
||||
32
ggml-cuda.h
32
ggml-cuda.h
@ -18,34 +18,34 @@ extern "C" {
|
||||
#define GGML_CUDA_MAX_DEVICES 16
|
||||
|
||||
// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
|
||||
GGML_API void ggml_init_cublas(void);
|
||||
GGML_API GGML_CALL void ggml_init_cublas(void);
|
||||
|
||||
// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
|
||||
GGML_API bool ggml_cublas_loaded(void);
|
||||
GGML_API GGML_CALL bool ggml_cublas_loaded(void);
|
||||
|
||||
GGML_API void * ggml_cuda_host_malloc(size_t size);
|
||||
GGML_API void ggml_cuda_host_free(void * ptr);
|
||||
GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size);
|
||||
GGML_API GGML_CALL void ggml_cuda_host_free(void * ptr);
|
||||
|
||||
GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
GGML_API GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API int ggml_cuda_get_device_count(void);
|
||||
GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
|
||||
GGML_API GGML_CALL int ggml_cuda_get_device_count(void);
|
||||
GGML_API GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
|
||||
|
||||
// backend API
|
||||
GGML_API ggml_backend_t ggml_backend_cuda_init(int device);
|
||||
GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device);
|
||||
|
||||
GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
|
||||
GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
|
||||
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
|
||||
// split tensor buffer that splits matrices by rows across multiple devices
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
|
||||
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
|
||||
|
||||
GGML_API int ggml_backend_cuda_get_device_count(void);
|
||||
GGML_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
|
||||
GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
|
||||
GGML_API GGML_CALL int ggml_backend_cuda_get_device_count(void);
|
||||
GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
|
||||
GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
||||
@ -27,7 +27,6 @@
|
||||
|
||||
// max memory buffers that can be mapped to the device
|
||||
#define GGML_METAL_MAX_BUFFERS 64
|
||||
#define GGML_METAL_MAX_COMMAND_BUFFERS 32
|
||||
|
||||
struct ggml_tensor;
|
||||
struct ggml_cgraph;
|
||||
@ -47,11 +46,11 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void);
|
||||
|
||||
GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
|
||||
|
||||
GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
|
||||
|
||||
GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
|
||||
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
|
||||
|
||||
// helper to check if the device supports a specific family
|
||||
// ideally, the user code should be doing these checks
|
||||
|
||||
3025
ggml-metal.m
3025
ggml-metal.m
File diff suppressed because it is too large
Load Diff
192
ggml-quants.c
192
ggml-quants.c
@ -515,6 +515,7 @@ void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
|
||||
quantize_row_q4_0_reference(x, y, k);
|
||||
}
|
||||
|
||||
|
||||
void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) {
|
||||
const int qk = QK4_1;
|
||||
|
||||
@ -3039,6 +3040,197 @@ size_t quantize_q6_K(const float * src, void * dst, int nrow, int n_per_row, int
|
||||
return nrow * row_size;
|
||||
}
|
||||
|
||||
static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restrict y, int n_per_row, const float * quant_weights) {
|
||||
static_assert(QK4_0 == 32, "QK4_0 must be 32");
|
||||
|
||||
if (!quant_weights) {
|
||||
quantize_row_q4_0_reference(x, y, n_per_row);
|
||||
return;
|
||||
}
|
||||
|
||||
float weight[QK4_0];
|
||||
int8_t L[QK4_0];
|
||||
|
||||
float sum_x2 = 0;
|
||||
for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
|
||||
float sigma2 = sum_x2/n_per_row;
|
||||
|
||||
const int nb = n_per_row/QK4_0;
|
||||
for (int ib = 0; ib < nb; ++ib) {
|
||||
const float * xb = x + QK4_0 * ib;
|
||||
const float * qw = quant_weights + QK4_0 * ib;
|
||||
for (int j = 0; j < QK4_0; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
|
||||
float d = make_qx_quants(QK4_0, 8, xb, L, 1, weight);
|
||||
y[ib].d = GGML_FP32_TO_FP16(d);
|
||||
for (int j = 0; j < 16; ++j) {
|
||||
y[ib].qs[j] = L[j] | (L[j+16] << 4);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_q4_0(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
|
||||
if (!quant_weights) {
|
||||
return ggml_quantize_q4_0(src, dst, nrow*n_per_row, n_per_row, hist);
|
||||
}
|
||||
int row_size = ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
|
||||
char * qrow = (char *)dst;
|
||||
for (int row = 0; row < nrow; ++row) {
|
||||
quantize_row_q4_0_impl(src, (block_q4_0*)qrow, n_per_row, quant_weights);
|
||||
src += n_per_row;
|
||||
qrow += row_size;
|
||||
}
|
||||
return nrow * row_size;
|
||||
}
|
||||
|
||||
static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restrict y, int n_per_row, const float * quant_weights) {
|
||||
static_assert(QK4_1 == 32, "QK4_1 must be 32");
|
||||
|
||||
if (!quant_weights) {
|
||||
quantize_row_q4_1_reference(x, y, n_per_row);
|
||||
return;
|
||||
}
|
||||
|
||||
float weight[QK4_1];
|
||||
uint8_t L[QK4_1], Laux[QK4_1];
|
||||
|
||||
float sum_x2 = 0;
|
||||
for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
|
||||
float sigma2 = sum_x2/n_per_row;
|
||||
|
||||
const int nb = n_per_row/QK4_1;
|
||||
for (int ib = 0; ib < nb; ++ib) {
|
||||
const float * xb = x + QK4_1 * ib;
|
||||
const float * qw = quant_weights + QK4_1 * ib;
|
||||
for (int j = 0; j < QK4_1; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
|
||||
float min;
|
||||
float d = make_qkx3_quants(QK4_1, 15, xb, weight, L, &min, Laux, -0.9f, 0.05f, 36, false);
|
||||
y[ib].d = GGML_FP32_TO_FP16(d);
|
||||
y[ib].m = GGML_FP32_TO_FP16(-min);
|
||||
for (int j = 0; j < 16; ++j) {
|
||||
y[ib].qs[j] = L[j] | (L[j+16] << 4);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_q4_1(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
|
||||
if (!quant_weights) {
|
||||
return ggml_quantize_q4_1(src, dst, nrow*n_per_row, n_per_row, hist);
|
||||
}
|
||||
int row_size = ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
|
||||
char * qrow = (char *)dst;
|
||||
for (int row = 0; row < nrow; ++row) {
|
||||
quantize_row_q4_1_impl(src, (block_q4_1*)qrow, n_per_row, quant_weights);
|
||||
src += n_per_row;
|
||||
qrow += row_size;
|
||||
}
|
||||
return nrow * row_size;
|
||||
}
|
||||
|
||||
static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restrict y, int n_per_row, const float * quant_weights) {
|
||||
static_assert(QK5_0 == 32, "QK5_0 must be 32");
|
||||
|
||||
if (!quant_weights) {
|
||||
quantize_row_q5_0_reference(x, y, n_per_row);
|
||||
return;
|
||||
}
|
||||
|
||||
float weight[QK5_0];
|
||||
int8_t L[QK5_0];
|
||||
|
||||
float sum_x2 = 0;
|
||||
for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
|
||||
float sigma2 = sum_x2/n_per_row;
|
||||
|
||||
const int nb = n_per_row/QK5_0;
|
||||
for (int ib = 0; ib < nb; ++ib) {
|
||||
const float * xb = x + QK5_0 * ib;
|
||||
const float * qw = quant_weights + QK5_0 * ib;
|
||||
for (int j = 0; j < QK5_0; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
|
||||
float d = make_qx_quants(QK5_0, 16, xb, L, 1, weight);
|
||||
y[ib].d = GGML_FP32_TO_FP16(d);
|
||||
|
||||
uint32_t qh = 0;
|
||||
|
||||
for (int j = 0; j < 16; ++j) {
|
||||
const uint8_t xi0 = L[j];
|
||||
const uint8_t xi1 = L[j+16];
|
||||
y[ib].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
|
||||
|
||||
// get the 5-th bit and store it in qh at the right position
|
||||
qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
|
||||
qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
|
||||
}
|
||||
|
||||
memcpy(&y[ib].qh, &qh, sizeof(qh));
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_q5_0(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
|
||||
if (!quant_weights) {
|
||||
return ggml_quantize_q5_0(src, dst, nrow*n_per_row, n_per_row, hist);
|
||||
}
|
||||
int row_size = ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
|
||||
char * qrow = (char *)dst;
|
||||
for (int row = 0; row < nrow; ++row) {
|
||||
quantize_row_q5_0_impl(src, (block_q5_0*)qrow, n_per_row, quant_weights);
|
||||
src += n_per_row;
|
||||
qrow += row_size;
|
||||
}
|
||||
return nrow * row_size;
|
||||
}
|
||||
|
||||
static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restrict y, int n_per_row, const float * quant_weights) {
|
||||
static_assert(QK5_1 == 32, "QK5_1 must be 32");
|
||||
|
||||
if (!quant_weights) {
|
||||
quantize_row_q5_1_reference(x, y, n_per_row);
|
||||
return;
|
||||
}
|
||||
|
||||
float weight[QK5_1];
|
||||
uint8_t L[QK5_1], Laux[QK5_1];
|
||||
|
||||
float sum_x2 = 0;
|
||||
for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
|
||||
float sigma2 = sum_x2/n_per_row;
|
||||
|
||||
const int nb = n_per_row/QK5_1;
|
||||
for (int ib = 0; ib < nb; ++ib) {
|
||||
const float * xb = x + QK5_1 * ib;
|
||||
const float * qw = quant_weights + QK5_1 * ib;
|
||||
for (int j = 0; j < QK5_1; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
|
||||
float min;
|
||||
float d = make_qkx3_quants(QK5_1, 31, xb, weight, L, &min, Laux, -0.9f, 0.05f, 36, false);
|
||||
y[ib].d = GGML_FP32_TO_FP16(d);
|
||||
y[ib].m = GGML_FP32_TO_FP16(-min);
|
||||
|
||||
uint32_t qh = 0;
|
||||
for (int j = 0; j < 16; ++j) {
|
||||
const uint8_t xi0 = L[j];
|
||||
const uint8_t xi1 = L[j+16];
|
||||
y[ib].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
|
||||
// get the 5-th bit and store it in qh at the right position
|
||||
qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
|
||||
qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
|
||||
}
|
||||
memcpy(&y[ib].qh, &qh, sizeof(qh));
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_q5_1(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
|
||||
if (!quant_weights) {
|
||||
return ggml_quantize_q5_1(src, dst, nrow*n_per_row, n_per_row, hist);
|
||||
}
|
||||
int row_size = ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
|
||||
char * qrow = (char *)dst;
|
||||
for (int row = 0; row < nrow; ++row) {
|
||||
quantize_row_q5_1_impl(src, (block_q5_1*)qrow, n_per_row, quant_weights);
|
||||
src += n_per_row;
|
||||
qrow += row_size;
|
||||
}
|
||||
return nrow * row_size;
|
||||
}
|
||||
|
||||
// ====================== "True" 2-bit (de)-quantization
|
||||
|
||||
static const uint64_t iq2xxs_grid[256] = {
|
||||
|
||||
@ -253,3 +253,7 @@ size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row,
|
||||
size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_q5_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_q6_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_q4_0 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_q4_1 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_q5_0 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_q5_1 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
|
||||
60
ggml.c
60
ggml.c
@ -1984,19 +1984,19 @@ void ggml_print_objects(const struct ggml_context * ctx) {
|
||||
GGML_PRINT("%s: --- end ---\n", __func__);
|
||||
}
|
||||
|
||||
int64_t ggml_nelements(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL int64_t ggml_nelements(const struct ggml_tensor * tensor) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
|
||||
}
|
||||
|
||||
int64_t ggml_nrows(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL 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];
|
||||
}
|
||||
|
||||
size_t ggml_nbytes(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL size_t ggml_nbytes(const struct ggml_tensor * tensor) {
|
||||
size_t nbytes;
|
||||
size_t blck_size = ggml_blck_size(tensor->type);
|
||||
if (blck_size == 1) {
|
||||
@ -2019,15 +2019,15 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
|
||||
return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN);
|
||||
}
|
||||
|
||||
int ggml_blck_size(enum ggml_type type) {
|
||||
GGML_CALL int ggml_blck_size(enum ggml_type type) {
|
||||
return type_traits[type].blck_size;
|
||||
}
|
||||
|
||||
size_t ggml_type_size(enum ggml_type type) {
|
||||
GGML_CALL size_t ggml_type_size(enum ggml_type type) {
|
||||
return type_traits[type].type_size;
|
||||
}
|
||||
|
||||
size_t ggml_row_size(enum ggml_type type, int64_t ne) {
|
||||
GGML_CALL size_t ggml_row_size(enum ggml_type type, int64_t ne) {
|
||||
assert(ne % ggml_blck_size(type) == 0);
|
||||
return ggml_type_size(type)*ne/ggml_blck_size(type);
|
||||
}
|
||||
@ -2036,15 +2036,15 @@ double ggml_type_sizef(enum ggml_type type) {
|
||||
return ((double)(type_traits[type].type_size))/type_traits[type].blck_size;
|
||||
}
|
||||
|
||||
const char * ggml_type_name(enum ggml_type type) {
|
||||
GGML_CALL const char * ggml_type_name(enum ggml_type type) {
|
||||
return type_traits[type].type_name;
|
||||
}
|
||||
|
||||
bool ggml_is_quantized(enum ggml_type type) {
|
||||
GGML_CALL bool ggml_is_quantized(enum ggml_type type) {
|
||||
return type_traits[type].is_quantized;
|
||||
}
|
||||
|
||||
const char * ggml_op_name(enum ggml_op op) {
|
||||
GGML_CALL const char * ggml_op_name(enum ggml_op op) {
|
||||
return GGML_OP_NAME[op];
|
||||
}
|
||||
|
||||
@ -2056,7 +2056,7 @@ const char * ggml_unary_op_name(enum ggml_unary_op op) {
|
||||
return GGML_UNARY_OP_NAME[op];
|
||||
}
|
||||
|
||||
const char * ggml_op_desc(const struct ggml_tensor * t) {
|
||||
GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t) {
|
||||
if (t->op == GGML_OP_UNARY) {
|
||||
enum ggml_unary_op uop = ggml_get_unary_op(t);
|
||||
return ggml_unary_op_name(uop);
|
||||
@ -2066,7 +2066,7 @@ const char * ggml_op_desc(const struct ggml_tensor * t) {
|
||||
}
|
||||
}
|
||||
|
||||
size_t ggml_element_size(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor) {
|
||||
return ggml_type_size(tensor->type);
|
||||
}
|
||||
|
||||
@ -2148,11 +2148,11 @@ size_t ggml_tensor_overhead(void) {
|
||||
return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE;
|
||||
}
|
||||
|
||||
bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
||||
return tensor->nb[0] > tensor->nb[1];
|
||||
}
|
||||
|
||||
bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL 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
|
||||
@ -2171,7 +2171,7 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te
|
||||
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
|
||||
}
|
||||
|
||||
bool ggml_is_permuted(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3];
|
||||
@ -3073,7 +3073,7 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
|
||||
return (float *)(tensor->data);
|
||||
}
|
||||
|
||||
enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
|
||||
GGML_ASSERT(tensor->op == GGML_OP_UNARY);
|
||||
return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0);
|
||||
}
|
||||
@ -11639,7 +11639,7 @@ static void ggml_rope_cache_init(
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_rope_yarn_corr_dims(
|
||||
GGML_CALL void ggml_rope_yarn_corr_dims(
|
||||
int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
|
||||
) {
|
||||
// start and end correction dims
|
||||
@ -18660,26 +18660,38 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
|
||||
case GGML_TYPE_Q4_0:
|
||||
{
|
||||
GGML_ASSERT(start % QK4_0 == 0);
|
||||
block_q4_0 * block = (block_q4_0*)dst + start / QK4_0;
|
||||
result = ggml_quantize_q4_0(src + start, block, n, n, hist);
|
||||
GGML_ASSERT(start % n_per_row == 0);
|
||||
size_t start_row = start / n_per_row;
|
||||
size_t row_size = ggml_row_size(type, n_per_row);
|
||||
result = quantize_q4_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
|
||||
GGML_ASSERT(result == row_size * nrows);
|
||||
} break;
|
||||
case GGML_TYPE_Q4_1:
|
||||
{
|
||||
GGML_ASSERT(start % QK4_1 == 0);
|
||||
block_q4_1 * block = (block_q4_1*)dst + start / QK4_1;
|
||||
result = ggml_quantize_q4_1(src + start, block, n, n, hist);
|
||||
GGML_ASSERT(start % n_per_row == 0);
|
||||
size_t start_row = start / n_per_row;
|
||||
size_t row_size = ggml_row_size(type, n_per_row);
|
||||
result = quantize_q4_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
|
||||
GGML_ASSERT(result == row_size * nrows);
|
||||
} break;
|
||||
case GGML_TYPE_Q5_0:
|
||||
{
|
||||
GGML_ASSERT(start % QK5_0 == 0);
|
||||
block_q5_0 * block = (block_q5_0*)dst + start / QK5_0;
|
||||
result = ggml_quantize_q5_0(src + start, block, n, n, hist);
|
||||
GGML_ASSERT(start % n_per_row == 0);
|
||||
size_t start_row = start / n_per_row;
|
||||
size_t row_size = ggml_row_size(type, n_per_row);
|
||||
result = quantize_q5_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
|
||||
GGML_ASSERT(result == row_size * nrows);
|
||||
} break;
|
||||
case GGML_TYPE_Q5_1:
|
||||
{
|
||||
GGML_ASSERT(start % QK5_1 == 0);
|
||||
block_q5_1 * block = (block_q5_1*)dst + start / QK5_1;
|
||||
result = ggml_quantize_q5_1(src + start, block, n, n, hist);
|
||||
GGML_ASSERT(start % n_per_row == 0);
|
||||
size_t start_row = start / n_per_row;
|
||||
size_t row_size = ggml_row_size(type, n_per_row);
|
||||
result = quantize_q5_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
|
||||
GGML_ASSERT(result == row_size * nrows);
|
||||
} break;
|
||||
case GGML_TYPE_Q8_0:
|
||||
{
|
||||
|
||||
58
ggml.h
58
ggml.h
@ -187,6 +187,16 @@
|
||||
# define GGML_API
|
||||
#endif
|
||||
|
||||
#ifdef GGML_MULTIPLATFORM
|
||||
# if defined(_WIN32)
|
||||
# define GGML_CALL
|
||||
# else
|
||||
# define GGML_CALL __attribute__((__ms_abi__))
|
||||
# endif
|
||||
#else
|
||||
# define GGML_CALL
|
||||
#endif
|
||||
|
||||
// TODO: support for clang
|
||||
#ifdef __GNUC__
|
||||
# define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
|
||||
@ -649,41 +659,41 @@ extern "C" {
|
||||
GGML_API void ggml_print_object (const struct ggml_object * obj);
|
||||
GGML_API void ggml_print_objects(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);
|
||||
GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
|
||||
GGML_API GGML_CALL int64_t ggml_nelements (const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL int64_t ggml_nrows (const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
|
||||
|
||||
GGML_API int ggml_blck_size(enum ggml_type type);
|
||||
GGML_API size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
|
||||
GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
|
||||
GGML_API GGML_CALL int ggml_blck_size(enum ggml_type type);
|
||||
GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
|
||||
GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
|
||||
|
||||
GGML_DEPRECATED(
|
||||
GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
|
||||
"use ggml_row_size() instead");
|
||||
|
||||
GGML_API const char * ggml_type_name(enum ggml_type type);
|
||||
GGML_API const char * ggml_op_name (enum ggml_op op);
|
||||
GGML_API const char * ggml_op_symbol(enum ggml_op op);
|
||||
GGML_API GGML_CALL const char * ggml_type_name(enum ggml_type type);
|
||||
GGML_API GGML_CALL const char * ggml_op_name (enum ggml_op op);
|
||||
GGML_API const char * ggml_op_symbol(enum ggml_op op);
|
||||
|
||||
GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
|
||||
GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
|
||||
GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
|
||||
GGML_API GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
|
||||
|
||||
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API bool ggml_is_quantized(enum ggml_type type);
|
||||
GGML_API GGML_CALL bool ggml_is_quantized(enum ggml_type type);
|
||||
|
||||
// TODO: temporary until model loading of ggml examples is refactored
|
||||
GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
|
||||
|
||||
GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor);
|
||||
GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars
|
||||
GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor);
|
||||
GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars
|
||||
|
||||
GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
|
||||
|
||||
@ -770,7 +780,7 @@ extern "C" {
|
||||
GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
|
||||
GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name);
|
||||
@ -1413,7 +1423,7 @@ extern "C" {
|
||||
float beta_slow);
|
||||
|
||||
// compute correction dims for YaRN RoPE scaling
|
||||
void ggml_rope_yarn_corr_dims(
|
||||
GGML_CALL void ggml_rope_yarn_corr_dims(
|
||||
int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]);
|
||||
|
||||
// xPos RoPE, in-place, returns view(a)
|
||||
|
||||
21
llama.cpp
21
llama.cpp
@ -8378,6 +8378,8 @@ struct quantize_state_internal {
|
||||
int n_k_quantized = 0;
|
||||
int n_fallback = 0;
|
||||
|
||||
bool has_imatrix = false;
|
||||
|
||||
quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
|
||||
: model(model)
|
||||
, params(params)
|
||||
@ -8479,7 +8481,12 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
|
||||
}
|
||||
else if (name == "token_embd.weight") new_type = GGML_TYPE_Q2_K;
|
||||
} else if (name.find("attn_v.weight") != std::string::npos) {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
|
||||
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
|
||||
new_type = GGML_TYPE_Q4_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
|
||||
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
|
||||
}
|
||||
@ -8550,6 +8557,13 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
|
||||
&& qs.has_imatrix && i_layer < n_layer/8) {
|
||||
// Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
|
||||
// We only do it when an imatrix is provided because a) we want to make sure that one can always get the
|
||||
// same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
|
||||
new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
|
||||
}
|
||||
++qs.i_feed_forward_w2;
|
||||
} else if (name.find("attn_output.weight") != std::string::npos) {
|
||||
if (arch != LLM_ARCH_FALCON) {
|
||||
@ -8673,6 +8687,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
|
||||
if (imatrix_data) {
|
||||
LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
|
||||
qs.has_imatrix = true;
|
||||
}
|
||||
}
|
||||
|
||||
@ -9265,14 +9280,14 @@ struct llama_context_params llama_context_default_params() {
|
||||
/*.yarn_beta_fast =*/ 32.0f,
|
||||
/*.yarn_beta_slow =*/ 1.0f,
|
||||
/*.yarn_orig_ctx =*/ 0,
|
||||
/*.cb_eval =*/ nullptr,
|
||||
/*.cb_eval_user_data =*/ nullptr,
|
||||
/*.type_k =*/ GGML_TYPE_F16,
|
||||
/*.type_v =*/ GGML_TYPE_F16,
|
||||
/*.mul_mat_q =*/ true,
|
||||
/*.logits_all =*/ false,
|
||||
/*.embedding =*/ false,
|
||||
/*.offload_kqv =*/ true,
|
||||
/*.cb_eval =*/ nullptr,
|
||||
/*.cb_eval_user_data =*/ nullptr,
|
||||
};
|
||||
|
||||
return result;
|
||||
|
||||
6
llama.h
6
llama.h
@ -232,6 +232,9 @@ extern "C" {
|
||||
float yarn_beta_slow; // YaRN high correction dim
|
||||
uint32_t yarn_orig_ctx; // YaRN original context size
|
||||
|
||||
ggml_backend_sched_eval_callback cb_eval;
|
||||
void * cb_eval_user_data;
|
||||
|
||||
enum ggml_type type_k; // data type for K cache
|
||||
enum ggml_type type_v; // data type for V cache
|
||||
|
||||
@ -240,9 +243,6 @@ extern "C" {
|
||||
bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
|
||||
bool embedding; // embedding mode only
|
||||
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
|
||||
|
||||
ggml_backend_sched_eval_callback cb_eval;
|
||||
void * cb_eval_user_data;
|
||||
};
|
||||
|
||||
// model quantization parameters
|
||||
|
||||
@ -49,6 +49,7 @@ llama_build_and_test_executable(test-llama-grammar.cpp)
|
||||
llama_build_and_test_executable(test-grad0.cpp)
|
||||
# llama_build_and_test_executable(test-opt.cpp) # SLOW
|
||||
llama_build_and_test_executable(test-backend-ops.cpp)
|
||||
llama_build_and_test_executable(test-autorelease.cpp)
|
||||
|
||||
llama_build_and_test_executable(test-rope.cpp)
|
||||
|
||||
|
||||
28
tests/test-autorelease.cpp
Normal file
28
tests/test-autorelease.cpp
Normal file
@ -0,0 +1,28 @@
|
||||
// ref: https://github.com/ggerganov/llama.cpp/issues/4952#issuecomment-1892864763
|
||||
|
||||
#include <cstdio>
|
||||
#include <string>
|
||||
#include <thread>
|
||||
|
||||
#include "llama.h"
|
||||
|
||||
// This creates a new context inside a pthread and then tries to exit cleanly.
|
||||
int main(int argc, char ** argv) {
|
||||
if (argc < 2) {
|
||||
printf("Usage: %s model.gguf\n", argv[0]);
|
||||
return 0; // intentionally return success
|
||||
}
|
||||
|
||||
const std::string fname = argv[1];
|
||||
|
||||
std::thread([&fname]() {
|
||||
llama_backend_init(false);
|
||||
auto * model = llama_load_model_from_file(fname.c_str(), llama_model_default_params());
|
||||
auto * ctx = llama_new_context_with_model(model, llama_context_default_params());
|
||||
llama_free(ctx);
|
||||
llama_free_model(model);
|
||||
llama_backend_free();
|
||||
}).join();
|
||||
|
||||
return 0;
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user